code stringlengths 82 54.1k | code_codestyle int64 0 699 | style_context stringlengths 111 35.6k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
def A__ (snake_case : list[list[float]] ) -> Tuple:
__UpperCamelCase : list[list[float]] = []
for data in source_data:
for i, el in enumerate(lowercase_ ):
if len(lowercase_ ) < i + 1:
data_lists.append([] )
data_lists[i].append(float(lowercase_ ) )
return data_lists
def A__ (snake_case : list[list[float]] , snake_case : list[int] ) -> Tuple:
__UpperCamelCase : list[list[float]] = []
for dlist, weight in zip(lowercase_ , lowercase_ ):
__UpperCamelCase : int = min(lowercase_ )
__UpperCamelCase : Optional[Any] = max(lowercase_ )
__UpperCamelCase : list[float] = []
# for weight 0 score is 1 - actual score
if weight == 0:
for item in dlist:
try:
score.append(1 - ((item - mind) / (maxd - mind)) )
except ZeroDivisionError:
score.append(1 )
elif weight == 1:
for item in dlist:
try:
score.append((item - mind) / (maxd - mind) )
except ZeroDivisionError:
score.append(0 )
# weight not 0 or 1
else:
__UpperCamelCase : Any = F'''Invalid weight of {weight:f} provided'''
raise ValueError(lowercase_ )
score_lists.append(lowercase_ )
return score_lists
def A__ (snake_case : list[list[float]] ) -> Tuple:
__UpperCamelCase : list[float] = [0 for i in range(len(score_lists[0] ) )]
for slist in score_lists:
for j, ele in enumerate(lowercase_ ):
__UpperCamelCase : Dict = final_scores[j] + ele
return final_scores
def A__ (snake_case : list[list[float]] , snake_case : list[int] ) -> str:
__UpperCamelCase : int = get_data(lowercase_ )
__UpperCamelCase : str = calculate_each_score(lowercase_ , lowercase_ )
__UpperCamelCase : Any = generate_final_scores(lowercase_ )
# append scores to source data
for i, ele in enumerate(lowercase_ ):
source_data[i].append(lowercase_ )
return source_data
| 279 |
"""simple docstring"""
from typing import Optional, Union
import torch
from torch import nn
from ...configuration_utils import ConfigMixin, register_to_config
from ...models.modeling_utils import ModelMixin
class snake_case ( __UpperCAmelCase , __UpperCAmelCase ):
@register_to_config
def __init__( self :List[str] , _lowerCamelCase :int = 7_6_8 , ):
super().__init__()
__SCREAMING_SNAKE_CASE : Union[str, Any] = nn.Parameter(torch.zeros(1 , _lowerCamelCase ) )
__SCREAMING_SNAKE_CASE : List[Any] = nn.Parameter(torch.ones(1 , _lowerCamelCase ) )
def SCREAMING_SNAKE_CASE_ ( self :Any , _lowerCamelCase :Optional[Union[str, torch.device]] = None , _lowerCamelCase :Optional[torch.dtype] = None , ):
__SCREAMING_SNAKE_CASE : Optional[int] = nn.Parameter(self.mean.to(_lowerCamelCase ).to(_lowerCamelCase ) )
__SCREAMING_SNAKE_CASE : Tuple = nn.Parameter(self.std.to(_lowerCamelCase ).to(_lowerCamelCase ) )
return self
def SCREAMING_SNAKE_CASE_ ( self :int , _lowerCamelCase :Tuple ):
__SCREAMING_SNAKE_CASE : Tuple = (embeds - self.mean) * 1.0 / self.std
return embeds
def SCREAMING_SNAKE_CASE_ ( self :Optional[Any] , _lowerCamelCase :Optional[int] ):
__SCREAMING_SNAKE_CASE : List[Any] = (embeds * self.std) + self.mean
return embeds
| 674 | 0 |
from dataclasses import dataclass, field
from typing import Optional
from transformers import AutoConfig, AutoImageProcessor, AutoTokenizer, FlaxVisionEncoderDecoderModel, HfArgumentParser
@dataclass
class lowerCamelCase :
UpperCamelCase_ : Tuple = field(
metadata={'help': 'The output directory where the model will be written.'} , )
UpperCamelCase_ : str = field(
metadata={
'help': (
'The encoder model checkpoint for weights initialization.'
'Don\'t set if you want to train an encoder model from scratch.'
)
} , )
UpperCamelCase_ : int = field(
metadata={
'help': (
'The decoder model checkpoint for weights initialization.'
'Don\'t set if you want to train a decoder model from scratch.'
)
} , )
UpperCamelCase_ : Union[str, Any] = field(
default=__UpperCAmelCase , metadata={'help': 'Pretrained encoder config name or path if not the same as encoder_model_name'} )
UpperCamelCase_ : List[Any] = field(
default=__UpperCAmelCase , metadata={'help': 'Pretrained decoder config name or path if not the same as decoder_model_name'} )
def a ( ) ->List[Any]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = HfArgumentParser((ModelArguments,) )
(SCREAMING_SNAKE_CASE ) = parser.parse_args_into_dataclasses()
# Load pretrained model and tokenizer
# Use explicit specified encoder config
if model_args.encoder_config_name:
SCREAMING_SNAKE_CASE = AutoConfig.from_pretrained(model_args.encoder_config_name )
# Use pretrained encoder model's config
else:
SCREAMING_SNAKE_CASE = AutoConfig.from_pretrained(model_args.encoder_model_name_or_path )
# Use explicit specified decoder config
if model_args.decoder_config_name:
SCREAMING_SNAKE_CASE = AutoConfig.from_pretrained(model_args.decoder_config_name )
# Use pretrained decoder model's config
else:
SCREAMING_SNAKE_CASE = AutoConfig.from_pretrained(model_args.decoder_model_name_or_path )
# necessary for `from_encoder_decoder_pretrained` when `decoder_config` is passed
SCREAMING_SNAKE_CASE = True
SCREAMING_SNAKE_CASE = True
SCREAMING_SNAKE_CASE = FlaxVisionEncoderDecoderModel.from_encoder_decoder_pretrained(
encoder_pretrained_model_name_or_path=model_args.encoder_model_name_or_path , decoder_pretrained_model_name_or_path=model_args.decoder_model_name_or_path , encoder_config=lowercase_ , decoder_config=lowercase_ , )
# GPT2 only has bos/eos tokens but not decoder_start/pad tokens
SCREAMING_SNAKE_CASE = decoder_config.decoder_start_token_id
SCREAMING_SNAKE_CASE = decoder_config.pad_token_id
if decoder_start_token_id is None:
SCREAMING_SNAKE_CASE = decoder_config.bos_token_id
if pad_token_id is None:
SCREAMING_SNAKE_CASE = decoder_config.eos_token_id
# This is necessary to make Flax's generate() work
SCREAMING_SNAKE_CASE = decoder_config.eos_token_id
SCREAMING_SNAKE_CASE = decoder_start_token_id
SCREAMING_SNAKE_CASE = pad_token_id
SCREAMING_SNAKE_CASE = AutoImageProcessor.from_pretrained(model_args.encoder_model_name_or_path )
SCREAMING_SNAKE_CASE = AutoTokenizer.from_pretrained(model_args.decoder_model_name_or_path )
SCREAMING_SNAKE_CASE = tokenizer.convert_ids_to_tokens(model.config.pad_token_id )
model.save_pretrained(model_args.output_dir )
image_processor.save_pretrained(model_args.output_dir )
tokenizer.save_pretrained(model_args.output_dir )
if __name__ == "__main__":
main() | 201 |
"""simple docstring"""
import argparse
import torch
from transformers import BertConfig, BertForPreTraining, load_tf_weights_in_bert
from transformers.utils import logging
logging.set_verbosity_info()
def lowerCAmelCase_ ( lowercase_ : Dict , lowercase_ : int , lowercase_ : Any ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : Tuple = BertConfig.from_json_file(lowercase_ )
print(F'''Building PyTorch model from configuration: {config}''' )
__SCREAMING_SNAKE_CASE : Optional[int] = BertForPreTraining(lowercase_ )
# Load weights from tf checkpoint
load_tf_weights_in_bert(lowercase_ , lowercase_ , lowercase_ )
# Save pytorch-model
print(F'''Save PyTorch model to {pytorch_dump_path}''' )
torch.save(model.state_dict() , lowercase_ )
if __name__ == "__main__":
_lowerCamelCase = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--tf_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
)
parser.add_argument(
'''--bert_config_file''',
default=None,
type=str,
required=True,
help=(
'''The config json file corresponding to the pre-trained BERT model. \n'''
'''This specifies the model architecture.'''
),
)
parser.add_argument(
'''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
)
_lowerCamelCase = parser.parse_args()
convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)
| 674 | 0 |
"""simple docstring"""
import numpy as np
import torch
from ..models.clipseg import CLIPSegForImageSegmentation
from ..utils import is_vision_available, requires_backends
from .base import PipelineTool
if is_vision_available():
from PIL import Image
class __UpperCAmelCase( __UpperCAmelCase ):
"""simple docstring"""
__lowerCamelCase = (
"This is a tool that creates a segmentation mask of an image according to a label. It cannot create an image."
"It takes two arguments named `image` which should be the original image, and `label` which should be a text "
"describing the elements what should be identified in the segmentation mask. The tool returns the mask."
)
__lowerCamelCase = "CIDAS/clipseg-rd64-refined"
__lowerCamelCase = "image_segmenter"
__lowerCamelCase = CLIPSegForImageSegmentation
__lowerCamelCase = ["image", "text"]
__lowerCamelCase = ["image"]
def __init__( self , *snake_case__ , **snake_case__ ):
'''simple docstring'''
requires_backends(self , ["vision"] )
super().__init__(*_lowerCamelCase , **_lowerCamelCase )
def UpperCAmelCase_ ( self , snake_case__ , snake_case__ ):
'''simple docstring'''
return self.pre_processor(text=[label] , images=[image] , padding=_lowerCamelCase , return_tensors="pt" )
def UpperCAmelCase_ ( self , snake_case__ ):
'''simple docstring'''
with torch.no_grad():
lowercase__ : List[Any]= self.model(**_lowerCamelCase ).logits
return logits
def UpperCAmelCase_ ( self , snake_case__ ):
'''simple docstring'''
lowercase__ : Optional[int]= outputs.cpu().detach().numpy()
lowercase__ : str= 0
lowercase__ : str= 1
return Image.fromarray((array * 255).astype(np.uinta ) )
| 218 |
"""simple docstring"""
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import numpy as np
import torch
from ..models.clipseg import CLIPSegForImageSegmentation
from ..utils import is_vision_available, requires_backends
from .base import PipelineTool
if is_vision_available():
from PIL import Image
class snake_case ( __UpperCAmelCase ):
lowerCamelCase__ = (
'''This is a tool that creates a segmentation mask of an image according to a label. It cannot create an image.'''
'''It takes two arguments named `image` which should be the original image, and `label` which should be a text '''
'''describing the elements what should be identified in the segmentation mask. The tool returns the mask.'''
)
lowerCamelCase__ = '''CIDAS/clipseg-rd64-refined'''
lowerCamelCase__ = '''image_segmenter'''
lowerCamelCase__ = CLIPSegForImageSegmentation
lowerCamelCase__ = ['''image''', '''text''']
lowerCamelCase__ = ['''image''']
def __init__( self :Dict , *_lowerCamelCase :Union[str, Any] , **_lowerCamelCase :Tuple ):
requires_backends(self , ['''vision'''] )
super().__init__(*_lowerCamelCase , **_lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :Tuple , _lowerCamelCase :"Image" , _lowerCamelCase :str ):
return self.pre_processor(text=[label] , images=[image] , padding=_lowerCamelCase , return_tensors='''pt''' )
def SCREAMING_SNAKE_CASE_ ( self :Optional[int] , _lowerCamelCase :Optional[int] ):
with torch.no_grad():
__SCREAMING_SNAKE_CASE : List[Any] = self.model(**_lowerCamelCase ).logits
return logits
def SCREAMING_SNAKE_CASE_ ( self :Optional[Any] , _lowerCamelCase :Tuple ):
__SCREAMING_SNAKE_CASE : Optional[int] = outputs.cpu().detach().numpy()
__SCREAMING_SNAKE_CASE : str = 0
__SCREAMING_SNAKE_CASE : str = 1
return Image.fromarray((array * 2_5_5).astype(np.uinta ) )
| 674 | 0 |
def __snake_case ( _UpperCamelCase ) -> Union[str, Any]: # noqa: E741
_a = len(lowercase_ )
_a = 0
_a = [0] * n
_a = [False] * n
_a = [False] * n
def dfs(_UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase ):
if parent == root:
out_edge_count += 1
_a = True
_a = at
for to in l[at]:
if to == parent:
pass
elif not visited[to]:
_a = dfs(lowercase_ , lowercase_ , lowercase_ , lowercase_ )
_a = min(low[at] , low[to] )
# AP found via bridge
if at < low[to]:
_a = True
# AP found via cycle
if at == low[to]:
_a = True
else:
_a = min(low[at] , lowercase_ )
return out_edge_count
for i in range(lowercase_ ):
if not visited[i]:
_a = 0
_a = dfs(lowercase_ , lowercase_ , -1 , lowercase_ )
_a = out_edge_count > 1
for x in range(len(lowercase_ ) ):
if is_art[x] is True:
print(lowercase_ )
# Adjacency list of graph
lowerCamelCase :Any = {
0: [1, 2],
1: [0, 2],
2: [0, 1, 3, 5],
3: [2, 4],
4: [3],
5: [2, 6, 8],
6: [5, 7],
7: [6, 8],
8: [5, 7],
}
compute_ap(data)
| 487 |
"""simple docstring"""
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
from transformers import BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES, BertTokenizer
from transformers.testing_utils import require_tokenizers, require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available
if is_vision_available():
from PIL import Image
from transformers import VisionTextDualEncoderProcessor, ViTImageProcessor
@require_tokenizers
@require_vision
class snake_case ( unittest.TestCase ):
def SCREAMING_SNAKE_CASE_ ( self :Union[str, Any] ):
__SCREAMING_SNAKE_CASE : Union[str, Any] = tempfile.mkdtemp()
# fmt: off
__SCREAMING_SNAKE_CASE : Optional[int] = ['''[UNK]''', '''[CLS]''', '''[SEP]''', '''[PAD]''', '''[MASK]''', '''want''', '''##want''', '''##ed''', '''wa''', '''un''', '''runn''', '''##ing''', ''',''', '''low''', '''lowest''']
# fmt: on
__SCREAMING_SNAKE_CASE : List[Any] = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['''vocab_file'''] )
with open(self.vocab_file , '''w''' , encoding='''utf-8''' ) as vocab_writer:
vocab_writer.write(''''''.join([x + '''\n''' for x in vocab_tokens] ) )
__SCREAMING_SNAKE_CASE : Optional[int] = {
'''do_resize''': True,
'''size''': {'''height''': 1_8, '''width''': 1_8},
'''do_normalize''': True,
'''image_mean''': [0.5, 0.5, 0.5],
'''image_std''': [0.5, 0.5, 0.5],
}
__SCREAMING_SNAKE_CASE : List[Any] = os.path.join(self.tmpdirname , _lowerCamelCase )
with open(self.image_processor_file , '''w''' , encoding='''utf-8''' ) as fp:
json.dump(_lowerCamelCase , _lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :Union[str, Any] , **_lowerCamelCase :List[str] ):
return BertTokenizer.from_pretrained(self.tmpdirname , **_lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :List[str] , **_lowerCamelCase :Optional[int] ):
return ViTImageProcessor.from_pretrained(self.tmpdirname , **_lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :Optional[Any] ):
shutil.rmtree(self.tmpdirname )
def SCREAMING_SNAKE_CASE_ ( self :Dict ):
__SCREAMING_SNAKE_CASE : Union[str, Any] = [np.random.randint(2_5_5 , size=(3, 3_0, 4_0_0) , dtype=np.uinta )]
__SCREAMING_SNAKE_CASE : Tuple = [Image.fromarray(np.moveaxis(_lowerCamelCase , 0 , -1 ) ) for x in image_inputs]
return image_inputs
def SCREAMING_SNAKE_CASE_ ( self :int ):
__SCREAMING_SNAKE_CASE : Optional[int] = self.get_tokenizer()
__SCREAMING_SNAKE_CASE : Optional[Any] = self.get_image_processor()
__SCREAMING_SNAKE_CASE : Optional[Any] = VisionTextDualEncoderProcessor(tokenizer=_lowerCamelCase , image_processor=_lowerCamelCase )
processor.save_pretrained(self.tmpdirname )
__SCREAMING_SNAKE_CASE : int = VisionTextDualEncoderProcessor.from_pretrained(self.tmpdirname )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer.get_vocab() )
self.assertIsInstance(processor.tokenizer , (BertTokenizer, BertTokenizerFast) )
self.assertEqual(processor.image_processor.to_json_string() , image_processor.to_json_string() )
self.assertIsInstance(processor.image_processor , _lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :List[Any] ):
__SCREAMING_SNAKE_CASE : str = VisionTextDualEncoderProcessor(
tokenizer=self.get_tokenizer() , image_processor=self.get_image_processor() )
processor.save_pretrained(self.tmpdirname )
__SCREAMING_SNAKE_CASE : Optional[int] = self.get_tokenizer(bos_token='''(BOS)''' , eos_token='''(EOS)''' )
__SCREAMING_SNAKE_CASE : List[str] = self.get_image_processor(do_normalize=_lowerCamelCase , padding_value=1.0 )
__SCREAMING_SNAKE_CASE : int = VisionTextDualEncoderProcessor.from_pretrained(
self.tmpdirname , bos_token='''(BOS)''' , eos_token='''(EOS)''' , do_normalize=_lowerCamelCase , padding_value=1.0 )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer_add_kwargs.get_vocab() )
self.assertIsInstance(processor.tokenizer , (BertTokenizer, BertTokenizerFast) )
self.assertEqual(processor.image_processor.to_json_string() , image_processor_add_kwargs.to_json_string() )
self.assertIsInstance(processor.image_processor , _lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :int ):
__SCREAMING_SNAKE_CASE : Union[str, Any] = self.get_image_processor()
__SCREAMING_SNAKE_CASE : Dict = self.get_tokenizer()
__SCREAMING_SNAKE_CASE : Tuple = VisionTextDualEncoderProcessor(tokenizer=_lowerCamelCase , image_processor=_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Dict = self.prepare_image_inputs()
__SCREAMING_SNAKE_CASE : Optional[int] = image_processor(_lowerCamelCase , return_tensors='''np''' )
__SCREAMING_SNAKE_CASE : Tuple = processor(images=_lowerCamelCase , return_tensors='''np''' )
for key in input_feat_extract.keys():
self.assertAlmostEqual(input_feat_extract[key].sum() , input_processor[key].sum() , delta=1e-2 )
def SCREAMING_SNAKE_CASE_ ( self :Optional[int] ):
__SCREAMING_SNAKE_CASE : Any = self.get_image_processor()
__SCREAMING_SNAKE_CASE : Any = self.get_tokenizer()
__SCREAMING_SNAKE_CASE : Tuple = VisionTextDualEncoderProcessor(tokenizer=_lowerCamelCase , image_processor=_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Union[str, Any] = '''lower newer'''
__SCREAMING_SNAKE_CASE : Optional[int] = processor(text=_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Any = tokenizer(_lowerCamelCase )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] , encoded_processor[key] )
def SCREAMING_SNAKE_CASE_ ( self :str ):
__SCREAMING_SNAKE_CASE : str = self.get_image_processor()
__SCREAMING_SNAKE_CASE : int = self.get_tokenizer()
__SCREAMING_SNAKE_CASE : int = VisionTextDualEncoderProcessor(tokenizer=_lowerCamelCase , image_processor=_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Any = '''lower newer'''
__SCREAMING_SNAKE_CASE : int = self.prepare_image_inputs()
__SCREAMING_SNAKE_CASE : int = processor(text=_lowerCamelCase , images=_lowerCamelCase )
self.assertListEqual(list(inputs.keys() ) , ['''input_ids''', '''token_type_ids''', '''attention_mask''', '''pixel_values'''] )
# test if it raises when no input is passed
with self.assertRaises(_lowerCamelCase ):
processor()
def SCREAMING_SNAKE_CASE_ ( self :Any ):
__SCREAMING_SNAKE_CASE : Dict = self.get_image_processor()
__SCREAMING_SNAKE_CASE : List[Any] = self.get_tokenizer()
__SCREAMING_SNAKE_CASE : Union[str, Any] = VisionTextDualEncoderProcessor(tokenizer=_lowerCamelCase , image_processor=_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Union[str, Any] = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
__SCREAMING_SNAKE_CASE : Tuple = processor.batch_decode(_lowerCamelCase )
__SCREAMING_SNAKE_CASE : List[str] = tokenizer.batch_decode(_lowerCamelCase )
self.assertListEqual(_lowerCamelCase , _lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :Dict ):
__SCREAMING_SNAKE_CASE : str = self.get_image_processor()
__SCREAMING_SNAKE_CASE : List[str] = self.get_tokenizer()
__SCREAMING_SNAKE_CASE : Optional[int] = VisionTextDualEncoderProcessor(tokenizer=_lowerCamelCase , image_processor=_lowerCamelCase )
__SCREAMING_SNAKE_CASE : List[Any] = '''lower newer'''
__SCREAMING_SNAKE_CASE : Dict = self.prepare_image_inputs()
__SCREAMING_SNAKE_CASE : str = processor(text=_lowerCamelCase , images=_lowerCamelCase )
self.assertListEqual(list(inputs.keys() ) , processor.model_input_names )
| 674 | 0 |
"""simple docstring"""
import argparse
import logging
import os
import time
import timeit
import datasets
import numpy as np
import pycuda.autoinit # noqa: F401
import pycuda.driver as cuda
import tensorrt as trt
import torch
from absl import logging as absl_logging
from accelerate import Accelerator
from datasets import load_dataset, load_metric
from torch.utils.data import DataLoader
from utils_qa import postprocess_qa_predictions
import transformers
from transformers import AutoTokenizer, EvalPrediction, default_data_collator, set_seed
from transformers.trainer_pt_utils import nested_concat, nested_truncate
a_ = trt.Logger(trt.Logger.WARNING)
a_ = absl_logging.get_absl_logger()
absl_logger.setLevel(logging.WARNING)
a_ = logging.getLogger(__name__)
a_ = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--onnx_model_path',
default=None,
type=str,
required=True,
help='Path to ONNX model: ',
)
parser.add_argument(
'--output_dir',
default=None,
type=str,
required=True,
help='The output directory where the model checkpoints and predictions will be written.',
)
# Other parameters
parser.add_argument(
'--tokenizer_name',
default='',
type=str,
required=True,
help='Pretrained tokenizer name or path if not the same as model_name',
)
parser.add_argument(
'--version_2_with_negative',
action='store_true',
help='If true, the SQuAD examples contain some that do not have an answer.',
)
parser.add_argument(
'--null_score_diff_threshold',
type=float,
default=0.0,
help='If null_score - best_non_null is greater than the threshold predict null.',
)
parser.add_argument(
'--max_seq_length',
default=3_8_4,
type=int,
help=(
'The maximum total input sequence length after WordPiece tokenization. Sequences '
'longer than this will be truncated, and sequences shorter than this will be padded.'
),
)
parser.add_argument(
'--doc_stride',
default=1_2_8,
type=int,
help='When splitting up a long document into chunks, how much stride to take between chunks.',
)
parser.add_argument('--per_device_eval_batch_size', default=8, type=int, help='Batch size per GPU/CPU for evaluation.')
parser.add_argument(
'--n_best_size',
default=2_0,
type=int,
help='The total number of n-best predictions to generate in the nbest_predictions.json output file.',
)
parser.add_argument(
'--max_answer_length',
default=3_0,
type=int,
help=(
'The maximum length of an answer that can be generated. This is needed because the start '
'and end predictions are not conditioned on one another.'
),
)
parser.add_argument('--seed', type=int, default=4_2, help='random seed for initialization')
parser.add_argument(
'--dataset_name',
type=str,
default=None,
required=True,
help='The name of the dataset to use (via the datasets library).',
)
parser.add_argument(
'--dataset_config_name',
type=str,
default=None,
help='The configuration name of the dataset to use (via the datasets library).',
)
parser.add_argument(
'--preprocessing_num_workers', type=int, default=4, help='A csv or a json file containing the training data.'
)
parser.add_argument('--overwrite_cache', action='store_true', help='Overwrite the cached training and evaluation sets')
parser.add_argument(
'--fp16',
action='store_true',
help='Whether to use 16-bit (mixed) precision instead of 32-bit',
)
parser.add_argument(
'--int8',
action='store_true',
help='Whether to use INT8',
)
a_ = parser.parse_args()
if args.tokenizer_name:
a_ = AutoTokenizer.from_pretrained(args.tokenizer_name, use_fast=True)
else:
raise ValueError(
'You are instantiating a new tokenizer from scratch. This is not supported by this script.'
'You can do it from another script, save it, and load it from here, using --tokenizer_name.'
)
logger.info('Training/evaluation parameters %s', args)
a_ = args.per_device_eval_batch_size
a_ = (args.eval_batch_size, args.max_seq_length)
# TRT Engine properties
a_ = True
a_ = 'temp_engine/bert-fp32.engine'
if args.fpaa:
a_ = 'temp_engine/bert-fp16.engine'
if args.inta:
a_ = 'temp_engine/bert-int8.engine'
# import ONNX file
if not os.path.exists('temp_engine'):
os.makedirs('temp_engine')
a_ = 1 << (int)(trt.NetworkDefinitionCreationFlag.EXPLICIT_BATCH)
with trt.Builder(TRT_LOGGER) as builder, builder.create_network(EXPLICIT_BATCH) as network, trt.OnnxParser(
network, TRT_LOGGER
) as parser:
with open(args.onnx_model_path, 'rb') as model:
if not parser.parse(model.read()):
for error in range(parser.num_errors):
print(parser.get_error(error))
# Query input names and shapes from parsed TensorRT network
a_ = [network.get_input(i) for i in range(network.num_inputs)]
a_ = [_input.name for _input in network_inputs] # ex: ["actual_input1"]
with builder.create_builder_config() as config:
a_ = 1 << 5_0
if STRICT_TYPES:
config.set_flag(trt.BuilderFlag.STRICT_TYPES)
if args.fpaa:
config.set_flag(trt.BuilderFlag.FPaa)
if args.inta:
config.set_flag(trt.BuilderFlag.INTa)
a_ = builder.create_optimization_profile()
config.add_optimization_profile(profile)
for i in range(len(input_names)):
profile.set_shape(input_names[i], INPUT_SHAPE, INPUT_SHAPE, INPUT_SHAPE)
a_ = builder.build_engine(network, config)
# serialize_engine and store in file (can be directly loaded and deserialized):
with open(engine_name, 'wb') as f:
f.write(engine.serialize())
def __UpperCAmelCase ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
__lowercase : Dict = np.asarray(inputs['''input_ids'''] , dtype=np.intaa )
__lowercase : Optional[Any] = np.asarray(inputs['''attention_mask'''] , dtype=np.intaa )
__lowercase : Optional[Any] = np.asarray(inputs['''token_type_ids'''] , dtype=np.intaa )
# Copy inputs
cuda.memcpy_htod_async(d_inputs[0] , input_ids.ravel() , lowercase_ )
cuda.memcpy_htod_async(d_inputs[1] , attention_mask.ravel() , lowercase_ )
cuda.memcpy_htod_async(d_inputs[2] , token_type_ids.ravel() , lowercase_ )
# start time
__lowercase : Tuple = time.time()
# Run inference
context.execute_async(
bindings=[int(lowercase_ ) for d_inp in d_inputs] + [int(lowercase_ ), int(lowercase_ )] , stream_handle=stream.handle )
# Transfer predictions back from GPU
cuda.memcpy_dtoh_async(lowercase_ , lowercase_ , lowercase_ )
cuda.memcpy_dtoh_async(lowercase_ , lowercase_ , lowercase_ )
# Synchronize the stream and take time
stream.synchronize()
# end time
__lowercase : List[str] = time.time()
__lowercase : int = end_time - start_time
__lowercase : int = (h_outputa, h_outputa)
# print(outputs)
return outputs, infer_time
# Initialize the accelerator. We will let the accelerator handle device placement for us in this example.
a_ = Accelerator()
# Make one log on every process with the configuration for debugging.
logging.basicConfig(
format='%(asctime)s - %(levelname)s - %(name)s - %(message)s',
datefmt='%m/%d/%Y %H:%M:%S',
level=logging.INFO,
)
# Setup logging, we only want one process per machine to log things on the screen.
# accelerator.is_local_main_process is only True for one process per machine.
logger.setLevel(logging.INFO if accelerator.is_local_main_process else logging.ERROR)
if accelerator.is_local_main_process:
datasets.utils.logging.set_verbosity_warning()
transformers.utils.logging.set_verbosity_info()
else:
datasets.utils.logging.set_verbosity_error()
transformers.utils.logging.set_verbosity_error()
# If passed along, set the training seed now.
if args.seed is not None:
set_seed(args.seed)
# Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below)
# or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
# (the dataset will be downloaded automatically from the datasets Hub).
#
# For CSV/JSON files, this script will use the column called 'text' or the first column if no column called
# 'text' is found. You can easily tweak this behavior (see below).
if args.dataset_name is not None:
# Downloading and loading a dataset from the hub.
a_ = load_dataset(args.dataset_name, args.dataset_config_name)
else:
raise ValueError('Evaluation requires a dataset name')
# See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
# https://huggingface.co/docs/datasets/loading_datasets.html.
# Preprocessing the datasets.
# Preprocessing is slighlty different for training and evaluation.
a_ = raw_datasets['validation'].column_names
a_ = 'question' if 'question' in column_names else column_names[0]
a_ = 'context' if 'context' in column_names else column_names[1]
a_ = 'answers' if 'answers' in column_names else column_names[2]
# Padding side determines if we do (question|context) or (context|question).
a_ = tokenizer.padding_side == 'right'
if args.max_seq_length > tokenizer.model_max_length:
logger.warning(
F"The max_seq_length passed ({args.max_seq_length}) is larger than the maximum length for the"
F"model ({tokenizer.model_max_length}). Using max_seq_length={tokenizer.model_max_length}."
)
a_ = min(args.max_seq_length, tokenizer.model_max_length)
def __UpperCAmelCase ( __UpperCamelCase ):
__lowercase : Any = [q.lstrip() for q in examples[question_column_name]]
# Tokenize our examples with truncation and maybe padding, but keep the overflows using a stride. This results
# in one example possible giving several features when a context is long, each of those features having a
# context that overlaps a bit the context of the previous feature.
__lowercase : Optional[Any] = tokenizer(
examples[question_column_name if pad_on_right else context_column_name] , examples[context_column_name if pad_on_right else question_column_name] , truncation='''only_second''' if pad_on_right else '''only_first''' , max_length=lowercase_ , stride=args.doc_stride , return_overflowing_tokens=lowercase_ , return_offsets_mapping=lowercase_ , padding='''max_length''' , )
# Since one example might give us several features if it has a long context, we need a map from a feature to
# its corresponding example. This key gives us just that.
__lowercase : Optional[int] = tokenized_examples.pop('''overflow_to_sample_mapping''' )
# For evaluation, we will need to convert our predictions to substrings of the context, so we keep the
# corresponding example_id and we will store the offset mappings.
__lowercase : Any = []
for i in range(len(tokenized_examples['''input_ids'''] ) ):
# Grab the sequence corresponding to that example (to know what is the context and what is the question).
__lowercase : int = tokenized_examples.sequence_ids(lowercase_ )
__lowercase : str = 1 if pad_on_right else 0
# One example can give several spans, this is the index of the example containing this span of text.
__lowercase : str = sample_mapping[i]
tokenized_examples["example_id"].append(examples['''id'''][sample_index] )
# Set to None the offset_mapping that are not part of the context so it's easy to determine if a token
# position is part of the context or not.
__lowercase : List[str] = [
(o if sequence_ids[k] == context_index else None)
for k, o in enumerate(tokenized_examples['''offset_mapping'''][i] )
]
return tokenized_examples
a_ = raw_datasets['validation']
# Validation Feature Creation
a_ = eval_examples.map(
prepare_validation_features,
batched=True,
num_proc=args.preprocessing_num_workers,
remove_columns=column_names,
load_from_cache_file=not args.overwrite_cache,
desc='Running tokenizer on validation dataset',
)
a_ = default_data_collator
a_ = eval_dataset.remove_columns(['example_id', 'offset_mapping'])
a_ = DataLoader(
eval_dataset_for_model, collate_fn=data_collator, batch_size=args.per_device_eval_batch_size
)
def __UpperCAmelCase ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase="eval" ):
__lowercase : Tuple = postprocess_qa_predictions(
examples=lowercase_ , features=lowercase_ , predictions=lowercase_ , version_2_with_negative=args.version_2_with_negative , n_best_size=args.n_best_size , max_answer_length=args.max_answer_length , null_score_diff_threshold=args.null_score_diff_threshold , output_dir=args.output_dir , prefix=lowercase_ , )
# Format the result to the format the metric expects.
if args.version_2_with_negative:
__lowercase : Union[str, Any] = [
{'''id''': k, '''prediction_text''': v, '''no_answer_probability''': 0.0} for k, v in predictions.items()
]
else:
__lowercase : int = [{'''id''': k, '''prediction_text''': v} for k, v in predictions.items()]
__lowercase : Any = [{'''id''': ex['''id'''], '''answers''': ex[answer_column_name]} for ex in examples]
return EvalPrediction(predictions=lowercase_ , label_ids=lowercase_ )
a_ = load_metric('squad_v2' if args.version_2_with_negative else 'squad')
# Evaluation!
logger.info('Loading ONNX model %s for evaluation', args.onnx_model_path)
with open(engine_name, 'rb') as f, trt.Runtime(TRT_LOGGER) as runtime, runtime.deserialize_cuda_engine(
f.read()
) as engine, engine.create_execution_context() as context:
# setup for TRT inferrence
for i in range(len(input_names)):
context.set_binding_shape(i, INPUT_SHAPE)
assert context.all_binding_shapes_specified
def __UpperCAmelCase ( __UpperCamelCase ):
return trt.volume(engine.get_binding_shape(lowercase_ ) ) * engine.get_binding_dtype(lowercase_ ).itemsize
# Allocate device memory for inputs and outputs.
a_ = [cuda.mem_alloc(binding_nbytes(binding)) for binding in engine if engine.binding_is_input(binding)]
# Allocate output buffer
a_ = cuda.pagelocked_empty(tuple(context.get_binding_shape(3)), dtype=np.floataa)
a_ = cuda.pagelocked_empty(tuple(context.get_binding_shape(4)), dtype=np.floataa)
a_ = cuda.mem_alloc(h_outputa.nbytes)
a_ = cuda.mem_alloc(h_outputa.nbytes)
# Create a stream in which to copy inputs/outputs and run inference.
a_ = cuda.Stream()
# Evaluation
logger.info('***** Running Evaluation *****')
logger.info(F" Num examples = {len(eval_dataset)}")
logger.info(F" Batch size = {args.per_device_eval_batch_size}")
a_ = 0.0
a_ = 0
a_ = timeit.default_timer()
a_ = None
for step, batch in enumerate(eval_dataloader):
a_ , a_ = model_infer(batch, context, d_inputs, h_outputa, h_outputa, d_outputa, d_outputa, stream)
total_time += infer_time
niter += 1
a_ , a_ = outputs
a_ = torch.tensor(start_logits)
a_ = torch.tensor(end_logits)
# necessary to pad predictions and labels for being gathered
a_ = accelerator.pad_across_processes(start_logits, dim=1, pad_index=-1_0_0)
a_ = accelerator.pad_across_processes(end_logits, dim=1, pad_index=-1_0_0)
a_ = (accelerator.gather(start_logits).cpu().numpy(), accelerator.gather(end_logits).cpu().numpy())
a_ = logits if all_preds is None else nested_concat(all_preds, logits, padding_index=-1_0_0)
if all_preds is not None:
a_ = nested_truncate(all_preds, len(eval_dataset))
a_ = timeit.default_timer() - start_time
logger.info(' Evaluation done in total %f secs (%f sec per example)', evalTime, evalTime / len(eval_dataset))
# Inference time from TRT
logger.info('Average Inference Time = {:.3f} ms'.format(total_time * 1_0_0_0 / niter))
logger.info('Total Inference Time = {:.3f} ms'.format(total_time * 1_0_0_0))
logger.info('Total Number of Inference = %d', niter)
a_ = post_processing_function(eval_examples, eval_dataset, all_preds)
a_ = metric.compute(predictions=prediction.predictions, references=prediction.label_ids)
logger.info(F"Evaluation metrics: {eval_metric}")
| 76 |
"""simple docstring"""
import inspect
import unittest
import numpy as np
from tests.test_modeling_common import floats_tensor
from transformers import DetrConfig, MaskFormerConfig, SwinConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import MaskFormerForInstanceSegmentation, MaskFormerModel
if is_vision_available():
from transformers import MaskFormerImageProcessor
if is_vision_available():
from PIL import Image
class snake_case :
def __init__( self :Optional[int] , _lowerCamelCase :int , _lowerCamelCase :Any=2 , _lowerCamelCase :List[str]=True , _lowerCamelCase :str=False , _lowerCamelCase :Tuple=1_0 , _lowerCamelCase :str=3 , _lowerCamelCase :str=3_2 * 4 , _lowerCamelCase :Dict=3_2 * 6 , _lowerCamelCase :str=4 , _lowerCamelCase :Any=3_2 , ):
__SCREAMING_SNAKE_CASE : List[str] = parent
__SCREAMING_SNAKE_CASE : Tuple = batch_size
__SCREAMING_SNAKE_CASE : Optional[Any] = is_training
__SCREAMING_SNAKE_CASE : Dict = use_auxiliary_loss
__SCREAMING_SNAKE_CASE : List[str] = num_queries
__SCREAMING_SNAKE_CASE : Optional[int] = num_channels
__SCREAMING_SNAKE_CASE : List[Any] = min_size
__SCREAMING_SNAKE_CASE : int = max_size
__SCREAMING_SNAKE_CASE : Any = num_labels
__SCREAMING_SNAKE_CASE : Union[str, Any] = mask_feature_size
def SCREAMING_SNAKE_CASE_ ( self :Any ):
__SCREAMING_SNAKE_CASE : str = floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size] ).to(
_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Any = torch.ones([self.batch_size, self.min_size, self.max_size] , device=_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Any = (
torch.rand([self.batch_size, self.num_labels, self.min_size, self.max_size] , device=_lowerCamelCase ) > 0.5
).float()
__SCREAMING_SNAKE_CASE : Dict = (torch.rand((self.batch_size, self.num_labels) , device=_lowerCamelCase ) > 0.5).long()
__SCREAMING_SNAKE_CASE : str = self.get_config()
return config, pixel_values, pixel_mask, mask_labels, class_labels
def SCREAMING_SNAKE_CASE_ ( self :List[Any] ):
return MaskFormerConfig.from_backbone_and_decoder_configs(
backbone_config=SwinConfig(
depths=[1, 1, 1, 1] , ) , decoder_config=DetrConfig(
decoder_ffn_dim=1_2_8 , num_queries=self.num_queries , decoder_attention_heads=2 , d_model=self.mask_feature_size , ) , mask_feature_size=self.mask_feature_size , fpn_feature_size=self.mask_feature_size , num_channels=self.num_channels , num_labels=self.num_labels , )
def SCREAMING_SNAKE_CASE_ ( self :str ):
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Optional[Any] = self.prepare_config_and_inputs()
__SCREAMING_SNAKE_CASE : Any = {'''pixel_values''': pixel_values, '''pixel_mask''': pixel_mask}
return config, inputs_dict
def SCREAMING_SNAKE_CASE_ ( self :int , _lowerCamelCase :int , _lowerCamelCase :Optional[int] ):
__SCREAMING_SNAKE_CASE : Union[str, Any] = output.encoder_hidden_states
__SCREAMING_SNAKE_CASE : int = output.pixel_decoder_hidden_states
__SCREAMING_SNAKE_CASE : Optional[int] = output.transformer_decoder_hidden_states
self.parent.assertTrue(len(_lowerCamelCase ) , len(config.backbone_config.depths ) )
self.parent.assertTrue(len(_lowerCamelCase ) , len(config.backbone_config.depths ) )
self.parent.assertTrue(len(_lowerCamelCase ) , config.decoder_config.decoder_layers )
def SCREAMING_SNAKE_CASE_ ( self :Optional[Any] , _lowerCamelCase :Dict , _lowerCamelCase :str , _lowerCamelCase :Any , _lowerCamelCase :Optional[Any]=False ):
with torch.no_grad():
__SCREAMING_SNAKE_CASE : List[str] = MaskFormerModel(config=_lowerCamelCase )
model.to(_lowerCamelCase )
model.eval()
__SCREAMING_SNAKE_CASE : str = model(pixel_values=_lowerCamelCase , pixel_mask=_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Any = model(_lowerCamelCase , output_hidden_states=_lowerCamelCase )
# the correct shape of output.transformer_decoder_hidden_states ensure the correcteness of the
# encoder and pixel decoder
self.parent.assertEqual(
output.transformer_decoder_last_hidden_state.shape , (self.batch_size, self.num_queries, self.mask_feature_size) , )
# let's ensure the other two hidden state exists
self.parent.assertTrue(output.pixel_decoder_last_hidden_state is not None )
self.parent.assertTrue(output.encoder_last_hidden_state is not None )
if output_hidden_states:
self.check_output_hidden_state(_lowerCamelCase , _lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :Dict , _lowerCamelCase :Optional[Any] , _lowerCamelCase :Optional[int] , _lowerCamelCase :Optional[Any] , _lowerCamelCase :List[str] , _lowerCamelCase :Dict ):
__SCREAMING_SNAKE_CASE : Optional[Any] = MaskFormerForInstanceSegmentation(config=_lowerCamelCase )
model.to(_lowerCamelCase )
model.eval()
def comm_check_on_output(_lowerCamelCase :Optional[Any] ):
# let's still check that all the required stuff is there
self.parent.assertTrue(result.transformer_decoder_last_hidden_state is not None )
self.parent.assertTrue(result.pixel_decoder_last_hidden_state is not None )
self.parent.assertTrue(result.encoder_last_hidden_state is not None )
# okay, now we need to check the logits shape
# due to the encoder compression, masks have a //4 spatial size
self.parent.assertEqual(
result.masks_queries_logits.shape , (self.batch_size, self.num_queries, self.min_size // 4, self.max_size // 4) , )
# + 1 for null class
self.parent.assertEqual(
result.class_queries_logits.shape , (self.batch_size, self.num_queries, self.num_labels + 1) )
with torch.no_grad():
__SCREAMING_SNAKE_CASE : List[str] = model(pixel_values=_lowerCamelCase , pixel_mask=_lowerCamelCase )
__SCREAMING_SNAKE_CASE : int = model(_lowerCamelCase )
comm_check_on_output(_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Dict = model(
pixel_values=_lowerCamelCase , pixel_mask=_lowerCamelCase , mask_labels=_lowerCamelCase , class_labels=_lowerCamelCase )
comm_check_on_output(_lowerCamelCase )
self.parent.assertTrue(result.loss is not None )
self.parent.assertEqual(result.loss.shape , torch.Size([1] ) )
@require_torch
class snake_case ( __UpperCAmelCase , __UpperCAmelCase , unittest.TestCase ):
lowerCamelCase__ = (MaskFormerModel, MaskFormerForInstanceSegmentation) if is_torch_available() else ()
lowerCamelCase__ = (
{'''feature-extraction''': MaskFormerModel, '''image-segmentation''': MaskFormerForInstanceSegmentation}
if is_torch_available()
else {}
)
lowerCamelCase__ = False
lowerCamelCase__ = False
lowerCamelCase__ = False
lowerCamelCase__ = False
def SCREAMING_SNAKE_CASE_ ( self :Dict ):
__SCREAMING_SNAKE_CASE : Optional[Any] = MaskFormerModelTester(self )
__SCREAMING_SNAKE_CASE : str = ConfigTester(self , config_class=_lowerCamelCase , has_text_modality=_lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :Union[str, Any] ):
self.config_tester.run_common_tests()
def SCREAMING_SNAKE_CASE_ ( self :Union[str, Any] ):
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Any = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.create_and_check_maskformer_model(_lowerCamelCase , **_lowerCamelCase , output_hidden_states=_lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :List[str] ):
__SCREAMING_SNAKE_CASE : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_maskformer_instance_segmentation_head_model(*_lowerCamelCase )
@unittest.skip(reason='''MaskFormer does not use inputs_embeds''' )
def SCREAMING_SNAKE_CASE_ ( self :List[Any] ):
pass
@unittest.skip(reason='''MaskFormer does not have a get_input_embeddings method''' )
def SCREAMING_SNAKE_CASE_ ( self :int ):
pass
@unittest.skip(reason='''MaskFormer is not a generative model''' )
def SCREAMING_SNAKE_CASE_ ( self :Union[str, Any] ):
pass
@unittest.skip(reason='''MaskFormer does not use token embeddings''' )
def SCREAMING_SNAKE_CASE_ ( self :Optional[int] ):
pass
@require_torch_multi_gpu
@unittest.skip(
reason='''MaskFormer has some layers using `add_module` which doesn\'t work well with `nn.DataParallel`''' )
def SCREAMING_SNAKE_CASE_ ( self :Dict ):
pass
@unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.''' )
def SCREAMING_SNAKE_CASE_ ( self :Dict ):
pass
def SCREAMING_SNAKE_CASE_ ( self :Union[str, Any] ):
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Any = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__SCREAMING_SNAKE_CASE : Tuple = model_class(_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Optional[int] = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
__SCREAMING_SNAKE_CASE : List[str] = [*signature.parameters.keys()]
__SCREAMING_SNAKE_CASE : Optional[Any] = ['''pixel_values''']
self.assertListEqual(arg_names[:1] , _lowerCamelCase )
@slow
def SCREAMING_SNAKE_CASE_ ( self :Optional[Any] ):
for model_name in ["facebook/maskformer-swin-small-coco"]:
__SCREAMING_SNAKE_CASE : Tuple = MaskFormerModel.from_pretrained(_lowerCamelCase )
self.assertIsNotNone(_lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :Union[str, Any] ):
__SCREAMING_SNAKE_CASE : Dict = (self.model_tester.min_size,) * 2
__SCREAMING_SNAKE_CASE : Dict = {
'''pixel_values''': torch.randn((2, 3, *size) , device=_lowerCamelCase ),
'''mask_labels''': torch.randn((2, 1_0, *size) , device=_lowerCamelCase ),
'''class_labels''': torch.zeros(2 , 1_0 , device=_lowerCamelCase ).long(),
}
__SCREAMING_SNAKE_CASE : Dict = MaskFormerForInstanceSegmentation(MaskFormerConfig() ).to(_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Optional[Any] = model(**_lowerCamelCase )
self.assertTrue(outputs.loss is not None )
def SCREAMING_SNAKE_CASE_ ( self :Union[str, Any] ):
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Tuple = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.create_and_check_maskformer_model(_lowerCamelCase , **_lowerCamelCase , output_hidden_states=_lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :Union[str, Any] ):
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Tuple = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__SCREAMING_SNAKE_CASE : Dict = model_class(_lowerCamelCase ).to(_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Optional[int] = model(**_lowerCamelCase , output_attentions=_lowerCamelCase )
self.assertTrue(outputs.attentions is not None )
def SCREAMING_SNAKE_CASE_ ( self :Dict ):
if not self.model_tester.is_training:
return
# only MaskFormerForInstanceSegmentation has the loss
__SCREAMING_SNAKE_CASE : List[Any] = self.all_model_classes[1]
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Dict = self.model_tester.prepare_config_and_inputs()
__SCREAMING_SNAKE_CASE : int = model_class(_lowerCamelCase )
model.to(_lowerCamelCase )
model.train()
__SCREAMING_SNAKE_CASE : Any = model(_lowerCamelCase , mask_labels=_lowerCamelCase , class_labels=_lowerCamelCase ).loss
loss.backward()
def SCREAMING_SNAKE_CASE_ ( self :Optional[int] ):
# only MaskFormerForInstanceSegmentation has the loss
__SCREAMING_SNAKE_CASE : Tuple = self.all_model_classes[1]
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Tuple = self.model_tester.prepare_config_and_inputs()
__SCREAMING_SNAKE_CASE : Tuple = True
__SCREAMING_SNAKE_CASE : List[str] = True
__SCREAMING_SNAKE_CASE : Any = model_class(_lowerCamelCase )
model.to(_lowerCamelCase )
model.train()
__SCREAMING_SNAKE_CASE : Optional[int] = model(_lowerCamelCase , mask_labels=_lowerCamelCase , class_labels=_lowerCamelCase )
__SCREAMING_SNAKE_CASE : List[str] = outputs.encoder_hidden_states[0]
encoder_hidden_states.retain_grad()
__SCREAMING_SNAKE_CASE : str = outputs.pixel_decoder_hidden_states[0]
pixel_decoder_hidden_states.retain_grad()
# we requires_grad=True in inputs_embeds (line 2152), the original implementation don't
__SCREAMING_SNAKE_CASE : Optional[int] = outputs.transformer_decoder_hidden_states[0]
transformer_decoder_hidden_states.retain_grad()
__SCREAMING_SNAKE_CASE : int = outputs.attentions[0]
attentions.retain_grad()
outputs.loss.backward(retain_graph=_lowerCamelCase )
self.assertIsNotNone(encoder_hidden_states.grad )
self.assertIsNotNone(pixel_decoder_hidden_states.grad )
self.assertIsNotNone(transformer_decoder_hidden_states.grad )
self.assertIsNotNone(attentions.grad )
_lowerCamelCase = 1e-4
def lowerCAmelCase_ ( ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : List[Any] = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
return image
@require_vision
@slow
class snake_case ( unittest.TestCase ):
@cached_property
def SCREAMING_SNAKE_CASE_ ( self :str ):
return (
MaskFormerImageProcessor.from_pretrained('''facebook/maskformer-swin-small-coco''' )
if is_vision_available()
else None
)
def SCREAMING_SNAKE_CASE_ ( self :List[Any] ):
__SCREAMING_SNAKE_CASE : Union[str, Any] = MaskFormerModel.from_pretrained('''facebook/maskformer-swin-small-coco''' ).to(_lowerCamelCase )
__SCREAMING_SNAKE_CASE : int = self.default_image_processor
__SCREAMING_SNAKE_CASE : Tuple = prepare_img()
__SCREAMING_SNAKE_CASE : List[Any] = image_processor(_lowerCamelCase , return_tensors='''pt''' ).to(_lowerCamelCase )
__SCREAMING_SNAKE_CASE : int = inputs['''pixel_values'''].shape
# check size is divisible by 32
self.assertTrue((inputs_shape[-1] % 3_2) == 0 and (inputs_shape[-2] % 3_2) == 0 )
# check size
self.assertEqual(_lowerCamelCase , (1, 3, 8_0_0, 1_0_8_8) )
with torch.no_grad():
__SCREAMING_SNAKE_CASE : Tuple = model(**_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Union[str, Any] = torch.tensor(
[[-0.0_4_8_2, 0.9_2_2_8, 0.4_9_5_1], [-0.2_5_4_7, 0.8_0_1_7, 0.8_5_2_7], [-0.0_0_6_9, 0.3_3_8_5, -0.0_0_8_9]] ).to(_lowerCamelCase )
self.assertTrue(
torch.allclose(
outputs.encoder_last_hidden_state[0, 0, :3, :3] , _lowerCamelCase , atol=_lowerCamelCase ) )
__SCREAMING_SNAKE_CASE : Any = torch.tensor(
[[-0.8_4_2_2, -0.8_4_3_4, -0.9_7_1_8], [-1.0_1_4_4, -0.5_5_6_5, -0.4_1_9_5], [-1.0_0_3_8, -0.4_4_8_4, -0.1_9_6_1]] ).to(_lowerCamelCase )
self.assertTrue(
torch.allclose(
outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3] , _lowerCamelCase , atol=_lowerCamelCase ) )
__SCREAMING_SNAKE_CASE : Optional[int] = torch.tensor(
[[0.2_8_5_2, -0.0_1_5_9, 0.9_7_3_5], [0.6_2_5_4, 0.1_8_5_8, 0.8_5_2_9], [-0.0_6_8_0, -0.4_1_1_6, 1.8_4_1_3]] ).to(_lowerCamelCase )
self.assertTrue(
torch.allclose(
outputs.transformer_decoder_last_hidden_state[0, :3, :3] , _lowerCamelCase , atol=_lowerCamelCase ) )
def SCREAMING_SNAKE_CASE_ ( self :Optional[Any] ):
__SCREAMING_SNAKE_CASE : Tuple = (
MaskFormerForInstanceSegmentation.from_pretrained('''facebook/maskformer-swin-small-coco''' )
.to(_lowerCamelCase )
.eval()
)
__SCREAMING_SNAKE_CASE : Union[str, Any] = self.default_image_processor
__SCREAMING_SNAKE_CASE : Tuple = prepare_img()
__SCREAMING_SNAKE_CASE : str = image_processor(_lowerCamelCase , return_tensors='''pt''' ).to(_lowerCamelCase )
__SCREAMING_SNAKE_CASE : List[str] = inputs['''pixel_values'''].shape
# check size is divisible by 32
self.assertTrue((inputs_shape[-1] % 3_2) == 0 and (inputs_shape[-2] % 3_2) == 0 )
# check size
self.assertEqual(_lowerCamelCase , (1, 3, 8_0_0, 1_0_8_8) )
with torch.no_grad():
__SCREAMING_SNAKE_CASE : str = model(**_lowerCamelCase )
# masks_queries_logits
__SCREAMING_SNAKE_CASE : Any = outputs.masks_queries_logits
self.assertEqual(
masks_queries_logits.shape , (1, model.config.decoder_config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4) , )
__SCREAMING_SNAKE_CASE : List[Any] = [
[-1.3_7_3_7_1_2_4, -1.7_7_2_4_9_3_7, -1.9_3_6_4_2_3_3],
[-1.5_9_7_7_2_8_1, -1.9_8_6_7_9_3_9, -2.1_5_2_3_6_9_5],
[-1.5_7_9_5_3_9_8, -1.9_2_6_9_8_3_2, -2.0_9_3_9_4_2],
]
__SCREAMING_SNAKE_CASE : Any = torch.tensor(_lowerCamelCase ).to(_lowerCamelCase )
self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3] , _lowerCamelCase , atol=_lowerCamelCase ) )
# class_queries_logits
__SCREAMING_SNAKE_CASE : List[Any] = outputs.class_queries_logits
self.assertEqual(
class_queries_logits.shape , (1, model.config.decoder_config.num_queries, model.config.num_labels + 1) )
__SCREAMING_SNAKE_CASE : List[str] = torch.tensor(
[
[1.65_12e00, -5.25_72e00, -3.35_19e00],
[3.61_69e-02, -5.90_25e00, -2.93_13e00],
[1.07_66e-04, -7.76_30e00, -5.12_63e00],
] ).to(_lowerCamelCase )
self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3] , _lowerCamelCase , atol=_lowerCamelCase ) )
def SCREAMING_SNAKE_CASE_ ( self :Any ):
__SCREAMING_SNAKE_CASE : Tuple = (
MaskFormerForInstanceSegmentation.from_pretrained('''facebook/maskformer-resnet101-coco-stuff''' )
.to(_lowerCamelCase )
.eval()
)
__SCREAMING_SNAKE_CASE : Tuple = self.default_image_processor
__SCREAMING_SNAKE_CASE : str = prepare_img()
__SCREAMING_SNAKE_CASE : List[Any] = image_processor(_lowerCamelCase , return_tensors='''pt''' ).to(_lowerCamelCase )
__SCREAMING_SNAKE_CASE : List[Any] = inputs['''pixel_values'''].shape
# check size is divisible by 32
self.assertTrue((inputs_shape[-1] % 3_2) == 0 and (inputs_shape[-2] % 3_2) == 0 )
# check size
self.assertEqual(_lowerCamelCase , (1, 3, 8_0_0, 1_0_8_8) )
with torch.no_grad():
__SCREAMING_SNAKE_CASE : List[Any] = model(**_lowerCamelCase )
# masks_queries_logits
__SCREAMING_SNAKE_CASE : Union[str, Any] = outputs.masks_queries_logits
self.assertEqual(
masks_queries_logits.shape , (1, model.config.decoder_config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4) , )
__SCREAMING_SNAKE_CASE : List[str] = [[-0.9_0_4_6, -2.6_3_6_6, -4.6_0_6_2], [-3.4_1_7_9, -5.7_8_9_0, -8.8_0_5_7], [-4.9_1_7_9, -7.6_5_6_0, -1_0.7_7_1_1]]
__SCREAMING_SNAKE_CASE : Any = torch.tensor(_lowerCamelCase ).to(_lowerCamelCase )
self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3] , _lowerCamelCase , atol=_lowerCamelCase ) )
# class_queries_logits
__SCREAMING_SNAKE_CASE : int = outputs.class_queries_logits
self.assertEqual(
class_queries_logits.shape , (1, model.config.decoder_config.num_queries, model.config.num_labels + 1) )
__SCREAMING_SNAKE_CASE : List[str] = torch.tensor(
[[4.7_1_8_8, -3.2_5_8_5, -2.8_8_5_7], [6.6_8_7_1, -2.9_1_8_1, -1.2_4_8_7], [7.2_4_4_9, -2.2_7_6_4, -2.1_8_7_4]] ).to(_lowerCamelCase )
self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3] , _lowerCamelCase , atol=_lowerCamelCase ) )
def SCREAMING_SNAKE_CASE_ ( self :int ):
__SCREAMING_SNAKE_CASE : int = (
MaskFormerForInstanceSegmentation.from_pretrained('''facebook/maskformer-swin-small-coco''' )
.to(_lowerCamelCase )
.eval()
)
__SCREAMING_SNAKE_CASE : Any = self.default_image_processor
__SCREAMING_SNAKE_CASE : int = image_processor(
[np.zeros((3, 8_0_0, 1_3_3_3) ), np.zeros((3, 8_0_0, 1_3_3_3) )] , segmentation_maps=[np.zeros((3_8_4, 3_8_4) ).astype(np.floataa ), np.zeros((3_8_4, 3_8_4) ).astype(np.floataa )] , return_tensors='''pt''' , )
__SCREAMING_SNAKE_CASE : Dict = inputs['''pixel_values'''].to(_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Dict = [el.to(_lowerCamelCase ) for el in inputs['''mask_labels''']]
__SCREAMING_SNAKE_CASE : str = [el.to(_lowerCamelCase ) for el in inputs['''class_labels''']]
with torch.no_grad():
__SCREAMING_SNAKE_CASE : int = model(**_lowerCamelCase )
self.assertTrue(outputs.loss is not None )
| 674 | 0 |
lowercase_ : int = {}
def A__ ( snake_case_ : int , snake_case_ : int , snake_case_ : int ):
if late == 3 or absent == 2:
return 0
# if we have no days left, and have not failed any other rules,
# we have a prize string
if days == 0:
return 1
# No easy solution, so now we need to do the recursive calculation
# First, check if the combination is already in the cache, and
# if yes, return the stored value from there since we already
# know the number of possible prize strings from this point on
SCREAMING_SNAKE_CASE__: List[Any]= (days, absent, late)
if key in cache:
return cache[key]
# now we calculate the three possible ways that can unfold from
# this point on, depending on our attendance today
# 1) if we are late (but not absent), the "absent" counter stays as
# it is, but the "late" counter increases by one
SCREAMING_SNAKE_CASE__: Optional[Any]= _calculate(days - 1 , lowercase_ , late + 1 )
# 2) if we are absent, the "absent" counter increases by 1, and the
# "late" counter resets to 0
SCREAMING_SNAKE_CASE__: Optional[Any]= _calculate(days - 1 , absent + 1 , 0 )
# 3) if we are on time, this resets the "late" counter and keeps the
# absent counter
SCREAMING_SNAKE_CASE__: List[str]= _calculate(days - 1 , lowercase_ , 0 )
SCREAMING_SNAKE_CASE__: Optional[int]= state_late + state_absent + state_ontime
SCREAMING_SNAKE_CASE__: Any= prizestrings
return prizestrings
def A__ ( snake_case_ : int = 30 ):
return _calculate(lowercase_ , absent=0 , late=0 )
if __name__ == "__main__":
print(solution())
| 64 |
"""simple docstring"""
import contextlib
import importlib
import io
import unittest
import transformers
# Try to import everything from transformers to ensure every object can be loaded.
from transformers import * # noqa F406
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, require_flax, require_tf, require_torch
from transformers.utils import ContextManagers, find_labels, is_flax_available, is_tf_available, is_torch_available
if is_torch_available():
from transformers import BertForPreTraining, BertForQuestionAnswering, BertForSequenceClassification
if is_tf_available():
from transformers import TFBertForPreTraining, TFBertForQuestionAnswering, TFBertForSequenceClassification
if is_flax_available():
from transformers import FlaxBertForPreTraining, FlaxBertForQuestionAnswering, FlaxBertForSequenceClassification
_lowerCamelCase = DUMMY_UNKNOWN_IDENTIFIER
# An actual model hosted on huggingface.co
_lowerCamelCase = '''main'''
# Default branch name
_lowerCamelCase = '''f2c752cfc5c0ab6f4bdec59acea69eefbee381c2'''
# One particular commit (not the top of `main`)
_lowerCamelCase = '''aaaaaaa'''
# This commit does not exist, so we should 404.
_lowerCamelCase = '''d9e9f15bc825e4b2c9249e9578f884bbcb5e3684'''
# Sha-1 of config.json on the top of `main`, for checking purposes
_lowerCamelCase = '''4b243c475af8d0a7754e87d7d096c92e5199ec2fe168a2ee7998e3b8e9bcb1d3'''
@contextlib.contextmanager
def lowerCAmelCase_ ( ):
'''simple docstring'''
print('''Welcome!''' )
yield
print('''Bye!''' )
@contextlib.contextmanager
def lowerCAmelCase_ ( ):
'''simple docstring'''
print('''Bonjour!''' )
yield
print('''Au revoir!''' )
class snake_case ( unittest.TestCase ):
def SCREAMING_SNAKE_CASE_ ( self :Union[str, Any] ):
# If the spec is missing, importlib would not be able to import the module dynamically.
assert transformers.__spec__ is not None
assert importlib.util.find_spec('''transformers''' ) is not None
class snake_case ( unittest.TestCase ):
@unittest.mock.patch('''sys.stdout''' , new_callable=io.StringIO )
def SCREAMING_SNAKE_CASE_ ( self :Dict , _lowerCamelCase :Optional[int] ):
with ContextManagers([] ):
print('''Transformers are awesome!''' )
# The print statement adds a new line at the end of the output
self.assertEqual(mock_stdout.getvalue() , '''Transformers are awesome!\n''' )
@unittest.mock.patch('''sys.stdout''' , new_callable=io.StringIO )
def SCREAMING_SNAKE_CASE_ ( self :str , _lowerCamelCase :Optional[int] ):
with ContextManagers([context_en()] ):
print('''Transformers are awesome!''' )
# The output should be wrapped with an English welcome and goodbye
self.assertEqual(mock_stdout.getvalue() , '''Welcome!\nTransformers are awesome!\nBye!\n''' )
@unittest.mock.patch('''sys.stdout''' , new_callable=io.StringIO )
def SCREAMING_SNAKE_CASE_ ( self :Dict , _lowerCamelCase :List[str] ):
with ContextManagers([context_fr(), context_en()] ):
print('''Transformers are awesome!''' )
# The output should be wrapped with an English and French welcome and goodbye
self.assertEqual(mock_stdout.getvalue() , '''Bonjour!\nWelcome!\nTransformers are awesome!\nBye!\nAu revoir!\n''' )
@require_torch
def SCREAMING_SNAKE_CASE_ ( self :List[str] ):
self.assertEqual(find_labels(_lowerCamelCase ) , ['''labels'''] )
self.assertEqual(find_labels(_lowerCamelCase ) , ['''labels''', '''next_sentence_label'''] )
self.assertEqual(find_labels(_lowerCamelCase ) , ['''start_positions''', '''end_positions'''] )
class snake_case ( __UpperCAmelCase ):
pass
self.assertEqual(find_labels(_lowerCamelCase ) , ['''labels'''] )
@require_tf
def SCREAMING_SNAKE_CASE_ ( self :int ):
self.assertEqual(find_labels(_lowerCamelCase ) , ['''labels'''] )
self.assertEqual(find_labels(_lowerCamelCase ) , ['''labels''', '''next_sentence_label'''] )
self.assertEqual(find_labels(_lowerCamelCase ) , ['''start_positions''', '''end_positions'''] )
class snake_case ( __UpperCAmelCase ):
pass
self.assertEqual(find_labels(_lowerCamelCase ) , ['''labels'''] )
@require_flax
def SCREAMING_SNAKE_CASE_ ( self :Dict ):
# Flax models don't have labels
self.assertEqual(find_labels(_lowerCamelCase ) , [] )
self.assertEqual(find_labels(_lowerCamelCase ) , [] )
self.assertEqual(find_labels(_lowerCamelCase ) , [] )
class snake_case ( __UpperCAmelCase ):
pass
self.assertEqual(find_labels(_lowerCamelCase ) , [] )
| 674 | 0 |
import numpy as np
import torch
from torch.utils.data import DataLoader
from accelerate.utils.dataclasses import DistributedType
class lowerCamelCase_ :
'''simple docstring'''
def __init__( self , snake_case_=2 , snake_case_=3 , snake_case_=6_4 , snake_case_=None ) -> Any:
'''simple docstring'''
__lowercase = np.random.default_rng(_lowerCamelCase )
__lowercase = length
__lowercase = rng.normal(size=(length,) ).astype(np.floataa )
__lowercase = a * self.x + b + rng.normal(scale=0.1 , size=(length,) ).astype(np.floataa )
def __len__( self ) -> Optional[Any]:
'''simple docstring'''
return self.length
def __getitem__( self , snake_case_ ) -> str:
'''simple docstring'''
return {"x": self.x[i], "y": self.y[i]}
class lowerCamelCase_ ( torch.nn.Module ):
'''simple docstring'''
def __init__( self , snake_case_=0 , snake_case_=0 , snake_case_=False ) -> List[Any]:
'''simple docstring'''
super().__init__()
__lowercase = torch.nn.Parameter(torch.tensor([2, 3] ).float() )
__lowercase = torch.nn.Parameter(torch.tensor([2, 3] ).float() )
__lowercase = True
def A ( self , snake_case_=None ) -> str:
'''simple docstring'''
if self.first_batch:
print(F'Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}' )
__lowercase = False
return x * self.a[0] + self.b[0]
class lowerCamelCase_ ( torch.nn.Module ):
'''simple docstring'''
def __init__( self , snake_case_=0 , snake_case_=0 , snake_case_=False ) -> Tuple:
'''simple docstring'''
super().__init__()
__lowercase = torch.nn.Parameter(torch.tensor(_lowerCamelCase ).float() )
__lowercase = torch.nn.Parameter(torch.tensor(_lowerCamelCase ).float() )
__lowercase = True
def A ( self , snake_case_=None ) -> List[Any]:
'''simple docstring'''
if self.first_batch:
print(F'Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}' )
__lowercase = False
return x * self.a + self.b
def lowercase_ ( _UpperCamelCase , _UpperCamelCase = 16 ):
'''simple docstring'''
from datasets import load_dataset
from transformers import AutoTokenizer
__lowercase = AutoTokenizer.from_pretrained('''bert-base-cased''' )
__lowercase = {'''train''': '''tests/test_samples/MRPC/train.csv''', '''validation''': '''tests/test_samples/MRPC/dev.csv'''}
__lowercase = load_dataset('''csv''' , data_files=lowercase_ )
__lowercase = datasets['''train'''].unique('''label''' )
__lowercase = {v: i for i, v in enumerate(lowercase_ )}
def tokenize_function(_UpperCamelCase ):
# max_length=None => use the model max length (it's actually the default)
__lowercase = tokenizer(
examples['''sentence1'''] , examples['''sentence2'''] , truncation=lowercase_ , max_length=lowercase_ , padding='''max_length''' )
if "label" in examples:
__lowercase = [label_to_id[l] for l in examples['''label''']]
return outputs
# Apply the method we just defined to all the examples in all the splits of the dataset
__lowercase = datasets.map(
lowercase_ , batched=lowercase_ , remove_columns=['''sentence1''', '''sentence2''', '''label'''] , )
def collate_fn(_UpperCamelCase ):
# On TPU it's best to pad everything to the same length or training will be very slow.
if accelerator.distributed_type == DistributedType.TPU:
return tokenizer.pad(lowercase_ , padding='''max_length''' , max_length=1_28 , return_tensors='''pt''' )
return tokenizer.pad(lowercase_ , padding='''longest''' , return_tensors='''pt''' )
# Instantiate dataloaders.
__lowercase = DataLoader(tokenized_datasets['''train'''] , shuffle=lowercase_ , collate_fn=lowercase_ , batch_size=2 )
__lowercase = DataLoader(tokenized_datasets['''validation'''] , shuffle=lowercase_ , collate_fn=lowercase_ , batch_size=1 )
return train_dataloader, eval_dataloader
| 639 |
"""simple docstring"""
import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import YolosImageProcessor
class snake_case ( unittest.TestCase ):
def __init__( self :List[Any] , _lowerCamelCase :Dict , _lowerCamelCase :Tuple=7 , _lowerCamelCase :Dict=3 , _lowerCamelCase :Optional[Any]=3_0 , _lowerCamelCase :List[str]=4_0_0 , _lowerCamelCase :Union[str, Any]=True , _lowerCamelCase :Union[str, Any]=None , _lowerCamelCase :List[Any]=True , _lowerCamelCase :Any=[0.5, 0.5, 0.5] , _lowerCamelCase :Dict=[0.5, 0.5, 0.5] , _lowerCamelCase :Dict=True , _lowerCamelCase :str=1 / 2_5_5 , _lowerCamelCase :Union[str, Any]=True , ):
# by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
__SCREAMING_SNAKE_CASE : Optional[Any] = size if size is not None else {'''shortest_edge''': 1_8, '''longest_edge''': 1_3_3_3}
__SCREAMING_SNAKE_CASE : List[str] = parent
__SCREAMING_SNAKE_CASE : Dict = batch_size
__SCREAMING_SNAKE_CASE : str = num_channels
__SCREAMING_SNAKE_CASE : Union[str, Any] = min_resolution
__SCREAMING_SNAKE_CASE : Union[str, Any] = max_resolution
__SCREAMING_SNAKE_CASE : Tuple = do_resize
__SCREAMING_SNAKE_CASE : Union[str, Any] = size
__SCREAMING_SNAKE_CASE : int = do_normalize
__SCREAMING_SNAKE_CASE : List[Any] = image_mean
__SCREAMING_SNAKE_CASE : Tuple = image_std
__SCREAMING_SNAKE_CASE : Dict = do_rescale
__SCREAMING_SNAKE_CASE : Optional[int] = rescale_factor
__SCREAMING_SNAKE_CASE : List[Any] = do_pad
def SCREAMING_SNAKE_CASE_ ( self :Optional[Any] ):
return {
"do_resize": self.do_resize,
"size": self.size,
"do_normalize": self.do_normalize,
"image_mean": self.image_mean,
"image_std": self.image_std,
"do_rescale": self.do_rescale,
"rescale_factor": self.rescale_factor,
"do_pad": self.do_pad,
}
def SCREAMING_SNAKE_CASE_ ( self :Tuple , _lowerCamelCase :Dict , _lowerCamelCase :List[Any]=False ):
if not batched:
__SCREAMING_SNAKE_CASE : str = image_inputs[0]
if isinstance(_lowerCamelCase , Image.Image ):
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Optional[int] = image.size
else:
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Dict = image.shape[1], image.shape[2]
if w < h:
__SCREAMING_SNAKE_CASE : str = int(self.size['''shortest_edge'''] * h / w )
__SCREAMING_SNAKE_CASE : int = self.size['''shortest_edge''']
elif w > h:
__SCREAMING_SNAKE_CASE : Optional[Any] = self.size['''shortest_edge''']
__SCREAMING_SNAKE_CASE : int = int(self.size['''shortest_edge'''] * w / h )
else:
__SCREAMING_SNAKE_CASE : List[str] = self.size['''shortest_edge''']
__SCREAMING_SNAKE_CASE : List[str] = self.size['''shortest_edge''']
else:
__SCREAMING_SNAKE_CASE : Optional[Any] = []
for image in image_inputs:
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Optional[Any] = self.get_expected_values([image] )
expected_values.append((expected_height, expected_width) )
__SCREAMING_SNAKE_CASE : Optional[int] = max(_lowerCamelCase , key=lambda _lowerCamelCase : item[0] )[0]
__SCREAMING_SNAKE_CASE : int = max(_lowerCamelCase , key=lambda _lowerCamelCase : item[1] )[1]
return expected_height, expected_width
@require_torch
@require_vision
class snake_case ( __UpperCAmelCase , unittest.TestCase ):
lowerCamelCase__ = YolosImageProcessor if is_vision_available() else None
def SCREAMING_SNAKE_CASE_ ( self :Optional[int] ):
__SCREAMING_SNAKE_CASE : List[Any] = YolosImageProcessingTester(self )
@property
def SCREAMING_SNAKE_CASE_ ( self :Optional[int] ):
return self.image_processor_tester.prepare_image_processor_dict()
def SCREAMING_SNAKE_CASE_ ( self :Tuple ):
__SCREAMING_SNAKE_CASE : Any = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(_lowerCamelCase , '''image_mean''' ) )
self.assertTrue(hasattr(_lowerCamelCase , '''image_std''' ) )
self.assertTrue(hasattr(_lowerCamelCase , '''do_normalize''' ) )
self.assertTrue(hasattr(_lowerCamelCase , '''do_resize''' ) )
self.assertTrue(hasattr(_lowerCamelCase , '''size''' ) )
def SCREAMING_SNAKE_CASE_ ( self :Tuple ):
__SCREAMING_SNAKE_CASE : Union[str, Any] = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {'''shortest_edge''': 1_8, '''longest_edge''': 1_3_3_3} )
self.assertEqual(image_processor.do_pad , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : Tuple = self.image_processing_class.from_dict(
self.image_processor_dict , size=4_2 , max_size=8_4 , pad_and_return_pixel_mask=_lowerCamelCase )
self.assertEqual(image_processor.size , {'''shortest_edge''': 4_2, '''longest_edge''': 8_4} )
self.assertEqual(image_processor.do_pad , _lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :List[str] ):
pass
def SCREAMING_SNAKE_CASE_ ( self :int ):
# Initialize image_processing
__SCREAMING_SNAKE_CASE : Optional[int] = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
__SCREAMING_SNAKE_CASE : List[str] = prepare_image_inputs(self.image_processor_tester , equal_resolution=_lowerCamelCase )
for image in image_inputs:
self.assertIsInstance(_lowerCamelCase , Image.Image )
# Test not batched input
__SCREAMING_SNAKE_CASE : str = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Dict = self.image_processor_tester.get_expected_values(_lowerCamelCase )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Tuple = self.image_processor_tester.get_expected_values(_lowerCamelCase , batched=_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Union[str, Any] = image_processing(_lowerCamelCase , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
def SCREAMING_SNAKE_CASE_ ( self :Optional[int] ):
# Initialize image_processing
__SCREAMING_SNAKE_CASE : Optional[Any] = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
__SCREAMING_SNAKE_CASE : Optional[int] = prepare_image_inputs(self.image_processor_tester , equal_resolution=_lowerCamelCase , numpify=_lowerCamelCase )
for image in image_inputs:
self.assertIsInstance(_lowerCamelCase , np.ndarray )
# Test not batched input
__SCREAMING_SNAKE_CASE : Dict = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : List[str] = self.image_processor_tester.get_expected_values(_lowerCamelCase )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
__SCREAMING_SNAKE_CASE : List[Any] = image_processing(_lowerCamelCase , return_tensors='''pt''' ).pixel_values
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Any = self.image_processor_tester.get_expected_values(_lowerCamelCase , batched=_lowerCamelCase )
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
def SCREAMING_SNAKE_CASE_ ( self :Any ):
# Initialize image_processing
__SCREAMING_SNAKE_CASE : str = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
__SCREAMING_SNAKE_CASE : Any = prepare_image_inputs(self.image_processor_tester , equal_resolution=_lowerCamelCase , torchify=_lowerCamelCase )
for image in image_inputs:
self.assertIsInstance(_lowerCamelCase , torch.Tensor )
# Test not batched input
__SCREAMING_SNAKE_CASE : List[str] = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Optional[Any] = self.image_processor_tester.get_expected_values(_lowerCamelCase )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
__SCREAMING_SNAKE_CASE : Optional[int] = image_processing(_lowerCamelCase , return_tensors='''pt''' ).pixel_values
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : List[Any] = self.image_processor_tester.get_expected_values(_lowerCamelCase , batched=_lowerCamelCase )
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
def SCREAMING_SNAKE_CASE_ ( self :List[str] ):
# Initialize image_processings
__SCREAMING_SNAKE_CASE : Dict = self.image_processing_class(**self.image_processor_dict )
__SCREAMING_SNAKE_CASE : Tuple = self.image_processing_class(do_resize=_lowerCamelCase , do_normalize=_lowerCamelCase , do_rescale=_lowerCamelCase )
# create random PyTorch tensors
__SCREAMING_SNAKE_CASE : int = prepare_image_inputs(self.image_processor_tester , equal_resolution=_lowerCamelCase , torchify=_lowerCamelCase )
for image in image_inputs:
self.assertIsInstance(_lowerCamelCase , torch.Tensor )
# Test whether the method "pad" and calling the image processor return the same tensors
__SCREAMING_SNAKE_CASE : Union[str, Any] = image_processing_a.pad(_lowerCamelCase , return_tensors='''pt''' )
__SCREAMING_SNAKE_CASE : Union[str, Any] = image_processing_a(_lowerCamelCase , return_tensors='''pt''' )
self.assertTrue(
torch.allclose(encoded_images_with_method['''pixel_values'''] , encoded_images['''pixel_values'''] , atol=1e-4 ) )
@slow
def SCREAMING_SNAKE_CASE_ ( self :int ):
# prepare image and target
__SCREAMING_SNAKE_CASE : Tuple = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
with open('''./tests/fixtures/tests_samples/COCO/coco_annotations.txt''' , '''r''' ) as f:
__SCREAMING_SNAKE_CASE : Tuple = json.loads(f.read() )
__SCREAMING_SNAKE_CASE : Union[str, Any] = {'''image_id''': 3_9_7_6_9, '''annotations''': target}
# encode them
__SCREAMING_SNAKE_CASE : List[Any] = YolosImageProcessor.from_pretrained('''hustvl/yolos-small''' )
__SCREAMING_SNAKE_CASE : Optional[Any] = image_processing(images=_lowerCamelCase , annotations=_lowerCamelCase , return_tensors='''pt''' )
# verify pixel values
__SCREAMING_SNAKE_CASE : Optional[int] = torch.Size([1, 3, 8_0_0, 1_0_6_6] )
self.assertEqual(encoding['''pixel_values'''].shape , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : List[str] = torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1] )
self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3] , _lowerCamelCase , atol=1e-4 ) )
# verify area
__SCREAMING_SNAKE_CASE : Union[str, Any] = torch.tensor([5_8_8_7.9_6_0_0, 1_1_2_5_0.2_0_6_1, 4_8_9_3_5_3.8_4_3_8, 8_3_7_1_2_2.7_5_0_0, 1_4_7_9_6_7.5_1_5_6, 1_6_5_7_3_2.3_4_3_8] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area'''] , _lowerCamelCase ) )
# verify boxes
__SCREAMING_SNAKE_CASE : List[str] = torch.Size([6, 4] )
self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : Any = torch.tensor([0.5_5_0_3, 0.2_7_6_5, 0.0_6_0_4, 0.2_2_1_5] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0] , _lowerCamelCase , atol=1e-3 ) )
# verify image_id
__SCREAMING_SNAKE_CASE : Optional[Any] = torch.tensor([3_9_7_6_9] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id'''] , _lowerCamelCase ) )
# verify is_crowd
__SCREAMING_SNAKE_CASE : str = torch.tensor([0, 0, 0, 0, 0, 0] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd'''] , _lowerCamelCase ) )
# verify class_labels
__SCREAMING_SNAKE_CASE : Dict = torch.tensor([7_5, 7_5, 6_3, 6_5, 1_7, 1_7] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels'''] , _lowerCamelCase ) )
# verify orig_size
__SCREAMING_SNAKE_CASE : Union[str, Any] = torch.tensor([4_8_0, 6_4_0] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size'''] , _lowerCamelCase ) )
# verify size
__SCREAMING_SNAKE_CASE : List[str] = torch.tensor([8_0_0, 1_0_6_6] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size'''] , _lowerCamelCase ) )
@slow
def SCREAMING_SNAKE_CASE_ ( self :Optional[int] ):
# prepare image, target and masks_path
__SCREAMING_SNAKE_CASE : List[str] = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
with open('''./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt''' , '''r''' ) as f:
__SCREAMING_SNAKE_CASE : Union[str, Any] = json.loads(f.read() )
__SCREAMING_SNAKE_CASE : Dict = {'''file_name''': '''000000039769.png''', '''image_id''': 3_9_7_6_9, '''segments_info''': target}
__SCREAMING_SNAKE_CASE : Optional[int] = pathlib.Path('''./tests/fixtures/tests_samples/COCO/coco_panoptic''' )
# encode them
__SCREAMING_SNAKE_CASE : Any = YolosImageProcessor(format='''coco_panoptic''' )
__SCREAMING_SNAKE_CASE : Dict = image_processing(images=_lowerCamelCase , annotations=_lowerCamelCase , masks_path=_lowerCamelCase , return_tensors='''pt''' )
# verify pixel values
__SCREAMING_SNAKE_CASE : str = torch.Size([1, 3, 8_0_0, 1_0_6_6] )
self.assertEqual(encoding['''pixel_values'''].shape , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : str = torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1] )
self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3] , _lowerCamelCase , atol=1e-4 ) )
# verify area
__SCREAMING_SNAKE_CASE : Any = torch.tensor([1_4_7_9_7_9.6_8_7_5, 1_6_5_5_2_7.0_4_6_9, 4_8_4_6_3_8.5_9_3_8, 1_1_2_9_2.9_3_7_5, 5_8_7_9.6_5_6_2, 7_6_3_4.1_1_4_7] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area'''] , _lowerCamelCase ) )
# verify boxes
__SCREAMING_SNAKE_CASE : List[str] = torch.Size([6, 4] )
self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : int = torch.tensor([0.2_6_2_5, 0.5_4_3_7, 0.4_6_8_8, 0.8_6_2_5] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0] , _lowerCamelCase , atol=1e-3 ) )
# verify image_id
__SCREAMING_SNAKE_CASE : Dict = torch.tensor([3_9_7_6_9] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id'''] , _lowerCamelCase ) )
# verify is_crowd
__SCREAMING_SNAKE_CASE : List[Any] = torch.tensor([0, 0, 0, 0, 0, 0] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd'''] , _lowerCamelCase ) )
# verify class_labels
__SCREAMING_SNAKE_CASE : Optional[Any] = torch.tensor([1_7, 1_7, 6_3, 7_5, 7_5, 9_3] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels'''] , _lowerCamelCase ) )
# verify masks
__SCREAMING_SNAKE_CASE : Optional[Any] = 8_2_2_8_7_3
self.assertEqual(encoding['''labels'''][0]['''masks'''].sum().item() , _lowerCamelCase )
# verify orig_size
__SCREAMING_SNAKE_CASE : List[str] = torch.tensor([4_8_0, 6_4_0] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size'''] , _lowerCamelCase ) )
# verify size
__SCREAMING_SNAKE_CASE : Any = torch.tensor([8_0_0, 1_0_6_6] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size'''] , _lowerCamelCase ) )
| 674 | 0 |
from __future__ import annotations
from math import pi, sqrt
def lowerCAmelCase_ ( lowercase: float , lowercase: float ) -> Union[str, Any]:
'''simple docstring'''
if inductance <= 0:
raise ValueError('''Inductance cannot be 0 or negative''' )
elif capacitance <= 0:
raise ValueError('''Capacitance cannot be 0 or negative''' )
else:
return (
"Resonant frequency",
float(1 / (2 * pi * (sqrt(inductance * capacitance ))) ),
)
if __name__ == "__main__":
import doctest
doctest.testmod() | 271 |
"""simple docstring"""
from __future__ import annotations
def lowerCAmelCase_ ( lowercase_ : int ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : Optional[int] = str(lowercase_ )
return len(lowercase_ ) == 9 and set(lowercase_ ) == set('''123456789''' )
def lowerCAmelCase_ ( ):
'''simple docstring'''
for base_num in range(9999 , 4999 , -1 ):
__SCREAMING_SNAKE_CASE : List[str] = 10_0002 * base_num
if is_9_pandigital(lowercase_ ):
return candidate
for base_num in range(333 , 99 , -1 ):
__SCREAMING_SNAKE_CASE : List[Any] = 100_2003 * base_num
if is_9_pandigital(lowercase_ ):
return candidate
return None
if __name__ == "__main__":
print(f'{solution() = }')
| 674 | 0 |
from __future__ import annotations
from typing import Any
class _lowerCAmelCase ( __UpperCAmelCase ):
"""simple docstring"""
pass
class _lowerCAmelCase :
"""simple docstring"""
def __init__( self : List[Any] , SCREAMING_SNAKE_CASE__ : Any ):
"""simple docstring"""
UpperCamelCase = data
UpperCamelCase = None
def __iter__( self : Union[str, Any] ):
"""simple docstring"""
UpperCamelCase = self
UpperCamelCase = []
while node:
if node in visited:
raise ContainsLoopError
visited.append(_lowerCamelCase )
yield node.data
UpperCamelCase = node.next_node
@property
def __lowerCAmelCase ( self : Any ):
"""simple docstring"""
try:
list(self )
return False
except ContainsLoopError:
return True
if __name__ == "__main__":
_snake_case = Node(1)
_snake_case = Node(2)
_snake_case = Node(3)
_snake_case = Node(4)
print(root_node.has_loop) # False
_snake_case = root_node.next_node
print(root_node.has_loop) # True
_snake_case = Node(5)
_snake_case = Node(6)
_snake_case = Node(5)
_snake_case = Node(6)
print(root_node.has_loop) # False
_snake_case = Node(1)
print(root_node.has_loop) # False
| 282 |
"""simple docstring"""
import json
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
from transformers import (
CONFIG_MAPPING,
IMAGE_PROCESSOR_MAPPING,
AutoConfig,
AutoImageProcessor,
CLIPConfig,
CLIPImageProcessor,
)
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER
sys.path.append(str(Path(__file__).parent.parent.parent.parent / '''utils'''))
from test_module.custom_configuration import CustomConfig # noqa E402
from test_module.custom_image_processing import CustomImageProcessor # noqa E402
class snake_case ( unittest.TestCase ):
def SCREAMING_SNAKE_CASE_ ( self :int ):
__SCREAMING_SNAKE_CASE : Tuple = 0
def SCREAMING_SNAKE_CASE_ ( self :Any ):
__SCREAMING_SNAKE_CASE : Optional[Any] = AutoImageProcessor.from_pretrained('''openai/clip-vit-base-patch32''' )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :int ):
with tempfile.TemporaryDirectory() as tmpdirname:
__SCREAMING_SNAKE_CASE : Optional[Any] = Path(_lowerCamelCase ) / '''preprocessor_config.json'''
__SCREAMING_SNAKE_CASE : Dict = Path(_lowerCamelCase ) / '''config.json'''
json.dump(
{'''image_processor_type''': '''CLIPImageProcessor''', '''processor_class''': '''CLIPProcessor'''} , open(_lowerCamelCase , '''w''' ) , )
json.dump({'''model_type''': '''clip'''} , open(_lowerCamelCase , '''w''' ) )
__SCREAMING_SNAKE_CASE : Dict = AutoImageProcessor.from_pretrained(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :Optional[int] ):
# Ensure we can load the image processor from the feature extractor config
with tempfile.TemporaryDirectory() as tmpdirname:
__SCREAMING_SNAKE_CASE : Tuple = Path(_lowerCamelCase ) / '''preprocessor_config.json'''
__SCREAMING_SNAKE_CASE : Union[str, Any] = Path(_lowerCamelCase ) / '''config.json'''
json.dump(
{'''feature_extractor_type''': '''CLIPFeatureExtractor''', '''processor_class''': '''CLIPProcessor'''} , open(_lowerCamelCase , '''w''' ) , )
json.dump({'''model_type''': '''clip'''} , open(_lowerCamelCase , '''w''' ) )
__SCREAMING_SNAKE_CASE : int = AutoImageProcessor.from_pretrained(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :int ):
with tempfile.TemporaryDirectory() as tmpdirname:
__SCREAMING_SNAKE_CASE : str = CLIPConfig()
# Create a dummy config file with image_proceesor_type
__SCREAMING_SNAKE_CASE : Tuple = Path(_lowerCamelCase ) / '''preprocessor_config.json'''
__SCREAMING_SNAKE_CASE : Any = Path(_lowerCamelCase ) / '''config.json'''
json.dump(
{'''image_processor_type''': '''CLIPImageProcessor''', '''processor_class''': '''CLIPProcessor'''} , open(_lowerCamelCase , '''w''' ) , )
json.dump({'''model_type''': '''clip'''} , open(_lowerCamelCase , '''w''' ) )
# remove image_processor_type to make sure config.json alone is enough to load image processor locally
__SCREAMING_SNAKE_CASE : int = AutoImageProcessor.from_pretrained(_lowerCamelCase ).to_dict()
config_dict.pop('''image_processor_type''' )
__SCREAMING_SNAKE_CASE : Union[str, Any] = CLIPImageProcessor(**_lowerCamelCase )
# save in new folder
model_config.save_pretrained(_lowerCamelCase )
config.save_pretrained(_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Tuple = AutoImageProcessor.from_pretrained(_lowerCamelCase )
# make sure private variable is not incorrectly saved
__SCREAMING_SNAKE_CASE : Tuple = json.loads(config.to_json_string() )
self.assertTrue('''_processor_class''' not in dict_as_saved )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :Tuple ):
with tempfile.TemporaryDirectory() as tmpdirname:
__SCREAMING_SNAKE_CASE : Dict = Path(_lowerCamelCase ) / '''preprocessor_config.json'''
json.dump(
{'''image_processor_type''': '''CLIPImageProcessor''', '''processor_class''': '''CLIPProcessor'''} , open(_lowerCamelCase , '''w''' ) , )
__SCREAMING_SNAKE_CASE : Optional[Any] = AutoImageProcessor.from_pretrained(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :Optional[int] ):
with self.assertRaisesRegex(
_lowerCamelCase , '''clip-base is not a local folder and is not a valid model identifier''' ):
__SCREAMING_SNAKE_CASE : Tuple = AutoImageProcessor.from_pretrained('''clip-base''' )
def SCREAMING_SNAKE_CASE_ ( self :Union[str, Any] ):
with self.assertRaisesRegex(
_lowerCamelCase , r'''aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)''' ):
__SCREAMING_SNAKE_CASE : Optional[int] = AutoImageProcessor.from_pretrained(_lowerCamelCase , revision='''aaaaaa''' )
def SCREAMING_SNAKE_CASE_ ( self :List[str] ):
with self.assertRaisesRegex(
_lowerCamelCase , '''hf-internal-testing/config-no-model does not appear to have a file named preprocessor_config.json.''' , ):
__SCREAMING_SNAKE_CASE : Optional[int] = AutoImageProcessor.from_pretrained('''hf-internal-testing/config-no-model''' )
def SCREAMING_SNAKE_CASE_ ( self :Tuple ):
# If remote code is not set, we will time out when asking whether to load the model.
with self.assertRaises(_lowerCamelCase ):
__SCREAMING_SNAKE_CASE : Union[str, Any] = AutoImageProcessor.from_pretrained('''hf-internal-testing/test_dynamic_image_processor''' )
# If remote code is disabled, we can't load this config.
with self.assertRaises(_lowerCamelCase ):
__SCREAMING_SNAKE_CASE : Optional[Any] = AutoImageProcessor.from_pretrained(
'''hf-internal-testing/test_dynamic_image_processor''' , trust_remote_code=_lowerCamelCase )
__SCREAMING_SNAKE_CASE : int = AutoImageProcessor.from_pretrained(
'''hf-internal-testing/test_dynamic_image_processor''' , trust_remote_code=_lowerCamelCase )
self.assertEqual(image_processor.__class__.__name__ , '''NewImageProcessor''' )
# Test image processor can be reloaded.
with tempfile.TemporaryDirectory() as tmp_dir:
image_processor.save_pretrained(_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Tuple = AutoImageProcessor.from_pretrained(_lowerCamelCase , trust_remote_code=_lowerCamelCase )
self.assertEqual(reloaded_image_processor.__class__.__name__ , '''NewImageProcessor''' )
def SCREAMING_SNAKE_CASE_ ( self :Tuple ):
try:
AutoConfig.register('''custom''' , _lowerCamelCase )
AutoImageProcessor.register(_lowerCamelCase , _lowerCamelCase )
# Trying to register something existing in the Transformers library will raise an error
with self.assertRaises(_lowerCamelCase ):
AutoImageProcessor.register(_lowerCamelCase , _lowerCamelCase )
with tempfile.TemporaryDirectory() as tmpdirname:
__SCREAMING_SNAKE_CASE : Dict = Path(_lowerCamelCase ) / '''preprocessor_config.json'''
__SCREAMING_SNAKE_CASE : Dict = Path(_lowerCamelCase ) / '''config.json'''
json.dump(
{'''feature_extractor_type''': '''CLIPFeatureExtractor''', '''processor_class''': '''CLIPProcessor'''} , open(_lowerCamelCase , '''w''' ) , )
json.dump({'''model_type''': '''clip'''} , open(_lowerCamelCase , '''w''' ) )
__SCREAMING_SNAKE_CASE : Optional[int] = CustomImageProcessor.from_pretrained(_lowerCamelCase )
# Now that the config is registered, it can be used as any other config with the auto-API
with tempfile.TemporaryDirectory() as tmp_dir:
image_processor.save_pretrained(_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Tuple = AutoImageProcessor.from_pretrained(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
finally:
if "custom" in CONFIG_MAPPING._extra_content:
del CONFIG_MAPPING._extra_content["custom"]
if CustomConfig in IMAGE_PROCESSOR_MAPPING._extra_content:
del IMAGE_PROCESSOR_MAPPING._extra_content[CustomConfig]
def SCREAMING_SNAKE_CASE_ ( self :Dict ):
class snake_case ( __UpperCAmelCase ):
lowerCamelCase__ = True
try:
AutoConfig.register('''custom''' , _lowerCamelCase )
AutoImageProcessor.register(_lowerCamelCase , _lowerCamelCase )
# If remote code is not set, the default is to use local
__SCREAMING_SNAKE_CASE : List[str] = AutoImageProcessor.from_pretrained('''hf-internal-testing/test_dynamic_image_processor''' )
self.assertEqual(image_processor.__class__.__name__ , '''NewImageProcessor''' )
self.assertTrue(image_processor.is_local )
# If remote code is disabled, we load the local one.
__SCREAMING_SNAKE_CASE : str = AutoImageProcessor.from_pretrained(
'''hf-internal-testing/test_dynamic_image_processor''' , trust_remote_code=_lowerCamelCase )
self.assertEqual(image_processor.__class__.__name__ , '''NewImageProcessor''' )
self.assertTrue(image_processor.is_local )
# If remote is enabled, we load from the Hub
__SCREAMING_SNAKE_CASE : Optional[int] = AutoImageProcessor.from_pretrained(
'''hf-internal-testing/test_dynamic_image_processor''' , trust_remote_code=_lowerCamelCase )
self.assertEqual(image_processor.__class__.__name__ , '''NewImageProcessor''' )
self.assertTrue(not hasattr(_lowerCamelCase , '''is_local''' ) )
finally:
if "custom" in CONFIG_MAPPING._extra_content:
del CONFIG_MAPPING._extra_content["custom"]
if CustomConfig in IMAGE_PROCESSOR_MAPPING._extra_content:
del IMAGE_PROCESSOR_MAPPING._extra_content[CustomConfig]
| 674 | 0 |
"""simple docstring"""
from collections.abc import Sequence
from queue import Queue
class __lowercase:
'''simple docstring'''
def __init__( self , __a , __a , __a , __a=None , __a=None ):
__lowerCamelCase : Any = start
__lowerCamelCase : List[Any] = end
__lowerCamelCase : str = val
__lowerCamelCase : str = (start + end) // 2
__lowerCamelCase : Any = left
__lowerCamelCase : Dict = right
def __repr__( self ):
return f'''SegmentTreeNode(start={self.start}, end={self.end}, val={self.val})'''
class __lowercase:
'''simple docstring'''
def __init__( self , __a , __a ):
__lowerCamelCase : Optional[int] = collection
__lowerCamelCase : List[str] = function
if self.collection:
__lowerCamelCase : List[str] = self._build_tree(0 , len(_lowerCamelCase ) - 1 )
def snake_case_ ( self , __a , __a ):
self._update_tree(self.root , _lowerCamelCase , _lowerCamelCase )
def snake_case_ ( self , __a , __a ):
return self._query_range(self.root , _lowerCamelCase , _lowerCamelCase )
def snake_case_ ( self , __a , __a ):
if start == end:
return SegmentTreeNode(_lowerCamelCase , _lowerCamelCase , self.collection[start] )
__lowerCamelCase : str = (start + end) // 2
__lowerCamelCase : str = self._build_tree(_lowerCamelCase , _lowerCamelCase )
__lowerCamelCase : int = self._build_tree(mid + 1 , _lowerCamelCase )
return SegmentTreeNode(_lowerCamelCase , _lowerCamelCase , self.fn(left.val , right.val ) , _lowerCamelCase , _lowerCamelCase )
def snake_case_ ( self , __a , __a , __a ):
if node.start == i and node.end == i:
__lowerCamelCase : Any = val
return
if i <= node.mid:
self._update_tree(node.left , _lowerCamelCase , _lowerCamelCase )
else:
self._update_tree(node.right , _lowerCamelCase , _lowerCamelCase )
__lowerCamelCase : str = self.fn(node.left.val , node.right.val )
def snake_case_ ( self , __a , __a , __a ):
if node.start == i and node.end == j:
return node.val
if i <= node.mid:
if j <= node.mid:
# range in left child tree
return self._query_range(node.left , _lowerCamelCase , _lowerCamelCase )
else:
# range in left child tree and right child tree
return self.fn(
self._query_range(node.left , _lowerCamelCase , node.mid ) , self._query_range(node.right , node.mid + 1 , _lowerCamelCase ) , )
else:
# range in right child tree
return self._query_range(node.right , _lowerCamelCase , _lowerCamelCase )
def snake_case_ ( self ):
if self.root is not None:
__lowerCamelCase : Any = Queue()
queue.put(self.root )
while not queue.empty():
__lowerCamelCase : Dict = queue.get()
yield node
if node.left is not None:
queue.put(node.left )
if node.right is not None:
queue.put(node.right )
if __name__ == "__main__":
import operator
for fn in [operator.add, max, min]:
print('''*''' * 50)
a_ : int = SegmentTree([2, 1, 5, 3, 4], fn)
for node in arr.traverse():
print(node)
print()
arr.update(1, 5)
for node in arr.traverse():
print(node)
print()
print(arr.query_range(3, 4)) # 7
print(arr.query_range(2, 2)) # 5
print(arr.query_range(1, 3)) # 13
print()
| 594 |
"""simple docstring"""
from __future__ import annotations
from typing import Any
class snake_case ( __UpperCAmelCase ):
pass
class snake_case :
def __init__( self :List[Any] , _lowerCamelCase :Any ):
__SCREAMING_SNAKE_CASE : Any = data
__SCREAMING_SNAKE_CASE : Node | None = None
def __iter__( self :Union[str, Any] ):
__SCREAMING_SNAKE_CASE : List[str] = self
__SCREAMING_SNAKE_CASE : List[str] = []
while node:
if node in visited:
raise ContainsLoopError
visited.append(_lowerCamelCase )
yield node.data
__SCREAMING_SNAKE_CASE : List[str] = node.next_node
@property
def SCREAMING_SNAKE_CASE_ ( self :Any ):
try:
list(self )
return False
except ContainsLoopError:
return True
if __name__ == "__main__":
_lowerCamelCase = Node(1)
_lowerCamelCase = Node(2)
_lowerCamelCase = Node(3)
_lowerCamelCase = Node(4)
print(root_node.has_loop) # False
_lowerCamelCase = root_node.next_node
print(root_node.has_loop) # True
_lowerCamelCase = Node(5)
_lowerCamelCase = Node(6)
_lowerCamelCase = Node(5)
_lowerCamelCase = Node(6)
print(root_node.has_loop) # False
_lowerCamelCase = Node(1)
print(root_node.has_loop) # False
| 674 | 0 |
import warnings
from ..trainer import Trainer
from ..utils import logging
__snake_case = logging.get_logger(__name__)
class UpperCAmelCase ( __UpperCAmelCase ):
def __init__( self : str , __magic_name__ : Optional[Any]=None , **__magic_name__ : Optional[int] ):
"""simple docstring"""
warnings.warn(
"""`SageMakerTrainer` is deprecated and will be removed in v5 of Transformers. You can use `Trainer` """
"""instead.""" , _lowerCamelCase , )
super().__init__(args=_lowerCamelCase , **_lowerCamelCase )
| 386 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCamelCase = logging.get_logger(__name__)
_lowerCamelCase = {
'''weiweishi/roc-bert-base-zh''': '''https://huggingface.co/weiweishi/roc-bert-base-zh/resolve/main/config.json''',
}
class snake_case ( __UpperCAmelCase ):
lowerCamelCase__ = '''roc_bert'''
def __init__( self :Union[str, Any] , _lowerCamelCase :Any=3_0_5_2_2 , _lowerCamelCase :str=7_6_8 , _lowerCamelCase :Optional[Any]=1_2 , _lowerCamelCase :List[str]=1_2 , _lowerCamelCase :str=3_0_7_2 , _lowerCamelCase :Tuple="gelu" , _lowerCamelCase :List[Any]=0.1 , _lowerCamelCase :List[str]=0.1 , _lowerCamelCase :Optional[int]=5_1_2 , _lowerCamelCase :Dict=2 , _lowerCamelCase :Any=0.0_2 , _lowerCamelCase :Optional[int]=1e-12 , _lowerCamelCase :str=True , _lowerCamelCase :Any=0 , _lowerCamelCase :List[str]="absolute" , _lowerCamelCase :List[Any]=None , _lowerCamelCase :Any=True , _lowerCamelCase :Union[str, Any]=True , _lowerCamelCase :str=7_6_8 , _lowerCamelCase :Union[str, Any]=9_1_0 , _lowerCamelCase :List[Any]=5_1_2 , _lowerCamelCase :Optional[int]=2_4_8_5_8 , _lowerCamelCase :Union[str, Any]=True , **_lowerCamelCase :str , ):
__SCREAMING_SNAKE_CASE : List[str] = vocab_size
__SCREAMING_SNAKE_CASE : int = max_position_embeddings
__SCREAMING_SNAKE_CASE : List[str] = hidden_size
__SCREAMING_SNAKE_CASE : str = num_hidden_layers
__SCREAMING_SNAKE_CASE : int = num_attention_heads
__SCREAMING_SNAKE_CASE : Any = intermediate_size
__SCREAMING_SNAKE_CASE : Optional[int] = hidden_act
__SCREAMING_SNAKE_CASE : List[Any] = hidden_dropout_prob
__SCREAMING_SNAKE_CASE : Optional[Any] = attention_probs_dropout_prob
__SCREAMING_SNAKE_CASE : Union[str, Any] = initializer_range
__SCREAMING_SNAKE_CASE : Union[str, Any] = type_vocab_size
__SCREAMING_SNAKE_CASE : List[str] = layer_norm_eps
__SCREAMING_SNAKE_CASE : Optional[int] = use_cache
__SCREAMING_SNAKE_CASE : str = enable_pronunciation
__SCREAMING_SNAKE_CASE : List[str] = enable_shape
__SCREAMING_SNAKE_CASE : Tuple = pronunciation_embed_dim
__SCREAMING_SNAKE_CASE : Optional[Any] = pronunciation_vocab_size
__SCREAMING_SNAKE_CASE : str = shape_embed_dim
__SCREAMING_SNAKE_CASE : Union[str, Any] = shape_vocab_size
__SCREAMING_SNAKE_CASE : Tuple = concat_input
__SCREAMING_SNAKE_CASE : Union[str, Any] = position_embedding_type
__SCREAMING_SNAKE_CASE : str = classifier_dropout
super().__init__(pad_token_id=_lowerCamelCase , **_lowerCamelCase )
| 674 | 0 |
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
UniSpeechConfig,
UniSpeechForCTC,
UniSpeechForPreTraining,
WavaVecaFeatureExtractor,
WavaVecaPhonemeCTCTokenizer,
WavaVecaProcessor,
logging,
)
logging.set_verbosity_info()
a__ = logging.get_logger(__name__)
a__ = {
'''post_extract_proj''': '''feature_projection.projection''',
'''encoder.pos_conv.0''': '''encoder.pos_conv_embed.conv''',
'''self_attn.k_proj''': '''encoder.layers.*.attention.k_proj''',
'''self_attn.v_proj''': '''encoder.layers.*.attention.v_proj''',
'''self_attn.q_proj''': '''encoder.layers.*.attention.q_proj''',
'''self_attn.out_proj''': '''encoder.layers.*.attention.out_proj''',
'''self_attn_layer_norm''': '''encoder.layers.*.layer_norm''',
'''fc1''': '''encoder.layers.*.feed_forward.intermediate_dense''',
'''fc2''': '''encoder.layers.*.feed_forward.output_dense''',
'''final_layer_norm''': '''encoder.layers.*.final_layer_norm''',
'''encoder.layer_norm''': '''encoder.layer_norm''',
'''w2v_model.layer_norm''': '''feature_projection.layer_norm''',
'''quantizer.weight_proj''': '''quantizer.weight_proj''',
'''quantizer.vars''': '''quantizer.codevectors''',
'''project_q''': '''project_q''',
'''final_proj''': '''project_hid''',
'''w2v_encoder.proj''': '''ctc_proj''',
'''mask_emb''': '''masked_spec_embed''',
}
a__ = [
'''ctc_proj''',
'''quantizer.weight_proj''',
'''quantizer.codevectors''',
'''project_q''',
'''project_hid''',
]
def A__ (snake_case : Union[str, Any] , snake_case : Tuple , snake_case : Union[str, Any] , snake_case : Any , snake_case : Optional[int] , snake_case : Any ) -> Dict:
for attribute in key.split(""".""" ):
if is_finetuned:
if attribute in ["quantizer", "project_q", "project_hid"]:
# those layers are only relevant for pretraining and should be dropped
return
if attribute == "ctc_proj":
# we should rename `ctc_proj` to `lm_head` for fine-tuned phoneme models
__UpperCamelCase : int = '''lm_head'''
__UpperCamelCase : Optional[Any] = getattr(lowercase_ , lowercase_ )
if weight_type is not None:
__UpperCamelCase : Any = getattr(lowercase_ , lowercase_ ).shape
else:
__UpperCamelCase : Tuple = hf_pointer.shape
assert hf_shape == value.shape, (
F'''Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be'''
F''' {value.shape} for {full_name}'''
)
if weight_type == "weight":
__UpperCamelCase : Dict = value
elif weight_type == "weight_g":
__UpperCamelCase : Optional[Any] = value
elif weight_type == "weight_v":
__UpperCamelCase : str = value
elif weight_type == "bias":
__UpperCamelCase : List[Any] = value
else:
__UpperCamelCase : int = value
logger.info(F'''{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.''' )
def A__ (snake_case : List[Any] , snake_case : List[str] , snake_case : Optional[int] ) -> Any:
__UpperCamelCase : str = []
__UpperCamelCase : Tuple = fairseq_model.state_dict()
__UpperCamelCase : Optional[int] = hf_model.unispeech.feature_extractor
for name, value in fairseq_dict.items():
__UpperCamelCase : List[str] = False
if "conv_layers" in name:
load_conv_layer(
lowercase_ , lowercase_ , lowercase_ , lowercase_ , hf_model.config.feat_extract_norm == """group""" , )
__UpperCamelCase : Any = True
else:
for key, mapped_key in MAPPING.items():
__UpperCamelCase : Optional[Any] = '''unispeech.''' + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
if key in name or key.split("""w2v_model.""" )[-1] == name.split(""".""" )[0]:
__UpperCamelCase : Dict = True
if "*" in mapped_key:
__UpperCamelCase : Tuple = name.split(lowercase_ )[0].split(""".""" )[-2]
__UpperCamelCase : Optional[int] = mapped_key.replace("""*""" , lowercase_ )
if "weight_g" in name:
__UpperCamelCase : Dict = '''weight_g'''
elif "weight_v" in name:
__UpperCamelCase : Optional[Any] = '''weight_v'''
elif "bias" in name:
__UpperCamelCase : int = '''bias'''
elif "weight" in name:
# TODO: don't match quantizer.weight_proj
__UpperCamelCase : Dict = '''weight'''
else:
__UpperCamelCase : Tuple = None
set_recursively(lowercase_ , lowercase_ , lowercase_ , lowercase_ , lowercase_ , lowercase_ )
continue
if not is_used:
unused_weights.append(lowercase_ )
logger.warning(F'''Unused weights: {unused_weights}''' )
def A__ (snake_case : Tuple , snake_case : Optional[int] , snake_case : Union[str, Any] , snake_case : str , snake_case : int ) -> Dict:
__UpperCamelCase : Union[str, Any] = full_name.split("""conv_layers.""" )[-1]
__UpperCamelCase : Tuple = name.split(""".""" )
__UpperCamelCase : List[Any] = int(items[0] )
__UpperCamelCase : Dict = int(items[1] )
if type_id == 0:
if "bias" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
F'''{full_name} has size {value.shape}, but'''
F''' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'''
)
__UpperCamelCase : str = value
logger.info(F'''Feat extract conv layer {layer_id} was initialized from {full_name}.''' )
elif "weight" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
F'''{full_name} has size {value.shape}, but'''
F''' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'''
)
__UpperCamelCase : Union[str, Any] = value
logger.info(F'''Feat extract conv layer {layer_id} was initialized from {full_name}.''' )
elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
if "bias" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
F'''{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was'''
" found."
)
__UpperCamelCase : List[Any] = value
logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.''' )
elif "weight" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
F'''{full_name} has size {value.shape}, but'''
F''' {feature_extractor[layer_id].layer_norm.weight.data.shape} was found.'''
)
__UpperCamelCase : Optional[int] = value
logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.''' )
else:
unused_weights.append(lowercase_ )
@torch.no_grad()
def A__ (snake_case : List[Any] , snake_case : Any , snake_case : Union[str, Any]=None , snake_case : Optional[Any]=None , snake_case : str=True ) -> List[str]:
if config_path is not None:
__UpperCamelCase : Union[str, Any] = UniSpeechConfig.from_pretrained(lowercase_ )
else:
__UpperCamelCase : Tuple = UniSpeechConfig()
if is_finetuned:
if dict_path:
__UpperCamelCase : Tuple = Dictionary.load_from_json(lowercase_ )
# important change bos & pad token id since CTC symbol is <pad> and
# not <s> as in fairseq
__UpperCamelCase : List[str] = target_dict.pad_index
__UpperCamelCase : Dict = target_dict.bos_index
__UpperCamelCase : Optional[Any] = target_dict.eos_index
__UpperCamelCase : List[Any] = len(target_dict.symbols )
__UpperCamelCase : List[str] = os.path.join(lowercase_ , """vocab.json""" )
if not os.path.isdir(lowercase_ ):
logger.error("""--pytorch_dump_folder_path ({}) should be a directory""".format(lowercase_ ) )
return
os.makedirs(lowercase_ , exist_ok=lowercase_ )
__UpperCamelCase : Dict = target_dict.indices
# fairseq has the <pad> and <s> switched
__UpperCamelCase : List[Any] = 42
__UpperCamelCase : Tuple = 43
with open(lowercase_ , """w""" , encoding="""utf-8""" ) as vocab_handle:
json.dump(lowercase_ , lowercase_ )
__UpperCamelCase : List[str] = WavaVecaPhonemeCTCTokenizer(
lowercase_ , unk_token=target_dict.unk_word , pad_token=target_dict.pad_word , bos_token=target_dict.bos_word , eos_token=target_dict.eos_word , word_delimiter_token="""|""" , do_lower_case=lowercase_ , )
__UpperCamelCase : str = True if config.feat_extract_norm == '''layer''' else False
__UpperCamelCase : Any = WavaVecaFeatureExtractor(
feature_size=1 , sampling_rate=1_60_00 , padding_value=0 , do_normalize=lowercase_ , return_attention_mask=lowercase_ , )
__UpperCamelCase : Tuple = WavaVecaProcessor(feature_extractor=lowercase_ , tokenizer=lowercase_ )
processor.save_pretrained(lowercase_ )
__UpperCamelCase : List[str] = UniSpeechForCTC(lowercase_ )
else:
__UpperCamelCase : Union[str, Any] = UniSpeechForPreTraining(lowercase_ )
if is_finetuned:
__UpperCamelCase : Optional[Any] = fairseq.checkpoint_utils.load_model_ensemble_and_task(
[checkpoint_path] , arg_overrides={"""data""": """/""".join(dict_path.split("""/""" )[:-1] ), """w2v_path""": checkpoint_path} )
else:
__UpperCamelCase : Any = fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path] )
__UpperCamelCase : Optional[Any] = model[0].eval()
recursively_load_weights(lowercase_ , lowercase_ , lowercase_ )
hf_unispeech.save_pretrained(lowercase_ )
if __name__ == "__main__":
a__ = argparse.ArgumentParser()
parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
parser.add_argument('''--checkpoint_path''', default=None, type=str, help='''Path to fairseq checkpoint''')
parser.add_argument('''--dict_path''', default=None, type=str, help='''Path to dict of fine-tuned model''')
parser.add_argument('''--config_path''', default=None, type=str, help='''Path to hf config.json of model to convert''')
parser.add_argument(
'''--not_finetuned''', action='''store_true''', help='''Whether the model to convert is a fine-tuned model or not'''
)
a__ = parser.parse_args()
convert_unispeech_checkpoint(
args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
)
| 279 |
"""simple docstring"""
import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List
import git
import torch
from torch.utils.data import Dataset
from transformers import BartTokenizer, RagTokenizer, TaTokenizer
def lowerCAmelCase_ ( lowercase_ : List[str] , lowercase_ : List[str] , lowercase_ : Dict , lowercase_ : Tuple , lowercase_ : Optional[Any]=True , lowercase_ : Any="pt" ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : Union[str, Any] = {'''add_prefix_space''': True} if isinstance(lowercase_ , lowercase_ ) and not line.startswith(''' ''' ) else {}
__SCREAMING_SNAKE_CASE : Optional[int] = padding_side
return tokenizer(
[line] , max_length=lowercase_ , padding='''max_length''' if pad_to_max_length else None , truncation=lowercase_ , return_tensors=lowercase_ , add_special_tokens=lowercase_ , **lowercase_ , )
def lowerCAmelCase_ ( lowercase_ : Optional[int] , lowercase_ : Tuple , lowercase_ : List[Any]=None , ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : Dict = input_ids.ne(lowercase_ ).any(dim=0 )
if attention_mask is None:
return input_ids[:, keep_column_mask]
else:
return (input_ids[:, keep_column_mask], attention_mask[:, keep_column_mask])
class snake_case ( __UpperCAmelCase ):
def __init__( self :Optional[Any] , _lowerCamelCase :Dict , _lowerCamelCase :Any , _lowerCamelCase :Optional[Any] , _lowerCamelCase :Tuple , _lowerCamelCase :Any="train" , _lowerCamelCase :str=None , _lowerCamelCase :Optional[Any]=None , _lowerCamelCase :List[Any]=None , _lowerCamelCase :Tuple="" , ):
super().__init__()
__SCREAMING_SNAKE_CASE : Dict = Path(_lowerCamelCase ).joinpath(type_path + '''.source''' )
__SCREAMING_SNAKE_CASE : Any = Path(_lowerCamelCase ).joinpath(type_path + '''.target''' )
__SCREAMING_SNAKE_CASE : Any = self.get_char_lens(self.src_file )
__SCREAMING_SNAKE_CASE : List[str] = max_source_length
__SCREAMING_SNAKE_CASE : Dict = max_target_length
assert min(self.src_lens ) > 0, f'''found empty line in {self.src_file}'''
__SCREAMING_SNAKE_CASE : Dict = tokenizer
__SCREAMING_SNAKE_CASE : Union[str, Any] = prefix
if n_obs is not None:
__SCREAMING_SNAKE_CASE : Any = self.src_lens[:n_obs]
__SCREAMING_SNAKE_CASE : List[str] = src_lang
__SCREAMING_SNAKE_CASE : str = tgt_lang
def __len__( self :int ):
return len(self.src_lens )
def __getitem__( self :Optional[Any] , _lowerCamelCase :Any ):
__SCREAMING_SNAKE_CASE : Optional[Any] = index + 1 # linecache starts at 1
__SCREAMING_SNAKE_CASE : Any = self.prefix + linecache.getline(str(self.src_file ) , _lowerCamelCase ).rstrip('''\n''' )
__SCREAMING_SNAKE_CASE : Dict = linecache.getline(str(self.tgt_file ) , _lowerCamelCase ).rstrip('''\n''' )
assert source_line, f'''empty source line for index {index}'''
assert tgt_line, f'''empty tgt line for index {index}'''
# Need to add eos token manually for T5
if isinstance(self.tokenizer , _lowerCamelCase ):
source_line += self.tokenizer.eos_token
tgt_line += self.tokenizer.eos_token
# Pad source and target to the right
__SCREAMING_SNAKE_CASE : Dict = (
self.tokenizer.question_encoder if isinstance(self.tokenizer , _lowerCamelCase ) else self.tokenizer
)
__SCREAMING_SNAKE_CASE : Optional[Any] = self.tokenizer.generator if isinstance(self.tokenizer , _lowerCamelCase ) else self.tokenizer
__SCREAMING_SNAKE_CASE : Dict = encode_line(_lowerCamelCase , _lowerCamelCase , self.max_source_length , '''right''' )
__SCREAMING_SNAKE_CASE : Dict = encode_line(_lowerCamelCase , _lowerCamelCase , self.max_target_length , '''right''' )
__SCREAMING_SNAKE_CASE : Any = source_inputs['''input_ids'''].squeeze()
__SCREAMING_SNAKE_CASE : Any = target_inputs['''input_ids'''].squeeze()
__SCREAMING_SNAKE_CASE : Dict = source_inputs['''attention_mask'''].squeeze()
return {
"input_ids": source_ids,
"attention_mask": src_mask,
"decoder_input_ids": target_ids,
}
@staticmethod
def SCREAMING_SNAKE_CASE_ ( _lowerCamelCase :Any ):
return [len(_lowerCamelCase ) for x in Path(_lowerCamelCase ).open().readlines()]
def SCREAMING_SNAKE_CASE_ ( self :List[str] , _lowerCamelCase :List[str] ):
__SCREAMING_SNAKE_CASE : int = torch.stack([x['''input_ids'''] for x in batch] )
__SCREAMING_SNAKE_CASE : str = torch.stack([x['''attention_mask'''] for x in batch] )
__SCREAMING_SNAKE_CASE : int = torch.stack([x['''decoder_input_ids'''] for x in batch] )
__SCREAMING_SNAKE_CASE : str = (
self.tokenizer.generator.pad_token_id
if isinstance(self.tokenizer , _lowerCamelCase )
else self.tokenizer.pad_token_id
)
__SCREAMING_SNAKE_CASE : Union[str, Any] = (
self.tokenizer.question_encoder.pad_token_id
if isinstance(self.tokenizer , _lowerCamelCase )
else self.tokenizer.pad_token_id
)
__SCREAMING_SNAKE_CASE : List[str] = trim_batch(_lowerCamelCase , _lowerCamelCase )
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Any = trim_batch(_lowerCamelCase , _lowerCamelCase , attention_mask=_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Dict = {
'''input_ids''': source_ids,
'''attention_mask''': source_mask,
'''decoder_input_ids''': y,
}
return batch
_lowerCamelCase = getLogger(__name__)
def lowerCAmelCase_ ( lowercase_ : List[List] ):
'''simple docstring'''
return list(itertools.chain.from_iterable(lowercase_ ) )
def lowerCAmelCase_ ( lowercase_ : str ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : Any = get_git_info()
save_json(lowercase_ , os.path.join(lowercase_ , '''git_log.json''' ) )
def lowerCAmelCase_ ( lowercase_ : Any , lowercase_ : Optional[int] , lowercase_ : str=4 , **lowercase_ : List[str] ):
'''simple docstring'''
with open(lowercase_ , '''w''' ) as f:
json.dump(lowercase_ , lowercase_ , indent=lowercase_ , **lowercase_ )
def lowerCAmelCase_ ( lowercase_ : Union[str, Any] ):
'''simple docstring'''
with open(lowercase_ ) as f:
return json.load(lowercase_ )
def lowerCAmelCase_ ( ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : Any = git.Repo(search_parent_directories=lowercase_ )
__SCREAMING_SNAKE_CASE : List[str] = {
'''repo_id''': str(lowercase_ ),
'''repo_sha''': str(repo.head.object.hexsha ),
'''repo_branch''': str(repo.active_branch ),
'''hostname''': str(socket.gethostname() ),
}
return repo_infos
def lowerCAmelCase_ ( lowercase_ : Callable , lowercase_ : Iterable ):
'''simple docstring'''
return list(map(lowercase_ , lowercase_ ) )
def lowerCAmelCase_ ( lowercase_ : Any , lowercase_ : Any ):
'''simple docstring'''
with open(lowercase_ , '''wb''' ) as f:
return pickle.dump(lowercase_ , lowercase_ )
def lowerCAmelCase_ ( lowercase_ : Any ):
'''simple docstring'''
def remove_articles(lowercase_ : Dict ):
return re.sub(r'''\b(a|an|the)\b''' , ''' ''' , lowercase_ )
def white_space_fix(lowercase_ : Optional[int] ):
return " ".join(text.split() )
def remove_punc(lowercase_ : Any ):
__SCREAMING_SNAKE_CASE : Optional[int] = set(string.punctuation )
return "".join(ch for ch in text if ch not in exclude )
def lower(lowercase_ : int ):
return text.lower()
return white_space_fix(remove_articles(remove_punc(lower(lowercase_ ) ) ) )
def lowerCAmelCase_ ( lowercase_ : Optional[int] , lowercase_ : int ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : Any = normalize_answer(lowercase_ ).split()
__SCREAMING_SNAKE_CASE : Any = normalize_answer(lowercase_ ).split()
__SCREAMING_SNAKE_CASE : Tuple = Counter(lowercase_ ) & Counter(lowercase_ )
__SCREAMING_SNAKE_CASE : Tuple = sum(common.values() )
if num_same == 0:
return 0
__SCREAMING_SNAKE_CASE : Any = 1.0 * num_same / len(lowercase_ )
__SCREAMING_SNAKE_CASE : List[str] = 1.0 * num_same / len(lowercase_ )
__SCREAMING_SNAKE_CASE : Optional[int] = (2 * precision * recall) / (precision + recall)
return fa
def lowerCAmelCase_ ( lowercase_ : str , lowercase_ : Union[str, Any] ):
'''simple docstring'''
return normalize_answer(lowercase_ ) == normalize_answer(lowercase_ )
def lowerCAmelCase_ ( lowercase_ : List[str] , lowercase_ : List[str] ):
'''simple docstring'''
assert len(lowercase_ ) == len(lowercase_ )
__SCREAMING_SNAKE_CASE : Union[str, Any] = 0
for hypo, pred in zip(lowercase_ , lowercase_ ):
em += exact_match_score(lowercase_ , lowercase_ )
if len(lowercase_ ) > 0:
em /= len(lowercase_ )
return {"em": em}
def lowerCAmelCase_ ( lowercase_ : str ):
'''simple docstring'''
return model_prefix.startswith('''rag''' )
def lowerCAmelCase_ ( lowercase_ : Optional[Any] , lowercase_ : Tuple , lowercase_ : Optional[int] ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : Optional[int] = {p: p for p in extra_params}
# T5 models don't have `dropout` param, they have `dropout_rate` instead
__SCREAMING_SNAKE_CASE : Any = '''dropout_rate'''
for p in extra_params:
if getattr(lowercase_ , lowercase_ , lowercase_ ):
if not hasattr(lowercase_ , lowercase_ ) and not hasattr(lowercase_ , equivalent_param[p] ):
logger.info('''config doesn\'t have a `{}` attribute'''.format(lowercase_ ) )
delattr(lowercase_ , lowercase_ )
continue
__SCREAMING_SNAKE_CASE : Optional[int] = p if hasattr(lowercase_ , lowercase_ ) else equivalent_param[p]
setattr(lowercase_ , lowercase_ , getattr(lowercase_ , lowercase_ ) )
delattr(lowercase_ , lowercase_ )
return hparams, config
| 674 | 0 |
import os
from math import logaa
def a ( a = "base_exp.txt" ) ->str:
'''simple docstring'''
SCREAMING_SNAKE_CASE = 0
SCREAMING_SNAKE_CASE = 0
for i, line in enumerate(open(os.path.join(os.path.dirname(lowercase_ ) , lowercase_ ) ) ):
SCREAMING_SNAKE_CASE = list(map(lowercase_ , line.split(''',''' ) ) )
if x * logaa(lowercase_ ) > largest:
SCREAMING_SNAKE_CASE = x * logaa(lowercase_ )
SCREAMING_SNAKE_CASE = i + 1
return result
if __name__ == "__main__":
print(solution()) | 201 |
"""simple docstring"""
from collections.abc import Callable
import numpy as np
def lowerCAmelCase_ ( lowercase_ : Callable , lowercase_ : float , lowercase_ : float , lowercase_ : float , lowercase_ : float ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : int = int(np.ceil((x_end - xa) / step_size ) )
__SCREAMING_SNAKE_CASE : Dict = np.zeros((n + 1,) )
__SCREAMING_SNAKE_CASE : List[Any] = ya
__SCREAMING_SNAKE_CASE : Dict = xa
for k in range(lowercase_ ):
__SCREAMING_SNAKE_CASE : str = y[k] + step_size * ode_func(lowercase_ , y[k] )
__SCREAMING_SNAKE_CASE : int = y[k] + (
(step_size / 2) * (ode_func(lowercase_ , y[k] ) + ode_func(x + step_size , lowercase_ ))
)
x += step_size
return y
if __name__ == "__main__":
import doctest
doctest.testmod()
| 674 | 0 |
"""simple docstring"""
import ast
import os
import re
import shutil
import tempfile
import unittest
from unittest import mock
import torch
from accelerate.test_utils.examples import compare_against_test
from accelerate.test_utils.testing import TempDirTestCase, require_trackers, run_command, slow
from accelerate.utils import write_basic_config
# DataLoaders built from `test_samples/MRPC` for quick testing
# Should mock `{script_name}.get_dataloaders` via:
# @mock.patch("{script_name}.get_dataloaders", mocked_dataloaders)
a : Union[str, Any] = [
"""cross_validation.py""",
"""gradient_accumulation.py""",
"""local_sgd.py""",
"""multi_process_metrics.py""",
"""memory.py""",
"""automatic_gradient_accumulation.py""",
"""fsdp_with_peak_mem_tracking.py""",
"""deepspeed_with_config_support.py""",
"""megatron_lm_gpt_pretraining.py""",
]
class __UpperCAmelCase( unittest.TestCase ):
"""simple docstring"""
def UpperCAmelCase_ ( self , snake_case__ , snake_case__ , snake_case__ = None , snake_case__ = None ):
'''simple docstring'''
lowercase__ : List[str]= None
lowercase__ : Optional[Any]= os.path.abspath(os.path.join("examples" , "by_feature" ) )
lowercase__ : Union[str, Any]= os.path.abspath("examples" )
for item in os.listdir(_lowerCamelCase ):
if item not in EXCLUDE_EXAMPLES:
lowercase__ : List[Any]= os.path.join(_lowerCamelCase , _lowerCamelCase )
if os.path.isfile(_lowerCamelCase ) and ".py" in item_path:
with self.subTest(
tested_script=_lowerCamelCase , feature_script=_lowerCamelCase , tested_section="main()" if parser_only else "training_function()" , ):
lowercase__ : Tuple= compare_against_test(
os.path.join(_lowerCamelCase , _lowerCamelCase ) , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase )
lowercase__ : Optional[Any]= '''\n'''.join(_lowerCamelCase )
if special_strings is not None:
for string in special_strings:
lowercase__ : List[Any]= diff.replace(_lowerCamelCase , "" )
self.assertEqual(_lowerCamelCase , "" )
def UpperCAmelCase_ ( self ):
'''simple docstring'''
self.one_complete_example("complete_nlp_example.py" , _lowerCamelCase )
self.one_complete_example("complete_nlp_example.py" , _lowerCamelCase )
def UpperCAmelCase_ ( self ):
'''simple docstring'''
lowercase__ : Union[str, Any]= os.path.abspath(os.path.join("examples" , "cv_example.py" ) )
lowercase__ : Optional[int]= [
''' ''' * 16 + '''{\n\n''',
''' ''' * 20 + '''"accuracy": eval_metric["accuracy"],\n\n''',
''' ''' * 20 + '''"f1": eval_metric["f1"],\n\n''',
''' ''' * 20 + '''"train_loss": total_loss.item() / len(train_dataloader),\n\n''',
''' ''' * 20 + '''"epoch": epoch,\n\n''',
''' ''' * 16 + '''},\n\n''',
''' ''' * 16 + '''step=epoch,\n''',
''' ''' * 12,
''' ''' * 8 + '''for step, batch in enumerate(active_dataloader):\n''',
]
self.one_complete_example("complete_cv_example.py" , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase )
self.one_complete_example("complete_cv_example.py" , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase )
@mock.patch.dict(os.environ , {"TESTING_MOCKED_DATALOADERS": "1"} )
class __UpperCAmelCase( __UpperCAmelCase ):
"""simple docstring"""
__lowerCamelCase = False
@classmethod
def UpperCAmelCase_ ( cls ):
'''simple docstring'''
super().setUpClass()
lowercase__ : Dict= tempfile.mkdtemp()
lowercase__ : str= os.path.join(cls._tmpdir , "default_config.yml" )
write_basic_config(save_location=cls.configPath )
lowercase__ : List[Any]= ['''accelerate''', '''launch''', '''--config_file''', cls.configPath]
@classmethod
def UpperCAmelCase_ ( cls ):
'''simple docstring'''
super().tearDownClass()
shutil.rmtree(cls._tmpdir )
def UpperCAmelCase_ ( self ):
'''simple docstring'''
lowercase__ : str= F'''
examples/by_feature/checkpointing.py
--checkpointing_steps epoch
--output_dir {self.tmpdir}
'''.split()
run_command(self._launch_args + testargs )
self.assertTrue(os.path.exists(os.path.join(self.tmpdir , "epoch_0" ) ) )
def UpperCAmelCase_ ( self ):
'''simple docstring'''
lowercase__ : Optional[Any]= F'''
examples/by_feature/checkpointing.py
--checkpointing_steps 1
--output_dir {self.tmpdir}
'''.split()
lowercase__ : Optional[int]= run_command(self._launch_args + testargs )
self.assertTrue(os.path.exists(os.path.join(self.tmpdir , "step_2" ) ) )
def UpperCAmelCase_ ( self ):
'''simple docstring'''
lowercase__ : Optional[int]= F'''
examples/by_feature/checkpointing.py
--resume_from_checkpoint {os.path.join(self.tmpdir , 'epoch_0' )}
'''.split()
lowercase__ : Any= run_command(self._launch_args + testargs , return_stdout=_lowerCamelCase )
self.assertNotIn("epoch 0:" , _lowerCamelCase )
self.assertIn("epoch 1:" , _lowerCamelCase )
def UpperCAmelCase_ ( self ):
'''simple docstring'''
lowercase__ : Optional[int]= F'''
examples/by_feature/checkpointing.py
--resume_from_checkpoint {os.path.join(self.tmpdir , 'step_2' )}
'''.split()
lowercase__ : List[str]= run_command(self._launch_args + testargs , return_stdout=_lowerCamelCase )
if torch.cuda.is_available():
lowercase__ : List[Any]= torch.cuda.device_count()
else:
lowercase__ : Optional[int]= 1
if num_processes > 1:
self.assertNotIn("epoch 0:" , _lowerCamelCase )
self.assertIn("epoch 1:" , _lowerCamelCase )
else:
self.assertIn("epoch 0:" , _lowerCamelCase )
self.assertIn("epoch 1:" , _lowerCamelCase )
@slow
def UpperCAmelCase_ ( self ):
'''simple docstring'''
lowercase__ : Optional[Any]= '''
examples/by_feature/cross_validation.py
--num_folds 2
'''.split()
with mock.patch.dict(os.environ , {"TESTING_MOCKED_DATALOADERS": "0"} ):
lowercase__ : Union[str, Any]= run_command(self._launch_args + testargs , return_stdout=_lowerCamelCase )
lowercase__ : Any= re.findall("({.+})" , _lowerCamelCase )
lowercase__ : List[Any]= [r for r in results if '''accuracy''' in r][-1]
lowercase__ : Tuple= ast.literal_eval(_lowerCamelCase )
self.assertGreaterEqual(results["accuracy"] , 0.75 )
def UpperCAmelCase_ ( self ):
'''simple docstring'''
lowercase__ : Optional[Any]= ['''examples/by_feature/multi_process_metrics.py''']
run_command(self._launch_args + testargs )
@require_trackers
@mock.patch.dict(os.environ , {"WANDB_MODE": "offline"} )
def UpperCAmelCase_ ( self ):
'''simple docstring'''
with tempfile.TemporaryDirectory() as tmpdir:
lowercase__ : int= F'''
examples/by_feature/tracking.py
--with_tracking
--project_dir {tmpdir}
'''.split()
run_command(self._launch_args + testargs )
self.assertTrue(os.path.exists(os.path.join(_lowerCamelCase , "tracking" ) ) )
def UpperCAmelCase_ ( self ):
'''simple docstring'''
lowercase__ : Union[str, Any]= ['''examples/by_feature/gradient_accumulation.py''']
run_command(self._launch_args + testargs )
def UpperCAmelCase_ ( self ):
'''simple docstring'''
lowercase__ : List[Any]= ['''examples/by_feature/local_sgd.py''']
run_command(self._launch_args + testargs )
| 218 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_torch_available
from ...utils import OptionalDependencyNotAvailable
_lowerCamelCase = {
'''configuration_gpt_neox_japanese''': ['''GPT_NEOX_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GPTNeoXJapaneseConfig'''],
'''tokenization_gpt_neox_japanese''': ['''GPTNeoXJapaneseTokenizer'''],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowerCamelCase = [
'''GPT_NEOX_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''GPTNeoXJapaneseForCausalLM''',
'''GPTNeoXJapaneseLayer''',
'''GPTNeoXJapaneseModel''',
'''GPTNeoXJapanesePreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_gpt_neox_japanese import GPT_NEOX_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoXJapaneseConfig
from .tokenization_gpt_neox_japanese import GPTNeoXJapaneseTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_gpt_neox_japanese import (
GPT_NEOX_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST,
GPTNeoXJapaneseForCausalLM,
GPTNeoXJapaneseLayer,
GPTNeoXJapaneseModel,
GPTNeoXJapanesePreTrainedModel,
)
else:
import sys
_lowerCamelCase = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 674 | 0 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from diffusers import (
DDIMScheduler,
KandinskyVaaInpaintPipeline,
KandinskyVaaPriorPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
enable_full_determinism()
class UpperCAmelCase ( __UpperCAmelCase , unittest.TestCase ):
a: Optional[int] = KandinskyVaaInpaintPipeline
a: str = ["image_embeds", "negative_image_embeds", "image", "mask_image"]
a: Any = [
"image_embeds",
"negative_image_embeds",
"image",
"mask_image",
]
a: Optional[Any] = [
"generator",
"height",
"width",
"latents",
"guidance_scale",
"num_inference_steps",
"return_dict",
"guidance_scale",
"num_images_per_prompt",
"output_type",
"return_dict",
]
a: List[Any] = False
@property
def _A ( self: List[Any] ):
return 32
@property
def _A ( self: Union[str, Any] ):
return 32
@property
def _A ( self: Optional[int] ):
return self.time_input_dim
@property
def _A ( self: int ):
return self.time_input_dim * 4
@property
def _A ( self: Union[str, Any] ):
return 100
@property
def _A ( self: Tuple ):
torch.manual_seed(0 )
_a = {
'''in_channels''': 9,
# Out channels is double in channels because predicts mean and variance
'''out_channels''': 8,
'''addition_embed_type''': '''image''',
'''down_block_types''': ('''ResnetDownsampleBlock2D''', '''SimpleCrossAttnDownBlock2D'''),
'''up_block_types''': ('''SimpleCrossAttnUpBlock2D''', '''ResnetUpsampleBlock2D'''),
'''mid_block_type''': '''UNetMidBlock2DSimpleCrossAttn''',
'''block_out_channels''': (self.block_out_channels_a, self.block_out_channels_a * 2),
'''layers_per_block''': 1,
'''encoder_hid_dim''': self.text_embedder_hidden_size,
'''encoder_hid_dim_type''': '''image_proj''',
'''cross_attention_dim''': self.cross_attention_dim,
'''attention_head_dim''': 4,
'''resnet_time_scale_shift''': '''scale_shift''',
'''class_embed_type''': None,
}
_a = UNetaDConditionModel(**_lowerCamelCase )
return model
@property
def _A ( self: Union[str, Any] ):
return {
"block_out_channels": [32, 64],
"down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
"in_channels": 3,
"latent_channels": 4,
"layers_per_block": 1,
"norm_num_groups": 8,
"norm_type": "spatial",
"num_vq_embeddings": 12,
"out_channels": 3,
"up_block_types": [
"AttnUpDecoderBlock2D",
"UpDecoderBlock2D",
],
"vq_embed_dim": 4,
}
@property
def _A ( self: Optional[int] ):
torch.manual_seed(0 )
_a = VQModel(**self.dummy_movq_kwargs )
return model
def _A ( self: List[str] ):
_a = self.dummy_unet
_a = self.dummy_movq
_a = DDIMScheduler(
num_train_timesteps=1000 , beta_schedule='''linear''' , beta_start=0.0_0_0_8_5 , beta_end=0.0_1_2 , clip_sample=_lowerCamelCase , set_alpha_to_one=_lowerCamelCase , steps_offset=1 , prediction_type='''epsilon''' , thresholding=_lowerCamelCase , )
_a = {
'''unet''': unet,
'''scheduler''': scheduler,
'''movq''': movq,
}
return components
def _A ( self: Union[str, Any] , __UpperCamelCase: Optional[int] , __UpperCamelCase: List[str]=0 ):
_a = floats_tensor((1, self.text_embedder_hidden_size) , rng=random.Random(_lowerCamelCase ) ).to(_lowerCamelCase )
_a = floats_tensor((1, self.text_embedder_hidden_size) , rng=random.Random(seed + 1 ) ).to(
_lowerCamelCase )
# create init_image
_a = floats_tensor((1, 3, 64, 64) , rng=random.Random(_lowerCamelCase ) ).to(_lowerCamelCase )
_a = image.cpu().permute(0 , 2 , 3 , 1 )[0]
_a = Image.fromarray(np.uinta(_lowerCamelCase ) ).convert('''RGB''' ).resize((256, 256) )
# create mask
_a = np.ones((64, 64) , dtype=np.floataa )
_a = 0
if str(_lowerCamelCase ).startswith('''mps''' ):
_a = torch.manual_seed(_lowerCamelCase )
else:
_a = torch.Generator(device=_lowerCamelCase ).manual_seed(_lowerCamelCase )
_a = {
'''image''': init_image,
'''mask_image''': mask,
'''image_embeds''': image_embeds,
'''negative_image_embeds''': negative_image_embeds,
'''generator''': generator,
'''height''': 64,
'''width''': 64,
'''num_inference_steps''': 2,
'''guidance_scale''': 4.0,
'''output_type''': '''np''',
}
return inputs
def _A ( self: Optional[Any] ):
_a = '''cpu'''
_a = self.get_dummy_components()
_a = self.pipeline_class(**_lowerCamelCase )
_a = pipe.to(_lowerCamelCase )
pipe.set_progress_bar_config(disable=_lowerCamelCase )
_a = pipe(**self.get_dummy_inputs(_lowerCamelCase ) )
_a = output.images
_a = pipe(
**self.get_dummy_inputs(_lowerCamelCase ) , return_dict=_lowerCamelCase , )[0]
_a = image[0, -3:, -3:, -1]
_a = image_from_tuple[0, -3:, -3:, -1]
print(f"image.shape {image.shape}" )
assert image.shape == (1, 64, 64, 3)
_a = np.array(
[0.5_0_7_7_5_9_0_3, 0.4_9_5_2_7_1_9_5, 0.4_8_8_2_4_5_4_3, 0.5_0_1_9_2_2_3_7, 0.4_8_6_4_4_9_0_6, 0.4_9_3_7_3_8_1_4, 0.4_7_8_0_5_9_8, 0.4_7_2_3_4_8_2_7, 0.4_8_3_2_7_8_4_8] )
assert (
np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
), f" expected_slice {expected_slice}, but got {image_slice.flatten()}"
assert (
np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1E-2
), f" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}"
def _A ( self: Any ):
super().test_inference_batch_single_identical(expected_max_diff=3E-3 )
@slow
@require_torch_gpu
class UpperCAmelCase ( unittest.TestCase ):
def _A ( self: List[str] ):
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def _A ( self: List[Any] ):
_a = load_numpy(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
'''/kandinskyv22/kandinskyv22_inpaint_cat_with_hat_fp16.npy''' )
_a = load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main''' '''/kandinsky/cat.png''' )
_a = np.ones((768, 768) , dtype=np.floataa )
_a = 0
_a = '''a hat'''
_a = KandinskyVaaPriorPipeline.from_pretrained(
'''kandinsky-community/kandinsky-2-2-prior''' , torch_dtype=torch.floataa )
pipe_prior.to(_lowerCamelCase )
_a = KandinskyVaaInpaintPipeline.from_pretrained(
'''kandinsky-community/kandinsky-2-2-decoder-inpaint''' , torch_dtype=torch.floataa )
_a = pipeline.to(_lowerCamelCase )
pipeline.set_progress_bar_config(disable=_lowerCamelCase )
_a = torch.Generator(device='''cpu''' ).manual_seed(0 )
_a = pipe_prior(
_lowerCamelCase , generator=_lowerCamelCase , num_inference_steps=5 , negative_prompt='''''' , ).to_tuple()
_a = pipeline(
image=_lowerCamelCase , mask_image=_lowerCamelCase , image_embeds=_lowerCamelCase , negative_image_embeds=_lowerCamelCase , generator=_lowerCamelCase , num_inference_steps=100 , height=768 , width=768 , output_type='''np''' , )
_a = output.images[0]
assert image.shape == (768, 768, 3)
assert_mean_pixel_difference(_lowerCamelCase , _lowerCamelCase )
| 487 |
"""simple docstring"""
from __future__ import annotations
from typing import Any
class snake_case :
def __init__( self :Optional[Any] , _lowerCamelCase :int ):
__SCREAMING_SNAKE_CASE : int = num_of_nodes
__SCREAMING_SNAKE_CASE : list[list[int]] = []
__SCREAMING_SNAKE_CASE : dict[int, int] = {}
def SCREAMING_SNAKE_CASE_ ( self :int , _lowerCamelCase :int , _lowerCamelCase :int , _lowerCamelCase :int ):
self.m_edges.append([u_node, v_node, weight] )
def SCREAMING_SNAKE_CASE_ ( self :Tuple , _lowerCamelCase :int ):
if self.m_component[u_node] == u_node:
return u_node
return self.find_component(self.m_component[u_node] )
def SCREAMING_SNAKE_CASE_ ( self :List[str] , _lowerCamelCase :int ):
if self.m_component[u_node] != u_node:
for k in self.m_component:
__SCREAMING_SNAKE_CASE : Optional[Any] = self.find_component(_lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :Union[str, Any] , _lowerCamelCase :list[int] , _lowerCamelCase :int , _lowerCamelCase :int ):
if component_size[u_node] <= component_size[v_node]:
__SCREAMING_SNAKE_CASE : List[Any] = v_node
component_size[v_node] += component_size[u_node]
self.set_component(_lowerCamelCase )
elif component_size[u_node] >= component_size[v_node]:
__SCREAMING_SNAKE_CASE : Dict = self.find_component(_lowerCamelCase )
component_size[u_node] += component_size[v_node]
self.set_component(_lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :List[str] ):
__SCREAMING_SNAKE_CASE : Optional[int] = []
__SCREAMING_SNAKE_CASE : str = 0
__SCREAMING_SNAKE_CASE : list[Any] = [-1] * self.m_num_of_nodes
# A list of components (initialized to all of the nodes)
for node in range(self.m_num_of_nodes ):
self.m_component.update({node: node} )
component_size.append(1 )
__SCREAMING_SNAKE_CASE : str = self.m_num_of_nodes
while num_of_components > 1:
for edge in self.m_edges:
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Optional[Any] = edge
__SCREAMING_SNAKE_CASE : Optional[Any] = self.m_component[u]
__SCREAMING_SNAKE_CASE : int = self.m_component[v]
if u_component != v_component:
for component in (u_component, v_component):
if (
minimum_weight_edge[component] == -1
or minimum_weight_edge[component][2] > w
):
__SCREAMING_SNAKE_CASE : Optional[Any] = [u, v, w]
for edge in minimum_weight_edge:
if isinstance(_lowerCamelCase , _lowerCamelCase ):
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : int = edge
__SCREAMING_SNAKE_CASE : Tuple = self.m_component[u]
__SCREAMING_SNAKE_CASE : int = self.m_component[v]
if u_component != v_component:
mst_weight += w
self.union(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase )
print(f'''Added edge [{u} - {v}]\nAdded weight: {w}\n''' )
num_of_components -= 1
__SCREAMING_SNAKE_CASE : Optional[Any] = [-1] * self.m_num_of_nodes
print(f'''The total weight of the minimal spanning tree is: {mst_weight}''' )
def lowerCAmelCase_ ( ):
'''simple docstring'''
if __name__ == "__main__":
import doctest
doctest.testmod()
| 674 | 0 |
"""simple docstring"""
import argparse
import torch
from transformers import BertConfig, BertForPreTraining, load_tf_weights_in_bert
from transformers.utils import logging
logging.set_verbosity_info()
def __UpperCAmelCase ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
__lowercase : Tuple = BertConfig.from_json_file(lowercase_ )
print(f"""Building PyTorch model from configuration: {config}""" )
__lowercase : Optional[int] = BertForPreTraining(lowercase_ )
# Load weights from tf checkpoint
load_tf_weights_in_bert(lowercase_ , lowercase_ , lowercase_ )
# Save pytorch-model
print(f"""Save PyTorch model to {pytorch_dump_path}""" )
torch.save(model.state_dict() , lowercase_ )
if __name__ == "__main__":
a_ = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--tf_checkpoint_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
)
parser.add_argument(
'--bert_config_file',
default=None,
type=str,
required=True,
help=(
'The config json file corresponding to the pre-trained BERT model. \n'
'This specifies the model architecture.'
),
)
parser.add_argument(
'--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
)
a_ = parser.parse_args()
convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)
| 76 |
"""simple docstring"""
import argparse
import pickle
import numpy as np
import torch
from torch import nn
from transformers import ReformerConfig, ReformerModelWithLMHead
from transformers.utils import logging
logging.set_verbosity_info()
def lowerCAmelCase_ ( lowercase_ : int , lowercase_ : Any , lowercase_ : int=None ):
'''simple docstring'''
assert torch_layer.weight.shape == weight.shape, F'''{torch_layer} layer.weight does not match'''
__SCREAMING_SNAKE_CASE : str = nn.Parameter(lowercase_ )
if bias is not None:
assert torch_layer.bias.shape == bias.shape, F'''{torch_layer} layer.bias does not match'''
__SCREAMING_SNAKE_CASE : Tuple = nn.Parameter(lowercase_ )
def lowerCAmelCase_ ( lowercase_ : Tuple , lowercase_ : int , lowercase_ : int ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : Optional[int] = np.asarray(weights[0] )
__SCREAMING_SNAKE_CASE : Optional[int] = np.asarray(weights[1] )
__SCREAMING_SNAKE_CASE : Union[str, Any] = np.asarray(weights[2] )
set_param(
torch_layer.self_attention.query_key , torch.tensor(lowercase_ ).transpose(1 , 2 ).contiguous().view(-1 , lowercase_ ) , )
set_param(
torch_layer.self_attention.value , torch.tensor(lowercase_ ).transpose(1 , 2 ).contiguous().view(-1 , lowercase_ ) , )
set_param(
torch_layer.output.dense , torch.tensor(lowercase_ ).view(-1 , lowercase_ ).contiguous().transpose(0 , 1 ) , )
def lowerCAmelCase_ ( lowercase_ : Dict , lowercase_ : List[str] , lowercase_ : List[str] ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : List[str] = np.asarray(weights[0] )
__SCREAMING_SNAKE_CASE : Any = np.asarray(weights[1] )
__SCREAMING_SNAKE_CASE : Union[str, Any] = np.asarray(weights[2] )
__SCREAMING_SNAKE_CASE : Tuple = np.asarray(weights[3] )
set_param(
torch_layer.self_attention.query , torch.tensor(lowercase_ ).transpose(1 , 2 ).contiguous().view(-1 , lowercase_ ) , )
set_param(
torch_layer.self_attention.key , torch.tensor(lowercase_ ).transpose(1 , 2 ).contiguous().view(-1 , lowercase_ ) , )
set_param(
torch_layer.self_attention.value , torch.tensor(lowercase_ ).transpose(1 , 2 ).contiguous().view(-1 , lowercase_ ) , )
set_param(
torch_layer.output.dense , torch.tensor(lowercase_ ).view(-1 , lowercase_ ).contiguous().transpose(0 , 1 ) , )
def lowerCAmelCase_ ( lowercase_ : int , lowercase_ : List[str] , lowercase_ : str ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : Any = weights[0][0][0]
__SCREAMING_SNAKE_CASE : Union[str, Any] = np.asarray(layer_norm_a[0] )
__SCREAMING_SNAKE_CASE : List[Any] = np.asarray(layer_norm_a[1] )
set_param(
torch_block.attention.layer_norm , torch.tensor(lowercase_ ) , torch.tensor(lowercase_ ) , )
# lsh weights + output
__SCREAMING_SNAKE_CASE : Tuple = weights[0][1]
if len(lowercase_ ) < 4:
set_layer_weights_in_torch_lsh(lowercase_ , torch_block.attention , lowercase_ )
else:
set_layer_weights_in_torch_local(lowercase_ , torch_block.attention , lowercase_ )
# intermediate weighs
__SCREAMING_SNAKE_CASE : Any = weights[2][0][1][2]
# Chunked Feed Forward
if len(lowercase_ ) == 4:
__SCREAMING_SNAKE_CASE : List[str] = intermediate_weights[2]
# layernorm 2
__SCREAMING_SNAKE_CASE : List[str] = np.asarray(intermediate_weights[0][0] )
__SCREAMING_SNAKE_CASE : Union[str, Any] = np.asarray(intermediate_weights[0][1] )
set_param(
torch_block.feed_forward.layer_norm , torch.tensor(lowercase_ ) , torch.tensor(lowercase_ ) , )
# intermediate dense
__SCREAMING_SNAKE_CASE : int = np.asarray(intermediate_weights[1][0] )
__SCREAMING_SNAKE_CASE : Optional[Any] = np.asarray(intermediate_weights[1][1] )
set_param(
torch_block.feed_forward.dense.dense , torch.tensor(lowercase_ ).transpose(0 , 1 ).contiguous() , torch.tensor(lowercase_ ) , )
# intermediate out
__SCREAMING_SNAKE_CASE : Optional[Any] = np.asarray(intermediate_weights[4][0] )
__SCREAMING_SNAKE_CASE : Any = np.asarray(intermediate_weights[4][1] )
set_param(
torch_block.feed_forward.output.dense , torch.tensor(lowercase_ ).transpose(0 , 1 ).contiguous() , torch.tensor(lowercase_ ) , )
def lowerCAmelCase_ ( lowercase_ : str , lowercase_ : Optional[Any] , lowercase_ : Optional[Any] ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : Dict = torch_model.reformer
# word embeds
__SCREAMING_SNAKE_CASE : int = np.asarray(weights[1] )
set_param(
torch_model_reformer.embeddings.word_embeddings , torch.tensor(lowercase_ ) , )
if isinstance(weights[3] , lowercase_ ):
__SCREAMING_SNAKE_CASE : int = torch_model_reformer.embeddings.position_embeddings
for emb_idx in range(len(position_embeddings.weights ) ):
__SCREAMING_SNAKE_CASE : Dict = np.asarray(weights[3][emb_idx][0] )
assert (
position_embeddings.weights[emb_idx].shape == emb_weights.shape
), F'''{position_embeddings[emb_idx]} emb does not match'''
__SCREAMING_SNAKE_CASE : str = nn.Parameter(torch.tensor(lowercase_ ) )
__SCREAMING_SNAKE_CASE : List[Any] = weights[5]
assert len(torch_model_reformer.encoder.layers ) * 4 == len(
lowercase_ ), "HF and trax model do not have the same number of layers"
for layer_idx, layer in enumerate(torch_model_reformer.encoder.layers ):
__SCREAMING_SNAKE_CASE : Union[str, Any] = trax_layer_weights[4 * layer_idx : 4 * (layer_idx + 1)]
set_block_weights_in_torch(lowercase_ , lowercase_ , lowercase_ )
# output layer norm
__SCREAMING_SNAKE_CASE : List[str] = np.asarray(weights[7][0] )
__SCREAMING_SNAKE_CASE : Optional[Any] = np.asarray(weights[7][1] )
set_param(
torch_model_reformer.encoder.layer_norm , torch.tensor(lowercase_ ) , torch.tensor(lowercase_ ) , )
# output embeddings
__SCREAMING_SNAKE_CASE : Optional[Any] = np.asarray(weights[9][0] )
__SCREAMING_SNAKE_CASE : List[Any] = np.asarray(weights[9][1] )
set_param(
torch_model.lm_head.decoder , torch.tensor(lowercase_ ).transpose(0 , 1 ).contiguous() , torch.tensor(lowercase_ ) , )
def lowerCAmelCase_ ( lowercase_ : List[str] , lowercase_ : Any , lowercase_ : int ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : Dict = ReformerConfig.from_json_file(lowercase_ )
print(F'''Building PyTorch model from configuration: {config}''' )
__SCREAMING_SNAKE_CASE : List[str] = ReformerModelWithLMHead(lowercase_ )
with open(lowercase_ , '''rb''' ) as f:
__SCREAMING_SNAKE_CASE : int = pickle.load(lowercase_ )['''weights''']
set_model_weights_in_torch(lowercase_ , lowercase_ , config.hidden_size )
# Save pytorch-model
print(F'''Save PyTorch model to {pytorch_dump_path}''' )
torch.save(model.state_dict() , lowercase_ )
if __name__ == "__main__":
_lowerCamelCase = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--trax_model_pkl_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
)
parser.add_argument(
'''--config_file''',
default=None,
type=str,
required=True,
help=(
'''The config json file corresponding to the pre-trained Reformer model. \n'''
'''This specifies the model architecture.'''
),
)
parser.add_argument(
'''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
)
_lowerCamelCase = parser.parse_args()
convert_trax_checkpoint_to_pytorch(args.trax_model_pkl_path, args.config_file, args.pytorch_dump_path)
| 674 | 0 |
def A__ ( snake_case_ : str ):
SCREAMING_SNAKE_CASE__: List[Any]= hex_num.strip()
if not hex_num:
raise ValueError('''No value was passed to the function''' )
SCREAMING_SNAKE_CASE__: Optional[int]= hex_num[0] == '''-'''
if is_negative:
SCREAMING_SNAKE_CASE__: Optional[int]= hex_num[1:]
try:
SCREAMING_SNAKE_CASE__: Union[str, Any]= int(lowercase_ , 16 )
except ValueError:
raise ValueError('''Invalid value was passed to the function''' )
SCREAMING_SNAKE_CASE__: List[str]= ''''''
while int_num > 0:
SCREAMING_SNAKE_CASE__: Union[str, Any]= str(int_num % 2 ) + bin_str
int_num >>= 1
return int(('''-''' + bin_str) if is_negative else bin_str )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 64 |
"""simple docstring"""
from typing import Callable, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCamelCase = logging.get_logger(__name__)
_lowerCamelCase = {
'''microsoft/xprophetnet-large-wiki100-cased''': (
'''https://huggingface.co/microsoft/xprophetnet-large-wiki100-cased/resolve/main/config.json'''
),
}
class snake_case ( __UpperCAmelCase ):
lowerCamelCase__ = '''xlm-prophetnet'''
lowerCamelCase__ = ['''past_key_values''']
lowerCamelCase__ = {
'''num_attention_heads''': '''num_encoder_attention_heads''',
}
def __init__( self :List[str] , _lowerCamelCase :Optional[float] = 0.1 , _lowerCamelCase :Optional[Union[str, Callable]] = "gelu" , _lowerCamelCase :Optional[int] = 3_0_5_2_2 , _lowerCamelCase :Optional[int] = 1_0_2_4 , _lowerCamelCase :Optional[int] = 4_0_9_6 , _lowerCamelCase :Optional[int] = 1_2 , _lowerCamelCase :Optional[int] = 1_6 , _lowerCamelCase :Optional[int] = 4_0_9_6 , _lowerCamelCase :Optional[int] = 1_2 , _lowerCamelCase :Optional[int] = 1_6 , _lowerCamelCase :Optional[float] = 0.1 , _lowerCamelCase :Optional[float] = 0.1 , _lowerCamelCase :Optional[int] = 5_1_2 , _lowerCamelCase :Optional[float] = 0.0_2 , _lowerCamelCase :Optional[bool] = True , _lowerCamelCase :Optional[bool] = True , _lowerCamelCase :Optional[int] = 0 , _lowerCamelCase :Optional[int] = 2 , _lowerCamelCase :Optional[int] = 3_2 , _lowerCamelCase :Optional[int] = 1_2_8 , _lowerCamelCase :Optional[bool] = False , _lowerCamelCase :Optional[float] = 0.0 , _lowerCamelCase :Optional[bool] = True , _lowerCamelCase :Optional[int] = 0 , _lowerCamelCase :Optional[int] = 1 , _lowerCamelCase :Optional[int] = 2 , **_lowerCamelCase :int , ):
__SCREAMING_SNAKE_CASE : Union[str, Any] = vocab_size
__SCREAMING_SNAKE_CASE : Optional[int] = hidden_size
__SCREAMING_SNAKE_CASE : List[Any] = encoder_ffn_dim
__SCREAMING_SNAKE_CASE : str = num_encoder_layers
__SCREAMING_SNAKE_CASE : Optional[Any] = num_encoder_attention_heads
__SCREAMING_SNAKE_CASE : str = decoder_ffn_dim
__SCREAMING_SNAKE_CASE : List[Any] = num_decoder_layers
__SCREAMING_SNAKE_CASE : List[str] = num_decoder_attention_heads
__SCREAMING_SNAKE_CASE : Dict = max_position_embeddings
__SCREAMING_SNAKE_CASE : Any = init_std # Normal(0, this parameter)
__SCREAMING_SNAKE_CASE : Any = activation_function
# parameters for xlmprophetnet
__SCREAMING_SNAKE_CASE : List[Any] = ngram
__SCREAMING_SNAKE_CASE : int = num_buckets
__SCREAMING_SNAKE_CASE : List[str] = relative_max_distance
__SCREAMING_SNAKE_CASE : str = disable_ngram_loss
__SCREAMING_SNAKE_CASE : Optional[int] = eps
# 3 Types of Dropout
__SCREAMING_SNAKE_CASE : int = attention_dropout
__SCREAMING_SNAKE_CASE : Optional[Any] = activation_dropout
__SCREAMING_SNAKE_CASE : Dict = dropout
__SCREAMING_SNAKE_CASE : Any = use_cache
super().__init__(
pad_token_id=_lowerCamelCase , bos_token_id=_lowerCamelCase , eos_token_id=_lowerCamelCase , is_encoder_decoder=_lowerCamelCase , add_cross_attention=_lowerCamelCase , decoder_start_token_id=_lowerCamelCase , **_lowerCamelCase , )
@property
def SCREAMING_SNAKE_CASE_ ( self :int ):
return self.num_encoder_layers + self.num_decoder_layers
@num_hidden_layers.setter
def SCREAMING_SNAKE_CASE_ ( self :Dict , _lowerCamelCase :List[Any] ):
raise NotImplementedError(
'''This model does not support the setting of `num_hidden_layers`. Please set `num_encoder_layers` and'''
''' `num_decoder_layers`.''' )
| 674 | 0 |
import unittest
from transformers import PegasusTokenizer, PegasusTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
a : int = get_tests_dir('''fixtures/test_sentencepiece_no_bos.model''')
@require_sentencepiece
@require_tokenizers
class lowerCamelCase_ ( __UpperCAmelCase , unittest.TestCase ):
'''simple docstring'''
__UpperCAmelCase = PegasusTokenizer
__UpperCAmelCase = PegasusTokenizerFast
__UpperCAmelCase = True
__UpperCAmelCase = True
def A ( self ) -> int:
'''simple docstring'''
super().setUp()
# We have a SentencePiece fixture for testing
__lowercase = PegasusTokenizer(_lowerCamelCase )
tokenizer.save_pretrained(self.tmpdirname )
@cached_property
def A ( self ) -> Any:
'''simple docstring'''
return PegasusTokenizer.from_pretrained('''google/pegasus-large''' )
def A ( self , **snake_case_ ) -> Optional[Any]:
'''simple docstring'''
return PegasusTokenizer.from_pretrained(self.tmpdirname , **_lowerCamelCase )
def A ( self , snake_case_ ) -> int:
'''simple docstring'''
return ("This is a test", "This is a test")
def A ( self ) -> Dict:
'''simple docstring'''
__lowercase = '''</s>'''
__lowercase = 1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(_lowerCamelCase ) , _lowerCamelCase )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(_lowerCamelCase ) , _lowerCamelCase )
def A ( self ) -> Dict:
'''simple docstring'''
__lowercase = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , '''<pad>''' )
self.assertEqual(vocab_keys[1] , '''</s>''' )
self.assertEqual(vocab_keys[-1] , '''v''' )
self.assertEqual(len(_lowerCamelCase ) , 1_1_0_3 )
def A ( self ) -> Optional[int]:
'''simple docstring'''
self.assertEqual(self.get_tokenizer().vocab_size , 1_1_0_3 )
def A ( self ) -> Tuple:
'''simple docstring'''
__lowercase = self.rust_tokenizer_class.from_pretrained(self.tmpdirname )
__lowercase = self.tokenizer_class.from_pretrained(self.tmpdirname )
__lowercase = (
'''Let\'s see which <unk> is the better <unk_token_11> one <mask_1> It seems like this <mask_2> was important'''
''' </s> <pad> <pad> <pad>'''
)
__lowercase = rust_tokenizer([raw_input_str] , return_tensors=_lowerCamelCase , add_special_tokens=_lowerCamelCase ).input_ids[0]
__lowercase = py_tokenizer([raw_input_str] , return_tensors=_lowerCamelCase , add_special_tokens=_lowerCamelCase ).input_ids[0]
self.assertListEqual(_lowerCamelCase , _lowerCamelCase )
def A ( self ) -> Optional[Any]:
'''simple docstring'''
__lowercase = self._large_tokenizer
# <mask_1> masks whole sentence while <mask_2> masks single word
__lowercase = '''<mask_1> To ensure a <mask_2> flow of bank resolutions.'''
__lowercase = [2, 4_1_3, 6_1_5, 1_1_4, 3, 1_9_7_1, 1_1_3, 1_6_7_9, 1_0_7_1_0, 1_0_7, 1]
__lowercase = tokenizer([raw_input_str] , return_tensors=_lowerCamelCase ).input_ids[0]
self.assertListEqual(_lowerCamelCase , _lowerCamelCase )
def A ( self ) -> Optional[Any]:
'''simple docstring'''
__lowercase = self._large_tokenizer
# The tracebacks for the following asserts are **better** without messages or self.assertEqual
assert tokenizer.vocab_size == 9_6_1_0_3
assert tokenizer.pad_token_id == 0
assert tokenizer.eos_token_id == 1
assert tokenizer.offset == 1_0_3
assert tokenizer.unk_token_id == tokenizer.offset + 2 == 1_0_5
assert tokenizer.unk_token == "<unk>"
assert tokenizer.model_max_length == 1_0_2_4
__lowercase = '''To ensure a smooth flow of bank resolutions.'''
__lowercase = [4_1_3, 6_1_5, 1_1_4, 2_2_9_1, 1_9_7_1, 1_1_3, 1_6_7_9, 1_0_7_1_0, 1_0_7, 1]
__lowercase = tokenizer([raw_input_str] , return_tensors=_lowerCamelCase ).input_ids[0]
self.assertListEqual(_lowerCamelCase , _lowerCamelCase )
assert tokenizer.convert_ids_to_tokens([0, 1, 2, 3] ) == ["<pad>", "</s>", "<mask_1>", "<mask_2>"]
@require_torch
def A ( self ) -> int:
'''simple docstring'''
__lowercase = ['''This is going to be way too long.''' * 1_5_0, '''short example''']
__lowercase = ['''not super long but more than 5 tokens''', '''tiny''']
__lowercase = self._large_tokenizer(_lowerCamelCase , padding=_lowerCamelCase , truncation=_lowerCamelCase , return_tensors='''pt''' )
__lowercase = self._large_tokenizer(
text_target=_lowerCamelCase , max_length=5 , padding=_lowerCamelCase , truncation=_lowerCamelCase , return_tensors='''pt''' )
assert batch.input_ids.shape == (2, 1_0_2_4)
assert batch.attention_mask.shape == (2, 1_0_2_4)
assert targets["input_ids"].shape == (2, 5)
assert len(_lowerCamelCase ) == 2 # input_ids, attention_mask.
@slow
def A ( self ) -> Any:
'''simple docstring'''
__lowercase = {'''input_ids''': [[3_8_9_7_9, 1_4_3, 1_8_4_8_5, 6_0_6, 1_3_0, 2_6_6_6_9, 8_7_6_8_6, 1_2_1, 5_4_1_8_9, 1_1_2_9, 1_1_1, 2_6_6_6_9, 8_7_6_8_6, 1_2_1, 9_1_1_4, 1_4_7_8_7, 1_2_1, 1_3_2_4_9, 1_5_8, 5_9_2, 9_5_6, 1_2_1, 1_4_6_2_1, 3_1_5_7_6, 1_4_3, 6_2_6_1_3, 1_0_8, 9_6_8_8, 9_3_0, 4_3_4_3_0, 1_1_5_6_2, 6_2_6_1_3, 3_0_4, 1_0_8, 1_1_4_4_3, 8_9_7, 1_0_8, 9_3_1_4, 1_7_4_1_5, 6_3_3_9_9, 1_0_8, 1_1_4_4_3, 7_6_1_4, 1_8_3_1_6, 1_1_8, 4_2_8_4, 7_1_4_8, 1_2_4_3_0, 1_4_3, 1_4_0_0, 2_5_7_0_3, 1_5_8, 1_1_1, 4_2_8_4, 7_1_4_8, 1_1_7_7_2, 1_4_3, 2_1_2_9_7, 1_0_6_4, 1_5_8, 1_2_2, 2_0_4, 3_5_0_6, 1_7_5_4, 1_1_3_3, 1_4_7_8_7, 1_5_8_1, 1_1_5, 3_3_2_2_4, 4_4_8_2, 1_1_1, 1_3_5_5, 1_1_0, 2_9_1_7_3, 3_1_7, 5_0_8_3_3, 1_0_8, 2_0_1_4_7, 9_4_6_6_5, 1_1_1, 7_7_1_9_8, 1_0_7, 1], [1_1_0, 6_2_6_1_3, 1_1_7, 6_3_8, 1_1_2, 1_1_3_3, 1_2_1, 2_0_0_9_8, 1_3_5_5, 7_9_0_5_0, 1_3_8_7_2, 1_3_5, 1_5_9_6, 5_3_5_4_1, 1_3_5_2, 1_4_1, 1_3_0_3_9, 5_5_4_2, 1_2_4, 3_0_2, 5_1_8, 1_1_1, 2_6_8, 2_9_5_6, 1_1_5, 1_4_9, 4_4_2_7, 1_0_7, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1_3_9, 1_2_3_5, 2_7_9_9, 1_8_2_8_9, 1_7_7_8_0, 2_0_4, 1_0_9, 9_4_7_4, 1_2_9_6, 1_0_7, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], '''attention_mask''': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]} # noqa: E501
# fmt: on
self.tokenizer_integration_test_util(
expected_encoding=_lowerCamelCase , model_name='''google/bigbird-pegasus-large-arxiv''' , revision='''ba85d0851d708441f91440d509690f1ab6353415''' , )
@require_sentencepiece
@require_tokenizers
class lowerCamelCase_ ( __UpperCAmelCase , unittest.TestCase ):
'''simple docstring'''
__UpperCAmelCase = PegasusTokenizer
__UpperCAmelCase = PegasusTokenizerFast
__UpperCAmelCase = True
__UpperCAmelCase = True
def A ( self ) -> Dict:
'''simple docstring'''
super().setUp()
# We have a SentencePiece fixture for testing
__lowercase = PegasusTokenizer(_lowerCamelCase , offset=0 , mask_token_sent=_lowerCamelCase , mask_token='''[MASK]''' )
tokenizer.save_pretrained(self.tmpdirname )
@cached_property
def A ( self ) -> Optional[Any]:
'''simple docstring'''
return PegasusTokenizer.from_pretrained('''google/bigbird-pegasus-large-arxiv''' )
def A ( self , **snake_case_ ) -> List[str]:
'''simple docstring'''
return PegasusTokenizer.from_pretrained(self.tmpdirname , **_lowerCamelCase )
def A ( self , snake_case_ ) -> Dict:
'''simple docstring'''
return ("This is a test", "This is a test")
def A ( self ) -> Dict:
'''simple docstring'''
__lowercase = self.rust_tokenizer_class.from_pretrained(self.tmpdirname )
__lowercase = self.tokenizer_class.from_pretrained(self.tmpdirname )
__lowercase = (
'''Let\'s see which <unk> is the better <unk_token> one [MASK] It seems like this [MASK] was important </s>'''
''' <pad> <pad> <pad>'''
)
__lowercase = rust_tokenizer([raw_input_str] , return_tensors=_lowerCamelCase , add_special_tokens=_lowerCamelCase ).input_ids[0]
__lowercase = py_tokenizer([raw_input_str] , return_tensors=_lowerCamelCase , add_special_tokens=_lowerCamelCase ).input_ids[0]
self.assertListEqual(_lowerCamelCase , _lowerCamelCase )
@require_torch
def A ( self ) -> str:
'''simple docstring'''
__lowercase = ['''This is going to be way too long.''' * 1_0_0_0, '''short example''']
__lowercase = ['''not super long but more than 5 tokens''', '''tiny''']
__lowercase = self._large_tokenizer(_lowerCamelCase , padding=_lowerCamelCase , truncation=_lowerCamelCase , return_tensors='''pt''' )
__lowercase = self._large_tokenizer(
text_target=_lowerCamelCase , max_length=5 , padding=_lowerCamelCase , truncation=_lowerCamelCase , return_tensors='''pt''' )
assert batch.input_ids.shape == (2, 4_0_9_6)
assert batch.attention_mask.shape == (2, 4_0_9_6)
assert targets["input_ids"].shape == (2, 5)
assert len(_lowerCamelCase ) == 2 # input_ids, attention_mask.
def A ( self ) -> str:
'''simple docstring'''
__lowercase = (
'''This is an example string that is used to test the original TF implementation against the HF'''
''' implementation'''
)
__lowercase = self._large_tokenizer(_lowerCamelCase ).input_ids
self.assertListEqual(
_lowerCamelCase , [1_8_2, 1_1_7, 1_4_2, 5_8_7, 4_2_1_1, 1_2_0, 1_1_7, 2_6_3, 1_1_2, 8_0_4, 1_0_9, 8_5_6, 2_5_0_1_6, 3_1_3_7, 4_6_4, 1_0_9, 2_6_9_5_5, 3_1_3_7, 1] , )
| 639 |
"""simple docstring"""
import ast
import os
import re
import shutil
import tempfile
import unittest
from unittest import mock
import torch
from accelerate.test_utils.examples import compare_against_test
from accelerate.test_utils.testing import TempDirTestCase, require_trackers, run_command, slow
from accelerate.utils import write_basic_config
# DataLoaders built from `test_samples/MRPC` for quick testing
# Should mock `{script_name}.get_dataloaders` via:
# @mock.patch("{script_name}.get_dataloaders", mocked_dataloaders)
_lowerCamelCase = [
'''cross_validation.py''',
'''gradient_accumulation.py''',
'''local_sgd.py''',
'''multi_process_metrics.py''',
'''memory.py''',
'''automatic_gradient_accumulation.py''',
'''fsdp_with_peak_mem_tracking.py''',
'''deepspeed_with_config_support.py''',
'''megatron_lm_gpt_pretraining.py''',
]
class snake_case ( unittest.TestCase ):
def SCREAMING_SNAKE_CASE_ ( self :List[str] , _lowerCamelCase :str , _lowerCamelCase :bool , _lowerCamelCase :str = None , _lowerCamelCase :list = None ):
__SCREAMING_SNAKE_CASE : List[str] = None
__SCREAMING_SNAKE_CASE : Optional[Any] = os.path.abspath(os.path.join('''examples''' , '''by_feature''' ) )
__SCREAMING_SNAKE_CASE : Union[str, Any] = os.path.abspath('''examples''' )
for item in os.listdir(_lowerCamelCase ):
if item not in EXCLUDE_EXAMPLES:
__SCREAMING_SNAKE_CASE : List[Any] = os.path.join(_lowerCamelCase , _lowerCamelCase )
if os.path.isfile(_lowerCamelCase ) and ".py" in item_path:
with self.subTest(
tested_script=_lowerCamelCase , feature_script=_lowerCamelCase , tested_section='''main()''' if parser_only else '''training_function()''' , ):
__SCREAMING_SNAKE_CASE : Tuple = compare_against_test(
os.path.join(_lowerCamelCase , _lowerCamelCase ) , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : Optional[Any] = '''\n'''.join(_lowerCamelCase )
if special_strings is not None:
for string in special_strings:
__SCREAMING_SNAKE_CASE : List[Any] = diff.replace(_lowerCamelCase , '''''' )
self.assertEqual(_lowerCamelCase , '''''' )
def SCREAMING_SNAKE_CASE_ ( self :Union[str, Any] ):
self.one_complete_example('''complete_nlp_example.py''' , _lowerCamelCase )
self.one_complete_example('''complete_nlp_example.py''' , _lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :List[str] ):
__SCREAMING_SNAKE_CASE : Union[str, Any] = os.path.abspath(os.path.join('''examples''' , '''cv_example.py''' ) )
__SCREAMING_SNAKE_CASE : Optional[int] = [
''' ''' * 1_6 + '''{\n\n''',
''' ''' * 2_0 + '''"accuracy": eval_metric["accuracy"],\n\n''',
''' ''' * 2_0 + '''"f1": eval_metric["f1"],\n\n''',
''' ''' * 2_0 + '''"train_loss": total_loss.item() / len(train_dataloader),\n\n''',
''' ''' * 2_0 + '''"epoch": epoch,\n\n''',
''' ''' * 1_6 + '''},\n\n''',
''' ''' * 1_6 + '''step=epoch,\n''',
''' ''' * 1_2,
''' ''' * 8 + '''for step, batch in enumerate(active_dataloader):\n''',
]
self.one_complete_example('''complete_cv_example.py''' , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase )
self.one_complete_example('''complete_cv_example.py''' , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase )
@mock.patch.dict(os.environ , {'''TESTING_MOCKED_DATALOADERS''': '''1'''} )
class snake_case ( __UpperCAmelCase ):
lowerCamelCase__ = False
@classmethod
def SCREAMING_SNAKE_CASE_ ( cls :Dict ):
super().setUpClass()
__SCREAMING_SNAKE_CASE : Dict = tempfile.mkdtemp()
__SCREAMING_SNAKE_CASE : str = os.path.join(cls._tmpdir , '''default_config.yml''' )
write_basic_config(save_location=cls.configPath )
__SCREAMING_SNAKE_CASE : List[Any] = ['''accelerate''', '''launch''', '''--config_file''', cls.configPath]
@classmethod
def SCREAMING_SNAKE_CASE_ ( cls :Dict ):
super().tearDownClass()
shutil.rmtree(cls._tmpdir )
def SCREAMING_SNAKE_CASE_ ( self :Tuple ):
__SCREAMING_SNAKE_CASE : str = f'''
examples/by_feature/checkpointing.py
--checkpointing_steps epoch
--output_dir {self.tmpdir}
'''.split()
run_command(self._launch_args + testargs )
self.assertTrue(os.path.exists(os.path.join(self.tmpdir , '''epoch_0''' ) ) )
def SCREAMING_SNAKE_CASE_ ( self :List[Any] ):
__SCREAMING_SNAKE_CASE : Optional[Any] = f'''
examples/by_feature/checkpointing.py
--checkpointing_steps 1
--output_dir {self.tmpdir}
'''.split()
__SCREAMING_SNAKE_CASE : Optional[int] = run_command(self._launch_args + testargs )
self.assertTrue(os.path.exists(os.path.join(self.tmpdir , '''step_2''' ) ) )
def SCREAMING_SNAKE_CASE_ ( self :List[str] ):
__SCREAMING_SNAKE_CASE : Optional[int] = f'''
examples/by_feature/checkpointing.py
--resume_from_checkpoint {os.path.join(self.tmpdir , 'epoch_0' )}
'''.split()
__SCREAMING_SNAKE_CASE : Any = run_command(self._launch_args + testargs , return_stdout=_lowerCamelCase )
self.assertNotIn('''epoch 0:''' , _lowerCamelCase )
self.assertIn('''epoch 1:''' , _lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :Union[str, Any] ):
__SCREAMING_SNAKE_CASE : Optional[int] = f'''
examples/by_feature/checkpointing.py
--resume_from_checkpoint {os.path.join(self.tmpdir , 'step_2' )}
'''.split()
__SCREAMING_SNAKE_CASE : List[str] = run_command(self._launch_args + testargs , return_stdout=_lowerCamelCase )
if torch.cuda.is_available():
__SCREAMING_SNAKE_CASE : List[Any] = torch.cuda.device_count()
else:
__SCREAMING_SNAKE_CASE : Optional[int] = 1
if num_processes > 1:
self.assertNotIn('''epoch 0:''' , _lowerCamelCase )
self.assertIn('''epoch 1:''' , _lowerCamelCase )
else:
self.assertIn('''epoch 0:''' , _lowerCamelCase )
self.assertIn('''epoch 1:''' , _lowerCamelCase )
@slow
def SCREAMING_SNAKE_CASE_ ( self :str ):
__SCREAMING_SNAKE_CASE : Optional[Any] = '''
examples/by_feature/cross_validation.py
--num_folds 2
'''.split()
with mock.patch.dict(os.environ , {'''TESTING_MOCKED_DATALOADERS''': '''0'''} ):
__SCREAMING_SNAKE_CASE : Union[str, Any] = run_command(self._launch_args + testargs , return_stdout=_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Any = re.findall('''({.+})''' , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : List[Any] = [r for r in results if '''accuracy''' in r][-1]
__SCREAMING_SNAKE_CASE : Tuple = ast.literal_eval(_lowerCamelCase )
self.assertGreaterEqual(results['''accuracy'''] , 0.7_5 )
def SCREAMING_SNAKE_CASE_ ( self :str ):
__SCREAMING_SNAKE_CASE : Optional[Any] = ['''examples/by_feature/multi_process_metrics.py''']
run_command(self._launch_args + testargs )
@require_trackers
@mock.patch.dict(os.environ , {'''WANDB_MODE''': '''offline'''} )
def SCREAMING_SNAKE_CASE_ ( self :List[str] ):
with tempfile.TemporaryDirectory() as tmpdir:
__SCREAMING_SNAKE_CASE : int = f'''
examples/by_feature/tracking.py
--with_tracking
--project_dir {tmpdir}
'''.split()
run_command(self._launch_args + testargs )
self.assertTrue(os.path.exists(os.path.join(_lowerCamelCase , '''tracking''' ) ) )
def SCREAMING_SNAKE_CASE_ ( self :Dict ):
__SCREAMING_SNAKE_CASE : Union[str, Any] = ['''examples/by_feature/gradient_accumulation.py''']
run_command(self._launch_args + testargs )
def SCREAMING_SNAKE_CASE_ ( self :Optional[int] ):
__SCREAMING_SNAKE_CASE : List[Any] = ['''examples/by_feature/local_sgd.py''']
run_command(self._launch_args + testargs )
| 674 | 0 |
import collections
import tempfile
import unittest
import numpy as np
from transformers.testing_utils import (
is_pt_flax_cross_test,
require_flax,
require_torch,
require_vision,
slow,
torch_device,
)
from transformers.utils import is_flax_available, is_torch_available, is_vision_available
from ...test_modeling_flax_common import floats_tensor, ids_tensor, random_attention_mask
from ..bert.test_modeling_flax_bert import FlaxBertModelTester
from ..clip.test_modeling_flax_clip import FlaxCLIPVisionModelTester
from ..vit.test_modeling_flax_vit import FlaxViTModelTester
if is_flax_available():
from transformers import (
FlaxBertModel,
FlaxCLIPVisionModel,
FlaxVisionTextDualEncoderModel,
FlaxViTModel,
VisionTextDualEncoderConfig,
VisionTextDualEncoderProcessor,
)
from transformers.modeling_flax_pytorch_utils import (
convert_pytorch_state_dict_to_flax,
load_flax_weights_in_pytorch_model,
)
if is_torch_available():
import torch
from transformers import VisionTextDualEncoderModel
if is_vision_available():
from PIL import Image
def lowerCAmelCase_ ( lowercase: Any ) -> List[Any]:
'''simple docstring'''
if isinstance(lowercase_ , collections.abc.Iterable ):
return x
return (x, x)
@require_flax
class __magic_name__ :
"""simple docstring"""
def lowerCAmelCase ( self : Union[str, Any] , _lowercase : Tuple , _lowercase : Tuple ):
"""simple docstring"""
pass
def lowerCAmelCase ( self : str ):
"""simple docstring"""
pass
def lowerCAmelCase ( self : List[Any] ):
"""simple docstring"""
pass
def lowerCAmelCase ( self : Dict , _lowercase : np.ndarray , _lowercase : np.ndarray , _lowercase : float ):
"""simple docstring"""
_UpperCamelCase: Dict = np.abs((a - b) ).max()
self.assertLessEqual(_lowerCamelCase , _lowerCamelCase , f"""Difference between torch and flax is {diff} (>= {tol}).""" )
def lowerCAmelCase ( self : str , _lowercase : List[str] , _lowercase : Dict , _lowercase : Optional[int] , _lowercase : List[str] , _lowercase : int=None , **_lowercase : Union[str, Any] ):
"""simple docstring"""
_UpperCamelCase: int = VisionTextDualEncoderConfig.from_vision_text_configs(_lowerCamelCase , _lowerCamelCase )
_UpperCamelCase: int = FlaxVisionTextDualEncoderModel(_lowerCamelCase )
_UpperCamelCase: Union[str, Any] = model(input_ids=_lowerCamelCase , pixel_values=_lowerCamelCase , attention_mask=_lowerCamelCase )
self.assertEqual(output['''text_embeds'''].shape , (input_ids.shape[0], config.projection_dim) )
self.assertEqual(output['''image_embeds'''].shape , (pixel_values.shape[0], config.projection_dim) )
def lowerCAmelCase ( self : str , _lowercase : List[str] , _lowercase : Tuple , _lowercase : List[str] , _lowercase : Dict , _lowercase : int=None , **_lowercase : int ):
"""simple docstring"""
_UpperCamelCase: Optional[Any] = self.get_vision_text_model(_lowerCamelCase , _lowerCamelCase )
_UpperCamelCase: str = {'''vision_model''': vision_model, '''text_model''': text_model}
_UpperCamelCase: Optional[int] = FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**_lowerCamelCase )
_UpperCamelCase: Dict = model(input_ids=_lowerCamelCase , pixel_values=_lowerCamelCase , attention_mask=_lowerCamelCase )
self.assertEqual(output['''text_embeds'''].shape , (input_ids.shape[0], model.config.projection_dim) )
self.assertEqual(output['''image_embeds'''].shape , (pixel_values.shape[0], model.config.projection_dim) )
def lowerCAmelCase ( self : Any , _lowercase : Dict , _lowercase : Optional[int] , _lowercase : Dict , _lowercase : str , _lowercase : List[Any]=None , **_lowercase : Dict ):
"""simple docstring"""
_UpperCamelCase: Tuple = self.get_vision_text_model(_lowerCamelCase , _lowerCamelCase )
_UpperCamelCase: int = {'''vision_model''': vision_model, '''text_model''': text_model}
_UpperCamelCase: Any = FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**_lowerCamelCase )
_UpperCamelCase: Any = model(input_ids=_lowerCamelCase , pixel_values=_lowerCamelCase , attention_mask=_lowerCamelCase )
_UpperCamelCase: Optional[Any] = output[0]
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(_lowerCamelCase )
_UpperCamelCase: List[Any] = FlaxVisionTextDualEncoderModel.from_pretrained(_lowerCamelCase )
_UpperCamelCase: str = model(input_ids=_lowerCamelCase , pixel_values=_lowerCamelCase , attention_mask=_lowerCamelCase )
_UpperCamelCase: Dict = after_output[0]
_UpperCamelCase: Tuple = np.amax(np.abs(out_a - out_a ) )
self.assertLessEqual(_lowerCamelCase , 1E-3 )
def lowerCAmelCase ( self : Optional[int] , _lowercase : List[Any] , _lowercase : int , _lowercase : int , _lowercase : List[str] , _lowercase : List[Any]=None , **_lowercase : Tuple ):
"""simple docstring"""
_UpperCamelCase: int = self.get_vision_text_model(_lowerCamelCase , _lowerCamelCase )
_UpperCamelCase: Dict = {'''vision_model''': vision_model, '''text_model''': text_model}
_UpperCamelCase: str = FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**_lowerCamelCase )
_UpperCamelCase: Union[str, Any] = model(
input_ids=_lowerCamelCase , pixel_values=_lowerCamelCase , attention_mask=_lowerCamelCase , output_attentions=_lowerCamelCase )
_UpperCamelCase: Optional[Any] = output.vision_model_output.attentions
self.assertEqual(len(_lowerCamelCase ) , vision_config.num_hidden_layers )
# in ViT, the seq_len equals the number of patches + 1 (we add 1 for the [CLS] token)
_UpperCamelCase: Tuple = to_atuple(vision_model.config.image_size )
_UpperCamelCase: List[Any] = to_atuple(vision_model.config.patch_size )
_UpperCamelCase: Tuple = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
_UpperCamelCase: int = num_patches + 1
self.assertEqual(vision_attentions[0].shape[-3:] , (vision_config.num_attention_heads, seq_len, seq_len) )
_UpperCamelCase: Optional[int] = output.text_model_output.attentions
self.assertEqual(len(_lowerCamelCase ) , text_config.num_hidden_layers )
self.assertEqual(
text_attentions[0].shape[-3:] , (text_config.num_attention_heads, input_ids.shape[-1], input_ids.shape[-1]) , )
def lowerCAmelCase ( self : Union[str, Any] , _lowercase : Optional[Any] , _lowercase : Any , _lowercase : int ):
"""simple docstring"""
pt_model.to(_lowerCamelCase )
pt_model.eval()
# prepare inputs
_UpperCamelCase: Union[str, Any] = inputs_dict
_UpperCamelCase: Tuple = {k: torch.tensor(v.tolist() ) for k, v in flax_inputs.items()}
with torch.no_grad():
_UpperCamelCase: Union[str, Any] = pt_model(**_lowerCamelCase ).to_tuple()
_UpperCamelCase: Optional[Any] = fx_model(**_lowerCamelCase ).to_tuple()
self.assertEqual(len(_lowerCamelCase ) , len(_lowerCamelCase ) , '''Output lengths differ between Flax and PyTorch''' )
for fx_output, pt_output in zip(fx_outputs[:4] , pt_outputs[:4] ):
self.assert_almost_equals(_lowerCamelCase , pt_output.numpy() , 4E-2 )
# PT -> Flax
with tempfile.TemporaryDirectory() as tmpdirname:
pt_model.save_pretrained(_lowerCamelCase )
_UpperCamelCase: Union[str, Any] = FlaxVisionTextDualEncoderModel.from_pretrained(_lowerCamelCase , from_pt=_lowerCamelCase )
_UpperCamelCase: Optional[Any] = fx_model_loaded(**_lowerCamelCase ).to_tuple()
self.assertEqual(len(_lowerCamelCase ) , len(_lowerCamelCase ) , '''Output lengths differ between Flax and PyTorch''' )
for fx_output_loaded, pt_output in zip(fx_outputs_loaded[:4] , pt_outputs[:4] ):
self.assert_almost_equals(_lowerCamelCase , pt_output.numpy() , 4E-2 )
# Flax -> PT
with tempfile.TemporaryDirectory() as tmpdirname:
fx_model.save_pretrained(_lowerCamelCase )
_UpperCamelCase: List[Any] = VisionTextDualEncoderModel.from_pretrained(_lowerCamelCase , from_flax=_lowerCamelCase )
pt_model_loaded.to(_lowerCamelCase )
pt_model_loaded.eval()
with torch.no_grad():
_UpperCamelCase: Optional[int] = pt_model_loaded(**_lowerCamelCase ).to_tuple()
self.assertEqual(len(_lowerCamelCase ) , len(_lowerCamelCase ) , '''Output lengths differ between Flax and PyTorch''' )
for fx_output, pt_output_loaded in zip(fx_outputs[:4] , pt_outputs_loaded[:4] ):
self.assert_almost_equals(_lowerCamelCase , pt_output_loaded.numpy() , 4E-2 )
def lowerCAmelCase ( self : Optional[int] , _lowercase : str , _lowercase : Optional[Any] , _lowercase : Dict ):
"""simple docstring"""
_UpperCamelCase: Any = VisionTextDualEncoderConfig.from_vision_text_configs(_lowerCamelCase , _lowerCamelCase )
_UpperCamelCase: Any = VisionTextDualEncoderModel(_lowerCamelCase )
_UpperCamelCase: str = FlaxVisionTextDualEncoderModel(_lowerCamelCase )
_UpperCamelCase: List[str] = convert_pytorch_state_dict_to_flax(pt_model.state_dict() , _lowerCamelCase )
_UpperCamelCase: str = fx_state
self.check_pt_flax_equivalence(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase )
def lowerCAmelCase ( self : Any , _lowercase : List[Any] , _lowercase : Optional[int] , _lowercase : List[Any] ):
"""simple docstring"""
_UpperCamelCase: str = VisionTextDualEncoderConfig.from_vision_text_configs(_lowerCamelCase , _lowerCamelCase )
_UpperCamelCase: Tuple = VisionTextDualEncoderModel(_lowerCamelCase )
_UpperCamelCase: Dict = FlaxVisionTextDualEncoderModel(_lowerCamelCase )
_UpperCamelCase: Optional[Any] = load_flax_weights_in_pytorch_model(_lowerCamelCase , fx_model.params )
self.check_pt_flax_equivalence(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase )
def lowerCAmelCase ( self : Optional[int] ):
"""simple docstring"""
_UpperCamelCase: List[Any] = self.prepare_config_and_inputs()
self.check_model_from_pretrained_configs(**_lowerCamelCase )
def lowerCAmelCase ( self : int ):
"""simple docstring"""
_UpperCamelCase: str = self.prepare_config_and_inputs()
self.check_vision_text_dual_encoder_from_pretrained(**_lowerCamelCase )
def lowerCAmelCase ( self : Any ):
"""simple docstring"""
_UpperCamelCase: int = self.prepare_config_and_inputs()
self.check_save_load(**_lowerCamelCase )
def lowerCAmelCase ( self : Tuple ):
"""simple docstring"""
_UpperCamelCase: Dict = self.prepare_config_and_inputs()
self.check_vision_text_output_attention(**_lowerCamelCase )
@is_pt_flax_cross_test
def lowerCAmelCase ( self : List[Any] ):
"""simple docstring"""
_UpperCamelCase: Union[str, Any] = self.prepare_config_and_inputs()
_UpperCamelCase: Union[str, Any] = config_inputs_dict.pop('''vision_config''' )
_UpperCamelCase: Optional[int] = config_inputs_dict.pop('''text_config''' )
_UpperCamelCase: Optional[Any] = config_inputs_dict
self.check_equivalence_pt_to_flax(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase )
self.check_equivalence_flax_to_pt(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase )
@slow
def lowerCAmelCase ( self : List[str] ):
"""simple docstring"""
_UpperCamelCase: List[Any] = self.get_pretrained_model_and_inputs()
_UpperCamelCase: List[Any] = model_a(**_lowerCamelCase )
_UpperCamelCase: List[str] = outputs[0]
with tempfile.TemporaryDirectory() as tmp_dirname:
model_a.save_pretrained(_lowerCamelCase )
_UpperCamelCase: Union[str, Any] = FlaxVisionTextDualEncoderModel.from_pretrained(_lowerCamelCase )
_UpperCamelCase: Union[str, Any] = model_a(**_lowerCamelCase )
_UpperCamelCase: Any = after_outputs[0]
_UpperCamelCase: Union[str, Any] = np.amax(np.abs(out_a - out_a ) )
self.assertLessEqual(_lowerCamelCase , 1E-5 )
@require_flax
class __magic_name__ ( __UpperCAmelCase , unittest.TestCase ):
"""simple docstring"""
def lowerCAmelCase ( self : Dict ):
"""simple docstring"""
_UpperCamelCase: Any = FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(
'''hf-internal-testing/tiny-random-vit''' , '''hf-internal-testing/tiny-bert''' , vision_from_pt=_lowerCamelCase , text_from_pt=_lowerCamelCase , )
_UpperCamelCase: List[str] = 13
_UpperCamelCase: Any = floats_tensor(
[
batch_size,
model.config.vision_config.num_channels,
model.config.vision_config.image_size,
model.config.vision_config.image_size,
] )
_UpperCamelCase: Optional[int] = ids_tensor([batch_size, 4] , model.config.text_config.vocab_size )
_UpperCamelCase: Optional[Any] = random_attention_mask([batch_size, 4] )
_UpperCamelCase: List[str] = {'''pixel_values''': pixel_values, '''input_ids''': input_ids, '''attention_mask''': attention_mask}
return model, inputs
def lowerCAmelCase ( self : List[Any] , _lowercase : Tuple , _lowercase : Optional[int] ):
"""simple docstring"""
_UpperCamelCase: Dict = FlaxViTModel(_lowerCamelCase )
_UpperCamelCase: List[str] = FlaxBertModel(_lowerCamelCase )
return vision_model, text_model
def lowerCAmelCase ( self : Any ):
"""simple docstring"""
_UpperCamelCase: List[Any] = FlaxViTModelTester(self )
_UpperCamelCase: Dict = FlaxBertModelTester(self )
_UpperCamelCase: Tuple = vit_model_tester.prepare_config_and_inputs()
_UpperCamelCase: Tuple = bert_model_tester.prepare_config_and_inputs()
_UpperCamelCase: Optional[int] = vision_config_and_inputs
_UpperCamelCase: Any = text_config_and_inputs
# make sure that cross attention layers are added
return {
"text_config": text_config,
"vision_config": vision_config,
"pixel_values": pixel_values,
"attention_mask": attention_mask,
"input_ids": input_ids,
"token_type_ids": token_type_ids,
}
@require_torch
class __magic_name__ ( __UpperCAmelCase , unittest.TestCase ):
"""simple docstring"""
def lowerCAmelCase ( self : Optional[int] ):
"""simple docstring"""
_UpperCamelCase: Optional[int] = FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(
'''hf-internal-testing/tiny-random-clip''' , '''hf-internal-testing/tiny-bert''' , vision_from_pt=_lowerCamelCase , text_from_pt=_lowerCamelCase , )
_UpperCamelCase: Tuple = 13
_UpperCamelCase: List[str] = floats_tensor(
[
batch_size,
model.config.vision_config.num_channels,
model.config.vision_config.image_size,
model.config.vision_config.image_size,
] )
_UpperCamelCase: Union[str, Any] = ids_tensor([batch_size, 4] , model.config.text_config.vocab_size )
_UpperCamelCase: List[str] = random_attention_mask([batch_size, 4] )
_UpperCamelCase: Optional[Any] = {'''pixel_values''': pixel_values, '''input_ids''': input_ids, '''attention_mask''': attention_mask}
return model, inputs
def lowerCAmelCase ( self : str , _lowercase : List[str] , _lowercase : int ):
"""simple docstring"""
_UpperCamelCase: List[Any] = FlaxCLIPVisionModel(_lowerCamelCase )
_UpperCamelCase: Any = FlaxBertModel(_lowerCamelCase )
return vision_model, text_model
def lowerCAmelCase ( self : List[str] ):
"""simple docstring"""
_UpperCamelCase: List[Any] = FlaxCLIPVisionModelTester(self )
_UpperCamelCase: int = FlaxBertModelTester(self )
_UpperCamelCase: List[str] = clip_model_tester.prepare_config_and_inputs()
_UpperCamelCase: Tuple = bert_model_tester.prepare_config_and_inputs()
_UpperCamelCase: Dict = vision_config_and_inputs
_UpperCamelCase: Optional[Any] = text_config_and_inputs
# make sure that cross attention layers are added
return {
"text_config": text_config,
"vision_config": vision_config,
"pixel_values": pixel_values,
"attention_mask": attention_mask,
"input_ids": input_ids,
"token_type_ids": token_type_ids,
}
@require_flax
@require_vision
class __magic_name__ ( unittest.TestCase ):
"""simple docstring"""
@slow
def lowerCAmelCase ( self : Dict ):
"""simple docstring"""
_UpperCamelCase: List[Any] = FlaxVisionTextDualEncoderModel.from_pretrained('''clip-italian/clip-italian''' , logit_scale_init_value=1.0 )
_UpperCamelCase: List[str] = VisionTextDualEncoderProcessor.from_pretrained('''clip-italian/clip-italian''' )
_UpperCamelCase: Union[str, Any] = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
_UpperCamelCase: Union[str, Any] = processor(
text=['''una foto di un gatto''', '''una foto di un cane'''] , images=_lowerCamelCase , padding=_lowerCamelCase , return_tensors='''np''' )
_UpperCamelCase: int = model(**_lowerCamelCase )
# verify the logits
self.assertEqual(outputs.logits_per_image.shape , (inputs.pixel_values.shape[0], inputs.input_ids.shape[0]) )
self.assertEqual(
outputs.logits_per_text.shape , (inputs.input_ids.shape[0], inputs.pixel_values.shape[0]) , )
_UpperCamelCase: Dict = np.array([[1.2284727, 0.3104122]] )
self.assertTrue(np.allclose(outputs.logits_per_image , _lowerCamelCase , atol=1E-3 ) ) | 271 |
"""simple docstring"""
import argparse
import logging
import os
import time
import timeit
import datasets
import numpy as np
import pycuda.autoinit # noqa: F401
import pycuda.driver as cuda
import tensorrt as trt
import torch
from absl import logging as absl_logging
from accelerate import Accelerator
from datasets import load_dataset, load_metric
from torch.utils.data import DataLoader
from utils_qa import postprocess_qa_predictions
import transformers
from transformers import AutoTokenizer, EvalPrediction, default_data_collator, set_seed
from transformers.trainer_pt_utils import nested_concat, nested_truncate
_lowerCamelCase = trt.Logger(trt.Logger.WARNING)
_lowerCamelCase = absl_logging.get_absl_logger()
absl_logger.setLevel(logging.WARNING)
_lowerCamelCase = logging.getLogger(__name__)
_lowerCamelCase = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--onnx_model_path''',
default=None,
type=str,
required=True,
help='''Path to ONNX model: ''',
)
parser.add_argument(
'''--output_dir''',
default=None,
type=str,
required=True,
help='''The output directory where the model checkpoints and predictions will be written.''',
)
# Other parameters
parser.add_argument(
'''--tokenizer_name''',
default='''''',
type=str,
required=True,
help='''Pretrained tokenizer name or path if not the same as model_name''',
)
parser.add_argument(
'''--version_2_with_negative''',
action='''store_true''',
help='''If true, the SQuAD examples contain some that do not have an answer.''',
)
parser.add_argument(
'''--null_score_diff_threshold''',
type=float,
default=0.0,
help='''If null_score - best_non_null is greater than the threshold predict null.''',
)
parser.add_argument(
'''--max_seq_length''',
default=3_84,
type=int,
help=(
'''The maximum total input sequence length after WordPiece tokenization. Sequences '''
'''longer than this will be truncated, and sequences shorter than this will be padded.'''
),
)
parser.add_argument(
'''--doc_stride''',
default=1_28,
type=int,
help='''When splitting up a long document into chunks, how much stride to take between chunks.''',
)
parser.add_argument('''--per_device_eval_batch_size''', default=8, type=int, help='''Batch size per GPU/CPU for evaluation.''')
parser.add_argument(
'''--n_best_size''',
default=20,
type=int,
help='''The total number of n-best predictions to generate in the nbest_predictions.json output file.''',
)
parser.add_argument(
'''--max_answer_length''',
default=30,
type=int,
help=(
'''The maximum length of an answer that can be generated. This is needed because the start '''
'''and end predictions are not conditioned on one another.'''
),
)
parser.add_argument('''--seed''', type=int, default=42, help='''random seed for initialization''')
parser.add_argument(
'''--dataset_name''',
type=str,
default=None,
required=True,
help='''The name of the dataset to use (via the datasets library).''',
)
parser.add_argument(
'''--dataset_config_name''',
type=str,
default=None,
help='''The configuration name of the dataset to use (via the datasets library).''',
)
parser.add_argument(
'''--preprocessing_num_workers''', type=int, default=4, help='''A csv or a json file containing the training data.'''
)
parser.add_argument('''--overwrite_cache''', action='''store_true''', help='''Overwrite the cached training and evaluation sets''')
parser.add_argument(
'''--fp16''',
action='''store_true''',
help='''Whether to use 16-bit (mixed) precision instead of 32-bit''',
)
parser.add_argument(
'''--int8''',
action='''store_true''',
help='''Whether to use INT8''',
)
_lowerCamelCase = parser.parse_args()
if args.tokenizer_name:
_lowerCamelCase = AutoTokenizer.from_pretrained(args.tokenizer_name, use_fast=True)
else:
raise ValueError(
'''You are instantiating a new tokenizer from scratch. This is not supported by this script.'''
'''You can do it from another script, save it, and load it from here, using --tokenizer_name.'''
)
logger.info('''Training/evaluation parameters %s''', args)
_lowerCamelCase = args.per_device_eval_batch_size
_lowerCamelCase = (args.eval_batch_size, args.max_seq_length)
# TRT Engine properties
_lowerCamelCase = True
_lowerCamelCase = '''temp_engine/bert-fp32.engine'''
if args.fpaa:
_lowerCamelCase = '''temp_engine/bert-fp16.engine'''
if args.inta:
_lowerCamelCase = '''temp_engine/bert-int8.engine'''
# import ONNX file
if not os.path.exists('''temp_engine'''):
os.makedirs('''temp_engine''')
_lowerCamelCase = 1 << (int)(trt.NetworkDefinitionCreationFlag.EXPLICIT_BATCH)
with trt.Builder(TRT_LOGGER) as builder, builder.create_network(EXPLICIT_BATCH) as network, trt.OnnxParser(
network, TRT_LOGGER
) as parser:
with open(args.onnx_model_path, '''rb''') as model:
if not parser.parse(model.read()):
for error in range(parser.num_errors):
print(parser.get_error(error))
# Query input names and shapes from parsed TensorRT network
_lowerCamelCase = [network.get_input(i) for i in range(network.num_inputs)]
_lowerCamelCase = [_input.name for _input in network_inputs] # ex: ["actual_input1"]
with builder.create_builder_config() as config:
_lowerCamelCase = 1 << 50
if STRICT_TYPES:
config.set_flag(trt.BuilderFlag.STRICT_TYPES)
if args.fpaa:
config.set_flag(trt.BuilderFlag.FPaa)
if args.inta:
config.set_flag(trt.BuilderFlag.INTa)
_lowerCamelCase = builder.create_optimization_profile()
config.add_optimization_profile(profile)
for i in range(len(input_names)):
profile.set_shape(input_names[i], INPUT_SHAPE, INPUT_SHAPE, INPUT_SHAPE)
_lowerCamelCase = builder.build_engine(network, config)
# serialize_engine and store in file (can be directly loaded and deserialized):
with open(engine_name, '''wb''') as f:
f.write(engine.serialize())
def lowerCAmelCase_ ( lowercase_ : List[Any] , lowercase_ : Tuple , lowercase_ : List[Any] , lowercase_ : List[Any] , lowercase_ : str , lowercase_ : Tuple , lowercase_ : Any , lowercase_ : Optional[int] ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : Dict = np.asarray(inputs['''input_ids'''] , dtype=np.intaa )
__SCREAMING_SNAKE_CASE : Optional[Any] = np.asarray(inputs['''attention_mask'''] , dtype=np.intaa )
__SCREAMING_SNAKE_CASE : Optional[Any] = np.asarray(inputs['''token_type_ids'''] , dtype=np.intaa )
# Copy inputs
cuda.memcpy_htod_async(d_inputs[0] , input_ids.ravel() , lowercase_ )
cuda.memcpy_htod_async(d_inputs[1] , attention_mask.ravel() , lowercase_ )
cuda.memcpy_htod_async(d_inputs[2] , token_type_ids.ravel() , lowercase_ )
# start time
__SCREAMING_SNAKE_CASE : Tuple = time.time()
# Run inference
context.execute_async(
bindings=[int(lowercase_ ) for d_inp in d_inputs] + [int(lowercase_ ), int(lowercase_ )] , stream_handle=stream.handle )
# Transfer predictions back from GPU
cuda.memcpy_dtoh_async(lowercase_ , lowercase_ , lowercase_ )
cuda.memcpy_dtoh_async(lowercase_ , lowercase_ , lowercase_ )
# Synchronize the stream and take time
stream.synchronize()
# end time
__SCREAMING_SNAKE_CASE : List[str] = time.time()
__SCREAMING_SNAKE_CASE : int = end_time - start_time
__SCREAMING_SNAKE_CASE : int = (h_outputa, h_outputa)
# print(outputs)
return outputs, infer_time
# Initialize the accelerator. We will let the accelerator handle device placement for us in this example.
_lowerCamelCase = Accelerator()
# Make one log on every process with the configuration for debugging.
logging.basicConfig(
format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''',
datefmt='''%m/%d/%Y %H:%M:%S''',
level=logging.INFO,
)
# Setup logging, we only want one process per machine to log things on the screen.
# accelerator.is_local_main_process is only True for one process per machine.
logger.setLevel(logging.INFO if accelerator.is_local_main_process else logging.ERROR)
if accelerator.is_local_main_process:
datasets.utils.logging.set_verbosity_warning()
transformers.utils.logging.set_verbosity_info()
else:
datasets.utils.logging.set_verbosity_error()
transformers.utils.logging.set_verbosity_error()
# If passed along, set the training seed now.
if args.seed is not None:
set_seed(args.seed)
# Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below)
# or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
# (the dataset will be downloaded automatically from the datasets Hub).
#
# For CSV/JSON files, this script will use the column called 'text' or the first column if no column called
# 'text' is found. You can easily tweak this behavior (see below).
if args.dataset_name is not None:
# Downloading and loading a dataset from the hub.
_lowerCamelCase = load_dataset(args.dataset_name, args.dataset_config_name)
else:
raise ValueError('''Evaluation requires a dataset name''')
# See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
# https://huggingface.co/docs/datasets/loading_datasets.html.
# Preprocessing the datasets.
# Preprocessing is slighlty different for training and evaluation.
_lowerCamelCase = raw_datasets['''validation'''].column_names
_lowerCamelCase = '''question''' if '''question''' in column_names else column_names[0]
_lowerCamelCase = '''context''' if '''context''' in column_names else column_names[1]
_lowerCamelCase = '''answers''' if '''answers''' in column_names else column_names[2]
# Padding side determines if we do (question|context) or (context|question).
_lowerCamelCase = tokenizer.padding_side == '''right'''
if args.max_seq_length > tokenizer.model_max_length:
logger.warning(
f'The max_seq_length passed ({args.max_seq_length}) is larger than the maximum length for the'
f'model ({tokenizer.model_max_length}). Using max_seq_length={tokenizer.model_max_length}.'
)
_lowerCamelCase = min(args.max_seq_length, tokenizer.model_max_length)
def lowerCAmelCase_ ( lowercase_ : Tuple ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : Any = [q.lstrip() for q in examples[question_column_name]]
# Tokenize our examples with truncation and maybe padding, but keep the overflows using a stride. This results
# in one example possible giving several features when a context is long, each of those features having a
# context that overlaps a bit the context of the previous feature.
__SCREAMING_SNAKE_CASE : Optional[Any] = tokenizer(
examples[question_column_name if pad_on_right else context_column_name] , examples[context_column_name if pad_on_right else question_column_name] , truncation='''only_second''' if pad_on_right else '''only_first''' , max_length=lowercase_ , stride=args.doc_stride , return_overflowing_tokens=lowercase_ , return_offsets_mapping=lowercase_ , padding='''max_length''' , )
# Since one example might give us several features if it has a long context, we need a map from a feature to
# its corresponding example. This key gives us just that.
__SCREAMING_SNAKE_CASE : Optional[int] = tokenized_examples.pop('''overflow_to_sample_mapping''' )
# For evaluation, we will need to convert our predictions to substrings of the context, so we keep the
# corresponding example_id and we will store the offset mappings.
__SCREAMING_SNAKE_CASE : Any = []
for i in range(len(tokenized_examples['''input_ids'''] ) ):
# Grab the sequence corresponding to that example (to know what is the context and what is the question).
__SCREAMING_SNAKE_CASE : int = tokenized_examples.sequence_ids(lowercase_ )
__SCREAMING_SNAKE_CASE : str = 1 if pad_on_right else 0
# One example can give several spans, this is the index of the example containing this span of text.
__SCREAMING_SNAKE_CASE : str = sample_mapping[i]
tokenized_examples["example_id"].append(examples['''id'''][sample_index] )
# Set to None the offset_mapping that are not part of the context so it's easy to determine if a token
# position is part of the context or not.
__SCREAMING_SNAKE_CASE : List[str] = [
(o if sequence_ids[k] == context_index else None)
for k, o in enumerate(tokenized_examples['''offset_mapping'''][i] )
]
return tokenized_examples
_lowerCamelCase = raw_datasets['''validation''']
# Validation Feature Creation
_lowerCamelCase = eval_examples.map(
prepare_validation_features,
batched=True,
num_proc=args.preprocessing_num_workers,
remove_columns=column_names,
load_from_cache_file=not args.overwrite_cache,
desc='''Running tokenizer on validation dataset''',
)
_lowerCamelCase = default_data_collator
_lowerCamelCase = eval_dataset.remove_columns(['''example_id''', '''offset_mapping'''])
_lowerCamelCase = DataLoader(
eval_dataset_for_model, collate_fn=data_collator, batch_size=args.per_device_eval_batch_size
)
def lowerCAmelCase_ ( lowercase_ : Dict , lowercase_ : Union[str, Any] , lowercase_ : Union[str, Any] , lowercase_ : List[Any]="eval" ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : Tuple = postprocess_qa_predictions(
examples=lowercase_ , features=lowercase_ , predictions=lowercase_ , version_2_with_negative=args.version_2_with_negative , n_best_size=args.n_best_size , max_answer_length=args.max_answer_length , null_score_diff_threshold=args.null_score_diff_threshold , output_dir=args.output_dir , prefix=lowercase_ , )
# Format the result to the format the metric expects.
if args.version_2_with_negative:
__SCREAMING_SNAKE_CASE : Union[str, Any] = [
{'''id''': k, '''prediction_text''': v, '''no_answer_probability''': 0.0} for k, v in predictions.items()
]
else:
__SCREAMING_SNAKE_CASE : int = [{'''id''': k, '''prediction_text''': v} for k, v in predictions.items()]
__SCREAMING_SNAKE_CASE : Any = [{'''id''': ex['''id'''], '''answers''': ex[answer_column_name]} for ex in examples]
return EvalPrediction(predictions=lowercase_ , label_ids=lowercase_ )
_lowerCamelCase = load_metric('''squad_v2''' if args.version_2_with_negative else '''squad''')
# Evaluation!
logger.info('''Loading ONNX model %s for evaluation''', args.onnx_model_path)
with open(engine_name, '''rb''') as f, trt.Runtime(TRT_LOGGER) as runtime, runtime.deserialize_cuda_engine(
f.read()
) as engine, engine.create_execution_context() as context:
# setup for TRT inferrence
for i in range(len(input_names)):
context.set_binding_shape(i, INPUT_SHAPE)
assert context.all_binding_shapes_specified
def lowerCAmelCase_ ( lowercase_ : Any ):
'''simple docstring'''
return trt.volume(engine.get_binding_shape(lowercase_ ) ) * engine.get_binding_dtype(lowercase_ ).itemsize
# Allocate device memory for inputs and outputs.
_lowerCamelCase = [cuda.mem_alloc(binding_nbytes(binding)) for binding in engine if engine.binding_is_input(binding)]
# Allocate output buffer
_lowerCamelCase = cuda.pagelocked_empty(tuple(context.get_binding_shape(3)), dtype=np.floataa)
_lowerCamelCase = cuda.pagelocked_empty(tuple(context.get_binding_shape(4)), dtype=np.floataa)
_lowerCamelCase = cuda.mem_alloc(h_outputa.nbytes)
_lowerCamelCase = cuda.mem_alloc(h_outputa.nbytes)
# Create a stream in which to copy inputs/outputs and run inference.
_lowerCamelCase = cuda.Stream()
# Evaluation
logger.info('''***** Running Evaluation *****''')
logger.info(f' Num examples = {len(eval_dataset)}')
logger.info(f' Batch size = {args.per_device_eval_batch_size}')
_lowerCamelCase = 0.0
_lowerCamelCase = 0
_lowerCamelCase = timeit.default_timer()
_lowerCamelCase = None
for step, batch in enumerate(eval_dataloader):
_lowerCamelCase , _lowerCamelCase = model_infer(batch, context, d_inputs, h_outputa, h_outputa, d_outputa, d_outputa, stream)
total_time += infer_time
niter += 1
_lowerCamelCase , _lowerCamelCase = outputs
_lowerCamelCase = torch.tensor(start_logits)
_lowerCamelCase = torch.tensor(end_logits)
# necessary to pad predictions and labels for being gathered
_lowerCamelCase = accelerator.pad_across_processes(start_logits, dim=1, pad_index=-1_00)
_lowerCamelCase = accelerator.pad_across_processes(end_logits, dim=1, pad_index=-1_00)
_lowerCamelCase = (accelerator.gather(start_logits).cpu().numpy(), accelerator.gather(end_logits).cpu().numpy())
_lowerCamelCase = logits if all_preds is None else nested_concat(all_preds, logits, padding_index=-1_00)
if all_preds is not None:
_lowerCamelCase = nested_truncate(all_preds, len(eval_dataset))
_lowerCamelCase = timeit.default_timer() - start_time
logger.info(''' Evaluation done in total %f secs (%f sec per example)''', evalTime, evalTime / len(eval_dataset))
# Inference time from TRT
logger.info('''Average Inference Time = {:.3f} ms'''.format(total_time * 10_00 / niter))
logger.info('''Total Inference Time = {:.3f} ms'''.format(total_time * 10_00))
logger.info('''Total Number of Inference = %d''', niter)
_lowerCamelCase = post_processing_function(eval_examples, eval_dataset, all_preds)
_lowerCamelCase = metric.compute(predictions=prediction.predictions, references=prediction.label_ids)
logger.info(f'Evaluation metrics: {eval_metric}')
| 674 | 0 |
import shutil
import tempfile
import unittest
from transformers import ClapFeatureExtractor, ClapProcessor, RobertaTokenizer, RobertaTokenizerFast
from transformers.testing_utils import require_sentencepiece, require_torchaudio
from .test_feature_extraction_clap import floats_list
@require_torchaudio
@require_sentencepiece
class _lowerCAmelCase ( unittest.TestCase ):
"""simple docstring"""
def __lowerCAmelCase ( self : List[Any] ):
"""simple docstring"""
UpperCamelCase = '''laion/clap-htsat-unfused'''
UpperCamelCase = tempfile.mkdtemp()
def __lowerCAmelCase ( self : Tuple , **SCREAMING_SNAKE_CASE__ : List[str] ):
"""simple docstring"""
return RobertaTokenizer.from_pretrained(self.checkpoint , **_lowerCamelCase )
def __lowerCAmelCase ( self : Union[str, Any] , **SCREAMING_SNAKE_CASE__ : Optional[int] ):
"""simple docstring"""
return ClapFeatureExtractor.from_pretrained(self.checkpoint , **_lowerCamelCase )
def __lowerCAmelCase ( self : List[Any] ):
"""simple docstring"""
shutil.rmtree(self.tmpdirname )
def __lowerCAmelCase ( self : Union[str, Any] ):
"""simple docstring"""
UpperCamelCase = self.get_tokenizer()
UpperCamelCase = self.get_feature_extractor()
UpperCamelCase = ClapProcessor(tokenizer=_lowerCamelCase , feature_extractor=_lowerCamelCase )
processor.save_pretrained(self.tmpdirname )
UpperCamelCase = ClapProcessor.from_pretrained(self.tmpdirname )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer.get_vocab() )
self.assertIsInstance(processor.tokenizer , _lowerCamelCase )
self.assertEqual(processor.feature_extractor.to_json_string() , feature_extractor.to_json_string() )
self.assertIsInstance(processor.feature_extractor , _lowerCamelCase )
def __lowerCAmelCase ( self : str ):
"""simple docstring"""
UpperCamelCase = ClapProcessor(tokenizer=self.get_tokenizer() , feature_extractor=self.get_feature_extractor() )
processor.save_pretrained(self.tmpdirname )
UpperCamelCase = self.get_tokenizer(bos_token='(BOS)' , eos_token='(EOS)' )
UpperCamelCase = self.get_feature_extractor(do_normalize=_lowerCamelCase , padding_value=1.0 )
UpperCamelCase = ClapProcessor.from_pretrained(
self.tmpdirname , bos_token='(BOS)' , eos_token='(EOS)' , do_normalize=_lowerCamelCase , padding_value=1.0 )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer_add_kwargs.get_vocab() )
self.assertIsInstance(processor.tokenizer , _lowerCamelCase )
self.assertEqual(processor.feature_extractor.to_json_string() , feature_extractor_add_kwargs.to_json_string() )
self.assertIsInstance(processor.feature_extractor , _lowerCamelCase )
def __lowerCAmelCase ( self : List[Any] ):
"""simple docstring"""
UpperCamelCase = self.get_feature_extractor()
UpperCamelCase = self.get_tokenizer()
UpperCamelCase = ClapProcessor(tokenizer=_lowerCamelCase , feature_extractor=_lowerCamelCase )
UpperCamelCase = floats_list((3, 10_00) )
UpperCamelCase = feature_extractor(_lowerCamelCase , return_tensors='np' )
UpperCamelCase = processor(audios=_lowerCamelCase , return_tensors='np' )
for key in input_feat_extract.keys():
self.assertAlmostEqual(input_feat_extract[key].sum() , input_processor[key].sum() , delta=1e-2 )
def __lowerCAmelCase ( self : int ):
"""simple docstring"""
UpperCamelCase = self.get_feature_extractor()
UpperCamelCase = self.get_tokenizer()
UpperCamelCase = ClapProcessor(tokenizer=_lowerCamelCase , feature_extractor=_lowerCamelCase )
UpperCamelCase = '''This is a test string'''
UpperCamelCase = processor(text=_lowerCamelCase )
UpperCamelCase = tokenizer(_lowerCamelCase )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] , encoded_processor[key] )
def __lowerCAmelCase ( self : str ):
"""simple docstring"""
UpperCamelCase = self.get_feature_extractor()
UpperCamelCase = self.get_tokenizer()
UpperCamelCase = ClapProcessor(tokenizer=_lowerCamelCase , feature_extractor=_lowerCamelCase )
UpperCamelCase = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
UpperCamelCase = processor.batch_decode(_lowerCamelCase )
UpperCamelCase = tokenizer.batch_decode(_lowerCamelCase )
self.assertListEqual(_lowerCamelCase , _lowerCamelCase )
def __lowerCAmelCase ( self : List[str] ):
"""simple docstring"""
UpperCamelCase = self.get_feature_extractor()
UpperCamelCase = self.get_tokenizer()
UpperCamelCase = ClapProcessor(tokenizer=_lowerCamelCase , feature_extractor=_lowerCamelCase )
self.assertListEqual(
processor.model_input_names[2:] , feature_extractor.model_input_names , msg='`processor` and `feature_extractor` model input names do not match' , )
| 282 |
"""simple docstring"""
from __future__ import annotations
import unittest
from transformers import is_tf_available, is_torch_available
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, SMALL_MODEL_IDENTIFIER, is_pt_tf_cross_test, slow
if is_tf_available():
from transformers import (
AutoConfig,
BertConfig,
GPTaConfig,
TaConfig,
TFAutoModel,
TFAutoModelForCausalLM,
TFAutoModelForMaskedLM,
TFAutoModelForPreTraining,
TFAutoModelForQuestionAnswering,
TFAutoModelForSeqaSeqLM,
TFAutoModelForSequenceClassification,
TFAutoModelWithLMHead,
TFBertForMaskedLM,
TFBertForPreTraining,
TFBertForQuestionAnswering,
TFBertForSequenceClassification,
TFBertModel,
TFGPTaLMHeadModel,
TFRobertaForMaskedLM,
TFTaForConditionalGeneration,
)
from transformers.models.bert.modeling_tf_bert import TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST
from transformers.models.gpta.modeling_tf_gpta import TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST
from transformers.models.ta.modeling_tf_ta import TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST
if is_torch_available():
from transformers import (
AutoModel,
AutoModelForCausalLM,
AutoModelForMaskedLM,
AutoModelForPreTraining,
AutoModelForQuestionAnswering,
AutoModelForSeqaSeqLM,
AutoModelForSequenceClassification,
AutoModelWithLMHead,
BertForMaskedLM,
BertForPreTraining,
BertForQuestionAnswering,
BertForSequenceClassification,
BertModel,
GPTaLMHeadModel,
RobertaForMaskedLM,
TaForConditionalGeneration,
)
@is_pt_tf_cross_test
class snake_case ( unittest.TestCase ):
@slow
def SCREAMING_SNAKE_CASE_ ( self :Tuple ):
# for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
for model_name in ["bert-base-uncased"]:
__SCREAMING_SNAKE_CASE : List[Any] = AutoConfig.from_pretrained(_lowerCamelCase )
self.assertIsNotNone(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : List[str] = TFAutoModel.from_pretrained(_lowerCamelCase , from_pt=_lowerCamelCase )
self.assertIsNotNone(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : Optional[Any] = AutoModel.from_pretrained(_lowerCamelCase , from_tf=_lowerCamelCase )
self.assertIsNotNone(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
@slow
def SCREAMING_SNAKE_CASE_ ( self :Optional[int] ):
# for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
for model_name in ["bert-base-uncased"]:
__SCREAMING_SNAKE_CASE : List[str] = AutoConfig.from_pretrained(_lowerCamelCase )
self.assertIsNotNone(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : Tuple = TFAutoModelForPreTraining.from_pretrained(_lowerCamelCase , from_pt=_lowerCamelCase )
self.assertIsNotNone(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : Optional[int] = AutoModelForPreTraining.from_pretrained(_lowerCamelCase , from_tf=_lowerCamelCase )
self.assertIsNotNone(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
@slow
def SCREAMING_SNAKE_CASE_ ( self :Any ):
for model_name in TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__SCREAMING_SNAKE_CASE : Optional[int] = AutoConfig.from_pretrained(_lowerCamelCase )
self.assertIsNotNone(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : int = TFAutoModelForCausalLM.from_pretrained(_lowerCamelCase , from_pt=_lowerCamelCase )
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : List[Any] = TFAutoModelForCausalLM.from_pretrained(
_lowerCamelCase , output_loading_info=_lowerCamelCase , from_pt=_lowerCamelCase )
self.assertIsNotNone(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : Tuple = AutoModelForCausalLM.from_pretrained(_lowerCamelCase , from_tf=_lowerCamelCase )
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Dict = AutoModelForCausalLM.from_pretrained(
_lowerCamelCase , output_loading_info=_lowerCamelCase , from_tf=_lowerCamelCase )
self.assertIsNotNone(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
@slow
def SCREAMING_SNAKE_CASE_ ( self :List[Any] ):
for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__SCREAMING_SNAKE_CASE : Any = AutoConfig.from_pretrained(_lowerCamelCase )
self.assertIsNotNone(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : Any = TFAutoModelWithLMHead.from_pretrained(_lowerCamelCase , from_pt=_lowerCamelCase )
self.assertIsNotNone(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : Optional[Any] = AutoModelWithLMHead.from_pretrained(_lowerCamelCase , from_tf=_lowerCamelCase )
self.assertIsNotNone(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
@slow
def SCREAMING_SNAKE_CASE_ ( self :Dict ):
for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__SCREAMING_SNAKE_CASE : Union[str, Any] = AutoConfig.from_pretrained(_lowerCamelCase )
self.assertIsNotNone(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : Optional[int] = TFAutoModelForMaskedLM.from_pretrained(_lowerCamelCase , from_pt=_lowerCamelCase )
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : List[str] = TFAutoModelForMaskedLM.from_pretrained(
_lowerCamelCase , output_loading_info=_lowerCamelCase , from_pt=_lowerCamelCase )
self.assertIsNotNone(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : Tuple = AutoModelForMaskedLM.from_pretrained(_lowerCamelCase , from_tf=_lowerCamelCase )
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : List[Any] = AutoModelForMaskedLM.from_pretrained(
_lowerCamelCase , output_loading_info=_lowerCamelCase , from_tf=_lowerCamelCase )
self.assertIsNotNone(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
@slow
def SCREAMING_SNAKE_CASE_ ( self :int ):
for model_name in TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__SCREAMING_SNAKE_CASE : str = AutoConfig.from_pretrained(_lowerCamelCase )
self.assertIsNotNone(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : Tuple = TFAutoModelForSeqaSeqLM.from_pretrained(_lowerCamelCase , from_pt=_lowerCamelCase )
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : str = TFAutoModelForSeqaSeqLM.from_pretrained(
_lowerCamelCase , output_loading_info=_lowerCamelCase , from_pt=_lowerCamelCase )
self.assertIsNotNone(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : Optional[int] = AutoModelForSeqaSeqLM.from_pretrained(_lowerCamelCase , from_tf=_lowerCamelCase )
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Dict = AutoModelForSeqaSeqLM.from_pretrained(
_lowerCamelCase , output_loading_info=_lowerCamelCase , from_tf=_lowerCamelCase )
self.assertIsNotNone(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
@slow
def SCREAMING_SNAKE_CASE_ ( self :Optional[Any] ):
# for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
for model_name in ["bert-base-uncased"]:
__SCREAMING_SNAKE_CASE : Optional[Any] = AutoConfig.from_pretrained(_lowerCamelCase )
self.assertIsNotNone(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : int = TFAutoModelForSequenceClassification.from_pretrained(_lowerCamelCase , from_pt=_lowerCamelCase )
self.assertIsNotNone(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : Optional[Any] = AutoModelForSequenceClassification.from_pretrained(_lowerCamelCase , from_tf=_lowerCamelCase )
self.assertIsNotNone(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
@slow
def SCREAMING_SNAKE_CASE_ ( self :List[Any] ):
# for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
for model_name in ["bert-base-uncased"]:
__SCREAMING_SNAKE_CASE : Optional[Any] = AutoConfig.from_pretrained(_lowerCamelCase )
self.assertIsNotNone(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : str = TFAutoModelForQuestionAnswering.from_pretrained(_lowerCamelCase , from_pt=_lowerCamelCase )
self.assertIsNotNone(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : List[Any] = AutoModelForQuestionAnswering.from_pretrained(_lowerCamelCase , from_tf=_lowerCamelCase )
self.assertIsNotNone(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :Union[str, Any] ):
__SCREAMING_SNAKE_CASE : Tuple = TFAutoModelWithLMHead.from_pretrained(_lowerCamelCase , from_pt=_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
self.assertEqual(model.num_parameters() , 1_4_4_1_0 )
self.assertEqual(model.num_parameters(only_trainable=_lowerCamelCase ) , 1_4_4_1_0 )
__SCREAMING_SNAKE_CASE : Tuple = AutoModelWithLMHead.from_pretrained(_lowerCamelCase , from_tf=_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
self.assertEqual(model.num_parameters() , 1_4_4_1_0 )
self.assertEqual(model.num_parameters(only_trainable=_lowerCamelCase ) , 1_4_4_1_0 )
def SCREAMING_SNAKE_CASE_ ( self :str ):
__SCREAMING_SNAKE_CASE : Optional[Any] = TFAutoModelWithLMHead.from_pretrained(_lowerCamelCase , from_pt=_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
self.assertEqual(model.num_parameters() , 1_4_4_1_0 )
self.assertEqual(model.num_parameters(only_trainable=_lowerCamelCase ) , 1_4_4_1_0 )
__SCREAMING_SNAKE_CASE : Union[str, Any] = AutoModelWithLMHead.from_pretrained(_lowerCamelCase , from_tf=_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
self.assertEqual(model.num_parameters() , 1_4_4_1_0 )
self.assertEqual(model.num_parameters(only_trainable=_lowerCamelCase ) , 1_4_4_1_0 )
| 674 | 0 |
"""simple docstring"""
import numpy as np
import torch
from imwatermark import WatermarkEncoder
# Copied from https://github.com/Stability-AI/generative-models/blob/613af104c6b85184091d42d374fef420eddb356d/scripts/demo/streamlit_helpers.py#L66
a_ : str = 0B101100111110110010010000011110111011000110011110
# bin(x)[2:] gives bits of x as str, use int to convert them to 0/1
a_ : Optional[int] = [int(bit) for bit in bin(WATERMARK_MESSAGE)[2:]]
class __lowercase:
'''simple docstring'''
def __init__( self ):
__lowerCamelCase : Any = WATERMARK_BITS
__lowerCamelCase : List[Any] = WatermarkEncoder()
self.encoder.set_watermark('bits' , self.watermark )
def snake_case_ ( self , __a ):
# can't encode images that are smaller than 256
if images.shape[-1] < 256:
return images
__lowerCamelCase : Dict = (255 * (images / 2 + 0.5)).cpu().permute(0 , 2 , 3 , 1 ).float().numpy()
__lowerCamelCase : Optional[Any] = [self.encoder.encode(_lowerCamelCase , 'dwtDct' ) for image in images]
__lowerCamelCase : Dict = torch.from_numpy(np.array(_lowerCamelCase ) ).permute(0 , 3 , 1 , 2 )
__lowerCamelCase : int = torch.clamp(2 * (images / 255 - 0.5) , min=-1.0 , max=1.0 )
return images
| 594 |
"""simple docstring"""
import os
import re
import warnings
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
if TYPE_CHECKING:
from ...tokenization_utils_base import TextInput
from ...utils import logging
_lowerCamelCase = logging.get_logger(__name__)
_lowerCamelCase = {'''vocab_file''': '''spiece.model'''}
_lowerCamelCase = {
'''vocab_file''': {
'''t5-small''': '''https://huggingface.co/t5-small/resolve/main/spiece.model''',
'''t5-base''': '''https://huggingface.co/t5-base/resolve/main/spiece.model''',
'''t5-large''': '''https://huggingface.co/t5-large/resolve/main/spiece.model''',
'''t5-3b''': '''https://huggingface.co/t5-3b/resolve/main/spiece.model''',
'''t5-11b''': '''https://huggingface.co/t5-11b/resolve/main/spiece.model''',
}
}
# TODO(PVP) - this should be removed in Transformers v5
_lowerCamelCase = {
'''t5-small''': 5_12,
'''t5-base''': 5_12,
'''t5-large''': 5_12,
'''t5-3b''': 5_12,
'''t5-11b''': 5_12,
}
_lowerCamelCase = '''▁'''
class snake_case ( __UpperCAmelCase ):
lowerCamelCase__ = VOCAB_FILES_NAMES
lowerCamelCase__ = PRETRAINED_VOCAB_FILES_MAP
lowerCamelCase__ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowerCamelCase__ = ['''input_ids''', '''attention_mask''']
def __init__( self :int , _lowerCamelCase :Optional[Any] , _lowerCamelCase :Union[str, Any]="</s>" , _lowerCamelCase :List[Any]="<unk>" , _lowerCamelCase :Union[str, Any]="<pad>" , _lowerCamelCase :int=1_0_0 , _lowerCamelCase :Union[str, Any]=None , _lowerCamelCase :Optional[Dict[str, Any]] = None , _lowerCamelCase :int=True , **_lowerCamelCase :List[Any] , ):
# Add extra_ids to the special token list
if extra_ids > 0 and additional_special_tokens is None:
__SCREAMING_SNAKE_CASE : Union[str, Any] = [f'''<extra_id_{i}>''' for i in range(_lowerCamelCase )]
elif extra_ids > 0 and additional_special_tokens is not None:
# Check that we have the right number of extra_id special tokens
__SCREAMING_SNAKE_CASE : Optional[int] = len(set(filter(lambda _lowerCamelCase : bool('''extra_id''' in str(_lowerCamelCase ) ) , _lowerCamelCase ) ) )
if extra_tokens != extra_ids:
raise ValueError(
f'''Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are'''
''' provided to T5Tokenizer. In this case the additional_special_tokens must include the extra_ids'''
''' tokens''' )
if legacy:
logger.warning_once(
f'''You are using the legacy behaviour of the {self.__class__}. This means that tokens that come after special tokens will not be properly handled. We recommend you to'''
''' read the related pull request available at https://github.com/huggingface/transformers/pull/24565''' )
__SCREAMING_SNAKE_CASE : Optional[Any] = legacy
__SCREAMING_SNAKE_CASE : Optional[int] = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
eos_token=_lowerCamelCase , unk_token=_lowerCamelCase , pad_token=_lowerCamelCase , extra_ids=_lowerCamelCase , additional_special_tokens=_lowerCamelCase , sp_model_kwargs=self.sp_model_kwargs , legacy=_lowerCamelCase , **_lowerCamelCase , )
__SCREAMING_SNAKE_CASE : Tuple = vocab_file
__SCREAMING_SNAKE_CASE : List[str] = extra_ids
__SCREAMING_SNAKE_CASE : Optional[int] = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(_lowerCamelCase )
@staticmethod
def SCREAMING_SNAKE_CASE_ ( _lowerCamelCase :str , _lowerCamelCase :Union[str, Any] , _lowerCamelCase :int ):
if pretrained_model_name_or_path in TaTokenizer.max_model_input_sizes:
__SCREAMING_SNAKE_CASE : Any = TaTokenizer.max_model_input_sizes[pretrained_model_name_or_path]
if init_max_model_length is not None and init_max_model_length != max_model_length:
return init_max_model_length
elif init_max_model_length is None:
warnings.warn(
'''This tokenizer was incorrectly instantiated with a model max length of'''
f''' {deprecated_max_model_length} which will be corrected in Transformers v5.\nFor now, this'''
''' behavior is kept to avoid breaking backwards compatibility when padding/encoding with'''
''' `truncation is True`.\n- Be aware that you SHOULD NOT rely on'''
f''' {pretrained_model_name_or_path} automatically truncating your input to'''
f''' {deprecated_max_model_length} when padding/encoding.\n- If you want to encode/pad to sequences'''
f''' longer than {deprecated_max_model_length} you can either instantiate this tokenizer with'''
''' `model_max_length` or pass `max_length` when encoding/padding.\n- To avoid this warning, please'''
''' instantiate this tokenizer with `model_max_length` set to your preferred value.''' , _lowerCamelCase , )
return max_model_length
@property
def SCREAMING_SNAKE_CASE_ ( self :Tuple ):
return self.sp_model.get_piece_size() + self._extra_ids
def SCREAMING_SNAKE_CASE_ ( self :Union[str, Any] ):
__SCREAMING_SNAKE_CASE : str = {self.convert_ids_to_tokens(_lowerCamelCase ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def SCREAMING_SNAKE_CASE_ ( self :Union[str, Any] , _lowerCamelCase :List[int] , _lowerCamelCase :Optional[List[int]] = None , _lowerCamelCase :bool = False ):
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=_lowerCamelCase , token_ids_a=_lowerCamelCase , already_has_special_tokens=_lowerCamelCase )
# normal case: some special tokens
if token_ids_a is None:
return ([0] * len(_lowerCamelCase )) + [1]
return ([0] * len(_lowerCamelCase )) + [1] + ([0] * len(_lowerCamelCase )) + [1]
def SCREAMING_SNAKE_CASE_ ( self :List[str] ):
return list(
set(filter(lambda _lowerCamelCase : bool(re.search(r'''<extra_id_\d+>''' , _lowerCamelCase ) ) is not None , self.additional_special_tokens ) ) )
def SCREAMING_SNAKE_CASE_ ( self :List[Any] ):
return [self._convert_token_to_id(_lowerCamelCase ) for token in self.get_sentinel_tokens()]
def SCREAMING_SNAKE_CASE_ ( self :Any , _lowerCamelCase :List[int] ):
if len(_lowerCamelCase ) > 0 and token_ids[-1] == self.eos_token_id:
warnings.warn(
f'''This sequence already has {self.eos_token}. In future versions this behavior may lead to duplicated'''
''' eos tokens being added.''' )
return token_ids
else:
return token_ids + [self.eos_token_id]
def SCREAMING_SNAKE_CASE_ ( self :Union[str, Any] , _lowerCamelCase :List[int] , _lowerCamelCase :Optional[List[int]] = None ):
__SCREAMING_SNAKE_CASE : List[str] = [self.eos_token_id]
if token_ids_a is None:
return len(token_ids_a + eos ) * [0]
return len(token_ids_a + eos + token_ids_a + eos ) * [0]
def SCREAMING_SNAKE_CASE_ ( self :List[Any] , _lowerCamelCase :List[int] , _lowerCamelCase :Optional[List[int]] = None ):
__SCREAMING_SNAKE_CASE : Optional[Any] = self._add_eos_if_not_present(_lowerCamelCase )
if token_ids_a is None:
return token_ids_a
else:
__SCREAMING_SNAKE_CASE : Union[str, Any] = self._add_eos_if_not_present(_lowerCamelCase )
return token_ids_a + token_ids_a
def __getstate__( self :Union[str, Any] ):
__SCREAMING_SNAKE_CASE : Any = self.__dict__.copy()
__SCREAMING_SNAKE_CASE : List[str] = None
return state
def __setstate__( self :Optional[Any] , _lowerCamelCase :List[str] ):
__SCREAMING_SNAKE_CASE : Tuple = d
# for backward compatibility
if not hasattr(self , '''sp_model_kwargs''' ):
__SCREAMING_SNAKE_CASE : Optional[int] = {}
__SCREAMING_SNAKE_CASE : Optional[int] = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(self.vocab_file )
def SCREAMING_SNAKE_CASE_ ( self :int , _lowerCamelCase :"TextInput" , **_lowerCamelCase :str ):
# Replace the SPIECE_UNDERLINE with a space to make sure SPIECE_UNDERLINE is only used at
# the beginning of the text
if not self.legacy:
__SCREAMING_SNAKE_CASE : Dict = SPIECE_UNDERLINE + text.replace(_lowerCamelCase , ''' ''' )
return super().tokenize(_lowerCamelCase , **_lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :Optional[Any] , _lowerCamelCase :List[Any] , **_lowerCamelCase :Dict ):
if not self.legacy:
__SCREAMING_SNAKE_CASE : str = text.startswith(_lowerCamelCase )
if is_first:
__SCREAMING_SNAKE_CASE : str = text[1:]
__SCREAMING_SNAKE_CASE : Tuple = self.sp_model.encode(_lowerCamelCase , out_type=_lowerCamelCase )
if not self.legacy and not is_first and not text.startswith(''' ''' ) and tokens[0].startswith(_lowerCamelCase ):
__SCREAMING_SNAKE_CASE : Optional[int] = ([tokens[0][1:]] if len(tokens[0] ) > 1 else []) + tokens[1:]
return tokens
def SCREAMING_SNAKE_CASE_ ( self :Optional[Any] , _lowerCamelCase :Optional[Any] ):
if token.startswith('''<extra_id_''' ):
__SCREAMING_SNAKE_CASE : Tuple = re.match(r'''<extra_id_(\d+)>''' , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : Union[str, Any] = int(match.group(1 ) )
return self.vocab_size - num - 1
return self.sp_model.piece_to_id(_lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :str , _lowerCamelCase :Optional[int] ):
if index < self.sp_model.get_piece_size():
__SCREAMING_SNAKE_CASE : List[Any] = self.sp_model.IdToPiece(_lowerCamelCase )
else:
__SCREAMING_SNAKE_CASE : Dict = f'''<extra_id_{self.vocab_size - 1 - index}>'''
return token
def SCREAMING_SNAKE_CASE_ ( self :Tuple , _lowerCamelCase :Any ):
__SCREAMING_SNAKE_CASE : str = []
__SCREAMING_SNAKE_CASE : Dict = ''''''
__SCREAMING_SNAKE_CASE : Dict = False
for token in tokens:
# make sure that special tokens are not decoded using sentencepiece model
if token in self.all_special_tokens:
if not prev_is_special:
out_string += " "
out_string += self.sp_model.decode(_lowerCamelCase ) + token
__SCREAMING_SNAKE_CASE : List[str] = True
__SCREAMING_SNAKE_CASE : str = []
else:
current_sub_tokens.append(_lowerCamelCase )
__SCREAMING_SNAKE_CASE : int = False
out_string += self.sp_model.decode(_lowerCamelCase )
return out_string.strip()
def SCREAMING_SNAKE_CASE_ ( self :Optional[Any] , _lowerCamelCase :str , _lowerCamelCase :Optional[str] = None ):
if not os.path.isdir(_lowerCamelCase ):
logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' )
return
__SCREAMING_SNAKE_CASE : List[str] = os.path.join(
_lowerCamelCase , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(_lowerCamelCase ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file , _lowerCamelCase )
elif not os.path.isfile(self.vocab_file ):
with open(_lowerCamelCase , '''wb''' ) as fi:
__SCREAMING_SNAKE_CASE : Any = self.sp_model.serialized_model_proto()
fi.write(_lowerCamelCase )
return (out_vocab_file,)
| 674 | 0 |
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__snake_case = logging.get_logger(__name__)
__snake_case = {
"google/mobilenet_v2_1.4_224": "https://huggingface.co/google/mobilenet_v2_1.4_224/resolve/main/config.json",
"google/mobilenet_v2_1.0_224": "https://huggingface.co/google/mobilenet_v2_1.0_224/resolve/main/config.json",
"google/mobilenet_v2_0.75_160": "https://huggingface.co/google/mobilenet_v2_0.75_160/resolve/main/config.json",
"google/mobilenet_v2_0.35_96": "https://huggingface.co/google/mobilenet_v2_0.35_96/resolve/main/config.json",
# See all MobileNetV2 models at https://huggingface.co/models?filter=mobilenet_v2
}
class UpperCAmelCase ( __UpperCAmelCase ):
lowercase = """mobilenet_v2"""
def __init__( self : Any , __magic_name__ : List[str]=3 , __magic_name__ : Tuple=2_2_4 , __magic_name__ : List[Any]=1.0 , __magic_name__ : List[Any]=8 , __magic_name__ : Tuple=8 , __magic_name__ : Optional[Any]=6 , __magic_name__ : Dict=3_2 , __magic_name__ : Dict=True , __magic_name__ : Union[str, Any]=True , __magic_name__ : List[str]="relu6" , __magic_name__ : Any=True , __magic_name__ : Any=0.8 , __magic_name__ : List[str]=0.02 , __magic_name__ : Any=0.001 , __magic_name__ : Union[str, Any]=2_5_5 , **__magic_name__ : str , ):
"""simple docstring"""
super().__init__(**_lowerCamelCase )
if depth_multiplier <= 0:
raise ValueError("""depth_multiplier must be greater than zero.""" )
UpperCamelCase = num_channels
UpperCamelCase = image_size
UpperCamelCase = depth_multiplier
UpperCamelCase = depth_divisible_by
UpperCamelCase = min_depth
UpperCamelCase = expand_ratio
UpperCamelCase = output_stride
UpperCamelCase = first_layer_is_expansion
UpperCamelCase = finegrained_output
UpperCamelCase = hidden_act
UpperCamelCase = tf_padding
UpperCamelCase = classifier_dropout_prob
UpperCamelCase = initializer_range
UpperCamelCase = layer_norm_eps
UpperCamelCase = semantic_loss_ignore_index
class UpperCAmelCase ( __UpperCAmelCase ):
lowercase = version.parse("""1.11""" )
@property
def lowerCamelCase_ ( self : Optional[int] ):
"""simple docstring"""
return OrderedDict([("""pixel_values""", {0: """batch"""})] )
@property
def lowerCamelCase_ ( self : Any ):
"""simple docstring"""
if self.task == "image-classification":
return OrderedDict([("""logits""", {0: """batch"""})] )
else:
return OrderedDict([("""last_hidden_state""", {0: """batch"""}), ("""pooler_output""", {0: """batch"""})] )
@property
def lowerCamelCase_ ( self : Optional[Any] ):
"""simple docstring"""
return 1e-4
| 386 |
"""simple docstring"""
import os
import pytest
import yaml
from datasets.features.features import Features, Value
from datasets.info import DatasetInfo, DatasetInfosDict
@pytest.mark.parametrize(
'''files''' , [
['''full:README.md''', '''dataset_infos.json'''],
['''empty:README.md''', '''dataset_infos.json'''],
['''dataset_infos.json'''],
['''full:README.md'''],
] , )
def lowerCAmelCase_ ( lowercase_ : List[str] , lowercase_ : str ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : Optional[int] = tmp_path_factory.mktemp('''dset_infos_dir''' )
if "full:README.md" in files:
with open(dataset_infos_dir / '''README.md''' , '''w''' ) as f:
f.write('''---\ndataset_info:\n dataset_size: 42\n---''' )
if "empty:README.md" in files:
with open(dataset_infos_dir / '''README.md''' , '''w''' ) as f:
f.write('''''' )
# we want to support dataset_infos.json for backward compatibility
if "dataset_infos.json" in files:
with open(dataset_infos_dir / '''dataset_infos.json''' , '''w''' ) as f:
f.write('''{"default": {"dataset_size": 42}}''' )
__SCREAMING_SNAKE_CASE : Optional[Any] = DatasetInfosDict.from_directory(lowercase_ )
assert dataset_infos
assert dataset_infos["default"].dataset_size == 42
@pytest.mark.parametrize(
'''dataset_info''' , [
DatasetInfo(),
DatasetInfo(
description='''foo''' , features=Features({'''a''': Value('''int32''' )} ) , builder_name='''builder''' , config_name='''config''' , version='''1.0.0''' , splits=[{'''name''': '''train'''}] , download_size=42 , ),
] , )
def lowerCAmelCase_ ( lowercase_ : Any , lowercase_ : DatasetInfo ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : List[str] = str(lowercase_ )
dataset_info.write_to_directory(lowercase_ )
__SCREAMING_SNAKE_CASE : Dict = DatasetInfo.from_directory(lowercase_ )
assert dataset_info == reloaded
assert os.path.exists(os.path.join(lowercase_ , '''dataset_info.json''' ) )
def lowerCAmelCase_ ( ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : Tuple = DatasetInfo(
description='''foo''' , citation='''bar''' , homepage='''https://foo.bar''' , license='''CC0''' , features=Features({'''a''': Value('''int32''' )} ) , post_processed={} , supervised_keys=() , task_templates=[] , builder_name='''builder''' , config_name='''config''' , version='''1.0.0''' , splits=[{'''name''': '''train''', '''num_examples''': 42}] , download_checksums={} , download_size=1337 , post_processing_size=442 , dataset_size=1234 , size_in_bytes=1337 + 442 + 1234 , )
__SCREAMING_SNAKE_CASE : Optional[int] = dataset_info._to_yaml_dict()
assert sorted(lowercase_ ) == sorted(DatasetInfo._INCLUDED_INFO_IN_YAML )
for key in DatasetInfo._INCLUDED_INFO_IN_YAML:
assert key in dataset_info_yaml_dict
assert isinstance(dataset_info_yaml_dict[key] , (list, dict, int, str) )
__SCREAMING_SNAKE_CASE : int = yaml.safe_dump(lowercase_ )
__SCREAMING_SNAKE_CASE : Union[str, Any] = yaml.safe_load(lowercase_ )
assert dataset_info_yaml_dict == reloaded
def lowerCAmelCase_ ( ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : Union[str, Any] = DatasetInfo()
__SCREAMING_SNAKE_CASE : Union[str, Any] = dataset_info._to_yaml_dict()
assert dataset_info_yaml_dict == {}
@pytest.mark.parametrize(
'''dataset_infos_dict''' , [
DatasetInfosDict(),
DatasetInfosDict({'''default''': DatasetInfo()} ),
DatasetInfosDict({'''my_config_name''': DatasetInfo()} ),
DatasetInfosDict(
{
'''default''': DatasetInfo(
description='''foo''' , features=Features({'''a''': Value('''int32''' )} ) , builder_name='''builder''' , config_name='''config''' , version='''1.0.0''' , splits=[{'''name''': '''train'''}] , download_size=42 , )
} ),
DatasetInfosDict(
{
'''v1''': DatasetInfo(dataset_size=42 ),
'''v2''': DatasetInfo(dataset_size=1337 ),
} ),
] , )
def lowerCAmelCase_ ( lowercase_ : List[Any] , lowercase_ : DatasetInfosDict ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : Optional[int] = str(lowercase_ )
dataset_infos_dict.write_to_directory(lowercase_ )
__SCREAMING_SNAKE_CASE : Optional[Any] = DatasetInfosDict.from_directory(lowercase_ )
# the config_name of the dataset_infos_dict take over the attribute
for config_name, dataset_info in dataset_infos_dict.items():
__SCREAMING_SNAKE_CASE : Optional[int] = config_name
# the yaml representation doesn't include fields like description or citation
# so we just test that we can recover what we can from the yaml
__SCREAMING_SNAKE_CASE : Tuple = DatasetInfo._from_yaml_dict(dataset_info._to_yaml_dict() )
assert dataset_infos_dict == reloaded
if dataset_infos_dict:
assert os.path.exists(os.path.join(lowercase_ , '''README.md''' ) )
| 674 | 0 |
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
a__ = {
'''configuration_cpmant''': ['''CPMANT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''CpmAntConfig'''],
'''tokenization_cpmant''': ['''CpmAntTokenizer'''],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a__ = [
'''CPMANT_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''CpmAntForCausalLM''',
'''CpmAntModel''',
'''CpmAntPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_cpmant import CPMANT_PRETRAINED_CONFIG_ARCHIVE_MAP, CpmAntConfig
from .tokenization_cpmant import CpmAntTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_cpmant import (
CPMANT_PRETRAINED_MODEL_ARCHIVE_LIST,
CpmAntForCausalLM,
CpmAntModel,
CpmAntPreTrainedModel,
)
else:
import sys
a__ = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 279 |
"""simple docstring"""
from typing import Optional, Union
import torch
from torch import nn
from ...configuration_utils import ConfigMixin, register_to_config
from ...models.modeling_utils import ModelMixin
class snake_case ( __UpperCAmelCase , __UpperCAmelCase ):
@register_to_config
def __init__( self :List[str] , _lowerCamelCase :int = 7_6_8 , ):
super().__init__()
__SCREAMING_SNAKE_CASE : Union[str, Any] = nn.Parameter(torch.zeros(1 , _lowerCamelCase ) )
__SCREAMING_SNAKE_CASE : List[Any] = nn.Parameter(torch.ones(1 , _lowerCamelCase ) )
def SCREAMING_SNAKE_CASE_ ( self :Any , _lowerCamelCase :Optional[Union[str, torch.device]] = None , _lowerCamelCase :Optional[torch.dtype] = None , ):
__SCREAMING_SNAKE_CASE : Optional[int] = nn.Parameter(self.mean.to(_lowerCamelCase ).to(_lowerCamelCase ) )
__SCREAMING_SNAKE_CASE : Tuple = nn.Parameter(self.std.to(_lowerCamelCase ).to(_lowerCamelCase ) )
return self
def SCREAMING_SNAKE_CASE_ ( self :int , _lowerCamelCase :Tuple ):
__SCREAMING_SNAKE_CASE : Tuple = (embeds - self.mean) * 1.0 / self.std
return embeds
def SCREAMING_SNAKE_CASE_ ( self :Optional[Any] , _lowerCamelCase :Optional[int] ):
__SCREAMING_SNAKE_CASE : List[Any] = (embeds * self.std) + self.mean
return embeds
| 674 | 0 |
from pathlib import Path
import fire
from tqdm import tqdm
def a ( a="ro" , a="en" , a="wmt16" , a=None ) ->List[Any]:
'''simple docstring'''
try:
import datasets
except (ModuleNotFoundError, ImportError):
raise ImportError('''run pip install datasets''' )
SCREAMING_SNAKE_CASE = F"""{src_lang}-{tgt_lang}"""
print(F"""Converting {dataset}-{pair}""" )
SCREAMING_SNAKE_CASE = datasets.load_dataset(lowercase_ , lowercase_ )
if save_dir is None:
SCREAMING_SNAKE_CASE = F"""{dataset}-{pair}"""
SCREAMING_SNAKE_CASE = Path(lowercase_ )
save_dir.mkdir(exist_ok=lowercase_ )
for split in ds.keys():
print(F"""Splitting {split} with {ds[split].num_rows} records""" )
# to save to val.source, val.target like summary datasets
SCREAMING_SNAKE_CASE = '''val''' if split == '''validation''' else split
SCREAMING_SNAKE_CASE = save_dir.joinpath(F"""{fn}.source""" )
SCREAMING_SNAKE_CASE = save_dir.joinpath(F"""{fn}.target""" )
SCREAMING_SNAKE_CASE = src_path.open('''w+''' )
SCREAMING_SNAKE_CASE = tgt_path.open('''w+''' )
# reader is the bottleneck so writing one record at a time doesn't slow things down
for x in tqdm(ds[split] ):
SCREAMING_SNAKE_CASE = x['''translation''']
src_fp.write(ex[src_lang] + '''\n''' )
tgt_fp.write(ex[tgt_lang] + '''\n''' )
print(F"""Saved {dataset} dataset to {save_dir}""" )
if __name__ == "__main__":
fire.Fire(download_wmt_dataset) | 201 |
"""simple docstring"""
import argparse
import torch
from transformers import BertConfig, BertForPreTraining, load_tf_weights_in_bert
from transformers.utils import logging
logging.set_verbosity_info()
def lowerCAmelCase_ ( lowercase_ : Dict , lowercase_ : int , lowercase_ : Any ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : Tuple = BertConfig.from_json_file(lowercase_ )
print(F'''Building PyTorch model from configuration: {config}''' )
__SCREAMING_SNAKE_CASE : Optional[int] = BertForPreTraining(lowercase_ )
# Load weights from tf checkpoint
load_tf_weights_in_bert(lowercase_ , lowercase_ , lowercase_ )
# Save pytorch-model
print(F'''Save PyTorch model to {pytorch_dump_path}''' )
torch.save(model.state_dict() , lowercase_ )
if __name__ == "__main__":
_lowerCamelCase = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--tf_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
)
parser.add_argument(
'''--bert_config_file''',
default=None,
type=str,
required=True,
help=(
'''The config json file corresponding to the pre-trained BERT model. \n'''
'''This specifies the model architecture.'''
),
)
parser.add_argument(
'''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
)
_lowerCamelCase = parser.parse_args()
convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)
| 674 | 0 |
"""simple docstring"""
import collections
import inspect
import unittest
from typing import Dict, List, Tuple
from transformers import MaskFormerSwinConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, torch_device
from transformers.utils import is_torch_available
from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import MaskFormerSwinBackbone
from transformers.models.maskformer import MaskFormerSwinModel
class __UpperCAmelCase:
"""simple docstring"""
def __init__( self , snake_case__ , snake_case__=13 , snake_case__=32 , snake_case__=2 , snake_case__=3 , snake_case__=16 , snake_case__=[1, 2, 1] , snake_case__=[2, 2, 4] , snake_case__=2 , snake_case__=2.0 , snake_case__=True , snake_case__=0.0 , snake_case__=0.0 , snake_case__=0.1 , snake_case__="gelu" , snake_case__=False , snake_case__=True , snake_case__=0.02 , snake_case__=1e-5 , snake_case__=True , snake_case__=None , snake_case__=True , snake_case__=10 , snake_case__=8 , snake_case__=["stage1", "stage2", "stage3"] , snake_case__=[1, 2, 3] , ):
'''simple docstring'''
lowercase__ : Union[str, Any]= parent
lowercase__ : Optional[int]= batch_size
lowercase__ : Any= image_size
lowercase__ : Optional[int]= patch_size
lowercase__ : str= num_channels
lowercase__ : List[Any]= embed_dim
lowercase__ : Optional[Any]= depths
lowercase__ : List[str]= num_heads
lowercase__ : Union[str, Any]= window_size
lowercase__ : int= mlp_ratio
lowercase__ : Any= qkv_bias
lowercase__ : Tuple= hidden_dropout_prob
lowercase__ : Optional[int]= attention_probs_dropout_prob
lowercase__ : Dict= drop_path_rate
lowercase__ : Dict= hidden_act
lowercase__ : Union[str, Any]= use_absolute_embeddings
lowercase__ : Optional[int]= patch_norm
lowercase__ : Any= layer_norm_eps
lowercase__ : Union[str, Any]= initializer_range
lowercase__ : Dict= is_training
lowercase__ : Optional[Any]= scope
lowercase__ : Union[str, Any]= use_labels
lowercase__ : Optional[Any]= type_sequence_label_size
lowercase__ : List[Any]= encoder_stride
lowercase__ : List[str]= out_features
lowercase__ : Optional[int]= out_indices
def UpperCAmelCase_ ( self ):
'''simple docstring'''
lowercase__ : str= floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
lowercase__ : int= None
if self.use_labels:
lowercase__ : Union[str, Any]= ids_tensor([self.batch_size] , self.type_sequence_label_size )
lowercase__ : Tuple= self.get_config()
return config, pixel_values, labels
def UpperCAmelCase_ ( self ):
'''simple docstring'''
return MaskFormerSwinConfig(
image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , embed_dim=self.embed_dim , depths=self.depths , num_heads=self.num_heads , window_size=self.window_size , mlp_ratio=self.mlp_ratio , qkv_bias=self.qkv_bias , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , drop_path_rate=self.drop_path_rate , hidden_act=self.hidden_act , use_absolute_embeddings=self.use_absolute_embeddings , path_norm=self.patch_norm , layer_norm_eps=self.layer_norm_eps , initializer_range=self.initializer_range , encoder_stride=self.encoder_stride , out_features=self.out_features , out_indices=self.out_indices , )
def UpperCAmelCase_ ( self , snake_case__ , snake_case__ , snake_case__ ):
'''simple docstring'''
lowercase__ : Union[str, Any]= MaskFormerSwinModel(config=_lowerCamelCase )
model.to(_lowerCamelCase )
model.eval()
lowercase__ : Tuple= model(_lowerCamelCase )
lowercase__ : List[str]= ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths ) - 1))
lowercase__ : Optional[int]= int(config.embed_dim * 2 ** (len(config.depths ) - 1) )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, expected_seq_len, expected_dim) )
def UpperCAmelCase_ ( self , snake_case__ , snake_case__ , snake_case__ ):
'''simple docstring'''
lowercase__ : Any= MaskFormerSwinBackbone(config=_lowerCamelCase )
model.to(_lowerCamelCase )
model.eval()
lowercase__ : Union[str, Any]= model(_lowerCamelCase )
# verify feature maps
self.parent.assertEqual(len(result.feature_maps ) , len(config.out_features ) )
self.parent.assertListEqual(list(result.feature_maps[0].shape ) , [13, 16, 16, 16] )
# verify channels
self.parent.assertEqual(len(model.channels ) , len(config.out_features ) )
self.parent.assertListEqual(model.channels , [16, 32, 64] )
# verify ValueError
with self.parent.assertRaises(_lowerCamelCase ):
lowercase__ : Optional[Any]= ['''stem''']
lowercase__ : Optional[Any]= MaskFormerSwinBackbone(config=_lowerCamelCase )
def UpperCAmelCase_ ( self ):
'''simple docstring'''
lowercase__ : Dict= self.prepare_config_and_inputs()
lowercase__ : int= config_and_inputs
lowercase__ : str= {'''pixel_values''': pixel_values}
return config, inputs_dict
@require_torch
class __UpperCAmelCase( __UpperCAmelCase , __UpperCAmelCase , unittest.TestCase ):
"""simple docstring"""
__lowerCamelCase = (
(
MaskFormerSwinModel,
MaskFormerSwinBackbone,
)
if is_torch_available()
else ()
)
__lowerCamelCase = {"feature-extraction": MaskFormerSwinModel} if is_torch_available() else {}
__lowerCamelCase = False
__lowerCamelCase = False
__lowerCamelCase = False
__lowerCamelCase = False
__lowerCamelCase = False
def UpperCAmelCase_ ( self ):
'''simple docstring'''
lowercase__ : Tuple= MaskFormerSwinModelTester(self )
lowercase__ : str= ConfigTester(self , config_class=_lowerCamelCase , embed_dim=37 )
@require_torch_multi_gpu
@unittest.skip(
reason=(
"`MaskFormerSwinModel` outputs `hidden_states_spatial_dimensions` which doesn\'t work well with"
" `nn.DataParallel`"
) )
def UpperCAmelCase_ ( self ):
'''simple docstring'''
pass
def UpperCAmelCase_ ( self ):
'''simple docstring'''
self.create_and_test_config_common_properties()
self.config_tester.create_and_test_config_to_json_string()
self.config_tester.create_and_test_config_to_json_file()
self.config_tester.create_and_test_config_from_and_save_pretrained()
self.config_tester.create_and_test_config_with_num_labels()
self.config_tester.check_config_can_be_init_without_params()
self.config_tester.check_config_arguments_init()
def UpperCAmelCase_ ( self ):
'''simple docstring'''
return
def UpperCAmelCase_ ( self ):
'''simple docstring'''
lowercase__ : Tuple= self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*_lowerCamelCase )
def UpperCAmelCase_ ( self ):
'''simple docstring'''
lowercase__ : Optional[int]= self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_backbone(*_lowerCamelCase )
@unittest.skip("Swin does not use inputs_embeds" )
def UpperCAmelCase_ ( self ):
'''simple docstring'''
pass
@unittest.skip("Swin does not support feedforward chunking" )
def UpperCAmelCase_ ( self ):
'''simple docstring'''
pass
def UpperCAmelCase_ ( self ):
'''simple docstring'''
lowercase__ : Dict= self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowercase__ : int= model_class(_lowerCamelCase )
self.assertIsInstance(model.get_input_embeddings() , (nn.Module) )
lowercase__ : List[str]= model.get_output_embeddings()
self.assertTrue(x is None or isinstance(_lowerCamelCase , nn.Linear ) )
def UpperCAmelCase_ ( self ):
'''simple docstring'''
lowercase__ : Union[str, Any]= self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowercase__ : Union[str, Any]= model_class(_lowerCamelCase )
lowercase__ : List[str]= inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
lowercase__ : Any= [*signature.parameters.keys()]
lowercase__ : Any= ['''pixel_values''']
self.assertListEqual(arg_names[:1] , _lowerCamelCase )
@unittest.skip(reason="MaskFormerSwin is only used as backbone and doesn\'t support output_attentions" )
def UpperCAmelCase_ ( self ):
'''simple docstring'''
pass
@unittest.skip(reason="MaskFormerSwin is only used as an internal backbone" )
def UpperCAmelCase_ ( self ):
'''simple docstring'''
pass
def UpperCAmelCase_ ( self , snake_case__ , snake_case__ , snake_case__ , snake_case__ ):
'''simple docstring'''
lowercase__ : Union[str, Any]= model_class(_lowerCamelCase )
model.to(_lowerCamelCase )
model.eval()
with torch.no_grad():
lowercase__ : Union[str, Any]= model(**self._prepare_for_class(_lowerCamelCase , _lowerCamelCase ) )
lowercase__ : Dict= outputs.hidden_states
lowercase__ : int= getattr(
self.model_tester , "expected_num_hidden_layers" , len(self.model_tester.depths ) + 1 )
self.assertEqual(len(_lowerCamelCase ) , _lowerCamelCase )
# Swin has a different seq_length
lowercase__ : Any= (
config.patch_size
if isinstance(config.patch_size , collections.abc.Iterable )
else (config.patch_size, config.patch_size)
)
lowercase__ : List[Any]= (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
self.assertListEqual(
list(hidden_states[0].shape[-2:] ) , [num_patches, self.model_tester.embed_dim] , )
def UpperCAmelCase_ ( self ):
'''simple docstring'''
lowercase__ : List[Any]= self.model_tester.prepare_config_and_inputs_for_common()
lowercase__ : Dict= (
self.model_tester.image_size
if isinstance(self.model_tester.image_size , collections.abc.Iterable )
else (self.model_tester.image_size, self.model_tester.image_size)
)
for model_class in self.all_model_classes:
lowercase__ : Union[str, Any]= True
self.check_hidden_states_output(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
lowercase__ : Optional[Any]= True
self.check_hidden_states_output(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase )
def UpperCAmelCase_ ( self ):
'''simple docstring'''
lowercase__ : Dict= self.model_tester.prepare_config_and_inputs_for_common()
lowercase__ : int= 3
lowercase__ : int= (
self.model_tester.image_size
if isinstance(self.model_tester.image_size , collections.abc.Iterable )
else (self.model_tester.image_size, self.model_tester.image_size)
)
lowercase__ : List[Any]= (
config.patch_size
if isinstance(config.patch_size , collections.abc.Iterable )
else (config.patch_size, config.patch_size)
)
lowercase__ : Any= image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
lowercase__ : List[Any]= image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])
for model_class in self.all_model_classes:
lowercase__ : Dict= True
self.check_hidden_states_output(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase , (padded_height, padded_width) )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
lowercase__ : Any= True
self.check_hidden_states_output(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase , (padded_height, padded_width) )
@unittest.skip(reason="MaskFormerSwin doesn\'t have pretrained checkpoints" )
def UpperCAmelCase_ ( self ):
'''simple docstring'''
pass
@unittest.skip(reason="This will be fixed once MaskFormerSwin is replaced by native Swin" )
def UpperCAmelCase_ ( self ):
'''simple docstring'''
pass
@unittest.skip(reason="This will be fixed once MaskFormerSwin is replaced by native Swin" )
def UpperCAmelCase_ ( self ):
'''simple docstring'''
pass
def UpperCAmelCase_ ( self ):
'''simple docstring'''
lowercase__ : List[Any]= self.model_tester.prepare_config_and_inputs_for_common()
def set_nan_tensor_to_zero(snake_case__ ):
lowercase__ : Any= 0
return t
def check_equivalence(snake_case__ , snake_case__ , snake_case__ , snake_case__={} ):
with torch.no_grad():
lowercase__ : List[str]= model(**_lowerCamelCase , return_dict=_lowerCamelCase , **_lowerCamelCase )
lowercase__ : Optional[int]= model(**_lowerCamelCase , return_dict=_lowerCamelCase , **_lowerCamelCase ).to_tuple()
def recursive_check(snake_case__ , snake_case__ ):
if isinstance(_lowerCamelCase , (List, Tuple) ):
for tuple_iterable_value, dict_iterable_value in zip(_lowerCamelCase , _lowerCamelCase ):
recursive_check(_lowerCamelCase , _lowerCamelCase )
elif isinstance(_lowerCamelCase , _lowerCamelCase ):
for tuple_iterable_value, dict_iterable_value in zip(
tuple_object.values() , dict_object.values() ):
recursive_check(_lowerCamelCase , _lowerCamelCase )
elif tuple_object is None:
return
else:
self.assertTrue(
torch.allclose(
set_nan_tensor_to_zero(_lowerCamelCase ) , set_nan_tensor_to_zero(_lowerCamelCase ) , atol=1e-5 ) , msg=(
"Tuple and dict output are not equal. Difference:"
F''' {torch.max(torch.abs(tuple_object - dict_object ) )}. Tuple has `nan`:'''
F''' {torch.isnan(_lowerCamelCase ).any()} and `inf`: {torch.isinf(_lowerCamelCase )}. Dict has'''
F''' `nan`: {torch.isnan(_lowerCamelCase ).any()} and `inf`: {torch.isinf(_lowerCamelCase )}.'''
) , )
recursive_check(_lowerCamelCase , _lowerCamelCase )
for model_class in self.all_model_classes:
lowercase__ : List[str]= model_class(_lowerCamelCase )
model.to(_lowerCamelCase )
model.eval()
lowercase__ : List[Any]= self._prepare_for_class(_lowerCamelCase , _lowerCamelCase )
lowercase__ : str= self._prepare_for_class(_lowerCamelCase , _lowerCamelCase )
check_equivalence(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase )
lowercase__ : int= self._prepare_for_class(_lowerCamelCase , _lowerCamelCase , return_labels=_lowerCamelCase )
lowercase__ : Optional[Any]= self._prepare_for_class(_lowerCamelCase , _lowerCamelCase , return_labels=_lowerCamelCase )
check_equivalence(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase )
lowercase__ : Any= self._prepare_for_class(_lowerCamelCase , _lowerCamelCase )
lowercase__ : Tuple= self._prepare_for_class(_lowerCamelCase , _lowerCamelCase )
check_equivalence(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase , {"output_hidden_states": True} )
lowercase__ : Any= self._prepare_for_class(_lowerCamelCase , _lowerCamelCase , return_labels=_lowerCamelCase )
lowercase__ : Union[str, Any]= self._prepare_for_class(_lowerCamelCase , _lowerCamelCase , return_labels=_lowerCamelCase )
check_equivalence(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase , {"output_hidden_states": True} )
@require_torch
class __UpperCAmelCase( unittest.TestCase , __UpperCAmelCase ):
"""simple docstring"""
__lowerCamelCase = (MaskFormerSwinBackbone,) if is_torch_available() else ()
__lowerCamelCase = MaskFormerSwinConfig
def UpperCAmelCase_ ( self ):
'''simple docstring'''
lowercase__ : Tuple= MaskFormerSwinModelTester(self )
def UpperCAmelCase_ ( self ):
'''simple docstring'''
lowercase__ : Dict= self.model_tester.prepare_config_and_inputs_for_common()
lowercase__ : List[str]= inputs_dict['''pixel_values'''].shape[0]
for backbone_class in self.all_model_classes:
lowercase__ : int= backbone_class(_lowerCamelCase )
backbone.to(_lowerCamelCase )
backbone.eval()
lowercase__ : Optional[int]= backbone(**_lowerCamelCase )
# Test default outputs and verify feature maps
self.assertIsInstance(outputs.feature_maps , _lowerCamelCase )
self.assertTrue(len(outputs.feature_maps ) == len(backbone.channels ) )
for feature_map, n_channels in zip(outputs.feature_maps , backbone.channels ):
self.assertTrue(feature_map.shape[:2] , (batch_size, n_channels) )
self.assertIsNone(outputs.hidden_states )
self.assertIsNone(outputs.attentions )
# Test output_hidden_states=True
lowercase__ : str= backbone(**_lowerCamelCase , output_hidden_states=_lowerCamelCase )
self.assertIsNotNone(outputs.hidden_states )
self.assertTrue(len(outputs.hidden_states ) , len(backbone.stage_names ) )
# We skip the stem layer
for hidden_states, n_channels in zip(outputs.hidden_states[1:] , backbone.channels ):
for hidden_state in hidden_states:
# Hidden states are in the format (batch_size, (height * width), n_channels)
lowercase__ : Tuple= hidden_state.shape
self.assertTrue((h_batch_size, h_n_channels) , (batch_size, n_channels) )
# Test output_attentions=True
if self.has_attentions:
lowercase__ : Any= backbone(**_lowerCamelCase , output_attentions=_lowerCamelCase )
self.assertIsNotNone(outputs.attentions )
| 218 |
"""simple docstring"""
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import numpy as np
import torch
from ..models.clipseg import CLIPSegForImageSegmentation
from ..utils import is_vision_available, requires_backends
from .base import PipelineTool
if is_vision_available():
from PIL import Image
class snake_case ( __UpperCAmelCase ):
lowerCamelCase__ = (
'''This is a tool that creates a segmentation mask of an image according to a label. It cannot create an image.'''
'''It takes two arguments named `image` which should be the original image, and `label` which should be a text '''
'''describing the elements what should be identified in the segmentation mask. The tool returns the mask.'''
)
lowerCamelCase__ = '''CIDAS/clipseg-rd64-refined'''
lowerCamelCase__ = '''image_segmenter'''
lowerCamelCase__ = CLIPSegForImageSegmentation
lowerCamelCase__ = ['''image''', '''text''']
lowerCamelCase__ = ['''image''']
def __init__( self :Dict , *_lowerCamelCase :Union[str, Any] , **_lowerCamelCase :Tuple ):
requires_backends(self , ['''vision'''] )
super().__init__(*_lowerCamelCase , **_lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :Tuple , _lowerCamelCase :"Image" , _lowerCamelCase :str ):
return self.pre_processor(text=[label] , images=[image] , padding=_lowerCamelCase , return_tensors='''pt''' )
def SCREAMING_SNAKE_CASE_ ( self :Optional[int] , _lowerCamelCase :Optional[int] ):
with torch.no_grad():
__SCREAMING_SNAKE_CASE : List[Any] = self.model(**_lowerCamelCase ).logits
return logits
def SCREAMING_SNAKE_CASE_ ( self :Optional[Any] , _lowerCamelCase :Tuple ):
__SCREAMING_SNAKE_CASE : Optional[int] = outputs.cpu().detach().numpy()
__SCREAMING_SNAKE_CASE : str = 0
__SCREAMING_SNAKE_CASE : str = 1
return Image.fromarray((array * 2_5_5).astype(np.uinta ) )
| 674 | 0 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowerCamelCase :str = logging.get_logger(__name__)
lowerCamelCase :Tuple = {
'bert-base-uncased': 'https://huggingface.co/bert-base-uncased/resolve/main/config.json',
'bert-large-uncased': 'https://huggingface.co/bert-large-uncased/resolve/main/config.json',
'bert-base-cased': 'https://huggingface.co/bert-base-cased/resolve/main/config.json',
'bert-large-cased': 'https://huggingface.co/bert-large-cased/resolve/main/config.json',
'bert-base-multilingual-uncased': 'https://huggingface.co/bert-base-multilingual-uncased/resolve/main/config.json',
'bert-base-multilingual-cased': 'https://huggingface.co/bert-base-multilingual-cased/resolve/main/config.json',
'bert-base-chinese': 'https://huggingface.co/bert-base-chinese/resolve/main/config.json',
'bert-base-german-cased': 'https://huggingface.co/bert-base-german-cased/resolve/main/config.json',
'bert-large-uncased-whole-word-masking': (
'https://huggingface.co/bert-large-uncased-whole-word-masking/resolve/main/config.json'
),
'bert-large-cased-whole-word-masking': (
'https://huggingface.co/bert-large-cased-whole-word-masking/resolve/main/config.json'
),
'bert-large-uncased-whole-word-masking-finetuned-squad': (
'https://huggingface.co/bert-large-uncased-whole-word-masking-finetuned-squad/resolve/main/config.json'
),
'bert-large-cased-whole-word-masking-finetuned-squad': (
'https://huggingface.co/bert-large-cased-whole-word-masking-finetuned-squad/resolve/main/config.json'
),
'bert-base-cased-finetuned-mrpc': 'https://huggingface.co/bert-base-cased-finetuned-mrpc/resolve/main/config.json',
'bert-base-german-dbmdz-cased': 'https://huggingface.co/bert-base-german-dbmdz-cased/resolve/main/config.json',
'bert-base-german-dbmdz-uncased': 'https://huggingface.co/bert-base-german-dbmdz-uncased/resolve/main/config.json',
'cl-tohoku/bert-base-japanese': 'https://huggingface.co/cl-tohoku/bert-base-japanese/resolve/main/config.json',
'cl-tohoku/bert-base-japanese-whole-word-masking': (
'https://huggingface.co/cl-tohoku/bert-base-japanese-whole-word-masking/resolve/main/config.json'
),
'cl-tohoku/bert-base-japanese-char': (
'https://huggingface.co/cl-tohoku/bert-base-japanese-char/resolve/main/config.json'
),
'cl-tohoku/bert-base-japanese-char-whole-word-masking': (
'https://huggingface.co/cl-tohoku/bert-base-japanese-char-whole-word-masking/resolve/main/config.json'
),
'TurkuNLP/bert-base-finnish-cased-v1': (
'https://huggingface.co/TurkuNLP/bert-base-finnish-cased-v1/resolve/main/config.json'
),
'TurkuNLP/bert-base-finnish-uncased-v1': (
'https://huggingface.co/TurkuNLP/bert-base-finnish-uncased-v1/resolve/main/config.json'
),
'wietsedv/bert-base-dutch-cased': 'https://huggingface.co/wietsedv/bert-base-dutch-cased/resolve/main/config.json',
# See all BERT models at https://huggingface.co/models?filter=bert
}
class UpperCAmelCase ( __UpperCAmelCase ):
a: List[Any] = "bert"
def __init__( self: Optional[int] , __UpperCamelCase: Optional[Any]=3_0522 , __UpperCamelCase: int=768 , __UpperCamelCase: Union[str, Any]=12 , __UpperCamelCase: Any=12 , __UpperCamelCase: List[str]=3072 , __UpperCamelCase: Optional[int]="gelu" , __UpperCamelCase: List[Any]=0.1 , __UpperCamelCase: Union[str, Any]=0.1 , __UpperCamelCase: List[str]=512 , __UpperCamelCase: Union[str, Any]=2 , __UpperCamelCase: Tuple=0.0_2 , __UpperCamelCase: Any=1E-12 , __UpperCamelCase: Tuple=0 , __UpperCamelCase: List[str]="absolute" , __UpperCamelCase: Optional[int]=True , __UpperCamelCase: Optional[int]=None , **__UpperCamelCase: Optional[Any] , ):
super().__init__(pad_token_id=_lowerCamelCase , **_lowerCamelCase )
_a = vocab_size
_a = hidden_size
_a = num_hidden_layers
_a = num_attention_heads
_a = hidden_act
_a = intermediate_size
_a = hidden_dropout_prob
_a = attention_probs_dropout_prob
_a = max_position_embeddings
_a = type_vocab_size
_a = initializer_range
_a = layer_norm_eps
_a = position_embedding_type
_a = use_cache
_a = classifier_dropout
class UpperCAmelCase ( __UpperCAmelCase ):
@property
def _A ( self: Any ):
if self.task == "multiple-choice":
_a = {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
else:
_a = {0: '''batch''', 1: '''sequence'''}
return OrderedDict(
[
('''input_ids''', dynamic_axis),
('''attention_mask''', dynamic_axis),
('''token_type_ids''', dynamic_axis),
] )
| 487 |
"""simple docstring"""
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
from transformers import BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES, BertTokenizer
from transformers.testing_utils import require_tokenizers, require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available
if is_vision_available():
from PIL import Image
from transformers import VisionTextDualEncoderProcessor, ViTImageProcessor
@require_tokenizers
@require_vision
class snake_case ( unittest.TestCase ):
def SCREAMING_SNAKE_CASE_ ( self :Union[str, Any] ):
__SCREAMING_SNAKE_CASE : Union[str, Any] = tempfile.mkdtemp()
# fmt: off
__SCREAMING_SNAKE_CASE : Optional[int] = ['''[UNK]''', '''[CLS]''', '''[SEP]''', '''[PAD]''', '''[MASK]''', '''want''', '''##want''', '''##ed''', '''wa''', '''un''', '''runn''', '''##ing''', ''',''', '''low''', '''lowest''']
# fmt: on
__SCREAMING_SNAKE_CASE : List[Any] = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['''vocab_file'''] )
with open(self.vocab_file , '''w''' , encoding='''utf-8''' ) as vocab_writer:
vocab_writer.write(''''''.join([x + '''\n''' for x in vocab_tokens] ) )
__SCREAMING_SNAKE_CASE : Optional[int] = {
'''do_resize''': True,
'''size''': {'''height''': 1_8, '''width''': 1_8},
'''do_normalize''': True,
'''image_mean''': [0.5, 0.5, 0.5],
'''image_std''': [0.5, 0.5, 0.5],
}
__SCREAMING_SNAKE_CASE : List[Any] = os.path.join(self.tmpdirname , _lowerCamelCase )
with open(self.image_processor_file , '''w''' , encoding='''utf-8''' ) as fp:
json.dump(_lowerCamelCase , _lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :Union[str, Any] , **_lowerCamelCase :List[str] ):
return BertTokenizer.from_pretrained(self.tmpdirname , **_lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :List[str] , **_lowerCamelCase :Optional[int] ):
return ViTImageProcessor.from_pretrained(self.tmpdirname , **_lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :Optional[Any] ):
shutil.rmtree(self.tmpdirname )
def SCREAMING_SNAKE_CASE_ ( self :Dict ):
__SCREAMING_SNAKE_CASE : Union[str, Any] = [np.random.randint(2_5_5 , size=(3, 3_0, 4_0_0) , dtype=np.uinta )]
__SCREAMING_SNAKE_CASE : Tuple = [Image.fromarray(np.moveaxis(_lowerCamelCase , 0 , -1 ) ) for x in image_inputs]
return image_inputs
def SCREAMING_SNAKE_CASE_ ( self :int ):
__SCREAMING_SNAKE_CASE : Optional[int] = self.get_tokenizer()
__SCREAMING_SNAKE_CASE : Optional[Any] = self.get_image_processor()
__SCREAMING_SNAKE_CASE : Optional[Any] = VisionTextDualEncoderProcessor(tokenizer=_lowerCamelCase , image_processor=_lowerCamelCase )
processor.save_pretrained(self.tmpdirname )
__SCREAMING_SNAKE_CASE : int = VisionTextDualEncoderProcessor.from_pretrained(self.tmpdirname )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer.get_vocab() )
self.assertIsInstance(processor.tokenizer , (BertTokenizer, BertTokenizerFast) )
self.assertEqual(processor.image_processor.to_json_string() , image_processor.to_json_string() )
self.assertIsInstance(processor.image_processor , _lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :List[Any] ):
__SCREAMING_SNAKE_CASE : str = VisionTextDualEncoderProcessor(
tokenizer=self.get_tokenizer() , image_processor=self.get_image_processor() )
processor.save_pretrained(self.tmpdirname )
__SCREAMING_SNAKE_CASE : Optional[int] = self.get_tokenizer(bos_token='''(BOS)''' , eos_token='''(EOS)''' )
__SCREAMING_SNAKE_CASE : List[str] = self.get_image_processor(do_normalize=_lowerCamelCase , padding_value=1.0 )
__SCREAMING_SNAKE_CASE : int = VisionTextDualEncoderProcessor.from_pretrained(
self.tmpdirname , bos_token='''(BOS)''' , eos_token='''(EOS)''' , do_normalize=_lowerCamelCase , padding_value=1.0 )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer_add_kwargs.get_vocab() )
self.assertIsInstance(processor.tokenizer , (BertTokenizer, BertTokenizerFast) )
self.assertEqual(processor.image_processor.to_json_string() , image_processor_add_kwargs.to_json_string() )
self.assertIsInstance(processor.image_processor , _lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :int ):
__SCREAMING_SNAKE_CASE : Union[str, Any] = self.get_image_processor()
__SCREAMING_SNAKE_CASE : Dict = self.get_tokenizer()
__SCREAMING_SNAKE_CASE : Tuple = VisionTextDualEncoderProcessor(tokenizer=_lowerCamelCase , image_processor=_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Dict = self.prepare_image_inputs()
__SCREAMING_SNAKE_CASE : Optional[int] = image_processor(_lowerCamelCase , return_tensors='''np''' )
__SCREAMING_SNAKE_CASE : Tuple = processor(images=_lowerCamelCase , return_tensors='''np''' )
for key in input_feat_extract.keys():
self.assertAlmostEqual(input_feat_extract[key].sum() , input_processor[key].sum() , delta=1e-2 )
def SCREAMING_SNAKE_CASE_ ( self :Optional[int] ):
__SCREAMING_SNAKE_CASE : Any = self.get_image_processor()
__SCREAMING_SNAKE_CASE : Any = self.get_tokenizer()
__SCREAMING_SNAKE_CASE : Tuple = VisionTextDualEncoderProcessor(tokenizer=_lowerCamelCase , image_processor=_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Union[str, Any] = '''lower newer'''
__SCREAMING_SNAKE_CASE : Optional[int] = processor(text=_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Any = tokenizer(_lowerCamelCase )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] , encoded_processor[key] )
def SCREAMING_SNAKE_CASE_ ( self :str ):
__SCREAMING_SNAKE_CASE : str = self.get_image_processor()
__SCREAMING_SNAKE_CASE : int = self.get_tokenizer()
__SCREAMING_SNAKE_CASE : int = VisionTextDualEncoderProcessor(tokenizer=_lowerCamelCase , image_processor=_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Any = '''lower newer'''
__SCREAMING_SNAKE_CASE : int = self.prepare_image_inputs()
__SCREAMING_SNAKE_CASE : int = processor(text=_lowerCamelCase , images=_lowerCamelCase )
self.assertListEqual(list(inputs.keys() ) , ['''input_ids''', '''token_type_ids''', '''attention_mask''', '''pixel_values'''] )
# test if it raises when no input is passed
with self.assertRaises(_lowerCamelCase ):
processor()
def SCREAMING_SNAKE_CASE_ ( self :Any ):
__SCREAMING_SNAKE_CASE : Dict = self.get_image_processor()
__SCREAMING_SNAKE_CASE : List[Any] = self.get_tokenizer()
__SCREAMING_SNAKE_CASE : Union[str, Any] = VisionTextDualEncoderProcessor(tokenizer=_lowerCamelCase , image_processor=_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Union[str, Any] = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
__SCREAMING_SNAKE_CASE : Tuple = processor.batch_decode(_lowerCamelCase )
__SCREAMING_SNAKE_CASE : List[str] = tokenizer.batch_decode(_lowerCamelCase )
self.assertListEqual(_lowerCamelCase , _lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :Dict ):
__SCREAMING_SNAKE_CASE : str = self.get_image_processor()
__SCREAMING_SNAKE_CASE : List[str] = self.get_tokenizer()
__SCREAMING_SNAKE_CASE : Optional[int] = VisionTextDualEncoderProcessor(tokenizer=_lowerCamelCase , image_processor=_lowerCamelCase )
__SCREAMING_SNAKE_CASE : List[Any] = '''lower newer'''
__SCREAMING_SNAKE_CASE : Dict = self.prepare_image_inputs()
__SCREAMING_SNAKE_CASE : str = processor(text=_lowerCamelCase , images=_lowerCamelCase )
self.assertListEqual(list(inputs.keys() ) , processor.model_input_names )
| 674 | 0 |
"""simple docstring"""
from typing import Any, Dict, Optional
import torch
import torch.nn.functional as F
from torch import nn
from ..utils import maybe_allow_in_graph
from .activations import get_activation
from .attention_processor import Attention
from .embeddings import CombinedTimestepLabelEmbeddings
@maybe_allow_in_graph
class UpperCAmelCase_ ( nn.Module ):
def __init__( self , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_=0.0 , UpperCamelCase_ = None , UpperCamelCase_ = "geglu" , UpperCamelCase_ = None , UpperCamelCase_ = False , UpperCamelCase_ = False , UpperCamelCase_ = False , UpperCamelCase_ = False , UpperCamelCase_ = True , UpperCamelCase_ = "layer_norm" , UpperCamelCase_ = False , ) -> str:
super().__init__()
__lowercase : Optional[Any] = only_cross_attention
__lowercase : Union[str, Any] = (num_embeds_ada_norm is not None) and norm_type == '''ada_norm_zero'''
__lowercase : Optional[int] = (num_embeds_ada_norm is not None) and norm_type == '''ada_norm'''
if norm_type in ("ada_norm", "ada_norm_zero") and num_embeds_ada_norm is None:
raise ValueError(
F"""`norm_type` is set to {norm_type}, but `num_embeds_ada_norm` is not defined. Please make sure to"""
F""" define `num_embeds_ada_norm` if setting `norm_type` to {norm_type}.""" )
# Define 3 blocks. Each block has its own normalization layer.
# 1. Self-Attn
if self.use_ada_layer_norm:
__lowercase : Union[str, Any] = AdaLayerNorm(_lowerCamelCase , _lowerCamelCase )
elif self.use_ada_layer_norm_zero:
__lowercase : Any = AdaLayerNormZero(_lowerCamelCase , _lowerCamelCase )
else:
__lowercase : List[Any] = nn.LayerNorm(_lowerCamelCase , elementwise_affine=_lowerCamelCase )
__lowercase : Optional[int] = Attention(
query_dim=_lowerCamelCase , heads=_lowerCamelCase , dim_head=_lowerCamelCase , dropout=_lowerCamelCase , bias=_lowerCamelCase , cross_attention_dim=cross_attention_dim if only_cross_attention else None , upcast_attention=_lowerCamelCase , )
# 2. Cross-Attn
if cross_attention_dim is not None or double_self_attention:
# We currently only use AdaLayerNormZero for self attention where there will only be one attention block.
# I.e. the number of returned modulation chunks from AdaLayerZero would not make sense if returned during
# the second cross attention block.
__lowercase : Union[str, Any] = (
AdaLayerNorm(_lowerCamelCase , _lowerCamelCase )
if self.use_ada_layer_norm
else nn.LayerNorm(_lowerCamelCase , elementwise_affine=_lowerCamelCase )
)
__lowercase : int = Attention(
query_dim=_lowerCamelCase , cross_attention_dim=cross_attention_dim if not double_self_attention else None , heads=_lowerCamelCase , dim_head=_lowerCamelCase , dropout=_lowerCamelCase , bias=_lowerCamelCase , upcast_attention=_lowerCamelCase , ) # is self-attn if encoder_hidden_states is none
else:
__lowercase : Tuple = None
__lowercase : List[str] = None
# 3. Feed-forward
__lowercase : Dict = nn.LayerNorm(_lowerCamelCase , elementwise_affine=_lowerCamelCase )
__lowercase : List[Any] = FeedForward(_lowerCamelCase , dropout=_lowerCamelCase , activation_fn=_lowerCamelCase , final_dropout=_lowerCamelCase )
# let chunk size default to None
__lowercase : Optional[Any] = None
__lowercase : Optional[Any] = 0
def _lowerCamelCase ( self , UpperCamelCase_ , UpperCamelCase_ ) -> Dict:
# Sets chunk feed-forward
__lowercase : Union[str, Any] = chunk_size
__lowercase : Any = dim
def _lowerCamelCase ( self , UpperCamelCase_ , UpperCamelCase_ = None , UpperCamelCase_ = None , UpperCamelCase_ = None , UpperCamelCase_ = None , UpperCamelCase_ = None , UpperCamelCase_ = None , ) -> int:
# Notice that normalization is always applied before the real computation in the following blocks.
# 1. Self-Attention
if self.use_ada_layer_norm:
__lowercase : Optional[Any] = self.norma(_lowerCamelCase , _lowerCamelCase )
elif self.use_ada_layer_norm_zero:
__lowercase : List[Any] = self.norma(
_lowerCamelCase , _lowerCamelCase , _lowerCamelCase , hidden_dtype=hidden_states.dtype )
else:
__lowercase : List[str] = self.norma(_lowerCamelCase )
__lowercase : int = cross_attention_kwargs if cross_attention_kwargs is not None else {}
__lowercase : Optional[int] = self.attna(
_lowerCamelCase , encoder_hidden_states=encoder_hidden_states if self.only_cross_attention else None , attention_mask=_lowerCamelCase , **_lowerCamelCase , )
if self.use_ada_layer_norm_zero:
__lowercase : List[Any] = gate_msa.unsqueeze(1 ) * attn_output
__lowercase : Tuple = attn_output + hidden_states
# 2. Cross-Attention
if self.attna is not None:
__lowercase : int = (
self.norma(_lowerCamelCase , _lowerCamelCase ) if self.use_ada_layer_norm else self.norma(_lowerCamelCase )
)
__lowercase : int = self.attna(
_lowerCamelCase , encoder_hidden_states=_lowerCamelCase , attention_mask=_lowerCamelCase , **_lowerCamelCase , )
__lowercase : Optional[int] = attn_output + hidden_states
# 3. Feed-forward
__lowercase : str = self.norma(_lowerCamelCase )
if self.use_ada_layer_norm_zero:
__lowercase : Tuple = norm_hidden_states * (1 + scale_mlp[:, None]) + shift_mlp[:, None]
if self._chunk_size is not None:
# "feed_forward_chunk_size" can be used to save memory
if norm_hidden_states.shape[self._chunk_dim] % self._chunk_size != 0:
raise ValueError(
F"""`hidden_states` dimension to be chunked: {norm_hidden_states.shape[self._chunk_dim]} has to be divisible by chunk size: {self._chunk_size}. Make sure to set an appropriate `chunk_size` when calling `unet.enable_forward_chunking`.""" )
__lowercase : Union[str, Any] = norm_hidden_states.shape[self._chunk_dim] // self._chunk_size
__lowercase : str = torch.cat(
[self.ff(_lowerCamelCase ) for hid_slice in norm_hidden_states.chunk(_lowerCamelCase , dim=self._chunk_dim )] , dim=self._chunk_dim , )
else:
__lowercase : int = self.ff(_lowerCamelCase )
if self.use_ada_layer_norm_zero:
__lowercase : Union[str, Any] = gate_mlp.unsqueeze(1 ) * ff_output
__lowercase : Optional[int] = ff_output + hidden_states
return hidden_states
class UpperCAmelCase_ ( nn.Module ):
def __init__( self , UpperCamelCase_ , UpperCamelCase_ = None , UpperCamelCase_ = 4 , UpperCamelCase_ = 0.0 , UpperCamelCase_ = "geglu" , UpperCamelCase_ = False , ) -> int:
super().__init__()
__lowercase : Optional[Any] = int(dim * mult )
__lowercase : Optional[int] = dim_out if dim_out is not None else dim
if activation_fn == "gelu":
__lowercase : Optional[Any] = GELU(_lowerCamelCase , _lowerCamelCase )
if activation_fn == "gelu-approximate":
__lowercase : Tuple = GELU(_lowerCamelCase , _lowerCamelCase , approximate='''tanh''' )
elif activation_fn == "geglu":
__lowercase : Tuple = GEGLU(_lowerCamelCase , _lowerCamelCase )
elif activation_fn == "geglu-approximate":
__lowercase : Optional[Any] = ApproximateGELU(_lowerCamelCase , _lowerCamelCase )
__lowercase : Optional[int] = nn.ModuleList([] )
# project in
self.net.append(_lowerCamelCase )
# project dropout
self.net.append(nn.Dropout(_lowerCamelCase ) )
# project out
self.net.append(nn.Linear(_lowerCamelCase , _lowerCamelCase ) )
# FF as used in Vision Transformer, MLP-Mixer, etc. have a final dropout
if final_dropout:
self.net.append(nn.Dropout(_lowerCamelCase ) )
def _lowerCamelCase ( self , UpperCamelCase_ ) -> Optional[Any]:
for module in self.net:
__lowercase : Any = module(_lowerCamelCase )
return hidden_states
class UpperCAmelCase_ ( nn.Module ):
def __init__( self , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ = "none" ) -> List[Any]:
super().__init__()
__lowercase : int = nn.Linear(_lowerCamelCase , _lowerCamelCase )
__lowercase : Optional[Any] = approximate
def _lowerCamelCase ( self , UpperCamelCase_ ) -> Any:
if gate.device.type != "mps":
return F.gelu(_lowerCamelCase , approximate=self.approximate )
# mps: gelu is not implemented for float16
return F.gelu(gate.to(dtype=torch.floataa ) , approximate=self.approximate ).to(dtype=gate.dtype )
def _lowerCamelCase ( self , UpperCamelCase_ ) -> Union[str, Any]:
__lowercase : Union[str, Any] = self.proj(_lowerCamelCase )
__lowercase : List[str] = self.gelu(_lowerCamelCase )
return hidden_states
class UpperCAmelCase_ ( nn.Module ):
def __init__( self , UpperCamelCase_ , UpperCamelCase_ ) -> int:
super().__init__()
__lowercase : Any = nn.Linear(_lowerCamelCase , dim_out * 2 )
def _lowerCamelCase ( self , UpperCamelCase_ ) -> int:
if gate.device.type != "mps":
return F.gelu(_lowerCamelCase )
# mps: gelu is not implemented for float16
return F.gelu(gate.to(dtype=torch.floataa ) ).to(dtype=gate.dtype )
def _lowerCamelCase ( self , UpperCamelCase_ ) -> List[Any]:
__lowercase : List[Any] = self.proj(_lowerCamelCase ).chunk(2 , dim=-1 )
return hidden_states * self.gelu(_lowerCamelCase )
class UpperCAmelCase_ ( nn.Module ):
def __init__( self , UpperCamelCase_ , UpperCamelCase_ ) -> Union[str, Any]:
super().__init__()
__lowercase : str = nn.Linear(_lowerCamelCase , _lowerCamelCase )
def _lowerCamelCase ( self , UpperCamelCase_ ) -> int:
__lowercase : str = self.proj(_lowerCamelCase )
return x * torch.sigmoid(1.7_0_2 * x )
class UpperCAmelCase_ ( nn.Module ):
def __init__( self , UpperCamelCase_ , UpperCamelCase_ ) -> Union[str, Any]:
super().__init__()
__lowercase : str = nn.Embedding(_lowerCamelCase , _lowerCamelCase )
__lowercase : Any = nn.SiLU()
__lowercase : Tuple = nn.Linear(_lowerCamelCase , embedding_dim * 2 )
__lowercase : Tuple = nn.LayerNorm(_lowerCamelCase , elementwise_affine=_lowerCamelCase )
def _lowerCamelCase ( self , UpperCamelCase_ , UpperCamelCase_ ) -> Union[str, Any]:
__lowercase : Optional[int] = self.linear(self.silu(self.emb(_lowerCamelCase ) ) )
__lowercase : Tuple = torch.chunk(_lowerCamelCase , 2 )
__lowercase : List[str] = self.norm(_lowerCamelCase ) * (1 + scale) + shift
return x
class UpperCAmelCase_ ( nn.Module ):
def __init__( self , UpperCamelCase_ , UpperCamelCase_ ) -> str:
super().__init__()
__lowercase : List[Any] = CombinedTimestepLabelEmbeddings(_lowerCamelCase , _lowerCamelCase )
__lowercase : int = nn.SiLU()
__lowercase : Any = nn.Linear(_lowerCamelCase , 6 * embedding_dim , bias=_lowerCamelCase )
__lowercase : List[Any] = nn.LayerNorm(_lowerCamelCase , elementwise_affine=_lowerCamelCase , eps=1E-6 )
def _lowerCamelCase ( self , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_=None ) -> Tuple:
__lowercase : Tuple = self.linear(self.silu(self.emb(_lowerCamelCase , _lowerCamelCase , hidden_dtype=_lowerCamelCase ) ) )
__lowercase : Union[str, Any] = emb.chunk(6 , dim=1 )
__lowercase : int = self.norm(_lowerCamelCase ) * (1 + scale_msa[:, None]) + shift_msa[:, None]
return x, gate_msa, shift_mlp, scale_mlp, gate_mlp
class UpperCAmelCase_ ( nn.Module ):
def __init__( self , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ = None , UpperCamelCase_ = 1E-5 ) -> Optional[Any]:
super().__init__()
__lowercase : int = num_groups
__lowercase : List[str] = eps
if act_fn is None:
__lowercase : Tuple = None
else:
__lowercase : int = get_activation(_lowerCamelCase )
__lowercase : Dict = nn.Linear(_lowerCamelCase , out_dim * 2 )
def _lowerCamelCase ( self , UpperCamelCase_ , UpperCamelCase_ ) -> Tuple:
if self.act:
__lowercase : int = self.act(_lowerCamelCase )
__lowercase : int = self.linear(_lowerCamelCase )
__lowercase : List[Any] = emb[:, :, None, None]
__lowercase : List[Any] = emb.chunk(2 , dim=1 )
__lowercase : Tuple = F.group_norm(_lowerCamelCase , self.num_groups , eps=self.eps )
__lowercase : Optional[Any] = x * (1 + scale) + shift
return x
| 76 |
"""simple docstring"""
import inspect
import unittest
import numpy as np
from tests.test_modeling_common import floats_tensor
from transformers import DetrConfig, MaskFormerConfig, SwinConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import MaskFormerForInstanceSegmentation, MaskFormerModel
if is_vision_available():
from transformers import MaskFormerImageProcessor
if is_vision_available():
from PIL import Image
class snake_case :
def __init__( self :Optional[int] , _lowerCamelCase :int , _lowerCamelCase :Any=2 , _lowerCamelCase :List[str]=True , _lowerCamelCase :str=False , _lowerCamelCase :Tuple=1_0 , _lowerCamelCase :str=3 , _lowerCamelCase :str=3_2 * 4 , _lowerCamelCase :Dict=3_2 * 6 , _lowerCamelCase :str=4 , _lowerCamelCase :Any=3_2 , ):
__SCREAMING_SNAKE_CASE : List[str] = parent
__SCREAMING_SNAKE_CASE : Tuple = batch_size
__SCREAMING_SNAKE_CASE : Optional[Any] = is_training
__SCREAMING_SNAKE_CASE : Dict = use_auxiliary_loss
__SCREAMING_SNAKE_CASE : List[str] = num_queries
__SCREAMING_SNAKE_CASE : Optional[int] = num_channels
__SCREAMING_SNAKE_CASE : List[Any] = min_size
__SCREAMING_SNAKE_CASE : int = max_size
__SCREAMING_SNAKE_CASE : Any = num_labels
__SCREAMING_SNAKE_CASE : Union[str, Any] = mask_feature_size
def SCREAMING_SNAKE_CASE_ ( self :Any ):
__SCREAMING_SNAKE_CASE : str = floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size] ).to(
_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Any = torch.ones([self.batch_size, self.min_size, self.max_size] , device=_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Any = (
torch.rand([self.batch_size, self.num_labels, self.min_size, self.max_size] , device=_lowerCamelCase ) > 0.5
).float()
__SCREAMING_SNAKE_CASE : Dict = (torch.rand((self.batch_size, self.num_labels) , device=_lowerCamelCase ) > 0.5).long()
__SCREAMING_SNAKE_CASE : str = self.get_config()
return config, pixel_values, pixel_mask, mask_labels, class_labels
def SCREAMING_SNAKE_CASE_ ( self :List[Any] ):
return MaskFormerConfig.from_backbone_and_decoder_configs(
backbone_config=SwinConfig(
depths=[1, 1, 1, 1] , ) , decoder_config=DetrConfig(
decoder_ffn_dim=1_2_8 , num_queries=self.num_queries , decoder_attention_heads=2 , d_model=self.mask_feature_size , ) , mask_feature_size=self.mask_feature_size , fpn_feature_size=self.mask_feature_size , num_channels=self.num_channels , num_labels=self.num_labels , )
def SCREAMING_SNAKE_CASE_ ( self :str ):
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Optional[Any] = self.prepare_config_and_inputs()
__SCREAMING_SNAKE_CASE : Any = {'''pixel_values''': pixel_values, '''pixel_mask''': pixel_mask}
return config, inputs_dict
def SCREAMING_SNAKE_CASE_ ( self :int , _lowerCamelCase :int , _lowerCamelCase :Optional[int] ):
__SCREAMING_SNAKE_CASE : Union[str, Any] = output.encoder_hidden_states
__SCREAMING_SNAKE_CASE : int = output.pixel_decoder_hidden_states
__SCREAMING_SNAKE_CASE : Optional[int] = output.transformer_decoder_hidden_states
self.parent.assertTrue(len(_lowerCamelCase ) , len(config.backbone_config.depths ) )
self.parent.assertTrue(len(_lowerCamelCase ) , len(config.backbone_config.depths ) )
self.parent.assertTrue(len(_lowerCamelCase ) , config.decoder_config.decoder_layers )
def SCREAMING_SNAKE_CASE_ ( self :Optional[Any] , _lowerCamelCase :Dict , _lowerCamelCase :str , _lowerCamelCase :Any , _lowerCamelCase :Optional[Any]=False ):
with torch.no_grad():
__SCREAMING_SNAKE_CASE : List[str] = MaskFormerModel(config=_lowerCamelCase )
model.to(_lowerCamelCase )
model.eval()
__SCREAMING_SNAKE_CASE : str = model(pixel_values=_lowerCamelCase , pixel_mask=_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Any = model(_lowerCamelCase , output_hidden_states=_lowerCamelCase )
# the correct shape of output.transformer_decoder_hidden_states ensure the correcteness of the
# encoder and pixel decoder
self.parent.assertEqual(
output.transformer_decoder_last_hidden_state.shape , (self.batch_size, self.num_queries, self.mask_feature_size) , )
# let's ensure the other two hidden state exists
self.parent.assertTrue(output.pixel_decoder_last_hidden_state is not None )
self.parent.assertTrue(output.encoder_last_hidden_state is not None )
if output_hidden_states:
self.check_output_hidden_state(_lowerCamelCase , _lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :Dict , _lowerCamelCase :Optional[Any] , _lowerCamelCase :Optional[int] , _lowerCamelCase :Optional[Any] , _lowerCamelCase :List[str] , _lowerCamelCase :Dict ):
__SCREAMING_SNAKE_CASE : Optional[Any] = MaskFormerForInstanceSegmentation(config=_lowerCamelCase )
model.to(_lowerCamelCase )
model.eval()
def comm_check_on_output(_lowerCamelCase :Optional[Any] ):
# let's still check that all the required stuff is there
self.parent.assertTrue(result.transformer_decoder_last_hidden_state is not None )
self.parent.assertTrue(result.pixel_decoder_last_hidden_state is not None )
self.parent.assertTrue(result.encoder_last_hidden_state is not None )
# okay, now we need to check the logits shape
# due to the encoder compression, masks have a //4 spatial size
self.parent.assertEqual(
result.masks_queries_logits.shape , (self.batch_size, self.num_queries, self.min_size // 4, self.max_size // 4) , )
# + 1 for null class
self.parent.assertEqual(
result.class_queries_logits.shape , (self.batch_size, self.num_queries, self.num_labels + 1) )
with torch.no_grad():
__SCREAMING_SNAKE_CASE : List[str] = model(pixel_values=_lowerCamelCase , pixel_mask=_lowerCamelCase )
__SCREAMING_SNAKE_CASE : int = model(_lowerCamelCase )
comm_check_on_output(_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Dict = model(
pixel_values=_lowerCamelCase , pixel_mask=_lowerCamelCase , mask_labels=_lowerCamelCase , class_labels=_lowerCamelCase )
comm_check_on_output(_lowerCamelCase )
self.parent.assertTrue(result.loss is not None )
self.parent.assertEqual(result.loss.shape , torch.Size([1] ) )
@require_torch
class snake_case ( __UpperCAmelCase , __UpperCAmelCase , unittest.TestCase ):
lowerCamelCase__ = (MaskFormerModel, MaskFormerForInstanceSegmentation) if is_torch_available() else ()
lowerCamelCase__ = (
{'''feature-extraction''': MaskFormerModel, '''image-segmentation''': MaskFormerForInstanceSegmentation}
if is_torch_available()
else {}
)
lowerCamelCase__ = False
lowerCamelCase__ = False
lowerCamelCase__ = False
lowerCamelCase__ = False
def SCREAMING_SNAKE_CASE_ ( self :Dict ):
__SCREAMING_SNAKE_CASE : Optional[Any] = MaskFormerModelTester(self )
__SCREAMING_SNAKE_CASE : str = ConfigTester(self , config_class=_lowerCamelCase , has_text_modality=_lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :Union[str, Any] ):
self.config_tester.run_common_tests()
def SCREAMING_SNAKE_CASE_ ( self :Union[str, Any] ):
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Any = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.create_and_check_maskformer_model(_lowerCamelCase , **_lowerCamelCase , output_hidden_states=_lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :List[str] ):
__SCREAMING_SNAKE_CASE : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_maskformer_instance_segmentation_head_model(*_lowerCamelCase )
@unittest.skip(reason='''MaskFormer does not use inputs_embeds''' )
def SCREAMING_SNAKE_CASE_ ( self :List[Any] ):
pass
@unittest.skip(reason='''MaskFormer does not have a get_input_embeddings method''' )
def SCREAMING_SNAKE_CASE_ ( self :int ):
pass
@unittest.skip(reason='''MaskFormer is not a generative model''' )
def SCREAMING_SNAKE_CASE_ ( self :Union[str, Any] ):
pass
@unittest.skip(reason='''MaskFormer does not use token embeddings''' )
def SCREAMING_SNAKE_CASE_ ( self :Optional[int] ):
pass
@require_torch_multi_gpu
@unittest.skip(
reason='''MaskFormer has some layers using `add_module` which doesn\'t work well with `nn.DataParallel`''' )
def SCREAMING_SNAKE_CASE_ ( self :Dict ):
pass
@unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.''' )
def SCREAMING_SNAKE_CASE_ ( self :Dict ):
pass
def SCREAMING_SNAKE_CASE_ ( self :Union[str, Any] ):
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Any = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__SCREAMING_SNAKE_CASE : Tuple = model_class(_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Optional[int] = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
__SCREAMING_SNAKE_CASE : List[str] = [*signature.parameters.keys()]
__SCREAMING_SNAKE_CASE : Optional[Any] = ['''pixel_values''']
self.assertListEqual(arg_names[:1] , _lowerCamelCase )
@slow
def SCREAMING_SNAKE_CASE_ ( self :Optional[Any] ):
for model_name in ["facebook/maskformer-swin-small-coco"]:
__SCREAMING_SNAKE_CASE : Tuple = MaskFormerModel.from_pretrained(_lowerCamelCase )
self.assertIsNotNone(_lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :Union[str, Any] ):
__SCREAMING_SNAKE_CASE : Dict = (self.model_tester.min_size,) * 2
__SCREAMING_SNAKE_CASE : Dict = {
'''pixel_values''': torch.randn((2, 3, *size) , device=_lowerCamelCase ),
'''mask_labels''': torch.randn((2, 1_0, *size) , device=_lowerCamelCase ),
'''class_labels''': torch.zeros(2 , 1_0 , device=_lowerCamelCase ).long(),
}
__SCREAMING_SNAKE_CASE : Dict = MaskFormerForInstanceSegmentation(MaskFormerConfig() ).to(_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Optional[Any] = model(**_lowerCamelCase )
self.assertTrue(outputs.loss is not None )
def SCREAMING_SNAKE_CASE_ ( self :Union[str, Any] ):
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Tuple = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.create_and_check_maskformer_model(_lowerCamelCase , **_lowerCamelCase , output_hidden_states=_lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :Union[str, Any] ):
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Tuple = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__SCREAMING_SNAKE_CASE : Dict = model_class(_lowerCamelCase ).to(_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Optional[int] = model(**_lowerCamelCase , output_attentions=_lowerCamelCase )
self.assertTrue(outputs.attentions is not None )
def SCREAMING_SNAKE_CASE_ ( self :Dict ):
if not self.model_tester.is_training:
return
# only MaskFormerForInstanceSegmentation has the loss
__SCREAMING_SNAKE_CASE : List[Any] = self.all_model_classes[1]
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Dict = self.model_tester.prepare_config_and_inputs()
__SCREAMING_SNAKE_CASE : int = model_class(_lowerCamelCase )
model.to(_lowerCamelCase )
model.train()
__SCREAMING_SNAKE_CASE : Any = model(_lowerCamelCase , mask_labels=_lowerCamelCase , class_labels=_lowerCamelCase ).loss
loss.backward()
def SCREAMING_SNAKE_CASE_ ( self :Optional[int] ):
# only MaskFormerForInstanceSegmentation has the loss
__SCREAMING_SNAKE_CASE : Tuple = self.all_model_classes[1]
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Tuple = self.model_tester.prepare_config_and_inputs()
__SCREAMING_SNAKE_CASE : Tuple = True
__SCREAMING_SNAKE_CASE : List[str] = True
__SCREAMING_SNAKE_CASE : Any = model_class(_lowerCamelCase )
model.to(_lowerCamelCase )
model.train()
__SCREAMING_SNAKE_CASE : Optional[int] = model(_lowerCamelCase , mask_labels=_lowerCamelCase , class_labels=_lowerCamelCase )
__SCREAMING_SNAKE_CASE : List[str] = outputs.encoder_hidden_states[0]
encoder_hidden_states.retain_grad()
__SCREAMING_SNAKE_CASE : str = outputs.pixel_decoder_hidden_states[0]
pixel_decoder_hidden_states.retain_grad()
# we requires_grad=True in inputs_embeds (line 2152), the original implementation don't
__SCREAMING_SNAKE_CASE : Optional[int] = outputs.transformer_decoder_hidden_states[0]
transformer_decoder_hidden_states.retain_grad()
__SCREAMING_SNAKE_CASE : int = outputs.attentions[0]
attentions.retain_grad()
outputs.loss.backward(retain_graph=_lowerCamelCase )
self.assertIsNotNone(encoder_hidden_states.grad )
self.assertIsNotNone(pixel_decoder_hidden_states.grad )
self.assertIsNotNone(transformer_decoder_hidden_states.grad )
self.assertIsNotNone(attentions.grad )
_lowerCamelCase = 1e-4
def lowerCAmelCase_ ( ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : List[Any] = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
return image
@require_vision
@slow
class snake_case ( unittest.TestCase ):
@cached_property
def SCREAMING_SNAKE_CASE_ ( self :str ):
return (
MaskFormerImageProcessor.from_pretrained('''facebook/maskformer-swin-small-coco''' )
if is_vision_available()
else None
)
def SCREAMING_SNAKE_CASE_ ( self :List[Any] ):
__SCREAMING_SNAKE_CASE : Union[str, Any] = MaskFormerModel.from_pretrained('''facebook/maskformer-swin-small-coco''' ).to(_lowerCamelCase )
__SCREAMING_SNAKE_CASE : int = self.default_image_processor
__SCREAMING_SNAKE_CASE : Tuple = prepare_img()
__SCREAMING_SNAKE_CASE : List[Any] = image_processor(_lowerCamelCase , return_tensors='''pt''' ).to(_lowerCamelCase )
__SCREAMING_SNAKE_CASE : int = inputs['''pixel_values'''].shape
# check size is divisible by 32
self.assertTrue((inputs_shape[-1] % 3_2) == 0 and (inputs_shape[-2] % 3_2) == 0 )
# check size
self.assertEqual(_lowerCamelCase , (1, 3, 8_0_0, 1_0_8_8) )
with torch.no_grad():
__SCREAMING_SNAKE_CASE : Tuple = model(**_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Union[str, Any] = torch.tensor(
[[-0.0_4_8_2, 0.9_2_2_8, 0.4_9_5_1], [-0.2_5_4_7, 0.8_0_1_7, 0.8_5_2_7], [-0.0_0_6_9, 0.3_3_8_5, -0.0_0_8_9]] ).to(_lowerCamelCase )
self.assertTrue(
torch.allclose(
outputs.encoder_last_hidden_state[0, 0, :3, :3] , _lowerCamelCase , atol=_lowerCamelCase ) )
__SCREAMING_SNAKE_CASE : Any = torch.tensor(
[[-0.8_4_2_2, -0.8_4_3_4, -0.9_7_1_8], [-1.0_1_4_4, -0.5_5_6_5, -0.4_1_9_5], [-1.0_0_3_8, -0.4_4_8_4, -0.1_9_6_1]] ).to(_lowerCamelCase )
self.assertTrue(
torch.allclose(
outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3] , _lowerCamelCase , atol=_lowerCamelCase ) )
__SCREAMING_SNAKE_CASE : Optional[int] = torch.tensor(
[[0.2_8_5_2, -0.0_1_5_9, 0.9_7_3_5], [0.6_2_5_4, 0.1_8_5_8, 0.8_5_2_9], [-0.0_6_8_0, -0.4_1_1_6, 1.8_4_1_3]] ).to(_lowerCamelCase )
self.assertTrue(
torch.allclose(
outputs.transformer_decoder_last_hidden_state[0, :3, :3] , _lowerCamelCase , atol=_lowerCamelCase ) )
def SCREAMING_SNAKE_CASE_ ( self :Optional[Any] ):
__SCREAMING_SNAKE_CASE : Tuple = (
MaskFormerForInstanceSegmentation.from_pretrained('''facebook/maskformer-swin-small-coco''' )
.to(_lowerCamelCase )
.eval()
)
__SCREAMING_SNAKE_CASE : Union[str, Any] = self.default_image_processor
__SCREAMING_SNAKE_CASE : Tuple = prepare_img()
__SCREAMING_SNAKE_CASE : str = image_processor(_lowerCamelCase , return_tensors='''pt''' ).to(_lowerCamelCase )
__SCREAMING_SNAKE_CASE : List[str] = inputs['''pixel_values'''].shape
# check size is divisible by 32
self.assertTrue((inputs_shape[-1] % 3_2) == 0 and (inputs_shape[-2] % 3_2) == 0 )
# check size
self.assertEqual(_lowerCamelCase , (1, 3, 8_0_0, 1_0_8_8) )
with torch.no_grad():
__SCREAMING_SNAKE_CASE : str = model(**_lowerCamelCase )
# masks_queries_logits
__SCREAMING_SNAKE_CASE : Any = outputs.masks_queries_logits
self.assertEqual(
masks_queries_logits.shape , (1, model.config.decoder_config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4) , )
__SCREAMING_SNAKE_CASE : List[Any] = [
[-1.3_7_3_7_1_2_4, -1.7_7_2_4_9_3_7, -1.9_3_6_4_2_3_3],
[-1.5_9_7_7_2_8_1, -1.9_8_6_7_9_3_9, -2.1_5_2_3_6_9_5],
[-1.5_7_9_5_3_9_8, -1.9_2_6_9_8_3_2, -2.0_9_3_9_4_2],
]
__SCREAMING_SNAKE_CASE : Any = torch.tensor(_lowerCamelCase ).to(_lowerCamelCase )
self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3] , _lowerCamelCase , atol=_lowerCamelCase ) )
# class_queries_logits
__SCREAMING_SNAKE_CASE : List[Any] = outputs.class_queries_logits
self.assertEqual(
class_queries_logits.shape , (1, model.config.decoder_config.num_queries, model.config.num_labels + 1) )
__SCREAMING_SNAKE_CASE : List[str] = torch.tensor(
[
[1.65_12e00, -5.25_72e00, -3.35_19e00],
[3.61_69e-02, -5.90_25e00, -2.93_13e00],
[1.07_66e-04, -7.76_30e00, -5.12_63e00],
] ).to(_lowerCamelCase )
self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3] , _lowerCamelCase , atol=_lowerCamelCase ) )
def SCREAMING_SNAKE_CASE_ ( self :Any ):
__SCREAMING_SNAKE_CASE : Tuple = (
MaskFormerForInstanceSegmentation.from_pretrained('''facebook/maskformer-resnet101-coco-stuff''' )
.to(_lowerCamelCase )
.eval()
)
__SCREAMING_SNAKE_CASE : Tuple = self.default_image_processor
__SCREAMING_SNAKE_CASE : str = prepare_img()
__SCREAMING_SNAKE_CASE : List[Any] = image_processor(_lowerCamelCase , return_tensors='''pt''' ).to(_lowerCamelCase )
__SCREAMING_SNAKE_CASE : List[Any] = inputs['''pixel_values'''].shape
# check size is divisible by 32
self.assertTrue((inputs_shape[-1] % 3_2) == 0 and (inputs_shape[-2] % 3_2) == 0 )
# check size
self.assertEqual(_lowerCamelCase , (1, 3, 8_0_0, 1_0_8_8) )
with torch.no_grad():
__SCREAMING_SNAKE_CASE : List[Any] = model(**_lowerCamelCase )
# masks_queries_logits
__SCREAMING_SNAKE_CASE : Union[str, Any] = outputs.masks_queries_logits
self.assertEqual(
masks_queries_logits.shape , (1, model.config.decoder_config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4) , )
__SCREAMING_SNAKE_CASE : List[str] = [[-0.9_0_4_6, -2.6_3_6_6, -4.6_0_6_2], [-3.4_1_7_9, -5.7_8_9_0, -8.8_0_5_7], [-4.9_1_7_9, -7.6_5_6_0, -1_0.7_7_1_1]]
__SCREAMING_SNAKE_CASE : Any = torch.tensor(_lowerCamelCase ).to(_lowerCamelCase )
self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3] , _lowerCamelCase , atol=_lowerCamelCase ) )
# class_queries_logits
__SCREAMING_SNAKE_CASE : int = outputs.class_queries_logits
self.assertEqual(
class_queries_logits.shape , (1, model.config.decoder_config.num_queries, model.config.num_labels + 1) )
__SCREAMING_SNAKE_CASE : List[str] = torch.tensor(
[[4.7_1_8_8, -3.2_5_8_5, -2.8_8_5_7], [6.6_8_7_1, -2.9_1_8_1, -1.2_4_8_7], [7.2_4_4_9, -2.2_7_6_4, -2.1_8_7_4]] ).to(_lowerCamelCase )
self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3] , _lowerCamelCase , atol=_lowerCamelCase ) )
def SCREAMING_SNAKE_CASE_ ( self :int ):
__SCREAMING_SNAKE_CASE : int = (
MaskFormerForInstanceSegmentation.from_pretrained('''facebook/maskformer-swin-small-coco''' )
.to(_lowerCamelCase )
.eval()
)
__SCREAMING_SNAKE_CASE : Any = self.default_image_processor
__SCREAMING_SNAKE_CASE : int = image_processor(
[np.zeros((3, 8_0_0, 1_3_3_3) ), np.zeros((3, 8_0_0, 1_3_3_3) )] , segmentation_maps=[np.zeros((3_8_4, 3_8_4) ).astype(np.floataa ), np.zeros((3_8_4, 3_8_4) ).astype(np.floataa )] , return_tensors='''pt''' , )
__SCREAMING_SNAKE_CASE : Dict = inputs['''pixel_values'''].to(_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Dict = [el.to(_lowerCamelCase ) for el in inputs['''mask_labels''']]
__SCREAMING_SNAKE_CASE : str = [el.to(_lowerCamelCase ) for el in inputs['''class_labels''']]
with torch.no_grad():
__SCREAMING_SNAKE_CASE : int = model(**_lowerCamelCase )
self.assertTrue(outputs.loss is not None )
| 674 | 0 |
import unittest
import numpy as np
import torch
from diffusers import PNDMPipeline, PNDMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class _lowerCamelCase ( unittest.TestCase ):
@property
def UpperCamelCase_ ( self ) -> List[Any]:
torch.manual_seed(0 )
SCREAMING_SNAKE_CASE__: str= UNetaDModel(
block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=3 , out_channels=3 , down_block_types=('''DownBlock2D''', '''AttnDownBlock2D''') , up_block_types=('''AttnUpBlock2D''', '''UpBlock2D''') , )
return model
def UpperCamelCase_ ( self ) -> Tuple:
SCREAMING_SNAKE_CASE__: Optional[int]= self.dummy_uncond_unet
SCREAMING_SNAKE_CASE__: int= PNDMScheduler()
SCREAMING_SNAKE_CASE__: Optional[int]= PNDMPipeline(unet=_lowerCamelCase , scheduler=_lowerCamelCase )
pndm.to(_lowerCamelCase )
pndm.set_progress_bar_config(disable=_lowerCamelCase )
SCREAMING_SNAKE_CASE__: int= torch.manual_seed(0 )
SCREAMING_SNAKE_CASE__: Optional[Any]= pndm(generator=_lowerCamelCase , num_inference_steps=20 , output_type='''numpy''' ).images
SCREAMING_SNAKE_CASE__: str= torch.manual_seed(0 )
SCREAMING_SNAKE_CASE__: str= pndm(generator=_lowerCamelCase , num_inference_steps=20 , output_type='''numpy''' , return_dict=_lowerCamelCase )[0]
SCREAMING_SNAKE_CASE__: List[str]= image[0, -3:, -3:, -1]
SCREAMING_SNAKE_CASE__: Dict= image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
SCREAMING_SNAKE_CASE__: Tuple= np.array([1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1e-2
@slow
@require_torch
class _lowerCamelCase ( unittest.TestCase ):
def UpperCamelCase_ ( self ) -> List[Any]:
SCREAMING_SNAKE_CASE__: List[str]= '''google/ddpm-cifar10-32'''
SCREAMING_SNAKE_CASE__: Tuple= UNetaDModel.from_pretrained(_lowerCamelCase )
SCREAMING_SNAKE_CASE__: str= PNDMScheduler()
SCREAMING_SNAKE_CASE__: Optional[int]= PNDMPipeline(unet=_lowerCamelCase , scheduler=_lowerCamelCase )
pndm.to(_lowerCamelCase )
pndm.set_progress_bar_config(disable=_lowerCamelCase )
SCREAMING_SNAKE_CASE__: int= torch.manual_seed(0 )
SCREAMING_SNAKE_CASE__: Tuple= pndm(generator=_lowerCamelCase , output_type='''numpy''' ).images
SCREAMING_SNAKE_CASE__: Dict= image[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
SCREAMING_SNAKE_CASE__: Dict= np.array([0.1564, 0.14645, 0.1406, 0.14715, 0.12425, 0.14045, 0.13115, 0.12175, 0.125] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
| 64 |
"""simple docstring"""
import contextlib
import importlib
import io
import unittest
import transformers
# Try to import everything from transformers to ensure every object can be loaded.
from transformers import * # noqa F406
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, require_flax, require_tf, require_torch
from transformers.utils import ContextManagers, find_labels, is_flax_available, is_tf_available, is_torch_available
if is_torch_available():
from transformers import BertForPreTraining, BertForQuestionAnswering, BertForSequenceClassification
if is_tf_available():
from transformers import TFBertForPreTraining, TFBertForQuestionAnswering, TFBertForSequenceClassification
if is_flax_available():
from transformers import FlaxBertForPreTraining, FlaxBertForQuestionAnswering, FlaxBertForSequenceClassification
_lowerCamelCase = DUMMY_UNKNOWN_IDENTIFIER
# An actual model hosted on huggingface.co
_lowerCamelCase = '''main'''
# Default branch name
_lowerCamelCase = '''f2c752cfc5c0ab6f4bdec59acea69eefbee381c2'''
# One particular commit (not the top of `main`)
_lowerCamelCase = '''aaaaaaa'''
# This commit does not exist, so we should 404.
_lowerCamelCase = '''d9e9f15bc825e4b2c9249e9578f884bbcb5e3684'''
# Sha-1 of config.json on the top of `main`, for checking purposes
_lowerCamelCase = '''4b243c475af8d0a7754e87d7d096c92e5199ec2fe168a2ee7998e3b8e9bcb1d3'''
@contextlib.contextmanager
def lowerCAmelCase_ ( ):
'''simple docstring'''
print('''Welcome!''' )
yield
print('''Bye!''' )
@contextlib.contextmanager
def lowerCAmelCase_ ( ):
'''simple docstring'''
print('''Bonjour!''' )
yield
print('''Au revoir!''' )
class snake_case ( unittest.TestCase ):
def SCREAMING_SNAKE_CASE_ ( self :Union[str, Any] ):
# If the spec is missing, importlib would not be able to import the module dynamically.
assert transformers.__spec__ is not None
assert importlib.util.find_spec('''transformers''' ) is not None
class snake_case ( unittest.TestCase ):
@unittest.mock.patch('''sys.stdout''' , new_callable=io.StringIO )
def SCREAMING_SNAKE_CASE_ ( self :Dict , _lowerCamelCase :Optional[int] ):
with ContextManagers([] ):
print('''Transformers are awesome!''' )
# The print statement adds a new line at the end of the output
self.assertEqual(mock_stdout.getvalue() , '''Transformers are awesome!\n''' )
@unittest.mock.patch('''sys.stdout''' , new_callable=io.StringIO )
def SCREAMING_SNAKE_CASE_ ( self :str , _lowerCamelCase :Optional[int] ):
with ContextManagers([context_en()] ):
print('''Transformers are awesome!''' )
# The output should be wrapped with an English welcome and goodbye
self.assertEqual(mock_stdout.getvalue() , '''Welcome!\nTransformers are awesome!\nBye!\n''' )
@unittest.mock.patch('''sys.stdout''' , new_callable=io.StringIO )
def SCREAMING_SNAKE_CASE_ ( self :Dict , _lowerCamelCase :List[str] ):
with ContextManagers([context_fr(), context_en()] ):
print('''Transformers are awesome!''' )
# The output should be wrapped with an English and French welcome and goodbye
self.assertEqual(mock_stdout.getvalue() , '''Bonjour!\nWelcome!\nTransformers are awesome!\nBye!\nAu revoir!\n''' )
@require_torch
def SCREAMING_SNAKE_CASE_ ( self :List[str] ):
self.assertEqual(find_labels(_lowerCamelCase ) , ['''labels'''] )
self.assertEqual(find_labels(_lowerCamelCase ) , ['''labels''', '''next_sentence_label'''] )
self.assertEqual(find_labels(_lowerCamelCase ) , ['''start_positions''', '''end_positions'''] )
class snake_case ( __UpperCAmelCase ):
pass
self.assertEqual(find_labels(_lowerCamelCase ) , ['''labels'''] )
@require_tf
def SCREAMING_SNAKE_CASE_ ( self :int ):
self.assertEqual(find_labels(_lowerCamelCase ) , ['''labels'''] )
self.assertEqual(find_labels(_lowerCamelCase ) , ['''labels''', '''next_sentence_label'''] )
self.assertEqual(find_labels(_lowerCamelCase ) , ['''start_positions''', '''end_positions'''] )
class snake_case ( __UpperCAmelCase ):
pass
self.assertEqual(find_labels(_lowerCamelCase ) , ['''labels'''] )
@require_flax
def SCREAMING_SNAKE_CASE_ ( self :Dict ):
# Flax models don't have labels
self.assertEqual(find_labels(_lowerCamelCase ) , [] )
self.assertEqual(find_labels(_lowerCamelCase ) , [] )
self.assertEqual(find_labels(_lowerCamelCase ) , [] )
class snake_case ( __UpperCAmelCase ):
pass
self.assertEqual(find_labels(_lowerCamelCase ) , [] )
| 674 | 0 |
def lowercase_ ( _UpperCamelCase , _UpperCamelCase ):
'''simple docstring'''
if discount_rate < 0:
raise ValueError('''Discount rate cannot be negative''' )
if not cash_flows:
raise ValueError('''Cash flows list cannot be empty''' )
__lowercase = sum(
cash_flow / ((1 + discount_rate) ** i) for i, cash_flow in enumerate(lowercase_ ) )
return round(lowercase_ , ndigits=2 )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 639 |
"""simple docstring"""
import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import YolosImageProcessor
class snake_case ( unittest.TestCase ):
def __init__( self :List[Any] , _lowerCamelCase :Dict , _lowerCamelCase :Tuple=7 , _lowerCamelCase :Dict=3 , _lowerCamelCase :Optional[Any]=3_0 , _lowerCamelCase :List[str]=4_0_0 , _lowerCamelCase :Union[str, Any]=True , _lowerCamelCase :Union[str, Any]=None , _lowerCamelCase :List[Any]=True , _lowerCamelCase :Any=[0.5, 0.5, 0.5] , _lowerCamelCase :Dict=[0.5, 0.5, 0.5] , _lowerCamelCase :Dict=True , _lowerCamelCase :str=1 / 2_5_5 , _lowerCamelCase :Union[str, Any]=True , ):
# by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
__SCREAMING_SNAKE_CASE : Optional[Any] = size if size is not None else {'''shortest_edge''': 1_8, '''longest_edge''': 1_3_3_3}
__SCREAMING_SNAKE_CASE : List[str] = parent
__SCREAMING_SNAKE_CASE : Dict = batch_size
__SCREAMING_SNAKE_CASE : str = num_channels
__SCREAMING_SNAKE_CASE : Union[str, Any] = min_resolution
__SCREAMING_SNAKE_CASE : Union[str, Any] = max_resolution
__SCREAMING_SNAKE_CASE : Tuple = do_resize
__SCREAMING_SNAKE_CASE : Union[str, Any] = size
__SCREAMING_SNAKE_CASE : int = do_normalize
__SCREAMING_SNAKE_CASE : List[Any] = image_mean
__SCREAMING_SNAKE_CASE : Tuple = image_std
__SCREAMING_SNAKE_CASE : Dict = do_rescale
__SCREAMING_SNAKE_CASE : Optional[int] = rescale_factor
__SCREAMING_SNAKE_CASE : List[Any] = do_pad
def SCREAMING_SNAKE_CASE_ ( self :Optional[Any] ):
return {
"do_resize": self.do_resize,
"size": self.size,
"do_normalize": self.do_normalize,
"image_mean": self.image_mean,
"image_std": self.image_std,
"do_rescale": self.do_rescale,
"rescale_factor": self.rescale_factor,
"do_pad": self.do_pad,
}
def SCREAMING_SNAKE_CASE_ ( self :Tuple , _lowerCamelCase :Dict , _lowerCamelCase :List[Any]=False ):
if not batched:
__SCREAMING_SNAKE_CASE : str = image_inputs[0]
if isinstance(_lowerCamelCase , Image.Image ):
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Optional[int] = image.size
else:
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Dict = image.shape[1], image.shape[2]
if w < h:
__SCREAMING_SNAKE_CASE : str = int(self.size['''shortest_edge'''] * h / w )
__SCREAMING_SNAKE_CASE : int = self.size['''shortest_edge''']
elif w > h:
__SCREAMING_SNAKE_CASE : Optional[Any] = self.size['''shortest_edge''']
__SCREAMING_SNAKE_CASE : int = int(self.size['''shortest_edge'''] * w / h )
else:
__SCREAMING_SNAKE_CASE : List[str] = self.size['''shortest_edge''']
__SCREAMING_SNAKE_CASE : List[str] = self.size['''shortest_edge''']
else:
__SCREAMING_SNAKE_CASE : Optional[Any] = []
for image in image_inputs:
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Optional[Any] = self.get_expected_values([image] )
expected_values.append((expected_height, expected_width) )
__SCREAMING_SNAKE_CASE : Optional[int] = max(_lowerCamelCase , key=lambda _lowerCamelCase : item[0] )[0]
__SCREAMING_SNAKE_CASE : int = max(_lowerCamelCase , key=lambda _lowerCamelCase : item[1] )[1]
return expected_height, expected_width
@require_torch
@require_vision
class snake_case ( __UpperCAmelCase , unittest.TestCase ):
lowerCamelCase__ = YolosImageProcessor if is_vision_available() else None
def SCREAMING_SNAKE_CASE_ ( self :Optional[int] ):
__SCREAMING_SNAKE_CASE : List[Any] = YolosImageProcessingTester(self )
@property
def SCREAMING_SNAKE_CASE_ ( self :Optional[int] ):
return self.image_processor_tester.prepare_image_processor_dict()
def SCREAMING_SNAKE_CASE_ ( self :Tuple ):
__SCREAMING_SNAKE_CASE : Any = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(_lowerCamelCase , '''image_mean''' ) )
self.assertTrue(hasattr(_lowerCamelCase , '''image_std''' ) )
self.assertTrue(hasattr(_lowerCamelCase , '''do_normalize''' ) )
self.assertTrue(hasattr(_lowerCamelCase , '''do_resize''' ) )
self.assertTrue(hasattr(_lowerCamelCase , '''size''' ) )
def SCREAMING_SNAKE_CASE_ ( self :Tuple ):
__SCREAMING_SNAKE_CASE : Union[str, Any] = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {'''shortest_edge''': 1_8, '''longest_edge''': 1_3_3_3} )
self.assertEqual(image_processor.do_pad , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : Tuple = self.image_processing_class.from_dict(
self.image_processor_dict , size=4_2 , max_size=8_4 , pad_and_return_pixel_mask=_lowerCamelCase )
self.assertEqual(image_processor.size , {'''shortest_edge''': 4_2, '''longest_edge''': 8_4} )
self.assertEqual(image_processor.do_pad , _lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :List[str] ):
pass
def SCREAMING_SNAKE_CASE_ ( self :int ):
# Initialize image_processing
__SCREAMING_SNAKE_CASE : Optional[int] = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
__SCREAMING_SNAKE_CASE : List[str] = prepare_image_inputs(self.image_processor_tester , equal_resolution=_lowerCamelCase )
for image in image_inputs:
self.assertIsInstance(_lowerCamelCase , Image.Image )
# Test not batched input
__SCREAMING_SNAKE_CASE : str = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Dict = self.image_processor_tester.get_expected_values(_lowerCamelCase )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Tuple = self.image_processor_tester.get_expected_values(_lowerCamelCase , batched=_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Union[str, Any] = image_processing(_lowerCamelCase , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
def SCREAMING_SNAKE_CASE_ ( self :Optional[int] ):
# Initialize image_processing
__SCREAMING_SNAKE_CASE : Optional[Any] = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
__SCREAMING_SNAKE_CASE : Optional[int] = prepare_image_inputs(self.image_processor_tester , equal_resolution=_lowerCamelCase , numpify=_lowerCamelCase )
for image in image_inputs:
self.assertIsInstance(_lowerCamelCase , np.ndarray )
# Test not batched input
__SCREAMING_SNAKE_CASE : Dict = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : List[str] = self.image_processor_tester.get_expected_values(_lowerCamelCase )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
__SCREAMING_SNAKE_CASE : List[Any] = image_processing(_lowerCamelCase , return_tensors='''pt''' ).pixel_values
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Any = self.image_processor_tester.get_expected_values(_lowerCamelCase , batched=_lowerCamelCase )
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
def SCREAMING_SNAKE_CASE_ ( self :Any ):
# Initialize image_processing
__SCREAMING_SNAKE_CASE : str = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
__SCREAMING_SNAKE_CASE : Any = prepare_image_inputs(self.image_processor_tester , equal_resolution=_lowerCamelCase , torchify=_lowerCamelCase )
for image in image_inputs:
self.assertIsInstance(_lowerCamelCase , torch.Tensor )
# Test not batched input
__SCREAMING_SNAKE_CASE : List[str] = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Optional[Any] = self.image_processor_tester.get_expected_values(_lowerCamelCase )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
__SCREAMING_SNAKE_CASE : Optional[int] = image_processing(_lowerCamelCase , return_tensors='''pt''' ).pixel_values
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : List[Any] = self.image_processor_tester.get_expected_values(_lowerCamelCase , batched=_lowerCamelCase )
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
def SCREAMING_SNAKE_CASE_ ( self :List[str] ):
# Initialize image_processings
__SCREAMING_SNAKE_CASE : Dict = self.image_processing_class(**self.image_processor_dict )
__SCREAMING_SNAKE_CASE : Tuple = self.image_processing_class(do_resize=_lowerCamelCase , do_normalize=_lowerCamelCase , do_rescale=_lowerCamelCase )
# create random PyTorch tensors
__SCREAMING_SNAKE_CASE : int = prepare_image_inputs(self.image_processor_tester , equal_resolution=_lowerCamelCase , torchify=_lowerCamelCase )
for image in image_inputs:
self.assertIsInstance(_lowerCamelCase , torch.Tensor )
# Test whether the method "pad" and calling the image processor return the same tensors
__SCREAMING_SNAKE_CASE : Union[str, Any] = image_processing_a.pad(_lowerCamelCase , return_tensors='''pt''' )
__SCREAMING_SNAKE_CASE : Union[str, Any] = image_processing_a(_lowerCamelCase , return_tensors='''pt''' )
self.assertTrue(
torch.allclose(encoded_images_with_method['''pixel_values'''] , encoded_images['''pixel_values'''] , atol=1e-4 ) )
@slow
def SCREAMING_SNAKE_CASE_ ( self :int ):
# prepare image and target
__SCREAMING_SNAKE_CASE : Tuple = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
with open('''./tests/fixtures/tests_samples/COCO/coco_annotations.txt''' , '''r''' ) as f:
__SCREAMING_SNAKE_CASE : Tuple = json.loads(f.read() )
__SCREAMING_SNAKE_CASE : Union[str, Any] = {'''image_id''': 3_9_7_6_9, '''annotations''': target}
# encode them
__SCREAMING_SNAKE_CASE : List[Any] = YolosImageProcessor.from_pretrained('''hustvl/yolos-small''' )
__SCREAMING_SNAKE_CASE : Optional[Any] = image_processing(images=_lowerCamelCase , annotations=_lowerCamelCase , return_tensors='''pt''' )
# verify pixel values
__SCREAMING_SNAKE_CASE : Optional[int] = torch.Size([1, 3, 8_0_0, 1_0_6_6] )
self.assertEqual(encoding['''pixel_values'''].shape , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : List[str] = torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1] )
self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3] , _lowerCamelCase , atol=1e-4 ) )
# verify area
__SCREAMING_SNAKE_CASE : Union[str, Any] = torch.tensor([5_8_8_7.9_6_0_0, 1_1_2_5_0.2_0_6_1, 4_8_9_3_5_3.8_4_3_8, 8_3_7_1_2_2.7_5_0_0, 1_4_7_9_6_7.5_1_5_6, 1_6_5_7_3_2.3_4_3_8] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area'''] , _lowerCamelCase ) )
# verify boxes
__SCREAMING_SNAKE_CASE : List[str] = torch.Size([6, 4] )
self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : Any = torch.tensor([0.5_5_0_3, 0.2_7_6_5, 0.0_6_0_4, 0.2_2_1_5] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0] , _lowerCamelCase , atol=1e-3 ) )
# verify image_id
__SCREAMING_SNAKE_CASE : Optional[Any] = torch.tensor([3_9_7_6_9] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id'''] , _lowerCamelCase ) )
# verify is_crowd
__SCREAMING_SNAKE_CASE : str = torch.tensor([0, 0, 0, 0, 0, 0] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd'''] , _lowerCamelCase ) )
# verify class_labels
__SCREAMING_SNAKE_CASE : Dict = torch.tensor([7_5, 7_5, 6_3, 6_5, 1_7, 1_7] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels'''] , _lowerCamelCase ) )
# verify orig_size
__SCREAMING_SNAKE_CASE : Union[str, Any] = torch.tensor([4_8_0, 6_4_0] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size'''] , _lowerCamelCase ) )
# verify size
__SCREAMING_SNAKE_CASE : List[str] = torch.tensor([8_0_0, 1_0_6_6] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size'''] , _lowerCamelCase ) )
@slow
def SCREAMING_SNAKE_CASE_ ( self :Optional[int] ):
# prepare image, target and masks_path
__SCREAMING_SNAKE_CASE : List[str] = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
with open('''./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt''' , '''r''' ) as f:
__SCREAMING_SNAKE_CASE : Union[str, Any] = json.loads(f.read() )
__SCREAMING_SNAKE_CASE : Dict = {'''file_name''': '''000000039769.png''', '''image_id''': 3_9_7_6_9, '''segments_info''': target}
__SCREAMING_SNAKE_CASE : Optional[int] = pathlib.Path('''./tests/fixtures/tests_samples/COCO/coco_panoptic''' )
# encode them
__SCREAMING_SNAKE_CASE : Any = YolosImageProcessor(format='''coco_panoptic''' )
__SCREAMING_SNAKE_CASE : Dict = image_processing(images=_lowerCamelCase , annotations=_lowerCamelCase , masks_path=_lowerCamelCase , return_tensors='''pt''' )
# verify pixel values
__SCREAMING_SNAKE_CASE : str = torch.Size([1, 3, 8_0_0, 1_0_6_6] )
self.assertEqual(encoding['''pixel_values'''].shape , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : str = torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1] )
self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3] , _lowerCamelCase , atol=1e-4 ) )
# verify area
__SCREAMING_SNAKE_CASE : Any = torch.tensor([1_4_7_9_7_9.6_8_7_5, 1_6_5_5_2_7.0_4_6_9, 4_8_4_6_3_8.5_9_3_8, 1_1_2_9_2.9_3_7_5, 5_8_7_9.6_5_6_2, 7_6_3_4.1_1_4_7] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area'''] , _lowerCamelCase ) )
# verify boxes
__SCREAMING_SNAKE_CASE : List[str] = torch.Size([6, 4] )
self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : int = torch.tensor([0.2_6_2_5, 0.5_4_3_7, 0.4_6_8_8, 0.8_6_2_5] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0] , _lowerCamelCase , atol=1e-3 ) )
# verify image_id
__SCREAMING_SNAKE_CASE : Dict = torch.tensor([3_9_7_6_9] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id'''] , _lowerCamelCase ) )
# verify is_crowd
__SCREAMING_SNAKE_CASE : List[Any] = torch.tensor([0, 0, 0, 0, 0, 0] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd'''] , _lowerCamelCase ) )
# verify class_labels
__SCREAMING_SNAKE_CASE : Optional[Any] = torch.tensor([1_7, 1_7, 6_3, 7_5, 7_5, 9_3] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels'''] , _lowerCamelCase ) )
# verify masks
__SCREAMING_SNAKE_CASE : Optional[Any] = 8_2_2_8_7_3
self.assertEqual(encoding['''labels'''][0]['''masks'''].sum().item() , _lowerCamelCase )
# verify orig_size
__SCREAMING_SNAKE_CASE : List[str] = torch.tensor([4_8_0, 6_4_0] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size'''] , _lowerCamelCase ) )
# verify size
__SCREAMING_SNAKE_CASE : Any = torch.tensor([8_0_0, 1_0_6_6] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size'''] , _lowerCamelCase ) )
| 674 | 0 |
def lowerCAmelCase_ ( lowercase: str ) -> Any:
'''simple docstring'''
return "".join(chr(ord(lowercase_ ) - 32 ) if '''a''' <= char <= '''z''' else char for char in word )
if __name__ == "__main__":
from doctest import testmod
testmod() | 271 |
"""simple docstring"""
from __future__ import annotations
def lowerCAmelCase_ ( lowercase_ : int ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : Optional[int] = str(lowercase_ )
return len(lowercase_ ) == 9 and set(lowercase_ ) == set('''123456789''' )
def lowerCAmelCase_ ( ):
'''simple docstring'''
for base_num in range(9999 , 4999 , -1 ):
__SCREAMING_SNAKE_CASE : List[str] = 10_0002 * base_num
if is_9_pandigital(lowercase_ ):
return candidate
for base_num in range(333 , 99 , -1 ):
__SCREAMING_SNAKE_CASE : List[Any] = 100_2003 * base_num
if is_9_pandigital(lowercase_ ):
return candidate
return None
if __name__ == "__main__":
print(f'{solution() = }')
| 674 | 0 |
import importlib.metadata
import warnings
from copy import deepcopy
from packaging import version
from ..utils import logging
from .import_utils import is_accelerate_available, is_bitsandbytes_available
if is_bitsandbytes_available():
import bitsandbytes as bnb
import torch
import torch.nn as nn
from ..pytorch_utils import ConvaD
if is_accelerate_available():
from accelerate import init_empty_weights
from accelerate.utils import find_tied_parameters
_snake_case = logging.get_logger(__name__)
def __lowerCamelCase ( _lowercase , _lowercase , _lowercase , _lowercase=None , _lowercase=None ) -> Optional[Any]:
if "." in tensor_name:
UpperCamelCase = tensor_name.split('.' )
for split in splits[:-1]:
UpperCamelCase = getattr(lowercase_ , lowercase_ )
if new_module is None:
raise ValueError(F'{module} has no attribute {split}.' )
UpperCamelCase = new_module
UpperCamelCase = splits[-1]
if tensor_name not in module._parameters and tensor_name not in module._buffers:
raise ValueError(F'{module} does not have a parameter or a buffer named {tensor_name}.' )
UpperCamelCase = tensor_name in module._buffers
UpperCamelCase = getattr(lowercase_ , lowercase_ )
if old_value.device == torch.device('meta' ) and device not in ["meta", torch.device('meta' )] and value is None:
raise ValueError(F'{tensor_name} is on the meta device, we need a `value` to put in on {device}.' )
UpperCamelCase = False
UpperCamelCase = False
if is_buffer or not is_bitsandbytes_available():
UpperCamelCase = False
UpperCamelCase = False
else:
UpperCamelCase = hasattr(bnb.nn , 'Params4bit' ) and isinstance(module._parameters[tensor_name] , bnb.nn.Paramsabit )
UpperCamelCase = isinstance(module._parameters[tensor_name] , bnb.nn.IntaParams )
if is_abit or is_abit:
UpperCamelCase = module._parameters[tensor_name]
if param.device.type != "cuda":
if value is None:
UpperCamelCase = old_value.to(lowercase_ )
elif isinstance(lowercase_ , torch.Tensor ):
UpperCamelCase = value.to('cpu' )
if value.dtype == torch.inta:
UpperCamelCase = version.parse(importlib.metadata.version('bitsandbytes' ) ) > version.parse(
'0.37.2' )
if not is_abit_serializable:
raise ValueError(
'Detected int8 weights but the version of bitsandbytes is not compatible with int8 serialization. '
'Make sure to download the latest `bitsandbytes` version. `pip install --upgrade bitsandbytes`.' )
else:
UpperCamelCase = torch.tensor(lowercase_ , device='cpu' )
# Support models using `Conv1D` in place of `nn.Linear` (e.g. gpt2) by transposing the weight matrix prior to quantization.
# Since weights are saved in the correct "orientation", we skip transposing when loading.
if issubclass(module.source_cls , lowercase_ ) and fpaa_statistics is None:
UpperCamelCase = new_value.T
UpperCamelCase = old_value.__dict__
if is_abit:
UpperCamelCase = bnb.nn.IntaParams(lowercase_ , requires_grad=lowercase_ , **lowercase_ ).to(lowercase_ )
elif is_abit:
UpperCamelCase = bnb.nn.Paramsabit(lowercase_ , requires_grad=lowercase_ , **lowercase_ ).to(lowercase_ )
UpperCamelCase = new_value
if fpaa_statistics is not None:
setattr(module.weight , 'SCB' , fpaa_statistics.to(lowercase_ ) )
else:
if value is None:
UpperCamelCase = old_value.to(lowercase_ )
elif isinstance(lowercase_ , torch.Tensor ):
UpperCamelCase = value.to(lowercase_ )
else:
UpperCamelCase = torch.tensor(lowercase_ , device=lowercase_ )
if is_buffer:
UpperCamelCase = new_value
else:
UpperCamelCase = nn.Parameter(lowercase_ , requires_grad=old_value.requires_grad )
UpperCamelCase = new_value
def __lowerCamelCase ( _lowercase , _lowercase=None , _lowercase=None , _lowercase=None , _lowercase=False ) -> Optional[Any]:
for name, module in model.named_children():
if current_key_name is None:
UpperCamelCase = []
current_key_name.append(lowercase_ )
if (isinstance(lowercase_ , nn.Linear ) or isinstance(lowercase_ , lowercase_ )) and name not in modules_to_not_convert:
# Check if the current key is not in the `modules_to_not_convert`
if not any(key in '.'.join(lowercase_ ) for key in modules_to_not_convert ):
with init_empty_weights():
if isinstance(lowercase_ , lowercase_ ):
UpperCamelCase = module.weight.shape
else:
UpperCamelCase = module.in_features
UpperCamelCase = module.out_features
if quantization_config.quantization_method() == "llm_int8":
UpperCamelCase = bnb.nn.LinearabitLt(
lowercase_ , lowercase_ , module.bias is not None , has_fpaa_weights=quantization_config.llm_inta_has_fpaa_weight , threshold=quantization_config.llm_inta_threshold , )
UpperCamelCase = True
else:
if (
quantization_config.llm_inta_skip_modules is not None
and name in quantization_config.llm_inta_skip_modules
):
pass
else:
UpperCamelCase = bnb.nn.Linearabit(
lowercase_ , lowercase_ , module.bias is not None , quantization_config.bnb_abit_compute_dtype , compress_statistics=quantization_config.bnb_abit_use_double_quant , quant_type=quantization_config.bnb_abit_quant_type , )
UpperCamelCase = True
# Store the module class in case we need to transpose the weight later
UpperCamelCase = type(lowercase_ )
# Force requires grad to False to avoid unexpected errors
model._modules[name].requires_grad_(lowercase_ )
if len(list(module.children() ) ) > 0:
UpperCamelCase = _replace_with_bnb_linear(
lowercase_ , lowercase_ , lowercase_ , lowercase_ , has_been_replaced=lowercase_ , )
# Remove the last key for recursion
current_key_name.pop(-1 )
return model, has_been_replaced
def __lowerCamelCase ( _lowercase , _lowercase=None , _lowercase=None , _lowercase=None ) -> Tuple:
UpperCamelCase = ['''lm_head'''] if modules_to_not_convert is None else modules_to_not_convert
UpperCamelCase = _replace_with_bnb_linear(
lowercase_ , lowercase_ , lowercase_ , lowercase_ )
if not has_been_replaced:
logger.warning(
'You are loading your model in 8bit or 4bit but no linear modules were found in your model.'
' Please double check your model architecture, or submit an issue on github if you think this is'
' a bug.' )
return model
def __lowerCamelCase ( *_lowercase , **_lowercase ) -> int:
warnings.warn(
'`replace_8bit_linear` will be deprecated in a future version, please use `replace_with_bnb_linear` instead' , lowercase_ , )
return replace_with_bnb_linear(*lowercase_ , **lowercase_ )
def __lowerCamelCase ( *_lowercase , **_lowercase ) -> int:
warnings.warn(
'`set_module_8bit_tensor_to_device` will be deprecated in a future version, please use `set_module_quantized_tensor_to_device` instead' , lowercase_ , )
return set_module_quantized_tensor_to_device(*lowercase_ , **lowercase_ )
def __lowerCamelCase ( _lowercase ) -> str:
UpperCamelCase = deepcopy(lowercase_ ) # this has 0 cost since it is done inside `init_empty_weights` context manager`
tied_model.tie_weights()
UpperCamelCase = find_tied_parameters(lowercase_ )
# For compatibility with Accelerate < 0.18
if isinstance(lowercase_ , lowercase_ ):
UpperCamelCase = sum(list(tied_params.values() ) , [] ) + list(tied_params.keys() )
else:
UpperCamelCase = sum(lowercase_ , [] )
UpperCamelCase = len(lowercase_ ) > 0
# Check if it is a base model
UpperCamelCase = not hasattr(lowercase_ , model.base_model_prefix )
# Ignore this for base models (BertModel, GPT2Model, etc.)
if (not has_tied_params) and is_base_model:
return []
# otherwise they have an attached head
UpperCamelCase = list(model.named_children() )
UpperCamelCase = [list_modules[-1][0]]
# add last module together with tied weights
UpperCamelCase = set(lowercase_ ) - set(lowercase_ )
UpperCamelCase = list(set(lowercase_ ) ) + list(lowercase_ )
# remove ".weight" from the keys
UpperCamelCase = ['''.weight''', '''.bias''']
UpperCamelCase = []
for name in list_untouched:
for name_to_remove in names_to_remove:
if name_to_remove in name:
UpperCamelCase = name.replace(lowercase_ , '' )
filtered_module_names.append(lowercase_ )
return filtered_module_names
| 282 |
"""simple docstring"""
import json
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
from transformers import (
CONFIG_MAPPING,
IMAGE_PROCESSOR_MAPPING,
AutoConfig,
AutoImageProcessor,
CLIPConfig,
CLIPImageProcessor,
)
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER
sys.path.append(str(Path(__file__).parent.parent.parent.parent / '''utils'''))
from test_module.custom_configuration import CustomConfig # noqa E402
from test_module.custom_image_processing import CustomImageProcessor # noqa E402
class snake_case ( unittest.TestCase ):
def SCREAMING_SNAKE_CASE_ ( self :int ):
__SCREAMING_SNAKE_CASE : Tuple = 0
def SCREAMING_SNAKE_CASE_ ( self :Any ):
__SCREAMING_SNAKE_CASE : Optional[Any] = AutoImageProcessor.from_pretrained('''openai/clip-vit-base-patch32''' )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :int ):
with tempfile.TemporaryDirectory() as tmpdirname:
__SCREAMING_SNAKE_CASE : Optional[Any] = Path(_lowerCamelCase ) / '''preprocessor_config.json'''
__SCREAMING_SNAKE_CASE : Dict = Path(_lowerCamelCase ) / '''config.json'''
json.dump(
{'''image_processor_type''': '''CLIPImageProcessor''', '''processor_class''': '''CLIPProcessor'''} , open(_lowerCamelCase , '''w''' ) , )
json.dump({'''model_type''': '''clip'''} , open(_lowerCamelCase , '''w''' ) )
__SCREAMING_SNAKE_CASE : Dict = AutoImageProcessor.from_pretrained(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :Optional[int] ):
# Ensure we can load the image processor from the feature extractor config
with tempfile.TemporaryDirectory() as tmpdirname:
__SCREAMING_SNAKE_CASE : Tuple = Path(_lowerCamelCase ) / '''preprocessor_config.json'''
__SCREAMING_SNAKE_CASE : Union[str, Any] = Path(_lowerCamelCase ) / '''config.json'''
json.dump(
{'''feature_extractor_type''': '''CLIPFeatureExtractor''', '''processor_class''': '''CLIPProcessor'''} , open(_lowerCamelCase , '''w''' ) , )
json.dump({'''model_type''': '''clip'''} , open(_lowerCamelCase , '''w''' ) )
__SCREAMING_SNAKE_CASE : int = AutoImageProcessor.from_pretrained(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :int ):
with tempfile.TemporaryDirectory() as tmpdirname:
__SCREAMING_SNAKE_CASE : str = CLIPConfig()
# Create a dummy config file with image_proceesor_type
__SCREAMING_SNAKE_CASE : Tuple = Path(_lowerCamelCase ) / '''preprocessor_config.json'''
__SCREAMING_SNAKE_CASE : Any = Path(_lowerCamelCase ) / '''config.json'''
json.dump(
{'''image_processor_type''': '''CLIPImageProcessor''', '''processor_class''': '''CLIPProcessor'''} , open(_lowerCamelCase , '''w''' ) , )
json.dump({'''model_type''': '''clip'''} , open(_lowerCamelCase , '''w''' ) )
# remove image_processor_type to make sure config.json alone is enough to load image processor locally
__SCREAMING_SNAKE_CASE : int = AutoImageProcessor.from_pretrained(_lowerCamelCase ).to_dict()
config_dict.pop('''image_processor_type''' )
__SCREAMING_SNAKE_CASE : Union[str, Any] = CLIPImageProcessor(**_lowerCamelCase )
# save in new folder
model_config.save_pretrained(_lowerCamelCase )
config.save_pretrained(_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Tuple = AutoImageProcessor.from_pretrained(_lowerCamelCase )
# make sure private variable is not incorrectly saved
__SCREAMING_SNAKE_CASE : Tuple = json.loads(config.to_json_string() )
self.assertTrue('''_processor_class''' not in dict_as_saved )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :Tuple ):
with tempfile.TemporaryDirectory() as tmpdirname:
__SCREAMING_SNAKE_CASE : Dict = Path(_lowerCamelCase ) / '''preprocessor_config.json'''
json.dump(
{'''image_processor_type''': '''CLIPImageProcessor''', '''processor_class''': '''CLIPProcessor'''} , open(_lowerCamelCase , '''w''' ) , )
__SCREAMING_SNAKE_CASE : Optional[Any] = AutoImageProcessor.from_pretrained(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :Optional[int] ):
with self.assertRaisesRegex(
_lowerCamelCase , '''clip-base is not a local folder and is not a valid model identifier''' ):
__SCREAMING_SNAKE_CASE : Tuple = AutoImageProcessor.from_pretrained('''clip-base''' )
def SCREAMING_SNAKE_CASE_ ( self :Union[str, Any] ):
with self.assertRaisesRegex(
_lowerCamelCase , r'''aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)''' ):
__SCREAMING_SNAKE_CASE : Optional[int] = AutoImageProcessor.from_pretrained(_lowerCamelCase , revision='''aaaaaa''' )
def SCREAMING_SNAKE_CASE_ ( self :List[str] ):
with self.assertRaisesRegex(
_lowerCamelCase , '''hf-internal-testing/config-no-model does not appear to have a file named preprocessor_config.json.''' , ):
__SCREAMING_SNAKE_CASE : Optional[int] = AutoImageProcessor.from_pretrained('''hf-internal-testing/config-no-model''' )
def SCREAMING_SNAKE_CASE_ ( self :Tuple ):
# If remote code is not set, we will time out when asking whether to load the model.
with self.assertRaises(_lowerCamelCase ):
__SCREAMING_SNAKE_CASE : Union[str, Any] = AutoImageProcessor.from_pretrained('''hf-internal-testing/test_dynamic_image_processor''' )
# If remote code is disabled, we can't load this config.
with self.assertRaises(_lowerCamelCase ):
__SCREAMING_SNAKE_CASE : Optional[Any] = AutoImageProcessor.from_pretrained(
'''hf-internal-testing/test_dynamic_image_processor''' , trust_remote_code=_lowerCamelCase )
__SCREAMING_SNAKE_CASE : int = AutoImageProcessor.from_pretrained(
'''hf-internal-testing/test_dynamic_image_processor''' , trust_remote_code=_lowerCamelCase )
self.assertEqual(image_processor.__class__.__name__ , '''NewImageProcessor''' )
# Test image processor can be reloaded.
with tempfile.TemporaryDirectory() as tmp_dir:
image_processor.save_pretrained(_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Tuple = AutoImageProcessor.from_pretrained(_lowerCamelCase , trust_remote_code=_lowerCamelCase )
self.assertEqual(reloaded_image_processor.__class__.__name__ , '''NewImageProcessor''' )
def SCREAMING_SNAKE_CASE_ ( self :Tuple ):
try:
AutoConfig.register('''custom''' , _lowerCamelCase )
AutoImageProcessor.register(_lowerCamelCase , _lowerCamelCase )
# Trying to register something existing in the Transformers library will raise an error
with self.assertRaises(_lowerCamelCase ):
AutoImageProcessor.register(_lowerCamelCase , _lowerCamelCase )
with tempfile.TemporaryDirectory() as tmpdirname:
__SCREAMING_SNAKE_CASE : Dict = Path(_lowerCamelCase ) / '''preprocessor_config.json'''
__SCREAMING_SNAKE_CASE : Dict = Path(_lowerCamelCase ) / '''config.json'''
json.dump(
{'''feature_extractor_type''': '''CLIPFeatureExtractor''', '''processor_class''': '''CLIPProcessor'''} , open(_lowerCamelCase , '''w''' ) , )
json.dump({'''model_type''': '''clip'''} , open(_lowerCamelCase , '''w''' ) )
__SCREAMING_SNAKE_CASE : Optional[int] = CustomImageProcessor.from_pretrained(_lowerCamelCase )
# Now that the config is registered, it can be used as any other config with the auto-API
with tempfile.TemporaryDirectory() as tmp_dir:
image_processor.save_pretrained(_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Tuple = AutoImageProcessor.from_pretrained(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
finally:
if "custom" in CONFIG_MAPPING._extra_content:
del CONFIG_MAPPING._extra_content["custom"]
if CustomConfig in IMAGE_PROCESSOR_MAPPING._extra_content:
del IMAGE_PROCESSOR_MAPPING._extra_content[CustomConfig]
def SCREAMING_SNAKE_CASE_ ( self :Dict ):
class snake_case ( __UpperCAmelCase ):
lowerCamelCase__ = True
try:
AutoConfig.register('''custom''' , _lowerCamelCase )
AutoImageProcessor.register(_lowerCamelCase , _lowerCamelCase )
# If remote code is not set, the default is to use local
__SCREAMING_SNAKE_CASE : List[str] = AutoImageProcessor.from_pretrained('''hf-internal-testing/test_dynamic_image_processor''' )
self.assertEqual(image_processor.__class__.__name__ , '''NewImageProcessor''' )
self.assertTrue(image_processor.is_local )
# If remote code is disabled, we load the local one.
__SCREAMING_SNAKE_CASE : str = AutoImageProcessor.from_pretrained(
'''hf-internal-testing/test_dynamic_image_processor''' , trust_remote_code=_lowerCamelCase )
self.assertEqual(image_processor.__class__.__name__ , '''NewImageProcessor''' )
self.assertTrue(image_processor.is_local )
# If remote is enabled, we load from the Hub
__SCREAMING_SNAKE_CASE : Optional[int] = AutoImageProcessor.from_pretrained(
'''hf-internal-testing/test_dynamic_image_processor''' , trust_remote_code=_lowerCamelCase )
self.assertEqual(image_processor.__class__.__name__ , '''NewImageProcessor''' )
self.assertTrue(not hasattr(_lowerCamelCase , '''is_local''' ) )
finally:
if "custom" in CONFIG_MAPPING._extra_content:
del CONFIG_MAPPING._extra_content["custom"]
if CustomConfig in IMAGE_PROCESSOR_MAPPING._extra_content:
del IMAGE_PROCESSOR_MAPPING._extra_content[CustomConfig]
| 674 | 0 |
"""simple docstring"""
import copy
import fnmatch
import json
import os
import pickle as pkl
import shutil
import sys
import tarfile
import tempfile
from collections import OrderedDict
from contextlib import contextmanager
from functools import partial
from hashlib import shaaaa
from io import BytesIO
from pathlib import Path
from urllib.parse import urlparse
from zipfile import ZipFile, is_zipfile
import cva
import numpy as np
import requests
import wget
from filelock import FileLock
from PIL import Image
from tqdm.auto import tqdm
from yaml import Loader, dump, load
try:
import torch
a_ : Union[str, Any] = True
except ImportError:
a_ : str = False
try:
from torch.hub import _get_torch_home
a_ : List[str] = _get_torch_home()
except ImportError:
a_ : int = os.path.expanduser(
os.getenv('''TORCH_HOME''', os.path.join(os.getenv('''XDG_CACHE_HOME''', '''~/.cache'''), '''torch'''))
)
a_ : Tuple = os.path.join(torch_cache_home, '''transformers''')
a_ : List[Any] = '''https://cdn.huggingface.co'''
a_ : Tuple = '''https://s3.amazonaws.com/models.huggingface.co/bert'''
a_ : Tuple = '''/'''.join(str(Path(__file__).resolve()).split('''/''')[:-1])
a_ : Optional[Any] = os.path.join(PATH, '''config.yaml''')
a_ : Optional[Any] = os.path.join(PATH, '''attributes.txt''')
a_ : List[str] = os.path.join(PATH, '''objects.txt''')
a_ : Optional[int] = os.getenv('''PYTORCH_PRETRAINED_BERT_CACHE''', default_cache_path)
a_ : Dict = os.getenv('''PYTORCH_TRANSFORMERS_CACHE''', PYTORCH_PRETRAINED_BERT_CACHE)
a_ : Dict = os.getenv('''TRANSFORMERS_CACHE''', PYTORCH_TRANSFORMERS_CACHE)
a_ : Optional[int] = '''pytorch_model.bin'''
a_ : int = '''config.yaml'''
def UpperCAmelCase ( A__: Optional[int]=OBJECTS , A__: Any=ATTRIBUTES ) -> str:
__lowerCamelCase : Union[str, Any] = []
with open(lowercase_ ) as f:
for object in f.readlines():
vg_classes.append(object.split(',' )[0].lower().strip() )
__lowerCamelCase : str = []
with open(lowercase_ ) as f:
for object in f.readlines():
vg_attrs.append(object.split(',' )[0].lower().strip() )
return vg_classes, vg_attrs
def UpperCAmelCase ( A__: Optional[Any] ) -> Any:
__lowerCamelCase : Optional[Any] = OrderedDict()
with open(lowercase_ , 'rb' ) as f:
__lowerCamelCase : str = pkl.load(lowercase_ )['''model''']
for k in copy.deepcopy(list(ckp.keys() ) ):
__lowerCamelCase : Union[str, Any] = ckp.pop(lowercase_ )
if isinstance(lowercase_ , np.ndarray ):
__lowerCamelCase : Optional[Any] = torch.tensor(lowercase_ )
else:
assert isinstance(lowercase_ , torch.tensor ), type(lowercase_ )
__lowerCamelCase : int = v
return r
class __lowercase:
'''simple docstring'''
__a : List[str] = {}
def __init__( self , __a , __a = "root" , __a=0 ):
__lowerCamelCase : Tuple = name
__lowerCamelCase : int = level
__lowerCamelCase : Any = {}
for k, v in dictionary.items():
if v is None:
raise ValueError()
__lowerCamelCase : Optional[int] = copy.deepcopy(_lowerCamelCase )
__lowerCamelCase : str = copy.deepcopy(_lowerCamelCase )
if isinstance(_lowerCamelCase , _lowerCamelCase ):
__lowerCamelCase : Optional[int] = Config(_lowerCamelCase , name=_lowerCamelCase , level=level + 1 )
__lowerCamelCase : Dict = v
setattr(self , _lowerCamelCase , _lowerCamelCase )
__lowerCamelCase : str = d
def __repr__( self ):
return str(list((self._pointer.keys()) ) )
def __setattr__( self , __a , __a ):
__lowerCamelCase : Optional[Any] = val
__lowerCamelCase : List[str] = val
__lowerCamelCase : Union[str, Any] = key.split('.' )
__lowerCamelCase : Dict = len(_lowerCamelCase ) - 1
__lowerCamelCase : str = self._pointer
if len(_lowerCamelCase ) > 1:
for i, l in enumerate(_lowerCamelCase ):
if hasattr(self , _lowerCamelCase ) and isinstance(getattr(self , _lowerCamelCase ) , _lowerCamelCase ):
setattr(getattr(self , _lowerCamelCase ) , '.'.join(levels[i:] ) , _lowerCamelCase )
if l == last_level:
__lowerCamelCase : Any = val
else:
__lowerCamelCase : List[Any] = pointer[l]
def snake_case_ ( self ):
return self._pointer
def snake_case_ ( self , __a , __a ):
with open(f'''{file_name}''' , 'w' ) as stream:
dump(_lowerCamelCase , _lowerCamelCase )
def snake_case_ ( self , __a , __a ):
with open(f'''{file_name}''' , 'w' ) as stream:
json.dump(_lowerCamelCase , _lowerCamelCase )
@staticmethod
def snake_case_ ( __a ):
with open(_lowerCamelCase ) as stream:
__lowerCamelCase : int = load(_lowerCamelCase , Loader=_lowerCamelCase )
return data
def __str__( self ):
__lowerCamelCase : Optional[int] = ''' '''
if self._name != "root":
__lowerCamelCase : List[Any] = f'''{t * (self._level-1)}{self._name}:\n'''
else:
__lowerCamelCase : Optional[int] = ''''''
__lowerCamelCase : Union[str, Any] = self._level
for i, (k, v) in enumerate(self._pointer.items() ):
if isinstance(_lowerCamelCase , _lowerCamelCase ):
r += f'''{t * (self._level)}{v}\n'''
self._level += 1
else:
r += f'''{t * (self._level)}{k}: {v} ({type(_lowerCamelCase ).__name__})\n'''
__lowerCamelCase : Union[str, Any] = level
return r[:-1]
@classmethod
def snake_case_ ( cls , __a , **__a ):
__lowerCamelCase : Dict = cls.get_config_dict(_lowerCamelCase , **_lowerCamelCase )
return cls(_lowerCamelCase )
@classmethod
def snake_case_ ( cls , __a , **__a ):
__lowerCamelCase : Any = kwargs.pop('cache_dir' , _lowerCamelCase )
__lowerCamelCase : List[str] = kwargs.pop('force_download' , _lowerCamelCase )
__lowerCamelCase : Optional[Any] = kwargs.pop('resume_download' , _lowerCamelCase )
__lowerCamelCase : int = kwargs.pop('proxies' , _lowerCamelCase )
__lowerCamelCase : str = kwargs.pop('local_files_only' , _lowerCamelCase )
if os.path.isdir(_lowerCamelCase ):
__lowerCamelCase : str = os.path.join(_lowerCamelCase , _lowerCamelCase )
elif os.path.isfile(_lowerCamelCase ) or is_remote_url(_lowerCamelCase ):
__lowerCamelCase : Tuple = pretrained_model_name_or_path
else:
__lowerCamelCase : Dict = hf_bucket_url(_lowerCamelCase , filename=_lowerCamelCase , use_cdn=_lowerCamelCase )
try:
# Load from URL or cache if already cached
__lowerCamelCase : List[Any] = cached_path(
_lowerCamelCase , cache_dir=_lowerCamelCase , force_download=_lowerCamelCase , proxies=_lowerCamelCase , resume_download=_lowerCamelCase , local_files_only=_lowerCamelCase , )
# Load config dict
if resolved_config_file is None:
raise EnvironmentError
__lowerCamelCase : List[str] = Config.load_yaml(_lowerCamelCase )
except EnvironmentError:
__lowerCamelCase : List[str] = '''Can\'t load config for'''
raise EnvironmentError(_lowerCamelCase )
if resolved_config_file == config_file:
print('loading configuration file from path' )
else:
print('loading configuration file cache' )
return Config.load_yaml(_lowerCamelCase ), kwargs
def UpperCAmelCase ( A__: Any ) -> str:
__lowerCamelCase : Optional[Any] = torch.load('dump.pt' , map_location=in_tensor.device )
__lowerCamelCase : Optional[int] = in_tensor.numpy()
__lowerCamelCase : Optional[Any] = out_tensor.numpy()[0]
print(na.shape , na[0, 0, :5] )
print(na.shape , na[0, 0, :5] )
assert np.allclose(lowercase_ , lowercase_ , rtol=0.01 , atol=0.1 ), (
f'''{sum([1 for x in np.isclose(lowercase_ , lowercase_ , rtol=0.01 , atol=0.1 ).flatten() if x is False] )/len(na.flatten() )*100:.4f} %'''
" element-wise mismatch"
)
raise Exception('tensors are all good' )
# Hugging face functions below
def UpperCAmelCase ( A__: Union[str, Any] ) -> Optional[Any]:
__lowerCamelCase : str = urlparse(lowercase_ )
return parsed.scheme in ("http", "https")
def UpperCAmelCase ( A__: str , A__: str , A__: str=True ) -> List[Any]:
__lowerCamelCase : str = CLOUDFRONT_DISTRIB_PREFIX if use_cdn else S3_BUCKET_PREFIX
__lowerCamelCase : Any = '''/''' not in model_id
if legacy_format:
return f'''{endpoint}/{model_id}-{filename}'''
else:
return f'''{endpoint}/{model_id}/{filename}'''
def UpperCAmelCase ( A__: Union[str, Any] , A__: int , A__: Union[str, Any]=None , A__: Optional[int]=0 , A__: str=None , ) -> Optional[int]:
__lowerCamelCase : Tuple = '''python/{}'''.format(sys.version.split()[0] )
if _torch_available:
ua += "; torch/{}".format(torch.__version__ )
if isinstance(lowercase_ , lowercase_ ):
ua += "; " + "; ".join('{}/{}'.format(lowercase_ , lowercase_ ) for k, v in user_agent.items() )
elif isinstance(lowercase_ , lowercase_ ):
ua += "; " + user_agent
__lowerCamelCase : List[Any] = {'''user-agent''': ua}
if resume_size > 0:
__lowerCamelCase : Optional[Any] = '''bytes=%d-''' % (resume_size,)
__lowerCamelCase : Union[str, Any] = requests.get(lowercase_ , stream=lowercase_ , proxies=lowercase_ , headers=lowercase_ )
if response.status_code == 416: # Range not satisfiable
return
__lowerCamelCase : List[Any] = response.headers.get('Content-Length' )
__lowerCamelCase : Any = resume_size + int(lowercase_ ) if content_length is not None else None
__lowerCamelCase : str = tqdm(
unit='B' , unit_scale=lowercase_ , total=lowercase_ , initial=lowercase_ , desc='Downloading' , )
for chunk in response.iter_content(chunk_size=1024 ):
if chunk: # filter out keep-alive new chunks
progress.update(len(lowercase_ ) )
temp_file.write(lowercase_ )
progress.close()
def UpperCAmelCase ( A__: Optional[int] , A__: int=None , A__: Dict=False , A__: Optional[Any]=None , A__: Any=10 , A__: List[Any]=False , A__: Optional[Any]=None , A__: Optional[Any]=False , ) -> Dict:
if cache_dir is None:
__lowerCamelCase : str = TRANSFORMERS_CACHE
if isinstance(lowercase_ , lowercase_ ):
__lowerCamelCase : Dict = str(lowercase_ )
os.makedirs(lowercase_ , exist_ok=lowercase_ )
__lowerCamelCase : Tuple = None
if not local_files_only:
try:
__lowerCamelCase : Any = requests.head(lowercase_ , allow_redirects=lowercase_ , proxies=lowercase_ , timeout=lowercase_ )
if response.status_code == 200:
__lowerCamelCase : Optional[int] = response.headers.get('ETag' )
except (EnvironmentError, requests.exceptions.Timeout):
# etag is already None
pass
__lowerCamelCase : Optional[int] = url_to_filename(lowercase_ , lowercase_ )
# get cache path to put the file
__lowerCamelCase : List[Any] = os.path.join(lowercase_ , lowercase_ )
# etag is None = we don't have a connection, or url doesn't exist, or is otherwise inaccessible.
# try to get the last downloaded one
if etag is None:
if os.path.exists(lowercase_ ):
return cache_path
else:
__lowerCamelCase : Union[str, Any] = [
file
for file in fnmatch.filter(os.listdir(lowercase_ ) , filename + '.*' )
if not file.endswith('.json' ) and not file.endswith('.lock' )
]
if len(lowercase_ ) > 0:
return os.path.join(lowercase_ , matching_files[-1] )
else:
# If files cannot be found and local_files_only=True,
# the models might've been found if local_files_only=False
# Notify the user about that
if local_files_only:
raise ValueError(
'Cannot find the requested files in the cached path and outgoing traffic has been'
' disabled. To enable model look-ups and downloads online, set \'local_files_only\''
' to False.' )
return None
# From now on, etag is not None.
if os.path.exists(lowercase_ ) and not force_download:
return cache_path
# Prevent parallel downloads of the same file with a lock.
__lowerCamelCase : Dict = cache_path + '''.lock'''
with FileLock(lowercase_ ):
# If the download just completed while the lock was activated.
if os.path.exists(lowercase_ ) and not force_download:
# Even if returning early like here, the lock will be released.
return cache_path
if resume_download:
__lowerCamelCase : List[str] = cache_path + '''.incomplete'''
@contextmanager
def _resumable_file_manager():
with open(lowercase_ , 'a+b' ) as f:
yield f
__lowerCamelCase : str = _resumable_file_manager
if os.path.exists(lowercase_ ):
__lowerCamelCase : Any = os.stat(lowercase_ ).st_size
else:
__lowerCamelCase : str = 0
else:
__lowerCamelCase : List[str] = partial(tempfile.NamedTemporaryFile , dir=lowercase_ , delete=lowercase_ )
__lowerCamelCase : Union[str, Any] = 0
# Download to temporary file, then copy to cache dir once finished.
# Otherwise you get corrupt cache entries if the download gets interrupted.
with temp_file_manager() as temp_file:
print(
'%s not found in cache or force_download set to True, downloading to %s' , lowercase_ , temp_file.name , )
http_get(
lowercase_ , lowercase_ , proxies=lowercase_ , resume_size=lowercase_ , user_agent=lowercase_ , )
os.replace(temp_file.name , lowercase_ )
__lowerCamelCase : List[str] = {'''url''': url, '''etag''': etag}
__lowerCamelCase : Union[str, Any] = cache_path + '''.json'''
with open(lowercase_ , 'w' ) as meta_file:
json.dump(lowercase_ , lowercase_ )
return cache_path
def UpperCAmelCase ( A__: List[str] , A__: Any=None ) -> Dict:
__lowerCamelCase : Optional[Any] = url.encode('utf-8' )
__lowerCamelCase : Any = shaaaa(lowercase_ )
__lowerCamelCase : Dict = url_hash.hexdigest()
if etag:
__lowerCamelCase : Tuple = etag.encode('utf-8' )
__lowerCamelCase : Optional[Any] = shaaaa(lowercase_ )
filename += "." + etag_hash.hexdigest()
if url.endswith('.h5' ):
filename += ".h5"
return filename
def UpperCAmelCase ( A__: Union[str, Any] , A__: Optional[int]=None , A__: int=False , A__: Optional[Any]=None , A__: List[Any]=False , A__: Any=None , A__: Optional[int]=False , A__: Tuple=False , A__: int=False , ) -> int:
if cache_dir is None:
__lowerCamelCase : Optional[Any] = TRANSFORMERS_CACHE
if isinstance(lowercase_ , lowercase_ ):
__lowerCamelCase : Tuple = str(lowercase_ )
if isinstance(lowercase_ , lowercase_ ):
__lowerCamelCase : Dict = str(lowercase_ )
if is_remote_url(lowercase_ ):
# URL, so get it from the cache (downloading if necessary)
__lowerCamelCase : Any = get_from_cache(
lowercase_ , cache_dir=lowercase_ , force_download=lowercase_ , proxies=lowercase_ , resume_download=lowercase_ , user_agent=lowercase_ , local_files_only=lowercase_ , )
elif os.path.exists(lowercase_ ):
# File, and it exists.
__lowerCamelCase : List[str] = url_or_filename
elif urlparse(lowercase_ ).scheme == "":
# File, but it doesn't exist.
raise EnvironmentError('file {} not found'.format(lowercase_ ) )
else:
# Something unknown
raise ValueError('unable to parse {} as a URL or as a local path'.format(lowercase_ ) )
if extract_compressed_file:
if not is_zipfile(lowercase_ ) and not tarfile.is_tarfile(lowercase_ ):
return output_path
# Path where we extract compressed archives
# We avoid '.' in dir name and add "-extracted" at the end: "./model.zip" => "./model-zip-extracted/"
__lowerCamelCase : List[str] = os.path.split(lowercase_ )
__lowerCamelCase : List[Any] = output_file.replace('.' , '-' ) + '''-extracted'''
__lowerCamelCase : Dict = os.path.join(lowercase_ , lowercase_ )
if os.path.isdir(lowercase_ ) and os.listdir(lowercase_ ) and not force_extract:
return output_path_extracted
# Prevent parallel extractions
__lowerCamelCase : Dict = output_path + '''.lock'''
with FileLock(lowercase_ ):
shutil.rmtree(lowercase_ , ignore_errors=lowercase_ )
os.makedirs(lowercase_ )
if is_zipfile(lowercase_ ):
with ZipFile(lowercase_ , 'r' ) as zip_file:
zip_file.extractall(lowercase_ )
zip_file.close()
elif tarfile.is_tarfile(lowercase_ ):
__lowerCamelCase : Optional[Any] = tarfile.open(lowercase_ )
tar_file.extractall(lowercase_ )
tar_file.close()
else:
raise EnvironmentError('Archive format of {} could not be identified'.format(lowercase_ ) )
return output_path_extracted
return output_path
def UpperCAmelCase ( A__: Any , A__: List[str]="," ) -> Optional[int]:
assert isinstance(lowercase_ , lowercase_ )
if os.path.isfile(lowercase_ ):
with open(lowercase_ ) as f:
__lowerCamelCase : str = eval(f.read() )
else:
__lowerCamelCase : Any = requests.get(lowercase_ )
try:
__lowerCamelCase : Optional[int] = requests.json()
except Exception:
__lowerCamelCase : Tuple = req.content.decode()
assert data is not None, "could not connect"
try:
__lowerCamelCase : List[Any] = eval(lowercase_ )
except Exception:
__lowerCamelCase : List[Any] = data.split('\n' )
req.close()
return data
def UpperCAmelCase ( A__: Tuple ) -> List[Any]:
__lowerCamelCase : Dict = requests.get(lowercase_ )
__lowerCamelCase : str = np.array(Image.open(BytesIO(response.content ) ) )
return img
def UpperCAmelCase ( A__: Tuple ) -> Union[str, Any]:
__lowerCamelCase : Dict = url.split('/' )[-1]
if fn not in os.listdir(os.getcwd() ):
wget.download(lowercase_ )
with open(lowercase_ , 'rb' ) as stream:
__lowerCamelCase : Tuple = pkl.load(lowercase_ )
__lowerCamelCase : Optional[Any] = weights.pop('model' )
__lowerCamelCase : str = {}
for k, v in model.items():
__lowerCamelCase : Optional[int] = torch.from_numpy(lowercase_ )
if "running_var" in k:
__lowerCamelCase : Union[str, Any] = torch.tensor([0] )
__lowerCamelCase : List[Any] = k.replace('running_var' , 'num_batches_tracked' )
__lowerCamelCase : Dict = zero
return new
def UpperCAmelCase ( ) -> Union[str, Any]:
print(f'''{os.path.abspath(os.path.join(lowercase_ , os.pardir ) )}/demo.ipynb''' )
def UpperCAmelCase ( A__: int , A__: List[str]="RGB" ) -> Dict:
assert isinstance(lowercase_ , lowercase_ )
if os.path.isfile(lowercase_ ):
__lowerCamelCase : Any = cva.imread(lowercase_ )
else:
__lowerCamelCase : Union[str, Any] = get_image_from_url(lowercase_ )
assert img is not None, f'''could not connect to: {im}'''
__lowerCamelCase : List[str] = cva.cvtColor(lowercase_ , cva.COLOR_BGR2RGB )
if input_format == "RGB":
__lowerCamelCase : List[Any] = img[:, :, ::-1]
return img
def UpperCAmelCase ( A__: Dict , A__: int=1 ) -> List[Any]:
return (images[i : i + batch] for i in range(0 , len(lowercase_ ) , lowercase_ ))
| 594 |
"""simple docstring"""
from __future__ import annotations
from typing import Any
class snake_case ( __UpperCAmelCase ):
pass
class snake_case :
def __init__( self :List[Any] , _lowerCamelCase :Any ):
__SCREAMING_SNAKE_CASE : Any = data
__SCREAMING_SNAKE_CASE : Node | None = None
def __iter__( self :Union[str, Any] ):
__SCREAMING_SNAKE_CASE : List[str] = self
__SCREAMING_SNAKE_CASE : List[str] = []
while node:
if node in visited:
raise ContainsLoopError
visited.append(_lowerCamelCase )
yield node.data
__SCREAMING_SNAKE_CASE : List[str] = node.next_node
@property
def SCREAMING_SNAKE_CASE_ ( self :Any ):
try:
list(self )
return False
except ContainsLoopError:
return True
if __name__ == "__main__":
_lowerCamelCase = Node(1)
_lowerCamelCase = Node(2)
_lowerCamelCase = Node(3)
_lowerCamelCase = Node(4)
print(root_node.has_loop) # False
_lowerCamelCase = root_node.next_node
print(root_node.has_loop) # True
_lowerCamelCase = Node(5)
_lowerCamelCase = Node(6)
_lowerCamelCase = Node(5)
_lowerCamelCase = Node(6)
print(root_node.has_loop) # False
_lowerCamelCase = Node(1)
print(root_node.has_loop) # False
| 674 | 0 |
import subprocess
import sys
from transformers import BertConfig, BertModel, BertTokenizer, pipeline
from transformers.testing_utils import TestCasePlus, require_torch
class UpperCAmelCase ( __UpperCAmelCase ):
@require_torch
def lowerCamelCase_ ( self : Tuple ):
"""simple docstring"""
UpperCamelCase = '''
from transformers import BertConfig, BertModel, BertTokenizer, pipeline
'''
UpperCamelCase = '''
mname = "hf-internal-testing/tiny-random-bert"
BertConfig.from_pretrained(mname)
BertModel.from_pretrained(mname)
BertTokenizer.from_pretrained(mname)
pipe = pipeline(task="fill-mask", model=mname)
print("success")
'''
UpperCamelCase = '''
import socket
def offline_socket(*args, **kwargs): raise RuntimeError("Offline mode is enabled, we shouldn\'t access internet")
socket.socket = offline_socket
'''
# Force fetching the files so that we can use the cache
UpperCamelCase = '''hf-internal-testing/tiny-random-bert'''
BertConfig.from_pretrained(_lowerCamelCase )
BertModel.from_pretrained(_lowerCamelCase )
BertTokenizer.from_pretrained(_lowerCamelCase )
pipeline(task="""fill-mask""" , model=_lowerCamelCase )
# baseline - just load from_pretrained with normal network
UpperCamelCase = [sys.executable, '''-c''', '''\n'''.join([load, run, mock] )]
# should succeed
UpperCamelCase = self.get_env()
# should succeed as TRANSFORMERS_OFFLINE=1 tells it to use local files
UpperCamelCase = '''1'''
UpperCamelCase = subprocess.run(_lowerCamelCase , env=_lowerCamelCase , check=_lowerCamelCase , capture_output=_lowerCamelCase )
self.assertEqual(result.returncode , 0 , result.stderr )
self.assertIn("""success""" , result.stdout.decode() )
@require_torch
def lowerCamelCase_ ( self : List[str] ):
"""simple docstring"""
UpperCamelCase = '''
from transformers import BertConfig, BertModel, BertTokenizer, pipeline
'''
UpperCamelCase = '''
mname = "hf-internal-testing/tiny-random-bert"
BertConfig.from_pretrained(mname)
BertModel.from_pretrained(mname)
BertTokenizer.from_pretrained(mname)
pipe = pipeline(task="fill-mask", model=mname)
print("success")
'''
UpperCamelCase = '''
import socket
def offline_socket(*args, **kwargs): raise socket.error("Faking flaky internet")
socket.socket = offline_socket
'''
# Force fetching the files so that we can use the cache
UpperCamelCase = '''hf-internal-testing/tiny-random-bert'''
BertConfig.from_pretrained(_lowerCamelCase )
BertModel.from_pretrained(_lowerCamelCase )
BertTokenizer.from_pretrained(_lowerCamelCase )
pipeline(task="""fill-mask""" , model=_lowerCamelCase )
# baseline - just load from_pretrained with normal network
UpperCamelCase = [sys.executable, '''-c''', '''\n'''.join([load, run, mock] )]
# should succeed
UpperCamelCase = self.get_env()
UpperCamelCase = subprocess.run(_lowerCamelCase , env=_lowerCamelCase , check=_lowerCamelCase , capture_output=_lowerCamelCase )
self.assertEqual(result.returncode , 0 , result.stderr )
self.assertIn("""success""" , result.stdout.decode() )
@require_torch
def lowerCamelCase_ ( self : List[Any] ):
"""simple docstring"""
UpperCamelCase = '''
from transformers import BertConfig, BertModel, BertTokenizer
'''
UpperCamelCase = '''
mname = "hf-internal-testing/tiny-random-bert-sharded"
BertConfig.from_pretrained(mname)
BertModel.from_pretrained(mname)
print("success")
'''
UpperCamelCase = '''
import socket
def offline_socket(*args, **kwargs): raise ValueError("Offline mode is enabled")
socket.socket = offline_socket
'''
# baseline - just load from_pretrained with normal network
UpperCamelCase = [sys.executable, '''-c''', '''\n'''.join([load, run] )]
# should succeed
UpperCamelCase = self.get_env()
UpperCamelCase = subprocess.run(_lowerCamelCase , env=_lowerCamelCase , check=_lowerCamelCase , capture_output=_lowerCamelCase )
self.assertEqual(result.returncode , 0 , result.stderr )
self.assertIn("""success""" , result.stdout.decode() )
# next emulate no network
UpperCamelCase = [sys.executable, '''-c''', '''\n'''.join([load, mock, run] )]
# Doesn't fail anymore since the model is in the cache due to other tests, so commenting this.
# env["TRANSFORMERS_OFFLINE"] = "0"
# result = subprocess.run(cmd, env=env, check=False, capture_output=True)
# self.assertEqual(result.returncode, 1, result.stderr)
# should succeed as TRANSFORMERS_OFFLINE=1 tells it to use local files
UpperCamelCase = '''1'''
UpperCamelCase = subprocess.run(_lowerCamelCase , env=_lowerCamelCase , check=_lowerCamelCase , capture_output=_lowerCamelCase )
self.assertEqual(result.returncode , 0 , result.stderr )
self.assertIn("""success""" , result.stdout.decode() )
@require_torch
def lowerCamelCase_ ( self : Optional[Any] ):
"""simple docstring"""
UpperCamelCase = '''
from transformers import pipeline
'''
UpperCamelCase = '''
mname = "hf-internal-testing/tiny-random-bert"
pipe = pipeline(model=mname)
'''
UpperCamelCase = '''
import socket
def offline_socket(*args, **kwargs): raise socket.error("Offline mode is enabled")
socket.socket = offline_socket
'''
UpperCamelCase = self.get_env()
UpperCamelCase = '''1'''
UpperCamelCase = [sys.executable, '''-c''', '''\n'''.join([load, mock, run] )]
UpperCamelCase = subprocess.run(_lowerCamelCase , env=_lowerCamelCase , check=_lowerCamelCase , capture_output=_lowerCamelCase )
self.assertEqual(result.returncode , 1 , result.stderr )
self.assertIn(
"""You cannot infer task automatically within `pipeline` when using offline mode""" , result.stderr.decode().replace("""\n""" , """""" ) , )
@require_torch
def lowerCamelCase_ ( self : Dict ):
"""simple docstring"""
UpperCamelCase = '''
from transformers import AutoModel
'''
UpperCamelCase = '''
mname = "hf-internal-testing/test_dynamic_model"
AutoModel.from_pretrained(mname, trust_remote_code=True)
print("success")
'''
# baseline - just load from_pretrained with normal network
UpperCamelCase = [sys.executable, '''-c''', '''\n'''.join([load, run] )]
# should succeed
UpperCamelCase = self.get_env()
UpperCamelCase = subprocess.run(_lowerCamelCase , env=_lowerCamelCase , check=_lowerCamelCase , capture_output=_lowerCamelCase )
self.assertEqual(result.returncode , 0 , result.stderr )
self.assertIn("""success""" , result.stdout.decode() )
# should succeed as TRANSFORMERS_OFFLINE=1 tells it to use local files
UpperCamelCase = '''1'''
UpperCamelCase = subprocess.run(_lowerCamelCase , env=_lowerCamelCase , check=_lowerCamelCase , capture_output=_lowerCamelCase )
self.assertEqual(result.returncode , 0 , result.stderr )
self.assertIn("""success""" , result.stdout.decode() )
| 386 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCamelCase = logging.get_logger(__name__)
_lowerCamelCase = {
'''weiweishi/roc-bert-base-zh''': '''https://huggingface.co/weiweishi/roc-bert-base-zh/resolve/main/config.json''',
}
class snake_case ( __UpperCAmelCase ):
lowerCamelCase__ = '''roc_bert'''
def __init__( self :Union[str, Any] , _lowerCamelCase :Any=3_0_5_2_2 , _lowerCamelCase :str=7_6_8 , _lowerCamelCase :Optional[Any]=1_2 , _lowerCamelCase :List[str]=1_2 , _lowerCamelCase :str=3_0_7_2 , _lowerCamelCase :Tuple="gelu" , _lowerCamelCase :List[Any]=0.1 , _lowerCamelCase :List[str]=0.1 , _lowerCamelCase :Optional[int]=5_1_2 , _lowerCamelCase :Dict=2 , _lowerCamelCase :Any=0.0_2 , _lowerCamelCase :Optional[int]=1e-12 , _lowerCamelCase :str=True , _lowerCamelCase :Any=0 , _lowerCamelCase :List[str]="absolute" , _lowerCamelCase :List[Any]=None , _lowerCamelCase :Any=True , _lowerCamelCase :Union[str, Any]=True , _lowerCamelCase :str=7_6_8 , _lowerCamelCase :Union[str, Any]=9_1_0 , _lowerCamelCase :List[Any]=5_1_2 , _lowerCamelCase :Optional[int]=2_4_8_5_8 , _lowerCamelCase :Union[str, Any]=True , **_lowerCamelCase :str , ):
__SCREAMING_SNAKE_CASE : List[str] = vocab_size
__SCREAMING_SNAKE_CASE : int = max_position_embeddings
__SCREAMING_SNAKE_CASE : List[str] = hidden_size
__SCREAMING_SNAKE_CASE : str = num_hidden_layers
__SCREAMING_SNAKE_CASE : int = num_attention_heads
__SCREAMING_SNAKE_CASE : Any = intermediate_size
__SCREAMING_SNAKE_CASE : Optional[int] = hidden_act
__SCREAMING_SNAKE_CASE : List[Any] = hidden_dropout_prob
__SCREAMING_SNAKE_CASE : Optional[Any] = attention_probs_dropout_prob
__SCREAMING_SNAKE_CASE : Union[str, Any] = initializer_range
__SCREAMING_SNAKE_CASE : Union[str, Any] = type_vocab_size
__SCREAMING_SNAKE_CASE : List[str] = layer_norm_eps
__SCREAMING_SNAKE_CASE : Optional[int] = use_cache
__SCREAMING_SNAKE_CASE : str = enable_pronunciation
__SCREAMING_SNAKE_CASE : List[str] = enable_shape
__SCREAMING_SNAKE_CASE : Tuple = pronunciation_embed_dim
__SCREAMING_SNAKE_CASE : Optional[Any] = pronunciation_vocab_size
__SCREAMING_SNAKE_CASE : str = shape_embed_dim
__SCREAMING_SNAKE_CASE : Union[str, Any] = shape_vocab_size
__SCREAMING_SNAKE_CASE : Tuple = concat_input
__SCREAMING_SNAKE_CASE : Union[str, Any] = position_embedding_type
__SCREAMING_SNAKE_CASE : str = classifier_dropout
super().__init__(pad_token_id=_lowerCamelCase , **_lowerCamelCase )
| 674 | 0 |
a__ = [4, 1, 7, 4, 2, 6, 4, 1, 5, 3, 7, 5]
a__ = [3, 7, 7, 4, 2, 6, 4, 1, 5, 3, 7, 5]
a__ = {
0: '''Sunday''',
1: '''Monday''',
2: '''Tuesday''',
3: '''Wednesday''',
4: '''Thursday''',
5: '''Friday''',
6: '''Saturday''',
}
def A__ (snake_case : int , snake_case : int , snake_case : int ) -> Optional[Any]:
assert len(str(lowercase_ ) ) > 2, "year should be in YYYY format"
assert 1 <= month <= 12, "month should be between 1 to 12"
assert 1 <= day <= 31, "day should be between 1 to 31"
# Doomsday algorithm:
__UpperCamelCase : List[str] = year // 1_00
__UpperCamelCase : str = (5 * (century % 4) + 2) % 7
__UpperCamelCase : Dict = year % 1_00
__UpperCamelCase : List[Any] = centurian % 12
__UpperCamelCase : Union[str, Any] = (
(centurian // 12) + centurian_m + (centurian_m // 4) + century_anchor
) % 7
__UpperCamelCase : List[Any] = (
DOOMSDAY_NOT_LEAP[month - 1]
if (year % 4 != 0) or (centurian == 0 and (year % 4_00) == 0)
else DOOMSDAY_LEAP[month - 1]
)
__UpperCamelCase : Union[str, Any] = (dooms_day + day - day_anchor) % 7
return WEEK_DAY_NAMES[week_day]
if __name__ == "__main__":
import doctest
doctest.testmod()
| 279 |
"""simple docstring"""
import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List
import git
import torch
from torch.utils.data import Dataset
from transformers import BartTokenizer, RagTokenizer, TaTokenizer
def lowerCAmelCase_ ( lowercase_ : List[str] , lowercase_ : List[str] , lowercase_ : Dict , lowercase_ : Tuple , lowercase_ : Optional[Any]=True , lowercase_ : Any="pt" ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : Union[str, Any] = {'''add_prefix_space''': True} if isinstance(lowercase_ , lowercase_ ) and not line.startswith(''' ''' ) else {}
__SCREAMING_SNAKE_CASE : Optional[int] = padding_side
return tokenizer(
[line] , max_length=lowercase_ , padding='''max_length''' if pad_to_max_length else None , truncation=lowercase_ , return_tensors=lowercase_ , add_special_tokens=lowercase_ , **lowercase_ , )
def lowerCAmelCase_ ( lowercase_ : Optional[int] , lowercase_ : Tuple , lowercase_ : List[Any]=None , ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : Dict = input_ids.ne(lowercase_ ).any(dim=0 )
if attention_mask is None:
return input_ids[:, keep_column_mask]
else:
return (input_ids[:, keep_column_mask], attention_mask[:, keep_column_mask])
class snake_case ( __UpperCAmelCase ):
def __init__( self :Optional[Any] , _lowerCamelCase :Dict , _lowerCamelCase :Any , _lowerCamelCase :Optional[Any] , _lowerCamelCase :Tuple , _lowerCamelCase :Any="train" , _lowerCamelCase :str=None , _lowerCamelCase :Optional[Any]=None , _lowerCamelCase :List[Any]=None , _lowerCamelCase :Tuple="" , ):
super().__init__()
__SCREAMING_SNAKE_CASE : Dict = Path(_lowerCamelCase ).joinpath(type_path + '''.source''' )
__SCREAMING_SNAKE_CASE : Any = Path(_lowerCamelCase ).joinpath(type_path + '''.target''' )
__SCREAMING_SNAKE_CASE : Any = self.get_char_lens(self.src_file )
__SCREAMING_SNAKE_CASE : List[str] = max_source_length
__SCREAMING_SNAKE_CASE : Dict = max_target_length
assert min(self.src_lens ) > 0, f'''found empty line in {self.src_file}'''
__SCREAMING_SNAKE_CASE : Dict = tokenizer
__SCREAMING_SNAKE_CASE : Union[str, Any] = prefix
if n_obs is not None:
__SCREAMING_SNAKE_CASE : Any = self.src_lens[:n_obs]
__SCREAMING_SNAKE_CASE : List[str] = src_lang
__SCREAMING_SNAKE_CASE : str = tgt_lang
def __len__( self :int ):
return len(self.src_lens )
def __getitem__( self :Optional[Any] , _lowerCamelCase :Any ):
__SCREAMING_SNAKE_CASE : Optional[Any] = index + 1 # linecache starts at 1
__SCREAMING_SNAKE_CASE : Any = self.prefix + linecache.getline(str(self.src_file ) , _lowerCamelCase ).rstrip('''\n''' )
__SCREAMING_SNAKE_CASE : Dict = linecache.getline(str(self.tgt_file ) , _lowerCamelCase ).rstrip('''\n''' )
assert source_line, f'''empty source line for index {index}'''
assert tgt_line, f'''empty tgt line for index {index}'''
# Need to add eos token manually for T5
if isinstance(self.tokenizer , _lowerCamelCase ):
source_line += self.tokenizer.eos_token
tgt_line += self.tokenizer.eos_token
# Pad source and target to the right
__SCREAMING_SNAKE_CASE : Dict = (
self.tokenizer.question_encoder if isinstance(self.tokenizer , _lowerCamelCase ) else self.tokenizer
)
__SCREAMING_SNAKE_CASE : Optional[Any] = self.tokenizer.generator if isinstance(self.tokenizer , _lowerCamelCase ) else self.tokenizer
__SCREAMING_SNAKE_CASE : Dict = encode_line(_lowerCamelCase , _lowerCamelCase , self.max_source_length , '''right''' )
__SCREAMING_SNAKE_CASE : Dict = encode_line(_lowerCamelCase , _lowerCamelCase , self.max_target_length , '''right''' )
__SCREAMING_SNAKE_CASE : Any = source_inputs['''input_ids'''].squeeze()
__SCREAMING_SNAKE_CASE : Any = target_inputs['''input_ids'''].squeeze()
__SCREAMING_SNAKE_CASE : Dict = source_inputs['''attention_mask'''].squeeze()
return {
"input_ids": source_ids,
"attention_mask": src_mask,
"decoder_input_ids": target_ids,
}
@staticmethod
def SCREAMING_SNAKE_CASE_ ( _lowerCamelCase :Any ):
return [len(_lowerCamelCase ) for x in Path(_lowerCamelCase ).open().readlines()]
def SCREAMING_SNAKE_CASE_ ( self :List[str] , _lowerCamelCase :List[str] ):
__SCREAMING_SNAKE_CASE : int = torch.stack([x['''input_ids'''] for x in batch] )
__SCREAMING_SNAKE_CASE : str = torch.stack([x['''attention_mask'''] for x in batch] )
__SCREAMING_SNAKE_CASE : int = torch.stack([x['''decoder_input_ids'''] for x in batch] )
__SCREAMING_SNAKE_CASE : str = (
self.tokenizer.generator.pad_token_id
if isinstance(self.tokenizer , _lowerCamelCase )
else self.tokenizer.pad_token_id
)
__SCREAMING_SNAKE_CASE : Union[str, Any] = (
self.tokenizer.question_encoder.pad_token_id
if isinstance(self.tokenizer , _lowerCamelCase )
else self.tokenizer.pad_token_id
)
__SCREAMING_SNAKE_CASE : List[str] = trim_batch(_lowerCamelCase , _lowerCamelCase )
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Any = trim_batch(_lowerCamelCase , _lowerCamelCase , attention_mask=_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Dict = {
'''input_ids''': source_ids,
'''attention_mask''': source_mask,
'''decoder_input_ids''': y,
}
return batch
_lowerCamelCase = getLogger(__name__)
def lowerCAmelCase_ ( lowercase_ : List[List] ):
'''simple docstring'''
return list(itertools.chain.from_iterable(lowercase_ ) )
def lowerCAmelCase_ ( lowercase_ : str ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : Any = get_git_info()
save_json(lowercase_ , os.path.join(lowercase_ , '''git_log.json''' ) )
def lowerCAmelCase_ ( lowercase_ : Any , lowercase_ : Optional[int] , lowercase_ : str=4 , **lowercase_ : List[str] ):
'''simple docstring'''
with open(lowercase_ , '''w''' ) as f:
json.dump(lowercase_ , lowercase_ , indent=lowercase_ , **lowercase_ )
def lowerCAmelCase_ ( lowercase_ : Union[str, Any] ):
'''simple docstring'''
with open(lowercase_ ) as f:
return json.load(lowercase_ )
def lowerCAmelCase_ ( ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : Any = git.Repo(search_parent_directories=lowercase_ )
__SCREAMING_SNAKE_CASE : List[str] = {
'''repo_id''': str(lowercase_ ),
'''repo_sha''': str(repo.head.object.hexsha ),
'''repo_branch''': str(repo.active_branch ),
'''hostname''': str(socket.gethostname() ),
}
return repo_infos
def lowerCAmelCase_ ( lowercase_ : Callable , lowercase_ : Iterable ):
'''simple docstring'''
return list(map(lowercase_ , lowercase_ ) )
def lowerCAmelCase_ ( lowercase_ : Any , lowercase_ : Any ):
'''simple docstring'''
with open(lowercase_ , '''wb''' ) as f:
return pickle.dump(lowercase_ , lowercase_ )
def lowerCAmelCase_ ( lowercase_ : Any ):
'''simple docstring'''
def remove_articles(lowercase_ : Dict ):
return re.sub(r'''\b(a|an|the)\b''' , ''' ''' , lowercase_ )
def white_space_fix(lowercase_ : Optional[int] ):
return " ".join(text.split() )
def remove_punc(lowercase_ : Any ):
__SCREAMING_SNAKE_CASE : Optional[int] = set(string.punctuation )
return "".join(ch for ch in text if ch not in exclude )
def lower(lowercase_ : int ):
return text.lower()
return white_space_fix(remove_articles(remove_punc(lower(lowercase_ ) ) ) )
def lowerCAmelCase_ ( lowercase_ : Optional[int] , lowercase_ : int ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : Any = normalize_answer(lowercase_ ).split()
__SCREAMING_SNAKE_CASE : Any = normalize_answer(lowercase_ ).split()
__SCREAMING_SNAKE_CASE : Tuple = Counter(lowercase_ ) & Counter(lowercase_ )
__SCREAMING_SNAKE_CASE : Tuple = sum(common.values() )
if num_same == 0:
return 0
__SCREAMING_SNAKE_CASE : Any = 1.0 * num_same / len(lowercase_ )
__SCREAMING_SNAKE_CASE : List[str] = 1.0 * num_same / len(lowercase_ )
__SCREAMING_SNAKE_CASE : Optional[int] = (2 * precision * recall) / (precision + recall)
return fa
def lowerCAmelCase_ ( lowercase_ : str , lowercase_ : Union[str, Any] ):
'''simple docstring'''
return normalize_answer(lowercase_ ) == normalize_answer(lowercase_ )
def lowerCAmelCase_ ( lowercase_ : List[str] , lowercase_ : List[str] ):
'''simple docstring'''
assert len(lowercase_ ) == len(lowercase_ )
__SCREAMING_SNAKE_CASE : Union[str, Any] = 0
for hypo, pred in zip(lowercase_ , lowercase_ ):
em += exact_match_score(lowercase_ , lowercase_ )
if len(lowercase_ ) > 0:
em /= len(lowercase_ )
return {"em": em}
def lowerCAmelCase_ ( lowercase_ : str ):
'''simple docstring'''
return model_prefix.startswith('''rag''' )
def lowerCAmelCase_ ( lowercase_ : Optional[Any] , lowercase_ : Tuple , lowercase_ : Optional[int] ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : Optional[int] = {p: p for p in extra_params}
# T5 models don't have `dropout` param, they have `dropout_rate` instead
__SCREAMING_SNAKE_CASE : Any = '''dropout_rate'''
for p in extra_params:
if getattr(lowercase_ , lowercase_ , lowercase_ ):
if not hasattr(lowercase_ , lowercase_ ) and not hasattr(lowercase_ , equivalent_param[p] ):
logger.info('''config doesn\'t have a `{}` attribute'''.format(lowercase_ ) )
delattr(lowercase_ , lowercase_ )
continue
__SCREAMING_SNAKE_CASE : Optional[int] = p if hasattr(lowercase_ , lowercase_ ) else equivalent_param[p]
setattr(lowercase_ , lowercase_ , getattr(lowercase_ , lowercase_ ) )
delattr(lowercase_ , lowercase_ )
return hparams, config
| 674 | 0 |
import os
import shutil
from pathlib import Path
from typing import Optional, Union
import numpy as np
from huggingface_hub import hf_hub_download
from ..utils import ONNX_EXTERNAL_WEIGHTS_NAME, ONNX_WEIGHTS_NAME, is_onnx_available, logging
if is_onnx_available():
import onnxruntime as ort
__lowerCAmelCase = logging.get_logger(__name__)
__lowerCAmelCase = {
'tensor(bool)': np.bool_,
'tensor(int8)': np.inta,
'tensor(uint8)': np.uinta,
'tensor(int16)': np.intaa,
'tensor(uint16)': np.uintaa,
'tensor(int32)': np.intaa,
'tensor(uint32)': np.uintaa,
'tensor(int64)': np.intaa,
'tensor(uint64)': np.uintaa,
'tensor(float16)': np.floataa,
'tensor(float)': np.floataa,
'tensor(double)': np.floataa,
}
class lowerCamelCase :
def __init__( self :Tuple , lowercase :Tuple=None , **lowercase :Optional[Any] ) -> List[Any]:
"""simple docstring"""
logger.info('''`diffusers.OnnxRuntimeModel` is experimental and might change in the future.''' )
SCREAMING_SNAKE_CASE = model
SCREAMING_SNAKE_CASE = kwargs.get('''model_save_dir''' , _lowerCamelCase )
SCREAMING_SNAKE_CASE = kwargs.get('''latest_model_name''' , _lowerCamelCase )
def __call__( self :Dict , **lowercase :List[Any] ) -> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE = {k: np.array(_lowerCamelCase ) for k, v in kwargs.items()}
return self.model.run(_lowerCamelCase , _lowerCamelCase )
@staticmethod
def snake_case__ ( lowercase :Union[str, Path] , lowercase :Any=None , lowercase :Any=None ) -> Tuple:
"""simple docstring"""
if provider is None:
logger.info('''No onnxruntime provider specified, using CPUExecutionProvider''' )
SCREAMING_SNAKE_CASE = '''CPUExecutionProvider'''
return ort.InferenceSession(_lowerCamelCase , providers=[provider] , sess_options=_lowerCamelCase )
def snake_case__ ( self :Dict , lowercase :Union[str, Path] , lowercase :Optional[str] = None , **lowercase :List[Any] ) -> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE = file_name if file_name is not None else ONNX_WEIGHTS_NAME
SCREAMING_SNAKE_CASE = self.model_save_dir.joinpath(self.latest_model_name )
SCREAMING_SNAKE_CASE = Path(_lowerCamelCase ).joinpath(_lowerCamelCase )
try:
shutil.copyfile(_lowerCamelCase , _lowerCamelCase )
except shutil.SameFileError:
pass
# copy external weights (for models >2GB)
SCREAMING_SNAKE_CASE = self.model_save_dir.joinpath(_lowerCamelCase )
if src_path.exists():
SCREAMING_SNAKE_CASE = Path(_lowerCamelCase ).joinpath(_lowerCamelCase )
try:
shutil.copyfile(_lowerCamelCase , _lowerCamelCase )
except shutil.SameFileError:
pass
def snake_case__ ( self :Any , lowercase :Union[str, os.PathLike] , **lowercase :Optional[int] , ) -> str:
"""simple docstring"""
if os.path.isfile(_lowerCamelCase ):
logger.error(f"""Provided path ({save_directory}) should be a directory, not a file""" )
return
os.makedirs(_lowerCamelCase , exist_ok=_lowerCamelCase )
# saving model weights/files
self._save_pretrained(_lowerCamelCase , **_lowerCamelCase )
@classmethod
def snake_case__ ( cls :Union[str, Any] , lowercase :Union[str, Path] , lowercase :Optional[Union[bool, str, None]] = None , lowercase :Optional[Union[str, None]] = None , lowercase :bool = False , lowercase :Optional[str] = None , lowercase :Optional[str] = None , lowercase :Optional[str] = None , lowercase :Optional["ort.SessionOptions"] = None , **lowercase :Optional[int] , ) -> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE = file_name if file_name is not None else ONNX_WEIGHTS_NAME
# load model from local directory
if os.path.isdir(_lowerCamelCase ):
SCREAMING_SNAKE_CASE = OnnxRuntimeModel.load_model(
os.path.join(_lowerCamelCase , _lowerCamelCase ) , provider=_lowerCamelCase , sess_options=_lowerCamelCase )
SCREAMING_SNAKE_CASE = Path(_lowerCamelCase )
# load model from hub
else:
# download model
SCREAMING_SNAKE_CASE = hf_hub_download(
repo_id=_lowerCamelCase , filename=_lowerCamelCase , use_auth_token=_lowerCamelCase , revision=_lowerCamelCase , cache_dir=_lowerCamelCase , force_download=_lowerCamelCase , )
SCREAMING_SNAKE_CASE = Path(_lowerCamelCase ).parent
SCREAMING_SNAKE_CASE = Path(_lowerCamelCase ).name
SCREAMING_SNAKE_CASE = OnnxRuntimeModel.load_model(_lowerCamelCase , provider=_lowerCamelCase , sess_options=_lowerCamelCase )
return cls(model=_lowerCamelCase , **_lowerCamelCase )
@classmethod
def snake_case__ ( cls :List[Any] , lowercase :Union[str, Path] , lowercase :bool = True , lowercase :Optional[str] = None , lowercase :Optional[str] = None , **lowercase :List[str] , ) -> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE = None
if len(str(_lowerCamelCase ).split('''@''' ) ) == 2:
SCREAMING_SNAKE_CASE = model_id.split('''@''' )
return cls._from_pretrained(
model_id=_lowerCamelCase , revision=_lowerCamelCase , cache_dir=_lowerCamelCase , force_download=_lowerCamelCase , use_auth_token=_lowerCamelCase , **_lowerCamelCase , ) | 201 |
"""simple docstring"""
from collections.abc import Callable
import numpy as np
def lowerCAmelCase_ ( lowercase_ : Callable , lowercase_ : float , lowercase_ : float , lowercase_ : float , lowercase_ : float ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : int = int(np.ceil((x_end - xa) / step_size ) )
__SCREAMING_SNAKE_CASE : Dict = np.zeros((n + 1,) )
__SCREAMING_SNAKE_CASE : List[Any] = ya
__SCREAMING_SNAKE_CASE : Dict = xa
for k in range(lowercase_ ):
__SCREAMING_SNAKE_CASE : str = y[k] + step_size * ode_func(lowercase_ , y[k] )
__SCREAMING_SNAKE_CASE : int = y[k] + (
(step_size / 2) * (ode_func(lowercase_ , y[k] ) + ode_func(x + step_size , lowercase_ ))
)
x += step_size
return y
if __name__ == "__main__":
import doctest
doctest.testmod()
| 674 | 0 |
"""simple docstring"""
from ....utils import logging
a : List[Any] = logging.get_logger(__name__)
class __UpperCAmelCase( __UpperCAmelCase ):
"""simple docstring"""
def __init__( self , snake_case__ , snake_case__=None , snake_case__=2048 ):
'''simple docstring'''
lowercase__ : Union[str, Any]= config.__dict__
lowercase__ : Union[str, Any]= modal_hidden_size
if num_labels:
lowercase__ : int= num_labels
| 218 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_torch_available
from ...utils import OptionalDependencyNotAvailable
_lowerCamelCase = {
'''configuration_gpt_neox_japanese''': ['''GPT_NEOX_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GPTNeoXJapaneseConfig'''],
'''tokenization_gpt_neox_japanese''': ['''GPTNeoXJapaneseTokenizer'''],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowerCamelCase = [
'''GPT_NEOX_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''GPTNeoXJapaneseForCausalLM''',
'''GPTNeoXJapaneseLayer''',
'''GPTNeoXJapaneseModel''',
'''GPTNeoXJapanesePreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_gpt_neox_japanese import GPT_NEOX_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoXJapaneseConfig
from .tokenization_gpt_neox_japanese import GPTNeoXJapaneseTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_gpt_neox_japanese import (
GPT_NEOX_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST,
GPTNeoXJapaneseForCausalLM,
GPTNeoXJapaneseLayer,
GPTNeoXJapaneseModel,
GPTNeoXJapanesePreTrainedModel,
)
else:
import sys
_lowerCamelCase = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 674 | 0 |
import sys
import warnings
from os.path import abspath, dirname, join
# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when switching between checkouts and running tests.
lowerCamelCase :Dict = abspath(join(dirname(dirname(dirname(__file__))), 'src'))
sys.path.insert(1, git_repo_path)
# silence FutureWarning warnings in tests since often we can't act on them until
# they become normal warnings - i.e. the tests still need to test the current functionality
warnings.simplefilter(action='ignore', category=FutureWarning)
def __snake_case ( _UpperCamelCase ) -> Tuple:
from transformers.testing_utils import pytest_addoption_shared
pytest_addoption_shared(lowercase_ )
def __snake_case ( _UpperCamelCase ) -> Any:
from transformers.testing_utils import pytest_terminal_summary_main
_a = terminalreporter.config.getoption('''--make-reports''' )
if make_reports:
pytest_terminal_summary_main(lowercase_ , id=lowercase_ )
| 487 |
"""simple docstring"""
from __future__ import annotations
from typing import Any
class snake_case :
def __init__( self :Optional[Any] , _lowerCamelCase :int ):
__SCREAMING_SNAKE_CASE : int = num_of_nodes
__SCREAMING_SNAKE_CASE : list[list[int]] = []
__SCREAMING_SNAKE_CASE : dict[int, int] = {}
def SCREAMING_SNAKE_CASE_ ( self :int , _lowerCamelCase :int , _lowerCamelCase :int , _lowerCamelCase :int ):
self.m_edges.append([u_node, v_node, weight] )
def SCREAMING_SNAKE_CASE_ ( self :Tuple , _lowerCamelCase :int ):
if self.m_component[u_node] == u_node:
return u_node
return self.find_component(self.m_component[u_node] )
def SCREAMING_SNAKE_CASE_ ( self :List[str] , _lowerCamelCase :int ):
if self.m_component[u_node] != u_node:
for k in self.m_component:
__SCREAMING_SNAKE_CASE : Optional[Any] = self.find_component(_lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :Union[str, Any] , _lowerCamelCase :list[int] , _lowerCamelCase :int , _lowerCamelCase :int ):
if component_size[u_node] <= component_size[v_node]:
__SCREAMING_SNAKE_CASE : List[Any] = v_node
component_size[v_node] += component_size[u_node]
self.set_component(_lowerCamelCase )
elif component_size[u_node] >= component_size[v_node]:
__SCREAMING_SNAKE_CASE : Dict = self.find_component(_lowerCamelCase )
component_size[u_node] += component_size[v_node]
self.set_component(_lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :List[str] ):
__SCREAMING_SNAKE_CASE : Optional[int] = []
__SCREAMING_SNAKE_CASE : str = 0
__SCREAMING_SNAKE_CASE : list[Any] = [-1] * self.m_num_of_nodes
# A list of components (initialized to all of the nodes)
for node in range(self.m_num_of_nodes ):
self.m_component.update({node: node} )
component_size.append(1 )
__SCREAMING_SNAKE_CASE : str = self.m_num_of_nodes
while num_of_components > 1:
for edge in self.m_edges:
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Optional[Any] = edge
__SCREAMING_SNAKE_CASE : Optional[Any] = self.m_component[u]
__SCREAMING_SNAKE_CASE : int = self.m_component[v]
if u_component != v_component:
for component in (u_component, v_component):
if (
minimum_weight_edge[component] == -1
or minimum_weight_edge[component][2] > w
):
__SCREAMING_SNAKE_CASE : Optional[Any] = [u, v, w]
for edge in minimum_weight_edge:
if isinstance(_lowerCamelCase , _lowerCamelCase ):
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : int = edge
__SCREAMING_SNAKE_CASE : Tuple = self.m_component[u]
__SCREAMING_SNAKE_CASE : int = self.m_component[v]
if u_component != v_component:
mst_weight += w
self.union(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase )
print(f'''Added edge [{u} - {v}]\nAdded weight: {w}\n''' )
num_of_components -= 1
__SCREAMING_SNAKE_CASE : Optional[Any] = [-1] * self.m_num_of_nodes
print(f'''The total weight of the minimal spanning tree is: {mst_weight}''' )
def lowerCAmelCase_ ( ):
'''simple docstring'''
if __name__ == "__main__":
import doctest
doctest.testmod()
| 674 | 0 |
"""simple docstring"""
a_ = 'Alexander Joslin'
import operator as op
from .stack import Stack
def __UpperCAmelCase ( __UpperCamelCase ):
__lowercase : str = {'''*''': op.mul, '''/''': op.truediv, '''+''': op.add, '''-''': op.sub}
__lowercase : Stack[int] = Stack()
__lowercase : Stack[str] = Stack()
for i in equation:
if i.isdigit():
# RULE 1
operand_stack.push(int(lowercase_ ) )
elif i in operators:
# RULE 2
operator_stack.push(lowercase_ )
elif i == ")":
# RULE 4
__lowercase : Any = operator_stack.peek()
operator_stack.pop()
__lowercase : List[Any] = operand_stack.peek()
operand_stack.pop()
__lowercase : Union[str, Any] = operand_stack.peek()
operand_stack.pop()
__lowercase : List[Any] = operators[opr](lowercase_ , lowercase_ )
operand_stack.push(lowercase_ )
# RULE 5
return operand_stack.peek()
if __name__ == "__main__":
a_ = '(5 + ((4 * 2) * (2 + 3)))'
# answer = 45
print(F"{equation} = {dijkstras_two_stack_algorithm(equation)}")
| 76 |
"""simple docstring"""
import argparse
import pickle
import numpy as np
import torch
from torch import nn
from transformers import ReformerConfig, ReformerModelWithLMHead
from transformers.utils import logging
logging.set_verbosity_info()
def lowerCAmelCase_ ( lowercase_ : int , lowercase_ : Any , lowercase_ : int=None ):
'''simple docstring'''
assert torch_layer.weight.shape == weight.shape, F'''{torch_layer} layer.weight does not match'''
__SCREAMING_SNAKE_CASE : str = nn.Parameter(lowercase_ )
if bias is not None:
assert torch_layer.bias.shape == bias.shape, F'''{torch_layer} layer.bias does not match'''
__SCREAMING_SNAKE_CASE : Tuple = nn.Parameter(lowercase_ )
def lowerCAmelCase_ ( lowercase_ : Tuple , lowercase_ : int , lowercase_ : int ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : Optional[int] = np.asarray(weights[0] )
__SCREAMING_SNAKE_CASE : Optional[int] = np.asarray(weights[1] )
__SCREAMING_SNAKE_CASE : Union[str, Any] = np.asarray(weights[2] )
set_param(
torch_layer.self_attention.query_key , torch.tensor(lowercase_ ).transpose(1 , 2 ).contiguous().view(-1 , lowercase_ ) , )
set_param(
torch_layer.self_attention.value , torch.tensor(lowercase_ ).transpose(1 , 2 ).contiguous().view(-1 , lowercase_ ) , )
set_param(
torch_layer.output.dense , torch.tensor(lowercase_ ).view(-1 , lowercase_ ).contiguous().transpose(0 , 1 ) , )
def lowerCAmelCase_ ( lowercase_ : Dict , lowercase_ : List[str] , lowercase_ : List[str] ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : List[str] = np.asarray(weights[0] )
__SCREAMING_SNAKE_CASE : Any = np.asarray(weights[1] )
__SCREAMING_SNAKE_CASE : Union[str, Any] = np.asarray(weights[2] )
__SCREAMING_SNAKE_CASE : Tuple = np.asarray(weights[3] )
set_param(
torch_layer.self_attention.query , torch.tensor(lowercase_ ).transpose(1 , 2 ).contiguous().view(-1 , lowercase_ ) , )
set_param(
torch_layer.self_attention.key , torch.tensor(lowercase_ ).transpose(1 , 2 ).contiguous().view(-1 , lowercase_ ) , )
set_param(
torch_layer.self_attention.value , torch.tensor(lowercase_ ).transpose(1 , 2 ).contiguous().view(-1 , lowercase_ ) , )
set_param(
torch_layer.output.dense , torch.tensor(lowercase_ ).view(-1 , lowercase_ ).contiguous().transpose(0 , 1 ) , )
def lowerCAmelCase_ ( lowercase_ : int , lowercase_ : List[str] , lowercase_ : str ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : Any = weights[0][0][0]
__SCREAMING_SNAKE_CASE : Union[str, Any] = np.asarray(layer_norm_a[0] )
__SCREAMING_SNAKE_CASE : List[Any] = np.asarray(layer_norm_a[1] )
set_param(
torch_block.attention.layer_norm , torch.tensor(lowercase_ ) , torch.tensor(lowercase_ ) , )
# lsh weights + output
__SCREAMING_SNAKE_CASE : Tuple = weights[0][1]
if len(lowercase_ ) < 4:
set_layer_weights_in_torch_lsh(lowercase_ , torch_block.attention , lowercase_ )
else:
set_layer_weights_in_torch_local(lowercase_ , torch_block.attention , lowercase_ )
# intermediate weighs
__SCREAMING_SNAKE_CASE : Any = weights[2][0][1][2]
# Chunked Feed Forward
if len(lowercase_ ) == 4:
__SCREAMING_SNAKE_CASE : List[str] = intermediate_weights[2]
# layernorm 2
__SCREAMING_SNAKE_CASE : List[str] = np.asarray(intermediate_weights[0][0] )
__SCREAMING_SNAKE_CASE : Union[str, Any] = np.asarray(intermediate_weights[0][1] )
set_param(
torch_block.feed_forward.layer_norm , torch.tensor(lowercase_ ) , torch.tensor(lowercase_ ) , )
# intermediate dense
__SCREAMING_SNAKE_CASE : int = np.asarray(intermediate_weights[1][0] )
__SCREAMING_SNAKE_CASE : Optional[Any] = np.asarray(intermediate_weights[1][1] )
set_param(
torch_block.feed_forward.dense.dense , torch.tensor(lowercase_ ).transpose(0 , 1 ).contiguous() , torch.tensor(lowercase_ ) , )
# intermediate out
__SCREAMING_SNAKE_CASE : Optional[Any] = np.asarray(intermediate_weights[4][0] )
__SCREAMING_SNAKE_CASE : Any = np.asarray(intermediate_weights[4][1] )
set_param(
torch_block.feed_forward.output.dense , torch.tensor(lowercase_ ).transpose(0 , 1 ).contiguous() , torch.tensor(lowercase_ ) , )
def lowerCAmelCase_ ( lowercase_ : str , lowercase_ : Optional[Any] , lowercase_ : Optional[Any] ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : Dict = torch_model.reformer
# word embeds
__SCREAMING_SNAKE_CASE : int = np.asarray(weights[1] )
set_param(
torch_model_reformer.embeddings.word_embeddings , torch.tensor(lowercase_ ) , )
if isinstance(weights[3] , lowercase_ ):
__SCREAMING_SNAKE_CASE : int = torch_model_reformer.embeddings.position_embeddings
for emb_idx in range(len(position_embeddings.weights ) ):
__SCREAMING_SNAKE_CASE : Dict = np.asarray(weights[3][emb_idx][0] )
assert (
position_embeddings.weights[emb_idx].shape == emb_weights.shape
), F'''{position_embeddings[emb_idx]} emb does not match'''
__SCREAMING_SNAKE_CASE : str = nn.Parameter(torch.tensor(lowercase_ ) )
__SCREAMING_SNAKE_CASE : List[Any] = weights[5]
assert len(torch_model_reformer.encoder.layers ) * 4 == len(
lowercase_ ), "HF and trax model do not have the same number of layers"
for layer_idx, layer in enumerate(torch_model_reformer.encoder.layers ):
__SCREAMING_SNAKE_CASE : Union[str, Any] = trax_layer_weights[4 * layer_idx : 4 * (layer_idx + 1)]
set_block_weights_in_torch(lowercase_ , lowercase_ , lowercase_ )
# output layer norm
__SCREAMING_SNAKE_CASE : List[str] = np.asarray(weights[7][0] )
__SCREAMING_SNAKE_CASE : Optional[Any] = np.asarray(weights[7][1] )
set_param(
torch_model_reformer.encoder.layer_norm , torch.tensor(lowercase_ ) , torch.tensor(lowercase_ ) , )
# output embeddings
__SCREAMING_SNAKE_CASE : Optional[Any] = np.asarray(weights[9][0] )
__SCREAMING_SNAKE_CASE : List[Any] = np.asarray(weights[9][1] )
set_param(
torch_model.lm_head.decoder , torch.tensor(lowercase_ ).transpose(0 , 1 ).contiguous() , torch.tensor(lowercase_ ) , )
def lowerCAmelCase_ ( lowercase_ : List[str] , lowercase_ : Any , lowercase_ : int ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : Dict = ReformerConfig.from_json_file(lowercase_ )
print(F'''Building PyTorch model from configuration: {config}''' )
__SCREAMING_SNAKE_CASE : List[str] = ReformerModelWithLMHead(lowercase_ )
with open(lowercase_ , '''rb''' ) as f:
__SCREAMING_SNAKE_CASE : int = pickle.load(lowercase_ )['''weights''']
set_model_weights_in_torch(lowercase_ , lowercase_ , config.hidden_size )
# Save pytorch-model
print(F'''Save PyTorch model to {pytorch_dump_path}''' )
torch.save(model.state_dict() , lowercase_ )
if __name__ == "__main__":
_lowerCamelCase = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--trax_model_pkl_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
)
parser.add_argument(
'''--config_file''',
default=None,
type=str,
required=True,
help=(
'''The config json file corresponding to the pre-trained Reformer model. \n'''
'''This specifies the model architecture.'''
),
)
parser.add_argument(
'''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
)
_lowerCamelCase = parser.parse_args()
convert_trax_checkpoint_to_pytorch(args.trax_model_pkl_path, args.config_file, args.pytorch_dump_path)
| 674 | 0 |
lowercase_ : Optional[Any] = [
'Audio',
'Array2D',
'Array3D',
'Array4D',
'Array5D',
'ClassLabel',
'Features',
'Sequence',
'Value',
'Image',
'Translation',
'TranslationVariableLanguages',
]
from .audio import Audio
from .features import ArrayaD, ArrayaD, ArrayaD, ArrayaD, ClassLabel, Features, Sequence, Value
from .image import Image
from .translation import Translation, TranslationVariableLanguages
| 64 |
"""simple docstring"""
from typing import Callable, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCamelCase = logging.get_logger(__name__)
_lowerCamelCase = {
'''microsoft/xprophetnet-large-wiki100-cased''': (
'''https://huggingface.co/microsoft/xprophetnet-large-wiki100-cased/resolve/main/config.json'''
),
}
class snake_case ( __UpperCAmelCase ):
lowerCamelCase__ = '''xlm-prophetnet'''
lowerCamelCase__ = ['''past_key_values''']
lowerCamelCase__ = {
'''num_attention_heads''': '''num_encoder_attention_heads''',
}
def __init__( self :List[str] , _lowerCamelCase :Optional[float] = 0.1 , _lowerCamelCase :Optional[Union[str, Callable]] = "gelu" , _lowerCamelCase :Optional[int] = 3_0_5_2_2 , _lowerCamelCase :Optional[int] = 1_0_2_4 , _lowerCamelCase :Optional[int] = 4_0_9_6 , _lowerCamelCase :Optional[int] = 1_2 , _lowerCamelCase :Optional[int] = 1_6 , _lowerCamelCase :Optional[int] = 4_0_9_6 , _lowerCamelCase :Optional[int] = 1_2 , _lowerCamelCase :Optional[int] = 1_6 , _lowerCamelCase :Optional[float] = 0.1 , _lowerCamelCase :Optional[float] = 0.1 , _lowerCamelCase :Optional[int] = 5_1_2 , _lowerCamelCase :Optional[float] = 0.0_2 , _lowerCamelCase :Optional[bool] = True , _lowerCamelCase :Optional[bool] = True , _lowerCamelCase :Optional[int] = 0 , _lowerCamelCase :Optional[int] = 2 , _lowerCamelCase :Optional[int] = 3_2 , _lowerCamelCase :Optional[int] = 1_2_8 , _lowerCamelCase :Optional[bool] = False , _lowerCamelCase :Optional[float] = 0.0 , _lowerCamelCase :Optional[bool] = True , _lowerCamelCase :Optional[int] = 0 , _lowerCamelCase :Optional[int] = 1 , _lowerCamelCase :Optional[int] = 2 , **_lowerCamelCase :int , ):
__SCREAMING_SNAKE_CASE : Union[str, Any] = vocab_size
__SCREAMING_SNAKE_CASE : Optional[int] = hidden_size
__SCREAMING_SNAKE_CASE : List[Any] = encoder_ffn_dim
__SCREAMING_SNAKE_CASE : str = num_encoder_layers
__SCREAMING_SNAKE_CASE : Optional[Any] = num_encoder_attention_heads
__SCREAMING_SNAKE_CASE : str = decoder_ffn_dim
__SCREAMING_SNAKE_CASE : List[Any] = num_decoder_layers
__SCREAMING_SNAKE_CASE : List[str] = num_decoder_attention_heads
__SCREAMING_SNAKE_CASE : Dict = max_position_embeddings
__SCREAMING_SNAKE_CASE : Any = init_std # Normal(0, this parameter)
__SCREAMING_SNAKE_CASE : Any = activation_function
# parameters for xlmprophetnet
__SCREAMING_SNAKE_CASE : List[Any] = ngram
__SCREAMING_SNAKE_CASE : int = num_buckets
__SCREAMING_SNAKE_CASE : List[str] = relative_max_distance
__SCREAMING_SNAKE_CASE : str = disable_ngram_loss
__SCREAMING_SNAKE_CASE : Optional[int] = eps
# 3 Types of Dropout
__SCREAMING_SNAKE_CASE : int = attention_dropout
__SCREAMING_SNAKE_CASE : Optional[Any] = activation_dropout
__SCREAMING_SNAKE_CASE : Dict = dropout
__SCREAMING_SNAKE_CASE : Any = use_cache
super().__init__(
pad_token_id=_lowerCamelCase , bos_token_id=_lowerCamelCase , eos_token_id=_lowerCamelCase , is_encoder_decoder=_lowerCamelCase , add_cross_attention=_lowerCamelCase , decoder_start_token_id=_lowerCamelCase , **_lowerCamelCase , )
@property
def SCREAMING_SNAKE_CASE_ ( self :int ):
return self.num_encoder_layers + self.num_decoder_layers
@num_hidden_layers.setter
def SCREAMING_SNAKE_CASE_ ( self :Dict , _lowerCamelCase :List[Any] ):
raise NotImplementedError(
'''This model does not support the setting of `num_hidden_layers`. Please set `num_encoder_layers` and'''
''' `num_decoder_layers`.''' )
| 674 | 0 |
import unittest
from transformers import JukeboxTokenizer
from transformers.testing_utils import require_torch
class lowerCamelCase_ ( unittest.TestCase ):
'''simple docstring'''
__UpperCAmelCase = JukeboxTokenizer
__UpperCAmelCase = {
"artist": "Zac Brown Band",
"genres": "Country",
"lyrics": "I met a traveller from an antique land,\n Who said \"Two vast and trunkless legs of stone\n Stand in the desert. . . . Near them, on the sand,\n Half sunk a shattered visage lies, whose frown,\n And wrinkled lip, and sneer of cold command,\n Tell that its sculptor well those passions read\n Which yet survive, stamped on these lifeless things,\n The hand that mocked them, and the heart that fed;\n And on the pedestal, these words appear:\n My name is Ozymandias, King of Kings;\n Look on my Works, ye Mighty, and despair!\n Nothing beside remains. Round the decay\n Of that colossal Wreck, boundless and bare\n The lone and level sands stretch far away\n ",
}
@require_torch
def A ( self ) -> str:
'''simple docstring'''
import torch
__lowercase = JukeboxTokenizer.from_pretrained('''openai/jukebox-1b-lyrics''' )
__lowercase = tokenizer(**self.metas )['''input_ids''']
# fmt: off
__lowercase = [
torch.tensor([[
0, 0, 0, 7_1_6_9, 5_0_7, 9, 7_6, 3_9, 3_1, 4_6, 7_6, 2_7,
7_6, 4_6, 4_4, 2_7, 4_8, 3_1, 3_8, 3_8, 3_1, 4_4, 7_6, 3_2,
4_4, 4_1, 3_9, 7_6, 2_7, 4_0, 7_6, 2_7, 4_0, 4_6, 3_5, 4_3,
4_7, 3_1, 7_6, 3_8, 2_7, 4_0, 3_0, 6_4, 7_8, 7_6, 7_6, 7_6,
7_6, 7_6, 7_6, 7_6, 7_6, 2_3, 3_4, 4_1, 7_6, 4_5, 2_7, 3_5,
3_0, 7_6, 7_1, 2_0, 4_9, 4_1, 7_6, 4_8, 2_7, 4_5, 4_6, 7_6,
2_7, 4_0, 3_0, 7_6, 4_6, 4_4, 4_7, 4_0, 3_7, 3_8, 3_1, 4_5,
4_5, 7_6, 3_8, 3_1, 3_3, 4_5, 7_6, 4_1, 3_2, 7_6, 4_5, 4_6,
4_1, 4_0, 3_1, 7_8, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6,
1_9, 4_6, 2_7, 4_0, 3_0, 7_6, 3_5, 4_0, 7_6, 4_6, 3_4, 3_1,
7_6, 3_0, 3_1, 4_5, 3_1, 4_4, 4_6, 6_3, 7_6, 6_3, 7_6, 6_3,
7_6, 6_3, 7_6, 1_4, 3_1, 2_7, 4_4, 7_6, 4_6, 3_4, 3_1, 3_9,
6_4, 7_6, 4_1, 4_0, 7_6, 4_6, 3_4, 3_1, 7_6, 4_5, 2_7, 4_0,
3_0, 6_4, 7_8, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 8,
2_7, 3_8, 3_2, 7_6, 4_5, 4_7, 4_0, 3_7, 7_6, 2_7, 7_6, 4_5,
3_4, 2_7, 4_6, 4_6, 3_1, 4_4, 3_1, 3_0, 7_6, 4_8, 3_5, 4_5,
2_7, 3_3, 3_1, 7_6, 3_8, 3_5, 3_1, 4_5, 6_4, 7_6, 4_9, 3_4,
4_1, 4_5, 3_1, 7_6, 3_2, 4_4, 4_1, 4_9, 4_0, 6_4, 7_8, 7_6,
7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 1, 4_0, 3_0, 7_6, 4_9,
4_4, 3_5, 4_0, 3_7, 3_8, 3_1, 3_0, 7_6, 3_8, 3_5, 4_2, 6_4,
7_6, 2_7, 4_0, 3_0, 7_6, 4_5, 4_0, 3_1, 3_1, 4_4, 7_6, 4_1,
3_2, 7_6, 2_9, 4_1, 3_8, 3_0, 7_6, 2_9, 4_1, 3_9, 3_9, 2_7,
4_0, 3_0, 6_4, 7_8, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6,
2_0, 3_1, 3_8, 3_8, 7_6, 4_6, 3_4, 2_7, 4_6, 7_6, 3_5, 4_6,
4_5, 7_6, 4_5, 2_9, 4_7, 3_8, 4_2, 4_6, 4_1, 4_4, 7_6, 4_9,
3_1, 3_8, 3_8, 7_6, 4_6, 3_4, 4_1, 4_5, 3_1, 7_6, 4_2, 2_7,
4_5, 4_5, 3_5, 4_1, 4_0, 4_5, 7_6, 4_4, 3_1, 2_7, 3_0, 7_8,
7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 2_3, 3_4, 3_5, 2_9,
3_4, 7_6, 5_1, 3_1, 4_6, 7_6, 4_5, 4_7, 4_4, 4_8, 3_5, 4_8,
3_1, 6_4, 7_6, 4_5, 4_6, 2_7, 3_9, 4_2, 3_1, 3_0, 7_6, 4_1,
4_0, 7_6, 4_6, 3_4, 3_1, 4_5, 3_1, 7_6, 3_8, 3_5, 3_2, 3_1,
3_8, 3_1, 4_5, 4_5, 7_6, 4_6, 3_4, 3_5, 4_0, 3_3, 4_5, 6_4,
7_8, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 2_0, 3_4, 3_1,
7_6, 3_4, 2_7, 4_0, 3_0, 7_6, 4_6, 3_4, 2_7, 4_6, 7_6, 3_9,
4_1, 2_9, 3_7, 3_1, 3_0, 7_6, 4_6, 3_4, 3_1, 3_9, 6_4, 7_6,
2_7, 4_0, 3_0, 7_6, 4_6, 3_4, 3_1, 7_6, 3_4, 3_1, 2_7, 4_4,
4_6, 7_6, 4_6, 3_4, 2_7, 4_6, 7_6, 3_2, 3_1, 3_0, 6_6, 7_8,
7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 1, 4_0, 3_0, 7_6,
4_1, 4_0, 7_6, 4_6, 3_4, 3_1, 7_6, 4_2, 3_1, 3_0, 3_1, 4_5,
4_6, 2_7, 3_8, 6_4, 7_6, 4_6, 3_4, 3_1, 4_5, 3_1, 7_6, 4_9,
4_1, 4_4, 3_0, 4_5, 7_6, 2_7, 4_2, 4_2, 3_1, 2_7, 4_4, 6_5,
7_8, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 1_3, 5_1, 7_6,
4_0, 2_7, 3_9, 3_1, 7_6, 3_5, 4_5, 7_6, 1_5, 5_2, 5_1, 3_9,
2_7, 4_0, 3_0, 3_5, 2_7, 4_5, 6_4, 7_6, 1_1, 3_5, 4_0, 3_3,
7_6, 4_1, 3_2, 7_6, 1_1, 3_5, 4_0, 3_3, 4_5, 6_6, 7_8, 7_6,
7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 1_2, 4_1, 4_1, 3_7, 7_6,
4_1, 4_0, 7_6, 3_9, 5_1, 7_6, 2_3, 4_1, 4_4, 3_7, 4_5, 6_4,
7_6, 5_1, 3_1, 7_6, 1_3, 3_5, 3_3, 3_4, 4_6, 5_1, 6_4, 7_6,
2_7, 4_0, 3_0, 7_6, 3_0, 3_1, 4_5, 4_2, 2_7, 3_5, 4_4, 6_7,
7_8, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 1_4, 4_1, 4_6,
3_4, 3_5, 4_0, 3_3, 7_6, 2_8, 3_1, 4_5, 3_5, 3_0, 3_1, 7_6,
4_4, 3_1, 3_9, 2_7, 3_5, 4_0, 4_5, 6_3, 7_6, 1_8, 4_1, 4_7,
4_0, 3_0, 7_6, 4_6, 3_4, 3_1, 7_6, 3_0, 3_1, 2_9, 2_7, 5_1,
7_8, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 1_5, 3_2, 7_6,
4_6, 3_4, 2_7, 4_6, 7_6, 2_9, 4_1, 3_8, 4_1, 4_5, 4_5, 2_7,
3_8, 7_6, 2_3, 4_4, 3_1, 2_9, 3_7, 6_4, 7_6, 2_8, 4_1, 4_7,
4_0, 3_0, 3_8, 3_1, 4_5, 4_5, 7_6, 2_7, 4_0, 3_0, 7_6, 2_8,
2_7, 4_4, 3_1, 7_8, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6,
2_0, 3_4, 3_1, 7_6, 3_8, 4_1, 4_0, 3_1, 7_6, 2_7, 4_0, 3_0,
7_6, 3_8, 3_1, 4_8, 3_1, 3_8, 7_6, 4_5, 2_7, 4_0, 3_0, 4_5,
7_6, 4_5, 4_6, 4_4, 3_1, 4_6, 2_9, 3_4, 7_6, 3_2, 2_7, 4_4,
7_6, 2_7, 4_9, 2_7, 5_1, 7_8, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6,
7_6, 7_6]] ),
torch.tensor([[0, 0, 0, 1_0_6_9, 1_1]] ),
torch.tensor([[0, 0, 0, 1_0_6_9, 1_1]] ),
]
# fmt: on
self.assertTrue(torch.allclose(tokens[0] , EXPECTED_OUTPUT[0] ) )
self.assertTrue(torch.allclose(tokens[1] , EXPECTED_OUTPUT[1] ) )
self.assertTrue(torch.allclose(tokens[2] , EXPECTED_OUTPUT[2] ) )
@require_torch
def A ( self ) -> List[str]:
'''simple docstring'''
import torch
__lowercase = JukeboxTokenizer.from_pretrained('''openai/jukebox-5b-lyrics''' )
__lowercase = tokenizer(**self.metas )['''input_ids''']
# fmt: off
__lowercase = [
torch.tensor([[
0, 0, 0, 1_0_6_9, 1_1, -1, -1, -1, -1, 9, 7_7, 3_9,
3_1, 4_6, 7_7, 2_7, 7_7, 4_6, 4_4, 2_7, 4_8, 3_1, 3_8, 3_8,
3_1, 4_4, 7_7, 3_2, 4_4, 4_1, 3_9, 7_7, 2_7, 4_0, 7_7, 2_7,
4_0, 4_6, 3_5, 4_3, 4_7, 3_1, 7_7, 3_8, 2_7, 4_0, 3_0, 6_4,
7_9, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7, 2_3, 3_4, 4_1,
7_7, 4_5, 2_7, 3_5, 3_0, 7_7, 7_2, 2_0, 4_9, 4_1, 7_7, 4_8,
2_7, 4_5, 4_6, 7_7, 2_7, 4_0, 3_0, 7_7, 4_6, 4_4, 4_7, 4_0,
3_7, 3_8, 3_1, 4_5, 4_5, 7_7, 3_8, 3_1, 3_3, 4_5, 7_7, 4_1,
3_2, 7_7, 4_5, 4_6, 4_1, 4_0, 3_1, 7_9, 7_7, 7_7, 7_7, 7_7,
7_7, 7_7, 7_7, 7_7, 1_9, 4_6, 2_7, 4_0, 3_0, 7_7, 3_5, 4_0,
7_7, 4_6, 3_4, 3_1, 7_7, 3_0, 3_1, 4_5, 3_1, 4_4, 4_6, 6_3,
7_7, 6_3, 7_7, 6_3, 7_7, 6_3, 7_7, 1_4, 3_1, 2_7, 4_4, 7_7,
4_6, 3_4, 3_1, 3_9, 6_4, 7_7, 4_1, 4_0, 7_7, 4_6, 3_4, 3_1,
7_7, 4_5, 2_7, 4_0, 3_0, 6_4, 7_9, 7_7, 7_7, 7_7, 7_7, 7_7,
7_7, 7_7, 7_7, 8, 2_7, 3_8, 3_2, 7_7, 4_5, 4_7, 4_0, 3_7,
7_7, 2_7, 7_7, 4_5, 3_4, 2_7, 4_6, 4_6, 3_1, 4_4, 3_1, 3_0,
7_7, 4_8, 3_5, 4_5, 2_7, 3_3, 3_1, 7_7, 3_8, 3_5, 3_1, 4_5,
6_4, 7_7, 4_9, 3_4, 4_1, 4_5, 3_1, 7_7, 3_2, 4_4, 4_1, 4_9,
4_0, 6_4, 7_9, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7, 1,
4_0, 3_0, 7_7, 4_9, 4_4, 3_5, 4_0, 3_7, 3_8, 3_1, 3_0, 7_7,
3_8, 3_5, 4_2, 6_4, 7_7, 2_7, 4_0, 3_0, 7_7, 4_5, 4_0, 3_1,
3_1, 4_4, 7_7, 4_1, 3_2, 7_7, 2_9, 4_1, 3_8, 3_0, 7_7, 2_9,
4_1, 3_9, 3_9, 2_7, 4_0, 3_0, 6_4, 7_9, 7_7, 7_7, 7_7, 7_7,
7_7, 7_7, 7_7, 7_7, 2_0, 3_1, 3_8, 3_8, 7_7, 4_6, 3_4, 2_7,
4_6, 7_7, 3_5, 4_6, 4_5, 7_7, 4_5, 2_9, 4_7, 3_8, 4_2, 4_6,
4_1, 4_4, 7_7, 4_9, 3_1, 3_8, 3_8, 7_7, 4_6, 3_4, 4_1, 4_5,
3_1, 7_7, 4_2, 2_7, 4_5, 4_5, 3_5, 4_1, 4_0, 4_5, 7_7, 4_4,
3_1, 2_7, 3_0, 7_9, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7,
2_3, 3_4, 3_5, 2_9, 3_4, 7_7, 5_1, 3_1, 4_6, 7_7, 4_5, 4_7,
4_4, 4_8, 3_5, 4_8, 3_1, 6_4, 7_7, 4_5, 4_6, 2_7, 3_9, 4_2,
3_1, 3_0, 7_7, 4_1, 4_0, 7_7, 4_6, 3_4, 3_1, 4_5, 3_1, 7_7,
3_8, 3_5, 3_2, 3_1, 3_8, 3_1, 4_5, 4_5, 7_7, 4_6, 3_4, 3_5,
4_0, 3_3, 4_5, 6_4, 7_9, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7,
7_7, 2_0, 3_4, 3_1, 7_7, 3_4, 2_7, 4_0, 3_0, 7_7, 4_6, 3_4,
2_7, 4_6, 7_7, 3_9, 4_1, 2_9, 3_7, 3_1, 3_0, 7_7, 4_6, 3_4,
3_1, 3_9, 6_4, 7_7, 2_7, 4_0, 3_0, 7_7, 4_6, 3_4, 3_1, 7_7,
3_4, 3_1, 2_7, 4_4, 4_6, 7_7, 4_6, 3_4, 2_7, 4_6, 7_7, 3_2,
3_1, 3_0, 6_6, 7_9, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7,
1, 4_0, 3_0, 7_7, 4_1, 4_0, 7_7, 4_6, 3_4, 3_1, 7_7, 4_2,
3_1, 3_0, 3_1, 4_5, 4_6, 2_7, 3_8, 6_4, 7_7, 4_6, 3_4, 3_1,
4_5, 3_1, 7_7, 4_9, 4_1, 4_4, 3_0, 4_5, 7_7, 2_7, 4_2, 4_2,
3_1, 2_7, 4_4, 6_5, 7_9, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7,
7_7, 1_3, 5_1, 7_7, 4_0, 2_7, 3_9, 3_1, 7_7, 3_5, 4_5, 7_7,
1_5, 5_2, 5_1, 3_9, 2_7, 4_0, 3_0, 3_5, 2_7, 4_5, 6_4, 7_7,
1_1, 3_5, 4_0, 3_3, 7_7, 4_1, 3_2, 7_7, 1_1, 3_5, 4_0, 3_3,
4_5, 6_6, 7_9, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7, 1_2,
4_1, 4_1, 3_7, 7_7, 4_1, 4_0, 7_7, 3_9, 5_1, 7_7, 2_3, 4_1,
4_4, 3_7, 4_5, 6_4, 7_7, 5_1, 3_1, 7_7, 1_3, 3_5, 3_3, 3_4,
4_6, 5_1, 6_4, 7_7, 2_7, 4_0, 3_0, 7_7, 3_0, 3_1, 4_5, 4_2,
2_7, 3_5, 4_4, 6_7, 7_9, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7,
7_7, 1_4, 4_1, 4_6, 3_4, 3_5, 4_0, 3_3, 7_7, 2_8, 3_1, 4_5,
3_5, 3_0, 3_1, 7_7, 4_4, 3_1, 3_9, 2_7, 3_5, 4_0, 4_5, 6_3,
7_7, 1_8, 4_1, 4_7, 4_0, 3_0, 7_7, 4_6, 3_4, 3_1, 7_7, 3_0,
3_1, 2_9, 2_7, 5_1, 7_9, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7,
7_7, 1_5, 3_2, 7_7, 4_6, 3_4, 2_7, 4_6, 7_7, 2_9, 4_1, 3_8,
4_1, 4_5, 4_5, 2_7, 3_8, 7_7, 2_3, 4_4, 3_1, 2_9, 3_7, 6_4,
7_7, 2_8, 4_1, 4_7, 4_0, 3_0, 3_8, 3_1, 4_5, 4_5, 7_7, 2_7,
4_0, 3_0, 7_7, 2_8, 2_7, 4_4, 3_1, 7_9, 7_7, 7_7, 7_7, 7_7,
7_7, 7_7, 7_7, 7_7, 2_0, 3_4, 3_1, 7_7, 3_8, 4_1, 4_0, 3_1,
7_7, 2_7, 4_0, 3_0, 7_7, 3_8, 3_1, 4_8, 3_1, 3_8, 7_7, 4_5,
2_7, 4_0, 3_0, 4_5, 7_7, 4_5, 4_6, 4_4, 3_1, 4_6, 2_9, 3_4,
7_7, 3_2, 2_7, 4_4, 7_7, 2_7, 4_9, 2_7, 5_1, 7_9, 7_7, 7_7,
7_7, 7_7, 7_7, 7_7, 7_7, 7_7]] ),
torch.tensor([[0, 0, 0, 1_0_6_9, 1_1, -1, -1, -1, -1]] ),
torch.tensor([[0, 0, 0, 1_0_6_9, 1_1, -1, -1, -1, -1]] ),
]
# fmt: on
self.assertTrue(torch.allclose(tokens[0] , EXPECTED_OUTPUT[0] ) )
self.assertTrue(torch.allclose(tokens[1] , EXPECTED_OUTPUT[1] ) )
self.assertTrue(torch.allclose(tokens[2] , EXPECTED_OUTPUT[2] ) )
| 639 |
"""simple docstring"""
import ast
import os
import re
import shutil
import tempfile
import unittest
from unittest import mock
import torch
from accelerate.test_utils.examples import compare_against_test
from accelerate.test_utils.testing import TempDirTestCase, require_trackers, run_command, slow
from accelerate.utils import write_basic_config
# DataLoaders built from `test_samples/MRPC` for quick testing
# Should mock `{script_name}.get_dataloaders` via:
# @mock.patch("{script_name}.get_dataloaders", mocked_dataloaders)
_lowerCamelCase = [
'''cross_validation.py''',
'''gradient_accumulation.py''',
'''local_sgd.py''',
'''multi_process_metrics.py''',
'''memory.py''',
'''automatic_gradient_accumulation.py''',
'''fsdp_with_peak_mem_tracking.py''',
'''deepspeed_with_config_support.py''',
'''megatron_lm_gpt_pretraining.py''',
]
class snake_case ( unittest.TestCase ):
def SCREAMING_SNAKE_CASE_ ( self :List[str] , _lowerCamelCase :str , _lowerCamelCase :bool , _lowerCamelCase :str = None , _lowerCamelCase :list = None ):
__SCREAMING_SNAKE_CASE : List[str] = None
__SCREAMING_SNAKE_CASE : Optional[Any] = os.path.abspath(os.path.join('''examples''' , '''by_feature''' ) )
__SCREAMING_SNAKE_CASE : Union[str, Any] = os.path.abspath('''examples''' )
for item in os.listdir(_lowerCamelCase ):
if item not in EXCLUDE_EXAMPLES:
__SCREAMING_SNAKE_CASE : List[Any] = os.path.join(_lowerCamelCase , _lowerCamelCase )
if os.path.isfile(_lowerCamelCase ) and ".py" in item_path:
with self.subTest(
tested_script=_lowerCamelCase , feature_script=_lowerCamelCase , tested_section='''main()''' if parser_only else '''training_function()''' , ):
__SCREAMING_SNAKE_CASE : Tuple = compare_against_test(
os.path.join(_lowerCamelCase , _lowerCamelCase ) , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : Optional[Any] = '''\n'''.join(_lowerCamelCase )
if special_strings is not None:
for string in special_strings:
__SCREAMING_SNAKE_CASE : List[Any] = diff.replace(_lowerCamelCase , '''''' )
self.assertEqual(_lowerCamelCase , '''''' )
def SCREAMING_SNAKE_CASE_ ( self :Union[str, Any] ):
self.one_complete_example('''complete_nlp_example.py''' , _lowerCamelCase )
self.one_complete_example('''complete_nlp_example.py''' , _lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :List[str] ):
__SCREAMING_SNAKE_CASE : Union[str, Any] = os.path.abspath(os.path.join('''examples''' , '''cv_example.py''' ) )
__SCREAMING_SNAKE_CASE : Optional[int] = [
''' ''' * 1_6 + '''{\n\n''',
''' ''' * 2_0 + '''"accuracy": eval_metric["accuracy"],\n\n''',
''' ''' * 2_0 + '''"f1": eval_metric["f1"],\n\n''',
''' ''' * 2_0 + '''"train_loss": total_loss.item() / len(train_dataloader),\n\n''',
''' ''' * 2_0 + '''"epoch": epoch,\n\n''',
''' ''' * 1_6 + '''},\n\n''',
''' ''' * 1_6 + '''step=epoch,\n''',
''' ''' * 1_2,
''' ''' * 8 + '''for step, batch in enumerate(active_dataloader):\n''',
]
self.one_complete_example('''complete_cv_example.py''' , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase )
self.one_complete_example('''complete_cv_example.py''' , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase )
@mock.patch.dict(os.environ , {'''TESTING_MOCKED_DATALOADERS''': '''1'''} )
class snake_case ( __UpperCAmelCase ):
lowerCamelCase__ = False
@classmethod
def SCREAMING_SNAKE_CASE_ ( cls :Dict ):
super().setUpClass()
__SCREAMING_SNAKE_CASE : Dict = tempfile.mkdtemp()
__SCREAMING_SNAKE_CASE : str = os.path.join(cls._tmpdir , '''default_config.yml''' )
write_basic_config(save_location=cls.configPath )
__SCREAMING_SNAKE_CASE : List[Any] = ['''accelerate''', '''launch''', '''--config_file''', cls.configPath]
@classmethod
def SCREAMING_SNAKE_CASE_ ( cls :Dict ):
super().tearDownClass()
shutil.rmtree(cls._tmpdir )
def SCREAMING_SNAKE_CASE_ ( self :Tuple ):
__SCREAMING_SNAKE_CASE : str = f'''
examples/by_feature/checkpointing.py
--checkpointing_steps epoch
--output_dir {self.tmpdir}
'''.split()
run_command(self._launch_args + testargs )
self.assertTrue(os.path.exists(os.path.join(self.tmpdir , '''epoch_0''' ) ) )
def SCREAMING_SNAKE_CASE_ ( self :List[Any] ):
__SCREAMING_SNAKE_CASE : Optional[Any] = f'''
examples/by_feature/checkpointing.py
--checkpointing_steps 1
--output_dir {self.tmpdir}
'''.split()
__SCREAMING_SNAKE_CASE : Optional[int] = run_command(self._launch_args + testargs )
self.assertTrue(os.path.exists(os.path.join(self.tmpdir , '''step_2''' ) ) )
def SCREAMING_SNAKE_CASE_ ( self :List[str] ):
__SCREAMING_SNAKE_CASE : Optional[int] = f'''
examples/by_feature/checkpointing.py
--resume_from_checkpoint {os.path.join(self.tmpdir , 'epoch_0' )}
'''.split()
__SCREAMING_SNAKE_CASE : Any = run_command(self._launch_args + testargs , return_stdout=_lowerCamelCase )
self.assertNotIn('''epoch 0:''' , _lowerCamelCase )
self.assertIn('''epoch 1:''' , _lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :Union[str, Any] ):
__SCREAMING_SNAKE_CASE : Optional[int] = f'''
examples/by_feature/checkpointing.py
--resume_from_checkpoint {os.path.join(self.tmpdir , 'step_2' )}
'''.split()
__SCREAMING_SNAKE_CASE : List[str] = run_command(self._launch_args + testargs , return_stdout=_lowerCamelCase )
if torch.cuda.is_available():
__SCREAMING_SNAKE_CASE : List[Any] = torch.cuda.device_count()
else:
__SCREAMING_SNAKE_CASE : Optional[int] = 1
if num_processes > 1:
self.assertNotIn('''epoch 0:''' , _lowerCamelCase )
self.assertIn('''epoch 1:''' , _lowerCamelCase )
else:
self.assertIn('''epoch 0:''' , _lowerCamelCase )
self.assertIn('''epoch 1:''' , _lowerCamelCase )
@slow
def SCREAMING_SNAKE_CASE_ ( self :str ):
__SCREAMING_SNAKE_CASE : Optional[Any] = '''
examples/by_feature/cross_validation.py
--num_folds 2
'''.split()
with mock.patch.dict(os.environ , {'''TESTING_MOCKED_DATALOADERS''': '''0'''} ):
__SCREAMING_SNAKE_CASE : Union[str, Any] = run_command(self._launch_args + testargs , return_stdout=_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Any = re.findall('''({.+})''' , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : List[Any] = [r for r in results if '''accuracy''' in r][-1]
__SCREAMING_SNAKE_CASE : Tuple = ast.literal_eval(_lowerCamelCase )
self.assertGreaterEqual(results['''accuracy'''] , 0.7_5 )
def SCREAMING_SNAKE_CASE_ ( self :str ):
__SCREAMING_SNAKE_CASE : Optional[Any] = ['''examples/by_feature/multi_process_metrics.py''']
run_command(self._launch_args + testargs )
@require_trackers
@mock.patch.dict(os.environ , {'''WANDB_MODE''': '''offline'''} )
def SCREAMING_SNAKE_CASE_ ( self :List[str] ):
with tempfile.TemporaryDirectory() as tmpdir:
__SCREAMING_SNAKE_CASE : int = f'''
examples/by_feature/tracking.py
--with_tracking
--project_dir {tmpdir}
'''.split()
run_command(self._launch_args + testargs )
self.assertTrue(os.path.exists(os.path.join(_lowerCamelCase , '''tracking''' ) ) )
def SCREAMING_SNAKE_CASE_ ( self :Dict ):
__SCREAMING_SNAKE_CASE : Union[str, Any] = ['''examples/by_feature/gradient_accumulation.py''']
run_command(self._launch_args + testargs )
def SCREAMING_SNAKE_CASE_ ( self :Optional[int] ):
__SCREAMING_SNAKE_CASE : List[Any] = ['''examples/by_feature/local_sgd.py''']
run_command(self._launch_args + testargs )
| 674 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCAmelCase_ = {
'''configuration_x_clip''': [
'''XCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''XCLIPConfig''',
'''XCLIPTextConfig''',
'''XCLIPVisionConfig''',
],
'''processing_x_clip''': ['''XCLIPProcessor'''],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase_ = [
'''XCLIP_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''XCLIPModel''',
'''XCLIPPreTrainedModel''',
'''XCLIPTextModel''',
'''XCLIPVisionModel''',
]
if TYPE_CHECKING:
from .configuration_x_clip import (
XCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
XCLIPConfig,
XCLIPTextConfig,
XCLIPVisionConfig,
)
from .processing_x_clip import XCLIPProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_x_clip import (
XCLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
XCLIPModel,
XCLIPPreTrainedModel,
XCLIPTextModel,
XCLIPVisionModel,
)
else:
import sys
UpperCAmelCase_ = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__) | 271 |
"""simple docstring"""
import argparse
import logging
import os
import time
import timeit
import datasets
import numpy as np
import pycuda.autoinit # noqa: F401
import pycuda.driver as cuda
import tensorrt as trt
import torch
from absl import logging as absl_logging
from accelerate import Accelerator
from datasets import load_dataset, load_metric
from torch.utils.data import DataLoader
from utils_qa import postprocess_qa_predictions
import transformers
from transformers import AutoTokenizer, EvalPrediction, default_data_collator, set_seed
from transformers.trainer_pt_utils import nested_concat, nested_truncate
_lowerCamelCase = trt.Logger(trt.Logger.WARNING)
_lowerCamelCase = absl_logging.get_absl_logger()
absl_logger.setLevel(logging.WARNING)
_lowerCamelCase = logging.getLogger(__name__)
_lowerCamelCase = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--onnx_model_path''',
default=None,
type=str,
required=True,
help='''Path to ONNX model: ''',
)
parser.add_argument(
'''--output_dir''',
default=None,
type=str,
required=True,
help='''The output directory where the model checkpoints and predictions will be written.''',
)
# Other parameters
parser.add_argument(
'''--tokenizer_name''',
default='''''',
type=str,
required=True,
help='''Pretrained tokenizer name or path if not the same as model_name''',
)
parser.add_argument(
'''--version_2_with_negative''',
action='''store_true''',
help='''If true, the SQuAD examples contain some that do not have an answer.''',
)
parser.add_argument(
'''--null_score_diff_threshold''',
type=float,
default=0.0,
help='''If null_score - best_non_null is greater than the threshold predict null.''',
)
parser.add_argument(
'''--max_seq_length''',
default=3_84,
type=int,
help=(
'''The maximum total input sequence length after WordPiece tokenization. Sequences '''
'''longer than this will be truncated, and sequences shorter than this will be padded.'''
),
)
parser.add_argument(
'''--doc_stride''',
default=1_28,
type=int,
help='''When splitting up a long document into chunks, how much stride to take between chunks.''',
)
parser.add_argument('''--per_device_eval_batch_size''', default=8, type=int, help='''Batch size per GPU/CPU for evaluation.''')
parser.add_argument(
'''--n_best_size''',
default=20,
type=int,
help='''The total number of n-best predictions to generate in the nbest_predictions.json output file.''',
)
parser.add_argument(
'''--max_answer_length''',
default=30,
type=int,
help=(
'''The maximum length of an answer that can be generated. This is needed because the start '''
'''and end predictions are not conditioned on one another.'''
),
)
parser.add_argument('''--seed''', type=int, default=42, help='''random seed for initialization''')
parser.add_argument(
'''--dataset_name''',
type=str,
default=None,
required=True,
help='''The name of the dataset to use (via the datasets library).''',
)
parser.add_argument(
'''--dataset_config_name''',
type=str,
default=None,
help='''The configuration name of the dataset to use (via the datasets library).''',
)
parser.add_argument(
'''--preprocessing_num_workers''', type=int, default=4, help='''A csv or a json file containing the training data.'''
)
parser.add_argument('''--overwrite_cache''', action='''store_true''', help='''Overwrite the cached training and evaluation sets''')
parser.add_argument(
'''--fp16''',
action='''store_true''',
help='''Whether to use 16-bit (mixed) precision instead of 32-bit''',
)
parser.add_argument(
'''--int8''',
action='''store_true''',
help='''Whether to use INT8''',
)
_lowerCamelCase = parser.parse_args()
if args.tokenizer_name:
_lowerCamelCase = AutoTokenizer.from_pretrained(args.tokenizer_name, use_fast=True)
else:
raise ValueError(
'''You are instantiating a new tokenizer from scratch. This is not supported by this script.'''
'''You can do it from another script, save it, and load it from here, using --tokenizer_name.'''
)
logger.info('''Training/evaluation parameters %s''', args)
_lowerCamelCase = args.per_device_eval_batch_size
_lowerCamelCase = (args.eval_batch_size, args.max_seq_length)
# TRT Engine properties
_lowerCamelCase = True
_lowerCamelCase = '''temp_engine/bert-fp32.engine'''
if args.fpaa:
_lowerCamelCase = '''temp_engine/bert-fp16.engine'''
if args.inta:
_lowerCamelCase = '''temp_engine/bert-int8.engine'''
# import ONNX file
if not os.path.exists('''temp_engine'''):
os.makedirs('''temp_engine''')
_lowerCamelCase = 1 << (int)(trt.NetworkDefinitionCreationFlag.EXPLICIT_BATCH)
with trt.Builder(TRT_LOGGER) as builder, builder.create_network(EXPLICIT_BATCH) as network, trt.OnnxParser(
network, TRT_LOGGER
) as parser:
with open(args.onnx_model_path, '''rb''') as model:
if not parser.parse(model.read()):
for error in range(parser.num_errors):
print(parser.get_error(error))
# Query input names and shapes from parsed TensorRT network
_lowerCamelCase = [network.get_input(i) for i in range(network.num_inputs)]
_lowerCamelCase = [_input.name for _input in network_inputs] # ex: ["actual_input1"]
with builder.create_builder_config() as config:
_lowerCamelCase = 1 << 50
if STRICT_TYPES:
config.set_flag(trt.BuilderFlag.STRICT_TYPES)
if args.fpaa:
config.set_flag(trt.BuilderFlag.FPaa)
if args.inta:
config.set_flag(trt.BuilderFlag.INTa)
_lowerCamelCase = builder.create_optimization_profile()
config.add_optimization_profile(profile)
for i in range(len(input_names)):
profile.set_shape(input_names[i], INPUT_SHAPE, INPUT_SHAPE, INPUT_SHAPE)
_lowerCamelCase = builder.build_engine(network, config)
# serialize_engine and store in file (can be directly loaded and deserialized):
with open(engine_name, '''wb''') as f:
f.write(engine.serialize())
def lowerCAmelCase_ ( lowercase_ : List[Any] , lowercase_ : Tuple , lowercase_ : List[Any] , lowercase_ : List[Any] , lowercase_ : str , lowercase_ : Tuple , lowercase_ : Any , lowercase_ : Optional[int] ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : Dict = np.asarray(inputs['''input_ids'''] , dtype=np.intaa )
__SCREAMING_SNAKE_CASE : Optional[Any] = np.asarray(inputs['''attention_mask'''] , dtype=np.intaa )
__SCREAMING_SNAKE_CASE : Optional[Any] = np.asarray(inputs['''token_type_ids'''] , dtype=np.intaa )
# Copy inputs
cuda.memcpy_htod_async(d_inputs[0] , input_ids.ravel() , lowercase_ )
cuda.memcpy_htod_async(d_inputs[1] , attention_mask.ravel() , lowercase_ )
cuda.memcpy_htod_async(d_inputs[2] , token_type_ids.ravel() , lowercase_ )
# start time
__SCREAMING_SNAKE_CASE : Tuple = time.time()
# Run inference
context.execute_async(
bindings=[int(lowercase_ ) for d_inp in d_inputs] + [int(lowercase_ ), int(lowercase_ )] , stream_handle=stream.handle )
# Transfer predictions back from GPU
cuda.memcpy_dtoh_async(lowercase_ , lowercase_ , lowercase_ )
cuda.memcpy_dtoh_async(lowercase_ , lowercase_ , lowercase_ )
# Synchronize the stream and take time
stream.synchronize()
# end time
__SCREAMING_SNAKE_CASE : List[str] = time.time()
__SCREAMING_SNAKE_CASE : int = end_time - start_time
__SCREAMING_SNAKE_CASE : int = (h_outputa, h_outputa)
# print(outputs)
return outputs, infer_time
# Initialize the accelerator. We will let the accelerator handle device placement for us in this example.
_lowerCamelCase = Accelerator()
# Make one log on every process with the configuration for debugging.
logging.basicConfig(
format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''',
datefmt='''%m/%d/%Y %H:%M:%S''',
level=logging.INFO,
)
# Setup logging, we only want one process per machine to log things on the screen.
# accelerator.is_local_main_process is only True for one process per machine.
logger.setLevel(logging.INFO if accelerator.is_local_main_process else logging.ERROR)
if accelerator.is_local_main_process:
datasets.utils.logging.set_verbosity_warning()
transformers.utils.logging.set_verbosity_info()
else:
datasets.utils.logging.set_verbosity_error()
transformers.utils.logging.set_verbosity_error()
# If passed along, set the training seed now.
if args.seed is not None:
set_seed(args.seed)
# Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below)
# or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
# (the dataset will be downloaded automatically from the datasets Hub).
#
# For CSV/JSON files, this script will use the column called 'text' or the first column if no column called
# 'text' is found. You can easily tweak this behavior (see below).
if args.dataset_name is not None:
# Downloading and loading a dataset from the hub.
_lowerCamelCase = load_dataset(args.dataset_name, args.dataset_config_name)
else:
raise ValueError('''Evaluation requires a dataset name''')
# See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
# https://huggingface.co/docs/datasets/loading_datasets.html.
# Preprocessing the datasets.
# Preprocessing is slighlty different for training and evaluation.
_lowerCamelCase = raw_datasets['''validation'''].column_names
_lowerCamelCase = '''question''' if '''question''' in column_names else column_names[0]
_lowerCamelCase = '''context''' if '''context''' in column_names else column_names[1]
_lowerCamelCase = '''answers''' if '''answers''' in column_names else column_names[2]
# Padding side determines if we do (question|context) or (context|question).
_lowerCamelCase = tokenizer.padding_side == '''right'''
if args.max_seq_length > tokenizer.model_max_length:
logger.warning(
f'The max_seq_length passed ({args.max_seq_length}) is larger than the maximum length for the'
f'model ({tokenizer.model_max_length}). Using max_seq_length={tokenizer.model_max_length}.'
)
_lowerCamelCase = min(args.max_seq_length, tokenizer.model_max_length)
def lowerCAmelCase_ ( lowercase_ : Tuple ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : Any = [q.lstrip() for q in examples[question_column_name]]
# Tokenize our examples with truncation and maybe padding, but keep the overflows using a stride. This results
# in one example possible giving several features when a context is long, each of those features having a
# context that overlaps a bit the context of the previous feature.
__SCREAMING_SNAKE_CASE : Optional[Any] = tokenizer(
examples[question_column_name if pad_on_right else context_column_name] , examples[context_column_name if pad_on_right else question_column_name] , truncation='''only_second''' if pad_on_right else '''only_first''' , max_length=lowercase_ , stride=args.doc_stride , return_overflowing_tokens=lowercase_ , return_offsets_mapping=lowercase_ , padding='''max_length''' , )
# Since one example might give us several features if it has a long context, we need a map from a feature to
# its corresponding example. This key gives us just that.
__SCREAMING_SNAKE_CASE : Optional[int] = tokenized_examples.pop('''overflow_to_sample_mapping''' )
# For evaluation, we will need to convert our predictions to substrings of the context, so we keep the
# corresponding example_id and we will store the offset mappings.
__SCREAMING_SNAKE_CASE : Any = []
for i in range(len(tokenized_examples['''input_ids'''] ) ):
# Grab the sequence corresponding to that example (to know what is the context and what is the question).
__SCREAMING_SNAKE_CASE : int = tokenized_examples.sequence_ids(lowercase_ )
__SCREAMING_SNAKE_CASE : str = 1 if pad_on_right else 0
# One example can give several spans, this is the index of the example containing this span of text.
__SCREAMING_SNAKE_CASE : str = sample_mapping[i]
tokenized_examples["example_id"].append(examples['''id'''][sample_index] )
# Set to None the offset_mapping that are not part of the context so it's easy to determine if a token
# position is part of the context or not.
__SCREAMING_SNAKE_CASE : List[str] = [
(o if sequence_ids[k] == context_index else None)
for k, o in enumerate(tokenized_examples['''offset_mapping'''][i] )
]
return tokenized_examples
_lowerCamelCase = raw_datasets['''validation''']
# Validation Feature Creation
_lowerCamelCase = eval_examples.map(
prepare_validation_features,
batched=True,
num_proc=args.preprocessing_num_workers,
remove_columns=column_names,
load_from_cache_file=not args.overwrite_cache,
desc='''Running tokenizer on validation dataset''',
)
_lowerCamelCase = default_data_collator
_lowerCamelCase = eval_dataset.remove_columns(['''example_id''', '''offset_mapping'''])
_lowerCamelCase = DataLoader(
eval_dataset_for_model, collate_fn=data_collator, batch_size=args.per_device_eval_batch_size
)
def lowerCAmelCase_ ( lowercase_ : Dict , lowercase_ : Union[str, Any] , lowercase_ : Union[str, Any] , lowercase_ : List[Any]="eval" ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : Tuple = postprocess_qa_predictions(
examples=lowercase_ , features=lowercase_ , predictions=lowercase_ , version_2_with_negative=args.version_2_with_negative , n_best_size=args.n_best_size , max_answer_length=args.max_answer_length , null_score_diff_threshold=args.null_score_diff_threshold , output_dir=args.output_dir , prefix=lowercase_ , )
# Format the result to the format the metric expects.
if args.version_2_with_negative:
__SCREAMING_SNAKE_CASE : Union[str, Any] = [
{'''id''': k, '''prediction_text''': v, '''no_answer_probability''': 0.0} for k, v in predictions.items()
]
else:
__SCREAMING_SNAKE_CASE : int = [{'''id''': k, '''prediction_text''': v} for k, v in predictions.items()]
__SCREAMING_SNAKE_CASE : Any = [{'''id''': ex['''id'''], '''answers''': ex[answer_column_name]} for ex in examples]
return EvalPrediction(predictions=lowercase_ , label_ids=lowercase_ )
_lowerCamelCase = load_metric('''squad_v2''' if args.version_2_with_negative else '''squad''')
# Evaluation!
logger.info('''Loading ONNX model %s for evaluation''', args.onnx_model_path)
with open(engine_name, '''rb''') as f, trt.Runtime(TRT_LOGGER) as runtime, runtime.deserialize_cuda_engine(
f.read()
) as engine, engine.create_execution_context() as context:
# setup for TRT inferrence
for i in range(len(input_names)):
context.set_binding_shape(i, INPUT_SHAPE)
assert context.all_binding_shapes_specified
def lowerCAmelCase_ ( lowercase_ : Any ):
'''simple docstring'''
return trt.volume(engine.get_binding_shape(lowercase_ ) ) * engine.get_binding_dtype(lowercase_ ).itemsize
# Allocate device memory for inputs and outputs.
_lowerCamelCase = [cuda.mem_alloc(binding_nbytes(binding)) for binding in engine if engine.binding_is_input(binding)]
# Allocate output buffer
_lowerCamelCase = cuda.pagelocked_empty(tuple(context.get_binding_shape(3)), dtype=np.floataa)
_lowerCamelCase = cuda.pagelocked_empty(tuple(context.get_binding_shape(4)), dtype=np.floataa)
_lowerCamelCase = cuda.mem_alloc(h_outputa.nbytes)
_lowerCamelCase = cuda.mem_alloc(h_outputa.nbytes)
# Create a stream in which to copy inputs/outputs and run inference.
_lowerCamelCase = cuda.Stream()
# Evaluation
logger.info('''***** Running Evaluation *****''')
logger.info(f' Num examples = {len(eval_dataset)}')
logger.info(f' Batch size = {args.per_device_eval_batch_size}')
_lowerCamelCase = 0.0
_lowerCamelCase = 0
_lowerCamelCase = timeit.default_timer()
_lowerCamelCase = None
for step, batch in enumerate(eval_dataloader):
_lowerCamelCase , _lowerCamelCase = model_infer(batch, context, d_inputs, h_outputa, h_outputa, d_outputa, d_outputa, stream)
total_time += infer_time
niter += 1
_lowerCamelCase , _lowerCamelCase = outputs
_lowerCamelCase = torch.tensor(start_logits)
_lowerCamelCase = torch.tensor(end_logits)
# necessary to pad predictions and labels for being gathered
_lowerCamelCase = accelerator.pad_across_processes(start_logits, dim=1, pad_index=-1_00)
_lowerCamelCase = accelerator.pad_across_processes(end_logits, dim=1, pad_index=-1_00)
_lowerCamelCase = (accelerator.gather(start_logits).cpu().numpy(), accelerator.gather(end_logits).cpu().numpy())
_lowerCamelCase = logits if all_preds is None else nested_concat(all_preds, logits, padding_index=-1_00)
if all_preds is not None:
_lowerCamelCase = nested_truncate(all_preds, len(eval_dataset))
_lowerCamelCase = timeit.default_timer() - start_time
logger.info(''' Evaluation done in total %f secs (%f sec per example)''', evalTime, evalTime / len(eval_dataset))
# Inference time from TRT
logger.info('''Average Inference Time = {:.3f} ms'''.format(total_time * 10_00 / niter))
logger.info('''Total Inference Time = {:.3f} ms'''.format(total_time * 10_00))
logger.info('''Total Number of Inference = %d''', niter)
_lowerCamelCase = post_processing_function(eval_examples, eval_dataset, all_preds)
_lowerCamelCase = metric.compute(predictions=prediction.predictions, references=prediction.label_ids)
logger.info(f'Evaluation metrics: {eval_metric}')
| 674 | 0 |
def __lowerCamelCase ( _lowercase = 100 ) -> List[Any]:
UpperCamelCase = (n * (n + 1) // 2) ** 2
UpperCamelCase = n * (n + 1) * (2 * n + 1) // 6
return sum_cubes - sum_squares
if __name__ == "__main__":
print(F"{solution() = }")
| 282 |
"""simple docstring"""
from __future__ import annotations
import unittest
from transformers import is_tf_available, is_torch_available
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, SMALL_MODEL_IDENTIFIER, is_pt_tf_cross_test, slow
if is_tf_available():
from transformers import (
AutoConfig,
BertConfig,
GPTaConfig,
TaConfig,
TFAutoModel,
TFAutoModelForCausalLM,
TFAutoModelForMaskedLM,
TFAutoModelForPreTraining,
TFAutoModelForQuestionAnswering,
TFAutoModelForSeqaSeqLM,
TFAutoModelForSequenceClassification,
TFAutoModelWithLMHead,
TFBertForMaskedLM,
TFBertForPreTraining,
TFBertForQuestionAnswering,
TFBertForSequenceClassification,
TFBertModel,
TFGPTaLMHeadModel,
TFRobertaForMaskedLM,
TFTaForConditionalGeneration,
)
from transformers.models.bert.modeling_tf_bert import TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST
from transformers.models.gpta.modeling_tf_gpta import TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST
from transformers.models.ta.modeling_tf_ta import TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST
if is_torch_available():
from transformers import (
AutoModel,
AutoModelForCausalLM,
AutoModelForMaskedLM,
AutoModelForPreTraining,
AutoModelForQuestionAnswering,
AutoModelForSeqaSeqLM,
AutoModelForSequenceClassification,
AutoModelWithLMHead,
BertForMaskedLM,
BertForPreTraining,
BertForQuestionAnswering,
BertForSequenceClassification,
BertModel,
GPTaLMHeadModel,
RobertaForMaskedLM,
TaForConditionalGeneration,
)
@is_pt_tf_cross_test
class snake_case ( unittest.TestCase ):
@slow
def SCREAMING_SNAKE_CASE_ ( self :Tuple ):
# for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
for model_name in ["bert-base-uncased"]:
__SCREAMING_SNAKE_CASE : List[Any] = AutoConfig.from_pretrained(_lowerCamelCase )
self.assertIsNotNone(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : List[str] = TFAutoModel.from_pretrained(_lowerCamelCase , from_pt=_lowerCamelCase )
self.assertIsNotNone(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : Optional[Any] = AutoModel.from_pretrained(_lowerCamelCase , from_tf=_lowerCamelCase )
self.assertIsNotNone(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
@slow
def SCREAMING_SNAKE_CASE_ ( self :Optional[int] ):
# for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
for model_name in ["bert-base-uncased"]:
__SCREAMING_SNAKE_CASE : List[str] = AutoConfig.from_pretrained(_lowerCamelCase )
self.assertIsNotNone(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : Tuple = TFAutoModelForPreTraining.from_pretrained(_lowerCamelCase , from_pt=_lowerCamelCase )
self.assertIsNotNone(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : Optional[int] = AutoModelForPreTraining.from_pretrained(_lowerCamelCase , from_tf=_lowerCamelCase )
self.assertIsNotNone(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
@slow
def SCREAMING_SNAKE_CASE_ ( self :Any ):
for model_name in TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__SCREAMING_SNAKE_CASE : Optional[int] = AutoConfig.from_pretrained(_lowerCamelCase )
self.assertIsNotNone(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : int = TFAutoModelForCausalLM.from_pretrained(_lowerCamelCase , from_pt=_lowerCamelCase )
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : List[Any] = TFAutoModelForCausalLM.from_pretrained(
_lowerCamelCase , output_loading_info=_lowerCamelCase , from_pt=_lowerCamelCase )
self.assertIsNotNone(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : Tuple = AutoModelForCausalLM.from_pretrained(_lowerCamelCase , from_tf=_lowerCamelCase )
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Dict = AutoModelForCausalLM.from_pretrained(
_lowerCamelCase , output_loading_info=_lowerCamelCase , from_tf=_lowerCamelCase )
self.assertIsNotNone(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
@slow
def SCREAMING_SNAKE_CASE_ ( self :List[Any] ):
for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__SCREAMING_SNAKE_CASE : Any = AutoConfig.from_pretrained(_lowerCamelCase )
self.assertIsNotNone(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : Any = TFAutoModelWithLMHead.from_pretrained(_lowerCamelCase , from_pt=_lowerCamelCase )
self.assertIsNotNone(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : Optional[Any] = AutoModelWithLMHead.from_pretrained(_lowerCamelCase , from_tf=_lowerCamelCase )
self.assertIsNotNone(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
@slow
def SCREAMING_SNAKE_CASE_ ( self :Dict ):
for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__SCREAMING_SNAKE_CASE : Union[str, Any] = AutoConfig.from_pretrained(_lowerCamelCase )
self.assertIsNotNone(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : Optional[int] = TFAutoModelForMaskedLM.from_pretrained(_lowerCamelCase , from_pt=_lowerCamelCase )
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : List[str] = TFAutoModelForMaskedLM.from_pretrained(
_lowerCamelCase , output_loading_info=_lowerCamelCase , from_pt=_lowerCamelCase )
self.assertIsNotNone(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : Tuple = AutoModelForMaskedLM.from_pretrained(_lowerCamelCase , from_tf=_lowerCamelCase )
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : List[Any] = AutoModelForMaskedLM.from_pretrained(
_lowerCamelCase , output_loading_info=_lowerCamelCase , from_tf=_lowerCamelCase )
self.assertIsNotNone(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
@slow
def SCREAMING_SNAKE_CASE_ ( self :int ):
for model_name in TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__SCREAMING_SNAKE_CASE : str = AutoConfig.from_pretrained(_lowerCamelCase )
self.assertIsNotNone(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : Tuple = TFAutoModelForSeqaSeqLM.from_pretrained(_lowerCamelCase , from_pt=_lowerCamelCase )
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : str = TFAutoModelForSeqaSeqLM.from_pretrained(
_lowerCamelCase , output_loading_info=_lowerCamelCase , from_pt=_lowerCamelCase )
self.assertIsNotNone(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : Optional[int] = AutoModelForSeqaSeqLM.from_pretrained(_lowerCamelCase , from_tf=_lowerCamelCase )
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Dict = AutoModelForSeqaSeqLM.from_pretrained(
_lowerCamelCase , output_loading_info=_lowerCamelCase , from_tf=_lowerCamelCase )
self.assertIsNotNone(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
@slow
def SCREAMING_SNAKE_CASE_ ( self :Optional[Any] ):
# for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
for model_name in ["bert-base-uncased"]:
__SCREAMING_SNAKE_CASE : Optional[Any] = AutoConfig.from_pretrained(_lowerCamelCase )
self.assertIsNotNone(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : int = TFAutoModelForSequenceClassification.from_pretrained(_lowerCamelCase , from_pt=_lowerCamelCase )
self.assertIsNotNone(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : Optional[Any] = AutoModelForSequenceClassification.from_pretrained(_lowerCamelCase , from_tf=_lowerCamelCase )
self.assertIsNotNone(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
@slow
def SCREAMING_SNAKE_CASE_ ( self :List[Any] ):
# for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
for model_name in ["bert-base-uncased"]:
__SCREAMING_SNAKE_CASE : Optional[Any] = AutoConfig.from_pretrained(_lowerCamelCase )
self.assertIsNotNone(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : str = TFAutoModelForQuestionAnswering.from_pretrained(_lowerCamelCase , from_pt=_lowerCamelCase )
self.assertIsNotNone(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : List[Any] = AutoModelForQuestionAnswering.from_pretrained(_lowerCamelCase , from_tf=_lowerCamelCase )
self.assertIsNotNone(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :Union[str, Any] ):
__SCREAMING_SNAKE_CASE : Tuple = TFAutoModelWithLMHead.from_pretrained(_lowerCamelCase , from_pt=_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
self.assertEqual(model.num_parameters() , 1_4_4_1_0 )
self.assertEqual(model.num_parameters(only_trainable=_lowerCamelCase ) , 1_4_4_1_0 )
__SCREAMING_SNAKE_CASE : Tuple = AutoModelWithLMHead.from_pretrained(_lowerCamelCase , from_tf=_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
self.assertEqual(model.num_parameters() , 1_4_4_1_0 )
self.assertEqual(model.num_parameters(only_trainable=_lowerCamelCase ) , 1_4_4_1_0 )
def SCREAMING_SNAKE_CASE_ ( self :str ):
__SCREAMING_SNAKE_CASE : Optional[Any] = TFAutoModelWithLMHead.from_pretrained(_lowerCamelCase , from_pt=_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
self.assertEqual(model.num_parameters() , 1_4_4_1_0 )
self.assertEqual(model.num_parameters(only_trainable=_lowerCamelCase ) , 1_4_4_1_0 )
__SCREAMING_SNAKE_CASE : Union[str, Any] = AutoModelWithLMHead.from_pretrained(_lowerCamelCase , from_tf=_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
self.assertEqual(model.num_parameters() , 1_4_4_1_0 )
self.assertEqual(model.num_parameters(only_trainable=_lowerCamelCase ) , 1_4_4_1_0 )
| 674 | 0 |
"""simple docstring"""
def UpperCAmelCase ( A__: int ) -> Any:
__lowerCamelCase : int = n ** (1 / 3)
return (val * val * val) == n
if __name__ == "__main__":
print(perfect_cube(27))
print(perfect_cube(4))
| 594 |
"""simple docstring"""
import os
import re
import warnings
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
if TYPE_CHECKING:
from ...tokenization_utils_base import TextInput
from ...utils import logging
_lowerCamelCase = logging.get_logger(__name__)
_lowerCamelCase = {'''vocab_file''': '''spiece.model'''}
_lowerCamelCase = {
'''vocab_file''': {
'''t5-small''': '''https://huggingface.co/t5-small/resolve/main/spiece.model''',
'''t5-base''': '''https://huggingface.co/t5-base/resolve/main/spiece.model''',
'''t5-large''': '''https://huggingface.co/t5-large/resolve/main/spiece.model''',
'''t5-3b''': '''https://huggingface.co/t5-3b/resolve/main/spiece.model''',
'''t5-11b''': '''https://huggingface.co/t5-11b/resolve/main/spiece.model''',
}
}
# TODO(PVP) - this should be removed in Transformers v5
_lowerCamelCase = {
'''t5-small''': 5_12,
'''t5-base''': 5_12,
'''t5-large''': 5_12,
'''t5-3b''': 5_12,
'''t5-11b''': 5_12,
}
_lowerCamelCase = '''▁'''
class snake_case ( __UpperCAmelCase ):
lowerCamelCase__ = VOCAB_FILES_NAMES
lowerCamelCase__ = PRETRAINED_VOCAB_FILES_MAP
lowerCamelCase__ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowerCamelCase__ = ['''input_ids''', '''attention_mask''']
def __init__( self :int , _lowerCamelCase :Optional[Any] , _lowerCamelCase :Union[str, Any]="</s>" , _lowerCamelCase :List[Any]="<unk>" , _lowerCamelCase :Union[str, Any]="<pad>" , _lowerCamelCase :int=1_0_0 , _lowerCamelCase :Union[str, Any]=None , _lowerCamelCase :Optional[Dict[str, Any]] = None , _lowerCamelCase :int=True , **_lowerCamelCase :List[Any] , ):
# Add extra_ids to the special token list
if extra_ids > 0 and additional_special_tokens is None:
__SCREAMING_SNAKE_CASE : Union[str, Any] = [f'''<extra_id_{i}>''' for i in range(_lowerCamelCase )]
elif extra_ids > 0 and additional_special_tokens is not None:
# Check that we have the right number of extra_id special tokens
__SCREAMING_SNAKE_CASE : Optional[int] = len(set(filter(lambda _lowerCamelCase : bool('''extra_id''' in str(_lowerCamelCase ) ) , _lowerCamelCase ) ) )
if extra_tokens != extra_ids:
raise ValueError(
f'''Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are'''
''' provided to T5Tokenizer. In this case the additional_special_tokens must include the extra_ids'''
''' tokens''' )
if legacy:
logger.warning_once(
f'''You are using the legacy behaviour of the {self.__class__}. This means that tokens that come after special tokens will not be properly handled. We recommend you to'''
''' read the related pull request available at https://github.com/huggingface/transformers/pull/24565''' )
__SCREAMING_SNAKE_CASE : Optional[Any] = legacy
__SCREAMING_SNAKE_CASE : Optional[int] = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
eos_token=_lowerCamelCase , unk_token=_lowerCamelCase , pad_token=_lowerCamelCase , extra_ids=_lowerCamelCase , additional_special_tokens=_lowerCamelCase , sp_model_kwargs=self.sp_model_kwargs , legacy=_lowerCamelCase , **_lowerCamelCase , )
__SCREAMING_SNAKE_CASE : Tuple = vocab_file
__SCREAMING_SNAKE_CASE : List[str] = extra_ids
__SCREAMING_SNAKE_CASE : Optional[int] = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(_lowerCamelCase )
@staticmethod
def SCREAMING_SNAKE_CASE_ ( _lowerCamelCase :str , _lowerCamelCase :Union[str, Any] , _lowerCamelCase :int ):
if pretrained_model_name_or_path in TaTokenizer.max_model_input_sizes:
__SCREAMING_SNAKE_CASE : Any = TaTokenizer.max_model_input_sizes[pretrained_model_name_or_path]
if init_max_model_length is not None and init_max_model_length != max_model_length:
return init_max_model_length
elif init_max_model_length is None:
warnings.warn(
'''This tokenizer was incorrectly instantiated with a model max length of'''
f''' {deprecated_max_model_length} which will be corrected in Transformers v5.\nFor now, this'''
''' behavior is kept to avoid breaking backwards compatibility when padding/encoding with'''
''' `truncation is True`.\n- Be aware that you SHOULD NOT rely on'''
f''' {pretrained_model_name_or_path} automatically truncating your input to'''
f''' {deprecated_max_model_length} when padding/encoding.\n- If you want to encode/pad to sequences'''
f''' longer than {deprecated_max_model_length} you can either instantiate this tokenizer with'''
''' `model_max_length` or pass `max_length` when encoding/padding.\n- To avoid this warning, please'''
''' instantiate this tokenizer with `model_max_length` set to your preferred value.''' , _lowerCamelCase , )
return max_model_length
@property
def SCREAMING_SNAKE_CASE_ ( self :Tuple ):
return self.sp_model.get_piece_size() + self._extra_ids
def SCREAMING_SNAKE_CASE_ ( self :Union[str, Any] ):
__SCREAMING_SNAKE_CASE : str = {self.convert_ids_to_tokens(_lowerCamelCase ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def SCREAMING_SNAKE_CASE_ ( self :Union[str, Any] , _lowerCamelCase :List[int] , _lowerCamelCase :Optional[List[int]] = None , _lowerCamelCase :bool = False ):
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=_lowerCamelCase , token_ids_a=_lowerCamelCase , already_has_special_tokens=_lowerCamelCase )
# normal case: some special tokens
if token_ids_a is None:
return ([0] * len(_lowerCamelCase )) + [1]
return ([0] * len(_lowerCamelCase )) + [1] + ([0] * len(_lowerCamelCase )) + [1]
def SCREAMING_SNAKE_CASE_ ( self :List[str] ):
return list(
set(filter(lambda _lowerCamelCase : bool(re.search(r'''<extra_id_\d+>''' , _lowerCamelCase ) ) is not None , self.additional_special_tokens ) ) )
def SCREAMING_SNAKE_CASE_ ( self :List[Any] ):
return [self._convert_token_to_id(_lowerCamelCase ) for token in self.get_sentinel_tokens()]
def SCREAMING_SNAKE_CASE_ ( self :Any , _lowerCamelCase :List[int] ):
if len(_lowerCamelCase ) > 0 and token_ids[-1] == self.eos_token_id:
warnings.warn(
f'''This sequence already has {self.eos_token}. In future versions this behavior may lead to duplicated'''
''' eos tokens being added.''' )
return token_ids
else:
return token_ids + [self.eos_token_id]
def SCREAMING_SNAKE_CASE_ ( self :Union[str, Any] , _lowerCamelCase :List[int] , _lowerCamelCase :Optional[List[int]] = None ):
__SCREAMING_SNAKE_CASE : List[str] = [self.eos_token_id]
if token_ids_a is None:
return len(token_ids_a + eos ) * [0]
return len(token_ids_a + eos + token_ids_a + eos ) * [0]
def SCREAMING_SNAKE_CASE_ ( self :List[Any] , _lowerCamelCase :List[int] , _lowerCamelCase :Optional[List[int]] = None ):
__SCREAMING_SNAKE_CASE : Optional[Any] = self._add_eos_if_not_present(_lowerCamelCase )
if token_ids_a is None:
return token_ids_a
else:
__SCREAMING_SNAKE_CASE : Union[str, Any] = self._add_eos_if_not_present(_lowerCamelCase )
return token_ids_a + token_ids_a
def __getstate__( self :Union[str, Any] ):
__SCREAMING_SNAKE_CASE : Any = self.__dict__.copy()
__SCREAMING_SNAKE_CASE : List[str] = None
return state
def __setstate__( self :Optional[Any] , _lowerCamelCase :List[str] ):
__SCREAMING_SNAKE_CASE : Tuple = d
# for backward compatibility
if not hasattr(self , '''sp_model_kwargs''' ):
__SCREAMING_SNAKE_CASE : Optional[int] = {}
__SCREAMING_SNAKE_CASE : Optional[int] = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(self.vocab_file )
def SCREAMING_SNAKE_CASE_ ( self :int , _lowerCamelCase :"TextInput" , **_lowerCamelCase :str ):
# Replace the SPIECE_UNDERLINE with a space to make sure SPIECE_UNDERLINE is only used at
# the beginning of the text
if not self.legacy:
__SCREAMING_SNAKE_CASE : Dict = SPIECE_UNDERLINE + text.replace(_lowerCamelCase , ''' ''' )
return super().tokenize(_lowerCamelCase , **_lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :Optional[Any] , _lowerCamelCase :List[Any] , **_lowerCamelCase :Dict ):
if not self.legacy:
__SCREAMING_SNAKE_CASE : str = text.startswith(_lowerCamelCase )
if is_first:
__SCREAMING_SNAKE_CASE : str = text[1:]
__SCREAMING_SNAKE_CASE : Tuple = self.sp_model.encode(_lowerCamelCase , out_type=_lowerCamelCase )
if not self.legacy and not is_first and not text.startswith(''' ''' ) and tokens[0].startswith(_lowerCamelCase ):
__SCREAMING_SNAKE_CASE : Optional[int] = ([tokens[0][1:]] if len(tokens[0] ) > 1 else []) + tokens[1:]
return tokens
def SCREAMING_SNAKE_CASE_ ( self :Optional[Any] , _lowerCamelCase :Optional[Any] ):
if token.startswith('''<extra_id_''' ):
__SCREAMING_SNAKE_CASE : Tuple = re.match(r'''<extra_id_(\d+)>''' , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : Union[str, Any] = int(match.group(1 ) )
return self.vocab_size - num - 1
return self.sp_model.piece_to_id(_lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :str , _lowerCamelCase :Optional[int] ):
if index < self.sp_model.get_piece_size():
__SCREAMING_SNAKE_CASE : List[Any] = self.sp_model.IdToPiece(_lowerCamelCase )
else:
__SCREAMING_SNAKE_CASE : Dict = f'''<extra_id_{self.vocab_size - 1 - index}>'''
return token
def SCREAMING_SNAKE_CASE_ ( self :Tuple , _lowerCamelCase :Any ):
__SCREAMING_SNAKE_CASE : str = []
__SCREAMING_SNAKE_CASE : Dict = ''''''
__SCREAMING_SNAKE_CASE : Dict = False
for token in tokens:
# make sure that special tokens are not decoded using sentencepiece model
if token in self.all_special_tokens:
if not prev_is_special:
out_string += " "
out_string += self.sp_model.decode(_lowerCamelCase ) + token
__SCREAMING_SNAKE_CASE : List[str] = True
__SCREAMING_SNAKE_CASE : str = []
else:
current_sub_tokens.append(_lowerCamelCase )
__SCREAMING_SNAKE_CASE : int = False
out_string += self.sp_model.decode(_lowerCamelCase )
return out_string.strip()
def SCREAMING_SNAKE_CASE_ ( self :Optional[Any] , _lowerCamelCase :str , _lowerCamelCase :Optional[str] = None ):
if not os.path.isdir(_lowerCamelCase ):
logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' )
return
__SCREAMING_SNAKE_CASE : List[str] = os.path.join(
_lowerCamelCase , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(_lowerCamelCase ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file , _lowerCamelCase )
elif not os.path.isfile(self.vocab_file ):
with open(_lowerCamelCase , '''wb''' ) as fi:
__SCREAMING_SNAKE_CASE : Any = self.sp_model.serialized_model_proto()
fi.write(_lowerCamelCase )
return (out_vocab_file,)
| 674 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__snake_case = {"configuration_xlnet": ["XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "XLNetConfig"]}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__snake_case = ["XLNetTokenizer"]
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__snake_case = ["XLNetTokenizerFast"]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__snake_case = [
"XLNET_PRETRAINED_MODEL_ARCHIVE_LIST",
"XLNetForMultipleChoice",
"XLNetForQuestionAnswering",
"XLNetForQuestionAnsweringSimple",
"XLNetForSequenceClassification",
"XLNetForTokenClassification",
"XLNetLMHeadModel",
"XLNetModel",
"XLNetPreTrainedModel",
"load_tf_weights_in_xlnet",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__snake_case = [
"TF_XLNET_PRETRAINED_MODEL_ARCHIVE_LIST",
"TFXLNetForMultipleChoice",
"TFXLNetForQuestionAnsweringSimple",
"TFXLNetForSequenceClassification",
"TFXLNetForTokenClassification",
"TFXLNetLMHeadModel",
"TFXLNetMainLayer",
"TFXLNetModel",
"TFXLNetPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_xlnet import XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP, XLNetConfig
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_xlnet import XLNetTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_xlnet_fast import XLNetTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_xlnet import (
XLNET_PRETRAINED_MODEL_ARCHIVE_LIST,
XLNetForMultipleChoice,
XLNetForQuestionAnswering,
XLNetForQuestionAnsweringSimple,
XLNetForSequenceClassification,
XLNetForTokenClassification,
XLNetLMHeadModel,
XLNetModel,
XLNetPreTrainedModel,
load_tf_weights_in_xlnet,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_xlnet import (
TF_XLNET_PRETRAINED_MODEL_ARCHIVE_LIST,
TFXLNetForMultipleChoice,
TFXLNetForQuestionAnsweringSimple,
TFXLNetForSequenceClassification,
TFXLNetForTokenClassification,
TFXLNetLMHeadModel,
TFXLNetMainLayer,
TFXLNetModel,
TFXLNetPreTrainedModel,
)
else:
import sys
__snake_case = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 386 |
"""simple docstring"""
import os
import pytest
import yaml
from datasets.features.features import Features, Value
from datasets.info import DatasetInfo, DatasetInfosDict
@pytest.mark.parametrize(
'''files''' , [
['''full:README.md''', '''dataset_infos.json'''],
['''empty:README.md''', '''dataset_infos.json'''],
['''dataset_infos.json'''],
['''full:README.md'''],
] , )
def lowerCAmelCase_ ( lowercase_ : List[str] , lowercase_ : str ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : Optional[int] = tmp_path_factory.mktemp('''dset_infos_dir''' )
if "full:README.md" in files:
with open(dataset_infos_dir / '''README.md''' , '''w''' ) as f:
f.write('''---\ndataset_info:\n dataset_size: 42\n---''' )
if "empty:README.md" in files:
with open(dataset_infos_dir / '''README.md''' , '''w''' ) as f:
f.write('''''' )
# we want to support dataset_infos.json for backward compatibility
if "dataset_infos.json" in files:
with open(dataset_infos_dir / '''dataset_infos.json''' , '''w''' ) as f:
f.write('''{"default": {"dataset_size": 42}}''' )
__SCREAMING_SNAKE_CASE : Optional[Any] = DatasetInfosDict.from_directory(lowercase_ )
assert dataset_infos
assert dataset_infos["default"].dataset_size == 42
@pytest.mark.parametrize(
'''dataset_info''' , [
DatasetInfo(),
DatasetInfo(
description='''foo''' , features=Features({'''a''': Value('''int32''' )} ) , builder_name='''builder''' , config_name='''config''' , version='''1.0.0''' , splits=[{'''name''': '''train'''}] , download_size=42 , ),
] , )
def lowerCAmelCase_ ( lowercase_ : Any , lowercase_ : DatasetInfo ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : List[str] = str(lowercase_ )
dataset_info.write_to_directory(lowercase_ )
__SCREAMING_SNAKE_CASE : Dict = DatasetInfo.from_directory(lowercase_ )
assert dataset_info == reloaded
assert os.path.exists(os.path.join(lowercase_ , '''dataset_info.json''' ) )
def lowerCAmelCase_ ( ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : Tuple = DatasetInfo(
description='''foo''' , citation='''bar''' , homepage='''https://foo.bar''' , license='''CC0''' , features=Features({'''a''': Value('''int32''' )} ) , post_processed={} , supervised_keys=() , task_templates=[] , builder_name='''builder''' , config_name='''config''' , version='''1.0.0''' , splits=[{'''name''': '''train''', '''num_examples''': 42}] , download_checksums={} , download_size=1337 , post_processing_size=442 , dataset_size=1234 , size_in_bytes=1337 + 442 + 1234 , )
__SCREAMING_SNAKE_CASE : Optional[int] = dataset_info._to_yaml_dict()
assert sorted(lowercase_ ) == sorted(DatasetInfo._INCLUDED_INFO_IN_YAML )
for key in DatasetInfo._INCLUDED_INFO_IN_YAML:
assert key in dataset_info_yaml_dict
assert isinstance(dataset_info_yaml_dict[key] , (list, dict, int, str) )
__SCREAMING_SNAKE_CASE : int = yaml.safe_dump(lowercase_ )
__SCREAMING_SNAKE_CASE : Union[str, Any] = yaml.safe_load(lowercase_ )
assert dataset_info_yaml_dict == reloaded
def lowerCAmelCase_ ( ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : Union[str, Any] = DatasetInfo()
__SCREAMING_SNAKE_CASE : Union[str, Any] = dataset_info._to_yaml_dict()
assert dataset_info_yaml_dict == {}
@pytest.mark.parametrize(
'''dataset_infos_dict''' , [
DatasetInfosDict(),
DatasetInfosDict({'''default''': DatasetInfo()} ),
DatasetInfosDict({'''my_config_name''': DatasetInfo()} ),
DatasetInfosDict(
{
'''default''': DatasetInfo(
description='''foo''' , features=Features({'''a''': Value('''int32''' )} ) , builder_name='''builder''' , config_name='''config''' , version='''1.0.0''' , splits=[{'''name''': '''train'''}] , download_size=42 , )
} ),
DatasetInfosDict(
{
'''v1''': DatasetInfo(dataset_size=42 ),
'''v2''': DatasetInfo(dataset_size=1337 ),
} ),
] , )
def lowerCAmelCase_ ( lowercase_ : List[Any] , lowercase_ : DatasetInfosDict ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : Optional[int] = str(lowercase_ )
dataset_infos_dict.write_to_directory(lowercase_ )
__SCREAMING_SNAKE_CASE : Optional[Any] = DatasetInfosDict.from_directory(lowercase_ )
# the config_name of the dataset_infos_dict take over the attribute
for config_name, dataset_info in dataset_infos_dict.items():
__SCREAMING_SNAKE_CASE : Optional[int] = config_name
# the yaml representation doesn't include fields like description or citation
# so we just test that we can recover what we can from the yaml
__SCREAMING_SNAKE_CASE : Tuple = DatasetInfo._from_yaml_dict(dataset_info._to_yaml_dict() )
assert dataset_infos_dict == reloaded
if dataset_infos_dict:
assert os.path.exists(os.path.join(lowercase_ , '''README.md''' ) )
| 674 | 0 |
def A__ (snake_case : int ) -> Union[str, Any]:
assert (
isinstance(lowercase_ , lowercase_ ) and number_of_steps > 0
), F'''number_of_steps needs to be positive integer, your input {number_of_steps}'''
if number_of_steps == 1:
return 1
__UpperCamelCase : Union[str, Any] = 1, 1
for _ in range(number_of_steps - 1 ):
__UpperCamelCase : Union[str, Any] = current + previous, current
return current
if __name__ == "__main__":
import doctest
doctest.testmod()
| 279 |
"""simple docstring"""
from typing import Optional, Union
import torch
from torch import nn
from ...configuration_utils import ConfigMixin, register_to_config
from ...models.modeling_utils import ModelMixin
class snake_case ( __UpperCAmelCase , __UpperCAmelCase ):
@register_to_config
def __init__( self :List[str] , _lowerCamelCase :int = 7_6_8 , ):
super().__init__()
__SCREAMING_SNAKE_CASE : Union[str, Any] = nn.Parameter(torch.zeros(1 , _lowerCamelCase ) )
__SCREAMING_SNAKE_CASE : List[Any] = nn.Parameter(torch.ones(1 , _lowerCamelCase ) )
def SCREAMING_SNAKE_CASE_ ( self :Any , _lowerCamelCase :Optional[Union[str, torch.device]] = None , _lowerCamelCase :Optional[torch.dtype] = None , ):
__SCREAMING_SNAKE_CASE : Optional[int] = nn.Parameter(self.mean.to(_lowerCamelCase ).to(_lowerCamelCase ) )
__SCREAMING_SNAKE_CASE : Tuple = nn.Parameter(self.std.to(_lowerCamelCase ).to(_lowerCamelCase ) )
return self
def SCREAMING_SNAKE_CASE_ ( self :int , _lowerCamelCase :Tuple ):
__SCREAMING_SNAKE_CASE : Tuple = (embeds - self.mean) * 1.0 / self.std
return embeds
def SCREAMING_SNAKE_CASE_ ( self :Optional[Any] , _lowerCamelCase :Optional[int] ):
__SCREAMING_SNAKE_CASE : List[Any] = (embeds * self.std) + self.mean
return embeds
| 674 | 0 |
import argparse
import torch
from safetensors.torch import load_file
from diffusers import StableDiffusionPipeline
def a ( a , a , a , a , a ) ->int:
'''simple docstring'''
SCREAMING_SNAKE_CASE = StableDiffusionPipeline.from_pretrained(lowercase_ , torch_dtype=torch.floataa )
# load LoRA weight from .safetensors
SCREAMING_SNAKE_CASE = load_file(lowercase_ )
SCREAMING_SNAKE_CASE = []
# directly update weight in diffusers model
for key in state_dict:
# it is suggested to print out the key, it usually will be something like below
# "lora_te_text_model_encoder_layers_0_self_attn_k_proj.lora_down.weight"
# as we have set the alpha beforehand, so just skip
if ".alpha" in key or key in visited:
continue
if "text" in key:
SCREAMING_SNAKE_CASE = key.split('''.''' )[0].split(LORA_PREFIX_TEXT_ENCODER + '''_''' )[-1].split('''_''' )
SCREAMING_SNAKE_CASE = pipeline.text_encoder
else:
SCREAMING_SNAKE_CASE = key.split('''.''' )[0].split(LORA_PREFIX_UNET + '''_''' )[-1].split('''_''' )
SCREAMING_SNAKE_CASE = pipeline.unet
# find the target layer
SCREAMING_SNAKE_CASE = layer_infos.pop(0 )
while len(lowercase_ ) > -1:
try:
SCREAMING_SNAKE_CASE = curr_layer.__getattr__(lowercase_ )
if len(lowercase_ ) > 0:
SCREAMING_SNAKE_CASE = layer_infos.pop(0 )
elif len(lowercase_ ) == 0:
break
except Exception:
if len(lowercase_ ) > 0:
temp_name += "_" + layer_infos.pop(0 )
else:
SCREAMING_SNAKE_CASE = layer_infos.pop(0 )
SCREAMING_SNAKE_CASE = []
if "lora_down" in key:
pair_keys.append(key.replace('''lora_down''' , '''lora_up''' ) )
pair_keys.append(lowercase_ )
else:
pair_keys.append(lowercase_ )
pair_keys.append(key.replace('''lora_up''' , '''lora_down''' ) )
# update weight
if len(state_dict[pair_keys[0]].shape ) == 4:
SCREAMING_SNAKE_CASE = state_dict[pair_keys[0]].squeeze(3 ).squeeze(2 ).to(torch.floataa )
SCREAMING_SNAKE_CASE = state_dict[pair_keys[1]].squeeze(3 ).squeeze(2 ).to(torch.floataa )
curr_layer.weight.data += alpha * torch.mm(lowercase_ , lowercase_ ).unsqueeze(2 ).unsqueeze(3 )
else:
SCREAMING_SNAKE_CASE = state_dict[pair_keys[0]].to(torch.floataa )
SCREAMING_SNAKE_CASE = state_dict[pair_keys[1]].to(torch.floataa )
curr_layer.weight.data += alpha * torch.mm(lowercase_ , lowercase_ )
# update visited list
for item in pair_keys:
visited.append(lowercase_ )
return pipeline
if __name__ == "__main__":
__lowerCAmelCase = argparse.ArgumentParser()
parser.add_argument(
'--base_model_path', default=None, type=str, required=True, help='Path to the base model in diffusers format.'
)
parser.add_argument(
'--checkpoint_path', default=None, type=str, required=True, help='Path to the checkpoint to convert.'
)
parser.add_argument('--dump_path', default=None, type=str, required=True, help='Path to the output model.')
parser.add_argument(
'--lora_prefix_unet', default='lora_unet', type=str, help='The prefix of UNet weight in safetensors'
)
parser.add_argument(
'--lora_prefix_text_encoder',
default='lora_te',
type=str,
help='The prefix of text encoder weight in safetensors',
)
parser.add_argument('--alpha', default=0.75, type=float, help='The merging ratio in W = W0 + alpha * deltaW')
parser.add_argument(
'--to_safetensors', action='store_true', help='Whether to store pipeline in safetensors format or not.'
)
parser.add_argument('--device', type=str, help='Device to use (e.g. cpu, cuda:0, cuda:1, etc.)')
__lowerCAmelCase = parser.parse_args()
__lowerCAmelCase = args.base_model_path
__lowerCAmelCase = args.checkpoint_path
__lowerCAmelCase = args.dump_path
__lowerCAmelCase = args.lora_prefix_unet
__lowerCAmelCase = args.lora_prefix_text_encoder
__lowerCAmelCase = args.alpha
__lowerCAmelCase = convert(base_model_path, checkpoint_path, lora_prefix_unet, lora_prefix_text_encoder, alpha)
__lowerCAmelCase = pipe.to(args.device)
pipe.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors) | 201 |
"""simple docstring"""
import argparse
import torch
from transformers import BertConfig, BertForPreTraining, load_tf_weights_in_bert
from transformers.utils import logging
logging.set_verbosity_info()
def lowerCAmelCase_ ( lowercase_ : Dict , lowercase_ : int , lowercase_ : Any ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : Tuple = BertConfig.from_json_file(lowercase_ )
print(F'''Building PyTorch model from configuration: {config}''' )
__SCREAMING_SNAKE_CASE : Optional[int] = BertForPreTraining(lowercase_ )
# Load weights from tf checkpoint
load_tf_weights_in_bert(lowercase_ , lowercase_ , lowercase_ )
# Save pytorch-model
print(F'''Save PyTorch model to {pytorch_dump_path}''' )
torch.save(model.state_dict() , lowercase_ )
if __name__ == "__main__":
_lowerCamelCase = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--tf_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
)
parser.add_argument(
'''--bert_config_file''',
default=None,
type=str,
required=True,
help=(
'''The config json file corresponding to the pre-trained BERT model. \n'''
'''This specifies the model architecture.'''
),
)
parser.add_argument(
'''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
)
_lowerCamelCase = parser.parse_args()
convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)
| 674 | 0 |
"""simple docstring"""
import unittest
from transformers import SPIECE_UNDERLINE, XLNetTokenizer, XLNetTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
a : List[str] = get_tests_dir("""fixtures/test_sentencepiece.model""")
@require_sentencepiece
@require_tokenizers
class __UpperCAmelCase( __UpperCAmelCase , unittest.TestCase ):
"""simple docstring"""
__lowerCamelCase = XLNetTokenizer
__lowerCamelCase = XLNetTokenizerFast
__lowerCamelCase = True
__lowerCamelCase = True
def UpperCAmelCase_ ( self ):
'''simple docstring'''
super().setUp()
# We have a SentencePiece fixture for testing
lowercase__ : Dict= XLNetTokenizer(_lowerCamelCase , keep_accents=_lowerCamelCase )
tokenizer.sanitize_special_tokens()
tokenizer.save_pretrained(self.tmpdirname )
def UpperCAmelCase_ ( self ):
'''simple docstring'''
lowercase__ : int= '''<s>'''
lowercase__ : List[Any]= 1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(_lowerCamelCase ) , _lowerCamelCase )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(_lowerCamelCase ) , _lowerCamelCase )
def UpperCAmelCase_ ( self ):
'''simple docstring'''
lowercase__ : int= list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , "<unk>" )
self.assertEqual(vocab_keys[1] , "<s>" )
self.assertEqual(vocab_keys[-1] , "<eod>" )
self.assertEqual(len(_lowerCamelCase ) , 1006 )
def UpperCAmelCase_ ( self ):
'''simple docstring'''
self.assertEqual(self.get_tokenizer().vocab_size , 1000 )
def UpperCAmelCase_ ( self ):
'''simple docstring'''
lowercase__ : List[Any]= XLNetTokenizer(_lowerCamelCase , keep_accents=_lowerCamelCase )
lowercase__ : str= tokenizer.tokenize("This is a test" )
self.assertListEqual(_lowerCamelCase , ["▁This", "▁is", "▁a", "▁t", "est"] )
self.assertListEqual(tokenizer.convert_tokens_to_ids(_lowerCamelCase ) , [285, 46, 10, 170, 382] )
lowercase__ : Dict= tokenizer.tokenize("I was born in 92000, and this is falsé." )
self.assertListEqual(
_lowerCamelCase , [
SPIECE_UNDERLINE + "I",
SPIECE_UNDERLINE + "was",
SPIECE_UNDERLINE + "b",
"or",
"n",
SPIECE_UNDERLINE + "in",
SPIECE_UNDERLINE + "",
"9",
"2",
"0",
"0",
"0",
",",
SPIECE_UNDERLINE + "and",
SPIECE_UNDERLINE + "this",
SPIECE_UNDERLINE + "is",
SPIECE_UNDERLINE + "f",
"al",
"s",
"é",
".",
] , )
lowercase__ : Dict= tokenizer.convert_tokens_to_ids(_lowerCamelCase )
self.assertListEqual(_lowerCamelCase , [8, 21, 84, 55, 24, 19, 7, 0, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 0, 4] )
lowercase__ : List[Any]= tokenizer.convert_ids_to_tokens(_lowerCamelCase )
self.assertListEqual(
_lowerCamelCase , [
SPIECE_UNDERLINE + "I",
SPIECE_UNDERLINE + "was",
SPIECE_UNDERLINE + "b",
"or",
"n",
SPIECE_UNDERLINE + "in",
SPIECE_UNDERLINE + "",
"<unk>",
"2",
"0",
"0",
"0",
",",
SPIECE_UNDERLINE + "and",
SPIECE_UNDERLINE + "this",
SPIECE_UNDERLINE + "is",
SPIECE_UNDERLINE + "f",
"al",
"s",
"<unk>",
".",
] , )
def UpperCAmelCase_ ( self ):
'''simple docstring'''
lowercase__ : List[Any]= XLNetTokenizer(_lowerCamelCase , do_lower_case=_lowerCamelCase )
lowercase__ : Union[str, Any]= tokenizer.tokenize("I was born in 92000, and this is falsé." )
self.assertListEqual(
_lowerCamelCase , [
SPIECE_UNDERLINE + "",
"i",
SPIECE_UNDERLINE + "was",
SPIECE_UNDERLINE + "b",
"or",
"n",
SPIECE_UNDERLINE + "in",
SPIECE_UNDERLINE + "",
"9",
"2",
"0",
"0",
"0",
",",
SPIECE_UNDERLINE + "and",
SPIECE_UNDERLINE + "this",
SPIECE_UNDERLINE + "is",
SPIECE_UNDERLINE + "f",
"al",
"se",
".",
] , )
self.assertListEqual(tokenizer.tokenize("H\u00E9llo" ) , ["▁he", "ll", "o"] )
def UpperCAmelCase_ ( self ):
'''simple docstring'''
lowercase__ : Union[str, Any]= XLNetTokenizer(_lowerCamelCase , do_lower_case=_lowerCamelCase )
lowercase__ : Tuple= tokenizer.tokenize("I was born in 92000, and this is falsé." )
self.assertListEqual(
_lowerCamelCase , [
SPIECE_UNDERLINE + "I",
SPIECE_UNDERLINE + "was",
SPIECE_UNDERLINE + "b",
"or",
"n",
SPIECE_UNDERLINE + "in",
SPIECE_UNDERLINE + "",
"9",
"2",
"0",
"0",
"0",
",",
SPIECE_UNDERLINE + "and",
SPIECE_UNDERLINE + "this",
SPIECE_UNDERLINE + "is",
SPIECE_UNDERLINE + "f",
"al",
"se",
".",
] , )
@slow
def UpperCAmelCase_ ( self ):
'''simple docstring'''
lowercase__ : str= XLNetTokenizer.from_pretrained("xlnet-base-cased" )
lowercase__ : Tuple= tokenizer.encode("sequence builders" , add_special_tokens=_lowerCamelCase )
lowercase__ : Optional[int]= tokenizer.encode("multi-sequence build" , add_special_tokens=_lowerCamelCase )
lowercase__ : str= tokenizer.build_inputs_with_special_tokens(_lowerCamelCase )
lowercase__ : int= tokenizer.build_inputs_with_special_tokens(_lowerCamelCase , _lowerCamelCase )
assert encoded_sentence == text + [4, 3]
assert encoded_pair == text + [4] + text_a + [4, 3]
@slow
def UpperCAmelCase_ ( self ):
'''simple docstring'''
# fmt: off
lowercase__ : Tuple= {'''input_ids''': [[17, 21442, 270, 17, 10, 14645, 318, 34, 17, 4546, 3145, 787, 13, 7752, 22018, 23, 21, 17, 4546, 3145, 787, 13, 3352, 14431, 13, 5500, 11, 1176, 580, 13, 16819, 4797, 23, 17, 10, 17135, 658, 19, 457, 7932, 13, 184, 19, 3154, 17135, 6468, 19, 1404, 12269, 19, 4229, 5356, 16264, 46, 19, 17, 20545, 10395, 9, 9, 9, 11, 28, 6421, 9531, 20729, 17, 10, 353, 17022, 11, 21, 6421, 9531, 16949, 17, 10, 11509, 753, 11, 33, 95, 2421, 7385, 956, 14431, 2626, 25, 842, 7385, 4836, 21, 1429, 2272, 9855, 3120, 161, 24738, 19, 13203, 658, 218, 787, 21, 430, 18482, 847, 2637, 9, 4, 3], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 322, 22178, 27, 1064, 22, 956, 13, 11101, 1429, 5854, 24313, 18953, 40, 422, 24366, 68, 1758, 37, 10483, 14257, 31, 207, 263, 21, 203, 3773, 25, 71, 9735, 9, 4, 3], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 32, 2049, 3442, 17, 13894, 3380, 23, 95, 18, 17634, 2288, 9, 4, 3]], '''token_type_ids''': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2], [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2], [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2]], '''attention_mask''': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]} # noqa: E501
# fmt: on
self.tokenizer_integration_test_util(
expected_encoding=_lowerCamelCase , model_name="xlnet-base-cased" , revision="c841166438c31ec7ca9a106dee7bb312b73ae511" , )
| 218 |
"""simple docstring"""
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import numpy as np
import torch
from ..models.clipseg import CLIPSegForImageSegmentation
from ..utils import is_vision_available, requires_backends
from .base import PipelineTool
if is_vision_available():
from PIL import Image
class snake_case ( __UpperCAmelCase ):
lowerCamelCase__ = (
'''This is a tool that creates a segmentation mask of an image according to a label. It cannot create an image.'''
'''It takes two arguments named `image` which should be the original image, and `label` which should be a text '''
'''describing the elements what should be identified in the segmentation mask. The tool returns the mask.'''
)
lowerCamelCase__ = '''CIDAS/clipseg-rd64-refined'''
lowerCamelCase__ = '''image_segmenter'''
lowerCamelCase__ = CLIPSegForImageSegmentation
lowerCamelCase__ = ['''image''', '''text''']
lowerCamelCase__ = ['''image''']
def __init__( self :Dict , *_lowerCamelCase :Union[str, Any] , **_lowerCamelCase :Tuple ):
requires_backends(self , ['''vision'''] )
super().__init__(*_lowerCamelCase , **_lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :Tuple , _lowerCamelCase :"Image" , _lowerCamelCase :str ):
return self.pre_processor(text=[label] , images=[image] , padding=_lowerCamelCase , return_tensors='''pt''' )
def SCREAMING_SNAKE_CASE_ ( self :Optional[int] , _lowerCamelCase :Optional[int] ):
with torch.no_grad():
__SCREAMING_SNAKE_CASE : List[Any] = self.model(**_lowerCamelCase ).logits
return logits
def SCREAMING_SNAKE_CASE_ ( self :Optional[Any] , _lowerCamelCase :Tuple ):
__SCREAMING_SNAKE_CASE : Optional[int] = outputs.cpu().detach().numpy()
__SCREAMING_SNAKE_CASE : str = 0
__SCREAMING_SNAKE_CASE : str = 1
return Image.fromarray((array * 2_5_5).astype(np.uinta ) )
| 674 | 0 |
import unittest
from transformers import MraConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_torch_available():
import torch
from transformers import (
MraForMaskedLM,
MraForMultipleChoice,
MraForQuestionAnswering,
MraForSequenceClassification,
MraForTokenClassification,
MraModel,
)
from transformers.models.mra.modeling_mra import MRA_PRETRAINED_MODEL_ARCHIVE_LIST
class UpperCAmelCase :
def __init__( self: Any , __UpperCamelCase: List[Any] , __UpperCamelCase: int=2 , __UpperCamelCase: Tuple=8 , __UpperCamelCase: Union[str, Any]=True , __UpperCamelCase: Optional[int]=True , __UpperCamelCase: Optional[int]=True , __UpperCamelCase: List[str]=True , __UpperCamelCase: Union[str, Any]=99 , __UpperCamelCase: int=16 , __UpperCamelCase: str=5 , __UpperCamelCase: List[Any]=2 , __UpperCamelCase: int=36 , __UpperCamelCase: Optional[Any]="gelu" , __UpperCamelCase: int=0.0 , __UpperCamelCase: Union[str, Any]=0.0 , __UpperCamelCase: List[Any]=512 , __UpperCamelCase: Any=16 , __UpperCamelCase: Any=2 , __UpperCamelCase: Union[str, Any]=0.0_2 , __UpperCamelCase: Union[str, Any]=3 , __UpperCamelCase: Tuple=4 , __UpperCamelCase: Dict=None , ):
_a = parent
_a = batch_size
_a = seq_length
_a = is_training
_a = use_input_mask
_a = use_token_type_ids
_a = use_labels
_a = vocab_size
_a = hidden_size
_a = num_hidden_layers
_a = num_attention_heads
_a = intermediate_size
_a = hidden_act
_a = hidden_dropout_prob
_a = attention_probs_dropout_prob
_a = max_position_embeddings
_a = type_vocab_size
_a = type_sequence_label_size
_a = initializer_range
_a = num_labels
_a = num_choices
_a = scope
def _A ( self: Optional[Any] ):
_a = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
_a = None
if self.use_input_mask:
_a = random_attention_mask([self.batch_size, self.seq_length] )
_a = None
if self.use_token_type_ids:
_a = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
_a = None
_a = None
_a = None
if self.use_labels:
_a = ids_tensor([self.batch_size] , self.type_sequence_label_size )
_a = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
_a = ids_tensor([self.batch_size] , self.num_choices )
_a = self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def _A ( self: int ):
return MraConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=_lowerCamelCase , initializer_range=self.initializer_range , )
def _A ( self: Dict ):
_a = self.get_config()
_a = 300
return config
def _A ( self: List[str] ):
(
_a
) = self.prepare_config_and_inputs()
_a = True
_a = floats_tensor([self.batch_size, self.seq_length, self.hidden_size] )
_a = ids_tensor([self.batch_size, self.seq_length] , vocab_size=2 )
return (
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
encoder_hidden_states,
encoder_attention_mask,
)
def _A ( self: Tuple , __UpperCamelCase: Optional[Any] , __UpperCamelCase: Optional[int] , __UpperCamelCase: Any , __UpperCamelCase: List[Any] , __UpperCamelCase: Tuple , __UpperCamelCase: List[str] , __UpperCamelCase: List[str] ):
_a = MraModel(config=_lowerCamelCase )
model.to(_lowerCamelCase )
model.eval()
_a = model(_lowerCamelCase , attention_mask=_lowerCamelCase , token_type_ids=_lowerCamelCase )
_a = model(_lowerCamelCase , token_type_ids=_lowerCamelCase )
_a = model(_lowerCamelCase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def _A ( self: List[str] , __UpperCamelCase: List[Any] , __UpperCamelCase: int , __UpperCamelCase: List[Any] , __UpperCamelCase: Any , __UpperCamelCase: List[str] , __UpperCamelCase: int , __UpperCamelCase: Optional[int] , __UpperCamelCase: Any , __UpperCamelCase: str , ):
_a = True
_a = MraModel(_lowerCamelCase )
model.to(_lowerCamelCase )
model.eval()
_a = model(
_lowerCamelCase , attention_mask=_lowerCamelCase , token_type_ids=_lowerCamelCase , encoder_hidden_states=_lowerCamelCase , encoder_attention_mask=_lowerCamelCase , )
_a = model(
_lowerCamelCase , attention_mask=_lowerCamelCase , token_type_ids=_lowerCamelCase , encoder_hidden_states=_lowerCamelCase , )
_a = model(_lowerCamelCase , attention_mask=_lowerCamelCase , token_type_ids=_lowerCamelCase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def _A ( self: Optional[int] , __UpperCamelCase: Optional[Any] , __UpperCamelCase: Dict , __UpperCamelCase: Union[str, Any] , __UpperCamelCase: str , __UpperCamelCase: int , __UpperCamelCase: Tuple , __UpperCamelCase: List[Any] ):
_a = MraForMaskedLM(config=_lowerCamelCase )
model.to(_lowerCamelCase )
model.eval()
_a = model(_lowerCamelCase , attention_mask=_lowerCamelCase , token_type_ids=_lowerCamelCase , labels=_lowerCamelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def _A ( self: int , __UpperCamelCase: Union[str, Any] , __UpperCamelCase: int , __UpperCamelCase: Dict , __UpperCamelCase: Optional[Any] , __UpperCamelCase: Any , __UpperCamelCase: List[str] , __UpperCamelCase: List[Any] ):
_a = MraForQuestionAnswering(config=_lowerCamelCase )
model.to(_lowerCamelCase )
model.eval()
_a = model(
_lowerCamelCase , attention_mask=_lowerCamelCase , token_type_ids=_lowerCamelCase , start_positions=_lowerCamelCase , end_positions=_lowerCamelCase , )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def _A ( self: Tuple , __UpperCamelCase: int , __UpperCamelCase: Union[str, Any] , __UpperCamelCase: Optional[Any] , __UpperCamelCase: Dict , __UpperCamelCase: List[Any] , __UpperCamelCase: str , __UpperCamelCase: Tuple ):
_a = self.num_labels
_a = MraForSequenceClassification(_lowerCamelCase )
model.to(_lowerCamelCase )
model.eval()
_a = model(_lowerCamelCase , attention_mask=_lowerCamelCase , token_type_ids=_lowerCamelCase , labels=_lowerCamelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def _A ( self: List[str] , __UpperCamelCase: str , __UpperCamelCase: List[str] , __UpperCamelCase: str , __UpperCamelCase: List[Any] , __UpperCamelCase: Any , __UpperCamelCase: Any , __UpperCamelCase: Tuple ):
_a = self.num_labels
_a = MraForTokenClassification(config=_lowerCamelCase )
model.to(_lowerCamelCase )
model.eval()
_a = model(_lowerCamelCase , attention_mask=_lowerCamelCase , token_type_ids=_lowerCamelCase , labels=_lowerCamelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def _A ( self: Any , __UpperCamelCase: List[str] , __UpperCamelCase: str , __UpperCamelCase: str , __UpperCamelCase: Any , __UpperCamelCase: Any , __UpperCamelCase: Union[str, Any] , __UpperCamelCase: Any ):
_a = self.num_choices
_a = MraForMultipleChoice(config=_lowerCamelCase )
model.to(_lowerCamelCase )
model.eval()
_a = input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
_a = token_type_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
_a = input_mask.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
_a = model(
_lowerCamelCase , attention_mask=_lowerCamelCase , token_type_ids=_lowerCamelCase , labels=_lowerCamelCase , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def _A ( self: str ):
_a = self.prepare_config_and_inputs()
(
_a
) = config_and_inputs
_a = {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
return config, inputs_dict
@require_torch
class UpperCAmelCase ( __UpperCAmelCase , unittest.TestCase ):
a: str = (
(
MraModel,
MraForMaskedLM,
MraForMultipleChoice,
MraForQuestionAnswering,
MraForSequenceClassification,
MraForTokenClassification,
)
if is_torch_available()
else ()
)
a: Union[str, Any] = False
a: List[Any] = False
a: int = False
a: Any = False
a: Dict = ()
def _A ( self: int ):
_a = MraModelTester(self )
_a = ConfigTester(self , config_class=_lowerCamelCase , hidden_size=37 )
def _A ( self: Any ):
self.config_tester.run_common_tests()
def _A ( self: Dict ):
_a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*_lowerCamelCase )
def _A ( self: List[str] ):
_a = self.model_tester.prepare_config_and_inputs()
for type in ["absolute", "relative_key", "relative_key_query"]:
_a = type
self.model_tester.create_and_check_model(*_lowerCamelCase )
def _A ( self: List[Any] ):
_a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*_lowerCamelCase )
def _A ( self: Any ):
_a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_multiple_choice(*_lowerCamelCase )
def _A ( self: Optional[Any] ):
_a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*_lowerCamelCase )
def _A ( self: int ):
_a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*_lowerCamelCase )
def _A ( self: str ):
_a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*_lowerCamelCase )
@slow
def _A ( self: Any ):
for model_name in MRA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_a = MraModel.from_pretrained(_lowerCamelCase )
self.assertIsNotNone(_lowerCamelCase )
@unittest.skip(reason='''MRA does not output attentions''' )
def _A ( self: Tuple ):
return
@require_torch
class UpperCAmelCase ( unittest.TestCase ):
@slow
def _A ( self: List[Any] ):
_a = MraModel.from_pretrained('''uw-madison/mra-base-512-4''' )
_a = torch.arange(256 ).unsqueeze(0 )
with torch.no_grad():
_a = model(_lowerCamelCase )[0]
_a = torch.Size((1, 256, 768) )
self.assertEqual(output.shape , _lowerCamelCase )
_a = torch.tensor(
[[[-0.0_1_4_0, 0.0_8_3_0, -0.0_3_8_1], [0.1_5_4_6, 0.1_4_0_2, 0.0_2_2_0], [0.1_1_6_2, 0.0_8_5_1, 0.0_1_6_5]]] )
self.assertTrue(torch.allclose(output[:, :3, :3] , _lowerCamelCase , atol=1E-4 ) )
@slow
def _A ( self: List[Any] ):
_a = MraForMaskedLM.from_pretrained('''uw-madison/mra-base-512-4''' )
_a = torch.arange(256 ).unsqueeze(0 )
with torch.no_grad():
_a = model(_lowerCamelCase )[0]
_a = 5_0265
_a = torch.Size((1, 256, vocab_size) )
self.assertEqual(output.shape , _lowerCamelCase )
_a = torch.tensor(
[[[9.2_5_9_5, -3.6_0_3_8, 11.8819], [9.3_8_6_9, -3.2_6_9_3, 11.0956], [11.8524, -3.4_9_3_8, 13.1210]]] )
self.assertTrue(torch.allclose(output[:, :3, :3] , _lowerCamelCase , atol=1E-4 ) )
@slow
def _A ( self: int ):
_a = MraForMaskedLM.from_pretrained('''uw-madison/mra-base-4096-8-d3''' )
_a = torch.arange(4096 ).unsqueeze(0 )
with torch.no_grad():
_a = model(_lowerCamelCase )[0]
_a = 5_0265
_a = torch.Size((1, 4096, vocab_size) )
self.assertEqual(output.shape , _lowerCamelCase )
_a = torch.tensor(
[[[5.4_7_8_9, -2.3_5_6_4, 7.5_0_6_4], [7.9_0_6_7, -1.3_3_6_9, 9.9_6_6_8], [9.0_7_1_2, -1.8_1_0_6, 7.0_3_8_0]]] )
self.assertTrue(torch.allclose(output[:, :3, :3] , _lowerCamelCase , atol=1E-4 ) )
| 487 |
"""simple docstring"""
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
from transformers import BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES, BertTokenizer
from transformers.testing_utils import require_tokenizers, require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available
if is_vision_available():
from PIL import Image
from transformers import VisionTextDualEncoderProcessor, ViTImageProcessor
@require_tokenizers
@require_vision
class snake_case ( unittest.TestCase ):
def SCREAMING_SNAKE_CASE_ ( self :Union[str, Any] ):
__SCREAMING_SNAKE_CASE : Union[str, Any] = tempfile.mkdtemp()
# fmt: off
__SCREAMING_SNAKE_CASE : Optional[int] = ['''[UNK]''', '''[CLS]''', '''[SEP]''', '''[PAD]''', '''[MASK]''', '''want''', '''##want''', '''##ed''', '''wa''', '''un''', '''runn''', '''##ing''', ''',''', '''low''', '''lowest''']
# fmt: on
__SCREAMING_SNAKE_CASE : List[Any] = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['''vocab_file'''] )
with open(self.vocab_file , '''w''' , encoding='''utf-8''' ) as vocab_writer:
vocab_writer.write(''''''.join([x + '''\n''' for x in vocab_tokens] ) )
__SCREAMING_SNAKE_CASE : Optional[int] = {
'''do_resize''': True,
'''size''': {'''height''': 1_8, '''width''': 1_8},
'''do_normalize''': True,
'''image_mean''': [0.5, 0.5, 0.5],
'''image_std''': [0.5, 0.5, 0.5],
}
__SCREAMING_SNAKE_CASE : List[Any] = os.path.join(self.tmpdirname , _lowerCamelCase )
with open(self.image_processor_file , '''w''' , encoding='''utf-8''' ) as fp:
json.dump(_lowerCamelCase , _lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :Union[str, Any] , **_lowerCamelCase :List[str] ):
return BertTokenizer.from_pretrained(self.tmpdirname , **_lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :List[str] , **_lowerCamelCase :Optional[int] ):
return ViTImageProcessor.from_pretrained(self.tmpdirname , **_lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :Optional[Any] ):
shutil.rmtree(self.tmpdirname )
def SCREAMING_SNAKE_CASE_ ( self :Dict ):
__SCREAMING_SNAKE_CASE : Union[str, Any] = [np.random.randint(2_5_5 , size=(3, 3_0, 4_0_0) , dtype=np.uinta )]
__SCREAMING_SNAKE_CASE : Tuple = [Image.fromarray(np.moveaxis(_lowerCamelCase , 0 , -1 ) ) for x in image_inputs]
return image_inputs
def SCREAMING_SNAKE_CASE_ ( self :int ):
__SCREAMING_SNAKE_CASE : Optional[int] = self.get_tokenizer()
__SCREAMING_SNAKE_CASE : Optional[Any] = self.get_image_processor()
__SCREAMING_SNAKE_CASE : Optional[Any] = VisionTextDualEncoderProcessor(tokenizer=_lowerCamelCase , image_processor=_lowerCamelCase )
processor.save_pretrained(self.tmpdirname )
__SCREAMING_SNAKE_CASE : int = VisionTextDualEncoderProcessor.from_pretrained(self.tmpdirname )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer.get_vocab() )
self.assertIsInstance(processor.tokenizer , (BertTokenizer, BertTokenizerFast) )
self.assertEqual(processor.image_processor.to_json_string() , image_processor.to_json_string() )
self.assertIsInstance(processor.image_processor , _lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :List[Any] ):
__SCREAMING_SNAKE_CASE : str = VisionTextDualEncoderProcessor(
tokenizer=self.get_tokenizer() , image_processor=self.get_image_processor() )
processor.save_pretrained(self.tmpdirname )
__SCREAMING_SNAKE_CASE : Optional[int] = self.get_tokenizer(bos_token='''(BOS)''' , eos_token='''(EOS)''' )
__SCREAMING_SNAKE_CASE : List[str] = self.get_image_processor(do_normalize=_lowerCamelCase , padding_value=1.0 )
__SCREAMING_SNAKE_CASE : int = VisionTextDualEncoderProcessor.from_pretrained(
self.tmpdirname , bos_token='''(BOS)''' , eos_token='''(EOS)''' , do_normalize=_lowerCamelCase , padding_value=1.0 )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer_add_kwargs.get_vocab() )
self.assertIsInstance(processor.tokenizer , (BertTokenizer, BertTokenizerFast) )
self.assertEqual(processor.image_processor.to_json_string() , image_processor_add_kwargs.to_json_string() )
self.assertIsInstance(processor.image_processor , _lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :int ):
__SCREAMING_SNAKE_CASE : Union[str, Any] = self.get_image_processor()
__SCREAMING_SNAKE_CASE : Dict = self.get_tokenizer()
__SCREAMING_SNAKE_CASE : Tuple = VisionTextDualEncoderProcessor(tokenizer=_lowerCamelCase , image_processor=_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Dict = self.prepare_image_inputs()
__SCREAMING_SNAKE_CASE : Optional[int] = image_processor(_lowerCamelCase , return_tensors='''np''' )
__SCREAMING_SNAKE_CASE : Tuple = processor(images=_lowerCamelCase , return_tensors='''np''' )
for key in input_feat_extract.keys():
self.assertAlmostEqual(input_feat_extract[key].sum() , input_processor[key].sum() , delta=1e-2 )
def SCREAMING_SNAKE_CASE_ ( self :Optional[int] ):
__SCREAMING_SNAKE_CASE : Any = self.get_image_processor()
__SCREAMING_SNAKE_CASE : Any = self.get_tokenizer()
__SCREAMING_SNAKE_CASE : Tuple = VisionTextDualEncoderProcessor(tokenizer=_lowerCamelCase , image_processor=_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Union[str, Any] = '''lower newer'''
__SCREAMING_SNAKE_CASE : Optional[int] = processor(text=_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Any = tokenizer(_lowerCamelCase )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] , encoded_processor[key] )
def SCREAMING_SNAKE_CASE_ ( self :str ):
__SCREAMING_SNAKE_CASE : str = self.get_image_processor()
__SCREAMING_SNAKE_CASE : int = self.get_tokenizer()
__SCREAMING_SNAKE_CASE : int = VisionTextDualEncoderProcessor(tokenizer=_lowerCamelCase , image_processor=_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Any = '''lower newer'''
__SCREAMING_SNAKE_CASE : int = self.prepare_image_inputs()
__SCREAMING_SNAKE_CASE : int = processor(text=_lowerCamelCase , images=_lowerCamelCase )
self.assertListEqual(list(inputs.keys() ) , ['''input_ids''', '''token_type_ids''', '''attention_mask''', '''pixel_values'''] )
# test if it raises when no input is passed
with self.assertRaises(_lowerCamelCase ):
processor()
def SCREAMING_SNAKE_CASE_ ( self :Any ):
__SCREAMING_SNAKE_CASE : Dict = self.get_image_processor()
__SCREAMING_SNAKE_CASE : List[Any] = self.get_tokenizer()
__SCREAMING_SNAKE_CASE : Union[str, Any] = VisionTextDualEncoderProcessor(tokenizer=_lowerCamelCase , image_processor=_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Union[str, Any] = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
__SCREAMING_SNAKE_CASE : Tuple = processor.batch_decode(_lowerCamelCase )
__SCREAMING_SNAKE_CASE : List[str] = tokenizer.batch_decode(_lowerCamelCase )
self.assertListEqual(_lowerCamelCase , _lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :Dict ):
__SCREAMING_SNAKE_CASE : str = self.get_image_processor()
__SCREAMING_SNAKE_CASE : List[str] = self.get_tokenizer()
__SCREAMING_SNAKE_CASE : Optional[int] = VisionTextDualEncoderProcessor(tokenizer=_lowerCamelCase , image_processor=_lowerCamelCase )
__SCREAMING_SNAKE_CASE : List[Any] = '''lower newer'''
__SCREAMING_SNAKE_CASE : Dict = self.prepare_image_inputs()
__SCREAMING_SNAKE_CASE : str = processor(text=_lowerCamelCase , images=_lowerCamelCase )
self.assertListEqual(list(inputs.keys() ) , processor.model_input_names )
| 674 | 0 |
"""simple docstring"""
import contextlib
import copy
import random
from typing import Any, Dict, Iterable, Optional, Union
import numpy as np
import torch
from .utils import deprecate, is_transformers_available
if is_transformers_available():
import transformers
def __UpperCAmelCase ( __UpperCamelCase ):
random.seed(lowercase_ )
np.random.seed(lowercase_ )
torch.manual_seed(lowercase_ )
torch.cuda.manual_seed_all(lowercase_ )
# ^^ safe to call this function even if cuda is not available
class UpperCAmelCase_ :
def __init__( self , UpperCamelCase_ , UpperCamelCase_ = 0.9_9_9_9 , UpperCamelCase_ = 0.0 , UpperCamelCase_ = 0 , UpperCamelCase_ = False , UpperCamelCase_ = 1.0 , UpperCamelCase_ = 2 / 3 , UpperCamelCase_ = None , UpperCamelCase_ = None , **UpperCamelCase_ , ) -> Tuple:
if isinstance(_lowerCamelCase , torch.nn.Module ):
__lowercase : int = (
'''Passing a `torch.nn.Module` to `ExponentialMovingAverage` is deprecated. '''
'''Please pass the parameters of the module instead.'''
)
deprecate(
'''passing a `torch.nn.Module` to `ExponentialMovingAverage`''' , '''1.0.0''' , _lowerCamelCase , standard_warn=_lowerCamelCase , )
__lowercase : List[Any] = parameters.parameters()
# set use_ema_warmup to True if a torch.nn.Module is passed for backwards compatibility
__lowercase : Optional[int] = True
if kwargs.get('''max_value''' , _lowerCamelCase ) is not None:
__lowercase : Tuple = '''The `max_value` argument is deprecated. Please use `decay` instead.'''
deprecate('''max_value''' , '''1.0.0''' , _lowerCamelCase , standard_warn=_lowerCamelCase )
__lowercase : str = kwargs['''max_value''']
if kwargs.get('''min_value''' , _lowerCamelCase ) is not None:
__lowercase : List[Any] = '''The `min_value` argument is deprecated. Please use `min_decay` instead.'''
deprecate('''min_value''' , '''1.0.0''' , _lowerCamelCase , standard_warn=_lowerCamelCase )
__lowercase : Optional[Any] = kwargs['''min_value''']
__lowercase : Optional[int] = list(_lowerCamelCase )
__lowercase : int = [p.clone().detach() for p in parameters]
if kwargs.get('''device''' , _lowerCamelCase ) is not None:
__lowercase : Union[str, Any] = '''The `device` argument is deprecated. Please use `to` instead.'''
deprecate('''device''' , '''1.0.0''' , _lowerCamelCase , standard_warn=_lowerCamelCase )
self.to(device=kwargs['''device'''] )
__lowercase : int = None
__lowercase : Tuple = decay
__lowercase : Any = min_decay
__lowercase : Optional[int] = update_after_step
__lowercase : Optional[Any] = use_ema_warmup
__lowercase : str = inv_gamma
__lowercase : Tuple = power
__lowercase : Any = 0
__lowercase : str = None # set in `step()`
__lowercase : Dict = model_cls
__lowercase : List[str] = model_config
@classmethod
def _lowerCamelCase ( cls , UpperCamelCase_ , UpperCamelCase_ ) -> str:
__lowercase : int = model_cls.load_config(_lowerCamelCase , return_unused_kwargs=_lowerCamelCase )
__lowercase : Union[str, Any] = model_cls.from_pretrained(_lowerCamelCase )
__lowercase : Tuple = cls(model.parameters() , model_cls=_lowerCamelCase , model_config=model.config )
ema_model.load_state_dict(_lowerCamelCase )
return ema_model
def _lowerCamelCase ( self , UpperCamelCase_ ) -> Tuple:
if self.model_cls is None:
raise ValueError('''`save_pretrained` can only be used if `model_cls` was defined at __init__.''' )
if self.model_config is None:
raise ValueError('''`save_pretrained` can only be used if `model_config` was defined at __init__.''' )
__lowercase : List[str] = self.model_cls.from_config(self.model_config )
__lowercase : List[str] = self.state_dict()
state_dict.pop('''shadow_params''' , _lowerCamelCase )
model.register_to_config(**_lowerCamelCase )
self.copy_to(model.parameters() )
model.save_pretrained(_lowerCamelCase )
def _lowerCamelCase ( self , UpperCamelCase_ ) -> List[Any]:
__lowercase : Dict = max(0 , optimization_step - self.update_after_step - 1 )
if step <= 0:
return 0.0
if self.use_ema_warmup:
__lowercase : List[str] = 1 - (1 + step / self.inv_gamma) ** -self.power
else:
__lowercase : Tuple = (1 + step) / (10 + step)
__lowercase : List[Any] = min(_lowerCamelCase , self.decay )
# make sure decay is not smaller than min_decay
__lowercase : Any = max(_lowerCamelCase , self.min_decay )
return cur_decay_value
@torch.no_grad()
def _lowerCamelCase ( self , UpperCamelCase_ ) -> Any:
if isinstance(_lowerCamelCase , torch.nn.Module ):
__lowercase : List[Any] = (
'''Passing a `torch.nn.Module` to `ExponentialMovingAverage.step` is deprecated. '''
'''Please pass the parameters of the module instead.'''
)
deprecate(
'''passing a `torch.nn.Module` to `ExponentialMovingAverage.step`''' , '''1.0.0''' , _lowerCamelCase , standard_warn=_lowerCamelCase , )
__lowercase : str = parameters.parameters()
__lowercase : Union[str, Any] = list(_lowerCamelCase )
self.optimization_step += 1
# Compute the decay factor for the exponential moving average.
__lowercase : Union[str, Any] = self.get_decay(self.optimization_step )
__lowercase : int = decay
__lowercase : List[Any] = 1 - decay
__lowercase : List[Any] = contextlib.nullcontext
if is_transformers_available() and transformers.deepspeed.is_deepspeed_zeroa_enabled():
import deepspeed
for s_param, param in zip(self.shadow_params , _lowerCamelCase ):
if is_transformers_available() and transformers.deepspeed.is_deepspeed_zeroa_enabled():
__lowercase : Any = deepspeed.zero.GatheredParameters(_lowerCamelCase , modifier_rank=_lowerCamelCase )
with context_manager():
if param.requires_grad:
s_param.sub_(one_minus_decay * (s_param - param) )
else:
s_param.copy_(_lowerCamelCase )
def _lowerCamelCase ( self , UpperCamelCase_ ) -> Tuple:
__lowercase : Any = list(_lowerCamelCase )
for s_param, param in zip(self.shadow_params , _lowerCamelCase ):
param.data.copy_(s_param.to(param.device ).data )
def _lowerCamelCase ( self , UpperCamelCase_=None , UpperCamelCase_=None ) -> Union[str, Any]:
__lowercase : List[str] = [
p.to(device=_lowerCamelCase , dtype=_lowerCamelCase ) if p.is_floating_point() else p.to(device=_lowerCamelCase )
for p in self.shadow_params
]
def _lowerCamelCase ( self ) -> str:
return {
"decay": self.decay,
"min_decay": self.min_decay,
"optimization_step": self.optimization_step,
"update_after_step": self.update_after_step,
"use_ema_warmup": self.use_ema_warmup,
"inv_gamma": self.inv_gamma,
"power": self.power,
"shadow_params": self.shadow_params,
}
def _lowerCamelCase ( self , UpperCamelCase_ ) -> int:
__lowercase : Tuple = [param.detach().cpu().clone() for param in parameters]
def _lowerCamelCase ( self , UpperCamelCase_ ) -> Union[str, Any]:
if self.temp_stored_params is None:
raise RuntimeError('''This ExponentialMovingAverage has no `store()`ed weights ''' '''to `restore()`''' )
for c_param, param in zip(self.temp_stored_params , _lowerCamelCase ):
param.data.copy_(c_param.data )
# Better memory-wise.
__lowercase : Dict = None
def _lowerCamelCase ( self , UpperCamelCase_ ) -> int:
__lowercase : Optional[Any] = copy.deepcopy(_lowerCamelCase )
__lowercase : List[str] = state_dict.get('''decay''' , self.decay )
if self.decay < 0.0 or self.decay > 1.0:
raise ValueError('''Decay must be between 0 and 1''' )
__lowercase : Dict = state_dict.get('''min_decay''' , self.min_decay )
if not isinstance(self.min_decay , _lowerCamelCase ):
raise ValueError('''Invalid min_decay''' )
__lowercase : List[str] = state_dict.get('''optimization_step''' , self.optimization_step )
if not isinstance(self.optimization_step , _lowerCamelCase ):
raise ValueError('''Invalid optimization_step''' )
__lowercase : Any = state_dict.get('''update_after_step''' , self.update_after_step )
if not isinstance(self.update_after_step , _lowerCamelCase ):
raise ValueError('''Invalid update_after_step''' )
__lowercase : Union[str, Any] = state_dict.get('''use_ema_warmup''' , self.use_ema_warmup )
if not isinstance(self.use_ema_warmup , _lowerCamelCase ):
raise ValueError('''Invalid use_ema_warmup''' )
__lowercase : int = state_dict.get('''inv_gamma''' , self.inv_gamma )
if not isinstance(self.inv_gamma , (float, int) ):
raise ValueError('''Invalid inv_gamma''' )
__lowercase : Tuple = state_dict.get('''power''' , self.power )
if not isinstance(self.power , (float, int) ):
raise ValueError('''Invalid power''' )
__lowercase : Optional[int] = state_dict.get('''shadow_params''' , _lowerCamelCase )
if shadow_params is not None:
__lowercase : str = shadow_params
if not isinstance(self.shadow_params , _lowerCamelCase ):
raise ValueError('''shadow_params must be a list''' )
if not all(isinstance(_lowerCamelCase , torch.Tensor ) for p in self.shadow_params ):
raise ValueError('''shadow_params must all be Tensors''' )
| 76 |
"""simple docstring"""
import inspect
import unittest
import numpy as np
from tests.test_modeling_common import floats_tensor
from transformers import DetrConfig, MaskFormerConfig, SwinConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import MaskFormerForInstanceSegmentation, MaskFormerModel
if is_vision_available():
from transformers import MaskFormerImageProcessor
if is_vision_available():
from PIL import Image
class snake_case :
def __init__( self :Optional[int] , _lowerCamelCase :int , _lowerCamelCase :Any=2 , _lowerCamelCase :List[str]=True , _lowerCamelCase :str=False , _lowerCamelCase :Tuple=1_0 , _lowerCamelCase :str=3 , _lowerCamelCase :str=3_2 * 4 , _lowerCamelCase :Dict=3_2 * 6 , _lowerCamelCase :str=4 , _lowerCamelCase :Any=3_2 , ):
__SCREAMING_SNAKE_CASE : List[str] = parent
__SCREAMING_SNAKE_CASE : Tuple = batch_size
__SCREAMING_SNAKE_CASE : Optional[Any] = is_training
__SCREAMING_SNAKE_CASE : Dict = use_auxiliary_loss
__SCREAMING_SNAKE_CASE : List[str] = num_queries
__SCREAMING_SNAKE_CASE : Optional[int] = num_channels
__SCREAMING_SNAKE_CASE : List[Any] = min_size
__SCREAMING_SNAKE_CASE : int = max_size
__SCREAMING_SNAKE_CASE : Any = num_labels
__SCREAMING_SNAKE_CASE : Union[str, Any] = mask_feature_size
def SCREAMING_SNAKE_CASE_ ( self :Any ):
__SCREAMING_SNAKE_CASE : str = floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size] ).to(
_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Any = torch.ones([self.batch_size, self.min_size, self.max_size] , device=_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Any = (
torch.rand([self.batch_size, self.num_labels, self.min_size, self.max_size] , device=_lowerCamelCase ) > 0.5
).float()
__SCREAMING_SNAKE_CASE : Dict = (torch.rand((self.batch_size, self.num_labels) , device=_lowerCamelCase ) > 0.5).long()
__SCREAMING_SNAKE_CASE : str = self.get_config()
return config, pixel_values, pixel_mask, mask_labels, class_labels
def SCREAMING_SNAKE_CASE_ ( self :List[Any] ):
return MaskFormerConfig.from_backbone_and_decoder_configs(
backbone_config=SwinConfig(
depths=[1, 1, 1, 1] , ) , decoder_config=DetrConfig(
decoder_ffn_dim=1_2_8 , num_queries=self.num_queries , decoder_attention_heads=2 , d_model=self.mask_feature_size , ) , mask_feature_size=self.mask_feature_size , fpn_feature_size=self.mask_feature_size , num_channels=self.num_channels , num_labels=self.num_labels , )
def SCREAMING_SNAKE_CASE_ ( self :str ):
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Optional[Any] = self.prepare_config_and_inputs()
__SCREAMING_SNAKE_CASE : Any = {'''pixel_values''': pixel_values, '''pixel_mask''': pixel_mask}
return config, inputs_dict
def SCREAMING_SNAKE_CASE_ ( self :int , _lowerCamelCase :int , _lowerCamelCase :Optional[int] ):
__SCREAMING_SNAKE_CASE : Union[str, Any] = output.encoder_hidden_states
__SCREAMING_SNAKE_CASE : int = output.pixel_decoder_hidden_states
__SCREAMING_SNAKE_CASE : Optional[int] = output.transformer_decoder_hidden_states
self.parent.assertTrue(len(_lowerCamelCase ) , len(config.backbone_config.depths ) )
self.parent.assertTrue(len(_lowerCamelCase ) , len(config.backbone_config.depths ) )
self.parent.assertTrue(len(_lowerCamelCase ) , config.decoder_config.decoder_layers )
def SCREAMING_SNAKE_CASE_ ( self :Optional[Any] , _lowerCamelCase :Dict , _lowerCamelCase :str , _lowerCamelCase :Any , _lowerCamelCase :Optional[Any]=False ):
with torch.no_grad():
__SCREAMING_SNAKE_CASE : List[str] = MaskFormerModel(config=_lowerCamelCase )
model.to(_lowerCamelCase )
model.eval()
__SCREAMING_SNAKE_CASE : str = model(pixel_values=_lowerCamelCase , pixel_mask=_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Any = model(_lowerCamelCase , output_hidden_states=_lowerCamelCase )
# the correct shape of output.transformer_decoder_hidden_states ensure the correcteness of the
# encoder and pixel decoder
self.parent.assertEqual(
output.transformer_decoder_last_hidden_state.shape , (self.batch_size, self.num_queries, self.mask_feature_size) , )
# let's ensure the other two hidden state exists
self.parent.assertTrue(output.pixel_decoder_last_hidden_state is not None )
self.parent.assertTrue(output.encoder_last_hidden_state is not None )
if output_hidden_states:
self.check_output_hidden_state(_lowerCamelCase , _lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :Dict , _lowerCamelCase :Optional[Any] , _lowerCamelCase :Optional[int] , _lowerCamelCase :Optional[Any] , _lowerCamelCase :List[str] , _lowerCamelCase :Dict ):
__SCREAMING_SNAKE_CASE : Optional[Any] = MaskFormerForInstanceSegmentation(config=_lowerCamelCase )
model.to(_lowerCamelCase )
model.eval()
def comm_check_on_output(_lowerCamelCase :Optional[Any] ):
# let's still check that all the required stuff is there
self.parent.assertTrue(result.transformer_decoder_last_hidden_state is not None )
self.parent.assertTrue(result.pixel_decoder_last_hidden_state is not None )
self.parent.assertTrue(result.encoder_last_hidden_state is not None )
# okay, now we need to check the logits shape
# due to the encoder compression, masks have a //4 spatial size
self.parent.assertEqual(
result.masks_queries_logits.shape , (self.batch_size, self.num_queries, self.min_size // 4, self.max_size // 4) , )
# + 1 for null class
self.parent.assertEqual(
result.class_queries_logits.shape , (self.batch_size, self.num_queries, self.num_labels + 1) )
with torch.no_grad():
__SCREAMING_SNAKE_CASE : List[str] = model(pixel_values=_lowerCamelCase , pixel_mask=_lowerCamelCase )
__SCREAMING_SNAKE_CASE : int = model(_lowerCamelCase )
comm_check_on_output(_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Dict = model(
pixel_values=_lowerCamelCase , pixel_mask=_lowerCamelCase , mask_labels=_lowerCamelCase , class_labels=_lowerCamelCase )
comm_check_on_output(_lowerCamelCase )
self.parent.assertTrue(result.loss is not None )
self.parent.assertEqual(result.loss.shape , torch.Size([1] ) )
@require_torch
class snake_case ( __UpperCAmelCase , __UpperCAmelCase , unittest.TestCase ):
lowerCamelCase__ = (MaskFormerModel, MaskFormerForInstanceSegmentation) if is_torch_available() else ()
lowerCamelCase__ = (
{'''feature-extraction''': MaskFormerModel, '''image-segmentation''': MaskFormerForInstanceSegmentation}
if is_torch_available()
else {}
)
lowerCamelCase__ = False
lowerCamelCase__ = False
lowerCamelCase__ = False
lowerCamelCase__ = False
def SCREAMING_SNAKE_CASE_ ( self :Dict ):
__SCREAMING_SNAKE_CASE : Optional[Any] = MaskFormerModelTester(self )
__SCREAMING_SNAKE_CASE : str = ConfigTester(self , config_class=_lowerCamelCase , has_text_modality=_lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :Union[str, Any] ):
self.config_tester.run_common_tests()
def SCREAMING_SNAKE_CASE_ ( self :Union[str, Any] ):
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Any = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.create_and_check_maskformer_model(_lowerCamelCase , **_lowerCamelCase , output_hidden_states=_lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :List[str] ):
__SCREAMING_SNAKE_CASE : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_maskformer_instance_segmentation_head_model(*_lowerCamelCase )
@unittest.skip(reason='''MaskFormer does not use inputs_embeds''' )
def SCREAMING_SNAKE_CASE_ ( self :List[Any] ):
pass
@unittest.skip(reason='''MaskFormer does not have a get_input_embeddings method''' )
def SCREAMING_SNAKE_CASE_ ( self :int ):
pass
@unittest.skip(reason='''MaskFormer is not a generative model''' )
def SCREAMING_SNAKE_CASE_ ( self :Union[str, Any] ):
pass
@unittest.skip(reason='''MaskFormer does not use token embeddings''' )
def SCREAMING_SNAKE_CASE_ ( self :Optional[int] ):
pass
@require_torch_multi_gpu
@unittest.skip(
reason='''MaskFormer has some layers using `add_module` which doesn\'t work well with `nn.DataParallel`''' )
def SCREAMING_SNAKE_CASE_ ( self :Dict ):
pass
@unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.''' )
def SCREAMING_SNAKE_CASE_ ( self :Dict ):
pass
def SCREAMING_SNAKE_CASE_ ( self :Union[str, Any] ):
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Any = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__SCREAMING_SNAKE_CASE : Tuple = model_class(_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Optional[int] = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
__SCREAMING_SNAKE_CASE : List[str] = [*signature.parameters.keys()]
__SCREAMING_SNAKE_CASE : Optional[Any] = ['''pixel_values''']
self.assertListEqual(arg_names[:1] , _lowerCamelCase )
@slow
def SCREAMING_SNAKE_CASE_ ( self :Optional[Any] ):
for model_name in ["facebook/maskformer-swin-small-coco"]:
__SCREAMING_SNAKE_CASE : Tuple = MaskFormerModel.from_pretrained(_lowerCamelCase )
self.assertIsNotNone(_lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :Union[str, Any] ):
__SCREAMING_SNAKE_CASE : Dict = (self.model_tester.min_size,) * 2
__SCREAMING_SNAKE_CASE : Dict = {
'''pixel_values''': torch.randn((2, 3, *size) , device=_lowerCamelCase ),
'''mask_labels''': torch.randn((2, 1_0, *size) , device=_lowerCamelCase ),
'''class_labels''': torch.zeros(2 , 1_0 , device=_lowerCamelCase ).long(),
}
__SCREAMING_SNAKE_CASE : Dict = MaskFormerForInstanceSegmentation(MaskFormerConfig() ).to(_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Optional[Any] = model(**_lowerCamelCase )
self.assertTrue(outputs.loss is not None )
def SCREAMING_SNAKE_CASE_ ( self :Union[str, Any] ):
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Tuple = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.create_and_check_maskformer_model(_lowerCamelCase , **_lowerCamelCase , output_hidden_states=_lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :Union[str, Any] ):
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Tuple = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__SCREAMING_SNAKE_CASE : Dict = model_class(_lowerCamelCase ).to(_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Optional[int] = model(**_lowerCamelCase , output_attentions=_lowerCamelCase )
self.assertTrue(outputs.attentions is not None )
def SCREAMING_SNAKE_CASE_ ( self :Dict ):
if not self.model_tester.is_training:
return
# only MaskFormerForInstanceSegmentation has the loss
__SCREAMING_SNAKE_CASE : List[Any] = self.all_model_classes[1]
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Dict = self.model_tester.prepare_config_and_inputs()
__SCREAMING_SNAKE_CASE : int = model_class(_lowerCamelCase )
model.to(_lowerCamelCase )
model.train()
__SCREAMING_SNAKE_CASE : Any = model(_lowerCamelCase , mask_labels=_lowerCamelCase , class_labels=_lowerCamelCase ).loss
loss.backward()
def SCREAMING_SNAKE_CASE_ ( self :Optional[int] ):
# only MaskFormerForInstanceSegmentation has the loss
__SCREAMING_SNAKE_CASE : Tuple = self.all_model_classes[1]
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Tuple = self.model_tester.prepare_config_and_inputs()
__SCREAMING_SNAKE_CASE : Tuple = True
__SCREAMING_SNAKE_CASE : List[str] = True
__SCREAMING_SNAKE_CASE : Any = model_class(_lowerCamelCase )
model.to(_lowerCamelCase )
model.train()
__SCREAMING_SNAKE_CASE : Optional[int] = model(_lowerCamelCase , mask_labels=_lowerCamelCase , class_labels=_lowerCamelCase )
__SCREAMING_SNAKE_CASE : List[str] = outputs.encoder_hidden_states[0]
encoder_hidden_states.retain_grad()
__SCREAMING_SNAKE_CASE : str = outputs.pixel_decoder_hidden_states[0]
pixel_decoder_hidden_states.retain_grad()
# we requires_grad=True in inputs_embeds (line 2152), the original implementation don't
__SCREAMING_SNAKE_CASE : Optional[int] = outputs.transformer_decoder_hidden_states[0]
transformer_decoder_hidden_states.retain_grad()
__SCREAMING_SNAKE_CASE : int = outputs.attentions[0]
attentions.retain_grad()
outputs.loss.backward(retain_graph=_lowerCamelCase )
self.assertIsNotNone(encoder_hidden_states.grad )
self.assertIsNotNone(pixel_decoder_hidden_states.grad )
self.assertIsNotNone(transformer_decoder_hidden_states.grad )
self.assertIsNotNone(attentions.grad )
_lowerCamelCase = 1e-4
def lowerCAmelCase_ ( ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : List[Any] = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
return image
@require_vision
@slow
class snake_case ( unittest.TestCase ):
@cached_property
def SCREAMING_SNAKE_CASE_ ( self :str ):
return (
MaskFormerImageProcessor.from_pretrained('''facebook/maskformer-swin-small-coco''' )
if is_vision_available()
else None
)
def SCREAMING_SNAKE_CASE_ ( self :List[Any] ):
__SCREAMING_SNAKE_CASE : Union[str, Any] = MaskFormerModel.from_pretrained('''facebook/maskformer-swin-small-coco''' ).to(_lowerCamelCase )
__SCREAMING_SNAKE_CASE : int = self.default_image_processor
__SCREAMING_SNAKE_CASE : Tuple = prepare_img()
__SCREAMING_SNAKE_CASE : List[Any] = image_processor(_lowerCamelCase , return_tensors='''pt''' ).to(_lowerCamelCase )
__SCREAMING_SNAKE_CASE : int = inputs['''pixel_values'''].shape
# check size is divisible by 32
self.assertTrue((inputs_shape[-1] % 3_2) == 0 and (inputs_shape[-2] % 3_2) == 0 )
# check size
self.assertEqual(_lowerCamelCase , (1, 3, 8_0_0, 1_0_8_8) )
with torch.no_grad():
__SCREAMING_SNAKE_CASE : Tuple = model(**_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Union[str, Any] = torch.tensor(
[[-0.0_4_8_2, 0.9_2_2_8, 0.4_9_5_1], [-0.2_5_4_7, 0.8_0_1_7, 0.8_5_2_7], [-0.0_0_6_9, 0.3_3_8_5, -0.0_0_8_9]] ).to(_lowerCamelCase )
self.assertTrue(
torch.allclose(
outputs.encoder_last_hidden_state[0, 0, :3, :3] , _lowerCamelCase , atol=_lowerCamelCase ) )
__SCREAMING_SNAKE_CASE : Any = torch.tensor(
[[-0.8_4_2_2, -0.8_4_3_4, -0.9_7_1_8], [-1.0_1_4_4, -0.5_5_6_5, -0.4_1_9_5], [-1.0_0_3_8, -0.4_4_8_4, -0.1_9_6_1]] ).to(_lowerCamelCase )
self.assertTrue(
torch.allclose(
outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3] , _lowerCamelCase , atol=_lowerCamelCase ) )
__SCREAMING_SNAKE_CASE : Optional[int] = torch.tensor(
[[0.2_8_5_2, -0.0_1_5_9, 0.9_7_3_5], [0.6_2_5_4, 0.1_8_5_8, 0.8_5_2_9], [-0.0_6_8_0, -0.4_1_1_6, 1.8_4_1_3]] ).to(_lowerCamelCase )
self.assertTrue(
torch.allclose(
outputs.transformer_decoder_last_hidden_state[0, :3, :3] , _lowerCamelCase , atol=_lowerCamelCase ) )
def SCREAMING_SNAKE_CASE_ ( self :Optional[Any] ):
__SCREAMING_SNAKE_CASE : Tuple = (
MaskFormerForInstanceSegmentation.from_pretrained('''facebook/maskformer-swin-small-coco''' )
.to(_lowerCamelCase )
.eval()
)
__SCREAMING_SNAKE_CASE : Union[str, Any] = self.default_image_processor
__SCREAMING_SNAKE_CASE : Tuple = prepare_img()
__SCREAMING_SNAKE_CASE : str = image_processor(_lowerCamelCase , return_tensors='''pt''' ).to(_lowerCamelCase )
__SCREAMING_SNAKE_CASE : List[str] = inputs['''pixel_values'''].shape
# check size is divisible by 32
self.assertTrue((inputs_shape[-1] % 3_2) == 0 and (inputs_shape[-2] % 3_2) == 0 )
# check size
self.assertEqual(_lowerCamelCase , (1, 3, 8_0_0, 1_0_8_8) )
with torch.no_grad():
__SCREAMING_SNAKE_CASE : str = model(**_lowerCamelCase )
# masks_queries_logits
__SCREAMING_SNAKE_CASE : Any = outputs.masks_queries_logits
self.assertEqual(
masks_queries_logits.shape , (1, model.config.decoder_config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4) , )
__SCREAMING_SNAKE_CASE : List[Any] = [
[-1.3_7_3_7_1_2_4, -1.7_7_2_4_9_3_7, -1.9_3_6_4_2_3_3],
[-1.5_9_7_7_2_8_1, -1.9_8_6_7_9_3_9, -2.1_5_2_3_6_9_5],
[-1.5_7_9_5_3_9_8, -1.9_2_6_9_8_3_2, -2.0_9_3_9_4_2],
]
__SCREAMING_SNAKE_CASE : Any = torch.tensor(_lowerCamelCase ).to(_lowerCamelCase )
self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3] , _lowerCamelCase , atol=_lowerCamelCase ) )
# class_queries_logits
__SCREAMING_SNAKE_CASE : List[Any] = outputs.class_queries_logits
self.assertEqual(
class_queries_logits.shape , (1, model.config.decoder_config.num_queries, model.config.num_labels + 1) )
__SCREAMING_SNAKE_CASE : List[str] = torch.tensor(
[
[1.65_12e00, -5.25_72e00, -3.35_19e00],
[3.61_69e-02, -5.90_25e00, -2.93_13e00],
[1.07_66e-04, -7.76_30e00, -5.12_63e00],
] ).to(_lowerCamelCase )
self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3] , _lowerCamelCase , atol=_lowerCamelCase ) )
def SCREAMING_SNAKE_CASE_ ( self :Any ):
__SCREAMING_SNAKE_CASE : Tuple = (
MaskFormerForInstanceSegmentation.from_pretrained('''facebook/maskformer-resnet101-coco-stuff''' )
.to(_lowerCamelCase )
.eval()
)
__SCREAMING_SNAKE_CASE : Tuple = self.default_image_processor
__SCREAMING_SNAKE_CASE : str = prepare_img()
__SCREAMING_SNAKE_CASE : List[Any] = image_processor(_lowerCamelCase , return_tensors='''pt''' ).to(_lowerCamelCase )
__SCREAMING_SNAKE_CASE : List[Any] = inputs['''pixel_values'''].shape
# check size is divisible by 32
self.assertTrue((inputs_shape[-1] % 3_2) == 0 and (inputs_shape[-2] % 3_2) == 0 )
# check size
self.assertEqual(_lowerCamelCase , (1, 3, 8_0_0, 1_0_8_8) )
with torch.no_grad():
__SCREAMING_SNAKE_CASE : List[Any] = model(**_lowerCamelCase )
# masks_queries_logits
__SCREAMING_SNAKE_CASE : Union[str, Any] = outputs.masks_queries_logits
self.assertEqual(
masks_queries_logits.shape , (1, model.config.decoder_config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4) , )
__SCREAMING_SNAKE_CASE : List[str] = [[-0.9_0_4_6, -2.6_3_6_6, -4.6_0_6_2], [-3.4_1_7_9, -5.7_8_9_0, -8.8_0_5_7], [-4.9_1_7_9, -7.6_5_6_0, -1_0.7_7_1_1]]
__SCREAMING_SNAKE_CASE : Any = torch.tensor(_lowerCamelCase ).to(_lowerCamelCase )
self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3] , _lowerCamelCase , atol=_lowerCamelCase ) )
# class_queries_logits
__SCREAMING_SNAKE_CASE : int = outputs.class_queries_logits
self.assertEqual(
class_queries_logits.shape , (1, model.config.decoder_config.num_queries, model.config.num_labels + 1) )
__SCREAMING_SNAKE_CASE : List[str] = torch.tensor(
[[4.7_1_8_8, -3.2_5_8_5, -2.8_8_5_7], [6.6_8_7_1, -2.9_1_8_1, -1.2_4_8_7], [7.2_4_4_9, -2.2_7_6_4, -2.1_8_7_4]] ).to(_lowerCamelCase )
self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3] , _lowerCamelCase , atol=_lowerCamelCase ) )
def SCREAMING_SNAKE_CASE_ ( self :int ):
__SCREAMING_SNAKE_CASE : int = (
MaskFormerForInstanceSegmentation.from_pretrained('''facebook/maskformer-swin-small-coco''' )
.to(_lowerCamelCase )
.eval()
)
__SCREAMING_SNAKE_CASE : Any = self.default_image_processor
__SCREAMING_SNAKE_CASE : int = image_processor(
[np.zeros((3, 8_0_0, 1_3_3_3) ), np.zeros((3, 8_0_0, 1_3_3_3) )] , segmentation_maps=[np.zeros((3_8_4, 3_8_4) ).astype(np.floataa ), np.zeros((3_8_4, 3_8_4) ).astype(np.floataa )] , return_tensors='''pt''' , )
__SCREAMING_SNAKE_CASE : Dict = inputs['''pixel_values'''].to(_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Dict = [el.to(_lowerCamelCase ) for el in inputs['''mask_labels''']]
__SCREAMING_SNAKE_CASE : str = [el.to(_lowerCamelCase ) for el in inputs['''class_labels''']]
with torch.no_grad():
__SCREAMING_SNAKE_CASE : int = model(**_lowerCamelCase )
self.assertTrue(outputs.loss is not None )
| 674 | 0 |
import unittest
import numpy as np
import timeout_decorator # noqa
from transformers import BlenderbotConfig, is_flax_available
from transformers.testing_utils import jax_device, require_flax, slow
from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor
if is_flax_available():
import os
# The slow tests are often failing with OOM error on GPU
# This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
# but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
lowercase_ : Optional[Any] = 'platform'
import jax
import jax.numpy as jnp
from transformers import BlenderbotTokenizer
from transformers.models.blenderbot.modeling_flax_blenderbot import (
FlaxBlenderbotForConditionalGeneration,
FlaxBlenderbotModel,
shift_tokens_right,
)
def A__ ( snake_case_ : Dict , snake_case_ : Optional[int] , snake_case_ : Optional[int]=None , snake_case_ : List[str]=None , snake_case_ : Optional[int]=None , snake_case_ : str=None , snake_case_ : Optional[int]=None , snake_case_ : Optional[int]=None , ):
if attention_mask is None:
SCREAMING_SNAKE_CASE__: int= np.where(input_ids != config.pad_token_id , 1 , 0 )
if decoder_attention_mask is None:
SCREAMING_SNAKE_CASE__: Any= np.where(decoder_input_ids != config.pad_token_id , 1 , 0 )
if head_mask is None:
SCREAMING_SNAKE_CASE__: str= np.ones((config.encoder_layers, config.encoder_attention_heads) )
if decoder_head_mask is None:
SCREAMING_SNAKE_CASE__: int= np.ones((config.decoder_layers, config.decoder_attention_heads) )
if cross_attn_head_mask is None:
SCREAMING_SNAKE_CASE__: Any= np.ones((config.decoder_layers, config.decoder_attention_heads) )
return {
"input_ids": input_ids,
"decoder_input_ids": decoder_input_ids,
"attention_mask": attention_mask,
"decoder_attention_mask": attention_mask,
}
class _lowerCamelCase :
def __init__( self , lowerCAmelCase , lowerCAmelCase=13 , lowerCAmelCase=7 , lowerCAmelCase=True , lowerCAmelCase=False , lowerCAmelCase=99 , lowerCAmelCase=16 , lowerCAmelCase=2 , lowerCAmelCase=4 , lowerCAmelCase=4 , lowerCAmelCase="gelu" , lowerCAmelCase=0.1 , lowerCAmelCase=0.1 , lowerCAmelCase=32 , lowerCAmelCase=2 , lowerCAmelCase=1 , lowerCAmelCase=0 , lowerCAmelCase=0.02 , ) -> Tuple:
SCREAMING_SNAKE_CASE__: Optional[Any]= parent
SCREAMING_SNAKE_CASE__: Union[str, Any]= batch_size
SCREAMING_SNAKE_CASE__: Union[str, Any]= seq_length
SCREAMING_SNAKE_CASE__: Optional[int]= is_training
SCREAMING_SNAKE_CASE__: Tuple= use_labels
SCREAMING_SNAKE_CASE__: Union[str, Any]= vocab_size
SCREAMING_SNAKE_CASE__: Optional[int]= hidden_size
SCREAMING_SNAKE_CASE__: List[str]= num_hidden_layers
SCREAMING_SNAKE_CASE__: str= num_attention_heads
SCREAMING_SNAKE_CASE__: int= intermediate_size
SCREAMING_SNAKE_CASE__: Optional[int]= hidden_act
SCREAMING_SNAKE_CASE__: Dict= hidden_dropout_prob
SCREAMING_SNAKE_CASE__: int= attention_probs_dropout_prob
SCREAMING_SNAKE_CASE__: Tuple= max_position_embeddings
SCREAMING_SNAKE_CASE__: List[Any]= eos_token_id
SCREAMING_SNAKE_CASE__: Optional[int]= pad_token_id
SCREAMING_SNAKE_CASE__: Tuple= bos_token_id
SCREAMING_SNAKE_CASE__: List[Any]= initializer_range
def UpperCamelCase_ ( self ) -> Tuple:
SCREAMING_SNAKE_CASE__: Optional[int]= np.clip(ids_tensor([self.batch_size, self.seq_length - 1] , self.vocab_size ) , 3 , self.vocab_size )
SCREAMING_SNAKE_CASE__: Dict= np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1) , dtype=np.intaa )) , -1 )
SCREAMING_SNAKE_CASE__: int= shift_tokens_right(_lowerCamelCase , 1 , 2 )
SCREAMING_SNAKE_CASE__: Union[str, Any]= BlenderbotConfig(
vocab_size=self.vocab_size , d_model=self.hidden_size , encoder_layers=self.num_hidden_layers , decoder_layers=self.num_hidden_layers , encoder_attention_heads=self.num_attention_heads , decoder_attention_heads=self.num_attention_heads , encoder_ffn_dim=self.intermediate_size , decoder_ffn_dim=self.intermediate_size , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , eos_token_id=self.eos_token_id , bos_token_id=self.bos_token_id , pad_token_id=self.pad_token_id , initializer_range=self.initializer_range , use_cache=_lowerCamelCase , )
SCREAMING_SNAKE_CASE__: Optional[int]= prepare_blenderbot_inputs_dict(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase )
return config, inputs_dict
def UpperCamelCase_ ( self ) -> Any:
SCREAMING_SNAKE_CASE__: List[str]= self.prepare_config_and_inputs()
return config, inputs_dict
def UpperCamelCase_ ( self , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase ) -> List[Any]:
SCREAMING_SNAKE_CASE__: Tuple= 20
SCREAMING_SNAKE_CASE__: List[str]= model_class_name(_lowerCamelCase )
SCREAMING_SNAKE_CASE__: Tuple= model.encode(inputs_dict['''input_ids'''] )
SCREAMING_SNAKE_CASE__: Dict= (
inputs_dict['''decoder_input_ids'''],
inputs_dict['''decoder_attention_mask'''],
)
SCREAMING_SNAKE_CASE__: Dict= model.init_cache(decoder_input_ids.shape[0] , _lowerCamelCase , _lowerCamelCase )
SCREAMING_SNAKE_CASE__: Union[str, Any]= jnp.ones((decoder_input_ids.shape[0], max_decoder_length) , dtype='''i4''' )
SCREAMING_SNAKE_CASE__: List[str]= jnp.broadcast_to(
jnp.arange(decoder_input_ids.shape[-1] - 1 )[None, :] , (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1) , )
SCREAMING_SNAKE_CASE__: Optional[int]= model.decode(
decoder_input_ids[:, :-1] , _lowerCamelCase , decoder_attention_mask=_lowerCamelCase , past_key_values=_lowerCamelCase , decoder_position_ids=_lowerCamelCase , )
SCREAMING_SNAKE_CASE__: str= jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]] , dtype='''i4''' )
SCREAMING_SNAKE_CASE__: Dict= model.decode(
decoder_input_ids[:, -1:] , _lowerCamelCase , decoder_attention_mask=_lowerCamelCase , past_key_values=outputs_cache.past_key_values , decoder_position_ids=_lowerCamelCase , )
SCREAMING_SNAKE_CASE__: Dict= model.decode(_lowerCamelCase , _lowerCamelCase )
SCREAMING_SNAKE_CASE__: Optional[Any]= np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5]) ) )
self.parent.assertTrue(diff < 1e-3 , msg=f'Max diff is {diff}' )
def UpperCamelCase_ ( self , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase ) -> str:
SCREAMING_SNAKE_CASE__: Union[str, Any]= 20
SCREAMING_SNAKE_CASE__: Optional[int]= model_class_name(_lowerCamelCase )
SCREAMING_SNAKE_CASE__: Optional[int]= model.encode(inputs_dict['''input_ids'''] )
SCREAMING_SNAKE_CASE__: List[Any]= (
inputs_dict['''decoder_input_ids'''],
inputs_dict['''decoder_attention_mask'''],
)
SCREAMING_SNAKE_CASE__: List[Any]= jnp.concatenate(
[
decoder_attention_mask,
jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1]) ),
] , axis=-1 , )
SCREAMING_SNAKE_CASE__: List[str]= model.init_cache(decoder_input_ids.shape[0] , _lowerCamelCase , _lowerCamelCase )
SCREAMING_SNAKE_CASE__: str= jnp.broadcast_to(
jnp.arange(decoder_input_ids.shape[-1] - 1 )[None, :] , (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1) , )
SCREAMING_SNAKE_CASE__: str= model.decode(
decoder_input_ids[:, :-1] , _lowerCamelCase , decoder_attention_mask=_lowerCamelCase , past_key_values=_lowerCamelCase , decoder_position_ids=_lowerCamelCase , )
SCREAMING_SNAKE_CASE__: Any= jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]] , dtype='''i4''' )
SCREAMING_SNAKE_CASE__: int= model.decode(
decoder_input_ids[:, -1:] , _lowerCamelCase , past_key_values=outputs_cache.past_key_values , decoder_attention_mask=_lowerCamelCase , decoder_position_ids=_lowerCamelCase , )
SCREAMING_SNAKE_CASE__: Dict= model.decode(_lowerCamelCase , _lowerCamelCase , decoder_attention_mask=_lowerCamelCase )
SCREAMING_SNAKE_CASE__: Optional[Any]= np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5]) ) )
self.parent.assertTrue(diff < 1e-3 , msg=f'Max diff is {diff}' )
@require_flax
class _lowerCamelCase ( unittest.TestCase ):
__a = 99
def UpperCamelCase_ ( self ) -> List[str]:
SCREAMING_SNAKE_CASE__: Optional[int]= np.array(
[
[71, 82, 18, 33, 46, 91, 2],
[68, 34, 26, 58, 30, 82, 2],
[5, 97, 17, 39, 94, 40, 2],
[76, 83, 94, 25, 70, 78, 2],
[87, 59, 41, 35, 48, 66, 2],
[55, 13, 16, 58, 5, 2, 1], # note padding
[64, 27, 31, 51, 12, 75, 2],
[52, 64, 86, 17, 83, 39, 2],
[48, 61, 9, 24, 71, 82, 2],
[26, 1, 60, 48, 22, 13, 2],
[21, 5, 62, 28, 14, 76, 2],
[45, 98, 37, 86, 59, 48, 2],
[70, 70, 50, 9, 28, 0, 2],
] , dtype=np.intaa , )
SCREAMING_SNAKE_CASE__: Tuple= input_ids.shape[0]
SCREAMING_SNAKE_CASE__: Optional[Any]= BlenderbotConfig(
vocab_size=self.vocab_size , d_model=24 , encoder_layers=2 , decoder_layers=2 , encoder_attention_heads=2 , decoder_attention_heads=2 , encoder_ffn_dim=32 , decoder_ffn_dim=32 , max_position_embeddings=48 , eos_token_id=2 , pad_token_id=1 , bos_token_id=0 , )
return config, input_ids, batch_size
def UpperCamelCase_ ( self ) -> Optional[Any]:
SCREAMING_SNAKE_CASE__: Dict= self._get_config_and_data()
SCREAMING_SNAKE_CASE__: List[Any]= FlaxBlenderbotForConditionalGeneration(_lowerCamelCase )
SCREAMING_SNAKE_CASE__: Optional[Any]= lm_model(input_ids=_lowerCamelCase )
SCREAMING_SNAKE_CASE__: Optional[Any]= (batch_size, input_ids.shape[1], config.vocab_size)
self.assertEqual(outputs['''logits'''].shape , _lowerCamelCase )
def UpperCamelCase_ ( self ) -> List[Any]:
SCREAMING_SNAKE_CASE__: int= BlenderbotConfig(
vocab_size=self.vocab_size , d_model=14 , encoder_layers=2 , decoder_layers=2 , encoder_attention_heads=2 , decoder_attention_heads=2 , encoder_ffn_dim=8 , decoder_ffn_dim=8 , max_position_embeddings=48 , )
SCREAMING_SNAKE_CASE__: str= FlaxBlenderbotForConditionalGeneration(_lowerCamelCase )
SCREAMING_SNAKE_CASE__: Dict= np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]] , dtype=np.intaa )
SCREAMING_SNAKE_CASE__: int= np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]] , dtype=np.intaa )
SCREAMING_SNAKE_CASE__: int= lm_model(input_ids=_lowerCamelCase , decoder_input_ids=_lowerCamelCase )
SCREAMING_SNAKE_CASE__: Optional[int]= (*summary.shape, config.vocab_size)
self.assertEqual(outputs['''logits'''].shape , _lowerCamelCase )
def UpperCamelCase_ ( self ) -> List[str]:
SCREAMING_SNAKE_CASE__: int= np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]] , dtype=np.intaa )
SCREAMING_SNAKE_CASE__: Any= shift_tokens_right(_lowerCamelCase , 1 , 2 )
SCREAMING_SNAKE_CASE__: List[str]= np.equal(_lowerCamelCase , 1 ).astype(np.floataa ).sum()
SCREAMING_SNAKE_CASE__: List[Any]= np.equal(_lowerCamelCase , 1 ).astype(np.floataa ).sum()
self.assertEqual(shifted.shape , input_ids.shape )
self.assertEqual(_lowerCamelCase , n_pad_before - 1 )
self.assertTrue(np.equal(shifted[:, 0] , 2 ).all() )
@require_flax
class _lowerCamelCase ( __UpperCAmelCase , unittest.TestCase , __UpperCAmelCase ):
__a = True
__a = (
(
FlaxBlenderbotModel,
FlaxBlenderbotForConditionalGeneration,
)
if is_flax_available()
else ()
)
__a = (FlaxBlenderbotForConditionalGeneration,) if is_flax_available() else ()
def UpperCamelCase_ ( self ) -> Union[str, Any]:
SCREAMING_SNAKE_CASE__: Union[str, Any]= FlaxBlenderbotModelTester(self )
def UpperCamelCase_ ( self ) -> Optional[Any]:
SCREAMING_SNAKE_CASE__: List[str]= self.model_tester.prepare_config_and_inputs()
for model_class in self.all_model_classes:
self.model_tester.check_use_cache_forward(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase )
def UpperCamelCase_ ( self ) -> Optional[int]:
SCREAMING_SNAKE_CASE__: str= self.model_tester.prepare_config_and_inputs()
for model_class in self.all_model_classes:
self.model_tester.check_use_cache_forward_with_attn_mask(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase )
def UpperCamelCase_ ( self ) -> Optional[int]:
SCREAMING_SNAKE_CASE__: List[Any]= self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__ ):
SCREAMING_SNAKE_CASE__: Optional[Any]= self._prepare_for_class(_lowerCamelCase , _lowerCamelCase )
SCREAMING_SNAKE_CASE__: str= model_class(_lowerCamelCase )
@jax.jit
def encode_jitted(lowerCAmelCase , lowerCAmelCase=None , **lowerCAmelCase ):
return model.encode(input_ids=_lowerCamelCase , attention_mask=_lowerCamelCase )
with self.subTest('''JIT Enabled''' ):
SCREAMING_SNAKE_CASE__: Dict= encode_jitted(**_lowerCamelCase ).to_tuple()
with self.subTest('''JIT Disabled''' ):
with jax.disable_jit():
SCREAMING_SNAKE_CASE__: Optional[int]= encode_jitted(**_lowerCamelCase ).to_tuple()
self.assertEqual(len(_lowerCamelCase ) , len(_lowerCamelCase ) )
for jitted_output, output in zip(_lowerCamelCase , _lowerCamelCase ):
self.assertEqual(jitted_output.shape , output.shape )
def UpperCamelCase_ ( self ) -> Tuple:
SCREAMING_SNAKE_CASE__: List[Any]= self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__ ):
SCREAMING_SNAKE_CASE__: Optional[Any]= model_class(_lowerCamelCase )
SCREAMING_SNAKE_CASE__: Union[str, Any]= model.encode(inputs_dict['''input_ids'''] , inputs_dict['''attention_mask'''] )
SCREAMING_SNAKE_CASE__: Union[str, Any]= {
'''decoder_input_ids''': inputs_dict['''decoder_input_ids'''],
'''decoder_attention_mask''': inputs_dict['''decoder_attention_mask'''],
'''encoder_outputs''': encoder_outputs,
}
@jax.jit
def decode_jitted(lowerCAmelCase , lowerCAmelCase , lowerCAmelCase ):
return model.decode(
decoder_input_ids=_lowerCamelCase , decoder_attention_mask=_lowerCamelCase , encoder_outputs=_lowerCamelCase , )
with self.subTest('''JIT Enabled''' ):
SCREAMING_SNAKE_CASE__: int= decode_jitted(**_lowerCamelCase ).to_tuple()
with self.subTest('''JIT Disabled''' ):
with jax.disable_jit():
SCREAMING_SNAKE_CASE__: Optional[int]= decode_jitted(**_lowerCamelCase ).to_tuple()
self.assertEqual(len(_lowerCamelCase ) , len(_lowerCamelCase ) )
for jitted_output, output in zip(_lowerCamelCase , _lowerCamelCase ):
self.assertEqual(jitted_output.shape , output.shape )
@slow
def UpperCamelCase_ ( self ) -> str:
for model_class_name in self.all_model_classes:
SCREAMING_SNAKE_CASE__: Optional[int]= model_class_name.from_pretrained('''facebook/blenderbot-400M-distill''' )
# FlaxBlenderbotForSequenceClassification expects eos token in input_ids
SCREAMING_SNAKE_CASE__: Union[str, Any]= np.ones((1, 1) ) * model.config.eos_token_id
SCREAMING_SNAKE_CASE__: int= model(_lowerCamelCase )
self.assertIsNotNone(_lowerCamelCase )
@unittest.skipUnless(jax_device != '''cpu''' , '''3B test too slow on CPU.''' )
@slow
def UpperCamelCase_ ( self ) -> Optional[int]:
SCREAMING_SNAKE_CASE__: int= {'''num_beams''': 1, '''early_stopping''': True, '''min_length''': 15, '''max_length''': 25}
SCREAMING_SNAKE_CASE__: List[str]= {'''skip_special_tokens''': True, '''clean_up_tokenization_spaces''': True}
SCREAMING_SNAKE_CASE__: Any= FlaxBlenderbotForConditionalGeneration.from_pretrained('''facebook/blenderbot-3B''' , from_pt=_lowerCamelCase )
SCREAMING_SNAKE_CASE__: Union[str, Any]= BlenderbotTokenizer.from_pretrained('''facebook/blenderbot-3B''' )
SCREAMING_SNAKE_CASE__: Union[str, Any]= ['''Sam''']
SCREAMING_SNAKE_CASE__: Any= tokenizer(_lowerCamelCase , return_tensors='''jax''' )
SCREAMING_SNAKE_CASE__: List[Any]= model.generate(**_lowerCamelCase , **_lowerCamelCase )
SCREAMING_SNAKE_CASE__: Union[str, Any]= '''Sam is a great name. It means "sun" in Gaelic.'''
SCREAMING_SNAKE_CASE__: str= tokenizer.batch_decode(_lowerCamelCase , **_lowerCamelCase )
assert generated_txt[0].strip() == tgt_text
| 64 |
"""simple docstring"""
import contextlib
import importlib
import io
import unittest
import transformers
# Try to import everything from transformers to ensure every object can be loaded.
from transformers import * # noqa F406
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, require_flax, require_tf, require_torch
from transformers.utils import ContextManagers, find_labels, is_flax_available, is_tf_available, is_torch_available
if is_torch_available():
from transformers import BertForPreTraining, BertForQuestionAnswering, BertForSequenceClassification
if is_tf_available():
from transformers import TFBertForPreTraining, TFBertForQuestionAnswering, TFBertForSequenceClassification
if is_flax_available():
from transformers import FlaxBertForPreTraining, FlaxBertForQuestionAnswering, FlaxBertForSequenceClassification
_lowerCamelCase = DUMMY_UNKNOWN_IDENTIFIER
# An actual model hosted on huggingface.co
_lowerCamelCase = '''main'''
# Default branch name
_lowerCamelCase = '''f2c752cfc5c0ab6f4bdec59acea69eefbee381c2'''
# One particular commit (not the top of `main`)
_lowerCamelCase = '''aaaaaaa'''
# This commit does not exist, so we should 404.
_lowerCamelCase = '''d9e9f15bc825e4b2c9249e9578f884bbcb5e3684'''
# Sha-1 of config.json on the top of `main`, for checking purposes
_lowerCamelCase = '''4b243c475af8d0a7754e87d7d096c92e5199ec2fe168a2ee7998e3b8e9bcb1d3'''
@contextlib.contextmanager
def lowerCAmelCase_ ( ):
'''simple docstring'''
print('''Welcome!''' )
yield
print('''Bye!''' )
@contextlib.contextmanager
def lowerCAmelCase_ ( ):
'''simple docstring'''
print('''Bonjour!''' )
yield
print('''Au revoir!''' )
class snake_case ( unittest.TestCase ):
def SCREAMING_SNAKE_CASE_ ( self :Union[str, Any] ):
# If the spec is missing, importlib would not be able to import the module dynamically.
assert transformers.__spec__ is not None
assert importlib.util.find_spec('''transformers''' ) is not None
class snake_case ( unittest.TestCase ):
@unittest.mock.patch('''sys.stdout''' , new_callable=io.StringIO )
def SCREAMING_SNAKE_CASE_ ( self :Dict , _lowerCamelCase :Optional[int] ):
with ContextManagers([] ):
print('''Transformers are awesome!''' )
# The print statement adds a new line at the end of the output
self.assertEqual(mock_stdout.getvalue() , '''Transformers are awesome!\n''' )
@unittest.mock.patch('''sys.stdout''' , new_callable=io.StringIO )
def SCREAMING_SNAKE_CASE_ ( self :str , _lowerCamelCase :Optional[int] ):
with ContextManagers([context_en()] ):
print('''Transformers are awesome!''' )
# The output should be wrapped with an English welcome and goodbye
self.assertEqual(mock_stdout.getvalue() , '''Welcome!\nTransformers are awesome!\nBye!\n''' )
@unittest.mock.patch('''sys.stdout''' , new_callable=io.StringIO )
def SCREAMING_SNAKE_CASE_ ( self :Dict , _lowerCamelCase :List[str] ):
with ContextManagers([context_fr(), context_en()] ):
print('''Transformers are awesome!''' )
# The output should be wrapped with an English and French welcome and goodbye
self.assertEqual(mock_stdout.getvalue() , '''Bonjour!\nWelcome!\nTransformers are awesome!\nBye!\nAu revoir!\n''' )
@require_torch
def SCREAMING_SNAKE_CASE_ ( self :List[str] ):
self.assertEqual(find_labels(_lowerCamelCase ) , ['''labels'''] )
self.assertEqual(find_labels(_lowerCamelCase ) , ['''labels''', '''next_sentence_label'''] )
self.assertEqual(find_labels(_lowerCamelCase ) , ['''start_positions''', '''end_positions'''] )
class snake_case ( __UpperCAmelCase ):
pass
self.assertEqual(find_labels(_lowerCamelCase ) , ['''labels'''] )
@require_tf
def SCREAMING_SNAKE_CASE_ ( self :int ):
self.assertEqual(find_labels(_lowerCamelCase ) , ['''labels'''] )
self.assertEqual(find_labels(_lowerCamelCase ) , ['''labels''', '''next_sentence_label'''] )
self.assertEqual(find_labels(_lowerCamelCase ) , ['''start_positions''', '''end_positions'''] )
class snake_case ( __UpperCAmelCase ):
pass
self.assertEqual(find_labels(_lowerCamelCase ) , ['''labels'''] )
@require_flax
def SCREAMING_SNAKE_CASE_ ( self :Dict ):
# Flax models don't have labels
self.assertEqual(find_labels(_lowerCamelCase ) , [] )
self.assertEqual(find_labels(_lowerCamelCase ) , [] )
self.assertEqual(find_labels(_lowerCamelCase ) , [] )
class snake_case ( __UpperCAmelCase ):
pass
self.assertEqual(find_labels(_lowerCamelCase ) , [] )
| 674 | 0 |
from __future__ import annotations
from PIL import Image
# Define glider example
a : List[Any] = [
[0, 1, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0, 0],
[1, 1, 1, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
]
# Define blinker example
a : Tuple = [[0, 1, 0], [0, 1, 0], [0, 1, 0]]
def lowercase_ ( _UpperCamelCase ):
'''simple docstring'''
__lowercase = []
for i in range(len(lowercase_ ) ):
__lowercase = []
for j in range(len(cells[i] ) ):
# Get the number of live neighbours
__lowercase = 0
if i > 0 and j > 0:
neighbour_count += cells[i - 1][j - 1]
if i > 0:
neighbour_count += cells[i - 1][j]
if i > 0 and j < len(cells[i] ) - 1:
neighbour_count += cells[i - 1][j + 1]
if j > 0:
neighbour_count += cells[i][j - 1]
if j < len(cells[i] ) - 1:
neighbour_count += cells[i][j + 1]
if i < len(lowercase_ ) - 1 and j > 0:
neighbour_count += cells[i + 1][j - 1]
if i < len(lowercase_ ) - 1:
neighbour_count += cells[i + 1][j]
if i < len(lowercase_ ) - 1 and j < len(cells[i] ) - 1:
neighbour_count += cells[i + 1][j + 1]
# Rules of the game of life (excerpt from Wikipedia):
# 1. Any live cell with two or three live neighbours survives.
# 2. Any dead cell with three live neighbours becomes a live cell.
# 3. All other live cells die in the next generation.
# Similarly, all other dead cells stay dead.
__lowercase = cells[i][j] == 1
if (
(alive and 2 <= neighbour_count <= 3)
or not alive
and neighbour_count == 3
):
next_generation_row.append(1 )
else:
next_generation_row.append(0 )
next_generation.append(lowercase_ )
return next_generation
def lowercase_ ( _UpperCamelCase , _UpperCamelCase ):
'''simple docstring'''
__lowercase = []
for _ in range(lowercase_ ):
# Create output image
__lowercase = Image.new('''RGB''' , (len(cells[0] ), len(lowercase_ )) )
__lowercase = img.load()
# Save cells to image
for x in range(len(lowercase_ ) ):
for y in range(len(cells[0] ) ):
__lowercase = 2_55 - cells[y][x] * 2_55
__lowercase = (colour, colour, colour)
# Save image
images.append(lowercase_ )
__lowercase = new_generation(lowercase_ )
return images
if __name__ == "__main__":
a : List[Any] = generate_images(GLIDER, 16)
images[0].save('''out.gif''', save_all=True, append_images=images[1:])
| 639 |
"""simple docstring"""
import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import YolosImageProcessor
class snake_case ( unittest.TestCase ):
def __init__( self :List[Any] , _lowerCamelCase :Dict , _lowerCamelCase :Tuple=7 , _lowerCamelCase :Dict=3 , _lowerCamelCase :Optional[Any]=3_0 , _lowerCamelCase :List[str]=4_0_0 , _lowerCamelCase :Union[str, Any]=True , _lowerCamelCase :Union[str, Any]=None , _lowerCamelCase :List[Any]=True , _lowerCamelCase :Any=[0.5, 0.5, 0.5] , _lowerCamelCase :Dict=[0.5, 0.5, 0.5] , _lowerCamelCase :Dict=True , _lowerCamelCase :str=1 / 2_5_5 , _lowerCamelCase :Union[str, Any]=True , ):
# by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
__SCREAMING_SNAKE_CASE : Optional[Any] = size if size is not None else {'''shortest_edge''': 1_8, '''longest_edge''': 1_3_3_3}
__SCREAMING_SNAKE_CASE : List[str] = parent
__SCREAMING_SNAKE_CASE : Dict = batch_size
__SCREAMING_SNAKE_CASE : str = num_channels
__SCREAMING_SNAKE_CASE : Union[str, Any] = min_resolution
__SCREAMING_SNAKE_CASE : Union[str, Any] = max_resolution
__SCREAMING_SNAKE_CASE : Tuple = do_resize
__SCREAMING_SNAKE_CASE : Union[str, Any] = size
__SCREAMING_SNAKE_CASE : int = do_normalize
__SCREAMING_SNAKE_CASE : List[Any] = image_mean
__SCREAMING_SNAKE_CASE : Tuple = image_std
__SCREAMING_SNAKE_CASE : Dict = do_rescale
__SCREAMING_SNAKE_CASE : Optional[int] = rescale_factor
__SCREAMING_SNAKE_CASE : List[Any] = do_pad
def SCREAMING_SNAKE_CASE_ ( self :Optional[Any] ):
return {
"do_resize": self.do_resize,
"size": self.size,
"do_normalize": self.do_normalize,
"image_mean": self.image_mean,
"image_std": self.image_std,
"do_rescale": self.do_rescale,
"rescale_factor": self.rescale_factor,
"do_pad": self.do_pad,
}
def SCREAMING_SNAKE_CASE_ ( self :Tuple , _lowerCamelCase :Dict , _lowerCamelCase :List[Any]=False ):
if not batched:
__SCREAMING_SNAKE_CASE : str = image_inputs[0]
if isinstance(_lowerCamelCase , Image.Image ):
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Optional[int] = image.size
else:
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Dict = image.shape[1], image.shape[2]
if w < h:
__SCREAMING_SNAKE_CASE : str = int(self.size['''shortest_edge'''] * h / w )
__SCREAMING_SNAKE_CASE : int = self.size['''shortest_edge''']
elif w > h:
__SCREAMING_SNAKE_CASE : Optional[Any] = self.size['''shortest_edge''']
__SCREAMING_SNAKE_CASE : int = int(self.size['''shortest_edge'''] * w / h )
else:
__SCREAMING_SNAKE_CASE : List[str] = self.size['''shortest_edge''']
__SCREAMING_SNAKE_CASE : List[str] = self.size['''shortest_edge''']
else:
__SCREAMING_SNAKE_CASE : Optional[Any] = []
for image in image_inputs:
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Optional[Any] = self.get_expected_values([image] )
expected_values.append((expected_height, expected_width) )
__SCREAMING_SNAKE_CASE : Optional[int] = max(_lowerCamelCase , key=lambda _lowerCamelCase : item[0] )[0]
__SCREAMING_SNAKE_CASE : int = max(_lowerCamelCase , key=lambda _lowerCamelCase : item[1] )[1]
return expected_height, expected_width
@require_torch
@require_vision
class snake_case ( __UpperCAmelCase , unittest.TestCase ):
lowerCamelCase__ = YolosImageProcessor if is_vision_available() else None
def SCREAMING_SNAKE_CASE_ ( self :Optional[int] ):
__SCREAMING_SNAKE_CASE : List[Any] = YolosImageProcessingTester(self )
@property
def SCREAMING_SNAKE_CASE_ ( self :Optional[int] ):
return self.image_processor_tester.prepare_image_processor_dict()
def SCREAMING_SNAKE_CASE_ ( self :Tuple ):
__SCREAMING_SNAKE_CASE : Any = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(_lowerCamelCase , '''image_mean''' ) )
self.assertTrue(hasattr(_lowerCamelCase , '''image_std''' ) )
self.assertTrue(hasattr(_lowerCamelCase , '''do_normalize''' ) )
self.assertTrue(hasattr(_lowerCamelCase , '''do_resize''' ) )
self.assertTrue(hasattr(_lowerCamelCase , '''size''' ) )
def SCREAMING_SNAKE_CASE_ ( self :Tuple ):
__SCREAMING_SNAKE_CASE : Union[str, Any] = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {'''shortest_edge''': 1_8, '''longest_edge''': 1_3_3_3} )
self.assertEqual(image_processor.do_pad , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : Tuple = self.image_processing_class.from_dict(
self.image_processor_dict , size=4_2 , max_size=8_4 , pad_and_return_pixel_mask=_lowerCamelCase )
self.assertEqual(image_processor.size , {'''shortest_edge''': 4_2, '''longest_edge''': 8_4} )
self.assertEqual(image_processor.do_pad , _lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :List[str] ):
pass
def SCREAMING_SNAKE_CASE_ ( self :int ):
# Initialize image_processing
__SCREAMING_SNAKE_CASE : Optional[int] = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
__SCREAMING_SNAKE_CASE : List[str] = prepare_image_inputs(self.image_processor_tester , equal_resolution=_lowerCamelCase )
for image in image_inputs:
self.assertIsInstance(_lowerCamelCase , Image.Image )
# Test not batched input
__SCREAMING_SNAKE_CASE : str = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Dict = self.image_processor_tester.get_expected_values(_lowerCamelCase )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Tuple = self.image_processor_tester.get_expected_values(_lowerCamelCase , batched=_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Union[str, Any] = image_processing(_lowerCamelCase , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
def SCREAMING_SNAKE_CASE_ ( self :Optional[int] ):
# Initialize image_processing
__SCREAMING_SNAKE_CASE : Optional[Any] = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
__SCREAMING_SNAKE_CASE : Optional[int] = prepare_image_inputs(self.image_processor_tester , equal_resolution=_lowerCamelCase , numpify=_lowerCamelCase )
for image in image_inputs:
self.assertIsInstance(_lowerCamelCase , np.ndarray )
# Test not batched input
__SCREAMING_SNAKE_CASE : Dict = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : List[str] = self.image_processor_tester.get_expected_values(_lowerCamelCase )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
__SCREAMING_SNAKE_CASE : List[Any] = image_processing(_lowerCamelCase , return_tensors='''pt''' ).pixel_values
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Any = self.image_processor_tester.get_expected_values(_lowerCamelCase , batched=_lowerCamelCase )
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
def SCREAMING_SNAKE_CASE_ ( self :Any ):
# Initialize image_processing
__SCREAMING_SNAKE_CASE : str = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
__SCREAMING_SNAKE_CASE : Any = prepare_image_inputs(self.image_processor_tester , equal_resolution=_lowerCamelCase , torchify=_lowerCamelCase )
for image in image_inputs:
self.assertIsInstance(_lowerCamelCase , torch.Tensor )
# Test not batched input
__SCREAMING_SNAKE_CASE : List[str] = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Optional[Any] = self.image_processor_tester.get_expected_values(_lowerCamelCase )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
__SCREAMING_SNAKE_CASE : Optional[int] = image_processing(_lowerCamelCase , return_tensors='''pt''' ).pixel_values
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : List[Any] = self.image_processor_tester.get_expected_values(_lowerCamelCase , batched=_lowerCamelCase )
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
def SCREAMING_SNAKE_CASE_ ( self :List[str] ):
# Initialize image_processings
__SCREAMING_SNAKE_CASE : Dict = self.image_processing_class(**self.image_processor_dict )
__SCREAMING_SNAKE_CASE : Tuple = self.image_processing_class(do_resize=_lowerCamelCase , do_normalize=_lowerCamelCase , do_rescale=_lowerCamelCase )
# create random PyTorch tensors
__SCREAMING_SNAKE_CASE : int = prepare_image_inputs(self.image_processor_tester , equal_resolution=_lowerCamelCase , torchify=_lowerCamelCase )
for image in image_inputs:
self.assertIsInstance(_lowerCamelCase , torch.Tensor )
# Test whether the method "pad" and calling the image processor return the same tensors
__SCREAMING_SNAKE_CASE : Union[str, Any] = image_processing_a.pad(_lowerCamelCase , return_tensors='''pt''' )
__SCREAMING_SNAKE_CASE : Union[str, Any] = image_processing_a(_lowerCamelCase , return_tensors='''pt''' )
self.assertTrue(
torch.allclose(encoded_images_with_method['''pixel_values'''] , encoded_images['''pixel_values'''] , atol=1e-4 ) )
@slow
def SCREAMING_SNAKE_CASE_ ( self :int ):
# prepare image and target
__SCREAMING_SNAKE_CASE : Tuple = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
with open('''./tests/fixtures/tests_samples/COCO/coco_annotations.txt''' , '''r''' ) as f:
__SCREAMING_SNAKE_CASE : Tuple = json.loads(f.read() )
__SCREAMING_SNAKE_CASE : Union[str, Any] = {'''image_id''': 3_9_7_6_9, '''annotations''': target}
# encode them
__SCREAMING_SNAKE_CASE : List[Any] = YolosImageProcessor.from_pretrained('''hustvl/yolos-small''' )
__SCREAMING_SNAKE_CASE : Optional[Any] = image_processing(images=_lowerCamelCase , annotations=_lowerCamelCase , return_tensors='''pt''' )
# verify pixel values
__SCREAMING_SNAKE_CASE : Optional[int] = torch.Size([1, 3, 8_0_0, 1_0_6_6] )
self.assertEqual(encoding['''pixel_values'''].shape , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : List[str] = torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1] )
self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3] , _lowerCamelCase , atol=1e-4 ) )
# verify area
__SCREAMING_SNAKE_CASE : Union[str, Any] = torch.tensor([5_8_8_7.9_6_0_0, 1_1_2_5_0.2_0_6_1, 4_8_9_3_5_3.8_4_3_8, 8_3_7_1_2_2.7_5_0_0, 1_4_7_9_6_7.5_1_5_6, 1_6_5_7_3_2.3_4_3_8] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area'''] , _lowerCamelCase ) )
# verify boxes
__SCREAMING_SNAKE_CASE : List[str] = torch.Size([6, 4] )
self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : Any = torch.tensor([0.5_5_0_3, 0.2_7_6_5, 0.0_6_0_4, 0.2_2_1_5] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0] , _lowerCamelCase , atol=1e-3 ) )
# verify image_id
__SCREAMING_SNAKE_CASE : Optional[Any] = torch.tensor([3_9_7_6_9] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id'''] , _lowerCamelCase ) )
# verify is_crowd
__SCREAMING_SNAKE_CASE : str = torch.tensor([0, 0, 0, 0, 0, 0] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd'''] , _lowerCamelCase ) )
# verify class_labels
__SCREAMING_SNAKE_CASE : Dict = torch.tensor([7_5, 7_5, 6_3, 6_5, 1_7, 1_7] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels'''] , _lowerCamelCase ) )
# verify orig_size
__SCREAMING_SNAKE_CASE : Union[str, Any] = torch.tensor([4_8_0, 6_4_0] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size'''] , _lowerCamelCase ) )
# verify size
__SCREAMING_SNAKE_CASE : List[str] = torch.tensor([8_0_0, 1_0_6_6] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size'''] , _lowerCamelCase ) )
@slow
def SCREAMING_SNAKE_CASE_ ( self :Optional[int] ):
# prepare image, target and masks_path
__SCREAMING_SNAKE_CASE : List[str] = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
with open('''./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt''' , '''r''' ) as f:
__SCREAMING_SNAKE_CASE : Union[str, Any] = json.loads(f.read() )
__SCREAMING_SNAKE_CASE : Dict = {'''file_name''': '''000000039769.png''', '''image_id''': 3_9_7_6_9, '''segments_info''': target}
__SCREAMING_SNAKE_CASE : Optional[int] = pathlib.Path('''./tests/fixtures/tests_samples/COCO/coco_panoptic''' )
# encode them
__SCREAMING_SNAKE_CASE : Any = YolosImageProcessor(format='''coco_panoptic''' )
__SCREAMING_SNAKE_CASE : Dict = image_processing(images=_lowerCamelCase , annotations=_lowerCamelCase , masks_path=_lowerCamelCase , return_tensors='''pt''' )
# verify pixel values
__SCREAMING_SNAKE_CASE : str = torch.Size([1, 3, 8_0_0, 1_0_6_6] )
self.assertEqual(encoding['''pixel_values'''].shape , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : str = torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1] )
self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3] , _lowerCamelCase , atol=1e-4 ) )
# verify area
__SCREAMING_SNAKE_CASE : Any = torch.tensor([1_4_7_9_7_9.6_8_7_5, 1_6_5_5_2_7.0_4_6_9, 4_8_4_6_3_8.5_9_3_8, 1_1_2_9_2.9_3_7_5, 5_8_7_9.6_5_6_2, 7_6_3_4.1_1_4_7] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area'''] , _lowerCamelCase ) )
# verify boxes
__SCREAMING_SNAKE_CASE : List[str] = torch.Size([6, 4] )
self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : int = torch.tensor([0.2_6_2_5, 0.5_4_3_7, 0.4_6_8_8, 0.8_6_2_5] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0] , _lowerCamelCase , atol=1e-3 ) )
# verify image_id
__SCREAMING_SNAKE_CASE : Dict = torch.tensor([3_9_7_6_9] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id'''] , _lowerCamelCase ) )
# verify is_crowd
__SCREAMING_SNAKE_CASE : List[Any] = torch.tensor([0, 0, 0, 0, 0, 0] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd'''] , _lowerCamelCase ) )
# verify class_labels
__SCREAMING_SNAKE_CASE : Optional[Any] = torch.tensor([1_7, 1_7, 6_3, 7_5, 7_5, 9_3] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels'''] , _lowerCamelCase ) )
# verify masks
__SCREAMING_SNAKE_CASE : Optional[Any] = 8_2_2_8_7_3
self.assertEqual(encoding['''labels'''][0]['''masks'''].sum().item() , _lowerCamelCase )
# verify orig_size
__SCREAMING_SNAKE_CASE : List[str] = torch.tensor([4_8_0, 6_4_0] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size'''] , _lowerCamelCase ) )
# verify size
__SCREAMING_SNAKE_CASE : Any = torch.tensor([8_0_0, 1_0_6_6] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size'''] , _lowerCamelCase ) )
| 674 | 0 |
import inspect
import unittest
from transformers import BitConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import BitBackbone, BitForImageClassification, BitImageProcessor, BitModel
from transformers.models.bit.modeling_bit import BIT_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
class __magic_name__ :
"""simple docstring"""
def __init__( self : Optional[int] , _lowercase : str , _lowercase : int=3 , _lowercase : List[Any]=32 , _lowercase : int=3 , _lowercase : Dict=10 , _lowercase : int=[8, 16, 32, 64] , _lowercase : List[Any]=[1, 1, 2, 1] , _lowercase : Tuple=True , _lowercase : List[str]=True , _lowercase : str="relu" , _lowercase : List[Any]=3 , _lowercase : Optional[int]=None , _lowercase : Optional[int]=["stage2", "stage3", "stage4"] , _lowercase : Optional[Any]=[2, 3, 4] , _lowercase : Tuple=1 , ):
"""simple docstring"""
_UpperCamelCase: Dict = parent
_UpperCamelCase: Union[str, Any] = batch_size
_UpperCamelCase: List[Any] = image_size
_UpperCamelCase: Union[str, Any] = num_channels
_UpperCamelCase: Any = embeddings_size
_UpperCamelCase: Any = hidden_sizes
_UpperCamelCase: int = depths
_UpperCamelCase: Any = is_training
_UpperCamelCase: List[str] = use_labels
_UpperCamelCase: Any = hidden_act
_UpperCamelCase: str = num_labels
_UpperCamelCase: Tuple = scope
_UpperCamelCase: Union[str, Any] = len(_lowerCamelCase )
_UpperCamelCase: int = out_features
_UpperCamelCase: Any = out_indices
_UpperCamelCase: int = num_groups
def lowerCAmelCase ( self : List[str] ):
"""simple docstring"""
_UpperCamelCase: List[str] = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
_UpperCamelCase: List[str] = None
if self.use_labels:
_UpperCamelCase: Any = ids_tensor([self.batch_size] , self.num_labels )
_UpperCamelCase: Dict = self.get_config()
return config, pixel_values, labels
def lowerCAmelCase ( self : Dict ):
"""simple docstring"""
return BitConfig(
num_channels=self.num_channels , embeddings_size=self.embeddings_size , hidden_sizes=self.hidden_sizes , depths=self.depths , hidden_act=self.hidden_act , num_labels=self.num_labels , out_features=self.out_features , out_indices=self.out_indices , num_groups=self.num_groups , )
def lowerCAmelCase ( self : Union[str, Any] , _lowercase : int , _lowercase : Optional[Any] , _lowercase : Tuple ):
"""simple docstring"""
_UpperCamelCase: Optional[int] = BitModel(config=_lowerCamelCase )
model.to(_lowerCamelCase )
model.eval()
_UpperCamelCase: Optional[Any] = model(_lowerCamelCase )
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32) , )
def lowerCAmelCase ( self : str , _lowercase : Any , _lowercase : Union[str, Any] , _lowercase : int ):
"""simple docstring"""
_UpperCamelCase: int = self.num_labels
_UpperCamelCase: int = BitForImageClassification(_lowerCamelCase )
model.to(_lowerCamelCase )
model.eval()
_UpperCamelCase: Dict = model(_lowerCamelCase , labels=_lowerCamelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def lowerCAmelCase ( self : Tuple , _lowercase : List[Any] , _lowercase : Union[str, Any] , _lowercase : str ):
"""simple docstring"""
_UpperCamelCase: Tuple = BitBackbone(config=_lowerCamelCase )
model.to(_lowerCamelCase )
model.eval()
_UpperCamelCase: Any = model(_lowerCamelCase )
# verify feature maps
self.parent.assertEqual(len(result.feature_maps ) , len(config.out_features ) )
self.parent.assertListEqual(list(result.feature_maps[0].shape ) , [self.batch_size, self.hidden_sizes[1], 4, 4] )
# verify channels
self.parent.assertEqual(len(model.channels ) , len(config.out_features ) )
self.parent.assertListEqual(model.channels , config.hidden_sizes[1:] )
# verify backbone works with out_features=None
_UpperCamelCase: Tuple = None
_UpperCamelCase: Tuple = BitBackbone(config=_lowerCamelCase )
model.to(_lowerCamelCase )
model.eval()
_UpperCamelCase: Tuple = model(_lowerCamelCase )
# verify feature maps
self.parent.assertEqual(len(result.feature_maps ) , 1 )
self.parent.assertListEqual(list(result.feature_maps[0].shape ) , [self.batch_size, self.hidden_sizes[-1], 1, 1] )
# verify channels
self.parent.assertEqual(len(model.channels ) , 1 )
self.parent.assertListEqual(model.channels , [config.hidden_sizes[-1]] )
def lowerCAmelCase ( self : Union[str, Any] ):
"""simple docstring"""
_UpperCamelCase: Any = self.prepare_config_and_inputs()
_UpperCamelCase: Tuple = config_and_inputs
_UpperCamelCase: Optional[Any] = {'''pixel_values''': pixel_values}
return config, inputs_dict
@require_torch
class __magic_name__ ( __UpperCAmelCase , __UpperCAmelCase , unittest.TestCase ):
"""simple docstring"""
lowerCAmelCase : str = (BitModel, BitForImageClassification, BitBackbone) if is_torch_available() else ()
lowerCAmelCase : Union[str, Any] = (
{'''feature-extraction''': BitModel, '''image-classification''': BitForImageClassification}
if is_torch_available()
else {}
)
lowerCAmelCase : Optional[Any] = False
lowerCAmelCase : List[str] = False
lowerCAmelCase : List[str] = False
lowerCAmelCase : Union[str, Any] = False
lowerCAmelCase : Tuple = False
def lowerCAmelCase ( self : Optional[int] ):
"""simple docstring"""
_UpperCamelCase: Union[str, Any] = BitModelTester(self )
_UpperCamelCase: Optional[Any] = ConfigTester(self , config_class=_lowerCamelCase , has_text_modality=_lowerCamelCase )
def lowerCAmelCase ( self : Optional[int] ):
"""simple docstring"""
self.create_and_test_config_common_properties()
self.config_tester.create_and_test_config_to_json_string()
self.config_tester.create_and_test_config_to_json_file()
self.config_tester.create_and_test_config_from_and_save_pretrained()
self.config_tester.create_and_test_config_with_num_labels()
self.config_tester.check_config_can_be_init_without_params()
self.config_tester.check_config_arguments_init()
def lowerCAmelCase ( self : Dict ):
"""simple docstring"""
return
@unittest.skip(reason='''Bit does not output attentions''' )
def lowerCAmelCase ( self : Dict ):
"""simple docstring"""
pass
@unittest.skip(reason='''Bit does not use inputs_embeds''' )
def lowerCAmelCase ( self : List[Any] ):
"""simple docstring"""
pass
@unittest.skip(reason='''Bit does not support input and output embeddings''' )
def lowerCAmelCase ( self : Union[str, Any] ):
"""simple docstring"""
pass
def lowerCAmelCase ( self : List[str] ):
"""simple docstring"""
_UpperCamelCase: Optional[Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_UpperCamelCase: str = model_class(_lowerCamelCase )
_UpperCamelCase: List[str] = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
_UpperCamelCase: str = [*signature.parameters.keys()]
_UpperCamelCase: Any = ['''pixel_values''']
self.assertListEqual(arg_names[:1] , _lowerCamelCase )
def lowerCAmelCase ( self : int ):
"""simple docstring"""
_UpperCamelCase: Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*_lowerCamelCase )
def lowerCAmelCase ( self : Any ):
"""simple docstring"""
_UpperCamelCase: str = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_backbone(*_lowerCamelCase )
def lowerCAmelCase ( self : Tuple ):
"""simple docstring"""
_UpperCamelCase: Optional[Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_UpperCamelCase: List[Any] = model_class(config=_lowerCamelCase )
for name, module in model.named_modules():
if isinstance(_lowerCamelCase , (nn.BatchNormad, nn.GroupNorm) ):
self.assertTrue(
torch.all(module.weight == 1 ) , msg=f"""Parameter {name} of model {model_class} seems not properly initialized""" , )
self.assertTrue(
torch.all(module.bias == 0 ) , msg=f"""Parameter {name} of model {model_class} seems not properly initialized""" , )
def lowerCAmelCase ( self : List[Any] ):
"""simple docstring"""
def check_hidden_states_output(_lowercase : Optional[int] , _lowercase : Tuple , _lowercase : List[Any] ):
_UpperCamelCase: Optional[int] = model_class(_lowerCamelCase )
model.to(_lowerCamelCase )
model.eval()
with torch.no_grad():
_UpperCamelCase: Union[str, Any] = model(**self._prepare_for_class(_lowerCamelCase , _lowerCamelCase ) )
_UpperCamelCase: int = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
_UpperCamelCase: Optional[Any] = self.model_tester.num_stages
self.assertEqual(len(_lowerCamelCase ) , expected_num_stages + 1 )
# Bit's feature maps are of shape (batch_size, num_channels, height, width)
self.assertListEqual(
list(hidden_states[0].shape[-2:] ) , [self.model_tester.image_size // 4, self.model_tester.image_size // 4] , )
_UpperCamelCase: List[str] = self.model_tester.prepare_config_and_inputs_for_common()
_UpperCamelCase: List[Any] = ['''preactivation''', '''bottleneck''']
for model_class in self.all_model_classes:
for layer_type in layers_type:
_UpperCamelCase: Optional[int] = layer_type
_UpperCamelCase: Any = True
check_hidden_states_output(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
_UpperCamelCase: Tuple = True
check_hidden_states_output(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase )
@unittest.skip(reason='''Bit does not use feedforward chunking''' )
def lowerCAmelCase ( self : Optional[int] ):
"""simple docstring"""
pass
def lowerCAmelCase ( self : List[Any] ):
"""simple docstring"""
_UpperCamelCase: Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*_lowerCamelCase )
@slow
def lowerCAmelCase ( self : Optional[Any] ):
"""simple docstring"""
for model_name in BIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_UpperCamelCase: str = BitModel.from_pretrained(_lowerCamelCase )
self.assertIsNotNone(_lowerCamelCase )
def lowerCAmelCase_ ( ) -> Optional[int]:
'''simple docstring'''
_UpperCamelCase: Union[str, Any] = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
return image
@require_torch
@require_vision
class __magic_name__ ( unittest.TestCase ):
"""simple docstring"""
@cached_property
def lowerCAmelCase ( self : Tuple ):
"""simple docstring"""
return (
BitImageProcessor.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0] ) if is_vision_available() else None
)
@slow
def lowerCAmelCase ( self : Any ):
"""simple docstring"""
_UpperCamelCase: Any = BitForImageClassification.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0] ).to(_lowerCamelCase )
_UpperCamelCase: Tuple = self.default_image_processor
_UpperCamelCase: str = prepare_img()
_UpperCamelCase: Any = image_processor(images=_lowerCamelCase , return_tensors='''pt''' ).to(_lowerCamelCase )
# forward pass
with torch.no_grad():
_UpperCamelCase: Any = model(**_lowerCamelCase )
# verify the logits
_UpperCamelCase: Optional[int] = torch.Size((1, 1_000) )
self.assertEqual(outputs.logits.shape , _lowerCamelCase )
_UpperCamelCase: Tuple = torch.tensor([[-0.6526, -0.5263, -1.4398]] ).to(_lowerCamelCase )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , _lowerCamelCase , atol=1E-4 ) )
@require_torch
class __magic_name__ ( __UpperCAmelCase , unittest.TestCase ):
"""simple docstring"""
lowerCAmelCase : Dict = (BitBackbone,) if is_torch_available() else ()
lowerCAmelCase : Optional[int] = BitConfig
lowerCAmelCase : Optional[Any] = False
def lowerCAmelCase ( self : str ):
"""simple docstring"""
_UpperCamelCase: Union[str, Any] = BitModelTester(self ) | 271 |
"""simple docstring"""
from __future__ import annotations
def lowerCAmelCase_ ( lowercase_ : int ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : Optional[int] = str(lowercase_ )
return len(lowercase_ ) == 9 and set(lowercase_ ) == set('''123456789''' )
def lowerCAmelCase_ ( ):
'''simple docstring'''
for base_num in range(9999 , 4999 , -1 ):
__SCREAMING_SNAKE_CASE : List[str] = 10_0002 * base_num
if is_9_pandigital(lowercase_ ):
return candidate
for base_num in range(333 , 99 , -1 ):
__SCREAMING_SNAKE_CASE : List[Any] = 100_2003 * base_num
if is_9_pandigital(lowercase_ ):
return candidate
return None
if __name__ == "__main__":
print(f'{solution() = }')
| 674 | 0 |
import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import YolosImageProcessor
class _lowerCAmelCase ( unittest.TestCase ):
"""simple docstring"""
def __init__( self : List[Any] , SCREAMING_SNAKE_CASE__ : Dict , SCREAMING_SNAKE_CASE__ : Tuple=7 , SCREAMING_SNAKE_CASE__ : Dict=3 , SCREAMING_SNAKE_CASE__ : Optional[Any]=30 , SCREAMING_SNAKE_CASE__ : List[str]=4_00 , SCREAMING_SNAKE_CASE__ : Union[str, Any]=True , SCREAMING_SNAKE_CASE__ : Union[str, Any]=None , SCREAMING_SNAKE_CASE__ : List[Any]=True , SCREAMING_SNAKE_CASE__ : Any=[0.5, 0.5, 0.5] , SCREAMING_SNAKE_CASE__ : Dict=[0.5, 0.5, 0.5] , SCREAMING_SNAKE_CASE__ : Dict=True , SCREAMING_SNAKE_CASE__ : str=1 / 2_55 , SCREAMING_SNAKE_CASE__ : Union[str, Any]=True , ):
"""simple docstring"""
UpperCamelCase = size if size is not None else {'''shortest_edge''': 18, '''longest_edge''': 13_33}
UpperCamelCase = parent
UpperCamelCase = batch_size
UpperCamelCase = num_channels
UpperCamelCase = min_resolution
UpperCamelCase = max_resolution
UpperCamelCase = do_resize
UpperCamelCase = size
UpperCamelCase = do_normalize
UpperCamelCase = image_mean
UpperCamelCase = image_std
UpperCamelCase = do_rescale
UpperCamelCase = rescale_factor
UpperCamelCase = do_pad
def __lowerCAmelCase ( self : Optional[Any] ):
"""simple docstring"""
return {
"do_resize": self.do_resize,
"size": self.size,
"do_normalize": self.do_normalize,
"image_mean": self.image_mean,
"image_std": self.image_std,
"do_rescale": self.do_rescale,
"rescale_factor": self.rescale_factor,
"do_pad": self.do_pad,
}
def __lowerCAmelCase ( self : Tuple , SCREAMING_SNAKE_CASE__ : Dict , SCREAMING_SNAKE_CASE__ : List[Any]=False ):
"""simple docstring"""
if not batched:
UpperCamelCase = image_inputs[0]
if isinstance(_lowerCamelCase , Image.Image ):
UpperCamelCase = image.size
else:
UpperCamelCase = image.shape[1], image.shape[2]
if w < h:
UpperCamelCase = int(self.size['shortest_edge'] * h / w )
UpperCamelCase = self.size['''shortest_edge''']
elif w > h:
UpperCamelCase = self.size['''shortest_edge''']
UpperCamelCase = int(self.size['shortest_edge'] * w / h )
else:
UpperCamelCase = self.size['''shortest_edge''']
UpperCamelCase = self.size['''shortest_edge''']
else:
UpperCamelCase = []
for image in image_inputs:
UpperCamelCase = self.get_expected_values([image] )
expected_values.append((expected_height, expected_width) )
UpperCamelCase = max(_lowerCamelCase , key=lambda SCREAMING_SNAKE_CASE__ : item[0] )[0]
UpperCamelCase = max(_lowerCamelCase , key=lambda SCREAMING_SNAKE_CASE__ : item[1] )[1]
return expected_height, expected_width
@require_torch
@require_vision
class _lowerCAmelCase ( __UpperCAmelCase , unittest.TestCase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Optional[int] =YolosImageProcessor if is_vision_available() else None
def __lowerCAmelCase ( self : Optional[int] ):
"""simple docstring"""
UpperCamelCase = YolosImageProcessingTester(self )
@property
def __lowerCAmelCase ( self : Optional[int] ):
"""simple docstring"""
return self.image_processor_tester.prepare_image_processor_dict()
def __lowerCAmelCase ( self : Tuple ):
"""simple docstring"""
UpperCamelCase = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(_lowerCamelCase , 'image_mean' ) )
self.assertTrue(hasattr(_lowerCamelCase , 'image_std' ) )
self.assertTrue(hasattr(_lowerCamelCase , 'do_normalize' ) )
self.assertTrue(hasattr(_lowerCamelCase , 'do_resize' ) )
self.assertTrue(hasattr(_lowerCamelCase , 'size' ) )
def __lowerCAmelCase ( self : Tuple ):
"""simple docstring"""
UpperCamelCase = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {'shortest_edge': 18, 'longest_edge': 13_33} )
self.assertEqual(image_processor.do_pad , _lowerCamelCase )
UpperCamelCase = self.image_processing_class.from_dict(
self.image_processor_dict , size=42 , max_size=84 , pad_and_return_pixel_mask=_lowerCamelCase )
self.assertEqual(image_processor.size , {'shortest_edge': 42, 'longest_edge': 84} )
self.assertEqual(image_processor.do_pad , _lowerCamelCase )
def __lowerCAmelCase ( self : List[str] ):
"""simple docstring"""
pass
def __lowerCAmelCase ( self : int ):
"""simple docstring"""
UpperCamelCase = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
UpperCamelCase = prepare_image_inputs(self.image_processor_tester , equal_resolution=_lowerCamelCase )
for image in image_inputs:
self.assertIsInstance(_lowerCamelCase , Image.Image )
# Test not batched input
UpperCamelCase = image_processing(image_inputs[0] , return_tensors='pt' ).pixel_values
UpperCamelCase = self.image_processor_tester.get_expected_values(_lowerCamelCase )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
UpperCamelCase = self.image_processor_tester.get_expected_values(_lowerCamelCase , batched=_lowerCamelCase )
UpperCamelCase = image_processing(_lowerCamelCase , return_tensors='pt' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
def __lowerCAmelCase ( self : Optional[int] ):
"""simple docstring"""
UpperCamelCase = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
UpperCamelCase = prepare_image_inputs(self.image_processor_tester , equal_resolution=_lowerCamelCase , numpify=_lowerCamelCase )
for image in image_inputs:
self.assertIsInstance(_lowerCamelCase , np.ndarray )
# Test not batched input
UpperCamelCase = image_processing(image_inputs[0] , return_tensors='pt' ).pixel_values
UpperCamelCase = self.image_processor_tester.get_expected_values(_lowerCamelCase )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
UpperCamelCase = image_processing(_lowerCamelCase , return_tensors='pt' ).pixel_values
UpperCamelCase = self.image_processor_tester.get_expected_values(_lowerCamelCase , batched=_lowerCamelCase )
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
def __lowerCAmelCase ( self : Any ):
"""simple docstring"""
UpperCamelCase = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
UpperCamelCase = prepare_image_inputs(self.image_processor_tester , equal_resolution=_lowerCamelCase , torchify=_lowerCamelCase )
for image in image_inputs:
self.assertIsInstance(_lowerCamelCase , torch.Tensor )
# Test not batched input
UpperCamelCase = image_processing(image_inputs[0] , return_tensors='pt' ).pixel_values
UpperCamelCase = self.image_processor_tester.get_expected_values(_lowerCamelCase )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
UpperCamelCase = image_processing(_lowerCamelCase , return_tensors='pt' ).pixel_values
UpperCamelCase = self.image_processor_tester.get_expected_values(_lowerCamelCase , batched=_lowerCamelCase )
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
def __lowerCAmelCase ( self : List[str] ):
"""simple docstring"""
UpperCamelCase = self.image_processing_class(**self.image_processor_dict )
UpperCamelCase = self.image_processing_class(do_resize=_lowerCamelCase , do_normalize=_lowerCamelCase , do_rescale=_lowerCamelCase )
# create random PyTorch tensors
UpperCamelCase = prepare_image_inputs(self.image_processor_tester , equal_resolution=_lowerCamelCase , torchify=_lowerCamelCase )
for image in image_inputs:
self.assertIsInstance(_lowerCamelCase , torch.Tensor )
# Test whether the method "pad" and calling the image processor return the same tensors
UpperCamelCase = image_processing_a.pad(_lowerCamelCase , return_tensors='pt' )
UpperCamelCase = image_processing_a(_lowerCamelCase , return_tensors='pt' )
self.assertTrue(
torch.allclose(encoded_images_with_method['pixel_values'] , encoded_images['pixel_values'] , atol=1e-4 ) )
@slow
def __lowerCAmelCase ( self : int ):
"""simple docstring"""
UpperCamelCase = Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
with open('./tests/fixtures/tests_samples/COCO/coco_annotations.txt' , 'r' ) as f:
UpperCamelCase = json.loads(f.read() )
UpperCamelCase = {'''image_id''': 3_97_69, '''annotations''': target}
# encode them
UpperCamelCase = YolosImageProcessor.from_pretrained('hustvl/yolos-small' )
UpperCamelCase = image_processing(images=_lowerCamelCase , annotations=_lowerCamelCase , return_tensors='pt' )
# verify pixel values
UpperCamelCase = torch.Size([1, 3, 8_00, 10_66] )
self.assertEqual(encoding['pixel_values'].shape , _lowerCamelCase )
UpperCamelCase = torch.tensor([0.2796, 0.3138, 0.3481] )
self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3] , _lowerCamelCase , atol=1e-4 ) )
# verify area
UpperCamelCase = torch.tensor([58_87.96_00, 1_12_50.20_61, 48_93_53.84_38, 83_71_22.75_00, 14_79_67.51_56, 16_57_32.34_38] )
self.assertTrue(torch.allclose(encoding['labels'][0]['area'] , _lowerCamelCase ) )
# verify boxes
UpperCamelCase = torch.Size([6, 4] )
self.assertEqual(encoding['labels'][0]['boxes'].shape , _lowerCamelCase )
UpperCamelCase = torch.tensor([0.5503, 0.2765, 0.0604, 0.2215] )
self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0] , _lowerCamelCase , atol=1e-3 ) )
# verify image_id
UpperCamelCase = torch.tensor([3_97_69] )
self.assertTrue(torch.allclose(encoding['labels'][0]['image_id'] , _lowerCamelCase ) )
# verify is_crowd
UpperCamelCase = torch.tensor([0, 0, 0, 0, 0, 0] )
self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd'] , _lowerCamelCase ) )
# verify class_labels
UpperCamelCase = torch.tensor([75, 75, 63, 65, 17, 17] )
self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels'] , _lowerCamelCase ) )
# verify orig_size
UpperCamelCase = torch.tensor([4_80, 6_40] )
self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size'] , _lowerCamelCase ) )
# verify size
UpperCamelCase = torch.tensor([8_00, 10_66] )
self.assertTrue(torch.allclose(encoding['labels'][0]['size'] , _lowerCamelCase ) )
@slow
def __lowerCAmelCase ( self : Optional[int] ):
"""simple docstring"""
UpperCamelCase = Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
with open('./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt' , 'r' ) as f:
UpperCamelCase = json.loads(f.read() )
UpperCamelCase = {'''file_name''': '''000000039769.png''', '''image_id''': 3_97_69, '''segments_info''': target}
UpperCamelCase = pathlib.Path('./tests/fixtures/tests_samples/COCO/coco_panoptic' )
# encode them
UpperCamelCase = YolosImageProcessor(format='coco_panoptic' )
UpperCamelCase = image_processing(images=_lowerCamelCase , annotations=_lowerCamelCase , masks_path=_lowerCamelCase , return_tensors='pt' )
# verify pixel values
UpperCamelCase = torch.Size([1, 3, 8_00, 10_66] )
self.assertEqual(encoding['pixel_values'].shape , _lowerCamelCase )
UpperCamelCase = torch.tensor([0.2796, 0.3138, 0.3481] )
self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3] , _lowerCamelCase , atol=1e-4 ) )
# verify area
UpperCamelCase = torch.tensor([14_79_79.68_75, 16_55_27.04_69, 48_46_38.59_38, 1_12_92.93_75, 58_79.65_62, 76_34.11_47] )
self.assertTrue(torch.allclose(encoding['labels'][0]['area'] , _lowerCamelCase ) )
# verify boxes
UpperCamelCase = torch.Size([6, 4] )
self.assertEqual(encoding['labels'][0]['boxes'].shape , _lowerCamelCase )
UpperCamelCase = torch.tensor([0.2625, 0.5437, 0.4688, 0.8625] )
self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0] , _lowerCamelCase , atol=1e-3 ) )
# verify image_id
UpperCamelCase = torch.tensor([3_97_69] )
self.assertTrue(torch.allclose(encoding['labels'][0]['image_id'] , _lowerCamelCase ) )
# verify is_crowd
UpperCamelCase = torch.tensor([0, 0, 0, 0, 0, 0] )
self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd'] , _lowerCamelCase ) )
# verify class_labels
UpperCamelCase = torch.tensor([17, 17, 63, 75, 75, 93] )
self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels'] , _lowerCamelCase ) )
# verify masks
UpperCamelCase = 82_28_73
self.assertEqual(encoding['labels'][0]['masks'].sum().item() , _lowerCamelCase )
# verify orig_size
UpperCamelCase = torch.tensor([4_80, 6_40] )
self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size'] , _lowerCamelCase ) )
# verify size
UpperCamelCase = torch.tensor([8_00, 10_66] )
self.assertTrue(torch.allclose(encoding['labels'][0]['size'] , _lowerCamelCase ) )
| 282 |
"""simple docstring"""
import json
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
from transformers import (
CONFIG_MAPPING,
IMAGE_PROCESSOR_MAPPING,
AutoConfig,
AutoImageProcessor,
CLIPConfig,
CLIPImageProcessor,
)
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER
sys.path.append(str(Path(__file__).parent.parent.parent.parent / '''utils'''))
from test_module.custom_configuration import CustomConfig # noqa E402
from test_module.custom_image_processing import CustomImageProcessor # noqa E402
class snake_case ( unittest.TestCase ):
def SCREAMING_SNAKE_CASE_ ( self :int ):
__SCREAMING_SNAKE_CASE : Tuple = 0
def SCREAMING_SNAKE_CASE_ ( self :Any ):
__SCREAMING_SNAKE_CASE : Optional[Any] = AutoImageProcessor.from_pretrained('''openai/clip-vit-base-patch32''' )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :int ):
with tempfile.TemporaryDirectory() as tmpdirname:
__SCREAMING_SNAKE_CASE : Optional[Any] = Path(_lowerCamelCase ) / '''preprocessor_config.json'''
__SCREAMING_SNAKE_CASE : Dict = Path(_lowerCamelCase ) / '''config.json'''
json.dump(
{'''image_processor_type''': '''CLIPImageProcessor''', '''processor_class''': '''CLIPProcessor'''} , open(_lowerCamelCase , '''w''' ) , )
json.dump({'''model_type''': '''clip'''} , open(_lowerCamelCase , '''w''' ) )
__SCREAMING_SNAKE_CASE : Dict = AutoImageProcessor.from_pretrained(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :Optional[int] ):
# Ensure we can load the image processor from the feature extractor config
with tempfile.TemporaryDirectory() as tmpdirname:
__SCREAMING_SNAKE_CASE : Tuple = Path(_lowerCamelCase ) / '''preprocessor_config.json'''
__SCREAMING_SNAKE_CASE : Union[str, Any] = Path(_lowerCamelCase ) / '''config.json'''
json.dump(
{'''feature_extractor_type''': '''CLIPFeatureExtractor''', '''processor_class''': '''CLIPProcessor'''} , open(_lowerCamelCase , '''w''' ) , )
json.dump({'''model_type''': '''clip'''} , open(_lowerCamelCase , '''w''' ) )
__SCREAMING_SNAKE_CASE : int = AutoImageProcessor.from_pretrained(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :int ):
with tempfile.TemporaryDirectory() as tmpdirname:
__SCREAMING_SNAKE_CASE : str = CLIPConfig()
# Create a dummy config file with image_proceesor_type
__SCREAMING_SNAKE_CASE : Tuple = Path(_lowerCamelCase ) / '''preprocessor_config.json'''
__SCREAMING_SNAKE_CASE : Any = Path(_lowerCamelCase ) / '''config.json'''
json.dump(
{'''image_processor_type''': '''CLIPImageProcessor''', '''processor_class''': '''CLIPProcessor'''} , open(_lowerCamelCase , '''w''' ) , )
json.dump({'''model_type''': '''clip'''} , open(_lowerCamelCase , '''w''' ) )
# remove image_processor_type to make sure config.json alone is enough to load image processor locally
__SCREAMING_SNAKE_CASE : int = AutoImageProcessor.from_pretrained(_lowerCamelCase ).to_dict()
config_dict.pop('''image_processor_type''' )
__SCREAMING_SNAKE_CASE : Union[str, Any] = CLIPImageProcessor(**_lowerCamelCase )
# save in new folder
model_config.save_pretrained(_lowerCamelCase )
config.save_pretrained(_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Tuple = AutoImageProcessor.from_pretrained(_lowerCamelCase )
# make sure private variable is not incorrectly saved
__SCREAMING_SNAKE_CASE : Tuple = json.loads(config.to_json_string() )
self.assertTrue('''_processor_class''' not in dict_as_saved )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :Tuple ):
with tempfile.TemporaryDirectory() as tmpdirname:
__SCREAMING_SNAKE_CASE : Dict = Path(_lowerCamelCase ) / '''preprocessor_config.json'''
json.dump(
{'''image_processor_type''': '''CLIPImageProcessor''', '''processor_class''': '''CLIPProcessor'''} , open(_lowerCamelCase , '''w''' ) , )
__SCREAMING_SNAKE_CASE : Optional[Any] = AutoImageProcessor.from_pretrained(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :Optional[int] ):
with self.assertRaisesRegex(
_lowerCamelCase , '''clip-base is not a local folder and is not a valid model identifier''' ):
__SCREAMING_SNAKE_CASE : Tuple = AutoImageProcessor.from_pretrained('''clip-base''' )
def SCREAMING_SNAKE_CASE_ ( self :Union[str, Any] ):
with self.assertRaisesRegex(
_lowerCamelCase , r'''aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)''' ):
__SCREAMING_SNAKE_CASE : Optional[int] = AutoImageProcessor.from_pretrained(_lowerCamelCase , revision='''aaaaaa''' )
def SCREAMING_SNAKE_CASE_ ( self :List[str] ):
with self.assertRaisesRegex(
_lowerCamelCase , '''hf-internal-testing/config-no-model does not appear to have a file named preprocessor_config.json.''' , ):
__SCREAMING_SNAKE_CASE : Optional[int] = AutoImageProcessor.from_pretrained('''hf-internal-testing/config-no-model''' )
def SCREAMING_SNAKE_CASE_ ( self :Tuple ):
# If remote code is not set, we will time out when asking whether to load the model.
with self.assertRaises(_lowerCamelCase ):
__SCREAMING_SNAKE_CASE : Union[str, Any] = AutoImageProcessor.from_pretrained('''hf-internal-testing/test_dynamic_image_processor''' )
# If remote code is disabled, we can't load this config.
with self.assertRaises(_lowerCamelCase ):
__SCREAMING_SNAKE_CASE : Optional[Any] = AutoImageProcessor.from_pretrained(
'''hf-internal-testing/test_dynamic_image_processor''' , trust_remote_code=_lowerCamelCase )
__SCREAMING_SNAKE_CASE : int = AutoImageProcessor.from_pretrained(
'''hf-internal-testing/test_dynamic_image_processor''' , trust_remote_code=_lowerCamelCase )
self.assertEqual(image_processor.__class__.__name__ , '''NewImageProcessor''' )
# Test image processor can be reloaded.
with tempfile.TemporaryDirectory() as tmp_dir:
image_processor.save_pretrained(_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Tuple = AutoImageProcessor.from_pretrained(_lowerCamelCase , trust_remote_code=_lowerCamelCase )
self.assertEqual(reloaded_image_processor.__class__.__name__ , '''NewImageProcessor''' )
def SCREAMING_SNAKE_CASE_ ( self :Tuple ):
try:
AutoConfig.register('''custom''' , _lowerCamelCase )
AutoImageProcessor.register(_lowerCamelCase , _lowerCamelCase )
# Trying to register something existing in the Transformers library will raise an error
with self.assertRaises(_lowerCamelCase ):
AutoImageProcessor.register(_lowerCamelCase , _lowerCamelCase )
with tempfile.TemporaryDirectory() as tmpdirname:
__SCREAMING_SNAKE_CASE : Dict = Path(_lowerCamelCase ) / '''preprocessor_config.json'''
__SCREAMING_SNAKE_CASE : Dict = Path(_lowerCamelCase ) / '''config.json'''
json.dump(
{'''feature_extractor_type''': '''CLIPFeatureExtractor''', '''processor_class''': '''CLIPProcessor'''} , open(_lowerCamelCase , '''w''' ) , )
json.dump({'''model_type''': '''clip'''} , open(_lowerCamelCase , '''w''' ) )
__SCREAMING_SNAKE_CASE : Optional[int] = CustomImageProcessor.from_pretrained(_lowerCamelCase )
# Now that the config is registered, it can be used as any other config with the auto-API
with tempfile.TemporaryDirectory() as tmp_dir:
image_processor.save_pretrained(_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Tuple = AutoImageProcessor.from_pretrained(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
finally:
if "custom" in CONFIG_MAPPING._extra_content:
del CONFIG_MAPPING._extra_content["custom"]
if CustomConfig in IMAGE_PROCESSOR_MAPPING._extra_content:
del IMAGE_PROCESSOR_MAPPING._extra_content[CustomConfig]
def SCREAMING_SNAKE_CASE_ ( self :Dict ):
class snake_case ( __UpperCAmelCase ):
lowerCamelCase__ = True
try:
AutoConfig.register('''custom''' , _lowerCamelCase )
AutoImageProcessor.register(_lowerCamelCase , _lowerCamelCase )
# If remote code is not set, the default is to use local
__SCREAMING_SNAKE_CASE : List[str] = AutoImageProcessor.from_pretrained('''hf-internal-testing/test_dynamic_image_processor''' )
self.assertEqual(image_processor.__class__.__name__ , '''NewImageProcessor''' )
self.assertTrue(image_processor.is_local )
# If remote code is disabled, we load the local one.
__SCREAMING_SNAKE_CASE : str = AutoImageProcessor.from_pretrained(
'''hf-internal-testing/test_dynamic_image_processor''' , trust_remote_code=_lowerCamelCase )
self.assertEqual(image_processor.__class__.__name__ , '''NewImageProcessor''' )
self.assertTrue(image_processor.is_local )
# If remote is enabled, we load from the Hub
__SCREAMING_SNAKE_CASE : Optional[int] = AutoImageProcessor.from_pretrained(
'''hf-internal-testing/test_dynamic_image_processor''' , trust_remote_code=_lowerCamelCase )
self.assertEqual(image_processor.__class__.__name__ , '''NewImageProcessor''' )
self.assertTrue(not hasattr(_lowerCamelCase , '''is_local''' ) )
finally:
if "custom" in CONFIG_MAPPING._extra_content:
del CONFIG_MAPPING._extra_content["custom"]
if CustomConfig in IMAGE_PROCESSOR_MAPPING._extra_content:
del IMAGE_PROCESSOR_MAPPING._extra_content[CustomConfig]
| 674 | 0 |
"""simple docstring"""
import os
import unittest
from transformers import LayoutLMTokenizer, LayoutLMTokenizerFast
from transformers.models.layoutlm.tokenization_layoutlm import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class __lowercase( __UpperCAmelCase , unittest.TestCase ):
'''simple docstring'''
__a : Optional[int] = LayoutLMTokenizer
__a : List[str] = LayoutLMTokenizerFast
__a : List[Any] = True
__a : List[Any] = True
def snake_case_ ( self ):
super().setUp()
__lowerCamelCase : Dict = [
'''[UNK]''',
'''[CLS]''',
'''[SEP]''',
'''want''',
'''##want''',
'''##ed''',
'''wa''',
'''un''',
'''runn''',
'''##ing''',
''',''',
'''low''',
'''lowest''',
]
__lowerCamelCase : Optional[Any] = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['vocab_file'] )
with open(self.vocab_file , 'w' , encoding='utf-8' ) as vocab_writer:
vocab_writer.write(''.join([x + '\n' for x in vocab_tokens] ) )
def snake_case_ ( self , **__a ):
return LayoutLMTokenizer.from_pretrained(self.tmpdirname , **_lowerCamelCase )
def snake_case_ ( self , __a ):
__lowerCamelCase : Optional[Any] = '''UNwant\u00E9d,running'''
__lowerCamelCase : Union[str, Any] = '''unwanted, running'''
return input_text, output_text
def snake_case_ ( self ):
__lowerCamelCase : Optional[int] = self.tokenizer_class(self.vocab_file )
__lowerCamelCase : Dict = tokenizer.tokenize('UNwant\u00E9d,running' )
self.assertListEqual(_lowerCamelCase , ['un', '##want', '##ed', ',', 'runn', '##ing'] )
self.assertListEqual(tokenizer.convert_tokens_to_ids(_lowerCamelCase ) , [7, 4, 5, 10, 8, 9] )
def snake_case_ ( self ):
pass
| 594 |
"""simple docstring"""
from __future__ import annotations
from typing import Any
class snake_case ( __UpperCAmelCase ):
pass
class snake_case :
def __init__( self :List[Any] , _lowerCamelCase :Any ):
__SCREAMING_SNAKE_CASE : Any = data
__SCREAMING_SNAKE_CASE : Node | None = None
def __iter__( self :Union[str, Any] ):
__SCREAMING_SNAKE_CASE : List[str] = self
__SCREAMING_SNAKE_CASE : List[str] = []
while node:
if node in visited:
raise ContainsLoopError
visited.append(_lowerCamelCase )
yield node.data
__SCREAMING_SNAKE_CASE : List[str] = node.next_node
@property
def SCREAMING_SNAKE_CASE_ ( self :Any ):
try:
list(self )
return False
except ContainsLoopError:
return True
if __name__ == "__main__":
_lowerCamelCase = Node(1)
_lowerCamelCase = Node(2)
_lowerCamelCase = Node(3)
_lowerCamelCase = Node(4)
print(root_node.has_loop) # False
_lowerCamelCase = root_node.next_node
print(root_node.has_loop) # True
_lowerCamelCase = Node(5)
_lowerCamelCase = Node(6)
_lowerCamelCase = Node(5)
_lowerCamelCase = Node(6)
print(root_node.has_loop) # False
_lowerCamelCase = Node(1)
print(root_node.has_loop) # False
| 674 | 0 |
import fcntl
import os
import socket
import torch
import torch.distributed as dist
def _lowercase ( *SCREAMING_SNAKE_CASE_ : Optional[Any] ):
"""simple docstring"""
with open(lowercase_ , """r""" ) as fh:
fcntl.flock(lowercase_ , fcntl.LOCK_EX )
try:
print(*lowercase_ )
finally:
fcntl.flock(lowercase_ , fcntl.LOCK_UN )
__snake_case = int(os.environ["LOCAL_RANK"])
torch.cuda.set_device(local_rank)
__snake_case = torch.device("cuda", local_rank)
__snake_case = socket.gethostname()
__snake_case = F'''[{hostname}-{local_rank}]'''
try:
# test distributed
dist.init_process_group("nccl")
dist.all_reduce(torch.ones(1).to(device), op=dist.ReduceOp.SUM)
dist.barrier()
# test cuda is available and can allocate memory
torch.cuda.is_available()
torch.ones(1).cuda(local_rank)
# global rank
__snake_case = dist.get_rank()
__snake_case = dist.get_world_size()
printflock(F'''{gpu} is OK (global rank: {rank}/{world_size})''')
dist.barrier()
if rank == 0:
printflock(F'''pt={torch.__version__}, cuda={torch.version.cuda}, nccl={torch.cuda.nccl.version()}''')
except Exception:
printflock(F'''{gpu} is broken''')
raise
| 386 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCamelCase = logging.get_logger(__name__)
_lowerCamelCase = {
'''weiweishi/roc-bert-base-zh''': '''https://huggingface.co/weiweishi/roc-bert-base-zh/resolve/main/config.json''',
}
class snake_case ( __UpperCAmelCase ):
lowerCamelCase__ = '''roc_bert'''
def __init__( self :Union[str, Any] , _lowerCamelCase :Any=3_0_5_2_2 , _lowerCamelCase :str=7_6_8 , _lowerCamelCase :Optional[Any]=1_2 , _lowerCamelCase :List[str]=1_2 , _lowerCamelCase :str=3_0_7_2 , _lowerCamelCase :Tuple="gelu" , _lowerCamelCase :List[Any]=0.1 , _lowerCamelCase :List[str]=0.1 , _lowerCamelCase :Optional[int]=5_1_2 , _lowerCamelCase :Dict=2 , _lowerCamelCase :Any=0.0_2 , _lowerCamelCase :Optional[int]=1e-12 , _lowerCamelCase :str=True , _lowerCamelCase :Any=0 , _lowerCamelCase :List[str]="absolute" , _lowerCamelCase :List[Any]=None , _lowerCamelCase :Any=True , _lowerCamelCase :Union[str, Any]=True , _lowerCamelCase :str=7_6_8 , _lowerCamelCase :Union[str, Any]=9_1_0 , _lowerCamelCase :List[Any]=5_1_2 , _lowerCamelCase :Optional[int]=2_4_8_5_8 , _lowerCamelCase :Union[str, Any]=True , **_lowerCamelCase :str , ):
__SCREAMING_SNAKE_CASE : List[str] = vocab_size
__SCREAMING_SNAKE_CASE : int = max_position_embeddings
__SCREAMING_SNAKE_CASE : List[str] = hidden_size
__SCREAMING_SNAKE_CASE : str = num_hidden_layers
__SCREAMING_SNAKE_CASE : int = num_attention_heads
__SCREAMING_SNAKE_CASE : Any = intermediate_size
__SCREAMING_SNAKE_CASE : Optional[int] = hidden_act
__SCREAMING_SNAKE_CASE : List[Any] = hidden_dropout_prob
__SCREAMING_SNAKE_CASE : Optional[Any] = attention_probs_dropout_prob
__SCREAMING_SNAKE_CASE : Union[str, Any] = initializer_range
__SCREAMING_SNAKE_CASE : Union[str, Any] = type_vocab_size
__SCREAMING_SNAKE_CASE : List[str] = layer_norm_eps
__SCREAMING_SNAKE_CASE : Optional[int] = use_cache
__SCREAMING_SNAKE_CASE : str = enable_pronunciation
__SCREAMING_SNAKE_CASE : List[str] = enable_shape
__SCREAMING_SNAKE_CASE : Tuple = pronunciation_embed_dim
__SCREAMING_SNAKE_CASE : Optional[Any] = pronunciation_vocab_size
__SCREAMING_SNAKE_CASE : str = shape_embed_dim
__SCREAMING_SNAKE_CASE : Union[str, Any] = shape_vocab_size
__SCREAMING_SNAKE_CASE : Tuple = concat_input
__SCREAMING_SNAKE_CASE : Union[str, Any] = position_embedding_type
__SCREAMING_SNAKE_CASE : str = classifier_dropout
super().__init__(pad_token_id=_lowerCamelCase , **_lowerCamelCase )
| 674 | 0 |
from ..utils import DummyObject, requires_backends
class SCREAMING_SNAKE_CASE_ ( metaclass=__UpperCAmelCase ):
"""simple docstring"""
__magic_name__ : Dict = ['torch', 'scipy']
def __init__( self : Optional[int] , *lowerCAmelCase : Tuple , **lowerCAmelCase : int ) -> Dict:
"""simple docstring"""
requires_backends(self , ["""torch""", """scipy"""] )
@classmethod
def lowerCamelCase__ ( cls : int , *lowerCAmelCase : Optional[int] , **lowerCAmelCase : int ) -> Tuple:
"""simple docstring"""
requires_backends(cls , ["""torch""", """scipy"""] )
@classmethod
def lowerCamelCase__ ( cls : Any , *lowerCAmelCase : Optional[Any] , **lowerCAmelCase : Optional[int] ) -> int:
"""simple docstring"""
requires_backends(cls , ["""torch""", """scipy"""] )
| 279 |
"""simple docstring"""
import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List
import git
import torch
from torch.utils.data import Dataset
from transformers import BartTokenizer, RagTokenizer, TaTokenizer
def lowerCAmelCase_ ( lowercase_ : List[str] , lowercase_ : List[str] , lowercase_ : Dict , lowercase_ : Tuple , lowercase_ : Optional[Any]=True , lowercase_ : Any="pt" ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : Union[str, Any] = {'''add_prefix_space''': True} if isinstance(lowercase_ , lowercase_ ) and not line.startswith(''' ''' ) else {}
__SCREAMING_SNAKE_CASE : Optional[int] = padding_side
return tokenizer(
[line] , max_length=lowercase_ , padding='''max_length''' if pad_to_max_length else None , truncation=lowercase_ , return_tensors=lowercase_ , add_special_tokens=lowercase_ , **lowercase_ , )
def lowerCAmelCase_ ( lowercase_ : Optional[int] , lowercase_ : Tuple , lowercase_ : List[Any]=None , ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : Dict = input_ids.ne(lowercase_ ).any(dim=0 )
if attention_mask is None:
return input_ids[:, keep_column_mask]
else:
return (input_ids[:, keep_column_mask], attention_mask[:, keep_column_mask])
class snake_case ( __UpperCAmelCase ):
def __init__( self :Optional[Any] , _lowerCamelCase :Dict , _lowerCamelCase :Any , _lowerCamelCase :Optional[Any] , _lowerCamelCase :Tuple , _lowerCamelCase :Any="train" , _lowerCamelCase :str=None , _lowerCamelCase :Optional[Any]=None , _lowerCamelCase :List[Any]=None , _lowerCamelCase :Tuple="" , ):
super().__init__()
__SCREAMING_SNAKE_CASE : Dict = Path(_lowerCamelCase ).joinpath(type_path + '''.source''' )
__SCREAMING_SNAKE_CASE : Any = Path(_lowerCamelCase ).joinpath(type_path + '''.target''' )
__SCREAMING_SNAKE_CASE : Any = self.get_char_lens(self.src_file )
__SCREAMING_SNAKE_CASE : List[str] = max_source_length
__SCREAMING_SNAKE_CASE : Dict = max_target_length
assert min(self.src_lens ) > 0, f'''found empty line in {self.src_file}'''
__SCREAMING_SNAKE_CASE : Dict = tokenizer
__SCREAMING_SNAKE_CASE : Union[str, Any] = prefix
if n_obs is not None:
__SCREAMING_SNAKE_CASE : Any = self.src_lens[:n_obs]
__SCREAMING_SNAKE_CASE : List[str] = src_lang
__SCREAMING_SNAKE_CASE : str = tgt_lang
def __len__( self :int ):
return len(self.src_lens )
def __getitem__( self :Optional[Any] , _lowerCamelCase :Any ):
__SCREAMING_SNAKE_CASE : Optional[Any] = index + 1 # linecache starts at 1
__SCREAMING_SNAKE_CASE : Any = self.prefix + linecache.getline(str(self.src_file ) , _lowerCamelCase ).rstrip('''\n''' )
__SCREAMING_SNAKE_CASE : Dict = linecache.getline(str(self.tgt_file ) , _lowerCamelCase ).rstrip('''\n''' )
assert source_line, f'''empty source line for index {index}'''
assert tgt_line, f'''empty tgt line for index {index}'''
# Need to add eos token manually for T5
if isinstance(self.tokenizer , _lowerCamelCase ):
source_line += self.tokenizer.eos_token
tgt_line += self.tokenizer.eos_token
# Pad source and target to the right
__SCREAMING_SNAKE_CASE : Dict = (
self.tokenizer.question_encoder if isinstance(self.tokenizer , _lowerCamelCase ) else self.tokenizer
)
__SCREAMING_SNAKE_CASE : Optional[Any] = self.tokenizer.generator if isinstance(self.tokenizer , _lowerCamelCase ) else self.tokenizer
__SCREAMING_SNAKE_CASE : Dict = encode_line(_lowerCamelCase , _lowerCamelCase , self.max_source_length , '''right''' )
__SCREAMING_SNAKE_CASE : Dict = encode_line(_lowerCamelCase , _lowerCamelCase , self.max_target_length , '''right''' )
__SCREAMING_SNAKE_CASE : Any = source_inputs['''input_ids'''].squeeze()
__SCREAMING_SNAKE_CASE : Any = target_inputs['''input_ids'''].squeeze()
__SCREAMING_SNAKE_CASE : Dict = source_inputs['''attention_mask'''].squeeze()
return {
"input_ids": source_ids,
"attention_mask": src_mask,
"decoder_input_ids": target_ids,
}
@staticmethod
def SCREAMING_SNAKE_CASE_ ( _lowerCamelCase :Any ):
return [len(_lowerCamelCase ) for x in Path(_lowerCamelCase ).open().readlines()]
def SCREAMING_SNAKE_CASE_ ( self :List[str] , _lowerCamelCase :List[str] ):
__SCREAMING_SNAKE_CASE : int = torch.stack([x['''input_ids'''] for x in batch] )
__SCREAMING_SNAKE_CASE : str = torch.stack([x['''attention_mask'''] for x in batch] )
__SCREAMING_SNAKE_CASE : int = torch.stack([x['''decoder_input_ids'''] for x in batch] )
__SCREAMING_SNAKE_CASE : str = (
self.tokenizer.generator.pad_token_id
if isinstance(self.tokenizer , _lowerCamelCase )
else self.tokenizer.pad_token_id
)
__SCREAMING_SNAKE_CASE : Union[str, Any] = (
self.tokenizer.question_encoder.pad_token_id
if isinstance(self.tokenizer , _lowerCamelCase )
else self.tokenizer.pad_token_id
)
__SCREAMING_SNAKE_CASE : List[str] = trim_batch(_lowerCamelCase , _lowerCamelCase )
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Any = trim_batch(_lowerCamelCase , _lowerCamelCase , attention_mask=_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Dict = {
'''input_ids''': source_ids,
'''attention_mask''': source_mask,
'''decoder_input_ids''': y,
}
return batch
_lowerCamelCase = getLogger(__name__)
def lowerCAmelCase_ ( lowercase_ : List[List] ):
'''simple docstring'''
return list(itertools.chain.from_iterable(lowercase_ ) )
def lowerCAmelCase_ ( lowercase_ : str ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : Any = get_git_info()
save_json(lowercase_ , os.path.join(lowercase_ , '''git_log.json''' ) )
def lowerCAmelCase_ ( lowercase_ : Any , lowercase_ : Optional[int] , lowercase_ : str=4 , **lowercase_ : List[str] ):
'''simple docstring'''
with open(lowercase_ , '''w''' ) as f:
json.dump(lowercase_ , lowercase_ , indent=lowercase_ , **lowercase_ )
def lowerCAmelCase_ ( lowercase_ : Union[str, Any] ):
'''simple docstring'''
with open(lowercase_ ) as f:
return json.load(lowercase_ )
def lowerCAmelCase_ ( ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : Any = git.Repo(search_parent_directories=lowercase_ )
__SCREAMING_SNAKE_CASE : List[str] = {
'''repo_id''': str(lowercase_ ),
'''repo_sha''': str(repo.head.object.hexsha ),
'''repo_branch''': str(repo.active_branch ),
'''hostname''': str(socket.gethostname() ),
}
return repo_infos
def lowerCAmelCase_ ( lowercase_ : Callable , lowercase_ : Iterable ):
'''simple docstring'''
return list(map(lowercase_ , lowercase_ ) )
def lowerCAmelCase_ ( lowercase_ : Any , lowercase_ : Any ):
'''simple docstring'''
with open(lowercase_ , '''wb''' ) as f:
return pickle.dump(lowercase_ , lowercase_ )
def lowerCAmelCase_ ( lowercase_ : Any ):
'''simple docstring'''
def remove_articles(lowercase_ : Dict ):
return re.sub(r'''\b(a|an|the)\b''' , ''' ''' , lowercase_ )
def white_space_fix(lowercase_ : Optional[int] ):
return " ".join(text.split() )
def remove_punc(lowercase_ : Any ):
__SCREAMING_SNAKE_CASE : Optional[int] = set(string.punctuation )
return "".join(ch for ch in text if ch not in exclude )
def lower(lowercase_ : int ):
return text.lower()
return white_space_fix(remove_articles(remove_punc(lower(lowercase_ ) ) ) )
def lowerCAmelCase_ ( lowercase_ : Optional[int] , lowercase_ : int ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : Any = normalize_answer(lowercase_ ).split()
__SCREAMING_SNAKE_CASE : Any = normalize_answer(lowercase_ ).split()
__SCREAMING_SNAKE_CASE : Tuple = Counter(lowercase_ ) & Counter(lowercase_ )
__SCREAMING_SNAKE_CASE : Tuple = sum(common.values() )
if num_same == 0:
return 0
__SCREAMING_SNAKE_CASE : Any = 1.0 * num_same / len(lowercase_ )
__SCREAMING_SNAKE_CASE : List[str] = 1.0 * num_same / len(lowercase_ )
__SCREAMING_SNAKE_CASE : Optional[int] = (2 * precision * recall) / (precision + recall)
return fa
def lowerCAmelCase_ ( lowercase_ : str , lowercase_ : Union[str, Any] ):
'''simple docstring'''
return normalize_answer(lowercase_ ) == normalize_answer(lowercase_ )
def lowerCAmelCase_ ( lowercase_ : List[str] , lowercase_ : List[str] ):
'''simple docstring'''
assert len(lowercase_ ) == len(lowercase_ )
__SCREAMING_SNAKE_CASE : Union[str, Any] = 0
for hypo, pred in zip(lowercase_ , lowercase_ ):
em += exact_match_score(lowercase_ , lowercase_ )
if len(lowercase_ ) > 0:
em /= len(lowercase_ )
return {"em": em}
def lowerCAmelCase_ ( lowercase_ : str ):
'''simple docstring'''
return model_prefix.startswith('''rag''' )
def lowerCAmelCase_ ( lowercase_ : Optional[Any] , lowercase_ : Tuple , lowercase_ : Optional[int] ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : Optional[int] = {p: p for p in extra_params}
# T5 models don't have `dropout` param, they have `dropout_rate` instead
__SCREAMING_SNAKE_CASE : Any = '''dropout_rate'''
for p in extra_params:
if getattr(lowercase_ , lowercase_ , lowercase_ ):
if not hasattr(lowercase_ , lowercase_ ) and not hasattr(lowercase_ , equivalent_param[p] ):
logger.info('''config doesn\'t have a `{}` attribute'''.format(lowercase_ ) )
delattr(lowercase_ , lowercase_ )
continue
__SCREAMING_SNAKE_CASE : Optional[int] = p if hasattr(lowercase_ , lowercase_ ) else equivalent_param[p]
setattr(lowercase_ , lowercase_ , getattr(lowercase_ , lowercase_ ) )
delattr(lowercase_ , lowercase_ )
return hparams, config
| 674 | 0 |
from typing import Optional, Union
import torch
from torch import nn
from ...configuration_utils import ConfigMixin, register_to_config
from ...models.modeling_utils import ModelMixin
class lowerCamelCase ( __UpperCAmelCase , __UpperCAmelCase ):
@register_to_config
def __init__( self :List[str] , lowercase :int = 7_6_8 , ) -> Dict:
"""simple docstring"""
super().__init__()
SCREAMING_SNAKE_CASE = nn.Parameter(torch.zeros(1 , _lowerCamelCase ) )
SCREAMING_SNAKE_CASE = nn.Parameter(torch.ones(1 , _lowerCamelCase ) )
def snake_case__ ( self :Any , lowercase :Optional[Union[str, torch.device]] = None , lowercase :Optional[torch.dtype] = None , ) -> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE = nn.Parameter(self.mean.to(_lowerCamelCase ).to(_lowerCamelCase ) )
SCREAMING_SNAKE_CASE = nn.Parameter(self.std.to(_lowerCamelCase ).to(_lowerCamelCase ) )
return self
def snake_case__ ( self :int , lowercase :Tuple ) -> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE = (embeds - self.mean) * 1.0 / self.std
return embeds
def snake_case__ ( self :Optional[Any] , lowercase :Optional[int] ) -> List[str]:
"""simple docstring"""
SCREAMING_SNAKE_CASE = (embeds * self.std) + self.mean
return embeds | 201 |
"""simple docstring"""
from collections.abc import Callable
import numpy as np
def lowerCAmelCase_ ( lowercase_ : Callable , lowercase_ : float , lowercase_ : float , lowercase_ : float , lowercase_ : float ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : int = int(np.ceil((x_end - xa) / step_size ) )
__SCREAMING_SNAKE_CASE : Dict = np.zeros((n + 1,) )
__SCREAMING_SNAKE_CASE : List[Any] = ya
__SCREAMING_SNAKE_CASE : Dict = xa
for k in range(lowercase_ ):
__SCREAMING_SNAKE_CASE : str = y[k] + step_size * ode_func(lowercase_ , y[k] )
__SCREAMING_SNAKE_CASE : int = y[k] + (
(step_size / 2) * (ode_func(lowercase_ , y[k] ) + ode_func(x + step_size , lowercase_ ))
)
x += step_size
return y
if __name__ == "__main__":
import doctest
doctest.testmod()
| 674 | 0 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
a : int = logging.get_logger(__name__)
a : int = {
"""microsoft/resnet-50""": """https://huggingface.co/microsoft/resnet-50/blob/main/config.json""",
}
class __UpperCAmelCase( __UpperCAmelCase , __UpperCAmelCase ):
"""simple docstring"""
__lowerCamelCase = "resnet"
__lowerCamelCase = ["basic", "bottleneck"]
def __init__( self , snake_case__=3 , snake_case__=64 , snake_case__=[256, 512, 1024, 2048] , snake_case__=[3, 4, 6, 3] , snake_case__="bottleneck" , snake_case__="relu" , snake_case__=False , snake_case__=None , snake_case__=None , **snake_case__ , ):
'''simple docstring'''
super().__init__(**_lowerCamelCase )
if layer_type not in self.layer_types:
raise ValueError(F'''layer_type={layer_type} is not one of {','.join(self.layer_types )}''' )
lowercase__ : List[Any]= num_channels
lowercase__ : List[str]= embedding_size
lowercase__ : Union[str, Any]= hidden_sizes
lowercase__ : List[str]= depths
lowercase__ : Optional[int]= layer_type
lowercase__ : Dict= hidden_act
lowercase__ : int= downsample_in_first_stage
lowercase__ : int= ['''stem'''] + [F'''stage{idx}''' for idx in range(1 , len(_lowerCamelCase ) + 1 )]
lowercase__ : List[str]= get_aligned_output_features_output_indices(
out_features=_lowerCamelCase , out_indices=_lowerCamelCase , stage_names=self.stage_names )
class __UpperCAmelCase( __UpperCAmelCase ):
"""simple docstring"""
__lowerCamelCase = version.parse("1.11" )
@property
def UpperCAmelCase_ ( self ):
'''simple docstring'''
return OrderedDict(
[
("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
] )
@property
def UpperCAmelCase_ ( self ):
'''simple docstring'''
return 1e-3
| 218 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_torch_available
from ...utils import OptionalDependencyNotAvailable
_lowerCamelCase = {
'''configuration_gpt_neox_japanese''': ['''GPT_NEOX_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GPTNeoXJapaneseConfig'''],
'''tokenization_gpt_neox_japanese''': ['''GPTNeoXJapaneseTokenizer'''],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowerCamelCase = [
'''GPT_NEOX_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''GPTNeoXJapaneseForCausalLM''',
'''GPTNeoXJapaneseLayer''',
'''GPTNeoXJapaneseModel''',
'''GPTNeoXJapanesePreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_gpt_neox_japanese import GPT_NEOX_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoXJapaneseConfig
from .tokenization_gpt_neox_japanese import GPTNeoXJapaneseTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_gpt_neox_japanese import (
GPT_NEOX_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST,
GPTNeoXJapaneseForCausalLM,
GPTNeoXJapaneseLayer,
GPTNeoXJapaneseModel,
GPTNeoXJapanesePreTrainedModel,
)
else:
import sys
_lowerCamelCase = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 674 | 0 |
import random
import timeit
from functools import wraps
from typing import Callable, Optional
from ..configuration_utils import PretrainedConfig
from ..models.auto.modeling_tf_auto import TF_MODEL_MAPPING, TF_MODEL_WITH_LM_HEAD_MAPPING
from ..utils import is_pyanvml_available, is_tf_available, logging
from .benchmark_utils import (
Benchmark,
Memory,
MemorySummary,
measure_peak_memory_cpu,
start_memory_tracing,
stop_memory_tracing,
)
if is_tf_available():
import tensorflow as tf
from tensorflow.python.framework.errors_impl import ResourceExhaustedError
from .benchmark_args_tf import TensorFlowBenchmarkArguments
if is_pyanvml_available():
import pyanvml.pyanvml as nvml
lowerCamelCase :str = logging.get_logger(__name__)
def __snake_case ( _UpperCamelCase , _UpperCamelCase ) -> List[str]:
def run_func(_UpperCamelCase ):
@wraps(lowercase_ )
def run_in_eager_mode(*_UpperCamelCase , **_UpperCamelCase ):
return func(*lowercase_ , **lowercase_ )
@wraps(lowercase_ )
@tf.function(experimental_compile=lowercase_ )
def run_in_graph_mode(*_UpperCamelCase , **_UpperCamelCase ):
return func(*lowercase_ , **lowercase_ )
if do_eager_mode is True:
if use_xla is not False:
raise ValueError(
'''Cannot run model in XLA, if `args.eager_mode` is set to `True`. Please set `args.eager_mode=False`.''' )
return run_in_eager_mode
else:
return run_in_graph_mode
return run_func
def __snake_case ( _UpperCamelCase , _UpperCamelCase , _UpperCamelCase ) -> Optional[Any]:
_a = random.Random()
_a = [rng.randint(0 , vocab_size - 1 ) for i in range(batch_size * sequence_length )]
return tf.constant(lowercase_ , shape=(batch_size, sequence_length) , dtype=tf.intaa )
class UpperCAmelCase ( __UpperCAmelCase ):
a: int = 42
a: Union[str, Any] = 42
a: Optional[int] = "TensorFlow"
@property
def _A ( self: Optional[Any] ):
return tf.__version__
def _A ( self: Tuple , __UpperCamelCase: str , __UpperCamelCase: int , __UpperCamelCase: int ):
# initialize GPU on separate process
_a = self.args.strategy
if strategy is None:
raise ValueError('''A device strategy has to be initialized before using TensorFlow.''' )
_a = self._prepare_inference_func(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase )
return self._measure_speed(_inference )
def _A ( self: Dict , __UpperCamelCase: str , __UpperCamelCase: int , __UpperCamelCase: int ):
_a = self.args.strategy
if strategy is None:
raise ValueError('''A device strategy has to be initialized before using TensorFlow.''' )
_a = self._prepare_train_func(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase )
return self._measure_speed(_train )
def _A ( self: Tuple , __UpperCamelCase: str , __UpperCamelCase: int , __UpperCamelCase: int ):
# initialize GPU on separate process
if self.args.is_gpu:
tf.config.experimental.set_memory_growth(self.args.gpu_list[self.args.device_idx] , _lowerCamelCase )
_a = self.args.strategy
if strategy is None:
raise ValueError('''A device strategy has to be initialized before using TensorFlow.''' )
_a = self._prepare_inference_func(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase )
return self._measure_memory(_inference )
def _A ( self: Union[str, Any] , __UpperCamelCase: str , __UpperCamelCase: int , __UpperCamelCase: int ):
if self.args.is_gpu:
tf.config.experimental.set_memory_growth(self.args.gpu_list[self.args.device_idx] , _lowerCamelCase )
_a = self.args.strategy
if strategy is None:
raise ValueError('''A device strategy has to be initialized before using TensorFlow.''' )
_a = self._prepare_train_func(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase )
return self._measure_memory(_train )
def _A ( self: Optional[Any] , __UpperCamelCase: str , __UpperCamelCase: int , __UpperCamelCase: int ):
_a = self.config_dict[model_name]
if self.args.fpaa:
raise NotImplementedError('''Mixed precision is currently not supported.''' )
_a = (
hasattr(_lowerCamelCase , '''architectures''' )
and isinstance(config.architectures , _lowerCamelCase )
and len(config.architectures ) > 0
)
if not self.args.only_pretrain_model and has_model_class_in_config:
try:
_a = '''TF''' + config.architectures[0] # prepend 'TF' for tensorflow model
_a = __import__('''transformers''' , fromlist=[model_class] )
_a = getattr(_lowerCamelCase , _lowerCamelCase )
_a = model_cls(_lowerCamelCase )
except ImportError:
raise ImportError(
f"{model_class} does not exist. If you just want to test the pretrained model, you might want to"
''' set `--only_pretrain_model` or `args.only_pretrain_model=True`.''' )
else:
_a = TF_MODEL_MAPPING[config.__class__](_lowerCamelCase )
# encoder-decoder has vocab size saved differently
_a = config.vocab_size if hasattr(_lowerCamelCase , '''vocab_size''' ) else config.encoder.vocab_size
_a = random_input_ids(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase )
@run_with_tf_optimizations(self.args.eager_mode , self.args.use_xla )
def encoder_decoder_forward():
return model(_lowerCamelCase , decoder_input_ids=_lowerCamelCase , training=_lowerCamelCase )
@run_with_tf_optimizations(self.args.eager_mode , self.args.use_xla )
def encoder_forward():
return model(_lowerCamelCase , training=_lowerCamelCase )
_a = encoder_decoder_forward if config.is_encoder_decoder else encoder_forward
return _inference
def _A ( self: List[Any] , __UpperCamelCase: str , __UpperCamelCase: int , __UpperCamelCase: int ):
_a = self.config_dict[model_name]
if self.args.eager_mode is not False:
raise ValueError('''Training cannot be done in eager mode. Please make sure that `args.eager_mode = False`.''' )
if self.args.fpaa:
raise NotImplementedError('''Mixed precision is currently not supported.''' )
_a = (
hasattr(_lowerCamelCase , '''architectures''' )
and isinstance(config.architectures , _lowerCamelCase )
and len(config.architectures ) > 0
)
if not self.args.only_pretrain_model and has_model_class_in_config:
try:
_a = '''TF''' + config.architectures[0] # prepend 'TF' for tensorflow model
_a = __import__('''transformers''' , fromlist=[model_class] )
_a = getattr(_lowerCamelCase , _lowerCamelCase )
_a = model_cls(_lowerCamelCase )
except ImportError:
raise ImportError(
f"{model_class} does not exist. If you just want to test the pretrained model, you might want to"
''' set `--only_pretrain_model` or `args.only_pretrain_model=True`.''' )
else:
_a = TF_MODEL_WITH_LM_HEAD_MAPPING[config.__class__](_lowerCamelCase )
# encoder-decoder has vocab size saved differently
_a = config.vocab_size if hasattr(_lowerCamelCase , '''vocab_size''' ) else config.encoder.vocab_size
_a = random_input_ids(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase )
@run_with_tf_optimizations(self.args.eager_mode , self.args.use_xla )
def encoder_decoder_train():
_a = model(_lowerCamelCase , decoder_input_ids=_lowerCamelCase , labels=_lowerCamelCase , training=_lowerCamelCase )[0]
_a = tf.gradients(_lowerCamelCase , model.trainable_variables )
return gradients
@run_with_tf_optimizations(self.args.eager_mode , self.args.use_xla )
def encoder_train():
_a = model(_lowerCamelCase , labels=_lowerCamelCase , training=_lowerCamelCase )[0]
_a = tf.gradients(_lowerCamelCase , model.trainable_variables )
return gradients
_a = encoder_decoder_train if config.is_encoder_decoder else encoder_train
return _train
def _A ( self: Dict , __UpperCamelCase: Dict ):
with self.args.strategy.scope():
try:
if self.args.is_tpu or self.args.use_xla:
# run additional 10 times to stabilize compilation for tpu
logger.info('''Do inference on TPU. Running model 5 times to stabilize compilation''' )
timeit.repeat(_lowerCamelCase , repeat=1 , number=5 )
# as written in https://docs.python.org/2/library/timeit.html#timeit.Timer.repeat, min should be taken rather than the average
_a = timeit.repeat(
_lowerCamelCase , repeat=self.args.repeat , number=10 , )
return min(_lowerCamelCase ) / 10.0
except ResourceExhaustedError as e:
self.print_fn(f"Doesn\'t fit on GPU. {e}" )
def _A ( self: Any , __UpperCamelCase: Callable[[], None] ):
logger.info(
'''Note that TensorFlow allocates more memory than '''
'''it might need to speed up computation. '''
'''The memory reported here corresponds to the memory '''
'''reported by `nvidia-smi`, which can vary depending '''
'''on total available memory on the GPU that is used.''' )
with self.args.strategy.scope():
try:
if self.args.trace_memory_line_by_line:
if not self.args.eager_mode:
raise ValueError(
'''`args.eager_mode` is set to `False`. Make sure to run model in eager mode to measure memory'''
''' consumption line by line.''' )
_a = start_memory_tracing('''transformers''' )
if self.args.is_tpu:
# tpu
raise NotImplementedError(
'''Memory Benchmarking is currently not implemented for TPU. Please disable memory benchmarking'''
''' with `args.memory=False`''' )
elif self.args.is_gpu:
# gpu
if not is_pyanvml_available():
logger.warning(
'''py3nvml not installed, we won\'t log GPU memory usage. '''
'''Install py3nvml (pip install py3nvml) to log information about GPU.''' )
_a = '''N/A'''
else:
logger.info(
'''Measuring total GPU usage on GPU device. Make sure to not have additional processes'''
''' running on the same GPU.''' )
# init nvml
nvml.nvmlInit()
func()
_a = nvml.nvmlDeviceGetHandleByIndex(self.args.device_idx )
_a = nvml.nvmlDeviceGetMemoryInfo(_lowerCamelCase )
_a = meminfo.used
_a = Memory(_lowerCamelCase )
# shutdown nvml
nvml.nvmlShutdown()
else:
# cpu
if self.args.trace_memory_line_by_line:
logger.info(
'''When enabling line by line tracing, the max peak memory for CPU is inaccurate in'''
''' TensorFlow.''' )
_a = None
else:
_a = measure_peak_memory_cpu(_lowerCamelCase )
_a = Memory(_lowerCamelCase ) if isinstance(_lowerCamelCase , _lowerCamelCase ) else memory_bytes
if self.args.trace_memory_line_by_line:
_a = stop_memory_tracing(_lowerCamelCase )
if memory is None:
_a = summary.total
else:
_a = None
return memory, summary
except ResourceExhaustedError as e:
self.print_fn(f"Doesn\'t fit on GPU. {e}" )
return "N/A", None
| 487 |
"""simple docstring"""
from __future__ import annotations
from typing import Any
class snake_case :
def __init__( self :Optional[Any] , _lowerCamelCase :int ):
__SCREAMING_SNAKE_CASE : int = num_of_nodes
__SCREAMING_SNAKE_CASE : list[list[int]] = []
__SCREAMING_SNAKE_CASE : dict[int, int] = {}
def SCREAMING_SNAKE_CASE_ ( self :int , _lowerCamelCase :int , _lowerCamelCase :int , _lowerCamelCase :int ):
self.m_edges.append([u_node, v_node, weight] )
def SCREAMING_SNAKE_CASE_ ( self :Tuple , _lowerCamelCase :int ):
if self.m_component[u_node] == u_node:
return u_node
return self.find_component(self.m_component[u_node] )
def SCREAMING_SNAKE_CASE_ ( self :List[str] , _lowerCamelCase :int ):
if self.m_component[u_node] != u_node:
for k in self.m_component:
__SCREAMING_SNAKE_CASE : Optional[Any] = self.find_component(_lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :Union[str, Any] , _lowerCamelCase :list[int] , _lowerCamelCase :int , _lowerCamelCase :int ):
if component_size[u_node] <= component_size[v_node]:
__SCREAMING_SNAKE_CASE : List[Any] = v_node
component_size[v_node] += component_size[u_node]
self.set_component(_lowerCamelCase )
elif component_size[u_node] >= component_size[v_node]:
__SCREAMING_SNAKE_CASE : Dict = self.find_component(_lowerCamelCase )
component_size[u_node] += component_size[v_node]
self.set_component(_lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :List[str] ):
__SCREAMING_SNAKE_CASE : Optional[int] = []
__SCREAMING_SNAKE_CASE : str = 0
__SCREAMING_SNAKE_CASE : list[Any] = [-1] * self.m_num_of_nodes
# A list of components (initialized to all of the nodes)
for node in range(self.m_num_of_nodes ):
self.m_component.update({node: node} )
component_size.append(1 )
__SCREAMING_SNAKE_CASE : str = self.m_num_of_nodes
while num_of_components > 1:
for edge in self.m_edges:
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Optional[Any] = edge
__SCREAMING_SNAKE_CASE : Optional[Any] = self.m_component[u]
__SCREAMING_SNAKE_CASE : int = self.m_component[v]
if u_component != v_component:
for component in (u_component, v_component):
if (
minimum_weight_edge[component] == -1
or minimum_weight_edge[component][2] > w
):
__SCREAMING_SNAKE_CASE : Optional[Any] = [u, v, w]
for edge in minimum_weight_edge:
if isinstance(_lowerCamelCase , _lowerCamelCase ):
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : int = edge
__SCREAMING_SNAKE_CASE : Tuple = self.m_component[u]
__SCREAMING_SNAKE_CASE : int = self.m_component[v]
if u_component != v_component:
mst_weight += w
self.union(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase )
print(f'''Added edge [{u} - {v}]\nAdded weight: {w}\n''' )
num_of_components -= 1
__SCREAMING_SNAKE_CASE : Optional[Any] = [-1] * self.m_num_of_nodes
print(f'''The total weight of the minimal spanning tree is: {mst_weight}''' )
def lowerCAmelCase_ ( ):
'''simple docstring'''
if __name__ == "__main__":
import doctest
doctest.testmod()
| 674 | 0 |
"""simple docstring"""
import unittest
import numpy as np
from transformers.testing_utils import require_pytesseract, require_torch
from transformers.utils import is_pytesseract_available, is_torch_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
if is_pytesseract_available():
from PIL import Image
from transformers import LayoutLMvaImageProcessor
class UpperCAmelCase_ ( unittest.TestCase ):
def __init__( self , UpperCamelCase_ , UpperCamelCase_=7 , UpperCamelCase_=3 , UpperCamelCase_=18 , UpperCamelCase_=30 , UpperCamelCase_=4_00 , UpperCamelCase_=True , UpperCamelCase_=None , UpperCamelCase_=True , ) -> List[str]:
__lowercase : Any = size if size is not None else {'''height''': 18, '''width''': 18}
__lowercase : List[Any] = parent
__lowercase : List[Any] = batch_size
__lowercase : int = num_channels
__lowercase : Tuple = image_size
__lowercase : Optional[int] = min_resolution
__lowercase : str = max_resolution
__lowercase : List[Any] = do_resize
__lowercase : List[Any] = size
__lowercase : List[Any] = apply_ocr
def _lowerCamelCase ( self ) -> List[Any]:
return {"do_resize": self.do_resize, "size": self.size, "apply_ocr": self.apply_ocr}
@require_torch
@require_pytesseract
class UpperCAmelCase_ ( __UpperCAmelCase , unittest.TestCase ):
UpperCamelCase =LayoutLMvaImageProcessor if is_pytesseract_available() else None
def _lowerCamelCase ( self ) -> Any:
__lowercase : Tuple = LayoutLMvaImageProcessingTester(self )
@property
def _lowerCamelCase ( self ) -> Dict:
return self.image_processor_tester.prepare_image_processor_dict()
def _lowerCamelCase ( self ) -> List[Any]:
__lowercase : Tuple = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(_lowerCamelCase , '''do_resize''' ) )
self.assertTrue(hasattr(_lowerCamelCase , '''size''' ) )
self.assertTrue(hasattr(_lowerCamelCase , '''apply_ocr''' ) )
def _lowerCamelCase ( self ) -> List[Any]:
__lowercase : Optional[int] = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {'''height''': 18, '''width''': 18} )
__lowercase : Any = self.image_processing_class.from_dict(self.image_processor_dict , size=42 )
self.assertEqual(image_processor.size , {'''height''': 42, '''width''': 42} )
def _lowerCamelCase ( self ) -> str:
pass
def _lowerCamelCase ( self ) -> List[Any]:
# Initialize image_processing
__lowercase : Union[str, Any] = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
__lowercase : Union[str, Any] = prepare_image_inputs(self.image_processor_tester , equal_resolution=_lowerCamelCase )
for image in image_inputs:
self.assertIsInstance(_lowerCamelCase , Image.Image )
# Test not batched input
__lowercase : int = image_processing(image_inputs[0] , return_tensors='''pt''' )
self.assertEqual(
encoding.pixel_values.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.size['''height'''],
self.image_processor_tester.size['''width'''],
) , )
self.assertIsInstance(encoding.words , _lowerCamelCase )
self.assertIsInstance(encoding.boxes , _lowerCamelCase )
# Test batched
__lowercase : int = image_processing(_lowerCamelCase , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.size['''height'''],
self.image_processor_tester.size['''width'''],
) , )
def _lowerCamelCase ( self ) -> Any:
# Initialize image_processing
__lowercase : List[Any] = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
__lowercase : Any = prepare_image_inputs(self.image_processor_tester , equal_resolution=_lowerCamelCase , numpify=_lowerCamelCase )
for image in image_inputs:
self.assertIsInstance(_lowerCamelCase , np.ndarray )
# Test not batched input
__lowercase : Union[str, Any] = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.size['''height'''],
self.image_processor_tester.size['''width'''],
) , )
# Test batched
__lowercase : List[str] = image_processing(_lowerCamelCase , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.size['''height'''],
self.image_processor_tester.size['''width'''],
) , )
def _lowerCamelCase ( self ) -> List[Any]:
# Initialize image_processing
__lowercase : List[Any] = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
__lowercase : Optional[Any] = prepare_image_inputs(self.image_processor_tester , equal_resolution=_lowerCamelCase , torchify=_lowerCamelCase )
for image in image_inputs:
self.assertIsInstance(_lowerCamelCase , torch.Tensor )
# Test not batched input
__lowercase : Dict = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.size['''height'''],
self.image_processor_tester.size['''width'''],
) , )
# Test batched
__lowercase : Dict = image_processing(_lowerCamelCase , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.size['''height'''],
self.image_processor_tester.size['''width'''],
) , )
def _lowerCamelCase ( self ) -> Dict:
# with apply_OCR = True
__lowercase : Any = LayoutLMvaImageProcessor()
from datasets import load_dataset
__lowercase : Union[str, Any] = load_dataset('''hf-internal-testing/fixtures_docvqa''' , split='''test''' )
__lowercase : Optional[int] = Image.open(ds[0]['''file'''] ).convert('''RGB''' )
__lowercase : Any = image_processing(_lowerCamelCase , return_tensors='''pt''' )
self.assertEqual(encoding.pixel_values.shape , (1, 3, 2_24, 2_24) )
self.assertEqual(len(encoding.words ) , len(encoding.boxes ) )
# fmt: off
# the words and boxes were obtained with Tesseract 4.1.1
__lowercase : Union[str, Any] = [['''11:14''', '''to''', '''11:39''', '''a.m''', '''11:39''', '''to''', '''11:44''', '''a.m.''', '''11:44''', '''a.m.''', '''to''', '''12:25''', '''p.m.''', '''12:25''', '''to''', '''12:58''', '''p.m.''', '''12:58''', '''to''', '''4:00''', '''p.m.''', '''2:00''', '''to''', '''5:00''', '''p.m.''', '''Coffee''', '''Break''', '''Coffee''', '''will''', '''be''', '''served''', '''for''', '''men''', '''and''', '''women''', '''in''', '''the''', '''lobby''', '''adjacent''', '''to''', '''exhibit''', '''area.''', '''Please''', '''move''', '''into''', '''exhibit''', '''area.''', '''(Exhibits''', '''Open)''', '''TRRF''', '''GENERAL''', '''SESSION''', '''(PART''', '''|)''', '''Presiding:''', '''Lee''', '''A.''', '''Waller''', '''TRRF''', '''Vice''', '''President''', '''“Introductory''', '''Remarks”''', '''Lee''', '''A.''', '''Waller,''', '''TRRF''', '''Vice''', '''Presi-''', '''dent''', '''Individual''', '''Interviews''', '''with''', '''TRRF''', '''Public''', '''Board''', '''Members''', '''and''', '''Sci-''', '''entific''', '''Advisory''', '''Council''', '''Mem-''', '''bers''', '''Conducted''', '''by''', '''TRRF''', '''Treasurer''', '''Philip''', '''G.''', '''Kuehn''', '''to''', '''get''', '''answers''', '''which''', '''the''', '''public''', '''refrigerated''', '''warehousing''', '''industry''', '''is''', '''looking''', '''for.''', '''Plus''', '''questions''', '''from''', '''the''', '''floor.''', '''Dr.''', '''Emil''', '''M.''', '''Mrak,''', '''University''', '''of''', '''Cal-''', '''ifornia,''', '''Chairman,''', '''TRRF''', '''Board;''', '''Sam''', '''R.''', '''Cecil,''', '''University''', '''of''', '''Georgia''', '''College''', '''of''', '''Agriculture;''', '''Dr.''', '''Stanley''', '''Charm,''', '''Tufts''', '''University''', '''School''', '''of''', '''Medicine;''', '''Dr.''', '''Robert''', '''H.''', '''Cotton,''', '''ITT''', '''Continental''', '''Baking''', '''Company;''', '''Dr.''', '''Owen''', '''Fennema,''', '''University''', '''of''', '''Wis-''', '''consin;''', '''Dr.''', '''Robert''', '''E.''', '''Hardenburg,''', '''USDA.''', '''Questions''', '''and''', '''Answers''', '''Exhibits''', '''Open''', '''Capt.''', '''Jack''', '''Stoney''', '''Room''', '''TRRF''', '''Scientific''', '''Advisory''', '''Council''', '''Meeting''', '''Ballroom''', '''Foyer''']] # noqa: E231
__lowercase : Any = [[[1_41, 57, 2_14, 69], [2_28, 58, 2_52, 69], [1_41, 75, 2_16, 88], [2_30, 79, 2_80, 88], [1_42, 2_60, 2_18, 2_73], [2_30, 2_61, 2_55, 2_73], [1_43, 2_79, 2_18, 2_90], [2_31, 2_82, 2_90, 2_91], [1_43, 3_42, 2_18, 3_54], [2_31, 3_45, 2_89, 3_55], [2_02, 3_62, 2_27, 3_73], [1_43, 3_79, 2_20, 3_92], [2_31, 3_82, 2_91, 3_94], [1_44, 7_14, 2_20, 7_26], [2_31, 7_15, 2_56, 7_26], [1_44, 7_32, 2_20, 7_45], [2_32, 7_36, 2_91, 7_47], [1_44, 7_69, 2_18, 7_82], [2_31, 7_70, 2_56, 7_82], [1_41, 7_88, 2_02, 8_01], [2_15, 7_91, 2_74, 8_04], [1_43, 8_26, 2_04, 8_38], [2_15, 8_26, 2_40, 8_38], [1_42, 8_44, 2_02, 8_57], [2_15, 8_47, 2_74, 8_59], [3_34, 57, 4_27, 69], [4_40, 57, 5_22, 69], [3_69, 75, 4_61, 88], [4_69, 75, 5_16, 88], [5_28, 76, 5_62, 88], [5_70, 76, 6_67, 88], [6_75, 75, 7_11, 87], [7_21, 79, 7_78, 88], [7_89, 75, 8_40, 88], [3_69, 97, 4_70, 1_07], [4_84, 94, 5_07, 1_06], [5_18, 94, 5_62, 1_07], [5_76, 94, 6_55, 1_10], [6_68, 94, 7_92, 1_09], [8_04, 95, 8_29, 1_07], [3_69, 1_13, 4_65, 1_25], [4_77, 1_16, 5_47, 1_25], [5_62, 1_13, 6_58, 1_25], [6_71, 1_16, 7_48, 1_25], [7_61, 1_13, 8_11, 1_25], [3_69, 1_31, 4_65, 1_43], [4_77, 1_33, 5_48, 1_43], [5_63, 1_30, 6_98, 1_45], [7_10, 1_30, 8_02, 1_46], [3_36, 1_71, 4_12, 1_83], [4_23, 1_71, 5_72, 1_83], [5_82, 1_70, 7_16, 1_84], [7_28, 1_71, 8_17, 1_87], [8_29, 1_71, 8_44, 1_86], [3_38, 1_97, 4_82, 2_12], [5_07, 1_96, 5_57, 2_09], [5_69, 1_96, 5_95, 2_08], [6_10, 1_96, 7_02, 2_09], [5_05, 2_14, 5_83, 2_26], [5_95, 2_14, 6_56, 2_27], [6_70, 2_15, 8_07, 2_27], [3_35, 2_59, 5_43, 2_74], [5_56, 2_59, 7_08, 2_72], [3_72, 2_79, 4_22, 2_91], [4_35, 2_79, 4_60, 2_91], [4_74, 2_79, 5_74, 2_92], [5_87, 2_78, 6_64, 2_91], [6_76, 2_78, 7_38, 2_91], [7_51, 2_79, 8_34, 2_91], [3_72, 2_98, 4_34, 3_10], [3_35, 3_41, 4_83, 3_54], [4_97, 3_41, 6_55, 3_54], [6_67, 3_41, 7_28, 3_54], [7_40, 3_41, 8_25, 3_54], [3_35, 3_60, 4_30, 3_72], [4_42, 3_60, 5_34, 3_72], [5_45, 3_59, 6_87, 3_72], [6_97, 3_60, 7_54, 3_72], [7_65, 3_60, 8_23, 3_73], [3_34, 3_78, 4_28, 3_91], [4_40, 3_78, 5_77, 3_94], [5_90, 3_78, 7_05, 3_91], [7_20, 3_78, 8_01, 3_91], [3_34, 3_97, 4_00, 4_09], [3_70, 4_16, 5_29, 4_29], [5_44, 4_16, 5_76, 4_32], [5_87, 4_16, 6_65, 4_28], [6_77, 4_16, 8_14, 4_29], [3_72, 4_35, 4_52, 4_50], [4_65, 4_34, 4_95, 4_47], [5_11, 4_34, 6_00, 4_47], [6_11, 4_36, 6_37, 4_47], [6_49, 4_36, 6_94, 4_51], [7_05, 4_38, 8_24, 4_47], [3_69, 4_53, 4_52, 4_66], [4_64, 4_54, 5_09, 4_66], [5_22, 4_53, 6_11, 4_69], [6_25, 4_53, 7_92, 4_69], [3_70, 4_72, 5_56, 4_88], [5_70, 4_72, 6_84, 4_87], [6_97, 4_72, 7_18, 4_85], [7_32, 4_72, 8_35, 4_88], [3_69, 4_90, 4_11, 5_03], [4_25, 4_90, 4_84, 5_03], [4_96, 4_90, 6_35, 5_06], [6_45, 4_90, 7_07, 5_03], [7_18, 4_91, 7_61, 5_03], [7_71, 4_90, 8_40, 5_03], [3_36, 5_10, 3_74, 5_21], [3_88, 5_10, 4_47, 5_22], [4_60, 5_10, 4_89, 5_21], [5_03, 5_10, 5_80, 5_22], [5_92, 5_09, 7_36, 5_25], [7_45, 5_09, 7_70, 5_22], [7_81, 5_09, 8_40, 5_22], [3_38, 5_28, 4_34, 5_41], [4_48, 5_28, 5_96, 5_41], [6_09, 5_27, 6_87, 5_40], [7_00, 5_28, 7_92, 5_41], [3_36, 5_46, 3_97, 5_59], [4_07, 5_46, 4_31, 5_59], [4_43, 5_46, 5_25, 5_60], [5_37, 5_46, 6_80, 5_62], [6_88, 5_46, 7_14, 5_59], [7_22, 5_46, 8_37, 5_62], [3_36, 5_65, 4_49, 5_81], [4_61, 5_65, 4_85, 5_77], [4_97, 5_65, 6_65, 5_81], [6_81, 5_65, 7_18, 5_77], [7_32, 5_65, 8_37, 5_80], [3_37, 5_84, 4_38, 5_97], [4_52, 5_83, 5_21, 5_96], [5_35, 5_84, 6_77, 5_99], [6_90, 5_83, 7_87, 5_96], [8_01, 5_83, 8_25, 5_96], [3_38, 6_02, 4_78, 6_15], [4_92, 6_02, 5_30, 6_14], [5_43, 6_02, 6_38, 6_15], [6_50, 6_02, 6_76, 6_14], [6_88, 6_02, 7_88, 6_15], [8_02, 6_02, 8_43, 6_14], [3_37, 6_21, 5_02, 6_33], [5_16, 6_21, 6_15, 6_37], [6_29, 6_21, 7_74, 6_36], [7_89, 6_21, 8_27, 6_33], [3_37, 6_39, 4_18, 6_52], [4_32, 6_40, 5_71, 6_53], [5_87, 6_39, 7_31, 6_55], [7_43, 6_39, 7_69, 6_52], [7_80, 6_39, 8_41, 6_52], [3_38, 6_58, 4_40, 6_73], [4_55, 6_58, 4_91, 6_70], [5_08, 6_58, 6_02, 6_71], [6_16, 6_58, 6_38, 6_70], [6_54, 6_58, 8_35, 6_74], [3_37, 6_77, 4_29, 6_89], [3_37, 7_14, 4_82, 7_26], [4_95, 7_14, 5_48, 7_26], [5_61, 7_14, 6_83, 7_26], [3_38, 7_70, 4_61, 7_82], [4_74, 7_69, 5_54, 7_85], [4_89, 7_88, 5_62, 8_03], [5_76, 7_88, 6_43, 8_01], [6_56, 7_87, 7_51, 8_04], [7_64, 7_88, 8_44, 8_01], [3_34, 8_25, 4_21, 8_38], [4_30, 8_24, 5_74, 8_38], [5_84, 8_24, 7_23, 8_41], [3_35, 8_44, 4_50, 8_57], [4_64, 8_43, 5_83, 8_60], [6_28, 8_62, 7_55, 8_75], [7_69, 8_61, 8_48, 8_78]]] # noqa: E231
# fmt: on
self.assertListEqual(encoding.words , _lowerCamelCase )
self.assertListEqual(encoding.boxes , _lowerCamelCase )
# with apply_OCR = False
__lowercase : Union[str, Any] = LayoutLMvaImageProcessor(apply_ocr=_lowerCamelCase )
__lowercase : Optional[Any] = image_processing(_lowerCamelCase , return_tensors='''pt''' )
self.assertEqual(encoding.pixel_values.shape , (1, 3, 2_24, 2_24) )
| 76 |
"""simple docstring"""
import argparse
import pickle
import numpy as np
import torch
from torch import nn
from transformers import ReformerConfig, ReformerModelWithLMHead
from transformers.utils import logging
logging.set_verbosity_info()
def lowerCAmelCase_ ( lowercase_ : int , lowercase_ : Any , lowercase_ : int=None ):
'''simple docstring'''
assert torch_layer.weight.shape == weight.shape, F'''{torch_layer} layer.weight does not match'''
__SCREAMING_SNAKE_CASE : str = nn.Parameter(lowercase_ )
if bias is not None:
assert torch_layer.bias.shape == bias.shape, F'''{torch_layer} layer.bias does not match'''
__SCREAMING_SNAKE_CASE : Tuple = nn.Parameter(lowercase_ )
def lowerCAmelCase_ ( lowercase_ : Tuple , lowercase_ : int , lowercase_ : int ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : Optional[int] = np.asarray(weights[0] )
__SCREAMING_SNAKE_CASE : Optional[int] = np.asarray(weights[1] )
__SCREAMING_SNAKE_CASE : Union[str, Any] = np.asarray(weights[2] )
set_param(
torch_layer.self_attention.query_key , torch.tensor(lowercase_ ).transpose(1 , 2 ).contiguous().view(-1 , lowercase_ ) , )
set_param(
torch_layer.self_attention.value , torch.tensor(lowercase_ ).transpose(1 , 2 ).contiguous().view(-1 , lowercase_ ) , )
set_param(
torch_layer.output.dense , torch.tensor(lowercase_ ).view(-1 , lowercase_ ).contiguous().transpose(0 , 1 ) , )
def lowerCAmelCase_ ( lowercase_ : Dict , lowercase_ : List[str] , lowercase_ : List[str] ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : List[str] = np.asarray(weights[0] )
__SCREAMING_SNAKE_CASE : Any = np.asarray(weights[1] )
__SCREAMING_SNAKE_CASE : Union[str, Any] = np.asarray(weights[2] )
__SCREAMING_SNAKE_CASE : Tuple = np.asarray(weights[3] )
set_param(
torch_layer.self_attention.query , torch.tensor(lowercase_ ).transpose(1 , 2 ).contiguous().view(-1 , lowercase_ ) , )
set_param(
torch_layer.self_attention.key , torch.tensor(lowercase_ ).transpose(1 , 2 ).contiguous().view(-1 , lowercase_ ) , )
set_param(
torch_layer.self_attention.value , torch.tensor(lowercase_ ).transpose(1 , 2 ).contiguous().view(-1 , lowercase_ ) , )
set_param(
torch_layer.output.dense , torch.tensor(lowercase_ ).view(-1 , lowercase_ ).contiguous().transpose(0 , 1 ) , )
def lowerCAmelCase_ ( lowercase_ : int , lowercase_ : List[str] , lowercase_ : str ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : Any = weights[0][0][0]
__SCREAMING_SNAKE_CASE : Union[str, Any] = np.asarray(layer_norm_a[0] )
__SCREAMING_SNAKE_CASE : List[Any] = np.asarray(layer_norm_a[1] )
set_param(
torch_block.attention.layer_norm , torch.tensor(lowercase_ ) , torch.tensor(lowercase_ ) , )
# lsh weights + output
__SCREAMING_SNAKE_CASE : Tuple = weights[0][1]
if len(lowercase_ ) < 4:
set_layer_weights_in_torch_lsh(lowercase_ , torch_block.attention , lowercase_ )
else:
set_layer_weights_in_torch_local(lowercase_ , torch_block.attention , lowercase_ )
# intermediate weighs
__SCREAMING_SNAKE_CASE : Any = weights[2][0][1][2]
# Chunked Feed Forward
if len(lowercase_ ) == 4:
__SCREAMING_SNAKE_CASE : List[str] = intermediate_weights[2]
# layernorm 2
__SCREAMING_SNAKE_CASE : List[str] = np.asarray(intermediate_weights[0][0] )
__SCREAMING_SNAKE_CASE : Union[str, Any] = np.asarray(intermediate_weights[0][1] )
set_param(
torch_block.feed_forward.layer_norm , torch.tensor(lowercase_ ) , torch.tensor(lowercase_ ) , )
# intermediate dense
__SCREAMING_SNAKE_CASE : int = np.asarray(intermediate_weights[1][0] )
__SCREAMING_SNAKE_CASE : Optional[Any] = np.asarray(intermediate_weights[1][1] )
set_param(
torch_block.feed_forward.dense.dense , torch.tensor(lowercase_ ).transpose(0 , 1 ).contiguous() , torch.tensor(lowercase_ ) , )
# intermediate out
__SCREAMING_SNAKE_CASE : Optional[Any] = np.asarray(intermediate_weights[4][0] )
__SCREAMING_SNAKE_CASE : Any = np.asarray(intermediate_weights[4][1] )
set_param(
torch_block.feed_forward.output.dense , torch.tensor(lowercase_ ).transpose(0 , 1 ).contiguous() , torch.tensor(lowercase_ ) , )
def lowerCAmelCase_ ( lowercase_ : str , lowercase_ : Optional[Any] , lowercase_ : Optional[Any] ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : Dict = torch_model.reformer
# word embeds
__SCREAMING_SNAKE_CASE : int = np.asarray(weights[1] )
set_param(
torch_model_reformer.embeddings.word_embeddings , torch.tensor(lowercase_ ) , )
if isinstance(weights[3] , lowercase_ ):
__SCREAMING_SNAKE_CASE : int = torch_model_reformer.embeddings.position_embeddings
for emb_idx in range(len(position_embeddings.weights ) ):
__SCREAMING_SNAKE_CASE : Dict = np.asarray(weights[3][emb_idx][0] )
assert (
position_embeddings.weights[emb_idx].shape == emb_weights.shape
), F'''{position_embeddings[emb_idx]} emb does not match'''
__SCREAMING_SNAKE_CASE : str = nn.Parameter(torch.tensor(lowercase_ ) )
__SCREAMING_SNAKE_CASE : List[Any] = weights[5]
assert len(torch_model_reformer.encoder.layers ) * 4 == len(
lowercase_ ), "HF and trax model do not have the same number of layers"
for layer_idx, layer in enumerate(torch_model_reformer.encoder.layers ):
__SCREAMING_SNAKE_CASE : Union[str, Any] = trax_layer_weights[4 * layer_idx : 4 * (layer_idx + 1)]
set_block_weights_in_torch(lowercase_ , lowercase_ , lowercase_ )
# output layer norm
__SCREAMING_SNAKE_CASE : List[str] = np.asarray(weights[7][0] )
__SCREAMING_SNAKE_CASE : Optional[Any] = np.asarray(weights[7][1] )
set_param(
torch_model_reformer.encoder.layer_norm , torch.tensor(lowercase_ ) , torch.tensor(lowercase_ ) , )
# output embeddings
__SCREAMING_SNAKE_CASE : Optional[Any] = np.asarray(weights[9][0] )
__SCREAMING_SNAKE_CASE : List[Any] = np.asarray(weights[9][1] )
set_param(
torch_model.lm_head.decoder , torch.tensor(lowercase_ ).transpose(0 , 1 ).contiguous() , torch.tensor(lowercase_ ) , )
def lowerCAmelCase_ ( lowercase_ : List[str] , lowercase_ : Any , lowercase_ : int ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : Dict = ReformerConfig.from_json_file(lowercase_ )
print(F'''Building PyTorch model from configuration: {config}''' )
__SCREAMING_SNAKE_CASE : List[str] = ReformerModelWithLMHead(lowercase_ )
with open(lowercase_ , '''rb''' ) as f:
__SCREAMING_SNAKE_CASE : int = pickle.load(lowercase_ )['''weights''']
set_model_weights_in_torch(lowercase_ , lowercase_ , config.hidden_size )
# Save pytorch-model
print(F'''Save PyTorch model to {pytorch_dump_path}''' )
torch.save(model.state_dict() , lowercase_ )
if __name__ == "__main__":
_lowerCamelCase = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--trax_model_pkl_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
)
parser.add_argument(
'''--config_file''',
default=None,
type=str,
required=True,
help=(
'''The config json file corresponding to the pre-trained Reformer model. \n'''
'''This specifies the model architecture.'''
),
)
parser.add_argument(
'''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
)
_lowerCamelCase = parser.parse_args()
convert_trax_checkpoint_to_pytorch(args.trax_model_pkl_path, args.config_file, args.pytorch_dump_path)
| 674 | 0 |
import numpy as np
import torch
import tqdm
from ...models.unet_ad import UNetaDModel
from ...pipelines import DiffusionPipeline
from ...utils import randn_tensor
from ...utils.dummy_pt_objects import DDPMScheduler
class _lowerCamelCase ( __UpperCAmelCase ):
def __init__( self , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , ) -> Optional[Any]:
super().__init__()
SCREAMING_SNAKE_CASE__: Tuple= value_function
SCREAMING_SNAKE_CASE__: List[Any]= unet
SCREAMING_SNAKE_CASE__: List[str]= scheduler
SCREAMING_SNAKE_CASE__: Optional[Any]= env
SCREAMING_SNAKE_CASE__: int= env.get_dataset()
SCREAMING_SNAKE_CASE__: Optional[int]= {}
for key in self.data.keys():
try:
SCREAMING_SNAKE_CASE__: Tuple= self.data[key].mean()
except: # noqa: E722
pass
SCREAMING_SNAKE_CASE__: str= {}
for key in self.data.keys():
try:
SCREAMING_SNAKE_CASE__: str= self.data[key].std()
except: # noqa: E722
pass
SCREAMING_SNAKE_CASE__: Tuple= env.observation_space.shape[0]
SCREAMING_SNAKE_CASE__: List[Any]= env.action_space.shape[0]
def UpperCamelCase_ ( self , lowerCAmelCase , lowerCAmelCase ) -> List[str]:
return (x_in - self.means[key]) / self.stds[key]
def UpperCamelCase_ ( self , lowerCAmelCase , lowerCAmelCase ) -> Union[str, Any]:
return x_in * self.stds[key] + self.means[key]
def UpperCamelCase_ ( self , lowerCAmelCase ) -> List[str]:
if type(_lowerCamelCase ) is dict:
return {k: self.to_torch(_lowerCamelCase ) for k, v in x_in.items()}
elif torch.is_tensor(_lowerCamelCase ):
return x_in.to(self.unet.device )
return torch.tensor(_lowerCamelCase , device=self.unet.device )
def UpperCamelCase_ ( self , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase ) -> Union[str, Any]:
for key, val in cond.items():
SCREAMING_SNAKE_CASE__: int= val.clone()
return x_in
def UpperCamelCase_ ( self , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase ) -> Any:
SCREAMING_SNAKE_CASE__: Any= x.shape[0]
SCREAMING_SNAKE_CASE__: int= None
for i in tqdm.tqdm(self.scheduler.timesteps ):
# create batch of timesteps to pass into model
SCREAMING_SNAKE_CASE__: Optional[int]= torch.full((batch_size,) , _lowerCamelCase , device=self.unet.device , dtype=torch.long )
for _ in range(_lowerCamelCase ):
with torch.enable_grad():
x.requires_grad_()
# permute to match dimension for pre-trained models
SCREAMING_SNAKE_CASE__: str= self.value_function(x.permute(0 , 2 , 1 ) , _lowerCamelCase ).sample
SCREAMING_SNAKE_CASE__: Dict= torch.autograd.grad([y.sum()] , [x] )[0]
SCREAMING_SNAKE_CASE__: Optional[int]= self.scheduler._get_variance(_lowerCamelCase )
SCREAMING_SNAKE_CASE__: Optional[Any]= torch.exp(0.5 * posterior_variance )
SCREAMING_SNAKE_CASE__: Union[str, Any]= model_std * grad
SCREAMING_SNAKE_CASE__: int= 0
SCREAMING_SNAKE_CASE__: Union[str, Any]= x.detach()
SCREAMING_SNAKE_CASE__: Optional[Any]= x + scale * grad
SCREAMING_SNAKE_CASE__: Any= self.reset_xa(_lowerCamelCase , _lowerCamelCase , self.action_dim )
SCREAMING_SNAKE_CASE__: Dict= self.unet(x.permute(0 , 2 , 1 ) , _lowerCamelCase ).sample.permute(0 , 2 , 1 )
# TODO: verify deprecation of this kwarg
SCREAMING_SNAKE_CASE__: Tuple= self.scheduler.step(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase , predict_epsilon=_lowerCamelCase )['''prev_sample''']
# apply conditions to the trajectory (set the initial state)
SCREAMING_SNAKE_CASE__: str= self.reset_xa(_lowerCamelCase , _lowerCamelCase , self.action_dim )
SCREAMING_SNAKE_CASE__: Optional[Any]= self.to_torch(_lowerCamelCase )
return x, y
def __call__( self , lowerCAmelCase , lowerCAmelCase=64 , lowerCAmelCase=32 , lowerCAmelCase=2 , lowerCAmelCase=0.1 ) -> Optional[Any]:
# normalize the observations and create batch dimension
SCREAMING_SNAKE_CASE__: Union[str, Any]= self.normalize(_lowerCamelCase , '''observations''' )
SCREAMING_SNAKE_CASE__: Dict= obs[None].repeat(_lowerCamelCase , axis=0 )
SCREAMING_SNAKE_CASE__: Optional[int]= {0: self.to_torch(_lowerCamelCase )}
SCREAMING_SNAKE_CASE__: Optional[int]= (batch_size, planning_horizon, self.state_dim + self.action_dim)
# generate initial noise and apply our conditions (to make the trajectories start at current state)
SCREAMING_SNAKE_CASE__: Optional[int]= randn_tensor(_lowerCamelCase , device=self.unet.device )
SCREAMING_SNAKE_CASE__: Optional[Any]= self.reset_xa(_lowerCamelCase , _lowerCamelCase , self.action_dim )
SCREAMING_SNAKE_CASE__: str= self.to_torch(_lowerCamelCase )
# run the diffusion process
SCREAMING_SNAKE_CASE__: Tuple= self.run_diffusion(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase )
# sort output trajectories by value
SCREAMING_SNAKE_CASE__: Optional[Any]= y.argsort(0 , descending=_lowerCamelCase ).squeeze()
SCREAMING_SNAKE_CASE__: Optional[Any]= x[sorted_idx]
SCREAMING_SNAKE_CASE__: Union[str, Any]= sorted_values[:, :, : self.action_dim]
SCREAMING_SNAKE_CASE__: Union[str, Any]= actions.detach().cpu().numpy()
SCREAMING_SNAKE_CASE__: str= self.de_normalize(_lowerCamelCase , key='''actions''' )
# select the action with the highest value
if y is not None:
SCREAMING_SNAKE_CASE__: Optional[int]= 0
else:
# if we didn't run value guiding, select a random action
SCREAMING_SNAKE_CASE__: str= np.random.randint(0 , _lowerCamelCase )
SCREAMING_SNAKE_CASE__: Tuple= denorm_actions[selected_index, 0]
return denorm_actions
| 64 |
"""simple docstring"""
from typing import Callable, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCamelCase = logging.get_logger(__name__)
_lowerCamelCase = {
'''microsoft/xprophetnet-large-wiki100-cased''': (
'''https://huggingface.co/microsoft/xprophetnet-large-wiki100-cased/resolve/main/config.json'''
),
}
class snake_case ( __UpperCAmelCase ):
lowerCamelCase__ = '''xlm-prophetnet'''
lowerCamelCase__ = ['''past_key_values''']
lowerCamelCase__ = {
'''num_attention_heads''': '''num_encoder_attention_heads''',
}
def __init__( self :List[str] , _lowerCamelCase :Optional[float] = 0.1 , _lowerCamelCase :Optional[Union[str, Callable]] = "gelu" , _lowerCamelCase :Optional[int] = 3_0_5_2_2 , _lowerCamelCase :Optional[int] = 1_0_2_4 , _lowerCamelCase :Optional[int] = 4_0_9_6 , _lowerCamelCase :Optional[int] = 1_2 , _lowerCamelCase :Optional[int] = 1_6 , _lowerCamelCase :Optional[int] = 4_0_9_6 , _lowerCamelCase :Optional[int] = 1_2 , _lowerCamelCase :Optional[int] = 1_6 , _lowerCamelCase :Optional[float] = 0.1 , _lowerCamelCase :Optional[float] = 0.1 , _lowerCamelCase :Optional[int] = 5_1_2 , _lowerCamelCase :Optional[float] = 0.0_2 , _lowerCamelCase :Optional[bool] = True , _lowerCamelCase :Optional[bool] = True , _lowerCamelCase :Optional[int] = 0 , _lowerCamelCase :Optional[int] = 2 , _lowerCamelCase :Optional[int] = 3_2 , _lowerCamelCase :Optional[int] = 1_2_8 , _lowerCamelCase :Optional[bool] = False , _lowerCamelCase :Optional[float] = 0.0 , _lowerCamelCase :Optional[bool] = True , _lowerCamelCase :Optional[int] = 0 , _lowerCamelCase :Optional[int] = 1 , _lowerCamelCase :Optional[int] = 2 , **_lowerCamelCase :int , ):
__SCREAMING_SNAKE_CASE : Union[str, Any] = vocab_size
__SCREAMING_SNAKE_CASE : Optional[int] = hidden_size
__SCREAMING_SNAKE_CASE : List[Any] = encoder_ffn_dim
__SCREAMING_SNAKE_CASE : str = num_encoder_layers
__SCREAMING_SNAKE_CASE : Optional[Any] = num_encoder_attention_heads
__SCREAMING_SNAKE_CASE : str = decoder_ffn_dim
__SCREAMING_SNAKE_CASE : List[Any] = num_decoder_layers
__SCREAMING_SNAKE_CASE : List[str] = num_decoder_attention_heads
__SCREAMING_SNAKE_CASE : Dict = max_position_embeddings
__SCREAMING_SNAKE_CASE : Any = init_std # Normal(0, this parameter)
__SCREAMING_SNAKE_CASE : Any = activation_function
# parameters for xlmprophetnet
__SCREAMING_SNAKE_CASE : List[Any] = ngram
__SCREAMING_SNAKE_CASE : int = num_buckets
__SCREAMING_SNAKE_CASE : List[str] = relative_max_distance
__SCREAMING_SNAKE_CASE : str = disable_ngram_loss
__SCREAMING_SNAKE_CASE : Optional[int] = eps
# 3 Types of Dropout
__SCREAMING_SNAKE_CASE : int = attention_dropout
__SCREAMING_SNAKE_CASE : Optional[Any] = activation_dropout
__SCREAMING_SNAKE_CASE : Dict = dropout
__SCREAMING_SNAKE_CASE : Any = use_cache
super().__init__(
pad_token_id=_lowerCamelCase , bos_token_id=_lowerCamelCase , eos_token_id=_lowerCamelCase , is_encoder_decoder=_lowerCamelCase , add_cross_attention=_lowerCamelCase , decoder_start_token_id=_lowerCamelCase , **_lowerCamelCase , )
@property
def SCREAMING_SNAKE_CASE_ ( self :int ):
return self.num_encoder_layers + self.num_decoder_layers
@num_hidden_layers.setter
def SCREAMING_SNAKE_CASE_ ( self :Dict , _lowerCamelCase :List[Any] ):
raise NotImplementedError(
'''This model does not support the setting of `num_hidden_layers`. Please set `num_encoder_layers` and'''
''' `num_decoder_layers`.''' )
| 674 | 0 |
import inspect
import unittest
from transformers import ConvNextVaConfig
from transformers.models.auto import get_values
from transformers.models.auto.modeling_auto import MODEL_FOR_BACKBONE_MAPPING_NAMES, MODEL_MAPPING_NAMES
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import ConvNextVaBackbone, ConvNextVaForImageClassification, ConvNextVaModel
from transformers.models.convnextva.modeling_convnextva import CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import AutoImageProcessor
class lowerCamelCase_ :
'''simple docstring'''
def __init__( self , snake_case_ , snake_case_=1_3 , snake_case_=3_2 , snake_case_=3 , snake_case_=4 , snake_case_=[1_0, 2_0, 3_0, 4_0] , snake_case_=[2, 2, 3, 2] , snake_case_=True , snake_case_=True , snake_case_=3_7 , snake_case_="gelu" , snake_case_=1_0 , snake_case_=0.0_2 , snake_case_=["stage2", "stage3", "stage4"] , snake_case_=[2, 3, 4] , snake_case_=None , ) -> Tuple:
'''simple docstring'''
__lowercase = parent
__lowercase = batch_size
__lowercase = image_size
__lowercase = num_channels
__lowercase = num_stages
__lowercase = hidden_sizes
__lowercase = depths
__lowercase = is_training
__lowercase = use_labels
__lowercase = intermediate_size
__lowercase = hidden_act
__lowercase = num_labels
__lowercase = initializer_range
__lowercase = out_features
__lowercase = out_indices
__lowercase = scope
def A ( self ) -> Optional[int]:
'''simple docstring'''
__lowercase = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
__lowercase = None
if self.use_labels:
__lowercase = ids_tensor([self.batch_size] , self.num_labels )
__lowercase = self.get_config()
return config, pixel_values, labels
def A ( self ) -> Optional[Any]:
'''simple docstring'''
return ConvNextVaConfig(
num_channels=self.num_channels , hidden_sizes=self.hidden_sizes , depths=self.depths , num_stages=self.num_stages , hidden_act=self.hidden_act , is_decoder=_lowerCamelCase , initializer_range=self.initializer_range , out_features=self.out_features , out_indices=self.out_indices , num_labels=self.num_labels , )
def A ( self , snake_case_ , snake_case_ , snake_case_ ) -> Optional[Any]:
'''simple docstring'''
__lowercase = ConvNextVaModel(config=_lowerCamelCase )
model.to(_lowerCamelCase )
model.eval()
__lowercase = model(_lowerCamelCase )
# expected last hidden states: B, C, H // 32, W // 32
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, self.hidden_sizes[-1], self.image_size // 3_2, self.image_size // 3_2) , )
def A ( self , snake_case_ , snake_case_ , snake_case_ ) -> Optional[Any]:
'''simple docstring'''
__lowercase = ConvNextVaForImageClassification(_lowerCamelCase )
model.to(_lowerCamelCase )
model.eval()
__lowercase = model(_lowerCamelCase , labels=_lowerCamelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def A ( self , snake_case_ , snake_case_ , snake_case_ ) -> Optional[int]:
'''simple docstring'''
__lowercase = ConvNextVaBackbone(config=_lowerCamelCase )
model.to(_lowerCamelCase )
model.eval()
__lowercase = model(_lowerCamelCase )
# verify hidden states
self.parent.assertEqual(len(result.feature_maps ) , len(config.out_features ) )
self.parent.assertListEqual(list(result.feature_maps[0].shape ) , [self.batch_size, self.hidden_sizes[1], 4, 4] )
# verify channels
self.parent.assertEqual(len(model.channels ) , len(config.out_features ) )
self.parent.assertListEqual(model.channels , config.hidden_sizes[1:] )
# verify backbone works with out_features=None
__lowercase = None
__lowercase = ConvNextVaBackbone(config=_lowerCamelCase )
model.to(_lowerCamelCase )
model.eval()
__lowercase = model(_lowerCamelCase )
# verify feature maps
self.parent.assertEqual(len(result.feature_maps ) , 1 )
self.parent.assertListEqual(list(result.feature_maps[0].shape ) , [self.batch_size, self.hidden_sizes[-1], 1, 1] )
# verify channels
self.parent.assertEqual(len(model.channels ) , 1 )
self.parent.assertListEqual(model.channels , [config.hidden_sizes[-1]] )
def A ( self ) -> int:
'''simple docstring'''
__lowercase = self.prepare_config_and_inputs()
__lowercase = config_and_inputs
__lowercase = {'''pixel_values''': pixel_values}
return config, inputs_dict
def A ( self ) -> Optional[Any]:
'''simple docstring'''
__lowercase = self.prepare_config_and_inputs()
__lowercase = config_and_inputs
__lowercase = {'''pixel_values''': pixel_values, '''labels''': labels}
return config, inputs_dict
@require_torch
class lowerCamelCase_ ( __UpperCAmelCase , __UpperCAmelCase , unittest.TestCase ):
'''simple docstring'''
__UpperCAmelCase = (
(
ConvNextVaModel,
ConvNextVaForImageClassification,
ConvNextVaBackbone,
)
if is_torch_available()
else ()
)
__UpperCAmelCase = (
{"feature-extraction": ConvNextVaModel, "image-classification": ConvNextVaForImageClassification}
if is_torch_available()
else {}
)
__UpperCAmelCase = False
__UpperCAmelCase = False
__UpperCAmelCase = False
__UpperCAmelCase = False
__UpperCAmelCase = False
def A ( self ) -> int:
'''simple docstring'''
__lowercase = ConvNextVaModelTester(self )
__lowercase = ConfigTester(self , config_class=_lowerCamelCase , has_text_modality=_lowerCamelCase , hidden_size=3_7 )
def A ( self ) -> Dict:
'''simple docstring'''
self.create_and_test_config_common_properties()
self.config_tester.create_and_test_config_to_json_string()
self.config_tester.create_and_test_config_to_json_file()
self.config_tester.create_and_test_config_from_and_save_pretrained()
self.config_tester.create_and_test_config_with_num_labels()
self.config_tester.check_config_can_be_init_without_params()
self.config_tester.check_config_arguments_init()
def A ( self ) -> Tuple:
'''simple docstring'''
return
@unittest.skip(reason='''ConvNextV2 does not use inputs_embeds''' )
def A ( self ) -> str:
'''simple docstring'''
pass
@unittest.skip(reason='''ConvNextV2 does not support input and output embeddings''' )
def A ( self ) -> Dict:
'''simple docstring'''
pass
@unittest.skip(reason='''ConvNextV2 does not use feedforward chunking''' )
def A ( self ) -> Any:
'''simple docstring'''
pass
def A ( self ) -> Union[str, Any]:
'''simple docstring'''
if not self.model_tester.is_training:
return
for model_class in self.all_model_classes:
__lowercase = self.model_tester.prepare_config_and_inputs_with_labels()
__lowercase = True
if model_class.__name__ in [
*get_values(_lowerCamelCase ),
*get_values(_lowerCamelCase ),
]:
continue
__lowercase = model_class(_lowerCamelCase )
model.to(_lowerCamelCase )
model.train()
__lowercase = self._prepare_for_class(_lowerCamelCase , _lowerCamelCase , return_labels=_lowerCamelCase )
__lowercase = model(**_lowerCamelCase ).loss
loss.backward()
def A ( self ) -> Dict:
'''simple docstring'''
if not self.model_tester.is_training:
return
for model_class in self.all_model_classes:
__lowercase = self.model_tester.prepare_config_and_inputs_with_labels()
__lowercase = False
__lowercase = True
if (
model_class.__name__
in [*get_values(_lowerCamelCase ), *get_values(_lowerCamelCase )]
or not model_class.supports_gradient_checkpointing
):
continue
__lowercase = model_class(_lowerCamelCase )
model.to(_lowerCamelCase )
model.gradient_checkpointing_enable()
model.train()
__lowercase = self._prepare_for_class(_lowerCamelCase , _lowerCamelCase , return_labels=_lowerCamelCase )
__lowercase = model(**_lowerCamelCase ).loss
loss.backward()
def A ( self ) -> Optional[int]:
'''simple docstring'''
__lowercase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__lowercase = model_class(_lowerCamelCase )
__lowercase = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
__lowercase = [*signature.parameters.keys()]
__lowercase = ['''pixel_values''']
self.assertListEqual(arg_names[:1] , _lowerCamelCase )
def A ( self ) -> str:
'''simple docstring'''
__lowercase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*_lowerCamelCase )
def A ( self ) -> int:
'''simple docstring'''
def check_hidden_states_output(snake_case_ , snake_case_ , snake_case_ ):
__lowercase = model_class(_lowerCamelCase )
model.to(_lowerCamelCase )
model.eval()
with torch.no_grad():
__lowercase = model(**self._prepare_for_class(_lowerCamelCase , _lowerCamelCase ) )
__lowercase = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
__lowercase = self.model_tester.num_stages
self.assertEqual(len(_lowerCamelCase ) , expected_num_stages + 1 )
# ConvNextV2's feature maps are of shape (batch_size, num_channels, height, width)
self.assertListEqual(
list(hidden_states[0].shape[-2:] ) , [self.model_tester.image_size // 4, self.model_tester.image_size // 4] , )
__lowercase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__lowercase = True
check_hidden_states_output(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
__lowercase = True
check_hidden_states_output(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase )
def A ( self ) -> Optional[int]:
'''simple docstring'''
__lowercase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*_lowerCamelCase )
@slow
def A ( self ) -> Union[str, Any]:
'''simple docstring'''
for model_name in CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__lowercase = ConvNextVaModel.from_pretrained(_lowerCamelCase )
self.assertIsNotNone(_lowerCamelCase )
def lowercase_ ( ):
'''simple docstring'''
__lowercase = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
return image
@require_torch
@require_vision
class lowerCamelCase_ ( unittest.TestCase ):
'''simple docstring'''
@cached_property
def A ( self ) -> Optional[Any]:
'''simple docstring'''
return AutoImageProcessor.from_pretrained('''facebook/convnextv2-tiny-1k-224''' ) if is_vision_available() else None
@slow
def A ( self ) -> Union[str, Any]:
'''simple docstring'''
__lowercase = ConvNextVaForImageClassification.from_pretrained('''facebook/convnextv2-tiny-1k-224''' ).to(_lowerCamelCase )
__lowercase = self.default_image_processor
__lowercase = prepare_img()
__lowercase = preprocessor(images=_lowerCamelCase , return_tensors='''pt''' ).to(_lowerCamelCase )
# forward pass
with torch.no_grad():
__lowercase = model(**_lowerCamelCase )
# verify the logits
__lowercase = torch.Size((1, 1_0_0_0) )
self.assertEqual(outputs.logits.shape , _lowerCamelCase )
__lowercase = torch.tensor([0.9_9_9_6, 0.1_9_6_6, -0.4_3_8_6] ).to(_lowerCamelCase )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , _lowerCamelCase , atol=1e-4 ) )
| 639 |
"""simple docstring"""
import ast
import os
import re
import shutil
import tempfile
import unittest
from unittest import mock
import torch
from accelerate.test_utils.examples import compare_against_test
from accelerate.test_utils.testing import TempDirTestCase, require_trackers, run_command, slow
from accelerate.utils import write_basic_config
# DataLoaders built from `test_samples/MRPC` for quick testing
# Should mock `{script_name}.get_dataloaders` via:
# @mock.patch("{script_name}.get_dataloaders", mocked_dataloaders)
_lowerCamelCase = [
'''cross_validation.py''',
'''gradient_accumulation.py''',
'''local_sgd.py''',
'''multi_process_metrics.py''',
'''memory.py''',
'''automatic_gradient_accumulation.py''',
'''fsdp_with_peak_mem_tracking.py''',
'''deepspeed_with_config_support.py''',
'''megatron_lm_gpt_pretraining.py''',
]
class snake_case ( unittest.TestCase ):
def SCREAMING_SNAKE_CASE_ ( self :List[str] , _lowerCamelCase :str , _lowerCamelCase :bool , _lowerCamelCase :str = None , _lowerCamelCase :list = None ):
__SCREAMING_SNAKE_CASE : List[str] = None
__SCREAMING_SNAKE_CASE : Optional[Any] = os.path.abspath(os.path.join('''examples''' , '''by_feature''' ) )
__SCREAMING_SNAKE_CASE : Union[str, Any] = os.path.abspath('''examples''' )
for item in os.listdir(_lowerCamelCase ):
if item not in EXCLUDE_EXAMPLES:
__SCREAMING_SNAKE_CASE : List[Any] = os.path.join(_lowerCamelCase , _lowerCamelCase )
if os.path.isfile(_lowerCamelCase ) and ".py" in item_path:
with self.subTest(
tested_script=_lowerCamelCase , feature_script=_lowerCamelCase , tested_section='''main()''' if parser_only else '''training_function()''' , ):
__SCREAMING_SNAKE_CASE : Tuple = compare_against_test(
os.path.join(_lowerCamelCase , _lowerCamelCase ) , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : Optional[Any] = '''\n'''.join(_lowerCamelCase )
if special_strings is not None:
for string in special_strings:
__SCREAMING_SNAKE_CASE : List[Any] = diff.replace(_lowerCamelCase , '''''' )
self.assertEqual(_lowerCamelCase , '''''' )
def SCREAMING_SNAKE_CASE_ ( self :Union[str, Any] ):
self.one_complete_example('''complete_nlp_example.py''' , _lowerCamelCase )
self.one_complete_example('''complete_nlp_example.py''' , _lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :List[str] ):
__SCREAMING_SNAKE_CASE : Union[str, Any] = os.path.abspath(os.path.join('''examples''' , '''cv_example.py''' ) )
__SCREAMING_SNAKE_CASE : Optional[int] = [
''' ''' * 1_6 + '''{\n\n''',
''' ''' * 2_0 + '''"accuracy": eval_metric["accuracy"],\n\n''',
''' ''' * 2_0 + '''"f1": eval_metric["f1"],\n\n''',
''' ''' * 2_0 + '''"train_loss": total_loss.item() / len(train_dataloader),\n\n''',
''' ''' * 2_0 + '''"epoch": epoch,\n\n''',
''' ''' * 1_6 + '''},\n\n''',
''' ''' * 1_6 + '''step=epoch,\n''',
''' ''' * 1_2,
''' ''' * 8 + '''for step, batch in enumerate(active_dataloader):\n''',
]
self.one_complete_example('''complete_cv_example.py''' , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase )
self.one_complete_example('''complete_cv_example.py''' , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase )
@mock.patch.dict(os.environ , {'''TESTING_MOCKED_DATALOADERS''': '''1'''} )
class snake_case ( __UpperCAmelCase ):
lowerCamelCase__ = False
@classmethod
def SCREAMING_SNAKE_CASE_ ( cls :Dict ):
super().setUpClass()
__SCREAMING_SNAKE_CASE : Dict = tempfile.mkdtemp()
__SCREAMING_SNAKE_CASE : str = os.path.join(cls._tmpdir , '''default_config.yml''' )
write_basic_config(save_location=cls.configPath )
__SCREAMING_SNAKE_CASE : List[Any] = ['''accelerate''', '''launch''', '''--config_file''', cls.configPath]
@classmethod
def SCREAMING_SNAKE_CASE_ ( cls :Dict ):
super().tearDownClass()
shutil.rmtree(cls._tmpdir )
def SCREAMING_SNAKE_CASE_ ( self :Tuple ):
__SCREAMING_SNAKE_CASE : str = f'''
examples/by_feature/checkpointing.py
--checkpointing_steps epoch
--output_dir {self.tmpdir}
'''.split()
run_command(self._launch_args + testargs )
self.assertTrue(os.path.exists(os.path.join(self.tmpdir , '''epoch_0''' ) ) )
def SCREAMING_SNAKE_CASE_ ( self :List[Any] ):
__SCREAMING_SNAKE_CASE : Optional[Any] = f'''
examples/by_feature/checkpointing.py
--checkpointing_steps 1
--output_dir {self.tmpdir}
'''.split()
__SCREAMING_SNAKE_CASE : Optional[int] = run_command(self._launch_args + testargs )
self.assertTrue(os.path.exists(os.path.join(self.tmpdir , '''step_2''' ) ) )
def SCREAMING_SNAKE_CASE_ ( self :List[str] ):
__SCREAMING_SNAKE_CASE : Optional[int] = f'''
examples/by_feature/checkpointing.py
--resume_from_checkpoint {os.path.join(self.tmpdir , 'epoch_0' )}
'''.split()
__SCREAMING_SNAKE_CASE : Any = run_command(self._launch_args + testargs , return_stdout=_lowerCamelCase )
self.assertNotIn('''epoch 0:''' , _lowerCamelCase )
self.assertIn('''epoch 1:''' , _lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :Union[str, Any] ):
__SCREAMING_SNAKE_CASE : Optional[int] = f'''
examples/by_feature/checkpointing.py
--resume_from_checkpoint {os.path.join(self.tmpdir , 'step_2' )}
'''.split()
__SCREAMING_SNAKE_CASE : List[str] = run_command(self._launch_args + testargs , return_stdout=_lowerCamelCase )
if torch.cuda.is_available():
__SCREAMING_SNAKE_CASE : List[Any] = torch.cuda.device_count()
else:
__SCREAMING_SNAKE_CASE : Optional[int] = 1
if num_processes > 1:
self.assertNotIn('''epoch 0:''' , _lowerCamelCase )
self.assertIn('''epoch 1:''' , _lowerCamelCase )
else:
self.assertIn('''epoch 0:''' , _lowerCamelCase )
self.assertIn('''epoch 1:''' , _lowerCamelCase )
@slow
def SCREAMING_SNAKE_CASE_ ( self :str ):
__SCREAMING_SNAKE_CASE : Optional[Any] = '''
examples/by_feature/cross_validation.py
--num_folds 2
'''.split()
with mock.patch.dict(os.environ , {'''TESTING_MOCKED_DATALOADERS''': '''0'''} ):
__SCREAMING_SNAKE_CASE : Union[str, Any] = run_command(self._launch_args + testargs , return_stdout=_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Any = re.findall('''({.+})''' , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : List[Any] = [r for r in results if '''accuracy''' in r][-1]
__SCREAMING_SNAKE_CASE : Tuple = ast.literal_eval(_lowerCamelCase )
self.assertGreaterEqual(results['''accuracy'''] , 0.7_5 )
def SCREAMING_SNAKE_CASE_ ( self :str ):
__SCREAMING_SNAKE_CASE : Optional[Any] = ['''examples/by_feature/multi_process_metrics.py''']
run_command(self._launch_args + testargs )
@require_trackers
@mock.patch.dict(os.environ , {'''WANDB_MODE''': '''offline'''} )
def SCREAMING_SNAKE_CASE_ ( self :List[str] ):
with tempfile.TemporaryDirectory() as tmpdir:
__SCREAMING_SNAKE_CASE : int = f'''
examples/by_feature/tracking.py
--with_tracking
--project_dir {tmpdir}
'''.split()
run_command(self._launch_args + testargs )
self.assertTrue(os.path.exists(os.path.join(_lowerCamelCase , '''tracking''' ) ) )
def SCREAMING_SNAKE_CASE_ ( self :Dict ):
__SCREAMING_SNAKE_CASE : Union[str, Any] = ['''examples/by_feature/gradient_accumulation.py''']
run_command(self._launch_args + testargs )
def SCREAMING_SNAKE_CASE_ ( self :Optional[int] ):
__SCREAMING_SNAKE_CASE : List[Any] = ['''examples/by_feature/local_sgd.py''']
run_command(self._launch_args + testargs )
| 674 | 0 |
import math
from typing import Dict, Iterable, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
IMAGENET_STANDARD_MEAN,
IMAGENET_STANDARD_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
get_image_size,
is_torch_available,
is_torch_tensor,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_vision_available, logging
if is_torch_available():
import torch
if is_vision_available():
import PIL
UpperCAmelCase_ = logging.get_logger(__name__)
def lowerCAmelCase_ ( lowercase: np.ndarray , lowercase: Union[int, Iterable[int]] , lowercase: bool , lowercase: int ) -> Optional[int]:
'''simple docstring'''
def constraint_to_multiple_of(lowercase: int , lowercase: str , lowercase: Any=0 , lowercase: Any=None ):
_UpperCamelCase: Tuple = round(val / multiple ) * multiple
if max_val is not None and x > max_val:
_UpperCamelCase: List[Any] = math.floor(val / multiple ) * multiple
if x < min_val:
_UpperCamelCase: List[Any] = math.ceil(val / multiple ) * multiple
return x
_UpperCamelCase: str = (output_size, output_size) if isinstance(lowercase_ , lowercase_ ) else output_size
_UpperCamelCase: Any = get_image_size(lowercase_ )
_UpperCamelCase: Union[str, Any] = output_size
# determine new height and width
_UpperCamelCase: List[Any] = output_height / input_height
_UpperCamelCase: int = output_width / input_width
if keep_aspect_ratio:
# scale as little as possible
if abs(1 - scale_width ) < abs(1 - scale_height ):
# fit width
_UpperCamelCase: str = scale_width
else:
# fit height
_UpperCamelCase: Union[str, Any] = scale_height
_UpperCamelCase: Optional[Any] = constraint_to_multiple_of(scale_height * input_height , multiple=lowercase_ )
_UpperCamelCase: int = constraint_to_multiple_of(scale_width * input_width , multiple=lowercase_ )
return (new_height, new_width)
class __magic_name__ ( __UpperCAmelCase ):
"""simple docstring"""
lowerCAmelCase : List[str] = ['''pixel_values''']
def __init__( self : Optional[Any] , _lowercase : bool = True , _lowercase : Dict[str, int] = None , _lowercase : PILImageResampling = PILImageResampling.BILINEAR , _lowercase : bool = False , _lowercase : int = 1 , _lowercase : bool = True , _lowercase : Union[int, float] = 1 / 255 , _lowercase : bool = True , _lowercase : Optional[Union[float, List[float]]] = None , _lowercase : Optional[Union[float, List[float]]] = None , **_lowercase : List[str] , ):
"""simple docstring"""
super().__init__(**_lowerCamelCase )
_UpperCamelCase: Dict = size if size is not None else {'''height''': 384, '''width''': 384}
_UpperCamelCase: List[Any] = get_size_dict(_lowerCamelCase )
_UpperCamelCase: List[Any] = do_resize
_UpperCamelCase: List[Any] = size
_UpperCamelCase: str = keep_aspect_ratio
_UpperCamelCase: Optional[int] = ensure_multiple_of
_UpperCamelCase: int = resample
_UpperCamelCase: Any = do_rescale
_UpperCamelCase: int = rescale_factor
_UpperCamelCase: Optional[int] = do_normalize
_UpperCamelCase: Optional[int] = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
_UpperCamelCase: Union[str, Any] = image_std if image_std is not None else IMAGENET_STANDARD_STD
def lowerCAmelCase ( self : Union[str, Any] , _lowercase : np.ndarray , _lowercase : Dict[str, int] , _lowercase : bool = False , _lowercase : int = 1 , _lowercase : PILImageResampling = PILImageResampling.BICUBIC , _lowercase : Optional[Union[str, ChannelDimension]] = None , **_lowercase : Tuple , ):
"""simple docstring"""
_UpperCamelCase: Optional[Any] = get_size_dict(_lowerCamelCase )
if "height" not in size or "width" not in size:
raise ValueError(f"""The size dictionary must contain the keys \'height\' and \'width\'. Got {size.keys()}""" )
_UpperCamelCase: Union[str, Any] = get_resize_output_image_size(
_lowerCamelCase , output_size=(size['''height'''], size['''width''']) , keep_aspect_ratio=_lowerCamelCase , multiple=_lowerCamelCase , )
return resize(_lowerCamelCase , size=_lowerCamelCase , resample=_lowerCamelCase , data_format=_lowerCamelCase , **_lowerCamelCase )
def lowerCAmelCase ( self : Union[str, Any] , _lowercase : np.ndarray , _lowercase : Union[int, float] , _lowercase : Optional[Union[str, ChannelDimension]] = None , **_lowercase : Tuple , ):
"""simple docstring"""
return rescale(_lowerCamelCase , scale=_lowerCamelCase , data_format=_lowerCamelCase , **_lowerCamelCase )
def lowerCAmelCase ( self : Optional[Any] , _lowercase : np.ndarray , _lowercase : Union[float, List[float]] , _lowercase : Union[float, List[float]] , _lowercase : Optional[Union[str, ChannelDimension]] = None , **_lowercase : Optional[int] , ):
"""simple docstring"""
return normalize(_lowerCamelCase , mean=_lowerCamelCase , std=_lowerCamelCase , data_format=_lowerCamelCase , **_lowerCamelCase )
def lowerCAmelCase ( self : Optional[int] , _lowercase : ImageInput , _lowercase : bool = None , _lowercase : int = None , _lowercase : bool = None , _lowercase : int = None , _lowercase : PILImageResampling = None , _lowercase : bool = None , _lowercase : float = None , _lowercase : bool = None , _lowercase : Optional[Union[float, List[float]]] = None , _lowercase : Optional[Union[float, List[float]]] = None , _lowercase : Optional[Union[str, TensorType]] = None , _lowercase : ChannelDimension = ChannelDimension.FIRST , **_lowercase : Dict , ):
"""simple docstring"""
_UpperCamelCase: int = do_resize if do_resize is not None else self.do_resize
_UpperCamelCase: int = size if size is not None else self.size
_UpperCamelCase: Dict = get_size_dict(_lowerCamelCase )
_UpperCamelCase: Tuple = keep_aspect_ratio if keep_aspect_ratio is not None else self.keep_aspect_ratio
_UpperCamelCase: List[Any] = ensure_multiple_of if ensure_multiple_of is not None else self.ensure_multiple_of
_UpperCamelCase: Optional[Any] = resample if resample is not None else self.resample
_UpperCamelCase: Optional[Any] = do_rescale if do_rescale is not None else self.do_rescale
_UpperCamelCase: Tuple = rescale_factor if rescale_factor is not None else self.rescale_factor
_UpperCamelCase: str = do_normalize if do_normalize is not None else self.do_normalize
_UpperCamelCase: Union[str, Any] = image_mean if image_mean is not None else self.image_mean
_UpperCamelCase: List[Any] = image_std if image_std is not None else self.image_std
_UpperCamelCase: Dict = make_list_of_images(_lowerCamelCase )
if not valid_images(_lowerCamelCase ):
raise ValueError(
'''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
'''torch.Tensor, tf.Tensor or jax.ndarray.''' )
if do_resize and size is None or resample is None:
raise ValueError('''Size and resample must be specified if do_resize is True.''' )
if do_rescale and rescale_factor is None:
raise ValueError('''Rescale factor must be specified if do_rescale is True.''' )
if do_normalize and (image_mean is None or image_std is None):
raise ValueError('''Image mean and std must be specified if do_normalize is True.''' )
# All transformations expect numpy arrays.
_UpperCamelCase: Optional[int] = [to_numpy_array(_lowerCamelCase ) for image in images]
if do_resize:
_UpperCamelCase: Optional[int] = [self.resize(image=_lowerCamelCase , size=_lowerCamelCase , resample=_lowerCamelCase ) for image in images]
if do_rescale:
_UpperCamelCase: int = [self.rescale(image=_lowerCamelCase , scale=_lowerCamelCase ) for image in images]
if do_normalize:
_UpperCamelCase: str = [self.normalize(image=_lowerCamelCase , mean=_lowerCamelCase , std=_lowerCamelCase ) for image in images]
_UpperCamelCase: int = [to_channel_dimension_format(_lowerCamelCase , _lowerCamelCase ) for image in images]
_UpperCamelCase: Tuple = {'''pixel_values''': images}
return BatchFeature(data=_lowerCamelCase , tensor_type=_lowerCamelCase )
def lowerCAmelCase ( self : Dict , _lowercase : List[Any] , _lowercase : List[Tuple] = None ):
"""simple docstring"""
_UpperCamelCase: int = outputs.logits
# Resize logits and compute semantic segmentation maps
if target_sizes is not None:
if len(_lowerCamelCase ) != len(_lowerCamelCase ):
raise ValueError(
'''Make sure that you pass in as many target sizes as the batch dimension of the logits''' )
if is_torch_tensor(_lowerCamelCase ):
_UpperCamelCase: Optional[int] = target_sizes.numpy()
_UpperCamelCase: Dict = []
for idx in range(len(_lowerCamelCase ) ):
_UpperCamelCase: int = torch.nn.functional.interpolate(
logits[idx].unsqueeze(dim=0 ) , size=target_sizes[idx] , mode='''bilinear''' , align_corners=_lowerCamelCase )
_UpperCamelCase: int = resized_logits[0].argmax(dim=0 )
semantic_segmentation.append(_lowerCamelCase )
else:
_UpperCamelCase: Any = logits.argmax(dim=1 )
_UpperCamelCase: Union[str, Any] = [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0] )]
return semantic_segmentation | 271 |
"""simple docstring"""
import argparse
import logging
import os
import time
import timeit
import datasets
import numpy as np
import pycuda.autoinit # noqa: F401
import pycuda.driver as cuda
import tensorrt as trt
import torch
from absl import logging as absl_logging
from accelerate import Accelerator
from datasets import load_dataset, load_metric
from torch.utils.data import DataLoader
from utils_qa import postprocess_qa_predictions
import transformers
from transformers import AutoTokenizer, EvalPrediction, default_data_collator, set_seed
from transformers.trainer_pt_utils import nested_concat, nested_truncate
_lowerCamelCase = trt.Logger(trt.Logger.WARNING)
_lowerCamelCase = absl_logging.get_absl_logger()
absl_logger.setLevel(logging.WARNING)
_lowerCamelCase = logging.getLogger(__name__)
_lowerCamelCase = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--onnx_model_path''',
default=None,
type=str,
required=True,
help='''Path to ONNX model: ''',
)
parser.add_argument(
'''--output_dir''',
default=None,
type=str,
required=True,
help='''The output directory where the model checkpoints and predictions will be written.''',
)
# Other parameters
parser.add_argument(
'''--tokenizer_name''',
default='''''',
type=str,
required=True,
help='''Pretrained tokenizer name or path if not the same as model_name''',
)
parser.add_argument(
'''--version_2_with_negative''',
action='''store_true''',
help='''If true, the SQuAD examples contain some that do not have an answer.''',
)
parser.add_argument(
'''--null_score_diff_threshold''',
type=float,
default=0.0,
help='''If null_score - best_non_null is greater than the threshold predict null.''',
)
parser.add_argument(
'''--max_seq_length''',
default=3_84,
type=int,
help=(
'''The maximum total input sequence length after WordPiece tokenization. Sequences '''
'''longer than this will be truncated, and sequences shorter than this will be padded.'''
),
)
parser.add_argument(
'''--doc_stride''',
default=1_28,
type=int,
help='''When splitting up a long document into chunks, how much stride to take between chunks.''',
)
parser.add_argument('''--per_device_eval_batch_size''', default=8, type=int, help='''Batch size per GPU/CPU for evaluation.''')
parser.add_argument(
'''--n_best_size''',
default=20,
type=int,
help='''The total number of n-best predictions to generate in the nbest_predictions.json output file.''',
)
parser.add_argument(
'''--max_answer_length''',
default=30,
type=int,
help=(
'''The maximum length of an answer that can be generated. This is needed because the start '''
'''and end predictions are not conditioned on one another.'''
),
)
parser.add_argument('''--seed''', type=int, default=42, help='''random seed for initialization''')
parser.add_argument(
'''--dataset_name''',
type=str,
default=None,
required=True,
help='''The name of the dataset to use (via the datasets library).''',
)
parser.add_argument(
'''--dataset_config_name''',
type=str,
default=None,
help='''The configuration name of the dataset to use (via the datasets library).''',
)
parser.add_argument(
'''--preprocessing_num_workers''', type=int, default=4, help='''A csv or a json file containing the training data.'''
)
parser.add_argument('''--overwrite_cache''', action='''store_true''', help='''Overwrite the cached training and evaluation sets''')
parser.add_argument(
'''--fp16''',
action='''store_true''',
help='''Whether to use 16-bit (mixed) precision instead of 32-bit''',
)
parser.add_argument(
'''--int8''',
action='''store_true''',
help='''Whether to use INT8''',
)
_lowerCamelCase = parser.parse_args()
if args.tokenizer_name:
_lowerCamelCase = AutoTokenizer.from_pretrained(args.tokenizer_name, use_fast=True)
else:
raise ValueError(
'''You are instantiating a new tokenizer from scratch. This is not supported by this script.'''
'''You can do it from another script, save it, and load it from here, using --tokenizer_name.'''
)
logger.info('''Training/evaluation parameters %s''', args)
_lowerCamelCase = args.per_device_eval_batch_size
_lowerCamelCase = (args.eval_batch_size, args.max_seq_length)
# TRT Engine properties
_lowerCamelCase = True
_lowerCamelCase = '''temp_engine/bert-fp32.engine'''
if args.fpaa:
_lowerCamelCase = '''temp_engine/bert-fp16.engine'''
if args.inta:
_lowerCamelCase = '''temp_engine/bert-int8.engine'''
# import ONNX file
if not os.path.exists('''temp_engine'''):
os.makedirs('''temp_engine''')
_lowerCamelCase = 1 << (int)(trt.NetworkDefinitionCreationFlag.EXPLICIT_BATCH)
with trt.Builder(TRT_LOGGER) as builder, builder.create_network(EXPLICIT_BATCH) as network, trt.OnnxParser(
network, TRT_LOGGER
) as parser:
with open(args.onnx_model_path, '''rb''') as model:
if not parser.parse(model.read()):
for error in range(parser.num_errors):
print(parser.get_error(error))
# Query input names and shapes from parsed TensorRT network
_lowerCamelCase = [network.get_input(i) for i in range(network.num_inputs)]
_lowerCamelCase = [_input.name for _input in network_inputs] # ex: ["actual_input1"]
with builder.create_builder_config() as config:
_lowerCamelCase = 1 << 50
if STRICT_TYPES:
config.set_flag(trt.BuilderFlag.STRICT_TYPES)
if args.fpaa:
config.set_flag(trt.BuilderFlag.FPaa)
if args.inta:
config.set_flag(trt.BuilderFlag.INTa)
_lowerCamelCase = builder.create_optimization_profile()
config.add_optimization_profile(profile)
for i in range(len(input_names)):
profile.set_shape(input_names[i], INPUT_SHAPE, INPUT_SHAPE, INPUT_SHAPE)
_lowerCamelCase = builder.build_engine(network, config)
# serialize_engine and store in file (can be directly loaded and deserialized):
with open(engine_name, '''wb''') as f:
f.write(engine.serialize())
def lowerCAmelCase_ ( lowercase_ : List[Any] , lowercase_ : Tuple , lowercase_ : List[Any] , lowercase_ : List[Any] , lowercase_ : str , lowercase_ : Tuple , lowercase_ : Any , lowercase_ : Optional[int] ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : Dict = np.asarray(inputs['''input_ids'''] , dtype=np.intaa )
__SCREAMING_SNAKE_CASE : Optional[Any] = np.asarray(inputs['''attention_mask'''] , dtype=np.intaa )
__SCREAMING_SNAKE_CASE : Optional[Any] = np.asarray(inputs['''token_type_ids'''] , dtype=np.intaa )
# Copy inputs
cuda.memcpy_htod_async(d_inputs[0] , input_ids.ravel() , lowercase_ )
cuda.memcpy_htod_async(d_inputs[1] , attention_mask.ravel() , lowercase_ )
cuda.memcpy_htod_async(d_inputs[2] , token_type_ids.ravel() , lowercase_ )
# start time
__SCREAMING_SNAKE_CASE : Tuple = time.time()
# Run inference
context.execute_async(
bindings=[int(lowercase_ ) for d_inp in d_inputs] + [int(lowercase_ ), int(lowercase_ )] , stream_handle=stream.handle )
# Transfer predictions back from GPU
cuda.memcpy_dtoh_async(lowercase_ , lowercase_ , lowercase_ )
cuda.memcpy_dtoh_async(lowercase_ , lowercase_ , lowercase_ )
# Synchronize the stream and take time
stream.synchronize()
# end time
__SCREAMING_SNAKE_CASE : List[str] = time.time()
__SCREAMING_SNAKE_CASE : int = end_time - start_time
__SCREAMING_SNAKE_CASE : int = (h_outputa, h_outputa)
# print(outputs)
return outputs, infer_time
# Initialize the accelerator. We will let the accelerator handle device placement for us in this example.
_lowerCamelCase = Accelerator()
# Make one log on every process with the configuration for debugging.
logging.basicConfig(
format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''',
datefmt='''%m/%d/%Y %H:%M:%S''',
level=logging.INFO,
)
# Setup logging, we only want one process per machine to log things on the screen.
# accelerator.is_local_main_process is only True for one process per machine.
logger.setLevel(logging.INFO if accelerator.is_local_main_process else logging.ERROR)
if accelerator.is_local_main_process:
datasets.utils.logging.set_verbosity_warning()
transformers.utils.logging.set_verbosity_info()
else:
datasets.utils.logging.set_verbosity_error()
transformers.utils.logging.set_verbosity_error()
# If passed along, set the training seed now.
if args.seed is not None:
set_seed(args.seed)
# Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below)
# or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
# (the dataset will be downloaded automatically from the datasets Hub).
#
# For CSV/JSON files, this script will use the column called 'text' or the first column if no column called
# 'text' is found. You can easily tweak this behavior (see below).
if args.dataset_name is not None:
# Downloading and loading a dataset from the hub.
_lowerCamelCase = load_dataset(args.dataset_name, args.dataset_config_name)
else:
raise ValueError('''Evaluation requires a dataset name''')
# See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
# https://huggingface.co/docs/datasets/loading_datasets.html.
# Preprocessing the datasets.
# Preprocessing is slighlty different for training and evaluation.
_lowerCamelCase = raw_datasets['''validation'''].column_names
_lowerCamelCase = '''question''' if '''question''' in column_names else column_names[0]
_lowerCamelCase = '''context''' if '''context''' in column_names else column_names[1]
_lowerCamelCase = '''answers''' if '''answers''' in column_names else column_names[2]
# Padding side determines if we do (question|context) or (context|question).
_lowerCamelCase = tokenizer.padding_side == '''right'''
if args.max_seq_length > tokenizer.model_max_length:
logger.warning(
f'The max_seq_length passed ({args.max_seq_length}) is larger than the maximum length for the'
f'model ({tokenizer.model_max_length}). Using max_seq_length={tokenizer.model_max_length}.'
)
_lowerCamelCase = min(args.max_seq_length, tokenizer.model_max_length)
def lowerCAmelCase_ ( lowercase_ : Tuple ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : Any = [q.lstrip() for q in examples[question_column_name]]
# Tokenize our examples with truncation and maybe padding, but keep the overflows using a stride. This results
# in one example possible giving several features when a context is long, each of those features having a
# context that overlaps a bit the context of the previous feature.
__SCREAMING_SNAKE_CASE : Optional[Any] = tokenizer(
examples[question_column_name if pad_on_right else context_column_name] , examples[context_column_name if pad_on_right else question_column_name] , truncation='''only_second''' if pad_on_right else '''only_first''' , max_length=lowercase_ , stride=args.doc_stride , return_overflowing_tokens=lowercase_ , return_offsets_mapping=lowercase_ , padding='''max_length''' , )
# Since one example might give us several features if it has a long context, we need a map from a feature to
# its corresponding example. This key gives us just that.
__SCREAMING_SNAKE_CASE : Optional[int] = tokenized_examples.pop('''overflow_to_sample_mapping''' )
# For evaluation, we will need to convert our predictions to substrings of the context, so we keep the
# corresponding example_id and we will store the offset mappings.
__SCREAMING_SNAKE_CASE : Any = []
for i in range(len(tokenized_examples['''input_ids'''] ) ):
# Grab the sequence corresponding to that example (to know what is the context and what is the question).
__SCREAMING_SNAKE_CASE : int = tokenized_examples.sequence_ids(lowercase_ )
__SCREAMING_SNAKE_CASE : str = 1 if pad_on_right else 0
# One example can give several spans, this is the index of the example containing this span of text.
__SCREAMING_SNAKE_CASE : str = sample_mapping[i]
tokenized_examples["example_id"].append(examples['''id'''][sample_index] )
# Set to None the offset_mapping that are not part of the context so it's easy to determine if a token
# position is part of the context or not.
__SCREAMING_SNAKE_CASE : List[str] = [
(o if sequence_ids[k] == context_index else None)
for k, o in enumerate(tokenized_examples['''offset_mapping'''][i] )
]
return tokenized_examples
_lowerCamelCase = raw_datasets['''validation''']
# Validation Feature Creation
_lowerCamelCase = eval_examples.map(
prepare_validation_features,
batched=True,
num_proc=args.preprocessing_num_workers,
remove_columns=column_names,
load_from_cache_file=not args.overwrite_cache,
desc='''Running tokenizer on validation dataset''',
)
_lowerCamelCase = default_data_collator
_lowerCamelCase = eval_dataset.remove_columns(['''example_id''', '''offset_mapping'''])
_lowerCamelCase = DataLoader(
eval_dataset_for_model, collate_fn=data_collator, batch_size=args.per_device_eval_batch_size
)
def lowerCAmelCase_ ( lowercase_ : Dict , lowercase_ : Union[str, Any] , lowercase_ : Union[str, Any] , lowercase_ : List[Any]="eval" ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : Tuple = postprocess_qa_predictions(
examples=lowercase_ , features=lowercase_ , predictions=lowercase_ , version_2_with_negative=args.version_2_with_negative , n_best_size=args.n_best_size , max_answer_length=args.max_answer_length , null_score_diff_threshold=args.null_score_diff_threshold , output_dir=args.output_dir , prefix=lowercase_ , )
# Format the result to the format the metric expects.
if args.version_2_with_negative:
__SCREAMING_SNAKE_CASE : Union[str, Any] = [
{'''id''': k, '''prediction_text''': v, '''no_answer_probability''': 0.0} for k, v in predictions.items()
]
else:
__SCREAMING_SNAKE_CASE : int = [{'''id''': k, '''prediction_text''': v} for k, v in predictions.items()]
__SCREAMING_SNAKE_CASE : Any = [{'''id''': ex['''id'''], '''answers''': ex[answer_column_name]} for ex in examples]
return EvalPrediction(predictions=lowercase_ , label_ids=lowercase_ )
_lowerCamelCase = load_metric('''squad_v2''' if args.version_2_with_negative else '''squad''')
# Evaluation!
logger.info('''Loading ONNX model %s for evaluation''', args.onnx_model_path)
with open(engine_name, '''rb''') as f, trt.Runtime(TRT_LOGGER) as runtime, runtime.deserialize_cuda_engine(
f.read()
) as engine, engine.create_execution_context() as context:
# setup for TRT inferrence
for i in range(len(input_names)):
context.set_binding_shape(i, INPUT_SHAPE)
assert context.all_binding_shapes_specified
def lowerCAmelCase_ ( lowercase_ : Any ):
'''simple docstring'''
return trt.volume(engine.get_binding_shape(lowercase_ ) ) * engine.get_binding_dtype(lowercase_ ).itemsize
# Allocate device memory for inputs and outputs.
_lowerCamelCase = [cuda.mem_alloc(binding_nbytes(binding)) for binding in engine if engine.binding_is_input(binding)]
# Allocate output buffer
_lowerCamelCase = cuda.pagelocked_empty(tuple(context.get_binding_shape(3)), dtype=np.floataa)
_lowerCamelCase = cuda.pagelocked_empty(tuple(context.get_binding_shape(4)), dtype=np.floataa)
_lowerCamelCase = cuda.mem_alloc(h_outputa.nbytes)
_lowerCamelCase = cuda.mem_alloc(h_outputa.nbytes)
# Create a stream in which to copy inputs/outputs and run inference.
_lowerCamelCase = cuda.Stream()
# Evaluation
logger.info('''***** Running Evaluation *****''')
logger.info(f' Num examples = {len(eval_dataset)}')
logger.info(f' Batch size = {args.per_device_eval_batch_size}')
_lowerCamelCase = 0.0
_lowerCamelCase = 0
_lowerCamelCase = timeit.default_timer()
_lowerCamelCase = None
for step, batch in enumerate(eval_dataloader):
_lowerCamelCase , _lowerCamelCase = model_infer(batch, context, d_inputs, h_outputa, h_outputa, d_outputa, d_outputa, stream)
total_time += infer_time
niter += 1
_lowerCamelCase , _lowerCamelCase = outputs
_lowerCamelCase = torch.tensor(start_logits)
_lowerCamelCase = torch.tensor(end_logits)
# necessary to pad predictions and labels for being gathered
_lowerCamelCase = accelerator.pad_across_processes(start_logits, dim=1, pad_index=-1_00)
_lowerCamelCase = accelerator.pad_across_processes(end_logits, dim=1, pad_index=-1_00)
_lowerCamelCase = (accelerator.gather(start_logits).cpu().numpy(), accelerator.gather(end_logits).cpu().numpy())
_lowerCamelCase = logits if all_preds is None else nested_concat(all_preds, logits, padding_index=-1_00)
if all_preds is not None:
_lowerCamelCase = nested_truncate(all_preds, len(eval_dataset))
_lowerCamelCase = timeit.default_timer() - start_time
logger.info(''' Evaluation done in total %f secs (%f sec per example)''', evalTime, evalTime / len(eval_dataset))
# Inference time from TRT
logger.info('''Average Inference Time = {:.3f} ms'''.format(total_time * 10_00 / niter))
logger.info('''Total Inference Time = {:.3f} ms'''.format(total_time * 10_00))
logger.info('''Total Number of Inference = %d''', niter)
_lowerCamelCase = post_processing_function(eval_examples, eval_dataset, all_preds)
_lowerCamelCase = metric.compute(predictions=prediction.predictions, references=prediction.label_ids)
logger.info(f'Evaluation metrics: {eval_metric}')
| 674 | 0 |
import copy
import os
from typing import TYPE_CHECKING, List, Union
if TYPE_CHECKING:
pass
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_snake_case = logging.get_logger(__name__)
_snake_case = {
'''kakaobrain/align-base''': '''https://huggingface.co/kakaobrain/align-base/resolve/main/config.json''',
}
class _lowerCAmelCase ( __UpperCAmelCase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Union[str, Any] ="align_text_model"
def __init__( self : Optional[Any] , SCREAMING_SNAKE_CASE__ : Any=3_05_22 , SCREAMING_SNAKE_CASE__ : Optional[Any]=7_68 , SCREAMING_SNAKE_CASE__ : Tuple=12 , SCREAMING_SNAKE_CASE__ : Any=12 , SCREAMING_SNAKE_CASE__ : Union[str, Any]=30_72 , SCREAMING_SNAKE_CASE__ : Optional[Any]="gelu" , SCREAMING_SNAKE_CASE__ : Optional[Any]=0.1 , SCREAMING_SNAKE_CASE__ : Tuple=0.1 , SCREAMING_SNAKE_CASE__ : Any=5_12 , SCREAMING_SNAKE_CASE__ : Dict=2 , SCREAMING_SNAKE_CASE__ : List[str]=0.02 , SCREAMING_SNAKE_CASE__ : Any=1e-12 , SCREAMING_SNAKE_CASE__ : Optional[int]=0 , SCREAMING_SNAKE_CASE__ : Tuple="absolute" , SCREAMING_SNAKE_CASE__ : Union[str, Any]=True , **SCREAMING_SNAKE_CASE__ : Optional[int] , ):
"""simple docstring"""
super().__init__(**_lowerCamelCase )
UpperCamelCase = vocab_size
UpperCamelCase = hidden_size
UpperCamelCase = num_hidden_layers
UpperCamelCase = num_attention_heads
UpperCamelCase = hidden_act
UpperCamelCase = intermediate_size
UpperCamelCase = hidden_dropout_prob
UpperCamelCase = attention_probs_dropout_prob
UpperCamelCase = max_position_embeddings
UpperCamelCase = type_vocab_size
UpperCamelCase = initializer_range
UpperCamelCase = layer_norm_eps
UpperCamelCase = position_embedding_type
UpperCamelCase = use_cache
UpperCamelCase = pad_token_id
@classmethod
def __lowerCAmelCase ( cls : Optional[int] , SCREAMING_SNAKE_CASE__ : Union[str, os.PathLike] , **SCREAMING_SNAKE_CASE__ : int ):
"""simple docstring"""
cls._set_token_in_kwargs(_lowerCamelCase )
UpperCamelCase = cls.get_config_dict(_lowerCamelCase , **_lowerCamelCase )
# get the text config dict if we are loading from AlignConfig
if config_dict.get('model_type' ) == "align":
UpperCamelCase = config_dict['''text_config''']
if "model_type" in config_dict and hasattr(cls , 'model_type' ) and config_dict["model_type"] != cls.model_type:
logger.warning(
F'You are using a model of type {config_dict["model_type"]} to instantiate a model of type '
F'{cls.model_type}. This is not supported for all configurations of models and can yield errors.' )
return cls.from_dict(_lowerCamelCase , **_lowerCamelCase )
class _lowerCAmelCase ( __UpperCAmelCase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Union[str, Any] ="align_vision_model"
def __init__( self : Tuple , SCREAMING_SNAKE_CASE__ : int = 3 , SCREAMING_SNAKE_CASE__ : int = 6_00 , SCREAMING_SNAKE_CASE__ : float = 2.0 , SCREAMING_SNAKE_CASE__ : float = 3.1 , SCREAMING_SNAKE_CASE__ : int = 8 , SCREAMING_SNAKE_CASE__ : List[int] = [3, 3, 5, 3, 5, 5, 3] , SCREAMING_SNAKE_CASE__ : List[int] = [32, 16, 24, 40, 80, 1_12, 1_92] , SCREAMING_SNAKE_CASE__ : List[int] = [16, 24, 40, 80, 1_12, 1_92, 3_20] , SCREAMING_SNAKE_CASE__ : List[int] = [] , SCREAMING_SNAKE_CASE__ : List[int] = [1, 2, 2, 2, 1, 2, 1] , SCREAMING_SNAKE_CASE__ : List[int] = [1, 2, 2, 3, 3, 4, 1] , SCREAMING_SNAKE_CASE__ : List[int] = [1, 6, 6, 6, 6, 6, 6] , SCREAMING_SNAKE_CASE__ : float = 0.25 , SCREAMING_SNAKE_CASE__ : str = "swish" , SCREAMING_SNAKE_CASE__ : int = 25_60 , SCREAMING_SNAKE_CASE__ : str = "mean" , SCREAMING_SNAKE_CASE__ : float = 0.02 , SCREAMING_SNAKE_CASE__ : float = 0.001 , SCREAMING_SNAKE_CASE__ : float = 0.99 , SCREAMING_SNAKE_CASE__ : float = 0.2 , **SCREAMING_SNAKE_CASE__ : List[Any] , ):
"""simple docstring"""
super().__init__(**_lowerCamelCase )
UpperCamelCase = num_channels
UpperCamelCase = image_size
UpperCamelCase = width_coefficient
UpperCamelCase = depth_coefficient
UpperCamelCase = depth_divisor
UpperCamelCase = kernel_sizes
UpperCamelCase = in_channels
UpperCamelCase = out_channels
UpperCamelCase = depthwise_padding
UpperCamelCase = strides
UpperCamelCase = num_block_repeats
UpperCamelCase = expand_ratios
UpperCamelCase = squeeze_expansion_ratio
UpperCamelCase = hidden_act
UpperCamelCase = hidden_dim
UpperCamelCase = pooling_type
UpperCamelCase = initializer_range
UpperCamelCase = batch_norm_eps
UpperCamelCase = batch_norm_momentum
UpperCamelCase = drop_connect_rate
UpperCamelCase = sum(_lowerCamelCase ) * 4
@classmethod
def __lowerCAmelCase ( cls : Optional[Any] , SCREAMING_SNAKE_CASE__ : Union[str, os.PathLike] , **SCREAMING_SNAKE_CASE__ : Union[str, Any] ):
"""simple docstring"""
cls._set_token_in_kwargs(_lowerCamelCase )
UpperCamelCase = cls.get_config_dict(_lowerCamelCase , **_lowerCamelCase )
# get the vision config dict if we are loading from AlignConfig
if config_dict.get('model_type' ) == "align":
UpperCamelCase = config_dict['''vision_config''']
if "model_type" in config_dict and hasattr(cls , 'model_type' ) and config_dict["model_type"] != cls.model_type:
logger.warning(
F'You are using a model of type {config_dict["model_type"]} to instantiate a model of type '
F'{cls.model_type}. This is not supported for all configurations of models and can yield errors.' )
return cls.from_dict(_lowerCamelCase , **_lowerCamelCase )
class _lowerCAmelCase ( __UpperCAmelCase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : List[str] ="align"
SCREAMING_SNAKE_CASE_ : str =True
def __init__( self : Union[str, Any] , SCREAMING_SNAKE_CASE__ : Optional[int]=None , SCREAMING_SNAKE_CASE__ : Optional[Any]=None , SCREAMING_SNAKE_CASE__ : List[str]=6_40 , SCREAMING_SNAKE_CASE__ : Any=1.0 , SCREAMING_SNAKE_CASE__ : Dict=0.02 , **SCREAMING_SNAKE_CASE__ : List[str] , ):
"""simple docstring"""
super().__init__(**_lowerCamelCase )
if text_config is None:
UpperCamelCase = {}
logger.info('text_config is None. Initializing the AlignTextConfig with default values.' )
if vision_config is None:
UpperCamelCase = {}
logger.info('vision_config is None. Initializing the AlignVisionConfig with default values.' )
UpperCamelCase = AlignTextConfig(**_lowerCamelCase )
UpperCamelCase = AlignVisionConfig(**_lowerCamelCase )
UpperCamelCase = projection_dim
UpperCamelCase = temperature_init_value
UpperCamelCase = initializer_range
@classmethod
def __lowerCAmelCase ( cls : str , SCREAMING_SNAKE_CASE__ : AlignTextConfig , SCREAMING_SNAKE_CASE__ : AlignVisionConfig , **SCREAMING_SNAKE_CASE__ : Optional[int] ):
"""simple docstring"""
return cls(text_config=text_config.to_dict() , vision_config=vision_config.to_dict() , **_lowerCamelCase )
def __lowerCAmelCase ( self : Tuple ):
"""simple docstring"""
UpperCamelCase = copy.deepcopy(self.__dict__ )
UpperCamelCase = self.text_config.to_dict()
UpperCamelCase = self.vision_config.to_dict()
UpperCamelCase = self.__class__.model_type
return output
| 282 |
"""simple docstring"""
from __future__ import annotations
import unittest
from transformers import is_tf_available, is_torch_available
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, SMALL_MODEL_IDENTIFIER, is_pt_tf_cross_test, slow
if is_tf_available():
from transformers import (
AutoConfig,
BertConfig,
GPTaConfig,
TaConfig,
TFAutoModel,
TFAutoModelForCausalLM,
TFAutoModelForMaskedLM,
TFAutoModelForPreTraining,
TFAutoModelForQuestionAnswering,
TFAutoModelForSeqaSeqLM,
TFAutoModelForSequenceClassification,
TFAutoModelWithLMHead,
TFBertForMaskedLM,
TFBertForPreTraining,
TFBertForQuestionAnswering,
TFBertForSequenceClassification,
TFBertModel,
TFGPTaLMHeadModel,
TFRobertaForMaskedLM,
TFTaForConditionalGeneration,
)
from transformers.models.bert.modeling_tf_bert import TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST
from transformers.models.gpta.modeling_tf_gpta import TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST
from transformers.models.ta.modeling_tf_ta import TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST
if is_torch_available():
from transformers import (
AutoModel,
AutoModelForCausalLM,
AutoModelForMaskedLM,
AutoModelForPreTraining,
AutoModelForQuestionAnswering,
AutoModelForSeqaSeqLM,
AutoModelForSequenceClassification,
AutoModelWithLMHead,
BertForMaskedLM,
BertForPreTraining,
BertForQuestionAnswering,
BertForSequenceClassification,
BertModel,
GPTaLMHeadModel,
RobertaForMaskedLM,
TaForConditionalGeneration,
)
@is_pt_tf_cross_test
class snake_case ( unittest.TestCase ):
@slow
def SCREAMING_SNAKE_CASE_ ( self :Tuple ):
# for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
for model_name in ["bert-base-uncased"]:
__SCREAMING_SNAKE_CASE : List[Any] = AutoConfig.from_pretrained(_lowerCamelCase )
self.assertIsNotNone(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : List[str] = TFAutoModel.from_pretrained(_lowerCamelCase , from_pt=_lowerCamelCase )
self.assertIsNotNone(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : Optional[Any] = AutoModel.from_pretrained(_lowerCamelCase , from_tf=_lowerCamelCase )
self.assertIsNotNone(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
@slow
def SCREAMING_SNAKE_CASE_ ( self :Optional[int] ):
# for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
for model_name in ["bert-base-uncased"]:
__SCREAMING_SNAKE_CASE : List[str] = AutoConfig.from_pretrained(_lowerCamelCase )
self.assertIsNotNone(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : Tuple = TFAutoModelForPreTraining.from_pretrained(_lowerCamelCase , from_pt=_lowerCamelCase )
self.assertIsNotNone(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : Optional[int] = AutoModelForPreTraining.from_pretrained(_lowerCamelCase , from_tf=_lowerCamelCase )
self.assertIsNotNone(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
@slow
def SCREAMING_SNAKE_CASE_ ( self :Any ):
for model_name in TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__SCREAMING_SNAKE_CASE : Optional[int] = AutoConfig.from_pretrained(_lowerCamelCase )
self.assertIsNotNone(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : int = TFAutoModelForCausalLM.from_pretrained(_lowerCamelCase , from_pt=_lowerCamelCase )
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : List[Any] = TFAutoModelForCausalLM.from_pretrained(
_lowerCamelCase , output_loading_info=_lowerCamelCase , from_pt=_lowerCamelCase )
self.assertIsNotNone(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : Tuple = AutoModelForCausalLM.from_pretrained(_lowerCamelCase , from_tf=_lowerCamelCase )
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Dict = AutoModelForCausalLM.from_pretrained(
_lowerCamelCase , output_loading_info=_lowerCamelCase , from_tf=_lowerCamelCase )
self.assertIsNotNone(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
@slow
def SCREAMING_SNAKE_CASE_ ( self :List[Any] ):
for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__SCREAMING_SNAKE_CASE : Any = AutoConfig.from_pretrained(_lowerCamelCase )
self.assertIsNotNone(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : Any = TFAutoModelWithLMHead.from_pretrained(_lowerCamelCase , from_pt=_lowerCamelCase )
self.assertIsNotNone(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : Optional[Any] = AutoModelWithLMHead.from_pretrained(_lowerCamelCase , from_tf=_lowerCamelCase )
self.assertIsNotNone(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
@slow
def SCREAMING_SNAKE_CASE_ ( self :Dict ):
for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__SCREAMING_SNAKE_CASE : Union[str, Any] = AutoConfig.from_pretrained(_lowerCamelCase )
self.assertIsNotNone(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : Optional[int] = TFAutoModelForMaskedLM.from_pretrained(_lowerCamelCase , from_pt=_lowerCamelCase )
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : List[str] = TFAutoModelForMaskedLM.from_pretrained(
_lowerCamelCase , output_loading_info=_lowerCamelCase , from_pt=_lowerCamelCase )
self.assertIsNotNone(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : Tuple = AutoModelForMaskedLM.from_pretrained(_lowerCamelCase , from_tf=_lowerCamelCase )
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : List[Any] = AutoModelForMaskedLM.from_pretrained(
_lowerCamelCase , output_loading_info=_lowerCamelCase , from_tf=_lowerCamelCase )
self.assertIsNotNone(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
@slow
def SCREAMING_SNAKE_CASE_ ( self :int ):
for model_name in TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__SCREAMING_SNAKE_CASE : str = AutoConfig.from_pretrained(_lowerCamelCase )
self.assertIsNotNone(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : Tuple = TFAutoModelForSeqaSeqLM.from_pretrained(_lowerCamelCase , from_pt=_lowerCamelCase )
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : str = TFAutoModelForSeqaSeqLM.from_pretrained(
_lowerCamelCase , output_loading_info=_lowerCamelCase , from_pt=_lowerCamelCase )
self.assertIsNotNone(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : Optional[int] = AutoModelForSeqaSeqLM.from_pretrained(_lowerCamelCase , from_tf=_lowerCamelCase )
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Dict = AutoModelForSeqaSeqLM.from_pretrained(
_lowerCamelCase , output_loading_info=_lowerCamelCase , from_tf=_lowerCamelCase )
self.assertIsNotNone(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
@slow
def SCREAMING_SNAKE_CASE_ ( self :Optional[Any] ):
# for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
for model_name in ["bert-base-uncased"]:
__SCREAMING_SNAKE_CASE : Optional[Any] = AutoConfig.from_pretrained(_lowerCamelCase )
self.assertIsNotNone(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : int = TFAutoModelForSequenceClassification.from_pretrained(_lowerCamelCase , from_pt=_lowerCamelCase )
self.assertIsNotNone(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : Optional[Any] = AutoModelForSequenceClassification.from_pretrained(_lowerCamelCase , from_tf=_lowerCamelCase )
self.assertIsNotNone(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
@slow
def SCREAMING_SNAKE_CASE_ ( self :List[Any] ):
# for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
for model_name in ["bert-base-uncased"]:
__SCREAMING_SNAKE_CASE : Optional[Any] = AutoConfig.from_pretrained(_lowerCamelCase )
self.assertIsNotNone(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : str = TFAutoModelForQuestionAnswering.from_pretrained(_lowerCamelCase , from_pt=_lowerCamelCase )
self.assertIsNotNone(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : List[Any] = AutoModelForQuestionAnswering.from_pretrained(_lowerCamelCase , from_tf=_lowerCamelCase )
self.assertIsNotNone(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :Union[str, Any] ):
__SCREAMING_SNAKE_CASE : Tuple = TFAutoModelWithLMHead.from_pretrained(_lowerCamelCase , from_pt=_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
self.assertEqual(model.num_parameters() , 1_4_4_1_0 )
self.assertEqual(model.num_parameters(only_trainable=_lowerCamelCase ) , 1_4_4_1_0 )
__SCREAMING_SNAKE_CASE : Tuple = AutoModelWithLMHead.from_pretrained(_lowerCamelCase , from_tf=_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
self.assertEqual(model.num_parameters() , 1_4_4_1_0 )
self.assertEqual(model.num_parameters(only_trainable=_lowerCamelCase ) , 1_4_4_1_0 )
def SCREAMING_SNAKE_CASE_ ( self :str ):
__SCREAMING_SNAKE_CASE : Optional[Any] = TFAutoModelWithLMHead.from_pretrained(_lowerCamelCase , from_pt=_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
self.assertEqual(model.num_parameters() , 1_4_4_1_0 )
self.assertEqual(model.num_parameters(only_trainable=_lowerCamelCase ) , 1_4_4_1_0 )
__SCREAMING_SNAKE_CASE : Union[str, Any] = AutoModelWithLMHead.from_pretrained(_lowerCamelCase , from_tf=_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
self.assertEqual(model.num_parameters() , 1_4_4_1_0 )
self.assertEqual(model.num_parameters(only_trainable=_lowerCamelCase ) , 1_4_4_1_0 )
| 674 | 0 |
"""simple docstring"""
import numpy as np
def UpperCAmelCase ( A__: np.ndarray , A__: np.ndarray , A__: float = 1E-12 , A__: int = 100 , ) -> Tuple:
assert np.shape(lowercase_ )[0] == np.shape(lowercase_ )[1]
# Ensure proper dimensionality.
assert np.shape(lowercase_ )[0] == np.shape(lowercase_ )[0]
# Ensure inputs are either both complex or both real
assert np.iscomplexobj(lowercase_ ) == np.iscomplexobj(lowercase_ )
__lowerCamelCase : Any = np.iscomplexobj(lowercase_ )
if is_complex:
# Ensure complex input_matrix is Hermitian
assert np.array_equal(lowercase_ , input_matrix.conj().T )
# Set convergence to False. Will define convergence when we exceed max_iterations
# or when we have small changes from one iteration to next.
__lowerCamelCase : Optional[Any] = False
__lowerCamelCase : Union[str, Any] = 0
__lowerCamelCase : Any = 0
__lowerCamelCase : Any = 1E12
while not convergence:
# Multiple matrix by the vector.
__lowerCamelCase : int = np.dot(lowercase_ , lowercase_ )
# Normalize the resulting output vector.
__lowerCamelCase : int = w / np.linalg.norm(lowercase_ )
# Find rayleigh quotient
# (faster than usual b/c we know vector is normalized already)
__lowerCamelCase : Optional[Any] = vector.conj().T if is_complex else vector.T
__lowerCamelCase : Optional[int] = np.dot(lowercase_ , np.dot(lowercase_ , lowercase_ ) )
# Check convergence.
__lowerCamelCase : int = np.abs(lambda_ - lambda_previous ) / lambda_
iterations += 1
if error <= error_tol or iterations >= max_iterations:
__lowerCamelCase : Optional[int] = True
__lowerCamelCase : str = lambda_
if is_complex:
__lowerCamelCase : Optional[int] = np.real(lambda_ )
return lambda_, vector
def UpperCAmelCase ( ) -> List[str]:
__lowerCamelCase : Union[str, Any] = np.array([[41, 4, 20], [4, 26, 30], [20, 30, 50]] )
__lowerCamelCase : Optional[Any] = np.array([41, 4, 20] )
__lowerCamelCase : Dict = real_input_matrix.astype(np.complexaaa )
__lowerCamelCase : List[str] = np.triu(1j * complex_input_matrix , 1 )
complex_input_matrix += imag_matrix
complex_input_matrix += -1 * imag_matrix.T
__lowerCamelCase : int = np.array([41, 4, 20] ).astype(np.complexaaa )
for problem_type in ["real", "complex"]:
if problem_type == "real":
__lowerCamelCase : int = real_input_matrix
__lowerCamelCase : str = real_vector
elif problem_type == "complex":
__lowerCamelCase : Dict = complex_input_matrix
__lowerCamelCase : Optional[int] = complex_vector
# Our implementation.
__lowerCamelCase : str = power_iteration(lowercase_ , lowercase_ )
# Numpy implementation.
# Get eigenvalues and eigenvectors using built-in numpy
# eigh (eigh used for symmetric or hermetian matrices).
__lowerCamelCase : int = np.linalg.eigh(lowercase_ )
# Last eigenvalue is the maximum one.
__lowerCamelCase : int = eigen_values[-1]
# Last column in this matrix is eigenvector corresponding to largest eigenvalue.
__lowerCamelCase : Dict = eigen_vectors[:, -1]
# Check our implementation and numpy gives close answers.
assert np.abs(eigen_value - eigen_value_max ) <= 1E-6
# Take absolute values element wise of each eigenvector.
# as they are only unique to a minus sign.
assert np.linalg.norm(np.abs(lowercase_ ) - np.abs(lowercase_ ) ) <= 1E-6
if __name__ == "__main__":
import doctest
doctest.testmod()
test_power_iteration()
| 594 |
"""simple docstring"""
import os
import re
import warnings
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
if TYPE_CHECKING:
from ...tokenization_utils_base import TextInput
from ...utils import logging
_lowerCamelCase = logging.get_logger(__name__)
_lowerCamelCase = {'''vocab_file''': '''spiece.model'''}
_lowerCamelCase = {
'''vocab_file''': {
'''t5-small''': '''https://huggingface.co/t5-small/resolve/main/spiece.model''',
'''t5-base''': '''https://huggingface.co/t5-base/resolve/main/spiece.model''',
'''t5-large''': '''https://huggingface.co/t5-large/resolve/main/spiece.model''',
'''t5-3b''': '''https://huggingface.co/t5-3b/resolve/main/spiece.model''',
'''t5-11b''': '''https://huggingface.co/t5-11b/resolve/main/spiece.model''',
}
}
# TODO(PVP) - this should be removed in Transformers v5
_lowerCamelCase = {
'''t5-small''': 5_12,
'''t5-base''': 5_12,
'''t5-large''': 5_12,
'''t5-3b''': 5_12,
'''t5-11b''': 5_12,
}
_lowerCamelCase = '''▁'''
class snake_case ( __UpperCAmelCase ):
lowerCamelCase__ = VOCAB_FILES_NAMES
lowerCamelCase__ = PRETRAINED_VOCAB_FILES_MAP
lowerCamelCase__ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowerCamelCase__ = ['''input_ids''', '''attention_mask''']
def __init__( self :int , _lowerCamelCase :Optional[Any] , _lowerCamelCase :Union[str, Any]="</s>" , _lowerCamelCase :List[Any]="<unk>" , _lowerCamelCase :Union[str, Any]="<pad>" , _lowerCamelCase :int=1_0_0 , _lowerCamelCase :Union[str, Any]=None , _lowerCamelCase :Optional[Dict[str, Any]] = None , _lowerCamelCase :int=True , **_lowerCamelCase :List[Any] , ):
# Add extra_ids to the special token list
if extra_ids > 0 and additional_special_tokens is None:
__SCREAMING_SNAKE_CASE : Union[str, Any] = [f'''<extra_id_{i}>''' for i in range(_lowerCamelCase )]
elif extra_ids > 0 and additional_special_tokens is not None:
# Check that we have the right number of extra_id special tokens
__SCREAMING_SNAKE_CASE : Optional[int] = len(set(filter(lambda _lowerCamelCase : bool('''extra_id''' in str(_lowerCamelCase ) ) , _lowerCamelCase ) ) )
if extra_tokens != extra_ids:
raise ValueError(
f'''Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are'''
''' provided to T5Tokenizer. In this case the additional_special_tokens must include the extra_ids'''
''' tokens''' )
if legacy:
logger.warning_once(
f'''You are using the legacy behaviour of the {self.__class__}. This means that tokens that come after special tokens will not be properly handled. We recommend you to'''
''' read the related pull request available at https://github.com/huggingface/transformers/pull/24565''' )
__SCREAMING_SNAKE_CASE : Optional[Any] = legacy
__SCREAMING_SNAKE_CASE : Optional[int] = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
eos_token=_lowerCamelCase , unk_token=_lowerCamelCase , pad_token=_lowerCamelCase , extra_ids=_lowerCamelCase , additional_special_tokens=_lowerCamelCase , sp_model_kwargs=self.sp_model_kwargs , legacy=_lowerCamelCase , **_lowerCamelCase , )
__SCREAMING_SNAKE_CASE : Tuple = vocab_file
__SCREAMING_SNAKE_CASE : List[str] = extra_ids
__SCREAMING_SNAKE_CASE : Optional[int] = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(_lowerCamelCase )
@staticmethod
def SCREAMING_SNAKE_CASE_ ( _lowerCamelCase :str , _lowerCamelCase :Union[str, Any] , _lowerCamelCase :int ):
if pretrained_model_name_or_path in TaTokenizer.max_model_input_sizes:
__SCREAMING_SNAKE_CASE : Any = TaTokenizer.max_model_input_sizes[pretrained_model_name_or_path]
if init_max_model_length is not None and init_max_model_length != max_model_length:
return init_max_model_length
elif init_max_model_length is None:
warnings.warn(
'''This tokenizer was incorrectly instantiated with a model max length of'''
f''' {deprecated_max_model_length} which will be corrected in Transformers v5.\nFor now, this'''
''' behavior is kept to avoid breaking backwards compatibility when padding/encoding with'''
''' `truncation is True`.\n- Be aware that you SHOULD NOT rely on'''
f''' {pretrained_model_name_or_path} automatically truncating your input to'''
f''' {deprecated_max_model_length} when padding/encoding.\n- If you want to encode/pad to sequences'''
f''' longer than {deprecated_max_model_length} you can either instantiate this tokenizer with'''
''' `model_max_length` or pass `max_length` when encoding/padding.\n- To avoid this warning, please'''
''' instantiate this tokenizer with `model_max_length` set to your preferred value.''' , _lowerCamelCase , )
return max_model_length
@property
def SCREAMING_SNAKE_CASE_ ( self :Tuple ):
return self.sp_model.get_piece_size() + self._extra_ids
def SCREAMING_SNAKE_CASE_ ( self :Union[str, Any] ):
__SCREAMING_SNAKE_CASE : str = {self.convert_ids_to_tokens(_lowerCamelCase ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def SCREAMING_SNAKE_CASE_ ( self :Union[str, Any] , _lowerCamelCase :List[int] , _lowerCamelCase :Optional[List[int]] = None , _lowerCamelCase :bool = False ):
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=_lowerCamelCase , token_ids_a=_lowerCamelCase , already_has_special_tokens=_lowerCamelCase )
# normal case: some special tokens
if token_ids_a is None:
return ([0] * len(_lowerCamelCase )) + [1]
return ([0] * len(_lowerCamelCase )) + [1] + ([0] * len(_lowerCamelCase )) + [1]
def SCREAMING_SNAKE_CASE_ ( self :List[str] ):
return list(
set(filter(lambda _lowerCamelCase : bool(re.search(r'''<extra_id_\d+>''' , _lowerCamelCase ) ) is not None , self.additional_special_tokens ) ) )
def SCREAMING_SNAKE_CASE_ ( self :List[Any] ):
return [self._convert_token_to_id(_lowerCamelCase ) for token in self.get_sentinel_tokens()]
def SCREAMING_SNAKE_CASE_ ( self :Any , _lowerCamelCase :List[int] ):
if len(_lowerCamelCase ) > 0 and token_ids[-1] == self.eos_token_id:
warnings.warn(
f'''This sequence already has {self.eos_token}. In future versions this behavior may lead to duplicated'''
''' eos tokens being added.''' )
return token_ids
else:
return token_ids + [self.eos_token_id]
def SCREAMING_SNAKE_CASE_ ( self :Union[str, Any] , _lowerCamelCase :List[int] , _lowerCamelCase :Optional[List[int]] = None ):
__SCREAMING_SNAKE_CASE : List[str] = [self.eos_token_id]
if token_ids_a is None:
return len(token_ids_a + eos ) * [0]
return len(token_ids_a + eos + token_ids_a + eos ) * [0]
def SCREAMING_SNAKE_CASE_ ( self :List[Any] , _lowerCamelCase :List[int] , _lowerCamelCase :Optional[List[int]] = None ):
__SCREAMING_SNAKE_CASE : Optional[Any] = self._add_eos_if_not_present(_lowerCamelCase )
if token_ids_a is None:
return token_ids_a
else:
__SCREAMING_SNAKE_CASE : Union[str, Any] = self._add_eos_if_not_present(_lowerCamelCase )
return token_ids_a + token_ids_a
def __getstate__( self :Union[str, Any] ):
__SCREAMING_SNAKE_CASE : Any = self.__dict__.copy()
__SCREAMING_SNAKE_CASE : List[str] = None
return state
def __setstate__( self :Optional[Any] , _lowerCamelCase :List[str] ):
__SCREAMING_SNAKE_CASE : Tuple = d
# for backward compatibility
if not hasattr(self , '''sp_model_kwargs''' ):
__SCREAMING_SNAKE_CASE : Optional[int] = {}
__SCREAMING_SNAKE_CASE : Optional[int] = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(self.vocab_file )
def SCREAMING_SNAKE_CASE_ ( self :int , _lowerCamelCase :"TextInput" , **_lowerCamelCase :str ):
# Replace the SPIECE_UNDERLINE with a space to make sure SPIECE_UNDERLINE is only used at
# the beginning of the text
if not self.legacy:
__SCREAMING_SNAKE_CASE : Dict = SPIECE_UNDERLINE + text.replace(_lowerCamelCase , ''' ''' )
return super().tokenize(_lowerCamelCase , **_lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :Optional[Any] , _lowerCamelCase :List[Any] , **_lowerCamelCase :Dict ):
if not self.legacy:
__SCREAMING_SNAKE_CASE : str = text.startswith(_lowerCamelCase )
if is_first:
__SCREAMING_SNAKE_CASE : str = text[1:]
__SCREAMING_SNAKE_CASE : Tuple = self.sp_model.encode(_lowerCamelCase , out_type=_lowerCamelCase )
if not self.legacy and not is_first and not text.startswith(''' ''' ) and tokens[0].startswith(_lowerCamelCase ):
__SCREAMING_SNAKE_CASE : Optional[int] = ([tokens[0][1:]] if len(tokens[0] ) > 1 else []) + tokens[1:]
return tokens
def SCREAMING_SNAKE_CASE_ ( self :Optional[Any] , _lowerCamelCase :Optional[Any] ):
if token.startswith('''<extra_id_''' ):
__SCREAMING_SNAKE_CASE : Tuple = re.match(r'''<extra_id_(\d+)>''' , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : Union[str, Any] = int(match.group(1 ) )
return self.vocab_size - num - 1
return self.sp_model.piece_to_id(_lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :str , _lowerCamelCase :Optional[int] ):
if index < self.sp_model.get_piece_size():
__SCREAMING_SNAKE_CASE : List[Any] = self.sp_model.IdToPiece(_lowerCamelCase )
else:
__SCREAMING_SNAKE_CASE : Dict = f'''<extra_id_{self.vocab_size - 1 - index}>'''
return token
def SCREAMING_SNAKE_CASE_ ( self :Tuple , _lowerCamelCase :Any ):
__SCREAMING_SNAKE_CASE : str = []
__SCREAMING_SNAKE_CASE : Dict = ''''''
__SCREAMING_SNAKE_CASE : Dict = False
for token in tokens:
# make sure that special tokens are not decoded using sentencepiece model
if token in self.all_special_tokens:
if not prev_is_special:
out_string += " "
out_string += self.sp_model.decode(_lowerCamelCase ) + token
__SCREAMING_SNAKE_CASE : List[str] = True
__SCREAMING_SNAKE_CASE : str = []
else:
current_sub_tokens.append(_lowerCamelCase )
__SCREAMING_SNAKE_CASE : int = False
out_string += self.sp_model.decode(_lowerCamelCase )
return out_string.strip()
def SCREAMING_SNAKE_CASE_ ( self :Optional[Any] , _lowerCamelCase :str , _lowerCamelCase :Optional[str] = None ):
if not os.path.isdir(_lowerCamelCase ):
logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' )
return
__SCREAMING_SNAKE_CASE : List[str] = os.path.join(
_lowerCamelCase , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(_lowerCamelCase ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file , _lowerCamelCase )
elif not os.path.isfile(self.vocab_file ):
with open(_lowerCamelCase , '''wb''' ) as fi:
__SCREAMING_SNAKE_CASE : Any = self.sp_model.serialized_model_proto()
fi.write(_lowerCamelCase )
return (out_vocab_file,)
| 674 | 0 |
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...models.auto.modeling_auto import MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
from ...utils import logging
from ..auto import CONFIG_MAPPING
__snake_case = logging.get_logger(__name__)
__snake_case = {
"Salesforce/instruct-blip-flan-t5": "https://huggingface.co/Salesforce/instruct-blip-flan-t5/resolve/main/config.json",
}
class UpperCAmelCase ( __UpperCAmelCase ):
lowercase = """instructblip_vision_model"""
def __init__( self : Any , __magic_name__ : Dict=1_4_0_8 , __magic_name__ : Dict=6_1_4_4 , __magic_name__ : Any=3_9 , __magic_name__ : Dict=1_6 , __magic_name__ : Optional[Any]=2_2_4 , __magic_name__ : Optional[Any]=1_4 , __magic_name__ : List[str]="gelu" , __magic_name__ : List[Any]=1e-6 , __magic_name__ : Optional[Any]=0.0 , __magic_name__ : List[Any]=1e-10 , __magic_name__ : Tuple=True , **__magic_name__ : Any , ):
"""simple docstring"""
super().__init__(**_lowerCamelCase )
UpperCamelCase = hidden_size
UpperCamelCase = intermediate_size
UpperCamelCase = num_hidden_layers
UpperCamelCase = num_attention_heads
UpperCamelCase = patch_size
UpperCamelCase = image_size
UpperCamelCase = initializer_range
UpperCamelCase = attention_dropout
UpperCamelCase = layer_norm_eps
UpperCamelCase = hidden_act
UpperCamelCase = qkv_bias
@classmethod
def lowerCamelCase_ ( cls : List[Any] , __magic_name__ : Union[str, os.PathLike] , **__magic_name__ : List[Any] ):
"""simple docstring"""
cls._set_token_in_kwargs(_lowerCamelCase )
UpperCamelCase = cls.get_config_dict(_lowerCamelCase , **_lowerCamelCase )
# get the vision config dict if we are loading from InstructBlipConfig
if config_dict.get("""model_type""" ) == "instructblip":
UpperCamelCase = config_dict['''vision_config''']
if "model_type" in config_dict and hasattr(cls , """model_type""" ) and config_dict["model_type"] != cls.model_type:
logger.warning(
F'You are using a model of type {config_dict["model_type"]} to instantiate a model of type '
F'{cls.model_type}. This is not supported for all configurations of models and can yield errors.' )
return cls.from_dict(_lowerCamelCase , **_lowerCamelCase )
class UpperCAmelCase ( __UpperCAmelCase ):
lowercase = """instructblip_qformer"""
def __init__( self : Optional[int] , __magic_name__ : Union[str, Any]=3_0_5_2_2 , __magic_name__ : List[Any]=7_6_8 , __magic_name__ : Dict=1_2 , __magic_name__ : Optional[int]=1_2 , __magic_name__ : int=3_0_7_2 , __magic_name__ : Tuple="gelu" , __magic_name__ : Optional[int]=0.1 , __magic_name__ : str=0.1 , __magic_name__ : Optional[Any]=5_1_2 , __magic_name__ : Tuple=0.02 , __magic_name__ : Optional[int]=1e-12 , __magic_name__ : Union[str, Any]=0 , __magic_name__ : Tuple="absolute" , __magic_name__ : Any=2 , __magic_name__ : int=1_4_0_8 , **__magic_name__ : str , ):
"""simple docstring"""
super().__init__(pad_token_id=_lowerCamelCase , **_lowerCamelCase )
UpperCamelCase = vocab_size
UpperCamelCase = hidden_size
UpperCamelCase = num_hidden_layers
UpperCamelCase = num_attention_heads
UpperCamelCase = hidden_act
UpperCamelCase = intermediate_size
UpperCamelCase = hidden_dropout_prob
UpperCamelCase = attention_probs_dropout_prob
UpperCamelCase = max_position_embeddings
UpperCamelCase = initializer_range
UpperCamelCase = layer_norm_eps
UpperCamelCase = position_embedding_type
UpperCamelCase = cross_attention_frequency
UpperCamelCase = encoder_hidden_size
@classmethod
def lowerCamelCase_ ( cls : str , __magic_name__ : Union[str, os.PathLike] , **__magic_name__ : Optional[int] ):
"""simple docstring"""
cls._set_token_in_kwargs(_lowerCamelCase )
UpperCamelCase = cls.get_config_dict(_lowerCamelCase , **_lowerCamelCase )
# get the qformer config dict if we are loading from InstructBlipConfig
if config_dict.get("""model_type""" ) == "instructblip":
UpperCamelCase = config_dict['''qformer_config''']
if "model_type" in config_dict and hasattr(cls , """model_type""" ) and config_dict["model_type"] != cls.model_type:
logger.warning(
F'You are using a model of type {config_dict["model_type"]} to instantiate a model of type '
F'{cls.model_type}. This is not supported for all configurations of models and can yield errors.' )
return cls.from_dict(_lowerCamelCase , **_lowerCamelCase )
class UpperCAmelCase ( __UpperCAmelCase ):
lowercase = """instructblip"""
lowercase = True
def __init__( self : Union[str, Any] , __magic_name__ : Union[str, Any]=None , __magic_name__ : Any=None , __magic_name__ : Optional[int]=None , __magic_name__ : Union[str, Any]=3_2 , **__magic_name__ : Dict ):
"""simple docstring"""
super().__init__(**_lowerCamelCase )
if vision_config is None:
UpperCamelCase = {}
logger.info("""vision_config is None. initializing the InstructBlipVisionConfig with default values.""" )
if qformer_config is None:
UpperCamelCase = {}
logger.info("""qformer_config is None. Initializing the InstructBlipQFormerConfig with default values.""" )
if text_config is None:
UpperCamelCase = {}
logger.info("""text_config is None. Initializing the text config with default values (`OPTConfig`).""" )
UpperCamelCase = InstructBlipVisionConfig(**_lowerCamelCase )
UpperCamelCase = InstructBlipQFormerConfig(**_lowerCamelCase )
UpperCamelCase = text_config['''model_type'''] if '''model_type''' in text_config else '''opt'''
UpperCamelCase = CONFIG_MAPPING[text_model_type](**_lowerCamelCase )
UpperCamelCase = self.text_config.tie_word_embeddings
UpperCamelCase = self.text_config.is_encoder_decoder
UpperCamelCase = num_query_tokens
UpperCamelCase = self.vision_config.hidden_size
UpperCamelCase = self.text_config.model_type in MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
UpperCamelCase = 1.0
UpperCamelCase = 0.02
@classmethod
def lowerCamelCase_ ( cls : int , __magic_name__ : InstructBlipVisionConfig , __magic_name__ : InstructBlipQFormerConfig , __magic_name__ : PretrainedConfig , **__magic_name__ : Union[str, Any] , ):
"""simple docstring"""
return cls(
vision_config=vision_config.to_dict() , qformer_config=qformer_config.to_dict() , text_config=text_config.to_dict() , **_lowerCamelCase , )
def lowerCamelCase_ ( self : Any ):
"""simple docstring"""
UpperCamelCase = copy.deepcopy(self.__dict__ )
UpperCamelCase = self.vision_config.to_dict()
UpperCamelCase = self.qformer_config.to_dict()
UpperCamelCase = self.text_config.to_dict()
UpperCamelCase = self.__class__.model_type
return output
| 386 |
"""simple docstring"""
import os
import pytest
import yaml
from datasets.features.features import Features, Value
from datasets.info import DatasetInfo, DatasetInfosDict
@pytest.mark.parametrize(
'''files''' , [
['''full:README.md''', '''dataset_infos.json'''],
['''empty:README.md''', '''dataset_infos.json'''],
['''dataset_infos.json'''],
['''full:README.md'''],
] , )
def lowerCAmelCase_ ( lowercase_ : List[str] , lowercase_ : str ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : Optional[int] = tmp_path_factory.mktemp('''dset_infos_dir''' )
if "full:README.md" in files:
with open(dataset_infos_dir / '''README.md''' , '''w''' ) as f:
f.write('''---\ndataset_info:\n dataset_size: 42\n---''' )
if "empty:README.md" in files:
with open(dataset_infos_dir / '''README.md''' , '''w''' ) as f:
f.write('''''' )
# we want to support dataset_infos.json for backward compatibility
if "dataset_infos.json" in files:
with open(dataset_infos_dir / '''dataset_infos.json''' , '''w''' ) as f:
f.write('''{"default": {"dataset_size": 42}}''' )
__SCREAMING_SNAKE_CASE : Optional[Any] = DatasetInfosDict.from_directory(lowercase_ )
assert dataset_infos
assert dataset_infos["default"].dataset_size == 42
@pytest.mark.parametrize(
'''dataset_info''' , [
DatasetInfo(),
DatasetInfo(
description='''foo''' , features=Features({'''a''': Value('''int32''' )} ) , builder_name='''builder''' , config_name='''config''' , version='''1.0.0''' , splits=[{'''name''': '''train'''}] , download_size=42 , ),
] , )
def lowerCAmelCase_ ( lowercase_ : Any , lowercase_ : DatasetInfo ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : List[str] = str(lowercase_ )
dataset_info.write_to_directory(lowercase_ )
__SCREAMING_SNAKE_CASE : Dict = DatasetInfo.from_directory(lowercase_ )
assert dataset_info == reloaded
assert os.path.exists(os.path.join(lowercase_ , '''dataset_info.json''' ) )
def lowerCAmelCase_ ( ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : Tuple = DatasetInfo(
description='''foo''' , citation='''bar''' , homepage='''https://foo.bar''' , license='''CC0''' , features=Features({'''a''': Value('''int32''' )} ) , post_processed={} , supervised_keys=() , task_templates=[] , builder_name='''builder''' , config_name='''config''' , version='''1.0.0''' , splits=[{'''name''': '''train''', '''num_examples''': 42}] , download_checksums={} , download_size=1337 , post_processing_size=442 , dataset_size=1234 , size_in_bytes=1337 + 442 + 1234 , )
__SCREAMING_SNAKE_CASE : Optional[int] = dataset_info._to_yaml_dict()
assert sorted(lowercase_ ) == sorted(DatasetInfo._INCLUDED_INFO_IN_YAML )
for key in DatasetInfo._INCLUDED_INFO_IN_YAML:
assert key in dataset_info_yaml_dict
assert isinstance(dataset_info_yaml_dict[key] , (list, dict, int, str) )
__SCREAMING_SNAKE_CASE : int = yaml.safe_dump(lowercase_ )
__SCREAMING_SNAKE_CASE : Union[str, Any] = yaml.safe_load(lowercase_ )
assert dataset_info_yaml_dict == reloaded
def lowerCAmelCase_ ( ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : Union[str, Any] = DatasetInfo()
__SCREAMING_SNAKE_CASE : Union[str, Any] = dataset_info._to_yaml_dict()
assert dataset_info_yaml_dict == {}
@pytest.mark.parametrize(
'''dataset_infos_dict''' , [
DatasetInfosDict(),
DatasetInfosDict({'''default''': DatasetInfo()} ),
DatasetInfosDict({'''my_config_name''': DatasetInfo()} ),
DatasetInfosDict(
{
'''default''': DatasetInfo(
description='''foo''' , features=Features({'''a''': Value('''int32''' )} ) , builder_name='''builder''' , config_name='''config''' , version='''1.0.0''' , splits=[{'''name''': '''train'''}] , download_size=42 , )
} ),
DatasetInfosDict(
{
'''v1''': DatasetInfo(dataset_size=42 ),
'''v2''': DatasetInfo(dataset_size=1337 ),
} ),
] , )
def lowerCAmelCase_ ( lowercase_ : List[Any] , lowercase_ : DatasetInfosDict ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : Optional[int] = str(lowercase_ )
dataset_infos_dict.write_to_directory(lowercase_ )
__SCREAMING_SNAKE_CASE : Optional[Any] = DatasetInfosDict.from_directory(lowercase_ )
# the config_name of the dataset_infos_dict take over the attribute
for config_name, dataset_info in dataset_infos_dict.items():
__SCREAMING_SNAKE_CASE : Optional[int] = config_name
# the yaml representation doesn't include fields like description or citation
# so we just test that we can recover what we can from the yaml
__SCREAMING_SNAKE_CASE : Tuple = DatasetInfo._from_yaml_dict(dataset_info._to_yaml_dict() )
assert dataset_infos_dict == reloaded
if dataset_infos_dict:
assert os.path.exists(os.path.join(lowercase_ , '''README.md''' ) )
| 674 | 0 |
import random
import unittest
import numpy as np
import transformers
from transformers import is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax
if is_flax_available():
import os
import jax.numpy as jnp
from jax import jit
from transformers import AutoTokenizer, FlaxAutoModelForCausalLM
from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_pytorch_model
a__ = '''0.12''' # assumed parallelism: 8
if is_torch_available():
import torch
def A__ (snake_case : List[Any] , snake_case : Union[str, Any] , snake_case : Optional[int]=None ) -> Any:
if rng is None:
__UpperCamelCase : Dict = random.Random()
__UpperCamelCase : List[str] = 1
for dim in shape:
total_dims *= dim
__UpperCamelCase : Tuple = []
for _ in range(lowercase_ ):
values.append(rng.randint(0 , vocab_size - 1 ) )
__UpperCamelCase : Union[str, Any] = np.array(lowercase_ , dtype=jnp.intaa ).reshape(lowercase_ )
return output
def A__ (snake_case : Optional[Any] , snake_case : Dict=None ) -> int:
__UpperCamelCase : Optional[int] = ids_tensor(lowercase_ , vocab_size=2 , rng=lowercase_ )
# make sure that at least one token is attended to for each batch
__UpperCamelCase : Tuple = 1
return attn_mask
@require_flax
class SCREAMING_SNAKE_CASE_ :
"""simple docstring"""
__magic_name__ : str = None
__magic_name__ : List[str] = ()
def lowerCamelCase__ ( self : Optional[int] ) -> Union[str, Any]:
"""simple docstring"""
__UpperCamelCase : Optional[int] = self.model_tester.prepare_config_and_inputs_for_common()
# cut to half length & take max batch_size 3
__UpperCamelCase : int = 2
__UpperCamelCase : Any = inputs['''input_ids'''].shape[-1] // 2
__UpperCamelCase : Optional[Any] = inputs['''input_ids'''][:max_batch_size, :sequence_length]
__UpperCamelCase : List[str] = jnp.ones_like(_lowerCamelCase )
__UpperCamelCase : Optional[Any] = attention_mask[:max_batch_size, :sequence_length]
# generate max 5 tokens
__UpperCamelCase : Union[str, Any] = input_ids.shape[-1] + 5
if config.eos_token_id is not None and config.pad_token_id is None:
# hack to allow generate for models such as GPT2 as is done in `generate()`
__UpperCamelCase : Optional[int] = config.eos_token_id
return config, input_ids, attention_mask, max_length
@is_pt_flax_cross_test
def lowerCamelCase__ ( self : int ) -> List[Any]:
"""simple docstring"""
__UpperCamelCase : List[Any] = self._get_input_ids_and_config()
__UpperCamelCase : Dict = False
__UpperCamelCase : Optional[Any] = max_length
__UpperCamelCase : List[Any] = 0
for model_class in self.all_generative_model_classes:
__UpperCamelCase : Optional[Any] = model_class(_lowerCamelCase )
__UpperCamelCase : List[Any] = model_class.__name__[4:] # Skip the "Flax" at the beginning
__UpperCamelCase : Optional[int] = getattr(_lowerCamelCase , _lowerCamelCase )
__UpperCamelCase : Optional[Any] = pt_model_class(_lowerCamelCase ).eval()
__UpperCamelCase : Any = load_flax_weights_in_pytorch_model(_lowerCamelCase , flax_model.params )
__UpperCamelCase : Any = flax_model.generate(_lowerCamelCase ).sequences
__UpperCamelCase : str = pt_model.generate(torch.tensor(_lowerCamelCase , dtype=torch.long ) )
if flax_generation_outputs.shape[-1] > pt_generation_outputs.shape[-1]:
__UpperCamelCase : Optional[Any] = flax_generation_outputs[:, : pt_generation_outputs.shape[-1]]
self.assertListEqual(pt_generation_outputs.numpy().tolist() , flax_generation_outputs.tolist() )
def lowerCamelCase__ ( self : List[Any] ) -> Optional[Any]:
"""simple docstring"""
__UpperCamelCase : int = self._get_input_ids_and_config()
__UpperCamelCase : List[str] = False
__UpperCamelCase : Dict = max_length
for model_class in self.all_generative_model_classes:
__UpperCamelCase : Dict = model_class(_lowerCamelCase )
__UpperCamelCase : str = model.generate(_lowerCamelCase ).sequences
self.assertEqual(generation_outputs.shape[-1] , _lowerCamelCase )
__UpperCamelCase : str = jit(model.generate )
__UpperCamelCase : List[str] = jit_generate(_lowerCamelCase ).sequences
self.assertListEqual(generation_outputs.tolist() , jit_generation_outputs.tolist() )
def lowerCamelCase__ ( self : List[Any] ) -> Union[str, Any]:
"""simple docstring"""
__UpperCamelCase : Dict = self._get_input_ids_and_config()
__UpperCamelCase : Optional[Any] = True
__UpperCamelCase : List[str] = max_length
for model_class in self.all_generative_model_classes:
__UpperCamelCase : List[Any] = model_class(_lowerCamelCase )
__UpperCamelCase : List[Any] = model.generate(_lowerCamelCase ).sequences
self.assertEqual(generation_outputs.shape[-1] , _lowerCamelCase )
__UpperCamelCase : Any = jit(model.generate )
__UpperCamelCase : Any = jit_generate(_lowerCamelCase ).sequences
self.assertListEqual(generation_outputs.tolist() , jit_generation_outputs.tolist() )
def lowerCamelCase__ ( self : Union[str, Any] ) -> Optional[Any]:
"""simple docstring"""
__UpperCamelCase : Optional[Any] = self._get_input_ids_and_config()
__UpperCamelCase : Dict = False
__UpperCamelCase : str = max_length
__UpperCamelCase : List[Any] = 2
for model_class in self.all_generative_model_classes:
__UpperCamelCase : List[str] = model_class(_lowerCamelCase )
__UpperCamelCase : str = model.generate(_lowerCamelCase ).sequences
self.assertEqual(generation_outputs.shape[-1] , _lowerCamelCase )
__UpperCamelCase : Any = jit(model.generate )
__UpperCamelCase : Optional[int] = jit_generate(_lowerCamelCase ).sequences
self.assertListEqual(generation_outputs.tolist() , jit_generation_outputs.tolist() )
def lowerCamelCase__ ( self : List[Any] ) -> int:
"""simple docstring"""
__UpperCamelCase : Optional[int] = self._get_input_ids_and_config()
__UpperCamelCase : List[Any] = False
__UpperCamelCase : Optional[Any] = max_length
__UpperCamelCase : Dict = 2
__UpperCamelCase : int = 2
for model_class in self.all_generative_model_classes:
__UpperCamelCase : Any = model_class(_lowerCamelCase )
__UpperCamelCase : List[Any] = model.generate(_lowerCamelCase ).sequences
self.assertEqual(generation_outputs.shape[0] , input_ids.shape[0] * config.num_return_sequences )
def lowerCamelCase__ ( self : List[Any] ) -> str:
"""simple docstring"""
__UpperCamelCase : str = self._get_input_ids_and_config()
__UpperCamelCase : Any = True
__UpperCamelCase : int = max_length
__UpperCamelCase : Dict = 0.8
__UpperCamelCase : int = 10
__UpperCamelCase : List[str] = 0.3
__UpperCamelCase : Optional[Any] = 1
__UpperCamelCase : Optional[Any] = 8
__UpperCamelCase : Optional[int] = 9
for model_class in self.all_generative_model_classes:
__UpperCamelCase : List[str] = model_class(_lowerCamelCase )
__UpperCamelCase : Dict = model.generate(_lowerCamelCase ).sequences
self.assertEqual(generation_outputs.shape[-1] , _lowerCamelCase )
__UpperCamelCase : Optional[int] = jit(model.generate )
__UpperCamelCase : str = jit_generate(_lowerCamelCase ).sequences
self.assertListEqual(generation_outputs.tolist() , jit_generation_outputs.tolist() )
def lowerCamelCase__ ( self : Union[str, Any] ) -> int:
"""simple docstring"""
__UpperCamelCase : Optional[int] = self._get_input_ids_and_config()
__UpperCamelCase : Tuple = max_length
__UpperCamelCase : Union[str, Any] = 1
__UpperCamelCase : str = 8
__UpperCamelCase : Optional[int] = 9
for model_class in self.all_generative_model_classes:
__UpperCamelCase : Optional[int] = model_class(_lowerCamelCase )
__UpperCamelCase : List[str] = model.generate(_lowerCamelCase ).sequences
self.assertEqual(generation_outputs.shape[-1] , _lowerCamelCase )
__UpperCamelCase : List[str] = jit(model.generate )
__UpperCamelCase : str = jit_generate(_lowerCamelCase ).sequences
self.assertListEqual(generation_outputs.tolist() , jit_generation_outputs.tolist() )
def lowerCamelCase__ ( self : Dict ) -> Optional[int]:
"""simple docstring"""
__UpperCamelCase : List[Any] = self._get_input_ids_and_config()
__UpperCamelCase : List[str] = max_length
__UpperCamelCase : List[Any] = 2
__UpperCamelCase : Optional[Any] = 1
__UpperCamelCase : int = 8
__UpperCamelCase : int = 9
for model_class in self.all_generative_model_classes:
__UpperCamelCase : Any = model_class(_lowerCamelCase )
__UpperCamelCase : List[str] = model.generate(_lowerCamelCase ).sequences
self.assertEqual(generation_outputs.shape[-1] , _lowerCamelCase )
__UpperCamelCase : Union[str, Any] = jit(model.generate )
__UpperCamelCase : Optional[int] = jit_generate(_lowerCamelCase ).sequences
self.assertListEqual(generation_outputs.tolist() , jit_generation_outputs.tolist() )
def lowerCamelCase__ ( self : int ) -> Optional[Any]:
"""simple docstring"""
__UpperCamelCase : Any = self._get_input_ids_and_config()
# pad attention mask on the left
__UpperCamelCase : Optional[int] = attention_mask.at[(0, 0)].set(0 )
__UpperCamelCase : Tuple = False
__UpperCamelCase : Optional[Any] = max_length
for model_class in self.all_generative_model_classes:
__UpperCamelCase : List[str] = model_class(_lowerCamelCase )
__UpperCamelCase : Optional[Any] = model.generate(_lowerCamelCase , attention_mask=_lowerCamelCase ).sequences
self.assertEqual(generation_outputs.shape[-1] , _lowerCamelCase )
__UpperCamelCase : Optional[int] = jit(model.generate )
__UpperCamelCase : Optional[Any] = jit_generate(_lowerCamelCase , attention_mask=_lowerCamelCase ).sequences
self.assertListEqual(generation_outputs.tolist() , jit_generation_outputs.tolist() )
def lowerCamelCase__ ( self : Dict ) -> Dict:
"""simple docstring"""
__UpperCamelCase : List[Any] = self._get_input_ids_and_config()
# pad attention mask on the left
__UpperCamelCase : List[Any] = attention_mask.at[(0, 0)].set(0 )
__UpperCamelCase : List[str] = True
__UpperCamelCase : List[str] = max_length
for model_class in self.all_generative_model_classes:
__UpperCamelCase : List[Any] = model_class(_lowerCamelCase )
__UpperCamelCase : Optional[Any] = model.generate(_lowerCamelCase , attention_mask=_lowerCamelCase ).sequences
self.assertEqual(generation_outputs.shape[-1] , _lowerCamelCase )
__UpperCamelCase : int = jit(model.generate )
__UpperCamelCase : List[str] = jit_generate(_lowerCamelCase , attention_mask=_lowerCamelCase ).sequences
self.assertListEqual(generation_outputs.tolist() , jit_generation_outputs.tolist() )
def lowerCamelCase__ ( self : int ) -> Union[str, Any]:
"""simple docstring"""
__UpperCamelCase : Tuple = self._get_input_ids_and_config()
# pad attention mask on the left
__UpperCamelCase : Dict = attention_mask.at[(0, 0)].set(0 )
__UpperCamelCase : Tuple = 2
__UpperCamelCase : Tuple = max_length
for model_class in self.all_generative_model_classes:
__UpperCamelCase : List[Any] = model_class(_lowerCamelCase )
__UpperCamelCase : int = model.generate(_lowerCamelCase , attention_mask=_lowerCamelCase ).sequences
self.assertEqual(generation_outputs.shape[-1] , _lowerCamelCase )
__UpperCamelCase : int = jit(model.generate )
__UpperCamelCase : Optional[Any] = jit_generate(_lowerCamelCase , attention_mask=_lowerCamelCase ).sequences
self.assertListEqual(generation_outputs.tolist() , jit_generation_outputs.tolist() )
@require_flax
class SCREAMING_SNAKE_CASE_ ( unittest.TestCase ):
"""simple docstring"""
def lowerCamelCase__ ( self : str ) -> Optional[int]:
"""simple docstring"""
__UpperCamelCase : Any = AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-bert""" )
__UpperCamelCase : Dict = FlaxAutoModelForCausalLM.from_pretrained("""hf-internal-testing/tiny-bert-flax-only""" )
__UpperCamelCase : Dict = '''Hello world'''
__UpperCamelCase : int = tokenizer(_lowerCamelCase , return_tensors="""np""" ).input_ids
# typos are quickly detected (the correct argument is `do_sample`)
with self.assertRaisesRegex(_lowerCamelCase , """do_samples""" ):
model.generate(_lowerCamelCase , do_samples=_lowerCamelCase )
# arbitrary arguments that will not be used anywhere are also not accepted
with self.assertRaisesRegex(_lowerCamelCase , """foo""" ):
__UpperCamelCase : int = {'''foo''': '''bar'''}
model.generate(_lowerCamelCase , **_lowerCamelCase )
| 279 |
"""simple docstring"""
from typing import Optional, Union
import torch
from torch import nn
from ...configuration_utils import ConfigMixin, register_to_config
from ...models.modeling_utils import ModelMixin
class snake_case ( __UpperCAmelCase , __UpperCAmelCase ):
@register_to_config
def __init__( self :List[str] , _lowerCamelCase :int = 7_6_8 , ):
super().__init__()
__SCREAMING_SNAKE_CASE : Union[str, Any] = nn.Parameter(torch.zeros(1 , _lowerCamelCase ) )
__SCREAMING_SNAKE_CASE : List[Any] = nn.Parameter(torch.ones(1 , _lowerCamelCase ) )
def SCREAMING_SNAKE_CASE_ ( self :Any , _lowerCamelCase :Optional[Union[str, torch.device]] = None , _lowerCamelCase :Optional[torch.dtype] = None , ):
__SCREAMING_SNAKE_CASE : Optional[int] = nn.Parameter(self.mean.to(_lowerCamelCase ).to(_lowerCamelCase ) )
__SCREAMING_SNAKE_CASE : Tuple = nn.Parameter(self.std.to(_lowerCamelCase ).to(_lowerCamelCase ) )
return self
def SCREAMING_SNAKE_CASE_ ( self :int , _lowerCamelCase :Tuple ):
__SCREAMING_SNAKE_CASE : Tuple = (embeds - self.mean) * 1.0 / self.std
return embeds
def SCREAMING_SNAKE_CASE_ ( self :Optional[Any] , _lowerCamelCase :Optional[int] ):
__SCREAMING_SNAKE_CASE : List[Any] = (embeds * self.std) + self.mean
return embeds
| 674 | 0 |
import os
import sys
import unittest
__lowerCAmelCase = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, 'utils'))
import check_dummies # noqa: E402
from check_dummies import create_dummy_files, create_dummy_object, find_backend, read_init # noqa: E402
# Align TRANSFORMERS_PATH in check_dummies with the current path
__lowerCAmelCase = os.path.join(git_repo_path, 'src', 'transformers')
__lowerCAmelCase = '\n{0} = None\n'
__lowerCAmelCase = '\nclass {0}(metaclass=DummyObject):\n _backends = {1}\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, {1})\n'
__lowerCAmelCase = '\ndef {0}(*args, **kwargs):\n requires_backends({0}, {1})\n'
class lowerCamelCase ( unittest.TestCase ):
def snake_case__ ( self :List[Any] ) -> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE = find_backend(''' _import_structure["models.albert"].append("AlbertTokenizerFast")''' )
self.assertIsNone(_lowerCamelCase )
SCREAMING_SNAKE_CASE = find_backend(''' if not is_tokenizers_available():''' )
self.assertEqual(_lowerCamelCase , '''tokenizers''' )
SCREAMING_SNAKE_CASE = find_backend(''' if not is_tensorflow_text_available():''' )
self.assertEqual(_lowerCamelCase , '''tensorflow_text''' )
SCREAMING_SNAKE_CASE = find_backend(''' if not (is_sentencepiece_available() and is_tokenizers_available()):''' )
self.assertEqual(_lowerCamelCase , '''sentencepiece_and_tokenizers''' )
SCREAMING_SNAKE_CASE = find_backend(
''' if not (is_sentencepiece_available() and is_tensorflow_text_available()):''' )
self.assertEqual(_lowerCamelCase , '''sentencepiece_and_tensorflow_text''' )
SCREAMING_SNAKE_CASE = find_backend(
''' if not (is_sentencepiece_available() and is_tokenizers_available() and is_vision_available()):''' )
self.assertEqual(_lowerCamelCase , '''sentencepiece_and_tokenizers_and_vision''' )
def snake_case__ ( self :int ) -> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE = read_init()
# We don't assert on the exact list of keys to allow for smooth grow of backend-specific objects
self.assertIn('''torch''' , _lowerCamelCase )
self.assertIn('''tensorflow_text''' , _lowerCamelCase )
self.assertIn('''sentencepiece_and_tokenizers''' , _lowerCamelCase )
# Likewise, we can't assert on the exact content of a key
self.assertIn('''BertModel''' , objects['''torch'''] )
self.assertIn('''TFBertModel''' , objects['''tf'''] )
self.assertIn('''FlaxBertModel''' , objects['''flax'''] )
self.assertIn('''BertModel''' , objects['''torch'''] )
self.assertIn('''TFBertTokenizer''' , objects['''tensorflow_text'''] )
self.assertIn('''convert_slow_tokenizer''' , objects['''sentencepiece_and_tokenizers'''] )
def snake_case__ ( self :Any ) -> Union[str, Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE = create_dummy_object('''CONSTANT''' , '''\'torch\'''' )
self.assertEqual(_lowerCamelCase , '''\nCONSTANT = None\n''' )
SCREAMING_SNAKE_CASE = create_dummy_object('''function''' , '''\'torch\'''' )
self.assertEqual(
_lowerCamelCase , '''\ndef function(*args, **kwargs):\n requires_backends(function, \'torch\')\n''' )
SCREAMING_SNAKE_CASE = '''
class FakeClass(metaclass=DummyObject):
_backends = \'torch\'
def __init__(self, *args, **kwargs):
requires_backends(self, \'torch\')
'''
SCREAMING_SNAKE_CASE = create_dummy_object('''FakeClass''' , '''\'torch\'''' )
self.assertEqual(_lowerCamelCase , _lowerCamelCase )
def snake_case__ ( self :Union[str, Any] ) -> Union[str, Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE = '''# This file is autogenerated by the command `make fix-copies`, do not edit.
from ..utils import DummyObject, requires_backends
CONSTANT = None
def function(*args, **kwargs):
requires_backends(function, ["torch"])
class FakeClass(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
'''
SCREAMING_SNAKE_CASE = create_dummy_files({'''torch''': ['''CONSTANT''', '''function''', '''FakeClass''']} )
self.assertEqual(dummy_files['''torch'''] , _lowerCamelCase ) | 201 |
"""simple docstring"""
import argparse
import torch
from transformers import BertConfig, BertForPreTraining, load_tf_weights_in_bert
from transformers.utils import logging
logging.set_verbosity_info()
def lowerCAmelCase_ ( lowercase_ : Dict , lowercase_ : int , lowercase_ : Any ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : Tuple = BertConfig.from_json_file(lowercase_ )
print(F'''Building PyTorch model from configuration: {config}''' )
__SCREAMING_SNAKE_CASE : Optional[int] = BertForPreTraining(lowercase_ )
# Load weights from tf checkpoint
load_tf_weights_in_bert(lowercase_ , lowercase_ , lowercase_ )
# Save pytorch-model
print(F'''Save PyTorch model to {pytorch_dump_path}''' )
torch.save(model.state_dict() , lowercase_ )
if __name__ == "__main__":
_lowerCamelCase = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--tf_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
)
parser.add_argument(
'''--bert_config_file''',
default=None,
type=str,
required=True,
help=(
'''The config json file corresponding to the pre-trained BERT model. \n'''
'''This specifies the model architecture.'''
),
)
parser.add_argument(
'''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
)
_lowerCamelCase = parser.parse_args()
convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)
| 674 | 0 |
"""simple docstring"""
from typing import List, Optional, TypeVar
from .arrow_dataset import Dataset, _concatenate_map_style_datasets, _interleave_map_style_datasets
from .dataset_dict import DatasetDict, IterableDatasetDict
from .info import DatasetInfo
from .iterable_dataset import IterableDataset, _concatenate_iterable_datasets, _interleave_iterable_datasets
from .splits import NamedSplit
from .utils import logging
from .utils.py_utils import Literal
a : List[str] = logging.get_logger(__name__)
a : Optional[Any] = TypeVar("""DatasetType""", Dataset, IterableDataset)
def lowercase__(A , A = None , A = None , A = None , A = None , A = "first_exhausted" , ) ->List[str]:
"""simple docstring"""
from .arrow_dataset import Dataset
from .iterable_dataset import IterableDataset
if not datasets:
raise ValueError("Unable to interleave an empty list of datasets." )
for i, dataset in enumerate(lowercase_ ):
if not isinstance(lowercase_ , (Dataset, IterableDataset) ):
if isinstance(lowercase_ , (DatasetDict, IterableDatasetDict) ):
if not dataset:
raise ValueError(
f'''Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} '''
"is an empty dataset dictionary." )
raise ValueError(
f'''Dataset at position {i} has at least one split: {list(lowercase_ )}\n'''
f'''Please pick one to interleave with the other datasets, for example: dataset[\'{next(iter(lowercase_ ) )}\']''' )
raise ValueError(
f'''Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} is a {type(lowercase_ ).__name__}.''' )
if i == 0:
lowercase__ : List[str]= (
(Dataset, IterableDataset) if isinstance(lowercase_ , lowercase_ ) else (IterableDataset, Dataset)
)
elif not isinstance(lowercase_ , lowercase_ ):
raise ValueError(
f'''Unable to interleave a {dataset_type.__name__} (at position 0) with a {other_type.__name__} (at position {i}). Expected a list of Dataset objects or a list of IterableDataset objects.''' )
if stopping_strategy not in ["first_exhausted", "all_exhausted"]:
raise ValueError(f'''{stopping_strategy} is not supported. Please enter a valid stopping_strategy.''' )
if dataset_type is Dataset:
return _interleave_map_style_datasets(
lowercase_ , lowercase_ , lowercase_ , info=lowercase_ , split=lowercase_ , stopping_strategy=lowercase_ )
else:
return _interleave_iterable_datasets(
lowercase_ , lowercase_ , lowercase_ , info=lowercase_ , split=lowercase_ , stopping_strategy=lowercase_ )
def lowercase__(A , A = None , A = None , A = 0 , ) ->List[str]:
"""simple docstring"""
if not dsets:
raise ValueError("Unable to concatenate an empty list of datasets." )
for i, dataset in enumerate(lowercase_ ):
if not isinstance(lowercase_ , (Dataset, IterableDataset) ):
if isinstance(lowercase_ , (DatasetDict, IterableDatasetDict) ):
if not dataset:
raise ValueError(
f'''Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} '''
"is an empty dataset dictionary." )
raise ValueError(
f'''Dataset at position {i} has at least one split: {list(lowercase_ )}\n'''
f'''Please pick one to interleave with the other datasets, for example: dataset[\'{next(iter(lowercase_ ) )}\']''' )
raise ValueError(
f'''Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} is a {type(lowercase_ ).__name__}.''' )
if i == 0:
lowercase__ : Optional[Any]= (
(Dataset, IterableDataset) if isinstance(lowercase_ , lowercase_ ) else (IterableDataset, Dataset)
)
elif not isinstance(lowercase_ , lowercase_ ):
raise ValueError(
f'''Unable to interleave a {dataset_type.__name__} (at position 0) with a {other_type.__name__} (at position {i}). Expected a list of Dataset objects or a list of IterableDataset objects.''' )
if dataset_type is Dataset:
return _concatenate_map_style_datasets(lowercase_ , info=lowercase_ , split=lowercase_ , axis=lowercase_ )
else:
return _concatenate_iterable_datasets(lowercase_ , info=lowercase_ , split=lowercase_ , axis=lowercase_ )
| 218 |
"""simple docstring"""
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import numpy as np
import torch
from ..models.clipseg import CLIPSegForImageSegmentation
from ..utils import is_vision_available, requires_backends
from .base import PipelineTool
if is_vision_available():
from PIL import Image
class snake_case ( __UpperCAmelCase ):
lowerCamelCase__ = (
'''This is a tool that creates a segmentation mask of an image according to a label. It cannot create an image.'''
'''It takes two arguments named `image` which should be the original image, and `label` which should be a text '''
'''describing the elements what should be identified in the segmentation mask. The tool returns the mask.'''
)
lowerCamelCase__ = '''CIDAS/clipseg-rd64-refined'''
lowerCamelCase__ = '''image_segmenter'''
lowerCamelCase__ = CLIPSegForImageSegmentation
lowerCamelCase__ = ['''image''', '''text''']
lowerCamelCase__ = ['''image''']
def __init__( self :Dict , *_lowerCamelCase :Union[str, Any] , **_lowerCamelCase :Tuple ):
requires_backends(self , ['''vision'''] )
super().__init__(*_lowerCamelCase , **_lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :Tuple , _lowerCamelCase :"Image" , _lowerCamelCase :str ):
return self.pre_processor(text=[label] , images=[image] , padding=_lowerCamelCase , return_tensors='''pt''' )
def SCREAMING_SNAKE_CASE_ ( self :Optional[int] , _lowerCamelCase :Optional[int] ):
with torch.no_grad():
__SCREAMING_SNAKE_CASE : List[Any] = self.model(**_lowerCamelCase ).logits
return logits
def SCREAMING_SNAKE_CASE_ ( self :Optional[Any] , _lowerCamelCase :Tuple ):
__SCREAMING_SNAKE_CASE : Optional[int] = outputs.cpu().detach().numpy()
__SCREAMING_SNAKE_CASE : str = 0
__SCREAMING_SNAKE_CASE : str = 1
return Image.fromarray((array * 2_5_5).astype(np.uinta ) )
| 674 | 0 |
import os
import unittest
from transformers.models.transfo_xl.tokenization_transfo_xl import VOCAB_FILES_NAMES, TransfoXLTokenizer
from ...test_tokenization_common import TokenizerTesterMixin
class UpperCAmelCase ( __UpperCAmelCase , unittest.TestCase ):
a: Union[str, Any] = TransfoXLTokenizer
a: Union[str, Any] = False
a: Optional[int] = False
def _A ( self: List[str] ):
super().setUp()
_a = [
'''<unk>''',
'''[CLS]''',
'''[SEP]''',
'''want''',
'''unwanted''',
'''wa''',
'''un''',
'''running''',
''',''',
'''low''',
'''l''',
]
_a = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['''vocab_file'''] )
with open(self.vocab_file , '''w''' , encoding='''utf-8''' ) as vocab_writer:
vocab_writer.write(''''''.join([x + '''\n''' for x in vocab_tokens] ) )
def _A ( self: Tuple , **__UpperCamelCase: Dict ):
_a = True
return TransfoXLTokenizer.from_pretrained(self.tmpdirname , **_lowerCamelCase )
def _A ( self: Optional[int] , __UpperCamelCase: int ):
_a = '''<unk> UNwanted , running'''
_a = '''<unk> unwanted, running'''
return input_text, output_text
def _A ( self: Union[str, Any] ):
_a = TransfoXLTokenizer(vocab_file=self.vocab_file , lower_case=_lowerCamelCase )
_a = tokenizer.tokenize('''<unk> UNwanted , running''' )
self.assertListEqual(_lowerCamelCase , ['''<unk>''', '''unwanted''', ''',''', '''running'''] )
self.assertListEqual(tokenizer.convert_tokens_to_ids(_lowerCamelCase ) , [0, 4, 8, 7] )
def _A ( self: Optional[Any] ):
_a = TransfoXLTokenizer(lower_case=_lowerCamelCase )
self.assertListEqual(
tokenizer.tokenize(''' \tHeLLo ! how \n Are yoU ? ''' ) , ['''hello''', '''!''', '''how''', '''are''', '''you''', '''?'''] )
def _A ( self: List[str] ):
_a = TransfoXLTokenizer(lower_case=_lowerCamelCase )
self.assertListEqual(
tokenizer.tokenize(''' \tHeLLo ! how \n Are yoU ? ''' ) , ['''HeLLo''', '''!''', '''how''', '''Are''', '''yoU''', '''?'''] )
def _A ( self: List[Any] ):
_a = TransfoXLTokenizer(lower_case=_lowerCamelCase )
_a = '''Hello (bracket) and side-scrolled [and] Henry\'s $5,000 with 3.34 m. What\'s up!?'''
_a = [
'''Hello''',
'''(''',
'''bracket''',
''')''',
'''and''',
'''side''',
'''@-@''',
'''scrolled''',
'''[''',
'''and''',
''']''',
'''Henry''',
'''\'s''',
'''$''',
'''5''',
'''@,@''',
'''000''',
'''with''',
'''3''',
'''@.@''',
'''34''',
'''m''',
'''.''',
'''What''',
'''\'s''',
'''up''',
'''!''',
'''?''',
]
self.assertListEqual(tokenizer.tokenize(_lowerCamelCase ) , _lowerCamelCase )
self.assertEqual(tokenizer.convert_tokens_to_string(_lowerCamelCase ) , _lowerCamelCase )
def _A ( self: Optional[int] ):
_a = self.get_tokenizer()
_a = len(_lowerCamelCase )
tokenizer.add_tokens(['''new1''', '''new2'''] )
tokenizer.move_added_token('''new1''' , 1 )
# Check that moved token is not copied (duplicate)
self.assertEqual(len(_lowerCamelCase ) , original_len + 2 )
# Check that token is moved to specified id
self.assertEqual(tokenizer.encode('''new1''' ) , [1] )
self.assertEqual(tokenizer.decode([1] ) , '''new1''' )
| 487 |
"""simple docstring"""
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
from transformers import BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES, BertTokenizer
from transformers.testing_utils import require_tokenizers, require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available
if is_vision_available():
from PIL import Image
from transformers import VisionTextDualEncoderProcessor, ViTImageProcessor
@require_tokenizers
@require_vision
class snake_case ( unittest.TestCase ):
def SCREAMING_SNAKE_CASE_ ( self :Union[str, Any] ):
__SCREAMING_SNAKE_CASE : Union[str, Any] = tempfile.mkdtemp()
# fmt: off
__SCREAMING_SNAKE_CASE : Optional[int] = ['''[UNK]''', '''[CLS]''', '''[SEP]''', '''[PAD]''', '''[MASK]''', '''want''', '''##want''', '''##ed''', '''wa''', '''un''', '''runn''', '''##ing''', ''',''', '''low''', '''lowest''']
# fmt: on
__SCREAMING_SNAKE_CASE : List[Any] = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['''vocab_file'''] )
with open(self.vocab_file , '''w''' , encoding='''utf-8''' ) as vocab_writer:
vocab_writer.write(''''''.join([x + '''\n''' for x in vocab_tokens] ) )
__SCREAMING_SNAKE_CASE : Optional[int] = {
'''do_resize''': True,
'''size''': {'''height''': 1_8, '''width''': 1_8},
'''do_normalize''': True,
'''image_mean''': [0.5, 0.5, 0.5],
'''image_std''': [0.5, 0.5, 0.5],
}
__SCREAMING_SNAKE_CASE : List[Any] = os.path.join(self.tmpdirname , _lowerCamelCase )
with open(self.image_processor_file , '''w''' , encoding='''utf-8''' ) as fp:
json.dump(_lowerCamelCase , _lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :Union[str, Any] , **_lowerCamelCase :List[str] ):
return BertTokenizer.from_pretrained(self.tmpdirname , **_lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :List[str] , **_lowerCamelCase :Optional[int] ):
return ViTImageProcessor.from_pretrained(self.tmpdirname , **_lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :Optional[Any] ):
shutil.rmtree(self.tmpdirname )
def SCREAMING_SNAKE_CASE_ ( self :Dict ):
__SCREAMING_SNAKE_CASE : Union[str, Any] = [np.random.randint(2_5_5 , size=(3, 3_0, 4_0_0) , dtype=np.uinta )]
__SCREAMING_SNAKE_CASE : Tuple = [Image.fromarray(np.moveaxis(_lowerCamelCase , 0 , -1 ) ) for x in image_inputs]
return image_inputs
def SCREAMING_SNAKE_CASE_ ( self :int ):
__SCREAMING_SNAKE_CASE : Optional[int] = self.get_tokenizer()
__SCREAMING_SNAKE_CASE : Optional[Any] = self.get_image_processor()
__SCREAMING_SNAKE_CASE : Optional[Any] = VisionTextDualEncoderProcessor(tokenizer=_lowerCamelCase , image_processor=_lowerCamelCase )
processor.save_pretrained(self.tmpdirname )
__SCREAMING_SNAKE_CASE : int = VisionTextDualEncoderProcessor.from_pretrained(self.tmpdirname )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer.get_vocab() )
self.assertIsInstance(processor.tokenizer , (BertTokenizer, BertTokenizerFast) )
self.assertEqual(processor.image_processor.to_json_string() , image_processor.to_json_string() )
self.assertIsInstance(processor.image_processor , _lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :List[Any] ):
__SCREAMING_SNAKE_CASE : str = VisionTextDualEncoderProcessor(
tokenizer=self.get_tokenizer() , image_processor=self.get_image_processor() )
processor.save_pretrained(self.tmpdirname )
__SCREAMING_SNAKE_CASE : Optional[int] = self.get_tokenizer(bos_token='''(BOS)''' , eos_token='''(EOS)''' )
__SCREAMING_SNAKE_CASE : List[str] = self.get_image_processor(do_normalize=_lowerCamelCase , padding_value=1.0 )
__SCREAMING_SNAKE_CASE : int = VisionTextDualEncoderProcessor.from_pretrained(
self.tmpdirname , bos_token='''(BOS)''' , eos_token='''(EOS)''' , do_normalize=_lowerCamelCase , padding_value=1.0 )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer_add_kwargs.get_vocab() )
self.assertIsInstance(processor.tokenizer , (BertTokenizer, BertTokenizerFast) )
self.assertEqual(processor.image_processor.to_json_string() , image_processor_add_kwargs.to_json_string() )
self.assertIsInstance(processor.image_processor , _lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :int ):
__SCREAMING_SNAKE_CASE : Union[str, Any] = self.get_image_processor()
__SCREAMING_SNAKE_CASE : Dict = self.get_tokenizer()
__SCREAMING_SNAKE_CASE : Tuple = VisionTextDualEncoderProcessor(tokenizer=_lowerCamelCase , image_processor=_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Dict = self.prepare_image_inputs()
__SCREAMING_SNAKE_CASE : Optional[int] = image_processor(_lowerCamelCase , return_tensors='''np''' )
__SCREAMING_SNAKE_CASE : Tuple = processor(images=_lowerCamelCase , return_tensors='''np''' )
for key in input_feat_extract.keys():
self.assertAlmostEqual(input_feat_extract[key].sum() , input_processor[key].sum() , delta=1e-2 )
def SCREAMING_SNAKE_CASE_ ( self :Optional[int] ):
__SCREAMING_SNAKE_CASE : Any = self.get_image_processor()
__SCREAMING_SNAKE_CASE : Any = self.get_tokenizer()
__SCREAMING_SNAKE_CASE : Tuple = VisionTextDualEncoderProcessor(tokenizer=_lowerCamelCase , image_processor=_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Union[str, Any] = '''lower newer'''
__SCREAMING_SNAKE_CASE : Optional[int] = processor(text=_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Any = tokenizer(_lowerCamelCase )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] , encoded_processor[key] )
def SCREAMING_SNAKE_CASE_ ( self :str ):
__SCREAMING_SNAKE_CASE : str = self.get_image_processor()
__SCREAMING_SNAKE_CASE : int = self.get_tokenizer()
__SCREAMING_SNAKE_CASE : int = VisionTextDualEncoderProcessor(tokenizer=_lowerCamelCase , image_processor=_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Any = '''lower newer'''
__SCREAMING_SNAKE_CASE : int = self.prepare_image_inputs()
__SCREAMING_SNAKE_CASE : int = processor(text=_lowerCamelCase , images=_lowerCamelCase )
self.assertListEqual(list(inputs.keys() ) , ['''input_ids''', '''token_type_ids''', '''attention_mask''', '''pixel_values'''] )
# test if it raises when no input is passed
with self.assertRaises(_lowerCamelCase ):
processor()
def SCREAMING_SNAKE_CASE_ ( self :Any ):
__SCREAMING_SNAKE_CASE : Dict = self.get_image_processor()
__SCREAMING_SNAKE_CASE : List[Any] = self.get_tokenizer()
__SCREAMING_SNAKE_CASE : Union[str, Any] = VisionTextDualEncoderProcessor(tokenizer=_lowerCamelCase , image_processor=_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Union[str, Any] = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
__SCREAMING_SNAKE_CASE : Tuple = processor.batch_decode(_lowerCamelCase )
__SCREAMING_SNAKE_CASE : List[str] = tokenizer.batch_decode(_lowerCamelCase )
self.assertListEqual(_lowerCamelCase , _lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :Dict ):
__SCREAMING_SNAKE_CASE : str = self.get_image_processor()
__SCREAMING_SNAKE_CASE : List[str] = self.get_tokenizer()
__SCREAMING_SNAKE_CASE : Optional[int] = VisionTextDualEncoderProcessor(tokenizer=_lowerCamelCase , image_processor=_lowerCamelCase )
__SCREAMING_SNAKE_CASE : List[Any] = '''lower newer'''
__SCREAMING_SNAKE_CASE : Dict = self.prepare_image_inputs()
__SCREAMING_SNAKE_CASE : str = processor(text=_lowerCamelCase , images=_lowerCamelCase )
self.assertListEqual(list(inputs.keys() ) , processor.model_input_names )
| 674 | 0 |
"""simple docstring"""
import inspect
import os
import unittest
from dataclasses import dataclass
import torch
from accelerate import Accelerator, DistributedDataParallelKwargs, GradScalerKwargs
from accelerate.state import AcceleratorState
from accelerate.test_utils import execute_subprocess_async, require_cuda, require_multi_gpu
from accelerate.utils import KwargsHandler
@dataclass
class UpperCAmelCase_ ( __UpperCAmelCase ):
UpperCamelCase =0
UpperCamelCase =False
UpperCamelCase =3.0
class UpperCAmelCase_ ( unittest.TestCase ):
def _lowerCamelCase ( self ) -> Union[str, Any]:
# If no defaults are changed, `to_kwargs` returns an empty dict.
self.assertDictEqual(MockClass().to_kwargs() , {} )
self.assertDictEqual(MockClass(a=2 ).to_kwargs() , {'''a''': 2} )
self.assertDictEqual(MockClass(a=2 , b=_lowerCamelCase ).to_kwargs() , {'''a''': 2, '''b''': True} )
self.assertDictEqual(MockClass(a=2 , c=2.2_5 ).to_kwargs() , {'''a''': 2, '''c''': 2.2_5} )
@require_cuda
def _lowerCamelCase ( self ) -> List[Any]:
# If no defaults are changed, `to_kwargs` returns an empty dict.
__lowercase : str = GradScalerKwargs(init_scale=10_24 , growth_factor=2 )
AcceleratorState._reset_state()
__lowercase : List[Any] = Accelerator(mixed_precision='''fp16''' , kwargs_handlers=[scaler_handler] )
print(accelerator.use_fpaa )
__lowercase : Union[str, Any] = accelerator.scaler
# Check the kwargs have been applied
self.assertEqual(scaler._init_scale , 10_24.0 )
self.assertEqual(scaler._growth_factor , 2.0 )
# Check the other values are at the default
self.assertEqual(scaler._backoff_factor , 0.5 )
self.assertEqual(scaler._growth_interval , 20_00 )
self.assertEqual(scaler._enabled , _lowerCamelCase )
@require_multi_gpu
def _lowerCamelCase ( self ) -> str:
__lowercase : str = ['''torchrun''', F"""--nproc_per_node={torch.cuda.device_count()}""", inspect.getfile(self.__class__ )]
execute_subprocess_async(_lowerCamelCase , env=os.environ.copy() )
if __name__ == "__main__":
a_ = DistributedDataParallelKwargs(bucket_cap_mb=1_5, find_unused_parameters=True)
a_ = Accelerator(kwargs_handlers=[ddp_scaler])
a_ = torch.nn.Linear(1_0_0, 2_0_0)
a_ = accelerator.prepare(model)
# Check the values changed in kwargs
a_ = ''
a_ = model.bucket_bytes_cap // (1_0_2_4 * 1_0_2_4)
if observed_bucket_cap_map != 1_5:
error_msg += F"Kwargs badly passed, should have `15` but found {observed_bucket_cap_map}.\n"
if model.find_unused_parameters is not True:
error_msg += F"Kwargs badly passed, should have `True` but found {model.find_unused_parameters}.\n"
# Check the values of the defaults
if model.dim != 0:
error_msg += F"Default value not respected, should have `0` but found {model.dim}.\n"
if model.broadcast_buffers is not True:
error_msg += F"Default value not respected, should have `True` but found {model.broadcast_buffers}.\n"
if model.gradient_as_bucket_view is not False:
error_msg += F"Default value not respected, should have `False` but found {model.gradient_as_bucket_view}.\n"
# Raise error at the end to make sure we don't stop at the first failure.
if len(error_msg) > 0:
raise ValueError(error_msg)
| 76 |
"""simple docstring"""
import inspect
import unittest
import numpy as np
from tests.test_modeling_common import floats_tensor
from transformers import DetrConfig, MaskFormerConfig, SwinConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import MaskFormerForInstanceSegmentation, MaskFormerModel
if is_vision_available():
from transformers import MaskFormerImageProcessor
if is_vision_available():
from PIL import Image
class snake_case :
def __init__( self :Optional[int] , _lowerCamelCase :int , _lowerCamelCase :Any=2 , _lowerCamelCase :List[str]=True , _lowerCamelCase :str=False , _lowerCamelCase :Tuple=1_0 , _lowerCamelCase :str=3 , _lowerCamelCase :str=3_2 * 4 , _lowerCamelCase :Dict=3_2 * 6 , _lowerCamelCase :str=4 , _lowerCamelCase :Any=3_2 , ):
__SCREAMING_SNAKE_CASE : List[str] = parent
__SCREAMING_SNAKE_CASE : Tuple = batch_size
__SCREAMING_SNAKE_CASE : Optional[Any] = is_training
__SCREAMING_SNAKE_CASE : Dict = use_auxiliary_loss
__SCREAMING_SNAKE_CASE : List[str] = num_queries
__SCREAMING_SNAKE_CASE : Optional[int] = num_channels
__SCREAMING_SNAKE_CASE : List[Any] = min_size
__SCREAMING_SNAKE_CASE : int = max_size
__SCREAMING_SNAKE_CASE : Any = num_labels
__SCREAMING_SNAKE_CASE : Union[str, Any] = mask_feature_size
def SCREAMING_SNAKE_CASE_ ( self :Any ):
__SCREAMING_SNAKE_CASE : str = floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size] ).to(
_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Any = torch.ones([self.batch_size, self.min_size, self.max_size] , device=_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Any = (
torch.rand([self.batch_size, self.num_labels, self.min_size, self.max_size] , device=_lowerCamelCase ) > 0.5
).float()
__SCREAMING_SNAKE_CASE : Dict = (torch.rand((self.batch_size, self.num_labels) , device=_lowerCamelCase ) > 0.5).long()
__SCREAMING_SNAKE_CASE : str = self.get_config()
return config, pixel_values, pixel_mask, mask_labels, class_labels
def SCREAMING_SNAKE_CASE_ ( self :List[Any] ):
return MaskFormerConfig.from_backbone_and_decoder_configs(
backbone_config=SwinConfig(
depths=[1, 1, 1, 1] , ) , decoder_config=DetrConfig(
decoder_ffn_dim=1_2_8 , num_queries=self.num_queries , decoder_attention_heads=2 , d_model=self.mask_feature_size , ) , mask_feature_size=self.mask_feature_size , fpn_feature_size=self.mask_feature_size , num_channels=self.num_channels , num_labels=self.num_labels , )
def SCREAMING_SNAKE_CASE_ ( self :str ):
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Optional[Any] = self.prepare_config_and_inputs()
__SCREAMING_SNAKE_CASE : Any = {'''pixel_values''': pixel_values, '''pixel_mask''': pixel_mask}
return config, inputs_dict
def SCREAMING_SNAKE_CASE_ ( self :int , _lowerCamelCase :int , _lowerCamelCase :Optional[int] ):
__SCREAMING_SNAKE_CASE : Union[str, Any] = output.encoder_hidden_states
__SCREAMING_SNAKE_CASE : int = output.pixel_decoder_hidden_states
__SCREAMING_SNAKE_CASE : Optional[int] = output.transformer_decoder_hidden_states
self.parent.assertTrue(len(_lowerCamelCase ) , len(config.backbone_config.depths ) )
self.parent.assertTrue(len(_lowerCamelCase ) , len(config.backbone_config.depths ) )
self.parent.assertTrue(len(_lowerCamelCase ) , config.decoder_config.decoder_layers )
def SCREAMING_SNAKE_CASE_ ( self :Optional[Any] , _lowerCamelCase :Dict , _lowerCamelCase :str , _lowerCamelCase :Any , _lowerCamelCase :Optional[Any]=False ):
with torch.no_grad():
__SCREAMING_SNAKE_CASE : List[str] = MaskFormerModel(config=_lowerCamelCase )
model.to(_lowerCamelCase )
model.eval()
__SCREAMING_SNAKE_CASE : str = model(pixel_values=_lowerCamelCase , pixel_mask=_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Any = model(_lowerCamelCase , output_hidden_states=_lowerCamelCase )
# the correct shape of output.transformer_decoder_hidden_states ensure the correcteness of the
# encoder and pixel decoder
self.parent.assertEqual(
output.transformer_decoder_last_hidden_state.shape , (self.batch_size, self.num_queries, self.mask_feature_size) , )
# let's ensure the other two hidden state exists
self.parent.assertTrue(output.pixel_decoder_last_hidden_state is not None )
self.parent.assertTrue(output.encoder_last_hidden_state is not None )
if output_hidden_states:
self.check_output_hidden_state(_lowerCamelCase , _lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :Dict , _lowerCamelCase :Optional[Any] , _lowerCamelCase :Optional[int] , _lowerCamelCase :Optional[Any] , _lowerCamelCase :List[str] , _lowerCamelCase :Dict ):
__SCREAMING_SNAKE_CASE : Optional[Any] = MaskFormerForInstanceSegmentation(config=_lowerCamelCase )
model.to(_lowerCamelCase )
model.eval()
def comm_check_on_output(_lowerCamelCase :Optional[Any] ):
# let's still check that all the required stuff is there
self.parent.assertTrue(result.transformer_decoder_last_hidden_state is not None )
self.parent.assertTrue(result.pixel_decoder_last_hidden_state is not None )
self.parent.assertTrue(result.encoder_last_hidden_state is not None )
# okay, now we need to check the logits shape
# due to the encoder compression, masks have a //4 spatial size
self.parent.assertEqual(
result.masks_queries_logits.shape , (self.batch_size, self.num_queries, self.min_size // 4, self.max_size // 4) , )
# + 1 for null class
self.parent.assertEqual(
result.class_queries_logits.shape , (self.batch_size, self.num_queries, self.num_labels + 1) )
with torch.no_grad():
__SCREAMING_SNAKE_CASE : List[str] = model(pixel_values=_lowerCamelCase , pixel_mask=_lowerCamelCase )
__SCREAMING_SNAKE_CASE : int = model(_lowerCamelCase )
comm_check_on_output(_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Dict = model(
pixel_values=_lowerCamelCase , pixel_mask=_lowerCamelCase , mask_labels=_lowerCamelCase , class_labels=_lowerCamelCase )
comm_check_on_output(_lowerCamelCase )
self.parent.assertTrue(result.loss is not None )
self.parent.assertEqual(result.loss.shape , torch.Size([1] ) )
@require_torch
class snake_case ( __UpperCAmelCase , __UpperCAmelCase , unittest.TestCase ):
lowerCamelCase__ = (MaskFormerModel, MaskFormerForInstanceSegmentation) if is_torch_available() else ()
lowerCamelCase__ = (
{'''feature-extraction''': MaskFormerModel, '''image-segmentation''': MaskFormerForInstanceSegmentation}
if is_torch_available()
else {}
)
lowerCamelCase__ = False
lowerCamelCase__ = False
lowerCamelCase__ = False
lowerCamelCase__ = False
def SCREAMING_SNAKE_CASE_ ( self :Dict ):
__SCREAMING_SNAKE_CASE : Optional[Any] = MaskFormerModelTester(self )
__SCREAMING_SNAKE_CASE : str = ConfigTester(self , config_class=_lowerCamelCase , has_text_modality=_lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :Union[str, Any] ):
self.config_tester.run_common_tests()
def SCREAMING_SNAKE_CASE_ ( self :Union[str, Any] ):
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Any = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.create_and_check_maskformer_model(_lowerCamelCase , **_lowerCamelCase , output_hidden_states=_lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :List[str] ):
__SCREAMING_SNAKE_CASE : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_maskformer_instance_segmentation_head_model(*_lowerCamelCase )
@unittest.skip(reason='''MaskFormer does not use inputs_embeds''' )
def SCREAMING_SNAKE_CASE_ ( self :List[Any] ):
pass
@unittest.skip(reason='''MaskFormer does not have a get_input_embeddings method''' )
def SCREAMING_SNAKE_CASE_ ( self :int ):
pass
@unittest.skip(reason='''MaskFormer is not a generative model''' )
def SCREAMING_SNAKE_CASE_ ( self :Union[str, Any] ):
pass
@unittest.skip(reason='''MaskFormer does not use token embeddings''' )
def SCREAMING_SNAKE_CASE_ ( self :Optional[int] ):
pass
@require_torch_multi_gpu
@unittest.skip(
reason='''MaskFormer has some layers using `add_module` which doesn\'t work well with `nn.DataParallel`''' )
def SCREAMING_SNAKE_CASE_ ( self :Dict ):
pass
@unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.''' )
def SCREAMING_SNAKE_CASE_ ( self :Dict ):
pass
def SCREAMING_SNAKE_CASE_ ( self :Union[str, Any] ):
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Any = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__SCREAMING_SNAKE_CASE : Tuple = model_class(_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Optional[int] = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
__SCREAMING_SNAKE_CASE : List[str] = [*signature.parameters.keys()]
__SCREAMING_SNAKE_CASE : Optional[Any] = ['''pixel_values''']
self.assertListEqual(arg_names[:1] , _lowerCamelCase )
@slow
def SCREAMING_SNAKE_CASE_ ( self :Optional[Any] ):
for model_name in ["facebook/maskformer-swin-small-coco"]:
__SCREAMING_SNAKE_CASE : Tuple = MaskFormerModel.from_pretrained(_lowerCamelCase )
self.assertIsNotNone(_lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :Union[str, Any] ):
__SCREAMING_SNAKE_CASE : Dict = (self.model_tester.min_size,) * 2
__SCREAMING_SNAKE_CASE : Dict = {
'''pixel_values''': torch.randn((2, 3, *size) , device=_lowerCamelCase ),
'''mask_labels''': torch.randn((2, 1_0, *size) , device=_lowerCamelCase ),
'''class_labels''': torch.zeros(2 , 1_0 , device=_lowerCamelCase ).long(),
}
__SCREAMING_SNAKE_CASE : Dict = MaskFormerForInstanceSegmentation(MaskFormerConfig() ).to(_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Optional[Any] = model(**_lowerCamelCase )
self.assertTrue(outputs.loss is not None )
def SCREAMING_SNAKE_CASE_ ( self :Union[str, Any] ):
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Tuple = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.create_and_check_maskformer_model(_lowerCamelCase , **_lowerCamelCase , output_hidden_states=_lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :Union[str, Any] ):
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Tuple = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__SCREAMING_SNAKE_CASE : Dict = model_class(_lowerCamelCase ).to(_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Optional[int] = model(**_lowerCamelCase , output_attentions=_lowerCamelCase )
self.assertTrue(outputs.attentions is not None )
def SCREAMING_SNAKE_CASE_ ( self :Dict ):
if not self.model_tester.is_training:
return
# only MaskFormerForInstanceSegmentation has the loss
__SCREAMING_SNAKE_CASE : List[Any] = self.all_model_classes[1]
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Dict = self.model_tester.prepare_config_and_inputs()
__SCREAMING_SNAKE_CASE : int = model_class(_lowerCamelCase )
model.to(_lowerCamelCase )
model.train()
__SCREAMING_SNAKE_CASE : Any = model(_lowerCamelCase , mask_labels=_lowerCamelCase , class_labels=_lowerCamelCase ).loss
loss.backward()
def SCREAMING_SNAKE_CASE_ ( self :Optional[int] ):
# only MaskFormerForInstanceSegmentation has the loss
__SCREAMING_SNAKE_CASE : Tuple = self.all_model_classes[1]
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Tuple = self.model_tester.prepare_config_and_inputs()
__SCREAMING_SNAKE_CASE : Tuple = True
__SCREAMING_SNAKE_CASE : List[str] = True
__SCREAMING_SNAKE_CASE : Any = model_class(_lowerCamelCase )
model.to(_lowerCamelCase )
model.train()
__SCREAMING_SNAKE_CASE : Optional[int] = model(_lowerCamelCase , mask_labels=_lowerCamelCase , class_labels=_lowerCamelCase )
__SCREAMING_SNAKE_CASE : List[str] = outputs.encoder_hidden_states[0]
encoder_hidden_states.retain_grad()
__SCREAMING_SNAKE_CASE : str = outputs.pixel_decoder_hidden_states[0]
pixel_decoder_hidden_states.retain_grad()
# we requires_grad=True in inputs_embeds (line 2152), the original implementation don't
__SCREAMING_SNAKE_CASE : Optional[int] = outputs.transformer_decoder_hidden_states[0]
transformer_decoder_hidden_states.retain_grad()
__SCREAMING_SNAKE_CASE : int = outputs.attentions[0]
attentions.retain_grad()
outputs.loss.backward(retain_graph=_lowerCamelCase )
self.assertIsNotNone(encoder_hidden_states.grad )
self.assertIsNotNone(pixel_decoder_hidden_states.grad )
self.assertIsNotNone(transformer_decoder_hidden_states.grad )
self.assertIsNotNone(attentions.grad )
_lowerCamelCase = 1e-4
def lowerCAmelCase_ ( ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : List[Any] = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
return image
@require_vision
@slow
class snake_case ( unittest.TestCase ):
@cached_property
def SCREAMING_SNAKE_CASE_ ( self :str ):
return (
MaskFormerImageProcessor.from_pretrained('''facebook/maskformer-swin-small-coco''' )
if is_vision_available()
else None
)
def SCREAMING_SNAKE_CASE_ ( self :List[Any] ):
__SCREAMING_SNAKE_CASE : Union[str, Any] = MaskFormerModel.from_pretrained('''facebook/maskformer-swin-small-coco''' ).to(_lowerCamelCase )
__SCREAMING_SNAKE_CASE : int = self.default_image_processor
__SCREAMING_SNAKE_CASE : Tuple = prepare_img()
__SCREAMING_SNAKE_CASE : List[Any] = image_processor(_lowerCamelCase , return_tensors='''pt''' ).to(_lowerCamelCase )
__SCREAMING_SNAKE_CASE : int = inputs['''pixel_values'''].shape
# check size is divisible by 32
self.assertTrue((inputs_shape[-1] % 3_2) == 0 and (inputs_shape[-2] % 3_2) == 0 )
# check size
self.assertEqual(_lowerCamelCase , (1, 3, 8_0_0, 1_0_8_8) )
with torch.no_grad():
__SCREAMING_SNAKE_CASE : Tuple = model(**_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Union[str, Any] = torch.tensor(
[[-0.0_4_8_2, 0.9_2_2_8, 0.4_9_5_1], [-0.2_5_4_7, 0.8_0_1_7, 0.8_5_2_7], [-0.0_0_6_9, 0.3_3_8_5, -0.0_0_8_9]] ).to(_lowerCamelCase )
self.assertTrue(
torch.allclose(
outputs.encoder_last_hidden_state[0, 0, :3, :3] , _lowerCamelCase , atol=_lowerCamelCase ) )
__SCREAMING_SNAKE_CASE : Any = torch.tensor(
[[-0.8_4_2_2, -0.8_4_3_4, -0.9_7_1_8], [-1.0_1_4_4, -0.5_5_6_5, -0.4_1_9_5], [-1.0_0_3_8, -0.4_4_8_4, -0.1_9_6_1]] ).to(_lowerCamelCase )
self.assertTrue(
torch.allclose(
outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3] , _lowerCamelCase , atol=_lowerCamelCase ) )
__SCREAMING_SNAKE_CASE : Optional[int] = torch.tensor(
[[0.2_8_5_2, -0.0_1_5_9, 0.9_7_3_5], [0.6_2_5_4, 0.1_8_5_8, 0.8_5_2_9], [-0.0_6_8_0, -0.4_1_1_6, 1.8_4_1_3]] ).to(_lowerCamelCase )
self.assertTrue(
torch.allclose(
outputs.transformer_decoder_last_hidden_state[0, :3, :3] , _lowerCamelCase , atol=_lowerCamelCase ) )
def SCREAMING_SNAKE_CASE_ ( self :Optional[Any] ):
__SCREAMING_SNAKE_CASE : Tuple = (
MaskFormerForInstanceSegmentation.from_pretrained('''facebook/maskformer-swin-small-coco''' )
.to(_lowerCamelCase )
.eval()
)
__SCREAMING_SNAKE_CASE : Union[str, Any] = self.default_image_processor
__SCREAMING_SNAKE_CASE : Tuple = prepare_img()
__SCREAMING_SNAKE_CASE : str = image_processor(_lowerCamelCase , return_tensors='''pt''' ).to(_lowerCamelCase )
__SCREAMING_SNAKE_CASE : List[str] = inputs['''pixel_values'''].shape
# check size is divisible by 32
self.assertTrue((inputs_shape[-1] % 3_2) == 0 and (inputs_shape[-2] % 3_2) == 0 )
# check size
self.assertEqual(_lowerCamelCase , (1, 3, 8_0_0, 1_0_8_8) )
with torch.no_grad():
__SCREAMING_SNAKE_CASE : str = model(**_lowerCamelCase )
# masks_queries_logits
__SCREAMING_SNAKE_CASE : Any = outputs.masks_queries_logits
self.assertEqual(
masks_queries_logits.shape , (1, model.config.decoder_config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4) , )
__SCREAMING_SNAKE_CASE : List[Any] = [
[-1.3_7_3_7_1_2_4, -1.7_7_2_4_9_3_7, -1.9_3_6_4_2_3_3],
[-1.5_9_7_7_2_8_1, -1.9_8_6_7_9_3_9, -2.1_5_2_3_6_9_5],
[-1.5_7_9_5_3_9_8, -1.9_2_6_9_8_3_2, -2.0_9_3_9_4_2],
]
__SCREAMING_SNAKE_CASE : Any = torch.tensor(_lowerCamelCase ).to(_lowerCamelCase )
self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3] , _lowerCamelCase , atol=_lowerCamelCase ) )
# class_queries_logits
__SCREAMING_SNAKE_CASE : List[Any] = outputs.class_queries_logits
self.assertEqual(
class_queries_logits.shape , (1, model.config.decoder_config.num_queries, model.config.num_labels + 1) )
__SCREAMING_SNAKE_CASE : List[str] = torch.tensor(
[
[1.65_12e00, -5.25_72e00, -3.35_19e00],
[3.61_69e-02, -5.90_25e00, -2.93_13e00],
[1.07_66e-04, -7.76_30e00, -5.12_63e00],
] ).to(_lowerCamelCase )
self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3] , _lowerCamelCase , atol=_lowerCamelCase ) )
def SCREAMING_SNAKE_CASE_ ( self :Any ):
__SCREAMING_SNAKE_CASE : Tuple = (
MaskFormerForInstanceSegmentation.from_pretrained('''facebook/maskformer-resnet101-coco-stuff''' )
.to(_lowerCamelCase )
.eval()
)
__SCREAMING_SNAKE_CASE : Tuple = self.default_image_processor
__SCREAMING_SNAKE_CASE : str = prepare_img()
__SCREAMING_SNAKE_CASE : List[Any] = image_processor(_lowerCamelCase , return_tensors='''pt''' ).to(_lowerCamelCase )
__SCREAMING_SNAKE_CASE : List[Any] = inputs['''pixel_values'''].shape
# check size is divisible by 32
self.assertTrue((inputs_shape[-1] % 3_2) == 0 and (inputs_shape[-2] % 3_2) == 0 )
# check size
self.assertEqual(_lowerCamelCase , (1, 3, 8_0_0, 1_0_8_8) )
with torch.no_grad():
__SCREAMING_SNAKE_CASE : List[Any] = model(**_lowerCamelCase )
# masks_queries_logits
__SCREAMING_SNAKE_CASE : Union[str, Any] = outputs.masks_queries_logits
self.assertEqual(
masks_queries_logits.shape , (1, model.config.decoder_config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4) , )
__SCREAMING_SNAKE_CASE : List[str] = [[-0.9_0_4_6, -2.6_3_6_6, -4.6_0_6_2], [-3.4_1_7_9, -5.7_8_9_0, -8.8_0_5_7], [-4.9_1_7_9, -7.6_5_6_0, -1_0.7_7_1_1]]
__SCREAMING_SNAKE_CASE : Any = torch.tensor(_lowerCamelCase ).to(_lowerCamelCase )
self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3] , _lowerCamelCase , atol=_lowerCamelCase ) )
# class_queries_logits
__SCREAMING_SNAKE_CASE : int = outputs.class_queries_logits
self.assertEqual(
class_queries_logits.shape , (1, model.config.decoder_config.num_queries, model.config.num_labels + 1) )
__SCREAMING_SNAKE_CASE : List[str] = torch.tensor(
[[4.7_1_8_8, -3.2_5_8_5, -2.8_8_5_7], [6.6_8_7_1, -2.9_1_8_1, -1.2_4_8_7], [7.2_4_4_9, -2.2_7_6_4, -2.1_8_7_4]] ).to(_lowerCamelCase )
self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3] , _lowerCamelCase , atol=_lowerCamelCase ) )
def SCREAMING_SNAKE_CASE_ ( self :int ):
__SCREAMING_SNAKE_CASE : int = (
MaskFormerForInstanceSegmentation.from_pretrained('''facebook/maskformer-swin-small-coco''' )
.to(_lowerCamelCase )
.eval()
)
__SCREAMING_SNAKE_CASE : Any = self.default_image_processor
__SCREAMING_SNAKE_CASE : int = image_processor(
[np.zeros((3, 8_0_0, 1_3_3_3) ), np.zeros((3, 8_0_0, 1_3_3_3) )] , segmentation_maps=[np.zeros((3_8_4, 3_8_4) ).astype(np.floataa ), np.zeros((3_8_4, 3_8_4) ).astype(np.floataa )] , return_tensors='''pt''' , )
__SCREAMING_SNAKE_CASE : Dict = inputs['''pixel_values'''].to(_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Dict = [el.to(_lowerCamelCase ) for el in inputs['''mask_labels''']]
__SCREAMING_SNAKE_CASE : str = [el.to(_lowerCamelCase ) for el in inputs['''class_labels''']]
with torch.no_grad():
__SCREAMING_SNAKE_CASE : int = model(**_lowerCamelCase )
self.assertTrue(outputs.loss is not None )
| 674 | 0 |
from sklearn.metrics import fa_score, matthews_corrcoef
import datasets
from .record_evaluation import evaluate as evaluate_record
lowercase_ : List[str] = '\\n@article{wang2019superglue,\n title={SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems},\n author={Wang, Alex and Pruksachatkun, Yada and Nangia, Nikita and Singh, Amanpreet and Michael, Julian and Hill, Felix and Levy, Omer and Bowman, Samuel R},\n journal={arXiv preprint arXiv:1905.00537},\n year={2019}\n}\n'
lowercase_ : Tuple = '\\nSuperGLUE (https://super.gluebenchmark.com/) is a new benchmark styled after\nGLUE with a new set of more difficult language understanding tasks, improved\nresources, and a new public leaderboard.\n'
lowercase_ : Any = '\nCompute SuperGLUE evaluation metric associated to each SuperGLUE dataset.\nArgs:\n predictions: list of predictions to score. Depending on the SuperGlUE subset:\n - for \'record\': list of question-answer dictionaries with the following keys:\n - \'idx\': index of the question as specified by the dataset\n - \'prediction_text\': the predicted answer text\n - for \'multirc\': list of question-answer dictionaries with the following keys:\n - \'idx\': index of the question-answer pair as specified by the dataset\n - \'prediction\': the predicted answer label\n - otherwise: list of predicted labels\n references: list of reference labels. Depending on the SuperGLUE subset:\n - for \'record\': list of question-answers dictionaries with the following keys:\n - \'idx\': index of the question as specified by the dataset\n - \'answers\': list of possible answers\n - otherwise: list of reference labels\nReturns: depending on the SuperGLUE subset:\n - for \'record\':\n - \'exact_match\': Exact match between answer and gold answer\n - \'f1\': F1 score\n - for \'multirc\':\n - \'exact_match\': Exact match between answer and gold answer\n - \'f1_m\': Per-question macro-F1 score\n - \'f1_a\': Average F1 score over all answers\n - for \'axb\':\n \'matthews_correlation\': Matthew Correlation\n - for \'cb\':\n - \'accuracy\': Accuracy\n - \'f1\': F1 score\n - for all others:\n - \'accuracy\': Accuracy\nExamples:\n\n >>> super_glue_metric = datasets.load_metric(\'super_glue\', \'copa\') # any of ["copa", "rte", "wic", "wsc", "wsc.fixed", "boolq", "axg"]\n >>> predictions = [0, 1]\n >>> references = [0, 1]\n >>> results = super_glue_metric.compute(predictions=predictions, references=references)\n >>> print(results)\n {\'accuracy\': 1.0}\n\n >>> super_glue_metric = datasets.load_metric(\'super_glue\', \'cb\')\n >>> predictions = [0, 1]\n >>> references = [0, 1]\n >>> results = super_glue_metric.compute(predictions=predictions, references=references)\n >>> print(results)\n {\'accuracy\': 1.0, \'f1\': 1.0}\n\n >>> super_glue_metric = datasets.load_metric(\'super_glue\', \'record\')\n >>> predictions = [{\'idx\': {\'passage\': 0, \'query\': 0}, \'prediction_text\': \'answer\'}]\n >>> references = [{\'idx\': {\'passage\': 0, \'query\': 0}, \'answers\': [\'answer\', \'another_answer\']}]\n >>> results = super_glue_metric.compute(predictions=predictions, references=references)\n >>> print(results)\n {\'exact_match\': 1.0, \'f1\': 1.0}\n\n >>> super_glue_metric = datasets.load_metric(\'super_glue\', \'multirc\')\n >>> predictions = [{\'idx\': {\'answer\': 0, \'paragraph\': 0, \'question\': 0}, \'prediction\': 0}, {\'idx\': {\'answer\': 1, \'paragraph\': 2, \'question\': 3}, \'prediction\': 1}]\n >>> references = [0, 1]\n >>> results = super_glue_metric.compute(predictions=predictions, references=references)\n >>> print(results)\n {\'exact_match\': 1.0, \'f1_m\': 1.0, \'f1_a\': 1.0}\n\n >>> super_glue_metric = datasets.load_metric(\'super_glue\', \'axb\')\n >>> references = [0, 1]\n >>> predictions = [0, 1]\n >>> results = super_glue_metric.compute(predictions=predictions, references=references)\n >>> print(results)\n {\'matthews_correlation\': 1.0}\n'
def A__ ( snake_case_ : Optional[Any] , snake_case_ : List[Any] ):
return float((preds == labels).mean() )
def A__ ( snake_case_ : int , snake_case_ : int , snake_case_ : List[Any]="binary" ):
SCREAMING_SNAKE_CASE__: Tuple= simple_accuracy(lowercase_ , lowercase_ )
SCREAMING_SNAKE_CASE__: str= float(fa_score(y_true=lowercase_ , y_pred=lowercase_ , average=lowercase_ ) )
return {
"accuracy": acc,
"f1": fa,
}
def A__ ( snake_case_ : str , snake_case_ : Union[str, Any] ):
SCREAMING_SNAKE_CASE__: Optional[Any]= {}
for id_pred, label in zip(lowercase_ , lowercase_ ):
SCREAMING_SNAKE_CASE__: Optional[Any]= F'{id_pred["idx"]["paragraph"]}-{id_pred["idx"]["question"]}'
SCREAMING_SNAKE_CASE__: str= id_pred['''prediction''']
if question_id in question_map:
question_map[question_id].append((pred, label) )
else:
SCREAMING_SNAKE_CASE__: Dict= [(pred, label)]
SCREAMING_SNAKE_CASE__: int= [], []
for question, preds_labels in question_map.items():
SCREAMING_SNAKE_CASE__: Tuple= zip(*lowercase_ )
SCREAMING_SNAKE_CASE__: List[str]= fa_score(y_true=lowercase_ , y_pred=lowercase_ , average='''macro''' )
fas.append(lowercase_ )
SCREAMING_SNAKE_CASE__: Optional[Any]= int(sum(pred == label for pred, label in preds_labels ) == len(lowercase_ ) )
ems.append(lowercase_ )
SCREAMING_SNAKE_CASE__: Optional[int]= float(sum(lowercase_ ) / len(lowercase_ ) )
SCREAMING_SNAKE_CASE__: List[str]= sum(lowercase_ ) / len(lowercase_ )
SCREAMING_SNAKE_CASE__: List[Any]= float(fa_score(y_true=lowercase_ , y_pred=[id_pred['''prediction'''] for id_pred in ids_preds] ) )
return {"exact_match": em, "f1_m": fa_m, "f1_a": fa_a}
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class _lowerCamelCase ( datasets.Metric ):
def UpperCamelCase_ ( self ) -> List[str]:
if self.config_name not in [
"boolq",
"cb",
"copa",
"multirc",
"record",
"rte",
"wic",
"wsc",
"wsc.fixed",
"axb",
"axg",
]:
raise KeyError(
'''You should supply a configuration name selected in '''
'''["boolq", "cb", "copa", "multirc", "record", "rte", "wic", "wsc", "wsc.fixed", "axb", "axg",]''' )
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(self._get_feature_types() ) , codebase_urls=[] , reference_urls=[] , format='''numpy''' if not self.config_name == '''record''' and not self.config_name == '''multirc''' else None , )
def UpperCamelCase_ ( self ) -> Optional[Any]:
if self.config_name == "record":
return {
"predictions": {
"idx": {
"passage": datasets.Value('''int64''' ),
"query": datasets.Value('''int64''' ),
},
"prediction_text": datasets.Value('''string''' ),
},
"references": {
"idx": {
"passage": datasets.Value('''int64''' ),
"query": datasets.Value('''int64''' ),
},
"answers": datasets.Sequence(datasets.Value('''string''' ) ),
},
}
elif self.config_name == "multirc":
return {
"predictions": {
"idx": {
"answer": datasets.Value('''int64''' ),
"paragraph": datasets.Value('''int64''' ),
"question": datasets.Value('''int64''' ),
},
"prediction": datasets.Value('''int64''' ),
},
"references": datasets.Value('''int64''' ),
}
else:
return {
"predictions": datasets.Value('''int64''' ),
"references": datasets.Value('''int64''' ),
}
def UpperCamelCase_ ( self , lowerCAmelCase , lowerCAmelCase ) -> List[str]:
if self.config_name == "axb":
return {"matthews_correlation": matthews_corrcoef(_lowerCamelCase , _lowerCamelCase )}
elif self.config_name == "cb":
return acc_and_fa(_lowerCamelCase , _lowerCamelCase , fa_avg='''macro''' )
elif self.config_name == "record":
SCREAMING_SNAKE_CASE__: Optional[int]= [
{
'''qas''': [
{'''id''': ref['''idx''']['''query'''], '''answers''': [{'''text''': ans} for ans in ref['''answers''']]}
for ref in references
]
}
]
SCREAMING_SNAKE_CASE__: Dict= {pred['''idx''']['''query''']: pred['''prediction_text'''] for pred in predictions}
return evaluate_record(_lowerCamelCase , _lowerCamelCase )[0]
elif self.config_name == "multirc":
return evaluate_multirc(_lowerCamelCase , _lowerCamelCase )
elif self.config_name in ["copa", "rte", "wic", "wsc", "wsc.fixed", "boolq", "axg"]:
return {"accuracy": simple_accuracy(_lowerCamelCase , _lowerCamelCase )}
else:
raise KeyError(
'''You should supply a configuration name selected in '''
'''["boolq", "cb", "copa", "multirc", "record", "rte", "wic", "wsc", "wsc.fixed", "axb", "axg",]''' )
| 64 |
"""simple docstring"""
import contextlib
import importlib
import io
import unittest
import transformers
# Try to import everything from transformers to ensure every object can be loaded.
from transformers import * # noqa F406
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, require_flax, require_tf, require_torch
from transformers.utils import ContextManagers, find_labels, is_flax_available, is_tf_available, is_torch_available
if is_torch_available():
from transformers import BertForPreTraining, BertForQuestionAnswering, BertForSequenceClassification
if is_tf_available():
from transformers import TFBertForPreTraining, TFBertForQuestionAnswering, TFBertForSequenceClassification
if is_flax_available():
from transformers import FlaxBertForPreTraining, FlaxBertForQuestionAnswering, FlaxBertForSequenceClassification
_lowerCamelCase = DUMMY_UNKNOWN_IDENTIFIER
# An actual model hosted on huggingface.co
_lowerCamelCase = '''main'''
# Default branch name
_lowerCamelCase = '''f2c752cfc5c0ab6f4bdec59acea69eefbee381c2'''
# One particular commit (not the top of `main`)
_lowerCamelCase = '''aaaaaaa'''
# This commit does not exist, so we should 404.
_lowerCamelCase = '''d9e9f15bc825e4b2c9249e9578f884bbcb5e3684'''
# Sha-1 of config.json on the top of `main`, for checking purposes
_lowerCamelCase = '''4b243c475af8d0a7754e87d7d096c92e5199ec2fe168a2ee7998e3b8e9bcb1d3'''
@contextlib.contextmanager
def lowerCAmelCase_ ( ):
'''simple docstring'''
print('''Welcome!''' )
yield
print('''Bye!''' )
@contextlib.contextmanager
def lowerCAmelCase_ ( ):
'''simple docstring'''
print('''Bonjour!''' )
yield
print('''Au revoir!''' )
class snake_case ( unittest.TestCase ):
def SCREAMING_SNAKE_CASE_ ( self :Union[str, Any] ):
# If the spec is missing, importlib would not be able to import the module dynamically.
assert transformers.__spec__ is not None
assert importlib.util.find_spec('''transformers''' ) is not None
class snake_case ( unittest.TestCase ):
@unittest.mock.patch('''sys.stdout''' , new_callable=io.StringIO )
def SCREAMING_SNAKE_CASE_ ( self :Dict , _lowerCamelCase :Optional[int] ):
with ContextManagers([] ):
print('''Transformers are awesome!''' )
# The print statement adds a new line at the end of the output
self.assertEqual(mock_stdout.getvalue() , '''Transformers are awesome!\n''' )
@unittest.mock.patch('''sys.stdout''' , new_callable=io.StringIO )
def SCREAMING_SNAKE_CASE_ ( self :str , _lowerCamelCase :Optional[int] ):
with ContextManagers([context_en()] ):
print('''Transformers are awesome!''' )
# The output should be wrapped with an English welcome and goodbye
self.assertEqual(mock_stdout.getvalue() , '''Welcome!\nTransformers are awesome!\nBye!\n''' )
@unittest.mock.patch('''sys.stdout''' , new_callable=io.StringIO )
def SCREAMING_SNAKE_CASE_ ( self :Dict , _lowerCamelCase :List[str] ):
with ContextManagers([context_fr(), context_en()] ):
print('''Transformers are awesome!''' )
# The output should be wrapped with an English and French welcome and goodbye
self.assertEqual(mock_stdout.getvalue() , '''Bonjour!\nWelcome!\nTransformers are awesome!\nBye!\nAu revoir!\n''' )
@require_torch
def SCREAMING_SNAKE_CASE_ ( self :List[str] ):
self.assertEqual(find_labels(_lowerCamelCase ) , ['''labels'''] )
self.assertEqual(find_labels(_lowerCamelCase ) , ['''labels''', '''next_sentence_label'''] )
self.assertEqual(find_labels(_lowerCamelCase ) , ['''start_positions''', '''end_positions'''] )
class snake_case ( __UpperCAmelCase ):
pass
self.assertEqual(find_labels(_lowerCamelCase ) , ['''labels'''] )
@require_tf
def SCREAMING_SNAKE_CASE_ ( self :int ):
self.assertEqual(find_labels(_lowerCamelCase ) , ['''labels'''] )
self.assertEqual(find_labels(_lowerCamelCase ) , ['''labels''', '''next_sentence_label'''] )
self.assertEqual(find_labels(_lowerCamelCase ) , ['''start_positions''', '''end_positions'''] )
class snake_case ( __UpperCAmelCase ):
pass
self.assertEqual(find_labels(_lowerCamelCase ) , ['''labels'''] )
@require_flax
def SCREAMING_SNAKE_CASE_ ( self :Dict ):
# Flax models don't have labels
self.assertEqual(find_labels(_lowerCamelCase ) , [] )
self.assertEqual(find_labels(_lowerCamelCase ) , [] )
self.assertEqual(find_labels(_lowerCamelCase ) , [] )
class snake_case ( __UpperCAmelCase ):
pass
self.assertEqual(find_labels(_lowerCamelCase ) , [] )
| 674 | 0 |
import math
import sys
def lowercase_ ( _UpperCamelCase ):
'''simple docstring'''
__lowercase = ''''''
try:
with open(lowercase_ , '''rb''' ) as binary_file:
__lowercase = binary_file.read()
for dat in data:
__lowercase = F'{dat:08b}'
result += curr_byte
return result
except OSError:
print('''File not accessible''' )
sys.exit()
def lowercase_ ( _UpperCamelCase ):
'''simple docstring'''
__lowercase = {'''0''': '''0''', '''1''': '''1'''}
__lowercase = '''''', ''''''
__lowercase = len(lowercase_ )
for i in range(len(lowercase_ ) ):
curr_string += data_bits[i]
if curr_string not in lexicon:
continue
__lowercase = lexicon[curr_string]
result += last_match_id
__lowercase = last_match_id + '''0'''
if math.loga(lowercase_ ).is_integer():
__lowercase = {}
for curr_key in list(lowercase_ ):
__lowercase = lexicon.pop(lowercase_ )
__lowercase = new_lex
__lowercase = last_match_id + '''1'''
index += 1
__lowercase = ''''''
return result
def lowercase_ ( _UpperCamelCase , _UpperCamelCase ):
'''simple docstring'''
__lowercase = 8
try:
with open(lowercase_ , '''wb''' ) as opened_file:
__lowercase = [
to_write[i : i + byte_length]
for i in range(0 , len(lowercase_ ) , lowercase_ )
]
if len(result_byte_array[-1] ) % byte_length == 0:
result_byte_array.append('''10000000''' )
else:
result_byte_array[-1] += "1" + "0" * (
byte_length - len(result_byte_array[-1] ) - 1
)
for elem in result_byte_array[:-1]:
opened_file.write(int(lowercase_ , 2 ).to_bytes(1 , byteorder='''big''' ) )
except OSError:
print('''File not accessible''' )
sys.exit()
def lowercase_ ( _UpperCamelCase ):
'''simple docstring'''
__lowercase = 0
for letter in data_bits:
if letter == "1":
break
counter += 1
__lowercase = data_bits[counter:]
__lowercase = data_bits[counter + 1 :]
return data_bits
def lowercase_ ( _UpperCamelCase , _UpperCamelCase ):
'''simple docstring'''
__lowercase = read_file_binary(lowercase_ )
__lowercase = remove_prefix(lowercase_ )
__lowercase = decompress_data(lowercase_ )
write_file_binary(lowercase_ , lowercase_ )
if __name__ == "__main__":
compress(sys.argv[1], sys.argv[2])
| 639 |
"""simple docstring"""
import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import YolosImageProcessor
class snake_case ( unittest.TestCase ):
def __init__( self :List[Any] , _lowerCamelCase :Dict , _lowerCamelCase :Tuple=7 , _lowerCamelCase :Dict=3 , _lowerCamelCase :Optional[Any]=3_0 , _lowerCamelCase :List[str]=4_0_0 , _lowerCamelCase :Union[str, Any]=True , _lowerCamelCase :Union[str, Any]=None , _lowerCamelCase :List[Any]=True , _lowerCamelCase :Any=[0.5, 0.5, 0.5] , _lowerCamelCase :Dict=[0.5, 0.5, 0.5] , _lowerCamelCase :Dict=True , _lowerCamelCase :str=1 / 2_5_5 , _lowerCamelCase :Union[str, Any]=True , ):
# by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
__SCREAMING_SNAKE_CASE : Optional[Any] = size if size is not None else {'''shortest_edge''': 1_8, '''longest_edge''': 1_3_3_3}
__SCREAMING_SNAKE_CASE : List[str] = parent
__SCREAMING_SNAKE_CASE : Dict = batch_size
__SCREAMING_SNAKE_CASE : str = num_channels
__SCREAMING_SNAKE_CASE : Union[str, Any] = min_resolution
__SCREAMING_SNAKE_CASE : Union[str, Any] = max_resolution
__SCREAMING_SNAKE_CASE : Tuple = do_resize
__SCREAMING_SNAKE_CASE : Union[str, Any] = size
__SCREAMING_SNAKE_CASE : int = do_normalize
__SCREAMING_SNAKE_CASE : List[Any] = image_mean
__SCREAMING_SNAKE_CASE : Tuple = image_std
__SCREAMING_SNAKE_CASE : Dict = do_rescale
__SCREAMING_SNAKE_CASE : Optional[int] = rescale_factor
__SCREAMING_SNAKE_CASE : List[Any] = do_pad
def SCREAMING_SNAKE_CASE_ ( self :Optional[Any] ):
return {
"do_resize": self.do_resize,
"size": self.size,
"do_normalize": self.do_normalize,
"image_mean": self.image_mean,
"image_std": self.image_std,
"do_rescale": self.do_rescale,
"rescale_factor": self.rescale_factor,
"do_pad": self.do_pad,
}
def SCREAMING_SNAKE_CASE_ ( self :Tuple , _lowerCamelCase :Dict , _lowerCamelCase :List[Any]=False ):
if not batched:
__SCREAMING_SNAKE_CASE : str = image_inputs[0]
if isinstance(_lowerCamelCase , Image.Image ):
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Optional[int] = image.size
else:
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Dict = image.shape[1], image.shape[2]
if w < h:
__SCREAMING_SNAKE_CASE : str = int(self.size['''shortest_edge'''] * h / w )
__SCREAMING_SNAKE_CASE : int = self.size['''shortest_edge''']
elif w > h:
__SCREAMING_SNAKE_CASE : Optional[Any] = self.size['''shortest_edge''']
__SCREAMING_SNAKE_CASE : int = int(self.size['''shortest_edge'''] * w / h )
else:
__SCREAMING_SNAKE_CASE : List[str] = self.size['''shortest_edge''']
__SCREAMING_SNAKE_CASE : List[str] = self.size['''shortest_edge''']
else:
__SCREAMING_SNAKE_CASE : Optional[Any] = []
for image in image_inputs:
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Optional[Any] = self.get_expected_values([image] )
expected_values.append((expected_height, expected_width) )
__SCREAMING_SNAKE_CASE : Optional[int] = max(_lowerCamelCase , key=lambda _lowerCamelCase : item[0] )[0]
__SCREAMING_SNAKE_CASE : int = max(_lowerCamelCase , key=lambda _lowerCamelCase : item[1] )[1]
return expected_height, expected_width
@require_torch
@require_vision
class snake_case ( __UpperCAmelCase , unittest.TestCase ):
lowerCamelCase__ = YolosImageProcessor if is_vision_available() else None
def SCREAMING_SNAKE_CASE_ ( self :Optional[int] ):
__SCREAMING_SNAKE_CASE : List[Any] = YolosImageProcessingTester(self )
@property
def SCREAMING_SNAKE_CASE_ ( self :Optional[int] ):
return self.image_processor_tester.prepare_image_processor_dict()
def SCREAMING_SNAKE_CASE_ ( self :Tuple ):
__SCREAMING_SNAKE_CASE : Any = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(_lowerCamelCase , '''image_mean''' ) )
self.assertTrue(hasattr(_lowerCamelCase , '''image_std''' ) )
self.assertTrue(hasattr(_lowerCamelCase , '''do_normalize''' ) )
self.assertTrue(hasattr(_lowerCamelCase , '''do_resize''' ) )
self.assertTrue(hasattr(_lowerCamelCase , '''size''' ) )
def SCREAMING_SNAKE_CASE_ ( self :Tuple ):
__SCREAMING_SNAKE_CASE : Union[str, Any] = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {'''shortest_edge''': 1_8, '''longest_edge''': 1_3_3_3} )
self.assertEqual(image_processor.do_pad , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : Tuple = self.image_processing_class.from_dict(
self.image_processor_dict , size=4_2 , max_size=8_4 , pad_and_return_pixel_mask=_lowerCamelCase )
self.assertEqual(image_processor.size , {'''shortest_edge''': 4_2, '''longest_edge''': 8_4} )
self.assertEqual(image_processor.do_pad , _lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :List[str] ):
pass
def SCREAMING_SNAKE_CASE_ ( self :int ):
# Initialize image_processing
__SCREAMING_SNAKE_CASE : Optional[int] = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
__SCREAMING_SNAKE_CASE : List[str] = prepare_image_inputs(self.image_processor_tester , equal_resolution=_lowerCamelCase )
for image in image_inputs:
self.assertIsInstance(_lowerCamelCase , Image.Image )
# Test not batched input
__SCREAMING_SNAKE_CASE : str = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Dict = self.image_processor_tester.get_expected_values(_lowerCamelCase )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Tuple = self.image_processor_tester.get_expected_values(_lowerCamelCase , batched=_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Union[str, Any] = image_processing(_lowerCamelCase , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
def SCREAMING_SNAKE_CASE_ ( self :Optional[int] ):
# Initialize image_processing
__SCREAMING_SNAKE_CASE : Optional[Any] = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
__SCREAMING_SNAKE_CASE : Optional[int] = prepare_image_inputs(self.image_processor_tester , equal_resolution=_lowerCamelCase , numpify=_lowerCamelCase )
for image in image_inputs:
self.assertIsInstance(_lowerCamelCase , np.ndarray )
# Test not batched input
__SCREAMING_SNAKE_CASE : Dict = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : List[str] = self.image_processor_tester.get_expected_values(_lowerCamelCase )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
__SCREAMING_SNAKE_CASE : List[Any] = image_processing(_lowerCamelCase , return_tensors='''pt''' ).pixel_values
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Any = self.image_processor_tester.get_expected_values(_lowerCamelCase , batched=_lowerCamelCase )
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
def SCREAMING_SNAKE_CASE_ ( self :Any ):
# Initialize image_processing
__SCREAMING_SNAKE_CASE : str = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
__SCREAMING_SNAKE_CASE : Any = prepare_image_inputs(self.image_processor_tester , equal_resolution=_lowerCamelCase , torchify=_lowerCamelCase )
for image in image_inputs:
self.assertIsInstance(_lowerCamelCase , torch.Tensor )
# Test not batched input
__SCREAMING_SNAKE_CASE : List[str] = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Optional[Any] = self.image_processor_tester.get_expected_values(_lowerCamelCase )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
__SCREAMING_SNAKE_CASE : Optional[int] = image_processing(_lowerCamelCase , return_tensors='''pt''' ).pixel_values
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : List[Any] = self.image_processor_tester.get_expected_values(_lowerCamelCase , batched=_lowerCamelCase )
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
def SCREAMING_SNAKE_CASE_ ( self :List[str] ):
# Initialize image_processings
__SCREAMING_SNAKE_CASE : Dict = self.image_processing_class(**self.image_processor_dict )
__SCREAMING_SNAKE_CASE : Tuple = self.image_processing_class(do_resize=_lowerCamelCase , do_normalize=_lowerCamelCase , do_rescale=_lowerCamelCase )
# create random PyTorch tensors
__SCREAMING_SNAKE_CASE : int = prepare_image_inputs(self.image_processor_tester , equal_resolution=_lowerCamelCase , torchify=_lowerCamelCase )
for image in image_inputs:
self.assertIsInstance(_lowerCamelCase , torch.Tensor )
# Test whether the method "pad" and calling the image processor return the same tensors
__SCREAMING_SNAKE_CASE : Union[str, Any] = image_processing_a.pad(_lowerCamelCase , return_tensors='''pt''' )
__SCREAMING_SNAKE_CASE : Union[str, Any] = image_processing_a(_lowerCamelCase , return_tensors='''pt''' )
self.assertTrue(
torch.allclose(encoded_images_with_method['''pixel_values'''] , encoded_images['''pixel_values'''] , atol=1e-4 ) )
@slow
def SCREAMING_SNAKE_CASE_ ( self :int ):
# prepare image and target
__SCREAMING_SNAKE_CASE : Tuple = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
with open('''./tests/fixtures/tests_samples/COCO/coco_annotations.txt''' , '''r''' ) as f:
__SCREAMING_SNAKE_CASE : Tuple = json.loads(f.read() )
__SCREAMING_SNAKE_CASE : Union[str, Any] = {'''image_id''': 3_9_7_6_9, '''annotations''': target}
# encode them
__SCREAMING_SNAKE_CASE : List[Any] = YolosImageProcessor.from_pretrained('''hustvl/yolos-small''' )
__SCREAMING_SNAKE_CASE : Optional[Any] = image_processing(images=_lowerCamelCase , annotations=_lowerCamelCase , return_tensors='''pt''' )
# verify pixel values
__SCREAMING_SNAKE_CASE : Optional[int] = torch.Size([1, 3, 8_0_0, 1_0_6_6] )
self.assertEqual(encoding['''pixel_values'''].shape , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : List[str] = torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1] )
self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3] , _lowerCamelCase , atol=1e-4 ) )
# verify area
__SCREAMING_SNAKE_CASE : Union[str, Any] = torch.tensor([5_8_8_7.9_6_0_0, 1_1_2_5_0.2_0_6_1, 4_8_9_3_5_3.8_4_3_8, 8_3_7_1_2_2.7_5_0_0, 1_4_7_9_6_7.5_1_5_6, 1_6_5_7_3_2.3_4_3_8] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area'''] , _lowerCamelCase ) )
# verify boxes
__SCREAMING_SNAKE_CASE : List[str] = torch.Size([6, 4] )
self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : Any = torch.tensor([0.5_5_0_3, 0.2_7_6_5, 0.0_6_0_4, 0.2_2_1_5] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0] , _lowerCamelCase , atol=1e-3 ) )
# verify image_id
__SCREAMING_SNAKE_CASE : Optional[Any] = torch.tensor([3_9_7_6_9] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id'''] , _lowerCamelCase ) )
# verify is_crowd
__SCREAMING_SNAKE_CASE : str = torch.tensor([0, 0, 0, 0, 0, 0] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd'''] , _lowerCamelCase ) )
# verify class_labels
__SCREAMING_SNAKE_CASE : Dict = torch.tensor([7_5, 7_5, 6_3, 6_5, 1_7, 1_7] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels'''] , _lowerCamelCase ) )
# verify orig_size
__SCREAMING_SNAKE_CASE : Union[str, Any] = torch.tensor([4_8_0, 6_4_0] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size'''] , _lowerCamelCase ) )
# verify size
__SCREAMING_SNAKE_CASE : List[str] = torch.tensor([8_0_0, 1_0_6_6] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size'''] , _lowerCamelCase ) )
@slow
def SCREAMING_SNAKE_CASE_ ( self :Optional[int] ):
# prepare image, target and masks_path
__SCREAMING_SNAKE_CASE : List[str] = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
with open('''./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt''' , '''r''' ) as f:
__SCREAMING_SNAKE_CASE : Union[str, Any] = json.loads(f.read() )
__SCREAMING_SNAKE_CASE : Dict = {'''file_name''': '''000000039769.png''', '''image_id''': 3_9_7_6_9, '''segments_info''': target}
__SCREAMING_SNAKE_CASE : Optional[int] = pathlib.Path('''./tests/fixtures/tests_samples/COCO/coco_panoptic''' )
# encode them
__SCREAMING_SNAKE_CASE : Any = YolosImageProcessor(format='''coco_panoptic''' )
__SCREAMING_SNAKE_CASE : Dict = image_processing(images=_lowerCamelCase , annotations=_lowerCamelCase , masks_path=_lowerCamelCase , return_tensors='''pt''' )
# verify pixel values
__SCREAMING_SNAKE_CASE : str = torch.Size([1, 3, 8_0_0, 1_0_6_6] )
self.assertEqual(encoding['''pixel_values'''].shape , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : str = torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1] )
self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3] , _lowerCamelCase , atol=1e-4 ) )
# verify area
__SCREAMING_SNAKE_CASE : Any = torch.tensor([1_4_7_9_7_9.6_8_7_5, 1_6_5_5_2_7.0_4_6_9, 4_8_4_6_3_8.5_9_3_8, 1_1_2_9_2.9_3_7_5, 5_8_7_9.6_5_6_2, 7_6_3_4.1_1_4_7] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area'''] , _lowerCamelCase ) )
# verify boxes
__SCREAMING_SNAKE_CASE : List[str] = torch.Size([6, 4] )
self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : int = torch.tensor([0.2_6_2_5, 0.5_4_3_7, 0.4_6_8_8, 0.8_6_2_5] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0] , _lowerCamelCase , atol=1e-3 ) )
# verify image_id
__SCREAMING_SNAKE_CASE : Dict = torch.tensor([3_9_7_6_9] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id'''] , _lowerCamelCase ) )
# verify is_crowd
__SCREAMING_SNAKE_CASE : List[Any] = torch.tensor([0, 0, 0, 0, 0, 0] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd'''] , _lowerCamelCase ) )
# verify class_labels
__SCREAMING_SNAKE_CASE : Optional[Any] = torch.tensor([1_7, 1_7, 6_3, 7_5, 7_5, 9_3] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels'''] , _lowerCamelCase ) )
# verify masks
__SCREAMING_SNAKE_CASE : Optional[Any] = 8_2_2_8_7_3
self.assertEqual(encoding['''labels'''][0]['''masks'''].sum().item() , _lowerCamelCase )
# verify orig_size
__SCREAMING_SNAKE_CASE : List[str] = torch.tensor([4_8_0, 6_4_0] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size'''] , _lowerCamelCase ) )
# verify size
__SCREAMING_SNAKE_CASE : Any = torch.tensor([8_0_0, 1_0_6_6] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size'''] , _lowerCamelCase ) )
| 674 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase_ = logging.get_logger(__name__)
UpperCAmelCase_ = {
'''weiweishi/roc-bert-base-zh''': '''https://huggingface.co/weiweishi/roc-bert-base-zh/resolve/main/config.json''',
}
class __magic_name__ ( __UpperCAmelCase ):
"""simple docstring"""
lowerCAmelCase : Optional[int] = '''roc_bert'''
def __init__( self : Union[str, Any] , _lowercase : Any=30_522 , _lowercase : str=768 , _lowercase : Optional[Any]=12 , _lowercase : List[str]=12 , _lowercase : str=3_072 , _lowercase : Tuple="gelu" , _lowercase : List[Any]=0.1 , _lowercase : List[str]=0.1 , _lowercase : Optional[int]=512 , _lowercase : Dict=2 , _lowercase : Any=0.02 , _lowercase : Optional[int]=1E-12 , _lowercase : str=True , _lowercase : Any=0 , _lowercase : List[str]="absolute" , _lowercase : List[Any]=None , _lowercase : Any=True , _lowercase : Union[str, Any]=True , _lowercase : str=768 , _lowercase : Union[str, Any]=910 , _lowercase : List[Any]=512 , _lowercase : Optional[int]=24_858 , _lowercase : Union[str, Any]=True , **_lowercase : str , ):
"""simple docstring"""
_UpperCamelCase: List[str] = vocab_size
_UpperCamelCase: int = max_position_embeddings
_UpperCamelCase: List[str] = hidden_size
_UpperCamelCase: str = num_hidden_layers
_UpperCamelCase: int = num_attention_heads
_UpperCamelCase: Any = intermediate_size
_UpperCamelCase: Optional[int] = hidden_act
_UpperCamelCase: List[Any] = hidden_dropout_prob
_UpperCamelCase: Optional[Any] = attention_probs_dropout_prob
_UpperCamelCase: Union[str, Any] = initializer_range
_UpperCamelCase: Union[str, Any] = type_vocab_size
_UpperCamelCase: List[str] = layer_norm_eps
_UpperCamelCase: Optional[int] = use_cache
_UpperCamelCase: str = enable_pronunciation
_UpperCamelCase: List[str] = enable_shape
_UpperCamelCase: Tuple = pronunciation_embed_dim
_UpperCamelCase: Optional[Any] = pronunciation_vocab_size
_UpperCamelCase: str = shape_embed_dim
_UpperCamelCase: Union[str, Any] = shape_vocab_size
_UpperCamelCase: Tuple = concat_input
_UpperCamelCase: Union[str, Any] = position_embedding_type
_UpperCamelCase: str = classifier_dropout
super().__init__(pad_token_id=_lowerCamelCase , **_lowerCamelCase ) | 271 |
"""simple docstring"""
from __future__ import annotations
def lowerCAmelCase_ ( lowercase_ : int ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : Optional[int] = str(lowercase_ )
return len(lowercase_ ) == 9 and set(lowercase_ ) == set('''123456789''' )
def lowerCAmelCase_ ( ):
'''simple docstring'''
for base_num in range(9999 , 4999 , -1 ):
__SCREAMING_SNAKE_CASE : List[str] = 10_0002 * base_num
if is_9_pandigital(lowercase_ ):
return candidate
for base_num in range(333 , 99 , -1 ):
__SCREAMING_SNAKE_CASE : List[Any] = 100_2003 * base_num
if is_9_pandigital(lowercase_ ):
return candidate
return None
if __name__ == "__main__":
print(f'{solution() = }')
| 674 | 0 |
import os
from tempfile import TemporaryDirectory
from unittest import TestCase
import pytest
from absl.testing import parameterized
from datasets import config
from datasets.arrow_reader import HF_GCP_BASE_URL
from datasets.builder import DatasetBuilder
from datasets.dataset_dict import IterableDatasetDict
from datasets.iterable_dataset import IterableDataset
from datasets.load import dataset_module_factory, import_main_class
from datasets.utils.file_utils import cached_path
_snake_case = [
{'''dataset''': '''wikipedia''', '''config_name''': '''20220301.de'''},
{'''dataset''': '''wikipedia''', '''config_name''': '''20220301.en'''},
{'''dataset''': '''wikipedia''', '''config_name''': '''20220301.fr'''},
{'''dataset''': '''wikipedia''', '''config_name''': '''20220301.frr'''},
{'''dataset''': '''wikipedia''', '''config_name''': '''20220301.it'''},
{'''dataset''': '''wikipedia''', '''config_name''': '''20220301.simple'''},
{'''dataset''': '''snli''', '''config_name''': '''plain_text'''},
{'''dataset''': '''eli5''', '''config_name''': '''LFQA_reddit'''},
{'''dataset''': '''wiki40b''', '''config_name''': '''en'''},
{'''dataset''': '''wiki_dpr''', '''config_name''': '''psgs_w100.nq.compressed'''},
{'''dataset''': '''wiki_dpr''', '''config_name''': '''psgs_w100.nq.no_index'''},
{'''dataset''': '''wiki_dpr''', '''config_name''': '''psgs_w100.multiset.no_index'''},
{'''dataset''': '''natural_questions''', '''config_name''': '''default'''},
]
def __lowerCamelCase ( _lowercase=True ) -> Optional[int]:
if with_config:
return [
{
"testcase_name": d["dataset"] + "/" + d["config_name"],
"dataset": d["dataset"],
"config_name": d["config_name"],
}
for d in DATASETS_ON_HF_GCP
]
else:
return [
{"testcase_name": dataset, "dataset": dataset} for dataset in {d["dataset"] for d in DATASETS_ON_HF_GCP}
]
@parameterized.named_parameters(list_datasets_on_hf_gcp_parameters(with_config=__UpperCAmelCase ) )
class _lowerCAmelCase ( __UpperCAmelCase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : str =None
SCREAMING_SNAKE_CASE_ : Optional[Any] =None
def __lowerCAmelCase ( self : Dict , SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : Tuple ):
"""simple docstring"""
with TemporaryDirectory() as tmp_dir:
UpperCamelCase = dataset_module_factory(_lowerCamelCase , cache_dir=_lowerCamelCase )
UpperCamelCase = import_main_class(dataset_module.module_path , dataset=_lowerCamelCase )
UpperCamelCase = builder_cls(
cache_dir=_lowerCamelCase , config_name=_lowerCamelCase , hash=dataset_module.hash , )
UpperCamelCase = '''/'''.join(
[
HF_GCP_BASE_URL,
builder_instance._relative_data_dir(with_hash=_lowerCamelCase ).replace(os.sep , '/' ),
config.DATASET_INFO_FILENAME,
] )
UpperCamelCase = cached_path(_lowerCamelCase , cache_dir=_lowerCamelCase )
self.assertTrue(os.path.exists(_lowerCamelCase ) )
@pytest.mark.integration
def __lowerCamelCase ( _lowercase ) -> Optional[Any]:
UpperCamelCase = tmp_path_factory.mktemp('test_hf_gcp' ) / '''test_wikipedia_simple'''
UpperCamelCase = dataset_module_factory('wikipedia' , cache_dir=lowercase_ )
UpperCamelCase = import_main_class(dataset_module.module_path )
UpperCamelCase = builder_cls(
cache_dir=lowercase_ , config_name='20220301.frr' , hash=dataset_module.hash , )
# use the HF cloud storage, not the original download_and_prepare that uses apache-beam
UpperCamelCase = None
builder_instance.download_and_prepare()
UpperCamelCase = builder_instance.as_dataset()
assert ds
@pytest.mark.integration
def __lowerCamelCase ( _lowercase ) -> str:
UpperCamelCase = dataset_module_factory('wikipedia' , cache_dir=lowercase_ )
UpperCamelCase = import_main_class(dataset_module.module_path , dataset=lowercase_ )
UpperCamelCase = builder_cls(
cache_dir=lowercase_ , config_name='20220301.frr' , hash=dataset_module.hash , )
UpperCamelCase = builder_instance.as_streaming_dataset()
assert ds
assert isinstance(lowercase_ , lowercase_ )
assert "train" in ds
assert isinstance(ds['train'] , lowercase_ )
assert next(iter(ds['train'] ) )
| 282 |
"""simple docstring"""
import json
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
from transformers import (
CONFIG_MAPPING,
IMAGE_PROCESSOR_MAPPING,
AutoConfig,
AutoImageProcessor,
CLIPConfig,
CLIPImageProcessor,
)
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER
sys.path.append(str(Path(__file__).parent.parent.parent.parent / '''utils'''))
from test_module.custom_configuration import CustomConfig # noqa E402
from test_module.custom_image_processing import CustomImageProcessor # noqa E402
class snake_case ( unittest.TestCase ):
def SCREAMING_SNAKE_CASE_ ( self :int ):
__SCREAMING_SNAKE_CASE : Tuple = 0
def SCREAMING_SNAKE_CASE_ ( self :Any ):
__SCREAMING_SNAKE_CASE : Optional[Any] = AutoImageProcessor.from_pretrained('''openai/clip-vit-base-patch32''' )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :int ):
with tempfile.TemporaryDirectory() as tmpdirname:
__SCREAMING_SNAKE_CASE : Optional[Any] = Path(_lowerCamelCase ) / '''preprocessor_config.json'''
__SCREAMING_SNAKE_CASE : Dict = Path(_lowerCamelCase ) / '''config.json'''
json.dump(
{'''image_processor_type''': '''CLIPImageProcessor''', '''processor_class''': '''CLIPProcessor'''} , open(_lowerCamelCase , '''w''' ) , )
json.dump({'''model_type''': '''clip'''} , open(_lowerCamelCase , '''w''' ) )
__SCREAMING_SNAKE_CASE : Dict = AutoImageProcessor.from_pretrained(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :Optional[int] ):
# Ensure we can load the image processor from the feature extractor config
with tempfile.TemporaryDirectory() as tmpdirname:
__SCREAMING_SNAKE_CASE : Tuple = Path(_lowerCamelCase ) / '''preprocessor_config.json'''
__SCREAMING_SNAKE_CASE : Union[str, Any] = Path(_lowerCamelCase ) / '''config.json'''
json.dump(
{'''feature_extractor_type''': '''CLIPFeatureExtractor''', '''processor_class''': '''CLIPProcessor'''} , open(_lowerCamelCase , '''w''' ) , )
json.dump({'''model_type''': '''clip'''} , open(_lowerCamelCase , '''w''' ) )
__SCREAMING_SNAKE_CASE : int = AutoImageProcessor.from_pretrained(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :int ):
with tempfile.TemporaryDirectory() as tmpdirname:
__SCREAMING_SNAKE_CASE : str = CLIPConfig()
# Create a dummy config file with image_proceesor_type
__SCREAMING_SNAKE_CASE : Tuple = Path(_lowerCamelCase ) / '''preprocessor_config.json'''
__SCREAMING_SNAKE_CASE : Any = Path(_lowerCamelCase ) / '''config.json'''
json.dump(
{'''image_processor_type''': '''CLIPImageProcessor''', '''processor_class''': '''CLIPProcessor'''} , open(_lowerCamelCase , '''w''' ) , )
json.dump({'''model_type''': '''clip'''} , open(_lowerCamelCase , '''w''' ) )
# remove image_processor_type to make sure config.json alone is enough to load image processor locally
__SCREAMING_SNAKE_CASE : int = AutoImageProcessor.from_pretrained(_lowerCamelCase ).to_dict()
config_dict.pop('''image_processor_type''' )
__SCREAMING_SNAKE_CASE : Union[str, Any] = CLIPImageProcessor(**_lowerCamelCase )
# save in new folder
model_config.save_pretrained(_lowerCamelCase )
config.save_pretrained(_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Tuple = AutoImageProcessor.from_pretrained(_lowerCamelCase )
# make sure private variable is not incorrectly saved
__SCREAMING_SNAKE_CASE : Tuple = json.loads(config.to_json_string() )
self.assertTrue('''_processor_class''' not in dict_as_saved )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :Tuple ):
with tempfile.TemporaryDirectory() as tmpdirname:
__SCREAMING_SNAKE_CASE : Dict = Path(_lowerCamelCase ) / '''preprocessor_config.json'''
json.dump(
{'''image_processor_type''': '''CLIPImageProcessor''', '''processor_class''': '''CLIPProcessor'''} , open(_lowerCamelCase , '''w''' ) , )
__SCREAMING_SNAKE_CASE : Optional[Any] = AutoImageProcessor.from_pretrained(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :Optional[int] ):
with self.assertRaisesRegex(
_lowerCamelCase , '''clip-base is not a local folder and is not a valid model identifier''' ):
__SCREAMING_SNAKE_CASE : Tuple = AutoImageProcessor.from_pretrained('''clip-base''' )
def SCREAMING_SNAKE_CASE_ ( self :Union[str, Any] ):
with self.assertRaisesRegex(
_lowerCamelCase , r'''aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)''' ):
__SCREAMING_SNAKE_CASE : Optional[int] = AutoImageProcessor.from_pretrained(_lowerCamelCase , revision='''aaaaaa''' )
def SCREAMING_SNAKE_CASE_ ( self :List[str] ):
with self.assertRaisesRegex(
_lowerCamelCase , '''hf-internal-testing/config-no-model does not appear to have a file named preprocessor_config.json.''' , ):
__SCREAMING_SNAKE_CASE : Optional[int] = AutoImageProcessor.from_pretrained('''hf-internal-testing/config-no-model''' )
def SCREAMING_SNAKE_CASE_ ( self :Tuple ):
# If remote code is not set, we will time out when asking whether to load the model.
with self.assertRaises(_lowerCamelCase ):
__SCREAMING_SNAKE_CASE : Union[str, Any] = AutoImageProcessor.from_pretrained('''hf-internal-testing/test_dynamic_image_processor''' )
# If remote code is disabled, we can't load this config.
with self.assertRaises(_lowerCamelCase ):
__SCREAMING_SNAKE_CASE : Optional[Any] = AutoImageProcessor.from_pretrained(
'''hf-internal-testing/test_dynamic_image_processor''' , trust_remote_code=_lowerCamelCase )
__SCREAMING_SNAKE_CASE : int = AutoImageProcessor.from_pretrained(
'''hf-internal-testing/test_dynamic_image_processor''' , trust_remote_code=_lowerCamelCase )
self.assertEqual(image_processor.__class__.__name__ , '''NewImageProcessor''' )
# Test image processor can be reloaded.
with tempfile.TemporaryDirectory() as tmp_dir:
image_processor.save_pretrained(_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Tuple = AutoImageProcessor.from_pretrained(_lowerCamelCase , trust_remote_code=_lowerCamelCase )
self.assertEqual(reloaded_image_processor.__class__.__name__ , '''NewImageProcessor''' )
def SCREAMING_SNAKE_CASE_ ( self :Tuple ):
try:
AutoConfig.register('''custom''' , _lowerCamelCase )
AutoImageProcessor.register(_lowerCamelCase , _lowerCamelCase )
# Trying to register something existing in the Transformers library will raise an error
with self.assertRaises(_lowerCamelCase ):
AutoImageProcessor.register(_lowerCamelCase , _lowerCamelCase )
with tempfile.TemporaryDirectory() as tmpdirname:
__SCREAMING_SNAKE_CASE : Dict = Path(_lowerCamelCase ) / '''preprocessor_config.json'''
__SCREAMING_SNAKE_CASE : Dict = Path(_lowerCamelCase ) / '''config.json'''
json.dump(
{'''feature_extractor_type''': '''CLIPFeatureExtractor''', '''processor_class''': '''CLIPProcessor'''} , open(_lowerCamelCase , '''w''' ) , )
json.dump({'''model_type''': '''clip'''} , open(_lowerCamelCase , '''w''' ) )
__SCREAMING_SNAKE_CASE : Optional[int] = CustomImageProcessor.from_pretrained(_lowerCamelCase )
# Now that the config is registered, it can be used as any other config with the auto-API
with tempfile.TemporaryDirectory() as tmp_dir:
image_processor.save_pretrained(_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Tuple = AutoImageProcessor.from_pretrained(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
finally:
if "custom" in CONFIG_MAPPING._extra_content:
del CONFIG_MAPPING._extra_content["custom"]
if CustomConfig in IMAGE_PROCESSOR_MAPPING._extra_content:
del IMAGE_PROCESSOR_MAPPING._extra_content[CustomConfig]
def SCREAMING_SNAKE_CASE_ ( self :Dict ):
class snake_case ( __UpperCAmelCase ):
lowerCamelCase__ = True
try:
AutoConfig.register('''custom''' , _lowerCamelCase )
AutoImageProcessor.register(_lowerCamelCase , _lowerCamelCase )
# If remote code is not set, the default is to use local
__SCREAMING_SNAKE_CASE : List[str] = AutoImageProcessor.from_pretrained('''hf-internal-testing/test_dynamic_image_processor''' )
self.assertEqual(image_processor.__class__.__name__ , '''NewImageProcessor''' )
self.assertTrue(image_processor.is_local )
# If remote code is disabled, we load the local one.
__SCREAMING_SNAKE_CASE : str = AutoImageProcessor.from_pretrained(
'''hf-internal-testing/test_dynamic_image_processor''' , trust_remote_code=_lowerCamelCase )
self.assertEqual(image_processor.__class__.__name__ , '''NewImageProcessor''' )
self.assertTrue(image_processor.is_local )
# If remote is enabled, we load from the Hub
__SCREAMING_SNAKE_CASE : Optional[int] = AutoImageProcessor.from_pretrained(
'''hf-internal-testing/test_dynamic_image_processor''' , trust_remote_code=_lowerCamelCase )
self.assertEqual(image_processor.__class__.__name__ , '''NewImageProcessor''' )
self.assertTrue(not hasattr(_lowerCamelCase , '''is_local''' ) )
finally:
if "custom" in CONFIG_MAPPING._extra_content:
del CONFIG_MAPPING._extra_content["custom"]
if CustomConfig in IMAGE_PROCESSOR_MAPPING._extra_content:
del IMAGE_PROCESSOR_MAPPING._extra_content[CustomConfig]
| 674 | 0 |
"""simple docstring"""
import unittest
import numpy as np
from transformers import BertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_flax_available():
from transformers.models.bert.modeling_flax_bert import (
FlaxBertForMaskedLM,
FlaxBertForMultipleChoice,
FlaxBertForNextSentencePrediction,
FlaxBertForPreTraining,
FlaxBertForQuestionAnswering,
FlaxBertForSequenceClassification,
FlaxBertForTokenClassification,
FlaxBertModel,
)
class __lowercase( unittest.TestCase ):
'''simple docstring'''
def __init__( self , __a , __a=13 , __a=7 , __a=True , __a=True , __a=True , __a=True , __a=99 , __a=32 , __a=5 , __a=4 , __a=37 , __a="gelu" , __a=0.1 , __a=0.1 , __a=512 , __a=16 , __a=2 , __a=0.02 , __a=4 , ):
__lowerCamelCase : List[Any] = parent
__lowerCamelCase : int = batch_size
__lowerCamelCase : int = seq_length
__lowerCamelCase : Optional[int] = is_training
__lowerCamelCase : List[Any] = use_attention_mask
__lowerCamelCase : List[Any] = use_token_type_ids
__lowerCamelCase : List[str] = use_labels
__lowerCamelCase : Optional[Any] = vocab_size
__lowerCamelCase : Union[str, Any] = hidden_size
__lowerCamelCase : Any = num_hidden_layers
__lowerCamelCase : str = num_attention_heads
__lowerCamelCase : str = intermediate_size
__lowerCamelCase : List[str] = hidden_act
__lowerCamelCase : Tuple = hidden_dropout_prob
__lowerCamelCase : Any = attention_probs_dropout_prob
__lowerCamelCase : Union[str, Any] = max_position_embeddings
__lowerCamelCase : str = type_vocab_size
__lowerCamelCase : Union[str, Any] = type_sequence_label_size
__lowerCamelCase : Any = initializer_range
__lowerCamelCase : int = num_choices
def snake_case_ ( self ):
__lowerCamelCase : int = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
__lowerCamelCase : Union[str, Any] = None
if self.use_attention_mask:
__lowerCamelCase : Optional[int] = random_attention_mask([self.batch_size, self.seq_length] )
__lowerCamelCase : Optional[Any] = None
if self.use_token_type_ids:
__lowerCamelCase : Union[str, Any] = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
__lowerCamelCase : Any = BertConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=_lowerCamelCase , initializer_range=self.initializer_range , )
return config, input_ids, token_type_ids, attention_mask
def snake_case_ ( self ):
__lowerCamelCase : int = self.prepare_config_and_inputs()
__lowerCamelCase : Optional[int] = config_and_inputs
__lowerCamelCase : Any = {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': attention_mask}
return config, inputs_dict
def snake_case_ ( self ):
__lowerCamelCase : Any = self.prepare_config_and_inputs()
__lowerCamelCase : Union[str, Any] = config_and_inputs
__lowerCamelCase : int = True
__lowerCamelCase : Tuple = floats_tensor([self.batch_size, self.seq_length, self.hidden_size] )
__lowerCamelCase : Union[str, Any] = ids_tensor([self.batch_size, self.seq_length] , vocab_size=2 )
return (
config,
input_ids,
attention_mask,
encoder_hidden_states,
encoder_attention_mask,
)
@require_flax
class __lowercase( __UpperCAmelCase , unittest.TestCase ):
'''simple docstring'''
__a : Optional[Any] = True
__a : Dict = (
(
FlaxBertModel,
FlaxBertForPreTraining,
FlaxBertForMaskedLM,
FlaxBertForMultipleChoice,
FlaxBertForQuestionAnswering,
FlaxBertForNextSentencePrediction,
FlaxBertForSequenceClassification,
FlaxBertForTokenClassification,
FlaxBertForQuestionAnswering,
)
if is_flax_available()
else ()
)
def snake_case_ ( self ):
__lowerCamelCase : Union[str, Any] = FlaxBertModelTester(self )
@slow
def snake_case_ ( self ):
# Only check this for base model, not necessary for all model classes.
# This will also help speed-up tests.
__lowerCamelCase : int = FlaxBertModel.from_pretrained('bert-base-cased' )
__lowerCamelCase : Tuple = model(np.ones((1, 1) ) )
self.assertIsNotNone(_lowerCamelCase )
| 594 |
"""simple docstring"""
from __future__ import annotations
from typing import Any
class snake_case ( __UpperCAmelCase ):
pass
class snake_case :
def __init__( self :List[Any] , _lowerCamelCase :Any ):
__SCREAMING_SNAKE_CASE : Any = data
__SCREAMING_SNAKE_CASE : Node | None = None
def __iter__( self :Union[str, Any] ):
__SCREAMING_SNAKE_CASE : List[str] = self
__SCREAMING_SNAKE_CASE : List[str] = []
while node:
if node in visited:
raise ContainsLoopError
visited.append(_lowerCamelCase )
yield node.data
__SCREAMING_SNAKE_CASE : List[str] = node.next_node
@property
def SCREAMING_SNAKE_CASE_ ( self :Any ):
try:
list(self )
return False
except ContainsLoopError:
return True
if __name__ == "__main__":
_lowerCamelCase = Node(1)
_lowerCamelCase = Node(2)
_lowerCamelCase = Node(3)
_lowerCamelCase = Node(4)
print(root_node.has_loop) # False
_lowerCamelCase = root_node.next_node
print(root_node.has_loop) # True
_lowerCamelCase = Node(5)
_lowerCamelCase = Node(6)
_lowerCamelCase = Node(5)
_lowerCamelCase = Node(6)
print(root_node.has_loop) # False
_lowerCamelCase = Node(1)
print(root_node.has_loop) # False
| 674 | 0 |
def _lowercase ( SCREAMING_SNAKE_CASE_ : Any , SCREAMING_SNAKE_CASE_ : Optional[int] ):
"""simple docstring"""
UpperCamelCase = ''''''
for i in table:
res += inp[i - 1]
return res
def _lowercase ( SCREAMING_SNAKE_CASE_ : str ):
"""simple docstring"""
return data[1:] + data[0]
def _lowercase ( SCREAMING_SNAKE_CASE_ : Optional[int] , SCREAMING_SNAKE_CASE_ : List[Any] ):
"""simple docstring"""
UpperCamelCase = ''''''
for i in range(len(lowercase_ ) ):
if a[i] == b[i]:
res += "0"
else:
res += "1"
return res
def _lowercase ( SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : Optional[Any] ):
"""simple docstring"""
UpperCamelCase = int("""0b""" + data[0] + data[-1] , 2 )
UpperCamelCase = int("""0b""" + data[1:3] , 2 )
return bin(s[row][col] )[2:]
def _lowercase ( SCREAMING_SNAKE_CASE_ : List[Any] , SCREAMING_SNAKE_CASE_ : Optional[Any] , SCREAMING_SNAKE_CASE_ : List[Any] , SCREAMING_SNAKE_CASE_ : Tuple , SCREAMING_SNAKE_CASE_ : Any ):
"""simple docstring"""
UpperCamelCase = message[:4]
UpperCamelCase = message[4:]
UpperCamelCase = apply_table(lowercase_ , lowercase_ )
UpperCamelCase = xor(lowercase_ , lowercase_ )
UpperCamelCase = apply_sbox(lowercase_ , temp[:4] ) # noqa: E741
UpperCamelCase = apply_sbox(lowercase_ , temp[4:] )
UpperCamelCase = '''0''' * (2 - len(lowercase_ )) + l # noqa: E741
UpperCamelCase = '''0''' * (2 - len(lowercase_ )) + r
UpperCamelCase = apply_table(l + r , lowercase_ )
UpperCamelCase = xor(lowercase_ , lowercase_ )
return temp + right
if __name__ == "__main__":
__snake_case = input("Enter 10 bit key: ")
__snake_case = input("Enter 8 bit message: ")
__snake_case = [6, 3, 7, 4, 8, 5, 10, 9]
__snake_case = [3, 5, 2, 7, 4, 10, 1, 9, 8, 6]
__snake_case = [2, 4, 3, 1]
__snake_case = [2, 6, 3, 1, 4, 8, 5, 7]
__snake_case = [4, 1, 3, 5, 7, 2, 8, 6]
__snake_case = [4, 1, 2, 3, 2, 3, 4, 1]
__snake_case = [[1, 0, 3, 2], [3, 2, 1, 0], [0, 2, 1, 3], [3, 1, 3, 2]]
__snake_case = [[0, 1, 2, 3], [2, 0, 1, 3], [3, 0, 1, 0], [2, 1, 0, 3]]
# key generation
__snake_case = apply_table(key, paa_table)
__snake_case = temp[:5]
__snake_case = temp[5:]
__snake_case = left_shift(left)
__snake_case = left_shift(right)
__snake_case = apply_table(left + right, pa_table)
__snake_case = left_shift(left)
__snake_case = left_shift(right)
__snake_case = left_shift(left)
__snake_case = left_shift(right)
__snake_case = apply_table(left + right, pa_table)
# encryption
__snake_case = apply_table(message, IP)
__snake_case = function(expansion, sa, sa, keya, temp)
__snake_case = temp[4:] + temp[:4]
__snake_case = function(expansion, sa, sa, keya, temp)
__snake_case = apply_table(temp, IP_inv)
print("Cipher text is:", CT)
# decryption
__snake_case = apply_table(CT, IP)
__snake_case = function(expansion, sa, sa, keya, temp)
__snake_case = temp[4:] + temp[:4]
__snake_case = function(expansion, sa, sa, keya, temp)
__snake_case = apply_table(temp, IP_inv)
print("Plain text after decypting is:", PT)
| 386 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCamelCase = logging.get_logger(__name__)
_lowerCamelCase = {
'''weiweishi/roc-bert-base-zh''': '''https://huggingface.co/weiweishi/roc-bert-base-zh/resolve/main/config.json''',
}
class snake_case ( __UpperCAmelCase ):
lowerCamelCase__ = '''roc_bert'''
def __init__( self :Union[str, Any] , _lowerCamelCase :Any=3_0_5_2_2 , _lowerCamelCase :str=7_6_8 , _lowerCamelCase :Optional[Any]=1_2 , _lowerCamelCase :List[str]=1_2 , _lowerCamelCase :str=3_0_7_2 , _lowerCamelCase :Tuple="gelu" , _lowerCamelCase :List[Any]=0.1 , _lowerCamelCase :List[str]=0.1 , _lowerCamelCase :Optional[int]=5_1_2 , _lowerCamelCase :Dict=2 , _lowerCamelCase :Any=0.0_2 , _lowerCamelCase :Optional[int]=1e-12 , _lowerCamelCase :str=True , _lowerCamelCase :Any=0 , _lowerCamelCase :List[str]="absolute" , _lowerCamelCase :List[Any]=None , _lowerCamelCase :Any=True , _lowerCamelCase :Union[str, Any]=True , _lowerCamelCase :str=7_6_8 , _lowerCamelCase :Union[str, Any]=9_1_0 , _lowerCamelCase :List[Any]=5_1_2 , _lowerCamelCase :Optional[int]=2_4_8_5_8 , _lowerCamelCase :Union[str, Any]=True , **_lowerCamelCase :str , ):
__SCREAMING_SNAKE_CASE : List[str] = vocab_size
__SCREAMING_SNAKE_CASE : int = max_position_embeddings
__SCREAMING_SNAKE_CASE : List[str] = hidden_size
__SCREAMING_SNAKE_CASE : str = num_hidden_layers
__SCREAMING_SNAKE_CASE : int = num_attention_heads
__SCREAMING_SNAKE_CASE : Any = intermediate_size
__SCREAMING_SNAKE_CASE : Optional[int] = hidden_act
__SCREAMING_SNAKE_CASE : List[Any] = hidden_dropout_prob
__SCREAMING_SNAKE_CASE : Optional[Any] = attention_probs_dropout_prob
__SCREAMING_SNAKE_CASE : Union[str, Any] = initializer_range
__SCREAMING_SNAKE_CASE : Union[str, Any] = type_vocab_size
__SCREAMING_SNAKE_CASE : List[str] = layer_norm_eps
__SCREAMING_SNAKE_CASE : Optional[int] = use_cache
__SCREAMING_SNAKE_CASE : str = enable_pronunciation
__SCREAMING_SNAKE_CASE : List[str] = enable_shape
__SCREAMING_SNAKE_CASE : Tuple = pronunciation_embed_dim
__SCREAMING_SNAKE_CASE : Optional[Any] = pronunciation_vocab_size
__SCREAMING_SNAKE_CASE : str = shape_embed_dim
__SCREAMING_SNAKE_CASE : Union[str, Any] = shape_vocab_size
__SCREAMING_SNAKE_CASE : Tuple = concat_input
__SCREAMING_SNAKE_CASE : Union[str, Any] = position_embedding_type
__SCREAMING_SNAKE_CASE : str = classifier_dropout
super().__init__(pad_token_id=_lowerCamelCase , **_lowerCamelCase )
| 674 | 0 |
import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import DetrImageProcessor
class SCREAMING_SNAKE_CASE_ ( unittest.TestCase ):
"""simple docstring"""
def __init__( self : Optional[int] , lowerCAmelCase : Any , lowerCAmelCase : Dict=7 , lowerCAmelCase : Tuple=3 , lowerCAmelCase : List[Any]=30 , lowerCAmelCase : List[str]=400 , lowerCAmelCase : Any=True , lowerCAmelCase : str=None , lowerCAmelCase : List[Any]=True , lowerCAmelCase : List[Any]=1 / 255 , lowerCAmelCase : Dict=True , lowerCAmelCase : Any=[0.5, 0.5, 0.5] , lowerCAmelCase : int=[0.5, 0.5, 0.5] , lowerCAmelCase : Union[str, Any]=True , ) -> Optional[int]:
"""simple docstring"""
__UpperCamelCase : str = size if size is not None else {'''shortest_edge''': 18, '''longest_edge''': 1333}
__UpperCamelCase : Dict = parent
__UpperCamelCase : Dict = batch_size
__UpperCamelCase : int = num_channels
__UpperCamelCase : int = min_resolution
__UpperCamelCase : int = max_resolution
__UpperCamelCase : Optional[int] = do_resize
__UpperCamelCase : List[str] = size
__UpperCamelCase : Dict = do_rescale
__UpperCamelCase : List[str] = rescale_factor
__UpperCamelCase : Tuple = do_normalize
__UpperCamelCase : List[Any] = image_mean
__UpperCamelCase : str = image_std
__UpperCamelCase : List[str] = do_pad
def lowerCamelCase__ ( self : str ) -> List[Any]:
"""simple docstring"""
return {
"do_resize": self.do_resize,
"size": self.size,
"do_rescale": self.do_rescale,
"rescale_factor": self.rescale_factor,
"do_normalize": self.do_normalize,
"image_mean": self.image_mean,
"image_std": self.image_std,
"do_pad": self.do_pad,
}
def lowerCamelCase__ ( self : Optional[Any] , lowerCAmelCase : int , lowerCAmelCase : Union[str, Any]=False ) -> int:
"""simple docstring"""
if not batched:
__UpperCamelCase : Optional[int] = image_inputs[0]
if isinstance(_lowerCamelCase , Image.Image ):
__UpperCamelCase : Tuple = image.size
else:
__UpperCamelCase : Any = image.shape[1], image.shape[2]
if w < h:
__UpperCamelCase : Optional[int] = int(self.size["""shortest_edge"""] * h / w )
__UpperCamelCase : Dict = self.size['''shortest_edge''']
elif w > h:
__UpperCamelCase : Tuple = self.size['''shortest_edge''']
__UpperCamelCase : Optional[Any] = int(self.size["""shortest_edge"""] * w / h )
else:
__UpperCamelCase : List[str] = self.size['''shortest_edge''']
__UpperCamelCase : List[Any] = self.size['''shortest_edge''']
else:
__UpperCamelCase : Optional[int] = []
for image in image_inputs:
__UpperCamelCase : Optional[int] = self.get_expected_values([image] )
expected_values.append((expected_height, expected_width) )
__UpperCamelCase : Optional[int] = max(_lowerCamelCase , key=lambda lowerCAmelCase : item[0] )[0]
__UpperCamelCase : Any = max(_lowerCamelCase , key=lambda lowerCAmelCase : item[1] )[1]
return expected_height, expected_width
@require_torch
@require_vision
class SCREAMING_SNAKE_CASE_ ( __UpperCAmelCase , unittest.TestCase ):
"""simple docstring"""
__magic_name__ : Tuple = DetrImageProcessor if is_vision_available() else None
def lowerCamelCase__ ( self : Optional[Any] ) -> Optional[Any]:
"""simple docstring"""
__UpperCamelCase : Tuple = DetrImageProcessingTester(self )
@property
def lowerCamelCase__ ( self : Union[str, Any] ) -> List[str]:
"""simple docstring"""
return self.image_processor_tester.prepare_image_processor_dict()
def lowerCamelCase__ ( self : Union[str, Any] ) -> Optional[Any]:
"""simple docstring"""
__UpperCamelCase : Optional[int] = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(_lowerCamelCase , """image_mean""" ) )
self.assertTrue(hasattr(_lowerCamelCase , """image_std""" ) )
self.assertTrue(hasattr(_lowerCamelCase , """do_normalize""" ) )
self.assertTrue(hasattr(_lowerCamelCase , """do_rescale""" ) )
self.assertTrue(hasattr(_lowerCamelCase , """rescale_factor""" ) )
self.assertTrue(hasattr(_lowerCamelCase , """do_resize""" ) )
self.assertTrue(hasattr(_lowerCamelCase , """size""" ) )
self.assertTrue(hasattr(_lowerCamelCase , """do_pad""" ) )
def lowerCamelCase__ ( self : int ) -> Dict:
"""simple docstring"""
__UpperCamelCase : int = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {"""shortest_edge""": 18, """longest_edge""": 1333} )
self.assertEqual(image_processor.do_pad , _lowerCamelCase )
__UpperCamelCase : int = self.image_processing_class.from_dict(
self.image_processor_dict , size=42 , max_size=84 , pad_and_return_pixel_mask=_lowerCamelCase )
self.assertEqual(image_processor.size , {"""shortest_edge""": 42, """longest_edge""": 84} )
self.assertEqual(image_processor.do_pad , _lowerCamelCase )
def lowerCamelCase__ ( self : Dict ) -> Tuple:
"""simple docstring"""
pass
def lowerCamelCase__ ( self : Optional[int] ) -> List[str]:
"""simple docstring"""
__UpperCamelCase : str = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
__UpperCamelCase : Dict = prepare_image_inputs(self.image_processor_tester , equal_resolution=_lowerCamelCase )
for image in image_inputs:
self.assertIsInstance(_lowerCamelCase , Image.Image )
# Test not batched input
__UpperCamelCase : Any = image_processing(image_inputs[0] , return_tensors="""pt""" ).pixel_values
__UpperCamelCase : List[Any] = self.image_processor_tester.get_expected_values(_lowerCamelCase )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
__UpperCamelCase : List[Any] = self.image_processor_tester.get_expected_values(_lowerCamelCase , batched=_lowerCamelCase )
__UpperCamelCase : Union[str, Any] = image_processing(_lowerCamelCase , return_tensors="""pt""" ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
def lowerCamelCase__ ( self : int ) -> List[Any]:
"""simple docstring"""
__UpperCamelCase : int = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
__UpperCamelCase : int = prepare_image_inputs(self.image_processor_tester , equal_resolution=_lowerCamelCase , numpify=_lowerCamelCase )
for image in image_inputs:
self.assertIsInstance(_lowerCamelCase , np.ndarray )
# Test not batched input
__UpperCamelCase : List[str] = image_processing(image_inputs[0] , return_tensors="""pt""" ).pixel_values
__UpperCamelCase : Any = self.image_processor_tester.get_expected_values(_lowerCamelCase )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
__UpperCamelCase : int = image_processing(_lowerCamelCase , return_tensors="""pt""" ).pixel_values
__UpperCamelCase : Optional[int] = self.image_processor_tester.get_expected_values(_lowerCamelCase , batched=_lowerCamelCase )
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
def lowerCamelCase__ ( self : str ) -> Optional[Any]:
"""simple docstring"""
__UpperCamelCase : List[str] = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
__UpperCamelCase : Union[str, Any] = prepare_image_inputs(self.image_processor_tester , equal_resolution=_lowerCamelCase , torchify=_lowerCamelCase )
for image in image_inputs:
self.assertIsInstance(_lowerCamelCase , torch.Tensor )
# Test not batched input
__UpperCamelCase : Dict = image_processing(image_inputs[0] , return_tensors="""pt""" ).pixel_values
__UpperCamelCase : Dict = self.image_processor_tester.get_expected_values(_lowerCamelCase )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
__UpperCamelCase : Tuple = image_processing(_lowerCamelCase , return_tensors="""pt""" ).pixel_values
__UpperCamelCase : Dict = self.image_processor_tester.get_expected_values(_lowerCamelCase , batched=_lowerCamelCase )
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
@slow
def lowerCamelCase__ ( self : Union[str, Any] ) -> Tuple:
"""simple docstring"""
__UpperCamelCase : Union[str, Any] = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
with open("""./tests/fixtures/tests_samples/COCO/coco_annotations.txt""" , """r""" ) as f:
__UpperCamelCase : Tuple = json.loads(f.read() )
__UpperCamelCase : Any = {'''image_id''': 39769, '''annotations''': target}
# encode them
__UpperCamelCase : str = DetrImageProcessor.from_pretrained("""facebook/detr-resnet-50""" )
__UpperCamelCase : List[Any] = image_processing(images=_lowerCamelCase , annotations=_lowerCamelCase , return_tensors="""pt""" )
# verify pixel values
__UpperCamelCase : Optional[int] = torch.Size([1, 3, 800, 1066] )
self.assertEqual(encoding["""pixel_values"""].shape , _lowerCamelCase )
__UpperCamelCase : int = torch.tensor([0.27_96, 0.31_38, 0.34_81] )
self.assertTrue(torch.allclose(encoding["""pixel_values"""][0, 0, 0, :3] , _lowerCamelCase , atol=1E-4 ) )
# verify area
__UpperCamelCase : Optional[int] = torch.tensor([5887.9600, 11250.2061, 489353.8438, 837122.7500, 147967.5156, 165732.3438] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""area"""] , _lowerCamelCase ) )
# verify boxes
__UpperCamelCase : str = torch.Size([6, 4] )
self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape , _lowerCamelCase )
__UpperCamelCase : List[str] = torch.tensor([0.55_03, 0.27_65, 0.06_04, 0.22_15] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0] , _lowerCamelCase , atol=1E-3 ) )
# verify image_id
__UpperCamelCase : str = torch.tensor([39769] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""] , _lowerCamelCase ) )
# verify is_crowd
__UpperCamelCase : List[str] = torch.tensor([0, 0, 0, 0, 0, 0] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""] , _lowerCamelCase ) )
# verify class_labels
__UpperCamelCase : List[str] = torch.tensor([75, 75, 63, 65, 17, 17] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""] , _lowerCamelCase ) )
# verify orig_size
__UpperCamelCase : Dict = torch.tensor([480, 640] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""] , _lowerCamelCase ) )
# verify size
__UpperCamelCase : int = torch.tensor([800, 1066] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""] , _lowerCamelCase ) )
@slow
def lowerCamelCase__ ( self : List[str] ) -> Optional[int]:
"""simple docstring"""
__UpperCamelCase : Dict = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
with open("""./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt""" , """r""" ) as f:
__UpperCamelCase : int = json.loads(f.read() )
__UpperCamelCase : Dict = {'''file_name''': '''000000039769.png''', '''image_id''': 39769, '''segments_info''': target}
__UpperCamelCase : Tuple = pathlib.Path("""./tests/fixtures/tests_samples/COCO/coco_panoptic""" )
# encode them
__UpperCamelCase : Any = DetrImageProcessor.from_pretrained("""facebook/detr-resnet-50-panoptic""" )
__UpperCamelCase : Union[str, Any] = image_processing(images=_lowerCamelCase , annotations=_lowerCamelCase , masks_path=_lowerCamelCase , return_tensors="""pt""" )
# verify pixel values
__UpperCamelCase : Optional[Any] = torch.Size([1, 3, 800, 1066] )
self.assertEqual(encoding["""pixel_values"""].shape , _lowerCamelCase )
__UpperCamelCase : Any = torch.tensor([0.27_96, 0.31_38, 0.34_81] )
self.assertTrue(torch.allclose(encoding["""pixel_values"""][0, 0, 0, :3] , _lowerCamelCase , atol=1E-4 ) )
# verify area
__UpperCamelCase : Tuple = torch.tensor([147979.6875, 165527.0469, 484638.5938, 11292.9375, 5879.6562, 7634.1147] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""area"""] , _lowerCamelCase ) )
# verify boxes
__UpperCamelCase : Tuple = torch.Size([6, 4] )
self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape , _lowerCamelCase )
__UpperCamelCase : Union[str, Any] = torch.tensor([0.26_25, 0.54_37, 0.46_88, 0.86_25] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0] , _lowerCamelCase , atol=1E-3 ) )
# verify image_id
__UpperCamelCase : Dict = torch.tensor([39769] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""] , _lowerCamelCase ) )
# verify is_crowd
__UpperCamelCase : Tuple = torch.tensor([0, 0, 0, 0, 0, 0] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""] , _lowerCamelCase ) )
# verify class_labels
__UpperCamelCase : str = torch.tensor([17, 17, 63, 75, 75, 93] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""] , _lowerCamelCase ) )
# verify masks
__UpperCamelCase : Union[str, Any] = 822873
self.assertEqual(encoding["""labels"""][0]["""masks"""].sum().item() , _lowerCamelCase )
# verify orig_size
__UpperCamelCase : List[Any] = torch.tensor([480, 640] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""] , _lowerCamelCase ) )
# verify size
__UpperCamelCase : str = torch.tensor([800, 1066] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""] , _lowerCamelCase ) )
| 279 |
"""simple docstring"""
import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List
import git
import torch
from torch.utils.data import Dataset
from transformers import BartTokenizer, RagTokenizer, TaTokenizer
def lowerCAmelCase_ ( lowercase_ : List[str] , lowercase_ : List[str] , lowercase_ : Dict , lowercase_ : Tuple , lowercase_ : Optional[Any]=True , lowercase_ : Any="pt" ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : Union[str, Any] = {'''add_prefix_space''': True} if isinstance(lowercase_ , lowercase_ ) and not line.startswith(''' ''' ) else {}
__SCREAMING_SNAKE_CASE : Optional[int] = padding_side
return tokenizer(
[line] , max_length=lowercase_ , padding='''max_length''' if pad_to_max_length else None , truncation=lowercase_ , return_tensors=lowercase_ , add_special_tokens=lowercase_ , **lowercase_ , )
def lowerCAmelCase_ ( lowercase_ : Optional[int] , lowercase_ : Tuple , lowercase_ : List[Any]=None , ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : Dict = input_ids.ne(lowercase_ ).any(dim=0 )
if attention_mask is None:
return input_ids[:, keep_column_mask]
else:
return (input_ids[:, keep_column_mask], attention_mask[:, keep_column_mask])
class snake_case ( __UpperCAmelCase ):
def __init__( self :Optional[Any] , _lowerCamelCase :Dict , _lowerCamelCase :Any , _lowerCamelCase :Optional[Any] , _lowerCamelCase :Tuple , _lowerCamelCase :Any="train" , _lowerCamelCase :str=None , _lowerCamelCase :Optional[Any]=None , _lowerCamelCase :List[Any]=None , _lowerCamelCase :Tuple="" , ):
super().__init__()
__SCREAMING_SNAKE_CASE : Dict = Path(_lowerCamelCase ).joinpath(type_path + '''.source''' )
__SCREAMING_SNAKE_CASE : Any = Path(_lowerCamelCase ).joinpath(type_path + '''.target''' )
__SCREAMING_SNAKE_CASE : Any = self.get_char_lens(self.src_file )
__SCREAMING_SNAKE_CASE : List[str] = max_source_length
__SCREAMING_SNAKE_CASE : Dict = max_target_length
assert min(self.src_lens ) > 0, f'''found empty line in {self.src_file}'''
__SCREAMING_SNAKE_CASE : Dict = tokenizer
__SCREAMING_SNAKE_CASE : Union[str, Any] = prefix
if n_obs is not None:
__SCREAMING_SNAKE_CASE : Any = self.src_lens[:n_obs]
__SCREAMING_SNAKE_CASE : List[str] = src_lang
__SCREAMING_SNAKE_CASE : str = tgt_lang
def __len__( self :int ):
return len(self.src_lens )
def __getitem__( self :Optional[Any] , _lowerCamelCase :Any ):
__SCREAMING_SNAKE_CASE : Optional[Any] = index + 1 # linecache starts at 1
__SCREAMING_SNAKE_CASE : Any = self.prefix + linecache.getline(str(self.src_file ) , _lowerCamelCase ).rstrip('''\n''' )
__SCREAMING_SNAKE_CASE : Dict = linecache.getline(str(self.tgt_file ) , _lowerCamelCase ).rstrip('''\n''' )
assert source_line, f'''empty source line for index {index}'''
assert tgt_line, f'''empty tgt line for index {index}'''
# Need to add eos token manually for T5
if isinstance(self.tokenizer , _lowerCamelCase ):
source_line += self.tokenizer.eos_token
tgt_line += self.tokenizer.eos_token
# Pad source and target to the right
__SCREAMING_SNAKE_CASE : Dict = (
self.tokenizer.question_encoder if isinstance(self.tokenizer , _lowerCamelCase ) else self.tokenizer
)
__SCREAMING_SNAKE_CASE : Optional[Any] = self.tokenizer.generator if isinstance(self.tokenizer , _lowerCamelCase ) else self.tokenizer
__SCREAMING_SNAKE_CASE : Dict = encode_line(_lowerCamelCase , _lowerCamelCase , self.max_source_length , '''right''' )
__SCREAMING_SNAKE_CASE : Dict = encode_line(_lowerCamelCase , _lowerCamelCase , self.max_target_length , '''right''' )
__SCREAMING_SNAKE_CASE : Any = source_inputs['''input_ids'''].squeeze()
__SCREAMING_SNAKE_CASE : Any = target_inputs['''input_ids'''].squeeze()
__SCREAMING_SNAKE_CASE : Dict = source_inputs['''attention_mask'''].squeeze()
return {
"input_ids": source_ids,
"attention_mask": src_mask,
"decoder_input_ids": target_ids,
}
@staticmethod
def SCREAMING_SNAKE_CASE_ ( _lowerCamelCase :Any ):
return [len(_lowerCamelCase ) for x in Path(_lowerCamelCase ).open().readlines()]
def SCREAMING_SNAKE_CASE_ ( self :List[str] , _lowerCamelCase :List[str] ):
__SCREAMING_SNAKE_CASE : int = torch.stack([x['''input_ids'''] for x in batch] )
__SCREAMING_SNAKE_CASE : str = torch.stack([x['''attention_mask'''] for x in batch] )
__SCREAMING_SNAKE_CASE : int = torch.stack([x['''decoder_input_ids'''] for x in batch] )
__SCREAMING_SNAKE_CASE : str = (
self.tokenizer.generator.pad_token_id
if isinstance(self.tokenizer , _lowerCamelCase )
else self.tokenizer.pad_token_id
)
__SCREAMING_SNAKE_CASE : Union[str, Any] = (
self.tokenizer.question_encoder.pad_token_id
if isinstance(self.tokenizer , _lowerCamelCase )
else self.tokenizer.pad_token_id
)
__SCREAMING_SNAKE_CASE : List[str] = trim_batch(_lowerCamelCase , _lowerCamelCase )
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Any = trim_batch(_lowerCamelCase , _lowerCamelCase , attention_mask=_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Dict = {
'''input_ids''': source_ids,
'''attention_mask''': source_mask,
'''decoder_input_ids''': y,
}
return batch
_lowerCamelCase = getLogger(__name__)
def lowerCAmelCase_ ( lowercase_ : List[List] ):
'''simple docstring'''
return list(itertools.chain.from_iterable(lowercase_ ) )
def lowerCAmelCase_ ( lowercase_ : str ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : Any = get_git_info()
save_json(lowercase_ , os.path.join(lowercase_ , '''git_log.json''' ) )
def lowerCAmelCase_ ( lowercase_ : Any , lowercase_ : Optional[int] , lowercase_ : str=4 , **lowercase_ : List[str] ):
'''simple docstring'''
with open(lowercase_ , '''w''' ) as f:
json.dump(lowercase_ , lowercase_ , indent=lowercase_ , **lowercase_ )
def lowerCAmelCase_ ( lowercase_ : Union[str, Any] ):
'''simple docstring'''
with open(lowercase_ ) as f:
return json.load(lowercase_ )
def lowerCAmelCase_ ( ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : Any = git.Repo(search_parent_directories=lowercase_ )
__SCREAMING_SNAKE_CASE : List[str] = {
'''repo_id''': str(lowercase_ ),
'''repo_sha''': str(repo.head.object.hexsha ),
'''repo_branch''': str(repo.active_branch ),
'''hostname''': str(socket.gethostname() ),
}
return repo_infos
def lowerCAmelCase_ ( lowercase_ : Callable , lowercase_ : Iterable ):
'''simple docstring'''
return list(map(lowercase_ , lowercase_ ) )
def lowerCAmelCase_ ( lowercase_ : Any , lowercase_ : Any ):
'''simple docstring'''
with open(lowercase_ , '''wb''' ) as f:
return pickle.dump(lowercase_ , lowercase_ )
def lowerCAmelCase_ ( lowercase_ : Any ):
'''simple docstring'''
def remove_articles(lowercase_ : Dict ):
return re.sub(r'''\b(a|an|the)\b''' , ''' ''' , lowercase_ )
def white_space_fix(lowercase_ : Optional[int] ):
return " ".join(text.split() )
def remove_punc(lowercase_ : Any ):
__SCREAMING_SNAKE_CASE : Optional[int] = set(string.punctuation )
return "".join(ch for ch in text if ch not in exclude )
def lower(lowercase_ : int ):
return text.lower()
return white_space_fix(remove_articles(remove_punc(lower(lowercase_ ) ) ) )
def lowerCAmelCase_ ( lowercase_ : Optional[int] , lowercase_ : int ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : Any = normalize_answer(lowercase_ ).split()
__SCREAMING_SNAKE_CASE : Any = normalize_answer(lowercase_ ).split()
__SCREAMING_SNAKE_CASE : Tuple = Counter(lowercase_ ) & Counter(lowercase_ )
__SCREAMING_SNAKE_CASE : Tuple = sum(common.values() )
if num_same == 0:
return 0
__SCREAMING_SNAKE_CASE : Any = 1.0 * num_same / len(lowercase_ )
__SCREAMING_SNAKE_CASE : List[str] = 1.0 * num_same / len(lowercase_ )
__SCREAMING_SNAKE_CASE : Optional[int] = (2 * precision * recall) / (precision + recall)
return fa
def lowerCAmelCase_ ( lowercase_ : str , lowercase_ : Union[str, Any] ):
'''simple docstring'''
return normalize_answer(lowercase_ ) == normalize_answer(lowercase_ )
def lowerCAmelCase_ ( lowercase_ : List[str] , lowercase_ : List[str] ):
'''simple docstring'''
assert len(lowercase_ ) == len(lowercase_ )
__SCREAMING_SNAKE_CASE : Union[str, Any] = 0
for hypo, pred in zip(lowercase_ , lowercase_ ):
em += exact_match_score(lowercase_ , lowercase_ )
if len(lowercase_ ) > 0:
em /= len(lowercase_ )
return {"em": em}
def lowerCAmelCase_ ( lowercase_ : str ):
'''simple docstring'''
return model_prefix.startswith('''rag''' )
def lowerCAmelCase_ ( lowercase_ : Optional[Any] , lowercase_ : Tuple , lowercase_ : Optional[int] ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : Optional[int] = {p: p for p in extra_params}
# T5 models don't have `dropout` param, they have `dropout_rate` instead
__SCREAMING_SNAKE_CASE : Any = '''dropout_rate'''
for p in extra_params:
if getattr(lowercase_ , lowercase_ , lowercase_ ):
if not hasattr(lowercase_ , lowercase_ ) and not hasattr(lowercase_ , equivalent_param[p] ):
logger.info('''config doesn\'t have a `{}` attribute'''.format(lowercase_ ) )
delattr(lowercase_ , lowercase_ )
continue
__SCREAMING_SNAKE_CASE : Optional[int] = p if hasattr(lowercase_ , lowercase_ ) else equivalent_param[p]
setattr(lowercase_ , lowercase_ , getattr(lowercase_ , lowercase_ ) )
delattr(lowercase_ , lowercase_ )
return hparams, config
| 674 | 0 |
import logging
import os
from logging import (
CRITICAL, # NOQA
DEBUG, # NOQA
ERROR, # NOQA
FATAL, # NOQA
INFO, # NOQA
NOTSET, # NOQA
WARN, # NOQA
WARNING, # NOQA
)
from typing import Optional
from tqdm import auto as tqdm_lib
__lowerCAmelCase = {
'debug': logging.DEBUG,
'info': logging.INFO,
'warning': logging.WARNING,
'error': logging.ERROR,
'critical': logging.CRITICAL,
}
__lowerCAmelCase = logging.WARNING
def a ( ) ->Any:
'''simple docstring'''
SCREAMING_SNAKE_CASE = os.getenv('''DATASETS_VERBOSITY''' , lowercase_ )
if env_level_str:
if env_level_str in log_levels:
return log_levels[env_level_str]
else:
logging.getLogger().warning(
F"""Unknown option DATASETS_VERBOSITY={env_level_str}, """
F"""has to be one of: { ", ".join(log_levels.keys() ) }""" )
return _default_log_level
def a ( ) ->Dict:
'''simple docstring'''
return __name__.split('''.''' )[0]
def a ( ) ->Any:
'''simple docstring'''
return logging.getLogger(_get_library_name() )
def a ( ) ->str:
'''simple docstring'''
SCREAMING_SNAKE_CASE = _get_library_root_logger()
library_root_logger.setLevel(_get_default_logging_level() )
def a ( ) ->int:
'''simple docstring'''
SCREAMING_SNAKE_CASE = _get_library_root_logger()
library_root_logger.setLevel(logging.NOTSET )
def a ( a = None ) ->Any:
'''simple docstring'''
if name is None:
SCREAMING_SNAKE_CASE = _get_library_name()
return logging.getLogger(lowercase_ )
def a ( ) ->int:
'''simple docstring'''
return _get_library_root_logger().getEffectiveLevel()
def a ( a ) ->List[Any]:
'''simple docstring'''
_get_library_root_logger().setLevel(lowercase_ )
def a ( ) ->List[str]:
'''simple docstring'''
return set_verbosity(lowercase_ )
def a ( ) ->Union[str, Any]:
'''simple docstring'''
return set_verbosity(lowercase_ )
def a ( ) ->Any:
'''simple docstring'''
return set_verbosity(lowercase_ )
def a ( ) ->str:
'''simple docstring'''
return set_verbosity(lowercase_ )
def a ( ) ->Optional[Any]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = False
def a ( ) ->Dict:
'''simple docstring'''
SCREAMING_SNAKE_CASE = True
# Configure the library root logger at the module level (singleton-like)
_configure_library_root_logger()
class lowerCamelCase :
def __init__( self :str , *lowercase :List[Any] , **lowercase :Any ) -> Union[str, Any]: # pylint: disable=unused-argument
"""simple docstring"""
SCREAMING_SNAKE_CASE = args[0] if args else None
def __iter__( self :Union[str, Any] ) -> Optional[int]:
"""simple docstring"""
return iter(self._iterator )
def __getattr__( self :Union[str, Any] , lowercase :Optional[Any] ) -> Optional[int]:
"""simple docstring"""
def empty_fn(*lowercase :List[Any] , **lowercase :Dict ): # pylint: disable=unused-argument
return
return empty_fn
def __enter__( self :List[Any] ) -> Union[str, Any]:
"""simple docstring"""
return self
def __exit__( self :List[str] , lowercase :Optional[Any] , lowercase :Union[str, Any] , lowercase :Tuple ) -> Any:
"""simple docstring"""
return
__lowerCAmelCase = True
class lowerCamelCase :
def __call__( self :Dict , *lowercase :Union[str, Any] , lowercase :str=False , **lowercase :Optional[int] ) -> Tuple:
"""simple docstring"""
if _tqdm_active and not disable:
return tqdm_lib.tqdm(*_lowerCamelCase , **_lowerCamelCase )
else:
return EmptyTqdm(*_lowerCamelCase , **_lowerCamelCase )
def snake_case__ ( self :int , *lowercase :List[Any] , **lowercase :Optional[int] ) -> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE = None
if _tqdm_active:
return tqdm_lib.tqdm.set_lock(*_lowerCamelCase , **_lowerCamelCase )
def snake_case__ ( self :int ) -> List[str]:
"""simple docstring"""
if _tqdm_active:
return tqdm_lib.tqdm.get_lock()
__lowerCAmelCase = _tqdm_cls()
def a ( ) ->List[Any]:
'''simple docstring'''
global _tqdm_active
return bool(_tqdm_active )
def a ( ) ->Any:
'''simple docstring'''
global _tqdm_active
SCREAMING_SNAKE_CASE = True
def a ( ) ->List[str]:
'''simple docstring'''
global _tqdm_active
SCREAMING_SNAKE_CASE = False | 201 |
"""simple docstring"""
from collections.abc import Callable
import numpy as np
def lowerCAmelCase_ ( lowercase_ : Callable , lowercase_ : float , lowercase_ : float , lowercase_ : float , lowercase_ : float ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : int = int(np.ceil((x_end - xa) / step_size ) )
__SCREAMING_SNAKE_CASE : Dict = np.zeros((n + 1,) )
__SCREAMING_SNAKE_CASE : List[Any] = ya
__SCREAMING_SNAKE_CASE : Dict = xa
for k in range(lowercase_ ):
__SCREAMING_SNAKE_CASE : str = y[k] + step_size * ode_func(lowercase_ , y[k] )
__SCREAMING_SNAKE_CASE : int = y[k] + (
(step_size / 2) * (ode_func(lowercase_ , y[k] ) + ode_func(x + step_size , lowercase_ ))
)
x += step_size
return y
if __name__ == "__main__":
import doctest
doctest.testmod()
| 674 | 0 |
"""simple docstring"""
import os
import re
import warnings
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
if TYPE_CHECKING:
from ...tokenization_utils_base import TextInput
from ...utils import logging
a : Union[str, Any] = logging.get_logger(__name__)
a : Any = {"""vocab_file""": """spiece.model"""}
a : Optional[int] = {
"""vocab_file""": {
"""t5-small""": """https://huggingface.co/t5-small/resolve/main/spiece.model""",
"""t5-base""": """https://huggingface.co/t5-base/resolve/main/spiece.model""",
"""t5-large""": """https://huggingface.co/t5-large/resolve/main/spiece.model""",
"""t5-3b""": """https://huggingface.co/t5-3b/resolve/main/spiece.model""",
"""t5-11b""": """https://huggingface.co/t5-11b/resolve/main/spiece.model""",
}
}
# TODO(PVP) - this should be removed in Transformers v5
a : Any = {
"""t5-small""": 512,
"""t5-base""": 512,
"""t5-large""": 512,
"""t5-3b""": 512,
"""t5-11b""": 512,
}
a : List[str] = """▁"""
class __UpperCAmelCase( __UpperCAmelCase ):
"""simple docstring"""
__lowerCamelCase = VOCAB_FILES_NAMES
__lowerCamelCase = PRETRAINED_VOCAB_FILES_MAP
__lowerCamelCase = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__lowerCamelCase = ["input_ids", "attention_mask"]
def __init__( self , snake_case__ , snake_case__="</s>" , snake_case__="<unk>" , snake_case__="<pad>" , snake_case__=100 , snake_case__=None , snake_case__ = None , snake_case__=True , **snake_case__ , ):
'''simple docstring'''
# Add extra_ids to the special token list
if extra_ids > 0 and additional_special_tokens is None:
lowercase__ : Union[str, Any]= [F'''<extra_id_{i}>''' for i in range(_lowerCamelCase )]
elif extra_ids > 0 and additional_special_tokens is not None:
# Check that we have the right number of extra_id special tokens
lowercase__ : Optional[int]= len(set(filter(lambda snake_case__ : bool("extra_id" in str(_lowerCamelCase ) ) , _lowerCamelCase ) ) )
if extra_tokens != extra_ids:
raise ValueError(
F'''Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are'''
" provided to T5Tokenizer. In this case the additional_special_tokens must include the extra_ids"
" tokens" )
if legacy:
logger.warning_once(
F'''You are using the legacy behaviour of the {self.__class__}. This means that tokens that come after special tokens will not be properly handled. We recommend you to'''
" read the related pull request available at https://github.com/huggingface/transformers/pull/24565" )
lowercase__ : Optional[Any]= legacy
lowercase__ : Optional[int]= {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
eos_token=_lowerCamelCase , unk_token=_lowerCamelCase , pad_token=_lowerCamelCase , extra_ids=_lowerCamelCase , additional_special_tokens=_lowerCamelCase , sp_model_kwargs=self.sp_model_kwargs , legacy=_lowerCamelCase , **_lowerCamelCase , )
lowercase__ : Tuple= vocab_file
lowercase__ : List[str]= extra_ids
lowercase__ : Optional[int]= spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(_lowerCamelCase )
@staticmethod
def UpperCAmelCase_ ( snake_case__ , snake_case__ , snake_case__ ):
'''simple docstring'''
if pretrained_model_name_or_path in TaTokenizer.max_model_input_sizes:
lowercase__ : Any= TaTokenizer.max_model_input_sizes[pretrained_model_name_or_path]
if init_max_model_length is not None and init_max_model_length != max_model_length:
return init_max_model_length
elif init_max_model_length is None:
warnings.warn(
"This tokenizer was incorrectly instantiated with a model max length of"
F''' {deprecated_max_model_length} which will be corrected in Transformers v5.\nFor now, this'''
" behavior is kept to avoid breaking backwards compatibility when padding/encoding with"
" `truncation is True`.\n- Be aware that you SHOULD NOT rely on"
F''' {pretrained_model_name_or_path} automatically truncating your input to'''
F''' {deprecated_max_model_length} when padding/encoding.\n- If you want to encode/pad to sequences'''
F''' longer than {deprecated_max_model_length} you can either instantiate this tokenizer with'''
" `model_max_length` or pass `max_length` when encoding/padding.\n- To avoid this warning, please"
" instantiate this tokenizer with `model_max_length` set to your preferred value." , _lowerCamelCase , )
return max_model_length
@property
def UpperCAmelCase_ ( self ):
'''simple docstring'''
return self.sp_model.get_piece_size() + self._extra_ids
def UpperCAmelCase_ ( self ):
'''simple docstring'''
lowercase__ : str= {self.convert_ids_to_tokens(_lowerCamelCase ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def UpperCAmelCase_ ( self , snake_case__ , snake_case__ = None , snake_case__ = False ):
'''simple docstring'''
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=_lowerCamelCase , token_ids_a=_lowerCamelCase , already_has_special_tokens=_lowerCamelCase )
# normal case: some special tokens
if token_ids_a is None:
return ([0] * len(_lowerCamelCase )) + [1]
return ([0] * len(_lowerCamelCase )) + [1] + ([0] * len(_lowerCamelCase )) + [1]
def UpperCAmelCase_ ( self ):
'''simple docstring'''
return list(
set(filter(lambda snake_case__ : bool(re.search(r"<extra_id_\d+>" , _lowerCamelCase ) ) is not None , self.additional_special_tokens ) ) )
def UpperCAmelCase_ ( self ):
'''simple docstring'''
return [self._convert_token_to_id(_lowerCamelCase ) for token in self.get_sentinel_tokens()]
def UpperCAmelCase_ ( self , snake_case__ ):
'''simple docstring'''
if len(_lowerCamelCase ) > 0 and token_ids[-1] == self.eos_token_id:
warnings.warn(
F'''This sequence already has {self.eos_token}. In future versions this behavior may lead to duplicated'''
" eos tokens being added." )
return token_ids
else:
return token_ids + [self.eos_token_id]
def UpperCAmelCase_ ( self , snake_case__ , snake_case__ = None ):
'''simple docstring'''
lowercase__ : List[str]= [self.eos_token_id]
if token_ids_a is None:
return len(token_ids_a + eos ) * [0]
return len(token_ids_a + eos + token_ids_a + eos ) * [0]
def UpperCAmelCase_ ( self , snake_case__ , snake_case__ = None ):
'''simple docstring'''
lowercase__ : Optional[Any]= self._add_eos_if_not_present(_lowerCamelCase )
if token_ids_a is None:
return token_ids_a
else:
lowercase__ : Union[str, Any]= self._add_eos_if_not_present(_lowerCamelCase )
return token_ids_a + token_ids_a
def __getstate__( self ):
'''simple docstring'''
lowercase__ : Any= self.__dict__.copy()
lowercase__ : List[str]= None
return state
def __setstate__( self , snake_case__ ):
'''simple docstring'''
lowercase__ : Tuple= d
# for backward compatibility
if not hasattr(self , "sp_model_kwargs" ):
lowercase__ : Optional[int]= {}
lowercase__ : Optional[int]= spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(self.vocab_file )
def UpperCAmelCase_ ( self , snake_case__ , **snake_case__ ):
'''simple docstring'''
# Replace the SPIECE_UNDERLINE with a space to make sure SPIECE_UNDERLINE is only used at
# the beginning of the text
if not self.legacy:
lowercase__ : Dict= SPIECE_UNDERLINE + text.replace(_lowerCamelCase , " " )
return super().tokenize(_lowerCamelCase , **_lowerCamelCase )
def UpperCAmelCase_ ( self , snake_case__ , **snake_case__ ):
'''simple docstring'''
if not self.legacy:
lowercase__ : str= text.startswith(_lowerCamelCase )
if is_first:
lowercase__ : str= text[1:]
lowercase__ : Tuple= self.sp_model.encode(_lowerCamelCase , out_type=_lowerCamelCase )
if not self.legacy and not is_first and not text.startswith(" " ) and tokens[0].startswith(_lowerCamelCase ):
lowercase__ : Optional[int]= ([tokens[0][1:]] if len(tokens[0] ) > 1 else []) + tokens[1:]
return tokens
def UpperCAmelCase_ ( self , snake_case__ ):
'''simple docstring'''
if token.startswith("<extra_id_" ):
lowercase__ : Tuple= re.match(r"<extra_id_(\d+)>" , _lowerCamelCase )
lowercase__ : Union[str, Any]= int(match.group(1 ) )
return self.vocab_size - num - 1
return self.sp_model.piece_to_id(_lowerCamelCase )
def UpperCAmelCase_ ( self , snake_case__ ):
'''simple docstring'''
if index < self.sp_model.get_piece_size():
lowercase__ : List[Any]= self.sp_model.IdToPiece(_lowerCamelCase )
else:
lowercase__ : Dict= F'''<extra_id_{self.vocab_size - 1 - index}>'''
return token
def UpperCAmelCase_ ( self , snake_case__ ):
'''simple docstring'''
lowercase__ : str= []
lowercase__ : Dict= ''''''
lowercase__ : Dict= False
for token in tokens:
# make sure that special tokens are not decoded using sentencepiece model
if token in self.all_special_tokens:
if not prev_is_special:
out_string += " "
out_string += self.sp_model.decode(_lowerCamelCase ) + token
lowercase__ : List[str]= True
lowercase__ : str= []
else:
current_sub_tokens.append(_lowerCamelCase )
lowercase__ : int= False
out_string += self.sp_model.decode(_lowerCamelCase )
return out_string.strip()
def UpperCAmelCase_ ( self , snake_case__ , snake_case__ = None ):
'''simple docstring'''
if not os.path.isdir(_lowerCamelCase ):
logger.error(F'''Vocabulary path ({save_directory}) should be a directory''' )
return
lowercase__ : List[str]= os.path.join(
_lowerCamelCase , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(_lowerCamelCase ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file , _lowerCamelCase )
elif not os.path.isfile(self.vocab_file ):
with open(_lowerCamelCase , "wb" ) as fi:
lowercase__ : Any= self.sp_model.serialized_model_proto()
fi.write(_lowerCamelCase )
return (out_vocab_file,)
| 218 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_torch_available
from ...utils import OptionalDependencyNotAvailable
_lowerCamelCase = {
'''configuration_gpt_neox_japanese''': ['''GPT_NEOX_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GPTNeoXJapaneseConfig'''],
'''tokenization_gpt_neox_japanese''': ['''GPTNeoXJapaneseTokenizer'''],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowerCamelCase = [
'''GPT_NEOX_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''GPTNeoXJapaneseForCausalLM''',
'''GPTNeoXJapaneseLayer''',
'''GPTNeoXJapaneseModel''',
'''GPTNeoXJapanesePreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_gpt_neox_japanese import GPT_NEOX_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoXJapaneseConfig
from .tokenization_gpt_neox_japanese import GPTNeoXJapaneseTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_gpt_neox_japanese import (
GPT_NEOX_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST,
GPTNeoXJapaneseForCausalLM,
GPTNeoXJapaneseLayer,
GPTNeoXJapaneseModel,
GPTNeoXJapanesePreTrainedModel,
)
else:
import sys
_lowerCamelCase = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 674 | 0 |
import tempfile
import unittest
import numpy as np
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import BertConfig, is_flax_available
from transformers.testing_utils import TOKEN, USER, is_staging_test, require_flax
if is_flax_available():
import os
from flax.core.frozen_dict import unfreeze
from flax.traverse_util import flatten_dict
from transformers import FlaxBertModel
lowerCamelCase :Any = '0.12' # assumed parallelism: 8
@require_flax
@is_staging_test
class UpperCAmelCase ( unittest.TestCase ):
@classmethod
def _A ( cls: Union[str, Any] ):
_a = TOKEN
HfFolder.save_token(_lowerCamelCase )
@classmethod
def _A ( cls: int ):
try:
delete_repo(token=cls._token , repo_id='''test-model-flax''' )
except HTTPError:
pass
try:
delete_repo(token=cls._token , repo_id='''valid_org/test-model-flax-org''' )
except HTTPError:
pass
def _A ( self: List[str] ):
_a = BertConfig(
vocab_size=99 , hidden_size=32 , num_hidden_layers=5 , num_attention_heads=4 , intermediate_size=37 )
_a = FlaxBertModel(_lowerCamelCase )
model.push_to_hub('''test-model-flax''' , use_auth_token=self._token )
_a = FlaxBertModel.from_pretrained(f"{USER}/test-model-flax" )
_a = flatten_dict(unfreeze(model.params ) )
_a = flatten_dict(unfreeze(new_model.params ) )
for key in base_params.keys():
_a = (base_params[key] - new_params[key]).sum().item()
self.assertLessEqual(_lowerCamelCase , 1E-3 , msg=f"{key} not identical" )
# Reset repo
delete_repo(token=self._token , repo_id='''test-model-flax''' )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
model.save_pretrained(_lowerCamelCase , repo_id='''test-model-flax''' , push_to_hub=_lowerCamelCase , use_auth_token=self._token )
_a = FlaxBertModel.from_pretrained(f"{USER}/test-model-flax" )
_a = flatten_dict(unfreeze(model.params ) )
_a = flatten_dict(unfreeze(new_model.params ) )
for key in base_params.keys():
_a = (base_params[key] - new_params[key]).sum().item()
self.assertLessEqual(_lowerCamelCase , 1E-3 , msg=f"{key} not identical" )
def _A ( self: Any ):
_a = BertConfig(
vocab_size=99 , hidden_size=32 , num_hidden_layers=5 , num_attention_heads=4 , intermediate_size=37 )
_a = FlaxBertModel(_lowerCamelCase )
model.push_to_hub('''valid_org/test-model-flax-org''' , use_auth_token=self._token )
_a = FlaxBertModel.from_pretrained('''valid_org/test-model-flax-org''' )
_a = flatten_dict(unfreeze(model.params ) )
_a = flatten_dict(unfreeze(new_model.params ) )
for key in base_params.keys():
_a = (base_params[key] - new_params[key]).sum().item()
self.assertLessEqual(_lowerCamelCase , 1E-3 , msg=f"{key} not identical" )
# Reset repo
delete_repo(token=self._token , repo_id='''valid_org/test-model-flax-org''' )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
model.save_pretrained(
_lowerCamelCase , repo_id='''valid_org/test-model-flax-org''' , push_to_hub=_lowerCamelCase , use_auth_token=self._token )
_a = FlaxBertModel.from_pretrained('''valid_org/test-model-flax-org''' )
_a = flatten_dict(unfreeze(model.params ) )
_a = flatten_dict(unfreeze(new_model.params ) )
for key in base_params.keys():
_a = (base_params[key] - new_params[key]).sum().item()
self.assertLessEqual(_lowerCamelCase , 1E-3 , msg=f"{key} not identical" )
def __snake_case ( _UpperCamelCase , _UpperCamelCase ) -> Optional[int]:
_a = True
_a = flatten_dict(modela.params )
_a = flatten_dict(modela.params )
for key in flat_params_a.keys():
if np.sum(np.abs(flat_params_a[key] - flat_params_a[key] ) ) > 1E-4:
_a = False
return models_are_equal
@require_flax
class UpperCAmelCase ( unittest.TestCase ):
def _A ( self: Optional[Any] ):
_a = BertConfig.from_pretrained('''hf-internal-testing/tiny-bert-flax-only''' )
_a = FlaxBertModel(_lowerCamelCase )
_a = '''bert'''
with tempfile.TemporaryDirectory() as tmp_dir:
model.save_pretrained(os.path.join(_lowerCamelCase , _lowerCamelCase ) )
with self.assertRaises(_lowerCamelCase ):
_a = FlaxBertModel.from_pretrained(_lowerCamelCase )
_a = FlaxBertModel.from_pretrained(_lowerCamelCase , subfolder=_lowerCamelCase )
self.assertTrue(check_models_equal(_lowerCamelCase , _lowerCamelCase ) )
def _A ( self: List[Any] ):
_a = BertConfig.from_pretrained('''hf-internal-testing/tiny-bert-flax-only''' )
_a = FlaxBertModel(_lowerCamelCase )
_a = '''bert'''
with tempfile.TemporaryDirectory() as tmp_dir:
model.save_pretrained(os.path.join(_lowerCamelCase , _lowerCamelCase ) , max_shard_size='''10KB''' )
with self.assertRaises(_lowerCamelCase ):
_a = FlaxBertModel.from_pretrained(_lowerCamelCase )
_a = FlaxBertModel.from_pretrained(_lowerCamelCase , subfolder=_lowerCamelCase )
self.assertTrue(check_models_equal(_lowerCamelCase , _lowerCamelCase ) )
def _A ( self: List[str] ):
_a = '''bert'''
_a = '''hf-internal-testing/tiny-random-bert-subfolder'''
with self.assertRaises(_lowerCamelCase ):
_a = FlaxBertModel.from_pretrained(_lowerCamelCase )
_a = FlaxBertModel.from_pretrained(_lowerCamelCase , subfolder=_lowerCamelCase )
self.assertIsNotNone(_lowerCamelCase )
def _A ( self: Optional[Any] ):
_a = '''bert'''
_a = '''hf-internal-testing/tiny-random-bert-sharded-subfolder'''
with self.assertRaises(_lowerCamelCase ):
_a = FlaxBertModel.from_pretrained(_lowerCamelCase )
_a = FlaxBertModel.from_pretrained(_lowerCamelCase , subfolder=_lowerCamelCase )
self.assertIsNotNone(_lowerCamelCase )
| 487 |
"""simple docstring"""
from __future__ import annotations
from typing import Any
class snake_case :
def __init__( self :Optional[Any] , _lowerCamelCase :int ):
__SCREAMING_SNAKE_CASE : int = num_of_nodes
__SCREAMING_SNAKE_CASE : list[list[int]] = []
__SCREAMING_SNAKE_CASE : dict[int, int] = {}
def SCREAMING_SNAKE_CASE_ ( self :int , _lowerCamelCase :int , _lowerCamelCase :int , _lowerCamelCase :int ):
self.m_edges.append([u_node, v_node, weight] )
def SCREAMING_SNAKE_CASE_ ( self :Tuple , _lowerCamelCase :int ):
if self.m_component[u_node] == u_node:
return u_node
return self.find_component(self.m_component[u_node] )
def SCREAMING_SNAKE_CASE_ ( self :List[str] , _lowerCamelCase :int ):
if self.m_component[u_node] != u_node:
for k in self.m_component:
__SCREAMING_SNAKE_CASE : Optional[Any] = self.find_component(_lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :Union[str, Any] , _lowerCamelCase :list[int] , _lowerCamelCase :int , _lowerCamelCase :int ):
if component_size[u_node] <= component_size[v_node]:
__SCREAMING_SNAKE_CASE : List[Any] = v_node
component_size[v_node] += component_size[u_node]
self.set_component(_lowerCamelCase )
elif component_size[u_node] >= component_size[v_node]:
__SCREAMING_SNAKE_CASE : Dict = self.find_component(_lowerCamelCase )
component_size[u_node] += component_size[v_node]
self.set_component(_lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :List[str] ):
__SCREAMING_SNAKE_CASE : Optional[int] = []
__SCREAMING_SNAKE_CASE : str = 0
__SCREAMING_SNAKE_CASE : list[Any] = [-1] * self.m_num_of_nodes
# A list of components (initialized to all of the nodes)
for node in range(self.m_num_of_nodes ):
self.m_component.update({node: node} )
component_size.append(1 )
__SCREAMING_SNAKE_CASE : str = self.m_num_of_nodes
while num_of_components > 1:
for edge in self.m_edges:
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Optional[Any] = edge
__SCREAMING_SNAKE_CASE : Optional[Any] = self.m_component[u]
__SCREAMING_SNAKE_CASE : int = self.m_component[v]
if u_component != v_component:
for component in (u_component, v_component):
if (
minimum_weight_edge[component] == -1
or minimum_weight_edge[component][2] > w
):
__SCREAMING_SNAKE_CASE : Optional[Any] = [u, v, w]
for edge in minimum_weight_edge:
if isinstance(_lowerCamelCase , _lowerCamelCase ):
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : int = edge
__SCREAMING_SNAKE_CASE : Tuple = self.m_component[u]
__SCREAMING_SNAKE_CASE : int = self.m_component[v]
if u_component != v_component:
mst_weight += w
self.union(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase )
print(f'''Added edge [{u} - {v}]\nAdded weight: {w}\n''' )
num_of_components -= 1
__SCREAMING_SNAKE_CASE : Optional[Any] = [-1] * self.m_num_of_nodes
print(f'''The total weight of the minimal spanning tree is: {mst_weight}''' )
def lowerCAmelCase_ ( ):
'''simple docstring'''
if __name__ == "__main__":
import doctest
doctest.testmod()
| 674 | 0 |
"""simple docstring"""
def __UpperCAmelCase ( __UpperCamelCase ):
assert isinstance(lowercase_ , lowercase_ ), f"""The input value of [n={number}] is not an integer"""
if number == 1:
return 2
elif number < 1:
__lowercase : Union[str, Any] = f"""The input value of [n={number}] has to be > 0"""
raise ValueError(lowercase_ )
else:
__lowercase : Tuple = sylvester(number - 1 )
__lowercase : Any = num - 1
__lowercase : Tuple = num
return lower * upper + 1
if __name__ == "__main__":
print(F"The 8th number in Sylvester\'s sequence: {sylvester(8)}")
| 76 |
"""simple docstring"""
import argparse
import pickle
import numpy as np
import torch
from torch import nn
from transformers import ReformerConfig, ReformerModelWithLMHead
from transformers.utils import logging
logging.set_verbosity_info()
def lowerCAmelCase_ ( lowercase_ : int , lowercase_ : Any , lowercase_ : int=None ):
'''simple docstring'''
assert torch_layer.weight.shape == weight.shape, F'''{torch_layer} layer.weight does not match'''
__SCREAMING_SNAKE_CASE : str = nn.Parameter(lowercase_ )
if bias is not None:
assert torch_layer.bias.shape == bias.shape, F'''{torch_layer} layer.bias does not match'''
__SCREAMING_SNAKE_CASE : Tuple = nn.Parameter(lowercase_ )
def lowerCAmelCase_ ( lowercase_ : Tuple , lowercase_ : int , lowercase_ : int ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : Optional[int] = np.asarray(weights[0] )
__SCREAMING_SNAKE_CASE : Optional[int] = np.asarray(weights[1] )
__SCREAMING_SNAKE_CASE : Union[str, Any] = np.asarray(weights[2] )
set_param(
torch_layer.self_attention.query_key , torch.tensor(lowercase_ ).transpose(1 , 2 ).contiguous().view(-1 , lowercase_ ) , )
set_param(
torch_layer.self_attention.value , torch.tensor(lowercase_ ).transpose(1 , 2 ).contiguous().view(-1 , lowercase_ ) , )
set_param(
torch_layer.output.dense , torch.tensor(lowercase_ ).view(-1 , lowercase_ ).contiguous().transpose(0 , 1 ) , )
def lowerCAmelCase_ ( lowercase_ : Dict , lowercase_ : List[str] , lowercase_ : List[str] ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : List[str] = np.asarray(weights[0] )
__SCREAMING_SNAKE_CASE : Any = np.asarray(weights[1] )
__SCREAMING_SNAKE_CASE : Union[str, Any] = np.asarray(weights[2] )
__SCREAMING_SNAKE_CASE : Tuple = np.asarray(weights[3] )
set_param(
torch_layer.self_attention.query , torch.tensor(lowercase_ ).transpose(1 , 2 ).contiguous().view(-1 , lowercase_ ) , )
set_param(
torch_layer.self_attention.key , torch.tensor(lowercase_ ).transpose(1 , 2 ).contiguous().view(-1 , lowercase_ ) , )
set_param(
torch_layer.self_attention.value , torch.tensor(lowercase_ ).transpose(1 , 2 ).contiguous().view(-1 , lowercase_ ) , )
set_param(
torch_layer.output.dense , torch.tensor(lowercase_ ).view(-1 , lowercase_ ).contiguous().transpose(0 , 1 ) , )
def lowerCAmelCase_ ( lowercase_ : int , lowercase_ : List[str] , lowercase_ : str ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : Any = weights[0][0][0]
__SCREAMING_SNAKE_CASE : Union[str, Any] = np.asarray(layer_norm_a[0] )
__SCREAMING_SNAKE_CASE : List[Any] = np.asarray(layer_norm_a[1] )
set_param(
torch_block.attention.layer_norm , torch.tensor(lowercase_ ) , torch.tensor(lowercase_ ) , )
# lsh weights + output
__SCREAMING_SNAKE_CASE : Tuple = weights[0][1]
if len(lowercase_ ) < 4:
set_layer_weights_in_torch_lsh(lowercase_ , torch_block.attention , lowercase_ )
else:
set_layer_weights_in_torch_local(lowercase_ , torch_block.attention , lowercase_ )
# intermediate weighs
__SCREAMING_SNAKE_CASE : Any = weights[2][0][1][2]
# Chunked Feed Forward
if len(lowercase_ ) == 4:
__SCREAMING_SNAKE_CASE : List[str] = intermediate_weights[2]
# layernorm 2
__SCREAMING_SNAKE_CASE : List[str] = np.asarray(intermediate_weights[0][0] )
__SCREAMING_SNAKE_CASE : Union[str, Any] = np.asarray(intermediate_weights[0][1] )
set_param(
torch_block.feed_forward.layer_norm , torch.tensor(lowercase_ ) , torch.tensor(lowercase_ ) , )
# intermediate dense
__SCREAMING_SNAKE_CASE : int = np.asarray(intermediate_weights[1][0] )
__SCREAMING_SNAKE_CASE : Optional[Any] = np.asarray(intermediate_weights[1][1] )
set_param(
torch_block.feed_forward.dense.dense , torch.tensor(lowercase_ ).transpose(0 , 1 ).contiguous() , torch.tensor(lowercase_ ) , )
# intermediate out
__SCREAMING_SNAKE_CASE : Optional[Any] = np.asarray(intermediate_weights[4][0] )
__SCREAMING_SNAKE_CASE : Any = np.asarray(intermediate_weights[4][1] )
set_param(
torch_block.feed_forward.output.dense , torch.tensor(lowercase_ ).transpose(0 , 1 ).contiguous() , torch.tensor(lowercase_ ) , )
def lowerCAmelCase_ ( lowercase_ : str , lowercase_ : Optional[Any] , lowercase_ : Optional[Any] ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : Dict = torch_model.reformer
# word embeds
__SCREAMING_SNAKE_CASE : int = np.asarray(weights[1] )
set_param(
torch_model_reformer.embeddings.word_embeddings , torch.tensor(lowercase_ ) , )
if isinstance(weights[3] , lowercase_ ):
__SCREAMING_SNAKE_CASE : int = torch_model_reformer.embeddings.position_embeddings
for emb_idx in range(len(position_embeddings.weights ) ):
__SCREAMING_SNAKE_CASE : Dict = np.asarray(weights[3][emb_idx][0] )
assert (
position_embeddings.weights[emb_idx].shape == emb_weights.shape
), F'''{position_embeddings[emb_idx]} emb does not match'''
__SCREAMING_SNAKE_CASE : str = nn.Parameter(torch.tensor(lowercase_ ) )
__SCREAMING_SNAKE_CASE : List[Any] = weights[5]
assert len(torch_model_reformer.encoder.layers ) * 4 == len(
lowercase_ ), "HF and trax model do not have the same number of layers"
for layer_idx, layer in enumerate(torch_model_reformer.encoder.layers ):
__SCREAMING_SNAKE_CASE : Union[str, Any] = trax_layer_weights[4 * layer_idx : 4 * (layer_idx + 1)]
set_block_weights_in_torch(lowercase_ , lowercase_ , lowercase_ )
# output layer norm
__SCREAMING_SNAKE_CASE : List[str] = np.asarray(weights[7][0] )
__SCREAMING_SNAKE_CASE : Optional[Any] = np.asarray(weights[7][1] )
set_param(
torch_model_reformer.encoder.layer_norm , torch.tensor(lowercase_ ) , torch.tensor(lowercase_ ) , )
# output embeddings
__SCREAMING_SNAKE_CASE : Optional[Any] = np.asarray(weights[9][0] )
__SCREAMING_SNAKE_CASE : List[Any] = np.asarray(weights[9][1] )
set_param(
torch_model.lm_head.decoder , torch.tensor(lowercase_ ).transpose(0 , 1 ).contiguous() , torch.tensor(lowercase_ ) , )
def lowerCAmelCase_ ( lowercase_ : List[str] , lowercase_ : Any , lowercase_ : int ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : Dict = ReformerConfig.from_json_file(lowercase_ )
print(F'''Building PyTorch model from configuration: {config}''' )
__SCREAMING_SNAKE_CASE : List[str] = ReformerModelWithLMHead(lowercase_ )
with open(lowercase_ , '''rb''' ) as f:
__SCREAMING_SNAKE_CASE : int = pickle.load(lowercase_ )['''weights''']
set_model_weights_in_torch(lowercase_ , lowercase_ , config.hidden_size )
# Save pytorch-model
print(F'''Save PyTorch model to {pytorch_dump_path}''' )
torch.save(model.state_dict() , lowercase_ )
if __name__ == "__main__":
_lowerCamelCase = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--trax_model_pkl_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
)
parser.add_argument(
'''--config_file''',
default=None,
type=str,
required=True,
help=(
'''The config json file corresponding to the pre-trained Reformer model. \n'''
'''This specifies the model architecture.'''
),
)
parser.add_argument(
'''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
)
_lowerCamelCase = parser.parse_args()
convert_trax_checkpoint_to_pytorch(args.trax_model_pkl_path, args.config_file, args.pytorch_dump_path)
| 674 | 0 |
from __future__ import annotations
import os
from typing import Any
import requests
lowercase_ : List[Any] = 'https://api.github.com'
# https://docs.github.com/en/free-pro-team@latest/rest/reference/users#get-the-authenticated-user
lowercase_ : Dict = BASE_URL + '/user'
# https://github.com/settings/tokens
lowercase_ : Optional[int] = os.environ.get('USER_TOKEN', '')
def A__ ( snake_case_ : str ):
SCREAMING_SNAKE_CASE__: Tuple= {
'''Authorization''': F'token {auth_token}',
'''Accept''': '''application/vnd.github.v3+json''',
}
return requests.get(lowercase_ , headers=lowercase_ ).json()
if __name__ == "__main__": # pragma: no cover
if USER_TOKEN:
for key, value in fetch_github_info(USER_TOKEN).items():
print(f'''{key}: {value}''')
else:
raise ValueError('\'USER_TOKEN\' field cannot be empty.')
| 64 |
"""simple docstring"""
from typing import Callable, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCamelCase = logging.get_logger(__name__)
_lowerCamelCase = {
'''microsoft/xprophetnet-large-wiki100-cased''': (
'''https://huggingface.co/microsoft/xprophetnet-large-wiki100-cased/resolve/main/config.json'''
),
}
class snake_case ( __UpperCAmelCase ):
lowerCamelCase__ = '''xlm-prophetnet'''
lowerCamelCase__ = ['''past_key_values''']
lowerCamelCase__ = {
'''num_attention_heads''': '''num_encoder_attention_heads''',
}
def __init__( self :List[str] , _lowerCamelCase :Optional[float] = 0.1 , _lowerCamelCase :Optional[Union[str, Callable]] = "gelu" , _lowerCamelCase :Optional[int] = 3_0_5_2_2 , _lowerCamelCase :Optional[int] = 1_0_2_4 , _lowerCamelCase :Optional[int] = 4_0_9_6 , _lowerCamelCase :Optional[int] = 1_2 , _lowerCamelCase :Optional[int] = 1_6 , _lowerCamelCase :Optional[int] = 4_0_9_6 , _lowerCamelCase :Optional[int] = 1_2 , _lowerCamelCase :Optional[int] = 1_6 , _lowerCamelCase :Optional[float] = 0.1 , _lowerCamelCase :Optional[float] = 0.1 , _lowerCamelCase :Optional[int] = 5_1_2 , _lowerCamelCase :Optional[float] = 0.0_2 , _lowerCamelCase :Optional[bool] = True , _lowerCamelCase :Optional[bool] = True , _lowerCamelCase :Optional[int] = 0 , _lowerCamelCase :Optional[int] = 2 , _lowerCamelCase :Optional[int] = 3_2 , _lowerCamelCase :Optional[int] = 1_2_8 , _lowerCamelCase :Optional[bool] = False , _lowerCamelCase :Optional[float] = 0.0 , _lowerCamelCase :Optional[bool] = True , _lowerCamelCase :Optional[int] = 0 , _lowerCamelCase :Optional[int] = 1 , _lowerCamelCase :Optional[int] = 2 , **_lowerCamelCase :int , ):
__SCREAMING_SNAKE_CASE : Union[str, Any] = vocab_size
__SCREAMING_SNAKE_CASE : Optional[int] = hidden_size
__SCREAMING_SNAKE_CASE : List[Any] = encoder_ffn_dim
__SCREAMING_SNAKE_CASE : str = num_encoder_layers
__SCREAMING_SNAKE_CASE : Optional[Any] = num_encoder_attention_heads
__SCREAMING_SNAKE_CASE : str = decoder_ffn_dim
__SCREAMING_SNAKE_CASE : List[Any] = num_decoder_layers
__SCREAMING_SNAKE_CASE : List[str] = num_decoder_attention_heads
__SCREAMING_SNAKE_CASE : Dict = max_position_embeddings
__SCREAMING_SNAKE_CASE : Any = init_std # Normal(0, this parameter)
__SCREAMING_SNAKE_CASE : Any = activation_function
# parameters for xlmprophetnet
__SCREAMING_SNAKE_CASE : List[Any] = ngram
__SCREAMING_SNAKE_CASE : int = num_buckets
__SCREAMING_SNAKE_CASE : List[str] = relative_max_distance
__SCREAMING_SNAKE_CASE : str = disable_ngram_loss
__SCREAMING_SNAKE_CASE : Optional[int] = eps
# 3 Types of Dropout
__SCREAMING_SNAKE_CASE : int = attention_dropout
__SCREAMING_SNAKE_CASE : Optional[Any] = activation_dropout
__SCREAMING_SNAKE_CASE : Dict = dropout
__SCREAMING_SNAKE_CASE : Any = use_cache
super().__init__(
pad_token_id=_lowerCamelCase , bos_token_id=_lowerCamelCase , eos_token_id=_lowerCamelCase , is_encoder_decoder=_lowerCamelCase , add_cross_attention=_lowerCamelCase , decoder_start_token_id=_lowerCamelCase , **_lowerCamelCase , )
@property
def SCREAMING_SNAKE_CASE_ ( self :int ):
return self.num_encoder_layers + self.num_decoder_layers
@num_hidden_layers.setter
def SCREAMING_SNAKE_CASE_ ( self :Dict , _lowerCamelCase :List[Any] ):
raise NotImplementedError(
'''This model does not support the setting of `num_hidden_layers`. Please set `num_encoder_layers` and'''
''' `num_decoder_layers`.''' )
| 674 | 0 |
import json
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
from transformers import (
CONFIG_MAPPING,
IMAGE_PROCESSOR_MAPPING,
AutoConfig,
AutoImageProcessor,
CLIPConfig,
CLIPImageProcessor,
)
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER
sys.path.append(str(Path(__file__).parent.parent.parent.parent / '''utils'''))
from test_module.custom_configuration import CustomConfig # noqa E402
from test_module.custom_image_processing import CustomImageProcessor # noqa E402
class lowerCamelCase_ ( unittest.TestCase ):
'''simple docstring'''
def A ( self ) -> List[Any]:
'''simple docstring'''
__lowercase = 0
def A ( self ) -> List[Any]:
'''simple docstring'''
__lowercase = AutoImageProcessor.from_pretrained('''openai/clip-vit-base-patch32''' )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
def A ( self ) -> Tuple:
'''simple docstring'''
with tempfile.TemporaryDirectory() as tmpdirname:
__lowercase = Path(_lowerCamelCase ) / '''preprocessor_config.json'''
__lowercase = Path(_lowerCamelCase ) / '''config.json'''
json.dump(
{'''image_processor_type''': '''CLIPImageProcessor''', '''processor_class''': '''CLIPProcessor'''} , open(_lowerCamelCase , '''w''' ) , )
json.dump({'''model_type''': '''clip'''} , open(_lowerCamelCase , '''w''' ) )
__lowercase = AutoImageProcessor.from_pretrained(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
def A ( self ) -> List[Any]:
'''simple docstring'''
with tempfile.TemporaryDirectory() as tmpdirname:
__lowercase = Path(_lowerCamelCase ) / '''preprocessor_config.json'''
__lowercase = Path(_lowerCamelCase ) / '''config.json'''
json.dump(
{'''feature_extractor_type''': '''CLIPFeatureExtractor''', '''processor_class''': '''CLIPProcessor'''} , open(_lowerCamelCase , '''w''' ) , )
json.dump({'''model_type''': '''clip'''} , open(_lowerCamelCase , '''w''' ) )
__lowercase = AutoImageProcessor.from_pretrained(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
def A ( self ) -> Optional[Any]:
'''simple docstring'''
with tempfile.TemporaryDirectory() as tmpdirname:
__lowercase = CLIPConfig()
# Create a dummy config file with image_proceesor_type
__lowercase = Path(_lowerCamelCase ) / '''preprocessor_config.json'''
__lowercase = Path(_lowerCamelCase ) / '''config.json'''
json.dump(
{'''image_processor_type''': '''CLIPImageProcessor''', '''processor_class''': '''CLIPProcessor'''} , open(_lowerCamelCase , '''w''' ) , )
json.dump({'''model_type''': '''clip'''} , open(_lowerCamelCase , '''w''' ) )
# remove image_processor_type to make sure config.json alone is enough to load image processor locally
__lowercase = AutoImageProcessor.from_pretrained(_lowerCamelCase ).to_dict()
config_dict.pop('''image_processor_type''' )
__lowercase = CLIPImageProcessor(**_lowerCamelCase )
# save in new folder
model_config.save_pretrained(_lowerCamelCase )
config.save_pretrained(_lowerCamelCase )
__lowercase = AutoImageProcessor.from_pretrained(_lowerCamelCase )
# make sure private variable is not incorrectly saved
__lowercase = json.loads(config.to_json_string() )
self.assertTrue('''_processor_class''' not in dict_as_saved )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
def A ( self ) -> List[str]:
'''simple docstring'''
with tempfile.TemporaryDirectory() as tmpdirname:
__lowercase = Path(_lowerCamelCase ) / '''preprocessor_config.json'''
json.dump(
{'''image_processor_type''': '''CLIPImageProcessor''', '''processor_class''': '''CLIPProcessor'''} , open(_lowerCamelCase , '''w''' ) , )
__lowercase = AutoImageProcessor.from_pretrained(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
def A ( self ) -> List[str]:
'''simple docstring'''
with self.assertRaisesRegex(
_lowerCamelCase , '''clip-base is not a local folder and is not a valid model identifier''' ):
__lowercase = AutoImageProcessor.from_pretrained('''clip-base''' )
def A ( self ) -> List[str]:
'''simple docstring'''
with self.assertRaisesRegex(
_lowerCamelCase , r'''aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)''' ):
__lowercase = AutoImageProcessor.from_pretrained(_lowerCamelCase , revision='''aaaaaa''' )
def A ( self ) -> Any:
'''simple docstring'''
with self.assertRaisesRegex(
_lowerCamelCase , '''hf-internal-testing/config-no-model does not appear to have a file named preprocessor_config.json.''' , ):
__lowercase = AutoImageProcessor.from_pretrained('''hf-internal-testing/config-no-model''' )
def A ( self ) -> Any:
'''simple docstring'''
with self.assertRaises(_lowerCamelCase ):
__lowercase = AutoImageProcessor.from_pretrained('''hf-internal-testing/test_dynamic_image_processor''' )
# If remote code is disabled, we can't load this config.
with self.assertRaises(_lowerCamelCase ):
__lowercase = AutoImageProcessor.from_pretrained(
'''hf-internal-testing/test_dynamic_image_processor''' , trust_remote_code=_lowerCamelCase )
__lowercase = AutoImageProcessor.from_pretrained(
'''hf-internal-testing/test_dynamic_image_processor''' , trust_remote_code=_lowerCamelCase )
self.assertEqual(image_processor.__class__.__name__ , '''NewImageProcessor''' )
# Test image processor can be reloaded.
with tempfile.TemporaryDirectory() as tmp_dir:
image_processor.save_pretrained(_lowerCamelCase )
__lowercase = AutoImageProcessor.from_pretrained(_lowerCamelCase , trust_remote_code=_lowerCamelCase )
self.assertEqual(reloaded_image_processor.__class__.__name__ , '''NewImageProcessor''' )
def A ( self ) -> Union[str, Any]:
'''simple docstring'''
try:
AutoConfig.register('''custom''' , _lowerCamelCase )
AutoImageProcessor.register(_lowerCamelCase , _lowerCamelCase )
# Trying to register something existing in the Transformers library will raise an error
with self.assertRaises(_lowerCamelCase ):
AutoImageProcessor.register(_lowerCamelCase , _lowerCamelCase )
with tempfile.TemporaryDirectory() as tmpdirname:
__lowercase = Path(_lowerCamelCase ) / '''preprocessor_config.json'''
__lowercase = Path(_lowerCamelCase ) / '''config.json'''
json.dump(
{'''feature_extractor_type''': '''CLIPFeatureExtractor''', '''processor_class''': '''CLIPProcessor'''} , open(_lowerCamelCase , '''w''' ) , )
json.dump({'''model_type''': '''clip'''} , open(_lowerCamelCase , '''w''' ) )
__lowercase = CustomImageProcessor.from_pretrained(_lowerCamelCase )
# Now that the config is registered, it can be used as any other config with the auto-API
with tempfile.TemporaryDirectory() as tmp_dir:
image_processor.save_pretrained(_lowerCamelCase )
__lowercase = AutoImageProcessor.from_pretrained(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
finally:
if "custom" in CONFIG_MAPPING._extra_content:
del CONFIG_MAPPING._extra_content["custom"]
if CustomConfig in IMAGE_PROCESSOR_MAPPING._extra_content:
del IMAGE_PROCESSOR_MAPPING._extra_content[CustomConfig]
def A ( self ) -> Optional[int]:
'''simple docstring'''
class lowerCamelCase_ ( __UpperCAmelCase ):
'''simple docstring'''
__UpperCAmelCase = True
try:
AutoConfig.register('''custom''' , _lowerCamelCase )
AutoImageProcessor.register(_lowerCamelCase , _lowerCamelCase )
# If remote code is not set, the default is to use local
__lowercase = AutoImageProcessor.from_pretrained('''hf-internal-testing/test_dynamic_image_processor''' )
self.assertEqual(image_processor.__class__.__name__ , '''NewImageProcessor''' )
self.assertTrue(image_processor.is_local )
# If remote code is disabled, we load the local one.
__lowercase = AutoImageProcessor.from_pretrained(
'''hf-internal-testing/test_dynamic_image_processor''' , trust_remote_code=_lowerCamelCase )
self.assertEqual(image_processor.__class__.__name__ , '''NewImageProcessor''' )
self.assertTrue(image_processor.is_local )
# If remote is enabled, we load from the Hub
__lowercase = AutoImageProcessor.from_pretrained(
'''hf-internal-testing/test_dynamic_image_processor''' , trust_remote_code=_lowerCamelCase )
self.assertEqual(image_processor.__class__.__name__ , '''NewImageProcessor''' )
self.assertTrue(not hasattr(_lowerCamelCase , '''is_local''' ) )
finally:
if "custom" in CONFIG_MAPPING._extra_content:
del CONFIG_MAPPING._extra_content["custom"]
if CustomConfig in IMAGE_PROCESSOR_MAPPING._extra_content:
del IMAGE_PROCESSOR_MAPPING._extra_content[CustomConfig]
| 639 |
"""simple docstring"""
import ast
import os
import re
import shutil
import tempfile
import unittest
from unittest import mock
import torch
from accelerate.test_utils.examples import compare_against_test
from accelerate.test_utils.testing import TempDirTestCase, require_trackers, run_command, slow
from accelerate.utils import write_basic_config
# DataLoaders built from `test_samples/MRPC` for quick testing
# Should mock `{script_name}.get_dataloaders` via:
# @mock.patch("{script_name}.get_dataloaders", mocked_dataloaders)
_lowerCamelCase = [
'''cross_validation.py''',
'''gradient_accumulation.py''',
'''local_sgd.py''',
'''multi_process_metrics.py''',
'''memory.py''',
'''automatic_gradient_accumulation.py''',
'''fsdp_with_peak_mem_tracking.py''',
'''deepspeed_with_config_support.py''',
'''megatron_lm_gpt_pretraining.py''',
]
class snake_case ( unittest.TestCase ):
def SCREAMING_SNAKE_CASE_ ( self :List[str] , _lowerCamelCase :str , _lowerCamelCase :bool , _lowerCamelCase :str = None , _lowerCamelCase :list = None ):
__SCREAMING_SNAKE_CASE : List[str] = None
__SCREAMING_SNAKE_CASE : Optional[Any] = os.path.abspath(os.path.join('''examples''' , '''by_feature''' ) )
__SCREAMING_SNAKE_CASE : Union[str, Any] = os.path.abspath('''examples''' )
for item in os.listdir(_lowerCamelCase ):
if item not in EXCLUDE_EXAMPLES:
__SCREAMING_SNAKE_CASE : List[Any] = os.path.join(_lowerCamelCase , _lowerCamelCase )
if os.path.isfile(_lowerCamelCase ) and ".py" in item_path:
with self.subTest(
tested_script=_lowerCamelCase , feature_script=_lowerCamelCase , tested_section='''main()''' if parser_only else '''training_function()''' , ):
__SCREAMING_SNAKE_CASE : Tuple = compare_against_test(
os.path.join(_lowerCamelCase , _lowerCamelCase ) , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : Optional[Any] = '''\n'''.join(_lowerCamelCase )
if special_strings is not None:
for string in special_strings:
__SCREAMING_SNAKE_CASE : List[Any] = diff.replace(_lowerCamelCase , '''''' )
self.assertEqual(_lowerCamelCase , '''''' )
def SCREAMING_SNAKE_CASE_ ( self :Union[str, Any] ):
self.one_complete_example('''complete_nlp_example.py''' , _lowerCamelCase )
self.one_complete_example('''complete_nlp_example.py''' , _lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :List[str] ):
__SCREAMING_SNAKE_CASE : Union[str, Any] = os.path.abspath(os.path.join('''examples''' , '''cv_example.py''' ) )
__SCREAMING_SNAKE_CASE : Optional[int] = [
''' ''' * 1_6 + '''{\n\n''',
''' ''' * 2_0 + '''"accuracy": eval_metric["accuracy"],\n\n''',
''' ''' * 2_0 + '''"f1": eval_metric["f1"],\n\n''',
''' ''' * 2_0 + '''"train_loss": total_loss.item() / len(train_dataloader),\n\n''',
''' ''' * 2_0 + '''"epoch": epoch,\n\n''',
''' ''' * 1_6 + '''},\n\n''',
''' ''' * 1_6 + '''step=epoch,\n''',
''' ''' * 1_2,
''' ''' * 8 + '''for step, batch in enumerate(active_dataloader):\n''',
]
self.one_complete_example('''complete_cv_example.py''' , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase )
self.one_complete_example('''complete_cv_example.py''' , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase )
@mock.patch.dict(os.environ , {'''TESTING_MOCKED_DATALOADERS''': '''1'''} )
class snake_case ( __UpperCAmelCase ):
lowerCamelCase__ = False
@classmethod
def SCREAMING_SNAKE_CASE_ ( cls :Dict ):
super().setUpClass()
__SCREAMING_SNAKE_CASE : Dict = tempfile.mkdtemp()
__SCREAMING_SNAKE_CASE : str = os.path.join(cls._tmpdir , '''default_config.yml''' )
write_basic_config(save_location=cls.configPath )
__SCREAMING_SNAKE_CASE : List[Any] = ['''accelerate''', '''launch''', '''--config_file''', cls.configPath]
@classmethod
def SCREAMING_SNAKE_CASE_ ( cls :Dict ):
super().tearDownClass()
shutil.rmtree(cls._tmpdir )
def SCREAMING_SNAKE_CASE_ ( self :Tuple ):
__SCREAMING_SNAKE_CASE : str = f'''
examples/by_feature/checkpointing.py
--checkpointing_steps epoch
--output_dir {self.tmpdir}
'''.split()
run_command(self._launch_args + testargs )
self.assertTrue(os.path.exists(os.path.join(self.tmpdir , '''epoch_0''' ) ) )
def SCREAMING_SNAKE_CASE_ ( self :List[Any] ):
__SCREAMING_SNAKE_CASE : Optional[Any] = f'''
examples/by_feature/checkpointing.py
--checkpointing_steps 1
--output_dir {self.tmpdir}
'''.split()
__SCREAMING_SNAKE_CASE : Optional[int] = run_command(self._launch_args + testargs )
self.assertTrue(os.path.exists(os.path.join(self.tmpdir , '''step_2''' ) ) )
def SCREAMING_SNAKE_CASE_ ( self :List[str] ):
__SCREAMING_SNAKE_CASE : Optional[int] = f'''
examples/by_feature/checkpointing.py
--resume_from_checkpoint {os.path.join(self.tmpdir , 'epoch_0' )}
'''.split()
__SCREAMING_SNAKE_CASE : Any = run_command(self._launch_args + testargs , return_stdout=_lowerCamelCase )
self.assertNotIn('''epoch 0:''' , _lowerCamelCase )
self.assertIn('''epoch 1:''' , _lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :Union[str, Any] ):
__SCREAMING_SNAKE_CASE : Optional[int] = f'''
examples/by_feature/checkpointing.py
--resume_from_checkpoint {os.path.join(self.tmpdir , 'step_2' )}
'''.split()
__SCREAMING_SNAKE_CASE : List[str] = run_command(self._launch_args + testargs , return_stdout=_lowerCamelCase )
if torch.cuda.is_available():
__SCREAMING_SNAKE_CASE : List[Any] = torch.cuda.device_count()
else:
__SCREAMING_SNAKE_CASE : Optional[int] = 1
if num_processes > 1:
self.assertNotIn('''epoch 0:''' , _lowerCamelCase )
self.assertIn('''epoch 1:''' , _lowerCamelCase )
else:
self.assertIn('''epoch 0:''' , _lowerCamelCase )
self.assertIn('''epoch 1:''' , _lowerCamelCase )
@slow
def SCREAMING_SNAKE_CASE_ ( self :str ):
__SCREAMING_SNAKE_CASE : Optional[Any] = '''
examples/by_feature/cross_validation.py
--num_folds 2
'''.split()
with mock.patch.dict(os.environ , {'''TESTING_MOCKED_DATALOADERS''': '''0'''} ):
__SCREAMING_SNAKE_CASE : Union[str, Any] = run_command(self._launch_args + testargs , return_stdout=_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Any = re.findall('''({.+})''' , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : List[Any] = [r for r in results if '''accuracy''' in r][-1]
__SCREAMING_SNAKE_CASE : Tuple = ast.literal_eval(_lowerCamelCase )
self.assertGreaterEqual(results['''accuracy'''] , 0.7_5 )
def SCREAMING_SNAKE_CASE_ ( self :str ):
__SCREAMING_SNAKE_CASE : Optional[Any] = ['''examples/by_feature/multi_process_metrics.py''']
run_command(self._launch_args + testargs )
@require_trackers
@mock.patch.dict(os.environ , {'''WANDB_MODE''': '''offline'''} )
def SCREAMING_SNAKE_CASE_ ( self :List[str] ):
with tempfile.TemporaryDirectory() as tmpdir:
__SCREAMING_SNAKE_CASE : int = f'''
examples/by_feature/tracking.py
--with_tracking
--project_dir {tmpdir}
'''.split()
run_command(self._launch_args + testargs )
self.assertTrue(os.path.exists(os.path.join(_lowerCamelCase , '''tracking''' ) ) )
def SCREAMING_SNAKE_CASE_ ( self :Dict ):
__SCREAMING_SNAKE_CASE : Union[str, Any] = ['''examples/by_feature/gradient_accumulation.py''']
run_command(self._launch_args + testargs )
def SCREAMING_SNAKE_CASE_ ( self :Optional[int] ):
__SCREAMING_SNAKE_CASE : List[Any] = ['''examples/by_feature/local_sgd.py''']
run_command(self._launch_args + testargs )
| 674 | 0 |
import gc
import unittest
import numpy as np
import torch
from diffusers import (
AudioDiffusionPipeline,
AutoencoderKL,
DDIMScheduler,
DDPMScheduler,
DiffusionPipeline,
Mel,
UNetaDConditionModel,
UNetaDModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
enable_full_determinism()
class __magic_name__ ( unittest.TestCase ):
"""simple docstring"""
def lowerCAmelCase ( self : str ):
"""simple docstring"""
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
@property
def lowerCAmelCase ( self : List[Any] ):
"""simple docstring"""
torch.manual_seed(0 )
_UpperCamelCase: List[str] = UNetaDModel(
sample_size=(32, 64) , in_channels=1 , out_channels=1 , layers_per_block=2 , block_out_channels=(128, 128) , down_block_types=('''AttnDownBlock2D''', '''DownBlock2D''') , up_block_types=('''UpBlock2D''', '''AttnUpBlock2D''') , )
return model
@property
def lowerCAmelCase ( self : Any ):
"""simple docstring"""
torch.manual_seed(0 )
_UpperCamelCase: str = UNetaDConditionModel(
sample_size=(64, 32) , in_channels=1 , out_channels=1 , layers_per_block=2 , block_out_channels=(128, 128) , down_block_types=('''CrossAttnDownBlock2D''', '''DownBlock2D''') , up_block_types=('''UpBlock2D''', '''CrossAttnUpBlock2D''') , cross_attention_dim=10 , )
return model
@property
def lowerCAmelCase ( self : Any ):
"""simple docstring"""
torch.manual_seed(0 )
_UpperCamelCase: Tuple = AutoencoderKL(
sample_size=(128, 64) , in_channels=1 , out_channels=1 , latent_channels=1 , layers_per_block=2 , block_out_channels=(128, 128) , down_block_types=('''DownEncoderBlock2D''', '''DownEncoderBlock2D''') , up_block_types=('''UpDecoderBlock2D''', '''UpDecoderBlock2D''') , )
_UpperCamelCase: List[str] = UNetaDModel(
sample_size=(64, 32) , in_channels=1 , out_channels=1 , layers_per_block=2 , block_out_channels=(128, 128) , down_block_types=('''AttnDownBlock2D''', '''DownBlock2D''') , up_block_types=('''UpBlock2D''', '''AttnUpBlock2D''') , )
return vqvae, unet
@slow
def lowerCAmelCase ( self : Tuple ):
"""simple docstring"""
_UpperCamelCase: str = '''cpu''' # ensure determinism for the device-dependent torch.Generator
_UpperCamelCase: Tuple = Mel(
x_res=self.dummy_unet.config.sample_size[1] , y_res=self.dummy_unet.config.sample_size[0] , )
_UpperCamelCase: List[str] = DDPMScheduler()
_UpperCamelCase: Optional[int] = AudioDiffusionPipeline(vqvae=_lowerCamelCase , unet=self.dummy_unet , mel=_lowerCamelCase , scheduler=_lowerCamelCase )
_UpperCamelCase: int = pipe.to(_lowerCamelCase )
pipe.set_progress_bar_config(disable=_lowerCamelCase )
_UpperCamelCase: int = torch.Generator(device=_lowerCamelCase ).manual_seed(42 )
_UpperCamelCase: Optional[int] = pipe(generator=_lowerCamelCase , steps=4 )
_UpperCamelCase: Tuple = output.audios[0]
_UpperCamelCase: List[Any] = output.images[0]
_UpperCamelCase: Optional[Any] = torch.Generator(device=_lowerCamelCase ).manual_seed(42 )
_UpperCamelCase: int = pipe(generator=_lowerCamelCase , steps=4 , return_dict=_lowerCamelCase )
_UpperCamelCase: List[str] = output[0][0]
assert audio.shape == (1, (self.dummy_unet.config.sample_size[1] - 1) * mel.hop_length)
assert (
image.height == self.dummy_unet.config.sample_size[0]
and image.width == self.dummy_unet.config.sample_size[1]
)
_UpperCamelCase: List[Any] = np.frombuffer(image.tobytes() , dtype='''uint8''' )[:10]
_UpperCamelCase: int = np.frombuffer(image_from_tuple.tobytes() , dtype='''uint8''' )[:10]
_UpperCamelCase: str = np.array([69, 255, 255, 255, 0, 0, 77, 181, 12, 127] )
assert np.abs(image_slice.flatten() - expected_slice ).max() == 0
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() == 0
_UpperCamelCase: Dict = Mel(
x_res=self.dummy_vqvae_and_unet[0].config.sample_size[1] , y_res=self.dummy_vqvae_and_unet[0].config.sample_size[0] , )
_UpperCamelCase: List[Any] = DDIMScheduler()
_UpperCamelCase: str = self.dummy_vqvae_and_unet
_UpperCamelCase: List[str] = AudioDiffusionPipeline(
vqvae=self.dummy_vqvae_and_unet[0] , unet=dummy_vqvae_and_unet[1] , mel=_lowerCamelCase , scheduler=_lowerCamelCase )
_UpperCamelCase: Tuple = pipe.to(_lowerCamelCase )
pipe.set_progress_bar_config(disable=_lowerCamelCase )
np.random.seed(0 )
_UpperCamelCase: int = np.random.uniform(-1 , 1 , ((dummy_vqvae_and_unet[0].config.sample_size[1] - 1) * mel.hop_length,) )
_UpperCamelCase: Union[str, Any] = torch.Generator(device=_lowerCamelCase ).manual_seed(42 )
_UpperCamelCase: Tuple = pipe(raw_audio=_lowerCamelCase , generator=_lowerCamelCase , start_step=5 , steps=10 )
_UpperCamelCase: Any = output.images[0]
assert (
image.height == self.dummy_vqvae_and_unet[0].config.sample_size[0]
and image.width == self.dummy_vqvae_and_unet[0].config.sample_size[1]
)
_UpperCamelCase: Any = np.frombuffer(image.tobytes() , dtype='''uint8''' )[:10]
_UpperCamelCase: Union[str, Any] = np.array([120, 117, 110, 109, 138, 167, 138, 148, 132, 121] )
assert np.abs(image_slice.flatten() - expected_slice ).max() == 0
_UpperCamelCase: Optional[Any] = self.dummy_unet_condition
_UpperCamelCase: Dict = AudioDiffusionPipeline(
vqvae=self.dummy_vqvae_and_unet[0] , unet=_lowerCamelCase , mel=_lowerCamelCase , scheduler=_lowerCamelCase )
_UpperCamelCase: List[str] = pipe.to(_lowerCamelCase )
pipe.set_progress_bar_config(disable=_lowerCamelCase )
np.random.seed(0 )
_UpperCamelCase: Optional[int] = torch.rand((1, 1, 10) )
_UpperCamelCase: Union[str, Any] = pipe(generator=_lowerCamelCase , encoding=_lowerCamelCase )
_UpperCamelCase: Optional[int] = output.images[0]
_UpperCamelCase: List[Any] = np.frombuffer(image.tobytes() , dtype='''uint8''' )[:10]
_UpperCamelCase: List[str] = np.array([107, 103, 120, 127, 142, 122, 113, 122, 97, 111] )
assert np.abs(image_slice.flatten() - expected_slice ).max() == 0
@slow
@require_torch_gpu
class __magic_name__ ( unittest.TestCase ):
"""simple docstring"""
def lowerCAmelCase ( self : Optional[int] ):
"""simple docstring"""
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def lowerCAmelCase ( self : List[str] ):
"""simple docstring"""
_UpperCamelCase: str = torch_device
_UpperCamelCase: Union[str, Any] = DiffusionPipeline.from_pretrained('''teticio/audio-diffusion-ddim-256''' )
_UpperCamelCase: int = pipe.to(_lowerCamelCase )
pipe.set_progress_bar_config(disable=_lowerCamelCase )
_UpperCamelCase: Optional[int] = torch.Generator(device=_lowerCamelCase ).manual_seed(42 )
_UpperCamelCase: Optional[int] = pipe(generator=_lowerCamelCase )
_UpperCamelCase: Optional[Any] = output.audios[0]
_UpperCamelCase: Union[str, Any] = output.images[0]
assert audio.shape == (1, (pipe.unet.config.sample_size[1] - 1) * pipe.mel.hop_length)
assert image.height == pipe.unet.config.sample_size[0] and image.width == pipe.unet.config.sample_size[1]
_UpperCamelCase: Dict = np.frombuffer(image.tobytes() , dtype='''uint8''' )[:10]
_UpperCamelCase: Any = np.array([151, 167, 154, 144, 122, 134, 121, 105, 70, 26] )
assert np.abs(image_slice.flatten() - expected_slice ).max() == 0 | 271 |
"""simple docstring"""
import argparse
import logging
import os
import time
import timeit
import datasets
import numpy as np
import pycuda.autoinit # noqa: F401
import pycuda.driver as cuda
import tensorrt as trt
import torch
from absl import logging as absl_logging
from accelerate import Accelerator
from datasets import load_dataset, load_metric
from torch.utils.data import DataLoader
from utils_qa import postprocess_qa_predictions
import transformers
from transformers import AutoTokenizer, EvalPrediction, default_data_collator, set_seed
from transformers.trainer_pt_utils import nested_concat, nested_truncate
_lowerCamelCase = trt.Logger(trt.Logger.WARNING)
_lowerCamelCase = absl_logging.get_absl_logger()
absl_logger.setLevel(logging.WARNING)
_lowerCamelCase = logging.getLogger(__name__)
_lowerCamelCase = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--onnx_model_path''',
default=None,
type=str,
required=True,
help='''Path to ONNX model: ''',
)
parser.add_argument(
'''--output_dir''',
default=None,
type=str,
required=True,
help='''The output directory where the model checkpoints and predictions will be written.''',
)
# Other parameters
parser.add_argument(
'''--tokenizer_name''',
default='''''',
type=str,
required=True,
help='''Pretrained tokenizer name or path if not the same as model_name''',
)
parser.add_argument(
'''--version_2_with_negative''',
action='''store_true''',
help='''If true, the SQuAD examples contain some that do not have an answer.''',
)
parser.add_argument(
'''--null_score_diff_threshold''',
type=float,
default=0.0,
help='''If null_score - best_non_null is greater than the threshold predict null.''',
)
parser.add_argument(
'''--max_seq_length''',
default=3_84,
type=int,
help=(
'''The maximum total input sequence length after WordPiece tokenization. Sequences '''
'''longer than this will be truncated, and sequences shorter than this will be padded.'''
),
)
parser.add_argument(
'''--doc_stride''',
default=1_28,
type=int,
help='''When splitting up a long document into chunks, how much stride to take between chunks.''',
)
parser.add_argument('''--per_device_eval_batch_size''', default=8, type=int, help='''Batch size per GPU/CPU for evaluation.''')
parser.add_argument(
'''--n_best_size''',
default=20,
type=int,
help='''The total number of n-best predictions to generate in the nbest_predictions.json output file.''',
)
parser.add_argument(
'''--max_answer_length''',
default=30,
type=int,
help=(
'''The maximum length of an answer that can be generated. This is needed because the start '''
'''and end predictions are not conditioned on one another.'''
),
)
parser.add_argument('''--seed''', type=int, default=42, help='''random seed for initialization''')
parser.add_argument(
'''--dataset_name''',
type=str,
default=None,
required=True,
help='''The name of the dataset to use (via the datasets library).''',
)
parser.add_argument(
'''--dataset_config_name''',
type=str,
default=None,
help='''The configuration name of the dataset to use (via the datasets library).''',
)
parser.add_argument(
'''--preprocessing_num_workers''', type=int, default=4, help='''A csv or a json file containing the training data.'''
)
parser.add_argument('''--overwrite_cache''', action='''store_true''', help='''Overwrite the cached training and evaluation sets''')
parser.add_argument(
'''--fp16''',
action='''store_true''',
help='''Whether to use 16-bit (mixed) precision instead of 32-bit''',
)
parser.add_argument(
'''--int8''',
action='''store_true''',
help='''Whether to use INT8''',
)
_lowerCamelCase = parser.parse_args()
if args.tokenizer_name:
_lowerCamelCase = AutoTokenizer.from_pretrained(args.tokenizer_name, use_fast=True)
else:
raise ValueError(
'''You are instantiating a new tokenizer from scratch. This is not supported by this script.'''
'''You can do it from another script, save it, and load it from here, using --tokenizer_name.'''
)
logger.info('''Training/evaluation parameters %s''', args)
_lowerCamelCase = args.per_device_eval_batch_size
_lowerCamelCase = (args.eval_batch_size, args.max_seq_length)
# TRT Engine properties
_lowerCamelCase = True
_lowerCamelCase = '''temp_engine/bert-fp32.engine'''
if args.fpaa:
_lowerCamelCase = '''temp_engine/bert-fp16.engine'''
if args.inta:
_lowerCamelCase = '''temp_engine/bert-int8.engine'''
# import ONNX file
if not os.path.exists('''temp_engine'''):
os.makedirs('''temp_engine''')
_lowerCamelCase = 1 << (int)(trt.NetworkDefinitionCreationFlag.EXPLICIT_BATCH)
with trt.Builder(TRT_LOGGER) as builder, builder.create_network(EXPLICIT_BATCH) as network, trt.OnnxParser(
network, TRT_LOGGER
) as parser:
with open(args.onnx_model_path, '''rb''') as model:
if not parser.parse(model.read()):
for error in range(parser.num_errors):
print(parser.get_error(error))
# Query input names and shapes from parsed TensorRT network
_lowerCamelCase = [network.get_input(i) for i in range(network.num_inputs)]
_lowerCamelCase = [_input.name for _input in network_inputs] # ex: ["actual_input1"]
with builder.create_builder_config() as config:
_lowerCamelCase = 1 << 50
if STRICT_TYPES:
config.set_flag(trt.BuilderFlag.STRICT_TYPES)
if args.fpaa:
config.set_flag(trt.BuilderFlag.FPaa)
if args.inta:
config.set_flag(trt.BuilderFlag.INTa)
_lowerCamelCase = builder.create_optimization_profile()
config.add_optimization_profile(profile)
for i in range(len(input_names)):
profile.set_shape(input_names[i], INPUT_SHAPE, INPUT_SHAPE, INPUT_SHAPE)
_lowerCamelCase = builder.build_engine(network, config)
# serialize_engine and store in file (can be directly loaded and deserialized):
with open(engine_name, '''wb''') as f:
f.write(engine.serialize())
def lowerCAmelCase_ ( lowercase_ : List[Any] , lowercase_ : Tuple , lowercase_ : List[Any] , lowercase_ : List[Any] , lowercase_ : str , lowercase_ : Tuple , lowercase_ : Any , lowercase_ : Optional[int] ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : Dict = np.asarray(inputs['''input_ids'''] , dtype=np.intaa )
__SCREAMING_SNAKE_CASE : Optional[Any] = np.asarray(inputs['''attention_mask'''] , dtype=np.intaa )
__SCREAMING_SNAKE_CASE : Optional[Any] = np.asarray(inputs['''token_type_ids'''] , dtype=np.intaa )
# Copy inputs
cuda.memcpy_htod_async(d_inputs[0] , input_ids.ravel() , lowercase_ )
cuda.memcpy_htod_async(d_inputs[1] , attention_mask.ravel() , lowercase_ )
cuda.memcpy_htod_async(d_inputs[2] , token_type_ids.ravel() , lowercase_ )
# start time
__SCREAMING_SNAKE_CASE : Tuple = time.time()
# Run inference
context.execute_async(
bindings=[int(lowercase_ ) for d_inp in d_inputs] + [int(lowercase_ ), int(lowercase_ )] , stream_handle=stream.handle )
# Transfer predictions back from GPU
cuda.memcpy_dtoh_async(lowercase_ , lowercase_ , lowercase_ )
cuda.memcpy_dtoh_async(lowercase_ , lowercase_ , lowercase_ )
# Synchronize the stream and take time
stream.synchronize()
# end time
__SCREAMING_SNAKE_CASE : List[str] = time.time()
__SCREAMING_SNAKE_CASE : int = end_time - start_time
__SCREAMING_SNAKE_CASE : int = (h_outputa, h_outputa)
# print(outputs)
return outputs, infer_time
# Initialize the accelerator. We will let the accelerator handle device placement for us in this example.
_lowerCamelCase = Accelerator()
# Make one log on every process with the configuration for debugging.
logging.basicConfig(
format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''',
datefmt='''%m/%d/%Y %H:%M:%S''',
level=logging.INFO,
)
# Setup logging, we only want one process per machine to log things on the screen.
# accelerator.is_local_main_process is only True for one process per machine.
logger.setLevel(logging.INFO if accelerator.is_local_main_process else logging.ERROR)
if accelerator.is_local_main_process:
datasets.utils.logging.set_verbosity_warning()
transformers.utils.logging.set_verbosity_info()
else:
datasets.utils.logging.set_verbosity_error()
transformers.utils.logging.set_verbosity_error()
# If passed along, set the training seed now.
if args.seed is not None:
set_seed(args.seed)
# Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below)
# or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
# (the dataset will be downloaded automatically from the datasets Hub).
#
# For CSV/JSON files, this script will use the column called 'text' or the first column if no column called
# 'text' is found. You can easily tweak this behavior (see below).
if args.dataset_name is not None:
# Downloading and loading a dataset from the hub.
_lowerCamelCase = load_dataset(args.dataset_name, args.dataset_config_name)
else:
raise ValueError('''Evaluation requires a dataset name''')
# See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
# https://huggingface.co/docs/datasets/loading_datasets.html.
# Preprocessing the datasets.
# Preprocessing is slighlty different for training and evaluation.
_lowerCamelCase = raw_datasets['''validation'''].column_names
_lowerCamelCase = '''question''' if '''question''' in column_names else column_names[0]
_lowerCamelCase = '''context''' if '''context''' in column_names else column_names[1]
_lowerCamelCase = '''answers''' if '''answers''' in column_names else column_names[2]
# Padding side determines if we do (question|context) or (context|question).
_lowerCamelCase = tokenizer.padding_side == '''right'''
if args.max_seq_length > tokenizer.model_max_length:
logger.warning(
f'The max_seq_length passed ({args.max_seq_length}) is larger than the maximum length for the'
f'model ({tokenizer.model_max_length}). Using max_seq_length={tokenizer.model_max_length}.'
)
_lowerCamelCase = min(args.max_seq_length, tokenizer.model_max_length)
def lowerCAmelCase_ ( lowercase_ : Tuple ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : Any = [q.lstrip() for q in examples[question_column_name]]
# Tokenize our examples with truncation and maybe padding, but keep the overflows using a stride. This results
# in one example possible giving several features when a context is long, each of those features having a
# context that overlaps a bit the context of the previous feature.
__SCREAMING_SNAKE_CASE : Optional[Any] = tokenizer(
examples[question_column_name if pad_on_right else context_column_name] , examples[context_column_name if pad_on_right else question_column_name] , truncation='''only_second''' if pad_on_right else '''only_first''' , max_length=lowercase_ , stride=args.doc_stride , return_overflowing_tokens=lowercase_ , return_offsets_mapping=lowercase_ , padding='''max_length''' , )
# Since one example might give us several features if it has a long context, we need a map from a feature to
# its corresponding example. This key gives us just that.
__SCREAMING_SNAKE_CASE : Optional[int] = tokenized_examples.pop('''overflow_to_sample_mapping''' )
# For evaluation, we will need to convert our predictions to substrings of the context, so we keep the
# corresponding example_id and we will store the offset mappings.
__SCREAMING_SNAKE_CASE : Any = []
for i in range(len(tokenized_examples['''input_ids'''] ) ):
# Grab the sequence corresponding to that example (to know what is the context and what is the question).
__SCREAMING_SNAKE_CASE : int = tokenized_examples.sequence_ids(lowercase_ )
__SCREAMING_SNAKE_CASE : str = 1 if pad_on_right else 0
# One example can give several spans, this is the index of the example containing this span of text.
__SCREAMING_SNAKE_CASE : str = sample_mapping[i]
tokenized_examples["example_id"].append(examples['''id'''][sample_index] )
# Set to None the offset_mapping that are not part of the context so it's easy to determine if a token
# position is part of the context or not.
__SCREAMING_SNAKE_CASE : List[str] = [
(o if sequence_ids[k] == context_index else None)
for k, o in enumerate(tokenized_examples['''offset_mapping'''][i] )
]
return tokenized_examples
_lowerCamelCase = raw_datasets['''validation''']
# Validation Feature Creation
_lowerCamelCase = eval_examples.map(
prepare_validation_features,
batched=True,
num_proc=args.preprocessing_num_workers,
remove_columns=column_names,
load_from_cache_file=not args.overwrite_cache,
desc='''Running tokenizer on validation dataset''',
)
_lowerCamelCase = default_data_collator
_lowerCamelCase = eval_dataset.remove_columns(['''example_id''', '''offset_mapping'''])
_lowerCamelCase = DataLoader(
eval_dataset_for_model, collate_fn=data_collator, batch_size=args.per_device_eval_batch_size
)
def lowerCAmelCase_ ( lowercase_ : Dict , lowercase_ : Union[str, Any] , lowercase_ : Union[str, Any] , lowercase_ : List[Any]="eval" ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : Tuple = postprocess_qa_predictions(
examples=lowercase_ , features=lowercase_ , predictions=lowercase_ , version_2_with_negative=args.version_2_with_negative , n_best_size=args.n_best_size , max_answer_length=args.max_answer_length , null_score_diff_threshold=args.null_score_diff_threshold , output_dir=args.output_dir , prefix=lowercase_ , )
# Format the result to the format the metric expects.
if args.version_2_with_negative:
__SCREAMING_SNAKE_CASE : Union[str, Any] = [
{'''id''': k, '''prediction_text''': v, '''no_answer_probability''': 0.0} for k, v in predictions.items()
]
else:
__SCREAMING_SNAKE_CASE : int = [{'''id''': k, '''prediction_text''': v} for k, v in predictions.items()]
__SCREAMING_SNAKE_CASE : Any = [{'''id''': ex['''id'''], '''answers''': ex[answer_column_name]} for ex in examples]
return EvalPrediction(predictions=lowercase_ , label_ids=lowercase_ )
_lowerCamelCase = load_metric('''squad_v2''' if args.version_2_with_negative else '''squad''')
# Evaluation!
logger.info('''Loading ONNX model %s for evaluation''', args.onnx_model_path)
with open(engine_name, '''rb''') as f, trt.Runtime(TRT_LOGGER) as runtime, runtime.deserialize_cuda_engine(
f.read()
) as engine, engine.create_execution_context() as context:
# setup for TRT inferrence
for i in range(len(input_names)):
context.set_binding_shape(i, INPUT_SHAPE)
assert context.all_binding_shapes_specified
def lowerCAmelCase_ ( lowercase_ : Any ):
'''simple docstring'''
return trt.volume(engine.get_binding_shape(lowercase_ ) ) * engine.get_binding_dtype(lowercase_ ).itemsize
# Allocate device memory for inputs and outputs.
_lowerCamelCase = [cuda.mem_alloc(binding_nbytes(binding)) for binding in engine if engine.binding_is_input(binding)]
# Allocate output buffer
_lowerCamelCase = cuda.pagelocked_empty(tuple(context.get_binding_shape(3)), dtype=np.floataa)
_lowerCamelCase = cuda.pagelocked_empty(tuple(context.get_binding_shape(4)), dtype=np.floataa)
_lowerCamelCase = cuda.mem_alloc(h_outputa.nbytes)
_lowerCamelCase = cuda.mem_alloc(h_outputa.nbytes)
# Create a stream in which to copy inputs/outputs and run inference.
_lowerCamelCase = cuda.Stream()
# Evaluation
logger.info('''***** Running Evaluation *****''')
logger.info(f' Num examples = {len(eval_dataset)}')
logger.info(f' Batch size = {args.per_device_eval_batch_size}')
_lowerCamelCase = 0.0
_lowerCamelCase = 0
_lowerCamelCase = timeit.default_timer()
_lowerCamelCase = None
for step, batch in enumerate(eval_dataloader):
_lowerCamelCase , _lowerCamelCase = model_infer(batch, context, d_inputs, h_outputa, h_outputa, d_outputa, d_outputa, stream)
total_time += infer_time
niter += 1
_lowerCamelCase , _lowerCamelCase = outputs
_lowerCamelCase = torch.tensor(start_logits)
_lowerCamelCase = torch.tensor(end_logits)
# necessary to pad predictions and labels for being gathered
_lowerCamelCase = accelerator.pad_across_processes(start_logits, dim=1, pad_index=-1_00)
_lowerCamelCase = accelerator.pad_across_processes(end_logits, dim=1, pad_index=-1_00)
_lowerCamelCase = (accelerator.gather(start_logits).cpu().numpy(), accelerator.gather(end_logits).cpu().numpy())
_lowerCamelCase = logits if all_preds is None else nested_concat(all_preds, logits, padding_index=-1_00)
if all_preds is not None:
_lowerCamelCase = nested_truncate(all_preds, len(eval_dataset))
_lowerCamelCase = timeit.default_timer() - start_time
logger.info(''' Evaluation done in total %f secs (%f sec per example)''', evalTime, evalTime / len(eval_dataset))
# Inference time from TRT
logger.info('''Average Inference Time = {:.3f} ms'''.format(total_time * 10_00 / niter))
logger.info('''Total Inference Time = {:.3f} ms'''.format(total_time * 10_00))
logger.info('''Total Number of Inference = %d''', niter)
_lowerCamelCase = post_processing_function(eval_examples, eval_dataset, all_preds)
_lowerCamelCase = metric.compute(predictions=prediction.predictions, references=prediction.label_ids)
logger.info(f'Evaluation metrics: {eval_metric}')
| 674 | 0 |
from datasets.utils.patching import _PatchedModuleObj, patch_submodule
from . import _test_patching
def __lowerCamelCase ( ) -> str:
import os as original_os
from os import path as original_path
from os import rename as original_rename
from os.path import dirname as original_dirname
from os.path import join as original_join
assert _test_patching.os is original_os
assert _test_patching.path is original_path
assert _test_patching.join is original_join
assert _test_patching.renamed_os is original_os
assert _test_patching.renamed_path is original_path
assert _test_patching.renamed_join is original_join
UpperCamelCase = '''__test_patch_submodule_mock__'''
with patch_submodule(_test_patching , 'os.path.join' , lowercase_ ):
# Every way to access os.path.join must be patched, and the rest must stay untouched
# check os.path.join
assert isinstance(_test_patching.os , _PatchedModuleObj )
assert isinstance(_test_patching.os.path , _PatchedModuleObj )
assert _test_patching.os.path.join is mock
# check path.join
assert isinstance(_test_patching.path , _PatchedModuleObj )
assert _test_patching.path.join is mock
# check join
assert _test_patching.join is mock
# check that the other attributes are untouched
assert _test_patching.os.rename is original_rename
assert _test_patching.path.dirname is original_dirname
assert _test_patching.os.path.dirname is original_dirname
# Even renamed modules or objects must be patched
# check renamed_os.path.join
assert isinstance(_test_patching.renamed_os , _PatchedModuleObj )
assert isinstance(_test_patching.renamed_os.path , _PatchedModuleObj )
assert _test_patching.renamed_os.path.join is mock
# check renamed_path.join
assert isinstance(_test_patching.renamed_path , _PatchedModuleObj )
assert _test_patching.renamed_path.join is mock
# check renamed_join
assert _test_patching.renamed_join is mock
# check that the other attributes are untouched
assert _test_patching.renamed_os.rename is original_rename
assert _test_patching.renamed_path.dirname is original_dirname
assert _test_patching.renamed_os.path.dirname is original_dirname
# check that everthing is back to normal when the patch is over
assert _test_patching.os is original_os
assert _test_patching.path is original_path
assert _test_patching.join is original_join
assert _test_patching.renamed_os is original_os
assert _test_patching.renamed_path is original_path
assert _test_patching.renamed_join is original_join
def __lowerCamelCase ( ) -> Optional[int]:
assert _test_patching.open is open
UpperCamelCase = '''__test_patch_submodule_builtin_mock__'''
# _test_patching has "open" in its globals
assert _test_patching.open is open
with patch_submodule(_test_patching , 'open' , lowercase_ ):
assert _test_patching.open is mock
# check that everthing is back to normal when the patch is over
assert _test_patching.open is open
def __lowerCamelCase ( ) -> Dict:
UpperCamelCase = '''__test_patch_submodule_missing_mock__'''
with patch_submodule(_test_patching , 'pandas.read_csv' , lowercase_ ):
pass
def __lowerCamelCase ( ) -> Any:
UpperCamelCase = '''__test_patch_submodule_missing_builtin_mock__'''
# _test_patching doesn't have "len" in its globals
assert getattr(_test_patching , 'len' , lowercase_ ) is None
with patch_submodule(_test_patching , 'len' , lowercase_ ):
assert _test_patching.len is mock
assert _test_patching.len is len
def __lowerCamelCase ( ) -> Optional[Any]:
UpperCamelCase = '''__test_patch_submodule_start_and_stop_mock__'''
UpperCamelCase = patch_submodule(_test_patching , 'open' , lowercase_ )
assert _test_patching.open is open
patch.start()
assert _test_patching.open is mock
patch.stop()
assert _test_patching.open is open
def __lowerCamelCase ( ) -> Optional[Any]:
from os import rename as original_rename
from os.path import dirname as original_dirname
from os.path import join as original_join
UpperCamelCase = '''__test_patch_submodule_successive_join__'''
UpperCamelCase = '''__test_patch_submodule_successive_dirname__'''
UpperCamelCase = '''__test_patch_submodule_successive_rename__'''
assert _test_patching.os.path.join is original_join
assert _test_patching.os.path.dirname is original_dirname
assert _test_patching.os.rename is original_rename
with patch_submodule(_test_patching , 'os.path.join' , lowercase_ ):
with patch_submodule(_test_patching , 'os.rename' , lowercase_ ):
with patch_submodule(_test_patching , 'os.path.dirname' , lowercase_ ):
assert _test_patching.os.path.join is mock_join
assert _test_patching.os.path.dirname is mock_dirname
assert _test_patching.os.rename is mock_rename
# try another order
with patch_submodule(_test_patching , 'os.rename' , lowercase_ ):
with patch_submodule(_test_patching , 'os.path.join' , lowercase_ ):
with patch_submodule(_test_patching , 'os.path.dirname' , lowercase_ ):
assert _test_patching.os.path.join is mock_join
assert _test_patching.os.path.dirname is mock_dirname
assert _test_patching.os.rename is mock_rename
assert _test_patching.os.path.join is original_join
assert _test_patching.os.path.dirname is original_dirname
assert _test_patching.os.rename is original_rename
def __lowerCamelCase ( ) -> Any:
UpperCamelCase = '''__test_patch_submodule_doesnt_exist_mock__'''
with patch_submodule(_test_patching , '__module_that_doesn_exist__.__attribute_that_doesn_exist__' , lowercase_ ):
pass
with patch_submodule(_test_patching , 'os.__attribute_that_doesn_exist__' , lowercase_ ):
pass
| 282 |
"""simple docstring"""
from __future__ import annotations
import unittest
from transformers import is_tf_available, is_torch_available
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, SMALL_MODEL_IDENTIFIER, is_pt_tf_cross_test, slow
if is_tf_available():
from transformers import (
AutoConfig,
BertConfig,
GPTaConfig,
TaConfig,
TFAutoModel,
TFAutoModelForCausalLM,
TFAutoModelForMaskedLM,
TFAutoModelForPreTraining,
TFAutoModelForQuestionAnswering,
TFAutoModelForSeqaSeqLM,
TFAutoModelForSequenceClassification,
TFAutoModelWithLMHead,
TFBertForMaskedLM,
TFBertForPreTraining,
TFBertForQuestionAnswering,
TFBertForSequenceClassification,
TFBertModel,
TFGPTaLMHeadModel,
TFRobertaForMaskedLM,
TFTaForConditionalGeneration,
)
from transformers.models.bert.modeling_tf_bert import TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST
from transformers.models.gpta.modeling_tf_gpta import TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST
from transformers.models.ta.modeling_tf_ta import TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST
if is_torch_available():
from transformers import (
AutoModel,
AutoModelForCausalLM,
AutoModelForMaskedLM,
AutoModelForPreTraining,
AutoModelForQuestionAnswering,
AutoModelForSeqaSeqLM,
AutoModelForSequenceClassification,
AutoModelWithLMHead,
BertForMaskedLM,
BertForPreTraining,
BertForQuestionAnswering,
BertForSequenceClassification,
BertModel,
GPTaLMHeadModel,
RobertaForMaskedLM,
TaForConditionalGeneration,
)
@is_pt_tf_cross_test
class snake_case ( unittest.TestCase ):
@slow
def SCREAMING_SNAKE_CASE_ ( self :Tuple ):
# for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
for model_name in ["bert-base-uncased"]:
__SCREAMING_SNAKE_CASE : List[Any] = AutoConfig.from_pretrained(_lowerCamelCase )
self.assertIsNotNone(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : List[str] = TFAutoModel.from_pretrained(_lowerCamelCase , from_pt=_lowerCamelCase )
self.assertIsNotNone(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : Optional[Any] = AutoModel.from_pretrained(_lowerCamelCase , from_tf=_lowerCamelCase )
self.assertIsNotNone(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
@slow
def SCREAMING_SNAKE_CASE_ ( self :Optional[int] ):
# for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
for model_name in ["bert-base-uncased"]:
__SCREAMING_SNAKE_CASE : List[str] = AutoConfig.from_pretrained(_lowerCamelCase )
self.assertIsNotNone(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : Tuple = TFAutoModelForPreTraining.from_pretrained(_lowerCamelCase , from_pt=_lowerCamelCase )
self.assertIsNotNone(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : Optional[int] = AutoModelForPreTraining.from_pretrained(_lowerCamelCase , from_tf=_lowerCamelCase )
self.assertIsNotNone(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
@slow
def SCREAMING_SNAKE_CASE_ ( self :Any ):
for model_name in TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__SCREAMING_SNAKE_CASE : Optional[int] = AutoConfig.from_pretrained(_lowerCamelCase )
self.assertIsNotNone(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : int = TFAutoModelForCausalLM.from_pretrained(_lowerCamelCase , from_pt=_lowerCamelCase )
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : List[Any] = TFAutoModelForCausalLM.from_pretrained(
_lowerCamelCase , output_loading_info=_lowerCamelCase , from_pt=_lowerCamelCase )
self.assertIsNotNone(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : Tuple = AutoModelForCausalLM.from_pretrained(_lowerCamelCase , from_tf=_lowerCamelCase )
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Dict = AutoModelForCausalLM.from_pretrained(
_lowerCamelCase , output_loading_info=_lowerCamelCase , from_tf=_lowerCamelCase )
self.assertIsNotNone(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
@slow
def SCREAMING_SNAKE_CASE_ ( self :List[Any] ):
for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__SCREAMING_SNAKE_CASE : Any = AutoConfig.from_pretrained(_lowerCamelCase )
self.assertIsNotNone(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : Any = TFAutoModelWithLMHead.from_pretrained(_lowerCamelCase , from_pt=_lowerCamelCase )
self.assertIsNotNone(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : Optional[Any] = AutoModelWithLMHead.from_pretrained(_lowerCamelCase , from_tf=_lowerCamelCase )
self.assertIsNotNone(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
@slow
def SCREAMING_SNAKE_CASE_ ( self :Dict ):
for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__SCREAMING_SNAKE_CASE : Union[str, Any] = AutoConfig.from_pretrained(_lowerCamelCase )
self.assertIsNotNone(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : Optional[int] = TFAutoModelForMaskedLM.from_pretrained(_lowerCamelCase , from_pt=_lowerCamelCase )
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : List[str] = TFAutoModelForMaskedLM.from_pretrained(
_lowerCamelCase , output_loading_info=_lowerCamelCase , from_pt=_lowerCamelCase )
self.assertIsNotNone(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : Tuple = AutoModelForMaskedLM.from_pretrained(_lowerCamelCase , from_tf=_lowerCamelCase )
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : List[Any] = AutoModelForMaskedLM.from_pretrained(
_lowerCamelCase , output_loading_info=_lowerCamelCase , from_tf=_lowerCamelCase )
self.assertIsNotNone(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
@slow
def SCREAMING_SNAKE_CASE_ ( self :int ):
for model_name in TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__SCREAMING_SNAKE_CASE : str = AutoConfig.from_pretrained(_lowerCamelCase )
self.assertIsNotNone(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : Tuple = TFAutoModelForSeqaSeqLM.from_pretrained(_lowerCamelCase , from_pt=_lowerCamelCase )
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : str = TFAutoModelForSeqaSeqLM.from_pretrained(
_lowerCamelCase , output_loading_info=_lowerCamelCase , from_pt=_lowerCamelCase )
self.assertIsNotNone(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : Optional[int] = AutoModelForSeqaSeqLM.from_pretrained(_lowerCamelCase , from_tf=_lowerCamelCase )
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Dict = AutoModelForSeqaSeqLM.from_pretrained(
_lowerCamelCase , output_loading_info=_lowerCamelCase , from_tf=_lowerCamelCase )
self.assertIsNotNone(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
@slow
def SCREAMING_SNAKE_CASE_ ( self :Optional[Any] ):
# for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
for model_name in ["bert-base-uncased"]:
__SCREAMING_SNAKE_CASE : Optional[Any] = AutoConfig.from_pretrained(_lowerCamelCase )
self.assertIsNotNone(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : int = TFAutoModelForSequenceClassification.from_pretrained(_lowerCamelCase , from_pt=_lowerCamelCase )
self.assertIsNotNone(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : Optional[Any] = AutoModelForSequenceClassification.from_pretrained(_lowerCamelCase , from_tf=_lowerCamelCase )
self.assertIsNotNone(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
@slow
def SCREAMING_SNAKE_CASE_ ( self :List[Any] ):
# for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
for model_name in ["bert-base-uncased"]:
__SCREAMING_SNAKE_CASE : Optional[Any] = AutoConfig.from_pretrained(_lowerCamelCase )
self.assertIsNotNone(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : str = TFAutoModelForQuestionAnswering.from_pretrained(_lowerCamelCase , from_pt=_lowerCamelCase )
self.assertIsNotNone(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : List[Any] = AutoModelForQuestionAnswering.from_pretrained(_lowerCamelCase , from_tf=_lowerCamelCase )
self.assertIsNotNone(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :Union[str, Any] ):
__SCREAMING_SNAKE_CASE : Tuple = TFAutoModelWithLMHead.from_pretrained(_lowerCamelCase , from_pt=_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
self.assertEqual(model.num_parameters() , 1_4_4_1_0 )
self.assertEqual(model.num_parameters(only_trainable=_lowerCamelCase ) , 1_4_4_1_0 )
__SCREAMING_SNAKE_CASE : Tuple = AutoModelWithLMHead.from_pretrained(_lowerCamelCase , from_tf=_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
self.assertEqual(model.num_parameters() , 1_4_4_1_0 )
self.assertEqual(model.num_parameters(only_trainable=_lowerCamelCase ) , 1_4_4_1_0 )
def SCREAMING_SNAKE_CASE_ ( self :str ):
__SCREAMING_SNAKE_CASE : Optional[Any] = TFAutoModelWithLMHead.from_pretrained(_lowerCamelCase , from_pt=_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
self.assertEqual(model.num_parameters() , 1_4_4_1_0 )
self.assertEqual(model.num_parameters(only_trainable=_lowerCamelCase ) , 1_4_4_1_0 )
__SCREAMING_SNAKE_CASE : Union[str, Any] = AutoModelWithLMHead.from_pretrained(_lowerCamelCase , from_tf=_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
self.assertEqual(model.num_parameters() , 1_4_4_1_0 )
self.assertEqual(model.num_parameters(only_trainable=_lowerCamelCase ) , 1_4_4_1_0 )
| 674 | 0 |
"""simple docstring"""
import argparse
import OmegaConf
import torch
from diffusers import DDIMScheduler, LDMPipeline, UNetLDMModel, VQModel
def UpperCAmelCase ( A__: List[Any] , A__: Optional[int] , A__: List[Any] ) -> int:
__lowerCamelCase : Any = OmegaConf.load(lowercase_ )
__lowerCamelCase : List[str] = torch.load(lowercase_ , map_location='cpu' )['''model''']
__lowerCamelCase : List[str] = list(state_dict.keys() )
# extract state_dict for VQVAE
__lowerCamelCase : Union[str, Any] = {}
__lowerCamelCase : List[Any] = '''first_stage_model.'''
for key in keys:
if key.startswith(lowercase_ ):
__lowerCamelCase : Union[str, Any] = state_dict[key]
# extract state_dict for UNetLDM
__lowerCamelCase : List[Any] = {}
__lowerCamelCase : List[str] = '''model.diffusion_model.'''
for key in keys:
if key.startswith(lowercase_ ):
__lowerCamelCase : Optional[int] = state_dict[key]
__lowerCamelCase : Dict = config.model.params.first_stage_config.params
__lowerCamelCase : Union[str, Any] = config.model.params.unet_config.params
__lowerCamelCase : Optional[Any] = VQModel(**lowercase_ ).eval()
vqvae.load_state_dict(lowercase_ )
__lowerCamelCase : Dict = UNetLDMModel(**lowercase_ ).eval()
unet.load_state_dict(lowercase_ )
__lowerCamelCase : Union[str, Any] = DDIMScheduler(
timesteps=config.model.params.timesteps , beta_schedule='scaled_linear' , beta_start=config.model.params.linear_start , beta_end=config.model.params.linear_end , clip_sample=lowercase_ , )
__lowerCamelCase : Tuple = LDMPipeline(lowercase_ , lowercase_ , lowercase_ )
pipeline.save_pretrained(lowercase_ )
if __name__ == "__main__":
a_ : Union[str, Any] = argparse.ArgumentParser()
parser.add_argument('''--checkpoint_path''', type=str, required=True)
parser.add_argument('''--config_path''', type=str, required=True)
parser.add_argument('''--output_path''', type=str, required=True)
a_ : int = parser.parse_args()
convert_ldm_original(args.checkpoint_path, args.config_path, args.output_path)
| 594 |
"""simple docstring"""
import os
import re
import warnings
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
if TYPE_CHECKING:
from ...tokenization_utils_base import TextInput
from ...utils import logging
_lowerCamelCase = logging.get_logger(__name__)
_lowerCamelCase = {'''vocab_file''': '''spiece.model'''}
_lowerCamelCase = {
'''vocab_file''': {
'''t5-small''': '''https://huggingface.co/t5-small/resolve/main/spiece.model''',
'''t5-base''': '''https://huggingface.co/t5-base/resolve/main/spiece.model''',
'''t5-large''': '''https://huggingface.co/t5-large/resolve/main/spiece.model''',
'''t5-3b''': '''https://huggingface.co/t5-3b/resolve/main/spiece.model''',
'''t5-11b''': '''https://huggingface.co/t5-11b/resolve/main/spiece.model''',
}
}
# TODO(PVP) - this should be removed in Transformers v5
_lowerCamelCase = {
'''t5-small''': 5_12,
'''t5-base''': 5_12,
'''t5-large''': 5_12,
'''t5-3b''': 5_12,
'''t5-11b''': 5_12,
}
_lowerCamelCase = '''▁'''
class snake_case ( __UpperCAmelCase ):
lowerCamelCase__ = VOCAB_FILES_NAMES
lowerCamelCase__ = PRETRAINED_VOCAB_FILES_MAP
lowerCamelCase__ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowerCamelCase__ = ['''input_ids''', '''attention_mask''']
def __init__( self :int , _lowerCamelCase :Optional[Any] , _lowerCamelCase :Union[str, Any]="</s>" , _lowerCamelCase :List[Any]="<unk>" , _lowerCamelCase :Union[str, Any]="<pad>" , _lowerCamelCase :int=1_0_0 , _lowerCamelCase :Union[str, Any]=None , _lowerCamelCase :Optional[Dict[str, Any]] = None , _lowerCamelCase :int=True , **_lowerCamelCase :List[Any] , ):
# Add extra_ids to the special token list
if extra_ids > 0 and additional_special_tokens is None:
__SCREAMING_SNAKE_CASE : Union[str, Any] = [f'''<extra_id_{i}>''' for i in range(_lowerCamelCase )]
elif extra_ids > 0 and additional_special_tokens is not None:
# Check that we have the right number of extra_id special tokens
__SCREAMING_SNAKE_CASE : Optional[int] = len(set(filter(lambda _lowerCamelCase : bool('''extra_id''' in str(_lowerCamelCase ) ) , _lowerCamelCase ) ) )
if extra_tokens != extra_ids:
raise ValueError(
f'''Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are'''
''' provided to T5Tokenizer. In this case the additional_special_tokens must include the extra_ids'''
''' tokens''' )
if legacy:
logger.warning_once(
f'''You are using the legacy behaviour of the {self.__class__}. This means that tokens that come after special tokens will not be properly handled. We recommend you to'''
''' read the related pull request available at https://github.com/huggingface/transformers/pull/24565''' )
__SCREAMING_SNAKE_CASE : Optional[Any] = legacy
__SCREAMING_SNAKE_CASE : Optional[int] = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
eos_token=_lowerCamelCase , unk_token=_lowerCamelCase , pad_token=_lowerCamelCase , extra_ids=_lowerCamelCase , additional_special_tokens=_lowerCamelCase , sp_model_kwargs=self.sp_model_kwargs , legacy=_lowerCamelCase , **_lowerCamelCase , )
__SCREAMING_SNAKE_CASE : Tuple = vocab_file
__SCREAMING_SNAKE_CASE : List[str] = extra_ids
__SCREAMING_SNAKE_CASE : Optional[int] = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(_lowerCamelCase )
@staticmethod
def SCREAMING_SNAKE_CASE_ ( _lowerCamelCase :str , _lowerCamelCase :Union[str, Any] , _lowerCamelCase :int ):
if pretrained_model_name_or_path in TaTokenizer.max_model_input_sizes:
__SCREAMING_SNAKE_CASE : Any = TaTokenizer.max_model_input_sizes[pretrained_model_name_or_path]
if init_max_model_length is not None and init_max_model_length != max_model_length:
return init_max_model_length
elif init_max_model_length is None:
warnings.warn(
'''This tokenizer was incorrectly instantiated with a model max length of'''
f''' {deprecated_max_model_length} which will be corrected in Transformers v5.\nFor now, this'''
''' behavior is kept to avoid breaking backwards compatibility when padding/encoding with'''
''' `truncation is True`.\n- Be aware that you SHOULD NOT rely on'''
f''' {pretrained_model_name_or_path} automatically truncating your input to'''
f''' {deprecated_max_model_length} when padding/encoding.\n- If you want to encode/pad to sequences'''
f''' longer than {deprecated_max_model_length} you can either instantiate this tokenizer with'''
''' `model_max_length` or pass `max_length` when encoding/padding.\n- To avoid this warning, please'''
''' instantiate this tokenizer with `model_max_length` set to your preferred value.''' , _lowerCamelCase , )
return max_model_length
@property
def SCREAMING_SNAKE_CASE_ ( self :Tuple ):
return self.sp_model.get_piece_size() + self._extra_ids
def SCREAMING_SNAKE_CASE_ ( self :Union[str, Any] ):
__SCREAMING_SNAKE_CASE : str = {self.convert_ids_to_tokens(_lowerCamelCase ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def SCREAMING_SNAKE_CASE_ ( self :Union[str, Any] , _lowerCamelCase :List[int] , _lowerCamelCase :Optional[List[int]] = None , _lowerCamelCase :bool = False ):
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=_lowerCamelCase , token_ids_a=_lowerCamelCase , already_has_special_tokens=_lowerCamelCase )
# normal case: some special tokens
if token_ids_a is None:
return ([0] * len(_lowerCamelCase )) + [1]
return ([0] * len(_lowerCamelCase )) + [1] + ([0] * len(_lowerCamelCase )) + [1]
def SCREAMING_SNAKE_CASE_ ( self :List[str] ):
return list(
set(filter(lambda _lowerCamelCase : bool(re.search(r'''<extra_id_\d+>''' , _lowerCamelCase ) ) is not None , self.additional_special_tokens ) ) )
def SCREAMING_SNAKE_CASE_ ( self :List[Any] ):
return [self._convert_token_to_id(_lowerCamelCase ) for token in self.get_sentinel_tokens()]
def SCREAMING_SNAKE_CASE_ ( self :Any , _lowerCamelCase :List[int] ):
if len(_lowerCamelCase ) > 0 and token_ids[-1] == self.eos_token_id:
warnings.warn(
f'''This sequence already has {self.eos_token}. In future versions this behavior may lead to duplicated'''
''' eos tokens being added.''' )
return token_ids
else:
return token_ids + [self.eos_token_id]
def SCREAMING_SNAKE_CASE_ ( self :Union[str, Any] , _lowerCamelCase :List[int] , _lowerCamelCase :Optional[List[int]] = None ):
__SCREAMING_SNAKE_CASE : List[str] = [self.eos_token_id]
if token_ids_a is None:
return len(token_ids_a + eos ) * [0]
return len(token_ids_a + eos + token_ids_a + eos ) * [0]
def SCREAMING_SNAKE_CASE_ ( self :List[Any] , _lowerCamelCase :List[int] , _lowerCamelCase :Optional[List[int]] = None ):
__SCREAMING_SNAKE_CASE : Optional[Any] = self._add_eos_if_not_present(_lowerCamelCase )
if token_ids_a is None:
return token_ids_a
else:
__SCREAMING_SNAKE_CASE : Union[str, Any] = self._add_eos_if_not_present(_lowerCamelCase )
return token_ids_a + token_ids_a
def __getstate__( self :Union[str, Any] ):
__SCREAMING_SNAKE_CASE : Any = self.__dict__.copy()
__SCREAMING_SNAKE_CASE : List[str] = None
return state
def __setstate__( self :Optional[Any] , _lowerCamelCase :List[str] ):
__SCREAMING_SNAKE_CASE : Tuple = d
# for backward compatibility
if not hasattr(self , '''sp_model_kwargs''' ):
__SCREAMING_SNAKE_CASE : Optional[int] = {}
__SCREAMING_SNAKE_CASE : Optional[int] = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(self.vocab_file )
def SCREAMING_SNAKE_CASE_ ( self :int , _lowerCamelCase :"TextInput" , **_lowerCamelCase :str ):
# Replace the SPIECE_UNDERLINE with a space to make sure SPIECE_UNDERLINE is only used at
# the beginning of the text
if not self.legacy:
__SCREAMING_SNAKE_CASE : Dict = SPIECE_UNDERLINE + text.replace(_lowerCamelCase , ''' ''' )
return super().tokenize(_lowerCamelCase , **_lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :Optional[Any] , _lowerCamelCase :List[Any] , **_lowerCamelCase :Dict ):
if not self.legacy:
__SCREAMING_SNAKE_CASE : str = text.startswith(_lowerCamelCase )
if is_first:
__SCREAMING_SNAKE_CASE : str = text[1:]
__SCREAMING_SNAKE_CASE : Tuple = self.sp_model.encode(_lowerCamelCase , out_type=_lowerCamelCase )
if not self.legacy and not is_first and not text.startswith(''' ''' ) and tokens[0].startswith(_lowerCamelCase ):
__SCREAMING_SNAKE_CASE : Optional[int] = ([tokens[0][1:]] if len(tokens[0] ) > 1 else []) + tokens[1:]
return tokens
def SCREAMING_SNAKE_CASE_ ( self :Optional[Any] , _lowerCamelCase :Optional[Any] ):
if token.startswith('''<extra_id_''' ):
__SCREAMING_SNAKE_CASE : Tuple = re.match(r'''<extra_id_(\d+)>''' , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : Union[str, Any] = int(match.group(1 ) )
return self.vocab_size - num - 1
return self.sp_model.piece_to_id(_lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :str , _lowerCamelCase :Optional[int] ):
if index < self.sp_model.get_piece_size():
__SCREAMING_SNAKE_CASE : List[Any] = self.sp_model.IdToPiece(_lowerCamelCase )
else:
__SCREAMING_SNAKE_CASE : Dict = f'''<extra_id_{self.vocab_size - 1 - index}>'''
return token
def SCREAMING_SNAKE_CASE_ ( self :Tuple , _lowerCamelCase :Any ):
__SCREAMING_SNAKE_CASE : str = []
__SCREAMING_SNAKE_CASE : Dict = ''''''
__SCREAMING_SNAKE_CASE : Dict = False
for token in tokens:
# make sure that special tokens are not decoded using sentencepiece model
if token in self.all_special_tokens:
if not prev_is_special:
out_string += " "
out_string += self.sp_model.decode(_lowerCamelCase ) + token
__SCREAMING_SNAKE_CASE : List[str] = True
__SCREAMING_SNAKE_CASE : str = []
else:
current_sub_tokens.append(_lowerCamelCase )
__SCREAMING_SNAKE_CASE : int = False
out_string += self.sp_model.decode(_lowerCamelCase )
return out_string.strip()
def SCREAMING_SNAKE_CASE_ ( self :Optional[Any] , _lowerCamelCase :str , _lowerCamelCase :Optional[str] = None ):
if not os.path.isdir(_lowerCamelCase ):
logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' )
return
__SCREAMING_SNAKE_CASE : List[str] = os.path.join(
_lowerCamelCase , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(_lowerCamelCase ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file , _lowerCamelCase )
elif not os.path.isfile(self.vocab_file ):
with open(_lowerCamelCase , '''wb''' ) as fi:
__SCREAMING_SNAKE_CASE : Any = self.sp_model.serialized_model_proto()
fi.write(_lowerCamelCase )
return (out_vocab_file,)
| 674 | 0 |
from __future__ import annotations
from collections import deque
from collections.abc import Sequence
from dataclasses import dataclass
from typing import Any
@dataclass
class UpperCAmelCase :
lowercase = 4_2
lowercase = None
lowercase = None
def _lowercase ( ):
"""simple docstring"""
UpperCamelCase = Node(1 )
UpperCamelCase = Node(2 )
UpperCamelCase = Node(3 )
UpperCamelCase = Node(4 )
UpperCamelCase = Node(5 )
return tree
def _lowercase ( SCREAMING_SNAKE_CASE_ : Node | None ):
"""simple docstring"""
return [root.data, *preorder(root.left ), *preorder(root.right )] if root else []
def _lowercase ( SCREAMING_SNAKE_CASE_ : Node | None ):
"""simple docstring"""
return postorder(root.left ) + postorder(root.right ) + [root.data] if root else []
def _lowercase ( SCREAMING_SNAKE_CASE_ : Node | None ):
"""simple docstring"""
return [*inorder(root.left ), root.data, *inorder(root.right )] if root else []
def _lowercase ( SCREAMING_SNAKE_CASE_ : Node | None ):
"""simple docstring"""
return (max(height(root.left ) , height(root.right ) ) + 1) if root else 0
def _lowercase ( SCREAMING_SNAKE_CASE_ : Node | None ):
"""simple docstring"""
UpperCamelCase = []
if root is None:
return output
UpperCamelCase = deque([root] )
while process_queue:
UpperCamelCase = process_queue.popleft()
output.append(node.data )
if node.left:
process_queue.append(node.left )
if node.right:
process_queue.append(node.right )
return output
def _lowercase ( SCREAMING_SNAKE_CASE_ : Node | None , SCREAMING_SNAKE_CASE_ : int ):
"""simple docstring"""
UpperCamelCase = []
def populate_output(SCREAMING_SNAKE_CASE_ : Node | None , SCREAMING_SNAKE_CASE_ : int ) -> None:
if not root:
return
if level == 1:
output.append(root.data )
elif level > 1:
populate_output(root.left , level - 1 )
populate_output(root.right , level - 1 )
populate_output(lowercase_ , lowercase_ )
return output
def _lowercase ( SCREAMING_SNAKE_CASE_ : Node | None , SCREAMING_SNAKE_CASE_ : int ):
"""simple docstring"""
UpperCamelCase = []
def populate_output(SCREAMING_SNAKE_CASE_ : Node | None , SCREAMING_SNAKE_CASE_ : int ) -> None:
if root is None:
return
if level == 1:
output.append(root.data )
elif level > 1:
populate_output(root.right , level - 1 )
populate_output(root.left , level - 1 )
populate_output(lowercase_ , lowercase_ )
return output
def _lowercase ( SCREAMING_SNAKE_CASE_ : Node | None ):
"""simple docstring"""
if root is None:
return []
UpperCamelCase = []
UpperCamelCase = 0
UpperCamelCase = height(lowercase_ )
for h in range(1 , height_tree + 1 ):
if not flag:
output.append(get_nodes_from_left_to_right(lowercase_ , lowercase_ ) )
UpperCamelCase = 1
else:
output.append(get_nodes_from_right_to_left(lowercase_ , lowercase_ ) )
UpperCamelCase = 0
return output
def _lowercase ( ): # Main function for testing.
"""simple docstring"""
UpperCamelCase = make_tree()
print(f'In-order Traversal: {inorder(lowercase_ )}' )
print(f'Pre-order Traversal: {preorder(lowercase_ )}' )
print(f'Post-order Traversal: {postorder(lowercase_ )}' , """\n""" )
print(f'Height of Tree: {height(lowercase_ )}' , """\n""" )
print("""Complete Level Order Traversal: """ )
print(level_order(lowercase_ ) , """\n""" )
print("""Level-wise order Traversal: """ )
for level in range(1 , height(lowercase_ ) + 1 ):
print(f'Level {level}:' , get_nodes_from_left_to_right(lowercase_ , level=lowercase_ ) )
print("""\nZigZag order Traversal: """ )
print(zigzag(lowercase_ ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
main()
| 386 |
"""simple docstring"""
import os
import pytest
import yaml
from datasets.features.features import Features, Value
from datasets.info import DatasetInfo, DatasetInfosDict
@pytest.mark.parametrize(
'''files''' , [
['''full:README.md''', '''dataset_infos.json'''],
['''empty:README.md''', '''dataset_infos.json'''],
['''dataset_infos.json'''],
['''full:README.md'''],
] , )
def lowerCAmelCase_ ( lowercase_ : List[str] , lowercase_ : str ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : Optional[int] = tmp_path_factory.mktemp('''dset_infos_dir''' )
if "full:README.md" in files:
with open(dataset_infos_dir / '''README.md''' , '''w''' ) as f:
f.write('''---\ndataset_info:\n dataset_size: 42\n---''' )
if "empty:README.md" in files:
with open(dataset_infos_dir / '''README.md''' , '''w''' ) as f:
f.write('''''' )
# we want to support dataset_infos.json for backward compatibility
if "dataset_infos.json" in files:
with open(dataset_infos_dir / '''dataset_infos.json''' , '''w''' ) as f:
f.write('''{"default": {"dataset_size": 42}}''' )
__SCREAMING_SNAKE_CASE : Optional[Any] = DatasetInfosDict.from_directory(lowercase_ )
assert dataset_infos
assert dataset_infos["default"].dataset_size == 42
@pytest.mark.parametrize(
'''dataset_info''' , [
DatasetInfo(),
DatasetInfo(
description='''foo''' , features=Features({'''a''': Value('''int32''' )} ) , builder_name='''builder''' , config_name='''config''' , version='''1.0.0''' , splits=[{'''name''': '''train'''}] , download_size=42 , ),
] , )
def lowerCAmelCase_ ( lowercase_ : Any , lowercase_ : DatasetInfo ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : List[str] = str(lowercase_ )
dataset_info.write_to_directory(lowercase_ )
__SCREAMING_SNAKE_CASE : Dict = DatasetInfo.from_directory(lowercase_ )
assert dataset_info == reloaded
assert os.path.exists(os.path.join(lowercase_ , '''dataset_info.json''' ) )
def lowerCAmelCase_ ( ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : Tuple = DatasetInfo(
description='''foo''' , citation='''bar''' , homepage='''https://foo.bar''' , license='''CC0''' , features=Features({'''a''': Value('''int32''' )} ) , post_processed={} , supervised_keys=() , task_templates=[] , builder_name='''builder''' , config_name='''config''' , version='''1.0.0''' , splits=[{'''name''': '''train''', '''num_examples''': 42}] , download_checksums={} , download_size=1337 , post_processing_size=442 , dataset_size=1234 , size_in_bytes=1337 + 442 + 1234 , )
__SCREAMING_SNAKE_CASE : Optional[int] = dataset_info._to_yaml_dict()
assert sorted(lowercase_ ) == sorted(DatasetInfo._INCLUDED_INFO_IN_YAML )
for key in DatasetInfo._INCLUDED_INFO_IN_YAML:
assert key in dataset_info_yaml_dict
assert isinstance(dataset_info_yaml_dict[key] , (list, dict, int, str) )
__SCREAMING_SNAKE_CASE : int = yaml.safe_dump(lowercase_ )
__SCREAMING_SNAKE_CASE : Union[str, Any] = yaml.safe_load(lowercase_ )
assert dataset_info_yaml_dict == reloaded
def lowerCAmelCase_ ( ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : Union[str, Any] = DatasetInfo()
__SCREAMING_SNAKE_CASE : Union[str, Any] = dataset_info._to_yaml_dict()
assert dataset_info_yaml_dict == {}
@pytest.mark.parametrize(
'''dataset_infos_dict''' , [
DatasetInfosDict(),
DatasetInfosDict({'''default''': DatasetInfo()} ),
DatasetInfosDict({'''my_config_name''': DatasetInfo()} ),
DatasetInfosDict(
{
'''default''': DatasetInfo(
description='''foo''' , features=Features({'''a''': Value('''int32''' )} ) , builder_name='''builder''' , config_name='''config''' , version='''1.0.0''' , splits=[{'''name''': '''train'''}] , download_size=42 , )
} ),
DatasetInfosDict(
{
'''v1''': DatasetInfo(dataset_size=42 ),
'''v2''': DatasetInfo(dataset_size=1337 ),
} ),
] , )
def lowerCAmelCase_ ( lowercase_ : List[Any] , lowercase_ : DatasetInfosDict ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : Optional[int] = str(lowercase_ )
dataset_infos_dict.write_to_directory(lowercase_ )
__SCREAMING_SNAKE_CASE : Optional[Any] = DatasetInfosDict.from_directory(lowercase_ )
# the config_name of the dataset_infos_dict take over the attribute
for config_name, dataset_info in dataset_infos_dict.items():
__SCREAMING_SNAKE_CASE : Optional[int] = config_name
# the yaml representation doesn't include fields like description or citation
# so we just test that we can recover what we can from the yaml
__SCREAMING_SNAKE_CASE : Tuple = DatasetInfo._from_yaml_dict(dataset_info._to_yaml_dict() )
assert dataset_infos_dict == reloaded
if dataset_infos_dict:
assert os.path.exists(os.path.join(lowercase_ , '''README.md''' ) )
| 674 | 0 |
import tempfile
import torch
from diffusers import IPNDMScheduler
from .test_schedulers import SchedulerCommonTest
class SCREAMING_SNAKE_CASE_ ( __UpperCAmelCase ):
"""simple docstring"""
__magic_name__ : Any = (IPNDMScheduler,)
__magic_name__ : List[Any] = (('num_inference_steps', 50),)
def lowerCamelCase__ ( self : Optional[int] , **lowerCAmelCase : Tuple ) -> Optional[int]:
"""simple docstring"""
__UpperCamelCase : Union[str, Any] = {'''num_train_timesteps''': 1000}
config.update(**_lowerCamelCase )
return config
def lowerCamelCase__ ( self : List[Any] , lowerCAmelCase : List[Any]=0 , **lowerCAmelCase : Union[str, Any] ) -> List[Any]:
"""simple docstring"""
__UpperCamelCase : int = dict(self.forward_default_kwargs )
__UpperCamelCase : List[Any] = kwargs.pop("""num_inference_steps""" , _lowerCamelCase )
__UpperCamelCase : Optional[int] = self.dummy_sample
__UpperCamelCase : Optional[int] = 0.1 * sample
__UpperCamelCase : Optional[Any] = [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
for scheduler_class in self.scheduler_classes:
__UpperCamelCase : List[str] = self.get_scheduler_config(**_lowerCamelCase )
__UpperCamelCase : Optional[Any] = scheduler_class(**_lowerCamelCase )
scheduler.set_timesteps(_lowerCamelCase )
# copy over dummy past residuals
__UpperCamelCase : Union[str, Any] = dummy_past_residuals[:]
if time_step is None:
__UpperCamelCase : Tuple = scheduler.timesteps[len(scheduler.timesteps ) // 2]
with tempfile.TemporaryDirectory() as tmpdirname:
scheduler.save_config(_lowerCamelCase )
__UpperCamelCase : Any = scheduler_class.from_pretrained(_lowerCamelCase )
new_scheduler.set_timesteps(_lowerCamelCase )
# copy over dummy past residuals
__UpperCamelCase : Any = dummy_past_residuals[:]
__UpperCamelCase : List[Any] = scheduler.step(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase , **_lowerCamelCase ).prev_sample
__UpperCamelCase : Optional[Any] = new_scheduler.step(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase , **_lowerCamelCase ).prev_sample
assert torch.sum(torch.abs(output - new_output ) ) < 1E-5, "Scheduler outputs are not identical"
__UpperCamelCase : Any = scheduler.step(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase , **_lowerCamelCase ).prev_sample
__UpperCamelCase : Tuple = new_scheduler.step(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase , **_lowerCamelCase ).prev_sample
assert torch.sum(torch.abs(output - new_output ) ) < 1E-5, "Scheduler outputs are not identical"
def lowerCamelCase__ ( self : List[str] ) -> List[str]:
"""simple docstring"""
pass
def lowerCamelCase__ ( self : Any , lowerCAmelCase : Any=0 , **lowerCAmelCase : List[str] ) -> Union[str, Any]:
"""simple docstring"""
__UpperCamelCase : Optional[int] = dict(self.forward_default_kwargs )
__UpperCamelCase : Any = kwargs.pop("""num_inference_steps""" , _lowerCamelCase )
__UpperCamelCase : Tuple = self.dummy_sample
__UpperCamelCase : List[Any] = 0.1 * sample
__UpperCamelCase : Optional[Any] = [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
for scheduler_class in self.scheduler_classes:
__UpperCamelCase : Dict = self.get_scheduler_config()
__UpperCamelCase : int = scheduler_class(**_lowerCamelCase )
scheduler.set_timesteps(_lowerCamelCase )
# copy over dummy past residuals (must be after setting timesteps)
__UpperCamelCase : Any = dummy_past_residuals[:]
if time_step is None:
__UpperCamelCase : str = scheduler.timesteps[len(scheduler.timesteps ) // 2]
with tempfile.TemporaryDirectory() as tmpdirname:
scheduler.save_config(_lowerCamelCase )
__UpperCamelCase : List[str] = scheduler_class.from_pretrained(_lowerCamelCase )
# copy over dummy past residuals
new_scheduler.set_timesteps(_lowerCamelCase )
# copy over dummy past residual (must be after setting timesteps)
__UpperCamelCase : Any = dummy_past_residuals[:]
__UpperCamelCase : Union[str, Any] = scheduler.step(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase , **_lowerCamelCase ).prev_sample
__UpperCamelCase : Union[str, Any] = new_scheduler.step(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase , **_lowerCamelCase ).prev_sample
assert torch.sum(torch.abs(output - new_output ) ) < 1E-5, "Scheduler outputs are not identical"
__UpperCamelCase : int = scheduler.step(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase , **_lowerCamelCase ).prev_sample
__UpperCamelCase : Dict = new_scheduler.step(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase , **_lowerCamelCase ).prev_sample
assert torch.sum(torch.abs(output - new_output ) ) < 1E-5, "Scheduler outputs are not identical"
def lowerCamelCase__ ( self : Dict , **lowerCAmelCase : Optional[Any] ) -> Union[str, Any]:
"""simple docstring"""
__UpperCamelCase : List[Any] = self.scheduler_classes[0]
__UpperCamelCase : Optional[int] = self.get_scheduler_config(**_lowerCamelCase )
__UpperCamelCase : Tuple = scheduler_class(**_lowerCamelCase )
__UpperCamelCase : Union[str, Any] = 10
__UpperCamelCase : Any = self.dummy_model()
__UpperCamelCase : Optional[int] = self.dummy_sample_deter
scheduler.set_timesteps(_lowerCamelCase )
for i, t in enumerate(scheduler.timesteps ):
__UpperCamelCase : Union[str, Any] = model(_lowerCamelCase , _lowerCamelCase )
__UpperCamelCase : List[Any] = scheduler.step(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase ).prev_sample
for i, t in enumerate(scheduler.timesteps ):
__UpperCamelCase : Union[str, Any] = model(_lowerCamelCase , _lowerCamelCase )
__UpperCamelCase : str = scheduler.step(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase ).prev_sample
return sample
def lowerCamelCase__ ( self : Dict ) -> Optional[Any]:
"""simple docstring"""
__UpperCamelCase : Optional[int] = dict(self.forward_default_kwargs )
__UpperCamelCase : int = kwargs.pop("""num_inference_steps""" , _lowerCamelCase )
for scheduler_class in self.scheduler_classes:
__UpperCamelCase : List[Any] = self.get_scheduler_config()
__UpperCamelCase : str = scheduler_class(**_lowerCamelCase )
__UpperCamelCase : str = self.dummy_sample
__UpperCamelCase : Dict = 0.1 * sample
if num_inference_steps is not None and hasattr(_lowerCamelCase , """set_timesteps""" ):
scheduler.set_timesteps(_lowerCamelCase )
elif num_inference_steps is not None and not hasattr(_lowerCamelCase , """set_timesteps""" ):
__UpperCamelCase : str = num_inference_steps
# copy over dummy past residuals (must be done after set_timesteps)
__UpperCamelCase : List[str] = [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
__UpperCamelCase : Dict = dummy_past_residuals[:]
__UpperCamelCase : Any = scheduler.timesteps[5]
__UpperCamelCase : Optional[int] = scheduler.timesteps[6]
__UpperCamelCase : List[str] = scheduler.step(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase , **_lowerCamelCase ).prev_sample
__UpperCamelCase : Dict = scheduler.step(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase , **_lowerCamelCase ).prev_sample
self.assertEqual(output_a.shape , sample.shape )
self.assertEqual(output_a.shape , output_a.shape )
__UpperCamelCase : List[Any] = scheduler.step(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase , **_lowerCamelCase ).prev_sample
__UpperCamelCase : Dict = scheduler.step(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase , **_lowerCamelCase ).prev_sample
self.assertEqual(output_a.shape , sample.shape )
self.assertEqual(output_a.shape , output_a.shape )
def lowerCamelCase__ ( self : Optional[int] ) -> Tuple:
"""simple docstring"""
for timesteps in [100, 1000]:
self.check_over_configs(num_train_timesteps=_lowerCamelCase , time_step=_lowerCamelCase )
def lowerCamelCase__ ( self : Tuple ) -> Any:
"""simple docstring"""
for t, num_inference_steps in zip([1, 5, 10] , [10, 50, 100] ):
self.check_over_forward(num_inference_steps=_lowerCamelCase , time_step=_lowerCamelCase )
def lowerCamelCase__ ( self : Optional[int] ) -> Tuple:
"""simple docstring"""
__UpperCamelCase : Dict = self.full_loop()
__UpperCamelCase : Dict = torch.mean(torch.abs(_lowerCamelCase ) )
assert abs(result_mean.item() - 2540529 ) < 10
| 279 |
"""simple docstring"""
from typing import Optional, Union
import torch
from torch import nn
from ...configuration_utils import ConfigMixin, register_to_config
from ...models.modeling_utils import ModelMixin
class snake_case ( __UpperCAmelCase , __UpperCAmelCase ):
@register_to_config
def __init__( self :List[str] , _lowerCamelCase :int = 7_6_8 , ):
super().__init__()
__SCREAMING_SNAKE_CASE : Union[str, Any] = nn.Parameter(torch.zeros(1 , _lowerCamelCase ) )
__SCREAMING_SNAKE_CASE : List[Any] = nn.Parameter(torch.ones(1 , _lowerCamelCase ) )
def SCREAMING_SNAKE_CASE_ ( self :Any , _lowerCamelCase :Optional[Union[str, torch.device]] = None , _lowerCamelCase :Optional[torch.dtype] = None , ):
__SCREAMING_SNAKE_CASE : Optional[int] = nn.Parameter(self.mean.to(_lowerCamelCase ).to(_lowerCamelCase ) )
__SCREAMING_SNAKE_CASE : Tuple = nn.Parameter(self.std.to(_lowerCamelCase ).to(_lowerCamelCase ) )
return self
def SCREAMING_SNAKE_CASE_ ( self :int , _lowerCamelCase :Tuple ):
__SCREAMING_SNAKE_CASE : Tuple = (embeds - self.mean) * 1.0 / self.std
return embeds
def SCREAMING_SNAKE_CASE_ ( self :Optional[Any] , _lowerCamelCase :Optional[int] ):
__SCREAMING_SNAKE_CASE : List[Any] = (embeds * self.std) + self.mean
return embeds
| 674 | 0 |
import tempfile
import numpy as np
import torch
from transformers import AutoTokenizer, TaEncoderModel
from diffusers import DDPMScheduler, UNetaDConditionModel
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.pipelines.deepfloyd_if import IFWatermarker
from diffusers.utils.testing_utils import torch_device
from ..test_pipelines_common import to_np
class lowerCamelCase :
def snake_case__ ( self :Any ) -> Dict:
"""simple docstring"""
torch.manual_seed(0 )
SCREAMING_SNAKE_CASE = TaEncoderModel.from_pretrained('''hf-internal-testing/tiny-random-t5''' )
torch.manual_seed(0 )
SCREAMING_SNAKE_CASE = AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-t5''' )
torch.manual_seed(0 )
SCREAMING_SNAKE_CASE = UNetaDConditionModel(
sample_size=3_2 , layers_per_block=1 , block_out_channels=[3_2, 6_4] , down_block_types=[
'''ResnetDownsampleBlock2D''',
'''SimpleCrossAttnDownBlock2D''',
] , mid_block_type='''UNetMidBlock2DSimpleCrossAttn''' , up_block_types=['''SimpleCrossAttnUpBlock2D''', '''ResnetUpsampleBlock2D'''] , in_channels=3 , out_channels=6 , cross_attention_dim=3_2 , encoder_hid_dim=3_2 , attention_head_dim=8 , addition_embed_type='''text''' , addition_embed_type_num_heads=2 , cross_attention_norm='''group_norm''' , resnet_time_scale_shift='''scale_shift''' , act_fn='''gelu''' , )
unet.set_attn_processor(AttnAddedKVProcessor() ) # For reproducibility tests
torch.manual_seed(0 )
SCREAMING_SNAKE_CASE = DDPMScheduler(
num_train_timesteps=1_0_0_0 , beta_schedule='''squaredcos_cap_v2''' , beta_start=0.00_01 , beta_end=0.02 , thresholding=_lowerCamelCase , dynamic_thresholding_ratio=0.95 , sample_max_value=1.0 , prediction_type='''epsilon''' , variance_type='''learned_range''' , )
torch.manual_seed(0 )
SCREAMING_SNAKE_CASE = IFWatermarker()
return {
"text_encoder": text_encoder,
"tokenizer": tokenizer,
"unet": unet,
"scheduler": scheduler,
"watermarker": watermarker,
"safety_checker": None,
"feature_extractor": None,
}
def snake_case__ ( self :Tuple ) -> Union[str, Any]:
"""simple docstring"""
torch.manual_seed(0 )
SCREAMING_SNAKE_CASE = TaEncoderModel.from_pretrained('''hf-internal-testing/tiny-random-t5''' )
torch.manual_seed(0 )
SCREAMING_SNAKE_CASE = AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-t5''' )
torch.manual_seed(0 )
SCREAMING_SNAKE_CASE = UNetaDConditionModel(
sample_size=3_2 , layers_per_block=[1, 2] , block_out_channels=[3_2, 6_4] , down_block_types=[
'''ResnetDownsampleBlock2D''',
'''SimpleCrossAttnDownBlock2D''',
] , mid_block_type='''UNetMidBlock2DSimpleCrossAttn''' , up_block_types=['''SimpleCrossAttnUpBlock2D''', '''ResnetUpsampleBlock2D'''] , in_channels=6 , out_channels=6 , cross_attention_dim=3_2 , encoder_hid_dim=3_2 , attention_head_dim=8 , addition_embed_type='''text''' , addition_embed_type_num_heads=2 , cross_attention_norm='''group_norm''' , resnet_time_scale_shift='''scale_shift''' , act_fn='''gelu''' , class_embed_type='''timestep''' , mid_block_scale_factor=1.4_14 , time_embedding_act_fn='''gelu''' , time_embedding_dim=3_2 , )
unet.set_attn_processor(AttnAddedKVProcessor() ) # For reproducibility tests
torch.manual_seed(0 )
SCREAMING_SNAKE_CASE = DDPMScheduler(
num_train_timesteps=1_0_0_0 , beta_schedule='''squaredcos_cap_v2''' , beta_start=0.00_01 , beta_end=0.02 , thresholding=_lowerCamelCase , dynamic_thresholding_ratio=0.95 , sample_max_value=1.0 , prediction_type='''epsilon''' , variance_type='''learned_range''' , )
torch.manual_seed(0 )
SCREAMING_SNAKE_CASE = DDPMScheduler(
num_train_timesteps=1_0_0_0 , beta_schedule='''squaredcos_cap_v2''' , beta_start=0.00_01 , beta_end=0.02 , )
torch.manual_seed(0 )
SCREAMING_SNAKE_CASE = IFWatermarker()
return {
"text_encoder": text_encoder,
"tokenizer": tokenizer,
"unet": unet,
"scheduler": scheduler,
"image_noising_scheduler": image_noising_scheduler,
"watermarker": watermarker,
"safety_checker": None,
"feature_extractor": None,
}
def snake_case__ ( self :Optional[Any] ) -> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE = self.get_dummy_components()
SCREAMING_SNAKE_CASE = self.pipeline_class(**_lowerCamelCase )
pipe.to(_lowerCamelCase )
pipe.set_progress_bar_config(disable=_lowerCamelCase )
SCREAMING_SNAKE_CASE = self.get_dummy_inputs(_lowerCamelCase )
SCREAMING_SNAKE_CASE = inputs['''prompt''']
SCREAMING_SNAKE_CASE = inputs['''generator''']
SCREAMING_SNAKE_CASE = inputs['''num_inference_steps''']
SCREAMING_SNAKE_CASE = inputs['''output_type''']
if "image" in inputs:
SCREAMING_SNAKE_CASE = inputs['''image''']
else:
SCREAMING_SNAKE_CASE = None
if "mask_image" in inputs:
SCREAMING_SNAKE_CASE = inputs['''mask_image''']
else:
SCREAMING_SNAKE_CASE = None
if "original_image" in inputs:
SCREAMING_SNAKE_CASE = inputs['''original_image''']
else:
SCREAMING_SNAKE_CASE = None
SCREAMING_SNAKE_CASE = pipe.encode_prompt(_lowerCamelCase )
# inputs with prompt converted to embeddings
SCREAMING_SNAKE_CASE = {
'''prompt_embeds''': prompt_embeds,
'''negative_prompt_embeds''': negative_prompt_embeds,
'''generator''': generator,
'''num_inference_steps''': num_inference_steps,
'''output_type''': output_type,
}
if image is not None:
SCREAMING_SNAKE_CASE = image
if mask_image is not None:
SCREAMING_SNAKE_CASE = mask_image
if original_image is not None:
SCREAMING_SNAKE_CASE = original_image
# set all optional components to None
for optional_component in pipe._optional_components:
setattr(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase )
SCREAMING_SNAKE_CASE = pipe(**_lowerCamelCase )[0]
with tempfile.TemporaryDirectory() as tmpdir:
pipe.save_pretrained(_lowerCamelCase )
SCREAMING_SNAKE_CASE = self.pipeline_class.from_pretrained(_lowerCamelCase )
pipe_loaded.to(_lowerCamelCase )
pipe_loaded.set_progress_bar_config(disable=_lowerCamelCase )
pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor() ) # For reproducibility tests
for optional_component in pipe._optional_components:
self.assertTrue(
getattr(_lowerCamelCase , _lowerCamelCase ) is None , f"""`{optional_component}` did not stay set to None after loading.""" , )
SCREAMING_SNAKE_CASE = self.get_dummy_inputs(_lowerCamelCase )
SCREAMING_SNAKE_CASE = inputs['''generator''']
SCREAMING_SNAKE_CASE = inputs['''num_inference_steps''']
SCREAMING_SNAKE_CASE = inputs['''output_type''']
# inputs with prompt converted to embeddings
SCREAMING_SNAKE_CASE = {
'''prompt_embeds''': prompt_embeds,
'''negative_prompt_embeds''': negative_prompt_embeds,
'''generator''': generator,
'''num_inference_steps''': num_inference_steps,
'''output_type''': output_type,
}
if image is not None:
SCREAMING_SNAKE_CASE = image
if mask_image is not None:
SCREAMING_SNAKE_CASE = mask_image
if original_image is not None:
SCREAMING_SNAKE_CASE = original_image
SCREAMING_SNAKE_CASE = pipe_loaded(**_lowerCamelCase )[0]
SCREAMING_SNAKE_CASE = np.abs(to_np(_lowerCamelCase ) - to_np(_lowerCamelCase ) ).max()
self.assertLess(_lowerCamelCase , 1e-4 )
def snake_case__ ( self :int ) -> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE = self.get_dummy_components()
SCREAMING_SNAKE_CASE = self.pipeline_class(**_lowerCamelCase )
pipe.to(_lowerCamelCase )
pipe.set_progress_bar_config(disable=_lowerCamelCase )
SCREAMING_SNAKE_CASE = self.get_dummy_inputs(_lowerCamelCase )
SCREAMING_SNAKE_CASE = pipe(**_lowerCamelCase )[0]
with tempfile.TemporaryDirectory() as tmpdir:
pipe.save_pretrained(_lowerCamelCase )
SCREAMING_SNAKE_CASE = self.pipeline_class.from_pretrained(_lowerCamelCase )
pipe_loaded.to(_lowerCamelCase )
pipe_loaded.set_progress_bar_config(disable=_lowerCamelCase )
pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor() ) # For reproducibility tests
SCREAMING_SNAKE_CASE = self.get_dummy_inputs(_lowerCamelCase )
SCREAMING_SNAKE_CASE = pipe_loaded(**_lowerCamelCase )[0]
SCREAMING_SNAKE_CASE = np.abs(to_np(_lowerCamelCase ) - to_np(_lowerCamelCase ) ).max()
self.assertLess(_lowerCamelCase , 1e-4 ) | 201 |
"""simple docstring"""
import argparse
import torch
from transformers import BertConfig, BertForPreTraining, load_tf_weights_in_bert
from transformers.utils import logging
logging.set_verbosity_info()
def lowerCAmelCase_ ( lowercase_ : Dict , lowercase_ : int , lowercase_ : Any ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : Tuple = BertConfig.from_json_file(lowercase_ )
print(F'''Building PyTorch model from configuration: {config}''' )
__SCREAMING_SNAKE_CASE : Optional[int] = BertForPreTraining(lowercase_ )
# Load weights from tf checkpoint
load_tf_weights_in_bert(lowercase_ , lowercase_ , lowercase_ )
# Save pytorch-model
print(F'''Save PyTorch model to {pytorch_dump_path}''' )
torch.save(model.state_dict() , lowercase_ )
if __name__ == "__main__":
_lowerCamelCase = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--tf_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
)
parser.add_argument(
'''--bert_config_file''',
default=None,
type=str,
required=True,
help=(
'''The config json file corresponding to the pre-trained BERT model. \n'''
'''This specifies the model architecture.'''
),
)
parser.add_argument(
'''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
)
_lowerCamelCase = parser.parse_args()
convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)
| 674 | 0 |
"""simple docstring"""
def lowercase__(A ) ->str:
"""simple docstring"""
lowercase__ : Dict= int(lowercase_ )
if decimal in (0, 1): # Exit cases for the recursion
return str(lowercase_ )
lowercase__ : List[Any]= divmod(lowercase_ , 2 )
return binary_recursive(lowercase_ ) + str(lowercase_ )
def lowercase__(A ) ->List[str]:
"""simple docstring"""
lowercase__ : int= str(lowercase_ ).strip()
if not number:
raise ValueError("No input value was provided" )
lowercase__ : Union[str, Any]= '''-''' if number.startswith("-" ) else ''''''
lowercase__ : str= number.lstrip("-" )
if not number.isnumeric():
raise ValueError("Input value is not an integer" )
return f'''{negative}0b{binary_recursive(int(lowercase_ ) )}'''
if __name__ == "__main__":
from doctest import testmod
testmod()
| 218 |
"""simple docstring"""
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import numpy as np
import torch
from ..models.clipseg import CLIPSegForImageSegmentation
from ..utils import is_vision_available, requires_backends
from .base import PipelineTool
if is_vision_available():
from PIL import Image
class snake_case ( __UpperCAmelCase ):
lowerCamelCase__ = (
'''This is a tool that creates a segmentation mask of an image according to a label. It cannot create an image.'''
'''It takes two arguments named `image` which should be the original image, and `label` which should be a text '''
'''describing the elements what should be identified in the segmentation mask. The tool returns the mask.'''
)
lowerCamelCase__ = '''CIDAS/clipseg-rd64-refined'''
lowerCamelCase__ = '''image_segmenter'''
lowerCamelCase__ = CLIPSegForImageSegmentation
lowerCamelCase__ = ['''image''', '''text''']
lowerCamelCase__ = ['''image''']
def __init__( self :Dict , *_lowerCamelCase :Union[str, Any] , **_lowerCamelCase :Tuple ):
requires_backends(self , ['''vision'''] )
super().__init__(*_lowerCamelCase , **_lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :Tuple , _lowerCamelCase :"Image" , _lowerCamelCase :str ):
return self.pre_processor(text=[label] , images=[image] , padding=_lowerCamelCase , return_tensors='''pt''' )
def SCREAMING_SNAKE_CASE_ ( self :Optional[int] , _lowerCamelCase :Optional[int] ):
with torch.no_grad():
__SCREAMING_SNAKE_CASE : List[Any] = self.model(**_lowerCamelCase ).logits
return logits
def SCREAMING_SNAKE_CASE_ ( self :Optional[Any] , _lowerCamelCase :Tuple ):
__SCREAMING_SNAKE_CASE : Optional[int] = outputs.cpu().detach().numpy()
__SCREAMING_SNAKE_CASE : str = 0
__SCREAMING_SNAKE_CASE : str = 1
return Image.fromarray((array * 2_5_5).astype(np.uinta ) )
| 674 | 0 |
import argparse
import os
import shutil
import torch
from emmental.modules import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer
def __snake_case ( _UpperCamelCase ) -> Optional[Any]:
_a = args.pruning_method
_a = args.threshold
_a = args.model_name_or_path.rstrip('''/''' )
_a = args.target_model_path
print(f"Load fine-pruned model from {model_name_or_path}" )
_a = torch.load(os.path.join(lowercase_ , '''pytorch_model.bin''' ) )
_a = {}
for name, tensor in model.items():
if "embeddings" in name or "LayerNorm" in name or "pooler" in name:
_a = tensor
print(f"Copied layer {name}" )
elif "classifier" in name or "qa_output" in name:
_a = tensor
print(f"Copied layer {name}" )
elif "bias" in name:
_a = tensor
print(f"Copied layer {name}" )
else:
if pruning_method == "magnitude":
_a = MagnitudeBinarizer.apply(inputs=lowercase_ , threshold=lowercase_ )
_a = tensor * mask
print(f"Pruned layer {name}" )
elif pruning_method == "topK":
if "mask_scores" in name:
continue
_a = name[:-6]
_a = model[f"{prefix_}mask_scores"]
_a = TopKBinarizer.apply(lowercase_ , lowercase_ )
_a = tensor * mask
print(f"Pruned layer {name}" )
elif pruning_method == "sigmoied_threshold":
if "mask_scores" in name:
continue
_a = name[:-6]
_a = model[f"{prefix_}mask_scores"]
_a = ThresholdBinarizer.apply(lowercase_ , lowercase_ , lowercase_ )
_a = tensor * mask
print(f"Pruned layer {name}" )
elif pruning_method == "l0":
if "mask_scores" in name:
continue
_a = name[:-6]
_a = model[f"{prefix_}mask_scores"]
_a = -0.1, 1.1
_a = torch.sigmoid(lowercase_ )
_a = s * (r - l) + l
_a = s_bar.clamp(min=0.0 , max=1.0 )
_a = tensor * mask
print(f"Pruned layer {name}" )
else:
raise ValueError('''Unknown pruning method''' )
if target_model_path is None:
_a = os.path.join(
os.path.dirname(lowercase_ ) , f"bertarized_{os.path.basename(lowercase_ )}" )
if not os.path.isdir(lowercase_ ):
shutil.copytree(lowercase_ , lowercase_ )
print(f"\nCreated folder {target_model_path}" )
torch.save(lowercase_ , os.path.join(lowercase_ , '''pytorch_model.bin''' ) )
print('''\nPruned model saved! See you later!''' )
if __name__ == "__main__":
lowerCamelCase :Any = argparse.ArgumentParser()
parser.add_argument(
'--pruning_method',
choices=['l0', 'magnitude', 'topK', 'sigmoied_threshold'],
type=str,
required=True,
help=(
'Pruning Method (l0 = L0 regularization, magnitude = Magnitude pruning, topK = Movement pruning,'
' sigmoied_threshold = Soft movement pruning)'
),
)
parser.add_argument(
'--threshold',
type=float,
required=False,
help=(
'For `magnitude` and `topK`, it is the level of remaining weights (in %) in the fine-pruned model.'
'For `sigmoied_threshold`, it is the threshold \tau against which the (sigmoied) scores are compared.'
'Not needed for `l0`'
),
)
parser.add_argument(
'--model_name_or_path',
type=str,
required=True,
help='Folder containing the model that was previously fine-pruned',
)
parser.add_argument(
'--target_model_path',
default=None,
type=str,
required=False,
help='Folder containing the model that was previously fine-pruned',
)
lowerCamelCase :int = parser.parse_args()
main(args)
| 487 |
"""simple docstring"""
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
from transformers import BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES, BertTokenizer
from transformers.testing_utils import require_tokenizers, require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available
if is_vision_available():
from PIL import Image
from transformers import VisionTextDualEncoderProcessor, ViTImageProcessor
@require_tokenizers
@require_vision
class snake_case ( unittest.TestCase ):
def SCREAMING_SNAKE_CASE_ ( self :Union[str, Any] ):
__SCREAMING_SNAKE_CASE : Union[str, Any] = tempfile.mkdtemp()
# fmt: off
__SCREAMING_SNAKE_CASE : Optional[int] = ['''[UNK]''', '''[CLS]''', '''[SEP]''', '''[PAD]''', '''[MASK]''', '''want''', '''##want''', '''##ed''', '''wa''', '''un''', '''runn''', '''##ing''', ''',''', '''low''', '''lowest''']
# fmt: on
__SCREAMING_SNAKE_CASE : List[Any] = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['''vocab_file'''] )
with open(self.vocab_file , '''w''' , encoding='''utf-8''' ) as vocab_writer:
vocab_writer.write(''''''.join([x + '''\n''' for x in vocab_tokens] ) )
__SCREAMING_SNAKE_CASE : Optional[int] = {
'''do_resize''': True,
'''size''': {'''height''': 1_8, '''width''': 1_8},
'''do_normalize''': True,
'''image_mean''': [0.5, 0.5, 0.5],
'''image_std''': [0.5, 0.5, 0.5],
}
__SCREAMING_SNAKE_CASE : List[Any] = os.path.join(self.tmpdirname , _lowerCamelCase )
with open(self.image_processor_file , '''w''' , encoding='''utf-8''' ) as fp:
json.dump(_lowerCamelCase , _lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :Union[str, Any] , **_lowerCamelCase :List[str] ):
return BertTokenizer.from_pretrained(self.tmpdirname , **_lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :List[str] , **_lowerCamelCase :Optional[int] ):
return ViTImageProcessor.from_pretrained(self.tmpdirname , **_lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :Optional[Any] ):
shutil.rmtree(self.tmpdirname )
def SCREAMING_SNAKE_CASE_ ( self :Dict ):
__SCREAMING_SNAKE_CASE : Union[str, Any] = [np.random.randint(2_5_5 , size=(3, 3_0, 4_0_0) , dtype=np.uinta )]
__SCREAMING_SNAKE_CASE : Tuple = [Image.fromarray(np.moveaxis(_lowerCamelCase , 0 , -1 ) ) for x in image_inputs]
return image_inputs
def SCREAMING_SNAKE_CASE_ ( self :int ):
__SCREAMING_SNAKE_CASE : Optional[int] = self.get_tokenizer()
__SCREAMING_SNAKE_CASE : Optional[Any] = self.get_image_processor()
__SCREAMING_SNAKE_CASE : Optional[Any] = VisionTextDualEncoderProcessor(tokenizer=_lowerCamelCase , image_processor=_lowerCamelCase )
processor.save_pretrained(self.tmpdirname )
__SCREAMING_SNAKE_CASE : int = VisionTextDualEncoderProcessor.from_pretrained(self.tmpdirname )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer.get_vocab() )
self.assertIsInstance(processor.tokenizer , (BertTokenizer, BertTokenizerFast) )
self.assertEqual(processor.image_processor.to_json_string() , image_processor.to_json_string() )
self.assertIsInstance(processor.image_processor , _lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :List[Any] ):
__SCREAMING_SNAKE_CASE : str = VisionTextDualEncoderProcessor(
tokenizer=self.get_tokenizer() , image_processor=self.get_image_processor() )
processor.save_pretrained(self.tmpdirname )
__SCREAMING_SNAKE_CASE : Optional[int] = self.get_tokenizer(bos_token='''(BOS)''' , eos_token='''(EOS)''' )
__SCREAMING_SNAKE_CASE : List[str] = self.get_image_processor(do_normalize=_lowerCamelCase , padding_value=1.0 )
__SCREAMING_SNAKE_CASE : int = VisionTextDualEncoderProcessor.from_pretrained(
self.tmpdirname , bos_token='''(BOS)''' , eos_token='''(EOS)''' , do_normalize=_lowerCamelCase , padding_value=1.0 )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer_add_kwargs.get_vocab() )
self.assertIsInstance(processor.tokenizer , (BertTokenizer, BertTokenizerFast) )
self.assertEqual(processor.image_processor.to_json_string() , image_processor_add_kwargs.to_json_string() )
self.assertIsInstance(processor.image_processor , _lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :int ):
__SCREAMING_SNAKE_CASE : Union[str, Any] = self.get_image_processor()
__SCREAMING_SNAKE_CASE : Dict = self.get_tokenizer()
__SCREAMING_SNAKE_CASE : Tuple = VisionTextDualEncoderProcessor(tokenizer=_lowerCamelCase , image_processor=_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Dict = self.prepare_image_inputs()
__SCREAMING_SNAKE_CASE : Optional[int] = image_processor(_lowerCamelCase , return_tensors='''np''' )
__SCREAMING_SNAKE_CASE : Tuple = processor(images=_lowerCamelCase , return_tensors='''np''' )
for key in input_feat_extract.keys():
self.assertAlmostEqual(input_feat_extract[key].sum() , input_processor[key].sum() , delta=1e-2 )
def SCREAMING_SNAKE_CASE_ ( self :Optional[int] ):
__SCREAMING_SNAKE_CASE : Any = self.get_image_processor()
__SCREAMING_SNAKE_CASE : Any = self.get_tokenizer()
__SCREAMING_SNAKE_CASE : Tuple = VisionTextDualEncoderProcessor(tokenizer=_lowerCamelCase , image_processor=_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Union[str, Any] = '''lower newer'''
__SCREAMING_SNAKE_CASE : Optional[int] = processor(text=_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Any = tokenizer(_lowerCamelCase )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] , encoded_processor[key] )
def SCREAMING_SNAKE_CASE_ ( self :str ):
__SCREAMING_SNAKE_CASE : str = self.get_image_processor()
__SCREAMING_SNAKE_CASE : int = self.get_tokenizer()
__SCREAMING_SNAKE_CASE : int = VisionTextDualEncoderProcessor(tokenizer=_lowerCamelCase , image_processor=_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Any = '''lower newer'''
__SCREAMING_SNAKE_CASE : int = self.prepare_image_inputs()
__SCREAMING_SNAKE_CASE : int = processor(text=_lowerCamelCase , images=_lowerCamelCase )
self.assertListEqual(list(inputs.keys() ) , ['''input_ids''', '''token_type_ids''', '''attention_mask''', '''pixel_values'''] )
# test if it raises when no input is passed
with self.assertRaises(_lowerCamelCase ):
processor()
def SCREAMING_SNAKE_CASE_ ( self :Any ):
__SCREAMING_SNAKE_CASE : Dict = self.get_image_processor()
__SCREAMING_SNAKE_CASE : List[Any] = self.get_tokenizer()
__SCREAMING_SNAKE_CASE : Union[str, Any] = VisionTextDualEncoderProcessor(tokenizer=_lowerCamelCase , image_processor=_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Union[str, Any] = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
__SCREAMING_SNAKE_CASE : Tuple = processor.batch_decode(_lowerCamelCase )
__SCREAMING_SNAKE_CASE : List[str] = tokenizer.batch_decode(_lowerCamelCase )
self.assertListEqual(_lowerCamelCase , _lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :Dict ):
__SCREAMING_SNAKE_CASE : str = self.get_image_processor()
__SCREAMING_SNAKE_CASE : List[str] = self.get_tokenizer()
__SCREAMING_SNAKE_CASE : Optional[int] = VisionTextDualEncoderProcessor(tokenizer=_lowerCamelCase , image_processor=_lowerCamelCase )
__SCREAMING_SNAKE_CASE : List[Any] = '''lower newer'''
__SCREAMING_SNAKE_CASE : Dict = self.prepare_image_inputs()
__SCREAMING_SNAKE_CASE : str = processor(text=_lowerCamelCase , images=_lowerCamelCase )
self.assertListEqual(list(inputs.keys() ) , processor.model_input_names )
| 674 | 0 |
"""simple docstring"""
from __future__ import annotations
import string
from itertools import cycle, product
from pathlib import Path
a_ = (
string.ascii_letters + string.digits + string.punctuation + string.whitespace
)
a_ = [ord(letter) for letter in string.ascii_lowercase]
a_ = {ord(char) for char in VALID_CHARS}
a_ = ['the', 'be', 'to', 'of', 'and', 'in', 'that', 'have']
def __UpperCAmelCase ( __UpperCamelCase , __UpperCamelCase ):
__lowercase : str = ""
__lowercase : int
__lowercase : int
__lowercase : int
for keychar, cipherchar in zip(cycle(lowercase_ ) , lowercase_ ):
__lowercase : List[str] = cipherchar ^ keychar
if decodedchar not in VALID_INTS:
return None
decoded += chr(lowercase_ )
return decoded
def __UpperCAmelCase ( __UpperCamelCase ):
__lowercase : list[str] = []
for key in product(lowercase_ , repeat=3 ):
__lowercase : List[Any] = try_key(lowercase_ , lowercase_ )
if encoded is not None:
possibles.append(lowercase_ )
return possibles
def __UpperCAmelCase ( __UpperCamelCase , __UpperCamelCase ):
return [possible for possible in possibles if common_word in possible.lower()]
def __UpperCAmelCase ( __UpperCamelCase = "p059_cipher.txt" ):
__lowercase : list[int]
__lowercase : list[str]
__lowercase : str
__lowercase : str
__lowercase : str = Path(lowercase_ ).parent.joinpath(lowercase_ ).read_text(encoding='''utf-8''' )
__lowercase : Optional[Any] = [int(lowercase_ ) for number in data.strip().split(''',''' )]
__lowercase : Optional[int] = filter_valid_chars(lowercase_ )
for common_word in COMMON_WORDS:
__lowercase : Union[str, Any] = filter_common_word(lowercase_ , lowercase_ )
if len(lowercase_ ) == 1:
break
__lowercase : List[str] = possibles[0]
return sum(ord(lowercase_ ) for char in decoded_text )
if __name__ == "__main__":
print(F"{solution() = }")
| 76 |
"""simple docstring"""
import inspect
import unittest
import numpy as np
from tests.test_modeling_common import floats_tensor
from transformers import DetrConfig, MaskFormerConfig, SwinConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import MaskFormerForInstanceSegmentation, MaskFormerModel
if is_vision_available():
from transformers import MaskFormerImageProcessor
if is_vision_available():
from PIL import Image
class snake_case :
def __init__( self :Optional[int] , _lowerCamelCase :int , _lowerCamelCase :Any=2 , _lowerCamelCase :List[str]=True , _lowerCamelCase :str=False , _lowerCamelCase :Tuple=1_0 , _lowerCamelCase :str=3 , _lowerCamelCase :str=3_2 * 4 , _lowerCamelCase :Dict=3_2 * 6 , _lowerCamelCase :str=4 , _lowerCamelCase :Any=3_2 , ):
__SCREAMING_SNAKE_CASE : List[str] = parent
__SCREAMING_SNAKE_CASE : Tuple = batch_size
__SCREAMING_SNAKE_CASE : Optional[Any] = is_training
__SCREAMING_SNAKE_CASE : Dict = use_auxiliary_loss
__SCREAMING_SNAKE_CASE : List[str] = num_queries
__SCREAMING_SNAKE_CASE : Optional[int] = num_channels
__SCREAMING_SNAKE_CASE : List[Any] = min_size
__SCREAMING_SNAKE_CASE : int = max_size
__SCREAMING_SNAKE_CASE : Any = num_labels
__SCREAMING_SNAKE_CASE : Union[str, Any] = mask_feature_size
def SCREAMING_SNAKE_CASE_ ( self :Any ):
__SCREAMING_SNAKE_CASE : str = floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size] ).to(
_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Any = torch.ones([self.batch_size, self.min_size, self.max_size] , device=_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Any = (
torch.rand([self.batch_size, self.num_labels, self.min_size, self.max_size] , device=_lowerCamelCase ) > 0.5
).float()
__SCREAMING_SNAKE_CASE : Dict = (torch.rand((self.batch_size, self.num_labels) , device=_lowerCamelCase ) > 0.5).long()
__SCREAMING_SNAKE_CASE : str = self.get_config()
return config, pixel_values, pixel_mask, mask_labels, class_labels
def SCREAMING_SNAKE_CASE_ ( self :List[Any] ):
return MaskFormerConfig.from_backbone_and_decoder_configs(
backbone_config=SwinConfig(
depths=[1, 1, 1, 1] , ) , decoder_config=DetrConfig(
decoder_ffn_dim=1_2_8 , num_queries=self.num_queries , decoder_attention_heads=2 , d_model=self.mask_feature_size , ) , mask_feature_size=self.mask_feature_size , fpn_feature_size=self.mask_feature_size , num_channels=self.num_channels , num_labels=self.num_labels , )
def SCREAMING_SNAKE_CASE_ ( self :str ):
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Optional[Any] = self.prepare_config_and_inputs()
__SCREAMING_SNAKE_CASE : Any = {'''pixel_values''': pixel_values, '''pixel_mask''': pixel_mask}
return config, inputs_dict
def SCREAMING_SNAKE_CASE_ ( self :int , _lowerCamelCase :int , _lowerCamelCase :Optional[int] ):
__SCREAMING_SNAKE_CASE : Union[str, Any] = output.encoder_hidden_states
__SCREAMING_SNAKE_CASE : int = output.pixel_decoder_hidden_states
__SCREAMING_SNAKE_CASE : Optional[int] = output.transformer_decoder_hidden_states
self.parent.assertTrue(len(_lowerCamelCase ) , len(config.backbone_config.depths ) )
self.parent.assertTrue(len(_lowerCamelCase ) , len(config.backbone_config.depths ) )
self.parent.assertTrue(len(_lowerCamelCase ) , config.decoder_config.decoder_layers )
def SCREAMING_SNAKE_CASE_ ( self :Optional[Any] , _lowerCamelCase :Dict , _lowerCamelCase :str , _lowerCamelCase :Any , _lowerCamelCase :Optional[Any]=False ):
with torch.no_grad():
__SCREAMING_SNAKE_CASE : List[str] = MaskFormerModel(config=_lowerCamelCase )
model.to(_lowerCamelCase )
model.eval()
__SCREAMING_SNAKE_CASE : str = model(pixel_values=_lowerCamelCase , pixel_mask=_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Any = model(_lowerCamelCase , output_hidden_states=_lowerCamelCase )
# the correct shape of output.transformer_decoder_hidden_states ensure the correcteness of the
# encoder and pixel decoder
self.parent.assertEqual(
output.transformer_decoder_last_hidden_state.shape , (self.batch_size, self.num_queries, self.mask_feature_size) , )
# let's ensure the other two hidden state exists
self.parent.assertTrue(output.pixel_decoder_last_hidden_state is not None )
self.parent.assertTrue(output.encoder_last_hidden_state is not None )
if output_hidden_states:
self.check_output_hidden_state(_lowerCamelCase , _lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :Dict , _lowerCamelCase :Optional[Any] , _lowerCamelCase :Optional[int] , _lowerCamelCase :Optional[Any] , _lowerCamelCase :List[str] , _lowerCamelCase :Dict ):
__SCREAMING_SNAKE_CASE : Optional[Any] = MaskFormerForInstanceSegmentation(config=_lowerCamelCase )
model.to(_lowerCamelCase )
model.eval()
def comm_check_on_output(_lowerCamelCase :Optional[Any] ):
# let's still check that all the required stuff is there
self.parent.assertTrue(result.transformer_decoder_last_hidden_state is not None )
self.parent.assertTrue(result.pixel_decoder_last_hidden_state is not None )
self.parent.assertTrue(result.encoder_last_hidden_state is not None )
# okay, now we need to check the logits shape
# due to the encoder compression, masks have a //4 spatial size
self.parent.assertEqual(
result.masks_queries_logits.shape , (self.batch_size, self.num_queries, self.min_size // 4, self.max_size // 4) , )
# + 1 for null class
self.parent.assertEqual(
result.class_queries_logits.shape , (self.batch_size, self.num_queries, self.num_labels + 1) )
with torch.no_grad():
__SCREAMING_SNAKE_CASE : List[str] = model(pixel_values=_lowerCamelCase , pixel_mask=_lowerCamelCase )
__SCREAMING_SNAKE_CASE : int = model(_lowerCamelCase )
comm_check_on_output(_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Dict = model(
pixel_values=_lowerCamelCase , pixel_mask=_lowerCamelCase , mask_labels=_lowerCamelCase , class_labels=_lowerCamelCase )
comm_check_on_output(_lowerCamelCase )
self.parent.assertTrue(result.loss is not None )
self.parent.assertEqual(result.loss.shape , torch.Size([1] ) )
@require_torch
class snake_case ( __UpperCAmelCase , __UpperCAmelCase , unittest.TestCase ):
lowerCamelCase__ = (MaskFormerModel, MaskFormerForInstanceSegmentation) if is_torch_available() else ()
lowerCamelCase__ = (
{'''feature-extraction''': MaskFormerModel, '''image-segmentation''': MaskFormerForInstanceSegmentation}
if is_torch_available()
else {}
)
lowerCamelCase__ = False
lowerCamelCase__ = False
lowerCamelCase__ = False
lowerCamelCase__ = False
def SCREAMING_SNAKE_CASE_ ( self :Dict ):
__SCREAMING_SNAKE_CASE : Optional[Any] = MaskFormerModelTester(self )
__SCREAMING_SNAKE_CASE : str = ConfigTester(self , config_class=_lowerCamelCase , has_text_modality=_lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :Union[str, Any] ):
self.config_tester.run_common_tests()
def SCREAMING_SNAKE_CASE_ ( self :Union[str, Any] ):
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Any = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.create_and_check_maskformer_model(_lowerCamelCase , **_lowerCamelCase , output_hidden_states=_lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :List[str] ):
__SCREAMING_SNAKE_CASE : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_maskformer_instance_segmentation_head_model(*_lowerCamelCase )
@unittest.skip(reason='''MaskFormer does not use inputs_embeds''' )
def SCREAMING_SNAKE_CASE_ ( self :List[Any] ):
pass
@unittest.skip(reason='''MaskFormer does not have a get_input_embeddings method''' )
def SCREAMING_SNAKE_CASE_ ( self :int ):
pass
@unittest.skip(reason='''MaskFormer is not a generative model''' )
def SCREAMING_SNAKE_CASE_ ( self :Union[str, Any] ):
pass
@unittest.skip(reason='''MaskFormer does not use token embeddings''' )
def SCREAMING_SNAKE_CASE_ ( self :Optional[int] ):
pass
@require_torch_multi_gpu
@unittest.skip(
reason='''MaskFormer has some layers using `add_module` which doesn\'t work well with `nn.DataParallel`''' )
def SCREAMING_SNAKE_CASE_ ( self :Dict ):
pass
@unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.''' )
def SCREAMING_SNAKE_CASE_ ( self :Dict ):
pass
def SCREAMING_SNAKE_CASE_ ( self :Union[str, Any] ):
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Any = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__SCREAMING_SNAKE_CASE : Tuple = model_class(_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Optional[int] = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
__SCREAMING_SNAKE_CASE : List[str] = [*signature.parameters.keys()]
__SCREAMING_SNAKE_CASE : Optional[Any] = ['''pixel_values''']
self.assertListEqual(arg_names[:1] , _lowerCamelCase )
@slow
def SCREAMING_SNAKE_CASE_ ( self :Optional[Any] ):
for model_name in ["facebook/maskformer-swin-small-coco"]:
__SCREAMING_SNAKE_CASE : Tuple = MaskFormerModel.from_pretrained(_lowerCamelCase )
self.assertIsNotNone(_lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :Union[str, Any] ):
__SCREAMING_SNAKE_CASE : Dict = (self.model_tester.min_size,) * 2
__SCREAMING_SNAKE_CASE : Dict = {
'''pixel_values''': torch.randn((2, 3, *size) , device=_lowerCamelCase ),
'''mask_labels''': torch.randn((2, 1_0, *size) , device=_lowerCamelCase ),
'''class_labels''': torch.zeros(2 , 1_0 , device=_lowerCamelCase ).long(),
}
__SCREAMING_SNAKE_CASE : Dict = MaskFormerForInstanceSegmentation(MaskFormerConfig() ).to(_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Optional[Any] = model(**_lowerCamelCase )
self.assertTrue(outputs.loss is not None )
def SCREAMING_SNAKE_CASE_ ( self :Union[str, Any] ):
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Tuple = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.create_and_check_maskformer_model(_lowerCamelCase , **_lowerCamelCase , output_hidden_states=_lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :Union[str, Any] ):
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Tuple = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__SCREAMING_SNAKE_CASE : Dict = model_class(_lowerCamelCase ).to(_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Optional[int] = model(**_lowerCamelCase , output_attentions=_lowerCamelCase )
self.assertTrue(outputs.attentions is not None )
def SCREAMING_SNAKE_CASE_ ( self :Dict ):
if not self.model_tester.is_training:
return
# only MaskFormerForInstanceSegmentation has the loss
__SCREAMING_SNAKE_CASE : List[Any] = self.all_model_classes[1]
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Dict = self.model_tester.prepare_config_and_inputs()
__SCREAMING_SNAKE_CASE : int = model_class(_lowerCamelCase )
model.to(_lowerCamelCase )
model.train()
__SCREAMING_SNAKE_CASE : Any = model(_lowerCamelCase , mask_labels=_lowerCamelCase , class_labels=_lowerCamelCase ).loss
loss.backward()
def SCREAMING_SNAKE_CASE_ ( self :Optional[int] ):
# only MaskFormerForInstanceSegmentation has the loss
__SCREAMING_SNAKE_CASE : Tuple = self.all_model_classes[1]
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Tuple = self.model_tester.prepare_config_and_inputs()
__SCREAMING_SNAKE_CASE : Tuple = True
__SCREAMING_SNAKE_CASE : List[str] = True
__SCREAMING_SNAKE_CASE : Any = model_class(_lowerCamelCase )
model.to(_lowerCamelCase )
model.train()
__SCREAMING_SNAKE_CASE : Optional[int] = model(_lowerCamelCase , mask_labels=_lowerCamelCase , class_labels=_lowerCamelCase )
__SCREAMING_SNAKE_CASE : List[str] = outputs.encoder_hidden_states[0]
encoder_hidden_states.retain_grad()
__SCREAMING_SNAKE_CASE : str = outputs.pixel_decoder_hidden_states[0]
pixel_decoder_hidden_states.retain_grad()
# we requires_grad=True in inputs_embeds (line 2152), the original implementation don't
__SCREAMING_SNAKE_CASE : Optional[int] = outputs.transformer_decoder_hidden_states[0]
transformer_decoder_hidden_states.retain_grad()
__SCREAMING_SNAKE_CASE : int = outputs.attentions[0]
attentions.retain_grad()
outputs.loss.backward(retain_graph=_lowerCamelCase )
self.assertIsNotNone(encoder_hidden_states.grad )
self.assertIsNotNone(pixel_decoder_hidden_states.grad )
self.assertIsNotNone(transformer_decoder_hidden_states.grad )
self.assertIsNotNone(attentions.grad )
_lowerCamelCase = 1e-4
def lowerCAmelCase_ ( ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : List[Any] = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
return image
@require_vision
@slow
class snake_case ( unittest.TestCase ):
@cached_property
def SCREAMING_SNAKE_CASE_ ( self :str ):
return (
MaskFormerImageProcessor.from_pretrained('''facebook/maskformer-swin-small-coco''' )
if is_vision_available()
else None
)
def SCREAMING_SNAKE_CASE_ ( self :List[Any] ):
__SCREAMING_SNAKE_CASE : Union[str, Any] = MaskFormerModel.from_pretrained('''facebook/maskformer-swin-small-coco''' ).to(_lowerCamelCase )
__SCREAMING_SNAKE_CASE : int = self.default_image_processor
__SCREAMING_SNAKE_CASE : Tuple = prepare_img()
__SCREAMING_SNAKE_CASE : List[Any] = image_processor(_lowerCamelCase , return_tensors='''pt''' ).to(_lowerCamelCase )
__SCREAMING_SNAKE_CASE : int = inputs['''pixel_values'''].shape
# check size is divisible by 32
self.assertTrue((inputs_shape[-1] % 3_2) == 0 and (inputs_shape[-2] % 3_2) == 0 )
# check size
self.assertEqual(_lowerCamelCase , (1, 3, 8_0_0, 1_0_8_8) )
with torch.no_grad():
__SCREAMING_SNAKE_CASE : Tuple = model(**_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Union[str, Any] = torch.tensor(
[[-0.0_4_8_2, 0.9_2_2_8, 0.4_9_5_1], [-0.2_5_4_7, 0.8_0_1_7, 0.8_5_2_7], [-0.0_0_6_9, 0.3_3_8_5, -0.0_0_8_9]] ).to(_lowerCamelCase )
self.assertTrue(
torch.allclose(
outputs.encoder_last_hidden_state[0, 0, :3, :3] , _lowerCamelCase , atol=_lowerCamelCase ) )
__SCREAMING_SNAKE_CASE : Any = torch.tensor(
[[-0.8_4_2_2, -0.8_4_3_4, -0.9_7_1_8], [-1.0_1_4_4, -0.5_5_6_5, -0.4_1_9_5], [-1.0_0_3_8, -0.4_4_8_4, -0.1_9_6_1]] ).to(_lowerCamelCase )
self.assertTrue(
torch.allclose(
outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3] , _lowerCamelCase , atol=_lowerCamelCase ) )
__SCREAMING_SNAKE_CASE : Optional[int] = torch.tensor(
[[0.2_8_5_2, -0.0_1_5_9, 0.9_7_3_5], [0.6_2_5_4, 0.1_8_5_8, 0.8_5_2_9], [-0.0_6_8_0, -0.4_1_1_6, 1.8_4_1_3]] ).to(_lowerCamelCase )
self.assertTrue(
torch.allclose(
outputs.transformer_decoder_last_hidden_state[0, :3, :3] , _lowerCamelCase , atol=_lowerCamelCase ) )
def SCREAMING_SNAKE_CASE_ ( self :Optional[Any] ):
__SCREAMING_SNAKE_CASE : Tuple = (
MaskFormerForInstanceSegmentation.from_pretrained('''facebook/maskformer-swin-small-coco''' )
.to(_lowerCamelCase )
.eval()
)
__SCREAMING_SNAKE_CASE : Union[str, Any] = self.default_image_processor
__SCREAMING_SNAKE_CASE : Tuple = prepare_img()
__SCREAMING_SNAKE_CASE : str = image_processor(_lowerCamelCase , return_tensors='''pt''' ).to(_lowerCamelCase )
__SCREAMING_SNAKE_CASE : List[str] = inputs['''pixel_values'''].shape
# check size is divisible by 32
self.assertTrue((inputs_shape[-1] % 3_2) == 0 and (inputs_shape[-2] % 3_2) == 0 )
# check size
self.assertEqual(_lowerCamelCase , (1, 3, 8_0_0, 1_0_8_8) )
with torch.no_grad():
__SCREAMING_SNAKE_CASE : str = model(**_lowerCamelCase )
# masks_queries_logits
__SCREAMING_SNAKE_CASE : Any = outputs.masks_queries_logits
self.assertEqual(
masks_queries_logits.shape , (1, model.config.decoder_config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4) , )
__SCREAMING_SNAKE_CASE : List[Any] = [
[-1.3_7_3_7_1_2_4, -1.7_7_2_4_9_3_7, -1.9_3_6_4_2_3_3],
[-1.5_9_7_7_2_8_1, -1.9_8_6_7_9_3_9, -2.1_5_2_3_6_9_5],
[-1.5_7_9_5_3_9_8, -1.9_2_6_9_8_3_2, -2.0_9_3_9_4_2],
]
__SCREAMING_SNAKE_CASE : Any = torch.tensor(_lowerCamelCase ).to(_lowerCamelCase )
self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3] , _lowerCamelCase , atol=_lowerCamelCase ) )
# class_queries_logits
__SCREAMING_SNAKE_CASE : List[Any] = outputs.class_queries_logits
self.assertEqual(
class_queries_logits.shape , (1, model.config.decoder_config.num_queries, model.config.num_labels + 1) )
__SCREAMING_SNAKE_CASE : List[str] = torch.tensor(
[
[1.65_12e00, -5.25_72e00, -3.35_19e00],
[3.61_69e-02, -5.90_25e00, -2.93_13e00],
[1.07_66e-04, -7.76_30e00, -5.12_63e00],
] ).to(_lowerCamelCase )
self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3] , _lowerCamelCase , atol=_lowerCamelCase ) )
def SCREAMING_SNAKE_CASE_ ( self :Any ):
__SCREAMING_SNAKE_CASE : Tuple = (
MaskFormerForInstanceSegmentation.from_pretrained('''facebook/maskformer-resnet101-coco-stuff''' )
.to(_lowerCamelCase )
.eval()
)
__SCREAMING_SNAKE_CASE : Tuple = self.default_image_processor
__SCREAMING_SNAKE_CASE : str = prepare_img()
__SCREAMING_SNAKE_CASE : List[Any] = image_processor(_lowerCamelCase , return_tensors='''pt''' ).to(_lowerCamelCase )
__SCREAMING_SNAKE_CASE : List[Any] = inputs['''pixel_values'''].shape
# check size is divisible by 32
self.assertTrue((inputs_shape[-1] % 3_2) == 0 and (inputs_shape[-2] % 3_2) == 0 )
# check size
self.assertEqual(_lowerCamelCase , (1, 3, 8_0_0, 1_0_8_8) )
with torch.no_grad():
__SCREAMING_SNAKE_CASE : List[Any] = model(**_lowerCamelCase )
# masks_queries_logits
__SCREAMING_SNAKE_CASE : Union[str, Any] = outputs.masks_queries_logits
self.assertEqual(
masks_queries_logits.shape , (1, model.config.decoder_config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4) , )
__SCREAMING_SNAKE_CASE : List[str] = [[-0.9_0_4_6, -2.6_3_6_6, -4.6_0_6_2], [-3.4_1_7_9, -5.7_8_9_0, -8.8_0_5_7], [-4.9_1_7_9, -7.6_5_6_0, -1_0.7_7_1_1]]
__SCREAMING_SNAKE_CASE : Any = torch.tensor(_lowerCamelCase ).to(_lowerCamelCase )
self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3] , _lowerCamelCase , atol=_lowerCamelCase ) )
# class_queries_logits
__SCREAMING_SNAKE_CASE : int = outputs.class_queries_logits
self.assertEqual(
class_queries_logits.shape , (1, model.config.decoder_config.num_queries, model.config.num_labels + 1) )
__SCREAMING_SNAKE_CASE : List[str] = torch.tensor(
[[4.7_1_8_8, -3.2_5_8_5, -2.8_8_5_7], [6.6_8_7_1, -2.9_1_8_1, -1.2_4_8_7], [7.2_4_4_9, -2.2_7_6_4, -2.1_8_7_4]] ).to(_lowerCamelCase )
self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3] , _lowerCamelCase , atol=_lowerCamelCase ) )
def SCREAMING_SNAKE_CASE_ ( self :int ):
__SCREAMING_SNAKE_CASE : int = (
MaskFormerForInstanceSegmentation.from_pretrained('''facebook/maskformer-swin-small-coco''' )
.to(_lowerCamelCase )
.eval()
)
__SCREAMING_SNAKE_CASE : Any = self.default_image_processor
__SCREAMING_SNAKE_CASE : int = image_processor(
[np.zeros((3, 8_0_0, 1_3_3_3) ), np.zeros((3, 8_0_0, 1_3_3_3) )] , segmentation_maps=[np.zeros((3_8_4, 3_8_4) ).astype(np.floataa ), np.zeros((3_8_4, 3_8_4) ).astype(np.floataa )] , return_tensors='''pt''' , )
__SCREAMING_SNAKE_CASE : Dict = inputs['''pixel_values'''].to(_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Dict = [el.to(_lowerCamelCase ) for el in inputs['''mask_labels''']]
__SCREAMING_SNAKE_CASE : str = [el.to(_lowerCamelCase ) for el in inputs['''class_labels''']]
with torch.no_grad():
__SCREAMING_SNAKE_CASE : int = model(**_lowerCamelCase )
self.assertTrue(outputs.loss is not None )
| 674 | 0 |
from typing import Dict, List
from nltk.translate import gleu_score
import datasets
from datasets import MetricInfo
lowercase_ : Tuple = '\\n@misc{wu2016googles,\n title={Google\'s Neural Machine Translation System: Bridging the Gap between Human and Machine Translation},\n author={Yonghui Wu and Mike Schuster and Zhifeng Chen and Quoc V. Le and Mohammad Norouzi and Wolfgang Macherey\n and Maxim Krikun and Yuan Cao and Qin Gao and Klaus Macherey and Jeff Klingner and Apurva Shah and Melvin\n Johnson and Xiaobing Liu and Łukasz Kaiser and Stephan Gouws and Yoshikiyo Kato and Taku Kudo and Hideto\n Kazawa and Keith Stevens and George Kurian and Nishant Patil and Wei Wang and Cliff Young and\n Jason Smith and Jason Riesa and Alex Rudnick and Oriol Vinyals and Greg Corrado and Macduff Hughes\n and Jeffrey Dean},\n year={2016},\n eprint={1609.08144},\n archivePrefix={arXiv},\n primaryClass={cs.CL}\n}\n'
lowercase_ : Optional[Any] = '\\nThe BLEU score has some undesirable properties when used for single\nsentences, as it was designed to be a corpus measure. We therefore\nuse a slightly different score for our RL experiments which we call\nthe \'GLEU score\'. For the GLEU score, we record all sub-sequences of\n1, 2, 3 or 4 tokens in output and target sequence (n-grams). We then\ncompute a recall, which is the ratio of the number of matching n-grams\nto the number of total n-grams in the target (ground truth) sequence,\nand a precision, which is the ratio of the number of matching n-grams\nto the number of total n-grams in the generated output sequence. Then\nGLEU score is simply the minimum of recall and precision. This GLEU\nscore\'s range is always between 0 (no matches) and 1 (all match) and\nit is symmetrical when switching output and target. According to\nour experiments, GLEU score correlates quite well with the BLEU\nmetric on a corpus level but does not have its drawbacks for our per\nsentence reward objective.\n'
lowercase_ : Optional[Any] = '\\nComputes corpus-level Google BLEU (GLEU) score of translated segments against one or more references.\nInstead of averaging the sentence level GLEU scores (i.e. macro-average precision), Wu et al. (2016) sum up the matching\ntokens and the max of hypothesis and reference tokens for each sentence, then compute using the aggregate values.\n\nArgs:\n predictions (list of str): list of translations to score.\n Each translation should be tokenized into a list of tokens.\n references (list of list of str): list of lists of references for each translation.\n Each reference should be tokenized into a list of tokens.\n min_len (int): The minimum order of n-gram this function should extract. Defaults to 1.\n max_len (int): The maximum order of n-gram this function should extract. Defaults to 4.\n\nReturns:\n \'google_bleu\': google_bleu score\n\nExamples:\n Example 1:\n >>> hyp1 = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'which\',\n ... \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'always\',\n ... \'disobeys\', \'the\', \'commands\', \'of\', \'the\', \'cat\']\n >>> ref1a = [\'It\', \'is\', \'the\', \'guiding\', \'principle\', \'which\',\n ... \'guarantees\', \'the\', \'rubber\', \'duck\', \'forces\', \'never\',\n ... \'being\', \'under\', \'the\', \'command\', \'of\', \'the\', \'cat\']\n\n >>> hyp2 = [\'he\', \'read\', \'the\', \'book\', \'because\', \'he\', \'was\',\n ... \'interested\', \'in\', \'world\', \'history\']\n >>> ref2a = [\'he\', \'was\', \'interested\', \'in\', \'world\', \'history\',\n ... \'because\', \'he\', \'read\', \'the\', \'book\']\n\n >>> list_of_references = [[ref1a], [ref2a]]\n >>> hypotheses = [hyp1, hyp2]\n >>> google_bleu = datasets.load_metric("google_bleu")\n >>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references)\n >>> print(round(results["google_bleu"], 2))\n 0.44\n\n Example 2:\n >>> hyp1 = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'which\',\n ... \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'always\',\n ... \'disobeys\', \'the\', \'commands\', \'of\', \'the\', \'cat\']\n >>> ref1a = [\'It\', \'is\', \'the\', \'guiding\', \'principle\', \'which\',\n ... \'guarantees\', \'the\', \'rubber\', \'duck\', \'forces\', \'never\',\n ... \'being\', \'under\', \'the\', \'command\', \'of\', \'the\', \'cat\']\n >>> ref1b = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'that\',\n ... \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'will\', \'never\',\n ... \'heed\', \'the\', \'cat\', \'commands\']\n >>> ref1c = [\'It\', \'is\', \'the\', \'practical\', \'guide\', \'for\', \'the\',\n ... \'rubber\', \'duck\', \'army\', \'never\', \'to\', \'heed\', \'the\', \'directions\',\n ... \'of\', \'the\', \'cat\']\n\n >>> hyp2 = [\'he\', \'read\', \'the\', \'book\', \'because\', \'he\', \'was\',\n ... \'interested\', \'in\', \'world\', \'history\']\n >>> ref2a = [\'he\', \'was\', \'interested\', \'in\', \'world\', \'history\',\n ... \'because\', \'he\', \'read\', \'the\', \'book\']\n\n >>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]\n >>> hypotheses = [hyp1, hyp2]\n >>> google_bleu = datasets.load_metric("google_bleu")\n >>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references)\n >>> print(round(results["google_bleu"], 2))\n 0.61\n\n Example 3:\n >>> hyp1 = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'which\',\n ... \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'always\',\n ... \'disobeys\', \'the\', \'commands\', \'of\', \'the\', \'cat\']\n >>> ref1a = [\'It\', \'is\', \'the\', \'guiding\', \'principle\', \'which\',\n ... \'guarantees\', \'the\', \'rubber\', \'duck\', \'forces\', \'never\',\n ... \'being\', \'under\', \'the\', \'command\', \'of\', \'the\', \'cat\']\n >>> ref1b = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'that\',\n ... \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'will\', \'never\',\n ... \'heed\', \'the\', \'cat\', \'commands\']\n >>> ref1c = [\'It\', \'is\', \'the\', \'practical\', \'guide\', \'for\', \'the\',\n ... \'rubber\', \'duck\', \'army\', \'never\', \'to\', \'heed\', \'the\', \'directions\',\n ... \'of\', \'the\', \'cat\']\n\n >>> hyp2 = [\'he\', \'read\', \'the\', \'book\', \'because\', \'he\', \'was\',\n ... \'interested\', \'in\', \'world\', \'history\']\n >>> ref2a = [\'he\', \'was\', \'interested\', \'in\', \'world\', \'history\',\n ... \'because\', \'he\', \'read\', \'the\', \'book\']\n\n >>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]\n >>> hypotheses = [hyp1, hyp2]\n >>> google_bleu = datasets.load_metric("google_bleu")\n >>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references, min_len=2)\n >>> print(round(results["google_bleu"], 2))\n 0.53\n\n Example 4:\n >>> hyp1 = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'which\',\n ... \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'always\',\n ... \'disobeys\', \'the\', \'commands\', \'of\', \'the\', \'cat\']\n >>> ref1a = [\'It\', \'is\', \'the\', \'guiding\', \'principle\', \'which\',\n ... \'guarantees\', \'the\', \'rubber\', \'duck\', \'forces\', \'never\',\n ... \'being\', \'under\', \'the\', \'command\', \'of\', \'the\', \'cat\']\n >>> ref1b = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'that\',\n ... \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'will\', \'never\',\n ... \'heed\', \'the\', \'cat\', \'commands\']\n >>> ref1c = [\'It\', \'is\', \'the\', \'practical\', \'guide\', \'for\', \'the\',\n ... \'rubber\', \'duck\', \'army\', \'never\', \'to\', \'heed\', \'the\', \'directions\',\n ... \'of\', \'the\', \'cat\']\n\n >>> hyp2 = [\'he\', \'read\', \'the\', \'book\', \'because\', \'he\', \'was\',\n ... \'interested\', \'in\', \'world\', \'history\']\n >>> ref2a = [\'he\', \'was\', \'interested\', \'in\', \'world\', \'history\',\n ... \'because\', \'he\', \'read\', \'the\', \'book\']\n\n >>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]\n >>> hypotheses = [hyp1, hyp2]\n >>> google_bleu = datasets.load_metric("google_bleu")\n >>> results = google_bleu.compute(predictions=hypotheses,references=list_of_references, min_len=2, max_len=6)\n >>> print(round(results["google_bleu"], 2))\n 0.4\n'
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class _lowerCamelCase ( datasets.Metric ):
def UpperCamelCase_ ( self ) -> int:
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
'''predictions''': datasets.Sequence(datasets.Value('''string''' , id='''token''' ) , id='''sequence''' ),
'''references''': datasets.Sequence(
datasets.Sequence(datasets.Value('''string''' , id='''token''' ) , id='''sequence''' ) , id='''references''' ),
} ) , )
def UpperCamelCase_ ( self , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase = 1 , lowerCAmelCase = 4 , ) -> int:
return {
"google_bleu": gleu_score.corpus_gleu(
list_of_references=_lowerCamelCase , hypotheses=_lowerCamelCase , min_len=_lowerCamelCase , max_len=_lowerCamelCase )
}
| 64 |
"""simple docstring"""
import contextlib
import importlib
import io
import unittest
import transformers
# Try to import everything from transformers to ensure every object can be loaded.
from transformers import * # noqa F406
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, require_flax, require_tf, require_torch
from transformers.utils import ContextManagers, find_labels, is_flax_available, is_tf_available, is_torch_available
if is_torch_available():
from transformers import BertForPreTraining, BertForQuestionAnswering, BertForSequenceClassification
if is_tf_available():
from transformers import TFBertForPreTraining, TFBertForQuestionAnswering, TFBertForSequenceClassification
if is_flax_available():
from transformers import FlaxBertForPreTraining, FlaxBertForQuestionAnswering, FlaxBertForSequenceClassification
_lowerCamelCase = DUMMY_UNKNOWN_IDENTIFIER
# An actual model hosted on huggingface.co
_lowerCamelCase = '''main'''
# Default branch name
_lowerCamelCase = '''f2c752cfc5c0ab6f4bdec59acea69eefbee381c2'''
# One particular commit (not the top of `main`)
_lowerCamelCase = '''aaaaaaa'''
# This commit does not exist, so we should 404.
_lowerCamelCase = '''d9e9f15bc825e4b2c9249e9578f884bbcb5e3684'''
# Sha-1 of config.json on the top of `main`, for checking purposes
_lowerCamelCase = '''4b243c475af8d0a7754e87d7d096c92e5199ec2fe168a2ee7998e3b8e9bcb1d3'''
@contextlib.contextmanager
def lowerCAmelCase_ ( ):
'''simple docstring'''
print('''Welcome!''' )
yield
print('''Bye!''' )
@contextlib.contextmanager
def lowerCAmelCase_ ( ):
'''simple docstring'''
print('''Bonjour!''' )
yield
print('''Au revoir!''' )
class snake_case ( unittest.TestCase ):
def SCREAMING_SNAKE_CASE_ ( self :Union[str, Any] ):
# If the spec is missing, importlib would not be able to import the module dynamically.
assert transformers.__spec__ is not None
assert importlib.util.find_spec('''transformers''' ) is not None
class snake_case ( unittest.TestCase ):
@unittest.mock.patch('''sys.stdout''' , new_callable=io.StringIO )
def SCREAMING_SNAKE_CASE_ ( self :Dict , _lowerCamelCase :Optional[int] ):
with ContextManagers([] ):
print('''Transformers are awesome!''' )
# The print statement adds a new line at the end of the output
self.assertEqual(mock_stdout.getvalue() , '''Transformers are awesome!\n''' )
@unittest.mock.patch('''sys.stdout''' , new_callable=io.StringIO )
def SCREAMING_SNAKE_CASE_ ( self :str , _lowerCamelCase :Optional[int] ):
with ContextManagers([context_en()] ):
print('''Transformers are awesome!''' )
# The output should be wrapped with an English welcome and goodbye
self.assertEqual(mock_stdout.getvalue() , '''Welcome!\nTransformers are awesome!\nBye!\n''' )
@unittest.mock.patch('''sys.stdout''' , new_callable=io.StringIO )
def SCREAMING_SNAKE_CASE_ ( self :Dict , _lowerCamelCase :List[str] ):
with ContextManagers([context_fr(), context_en()] ):
print('''Transformers are awesome!''' )
# The output should be wrapped with an English and French welcome and goodbye
self.assertEqual(mock_stdout.getvalue() , '''Bonjour!\nWelcome!\nTransformers are awesome!\nBye!\nAu revoir!\n''' )
@require_torch
def SCREAMING_SNAKE_CASE_ ( self :List[str] ):
self.assertEqual(find_labels(_lowerCamelCase ) , ['''labels'''] )
self.assertEqual(find_labels(_lowerCamelCase ) , ['''labels''', '''next_sentence_label'''] )
self.assertEqual(find_labels(_lowerCamelCase ) , ['''start_positions''', '''end_positions'''] )
class snake_case ( __UpperCAmelCase ):
pass
self.assertEqual(find_labels(_lowerCamelCase ) , ['''labels'''] )
@require_tf
def SCREAMING_SNAKE_CASE_ ( self :int ):
self.assertEqual(find_labels(_lowerCamelCase ) , ['''labels'''] )
self.assertEqual(find_labels(_lowerCamelCase ) , ['''labels''', '''next_sentence_label'''] )
self.assertEqual(find_labels(_lowerCamelCase ) , ['''start_positions''', '''end_positions'''] )
class snake_case ( __UpperCAmelCase ):
pass
self.assertEqual(find_labels(_lowerCamelCase ) , ['''labels'''] )
@require_flax
def SCREAMING_SNAKE_CASE_ ( self :Dict ):
# Flax models don't have labels
self.assertEqual(find_labels(_lowerCamelCase ) , [] )
self.assertEqual(find_labels(_lowerCamelCase ) , [] )
self.assertEqual(find_labels(_lowerCamelCase ) , [] )
class snake_case ( __UpperCAmelCase ):
pass
self.assertEqual(find_labels(_lowerCamelCase ) , [] )
| 674 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a : Tuple = logging.get_logger(__name__)
a : List[str] = {
'''abeja/gpt-neox-japanese-2.7b''': '''https://huggingface.co/abeja/gpt-neox-japanese-2.7b/resolve/main/config.json''',
}
class lowerCamelCase_ ( __UpperCAmelCase ):
'''simple docstring'''
__UpperCAmelCase = "gpt_neox_japanese"
def __init__( self , snake_case_=3_2_0_0_0 , snake_case_=2_5_6_0 , snake_case_=3_2 , snake_case_=3_2 , snake_case_=4 , snake_case_="gelu" , snake_case_=1.0_0 , snake_case_=1_0_0_0_0 , snake_case_=2_0_4_8 , snake_case_=0.0_2 , snake_case_=1e-5 , snake_case_=True , snake_case_=3_1_9_9_6 , snake_case_=3_1_9_9_9 , snake_case_=0.1 , snake_case_=0.0 , **snake_case_ , ) -> int:
'''simple docstring'''
super().__init__(bos_token_id=_lowerCamelCase , eos_token_id=_lowerCamelCase , **_lowerCamelCase )
__lowercase = vocab_size
__lowercase = max_position_embeddings
__lowercase = hidden_size
__lowercase = num_hidden_layers
__lowercase = num_attention_heads
__lowercase = intermediate_multiple_size
__lowercase = hidden_act
__lowercase = rotary_pct
__lowercase = rotary_emb_base
__lowercase = initializer_range
__lowercase = layer_norm_eps
__lowercase = use_cache
__lowercase = attention_dropout
__lowercase = hidden_dropout
| 639 |
"""simple docstring"""
import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import YolosImageProcessor
class snake_case ( unittest.TestCase ):
def __init__( self :List[Any] , _lowerCamelCase :Dict , _lowerCamelCase :Tuple=7 , _lowerCamelCase :Dict=3 , _lowerCamelCase :Optional[Any]=3_0 , _lowerCamelCase :List[str]=4_0_0 , _lowerCamelCase :Union[str, Any]=True , _lowerCamelCase :Union[str, Any]=None , _lowerCamelCase :List[Any]=True , _lowerCamelCase :Any=[0.5, 0.5, 0.5] , _lowerCamelCase :Dict=[0.5, 0.5, 0.5] , _lowerCamelCase :Dict=True , _lowerCamelCase :str=1 / 2_5_5 , _lowerCamelCase :Union[str, Any]=True , ):
# by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
__SCREAMING_SNAKE_CASE : Optional[Any] = size if size is not None else {'''shortest_edge''': 1_8, '''longest_edge''': 1_3_3_3}
__SCREAMING_SNAKE_CASE : List[str] = parent
__SCREAMING_SNAKE_CASE : Dict = batch_size
__SCREAMING_SNAKE_CASE : str = num_channels
__SCREAMING_SNAKE_CASE : Union[str, Any] = min_resolution
__SCREAMING_SNAKE_CASE : Union[str, Any] = max_resolution
__SCREAMING_SNAKE_CASE : Tuple = do_resize
__SCREAMING_SNAKE_CASE : Union[str, Any] = size
__SCREAMING_SNAKE_CASE : int = do_normalize
__SCREAMING_SNAKE_CASE : List[Any] = image_mean
__SCREAMING_SNAKE_CASE : Tuple = image_std
__SCREAMING_SNAKE_CASE : Dict = do_rescale
__SCREAMING_SNAKE_CASE : Optional[int] = rescale_factor
__SCREAMING_SNAKE_CASE : List[Any] = do_pad
def SCREAMING_SNAKE_CASE_ ( self :Optional[Any] ):
return {
"do_resize": self.do_resize,
"size": self.size,
"do_normalize": self.do_normalize,
"image_mean": self.image_mean,
"image_std": self.image_std,
"do_rescale": self.do_rescale,
"rescale_factor": self.rescale_factor,
"do_pad": self.do_pad,
}
def SCREAMING_SNAKE_CASE_ ( self :Tuple , _lowerCamelCase :Dict , _lowerCamelCase :List[Any]=False ):
if not batched:
__SCREAMING_SNAKE_CASE : str = image_inputs[0]
if isinstance(_lowerCamelCase , Image.Image ):
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Optional[int] = image.size
else:
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Dict = image.shape[1], image.shape[2]
if w < h:
__SCREAMING_SNAKE_CASE : str = int(self.size['''shortest_edge'''] * h / w )
__SCREAMING_SNAKE_CASE : int = self.size['''shortest_edge''']
elif w > h:
__SCREAMING_SNAKE_CASE : Optional[Any] = self.size['''shortest_edge''']
__SCREAMING_SNAKE_CASE : int = int(self.size['''shortest_edge'''] * w / h )
else:
__SCREAMING_SNAKE_CASE : List[str] = self.size['''shortest_edge''']
__SCREAMING_SNAKE_CASE : List[str] = self.size['''shortest_edge''']
else:
__SCREAMING_SNAKE_CASE : Optional[Any] = []
for image in image_inputs:
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Optional[Any] = self.get_expected_values([image] )
expected_values.append((expected_height, expected_width) )
__SCREAMING_SNAKE_CASE : Optional[int] = max(_lowerCamelCase , key=lambda _lowerCamelCase : item[0] )[0]
__SCREAMING_SNAKE_CASE : int = max(_lowerCamelCase , key=lambda _lowerCamelCase : item[1] )[1]
return expected_height, expected_width
@require_torch
@require_vision
class snake_case ( __UpperCAmelCase , unittest.TestCase ):
lowerCamelCase__ = YolosImageProcessor if is_vision_available() else None
def SCREAMING_SNAKE_CASE_ ( self :Optional[int] ):
__SCREAMING_SNAKE_CASE : List[Any] = YolosImageProcessingTester(self )
@property
def SCREAMING_SNAKE_CASE_ ( self :Optional[int] ):
return self.image_processor_tester.prepare_image_processor_dict()
def SCREAMING_SNAKE_CASE_ ( self :Tuple ):
__SCREAMING_SNAKE_CASE : Any = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(_lowerCamelCase , '''image_mean''' ) )
self.assertTrue(hasattr(_lowerCamelCase , '''image_std''' ) )
self.assertTrue(hasattr(_lowerCamelCase , '''do_normalize''' ) )
self.assertTrue(hasattr(_lowerCamelCase , '''do_resize''' ) )
self.assertTrue(hasattr(_lowerCamelCase , '''size''' ) )
def SCREAMING_SNAKE_CASE_ ( self :Tuple ):
__SCREAMING_SNAKE_CASE : Union[str, Any] = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {'''shortest_edge''': 1_8, '''longest_edge''': 1_3_3_3} )
self.assertEqual(image_processor.do_pad , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : Tuple = self.image_processing_class.from_dict(
self.image_processor_dict , size=4_2 , max_size=8_4 , pad_and_return_pixel_mask=_lowerCamelCase )
self.assertEqual(image_processor.size , {'''shortest_edge''': 4_2, '''longest_edge''': 8_4} )
self.assertEqual(image_processor.do_pad , _lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :List[str] ):
pass
def SCREAMING_SNAKE_CASE_ ( self :int ):
# Initialize image_processing
__SCREAMING_SNAKE_CASE : Optional[int] = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
__SCREAMING_SNAKE_CASE : List[str] = prepare_image_inputs(self.image_processor_tester , equal_resolution=_lowerCamelCase )
for image in image_inputs:
self.assertIsInstance(_lowerCamelCase , Image.Image )
# Test not batched input
__SCREAMING_SNAKE_CASE : str = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Dict = self.image_processor_tester.get_expected_values(_lowerCamelCase )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Tuple = self.image_processor_tester.get_expected_values(_lowerCamelCase , batched=_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Union[str, Any] = image_processing(_lowerCamelCase , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
def SCREAMING_SNAKE_CASE_ ( self :Optional[int] ):
# Initialize image_processing
__SCREAMING_SNAKE_CASE : Optional[Any] = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
__SCREAMING_SNAKE_CASE : Optional[int] = prepare_image_inputs(self.image_processor_tester , equal_resolution=_lowerCamelCase , numpify=_lowerCamelCase )
for image in image_inputs:
self.assertIsInstance(_lowerCamelCase , np.ndarray )
# Test not batched input
__SCREAMING_SNAKE_CASE : Dict = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : List[str] = self.image_processor_tester.get_expected_values(_lowerCamelCase )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
__SCREAMING_SNAKE_CASE : List[Any] = image_processing(_lowerCamelCase , return_tensors='''pt''' ).pixel_values
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Any = self.image_processor_tester.get_expected_values(_lowerCamelCase , batched=_lowerCamelCase )
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
def SCREAMING_SNAKE_CASE_ ( self :Any ):
# Initialize image_processing
__SCREAMING_SNAKE_CASE : str = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
__SCREAMING_SNAKE_CASE : Any = prepare_image_inputs(self.image_processor_tester , equal_resolution=_lowerCamelCase , torchify=_lowerCamelCase )
for image in image_inputs:
self.assertIsInstance(_lowerCamelCase , torch.Tensor )
# Test not batched input
__SCREAMING_SNAKE_CASE : List[str] = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Optional[Any] = self.image_processor_tester.get_expected_values(_lowerCamelCase )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
__SCREAMING_SNAKE_CASE : Optional[int] = image_processing(_lowerCamelCase , return_tensors='''pt''' ).pixel_values
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : List[Any] = self.image_processor_tester.get_expected_values(_lowerCamelCase , batched=_lowerCamelCase )
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
def SCREAMING_SNAKE_CASE_ ( self :List[str] ):
# Initialize image_processings
__SCREAMING_SNAKE_CASE : Dict = self.image_processing_class(**self.image_processor_dict )
__SCREAMING_SNAKE_CASE : Tuple = self.image_processing_class(do_resize=_lowerCamelCase , do_normalize=_lowerCamelCase , do_rescale=_lowerCamelCase )
# create random PyTorch tensors
__SCREAMING_SNAKE_CASE : int = prepare_image_inputs(self.image_processor_tester , equal_resolution=_lowerCamelCase , torchify=_lowerCamelCase )
for image in image_inputs:
self.assertIsInstance(_lowerCamelCase , torch.Tensor )
# Test whether the method "pad" and calling the image processor return the same tensors
__SCREAMING_SNAKE_CASE : Union[str, Any] = image_processing_a.pad(_lowerCamelCase , return_tensors='''pt''' )
__SCREAMING_SNAKE_CASE : Union[str, Any] = image_processing_a(_lowerCamelCase , return_tensors='''pt''' )
self.assertTrue(
torch.allclose(encoded_images_with_method['''pixel_values'''] , encoded_images['''pixel_values'''] , atol=1e-4 ) )
@slow
def SCREAMING_SNAKE_CASE_ ( self :int ):
# prepare image and target
__SCREAMING_SNAKE_CASE : Tuple = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
with open('''./tests/fixtures/tests_samples/COCO/coco_annotations.txt''' , '''r''' ) as f:
__SCREAMING_SNAKE_CASE : Tuple = json.loads(f.read() )
__SCREAMING_SNAKE_CASE : Union[str, Any] = {'''image_id''': 3_9_7_6_9, '''annotations''': target}
# encode them
__SCREAMING_SNAKE_CASE : List[Any] = YolosImageProcessor.from_pretrained('''hustvl/yolos-small''' )
__SCREAMING_SNAKE_CASE : Optional[Any] = image_processing(images=_lowerCamelCase , annotations=_lowerCamelCase , return_tensors='''pt''' )
# verify pixel values
__SCREAMING_SNAKE_CASE : Optional[int] = torch.Size([1, 3, 8_0_0, 1_0_6_6] )
self.assertEqual(encoding['''pixel_values'''].shape , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : List[str] = torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1] )
self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3] , _lowerCamelCase , atol=1e-4 ) )
# verify area
__SCREAMING_SNAKE_CASE : Union[str, Any] = torch.tensor([5_8_8_7.9_6_0_0, 1_1_2_5_0.2_0_6_1, 4_8_9_3_5_3.8_4_3_8, 8_3_7_1_2_2.7_5_0_0, 1_4_7_9_6_7.5_1_5_6, 1_6_5_7_3_2.3_4_3_8] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area'''] , _lowerCamelCase ) )
# verify boxes
__SCREAMING_SNAKE_CASE : List[str] = torch.Size([6, 4] )
self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : Any = torch.tensor([0.5_5_0_3, 0.2_7_6_5, 0.0_6_0_4, 0.2_2_1_5] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0] , _lowerCamelCase , atol=1e-3 ) )
# verify image_id
__SCREAMING_SNAKE_CASE : Optional[Any] = torch.tensor([3_9_7_6_9] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id'''] , _lowerCamelCase ) )
# verify is_crowd
__SCREAMING_SNAKE_CASE : str = torch.tensor([0, 0, 0, 0, 0, 0] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd'''] , _lowerCamelCase ) )
# verify class_labels
__SCREAMING_SNAKE_CASE : Dict = torch.tensor([7_5, 7_5, 6_3, 6_5, 1_7, 1_7] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels'''] , _lowerCamelCase ) )
# verify orig_size
__SCREAMING_SNAKE_CASE : Union[str, Any] = torch.tensor([4_8_0, 6_4_0] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size'''] , _lowerCamelCase ) )
# verify size
__SCREAMING_SNAKE_CASE : List[str] = torch.tensor([8_0_0, 1_0_6_6] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size'''] , _lowerCamelCase ) )
@slow
def SCREAMING_SNAKE_CASE_ ( self :Optional[int] ):
# prepare image, target and masks_path
__SCREAMING_SNAKE_CASE : List[str] = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
with open('''./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt''' , '''r''' ) as f:
__SCREAMING_SNAKE_CASE : Union[str, Any] = json.loads(f.read() )
__SCREAMING_SNAKE_CASE : Dict = {'''file_name''': '''000000039769.png''', '''image_id''': 3_9_7_6_9, '''segments_info''': target}
__SCREAMING_SNAKE_CASE : Optional[int] = pathlib.Path('''./tests/fixtures/tests_samples/COCO/coco_panoptic''' )
# encode them
__SCREAMING_SNAKE_CASE : Any = YolosImageProcessor(format='''coco_panoptic''' )
__SCREAMING_SNAKE_CASE : Dict = image_processing(images=_lowerCamelCase , annotations=_lowerCamelCase , masks_path=_lowerCamelCase , return_tensors='''pt''' )
# verify pixel values
__SCREAMING_SNAKE_CASE : str = torch.Size([1, 3, 8_0_0, 1_0_6_6] )
self.assertEqual(encoding['''pixel_values'''].shape , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : str = torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1] )
self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3] , _lowerCamelCase , atol=1e-4 ) )
# verify area
__SCREAMING_SNAKE_CASE : Any = torch.tensor([1_4_7_9_7_9.6_8_7_5, 1_6_5_5_2_7.0_4_6_9, 4_8_4_6_3_8.5_9_3_8, 1_1_2_9_2.9_3_7_5, 5_8_7_9.6_5_6_2, 7_6_3_4.1_1_4_7] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area'''] , _lowerCamelCase ) )
# verify boxes
__SCREAMING_SNAKE_CASE : List[str] = torch.Size([6, 4] )
self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : int = torch.tensor([0.2_6_2_5, 0.5_4_3_7, 0.4_6_8_8, 0.8_6_2_5] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0] , _lowerCamelCase , atol=1e-3 ) )
# verify image_id
__SCREAMING_SNAKE_CASE : Dict = torch.tensor([3_9_7_6_9] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id'''] , _lowerCamelCase ) )
# verify is_crowd
__SCREAMING_SNAKE_CASE : List[Any] = torch.tensor([0, 0, 0, 0, 0, 0] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd'''] , _lowerCamelCase ) )
# verify class_labels
__SCREAMING_SNAKE_CASE : Optional[Any] = torch.tensor([1_7, 1_7, 6_3, 7_5, 7_5, 9_3] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels'''] , _lowerCamelCase ) )
# verify masks
__SCREAMING_SNAKE_CASE : Optional[Any] = 8_2_2_8_7_3
self.assertEqual(encoding['''labels'''][0]['''masks'''].sum().item() , _lowerCamelCase )
# verify orig_size
__SCREAMING_SNAKE_CASE : List[str] = torch.tensor([4_8_0, 6_4_0] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size'''] , _lowerCamelCase ) )
# verify size
__SCREAMING_SNAKE_CASE : Any = torch.tensor([8_0_0, 1_0_6_6] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size'''] , _lowerCamelCase ) )
| 674 | 0 |
from __future__ import annotations
from collections import namedtuple
def lowerCAmelCase_ ( lowercase: float , lowercase: float , lowercase: float ) -> Union[str, Any]:
'''simple docstring'''
_UpperCamelCase: List[Any] = namedtuple('''result''' , '''name value''' )
if (voltage, current, power).count(0 ) != 1:
raise ValueError('''Only one argument must be 0''' )
elif power < 0:
raise ValueError(
'''Power cannot be negative in any electrical/electronics system''' )
elif voltage == 0:
return result('''voltage''' , power / current )
elif current == 0:
return result('''current''' , power / voltage )
elif power == 0:
return result('''power''' , float(round(abs(voltage * current ) , 2 ) ) )
else:
raise ValueError('''Exactly one argument must be 0''' )
if __name__ == "__main__":
import doctest
doctest.testmod() | 271 |
"""simple docstring"""
from __future__ import annotations
def lowerCAmelCase_ ( lowercase_ : int ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : Optional[int] = str(lowercase_ )
return len(lowercase_ ) == 9 and set(lowercase_ ) == set('''123456789''' )
def lowerCAmelCase_ ( ):
'''simple docstring'''
for base_num in range(9999 , 4999 , -1 ):
__SCREAMING_SNAKE_CASE : List[str] = 10_0002 * base_num
if is_9_pandigital(lowercase_ ):
return candidate
for base_num in range(333 , 99 , -1 ):
__SCREAMING_SNAKE_CASE : List[Any] = 100_2003 * base_num
if is_9_pandigital(lowercase_ ):
return candidate
return None
if __name__ == "__main__":
print(f'{solution() = }')
| 674 | 0 |
def __lowerCamelCase ( _lowercase , _lowercase , _lowercase , _lowercase ) -> List[Any]:
if graph[path[curr_ind - 1]][next_ver] == 0:
return False
# 2. Validate that next vertex is not already in path
return not any(vertex == next_ver for vertex in path )
def __lowerCamelCase ( _lowercase , _lowercase , _lowercase ) -> str:
if curr_ind == len(lowercase_ ):
# return whether path exists between current and starting vertices
return graph[path[curr_ind - 1]][path[0]] == 1
# Recursive Step
for next_ver in range(0 , len(lowercase_ ) ):
if valid_connection(lowercase_ , lowercase_ , lowercase_ , lowercase_ ):
# Insert current vertex into path as next transition
UpperCamelCase = next_ver
# Validate created path
if util_hamilton_cycle(lowercase_ , lowercase_ , curr_ind + 1 ):
return True
# Backtrack
UpperCamelCase = -1
return False
def __lowerCamelCase ( _lowercase , _lowercase = 0 ) -> int:
UpperCamelCase = [-1] * (len(lowercase_ ) + 1)
# initialize start and end of path with starting index
UpperCamelCase = start_index
# evaluate and if we find answer return path either return empty array
return path if util_hamilton_cycle(lowercase_ , lowercase_ , 1 ) else []
| 282 |
"""simple docstring"""
import json
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
from transformers import (
CONFIG_MAPPING,
IMAGE_PROCESSOR_MAPPING,
AutoConfig,
AutoImageProcessor,
CLIPConfig,
CLIPImageProcessor,
)
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER
sys.path.append(str(Path(__file__).parent.parent.parent.parent / '''utils'''))
from test_module.custom_configuration import CustomConfig # noqa E402
from test_module.custom_image_processing import CustomImageProcessor # noqa E402
class snake_case ( unittest.TestCase ):
def SCREAMING_SNAKE_CASE_ ( self :int ):
__SCREAMING_SNAKE_CASE : Tuple = 0
def SCREAMING_SNAKE_CASE_ ( self :Any ):
__SCREAMING_SNAKE_CASE : Optional[Any] = AutoImageProcessor.from_pretrained('''openai/clip-vit-base-patch32''' )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :int ):
with tempfile.TemporaryDirectory() as tmpdirname:
__SCREAMING_SNAKE_CASE : Optional[Any] = Path(_lowerCamelCase ) / '''preprocessor_config.json'''
__SCREAMING_SNAKE_CASE : Dict = Path(_lowerCamelCase ) / '''config.json'''
json.dump(
{'''image_processor_type''': '''CLIPImageProcessor''', '''processor_class''': '''CLIPProcessor'''} , open(_lowerCamelCase , '''w''' ) , )
json.dump({'''model_type''': '''clip'''} , open(_lowerCamelCase , '''w''' ) )
__SCREAMING_SNAKE_CASE : Dict = AutoImageProcessor.from_pretrained(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :Optional[int] ):
# Ensure we can load the image processor from the feature extractor config
with tempfile.TemporaryDirectory() as tmpdirname:
__SCREAMING_SNAKE_CASE : Tuple = Path(_lowerCamelCase ) / '''preprocessor_config.json'''
__SCREAMING_SNAKE_CASE : Union[str, Any] = Path(_lowerCamelCase ) / '''config.json'''
json.dump(
{'''feature_extractor_type''': '''CLIPFeatureExtractor''', '''processor_class''': '''CLIPProcessor'''} , open(_lowerCamelCase , '''w''' ) , )
json.dump({'''model_type''': '''clip'''} , open(_lowerCamelCase , '''w''' ) )
__SCREAMING_SNAKE_CASE : int = AutoImageProcessor.from_pretrained(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :int ):
with tempfile.TemporaryDirectory() as tmpdirname:
__SCREAMING_SNAKE_CASE : str = CLIPConfig()
# Create a dummy config file with image_proceesor_type
__SCREAMING_SNAKE_CASE : Tuple = Path(_lowerCamelCase ) / '''preprocessor_config.json'''
__SCREAMING_SNAKE_CASE : Any = Path(_lowerCamelCase ) / '''config.json'''
json.dump(
{'''image_processor_type''': '''CLIPImageProcessor''', '''processor_class''': '''CLIPProcessor'''} , open(_lowerCamelCase , '''w''' ) , )
json.dump({'''model_type''': '''clip'''} , open(_lowerCamelCase , '''w''' ) )
# remove image_processor_type to make sure config.json alone is enough to load image processor locally
__SCREAMING_SNAKE_CASE : int = AutoImageProcessor.from_pretrained(_lowerCamelCase ).to_dict()
config_dict.pop('''image_processor_type''' )
__SCREAMING_SNAKE_CASE : Union[str, Any] = CLIPImageProcessor(**_lowerCamelCase )
# save in new folder
model_config.save_pretrained(_lowerCamelCase )
config.save_pretrained(_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Tuple = AutoImageProcessor.from_pretrained(_lowerCamelCase )
# make sure private variable is not incorrectly saved
__SCREAMING_SNAKE_CASE : Tuple = json.loads(config.to_json_string() )
self.assertTrue('''_processor_class''' not in dict_as_saved )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :Tuple ):
with tempfile.TemporaryDirectory() as tmpdirname:
__SCREAMING_SNAKE_CASE : Dict = Path(_lowerCamelCase ) / '''preprocessor_config.json'''
json.dump(
{'''image_processor_type''': '''CLIPImageProcessor''', '''processor_class''': '''CLIPProcessor'''} , open(_lowerCamelCase , '''w''' ) , )
__SCREAMING_SNAKE_CASE : Optional[Any] = AutoImageProcessor.from_pretrained(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :Optional[int] ):
with self.assertRaisesRegex(
_lowerCamelCase , '''clip-base is not a local folder and is not a valid model identifier''' ):
__SCREAMING_SNAKE_CASE : Tuple = AutoImageProcessor.from_pretrained('''clip-base''' )
def SCREAMING_SNAKE_CASE_ ( self :Union[str, Any] ):
with self.assertRaisesRegex(
_lowerCamelCase , r'''aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)''' ):
__SCREAMING_SNAKE_CASE : Optional[int] = AutoImageProcessor.from_pretrained(_lowerCamelCase , revision='''aaaaaa''' )
def SCREAMING_SNAKE_CASE_ ( self :List[str] ):
with self.assertRaisesRegex(
_lowerCamelCase , '''hf-internal-testing/config-no-model does not appear to have a file named preprocessor_config.json.''' , ):
__SCREAMING_SNAKE_CASE : Optional[int] = AutoImageProcessor.from_pretrained('''hf-internal-testing/config-no-model''' )
def SCREAMING_SNAKE_CASE_ ( self :Tuple ):
# If remote code is not set, we will time out when asking whether to load the model.
with self.assertRaises(_lowerCamelCase ):
__SCREAMING_SNAKE_CASE : Union[str, Any] = AutoImageProcessor.from_pretrained('''hf-internal-testing/test_dynamic_image_processor''' )
# If remote code is disabled, we can't load this config.
with self.assertRaises(_lowerCamelCase ):
__SCREAMING_SNAKE_CASE : Optional[Any] = AutoImageProcessor.from_pretrained(
'''hf-internal-testing/test_dynamic_image_processor''' , trust_remote_code=_lowerCamelCase )
__SCREAMING_SNAKE_CASE : int = AutoImageProcessor.from_pretrained(
'''hf-internal-testing/test_dynamic_image_processor''' , trust_remote_code=_lowerCamelCase )
self.assertEqual(image_processor.__class__.__name__ , '''NewImageProcessor''' )
# Test image processor can be reloaded.
with tempfile.TemporaryDirectory() as tmp_dir:
image_processor.save_pretrained(_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Tuple = AutoImageProcessor.from_pretrained(_lowerCamelCase , trust_remote_code=_lowerCamelCase )
self.assertEqual(reloaded_image_processor.__class__.__name__ , '''NewImageProcessor''' )
def SCREAMING_SNAKE_CASE_ ( self :Tuple ):
try:
AutoConfig.register('''custom''' , _lowerCamelCase )
AutoImageProcessor.register(_lowerCamelCase , _lowerCamelCase )
# Trying to register something existing in the Transformers library will raise an error
with self.assertRaises(_lowerCamelCase ):
AutoImageProcessor.register(_lowerCamelCase , _lowerCamelCase )
with tempfile.TemporaryDirectory() as tmpdirname:
__SCREAMING_SNAKE_CASE : Dict = Path(_lowerCamelCase ) / '''preprocessor_config.json'''
__SCREAMING_SNAKE_CASE : Dict = Path(_lowerCamelCase ) / '''config.json'''
json.dump(
{'''feature_extractor_type''': '''CLIPFeatureExtractor''', '''processor_class''': '''CLIPProcessor'''} , open(_lowerCamelCase , '''w''' ) , )
json.dump({'''model_type''': '''clip'''} , open(_lowerCamelCase , '''w''' ) )
__SCREAMING_SNAKE_CASE : Optional[int] = CustomImageProcessor.from_pretrained(_lowerCamelCase )
# Now that the config is registered, it can be used as any other config with the auto-API
with tempfile.TemporaryDirectory() as tmp_dir:
image_processor.save_pretrained(_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Tuple = AutoImageProcessor.from_pretrained(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
finally:
if "custom" in CONFIG_MAPPING._extra_content:
del CONFIG_MAPPING._extra_content["custom"]
if CustomConfig in IMAGE_PROCESSOR_MAPPING._extra_content:
del IMAGE_PROCESSOR_MAPPING._extra_content[CustomConfig]
def SCREAMING_SNAKE_CASE_ ( self :Dict ):
class snake_case ( __UpperCAmelCase ):
lowerCamelCase__ = True
try:
AutoConfig.register('''custom''' , _lowerCamelCase )
AutoImageProcessor.register(_lowerCamelCase , _lowerCamelCase )
# If remote code is not set, the default is to use local
__SCREAMING_SNAKE_CASE : List[str] = AutoImageProcessor.from_pretrained('''hf-internal-testing/test_dynamic_image_processor''' )
self.assertEqual(image_processor.__class__.__name__ , '''NewImageProcessor''' )
self.assertTrue(image_processor.is_local )
# If remote code is disabled, we load the local one.
__SCREAMING_SNAKE_CASE : str = AutoImageProcessor.from_pretrained(
'''hf-internal-testing/test_dynamic_image_processor''' , trust_remote_code=_lowerCamelCase )
self.assertEqual(image_processor.__class__.__name__ , '''NewImageProcessor''' )
self.assertTrue(image_processor.is_local )
# If remote is enabled, we load from the Hub
__SCREAMING_SNAKE_CASE : Optional[int] = AutoImageProcessor.from_pretrained(
'''hf-internal-testing/test_dynamic_image_processor''' , trust_remote_code=_lowerCamelCase )
self.assertEqual(image_processor.__class__.__name__ , '''NewImageProcessor''' )
self.assertTrue(not hasattr(_lowerCamelCase , '''is_local''' ) )
finally:
if "custom" in CONFIG_MAPPING._extra_content:
del CONFIG_MAPPING._extra_content["custom"]
if CustomConfig in IMAGE_PROCESSOR_MAPPING._extra_content:
del IMAGE_PROCESSOR_MAPPING._extra_content[CustomConfig]
| 674 | 0 |
"""simple docstring"""
import unittest
import numpy as np
import torch
from diffusers import DDIMPipeline, DDIMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device
from ..pipeline_params import UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS, UNCONDITIONAL_IMAGE_GENERATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin
enable_full_determinism()
class __lowercase( __UpperCAmelCase , unittest.TestCase ):
'''simple docstring'''
__a : Tuple = DDIMPipeline
__a : int = UNCONDITIONAL_IMAGE_GENERATION_PARAMS
__a : List[Any] = PipelineTesterMixin.required_optional_params - {
'num_images_per_prompt',
'latents',
'callback',
'callback_steps',
}
__a : List[Any] = UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS
__a : Optional[int] = False
def snake_case_ ( self ):
torch.manual_seed(0 )
__lowerCamelCase : Optional[int] = UNetaDModel(
block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=3 , out_channels=3 , down_block_types=('DownBlock2D', 'AttnDownBlock2D') , up_block_types=('AttnUpBlock2D', 'UpBlock2D') , )
__lowerCamelCase : List[Any] = DDIMScheduler()
__lowerCamelCase : Optional[int] = {'''unet''': unet, '''scheduler''': scheduler}
return components
def snake_case_ ( self , __a , __a=0 ):
if str(_lowerCamelCase ).startswith('mps' ):
__lowerCamelCase : Tuple = torch.manual_seed(_lowerCamelCase )
else:
__lowerCamelCase : List[str] = torch.Generator(device=_lowerCamelCase ).manual_seed(_lowerCamelCase )
__lowerCamelCase : Optional[int] = {
'''batch_size''': 1,
'''generator''': generator,
'''num_inference_steps''': 2,
'''output_type''': '''numpy''',
}
return inputs
def snake_case_ ( self ):
__lowerCamelCase : str = '''cpu'''
__lowerCamelCase : List[str] = self.get_dummy_components()
__lowerCamelCase : int = self.pipeline_class(**_lowerCamelCase )
pipe.to(_lowerCamelCase )
pipe.set_progress_bar_config(disable=_lowerCamelCase )
__lowerCamelCase : List[Any] = self.get_dummy_inputs(_lowerCamelCase )
__lowerCamelCase : Any = pipe(**_lowerCamelCase ).images
__lowerCamelCase : List[Any] = image[0, -3:, -3:, -1]
self.assertEqual(image.shape , (1, 32, 32, 3) )
__lowerCamelCase : Any = np.array(
[1.000E00, 5.717E-01, 4.717E-01, 1.000E00, 0.000E00, 1.000E00, 3.000E-04, 0.000E00, 9.000E-04] )
__lowerCamelCase : Any = np.abs(image_slice.flatten() - expected_slice ).max()
self.assertLessEqual(_lowerCamelCase , 1E-3 )
def snake_case_ ( self ):
super().test_dict_tuple_outputs_equivalent(expected_max_difference=3E-3 )
def snake_case_ ( self ):
super().test_save_load_local(expected_max_difference=3E-3 )
def snake_case_ ( self ):
super().test_save_load_optional_components(expected_max_difference=3E-3 )
def snake_case_ ( self ):
super().test_inference_batch_single_identical(expected_max_diff=3E-3 )
@slow
@require_torch_gpu
class __lowercase( unittest.TestCase ):
'''simple docstring'''
def snake_case_ ( self ):
__lowerCamelCase : Union[str, Any] = '''google/ddpm-cifar10-32'''
__lowerCamelCase : Any = UNetaDModel.from_pretrained(_lowerCamelCase )
__lowerCamelCase : Dict = DDIMScheduler()
__lowerCamelCase : Dict = DDIMPipeline(unet=_lowerCamelCase , scheduler=_lowerCamelCase )
ddim.to(_lowerCamelCase )
ddim.set_progress_bar_config(disable=_lowerCamelCase )
__lowerCamelCase : Dict = torch.manual_seed(0 )
__lowerCamelCase : str = ddim(generator=_lowerCamelCase , eta=0.0 , output_type='numpy' ).images
__lowerCamelCase : Tuple = image[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
__lowerCamelCase : int = np.array([0.1_723, 0.1_617, 0.1_600, 0.1_626, 0.1_497, 0.1_513, 0.1_505, 0.1_442, 0.1_453] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
def snake_case_ ( self ):
__lowerCamelCase : List[str] = '''google/ddpm-ema-bedroom-256'''
__lowerCamelCase : str = UNetaDModel.from_pretrained(_lowerCamelCase )
__lowerCamelCase : Union[str, Any] = DDIMScheduler.from_pretrained(_lowerCamelCase )
__lowerCamelCase : Union[str, Any] = DDIMPipeline(unet=_lowerCamelCase , scheduler=_lowerCamelCase )
ddpm.to(_lowerCamelCase )
ddpm.set_progress_bar_config(disable=_lowerCamelCase )
__lowerCamelCase : Any = torch.manual_seed(0 )
__lowerCamelCase : Optional[int] = ddpm(generator=_lowerCamelCase , output_type='numpy' ).images
__lowerCamelCase : Optional[int] = image[0, -3:, -3:, -1]
assert image.shape == (1, 256, 256, 3)
__lowerCamelCase : List[Any] = np.array([0.0_060, 0.0_201, 0.0_344, 0.0_024, 0.0_018, 0.0_002, 0.0_022, 0.0_000, 0.0_069] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
| 594 |
"""simple docstring"""
from __future__ import annotations
from typing import Any
class snake_case ( __UpperCAmelCase ):
pass
class snake_case :
def __init__( self :List[Any] , _lowerCamelCase :Any ):
__SCREAMING_SNAKE_CASE : Any = data
__SCREAMING_SNAKE_CASE : Node | None = None
def __iter__( self :Union[str, Any] ):
__SCREAMING_SNAKE_CASE : List[str] = self
__SCREAMING_SNAKE_CASE : List[str] = []
while node:
if node in visited:
raise ContainsLoopError
visited.append(_lowerCamelCase )
yield node.data
__SCREAMING_SNAKE_CASE : List[str] = node.next_node
@property
def SCREAMING_SNAKE_CASE_ ( self :Any ):
try:
list(self )
return False
except ContainsLoopError:
return True
if __name__ == "__main__":
_lowerCamelCase = Node(1)
_lowerCamelCase = Node(2)
_lowerCamelCase = Node(3)
_lowerCamelCase = Node(4)
print(root_node.has_loop) # False
_lowerCamelCase = root_node.next_node
print(root_node.has_loop) # True
_lowerCamelCase = Node(5)
_lowerCamelCase = Node(6)
_lowerCamelCase = Node(5)
_lowerCamelCase = Node(6)
print(root_node.has_loop) # False
_lowerCamelCase = Node(1)
print(root_node.has_loop) # False
| 674 | 0 |
def _lowercase ( SCREAMING_SNAKE_CASE_ : int = 50 ):
"""simple docstring"""
UpperCamelCase = [1] * (length + 1)
for row_length in range(length + 1 ):
for tile_length in range(2 , 5 ):
for tile_start in range(row_length - tile_length + 1 ):
ways_number[row_length] += ways_number[
row_length - tile_start - tile_length
]
return ways_number[length]
if __name__ == "__main__":
print(F'''{solution() = }''')
| 386 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCamelCase = logging.get_logger(__name__)
_lowerCamelCase = {
'''weiweishi/roc-bert-base-zh''': '''https://huggingface.co/weiweishi/roc-bert-base-zh/resolve/main/config.json''',
}
class snake_case ( __UpperCAmelCase ):
lowerCamelCase__ = '''roc_bert'''
def __init__( self :Union[str, Any] , _lowerCamelCase :Any=3_0_5_2_2 , _lowerCamelCase :str=7_6_8 , _lowerCamelCase :Optional[Any]=1_2 , _lowerCamelCase :List[str]=1_2 , _lowerCamelCase :str=3_0_7_2 , _lowerCamelCase :Tuple="gelu" , _lowerCamelCase :List[Any]=0.1 , _lowerCamelCase :List[str]=0.1 , _lowerCamelCase :Optional[int]=5_1_2 , _lowerCamelCase :Dict=2 , _lowerCamelCase :Any=0.0_2 , _lowerCamelCase :Optional[int]=1e-12 , _lowerCamelCase :str=True , _lowerCamelCase :Any=0 , _lowerCamelCase :List[str]="absolute" , _lowerCamelCase :List[Any]=None , _lowerCamelCase :Any=True , _lowerCamelCase :Union[str, Any]=True , _lowerCamelCase :str=7_6_8 , _lowerCamelCase :Union[str, Any]=9_1_0 , _lowerCamelCase :List[Any]=5_1_2 , _lowerCamelCase :Optional[int]=2_4_8_5_8 , _lowerCamelCase :Union[str, Any]=True , **_lowerCamelCase :str , ):
__SCREAMING_SNAKE_CASE : List[str] = vocab_size
__SCREAMING_SNAKE_CASE : int = max_position_embeddings
__SCREAMING_SNAKE_CASE : List[str] = hidden_size
__SCREAMING_SNAKE_CASE : str = num_hidden_layers
__SCREAMING_SNAKE_CASE : int = num_attention_heads
__SCREAMING_SNAKE_CASE : Any = intermediate_size
__SCREAMING_SNAKE_CASE : Optional[int] = hidden_act
__SCREAMING_SNAKE_CASE : List[Any] = hidden_dropout_prob
__SCREAMING_SNAKE_CASE : Optional[Any] = attention_probs_dropout_prob
__SCREAMING_SNAKE_CASE : Union[str, Any] = initializer_range
__SCREAMING_SNAKE_CASE : Union[str, Any] = type_vocab_size
__SCREAMING_SNAKE_CASE : List[str] = layer_norm_eps
__SCREAMING_SNAKE_CASE : Optional[int] = use_cache
__SCREAMING_SNAKE_CASE : str = enable_pronunciation
__SCREAMING_SNAKE_CASE : List[str] = enable_shape
__SCREAMING_SNAKE_CASE : Tuple = pronunciation_embed_dim
__SCREAMING_SNAKE_CASE : Optional[Any] = pronunciation_vocab_size
__SCREAMING_SNAKE_CASE : str = shape_embed_dim
__SCREAMING_SNAKE_CASE : Union[str, Any] = shape_vocab_size
__SCREAMING_SNAKE_CASE : Tuple = concat_input
__SCREAMING_SNAKE_CASE : Union[str, Any] = position_embedding_type
__SCREAMING_SNAKE_CASE : str = classifier_dropout
super().__init__(pad_token_id=_lowerCamelCase , **_lowerCamelCase )
| 674 | 0 |
import gc
import tempfile
import unittest
import numpy as np
import torch
from diffusers import VersatileDiffusionPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device
a__ = False
class SCREAMING_SNAKE_CASE_ ( unittest.TestCase ):
"""simple docstring"""
pass
@nightly
@require_torch_gpu
class SCREAMING_SNAKE_CASE_ ( unittest.TestCase ):
"""simple docstring"""
def lowerCamelCase__ ( self : int ) -> str:
"""simple docstring"""
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def lowerCamelCase__ ( self : int ) -> Tuple:
"""simple docstring"""
__UpperCamelCase : int = VersatileDiffusionPipeline.from_pretrained("""shi-labs/versatile-diffusion""" , torch_dtype=torch.floataa )
pipe.to(_lowerCamelCase )
pipe.set_progress_bar_config(disable=_lowerCamelCase )
__UpperCamelCase : Dict = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg""" )
__UpperCamelCase : List[str] = torch.manual_seed(0 )
__UpperCamelCase : Union[str, Any] = pipe.dual_guided(
prompt="""first prompt""" , image=_lowerCamelCase , text_to_image_strength=0.75 , generator=_lowerCamelCase , guidance_scale=7.5 , num_inference_steps=2 , output_type="""numpy""" , ).images
with tempfile.TemporaryDirectory() as tmpdirname:
pipe.save_pretrained(_lowerCamelCase )
__UpperCamelCase : List[Any] = VersatileDiffusionPipeline.from_pretrained(_lowerCamelCase , torch_dtype=torch.floataa )
pipe.to(_lowerCamelCase )
pipe.set_progress_bar_config(disable=_lowerCamelCase )
__UpperCamelCase : List[str] = generator.manual_seed(0 )
__UpperCamelCase : int = pipe.dual_guided(
prompt="""first prompt""" , image=_lowerCamelCase , text_to_image_strength=0.75 , generator=_lowerCamelCase , guidance_scale=7.5 , num_inference_steps=2 , output_type="""numpy""" , ).images
assert np.abs(image - new_image ).sum() < 1E-5, "Models don't have the same forward pass"
def lowerCamelCase__ ( self : int ) -> Tuple:
"""simple docstring"""
__UpperCamelCase : Tuple = VersatileDiffusionPipeline.from_pretrained("""shi-labs/versatile-diffusion""" , torch_dtype=torch.floataa )
pipe.to(_lowerCamelCase )
pipe.set_progress_bar_config(disable=_lowerCamelCase )
__UpperCamelCase : int = '''cyberpunk 2077'''
__UpperCamelCase : Any = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg""" )
__UpperCamelCase : str = torch.manual_seed(0 )
__UpperCamelCase : Dict = pipe.dual_guided(
prompt=_lowerCamelCase , image=_lowerCamelCase , text_to_image_strength=0.75 , generator=_lowerCamelCase , guidance_scale=7.5 , num_inference_steps=50 , output_type="""numpy""" , ).images
__UpperCamelCase : List[Any] = image[0, 253:256, 253:256, -1]
assert image.shape == (1, 512, 512, 3)
__UpperCamelCase : List[str] = np.array([0.14_48, 0.16_19, 0.17_41, 0.10_86, 0.11_47, 0.11_28, 0.11_99, 0.11_65, 0.10_01] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-1
__UpperCamelCase : List[str] = '''A painting of a squirrel eating a burger '''
__UpperCamelCase : str = torch.manual_seed(0 )
__UpperCamelCase : Dict = pipe.text_to_image(
prompt=_lowerCamelCase , generator=_lowerCamelCase , guidance_scale=7.5 , num_inference_steps=50 , output_type="""numpy""" ).images
__UpperCamelCase : Any = image[0, 253:256, 253:256, -1]
assert image.shape == (1, 512, 512, 3)
__UpperCamelCase : List[Any] = np.array([0.33_67, 0.31_69, 0.26_56, 0.38_70, 0.47_90, 0.37_96, 0.40_09, 0.48_78, 0.47_78] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-1
__UpperCamelCase : str = pipe.image_variation(_lowerCamelCase , generator=_lowerCamelCase , output_type="""numpy""" ).images
__UpperCamelCase : List[Any] = image[0, 253:256, 253:256, -1]
assert image.shape == (1, 512, 512, 3)
__UpperCamelCase : Tuple = np.array([0.30_76, 0.31_23, 0.32_84, 0.37_82, 0.37_70, 0.38_94, 0.42_97, 0.43_31, 0.44_56] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-1
| 279 |
"""simple docstring"""
import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List
import git
import torch
from torch.utils.data import Dataset
from transformers import BartTokenizer, RagTokenizer, TaTokenizer
def lowerCAmelCase_ ( lowercase_ : List[str] , lowercase_ : List[str] , lowercase_ : Dict , lowercase_ : Tuple , lowercase_ : Optional[Any]=True , lowercase_ : Any="pt" ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : Union[str, Any] = {'''add_prefix_space''': True} if isinstance(lowercase_ , lowercase_ ) and not line.startswith(''' ''' ) else {}
__SCREAMING_SNAKE_CASE : Optional[int] = padding_side
return tokenizer(
[line] , max_length=lowercase_ , padding='''max_length''' if pad_to_max_length else None , truncation=lowercase_ , return_tensors=lowercase_ , add_special_tokens=lowercase_ , **lowercase_ , )
def lowerCAmelCase_ ( lowercase_ : Optional[int] , lowercase_ : Tuple , lowercase_ : List[Any]=None , ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : Dict = input_ids.ne(lowercase_ ).any(dim=0 )
if attention_mask is None:
return input_ids[:, keep_column_mask]
else:
return (input_ids[:, keep_column_mask], attention_mask[:, keep_column_mask])
class snake_case ( __UpperCAmelCase ):
def __init__( self :Optional[Any] , _lowerCamelCase :Dict , _lowerCamelCase :Any , _lowerCamelCase :Optional[Any] , _lowerCamelCase :Tuple , _lowerCamelCase :Any="train" , _lowerCamelCase :str=None , _lowerCamelCase :Optional[Any]=None , _lowerCamelCase :List[Any]=None , _lowerCamelCase :Tuple="" , ):
super().__init__()
__SCREAMING_SNAKE_CASE : Dict = Path(_lowerCamelCase ).joinpath(type_path + '''.source''' )
__SCREAMING_SNAKE_CASE : Any = Path(_lowerCamelCase ).joinpath(type_path + '''.target''' )
__SCREAMING_SNAKE_CASE : Any = self.get_char_lens(self.src_file )
__SCREAMING_SNAKE_CASE : List[str] = max_source_length
__SCREAMING_SNAKE_CASE : Dict = max_target_length
assert min(self.src_lens ) > 0, f'''found empty line in {self.src_file}'''
__SCREAMING_SNAKE_CASE : Dict = tokenizer
__SCREAMING_SNAKE_CASE : Union[str, Any] = prefix
if n_obs is not None:
__SCREAMING_SNAKE_CASE : Any = self.src_lens[:n_obs]
__SCREAMING_SNAKE_CASE : List[str] = src_lang
__SCREAMING_SNAKE_CASE : str = tgt_lang
def __len__( self :int ):
return len(self.src_lens )
def __getitem__( self :Optional[Any] , _lowerCamelCase :Any ):
__SCREAMING_SNAKE_CASE : Optional[Any] = index + 1 # linecache starts at 1
__SCREAMING_SNAKE_CASE : Any = self.prefix + linecache.getline(str(self.src_file ) , _lowerCamelCase ).rstrip('''\n''' )
__SCREAMING_SNAKE_CASE : Dict = linecache.getline(str(self.tgt_file ) , _lowerCamelCase ).rstrip('''\n''' )
assert source_line, f'''empty source line for index {index}'''
assert tgt_line, f'''empty tgt line for index {index}'''
# Need to add eos token manually for T5
if isinstance(self.tokenizer , _lowerCamelCase ):
source_line += self.tokenizer.eos_token
tgt_line += self.tokenizer.eos_token
# Pad source and target to the right
__SCREAMING_SNAKE_CASE : Dict = (
self.tokenizer.question_encoder if isinstance(self.tokenizer , _lowerCamelCase ) else self.tokenizer
)
__SCREAMING_SNAKE_CASE : Optional[Any] = self.tokenizer.generator if isinstance(self.tokenizer , _lowerCamelCase ) else self.tokenizer
__SCREAMING_SNAKE_CASE : Dict = encode_line(_lowerCamelCase , _lowerCamelCase , self.max_source_length , '''right''' )
__SCREAMING_SNAKE_CASE : Dict = encode_line(_lowerCamelCase , _lowerCamelCase , self.max_target_length , '''right''' )
__SCREAMING_SNAKE_CASE : Any = source_inputs['''input_ids'''].squeeze()
__SCREAMING_SNAKE_CASE : Any = target_inputs['''input_ids'''].squeeze()
__SCREAMING_SNAKE_CASE : Dict = source_inputs['''attention_mask'''].squeeze()
return {
"input_ids": source_ids,
"attention_mask": src_mask,
"decoder_input_ids": target_ids,
}
@staticmethod
def SCREAMING_SNAKE_CASE_ ( _lowerCamelCase :Any ):
return [len(_lowerCamelCase ) for x in Path(_lowerCamelCase ).open().readlines()]
def SCREAMING_SNAKE_CASE_ ( self :List[str] , _lowerCamelCase :List[str] ):
__SCREAMING_SNAKE_CASE : int = torch.stack([x['''input_ids'''] for x in batch] )
__SCREAMING_SNAKE_CASE : str = torch.stack([x['''attention_mask'''] for x in batch] )
__SCREAMING_SNAKE_CASE : int = torch.stack([x['''decoder_input_ids'''] for x in batch] )
__SCREAMING_SNAKE_CASE : str = (
self.tokenizer.generator.pad_token_id
if isinstance(self.tokenizer , _lowerCamelCase )
else self.tokenizer.pad_token_id
)
__SCREAMING_SNAKE_CASE : Union[str, Any] = (
self.tokenizer.question_encoder.pad_token_id
if isinstance(self.tokenizer , _lowerCamelCase )
else self.tokenizer.pad_token_id
)
__SCREAMING_SNAKE_CASE : List[str] = trim_batch(_lowerCamelCase , _lowerCamelCase )
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Any = trim_batch(_lowerCamelCase , _lowerCamelCase , attention_mask=_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Dict = {
'''input_ids''': source_ids,
'''attention_mask''': source_mask,
'''decoder_input_ids''': y,
}
return batch
_lowerCamelCase = getLogger(__name__)
def lowerCAmelCase_ ( lowercase_ : List[List] ):
'''simple docstring'''
return list(itertools.chain.from_iterable(lowercase_ ) )
def lowerCAmelCase_ ( lowercase_ : str ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : Any = get_git_info()
save_json(lowercase_ , os.path.join(lowercase_ , '''git_log.json''' ) )
def lowerCAmelCase_ ( lowercase_ : Any , lowercase_ : Optional[int] , lowercase_ : str=4 , **lowercase_ : List[str] ):
'''simple docstring'''
with open(lowercase_ , '''w''' ) as f:
json.dump(lowercase_ , lowercase_ , indent=lowercase_ , **lowercase_ )
def lowerCAmelCase_ ( lowercase_ : Union[str, Any] ):
'''simple docstring'''
with open(lowercase_ ) as f:
return json.load(lowercase_ )
def lowerCAmelCase_ ( ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : Any = git.Repo(search_parent_directories=lowercase_ )
__SCREAMING_SNAKE_CASE : List[str] = {
'''repo_id''': str(lowercase_ ),
'''repo_sha''': str(repo.head.object.hexsha ),
'''repo_branch''': str(repo.active_branch ),
'''hostname''': str(socket.gethostname() ),
}
return repo_infos
def lowerCAmelCase_ ( lowercase_ : Callable , lowercase_ : Iterable ):
'''simple docstring'''
return list(map(lowercase_ , lowercase_ ) )
def lowerCAmelCase_ ( lowercase_ : Any , lowercase_ : Any ):
'''simple docstring'''
with open(lowercase_ , '''wb''' ) as f:
return pickle.dump(lowercase_ , lowercase_ )
def lowerCAmelCase_ ( lowercase_ : Any ):
'''simple docstring'''
def remove_articles(lowercase_ : Dict ):
return re.sub(r'''\b(a|an|the)\b''' , ''' ''' , lowercase_ )
def white_space_fix(lowercase_ : Optional[int] ):
return " ".join(text.split() )
def remove_punc(lowercase_ : Any ):
__SCREAMING_SNAKE_CASE : Optional[int] = set(string.punctuation )
return "".join(ch for ch in text if ch not in exclude )
def lower(lowercase_ : int ):
return text.lower()
return white_space_fix(remove_articles(remove_punc(lower(lowercase_ ) ) ) )
def lowerCAmelCase_ ( lowercase_ : Optional[int] , lowercase_ : int ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : Any = normalize_answer(lowercase_ ).split()
__SCREAMING_SNAKE_CASE : Any = normalize_answer(lowercase_ ).split()
__SCREAMING_SNAKE_CASE : Tuple = Counter(lowercase_ ) & Counter(lowercase_ )
__SCREAMING_SNAKE_CASE : Tuple = sum(common.values() )
if num_same == 0:
return 0
__SCREAMING_SNAKE_CASE : Any = 1.0 * num_same / len(lowercase_ )
__SCREAMING_SNAKE_CASE : List[str] = 1.0 * num_same / len(lowercase_ )
__SCREAMING_SNAKE_CASE : Optional[int] = (2 * precision * recall) / (precision + recall)
return fa
def lowerCAmelCase_ ( lowercase_ : str , lowercase_ : Union[str, Any] ):
'''simple docstring'''
return normalize_answer(lowercase_ ) == normalize_answer(lowercase_ )
def lowerCAmelCase_ ( lowercase_ : List[str] , lowercase_ : List[str] ):
'''simple docstring'''
assert len(lowercase_ ) == len(lowercase_ )
__SCREAMING_SNAKE_CASE : Union[str, Any] = 0
for hypo, pred in zip(lowercase_ , lowercase_ ):
em += exact_match_score(lowercase_ , lowercase_ )
if len(lowercase_ ) > 0:
em /= len(lowercase_ )
return {"em": em}
def lowerCAmelCase_ ( lowercase_ : str ):
'''simple docstring'''
return model_prefix.startswith('''rag''' )
def lowerCAmelCase_ ( lowercase_ : Optional[Any] , lowercase_ : Tuple , lowercase_ : Optional[int] ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : Optional[int] = {p: p for p in extra_params}
# T5 models don't have `dropout` param, they have `dropout_rate` instead
__SCREAMING_SNAKE_CASE : Any = '''dropout_rate'''
for p in extra_params:
if getattr(lowercase_ , lowercase_ , lowercase_ ):
if not hasattr(lowercase_ , lowercase_ ) and not hasattr(lowercase_ , equivalent_param[p] ):
logger.info('''config doesn\'t have a `{}` attribute'''.format(lowercase_ ) )
delattr(lowercase_ , lowercase_ )
continue
__SCREAMING_SNAKE_CASE : Optional[int] = p if hasattr(lowercase_ , lowercase_ ) else equivalent_param[p]
setattr(lowercase_ , lowercase_ , getattr(lowercase_ , lowercase_ ) )
delattr(lowercase_ , lowercase_ )
return hparams, config
| 674 | 0 |
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