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"""simple docstring"""
from typing import List, Optional, Tuple, Union
import torch
from ...utils import logging, randn_tensor
from ..pipeline_utils import AudioPipelineOutput, DiffusionPipeline
__UpperCAmelCase =logging.get_logger(__name__) # pylint: disable=invalid-name
class lowerCAmelCase__ ( __a ):
def __init__( self , UpperCamelCase__ , UpperCamelCase__ ):
'''simple docstring'''
super().__init__()
self.register_modules(unet=_lowerCamelCase , scheduler=_lowerCamelCase )
@torch.no_grad()
def __call__( self , UpperCamelCase__ = 1 , UpperCamelCase__ = 1_00 , UpperCamelCase__ = None , UpperCamelCase__ = None , UpperCamelCase__ = True , ):
'''simple docstring'''
if audio_length_in_s is None:
A__ = self.unet.config.sample_size / self.unet.config.sample_rate
A__ = audio_length_in_s * self.unet.config.sample_rate
A__ = 2 ** len(self.unet.up_blocks )
if sample_size < 3 * down_scale_factor:
raise ValueError(
f"""{audio_length_in_s} is too small. Make sure it\'s bigger or equal to"""
f""" {3 * down_scale_factor / self.unet.config.sample_rate}.""" )
A__ = int(_lowerCamelCase )
if sample_size % down_scale_factor != 0:
A__ = (
(audio_length_in_s * self.unet.config.sample_rate) // down_scale_factor + 1
) * down_scale_factor
logger.info(
f"""{audio_length_in_s} is increased to {sample_size / self.unet.config.sample_rate} so that it can be handled"""
f""" by the model. It will be cut to {original_sample_size / self.unet.config.sample_rate} after the denoising"""
" process." )
A__ = int(_lowerCamelCase )
A__ = next(iter(self.unet.parameters() ) ).dtype
A__ = (batch_size, self.unet.config.in_channels, sample_size)
if isinstance(_lowerCamelCase , _lowerCamelCase ) and len(_lowerCamelCase ) != batch_size:
raise ValueError(
f"""You have passed a list of generators of length {len(_lowerCamelCase )}, but requested an effective batch"""
f""" size of {batch_size}. Make sure the batch size matches the length of the generators.""" )
A__ = randn_tensor(_lowerCamelCase , generator=_lowerCamelCase , device=self.device , dtype=_lowerCamelCase )
# set step values
self.scheduler.set_timesteps(_lowerCamelCase , device=audio.device )
A__ = self.scheduler.timesteps.to(_lowerCamelCase )
for t in self.progress_bar(self.scheduler.timesteps ):
# 1. predict noise model_output
A__ = self.unet(_lowerCamelCase , _lowerCamelCase ).sample
# 2. compute previous image: x_t -> t_t-1
A__ = self.scheduler.step(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase ).prev_sample
A__ = audio.clamp(-1 , 1 ).float().cpu().numpy()
A__ = audio[:, :, :original_sample_size]
if not return_dict:
return (audio,)
return AudioPipelineOutput(audios=_lowerCamelCase )
| 337
|
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
_lowercase = {
'''configuration_mask2former''': [
'''MASK2FORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''Mask2FormerConfig''',
],
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowercase = ['''Mask2FormerImageProcessor''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowercase = [
'''MASK2FORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''Mask2FormerForUniversalSegmentation''',
'''Mask2FormerModel''',
'''Mask2FormerPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_maskaformer import MASK2FORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, MaskaFormerConfig
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .image_processing_maskaformer import MaskaFormerImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_maskaformer import (
MASK2FORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
MaskaFormerForUniversalSegmentation,
MaskaFormerModel,
MaskaFormerPreTrainedModel,
)
else:
import sys
_lowercase = _LazyModule(__name__, globals()['''__file__'''], _import_structure)
| 118
| 0
|
def __UpperCamelCase ( lowerCAmelCase__ : int = 1_0**9 ):
__a : Any = 1
__a : List[Any] = 2
__a : List[str] = 0
__a : Optional[int] = 0
__a : Dict = 0
while perimeter <= max_perimeter:
perimeters_sum += perimeter
prev_value += 2 * value
value += prev_value
__a : Dict = 2 * value + 2 if i % 2 == 0 else 2 * value - 2
i += 1
return perimeters_sum
if __name__ == "__main__":
print(F"""{solution() = }""")
| 326
|
import logging
from pathlib import Path
import numpy as np
import pytorch_lightning as pl
import torch
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
from pytorch_lightning.utilities import rank_zero_only
from utils_rag import save_json
def __UpperCamelCase ( lowerCAmelCase__ : Any ):
__a : Dict = filter(lambda lowerCAmelCase__ : p.requires_grad , model.parameters() )
__a : Tuple = sum([np.prod(p.size() ) for p in model_parameters] )
return params
lowercase__ =logging.getLogger(__name__)
def __UpperCamelCase ( lowerCAmelCase__ : List[str] , lowerCAmelCase__ : Optional[Any] ):
if metric == "rouge2":
__a : List[Any] = '''{val_avg_rouge2:.4f}-{step_count}'''
elif metric == "bleu":
__a : List[str] = '''{val_avg_bleu:.4f}-{step_count}'''
elif metric == "em":
__a : Optional[Any] = '''{val_avg_em:.4f}-{step_count}'''
else:
raise NotImplementedError(
f"seq2seq callbacks only support rouge2 and bleu, got {metric}, You can make your own by adding to this"
''' function.''' )
__a : List[Any] = ModelCheckpoint(
dirpath=lowerCAmelCase__ , filename=lowerCAmelCase__ , monitor=f"val_{metric}" , mode='''max''' , save_top_k=3 , every_n_epochs=1 , )
return checkpoint_callback
def __UpperCamelCase ( lowerCAmelCase__ : Dict , lowerCAmelCase__ : Union[str, Any] ):
return EarlyStopping(
monitor=f"val_{metric}" , mode='''min''' if '''loss''' in metric else '''max''' , patience=lowerCAmelCase__ , verbose=lowerCAmelCase__ , )
class UpperCamelCase__ ( pl.Callback ):
def lowerCAmelCase (self : List[str] , snake_case_ : Any , snake_case_ : Any ):
__a : Optional[int] = {f"lr_group_{i}": param['''lr'''] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups )}
pl_module.logger.log_metrics(snake_case_ )
@rank_zero_only
def lowerCAmelCase (self : str , snake_case_ : pl.Trainer , snake_case_ : pl.LightningModule , snake_case_ : str , snake_case_ : Dict=True ):
logger.info(f"***** {type_path} results at step {trainer.global_step:05d} *****" )
__a : List[str] = trainer.callback_metrics
trainer.logger.log_metrics({k: v for k, v in metrics.items() if k not in ['''log''', '''progress_bar''', '''preds''']} )
# Log results
__a : Union[str, Any] = Path(pl_module.hparams.output_dir )
if type_path == "test":
__a : Union[str, Any] = od / '''test_results.txt'''
__a : Optional[Any] = od / '''test_generations.txt'''
else:
# this never gets hit. I prefer not to save intermediate generations, and results are in metrics.json
# If people want this it will be easy enough to add back.
__a : Optional[int] = od / f"{type_path}_results/{trainer.global_step:05d}.txt"
__a : List[str] = od / f"{type_path}_generations/{trainer.global_step:05d}.txt"
results_file.parent.mkdir(exist_ok=snake_case_ )
generations_file.parent.mkdir(exist_ok=snake_case_ )
with open(snake_case_ , '''a+''' ) as writer:
for key in sorted(snake_case_ ):
if key in ["log", "progress_bar", "preds"]:
continue
__a : Tuple = metrics[key]
if isinstance(snake_case_ , torch.Tensor ):
__a : Optional[int] = val.item()
__a : List[str] = f"{key}: {val:.6f}\n"
writer.write(snake_case_ )
if not save_generations:
return
if "preds" in metrics:
__a : Optional[Any] = '''\n'''.join(metrics['''preds'''] )
generations_file.open('''w+''' ).write(snake_case_ )
@rank_zero_only
def lowerCAmelCase (self : List[Any] , snake_case_ : Union[str, Any] , snake_case_ : Tuple ):
try:
__a : Union[str, Any] = pl_module.model.model.num_parameters()
except AttributeError:
__a : int = pl_module.model.num_parameters()
__a : Any = count_trainable_parameters(snake_case_ )
# mp stands for million parameters
trainer.logger.log_metrics({'''n_params''': npars, '''mp''': npars / 1E6, '''grad_mp''': n_trainable_pars / 1E6} )
@rank_zero_only
def lowerCAmelCase (self : Optional[int] , snake_case_ : pl.Trainer , snake_case_ : pl.LightningModule ):
save_json(pl_module.metrics , pl_module.metrics_save_path )
return self._write_logs(snake_case_ , snake_case_ , '''test''' )
@rank_zero_only
def lowerCAmelCase (self : Union[str, Any] , snake_case_ : pl.Trainer , snake_case_ : str ):
save_json(pl_module.metrics , pl_module.metrics_save_path )
# Uncommenting this will save val generations
# return self._write_logs(trainer, pl_module, "valid")
| 326
| 1
|
"""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 _snake_case ( snake_case__ : str , snake_case__ : Any , snake_case__ : Any ):
# Initialise PyTorch model
A = BertConfig.from_json_file(snake_case_ )
print(F'Building PyTorch model from configuration: {config}' )
A = BertForPreTraining(snake_case_ )
# Load weights from tf checkpoint
load_tf_weights_in_bert(snake_case_ , snake_case_ , snake_case_ )
# Save pytorch-model
print(F'Save PyTorch model to {pytorch_dump_path}' )
torch.save(model.state_dict() , snake_case_ )
if __name__ == "__main__":
_lowercase = 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.'''
)
_lowercase = parser.parse_args()
convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)
| 91
|
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 a ( unittest.TestCase ):
@slow
def lowerCAmelCase_ ( self )-> str:
'''simple docstring'''
for model_name in ["bert-base-uncased"]:
A__ : Tuple =AutoConfig.from_pretrained(__UpperCamelCase )
self.assertIsNotNone(__UpperCamelCase )
self.assertIsInstance(__UpperCamelCase , __UpperCamelCase )
A__ : Dict =TFAutoModel.from_pretrained(__UpperCamelCase , from_pt=__UpperCamelCase )
self.assertIsNotNone(__UpperCamelCase )
self.assertIsInstance(__UpperCamelCase , __UpperCamelCase )
A__ : Any =AutoModel.from_pretrained(__UpperCamelCase , from_tf=__UpperCamelCase )
self.assertIsNotNone(__UpperCamelCase )
self.assertIsInstance(__UpperCamelCase , __UpperCamelCase )
@slow
def lowerCAmelCase_ ( self )-> Union[str, Any]:
'''simple docstring'''
for model_name in ["bert-base-uncased"]:
A__ : Tuple =AutoConfig.from_pretrained(__UpperCamelCase )
self.assertIsNotNone(__UpperCamelCase )
self.assertIsInstance(__UpperCamelCase , __UpperCamelCase )
A__ : Tuple =TFAutoModelForPreTraining.from_pretrained(__UpperCamelCase , from_pt=__UpperCamelCase )
self.assertIsNotNone(__UpperCamelCase )
self.assertIsInstance(__UpperCamelCase , __UpperCamelCase )
A__ : List[Any] =AutoModelForPreTraining.from_pretrained(__UpperCamelCase , from_tf=__UpperCamelCase )
self.assertIsNotNone(__UpperCamelCase )
self.assertIsInstance(__UpperCamelCase , __UpperCamelCase )
@slow
def lowerCAmelCase_ ( self )-> Optional[Any]:
'''simple docstring'''
for model_name in TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
A__ : Optional[int] =AutoConfig.from_pretrained(__UpperCamelCase )
self.assertIsNotNone(__UpperCamelCase )
self.assertIsInstance(__UpperCamelCase , __UpperCamelCase )
A__ : Optional[int] =TFAutoModelForCausalLM.from_pretrained(__UpperCamelCase , from_pt=__UpperCamelCase )
A__ , A__ : List[Any] =TFAutoModelForCausalLM.from_pretrained(
__UpperCamelCase , output_loading_info=__UpperCamelCase , from_pt=__UpperCamelCase )
self.assertIsNotNone(__UpperCamelCase )
self.assertIsInstance(__UpperCamelCase , __UpperCamelCase )
A__ : Dict =AutoModelForCausalLM.from_pretrained(__UpperCamelCase , from_tf=__UpperCamelCase )
A__ , A__ : List[Any] =AutoModelForCausalLM.from_pretrained(
__UpperCamelCase , output_loading_info=__UpperCamelCase , from_tf=__UpperCamelCase )
self.assertIsNotNone(__UpperCamelCase )
self.assertIsInstance(__UpperCamelCase , __UpperCamelCase )
@slow
def lowerCAmelCase_ ( self )-> Any:
'''simple docstring'''
for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
A__ : List[str] =AutoConfig.from_pretrained(__UpperCamelCase )
self.assertIsNotNone(__UpperCamelCase )
self.assertIsInstance(__UpperCamelCase , __UpperCamelCase )
A__ : Optional[int] =TFAutoModelWithLMHead.from_pretrained(__UpperCamelCase , from_pt=__UpperCamelCase )
self.assertIsNotNone(__UpperCamelCase )
self.assertIsInstance(__UpperCamelCase , __UpperCamelCase )
A__ : int =AutoModelWithLMHead.from_pretrained(__UpperCamelCase , from_tf=__UpperCamelCase )
self.assertIsNotNone(__UpperCamelCase )
self.assertIsInstance(__UpperCamelCase , __UpperCamelCase )
@slow
def lowerCAmelCase_ ( self )-> Optional[Any]:
'''simple docstring'''
for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
A__ : Dict =AutoConfig.from_pretrained(__UpperCamelCase )
self.assertIsNotNone(__UpperCamelCase )
self.assertIsInstance(__UpperCamelCase , __UpperCamelCase )
A__ : Union[str, Any] =TFAutoModelForMaskedLM.from_pretrained(__UpperCamelCase , from_pt=__UpperCamelCase )
A__ , A__ : Union[str, Any] =TFAutoModelForMaskedLM.from_pretrained(
__UpperCamelCase , output_loading_info=__UpperCamelCase , from_pt=__UpperCamelCase )
self.assertIsNotNone(__UpperCamelCase )
self.assertIsInstance(__UpperCamelCase , __UpperCamelCase )
A__ : List[Any] =AutoModelForMaskedLM.from_pretrained(__UpperCamelCase , from_tf=__UpperCamelCase )
A__ , A__ : Union[str, Any] =AutoModelForMaskedLM.from_pretrained(
__UpperCamelCase , output_loading_info=__UpperCamelCase , from_tf=__UpperCamelCase )
self.assertIsNotNone(__UpperCamelCase )
self.assertIsInstance(__UpperCamelCase , __UpperCamelCase )
@slow
def lowerCAmelCase_ ( self )-> Any:
'''simple docstring'''
for model_name in TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
A__ : Union[str, Any] =AutoConfig.from_pretrained(__UpperCamelCase )
self.assertIsNotNone(__UpperCamelCase )
self.assertIsInstance(__UpperCamelCase , __UpperCamelCase )
A__ : str =TFAutoModelForSeqaSeqLM.from_pretrained(__UpperCamelCase , from_pt=__UpperCamelCase )
A__ , A__ : List[str] =TFAutoModelForSeqaSeqLM.from_pretrained(
__UpperCamelCase , output_loading_info=__UpperCamelCase , from_pt=__UpperCamelCase )
self.assertIsNotNone(__UpperCamelCase )
self.assertIsInstance(__UpperCamelCase , __UpperCamelCase )
A__ : Any =AutoModelForSeqaSeqLM.from_pretrained(__UpperCamelCase , from_tf=__UpperCamelCase )
A__ , A__ : Dict =AutoModelForSeqaSeqLM.from_pretrained(
__UpperCamelCase , output_loading_info=__UpperCamelCase , from_tf=__UpperCamelCase )
self.assertIsNotNone(__UpperCamelCase )
self.assertIsInstance(__UpperCamelCase , __UpperCamelCase )
@slow
def lowerCAmelCase_ ( self )-> List[str]:
'''simple docstring'''
for model_name in ["bert-base-uncased"]:
A__ : Dict =AutoConfig.from_pretrained(__UpperCamelCase )
self.assertIsNotNone(__UpperCamelCase )
self.assertIsInstance(__UpperCamelCase , __UpperCamelCase )
A__ : Optional[Any] =TFAutoModelForSequenceClassification.from_pretrained(__UpperCamelCase , from_pt=__UpperCamelCase )
self.assertIsNotNone(__UpperCamelCase )
self.assertIsInstance(__UpperCamelCase , __UpperCamelCase )
A__ : Optional[int] =AutoModelForSequenceClassification.from_pretrained(__UpperCamelCase , from_tf=__UpperCamelCase )
self.assertIsNotNone(__UpperCamelCase )
self.assertIsInstance(__UpperCamelCase , __UpperCamelCase )
@slow
def lowerCAmelCase_ ( self )-> Tuple:
'''simple docstring'''
for model_name in ["bert-base-uncased"]:
A__ : Optional[Any] =AutoConfig.from_pretrained(__UpperCamelCase )
self.assertIsNotNone(__UpperCamelCase )
self.assertIsInstance(__UpperCamelCase , __UpperCamelCase )
A__ : Tuple =TFAutoModelForQuestionAnswering.from_pretrained(__UpperCamelCase , from_pt=__UpperCamelCase )
self.assertIsNotNone(__UpperCamelCase )
self.assertIsInstance(__UpperCamelCase , __UpperCamelCase )
A__ : int =AutoModelForQuestionAnswering.from_pretrained(__UpperCamelCase , from_tf=__UpperCamelCase )
self.assertIsNotNone(__UpperCamelCase )
self.assertIsInstance(__UpperCamelCase , __UpperCamelCase )
def lowerCAmelCase_ ( self )-> Optional[int]:
'''simple docstring'''
A__ : str =TFAutoModelWithLMHead.from_pretrained(__UpperCamelCase , from_pt=__UpperCamelCase )
self.assertIsInstance(__UpperCamelCase , __UpperCamelCase )
self.assertEqual(model.num_parameters() , 1_44_10 )
self.assertEqual(model.num_parameters(only_trainable=__UpperCamelCase ) , 1_44_10 )
A__ : List[str] =AutoModelWithLMHead.from_pretrained(__UpperCamelCase , from_tf=__UpperCamelCase )
self.assertIsInstance(__UpperCamelCase , __UpperCamelCase )
self.assertEqual(model.num_parameters() , 1_44_10 )
self.assertEqual(model.num_parameters(only_trainable=__UpperCamelCase ) , 1_44_10 )
def lowerCAmelCase_ ( self )-> List[Any]:
'''simple docstring'''
A__ : Any =TFAutoModelWithLMHead.from_pretrained(__UpperCamelCase , from_pt=__UpperCamelCase )
self.assertIsInstance(__UpperCamelCase , __UpperCamelCase )
self.assertEqual(model.num_parameters() , 1_44_10 )
self.assertEqual(model.num_parameters(only_trainable=__UpperCamelCase ) , 1_44_10 )
A__ : Tuple =AutoModelWithLMHead.from_pretrained(__UpperCamelCase , from_tf=__UpperCamelCase )
self.assertIsInstance(__UpperCamelCase , __UpperCamelCase )
self.assertEqual(model.num_parameters() , 1_44_10 )
self.assertEqual(model.num_parameters(only_trainable=__UpperCamelCase ) , 1_44_10 )
| 416
| 0
|
import argparse
import torch
from transformers import BertForMaskedLM
if __name__ == "__main__":
_lowerCamelCase = argparse.ArgumentParser(
description=(
"""Extraction some layers of the full BertForMaskedLM or RObertaForMaskedLM for Transfer Learned"""
""" Distillation"""
)
)
parser.add_argument("""--model_type""", default="""bert""", choices=["""bert"""])
parser.add_argument("""--model_name""", default="""bert-base-uncased""", type=str)
parser.add_argument("""--dump_checkpoint""", default="""serialization_dir/tf_bert-base-uncased_0247911.pth""", type=str)
parser.add_argument("""--vocab_transform""", action="""store_true""")
_lowerCamelCase = parser.parse_args()
if args.model_type == "bert":
_lowerCamelCase = BertForMaskedLM.from_pretrained(args.model_name)
_lowerCamelCase = """bert"""
else:
raise ValueError("""args.model_type should be \"bert\".""")
_lowerCamelCase = model.state_dict()
_lowerCamelCase = {}
for w in ["word_embeddings", "position_embeddings"]:
_lowerCamelCase = state_dict[f'''{prefix}.embeddings.{w}.weight''']
for w in ["weight", "bias"]:
_lowerCamelCase = state_dict[f'''{prefix}.embeddings.LayerNorm.{w}''']
_lowerCamelCase = 0
for teacher_idx in [0, 2, 4, 7, 9, 11]:
for w in ["weight", "bias"]:
_lowerCamelCase = state_dict[
f'''{prefix}.encoder.layer.{teacher_idx}.attention.self.query.{w}'''
]
_lowerCamelCase = state_dict[
f'''{prefix}.encoder.layer.{teacher_idx}.attention.self.key.{w}'''
]
_lowerCamelCase = state_dict[
f'''{prefix}.encoder.layer.{teacher_idx}.attention.self.value.{w}'''
]
_lowerCamelCase = state_dict[
f'''{prefix}.encoder.layer.{teacher_idx}.attention.output.dense.{w}'''
]
_lowerCamelCase = state_dict[
f'''{prefix}.encoder.layer.{teacher_idx}.attention.output.LayerNorm.{w}'''
]
_lowerCamelCase = state_dict[
f'''{prefix}.encoder.layer.{teacher_idx}.intermediate.dense.{w}'''
]
_lowerCamelCase = state_dict[
f'''{prefix}.encoder.layer.{teacher_idx}.output.dense.{w}'''
]
_lowerCamelCase = state_dict[
f'''{prefix}.encoder.layer.{teacher_idx}.output.LayerNorm.{w}'''
]
std_idx += 1
_lowerCamelCase = state_dict["""cls.predictions.decoder.weight"""]
_lowerCamelCase = state_dict["""cls.predictions.bias"""]
if args.vocab_transform:
for w in ["weight", "bias"]:
_lowerCamelCase = state_dict[f'''cls.predictions.transform.dense.{w}''']
_lowerCamelCase = state_dict[f'''cls.predictions.transform.LayerNorm.{w}''']
print(f'''N layers selected for distillation: {std_idx}''')
print(f'''Number of params transferred for distillation: {len(compressed_sd.keys())}''')
print(f'''Save transferred checkpoint to {args.dump_checkpoint}.''')
torch.save(compressed_sd, args.dump_checkpoint)
| 718
|
import argparse
import json
import os
from pathlib import Path
import requests
import torch
from transformers import JukeboxConfig, JukeboxModel
from transformers.utils import logging
logging.set_verbosity_info()
_lowerCamelCase = logging.get_logger(__name__)
_lowerCamelCase = """https://openaipublic.azureedge.net/jukebox/models/"""
_lowerCamelCase = {
"""jukebox-1b-lyrics""": [
"""5b/vqvae.pth.tar""",
"""5b/prior_level_0.pth.tar""",
"""5b/prior_level_1.pth.tar""",
"""1b_lyrics/prior_level_2.pth.tar""",
],
"""jukebox-5b-lyrics""": [
"""5b/vqvae.pth.tar""",
"""5b/prior_level_0.pth.tar""",
"""5b/prior_level_1.pth.tar""",
"""5b_lyrics/prior_level_2.pth.tar""",
],
}
def _lowerCAmelCase ( __lowerCamelCase : Any ):
"""simple docstring"""
if key.endswith(".model.1.bias" ) and len(key.split("." ) ) > 10:
__SCREAMING_SNAKE_CASE : Dict = key.replace(".model.1.bias" , ".conv1d_1.bias" )
elif key.endswith(".model.1.weight" ) and len(key.split("." ) ) > 10:
__SCREAMING_SNAKE_CASE : Tuple = key.replace(".model.1.weight" , ".conv1d_1.weight" )
elif key.endswith(".model.3.bias" ) and len(key.split("." ) ) > 10:
__SCREAMING_SNAKE_CASE : Dict = key.replace(".model.3.bias" , ".conv1d_2.bias" )
elif key.endswith(".model.3.weight" ) and len(key.split("." ) ) > 10:
__SCREAMING_SNAKE_CASE : List[str] = key.replace(".model.3.weight" , ".conv1d_2.weight" )
if "conditioner_blocks.0." in key:
__SCREAMING_SNAKE_CASE : List[Any] = key.replace("conditioner_blocks.0" , "conditioner_blocks" )
if "prime_prior" in key:
__SCREAMING_SNAKE_CASE : Dict = key.replace("prime_prior" , "encoder" )
if ".emb." in key and "total" not in key and "absolute" not in key and "relative" not in key:
__SCREAMING_SNAKE_CASE : Optional[Any] = key.replace(".emb." , "." )
if key.endswith("k" ): # replace vqvae.X.k with vqvae.X.codebook
return key.replace(".k" , ".codebook" )
if "y_emb." in key:
return key.replace("y_emb." , "metadata_embedding." )
if "x_emb.emb." in key:
__SCREAMING_SNAKE_CASE : Any = key.replace("0.x_emb.emb" , "embed_tokens" )
if "prime_state_ln" in key:
return key.replace("prime_state_ln" , "encoder.final_layer_norm" )
if ".ln" in key:
return key.replace(".ln" , ".layer_norm" )
if "_ln" in key:
return key.replace("_ln" , "_layer_norm" )
if "prime_state_proj" in key:
return key.replace("prime_state_proj" , "encoder.proj_in" )
if "prime_x_out" in key:
return key.replace("prime_x_out" , "encoder.lm_head" )
if "prior.x_out" in key:
return key.replace("x_out" , "fc_proj_out" )
if "x_emb" in key:
return key.replace("x_emb" , "embed_tokens" )
return key
def _lowerCAmelCase ( __lowerCamelCase : Tuple , __lowerCamelCase : Union[str, Any] , __lowerCamelCase : List[str] , __lowerCamelCase : Optional[Any] ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : str = {}
import re
__SCREAMING_SNAKE_CASE : str = re.compile(r"encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)" )
__SCREAMING_SNAKE_CASE : Tuple = re.compile(
r"encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)" )
__SCREAMING_SNAKE_CASE : Tuple = re.compile(r"encoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)" )
__SCREAMING_SNAKE_CASE : int = re.compile(r"decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)" )
__SCREAMING_SNAKE_CASE : Union[str, Any] = re.compile(
r"decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)" )
__SCREAMING_SNAKE_CASE : List[str] = re.compile(r"decoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)" )
__SCREAMING_SNAKE_CASE : int = re.compile(r"conditioner_blocks.(\d*).cond.model.(\d*).(\d).(bias|weight)" )
__SCREAMING_SNAKE_CASE : List[str] = re.compile(
r"conditioner_blocks.(\d*).cond.model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)" )
__SCREAMING_SNAKE_CASE : Dict = re.compile(r"conditioner_blocks.(\d*).cond.model.(\d*).(bias|weight)" )
for original_key, value in state_dict.items():
# rename vqvae.encoder keys
if re_encoder_block_conv_in.fullmatch(__lowerCamelCase ):
__SCREAMING_SNAKE_CASE : List[Any] = re_encoder_block_conv_in.match(__lowerCamelCase )
__SCREAMING_SNAKE_CASE : List[str] = regex_match.groups()
__SCREAMING_SNAKE_CASE : Optional[int] = int(groups[2] ) * 2 + int(groups[3] )
__SCREAMING_SNAKE_CASE : int = F"""encoders.{groups[0]}.level_blocks.{groups[1]}.downsample_block.{block_index}.{groups[-1]}"""
__SCREAMING_SNAKE_CASE : Union[str, Any] = re_encoder_block_conv_in.sub(__lowerCamelCase , __lowerCamelCase )
elif re_encoder_block_resnet.fullmatch(__lowerCamelCase ):
__SCREAMING_SNAKE_CASE : Dict = re_encoder_block_resnet.match(__lowerCamelCase )
__SCREAMING_SNAKE_CASE : Optional[int] = regex_match.groups()
__SCREAMING_SNAKE_CASE : List[str] = int(groups[2] ) * 2 + int(groups[3] )
__SCREAMING_SNAKE_CASE : Any = {"1": 1, "3": 2}[groups[-2]]
__SCREAMING_SNAKE_CASE : Union[str, Any] = F"""encoders.{groups[0]}.level_blocks.{groups[1]}.downsample_block.{block_index}."""
__SCREAMING_SNAKE_CASE : List[str] = F"""resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}"""
__SCREAMING_SNAKE_CASE : Any = prefix + resnet_block
__SCREAMING_SNAKE_CASE : List[str] = re_encoder_block_resnet.sub(__lowerCamelCase , __lowerCamelCase )
elif re_encoder_block_proj_out.fullmatch(__lowerCamelCase ):
__SCREAMING_SNAKE_CASE : Any = re_encoder_block_proj_out.match(__lowerCamelCase )
__SCREAMING_SNAKE_CASE : int = regex_match.groups()
__SCREAMING_SNAKE_CASE : Optional[int] = F"""encoders.{groups[0]}.level_blocks.{groups[1]}.proj_out.{groups[-1]}"""
__SCREAMING_SNAKE_CASE : Dict = re_encoder_block_proj_out.sub(__lowerCamelCase , __lowerCamelCase )
# rename vqvae.decoder keys
elif re_decoder_block_conv_out.fullmatch(__lowerCamelCase ):
__SCREAMING_SNAKE_CASE : List[str] = re_decoder_block_conv_out.match(__lowerCamelCase )
__SCREAMING_SNAKE_CASE : str = regex_match.groups()
__SCREAMING_SNAKE_CASE : Optional[Any] = int(groups[2] ) * 2 + int(groups[3] ) - 2
__SCREAMING_SNAKE_CASE : List[Any] = F"""decoders.{groups[0]}.level_blocks.{groups[1]}.upsample_block.{block_index}.{groups[-1]}"""
__SCREAMING_SNAKE_CASE : Optional[Any] = re_decoder_block_conv_out.sub(__lowerCamelCase , __lowerCamelCase )
elif re_decoder_block_resnet.fullmatch(__lowerCamelCase ):
__SCREAMING_SNAKE_CASE : Union[str, Any] = re_decoder_block_resnet.match(__lowerCamelCase )
__SCREAMING_SNAKE_CASE : int = regex_match.groups()
__SCREAMING_SNAKE_CASE : Union[str, Any] = int(groups[2] ) * 2 + int(groups[3] ) - 2
__SCREAMING_SNAKE_CASE : Any = {"1": 1, "3": 2}[groups[-2]]
__SCREAMING_SNAKE_CASE : Tuple = F"""decoders.{groups[0]}.level_blocks.{groups[1]}.upsample_block.{block_index}."""
__SCREAMING_SNAKE_CASE : List[Any] = F"""resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}"""
__SCREAMING_SNAKE_CASE : List[Any] = prefix + resnet_block
__SCREAMING_SNAKE_CASE : Union[str, Any] = re_decoder_block_resnet.sub(__lowerCamelCase , __lowerCamelCase )
elif re_decoder_block_proj_in.fullmatch(__lowerCamelCase ):
__SCREAMING_SNAKE_CASE : Optional[Any] = re_decoder_block_proj_in.match(__lowerCamelCase )
__SCREAMING_SNAKE_CASE : Optional[Any] = regex_match.groups()
__SCREAMING_SNAKE_CASE : List[str] = F"""decoders.{groups[0]}.level_blocks.{groups[1]}.proj_in.{groups[-1]}"""
__SCREAMING_SNAKE_CASE : int = re_decoder_block_proj_in.sub(__lowerCamelCase , __lowerCamelCase )
# rename prior cond.model to upsampler.upsample_block and resnet
elif re_prior_cond_conv_out.fullmatch(__lowerCamelCase ):
__SCREAMING_SNAKE_CASE : List[Any] = re_prior_cond_conv_out.match(__lowerCamelCase )
__SCREAMING_SNAKE_CASE : Optional[int] = regex_match.groups()
__SCREAMING_SNAKE_CASE : str = int(groups[1] ) * 2 + int(groups[2] ) - 2
__SCREAMING_SNAKE_CASE : Union[str, Any] = F"""conditioner_blocks.upsampler.upsample_block.{block_index}.{groups[-1]}"""
__SCREAMING_SNAKE_CASE : Any = re_prior_cond_conv_out.sub(__lowerCamelCase , __lowerCamelCase )
elif re_prior_cond_resnet.fullmatch(__lowerCamelCase ):
__SCREAMING_SNAKE_CASE : Union[str, Any] = re_prior_cond_resnet.match(__lowerCamelCase )
__SCREAMING_SNAKE_CASE : Optional[int] = regex_match.groups()
__SCREAMING_SNAKE_CASE : Any = int(groups[1] ) * 2 + int(groups[2] ) - 2
__SCREAMING_SNAKE_CASE : str = {"1": 1, "3": 2}[groups[-2]]
__SCREAMING_SNAKE_CASE : Any = F"""conditioner_blocks.upsampler.upsample_block.{block_index}."""
__SCREAMING_SNAKE_CASE : Union[str, Any] = F"""resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}"""
__SCREAMING_SNAKE_CASE : Any = prefix + resnet_block
__SCREAMING_SNAKE_CASE : Union[str, Any] = re_prior_cond_resnet.sub(__lowerCamelCase , __lowerCamelCase )
elif re_prior_cond_proj_in.fullmatch(__lowerCamelCase ):
__SCREAMING_SNAKE_CASE : Tuple = re_prior_cond_proj_in.match(__lowerCamelCase )
__SCREAMING_SNAKE_CASE : Dict = regex_match.groups()
__SCREAMING_SNAKE_CASE : Dict = F"""conditioner_blocks.upsampler.proj_in.{groups[-1]}"""
__SCREAMING_SNAKE_CASE : int = re_prior_cond_proj_in.sub(__lowerCamelCase , __lowerCamelCase )
# keep original key
else:
__SCREAMING_SNAKE_CASE : Tuple = original_key
__SCREAMING_SNAKE_CASE : Union[str, Any] = replace_key(__lowerCamelCase )
if F"""{key_prefix}.{key}""" not in model_state_dict or key is None:
print(F"""failed converting {original_key} to {key}, does not match""" )
# handle missmatched shape
elif value.shape != model_state_dict[F"""{key_prefix}.{key}"""].shape:
__SCREAMING_SNAKE_CASE : List[str] = model_state_dict[F"""{key_prefix}.{key}"""]
print(F"""{original_key}-> {key} : \nshape {val.shape} and { value.shape}, do not match""" )
__SCREAMING_SNAKE_CASE : str = original_key
__SCREAMING_SNAKE_CASE : List[str] = original_key
__SCREAMING_SNAKE_CASE : Union[str, Any] = value
return new_dict
@torch.no_grad()
def _lowerCAmelCase ( __lowerCamelCase : Optional[int]=None , __lowerCamelCase : List[Any]=None ):
"""simple docstring"""
for file in MODEL_MAPPING[model_name]:
if not os.path.isfile(F"""{pytorch_dump_folder_path}/{file.split("/" )[-1]}""" ):
__SCREAMING_SNAKE_CASE : Dict = requests.get(F"""{PREFIX}{file}""" , allow_redirects=__lowerCamelCase )
os.makedirs(F"""{pytorch_dump_folder_path}/""" , exist_ok=__lowerCamelCase )
open(F"""{pytorch_dump_folder_path}/{file.split("/" )[-1]}""" , "wb" ).write(r.content )
__SCREAMING_SNAKE_CASE : int = MODEL_MAPPING[model_name.split("/" )[-1]]
__SCREAMING_SNAKE_CASE : List[str] = JukeboxConfig.from_pretrained(__lowerCamelCase )
__SCREAMING_SNAKE_CASE : str = JukeboxModel(__lowerCamelCase )
__SCREAMING_SNAKE_CASE : int = []
__SCREAMING_SNAKE_CASE : Optional[int] = {}
for i, dict_name in enumerate(__lowerCamelCase ):
__SCREAMING_SNAKE_CASE : str = torch.load(F"""{pytorch_dump_folder_path}/{dict_name.split("/" )[-1]}""" )["model"]
__SCREAMING_SNAKE_CASE : Optional[int] = {}
for k in old_dic.keys():
if k.endswith(".b" ):
__SCREAMING_SNAKE_CASE : Optional[int] = old_dic[k]
elif k.endswith(".w" ):
__SCREAMING_SNAKE_CASE : int = old_dic[k]
elif "level_2" not in dict_name and "cond.model." in k:
__SCREAMING_SNAKE_CASE : Optional[Any] = old_dic[k]
else:
__SCREAMING_SNAKE_CASE : Optional[int] = old_dic[k]
__SCREAMING_SNAKE_CASE : Optional[Any] = "vqvae" if i == 0 else F"""priors.{3 - i}"""
__SCREAMING_SNAKE_CASE : int = fix_jukebox_keys(__lowerCamelCase , model.state_dict() , __lowerCamelCase , __lowerCamelCase )
weight_dict.append(__lowerCamelCase )
__SCREAMING_SNAKE_CASE : Dict = weight_dict.pop(0 )
model.vqvae.load_state_dict(__lowerCamelCase )
for i in range(len(__lowerCamelCase ) ):
model.priors[i].load_state_dict(weight_dict[2 - i] )
Path(__lowerCamelCase ).mkdir(exist_ok=__lowerCamelCase )
with open(F"""{pytorch_dump_folder_path}/mapping.json""" , "w" ) as txtfile:
json.dump(__lowerCamelCase , __lowerCamelCase )
print(F"""Saving model {model_name} to {pytorch_dump_folder_path}""" )
model.save_pretrained(__lowerCamelCase )
return weight_dict
if __name__ == "__main__":
_lowerCamelCase = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--model_name""",
default="""jukebox-5b-lyrics""",
type=str,
help="""Name of the model you'd like to convert.""",
)
parser.add_argument(
"""--pytorch_dump_folder_path""",
default="""jukebox-5b-lyrics-converted""",
type=str,
help="""Path to the output PyTorch model directory.""",
)
_lowerCamelCase = parser.parse_args()
convert_openai_checkpoint(args.model_name, args.pytorch_dump_folder_path)
| 447
| 0
|
'''simple docstring'''
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Features, Value
from .base import TaskTemplate
@dataclass(frozen=a_ )
class lowerCAmelCase_ ( a_ ):
__UpperCAmelCase = field(default='language-modeling' , metadata={'include_in_asdict_even_if_is_default': True} )
__UpperCAmelCase = Features({'text': Value('string' )} )
__UpperCAmelCase = Features({} )
__UpperCAmelCase = "text"
@property
def __snake_case ( self : Tuple ):
'''simple docstring'''
return {self.text_column: "text"}
| 349
|
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
A : Optional[Any] = {
"""configuration_megatron_bert""": ["""MEGATRON_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MegatronBertConfig"""],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A : Any = [
"""MEGATRON_BERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""MegatronBertForCausalLM""",
"""MegatronBertForMaskedLM""",
"""MegatronBertForMultipleChoice""",
"""MegatronBertForNextSentencePrediction""",
"""MegatronBertForPreTraining""",
"""MegatronBertForQuestionAnswering""",
"""MegatronBertForSequenceClassification""",
"""MegatronBertForTokenClassification""",
"""MegatronBertModel""",
"""MegatronBertPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_megatron_bert import MEGATRON_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, MegatronBertConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_megatron_bert import (
MEGATRON_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
MegatronBertForCausalLM,
MegatronBertForMaskedLM,
MegatronBertForMultipleChoice,
MegatronBertForNextSentencePrediction,
MegatronBertForPreTraining,
MegatronBertForQuestionAnswering,
MegatronBertForSequenceClassification,
MegatronBertForTokenClassification,
MegatronBertModel,
MegatronBertPreTrainedModel,
)
else:
import sys
A : Dict = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 349
| 1
|
'''simple docstring'''
import unittest
import numpy as np
from transformers.file_utils import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision
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 DPTImageProcessor
class __lowerCamelCase ( unittest.TestCase ):
'''simple docstring'''
def __init__( self , a__ , a__=7 , a__=3 , a__=18 , a__=30 , a__=400 , a__=True , a__=None , a__=True , a__=[0.5, 0.5, 0.5] , a__=[0.5, 0.5, 0.5] , ):
__SCREAMING_SNAKE_CASE : int = size if size is not None else {"height": 18, "width": 18}
__SCREAMING_SNAKE_CASE : Tuple = parent
__SCREAMING_SNAKE_CASE : List[str] = batch_size
__SCREAMING_SNAKE_CASE : List[Any] = num_channels
__SCREAMING_SNAKE_CASE : List[str] = image_size
__SCREAMING_SNAKE_CASE : Any = min_resolution
__SCREAMING_SNAKE_CASE : Tuple = max_resolution
__SCREAMING_SNAKE_CASE : Dict = do_resize
__SCREAMING_SNAKE_CASE : Optional[int] = size
__SCREAMING_SNAKE_CASE : Union[str, Any] = do_normalize
__SCREAMING_SNAKE_CASE : Union[str, Any] = image_mean
__SCREAMING_SNAKE_CASE : List[str] = image_std
def a_ ( self ):
return {
"image_mean": self.image_mean,
"image_std": self.image_std,
"do_normalize": self.do_normalize,
"do_resize": self.do_resize,
"size": self.size,
}
@require_torch
@require_vision
class __lowerCamelCase ( __snake_case , unittest.TestCase ):
'''simple docstring'''
snake_case__ : Optional[Any] = DPTImageProcessor if is_vision_available() else None
def a_ ( self ):
__SCREAMING_SNAKE_CASE : Optional[int] = DPTImageProcessingTester(self )
@property
def a_ ( self ):
return self.image_processor_tester.prepare_image_processor_dict()
def a_ ( self ):
__SCREAMING_SNAKE_CASE : Tuple = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(A_ , "image_mean" ) )
self.assertTrue(hasattr(A_ , "image_std" ) )
self.assertTrue(hasattr(A_ , "do_normalize" ) )
self.assertTrue(hasattr(A_ , "do_resize" ) )
self.assertTrue(hasattr(A_ , "size" ) )
def a_ ( self ):
__SCREAMING_SNAKE_CASE : Dict = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {"height": 18, "width": 18} )
__SCREAMING_SNAKE_CASE : str = self.image_processing_class.from_dict(self.image_processor_dict , size=42 )
self.assertEqual(image_processor.size , {"height": 42, "width": 42} )
def a_ ( self ):
__SCREAMING_SNAKE_CASE : str = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
__SCREAMING_SNAKE_CASE : Any = prepare_image_inputs(self.image_processor_tester , equal_resolution=A_ )
for image in image_inputs:
self.assertIsInstance(A_ , Image.Image )
# Test not batched input
__SCREAMING_SNAKE_CASE : List[str] = 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
__SCREAMING_SNAKE_CASE : Union[str, Any] = image_processing(A_ , 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 a_ ( self ):
__SCREAMING_SNAKE_CASE : int = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
__SCREAMING_SNAKE_CASE : Optional[Any] = prepare_image_inputs(self.image_processor_tester , equal_resolution=A_ , numpify=A_ )
for image in image_inputs:
self.assertIsInstance(A_ , np.ndarray )
# Test not batched input
__SCREAMING_SNAKE_CASE : Tuple = 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
__SCREAMING_SNAKE_CASE : List[str] = image_processing(A_ , 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 a_ ( self ):
__SCREAMING_SNAKE_CASE : Union[str, Any] = 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=A_ , torchify=A_ )
for image in image_inputs:
self.assertIsInstance(A_ , torch.Tensor )
# Test not batched input
__SCREAMING_SNAKE_CASE : List[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
__SCREAMING_SNAKE_CASE : List[str] = image_processing(A_ , 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"],
) , )
| 716
|
'''simple docstring'''
import os
import numpy
import onnx
def __A ( _SCREAMING_SNAKE_CASE : List[Any] , _SCREAMING_SNAKE_CASE : List[Any] ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : int = a.name
__SCREAMING_SNAKE_CASE : List[Any] = b.name
__SCREAMING_SNAKE_CASE : int = ""
__SCREAMING_SNAKE_CASE : str = ""
__SCREAMING_SNAKE_CASE : List[Any] = a == b
__SCREAMING_SNAKE_CASE : Any = name_a
__SCREAMING_SNAKE_CASE : Optional[Any] = name_b
return res
def __A ( _SCREAMING_SNAKE_CASE : Optional[Any] , _SCREAMING_SNAKE_CASE : Optional[int] , _SCREAMING_SNAKE_CASE : Union[str, Any] ):
"""simple docstring"""
for i, input_name in enumerate(node_proto.input ):
if input_name == name:
node_proto.input.insert(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
node_proto.input.pop(i + 1 )
if node_proto.op_type == "If":
_graph_replace_input_with(node_proto.attribute[0].g , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
_graph_replace_input_with(node_proto.attribute[1].g , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
if node_proto.op_type == "Loop":
_graph_replace_input_with(node_proto.attribute[0].g , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
def __A ( _SCREAMING_SNAKE_CASE : int , _SCREAMING_SNAKE_CASE : Dict , _SCREAMING_SNAKE_CASE : Optional[Any] ):
"""simple docstring"""
for n in graph_proto.node:
_node_replace_input_with(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
def __A ( _SCREAMING_SNAKE_CASE : str , _SCREAMING_SNAKE_CASE : str , _SCREAMING_SNAKE_CASE : Optional[int] ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : List[str] = list(model.graph.initializer )
__SCREAMING_SNAKE_CASE : Tuple = list(model_without_ext.graph.initializer )
for i, ref_i in ind_to_replace:
assert inits_with_data[i].name == inits[i].name
assert inits_with_data[ref_i].name == inits[ref_i].name
assert i > ref_i
__SCREAMING_SNAKE_CASE : str = inits[i].name
__SCREAMING_SNAKE_CASE : str = inits[ref_i].name
model_without_ext.graph.initializer.remove(inits[i] )
# for n in model.graph.node:
_graph_replace_input_with(model_without_ext.graph , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
def __A ( _SCREAMING_SNAKE_CASE : Dict ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : Any = os.path.dirname(_SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE : Tuple = os.path.basename(_SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE : Dict = onnx.load(os.path.join(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) )
__SCREAMING_SNAKE_CASE : Dict = list(model.graph.initializer )
__SCREAMING_SNAKE_CASE : int = set()
__SCREAMING_SNAKE_CASE : Optional[Any] = {}
__SCREAMING_SNAKE_CASE : Any = []
__SCREAMING_SNAKE_CASE : str = 0
for i in range(len(_SCREAMING_SNAKE_CASE ) ):
if i in dup_set:
continue
for j in range(i + 1 , len(_SCREAMING_SNAKE_CASE ) ):
if j in dup_set:
continue
if _is_equal_tensor_proto(inits[i] , inits[j] ):
dup_set.add(_SCREAMING_SNAKE_CASE )
dup_set.add(_SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE : int = inits[j].data_type
__SCREAMING_SNAKE_CASE : Any = numpy.prod(inits[j].dims )
if dtype == 1:
mem_size *= 4
elif dtype == 6:
mem_size *= 4
elif dtype == 7 or dtype == 1_1:
mem_size *= 8
else:
print("unexpected data type: " , _SCREAMING_SNAKE_CASE )
total_reduced_size += mem_size
__SCREAMING_SNAKE_CASE : Any = inits[i].name
__SCREAMING_SNAKE_CASE : Optional[int] = inits[j].name
if name_i in dup_map:
dup_map[name_i].append(_SCREAMING_SNAKE_CASE )
else:
__SCREAMING_SNAKE_CASE : List[Any] = [name_j]
ind_to_replace.append((j, i) )
print("total reduced size: " , total_reduced_size / 1_0_2_4 / 1_0_2_4 / 1_0_2_4 , "GB" )
__SCREAMING_SNAKE_CASE : Optional[int] = sorted(_SCREAMING_SNAKE_CASE )
_remove_dup_initializers_from_model(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE : Any = "optimized_" + model_file_name
__SCREAMING_SNAKE_CASE : Union[str, Any] = os.path.join(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
onnx.save(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
return new_model
| 564
| 0
|
import gc
import tempfile
import unittest
import numpy as np
import torch
from diffusers import VersatileDiffusionTextToImagePipeline
from diffusers.utils.testing_utils import nightly, require_torch_gpu, torch_device
a = False
class _A ( unittest.TestCase ):
pass
@nightly
@require_torch_gpu
class _A ( unittest.TestCase ):
def UpperCAmelCase ( self ):
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def UpperCAmelCase ( self ):
_UpperCAmelCase = VersatileDiffusionTextToImagePipeline.from_pretrained("""shi-labs/versatile-diffusion""" )
# remove text_unet
pipe.remove_unused_weights()
pipe.to(_SCREAMING_SNAKE_CASE )
pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = """A painting of a squirrel eating a burger """
_UpperCAmelCase = torch.manual_seed(0 )
_UpperCAmelCase = pipe(
prompt=_SCREAMING_SNAKE_CASE , generator=_SCREAMING_SNAKE_CASE , guidance_scale=7.5 , num_inference_steps=2 , output_type="""numpy""" ).images
with tempfile.TemporaryDirectory() as tmpdirname:
pipe.save_pretrained(_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = VersatileDiffusionTextToImagePipeline.from_pretrained(_SCREAMING_SNAKE_CASE )
pipe.to(_SCREAMING_SNAKE_CASE )
pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = generator.manual_seed(0 )
_UpperCAmelCase = pipe(
prompt=_SCREAMING_SNAKE_CASE , generator=_SCREAMING_SNAKE_CASE , 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 UpperCAmelCase ( self ):
_UpperCAmelCase = VersatileDiffusionTextToImagePipeline.from_pretrained(
"""shi-labs/versatile-diffusion""" , torch_dtype=torch.floataa )
pipe.to(_SCREAMING_SNAKE_CASE )
pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = """A painting of a squirrel eating a burger """
_UpperCAmelCase = torch.manual_seed(0 )
_UpperCAmelCase = pipe(
prompt=_SCREAMING_SNAKE_CASE , generator=_SCREAMING_SNAKE_CASE , guidance_scale=7.5 , num_inference_steps=50 , output_type="""numpy""" ).images
_UpperCAmelCase = image[0, 253:256, 253:256, -1]
assert image.shape == (1, 512, 512, 3)
_UpperCAmelCase = np.array([0.3367, 0.3169, 0.2656, 0.3870, 0.4790, 0.3796, 0.4009, 0.4878, 0.4778] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
| 518
|
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_torch_available,
)
a = {
"configuration_speecht5": [
"SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP",
"SPEECHT5_PRETRAINED_HIFIGAN_CONFIG_ARCHIVE_MAP",
"SpeechT5Config",
"SpeechT5HifiGanConfig",
],
"feature_extraction_speecht5": ["SpeechT5FeatureExtractor"],
"processing_speecht5": ["SpeechT5Processor"],
}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a = ["SpeechT5Tokenizer"]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a = [
"SPEECHT5_PRETRAINED_MODEL_ARCHIVE_LIST",
"SpeechT5ForSpeechToText",
"SpeechT5ForSpeechToSpeech",
"SpeechT5ForTextToSpeech",
"SpeechT5Model",
"SpeechT5PreTrainedModel",
"SpeechT5HifiGan",
]
if TYPE_CHECKING:
from .configuration_speechta import (
SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP,
SPEECHT5_PRETRAINED_HIFIGAN_CONFIG_ARCHIVE_MAP,
SpeechTaConfig,
SpeechTaHifiGanConfig,
)
from .feature_extraction_speechta import SpeechTaFeatureExtractor
from .processing_speechta import SpeechTaProcessor
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_speechta import SpeechTaTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_speechta import (
SPEECHT5_PRETRAINED_MODEL_ARCHIVE_LIST,
SpeechTaForSpeechToSpeech,
SpeechTaForSpeechToText,
SpeechTaForTextToSpeech,
SpeechTaHifiGan,
SpeechTaModel,
SpeechTaPreTrainedModel,
)
else:
import sys
a = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 518
| 1
|
def lowercase ( _a ) -> int:
return 1 if digit in (0, 1) else (digit * factorial(digit - 1 ))
def lowercase ( _a ) -> bool:
UpperCAmelCase_: Any = 0
UpperCAmelCase_: List[str] = number
while duplicate > 0:
UpperCAmelCase_: List[Any] = divmod(_a ,10 )
fact_sum += factorial(_a )
return fact_sum == number
if __name__ == "__main__":
print("""Program to check whether a number is a Krisnamurthy Number or not.""")
_lowerCAmelCase = int(input("""Enter number: """).strip())
print(
F"""{number} is {'' if krishnamurthy(number) else 'not '}a Krishnamurthy Number."""
)
| 704
|
from __future__ import annotations
_lowerCAmelCase = [-10, -5, 0, 5, 5.1, 11, 13, 21, 3, 4, -21, -10, -5, -1, 0]
_lowerCAmelCase = [-5, 0, 5, 5.1, 11, 13, 21, -1, 4, -1, -10, -5, -1, 0, -1]
def lowercase ( _a ) -> list[float]:
UpperCAmelCase_: Dict = []
UpperCAmelCase_: List[Any] = len(_a )
for i in range(_a ):
UpperCAmelCase_: float = -1
for j in range(i + 1 ,_a ):
if arr[i] < arr[j]:
UpperCAmelCase_: List[str] = arr[j]
break
result.append(_a )
return result
def lowercase ( _a ) -> list[float]:
UpperCAmelCase_: List[Any] = []
for i, outer in enumerate(_a ):
UpperCAmelCase_: float = -1
for inner in arr[i + 1 :]:
if outer < inner:
UpperCAmelCase_: Union[str, Any] = inner
break
result.append(_a )
return result
def lowercase ( _a ) -> list[float]:
UpperCAmelCase_: Union[str, Any] = len(_a )
UpperCAmelCase_: list[float] = []
UpperCAmelCase_: list[float] = [-1] * arr_size
for index in reversed(range(_a ) ):
if stack:
while stack[-1] <= arr[index]:
stack.pop()
if not stack:
break
if stack:
UpperCAmelCase_: Union[str, Any] = stack[-1]
stack.append(arr[index] )
return result
if __name__ == "__main__":
from doctest import testmod
from timeit import timeit
testmod()
print(next_greatest_element_slow(arr))
print(next_greatest_element_fast(arr))
print(next_greatest_element(arr))
_lowerCAmelCase = (
"""from __main__ import arr, next_greatest_element_slow, """
"""next_greatest_element_fast, next_greatest_element"""
)
print(
"""next_greatest_element_slow():""",
timeit("""next_greatest_element_slow(arr)""", setup=setup),
)
print(
"""next_greatest_element_fast():""",
timeit("""next_greatest_element_fast(arr)""", setup=setup),
)
print(
""" next_greatest_element():""",
timeit("""next_greatest_element(arr)""", setup=setup),
)
| 306
| 0
|
'''simple docstring'''
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
lowerCAmelCase : Dict = '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_( A : List[str] , A : Union[str, Any] , A : List[str]=None , A : Dict=None , A : List[Any]=None , A : int=None , A : Tuple=None , A : int=None , ):
if attention_mask is None:
UpperCamelCase = np.where(input_ids != config.pad_token_id , 1 , 0)
if decoder_attention_mask is None:
UpperCamelCase = np.where(decoder_input_ids != config.pad_token_id , 1 , 0)
if head_mask is None:
UpperCamelCase = np.ones((config.encoder_layers, config.encoder_attention_heads))
if decoder_head_mask is None:
UpperCamelCase = np.ones((config.decoder_layers, config.decoder_attention_heads))
if cross_attn_head_mask is None:
UpperCamelCase = 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 SCREAMING_SNAKE_CASE__ :
def __init__( self , A_ , A_=13 , A_=7 , A_=True , A_=False , A_=99 , A_=16 , A_=2 , A_=4 , A_=4 , A_="gelu" , A_=0.1 , A_=0.1 , A_=32 , A_=2 , A_=1 , A_=0 , A_=0.02 , )-> Any:
'''simple docstring'''
UpperCamelCase = parent
UpperCamelCase = batch_size
UpperCamelCase = seq_length
UpperCamelCase = is_training
UpperCamelCase = use_labels
UpperCamelCase = vocab_size
UpperCamelCase = hidden_size
UpperCamelCase = num_hidden_layers
UpperCamelCase = num_attention_heads
UpperCamelCase = intermediate_size
UpperCamelCase = hidden_act
UpperCamelCase = hidden_dropout_prob
UpperCamelCase = attention_probs_dropout_prob
UpperCamelCase = max_position_embeddings
UpperCamelCase = eos_token_id
UpperCamelCase = pad_token_id
UpperCamelCase = bos_token_id
UpperCamelCase = initializer_range
def UpperCAmelCase_ ( self )-> Any:
'''simple docstring'''
UpperCamelCase = np.clip(ids_tensor([self.batch_size, self.seq_length - 1] , self.vocab_size ) , 3 , self.vocab_size )
UpperCamelCase = np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1) , dtype=np.intaa )) , -1 )
UpperCamelCase = shift_tokens_right(UpperCamelCase__ , 1 , 2 )
UpperCamelCase = 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=UpperCamelCase__ , )
UpperCamelCase = prepare_blenderbot_inputs_dict(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
return config, inputs_dict
def UpperCAmelCase_ ( self )-> Any:
'''simple docstring'''
UpperCamelCase , UpperCamelCase = self.prepare_config_and_inputs()
return config, inputs_dict
def UpperCAmelCase_ ( self , A_ , A_ , A_ )-> Dict:
'''simple docstring'''
UpperCamelCase = 20
UpperCamelCase = model_class_name(UpperCamelCase__ )
UpperCamelCase = model.encode(inputs_dict['input_ids'] )
UpperCamelCase , UpperCamelCase = (
inputs_dict['decoder_input_ids'],
inputs_dict['decoder_attention_mask'],
)
UpperCamelCase = model.init_cache(decoder_input_ids.shape[0] , UpperCamelCase__ , UpperCamelCase__ )
UpperCamelCase = jnp.ones((decoder_input_ids.shape[0], max_decoder_length) , dtype='i4' )
UpperCamelCase = jnp.broadcast_to(
jnp.arange(decoder_input_ids.shape[-1] - 1 )[None, :] , (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1) , )
UpperCamelCase = model.decode(
decoder_input_ids[:, :-1] , UpperCamelCase__ , decoder_attention_mask=UpperCamelCase__ , past_key_values=UpperCamelCase__ , decoder_position_ids=UpperCamelCase__ , )
UpperCamelCase = jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]] , dtype='i4' )
UpperCamelCase = model.decode(
decoder_input_ids[:, -1:] , UpperCamelCase__ , decoder_attention_mask=UpperCamelCase__ , past_key_values=outputs_cache.past_key_values , decoder_position_ids=UpperCamelCase__ , )
UpperCamelCase = model.decode(UpperCamelCase__ , UpperCamelCase__ )
UpperCamelCase = 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 , A_ , A_ , A_ )-> int:
'''simple docstring'''
UpperCamelCase = 20
UpperCamelCase = model_class_name(UpperCamelCase__ )
UpperCamelCase = model.encode(inputs_dict['input_ids'] )
UpperCamelCase , UpperCamelCase = (
inputs_dict['decoder_input_ids'],
inputs_dict['decoder_attention_mask'],
)
UpperCamelCase = jnp.concatenate(
[
decoder_attention_mask,
jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1]) ),
] , axis=-1 , )
UpperCamelCase = model.init_cache(decoder_input_ids.shape[0] , UpperCamelCase__ , UpperCamelCase__ )
UpperCamelCase = jnp.broadcast_to(
jnp.arange(decoder_input_ids.shape[-1] - 1 )[None, :] , (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1) , )
UpperCamelCase = model.decode(
decoder_input_ids[:, :-1] , UpperCamelCase__ , decoder_attention_mask=UpperCamelCase__ , past_key_values=UpperCamelCase__ , decoder_position_ids=UpperCamelCase__ , )
UpperCamelCase = jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]] , dtype='i4' )
UpperCamelCase = model.decode(
decoder_input_ids[:, -1:] , UpperCamelCase__ , past_key_values=outputs_cache.past_key_values , decoder_attention_mask=UpperCamelCase__ , decoder_position_ids=UpperCamelCase__ , )
UpperCamelCase = model.decode(UpperCamelCase__ , UpperCamelCase__ , decoder_attention_mask=UpperCamelCase__ )
UpperCamelCase = 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 SCREAMING_SNAKE_CASE__ ( unittest.TestCase):
lowerCAmelCase_ = 99
def UpperCAmelCase_ ( self )-> List[str]:
'''simple docstring'''
UpperCamelCase = 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 , )
UpperCamelCase = input_ids.shape[0]
UpperCamelCase = 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 )-> List[Any]:
'''simple docstring'''
UpperCamelCase , UpperCamelCase , UpperCamelCase = self._get_config_and_data()
UpperCamelCase = FlaxBlenderbotForConditionalGeneration(UpperCamelCase__ )
UpperCamelCase = lm_model(input_ids=UpperCamelCase__ )
UpperCamelCase = (batch_size, input_ids.shape[1], config.vocab_size)
self.assertEqual(outputs['logits'].shape , UpperCamelCase__ )
def UpperCAmelCase_ ( self )-> Any:
'''simple docstring'''
UpperCamelCase = 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 , )
UpperCamelCase = FlaxBlenderbotForConditionalGeneration(UpperCamelCase__ )
UpperCamelCase = np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]] , dtype=np.intaa )
UpperCamelCase = np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]] , dtype=np.intaa )
UpperCamelCase = lm_model(input_ids=UpperCamelCase__ , decoder_input_ids=UpperCamelCase__ )
UpperCamelCase = (*summary.shape, config.vocab_size)
self.assertEqual(outputs['logits'].shape , UpperCamelCase__ )
def UpperCAmelCase_ ( self )-> Union[str, Any]:
'''simple docstring'''
UpperCamelCase = np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]] , dtype=np.intaa )
UpperCamelCase = shift_tokens_right(UpperCamelCase__ , 1 , 2 )
UpperCamelCase = np.equal(UpperCamelCase__ , 1 ).astype(np.floataa ).sum()
UpperCamelCase = np.equal(UpperCamelCase__ , 1 ).astype(np.floataa ).sum()
self.assertEqual(shifted.shape , input_ids.shape )
self.assertEqual(UpperCamelCase__ , n_pad_before - 1 )
self.assertTrue(np.equal(shifted[:, 0] , 2 ).all() )
@require_flax
class SCREAMING_SNAKE_CASE__ ( lowercase_ , unittest.TestCase , lowercase_):
lowerCAmelCase_ = True
lowerCAmelCase_ = (
(
FlaxBlenderbotModel,
FlaxBlenderbotForConditionalGeneration,
)
if is_flax_available()
else ()
)
lowerCAmelCase_ = (FlaxBlenderbotForConditionalGeneration,) if is_flax_available() else ()
def UpperCAmelCase_ ( self )-> Tuple:
'''simple docstring'''
UpperCamelCase = FlaxBlenderbotModelTester(self )
def UpperCAmelCase_ ( self )-> Optional[Any]:
'''simple docstring'''
UpperCamelCase , UpperCamelCase = self.model_tester.prepare_config_and_inputs()
for model_class in self.all_model_classes:
self.model_tester.check_use_cache_forward(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
def UpperCAmelCase_ ( self )-> List[Any]:
'''simple docstring'''
UpperCamelCase , UpperCamelCase = 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(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
def UpperCAmelCase_ ( self )-> Optional[int]:
'''simple docstring'''
UpperCamelCase , UpperCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__ ):
UpperCamelCase = self._prepare_for_class(UpperCamelCase__ , UpperCamelCase__ )
UpperCamelCase = model_class(UpperCamelCase__ )
@jax.jit
def encode_jitted(A_ , A_=None , **A_ ):
return model.encode(input_ids=UpperCamelCase__ , attention_mask=UpperCamelCase__ )
with self.subTest('JIT Enabled' ):
UpperCamelCase = encode_jitted(**UpperCamelCase__ ).to_tuple()
with self.subTest('JIT Disabled' ):
with jax.disable_jit():
UpperCamelCase = encode_jitted(**UpperCamelCase__ ).to_tuple()
self.assertEqual(len(UpperCamelCase__ ) , len(UpperCamelCase__ ) )
for jitted_output, output in zip(UpperCamelCase__ , UpperCamelCase__ ):
self.assertEqual(jitted_output.shape , output.shape )
def UpperCAmelCase_ ( self )-> List[Any]:
'''simple docstring'''
UpperCamelCase , UpperCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__ ):
UpperCamelCase = model_class(UpperCamelCase__ )
UpperCamelCase = model.encode(inputs_dict['input_ids'] , inputs_dict['attention_mask'] )
UpperCamelCase = {
'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(A_ , A_ , A_ ):
return model.decode(
decoder_input_ids=UpperCamelCase__ , decoder_attention_mask=UpperCamelCase__ , encoder_outputs=UpperCamelCase__ , )
with self.subTest('JIT Enabled' ):
UpperCamelCase = decode_jitted(**UpperCamelCase__ ).to_tuple()
with self.subTest('JIT Disabled' ):
with jax.disable_jit():
UpperCamelCase = decode_jitted(**UpperCamelCase__ ).to_tuple()
self.assertEqual(len(UpperCamelCase__ ) , len(UpperCamelCase__ ) )
for jitted_output, output in zip(UpperCamelCase__ , UpperCamelCase__ ):
self.assertEqual(jitted_output.shape , output.shape )
@slow
def UpperCAmelCase_ ( self )-> str:
'''simple docstring'''
for model_class_name in self.all_model_classes:
UpperCamelCase = model_class_name.from_pretrained('facebook/blenderbot-400M-distill' )
# FlaxBlenderbotForSequenceClassification expects eos token in input_ids
UpperCamelCase = np.ones((1, 1) ) * model.config.eos_token_id
UpperCamelCase = model(UpperCamelCase__ )
self.assertIsNotNone(UpperCamelCase__ )
@unittest.skipUnless(jax_device != 'cpu' , '3B test too slow on CPU.' )
@slow
def UpperCAmelCase_ ( self )-> Optional[int]:
'''simple docstring'''
UpperCamelCase = {'num_beams': 1, 'early_stopping': True, 'min_length': 15, 'max_length': 25}
UpperCamelCase = {'skip_special_tokens': True, 'clean_up_tokenization_spaces': True}
UpperCamelCase = FlaxBlenderbotForConditionalGeneration.from_pretrained('facebook/blenderbot-3B' , from_pt=UpperCamelCase__ )
UpperCamelCase = BlenderbotTokenizer.from_pretrained('facebook/blenderbot-3B' )
UpperCamelCase = ['Sam']
UpperCamelCase = tokenizer(UpperCamelCase__ , return_tensors='jax' )
UpperCamelCase = model.generate(**UpperCamelCase__ , **UpperCamelCase__ )
UpperCamelCase = 'Sam is a great name. It means \"sun\" in Gaelic.'
UpperCamelCase = tokenizer.batch_decode(UpperCamelCase__ , **UpperCamelCase__ )
assert generated_txt[0].strip() == tgt_text
| 3
|
from typing import List, Optional, Tuple, Union
import torch
from ...models import UNetaDModel
from ...schedulers import ScoreSdeVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class _lowerCAmelCase ( lowercase_ ):
"""simple docstring"""
_lowercase : UNetaDModel
_lowercase : ScoreSdeVeScheduler
def __init__( self : Union[str, Any] , UpperCamelCase__ : UNetaDModel , UpperCamelCase__ : ScoreSdeVeScheduler):
'''simple docstring'''
super().__init__()
self.register_modules(unet=UpperCamelCase__ , scheduler=UpperCamelCase__)
@torch.no_grad()
def __call__( self : Union[str, Any] , UpperCamelCase__ : int = 1 , UpperCamelCase__ : int = 2_0_0_0 , UpperCamelCase__ : Optional[Union[torch.Generator, List[torch.Generator]]] = None , UpperCamelCase__ : Optional[str] = "pil" , UpperCamelCase__ : bool = True , **UpperCamelCase__ : List[str] , ):
'''simple docstring'''
snake_case__ = self.unet.config.sample_size
snake_case__ = (batch_size, 3, img_size, img_size)
snake_case__ = self.unet
snake_case__ = randn_tensor(UpperCamelCase__ , generator=UpperCamelCase__) * self.scheduler.init_noise_sigma
snake_case__ = sample.to(self.device)
self.scheduler.set_timesteps(UpperCamelCase__)
self.scheduler.set_sigmas(UpperCamelCase__)
for i, t in enumerate(self.progress_bar(self.scheduler.timesteps)):
snake_case__ = self.scheduler.sigmas[i] * torch.ones(shape[0] , device=self.device)
# correction step
for _ in range(self.scheduler.config.correct_steps):
snake_case__ = self.unet(UpperCamelCase__ , UpperCamelCase__).sample
snake_case__ = self.scheduler.step_correct(UpperCamelCase__ , UpperCamelCase__ , generator=UpperCamelCase__).prev_sample
# prediction step
snake_case__ = model(UpperCamelCase__ , UpperCamelCase__).sample
snake_case__ = self.scheduler.step_pred(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , generator=UpperCamelCase__)
snake_case__ , snake_case__ = output.prev_sample, output.prev_sample_mean
snake_case__ = sample_mean.clamp(0 , 1)
snake_case__ = sample.cpu().permute(0 , 2 , 3 , 1).numpy()
if output_type == "pil":
snake_case__ = self.numpy_to_pil(UpperCamelCase__)
if not return_dict:
return (sample,)
return ImagePipelineOutput(images=UpperCamelCase__)
| 654
| 0
|
"""simple docstring"""
def lowercase_ ( _lowerCamelCase: int ) -> int:
'''simple docstring'''
__lowerCamelCase : Any = abs(_lowerCamelCase )
__lowerCamelCase : List[str] = 0
while n > 0:
res += n % 10
n //= 10
return res
def lowercase_ ( _lowerCamelCase: int ) -> int:
'''simple docstring'''
__lowerCamelCase : List[str] = abs(_lowerCamelCase )
return n if n < 10 else n % 10 + sum_of_digits(n // 10 )
def lowercase_ ( _lowerCamelCase: int ) -> int:
'''simple docstring'''
return sum(int(_lowerCamelCase ) for c in str(abs(_lowerCamelCase ) ) )
def lowercase_ ( ) -> None:
'''simple docstring'''
from collections.abc import Callable
from timeit import timeit
def benchmark_a_function(_lowerCamelCase: Callable , _lowerCamelCase: int ) -> None:
__lowerCamelCase : Any = F"""{func.__name__}({value})"""
__lowerCamelCase : Union[str, Any] = timeit(F"""__main__.{call}""" , setup="import __main__" )
print(F"""{call:56} = {func(_lowerCamelCase )} -- {timing:.4f} seconds""" )
for value in (262144, 1125899906842624, 1267650600228229401496703205376):
for func in (sum_of_digits, sum_of_digits_recursion, sum_of_digits_compact):
benchmark_a_function(_lowerCamelCase , _lowerCamelCase )
print()
if __name__ == "__main__":
import doctest
doctest.testmod()
benchmark()
| 366
|
"""simple docstring"""
import gc
import threading
import time
import psutil
import torch
class _snake_case :
def __init__( self : str ):
__lowerCamelCase : Optional[Any] = psutil.Process()
__lowerCamelCase : List[Any] = False
def lowerCamelCase__ ( self : str ):
__lowerCamelCase : List[Any] = -1
while True:
__lowerCamelCase : Union[str, Any] = max(self.process.memory_info().rss , self.cpu_memory_peak )
# can't sleep or will not catch the peak right (this comment is here on purpose)
if not self.peak_monitoring:
break
def lowerCamelCase__ ( self : Any ):
__lowerCamelCase : Optional[Any] = True
__lowerCamelCase : Union[str, Any] = threading.Thread(target=self.peak_monitor )
__lowerCamelCase : Optional[Any] = True
self.thread.start()
def lowerCamelCase__ ( self : Dict ):
__lowerCamelCase : str = False
self.thread.join()
return self.cpu_memory_peak
__A = PeakCPUMemory()
def lowercase_ ( ) -> Union[str, Any]:
'''simple docstring'''
__lowerCamelCase : Any = {"time": time.time()}
gc.collect()
torch.cuda.empty_cache()
# CPU mem
__lowerCamelCase : Optional[int] = psutil.Process().memory_info().rss
cpu_peak_tracker.start()
# GPU mem
for i in range(torch.cuda.device_count() ):
__lowerCamelCase : int = torch.cuda.memory_allocated(_lowerCamelCase )
torch.cuda.reset_peak_memory_stats()
return measures
def lowercase_ ( _lowerCamelCase: Optional[Any] ) -> Tuple:
'''simple docstring'''
__lowerCamelCase : Tuple = {"time": time.time() - start_measures["time"]}
gc.collect()
torch.cuda.empty_cache()
# CPU mem
__lowerCamelCase : Dict = (psutil.Process().memory_info().rss - start_measures["cpu"]) / 2**20
__lowerCamelCase : Tuple = (cpu_peak_tracker.stop() - start_measures["cpu"]) / 2**20
# GPU mem
for i in range(torch.cuda.device_count() ):
__lowerCamelCase : Any = (torch.cuda.memory_allocated(_lowerCamelCase ) - start_measures[str(_lowerCamelCase )]) / 2**20
__lowerCamelCase : Optional[int] = (torch.cuda.max_memory_allocated(_lowerCamelCase ) - start_measures[str(_lowerCamelCase )]) / 2**20
return measures
def lowercase_ ( _lowerCamelCase: int , _lowerCamelCase: List[Any] ) -> Optional[int]:
'''simple docstring'''
print(F"""{description}:""" )
print(F"""- Time: {measures["time"]:.2f}s""" )
for i in range(torch.cuda.device_count() ):
print(F"""- GPU {i} allocated: {measures[str(_lowerCamelCase )]:.2f}MiB""" )
__lowerCamelCase : List[Any] = measures[F"""{i}-peak"""]
print(F"""- GPU {i} peak: {peak:.2f}MiB""" )
print(F"""- CPU RAM allocated: {measures["cpu"]:.2f}MiB""" )
print(F"""- CPU RAM peak: {measures["cpu-peak"]:.2f}MiB""" )
| 366
| 1
|
'''simple docstring'''
import random
import unittest
import numpy as np
from diffusers import (
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionImgaImgPipeline,
PNDMScheduler,
)
from diffusers.utils import floats_tensor
from diffusers.utils.testing_utils import (
is_onnx_available,
load_image,
nightly,
require_onnxruntime,
require_torch_gpu,
)
from ..test_pipelines_onnx_common import OnnxPipelineTesterMixin
if is_onnx_available():
import onnxruntime as ort
class _SCREAMING_SNAKE_CASE( snake_case__ , unittest.TestCase ):
A_ : List[Any] = "hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline"
def __lowerCamelCase ( self : Any , UpperCamelCase_ : Tuple=0 ) -> Union[str, Any]:
SCREAMING_SNAKE_CASE__ :Dict = floats_tensor((1, 3, 1_28, 1_28) , rng=random.Random(snake_case__ ) )
SCREAMING_SNAKE_CASE__ :int = np.random.RandomState(snake_case__ )
SCREAMING_SNAKE_CASE__ :List[str] = {
"prompt": "A painting of a squirrel eating a burger",
"image": image,
"generator": generator,
"num_inference_steps": 3,
"strength": 0.75,
"guidance_scale": 7.5,
"output_type": "numpy",
}
return inputs
def __lowerCamelCase ( self : List[str] ) -> List[str]:
SCREAMING_SNAKE_CASE__ :int = OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint , provider='CPUExecutionProvider' )
pipe.set_progress_bar_config(disable=snake_case__ )
SCREAMING_SNAKE_CASE__ :Tuple = self.get_dummy_inputs()
SCREAMING_SNAKE_CASE__ :Optional[Any] = pipe(**snake_case__ ).images
SCREAMING_SNAKE_CASE__ :List[str] = image[0, -3:, -3:, -1].flatten()
assert image.shape == (1, 1_28, 1_28, 3)
SCREAMING_SNAKE_CASE__ :str = np.array([0.6_9643, 0.5_8484, 0.5_0314, 0.5_8760, 0.5_5368, 0.5_9643, 0.5_1529, 0.4_1217, 0.4_9087] )
assert np.abs(image_slice - expected_slice ).max() < 1e-1
def __lowerCamelCase ( self : Dict ) -> Any:
SCREAMING_SNAKE_CASE__ :List[str] = OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint , provider='CPUExecutionProvider' )
SCREAMING_SNAKE_CASE__ :int = PNDMScheduler.from_config(pipe.scheduler.config , skip_prk_steps=snake_case__ )
pipe.set_progress_bar_config(disable=snake_case__ )
SCREAMING_SNAKE_CASE__ :Dict = self.get_dummy_inputs()
SCREAMING_SNAKE_CASE__ :Optional[int] = pipe(**snake_case__ ).images
SCREAMING_SNAKE_CASE__ :int = image[0, -3:, -3:, -1]
assert image.shape == (1, 1_28, 1_28, 3)
SCREAMING_SNAKE_CASE__ :Any = np.array([0.6_1737, 0.5_4642, 0.5_3183, 0.5_4465, 0.5_2742, 0.6_0525, 0.4_9969, 0.4_0655, 0.4_8154] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-1
def __lowerCamelCase ( self : Union[str, Any] ) -> Optional[int]:
SCREAMING_SNAKE_CASE__ :int = OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint , provider='CPUExecutionProvider' )
SCREAMING_SNAKE_CASE__ :Optional[int] = LMSDiscreteScheduler.from_config(pipe.scheduler.config )
pipe.set_progress_bar_config(disable=snake_case__ )
# warmup pass to apply optimizations
SCREAMING_SNAKE_CASE__ :List[Any] = pipe(**self.get_dummy_inputs() )
SCREAMING_SNAKE_CASE__ :int = self.get_dummy_inputs()
SCREAMING_SNAKE_CASE__ :Dict = pipe(**snake_case__ ).images
SCREAMING_SNAKE_CASE__ :List[str] = image[0, -3:, -3:, -1]
assert image.shape == (1, 1_28, 1_28, 3)
SCREAMING_SNAKE_CASE__ :str = np.array([0.5_2761, 0.5_9977, 0.4_9033, 0.4_9619, 0.5_4282, 0.5_0311, 0.4_7600, 0.4_0918, 0.4_5203] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-1
def __lowerCamelCase ( self : Optional[int] ) -> List[str]:
SCREAMING_SNAKE_CASE__ :int = OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint , provider='CPUExecutionProvider' )
SCREAMING_SNAKE_CASE__ :Optional[int] = EulerDiscreteScheduler.from_config(pipe.scheduler.config )
pipe.set_progress_bar_config(disable=snake_case__ )
SCREAMING_SNAKE_CASE__ :Optional[int] = self.get_dummy_inputs()
SCREAMING_SNAKE_CASE__ :Optional[int] = pipe(**snake_case__ ).images
SCREAMING_SNAKE_CASE__ :int = image[0, -3:, -3:, -1]
assert image.shape == (1, 1_28, 1_28, 3)
SCREAMING_SNAKE_CASE__ :Tuple = np.array([0.5_2911, 0.6_0004, 0.4_9229, 0.4_9805, 0.5_4502, 0.5_0680, 0.4_7777, 0.4_1028, 0.4_5304] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-1
def __lowerCamelCase ( self : str ) -> Any:
SCREAMING_SNAKE_CASE__ :Tuple = OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint , provider='CPUExecutionProvider' )
SCREAMING_SNAKE_CASE__ :List[Any] = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config )
pipe.set_progress_bar_config(disable=snake_case__ )
SCREAMING_SNAKE_CASE__ :int = self.get_dummy_inputs()
SCREAMING_SNAKE_CASE__ :str = pipe(**snake_case__ ).images
SCREAMING_SNAKE_CASE__ :Optional[Any] = image[0, -3:, -3:, -1]
assert image.shape == (1, 1_28, 1_28, 3)
SCREAMING_SNAKE_CASE__ :Tuple = np.array([0.5_2911, 0.6_0004, 0.4_9229, 0.4_9805, 0.5_4502, 0.5_0680, 0.4_7777, 0.4_1028, 0.4_5304] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-1
def __lowerCamelCase ( self : str ) -> Optional[Any]:
SCREAMING_SNAKE_CASE__ :Optional[int] = OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint , provider='CPUExecutionProvider' )
SCREAMING_SNAKE_CASE__ :List[Any] = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config )
pipe.set_progress_bar_config(disable=snake_case__ )
SCREAMING_SNAKE_CASE__ :Dict = self.get_dummy_inputs()
SCREAMING_SNAKE_CASE__ :int = pipe(**snake_case__ ).images
SCREAMING_SNAKE_CASE__ :Any = image[0, -3:, -3:, -1]
assert image.shape == (1, 1_28, 1_28, 3)
SCREAMING_SNAKE_CASE__ :str = np.array([0.6_5331, 0.5_8277, 0.4_8204, 0.5_6059, 0.5_3665, 0.5_6235, 0.5_0969, 0.4_0009, 0.4_6552] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-1
@nightly
@require_onnxruntime
@require_torch_gpu
class _SCREAMING_SNAKE_CASE( unittest.TestCase ):
@property
def __lowerCamelCase ( self : int ) -> Union[str, Any]:
return (
"CUDAExecutionProvider",
{
"gpu_mem_limit": "15000000000", # 15GB
"arena_extend_strategy": "kSameAsRequested",
},
)
@property
def __lowerCamelCase ( self : List[Any] ) -> List[str]:
SCREAMING_SNAKE_CASE__ :Optional[int] = ort.SessionOptions()
SCREAMING_SNAKE_CASE__ :Any = False
return options
def __lowerCamelCase ( self : int ) -> Optional[Any]:
SCREAMING_SNAKE_CASE__ :Any = load_image(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
'/img2img/sketch-mountains-input.jpg' )
SCREAMING_SNAKE_CASE__ :Optional[Any] = init_image.resize((7_68, 5_12) )
# using the PNDM scheduler by default
SCREAMING_SNAKE_CASE__ :str = OnnxStableDiffusionImgaImgPipeline.from_pretrained(
'CompVis/stable-diffusion-v1-4' , revision='onnx' , safety_checker=snake_case__ , feature_extractor=snake_case__ , provider=self.gpu_provider , sess_options=self.gpu_options , )
pipe.set_progress_bar_config(disable=snake_case__ )
SCREAMING_SNAKE_CASE__ :Union[str, Any] = "A fantasy landscape, trending on artstation"
SCREAMING_SNAKE_CASE__ :Optional[Any] = np.random.RandomState(0 )
SCREAMING_SNAKE_CASE__ :Any = pipe(
prompt=snake_case__ , image=snake_case__ , strength=0.75 , guidance_scale=7.5 , num_inference_steps=10 , generator=snake_case__ , output_type='np' , )
SCREAMING_SNAKE_CASE__ :int = output.images
SCREAMING_SNAKE_CASE__ :int = images[0, 2_55:2_58, 3_83:3_86, -1]
assert images.shape == (1, 5_12, 7_68, 3)
SCREAMING_SNAKE_CASE__ :Optional[Any] = np.array([0.4909, 0.5059, 0.5372, 0.4623, 0.4876, 0.5049, 0.4820, 0.4956, 0.5019] )
# TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues
assert np.abs(image_slice.flatten() - expected_slice ).max() < 2e-2
def __lowerCamelCase ( self : Optional[Any] ) -> Tuple:
SCREAMING_SNAKE_CASE__ :Optional[Any] = load_image(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
'/img2img/sketch-mountains-input.jpg' )
SCREAMING_SNAKE_CASE__ :Dict = init_image.resize((7_68, 5_12) )
SCREAMING_SNAKE_CASE__ :Optional[Any] = LMSDiscreteScheduler.from_pretrained(
'runwayml/stable-diffusion-v1-5' , subfolder='scheduler' , revision='onnx' )
SCREAMING_SNAKE_CASE__ :Union[str, Any] = OnnxStableDiffusionImgaImgPipeline.from_pretrained(
'runwayml/stable-diffusion-v1-5' , revision='onnx' , scheduler=snake_case__ , safety_checker=snake_case__ , feature_extractor=snake_case__ , provider=self.gpu_provider , sess_options=self.gpu_options , )
pipe.set_progress_bar_config(disable=snake_case__ )
SCREAMING_SNAKE_CASE__ :Any = "A fantasy landscape, trending on artstation"
SCREAMING_SNAKE_CASE__ :Optional[Any] = np.random.RandomState(0 )
SCREAMING_SNAKE_CASE__ :int = pipe(
prompt=snake_case__ , image=snake_case__ , strength=0.75 , guidance_scale=7.5 , num_inference_steps=20 , generator=snake_case__ , output_type='np' , )
SCREAMING_SNAKE_CASE__ :int = output.images
SCREAMING_SNAKE_CASE__ :Optional[Any] = images[0, 2_55:2_58, 3_83:3_86, -1]
assert images.shape == (1, 5_12, 7_68, 3)
SCREAMING_SNAKE_CASE__ :int = np.array([0.8043, 0.926, 0.9581, 0.8119, 0.8954, 0.913, 0.7209, 0.7463, 0.7431] )
# TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues
assert np.abs(image_slice.flatten() - expected_slice ).max() < 2e-2
| 209
|
"""simple docstring"""
from typing import Optional, Tuple, Union
import tensorflow as tf
from ...activations_tf import ACTaFN
from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward
from ...modeling_tf_outputs import (
TFBaseModelOutputWithNoAttention,
TFBaseModelOutputWithPoolingAndNoAttention,
TFSequenceClassifierOutput,
)
from ...modeling_tf_utils import TFPreTrainedModel, TFSequenceClassificationLoss, keras_serializable, unpack_inputs
from ...tf_utils import shape_list
from ...utils import logging
from .configuration_regnet import RegNetConfig
A__ : Optional[Any] = logging.get_logger(__name__)
# General docstring
A__ : List[str] = 'RegNetConfig'
# Base docstring
A__ : List[Any] = 'facebook/regnet-y-040'
A__ : Any = [1, 1_088, 7, 7]
# Image classification docstring
A__ : Any = 'facebook/regnet-y-040'
A__ : int = 'tabby, tabby cat'
A__ : Any = [
'facebook/regnet-y-040',
# See all regnet models at https://huggingface.co/models?filter=regnet
]
class lowercase__ ( tf.keras.layers.Layer ):
def __init__( self : Optional[Any] , snake_case__ : int , snake_case__ : int = 3 , snake_case__ : int = 1 , snake_case__ : int = 1 , snake_case__ : Optional[str] = "relu" , **snake_case__ : Optional[int] , ):
super().__init__(**snake_case__ )
# The padding and conv has been verified in
# https://colab.research.google.com/gist/sayakpaul/854bc10eeaf21c9ee2119e0b9f3841a7/scratchpad.ipynb
lowerCamelCase_ : Tuple =tf.keras.layers.ZeroPaddingaD(padding=kernel_size // 2 )
lowerCamelCase_ : Optional[Any] =tf.keras.layers.ConvaD(
filters=snake_case__ , kernel_size=snake_case__ , strides=snake_case__ , padding="VALID" , groups=snake_case__ , use_bias=snake_case__ , name="convolution" , )
lowerCamelCase_ : List[str] =tf.keras.layers.BatchNormalization(epsilon=1E-5 , momentum=0.9 , name="normalization" )
lowerCamelCase_ : List[Any] =ACTaFN[activation] if activation is not None else tf.identity
def UpperCAmelCase__ ( self : Optional[Any] , snake_case__ : str ):
lowerCamelCase_ : str =self.convolution(self.padding(snake_case__ ) )
lowerCamelCase_ : int =self.normalization(snake_case__ )
lowerCamelCase_ : int =self.activation(snake_case__ )
return hidden_state
class lowercase__ ( tf.keras.layers.Layer ):
def __init__( self : List[str] , snake_case__ : RegNetConfig , **snake_case__ : List[Any] ):
super().__init__(**snake_case__ )
lowerCamelCase_ : Union[str, Any] =config.num_channels
lowerCamelCase_ : str =TFRegNetConvLayer(
out_channels=config.embedding_size , kernel_size=3 , stride=2 , activation=config.hidden_act , name="embedder" , )
def UpperCAmelCase__ ( self : Union[str, Any] , snake_case__ : str ):
lowerCamelCase_ : str =shape_list(snake_case__ )[1]
if tf.executing_eagerly() and num_channels != self.num_channels:
raise ValueError(
"Make sure that the channel dimension of the pixel values match with the one set in the configuration." )
# When running on CPU, `tf.keras.layers.Conv2D` doesn't support `NCHW` format.
# So change the input format from `NCHW` to `NHWC`.
# shape = (batch_size, in_height, in_width, in_channels=num_channels)
lowerCamelCase_ : str =tf.transpose(snake_case__ , perm=(0, 2, 3, 1) )
lowerCamelCase_ : List[str] =self.embedder(snake_case__ )
return hidden_state
class lowercase__ ( tf.keras.layers.Layer ):
def __init__( self : List[str] , snake_case__ : int , snake_case__ : int = 2 , **snake_case__ : Tuple ):
super().__init__(**snake_case__ )
lowerCamelCase_ : Optional[int] =tf.keras.layers.ConvaD(
filters=snake_case__ , kernel_size=1 , strides=snake_case__ , use_bias=snake_case__ , name="convolution" )
lowerCamelCase_ : List[str] =tf.keras.layers.BatchNormalization(epsilon=1E-5 , momentum=0.9 , name="normalization" )
def UpperCAmelCase__ ( self : str , snake_case__ : tf.Tensor , snake_case__ : bool = False ):
return self.normalization(self.convolution(snake_case__ ) , training=snake_case__ )
class lowercase__ ( tf.keras.layers.Layer ):
def __init__( self : List[str] , snake_case__ : int , snake_case__ : int , **snake_case__ : Optional[int] ):
super().__init__(**snake_case__ )
lowerCamelCase_ : int =tf.keras.layers.GlobalAveragePoolingaD(keepdims=snake_case__ , name="pooler" )
lowerCamelCase_ : Tuple =[
tf.keras.layers.ConvaD(filters=snake_case__ , kernel_size=1 , activation="relu" , name="attention.0" ),
tf.keras.layers.ConvaD(filters=snake_case__ , kernel_size=1 , activation="sigmoid" , name="attention.2" ),
]
def UpperCAmelCase__ ( self : Tuple , snake_case__ : Tuple ):
# [batch_size, h, w, num_channels] -> [batch_size, 1, 1, num_channels]
lowerCamelCase_ : Any =self.pooler(snake_case__ )
for layer_module in self.attention:
lowerCamelCase_ : List[str] =layer_module(snake_case__ )
lowerCamelCase_ : str =hidden_state * pooled
return hidden_state
class lowercase__ ( tf.keras.layers.Layer ):
def __init__( self : str , snake_case__ : RegNetConfig , snake_case__ : int , snake_case__ : int , snake_case__ : int = 1 , **snake_case__ : Tuple ):
super().__init__(**snake_case__ )
lowerCamelCase_ : Any =in_channels != out_channels or stride != 1
lowerCamelCase_ : str =max(1 , out_channels // config.groups_width )
lowerCamelCase_ : Union[str, Any] =(
TFRegNetShortCut(snake_case__ , stride=snake_case__ , name="shortcut" )
if should_apply_shortcut
else tf.keras.layers.Activation("linear" , name="shortcut" )
)
# `self.layers` instead of `self.layer` because that is a reserved argument.
lowerCamelCase_ : int =[
TFRegNetConvLayer(snake_case__ , kernel_size=1 , activation=config.hidden_act , name="layer.0" ),
TFRegNetConvLayer(
snake_case__ , stride=snake_case__ , groups=snake_case__ , activation=config.hidden_act , name="layer.1" ),
TFRegNetConvLayer(snake_case__ , kernel_size=1 , activation=snake_case__ , name="layer.2" ),
]
lowerCamelCase_ : Tuple =ACTaFN[config.hidden_act]
def UpperCAmelCase__ ( self : Optional[Any] , snake_case__ : Optional[Any] ):
lowerCamelCase_ : Dict =hidden_state
for layer_module in self.layers:
lowerCamelCase_ : List[str] =layer_module(snake_case__ )
lowerCamelCase_ : str =self.shortcut(snake_case__ )
hidden_state += residual
lowerCamelCase_ : Optional[int] =self.activation(snake_case__ )
return hidden_state
class lowercase__ ( tf.keras.layers.Layer ):
def __init__( self : Union[str, Any] , snake_case__ : RegNetConfig , snake_case__ : int , snake_case__ : int , snake_case__ : int = 1 , **snake_case__ : str ):
super().__init__(**snake_case__ )
lowerCamelCase_ : str =in_channels != out_channels or stride != 1
lowerCamelCase_ : Union[str, Any] =max(1 , out_channels // config.groups_width )
lowerCamelCase_ : Any =(
TFRegNetShortCut(snake_case__ , stride=snake_case__ , name="shortcut" )
if should_apply_shortcut
else tf.keras.layers.Activation("linear" , name="shortcut" )
)
lowerCamelCase_ : Dict =[
TFRegNetConvLayer(snake_case__ , kernel_size=1 , activation=config.hidden_act , name="layer.0" ),
TFRegNetConvLayer(
snake_case__ , stride=snake_case__ , groups=snake_case__ , activation=config.hidden_act , name="layer.1" ),
TFRegNetSELayer(snake_case__ , reduced_channels=int(round(in_channels / 4 ) ) , name="layer.2" ),
TFRegNetConvLayer(snake_case__ , kernel_size=1 , activation=snake_case__ , name="layer.3" ),
]
lowerCamelCase_ : Tuple =ACTaFN[config.hidden_act]
def UpperCAmelCase__ ( self : Tuple , snake_case__ : List[Any] ):
lowerCamelCase_ : str =hidden_state
for layer_module in self.layers:
lowerCamelCase_ : List[Any] =layer_module(snake_case__ )
lowerCamelCase_ : Dict =self.shortcut(snake_case__ )
hidden_state += residual
lowerCamelCase_ : List[Any] =self.activation(snake_case__ )
return hidden_state
class lowercase__ ( tf.keras.layers.Layer ):
def __init__( self : str , snake_case__ : RegNetConfig , snake_case__ : int , snake_case__ : int , snake_case__ : int = 2 , snake_case__ : int = 2 , **snake_case__ : Any ):
super().__init__(**snake_case__ )
lowerCamelCase_ : List[Any] =TFRegNetXLayer if config.layer_type == "x" else TFRegNetYLayer
lowerCamelCase_ : str =[
# downsampling is done in the first layer with stride of 2
layer(snake_case__ , snake_case__ , snake_case__ , stride=snake_case__ , name="layers.0" ),
*[layer(snake_case__ , snake_case__ , snake_case__ , name=F"""layers.{i+1}""" ) for i in range(depth - 1 )],
]
def UpperCAmelCase__ ( self : Optional[Any] , snake_case__ : Optional[Any] ):
for layer_module in self.layers:
lowerCamelCase_ : int =layer_module(snake_case__ )
return hidden_state
class lowercase__ ( tf.keras.layers.Layer ):
def __init__( self : Optional[Any] , snake_case__ : RegNetConfig , **snake_case__ : Union[str, Any] ):
super().__init__(**snake_case__ )
lowerCamelCase_ : Dict =[]
# based on `downsample_in_first_stage`, the first layer of the first stage may or may not downsample the input
self.stages.append(
TFRegNetStage(
snake_case__ , config.embedding_size , config.hidden_sizes[0] , stride=2 if config.downsample_in_first_stage else 1 , depth=config.depths[0] , name="stages.0" , ) )
lowerCamelCase_ : Optional[Any] =zip(config.hidden_sizes , config.hidden_sizes[1:] )
for i, ((in_channels, out_channels), depth) in enumerate(zip(snake_case__ , config.depths[1:] ) ):
self.stages.append(TFRegNetStage(snake_case__ , snake_case__ , snake_case__ , depth=snake_case__ , name=F"""stages.{i+1}""" ) )
def UpperCAmelCase__ ( self : Optional[int] , snake_case__ : tf.Tensor , snake_case__ : bool = False , snake_case__ : bool = True ):
lowerCamelCase_ : List[Any] =() if output_hidden_states else None
for stage_module in self.stages:
if output_hidden_states:
lowerCamelCase_ : Optional[int] =hidden_states + (hidden_state,)
lowerCamelCase_ : Dict =stage_module(snake_case__ )
if output_hidden_states:
lowerCamelCase_ : Dict =hidden_states + (hidden_state,)
if not return_dict:
return tuple(v for v in [hidden_state, hidden_states] if v is not None )
return TFBaseModelOutputWithNoAttention(last_hidden_state=snake_case__ , hidden_states=snake_case__ )
@keras_serializable
class lowercase__ ( tf.keras.layers.Layer ):
_UpperCAmelCase :Any = RegNetConfig
def __init__( self : Optional[Any] , snake_case__ : Union[str, Any] , **snake_case__ : Union[str, Any] ):
super().__init__(**snake_case__ )
lowerCamelCase_ : List[str] =config
lowerCamelCase_ : List[str] =TFRegNetEmbeddings(snake_case__ , name="embedder" )
lowerCamelCase_ : Union[str, Any] =TFRegNetEncoder(snake_case__ , name="encoder" )
lowerCamelCase_ : Union[str, Any] =tf.keras.layers.GlobalAveragePoolingaD(keepdims=snake_case__ , name="pooler" )
@unpack_inputs
def UpperCAmelCase__ ( self : List[str] , snake_case__ : tf.Tensor , snake_case__ : Optional[bool] = None , snake_case__ : Optional[bool] = None , snake_case__ : bool = False , ):
lowerCamelCase_ : List[Any] =(
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
lowerCamelCase_ : Tuple =return_dict if return_dict is not None else self.config.use_return_dict
lowerCamelCase_ : str =self.embedder(snake_case__ , training=snake_case__ )
lowerCamelCase_ : Dict =self.encoder(
snake_case__ , output_hidden_states=snake_case__ , return_dict=snake_case__ , training=snake_case__ )
lowerCamelCase_ : Optional[int] =encoder_outputs[0]
lowerCamelCase_ : List[Any] =self.pooler(snake_case__ )
# Change to NCHW output format have uniformity in the modules
lowerCamelCase_ : str =tf.transpose(snake_case__ , perm=(0, 3, 1, 2) )
lowerCamelCase_ : Any =tf.transpose(snake_case__ , perm=(0, 3, 1, 2) )
# Change the other hidden state outputs to NCHW as well
if output_hidden_states:
lowerCamelCase_ : Optional[int] =tuple([tf.transpose(snake_case__ , perm=(0, 3, 1, 2) ) for h in encoder_outputs[1]] )
if not return_dict:
return (last_hidden_state, pooled_output) + encoder_outputs[1:]
return TFBaseModelOutputWithPoolingAndNoAttention(
last_hidden_state=snake_case__ , pooler_output=snake_case__ , hidden_states=hidden_states if output_hidden_states else encoder_outputs.hidden_states , )
class lowercase__ ( snake_case__ ):
_UpperCAmelCase :Union[str, Any] = RegNetConfig
_UpperCAmelCase :str = "regnet"
_UpperCAmelCase :List[Any] = "pixel_values"
@property
def UpperCAmelCase__ ( self : int ):
return {"pixel_values": tf.TensorSpec(shape=(None, self.config.num_channels, 224, 224) , dtype=tf.floataa )}
A__ : Dict = r'\n Parameters:\n This model is a Tensorflow\n [tf.keras.layers.Layer](https://www.tensorflow.org/api_docs/python/tf/keras/layers/Layer) sub-class. Use it as a\n regular Tensorflow Module and refer to the Tensorflow documentation for all matter related to general usage and\n behavior.\n config ([`RegNetConfig`]): Model configuration class with all the parameters of the model.\n Initializing with a config file does not load the weights associated with the model, only the\n configuration. Check out the [`~TFPreTrainedModel.from_pretrained`] method to load the model weights.\n'
A__ : List[str] = r'\n Args:\n pixel_values (`tf.Tensor` of shape `(batch_size, num_channels, height, width)`):\n Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See\n [`ConveNextImageProcessor.__call__`] for details.\n output_hidden_states (`bool`, *optional*):\n Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for\n more detail.\n return_dict (`bool`, *optional*):\n Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.\n'
@add_start_docstrings(
"The bare RegNet model outputting raw features without any specific head on top.", snake_case__, )
class lowercase__ ( snake_case__ ):
def __init__( self : List[str] , snake_case__ : RegNetConfig , *snake_case__ : str , **snake_case__ : Dict ):
super().__init__(snake_case__ , *snake_case__ , **snake_case__ )
lowerCamelCase_ : Union[str, Any] =TFRegNetMainLayer(snake_case__ , name="regnet" )
@unpack_inputs
@add_start_docstrings_to_model_forward(snake_case__ )
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC , output_type=snake_case__ , config_class=_CONFIG_FOR_DOC , modality="vision" , expected_output=_EXPECTED_OUTPUT_SHAPE , )
def UpperCAmelCase__ ( self : Optional[int] , snake_case__ : tf.Tensor , snake_case__ : Optional[bool] = None , snake_case__ : Optional[bool] = None , snake_case__ : Any=False , ):
lowerCamelCase_ : Optional[Any] =(
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
lowerCamelCase_ : List[Any] =return_dict if return_dict is not None else self.config.use_return_dict
lowerCamelCase_ : Optional[int] =self.regnet(
pixel_values=snake_case__ , output_hidden_states=snake_case__ , return_dict=snake_case__ , training=snake_case__ , )
if not return_dict:
return (outputs[0],) + outputs[1:]
return TFBaseModelOutputWithPoolingAndNoAttention(
last_hidden_state=outputs.last_hidden_state , pooler_output=outputs.pooler_output , hidden_states=outputs.hidden_states , )
@add_start_docstrings(
"\n RegNet Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for\n ImageNet.\n ", snake_case__, )
class lowercase__ ( snake_case__, snake_case__ ):
def __init__( self : int , snake_case__ : RegNetConfig , *snake_case__ : Optional[Any] , **snake_case__ : Optional[int] ):
super().__init__(snake_case__ , *snake_case__ , **snake_case__ )
lowerCamelCase_ : Tuple =config.num_labels
lowerCamelCase_ : Any =TFRegNetMainLayer(snake_case__ , name="regnet" )
# classification head
lowerCamelCase_ : Tuple =[
tf.keras.layers.Flatten(),
tf.keras.layers.Dense(config.num_labels , name="classifier.1" ) if config.num_labels > 0 else tf.identity,
]
@unpack_inputs
@add_start_docstrings_to_model_forward(snake_case__ )
@add_code_sample_docstrings(
checkpoint=_IMAGE_CLASS_CHECKPOINT , output_type=snake_case__ , config_class=_CONFIG_FOR_DOC , expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT , )
def UpperCAmelCase__ ( self : Any , snake_case__ : tf.Tensor = None , snake_case__ : tf.Tensor = None , snake_case__ : bool = None , snake_case__ : bool = None , snake_case__ : List[str]=False , ):
lowerCamelCase_ : List[Any] =(
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
lowerCamelCase_ : str =return_dict if return_dict is not None else self.config.use_return_dict
lowerCamelCase_ : int =self.regnet(
snake_case__ , output_hidden_states=snake_case__ , return_dict=snake_case__ , training=snake_case__ )
lowerCamelCase_ : str =outputs.pooler_output if return_dict else outputs[1]
lowerCamelCase_ : Dict =self.classifier[0](snake_case__ )
lowerCamelCase_ : Optional[int] =self.classifier[1](snake_case__ )
lowerCamelCase_ : Any =None if labels is None else self.hf_compute_loss(labels=snake_case__ , logits=snake_case__ )
if not return_dict:
lowerCamelCase_ : Optional[int] =(logits,) + outputs[2:]
return ((loss,) + output) if loss is not None else output
return TFSequenceClassifierOutput(loss=snake_case__ , logits=snake_case__ , hidden_states=outputs.hidden_states )
| 153
| 0
|
import random
import unittest
import torch
from diffusers import IFImgaImgSuperResolutionPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device
from ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin
@skip_mps
class lowerCAmelCase ( __snake_case , __snake_case , unittest.TestCase ):
_SCREAMING_SNAKE_CASE : List[Any] =IFImgaImgSuperResolutionPipeline
_SCREAMING_SNAKE_CASE : Optional[int] =TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'width', 'height'}
_SCREAMING_SNAKE_CASE : Union[str, Any] =TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({"""original_image"""} )
_SCREAMING_SNAKE_CASE : Optional[Any] =PipelineTesterMixin.required_optional_params - {'latents'}
def a__ ( self ):
return self._get_superresolution_dummy_components()
def a__ ( self , lowerCAmelCase__ , lowerCAmelCase__=0 ):
if str(A_ ).startswith('mps' ):
_A= torch.manual_seed(A_ )
else:
_A= torch.Generator(device=A_ ).manual_seed(A_ )
_A= floats_tensor((1, 3, 32, 32) , rng=random.Random(A_ ) ).to(A_ )
_A= floats_tensor((1, 3, 16, 16) , rng=random.Random(A_ ) ).to(A_ )
_A= {
"prompt": "A painting of a squirrel eating a burger",
"image": image,
"original_image": original_image,
"generator": generator,
"num_inference_steps": 2,
"output_type": "numpy",
}
return inputs
@unittest.skipIf(
torch_device != 'cuda' or not is_xformers_available() , reason='XFormers attention is only available with CUDA and `xformers` installed' , )
def a__ ( self ):
self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3 )
def a__ ( self ):
self._test_save_load_optional_components()
@unittest.skipIf(torch_device != 'cuda' , reason='float16 requires CUDA' )
def a__ ( self ):
super().test_save_load_floataa(expected_max_diff=1E-1 )
def a__ ( self ):
self._test_attention_slicing_forward_pass(expected_max_diff=1E-2 )
def a__ ( self ):
self._test_save_load_local()
def a__ ( self ):
self._test_inference_batch_single_identical(
expected_max_diff=1E-2 , )
| 716
|
from dataclasses import dataclass
from typing import Tuple
import numpy as np
import torch
@dataclass
class lowerCAmelCase :
_SCREAMING_SNAKE_CASE : torch.Tensor # [batch_size x 3]
_SCREAMING_SNAKE_CASE : torch.Tensor # [batch_size x 3]
_SCREAMING_SNAKE_CASE : torch.Tensor # [batch_size x 3]
_SCREAMING_SNAKE_CASE : torch.Tensor # [batch_size x 3]
_SCREAMING_SNAKE_CASE : int
_SCREAMING_SNAKE_CASE : int
_SCREAMING_SNAKE_CASE : float
_SCREAMING_SNAKE_CASE : float
_SCREAMING_SNAKE_CASE : Tuple[int]
def a__ ( self ):
assert self.x.shape[0] == self.y.shape[0] == self.z.shape[0] == self.origin.shape[0]
assert self.x.shape[1] == self.y.shape[1] == self.z.shape[1] == self.origin.shape[1] == 3
assert len(self.x.shape ) == len(self.y.shape ) == len(self.z.shape ) == len(self.origin.shape ) == 2
def a__ ( self ):
return torch.from_numpy(np.array([self.width, self.height] , dtype=np.floataa ) )
def a__ ( self ):
return torch.from_numpy(np.array([self.x_fov, self.y_fov] , dtype=np.floataa ) )
def a__ ( self ):
_A= torch.arange(self.height * self.width )
_A= torch.stack(
[
pixel_indices % self.width,
torch.div(lowerCAmelCase__ , self.width , rounding_mode='trunc' ),
] , axis=1 , )
return coords
@property
def a__ ( self ):
_A, *_A= self.shape
_A= int(np.prod(lowerCAmelCase__ ) )
_A= self.get_image_coords()
_A= torch.broadcast_to(coords.unsqueeze(0 ) , [batch_size * inner_batch_size, *coords.shape] )
_A= self.get_camera_rays(lowerCAmelCase__ )
_A= rays.view(lowerCAmelCase__ , inner_batch_size * self.height * self.width , 2 , 3 )
return rays
def a__ ( self , lowerCAmelCase__ ):
_A, *_A, _A= coords.shape
assert n_coords == 2
assert batch_size == self.origin.shape[0]
_A= coords.view(lowerCAmelCase__ , -1 , 2 )
_A= self.resolution()
_A= self.fov()
_A= (flat.float() / (res - 1)) * 2 - 1
_A= fracs * torch.tan(fov / 2 )
_A= fracs.view(lowerCAmelCase__ , -1 , 2 )
_A= (
self.z.view(lowerCAmelCase__ , 1 , 3 )
+ self.x.view(lowerCAmelCase__ , 1 , 3 ) * fracs[:, :, :1]
+ self.y.view(lowerCAmelCase__ , 1 , 3 ) * fracs[:, :, 1:]
)
_A= directions / directions.norm(dim=-1 , keepdim=lowerCAmelCase__ )
_A= torch.stack(
[
torch.broadcast_to(self.origin.view(lowerCAmelCase__ , 1 , 3 ) , [batch_size, directions.shape[1], 3] ),
directions,
] , dim=2 , )
return rays.view(lowerCAmelCase__ , *lowerCAmelCase__ , 2 , 3 )
def a__ ( self , lowerCAmelCase__ , lowerCAmelCase__ ):
assert width * self.height == height * self.width, "The aspect ratio should not change."
return DifferentiableProjectiveCamera(
origin=self.origin , x=self.x , y=self.y , z=self.z , width=lowerCAmelCase__ , height=lowerCAmelCase__ , x_fov=self.x_fov , y_fov=self.y_fov , )
def UpperCamelCase ( lowerCAmelCase_ ) -> DifferentiableProjectiveCamera:
'''simple docstring'''
_A= []
_A= []
_A= []
_A= []
for theta in np.linspace(0 , 2 * np.pi , num=20 ):
_A= np.array([np.sin(lowerCAmelCase_ ), np.cos(lowerCAmelCase_ ), -0.5] )
z /= np.sqrt(np.sum(z**2 ) )
_A= -z * 4
_A= np.array([np.cos(lowerCAmelCase_ ), -np.sin(lowerCAmelCase_ ), 0.0] )
_A= np.cross(lowerCAmelCase_ , lowerCAmelCase_ )
origins.append(lowerCAmelCase_ )
xs.append(lowerCAmelCase_ )
ys.append(lowerCAmelCase_ )
zs.append(lowerCAmelCase_ )
return DifferentiableProjectiveCamera(
origin=torch.from_numpy(np.stack(lowerCAmelCase_ , axis=0 ) ).float() , x=torch.from_numpy(np.stack(lowerCAmelCase_ , axis=0 ) ).float() , y=torch.from_numpy(np.stack(lowerCAmelCase_ , axis=0 ) ).float() , z=torch.from_numpy(np.stack(lowerCAmelCase_ , axis=0 ) ).float() , width=lowerCAmelCase_ , height=lowerCAmelCase_ , x_fov=0.7 , y_fov=0.7 , shape=(1, len(lowerCAmelCase_ )) , )
| 476
| 0
|
import gc
import threading
import time
import psutil
import torch
class A_ :
def __init__( self ):
'''simple docstring'''
UpperCAmelCase = psutil.Process()
UpperCAmelCase = False
def _lowercase ( self ):
'''simple docstring'''
UpperCAmelCase = -1
while True:
UpperCAmelCase = max(self.process.memory_info().rss , self.cpu_memory_peak )
# can't sleep or will not catch the peak right (this comment is here on purpose)
if not self.peak_monitoring:
break
def _lowercase ( self ):
'''simple docstring'''
UpperCAmelCase = True
UpperCAmelCase = threading.Thread(target=self.peak_monitor )
UpperCAmelCase = True
self.thread.start()
def _lowercase ( self ):
'''simple docstring'''
UpperCAmelCase = False
self.thread.join()
return self.cpu_memory_peak
__A : Union[str, Any] = PeakCPUMemory()
def __SCREAMING_SNAKE_CASE ( ) -> Optional[Any]:
'''simple docstring'''
UpperCAmelCase = {'''time''': time.time()}
gc.collect()
torch.cuda.empty_cache()
# CPU mem
UpperCAmelCase = psutil.Process().memory_info().rss
cpu_peak_tracker.start()
# GPU mem
for i in range(torch.cuda.device_count() ):
UpperCAmelCase = torch.cuda.memory_allocated(UpperCamelCase__ )
torch.cuda.reset_peak_memory_stats()
return measures
def __SCREAMING_SNAKE_CASE ( UpperCamelCase__ ) -> Tuple:
'''simple docstring'''
UpperCAmelCase = {'''time''': time.time() - start_measures['''time''']}
gc.collect()
torch.cuda.empty_cache()
# CPU mem
UpperCAmelCase = (psutil.Process().memory_info().rss - start_measures['''cpu''']) / 2**20
UpperCAmelCase = (cpu_peak_tracker.stop() - start_measures['''cpu''']) / 2**20
# GPU mem
for i in range(torch.cuda.device_count() ):
UpperCAmelCase = (torch.cuda.memory_allocated(UpperCamelCase__ ) - start_measures[str(UpperCamelCase__ )]) / 2**20
UpperCAmelCase = (torch.cuda.max_memory_allocated(UpperCamelCase__ ) - start_measures[str(UpperCamelCase__ )]) / 2**20
return measures
def __SCREAMING_SNAKE_CASE ( UpperCamelCase__ , UpperCamelCase__ ) -> Any:
'''simple docstring'''
print(F"""{description}:""" )
print(F"""- Time: {measures["time"]:.2f}s""" )
for i in range(torch.cuda.device_count() ):
print(F"""- GPU {i} allocated: {measures[str(UpperCamelCase__ )]:.2f}MiB""" )
UpperCAmelCase = measures[F"""{i}-peak"""]
print(F"""- GPU {i} peak: {peak:.2f}MiB""" )
print(F"""- CPU RAM allocated: {measures["cpu"]:.2f}MiB""" )
print(F"""- CPU RAM peak: {measures["cpu-peak"]:.2f}MiB""" )
| 130
|
import json
from typing import Iterator, List, Union
from tokenizers import AddedToken, Regex, Tokenizer, decoders, normalizers, pre_tokenizers, trainers
from tokenizers.implementations.base_tokenizer import BaseTokenizer
from tokenizers.models import Unigram
from tokenizers.processors import TemplateProcessing
class A_ (a_ ):
def __init__( self , _A = "▁" , _A = True , _A = "<unk>" , _A = "</s>" , _A = "<pad>" , ):
'''simple docstring'''
UpperCAmelCase = {
'''pad''': {'''id''': 0, '''token''': pad_token},
'''eos''': {'''id''': 1, '''token''': eos_token},
'''unk''': {'''id''': 2, '''token''': unk_token},
}
UpperCAmelCase = [None] * len(self.special_tokens )
for token_dict in self.special_tokens.values():
UpperCAmelCase = token_dict['''token''']
UpperCAmelCase = Tokenizer(Unigram() )
UpperCAmelCase = normalizers.Sequence(
[
normalizers.Nmt(),
normalizers.NFKC(),
normalizers.Replace(Regex(''' {2,}''' ) , ''' ''' ),
normalizers.Lowercase(),
] )
UpperCAmelCase = pre_tokenizers.Sequence(
[
pre_tokenizers.Metaspace(replacement=_A , add_prefix_space=_A ),
pre_tokenizers.Digits(individual_digits=_A ),
pre_tokenizers.Punctuation(),
] )
UpperCAmelCase = decoders.Metaspace(replacement=_A , add_prefix_space=_A )
UpperCAmelCase = TemplateProcessing(
single=F"""$A {self.special_tokens["eos"]["token"]}""" , special_tokens=[(self.special_tokens['''eos''']['''token'''], self.special_tokens['''eos''']['''id'''])] , )
UpperCAmelCase = {
'''model''': '''SentencePieceUnigram''',
'''replacement''': replacement,
'''add_prefix_space''': add_prefix_space,
}
super().__init__(_A , _A )
def _lowercase ( self , _A , _A = 8_0_0_0 , _A = True , ):
'''simple docstring'''
UpperCAmelCase = trainers.UnigramTrainer(
vocab_size=_A , special_tokens=self.special_tokens_list , show_progress=_A , )
if isinstance(_A , _A ):
UpperCAmelCase = [files]
self._tokenizer.train(_A , trainer=_A )
self.add_unk_id()
def _lowercase ( self , _A , _A = 8_0_0_0 , _A = True , ):
'''simple docstring'''
UpperCAmelCase = trainers.UnigramTrainer(
vocab_size=_A , special_tokens=self.special_tokens_list , show_progress=_A , )
self._tokenizer.train_from_iterator(_A , trainer=_A )
self.add_unk_id()
def _lowercase ( self ):
'''simple docstring'''
UpperCAmelCase = json.loads(self._tokenizer.to_str() )
UpperCAmelCase = self.special_tokens['''unk''']['''id''']
UpperCAmelCase = Tokenizer.from_str(json.dumps(_A ) )
| 130
| 1
|
'''simple docstring'''
from typing import List, Optional, Union
import numpy as np
import PIL.Image
from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
ChannelDimension,
PILImageResampling,
get_image_size,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, logging
lowercase__ = logging.get_logger(__name__)
class A_ ( _snake_case ):
UpperCAmelCase_ : Union[str, Any] = ["""pixel_values"""]
def __init__( self : List[Any] , lowercase_ : bool = True , lowercase_ : int = 32 , lowercase_ : Union[str, Any]=PILImageResampling.BILINEAR , lowercase_ : bool = True , **lowercase_ : Optional[Any] , ) -> None:
UpperCAmelCase : List[Any] = do_resize
UpperCAmelCase : int = do_rescale
UpperCAmelCase : Tuple = size_divisor
UpperCAmelCase : Any = resample
super().__init__(**lowercase_ )
def UpperCAmelCase_ ( self : Optional[int] , lowercase_ : np.ndarray , lowercase_ : int , lowercase_ : str , lowercase_ : Optional[ChannelDimension] = None , **lowercase_ : Tuple ) -> np.ndarray:
UpperCAmelCase : Optional[Any] = get_image_size(lowercase_ )
# Rounds the height and width down to the closest multiple of size_divisor
UpperCAmelCase : List[str] = height // size_divisor * size_divisor
UpperCAmelCase : Union[str, Any] = width // size_divisor * size_divisor
UpperCAmelCase : List[str] = resize(lowercase_ , (new_h, new_w) , resample=lowercase_ , data_format=lowercase_ , **lowercase_ )
return image
def UpperCAmelCase_ ( self : Dict , lowercase_ : np.ndarray , lowercase_ : float , lowercase_ : Optional[ChannelDimension] = None , **lowercase_ : Optional[Any] ) -> np.ndarray:
return rescale(image=lowercase_ , scale=lowercase_ , data_format=lowercase_ , **lowercase_ )
def UpperCAmelCase_ ( self : List[str] , lowercase_ : Union["PIL.Image.Image", TensorType, List["PIL.Image.Image"], List[TensorType]] , lowercase_ : Optional[bool] = None , lowercase_ : Optional[int] = None , lowercase_ : int=None , lowercase_ : Optional[bool] = None , lowercase_ : Optional[Union[TensorType, str]] = None , lowercase_ : ChannelDimension = ChannelDimension.FIRST , **lowercase_ : List[Any] , ) -> BatchFeature:
UpperCAmelCase : Dict = do_resize if do_resize is not None else self.do_resize
UpperCAmelCase : int = do_rescale if do_rescale is not None else self.do_rescale
UpperCAmelCase : Dict = size_divisor if size_divisor is not None else self.size_divisor
UpperCAmelCase : Tuple = resample if resample is not None else self.resample
if do_resize and size_divisor is None:
raise ValueError('size_divisor is required for resizing' )
UpperCAmelCase : List[Any] = make_list_of_images(lowercase_ )
if not valid_images(lowercase_ ):
raise ValueError('Invalid image(s)' )
# All transformations expect numpy arrays.
UpperCAmelCase : Any = [to_numpy_array(lowercase_ ) for img in images]
if do_resize:
UpperCAmelCase : Union[str, Any] = [self.resize(lowercase_ , size_divisor=lowercase_ , resample=lowercase_ ) for image in images]
if do_rescale:
UpperCAmelCase : str = [self.rescale(lowercase_ , scale=1 / 255 ) for image in images]
UpperCAmelCase : Dict = [to_channel_dimension_format(lowercase_ , lowercase_ ) for image in images]
UpperCAmelCase : Tuple = {'pixel_values': images}
return BatchFeature(data=lowercase_ , tensor_type=lowercase_ )
| 706
|
'''simple docstring'''
# Copyright 2021 The HuggingFace 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.
from pathlib import Path
import torch
from ...utils import is_npu_available, is_xpu_available
from .config_args import ClusterConfig, default_json_config_file
from .config_utils import SubcommandHelpFormatter
lowercase__ = "Create a default config file for Accelerate with only a few flags set."
def UpperCamelCase( UpperCAmelCase_="no" , UpperCAmelCase_ = default_json_config_file , UpperCAmelCase_ = False ):
UpperCAmelCase : Any = Path(UpperCAmelCase_ )
path.parent.mkdir(parents=UpperCAmelCase_ , exist_ok=UpperCAmelCase_ )
if path.exists():
print(
F"""Configuration already exists at {save_location}, will not override. Run `accelerate config` manually or pass a different `save_location`.""" )
return False
UpperCAmelCase : Optional[int] = mixed_precision.lower()
if mixed_precision not in ["no", "fp16", "bf16", "fp8"]:
raise ValueError(
F"""`mixed_precision` should be one of 'no', 'fp16', 'bf16', or 'fp8'. Received {mixed_precision}""" )
UpperCAmelCase : Dict = {
'compute_environment': 'LOCAL_MACHINE',
'mixed_precision': mixed_precision,
}
if torch.cuda.is_available():
UpperCAmelCase : Dict = torch.cuda.device_count()
UpperCAmelCase : List[Any] = num_gpus
UpperCAmelCase : List[Any] = False
if num_gpus > 1:
UpperCAmelCase : Tuple = 'MULTI_GPU'
else:
UpperCAmelCase : Optional[Any] = 'NO'
elif is_xpu_available() and use_xpu:
UpperCAmelCase : Optional[int] = torch.xpu.device_count()
UpperCAmelCase : Optional[int] = num_xpus
UpperCAmelCase : Any = False
if num_xpus > 1:
UpperCAmelCase : Tuple = 'MULTI_XPU'
else:
UpperCAmelCase : str = 'NO'
elif is_npu_available():
UpperCAmelCase : Optional[int] = torch.npu.device_count()
UpperCAmelCase : str = num_npus
UpperCAmelCase : int = False
if num_npus > 1:
UpperCAmelCase : int = 'MULTI_NPU'
else:
UpperCAmelCase : List[str] = 'NO'
else:
UpperCAmelCase : str = 0
UpperCAmelCase : int = True
UpperCAmelCase : str = 1
UpperCAmelCase : str = 'NO'
UpperCAmelCase : Any = ClusterConfig(**UpperCAmelCase_ )
config.to_json_file(UpperCAmelCase_ )
return path
def UpperCamelCase( UpperCAmelCase_ , UpperCAmelCase_ ):
UpperCAmelCase : Tuple = parser.add_parser('default' , parents=UpperCAmelCase_ , help=UpperCAmelCase_ , formatter_class=UpperCAmelCase_ )
parser.add_argument(
'--config_file' , default=UpperCAmelCase_ , help=(
'The path to use to store the config file. Will default to a file named default_config.yaml in the cache '
'location, which is the content of the environment `HF_HOME` suffixed with \'accelerate\', or if you don\'t have '
'such an environment variable, your cache directory (\'~/.cache\' or the content of `XDG_CACHE_HOME`) suffixed '
'with \'huggingface\'.'
) , dest='save_location' , )
parser.add_argument(
'--mixed_precision' , choices=['no', 'fp16', 'bf16'] , type=UpperCAmelCase_ , help='Whether or not to use mixed precision training. '
'Choose between FP16 and BF16 (bfloat16) training. '
'BF16 training is only supported on Nvidia Ampere GPUs and PyTorch 1.10 or later.' , default='no' , )
parser.set_defaults(func=UpperCAmelCase_ )
return parser
def UpperCamelCase( UpperCAmelCase_ ):
UpperCAmelCase : Union[str, Any] = write_basic_config(args.mixed_precision , args.save_location )
if config_file:
print(F"""accelerate configuration saved at {config_file}""" )
| 695
| 0
|
import argparse
import os
import re
import numpy as np
import PIL
import torch
from timm import create_model
from torch.optim.lr_scheduler import OneCycleLR
from torch.utils.data import DataLoader, Dataset
from torchvision.transforms import Compose, RandomResizedCrop, Resize, ToTensor
from accelerate import Accelerator
def UpperCamelCase ( _a ) -> List[str]:
'''simple docstring'''
lowercase_ :Tuple = fname.split(os.path.sep )[-1]
return re.search(R'''^(.*)_\d+\.jpg$''' , _a ).groups()[0]
class UpperCamelCase ( lowercase__ ):
'''simple docstring'''
def __init__( self , UpperCamelCase_ , UpperCamelCase_=None , UpperCamelCase_=None ):
lowercase_ :Any = file_names
lowercase_ :List[Any] = image_transform
lowercase_ :List[Any] = label_to_id
def __len__( self ):
return len(self.file_names )
def __getitem__( self , UpperCamelCase_ ):
lowercase_ :int = self.file_names[idx]
lowercase_ :List[Any] = PIL.Image.open(UpperCamelCase_ )
lowercase_ :Any = raw_image.convert('''RGB''' )
if self.image_transform is not None:
lowercase_ :Union[str, Any] = self.image_transform(UpperCamelCase_ )
lowercase_ :Tuple = extract_label(UpperCamelCase_ )
if self.label_to_id is not None:
lowercase_ :str = self.label_to_id[label]
return {"image": image, "label": label}
def UpperCamelCase ( _a , _a ) -> List[Any]:
'''simple docstring'''
if args.with_tracking:
lowercase_ :Optional[int] = Accelerator(
cpu=args.cpu , mixed_precision=args.mixed_precision , log_with='''all''' , project_dir=args.project_dir )
else:
lowercase_ :List[Any] = Accelerator(cpu=args.cpu , mixed_precision=args.mixed_precision )
# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
lowercase_ :int = config['''lr''']
lowercase_ :Optional[int] = int(config['''num_epochs'''] )
lowercase_ :int = int(config['''seed'''] )
lowercase_ :List[Any] = int(config['''batch_size'''] )
lowercase_ :Union[str, Any] = config['''image_size''']
if not isinstance(_a , (list, tuple) ):
lowercase_ :Tuple = (image_size, image_size)
# Parse out whether we are saving every epoch or after a certain number of batches
if hasattr(args.checkpointing_steps , '''isdigit''' ):
if args.checkpointing_steps == "epoch":
lowercase_ :Union[str, Any] = args.checkpointing_steps
elif args.checkpointing_steps.isdigit():
lowercase_ :Optional[int] = int(args.checkpointing_steps )
else:
raise ValueError(
f"Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed." )
else:
lowercase_ :List[str] = None
# We need to initialize the trackers we use, and also store our configuration
if args.with_tracking:
lowercase_ :Union[str, Any] = os.path.split(_a )[-1].split('''.''' )[0]
accelerator.init_trackers(_a , _a )
# Grab all the image filenames
lowercase_ :Tuple = [os.path.join(args.data_dir , _a ) for fname in os.listdir(args.data_dir ) if fname.endswith('''.jpg''' )]
# Build the label correspondences
lowercase_ :Any = [extract_label(_a ) for fname in file_names]
lowercase_ :Optional[Any] = list(set(_a ) )
id_to_label.sort()
lowercase_ :Tuple = {lbl: i for i, lbl in enumerate(_a )}
# Set the seed before splitting the data.
np.random.seed(_a )
torch.manual_seed(_a )
torch.cuda.manual_seed_all(_a )
# Split our filenames between train and validation
lowercase_ :Tuple = np.random.permutation(len(_a ) )
lowercase_ :Optional[int] = int(0.8 * len(_a ) )
lowercase_ :List[Any] = random_perm[:cut]
lowercase_ :Any = random_perm[cut:]
# For training we use a simple RandomResizedCrop
lowercase_ :Union[str, Any] = Compose([RandomResizedCrop(_a , scale=(0.5, 1.0) ), ToTensor()] )
lowercase_ :str = PetsDataset(
[file_names[i] for i in train_split] , image_transform=_a , label_to_id=_a )
# For evaluation, we use a deterministic Resize
lowercase_ :Union[str, Any] = Compose([Resize(_a ), ToTensor()] )
lowercase_ :List[Any] = PetsDataset([file_names[i] for i in eval_split] , image_transform=_a , label_to_id=_a )
# Instantiate dataloaders.
lowercase_ :Tuple = DataLoader(_a , shuffle=_a , batch_size=_a , num_workers=4 )
lowercase_ :Union[str, Any] = DataLoader(_a , shuffle=_a , batch_size=_a , num_workers=4 )
# Instantiate the model (we build the model here so that the seed also control new weights initialization)
lowercase_ :Dict = create_model('''resnet50d''' , pretrained=_a , num_classes=len(_a ) )
# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
lowercase_ :str = model.to(accelerator.device )
# Freezing the base model
for param in model.parameters():
lowercase_ :Any = False
for param in model.get_classifier().parameters():
lowercase_ :Tuple = True
# We normalize the batches of images to be a bit faster.
lowercase_ :Optional[int] = torch.tensor(model.default_cfg['''mean'''] )[None, :, None, None].to(accelerator.device )
lowercase_ :str = torch.tensor(model.default_cfg['''std'''] )[None, :, None, None].to(accelerator.device )
# Instantiate optimizer
lowercase_ :Dict = torch.optim.Adam(params=model.parameters() , lr=lr / 2_5 )
# Instantiate learning rate scheduler
lowercase_ :List[str] = OneCycleLR(optimizer=_a , max_lr=_a , epochs=_a , steps_per_epoch=len(_a ) )
# Prepare everything
# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
# prepare method.
lowercase_ , lowercase_ , lowercase_ , lowercase_ , lowercase_ :List[Any] = accelerator.prepare(
_a , _a , _a , _a , _a )
# We need to keep track of how many total steps we have iterated over
lowercase_ :List[Any] = 0
# We also need to keep track of the starting epoch so files are named properly
lowercase_ :List[Any] = 0
# Potentially load in the weights and states from a previous save
if args.resume_from_checkpoint:
if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "":
accelerator.print(f"Resumed from checkpoint: {args.resume_from_checkpoint}" )
accelerator.load_state(args.resume_from_checkpoint )
lowercase_ :List[str] = os.path.basename(args.resume_from_checkpoint )
else:
# Get the most recent checkpoint
lowercase_ :Optional[int] = [f.name for f in os.scandir(os.getcwd() ) if f.is_dir()]
dirs.sort(key=os.path.getctime )
lowercase_ :str = dirs[-1] # Sorts folders by date modified, most recent checkpoint is the last
# Extract `epoch_{i}` or `step_{i}`
lowercase_ :Tuple = os.path.splitext(_a )[0]
if "epoch" in training_difference:
lowercase_ :Optional[Any] = int(training_difference.replace('''epoch_''' , '''''' ) ) + 1
lowercase_ :int = None
else:
lowercase_ :Optional[Any] = int(training_difference.replace('''step_''' , '''''' ) )
lowercase_ :List[Any] = resume_step // len(_a )
resume_step -= starting_epoch * len(_a )
# Now we train the model
for epoch in range(_a , _a ):
model.train()
if args.with_tracking:
lowercase_ :List[str] = 0
if args.resume_from_checkpoint and epoch == starting_epoch and resume_step is not None:
# We need to skip steps until we reach the resumed step
lowercase_ :Union[str, Any] = accelerator.skip_first_batches(_a , _a )
overall_step += resume_step
else:
# After the first iteration though, we need to go back to the original dataloader
lowercase_ :Optional[Any] = train_dataloader
for batch in active_dataloader:
# We could avoid this line since we set the accelerator with `device_placement=True`.
lowercase_ :List[Any] = {k: v.to(accelerator.device ) for k, v in batch.items()}
lowercase_ :int = (batch['''image'''] - mean) / std
lowercase_ :Any = model(_a )
lowercase_ :List[str] = torch.nn.functional.cross_entropy(_a , batch['''label'''] )
# We keep track of the loss at each epoch
if args.with_tracking:
total_loss += loss.detach().float()
accelerator.backward(_a )
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
overall_step += 1
if isinstance(_a , _a ):
lowercase_ :Optional[Any] = f"step_{overall_step}"
if overall_step % checkpointing_steps == 0:
if args.output_dir is not None:
lowercase_ :Dict = os.path.join(args.output_dir , _a )
accelerator.save_state(_a )
model.eval()
lowercase_ :Dict = 0
lowercase_ :Optional[int] = 0
for step, batch in enumerate(_a ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
lowercase_ :List[Any] = {k: v.to(accelerator.device ) for k, v in batch.items()}
lowercase_ :Dict = (batch['''image'''] - mean) / std
with torch.no_grad():
lowercase_ :str = model(_a )
lowercase_ :Optional[Any] = outputs.argmax(dim=-1 )
lowercase_ , lowercase_ :List[Any] = accelerator.gather_for_metrics((predictions, batch['''label''']) )
lowercase_ :List[str] = predictions == references
num_elems += accurate_preds.shape[0]
accurate += accurate_preds.long().sum()
lowercase_ :str = accurate.item() / num_elems
# Use accelerator.print to print only on the main process.
accelerator.print(f"epoch {epoch}: {1_0_0 * eval_metric:.2f}" )
if args.with_tracking:
accelerator.log(
{
'''accuracy''': 1_0_0 * eval_metric,
'''train_loss''': total_loss.item() / len(_a ),
'''epoch''': epoch,
} , step=_a , )
if checkpointing_steps == "epoch":
lowercase_ :str = f"epoch_{epoch}"
if args.output_dir is not None:
lowercase_ :Any = os.path.join(args.output_dir , _a )
accelerator.save_state(_a )
if args.with_tracking:
accelerator.end_training()
def UpperCamelCase ( ) -> Dict:
'''simple docstring'''
lowercase_ :List[str] = argparse.ArgumentParser(description='''Simple example of training script.''' )
parser.add_argument('''--data_dir''' , required=_a , help='''The data folder on disk.''' )
parser.add_argument('''--fp16''' , action='''store_true''' , help='''If passed, will use FP16 training.''' )
parser.add_argument(
'''--mixed_precision''' , type=_a , default=_a , choices=['''no''', '''fp16''', '''bf16''', '''fp8'''] , help='''Whether to use mixed precision. Choose'''
'''between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.'''
'''and an Nvidia Ampere GPU.''' , )
parser.add_argument('''--cpu''' , action='''store_true''' , help='''If passed, will train on the CPU.''' )
parser.add_argument(
'''--checkpointing_steps''' , type=_a , default=_a , help='''Whether the various states should be saved at the end of every n steps, or \'epoch\' for each epoch.''' , )
parser.add_argument(
'''--output_dir''' , type=_a , default='''.''' , help='''Optional save directory where all checkpoint folders will be stored. Default is the current working directory.''' , )
parser.add_argument(
'''--resume_from_checkpoint''' , type=_a , default=_a , help='''If the training should continue from a checkpoint folder.''' , )
parser.add_argument(
'''--with_tracking''' , action='''store_true''' , help='''Whether to load in all available experiment trackers from the environment and use them for logging.''' , )
parser.add_argument(
'''--project_dir''' , type=_a , default='''logs''' , help='''Location on where to store experiment tracking logs` and relevent project information''' , )
lowercase_ :Optional[Any] = parser.parse_args()
lowercase_ :Optional[Any] = {'''lr''': 3E-2, '''num_epochs''': 3, '''seed''': 4_2, '''batch_size''': 6_4, '''image_size''': 2_2_4}
training_function(_a , _a )
if __name__ == "__main__":
main()
| 257
|
import torch
from diffusers import DPMSolverSDEScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import require_torchsde
from .test_schedulers import SchedulerCommonTest
@require_torchsde
class UpperCamelCase ( lowercase__ ):
'''simple docstring'''
lowercase : Union[str, Any] =(DPMSolverSDEScheduler,)
lowercase : Any =10
def UpperCamelCase ( self , **UpperCamelCase_ ):
lowercase_ :Union[str, Any] = {
'''num_train_timesteps''': 1100,
'''beta_start''': 0.0001,
'''beta_end''': 0.02,
'''beta_schedule''': '''linear''',
'''noise_sampler_seed''': 0,
}
config.update(**UpperCamelCase_ )
return config
def UpperCamelCase ( self ):
for timesteps in [10, 50, 100, 1000]:
self.check_over_configs(num_train_timesteps=UpperCamelCase_ )
def UpperCamelCase ( self ):
for beta_start, beta_end in zip([0.0_0001, 0.0001, 0.001] , [0.0002, 0.002, 0.02] ):
self.check_over_configs(beta_start=UpperCamelCase_ , beta_end=UpperCamelCase_ )
def UpperCamelCase ( self ):
for schedule in ["linear", "scaled_linear"]:
self.check_over_configs(beta_schedule=UpperCamelCase_ )
def UpperCamelCase ( self ):
for prediction_type in ["epsilon", "v_prediction"]:
self.check_over_configs(prediction_type=UpperCamelCase_ )
def UpperCamelCase ( self ):
lowercase_ :Any = self.scheduler_classes[0]
lowercase_ :Tuple = self.get_scheduler_config()
lowercase_ :int = scheduler_class(**UpperCamelCase_ )
scheduler.set_timesteps(self.num_inference_steps )
lowercase_ :Union[str, Any] = self.dummy_model()
lowercase_ :Dict = self.dummy_sample_deter * scheduler.init_noise_sigma
lowercase_ :int = sample.to(UpperCamelCase_ )
for i, t in enumerate(scheduler.timesteps ):
lowercase_ :Optional[Any] = scheduler.scale_model_input(UpperCamelCase_ , UpperCamelCase_ )
lowercase_ :Any = model(UpperCamelCase_ , UpperCamelCase_ )
lowercase_ :Tuple = scheduler.step(UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ )
lowercase_ :Dict = output.prev_sample
lowercase_ :Dict = torch.sum(torch.abs(UpperCamelCase_ ) )
lowercase_ :Union[str, Any] = torch.mean(torch.abs(UpperCamelCase_ ) )
if torch_device in ["mps"]:
assert abs(result_sum.item() - 167.47_8210_4492_1875 ) < 1E-2
assert abs(result_mean.item() - 0.2178_7059_6456_5277 ) < 1E-3
elif torch_device in ["cuda"]:
assert abs(result_sum.item() - 171.59_3521_1181_6406 ) < 1E-2
assert abs(result_mean.item() - 0.2_2342_9068_9229_9652 ) < 1E-3
else:
assert abs(result_sum.item() - 162.52_3834_2285_1562 ) < 1E-2
assert abs(result_mean.item() - 0.211_6195_7085_1326 ) < 1E-3
def UpperCamelCase ( self ):
lowercase_ :Union[str, Any] = self.scheduler_classes[0]
lowercase_ :Union[str, Any] = self.get_scheduler_config(prediction_type='''v_prediction''' )
lowercase_ :Union[str, Any] = scheduler_class(**UpperCamelCase_ )
scheduler.set_timesteps(self.num_inference_steps )
lowercase_ :List[str] = self.dummy_model()
lowercase_ :Dict = self.dummy_sample_deter * scheduler.init_noise_sigma
lowercase_ :Optional[int] = sample.to(UpperCamelCase_ )
for i, t in enumerate(scheduler.timesteps ):
lowercase_ :Any = scheduler.scale_model_input(UpperCamelCase_ , UpperCamelCase_ )
lowercase_ :str = model(UpperCamelCase_ , UpperCamelCase_ )
lowercase_ :Optional[int] = scheduler.step(UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ )
lowercase_ :List[str] = output.prev_sample
lowercase_ :Union[str, Any] = torch.sum(torch.abs(UpperCamelCase_ ) )
lowercase_ :int = torch.mean(torch.abs(UpperCamelCase_ ) )
if torch_device in ["mps"]:
assert abs(result_sum.item() - 124.77_1492_0043_9453 ) < 1E-2
assert abs(result_mean.item() - 0.1_6226_2890_1481_6284 ) < 1E-3
elif torch_device in ["cuda"]:
assert abs(result_sum.item() - 128.1_6633_6059_5703 ) < 1E-2
assert abs(result_mean.item() - 0.1_6688_3260_0116_7297 ) < 1E-3
else:
assert abs(result_sum.item() - 119.8_4875_4882_8125 ) < 1E-2
assert abs(result_mean.item() - 0.1560_5306_6253_6621 ) < 1E-3
def UpperCamelCase ( self ):
lowercase_ :Union[str, Any] = self.scheduler_classes[0]
lowercase_ :List[str] = self.get_scheduler_config()
lowercase_ :Tuple = scheduler_class(**UpperCamelCase_ )
scheduler.set_timesteps(self.num_inference_steps , device=UpperCamelCase_ )
lowercase_ :Tuple = self.dummy_model()
lowercase_ :str = self.dummy_sample_deter.to(UpperCamelCase_ ) * scheduler.init_noise_sigma
for t in scheduler.timesteps:
lowercase_ :str = scheduler.scale_model_input(UpperCamelCase_ , UpperCamelCase_ )
lowercase_ :int = model(UpperCamelCase_ , UpperCamelCase_ )
lowercase_ :int = scheduler.step(UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ )
lowercase_ :List[str] = output.prev_sample
lowercase_ :Dict = torch.sum(torch.abs(UpperCamelCase_ ) )
lowercase_ :Dict = torch.mean(torch.abs(UpperCamelCase_ ) )
if torch_device in ["mps"]:
assert abs(result_sum.item() - 167.46_9573_9746_0938 ) < 1E-2
assert abs(result_mean.item() - 0.2_1805_9346_0798_2635 ) < 1E-3
elif torch_device in ["cuda"]:
assert abs(result_sum.item() - 171.59_3536_3769_5312 ) < 1E-2
assert abs(result_mean.item() - 0.2_2342_9083_8241_5771 ) < 1E-3
else:
assert abs(result_sum.item() - 162.52_3834_2285_1562 ) < 1E-2
assert abs(result_mean.item() - 0.211_6195_7085_1326 ) < 1E-3
def UpperCamelCase ( self ):
lowercase_ :Any = self.scheduler_classes[0]
lowercase_ :Optional[int] = self.get_scheduler_config()
lowercase_ :Tuple = scheduler_class(**UpperCamelCase_ , use_karras_sigmas=UpperCamelCase_ )
scheduler.set_timesteps(self.num_inference_steps , device=UpperCamelCase_ )
lowercase_ :List[str] = self.dummy_model()
lowercase_ :Dict = self.dummy_sample_deter.to(UpperCamelCase_ ) * scheduler.init_noise_sigma
lowercase_ :Union[str, Any] = sample.to(UpperCamelCase_ )
for t in scheduler.timesteps:
lowercase_ :List[Any] = scheduler.scale_model_input(UpperCamelCase_ , UpperCamelCase_ )
lowercase_ :Optional[int] = model(UpperCamelCase_ , UpperCamelCase_ )
lowercase_ :int = scheduler.step(UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ )
lowercase_ :int = output.prev_sample
lowercase_ :List[Any] = torch.sum(torch.abs(UpperCamelCase_ ) )
lowercase_ :Tuple = torch.mean(torch.abs(UpperCamelCase_ ) )
if torch_device in ["mps"]:
assert abs(result_sum.item() - 176.66_9741_3574_2188 ) < 1E-2
assert abs(result_mean.item() - 0.2_3003_8727_3098_1811 ) < 1E-2
elif torch_device in ["cuda"]:
assert abs(result_sum.item() - 177.63_6535_6445_3125 ) < 1E-2
assert abs(result_mean.item() - 0.2_3003_8727_3098_1811 ) < 1E-2
else:
assert abs(result_sum.item() - 170.3_1352_2338_8672 ) < 1E-2
assert abs(result_mean.item() - 0.2_3003_8727_3098_1811 ) < 1E-2
| 257
| 1
|
"""simple docstring"""
import argparse
import re
import requests
import torch
# git clone https://github.com/salesforce/BLIP.git
from models.blip import blip_decoder
from models.blip_itm import blip_itm
from models.blip_vqa import blip_vqa
from PIL import Image
from torchvision import transforms
from torchvision.transforms.functional import InterpolationMode
from transformers import (
BertTokenizer,
BlipConfig,
BlipForConditionalGeneration,
BlipForImageTextRetrieval,
BlipForQuestionAnswering,
)
def lowercase_ ( _snake_case ,_snake_case ):
SCREAMING_SNAKE_CASE__ : str = """https://storage.googleapis.com/sfr-vision-language-research/BLIP/demo.jpg"""
SCREAMING_SNAKE_CASE__ : Dict = Image.open(requests.get(_snake_case ,stream=_snake_case ).raw ).convert("""RGB""" )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = transforms.Compose(
[
transforms.Resize((image_size, image_size) ,interpolation=InterpolationMode.BICUBIC ),
transforms.ToTensor(),
transforms.Normalize((0.48145466, 0.4578275, 0.40821073) ,(0.26862954, 0.26130258, 0.27577711) ),
] )
SCREAMING_SNAKE_CASE__ : Any = transform(_snake_case ).unsqueeze(0 ).to(_snake_case )
return image
def lowercase_ ( _snake_case ):
if "visual_encoder" in key:
SCREAMING_SNAKE_CASE__ : Optional[Any] = re.sub("""visual_encoder*""" ,"""vision_model.encoder""" ,_snake_case )
if "blocks" in key:
SCREAMING_SNAKE_CASE__ : List[str] = re.sub(R"""blocks""" ,"""layers""" ,_snake_case )
if "attn" in key:
SCREAMING_SNAKE_CASE__ : Dict = re.sub(R"""attn""" ,"""self_attn""" ,_snake_case )
if "norm1" in key:
SCREAMING_SNAKE_CASE__ : List[str] = re.sub(R"""norm1""" ,"""layer_norm1""" ,_snake_case )
if "norm2" in key:
SCREAMING_SNAKE_CASE__ : Tuple = re.sub(R"""norm2""" ,"""layer_norm2""" ,_snake_case )
if "encoder.norm" in key:
SCREAMING_SNAKE_CASE__ : List[str] = re.sub(R"""encoder.norm""" ,"""post_layernorm""" ,_snake_case )
if "encoder.patch_embed.proj" in key:
SCREAMING_SNAKE_CASE__ : int = re.sub(R"""encoder.patch_embed.proj""" ,"""embeddings.patch_embedding""" ,_snake_case )
if "encoder.pos_embed" in key:
SCREAMING_SNAKE_CASE__ : List[str] = re.sub(R"""encoder.pos_embed""" ,"""embeddings.position_embedding""" ,_snake_case )
if "encoder.cls_token" in key:
SCREAMING_SNAKE_CASE__ : Union[str, Any] = re.sub(R"""encoder.cls_token""" ,"""embeddings.class_embedding""" ,_snake_case )
if "self_attn" in key:
SCREAMING_SNAKE_CASE__ : str = re.sub(R"""self_attn.proj""" ,"""self_attn.projection""" ,_snake_case )
return key
@torch.no_grad()
def lowercase_ ( _snake_case ,_snake_case=None ):
if config_path is not None:
SCREAMING_SNAKE_CASE__ : Optional[Any] = BlipConfig.from_pretrained(_snake_case )
else:
SCREAMING_SNAKE_CASE__ : List[str] = BlipConfig(projection_dim=512 ,text_config={} ,vision_config={} )
SCREAMING_SNAKE_CASE__ : Optional[int] = BlipForConditionalGeneration(_snake_case ).eval()
SCREAMING_SNAKE_CASE__ : Tuple = """https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_capfilt_large.pth"""
SCREAMING_SNAKE_CASE__ : Any = blip_decoder(pretrained=_snake_case ,image_size=384 ,vit="""base""" )
SCREAMING_SNAKE_CASE__ : List[str] = pt_model.eval()
SCREAMING_SNAKE_CASE__ : List[Any] = pt_model.state_dict()
for key in modified_state_dict.copy():
SCREAMING_SNAKE_CASE__ : str = modified_state_dict.pop(_snake_case )
SCREAMING_SNAKE_CASE__ : Optional[int] = rename_key(_snake_case )
SCREAMING_SNAKE_CASE__ : Dict = value
hf_model.load_state_dict(_snake_case )
SCREAMING_SNAKE_CASE__ : List[str] = 384
SCREAMING_SNAKE_CASE__ : int = load_demo_image(image_size=_snake_case ,device="""cpu""" )
SCREAMING_SNAKE_CASE__ : Optional[Any] = BertTokenizer.from_pretrained("""bert-base-uncased""" )
SCREAMING_SNAKE_CASE__ : str = tokenizer(["""a picture of"""] ).input_ids
SCREAMING_SNAKE_CASE__ : Any = hf_model.generate(_snake_case ,_snake_case )
assert out[0].tolist() == [30_522, 1_037, 3_861, 1_997, 1_037, 2_450, 3_564, 2_006, 1_996, 3_509, 2_007, 2_014, 3_899, 102]
SCREAMING_SNAKE_CASE__ : Tuple = hf_model.generate(_snake_case )
assert out[0].tolist() == [30_522, 1_037, 2_450, 3_564, 2_006, 1_996, 3_509, 2_007, 2_014, 3_899, 102]
if pytorch_dump_folder_path is not None:
hf_model.save_pretrained(_snake_case )
# model_url = 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_vqa.pth'
SCREAMING_SNAKE_CASE__ : Optional[int] = (
"""https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_vqa_capfilt_large.pth"""
)
SCREAMING_SNAKE_CASE__ : Optional[int] = blip_vqa(pretrained=_snake_case ,image_size=_snake_case ,vit="""base""" )
vqa_model.eval()
SCREAMING_SNAKE_CASE__ : Optional[Any] = vqa_model.state_dict()
for key in modified_state_dict.copy():
SCREAMING_SNAKE_CASE__ : Optional[int] = modified_state_dict.pop(_snake_case )
SCREAMING_SNAKE_CASE__ : Tuple = rename_key(_snake_case )
SCREAMING_SNAKE_CASE__ : Optional[Any] = value
SCREAMING_SNAKE_CASE__ : List[Any] = BlipForQuestionAnswering(_snake_case )
hf_vqa_model.load_state_dict(_snake_case )
SCREAMING_SNAKE_CASE__ : Tuple = ["""How many dogs are in this image?"""]
SCREAMING_SNAKE_CASE__ : str = tokenizer(_snake_case ,return_tensors="""pt""" ).input_ids
SCREAMING_SNAKE_CASE__ : Optional[Any] = hf_vqa_model.generate(_snake_case ,_snake_case )
print(tokenizer.decode(answer[0] ) )
assert tokenizer.decode(answer[0] ) == "[UNK] 1 [SEP]"
if pytorch_dump_folder_path is not None:
hf_vqa_model.save_pretrained(pytorch_dump_folder_path + """_vqa""" )
SCREAMING_SNAKE_CASE__ : Any = """https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_retrieval_coco.pth"""
SCREAMING_SNAKE_CASE__ : Optional[int] = blip_itm(pretrained=_snake_case ,image_size=_snake_case ,vit="""base""" )
itm_model.eval()
SCREAMING_SNAKE_CASE__ : List[Any] = itm_model.state_dict()
for key in modified_state_dict.copy():
SCREAMING_SNAKE_CASE__ : List[Any] = modified_state_dict.pop(_snake_case )
SCREAMING_SNAKE_CASE__ : Dict = rename_key(_snake_case )
SCREAMING_SNAKE_CASE__ : Dict = value
SCREAMING_SNAKE_CASE__ : str = BlipForImageTextRetrieval(_snake_case )
SCREAMING_SNAKE_CASE__ : Dict = ["""A picture of a woman with a dog sitting in a beach"""]
SCREAMING_SNAKE_CASE__ : int = tokenizer(
_snake_case ,return_tensors="""pt""" ,padding="""max_length""" ,truncation=_snake_case ,max_length=35 ,).input_ids
hf_itm_model.load_state_dict(_snake_case )
hf_itm_model.eval()
SCREAMING_SNAKE_CASE__ : Optional[int] = hf_itm_model(_snake_case ,_snake_case ,use_itm_head=_snake_case )
SCREAMING_SNAKE_CASE__ : List[str] = hf_itm_model(_snake_case ,_snake_case ,use_itm_head=_snake_case )
assert out[0].item() == 0.2110687494277954
assert torch.nn.functional.softmax(out_itm[0] ,dim=1 )[:, 1].item() == 0.45698845386505127
if pytorch_dump_folder_path is not None:
hf_itm_model.save_pretrained(pytorch_dump_folder_path + """_itm""" )
if __name__ == "__main__":
UpperCAmelCase__ : Tuple = argparse.ArgumentParser()
parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
parser.add_argument('--config_path', default=None, type=str, help='Path to hf config.json of model to convert')
UpperCAmelCase__ : str = parser.parse_args()
convert_blip_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)
| 545
|
"""simple docstring"""
import copy
from collections import OrderedDict
from typing import Dict, Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
UpperCAmelCase__ : Optional[Any] = logging.get_logger(__name__)
UpperCAmelCase__ : str = {
'facebook/detr-resnet-50': 'https://huggingface.co/facebook/detr-resnet-50/resolve/main/config.json',
# See all DETR models at https://huggingface.co/models?filter=detr
}
class lowerCAmelCase_ (a__ ):
"""simple docstring"""
__UpperCamelCase : List[Any] = '''detr'''
__UpperCamelCase : List[Any] = ['''past_key_values''']
__UpperCamelCase : List[Any] = {
'''hidden_size''': '''d_model''',
'''num_attention_heads''': '''encoder_attention_heads''',
}
def __init__(self , SCREAMING_SNAKE_CASE__=True , SCREAMING_SNAKE_CASE__=None , SCREAMING_SNAKE_CASE__=3 , SCREAMING_SNAKE_CASE__=1_00 , SCREAMING_SNAKE_CASE__=6 , SCREAMING_SNAKE_CASE__=20_48 , SCREAMING_SNAKE_CASE__=8 , SCREAMING_SNAKE_CASE__=6 , SCREAMING_SNAKE_CASE__=20_48 , SCREAMING_SNAKE_CASE__=8 , SCREAMING_SNAKE_CASE__=0.0 , SCREAMING_SNAKE_CASE__=0.0 , SCREAMING_SNAKE_CASE__=True , SCREAMING_SNAKE_CASE__="relu" , SCREAMING_SNAKE_CASE__=2_56 , SCREAMING_SNAKE_CASE__=0.1 , SCREAMING_SNAKE_CASE__=0.0 , SCREAMING_SNAKE_CASE__=0.0 , SCREAMING_SNAKE_CASE__=0.02 , SCREAMING_SNAKE_CASE__=1.0 , SCREAMING_SNAKE_CASE__=False , SCREAMING_SNAKE_CASE__="sine" , SCREAMING_SNAKE_CASE__="resnet50" , SCREAMING_SNAKE_CASE__=True , SCREAMING_SNAKE_CASE__=False , SCREAMING_SNAKE_CASE__=1 , SCREAMING_SNAKE_CASE__=5 , SCREAMING_SNAKE_CASE__=2 , SCREAMING_SNAKE_CASE__=1 , SCREAMING_SNAKE_CASE__=1 , SCREAMING_SNAKE_CASE__=5 , SCREAMING_SNAKE_CASE__=2 , SCREAMING_SNAKE_CASE__=0.1 , **SCREAMING_SNAKE_CASE__ , ) -> Optional[Any]:
"""simple docstring"""
if backbone_config is not None and use_timm_backbone:
raise ValueError("""You can't specify both `backbone_config` and `use_timm_backbone`.""" )
if not use_timm_backbone:
if backbone_config is None:
logger.info("""`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.""" )
SCREAMING_SNAKE_CASE__ : Any = CONFIG_MAPPING["""resnet"""](out_features=["""stage4"""] )
elif isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
SCREAMING_SNAKE_CASE__ : Union[str, Any] = backbone_config.get("""model_type""" )
SCREAMING_SNAKE_CASE__ : int = CONFIG_MAPPING[backbone_model_type]
SCREAMING_SNAKE_CASE__ : str = config_class.from_dict(SCREAMING_SNAKE_CASE__ )
# set timm attributes to None
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : List[str] = None, None, None
SCREAMING_SNAKE_CASE__ : Optional[int] = use_timm_backbone
SCREAMING_SNAKE_CASE__ : Tuple = backbone_config
SCREAMING_SNAKE_CASE__ : List[Any] = num_channels
SCREAMING_SNAKE_CASE__ : Tuple = num_queries
SCREAMING_SNAKE_CASE__ : Optional[int] = d_model
SCREAMING_SNAKE_CASE__ : str = encoder_ffn_dim
SCREAMING_SNAKE_CASE__ : str = encoder_layers
SCREAMING_SNAKE_CASE__ : Optional[int] = encoder_attention_heads
SCREAMING_SNAKE_CASE__ : Any = decoder_ffn_dim
SCREAMING_SNAKE_CASE__ : Optional[Any] = decoder_layers
SCREAMING_SNAKE_CASE__ : Dict = decoder_attention_heads
SCREAMING_SNAKE_CASE__ : List[str] = dropout
SCREAMING_SNAKE_CASE__ : Tuple = attention_dropout
SCREAMING_SNAKE_CASE__ : Tuple = activation_dropout
SCREAMING_SNAKE_CASE__ : Union[str, Any] = activation_function
SCREAMING_SNAKE_CASE__ : Any = init_std
SCREAMING_SNAKE_CASE__ : Dict = init_xavier_std
SCREAMING_SNAKE_CASE__ : Any = encoder_layerdrop
SCREAMING_SNAKE_CASE__ : Union[str, Any] = decoder_layerdrop
SCREAMING_SNAKE_CASE__ : Dict = encoder_layers
SCREAMING_SNAKE_CASE__ : List[str] = auxiliary_loss
SCREAMING_SNAKE_CASE__ : List[Any] = position_embedding_type
SCREAMING_SNAKE_CASE__ : List[str] = backbone
SCREAMING_SNAKE_CASE__ : Dict = use_pretrained_backbone
SCREAMING_SNAKE_CASE__ : Any = dilation
# Hungarian matcher
SCREAMING_SNAKE_CASE__ : Optional[Any] = class_cost
SCREAMING_SNAKE_CASE__ : Tuple = bbox_cost
SCREAMING_SNAKE_CASE__ : List[Any] = giou_cost
# Loss coefficients
SCREAMING_SNAKE_CASE__ : Optional[int] = mask_loss_coefficient
SCREAMING_SNAKE_CASE__ : Optional[Any] = dice_loss_coefficient
SCREAMING_SNAKE_CASE__ : Optional[int] = bbox_loss_coefficient
SCREAMING_SNAKE_CASE__ : Any = giou_loss_coefficient
SCREAMING_SNAKE_CASE__ : List[str] = eos_coefficient
super().__init__(is_encoder_decoder=SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ )
@property
def __magic_name__ (self ) -> int:
"""simple docstring"""
return self.encoder_attention_heads
@property
def __magic_name__ (self ) -> int:
"""simple docstring"""
return self.d_model
@classmethod
def __magic_name__ (cls , SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ ) -> Tuple:
"""simple docstring"""
return cls(backbone_config=SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ )
def __magic_name__ (self ) -> Dict[str, any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Dict = copy.deepcopy(self.__dict__ )
if output["backbone_config"] is not None:
SCREAMING_SNAKE_CASE__ : Any = self.backbone_config.to_dict()
SCREAMING_SNAKE_CASE__ : Dict = self.__class__.model_type
return output
class lowerCAmelCase_ (a__ ):
"""simple docstring"""
__UpperCamelCase : Any = version.parse('''1.11''' )
@property
def __magic_name__ (self ) -> Mapping[str, Mapping[int, str]]:
"""simple docstring"""
return OrderedDict(
[
("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
("""pixel_mask""", {0: """batch"""}),
] )
@property
def __magic_name__ (self ) -> float:
"""simple docstring"""
return 1E-5
@property
def __magic_name__ (self ) -> int:
"""simple docstring"""
return 12
| 545
| 1
|
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
A = {
'''configuration_layoutlmv2''': ['''LAYOUTLMV2_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''LayoutLMv2Config'''],
'''processing_layoutlmv2''': ['''LayoutLMv2Processor'''],
'''tokenization_layoutlmv2''': ['''LayoutLMv2Tokenizer'''],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A = ['''LayoutLMv2TokenizerFast''']
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A = ['''LayoutLMv2FeatureExtractor''']
A = ['''LayoutLMv2ImageProcessor''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A = [
'''LAYOUTLMV2_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''LayoutLMv2ForQuestionAnswering''',
'''LayoutLMv2ForSequenceClassification''',
'''LayoutLMv2ForTokenClassification''',
'''LayoutLMv2Layer''',
'''LayoutLMv2Model''',
'''LayoutLMv2PreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_layoutlmva import LAYOUTLMV2_PRETRAINED_CONFIG_ARCHIVE_MAP, LayoutLMvaConfig
from .processing_layoutlmva import LayoutLMvaProcessor
from .tokenization_layoutlmva import LayoutLMvaTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_layoutlmva_fast import LayoutLMvaTokenizerFast
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_layoutlmva import LayoutLMvaFeatureExtractor, LayoutLMvaImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_layoutlmva import (
LAYOUTLMV2_PRETRAINED_MODEL_ARCHIVE_LIST,
LayoutLMvaForQuestionAnswering,
LayoutLMvaForSequenceClassification,
LayoutLMvaForTokenClassification,
LayoutLMvaLayer,
LayoutLMvaModel,
LayoutLMvaPreTrainedModel,
)
else:
import sys
A = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 52
|
"""simple docstring"""
import json
import os
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union
import sentencepiece
from ...tokenization_utils import BatchEncoding, PreTrainedTokenizer
from ...utils import logging
UpperCAmelCase__ =logging.get_logger(__name__)
UpperCAmelCase__ ="▁"
UpperCAmelCase__ ={
"vocab_file": "vocab.json",
"spm_file": "sentencepiece.bpe.model",
"tokenizer_config_file": "tokenizer_config.json",
}
UpperCAmelCase__ ={
"vocab_file": {
"facebook/m2m100_418M": "https://huggingface.co/facebook/m2m100_418M/resolve/main/vocab.json",
"facebook/m2m100_1.2B": "https://huggingface.co/facebook/m2m100_1.2B/resolve/main/vocab.json",
},
"spm_file": {
"facebook/m2m100_418M": "https://huggingface.co/facebook/m2m100_418M/resolve/main/sentencepiece.bpe.model",
"facebook/m2m100_1.2B": "https://huggingface.co/facebook/m2m100_1.2B/resolve/main/sentencepiece.bpe.model",
},
"tokenizer_config_file": {
"facebook/m2m100_418M": "https://huggingface.co/facebook/m2m100_418M/resolve/main/tokenizer_config.json",
"facebook/m2m100_1.2B": "https://huggingface.co/facebook/m2m100_1.2B/resolve/main/tokenizer_config.json",
},
}
UpperCAmelCase__ ={
"facebook/m2m100_418M": 1024,
}
# fmt: off
UpperCAmelCase__ ={
"m2m100": ["af", "am", "ar", "ast", "az", "ba", "be", "bg", "bn", "br", "bs", "ca", "ceb", "cs", "cy", "da", "de", "el", "en", "es", "et", "fa", "ff", "fi", "fr", "fy", "ga", "gd", "gl", "gu", "ha", "he", "hi", "hr", "ht", "hu", "hy", "id", "ig", "ilo", "is", "it", "ja", "jv", "ka", "kk", "km", "kn", "ko", "lb", "lg", "ln", "lo", "lt", "lv", "mg", "mk", "ml", "mn", "mr", "ms", "my", "ne", "nl", "no", "ns", "oc", "or", "pa", "pl", "ps", "pt", "ro", "ru", "sd", "si", "sk", "sl", "so", "sq", "sr", "ss", "su", "sv", "sw", "ta", "th", "tl", "tn", "tr", "uk", "ur", "uz", "vi", "wo", "xh", "yi", "yo", "zh", "zu"],
"wmt21": ["en", "ha", "is", "ja", "cs", "ru", "zh", "de"]
}
class lowerCamelCase__ ( _a ):
a : str = VOCAB_FILES_NAMES
a : Dict = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
a : int = PRETRAINED_VOCAB_FILES_MAP
a : Union[str, Any] = ["""input_ids""", """attention_mask"""]
a : List[int] = []
a : List[int] = []
def __init__( self : Optional[Any] , A_ : str , A_ : Dict , A_ : str=None , A_ : Dict=None , A_ : str="<s>" , A_ : Any="</s>" , A_ : List[Any]="</s>" , A_ : List[str]="<pad>" , A_ : Optional[int]="<unk>" , A_ : str="m2m100" , A_ : Optional[Dict[str, Any]] = None , A_ : Tuple=8 , **A_ : Dict , ):
'''simple docstring'''
__lowercase = {} if sp_model_kwargs is None else sp_model_kwargs
__lowercase = language_codes
__lowercase = FAIRSEQ_LANGUAGE_CODES[language_codes]
__lowercase = {lang_code: F'''__{lang_code}__''' for lang_code in fairseq_language_code}
__lowercase = kwargs.get("""additional_special_tokens""" , [] )
kwargs["additional_special_tokens"] += [
self.get_lang_token(A_ )
for lang_code in fairseq_language_code
if self.get_lang_token(A_ ) not in kwargs["additional_special_tokens"]
]
super().__init__(
src_lang=A_ , tgt_lang=A_ , bos_token=A_ , eos_token=A_ , sep_token=A_ , unk_token=A_ , pad_token=A_ , language_codes=A_ , sp_model_kwargs=self.sp_model_kwargs , num_madeup_words=A_ , **A_ , )
__lowercase = vocab_file
__lowercase = load_json(A_ )
__lowercase = {v: k for k, v in self.encoder.items()}
__lowercase = spm_file
__lowercase = load_spm(A_ , self.sp_model_kwargs )
__lowercase = len(self.encoder )
__lowercase = {
self.get_lang_token(A_ ): self.encoder_size + i for i, lang_code in enumerate(A_ )
}
__lowercase = {lang_code: self.encoder_size + i for i, lang_code in enumerate(A_ )}
__lowercase = {v: k for k, v in self.lang_token_to_id.items()}
__lowercase = src_lang if src_lang is not None else """en"""
__lowercase = tgt_lang
__lowercase = self.get_lang_id(self._src_lang )
self.set_src_lang_special_tokens(self._src_lang )
__lowercase = num_madeup_words
@property
def SCREAMING_SNAKE_CASE_ ( self : Union[str, Any] ):
'''simple docstring'''
return len(self.encoder ) + len(self.lang_token_to_id )
@property
def SCREAMING_SNAKE_CASE_ ( self : Dict ):
'''simple docstring'''
return self._src_lang
@src_lang.setter
def SCREAMING_SNAKE_CASE_ ( self : Dict , A_ : str ):
'''simple docstring'''
__lowercase = new_src_lang
self.set_src_lang_special_tokens(self._src_lang )
def SCREAMING_SNAKE_CASE_ ( self : Dict , A_ : str ):
'''simple docstring'''
return self.sp_model.encode(A_ , out_type=A_ )
def SCREAMING_SNAKE_CASE_ ( self : Optional[Any] , A_ : str ):
'''simple docstring'''
if token in self.lang_token_to_id:
return self.lang_token_to_id[token]
return self.encoder.get(A_ , self.encoder[self.unk_token] )
def SCREAMING_SNAKE_CASE_ ( self : List[str] , A_ : int ):
'''simple docstring'''
if index in self.id_to_lang_token:
return self.id_to_lang_token[index]
return self.decoder.get(A_ , self.unk_token )
def SCREAMING_SNAKE_CASE_ ( self : Tuple , A_ : Union[str, Any] ):
'''simple docstring'''
__lowercase = []
__lowercase = """"""
for token in tokens:
# make sure that special tokens are not decoded using sentencepiece model
if token in self.all_special_tokens:
out_string += self.sp_model.decode(A_ ) + token
__lowercase = []
else:
current_sub_tokens.append(A_ )
out_string += self.sp_model.decode(A_ )
return out_string.strip()
def SCREAMING_SNAKE_CASE_ ( self : List[str] , A_ : List[int] , A_ : Optional[List[int]] = None , A_ : bool = False ):
'''simple docstring'''
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=A_ , token_ids_a=A_ , already_has_special_tokens=A_ )
__lowercase = [1] * len(self.prefix_tokens )
__lowercase = [1] * len(self.suffix_tokens )
if token_ids_a is None:
return prefix_ones + ([0] * len(A_ )) + suffix_ones
return prefix_ones + ([0] * len(A_ )) + ([0] * len(A_ )) + suffix_ones
def SCREAMING_SNAKE_CASE_ ( self : Any , A_ : List[int] , A_ : Optional[List[int]] = None ):
'''simple docstring'''
if token_ids_a is None:
return self.prefix_tokens + token_ids_a + self.suffix_tokens
# We don't expect to process pairs, but leave the pair logic for API consistency
return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens
def SCREAMING_SNAKE_CASE_ ( self : List[str] ):
'''simple docstring'''
__lowercase = {self.convert_ids_to_tokens(A_ ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def __getstate__( self : List[str] ):
'''simple docstring'''
__lowercase = self.__dict__.copy()
__lowercase = None
return state
def __setstate__( self : Optional[Any] , A_ : Dict ):
'''simple docstring'''
__lowercase = d
# for backward compatibility
if not hasattr(self , """sp_model_kwargs""" ):
__lowercase = {}
__lowercase = load_spm(self.spm_file , self.sp_model_kwargs )
def SCREAMING_SNAKE_CASE_ ( self : Tuple , A_ : str , A_ : Optional[str] = None ):
'''simple docstring'''
__lowercase = Path(A_ )
if not save_dir.is_dir():
raise OSError(F'''{save_directory} should be a directory''' )
__lowercase = save_dir / (
(filename_prefix + """-""" if filename_prefix else """""") + self.vocab_files_names["""vocab_file"""]
)
__lowercase = save_dir / (
(filename_prefix + """-""" if filename_prefix else """""") + self.vocab_files_names["""spm_file"""]
)
save_json(self.encoder , A_ )
if os.path.abspath(self.spm_file ) != os.path.abspath(A_ ) and os.path.isfile(self.spm_file ):
copyfile(self.spm_file , A_ )
elif not os.path.isfile(self.spm_file ):
with open(A_ , """wb""" ) as fi:
__lowercase = self.sp_model.serialized_model_proto()
fi.write(A_ )
return (str(A_ ), str(A_ ))
def SCREAMING_SNAKE_CASE_ ( self : List[Any] , A_ : List[str] , A_ : str = "en" , A_ : Optional[List[str]] = None , A_ : str = "ro" , **A_ : List[Any] , ):
'''simple docstring'''
__lowercase = src_lang
__lowercase = tgt_lang
self.set_src_lang_special_tokens(self.src_lang )
return super().prepare_seqaseq_batch(A_ , A_ , **A_ )
def SCREAMING_SNAKE_CASE_ ( self : Optional[int] , A_ : List[Any] , A_ : Optional[str] , A_ : Optional[str] , **A_ : Optional[Any] ):
'''simple docstring'''
if src_lang is None or tgt_lang is None:
raise ValueError("""Translation requires a `src_lang` and a `tgt_lang` for this model""" )
__lowercase = src_lang
__lowercase = self(A_ , add_special_tokens=A_ , **A_ )
__lowercase = self.get_lang_id(A_ )
__lowercase = tgt_lang_id
return inputs
def SCREAMING_SNAKE_CASE_ ( self : List[str] ):
'''simple docstring'''
self.set_src_lang_special_tokens(self.src_lang )
def SCREAMING_SNAKE_CASE_ ( self : Optional[int] ):
'''simple docstring'''
self.set_tgt_lang_special_tokens(self.tgt_lang )
def SCREAMING_SNAKE_CASE_ ( self : Optional[Any] , A_ : str ):
'''simple docstring'''
__lowercase = self.get_lang_token(A_ )
__lowercase = self.lang_token_to_id[lang_token]
__lowercase = [self.cur_lang_id]
__lowercase = [self.eos_token_id]
def SCREAMING_SNAKE_CASE_ ( self : Optional[int] , A_ : str ):
'''simple docstring'''
__lowercase = self.get_lang_token(A_ )
__lowercase = self.lang_token_to_id[lang_token]
__lowercase = [self.cur_lang_id]
__lowercase = [self.eos_token_id]
def SCREAMING_SNAKE_CASE_ ( self : Dict , A_ : str ):
'''simple docstring'''
return self.lang_code_to_token[lang]
def SCREAMING_SNAKE_CASE_ ( self : Dict , A_ : str ):
'''simple docstring'''
__lowercase = self.get_lang_token(A_ )
return self.lang_token_to_id[lang_token]
def lowerCAmelCase_ ( UpperCamelCase__ : str , UpperCamelCase__ : Dict[str, Any] ):
"""simple docstring"""
__lowercase = sentencepiece.SentencePieceProcessor(**UpperCamelCase__ )
spm.Load(str(UpperCamelCase__ ) )
return spm
def lowerCAmelCase_ ( UpperCamelCase__ : str ):
"""simple docstring"""
with open(UpperCamelCase__ , """r""" ) as f:
return json.load(UpperCamelCase__ )
def lowerCAmelCase_ ( UpperCamelCase__ : List[str] , UpperCamelCase__ : str ):
"""simple docstring"""
with open(UpperCamelCase__ , """w""" ) as f:
json.dump(UpperCamelCase__ , UpperCamelCase__ , indent=2 )
| 616
| 0
|
"""simple docstring"""
import math
import qiskit
def UpperCAmelCase ( snake_case : int = 1 , snake_case : int = 1 , snake_case : int = 1 ):
if (
isinstance(snake_case , snake_case )
or isinstance(snake_case , snake_case )
or isinstance(snake_case , snake_case )
):
raise TypeError('''inputs must be integers.''' )
if (input_a < 0) or (input_a < 0) or (carry_in < 0):
raise ValueError('''inputs must be positive.''' )
if (
(math.floor(snake_case ) != input_a)
or (math.floor(snake_case ) != input_a)
or (math.floor(snake_case ) != carry_in)
):
raise ValueError('''inputs must be exact integers.''' )
if (input_a > 2) or (input_a > 2) or (carry_in > 2):
raise ValueError('''inputs must be less or equal to 2.''' )
# build registers
_lowerCAmelCase:List[str] = qiskit.QuantumRegister(4 , '''qr''' )
_lowerCAmelCase:Optional[int] = qiskit.ClassicalRegister(2 , '''cr''' )
# list the entries
_lowerCAmelCase:str = [input_a, input_a, carry_in]
_lowerCAmelCase:Tuple = qiskit.QuantumCircuit(snake_case , snake_case )
for i in range(0 , 3 ):
if entry[i] == 2:
quantum_circuit.h(snake_case ) # for hadamard entries
elif entry[i] == 1:
quantum_circuit.x(snake_case ) # for 1 entries
elif entry[i] == 0:
quantum_circuit.i(snake_case ) # for 0 entries
# build the circuit
quantum_circuit.ccx(0 , 1 , 3 ) # ccx = toffoli gate
quantum_circuit.cx(0 , 1 )
quantum_circuit.ccx(1 , 2 , 3 )
quantum_circuit.cx(1 , 2 )
quantum_circuit.cx(0 , 1 )
quantum_circuit.measure([2, 3] , snake_case ) # measure the last two qbits
_lowerCAmelCase:Any = qiskit.Aer.get_backend('''aer_simulator''' )
_lowerCAmelCase:List[Any] = qiskit.execute(snake_case , snake_case , shots=1000 )
return job.result().get_counts(snake_case )
if __name__ == "__main__":
print(F"Total sum count for state is: {quantum_full_adder(1, 1, 1)}")
| 439
|
"""simple docstring"""
from typing import List, Union
import numpy as np
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
if is_torch_available():
import torch
from ..models.auto.modeling_auto import MODEL_FOR_DEPTH_ESTIMATION_MAPPING
UpperCamelCase__ = logging.get_logger(__name__)
@add_end_docstrings(UpperCamelCase_ )
class a__ ( UpperCamelCase_ ):
def __init__( self : int ,*a__ : Optional[Any] ,**a__ : Union[str, Any]) -> Tuple:
"""simple docstring"""
super().__init__(*a__ ,**a__)
requires_backends(self ,'''vision''')
self.check_model_type(a__)
def __call__( self : str ,a__ : Union[str, List[str], "Image.Image", List["Image.Image"]] ,**a__ : List[str]) -> Optional[int]:
"""simple docstring"""
return super().__call__(a__ ,**a__)
def __UpperCamelCase ( self : Union[str, Any] ,**a__ : List[Any]) -> Any:
"""simple docstring"""
return {}, {}, {}
def __UpperCamelCase ( self : Tuple ,a__ : Optional[int]) -> Optional[Any]:
"""simple docstring"""
_lowerCAmelCase:List[str] = load_image(a__)
_lowerCAmelCase:int = image.size
_lowerCAmelCase:int = self.image_processor(images=a__ ,return_tensors=self.framework)
return model_inputs
def __UpperCamelCase ( self : Dict ,a__ : List[str]) -> Union[str, Any]:
"""simple docstring"""
_lowerCAmelCase:Any = self.model(**a__)
return model_outputs
def __UpperCamelCase ( self : List[Any] ,a__ : Dict) -> Any:
"""simple docstring"""
_lowerCAmelCase:Optional[int] = model_outputs.predicted_depth
_lowerCAmelCase:Union[str, Any] = torch.nn.functional.interpolate(
predicted_depth.unsqueeze(1) ,size=self.image_size[::-1] ,mode='''bicubic''' ,align_corners=a__)
_lowerCAmelCase:List[str] = prediction.squeeze().cpu().numpy()
_lowerCAmelCase:Any = (output * 255 / np.max(a__)).astype('''uint8''')
_lowerCAmelCase:Dict = Image.fromarray(a__)
_lowerCAmelCase:Tuple = {}
_lowerCAmelCase:Optional[int] = predicted_depth
_lowerCAmelCase:str = depth
return output_dict
| 439
| 1
|
"""simple docstring"""
import argparse
import os
import re
__lowerCAmelCase : Any = '''src/diffusers'''
# Pattern that looks at the indentation in a line.
__lowerCAmelCase : Any = re.compile(R'''^(\s*)\S''')
# Pattern that matches `"key":" and puts `key` in group 0.
__lowerCAmelCase : Union[str, Any] = re.compile(R'''^\s*"([^"]+)":''')
# Pattern that matches `_import_structure["key"]` and puts `key` in group 0.
__lowerCAmelCase : Tuple = re.compile(R'''^\s*_import_structure\["([^"]+)"\]''')
# Pattern that matches `"key",` and puts `key` in group 0.
__lowerCAmelCase : Any = re.compile(R'''^\s*"([^"]+)",\s*$''')
# Pattern that matches any `[stuff]` and puts `stuff` in group 0.
__lowerCAmelCase : List[Any] = re.compile(R'''\[([^\]]+)\]''')
def __lowerCAmelCase ( __UpperCamelCase : Dict ):
'''simple docstring'''
snake_case_ : Any = _re_indent.search(__UpperCamelCase )
return "" if search is None else search.groups()[0]
def __lowerCAmelCase ( __UpperCamelCase : Union[str, Any] , __UpperCamelCase : List[Any]="" , __UpperCamelCase : Union[str, Any]=None , __UpperCamelCase : List[Any]=None ):
'''simple docstring'''
snake_case_ : int = 0
snake_case_ : Dict = code.split("""\n""" )
if start_prompt is not None:
while not lines[index].startswith(__UpperCamelCase ):
index += 1
snake_case_ : Optional[int] = ["""\n""".join(lines[:index] )]
else:
snake_case_ : List[str] = []
# We split into blocks until we get to the `end_prompt` (or the end of the block).
snake_case_ : Any = [lines[index]]
index += 1
while index < len(__UpperCamelCase ) and (end_prompt is None or not lines[index].startswith(__UpperCamelCase )):
if len(lines[index] ) > 0 and get_indent(lines[index] ) == indent_level:
if len(__UpperCamelCase ) > 0 and get_indent(current_block[-1] ).startswith(indent_level + """ """ ):
current_block.append(lines[index] )
blocks.append("""\n""".join(__UpperCamelCase ) )
if index < len(__UpperCamelCase ) - 1:
snake_case_ : Union[str, Any] = [lines[index + 1]]
index += 1
else:
snake_case_ : Optional[int] = []
else:
blocks.append("""\n""".join(__UpperCamelCase ) )
snake_case_ : Union[str, Any] = [lines[index]]
else:
current_block.append(lines[index] )
index += 1
# Adds current block if it's nonempty.
if len(__UpperCamelCase ) > 0:
blocks.append("""\n""".join(__UpperCamelCase ) )
# Add final block after end_prompt if provided.
if end_prompt is not None and index < len(__UpperCamelCase ):
blocks.append("""\n""".join(lines[index:] ) )
return blocks
def __lowerCAmelCase ( __UpperCamelCase : List[str] ):
'''simple docstring'''
def _inner(__UpperCamelCase : Optional[int] ):
return key(__UpperCamelCase ).lower().replace("""_""" , """""" )
return _inner
def __lowerCAmelCase ( __UpperCamelCase : Union[str, Any] , __UpperCamelCase : List[Any]=None ):
'''simple docstring'''
def noop(__UpperCamelCase : Any ):
return x
if key is None:
snake_case_ : Tuple = noop
# Constants are all uppercase, they go first.
snake_case_ : Dict = [obj for obj in objects if key(__UpperCamelCase ).isupper()]
# Classes are not all uppercase but start with a capital, they go second.
snake_case_ : str = [obj for obj in objects if key(__UpperCamelCase )[0].isupper() and not key(__UpperCamelCase ).isupper()]
# Functions begin with a lowercase, they go last.
snake_case_ : int = [obj for obj in objects if not key(__UpperCamelCase )[0].isupper()]
snake_case_ : Optional[int] = ignore_underscore(__UpperCamelCase )
return sorted(__UpperCamelCase , key=__UpperCamelCase ) + sorted(__UpperCamelCase , key=__UpperCamelCase ) + sorted(__UpperCamelCase , key=__UpperCamelCase )
def __lowerCAmelCase ( __UpperCamelCase : Union[str, Any] ):
'''simple docstring'''
def _replace(__UpperCamelCase : int ):
snake_case_ : List[Any] = match.groups()[0]
if "," not in imports:
return F'[{imports}]'
snake_case_ : Tuple = [part.strip().replace("""\"""" , """""" ) for part in imports.split(""",""" )]
# We will have a final empty element if the line finished with a comma.
if len(keys[-1] ) == 0:
snake_case_ : Optional[Any] = keys[:-1]
return "[" + ", ".join([F'"{k}"' for k in sort_objects(__UpperCamelCase )] ) + "]"
snake_case_ : Optional[Any] = import_statement.split("""\n""" )
if len(__UpperCamelCase ) > 3:
# Here we have to sort internal imports that are on several lines (one per name):
# key: [
# "object1",
# "object2",
# ...
# ]
# We may have to ignore one or two lines on each side.
snake_case_ : Any = 2 if lines[1].strip() == """[""" else 1
snake_case_ : Any = [(i, _re_strip_line.search(__UpperCamelCase ).groups()[0]) for i, line in enumerate(lines[idx:-idx] )]
snake_case_ : List[str] = sort_objects(__UpperCamelCase , key=lambda __UpperCamelCase : x[1] )
snake_case_ : Union[str, Any] = [lines[x[0] + idx] for x in sorted_indices]
return "\n".join(lines[:idx] + sorted_lines + lines[-idx:] )
elif len(__UpperCamelCase ) == 3:
# Here we have to sort internal imports that are on one separate line:
# key: [
# "object1", "object2", ...
# ]
if _re_bracket_content.search(lines[1] ) is not None:
snake_case_ : Any = _re_bracket_content.sub(_replace , lines[1] )
else:
snake_case_ : List[str] = [part.strip().replace("""\"""" , """""" ) for part in lines[1].split(""",""" )]
# We will have a final empty element if the line finished with a comma.
if len(keys[-1] ) == 0:
snake_case_ : Tuple = keys[:-1]
snake_case_ : List[str] = get_indent(lines[1] ) + """, """.join([F'"{k}"' for k in sort_objects(__UpperCamelCase )] )
return "\n".join(__UpperCamelCase )
else:
# Finally we have to deal with imports fitting on one line
snake_case_ : Any = _re_bracket_content.sub(_replace , __UpperCamelCase )
return import_statement
def __lowerCAmelCase ( __UpperCamelCase : str , __UpperCamelCase : str=True ):
'''simple docstring'''
with open(__UpperCamelCase , """r""" ) as f:
snake_case_ : str = f.read()
if "_import_structure" not in code:
return
# Blocks of indent level 0
snake_case_ : int = split_code_in_indented_blocks(
__UpperCamelCase , start_prompt="""_import_structure = {""" , end_prompt="""if TYPE_CHECKING:""" )
# We ignore block 0 (everything until start_prompt) and the last block (everything after end_prompt).
for block_idx in range(1 , len(__UpperCamelCase ) - 1 ):
# Check if the block contains some `_import_structure`s thingy to sort.
snake_case_ : List[str] = main_blocks[block_idx]
snake_case_ : List[str] = block.split("""\n""" )
# Get to the start of the imports.
snake_case_ : int = 0
while line_idx < len(__UpperCamelCase ) and "_import_structure" not in block_lines[line_idx]:
# Skip dummy import blocks
if "import dummy" in block_lines[line_idx]:
snake_case_ : int = len(__UpperCamelCase )
else:
line_idx += 1
if line_idx >= len(__UpperCamelCase ):
continue
# Ignore beginning and last line: they don't contain anything.
snake_case_ : Dict = """\n""".join(block_lines[line_idx:-1] )
snake_case_ : Tuple = get_indent(block_lines[1] )
# Slit the internal block into blocks of indent level 1.
snake_case_ : List[str] = split_code_in_indented_blocks(__UpperCamelCase , indent_level=__UpperCamelCase )
# We have two categories of import key: list or _import_structure[key].append/extend
snake_case_ : Dict = _re_direct_key if """_import_structure""" in block_lines[0] else _re_indirect_key
# Grab the keys, but there is a trap: some lines are empty or just comments.
snake_case_ : str = [(pattern.search(__UpperCamelCase ).groups()[0] if pattern.search(__UpperCamelCase ) is not None else None) for b in internal_blocks]
# We only sort the lines with a key.
snake_case_ : str = [(i, key) for i, key in enumerate(__UpperCamelCase ) if key is not None]
snake_case_ : int = [x[0] for x in sorted(__UpperCamelCase , key=lambda __UpperCamelCase : x[1] )]
# We reorder the blocks by leaving empty lines/comments as they were and reorder the rest.
snake_case_ : int = 0
snake_case_ : int = []
for i in range(len(__UpperCamelCase ) ):
if keys[i] is None:
reordered_blocks.append(internal_blocks[i] )
else:
snake_case_ : str = sort_objects_in_import(internal_blocks[sorted_indices[count]] )
reordered_blocks.append(__UpperCamelCase )
count += 1
# And we put our main block back together with its first and last line.
snake_case_ : Any = """\n""".join(block_lines[:line_idx] + reordered_blocks + [block_lines[-1]] )
if code != "\n".join(__UpperCamelCase ):
if check_only:
return True
else:
print(F'Overwriting {file}.' )
with open(__UpperCamelCase , """w""" ) as f:
f.write("""\n""".join(__UpperCamelCase ) )
def __lowerCAmelCase ( __UpperCamelCase : Union[str, Any]=True ):
'''simple docstring'''
snake_case_ : Any = []
for root, _, files in os.walk(__UpperCamelCase ):
if "__init__.py" in files:
snake_case_ : Tuple = sort_imports(os.path.join(__UpperCamelCase , """__init__.py""" ) , check_only=__UpperCamelCase )
if result:
snake_case_ : List[str] = [os.path.join(__UpperCamelCase , """__init__.py""" )]
if len(__UpperCamelCase ) > 0:
raise ValueError(F'Would overwrite {len(__UpperCamelCase )} files, run `make style`.' )
if __name__ == "__main__":
__lowerCAmelCase : Tuple = argparse.ArgumentParser()
parser.add_argument('''--check_only''', action='''store_true''', help='''Whether to only check or fix style.''')
__lowerCAmelCase : Dict = parser.parse_args()
sort_imports_in_all_inits(check_only=args.check_only)
| 58
|
import asyncio
import os
import shutil
import subprocess
import sys
import tempfile
import unittest
from distutils.util import strtobool
from functools import partial
from pathlib import Path
from typing import List, Union
from unittest import mock
import torch
from ..state import AcceleratorState, PartialState
from ..utils import (
gather,
is_bnb_available,
is_comet_ml_available,
is_datasets_available,
is_deepspeed_available,
is_mps_available,
is_safetensors_available,
is_tensorboard_available,
is_torch_version,
is_tpu_available,
is_transformers_available,
is_wandb_available,
is_xpu_available,
)
def SCREAMING_SNAKE_CASE__ ( lowercase ,lowercase=False ) -> List[Any]:
try:
snake_case : Tuple = os.environ[key]
except KeyError:
# KEY isn't set, default to `default`.
snake_case : Tuple = default
else:
# KEY is set, convert it to True or False.
try:
snake_case : Tuple = strtobool(lowercase )
except ValueError:
# More values are supported, but let's keep the message simple.
raise ValueError(f"""If set, {key} must be yes or no.""" )
return _value
lowerCamelCase : Tuple = parse_flag_from_env('RUN_SLOW', default=False)
def SCREAMING_SNAKE_CASE__ ( lowercase ) -> Optional[int]:
return unittest.skip("""Test was skipped""" )(lowercase )
def SCREAMING_SNAKE_CASE__ ( lowercase ) -> Union[str, Any]:
return unittest.skipUnless(_run_slow_tests ,"""test is slow""" )(lowercase )
def SCREAMING_SNAKE_CASE__ ( lowercase ) -> Any:
return unittest.skipUnless(not torch.cuda.is_available() ,"""test requires only a CPU""" )(lowercase )
def SCREAMING_SNAKE_CASE__ ( lowercase ) -> Union[str, Any]:
return unittest.skipUnless(torch.cuda.is_available() ,"""test requires a GPU""" )(lowercase )
def SCREAMING_SNAKE_CASE__ ( lowercase ) -> Optional[int]:
return unittest.skipUnless(is_xpu_available() ,"""test requires a XPU""" )(lowercase )
def SCREAMING_SNAKE_CASE__ ( lowercase ) -> Optional[Any]:
return unittest.skipUnless(is_mps_available() ,"""test requires a `mps` backend support in `torch`""" )(lowercase )
def SCREAMING_SNAKE_CASE__ ( lowercase ) -> Union[str, Any]:
return unittest.skipUnless(
is_transformers_available() and is_datasets_available() ,"""test requires the Hugging Face suite""" )(lowercase )
def SCREAMING_SNAKE_CASE__ ( lowercase ) -> Optional[Any]:
return unittest.skipUnless(is_bnb_available() ,"""test requires the bitsandbytes library""" )(lowercase )
def SCREAMING_SNAKE_CASE__ ( lowercase ) -> Optional[int]:
return unittest.skipUnless(is_tpu_available() ,"""test requires TPU""" )(lowercase )
def SCREAMING_SNAKE_CASE__ ( lowercase ) -> Any:
return unittest.skipUnless(torch.cuda.device_count() == 1 ,"""test requires a GPU""" )(lowercase )
def SCREAMING_SNAKE_CASE__ ( lowercase ) -> List[str]:
return unittest.skipUnless(torch.xpu.device_count() == 1 ,"""test requires a XPU""" )(lowercase )
def SCREAMING_SNAKE_CASE__ ( lowercase ) -> Dict:
return unittest.skipUnless(torch.cuda.device_count() > 1 ,"""test requires multiple GPUs""" )(lowercase )
def SCREAMING_SNAKE_CASE__ ( lowercase ) -> List[Any]:
return unittest.skipUnless(torch.xpu.device_count() > 1 ,"""test requires multiple XPUs""" )(lowercase )
def SCREAMING_SNAKE_CASE__ ( lowercase ) -> Optional[Any]:
return unittest.skipUnless(is_safetensors_available() ,"""test requires safetensors""" )(lowercase )
def SCREAMING_SNAKE_CASE__ ( lowercase ) -> Any:
return unittest.skipUnless(is_deepspeed_available() ,"""test requires DeepSpeed""" )(lowercase )
def SCREAMING_SNAKE_CASE__ ( lowercase ) -> Union[str, Any]:
return unittest.skipUnless(is_torch_version(""">=""" ,"""1.12.0""" ) ,"""test requires torch version >= 1.12.0""" )(lowercase )
def SCREAMING_SNAKE_CASE__ ( lowercase=None ,lowercase=None ) -> Optional[int]:
if test_case is None:
return partial(lowercase ,version=lowercase )
return unittest.skipUnless(is_torch_version(""">=""" ,lowercase ) ,f"""test requires torch version >= {version}""" )(lowercase )
def SCREAMING_SNAKE_CASE__ ( lowercase ) -> Tuple:
return unittest.skipUnless(is_tensorboard_available() ,"""test requires Tensorboard""" )(lowercase )
def SCREAMING_SNAKE_CASE__ ( lowercase ) -> Union[str, Any]:
return unittest.skipUnless(is_wandb_available() ,"""test requires wandb""" )(lowercase )
def SCREAMING_SNAKE_CASE__ ( lowercase ) -> Union[str, Any]:
return unittest.skipUnless(is_comet_ml_available() ,"""test requires comet_ml""" )(lowercase )
lowerCamelCase : Union[str, Any] = (
any([is_wandb_available(), is_tensorboard_available()]) and not is_comet_ml_available()
)
def SCREAMING_SNAKE_CASE__ ( lowercase ) -> Optional[int]:
return unittest.skipUnless(
_atleast_one_tracker_available ,"""test requires at least one tracker to be available and for `comet_ml` to not be installed""" ,)(lowercase )
class __lowercase (unittest.TestCase ):
"""simple docstring"""
_snake_case = True
@classmethod
def UpperCAmelCase ( cls ) -> int:
snake_case : int = tempfile.mkdtemp()
@classmethod
def UpperCAmelCase ( cls ) -> str:
if os.path.exists(cls.tmpdir ):
shutil.rmtree(cls.tmpdir )
def UpperCAmelCase ( self ) -> Tuple:
if self.clear_on_setup:
for path in Path(self.tmpdir ).glob("""**/*""" ):
if path.is_file():
path.unlink()
elif path.is_dir():
shutil.rmtree(A )
class __lowercase (unittest.TestCase ):
"""simple docstring"""
def UpperCAmelCase ( self ) -> Optional[Any]:
super().tearDown()
# Reset the state of the AcceleratorState singleton.
AcceleratorState._reset_state()
PartialState._reset_state()
class __lowercase (unittest.TestCase ):
"""simple docstring"""
def UpperCAmelCase ( self , A ) -> Union[str, Any]:
snake_case : List[str] = mocks if isinstance(A , (tuple, list) ) else [mocks]
for m in self.mocks:
m.start()
self.addCleanup(m.stop )
def SCREAMING_SNAKE_CASE__ ( lowercase ) -> str:
snake_case : Optional[int] = AcceleratorState()
snake_case : int = tensor[None].clone().to(state.device )
snake_case : Dict = gather(lowercase ).cpu()
snake_case : str = tensor[0].cpu()
for i in range(tensors.shape[0] ):
if not torch.equal(tensors[i] ,lowercase ):
return False
return True
class __lowercase :
"""simple docstring"""
def __init__( self , A , A , A ) -> Optional[int]:
snake_case : Tuple = returncode
snake_case : str = stdout
snake_case : int = stderr
async def SCREAMING_SNAKE_CASE__ ( lowercase ,lowercase ) -> str:
while True:
snake_case : Any = await stream.readline()
if line:
callback(lowercase )
else:
break
async def SCREAMING_SNAKE_CASE__ ( lowercase ,lowercase=None ,lowercase=None ,lowercase=None ,lowercase=False ,lowercase=False ) -> _RunOutput:
if echo:
print("""\nRunning: """ ,""" """.join(lowercase ) )
snake_case : Optional[int] = await asyncio.create_subprocess_exec(
cmd[0] ,*cmd[1:] ,stdin=lowercase ,stdout=asyncio.subprocess.PIPE ,stderr=asyncio.subprocess.PIPE ,env=lowercase ,)
# note: there is a warning for a possible deadlock when using `wait` with huge amounts of data in the pipe
# https://docs.python.org/3/library/asyncio-subprocess.html#asyncio.asyncio.subprocess.Process.wait
#
# If it starts hanging, will need to switch to the following code. The problem is that no data
# will be seen until it's done and if it hangs for example there will be no debug info.
# out, err = await p.communicate()
# return _RunOutput(p.returncode, out, err)
snake_case : Dict = []
snake_case : Union[str, Any] = []
def tee(lowercase ,lowercase ,lowercase ,lowercase="" ):
snake_case : str = line.decode("""utf-8""" ).rstrip()
sink.append(lowercase )
if not quiet:
print(lowercase ,lowercase ,file=lowercase )
# XXX: the timeout doesn't seem to make any difference here
await asyncio.wait(
[
asyncio.create_task(_read_stream(p.stdout ,lambda lowercase : tee(lowercase ,lowercase ,sys.stdout ,label="""stdout:""" ) ) ),
asyncio.create_task(_read_stream(p.stderr ,lambda lowercase : tee(lowercase ,lowercase ,sys.stderr ,label="""stderr:""" ) ) ),
] ,timeout=lowercase ,)
return _RunOutput(await p.wait() ,lowercase ,lowercase )
def SCREAMING_SNAKE_CASE__ ( lowercase ,lowercase=None ,lowercase=None ,lowercase=180 ,lowercase=False ,lowercase=True ) -> _RunOutput:
snake_case : str = asyncio.get_event_loop()
snake_case : Union[str, Any] = loop.run_until_complete(
_stream_subprocess(lowercase ,env=lowercase ,stdin=lowercase ,timeout=lowercase ,quiet=lowercase ,echo=lowercase ) )
snake_case : List[str] = """ """.join(lowercase )
if result.returncode > 0:
snake_case : List[Any] = """\n""".join(result.stderr )
raise RuntimeError(
f"""'{cmd_str}' failed with returncode {result.returncode}\n\n"""
f"""The combined stderr from workers follows:\n{stderr}""" )
return result
class __lowercase (UpperCamelCase__ ):
"""simple docstring"""
pass
def SCREAMING_SNAKE_CASE__ ( lowercase ,lowercase=False ) -> List[str]:
try:
snake_case : List[str] = subprocess.check_output(lowercase ,stderr=subprocess.STDOUT )
if return_stdout:
if hasattr(lowercase ,"""decode""" ):
snake_case : List[str] = output.decode("""utf-8""" )
return output
except subprocess.CalledProcessError as e:
raise SubprocessCallException(
f"""Command `{" ".join(lowercase )}` failed with the following error:\n\n{e.output.decode()}""" ) from e
| 587
| 0
|
'''simple docstring'''
from manim import *
class __SCREAMING_SNAKE_CASE (lowerCamelCase_ ):
"""simple docstring"""
def UpperCamelCase__ ( self : str ):
_a = Rectangle(height=0.5 , width=0.5 )
_a = Rectangle(height=0.25 , width=0.25 )
_a = Rectangle(height=0.46 , width=0.46 ).set_stroke(width=0 )
_a = [mem.copy() for i in range(6 )]
_a = [mem.copy() for i in range(6 )]
_a = VGroup(*__a ).arrange(__a , buff=0 )
_a = VGroup(*__a ).arrange(__a , buff=0 )
_a = VGroup(__a , __a ).arrange(__a , buff=0 )
_a = Text("CPU" , font_size=24 )
_a = Group(__a , __a ).arrange(__a , buff=0.5 , aligned_edge=__a )
cpu.move_to([-2.5, -0.5, 0] )
self.add(__a )
_a = [mem.copy() for i in range(4 )]
_a = VGroup(*__a ).arrange(__a , buff=0 )
_a = Text("GPU" , font_size=24 )
_a = Group(__a , __a ).arrange(__a , buff=0.5 , aligned_edge=__a )
gpu.move_to([-1, -1, 0] )
self.add(__a )
_a = [mem.copy() for i in range(6 )]
_a = VGroup(*__a ).arrange(__a , buff=0 )
_a = Text("Model" , font_size=24 )
_a = Group(__a , __a ).arrange(__a , buff=0.5 , aligned_edge=__a )
model.move_to([3, -1.0, 0] )
self.add(__a )
_a = []
_a = []
_a = []
for i, rect in enumerate(__a ):
rect.set_stroke(__a )
_a = Rectangle(height=0.46 / 4 , width=0.46 / 3 ).set_stroke(width=0.0 ).set_fill(__a , opacity=0.7 )
if i == 0:
cpu_target.next_to(cpu_left_col_base[0].get_corner(DOWN + LEFT ) , buff=0.02 , direction=__a )
cpu_target.set_x(cpu_target.get_x() + 0.1 )
elif i == 3:
cpu_target.next_to(model_cpu_arr[0] , direction=__a , buff=0.0 )
else:
cpu_target.next_to(model_cpu_arr[i - 1] , direction=__a , buff=0.0 )
self.add(__a )
model_cpu_arr.append(__a )
self.add(*__a , *__a , *__a )
_a = [mem.copy() for i in range(6 )]
_a = VGroup(*__a ).arrange(__a , buff=0 )
_a = Text("Loaded Checkpoint" , font_size=24 )
_a = Group(__a , __a ).arrange(__a , buff=0.5 , aligned_edge=__a )
checkpoint.move_to([3, 0.5, 0] )
self.add(__a )
_a = []
_a = []
for i, rect in enumerate(__a ):
_a = fill.copy().set_fill(__a , opacity=0.7 )
target.move_to(__a )
ckpt_arr.append(__a )
_a = target.copy()
if i < 5:
cpu_target.move_to(cpu_left_col_base[i + 1] )
else:
cpu_target.move_to(cpu_right_col_base[i - 5] )
ckpt_cpu_arr.append(__a )
self.add(*__a , *__a )
_a = Square(side_length=2.2 )
key.move_to([-5, 2, 0] )
_a = MarkupText(
f'<b>Key:</b>\n\n<span fgcolor=\'{YELLOW}\'>●</span> Empty Model' , font_size=18 , )
key_text.move_to([-5, 2.4, 0] )
self.add(__a , __a )
_a = MarkupText(
f'<span fgcolor=\'{BLUE}\'>●</span> Checkpoint' , font_size=18 , )
blue_text.next_to(__a , DOWN * 2.4 , aligned_edge=key_text.get_left() )
self.add(__a )
_a = MarkupText(
f'Based on the passed in configuration, weights are stored in\na variety of np.memmaps on disk or to a particular device.' , font_size=24 , )
step_a.move_to([2, 2, 0] )
_a = [meta_mem.copy() for i in range(6 )]
_a = [meta_mem.copy() for i in range(6 )]
_a = VGroup(*__a ).arrange(__a , buff=0 )
_a = VGroup(*__a ).arrange(__a , buff=0 )
_a = VGroup(__a , __a ).arrange(__a , buff=0 )
_a = Text("Disk" , font_size=24 )
_a = Group(__a , __a ).arrange(__a , buff=0.5 , aligned_edge=__a )
disk.move_to([-4.0, -1.25, 0] )
self.play(Write(__a , run_time=3 ) , Write(__a , run_time=1 ) , Create(__a , run_time=1 ) )
_a = []
for i, rect in enumerate(__a ):
_a = rect.copy()
target.generate_target()
target.target.move_to(disk_left_col_base[i] ).scale(0.5 )
animations.append(MoveToTarget(__a , run_time=1.5 ) )
self.play(*__a )
self.play(FadeOut(__a ) )
_a = MarkupText(f'Then, the checkpoint is removed from memory\nthrough garbage collection.' , font_size=24 )
step_a.move_to([2, 2, 0] )
self.play(Write(__a , run_time=3 ) )
self.play(
FadeOut(__a , __a , *__a , *__a ) , )
self.wait()
| 521
|
'''simple docstring'''
lowerCAmelCase_ : Optional[Any] = [
[0, 16, 13, 0, 0, 0],
[0, 0, 10, 12, 0, 0],
[0, 4, 0, 0, 14, 0],
[0, 0, 9, 0, 0, 20],
[0, 0, 0, 7, 0, 4],
[0, 0, 0, 0, 0, 0],
]
def _lowerCamelCase ( lowercase : Union[str, Any] , lowercase : Optional[Any] , lowercase : Any , lowercase : str ) -> str:
# Return True if there is node that has not iterated.
_a = [False] * len(lowercase )
_a = [s]
_a = True
while queue:
_a = queue.pop(0 )
for ind in range(len(graph[u] ) ):
if visited[ind] is False and graph[u][ind] > 0:
queue.append(lowercase )
_a = True
_a = u
return visited[t]
def _lowerCamelCase ( lowercase : Dict , lowercase : Optional[Any] , lowercase : Dict ) -> Union[str, Any]:
_a = [-1] * (len(lowercase ))
_a = 0
_a = []
_a = [i[:] for i in graph] # Record original cut, copy.
while bfs(lowercase , lowercase , lowercase , lowercase ):
_a = float("Inf" )
_a = sink
while s != source:
# Find the minimum value in select path
_a = min(lowercase , graph[parent[s]][s] )
_a = parent[s]
max_flow += path_flow
_a = sink
while v != source:
_a = parent[v]
graph[u][v] -= path_flow
graph[v][u] += path_flow
_a = parent[v]
for i in range(len(lowercase ) ):
for j in range(len(graph[0] ) ):
if graph[i][j] == 0 and temp[i][j] > 0:
res.append((i, j) )
return res
if __name__ == "__main__":
print(mincut(test_graph, source=0, sink=5))
| 521
| 1
|
'''simple docstring'''
import logging
import os
from .state import PartialState
class __lowerCAmelCase ( logging.LoggerAdapter ):
"""simple docstring"""
@staticmethod
def snake_case__ ( lowerCAmelCase__ : Tuple ) -> List[Any]:
'''simple docstring'''
_UpperCamelCase = PartialState()
return not main_process_only or (main_process_only and state.is_main_process)
def snake_case__ ( self : Dict , lowerCAmelCase__ : List[Any] , lowerCAmelCase__ : Union[str, Any] , *lowerCAmelCase__ : int , **lowerCAmelCase__ : str ) -> Dict:
'''simple docstring'''
if PartialState._shared_state == {}:
raise RuntimeError(
'''You must initialize the accelerate state by calling either `PartialState()` or `Accelerator()` before using the logging utility.''' )
_UpperCamelCase = kwargs.pop('''main_process_only''' , lowerCAmelCase__ )
_UpperCamelCase = kwargs.pop('''in_order''' , lowerCAmelCase__ )
if self.isEnabledFor(lowerCAmelCase__ ):
if self._should_log(lowerCAmelCase__ ):
_UpperCamelCase , _UpperCamelCase = self.process(lowerCAmelCase__ , lowerCAmelCase__ )
self.logger.log(lowerCAmelCase__ , lowerCAmelCase__ , *lowerCAmelCase__ , **lowerCAmelCase__ )
elif in_order:
_UpperCamelCase = PartialState()
for i in range(state.num_processes ):
if i == state.process_index:
_UpperCamelCase , _UpperCamelCase = self.process(lowerCAmelCase__ , lowerCAmelCase__ )
self.logger.log(lowerCAmelCase__ , lowerCAmelCase__ , *lowerCAmelCase__ , **lowerCAmelCase__ )
state.wait_for_everyone()
def a__ ( lowercase : str, lowercase : str = None ) -> int:
"""simple docstring"""
if log_level is None:
_UpperCamelCase = os.environ.get('''ACCELERATE_LOG_LEVEL''', lowercase )
_UpperCamelCase = logging.getLogger(lowercase )
if log_level is not None:
logger.setLevel(log_level.upper() )
logger.root.setLevel(log_level.upper() )
return MultiProcessAdapter(lowercase, {} )
| 98
|
def __SCREAMING_SNAKE_CASE ( lowerCAmelCase: int , lowerCAmelCase: int ) -> Dict:
if b == 0:
return 1
if (b % 2) == 0:
return actual_power(lowerCAmelCase , int(b / 2 ) ) * actual_power(lowerCAmelCase , int(b / 2 ) )
else:
return a * actual_power(lowerCAmelCase , int(b / 2 ) ) * actual_power(lowerCAmelCase , int(b / 2 ) )
def __SCREAMING_SNAKE_CASE ( lowerCAmelCase: int , lowerCAmelCase: int ) -> float:
if b < 0:
return 1 / actual_power(lowerCAmelCase , lowerCAmelCase )
return actual_power(lowerCAmelCase , lowerCAmelCase )
if __name__ == "__main__":
print(power(-2, -3))
| 300
| 0
|
"""simple docstring"""
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class _lowercase ( __a ):
"""simple docstring"""
lowercase__ = ['''image_processor''', '''tokenizer''']
lowercase__ = '''ChineseCLIPImageProcessor'''
lowercase__ = ('''BertTokenizer''', '''BertTokenizerFast''')
def __init__( self : int , UpperCamelCase__ : Union[str, Any]=None , UpperCamelCase__ : List[Any]=None , **UpperCamelCase__ : int ) -> Union[str, Any]:
'''simple docstring'''
__UpperCamelCase =None
if "feature_extractor" in kwargs:
warnings.warn(
'''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
''' instead.''' , UpperCamelCase__ , )
__UpperCamelCase =kwargs.pop('''feature_extractor''' )
__UpperCamelCase =image_processor if image_processor is not None else feature_extractor
if image_processor is None:
raise ValueError('''You need to specify an `image_processor`.''' )
if tokenizer is None:
raise ValueError('''You need to specify a `tokenizer`.''' )
super().__init__(UpperCamelCase__ , UpperCamelCase__ )
__UpperCamelCase =self.image_processor
def __call__( self : List[str] , UpperCamelCase__ : str=None , UpperCamelCase__ : Union[str, Any]=None , UpperCamelCase__ : int=None , **UpperCamelCase__ : Dict ) -> Dict:
'''simple docstring'''
if text is None and images is None:
raise ValueError('''You have to specify either text or images. Both cannot be none.''' )
if text is not None:
__UpperCamelCase =self.tokenizer(UpperCamelCase__ , return_tensors=UpperCamelCase__ , **UpperCamelCase__ )
if images is not None:
__UpperCamelCase =self.image_processor(UpperCamelCase__ , return_tensors=UpperCamelCase__ , **UpperCamelCase__ )
if text is not None and images is not None:
__UpperCamelCase =image_features.pixel_values
return encoding
elif text is not None:
return encoding
else:
return BatchEncoding(data=dict(**UpperCamelCase__ ) , tensor_type=UpperCamelCase__ )
def UpperCAmelCase_ ( self : Any , *UpperCamelCase__ : Dict , **UpperCamelCase__ : str ) -> int:
'''simple docstring'''
return self.tokenizer.batch_decode(*UpperCamelCase__ , **UpperCamelCase__ )
def UpperCAmelCase_ ( self : int , *UpperCamelCase__ : Optional[Any] , **UpperCamelCase__ : List[str] ) -> str:
'''simple docstring'''
return self.tokenizer.decode(*UpperCamelCase__ , **UpperCamelCase__ )
@property
def UpperCAmelCase_ ( self : Any ) -> str:
'''simple docstring'''
__UpperCamelCase =self.tokenizer.model_input_names
__UpperCamelCase =self.image_processor.model_input_names
return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) )
@property
def UpperCAmelCase_ ( self : Any ) -> int:
'''simple docstring'''
warnings.warn(
'''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.''' , UpperCamelCase__ , )
return self.image_processor_class
| 700
|
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__lowercase = logging.get_logger(__name__)
__lowercase = {
'''YituTech/conv-bert-base''': '''https://huggingface.co/YituTech/conv-bert-base/resolve/main/config.json''',
'''YituTech/conv-bert-medium-small''': (
'''https://huggingface.co/YituTech/conv-bert-medium-small/resolve/main/config.json'''
),
'''YituTech/conv-bert-small''': '''https://huggingface.co/YituTech/conv-bert-small/resolve/main/config.json''',
# See all ConvBERT models at https://huggingface.co/models?filter=convbert
}
class _lowercase ( __a ):
"""simple docstring"""
lowercase__ = '''convbert'''
def __init__( self : Optional[Any] , UpperCamelCase__ : int=30522 , UpperCamelCase__ : int=768 , UpperCamelCase__ : str=12 , UpperCamelCase__ : Optional[Any]=12 , UpperCamelCase__ : Optional[Any]=3072 , UpperCamelCase__ : Optional[int]="gelu" , UpperCamelCase__ : str=0.1 , UpperCamelCase__ : List[str]=0.1 , UpperCamelCase__ : List[Any]=512 , UpperCamelCase__ : Dict=2 , UpperCamelCase__ : Union[str, Any]=0.02 , UpperCamelCase__ : Dict=1E-12 , UpperCamelCase__ : Any=1 , UpperCamelCase__ : Optional[Any]=0 , UpperCamelCase__ : Any=2 , UpperCamelCase__ : List[Any]=768 , UpperCamelCase__ : Any=2 , UpperCamelCase__ : List[str]=9 , UpperCamelCase__ : str=1 , UpperCamelCase__ : str=None , **UpperCamelCase__ : str , ) -> Optional[int]:
'''simple docstring'''
super().__init__(
pad_token_id=UpperCamelCase__ , bos_token_id=UpperCamelCase__ , eos_token_id=UpperCamelCase__ , **UpperCamelCase__ , )
__UpperCamelCase =vocab_size
__UpperCamelCase =hidden_size
__UpperCamelCase =num_hidden_layers
__UpperCamelCase =num_attention_heads
__UpperCamelCase =intermediate_size
__UpperCamelCase =hidden_act
__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 =embedding_size
__UpperCamelCase =head_ratio
__UpperCamelCase =conv_kernel_size
__UpperCamelCase =num_groups
__UpperCamelCase =classifier_dropout
class _lowercase ( __a ):
"""simple docstring"""
@property
def UpperCAmelCase_ ( self : List[str] ) -> Mapping[str, Mapping[int, str]]:
'''simple docstring'''
if self.task == "multiple-choice":
__UpperCamelCase ={0: '''batch''', 1: '''choice''', 2: '''sequence'''}
else:
__UpperCamelCase ={0: '''batch''', 1: '''sequence'''}
return OrderedDict(
[
('''input_ids''', dynamic_axis),
('''attention_mask''', dynamic_axis),
('''token_type_ids''', dynamic_axis),
] )
| 296
| 0
|
import argparse
import math
import traceback
import dateutil.parser as date_parser
import requests
def __lowerCAmelCase ( A ):
UpperCAmelCase_ = {}
UpperCAmelCase_ = job["started_at"]
UpperCAmelCase_ = job["completed_at"]
UpperCAmelCase_ = date_parser.parse(A )
UpperCAmelCase_ = date_parser.parse(A )
UpperCAmelCase_ = round((end_datetime - start_datetime).total_seconds() / 60.0 )
UpperCAmelCase_ = start
UpperCAmelCase_ = end
UpperCAmelCase_ = duration_in_min
return job_info
def __lowerCAmelCase ( A , A=None ):
UpperCAmelCase_ = None
if token is not None:
UpperCAmelCase_ = {"Accept": "application/vnd.github+json", "Authorization": F"Bearer {token}"}
UpperCAmelCase_ = F"https://api.github.com/repos/huggingface/transformers/actions/runs/{workflow_run_id}/jobs?per_page=100"
UpperCAmelCase_ = requests.get(A , headers=A ).json()
UpperCAmelCase_ = {}
try:
job_time.update({job["name"]: extract_time_from_single_job(A ) for job in result["jobs"]} )
UpperCAmelCase_ = math.ceil((result["total_count"] - 100) / 100 )
for i in range(A ):
UpperCAmelCase_ = requests.get(url + F"&page={i + 2}" , headers=A ).json()
job_time.update({job["name"]: extract_time_from_single_job(A ) for job in result["jobs"]} )
return job_time
except Exception:
print(F"Unknown error, could not fetch links:\n{traceback.format_exc()}" )
return {}
if __name__ == "__main__":
_a: Optional[int] = argparse.ArgumentParser()
# Required parameters
parser.add_argument("""--workflow_run_id""", type=str, required=True, help="""A GitHub Actions workflow run id.""")
_a: Tuple = parser.parse_args()
_a: List[str] = get_job_time(args.workflow_run_id)
_a: Union[str, Any] = dict(sorted(job_time.items(), key=lambda item: item[1]["duration"], reverse=True))
for k, v in job_time.items():
print(F'{k}: {v["duration"]}')
| 162
|
def __lowerCAmelCase ( A , A , A , A ):
# Return True if there is node that has not iterated.
UpperCAmelCase_ = [False] * len(A )
UpperCAmelCase_ = []
queue.append(A )
UpperCAmelCase_ = True
while queue:
UpperCAmelCase_ = queue.pop(0 )
for ind in range(len(graph[u] ) ):
if visited[ind] is False and graph[u][ind] > 0:
queue.append(A )
UpperCAmelCase_ = True
UpperCAmelCase_ = u
return visited[t]
def __lowerCAmelCase ( A , A , A ):
# This array is filled by BFS and to store path
UpperCAmelCase_ = [-1] * (len(A ))
UpperCAmelCase_ = 0
while bfs(A , A , A , A ):
UpperCAmelCase_ = float("Inf" )
UpperCAmelCase_ = sink
while s != source:
# Find the minimum value in select path
UpperCAmelCase_ = min(A , graph[parent[s]][s] )
UpperCAmelCase_ = parent[s]
max_flow += path_flow
UpperCAmelCase_ = sink
while v != source:
UpperCAmelCase_ = parent[v]
graph[u][v] -= path_flow
graph[v][u] += path_flow
UpperCAmelCase_ = parent[v]
return max_flow
_a: Any = [
[0, 16, 13, 0, 0, 0],
[0, 0, 10, 12, 0, 0],
[0, 4, 0, 0, 14, 0],
[0, 0, 9, 0, 0, 20],
[0, 0, 0, 7, 0, 4],
[0, 0, 0, 0, 0, 0],
]
_a , _a: Optional[int] = 0, 5
print(ford_fulkerson(graph, source, sink))
| 162
| 1
|
"""simple docstring"""
def __lowercase ( lowerCamelCase_ : int = 1000000 ):
SCREAMING_SNAKE_CASE__ = limit + 1
SCREAMING_SNAKE_CASE__ = [0] * limit
for first_term in range(1 , lowerCamelCase_ ):
for n in range(lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ ):
SCREAMING_SNAKE_CASE__ = first_term + n / first_term
if common_difference % 4: # d must be divisble by 4
continue
else:
common_difference /= 4
if (
first_term > common_difference
and first_term < 4 * common_difference
): # since x,y,z are positive integers
frequency[n] += 1 # so z>0 and a>d ,also 4d<a
SCREAMING_SNAKE_CASE__ = sum(1 for x in frequency[1:limit] if x == 10 )
return count
if __name__ == "__main__":
print(f"""{solution() = }""")
| 112
|
"""simple docstring"""
from __future__ import annotations
def __lowercase ( lowerCamelCase_ : list , lowerCamelCase_ : int , lowerCamelCase_ : int , lowerCamelCase_ : int ):
SCREAMING_SNAKE_CASE__ = []
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = input_list[low:mid], input_list[mid : high + 1]
while left and right:
result.append((left if left[0] <= right[0] else right).pop(0 ) )
SCREAMING_SNAKE_CASE__ = result + left + right
return input_list
def __lowercase ( lowerCamelCase_ : list ):
if len(lowerCamelCase_ ) <= 1:
return input_list
SCREAMING_SNAKE_CASE__ = list(lowerCamelCase_ )
# iteration for two-way merging
SCREAMING_SNAKE_CASE__ = 2
while p <= len(lowerCamelCase_ ):
# getting low, high and middle value for merge-sort of single list
for i in range(0 , len(lowerCamelCase_ ) , lowerCamelCase_ ):
SCREAMING_SNAKE_CASE__ = i
SCREAMING_SNAKE_CASE__ = i + p - 1
SCREAMING_SNAKE_CASE__ = (low + high + 1) // 2
SCREAMING_SNAKE_CASE__ = merge(lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ )
# final merge of last two parts
if p * 2 >= len(lowerCamelCase_ ):
SCREAMING_SNAKE_CASE__ = i
SCREAMING_SNAKE_CASE__ = merge(lowerCamelCase_ , 0 , lowerCamelCase_ , len(lowerCamelCase_ ) - 1 )
break
p *= 2
return input_list
if __name__ == "__main__":
_lowerCamelCase = input('Enter numbers separated by a comma:\n').strip()
if user_input == "":
_lowerCamelCase = []
else:
_lowerCamelCase = [int(item.strip()) for item in user_input.split(',')]
print(iter_merge_sort(unsorted))
| 112
| 1
|
'''simple docstring'''
import importlib
import json
import os
from collections import OrderedDict
from typing import Dict, Optional, Union
# Build the list of all image processors
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic_module, resolve_trust_remote_code
from ...image_processing_utils import ImageProcessingMixin
from ...utils import CONFIG_NAME, IMAGE_PROCESSOR_NAME, get_file_from_repo, logging
from .auto_factory import _LazyAutoMapping
from .configuration_auto import (
CONFIG_MAPPING_NAMES,
AutoConfig,
model_type_to_module_name,
replace_list_option_in_docstrings,
)
lowerCAmelCase: Optional[Any] = logging.get_logger(__name__)
lowerCAmelCase: Dict = OrderedDict(
[
('align', 'EfficientNetImageProcessor'),
('beit', 'BeitImageProcessor'),
('bit', 'BitImageProcessor'),
('blip', 'BlipImageProcessor'),
('blip-2', 'BlipImageProcessor'),
('bridgetower', 'BridgeTowerImageProcessor'),
('chinese_clip', 'ChineseCLIPImageProcessor'),
('clip', 'CLIPImageProcessor'),
('clipseg', 'ViTImageProcessor'),
('conditional_detr', 'ConditionalDetrImageProcessor'),
('convnext', 'ConvNextImageProcessor'),
('convnextv2', 'ConvNextImageProcessor'),
('cvt', 'ConvNextImageProcessor'),
('data2vec-vision', 'BeitImageProcessor'),
('deformable_detr', 'DeformableDetrImageProcessor'),
('deit', 'DeiTImageProcessor'),
('deta', 'DetaImageProcessor'),
('detr', 'DetrImageProcessor'),
('dinat', 'ViTImageProcessor'),
('donut-swin', 'DonutImageProcessor'),
('dpt', 'DPTImageProcessor'),
('efficientformer', 'EfficientFormerImageProcessor'),
('efficientnet', 'EfficientNetImageProcessor'),
('flava', 'FlavaImageProcessor'),
('focalnet', 'BitImageProcessor'),
('git', 'CLIPImageProcessor'),
('glpn', 'GLPNImageProcessor'),
('groupvit', 'CLIPImageProcessor'),
('imagegpt', 'ImageGPTImageProcessor'),
('instructblip', 'BlipImageProcessor'),
('layoutlmv2', 'LayoutLMv2ImageProcessor'),
('layoutlmv3', 'LayoutLMv3ImageProcessor'),
('levit', 'LevitImageProcessor'),
('mask2former', 'Mask2FormerImageProcessor'),
('maskformer', 'MaskFormerImageProcessor'),
('mgp-str', 'ViTImageProcessor'),
('mobilenet_v1', 'MobileNetV1ImageProcessor'),
('mobilenet_v2', 'MobileNetV2ImageProcessor'),
('mobilevit', 'MobileViTImageProcessor'),
('mobilevit', 'MobileViTImageProcessor'),
('mobilevitv2', 'MobileViTImageProcessor'),
('nat', 'ViTImageProcessor'),
('oneformer', 'OneFormerImageProcessor'),
('owlvit', 'OwlViTImageProcessor'),
('perceiver', 'PerceiverImageProcessor'),
('pix2struct', 'Pix2StructImageProcessor'),
('poolformer', 'PoolFormerImageProcessor'),
('regnet', 'ConvNextImageProcessor'),
('resnet', 'ConvNextImageProcessor'),
('sam', 'SamImageProcessor'),
('segformer', 'SegformerImageProcessor'),
('swiftformer', 'ViTImageProcessor'),
('swin', 'ViTImageProcessor'),
('swin2sr', 'Swin2SRImageProcessor'),
('swinv2', 'ViTImageProcessor'),
('table-transformer', 'DetrImageProcessor'),
('timesformer', 'VideoMAEImageProcessor'),
('tvlt', 'TvltImageProcessor'),
('upernet', 'SegformerImageProcessor'),
('van', 'ConvNextImageProcessor'),
('videomae', 'VideoMAEImageProcessor'),
('vilt', 'ViltImageProcessor'),
('vit', 'ViTImageProcessor'),
('vit_hybrid', 'ViTHybridImageProcessor'),
('vit_mae', 'ViTImageProcessor'),
('vit_msn', 'ViTImageProcessor'),
('xclip', 'CLIPImageProcessor'),
('yolos', 'YolosImageProcessor'),
]
)
lowerCAmelCase: List[str] = _LazyAutoMapping(CONFIG_MAPPING_NAMES, IMAGE_PROCESSOR_MAPPING_NAMES)
def lowerCamelCase__ ( _A ):
for module_name, extractors in IMAGE_PROCESSOR_MAPPING_NAMES.items():
if class_name in extractors:
a : int = model_type_to_module_name(_A )
a : List[Any] = importlib.import_module(f""".{module_name}""" , 'transformers.models' )
try:
return getattr(_A , _A )
except AttributeError:
continue
for _, extractor in IMAGE_PROCESSOR_MAPPING._extra_content.items():
if getattr(_A , '__name__' , _A ) == class_name:
return extractor
# We did not fine the class, but maybe it's because a dep is missing. In that case, the class will be in the main
# init and we return the proper dummy to get an appropriate error message.
a : Optional[Any] = importlib.import_module('transformers' )
if hasattr(_A , _A ):
return getattr(_A , _A )
return None
def lowerCamelCase__ ( _A , _A = None , _A = False , _A = False , _A = None , _A = None , _A = None , _A = False , **_A , ):
a : int = get_file_from_repo(
_A , _A , cache_dir=_A , force_download=_A , resume_download=_A , proxies=_A , use_auth_token=_A , revision=_A , local_files_only=_A , )
if resolved_config_file is None:
logger.info(
'Could not locate the image processor configuration file, will try to use the model config instead.' )
return {}
with open(_A , encoding='utf-8' ) as reader:
return json.load(_A )
class a__:
def __init__( self : Optional[int] ):
raise EnvironmentError(
'AutoImageProcessor is designed to be instantiated '
'using the `AutoImageProcessor.from_pretrained(pretrained_model_name_or_path)` method.' )
@classmethod
@replace_list_option_in_docstrings(__snake_case )
def lowercase_ ( cls : Tuple , __snake_case : List[str] , **__snake_case : Any ):
a : Optional[int] = kwargs.pop('config' , __snake_case )
a : Optional[Any] = kwargs.pop('trust_remote_code' , __snake_case )
a : List[str] = True
a , a : Tuple = ImageProcessingMixin.get_image_processor_dict(__snake_case , **__snake_case )
a : str = config_dict.get('image_processor_type' , __snake_case )
a : Optional[int] = None
if "AutoImageProcessor" in config_dict.get('auto_map' , {} ):
a : str = config_dict['auto_map']['AutoImageProcessor']
# If we still don't have the image processor class, check if we're loading from a previous feature extractor config
# and if so, infer the image processor class from there.
if image_processor_class is None and image_processor_auto_map is None:
a : List[Any] = config_dict.pop('feature_extractor_type' , __snake_case )
if feature_extractor_class is not None:
logger.warning(
'Could not find image processor class in the image processor config or the model config. Loading'
' based on pattern matching with the model\'s feature extractor configuration.' )
a : List[Any] = feature_extractor_class.replace('FeatureExtractor' , 'ImageProcessor' )
if "AutoFeatureExtractor" in config_dict.get('auto_map' , {} ):
a : Optional[Any] = config_dict['auto_map']['AutoFeatureExtractor']
a : Optional[Any] = feature_extractor_auto_map.replace('FeatureExtractor' , 'ImageProcessor' )
logger.warning(
'Could not find image processor auto map in the image processor config or the model config.'
' Loading based on pattern matching with the model\'s feature extractor configuration.' )
# If we don't find the image processor class in the image processor config, let's try the model config.
if image_processor_class is None and image_processor_auto_map is None:
if not isinstance(__snake_case , __snake_case ):
a : Dict = AutoConfig.from_pretrained(__snake_case , **__snake_case )
# It could be in `config.image_processor_type``
a : List[Any] = getattr(__snake_case , 'image_processor_type' , __snake_case )
if hasattr(__snake_case , 'auto_map' ) and "AutoImageProcessor" in config.auto_map:
a : str = config.auto_map['AutoImageProcessor']
if image_processor_class is not None:
a : Tuple = image_processor_class_from_name(__snake_case )
a : Optional[Any] = image_processor_auto_map is not None
a : List[str] = image_processor_class is not None or type(__snake_case ) in IMAGE_PROCESSOR_MAPPING
a : Dict = resolve_trust_remote_code(
__snake_case , __snake_case , __snake_case , __snake_case )
if has_remote_code and trust_remote_code:
a : Dict = get_class_from_dynamic_module(
__snake_case , __snake_case , **__snake_case )
a : List[str] = kwargs.pop('code_revision' , __snake_case )
if os.path.isdir(__snake_case ):
image_processor_class.register_for_auto_class()
return image_processor_class.from_dict(__snake_case , **__snake_case )
elif image_processor_class is not None:
return image_processor_class.from_dict(__snake_case , **__snake_case )
# Last try: we use the IMAGE_PROCESSOR_MAPPING.
elif type(__snake_case ) in IMAGE_PROCESSOR_MAPPING:
a : str = IMAGE_PROCESSOR_MAPPING[type(__snake_case )]
return image_processor_class.from_dict(__snake_case , **__snake_case )
raise ValueError(
F"""Unrecognized image processor in {pretrained_model_name_or_path}. Should have a """
F"""`image_processor_type` key in its {IMAGE_PROCESSOR_NAME} of {CONFIG_NAME}, or one of the following """
F"""`model_type` keys in its {CONFIG_NAME}: {", ".join(c for c in IMAGE_PROCESSOR_MAPPING_NAMES.keys() )}""" )
@staticmethod
def lowercase_ ( __snake_case : List[Any] , __snake_case : Optional[int] ):
IMAGE_PROCESSOR_MAPPING.register(__snake_case , __snake_case )
| 526
|
'''simple docstring'''
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
import numpy as np
import torch
from datasets import load_dataset
from torchvision.transforms import Compose, Lambda, Normalize, RandomHorizontalFlip, RandomResizedCrop, ToTensor
import transformers
from transformers import (
CONFIG_MAPPING,
IMAGE_PROCESSOR_MAPPING,
MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING,
AutoConfig,
AutoImageProcessor,
AutoModelForMaskedImageModeling,
HfArgumentParser,
Trainer,
TrainingArguments,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version
lowerCAmelCase: Optional[Any] = logging.getLogger(__name__)
# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version('4.31.0')
require_version('datasets>=1.8.0', 'To fix: pip install -r examples/pytorch/image-pretraining/requirements.txt')
lowerCAmelCase: Optional[int] = list(MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING.keys())
lowerCAmelCase: int = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)
@dataclass
class a__:
lowercase__ = field(
default="""cifar10""" , metadata={"""help""": """Name of a dataset from the datasets package"""} )
lowercase__ = field(
default=lowerCamelCase__ , metadata={"""help""": """The configuration name of the dataset to use (via the datasets library)."""} )
lowercase__ = field(
default=lowerCamelCase__ , metadata={"""help""": """The column name of the images in the files. If not set, will try to use 'image' or 'img'."""} , )
lowercase__ = field(default=lowerCamelCase__ , metadata={"""help""": """A folder containing the training data."""} )
lowercase__ = field(default=lowerCamelCase__ , metadata={"""help""": """A folder containing the validation data."""} )
lowercase__ = field(
default=0.15 , metadata={"""help""": """Percent to split off of train for validation."""} )
lowercase__ = field(default=32 , metadata={"""help""": """The size of the square patches to use for masking."""} )
lowercase__ = field(
default=0.6 , metadata={"""help""": """Percentage of patches to mask."""} , )
lowercase__ = field(
default=lowerCamelCase__ , metadata={
"""help""": (
"""For debugging purposes or quicker training, truncate the number of training examples to this """
"""value if set."""
)
} , )
lowercase__ = field(
default=lowerCamelCase__ , metadata={
"""help""": (
"""For debugging purposes or quicker training, truncate the number of evaluation examples to this """
"""value if set."""
)
} , )
def lowercase_ ( self : List[Any] ):
a : Any = {}
if self.train_dir is not None:
a : Dict = self.train_dir
if self.validation_dir is not None:
a : Union[str, Any] = self.validation_dir
a : Any = data_files if data_files else None
@dataclass
class a__:
lowercase__ = field(
default=lowerCamelCase__ , metadata={
"""help""": (
"""The model checkpoint for weights initialization. Can be a local path to a pytorch_model.bin or a """
"""checkpoint identifier on the hub. """
"""Don't set if you want to train a model from scratch."""
)
} , )
lowercase__ = field(
default=lowerCamelCase__ , metadata={"""help""": """If training from scratch, pass a model type from the list: """ + """, """.join(lowerCamelCase__ )} , )
lowercase__ = field(
default=lowerCamelCase__ , metadata={"""help""": """Pretrained config name or path if not the same as model_name"""} )
lowercase__ = field(
default=lowerCamelCase__ , metadata={
"""help""": (
"""Override some existing default config settings when a model is trained from scratch. Example: """
"""n_embd=10,resid_pdrop=0.2,scale_attn_weights=false,summary_type=cls_index"""
)
} , )
lowercase__ = field(
default=lowerCamelCase__ , metadata={"""help""": """Where do you want to store (cache) the pretrained models/datasets downloaded from the hub"""} , )
lowercase__ = field(
default="""main""" , metadata={"""help""": """The specific model version to use (can be a branch name, tag name or commit id)."""} , )
lowercase__ = field(default=lowerCamelCase__ , metadata={"""help""": """Name or path of preprocessor config."""} )
lowercase__ = field(
default=lowerCamelCase__ , metadata={
"""help""": (
"""Will use the token generated when running `huggingface-cli login` (necessary to use this script """
"""with private models)."""
)
} , )
lowercase__ = field(
default=lowerCamelCase__ , metadata={
"""help""": (
"""The size (resolution) of each image. If not specified, will use `image_size` of the configuration."""
)
} , )
lowercase__ = field(
default=lowerCamelCase__ , metadata={
"""help""": (
"""The size (resolution) of each patch. If not specified, will use `patch_size` of the configuration."""
)
} , )
lowercase__ = field(
default=lowerCamelCase__ , metadata={"""help""": """Stride to use for the encoder."""} , )
class a__:
def __init__( self : List[str] , __snake_case : int=1_92 , __snake_case : int=32 , __snake_case : List[str]=4 , __snake_case : Union[str, Any]=0.6 ):
a : Any = input_size
a : Union[str, Any] = mask_patch_size
a : int = model_patch_size
a : Tuple = mask_ratio
if self.input_size % self.mask_patch_size != 0:
raise ValueError('Input size must be divisible by mask patch size' )
if self.mask_patch_size % self.model_patch_size != 0:
raise ValueError('Mask patch size must be divisible by model patch size' )
a : str = self.input_size // self.mask_patch_size
a : Union[str, Any] = self.mask_patch_size // self.model_patch_size
a : str = self.rand_size**2
a : Tuple = int(np.ceil(self.token_count * self.mask_ratio ) )
def __call__( self : str ):
a : List[str] = np.random.permutation(self.token_count )[: self.mask_count]
a : List[str] = np.zeros(self.token_count , dtype=__snake_case )
a : Any = 1
a : List[str] = mask.reshape((self.rand_size, self.rand_size) )
a : Tuple = mask.repeat(self.scale , axis=0 ).repeat(self.scale , axis=1 )
return torch.tensor(mask.flatten() )
def lowerCamelCase__ ( _A ):
a : str = torch.stack([example['pixel_values'] for example in examples] )
a : List[str] = torch.stack([example['mask'] for example in examples] )
return {"pixel_values": pixel_values, "bool_masked_pos": mask}
def lowerCamelCase__ ( ):
# See all possible arguments in src/transformers/training_args.py
# or by passing the --help flag to this script.
# We now keep distinct sets of args, for a cleaner separation of concerns.
a : int = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
if len(sys.argv ) == 2 and sys.argv[1].endswith('.json' ):
# If we pass only one argument to the script and it's the path to a json file,
# let's parse it to get our arguments.
a , a , a : Dict = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
else:
a , a , a : int = parser.parse_args_into_dataclasses()
# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
# information sent is the one passed as arguments along with your Python/PyTorch versions.
send_example_telemetry('run_mim' , _A , _A )
# Setup logging
logging.basicConfig(
format='%(asctime)s - %(levelname)s - %(name)s - %(message)s' , datefmt='%m/%d/%Y %H:%M:%S' , handlers=[logging.StreamHandler(sys.stdout )] , )
if training_args.should_log:
# The default of training_args.log_level is passive, so we set log level at info here to have that default.
transformers.utils.logging.set_verbosity_info()
a : int = training_args.get_process_log_level()
logger.setLevel(_A )
transformers.utils.logging.set_verbosity(_A )
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
# Log on each process the small summary:
logger.warning(
f"""Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"""
+ f"""distributed training: {bool(training_args.local_rank != -1 )}, 16-bits training: {training_args.fpaa}""" )
logger.info(f"""Training/evaluation parameters {training_args}""" )
# Detecting last checkpoint.
a : Tuple = None
if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
a : List[Any] = get_last_checkpoint(training_args.output_dir )
if last_checkpoint is None and len(os.listdir(training_args.output_dir ) ) > 0:
raise ValueError(
f"""Output directory ({training_args.output_dir}) already exists and is not empty. """
'Use --overwrite_output_dir to overcome.' )
elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
logger.info(
f"""Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change """
'the `--output_dir` or add `--overwrite_output_dir` to train from scratch.' )
# Initialize our dataset.
a : Any = load_dataset(
data_args.dataset_name , data_args.dataset_config_name , data_files=data_args.data_files , cache_dir=model_args.cache_dir , use_auth_token=True if model_args.use_auth_token else None , )
# If we don't have a validation split, split off a percentage of train as validation.
a : Union[str, Any] = None if 'validation' in ds.keys() else data_args.train_val_split
if isinstance(data_args.train_val_split , _A ) and data_args.train_val_split > 0.0:
a : Any = ds['train'].train_test_split(data_args.train_val_split )
a : str = split['train']
a : Any = split['test']
# Create config
# Distributed training:
# The .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
a : Tuple = {
'cache_dir': model_args.cache_dir,
'revision': model_args.model_revision,
'use_auth_token': True if model_args.use_auth_token else None,
}
if model_args.config_name_or_path:
a : Any = AutoConfig.from_pretrained(model_args.config_name_or_path , **_A )
elif model_args.model_name_or_path:
a : Optional[int] = AutoConfig.from_pretrained(model_args.model_name_or_path , **_A )
else:
a : Optional[int] = CONFIG_MAPPING[model_args.model_type]()
logger.warning('You are instantiating a new config instance from scratch.' )
if model_args.config_overrides is not None:
logger.info(f"""Overriding config: {model_args.config_overrides}""" )
config.update_from_string(model_args.config_overrides )
logger.info(f"""New config: {config}""" )
# make sure the decoder_type is "simmim" (only relevant for BEiT)
if hasattr(_A , 'decoder_type' ):
a : List[str] = 'simmim'
# adapt config
a : str = model_args.image_size if model_args.image_size is not None else config.image_size
a : Union[str, Any] = model_args.patch_size if model_args.patch_size is not None else config.patch_size
a : Any = (
model_args.encoder_stride if model_args.encoder_stride is not None else config.encoder_stride
)
config.update(
{
'image_size': model_args.image_size,
'patch_size': model_args.patch_size,
'encoder_stride': model_args.encoder_stride,
} )
# create image processor
if model_args.image_processor_name:
a : Union[str, Any] = AutoImageProcessor.from_pretrained(model_args.image_processor_name , **_A )
elif model_args.model_name_or_path:
a : Union[str, Any] = AutoImageProcessor.from_pretrained(model_args.model_name_or_path , **_A )
else:
a : List[Any] = {
conf.model_type: image_processor_class for conf, image_processor_class in IMAGE_PROCESSOR_MAPPING.items()
}
a : int = IMAGE_PROCESSOR_TYPES[model_args.model_type]()
# create model
if model_args.model_name_or_path:
a : Tuple = AutoModelForMaskedImageModeling.from_pretrained(
model_args.model_name_or_path , from_tf=bool('.ckpt' in model_args.model_name_or_path ) , config=_A , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
else:
logger.info('Training new model from scratch' )
a : Union[str, Any] = AutoModelForMaskedImageModeling.from_config(_A )
if training_args.do_train:
a : Tuple = ds['train'].column_names
else:
a : Dict = ds['validation'].column_names
if data_args.image_column_name is not None:
a : Optional[Any] = data_args.image_column_name
elif "image" in column_names:
a : str = 'image'
elif "img" in column_names:
a : Union[str, Any] = 'img'
else:
a : str = column_names[0]
# transformations as done in original SimMIM paper
# source: https://github.com/microsoft/SimMIM/blob/main/data/data_simmim.py
a : Optional[int] = Compose(
[
Lambda(lambda _A : img.convert('RGB' ) if img.mode != "RGB" else img ),
RandomResizedCrop(model_args.image_size , scale=(0.67, 1.0) , ratio=(3.0 / 4.0, 4.0 / 3.0) ),
RandomHorizontalFlip(),
ToTensor(),
Normalize(mean=image_processor.image_mean , std=image_processor.image_std ),
] )
# create mask generator
a : Dict = MaskGenerator(
input_size=model_args.image_size , mask_patch_size=data_args.mask_patch_size , model_patch_size=model_args.patch_size , mask_ratio=data_args.mask_ratio , )
def preprocess_images(_A ):
a : Dict = [transforms(_A ) for image in examples[image_column_name]]
a : Optional[int] = [mask_generator() for i in range(len(examples[image_column_name] ) )]
return examples
if training_args.do_train:
if "train" not in ds:
raise ValueError('--do_train requires a train dataset' )
if data_args.max_train_samples is not None:
a : List[Any] = ds['train'].shuffle(seed=training_args.seed ).select(range(data_args.max_train_samples ) )
# Set the training transforms
ds["train"].set_transform(_A )
if training_args.do_eval:
if "validation" not in ds:
raise ValueError('--do_eval requires a validation dataset' )
if data_args.max_eval_samples is not None:
a : str = (
ds['validation'].shuffle(seed=training_args.seed ).select(range(data_args.max_eval_samples ) )
)
# Set the validation transforms
ds["validation"].set_transform(_A )
# Initialize our trainer
a : List[str] = Trainer(
model=_A , args=_A , train_dataset=ds['train'] if training_args.do_train else None , eval_dataset=ds['validation'] if training_args.do_eval else None , tokenizer=_A , data_collator=_A , )
# Training
if training_args.do_train:
a : str = None
if training_args.resume_from_checkpoint is not None:
a : Any = training_args.resume_from_checkpoint
elif last_checkpoint is not None:
a : Dict = last_checkpoint
a : Optional[int] = trainer.train(resume_from_checkpoint=_A )
trainer.save_model()
trainer.log_metrics('train' , train_result.metrics )
trainer.save_metrics('train' , train_result.metrics )
trainer.save_state()
# Evaluation
if training_args.do_eval:
a : List[Any] = trainer.evaluate()
trainer.log_metrics('eval' , _A )
trainer.save_metrics('eval' , _A )
# Write model card and (optionally) push to hub
a : str = {
'finetuned_from': model_args.model_name_or_path,
'tasks': 'masked-image-modeling',
'dataset': data_args.dataset_name,
'tags': ['masked-image-modeling'],
}
if training_args.push_to_hub:
trainer.push_to_hub(**_A )
else:
trainer.create_model_card(**_A )
if __name__ == "__main__":
main()
| 526
| 1
|
"""simple docstring"""
import numpy as np
from transformers import BatchFeature
from transformers.testing_utils import require_tf, require_torch
from .test_feature_extraction_common import FeatureExtractionSavingTestMixin
class _A ( _UpperCAmelCase ):
"""simple docstring"""
UpperCamelCase_ : List[Any] = None
UpperCamelCase_ : Optional[Any] = None
@property
def lowercase ( self : Optional[Any] ) -> Union[str, Any]:
return self.feat_extract_tester.prepare_feat_extract_dict()
def lowercase ( self : List[Any] ) -> Optional[Any]:
__snake_case = self.feature_extraction_class(**self.feat_extract_dict )
self.assertTrue(hasattr(A_ , '''feature_size''' ) )
self.assertTrue(hasattr(A_ , '''sampling_rate''' ) )
self.assertTrue(hasattr(A_ , '''padding_value''' ) )
def lowercase ( self : str ) -> List[str]:
__snake_case = self.feat_extract_tester.prepare_inputs_for_common()
__snake_case = self.feature_extraction_class(**self.feat_extract_dict )
__snake_case = feat_extract.model_input_names[0]
__snake_case = BatchFeature({input_name: speech_inputs} )
self.assertTrue(all(len(A_ ) == len(A_ ) for x, y in zip(A_ , processed_features[input_name] ) ) )
__snake_case = self.feat_extract_tester.prepare_inputs_for_common(equal_length=A_ )
__snake_case = BatchFeature({input_name: speech_inputs} , tensor_type='''np''' )
__snake_case = processed_features[input_name]
if len(batch_features_input.shape ) < 3:
__snake_case = batch_features_input[:, :, None]
self.assertTrue(
batch_features_input.shape
== (self.feat_extract_tester.batch_size, len(speech_inputs[0] ), self.feat_extract_tester.feature_size) )
@require_torch
def lowercase ( self : List[str] ) -> Optional[int]:
__snake_case = self.feat_extract_tester.prepare_inputs_for_common(equal_length=A_ )
__snake_case = self.feature_extraction_class(**self.feat_extract_dict )
__snake_case = feat_extract.model_input_names[0]
__snake_case = BatchFeature({input_name: speech_inputs} , tensor_type='''pt''' )
__snake_case = processed_features[input_name]
if len(batch_features_input.shape ) < 3:
__snake_case = batch_features_input[:, :, None]
self.assertTrue(
batch_features_input.shape
== (self.feat_extract_tester.batch_size, len(speech_inputs[0] ), self.feat_extract_tester.feature_size) )
@require_tf
def lowercase ( self : Union[str, Any] ) -> List[Any]:
__snake_case = self.feat_extract_tester.prepare_inputs_for_common(equal_length=A_ )
__snake_case = self.feature_extraction_class(**self.feat_extract_dict )
__snake_case = feat_extract.model_input_names[0]
__snake_case = BatchFeature({input_name: speech_inputs} , tensor_type='''tf''' )
__snake_case = processed_features[input_name]
if len(batch_features_input.shape ) < 3:
__snake_case = batch_features_input[:, :, None]
self.assertTrue(
batch_features_input.shape
== (self.feat_extract_tester.batch_size, len(speech_inputs[0] ), self.feat_extract_tester.feature_size) )
def lowercase ( self : int , A_ : Union[str, Any]=False ) -> Optional[Any]:
def _inputs_have_equal_length(A_ : int ):
__snake_case = len(input[0] )
for input_slice in input[1:]:
if len(A_ ) != length:
return False
return True
def _inputs_are_equal(A_ : int , A_ : Optional[int] ):
if len(A_ ) != len(A_ ):
return False
for input_slice_a, input_slice_a in zip(A_ , A_ ):
if not np.allclose(np.asarray(A_ ) , np.asarray(A_ ) , atol=1E-3 ):
return False
return True
__snake_case = self.feature_extraction_class(**self.feat_extract_dict )
__snake_case = self.feat_extract_tester.prepare_inputs_for_common(numpify=A_ )
__snake_case = feat_extract.model_input_names[0]
__snake_case = BatchFeature({input_name: speech_inputs} )
__snake_case = self.feat_extract_tester.seq_length_diff
__snake_case = self.feat_extract_tester.max_seq_length + pad_diff
__snake_case = self.feat_extract_tester.min_seq_length
__snake_case = self.feat_extract_tester.batch_size
__snake_case = self.feat_extract_tester.feature_size
# test padding for List[int] + numpy
__snake_case = feat_extract.pad(A_ , padding=A_ )
__snake_case = input_a[input_name]
__snake_case = feat_extract.pad(A_ , padding='''longest''' )
__snake_case = input_a[input_name]
__snake_case = feat_extract.pad(A_ , padding='''max_length''' , max_length=len(speech_inputs[-1] ) )
__snake_case = input_a[input_name]
__snake_case = feat_extract.pad(A_ , padding='''longest''' , return_tensors='''np''' )
__snake_case = input_a[input_name]
# max_length parameter has to be provided when setting `padding="max_length"`
with self.assertRaises(A_ ):
feat_extract.pad(A_ , padding='''max_length''' )[input_name]
__snake_case = feat_extract.pad(
A_ , padding='''max_length''' , max_length=A_ , return_tensors='''np''' )
__snake_case = input_a[input_name]
self.assertFalse(_inputs_have_equal_length(A_ ) )
self.assertTrue(_inputs_have_equal_length(A_ ) )
self.assertTrue(_inputs_have_equal_length(A_ ) )
self.assertTrue(_inputs_are_equal(A_ , A_ ) )
self.assertTrue(len(input_a[0] ) == pad_min_length )
self.assertTrue(len(input_a[1] ) == pad_min_length + pad_diff )
self.assertTrue(input_a.shape[:2] == (batch_size, len(input_a[0] )) )
self.assertTrue(input_a.shape[:2] == (batch_size, pad_max_length) )
if feature_size > 1:
self.assertTrue(input_a.shape[2] == input_a.shape[2] == feature_size )
# test padding for `pad_to_multiple_of` for List[int] + numpy
__snake_case = feat_extract.pad(A_ , pad_to_multiple_of=10 )
__snake_case = input_a[input_name]
__snake_case = feat_extract.pad(A_ , padding='''longest''' , pad_to_multiple_of=10 )
__snake_case = input_a[input_name]
__snake_case = feat_extract.pad(
A_ , padding='''max_length''' , pad_to_multiple_of=10 , max_length=A_ )
__snake_case = input_a[input_name]
__snake_case = feat_extract.pad(
A_ , padding='''max_length''' , pad_to_multiple_of=10 , max_length=A_ , return_tensors='''np''' , )
__snake_case = input_a[input_name]
self.assertTrue(all(len(A_ ) % 10 == 0 for x in input_a ) )
self.assertTrue(_inputs_are_equal(A_ , A_ ) )
__snake_case = pad_max_length if pad_max_length % 10 == 0 else (pad_max_length // 10 + 1) * 10
self.assertTrue(all(len(A_ ) == expected_mult_pad_length for x in input_a ) )
self.assertEqual(input_a.shape[:2] , (batch_size, expected_mult_pad_length) )
if feature_size > 1:
self.assertTrue(input_a.shape[2] == feature_size )
# Check padding value is correct
__snake_case = (np.ones(self.feat_extract_tester.feature_size ) * feat_extract.padding_value).sum()
self.assertTrue(
abs(np.asarray(input_a[0] )[pad_min_length:].sum() - padding_vector_sum * (pad_max_length - pad_min_length) )
< 1E-3 )
self.assertTrue(
abs(
np.asarray(input_a[1] )[pad_min_length + pad_diff :].sum()
- padding_vector_sum * (pad_max_length - pad_min_length - pad_diff) )
< 1E-3 )
self.assertTrue(
abs(
np.asarray(input_a[2] )[pad_min_length + 2 * pad_diff :].sum()
- padding_vector_sum * (pad_max_length - pad_min_length - 2 * pad_diff) )
< 1E-3 )
self.assertTrue(
abs(input_a[0, pad_min_length:].sum() - padding_vector_sum * (pad_max_length - pad_min_length) ) < 1E-3 )
self.assertTrue(
abs(input_a[0, pad_min_length:].sum() - padding_vector_sum * (expected_mult_pad_length - pad_min_length) )
< 1E-3 )
def lowercase ( self : List[Any] , A_ : Optional[int]=False ) -> Dict:
def _inputs_have_equal_length(A_ : Any ):
__snake_case = len(input[0] )
for input_slice in input[1:]:
if len(A_ ) != length:
return False
return True
def _inputs_are_equal(A_ : str , A_ : str ):
if len(A_ ) != len(A_ ):
return False
for input_slice_a, input_slice_a in zip(A_ , A_ ):
if not np.allclose(np.asarray(A_ ) , np.asarray(A_ ) , atol=1E-3 ):
return False
return True
__snake_case = self.feature_extraction_class(**self.feat_extract_dict )
__snake_case = self.feat_extract_tester.prepare_inputs_for_common(numpify=A_ )
__snake_case = feat_extract.model_input_names[0]
__snake_case = BatchFeature({input_name: speech_inputs} )
# truncate to smallest
__snake_case = feat_extract.pad(
A_ , padding='''max_length''' , max_length=len(speech_inputs[0] ) , truncation=A_ )
__snake_case = input_a[input_name]
__snake_case = feat_extract.pad(A_ , padding='''max_length''' , max_length=len(speech_inputs[0] ) )
__snake_case = input_a[input_name]
self.assertTrue(_inputs_have_equal_length(A_ ) )
self.assertFalse(_inputs_have_equal_length(A_ ) )
# truncate to smallest with np
__snake_case = feat_extract.pad(
A_ , padding='''max_length''' , max_length=len(speech_inputs[0] ) , return_tensors='''np''' , truncation=A_ , )
__snake_case = input_a[input_name]
__snake_case = feat_extract.pad(
A_ , padding='''max_length''' , max_length=len(speech_inputs[0] ) , return_tensors='''np''' )
__snake_case = input_a[input_name]
self.assertTrue(_inputs_have_equal_length(A_ ) )
self.assertTrue(input_a.shape[1] == len(speech_inputs[0] ) )
# since truncation forces padding to be smaller than longest input
# function can't return `np.ndarray`, but has to return list
self.assertFalse(_inputs_have_equal_length(A_ ) )
# truncate to middle
__snake_case = feat_extract.pad(
A_ , padding='''max_length''' , max_length=len(speech_inputs[1] ) , truncation=A_ , return_tensors='''np''' , )
__snake_case = input_a[input_name]
__snake_case = feat_extract.pad(
A_ , padding='''max_length''' , max_length=len(speech_inputs[1] ) , truncation=A_ )
__snake_case = input_a[input_name]
__snake_case = feat_extract.pad(
A_ , padding='''max_length''' , max_length=len(speech_inputs[1] ) , return_tensors='''np''' )
__snake_case = input_a[input_name]
self.assertTrue(input_a.shape[1] == len(speech_inputs[1] ) )
self.assertTrue(_inputs_have_equal_length(A_ ) )
self.assertTrue(_inputs_have_equal_length(A_ ) )
self.assertTrue(_inputs_are_equal(A_ , A_ ) )
# since truncation forces padding to be smaller than longest input
# function can't return `np.ndarray`, but has to return list
self.assertFalse(_inputs_have_equal_length(A_ ) )
self.assertTrue(len(input_a[-1] ) == len(speech_inputs[-1] ) )
# padding has to be max_length when setting `truncation=True`
with self.assertRaises(A_ ):
feat_extract.pad(A_ , truncation=A_ )[input_name]
# padding has to be max_length when setting `truncation=True`
with self.assertRaises(A_ ):
feat_extract.pad(A_ , padding='''longest''' , truncation=A_ )[input_name]
# padding has to be max_length when setting `truncation=True`
with self.assertRaises(A_ ):
feat_extract.pad(A_ , padding='''longest''' , truncation=A_ )[input_name]
# max_length parameter has to be provided when setting `truncation=True` and padding="max_length"
with self.assertRaises(A_ ):
feat_extract.pad(A_ , padding='''max_length''' , truncation=A_ )[input_name]
# test truncation for `pad_to_multiple_of` for List[int] + numpy
__snake_case = 12
__snake_case = feat_extract.pad(
A_ , padding='''max_length''' , max_length=len(speech_inputs[0] ) , pad_to_multiple_of=A_ , truncation=A_ , )
__snake_case = input_a[input_name]
__snake_case = feat_extract.pad(
A_ , padding='''max_length''' , max_length=len(speech_inputs[0] ) , pad_to_multiple_of=A_ , )
__snake_case = input_a[input_name]
# retrieve expected_length as multiple of pad_to_multiple_of
__snake_case = len(speech_inputs[0] )
if expected_length % pad_to_multiple_of != 0:
__snake_case = ((len(speech_inputs[0] ) // pad_to_multiple_of) + 1) * pad_to_multiple_of
self.assertTrue(len(input_a[0] ) == expected_length )
self.assertTrue(_inputs_have_equal_length(A_ ) )
self.assertFalse(_inputs_have_equal_length(A_ ) )
def lowercase ( self : int ) -> str:
self._check_padding(numpify=A_ )
def lowercase ( self : Optional[int] ) -> Optional[Any]:
self._check_padding(numpify=A_ )
def lowercase ( self : List[Any] ) -> Dict:
self._check_truncation(numpify=A_ )
def lowercase ( self : int ) -> str:
self._check_truncation(numpify=A_ )
@require_torch
def lowercase ( self : Optional[Any] ) -> List[str]:
__snake_case = self.feature_extraction_class(**self.feat_extract_dict )
__snake_case = self.feat_extract_tester.prepare_inputs_for_common()
__snake_case = feat_extract.model_input_names[0]
__snake_case = BatchFeature({input_name: speech_inputs} )
__snake_case = feat_extract.pad(A_ , padding='''longest''' , return_tensors='''np''' )[input_name]
__snake_case = feat_extract.pad(A_ , padding='''longest''' , return_tensors='''pt''' )[input_name]
self.assertTrue(abs(input_np.astype(np.floataa ).sum() - input_pt.numpy().astype(np.floataa ).sum() ) < 1E-2 )
@require_tf
def lowercase ( self : int ) -> Tuple:
__snake_case = self.feature_extraction_class(**self.feat_extract_dict )
__snake_case = self.feat_extract_tester.prepare_inputs_for_common()
__snake_case = feat_extract.model_input_names[0]
__snake_case = BatchFeature({input_name: speech_inputs} )
__snake_case = feat_extract.pad(A_ , padding='''longest''' , return_tensors='''np''' )[input_name]
__snake_case = feat_extract.pad(A_ , padding='''longest''' , return_tensors='''tf''' )[input_name]
self.assertTrue(abs(input_np.astype(np.floataa ).sum() - input_tf.numpy().astype(np.floataa ).sum() ) < 1E-2 )
def lowercase ( self : Dict ) -> List[Any]:
__snake_case = self.feat_extract_dict
__snake_case = True
__snake_case = self.feature_extraction_class(**A_ )
__snake_case = self.feat_extract_tester.prepare_inputs_for_common()
__snake_case = [len(A_ ) for x in speech_inputs]
__snake_case = feat_extract.model_input_names[0]
__snake_case = BatchFeature({input_name: speech_inputs} )
__snake_case = feat_extract.pad(A_ , padding='''longest''' , return_tensors='''np''' )
self.assertIn('''attention_mask''' , A_ )
self.assertListEqual(list(processed.attention_mask.shape ) , list(processed[input_name].shape[:2] ) )
self.assertListEqual(processed.attention_mask.sum(-1 ).tolist() , A_ )
def lowercase ( self : Union[str, Any] ) -> Union[str, Any]:
__snake_case = self.feat_extract_dict
__snake_case = True
__snake_case = self.feature_extraction_class(**A_ )
__snake_case = self.feat_extract_tester.prepare_inputs_for_common()
__snake_case = [len(A_ ) for x in speech_inputs]
__snake_case = feat_extract.model_input_names[0]
__snake_case = BatchFeature({input_name: speech_inputs} )
__snake_case = min(A_ )
__snake_case = feat_extract.pad(
A_ , padding='''max_length''' , max_length=A_ , truncation=A_ , return_tensors='''np''' )
self.assertIn('''attention_mask''' , A_ )
self.assertListEqual(
list(processed_pad.attention_mask.shape ) , [processed_pad[input_name].shape[0], max_length] )
self.assertListEqual(
processed_pad.attention_mask[:, :max_length].sum(-1 ).tolist() , [max_length for x in speech_inputs] )
| 702
|
"""simple docstring"""
import re
def SCREAMING_SNAKE_CASE ( snake_case):
return [char.split() for char in re.split(R'''[^ a-z A-Z 0-9 \s]''', str_)]
def SCREAMING_SNAKE_CASE ( snake_case):
__snake_case = split_input(str_)
return "".join(
[''''''.join([char.capitalize() for char in sub_str]) for sub_str in string_split])
def SCREAMING_SNAKE_CASE ( snake_case, snake_case, snake_case):
try:
__snake_case = split_input(snake_case)
if upper:
__snake_case = ''''''.join(
[
separator.join([char.upper() for char in sub_str])
for sub_str in string_split
])
else:
__snake_case = ''''''.join(
[
separator.join([char.lower() for char in sub_str])
for sub_str in string_split
])
return res_str
except IndexError:
return "not valid string"
def SCREAMING_SNAKE_CASE ( snake_case):
return to_simple_case(snake_case)
def SCREAMING_SNAKE_CASE ( snake_case):
try:
__snake_case = to_simple_case(snake_case)
return res_str[0].lower() + res_str[1:]
except IndexError:
return "not valid string"
def SCREAMING_SNAKE_CASE ( snake_case, snake_case):
return to_complex_case(snake_case, snake_case, '''_''')
def SCREAMING_SNAKE_CASE ( snake_case, snake_case):
return to_complex_case(snake_case, snake_case, '''-''')
if __name__ == "__main__":
__import__("doctest").testmod()
| 93
| 0
|
from typing import List
import datasets
from datasets.tasks import AudioClassification
from ..folder_based_builder import folder_based_builder
UpperCAmelCase = datasets.utils.logging.get_logger(__name__)
class snake_case__ ( folder_based_builder.FolderBasedBuilderConfig ):
_SCREAMING_SNAKE_CASE : bool = None
_SCREAMING_SNAKE_CASE : bool = None
class snake_case__ ( folder_based_builder.FolderBasedBuilder ):
_SCREAMING_SNAKE_CASE : List[Any] = datasets.Audio()
_SCREAMING_SNAKE_CASE : Union[str, Any] = "audio"
_SCREAMING_SNAKE_CASE : Any = AudioFolderConfig
_SCREAMING_SNAKE_CASE : List[str] # definition at the bottom of the script
_SCREAMING_SNAKE_CASE : List[Any] = AudioClassification(audio_column="audio" , label_column="label" )
UpperCAmelCase = [
".aiff",
".au",
".avr",
".caf",
".flac",
".htk",
".svx",
".mat4",
".mat5",
".mpc2k",
".ogg",
".paf",
".pvf",
".raw",
".rf64",
".sd2",
".sds",
".ircam",
".voc",
".w64",
".wav",
".nist",
".wavex",
".wve",
".xi",
".mp3",
".opus",
]
UpperCAmelCase = AUDIO_EXTENSIONS
| 666
|
from ...configuration_utils import PretrainedConfig
UpperCAmelCase = {
"google/tapas-base-finetuned-sqa": (
"https://huggingface.co/google/tapas-base-finetuned-sqa/resolve/main/config.json"
),
"google/tapas-base-finetuned-wtq": (
"https://huggingface.co/google/tapas-base-finetuned-wtq/resolve/main/config.json"
),
"google/tapas-base-finetuned-wikisql-supervised": (
"https://huggingface.co/google/tapas-base-finetuned-wikisql-supervised/resolve/main/config.json"
),
"google/tapas-base-finetuned-tabfact": (
"https://huggingface.co/google/tapas-base-finetuned-tabfact/resolve/main/config.json"
),
}
class snake_case__ ( _UpperCamelCase ):
_SCREAMING_SNAKE_CASE : Dict = "tapas"
def __init__( self : List[Any] , A__ : str=3_05_22 , A__ : Tuple=7_68 , A__ : List[Any]=12 , A__ : Optional[Any]=12 , A__ : Union[str, Any]=30_72 , A__ : Dict="gelu" , A__ : List[Any]=0.1 , A__ : str=0.1 , A__ : List[Any]=10_24 , A__ : Optional[int]=[3, 2_56, 2_56, 2, 2_56, 2_56, 10] , A__ : Union[str, Any]=0.02 , A__ : Tuple=1E-12 , A__ : Tuple=0 , A__ : Any=10.0 , A__ : List[str]=0 , A__ : List[str]=1.0 , A__ : Optional[Any]=None , A__ : Tuple=1.0 , A__ : Union[str, Any]=False , A__ : Any=None , A__ : Union[str, Any]=1.0 , A__ : int=1.0 , A__ : str=False , A__ : int=False , A__ : Optional[Any]="ratio" , A__ : str=None , A__ : int=None , A__ : Dict=64 , A__ : int=32 , A__ : Optional[Any]=False , A__ : List[str]=True , A__ : List[Any]=False , A__ : str=False , A__ : Any=True , A__ : Tuple=False , A__ : str=None , A__ : str=None , **A__ : List[str] , ) -> List[str]:
'''simple docstring'''
super().__init__(pad_token_id=A__ , **A__ )
# BERT hyperparameters (with updated max_position_embeddings and type_vocab_sizes)
snake_case_ : int = vocab_size
snake_case_ : int = hidden_size
snake_case_ : Optional[Any] = num_hidden_layers
snake_case_ : int = num_attention_heads
snake_case_ : Optional[int] = hidden_act
snake_case_ : Optional[int] = intermediate_size
snake_case_ : str = hidden_dropout_prob
snake_case_ : Dict = attention_probs_dropout_prob
snake_case_ : Any = max_position_embeddings
snake_case_ : List[Any] = type_vocab_sizes
snake_case_ : str = initializer_range
snake_case_ : Optional[Any] = layer_norm_eps
# Fine-tuning task hyperparameters
snake_case_ : Optional[int] = positive_label_weight
snake_case_ : Dict = num_aggregation_labels
snake_case_ : List[str] = aggregation_loss_weight
snake_case_ : str = use_answer_as_supervision
snake_case_ : int = answer_loss_importance
snake_case_ : Any = use_normalized_answer_loss
snake_case_ : int = huber_loss_delta
snake_case_ : List[Any] = temperature
snake_case_ : str = aggregation_temperature
snake_case_ : List[str] = use_gumbel_for_cells
snake_case_ : List[str] = use_gumbel_for_aggregation
snake_case_ : Dict = average_approximation_function
snake_case_ : List[str] = cell_selection_preference
snake_case_ : Dict = answer_loss_cutoff
snake_case_ : List[str] = max_num_rows
snake_case_ : Union[str, Any] = max_num_columns
snake_case_ : str = average_logits_per_cell
snake_case_ : Union[str, Any] = select_one_column
snake_case_ : Dict = allow_empty_column_selection
snake_case_ : List[Any] = init_cell_selection_weights_to_zero
snake_case_ : str = reset_position_index_per_cell
snake_case_ : List[Any] = disable_per_token_loss
# Aggregation hyperparameters
snake_case_ : List[str] = aggregation_labels
snake_case_ : Union[str, Any] = no_aggregation_label_index
if isinstance(self.aggregation_labels , A__ ):
snake_case_ : Optional[int] = {int(A__ ): v for k, v in aggregation_labels.items()}
| 666
| 1
|
import unittest
from datasets import load_dataset
from transformers.pipelines import pipeline
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_torch, slow
@is_pipeline_test
@require_torch
class lowerCamelCase ( unittest.TestCase ):
@require_torch
def snake_case_ ( self : Any ) -> List[Any]:
_a : Union[str, Any] = pipeline(
task='''zero-shot-audio-classification''' , model='''hf-internal-testing/tiny-clap-htsat-unfused''' )
_a : Any = load_dataset('''ashraq/esc50''' )
_a : str = dataset['''train''']['''audio'''][-1]['''array''']
_a : List[Any] = audio_classifier(__snake_case , candidate_labels=['''Sound of a dog''', '''Sound of vaccum cleaner'''] )
self.assertEqual(
nested_simplify(__snake_case ) , [{'''score''': 0.501, '''label''': '''Sound of a dog'''}, {'''score''': 0.499, '''label''': '''Sound of vaccum cleaner'''}] , )
@unittest.skip('''No models are available in TF''' )
def snake_case_ ( self : str ) -> Dict:
pass
@slow
@require_torch
def snake_case_ ( self : Optional[int] ) -> List[Any]:
_a : Optional[int] = pipeline(
task='''zero-shot-audio-classification''' , model='''laion/clap-htsat-unfused''' , )
# This is an audio of a dog
_a : str = load_dataset('''ashraq/esc50''' )
_a : Optional[Any] = dataset['''train''']['''audio'''][-1]['''array''']
_a : Optional[Any] = audio_classifier(__snake_case , candidate_labels=['''Sound of a dog''', '''Sound of vaccum cleaner'''] )
self.assertEqual(
nested_simplify(__snake_case ) , [
{'''score''': 0.999, '''label''': '''Sound of a dog'''},
{'''score''': 0.001, '''label''': '''Sound of vaccum cleaner'''},
] , )
_a : Union[str, Any] = audio_classifier([audio] * 5 , candidate_labels=['''Sound of a dog''', '''Sound of vaccum cleaner'''] )
self.assertEqual(
nested_simplify(__snake_case ) , [
[
{'''score''': 0.999, '''label''': '''Sound of a dog'''},
{'''score''': 0.001, '''label''': '''Sound of vaccum cleaner'''},
],
]
* 5 , )
_a : Dict = audio_classifier(
[audio] * 5 , candidate_labels=['''Sound of a dog''', '''Sound of vaccum cleaner'''] , batch_size=5 )
self.assertEqual(
nested_simplify(__snake_case ) , [
[
{'''score''': 0.999, '''label''': '''Sound of a dog'''},
{'''score''': 0.001, '''label''': '''Sound of vaccum cleaner'''},
],
]
* 5 , )
@unittest.skip('''No models are available in TF''' )
def snake_case_ ( self : List[str] ) -> List[Any]:
pass
| 714
|
from manim import *
class lowerCamelCase ( SCREAMING_SNAKE_CASE ):
def snake_case_ ( self : int ) -> Tuple:
_a : Optional[int] = Rectangle(height=0.5 , width=0.5 )
_a : Dict = Rectangle(height=0.25 , width=0.25 )
_a : str = Rectangle(height=0.46 , width=0.46 ).set_stroke(width=0 )
_a : Optional[int] = [mem.copy() for i in range(6 )]
_a : Tuple = [mem.copy() for i in range(6 )]
_a : Dict = VGroup(*__snake_case ).arrange(__snake_case , buff=0 )
_a : Any = VGroup(*__snake_case ).arrange(__snake_case , buff=0 )
_a : str = VGroup(__snake_case , __snake_case ).arrange(__snake_case , buff=0 )
_a : List[str] = Text('''CPU''' , font_size=24 )
_a : Union[str, Any] = Group(__snake_case , __snake_case ).arrange(__snake_case , buff=0.5 , aligned_edge=__snake_case )
cpu.move_to([-2.5, -0.5, 0] )
self.add(__snake_case )
_a : Union[str, Any] = [mem.copy() for i in range(4 )]
_a : Tuple = VGroup(*__snake_case ).arrange(__snake_case , buff=0 )
_a : Tuple = Text('''GPU''' , font_size=24 )
_a : List[Any] = Group(__snake_case , __snake_case ).arrange(__snake_case , buff=0.5 , aligned_edge=__snake_case )
gpu.move_to([-1, -1, 0] )
self.add(__snake_case )
_a : Optional[int] = [mem.copy() for i in range(6 )]
_a : Any = VGroup(*__snake_case ).arrange(__snake_case , buff=0 )
_a : Union[str, Any] = Text('''Model''' , font_size=24 )
_a : str = Group(__snake_case , __snake_case ).arrange(__snake_case , buff=0.5 , aligned_edge=__snake_case )
model.move_to([3, -1.0, 0] )
self.add(__snake_case )
_a : Optional[Any] = []
_a : Optional[Any] = []
_a : Any = []
for i, rect in enumerate(__snake_case ):
rect.set_stroke(__snake_case )
_a : Optional[Any] = Rectangle(height=0.46 / 4 , width=0.46 / 3 ).set_stroke(width=0.0 ).set_fill(__snake_case , opacity=0.7 )
if i == 0:
cpu_target.next_to(cpu_left_col_base[0].get_corner(DOWN + LEFT ) , buff=0.02 , direction=__snake_case )
cpu_target.set_x(cpu_target.get_x() + 0.1 )
elif i == 3:
cpu_target.next_to(model_cpu_arr[0] , direction=__snake_case , buff=0.0 )
else:
cpu_target.next_to(model_cpu_arr[i - 1] , direction=__snake_case , buff=0.0 )
self.add(__snake_case )
model_cpu_arr.append(__snake_case )
self.add(*__snake_case , *__snake_case , *__snake_case )
_a : List[Any] = [mem.copy() for i in range(6 )]
_a : str = VGroup(*__snake_case ).arrange(__snake_case , buff=0 )
_a : Union[str, Any] = Text('''Loaded Checkpoint''' , font_size=24 )
_a : Any = Group(__snake_case , __snake_case ).arrange(__snake_case , buff=0.5 , aligned_edge=__snake_case )
checkpoint.move_to([3, 0.5, 0] )
self.add(__snake_case )
_a : Dict = []
_a : Tuple = []
for i, rect in enumerate(__snake_case ):
_a : str = fill.copy().set_fill(__snake_case , opacity=0.7 )
target.move_to(__snake_case )
ckpt_arr.append(__snake_case )
_a : Optional[int] = target.copy()
if i < 5:
cpu_target.move_to(cpu_left_col_base[i + 1] )
else:
cpu_target.move_to(cpu_right_col_base[i - 5] )
ckpt_cpu_arr.append(__snake_case )
self.add(*__snake_case , *__snake_case )
_a : int = Square(side_length=2.2 )
key.move_to([-5, 2, 0] )
_a : Union[str, Any] = MarkupText(
f"""<b>Key:</b>\n\n<span fgcolor='{YELLOW}'>●</span> Empty Model""" , font_size=18 , )
key_text.move_to([-5, 2.4, 0] )
self.add(__snake_case , __snake_case )
_a : Any = MarkupText(
f"""<span fgcolor='{BLUE}'>●</span> Checkpoint""" , font_size=18 , )
blue_text.next_to(__snake_case , DOWN * 2.4 , aligned_edge=key_text.get_left() )
self.add(__snake_case )
_a : str = MarkupText(
f"""Based on the passed in configuration, weights are stored in\na variety of np.memmaps on disk or to a particular device.""" , font_size=24 , )
step_a.move_to([2, 2, 0] )
_a : Optional[Any] = [meta_mem.copy() for i in range(6 )]
_a : Union[str, Any] = [meta_mem.copy() for i in range(6 )]
_a : Union[str, Any] = VGroup(*__snake_case ).arrange(__snake_case , buff=0 )
_a : Optional[int] = VGroup(*__snake_case ).arrange(__snake_case , buff=0 )
_a : Optional[int] = VGroup(__snake_case , __snake_case ).arrange(__snake_case , buff=0 )
_a : Dict = Text('''Disk''' , font_size=24 )
_a : List[str] = Group(__snake_case , __snake_case ).arrange(__snake_case , buff=0.5 , aligned_edge=__snake_case )
disk.move_to([-4.0, -1.25, 0] )
self.play(Write(__snake_case , run_time=3 ) , Write(__snake_case , run_time=1 ) , Create(__snake_case , run_time=1 ) )
_a : List[Any] = []
for i, rect in enumerate(__snake_case ):
_a : Dict = rect.copy()
target.generate_target()
target.target.move_to(disk_left_col_base[i] ).scale(0.5 )
animations.append(MoveToTarget(__snake_case , run_time=1.5 ) )
self.play(*__snake_case )
self.play(FadeOut(__snake_case ) )
_a : Optional[int] = MarkupText(f"""Then, the checkpoint is removed from memory\nthrough garbage collection.""" , font_size=24 )
step_a.move_to([2, 2, 0] )
self.play(Write(__snake_case , run_time=3 ) )
self.play(
FadeOut(__snake_case , __snake_case , *__snake_case , *__snake_case ) , )
self.wait()
| 249
| 0
|
"""simple docstring"""
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
HubertConfig,
HubertForCTC,
HubertModel,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
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',
'w2v_encoder.proj': 'lm_head',
'mask_emb': 'masked_spec_embed',
}
def __UpperCAmelCase ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
for attribute in key.split('''.''' ):
__lowercase : str = getattr(__UpperCamelCase , __UpperCamelCase )
if weight_type is not None:
__lowercase : int = getattr(__UpperCamelCase , __UpperCamelCase ).shape
else:
__lowercase : int = 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":
__lowercase : List[str] = value
elif weight_type == "weight_g":
__lowercase : Optional[Any] = value
elif weight_type == "weight_v":
__lowercase : Tuple = value
elif weight_type == "bias":
__lowercase : Dict = value
else:
__lowercase : Union[str, Any] = value
logger.info(f"""{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.""" )
def __UpperCAmelCase ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
__lowercase : Tuple = []
__lowercase : Union[str, Any] = fairseq_model.state_dict()
__lowercase : Optional[Any] = hf_model.hubert.feature_extractor if is_finetuned else hf_model.feature_extractor
for name, value in fairseq_dict.items():
__lowercase : Union[str, Any] = False
if "conv_layers" in name:
load_conv_layer(
__UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , hf_model.config.feat_extract_norm == '''group''' , )
__lowercase : List[str] = True
else:
for key, mapped_key in MAPPING.items():
__lowercase : List[str] = '''hubert.''' + mapped_key if (is_finetuned and mapped_key != '''lm_head''') else mapped_key
if key in name or (key.split('''w2v_model.''' )[-1] == name.split('''.''' )[0] and not is_finetuned):
__lowercase : int = True
if "*" in mapped_key:
__lowercase : Union[str, Any] = name.split(__UpperCamelCase )[0].split('''.''' )[-2]
__lowercase : Tuple = mapped_key.replace('''*''' , __UpperCamelCase )
if "weight_g" in name:
__lowercase : Tuple = '''weight_g'''
elif "weight_v" in name:
__lowercase : Optional[int] = '''weight_v'''
elif "weight" in name:
__lowercase : str = '''weight'''
elif "bias" in name:
__lowercase : Optional[int] = '''bias'''
else:
__lowercase : List[str] = None
set_recursively(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase )
continue
if not is_used:
unused_weights.append(__UpperCamelCase )
logger.warning(f"""Unused weights: {unused_weights}""" )
def __UpperCAmelCase ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
__lowercase : List[Any] = full_name.split('''conv_layers.''' )[-1]
__lowercase : str = name.split('''.''' )
__lowercase : Dict = int(items[0] )
__lowercase : Any = 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."""
)
__lowercase : List[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."""
)
__lowercase : Tuple = 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."
)
__lowercase : Union[str, 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."""
)
__lowercase : Tuple = value
logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.""" )
else:
unused_weights.append(__UpperCamelCase )
@torch.no_grad()
def __UpperCAmelCase ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase=None , __UpperCamelCase=None , __UpperCamelCase=True ):
if config_path is not None:
__lowercase : Dict = HubertConfig.from_pretrained(__UpperCamelCase )
else:
__lowercase : str = HubertConfig()
if is_finetuned:
if dict_path:
__lowercase : Tuple = Dictionary.load(__UpperCamelCase )
# important change bos & pad token id since CTC symbol is <pad> and
# not <s> as in fairseq
__lowercase : int = target_dict.pad_index
__lowercase : Union[str, Any] = target_dict.bos_index
__lowercase : int = target_dict.eos_index
__lowercase : int = len(target_dict.symbols )
__lowercase : Dict = os.path.join(__UpperCamelCase , '''vocab.json''' )
if not os.path.isdir(__UpperCamelCase ):
logger.error('''--pytorch_dump_folder_path ({}) should be a directory'''.format(__UpperCamelCase ) )
return
os.makedirs(__UpperCamelCase , exist_ok=__UpperCamelCase )
with open(__UpperCamelCase , '''w''' , encoding='''utf-8''' ) as vocab_handle:
json.dump(target_dict.indices , __UpperCamelCase )
__lowercase : str = WavaVecaCTCTokenizer(
__UpperCamelCase , 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=__UpperCamelCase , )
__lowercase : str = True if config.feat_extract_norm == '''layer''' else False
__lowercase : Any = WavaVecaFeatureExtractor(
feature_size=1 , sampling_rate=1_60_00 , padding_value=0 , do_normalize=__UpperCamelCase , return_attention_mask=__UpperCamelCase , )
__lowercase : Union[str, Any] = WavaVecaProcessor(feature_extractor=__UpperCamelCase , tokenizer=__UpperCamelCase )
processor.save_pretrained(__UpperCamelCase )
__lowercase : Optional[Any] = HubertForCTC(__UpperCamelCase )
else:
__lowercase : Union[str, Any] = HubertModel(__UpperCamelCase )
if is_finetuned:
__lowercase ,__lowercase ,__lowercase : Any = fairseq.checkpoint_utils.load_model_ensemble_and_task(
[checkpoint_path] , arg_overrides={'''data''': '''/'''.join(dict_path.split('''/''' )[:-1] )} )
else:
__lowercase ,__lowercase ,__lowercase : Optional[Any] = fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path] )
__lowercase : Union[str, Any] = model[0].eval()
recursively_load_weights(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase )
hf_wavavec.save_pretrained(__UpperCamelCase )
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_hubert_checkpoint(
args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
)
| 76
|
'''simple docstring'''
def a__ ( _SCREAMING_SNAKE_CASE : list[int] , _SCREAMING_SNAKE_CASE : list[int] ) -> tuple[float, float]:
"""simple docstring"""
if not len(_SCREAMING_SNAKE_CASE ) == len(_SCREAMING_SNAKE_CASE ) == 3:
raise ValueError("Please enter a valid equation." )
if equationa[0] == equationa[1] == equationa[0] == equationa[1] == 0:
raise ValueError("Both a & b of two equations can't be zero." )
# Extract the coefficients
UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ : List[str] = equationa
UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ : List[Any] = equationa
# Calculate the determinants of the matrices
UpperCAmelCase_ : Optional[int] = aa * ba - aa * ba
UpperCAmelCase_ : Optional[int] = ca * ba - ca * ba
UpperCAmelCase_ : Any = aa * ca - aa * ca
# Check if the system of linear equations has a solution (using Cramer's rule)
if determinant == 0:
if determinant_x == determinant_y == 0:
raise ValueError("Infinite solutions. (Consistent system)" )
else:
raise ValueError("No solution. (Inconsistent system)" )
else:
if determinant_x == determinant_y == 0:
# Trivial solution (Inconsistent system)
return (0.0, 0.0)
else:
UpperCAmelCase_ : Optional[int] = determinant_x / determinant
UpperCAmelCase_ : List[Any] = determinant_y / determinant
# Non-Trivial Solution (Consistent system)
return (x, y)
| 71
| 0
|
'''simple docstring'''
import logging
from pathlib import Path
import numpy as np
import pytorch_lightning as pl
import torch
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
from pytorch_lightning.utilities import rank_zero_only
from utils_rag import save_json
def __lowerCamelCase ( __snake_case : List[str] ) -> Tuple:
"""simple docstring"""
A__ : int =filter(lambda __snake_case : p.requires_grad, model.parameters() )
A__ : Dict =sum([np.prod(p.size() ) for p in model_parameters] )
return params
__snake_case : int = logging.getLogger(__name__)
def __lowerCamelCase ( __snake_case : Optional[Any], __snake_case : List[Any] ) -> int:
"""simple docstring"""
if metric == "rouge2":
A__ : str ="""{val_avg_rouge2:.4f}-{step_count}"""
elif metric == "bleu":
A__ : Union[str, Any] ="""{val_avg_bleu:.4f}-{step_count}"""
elif metric == "em":
A__ : Union[str, Any] ="""{val_avg_em:.4f}-{step_count}"""
elif metric == "loss":
A__ : Union[str, Any] ="""{val_avg_loss:.4f}-{step_count}"""
else:
raise NotImplementedError(
f"seq2seq callbacks only support rouge2 and bleu, got {metric}, You can make your own by adding to this"
""" function.""" )
A__ : List[str] =ModelCheckpoint(
dirpath=__snake_case, filename=__snake_case, monitor=f"val_{metric}", mode="""max""", save_top_k=1, every_n_epochs=1, )
return checkpoint_callback
def __lowerCamelCase ( __snake_case : List[Any], __snake_case : str ) -> int:
"""simple docstring"""
return EarlyStopping(
monitor=f"val_{metric}", mode="""min""" if """loss""" in metric else """max""", patience=__snake_case, verbose=__snake_case, )
class lowerCamelCase ( pl.Callback ):
'''simple docstring'''
def lowercase__ ( self : Dict , lowerCAmelCase_ : Any , lowerCAmelCase_ : Union[str, Any] ) -> Optional[Any]:
'''simple docstring'''
A__ : Optional[Any] ={f"lr_group_{i}": param["""lr"""] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups )}
pl_module.logger.log_metrics(lowerCAmelCase_ )
@rank_zero_only
def lowercase__ ( self : Union[str, Any] , lowerCAmelCase_ : pl.Trainer , lowerCAmelCase_ : pl.LightningModule , lowerCAmelCase_ : str , lowerCAmelCase_ : Optional[int]=True ) -> None:
'''simple docstring'''
logger.info(f"***** {type_path} results at step {trainer.global_step:05d} *****" )
A__ : Optional[Any] =trainer.callback_metrics
trainer.logger.log_metrics({k: v for k, v in metrics.items() if k not in ["""log""", """progress_bar""", """preds"""]} )
# Log results
A__ : Any =Path(pl_module.hparams.output_dir )
if type_path == "test":
A__ : Union[str, Any] =od / """test_results.txt"""
A__ : str =od / """test_generations.txt"""
else:
# this never gets hit. I prefer not to save intermediate generations, and results are in metrics.json
# If people want this it will be easy enough to add back.
A__ : List[str] =od / f"{type_path}_results/{trainer.global_step:05d}.txt"
A__ : Any =od / f"{type_path}_generations/{trainer.global_step:05d}.txt"
results_file.parent.mkdir(exist_ok=lowerCAmelCase_ )
generations_file.parent.mkdir(exist_ok=lowerCAmelCase_ )
with open(lowerCAmelCase_ , """a+""" ) as writer:
for key in sorted(lowerCAmelCase_ ):
if key in ["log", "progress_bar", "preds"]:
continue
A__ : str =metrics[key]
if isinstance(lowerCAmelCase_ , torch.Tensor ):
A__ : Optional[Any] =val.item()
A__ : List[str] =f"{key}: {val:.6f}\n"
writer.write(lowerCAmelCase_ )
if not save_generations:
return
if "preds" in metrics:
A__ : Optional[int] ="""\n""".join(metrics["""preds"""] )
generations_file.open("""w+""" ).write(lowerCAmelCase_ )
@rank_zero_only
def lowercase__ ( self : Optional[Any] , lowerCAmelCase_ : List[str] , lowerCAmelCase_ : Optional[Any] ) -> Optional[int]:
'''simple docstring'''
try:
A__ : List[str] =pl_module.model.model.num_parameters()
except AttributeError:
A__ : int =pl_module.model.num_parameters()
A__ : int =count_trainable_parameters(lowerCAmelCase_ )
# mp stands for million parameters
trainer.logger.log_metrics({"""n_params""": npars, """mp""": npars / 1e6, """grad_mp""": n_trainable_pars / 1e6} )
@rank_zero_only
def lowercase__ ( self : Union[str, Any] , lowerCAmelCase_ : pl.Trainer , lowerCAmelCase_ : pl.LightningModule ) -> Optional[int]:
'''simple docstring'''
save_json(pl_module.metrics , pl_module.metrics_save_path )
return self._write_logs(lowerCAmelCase_ , lowerCAmelCase_ , """test""" )
@rank_zero_only
def lowercase__ ( self : str , lowerCAmelCase_ : pl.Trainer , lowerCAmelCase_ : List[str] ) -> List[str]:
'''simple docstring'''
save_json(pl_module.metrics , pl_module.metrics_save_path )
# Uncommenting this will save val generations
# return self._write_logs(trainer, pl_module, "valid")
| 715
|
'''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 __lowerCamelCase ( __snake_case : int ) -> Optional[int]:
"""simple docstring"""
random.seed(__snake_case )
np.random.seed(__snake_case )
torch.manual_seed(__snake_case )
torch.cuda.manual_seed_all(__snake_case )
# ^^ safe to call this function even if cuda is not available
class lowerCamelCase :
'''simple docstring'''
def __init__( self : Optional[Any] , lowerCAmelCase_ : Iterable[torch.nn.Parameter] , lowerCAmelCase_ : float = 0.9999 , lowerCAmelCase_ : float = 0.0 , lowerCAmelCase_ : int = 0 , lowerCAmelCase_ : bool = False , lowerCAmelCase_ : Union[float, int] = 1.0 , lowerCAmelCase_ : Union[float, int] = 2 / 3 , lowerCAmelCase_ : Optional[Any] = None , lowerCAmelCase_ : Dict[str, Any] = None , **lowerCAmelCase_ : Optional[Any] , ) -> List[str]:
'''simple docstring'''
if isinstance(lowerCAmelCase_ , torch.nn.Module ):
A__ : Optional[Any] =(
"""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_ , )
A__ : List[str] =parameters.parameters()
# set use_ema_warmup to True if a torch.nn.Module is passed for backwards compatibility
A__ : int =True
if kwargs.get("""max_value""" , lowerCAmelCase_ ) is not None:
A__ : Tuple ="""The `max_value` argument is deprecated. Please use `decay` instead."""
deprecate("""max_value""" , """1.0.0""" , lowerCAmelCase_ , standard_warn=lowerCAmelCase_ )
A__ : Union[str, Any] =kwargs["""max_value"""]
if kwargs.get("""min_value""" , lowerCAmelCase_ ) is not None:
A__ : List[str] ="""The `min_value` argument is deprecated. Please use `min_decay` instead."""
deprecate("""min_value""" , """1.0.0""" , lowerCAmelCase_ , standard_warn=lowerCAmelCase_ )
A__ : Optional[Any] =kwargs["""min_value"""]
A__ : Any =list(lowerCAmelCase_ )
A__ : int =[p.clone().detach() for p in parameters]
if kwargs.get("""device""" , lowerCAmelCase_ ) is not None:
A__ : List[str] ="""The `device` argument is deprecated. Please use `to` instead."""
deprecate("""device""" , """1.0.0""" , lowerCAmelCase_ , standard_warn=lowerCAmelCase_ )
self.to(device=kwargs["""device"""] )
A__ : Optional[int] =None
A__ : Any =decay
A__ : List[Any] =min_decay
A__ : Optional[int] =update_after_step
A__ : List[str] =use_ema_warmup
A__ : str =inv_gamma
A__ : Union[str, Any] =power
A__ : str =0
A__ : str =None # set in `step()`
A__ : List[str] =model_cls
A__ : Optional[int] =model_config
@classmethod
def lowercase__ ( cls : Dict , lowerCAmelCase_ : List[str] , lowerCAmelCase_ : Dict ) -> "EMAModel":
'''simple docstring'''
A__ , A__ : Tuple =model_cls.load_config(lowerCAmelCase_ , return_unused_kwargs=lowerCAmelCase_ )
A__ : Optional[Any] =model_cls.from_pretrained(lowerCAmelCase_ )
A__ : Optional[Any] =cls(model.parameters() , model_cls=lowerCAmelCase_ , model_config=model.config )
ema_model.load_state_dict(lowerCAmelCase_ )
return ema_model
def lowercase__ ( self : List[str] , lowerCAmelCase_ : Tuple ) -> List[Any]:
'''simple docstring'''
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__.""" )
A__ : Optional[int] =self.model_cls.from_config(self.model_config )
A__ : Optional[Any] =self.state_dict()
state_dict.pop("""shadow_params""" , lowerCAmelCase_ )
model.register_to_config(**lowerCAmelCase_ )
self.copy_to(model.parameters() )
model.save_pretrained(lowerCAmelCase_ )
def lowercase__ ( self : Dict , lowerCAmelCase_ : int ) -> float:
'''simple docstring'''
A__ : Optional[int] =max(0 , optimization_step - self.update_after_step - 1 )
if step <= 0:
return 0.0
if self.use_ema_warmup:
A__ : List[Any] =1 - (1 + step / self.inv_gamma) ** -self.power
else:
A__ : Union[str, Any] =(1 + step) / (10 + step)
A__ : str =min(lowerCAmelCase_ , self.decay )
# make sure decay is not smaller than min_decay
A__ : int =max(lowerCAmelCase_ , self.min_decay )
return cur_decay_value
@torch.no_grad()
def lowercase__ ( self : Optional[int] , lowerCAmelCase_ : Iterable[torch.nn.Parameter] ) -> Optional[Any]:
'''simple docstring'''
if isinstance(lowerCAmelCase_ , torch.nn.Module ):
A__ : 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_ , )
A__ : Optional[int] =parameters.parameters()
A__ : Dict =list(lowerCAmelCase_ )
self.optimization_step += 1
# Compute the decay factor for the exponential moving average.
A__ : Any =self.get_decay(self.optimization_step )
A__ : Optional[int] =decay
A__ : List[str] =1 - decay
A__ : str =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():
A__ : List[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 lowercase__ ( self : Tuple , lowerCAmelCase_ : Iterable[torch.nn.Parameter] ) -> None:
'''simple docstring'''
A__ : Optional[Any] =list(lowerCAmelCase_ )
for s_param, param in zip(self.shadow_params , lowerCAmelCase_ ):
param.data.copy_(s_param.to(param.device ).data )
def lowercase__ ( self : int , lowerCAmelCase_ : Dict=None , lowerCAmelCase_ : List[Any]=None ) -> None:
'''simple docstring'''
A__ : str =[
p.to(device=lowerCAmelCase_ , dtype=lowerCAmelCase_ ) if p.is_floating_point() else p.to(device=lowerCAmelCase_ )
for p in self.shadow_params
]
def lowercase__ ( self : Optional[Any] ) -> dict:
'''simple docstring'''
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 lowercase__ ( self : Tuple , lowerCAmelCase_ : Iterable[torch.nn.Parameter] ) -> None:
'''simple docstring'''
A__ : List[str] =[param.detach().cpu().clone() for param in parameters]
def lowercase__ ( self : List[str] , lowerCAmelCase_ : Iterable[torch.nn.Parameter] ) -> None:
'''simple docstring'''
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.
A__ : List[str] =None
def lowercase__ ( self : List[str] , lowerCAmelCase_ : dict ) -> None:
'''simple docstring'''
A__ : List[Any] =copy.deepcopy(lowerCAmelCase_ )
A__ : List[Any] =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""" )
A__ : List[Any] =state_dict.get("""min_decay""" , self.min_decay )
if not isinstance(self.min_decay , lowerCAmelCase_ ):
raise ValueError("""Invalid min_decay""" )
A__ : Tuple =state_dict.get("""optimization_step""" , self.optimization_step )
if not isinstance(self.optimization_step , lowerCAmelCase_ ):
raise ValueError("""Invalid optimization_step""" )
A__ : 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""" )
A__ : str =state_dict.get("""use_ema_warmup""" , self.use_ema_warmup )
if not isinstance(self.use_ema_warmup , lowerCAmelCase_ ):
raise ValueError("""Invalid use_ema_warmup""" )
A__ : str =state_dict.get("""inv_gamma""" , self.inv_gamma )
if not isinstance(self.inv_gamma , (float, int) ):
raise ValueError("""Invalid inv_gamma""" )
A__ : Tuple =state_dict.get("""power""" , self.power )
if not isinstance(self.power , (float, int) ):
raise ValueError("""Invalid power""" )
A__ : Tuple =state_dict.get("""shadow_params""" , lowerCAmelCase_ )
if shadow_params is not None:
A__ : List[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""" )
| 687
| 0
|
import subprocess
import sys
from transformers import BertConfig, BertModel, BertTokenizer, pipeline
from transformers.testing_utils import TestCasePlus, require_torch
class __a ( SCREAMING_SNAKE_CASE ):
@require_torch
def UpperCamelCase ( self : List[str])-> Union[str, Any]:
# this test is a bit tricky since TRANSFORMERS_OFFLINE can only be changed before
# `transformers` is loaded, and it's too late for inside pytest - so we are changing it
# while running an external program
# python one-liner segments
# this must be loaded before socket.socket is monkey-patched
__lowerCAmelCase ="""
from transformers import BertConfig, BertModel, BertTokenizer, pipeline
"""
__lowerCAmelCase ="""
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\")
"""
__lowerCAmelCase ="""
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
__lowerCAmelCase ="""hf-internal-testing/tiny-random-bert"""
BertConfig.from_pretrained(snake_case_)
BertModel.from_pretrained(snake_case_)
BertTokenizer.from_pretrained(snake_case_)
pipeline(task="""fill-mask""" , model=snake_case_)
# baseline - just load from_pretrained with normal network
__lowerCAmelCase =[sys.executable, """-c""", """\n""".join([load, run, mock])]
# should succeed
__lowerCAmelCase =self.get_env()
# should succeed as TRANSFORMERS_OFFLINE=1 tells it to use local files
__lowerCAmelCase ="""1"""
__lowerCAmelCase =subprocess.run(snake_case_ , env=snake_case_ , check=snake_case_ , capture_output=snake_case_)
self.assertEqual(result.returncode , 0 , result.stderr)
self.assertIn("""success""" , result.stdout.decode())
@require_torch
def UpperCamelCase ( self : Union[str, Any])-> List[Any]:
# python one-liner segments
# this must be loaded before socket.socket is monkey-patched
__lowerCAmelCase ="""
from transformers import BertConfig, BertModel, BertTokenizer, pipeline
"""
__lowerCAmelCase ="""
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\")
"""
__lowerCAmelCase ="""
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
__lowerCAmelCase ="""hf-internal-testing/tiny-random-bert"""
BertConfig.from_pretrained(snake_case_)
BertModel.from_pretrained(snake_case_)
BertTokenizer.from_pretrained(snake_case_)
pipeline(task="""fill-mask""" , model=snake_case_)
# baseline - just load from_pretrained with normal network
__lowerCAmelCase =[sys.executable, """-c""", """\n""".join([load, run, mock])]
# should succeed
__lowerCAmelCase =self.get_env()
__lowerCAmelCase =subprocess.run(snake_case_ , env=snake_case_ , check=snake_case_ , capture_output=snake_case_)
self.assertEqual(result.returncode , 0 , result.stderr)
self.assertIn("""success""" , result.stdout.decode())
@require_torch
def UpperCamelCase ( self : Dict)-> Dict:
# this test is a bit tricky since TRANSFORMERS_OFFLINE can only be changed before
# `transformers` is loaded, and it's too late for inside pytest - so we are changing it
# while running an external program
# python one-liner segments
# this must be loaded before socket.socket is monkey-patched
__lowerCAmelCase ="""
from transformers import BertConfig, BertModel, BertTokenizer
"""
__lowerCAmelCase ="""
mname = \"hf-internal-testing/tiny-random-bert-sharded\"
BertConfig.from_pretrained(mname)
BertModel.from_pretrained(mname)
print(\"success\")
"""
__lowerCAmelCase ="""
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
__lowerCAmelCase =[sys.executable, """-c""", """\n""".join([load, run])]
# should succeed
__lowerCAmelCase =self.get_env()
__lowerCAmelCase =subprocess.run(snake_case_ , env=snake_case_ , check=snake_case_ , capture_output=snake_case_)
self.assertEqual(result.returncode , 0 , result.stderr)
self.assertIn("""success""" , result.stdout.decode())
# next emulate no network
__lowerCAmelCase =[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
__lowerCAmelCase ="""1"""
__lowerCAmelCase =subprocess.run(snake_case_ , env=snake_case_ , check=snake_case_ , capture_output=snake_case_)
self.assertEqual(result.returncode , 0 , result.stderr)
self.assertIn("""success""" , result.stdout.decode())
@require_torch
def UpperCamelCase ( self : Tuple)-> Union[str, Any]:
__lowerCAmelCase ="""
from transformers import pipeline
"""
__lowerCAmelCase ="""
mname = \"hf-internal-testing/tiny-random-bert\"
pipe = pipeline(model=mname)
"""
__lowerCAmelCase ="""
import socket
def offline_socket(*args, **kwargs): raise socket.error(\"Offline mode is enabled\")
socket.socket = offline_socket
"""
__lowerCAmelCase =self.get_env()
__lowerCAmelCase ="""1"""
__lowerCAmelCase =[sys.executable, """-c""", """\n""".join([load, mock, run])]
__lowerCAmelCase =subprocess.run(snake_case_ , env=snake_case_ , check=snake_case_ , capture_output=snake_case_)
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 UpperCamelCase ( self : Optional[Any])-> Optional[int]:
__lowerCAmelCase ="""
from transformers import AutoModel
"""
__lowerCAmelCase ="""
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
__lowerCAmelCase =[sys.executable, """-c""", """\n""".join([load, run])]
# should succeed
__lowerCAmelCase =self.get_env()
__lowerCAmelCase =subprocess.run(snake_case_ , env=snake_case_ , check=snake_case_ , capture_output=snake_case_)
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
__lowerCAmelCase ="""1"""
__lowerCAmelCase =subprocess.run(snake_case_ , env=snake_case_ , check=snake_case_ , capture_output=snake_case_)
self.assertEqual(result.returncode , 0 , result.stderr)
self.assertIn("""success""" , result.stdout.decode())
| 354
|
import json
import os
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union
import sentencepiece
from ...tokenization_utils import BatchEncoding, PreTrainedTokenizer
from ...utils import logging
lowercase_ = logging.get_logger(__name__)
lowercase_ = '''▁'''
lowercase_ = {
'''vocab_file''': '''vocab.json''',
'''spm_file''': '''sentencepiece.bpe.model''',
'''tokenizer_config_file''': '''tokenizer_config.json''',
}
lowercase_ = {
'''vocab_file''': {
'''facebook/m2m100_418M''': '''https://huggingface.co/facebook/m2m100_418M/resolve/main/vocab.json''',
'''facebook/m2m100_1.2B''': '''https://huggingface.co/facebook/m2m100_1.2B/resolve/main/vocab.json''',
},
'''spm_file''': {
'''facebook/m2m100_418M''': '''https://huggingface.co/facebook/m2m100_418M/resolve/main/sentencepiece.bpe.model''',
'''facebook/m2m100_1.2B''': '''https://huggingface.co/facebook/m2m100_1.2B/resolve/main/sentencepiece.bpe.model''',
},
'''tokenizer_config_file''': {
'''facebook/m2m100_418M''': '''https://huggingface.co/facebook/m2m100_418M/resolve/main/tokenizer_config.json''',
'''facebook/m2m100_1.2B''': '''https://huggingface.co/facebook/m2m100_1.2B/resolve/main/tokenizer_config.json''',
},
}
lowercase_ = {
'''facebook/m2m100_418M''': 10_24,
}
# fmt: off
lowercase_ = {
'''m2m100''': ['''af''', '''am''', '''ar''', '''ast''', '''az''', '''ba''', '''be''', '''bg''', '''bn''', '''br''', '''bs''', '''ca''', '''ceb''', '''cs''', '''cy''', '''da''', '''de''', '''el''', '''en''', '''es''', '''et''', '''fa''', '''ff''', '''fi''', '''fr''', '''fy''', '''ga''', '''gd''', '''gl''', '''gu''', '''ha''', '''he''', '''hi''', '''hr''', '''ht''', '''hu''', '''hy''', '''id''', '''ig''', '''ilo''', '''is''', '''it''', '''ja''', '''jv''', '''ka''', '''kk''', '''km''', '''kn''', '''ko''', '''lb''', '''lg''', '''ln''', '''lo''', '''lt''', '''lv''', '''mg''', '''mk''', '''ml''', '''mn''', '''mr''', '''ms''', '''my''', '''ne''', '''nl''', '''no''', '''ns''', '''oc''', '''or''', '''pa''', '''pl''', '''ps''', '''pt''', '''ro''', '''ru''', '''sd''', '''si''', '''sk''', '''sl''', '''so''', '''sq''', '''sr''', '''ss''', '''su''', '''sv''', '''sw''', '''ta''', '''th''', '''tl''', '''tn''', '''tr''', '''uk''', '''ur''', '''uz''', '''vi''', '''wo''', '''xh''', '''yi''', '''yo''', '''zh''', '''zu'''],
'''wmt21''': ['''en''', '''ha''', '''is''', '''ja''', '''cs''', '''ru''', '''zh''', '''de''']
}
class __a ( SCREAMING_SNAKE_CASE ):
SCREAMING_SNAKE_CASE = VOCAB_FILES_NAMES
SCREAMING_SNAKE_CASE = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
SCREAMING_SNAKE_CASE = PRETRAINED_VOCAB_FILES_MAP
SCREAMING_SNAKE_CASE = ["input_ids", "attention_mask"]
SCREAMING_SNAKE_CASE = []
SCREAMING_SNAKE_CASE = []
def __init__( self : int , snake_case_ : List[str] , snake_case_ : Any , snake_case_ : Union[str, Any]=None , snake_case_ : Tuple=None , snake_case_ : List[str]="<s>" , snake_case_ : List[str]="</s>" , snake_case_ : Optional[Any]="</s>" , snake_case_ : Dict="<pad>" , snake_case_ : Any="<unk>" , snake_case_ : Tuple="m2m100" , snake_case_ : Optional[Dict[str, Any]] = None , snake_case_ : Dict=8 , **snake_case_ : List[Any] , )-> None:
__lowerCAmelCase ={} if sp_model_kwargs is None else sp_model_kwargs
__lowerCAmelCase =language_codes
__lowerCAmelCase =FAIRSEQ_LANGUAGE_CODES[language_codes]
__lowerCAmelCase ={lang_code: F"""__{lang_code}__""" for lang_code in fairseq_language_code}
__lowerCAmelCase =kwargs.get("""additional_special_tokens""" , [])
kwargs["additional_special_tokens"] += [
self.get_lang_token(snake_case_)
for lang_code in fairseq_language_code
if self.get_lang_token(snake_case_) not in kwargs["additional_special_tokens"]
]
super().__init__(
src_lang=snake_case_ , tgt_lang=snake_case_ , bos_token=snake_case_ , eos_token=snake_case_ , sep_token=snake_case_ , unk_token=snake_case_ , pad_token=snake_case_ , language_codes=snake_case_ , sp_model_kwargs=self.sp_model_kwargs , num_madeup_words=snake_case_ , **snake_case_ , )
__lowerCAmelCase =vocab_file
__lowerCAmelCase =load_json(snake_case_)
__lowerCAmelCase ={v: k for k, v in self.encoder.items()}
__lowerCAmelCase =spm_file
__lowerCAmelCase =load_spm(snake_case_ , self.sp_model_kwargs)
__lowerCAmelCase =len(self.encoder)
__lowerCAmelCase ={
self.get_lang_token(snake_case_): self.encoder_size + i for i, lang_code in enumerate(snake_case_)
}
__lowerCAmelCase ={lang_code: self.encoder_size + i for i, lang_code in enumerate(snake_case_)}
__lowerCAmelCase ={v: k for k, v in self.lang_token_to_id.items()}
__lowerCAmelCase =src_lang if src_lang is not None else """en"""
__lowerCAmelCase =tgt_lang
__lowerCAmelCase =self.get_lang_id(self._src_lang)
self.set_src_lang_special_tokens(self._src_lang)
__lowerCAmelCase =num_madeup_words
@property
def UpperCamelCase ( self : int)-> int:
return len(self.encoder) + len(self.lang_token_to_id)
@property
def UpperCamelCase ( self : Optional[Any])-> str:
return self._src_lang
@src_lang.setter
def UpperCamelCase ( self : List[Any] , snake_case_ : str)-> None:
__lowerCAmelCase =new_src_lang
self.set_src_lang_special_tokens(self._src_lang)
def UpperCamelCase ( self : str , snake_case_ : str)-> List[str]:
return self.sp_model.encode(snake_case_ , out_type=snake_case_)
def UpperCamelCase ( self : Optional[Any] , snake_case_ : List[str])-> str:
if token in self.lang_token_to_id:
return self.lang_token_to_id[token]
return self.encoder.get(snake_case_ , self.encoder[self.unk_token])
def UpperCamelCase ( self : Dict , snake_case_ : int)-> str:
if index in self.id_to_lang_token:
return self.id_to_lang_token[index]
return self.decoder.get(snake_case_ , self.unk_token)
def UpperCamelCase ( self : Union[str, Any] , snake_case_ : Tuple)-> str:
__lowerCAmelCase =[]
__lowerCAmelCase =""""""
for token in tokens:
# make sure that special tokens are not decoded using sentencepiece model
if token in self.all_special_tokens:
out_string += self.sp_model.decode(snake_case_) + token
__lowerCAmelCase =[]
else:
current_sub_tokens.append(snake_case_)
out_string += self.sp_model.decode(snake_case_)
return out_string.strip()
def UpperCamelCase ( self : Union[str, Any] , snake_case_ : List[int] , snake_case_ : Optional[List[int]] = None , snake_case_ : bool = False)-> List[int]:
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=snake_case_ , token_ids_a=snake_case_ , already_has_special_tokens=snake_case_)
__lowerCAmelCase =[1] * len(self.prefix_tokens)
__lowerCAmelCase =[1] * len(self.suffix_tokens)
if token_ids_a is None:
return prefix_ones + ([0] * len(snake_case_)) + suffix_ones
return prefix_ones + ([0] * len(snake_case_)) + ([0] * len(snake_case_)) + suffix_ones
def UpperCamelCase ( self : Any , snake_case_ : List[int] , snake_case_ : Optional[List[int]] = None)-> List[int]:
if token_ids_a is None:
return self.prefix_tokens + token_ids_a + self.suffix_tokens
# We don't expect to process pairs, but leave the pair logic for API consistency
return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens
def UpperCamelCase ( self : Optional[Any])-> Dict:
__lowerCAmelCase ={self.convert_ids_to_tokens(snake_case_): i for i in range(self.vocab_size)}
vocab.update(self.added_tokens_encoder)
return vocab
def __getstate__( self : Tuple)-> Dict:
__lowerCAmelCase =self.__dict__.copy()
__lowerCAmelCase =None
return state
def __setstate__( self : List[Any] , snake_case_ : Dict)-> None:
__lowerCAmelCase =d
# for backward compatibility
if not hasattr(self , """sp_model_kwargs"""):
__lowerCAmelCase ={}
__lowerCAmelCase =load_spm(self.spm_file , self.sp_model_kwargs)
def UpperCamelCase ( self : Union[str, Any] , snake_case_ : str , snake_case_ : Optional[str] = None)-> Tuple[str]:
__lowerCAmelCase =Path(snake_case_)
if not save_dir.is_dir():
raise OSError(F"""{save_directory} should be a directory""")
__lowerCAmelCase =save_dir / (
(filename_prefix + """-""" if filename_prefix else """""") + self.vocab_files_names["""vocab_file"""]
)
__lowerCAmelCase =save_dir / (
(filename_prefix + """-""" if filename_prefix else """""") + self.vocab_files_names["""spm_file"""]
)
save_json(self.encoder , snake_case_)
if os.path.abspath(self.spm_file) != os.path.abspath(snake_case_) and os.path.isfile(self.spm_file):
copyfile(self.spm_file , snake_case_)
elif not os.path.isfile(self.spm_file):
with open(snake_case_ , """wb""") as fi:
__lowerCAmelCase =self.sp_model.serialized_model_proto()
fi.write(snake_case_)
return (str(snake_case_), str(snake_case_))
def UpperCamelCase ( self : str , snake_case_ : List[str] , snake_case_ : str = "en" , snake_case_ : Optional[List[str]] = None , snake_case_ : str = "ro" , **snake_case_ : int , )-> BatchEncoding:
__lowerCAmelCase =src_lang
__lowerCAmelCase =tgt_lang
self.set_src_lang_special_tokens(self.src_lang)
return super().prepare_seqaseq_batch(snake_case_ , snake_case_ , **snake_case_)
def UpperCamelCase ( self : Optional[int] , snake_case_ : int , snake_case_ : Optional[str] , snake_case_ : Optional[str] , **snake_case_ : List[str])-> List[Any]:
if src_lang is None or tgt_lang is None:
raise ValueError("""Translation requires a `src_lang` and a `tgt_lang` for this model""")
__lowerCAmelCase =src_lang
__lowerCAmelCase =self(snake_case_ , add_special_tokens=snake_case_ , **snake_case_)
__lowerCAmelCase =self.get_lang_id(snake_case_)
__lowerCAmelCase =tgt_lang_id
return inputs
def UpperCamelCase ( self : Optional[int])-> Union[str, Any]:
self.set_src_lang_special_tokens(self.src_lang)
def UpperCamelCase ( self : Dict)-> Dict:
self.set_tgt_lang_special_tokens(self.tgt_lang)
def UpperCamelCase ( self : Union[str, Any] , snake_case_ : str)-> None:
__lowerCAmelCase =self.get_lang_token(snake_case_)
__lowerCAmelCase =self.lang_token_to_id[lang_token]
__lowerCAmelCase =[self.cur_lang_id]
__lowerCAmelCase =[self.eos_token_id]
def UpperCamelCase ( self : str , snake_case_ : str)-> None:
__lowerCAmelCase =self.get_lang_token(snake_case_)
__lowerCAmelCase =self.lang_token_to_id[lang_token]
__lowerCAmelCase =[self.cur_lang_id]
__lowerCAmelCase =[self.eos_token_id]
def UpperCamelCase ( self : int , snake_case_ : str)-> str:
return self.lang_code_to_token[lang]
def UpperCamelCase ( self : List[str] , snake_case_ : str)-> int:
__lowerCAmelCase =self.get_lang_token(snake_case_)
return self.lang_token_to_id[lang_token]
def __lowerCAmelCase ( __lowerCamelCase : str , __lowerCamelCase : Dict[str, Any] ) -> sentencepiece.SentencePieceProcessor:
__lowerCAmelCase =sentencepiece.SentencePieceProcessor(**__lowerCamelCase )
spm.Load(str(__lowerCamelCase ) )
return spm
def __lowerCAmelCase ( __lowerCamelCase : str ) -> Union[Dict, List]:
with open(__lowerCamelCase , """r""" ) as f:
return json.load(__lowerCamelCase )
def __lowerCAmelCase ( __lowerCamelCase : Any , __lowerCamelCase : str ) -> None:
with open(__lowerCamelCase , """w""" ) as f:
json.dump(__lowerCamelCase , __lowerCamelCase , indent=2 )
| 354
| 1
|
'''simple docstring'''
import warnings
from transformers import AutoTokenizer
from transformers.utils import is_torch_available
from transformers.utils.generic import ExplicitEnum
from ...processing_utils import ProcessorMixin
if is_torch_available():
import torch
class __a ( _snake_case ):
__UpperCamelCase : Tuple = 'char'
__UpperCamelCase : Optional[Any] = 'bpe'
__UpperCamelCase : Tuple = 'wp'
a = (DecodeType.CHARACTER, DecodeType.BPE, DecodeType.WORDPIECE)
class __a ( _snake_case ):
__UpperCamelCase : int = ['image_processor', 'char_tokenizer']
__UpperCamelCase : int = 'ViTImageProcessor'
__UpperCamelCase : Optional[Any] = 'MgpstrTokenizer'
def __init__( self : Union[str, Any] ,lowerCamelCase : Union[str, Any]=None ,lowerCamelCase : int=None ,**lowerCamelCase : Optional[Any] ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE = None
if "feature_extractor" in kwargs:
warnings.warn(
"""The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
""" instead.""" ,lowerCamelCase ,)
__SCREAMING_SNAKE_CASE = kwargs.pop("""feature_extractor""" )
__SCREAMING_SNAKE_CASE = image_processor if image_processor is not None else feature_extractor
if image_processor is None:
raise ValueError("""You need to specify an `image_processor`.""" )
if tokenizer is None:
raise ValueError("""You need to specify a `tokenizer`.""" )
__SCREAMING_SNAKE_CASE = tokenizer
__SCREAMING_SNAKE_CASE = AutoTokenizer.from_pretrained("""gpt2""" )
__SCREAMING_SNAKE_CASE = AutoTokenizer.from_pretrained("""bert-base-uncased""" )
super().__init__(lowerCamelCase ,lowerCamelCase )
def __call__( self : int ,lowerCamelCase : Optional[Any]=None ,lowerCamelCase : int=None ,lowerCamelCase : Any=None ,**lowerCamelCase : Any ):
'''simple docstring'''
if images is None and text is None:
raise ValueError("""You need to specify either an `images` or `text` input to process.""" )
if images is not None:
__SCREAMING_SNAKE_CASE = self.image_processor(lowerCamelCase ,return_tensors=lowerCamelCase ,**lowerCamelCase )
if text is not None:
__SCREAMING_SNAKE_CASE = self.char_tokenizer(lowerCamelCase ,return_tensors=lowerCamelCase ,**lowerCamelCase )
if text is None:
return inputs
elif images is None:
return encodings
else:
__SCREAMING_SNAKE_CASE = encodings["""input_ids"""]
return inputs
def UpperCAmelCase__ ( self : Union[str, Any] ,lowerCamelCase : List[Any] ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = sequences
__SCREAMING_SNAKE_CASE = char_preds.size(0 )
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = self._decode_helper(lowerCamelCase ,"""char""" )
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = self._decode_helper(lowerCamelCase ,"""bpe""" )
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = self._decode_helper(lowerCamelCase ,"""wp""" )
__SCREAMING_SNAKE_CASE = []
__SCREAMING_SNAKE_CASE = []
for i in range(lowerCamelCase ):
__SCREAMING_SNAKE_CASE = [char_scores[i], bpe_scores[i], wp_scores[i]]
__SCREAMING_SNAKE_CASE = [char_strs[i], bpe_strs[i], wp_strs[i]]
__SCREAMING_SNAKE_CASE = scores.index(max(lowerCamelCase ) )
final_strs.append(strs[max_score_index] )
final_scores.append(scores[max_score_index] )
__SCREAMING_SNAKE_CASE = {}
__SCREAMING_SNAKE_CASE = final_strs
__SCREAMING_SNAKE_CASE = final_scores
__SCREAMING_SNAKE_CASE = char_strs
__SCREAMING_SNAKE_CASE = bpe_strs
__SCREAMING_SNAKE_CASE = wp_strs
return out
def UpperCAmelCase__ ( self : Optional[Any] ,lowerCamelCase : List[Any] ,lowerCamelCase : str ):
'''simple docstring'''
if format == DecodeType.CHARACTER:
__SCREAMING_SNAKE_CASE = self.char_decode
__SCREAMING_SNAKE_CASE = 1
__SCREAMING_SNAKE_CASE = """[s]"""
elif format == DecodeType.BPE:
__SCREAMING_SNAKE_CASE = self.bpe_decode
__SCREAMING_SNAKE_CASE = 2
__SCREAMING_SNAKE_CASE = """#"""
elif format == DecodeType.WORDPIECE:
__SCREAMING_SNAKE_CASE = self.wp_decode
__SCREAMING_SNAKE_CASE = 102
__SCREAMING_SNAKE_CASE = """[SEP]"""
else:
raise ValueError(f"""Format {format} is not supported.""" )
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = [], []
__SCREAMING_SNAKE_CASE = pred_logits.size(0 )
__SCREAMING_SNAKE_CASE = pred_logits.size(1 )
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = pred_logits.topk(1 ,dim=-1 ,largest=lowerCamelCase ,sorted=lowerCamelCase )
__SCREAMING_SNAKE_CASE = preds_index.view(-1 ,lowerCamelCase )[:, 1:]
__SCREAMING_SNAKE_CASE = decoder(lowerCamelCase )
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = torch.nn.functional.softmax(lowerCamelCase ,dim=2 ).max(dim=2 )
__SCREAMING_SNAKE_CASE = preds_max_prob[:, 1:]
for index in range(lowerCamelCase ):
__SCREAMING_SNAKE_CASE = preds_str[index].find(lowerCamelCase )
__SCREAMING_SNAKE_CASE = preds_str[index][:pred_eos]
__SCREAMING_SNAKE_CASE = preds_index[index].cpu().tolist()
__SCREAMING_SNAKE_CASE = pred_index.index(lowerCamelCase ) if eos_token in pred_index else -1
__SCREAMING_SNAKE_CASE = preds_max_prob[index][: pred_eos_index + 1]
__SCREAMING_SNAKE_CASE = pred_max_prob.cumprod(dim=0 )[-1] if pred_max_prob.nelement() != 0 else 0.0
dec_strs.append(lowerCamelCase )
conf_scores.append(lowerCamelCase )
return dec_strs, conf_scores
def UpperCAmelCase__ ( self : str ,lowerCamelCase : Tuple ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE = [seq.replace(""" """ ,"""""" ) for seq in self.char_tokenizer.batch_decode(lowerCamelCase )]
return decode_strs
def UpperCAmelCase__ ( self : List[Any] ,lowerCamelCase : List[str] ):
'''simple docstring'''
return self.bpe_tokenizer.batch_decode(lowerCamelCase )
def UpperCAmelCase__ ( self : Tuple ,lowerCamelCase : str ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE = [seq.replace(""" """ ,"""""" ) for seq in self.wp_tokenizer.batch_decode(lowerCamelCase )]
return decode_strs
| 13
|
'''simple docstring'''
import os
import string
import sys
a = 1 << 8
a = {
"tab": ord("\t"),
"newline": ord("\r"),
"esc": 27,
"up": 65 + ARROW_KEY_FLAG,
"down": 66 + ARROW_KEY_FLAG,
"right": 67 + ARROW_KEY_FLAG,
"left": 68 + ARROW_KEY_FLAG,
"mod_int": 91,
"undefined": sys.maxsize,
"interrupt": 3,
"insert": 50,
"delete": 51,
"pg_up": 53,
"pg_down": 54,
}
a = KEYMAP["up"]
a = KEYMAP["left"]
if sys.platform == "win32":
a = []
a = {
b"\xe0H": KEYMAP["up"] - ARROW_KEY_FLAG,
b"\x00H": KEYMAP["up"] - ARROW_KEY_FLAG,
b"\xe0P": KEYMAP["down"] - ARROW_KEY_FLAG,
b"\x00P": KEYMAP["down"] - ARROW_KEY_FLAG,
b"\xe0M": KEYMAP["right"] - ARROW_KEY_FLAG,
b"\x00M": KEYMAP["right"] - ARROW_KEY_FLAG,
b"\xe0K": KEYMAP["left"] - ARROW_KEY_FLAG,
b"\x00K": KEYMAP["left"] - ARROW_KEY_FLAG,
}
for i in range(10):
a = ord(str(i))
def __magic_name__ ( ) -> Union[str, Any]:
'''simple docstring'''
if os.name == "nt":
import msvcrt
__SCREAMING_SNAKE_CASE = """mbcs"""
# Flush the keyboard buffer
while msvcrt.kbhit():
msvcrt.getch()
if len(__UpperCAmelCase ) == 0:
# Read the keystroke
__SCREAMING_SNAKE_CASE = msvcrt.getch()
# If it is a prefix char, get second part
if ch in (b"\x00", b"\xe0"):
__SCREAMING_SNAKE_CASE = ch + msvcrt.getch()
# Translate actual Win chars to bullet char types
try:
__SCREAMING_SNAKE_CASE = chr(WIN_KEYMAP[cha] )
WIN_CH_BUFFER.append(chr(KEYMAP["""mod_int"""] ) )
WIN_CH_BUFFER.append(__UpperCAmelCase )
if ord(__UpperCAmelCase ) in (
KEYMAP["insert"] - 1 << 9,
KEYMAP["delete"] - 1 << 9,
KEYMAP["pg_up"] - 1 << 9,
KEYMAP["pg_down"] - 1 << 9,
):
WIN_CH_BUFFER.append(chr(126 ) )
__SCREAMING_SNAKE_CASE = chr(KEYMAP["""esc"""] )
except KeyError:
__SCREAMING_SNAKE_CASE = cha[1]
else:
__SCREAMING_SNAKE_CASE = ch.decode(__UpperCAmelCase )
else:
__SCREAMING_SNAKE_CASE = WIN_CH_BUFFER.pop(0 )
elif os.name == "posix":
import termios
import tty
__SCREAMING_SNAKE_CASE = sys.stdin.fileno()
__SCREAMING_SNAKE_CASE = termios.tcgetattr(__UpperCAmelCase )
try:
tty.setraw(__UpperCAmelCase )
__SCREAMING_SNAKE_CASE = sys.stdin.read(1 )
finally:
termios.tcsetattr(__UpperCAmelCase , termios.TCSADRAIN , __UpperCAmelCase )
return ch
def __magic_name__ ( ) -> List[str]:
'''simple docstring'''
__SCREAMING_SNAKE_CASE = get_raw_chars()
if ord(__UpperCAmelCase ) in [KEYMAP["interrupt"], KEYMAP["newline"]]:
return char
elif ord(__UpperCAmelCase ) == KEYMAP["esc"]:
__SCREAMING_SNAKE_CASE = get_raw_chars()
if ord(__UpperCAmelCase ) == KEYMAP["mod_int"]:
__SCREAMING_SNAKE_CASE = get_raw_chars()
if ord(__UpperCAmelCase ) >= KEYMAP["arrow_begin"] - ARROW_KEY_FLAG and ord(__UpperCAmelCase ) <= KEYMAP["arrow_end"] - ARROW_KEY_FLAG:
return chr(ord(__UpperCAmelCase ) + ARROW_KEY_FLAG )
else:
return KEYMAP["undefined"]
else:
return get_raw_chars()
else:
if char in string.printable:
return char
else:
return KEYMAP["undefined"]
| 13
| 1
|
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__SCREAMING_SNAKE_CASE : Tuple = logging.get_logger(__name__)
__SCREAMING_SNAKE_CASE : Union[str, Any] = {
'''transfo-xl-wt103''': '''https://huggingface.co/transfo-xl-wt103/resolve/main/config.json''',
}
class __lowerCAmelCase ( lowercase ):
"""simple docstring"""
_UpperCAmelCase : Optional[Any] ="transfo-xl"
_UpperCAmelCase : str =["mems"]
_UpperCAmelCase : Optional[int] ={
"n_token": "vocab_size",
"hidden_size": "d_model",
"num_attention_heads": "n_head",
"num_hidden_layers": "n_layer",
}
def __init__( self : str , lowerCAmelCase : Tuple=26_77_35 , lowerCAmelCase : List[Any]=[2_00_00, 4_00_00, 20_00_00] , lowerCAmelCase : Dict=10_24 , lowerCAmelCase : List[str]=10_24 , lowerCAmelCase : int=16 , lowerCAmelCase : Optional[Any]=64 , lowerCAmelCase : str=40_96 , lowerCAmelCase : Optional[int]=4 , lowerCAmelCase : Any=False , lowerCAmelCase : List[str]=18 , lowerCAmelCase : Any=16_00 , lowerCAmelCase : List[str]=10_00 , lowerCAmelCase : int=True , lowerCAmelCase : List[str]=True , lowerCAmelCase : List[str]=0 , lowerCAmelCase : Tuple=-1 , lowerCAmelCase : Optional[int]=True , lowerCAmelCase : List[str]=0.1 , lowerCAmelCase : int=0.0 , lowerCAmelCase : Tuple=True , lowerCAmelCase : List[str]="normal" , lowerCAmelCase : Tuple=0.0_1 , lowerCAmelCase : Optional[int]=0.0_1 , lowerCAmelCase : List[Any]=0.0_2 , lowerCAmelCase : Dict=1e-5 , lowerCAmelCase : Optional[int]=0 , **lowerCAmelCase : List[str] , ):
A_ = vocab_size
A_ = []
self.cutoffs.extend(lowerCAmelCase )
if proj_share_all_but_first:
A_ = [False] + [True] * len(self.cutoffs )
else:
A_ = [False] + [False] * len(self.cutoffs )
A_ = d_model
A_ = d_embed
A_ = d_head
A_ = d_inner
A_ = div_val
A_ = pre_lnorm
A_ = n_layer
A_ = n_head
A_ = mem_len
A_ = same_length
A_ = attn_type
A_ = clamp_len
A_ = sample_softmax
A_ = adaptive
A_ = dropout
A_ = dropatt
A_ = untie_r
A_ = init
A_ = init_range
A_ = proj_init_std
A_ = init_std
A_ = layer_norm_epsilon
super().__init__(eos_token_id=lowerCAmelCase , **lowerCAmelCase )
@property
def _UpperCAmelCase ( self : str ):
# Message copied from Transformer-XL documentation
logger.info(F"The model {self.model_type} is one of the few models that has no sequence length limit." )
return -1
@max_position_embeddings.setter
def _UpperCAmelCase ( self : Tuple , lowerCAmelCase : Dict ):
# Message copied from Transformer-XL documentation
raise NotImplementedError(
F"The model {self.model_type} is one of the few models that has no sequence length limit." )
| 452
|
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__SCREAMING_SNAKE_CASE : Tuple = logging.get_logger(__name__)
__SCREAMING_SNAKE_CASE : Union[str, Any] = {
'''transfo-xl-wt103''': '''https://huggingface.co/transfo-xl-wt103/resolve/main/config.json''',
}
class __lowerCAmelCase ( lowercase ):
"""simple docstring"""
_UpperCAmelCase : Optional[Any] ="transfo-xl"
_UpperCAmelCase : str =["mems"]
_UpperCAmelCase : Optional[int] ={
"n_token": "vocab_size",
"hidden_size": "d_model",
"num_attention_heads": "n_head",
"num_hidden_layers": "n_layer",
}
def __init__( self : str , lowerCAmelCase : Tuple=26_77_35 , lowerCAmelCase : List[Any]=[2_00_00, 4_00_00, 20_00_00] , lowerCAmelCase : Dict=10_24 , lowerCAmelCase : List[str]=10_24 , lowerCAmelCase : int=16 , lowerCAmelCase : Optional[Any]=64 , lowerCAmelCase : str=40_96 , lowerCAmelCase : Optional[int]=4 , lowerCAmelCase : Any=False , lowerCAmelCase : List[str]=18 , lowerCAmelCase : Any=16_00 , lowerCAmelCase : List[str]=10_00 , lowerCAmelCase : int=True , lowerCAmelCase : List[str]=True , lowerCAmelCase : List[str]=0 , lowerCAmelCase : Tuple=-1 , lowerCAmelCase : Optional[int]=True , lowerCAmelCase : List[str]=0.1 , lowerCAmelCase : int=0.0 , lowerCAmelCase : Tuple=True , lowerCAmelCase : List[str]="normal" , lowerCAmelCase : Tuple=0.0_1 , lowerCAmelCase : Optional[int]=0.0_1 , lowerCAmelCase : List[Any]=0.0_2 , lowerCAmelCase : Dict=1e-5 , lowerCAmelCase : Optional[int]=0 , **lowerCAmelCase : List[str] , ):
A_ = vocab_size
A_ = []
self.cutoffs.extend(lowerCAmelCase )
if proj_share_all_but_first:
A_ = [False] + [True] * len(self.cutoffs )
else:
A_ = [False] + [False] * len(self.cutoffs )
A_ = d_model
A_ = d_embed
A_ = d_head
A_ = d_inner
A_ = div_val
A_ = pre_lnorm
A_ = n_layer
A_ = n_head
A_ = mem_len
A_ = same_length
A_ = attn_type
A_ = clamp_len
A_ = sample_softmax
A_ = adaptive
A_ = dropout
A_ = dropatt
A_ = untie_r
A_ = init
A_ = init_range
A_ = proj_init_std
A_ = init_std
A_ = layer_norm_epsilon
super().__init__(eos_token_id=lowerCAmelCase , **lowerCAmelCase )
@property
def _UpperCAmelCase ( self : str ):
# Message copied from Transformer-XL documentation
logger.info(F"The model {self.model_type} is one of the few models that has no sequence length limit." )
return -1
@max_position_embeddings.setter
def _UpperCAmelCase ( self : Tuple , lowerCAmelCase : Dict ):
# Message copied from Transformer-XL documentation
raise NotImplementedError(
F"The model {self.model_type} is one of the few models that has no sequence length limit." )
| 452
| 1
|
'''simple docstring'''
def _a ( __lowerCAmelCase : int , __lowerCAmelCase : int ):
"""simple docstring"""
return abs(__lowerCAmelCase ) if a == 0 else greatest_common_divisor(b % a , __lowerCAmelCase )
def _a ( __lowerCAmelCase : int , __lowerCAmelCase : int ):
"""simple docstring"""
while y: # --> when y=0 then loop will terminate and return x as final GCD.
snake_case__ , snake_case__ : str = y, x % y
return abs(__lowerCAmelCase )
def _a ( ):
"""simple docstring"""
try:
snake_case__ : Optional[int] = input('''Enter two integers separated by comma (,): ''' ).split(''',''' )
snake_case__ : List[Any] = int(nums[0] )
snake_case__ : List[str] = int(nums[1] )
print(
F"""greatest_common_divisor({num_a}, {num_a}) = """
F"""{greatest_common_divisor(__lowerCAmelCase , __lowerCAmelCase )}""" )
print(F"""By iterative gcd({num_a}, {num_a}) = {gcd_by_iterative(__lowerCAmelCase , __lowerCAmelCase )}""" )
except (IndexError, UnboundLocalError, ValueError):
print('''Wrong input''' )
if __name__ == "__main__":
main()
| 502
|
'''simple docstring'''
from argparse import ArgumentParser, Namespace
from typing import Any, List, Optional
from ..pipelines import Pipeline, get_supported_tasks, pipeline
from ..utils import logging
from . import BaseTransformersCLICommand
try:
from fastapi import Body, FastAPI, HTTPException
from fastapi.routing import APIRoute
from pydantic import BaseModel
from starlette.responses import JSONResponse
from uvicorn import run
lowerCAmelCase__ : Any = True
except (ImportError, AttributeError):
lowerCAmelCase__ : Dict = object
def _a ( *__lowerCAmelCase : List[str] , **__lowerCAmelCase : Tuple ):
"""simple docstring"""
pass
lowerCAmelCase__ : str = False
lowerCAmelCase__ : List[str] = logging.get_logger("""transformers-cli/serving""")
def _a ( __lowerCAmelCase : Namespace ):
"""simple docstring"""
snake_case__ : Union[str, Any] = pipeline(
task=args.task , model=args.model if args.model else None , config=args.config , tokenizer=args.tokenizer , device=args.device , )
return ServeCommand(__lowerCAmelCase , args.host , args.port , args.workers )
class a ( SCREAMING_SNAKE_CASE ):
"""simple docstring"""
__UpperCAmelCase = 42
class a ( SCREAMING_SNAKE_CASE ):
"""simple docstring"""
__UpperCAmelCase = 42
__UpperCAmelCase = 42
class a ( SCREAMING_SNAKE_CASE ):
"""simple docstring"""
__UpperCAmelCase = 42
class a ( SCREAMING_SNAKE_CASE ):
"""simple docstring"""
__UpperCAmelCase = 42
class a ( SCREAMING_SNAKE_CASE ):
"""simple docstring"""
@staticmethod
def __magic_name__ ( snake_case_ : ArgumentParser ):
'''simple docstring'''
snake_case__ : Optional[Any] = parser.add_parser(
'''serve''' , help='''CLI tool to run inference requests through REST and GraphQL endpoints.''' )
serve_parser.add_argument(
'''--task''' , type=snake_case_ , choices=get_supported_tasks() , help='''The task to run the pipeline on''' , )
serve_parser.add_argument('''--host''' , type=snake_case_ , default='''localhost''' , help='''Interface the server will listen on.''' )
serve_parser.add_argument('''--port''' , type=snake_case_ , default=8_8_8_8 , help='''Port the serving will listen to.''' )
serve_parser.add_argument('''--workers''' , type=snake_case_ , default=1 , help='''Number of http workers''' )
serve_parser.add_argument('''--model''' , type=snake_case_ , help='''Model\'s name or path to stored model.''' )
serve_parser.add_argument('''--config''' , type=snake_case_ , help='''Model\'s config name or path to stored model.''' )
serve_parser.add_argument('''--tokenizer''' , type=snake_case_ , help='''Tokenizer name to use.''' )
serve_parser.add_argument(
'''--device''' , type=snake_case_ , default=-1 , help='''Indicate the device to run onto, -1 indicates CPU, >= 0 indicates GPU (default: -1)''' , )
serve_parser.set_defaults(func=snake_case_ )
def __init__( self : Union[str, Any] , snake_case_ : Pipeline , snake_case_ : str , snake_case_ : int , snake_case_ : int ):
'''simple docstring'''
snake_case__ : Any = pipeline
snake_case__ : Tuple = host
snake_case__ : Optional[Any] = port
snake_case__ : Tuple = workers
if not _serve_dependencies_installed:
raise RuntimeError(
'''Using serve command requires FastAPI and uvicorn. '''
'''Please install transformers with [serving]: pip install "transformers[serving]".'''
'''Or install FastAPI and uvicorn separately.''' )
else:
logger.info(F"""Serving model over {host}:{port}""" )
snake_case__ : str = FastAPI(
routes=[
APIRoute(
'''/''' , self.model_info , response_model=snake_case_ , response_class=snake_case_ , methods=['''GET'''] , ),
APIRoute(
'''/tokenize''' , self.tokenize , response_model=snake_case_ , response_class=snake_case_ , methods=['''POST'''] , ),
APIRoute(
'''/detokenize''' , self.detokenize , response_model=snake_case_ , response_class=snake_case_ , methods=['''POST'''] , ),
APIRoute(
'''/forward''' , self.forward , response_model=snake_case_ , response_class=snake_case_ , methods=['''POST'''] , ),
] , timeout=6_0_0 , )
def __magic_name__ ( self : str ):
'''simple docstring'''
run(self._app , host=self.host , port=self.port , workers=self.workers )
def __magic_name__ ( self : Optional[Any] ):
'''simple docstring'''
return ServeModelInfoResult(infos=vars(self._pipeline.model.config ) )
def __magic_name__ ( self : List[str] , snake_case_ : str = Body(snake_case_ , embed=snake_case_ ) , snake_case_ : bool = Body(snake_case_ , embed=snake_case_ ) ):
'''simple docstring'''
try:
snake_case__ : Optional[Any] = self._pipeline.tokenizer.tokenize(snake_case_ )
if return_ids:
snake_case__ : Optional[int] = self._pipeline.tokenizer.convert_tokens_to_ids(snake_case_ )
return ServeTokenizeResult(tokens=snake_case_ , tokens_ids=snake_case_ )
else:
return ServeTokenizeResult(tokens=snake_case_ )
except Exception as e:
raise HTTPException(status_code=5_0_0 , detail={'''model''': '''''', '''error''': str(snake_case_ )} )
def __magic_name__ ( self : List[Any] , snake_case_ : List[int] = Body(snake_case_ , embed=snake_case_ ) , snake_case_ : bool = Body(snake_case_ , embed=snake_case_ ) , snake_case_ : bool = Body(snake_case_ , embed=snake_case_ ) , ):
'''simple docstring'''
try:
snake_case__ : Optional[int] = self._pipeline.tokenizer.decode(snake_case_ , snake_case_ , snake_case_ )
return ServeDeTokenizeResult(model='''''' , text=snake_case_ )
except Exception as e:
raise HTTPException(status_code=5_0_0 , detail={'''model''': '''''', '''error''': str(snake_case_ )} )
async def __magic_name__ ( self : Tuple , snake_case_ : List[str]=Body(snake_case_ , embed=snake_case_ ) ):
'''simple docstring'''
if len(snake_case_ ) == 0:
return ServeForwardResult(output=[] , attention=[] )
try:
# Forward through the model
snake_case__ : Tuple = self._pipeline(snake_case_ )
return ServeForwardResult(output=snake_case_ )
except Exception as e:
raise HTTPException(5_0_0 , {'''error''': str(snake_case_ )} )
| 502
| 1
|
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
import diffusers
from diffusers import (
AutoencoderKL,
EulerDiscreteScheduler,
StableDiffusionLatentUpscalePipeline,
StableDiffusionPipeline,
UNetaDConditionModel,
)
from diffusers.schedulers import KarrasDiffusionSchedulers
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 ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin
enable_full_determinism()
def __magic_name__ ( lowerCAmelCase_):
'''simple docstring'''
lowerCamelCase_ : List[Any] = [tensor.shape for tensor in tensor_list]
return all(shape == shapes[0] for shape in shapes[1:])
class lowerCAmelCase__ ( a_, a_, a_, unittest.TestCase ):
"""simple docstring"""
__UpperCAmelCase : Tuple = StableDiffusionLatentUpscalePipeline
__UpperCAmelCase : List[Any] = TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {
"height",
"width",
"cross_attention_kwargs",
"negative_prompt_embeds",
"prompt_embeds",
}
__UpperCAmelCase : List[Any] = PipelineTesterMixin.required_optional_params - {"num_images_per_prompt"}
__UpperCAmelCase : List[str] = TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
__UpperCAmelCase : str = frozenset(
[] ) # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
__UpperCAmelCase : Union[str, Any] = frozenset([] )
__UpperCAmelCase : int = True
@property
def _UpperCamelCase ( self ):
lowerCamelCase_ : Dict = 1
lowerCamelCase_ : str = 4
lowerCamelCase_ : List[Any] = (16, 16)
lowerCamelCase_ : List[Any] = floats_tensor((batch_size, num_channels) + sizes , rng=random.Random(0 ) ).to(a_ )
return image
def _UpperCamelCase ( self ):
torch.manual_seed(0 )
lowerCamelCase_ : Optional[int] = UNetaDConditionModel(
act_fn="gelu" , attention_head_dim=8 , norm_num_groups=a_ , block_out_channels=[32, 32, 64, 64] , time_cond_proj_dim=160 , conv_in_kernel=1 , conv_out_kernel=1 , cross_attention_dim=32 , down_block_types=(
"KDownBlock2D",
"KCrossAttnDownBlock2D",
"KCrossAttnDownBlock2D",
"KCrossAttnDownBlock2D",
) , in_channels=8 , mid_block_type=a_ , only_cross_attention=a_ , out_channels=5 , resnet_time_scale_shift="scale_shift" , time_embedding_type="fourier" , timestep_post_act="gelu" , up_block_types=("KCrossAttnUpBlock2D", "KCrossAttnUpBlock2D", "KCrossAttnUpBlock2D", "KUpBlock2D") , )
lowerCamelCase_ : Union[str, Any] = AutoencoderKL(
block_out_channels=[32, 32, 64, 64] , in_channels=3 , out_channels=3 , down_block_types=[
"DownEncoderBlock2D",
"DownEncoderBlock2D",
"DownEncoderBlock2D",
"DownEncoderBlock2D",
] , up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D"] , latent_channels=4 , )
lowerCamelCase_ : Any = EulerDiscreteScheduler(prediction_type="sample" )
lowerCamelCase_ : List[Any] = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=32 , intermediate_size=37 , layer_norm_eps=1E-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1000 , hidden_act="quick_gelu" , projection_dim=512 , )
lowerCamelCase_ : List[Any] = CLIPTextModel(a_ )
lowerCamelCase_ : Dict = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip" )
lowerCamelCase_ : Union[str, Any] = {
"""unet""": model.eval(),
"""vae""": vae.eval(),
"""scheduler""": scheduler,
"""text_encoder""": text_encoder,
"""tokenizer""": tokenizer,
}
return components
def _UpperCamelCase ( self , a_ , a_=0 ):
if str(a_ ).startswith("mps" ):
lowerCamelCase_ : str = torch.manual_seed(a_ )
else:
lowerCamelCase_ : List[Any] = torch.Generator(device=a_ ).manual_seed(a_ )
lowerCamelCase_ : int = {
"""prompt""": """A painting of a squirrel eating a burger""",
"""image""": self.dummy_image.cpu(),
"""generator""": generator,
"""num_inference_steps""": 2,
"""output_type""": """numpy""",
}
return inputs
def _UpperCamelCase ( self ):
lowerCamelCase_ : str = """cpu"""
lowerCamelCase_ : List[str] = self.get_dummy_components()
lowerCamelCase_ : Any = self.pipeline_class(**a_ )
pipe.to(a_ )
pipe.set_progress_bar_config(disable=a_ )
lowerCamelCase_ : str = self.get_dummy_inputs(a_ )
lowerCamelCase_ : Any = pipe(**a_ ).images
lowerCamelCase_ : Dict = image[0, -3:, -3:, -1]
self.assertEqual(image.shape , (1, 256, 256, 3) )
lowerCamelCase_ : str = np.array(
[0.47_22_24_12, 0.41_92_16_33, 0.44_71_74_34, 0.46_87_41_92, 0.42_58_82_58, 0.46_15_07_26, 0.4_67_75_34, 0.45_58_38_32, 0.48_57_90_55] )
lowerCamelCase_ : List[Any] = np.abs(image_slice.flatten() - expected_slice ).max()
self.assertLessEqual(a_ , 1E-3 )
def _UpperCamelCase ( self ):
super().test_attention_slicing_forward_pass(expected_max_diff=7E-3 )
def _UpperCamelCase ( self ):
super().test_cpu_offload_forward_pass(expected_max_diff=3E-3 )
def _UpperCamelCase ( self ):
super().test_dict_tuple_outputs_equivalent(expected_max_difference=3E-3 )
def _UpperCamelCase ( self ):
super().test_inference_batch_single_identical(expected_max_diff=7E-3 )
def _UpperCamelCase ( self ):
super().test_pt_np_pil_outputs_equivalent(expected_max_diff=3E-3 )
def _UpperCamelCase ( self ):
super().test_save_load_local(expected_max_difference=3E-3 )
def _UpperCamelCase ( self ):
super().test_save_load_optional_components(expected_max_difference=3E-3 )
def _UpperCamelCase ( self ):
lowerCamelCase_ : Dict = [
"""DDIMScheduler""",
"""DDPMScheduler""",
"""PNDMScheduler""",
"""HeunDiscreteScheduler""",
"""EulerAncestralDiscreteScheduler""",
"""KDPM2DiscreteScheduler""",
"""KDPM2AncestralDiscreteScheduler""",
"""DPMSolverSDEScheduler""",
]
lowerCamelCase_ : Union[str, Any] = self.get_dummy_components()
lowerCamelCase_ : int = self.pipeline_class(**a_ )
# make sure that PNDM does not need warm-up
pipe.scheduler.register_to_config(skip_prk_steps=a_ )
pipe.to(a_ )
pipe.set_progress_bar_config(disable=a_ )
lowerCamelCase_ : List[str] = self.get_dummy_inputs(a_ )
lowerCamelCase_ : Optional[int] = 2
lowerCamelCase_ : Any = []
for scheduler_enum in KarrasDiffusionSchedulers:
if scheduler_enum.name in skip_schedulers:
# no sigma schedulers are not supported
# no schedulers
continue
lowerCamelCase_ : str = getattr(a_ , scheduler_enum.name )
lowerCamelCase_ : Dict = scheduler_cls.from_config(pipe.scheduler.config )
lowerCamelCase_ : int = pipe(**a_ )[0]
outputs.append(a_ )
assert check_same_shape(a_ )
@require_torch_gpu
@slow
class lowerCAmelCase__ ( unittest.TestCase ):
"""simple docstring"""
def _UpperCamelCase ( self ):
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def _UpperCamelCase ( self ):
lowerCamelCase_ : Optional[int] = torch.manual_seed(33 )
lowerCamelCase_ : List[Any] = StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4" , torch_dtype=torch.floataa )
pipe.to("cuda" )
lowerCamelCase_ : Tuple = StableDiffusionLatentUpscalePipeline.from_pretrained(
"stabilityai/sd-x2-latent-upscaler" , torch_dtype=torch.floataa )
upscaler.to("cuda" )
lowerCamelCase_ : str = """a photo of an astronaut high resolution, unreal engine, ultra realistic"""
lowerCamelCase_ : Optional[Any] = pipe(a_ , generator=a_ , output_type="latent" ).images
lowerCamelCase_ : str = upscaler(
prompt=a_ , image=a_ , num_inference_steps=20 , guidance_scale=0 , generator=a_ , output_type="np" , ).images[0]
lowerCamelCase_ : Dict = load_numpy(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/astronaut_1024.npy" )
assert np.abs((expected_image - image).mean() ) < 5E-2
def _UpperCamelCase ( self ):
lowerCamelCase_ : List[str] = torch.manual_seed(33 )
lowerCamelCase_ : Dict = StableDiffusionLatentUpscalePipeline.from_pretrained(
"stabilityai/sd-x2-latent-upscaler" , torch_dtype=torch.floataa )
upscaler.to("cuda" )
lowerCamelCase_ : Dict = """the temple of fire by Ross Tran and Gerardo Dottori, oil on canvas"""
lowerCamelCase_ : Optional[Any] = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/fire_temple_512.png" )
lowerCamelCase_ : Dict = upscaler(
prompt=a_ , image=a_ , num_inference_steps=20 , guidance_scale=0 , generator=a_ , output_type="np" , ).images[0]
lowerCamelCase_ : Any = load_numpy(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/fire_temple_1024.npy" )
assert np.abs((expected_image - image).max() ) < 5E-2
| 250
|
"""simple docstring"""
from typing import Optional
import pyspark
from .. import Features, NamedSplit
from ..download import DownloadMode
from ..packaged_modules.spark.spark import Spark
from .abc import AbstractDatasetReader
class _lowerCamelCase ( a_ ):
def __init__( self : Optional[Any] , UpperCamelCase : pyspark.sql.DataFrame , UpperCamelCase : Optional[NamedSplit] = None , UpperCamelCase : Optional[Features] = None , UpperCamelCase : bool = True , UpperCamelCase : str = None , UpperCamelCase : bool = False , UpperCamelCase : str = None , UpperCamelCase : bool = True , UpperCamelCase : str = "arrow" , **UpperCamelCase : Optional[int] , ) -> List[str]:
"""simple docstring"""
super().__init__(
split=UpperCamelCase , features=UpperCamelCase , cache_dir=UpperCamelCase , keep_in_memory=UpperCamelCase , streaming=UpperCamelCase , **UpperCamelCase , )
lowerCAmelCase__ : Union[str, Any] = load_from_cache_file
lowerCAmelCase__ : List[str] = file_format
lowerCAmelCase__ : Any = Spark(
df=UpperCamelCase , features=UpperCamelCase , cache_dir=UpperCamelCase , working_dir=UpperCamelCase , **UpperCamelCase , )
def _lowerCAmelCase ( self : int ) -> int:
"""simple docstring"""
if self.streaming:
return self.builder.as_streaming_dataset(split=self.split )
lowerCAmelCase__ : List[str] = None if self._load_from_cache_file else DownloadMode.FORCE_REDOWNLOAD
self.builder.download_and_prepare(
download_mode=UpperCamelCase , file_format=self._file_format , )
return self.builder.as_dataset(split=self.split )
| 299
| 0
|
'''simple docstring'''
import argparse
import os
import subprocess
from packaging.version import Version, parse
from accelerate.commands.config.config_args import default_config_file, load_config_from_file
__A : Dict = 'Run commands across TPU VMs for initial setup before running `accelerate launch`.'
def lowerCAmelCase_ ( a : Any=None ):
if subparsers is not None:
a__ = subparsers.add_parser('tpu-config' , description=_description )
else:
a__ = argparse.ArgumentParser('Accelerate tpu-config command' , description=_description )
# Core arguments
a__ = parser.add_argument_group(
'Config Arguments' , 'Arguments that can be configured through `accelerate config`.' )
config_args.add_argument(
'--config_file' , type=a , default=a , help='Path to the config file to use for accelerate.' , )
config_args.add_argument(
'--tpu_name' , default=a , help='The name of the TPU to use. If not specified, will use the TPU specified in the config file.' , )
config_args.add_argument(
'--tpu_zone' , default=a , help='The zone of the TPU to use. If not specified, will use the zone specified in the config file.' , )
a__ = parser.add_argument_group('TPU Arguments' , 'Arguments for options ran inside the TPU.' )
pod_args.add_argument(
'--use_alpha' , action='store_true' , help='Whether to use `gcloud alpha` when running the TPU training script instead of `gcloud`.' , )
pod_args.add_argument(
'--command_file' , default=a , help='The path to the file containing the commands to run on the pod on startup.' , )
pod_args.add_argument(
'--command' , action='append' , nargs='+' , help='A command to run on the pod. Can be passed multiple times.' , )
pod_args.add_argument(
'--install_accelerate' , action='store_true' , help='Whether to install accelerate on the pod. Defaults to False.' , )
pod_args.add_argument(
'--accelerate_version' , default='latest' , help='The version of accelerate to install on the pod. If not specified, will use the latest pypi version. Specify \'dev\' to install from GitHub.' , )
pod_args.add_argument(
'--debug' , action='store_true' , help='If set, will print the command that would be run instead of running it.' )
if subparsers is not None:
parser.set_defaults(func=a )
return parser
def lowerCAmelCase_ ( a : Any ):
a__ = None
# Get the default from the config file if it exists.
if args.config_file is not None or os.path.isfile(a ):
a__ = load_config_from_file(args.config_file )
if not args.command_file and defaults.command_file is not None and not args.command:
a__ = defaults.command_file
if not args.command and defaults.commands is not None:
a__ = defaults.commands
if not args.tpu_name:
a__ = defaults.tpu_name
if not args.tpu_zone:
a__ = defaults.tpu_zone
if args.accelerate_version == "dev":
a__ = 'git+https://github.com/huggingface/accelerate.git'
elif args.accelerate_version == "latest":
a__ = 'accelerate -U'
elif isinstance(parse(args.accelerate_version ) , a ):
a__ = f'''accelerate=={args.accelerate_version}'''
if not args.command_file and not args.command:
raise ValueError('You must specify either a command file or a command to run on the pod.' )
if args.command_file:
with open(args.command_file , 'r' ) as f:
a__ = [f.read().splitlines()]
# To turn list of lists into list of strings
if isinstance(args.command[0] , a ):
a__ = [line for cmd in args.command for line in cmd]
# Default to the shared folder and install accelerate
a__ = ['cd /usr/share']
if args.install_accelerate:
new_cmd += [f'''pip install {args.accelerate_version}''']
new_cmd += args.command
a__ = '; '.join(a )
# Then send it to gcloud
# Eventually try to use google-api-core to do this instead of subprocess
a__ = ['gcloud']
if args.use_alpha:
cmd += ["alpha"]
cmd += [
"compute",
"tpus",
"tpu-vm",
"ssh",
args.tpu_name,
"--zone",
args.tpu_zone,
"--command",
args.command,
"--worker",
"all",
]
if args.debug:
print(f'''Running {' '.join(a )}''' )
return
subprocess.run(a )
print('Successfully setup pod.' )
def lowerCAmelCase_ ( ):
a__ = tpu_command_parser()
a__ = parser.parse_args()
tpu_command_launcher(a )
| 703
|
'''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 DetrImageProcessor
class _UpperCamelCase ( unittest.TestCase ):
'''simple docstring'''
def __init__( self , _a , _a=7 , _a=3 , _a=30 , _a=400 , _a=True , _a=None , _a=True , _a=1 / 255 , _a=True , _a=[0.5, 0.5, 0.5] , _a=[0.5, 0.5, 0.5] , _a=True , ):
"""simple docstring"""
# by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
a__ = size if size is not None else {'shortest_edge': 18, 'longest_edge': 1333}
a__ = parent
a__ = batch_size
a__ = num_channels
a__ = min_resolution
a__ = max_resolution
a__ = do_resize
a__ = size
a__ = do_rescale
a__ = rescale_factor
a__ = do_normalize
a__ = image_mean
a__ = image_std
a__ = do_pad
def lowercase__ ( self ):
"""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 lowercase__ ( self , _a , _a=False ):
"""simple docstring"""
if not batched:
a__ = image_inputs[0]
if isinstance(_a , Image.Image ):
a__ , a__ = image.size
else:
a__ , a__ = image.shape[1], image.shape[2]
if w < h:
a__ = int(self.size['shortest_edge'] * h / w )
a__ = self.size['shortest_edge']
elif w > h:
a__ = self.size['shortest_edge']
a__ = int(self.size['shortest_edge'] * w / h )
else:
a__ = self.size['shortest_edge']
a__ = self.size['shortest_edge']
else:
a__ = []
for image in image_inputs:
a__ , a__ = self.get_expected_values([image] )
expected_values.append((expected_height, expected_width) )
a__ = max(_a , key=lambda _a : item[0] )[0]
a__ = max(_a , key=lambda _a : item[1] )[1]
return expected_height, expected_width
@require_torch
@require_vision
class _UpperCamelCase ( _A , unittest.TestCase ):
'''simple docstring'''
SCREAMING_SNAKE_CASE:str = DetrImageProcessor if is_vision_available() else None
def lowercase__ ( self ):
"""simple docstring"""
a__ = DetrImageProcessingTester(self )
@property
def lowercase__ ( self ):
"""simple docstring"""
return self.image_processor_tester.prepare_image_processor_dict()
def lowercase__ ( self ):
"""simple docstring"""
a__ = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(_a , 'image_mean' ) )
self.assertTrue(hasattr(_a , 'image_std' ) )
self.assertTrue(hasattr(_a , 'do_normalize' ) )
self.assertTrue(hasattr(_a , 'do_rescale' ) )
self.assertTrue(hasattr(_a , 'rescale_factor' ) )
self.assertTrue(hasattr(_a , 'do_resize' ) )
self.assertTrue(hasattr(_a , 'size' ) )
self.assertTrue(hasattr(_a , 'do_pad' ) )
def lowercase__ ( self ):
"""simple docstring"""
a__ = 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 , _a )
a__ = self.image_processing_class.from_dict(
self.image_processor_dict , size=42 , max_size=84 , pad_and_return_pixel_mask=_a )
self.assertEqual(image_processor.size , {'shortest_edge': 42, 'longest_edge': 84} )
self.assertEqual(image_processor.do_pad , _a )
def lowercase__ ( self ):
"""simple docstring"""
pass
def lowercase__ ( self ):
"""simple docstring"""
# Initialize image_processing
a__ = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
a__ = prepare_image_inputs(self.image_processor_tester , equal_resolution=_a )
for image in image_inputs:
self.assertIsInstance(_a , Image.Image )
# Test not batched input
a__ = image_processing(image_inputs[0] , return_tensors='pt' ).pixel_values
a__ , a__ = self.image_processor_tester.get_expected_values(_a )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
a__ , a__ = self.image_processor_tester.get_expected_values(_a , batched=_a )
a__ = image_processing(_a , 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 lowercase__ ( self ):
"""simple docstring"""
# Initialize image_processing
a__ = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
a__ = prepare_image_inputs(self.image_processor_tester , equal_resolution=_a , numpify=_a )
for image in image_inputs:
self.assertIsInstance(_a , np.ndarray )
# Test not batched input
a__ = image_processing(image_inputs[0] , return_tensors='pt' ).pixel_values
a__ , a__ = self.image_processor_tester.get_expected_values(_a )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
a__ = image_processing(_a , return_tensors='pt' ).pixel_values
a__ , a__ = self.image_processor_tester.get_expected_values(_a , batched=_a )
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
def lowercase__ ( self ):
"""simple docstring"""
# Initialize image_processing
a__ = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
a__ = prepare_image_inputs(self.image_processor_tester , equal_resolution=_a , torchify=_a )
for image in image_inputs:
self.assertIsInstance(_a , torch.Tensor )
# Test not batched input
a__ = image_processing(image_inputs[0] , return_tensors='pt' ).pixel_values
a__ , a__ = self.image_processor_tester.get_expected_values(_a )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
a__ = image_processing(_a , return_tensors='pt' ).pixel_values
a__ , a__ = self.image_processor_tester.get_expected_values(_a , batched=_a )
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
@slow
def lowercase__ ( self ):
"""simple docstring"""
# prepare image and target
a__ = Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
with open('./tests/fixtures/tests_samples/COCO/coco_annotations.txt' , 'r' ) as f:
a__ = json.loads(f.read() )
a__ = {'image_id': 3_9769, 'annotations': target}
# encode them
a__ = DetrImageProcessor.from_pretrained('facebook/detr-resnet-50' )
a__ = image_processing(images=_a , annotations=_a , return_tensors='pt' )
# verify pixel values
a__ = torch.Size([1, 3, 800, 1066] )
self.assertEqual(encoding['pixel_values'].shape , _a )
a__ = torch.tensor([0.2796, 0.3138, 0.3481] )
self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3] , _a , atol=1e-4 ) )
# verify area
a__ = torch.tensor([5887.9600, 1_1250.2061, 48_9353.8438, 83_7122.7500, 14_7967.5156, 16_5732.3438] )
self.assertTrue(torch.allclose(encoding['labels'][0]['area'] , _a ) )
# verify boxes
a__ = torch.Size([6, 4] )
self.assertEqual(encoding['labels'][0]['boxes'].shape , _a )
a__ = torch.tensor([0.5503, 0.2765, 0.0604, 0.2215] )
self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0] , _a , atol=1e-3 ) )
# verify image_id
a__ = torch.tensor([3_9769] )
self.assertTrue(torch.allclose(encoding['labels'][0]['image_id'] , _a ) )
# verify is_crowd
a__ = torch.tensor([0, 0, 0, 0, 0, 0] )
self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd'] , _a ) )
# verify class_labels
a__ = torch.tensor([75, 75, 63, 65, 17, 17] )
self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels'] , _a ) )
# verify orig_size
a__ = torch.tensor([480, 640] )
self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size'] , _a ) )
# verify size
a__ = torch.tensor([800, 1066] )
self.assertTrue(torch.allclose(encoding['labels'][0]['size'] , _a ) )
@slow
def lowercase__ ( self ):
"""simple docstring"""
# prepare image, target and masks_path
a__ = Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
with open('./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt' , 'r' ) as f:
a__ = json.loads(f.read() )
a__ = {'file_name': '000000039769.png', 'image_id': 3_9769, 'segments_info': target}
a__ = pathlib.Path('./tests/fixtures/tests_samples/COCO/coco_panoptic' )
# encode them
a__ = DetrImageProcessor.from_pretrained('facebook/detr-resnet-50-panoptic' )
a__ = image_processing(images=_a , annotations=_a , masks_path=_a , return_tensors='pt' )
# verify pixel values
a__ = torch.Size([1, 3, 800, 1066] )
self.assertEqual(encoding['pixel_values'].shape , _a )
a__ = torch.tensor([0.2796, 0.3138, 0.3481] )
self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3] , _a , atol=1e-4 ) )
# verify area
a__ = torch.tensor([14_7979.6875, 16_5527.0469, 48_4638.5938, 1_1292.9375, 5879.6562, 7634.1147] )
self.assertTrue(torch.allclose(encoding['labels'][0]['area'] , _a ) )
# verify boxes
a__ = torch.Size([6, 4] )
self.assertEqual(encoding['labels'][0]['boxes'].shape , _a )
a__ = torch.tensor([0.2625, 0.5437, 0.4688, 0.8625] )
self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0] , _a , atol=1e-3 ) )
# verify image_id
a__ = torch.tensor([3_9769] )
self.assertTrue(torch.allclose(encoding['labels'][0]['image_id'] , _a ) )
# verify is_crowd
a__ = torch.tensor([0, 0, 0, 0, 0, 0] )
self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd'] , _a ) )
# verify class_labels
a__ = torch.tensor([17, 17, 63, 75, 75, 93] )
self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels'] , _a ) )
# verify masks
a__ = 82_2873
self.assertEqual(encoding['labels'][0]['masks'].sum().item() , _a )
# verify orig_size
a__ = torch.tensor([480, 640] )
self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size'] , _a ) )
# verify size
a__ = torch.tensor([800, 1066] )
self.assertTrue(torch.allclose(encoding['labels'][0]['size'] , _a ) )
| 126
| 0
|
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
if is_tf_available():
import numpy as np
import tensorflow as tf
from transformers import TFCamembertModel
@require_tf
@require_sentencepiece
@require_tokenizers
class lowerCamelCase_ ( unittest.TestCase ):
'''simple docstring'''
@slow
def lowerCAmelCase_ ( self : List[Any] ):
SCREAMING_SNAKE_CASE_ = TFCamembertModel.from_pretrained('jplu/tf-camembert-base' )
SCREAMING_SNAKE_CASE_ = tf.convert_to_tensor(
[[5, 121, 11, 660, 16, 730, 25_543, 110, 83, 6]] , dtype=tf.intaa , ) # J'aime le camembert !"
SCREAMING_SNAKE_CASE_ = model(_lowerCAmelCase )['last_hidden_state']
SCREAMING_SNAKE_CASE_ = tf.TensorShape((1, 10, 768) )
self.assertEqual(output.shape , _lowerCAmelCase )
# compare the actual values for a slice.
SCREAMING_SNAKE_CASE_ = tf.convert_to_tensor(
[[[-0.0254, 0.0235, 0.1027], [0.0606, -0.1811, -0.0418], [-0.1561, -0.1127, 0.2687]]] , dtype=tf.floataa , )
# camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0')
# camembert.eval()
# expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()
self.assertTrue(np.allclose(output[:, :3, :3].numpy() , expected_slice.numpy() , atol=1E-4 ) )
| 31
|
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_clip import CLIPImageProcessor
_A = logging.get_logger(__name__)
class _lowerCamelCase ( a_ ):
def __init__( self : str , *UpperCamelCase : int , **UpperCamelCase : str ) -> None:
"""simple docstring"""
warnings.warn(
"""The class CLIPFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"""
""" use CLIPImageProcessor instead.""" , UpperCamelCase , )
super().__init__(*UpperCamelCase , **UpperCamelCase )
| 299
| 0
|
'''simple docstring'''
class UpperCAmelCase_ :
"""simple docstring"""
def __init__( self : List[Any] , UpperCAmelCase : list ) -> None:
'''simple docstring'''
lowercase : Union[str, Any] =set_counts
lowercase : Any =max(UpperCAmelCase )
lowercase : Optional[Any] =len(UpperCAmelCase )
lowercase : Tuple =[1] * num_sets
lowercase : List[Any] =list(range(UpperCAmelCase ) )
def A__ ( self : Optional[int] , UpperCAmelCase : int , UpperCAmelCase : int ) -> bool:
'''simple docstring'''
lowercase : int =self.get_parent(UpperCAmelCase )
lowercase : int =self.get_parent(UpperCAmelCase )
if src_parent == dst_parent:
return False
if self.ranks[dst_parent] >= self.ranks[src_parent]:
self.set_counts[dst_parent] += self.set_counts[src_parent]
lowercase : str =0
lowercase : Dict =dst_parent
if self.ranks[dst_parent] == self.ranks[src_parent]:
self.ranks[dst_parent] += 1
lowercase : Optional[Any] =self.set_counts[dst_parent]
else:
self.set_counts[src_parent] += self.set_counts[dst_parent]
lowercase : List[str] =0
lowercase : Tuple =src_parent
lowercase : int =self.set_counts[src_parent]
lowercase : Union[str, Any] =max(self.max_set , UpperCAmelCase )
return True
def A__ ( self : int , UpperCAmelCase : int ) -> int:
'''simple docstring'''
if self.parents[disj_set] == disj_set:
return disj_set
lowercase : Tuple =self.get_parent(self.parents[disj_set] )
return self.parents[disj_set]
| 8
|
'''simple docstring'''
import importlib.metadata
from typing import Union
from packaging.version import Version, parse
from .constants import STR_OPERATION_TO_FUNC
SCREAMING_SNAKE_CASE = parse(importlib.metadata.version('torch'))
def lowercase_ ( __A : Union[str, Version] , __A : str , __A : str ) -> Union[str, Any]:
"""simple docstring"""
if operation not in STR_OPERATION_TO_FUNC.keys():
raise ValueError(F'`operation` must be one of {list(STR_OPERATION_TO_FUNC.keys() )}, received {operation}' )
lowercase : Any =STR_OPERATION_TO_FUNC[operation]
if isinstance(__A , __A ):
lowercase : List[Any] =parse(importlib.metadata.version(__A ) )
return operation(__A , parse(__A ) )
def lowercase_ ( __A : str , __A : str ) -> Tuple:
"""simple docstring"""
return compare_versions(__A , __A , __A )
| 8
| 1
|
"""simple docstring"""
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, BatchEncoding, PreTrainedTokenizer
from ...utils import logging
UpperCAmelCase =logging.get_logger(__name__)
UpperCAmelCase ="""▁"""
UpperCAmelCase ={"""vocab_file""": """sentencepiece.bpe.model"""}
UpperCAmelCase ={
"""vocab_file""": {
"""facebook/nllb-200-distilled-600M""": (
"""https://huggingface.co/facebook/nllb-200-distilled-600M/blob/main/sentencepiece.bpe.model"""
),
}
}
UpperCAmelCase ={
"""facebook/nllb-200-distilled-600M""": 1_024,
}
# fmt: off
UpperCAmelCase =["""ace_Arab""", """ace_Latn""", """acm_Arab""", """acq_Arab""", """aeb_Arab""", """afr_Latn""", """ajp_Arab""", """aka_Latn""", """amh_Ethi""", """apc_Arab""", """arb_Arab""", """ars_Arab""", """ary_Arab""", """arz_Arab""", """asm_Beng""", """ast_Latn""", """awa_Deva""", """ayr_Latn""", """azb_Arab""", """azj_Latn""", """bak_Cyrl""", """bam_Latn""", """ban_Latn""", """bel_Cyrl""", """bem_Latn""", """ben_Beng""", """bho_Deva""", """bjn_Arab""", """bjn_Latn""", """bod_Tibt""", """bos_Latn""", """bug_Latn""", """bul_Cyrl""", """cat_Latn""", """ceb_Latn""", """ces_Latn""", """cjk_Latn""", """ckb_Arab""", """crh_Latn""", """cym_Latn""", """dan_Latn""", """deu_Latn""", """dik_Latn""", """dyu_Latn""", """dzo_Tibt""", """ell_Grek""", """eng_Latn""", """epo_Latn""", """est_Latn""", """eus_Latn""", """ewe_Latn""", """fao_Latn""", """pes_Arab""", """fij_Latn""", """fin_Latn""", """fon_Latn""", """fra_Latn""", """fur_Latn""", """fuv_Latn""", """gla_Latn""", """gle_Latn""", """glg_Latn""", """grn_Latn""", """guj_Gujr""", """hat_Latn""", """hau_Latn""", """heb_Hebr""", """hin_Deva""", """hne_Deva""", """hrv_Latn""", """hun_Latn""", """hye_Armn""", """ibo_Latn""", """ilo_Latn""", """ind_Latn""", """isl_Latn""", """ita_Latn""", """jav_Latn""", """jpn_Jpan""", """kab_Latn""", """kac_Latn""", """kam_Latn""", """kan_Knda""", """kas_Arab""", """kas_Deva""", """kat_Geor""", """knc_Arab""", """knc_Latn""", """kaz_Cyrl""", """kbp_Latn""", """kea_Latn""", """khm_Khmr""", """kik_Latn""", """kin_Latn""", """kir_Cyrl""", """kmb_Latn""", """kon_Latn""", """kor_Hang""", """kmr_Latn""", """lao_Laoo""", """lvs_Latn""", """lij_Latn""", """lim_Latn""", """lin_Latn""", """lit_Latn""", """lmo_Latn""", """ltg_Latn""", """ltz_Latn""", """lua_Latn""", """lug_Latn""", """luo_Latn""", """lus_Latn""", """mag_Deva""", """mai_Deva""", """mal_Mlym""", """mar_Deva""", """min_Latn""", """mkd_Cyrl""", """plt_Latn""", """mlt_Latn""", """mni_Beng""", """khk_Cyrl""", """mos_Latn""", """mri_Latn""", """zsm_Latn""", """mya_Mymr""", """nld_Latn""", """nno_Latn""", """nob_Latn""", """npi_Deva""", """nso_Latn""", """nus_Latn""", """nya_Latn""", """oci_Latn""", """gaz_Latn""", """ory_Orya""", """pag_Latn""", """pan_Guru""", """pap_Latn""", """pol_Latn""", """por_Latn""", """prs_Arab""", """pbt_Arab""", """quy_Latn""", """ron_Latn""", """run_Latn""", """rus_Cyrl""", """sag_Latn""", """san_Deva""", """sat_Beng""", """scn_Latn""", """shn_Mymr""", """sin_Sinh""", """slk_Latn""", """slv_Latn""", """smo_Latn""", """sna_Latn""", """snd_Arab""", """som_Latn""", """sot_Latn""", """spa_Latn""", """als_Latn""", """srd_Latn""", """srp_Cyrl""", """ssw_Latn""", """sun_Latn""", """swe_Latn""", """swh_Latn""", """szl_Latn""", """tam_Taml""", """tat_Cyrl""", """tel_Telu""", """tgk_Cyrl""", """tgl_Latn""", """tha_Thai""", """tir_Ethi""", """taq_Latn""", """taq_Tfng""", """tpi_Latn""", """tsn_Latn""", """tso_Latn""", """tuk_Latn""", """tum_Latn""", """tur_Latn""", """twi_Latn""", """tzm_Tfng""", """uig_Arab""", """ukr_Cyrl""", """umb_Latn""", """urd_Arab""", """uzn_Latn""", """vec_Latn""", """vie_Latn""", """war_Latn""", """wol_Latn""", """xho_Latn""", """ydd_Hebr""", """yor_Latn""", """yue_Hant""", """zho_Hans""", """zho_Hant""", """zul_Latn"""]
class lowerCamelCase__ ( a__ ):
'''simple docstring'''
_lowerCamelCase = VOCAB_FILES_NAMES
_lowerCamelCase = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
_lowerCamelCase = PRETRAINED_VOCAB_FILES_MAP
_lowerCamelCase = ['''input_ids''', '''attention_mask''']
_lowerCamelCase = []
_lowerCamelCase = []
def __init__( self ,lowerCamelCase_ ,lowerCamelCase_="<s>" ,lowerCamelCase_="</s>" ,lowerCamelCase_="</s>" ,lowerCamelCase_="<s>" ,lowerCamelCase_="<unk>" ,lowerCamelCase_="<pad>" ,lowerCamelCase_="<mask>" ,lowerCamelCase_=None ,lowerCamelCase_=None ,lowerCamelCase_=None ,lowerCamelCase_ = None ,lowerCamelCase_=None ,lowerCamelCase_=False ,**lowerCamelCase_ ,) -> int:
# Mask token behave like a normal word, i.e. include the space before it
A = AddedToken(__lowerCAmelCase ,lstrip=__lowerCAmelCase ,rstrip=__lowerCAmelCase ) if isinstance(__lowerCAmelCase ,__lowerCAmelCase ) else mask_token
A = {} if sp_model_kwargs is None else sp_model_kwargs
A = legacy_behaviour
super().__init__(
bos_token=__lowerCAmelCase ,eos_token=__lowerCAmelCase ,unk_token=__lowerCAmelCase ,sep_token=__lowerCAmelCase ,cls_token=__lowerCAmelCase ,pad_token=__lowerCAmelCase ,mask_token=__lowerCAmelCase ,tokenizer_file=__lowerCAmelCase ,src_lang=__lowerCAmelCase ,tgt_lang=__lowerCAmelCase ,additional_special_tokens=__lowerCAmelCase ,sp_model_kwargs=self.sp_model_kwargs ,legacy_behaviour=__lowerCAmelCase ,**__lowerCAmelCase ,)
A = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(str(__lowerCAmelCase ) )
A = vocab_file
# Original fairseq vocab and spm vocab must be "aligned":
# Vocab | 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9
# -------- | ------- | ------- | ------ | ------- | ---- | ---- | ---- | ---- | ---- | ----
# fairseq | '<s>' | '<pad>' | '</s>' | '<unk>' | 'an' | '▁n' | '▁m' | '▁t' | '▁k' | '▁a'
# spm | '<unk>' | '<s>' | '</s>' | 'an' | '▁n' | '▁m' | '▁t' | '▁k' | '▁a' | '▁s'
# Mimic fairseq token-to-id alignment for the first 4 token
A = {'''<s>''': 0, '''<pad>''': 1, '''</s>''': 2, '''<unk>''': 3}
# The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
A = 1
A = len(self.sp_model )
A = {
code: self.sp_model_size + i + self.fairseq_offset for i, code in enumerate(__lowerCAmelCase )
}
A = {v: k for k, v in self.lang_code_to_id.items()}
A = len(self.sp_model ) + len(self.lang_code_to_id ) + self.fairseq_offset
self.fairseq_tokens_to_ids.update(self.lang_code_to_id )
A = {v: k for k, v in self.fairseq_tokens_to_ids.items()}
A = list(self.lang_code_to_id.keys() )
if additional_special_tokens is not None:
# Only add those special tokens if they are not already there.
self._additional_special_tokens.extend(
[t for t in additional_special_tokens if t not in self._additional_special_tokens] )
A = src_lang if src_lang is not None else '''eng_Latn'''
A = self.lang_code_to_id[self._src_lang]
A = tgt_lang
self.set_src_lang_special_tokens(self._src_lang )
def __getstate__( self ) -> Union[str, Any]:
A = self.__dict__.copy()
A = None
A = self.sp_model.serialized_model_proto()
return state
def __setstate__( self ,lowerCamelCase_ ) -> Tuple:
A = d
# for backward compatibility
if not hasattr(self ,"""sp_model_kwargs""" ):
A = {}
A = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.LoadFromSerializedProto(self.sp_model_proto )
@property
def UpperCamelCase__ ( self ) -> int:
return len(self.sp_model ) + len(self.lang_code_to_id ) + self.fairseq_offset + 1 # Plus 1 for the mask token
@property
def UpperCamelCase__ ( self ) -> str:
return self._src_lang
@src_lang.setter
def UpperCamelCase__ ( self ,lowerCamelCase_ ) -> None:
A = new_src_lang
self.set_src_lang_special_tokens(self._src_lang )
def UpperCamelCase__ ( self ,lowerCamelCase_ ,lowerCamelCase_ = None ,lowerCamelCase_ = False ) -> List[int]:
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=__lowerCAmelCase ,token_ids_a=__lowerCAmelCase ,already_has_special_tokens=__lowerCAmelCase )
A = [1] * len(self.prefix_tokens )
A = [1] * len(self.suffix_tokens )
if token_ids_a is None:
return prefix_ones + ([0] * len(__lowerCAmelCase )) + suffix_ones
return prefix_ones + ([0] * len(__lowerCAmelCase )) + ([0] * len(__lowerCAmelCase )) + suffix_ones
def UpperCamelCase__ ( self ,lowerCamelCase_ ,lowerCamelCase_ = None ) -> List[int]:
if token_ids_a is None:
return self.prefix_tokens + token_ids_a + self.suffix_tokens
# We don't expect to process pairs, but leave the pair logic for API consistency
return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens
def UpperCamelCase__ ( self ,lowerCamelCase_ ,lowerCamelCase_ = None ) -> List[int]:
A = [self.sep_token_id]
A = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]
def UpperCamelCase__ ( self ,lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ ,**lowerCamelCase_ ) -> List[str]:
if src_lang is None or tgt_lang is None:
raise ValueError("""Translation requires a `src_lang` and a `tgt_lang` for this model""" )
A = src_lang
A = self(__lowerCAmelCase ,add_special_tokens=__lowerCAmelCase ,return_tensors=__lowerCAmelCase ,**__lowerCAmelCase )
A = self.convert_tokens_to_ids(__lowerCAmelCase )
A = tgt_lang_id
return inputs
def UpperCamelCase__ ( self ) -> Tuple:
A = {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 ,lowerCamelCase_ ) -> List[str]:
return self.sp_model.encode(__lowerCAmelCase ,out_type=__lowerCAmelCase )
def UpperCamelCase__ ( self ,lowerCamelCase_ ) -> str:
if token in self.fairseq_tokens_to_ids:
return self.fairseq_tokens_to_ids[token]
A = self.sp_model.PieceToId(__lowerCAmelCase )
# Need to return unknown token if the SP model returned 0
return spm_id + self.fairseq_offset if spm_id else self.unk_token_id
def UpperCamelCase__ ( self ,lowerCamelCase_ ) -> Any:
if index in self.fairseq_ids_to_tokens:
return self.fairseq_ids_to_tokens[index]
return self.sp_model.IdToPiece(index - self.fairseq_offset )
def UpperCamelCase__ ( self ,lowerCamelCase_ ) -> List[Any]:
A = ''''''.join(__lowerCAmelCase ).replace(__lowerCAmelCase ,""" """ ).strip()
return out_string
def UpperCamelCase__ ( self ,lowerCamelCase_ ,lowerCamelCase_ = None ) -> Tuple[str]:
if not os.path.isdir(__lowerCAmelCase ):
logger.error(f'Vocabulary path ({save_directory}) should be a directory' )
return
A = 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:
A = self.sp_model.serialized_model_proto()
fi.write(__lowerCAmelCase )
return (out_vocab_file,)
def UpperCamelCase__ ( self ,lowerCamelCase_ ,lowerCamelCase_ = "eng_Latn" ,lowerCamelCase_ = None ,lowerCamelCase_ = "fra_Latn" ,**lowerCamelCase_ ,) -> BatchEncoding:
A = src_lang
A = tgt_lang
return super().prepare_seqaseq_batch(__lowerCAmelCase ,__lowerCAmelCase ,**__lowerCAmelCase )
def UpperCamelCase__ ( self ) -> str:
return self.set_src_lang_special_tokens(self.src_lang )
def UpperCamelCase__ ( self ) -> Optional[int]:
return self.set_tgt_lang_special_tokens(self.tgt_lang )
def UpperCamelCase__ ( self ,lowerCamelCase_ ) -> None:
A = self.lang_code_to_id[src_lang]
if self.legacy_behaviour:
A = []
A = [self.eos_token_id, self.cur_lang_code]
else:
A = [self.cur_lang_code]
A = [self.eos_token_id]
def UpperCamelCase__ ( self ,lowerCamelCase_ ) -> None:
A = self.lang_code_to_id[lang]
if self.legacy_behaviour:
A = []
A = [self.eos_token_id, self.cur_lang_code]
else:
A = [self.cur_lang_code]
A = [self.eos_token_id]
| 617
|
'''simple docstring'''
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging
__a: Optional[int] = logging.get_logger(__name__)
__a: Any = {
"""t5-small""": """https://huggingface.co/t5-small/resolve/main/config.json""",
"""t5-base""": """https://huggingface.co/t5-base/resolve/main/config.json""",
"""t5-large""": """https://huggingface.co/t5-large/resolve/main/config.json""",
"""t5-3b""": """https://huggingface.co/t5-3b/resolve/main/config.json""",
"""t5-11b""": """https://huggingface.co/t5-11b/resolve/main/config.json""",
}
class UpperCAmelCase ( a__ ):
'''simple docstring'''
SCREAMING_SNAKE_CASE = "t5"
SCREAMING_SNAKE_CASE = ["past_key_values"]
SCREAMING_SNAKE_CASE = {"hidden_size": "d_model", "num_attention_heads": "num_heads", "num_hidden_layers": "num_layers"}
def __init__( self , __lowerCAmelCase=32128 , __lowerCAmelCase=512 , __lowerCAmelCase=64 , __lowerCAmelCase=2048 , __lowerCAmelCase=6 , __lowerCAmelCase=None , __lowerCAmelCase=8 , __lowerCAmelCase=32 , __lowerCAmelCase=128 , __lowerCAmelCase=0.1 , __lowerCAmelCase=1E-6 , __lowerCAmelCase=1.0 , __lowerCAmelCase="relu" , __lowerCAmelCase=True , __lowerCAmelCase=True , __lowerCAmelCase=0 , __lowerCAmelCase=1 , **__lowerCAmelCase , ) -> Optional[int]:
lowercase__ : Union[str, Any] = vocab_size
lowercase__ : List[Any] = d_model
lowercase__ : int = d_kv
lowercase__ : List[str] = d_ff
lowercase__ : Optional[Any] = num_layers
lowercase__ : Any = (
num_decoder_layers if num_decoder_layers is not None else self.num_layers
) # default = symmetry
lowercase__ : Optional[Any] = num_heads
lowercase__ : int = relative_attention_num_buckets
lowercase__ : Optional[Any] = relative_attention_max_distance
lowercase__ : str = dropout_rate
lowercase__ : Tuple = layer_norm_epsilon
lowercase__ : List[str] = initializer_factor
lowercase__ : Dict = feed_forward_proj
lowercase__ : Any = use_cache
lowercase__ : Optional[int] = self.feed_forward_proj.split('''-''' )
lowercase__ : List[Any] = act_info[-1]
lowercase__ : Optional[int] = act_info[0] == '''gated'''
if len(__lowerCAmelCase ) > 1 and act_info[0] != "gated" or len(__lowerCAmelCase ) > 2:
raise ValueError(
F"""`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer."""
'''Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. '''
'''\'gated-gelu\' or \'relu\'''' )
# for backwards compatibility
if feed_forward_proj == "gated-gelu":
lowercase__ : Optional[Any] = '''gelu_new'''
super().__init__(
pad_token_id=__lowerCAmelCase , eos_token_id=__lowerCAmelCase , is_encoder_decoder=__lowerCAmelCase , **__lowerCAmelCase , )
class UpperCAmelCase ( a__ ):
'''simple docstring'''
@property
def _lowerCAmelCase( self ) -> Mapping[str, Mapping[int, str]]:
lowercase__ : int = {
'''input_ids''': {0: '''batch''', 1: '''encoder_sequence'''},
'''attention_mask''': {0: '''batch''', 1: '''encoder_sequence'''},
}
if self.use_past:
lowercase__ : Any = '''past_encoder_sequence + sequence'''
lowercase__ : List[Any] = {0: '''batch'''}
lowercase__ : int = {0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
else:
lowercase__ : Dict = {0: '''batch''', 1: '''decoder_sequence'''}
lowercase__ : Any = {0: '''batch''', 1: '''decoder_sequence'''}
if self.use_past:
self.fill_with_past_key_values_(__lowerCAmelCase , direction='''inputs''' )
return common_inputs
@property
def _lowerCAmelCase( self ) -> int:
return 13
| 152
| 0
|
'''simple docstring'''
import math
from numpy import inf
from scipy.integrate import quad
def UpperCAmelCase_ ( lowercase__ ):
'''simple docstring'''
if num <= 0:
raise ValueError("math domain error" )
return quad(lowercase__ , 0 , lowercase__ , args=(lowercase__) )[0]
def UpperCAmelCase_ ( lowercase__ , lowercase__ ):
'''simple docstring'''
return math.pow(lowercase__ , z - 1 ) * math.exp(-x )
if __name__ == "__main__":
from doctest import testmod
testmod()
| 41
|
'''simple docstring'''
from __future__ import annotations
def UpperCAmelCase_ ( lowercase__ ):
'''simple docstring'''
a_ =str(lowercase__ )
return len(lowercase__ ) == 9 and set(lowercase__ ) == set("123456789" )
def UpperCAmelCase_ ( ):
'''simple docstring'''
for base_num in range(9_9_9_9 , 4_9_9_9 , -1 ):
a_ =1_0_0_0_0_2 * base_num
if is_9_pandigital(lowercase__ ):
return candidate
for base_num in range(3_3_3 , 9_9 , -1 ):
a_ =1_0_0_2_0_0_3 * base_num
if is_9_pandigital(lowercase__ ):
return candidate
return None
if __name__ == "__main__":
print(F"""{solution() = }""")
| 41
| 1
|
import sys
from .dependency_versions_table import deps
from .utils.versions import require_version, require_version_core
# define which module versions we always want to check at run time
# (usually the ones defined in `install_requires` in setup.py)
#
# order specific notes:
# - tqdm must be checked before tokenizers
UpperCAmelCase_ : int = '''python tqdm regex requests packaging filelock numpy tokenizers'''.split()
if sys.version_info < (3, 7):
pkgs_to_check_at_runtime.append('''dataclasses''')
if sys.version_info < (3, 8):
pkgs_to_check_at_runtime.append('''importlib_metadata''')
for pkg in pkgs_to_check_at_runtime:
if pkg in deps:
if pkg == "tokenizers":
# must be loaded here, or else tqdm check may fail
from .utils import is_tokenizers_available
if not is_tokenizers_available():
continue # not required, check version only if installed
require_version_core(deps[pkg])
else:
raise ValueError(f"""can't find {pkg} in {deps.keys()}, check dependency_versions_table.py""")
def __SCREAMING_SNAKE_CASE ( a__ : Dict ,a__ : Any=None ) -> List[str]:
require_version(deps[pkg] ,a__ )
| 17
|
"""simple docstring"""
from sklearn.metrics import matthews_corrcoef
import datasets
lowercase_ : Tuple = '''
Compute the Matthews correlation coefficient (MCC)
The Matthews correlation coefficient is used in machine learning as a
measure of the quality of binary and multiclass classifications. It takes
into account true and false positives and negatives and is generally
regarded as a balanced measure which can be used even if the classes are of
very different sizes. The MCC is in essence a correlation coefficient value
between -1 and +1. A coefficient of +1 represents a perfect prediction, 0
an average random prediction and -1 an inverse prediction. The statistic
is also known as the phi coefficient. [source: Wikipedia]
'''
lowercase_ : Dict = '''
Args:
predictions (list of int): Predicted labels, as returned by a model.
references (list of int): Ground truth labels.
sample_weight (list of int, float, or bool): Sample weights. Defaults to `None`.
Returns:
matthews_correlation (dict containing float): Matthews correlation.
Examples:
Example 1, a basic example with only predictions and references as inputs:
>>> matthews_metric = datasets.load_metric("matthews_correlation")
>>> results = matthews_metric.compute(references=[1, 3, 2, 0, 3, 2],
... predictions=[1, 2, 2, 0, 3, 3])
>>> print(round(results[\'matthews_correlation\'], 2))
0.54
Example 2, the same example as above, but also including sample weights:
>>> matthews_metric = datasets.load_metric("matthews_correlation")
>>> results = matthews_metric.compute(references=[1, 3, 2, 0, 3, 2],
... predictions=[1, 2, 2, 0, 3, 3],
... sample_weight=[0.5, 3, 1, 1, 1, 2])
>>> print(round(results[\'matthews_correlation\'], 2))
0.1
Example 3, the same example as above, but with sample weights that cause a negative correlation:
>>> matthews_metric = datasets.load_metric("matthews_correlation")
>>> results = matthews_metric.compute(references=[1, 3, 2, 0, 3, 2],
... predictions=[1, 2, 2, 0, 3, 3],
... sample_weight=[0.5, 1, 0, 0, 0, 1])
>>> print(round(results[\'matthews_correlation\'], 2))
-0.25
'''
lowercase_ : List[str] = '''\
@article{scikit-learn,
title={Scikit-learn: Machine Learning in {P}ython},
author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.
and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and
Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},
journal={Journal of Machine Learning Research},
volume={12},
pages={2825--2830},
year={2011}
}
'''
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class UpperCamelCase ( datasets.Metric ):
def __SCREAMING_SNAKE_CASE ( self ):
"""simple docstring"""
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
"predictions": datasets.Value("int32" ),
"references": datasets.Value("int32" ),
} ) , reference_urls=[
"https://scikit-learn.org/stable/modules/generated/sklearn.metrics.matthews_corrcoef.html"
] , )
def __SCREAMING_SNAKE_CASE ( self , snake_case__ , snake_case__ , snake_case__=None ):
"""simple docstring"""
return {
"matthews_correlation": float(matthews_corrcoef(snake_case__ , snake_case__ , sample_weight=snake_case__ ) ),
}
| 572
| 0
|
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
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 ViTImageProcessor
class _UpperCamelCase ( unittest.TestCase ):
'''simple docstring'''
def __init__( self : Tuple , _lowerCamelCase : int , _lowerCamelCase : List[str]=1_3 , _lowerCamelCase : Tuple=3 , _lowerCamelCase : Any=2_2_4 , _lowerCamelCase : Optional[int]=3_0 , _lowerCamelCase : Optional[Any]=4_0_0 , _lowerCamelCase : Union[str, Any]=True , _lowerCamelCase : Optional[Any]=None , _lowerCamelCase : List[str]=True , _lowerCamelCase : Optional[int]=[0.5, 0.5, 0.5] , _lowerCamelCase : Union[str, Any]=[0.5, 0.5, 0.5] , ):
'''simple docstring'''
__lowerCamelCase : int = size if size is not None else {"""height""": 1_8, """width""": 1_8}
__lowerCamelCase : Optional[int] = parent
__lowerCamelCase : Dict = batch_size
__lowerCamelCase : List[Any] = num_channels
__lowerCamelCase : Tuple = image_size
__lowerCamelCase : int = min_resolution
__lowerCamelCase : List[Any] = max_resolution
__lowerCamelCase : List[str] = do_resize
__lowerCamelCase : str = size
__lowerCamelCase : Optional[Any] = do_normalize
__lowerCamelCase : List[str] = image_mean
__lowerCamelCase : List[str] = image_std
def _snake_case ( self : Optional[int] ):
'''simple docstring'''
return {
"image_mean": self.image_mean,
"image_std": self.image_std,
"do_normalize": self.do_normalize,
"do_resize": self.do_resize,
"size": self.size,
}
@require_torch
@require_vision
class _UpperCamelCase ( A,unittest.TestCase ):
'''simple docstring'''
a_ : Tuple = ViTImageProcessor if is_vision_available() else None
def _snake_case ( self : Optional[int] ):
'''simple docstring'''
__lowerCamelCase : int = EfficientFormerImageProcessorTester(self )
@property
def _snake_case ( self : Tuple ):
'''simple docstring'''
return self.image_proc_tester.prepare_image_processor_dict()
def _snake_case ( self : Dict ):
'''simple docstring'''
__lowerCamelCase : 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_resize""" ) )
self.assertTrue(hasattr(_lowerCamelCase , """size""" ) )
def _snake_case ( self : List[Any] ):
'''simple docstring'''
pass
def _snake_case ( self : Optional[int] ):
'''simple docstring'''
__lowerCamelCase : str = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
__lowerCamelCase : Union[str, Any] = prepare_image_inputs(self.image_proc_tester , equal_resolution=_lowerCamelCase )
for image in image_inputs:
self.assertIsInstance(_lowerCamelCase , Image.Image )
# Test not batched input
__lowerCamelCase : List[Any] = image_processor(image_inputs[0] , return_tensors="""pt""" ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_proc_tester.num_channels,
self.image_proc_tester.size["""height"""],
self.image_proc_tester.size["""width"""],
) , )
# Test batched
__lowerCamelCase : Tuple = image_processor(_lowerCamelCase , return_tensors="""pt""" ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_proc_tester.batch_size,
self.image_proc_tester.num_channels,
self.image_proc_tester.size["""height"""],
self.image_proc_tester.size["""width"""],
) , )
def _snake_case ( self : Union[str, Any] ):
'''simple docstring'''
__lowerCamelCase : int = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
__lowerCamelCase : List[str] = prepare_image_inputs(self.image_proc_tester , equal_resolution=_lowerCamelCase , numpify=_lowerCamelCase )
for image in image_inputs:
self.assertIsInstance(_lowerCamelCase , np.ndarray )
# Test not batched input
__lowerCamelCase : List[str] = image_processor(image_inputs[0] , return_tensors="""pt""" ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_proc_tester.num_channels,
self.image_proc_tester.size["""height"""],
self.image_proc_tester.size["""width"""],
) , )
# Test batched
__lowerCamelCase : Dict = image_processor(_lowerCamelCase , return_tensors="""pt""" ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_proc_tester.batch_size,
self.image_proc_tester.num_channels,
self.image_proc_tester.size["""height"""],
self.image_proc_tester.size["""width"""],
) , )
def _snake_case ( self : List[str] ):
'''simple docstring'''
__lowerCamelCase : Any = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
__lowerCamelCase : Dict = prepare_image_inputs(self.image_proc_tester , equal_resolution=_lowerCamelCase , torchify=_lowerCamelCase )
for image in image_inputs:
self.assertIsInstance(_lowerCamelCase , torch.Tensor )
# Test not batched input
__lowerCamelCase : Any = image_processor(image_inputs[0] , return_tensors="""pt""" ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_proc_tester.num_channels,
self.image_proc_tester.size["""height"""],
self.image_proc_tester.size["""width"""],
) , )
# Test batched
__lowerCamelCase : List[Any] = image_processor(_lowerCamelCase , return_tensors="""pt""" ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_proc_tester.batch_size,
self.image_proc_tester.num_channels,
self.image_proc_tester.size["""height"""],
self.image_proc_tester.size["""width"""],
) , )
| 713
|
import argparse
import torch
# Step 1. clone https://github.com/microsoft/unilm
# Step 2. git checkout to https://github.com/microsoft/unilm/commit/b94ec76c36f02fb2b0bf0dcb0b8554a2185173cd
# Step 3. cd unilm
# Step 4. ln -s $(realpath wavlm/modules.py) ./ # create simlink
# import classes
from unilm.wavlm.WavLM import WavLM as WavLMOrig
from unilm.wavlm.WavLM import WavLMConfig as WavLMConfigOrig
from transformers import WavLMConfig, WavLMModel, logging
logging.set_verbosity_info()
__UpperCamelCase : Any = logging.get_logger(__name__)
__UpperCamelCase : str = {
'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.grep_linear': 'encoder.layers.*.attention.gru_rel_pos_linear',
'self_attn.relative_attention_bias': 'encoder.layers.*.attention.rel_attn_embed',
'self_attn.grep_a': 'encoder.layers.*.attention.gru_rel_pos_const',
'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',
}
__UpperCamelCase : Optional[int] = [
'ctc_proj',
'quantizer.weight_proj',
'quantizer.codevectors',
'project_q',
'project_hid',
]
def _UpperCAmelCase ( UpperCAmelCase : Optional[Any] , UpperCAmelCase : Tuple , UpperCAmelCase : List[str] , UpperCAmelCase : Optional[int] , UpperCAmelCase : Tuple ):
"""simple docstring"""
for attribute in key.split(""".""" ):
__lowerCamelCase : Union[str, Any] = getattr(UpperCAmelCase , UpperCAmelCase )
if weight_type is not None:
__lowerCamelCase : Union[str, Any] = getattr(UpperCAmelCase , UpperCAmelCase ).shape
else:
__lowerCamelCase : Any = 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":
__lowerCamelCase : List[str] = value
elif weight_type == "weight_g":
__lowerCamelCase : Dict = value
elif weight_type == "weight_v":
__lowerCamelCase : str = value
elif weight_type == "bias":
__lowerCamelCase : Optional[Any] = value
else:
__lowerCamelCase : str = value
logger.info(f"""{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.""" )
def _UpperCAmelCase ( UpperCAmelCase : Tuple , UpperCAmelCase : List[str] ):
"""simple docstring"""
__lowerCamelCase : int = []
__lowerCamelCase : int = fairseq_model.state_dict()
__lowerCamelCase : List[str] = hf_model.feature_extractor
for name, value in fairseq_dict.items():
__lowerCamelCase : str = False
if "conv_layers" in name:
load_conv_layer(
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , hf_model.config.feat_extract_norm == """group""" , )
__lowerCamelCase : Optional[int] = True
else:
for key, mapped_key in MAPPING.items():
if key in name or key.split("""w2v_model.""" )[-1] == name.split(""".""" )[0]:
__lowerCamelCase : int = True
if "*" in mapped_key:
__lowerCamelCase : Optional[int] = name.split(UpperCAmelCase )[0].split(""".""" )[-2]
__lowerCamelCase : Optional[int] = mapped_key.replace("""*""" , UpperCAmelCase )
if "weight_g" in name:
__lowerCamelCase : Optional[Any] = """weight_g"""
elif "weight_v" in name:
__lowerCamelCase : Dict = """weight_v"""
elif "bias" in name and "relative_attention_bias" not in name:
__lowerCamelCase : List[Any] = """bias"""
elif "weight" in name:
# TODO: don't match quantizer.weight_proj
__lowerCamelCase : List[str] = """weight"""
else:
__lowerCamelCase : List[str] = None
set_recursively(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase )
continue
if not is_used:
unused_weights.append(UpperCAmelCase )
logger.warning(f"""Unused weights: {unused_weights}""" )
def _UpperCAmelCase ( UpperCAmelCase : Optional[int] , UpperCAmelCase : List[Any] , UpperCAmelCase : Any , UpperCAmelCase : Optional[int] , UpperCAmelCase : Optional[int] ):
"""simple docstring"""
__lowerCamelCase : List[Any] = full_name.split("""conv_layers.""" )[-1]
__lowerCamelCase : List[Any] = name.split(""".""" )
__lowerCamelCase : Tuple = int(items[0] )
__lowerCamelCase : List[Any] = 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."""
)
__lowerCamelCase : List[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."""
)
__lowerCamelCase : str = 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."
)
__lowerCamelCase : 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."""
)
__lowerCamelCase : Tuple = value
logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.""" )
else:
unused_weights.append(UpperCAmelCase )
@torch.no_grad()
def _UpperCAmelCase ( UpperCAmelCase : List[str] , UpperCAmelCase : Dict , UpperCAmelCase : Optional[int]=None ):
"""simple docstring"""
__lowerCamelCase : Any = torch.load(UpperCAmelCase )
__lowerCamelCase : Optional[int] = WavLMConfigOrig(checkpoint["""cfg"""] )
__lowerCamelCase : Any = WavLMOrig(UpperCAmelCase )
model.load_state_dict(checkpoint["""model"""] )
model.eval()
if config_path is not None:
__lowerCamelCase : Union[str, Any] = WavLMConfig.from_pretrained(UpperCAmelCase )
else:
__lowerCamelCase : str = WavLMConfig()
__lowerCamelCase : Dict = WavLMModel(UpperCAmelCase )
recursively_load_weights(UpperCAmelCase , UpperCAmelCase )
hf_wavlm.save_pretrained(UpperCAmelCase )
if __name__ == "__main__":
__UpperCamelCase : List[str] = 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('--config_path', default=None, type=str, help='Path to hf config.json of model to convert')
__UpperCamelCase : Tuple = parser.parse_args()
convert_wavlm_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)
| 458
| 0
|
"""simple docstring"""
import importlib
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Union
import torch
from ..utils import BaseOutput
lowerCAmelCase__ ="scheduler_config.json"
class A__( __magic_name__ ):
lowerCAmelCase = 1
lowerCAmelCase = 2
lowerCAmelCase = 3
lowerCAmelCase = 4
lowerCAmelCase = 5
lowerCAmelCase = 6
lowerCAmelCase = 7
lowerCAmelCase = 8
lowerCAmelCase = 9
lowerCAmelCase = 10
lowerCAmelCase = 11
lowerCAmelCase = 12
lowerCAmelCase = 13
lowerCAmelCase = 14
@dataclass
class A__( __magic_name__ ):
lowerCAmelCase = 42
class A__:
lowerCAmelCase = SCHEDULER_CONFIG_NAME
lowerCAmelCase = []
lowerCAmelCase = True
@classmethod
def _a ( cls : List[str] , __SCREAMING_SNAKE_CASE : Dict[str, Any] = None , __SCREAMING_SNAKE_CASE : Optional[str] = None , __SCREAMING_SNAKE_CASE : Union[str, Any]=False , **__SCREAMING_SNAKE_CASE : Tuple , ) -> Union[str, Any]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = cls.load_config(
pretrained_model_name_or_path=__SCREAMING_SNAKE_CASE , subfolder=__SCREAMING_SNAKE_CASE , return_unused_kwargs=__SCREAMING_SNAKE_CASE , return_commit_hash=__SCREAMING_SNAKE_CASE , **__SCREAMING_SNAKE_CASE , )
return cls.from_config(__SCREAMING_SNAKE_CASE , return_unused_kwargs=__SCREAMING_SNAKE_CASE , **__SCREAMING_SNAKE_CASE )
def _a ( self : List[str] , __SCREAMING_SNAKE_CASE : Union[str, os.PathLike] , __SCREAMING_SNAKE_CASE : bool = False , **__SCREAMING_SNAKE_CASE : str ) -> Tuple:
"""simple docstring"""
self.save_config(save_directory=__SCREAMING_SNAKE_CASE , push_to_hub=__SCREAMING_SNAKE_CASE , **__SCREAMING_SNAKE_CASE )
@property
def _a ( self : List[str] ) -> int:
"""simple docstring"""
return self._get_compatibles()
@classmethod
def _a ( cls : str ) -> Optional[int]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = list(set([cls.__name__] + cls._compatibles ) )
__SCREAMING_SNAKE_CASE = importlib.import_module(__name__.split('''.''' )[0] )
__SCREAMING_SNAKE_CASE = [
getattr(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ) for c in compatible_classes_str if hasattr(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
]
return compatible_classes
| 482
|
"""simple docstring"""
def _a ( UpperCAmelCase__ ) -> int:
if not isinstance(UpperCAmelCase__ , UpperCAmelCase__ ) or number < 0:
raise ValueError('''Input must be a non-negative integer''' )
__SCREAMING_SNAKE_CASE = 0
while number:
# This way we arrive at next set bit (next 1) instead of looping
# through each bit and checking for 1s hence the
# loop won't run 32 times it will only run the number of `1` times
number &= number - 1
count += 1
return count
if __name__ == "__main__":
import doctest
doctest.testmod()
| 482
| 1
|
'''simple docstring'''
class a :
def __init__( self , __magic_name__ , __magic_name__ , __magic_name__ ) -> Optional[Any]:
_a = None
_a = None
_a = graph
self._normalize_graph(__magic_name__ , __magic_name__ )
_a = len(__magic_name__ )
_a = None
def __UpperCAmelCase ( self , __magic_name__ , __magic_name__ ) -> int:
if sources is int:
_a = [sources]
if sinks is int:
_a = [sinks]
if len(__magic_name__ ) == 0 or len(__magic_name__ ) == 0:
return
_a = sources[0]
_a = sinks[0]
# make fake vertex if there are more
# than one source or sink
if len(__magic_name__ ) > 1 or len(__magic_name__ ) > 1:
_a = 0
for i in sources:
max_input_flow += sum(self.graph[i] )
_a = len(self.graph ) + 1
for room in self.graph:
room.insert(0 , 0 )
self.graph.insert(0 , [0] * size )
for i in sources:
_a = max_input_flow
_a = 0
_a = len(self.graph ) + 1
for room in self.graph:
room.append(0 )
self.graph.append([0] * size )
for i in sinks:
_a = max_input_flow
_a = size - 1
def __UpperCAmelCase ( self ) -> List[str]:
if self.maximum_flow_algorithm is None:
raise Exception('You need to set maximum flow algorithm before.' )
if self.source_index is None or self.sink_index is None:
return 0
self.maximum_flow_algorithm.execute()
return self.maximum_flow_algorithm.getMaximumFlow()
def __UpperCAmelCase ( self , __magic_name__ ) -> Tuple:
_a = algorithm(self )
class a :
def __init__( self , __magic_name__ ) -> Any:
_a = flow_network
_a = flow_network.verticesCount
_a = flow_network.sourceIndex
_a = flow_network.sinkIndex
# it's just a reference, so you shouldn't change
# it in your algorithms, use deep copy before doing that
_a = flow_network.graph
_a = False
def __UpperCAmelCase ( self ) -> Any:
if not self.executed:
self._algorithm()
_a = True
def __UpperCAmelCase ( self ) -> List[Any]:
pass
class a ( _SCREAMING_SNAKE_CASE ):
def __init__( self , __magic_name__ ) -> Tuple:
super().__init__(__magic_name__ )
# use this to save your result
_a = -1
def __UpperCAmelCase ( self ) -> Dict:
if not self.executed:
raise Exception('You should execute algorithm before using its result!' )
return self.maximum_flow
class a ( _SCREAMING_SNAKE_CASE ):
def __init__( self , __magic_name__ ) -> Tuple:
super().__init__(__magic_name__ )
_a = [[0] * self.verticies_count for i in range(self.verticies_count )]
_a = [0] * self.verticies_count
_a = [0] * self.verticies_count
def __UpperCAmelCase ( self ) -> Dict:
_a = self.verticies_count
# push some substance to graph
for nextvertex_index, bandwidth in enumerate(self.graph[self.source_index] ):
self.preflow[self.source_index][nextvertex_index] += bandwidth
self.preflow[nextvertex_index][self.source_index] -= bandwidth
self.excesses[nextvertex_index] += bandwidth
# Relabel-to-front selection rule
_a = [
i
for i in range(self.verticies_count )
if i != self.source_index and i != self.sink_index
]
# move through list
_a = 0
while i < len(__magic_name__ ):
_a = vertices_list[i]
_a = self.heights[vertex_index]
self.process_vertex(__magic_name__ )
if self.heights[vertex_index] > previous_height:
# if it was relabeled, swap elements
# and start from 0 index
vertices_list.insert(0 , vertices_list.pop(__magic_name__ ) )
_a = 0
else:
i += 1
_a = sum(self.preflow[self.source_index] )
def __UpperCAmelCase ( self , __magic_name__ ) -> List[str]:
while self.excesses[vertex_index] > 0:
for neighbour_index in range(self.verticies_count ):
# if it's neighbour and current vertex is higher
if (
self.graph[vertex_index][neighbour_index]
- self.preflow[vertex_index][neighbour_index]
> 0
and self.heights[vertex_index] > self.heights[neighbour_index]
):
self.push(__magic_name__ , __magic_name__ )
self.relabel(__magic_name__ )
def __UpperCAmelCase ( self , __magic_name__ , __magic_name__ ) -> Any:
_a = min(
self.excesses[from_index] , self.graph[from_index][to_index] - self.preflow[from_index][to_index] , )
self.preflow[from_index][to_index] += preflow_delta
self.preflow[to_index][from_index] -= preflow_delta
self.excesses[from_index] -= preflow_delta
self.excesses[to_index] += preflow_delta
def __UpperCAmelCase ( self , __magic_name__ ) -> int:
_a = None
for to_index in range(self.verticies_count ):
if (
self.graph[vertex_index][to_index]
- self.preflow[vertex_index][to_index]
> 0
) and (min_height is None or self.heights[to_index] < min_height):
_a = self.heights[to_index]
if min_height is not None:
_a = min_height + 1
if __name__ == "__main__":
a_ : str = [0]
a_ : Union[str, Any] = [3]
# graph = [
# [0, 0, 4, 6, 0, 0],
# [0, 0, 5, 2, 0, 0],
# [0, 0, 0, 0, 4, 4],
# [0, 0, 0, 0, 6, 6],
# [0, 0, 0, 0, 0, 0],
# [0, 0, 0, 0, 0, 0],
# ]
a_ : Union[str, Any] = [[0, 7, 0, 0], [0, 0, 6, 0], [0, 0, 0, 8], [9, 0, 0, 0]]
# prepare our network
a_ : List[Any] = FlowNetwork(graph, entrances, exits)
# set algorithm
flow_network.set_maximum_flow_algorithm(PushRelabelExecutor)
# and calculate
a_ : int = flow_network.find_maximum_flow()
print(f'''maximum flow is {maximum_flow}''')
| 532
|
'''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.
from typing import TYPE_CHECKING
import torch
from ..models.auto import AutoModelForVisualQuestionAnswering, AutoProcessor
from ..utils import requires_backends
from .base import PipelineTool
if TYPE_CHECKING:
from PIL import Image
class a ( _SCREAMING_SNAKE_CASE ):
_lowerCAmelCase = """dandelin/vilt-b32-finetuned-vqa"""
_lowerCAmelCase = (
"""This is a tool that answers a question about an image. It takes an input named `image` which should be the """
"""image containing the information, as well as a `question` which should be the question in English. It """
"""returns a text that is the answer to the question."""
)
_lowerCAmelCase = """image_qa"""
_lowerCAmelCase = AutoProcessor
_lowerCAmelCase = AutoModelForVisualQuestionAnswering
_lowerCAmelCase = ["""image""", """text"""]
_lowerCAmelCase = ["""text"""]
def __init__( self , *__magic_name__ , **__magic_name__ ) -> Tuple:
requires_backends(self , ['vision'] )
super().__init__(*__magic_name__ , **__magic_name__ )
def __UpperCAmelCase ( self , __magic_name__ , __magic_name__ ) -> Tuple:
return self.pre_processor(__magic_name__ , __magic_name__ , return_tensors='pt' )
def __UpperCAmelCase ( self , __magic_name__ ) -> Any:
with torch.no_grad():
return self.model(**__magic_name__ ).logits
def __UpperCAmelCase ( self , __magic_name__ ) -> Optional[int]:
_a = outputs.argmax(-1 ).item()
return self.model.config.idalabel[idx]
| 532
| 1
|
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE = {
"""funnel-transformer/small""": """https://huggingface.co/funnel-transformer/small/resolve/main/config.json""",
"""funnel-transformer/small-base""": """https://huggingface.co/funnel-transformer/small-base/resolve/main/config.json""",
"""funnel-transformer/medium""": """https://huggingface.co/funnel-transformer/medium/resolve/main/config.json""",
"""funnel-transformer/medium-base""": """https://huggingface.co/funnel-transformer/medium-base/resolve/main/config.json""",
"""funnel-transformer/intermediate""": (
"""https://huggingface.co/funnel-transformer/intermediate/resolve/main/config.json"""
),
"""funnel-transformer/intermediate-base""": (
"""https://huggingface.co/funnel-transformer/intermediate-base/resolve/main/config.json"""
),
"""funnel-transformer/large""": """https://huggingface.co/funnel-transformer/large/resolve/main/config.json""",
"""funnel-transformer/large-base""": """https://huggingface.co/funnel-transformer/large-base/resolve/main/config.json""",
"""funnel-transformer/xlarge""": """https://huggingface.co/funnel-transformer/xlarge/resolve/main/config.json""",
"""funnel-transformer/xlarge-base""": """https://huggingface.co/funnel-transformer/xlarge-base/resolve/main/config.json""",
}
class __UpperCAmelCase ( a_ ):
"""simple docstring"""
_lowerCamelCase = """funnel"""
_lowerCamelCase = {
"""hidden_size""": """d_model""",
"""num_attention_heads""": """n_head""",
}
def __init__( self , __A=30522 , __A=[4, 4, 4] , __A=None , __A=2 , __A=768 , __A=12 , __A=64 , __A=3072 , __A="gelu_new" , __A=0.1 , __A=0.1 , __A=0.0 , __A=0.1 , __A=None , __A=1E-9 , __A="mean" , __A="relative_shift" , __A=True , __A=True , __A=True , **__A , ):
__a = vocab_size
__a = block_sizes
__a = [1] * len(__A ) if block_repeats is None else block_repeats
assert len(__A ) == len(
self.block_repeats ), "`block_sizes` and `block_repeats` should have the same length."
__a = num_decoder_layers
__a = d_model
__a = n_head
__a = d_head
__a = d_inner
__a = hidden_act
__a = hidden_dropout
__a = attention_dropout
__a = activation_dropout
__a = initializer_range
__a = initializer_std
__a = layer_norm_eps
assert pooling_type in [
"mean",
"max",
], f'''Got {pooling_type} for `pooling_type` but only \'mean\' and \'max\' are supported.'''
__a = pooling_type
assert attention_type in [
"relative_shift",
"factorized",
], f'''Got {attention_type} for `attention_type` but only \'relative_shift\' and \'factorized\' are supported.'''
__a = attention_type
__a = separate_cls
__a = truncate_seq
__a = pool_q_only
super().__init__(**__A )
@property
def snake_case_ ( self ):
return sum(self.block_sizes )
@num_hidden_layers.setter
def snake_case_ ( self , __A ):
raise NotImplementedError(
"""This model does not support the setting of `num_hidden_layers`. Please set `block_sizes`.""" )
@property
def snake_case_ ( self ):
return len(self.block_sizes )
@num_blocks.setter
def snake_case_ ( self , __A ):
raise NotImplementedError("""This model does not support the setting of `num_blocks`. Please set `block_sizes`.""" )
| 99
|
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__UpperCamelCase : Optional[Any] = {
"""configuration_swinv2""": ["""SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP""", """Swinv2Config"""],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCamelCase : List[str] = [
"""SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""Swinv2ForImageClassification""",
"""Swinv2ForMaskedImageModeling""",
"""Swinv2Model""",
"""Swinv2PreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_swinva import SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP, SwinvaConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_swinva import (
SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST,
SwinvaForImageClassification,
SwinvaForMaskedImageModeling,
SwinvaModel,
SwinvaPreTrainedModel,
)
else:
import sys
__UpperCamelCase : Optional[int] = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 328
| 0
|
lowercase_ = [
'''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
| 336
|
import argparse
from pathlib import Path
from transformers import AutoConfig, AutoTokenizer, RagConfig, RagSequenceForGeneration, RagTokenForGeneration
def lowerCAmelCase ( UpperCAmelCase, UpperCAmelCase, UpperCAmelCase, UpperCAmelCase, UpperCAmelCase = None, UpperCAmelCase = None, UpperCAmelCase = None, ) ->Tuple:
"""simple docstring"""
if config_name_or_path is None:
__magic_name__ : Dict = '''facebook/rag-token-base''' if model_type == '''rag_token''' else '''facebook/rag-sequence-base'''
if generator_tokenizer_name_or_path is None:
__magic_name__ : int = generator_name_or_path
if question_encoder_tokenizer_name_or_path is None:
__magic_name__ : Union[str, Any] = question_encoder_name_or_path
__magic_name__ : Dict = RagTokenForGeneration if model_type == '''rag_token''' else RagSequenceForGeneration
# Save model.
__magic_name__ : str = RagConfig.from_pretrained(UpperCAmelCase )
__magic_name__ : Dict = AutoConfig.from_pretrained(UpperCAmelCase )
__magic_name__ : str = AutoConfig.from_pretrained(UpperCAmelCase )
__magic_name__ : Tuple = gen_config
__magic_name__ : str = question_encoder_config
__magic_name__ : str = model_class.from_pretrained_question_encoder_generator(
UpperCAmelCase, UpperCAmelCase, config=UpperCAmelCase )
rag_model.save_pretrained(UpperCAmelCase )
# Sanity check.
model_class.from_pretrained(UpperCAmelCase )
# Save tokenizers.
__magic_name__ : Any = AutoTokenizer.from_pretrained(UpperCAmelCase )
gen_tokenizer.save_pretrained(dest_dir / '''generator_tokenizer/''' )
__magic_name__ : List[str] = AutoTokenizer.from_pretrained(UpperCAmelCase )
question_encoder_tokenizer.save_pretrained(dest_dir / '''question_encoder_tokenizer/''' )
if __name__ == "__main__":
lowercase_ = argparse.ArgumentParser()
parser.add_argument(
'''--model_type''',
choices=['''rag_sequence''', '''rag_token'''],
required=True,
type=str,
help='''RAG model type: rag_sequence, rag_token''',
)
parser.add_argument('''--dest''', type=str, required=True, help='''Path to the output checkpoint directory.''')
parser.add_argument('''--generator_name_or_path''', type=str, required=True, help='''Generator model identifier''')
parser.add_argument(
'''--question_encoder_name_or_path''', type=str, required=True, help='''Question encoder model identifier'''
)
parser.add_argument(
'''--generator_tokenizer_name_or_path''',
type=str,
help='''Generator tokenizer identifier, if not specified, resolves to ``generator_name_or_path``''',
)
parser.add_argument(
'''--question_encoder_tokenizer_name_or_path''',
type=str,
help='''Question encoder tokenizer identifier, if not specified, resolves to ``question_encoder_name_or_path``''',
)
parser.add_argument(
'''--config_name_or_path''',
type=str,
help=(
'''Identifier of the model config to use, if not provided, resolves to a base config for a given'''
''' ``model_type``'''
),
)
lowercase_ = parser.parse_args()
lowercase_ = Path(args.dest)
dest_dir.mkdir(exist_ok=True)
consolidate(
args.model_type,
args.generator_name_or_path,
args.question_encoder_name_or_path,
dest_dir,
args.config_name_or_path,
args.generator_tokenizer_name_or_path,
args.question_encoder_tokenizer_name_or_path,
)
| 336
| 1
|
import random
from .binary_exp_mod import bin_exp_mod
def a__ ( lowercase__ , lowercase__=1_0_0_0 ):
'''simple docstring'''
if n < 2:
return False
if n % 2 == 0:
return n == 2
# this means n is odd
UpperCAmelCase_ =n - 1
UpperCAmelCase_ =0
while d % 2 == 0:
d /= 2
exp += 1
# n - 1=d*(2**exp)
UpperCAmelCase_ =0
while count < prec:
UpperCAmelCase_ =random.randint(2 , n - 1 )
UpperCAmelCase_ =bin_exp_mod(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase )
if b != 1:
UpperCAmelCase_ =True
for _ in range(_lowerCamelCase ):
if b == n - 1:
UpperCAmelCase_ =False
break
UpperCAmelCase_ =b * b
b %= n
if flag:
return False
count += 1
return True
if __name__ == "__main__":
__lowercase : Any =abs(int(input("""Enter bound : """).strip()))
print("""Here\'s the list of primes:""")
print(""", """.join(str(i) for i in range(n + 1) if is_prime_big(i)))
| 54
|
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCamelCase__ : str = {'''configuration_vit_msn''': ['''VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ViTMSNConfig''']}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase__ : Tuple = [
'''VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''ViTMSNModel''',
'''ViTMSNForImageClassification''',
'''ViTMSNPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_vit_msn import VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTMSNConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_vit_msn import (
VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST,
ViTMSNForImageClassification,
ViTMSNModel,
ViTMSNPreTrainedModel,
)
else:
import sys
UpperCamelCase__ : Any = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 578
| 0
|
import unittest
from transformers import MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING, AutoTokenizer, is_vision_available
from transformers.pipelines import pipeline
from transformers.pipelines.document_question_answering import apply_tesseract
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_detectrona,
require_pytesseract,
require_tf,
require_torch,
require_vision,
slow,
)
from .test_pipelines_common import ANY
if is_vision_available():
from PIL import Image
from transformers.image_utils import load_image
else:
class _UpperCamelCase :
'''simple docstring'''
@staticmethod
def __UpperCamelCase ( *a : Any , **a : Optional[int] ) -> Optional[Any]:
"""simple docstring"""
pass
def lowerCamelCase__ ( _a):
return None
# This is a pinned image from a specific revision of a document question answering space, hosted by HuggingFace,
# so we can expect it to be available.
a_ = (
'https://huggingface.co/spaces/impira/docquery/resolve/2f6c96314dc84dfda62d40de9da55f2f5165d403/invoice.png'
)
@is_pipeline_test
@require_torch
@require_vision
class _UpperCamelCase ( unittest.TestCase ):
'''simple docstring'''
lowerCamelCase__ =MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING
@require_pytesseract
@require_vision
def __UpperCamelCase ( self : Tuple , a : Union[str, Any] , a : Any , a : Optional[Any] ) -> List[str]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : int = pipeline(
"document-question-answering" , model=a , tokenizer=a , image_processor=a )
SCREAMING_SNAKE_CASE : Dict = INVOICE_URL
SCREAMING_SNAKE_CASE : int = list(zip(*apply_tesseract(load_image(a ) , a , "" ) ) )
SCREAMING_SNAKE_CASE : Union[str, Any] = "What is the placebo?"
SCREAMING_SNAKE_CASE : List[str] = [
{
"image": load_image(a ),
"question": question,
},
{
"image": image,
"question": question,
},
{
"image": image,
"question": question,
"word_boxes": word_boxes,
},
]
return dqa_pipeline, examples
def __UpperCamelCase ( self : List[str] , a : List[Any] , a : Any ) -> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[int] = dqa_pipeline(a , top_k=2 )
self.assertEqual(
a , [
[
{"score": ANY(a ), "answer": ANY(a ), "start": ANY(a ), "end": ANY(a )},
{"score": ANY(a ), "answer": ANY(a ), "start": ANY(a ), "end": ANY(a )},
]
]
* 3 , )
@require_torch
@require_detectrona
@require_pytesseract
def __UpperCamelCase ( self : Optional[int] ) -> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : List[Any] = pipeline("document-question-answering" , model="hf-internal-testing/tiny-random-layoutlmv2" )
SCREAMING_SNAKE_CASE : int = INVOICE_URL
SCREAMING_SNAKE_CASE : int = "How many cats are there?"
SCREAMING_SNAKE_CASE : Optional[int] = [
{"score": 0.0001, "answer": "oy 2312/2019", "start": 38, "end": 39},
{"score": 0.0001, "answer": "oy 2312/2019 DUE", "start": 38, "end": 40},
]
SCREAMING_SNAKE_CASE : Union[str, Any] = dqa_pipeline(image=a , question=a , top_k=2 )
self.assertEqual(nested_simplify(a , decimals=4 ) , a )
SCREAMING_SNAKE_CASE : Any = dqa_pipeline({"image": image, "question": question} , top_k=2 )
self.assertEqual(nested_simplify(a , decimals=4 ) , a )
# This image does not detect ANY text in it, meaning layoutlmv2 should fail.
# Empty answer probably
SCREAMING_SNAKE_CASE : Tuple = "./tests/fixtures/tests_samples/COCO/000000039769.png"
SCREAMING_SNAKE_CASE : str = dqa_pipeline(image=a , question=a , top_k=2 )
self.assertEqual(a , [] )
# We can optionnally pass directly the words and bounding boxes
SCREAMING_SNAKE_CASE : Optional[int] = "./tests/fixtures/tests_samples/COCO/000000039769.png"
SCREAMING_SNAKE_CASE : Union[str, Any] = []
SCREAMING_SNAKE_CASE : int = []
SCREAMING_SNAKE_CASE : str = dqa_pipeline(image=a , question=a , words=a , boxes=a , top_k=2 )
self.assertEqual(a , [] )
@slow
@require_torch
@require_detectrona
@require_pytesseract
def __UpperCamelCase ( self : Optional[int] ) -> Union[str, Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : List[Any] = pipeline(
"document-question-answering" , model="tiennvcs/layoutlmv2-base-uncased-finetuned-docvqa" , revision="9977165" , )
SCREAMING_SNAKE_CASE : Any = INVOICE_URL
SCREAMING_SNAKE_CASE : Any = "What is the invoice number?"
SCREAMING_SNAKE_CASE : Dict = dqa_pipeline(image=a , question=a , top_k=2 )
self.assertEqual(
nested_simplify(a , decimals=4 ) , [
{"score": 0.9944, "answer": "us-001", "start": 16, "end": 16},
{"score": 0.0009, "answer": "us-001", "start": 16, "end": 16},
] , )
SCREAMING_SNAKE_CASE : Optional[Any] = dqa_pipeline({"image": image, "question": question} , top_k=2 )
self.assertEqual(
nested_simplify(a , decimals=4 ) , [
{"score": 0.9944, "answer": "us-001", "start": 16, "end": 16},
{"score": 0.0009, "answer": "us-001", "start": 16, "end": 16},
] , )
SCREAMING_SNAKE_CASE : Any = dqa_pipeline(
[{"image": image, "question": question}, {"image": image, "question": question}] , top_k=2 )
self.assertEqual(
nested_simplify(a , decimals=4 ) , [
[
{"score": 0.9944, "answer": "us-001", "start": 16, "end": 16},
{"score": 0.0009, "answer": "us-001", "start": 16, "end": 16},
],
]
* 2 , )
@slow
@require_torch
@require_detectrona
@require_pytesseract
def __UpperCamelCase ( self : Tuple ) -> Union[str, Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[Any] = pipeline(
"document-question-answering" , model="tiennvcs/layoutlmv2-base-uncased-finetuned-docvqa" , revision="9977165" , max_seq_len=50 , )
SCREAMING_SNAKE_CASE : Optional[Any] = INVOICE_URL
SCREAMING_SNAKE_CASE : int = "What is the invoice number?"
SCREAMING_SNAKE_CASE : Any = dqa_pipeline(image=a , question=a , top_k=2 )
self.assertEqual(
nested_simplify(a , decimals=4 ) , [
{"score": 0.9974, "answer": "1110212019", "start": 23, "end": 23},
{"score": 0.9948, "answer": "us-001", "start": 16, "end": 16},
] , )
SCREAMING_SNAKE_CASE : Tuple = dqa_pipeline({"image": image, "question": question} , top_k=2 )
self.assertEqual(
nested_simplify(a , decimals=4 ) , [
{"score": 0.9974, "answer": "1110212019", "start": 23, "end": 23},
{"score": 0.9948, "answer": "us-001", "start": 16, "end": 16},
] , )
SCREAMING_SNAKE_CASE : Union[str, Any] = dqa_pipeline(
[{"image": image, "question": question}, {"image": image, "question": question}] , top_k=2 )
self.assertEqual(
nested_simplify(a , decimals=4 ) , [
[
{"score": 0.9974, "answer": "1110212019", "start": 23, "end": 23},
{"score": 0.9948, "answer": "us-001", "start": 16, "end": 16},
]
]
* 2 , )
@slow
@require_torch
@require_pytesseract
@require_vision
def __UpperCamelCase ( self : Union[str, Any] ) -> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[int] = AutoTokenizer.from_pretrained(
"impira/layoutlm-document-qa" , revision="3dc6de3" , add_prefix_space=a )
SCREAMING_SNAKE_CASE : List[str] = pipeline(
"document-question-answering" , model="impira/layoutlm-document-qa" , tokenizer=a , revision="3dc6de3" , )
SCREAMING_SNAKE_CASE : List[str] = INVOICE_URL
SCREAMING_SNAKE_CASE : int = "What is the invoice number?"
SCREAMING_SNAKE_CASE : str = dqa_pipeline(image=a , question=a , top_k=2 )
self.assertEqual(
nested_simplify(a , decimals=4 ) , [
{"score": 0.4251, "answer": "us-001", "start": 16, "end": 16},
{"score": 0.0819, "answer": "1110212019", "start": 23, "end": 23},
] , )
SCREAMING_SNAKE_CASE : Union[str, Any] = dqa_pipeline({"image": image, "question": question} , top_k=2 )
self.assertEqual(
nested_simplify(a , decimals=4 ) , [
{"score": 0.4251, "answer": "us-001", "start": 16, "end": 16},
{"score": 0.0819, "answer": "1110212019", "start": 23, "end": 23},
] , )
SCREAMING_SNAKE_CASE : Dict = dqa_pipeline(
[{"image": image, "question": question}, {"image": image, "question": question}] , top_k=2 )
self.assertEqual(
nested_simplify(a , decimals=4 ) , [
[
{"score": 0.4251, "answer": "us-001", "start": 16, "end": 16},
{"score": 0.0819, "answer": "1110212019", "start": 23, "end": 23},
]
]
* 2 , )
SCREAMING_SNAKE_CASE : int = list(zip(*apply_tesseract(load_image(a ) , a , "" ) ) )
# This model should also work if `image` is set to None
SCREAMING_SNAKE_CASE : List[Any] = dqa_pipeline({"image": None, "word_boxes": word_boxes, "question": question} , top_k=2 )
self.assertEqual(
nested_simplify(a , decimals=4 ) , [
{"score": 0.4251, "answer": "us-001", "start": 16, "end": 16},
{"score": 0.0819, "answer": "1110212019", "start": 23, "end": 23},
] , )
@slow
@require_torch
@require_pytesseract
@require_vision
def __UpperCamelCase ( self : Tuple ) -> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : int = AutoTokenizer.from_pretrained(
"impira/layoutlm-document-qa" , revision="3dc6de3" , add_prefix_space=a )
SCREAMING_SNAKE_CASE : Dict = pipeline(
"document-question-answering" , model="impira/layoutlm-document-qa" , tokenizer=a , revision="3dc6de3" , max_seq_len=50 , )
SCREAMING_SNAKE_CASE : Union[str, Any] = INVOICE_URL
SCREAMING_SNAKE_CASE : List[Any] = "What is the invoice number?"
SCREAMING_SNAKE_CASE : Dict = dqa_pipeline(image=a , question=a , top_k=2 )
self.assertEqual(
nested_simplify(a , decimals=4 ) , [
{"score": 0.9999, "answer": "us-001", "start": 16, "end": 16},
{"score": 0.9998, "answer": "us-001", "start": 16, "end": 16},
] , )
SCREAMING_SNAKE_CASE : int = dqa_pipeline(
[{"image": image, "question": question}, {"image": image, "question": question}] , top_k=2 )
self.assertEqual(
nested_simplify(a , decimals=4 ) , [
[
{"score": 0.9999, "answer": "us-001", "start": 16, "end": 16},
{"score": 0.9998, "answer": "us-001", "start": 16, "end": 16},
]
]
* 2 , )
SCREAMING_SNAKE_CASE : int = list(zip(*apply_tesseract(load_image(a ) , a , "" ) ) )
# This model should also work if `image` is set to None
SCREAMING_SNAKE_CASE : Any = dqa_pipeline({"image": None, "word_boxes": word_boxes, "question": question} , top_k=2 )
self.assertEqual(
nested_simplify(a , decimals=4 ) , [
{"score": 0.9999, "answer": "us-001", "start": 16, "end": 16},
{"score": 0.9998, "answer": "us-001", "start": 16, "end": 16},
] , )
@slow
@require_torch
def __UpperCamelCase ( self : str ) -> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE : List[str] = pipeline(
"document-question-answering" , model="naver-clova-ix/donut-base-finetuned-docvqa" , tokenizer=AutoTokenizer.from_pretrained("naver-clova-ix/donut-base-finetuned-docvqa" ) , feature_extractor="naver-clova-ix/donut-base-finetuned-docvqa" , )
SCREAMING_SNAKE_CASE : int = INVOICE_URL
SCREAMING_SNAKE_CASE : Union[str, Any] = "What is the invoice number?"
SCREAMING_SNAKE_CASE : Union[str, Any] = dqa_pipeline(image=a , question=a , top_k=2 )
self.assertEqual(nested_simplify(a , decimals=4 ) , [{"answer": "us-001"}] )
@require_tf
@unittest.skip("Document question answering not implemented in TF" )
def __UpperCamelCase ( self : List[str] ) -> int:
"""simple docstring"""
pass
| 193
|
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a_ = logging.get_logger(__name__)
a_ = {
'microsoft/wavlm-base': 'https://huggingface.co/microsoft/wavlm-base/resolve/main/config.json',
# See all WavLM models at https://huggingface.co/models?filter=wavlm
}
class _UpperCamelCase ( __A ):
'''simple docstring'''
lowerCamelCase__ ='wavlm'
def __init__( self : Optional[int] , a : Optional[Any]=32 , a : int=768 , a : Tuple=12 , a : List[str]=12 , a : str=3072 , a : Any="gelu" , a : Dict=0.1 , a : int=0.1 , a : str=0.1 , a : Optional[Any]=0.0 , a : Any=0.1 , a : Any=0.1 , a : List[str]=0.02 , a : List[Any]=1e-5 , a : Any="group" , a : Optional[int]="gelu" , a : List[str]=(512, 512, 512, 512, 512, 512, 512) , a : Any=(5, 2, 2, 2, 2, 2, 2) , a : Union[str, Any]=(10, 3, 3, 3, 3, 2, 2) , a : Optional[Any]=False , a : Dict=128 , a : Optional[Any]=16 , a : Optional[Any]=320 , a : str=800 , a : Optional[int]=False , a : Tuple=True , a : Optional[Any]=0.05 , a : Any=10 , a : Optional[int]=2 , a : Dict=0.0 , a : str=10 , a : Tuple=320 , a : Optional[int]=2 , a : int=0.1 , a : List[str]=100 , a : Tuple=256 , a : str=256 , a : Tuple=0.1 , a : str="mean" , a : int=False , a : int=False , a : Optional[Any]=256 , a : Any=(512, 512, 512, 512, 1500) , a : Tuple=(5, 3, 3, 1, 1) , a : str=(1, 2, 3, 1, 1) , a : Optional[Any]=512 , a : Optional[Any]=80 , a : Tuple=0 , a : Any=1 , a : Optional[Any]=2 , a : int=False , a : Dict=3 , a : Any=2 , a : List[Any]=3 , a : int=None , **a : Any , ) -> Dict:
"""simple docstring"""
super().__init__(**a , pad_token_id=a , bos_token_id=a , eos_token_id=a )
SCREAMING_SNAKE_CASE : List[str] = hidden_size
SCREAMING_SNAKE_CASE : Union[str, Any] = feat_extract_norm
SCREAMING_SNAKE_CASE : Any = feat_extract_activation
SCREAMING_SNAKE_CASE : Any = list(a )
SCREAMING_SNAKE_CASE : Optional[int] = list(a )
SCREAMING_SNAKE_CASE : Optional[Any] = list(a )
SCREAMING_SNAKE_CASE : Any = conv_bias
SCREAMING_SNAKE_CASE : str = num_buckets
SCREAMING_SNAKE_CASE : str = max_bucket_distance
SCREAMING_SNAKE_CASE : List[str] = num_conv_pos_embeddings
SCREAMING_SNAKE_CASE : Any = num_conv_pos_embedding_groups
SCREAMING_SNAKE_CASE : Union[str, Any] = len(self.conv_dim )
SCREAMING_SNAKE_CASE : List[Any] = num_hidden_layers
SCREAMING_SNAKE_CASE : List[str] = intermediate_size
SCREAMING_SNAKE_CASE : Dict = hidden_act
SCREAMING_SNAKE_CASE : Union[str, Any] = num_attention_heads
SCREAMING_SNAKE_CASE : int = hidden_dropout
SCREAMING_SNAKE_CASE : Optional[int] = attention_dropout
SCREAMING_SNAKE_CASE : List[str] = activation_dropout
SCREAMING_SNAKE_CASE : int = feat_proj_dropout
SCREAMING_SNAKE_CASE : Any = final_dropout
SCREAMING_SNAKE_CASE : Optional[int] = layerdrop
SCREAMING_SNAKE_CASE : Optional[int] = layer_norm_eps
SCREAMING_SNAKE_CASE : int = initializer_range
SCREAMING_SNAKE_CASE : Dict = num_ctc_classes
SCREAMING_SNAKE_CASE : Tuple = vocab_size
SCREAMING_SNAKE_CASE : int = do_stable_layer_norm
SCREAMING_SNAKE_CASE : List[Any] = use_weighted_layer_sum
SCREAMING_SNAKE_CASE : List[Any] = classifier_proj_size
if (
(len(self.conv_stride ) != self.num_feat_extract_layers)
or (len(self.conv_kernel ) != self.num_feat_extract_layers)
or (len(self.conv_dim ) != self.num_feat_extract_layers)
):
raise ValueError(
"Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =="
" `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ="
F" {len(self.conv_dim )}`, `len(config.conv_stride) = {len(self.conv_stride )}`,"
F" `len(config.conv_kernel) = {len(self.conv_kernel )}`." )
# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
SCREAMING_SNAKE_CASE : int = apply_spec_augment
SCREAMING_SNAKE_CASE : int = mask_time_prob
SCREAMING_SNAKE_CASE : Union[str, Any] = mask_time_length
SCREAMING_SNAKE_CASE : List[Any] = mask_time_min_masks
SCREAMING_SNAKE_CASE : Tuple = mask_feature_prob
SCREAMING_SNAKE_CASE : List[str] = mask_feature_length
# parameters for pretraining with codevector quantized representations
SCREAMING_SNAKE_CASE : str = num_codevectors_per_group
SCREAMING_SNAKE_CASE : Dict = num_codevector_groups
SCREAMING_SNAKE_CASE : Tuple = contrastive_logits_temperature
SCREAMING_SNAKE_CASE : List[Any] = num_negatives
SCREAMING_SNAKE_CASE : Optional[int] = codevector_dim
SCREAMING_SNAKE_CASE : int = proj_codevector_dim
SCREAMING_SNAKE_CASE : List[Any] = diversity_loss_weight
# ctc loss
SCREAMING_SNAKE_CASE : Any = ctc_loss_reduction
SCREAMING_SNAKE_CASE : Dict = ctc_zero_infinity
# adapter
SCREAMING_SNAKE_CASE : Any = add_adapter
SCREAMING_SNAKE_CASE : Optional[int] = adapter_kernel_size
SCREAMING_SNAKE_CASE : Any = adapter_stride
SCREAMING_SNAKE_CASE : List[Any] = num_adapter_layers
SCREAMING_SNAKE_CASE : int = output_hidden_size or hidden_size
# SequenceClassification-specific parameter. Feel free to ignore for other classes.
SCREAMING_SNAKE_CASE : List[Any] = classifier_proj_size
# XVector-specific parameters. Feel free to ignore for other classes.
SCREAMING_SNAKE_CASE : Union[str, Any] = list(a )
SCREAMING_SNAKE_CASE : Union[str, Any] = list(a )
SCREAMING_SNAKE_CASE : Union[str, Any] = list(a )
SCREAMING_SNAKE_CASE : Tuple = xvector_output_dim
@property
def __UpperCamelCase ( self : int ) -> Union[str, Any]:
"""simple docstring"""
return functools.reduce(operator.mul , self.conv_stride , 1 )
| 193
| 1
|
import argparse
import collections
import os
import re
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_table.py
snake_case__ : str = 'src/transformers'
snake_case__ : str = 'docs/source/en'
snake_case__ : int = '.'
def __lowerCamelCase ( A__ : List[Any] , A__ : Any , A__ : Optional[Any] ) -> Dict:
with open(a__ , """r""" , encoding="""utf-8""" , newline="""\n""" ) as f:
lowerCamelCase_ : Dict = f.readlines()
# Find the start prompt.
lowerCamelCase_ : Tuple = 0
while not lines[start_index].startswith(a__ ):
start_index += 1
start_index += 1
lowerCamelCase_ : List[Any] = start_index
while not lines[end_index].startswith(a__ ):
end_index += 1
end_index -= 1
while len(lines[start_index] ) <= 1:
start_index += 1
while len(lines[end_index] ) <= 1:
end_index -= 1
end_index += 1
return "".join(lines[start_index:end_index] ), start_index, end_index, lines
# Add here suffixes that are used to identify models, separated by |
snake_case__ : Optional[Any] = 'Model|Encoder|Decoder|ForConditionalGeneration'
# Regexes that match TF/Flax/PT model names.
snake_case__ : Optional[int] = re.compile(R'TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')
snake_case__ : Optional[int] = re.compile(R'Flax(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')
# Will match any TF or Flax model too so need to be in an else branch afterthe two previous regexes.
snake_case__ : int = re.compile(R'(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')
# This is to make sure the transformers module imported is the one in the repo.
snake_case__ : Optional[Any] = direct_transformers_import(TRANSFORMERS_PATH)
def __lowerCamelCase ( A__ : int ) -> Optional[int]:
lowerCamelCase_ : List[str] = re.finditer(""".+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)""" , a__ )
return [m.group(0 ) for m in matches]
def __lowerCamelCase ( A__ : List[str] , A__ : List[str] ) -> int:
lowerCamelCase_ : Optional[Any] = 2 if text == """✅""" or text == """❌""" else len(a__ )
lowerCamelCase_ : Optional[int] = (width - text_length) // 2
lowerCamelCase_ : Tuple = width - text_length - left_indent
return " " * left_indent + text + " " * right_indent
def __lowerCamelCase ( ) -> Any:
lowerCamelCase_ : List[str] = transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
lowerCamelCase_ : Any = {
name: config_maping_names[code]
for code, name in transformers_module.MODEL_NAMES_MAPPING.items()
if code in config_maping_names
}
lowerCamelCase_ : List[Any] = {name: config.replace("""Config""" , """""" ) for name, config in model_name_to_config.items()}
# Dictionaries flagging if each model prefix has a slow/fast tokenizer, backend in PT/TF/Flax.
lowerCamelCase_ : Optional[Any] = collections.defaultdict(a__ )
lowerCamelCase_ : Optional[int] = collections.defaultdict(a__ )
lowerCamelCase_ : int = collections.defaultdict(a__ )
lowerCamelCase_ : str = collections.defaultdict(a__ )
lowerCamelCase_ : Union[str, Any] = collections.defaultdict(a__ )
# Let's lookup through all transformers object (once).
for attr_name in dir(a__ ):
lowerCamelCase_ : int = None
if attr_name.endswith("""Tokenizer""" ):
lowerCamelCase_ : str = slow_tokenizers
lowerCamelCase_ : Any = attr_name[:-9]
elif attr_name.endswith("""TokenizerFast""" ):
lowerCamelCase_ : List[Any] = fast_tokenizers
lowerCamelCase_ : Dict = attr_name[:-13]
elif _re_tf_models.match(a__ ) is not None:
lowerCamelCase_ : Any = tf_models
lowerCamelCase_ : Any = _re_tf_models.match(a__ ).groups()[0]
elif _re_flax_models.match(a__ ) is not None:
lowerCamelCase_ : Dict = flax_models
lowerCamelCase_ : Dict = _re_flax_models.match(a__ ).groups()[0]
elif _re_pt_models.match(a__ ) is not None:
lowerCamelCase_ : List[Any] = pt_models
lowerCamelCase_ : int = _re_pt_models.match(a__ ).groups()[0]
if lookup_dict is not None:
while len(a__ ) > 0:
if attr_name in model_name_to_prefix.values():
lowerCamelCase_ : List[str] = True
break
# Try again after removing the last word in the name
lowerCamelCase_ : int = """""".join(camel_case_split(a__ )[:-1] )
# Let's build that table!
lowerCamelCase_ : Optional[int] = list(model_name_to_config.keys() )
model_names.sort(key=str.lower )
lowerCamelCase_ : Optional[int] = ["""Model""", """Tokenizer slow""", """Tokenizer fast""", """PyTorch support""", """TensorFlow support""", """Flax Support"""]
# We'll need widths to properly display everything in the center (+2 is to leave one extra space on each side).
lowerCamelCase_ : Union[str, Any] = [len(a__ ) + 2 for c in columns]
lowerCamelCase_ : Union[str, Any] = max([len(a__ ) for name in model_names] ) + 2
# Build the table per se
lowerCamelCase_ : str = """|""" + """|""".join([_center_text(a__ , a__ ) for c, w in zip(a__ , a__ )] ) + """|\n"""
# Use ":-----:" format to center-aligned table cell texts
table += "|" + "|".join([""":""" + """-""" * (w - 2) + """:""" for w in widths] ) + "|\n"
lowerCamelCase_ : Union[str, Any] = {True: """✅""", False: """❌"""}
for name in model_names:
lowerCamelCase_ : Union[str, Any] = model_name_to_prefix[name]
lowerCamelCase_ : List[Any] = [
name,
check[slow_tokenizers[prefix]],
check[fast_tokenizers[prefix]],
check[pt_models[prefix]],
check[tf_models[prefix]],
check[flax_models[prefix]],
]
table += "|" + "|".join([_center_text(a__ , a__ ) for l, w in zip(a__ , a__ )] ) + "|\n"
return table
def __lowerCamelCase ( A__ : Dict=False ) -> List[Any]:
lowerCamelCase_, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_ : List[str] = _find_text_in_file(
filename=os.path.join(a__ , """index.md""" ) , start_prompt="""<!--This table is updated automatically from the auto modules""" , end_prompt="""<!-- End table-->""" , )
lowerCamelCase_ : str = get_model_table_from_auto_modules()
if current_table != new_table:
if overwrite:
with open(os.path.join(a__ , """index.md""" ) , """w""" , encoding="""utf-8""" , newline="""\n""" ) as f:
f.writelines(lines[:start_index] + [new_table] + lines[end_index:] )
else:
raise ValueError(
"""The model table in the `index.md` has not been updated. Run `make fix-copies` to fix this.""" )
if __name__ == "__main__":
snake_case__ : str = argparse.ArgumentParser()
parser.add_argument('--fix_and_overwrite', action='store_true', help='Whether to fix inconsistencies.')
snake_case__ : int = parser.parse_args()
check_model_table(args.fix_and_overwrite)
| 278
|
from __future__ import annotations
from bisect import bisect_left
from functools import total_ordering
from heapq import merge
@total_ordering
class __A( a ):
def __lt__( self , _snake_case ) -> Dict:
'''simple docstring'''
return self[-1] < other[-1]
def __eq__( self , _snake_case ) -> Dict:
'''simple docstring'''
return self[-1] == other[-1]
def __lowerCAmelCase ( a__ ) -> list:
__a = []
# sort into stacks
for element in collection:
__a = Stack([element] )
__a = bisect_left(a__ , a__ )
if i != len(a__ ):
stacks[i].append(a__ )
else:
stacks.append(a__ )
# use a heap-based merge to merge stack efficiently
__a = merge(*(reversed(a__ ) for stack in stacks) )
return collection
if __name__ == "__main__":
A : List[Any] = input('Enter numbers separated by a comma:\n').strip()
A : Any = [int(item) for item in user_input.split(',')]
print(patience_sort(unsorted))
| 219
| 0
|
'''simple docstring'''
import logging
from pathlib import Path
import numpy as np
import pytorch_lightning as pl
import torch
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
from pytorch_lightning.utilities import rank_zero_only
from utils_rag import save_json
def __A ( lowerCAmelCase_ ):
_UpperCAmelCase : Optional[int] = filter(lambda lowerCAmelCase_ : p.requires_grad , model.parameters() )
_UpperCAmelCase : Any = sum([np.prod(p.size() ) for p in model_parameters] )
return params
lowerCAmelCase_ : Optional[int] = logging.getLogger(__name__)
def __A ( lowerCAmelCase_ , lowerCAmelCase_ ):
if metric == "rouge2":
_UpperCAmelCase : List[Any] = """{val_avg_rouge2:.4f}-{step_count}"""
elif metric == "bleu":
_UpperCAmelCase : str = """{val_avg_bleu:.4f}-{step_count}"""
elif metric == "em":
_UpperCAmelCase : Tuple = """{val_avg_em:.4f}-{step_count}"""
elif metric == "loss":
_UpperCAmelCase : Any = """{val_avg_loss:.4f}-{step_count}"""
else:
raise NotImplementedError(
f"seq2seq callbacks only support rouge2 and bleu, got {metric}, You can make your own by adding to this"
""" function.""" )
_UpperCAmelCase : Union[str, Any] = ModelCheckpoint(
dirpath=lowerCAmelCase_ , filename=lowerCAmelCase_ , monitor=f"val_{metric}" , mode="""max""" , save_top_k=1 , every_n_epochs=1 , )
return checkpoint_callback
def __A ( lowerCAmelCase_ , lowerCAmelCase_ ):
return EarlyStopping(
monitor=f"val_{metric}" , mode="""min""" if """loss""" in metric else """max""" , patience=lowerCAmelCase_ , verbose=lowerCAmelCase_ , )
class __lowerCAmelCase ( pl.Callback ):
def snake_case_ (self , lowerCAmelCase__ , lowerCAmelCase__ ):
_UpperCAmelCase : Dict = {F"lr_group_{i}": param["""lr"""] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups )}
pl_module.logger.log_metrics(lowerCAmelCase__ )
@rank_zero_only
def snake_case_ (self , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__=True ):
logger.info(F"***** {type_path} results at step {trainer.global_step:05d} *****" )
_UpperCAmelCase : Optional[Any] = trainer.callback_metrics
trainer.logger.log_metrics({k: v for k, v in metrics.items() if k not in ["""log""", """progress_bar""", """preds"""]} )
# Log results
_UpperCAmelCase : List[str] = Path(pl_module.hparams.output_dir )
if type_path == "test":
_UpperCAmelCase : int = od / """test_results.txt"""
_UpperCAmelCase : Dict = od / """test_generations.txt"""
else:
# this never gets hit. I prefer not to save intermediate generations, and results are in metrics.json
# If people want this it will be easy enough to add back.
_UpperCAmelCase : Union[str, Any] = od / F"{type_path}_results/{trainer.global_step:05d}.txt"
_UpperCAmelCase : int = od / F"{type_path}_generations/{trainer.global_step:05d}.txt"
results_file.parent.mkdir(exist_ok=lowerCAmelCase__ )
generations_file.parent.mkdir(exist_ok=lowerCAmelCase__ )
with open(lowerCAmelCase__ , """a+""" ) as writer:
for key in sorted(lowerCAmelCase__ ):
if key in ["log", "progress_bar", "preds"]:
continue
_UpperCAmelCase : Union[str, Any] = metrics[key]
if isinstance(lowerCAmelCase__ , torch.Tensor ):
_UpperCAmelCase : Union[str, Any] = val.item()
_UpperCAmelCase : str = F"{key}: {val:.6f}\n"
writer.write(lowerCAmelCase__ )
if not save_generations:
return
if "preds" in metrics:
_UpperCAmelCase : Dict = """\n""".join(metrics["""preds"""] )
generations_file.open("""w+""" ).write(lowerCAmelCase__ )
@rank_zero_only
def snake_case_ (self , lowerCAmelCase__ , lowerCAmelCase__ ):
try:
_UpperCAmelCase : Any = pl_module.model.model.num_parameters()
except AttributeError:
_UpperCAmelCase : Optional[Any] = pl_module.model.num_parameters()
_UpperCAmelCase : List[Any] = count_trainable_parameters(lowerCAmelCase__ )
# mp stands for million parameters
trainer.logger.log_metrics({"""n_params""": npars, """mp""": npars / 1e6, """grad_mp""": n_trainable_pars / 1e6} )
@rank_zero_only
def snake_case_ (self , lowerCAmelCase__ , lowerCAmelCase__ ):
save_json(pl_module.metrics , pl_module.metrics_save_path )
return self._write_logs(lowerCAmelCase__ , lowerCAmelCase__ , """test""" )
@rank_zero_only
def snake_case_ (self , lowerCAmelCase__ , lowerCAmelCase__ ):
save_json(pl_module.metrics , pl_module.metrics_save_path )
# Uncommenting this will save val generations
# return self._write_logs(trainer, pl_module, "valid")
| 156
|
'''simple docstring'''
import argparse
import re
import torch
from CLAP import create_model
from transformers import AutoFeatureExtractor, ClapConfig, ClapModel
lowerCAmelCase_ : List[Any] = {
'''text_branch''': '''text_model''',
'''audio_branch''': '''audio_model.audio_encoder''',
'''attn''': '''attention.self''',
'''self.proj''': '''output.dense''',
'''attention.self_mask''': '''attn_mask''',
'''mlp.fc1''': '''intermediate.dense''',
'''mlp.fc2''': '''output.dense''',
'''norm1''': '''layernorm_before''',
'''norm2''': '''layernorm_after''',
'''bn0''': '''batch_norm''',
}
lowerCAmelCase_ : Any = AutoFeatureExtractor.from_pretrained('''laion/clap-htsat-unfused''', truncation='''rand_trunc''')
def __A ( lowerCAmelCase_ , lowerCAmelCase_=False ):
_UpperCAmelCase , _UpperCAmelCase : Optional[Any] = create_model(
"""HTSAT-tiny""" , """roberta""" , lowerCAmelCase_ , precision="""fp32""" , device="""cuda:0""" if torch.cuda.is_available() else """cpu""" , enable_fusion=lowerCAmelCase_ , fusion_type="""aff_2d""" if enable_fusion else None , )
return model, model_cfg
def __A ( lowerCAmelCase_ ):
_UpperCAmelCase : Any = {}
_UpperCAmelCase : List[str] = r""".*sequential.(\d+).*"""
_UpperCAmelCase : int = r""".*_projection.(\d+).*"""
for key, value in state_dict.items():
# check if any key needs to be modified
for key_to_modify, new_key in KEYS_TO_MODIFY_MAPPING.items():
if key_to_modify in key:
_UpperCAmelCase : Optional[int] = key.replace(lowerCAmelCase_ , lowerCAmelCase_ )
if re.match(lowerCAmelCase_ , lowerCAmelCase_ ):
# replace sequential layers with list
_UpperCAmelCase : int = re.match(lowerCAmelCase_ , lowerCAmelCase_ ).group(1 )
_UpperCAmelCase : Any = key.replace(f"sequential.{sequential_layer}." , f"layers.{int(lowerCAmelCase_ )//3}.linear." )
elif re.match(lowerCAmelCase_ , lowerCAmelCase_ ):
_UpperCAmelCase : Tuple = int(re.match(lowerCAmelCase_ , lowerCAmelCase_ ).group(1 ) )
# Because in CLAP they use `nn.Sequential`...
_UpperCAmelCase : str = 1 if projecton_layer == 0 else 2
_UpperCAmelCase : Dict = key.replace(f"_projection.{projecton_layer}." , f"_projection.linear{transformers_projection_layer}." )
if "audio" and "qkv" in key:
# split qkv into query key and value
_UpperCAmelCase : Dict = value
_UpperCAmelCase : Optional[Any] = mixed_qkv.size(0 ) // 3
_UpperCAmelCase : Dict = mixed_qkv[:qkv_dim]
_UpperCAmelCase : Union[str, Any] = mixed_qkv[qkv_dim : qkv_dim * 2]
_UpperCAmelCase : Any = mixed_qkv[qkv_dim * 2 :]
_UpperCAmelCase : Dict = query_layer
_UpperCAmelCase : Optional[Any] = key_layer
_UpperCAmelCase : Tuple = value_layer
else:
_UpperCAmelCase : str = value
return model_state_dict
def __A ( lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_=False ):
_UpperCAmelCase , _UpperCAmelCase : Union[str, Any] = init_clap(lowerCAmelCase_ , enable_fusion=lowerCAmelCase_ )
clap_model.eval()
_UpperCAmelCase : Union[str, Any] = clap_model.state_dict()
_UpperCAmelCase : Optional[Any] = rename_state_dict(lowerCAmelCase_ )
_UpperCAmelCase : Any = ClapConfig()
_UpperCAmelCase : Dict = enable_fusion
_UpperCAmelCase : List[Any] = ClapModel(lowerCAmelCase_ )
# ignore the spectrogram embedding layer
model.load_state_dict(lowerCAmelCase_ , strict=lowerCAmelCase_ )
model.save_pretrained(lowerCAmelCase_ )
transformers_config.save_pretrained(lowerCAmelCase_ )
if __name__ == "__main__":
lowerCAmelCase_ : List[str] = 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('''--config_path''', default=None, type=str, help='''Path to hf config.json of model to convert''')
parser.add_argument('''--enable_fusion''', action='''store_true''', help='''Whether to enable fusion or not''')
lowerCAmelCase_ : Dict = parser.parse_args()
convert_clap_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.enable_fusion)
| 156
| 1
|
import pytest
from datasets import inspect_metric, list_metrics, load_metric
@pytest.fixture
def _A ( _lowercase ) -> Tuple:
"""simple docstring"""
monkeypatch.setattr('datasets.utils.deprecation_utils._emitted_deprecation_warnings' , set() )
@pytest.fixture
def _A ( _lowercase ) -> int:
"""simple docstring"""
class __lowerCamelCase :
def __init__( self: Optional[Any],A_: Tuple ):
'''simple docstring'''
__UpperCamelCase = metric_id
class __lowerCamelCase :
_lowercase = [MetricMock(_a ) for metric_id in ["""accuracy""", """mse""", """precision""", """codeparrot/apps_metric"""]]
def snake_case_ ( self: Any ):
'''simple docstring'''
return self._metrics
monkeypatch.setattr('datasets.inspect.huggingface_hub' , HfhMock() )
@pytest.mark.parametrize(
'func, args' , [(load_metric, ('metrics/mse',)), (list_metrics, ()), (inspect_metric, ('metrics/mse', 'tmp_path'))] )
def _A ( _lowercase , _lowercase , _lowercase , _lowercase , _lowercase ) -> str:
"""simple docstring"""
if "tmp_path" in args:
__UpperCamelCase = tuple(arg if arg != 'tmp_path' else tmp_path for arg in args )
with pytest.warns(_lowercase , match='https://huggingface.co/docs/evaluate' ):
func(*_lowercase )
| 1
|
"""simple docstring"""
from typing import List, Optional, Tuple, Union
import torch
from ...utils import logging, randn_tensor
from ..pipeline_utils import AudioPipelineOutput, DiffusionPipeline
_lowerCAmelCase = logging.get_logger(__name__) # pylint: disable=invalid-name
class __UpperCamelCase ( a__ ):
def __init__( self ,_A ,_A ):
'''simple docstring'''
super().__init__()
self.register_modules(unet=_A ,scheduler=_A )
@torch.no_grad()
def __call__( self ,_A = 1 ,_A = 100 ,_A = None ,_A = None ,_A = True ,):
'''simple docstring'''
if audio_length_in_s is None:
_lowerCAmelCase : str = self.unet.config.sample_size / self.unet.config.sample_rate
_lowerCAmelCase : List[str] = audio_length_in_s * self.unet.config.sample_rate
_lowerCAmelCase : int = 2 ** len(self.unet.up_blocks )
if sample_size < 3 * down_scale_factor:
raise ValueError(
F"""{audio_length_in_s} is too small. Make sure it's bigger or equal to"""
F""" {3 * down_scale_factor / self.unet.config.sample_rate}.""" )
_lowerCAmelCase : Tuple = int(_A )
if sample_size % down_scale_factor != 0:
_lowerCAmelCase : Any = (
(audio_length_in_s * self.unet.config.sample_rate) // down_scale_factor + 1
) * down_scale_factor
logger.info(
F"""{audio_length_in_s} is increased to {sample_size / self.unet.config.sample_rate} so that it can be handled"""
F""" by the model. It will be cut to {original_sample_size / self.unet.config.sample_rate} after the denoising"""
' process.' )
_lowerCAmelCase : List[Any] = int(_A )
_lowerCAmelCase : Dict = next(iter(self.unet.parameters() ) ).dtype
_lowerCAmelCase : Optional[int] = (batch_size, self.unet.config.in_channels, sample_size)
if isinstance(_A ,_A ) and len(_A ) != batch_size:
raise ValueError(
F"""You have passed a list of generators of length {len(_A )}, but requested an effective batch"""
F""" size of {batch_size}. Make sure the batch size matches the length of the generators.""" )
_lowerCAmelCase : List[Any] = randn_tensor(_A ,generator=_A ,device=self.device ,dtype=_A )
# set step values
self.scheduler.set_timesteps(_A ,device=audio.device )
_lowerCAmelCase : List[Any] = self.scheduler.timesteps.to(_A )
for t in self.progress_bar(self.scheduler.timesteps ):
# 1. predict noise model_output
_lowerCAmelCase : Any = self.unet(_A ,_A ).sample
# 2. compute previous image: x_t -> t_t-1
_lowerCAmelCase : Tuple = self.scheduler.step(_A ,_A ,_A ).prev_sample
_lowerCAmelCase : List[Any] = audio.clamp(-1 ,1 ).float().cpu().numpy()
_lowerCAmelCase : Optional[Any] = audio[:, :, :original_sample_size]
if not return_dict:
return (audio,)
return AudioPipelineOutput(audios=_A )
| 259
| 0
|
'''simple docstring'''
def SCREAMING_SNAKE_CASE__ ( lowerCAmelCase_ : int ,lowerCAmelCase_ : list[int] ,lowerCAmelCase_ : int ) -> int:
"""simple docstring"""
def count_of_possible_combinations(lowerCAmelCase_ : int ) -> int:
if target < 0:
return 0
if target == 0:
return 1
return sum(count_of_possible_combinations(target - item ) for item in array )
return count_of_possible_combinations(lowerCAmelCase_ )
def SCREAMING_SNAKE_CASE__ ( lowerCAmelCase_ : int ,lowerCAmelCase_ : list[int] ,lowerCAmelCase_ : int ) -> int:
"""simple docstring"""
def count_of_possible_combinations_with_dp_array(
lowerCAmelCase_ : int ,lowerCAmelCase_ : list[int] ) -> int:
if target < 0:
return 0
if target == 0:
return 1
if dp_array[target] != -1:
return dp_array[target]
SCREAMING_SNAKE_CASE_ : List[Any] =sum(
count_of_possible_combinations_with_dp_array(target - item ,lowerCAmelCase_ )
for item in array )
SCREAMING_SNAKE_CASE_ : List[str] =answer
return answer
SCREAMING_SNAKE_CASE_ : Optional[int] =[-1] * (target + 1)
return count_of_possible_combinations_with_dp_array(lowerCAmelCase_ ,lowerCAmelCase_ )
def SCREAMING_SNAKE_CASE__ ( lowerCAmelCase_ : int ,lowerCAmelCase_ : list[int] ,lowerCAmelCase_ : int ) -> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Tuple =[0] * (target + 1)
SCREAMING_SNAKE_CASE_ : List[Any] =1
for i in range(1 ,target + 1 ):
for j in range(lowerCAmelCase_ ):
if i - array[j] >= 0:
dp_array[i] += dp_array[i - array[j]]
return dp_array[target]
if __name__ == "__main__":
import doctest
doctest.testmod()
__SCREAMING_SNAKE_CASE = 3
__SCREAMING_SNAKE_CASE = 5
__SCREAMING_SNAKE_CASE = [1, 2, 5]
print(combination_sum_iv(n, array, target))
| 720
|
import os
import textwrap
import pyarrow as pa
import pytest
from datasets import ClassLabel, Features, Image
from datasets.packaged_modules.csv.csv import Csv
from ..utils import require_pil
@pytest.fixture
def SCREAMING_SNAKE_CASE__ ( lowerCAmelCase_ : int ) -> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : int =tmp_path / 'file.csv'
SCREAMING_SNAKE_CASE_ : Union[str, Any] =textwrap.dedent(
'\\n header1,header2\n 1,2\n 10,20\n ' )
with open(lowerCAmelCase_ ,'w' ) as f:
f.write(lowerCAmelCase_ )
return str(lowerCAmelCase_ )
@pytest.fixture
def SCREAMING_SNAKE_CASE__ ( lowerCAmelCase_ : int ) -> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : List[str] =tmp_path / 'malformed_file.csv'
SCREAMING_SNAKE_CASE_ : Tuple =textwrap.dedent(
'\\n header1,header2\n 1,2\n 10,20,\n ' )
with open(lowerCAmelCase_ ,'w' ) as f:
f.write(lowerCAmelCase_ )
return str(lowerCAmelCase_ )
@pytest.fixture
def SCREAMING_SNAKE_CASE__ ( lowerCAmelCase_ : List[Any] ,lowerCAmelCase_ : List[Any] ) -> List[str]:
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Tuple =tmp_path / 'csv_with_image.csv'
SCREAMING_SNAKE_CASE_ : str =textwrap.dedent(
F"""\
image
{image_file}
""" )
with open(lowerCAmelCase_ ,'w' ) as f:
f.write(lowerCAmelCase_ )
return str(lowerCAmelCase_ )
@pytest.fixture
def SCREAMING_SNAKE_CASE__ ( lowerCAmelCase_ : Union[str, Any] ) -> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Optional[int] =tmp_path / 'csv_with_label.csv'
SCREAMING_SNAKE_CASE_ : str =textwrap.dedent(
'\\n label\n good\n bad\n good\n ' )
with open(lowerCAmelCase_ ,'w' ) as f:
f.write(lowerCAmelCase_ )
return str(lowerCAmelCase_ )
@pytest.fixture
def SCREAMING_SNAKE_CASE__ ( lowerCAmelCase_ : Any ) -> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : int =tmp_path / 'csv_with_int_list.csv'
SCREAMING_SNAKE_CASE_ : List[str] =textwrap.dedent(
'\\n int_list\n 1 2 3\n 4 5 6\n 7 8 9\n ' )
with open(lowerCAmelCase_ ,'w' ) as f:
f.write(lowerCAmelCase_ )
return str(lowerCAmelCase_ )
def SCREAMING_SNAKE_CASE__ ( lowerCAmelCase_ : Union[str, Any] ,lowerCAmelCase_ : List[str] ,lowerCAmelCase_ : List[Any] ) -> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : int =Csv()
SCREAMING_SNAKE_CASE_ : int =csv._generate_tables([[csv_file, malformed_csv_file]] )
with pytest.raises(lowerCAmelCase_ ,match='Error tokenizing data' ):
for _ in generator:
pass
assert any(
record.levelname == 'ERROR'
and 'Failed to read file' in record.message
and os.path.basename(lowerCAmelCase_ ) in record.message
for record in caplog.records )
@require_pil
def SCREAMING_SNAKE_CASE__ ( lowerCAmelCase_ : List[Any] ) -> Optional[Any]:
"""simple docstring"""
with open(lowerCAmelCase_ ,encoding='utf-8' ) as f:
SCREAMING_SNAKE_CASE_ : Optional[int] =f.read().splitlines()[1]
SCREAMING_SNAKE_CASE_ : Optional[Any] =Csv(encoding='utf-8' ,features=Features({'image': Image()} ) )
SCREAMING_SNAKE_CASE_ : Tuple =csv._generate_tables([[csv_file_with_image]] )
SCREAMING_SNAKE_CASE_ : List[Any] =pa.concat_tables([table for _, table in generator] )
assert pa_table.schema.field('image' ).type == Image()()
SCREAMING_SNAKE_CASE_ : int =pa_table.to_pydict()['image']
assert generated_content == [{"path": image_file, "bytes": None}]
def SCREAMING_SNAKE_CASE__ ( lowerCAmelCase_ : List[str] ) -> str:
"""simple docstring"""
with open(lowerCAmelCase_ ,encoding='utf-8' ) as f:
SCREAMING_SNAKE_CASE_ : Optional[Any] =f.read().splitlines()[1:]
SCREAMING_SNAKE_CASE_ : List[str] =Csv(encoding='utf-8' ,features=Features({'label': ClassLabel(names=['good', 'bad'] )} ) )
SCREAMING_SNAKE_CASE_ : Optional[Any] =csv._generate_tables([[csv_file_with_label]] )
SCREAMING_SNAKE_CASE_ : Union[str, Any] =pa.concat_tables([table for _, table in generator] )
assert pa_table.schema.field('label' ).type == ClassLabel(names=['good', 'bad'] )()
SCREAMING_SNAKE_CASE_ : Union[str, Any] =pa_table.to_pydict()['label']
assert generated_content == [ClassLabel(names=['good', 'bad'] ).straint(lowerCAmelCase_ ) for label in labels]
def SCREAMING_SNAKE_CASE__ ( lowerCAmelCase_ : List[Any] ) -> Union[str, Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : int =Csv(encoding='utf-8' ,sep=',' ,converters={'int_list': lambda lowerCAmelCase_ : [int(lowerCAmelCase_ ) for i in x.split()]} )
SCREAMING_SNAKE_CASE_ : Optional[int] =csv._generate_tables([[csv_file_with_int_list]] )
SCREAMING_SNAKE_CASE_ : List[str] =pa.concat_tables([table for _, table in generator] )
assert pa.types.is_list(pa_table.schema.field('int_list' ).type )
SCREAMING_SNAKE_CASE_ : int =pa_table.to_pydict()['int_list']
assert generated_content == [[1, 2, 3], [4, 5, 6], [7, 8, 9]]
| 153
| 0
|
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase_ : List[Any] = logging.get_logger(__name__)
UpperCAmelCase_ : Dict = {
'funnel-transformer/small': 'https://huggingface.co/funnel-transformer/small/resolve/main/config.json',
'funnel-transformer/small-base': 'https://huggingface.co/funnel-transformer/small-base/resolve/main/config.json',
'funnel-transformer/medium': 'https://huggingface.co/funnel-transformer/medium/resolve/main/config.json',
'funnel-transformer/medium-base': 'https://huggingface.co/funnel-transformer/medium-base/resolve/main/config.json',
'funnel-transformer/intermediate': (
'https://huggingface.co/funnel-transformer/intermediate/resolve/main/config.json'
),
'funnel-transformer/intermediate-base': (
'https://huggingface.co/funnel-transformer/intermediate-base/resolve/main/config.json'
),
'funnel-transformer/large': 'https://huggingface.co/funnel-transformer/large/resolve/main/config.json',
'funnel-transformer/large-base': 'https://huggingface.co/funnel-transformer/large-base/resolve/main/config.json',
'funnel-transformer/xlarge': 'https://huggingface.co/funnel-transformer/xlarge/resolve/main/config.json',
'funnel-transformer/xlarge-base': 'https://huggingface.co/funnel-transformer/xlarge-base/resolve/main/config.json',
}
class UpperCAmelCase__ ( A ):
lowerCAmelCase_ = 'funnel'
lowerCAmelCase_ = {
'hidden_size': 'd_model',
'num_attention_heads': 'n_head',
}
def __init__( self : Optional[Any],__A : int=3_0_5_2_2,__A : Dict=[4, 4, 4],__A : int=None,__A : Union[str, Any]=2,__A : int=7_6_8,__A : str=1_2,__A : Dict=6_4,__A : int=3_0_7_2,__A : Optional[int]="gelu_new",__A : Union[str, Any]=0.1,__A : List[Any]=0.1,__A : List[str]=0.0,__A : int=0.1,__A : int=None,__A : Optional[int]=1e-9,__A : str="mean",__A : List[str]="relative_shift",__A : List[str]=True,__A : Optional[Any]=True,__A : Dict=True,**__A : Dict,):
_lowerCamelCase : int = vocab_size
_lowerCamelCase : Any = block_sizes
_lowerCamelCase : List[str] = [1] * len(__A ) if block_repeats is None else block_repeats
assert len(__A ) == len(
self.block_repeats ), "`block_sizes` and `block_repeats` should have the same length."
_lowerCamelCase : Optional[Any] = num_decoder_layers
_lowerCamelCase : Dict = d_model
_lowerCamelCase : List[Any] = n_head
_lowerCamelCase : Tuple = d_head
_lowerCamelCase : List[str] = d_inner
_lowerCamelCase : Any = hidden_act
_lowerCamelCase : Tuple = hidden_dropout
_lowerCamelCase : Dict = attention_dropout
_lowerCamelCase : Tuple = activation_dropout
_lowerCamelCase : List[Any] = initializer_range
_lowerCamelCase : str = initializer_std
_lowerCamelCase : str = layer_norm_eps
assert pooling_type in [
"mean",
"max",
], f'Got {pooling_type} for `pooling_type` but only \'mean\' and \'max\' are supported.'
_lowerCamelCase : Dict = pooling_type
assert attention_type in [
"relative_shift",
"factorized",
], f'Got {attention_type} for `attention_type` but only \'relative_shift\' and \'factorized\' are supported.'
_lowerCamelCase : List[str] = attention_type
_lowerCamelCase : Dict = separate_cls
_lowerCamelCase : int = truncate_seq
_lowerCamelCase : List[str] = pool_q_only
super().__init__(**__A )
@property
def lowerCamelCase_ ( self : str ):
return sum(self.block_sizes )
@num_hidden_layers.setter
def lowerCamelCase_ ( self : Any,__A : int ):
raise NotImplementedError(
"This model does not support the setting of `num_hidden_layers`. Please set `block_sizes`." )
@property
def lowerCamelCase_ ( self : Tuple ):
return len(self.block_sizes )
@num_blocks.setter
def lowerCamelCase_ ( self : Optional[int],__A : Optional[int] ):
raise NotImplementedError("This model does not support the setting of `num_blocks`. Please set `block_sizes`." )
| 44
|
'''simple docstring'''
class UpperCAmelCase__ :
def __init__( self : Any,__A : Any,__A : Any,__A : Any ):
_lowerCamelCase : List[Any] = name
_lowerCamelCase : Union[str, Any] = value
_lowerCamelCase : str = weight
def __repr__( self : Any ):
return f'{self.__class__.__name__}({self.name}, {self.value}, {self.weight})'
def lowerCamelCase_ ( self : Optional[int] ):
return self.value
def lowerCamelCase_ ( self : Any ):
return self.name
def lowerCamelCase_ ( self : List[Any] ):
return self.weight
def lowerCamelCase_ ( self : str ):
return self.value / self.weight
def A_ ( _lowerCAmelCase : Any , _lowerCAmelCase : Any , _lowerCAmelCase : Any ):
"""simple docstring"""
_lowerCamelCase : str = []
for i in range(len(_lowerCAmelCase ) ):
menu.append(Things(name[i] , value[i] , weight[i] ) )
return menu
def A_ ( _lowerCAmelCase : Any , _lowerCAmelCase : List[str] , _lowerCAmelCase : Union[str, Any] ):
"""simple docstring"""
_lowerCamelCase : Dict = sorted(_lowerCAmelCase , key=_lowerCAmelCase , reverse=_lowerCAmelCase )
_lowerCamelCase : Optional[int] = []
_lowerCamelCase , _lowerCamelCase : Optional[int] = 0.0, 0.0
for i in range(len(_lowerCAmelCase ) ):
if (total_cost + items_copy[i].get_weight()) <= max_cost:
result.append(items_copy[i] )
total_cost += items_copy[i].get_weight()
total_value += items_copy[i].get_value()
return (result, total_value)
def A_ ( ):
"""simple docstring"""
if __name__ == "__main__":
import doctest
doctest.testmod()
| 44
| 1
|
'''simple docstring'''
import itertools
import os
from collections import Counter, defaultdict
from concurrent.futures import ThreadPoolExecutor, as_completed
import numpy as np
import datasets
from .execute import check_correctness
_snake_case = '\\n@misc{chen2021evaluating,\n title={Evaluating Large Language Models Trained on Code},\n author={Mark Chen and Jerry Tworek and Heewoo Jun and Qiming Yuan \\nand Henrique Ponde de Oliveira Pinto and Jared Kaplan and Harri Edwards \\nand Yuri Burda and Nicholas Joseph and Greg Brockman and Alex Ray \\nand Raul Puri and Gretchen Krueger and Michael Petrov and Heidy Khlaaf \\nand Girish Sastry and Pamela Mishkin and Brooke Chan and Scott Gray \\nand Nick Ryder and Mikhail Pavlov and Alethea Power and Lukasz Kaiser \\nand Mohammad Bavarian and Clemens Winter and Philippe Tillet \\nand Felipe Petroski Such and Dave Cummings and Matthias Plappert \\nand Fotios Chantzis and Elizabeth Barnes and Ariel Herbert-Voss \\nand William Hebgen Guss and Alex Nichol and Alex Paino and Nikolas Tezak \\nand Jie Tang and Igor Babuschkin and Suchir Balaji and Shantanu Jain \\nand William Saunders and Christopher Hesse and Andrew N. Carr \\nand Jan Leike and Josh Achiam and Vedant Misra and Evan Morikawa \\nand Alec Radford and Matthew Knight and Miles Brundage and Mira Murati \\nand Katie Mayer and Peter Welinder and Bob McGrew and Dario Amodei \\nand Sam McCandlish and Ilya Sutskever and Wojciech Zaremba},\n year={2021},\n eprint={2107.03374},\n archivePrefix={arXiv},\n primaryClass={cs.LG}\n}\n'
_snake_case = '\\nThis metric implements the evaluation harness for the HumanEval problem solving dataset\ndescribed in the paper "Evaluating Large Language Models Trained on Code"\n(https://arxiv.org/abs/2107.03374).\n'
_snake_case = '\nCalculates how good are predictions given some references, using certain scores\nArgs:\n predictions: list of candidates to evaluate. Each candidates should be a list\n of strings with several code candidates to solve the problem.\n references: a list with a test for each prediction. Each test should evaluate the\n correctness of a code candidate.\n k: number of code candidates to consider in the evaluation (Default: [1, 10, 100])\n num_workers: number of workers used to evaluate the canidate programs (Default: 4).\n timeout:\nReturns:\n pass_at_k: dict with pass rates for each k\n results: dict with granular results of each unittest\nExamples:\n >>> code_eval = datasets.load_metric("code_eval")\n >>> test_cases = ["assert add(2,3)==5"]\n >>> candidates = [["def add(a,b): return a*b", "def add(a, b): return a+b"]]\n >>> pass_at_k, results = code_eval.compute(references=test_cases, predictions=candidates, k=[1, 2])\n >>> print(pass_at_k)\n {\'pass@1\': 0.5, \'pass@2\': 1.0}\n'
_snake_case = '\n################################################################################\n !!!WARNING!!!\n################################################################################\nThe "code_eval" metric executes untrusted model-generated code in Python.\nAlthough it is highly unlikely that model-generated code will do something\novertly malicious in response to this test suite, model-generated code may act\ndestructively due to a lack of model capability or alignment.\nUsers are strongly encouraged to sandbox this evaluation suite so that it\ndoes not perform destructive actions on their host or network. For more\ninformation on how OpenAI sandboxes its code, see the paper "Evaluating Large\nLanguage Models Trained on Code" (https://arxiv.org/abs/2107.03374).\n\nOnce you have read this disclaimer and taken appropriate precautions,\nset the environment variable HF_ALLOW_CODE_EVAL="1". Within Python you can to this\nwith:\n\n>>> import os\n>>> os.environ["HF_ALLOW_CODE_EVAL"] = "1"\n\n################################################################################\\n'
_snake_case = 'The MIT License\n\nCopyright (c) OpenAI (https://openai.com)\n\nPermission is hereby granted, free of charge, to any person obtaining a copy\nof this software and associated documentation files (the "Software"), to deal\nin the Software without restriction, including without limitation the rights\nto use, copy, modify, merge, publish, distribute, sublicense, and/or sell\ncopies of the Software, and to permit persons to whom the Software is\nfurnished to do so, subject to the following conditions:\n\nThe above copyright notice and this permission notice shall be included in\nall copies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\nIMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\nFITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\nAUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\nLIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\nOUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN\nTHE SOFTWARE.'
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION ,_KWARGS_DESCRIPTION )
class A ( datasets.Metric ):
def __lowerCAmelCase ( self : Union[str, Any] ) -> Any:
"""simple docstring"""
return datasets.MetricInfo(
# This is the description that will appear on the metrics page.
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
'''predictions''': datasets.Sequence(datasets.Value('''string''' ) ),
'''references''': datasets.Value('''string''' ),
} ) , homepage='''https://github.com/openai/human-eval''' , codebase_urls=['''https://github.com/openai/human-eval'''] , reference_urls=['''https://github.com/openai/human-eval'''] , license=_LICENSE , )
def __lowerCAmelCase ( self : List[Any] , lowerCAmelCase_ : Optional[int] , lowerCAmelCase_ : Dict , lowerCAmelCase_ : Optional[int]=[1, 10, 1_00] , lowerCAmelCase_ : int=4 , lowerCAmelCase_ : Dict=3.0 ) -> Optional[Any]:
"""simple docstring"""
if os.getenv('''HF_ALLOW_CODE_EVAL''' , 0 ) != "1":
raise ValueError(_WARNING )
if os.name == "nt":
raise NotImplementedError('''This metric is currently not supported on Windows.''' )
with ThreadPoolExecutor(max_workers=lowerCAmelCase_ ) as executor:
_a = []
_a = Counter()
_a = 0
_a = defaultdict(lowerCAmelCase_ )
for task_id, (candidates, test_case) in enumerate(zip(lowerCAmelCase_ , lowerCAmelCase_ ) ):
for candidate in candidates:
_a = candidate + '''\n''' + test_case
_a = (test_program, timeout, task_id, completion_id[task_id])
_a = executor.submit(lowerCAmelCase_ , *lowerCAmelCase_ )
futures.append(lowerCAmelCase_ )
completion_id[task_id] += 1
n_samples += 1
for future in as_completed(lowerCAmelCase_ ):
_a = future.result()
results[result["task_id"]].append((result['''completion_id'''], result) )
_a , _a = [], []
for result in results.values():
result.sort()
_a = [r[1]['''passed'''] for r in result]
total.append(len(lowerCAmelCase_ ) )
correct.append(sum(lowerCAmelCase_ ) )
_a = np.array(lowerCAmelCase_ )
_a = np.array(lowerCAmelCase_ )
_a = k
_a = {F'pass@{k}': estimate_pass_at_k(lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ ).mean() for k in ks if (total >= k).all()}
return pass_at_k, results
def snake_case_ (UpperCamelCase : int , UpperCamelCase : Optional[Any] , UpperCamelCase : Optional[Any] ):
'''simple docstring'''
def estimator(UpperCamelCase : int , UpperCamelCase : int , UpperCamelCase : int ) -> float:
if n - c < k:
return 1.0
return 1.0 - np.prod(1.0 - k / np.arange(n - c + 1 , n + 1 ) )
if isinstance(UpperCamelCase , UpperCamelCase ):
_a = itertools.repeat(UpperCamelCase , len(UpperCamelCase ) )
else:
assert len(UpperCamelCase ) == len(UpperCamelCase )
_a = iter(UpperCamelCase )
return np.array([estimator(int(UpperCamelCase ) , int(UpperCamelCase ) , UpperCamelCase ) for n, c in zip(UpperCamelCase , UpperCamelCase )] )
| 704
|
'''simple docstring'''
import math
import unittest
def snake_case_ (UpperCamelCase : int ):
'''simple docstring'''
assert isinstance(UpperCamelCase , UpperCamelCase ) and (
number >= 0
), "'number' must been an int and positive"
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
return False
# All primes number are in format of 6k +/- 1
for i in range(5 , int(math.sqrt(UpperCamelCase ) + 1 ) , 6 ):
if number % i == 0 or number % (i + 2) == 0:
return False
return True
class A ( unittest.TestCase ):
def __lowerCAmelCase ( self : Union[str, Any] ) -> Optional[Any]:
"""simple docstring"""
self.assertTrue(is_prime(2 ) )
self.assertTrue(is_prime(3 ) )
self.assertTrue(is_prime(5 ) )
self.assertTrue(is_prime(7 ) )
self.assertTrue(is_prime(11 ) )
self.assertTrue(is_prime(13 ) )
self.assertTrue(is_prime(17 ) )
self.assertTrue(is_prime(19 ) )
self.assertTrue(is_prime(23 ) )
self.assertTrue(is_prime(29 ) )
def __lowerCAmelCase ( self : Any ) -> Dict:
"""simple docstring"""
with self.assertRaises(lowerCAmelCase_ ):
is_prime(-19 )
self.assertFalse(
is_prime(0 ) , '''Zero doesn\'t have any positive factors, primes must have exactly two.''' , )
self.assertFalse(
is_prime(1 ) , '''One only has 1 positive factor, primes must have exactly two.''' , )
self.assertFalse(is_prime(2 * 2 ) )
self.assertFalse(is_prime(2 * 3 ) )
self.assertFalse(is_prime(3 * 3 ) )
self.assertFalse(is_prime(3 * 5 ) )
self.assertFalse(is_prime(3 * 5 * 7 ) )
if __name__ == "__main__":
unittest.main()
| 377
| 0
|
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__UpperCamelCase : List[str] = logging.get_logger(__name__)
__UpperCamelCase : str = {'ctrl': 'https://huggingface.co/ctrl/resolve/main/config.json'}
class lowercase__ ( UpperCamelCase_):
UpperCamelCase_ = """ctrl"""
UpperCamelCase_ = ["""past_key_values"""]
UpperCamelCase_ = {
"""max_position_embeddings""": """n_positions""",
"""hidden_size""": """n_embd""",
"""num_attention_heads""": """n_head""",
"""num_hidden_layers""": """n_layer""",
}
def __init__( self : List[str] , UpperCamelCase__ : str=24_6534 , UpperCamelCase__ : List[str]=256 , UpperCamelCase__ : List[Any]=1280 , UpperCamelCase__ : Optional[int]=8192 , UpperCamelCase__ : int=48 , UpperCamelCase__ : str=16 , UpperCamelCase__ : List[str]=0.1 , UpperCamelCase__ : Tuple=0.1 , UpperCamelCase__ : List[Any]=1E-6 , UpperCamelCase__ : List[str]=0.02 , UpperCamelCase__ : List[str]=True , **UpperCamelCase__ : Any , ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : List[str] = vocab_size
SCREAMING_SNAKE_CASE : List[Any] = n_positions
SCREAMING_SNAKE_CASE : Dict = n_embd
SCREAMING_SNAKE_CASE : int = n_layer
SCREAMING_SNAKE_CASE : str = n_head
SCREAMING_SNAKE_CASE : str = dff
SCREAMING_SNAKE_CASE : Any = resid_pdrop
SCREAMING_SNAKE_CASE : List[Any] = embd_pdrop
SCREAMING_SNAKE_CASE : Any = layer_norm_epsilon
SCREAMING_SNAKE_CASE : List[str] = initializer_range
SCREAMING_SNAKE_CASE : Tuple = use_cache
super().__init__(**UpperCamelCase__ )
| 248
|
"""simple docstring"""
import os
import tempfile
import unittest
from transformers import DistilBertConfig, is_torch_available
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
DistilBertForMaskedLM,
DistilBertForMultipleChoice,
DistilBertForQuestionAnswering,
DistilBertForSequenceClassification,
DistilBertForTokenClassification,
DistilBertModel,
)
class _UpperCAmelCase ( a ):
'''simple docstring'''
def __init__( self , A , A=1_3 , A=7 , A=True , A=True , A=False , A=True , A=9_9 , A=3_2 , A=5 , A=4 , A=3_7 , A="gelu" , A=0.1 , A=0.1 , A=5_1_2 , A=1_6 , A=2 , A=0.02 , A=3 , A=4 , A=None , ) -> Tuple:
_UpperCAmelCase : int = parent
_UpperCAmelCase : List[Any] = batch_size
_UpperCAmelCase : Union[str, Any] = seq_length
_UpperCAmelCase : Any = is_training
_UpperCAmelCase : str = use_input_mask
_UpperCAmelCase : Tuple = use_token_type_ids
_UpperCAmelCase : Tuple = use_labels
_UpperCAmelCase : Tuple = vocab_size
_UpperCAmelCase : Optional[Any] = hidden_size
_UpperCAmelCase : Dict = num_hidden_layers
_UpperCAmelCase : List[Any] = num_attention_heads
_UpperCAmelCase : Optional[int] = intermediate_size
_UpperCAmelCase : List[str] = hidden_act
_UpperCAmelCase : Tuple = hidden_dropout_prob
_UpperCAmelCase : Optional[Any] = attention_probs_dropout_prob
_UpperCAmelCase : List[Any] = max_position_embeddings
_UpperCAmelCase : Optional[Any] = type_vocab_size
_UpperCAmelCase : Dict = type_sequence_label_size
_UpperCAmelCase : Tuple = initializer_range
_UpperCAmelCase : Optional[int] = num_labels
_UpperCAmelCase : int = num_choices
_UpperCAmelCase : Any = scope
def __lowerCAmelCase ( self ) -> int:
_UpperCAmelCase : int = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
_UpperCAmelCase : Dict = None
if self.use_input_mask:
_UpperCAmelCase : str = random_attention_mask([self.batch_size, self.seq_length] )
_UpperCAmelCase : str = None
_UpperCAmelCase : Any = None
_UpperCAmelCase : Any = None
if self.use_labels:
_UpperCAmelCase : str = ids_tensor([self.batch_size] , self.type_sequence_label_size )
_UpperCAmelCase : int = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
_UpperCAmelCase : List[str] = ids_tensor([self.batch_size] , self.num_choices )
_UpperCAmelCase : Optional[int] = self.get_config()
return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
def __lowerCAmelCase ( self ) -> Dict:
return DistilBertConfig(
vocab_size=self.vocab_size , dim=self.hidden_size , n_layers=self.num_hidden_layers , n_heads=self.num_attention_heads , hidden_dim=self.intermediate_size , hidden_act=self.hidden_act , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , initializer_range=self.initializer_range , )
def __lowerCAmelCase ( self , A , A , A , A , A , A ) -> int:
_UpperCAmelCase : List[str] = DistilBertModel(config=A )
model.to(A )
model.eval()
_UpperCAmelCase : List[Any] = model(A , A )
_UpperCAmelCase : Tuple = model(A )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def __lowerCAmelCase ( self , A , A , A , A , A , A ) -> List[Any]:
_UpperCAmelCase : Tuple = DistilBertForMaskedLM(config=A )
model.to(A )
model.eval()
_UpperCAmelCase : Tuple = model(A , attention_mask=A , labels=A )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def __lowerCAmelCase ( self , A , A , A , A , A , A ) -> List[str]:
_UpperCAmelCase : Optional[Any] = DistilBertForQuestionAnswering(config=A )
model.to(A )
model.eval()
_UpperCAmelCase : str = model(
A , attention_mask=A , start_positions=A , end_positions=A )
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 __lowerCAmelCase ( self , A , A , A , A , A , A ) -> List[Any]:
_UpperCAmelCase : Optional[Any] = self.num_labels
_UpperCAmelCase : Dict = DistilBertForSequenceClassification(A )
model.to(A )
model.eval()
_UpperCAmelCase : Dict = model(A , attention_mask=A , labels=A )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def __lowerCAmelCase ( self , A , A , A , A , A , A ) -> int:
_UpperCAmelCase : str = self.num_labels
_UpperCAmelCase : int = DistilBertForTokenClassification(config=A )
model.to(A )
model.eval()
_UpperCAmelCase : int = model(A , attention_mask=A , labels=A )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def __lowerCAmelCase ( self , A , A , A , A , A , A ) -> str:
_UpperCAmelCase : List[str] = self.num_choices
_UpperCAmelCase : Optional[int] = DistilBertForMultipleChoice(config=A )
model.to(A )
model.eval()
_UpperCAmelCase : Optional[int] = input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
_UpperCAmelCase : Dict = input_mask.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
_UpperCAmelCase : Optional[Any] = model(
A , attention_mask=A , labels=A , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def __lowerCAmelCase ( self ) -> Optional[Any]:
_UpperCAmelCase : Optional[Any] = self.prepare_config_and_inputs()
((_UpperCAmelCase) , (_UpperCAmelCase) , (_UpperCAmelCase) , (_UpperCAmelCase) , (_UpperCAmelCase) , (_UpperCAmelCase)) : List[str] = config_and_inputs
_UpperCAmelCase : Tuple = {'''input_ids''': input_ids, '''attention_mask''': input_mask}
return config, inputs_dict
@require_torch
class _UpperCAmelCase ( a ,a ,unittest.TestCase ):
'''simple docstring'''
a__ =(
(
DistilBertModel,
DistilBertForMaskedLM,
DistilBertForMultipleChoice,
DistilBertForQuestionAnswering,
DistilBertForSequenceClassification,
DistilBertForTokenClassification,
)
if is_torch_available()
else None
)
a__ =(
{
'''feature-extraction''': DistilBertModel,
'''fill-mask''': DistilBertForMaskedLM,
'''question-answering''': DistilBertForQuestionAnswering,
'''text-classification''': DistilBertForSequenceClassification,
'''token-classification''': DistilBertForTokenClassification,
'''zero-shot''': DistilBertForSequenceClassification,
}
if is_torch_available()
else {}
)
a__ =True
a__ =True
a__ =True
a__ =True
def __lowerCAmelCase ( self ) -> Tuple:
_UpperCAmelCase : Optional[Any] = DistilBertModelTester(self )
_UpperCAmelCase : Optional[int] = ConfigTester(self , config_class=A , dim=3_7 )
def __lowerCAmelCase ( self ) -> Tuple:
self.config_tester.run_common_tests()
def __lowerCAmelCase ( self ) -> Optional[int]:
_UpperCAmelCase : Any = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_model(*A )
def __lowerCAmelCase ( self ) -> Optional[Any]:
_UpperCAmelCase : List[str] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_for_masked_lm(*A )
def __lowerCAmelCase ( self ) -> Any:
_UpperCAmelCase : Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_for_question_answering(*A )
def __lowerCAmelCase ( self ) -> List[Any]:
_UpperCAmelCase : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_for_sequence_classification(*A )
def __lowerCAmelCase ( self ) -> Union[str, Any]:
_UpperCAmelCase : List[str] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_for_token_classification(*A )
def __lowerCAmelCase ( self ) -> Dict:
_UpperCAmelCase : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_for_multiple_choice(*A )
@slow
def __lowerCAmelCase ( self ) -> str:
for model_name in DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_UpperCAmelCase : List[Any] = DistilBertModel.from_pretrained(A )
self.assertIsNotNone(A )
@slow
@require_torch_gpu
def __lowerCAmelCase ( self ) -> List[str]:
_UpperCAmelCase , _UpperCAmelCase : List[Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
# BertForMultipleChoice behaves incorrectly in JIT environments.
if model_class == DistilBertForMultipleChoice:
return
_UpperCAmelCase : Dict = True
_UpperCAmelCase : Dict = model_class(config=A )
_UpperCAmelCase : Optional[Any] = self._prepare_for_class(A , A )
_UpperCAmelCase : List[Any] = torch.jit.trace(
A , (inputs_dict['''input_ids'''].to('''cpu''' ), inputs_dict['''attention_mask'''].to('''cpu''' )) )
with tempfile.TemporaryDirectory() as tmp:
torch.jit.save(A , os.path.join(A , '''traced_model.pt''' ) )
_UpperCAmelCase : Optional[Any] = torch.jit.load(os.path.join(A , '''traced_model.pt''' ) , map_location=A )
loaded(inputs_dict['''input_ids'''].to(A ) , inputs_dict['''attention_mask'''].to(A ) )
@require_torch
class _UpperCAmelCase ( unittest.TestCase ):
'''simple docstring'''
@slow
def __lowerCAmelCase ( self ) -> Dict:
_UpperCAmelCase : Optional[int] = DistilBertModel.from_pretrained('''distilbert-base-uncased''' )
_UpperCAmelCase : int = torch.tensor([[0, 3_4_5, 2_3_2, 3_2_8, 7_4_0, 1_4_0, 1_6_9_5, 6_9, 6_0_7_8, 1_5_8_8, 2]] )
_UpperCAmelCase : str = torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]] )
with torch.no_grad():
_UpperCAmelCase : Optional[Any] = model(A , attention_mask=A )[0]
_UpperCAmelCase : int = torch.Size((1, 1_1, 7_6_8) )
self.assertEqual(output.shape , A )
_UpperCAmelCase : Optional[Any] = torch.tensor(
[[[-0.1_639, 0.3_299, 0.1_648], [-0.1_746, 0.3_289, 0.1_710], [-0.1_884, 0.3_357, 0.1_810]]] )
self.assertTrue(torch.allclose(output[:, 1:4, 1:4] , A , atol=1E-4 ) )
| 506
| 0
|
from operator import delitem, getitem, setitem
import pytest
from data_structures.hashing.hash_map import HashMap
def _lowercase ( UpperCAmelCase_):
"""simple docstring"""
return getitem, k
def _lowercase ( UpperCAmelCase_ , UpperCAmelCase_):
"""simple docstring"""
return setitem, k, v
def _lowercase ( UpperCAmelCase_):
"""simple docstring"""
return delitem, k
def _lowercase ( UpperCAmelCase_ , UpperCAmelCase_ , *UpperCAmelCase_):
"""simple docstring"""
try:
return fun(UpperCAmelCase_ , *UpperCAmelCase_), None
except Exception as e:
return None, e
lowercase_: Union[str, Any] = (
_set('key_a', 'val_a'),
_set('key_b', 'val_b'),
)
lowercase_: str = [
_set('key_a', 'val_a'),
_set('key_a', 'val_b'),
]
lowercase_: str = [
_set('key_a', 'val_a'),
_set('key_b', 'val_b'),
_del('key_a'),
_del('key_b'),
_set('key_a', 'val_a'),
_del('key_a'),
]
lowercase_: int = [
_get('key_a'),
_del('key_a'),
_set('key_a', 'val_a'),
_del('key_a'),
_del('key_a'),
_get('key_a'),
]
lowercase_: Union[str, Any] = [
*[_set(x, x) for x in range(5)], # guaranteed upsize
]
lowercase_: List[str] = [
*[_set(x, x) for x in range(5)], # guaranteed upsize
*[_del(x) for x in range(5)],
_set('key_a', 'val_b'),
]
@pytest.mark.parametrize(
"""operations""" , (
pytest.param(_add_items , id="""add items"""),
pytest.param(_overwrite_items , id="""overwrite items"""),
pytest.param(_delete_items , id="""delete items"""),
pytest.param(_access_absent_items , id="""access absent items"""),
pytest.param(_add_with_resize_up , id="""add with resize up"""),
pytest.param(_add_with_resize_down , id="""add with resize down"""),
) , )
def _lowercase ( UpperCAmelCase_):
"""simple docstring"""
snake_case__ : Optional[Any] = HashMap(initial_block_size=4)
snake_case__ : Union[str, Any] = {}
for _, (fun, *args) in enumerate(UpperCAmelCase_):
snake_case__ , snake_case__ : List[str] = _run_operation(UpperCAmelCase_ , UpperCAmelCase_ , *UpperCAmelCase_)
snake_case__ , snake_case__ : List[Any] = _run_operation(UpperCAmelCase_ , UpperCAmelCase_ , *UpperCAmelCase_)
assert my_res == py_res
assert str(UpperCAmelCase_) == str(UpperCAmelCase_)
assert set(UpperCAmelCase_) == set(UpperCAmelCase_)
assert len(UpperCAmelCase_) == len(UpperCAmelCase_)
assert set(my.items()) == set(py.items())
def _lowercase ( ):
"""simple docstring"""
def is_public(UpperCAmelCase_) -> bool:
return not name.startswith("""_""")
snake_case__ : Union[str, Any] = {name for name in dir({}) if is_public(UpperCAmelCase_)}
snake_case__ : str = {name for name in dir(HashMap()) if is_public(UpperCAmelCase_)}
assert dict_public_names > hash_public_names
| 705
|
import argparse
import requests
import torch
from PIL import Image
from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel
def _lowercase ( UpperCAmelCase_):
"""simple docstring"""
if "img_encoder.pos_embed" in name:
snake_case__ : Dict = name.replace("""img_encoder.pos_embed""" , """vision_model.embeddings.position_embeddings""")
if "img_encoder.patch_embed.proj" in name:
snake_case__ : Optional[Any] = name.replace("""img_encoder.patch_embed.proj""" , """vision_model.embeddings.patch_embeddings.projection""")
if "img_encoder.patch_embed.norm" in name:
snake_case__ : Dict = name.replace("""img_encoder.patch_embed.norm""" , """vision_model.embeddings.layernorm""")
if "img_encoder.layers" in name:
snake_case__ : List[str] = name.replace("""img_encoder.layers""" , """vision_model.encoder.stages""")
if "blocks" in name and "res" not in name:
snake_case__ : Optional[int] = name.replace("""blocks""" , """layers""")
if "attn" in name and "pre_assign" not in name:
snake_case__ : str = name.replace("""attn""" , """self_attn""")
if "proj" in name and "self_attn" in name and "text" not in name:
snake_case__ : Optional[int] = name.replace("""proj""" , """out_proj""")
if "pre_assign_attn.attn.proj" in name:
snake_case__ : Optional[int] = name.replace("""pre_assign_attn.attn.proj""" , """pre_assign_attn.attn.out_proj""")
if "norm1" in name:
snake_case__ : Union[str, Any] = name.replace("""norm1""" , """layer_norm1""")
if "norm2" in name and "pre_assign" not in name:
snake_case__ : List[str] = name.replace("""norm2""" , """layer_norm2""")
if "img_encoder.norm" in name:
snake_case__ : Any = name.replace("""img_encoder.norm""" , """vision_model.layernorm""")
# text encoder
if "text_encoder.token_embedding" in name:
snake_case__ : Optional[Any] = name.replace("""text_encoder.token_embedding""" , """text_model.embeddings.token_embedding""")
if "text_encoder.positional_embedding" in name:
snake_case__ : int = name.replace("""text_encoder.positional_embedding""" , """text_model.embeddings.position_embedding.weight""")
if "text_encoder.transformer.resblocks." in name:
snake_case__ : Tuple = name.replace("""text_encoder.transformer.resblocks.""" , """text_model.encoder.layers.""")
if "ln_1" in name:
snake_case__ : Optional[Any] = name.replace("""ln_1""" , """layer_norm1""")
if "ln_2" in name:
snake_case__ : Optional[int] = name.replace("""ln_2""" , """layer_norm2""")
if "c_fc" in name:
snake_case__ : int = name.replace("""c_fc""" , """fc1""")
if "c_proj" in name:
snake_case__ : List[str] = name.replace("""c_proj""" , """fc2""")
if "text_encoder" in name:
snake_case__ : Dict = name.replace("""text_encoder""" , """text_model""")
if "ln_final" in name:
snake_case__ : Union[str, Any] = name.replace("""ln_final""" , """final_layer_norm""")
# projection layers
if "img_projector.linear_hidden." in name:
snake_case__ : int = name.replace("""img_projector.linear_hidden.""" , """visual_projection.""")
if "img_projector.linear_out." in name:
snake_case__ : Optional[Any] = name.replace("""img_projector.linear_out.""" , """visual_projection.3.""")
if "text_projector.linear_hidden" in name:
snake_case__ : Optional[int] = name.replace("""text_projector.linear_hidden""" , """text_projection""")
if "text_projector.linear_out" in name:
snake_case__ : Tuple = name.replace("""text_projector.linear_out""" , """text_projection.3""")
return name
def _lowercase ( UpperCAmelCase_ , UpperCAmelCase_):
"""simple docstring"""
for key in orig_state_dict.copy().keys():
snake_case__ : List[Any] = orig_state_dict.pop(UpperCAmelCase_)
if "qkv" in key:
# weights and biases of the key, value and query projections of vision encoder's attention layers require special treatment:
# we need to split them up into separate matrices/vectors
snake_case__ : Optional[Any] = key.split(""".""")
snake_case__ , snake_case__ : str = int(key_split[2]), int(key_split[4])
snake_case__ : Tuple = config.vision_config.hidden_size
if "weight" in key:
snake_case__ : Tuple = val[:dim, :]
snake_case__ : List[Any] = val[dim : dim * 2, :]
snake_case__ : Optional[int] = val[-dim:, :]
else:
snake_case__ : Dict = val[:dim]
snake_case__ : List[Any] = val[dim : dim * 2]
snake_case__ : Union[str, Any] = val[-dim:]
elif "in_proj" in key:
# weights and biases of the key, value and query projections of text encoder's attention layers require special treatment:
# we need to split them up into separate matrices/vectors
snake_case__ : List[str] = key.split(""".""")
snake_case__ : str = int(key_split[3])
snake_case__ : Tuple = config.text_config.hidden_size
if "weight" in key:
snake_case__ : Any = val[:dim, :]
snake_case__ : Optional[int] = val[
dim : dim * 2, :
]
snake_case__ : List[str] = val[-dim:, :]
else:
snake_case__ : Tuple = val[:dim]
snake_case__ : List[str] = val[dim : dim * 2]
snake_case__ : Optional[int] = val[-dim:]
else:
snake_case__ : List[str] = rename_key(UpperCAmelCase_)
# squeeze if necessary
if (
"text_projection.0" in new_name
or "text_projection.3" in new_name
or "visual_projection.0" in new_name
or "visual_projection.3" in new_name
):
snake_case__ : Union[str, Any] = val.squeeze_()
else:
snake_case__ : List[str] = val
return orig_state_dict
def _lowercase ( ):
"""simple docstring"""
snake_case__ : Union[str, Any] = """http://images.cocodataset.org/val2017/000000039769.jpg"""
snake_case__ : Any = Image.open(requests.get(UpperCAmelCase_ , stream=UpperCAmelCase_).raw)
return im
@torch.no_grad()
def _lowercase ( UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_="groupvit-gcc-yfcc" , UpperCAmelCase_=False):
"""simple docstring"""
snake_case__ : Any = GroupViTConfig()
snake_case__ : List[Any] = GroupViTModel(UpperCAmelCase_).eval()
snake_case__ : List[str] = torch.load(UpperCAmelCase_ , map_location="""cpu""")["""model"""]
snake_case__ : List[Any] = convert_state_dict(UpperCAmelCase_ , UpperCAmelCase_)
snake_case__ , snake_case__ : Tuple = model.load_state_dict(UpperCAmelCase_ , strict=UpperCAmelCase_)
assert missing_keys == ["text_model.embeddings.position_ids"]
assert (unexpected_keys == ["multi_label_logit_scale"]) or (len(UpperCAmelCase_) == 0)
# verify result
snake_case__ : Optional[Any] = CLIPProcessor.from_pretrained("""openai/clip-vit-base-patch32""")
snake_case__ : str = prepare_img()
snake_case__ : int = processor(text=["""a photo of a cat""", """a photo of a dog"""] , images=UpperCAmelCase_ , padding=UpperCAmelCase_ , return_tensors="""pt""")
with torch.no_grad():
snake_case__ : Dict = model(**UpperCAmelCase_)
if model_name == "groupvit-gcc-yfcc":
snake_case__ : List[str] = torch.tensor([[13.3523, 6.3629]])
elif model_name == "groupvit-gcc-redcaps":
snake_case__ : List[str] = torch.tensor([[16.1873, 8.6230]])
else:
raise ValueError(F'Model name {model_name} not supported.')
assert torch.allclose(outputs.logits_per_image , UpperCAmelCase_ , atol=1e-3)
processor.save_pretrained(UpperCAmelCase_)
model.save_pretrained(UpperCAmelCase_)
print("""Successfully saved processor and model to""" , UpperCAmelCase_)
if push_to_hub:
print("""Pushing to the hub...""")
processor.push_to_hub(UpperCAmelCase_ , organization="""nielsr""")
model.push_to_hub(UpperCAmelCase_ , organization="""nielsr""")
if __name__ == "__main__":
lowercase_: List[Any] = argparse.ArgumentParser()
parser.add_argument(
'--pytorch_dump_folder_path', default=None, type=str, help='Path to dump the processor and PyTorch model.'
)
parser.add_argument('--checkpoint_path', default=None, type=str, help='Path to GroupViT checkpoint')
parser.add_argument(
'--model_name',
default='groupvit-gccy-fcc',
type=str,
help='Name of the model. Expecting either \'groupvit-gcc-yfcc\' or \'groupvit-gcc-redcaps\'',
)
parser.add_argument(
'--push_to_hub',
action='store_true',
help='Whether or not to push the converted model and processor to the 🤗 hub using the provided `model_name`.',
)
lowercase_: Any = parser.parse_args()
convert_groupvit_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.model_name, args.push_to_hub)
| 127
| 0
|
"""simple docstring"""
import unittest
from transformers import MPNetConfig, 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, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
MPNetForMaskedLM,
MPNetForMultipleChoice,
MPNetForQuestionAnswering,
MPNetForSequenceClassification,
MPNetForTokenClassification,
MPNetModel,
)
class A:
"""simple docstring"""
def __init__( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__=13 , SCREAMING_SNAKE_CASE__=7 , SCREAMING_SNAKE_CASE__=True , SCREAMING_SNAKE_CASE__=True , SCREAMING_SNAKE_CASE__=False , SCREAMING_SNAKE_CASE__=True , SCREAMING_SNAKE_CASE__=99 , SCREAMING_SNAKE_CASE__=64 , SCREAMING_SNAKE_CASE__=5 , SCREAMING_SNAKE_CASE__=4 , SCREAMING_SNAKE_CASE__=64 , SCREAMING_SNAKE_CASE__="gelu" , SCREAMING_SNAKE_CASE__=0.1 , SCREAMING_SNAKE_CASE__=0.1 , SCREAMING_SNAKE_CASE__=5_12 , SCREAMING_SNAKE_CASE__=16 , SCREAMING_SNAKE_CASE__=2 , SCREAMING_SNAKE_CASE__=0.0_2 , SCREAMING_SNAKE_CASE__=3 , SCREAMING_SNAKE_CASE__=4 , SCREAMING_SNAKE_CASE__=None , ) -> Dict:
"""simple docstring"""
_UpperCamelCase :List[str] = parent
_UpperCamelCase :Optional[int] = batch_size
_UpperCamelCase :int = seq_length
_UpperCamelCase :str = is_training
_UpperCamelCase :Dict = use_input_mask
_UpperCamelCase :Any = use_token_type_ids
_UpperCamelCase :Any = use_labels
_UpperCamelCase :List[str] = vocab_size
_UpperCamelCase :List[str] = hidden_size
_UpperCamelCase :Union[str, Any] = num_hidden_layers
_UpperCamelCase :List[Any] = num_attention_heads
_UpperCamelCase :List[str] = intermediate_size
_UpperCamelCase :Any = hidden_act
_UpperCamelCase :Optional[Any] = hidden_dropout_prob
_UpperCamelCase :Any = attention_probs_dropout_prob
_UpperCamelCase :str = max_position_embeddings
_UpperCamelCase :List[str] = type_vocab_size
_UpperCamelCase :List[Any] = type_sequence_label_size
_UpperCamelCase :Dict = initializer_range
_UpperCamelCase :Any = num_labels
_UpperCamelCase :Optional[Any] = num_choices
_UpperCamelCase :Optional[int] = scope
def _UpperCamelCase( self ) -> int:
"""simple docstring"""
return MPNetConfig.from_pretrained('''microsoft/mpnet-base''' )
def _UpperCamelCase( self ) -> List[Any]:
"""simple docstring"""
_UpperCamelCase :Union[str, Any] = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
_UpperCamelCase :Dict = None
if self.use_input_mask:
_UpperCamelCase :Union[str, Any] = random_attention_mask([self.batch_size, self.seq_length] )
_UpperCamelCase :List[Any] = None
_UpperCamelCase :Tuple = None
_UpperCamelCase :int = None
if self.use_labels:
_UpperCamelCase :Union[str, Any] = ids_tensor([self.batch_size] , self.type_sequence_label_size )
_UpperCamelCase :Dict = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
_UpperCamelCase :Optional[Any] = ids_tensor([self.batch_size] , self.num_choices )
_UpperCamelCase :str = self.get_config()
return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
def _UpperCamelCase( self ) -> Union[str, Any]:
"""simple docstring"""
return MPNetConfig(
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 , initializer_range=self.initializer_range , )
def _UpperCamelCase( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) -> str:
"""simple docstring"""
_UpperCamelCase :Optional[int] = MPNetModel(config=__a )
model.to(__a )
model.eval()
_UpperCamelCase :Any = model(__a , __a )
_UpperCamelCase :Optional[int] = model(__a )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
self.parent.assertEqual(result.pooler_output.shape , (self.batch_size, self.hidden_size) )
def _UpperCamelCase( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) -> Tuple:
"""simple docstring"""
_UpperCamelCase :str = MPNetForQuestionAnswering(config=__a )
model.to(__a )
model.eval()
_UpperCamelCase :Dict = model(
__a , attention_mask=__a , start_positions=__a , end_positions=__a , )
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 _UpperCamelCase( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) -> List[str]:
"""simple docstring"""
_UpperCamelCase :List[str] = self.num_labels
_UpperCamelCase :List[Any] = MPNetForSequenceClassification(__a )
model.to(__a )
model.eval()
_UpperCamelCase :Tuple = model(__a , attention_mask=__a , labels=__a )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def _UpperCamelCase( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) -> Union[str, Any]:
"""simple docstring"""
_UpperCamelCase :List[str] = self.num_choices
_UpperCamelCase :Tuple = MPNetForMultipleChoice(config=__a )
model.to(__a )
model.eval()
_UpperCamelCase :List[str] = input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
_UpperCamelCase :Optional[Any] = input_mask.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
_UpperCamelCase :List[Any] = model(
__a , attention_mask=__a , labels=__a , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def _UpperCamelCase( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) -> str:
"""simple docstring"""
_UpperCamelCase :List[Any] = self.num_labels
_UpperCamelCase :Optional[Any] = MPNetForTokenClassification(config=__a )
model.to(__a )
model.eval()
_UpperCamelCase :Any = model(__a , attention_mask=__a , labels=__a )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def _UpperCamelCase( self ) -> Any:
"""simple docstring"""
_UpperCamelCase :Dict = self.prepare_config_and_inputs()
((_UpperCamelCase) , (_UpperCamelCase) , (_UpperCamelCase) , (_UpperCamelCase) , (_UpperCamelCase) , (_UpperCamelCase)) :List[str] = config_and_inputs
_UpperCamelCase :Union[str, Any] = {'''input_ids''': input_ids, '''attention_mask''': input_mask}
return config, inputs_dict
@require_torch
class A( UpperCamelCase__ , UpperCamelCase__ , unittest.TestCase ):
"""simple docstring"""
A = (
(
MPNetForMaskedLM,
MPNetForMultipleChoice,
MPNetForQuestionAnswering,
MPNetForSequenceClassification,
MPNetForTokenClassification,
MPNetModel,
)
if is_torch_available()
else ()
)
A = (
{
"""feature-extraction""": MPNetModel,
"""fill-mask""": MPNetForMaskedLM,
"""question-answering""": MPNetForQuestionAnswering,
"""text-classification""": MPNetForSequenceClassification,
"""token-classification""": MPNetForTokenClassification,
"""zero-shot""": MPNetForSequenceClassification,
}
if is_torch_available()
else {}
)
A = False
A = True
def _UpperCamelCase( self ) -> List[Any]:
"""simple docstring"""
_UpperCamelCase :Dict = MPNetModelTester(self )
_UpperCamelCase :List[str] = ConfigTester(self , config_class=__a , hidden_size=37 )
def _UpperCamelCase( self ) -> Optional[int]:
"""simple docstring"""
self.config_tester.run_common_tests()
def _UpperCamelCase( self ) -> List[str]:
"""simple docstring"""
_UpperCamelCase :List[str] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mpnet_model(*__a )
def _UpperCamelCase( self ) -> Any:
"""simple docstring"""
_UpperCamelCase :int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mpnet_for_sequence_classification(*__a )
def _UpperCamelCase( self ) -> int:
"""simple docstring"""
_UpperCamelCase :str = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mpnet_for_multiple_choice(*__a )
def _UpperCamelCase( self ) -> Optional[Any]:
"""simple docstring"""
_UpperCamelCase :Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mpnet_for_token_classification(*__a )
def _UpperCamelCase( self ) -> List[str]:
"""simple docstring"""
_UpperCamelCase :Any = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mpnet_for_question_answering(*__a )
@require_torch
class A( unittest.TestCase ):
"""simple docstring"""
@slow
def _UpperCamelCase( self ) -> Optional[Any]:
"""simple docstring"""
_UpperCamelCase :List[Any] = MPNetModel.from_pretrained('''microsoft/mpnet-base''' )
_UpperCamelCase :Any = torch.tensor([[0, 3_45, 2_32, 3_28, 7_40, 1_40, 16_95, 69, 60_78, 15_88, 2]] )
_UpperCamelCase :Any = model(__a )[0]
_UpperCamelCase :Tuple = torch.Size((1, 11, 7_68) )
self.assertEqual(output.shape , __a )
_UpperCamelCase :Optional[int] = torch.tensor(
[[[-0.0_5_5_0, 0.1_9_4_3, -0.0_7_4_0], [-0.0_5_6_2, 0.2_2_1_1, -0.0_5_7_9], [-0.0_4_3_7, 0.3_3_3_7, -0.0_6_4_1]]] )
# compare the actual values for a slice.
self.assertTrue(torch.allclose(output[:, :3, :3] , __a , atol=1E-4 ) )
| 355
|
'''simple docstring'''
import requests
def lowerCAmelCase_ ( snake_case_ : str , snake_case_ : str ) -> None:
'''simple docstring'''
UpperCAmelCase_ = {"Content-Type": "application/json"}
UpperCAmelCase_ = requests.post(snake_case_ , json={"text": message_body} , headers=snake_case_ )
if response.status_code != 2_00:
UpperCAmelCase_ = (
"Request to slack returned an error "
f"""{response.status_code}, the response is:\n{response.text}"""
)
raise ValueError(snake_case_ )
if __name__ == "__main__":
# Set the slack url to the one provided by Slack when you create the webhook at
# https://my.slack.com/services/new/incoming-webhook/
send_slack_message('<YOUR MESSAGE BODY>', '<SLACK CHANNEL URL>')
| 78
| 0
|
'''simple docstring'''
import argparse
import torch
from transformers import (
SpeechTaConfig,
SpeechTaFeatureExtractor,
SpeechTaForSpeechToSpeech,
SpeechTaForSpeechToText,
SpeechTaForTextToSpeech,
SpeechTaProcessor,
SpeechTaTokenizer,
logging,
)
from transformers.tokenization_utils import AddedToken
logging.set_verbosity_info()
_UpperCAmelCase : Union[str, Any] = logging.get_logger('''transformers.models.speecht5''')
_UpperCAmelCase : List[Any] = {
'''speech_encoder_prenet.layer_norm''': '''speecht5.encoder.prenet.feature_projection.layer_norm''',
'''speech_encoder_prenet.post_extract_proj''': '''speecht5.encoder.prenet.feature_projection.projection''',
'''speech_encoder_prenet.pos_conv.0''': '''speecht5.encoder.prenet.pos_conv_embed.conv''',
'''speech_encoder_prenet.mask_emb''': '''speecht5.encoder.prenet.masked_spec_embed''',
}
_UpperCAmelCase : str = {
'''text_encoder_prenet.encoder_prenet.0''': '''speecht5.encoder.prenet.embed_tokens''',
'''text_encoder_prenet.encoder_prenet.1.alpha''': '''speecht5.encoder.prenet.encode_positions.alpha''',
}
_UpperCAmelCase : Tuple = {
'''speech_decoder_prenet.decoder_prenet.0.0.prenet.0.0''': '''speecht5.decoder.prenet.layers.0''',
'''speech_decoder_prenet.decoder_prenet.0.0.prenet.1.0''': '''speecht5.decoder.prenet.layers.1''',
'''speech_decoder_prenet.decoder_prenet.0.1''': '''speecht5.decoder.prenet.final_layer''',
'''speech_decoder_prenet.decoder_prenet.1.alpha''': '''speecht5.decoder.prenet.encode_positions.alpha''',
'''speech_decoder_prenet.spkembs_layer.0''': '''speecht5.decoder.prenet.speaker_embeds_layer''',
}
_UpperCAmelCase : List[str] = {
'''speech_decoder_postnet.feat_out''': '''speech_decoder_postnet.feat_out''',
'''speech_decoder_postnet.prob_out''': '''speech_decoder_postnet.prob_out''',
'''speech_decoder_postnet.postnet.postnet.0.0''': '''speech_decoder_postnet.layers.0.conv''',
'''speech_decoder_postnet.postnet.postnet.0.1''': '''speech_decoder_postnet.layers.0.batch_norm''',
'''speech_decoder_postnet.postnet.postnet.1.0''': '''speech_decoder_postnet.layers.1.conv''',
'''speech_decoder_postnet.postnet.postnet.1.1''': '''speech_decoder_postnet.layers.1.batch_norm''',
'''speech_decoder_postnet.postnet.postnet.2.0''': '''speech_decoder_postnet.layers.2.conv''',
'''speech_decoder_postnet.postnet.postnet.2.1''': '''speech_decoder_postnet.layers.2.batch_norm''',
'''speech_decoder_postnet.postnet.postnet.3.0''': '''speech_decoder_postnet.layers.3.conv''',
'''speech_decoder_postnet.postnet.postnet.3.1''': '''speech_decoder_postnet.layers.3.batch_norm''',
'''speech_decoder_postnet.postnet.postnet.4.0''': '''speech_decoder_postnet.layers.4.conv''',
'''speech_decoder_postnet.postnet.postnet.4.1''': '''speech_decoder_postnet.layers.4.batch_norm''',
}
_UpperCAmelCase : Optional[int] = {
'''text_decoder_prenet.embed_tokens''': '''speecht5.decoder.prenet.embed_tokens''',
}
_UpperCAmelCase : Optional[Any] = {
'''text_decoder_postnet.output_projection''': '''text_decoder_postnet.lm_head''',
}
_UpperCAmelCase : List[str] = {
'''encoder.layers.*.self_attn.k_proj''': '''speecht5.encoder.wrapped_encoder.layers.*.attention.k_proj''',
'''encoder.layers.*.self_attn.v_proj''': '''speecht5.encoder.wrapped_encoder.layers.*.attention.v_proj''',
'''encoder.layers.*.self_attn.q_proj''': '''speecht5.encoder.wrapped_encoder.layers.*.attention.q_proj''',
'''encoder.layers.*.self_attn.out_proj''': '''speecht5.encoder.wrapped_encoder.layers.*.attention.out_proj''',
'''encoder.layers.*.self_attn_layer_norm''': '''speecht5.encoder.wrapped_encoder.layers.*.layer_norm''',
'''encoder.layers.*.fc1''': '''speecht5.encoder.wrapped_encoder.layers.*.feed_forward.intermediate_dense''',
'''encoder.layers.*.fc2''': '''speecht5.encoder.wrapped_encoder.layers.*.feed_forward.output_dense''',
'''encoder.layers.*.final_layer_norm''': '''speecht5.encoder.wrapped_encoder.layers.*.final_layer_norm''',
'''encoder.layer_norm''': '''speecht5.encoder.wrapped_encoder.layer_norm''',
'''encoder.pos_emb.pe_k''': '''speecht5.encoder.wrapped_encoder.embed_positions.pe_k''',
}
_UpperCAmelCase : Tuple = {
'''decoder.layers.*.self_attn.k_proj''': '''speecht5.decoder.wrapped_decoder.layers.*.self_attn.k_proj''',
'''decoder.layers.*.self_attn.v_proj''': '''speecht5.decoder.wrapped_decoder.layers.*.self_attn.v_proj''',
'''decoder.layers.*.self_attn.q_proj''': '''speecht5.decoder.wrapped_decoder.layers.*.self_attn.q_proj''',
'''decoder.layers.*.self_attn.out_proj''': '''speecht5.decoder.wrapped_decoder.layers.*.self_attn.out_proj''',
'''decoder.layers.*.self_attn_layer_norm''': '''speecht5.decoder.wrapped_decoder.layers.*.self_attn_layer_norm''',
'''decoder.layers.*.encoder_attn.k_proj''': '''speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.k_proj''',
'''decoder.layers.*.encoder_attn.v_proj''': '''speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.v_proj''',
'''decoder.layers.*.encoder_attn.q_proj''': '''speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.q_proj''',
'''decoder.layers.*.encoder_attn.out_proj''': '''speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.out_proj''',
'''decoder.layers.*.encoder_attn_layer_norm''': '''speecht5.decoder.wrapped_decoder.layers.*.encoder_attn_layer_norm''',
'''decoder.layers.*.fc1''': '''speecht5.decoder.wrapped_decoder.layers.*.feed_forward.intermediate_dense''',
'''decoder.layers.*.fc2''': '''speecht5.decoder.wrapped_decoder.layers.*.feed_forward.output_dense''',
'''decoder.layers.*.final_layer_norm''': '''speecht5.decoder.wrapped_decoder.layers.*.final_layer_norm''',
}
_UpperCAmelCase : str = {
**MAPPING_SPEECH_ENCODER_PRENET,
**MAPPING_ENCODER,
**MAPPING_DECODER,
**MAPPING_TEXT_DECODER_PRENET,
**MAPPING_TEXT_DECODER_POSTNET,
}
_UpperCAmelCase : Optional[Any] = {
**MAPPING_TEXT_ENCODER_PRENET,
**MAPPING_ENCODER,
**MAPPING_DECODER,
**MAPPING_SPEECH_DECODER_PRENET,
**MAPPING_SPEECH_DECODER_POSTNET,
}
_UpperCAmelCase : Tuple = {
**MAPPING_SPEECH_ENCODER_PRENET,
**MAPPING_ENCODER,
**MAPPING_DECODER,
**MAPPING_SPEECH_DECODER_PRENET,
**MAPPING_SPEECH_DECODER_POSTNET,
}
_UpperCAmelCase : int = []
_UpperCAmelCase : Optional[int] = [
'''encoder.version''',
'''encoder.layers.*.norm_k.weight''',
'''encoder.layers.*.norm_k.bias''',
'''decoder.version''',
'''decoder.layers.*.norm_k.weight''',
'''decoder.layers.*.norm_k.bias''',
'''decoder.pos_emb.pe_k''',
'''speech_encoder_prenet.embed_positions._float_tensor''',
'''text_decoder_prenet.embed_positions._float_tensor''',
]
_UpperCAmelCase : Any = IGNORE_KEYS + [
'''encoder.proj''',
'''text_encoder_prenet.*''',
'''speech_decoder_prenet.*''',
'''speech_decoder_postnet.*''',
]
_UpperCAmelCase : Union[str, Any] = IGNORE_KEYS + [
'''encoder.proj''',
'''speech_encoder_prenet.*''',
'''text_decoder_prenet.*''',
'''text_decoder_postnet.*''',
]
_UpperCAmelCase : Tuple = IGNORE_KEYS + [
'''encoder.proj''',
'''text_encoder_prenet.*''',
'''text_decoder_prenet.*''',
'''text_decoder_postnet.*''',
]
def UpperCamelCase ( lowercase_ : Dict , lowercase_ : List[str] , lowercase_ : Union[str, Any] , lowercase_ : Any , lowercase_ : Union[str, Any] ) -> Dict:
'''simple docstring'''
for attribute in key.split('''.''' ):
lowercase =getattr(lowercase_ , lowercase_ )
if weight_type is not None:
lowercase =getattr(lowercase_ , lowercase_ ).shape
else:
lowercase =hf_pointer.shape
if hf_shape != value.shape:
raise ValueError(
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":
lowercase =value
elif weight_type == "weight_g":
lowercase =value
elif weight_type == "weight_v":
lowercase =value
elif weight_type == "bias":
lowercase =value
elif weight_type == "running_mean":
lowercase =value
elif weight_type == "running_var":
lowercase =value
elif weight_type == "num_batches_tracked":
lowercase =value
else:
lowercase =value
logger.info(f'{key + ("." + weight_type if weight_type is not None else "")} was initialized from {full_name}.' )
def UpperCamelCase ( lowercase_ : Optional[int] , lowercase_ : Dict ) -> Any:
'''simple docstring'''
for key in ignore_keys:
if key.endswith('''.*''' ):
if name.startswith(key[:-1] ):
return True
elif ".*." in key:
lowercase , lowercase =key.split('''.*.''' )
if prefix in name and suffix in name:
return True
elif key in name:
return True
return False
def UpperCamelCase ( lowercase_ : Optional[Any] , lowercase_ : Optional[int] , lowercase_ : Tuple ) -> int:
'''simple docstring'''
lowercase =[]
if task == "s2t":
lowercase =hf_model.speechta.encoder.prenet.feature_encoder
lowercase =MAPPING_S2T
lowercase =IGNORE_KEYS_S2T
elif task == "t2s":
lowercase =None
lowercase =MAPPING_T2S
lowercase =IGNORE_KEYS_T2S
elif task == "s2s":
lowercase =hf_model.speechta.encoder.prenet.feature_encoder
lowercase =MAPPING_S2S
lowercase =IGNORE_KEYS_S2S
else:
raise ValueError(f'Unsupported task: {task}' )
for name, value in fairseq_dict.items():
if should_ignore(lowercase_ , lowercase_ ):
logger.info(f'{name} was ignored' )
continue
lowercase =False
if "conv_layers" in name:
load_conv_layer(
lowercase_ , lowercase_ , lowercase_ , lowercase_ , hf_model.config.feat_extract_norm == '''group''' , )
lowercase =True
else:
for key, mapped_key in MAPPING.items():
# mapped_key = "speecht5." + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
if "*" in key:
lowercase , lowercase =key.split('''.*.''' )
if prefix in name and suffix in name:
lowercase =suffix
# if key in name or key.split("w2v_model.")[-1] == name.split(".")[0]:
if key in name:
lowercase =True
if "*" in mapped_key:
lowercase =name.split(lowercase_ )[0].split('''.''' )[-2]
lowercase =mapped_key.replace('''*''' , lowercase_ )
if "weight_g" in name:
lowercase ='''weight_g'''
elif "weight_v" in name:
lowercase ='''weight_v'''
elif "bias" in name:
lowercase ='''bias'''
elif "weight" in name:
lowercase ='''weight'''
elif "running_mean" in name:
lowercase ='''running_mean'''
elif "running_var" in name:
lowercase ='''running_var'''
elif "num_batches_tracked" in name:
lowercase ='''num_batches_tracked'''
else:
lowercase =None
set_recursively(lowercase_ , lowercase_ , lowercase_ , lowercase_ , lowercase_ )
continue
if not is_used:
unused_weights.append(lowercase_ )
logger.warning(f'Unused weights: {unused_weights}' )
def UpperCamelCase ( lowercase_ : Union[str, Any] , lowercase_ : Optional[Any] , lowercase_ : Optional[Any] , lowercase_ : Optional[Any] , lowercase_ : Optional[Any] ) -> Union[str, Any]:
'''simple docstring'''
lowercase =full_name.split('''conv_layers.''' )[-1]
lowercase =name.split('''.''' )
lowercase =int(items[0] )
lowercase =int(items[1] )
if type_id == 0:
if "bias" in name:
if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
raise ValueError(
f'{full_name} has size {value.shape}, but'
f' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.' )
lowercase =value
logger.info(f'Feat extract conv layer {layer_id} was initialized from {full_name}.' )
elif "weight" in name:
if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
raise ValueError(
f'{full_name} has size {value.shape}, but'
f' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.' )
lowercase =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:
if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
raise ValueError(
f'{full_name} has size {value.shape}, but'
f' {feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape} was found.' )
lowercase =value
logger.info(f'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.' )
elif "weight" in name:
if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
raise ValueError(
f'{full_name} has size {value.shape}, but'
f' {feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape} was found.' )
lowercase =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 UpperCamelCase ( lowercase_ : Tuple , lowercase_ : List[str] , lowercase_ : List[Any] , lowercase_ : Optional[int]=None , lowercase_ : List[Any]=None , lowercase_ : Optional[int]=None , ) -> Optional[int]:
'''simple docstring'''
if config_path is not None:
lowercase =SpeechTaConfig.from_pretrained(lowercase_ )
else:
lowercase =SpeechTaConfig()
if task == "s2t":
lowercase =config.max_text_positions
lowercase =SpeechTaForSpeechToText(lowercase_ )
elif task == "t2s":
lowercase =1_8_7_6
lowercase =6_0_0
lowercase =config.max_speech_positions
lowercase =SpeechTaForTextToSpeech(lowercase_ )
elif task == "s2s":
lowercase =1_8_7_6
lowercase =config.max_speech_positions
lowercase =SpeechTaForSpeechToSpeech(lowercase_ )
else:
raise ValueError(f'Unknown task name: {task}' )
if vocab_path:
lowercase =SpeechTaTokenizer(lowercase_ , model_max_length=config.max_text_positions )
# Mask token behaves like a normal word, i.e. include the space before it
lowercase =AddedToken('''<mask>''' , lstrip=lowercase_ , rstrip=lowercase_ )
lowercase =mask_token
tokenizer.add_special_tokens({'''mask_token''': mask_token} )
tokenizer.add_tokens(['''<ctc_blank>'''] )
lowercase =SpeechTaFeatureExtractor()
lowercase =SpeechTaProcessor(tokenizer=lowercase_ , feature_extractor=lowercase_ )
processor.save_pretrained(lowercase_ )
lowercase =torch.load(lowercase_ )
recursively_load_weights(fairseq_checkpoint['''model'''] , lowercase_ , lowercase_ )
model.save_pretrained(lowercase_ )
if repo_id:
print('''Pushing to the hub...''' )
processor.push_to_hub(lowercase_ )
model.push_to_hub(lowercase_ )
if __name__ == "__main__":
_UpperCAmelCase : Optional[int] = argparse.ArgumentParser()
parser.add_argument(
'''--task''',
default='''s2t''',
type=str,
help='''Type of the SpeechT5 model you\'d like to convert. Should be one of \'s2t\', \'t2s\', \'s2s\'.''',
)
parser.add_argument('''--checkpoint_path''', required=True, default=None, type=str, help='''Path to fairseq checkpoint''')
parser.add_argument('''--vocab_path''', default=None, type=str, help='''Path to SentencePiece model''')
parser.add_argument('''--config_path''', default=None, type=str, help='''Path to hf config.json of model to convert''')
parser.add_argument(
'''--pytorch_dump_folder_path''', required=True, default=None, type=str, help='''Path to the output PyTorch model.'''
)
parser.add_argument(
'''--push_to_hub''', default=None, type=str, help='''Where to upload the converted model on the 🤗 hub.'''
)
_UpperCAmelCase : Dict = parser.parse_args()
convert_speechta_checkpoint(
args.task,
args.checkpoint_path,
args.pytorch_dump_folder_path,
args.config_path,
args.vocab_path,
args.push_to_hub,
)
| 718
|
'''simple docstring'''
from __future__ import annotations
import os
import tempfile
import unittest
from transformers import ConvBertConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import (
TFConvBertForMaskedLM,
TFConvBertForMultipleChoice,
TFConvBertForQuestionAnswering,
TFConvBertForSequenceClassification,
TFConvBertForTokenClassification,
TFConvBertModel,
)
class __magic_name__ :
def __init__( self , snake_case_ , snake_case_=13 , snake_case_=7 , snake_case_=True , snake_case_=True , snake_case_=True , snake_case_=True , snake_case_=99 , snake_case_=32 , snake_case_=2 , snake_case_=4 , snake_case_=37 , snake_case_="gelu" , snake_case_=0.1 , snake_case_=0.1 , snake_case_=5_12 , snake_case_=16 , snake_case_=2 , snake_case_=0.02 , snake_case_=3 , snake_case_=4 , snake_case_=None , ):
lowercase =parent
lowercase =13
lowercase =7
lowercase =True
lowercase =True
lowercase =True
lowercase =True
lowercase =99
lowercase =3_84
lowercase =2
lowercase =4
lowercase =37
lowercase ='''gelu'''
lowercase =0.1
lowercase =0.1
lowercase =5_12
lowercase =16
lowercase =2
lowercase =0.02
lowercase =3
lowercase =4
lowercase =1_28
lowercase =2
lowercase =9
lowercase =1
lowercase =None
def _A( self ):
lowercase =ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
lowercase =None
if self.use_input_mask:
lowercase =random_attention_mask([self.batch_size, self.seq_length] )
lowercase =None
if self.use_token_type_ids:
lowercase =ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
lowercase =None
lowercase =None
lowercase =None
if self.use_labels:
lowercase =ids_tensor([self.batch_size] , self.type_sequence_label_size )
lowercase =ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
lowercase =ids_tensor([self.batch_size] , self.num_choices )
lowercase =ConvBertConfig(
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 , initializer_range=self.initializer_range , return_dict=snake_case_ , )
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def _A( self , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ ):
lowercase =TFConvBertModel(config=snake_case_ )
lowercase ={'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
lowercase =[input_ids, input_mask]
lowercase =model(snake_case_ )
lowercase =model(snake_case_ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def _A( self , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ ):
lowercase =TFConvBertForMaskedLM(config=snake_case_ )
lowercase ={
'''input_ids''': input_ids,
'''attention_mask''': input_mask,
'''token_type_ids''': token_type_ids,
}
lowercase =model(snake_case_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def _A( self , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ ):
lowercase =self.num_labels
lowercase =TFConvBertForSequenceClassification(config=snake_case_ )
lowercase ={
'''input_ids''': input_ids,
'''attention_mask''': input_mask,
'''token_type_ids''': token_type_ids,
}
lowercase =model(snake_case_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def _A( self , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ ):
lowercase =self.num_choices
lowercase =TFConvBertForMultipleChoice(config=snake_case_ )
lowercase =tf.tile(tf.expand_dims(snake_case_ , 1 ) , (1, self.num_choices, 1) )
lowercase =tf.tile(tf.expand_dims(snake_case_ , 1 ) , (1, self.num_choices, 1) )
lowercase =tf.tile(tf.expand_dims(snake_case_ , 1 ) , (1, self.num_choices, 1) )
lowercase ={
'''input_ids''': multiple_choice_inputs_ids,
'''attention_mask''': multiple_choice_input_mask,
'''token_type_ids''': multiple_choice_token_type_ids,
}
lowercase =model(snake_case_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def _A( self , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ ):
lowercase =self.num_labels
lowercase =TFConvBertForTokenClassification(config=snake_case_ )
lowercase ={
'''input_ids''': input_ids,
'''attention_mask''': input_mask,
'''token_type_ids''': token_type_ids,
}
lowercase =model(snake_case_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def _A( self , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ ):
lowercase =TFConvBertForQuestionAnswering(config=snake_case_ )
lowercase ={
'''input_ids''': input_ids,
'''attention_mask''': input_mask,
'''token_type_ids''': token_type_ids,
}
lowercase =model(snake_case_ )
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 ):
lowercase =self.prepare_config_and_inputs()
(
(
lowercase
) , (
lowercase
) , (
lowercase
) , (
lowercase
) , (
lowercase
) , (
lowercase
) , (
lowercase
) ,
) =config_and_inputs
lowercase ={'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
return config, inputs_dict
@require_tf
class __magic_name__ ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , unittest.TestCase ):
UpperCamelCase__ = (
(
TFConvBertModel,
TFConvBertForMaskedLM,
TFConvBertForQuestionAnswering,
TFConvBertForSequenceClassification,
TFConvBertForTokenClassification,
TFConvBertForMultipleChoice,
)
if is_tf_available()
else ()
)
UpperCamelCase__ = (
{
'feature-extraction': TFConvBertModel,
'fill-mask': TFConvBertForMaskedLM,
'question-answering': TFConvBertForQuestionAnswering,
'text-classification': TFConvBertForSequenceClassification,
'token-classification': TFConvBertForTokenClassification,
'zero-shot': TFConvBertForSequenceClassification,
}
if is_tf_available()
else {}
)
UpperCamelCase__ = False
UpperCamelCase__ = False
UpperCamelCase__ = False
def _A( self ):
lowercase =TFConvBertModelTester(self )
lowercase =ConfigTester(self , config_class=snake_case_ , hidden_size=37 )
def _A( self ):
self.config_tester.run_common_tests()
def _A( self ):
lowercase =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*snake_case_ )
def _A( self ):
lowercase =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*snake_case_ )
def _A( self ):
lowercase =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_multiple_choice(*snake_case_ )
def _A( self ):
lowercase =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*snake_case_ )
def _A( self ):
lowercase =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*snake_case_ )
def _A( self ):
lowercase =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*snake_case_ )
@slow
def _A( self ):
lowercase , lowercase =self.model_tester.prepare_config_and_inputs_for_common()
lowercase =True
lowercase =True
if hasattr(snake_case_ , '''use_cache''' ):
lowercase =True
lowercase =getattr(self.model_tester , '''encoder_seq_length''' , self.model_tester.seq_length )
lowercase =getattr(self.model_tester , '''key_length''' , snake_case_ )
for model_class in self.all_model_classes:
lowercase =self._prepare_for_class(snake_case_ , snake_case_ )
lowercase =model_class(snake_case_ )
lowercase =len(model(snake_case_ ) )
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(snake_case_ , saved_model=snake_case_ )
lowercase =os.path.join(snake_case_ , '''saved_model''' , '''1''' )
lowercase =tf.keras.models.load_model(snake_case_ )
lowercase =model(snake_case_ )
if self.is_encoder_decoder:
lowercase =outputs['''encoder_hidden_states''']
lowercase =outputs['''encoder_attentions''']
else:
lowercase =outputs['''hidden_states''']
lowercase =outputs['''attentions''']
self.assertEqual(len(snake_case_ ) , snake_case_ )
lowercase =getattr(
self.model_tester , '''expected_num_hidden_layers''' , self.model_tester.num_hidden_layers + 1 )
self.assertEqual(len(snake_case_ ) , snake_case_ )
self.assertListEqual(
list(output_hidden_states[0].shape[-2:] ) , [self.model_tester.seq_length, self.model_tester.hidden_size] , )
self.assertEqual(len(snake_case_ ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(output_attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads / 2, encoder_seq_length, encoder_key_length] , )
@slow
def _A( self ):
lowercase =TFConvBertModel.from_pretrained('''YituTech/conv-bert-base''' )
self.assertIsNotNone(snake_case_ )
def _A( self ):
lowercase , lowercase =self.model_tester.prepare_config_and_inputs_for_common()
lowercase =True
lowercase =getattr(self.model_tester , '''decoder_seq_length''' , self.model_tester.seq_length )
lowercase =getattr(self.model_tester , '''encoder_seq_length''' , self.model_tester.seq_length )
lowercase =getattr(self.model_tester , '''key_length''' , snake_case_ )
lowercase =getattr(self.model_tester , '''key_length''' , snake_case_ )
def check_decoder_attentions_output(snake_case_ ):
lowercase =len(snake_case_ )
self.assertEqual(out_len % 2 , 0 )
lowercase =outputs.decoder_attentions
self.assertEqual(len(snake_case_ ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(decoder_attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads / 2, decoder_seq_length, decoder_key_length] , )
def check_encoder_attentions_output(snake_case_ ):
lowercase =[
t.numpy() for t in (outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions)
]
self.assertEqual(len(snake_case_ ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads / 2, encoder_seq_length, encoder_key_length] , )
for model_class in self.all_model_classes:
lowercase =True
lowercase =False
lowercase =model_class(snake_case_ )
lowercase =model(self._prepare_for_class(snake_case_ , snake_case_ ) )
lowercase =len(snake_case_ )
self.assertEqual(config.output_hidden_states , snake_case_ )
check_encoder_attentions_output(snake_case_ )
if self.is_encoder_decoder:
lowercase =model_class(snake_case_ )
lowercase =model(self._prepare_for_class(snake_case_ , snake_case_ ) )
self.assertEqual(config.output_hidden_states , snake_case_ )
check_decoder_attentions_output(snake_case_ )
# Check that output attentions can also be changed via the config
del inputs_dict["output_attentions"]
lowercase =True
lowercase =model_class(snake_case_ )
lowercase =model(self._prepare_for_class(snake_case_ , snake_case_ ) )
self.assertEqual(config.output_hidden_states , snake_case_ )
check_encoder_attentions_output(snake_case_ )
# Check attention is always last and order is fine
lowercase =True
lowercase =True
lowercase =model_class(snake_case_ )
lowercase =model(self._prepare_for_class(snake_case_ , snake_case_ ) )
self.assertEqual(out_len + (2 if self.is_encoder_decoder else 1) , len(snake_case_ ) )
self.assertEqual(model.config.output_hidden_states , snake_case_ )
check_encoder_attentions_output(snake_case_ )
@require_tf
class __magic_name__ ( unittest.TestCase ):
@slow
def _A( self ):
lowercase =TFConvBertModel.from_pretrained('''YituTech/conv-bert-base''' )
lowercase =tf.constant([[0, 1, 2, 3, 4, 5]] )
lowercase =model(snake_case_ )[0]
lowercase =[1, 6, 7_68]
self.assertEqual(output.shape , snake_case_ )
lowercase =tf.constant(
[
[
[-0.03_47_54_93, -0.4_68_60_34, -0.30_63_88_32],
[0.22_63_72_48, -0.26_98_86_46, -0.7_42_34_24],
[0.10_32_48_68, -0.45_01_35_08, -0.58_28_07_84],
]
] )
tf.debugging.assert_near(output[:, :3, :3] , snake_case_ , atol=1E-4 )
| 145
| 0
|
'''simple docstring'''
import copy
from typing import Any, Dict, List, Optional, Union
import numpy as np
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import TensorType, logging
__lowerCAmelCase : List[Any] = logging.get_logger(__name__)
class A ( UpperCAmelCase ):
a_ = ['input_features']
def __init__( self : Union[str, Any] , __a : Union[str, Any]=8_0 , __a : List[Any]=1_6_0_0_0 , __a : int=1_6_0 , __a : Optional[int]=3_0 , __a : str=4_0_0 , __a : List[Any]=0.0 , __a : List[str]=False , **__a : List[str] , ) -> Union[str, Any]:
super().__init__(
feature_size=__SCREAMING_SNAKE_CASE , sampling_rate=__SCREAMING_SNAKE_CASE , padding_value=__SCREAMING_SNAKE_CASE , return_attention_mask=__SCREAMING_SNAKE_CASE , **__SCREAMING_SNAKE_CASE , )
__UpperCAmelCase = n_fft
__UpperCAmelCase = hop_length
__UpperCAmelCase = chunk_length
__UpperCAmelCase = chunk_length * sampling_rate
__UpperCAmelCase = self.n_samples // hop_length
__UpperCAmelCase = sampling_rate
__UpperCAmelCase = mel_filter_bank(
num_frequency_bins=1 + n_fft // 2 , num_mel_filters=__SCREAMING_SNAKE_CASE , min_frequency=0.0 , max_frequency=8_0_0_0.0 , sampling_rate=__SCREAMING_SNAKE_CASE , norm='''slaney''' , mel_scale='''slaney''' , )
def snake_case__ ( self : Any , __a : np.array ) -> np.ndarray:
__UpperCAmelCase = spectrogram(
__SCREAMING_SNAKE_CASE , window_function(self.n_fft , '''hann''' ) , frame_length=self.n_fft , hop_length=self.hop_length , power=2.0 , mel_filters=self.mel_filters , log_mel='''log10''' , )
__UpperCAmelCase = log_spec[:, :-1]
__UpperCAmelCase = np.maximum(__SCREAMING_SNAKE_CASE , log_spec.max() - 8.0 )
__UpperCAmelCase = (log_spec + 4.0) / 4.0
return log_spec
@staticmethod
# Copied from transformers.models.wav2vec2.feature_extraction_wav2vec2.Wav2Vec2FeatureExtractor.zero_mean_unit_var_norm
def snake_case__ ( __a : List[np.ndarray] , __a : List[np.ndarray] , __a : float = 0.0 ) -> List[np.ndarray]:
if attention_mask is not None:
__UpperCAmelCase = np.array(__SCREAMING_SNAKE_CASE , np.intaa )
__UpperCAmelCase = []
for vector, length in zip(__SCREAMING_SNAKE_CASE , attention_mask.sum(-1 ) ):
__UpperCAmelCase = (vector - vector[:length].mean()) / np.sqrt(vector[:length].var() + 1e-7 )
if length < normed_slice.shape[0]:
__UpperCAmelCase = padding_value
normed_input_values.append(__SCREAMING_SNAKE_CASE )
else:
__UpperCAmelCase = [(x - x.mean()) / np.sqrt(x.var() + 1e-7 ) for x in input_values]
return normed_input_values
def __call__( self : Optional[Any] , __a : Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]] , __a : bool = True , __a : Optional[int] = None , __a : Optional[Union[str, TensorType]] = None , __a : Optional[bool] = None , __a : Optional[str] = "max_length" , __a : Optional[int] = None , __a : Optional[int] = None , __a : Optional[bool] = None , **__a : Any , ) -> BatchFeature:
if sampling_rate is not None:
if sampling_rate != self.sampling_rate:
raise ValueError(
f"""The model corresponding to this feature extractor: {self.__class__.__name__} was trained using a"""
f""" sampling rate of {self.sampling_rate}. Please make sure that the provided `raw_speech` input"""
f""" was sampled with {self.sampling_rate} and not {sampling_rate}.""" )
else:
logger.warning(
'''It is strongly recommended to pass the `sampling_rate` argument to this function. '''
'''Failing to do so can result in silent errors that might be hard to debug.''' )
__UpperCAmelCase = isinstance(__SCREAMING_SNAKE_CASE , np.ndarray ) and len(raw_speech.shape ) > 1
if is_batched_numpy and len(raw_speech.shape ) > 2:
raise ValueError(f"""Only mono-channel audio is supported for input to {self}""" )
__UpperCAmelCase = is_batched_numpy or (
isinstance(__SCREAMING_SNAKE_CASE , (list, tuple) ) and (isinstance(raw_speech[0] , (np.ndarray, tuple, list) ))
)
if is_batched:
__UpperCAmelCase = [np.asarray([speech] , dtype=np.floataa ).T for speech in raw_speech]
elif not is_batched and not isinstance(__SCREAMING_SNAKE_CASE , np.ndarray ):
__UpperCAmelCase = np.asarray(__SCREAMING_SNAKE_CASE , dtype=np.floataa )
elif isinstance(__SCREAMING_SNAKE_CASE , np.ndarray ) and raw_speech.dtype is np.dtype(np.floataa ):
__UpperCAmelCase = raw_speech.astype(np.floataa )
# always return batch
if not is_batched:
__UpperCAmelCase = [np.asarray([raw_speech] ).T]
__UpperCAmelCase = BatchFeature({'''input_features''': raw_speech} )
# convert into correct format for padding
__UpperCAmelCase = self.pad(
__SCREAMING_SNAKE_CASE , padding=__SCREAMING_SNAKE_CASE , max_length=max_length if max_length else self.n_samples , truncation=__SCREAMING_SNAKE_CASE , pad_to_multiple_of=__SCREAMING_SNAKE_CASE , return_attention_mask=return_attention_mask or do_normalize , )
# zero-mean and unit-variance normalization
if do_normalize:
__UpperCAmelCase = self.zero_mean_unit_var_norm(
padded_inputs['''input_features'''] , attention_mask=padded_inputs['''attention_mask'''] , padding_value=self.padding_value , )
__UpperCAmelCase = np.stack(padded_inputs['''input_features'''] , axis=0 )
# make sure list is in array format
__UpperCAmelCase = padded_inputs.get('''input_features''' ).transpose(2 , 0 , 1 )
__UpperCAmelCase = [self._np_extract_fbank_features(__SCREAMING_SNAKE_CASE ) for waveform in input_features[0]]
if isinstance(input_features[0] , __SCREAMING_SNAKE_CASE ):
__UpperCAmelCase = [np.asarray(__SCREAMING_SNAKE_CASE , dtype=np.floataa ) for feature in input_features]
else:
__UpperCAmelCase = input_features
if return_attention_mask:
# rescale from sample (48000) to feature (3000)
__UpperCAmelCase = padded_inputs['''attention_mask'''][:, :: self.hop_length]
if return_tensors is not None:
__UpperCAmelCase = padded_inputs.convert_to_tensors(__SCREAMING_SNAKE_CASE )
return padded_inputs
def snake_case__ ( self : int ) -> Dict[str, Any]:
__UpperCAmelCase = copy.deepcopy(self.__dict__ )
__UpperCAmelCase = self.__class__.__name__
if "mel_filters" in output:
del output["mel_filters"]
return output
| 262
|
import collections
import inspect
import unittest
from transformers import SwinvaConfig
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, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import SwinvaForImageClassification, SwinvaForMaskedImageModeling, SwinvaModel
from transformers.models.swinva.modeling_swinva import SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import AutoImageProcessor
class _A :
"""simple docstring"""
def __init__( self : Dict , __SCREAMING_SNAKE_CASE : str , __SCREAMING_SNAKE_CASE : List[Any]=13 , __SCREAMING_SNAKE_CASE : Dict=32 , __SCREAMING_SNAKE_CASE : Optional[int]=2 , __SCREAMING_SNAKE_CASE : Optional[int]=3 , __SCREAMING_SNAKE_CASE : List[str]=16 , __SCREAMING_SNAKE_CASE : Union[str, Any]=[1, 2, 1] , __SCREAMING_SNAKE_CASE : List[Any]=[2, 2, 4] , __SCREAMING_SNAKE_CASE : str=2 , __SCREAMING_SNAKE_CASE : Any=2.0 , __SCREAMING_SNAKE_CASE : Optional[int]=True , __SCREAMING_SNAKE_CASE : int=0.0 , __SCREAMING_SNAKE_CASE : Dict=0.0 , __SCREAMING_SNAKE_CASE : Union[str, Any]=0.1 , __SCREAMING_SNAKE_CASE : Any="gelu" , __SCREAMING_SNAKE_CASE : Optional[Any]=False , __SCREAMING_SNAKE_CASE : str=True , __SCREAMING_SNAKE_CASE : List[Any]=0.02 , __SCREAMING_SNAKE_CASE : Tuple=1e-5 , __SCREAMING_SNAKE_CASE : Optional[Any]=True , __SCREAMING_SNAKE_CASE : Optional[int]=None , __SCREAMING_SNAKE_CASE : str=True , __SCREAMING_SNAKE_CASE : Any=10 , __SCREAMING_SNAKE_CASE : Dict=8 , ) -> List[Any]:
__UpperCAmelCase =parent
__UpperCAmelCase =batch_size
__UpperCAmelCase =image_size
__UpperCAmelCase =patch_size
__UpperCAmelCase =num_channels
__UpperCAmelCase =embed_dim
__UpperCAmelCase =depths
__UpperCAmelCase =num_heads
__UpperCAmelCase =window_size
__UpperCAmelCase =mlp_ratio
__UpperCAmelCase =qkv_bias
__UpperCAmelCase =hidden_dropout_prob
__UpperCAmelCase =attention_probs_dropout_prob
__UpperCAmelCase =drop_path_rate
__UpperCAmelCase =hidden_act
__UpperCAmelCase =use_absolute_embeddings
__UpperCAmelCase =patch_norm
__UpperCAmelCase =layer_norm_eps
__UpperCAmelCase =initializer_range
__UpperCAmelCase =is_training
__UpperCAmelCase =scope
__UpperCAmelCase =use_labels
__UpperCAmelCase =type_sequence_label_size
__UpperCAmelCase =encoder_stride
def _a ( self : Tuple ) -> Optional[int]:
__UpperCAmelCase =floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
__UpperCAmelCase =None
if self.use_labels:
__UpperCAmelCase =ids_tensor([self.batch_size] , self.type_sequence_label_size )
__UpperCAmelCase =self.get_config()
return config, pixel_values, labels
def _a ( self : List[Any] ) -> Optional[Any]:
return SwinvaConfig(
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 , )
def _a ( self : int , __SCREAMING_SNAKE_CASE : Any , __SCREAMING_SNAKE_CASE : Tuple , __SCREAMING_SNAKE_CASE : Dict ) -> Optional[int]:
__UpperCAmelCase =SwinvaModel(config=__SCREAMING_SNAKE_CASE )
model.to(__SCREAMING_SNAKE_CASE )
model.eval()
__UpperCAmelCase =model(__SCREAMING_SNAKE_CASE )
__UpperCAmelCase =((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths ) - 1))
__UpperCAmelCase =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 _a ( self : int , __SCREAMING_SNAKE_CASE : Union[str, Any] , __SCREAMING_SNAKE_CASE : List[Any] , __SCREAMING_SNAKE_CASE : int ) -> Tuple:
__UpperCAmelCase =SwinvaForMaskedImageModeling(config=__SCREAMING_SNAKE_CASE )
model.to(__SCREAMING_SNAKE_CASE )
model.eval()
__UpperCAmelCase =model(__SCREAMING_SNAKE_CASE )
self.parent.assertEqual(
result.logits.shape , (self.batch_size, self.num_channels, self.image_size, self.image_size) )
# test greyscale images
__UpperCAmelCase =1
__UpperCAmelCase =SwinvaForMaskedImageModeling(__SCREAMING_SNAKE_CASE )
model.to(__SCREAMING_SNAKE_CASE )
model.eval()
__UpperCAmelCase =floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
__UpperCAmelCase =model(__SCREAMING_SNAKE_CASE )
self.parent.assertEqual(result.logits.shape , (self.batch_size, 1, self.image_size, self.image_size) )
def _a ( self : Optional[int] , __SCREAMING_SNAKE_CASE : str , __SCREAMING_SNAKE_CASE : Optional[int] , __SCREAMING_SNAKE_CASE : Union[str, Any] ) -> Tuple:
__UpperCAmelCase =self.type_sequence_label_size
__UpperCAmelCase =SwinvaForImageClassification(__SCREAMING_SNAKE_CASE )
model.to(__SCREAMING_SNAKE_CASE )
model.eval()
__UpperCAmelCase =model(__SCREAMING_SNAKE_CASE , labels=__SCREAMING_SNAKE_CASE )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
def _a ( self : List[str] ) -> Tuple:
__UpperCAmelCase =self.prepare_config_and_inputs()
__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase =config_and_inputs
__UpperCAmelCase ={"""pixel_values""": pixel_values}
return config, inputs_dict
@require_torch
class _A ( UpperCamelCase , UpperCamelCase , unittest.TestCase ):
"""simple docstring"""
lowerCamelCase : Optional[int] = (
(SwinvaModel, SwinvaForImageClassification, SwinvaForMaskedImageModeling) if is_torch_available() else ()
)
lowerCamelCase : Tuple = (
{'feature-extraction': SwinvaModel, 'image-classification': SwinvaForImageClassification}
if is_torch_available()
else {}
)
lowerCamelCase : Dict = False
lowerCamelCase : Tuple = False
lowerCamelCase : List[str] = False
lowerCamelCase : Tuple = False
def _a ( self : str ) -> str:
__UpperCAmelCase =SwinvaModelTester(self )
__UpperCAmelCase =ConfigTester(self , config_class=__SCREAMING_SNAKE_CASE , embed_dim=37 )
def _a ( self : List[Any] ) -> Optional[int]:
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 : str ) -> str:
__UpperCAmelCase =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__SCREAMING_SNAKE_CASE )
@unittest.skip(reason="""Got `CUDA error: misaligned address` with PyTorch 2.0.0.""" )
def _a ( self : Tuple ) -> Tuple:
pass
@unittest.skip(reason="""Swinv2 does not use inputs_embeds""" )
def _a ( self : Optional[Any] ) -> int:
pass
def _a ( self : Tuple ) -> int:
__UpperCAmelCase , __UpperCAmelCase =self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__UpperCAmelCase =model_class(__SCREAMING_SNAKE_CASE )
self.assertIsInstance(model.get_input_embeddings() , (nn.Module) )
__UpperCAmelCase =model.get_output_embeddings()
self.assertTrue(x is None or isinstance(__SCREAMING_SNAKE_CASE , nn.Linear ) )
def _a ( self : str ) -> List[str]:
__UpperCAmelCase , __UpperCAmelCase =self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__UpperCAmelCase =model_class(__SCREAMING_SNAKE_CASE )
__UpperCAmelCase =inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
__UpperCAmelCase =[*signature.parameters.keys()]
__UpperCAmelCase =["""pixel_values"""]
self.assertListEqual(arg_names[:1] , __SCREAMING_SNAKE_CASE )
def _a ( self : Tuple ) -> Tuple:
__UpperCAmelCase , __UpperCAmelCase =self.model_tester.prepare_config_and_inputs_for_common()
__UpperCAmelCase =True
for model_class in self.all_model_classes:
__UpperCAmelCase =True
__UpperCAmelCase =False
__UpperCAmelCase =True
__UpperCAmelCase =model_class(__SCREAMING_SNAKE_CASE )
model.to(__SCREAMING_SNAKE_CASE )
model.eval()
with torch.no_grad():
__UpperCAmelCase =model(**self._prepare_for_class(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ) )
__UpperCAmelCase =outputs.attentions
__UpperCAmelCase =len(self.model_tester.depths )
self.assertEqual(len(__SCREAMING_SNAKE_CASE ) , __SCREAMING_SNAKE_CASE )
# check that output_attentions also work using config
del inputs_dict["output_attentions"]
__UpperCAmelCase =True
__UpperCAmelCase =config.window_size**2
__UpperCAmelCase =model_class(__SCREAMING_SNAKE_CASE )
model.to(__SCREAMING_SNAKE_CASE )
model.eval()
with torch.no_grad():
__UpperCAmelCase =model(**self._prepare_for_class(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ) )
__UpperCAmelCase =outputs.attentions
self.assertEqual(len(__SCREAMING_SNAKE_CASE ) , __SCREAMING_SNAKE_CASE )
self.assertListEqual(
list(attentions[0].shape[-3:] ) , [self.model_tester.num_heads[0], window_size_squared, window_size_squared] , )
__UpperCAmelCase =len(__SCREAMING_SNAKE_CASE )
# Check attention is always last and order is fine
__UpperCAmelCase =True
__UpperCAmelCase =True
__UpperCAmelCase =model_class(__SCREAMING_SNAKE_CASE )
model.to(__SCREAMING_SNAKE_CASE )
model.eval()
with torch.no_grad():
__UpperCAmelCase =model(**self._prepare_for_class(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ) )
if hasattr(self.model_tester , """num_hidden_states_types""" ):
__UpperCAmelCase =self.model_tester.num_hidden_states_types
else:
# also another +1 for reshaped_hidden_states
__UpperCAmelCase =2
self.assertEqual(out_len + added_hidden_states , len(__SCREAMING_SNAKE_CASE ) )
__UpperCAmelCase =outputs.attentions
self.assertEqual(len(__SCREAMING_SNAKE_CASE ) , __SCREAMING_SNAKE_CASE )
self.assertListEqual(
list(self_attentions[0].shape[-3:] ) , [self.model_tester.num_heads[0], window_size_squared, window_size_squared] , )
def _a ( self : Optional[int] , __SCREAMING_SNAKE_CASE : int , __SCREAMING_SNAKE_CASE : Optional[Any] , __SCREAMING_SNAKE_CASE : List[str] , __SCREAMING_SNAKE_CASE : Optional[Any] ) -> int:
__UpperCAmelCase =model_class(__SCREAMING_SNAKE_CASE )
model.to(__SCREAMING_SNAKE_CASE )
model.eval()
with torch.no_grad():
__UpperCAmelCase =model(**self._prepare_for_class(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ) )
__UpperCAmelCase =outputs.hidden_states
__UpperCAmelCase =getattr(
self.model_tester , """expected_num_hidden_layers""" , len(self.model_tester.depths ) + 1 )
self.assertEqual(len(__SCREAMING_SNAKE_CASE ) , __SCREAMING_SNAKE_CASE )
# Swinv2 has a different seq_length
__UpperCAmelCase =(
config.patch_size
if isinstance(config.patch_size , collections.abc.Iterable )
else (config.patch_size, config.patch_size)
)
__UpperCAmelCase =(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] , )
__UpperCAmelCase =outputs.reshaped_hidden_states
self.assertEqual(len(__SCREAMING_SNAKE_CASE ) , __SCREAMING_SNAKE_CASE )
__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase =reshaped_hidden_states[0].shape
__UpperCAmelCase =(
reshaped_hidden_states[0].view(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , height * width ).permute(0 , 2 , 1 )
)
self.assertListEqual(
list(reshaped_hidden_states.shape[-2:] ) , [num_patches, self.model_tester.embed_dim] , )
def _a ( self : str ) -> Union[str, Any]:
__UpperCAmelCase , __UpperCAmelCase =self.model_tester.prepare_config_and_inputs_for_common()
__UpperCAmelCase =(
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:
__UpperCAmelCase =True
self.check_hidden_states_output(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
__UpperCAmelCase =True
self.check_hidden_states_output(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
def _a ( self : Optional[int] ) -> Tuple:
__UpperCAmelCase , __UpperCAmelCase =self.model_tester.prepare_config_and_inputs_for_common()
__UpperCAmelCase =3
__UpperCAmelCase =(
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)
)
__UpperCAmelCase =(
config.patch_size
if isinstance(config.patch_size , collections.abc.Iterable )
else (config.patch_size, config.patch_size)
)
__UpperCAmelCase =image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
__UpperCAmelCase =image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])
for model_class in self.all_model_classes:
__UpperCAmelCase =True
self.check_hidden_states_output(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , (padded_height, padded_width) )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
__UpperCAmelCase =True
self.check_hidden_states_output(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , (padded_height, padded_width) )
def _a ( self : Optional[int] ) -> Tuple:
__UpperCAmelCase =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_image_modeling(*__SCREAMING_SNAKE_CASE )
def _a ( self : Tuple ) -> Dict:
__UpperCAmelCase =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*__SCREAMING_SNAKE_CASE )
@slow
def _a ( self : int ) -> Dict:
for model_name in SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__UpperCAmelCase =SwinvaModel.from_pretrained(__SCREAMING_SNAKE_CASE )
self.assertIsNotNone(__SCREAMING_SNAKE_CASE )
def _a ( self : Dict ) -> Union[str, Any]:
__UpperCAmelCase , __UpperCAmelCase =self.model_tester.prepare_config_and_inputs_for_common()
__UpperCAmelCase =_config_zero_init(__SCREAMING_SNAKE_CASE )
for model_class in self.all_model_classes:
__UpperCAmelCase =model_class(config=__SCREAMING_SNAKE_CASE )
for name, param in model.named_parameters():
if "embeddings" not in name and "logit_scale" not in name and param.requires_grad:
self.assertIn(
((param.data.mean() * 1e9).round() / 1e9).item() , [0.0, 1.0] , msg=f'''Parameter {name} of model {model_class} seems not properly initialized''' , )
@require_vision
@require_torch
class _A ( unittest.TestCase ):
"""simple docstring"""
@cached_property
def _a ( self : Tuple ) -> Dict:
return (
AutoImageProcessor.from_pretrained("""microsoft/swinv2-tiny-patch4-window8-256""" )
if is_vision_available()
else None
)
@slow
def _a ( self : int ) -> Optional[int]:
__UpperCAmelCase =SwinvaForImageClassification.from_pretrained("""microsoft/swinv2-tiny-patch4-window8-256""" ).to(
__SCREAMING_SNAKE_CASE )
__UpperCAmelCase =self.default_image_processor
__UpperCAmelCase =Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
__UpperCAmelCase =image_processor(images=__SCREAMING_SNAKE_CASE , return_tensors="""pt""" ).to(__SCREAMING_SNAKE_CASE )
# forward pass
with torch.no_grad():
__UpperCAmelCase =model(**__SCREAMING_SNAKE_CASE )
# verify the logits
__UpperCAmelCase =torch.Size((1, 1000) )
self.assertEqual(outputs.logits.shape , __SCREAMING_SNAKE_CASE )
__UpperCAmelCase =torch.tensor([-0.3_947, -0.4_306, 0.0_026] ).to(__SCREAMING_SNAKE_CASE )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , __SCREAMING_SNAKE_CASE , atol=1e-4 ) )
| 68
| 0
|
"""simple docstring"""
import argparse
import struct
import unittest
class lowercase__ :
'''simple docstring'''
def __init__( self : Tuple , _UpperCAmelCase : bytes ) -> None:
'''simple docstring'''
UpperCAmelCase_ = data
# Initialize hash values
UpperCAmelCase_ = [
0x6a_09_e6_67,
0xbb_67_ae_85,
0x3c_6e_f3_72,
0xa5_4f_f5_3a,
0x51_0e_52_7f,
0x9b_05_68_8c,
0x1f_83_d9_ab,
0x5b_e0_cd_19,
]
# Initialize round constants
UpperCAmelCase_ = [
0x42_8a_2f_98,
0x71_37_44_91,
0xb5_c0_fb_cf,
0xe9_b5_db_a5,
0x39_56_c2_5b,
0x59_f1_11_f1,
0x92_3f_82_a4,
0xab_1c_5e_d5,
0xd8_07_aa_98,
0x12_83_5b_01,
0x24_31_85_be,
0x55_0c_7d_c3,
0x72_be_5d_74,
0x80_de_b1_fe,
0x9b_dc_06_a7,
0xc1_9b_f1_74,
0xe4_9b_69_c1,
0xef_be_47_86,
0x0f_c1_9d_c6,
0x24_0c_a1_cc,
0x2d_e9_2c_6f,
0x4a_74_84_aa,
0x5c_b0_a9_dc,
0x76_f9_88_da,
0x98_3e_51_52,
0xa8_31_c6_6d,
0xb0_03_27_c8,
0xbf_59_7f_c7,
0xc6_e0_0b_f3,
0xd5_a7_91_47,
0x06_ca_63_51,
0x14_29_29_67,
0x27_b7_0a_85,
0x2e_1b_21_38,
0x4d_2c_6d_fc,
0x53_38_0d_13,
0x65_0a_73_54,
0x76_6a_0a_bb,
0x81_c2_c9_2e,
0x92_72_2c_85,
0xa2_bf_e8_a1,
0xa8_1a_66_4b,
0xc2_4b_8b_70,
0xc7_6c_51_a3,
0xd1_92_e8_19,
0xd6_99_06_24,
0xf4_0e_35_85,
0x10_6a_a0_70,
0x19_a4_c1_16,
0x1e_37_6c_08,
0x27_48_77_4c,
0x34_b0_bc_b5,
0x39_1c_0c_b3,
0x4e_d8_aa_4a,
0x5b_9c_ca_4f,
0x68_2e_6f_f3,
0x74_8f_82_ee,
0x78_a5_63_6f,
0x84_c8_78_14,
0x8c_c7_02_08,
0x90_be_ff_fa,
0xa4_50_6c_eb,
0xbe_f9_a3_f7,
0xc6_71_78_f2,
]
UpperCAmelCase_ = self.preprocessing(self.data )
self.final_hash()
@staticmethod
def lowercase__ ( _UpperCAmelCase : bytes ) -> bytes:
'''simple docstring'''
UpperCAmelCase_ = B"\x80" + (B"\x00" * (63 - (len(_UpperCAmelCase ) + 8) % 64))
UpperCAmelCase_ = struct.pack(">Q" , (len(_UpperCAmelCase ) * 8) )
return data + padding + big_endian_integer
def lowercase__ ( self : Tuple ) -> None:
'''simple docstring'''
UpperCAmelCase_ = [
self.preprocessed_data[x : x + 64]
for x in range(0 , len(self.preprocessed_data ) , 64 )
]
for block in self.blocks:
# Convert the given block into a list of 4 byte integers
UpperCAmelCase_ = list(struct.unpack(">16L" , _UpperCAmelCase ) )
# add 48 0-ed integers
words += [0] * 48
UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ = self.hashes
for index in range(0 , 64 ):
if index > 15:
# modify the zero-ed indexes at the end of the array
UpperCAmelCase_ = (
self.ror(words[index - 15] , 7 )
^ self.ror(words[index - 15] , 18 )
^ (words[index - 15] >> 3)
)
UpperCAmelCase_ = (
self.ror(words[index - 2] , 17 )
^ self.ror(words[index - 2] , 19 )
^ (words[index - 2] >> 10)
)
UpperCAmelCase_ = (
words[index - 16] + sa + words[index - 7] + sa
) % 0x1_00_00_00_00
# Compression
UpperCAmelCase_ = self.ror(_UpperCAmelCase , 6 ) ^ self.ror(_UpperCAmelCase , 11 ) ^ self.ror(_UpperCAmelCase , 25 )
UpperCAmelCase_ = (e & f) ^ ((~e & 0xff_ff_ff_ff) & g)
UpperCAmelCase_ = (
h + sa + ch + self.round_constants[index] + words[index]
) % 0x1_00_00_00_00
UpperCAmelCase_ = self.ror(_UpperCAmelCase , 2 ) ^ self.ror(_UpperCAmelCase , 13 ) ^ self.ror(_UpperCAmelCase , 22 )
UpperCAmelCase_ = (a & b) ^ (a & c) ^ (b & c)
UpperCAmelCase_ = (sa + maj) % 0x1_00_00_00_00
UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ = (
g,
f,
e,
((d + tempa) % 0x1_00_00_00_00),
c,
b,
a,
((tempa + tempa) % 0x1_00_00_00_00),
)
UpperCAmelCase_ = [a, b, c, d, e, f, g, h]
# Modify final values
UpperCAmelCase_ = [
((element + mutated_hash_values[index]) % 0x1_00_00_00_00)
for index, element in enumerate(self.hashes )
]
UpperCAmelCase_ = "".join([hex(_UpperCAmelCase )[2:].zfill(8 ) for value in self.hashes] )
def lowercase__ ( self : int , _UpperCAmelCase : int , _UpperCAmelCase : int ) -> int:
'''simple docstring'''
return 0xff_ff_ff_ff & (value << (32 - rotations)) | (value >> rotations)
class lowercase__ ( unittest.TestCase ):
'''simple docstring'''
def lowercase__ ( self : Optional[Any] ) -> None:
'''simple docstring'''
import hashlib
UpperCAmelCase_ = bytes("Test String" , "utf-8" )
self.assertEqual(SHAaaa(_UpperCAmelCase ).hash , hashlib.shaaaa(_UpperCAmelCase ).hexdigest() )
def a__ ( ):
import doctest
doctest.testmod()
UpperCAmelCase_ = argparse.ArgumentParser()
parser.add_argument(
"-s" , "--string" , dest="input_string" , default="Hello World!! Welcome to Cryptography" , help="Hash the string" , )
parser.add_argument(
"-f" , "--file" , dest="input_file" , help="Hash contents of a file" )
UpperCAmelCase_ = parser.parse_args()
UpperCAmelCase_ = args.input_string
# hash input should be a bytestring
if args.input_file:
with open(args.input_file , "rb" ) as f:
UpperCAmelCase_ = f.read()
else:
UpperCAmelCase_ = bytes(lowerCAmelCase__ , "utf-8" )
print(SHAaaa(lowerCAmelCase__ ).hash )
if __name__ == "__main__":
main()
| 14
|
"""simple docstring"""
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
from ...image_utils import (
IMAGENET_STANDARD_MEAN,
IMAGENET_STANDARD_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_vision_available, logging
if is_vision_available():
import PIL
lowerCamelCase = logging.get_logger(__name__)
class lowercase__ ( SCREAMING_SNAKE_CASE ):
'''simple docstring'''
UpperCamelCase = ['''pixel_values''']
def __init__( self : str , _UpperCAmelCase : bool = True , _UpperCAmelCase : Dict[str, int] = None , _UpperCAmelCase : float = None , _UpperCAmelCase : PILImageResampling = PILImageResampling.BILINEAR , _UpperCAmelCase : bool = True , _UpperCAmelCase : Union[int, float] = 1 / 255 , _UpperCAmelCase : bool = True , _UpperCAmelCase : Optional[Union[float, List[float]]] = None , _UpperCAmelCase : Optional[Union[float, List[float]]] = None , **_UpperCAmelCase : Optional[int] , ) -> None:
'''simple docstring'''
super().__init__(**_UpperCAmelCase )
UpperCAmelCase_ = size if size is not None else {"shortest_edge": 384}
UpperCAmelCase_ = get_size_dict(_UpperCAmelCase , default_to_square=_UpperCAmelCase )
UpperCAmelCase_ = do_resize
UpperCAmelCase_ = size
# Default value set here for backwards compatibility where the value in config is None
UpperCAmelCase_ = crop_pct if crop_pct is not None else 224 / 256
UpperCAmelCase_ = resample
UpperCAmelCase_ = do_rescale
UpperCAmelCase_ = rescale_factor
UpperCAmelCase_ = do_normalize
UpperCAmelCase_ = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
UpperCAmelCase_ = image_std if image_std is not None else IMAGENET_STANDARD_STD
def lowercase__ ( self : Union[str, Any] , _UpperCAmelCase : np.ndarray , _UpperCAmelCase : Dict[str, int] , _UpperCAmelCase : float , _UpperCAmelCase : PILImageResampling = PILImageResampling.BICUBIC , _UpperCAmelCase : Optional[Union[str, ChannelDimension]] = None , **_UpperCAmelCase : Optional[int] , ) -> np.ndarray:
'''simple docstring'''
UpperCAmelCase_ = get_size_dict(_UpperCAmelCase , default_to_square=_UpperCAmelCase )
if "shortest_edge" not in size:
raise ValueError(F"""Size dictionary must contain 'shortest_edge' key. Got {size.keys()}""" )
UpperCAmelCase_ = size["shortest_edge"]
if shortest_edge < 384:
# maintain same ratio, resizing shortest edge to shortest_edge/crop_pct
UpperCAmelCase_ = int(shortest_edge / crop_pct )
UpperCAmelCase_ = get_resize_output_image_size(_UpperCAmelCase , size=_UpperCAmelCase , default_to_square=_UpperCAmelCase )
UpperCAmelCase_ = resize(image=_UpperCAmelCase , size=_UpperCAmelCase , resample=_UpperCAmelCase , data_format=_UpperCAmelCase , **_UpperCAmelCase )
# then crop to (shortest_edge, shortest_edge)
return center_crop(image=_UpperCAmelCase , size=(shortest_edge, shortest_edge) , data_format=_UpperCAmelCase , **_UpperCAmelCase )
else:
# warping (no cropping) when evaluated at 384 or larger
return resize(
_UpperCAmelCase , size=(shortest_edge, shortest_edge) , resample=_UpperCAmelCase , data_format=_UpperCAmelCase , **_UpperCAmelCase )
def lowercase__ ( self : Tuple , _UpperCAmelCase : np.ndarray , _UpperCAmelCase : Union[int, float] , _UpperCAmelCase : Optional[Union[str, ChannelDimension]] = None , **_UpperCAmelCase : List[Any] , ) -> Any:
'''simple docstring'''
return rescale(_UpperCAmelCase , scale=_UpperCAmelCase , data_format=_UpperCAmelCase , **_UpperCAmelCase )
def lowercase__ ( self : Dict , _UpperCAmelCase : np.ndarray , _UpperCAmelCase : Union[float, List[float]] , _UpperCAmelCase : Union[float, List[float]] , _UpperCAmelCase : Optional[Union[str, ChannelDimension]] = None , **_UpperCAmelCase : List[str] , ) -> np.ndarray:
'''simple docstring'''
return normalize(_UpperCAmelCase , mean=_UpperCAmelCase , std=_UpperCAmelCase , data_format=_UpperCAmelCase , **_UpperCAmelCase )
def lowercase__ ( self : Union[str, Any] , _UpperCAmelCase : ImageInput , _UpperCAmelCase : bool = None , _UpperCAmelCase : Dict[str, int] = None , _UpperCAmelCase : float = None , _UpperCAmelCase : PILImageResampling = None , _UpperCAmelCase : bool = None , _UpperCAmelCase : float = None , _UpperCAmelCase : bool = None , _UpperCAmelCase : Optional[Union[float, List[float]]] = None , _UpperCAmelCase : Optional[Union[float, List[float]]] = None , _UpperCAmelCase : Optional[Union[str, TensorType]] = None , _UpperCAmelCase : ChannelDimension = ChannelDimension.FIRST , **_UpperCAmelCase : Optional[int] , ) -> PIL.Image.Image:
'''simple docstring'''
UpperCAmelCase_ = do_resize if do_resize is not None else self.do_resize
UpperCAmelCase_ = crop_pct if crop_pct is not None else self.crop_pct
UpperCAmelCase_ = resample if resample is not None else self.resample
UpperCAmelCase_ = do_rescale if do_rescale is not None else self.do_rescale
UpperCAmelCase_ = rescale_factor if rescale_factor is not None else self.rescale_factor
UpperCAmelCase_ = do_normalize if do_normalize is not None else self.do_normalize
UpperCAmelCase_ = image_mean if image_mean is not None else self.image_mean
UpperCAmelCase_ = image_std if image_std is not None else self.image_std
UpperCAmelCase_ = size if size is not None else self.size
UpperCAmelCase_ = get_size_dict(_UpperCAmelCase , default_to_square=_UpperCAmelCase )
UpperCAmelCase_ = make_list_of_images(_UpperCAmelCase )
if not valid_images(_UpperCAmelCase ):
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_resize and size["shortest_edge"] < 384 and crop_pct is None:
raise ValueError("crop_pct must be specified if size < 384." )
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_ = [to_numpy_array(_UpperCAmelCase ) for image in images]
if do_resize:
UpperCAmelCase_ = [self.resize(image=_UpperCAmelCase , size=_UpperCAmelCase , crop_pct=_UpperCAmelCase , resample=_UpperCAmelCase ) for image in images]
if do_rescale:
UpperCAmelCase_ = [self.rescale(image=_UpperCAmelCase , scale=_UpperCAmelCase ) for image in images]
if do_normalize:
UpperCAmelCase_ = [self.normalize(image=_UpperCAmelCase , mean=_UpperCAmelCase , std=_UpperCAmelCase ) for image in images]
UpperCAmelCase_ = [to_channel_dimension_format(_UpperCAmelCase , _UpperCAmelCase ) for image in images]
UpperCAmelCase_ = {"pixel_values": images}
return BatchFeature(data=_UpperCAmelCase , tensor_type=_UpperCAmelCase )
| 14
| 1
|
from datetime import datetime
import requests
from bsa import BeautifulSoup
if __name__ == "__main__":
__lowerCAmelCase = input('Enter image url: ').strip()
print(F'''Downloading image from {url} ...''')
__lowerCAmelCase = BeautifulSoup(requests.get(url).content, 'html.parser')
# The image URL is in the content field of the first meta tag with property og:image
__lowerCAmelCase = soup.find('meta', {'property': 'og:image'})['content']
__lowerCAmelCase = requests.get(image_url).content
__lowerCAmelCase = F'''{datetime.now():%Y-%m-%d_%H:%M:%S}.jpg'''
with open(file_name, 'wb') as fp:
fp.write(image_data)
print(F'''Done. Image saved to disk as {file_name}.''')
| 201
|
import os
def a ( a = "matrix.txt" ) ->int:
'''simple docstring'''
with open(os.path.join(os.path.dirname(a ) , a ) ) as in_file:
SCREAMING_SNAKE_CASE = in_file.read()
SCREAMING_SNAKE_CASE = [[int(a ) for cell in row.split(''',''' )] for row in data.strip().splitlines()]
SCREAMING_SNAKE_CASE = [[0 for cell in row] for row in grid]
SCREAMING_SNAKE_CASE = len(grid[0] )
SCREAMING_SNAKE_CASE = [[0 for i in range(a )] for j in range(a )]
SCREAMING_SNAKE_CASE = grid[0][0]
for i in range(1 , a ):
SCREAMING_SNAKE_CASE = grid[0][i] + dp[0][i - 1]
for i in range(1 , a ):
SCREAMING_SNAKE_CASE = grid[i][0] + dp[i - 1][0]
for i in range(1 , a ):
for j in range(1 , a ):
SCREAMING_SNAKE_CASE = grid[i][j] + min(dp[i - 1][j] , dp[i][j - 1] )
return dp[-1][-1]
if __name__ == "__main__":
print(F'''{solution() = }''')
| 201
| 1
|
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCAmelCase : Any = {
'''configuration_autoformer''': [
'''AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''AutoformerConfig''',
],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase : Dict = [
'''AUTOFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''AutoformerForPrediction''',
'''AutoformerModel''',
'''AutoformerPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_autoformer import (
AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
AutoformerConfig,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_autoformer import (
AUTOFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
AutoformerForPrediction,
AutoformerModel,
AutoformerPreTrainedModel,
)
else:
import sys
UpperCAmelCase : int = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 77
|
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils import load_numpy, slow
from diffusers.utils.testing_utils import require_torch_gpu, torch_device
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
class _A( snake_case__ , unittest.TestCase ):
"""simple docstring"""
UpperCamelCase : Any = ShapEPipeline
UpperCamelCase : str = ['''prompt''']
UpperCamelCase : Tuple = ['''prompt''']
UpperCamelCase : Optional[int] = [
'''num_images_per_prompt''',
'''num_inference_steps''',
'''generator''',
'''latents''',
'''guidance_scale''',
'''frame_size''',
'''output_type''',
'''return_dict''',
]
UpperCamelCase : int = False
@property
def UpperCAmelCase_ ( self ):
return 32
@property
def UpperCAmelCase_ ( self ):
return 32
@property
def UpperCAmelCase_ ( self ):
return self.time_input_dim * 4
@property
def UpperCAmelCase_ ( self ):
return 8
@property
def UpperCAmelCase_ ( self ):
__A : List[Any] = CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip' )
return tokenizer
@property
def UpperCAmelCase_ ( self ):
torch.manual_seed(0 )
__A : Optional[int] = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=self.text_embedder_hidden_size , projection_dim=self.text_embedder_hidden_size , intermediate_size=37 , layer_norm_eps=1e-0_5 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1000 , )
return CLIPTextModelWithProjection(_A )
@property
def UpperCAmelCase_ ( self ):
torch.manual_seed(0 )
__A : int = {
'num_attention_heads': 2,
'attention_head_dim': 16,
'embedding_dim': self.time_input_dim,
'num_embeddings': 32,
'embedding_proj_dim': self.text_embedder_hidden_size,
'time_embed_dim': self.time_embed_dim,
'num_layers': 1,
'clip_embed_dim': self.time_input_dim * 2,
'additional_embeddings': 0,
'time_embed_act_fn': 'gelu',
'norm_in_type': 'layer',
'encoder_hid_proj_type': None,
'added_emb_type': None,
}
__A : Optional[Any] = PriorTransformer(**_A )
return model
@property
def UpperCAmelCase_ ( self ):
torch.manual_seed(0 )
__A : List[str] = {
'param_shapes': (
(self.renderer_dim, 93),
(self.renderer_dim, 8),
(self.renderer_dim, 8),
(self.renderer_dim, 8),
),
'd_latent': self.time_input_dim,
'd_hidden': self.renderer_dim,
'n_output': 12,
'background': (
0.1,
0.1,
0.1,
),
}
__A : List[Any] = ShapERenderer(**_A )
return model
def UpperCAmelCase_ ( self ):
__A : List[str] = self.dummy_prior
__A : Optional[int] = self.dummy_text_encoder
__A : List[Any] = self.dummy_tokenizer
__A : str = self.dummy_renderer
__A : List[Any] = HeunDiscreteScheduler(
beta_schedule='exp' , num_train_timesteps=1024 , prediction_type='sample' , use_karras_sigmas=_A , clip_sample=_A , clip_sample_range=1.0 , )
__A : Any = {
'prior': prior,
'text_encoder': text_encoder,
'tokenizer': tokenizer,
'renderer': renderer,
'scheduler': scheduler,
}
return components
def UpperCAmelCase_ ( self , _A , _A=0 ):
if str(_A ).startswith('mps' ):
__A : List[Any] = torch.manual_seed(_A )
else:
__A : Dict = torch.Generator(device=_A ).manual_seed(_A )
__A : int = {
'prompt': 'horse',
'generator': generator,
'num_inference_steps': 1,
'frame_size': 32,
'output_type': 'np',
}
return inputs
def UpperCAmelCase_ ( self ):
__A : Tuple = 'cpu'
__A : Any = self.get_dummy_components()
__A : Tuple = self.pipeline_class(**_A )
__A : List[str] = pipe.to(_A )
pipe.set_progress_bar_config(disable=_A )
__A : Tuple = pipe(**self.get_dummy_inputs(_A ) )
__A : int = output.images[0]
__A : str = image[0, -3:, -3:, -1]
assert image.shape == (20, 32, 32, 3)
__A : Any = np.array(
[
0.0_0_0_3_9_2_1_6,
0.0_0_0_3_9_2_1_6,
0.0_0_0_3_9_2_1_6,
0.0_0_0_3_9_2_1_6,
0.0_0_0_3_9_2_1_6,
0.0_0_0_3_9_2_1_6,
0.0_0_0_3_9_2_1_6,
0.0_0_0_3_9_2_1_6,
0.0_0_0_3_9_2_1_6,
] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
def UpperCAmelCase_ ( self ):
# NOTE: Larger batch sizes cause this test to timeout, only test on smaller batches
self._test_inference_batch_consistent(batch_sizes=[1, 2] )
def UpperCAmelCase_ ( self ):
__A : List[str] = torch_device == 'cpu'
__A : Any = True
self._test_inference_batch_single_identical(
batch_size=2 , test_max_difference=_A , relax_max_difference=_A , )
def UpperCAmelCase_ ( self ):
__A : Any = self.get_dummy_components()
__A : Any = self.pipeline_class(**_A )
__A : Dict = pipe.to(_A )
pipe.set_progress_bar_config(disable=_A )
__A : Any = 1
__A : Dict = 2
__A : Tuple = self.get_dummy_inputs(_A )
for key in inputs.keys():
if key in self.batch_params:
__A : Optional[int] = batch_size * [inputs[key]]
__A : Optional[int] = pipe(**_A , num_images_per_prompt=_A )[0]
assert images.shape[0] == batch_size * num_images_per_prompt
@slow
@require_torch_gpu
class _A( unittest.TestCase ):
"""simple docstring"""
def UpperCAmelCase_ ( self ):
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def UpperCAmelCase_ ( self ):
__A : List[str] = load_numpy(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
'/shap_e/test_shap_e_np_out.npy' )
__A : Dict = ShapEPipeline.from_pretrained('openai/shap-e' )
__A : int = pipe.to(_A )
pipe.set_progress_bar_config(disable=_A )
__A : str = torch.Generator(device=_A ).manual_seed(0 )
__A : Tuple = pipe(
'a shark' , generator=_A , guidance_scale=1_5.0 , num_inference_steps=64 , frame_size=64 , output_type='np' , ).images[0]
assert images.shape == (20, 64, 64, 3)
assert_mean_pixel_difference(_A , _A )
| 77
| 1
|
'''simple docstring'''
from itertools import product
from cva import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey
from numpy import dot, exp, mgrid, pi, ravel, square, uinta, zeros
def SCREAMING_SNAKE_CASE ( lowerCAmelCase__ : List[Any] , lowerCAmelCase__ : int) -> Tuple:
'''simple docstring'''
_lowercase : Optional[Any] = k_size // 2
_lowercase , _lowercase : Any = mgrid[0 - center : k_size - center, 0 - center : k_size - center]
_lowercase : Tuple = 1 / (2 * pi * sigma) * exp(-(square(lowerCAmelCase__) + square(lowerCAmelCase__)) / (2 * square(lowerCAmelCase__)))
return g
def SCREAMING_SNAKE_CASE ( lowerCAmelCase__ : Optional[int] , lowerCAmelCase__ : List[str] , lowerCAmelCase__ : Tuple) -> str:
'''simple docstring'''
_lowercase , _lowercase : Optional[int] = image.shape[0], image.shape[1]
# dst image height and width
_lowercase : Optional[Any] = height - k_size + 1
_lowercase : int = width - k_size + 1
# im2col, turn the k_size*k_size pixels into a row and np.vstack all rows
_lowercase : List[Any] = zeros((dst_height * dst_width, k_size * k_size))
_lowercase : int = 0
for i, j in product(range(lowerCAmelCase__) , range(lowerCAmelCase__)):
_lowercase : Optional[Any] = ravel(image[i : i + k_size, j : j + k_size])
_lowercase : str = window
row += 1
# turn the kernel into shape(k*k, 1)
_lowercase : Optional[int] = gen_gaussian_kernel(lowerCAmelCase__ , lowerCAmelCase__)
_lowercase : Tuple = ravel(lowerCAmelCase__)
# reshape and get the dst image
_lowercase : Dict = dot(lowerCAmelCase__ , lowerCAmelCase__).reshape(lowerCAmelCase__ , lowerCAmelCase__).astype(lowerCAmelCase__)
return dst
if __name__ == "__main__":
# read original image
A = imread(R'''../image_data/lena.jpg''')
# turn image in gray scale value
A = cvtColor(img, COLOR_BGR2GRAY)
# get values with two different mask size
A = gaussian_filter(gray, 3, sigma=1)
A = gaussian_filter(gray, 5, sigma=0.8)
# show result images
imshow('''gaussian filter with 3x3 mask''', gaussianaxa)
imshow('''gaussian filter with 5x5 mask''', gaussianaxa)
waitKey()
| 125
|
'''simple docstring'''
import math
A = 10
A = 7
A = BALLS_PER_COLOUR * NUM_COLOURS
def SCREAMING_SNAKE_CASE ( lowerCAmelCase__ : int = 20) -> str:
'''simple docstring'''
_lowercase : Union[str, Any] = math.comb(lowerCAmelCase__ , lowerCAmelCase__)
_lowercase : Tuple = math.comb(NUM_BALLS - BALLS_PER_COLOUR , lowerCAmelCase__)
_lowercase : List[str] = NUM_COLOURS * (1 - missing_colour / total)
return F'''{result:.9f}'''
if __name__ == "__main__":
print(solution(20))
| 125
| 1
|
"""simple docstring"""
def _snake_case ( snake_case__ : list[list[int]] , snake_case__ : int , snake_case__ : int , snake_case__ : set ):
A , A = len(snake_case__ ), len(grid[0] )
if (
min(snake_case__ , snake_case__ ) < 0
or row == row_length
or col == col_length
or (row, col) in visit
or grid[row][col] == 1
):
return 0
if row == row_length - 1 and col == col_length - 1:
return 1
visit.add((row, col) )
A = 0
count += depth_first_search(snake_case__ , row + 1 , snake_case__ , snake_case__ )
count += depth_first_search(snake_case__ , row - 1 , snake_case__ , snake_case__ )
count += depth_first_search(snake_case__ , snake_case__ , col + 1 , snake_case__ )
count += depth_first_search(snake_case__ , snake_case__ , col - 1 , snake_case__ )
visit.remove((row, col) )
return count
if __name__ == "__main__":
import doctest
doctest.testmod()
| 721
|
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
_lowercase = {
'''configuration_clip''': [
'''CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''CLIPConfig''',
'''CLIPOnnxConfig''',
'''CLIPTextConfig''',
'''CLIPVisionConfig''',
],
'''processing_clip''': ['''CLIPProcessor'''],
'''tokenization_clip''': ['''CLIPTokenizer'''],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowercase = ['''CLIPTokenizerFast''']
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowercase = ['''CLIPFeatureExtractor''']
_lowercase = ['''CLIPImageProcessor''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowercase = [
'''CLIP_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''CLIPModel''',
'''CLIPPreTrainedModel''',
'''CLIPTextModel''',
'''CLIPTextModelWithProjection''',
'''CLIPVisionModel''',
'''CLIPVisionModelWithProjection''',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowercase = [
'''TF_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''TFCLIPModel''',
'''TFCLIPPreTrainedModel''',
'''TFCLIPTextModel''',
'''TFCLIPVisionModel''',
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowercase = [
'''FlaxCLIPModel''',
'''FlaxCLIPPreTrainedModel''',
'''FlaxCLIPTextModel''',
'''FlaxCLIPTextPreTrainedModel''',
'''FlaxCLIPVisionModel''',
'''FlaxCLIPVisionPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_clip import (
CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
CLIPConfig,
CLIPOnnxConfig,
CLIPTextConfig,
CLIPVisionConfig,
)
from .processing_clip import CLIPProcessor
from .tokenization_clip import CLIPTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_clip_fast import CLIPTokenizerFast
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_clip import CLIPFeatureExtractor
from .image_processing_clip import CLIPImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_clip import (
CLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
CLIPModel,
CLIPPreTrainedModel,
CLIPTextModel,
CLIPTextModelWithProjection,
CLIPVisionModel,
CLIPVisionModelWithProjection,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_clip import (
TF_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
TFCLIPModel,
TFCLIPPreTrainedModel,
TFCLIPTextModel,
TFCLIPVisionModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_clip import (
FlaxCLIPModel,
FlaxCLIPPreTrainedModel,
FlaxCLIPTextModel,
FlaxCLIPTextPreTrainedModel,
FlaxCLIPVisionModel,
FlaxCLIPVisionPreTrainedModel,
)
else:
import sys
_lowercase = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 22
| 0
|
'''simple docstring'''
def lowerCamelCase ( __lowerCamelCase : str , __lowerCamelCase : str ) ->str:
if not (isinstance(__lowerCamelCase , __lowerCamelCase ) and isinstance(__lowerCamelCase , __lowerCamelCase )):
raise ValueError("""longest_common_substring() takes two strings for inputs""" )
_SCREAMING_SNAKE_CASE = len(__lowerCamelCase )
_SCREAMING_SNAKE_CASE = len(__lowerCamelCase )
_SCREAMING_SNAKE_CASE = [[0] * (texta_length + 1) for _ in range(texta_length + 1 )]
_SCREAMING_SNAKE_CASE = 0
_SCREAMING_SNAKE_CASE = 0
for i in range(1 , texta_length + 1 ):
for j in range(1 , texta_length + 1 ):
if texta[i - 1] == texta[j - 1]:
_SCREAMING_SNAKE_CASE = 1 + dp[i - 1][j - 1]
if dp[i][j] > ans_length:
_SCREAMING_SNAKE_CASE = i
_SCREAMING_SNAKE_CASE = dp[i][j]
return texta[ans_index - ans_length : ans_index]
if __name__ == "__main__":
import doctest
doctest.testmod()
| 314
|
'''simple docstring'''
from __future__ import annotations
import os
from collections.abc import Mapping
lowercase_ = tuple[int, int]
class a_ :
'''simple docstring'''
def __init__( self , A , A ) -> None:
_SCREAMING_SNAKE_CASE = vertices
_SCREAMING_SNAKE_CASE = {
(min(A ), max(A )): weight for edge, weight in edges.items()
}
def snake_case_( self , A , A ) -> None:
self.vertices.add(edge[0] )
self.vertices.add(edge[1] )
_SCREAMING_SNAKE_CASE = weight
def snake_case_( self ) -> Graph:
_SCREAMING_SNAKE_CASE = Graph({min(self.vertices )} , {} )
_SCREAMING_SNAKE_CASE = 42
_SCREAMING_SNAKE_CASE = 42
_SCREAMING_SNAKE_CASE = 42
_SCREAMING_SNAKE_CASE = 42
while len(subgraph.vertices ) < len(self.vertices ):
_SCREAMING_SNAKE_CASE = max(self.edges.values() ) + 1
for edge, weight in self.edges.items():
if (edge[0] in subgraph.vertices) ^ (edge[1] in subgraph.vertices):
if weight < min_weight:
_SCREAMING_SNAKE_CASE = edge
_SCREAMING_SNAKE_CASE = weight
subgraph.add_edge(A , A )
return subgraph
def lowerCamelCase ( __lowerCamelCase : str = "p107_network.txt" ) ->int:
_SCREAMING_SNAKE_CASE = os.path.abspath(os.path.dirname(__lowerCamelCase ) )
_SCREAMING_SNAKE_CASE = os.path.join(__lowerCamelCase , __lowerCamelCase )
_SCREAMING_SNAKE_CASE = {}
_SCREAMING_SNAKE_CASE = 42
_SCREAMING_SNAKE_CASE = 42
_SCREAMING_SNAKE_CASE = 42
with open(__lowerCamelCase ) as f:
_SCREAMING_SNAKE_CASE = f.read().strip().split("""\n""" )
_SCREAMING_SNAKE_CASE = [line.split(""",""" ) for line in data]
for edgea in range(1 , len(__lowerCamelCase ) ):
for edgea in range(__lowerCamelCase ):
if adjaceny_matrix[edgea][edgea] != "-":
_SCREAMING_SNAKE_CASE = int(adjaceny_matrix[edgea][edgea] )
_SCREAMING_SNAKE_CASE = Graph(set(range(len(__lowerCamelCase ) ) ) , __lowerCamelCase )
_SCREAMING_SNAKE_CASE = graph.prims_algorithm()
_SCREAMING_SNAKE_CASE = sum(graph.edges.values() )
_SCREAMING_SNAKE_CASE = sum(subgraph.edges.values() )
return initial_total - optimal_total
if __name__ == "__main__":
print(f"""{solution() = }""")
| 314
| 1
|
'''simple docstring'''
import inspect
import unittest
from transformers import MobileNetVaConfig
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 MobileNetVaForImageClassification, MobileNetVaModel
from transformers.models.mobilenet_va.modeling_mobilenet_va import MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import MobileNetVaImageProcessor
class a_ ( lowerCamelCase ):
def A__ ( self ) -> int:
"""simple docstring"""
UpperCamelCase = self.config_class(**self.inputs_dict )
self.parent.assertTrue(hasattr(_SCREAMING_SNAKE_CASE , """tf_padding""" ) )
self.parent.assertTrue(hasattr(_SCREAMING_SNAKE_CASE , """depth_multiplier""" ) )
class a_ :
def __init__( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=13 , _SCREAMING_SNAKE_CASE=3 , _SCREAMING_SNAKE_CASE=32 , _SCREAMING_SNAKE_CASE=0.2_5 , _SCREAMING_SNAKE_CASE=8 , _SCREAMING_SNAKE_CASE=True , _SCREAMING_SNAKE_CASE=1024 , _SCREAMING_SNAKE_CASE=32 , _SCREAMING_SNAKE_CASE="relu6" , _SCREAMING_SNAKE_CASE=0.1 , _SCREAMING_SNAKE_CASE=0.0_2 , _SCREAMING_SNAKE_CASE=True , _SCREAMING_SNAKE_CASE=True , _SCREAMING_SNAKE_CASE=10 , _SCREAMING_SNAKE_CASE=None , ) -> List[str]:
"""simple docstring"""
UpperCamelCase = parent
UpperCamelCase = batch_size
UpperCamelCase = num_channels
UpperCamelCase = image_size
UpperCamelCase = depth_multiplier
UpperCamelCase = min_depth
UpperCamelCase = tf_padding
UpperCamelCase = int(last_hidden_size * depth_multiplier )
UpperCamelCase = output_stride
UpperCamelCase = hidden_act
UpperCamelCase = classifier_dropout_prob
UpperCamelCase = use_labels
UpperCamelCase = is_training
UpperCamelCase = num_labels
UpperCamelCase = initializer_range
UpperCamelCase = scope
def A__ ( self ) -> int:
"""simple docstring"""
UpperCamelCase = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
UpperCamelCase = None
UpperCamelCase = None
if self.use_labels:
UpperCamelCase = ids_tensor([self.batch_size] , self.num_labels )
UpperCamelCase = ids_tensor([self.batch_size, self.image_size, self.image_size] , self.num_labels )
UpperCamelCase = self.get_config()
return config, pixel_values, labels, pixel_labels
def A__ ( self ) -> Optional[Any]:
"""simple docstring"""
return MobileNetVaConfig(
num_channels=self.num_channels , image_size=self.image_size , depth_multiplier=self.depth_multiplier , min_depth=self.min_depth , tf_padding=self.tf_padding , hidden_act=self.hidden_act , classifier_dropout_prob=self.classifier_dropout_prob , initializer_range=self.initializer_range , )
def A__ ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> int:
"""simple docstring"""
UpperCamelCase = MobileNetVaModel(config=_SCREAMING_SNAKE_CASE )
model.to(_SCREAMING_SNAKE_CASE )
model.eval()
UpperCamelCase = model(_SCREAMING_SNAKE_CASE )
self.parent.assertEqual(
result.last_hidden_state.shape , (
self.batch_size,
self.last_hidden_size,
self.image_size // self.output_stride,
self.image_size // self.output_stride,
) , )
def A__ ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> List[Any]:
"""simple docstring"""
UpperCamelCase = self.num_labels
UpperCamelCase = MobileNetVaForImageClassification(_SCREAMING_SNAKE_CASE )
model.to(_SCREAMING_SNAKE_CASE )
model.eval()
UpperCamelCase = model(_SCREAMING_SNAKE_CASE , labels=_SCREAMING_SNAKE_CASE )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def A__ ( self ) -> Any:
"""simple docstring"""
UpperCamelCase = self.prepare_config_and_inputs()
UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase = config_and_inputs
UpperCamelCase = {"""pixel_values""": pixel_values}
return config, inputs_dict
@require_torch
class a_ ( lowerCamelCase , lowerCamelCase , unittest.TestCase ):
lowercase = (MobileNetVaModel, MobileNetVaForImageClassification) if is_torch_available() else ()
lowercase = (
{"""feature-extraction""": MobileNetVaModel, """image-classification""": MobileNetVaForImageClassification}
if is_torch_available()
else {}
)
lowercase = False
lowercase = False
lowercase = False
lowercase = False
def A__ ( self ) -> int:
"""simple docstring"""
UpperCamelCase = MobileNetVaModelTester(self )
UpperCamelCase = MobileNetVaConfigTester(self , config_class=_SCREAMING_SNAKE_CASE , has_text_modality=_SCREAMING_SNAKE_CASE )
def A__ ( self ) -> Optional[Any]:
"""simple docstring"""
self.config_tester.run_common_tests()
@unittest.skip(reason="""MobileNetV1 does not use inputs_embeds""" )
def A__ ( self ) -> int:
"""simple docstring"""
pass
@unittest.skip(reason="""MobileNetV1 does not support input and output embeddings""" )
def A__ ( self ) -> int:
"""simple docstring"""
pass
@unittest.skip(reason="""MobileNetV1 does not output attentions""" )
def A__ ( self ) -> Dict:
"""simple docstring"""
pass
def A__ ( self ) -> Union[str, Any]:
"""simple docstring"""
UpperCamelCase ,UpperCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
UpperCamelCase = model_class(_SCREAMING_SNAKE_CASE )
UpperCamelCase = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
UpperCamelCase = [*signature.parameters.keys()]
UpperCamelCase = ["""pixel_values"""]
self.assertListEqual(arg_names[:1] , _SCREAMING_SNAKE_CASE )
def A__ ( self ) -> str:
"""simple docstring"""
UpperCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*_SCREAMING_SNAKE_CASE )
def A__ ( self ) -> int:
"""simple docstring"""
def check_hidden_states_output(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
UpperCamelCase = model_class(_SCREAMING_SNAKE_CASE )
model.to(_SCREAMING_SNAKE_CASE )
model.eval()
with torch.no_grad():
UpperCamelCase = model(**self._prepare_for_class(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) )
UpperCamelCase = outputs.hidden_states
UpperCamelCase = 26
self.assertEqual(len(_SCREAMING_SNAKE_CASE ) , _SCREAMING_SNAKE_CASE )
UpperCamelCase ,UpperCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
UpperCamelCase = True
check_hidden_states_output(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
UpperCamelCase = True
check_hidden_states_output(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
def A__ ( self ) -> str:
"""simple docstring"""
UpperCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*_SCREAMING_SNAKE_CASE )
@slow
def A__ ( self ) -> Dict:
"""simple docstring"""
for model_name in MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
UpperCamelCase = MobileNetVaModel.from_pretrained(_SCREAMING_SNAKE_CASE )
self.assertIsNotNone(_SCREAMING_SNAKE_CASE )
def lowercase__ ( )-> Optional[Any]:
UpperCamelCase = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
return image
@require_torch
@require_vision
class a_ ( unittest.TestCase ):
@cached_property
def A__ ( self ) -> Dict:
"""simple docstring"""
return (
MobileNetVaImageProcessor.from_pretrained("""google/mobilenet_v1_1.0_224""" ) if is_vision_available() else None
)
@slow
def A__ ( self ) -> Union[str, Any]:
"""simple docstring"""
UpperCamelCase = MobileNetVaForImageClassification.from_pretrained("""google/mobilenet_v1_1.0_224""" ).to(_SCREAMING_SNAKE_CASE )
UpperCamelCase = self.default_image_processor
UpperCamelCase = prepare_img()
UpperCamelCase = image_processor(images=_SCREAMING_SNAKE_CASE , return_tensors="""pt""" ).to(_SCREAMING_SNAKE_CASE )
# forward pass
with torch.no_grad():
UpperCamelCase = model(**_SCREAMING_SNAKE_CASE )
# verify the logits
UpperCamelCase = torch.Size((1, 1001) )
self.assertEqual(outputs.logits.shape , _SCREAMING_SNAKE_CASE )
UpperCamelCase = torch.tensor([-4.1_7_3_9, -1.1_2_3_3, 3.1_2_0_5] ).to(_SCREAMING_SNAKE_CASE )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , _SCREAMING_SNAKE_CASE , atol=1e-4 ) )
| 35
|
'''simple docstring'''
from __future__ import annotations
def lowercase__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase )-> tuple[float, list[float]]:
UpperCamelCase = list(range(len(__UpperCamelCase ) ) )
UpperCamelCase = [v / w for v, w in zip(__UpperCamelCase , __UpperCamelCase )]
index.sort(key=lambda __UpperCamelCase : ratio[i] , reverse=__UpperCamelCase )
UpperCamelCase = 0
UpperCamelCase = [0] * len(__UpperCamelCase )
for i in index:
if weight[i] <= capacity:
UpperCamelCase = 1
max_value += value[i]
capacity -= weight[i]
else:
UpperCamelCase = capacity / weight[i]
max_value += value[i] * capacity / weight[i]
break
return max_value, fractions
if __name__ == "__main__":
import doctest
doctest.testmod()
| 35
| 1
|
'''simple docstring'''
def lowerCAmelCase_ ( a : list , a : int , a : int = 0 , a : int = 0 ):
a__ = right or len(a ) - 1
if left > right:
return -1
elif list_data[left] == key:
return left
elif list_data[right] == key:
return right
else:
return search(a , a , left + 1 , right - 1 )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 394
|
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A : Any = logging.get_logger(__name__)
__A : Union[str, Any] = {
'tanreinama/GPTSAN-2.8B-spout_is_uniform': (
'https://huggingface.co/tanreinama/GPTSAN-2.8B-spout_is_uniform/resolve/main/config.json'
),
}
class _UpperCamelCase ( _A ):
'''simple docstring'''
SCREAMING_SNAKE_CASE:List[Any] = 'gptsan-japanese'
SCREAMING_SNAKE_CASE:str = [
'past_key_values',
]
SCREAMING_SNAKE_CASE:int = {
'hidden_size': 'd_model',
'num_attention_heads': 'num_heads',
'num_hidden_layers': 'num_layers',
}
def __init__( self , _a=3_6000 , _a=1280 , _a=1024 , _a=8192 , _a=4096 , _a=128 , _a=10 , _a=0 , _a=16 , _a=16 , _a=128 , _a=0.0 , _a=1e-5 , _a=False , _a=0.0 , _a="float32" , _a=False , _a=False , _a=False , _a=0.002 , _a=False , _a=True , _a=3_5998 , _a=3_5995 , _a=3_5999 , **_a , ):
"""simple docstring"""
a__ = vocab_size
a__ = max_position_embeddings
a__ = d_model
a__ = d_ff
a__ = d_ext
a__ = d_spout
a__ = num_switch_layers
a__ = num_ext_layers
a__ = num_switch_layers + num_ext_layers
a__ = num_heads
a__ = num_experts
a__ = expert_capacity
a__ = dropout_rate
a__ = layer_norm_epsilon
a__ = router_bias
a__ = router_jitter_noise
a__ = router_dtype
a__ = router_ignore_padding_tokens
a__ = output_hidden_states
a__ = output_attentions
a__ = initializer_factor
a__ = output_router_logits
a__ = use_cache
super().__init__(
separator_token_id=_a , pad_token_id=_a , eos_token_id=_a , **_a , )
| 394
| 1
|
import re
from filelock import FileLock
try:
import nltk
__A = True
except (ImportError, ModuleNotFoundError):
__A = False
if NLTK_AVAILABLE:
with FileLock('''.lock''') as lock:
nltk.download('''punkt''', quiet=True)
def _SCREAMING_SNAKE_CASE ( A : str ) -> str:
"""simple docstring"""
re.sub('<n>' , '' , A ) # remove pegasus newline char
assert NLTK_AVAILABLE, "nltk must be installed to separate newlines between sentences. (pip install nltk)"
return "\n".join(nltk.sent_tokenize(A ) )
| 710
|
'''simple docstring'''
from functools import lru_cache
@lru_cache
def _SCREAMING_SNAKE_CASE ( A : int ) -> int:
"""simple docstring"""
if num < 0:
raise ValueError('Number should not be negative.' )
return 1 if num in (0, 1) else num * factorial(num - 1 )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 61
| 0
|
import csv
import tweepy
# Twitter API credentials
SCREAMING_SNAKE_CASE__ = ''''''
SCREAMING_SNAKE_CASE__ = ''''''
SCREAMING_SNAKE_CASE__ = ''''''
SCREAMING_SNAKE_CASE__ = ''''''
def A ( __UpperCamelCase ) -> None:
# authorize twitter, initialize tweepy
A__ = tweepy.OAuthHandler(__UpperCamelCase , __UpperCamelCase )
auth.set_access_token(__UpperCamelCase , __UpperCamelCase )
A__ = tweepy.API(__UpperCamelCase )
# initialize a list to hold all the tweepy Tweets
A__ = []
# make initial request for most recent tweets (200 is the maximum allowed count)
A__ = api.user_timeline(screen_name=__UpperCamelCase , count=200 )
# save most recent tweets
alltweets.extend(__UpperCamelCase )
# save the id of the oldest tweet less one
A__ = alltweets[-1].id - 1
# keep grabbing tweets until there are no tweets left to grab
while len(__UpperCamelCase ) > 0:
print(f'''getting tweets before {oldest}''' )
# all subsequent requests use the max_id param to prevent duplicates
A__ = api.user_timeline(
screen_name=__UpperCamelCase , count=200 , max_id=__UpperCamelCase )
# save most recent tweets
alltweets.extend(__UpperCamelCase )
# update the id of the oldest tweet less one
A__ = alltweets[-1].id - 1
print(f'''...{len(__UpperCamelCase )} tweets downloaded so far''' )
# transform the tweepy tweets into a 2D array that will populate the csv
A__ = [[tweet.id_str, tweet.created_at, tweet.text] for tweet in alltweets]
# write the csv
with open(f'''new_{screen_name}_tweets.csv''' , 'w' ) as f:
A__ = csv.writer(__UpperCamelCase )
writer.writerow(['id', 'created_at', 'text'] )
writer.writerows(__UpperCamelCase )
if __name__ == "__main__":
# pass in the username of the account you want to download
get_all_tweets('''FirePing32''')
| 9
|
SCREAMING_SNAKE_CASE__ = {
'''A''': ['''B''', '''C''', '''E'''],
'''B''': ['''A''', '''D''', '''E'''],
'''C''': ['''A''', '''F''', '''G'''],
'''D''': ['''B'''],
'''E''': ['''A''', '''B''', '''D'''],
'''F''': ['''C'''],
'''G''': ['''C'''],
}
def A ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ) -> list[str]:
A__ = set()
# keep track of all the paths to be checked
A__ = [[start]]
# return path if start is goal
if start == goal:
return [start]
# keeps looping until all possible paths have been checked
while queue:
# pop the first path from the queue
A__ = queue.pop(0 )
# get the last node from the path
A__ = path[-1]
if node not in explored:
A__ = graph[node]
# go through all neighbour nodes, construct a new path and
# push it into the queue
for neighbour in neighbours:
A__ = list(__UpperCamelCase )
new_path.append(__UpperCamelCase )
queue.append(__UpperCamelCase )
# return path if neighbour is goal
if neighbour == goal:
return new_path
# mark node as explored
explored.add(__UpperCamelCase )
# in case there's no path between the 2 nodes
return []
def A ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ) -> int:
if not graph or start not in graph or target not in graph:
return -1
if start == target:
return 0
A__ = [start]
A__ = set(__UpperCamelCase )
# Keep tab on distances from `start` node.
A__ = {start: 0, target: -1}
while queue:
A__ = queue.pop(0 )
if node == target:
A__ = (
dist[node] if dist[target] == -1 else min(dist[target] , dist[node] )
)
for adjacent in graph[node]:
if adjacent not in visited:
visited.add(__UpperCamelCase )
queue.append(__UpperCamelCase )
A__ = dist[node] + 1
return dist[target]
if __name__ == "__main__":
print(bfs_shortest_path(demo_graph, '''G''', '''D''')) # returns ['G', 'C', 'A', 'B', 'D']
print(bfs_shortest_path_distance(demo_graph, '''G''', '''D''')) # returns 4
| 9
| 1
|
def SCREAMING_SNAKE_CASE__ ( __A=28_123 ) -> Optional[Any]:
_snake_case = [1] * (limit + 1)
for i in range(2 , int(limit**0.5 ) + 1 ):
sum_divs[i * i] += i
for k in range(i + 1 , limit // i + 1 ):
sum_divs[k * i] += k + i
_snake_case = set()
_snake_case = 0
for n in range(1 , limit + 1 ):
if sum_divs[n] > n:
abundants.add(__lowerCAmelCase )
if not any((n - a in abundants) for a in abundants ):
res += n
return res
if __name__ == "__main__":
print(solution())
| 712
|
'''simple docstring'''
import torch
from transformers import CamembertForMaskedLM, CamembertTokenizer
def SCREAMING_SNAKE_CASE__ ( __A , __A , __A , __A=5 ) -> Dict:
# Adapted from https://github.com/pytorch/fairseq/blob/master/fairseq/models/roberta/hub_interface.py
assert masked_input.count('<mask>' ) == 1
_snake_case = torch.tensor(tokenizer.encode(__A , add_special_tokens=__A ) ).unsqueeze(0 ) # Batch size 1
_snake_case = model(__A )[0] # The last hidden-state is the first element of the output tuple
_snake_case = (input_ids.squeeze() == tokenizer.mask_token_id).nonzero().item()
_snake_case = logits[0, masked_index, :]
_snake_case = logits.softmax(dim=0 )
_snake_case , _snake_case = prob.topk(k=__A , dim=0 )
_snake_case = ' '.join(
[tokenizer.convert_ids_to_tokens(indices[i].item() ) for i in range(len(__A ) )] )
_snake_case = tokenizer.mask_token
_snake_case = []
for index, predicted_token_bpe in enumerate(topk_predicted_token_bpe.split(' ' ) ):
_snake_case = predicted_token_bpe.replace('\u2581' , ' ' )
if " {0}".format(__A ) in masked_input:
topk_filled_outputs.append(
(
masked_input.replace(' {0}'.format(__A ) , __A ),
values[index].item(),
predicted_token,
) )
else:
topk_filled_outputs.append(
(
masked_input.replace(__A , __A ),
values[index].item(),
predicted_token,
) )
return topk_filled_outputs
lowercase : str = CamembertTokenizer.from_pretrained("camembert-base")
lowercase : str = CamembertForMaskedLM.from_pretrained("camembert-base")
model.eval()
lowercase : Tuple = "Le camembert est <mask> :)"
print(fill_mask(masked_input, model, tokenizer, topk=3))
| 542
| 0
|
lowerCamelCase ="\n# Transformers installation\n! pip install transformers datasets\n# To install from source instead of the last release, comment the command above and uncomment the following one.\n# ! pip install git+https://github.com/huggingface/transformers.git\n"
lowerCamelCase =[{"type": "code", "content": INSTALL_CONTENT}]
lowerCamelCase ={
"{processor_class}": "FakeProcessorClass",
"{model_class}": "FakeModelClass",
"{object_class}": "FakeObjectClass",
}
| 285
|
from typing import Any
def SCREAMING_SNAKE_CASE_ ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , ):
_validation(
UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , )
# Creates data structures and fill initial step
UpperCamelCase__ : dict = {}
UpperCamelCase__ : dict = {}
for state in states_space:
UpperCamelCase__ : Optional[int] = observations_space[0]
UpperCamelCase__ : Any = (
initial_probabilities[state] * emission_probabilities[state][observation]
)
UpperCamelCase__ : Union[str, Any] = None
# Fills the data structure with the probabilities of
# different transitions and pointers to previous states
for o in range(1 , len(UpperCamelCase__ ) ):
UpperCamelCase__ : str = observations_space[o]
UpperCamelCase__ : Union[str, Any] = observations_space[o - 1]
for state in states_space:
# Calculates the argmax for probability function
UpperCamelCase__ : int = ''''''
UpperCamelCase__ : List[str] = -1
for k_state in states_space:
UpperCamelCase__ : Union[str, Any] = (
probabilities[(k_state, prior_observation)]
* transition_probabilities[k_state][state]
* emission_probabilities[state][observation]
)
if probability > max_probability:
UpperCamelCase__ : Tuple = probability
UpperCamelCase__ : Union[str, Any] = k_state
# Update probabilities and pointers dicts
UpperCamelCase__ : Tuple = (
probabilities[(arg_max, prior_observation)]
* transition_probabilities[arg_max][state]
* emission_probabilities[state][observation]
)
UpperCamelCase__ : Optional[Any] = arg_max
# The final observation
UpperCamelCase__ : List[str] = observations_space[len(UpperCamelCase__ ) - 1]
# argmax for given final observation
UpperCamelCase__ : Dict = ''''''
UpperCamelCase__ : Tuple = -1
for k_state in states_space:
UpperCamelCase__ : Any = probabilities[(k_state, final_observation)]
if probability > max_probability:
UpperCamelCase__ : List[str] = probability
UpperCamelCase__ : Tuple = k_state
UpperCamelCase__ : Any = arg_max
# Process pointers backwards
UpperCamelCase__ : List[Any] = last_state
UpperCamelCase__ : int = []
for o in range(len(UpperCamelCase__ ) - 1 , -1 , -1 ):
result.append(UpperCamelCase__ )
UpperCamelCase__ : int = pointers[previous, observations_space[o]]
result.reverse()
return result
def SCREAMING_SNAKE_CASE_ ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , ):
_validate_not_empty(
UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , )
_validate_lists(UpperCamelCase__ , UpperCamelCase__ )
_validate_dicts(
UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
def SCREAMING_SNAKE_CASE_ ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , ):
if not all(
[
observations_space,
states_space,
initial_probabilities,
transition_probabilities,
emission_probabilities,
] ):
raise ValueError('''There\'s an empty parameter''' )
def SCREAMING_SNAKE_CASE_ ( UpperCamelCase__ , UpperCamelCase__ ):
_validate_list(UpperCamelCase__ , '''observations_space''' )
_validate_list(UpperCamelCase__ , '''states_space''' )
def SCREAMING_SNAKE_CASE_ ( UpperCamelCase__ , UpperCamelCase__ ):
if not isinstance(_object , UpperCamelCase__ ):
UpperCamelCase__ : List[Any] = f'''{var_name} must be a list'''
raise ValueError(UpperCamelCase__ )
else:
for x in _object:
if not isinstance(UpperCamelCase__ , UpperCamelCase__ ):
UpperCamelCase__ : List[Any] = f'''{var_name} must be a list of strings'''
raise ValueError(UpperCamelCase__ )
def SCREAMING_SNAKE_CASE_ ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , ):
_validate_dict(UpperCamelCase__ , '''initial_probabilities''' , UpperCamelCase__ )
_validate_nested_dict(UpperCamelCase__ , '''transition_probabilities''' )
_validate_nested_dict(UpperCamelCase__ , '''emission_probabilities''' )
def SCREAMING_SNAKE_CASE_ ( UpperCamelCase__ , UpperCamelCase__ ):
_validate_dict(_object , UpperCamelCase__ , UpperCamelCase__ )
for x in _object.values():
_validate_dict(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
def SCREAMING_SNAKE_CASE_ ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ = False ):
if not isinstance(_object , UpperCamelCase__ ):
UpperCamelCase__ : List[str] = f'''{var_name} must be a dict'''
raise ValueError(UpperCamelCase__ )
if not all(isinstance(UpperCamelCase__ , UpperCamelCase__ ) for x in _object ):
UpperCamelCase__ : Dict = f'''{var_name} all keys must be strings'''
raise ValueError(UpperCamelCase__ )
if not all(isinstance(UpperCamelCase__ , UpperCamelCase__ ) for x in _object.values() ):
UpperCamelCase__ : Optional[Any] = '''nested dictionary ''' if nested else ''''''
UpperCamelCase__ : Optional[Any] = f'''{var_name} {nested_text}all values must be {value_type.__name__}'''
raise ValueError(UpperCamelCase__ )
if __name__ == "__main__":
from doctest import testmod
testmod()
| 285
| 1
|
'''simple docstring'''
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available, is_vision_available
from ...utils import OptionalDependencyNotAvailable
UpperCamelCase__ : Union[str, Any] = {'''configuration_dpt''': ['''DPT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''DPTConfig''']}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase__ : List[Any] = ['''DPTFeatureExtractor''']
UpperCamelCase__ : Union[str, Any] = ['''DPTImageProcessor''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase__ : Optional[int] = [
'''DPT_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''DPTForDepthEstimation''',
'''DPTForSemanticSegmentation''',
'''DPTModel''',
'''DPTPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_dpt import DPT_PRETRAINED_CONFIG_ARCHIVE_MAP, DPTConfig
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_dpt import DPTFeatureExtractor
from .image_processing_dpt import DPTImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_dpt import (
DPT_PRETRAINED_MODEL_ARCHIVE_LIST,
DPTForDepthEstimation,
DPTForSemanticSegmentation,
DPTModel,
DPTPreTrainedModel,
)
else:
import sys
UpperCamelCase__ : Optional[Any] = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 701
|
'''simple docstring'''
import math
import torch
from torch import nn
from ..configuration_utils import ConfigMixin, register_to_config
from .attention_processor import Attention
from .embeddings import get_timestep_embedding
from .modeling_utils import ModelMixin
class _UpperCamelCase ( lowerCamelCase__ , lowerCamelCase__ ):
'''simple docstring'''
@register_to_config
def __init__( self : Any , lowerCAmelCase__ : int = 1_2_8 , lowerCAmelCase__ : int = 2_5_6 , lowerCAmelCase__ : float = 20_00.0 , lowerCAmelCase__ : int = 7_6_8 , lowerCAmelCase__ : int = 1_2 , lowerCAmelCase__ : int = 1_2 , lowerCAmelCase__ : int = 6_4 , lowerCAmelCase__ : int = 2_0_4_8 , lowerCAmelCase__ : float = 0.1 , ):
"""simple docstring"""
super().__init__()
__SCREAMING_SNAKE_CASE : Optional[int] = nn.Sequential(
nn.Linear(lowerCAmelCase__ , d_model * 4 , bias=lowerCAmelCase__ ) , nn.SiLU() , nn.Linear(d_model * 4 , d_model * 4 , bias=lowerCAmelCase__ ) , nn.SiLU() , )
__SCREAMING_SNAKE_CASE : Dict = nn.Embedding(lowerCAmelCase__ , lowerCAmelCase__ )
__SCREAMING_SNAKE_CASE : Optional[Any] = False
__SCREAMING_SNAKE_CASE : Optional[Any] = nn.Linear(lowerCAmelCase__ , lowerCAmelCase__ , bias=lowerCAmelCase__ )
__SCREAMING_SNAKE_CASE : str = nn.Dropout(p=lowerCAmelCase__ )
__SCREAMING_SNAKE_CASE : List[Any] = nn.ModuleList()
for lyr_num in range(lowerCAmelCase__ ):
# FiLM conditional T5 decoder
__SCREAMING_SNAKE_CASE : Optional[Any] = DecoderLayer(d_model=lowerCAmelCase__ , d_kv=lowerCAmelCase__ , num_heads=lowerCAmelCase__ , d_ff=lowerCAmelCase__ , dropout_rate=lowerCAmelCase__ )
self.decoders.append(lowerCAmelCase__ )
__SCREAMING_SNAKE_CASE : List[str] = TaLayerNorm(lowerCAmelCase__ )
__SCREAMING_SNAKE_CASE : int = nn.Dropout(p=lowerCAmelCase__ )
__SCREAMING_SNAKE_CASE : List[Any] = nn.Linear(lowerCAmelCase__ , lowerCAmelCase__ , bias=lowerCAmelCase__ )
def UpperCamelCase__ ( self : Optional[int] , lowerCAmelCase__ : Optional[Any] , lowerCAmelCase__ : List[Any] ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : List[Any] = torch.mul(query_input.unsqueeze(-1 ) , key_input.unsqueeze(-2 ) )
return mask.unsqueeze(-3 )
def UpperCamelCase__ ( self : Dict , lowerCAmelCase__ : Optional[int] , lowerCAmelCase__ : Union[str, Any] , lowerCAmelCase__ : List[str] ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : List[str] = decoder_input_tokens.shape
assert decoder_noise_time.shape == (batch,)
# decoder_noise_time is in [0, 1), so rescale to expected timing range.
__SCREAMING_SNAKE_CASE : Tuple = get_timestep_embedding(
decoder_noise_time * self.config.max_decoder_noise_time , embedding_dim=self.config.d_model , max_period=self.config.max_decoder_noise_time , ).to(dtype=self.dtype )
__SCREAMING_SNAKE_CASE : Optional[int] = self.conditioning_emb(lowerCAmelCase__ ).unsqueeze(1 )
assert conditioning_emb.shape == (batch, 1, self.config.d_model * 4)
__SCREAMING_SNAKE_CASE : Union[str, Any] = decoder_input_tokens.shape[1]
# If we want to use relative positions for audio context, we can just offset
# this sequence by the length of encodings_and_masks.
__SCREAMING_SNAKE_CASE : Tuple = torch.broadcast_to(
torch.arange(lowerCAmelCase__ , device=decoder_input_tokens.device ) , (batch, seq_length) , )
__SCREAMING_SNAKE_CASE : str = self.position_encoding(lowerCAmelCase__ )
__SCREAMING_SNAKE_CASE : Tuple = self.continuous_inputs_projection(lowerCAmelCase__ )
inputs += position_encodings
__SCREAMING_SNAKE_CASE : Optional[Any] = self.dropout(lowerCAmelCase__ )
# decoder: No padding present.
__SCREAMING_SNAKE_CASE : Tuple = torch.ones(
decoder_input_tokens.shape[:2] , device=decoder_input_tokens.device , dtype=inputs.dtype )
# Translate encoding masks to encoder-decoder masks.
__SCREAMING_SNAKE_CASE : List[Any] = [(x, self.encoder_decoder_mask(lowerCAmelCase__ , lowerCAmelCase__ )) for x, y in encodings_and_masks]
# cross attend style: concat encodings
__SCREAMING_SNAKE_CASE : Optional[Any] = torch.cat([x[0] for x in encodings_and_encdec_masks] , dim=1 )
__SCREAMING_SNAKE_CASE : List[str] = torch.cat([x[1] for x in encodings_and_encdec_masks] , dim=-1 )
for lyr in self.decoders:
__SCREAMING_SNAKE_CASE : Dict = lyr(
lowerCAmelCase__ , conditioning_emb=lowerCAmelCase__ , encoder_hidden_states=lowerCAmelCase__ , encoder_attention_mask=lowerCAmelCase__ , )[0]
__SCREAMING_SNAKE_CASE : List[str] = self.decoder_norm(lowerCAmelCase__ )
__SCREAMING_SNAKE_CASE : List[Any] = self.post_dropout(lowerCAmelCase__ )
__SCREAMING_SNAKE_CASE : str = self.spec_out(lowerCAmelCase__ )
return spec_out
class _UpperCamelCase ( nn.Module ):
'''simple docstring'''
def __init__( self : Dict , lowerCAmelCase__ : int , lowerCAmelCase__ : Any , lowerCAmelCase__ : Optional[Any] , lowerCAmelCase__ : Optional[int] , lowerCAmelCase__ : str , lowerCAmelCase__ : Tuple=1E-6 ):
"""simple docstring"""
super().__init__()
__SCREAMING_SNAKE_CASE : Dict = nn.ModuleList()
# cond self attention: layer 0
self.layer.append(
TaLayerSelfAttentionCond(d_model=lowerCAmelCase__ , d_kv=lowerCAmelCase__ , num_heads=lowerCAmelCase__ , dropout_rate=lowerCAmelCase__ ) )
# cross attention: layer 1
self.layer.append(
TaLayerCrossAttention(
d_model=lowerCAmelCase__ , d_kv=lowerCAmelCase__ , num_heads=lowerCAmelCase__ , dropout_rate=lowerCAmelCase__ , layer_norm_epsilon=lowerCAmelCase__ , ) )
# Film Cond MLP + dropout: last layer
self.layer.append(
TaLayerFFCond(d_model=lowerCAmelCase__ , d_ff=lowerCAmelCase__ , dropout_rate=lowerCAmelCase__ , layer_norm_epsilon=lowerCAmelCase__ ) )
def UpperCamelCase__ ( self : int , lowerCAmelCase__ : Dict , lowerCAmelCase__ : Optional[Any]=None , lowerCAmelCase__ : List[Any]=None , lowerCAmelCase__ : Any=None , lowerCAmelCase__ : Dict=None , lowerCAmelCase__ : Optional[int]=None , ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : str = self.layer[0](
lowerCAmelCase__ , conditioning_emb=lowerCAmelCase__ , attention_mask=lowerCAmelCase__ , )
if encoder_hidden_states is not None:
__SCREAMING_SNAKE_CASE : List[str] = torch.where(encoder_attention_mask > 0 , 0 , -1E10 ).to(
encoder_hidden_states.dtype )
__SCREAMING_SNAKE_CASE : Union[str, Any] = self.layer[1](
lowerCAmelCase__ , key_value_states=lowerCAmelCase__ , attention_mask=lowerCAmelCase__ , )
# Apply Film Conditional Feed Forward layer
__SCREAMING_SNAKE_CASE : Union[str, Any] = self.layer[-1](lowerCAmelCase__ , lowerCAmelCase__ )
return (hidden_states,)
class _UpperCamelCase ( nn.Module ):
'''simple docstring'''
def __init__( self : List[str] , lowerCAmelCase__ : List[str] , lowerCAmelCase__ : str , lowerCAmelCase__ : int , lowerCAmelCase__ : Union[str, Any] ):
"""simple docstring"""
super().__init__()
__SCREAMING_SNAKE_CASE : List[Any] = TaLayerNorm(lowerCAmelCase__ )
__SCREAMING_SNAKE_CASE : Any = TaFiLMLayer(in_features=d_model * 4 , out_features=lowerCAmelCase__ )
__SCREAMING_SNAKE_CASE : List[str] = Attention(query_dim=lowerCAmelCase__ , heads=lowerCAmelCase__ , dim_head=lowerCAmelCase__ , out_bias=lowerCAmelCase__ , scale_qk=lowerCAmelCase__ )
__SCREAMING_SNAKE_CASE : Optional[int] = nn.Dropout(lowerCAmelCase__ )
def UpperCamelCase__ ( self : int , lowerCAmelCase__ : Union[str, Any] , lowerCAmelCase__ : List[str]=None , lowerCAmelCase__ : int=None , ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : Tuple = self.layer_norm(lowerCAmelCase__ )
if conditioning_emb is not None:
__SCREAMING_SNAKE_CASE : str = self.FiLMLayer(lowerCAmelCase__ , lowerCAmelCase__ )
# Self-attention block
__SCREAMING_SNAKE_CASE : List[Any] = self.attention(lowerCAmelCase__ )
__SCREAMING_SNAKE_CASE : str = hidden_states + self.dropout(lowerCAmelCase__ )
return hidden_states
class _UpperCamelCase ( nn.Module ):
'''simple docstring'''
def __init__( self : Union[str, Any] , lowerCAmelCase__ : int , lowerCAmelCase__ : Dict , lowerCAmelCase__ : Tuple , lowerCAmelCase__ : Dict , lowerCAmelCase__ : str ):
"""simple docstring"""
super().__init__()
__SCREAMING_SNAKE_CASE : Dict = Attention(query_dim=lowerCAmelCase__ , heads=lowerCAmelCase__ , dim_head=lowerCAmelCase__ , out_bias=lowerCAmelCase__ , scale_qk=lowerCAmelCase__ )
__SCREAMING_SNAKE_CASE : Any = TaLayerNorm(lowerCAmelCase__ , eps=lowerCAmelCase__ )
__SCREAMING_SNAKE_CASE : Optional[int] = nn.Dropout(lowerCAmelCase__ )
def UpperCamelCase__ ( self : List[Any] , lowerCAmelCase__ : Tuple , lowerCAmelCase__ : List[str]=None , lowerCAmelCase__ : Any=None , ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : Dict = self.layer_norm(lowerCAmelCase__ )
__SCREAMING_SNAKE_CASE : List[str] = self.attention(
lowerCAmelCase__ , encoder_hidden_states=lowerCAmelCase__ , attention_mask=attention_mask.squeeze(1 ) , )
__SCREAMING_SNAKE_CASE : Dict = hidden_states + self.dropout(lowerCAmelCase__ )
return layer_output
class _UpperCamelCase ( nn.Module ):
'''simple docstring'''
def __init__( self : List[str] , lowerCAmelCase__ : Dict , lowerCAmelCase__ : Any , lowerCAmelCase__ : Tuple , lowerCAmelCase__ : int ):
"""simple docstring"""
super().__init__()
__SCREAMING_SNAKE_CASE : Any = TaDenseGatedActDense(d_model=lowerCAmelCase__ , d_ff=lowerCAmelCase__ , dropout_rate=lowerCAmelCase__ )
__SCREAMING_SNAKE_CASE : Union[str, Any] = TaFiLMLayer(in_features=d_model * 4 , out_features=lowerCAmelCase__ )
__SCREAMING_SNAKE_CASE : Tuple = TaLayerNorm(lowerCAmelCase__ , eps=lowerCAmelCase__ )
__SCREAMING_SNAKE_CASE : Tuple = nn.Dropout(lowerCAmelCase__ )
def UpperCamelCase__ ( self : int , lowerCAmelCase__ : List[Any] , lowerCAmelCase__ : Tuple=None ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : str = self.layer_norm(lowerCAmelCase__ )
if conditioning_emb is not None:
__SCREAMING_SNAKE_CASE : Optional[Any] = self.film(lowerCAmelCase__ , lowerCAmelCase__ )
__SCREAMING_SNAKE_CASE : int = self.DenseReluDense(lowerCAmelCase__ )
__SCREAMING_SNAKE_CASE : Any = hidden_states + self.dropout(lowerCAmelCase__ )
return hidden_states
class _UpperCamelCase ( nn.Module ):
'''simple docstring'''
def __init__( self : Tuple , lowerCAmelCase__ : List[str] , lowerCAmelCase__ : Optional[int] , lowerCAmelCase__ : Dict ):
"""simple docstring"""
super().__init__()
__SCREAMING_SNAKE_CASE : Tuple = nn.Linear(lowerCAmelCase__ , lowerCAmelCase__ , bias=lowerCAmelCase__ )
__SCREAMING_SNAKE_CASE : Any = nn.Linear(lowerCAmelCase__ , lowerCAmelCase__ , bias=lowerCAmelCase__ )
__SCREAMING_SNAKE_CASE : Dict = nn.Linear(lowerCAmelCase__ , lowerCAmelCase__ , bias=lowerCAmelCase__ )
__SCREAMING_SNAKE_CASE : Optional[Any] = nn.Dropout(lowerCAmelCase__ )
__SCREAMING_SNAKE_CASE : Dict = NewGELUActivation()
def UpperCamelCase__ ( self : List[str] , lowerCAmelCase__ : Optional[Any] ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : int = self.act(self.wi_a(lowerCAmelCase__ ) )
__SCREAMING_SNAKE_CASE : Any = self.wi_a(lowerCAmelCase__ )
__SCREAMING_SNAKE_CASE : Union[str, Any] = hidden_gelu * hidden_linear
__SCREAMING_SNAKE_CASE : List[Any] = self.dropout(lowerCAmelCase__ )
__SCREAMING_SNAKE_CASE : List[str] = self.wo(lowerCAmelCase__ )
return hidden_states
class _UpperCamelCase ( nn.Module ):
'''simple docstring'''
def __init__( self : List[Any] , lowerCAmelCase__ : Union[str, Any] , lowerCAmelCase__ : Tuple=1E-6 ):
"""simple docstring"""
super().__init__()
__SCREAMING_SNAKE_CASE : Dict = nn.Parameter(torch.ones(lowerCAmelCase__ ) )
__SCREAMING_SNAKE_CASE : str = eps
def UpperCamelCase__ ( self : Dict , lowerCAmelCase__ : Optional[Any] ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : Optional[int] = hidden_states.to(torch.floataa ).pow(2 ).mean(-1 , keepdim=lowerCAmelCase__ )
__SCREAMING_SNAKE_CASE : Any = hidden_states * torch.rsqrt(variance + self.variance_epsilon )
# convert into half-precision if necessary
if self.weight.dtype in [torch.floataa, torch.bfloataa]:
__SCREAMING_SNAKE_CASE : Optional[Any] = hidden_states.to(self.weight.dtype )
return self.weight * hidden_states
class _UpperCamelCase ( nn.Module ):
'''simple docstring'''
def UpperCamelCase__ ( self : Union[str, Any] , lowerCAmelCase__ : torch.Tensor ):
"""simple docstring"""
return 0.5 * input * (1.0 + torch.tanh(math.sqrt(2.0 / math.pi ) * (input + 0.04_47_15 * torch.pow(lowerCAmelCase__ , 3.0 )) ))
class _UpperCamelCase ( nn.Module ):
'''simple docstring'''
def __init__( self : Any , lowerCAmelCase__ : Dict , lowerCAmelCase__ : Union[str, Any] ):
"""simple docstring"""
super().__init__()
__SCREAMING_SNAKE_CASE : Tuple = nn.Linear(lowerCAmelCase__ , out_features * 2 , bias=lowerCAmelCase__ )
def UpperCamelCase__ ( self : List[Any] , lowerCAmelCase__ : List[str] , lowerCAmelCase__ : Tuple ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : Dict = self.scale_bias(lowerCAmelCase__ )
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Tuple = torch.chunk(lowerCAmelCase__ , 2 , -1 )
__SCREAMING_SNAKE_CASE : Dict = x * (1 + scale) + shift
return x
| 178
| 0
|
import argparse
import json
import os
from collections import OrderedDict
import torch
from transformers import LukeConfig, LukeForMaskedLM, MLukeTokenizer, XLMRobertaTokenizer
from transformers.tokenization_utils_base import AddedToken
@torch.no_grad()
def lowerCAmelCase_ (lowercase__ : str , lowercase__ : Union[str, Any] , lowercase__ : List[str] , lowercase__ : Any , lowercase__ : List[str] ) -> Any:
'''simple docstring'''
with open(lowercase__ ) as metadata_file:
lowerCAmelCase__ = json.load(lowercase__ )
lowerCAmelCase__ = LukeConfig(use_entity_aware_attention=lowercase__ , **metadata['''model_config'''] )
# Load in the weights from the checkpoint_path
lowerCAmelCase__ = torch.load(lowercase__ , map_location='''cpu''' )['''module''']
# Load the entity vocab file
lowerCAmelCase__ = load_original_entity_vocab(lowercase__ )
# add an entry for [MASK2]
lowerCAmelCase__ = max(entity_vocab.values() ) + 1
config.entity_vocab_size += 1
lowerCAmelCase__ = XLMRobertaTokenizer.from_pretrained(metadata['''model_config''']['''bert_model_name'''] )
# Add special tokens to the token vocabulary for downstream tasks
lowerCAmelCase__ = AddedToken('''<ent>''' , lstrip=lowercase__ , rstrip=lowercase__ )
lowerCAmelCase__ = AddedToken('''<ent2>''' , lstrip=lowercase__ , rstrip=lowercase__ )
tokenizer.add_special_tokens({'''additional_special_tokens''': [entity_token_a, entity_token_a]} )
config.vocab_size += 2
print(f'Saving tokenizer to {pytorch_dump_folder_path}' )
tokenizer.save_pretrained(lowercase__ )
with open(os.path.join(lowercase__ , '''tokenizer_config.json''' ) , '''r''' ) as f:
lowerCAmelCase__ = json.load(lowercase__ )
lowerCAmelCase__ = '''MLukeTokenizer'''
with open(os.path.join(lowercase__ , '''tokenizer_config.json''' ) , '''w''' ) as f:
json.dump(lowercase__ , lowercase__ )
with open(os.path.join(lowercase__ , MLukeTokenizer.vocab_files_names['''entity_vocab_file'''] ) , '''w''' ) as f:
json.dump(lowercase__ , lowercase__ )
lowerCAmelCase__ = MLukeTokenizer.from_pretrained(lowercase__ )
# Initialize the embeddings of the special tokens
lowerCAmelCase__ = tokenizer.convert_tokens_to_ids(['''@'''] )[0]
lowerCAmelCase__ = tokenizer.convert_tokens_to_ids(['''#'''] )[0]
lowerCAmelCase__ = state_dict['''embeddings.word_embeddings.weight''']
lowerCAmelCase__ = word_emb[ent_init_index].unsqueeze(0 )
lowerCAmelCase__ = word_emb[enta_init_index].unsqueeze(0 )
lowerCAmelCase__ = torch.cat([word_emb, ent_emb, enta_emb] )
# add special tokens for 'entity_predictions.bias'
for bias_name in ["lm_head.decoder.bias", "lm_head.bias"]:
lowerCAmelCase__ = state_dict[bias_name]
lowerCAmelCase__ = decoder_bias[ent_init_index].unsqueeze(0 )
lowerCAmelCase__ = decoder_bias[enta_init_index].unsqueeze(0 )
lowerCAmelCase__ = torch.cat([decoder_bias, ent_decoder_bias, enta_decoder_bias] )
# Initialize the query layers of the entity-aware self-attention mechanism
for layer_index in range(config.num_hidden_layers ):
for matrix_name in ["query.weight", "query.bias"]:
lowerCAmelCase__ = f'encoder.layer.{layer_index}.attention.self.'
lowerCAmelCase__ = state_dict[prefix + matrix_name]
lowerCAmelCase__ = state_dict[prefix + matrix_name]
lowerCAmelCase__ = state_dict[prefix + matrix_name]
# Initialize the embedding of the [MASK2] entity using that of the [MASK] entity for downstream tasks
lowerCAmelCase__ = state_dict['''entity_embeddings.entity_embeddings.weight''']
lowerCAmelCase__ = entity_emb[entity_vocab['''[MASK]''']].unsqueeze(0 )
lowerCAmelCase__ = torch.cat([entity_emb, entity_mask_emb] )
# add [MASK2] for 'entity_predictions.bias'
lowerCAmelCase__ = state_dict['''entity_predictions.bias''']
lowerCAmelCase__ = entity_prediction_bias[entity_vocab['''[MASK]''']].unsqueeze(0 )
lowerCAmelCase__ = torch.cat([entity_prediction_bias, entity_mask_bias] )
lowerCAmelCase__ = LukeForMaskedLM(config=lowercase__ ).eval()
state_dict.pop('''entity_predictions.decoder.weight''' )
state_dict.pop('''lm_head.decoder.weight''' )
state_dict.pop('''lm_head.decoder.bias''' )
lowerCAmelCase__ = OrderedDict()
for key, value in state_dict.items():
if not (key.startswith('''lm_head''' ) or key.startswith('''entity_predictions''' )):
lowerCAmelCase__ = state_dict[key]
else:
lowerCAmelCase__ = state_dict[key]
lowerCAmelCase__ , lowerCAmelCase__ = model.load_state_dict(lowercase__ , strict=lowercase__ )
if set(lowercase__ ) != {"luke.embeddings.position_ids"}:
raise ValueError(f'Unexpected unexpected_keys: {unexpected_keys}' )
if set(lowercase__ ) != {
"lm_head.decoder.weight",
"lm_head.decoder.bias",
"entity_predictions.decoder.weight",
}:
raise ValueError(f'Unexpected missing_keys: {missing_keys}' )
model.tie_weights()
assert (model.luke.embeddings.word_embeddings.weight == model.lm_head.decoder.weight).all()
assert (model.luke.entity_embeddings.entity_embeddings.weight == model.entity_predictions.decoder.weight).all()
# Check outputs
lowerCAmelCase__ = MLukeTokenizer.from_pretrained(lowercase__ , task='''entity_classification''' )
lowerCAmelCase__ = '''ISO 639-3 uses the code fas for the dialects spoken across Iran and アフガニスタン (Afghanistan).'''
lowerCAmelCase__ = (0, 9)
lowerCAmelCase__ = tokenizer(lowercase__ , entity_spans=[span] , return_tensors='''pt''' )
lowerCAmelCase__ = model(**lowercase__ )
# Verify word hidden states
if model_size == "large":
raise NotImplementedError
else: # base
lowerCAmelCase__ = torch.Size((1, 33, 7_68) )
lowerCAmelCase__ = torch.tensor([[0.0892, 0.0596, -0.2819], [0.0134, 0.1199, 0.0573], [-0.0169, 0.0927, 0.0644]] )
if not (outputs.last_hidden_state.shape == expected_shape):
raise ValueError(
f'Outputs.last_hidden_state.shape is {outputs.last_hidden_state.shape}, Expected shape is {expected_shape}' )
if not torch.allclose(outputs.last_hidden_state[0, :3, :3] , lowercase__ , atol=1e-4 ):
raise ValueError
# Verify entity hidden states
if model_size == "large":
raise NotImplementedError
else: # base
lowerCAmelCase__ = torch.Size((1, 1, 7_68) )
lowerCAmelCase__ = torch.tensor([[-0.1482, 0.0609, 0.0322]] )
if not (outputs.entity_last_hidden_state.shape == expected_shape):
raise ValueError(
f'Outputs.entity_last_hidden_state.shape is {outputs.entity_last_hidden_state.shape}, Expected shape is'
f' {expected_shape}' )
if not torch.allclose(outputs.entity_last_hidden_state[0, :3, :3] , lowercase__ , atol=1e-4 ):
raise ValueError
# Verify masked word/entity prediction
lowerCAmelCase__ = MLukeTokenizer.from_pretrained(lowercase__ )
lowerCAmelCase__ = '''Tokyo is the capital of <mask>.'''
lowerCAmelCase__ = (24, 30)
lowerCAmelCase__ = tokenizer(lowercase__ , entity_spans=[span] , return_tensors='''pt''' )
lowerCAmelCase__ = model(**lowercase__ )
lowerCAmelCase__ = encoding['''input_ids'''][0].tolist()
lowerCAmelCase__ = input_ids.index(tokenizer.convert_tokens_to_ids('''<mask>''' ) )
lowerCAmelCase__ = outputs.logits[0][mask_position_id].argmax(dim=-1 )
assert "Japan" == tokenizer.decode(lowercase__ )
lowerCAmelCase__ = outputs.entity_logits[0][0].argmax().item()
lowerCAmelCase__ = [
entity for entity, entity_id in tokenizer.entity_vocab.items() if entity_id == predicted_entity_id
]
assert [e for e in multilingual_predicted_entities if e.startswith('''en:''' )][0] == "en:Japan"
# Finally, save our PyTorch model and tokenizer
print('''Saving PyTorch model to {}'''.format(lowercase__ ) )
model.save_pretrained(lowercase__ )
def lowerCAmelCase_ (lowercase__ : Tuple ) -> int:
'''simple docstring'''
lowerCAmelCase__ = ['''[MASK]''', '''[PAD]''', '''[UNK]''']
lowerCAmelCase__ = [json.loads(lowercase__ ) for line in open(lowercase__ )]
lowerCAmelCase__ = {}
for entry in data:
lowerCAmelCase__ = entry['''id''']
for entity_name, language in entry["entities"]:
if entity_name in SPECIAL_TOKENS:
lowerCAmelCase__ = entity_id
break
lowerCAmelCase__ = f'{language}:{entity_name}'
lowerCAmelCase__ = entity_id
return new_mapping
if __name__ == "__main__":
_UpperCAmelCase : List[Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument("--checkpoint_path", type=str, help="Path to a pytorch_model.bin file.")
parser.add_argument(
"--metadata_path", default=None, type=str, help="Path to a metadata.json file, defining the configuration."
)
parser.add_argument(
"--entity_vocab_path",
default=None,
type=str,
help="Path to an entity_vocab.tsv file, containing the entity vocabulary.",
)
parser.add_argument(
"--pytorch_dump_folder_path", default=None, type=str, help="Path to where to dump the output PyTorch model."
)
parser.add_argument(
"--model_size", default="base", type=str, choices=["base", "large"], help="Size of the model to be converted."
)
_UpperCAmelCase : Tuple = parser.parse_args()
convert_luke_checkpoint(
args.checkpoint_path,
args.metadata_path,
args.entity_vocab_path,
args.pytorch_dump_folder_path,
args.model_size,
)
| 668
|
import json
import os
from typing import Optional, Tuple
import regex as re
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
_UpperCAmelCase : Optional[int] = logging.get_logger(__name__)
_UpperCAmelCase : str = {
"vocab_file": "vocab.json",
"merges_file": "merges.txt",
}
_UpperCAmelCase : str = {
"vocab_file": {"ctrl": "https://raw.githubusercontent.com/salesforce/ctrl/master/ctrl-vocab.json"},
"merges_file": {"ctrl": "https://raw.githubusercontent.com/salesforce/ctrl/master/ctrl-merges.txt"},
}
_UpperCAmelCase : List[str] = {
"ctrl": 256,
}
_UpperCAmelCase : int = {
"Pregnancy": 168_629,
"Christianity": 7_675,
"Explain": 106_423,
"Fitness": 63_440,
"Saving": 63_163,
"Ask": 27_171,
"Ass": 95_985,
"Joke": 163_509,
"Questions": 45_622,
"Thoughts": 49_605,
"Retail": 52_342,
"Feminism": 164_338,
"Writing": 11_992,
"Atheism": 192_263,
"Netflix": 48_616,
"Computing": 39_639,
"Opinion": 43_213,
"Alone": 44_967,
"Funny": 58_917,
"Gaming": 40_358,
"Human": 4_088,
"India": 1_331,
"Joker": 77_138,
"Diet": 36_206,
"Legal": 11_859,
"Norman": 4_939,
"Tip": 72_689,
"Weight": 52_343,
"Movies": 46_273,
"Running": 23_425,
"Science": 2_090,
"Horror": 37_793,
"Confession": 60_572,
"Finance": 12_250,
"Politics": 16_360,
"Scary": 191_985,
"Support": 12_654,
"Technologies": 32_516,
"Teenage": 66_160,
"Event": 32_769,
"Learned": 67_460,
"Notion": 182_770,
"Wikipedia": 37_583,
"Books": 6_665,
"Extract": 76_050,
"Confessions": 102_701,
"Conspiracy": 75_932,
"Links": 63_674,
"Narcissus": 150_425,
"Relationship": 54_766,
"Relationships": 134_796,
"Reviews": 41_671,
"News": 4_256,
"Translation": 26_820,
"multilingual": 128_406,
}
def lowerCAmelCase_ (lowercase__ : Optional[int] ) -> Any:
'''simple docstring'''
lowerCAmelCase__ = set()
lowerCAmelCase__ = word[0]
for char in word[1:]:
pairs.add((prev_char, char) )
lowerCAmelCase__ = char
lowerCAmelCase__ = set(lowercase__ )
return pairs
class lowerCAmelCase_ ( snake_case__ ):
UpperCamelCase_ :int = VOCAB_FILES_NAMES
UpperCamelCase_ :str = PRETRAINED_VOCAB_FILES_MAP
UpperCamelCase_ :Dict = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
UpperCamelCase_ :Optional[int] = CONTROL_CODES
def __init__( self : Any , SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : Dict , SCREAMING_SNAKE_CASE_ : Union[str, Any]="<unk>" , **SCREAMING_SNAKE_CASE_ : Tuple ):
super().__init__(unk_token=SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
with open(SCREAMING_SNAKE_CASE_ , encoding='''utf-8''' ) as vocab_handle:
lowerCAmelCase__ = json.load(SCREAMING_SNAKE_CASE_ )
lowerCAmelCase__ = {v: k for k, v in self.encoder.items()}
with open(SCREAMING_SNAKE_CASE_ , encoding='''utf-8''' ) as merges_handle:
lowerCAmelCase__ = merges_handle.read().split('''\n''' )[1:-1]
lowerCAmelCase__ = [tuple(merge.split() ) for merge in merges]
lowerCAmelCase__ = dict(zip(SCREAMING_SNAKE_CASE_ , range(len(SCREAMING_SNAKE_CASE_ ) ) ) )
lowerCAmelCase__ = {}
@property
def __snake_case ( self : List[str] ):
return len(self.encoder )
def __snake_case ( self : Union[str, Any] ):
return dict(self.encoder , **self.added_tokens_encoder )
def __snake_case ( self : Any , SCREAMING_SNAKE_CASE_ : Any ):
if token in self.cache:
return self.cache[token]
lowerCAmelCase__ = tuple(SCREAMING_SNAKE_CASE_ )
lowerCAmelCase__ = tuple(list(word[:-1] ) + [word[-1] + '''</w>'''] )
lowerCAmelCase__ = get_pairs(SCREAMING_SNAKE_CASE_ )
if not pairs:
return token
while True:
lowerCAmelCase__ = min(SCREAMING_SNAKE_CASE_ , key=lambda SCREAMING_SNAKE_CASE_ : self.bpe_ranks.get(SCREAMING_SNAKE_CASE_ , float('''inf''' ) ) )
if bigram not in self.bpe_ranks:
break
lowerCAmelCase__ , lowerCAmelCase__ = bigram
lowerCAmelCase__ = []
lowerCAmelCase__ = 0
while i < len(SCREAMING_SNAKE_CASE_ ):
try:
lowerCAmelCase__ = word.index(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
except ValueError:
new_word.extend(word[i:] )
break
else:
new_word.extend(word[i:j] )
lowerCAmelCase__ = j
if word[i] == first and i < len(SCREAMING_SNAKE_CASE_ ) - 1 and word[i + 1] == second:
new_word.append(first + second )
i += 2
else:
new_word.append(word[i] )
i += 1
lowerCAmelCase__ = tuple(SCREAMING_SNAKE_CASE_ )
lowerCAmelCase__ = new_word
if len(SCREAMING_SNAKE_CASE_ ) == 1:
break
else:
lowerCAmelCase__ = get_pairs(SCREAMING_SNAKE_CASE_ )
lowerCAmelCase__ = '''@@ '''.join(SCREAMING_SNAKE_CASE_ )
lowerCAmelCase__ = word[:-4]
lowerCAmelCase__ = word
return word
def __snake_case ( self : Optional[Any] , SCREAMING_SNAKE_CASE_ : Optional[int] ):
lowerCAmelCase__ = []
lowerCAmelCase__ = re.findall(R'''\S+\n?''' , SCREAMING_SNAKE_CASE_ )
for token in words:
split_tokens.extend(list(self.bpe(SCREAMING_SNAKE_CASE_ ).split(''' ''' ) ) )
return split_tokens
def __snake_case ( self : Tuple , SCREAMING_SNAKE_CASE_ : Any ):
return self.encoder.get(SCREAMING_SNAKE_CASE_ , self.encoder.get(self.unk_token ) )
def __snake_case ( self : Optional[int] , SCREAMING_SNAKE_CASE_ : List[Any] ):
return self.decoder.get(SCREAMING_SNAKE_CASE_ , self.unk_token )
def __snake_case ( self : str , SCREAMING_SNAKE_CASE_ : str ):
lowerCAmelCase__ = ''' '''.join(SCREAMING_SNAKE_CASE_ ).replace('''@@ ''' , '''''' ).strip()
return out_string
def __snake_case ( self : Optional[Any] , SCREAMING_SNAKE_CASE_ : str , SCREAMING_SNAKE_CASE_ : Optional[str] = None ):
if not os.path.isdir(SCREAMING_SNAKE_CASE_ ):
logger.error(f'Vocabulary path ({save_directory}) should be a directory' )
return
lowerCAmelCase__ = os.path.join(
SCREAMING_SNAKE_CASE_ , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )
lowerCAmelCase__ = os.path.join(
SCREAMING_SNAKE_CASE_ , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''merges_file'''] )
with open(SCREAMING_SNAKE_CASE_ , '''w''' , encoding='''utf-8''' ) as f:
f.write(json.dumps(self.encoder , indent=2 , sort_keys=SCREAMING_SNAKE_CASE_ , ensure_ascii=SCREAMING_SNAKE_CASE_ ) + '''\n''' )
lowerCAmelCase__ = 0
with open(SCREAMING_SNAKE_CASE_ , '''w''' , encoding='''utf-8''' ) as writer:
writer.write('''#version: 0.2\n''' )
for bpe_tokens, token_index in sorted(self.bpe_ranks.items() , key=lambda SCREAMING_SNAKE_CASE_ : kv[1] ):
if index != token_index:
logger.warning(
f'Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'
''' Please check that the tokenizer is not corrupted!''' )
lowerCAmelCase__ = token_index
writer.write(''' '''.join(SCREAMING_SNAKE_CASE_ ) + '''\n''' )
index += 1
return vocab_file, merge_file
# def decode(self, token_ids, skip_special_tokens=False, clean_up_tokenization_spaces=True):
# filtered_tokens = ' '.join(self.convert_ids_to_tokens(token_ids, skip_special_tokens=skip_special_tokens))
# tokens_generated_so_far = re.sub('(@@ )', '', string=filtered_tokens)
# tokens_generated_so_far = re.sub('(@@ ?$)', '', string=tokens_generated_so_far)
# return ''.join(tokens_generated_so_far)
| 668
| 1
|
import tempfile
import unittest
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from transformers.testing_utils import (
is_torch_available,
require_optimum,
require_torch,
slow,
)
if is_torch_available():
import torch
@require_torch
@require_optimum
@slow
class _SCREAMING_SNAKE_CASE ( unittest.TestCase ):
'''simple docstring'''
def _snake_case ( self : int ):
SCREAMING_SNAKE_CASE = "hf-internal-testing/tiny-random-t5"
SCREAMING_SNAKE_CASE = AutoTokenizer.from_pretrained(__lowerCamelCase )
SCREAMING_SNAKE_CASE = AutoModelForSeqaSeqLM.from_pretrained(__lowerCamelCase )
SCREAMING_SNAKE_CASE = tokenizer("This is me" , return_tensors="pt" )
SCREAMING_SNAKE_CASE = model.to_bettertransformer()
self.assertTrue(any("BetterTransformer" in mod.__class__.__name__ for _, mod in model.named_modules() ) )
SCREAMING_SNAKE_CASE = model.generate(**__lowerCamelCase )
SCREAMING_SNAKE_CASE = model.reverse_bettertransformer()
self.assertFalse(any("BetterTransformer" in mod.__class__.__name__ for _, mod in model.named_modules() ) )
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(__lowerCamelCase )
SCREAMING_SNAKE_CASE = AutoModelForSeqaSeqLM.from_pretrained(__lowerCamelCase )
self.assertFalse(
any("BetterTransformer" in mod.__class__.__name__ for _, mod in model_reloaded.named_modules() ) )
SCREAMING_SNAKE_CASE = model_reloaded.generate(**__lowerCamelCase )
self.assertTrue(torch.allclose(__lowerCamelCase , __lowerCamelCase ) )
def _snake_case ( self : List[str] ):
SCREAMING_SNAKE_CASE = "hf-internal-testing/tiny-random-t5"
SCREAMING_SNAKE_CASE = AutoModelForSeqaSeqLM.from_pretrained(__lowerCamelCase )
SCREAMING_SNAKE_CASE = model.to_bettertransformer()
with tempfile.TemporaryDirectory() as tmpdirname:
with self.assertRaises(__lowerCamelCase ):
model.save_pretrained(__lowerCamelCase )
SCREAMING_SNAKE_CASE = model.reverse_bettertransformer()
model.save_pretrained(__lowerCamelCase )
| 698
|
__A : dict[str, float] = {
"joule": 1.0,
"kilojoule": 1_0_0_0,
"megajoule": 1_0_0_0_0_0_0,
"gigajoule": 1_0_0_0_0_0_0_0_0_0,
"wattsecond": 1.0,
"watthour": 3_6_0_0,
"kilowatthour": 3_6_0_0_0_0_0,
"newtonmeter": 1.0,
"calorie_nutr": 4_1_8_6.8,
"kilocalorie_nutr": 4_1_8_6_8_0_0.0_0,
"electronvolt": 1.6_0217_6634e-19,
"britishthermalunit_it": 1_0_5_5.0_5_5_8_5,
"footpound": 1.355_818,
}
def __a ( A__ : str , A__ : str , A__ : float ):
if to_type not in ENERGY_CONVERSION or from_type not in ENERGY_CONVERSION:
SCREAMING_SNAKE_CASE = (
F"Incorrect 'from_type' or 'to_type' value: {from_type!r}, {to_type!r}\n"
F"Valid values are: {', '.join(A__ )}"
)
raise ValueError(A__ )
return value * ENERGY_CONVERSION[from_type] / ENERGY_CONVERSION[to_type]
if __name__ == "__main__":
import doctest
doctest.testmod()
| 698
| 1
|
from sklearn.metrics import mean_squared_error
import datasets
lowerCamelCase_ = """\\n@article{scikit-learn,\n title={Scikit-learn: Machine Learning in {P}ython},\n author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.\n and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.\n and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and\n Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},\n journal={Journal of Machine Learning Research},\n volume={12},\n pages={2825--2830},\n year={2011}\n}\n"""
lowerCamelCase_ = """\\nMean Squared Error(MSE) is the average of the square of difference between the predicted\nand actual values.\n"""
lowerCamelCase_ = """\nArgs:\n predictions: array-like of shape (n_samples,) or (n_samples, n_outputs)\n Estimated target values.\n references: array-like of shape (n_samples,) or (n_samples, n_outputs)\n Ground truth (correct) target values.\n sample_weight: array-like of shape (n_samples,), default=None\n Sample weights.\n multioutput: {\"raw_values\", \"uniform_average\"} or array-like of shape (n_outputs,), default=\"uniform_average\"\n Defines aggregating of multiple output values. Array-like value defines weights used to average errors.\n\n \"raw_values\" : Returns a full set of errors in case of multioutput input.\n\n \"uniform_average\" : Errors of all outputs are averaged with uniform weight.\n\n squared : bool, default=True\n If True returns MSE value, if False returns RMSE (Root Mean Squared Error) value.\n\nReturns:\n mse : mean squared error.\nExamples:\n\n >>> mse_metric = datasets.load_metric(\"mse\")\n >>> predictions = [2.5, 0.0, 2, 8]\n >>> references = [3, -0.5, 2, 7]\n >>> results = mse_metric.compute(predictions=predictions, references=references)\n >>> print(results)\n {'mse': 0.375}\n >>> rmse_result = mse_metric.compute(predictions=predictions, references=references, squared=False)\n >>> print(rmse_result)\n {'mse': 0.6123724356957945}\n\n If you're using multi-dimensional lists, then set the config as follows :\n\n >>> mse_metric = datasets.load_metric(\"mse\", \"multilist\")\n >>> predictions = [[0.5, 1], [-1, 1], [7, -6]]\n >>> references = [[0, 2], [-1, 2], [8, -5]]\n >>> results = mse_metric.compute(predictions=predictions, references=references)\n >>> print(results)\n {'mse': 0.7083333333333334}\n >>> results = mse_metric.compute(predictions=predictions, references=references, multioutput='raw_values')\n >>> print(results) # doctest: +NORMALIZE_WHITESPACE\n {'mse': array([0.41666667, 1. ])}\n"""
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class a_ ( datasets.Metric ):
'''simple docstring'''
def _lowercase ( self ) -> Tuple:
'''simple docstring'''
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(self._get_feature_types() ) , reference_urls=[
'https://scikit-learn.org/stable/modules/generated/sklearn.metrics.mean_squared_error.html'
] , )
def _lowercase ( self ) -> List[str]:
'''simple docstring'''
if self.config_name == "multilist":
return {
"predictions": datasets.Sequence(datasets.Value('float' ) ),
"references": datasets.Sequence(datasets.Value('float' ) ),
}
else:
return {
"predictions": datasets.Value('float' ),
"references": datasets.Value('float' ),
}
def _lowercase ( self , lowercase_ , lowercase_ , lowercase_=None , lowercase_="uniform_average" , lowercase_=True ) -> Any:
'''simple docstring'''
lowerCAmelCase_ = mean_squared_error(
lowerCAmelCase__ , lowerCAmelCase__ , sample_weight=lowerCAmelCase__ , multioutput=lowerCAmelCase__ , squared=lowerCAmelCase__ )
return {"mse": mse}
| 318
|
"""simple docstring"""
import os
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
import torch
from torch import nn
from ...models.controlnet import ControlNetModel, ControlNetOutput
from ...models.modeling_utils import ModelMixin
from ...utils import logging
__magic_name__ = logging.get_logger(__name__)
class SCREAMING_SNAKE_CASE_ ( __a ):
"""simple docstring"""
def __init__( self , lowerCAmelCase__):
super().__init__()
__SCREAMING_SNAKE_CASE = nn.ModuleList(lowerCAmelCase__)
def snake_case_ ( self , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ = None , lowerCAmelCase__ = None , lowerCAmelCase__ = None , lowerCAmelCase__ = None , lowerCAmelCase__ = False , lowerCAmelCase__ = True , ):
for i, (image, scale, controlnet) in enumerate(zip(lowerCAmelCase__ , lowerCAmelCase__ , self.nets)):
__SCREAMING_SNAKE_CASE ,__SCREAMING_SNAKE_CASE = controlnet(
lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , )
# merge samples
if i == 0:
__SCREAMING_SNAKE_CASE ,__SCREAMING_SNAKE_CASE = down_samples, mid_sample
else:
__SCREAMING_SNAKE_CASE = [
samples_prev + samples_curr
for samples_prev, samples_curr in zip(lowerCAmelCase__ , lowerCAmelCase__)
]
mid_block_res_sample += mid_sample
return down_block_res_samples, mid_block_res_sample
def snake_case_ ( self , lowerCAmelCase__ , lowerCAmelCase__ = True , lowerCAmelCase__ = None , lowerCAmelCase__ = False , lowerCAmelCase__ = None , ):
__SCREAMING_SNAKE_CASE = 0
__SCREAMING_SNAKE_CASE = save_directory
for controlnet in self.nets:
controlnet.save_pretrained(
lowerCAmelCase__ , is_main_process=lowerCAmelCase__ , save_function=lowerCAmelCase__ , safe_serialization=lowerCAmelCase__ , variant=lowerCAmelCase__ , )
idx += 1
__SCREAMING_SNAKE_CASE = model_path_to_save + f"_{idx}"
@classmethod
def snake_case_ ( cls , lowerCAmelCase__ , **lowerCAmelCase__):
__SCREAMING_SNAKE_CASE = 0
__SCREAMING_SNAKE_CASE = []
# load controlnet and append to list until no controlnet directory exists anymore
# first controlnet has to be saved under `./mydirectory/controlnet` to be compliant with `DiffusionPipeline.from_prertained`
# second, third, ... controlnets have to be saved under `./mydirectory/controlnet_1`, `./mydirectory/controlnet_2`, ...
__SCREAMING_SNAKE_CASE = pretrained_model_path
while os.path.isdir(lowerCAmelCase__):
__SCREAMING_SNAKE_CASE = ControlNetModel.from_pretrained(lowerCAmelCase__ , **lowerCAmelCase__)
controlnets.append(lowerCAmelCase__)
idx += 1
__SCREAMING_SNAKE_CASE = pretrained_model_path + f"_{idx}"
logger.info(f"{len(lowerCAmelCase__)} controlnets loaded from {pretrained_model_path}.")
if len(lowerCAmelCase__) == 0:
raise ValueError(
f"No ControlNets found under {os.path.dirname(lowerCAmelCase__)}. Expected at least {pretrained_model_path + '_0'}.")
return cls(lowerCAmelCase__)
| 155
| 0
|
import colorsys
from PIL import Image # type: ignore
def a__ ( lowercase__ , lowercase__ , lowercase__ ):
'''simple docstring'''
UpperCAmelCase_ : List[Any] =x
UpperCAmelCase_ : List[Any] =y
for step in range(lowercase__ ): # noqa: B007
UpperCAmelCase_ : Union[str, Any] =a * a - b * b + x
UpperCAmelCase_ : str =2 * a * b + y
UpperCAmelCase_ : List[Any] =a_new
# divergence happens for all complex number with an absolute value
# greater than 4
if a * a + b * b > 4:
break
return step / (max_step - 1)
def a__ ( lowercase__ ):
'''simple docstring'''
if distance == 1:
return (0, 0, 0)
else:
return (2_5_5, 2_5_5, 2_5_5)
def a__ ( lowercase__ ):
'''simple docstring'''
if distance == 1:
return (0, 0, 0)
else:
return tuple(round(i * 2_5_5 ) for i in colorsys.hsv_to_rgb(lowercase__ , 1 , 1 ) )
def a__ ( lowercase__ = 8_0_0 , lowercase__ = 6_0_0 , lowercase__ = -0.6 , lowercase__ = 0 , lowercase__ = 3.2 , lowercase__ = 5_0 , lowercase__ = True , ):
'''simple docstring'''
UpperCAmelCase_ : Any =Image.new("RGB" , (image_width, image_height) )
UpperCAmelCase_ : Tuple =img.load()
# loop through the image-coordinates
for image_x in range(lowercase__ ):
for image_y in range(lowercase__ ):
# determine the figure-coordinates based on the image-coordinates
UpperCAmelCase_ : int =figure_width / image_width * image_height
UpperCAmelCase_ : str =figure_center_x + (image_x / image_width - 0.5) * figure_width
UpperCAmelCase_ : Optional[Any] =figure_center_y + (image_y / image_height - 0.5) * figure_height
UpperCAmelCase_ : str =get_distance(lowercase__ , lowercase__ , lowercase__ )
# color the corresponding pixel based on the selected coloring-function
if use_distance_color_coding:
UpperCAmelCase_ : Union[str, Any] =get_color_coded_rgb(lowercase__ )
else:
UpperCAmelCase_ : int =get_black_and_white_rgb(lowercase__ )
return img
if __name__ == "__main__":
import doctest
doctest.testmod()
# colored version, full figure
__lowercase : Tuple =get_image()
# uncomment for colored version, different section, zoomed in
# img = get_image(figure_center_x = -0.6, figure_center_y = -0.4,
# figure_width = 0.8)
# uncomment for black and white version, full figure
# img = get_image(use_distance_color_coding = False)
# uncomment to save the image
# img.save("mandelbrot.png")
img.show()
| 700
|
from __future__ import annotations
from sys import maxsize
from typing import Generic, TypeVar
__lowercase : Optional[int] =TypeVar("""T""")
def a__ ( lowercase__ ):
'''simple docstring'''
return (position - 1) // 2
def a__ ( lowercase__ ):
'''simple docstring'''
return (2 * position) + 1
def a__ ( lowercase__ ):
'''simple docstring'''
return (2 * position) + 2
class A ( Generic[T] ):
def __init__( self: List[str] ) -> None:
'''simple docstring'''
UpperCAmelCase_ =[]
UpperCAmelCase_ ={}
UpperCAmelCase_ =0
def __len__( self: Union[str, Any] ) -> int:
'''simple docstring'''
return self.elements
def __repr__( self: Dict ) -> str:
'''simple docstring'''
return str(self.heap )
def lowerCAmelCase__ ( self: Any ) -> bool:
'''simple docstring'''
return self.elements == 0
def lowerCAmelCase__ ( self: Optional[int] , _lowerCAmelCase: T , _lowerCAmelCase: int ) -> None:
'''simple docstring'''
self.heap.append((elem, weight) )
UpperCAmelCase_ =self.elements
self.elements += 1
self._bubble_up(_lowerCAmelCase )
def lowerCAmelCase__ ( self: Tuple ) -> T:
'''simple docstring'''
if self.elements > 1:
self._swap_nodes(0 , self.elements - 1 )
UpperCAmelCase_ , UpperCAmelCase_ =self.heap.pop()
del self.position_map[elem]
self.elements -= 1
if self.elements > 0:
UpperCAmelCase_ , UpperCAmelCase_ =self.heap[0]
self._bubble_down(_lowerCAmelCase )
return elem
def lowerCAmelCase__ ( self: Optional[Any] , _lowerCAmelCase: T , _lowerCAmelCase: int ) -> None:
'''simple docstring'''
UpperCAmelCase_ =self.position_map[elem]
UpperCAmelCase_ =(elem, weight)
if position > 0:
UpperCAmelCase_ =get_parent_position(_lowerCAmelCase )
UpperCAmelCase_ , UpperCAmelCase_ =self.heap[parent_position]
if parent_weight > weight:
self._bubble_up(_lowerCAmelCase )
else:
self._bubble_down(_lowerCAmelCase )
else:
self._bubble_down(_lowerCAmelCase )
def lowerCAmelCase__ ( self: Any , _lowerCAmelCase: T ) -> None:
'''simple docstring'''
UpperCAmelCase_ =self.position_map[elem]
if curr_pos == 0:
return None
UpperCAmelCase_ =get_parent_position(_lowerCAmelCase )
UpperCAmelCase_ , UpperCAmelCase_ =self.heap[curr_pos]
UpperCAmelCase_ , UpperCAmelCase_ =self.heap[parent_position]
if parent_weight > weight:
self._swap_nodes(_lowerCAmelCase , _lowerCAmelCase )
return self._bubble_up(_lowerCAmelCase )
return None
def lowerCAmelCase__ ( self: Optional[Any] , _lowerCAmelCase: T ) -> None:
'''simple docstring'''
UpperCAmelCase_ =self.position_map[elem]
UpperCAmelCase_ , UpperCAmelCase_ =self.heap[curr_pos]
UpperCAmelCase_ =get_child_left_position(_lowerCAmelCase )
UpperCAmelCase_ =get_child_right_position(_lowerCAmelCase )
if child_left_position < self.elements and child_right_position < self.elements:
UpperCAmelCase_ , UpperCAmelCase_ =self.heap[child_left_position]
UpperCAmelCase_ , UpperCAmelCase_ =self.heap[child_right_position]
if child_right_weight < child_left_weight and child_right_weight < weight:
self._swap_nodes(_lowerCAmelCase , _lowerCAmelCase )
return self._bubble_down(_lowerCAmelCase )
if child_left_position < self.elements:
UpperCAmelCase_ , UpperCAmelCase_ =self.heap[child_left_position]
if child_left_weight < weight:
self._swap_nodes(_lowerCAmelCase , _lowerCAmelCase )
return self._bubble_down(_lowerCAmelCase )
else:
return None
if child_right_position < self.elements:
UpperCAmelCase_ , UpperCAmelCase_ =self.heap[child_right_position]
if child_right_weight < weight:
self._swap_nodes(_lowerCAmelCase , _lowerCAmelCase )
return self._bubble_down(_lowerCAmelCase )
return None
def lowerCAmelCase__ ( self: Optional[Any] , _lowerCAmelCase: int , _lowerCAmelCase: int ) -> None:
'''simple docstring'''
UpperCAmelCase_ =self.heap[nodea_pos][0]
UpperCAmelCase_ =self.heap[nodea_pos][0]
UpperCAmelCase_ , UpperCAmelCase_ =(
self.heap[nodea_pos],
self.heap[nodea_pos],
)
UpperCAmelCase_ =nodea_pos
UpperCAmelCase_ =nodea_pos
class A ( Generic[T] ):
def __init__( self: Tuple ) -> None:
'''simple docstring'''
UpperCAmelCase_ ={}
UpperCAmelCase_ =0
def __repr__( self: List[str] ) -> str:
'''simple docstring'''
return str(self.connections )
def __len__( self: Optional[Any] ) -> int:
'''simple docstring'''
return self.nodes
def lowerCAmelCase__ ( self: Union[str, Any] , _lowerCAmelCase: T ) -> None:
'''simple docstring'''
if node not in self.connections:
UpperCAmelCase_ ={}
self.nodes += 1
def lowerCAmelCase__ ( self: Optional[Any] , _lowerCAmelCase: T , _lowerCAmelCase: T , _lowerCAmelCase: int ) -> None:
'''simple docstring'''
self.add_node(_lowerCAmelCase )
self.add_node(_lowerCAmelCase )
UpperCAmelCase_ =weight
UpperCAmelCase_ =weight
def a__ ( lowercase__ , ):
'''simple docstring'''
UpperCAmelCase_ ={node: maxsize for node in graph.connections}
UpperCAmelCase_ ={node: None for node in graph.connections}
UpperCAmelCase_ =MinPriorityQueue()
for node, weight in dist.items():
priority_queue.push(lowercase__ , lowercase__ )
if priority_queue.is_empty():
return dist, parent
# initialization
UpperCAmelCase_ =priority_queue.extract_min()
UpperCAmelCase_ =0
for neighbour in graph.connections[node]:
if dist[neighbour] > dist[node] + graph.connections[node][neighbour]:
UpperCAmelCase_ =dist[node] + graph.connections[node][neighbour]
priority_queue.update_key(lowercase__ , dist[neighbour] )
UpperCAmelCase_ =node
# running prim's algorithm
while not priority_queue.is_empty():
UpperCAmelCase_ =priority_queue.extract_min()
for neighbour in graph.connections[node]:
if dist[neighbour] > dist[node] + graph.connections[node][neighbour]:
UpperCAmelCase_ =dist[node] + graph.connections[node][neighbour]
priority_queue.update_key(lowercase__ , dist[neighbour] )
UpperCAmelCase_ =node
return dist, parent
| 550
| 0
|
'''simple docstring'''
def a ( __a , __a ) -> str:
'''simple docstring'''
if not (isinstance(__a , __a ) and isinstance(__a , __a )):
raise ValueError('''longest_common_substring() takes two strings for inputs''' )
UpperCamelCase__ :str = len(__a )
UpperCamelCase__ :Any = len(__a )
UpperCamelCase__ :Union[str, Any] = [[0] * (texta_length + 1) for _ in range(texta_length + 1 )]
UpperCamelCase__ :List[str] = 0
UpperCamelCase__ :List[Any] = 0
for i in range(1 , texta_length + 1 ):
for j in range(1 , texta_length + 1 ):
if texta[i - 1] == texta[j - 1]:
UpperCamelCase__ :Optional[int] = 1 + dp[i - 1][j - 1]
if dp[i][j] > ans_length:
UpperCamelCase__ :Any = i
UpperCamelCase__ :int = dp[i][j]
return texta[ans_index - ans_length : ans_index]
if __name__ == "__main__":
import doctest
doctest.testmod()
| 189
|
'''simple docstring'''
from .glue import glue_convert_examples_to_features, glue_output_modes, glue_processors, glue_tasks_num_labels
from .squad import SquadExample, SquadFeatures, SquadVaProcessor, SquadVaProcessor, squad_convert_examples_to_features
from .utils import DataProcessor, InputExample, InputFeatures, SingleSentenceClassificationProcessor
from .xnli import xnli_output_modes, xnli_processors, xnli_tasks_num_labels
| 189
| 1
|
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_torch_available, is_vision_available
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import MgpstrProcessor, ViTImageProcessor
@require_torch
@require_vision
class __lowerCamelCase ( unittest.TestCase ):
UpperCAmelCase : str = ViTImageProcessor if is_vision_available() else None
@property
def lowerCAmelCase_ ( self ) -> str:
return self.image_processor_tester.prepare_image_processor_dict()
def lowerCAmelCase_ ( self ) -> Optional[Any]:
snake_case_ = (3, 32, 128)
snake_case_ = tempfile.mkdtemp()
# fmt: off
snake_case_ = ["""[GO]""", """[s]""", """0""", """1""", """2""", """3""", """4""", """5""", """6""", """7""", """8""", """9""", """a""", """b""", """c""", """d""", """e""", """f""", """g""", """h""", """i""", """j""", """k""", """l""", """m""", """n""", """o""", """p""", """q""", """r""", """s""", """t""", """u""", """v""", """w""", """x""", """y""", """z"""]
# fmt: on
snake_case_ = dict(zip(lowerCamelCase , range(len(lowerCamelCase ) ) ) )
snake_case_ = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["""vocab_file"""] )
with open(self.vocab_file , """w""" , encoding="""utf-8""" ) as fp:
fp.write(json.dumps(lowerCamelCase ) + """\n""" )
snake_case_ = {
"""do_normalize""": False,
"""do_resize""": True,
"""image_processor_type""": """ViTImageProcessor""",
"""resample""": 3,
"""size""": {"""height""": 32, """width""": 128},
}
snake_case_ = os.path.join(self.tmpdirname , lowerCamelCase )
with open(self.image_processor_file , """w""" , encoding="""utf-8""" ) as fp:
json.dump(lowerCamelCase , lowerCamelCase )
def lowerCAmelCase_ ( self , **lowerCamelCase ) -> Optional[int]:
return MgpstrTokenizer.from_pretrained(self.tmpdirname , **lowerCamelCase )
def lowerCAmelCase_ ( self , **lowerCamelCase ) -> Union[str, Any]:
return ViTImageProcessor.from_pretrained(self.tmpdirname , **lowerCamelCase )
def lowerCAmelCase_ ( self ) -> Union[str, Any]:
shutil.rmtree(self.tmpdirname )
def lowerCAmelCase_ ( self ) -> int:
snake_case_ = np.random.randint(255 , size=(3, 30, 400) , dtype=np.uinta )
snake_case_ = Image.fromarray(np.moveaxis(lowerCamelCase , 0 , -1 ) )
return image_input
def lowerCAmelCase_ ( self ) -> Union[str, Any]:
snake_case_ = self.get_tokenizer()
snake_case_ = self.get_image_processor()
snake_case_ = MgpstrProcessor(tokenizer=lowerCamelCase , image_processor=lowerCamelCase )
processor.save_pretrained(self.tmpdirname )
snake_case_ = MgpstrProcessor.from_pretrained(self.tmpdirname , use_fast=lowerCamelCase )
self.assertEqual(processor.char_tokenizer.get_vocab() , tokenizer.get_vocab() )
self.assertIsInstance(processor.char_tokenizer , lowerCamelCase )
self.assertEqual(processor.image_processor.to_json_string() , image_processor.to_json_string() )
self.assertIsInstance(processor.image_processor , lowerCamelCase )
def lowerCAmelCase_ ( self ) -> Dict:
snake_case_ = self.get_tokenizer()
snake_case_ = self.get_image_processor()
snake_case_ = MgpstrProcessor(tokenizer=lowerCamelCase , image_processor=lowerCamelCase )
processor.save_pretrained(self.tmpdirname )
snake_case_ = self.get_tokenizer(bos_token="""(BOS)""" , eos_token="""(EOS)""" )
snake_case_ = self.get_image_processor(do_normalize=lowerCamelCase , padding_value=1.0 )
snake_case_ = MgpstrProcessor.from_pretrained(
self.tmpdirname , bos_token="""(BOS)""" , eos_token="""(EOS)""" , do_normalize=lowerCamelCase , padding_value=1.0 )
self.assertEqual(processor.char_tokenizer.get_vocab() , tokenizer_add_kwargs.get_vocab() )
self.assertIsInstance(processor.char_tokenizer , lowerCamelCase )
self.assertEqual(processor.image_processor.to_json_string() , image_processor_add_kwargs.to_json_string() )
self.assertIsInstance(processor.image_processor , lowerCamelCase )
def lowerCAmelCase_ ( self ) -> str:
snake_case_ = self.get_image_processor()
snake_case_ = self.get_tokenizer()
snake_case_ = MgpstrProcessor(tokenizer=lowerCamelCase , image_processor=lowerCamelCase )
snake_case_ = self.prepare_image_inputs()
snake_case_ = image_processor(lowerCamelCase , return_tensors="""np""" )
snake_case_ = processor(images=lowerCamelCase , return_tensors="""np""" )
for key in input_image_proc.keys():
self.assertAlmostEqual(input_image_proc[key].sum() , input_processor[key].sum() , delta=1e-2 )
def lowerCAmelCase_ ( self ) -> str:
snake_case_ = self.get_image_processor()
snake_case_ = self.get_tokenizer()
snake_case_ = MgpstrProcessor(tokenizer=lowerCamelCase , image_processor=lowerCamelCase )
snake_case_ = """test"""
snake_case_ = processor(text=lowerCamelCase )
snake_case_ = tokenizer(lowerCamelCase )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] , encoded_processor[key] )
def lowerCAmelCase_ ( self ) -> Dict:
snake_case_ = self.get_image_processor()
snake_case_ = self.get_tokenizer()
snake_case_ = MgpstrProcessor(tokenizer=lowerCamelCase , image_processor=lowerCamelCase )
snake_case_ = """test"""
snake_case_ = self.prepare_image_inputs()
snake_case_ = processor(text=lowerCamelCase , images=lowerCamelCase )
self.assertListEqual(list(inputs.keys() ) , ["""pixel_values""", """labels"""] )
# test if it raises when no input is passed
with pytest.raises(lowerCamelCase ):
processor()
def lowerCAmelCase_ ( self ) -> Optional[Any]:
snake_case_ = self.get_image_processor()
snake_case_ = self.get_tokenizer()
snake_case_ = MgpstrProcessor(tokenizer=lowerCamelCase , image_processor=lowerCamelCase )
snake_case_ = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9], [3, 4, 3, 1, 1, 8, 9]]
snake_case_ = processor.char_decode(lowerCamelCase )
snake_case_ = tokenizer.batch_decode(lowerCamelCase )
snake_case_ = [seq.replace(""" """ , """""" ) for seq in decoded_tok]
self.assertListEqual(lowerCamelCase , lowerCamelCase )
def lowerCAmelCase_ ( self ) -> str:
snake_case_ = self.get_image_processor()
snake_case_ = self.get_tokenizer()
snake_case_ = MgpstrProcessor(tokenizer=lowerCamelCase , image_processor=lowerCamelCase )
snake_case_ = None
snake_case_ = self.prepare_image_inputs()
snake_case_ = processor(text=lowerCamelCase , images=lowerCamelCase )
self.assertListEqual(list(inputs.keys() ) , processor.model_input_names )
def lowerCAmelCase_ ( self ) -> List[Any]:
snake_case_ = self.get_image_processor()
snake_case_ = self.get_tokenizer()
snake_case_ = MgpstrProcessor(tokenizer=lowerCamelCase , image_processor=lowerCamelCase )
snake_case_ = torch.randn(1 , 27 , 38 )
snake_case_ = torch.randn(1 , 27 , 50257 )
snake_case_ = torch.randn(1 , 27 , 30522 )
snake_case_ = processor.batch_decode([char_input, bpe_input, wp_input] )
self.assertListEqual(list(results.keys() ) , ["""generated_text""", """scores""", """char_preds""", """bpe_preds""", """wp_preds"""] )
| 708
|
from itertools import count
def UpperCamelCase( lowercase_ = 50 ) -> int:
'''simple docstring'''
snake_case_ = [1] * min_block_length
for n in count(lowercase_ ):
fill_count_functions.append(1 )
for block_length in range(lowercase_ , n + 1 ):
for block_start in range(n - block_length ):
fill_count_functions[n] += fill_count_functions[
n - block_start - block_length - 1
]
fill_count_functions[n] += 1
if fill_count_functions[n] > 1000000:
break
return n
if __name__ == "__main__":
print(f"""{solution() = }""")
| 161
| 0
|
from __future__ import annotations
def __UpperCamelCase ( A ):
UpperCamelCase__ = 0.00
UpperCamelCase__ = 0
for resistor in resistors:
if resistor <= 0:
UpperCamelCase__ = f"Resistor at index {index} has a negative or zero value!"
raise ValueError(A )
first_sum += 1 / float(A )
index += 1
return 1 / first_sum
def __UpperCamelCase ( A ):
UpperCamelCase__ = 0.00
UpperCamelCase__ = 0
for resistor in resistors:
sum_r += resistor
if resistor < 0:
UpperCamelCase__ = f"Resistor at index {index} has a negative value!"
raise ValueError(A )
index += 1
return sum_r
if __name__ == "__main__":
import doctest
doctest.testmod()
| 415
|
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 DetaImageProcessor
class _A ( unittest.TestCase ):
def __init__(self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=7 , SCREAMING_SNAKE_CASE_=3 , SCREAMING_SNAKE_CASE_=30 , SCREAMING_SNAKE_CASE_=400 , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_=None , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_=[0.5, 0.5, 0.5] , SCREAMING_SNAKE_CASE_=[0.5, 0.5, 0.5] , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_=1 / 255 , SCREAMING_SNAKE_CASE_=True , ) -> List[Any]:
'''simple docstring'''
UpperCamelCase__ = size if size is not None else {'''shortest_edge''': 18, '''longest_edge''': 1333}
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 _a (self ) -> List[str]:
'''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 _a (self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=False ) -> str:
'''simple docstring'''
if not batched:
UpperCamelCase__ = image_inputs[0]
if isinstance(SCREAMING_SNAKE_CASE_ , Image.Image ):
UpperCamelCase__ , UpperCamelCase__ = image.size
else:
UpperCamelCase__ , 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__ , UpperCamelCase__ = self.get_expected_values([image] )
expected_values.append((expected_height, expected_width) )
UpperCamelCase__ = max(SCREAMING_SNAKE_CASE_ , key=lambda SCREAMING_SNAKE_CASE_ : item[0] )[0]
UpperCamelCase__ = max(SCREAMING_SNAKE_CASE_ , key=lambda SCREAMING_SNAKE_CASE_ : item[1] )[1]
return expected_height, expected_width
@require_torch
@require_vision
class _A ( __UpperCamelCase , unittest.TestCase ):
SCREAMING_SNAKE_CASE_ : Optional[int] =DetaImageProcessor if is_vision_available() else None
def _a (self ) -> Tuple:
'''simple docstring'''
UpperCamelCase__ = DetaImageProcessingTester(self )
@property
def _a (self ) -> List[str]:
'''simple docstring'''
return self.image_processor_tester.prepare_image_processor_dict()
def _a (self ) -> Dict:
'''simple docstring'''
UpperCamelCase__ = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_ , '''image_mean''' ) )
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_ , '''image_std''' ) )
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_ , '''do_normalize''' ) )
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_ , '''do_resize''' ) )
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_ , '''do_rescale''' ) )
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_ , '''do_pad''' ) )
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_ , '''size''' ) )
def _a (self ) -> Dict:
'''simple docstring'''
UpperCamelCase__ = 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 , SCREAMING_SNAKE_CASE_ )
def _a (self ) -> List[Any]:
'''simple docstring'''
pass
def _a (self ) -> List[str]:
'''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=SCREAMING_SNAKE_CASE_ )
for image in image_inputs:
self.assertIsInstance(SCREAMING_SNAKE_CASE_ , Image.Image )
# Test not batched input
UpperCamelCase__ = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
UpperCamelCase__ , UpperCamelCase__ = self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE_ )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
UpperCamelCase__ , UpperCamelCase__ = self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE_ , batched=SCREAMING_SNAKE_CASE_ )
UpperCamelCase__ = image_processing(SCREAMING_SNAKE_CASE_ , 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 _a (self ) -> 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=SCREAMING_SNAKE_CASE_ , numpify=SCREAMING_SNAKE_CASE_ )
for image in image_inputs:
self.assertIsInstance(SCREAMING_SNAKE_CASE_ , np.ndarray )
# Test not batched input
UpperCamelCase__ = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
UpperCamelCase__ , UpperCamelCase__ = self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE_ )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
UpperCamelCase__ = image_processing(SCREAMING_SNAKE_CASE_ , return_tensors='''pt''' ).pixel_values
UpperCamelCase__ , UpperCamelCase__ = self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE_ , batched=SCREAMING_SNAKE_CASE_ )
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
def _a (self ) -> Optional[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=SCREAMING_SNAKE_CASE_ , torchify=SCREAMING_SNAKE_CASE_ )
for image in image_inputs:
self.assertIsInstance(SCREAMING_SNAKE_CASE_ , torch.Tensor )
# Test not batched input
UpperCamelCase__ = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
UpperCamelCase__ , UpperCamelCase__ = self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE_ )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
UpperCamelCase__ = image_processing(SCREAMING_SNAKE_CASE_ , return_tensors='''pt''' ).pixel_values
UpperCamelCase__ , UpperCamelCase__ = self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE_ , batched=SCREAMING_SNAKE_CASE_ )
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
@slow
def _a (self ) -> Optional[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_9769, '''annotations''': target}
# encode them
UpperCamelCase__ = DetaImageProcessor()
UpperCamelCase__ = image_processing(images=SCREAMING_SNAKE_CASE_ , annotations=SCREAMING_SNAKE_CASE_ , return_tensors='''pt''' )
# verify pixel values
UpperCamelCase__ = torch.Size([1, 3, 800, 1066] )
self.assertEqual(encoding['''pixel_values'''].shape , SCREAMING_SNAKE_CASE_ )
UpperCamelCase__ = torch.tensor([0.2796, 0.3138, 0.3481] )
self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3] , SCREAMING_SNAKE_CASE_ , atol=1E-4 ) )
# verify area
UpperCamelCase__ = torch.tensor([5887.9600, 11250.2061, 489353.8438, 837122.7500, 147967.5156, 165732.3438] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area'''] , SCREAMING_SNAKE_CASE_ ) )
# verify boxes
UpperCamelCase__ = torch.Size([6, 4] )
self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape , SCREAMING_SNAKE_CASE_ )
UpperCamelCase__ = torch.tensor([0.5503, 0.2765, 0.0604, 0.2215] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0] , SCREAMING_SNAKE_CASE_ , atol=1E-3 ) )
# verify image_id
UpperCamelCase__ = torch.tensor([3_9769] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id'''] , SCREAMING_SNAKE_CASE_ ) )
# verify is_crowd
UpperCamelCase__ = torch.tensor([0, 0, 0, 0, 0, 0] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd'''] , SCREAMING_SNAKE_CASE_ ) )
# verify class_labels
UpperCamelCase__ = torch.tensor([75, 75, 63, 65, 17, 17] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels'''] , SCREAMING_SNAKE_CASE_ ) )
# verify orig_size
UpperCamelCase__ = torch.tensor([480, 640] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size'''] , SCREAMING_SNAKE_CASE_ ) )
# verify size
UpperCamelCase__ = torch.tensor([800, 1066] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size'''] , SCREAMING_SNAKE_CASE_ ) )
@slow
def _a (self ) -> Optional[Any]:
'''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_9769, '''segments_info''': target}
UpperCamelCase__ = pathlib.Path('''./tests/fixtures/tests_samples/COCO/coco_panoptic''' )
# encode them
UpperCamelCase__ = DetaImageProcessor(format='''coco_panoptic''' )
UpperCamelCase__ = image_processing(images=SCREAMING_SNAKE_CASE_ , annotations=SCREAMING_SNAKE_CASE_ , masks_path=SCREAMING_SNAKE_CASE_ , return_tensors='''pt''' )
# verify pixel values
UpperCamelCase__ = torch.Size([1, 3, 800, 1066] )
self.assertEqual(encoding['''pixel_values'''].shape , SCREAMING_SNAKE_CASE_ )
UpperCamelCase__ = torch.tensor([0.2796, 0.3138, 0.3481] )
self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3] , SCREAMING_SNAKE_CASE_ , atol=1E-4 ) )
# verify area
UpperCamelCase__ = torch.tensor([147979.6875, 165527.0469, 484638.5938, 11292.9375, 5879.6562, 7634.1147] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area'''] , SCREAMING_SNAKE_CASE_ ) )
# verify boxes
UpperCamelCase__ = torch.Size([6, 4] )
self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape , SCREAMING_SNAKE_CASE_ )
UpperCamelCase__ = torch.tensor([0.2625, 0.5437, 0.4688, 0.8625] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0] , SCREAMING_SNAKE_CASE_ , atol=1E-3 ) )
# verify image_id
UpperCamelCase__ = torch.tensor([3_9769] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id'''] , SCREAMING_SNAKE_CASE_ ) )
# verify is_crowd
UpperCamelCase__ = torch.tensor([0, 0, 0, 0, 0, 0] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd'''] , SCREAMING_SNAKE_CASE_ ) )
# verify class_labels
UpperCamelCase__ = torch.tensor([17, 17, 63, 75, 75, 93] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels'''] , SCREAMING_SNAKE_CASE_ ) )
# verify masks
UpperCamelCase__ = 82_2873
self.assertEqual(encoding['''labels'''][0]['''masks'''].sum().item() , SCREAMING_SNAKE_CASE_ )
# verify orig_size
UpperCamelCase__ = torch.tensor([480, 640] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size'''] , SCREAMING_SNAKE_CASE_ ) )
# verify size
UpperCamelCase__ = torch.tensor([800, 1066] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size'''] , SCREAMING_SNAKE_CASE_ ) )
| 415
| 1
|
'''simple docstring'''
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
from ...image_utils import (
IMAGENET_STANDARD_MEAN,
IMAGENET_STANDARD_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_vision_available, logging
if is_vision_available():
import PIL
__A : List[str] = logging.get_logger(__name__)
class __snake_case ( _SCREAMING_SNAKE_CASE):
"""simple docstring"""
lowercase = ['pixel_values']
def __init__( self : Optional[Any] , lowerCamelCase : bool = True , lowerCamelCase : Dict[str, int] = None , lowerCamelCase : float = None , lowerCamelCase : PILImageResampling = PILImageResampling.BILINEAR , lowerCamelCase : bool = True , lowerCamelCase : Union[int, float] = 1 / 2_55 , lowerCamelCase : bool = True , lowerCamelCase : Optional[Union[float, List[float]]] = None , lowerCamelCase : Optional[Union[float, List[float]]] = None , **lowerCamelCase : Union[str, Any] , ) -> None:
super().__init__(**lowerCamelCase )
lowerCAmelCase_ : Dict = size if size is not None else {"""shortest_edge""": 3_84}
lowerCAmelCase_ : Any = get_size_dict(lowerCamelCase , default_to_square=lowerCamelCase )
lowerCAmelCase_ : Optional[int] = do_resize
lowerCAmelCase_ : Tuple = size
# Default value set here for backwards compatibility where the value in config is None
lowerCAmelCase_ : Any = crop_pct if crop_pct is not None else 2_24 / 2_56
lowerCAmelCase_ : Union[str, Any] = resample
lowerCAmelCase_ : Any = do_rescale
lowerCAmelCase_ : Dict = rescale_factor
lowerCAmelCase_ : Any = do_normalize
lowerCAmelCase_ : Tuple = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
lowerCAmelCase_ : int = image_std if image_std is not None else IMAGENET_STANDARD_STD
def __lowercase ( self : Tuple , lowerCamelCase : np.ndarray , lowerCamelCase : Dict[str, int] , lowerCamelCase : float , lowerCamelCase : PILImageResampling = PILImageResampling.BICUBIC , lowerCamelCase : Optional[Union[str, ChannelDimension]] = None , **lowerCamelCase : List[str] , ) -> np.ndarray:
lowerCAmelCase_ : str = get_size_dict(lowerCamelCase , default_to_square=lowerCamelCase )
if "shortest_edge" not in size:
raise ValueError(F'Size dictionary must contain \'shortest_edge\' key. Got {size.keys()}' )
lowerCAmelCase_ : Optional[int] = size["""shortest_edge"""]
if shortest_edge < 3_84:
# maintain same ratio, resizing shortest edge to shortest_edge/crop_pct
lowerCAmelCase_ : Any = int(shortest_edge / crop_pct )
lowerCAmelCase_ : Dict = get_resize_output_image_size(lowerCamelCase , size=lowerCamelCase , default_to_square=lowerCamelCase )
lowerCAmelCase_ : str = resize(image=lowerCamelCase , size=lowerCamelCase , resample=lowerCamelCase , data_format=lowerCamelCase , **lowerCamelCase )
# then crop to (shortest_edge, shortest_edge)
return center_crop(image=lowerCamelCase , size=(shortest_edge, shortest_edge) , data_format=lowerCamelCase , **lowerCamelCase )
else:
# warping (no cropping) when evaluated at 384 or larger
return resize(
lowerCamelCase , size=(shortest_edge, shortest_edge) , resample=lowerCamelCase , data_format=lowerCamelCase , **lowerCamelCase )
def __lowercase ( self : Any , lowerCamelCase : np.ndarray , lowerCamelCase : Union[int, float] , lowerCamelCase : Optional[Union[str, ChannelDimension]] = None , **lowerCamelCase : Any , ) -> Optional[Any]:
return rescale(lowerCamelCase , scale=lowerCamelCase , data_format=lowerCamelCase , **lowerCamelCase )
def __lowercase ( self : Tuple , lowerCamelCase : np.ndarray , lowerCamelCase : Union[float, List[float]] , lowerCamelCase : Union[float, List[float]] , lowerCamelCase : Optional[Union[str, ChannelDimension]] = None , **lowerCamelCase : Optional[int] , ) -> np.ndarray:
return normalize(lowerCamelCase , mean=lowerCamelCase , std=lowerCamelCase , data_format=lowerCamelCase , **lowerCamelCase )
def __lowercase ( self : List[Any] , lowerCamelCase : ImageInput , lowerCamelCase : bool = None , lowerCamelCase : Dict[str, int] = None , lowerCamelCase : float = None , lowerCamelCase : PILImageResampling = None , lowerCamelCase : bool = None , lowerCamelCase : float = None , lowerCamelCase : bool = None , lowerCamelCase : Optional[Union[float, List[float]]] = None , lowerCamelCase : Optional[Union[float, List[float]]] = None , lowerCamelCase : Optional[Union[str, TensorType]] = None , lowerCamelCase : ChannelDimension = ChannelDimension.FIRST , **lowerCamelCase : str , ) -> PIL.Image.Image:
lowerCAmelCase_ : Tuple = do_resize if do_resize is not None else self.do_resize
lowerCAmelCase_ : List[str] = crop_pct if crop_pct is not None else self.crop_pct
lowerCAmelCase_ : List[str] = resample if resample is not None else self.resample
lowerCAmelCase_ : Any = do_rescale if do_rescale is not None else self.do_rescale
lowerCAmelCase_ : Any = rescale_factor if rescale_factor is not None else self.rescale_factor
lowerCAmelCase_ : int = do_normalize if do_normalize is not None else self.do_normalize
lowerCAmelCase_ : List[Any] = image_mean if image_mean is not None else self.image_mean
lowerCAmelCase_ : Optional[Any] = image_std if image_std is not None else self.image_std
lowerCAmelCase_ : Tuple = size if size is not None else self.size
lowerCAmelCase_ : str = get_size_dict(lowerCamelCase , default_to_square=lowerCamelCase )
lowerCAmelCase_ : List[str] = 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_resize and size["shortest_edge"] < 3_84 and crop_pct is None:
raise ValueError("""crop_pct must be specified if size < 384.""" )
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.
lowerCAmelCase_ : Tuple = [to_numpy_array(lowerCamelCase ) for image in images]
if do_resize:
lowerCAmelCase_ : Union[str, Any] = [self.resize(image=lowerCamelCase , size=lowerCamelCase , crop_pct=lowerCamelCase , resample=lowerCamelCase ) for image in images]
if do_rescale:
lowerCAmelCase_ : List[str] = [self.rescale(image=lowerCamelCase , scale=lowerCamelCase ) for image in images]
if do_normalize:
lowerCAmelCase_ : Optional[Any] = [self.normalize(image=lowerCamelCase , mean=lowerCamelCase , std=lowerCamelCase ) for image in images]
lowerCAmelCase_ : str = [to_channel_dimension_format(lowerCamelCase , lowerCamelCase ) for image in images]
lowerCAmelCase_ : Optional[Any] = {"""pixel_values""": images}
return BatchFeature(data=lowerCamelCase , tensor_type=lowerCamelCase )
| 398
|
'''simple docstring'''
from typing import List, Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A : str = logging.get_logger(__name__)
__A : str = {
"huggingface/autoformer-tourism-monthly": "https://huggingface.co/huggingface/autoformer-tourism-monthly/resolve/main/config.json",
}
class __snake_case ( _SCREAMING_SNAKE_CASE):
"""simple docstring"""
lowercase = 'autoformer'
lowercase = {
'hidden_size': 'd_model',
'num_attention_heads': 'encoder_attention_heads',
'num_hidden_layers': 'encoder_layers',
}
def __init__( self : List[str] , lowerCamelCase : Optional[int] = None , lowerCamelCase : Optional[int] = None , lowerCamelCase : str = "student_t" , lowerCamelCase : str = "nll" , lowerCamelCase : int = 1 , lowerCamelCase : List[int] = [1, 2, 3, 4, 5, 6, 7] , lowerCamelCase : bool = True , lowerCamelCase : int = 0 , lowerCamelCase : int = 0 , lowerCamelCase : int = 0 , lowerCamelCase : int = 0 , lowerCamelCase : Optional[List[int]] = None , lowerCamelCase : Optional[List[int]] = None , lowerCamelCase : int = 64 , lowerCamelCase : int = 2 , lowerCamelCase : int = 2 , lowerCamelCase : int = 2 , lowerCamelCase : int = 2 , lowerCamelCase : int = 32 , lowerCamelCase : int = 32 , lowerCamelCase : str = "gelu" , lowerCamelCase : float = 0.1 , lowerCamelCase : float = 0.1 , lowerCamelCase : float = 0.1 , lowerCamelCase : float = 0.1 , lowerCamelCase : float = 0.1 , lowerCamelCase : int = 1_00 , lowerCamelCase : float = 0.02 , lowerCamelCase : bool = True , lowerCamelCase : Optional[int]=True , lowerCamelCase : int = 10 , lowerCamelCase : int = 25 , lowerCamelCase : int = 3 , **lowerCamelCase : Dict , ) -> List[Any]:
# time series specific configuration
lowerCAmelCase_ : str = prediction_length
lowerCAmelCase_ : str = context_length if context_length is not None else prediction_length
lowerCAmelCase_ : List[Any] = distribution_output
lowerCAmelCase_ : Optional[Any] = loss
lowerCAmelCase_ : List[str] = input_size
lowerCAmelCase_ : Optional[int] = num_time_features
lowerCAmelCase_ : List[Any] = lags_sequence
lowerCAmelCase_ : str = scaling
lowerCAmelCase_ : Optional[Any] = num_dynamic_real_features
lowerCAmelCase_ : str = num_static_real_features
lowerCAmelCase_ : Dict = num_static_categorical_features
if cardinality is not None and num_static_categorical_features > 0:
if len(lowerCamelCase ) != num_static_categorical_features:
raise ValueError(
"""The cardinality should be a list of the same length as `num_static_categorical_features`""" )
lowerCAmelCase_ : Union[str, Any] = cardinality
else:
lowerCAmelCase_ : int = [0]
if embedding_dimension is not None and num_static_categorical_features > 0:
if len(lowerCamelCase ) != num_static_categorical_features:
raise ValueError(
"""The embedding dimension should be a list of the same length as `num_static_categorical_features`""" )
lowerCAmelCase_ : Optional[int] = embedding_dimension
else:
lowerCAmelCase_ : Any = [min(50 , (cat + 1) // 2 ) for cat in self.cardinality]
lowerCAmelCase_ : List[str] = num_parallel_samples
# Transformer architecture configuration
lowerCAmelCase_ : Any = input_size * len(self.lags_sequence ) + self._number_of_features
lowerCAmelCase_ : List[str] = d_model
lowerCAmelCase_ : Optional[int] = encoder_attention_heads
lowerCAmelCase_ : List[str] = decoder_attention_heads
lowerCAmelCase_ : Union[str, Any] = encoder_ffn_dim
lowerCAmelCase_ : List[Any] = decoder_ffn_dim
lowerCAmelCase_ : Dict = encoder_layers
lowerCAmelCase_ : int = decoder_layers
lowerCAmelCase_ : Tuple = dropout
lowerCAmelCase_ : Optional[Any] = attention_dropout
lowerCAmelCase_ : str = activation_dropout
lowerCAmelCase_ : List[Any] = encoder_layerdrop
lowerCAmelCase_ : Union[str, Any] = decoder_layerdrop
lowerCAmelCase_ : Optional[int] = activation_function
lowerCAmelCase_ : Union[str, Any] = init_std
lowerCAmelCase_ : Union[str, Any] = use_cache
# Autoformer
lowerCAmelCase_ : Optional[Any] = label_length
lowerCAmelCase_ : List[Any] = moving_average
lowerCAmelCase_ : List[str] = autocorrelation_factor
super().__init__(is_encoder_decoder=lowerCamelCase , **lowerCamelCase )
@property
def __lowercase ( self : Union[str, Any] ) -> int:
return (
sum(self.embedding_dimension )
+ self.num_dynamic_real_features
+ self.num_time_features
+ self.num_static_real_features
+ self.input_size * 2 # the log1p(abs(loc)) and log(scale) features
)
| 398
| 1
|
'''simple docstring'''
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
_UpperCAmelCase : List[str] = [
{"""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 _SCREAMING_SNAKE_CASE ( __snake_case : str=True ):
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=snake_case__ ) )
class lowercase_ ( snake_case__ ):
"""simple docstring"""
__lowerCAmelCase = None
__lowerCAmelCase = None
def __UpperCAmelCase ( self : int, UpperCamelCase__ : Any, UpperCamelCase__ : str ) -> int:
with TemporaryDirectory() as tmp_dir:
_A = dataset_module_factory(UpperCamelCase__, cache_dir=UpperCamelCase__ )
_A = import_main_class(dataset_module.module_path, dataset=UpperCamelCase__ )
_A = builder_cls(
cache_dir=UpperCamelCase__, config_name=UpperCamelCase__, hash=dataset_module.hash, )
_A = "/".join(
[
HF_GCP_BASE_URL,
builder_instance._relative_data_dir(with_hash=UpperCamelCase__ ).replace(os.sep, '/' ),
config.DATASET_INFO_FILENAME,
] )
_A = cached_path(UpperCamelCase__, cache_dir=UpperCamelCase__ )
self.assertTrue(os.path.exists(UpperCamelCase__ ) )
@pytest.mark.integration
def _SCREAMING_SNAKE_CASE ( __snake_case : str ):
_A = tmp_path_factory.mktemp('test_hf_gcp' ) / "test_wikipedia_simple"
_A = dataset_module_factory('wikipedia' , cache_dir=_UpperCAmelCase )
_A = import_main_class(dataset_module.module_path )
_A = builder_cls(
cache_dir=_UpperCAmelCase , config_name='20220301.frr' , hash=dataset_module.hash , )
# use the HF cloud storage, not the original download_and_prepare that uses apache-beam
_A = None
builder_instance.download_and_prepare()
_A = builder_instance.as_dataset()
assert ds
@pytest.mark.integration
def _SCREAMING_SNAKE_CASE ( __snake_case : List[Any] ):
_A = dataset_module_factory('wikipedia' , cache_dir=_UpperCAmelCase )
_A = import_main_class(dataset_module.module_path , dataset=_UpperCAmelCase )
_A = builder_cls(
cache_dir=_UpperCAmelCase , config_name='20220301.frr' , hash=dataset_module.hash , )
_A = builder_instance.as_streaming_dataset()
assert ds
assert isinstance(_UpperCAmelCase , _UpperCAmelCase )
assert "train" in ds
assert isinstance(ds['train'] , _UpperCAmelCase )
assert next(iter(ds['train'] ) )
| 107
|
'''simple docstring'''
import torch
from diffusers import DDPMScheduler
from .test_schedulers import SchedulerCommonTest
class lowerCamelCase_ (snake_case__ ):
'''simple docstring'''
__UpperCamelCase: Union[str, Any] = (DDPMScheduler,)
def _A ( self : Any , **A : List[str] ):
_UpperCAmelCase : int = {
"num_train_timesteps": 1000,
"beta_start": 0.0_001,
"beta_end": 0.02,
"beta_schedule": "linear",
"variance_type": "fixed_small",
"clip_sample": True,
}
config.update(**A )
return config
def _A ( self : List[Any] ):
for timesteps in [1, 5, 100, 1000]:
self.check_over_configs(num_train_timesteps=A )
def _A ( self : Union[str, Any] ):
for beta_start, beta_end in zip([0.0_001, 0.001, 0.01, 0.1] , [0.002, 0.02, 0.2, 2] ):
self.check_over_configs(beta_start=A , beta_end=A )
def _A ( self : Optional[Any] ):
for schedule in ["linear", "squaredcos_cap_v2"]:
self.check_over_configs(beta_schedule=A )
def _A ( self : int ):
for variance in ["fixed_small", "fixed_large", "other"]:
self.check_over_configs(variance_type=A )
def _A ( self : Any ):
for clip_sample in [True, False]:
self.check_over_configs(clip_sample=A )
def _A ( self : Union[str, Any] ):
self.check_over_configs(thresholding=A )
for threshold in [0.5, 1.0, 2.0]:
for prediction_type in ["epsilon", "sample", "v_prediction"]:
self.check_over_configs(
thresholding=A , prediction_type=A , sample_max_value=A , )
def _A ( self : List[str] ):
for prediction_type in ["epsilon", "sample", "v_prediction"]:
self.check_over_configs(prediction_type=A )
def _A ( self : Union[str, Any] ):
for t in [0, 500, 999]:
self.check_over_forward(time_step=A )
def _A ( self : Tuple ):
_UpperCAmelCase : Union[str, Any] = self.scheduler_classes[0]
_UpperCAmelCase : List[Any] = self.get_scheduler_config()
_UpperCAmelCase : int = scheduler_class(**A )
assert torch.sum(torch.abs(scheduler._get_variance(0 ) - 0.0 ) ) < 1E-5
assert torch.sum(torch.abs(scheduler._get_variance(487 ) - 0.00_979 ) ) < 1E-5
assert torch.sum(torch.abs(scheduler._get_variance(999 ) - 0.02 ) ) < 1E-5
def _A ( self : List[Any] ):
_UpperCAmelCase : Optional[Any] = self.scheduler_classes[0]
_UpperCAmelCase : Optional[Any] = self.get_scheduler_config()
_UpperCAmelCase : int = scheduler_class(**A )
_UpperCAmelCase : Optional[Any] = len(A )
_UpperCAmelCase : List[Any] = self.dummy_model()
_UpperCAmelCase : List[str] = self.dummy_sample_deter
_UpperCAmelCase : List[str] = torch.manual_seed(0 )
for t in reversed(range(A ) ):
# 1. predict noise residual
_UpperCAmelCase : List[Any] = model(A , A )
# 2. predict previous mean of sample x_t-1
_UpperCAmelCase : List[Any] = scheduler.step(A , A , A , generator=A ).prev_sample
# if t > 0:
# noise = self.dummy_sample_deter
# variance = scheduler.get_variance(t) ** (0.5) * noise
#
# sample = pred_prev_sample + variance
_UpperCAmelCase : Any = pred_prev_sample
_UpperCAmelCase : str = torch.sum(torch.abs(A ) )
_UpperCAmelCase : Tuple = torch.mean(torch.abs(A ) )
assert abs(result_sum.item() - 258.9_606 ) < 1E-2
assert abs(result_mean.item() - 0.3_372 ) < 1E-3
def _A ( self : Union[str, Any] ):
_UpperCAmelCase : List[Any] = self.scheduler_classes[0]
_UpperCAmelCase : Dict = self.get_scheduler_config(prediction_type="v_prediction" )
_UpperCAmelCase : Optional[int] = scheduler_class(**A )
_UpperCAmelCase : Union[str, Any] = len(A )
_UpperCAmelCase : Optional[int] = self.dummy_model()
_UpperCAmelCase : Optional[Any] = self.dummy_sample_deter
_UpperCAmelCase : List[Any] = torch.manual_seed(0 )
for t in reversed(range(A ) ):
# 1. predict noise residual
_UpperCAmelCase : Tuple = model(A , A )
# 2. predict previous mean of sample x_t-1
_UpperCAmelCase : List[Any] = scheduler.step(A , A , A , generator=A ).prev_sample
# if t > 0:
# noise = self.dummy_sample_deter
# variance = scheduler.get_variance(t) ** (0.5) * noise
#
# sample = pred_prev_sample + variance
_UpperCAmelCase : Tuple = pred_prev_sample
_UpperCAmelCase : List[str] = torch.sum(torch.abs(A ) )
_UpperCAmelCase : int = torch.mean(torch.abs(A ) )
assert abs(result_sum.item() - 202.0_296 ) < 1E-2
assert abs(result_mean.item() - 0.2_631 ) < 1E-3
def _A ( self : Optional[Any] ):
_UpperCAmelCase : Optional[Any] = self.scheduler_classes[0]
_UpperCAmelCase : Optional[int] = self.get_scheduler_config()
_UpperCAmelCase : int = scheduler_class(**A )
_UpperCAmelCase : Any = [100, 87, 50, 1, 0]
scheduler.set_timesteps(timesteps=A )
_UpperCAmelCase : Optional[Any] = scheduler.timesteps
for i, timestep in enumerate(A ):
if i == len(A ) - 1:
_UpperCAmelCase : int = -1
else:
_UpperCAmelCase : str = timesteps[i + 1]
_UpperCAmelCase : Any = scheduler.previous_timestep(A )
_UpperCAmelCase : Optional[Any] = prev_t.item()
self.assertEqual(A , A )
def _A ( self : Optional[int] ):
_UpperCAmelCase : List[Any] = self.scheduler_classes[0]
_UpperCAmelCase : Union[str, Any] = self.get_scheduler_config()
_UpperCAmelCase : Optional[Any] = scheduler_class(**A )
_UpperCAmelCase : Optional[int] = [100, 87, 50, 51, 0]
with self.assertRaises(A , msg="`custom_timesteps` must be in descending order." ):
scheduler.set_timesteps(timesteps=A )
def _A ( self : Dict ):
_UpperCAmelCase : Optional[int] = self.scheduler_classes[0]
_UpperCAmelCase : Tuple = self.get_scheduler_config()
_UpperCAmelCase : str = scheduler_class(**A )
_UpperCAmelCase : str = [100, 87, 50, 1, 0]
_UpperCAmelCase : Tuple = len(A )
with self.assertRaises(A , msg="Can only pass one of `num_inference_steps` or `custom_timesteps`." ):
scheduler.set_timesteps(num_inference_steps=A , timesteps=A )
def _A ( self : List[str] ):
_UpperCAmelCase : List[str] = self.scheduler_classes[0]
_UpperCAmelCase : str = self.get_scheduler_config()
_UpperCAmelCase : int = scheduler_class(**A )
_UpperCAmelCase : List[Any] = [scheduler.config.num_train_timesteps]
with self.assertRaises(
A , msg="`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}" , ):
scheduler.set_timesteps(timesteps=A )
| 244
| 0
|
import string
def UpperCamelCase ( _A : str )-> None:
"""simple docstring"""
for key in range(len(string.ascii_uppercase ) ):
A__ = ""
for symbol in message:
if symbol in string.ascii_uppercase:
A__ = string.ascii_uppercase.find(_A )
A__ = num - key
if num < 0:
A__ = num + len(string.ascii_uppercase )
A__ = translated + string.ascii_uppercase[num]
else:
A__ = translated + symbol
print(f"""Decryption using Key #{key}: {translated}""" )
def UpperCamelCase ( )-> None:
"""simple docstring"""
A__ = input("Encrypted message: " )
A__ = message.upper()
decrypt(_A )
if __name__ == "__main__":
import doctest
doctest.testmod()
main()
| 711
|
import importlib
import inspect
import os
import re
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstrings.py
UpperCAmelCase_ : Optional[Any] = "src/transformers"
# This is to make sure the transformers module imported is the one in the repo.
UpperCAmelCase_ : Optional[Any] = importlib.util.spec_from_file_location(
"transformers",
os.path.join(PATH_TO_TRANSFORMERS, "__init__.py"),
submodule_search_locations=[PATH_TO_TRANSFORMERS],
)
UpperCAmelCase_ : Dict = spec.loader.load_module()
UpperCAmelCase_ : Optional[int] = transformers.models.auto.configuration_auto.CONFIG_MAPPING
# Regex pattern used to find the checkpoint mentioned in the docstring of `config_class`.
# For example, `[bert-base-uncased](https://huggingface.co/bert-base-uncased)`
UpperCAmelCase_ : Optional[int] = re.compile("\[(.+?)\]\((https://huggingface\.co/.+?)\)")
UpperCAmelCase_ : str = {
"CLIPConfigMixin",
"DecisionTransformerConfigMixin",
"EncoderDecoderConfigMixin",
"RagConfigMixin",
"SpeechEncoderDecoderConfigMixin",
"VisionEncoderDecoderConfigMixin",
"VisionTextDualEncoderConfigMixin",
}
def UpperCamelCase ( )-> Union[str, Any]:
"""simple docstring"""
A__ = []
for config_class in list(CONFIG_MAPPING.values() ):
A__ = False
# source code of `config_class`
A__ = inspect.getsource(_A )
A__ = _re_checkpoint.findall(_A )
for checkpoint in checkpoints:
# Each `checkpoint` is a tuple of a checkpoint name and a checkpoint link.
# For example, `('bert-base-uncased', 'https://huggingface.co/bert-base-uncased')`
A__ , A__ = checkpoint
# verify the checkpoint name corresponds to the checkpoint link
A__ = f"""https://huggingface.co/{ckpt_name}"""
if ckpt_link == ckpt_link_from_name:
A__ = True
break
A__ = config_class.__name__
if not checkpoint_found and name not in CONFIG_CLASSES_TO_IGNORE_FOR_DOCSTRING_CHECKPOINT_CHECK:
configs_without_checkpoint.append(_A )
if len(_A ) > 0:
A__ = "\n".join(sorted(_A ) )
raise ValueError(f"""The following configurations don't contain any valid checkpoint:\n{message}""" )
if __name__ == "__main__":
check_config_docstrings_have_checkpoints()
| 232
| 0
|
from __future__ import annotations
import unittest
from transformers import LEDConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import TFLEDForConditionalGeneration, TFLEDModel
@require_tf
class A_ :
'''simple docstring'''
_SCREAMING_SNAKE_CASE : List[Any] = LEDConfig
_SCREAMING_SNAKE_CASE : Union[str, Any] = {}
_SCREAMING_SNAKE_CASE : Union[str, Any] = "gelu"
def __init__( self , _A , _A=13 , _A=7 , _A=True , _A=False , _A=99 , _A=32 , _A=2 , _A=4 , _A=37 , _A=0.1 , _A=0.1 , _A=20 , _A=2 , _A=1 , _A=0 , _A=4 , ) -> Dict:
"""simple docstring"""
_UpperCAmelCase : Dict = parent
_UpperCAmelCase : str = batch_size
_UpperCAmelCase : Tuple = seq_length
_UpperCAmelCase : Tuple = is_training
_UpperCAmelCase : Tuple = use_labels
_UpperCAmelCase : Union[str, Any] = vocab_size
_UpperCAmelCase : Dict = hidden_size
_UpperCAmelCase : Dict = num_hidden_layers
_UpperCAmelCase : Dict = num_attention_heads
_UpperCAmelCase : Optional[Any] = intermediate_size
_UpperCAmelCase : List[Any] = hidden_dropout_prob
_UpperCAmelCase : Optional[int] = attention_probs_dropout_prob
_UpperCAmelCase : str = max_position_embeddings
_UpperCAmelCase : List[Any] = eos_token_id
_UpperCAmelCase : Any = pad_token_id
_UpperCAmelCase : List[Any] = bos_token_id
_UpperCAmelCase : Tuple = attention_window
# `ModelTesterMixin.test_attention_outputs` is expecting attention tensors to be of size
# [num_attention_heads, encoder_seq_length, encoder_key_length], but TFLongformerSelfAttention
# returns attention of shape [num_attention_heads, encoder_seq_length, self.attention_window + 1]
# because its local attention only attends to `self.attention_window` and one before and one after
_UpperCAmelCase : int = self.attention_window + 2
# because of padding `encoder_seq_length`, is different from `seq_length`. Relevant for
# the `test_attention_outputs` and `test_hidden_states_output` tests
_UpperCAmelCase : Any = (
self.seq_length + (self.attention_window - self.seq_length % self.attention_window) % self.attention_window
)
def snake_case__ ( self) -> int:
"""simple docstring"""
_UpperCAmelCase : List[str] = ids_tensor([self.batch_size, self.seq_length - 1] , self.vocab_size)
_UpperCAmelCase : Optional[int] = tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size) , 1)
_UpperCAmelCase : Tuple = tf.concat([input_ids, eos_tensor] , axis=1)
_UpperCAmelCase : Optional[Any] = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size)
_UpperCAmelCase : List[str] = self.config_cls(
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_ids=[2] , bos_token_id=self.bos_token_id , pad_token_id=self.pad_token_id , decoder_start_token_id=self.pad_token_id , attention_window=self.attention_window , **self.config_updates , )
_UpperCAmelCase : List[str] = prepare_led_inputs_dict(_A , _A , _A)
_UpperCAmelCase : Any = tf.concat(
[tf.zeros_like(_A)[:, :-1], tf.ones_like(_A)[:, -1:]] , axis=-1 , )
_UpperCAmelCase : List[Any] = global_attention_mask
return config, inputs_dict
def snake_case__ ( self , _A , _A) -> Any:
"""simple docstring"""
_UpperCAmelCase : Optional[int] = TFLEDModel(config=_A).get_decoder()
_UpperCAmelCase : Any = inputs_dict['''input_ids''']
_UpperCAmelCase : Optional[int] = input_ids[:1, :]
_UpperCAmelCase : Any = inputs_dict['''attention_mask'''][:1, :]
_UpperCAmelCase : int = 1
# first forward pass
_UpperCAmelCase : Union[str, Any] = model(_A , attention_mask=_A , use_cache=_A)
_UpperCAmelCase , _UpperCAmelCase : Optional[int] = outputs.to_tuple()
# create hypothetical next token and extent to next_input_ids
_UpperCAmelCase : Optional[int] = ids_tensor((self.batch_size, 3) , config.vocab_size)
_UpperCAmelCase : Tuple = tf.cast(ids_tensor((self.batch_size, 3) , 2) , tf.inta)
# append to next input_ids and
_UpperCAmelCase : List[str] = tf.concat([input_ids, next_tokens] , axis=-1)
_UpperCAmelCase : int = tf.concat([attention_mask, next_attn_mask] , axis=-1)
_UpperCAmelCase : str = model(_A , attention_mask=_A)[0]
_UpperCAmelCase : Optional[Any] = model(_A , attention_mask=_A , past_key_values=_A)[0]
self.parent.assertEqual(next_tokens.shape[1] , output_from_past.shape[1])
# select random slice
_UpperCAmelCase : List[str] = int(ids_tensor((1,) , output_from_past.shape[-1]))
_UpperCAmelCase : int = output_from_no_past[:, -3:, random_slice_idx]
_UpperCAmelCase : List[str] = output_from_past[:, :, random_slice_idx]
# test that outputs are equal for slice
tf.debugging.assert_near(_A , _A , rtol=1e-3)
def _lowerCamelCase ( __A : Union[str, Any] , __A : Tuple , __A : int , __A : Optional[Any]=None , __A : str=None , __A : List[Any]=None , __A : List[str]=None , ) -> str:
if attention_mask is None:
_UpperCAmelCase : int = tf.cast(tf.math.not_equal(__A , config.pad_token_id ) , tf.inta )
if decoder_attention_mask is None:
_UpperCAmelCase : Tuple = tf.concat(
[
tf.ones(decoder_input_ids[:, :1].shape , dtype=tf.inta ),
tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:] , config.pad_token_id ) , tf.inta ),
] , axis=-1 , )
if head_mask is None:
_UpperCAmelCase : Any = tf.ones((config.encoder_layers, config.encoder_attention_heads) )
if decoder_head_mask is None:
_UpperCAmelCase : List[Any] = tf.ones((config.decoder_layers, config.decoder_attention_heads) )
return {
"input_ids": input_ids,
"attention_mask": attention_mask,
"decoder_input_ids": decoder_input_ids,
"decoder_attention_mask": decoder_attention_mask,
"head_mask": head_mask,
"decoder_head_mask": decoder_head_mask,
}
@require_tf
class A_ ( __lowercase , __lowercase , unittest.TestCase ):
'''simple docstring'''
_SCREAMING_SNAKE_CASE : Optional[Any] = (TFLEDForConditionalGeneration, TFLEDModel) if is_tf_available() else ()
_SCREAMING_SNAKE_CASE : Optional[int] = (TFLEDForConditionalGeneration,) if is_tf_available() else ()
_SCREAMING_SNAKE_CASE : List[Any] = (
{
"conversational": TFLEDForConditionalGeneration,
"feature-extraction": TFLEDModel,
"summarization": TFLEDForConditionalGeneration,
"text2text-generation": TFLEDForConditionalGeneration,
"translation": TFLEDForConditionalGeneration,
}
if is_tf_available()
else {}
)
_SCREAMING_SNAKE_CASE : Any = True
_SCREAMING_SNAKE_CASE : List[str] = False
_SCREAMING_SNAKE_CASE : Optional[int] = False
_SCREAMING_SNAKE_CASE : List[Any] = False
def snake_case__ ( self) -> int:
"""simple docstring"""
_UpperCAmelCase : Optional[Any] = TFLEDModelTester(self)
_UpperCAmelCase : Union[str, Any] = ConfigTester(self , config_class=_A)
def snake_case__ ( self) -> Any:
"""simple docstring"""
self.config_tester.run_common_tests()
def snake_case__ ( self) -> Any:
"""simple docstring"""
_UpperCAmelCase : List[str] = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.check_decoder_model_past_large_inputs(*_A)
def snake_case__ ( self) -> List[Any]:
"""simple docstring"""
_UpperCAmelCase , _UpperCAmelCase : Optional[int] = self.model_tester.prepare_config_and_inputs_for_common()
_UpperCAmelCase : Tuple = tf.zeros_like(inputs_dict['''attention_mask'''])
_UpperCAmelCase : List[Any] = 2
_UpperCAmelCase : Optional[Any] = tf.where(
tf.range(self.model_tester.seq_length)[None, :] < num_global_attn_indices , 1 , inputs_dict['''global_attention_mask'''] , )
_UpperCAmelCase : Optional[int] = True
_UpperCAmelCase : Optional[Any] = self.model_tester.seq_length
_UpperCAmelCase : List[Any] = self.model_tester.encoder_seq_length
def check_decoder_attentions_output(_A):
_UpperCAmelCase : Any = outputs.decoder_attentions
self.assertEqual(len(_A) , self.model_tester.num_hidden_layers)
self.assertListEqual(
list(decoder_attentions[0].shape[-3:]) , [self.model_tester.num_attention_heads, seq_length, seq_length] , )
def check_encoder_attentions_output(_A):
_UpperCAmelCase : Union[str, Any] = [t.numpy() for t in outputs.encoder_attentions]
_UpperCAmelCase : Optional[int] = [t.numpy() for t in outputs.encoder_global_attentions]
self.assertEqual(len(_A) , self.model_tester.num_hidden_layers)
self.assertEqual(len(_A) , self.model_tester.num_hidden_layers)
self.assertListEqual(
list(attentions[0].shape[-3:]) , [self.model_tester.num_attention_heads, seq_length, seq_length] , )
self.assertListEqual(
list(global_attentions[0].shape[-3:]) , [self.model_tester.num_attention_heads, encoder_seq_length, num_global_attn_indices] , )
for model_class in self.all_model_classes:
_UpperCAmelCase : Optional[int] = True
_UpperCAmelCase : List[Any] = False
_UpperCAmelCase : List[Any] = False
_UpperCAmelCase : str = model_class(_A)
_UpperCAmelCase : Any = model(self._prepare_for_class(_A , _A))
_UpperCAmelCase : Optional[int] = len(_A)
self.assertEqual(config.output_hidden_states , _A)
check_encoder_attentions_output(_A)
if self.is_encoder_decoder:
_UpperCAmelCase : Optional[int] = model_class(_A)
_UpperCAmelCase : Union[str, Any] = model(self._prepare_for_class(_A , _A))
self.assertEqual(config.output_hidden_states , _A)
check_decoder_attentions_output(_A)
# Check that output attentions can also be changed via the config
del inputs_dict["output_attentions"]
_UpperCAmelCase : Any = True
_UpperCAmelCase : List[str] = model_class(_A)
_UpperCAmelCase : Optional[int] = model(self._prepare_for_class(_A , _A))
self.assertEqual(config.output_hidden_states , _A)
check_encoder_attentions_output(_A)
# Check attention is always last and order is fine
_UpperCAmelCase : Dict = True
_UpperCAmelCase : str = True
_UpperCAmelCase : List[str] = model_class(_A)
_UpperCAmelCase : Optional[Any] = model(self._prepare_for_class(_A , _A))
self.assertEqual(out_len + (2 if self.is_encoder_decoder else 1) , len(_A))
self.assertEqual(model.config.output_hidden_states , _A)
check_encoder_attentions_output(_A)
@unittest.skip('''LED keeps using potentially symbolic tensors in conditionals and breaks tracing.''')
def snake_case__ ( self) -> int:
"""simple docstring"""
pass
def snake_case__ ( self) -> Any:
"""simple docstring"""
pass
def _lowerCamelCase ( __A : Optional[Any] ) -> int:
return tf.constant(__A , dtype=tf.intaa )
SCREAMING_SNAKE_CASE = 1e-4
@slow
@require_tf
class A_ ( unittest.TestCase ):
'''simple docstring'''
def snake_case__ ( self) -> Any:
"""simple docstring"""
_UpperCAmelCase : str = TFLEDForConditionalGeneration.from_pretrained('''allenai/led-base-16384''').led
# change to intended input here
_UpperCAmelCase : Union[str, Any] = _long_tensor([512 * [0, 31414, 232, 328, 740, 1140, 12695, 69]])
_UpperCAmelCase : List[Any] = _long_tensor([128 * [0, 31414, 232, 328, 740, 1140, 12695, 69]])
_UpperCAmelCase : List[str] = prepare_led_inputs_dict(model.config , _A , _A)
_UpperCAmelCase : Union[str, Any] = model(**_A)[0]
_UpperCAmelCase : Optional[Any] = (1, 1024, 768)
self.assertEqual(output.shape , _A)
# change to expected output here
_UpperCAmelCase : Optional[Any] = tf.convert_to_tensor(
[[2.3050, 2.8279, 0.6531], [-1.8457, -0.1455, -3.5661], [-1.0186, 0.4586, -2.2043]] , )
tf.debugging.assert_near(output[:, :3, :3] , _A , atol=1e-3)
def snake_case__ ( self) -> Optional[int]:
"""simple docstring"""
_UpperCAmelCase : List[str] = TFLEDForConditionalGeneration.from_pretrained('''allenai/led-base-16384''')
# change to intended input here
_UpperCAmelCase : List[str] = _long_tensor([512 * [0, 31414, 232, 328, 740, 1140, 12695, 69]])
_UpperCAmelCase : str = _long_tensor([128 * [0, 31414, 232, 328, 740, 1140, 12695, 69]])
_UpperCAmelCase : Optional[int] = prepare_led_inputs_dict(model.config , _A , _A)
_UpperCAmelCase : str = model(**_A)[0]
_UpperCAmelCase : Any = (1, 1024, model.config.vocab_size)
self.assertEqual(output.shape , _A)
# change to expected output here
_UpperCAmelCase : Tuple = tf.convert_to_tensor(
[[33.6507, 6.4572, 16.8089], [5.8739, -2.4238, 11.2902], [-3.2139, -4.3149, 4.2783]] , )
tf.debugging.assert_near(output[:, :3, :3] , _A , atol=1e-3 , rtol=1e-3)
| 485
|
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE = {
'junnyu/roformer_chinese_small': 'https://huggingface.co/junnyu/roformer_chinese_small/resolve/main/config.json',
'junnyu/roformer_chinese_base': 'https://huggingface.co/junnyu/roformer_chinese_base/resolve/main/config.json',
'junnyu/roformer_chinese_char_small': (
'https://huggingface.co/junnyu/roformer_chinese_char_small/resolve/main/config.json'
),
'junnyu/roformer_chinese_char_base': (
'https://huggingface.co/junnyu/roformer_chinese_char_base/resolve/main/config.json'
),
'junnyu/roformer_small_discriminator': (
'https://huggingface.co/junnyu/roformer_small_discriminator/resolve/main/config.json'
),
'junnyu/roformer_small_generator': (
'https://huggingface.co/junnyu/roformer_small_generator/resolve/main/config.json'
),
# See all RoFormer models at https://huggingface.co/models?filter=roformer
}
class A_ ( __lowercase ):
'''simple docstring'''
_SCREAMING_SNAKE_CASE : List[Any] = "roformer"
def __init__( self , _A=50000 , _A=None , _A=768 , _A=12 , _A=12 , _A=3072 , _A="gelu" , _A=0.1 , _A=0.1 , _A=1536 , _A=2 , _A=0.02 , _A=1e-12 , _A=0 , _A=False , _A=True , **_A , ) -> Optional[Any]:
"""simple docstring"""
super().__init__(pad_token_id=_A , **_A)
_UpperCAmelCase : Tuple = vocab_size
_UpperCAmelCase : Any = hidden_size if embedding_size is None else embedding_size
_UpperCAmelCase : Any = hidden_size
_UpperCAmelCase : int = num_hidden_layers
_UpperCAmelCase : Dict = num_attention_heads
_UpperCAmelCase : int = hidden_act
_UpperCAmelCase : Optional[Any] = intermediate_size
_UpperCAmelCase : str = hidden_dropout_prob
_UpperCAmelCase : List[Any] = attention_probs_dropout_prob
_UpperCAmelCase : Tuple = max_position_embeddings
_UpperCAmelCase : List[Any] = type_vocab_size
_UpperCAmelCase : Any = initializer_range
_UpperCAmelCase : Any = layer_norm_eps
_UpperCAmelCase : Optional[Any] = rotary_value
_UpperCAmelCase : int = use_cache
class A_ ( __lowercase ):
'''simple docstring'''
@property
def snake_case__ ( self) -> Mapping[str, Mapping[int, str]]:
"""simple docstring"""
if self.task == "multiple-choice":
_UpperCAmelCase : Optional[int] = {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
else:
_UpperCAmelCase : Optional[Any] = {0: '''batch''', 1: '''sequence'''}
_UpperCAmelCase : Dict = {0: '''batch''', 1: '''sequence'''}
return OrderedDict(
[
('''input_ids''', dynamic_axis),
('''attention_mask''', dynamic_axis),
('''token_type_ids''', dynamic_axis),
])
| 485
| 1
|
'''simple docstring'''
def A_ ( _lowerCAmelCase : str , _lowerCAmelCase : int ):
"""simple docstring"""
_lowerCamelCase : list[list[str]] = [[] for _ in range(snake_case_ )]
_lowerCamelCase : Optional[int] = key - 1
if key <= 0:
raise ValueError("Height of grid can't be 0 or negative" )
if key == 1 or len(snake_case_ ) <= key:
return input_string
for position, character in enumerate(snake_case_ ):
_lowerCamelCase : Union[str, Any] = position % (lowest * 2) # puts it in bounds
_lowerCamelCase : Dict = min(snake_case_ , lowest * 2 - num ) # creates zigzag pattern
temp_grid[num].append(snake_case_ )
_lowerCamelCase : Tuple = ["".join(snake_case_ ) for row in temp_grid]
_lowerCamelCase : List[str] = "".join(snake_case_ )
return output_string
def A_ ( _lowerCAmelCase : str , _lowerCAmelCase : int ):
"""simple docstring"""
_lowerCamelCase : Any = []
_lowerCamelCase : Optional[Any] = key - 1
if key <= 0:
raise ValueError("Height of grid can't be 0 or negative" )
if key == 1:
return input_string
_lowerCamelCase : list[list[str]] = [[] for _ in range(snake_case_ )] # generates template
for position in range(len(snake_case_ ) ):
_lowerCamelCase : Optional[Any] = position % (lowest * 2) # puts it in bounds
_lowerCamelCase : str = min(snake_case_ , lowest * 2 - num ) # creates zigzag pattern
temp_grid[num].append("*" )
_lowerCamelCase : int = 0
for row in temp_grid: # fills in the characters
_lowerCamelCase : Dict = input_string[counter : counter + len(snake_case_ )]
grid.append(list(snake_case_ ) )
counter += len(snake_case_ )
_lowerCamelCase : Dict = "" # reads as zigzag
for position in range(len(snake_case_ ) ):
_lowerCamelCase : List[Any] = position % (lowest * 2) # puts it in bounds
_lowerCamelCase : Tuple = min(snake_case_ , lowest * 2 - num ) # creates zigzag pattern
output_string += grid[num][0]
grid[num].pop(0 )
return output_string
def A_ ( _lowerCAmelCase : str ):
"""simple docstring"""
_lowerCamelCase : Optional[int] = {}
for key_guess in range(1 , len(snake_case_ ) ): # tries every key
_lowerCamelCase : Tuple = decrypt(snake_case_ , snake_case_ )
return results
if __name__ == "__main__":
import doctest
doctest.testmod()
| 702
|
'''simple docstring'''
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import PIL
import torch
from transformers import CLIPImageProcessor, CLIPVisionModel
from ...models import PriorTransformer
from ...pipelines import DiffusionPipeline
from ...schedulers import HeunDiscreteScheduler
from ...utils import (
BaseOutput,
is_accelerate_available,
logging,
randn_tensor,
replace_example_docstring,
)
from .renderer import ShapERenderer
UpperCAmelCase_ : str = logging.get_logger(__name__) # pylint: disable=invalid-name
UpperCAmelCase_ : str = '\n Examples:\n ```py\n >>> from PIL import Image\n >>> import torch\n >>> from diffusers import DiffusionPipeline\n >>> from diffusers.utils import export_to_gif, load_image\n\n >>> device = torch.device("cuda" if torch.cuda.is_available() else "cpu")\n\n >>> repo = "openai/shap-e-img2img"\n >>> pipe = DiffusionPipeline.from_pretrained(repo, torch_dtype=torch.float16)\n >>> pipe = pipe.to(device)\n\n >>> guidance_scale = 3.0\n >>> image_url = "https://hf.co/datasets/diffusers/docs-images/resolve/main/shap-e/corgi.png"\n >>> image = load_image(image_url).convert("RGB")\n\n >>> images = pipe(\n ... image,\n ... guidance_scale=guidance_scale,\n ... num_inference_steps=64,\n ... frame_size=256,\n ... ).images\n\n >>> gif_path = export_to_gif(images[0], "corgi_3d.gif")\n ```\n'
@dataclass
class UpperCAmelCase__ ( A ):
lowerCAmelCase_ = 42
class UpperCAmelCase__ ( A ):
def __init__( self : str,__A : PriorTransformer,__A : CLIPVisionModel,__A : CLIPImageProcessor,__A : HeunDiscreteScheduler,__A : ShapERenderer,):
super().__init__()
self.register_modules(
prior=__A,image_encoder=__A,image_processor=__A,scheduler=__A,renderer=__A,)
def lowerCamelCase_ ( self : Optional[int],__A : str,__A : Tuple,__A : Any,__A : Optional[int],__A : Tuple,__A : Union[str, Any] ):
if latents is None:
_lowerCamelCase : int = randn_tensor(__A,generator=__A,device=__A,dtype=__A )
else:
if latents.shape != shape:
raise ValueError(f'Unexpected latents shape, got {latents.shape}, expected {shape}' )
_lowerCamelCase : Union[str, Any] = latents.to(__A )
_lowerCamelCase : Tuple = latents * scheduler.init_noise_sigma
return latents
def lowerCamelCase_ ( self : List[str],__A : List[Any]=0 ):
if is_accelerate_available():
from accelerate import cpu_offload
else:
raise ImportError("Please install accelerate via `pip install accelerate`" )
_lowerCamelCase : List[Any] = torch.device(f'cuda:{gpu_id}' )
_lowerCamelCase : Tuple = [self.image_encoder, self.prior]
for cpu_offloaded_model in models:
if cpu_offloaded_model is not None:
cpu_offload(__A,__A )
@property
def lowerCamelCase_ ( self : Optional[Any] ):
if self.device != torch.device("meta" ) or not hasattr(self.image_encoder,"_hf_hook" ):
return self.device
for module in self.image_encoder.modules():
if (
hasattr(__A,"_hf_hook" )
and hasattr(module._hf_hook,"execution_device" )
and module._hf_hook.execution_device is not None
):
return torch.device(module._hf_hook.execution_device )
return self.device
def lowerCamelCase_ ( self : Tuple,__A : int,__A : Any,__A : str,__A : int,):
if isinstance(__A,__A ) and isinstance(image[0],torch.Tensor ):
_lowerCamelCase : Dict = torch.cat(__A,axis=0 ) if image[0].ndim == 4 else torch.stack(__A,axis=0 )
if not isinstance(__A,torch.Tensor ):
_lowerCamelCase : str = self.image_processor(__A,return_tensors="pt" ).pixel_values[0].unsqueeze(0 )
_lowerCamelCase : Any = image.to(dtype=self.image_encoder.dtype,device=__A )
_lowerCamelCase : List[str] = self.image_encoder(__A )["last_hidden_state"]
_lowerCamelCase : Optional[Any] = image_embeds[:, 1:, :].contiguous() # batch_size, dim, 256
_lowerCamelCase : Any = image_embeds.repeat_interleave(__A,dim=0 )
if do_classifier_free_guidance:
_lowerCamelCase : int = torch.zeros_like(__A )
# For classifier free guidance, we need to do two forward passes.
# Here we concatenate the unconditional and text embeddings into a single batch
# to avoid doing two forward passes
_lowerCamelCase : Union[str, Any] = torch.cat([negative_image_embeds, image_embeds] )
return image_embeds
@torch.no_grad()
@replace_example_docstring(__A )
def __call__( self : Tuple,__A : Union[PIL.Image.Image, List[PIL.Image.Image]],__A : int = 1,__A : int = 2_5,__A : Optional[Union[torch.Generator, List[torch.Generator]]] = None,__A : Optional[torch.FloatTensor] = None,__A : float = 4.0,__A : int = 6_4,__A : Optional[str] = "pil",__A : bool = True,):
if isinstance(__A,PIL.Image.Image ):
_lowerCamelCase : Optional[int] = 1
elif isinstance(__A,torch.Tensor ):
_lowerCamelCase : Optional[Any] = image.shape[0]
elif isinstance(__A,__A ) and isinstance(image[0],(torch.Tensor, PIL.Image.Image) ):
_lowerCamelCase : str = len(__A )
else:
raise ValueError(
f'`image` has to be of type `PIL.Image.Image`, `torch.Tensor`, `List[PIL.Image.Image]` or `List[torch.Tensor]` but is {type(__A )}' )
_lowerCamelCase : Optional[int] = self._execution_device
_lowerCamelCase : int = batch_size * num_images_per_prompt
_lowerCamelCase : Union[str, Any] = guidance_scale > 1.0
_lowerCamelCase : Tuple = self._encode_image(__A,__A,__A,__A )
# prior
self.scheduler.set_timesteps(__A,device=__A )
_lowerCamelCase : List[Any] = self.scheduler.timesteps
_lowerCamelCase : Dict = self.prior.config.num_embeddings
_lowerCamelCase : Tuple = self.prior.config.embedding_dim
_lowerCamelCase : List[str] = self.prepare_latents(
(batch_size, num_embeddings * embedding_dim),image_embeds.dtype,__A,__A,__A,self.scheduler,)
# YiYi notes: for testing only to match ldm, we can directly create a latents with desired shape: batch_size, num_embeddings, embedding_dim
_lowerCamelCase : Tuple = latents.reshape(latents.shape[0],__A,__A )
for i, t in enumerate(self.progress_bar(__A ) ):
# expand the latents if we are doing classifier free guidance
_lowerCamelCase : Optional[int] = torch.cat([latents] * 2 ) if do_classifier_free_guidance else latents
_lowerCamelCase : Tuple = self.scheduler.scale_model_input(__A,__A )
_lowerCamelCase : List[str] = self.prior(
__A,timestep=__A,proj_embedding=__A,).predicted_image_embedding
# remove the variance
_lowerCamelCase , _lowerCamelCase : Optional[Any] = noise_pred.split(
scaled_model_input.shape[2],dim=2 ) # batch_size, num_embeddings, embedding_dim
if do_classifier_free_guidance is not None:
_lowerCamelCase , _lowerCamelCase : int = noise_pred.chunk(2 )
_lowerCamelCase : Optional[int] = noise_pred_uncond + guidance_scale * (noise_pred - noise_pred_uncond)
_lowerCamelCase : Any = self.scheduler.step(
__A,timestep=__A,sample=__A,).prev_sample
if output_type == "latent":
return ShapEPipelineOutput(images=__A )
_lowerCamelCase : Any = []
for i, latent in enumerate(__A ):
print()
_lowerCamelCase : int = self.renderer.decode(
latent[None, :],__A,size=__A,ray_batch_size=4_0_9_6,n_coarse_samples=6_4,n_fine_samples=1_2_8,)
images.append(__A )
_lowerCamelCase : List[Any] = torch.stack(__A )
if output_type not in ["np", "pil"]:
raise ValueError(f'Only the output types `pil` and `np` are supported not output_type={output_type}' )
_lowerCamelCase : Union[str, Any] = images.cpu().numpy()
if output_type == "pil":
_lowerCamelCase : List[str] = [self.numpy_to_pil(__A ) for image in images]
# Offload last model to CPU
if hasattr(self,"final_offload_hook" ) and self.final_offload_hook is not None:
self.final_offload_hook.offload()
if not return_dict:
return (images,)
return ShapEPipelineOutput(images=__A )
| 11
| 0
|
"""simple docstring"""
from collections.abc import Callable
def lowercase (_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ):
__lowerCAmelCase = a
__lowerCAmelCase = b
if function(__lowerCAmelCase ) == 0: # one of the a or b is a root for the function
return a
elif function(__lowerCAmelCase ) == 0:
return b
elif (
function(__lowerCAmelCase ) * function(__lowerCAmelCase ) > 0
): # if none of these are root and they are both positive or negative,
# then this algorithm can't find the root
raise ValueError("""could not find root in given interval.""" )
else:
__lowerCAmelCase = start + (end - start) / 2.0
while abs(start - mid ) > 10**-7: # until precisely equals to 10^-7
if function(__lowerCAmelCase ) == 0:
return mid
elif function(__lowerCAmelCase ) * function(__lowerCAmelCase ) < 0:
__lowerCAmelCase = mid
else:
__lowerCAmelCase = mid
__lowerCAmelCase = start + (end - start) / 2.0
return mid
def lowercase (_lowerCAmelCase ):
return x**3 - 2 * x - 5
if __name__ == "__main__":
print(bisection(f, 1, 1_000))
import doctest
doctest.testmod()
| 465
|
def _lowerCAmelCase ( __lowerCAmelCase = 200 ) -> int:
"""simple docstring"""
snake_case__ : Optional[int] = [1, 2, 5, 10, 20, 50, 100, 200]
snake_case__ : List[Any] = [0] * (pence + 1)
snake_case__ : str = 1 # base case: 1 way to make 0 pence
for coin in coins:
for i in range(__lowerCAmelCase , pence + 1 , 1 ):
number_of_ways[i] += number_of_ways[i - coin]
return number_of_ways[pence]
if __name__ == "__main__":
assert solution(200) == 7_3682
| 252
| 0
|
"""simple docstring"""
import pytest
import datasets.config
from datasets.utils.info_utils import is_small_dataset
@pytest.mark.parametrize("dataset_size" , [None, 4_00 * 2**20, 6_00 * 2**20] )
@pytest.mark.parametrize("input_in_memory_max_size" , ["default", 0, 1_00 * 2**20, 9_00 * 2**20] )
def lowercase_ ( _lowercase : Union[str, Any] , _lowercase : int , _lowercase : Union[str, Any] ):
'''simple docstring'''
if input_in_memory_max_size != "default":
monkeypatch.setattr(datasets.config , "IN_MEMORY_MAX_SIZE" , _lowercase )
UpperCAmelCase : Optional[int] = datasets.config.IN_MEMORY_MAX_SIZE
if input_in_memory_max_size == "default":
assert in_memory_max_size == 0
else:
assert in_memory_max_size == input_in_memory_max_size
if dataset_size and in_memory_max_size:
UpperCAmelCase : Tuple = dataset_size < in_memory_max_size
else:
UpperCAmelCase : Optional[int] = False
UpperCAmelCase : Union[str, Any] = is_small_dataset(_lowercase )
assert result == expected
| 292
|
"""simple docstring"""
snake_case_ : str = [
"""DownloadConfig""",
"""DownloadManager""",
"""DownloadMode""",
"""StreamingDownloadManager""",
]
from .download_config import DownloadConfig
from .download_manager import DownloadManager, DownloadMode
from .streaming_download_manager import StreamingDownloadManager
| 292
| 1
|
'''simple docstring'''
from __future__ import annotations
def _SCREAMING_SNAKE_CASE (A ) -> list[int]:
"""simple docstring"""
lowercase__ = 2
lowercase__ = []
while i * i <= n:
if n % i:
i += 1
else:
n //= i
factors.append(A )
if n > 1:
factors.append(A )
return factors
if __name__ == "__main__":
import doctest
doctest.testmod()
| 460
|
'''simple docstring'''
from __future__ import annotations
lowerCamelCase : List[str] = []
def _SCREAMING_SNAKE_CASE (A , A , A ) -> bool:
"""simple docstring"""
for i in range(len(A ) ):
if board[row][i] == 1:
return False
for i in range(len(A ) ):
if board[i][column] == 1:
return False
for i, j in zip(range(A , -1 , -1 ) , range(A , -1 , -1 ) ):
if board[i][j] == 1:
return False
for i, j in zip(range(A , -1 , -1 ) , range(A , len(A ) ) ):
if board[i][j] == 1:
return False
return True
def _SCREAMING_SNAKE_CASE (A , A ) -> bool:
"""simple docstring"""
if row >= len(A ):
solution.append(A )
printboard(A )
print()
return True
for i in range(len(A ) ):
if is_safe(A , A , A ):
lowercase__ = 1
solve(A , row + 1 )
lowercase__ = 0
return False
def _SCREAMING_SNAKE_CASE (A ) -> None:
"""simple docstring"""
for i in range(len(A ) ):
for j in range(len(A ) ):
if board[i][j] == 1:
print('''Q''' , end=''' ''' )
else:
print('''.''' , end=''' ''' )
print()
# n=int(input("The no. of queens"))
lowerCamelCase : Optional[Any] = 8
lowerCamelCase : Optional[int] = [[0 for i in range(n)] for j in range(n)]
solve(board, 0)
print('The total no. of solutions are :', len(solution))
| 460
| 1
|
# Copyright 2023 The HuggingFace 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 torch
from accelerate import PartialState
from accelerate.utils.operations import broadcast, gather, gather_object, pad_across_processes, reduce
def UpperCAmelCase_ ( _A ):
'''simple docstring'''
return (torch.arange(state.num_processes ) + 1.0 + (state.num_processes * state.process_index)).to(state.device )
def UpperCAmelCase_ ( _A ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ = create_tensor(_A )
SCREAMING_SNAKE_CASE__ = gather(_A )
assert gathered_tensor.tolist() == list(range(1 , state.num_processes**2 + 1 ) )
def UpperCAmelCase_ ( _A ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ = [state.process_index]
SCREAMING_SNAKE_CASE__ = gather_object(_A )
assert len(_A ) == state.num_processes, F'''{gathered_obj}, {len(_A )} != {state.num_processes}'''
assert gathered_obj == list(range(state.num_processes ) ), F'''{gathered_obj} != {list(range(state.num_processes ) )}'''
def UpperCAmelCase_ ( _A ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ = create_tensor(_A )
SCREAMING_SNAKE_CASE__ = broadcast(_A )
assert broadcasted_tensor.shape == torch.Size([state.num_processes] )
assert broadcasted_tensor.tolist() == list(range(1 , state.num_processes + 1 ) )
def UpperCAmelCase_ ( _A ):
'''simple docstring'''
if state.is_main_process:
SCREAMING_SNAKE_CASE__ = torch.arange(state.num_processes + 1 ).to(state.device )
else:
SCREAMING_SNAKE_CASE__ = torch.arange(state.num_processes ).to(state.device )
SCREAMING_SNAKE_CASE__ = pad_across_processes(_A )
assert padded_tensor.shape == torch.Size([state.num_processes + 1] )
if not state.is_main_process:
assert padded_tensor.tolist() == list(range(0 , state.num_processes ) ) + [0]
def UpperCAmelCase_ ( _A ):
'''simple docstring'''
if state.num_processes != 2:
return
SCREAMING_SNAKE_CASE__ = create_tensor(_A )
SCREAMING_SNAKE_CASE__ = reduce(_A , '''sum''' )
SCREAMING_SNAKE_CASE__ = torch.tensor([4.0, 6] ).to(state.device )
assert torch.allclose(_A , _A ), F'''{reduced_tensor} != {truth_tensor}'''
def UpperCAmelCase_ ( _A ):
'''simple docstring'''
if state.num_processes != 2:
return
SCREAMING_SNAKE_CASE__ = create_tensor(_A )
SCREAMING_SNAKE_CASE__ = reduce(_A , '''mean''' )
SCREAMING_SNAKE_CASE__ = torch.tensor([2.0, 3] ).to(state.device )
assert torch.allclose(_A , _A ), F'''{reduced_tensor} != {truth_tensor}'''
def UpperCAmelCase_ ( _A ):
'''simple docstring'''
main()
def UpperCAmelCase_ ( ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ = PartialState()
state.print(F'''State: {state}''' )
state.print('''testing gather''' )
test_gather(_A )
state.print('''testing gather_object''' )
test_gather_object(_A )
state.print('''testing broadcast''' )
test_broadcast(_A )
state.print('''testing pad_across_processes''' )
test_pad_across_processes(_A )
state.print('''testing reduce_sum''' )
test_reduce_sum(_A )
state.print('''testing reduce_mean''' )
test_reduce_mean(_A )
if __name__ == "__main__":
main()
| 714
|
from importlib import import_module
from .logging import get_logger
_SCREAMING_SNAKE_CASE : Optional[int] = get_logger(__name__)
class UpperCAmelCase__ :
"""simple docstring"""
def __init__( self : Optional[int] , __lowerCamelCase : Optional[Any] , __lowerCamelCase : Any=None ) -> int:
SCREAMING_SNAKE_CASE__ = attrs or []
if module is not None:
for key in module.__dict__:
if key in attrs or not key.startswith('''__''' ):
setattr(self , __lowerCamelCase , getattr(__lowerCamelCase , __lowerCamelCase ) )
SCREAMING_SNAKE_CASE__ = module._original_module if isinstance(__lowerCamelCase , _PatchedModuleObj ) else module
class UpperCAmelCase__ :
"""simple docstring"""
a = []
def __init__( self : int , __lowerCamelCase : Union[str, Any] , __lowerCamelCase : str , __lowerCamelCase : Optional[int] , __lowerCamelCase : List[Any]=None ) -> Any:
SCREAMING_SNAKE_CASE__ = obj
SCREAMING_SNAKE_CASE__ = target
SCREAMING_SNAKE_CASE__ = new
SCREAMING_SNAKE_CASE__ = target.split('''.''' )[0]
SCREAMING_SNAKE_CASE__ = {}
SCREAMING_SNAKE_CASE__ = attrs or []
def __enter__( self : int ) -> Tuple:
*SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__ = self.target.split('''.''' )
# Patch modules:
# it's used to patch attributes of submodules like "os.path.join";
# in this case we need to patch "os" and "os.path"
for i in range(len(__lowerCamelCase ) ):
try:
SCREAMING_SNAKE_CASE__ = import_module('''.'''.join(submodules[: i + 1] ) )
except ModuleNotFoundError:
continue
# We iterate over all the globals in self.obj in case we find "os" or "os.path"
for attr in self.obj.__dir__():
SCREAMING_SNAKE_CASE__ = getattr(self.obj , __lowerCamelCase )
# We don't check for the name of the global, but rather if its value *is* "os" or "os.path".
# This allows to patch renamed modules like "from os import path as ospath".
if obj_attr is submodule or (
(isinstance(__lowerCamelCase , _PatchedModuleObj ) and obj_attr._original_module is submodule)
):
SCREAMING_SNAKE_CASE__ = obj_attr
# patch at top level
setattr(self.obj , __lowerCamelCase , _PatchedModuleObj(__lowerCamelCase , attrs=self.attrs ) )
SCREAMING_SNAKE_CASE__ = getattr(self.obj , __lowerCamelCase )
# construct lower levels patches
for key in submodules[i + 1 :]:
setattr(__lowerCamelCase , __lowerCamelCase , _PatchedModuleObj(getattr(__lowerCamelCase , __lowerCamelCase , __lowerCamelCase ) , attrs=self.attrs ) )
SCREAMING_SNAKE_CASE__ = getattr(__lowerCamelCase , __lowerCamelCase )
# finally set the target attribute
setattr(__lowerCamelCase , __lowerCamelCase , self.new )
# Patch attribute itself:
# it's used for builtins like "open",
# and also to patch "os.path.join" we may also need to patch "join"
# itself if it was imported as "from os.path import join".
if submodules: # if it's an attribute of a submodule like "os.path.join"
try:
SCREAMING_SNAKE_CASE__ = getattr(import_module('''.'''.join(__lowerCamelCase ) ) , __lowerCamelCase )
except (AttributeError, ModuleNotFoundError):
return
# We iterate over all the globals in self.obj in case we find "os.path.join"
for attr in self.obj.__dir__():
# We don't check for the name of the global, but rather if its value *is* "os.path.join".
# This allows to patch renamed attributes like "from os.path import join as pjoin".
if getattr(self.obj , __lowerCamelCase ) is attr_value:
SCREAMING_SNAKE_CASE__ = getattr(self.obj , __lowerCamelCase )
setattr(self.obj , __lowerCamelCase , self.new )
elif target_attr in globals()["__builtins__"]: # if it'a s builtin like "open"
SCREAMING_SNAKE_CASE__ = globals()['''__builtins__'''][target_attr]
setattr(self.obj , __lowerCamelCase , self.new )
else:
raise RuntimeError(f'''Tried to patch attribute {target_attr} instead of a submodule.''' )
def __exit__( self : Optional[Any] , *__lowerCamelCase : Tuple ) -> List[str]:
for attr in list(self.original ):
setattr(self.obj , __lowerCamelCase , self.original.pop(__lowerCamelCase ) )
def lowercase_ ( self : List[Any] ) -> Optional[Any]:
self.__enter__()
self._active_patches.append(self )
def lowercase_ ( self : int ) -> int:
try:
self._active_patches.remove(self )
except ValueError:
# If the patch hasn't been started this will fail
return None
return self.__exit__()
| 472
| 0
|
'''simple docstring'''
from collections.abc import Sequence
from queue import Queue
class a__ :
def __init__( self , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase=None , _UpperCamelCase=None ):
"""simple docstring"""
_lowercase : Tuple = start
_lowercase : List[str] = end
_lowercase : List[str] = val
_lowercase : Tuple = (start + end) // 2
_lowercase : List[str] = left
_lowercase : List[str] = right
def __repr__( self ):
"""simple docstring"""
return f'''SegmentTreeNode(start={self.start}, end={self.end}, val={self.val})'''
class a__ :
def __init__( self , _UpperCamelCase , _UpperCamelCase ):
"""simple docstring"""
_lowercase : Union[str, Any] = collection
_lowercase : Union[str, Any] = function
if self.collection:
_lowercase : List[str] = self._build_tree(0 , len(_UpperCamelCase ) - 1 )
def _lowerCamelCase ( self , _UpperCamelCase , _UpperCamelCase ):
"""simple docstring"""
self._update_tree(self.root , _UpperCamelCase , _UpperCamelCase )
def _lowerCamelCase ( self , _UpperCamelCase , _UpperCamelCase ):
"""simple docstring"""
return self._query_range(self.root , _UpperCamelCase , _UpperCamelCase )
def _lowerCamelCase ( self , _UpperCamelCase , _UpperCamelCase ):
"""simple docstring"""
if start == end:
return SegmentTreeNode(_UpperCamelCase , _UpperCamelCase , self.collection[start] )
_lowercase : Optional[int] = (start + end) // 2
_lowercase : str = self._build_tree(_UpperCamelCase , _UpperCamelCase )
_lowercase : List[Any] = self._build_tree(mid + 1 , _UpperCamelCase )
return SegmentTreeNode(_UpperCamelCase , _UpperCamelCase , self.fn(left.val , right.val ) , _UpperCamelCase , _UpperCamelCase )
def _lowerCamelCase ( self , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase ):
"""simple docstring"""
if node.start == i and node.end == i:
_lowercase : str = val
return
if i <= node.mid:
self._update_tree(node.left , _UpperCamelCase , _UpperCamelCase )
else:
self._update_tree(node.right , _UpperCamelCase , _UpperCamelCase )
_lowercase : str = self.fn(node.left.val , node.right.val )
def _lowerCamelCase ( self , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase ):
"""simple docstring"""
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 , _UpperCamelCase , _UpperCamelCase )
else:
# range in left child tree and right child tree
return self.fn(
self._query_range(node.left , _UpperCamelCase , node.mid ) , self._query_range(node.right , node.mid + 1 , _UpperCamelCase ) , )
else:
# range in right child tree
return self._query_range(node.right , _UpperCamelCase , _UpperCamelCase )
def _lowerCamelCase ( self ):
"""simple docstring"""
if self.root is not None:
_lowercase : Tuple = Queue()
queue.put(self.root )
while not queue.empty():
_lowercase : Any = 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)
_snake_case = 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()
| 245
|
'''simple docstring'''
import argparse
from pathlib import Path
import torch
from transformers import OPTConfig, OPTModel
from transformers.utils import logging
logging.set_verbosity_info()
_snake_case = logging.get_logger(__name__)
def _A ( snake_case ) -> str:
_lowercase : Dict = torch.load(snake_case , map_location="cpu" )
if "model" in sd.keys():
_lowercase : Tuple = torch.load(snake_case , map_location="cpu" )["model"]
# pop unnecessary weights
_lowercase : Any = [
"decoder.version",
"decoder.output_projection.weight",
]
for key in keys_to_delete:
if key in sd:
sd.pop(snake_case )
_lowercase : List[Any] = {
"decoder.project_in_dim.weight": "decoder.project_in.weight",
"decoder.project_out_dim.weight": "decoder.project_out.weight",
"decoder.layer_norm.weight": "decoder.final_layer_norm.weight",
"decoder.layer_norm.bias": "decoder.final_layer_norm.bias",
}
for old_key, new_key in keys_to_rename.items():
if old_key in sd:
_lowercase : Dict = sd.pop(snake_case )
_lowercase : List[str] = list(sd.keys() )
for key in keys:
if ".qkv_proj." in key:
_lowercase : List[Any] = sd[key]
# We split QKV in separate Q,K,V
_lowercase : str = key.replace(".qkv_proj." , ".q_proj." )
_lowercase : List[str] = key.replace(".qkv_proj." , ".k_proj." )
_lowercase : Optional[Any] = key.replace(".qkv_proj." , ".v_proj." )
_lowercase : Union[str, Any] = value.shape[0]
assert depth % 3 == 0
# `SequeuceParallelTransformerBlock` has QKV weight is separated in K,V,Q despite the naming:
# https://cs.github.com/facebookresearch/metaseq/blob/51871bd73cd04c038f239ea2a26db1d7f6b37927/metaseq/modules/sequence_parallel_transformer_layer.py#L97
_lowercase , _lowercase , _lowercase : Dict = torch.split(snake_case , depth // 3 , dim=0 )
_lowercase : Optional[int] = q
_lowercase : str = k
_lowercase : List[str] = v
del sd[key]
return sd
@torch.no_grad()
def _A ( snake_case , snake_case , snake_case=None ) -> Any:
_lowercase : Union[str, Any] = load_checkpoint(snake_case )
if config is not None:
_lowercase : Tuple = OPTConfig.from_pretrained(snake_case )
else:
_lowercase : Optional[int] = OPTConfig()
_lowercase : List[Any] = OPTModel(snake_case ).half().eval()
model.load_state_dict(snake_case )
# Check results
Path(snake_case ).mkdir(exist_ok=snake_case )
model.save_pretrained(snake_case )
if __name__ == "__main__":
_snake_case = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--fairseq_path',
type=str,
help=(
'path to fairseq checkpoint in correct format. You can find all checkpoints in the correct format here:'
' https://huggingface.co/models?other=opt_metasq'
),
)
parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
parser.add_argument('--hf_config', default=None, type=str, help='Define HF config.')
_snake_case = parser.parse_args()
convert_opt_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, config=args.hf_config)
| 245
| 1
|
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
__a = logging.get_logger(__name__)
__a = {
"facebook/data2vec-vision-base-ft": (
"https://huggingface.co/facebook/data2vec-vision-base-ft/resolve/main/config.json"
),
}
class __lowercase ( lowercase_ ):
'''simple docstring'''
SCREAMING_SNAKE_CASE = "data2vec-vision"
def __init__( self : List[str] , UpperCamelCase_ : Optional[int]=768 , UpperCamelCase_ : int=12 , UpperCamelCase_ : Union[str, Any]=12 , UpperCamelCase_ : List[Any]=3_072 , UpperCamelCase_ : List[str]="gelu" , UpperCamelCase_ : Any=0.0 , UpperCamelCase_ : Tuple=0.0 , UpperCamelCase_ : Any=0.02 , UpperCamelCase_ : Optional[Any]=1e-12 , UpperCamelCase_ : Dict=224 , UpperCamelCase_ : Any=16 , UpperCamelCase_ : Dict=3 , UpperCamelCase_ : Any=False , UpperCamelCase_ : List[str]=False , UpperCamelCase_ : Optional[int]=False , UpperCamelCase_ : str=False , UpperCamelCase_ : Union[str, Any]=0.1 , UpperCamelCase_ : Union[str, Any]=0.1 , UpperCamelCase_ : Optional[int]=True , UpperCamelCase_ : int=[3, 5, 7, 11] , UpperCamelCase_ : Tuple=[1, 2, 3, 6] , UpperCamelCase_ : int=True , UpperCamelCase_ : str=0.4 , UpperCamelCase_ : Tuple=256 , UpperCamelCase_ : List[Any]=1 , UpperCamelCase_ : str=False , UpperCamelCase_ : Optional[Any]=255 , **UpperCamelCase_ : List[str] , ):
"""simple docstring"""
super().__init__(**UpperCamelCase_ )
__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 = initializer_range
__A = layer_norm_eps
__A = image_size
__A = patch_size
__A = num_channels
__A = use_mask_token
__A = use_absolute_position_embeddings
__A = use_relative_position_bias
__A = use_shared_relative_position_bias
__A = layer_scale_init_value
__A = drop_path_rate
__A = use_mean_pooling
# decode head attributes (semantic segmentation)
__A = out_indices
__A = pool_scales
# auxiliary head attributes (semantic segmentation)
__A = use_auxiliary_head
__A = auxiliary_loss_weight
__A = auxiliary_channels
__A = auxiliary_num_convs
__A = auxiliary_concat_input
__A = semantic_loss_ignore_index
class __lowercase ( lowercase_ ):
'''simple docstring'''
SCREAMING_SNAKE_CASE = version.parse("1.11" )
@property
def lowerCAmelCase_ ( self : int ):
"""simple docstring"""
return OrderedDict(
[
("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
] )
@property
def lowerCAmelCase_ ( self : Tuple ):
"""simple docstring"""
return 1e-4
| 704
|
def _SCREAMING_SNAKE_CASE ( __lowercase : str = "The quick brown fox jumps over the lazy dog" , ) -> bool:
"""simple docstring"""
__A = set()
# Replace all the whitespace in our sentence
__A = input_str.replace(""" """ , """""" )
for alpha in input_str:
if "a" <= alpha.lower() <= "z":
frequency.add(alpha.lower() )
return len(__lowercase ) == 2_6
def _SCREAMING_SNAKE_CASE ( __lowercase : str = "The quick brown fox jumps over the lazy dog" , ) -> bool:
"""simple docstring"""
__A = [False] * 2_6
for char in input_str:
if char.islower():
__A = True
elif char.isupper():
__A = True
return all(__lowercase )
def _SCREAMING_SNAKE_CASE ( __lowercase : str = "The quick brown fox jumps over the lazy dog" , ) -> bool:
"""simple docstring"""
return len({char for char in input_str.lower() if char.isalpha()} ) == 2_6
def _SCREAMING_SNAKE_CASE ( ) -> None:
"""simple docstring"""
from timeit import timeit
__A = """from __main__ import is_pangram, is_pangram_faster, is_pangram_fastest"""
print(timeit("""is_pangram()""" , setup=__lowercase ) )
print(timeit("""is_pangram_faster()""" , setup=__lowercase ) )
print(timeit("""is_pangram_fastest()""" , setup=__lowercase ) )
# 5.348480500048026, 2.6477354579837993, 1.8470395830227062
# 5.036091582966037, 2.644472333951853, 1.8869528750656173
if __name__ == "__main__":
import doctest
doctest.testmod()
benchmark()
| 199
| 0
|
import argparse
import logging
import os
import re
import tensorflow as tf
from transformers import (
AutoConfig,
AutoTokenizer,
DataCollatorForLanguageModeling,
PushToHubCallback,
TFAutoModelForMaskedLM,
create_optimizer,
)
__a = logging.getLogger(__name__)
__a = tf.data.AUTOTUNE
def lowerCamelCase__ ( ):
'''simple docstring'''
UpperCAmelCase_ : int = argparse.ArgumentParser(description='''Train a masked language model on TPU.''' )
parser.add_argument(
'''--pretrained_model_config''' , type=_lowercase , default='''roberta-base''' , help='''The model config to use. Note that we don\'t copy the model\'s weights, only the config!''' , )
parser.add_argument(
'''--tokenizer''' , type=_lowercase , default='''unigram-tokenizer-wikitext''' , help='''The name of the tokenizer to load. We use the pretrained tokenizer to initialize the model\'s vocab size.''' , )
parser.add_argument(
'''--per_replica_batch_size''' , type=_lowercase , default=8 , help='''Batch size per TPU core.''' , )
parser.add_argument(
'''--no_tpu''' , action='''store_true''' , help='''If set, run on CPU and don\'t try to initialize a TPU. Useful for debugging on non-TPU instances.''' , )
parser.add_argument(
'''--tpu_name''' , type=_lowercase , help='''Name of TPU resource to initialize. Should be blank on Colab, and \'local\' on TPU VMs.''' , default='''local''' , )
parser.add_argument(
'''--tpu_zone''' , type=_lowercase , help='''Google cloud zone that TPU resource is located in. Only used for non-Colab TPU nodes.''' , )
parser.add_argument(
'''--gcp_project''' , type=_lowercase , help='''Google cloud project name. Only used for non-Colab TPU nodes.''' )
parser.add_argument(
'''--bfloat16''' , action='''store_true''' , help='''Use mixed-precision bfloat16 for training. This is the recommended lower-precision format for TPU.''' , )
parser.add_argument(
'''--train_dataset''' , type=_lowercase , help='''Path to training dataset to load. If the path begins with `gs://`'''
''' then the dataset will be loaded from a Google Cloud Storage bucket.''' , )
parser.add_argument(
'''--shuffle_buffer_size''' , type=_lowercase , default=2**18 , help='''Size of the shuffle buffer (in samples)''' , )
parser.add_argument(
'''--eval_dataset''' , type=_lowercase , help='''Path to evaluation dataset to load. If the path begins with `gs://`'''
''' then the dataset will be loaded from a Google Cloud Storage bucket.''' , )
parser.add_argument(
'''--num_epochs''' , type=_lowercase , default=1 , help='''Number of epochs to train for.''' , )
parser.add_argument(
'''--learning_rate''' , type=_lowercase , default=1E-4 , help='''Learning rate to use for training.''' , )
parser.add_argument(
'''--weight_decay_rate''' , type=_lowercase , default=1E-3 , help='''Weight decay rate to use for training.''' , )
parser.add_argument(
'''--max_length''' , type=_lowercase , default=512 , help='''Maximum length of tokenized sequences. Should match the setting used in prepare_tfrecord_shards.py''' , )
parser.add_argument(
'''--mlm_probability''' , type=_lowercase , default=0.15 , help='''Fraction of tokens to mask during training.''' , )
parser.add_argument('''--output_dir''' , type=_lowercase , required=_lowercase , help='''Path to save model checkpoints to.''' )
parser.add_argument('''--hub_model_id''' , type=_lowercase , help='''Model ID to upload to on the Hugging Face Hub.''' )
UpperCAmelCase_ : Optional[int] = parser.parse_args()
return args
def lowerCamelCase__ ( _lowercase ):
'''simple docstring'''
try:
if args.tpu_name:
UpperCAmelCase_ : List[Any] = tf.distribute.cluster_resolver.TPUClusterResolver(
args.tpu_name , zone=args.tpu_zone , project=args.gcp_project )
else:
UpperCAmelCase_ : Optional[Any] = tf.distribute.cluster_resolver.TPUClusterResolver()
except ValueError:
raise RuntimeError(
'''Couldn\'t connect to TPU! Most likely you need to specify --tpu_name, --tpu_zone, or '''
'''--gcp_project. When running on a TPU VM, use --tpu_name local.''' )
tf.config.experimental_connect_to_cluster(_lowercase )
tf.tpu.experimental.initialize_tpu_system(_lowercase )
return tpu
def lowerCamelCase__ ( _lowercase ):
'''simple docstring'''
UpperCAmelCase_ : List[str] = 0
for file in file_list:
UpperCAmelCase_ : Optional[int] = file.split('''/''' )[-1]
UpperCAmelCase_ : Tuple = re.search(r'''-\d+-(\d+)\.tfrecord''' , _lowercase ).group(1 )
UpperCAmelCase_ : Dict = int(_lowercase )
num_samples += sample_count
return num_samples
def lowerCamelCase__ ( _lowercase , _lowercase , _lowercase , _lowercase , _lowercase , _lowercase=None ):
'''simple docstring'''
UpperCAmelCase_ : Tuple = count_samples(_lowercase )
UpperCAmelCase_ : List[Any] = tf.data.Dataset.from_tensor_slices(_lowercase )
if shuffle:
UpperCAmelCase_ : List[Any] = dataset.shuffle(len(_lowercase ) )
UpperCAmelCase_ : Any = tf.data.TFRecordDataset(_lowercase , num_parallel_reads=_lowercase )
# TF can't infer the total sample count because it doesn't read all the records yet, so we assert it here
UpperCAmelCase_ : Any = dataset.apply(tf.data.experimental.assert_cardinality(_lowercase ) )
UpperCAmelCase_ : List[Any] = dataset.map(_lowercase , num_parallel_calls=_lowercase )
if shuffle:
assert shuffle_buffer_size is not None
UpperCAmelCase_ : Tuple = dataset.shuffle(args.shuffle_buffer_size )
UpperCAmelCase_ : Optional[Any] = dataset.batch(_lowercase , drop_remainder=_lowercase )
UpperCAmelCase_ : Optional[Any] = dataset.map(_lowercase , num_parallel_calls=_lowercase )
UpperCAmelCase_ : Tuple = dataset.prefetch(_lowercase )
return dataset
def lowerCamelCase__ ( _lowercase ):
'''simple docstring'''
if not args.no_tpu:
UpperCAmelCase_ : List[Any] = initialize_tpu(_lowercase )
UpperCAmelCase_ : Optional[int] = tf.distribute.TPUStrategy(_lowercase )
else:
UpperCAmelCase_ : Dict = tf.distribute.OneDeviceStrategy(device='''/gpu:0''' )
if args.bfloataa:
tf.keras.mixed_precision.set_global_policy('''mixed_bfloat16''' )
UpperCAmelCase_ : str = AutoTokenizer.from_pretrained(args.tokenizer )
UpperCAmelCase_ : List[str] = AutoConfig.from_pretrained(args.pretrained_model_config )
UpperCAmelCase_ : int = tokenizer.vocab_size
UpperCAmelCase_ : Union[str, Any] = tf.io.gfile.glob(os.path.join(args.train_dataset , '''*.tfrecord''' ) )
if not training_records:
raise ValueError(f'''No .tfrecord files found in {args.train_dataset}.''' )
UpperCAmelCase_ : Tuple = tf.io.gfile.glob(os.path.join(args.eval_dataset , '''*.tfrecord''' ) )
if not eval_records:
raise ValueError(f'''No .tfrecord files found in {args.eval_dataset}.''' )
UpperCAmelCase_ : Dict = count_samples(_lowercase )
UpperCAmelCase_ : Any = num_train_samples // (args.per_replica_batch_size * strategy.num_replicas_in_sync)
UpperCAmelCase_ : Optional[int] = steps_per_epoch * args.num_epochs
with strategy.scope():
UpperCAmelCase_ : Any = TFAutoModelForMaskedLM.from_config(_lowercase )
model(model.dummy_inputs ) # Pass some dummy inputs through the model to ensure all the weights are built
UpperCAmelCase_, UpperCAmelCase_ : Any = create_optimizer(
num_train_steps=_lowercase , num_warmup_steps=total_train_steps // 20 , init_lr=args.learning_rate , weight_decay_rate=args.weight_decay_rate , )
# Transformers models compute the right loss for their task by default when labels are passed, and will
# use this for training unless you specify your own loss function in compile().
model.compile(optimizer=_lowercase , metrics=['''accuracy'''] )
def decode_fn(_lowercase ):
UpperCAmelCase_ : List[Any] = {
'''input_ids''': tf.io.FixedLenFeature(dtype=tf.intaa , shape=(args.max_length,) ),
'''attention_mask''': tf.io.FixedLenFeature(dtype=tf.intaa , shape=(args.max_length,) ),
}
return tf.io.parse_single_example(_lowercase , _lowercase )
# Many of the data collators in Transformers are TF-compilable when return_tensors == "tf", so we can
# use their methods in our data pipeline.
UpperCAmelCase_ : str = DataCollatorForLanguageModeling(
tokenizer=_lowercase , mlm_probability=args.mlm_probability , mlm=_lowercase , return_tensors='''tf''' )
def mask_with_collator(_lowercase ):
# TF really needs an isin() function
UpperCAmelCase_ : Dict = (
~tf.cast(batch['''attention_mask'''] , tf.bool )
| (batch['''input_ids'''] == tokenizer.cls_token_id)
| (batch['''input_ids'''] == tokenizer.sep_token_id)
)
UpperCAmelCase_, UpperCAmelCase_ : List[Any] = data_collator.tf_mask_tokens(
batch['''input_ids'''] , vocab_size=len(_lowercase ) , mask_token_id=tokenizer.mask_token_id , special_tokens_mask=_lowercase , )
return batch
UpperCAmelCase_ : Any = args.per_replica_batch_size * strategy.num_replicas_in_sync
UpperCAmelCase_ : Any = prepare_dataset(
_lowercase , decode_fn=_lowercase , mask_fn=_lowercase , batch_size=_lowercase , shuffle=_lowercase , shuffle_buffer_size=args.shuffle_buffer_size , )
UpperCAmelCase_ : Dict = prepare_dataset(
_lowercase , decode_fn=_lowercase , mask_fn=_lowercase , batch_size=_lowercase , shuffle=_lowercase , )
UpperCAmelCase_ : Optional[Any] = []
if args.hub_model_id:
callbacks.append(
PushToHubCallback(output_dir=args.output_dir , hub_model_id=args.hub_model_id , tokenizer=_lowercase ) )
model.fit(
_lowercase , validation_data=_lowercase , epochs=args.num_epochs , callbacks=_lowercase , )
model.save_pretrained(args.output_dir )
if __name__ == "__main__":
__a = parse_args()
main(args)
| 30
|
from functools import reduce
__a = (
'73167176531330624919225119674426574742355349194934'
'96983520312774506326239578318016984801869478851843'
'85861560789112949495459501737958331952853208805511'
'12540698747158523863050715693290963295227443043557'
'66896648950445244523161731856403098711121722383113'
'62229893423380308135336276614282806444486645238749'
'30358907296290491560440772390713810515859307960866'
'70172427121883998797908792274921901699720888093776'
'65727333001053367881220235421809751254540594752243'
'52584907711670556013604839586446706324415722155397'
'53697817977846174064955149290862569321978468622482'
'83972241375657056057490261407972968652414535100474'
'82166370484403199890008895243450658541227588666881'
'16427171479924442928230863465674813919123162824586'
'17866458359124566529476545682848912883142607690042'
'24219022671055626321111109370544217506941658960408'
'07198403850962455444362981230987879927244284909188'
'84580156166097919133875499200524063689912560717606'
'05886116467109405077541002256983155200055935729725'
'71636269561882670428252483600823257530420752963450'
)
def lowerCamelCase__ ( _lowercase = N ):
'''simple docstring'''
return max(
# mypy cannot properly interpret reduce
int(reduce(lambda _lowercase , _lowercase : str(int(_lowercase ) * int(_lowercase ) ) , n[i : i + 13] ) )
for i in range(len(_lowercase ) - 12 ) )
if __name__ == "__main__":
print(F"""{solution() = }""")
| 30
| 1
|
from abc import ABC, abstractmethod
from argparse import ArgumentParser
class lowercase__ ( UpperCamelCase_):
@staticmethod
@abstractmethod
def __A ( UpperCamelCase__ : ArgumentParser ):
'''simple docstring'''
raise NotImplementedError()
@abstractmethod
def __A ( self : Optional[Any] ):
'''simple docstring'''
raise NotImplementedError()
| 34
|
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 : int = logging.get_logger(__name__)
def A ( _lowercase , _lowercase , _lowercase , _lowercase ):
def constraint_to_multiple_of(_lowercase , _lowercase , _lowercase=0 , _lowercase=None ):
SCREAMING_SNAKE_CASE : int = round(val / multiple ) * multiple
if max_val is not None and x > max_val:
SCREAMING_SNAKE_CASE : Dict = math.floor(val / multiple ) * multiple
if x < min_val:
SCREAMING_SNAKE_CASE : Optional[Any] = math.ceil(val / multiple ) * multiple
return x
SCREAMING_SNAKE_CASE : Optional[Any] = (output_size, output_size) if isinstance(_lowercase , _lowercase ) else output_size
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : Optional[int] = get_image_size(_lowercase )
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : Optional[int] = output_size
# determine new height and width
SCREAMING_SNAKE_CASE : Dict = output_height / input_height
SCREAMING_SNAKE_CASE : Optional[Any] = output_width / input_width
if keep_aspect_ratio:
# scale as little as possible
if abs(1 - scale_width ) < abs(1 - scale_height ):
# fit width
SCREAMING_SNAKE_CASE : List[Any] = scale_width
else:
# fit height
SCREAMING_SNAKE_CASE : List[Any] = scale_height
SCREAMING_SNAKE_CASE : List[str] = constraint_to_multiple_of(scale_height * input_height , multiple=_lowercase )
SCREAMING_SNAKE_CASE : Optional[int] = constraint_to_multiple_of(scale_width * input_width , multiple=_lowercase )
return (new_height, new_width)
class lowercase__ ( UpperCamelCase_):
UpperCamelCase_ = ["""pixel_values"""]
def __init__( self : int , UpperCamelCase__ : bool = True , UpperCamelCase__ : Dict[str, int] = None , UpperCamelCase__ : PILImageResampling = PILImageResampling.BILINEAR , UpperCamelCase__ : bool = False , UpperCamelCase__ : int = 1 , UpperCamelCase__ : bool = True , UpperCamelCase__ : Union[int, float] = 1 / 255 , UpperCamelCase__ : bool = True , UpperCamelCase__ : Optional[Union[float, List[float]]] = None , UpperCamelCase__ : Optional[Union[float, List[float]]] = None , **UpperCamelCase__ : Optional[int] , ):
'''simple docstring'''
super().__init__(**UpperCamelCase__ )
SCREAMING_SNAKE_CASE : List[str] = size if size is not None else {'''height''': 384, '''width''': 384}
SCREAMING_SNAKE_CASE : Any = get_size_dict(UpperCamelCase__ )
SCREAMING_SNAKE_CASE : Dict = do_resize
SCREAMING_SNAKE_CASE : Any = size
SCREAMING_SNAKE_CASE : str = keep_aspect_ratio
SCREAMING_SNAKE_CASE : List[str] = ensure_multiple_of
SCREAMING_SNAKE_CASE : int = resample
SCREAMING_SNAKE_CASE : Any = do_rescale
SCREAMING_SNAKE_CASE : List[Any] = rescale_factor
SCREAMING_SNAKE_CASE : Optional[int] = do_normalize
SCREAMING_SNAKE_CASE : str = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
SCREAMING_SNAKE_CASE : Union[str, Any] = image_std if image_std is not None else IMAGENET_STANDARD_STD
def __A ( self : Optional[Any] , UpperCamelCase__ : np.ndarray , UpperCamelCase__ : Dict[str, int] , UpperCamelCase__ : bool = False , UpperCamelCase__ : int = 1 , UpperCamelCase__ : PILImageResampling = PILImageResampling.BICUBIC , UpperCamelCase__ : Optional[Union[str, ChannelDimension]] = None , **UpperCamelCase__ : Union[str, Any] , ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : List[Any] = get_size_dict(UpperCamelCase__ )
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()}""" )
SCREAMING_SNAKE_CASE : Any = get_resize_output_image_size(
UpperCamelCase__ , output_size=(size['''height'''], size['''width''']) , keep_aspect_ratio=UpperCamelCase__ , multiple=UpperCamelCase__ , )
return resize(UpperCamelCase__ , size=UpperCamelCase__ , resample=UpperCamelCase__ , data_format=UpperCamelCase__ , **UpperCamelCase__ )
def __A ( self : Dict , UpperCamelCase__ : np.ndarray , UpperCamelCase__ : Union[int, float] , UpperCamelCase__ : Optional[Union[str, ChannelDimension]] = None , **UpperCamelCase__ : str , ):
'''simple docstring'''
return rescale(UpperCamelCase__ , scale=UpperCamelCase__ , data_format=UpperCamelCase__ , **UpperCamelCase__ )
def __A ( self : Any , UpperCamelCase__ : np.ndarray , UpperCamelCase__ : Union[float, List[float]] , UpperCamelCase__ : Union[float, List[float]] , UpperCamelCase__ : Optional[Union[str, ChannelDimension]] = None , **UpperCamelCase__ : List[str] , ):
'''simple docstring'''
return normalize(UpperCamelCase__ , mean=UpperCamelCase__ , std=UpperCamelCase__ , data_format=UpperCamelCase__ , **UpperCamelCase__ )
def __A ( self : Optional[Any] , UpperCamelCase__ : ImageInput , UpperCamelCase__ : bool = None , UpperCamelCase__ : int = None , UpperCamelCase__ : bool = None , UpperCamelCase__ : int = None , UpperCamelCase__ : PILImageResampling = None , UpperCamelCase__ : bool = None , UpperCamelCase__ : float = None , UpperCamelCase__ : bool = None , UpperCamelCase__ : Optional[Union[float, List[float]]] = None , UpperCamelCase__ : Optional[Union[float, List[float]]] = None , UpperCamelCase__ : Optional[Union[str, TensorType]] = None , UpperCamelCase__ : ChannelDimension = ChannelDimension.FIRST , **UpperCamelCase__ : Optional[int] , ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Any = do_resize if do_resize is not None else self.do_resize
SCREAMING_SNAKE_CASE : Optional[Any] = size if size is not None else self.size
SCREAMING_SNAKE_CASE : Union[str, Any] = get_size_dict(UpperCamelCase__ )
SCREAMING_SNAKE_CASE : Optional[Any] = keep_aspect_ratio if keep_aspect_ratio is not None else self.keep_aspect_ratio
SCREAMING_SNAKE_CASE : List[str] = ensure_multiple_of if ensure_multiple_of is not None else self.ensure_multiple_of
SCREAMING_SNAKE_CASE : Tuple = resample if resample is not None else self.resample
SCREAMING_SNAKE_CASE : Optional[Any] = do_rescale if do_rescale is not None else self.do_rescale
SCREAMING_SNAKE_CASE : Union[str, Any] = rescale_factor if rescale_factor is not None else self.rescale_factor
SCREAMING_SNAKE_CASE : str = do_normalize if do_normalize is not None else self.do_normalize
SCREAMING_SNAKE_CASE : List[Any] = image_mean if image_mean is not None else self.image_mean
SCREAMING_SNAKE_CASE : List[Any] = image_std if image_std is not None else self.image_std
SCREAMING_SNAKE_CASE : Dict = make_list_of_images(UpperCamelCase__ )
if not valid_images(UpperCamelCase__ ):
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.
SCREAMING_SNAKE_CASE : Tuple = [to_numpy_array(UpperCamelCase__ ) for image in images]
if do_resize:
SCREAMING_SNAKE_CASE : Dict = [self.resize(image=UpperCamelCase__ , size=UpperCamelCase__ , resample=UpperCamelCase__ ) for image in images]
if do_rescale:
SCREAMING_SNAKE_CASE : Any = [self.rescale(image=UpperCamelCase__ , scale=UpperCamelCase__ ) for image in images]
if do_normalize:
SCREAMING_SNAKE_CASE : Any = [self.normalize(image=UpperCamelCase__ , mean=UpperCamelCase__ , std=UpperCamelCase__ ) for image in images]
SCREAMING_SNAKE_CASE : Optional[int] = [to_channel_dimension_format(UpperCamelCase__ , UpperCamelCase__ ) for image in images]
SCREAMING_SNAKE_CASE : Tuple = {'''pixel_values''': images}
return BatchFeature(data=UpperCamelCase__ , tensor_type=UpperCamelCase__ )
def __A ( self : Tuple , UpperCamelCase__ : Union[str, Any] , UpperCamelCase__ : List[Tuple] = None ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Dict = outputs.logits
# Resize logits and compute semantic segmentation maps
if target_sizes is not None:
if len(UpperCamelCase__ ) != len(UpperCamelCase__ ):
raise ValueError(
'''Make sure that you pass in as many target sizes as the batch dimension of the logits''' )
if is_torch_tensor(UpperCamelCase__ ):
SCREAMING_SNAKE_CASE : List[Any] = target_sizes.numpy()
SCREAMING_SNAKE_CASE : Optional[int] = []
for idx in range(len(UpperCamelCase__ ) ):
SCREAMING_SNAKE_CASE : List[str] = torch.nn.functional.interpolate(
logits[idx].unsqueeze(dim=0 ) , size=target_sizes[idx] , mode='''bilinear''' , align_corners=UpperCamelCase__ )
SCREAMING_SNAKE_CASE : Dict = resized_logits[0].argmax(dim=0 )
semantic_segmentation.append(UpperCamelCase__ )
else:
SCREAMING_SNAKE_CASE : List[Any] = logits.argmax(dim=1 )
SCREAMING_SNAKE_CASE : List[Any] = [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0] )]
return semantic_segmentation
| 34
| 1
|
"""simple docstring"""
from collections import defaultdict
def lowerCamelCase_( _lowerCamelCase , _lowerCamelCase ) -> bool:
'''simple docstring'''
_lowerCamelCase : str = first_str.lower().strip()
_lowerCamelCase : Any = second_str.lower().strip()
# Remove whitespace
_lowerCamelCase : int = first_str.replace(" " , "" )
_lowerCamelCase : List[Any] = second_str.replace(" " , "" )
# Strings of different lengths are not anagrams
if len(_lowerCamelCase ) != len(_lowerCamelCase ):
return False
# Default values for count should be 0
_lowerCamelCase : defaultdict[str, int] = defaultdict(_lowerCamelCase )
# For each character in input strings,
# increment count in the corresponding
for i in range(len(_lowerCamelCase ) ):
count[first_str[i]] += 1
count[second_str[i]] -= 1
return all(_count == 0 for _count in count.values() )
if __name__ == "__main__":
from doctest import testmod
testmod()
_lowerCAmelCase : Union[str, Any] = input('''Enter the first string ''').strip()
_lowerCAmelCase : Optional[Any] = input('''Enter the second string ''').strip()
_lowerCAmelCase : Optional[Any] = check_anagrams(input_a, input_b)
print(f'''{input_a} and {input_b} are {'' if status else 'not '}anagrams.''')
| 46
|
from graphs.minimum_spanning_tree_kruskal import kruskal
def lowerCamelCase_ ( ):
'''simple docstring'''
UpperCamelCase__ = 9
UpperCamelCase__ = [
[0, 1, 4],
[0, 7, 8],
[1, 2, 8],
[7, 8, 7],
[7, 6, 1],
[2, 8, 2],
[8, 6, 6],
[2, 3, 7],
[2, 5, 4],
[6, 5, 2],
[3, 5, 14],
[3, 4, 9],
[5, 4, 10],
[1, 7, 11],
]
UpperCamelCase__ = kruskal(UpperCamelCase__, UpperCamelCase__ )
UpperCamelCase__ = [
[7, 6, 1],
[2, 8, 2],
[6, 5, 2],
[0, 1, 4],
[2, 5, 4],
[2, 3, 7],
[0, 7, 8],
[3, 4, 9],
]
assert sorted(UpperCamelCase__ ) == sorted(UpperCamelCase__ )
| 240
| 0
|
from __future__ import annotations
def a ( A__ : list[list[int]] ) -> int:
"""simple docstring"""
for i in range(1 , len(matrix[0] ) ):
matrix[0][i] += matrix[0][i - 1]
# preprocessing the first column
for i in range(1 , len(A__ ) ):
matrix[i][0] += matrix[i - 1][0]
# updating the path cost for current position
for i in range(1 , len(A__ ) ):
for j in range(1 , len(matrix[0] ) ):
matrix[i][j] += min(matrix[i - 1][j] , matrix[i][j - 1] )
return matrix[-1][-1]
if __name__ == "__main__":
import doctest
doctest.testmod()
| 380
|
import argparse
import os
from . import (
ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
BART_PRETRAINED_MODEL_ARCHIVE_LIST,
BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP,
DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP,
FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP,
LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP,
ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
T5_PRETRAINED_CONFIG_ARCHIVE_MAP,
TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP,
WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP,
XLM_PRETRAINED_CONFIG_ARCHIVE_MAP,
XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP,
AlbertConfig,
BartConfig,
BertConfig,
CamembertConfig,
CTRLConfig,
DistilBertConfig,
DPRConfig,
ElectraConfig,
FlaubertConfig,
GPTaConfig,
LayoutLMConfig,
LxmertConfig,
OpenAIGPTConfig,
RobertaConfig,
TaConfig,
TFAlbertForPreTraining,
TFBartForConditionalGeneration,
TFBartForSequenceClassification,
TFBertForPreTraining,
TFBertForQuestionAnswering,
TFBertForSequenceClassification,
TFCamembertForMaskedLM,
TFCTRLLMHeadModel,
TFDistilBertForMaskedLM,
TFDistilBertForQuestionAnswering,
TFDPRContextEncoder,
TFDPRQuestionEncoder,
TFDPRReader,
TFElectraForPreTraining,
TFFlaubertWithLMHeadModel,
TFGPTaLMHeadModel,
TFLayoutLMForMaskedLM,
TFLxmertForPreTraining,
TFLxmertVisualFeatureEncoder,
TFOpenAIGPTLMHeadModel,
TFRobertaForCausalLM,
TFRobertaForMaskedLM,
TFRobertaForSequenceClassification,
TFTaForConditionalGeneration,
TFTransfoXLLMHeadModel,
TFWavaVecaModel,
TFXLMRobertaForMaskedLM,
TFXLMWithLMHeadModel,
TFXLNetLMHeadModel,
TransfoXLConfig,
WavaVecaConfig,
WavaVecaModel,
XLMConfig,
XLMRobertaConfig,
XLNetConfig,
is_torch_available,
load_pytorch_checkpoint_in_tfa_model,
)
from .utils import CONFIG_NAME, WEIGHTS_NAME, cached_file, logging
if is_torch_available():
import numpy as np
import torch
from . import (
AlbertForPreTraining,
BartForConditionalGeneration,
BertForPreTraining,
BertForQuestionAnswering,
BertForSequenceClassification,
CamembertForMaskedLM,
CTRLLMHeadModel,
DistilBertForMaskedLM,
DistilBertForQuestionAnswering,
DPRContextEncoder,
DPRQuestionEncoder,
DPRReader,
ElectraForPreTraining,
FlaubertWithLMHeadModel,
GPTaLMHeadModel,
LayoutLMForMaskedLM,
LxmertForPreTraining,
LxmertVisualFeatureEncoder,
OpenAIGPTLMHeadModel,
RobertaForMaskedLM,
RobertaForSequenceClassification,
TaForConditionalGeneration,
TransfoXLLMHeadModel,
XLMRobertaForMaskedLM,
XLMWithLMHeadModel,
XLNetLMHeadModel,
)
logging.set_verbosity_info()
lowercase_ = {
'bart': (
BartConfig,
TFBartForConditionalGeneration,
TFBartForSequenceClassification,
BartForConditionalGeneration,
BART_PRETRAINED_MODEL_ARCHIVE_LIST,
),
'bert': (
BertConfig,
TFBertForPreTraining,
BertForPreTraining,
BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
),
'bert-large-uncased-whole-word-masking-finetuned-squad': (
BertConfig,
TFBertForQuestionAnswering,
BertForQuestionAnswering,
BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
),
'bert-large-cased-whole-word-masking-finetuned-squad': (
BertConfig,
TFBertForQuestionAnswering,
BertForQuestionAnswering,
BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
),
'bert-base-cased-finetuned-mrpc': (
BertConfig,
TFBertForSequenceClassification,
BertForSequenceClassification,
BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
),
'dpr': (
DPRConfig,
TFDPRQuestionEncoder,
TFDPRContextEncoder,
TFDPRReader,
DPRQuestionEncoder,
DPRContextEncoder,
DPRReader,
DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
),
'gpt2': (
GPTaConfig,
TFGPTaLMHeadModel,
GPTaLMHeadModel,
GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP,
),
'xlnet': (
XLNetConfig,
TFXLNetLMHeadModel,
XLNetLMHeadModel,
XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP,
),
'xlm': (
XLMConfig,
TFXLMWithLMHeadModel,
XLMWithLMHeadModel,
XLM_PRETRAINED_CONFIG_ARCHIVE_MAP,
),
'xlm-roberta': (
XLMRobertaConfig,
TFXLMRobertaForMaskedLM,
XLMRobertaForMaskedLM,
XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
),
'transfo-xl': (
TransfoXLConfig,
TFTransfoXLLMHeadModel,
TransfoXLLMHeadModel,
TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP,
),
'openai-gpt': (
OpenAIGPTConfig,
TFOpenAIGPTLMHeadModel,
OpenAIGPTLMHeadModel,
OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP,
),
'roberta': (
RobertaConfig,
TFRobertaForCausalLM,
TFRobertaForMaskedLM,
RobertaForMaskedLM,
ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
),
'layoutlm': (
LayoutLMConfig,
TFLayoutLMForMaskedLM,
LayoutLMForMaskedLM,
LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
),
'roberta-large-mnli': (
RobertaConfig,
TFRobertaForSequenceClassification,
RobertaForSequenceClassification,
ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
),
'camembert': (
CamembertConfig,
TFCamembertForMaskedLM,
CamembertForMaskedLM,
CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
),
'flaubert': (
FlaubertConfig,
TFFlaubertWithLMHeadModel,
FlaubertWithLMHeadModel,
FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
),
'distilbert': (
DistilBertConfig,
TFDistilBertForMaskedLM,
DistilBertForMaskedLM,
DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
),
'distilbert-base-distilled-squad': (
DistilBertConfig,
TFDistilBertForQuestionAnswering,
DistilBertForQuestionAnswering,
DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
),
'lxmert': (
LxmertConfig,
TFLxmertForPreTraining,
LxmertForPreTraining,
LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
),
'lxmert-visual-feature-encoder': (
LxmertConfig,
TFLxmertVisualFeatureEncoder,
LxmertVisualFeatureEncoder,
LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
),
'ctrl': (
CTRLConfig,
TFCTRLLMHeadModel,
CTRLLMHeadModel,
CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP,
),
'albert': (
AlbertConfig,
TFAlbertForPreTraining,
AlbertForPreTraining,
ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
),
't5': (
TaConfig,
TFTaForConditionalGeneration,
TaForConditionalGeneration,
T5_PRETRAINED_CONFIG_ARCHIVE_MAP,
),
'electra': (
ElectraConfig,
TFElectraForPreTraining,
ElectraForPreTraining,
ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP,
),
'wav2vec2': (
WavaVecaConfig,
TFWavaVecaModel,
WavaVecaModel,
WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP,
),
}
def a ( A__ : Tuple , A__ : List[Any] , A__ : Optional[int] , A__ : Dict , A__ : Any=False , A__ : str=True ) -> str:
"""simple docstring"""
if model_type not in MODEL_CLASSES:
raise ValueError(F'''Unrecognized model type, should be one of {list(MODEL_CLASSES.keys() )}.''' )
_lowercase , _lowercase , _lowercase , _lowercase =MODEL_CLASSES[model_type]
# Initialise TF model
if config_file in aws_config_map:
_lowercase =cached_file(A__ , A__ , force_download=not use_cached_models )
_lowercase =config_class.from_json_file(A__ )
_lowercase =True
_lowercase =True
print(F'''Building TensorFlow model from configuration: {config}''' )
_lowercase =model_class(A__ )
# Load weights from tf checkpoint
if pytorch_checkpoint_path in aws_config_map.keys():
_lowercase =cached_file(
A__ , A__ , force_download=not use_cached_models )
# Load PyTorch checkpoint in tf2 model:
_lowercase =load_pytorch_checkpoint_in_tfa_model(A__ , A__ )
if compare_with_pt_model:
_lowercase =tf_model(tf_model.dummy_inputs , training=A__ ) # build the network
_lowercase =torch.load(A__ , map_location='cpu' )
_lowercase =pt_model_class.from_pretrained(
pretrained_model_name_or_path=A__ , config=A__ , state_dict=A__ )
with torch.no_grad():
_lowercase =pt_model(**pt_model.dummy_inputs )
_lowercase =pto[0].numpy()
_lowercase =tfo[0].numpy()
_lowercase =np.amax(np.abs(np_pt - np_tf ) )
print(F'''Max absolute difference between models outputs {diff}''' )
assert diff <= 2e-2, F'''Error, model absolute difference is >2e-2: {diff}'''
# Save pytorch-model
print(F'''Save TensorFlow model to {tf_dump_path}''' )
tf_model.save_weights(A__ , save_format='h5' )
def a ( A__ : str , A__ : str , A__ : Optional[Any]=None , A__ : Any=None , A__ : Optional[int]=False , A__ : Optional[int]=False , A__ : int=False , A__ : str=False , ) -> List[Any]:
"""simple docstring"""
if args_model_type is None:
_lowercase =list(MODEL_CLASSES.keys() )
else:
_lowercase =[args_model_type]
for j, model_type in enumerate(A__ , start=1 ):
print('=' * 100 )
print(F''' Converting model type {j}/{len(A__ )}: {model_type}''' )
print('=' * 100 )
if model_type not in MODEL_CLASSES:
raise ValueError(F'''Unrecognized model type {model_type}, should be one of {list(MODEL_CLASSES.keys() )}.''' )
_lowercase , _lowercase , _lowercase , _lowercase , _lowercase =MODEL_CLASSES[model_type]
if model_shortcut_names_or_path is None:
_lowercase =list(aws_model_maps.keys() )
if config_shortcut_names_or_path is None:
_lowercase =model_shortcut_names_or_path
for i, (model_shortcut_name, config_shortcut_name) in enumerate(
zip(A__ , A__ ) , start=1 ):
print('-' * 100 )
if "-squad" in model_shortcut_name or "-mrpc" in model_shortcut_name or "-mnli" in model_shortcut_name:
if not only_convert_finetuned_models:
print(F''' Skipping finetuned checkpoint {model_shortcut_name}''' )
continue
_lowercase =model_shortcut_name
elif only_convert_finetuned_models:
print(F''' Skipping not finetuned checkpoint {model_shortcut_name}''' )
continue
print(
F''' Converting checkpoint {i}/{len(A__ )}: {model_shortcut_name} - model_type {model_type}''' )
print('-' * 100 )
if config_shortcut_name in aws_config_map:
_lowercase =cached_file(A__ , A__ , force_download=not use_cached_models )
else:
_lowercase =config_shortcut_name
if model_shortcut_name in aws_model_maps:
_lowercase =cached_file(A__ , A__ , force_download=not use_cached_models )
else:
_lowercase =model_shortcut_name
if os.path.isfile(A__ ):
_lowercase ='converted_model'
convert_pt_checkpoint_to_tf(
model_type=A__ , pytorch_checkpoint_path=A__ , config_file=A__ , tf_dump_path=os.path.join(A__ , model_shortcut_name + '-tf_model.h5' ) , compare_with_pt_model=A__ , )
if remove_cached_files:
os.remove(A__ )
os.remove(A__ )
if __name__ == "__main__":
lowercase_ = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--tf_dump_path', default=None, type=str, required=True, help='Path to the output Tensorflow dump file.'
)
parser.add_argument(
'--model_type',
default=None,
type=str,
help=(
f"Model type selected in the list of {list(MODEL_CLASSES.keys())}. If not given, will download and "
'convert all the models from AWS.'
),
)
parser.add_argument(
'--pytorch_checkpoint_path',
default=None,
type=str,
help=(
'Path to the PyTorch checkpoint path or shortcut name to download from AWS. '
'If not given, will download and convert all the checkpoints from AWS.'
),
)
parser.add_argument(
'--config_file',
default=None,
type=str,
help=(
'The config json file corresponding to the pre-trained model. \n'
'This specifies the model architecture. If not given and '
'--pytorch_checkpoint_path is not given or is a shortcut name '
'use the configuration associated to the shortcut name on the AWS'
),
)
parser.add_argument(
'--compare_with_pt_model', action='store_true', help='Compare Tensorflow and PyTorch model predictions.'
)
parser.add_argument(
'--use_cached_models',
action='store_true',
help='Use cached models if possible instead of updating to latest checkpoint versions.',
)
parser.add_argument(
'--remove_cached_files',
action='store_true',
help='Remove pytorch models after conversion (save memory when converting in batches).',
)
parser.add_argument('--only_convert_finetuned_models', action='store_true', help='Only convert finetuned models.')
lowercase_ = parser.parse_args()
# if args.pytorch_checkpoint_path is not None:
# convert_pt_checkpoint_to_tf(args.model_type.lower(),
# args.pytorch_checkpoint_path,
# args.config_file if args.config_file is not None else args.pytorch_checkpoint_path,
# args.tf_dump_path,
# compare_with_pt_model=args.compare_with_pt_model,
# use_cached_models=args.use_cached_models)
# else:
convert_all_pt_checkpoints_to_tf(
args.model_type.lower() if args.model_type is not None else None,
args.tf_dump_path,
model_shortcut_names_or_path=[args.pytorch_checkpoint_path]
if args.pytorch_checkpoint_path is not None
else None,
config_shortcut_names_or_path=[args.config_file] if args.config_file is not None else None,
compare_with_pt_model=args.compare_with_pt_model,
use_cached_models=args.use_cached_models,
remove_cached_files=args.remove_cached_files,
only_convert_finetuned_models=args.only_convert_finetuned_models,
)
| 380
| 1
|
'''simple docstring'''
import numpy
class SCREAMING_SNAKE_CASE__ :
def __init__( self : Any , a_ : numpy.ndarray , a_ : numpy.ndarray ):
"""simple docstring"""
__snake_case = input_array
# Random initial weights are assigned where first argument is the
# number of nodes in previous layer and second argument is the
# number of nodes in the next layer.
# Random initial weights are assigned.
# self.input_array.shape[1] is used to represent number of nodes in input layer.
# First hidden layer consists of 4 nodes.
__snake_case = numpy.random.rand(
self.input_array.shape[1] , 4 )
# Random initial values for the first hidden layer.
# First hidden layer has 4 nodes.
# Second hidden layer has 3 nodes.
__snake_case = numpy.random.rand(
4 , 3 )
# Random initial values for the second hidden layer.
# Second hidden layer has 3 nodes.
# Output layer has 1 node.
__snake_case = numpy.random.rand(3 , 1 )
# Real output values provided.
__snake_case = output_array
# Predicted output values by the neural network.
# Predicted_output array initially consists of zeroes.
__snake_case = numpy.zeros(output_array.shape )
def A ( self : Union[str, Any] ):
"""simple docstring"""
__snake_case = sigmoid(
numpy.dot(self.input_array , self.input_layer_and_first_hidden_layer_weights ) )
# layer_between_first_hidden_layer_and_second_hidden_layer is the layer
# connecting the first hidden set of nodes with the second hidden set of nodes.
__snake_case = sigmoid(
numpy.dot(
self.layer_between_input_and_first_hidden_layer , self.first_hidden_layer_and_second_hidden_layer_weights , ) )
# layer_between_second_hidden_layer_and_output is the layer connecting
# second hidden layer with the output node.
__snake_case = sigmoid(
numpy.dot(
self.layer_between_first_hidden_layer_and_second_hidden_layer , self.second_hidden_layer_and_output_layer_weights , ) )
return self.layer_between_second_hidden_layer_and_output
def A ( self : Optional[Any] ):
"""simple docstring"""
__snake_case = numpy.dot(
self.layer_between_first_hidden_layer_and_second_hidden_layer.T , 2
* (self.output_array - self.predicted_output)
* sigmoid_derivative(self.predicted_output ) , )
__snake_case = numpy.dot(
self.layer_between_input_and_first_hidden_layer.T , numpy.dot(
2
* (self.output_array - self.predicted_output)
* sigmoid_derivative(self.predicted_output ) , self.second_hidden_layer_and_output_layer_weights.T , )
* sigmoid_derivative(
self.layer_between_first_hidden_layer_and_second_hidden_layer ) , )
__snake_case = numpy.dot(
self.input_array.T , numpy.dot(
numpy.dot(
2
* (self.output_array - self.predicted_output)
* sigmoid_derivative(self.predicted_output ) , self.second_hidden_layer_and_output_layer_weights.T , )
* sigmoid_derivative(
self.layer_between_first_hidden_layer_and_second_hidden_layer ) , self.first_hidden_layer_and_second_hidden_layer_weights.T , )
* sigmoid_derivative(self.layer_between_input_and_first_hidden_layer ) , )
self.input_layer_and_first_hidden_layer_weights += (
updated_input_layer_and_first_hidden_layer_weights
)
self.first_hidden_layer_and_second_hidden_layer_weights += (
updated_first_hidden_layer_and_second_hidden_layer_weights
)
self.second_hidden_layer_and_output_layer_weights += (
updated_second_hidden_layer_and_output_layer_weights
)
def A ( self : Union[str, Any] , a_ : numpy.ndarray , a_ : int , a_ : bool ):
"""simple docstring"""
for iteration in range(1 , iterations + 1 ):
__snake_case = self.feedforward()
self.back_propagation()
if give_loss:
__snake_case = numpy.mean(numpy.square(output - self.feedforward() ) )
print(f'''Iteration {iteration} Loss: {loss}''' )
def A ( self : Optional[Any] , a_ : numpy.ndarray ):
"""simple docstring"""
__snake_case = input_arr
__snake_case = sigmoid(
numpy.dot(self.array , self.input_layer_and_first_hidden_layer_weights ) )
__snake_case = sigmoid(
numpy.dot(
self.layer_between_input_and_first_hidden_layer , self.first_hidden_layer_and_second_hidden_layer_weights , ) )
__snake_case = sigmoid(
numpy.dot(
self.layer_between_first_hidden_layer_and_second_hidden_layer , self.second_hidden_layer_and_output_layer_weights , ) )
return int(self.layer_between_second_hidden_layer_and_output > 0.6 )
def __UpperCAmelCase ( _UpperCAmelCase : numpy.ndarray ) -> numpy.ndarray:
return 1 / (1 + numpy.exp(-value ))
def __UpperCAmelCase ( _UpperCAmelCase : numpy.ndarray ) -> numpy.ndarray:
return (value) * (1 - (value))
def __UpperCAmelCase ( ) -> int:
__snake_case = numpy.array(
(
[0, 0, 0],
[0, 0, 1],
[0, 1, 0],
[0, 1, 1],
[1, 0, 0],
[1, 0, 1],
[1, 1, 0],
[1, 1, 1],
) , dtype=numpy.floataa , )
# True output values for the given input values.
__snake_case = numpy.array(([0], [1], [1], [0], [1], [0], [0], [1]) , dtype=numpy.floataa )
# Calling neural network class.
__snake_case = TwoHiddenLayerNeuralNetwork(
input_array=_UpperCAmelCase , output_array=_UpperCAmelCase )
# Calling training function.
# Set give_loss to True if you want to see loss in every iteration.
neural_network.train(output=_UpperCAmelCase , iterations=10 , give_loss=_UpperCAmelCase )
return neural_network.predict(numpy.array(([1, 1, 1]) , dtype=numpy.floataa ) )
if __name__ == "__main__":
example()
| 69
|
def __lowerCamelCase ( _lowerCAmelCase ) -> list:
_UpperCAmelCase = len(_lowerCAmelCase )
for i in range(1 , _lowerCAmelCase ):
_UpperCAmelCase = collection[i]
_UpperCAmelCase = 0
_UpperCAmelCase = i - 1
while low <= high:
_UpperCAmelCase = (low + high) // 2
if val < collection[mid]:
_UpperCAmelCase = mid - 1
else:
_UpperCAmelCase = mid + 1
for j in range(_lowerCAmelCase , _lowerCAmelCase , -1 ):
_UpperCAmelCase = collection[j - 1]
_UpperCAmelCase = val
return collection
if __name__ == "__main__":
__lowerCAmelCase = input("Enter numbers separated by a comma:\n").strip()
__lowerCAmelCase = [int(item) for item in user_input.split(",")]
print(binary_insertion_sort(unsorted))
| 684
| 0
|
from typing import Optional, Tuple, Union
import tensorflow as tf
from ...activations_tf import ACTaFN
from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward
from ...modeling_tf_outputs import (
TFBaseModelOutputWithNoAttention,
TFBaseModelOutputWithPoolingAndNoAttention,
TFSequenceClassifierOutput,
)
from ...modeling_tf_utils import TFPreTrainedModel, TFSequenceClassificationLoss, keras_serializable, unpack_inputs
from ...tf_utils import shape_list
from ...utils import logging
from .configuration_regnet import RegNetConfig
__lowerCamelCase : Any = logging.get_logger(__name__)
# General docstring
__lowerCamelCase : List[Any] = '''RegNetConfig'''
# Base docstring
__lowerCamelCase : Optional[int] = '''facebook/regnet-y-040'''
__lowerCamelCase : Tuple = [1, 1088, 7, 7]
# Image classification docstring
__lowerCamelCase : Any = '''facebook/regnet-y-040'''
__lowerCamelCase : Tuple = '''tabby, tabby cat'''
__lowerCamelCase : Optional[Any] = [
'''facebook/regnet-y-040''',
# See all regnet models at https://huggingface.co/models?filter=regnet
]
class __snake_case ( tf.keras.layers.Layer ):
def __init__( self : Any , _lowercase : int , _lowercase : int = 3 , _lowercase : int = 1 , _lowercase : int = 1 , _lowercase : Optional[str] = "relu" , **_lowercase : Optional[Any] , ):
"""simple docstring"""
super().__init__(**_lowercase )
# The padding and conv has been verified in
# https://colab.research.google.com/gist/sayakpaul/854bc10eeaf21c9ee2119e0b9f3841a7/scratchpad.ipynb
SCREAMING_SNAKE_CASE__ = tf.keras.layers.ZeroPaddingaD(padding=kernel_size // 2 )
SCREAMING_SNAKE_CASE__ = tf.keras.layers.ConvaD(
filters=_lowercase , kernel_size=_lowercase , strides=_lowercase , padding="""VALID""" , groups=_lowercase , use_bias=_lowercase , name="""convolution""" , )
SCREAMING_SNAKE_CASE__ = tf.keras.layers.BatchNormalization(epsilon=1E-5 , momentum=0.9 , name="""normalization""" )
SCREAMING_SNAKE_CASE__ = ACTaFN[activation] if activation is not None else tf.identity
def __a ( self : Any , _lowercase : Any ):
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = self.convolution(self.padding(_lowercase ) )
SCREAMING_SNAKE_CASE__ = self.normalization(_lowercase )
SCREAMING_SNAKE_CASE__ = self.activation(_lowercase )
return hidden_state
class __snake_case ( tf.keras.layers.Layer ):
def __init__( self : Union[str, Any] , _lowercase : RegNetConfig , **_lowercase : Union[str, Any] ):
"""simple docstring"""
super().__init__(**_lowercase )
SCREAMING_SNAKE_CASE__ = config.num_channels
SCREAMING_SNAKE_CASE__ = TFRegNetConvLayer(
out_channels=config.embedding_size , kernel_size=3 , stride=2 , activation=config.hidden_act , name="""embedder""" , )
def __a ( self : List[str] , _lowercase : Optional[int] ):
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = shape_list(_lowercase )[1]
if tf.executing_eagerly() and num_channels != self.num_channels:
raise ValueError(
"""Make sure that the channel dimension of the pixel values match with the one set in the configuration.""" )
# When running on CPU, `tf.keras.layers.Conv2D` doesn't support `NCHW` format.
# So change the input format from `NCHW` to `NHWC`.
# shape = (batch_size, in_height, in_width, in_channels=num_channels)
SCREAMING_SNAKE_CASE__ = tf.transpose(_lowercase , perm=(0, 2, 3, 1) )
SCREAMING_SNAKE_CASE__ = self.embedder(_lowercase )
return hidden_state
class __snake_case ( tf.keras.layers.Layer ):
def __init__( self : Tuple , _lowercase : int , _lowercase : int = 2 , **_lowercase : Optional[int] ):
"""simple docstring"""
super().__init__(**_lowercase )
SCREAMING_SNAKE_CASE__ = tf.keras.layers.ConvaD(
filters=_lowercase , kernel_size=1 , strides=_lowercase , use_bias=_lowercase , name="""convolution""" )
SCREAMING_SNAKE_CASE__ = tf.keras.layers.BatchNormalization(epsilon=1E-5 , momentum=0.9 , name="""normalization""" )
def __a ( self : List[Any] , _lowercase : tf.Tensor , _lowercase : bool = False ):
"""simple docstring"""
return self.normalization(self.convolution(_lowercase ) , training=_lowercase )
class __snake_case ( tf.keras.layers.Layer ):
def __init__( self : int , _lowercase : int , _lowercase : int , **_lowercase : Optional[Any] ):
"""simple docstring"""
super().__init__(**_lowercase )
SCREAMING_SNAKE_CASE__ = tf.keras.layers.GlobalAveragePoolingaD(keepdims=_lowercase , name="""pooler""" )
SCREAMING_SNAKE_CASE__ = [
tf.keras.layers.ConvaD(filters=_lowercase , kernel_size=1 , activation="""relu""" , name="""attention.0""" ),
tf.keras.layers.ConvaD(filters=_lowercase , kernel_size=1 , activation="""sigmoid""" , name="""attention.2""" ),
]
def __a ( self : str , _lowercase : Tuple ):
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = self.pooler(_lowercase )
for layer_module in self.attention:
SCREAMING_SNAKE_CASE__ = layer_module(_lowercase )
SCREAMING_SNAKE_CASE__ = hidden_state * pooled
return hidden_state
class __snake_case ( tf.keras.layers.Layer ):
def __init__( self : List[str] , _lowercase : RegNetConfig , _lowercase : int , _lowercase : int , _lowercase : int = 1 , **_lowercase : List[str] ):
"""simple docstring"""
super().__init__(**_lowercase )
SCREAMING_SNAKE_CASE__ = in_channels != out_channels or stride != 1
SCREAMING_SNAKE_CASE__ = max(1 , out_channels // config.groups_width )
SCREAMING_SNAKE_CASE__ = (
TFRegNetShortCut(_lowercase , stride=_lowercase , name="""shortcut""" )
if should_apply_shortcut
else tf.keras.layers.Activation("""linear""" , name="""shortcut""" )
)
# `self.layers` instead of `self.layer` because that is a reserved argument.
SCREAMING_SNAKE_CASE__ = [
TFRegNetConvLayer(_lowercase , kernel_size=1 , activation=config.hidden_act , name="""layer.0""" ),
TFRegNetConvLayer(
_lowercase , stride=_lowercase , groups=_lowercase , activation=config.hidden_act , name="""layer.1""" ),
TFRegNetConvLayer(_lowercase , kernel_size=1 , activation=_lowercase , name="""layer.2""" ),
]
SCREAMING_SNAKE_CASE__ = ACTaFN[config.hidden_act]
def __a ( self : int , _lowercase : str ):
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = hidden_state
for layer_module in self.layers:
SCREAMING_SNAKE_CASE__ = layer_module(_lowercase )
SCREAMING_SNAKE_CASE__ = self.shortcut(_lowercase )
hidden_state += residual
SCREAMING_SNAKE_CASE__ = self.activation(_lowercase )
return hidden_state
class __snake_case ( tf.keras.layers.Layer ):
def __init__( self : str , _lowercase : RegNetConfig , _lowercase : int , _lowercase : int , _lowercase : int = 1 , **_lowercase : int ):
"""simple docstring"""
super().__init__(**_lowercase )
SCREAMING_SNAKE_CASE__ = in_channels != out_channels or stride != 1
SCREAMING_SNAKE_CASE__ = max(1 , out_channels // config.groups_width )
SCREAMING_SNAKE_CASE__ = (
TFRegNetShortCut(_lowercase , stride=_lowercase , name="""shortcut""" )
if should_apply_shortcut
else tf.keras.layers.Activation("""linear""" , name="""shortcut""" )
)
SCREAMING_SNAKE_CASE__ = [
TFRegNetConvLayer(_lowercase , kernel_size=1 , activation=config.hidden_act , name="""layer.0""" ),
TFRegNetConvLayer(
_lowercase , stride=_lowercase , groups=_lowercase , activation=config.hidden_act , name="""layer.1""" ),
TFRegNetSELayer(_lowercase , reduced_channels=int(round(in_channels / 4 ) ) , name="""layer.2""" ),
TFRegNetConvLayer(_lowercase , kernel_size=1 , activation=_lowercase , name="""layer.3""" ),
]
SCREAMING_SNAKE_CASE__ = ACTaFN[config.hidden_act]
def __a ( self : List[Any] , _lowercase : Optional[Any] ):
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = hidden_state
for layer_module in self.layers:
SCREAMING_SNAKE_CASE__ = layer_module(_lowercase )
SCREAMING_SNAKE_CASE__ = self.shortcut(_lowercase )
hidden_state += residual
SCREAMING_SNAKE_CASE__ = self.activation(_lowercase )
return hidden_state
class __snake_case ( tf.keras.layers.Layer ):
def __init__( self : Optional[int] , _lowercase : RegNetConfig , _lowercase : int , _lowercase : int , _lowercase : int = 2 , _lowercase : int = 2 , **_lowercase : Tuple ):
"""simple docstring"""
super().__init__(**_lowercase )
SCREAMING_SNAKE_CASE__ = TFRegNetXLayer if config.layer_type == """x""" else TFRegNetYLayer
SCREAMING_SNAKE_CASE__ = [
# downsampling is done in the first layer with stride of 2
layer(_lowercase , _lowercase , _lowercase , stride=_lowercase , name="""layers.0""" ),
*[layer(_lowercase , _lowercase , _lowercase , name=f"""layers.{i+1}""" ) for i in range(depth - 1 )],
]
def __a ( self : Optional[Any] , _lowercase : Tuple ):
"""simple docstring"""
for layer_module in self.layers:
SCREAMING_SNAKE_CASE__ = layer_module(_lowercase )
return hidden_state
class __snake_case ( tf.keras.layers.Layer ):
def __init__( self : int , _lowercase : RegNetConfig , **_lowercase : int ):
"""simple docstring"""
super().__init__(**_lowercase )
SCREAMING_SNAKE_CASE__ = []
# based on `downsample_in_first_stage`, the first layer of the first stage may or may not downsample the input
self.stages.append(
TFRegNetStage(
_lowercase , config.embedding_size , config.hidden_sizes[0] , stride=2 if config.downsample_in_first_stage else 1 , depth=config.depths[0] , name="""stages.0""" , ) )
SCREAMING_SNAKE_CASE__ = zip(config.hidden_sizes , config.hidden_sizes[1:] )
for i, ((in_channels, out_channels), depth) in enumerate(zip(_lowercase , config.depths[1:] ) ):
self.stages.append(TFRegNetStage(_lowercase , _lowercase , _lowercase , depth=_lowercase , name=f"""stages.{i+1}""" ) )
def __a ( self : List[Any] , _lowercase : tf.Tensor , _lowercase : bool = False , _lowercase : bool = True ):
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = () if output_hidden_states else None
for stage_module in self.stages:
if output_hidden_states:
SCREAMING_SNAKE_CASE__ = hidden_states + (hidden_state,)
SCREAMING_SNAKE_CASE__ = stage_module(_lowercase )
if output_hidden_states:
SCREAMING_SNAKE_CASE__ = hidden_states + (hidden_state,)
if not return_dict:
return tuple(v for v in [hidden_state, hidden_states] if v is not None )
return TFBaseModelOutputWithNoAttention(last_hidden_state=_lowercase , hidden_states=_lowercase )
@keras_serializable
class __snake_case ( tf.keras.layers.Layer ):
lowerCAmelCase_ = RegNetConfig
def __init__( self : Dict , _lowercase : List[str] , **_lowercase : Union[str, Any] ):
"""simple docstring"""
super().__init__(**_lowercase )
SCREAMING_SNAKE_CASE__ = config
SCREAMING_SNAKE_CASE__ = TFRegNetEmbeddings(_lowercase , name="""embedder""" )
SCREAMING_SNAKE_CASE__ = TFRegNetEncoder(_lowercase , name="""encoder""" )
SCREAMING_SNAKE_CASE__ = tf.keras.layers.GlobalAveragePoolingaD(keepdims=_lowercase , name="""pooler""" )
@unpack_inputs
def __a ( self : int , _lowercase : tf.Tensor , _lowercase : Optional[bool] = None , _lowercase : Optional[bool] = None , _lowercase : bool = False , ):
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
SCREAMING_SNAKE_CASE__ = return_dict if return_dict is not None else self.config.use_return_dict
SCREAMING_SNAKE_CASE__ = self.embedder(_lowercase , training=_lowercase )
SCREAMING_SNAKE_CASE__ = self.encoder(
_lowercase , output_hidden_states=_lowercase , return_dict=_lowercase , training=_lowercase )
SCREAMING_SNAKE_CASE__ = encoder_outputs[0]
SCREAMING_SNAKE_CASE__ = self.pooler(_lowercase )
# Change to NCHW output format have uniformity in the modules
SCREAMING_SNAKE_CASE__ = tf.transpose(_lowercase , perm=(0, 3, 1, 2) )
SCREAMING_SNAKE_CASE__ = tf.transpose(_lowercase , perm=(0, 3, 1, 2) )
# Change the other hidden state outputs to NCHW as well
if output_hidden_states:
SCREAMING_SNAKE_CASE__ = tuple([tf.transpose(_lowercase , perm=(0, 3, 1, 2) ) for h in encoder_outputs[1]] )
if not return_dict:
return (last_hidden_state, pooled_output) + encoder_outputs[1:]
return TFBaseModelOutputWithPoolingAndNoAttention(
last_hidden_state=_lowercase , pooler_output=_lowercase , hidden_states=hidden_states if output_hidden_states else encoder_outputs.hidden_states , )
class __snake_case ( lowerCamelCase_ ):
lowerCAmelCase_ = RegNetConfig
lowerCAmelCase_ = "regnet"
lowerCAmelCase_ = "pixel_values"
@property
def __a ( self : str ):
"""simple docstring"""
return {"pixel_values": tf.TensorSpec(shape=(None, self.config.num_channels, 2_24, 2_24) , dtype=tf.floataa )}
__lowerCamelCase : Optional[int] = r'''
Parameters:
This model is a Tensorflow
[tf.keras.layers.Layer](https://www.tensorflow.org/api_docs/python/tf/keras/layers/Layer) sub-class. Use it as a
regular Tensorflow Module and refer to the Tensorflow documentation for all matter related to general usage and
behavior.
config ([`RegNetConfig`]): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the [`~TFPreTrainedModel.from_pretrained`] method to load the model weights.
'''
__lowerCamelCase : Optional[Any] = r'''
Args:
pixel_values (`tf.Tensor` of shape `(batch_size, num_channels, height, width)`):
Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See
[`ConveNextImageProcessor.__call__`] for details.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
more detail.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
'''
@add_start_docstrings(
"The bare RegNet model outputting raw features without any specific head on top." , lowerCamelCase_ , )
class __snake_case ( lowerCamelCase_ ):
def __init__( self : Dict , _lowercase : RegNetConfig , *_lowercase : List[Any] , **_lowercase : Optional[int] ):
"""simple docstring"""
super().__init__(_lowercase , *_lowercase , **_lowercase )
SCREAMING_SNAKE_CASE__ = TFRegNetMainLayer(_lowercase , name="""regnet""" )
@unpack_inputs
@add_start_docstrings_to_model_forward(_lowercase )
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC , output_type=_lowercase , config_class=_CONFIG_FOR_DOC , modality="""vision""" , expected_output=_EXPECTED_OUTPUT_SHAPE , )
def __a ( self : str , _lowercase : tf.Tensor , _lowercase : Optional[bool] = None , _lowercase : Optional[bool] = None , _lowercase : List[Any]=False , ):
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
SCREAMING_SNAKE_CASE__ = return_dict if return_dict is not None else self.config.use_return_dict
SCREAMING_SNAKE_CASE__ = self.regnet(
pixel_values=_lowercase , output_hidden_states=_lowercase , return_dict=_lowercase , training=_lowercase , )
if not return_dict:
return (outputs[0],) + outputs[1:]
return TFBaseModelOutputWithPoolingAndNoAttention(
last_hidden_state=outputs.last_hidden_state , pooler_output=outputs.pooler_output , hidden_states=outputs.hidden_states , )
@add_start_docstrings(
"\n RegNet Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for\n ImageNet.\n " , lowerCamelCase_ , )
class __snake_case ( lowerCamelCase_ , lowerCamelCase_ ):
def __init__( self : Tuple , _lowercase : RegNetConfig , *_lowercase : List[str] , **_lowercase : Tuple ):
"""simple docstring"""
super().__init__(_lowercase , *_lowercase , **_lowercase )
SCREAMING_SNAKE_CASE__ = config.num_labels
SCREAMING_SNAKE_CASE__ = TFRegNetMainLayer(_lowercase , name="""regnet""" )
# classification head
SCREAMING_SNAKE_CASE__ = [
tf.keras.layers.Flatten(),
tf.keras.layers.Dense(config.num_labels , name="""classifier.1""" ) if config.num_labels > 0 else tf.identity,
]
@unpack_inputs
@add_start_docstrings_to_model_forward(_lowercase )
@add_code_sample_docstrings(
checkpoint=_IMAGE_CLASS_CHECKPOINT , output_type=_lowercase , config_class=_CONFIG_FOR_DOC , expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT , )
def __a ( self : int , _lowercase : tf.Tensor = None , _lowercase : tf.Tensor = None , _lowercase : bool = None , _lowercase : bool = None , _lowercase : Optional[int]=False , ):
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
SCREAMING_SNAKE_CASE__ = return_dict if return_dict is not None else self.config.use_return_dict
SCREAMING_SNAKE_CASE__ = self.regnet(
_lowercase , output_hidden_states=_lowercase , return_dict=_lowercase , training=_lowercase )
SCREAMING_SNAKE_CASE__ = outputs.pooler_output if return_dict else outputs[1]
SCREAMING_SNAKE_CASE__ = self.classifier[0](_lowercase )
SCREAMING_SNAKE_CASE__ = self.classifier[1](_lowercase )
SCREAMING_SNAKE_CASE__ = None if labels is None else self.hf_compute_loss(labels=_lowercase , logits=_lowercase )
if not return_dict:
SCREAMING_SNAKE_CASE__ = (logits,) + outputs[2:]
return ((loss,) + output) if loss is not None else output
return TFSequenceClassifierOutput(loss=_lowercase , logits=_lowercase , hidden_states=outputs.hidden_states )
| 379
|
import unittest
from pathlib import Path
from tempfile import NamedTemporaryFile, TemporaryDirectory
from transformers import BertConfig, BertTokenizerFast, FeatureExtractionPipeline
from transformers.convert_graph_to_onnx import (
convert,
ensure_valid_input,
generate_identified_filename,
infer_shapes,
quantize,
)
from transformers.testing_utils import require_tf, require_tokenizers, require_torch, slow
class __snake_case :
def __a ( self : str , _lowercase : str , _lowercase : Dict , _lowercase : List[Any] ):
"""simple docstring"""
return None
class __snake_case :
def __a ( self : List[str] , _lowercase : List[str] , _lowercase : str , _lowercase : List[Any] , _lowercase : str ):
"""simple docstring"""
return None
class __snake_case ( unittest.TestCase ):
lowerCAmelCase_ = [
# (model_name, model_kwargs)
("bert-base-cased", {}),
("gpt2", {"use_cache": False}), # We don't support exporting GPT2 past keys anymore
]
@require_tf
@slow
def __a ( self : List[Any] ):
"""simple docstring"""
for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
self._test_export(_lowercase , """tf""" , 12 , **_lowercase )
@require_torch
@slow
def __a ( self : Optional[Any] ):
"""simple docstring"""
for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
self._test_export(_lowercase , """pt""" , 12 , **_lowercase )
@require_torch
@slow
def __a ( self : Dict ):
"""simple docstring"""
from transformers import BertModel
SCREAMING_SNAKE_CASE__ = ["""[UNK]""", """[SEP]""", """[CLS]""", """[PAD]""", """[MASK]""", """some""", """other""", """words"""]
with NamedTemporaryFile(mode="""w+t""" ) as vocab_file:
vocab_file.write("""\n""".join(_lowercase ) )
vocab_file.flush()
SCREAMING_SNAKE_CASE__ = BertTokenizerFast(vocab_file.name )
with TemporaryDirectory() as bert_save_dir:
SCREAMING_SNAKE_CASE__ = BertModel(BertConfig(vocab_size=len(_lowercase ) ) )
model.save_pretrained(_lowercase )
self._test_export(_lowercase , """pt""" , 12 , _lowercase )
@require_tf
@slow
def __a ( self : Dict ):
"""simple docstring"""
for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
SCREAMING_SNAKE_CASE__ = self._test_export(_lowercase , """tf""" , 12 , **_lowercase )
SCREAMING_SNAKE_CASE__ = quantize(Path(_lowercase ) )
# Ensure the actual quantized model is not bigger than the original one
if quantized_path.stat().st_size >= Path(_lowercase ).stat().st_size:
self.fail("""Quantized model is bigger than initial ONNX model""" )
@require_torch
@slow
def __a ( self : Any ):
"""simple docstring"""
for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
SCREAMING_SNAKE_CASE__ = self._test_export(_lowercase , """pt""" , 12 , **_lowercase )
SCREAMING_SNAKE_CASE__ = quantize(_lowercase )
# Ensure the actual quantized model is not bigger than the original one
if quantized_path.stat().st_size >= Path(_lowercase ).stat().st_size:
self.fail("""Quantized model is bigger than initial ONNX model""" )
def __a ( self : Union[str, Any] , _lowercase : List[str] , _lowercase : Tuple , _lowercase : Optional[Any] , _lowercase : str=None , **_lowercase : List[str] ):
"""simple docstring"""
try:
# Compute path
with TemporaryDirectory() as tempdir:
SCREAMING_SNAKE_CASE__ = Path(_lowercase ).joinpath("""model.onnx""" )
# Remove folder if exists
if path.parent.exists():
path.parent.rmdir()
# Export
convert(_lowercase , _lowercase , _lowercase , _lowercase , _lowercase , **_lowercase )
return path
except Exception as e:
self.fail(_lowercase )
@require_torch
@require_tokenizers
@slow
def __a ( self : List[str] ):
"""simple docstring"""
from transformers import BertModel
SCREAMING_SNAKE_CASE__ = BertModel(BertConfig.from_pretrained("""lysandre/tiny-bert-random""" ) )
SCREAMING_SNAKE_CASE__ = BertTokenizerFast.from_pretrained("""lysandre/tiny-bert-random""" )
self._test_infer_dynamic_axis(_lowercase , _lowercase , """pt""" )
@require_tf
@require_tokenizers
@slow
def __a ( self : Dict ):
"""simple docstring"""
from transformers import TFBertModel
SCREAMING_SNAKE_CASE__ = TFBertModel(BertConfig.from_pretrained("""lysandre/tiny-bert-random""" ) )
SCREAMING_SNAKE_CASE__ = BertTokenizerFast.from_pretrained("""lysandre/tiny-bert-random""" )
self._test_infer_dynamic_axis(_lowercase , _lowercase , """tf""" )
def __a ( self : List[Any] , _lowercase : List[Any] , _lowercase : Optional[int] , _lowercase : Dict ):
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = FeatureExtractionPipeline(_lowercase , _lowercase )
SCREAMING_SNAKE_CASE__ = ["""input_ids""", """token_type_ids""", """attention_mask""", """output_0""", """output_1"""]
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = infer_shapes(_lowercase , _lowercase )
# Assert all variables are present
self.assertEqual(len(_lowercase ) , len(_lowercase ) )
self.assertTrue(all(var_name in shapes for var_name in variable_names ) )
self.assertSequenceEqual(variable_names[:3] , _lowercase )
self.assertSequenceEqual(variable_names[3:] , _lowercase )
# Assert inputs are {0: batch, 1: sequence}
for var_name in ["input_ids", "token_type_ids", "attention_mask"]:
self.assertDictEqual(shapes[var_name] , {0: """batch""", 1: """sequence"""} )
# Assert outputs are {0: batch, 1: sequence} and {0: batch}
self.assertDictEqual(shapes["""output_0"""] , {0: """batch""", 1: """sequence"""} )
self.assertDictEqual(shapes["""output_1"""] , {0: """batch"""} )
def __a ( self : Dict ):
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = ["""input_ids""", """attention_mask""", """token_type_ids"""]
SCREAMING_SNAKE_CASE__ = {"""input_ids""": [1, 2, 3, 4], """attention_mask""": [0, 0, 0, 0], """token_type_ids""": [1, 1, 1, 1]}
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = ensure_valid_input(FuncContiguousArgs() , _lowercase , _lowercase )
# Should have exactly the same number of args (all are valid)
self.assertEqual(len(_lowercase ) , 3 )
# Should have exactly the same input names
self.assertEqual(set(_lowercase ) , set(_lowercase ) )
# Parameter should be reordered according to their respective place in the function:
# (input_ids, token_type_ids, attention_mask)
self.assertEqual(_lowercase , (tokens["""input_ids"""], tokens["""token_type_ids"""], tokens["""attention_mask"""]) )
# Generated args are interleaved with another args (for instance parameter "past" in GPT2)
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = ensure_valid_input(FuncNonContiguousArgs() , _lowercase , _lowercase )
# Should have exactly the one arg (all before the one not provided "some_other_args")
self.assertEqual(len(_lowercase ) , 1 )
self.assertEqual(len(_lowercase ) , 1 )
# Should have only "input_ids"
self.assertEqual(inputs_args[0] , tokens["""input_ids"""] )
self.assertEqual(ordered_input_names[0] , """input_ids""" )
def __a ( self : List[str] ):
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = generate_identified_filename(Path("""/home/something/my_fake_model.onnx""" ) , """-test""" )
self.assertEqual("""/home/something/my_fake_model-test.onnx""" , generated.as_posix() )
| 379
| 1
|
'''simple docstring'''
from __future__ import annotations
from random import choice
def __magic_name__ ( __UpperCAmelCase ) -> Union[str, Any]:
'''simple docstring'''
return choice(__UpperCAmelCase )
def __magic_name__ ( __UpperCAmelCase , __UpperCAmelCase ) -> int:
'''simple docstring'''
__SCREAMING_SNAKE_CASE = random_pivot(__UpperCAmelCase )
# partition based on pivot
# linear time
__SCREAMING_SNAKE_CASE = [e for e in lst if e < pivot]
__SCREAMING_SNAKE_CASE = [e for e in lst if e > pivot]
# if we get lucky, pivot might be the element we want.
# we can easily see this:
# small (elements smaller than k)
# + pivot (kth element)
# + big (elements larger than k)
if len(__UpperCAmelCase ) == k - 1:
return pivot
# pivot is in elements bigger than k
elif len(__UpperCAmelCase ) < k - 1:
return kth_number(__UpperCAmelCase , k - len(__UpperCAmelCase ) - 1 )
# pivot is in elements smaller than k
else:
return kth_number(__UpperCAmelCase , __UpperCAmelCase )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 109
|
'''simple docstring'''
from pathlib import PurePosixPath
from typing import Optional
import fsspec
from fsspec import AbstractFileSystem
from huggingface_hub.hf_api import DatasetInfo
from ..utils.file_utils import get_authentication_headers_for_url
from ..utils.hub import hf_hub_url
class __a ( _snake_case ):
__UpperCamelCase : Any = ''
__UpperCamelCase : int = 'hf-legacy' # "hf://"" is reserved for hffs
def __init__( self : Any ,lowerCamelCase : Optional[DatasetInfo] = None ,lowerCamelCase : Optional[str] = None ,**lowerCamelCase : Dict ,):
'''simple docstring'''
super().__init__(self ,**lowerCamelCase )
__SCREAMING_SNAKE_CASE = repo_info
__SCREAMING_SNAKE_CASE = token
__SCREAMING_SNAKE_CASE = None
def UpperCAmelCase__ ( self : Optional[int] ):
'''simple docstring'''
if self.dir_cache is None:
__SCREAMING_SNAKE_CASE = {}
for hf_file in self.repo_info.siblings:
# TODO(QL): add sizes
__SCREAMING_SNAKE_CASE = {
"""name""": hf_file.rfilename,
"""size""": None,
"""type""": """file""",
}
self.dir_cache.update(
{
str(lowerCamelCase ): {"""name""": str(lowerCamelCase ), """size""": None, """type""": """directory"""}
for d in list(PurePosixPath(hf_file.rfilename ).parents )[:-1]
} )
def UpperCAmelCase__ ( self : List[Any] ,lowerCamelCase : str ,lowerCamelCase : str = "rb" ,**lowerCamelCase : Optional[Any] ,):
'''simple docstring'''
if not isinstance(self.repo_info ,lowerCamelCase ):
raise NotImplementedError(f"""Open is only implemented for dataset repositories, but got {self.repo_info}""" )
__SCREAMING_SNAKE_CASE = hf_hub_url(self.repo_info.id ,lowerCamelCase ,revision=self.repo_info.sha )
return fsspec.open(
lowerCamelCase ,mode=lowerCamelCase ,headers=get_authentication_headers_for_url(lowerCamelCase ,use_auth_token=self.token ) ,client_kwargs={"""trust_env""": True} ,).open()
def UpperCAmelCase__ ( self : Tuple ,lowerCamelCase : Any ,**lowerCamelCase : Optional[Any] ):
'''simple docstring'''
self._get_dirs()
__SCREAMING_SNAKE_CASE = self._strip_protocol(lowerCamelCase )
if path in self.dir_cache:
return self.dir_cache[path]
else:
raise FileNotFoundError(lowerCamelCase )
def UpperCAmelCase__ ( self : str ,lowerCamelCase : Any ,lowerCamelCase : str=False ,**lowerCamelCase : Any ):
'''simple docstring'''
self._get_dirs()
__SCREAMING_SNAKE_CASE = PurePosixPath(path.strip("""/""" ) )
__SCREAMING_SNAKE_CASE = {}
for p, f in self.dir_cache.items():
__SCREAMING_SNAKE_CASE = PurePosixPath(p.strip("""/""" ) )
__SCREAMING_SNAKE_CASE = p.parent
if root == path:
__SCREAMING_SNAKE_CASE = f
__SCREAMING_SNAKE_CASE = list(paths.values() )
if detail:
return out
else:
return sorted(f["""name"""] for f in out )
| 109
| 1
|
'''simple docstring'''
import argparse
import random
import joblib
import numpy as np
import torch
from igf.igf import (
SecondaryLearner,
collect_objective_set,
compute_perplexity,
generate_datasets,
load_gpta,
recopy_gpta,
set_seed,
train_secondary_learner,
)
from torch.utils.data import DataLoader, RandomSampler
from transformers import GPTaLMHeadModel
def __magic_name__( lowerCamelCase=3_2, lowerCamelCase=1_0, lowerCamelCase=1_0_0, lowerCamelCase=1_0_2_6, lowerCamelCase=True, lowerCamelCase="data/tokenized_stories_train_wikitext103.jbl", lowerCamelCase="igf_context_pairs.jbl", ):
set_seed(3)
# generate train_data and objective_set
__lowerCAmelCase , __lowerCAmelCase = generate_datasets(
lowerCamelCase, lowerCamelCase, number=lowerCamelCase, min_len=1_0_2_6, trim=lowerCamelCase)
# keeps model same across runs
set_seed(4)
# model, lm_optimizer, lm_scheduler = recopy_gpt2(model, device, max_steps) # store original model weights
# can we train on GPU?
__lowerCAmelCase = torch.device('''cuda:0''' if torch.cuda.is_available() else '''cpu''')
# load pretrained model
__lowerCAmelCase = load_gpta('''gpt2''').to(lowerCamelCase)
print('''computing perplexity on objective set''')
__lowerCAmelCase = compute_perplexity(lowerCamelCase, lowerCamelCase, lowerCamelCase).item()
print('''perplexity on objective set:''', lowerCamelCase)
# collect igf pairs and save to file demo.jbl
collect_objective_set(lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase)
# clean up, delete model and data we don't need anymore
del model, train_data, objective_set
torch.cuda.empty_cache()
def __magic_name__( lowerCamelCase, lowerCamelCase=1_5, lowerCamelCase=1_2_8, lowerCamelCase=1_0_0, lowerCamelCase="igf_model.pt", ):
set_seed(4_2)
# Load pre-trained model
__lowerCAmelCase = GPTaLMHeadModel.from_pretrained('''gpt2''')
# Initialize secondary learner to use embedding weights of model
__lowerCAmelCase = SecondaryLearner(lowerCamelCase)
# Train secondary learner
__lowerCAmelCase = train_secondary_learner(
lowerCamelCase, lowerCamelCase, max_epochs=lowerCamelCase, batch_size=lowerCamelCase, eval_freq=1_0_0, igf_model_path=lowerCamelCase, )
del model, secondary_learner_train_data
torch.cuda.empty_cache()
return secondary_learner
def __magic_name__( lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase=3_2, lowerCamelCase=1_0_0_0, lowerCamelCase=1_6, lowerCamelCase=1.0, lowerCamelCase=recopy_gpta, lowerCamelCase=None, lowerCamelCase=1_0, lowerCamelCase="gpt2_finetuned.pt", ):
__lowerCAmelCase = torch.device('''cuda:0''' if torch.cuda.is_available() else '''cpu''')
__lowerCAmelCase = RandomSampler(lowerCamelCase)
__lowerCAmelCase = DataLoader(lowerCamelCase, sampler=lowerCamelCase)
__lowerCAmelCase = max_steps // (len(lowerCamelCase)) + 1
__lowerCAmelCase = 0
__lowerCAmelCase = torch.zeros((1, context_len), dtype=torch.long, device=lowerCamelCase)
__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase = recopy_model(lowerCamelCase, lowerCamelCase, lowerCamelCase)
model.train()
if secondary_learner is not None:
secondary_learner.to(lowerCamelCase)
secondary_learner.eval()
__lowerCAmelCase = []
__lowerCAmelCase = 0
__lowerCAmelCase = []
__lowerCAmelCase = []
# Compute the performance of the transformer model at the beginning
__lowerCAmelCase = compute_perplexity(lowerCamelCase, lowerCamelCase, lowerCamelCase)
test_perps.append(lowerCamelCase)
print('''Test perplexity, step''', lowerCamelCase, ''':''', lowerCamelCase)
for epoch in range(int(lowerCamelCase)):
for step, example in enumerate(lowerCamelCase):
torch.cuda.empty_cache()
__lowerCAmelCase = random.randint(0, example.size(2) - context_len - 1)
__lowerCAmelCase = example[0, 0, start : start + context_len]
lm_optimizer.zero_grad()
__lowerCAmelCase = model(lowerCamelCase, labels=lowerCamelCase)
__lowerCAmelCase = True
if secondary_learner is not None:
__lowerCAmelCase = secondary_learner.forward(
torch.tensor(lowerCamelCase, dtype=torch.long, device=lowerCamelCase).unsqueeze(0))[0].item()
observed_qs.append(float(lowerCamelCase))
# Here we implement the simple non-constant threshold for the predicted IG(X) value
# We will decay the selectivity of our secondary learner filter from
# 1 standard deviation above average to 1 below average after 10 batches.
if global_step == 1_0:
__lowerCAmelCase = -1
if predicted_q < threshold:
__lowerCAmelCase = False
# If we passed the filter, add the context to the batch!
if do_backprop:
contexts.append(np.array(context.cpu()))
__lowerCAmelCase = outputs[0]
lm_loss.backward()
examples += 1
del outputs
# Once the batch is filled with enough contexts, backprop on the batch.
if examples == batch_size:
torch.cuda.empty_cache()
__lowerCAmelCase = 0
# Do LM backprop
torch.nn.utils.clip_grad_norm_(model.parameters(), 3.0)
lm_optimizer.step()
lm_scheduler.step() # Update learning rate schedule
global_step += 1
# Compute the performance of the transformer model at this batch
if global_step % eval_interval == 0:
__lowerCAmelCase = compute_perplexity(lowerCamelCase, lowerCamelCase, lowerCamelCase)
test_perps.append(lowerCamelCase)
print('''Test perplexity, step''', lowerCamelCase, ''':''', lowerCamelCase)
# Break out of the loop after 60 batches
if max_steps > 0 and global_step > 6_0:
break
if max_steps > 0 and global_step > 6_0:
break
# save finetuned transformer model
torch.save(model.state_dict(), lowerCamelCase)
torch.cuda.empty_cache()
# Do some cleaning up so we can reinitialize for the next run of this function
del lm_optimizer
del lm_scheduler
return model
def __magic_name__( ):
__lowerCAmelCase = argparse.ArgumentParser(description='''Fine-tune a transformer model with IGF on a language modeling task''')
# Required parameters
parser.add_argument(
'''--data_dir''', default=lowerCamelCase, type=lowerCamelCase, required=lowerCamelCase, help='''The input data dir. Should contain data files for WikiText.''', )
parser.add_argument(
'''--model_name_or_path''', default=lowerCamelCase, type=lowerCamelCase, required=lowerCamelCase, help='''Path to pretrained model or model identifier from huggingface.co/models''', )
parser.add_argument(
'''--data_file''', type=lowerCamelCase, default=lowerCamelCase, help=(
'''A jbl file containing tokenized data which can be split as objective dataset, '''
'''train_dataset and test_dataset.'''
), )
parser.add_argument(
'''--igf_data_file''', type=lowerCamelCase, default=lowerCamelCase, help='''A jbl file containing the context and information gain pairs to train secondary learner.''', )
parser.add_argument(
'''--output_dir''', default=lowerCamelCase, type=lowerCamelCase, required=lowerCamelCase, help='''The output directory where the final fine-tuned model is stored.''', )
parser.add_argument(
'''--tokenizer_name''', default=lowerCamelCase, type=lowerCamelCase, help='''Pretrained tokenizer name or path if not the same as model_name''', )
parser.add_argument('''--seed''', type=lowerCamelCase, default=lowerCamelCase, help='''A seed for reproducible training.''')
parser.add_argument(
'''--context_len''', default=3_2, type=lowerCamelCase, help=(
'''The maximum total input sequence length after tokenization. Sequences longer '''
'''than this will be truncated, sequences shorter will be padded.'''
), )
parser.add_argument(
'''--size_objective_set''', default=1_0_0, type=lowerCamelCase, help='''number of articles that are long enough to be used as our objective set''', )
parser.add_argument(
'''--eval_freq''', default=1_0_0, type=lowerCamelCase, help='''secondary model evaluation is triggered at eval_freq''')
parser.add_argument('''--max_steps''', default=1_0_0_0, type=lowerCamelCase, help='''To calculate training epochs''')
parser.add_argument(
'''--secondary_learner_batch_size''', default=1_2_8, type=lowerCamelCase, help='''batch size of training data for secondary learner''', )
parser.add_argument(
'''--batch_size''', default=1_6, type=lowerCamelCase, help='''batch size of training data of language model(gpt2) ''')
parser.add_argument(
'''--eval_interval''', default=1_0, type=lowerCamelCase, help=(
'''decay the selectivity of our secondary learner filter from'''
'''1 standard deviation above average to 1 below average after 10 batches'''
), )
parser.add_argument(
'''--number''', default=1_0_0, type=lowerCamelCase, help='''The number of examples split to be used as objective_set/test_data''')
parser.add_argument(
'''--min_len''', default=1_0_2_6, type=lowerCamelCase, help='''The minimum length of the article to be used as objective set''')
parser.add_argument(
'''--secondary_learner_max_epochs''', default=1_5, type=lowerCamelCase, help='''number of epochs to train secondary learner''')
parser.add_argument('''--trim''', default=lowerCamelCase, type=lowerCamelCase, help='''truncate the example if it exceeds context length''')
parser.add_argument(
'''--threshold''', default=1.0, type=lowerCamelCase, help=(
'''The threshold value used by secondary learner to filter the train_data and allow only'''
''' informative data as input to the model'''
), )
parser.add_argument('''--finetuned_model_name''', default='''gpt2_finetuned.pt''', type=lowerCamelCase, help='''finetuned_model_name''')
parser.add_argument(
'''--recopy_model''', default=lowerCamelCase, type=lowerCamelCase, help='''Reset the model to the original pretrained GPT-2 weights after each iteration''', )
# function calls
# Collecting *n* pairs of context and information gain(X, IG(X)) for training the secondary learner
generate_n_pairs(
context_len=3_2, max_steps=1_0, size_objective_set=1_0_0, min_len=1_0_2_6, trim=lowerCamelCase, data_file='''data/tokenized_stories_train_wikitext103.jbl''', igf_data_file='''igf_context_pairs.jbl''', )
# Load train data for secondary learner
__lowerCAmelCase = joblib.load('''data/IGF_values.jbl''')
# Train secondary learner
__lowerCAmelCase = training_secondary_learner(
lowerCamelCase, secondary_learner_max_epochs=1_5, secondary_learner_batch_size=1_2_8, eval_freq=1_0_0, igf_model_path='''igf_model.pt''', )
# load pretrained gpt2 model
__lowerCAmelCase = GPTaLMHeadModel.from_pretrained('''gpt2''')
set_seed(4_2)
# Generate train and test data to train and evaluate gpt2 model
__lowerCAmelCase , __lowerCAmelCase = generate_datasets(
context_len=3_2, file='''data/tokenized_stories_train_wikitext103.jbl''', number=1_0_0, min_len=1_0_2_6, trim=lowerCamelCase)
# fine-tuning of the gpt2 model using igf (Information Gain Filtration)
finetune(
lowerCamelCase, lowerCamelCase, lowerCamelCase, context_len=3_2, max_steps=1_0_0_0, batch_size=1_6, threshold=1.0, recopy_model=lowerCamelCase, secondary_learner=lowerCamelCase, eval_interval=1_0, finetuned_model_name='''gpt2_finetuned.pt''', )
if __name__ == "__main__":
main()
| 713
|
'''simple docstring'''
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_torch_available
from ...utils import OptionalDependencyNotAvailable
_UpperCAmelCase : str = {
"""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:
_UpperCAmelCase : Union[str, Any] = [
"""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
_UpperCAmelCase : int = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 474
| 0
|
'''simple docstring'''
import re
import string
from collections import Counter
import sacrebleu
import sacremoses
from packaging import version
import datasets
UpperCamelCase__: Dict = "\n@inproceedings{xu-etal-2016-optimizing,\n title = {Optimizing Statistical Machine Translation for Text Simplification},\n authors={Xu, Wei and Napoles, Courtney and Pavlick, Ellie and Chen, Quanze and Callison-Burch, Chris},\n journal = {Transactions of the Association for Computational Linguistics},\n volume = {4},\n year={2016},\n url = {https://www.aclweb.org/anthology/Q16-1029},\n pages = {401--415\n},\n@inproceedings{post-2018-call,\n title = \"A Call for Clarity in Reporting {BLEU} Scores\",\n author = \"Post, Matt\",\n booktitle = \"Proceedings of the Third Conference on Machine Translation: Research Papers\",\n month = oct,\n year = \"2018\",\n address = \"Belgium, Brussels\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://www.aclweb.org/anthology/W18-6319\",\n pages = \"186--191\",\n}\n"
UpperCamelCase__: int = "\\nWIKI_SPLIT is the combination of three metrics SARI, EXACT and SACREBLEU\nIt can be used to evaluate the quality of machine-generated texts.\n"
UpperCamelCase__: List[Any] = "\nCalculates sari score (between 0 and 100) given a list of source and predicted\nsentences, and a list of lists of reference sentences. It also computes the BLEU score as well as the exact match score.\nArgs:\n sources: list of source sentences where each sentence should be a string.\n predictions: list of predicted sentences where each sentence should be a string.\n references: list of lists of reference sentences where each sentence should be a string.\nReturns:\n sari: sari score\n sacrebleu: sacrebleu score\n exact: exact score\n\nExamples:\n >>> sources=[\"About 95 species are currently accepted .\"]\n >>> predictions=[\"About 95 you now get in .\"]\n >>> references=[[\"About 95 species are currently known .\"]]\n >>> wiki_split = datasets.load_metric(\"wiki_split\")\n >>> results = wiki_split.compute(sources=sources, predictions=predictions, references=references)\n >>> print(results)\n {'sari': 21.805555555555557, 'sacrebleu': 14.535768424205482, 'exact': 0.0}\n"
def snake_case_ ( _lowerCAmelCase : List[Any] ) -> Tuple:
def remove_articles(_lowerCAmelCase : Optional[Any] ):
UpperCAmelCase : str = re.compile(R'''\b(a|an|the)\b''' , re.UNICODE )
return re.sub(_lowerCAmelCase , ''' ''' , _lowerCAmelCase )
def white_space_fix(_lowerCAmelCase : Union[str, Any] ):
return " ".join(text.split() )
def remove_punc(_lowerCAmelCase : str ):
UpperCAmelCase : Tuple = set(string.punctuation )
return "".join(ch for ch in text if ch not in exclude )
def lower(_lowerCAmelCase : str ):
return text.lower()
return white_space_fix(remove_articles(remove_punc(lower(_lowerCAmelCase ) ) ) )
def snake_case_ ( _lowerCAmelCase : Optional[int] , _lowerCAmelCase : str ) -> List[str]:
return int(normalize_answer(_lowerCAmelCase ) == normalize_answer(_lowerCAmelCase ) )
def snake_case_ ( _lowerCAmelCase : str , _lowerCAmelCase : Union[str, Any] ) -> str:
UpperCAmelCase : List[str] = [any(compute_exact(_lowerCAmelCase , _lowerCAmelCase ) for ref in refs ) for pred, refs in zip(_lowerCAmelCase , _lowerCAmelCase )]
return (sum(_lowerCAmelCase ) / len(_lowerCAmelCase )) * 100
def snake_case_ ( _lowerCAmelCase : Optional[Any] , _lowerCAmelCase : Any , _lowerCAmelCase : List[Any] , _lowerCAmelCase : int ) -> List[Any]:
UpperCAmelCase : Union[str, Any] = [rgram for rgrams in rgramslist for rgram in rgrams]
UpperCAmelCase : int = Counter(_lowerCAmelCase )
UpperCAmelCase : Optional[int] = Counter(_lowerCAmelCase )
UpperCAmelCase : Dict = Counter()
for sgram, scount in sgramcounter.items():
UpperCAmelCase : str = scount * numref
UpperCAmelCase : List[Any] = Counter(_lowerCAmelCase )
UpperCAmelCase : List[str] = Counter()
for cgram, ccount in cgramcounter.items():
UpperCAmelCase : Optional[Any] = ccount * numref
# KEEP
UpperCAmelCase : int = sgramcounter_rep & cgramcounter_rep
UpperCAmelCase : Any = keepgramcounter_rep & rgramcounter
UpperCAmelCase : str = sgramcounter_rep & rgramcounter
UpperCAmelCase : Optional[int] = 0
UpperCAmelCase : Tuple = 0
for keepgram in keepgramcountergood_rep:
keeptmpscorea += keepgramcountergood_rep[keepgram] / keepgramcounter_rep[keepgram]
# Fix an alleged bug [2] in the keep score computation.
# keeptmpscore2 += keepgramcountergood_rep[keepgram] / keepgramcounterall_rep[keepgram]
keeptmpscorea += keepgramcountergood_rep[keepgram]
# Define 0/0=1 instead of 0 to give higher scores for predictions that match
# a target exactly.
UpperCAmelCase : Optional[Any] = 1
UpperCAmelCase : Union[str, Any] = 1
if len(_lowerCAmelCase ) > 0:
UpperCAmelCase : List[Any] = keeptmpscorea / len(_lowerCAmelCase )
if len(_lowerCAmelCase ) > 0:
# Fix an alleged bug [2] in the keep score computation.
# keepscore_recall = keeptmpscore2 / len(keepgramcounterall_rep)
UpperCAmelCase : List[Any] = keeptmpscorea / sum(keepgramcounterall_rep.values() )
UpperCAmelCase : Optional[Any] = 0
if keepscore_precision > 0 or keepscore_recall > 0:
UpperCAmelCase : Optional[int] = 2 * keepscore_precision * keepscore_recall / (keepscore_precision + keepscore_recall)
# DELETION
UpperCAmelCase : List[str] = sgramcounter_rep - cgramcounter_rep
UpperCAmelCase : Tuple = delgramcounter_rep - rgramcounter
UpperCAmelCase : Any = sgramcounter_rep - rgramcounter
UpperCAmelCase : Optional[Any] = 0
UpperCAmelCase : Optional[int] = 0
for delgram in delgramcountergood_rep:
deltmpscorea += delgramcountergood_rep[delgram] / delgramcounter_rep[delgram]
deltmpscorea += delgramcountergood_rep[delgram] / delgramcounterall_rep[delgram]
# Define 0/0=1 instead of 0 to give higher scores for predictions that match
# a target exactly.
UpperCAmelCase : List[Any] = 1
if len(_lowerCAmelCase ) > 0:
UpperCAmelCase : str = deltmpscorea / len(_lowerCAmelCase )
# ADDITION
UpperCAmelCase : str = set(_lowerCAmelCase ) - set(_lowerCAmelCase )
UpperCAmelCase : Dict = set(_lowerCAmelCase ) & set(_lowerCAmelCase )
UpperCAmelCase : Optional[Any] = set(_lowerCAmelCase ) - set(_lowerCAmelCase )
UpperCAmelCase : List[str] = 0
for addgram in addgramcountergood:
addtmpscore += 1
# Define 0/0=1 instead of 0 to give higher scores for predictions that match
# a target exactly.
UpperCAmelCase : Optional[int] = 1
UpperCAmelCase : str = 1
if len(_lowerCAmelCase ) > 0:
UpperCAmelCase : List[Any] = addtmpscore / len(_lowerCAmelCase )
if len(_lowerCAmelCase ) > 0:
UpperCAmelCase : Tuple = addtmpscore / len(_lowerCAmelCase )
UpperCAmelCase : List[str] = 0
if addscore_precision > 0 or addscore_recall > 0:
UpperCAmelCase : Dict = 2 * addscore_precision * addscore_recall / (addscore_precision + addscore_recall)
return (keepscore, delscore_precision, addscore)
def snake_case_ ( _lowerCAmelCase : int , _lowerCAmelCase : str , _lowerCAmelCase : Union[str, Any] ) -> List[Any]:
UpperCAmelCase : Optional[int] = len(_lowerCAmelCase )
UpperCAmelCase : List[str] = ssent.split(''' ''' )
UpperCAmelCase : Any = csent.split(''' ''' )
UpperCAmelCase : Tuple = []
UpperCAmelCase : List[str] = []
UpperCAmelCase : str = []
UpperCAmelCase : List[Any] = []
UpperCAmelCase : Tuple = []
UpperCAmelCase : List[Any] = []
UpperCAmelCase : Dict = []
UpperCAmelCase : Optional[int] = []
UpperCAmelCase : List[Any] = []
UpperCAmelCase : Tuple = []
for rsent in rsents:
UpperCAmelCase : str = rsent.split(''' ''' )
UpperCAmelCase : List[Any] = []
UpperCAmelCase : Optional[int] = []
UpperCAmelCase : Optional[Any] = []
ragramslist.append(_lowerCAmelCase )
for i in range(0 , len(_lowerCAmelCase ) - 1 ):
if i < len(_lowerCAmelCase ) - 1:
UpperCAmelCase : Optional[int] = ragrams[i] + ''' ''' + ragrams[i + 1]
ragrams.append(_lowerCAmelCase )
if i < len(_lowerCAmelCase ) - 2:
UpperCAmelCase : str = ragrams[i] + ''' ''' + ragrams[i + 1] + ''' ''' + ragrams[i + 2]
ragrams.append(_lowerCAmelCase )
if i < len(_lowerCAmelCase ) - 3:
UpperCAmelCase : Union[str, Any] = ragrams[i] + ''' ''' + ragrams[i + 1] + ''' ''' + ragrams[i + 2] + ''' ''' + ragrams[i + 3]
ragrams.append(_lowerCAmelCase )
ragramslist.append(_lowerCAmelCase )
ragramslist.append(_lowerCAmelCase )
ragramslist.append(_lowerCAmelCase )
for i in range(0 , len(_lowerCAmelCase ) - 1 ):
if i < len(_lowerCAmelCase ) - 1:
UpperCAmelCase : int = sagrams[i] + ''' ''' + sagrams[i + 1]
sagrams.append(_lowerCAmelCase )
if i < len(_lowerCAmelCase ) - 2:
UpperCAmelCase : Dict = sagrams[i] + ''' ''' + sagrams[i + 1] + ''' ''' + sagrams[i + 2]
sagrams.append(_lowerCAmelCase )
if i < len(_lowerCAmelCase ) - 3:
UpperCAmelCase : Dict = sagrams[i] + ''' ''' + sagrams[i + 1] + ''' ''' + sagrams[i + 2] + ''' ''' + sagrams[i + 3]
sagrams.append(_lowerCAmelCase )
for i in range(0 , len(_lowerCAmelCase ) - 1 ):
if i < len(_lowerCAmelCase ) - 1:
UpperCAmelCase : Optional[Any] = cagrams[i] + ''' ''' + cagrams[i + 1]
cagrams.append(_lowerCAmelCase )
if i < len(_lowerCAmelCase ) - 2:
UpperCAmelCase : List[Any] = cagrams[i] + ''' ''' + cagrams[i + 1] + ''' ''' + cagrams[i + 2]
cagrams.append(_lowerCAmelCase )
if i < len(_lowerCAmelCase ) - 3:
UpperCAmelCase : Dict = cagrams[i] + ''' ''' + cagrams[i + 1] + ''' ''' + cagrams[i + 2] + ''' ''' + cagrams[i + 3]
cagrams.append(_lowerCAmelCase )
((UpperCAmelCase) , (UpperCAmelCase) , (UpperCAmelCase)) : Optional[int] = SARIngram(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
((UpperCAmelCase) , (UpperCAmelCase) , (UpperCAmelCase)) : Optional[int] = SARIngram(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
((UpperCAmelCase) , (UpperCAmelCase) , (UpperCAmelCase)) : Dict = SARIngram(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
((UpperCAmelCase) , (UpperCAmelCase) , (UpperCAmelCase)) : Any = SARIngram(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
UpperCAmelCase : Any = sum([keepascore, keepascore, keepascore, keepascore] ) / 4
UpperCAmelCase : Dict = sum([delascore, delascore, delascore, delascore] ) / 4
UpperCAmelCase : int = sum([addascore, addascore, addascore, addascore] ) / 4
UpperCAmelCase : Optional[Any] = (avgkeepscore + avgdelscore + avgaddscore) / 3
return finalscore
def snake_case_ ( _lowerCAmelCase : Dict , _lowerCAmelCase : bool = True , _lowerCAmelCase : str = "13a" , _lowerCAmelCase : bool = True ) -> Any:
# Normalization is requried for the ASSET dataset (one of the primary
# datasets in sentence simplification) to allow using space
# to split the sentence. Even though Wiki-Auto and TURK datasets,
# do not require normalization, we do it for consistency.
# Code adapted from the EASSE library [1] written by the authors of the ASSET dataset.
# [1] https://github.com/feralvam/easse/blob/580bba7e1378fc8289c663f864e0487188fe8067/easse/utils/preprocessing.py#L7
if lowercase:
UpperCAmelCase : int = sentence.lower()
if tokenizer in ["13a", "intl"]:
if version.parse(sacrebleu.__version__ ).major >= 2:
UpperCAmelCase : Union[str, Any] = sacrebleu.metrics.bleu._get_tokenizer(_lowerCAmelCase )()(_lowerCAmelCase )
else:
UpperCAmelCase : Tuple = sacrebleu.TOKENIZERS[tokenizer]()(_lowerCAmelCase )
elif tokenizer == "moses":
UpperCAmelCase : List[str] = sacremoses.MosesTokenizer().tokenize(_lowerCAmelCase , return_str=_lowerCAmelCase , escape=_lowerCAmelCase )
elif tokenizer == "penn":
UpperCAmelCase : Any = sacremoses.MosesTokenizer().penn_tokenize(_lowerCAmelCase , return_str=_lowerCAmelCase )
else:
UpperCAmelCase : str = sentence
if not return_str:
UpperCAmelCase : Optional[Any] = normalized_sent.split()
return normalized_sent
def snake_case_ ( _lowerCAmelCase : str , _lowerCAmelCase : Optional[int] , _lowerCAmelCase : List[str] ) -> str:
if not (len(_lowerCAmelCase ) == len(_lowerCAmelCase ) == len(_lowerCAmelCase )):
raise ValueError('''Sources length must match predictions and references lengths.''' )
UpperCAmelCase : Union[str, Any] = 0
for src, pred, refs in zip(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ):
sari_score += SARIsent(normalize(_lowerCAmelCase ) , normalize(_lowerCAmelCase ) , [normalize(_lowerCAmelCase ) for sent in refs] )
UpperCAmelCase : List[Any] = sari_score / len(_lowerCAmelCase )
return 100 * sari_score
def snake_case_ ( _lowerCAmelCase : Any , _lowerCAmelCase : Optional[int] , _lowerCAmelCase : Dict="exp" , _lowerCAmelCase : Dict=None , _lowerCAmelCase : Optional[int]=False , _lowerCAmelCase : List[Any]=False , _lowerCAmelCase : Tuple=False , ) -> int:
UpperCAmelCase : List[Any] = len(references[0] )
if any(len(_lowerCAmelCase ) != references_per_prediction for refs in references ):
raise ValueError('''Sacrebleu requires the same number of references for each prediction''' )
UpperCAmelCase : Optional[Any] = [[refs[i] for refs in references] for i in range(_lowerCAmelCase )]
UpperCAmelCase : Tuple = sacrebleu.corpus_bleu(
_lowerCAmelCase , _lowerCAmelCase , smooth_method=_lowerCAmelCase , smooth_value=_lowerCAmelCase , force=_lowerCAmelCase , lowercase=_lowerCAmelCase , use_effective_order=_lowerCAmelCase , )
return output.score
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class SCREAMING_SNAKE_CASE( datasets.Metric ):
"""simple docstring"""
def A ( self : Tuple ) -> Optional[Any]:
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
'''predictions''': datasets.Value('''string''' , id='''sequence''' ),
'''references''': datasets.Sequence(datasets.Value('''string''' , id='''sequence''' ) , id='''references''' ),
} ) , codebase_urls=[
'''https://github.com/huggingface/transformers/blob/master/src/transformers/data/metrics/squad_metrics.py''',
'''https://github.com/cocoxu/simplification/blob/master/SARI.py''',
'''https://github.com/tensorflow/tensor2tensor/blob/master/tensor2tensor/utils/sari_hook.py''',
'''https://github.com/mjpost/sacreBLEU''',
] , reference_urls=[
'''https://www.aclweb.org/anthology/Q16-1029.pdf''',
'''https://github.com/mjpost/sacreBLEU''',
'''https://en.wikipedia.org/wiki/BLEU''',
'''https://towardsdatascience.com/evaluating-text-output-in-nlp-bleu-at-your-own-risk-e8609665a213''',
] , )
def A ( self : str , __snake_case : int , __snake_case : str , __snake_case : int ) -> str:
UpperCAmelCase : Dict = {}
result.update({'''sari''': compute_sari(sources=__snake_case , predictions=__snake_case , references=__snake_case )} )
result.update({'''sacrebleu''': compute_sacrebleu(predictions=__snake_case , references=__snake_case )} )
result.update({'''exact''': compute_em(predictions=__snake_case , references=__snake_case )} )
return result
| 127
|
'''simple docstring'''
import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
UpperCamelCase__: str = logging.get_logger(__name__)
UpperCamelCase__: Optional[int] = {"vocab_file": "vocab.json", "merges_file": "merges.txt"}
# See all BART models at https://huggingface.co/models?filter=bart
UpperCamelCase__: Dict = {
"vocab_file": {
"facebook/bart-base": "https://huggingface.co/facebook/bart-base/resolve/main/vocab.json",
"facebook/bart-large": "https://huggingface.co/facebook/bart-large/resolve/main/vocab.json",
"facebook/bart-large-mnli": "https://huggingface.co/facebook/bart-large-mnli/resolve/main/vocab.json",
"facebook/bart-large-cnn": "https://huggingface.co/facebook/bart-large-cnn/resolve/main/vocab.json",
"facebook/bart-large-xsum": "https://huggingface.co/facebook/bart-large-xsum/resolve/main/vocab.json",
"yjernite/bart_eli5": "https://huggingface.co/yjernite/bart_eli5/resolve/main/vocab.json",
},
"merges_file": {
"facebook/bart-base": "https://huggingface.co/facebook/bart-base/resolve/main/merges.txt",
"facebook/bart-large": "https://huggingface.co/facebook/bart-large/resolve/main/merges.txt",
"facebook/bart-large-mnli": "https://huggingface.co/facebook/bart-large-mnli/resolve/main/merges.txt",
"facebook/bart-large-cnn": "https://huggingface.co/facebook/bart-large-cnn/resolve/main/merges.txt",
"facebook/bart-large-xsum": "https://huggingface.co/facebook/bart-large-xsum/resolve/main/merges.txt",
"yjernite/bart_eli5": "https://huggingface.co/yjernite/bart_eli5/resolve/main/merges.txt",
},
}
UpperCamelCase__: Dict = {
"facebook/bart-base": 1024,
"facebook/bart-large": 1024,
"facebook/bart-large-mnli": 1024,
"facebook/bart-large-cnn": 1024,
"facebook/bart-large-xsum": 1024,
"yjernite/bart_eli5": 1024,
}
@lru_cache()
def snake_case_ ( ) -> str:
UpperCAmelCase : Tuple = (
list(range(ord('''!''' ) , ord('''~''' ) + 1 ) ) + list(range(ord('''¡''' ) , ord('''¬''' ) + 1 ) ) + list(range(ord('''®''' ) , ord('''ÿ''' ) + 1 ) )
)
UpperCAmelCase : Any = bs[:]
UpperCAmelCase : Optional[int] = 0
for b in range(2**8 ):
if b not in bs:
bs.append(_lowerCAmelCase )
cs.append(2**8 + n )
n += 1
UpperCAmelCase : List[Any] = [chr(_lowerCAmelCase ) for n in cs]
return dict(zip(_lowerCAmelCase , _lowerCAmelCase ) )
def snake_case_ ( _lowerCAmelCase : Any ) -> Optional[Any]:
UpperCAmelCase : Optional[int] = set()
UpperCAmelCase : Dict = word[0]
for char in word[1:]:
pairs.add((prev_char, char) )
UpperCAmelCase : List[str] = char
return pairs
class SCREAMING_SNAKE_CASE( A__ ):
"""simple docstring"""
lowerCamelCase__ = VOCAB_FILES_NAMES
lowerCamelCase__ = PRETRAINED_VOCAB_FILES_MAP
lowerCamelCase__ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowerCamelCase__ = ["""input_ids""", """attention_mask"""]
def __init__( self : Tuple , __snake_case : int , __snake_case : Dict , __snake_case : int="replace" , __snake_case : str="<s>" , __snake_case : List[Any]="</s>" , __snake_case : Optional[int]="</s>" , __snake_case : List[Any]="<s>" , __snake_case : Union[str, Any]="<unk>" , __snake_case : Optional[Any]="<pad>" , __snake_case : List[Any]="<mask>" , __snake_case : int=False , **__snake_case : Any , ) -> str:
UpperCAmelCase : Optional[Any] = AddedToken(__snake_case , lstrip=__snake_case , rstrip=__snake_case ) if isinstance(__snake_case , __snake_case ) else bos_token
UpperCAmelCase : Optional[int] = AddedToken(__snake_case , lstrip=__snake_case , rstrip=__snake_case ) if isinstance(__snake_case , __snake_case ) else eos_token
UpperCAmelCase : Dict = AddedToken(__snake_case , lstrip=__snake_case , rstrip=__snake_case ) if isinstance(__snake_case , __snake_case ) else sep_token
UpperCAmelCase : Optional[int] = AddedToken(__snake_case , lstrip=__snake_case , rstrip=__snake_case ) if isinstance(__snake_case , __snake_case ) else cls_token
UpperCAmelCase : Union[str, Any] = AddedToken(__snake_case , lstrip=__snake_case , rstrip=__snake_case ) if isinstance(__snake_case , __snake_case ) else unk_token
UpperCAmelCase : Tuple = AddedToken(__snake_case , lstrip=__snake_case , rstrip=__snake_case ) if isinstance(__snake_case , __snake_case ) else pad_token
# Mask token behave like a normal word, i.e. include the space before it
UpperCAmelCase : Optional[Any] = AddedToken(__snake_case , lstrip=__snake_case , rstrip=__snake_case ) if isinstance(__snake_case , __snake_case ) else mask_token
super().__init__(
errors=__snake_case , bos_token=__snake_case , eos_token=__snake_case , unk_token=__snake_case , sep_token=__snake_case , cls_token=__snake_case , pad_token=__snake_case , mask_token=__snake_case , add_prefix_space=__snake_case , **__snake_case , )
with open(__snake_case , encoding='''utf-8''' ) as vocab_handle:
UpperCAmelCase : Union[str, Any] = json.load(__snake_case )
UpperCAmelCase : str = {v: k for k, v in self.encoder.items()}
UpperCAmelCase : Any = errors # how to handle errors in decoding
UpperCAmelCase : List[Any] = bytes_to_unicode()
UpperCAmelCase : int = {v: k for k, v in self.byte_encoder.items()}
with open(__snake_case , encoding='''utf-8''' ) as merges_handle:
UpperCAmelCase : Union[str, Any] = merges_handle.read().split('''\n''' )[1:-1]
UpperCAmelCase : List[Any] = [tuple(merge.split() ) for merge in bpe_merges]
UpperCAmelCase : Optional[Any] = dict(zip(__snake_case , range(len(__snake_case ) ) ) )
UpperCAmelCase : Union[str, Any] = {}
UpperCAmelCase : List[Any] = add_prefix_space
# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
UpperCAmelCase : Union[str, Any] = re.compile(r'''\'s|\'t|\'re|\'ve|\'m|\'ll|\'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+''' )
@property
def A ( self : Tuple ) -> Dict:
return len(self.encoder )
def A ( self : Any ) -> Any:
return dict(self.encoder , **self.added_tokens_encoder )
def A ( self : Any , __snake_case : List[str] ) -> Optional[Any]:
if token in self.cache:
return self.cache[token]
UpperCAmelCase : Optional[int] = tuple(__snake_case )
UpperCAmelCase : List[str] = get_pairs(__snake_case )
if not pairs:
return token
while True:
UpperCAmelCase : int = min(__snake_case , key=lambda __snake_case : self.bpe_ranks.get(__snake_case , float('''inf''' ) ) )
if bigram not in self.bpe_ranks:
break
UpperCAmelCase , UpperCAmelCase : int = bigram
UpperCAmelCase : Any = []
UpperCAmelCase : List[str] = 0
while i < len(__snake_case ):
try:
UpperCAmelCase : int = word.index(__snake_case , __snake_case )
except ValueError:
new_word.extend(word[i:] )
break
else:
new_word.extend(word[i:j] )
UpperCAmelCase : Optional[int] = j
if word[i] == first and i < len(__snake_case ) - 1 and word[i + 1] == second:
new_word.append(first + second )
i += 2
else:
new_word.append(word[i] )
i += 1
UpperCAmelCase : Optional[int] = tuple(__snake_case )
UpperCAmelCase : Optional[Any] = new_word
if len(__snake_case ) == 1:
break
else:
UpperCAmelCase : List[str] = get_pairs(__snake_case )
UpperCAmelCase : str = ''' '''.join(__snake_case )
UpperCAmelCase : Union[str, Any] = word
return word
def A ( self : List[Any] , __snake_case : Optional[Any] ) -> Optional[int]:
UpperCAmelCase : int = []
for token in re.findall(self.pat , __snake_case ):
UpperCAmelCase : Dict = ''''''.join(
self.byte_encoder[b] for b in token.encode('''utf-8''' ) ) # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
bpe_tokens.extend(bpe_token for bpe_token in self.bpe(__snake_case ).split(''' ''' ) )
return bpe_tokens
def A ( self : Any , __snake_case : List[Any] ) -> List[str]:
return self.encoder.get(__snake_case , self.encoder.get(self.unk_token ) )
def A ( self : Tuple , __snake_case : Any ) -> Any:
return self.decoder.get(__snake_case )
def A ( self : List[str] , __snake_case : List[str] ) -> Dict:
UpperCAmelCase : Tuple = ''''''.join(__snake_case )
UpperCAmelCase : Union[str, Any] = bytearray([self.byte_decoder[c] for c in text] ).decode('''utf-8''' , errors=self.errors )
return text
def A ( self : Dict , __snake_case : str , __snake_case : Optional[str] = None ) -> Tuple[str]:
if not os.path.isdir(__snake_case ):
logger.error(F"""Vocabulary path ({save_directory}) should be a directory""" )
return
UpperCAmelCase : Any = os.path.join(
__snake_case , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )
UpperCAmelCase : Dict = os.path.join(
__snake_case , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''merges_file'''] )
with open(__snake_case , '''w''' , encoding='''utf-8''' ) as f:
f.write(json.dumps(self.encoder , indent=2 , sort_keys=__snake_case , ensure_ascii=__snake_case ) + '''\n''' )
UpperCAmelCase : List[str] = 0
with open(__snake_case , '''w''' , encoding='''utf-8''' ) as writer:
writer.write('''#version: 0.2\n''' )
for bpe_tokens, token_index in sorted(self.bpe_ranks.items() , key=lambda __snake_case : kv[1] ):
if index != token_index:
logger.warning(
F"""Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."""
''' Please check that the tokenizer is not corrupted!''' )
UpperCAmelCase : Dict = token_index
writer.write(''' '''.join(__snake_case ) + '''\n''' )
index += 1
return vocab_file, merge_file
def A ( self : Tuple , __snake_case : List[int] , __snake_case : Optional[List[int]] = None ) -> List[int]:
if token_ids_a is None:
return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
UpperCAmelCase : List[str] = [self.cls_token_id]
UpperCAmelCase : Optional[Any] = [self.sep_token_id]
return cls + token_ids_a + sep + sep + token_ids_a + sep
def A ( self : Tuple , __snake_case : List[int] , __snake_case : Optional[List[int]] = None , __snake_case : bool = False ) -> List[int]:
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=__snake_case , token_ids_a=__snake_case , already_has_special_tokens=__snake_case )
if token_ids_a is None:
return [1] + ([0] * len(__snake_case )) + [1]
return [1] + ([0] * len(__snake_case )) + [1, 1] + ([0] * len(__snake_case )) + [1]
def A ( self : Dict , __snake_case : List[int] , __snake_case : Optional[List[int]] = None ) -> List[int]:
UpperCAmelCase : Union[str, Any] = [self.sep_token_id]
UpperCAmelCase : Optional[int] = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]
def A ( self : Tuple , __snake_case : Optional[Any] , __snake_case : Tuple=False , **__snake_case : Optional[Any] ) -> Any:
UpperCAmelCase : Optional[int] = kwargs.pop('''add_prefix_space''' , self.add_prefix_space )
if (is_split_into_words or add_prefix_space) and (len(__snake_case ) > 0 and not text[0].isspace()):
UpperCAmelCase : List[Any] = ''' ''' + text
return (text, kwargs)
| 127
| 1
|
"""simple docstring"""
from .constants import (
MODEL_NAME,
OPTIMIZER_NAME,
RNG_STATE_NAME,
SAFE_WEIGHTS_INDEX_NAME,
SAFE_WEIGHTS_NAME,
SCALER_NAME,
SCHEDULER_NAME,
TORCH_LAUNCH_PARAMS,
WEIGHTS_INDEX_NAME,
WEIGHTS_NAME,
)
from .dataclasses import (
BnbQuantizationConfig,
ComputeEnvironment,
CustomDtype,
DeepSpeedPlugin,
DistributedDataParallelKwargs,
DistributedType,
DynamoBackend,
FPaRecipeKwargs,
FullyShardedDataParallelPlugin,
GradientAccumulationPlugin,
GradScalerKwargs,
InitProcessGroupKwargs,
KwargsHandler,
LoggerType,
MegatronLMPlugin,
PrecisionType,
ProjectConfiguration,
RNGType,
SageMakerDistributedType,
TensorInformation,
TorchDynamoPlugin,
)
from .environment import get_int_from_env, parse_choice_from_env, parse_flag_from_env
from .imports import (
get_ccl_version,
is_abit_bnb_available,
is_abit_bnb_available,
is_aim_available,
is_bfaa_available,
is_bnb_available,
is_botoa_available,
is_ccl_available,
is_comet_ml_available,
is_datasets_available,
is_deepspeed_available,
is_fpa_available,
is_ipex_available,
is_megatron_lm_available,
is_mlflow_available,
is_mps_available,
is_npu_available,
is_rich_available,
is_safetensors_available,
is_sagemaker_available,
is_tensorboard_available,
is_tpu_available,
is_transformers_available,
is_wandb_available,
is_xpu_available,
)
from .modeling import (
check_device_map,
check_tied_parameters_in_config,
check_tied_parameters_on_same_device,
compute_module_sizes,
convert_file_size_to_int,
dtype_byte_size,
find_tied_parameters,
get_balanced_memory,
get_max_layer_size,
get_max_memory,
get_mixed_precision_context_manager,
id_tensor_storage,
infer_auto_device_map,
load_checkpoint_in_model,
load_offloaded_weights,
load_state_dict,
named_module_tensors,
retie_parameters,
set_module_tensor_to_device,
shard_checkpoint,
)
from .offload import (
OffloadedWeightsLoader,
PrefixedDataset,
extract_submodules_state_dict,
load_offloaded_weight,
offload_state_dict,
offload_weight,
save_offload_index,
)
from .operations import (
broadcast,
broadcast_object_list,
concatenate,
convert_outputs_to_fpaa,
convert_to_fpaa,
find_batch_size,
find_device,
gather,
gather_object,
get_data_structure,
honor_type,
initialize_tensors,
is_namedtuple,
is_tensor_information,
is_torch_tensor,
listify,
pad_across_processes,
recursively_apply,
reduce,
send_to_device,
slice_tensors,
)
from .versions import compare_versions, is_torch_version
if is_deepspeed_available():
from .deepspeed import (
DeepSpeedEngineWrapper,
DeepSpeedOptimizerWrapper,
DeepSpeedSchedulerWrapper,
DummyOptim,
DummyScheduler,
HfDeepSpeedConfig,
)
from .bnb import has_abit_bnb_layers, load_and_quantize_model
from .fsdp_utils import load_fsdp_model, load_fsdp_optimizer, save_fsdp_model, save_fsdp_optimizer
from .launch import (
PrepareForLaunch,
_filter_args,
prepare_deepspeed_cmd_env,
prepare_multi_gpu_env,
prepare_sagemager_args_inputs,
prepare_simple_launcher_cmd_env,
prepare_tpu,
)
from .megatron_lm import (
AbstractTrainStep,
BertTrainStep,
GPTTrainStep,
MegatronEngine,
MegatronLMDummyDataLoader,
MegatronLMDummyScheduler,
MegatronLMOptimizerWrapper,
MegatronLMSchedulerWrapper,
TaTrainStep,
avg_losses_across_data_parallel_group,
gather_across_data_parallel_groups,
)
from .megatron_lm import initialize as megatron_lm_initialize
from .megatron_lm import prepare_data_loader as megatron_lm_prepare_data_loader
from .megatron_lm import prepare_model as megatron_lm_prepare_model
from .megatron_lm import prepare_optimizer as megatron_lm_prepare_optimizer
from .megatron_lm import prepare_scheduler as megatron_lm_prepare_scheduler
from .memory import find_executable_batch_size, release_memory
from .other import (
extract_model_from_parallel,
get_pretty_name,
is_port_in_use,
merge_dicts,
patch_environment,
save,
wait_for_everyone,
write_basic_config,
)
from .random import set_seed, synchronize_rng_state, synchronize_rng_states
from .torch_xla import install_xla
from .tqdm import tqdm
from .transformer_engine import convert_model, has_transformer_engine_layers
| 712
|
"""simple docstring"""
def UpperCamelCase ( _A , _A ) -> str:
lowercase : list[list[str]] = [[] for _ in range(_A )]
lowercase : Any = key - 1
if key <= 0:
raise ValueError("""Height of grid can't be 0 or negative""" )
if key == 1 or len(_A ) <= key:
return input_string
for position, character in enumerate(_A ):
lowercase : Optional[int] = position % (lowest * 2) # puts it in bounds
lowercase : Dict = min(_A , lowest * 2 - num ) # creates zigzag pattern
temp_grid[num].append(_A )
lowercase : Optional[Any] = ["""""".join(_A ) for row in temp_grid]
lowercase : int = """""".join(_A )
return output_string
def UpperCamelCase ( _A , _A ) -> str:
lowercase : Optional[Any] = []
lowercase : int = key - 1
if key <= 0:
raise ValueError("""Height of grid can't be 0 or negative""" )
if key == 1:
return input_string
lowercase : list[list[str]] = [[] for _ in range(_A )] # generates template
for position in range(len(_A ) ):
lowercase : Union[str, Any] = position % (lowest * 2) # puts it in bounds
lowercase : Union[str, Any] = min(_A , lowest * 2 - num ) # creates zigzag pattern
temp_grid[num].append("""*""" )
lowercase : Any = 0
for row in temp_grid: # fills in the characters
lowercase : Dict = input_string[counter : counter + len(_A )]
grid.append(list(_A ) )
counter += len(_A )
lowercase : Union[str, Any] = """""" # reads as zigzag
for position in range(len(_A ) ):
lowercase : List[str] = position % (lowest * 2) # puts it in bounds
lowercase : str = min(_A , lowest * 2 - num ) # creates zigzag pattern
output_string += grid[num][0]
grid[num].pop(0 )
return output_string
def UpperCamelCase ( _A ) -> dict[int, str]:
lowercase : int = {}
for key_guess in range(1 , len(_A ) ): # tries every key
lowercase : Dict = decrypt(_A , _A )
return results
if __name__ == "__main__":
import doctest
doctest.testmod()
| 348
| 0
|
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