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'''simple docstring'''
import os
import sys
import tempfile
import torch
from .state import AcceleratorState
from .utils import PrecisionType, PrepareForLaunch, is_mps_available, patch_environment
def __UpperCamelCase ( a : Any , a : List[str]=() , a : Tuple=None , a : List[str]="no" , a : Dict="29500" ) ->int:
snake_case = False
snake_case = False
if any(key.startswith('''KAGGLE''' ) for key in os.environ.keys() ):
snake_case = True
elif "IPython" in sys.modules:
snake_case = '''google.colab''' in str(sys.modules['''IPython'''].get_ipython() )
try:
snake_case = PrecisionType(mixed_precision.lower() )
except ValueError:
raise ValueError(
f"""Unknown mixed_precision mode: {args.mixed_precision.lower()}. Choose between {PrecisionType.list()}.""" )
if (in_colab or in_kaggle) and (os.environ.get('''TPU_NAME''' , a ) is not None):
# TPU launch
import torch_xla.distributed.xla_multiprocessing as xmp
if len(AcceleratorState._shared_state ) > 0:
raise ValueError(
'''To train on TPU in Colab or Kaggle Kernel, the `Accelerator` should only be initialized inside '''
'''your training function. Restart your notebook and make sure no cells initializes an '''
'''`Accelerator`.''' )
if num_processes is None:
snake_case = 8
snake_case = PrepareForLaunch(a , distributed_type='''TPU''' )
print(f"""Launching a training on {num_processes} TPU cores.""" )
xmp.spawn(a , args=a , nprocs=a , start_method='''fork''' )
elif in_colab:
# No need for a distributed launch otherwise as it's either CPU or one GPU.
if torch.cuda.is_available():
print('''Launching training on one GPU.''' )
else:
print('''Launching training on one CPU.''' )
function(*a )
else:
if num_processes is None:
raise ValueError(
'''You have to specify the number of GPUs you would like to use, add `num_processes=...` to your call.''' )
if num_processes > 1:
# Multi-GPU launch
from torch.multiprocessing import start_processes
from torch.multiprocessing.spawn import ProcessRaisedException
if len(AcceleratorState._shared_state ) > 0:
raise ValueError(
'''To launch a multi-GPU training from your notebook, the `Accelerator` should only be initialized '''
'''inside your training function. Restart your notebook and make sure no cells initializes an '''
'''`Accelerator`.''' )
if torch.cuda.is_initialized():
raise ValueError(
'''To launch a multi-GPU training from your notebook, you need to avoid running any instruction '''
'''using `torch.cuda` in any cell. Restart your notebook and make sure no cells use any CUDA '''
'''function.''' )
# torch.distributed will expect a few environment variable to be here. We set the ones common to each
# process here (the other ones will be set be the launcher).
with patch_environment(
world_size=a , master_addr='''127.0.01''' , master_port=a , mixed_precision=a ):
snake_case = PrepareForLaunch(a , distributed_type='''MULTI_GPU''' )
print(f"""Launching training on {num_processes} GPUs.""" )
try:
start_processes(a , args=a , nprocs=a , start_method='''fork''' )
except ProcessRaisedException as e:
if "Cannot re-initialize CUDA in forked subprocess" in e.args[0]:
raise RuntimeError(
'''CUDA has been initialized before the `notebook_launcher` could create a forked subprocess. '''
'''This likely stems from an outside import causing issues once the `notebook_launcher()` is called. '''
'''Please review your imports and test them when running the `notebook_launcher()` to identify '''
'''which one is problematic.''' ) from e
else:
# No need for a distributed launch otherwise as it's either CPU, GPU or MPS.
if is_mps_available():
snake_case = '''1'''
print('''Launching training on MPS.''' )
elif torch.cuda.is_available():
print('''Launching training on one GPU.''' )
else:
print('''Launching training on CPU.''' )
function(*a )
def __UpperCamelCase ( a : Dict , a : Optional[int]=() , a : Dict=2 ) ->int:
from torch.multiprocessing import start_processes
with tempfile.NamedTemporaryFile() as tmp_file:
# torch.distributed will expect a few environment variable to be here. We set the ones common to each
# process here (the other ones will be set be the launcher).
with patch_environment(
world_size=a , master_addr='''127.0.01''' , master_port='''29500''' , accelerate_mixed_precision='''no''' , accelerate_debug_rdv_file=tmp_file.name , accelerate_use_cpu='''yes''' , ):
snake_case = PrepareForLaunch(a , debug=a )
start_processes(a , args=a , nprocs=a , start_method='''fork''' )
| 44
|
'''simple docstring'''
import argparse
from collections import OrderedDict
from pathlib import Path
import torch
from transformers import (
VisualBertConfig,
VisualBertForMultipleChoice,
VisualBertForPreTraining,
VisualBertForQuestionAnswering,
VisualBertForVisualReasoning,
)
from transformers.utils import logging
logging.set_verbosity_info()
_lowercase = logging.get_logger(__name__)
_lowercase = [
('bert.bert', 'visual_bert'),
('bert.cls', 'cls'),
('bert.classifier', 'cls'),
('token_type_embeddings_visual', 'visual_token_type_embeddings'),
('position_embeddings_visual', 'visual_position_embeddings'),
('projection', 'visual_projection'),
]
_lowercase = [
'nlvr2_coco_pre_trained.th',
'nlvr2_fine_tuned.th',
'nlvr2_pre_trained.th',
'vcr_coco_pre_train.th',
'vcr_fine_tune.th',
'vcr_pre_train.th',
'vqa_coco_pre_trained.th',
'vqa_fine_tuned.th',
'vqa_pre_trained.th',
]
def __UpperCamelCase ( a : List[str] ) ->Optional[int]:
snake_case = torch.load(a , map_location='''cpu''' )
return sd
def __UpperCamelCase ( a : Optional[int] , a : Union[str, Any] , a : int=rename_keys_prefix ) ->Tuple:
snake_case = OrderedDict()
snake_case = torch.arange(config.max_position_embeddings ).expand((1, -1) )
# detector_d = OrderedDict()
for key in d:
if "detector" in key:
# detector_d[key.replace('detector.','')] = d[key]
continue
snake_case = key
for name_pair in rename_keys_prefix:
snake_case = new_key.replace(name_pair[0] , name_pair[1] )
snake_case = d[key]
if key == "bert.cls.predictions.decoder.weight":
# Old bert code didn't have `decoder.bias`, but was added separately
snake_case = new_d['''cls.predictions.bias''']
return new_d
@torch.no_grad()
def __UpperCamelCase ( a : Optional[int] , a : int ) ->Union[str, Any]:
assert (
checkpoint_path.split('''/''' )[-1] in ACCEPTABLE_CHECKPOINTS
), f"""The checkpoint provided must be in {ACCEPTABLE_CHECKPOINTS}."""
# Get Config
if "pre" in checkpoint_path:
snake_case = '''pretraining'''
if "vcr" in checkpoint_path:
snake_case = {'''visual_embedding_dim''': 512}
elif "vqa_advanced" in checkpoint_path:
snake_case = {'''visual_embedding_dim''': 2048}
elif "vqa" in checkpoint_path:
snake_case = {'''visual_embedding_dim''': 2048}
elif "nlvr" in checkpoint_path:
snake_case = {'''visual_embedding_dim''': 1024}
else:
raise NotImplementedError(f"""No implementation found for `{checkpoint_path}`.""" )
else:
if "vcr" in checkpoint_path:
snake_case = {'''visual_embedding_dim''': 512}
snake_case = '''multichoice'''
elif "vqa_advanced" in checkpoint_path:
snake_case = {'''visual_embedding_dim''': 2048}
snake_case = '''vqa_advanced'''
elif "vqa" in checkpoint_path:
snake_case = {'''visual_embedding_dim''': 2048, '''num_labels''': 3129}
snake_case = '''vqa'''
elif "nlvr" in checkpoint_path:
snake_case = {
'''visual_embedding_dim''': 1024,
'''num_labels''': 2,
}
snake_case = '''nlvr'''
snake_case = VisualBertConfig(**a )
# Load State Dict
snake_case = load_state_dict(a )
snake_case = get_new_dict(a , a )
if model_type == "pretraining":
snake_case = VisualBertForPreTraining(a )
elif model_type == "vqa":
snake_case = VisualBertForQuestionAnswering(a )
elif model_type == "nlvr":
snake_case = VisualBertForVisualReasoning(a )
elif model_type == "multichoice":
snake_case = VisualBertForMultipleChoice(a )
model.load_state_dict(a )
# Save Checkpoints
Path(a ).mkdir(exist_ok=a )
model.save_pretrained(a )
if __name__ == "__main__":
_lowercase = argparse.ArgumentParser()
# Required parameters
parser.add_argument('orig_checkpoint_path', type=str, help='A path to .th on local filesystem.')
parser.add_argument('pytorch_dump_folder_path', type=str, help='Path to the output PyTorch model.')
_lowercase = parser.parse_args()
convert_visual_bert_checkpoint(args.orig_checkpoint_path, args.pytorch_dump_folder_path)
| 44
| 1
|
'''simple docstring'''
import argparse
import copy
def __UpperCamelCase ( a : Union[str, Any] ) ->Tuple:
snake_case = {}
with open(a ) as f:
for line in f:
if line.split()[0] not in dict_of_neighbours:
snake_case = []
_list.append([line.split()[1], line.split()[2]] )
snake_case = _list
else:
dict_of_neighbours[line.split()[0]].append(
[line.split()[1], line.split()[2]] )
if line.split()[1] not in dict_of_neighbours:
snake_case = []
_list.append([line.split()[0], line.split()[2]] )
snake_case = _list
else:
dict_of_neighbours[line.split()[1]].append(
[line.split()[0], line.split()[2]] )
return dict_of_neighbours
def __UpperCamelCase ( a : Dict , a : Tuple ) ->int:
with open(a ) as f:
snake_case = f.read(1 )
snake_case = start_node
snake_case = []
snake_case = start_node
snake_case = 0
while visiting not in first_solution:
snake_case = 1_0000
for k in dict_of_neighbours[visiting]:
if int(k[1] ) < int(a ) and k[0] not in first_solution:
snake_case = k[1]
snake_case = k[0]
first_solution.append(a )
snake_case = distance_of_first_solution + int(a )
snake_case = best_node
first_solution.append(a )
snake_case = 0
for k in dict_of_neighbours[first_solution[-2]]:
if k[0] == start_node:
break
position += 1
snake_case = (
distance_of_first_solution
+ int(dict_of_neighbours[first_solution[-2]][position][1] )
- 1_0000
)
return first_solution, distance_of_first_solution
def __UpperCamelCase ( a : Optional[int] , a : str ) ->str:
snake_case = []
for n in solution[1:-1]:
snake_case = solution.index(a )
for kn in solution[1:-1]:
snake_case = solution.index(a )
if n == kn:
continue
snake_case = copy.deepcopy(a )
snake_case = kn
snake_case = n
snake_case = 0
for k in _tmp[:-1]:
snake_case = _tmp[_tmp.index(a ) + 1]
for i in dict_of_neighbours[k]:
if i[0] == next_node:
snake_case = distance + int(i[1] )
_tmp.append(a )
if _tmp not in neighborhood_of_solution:
neighborhood_of_solution.append(_tmp )
snake_case = len(neighborhood_of_solution[0] ) - 1
neighborhood_of_solution.sort(key=lambda a : x[index_of_last_item_in_the_list] )
return neighborhood_of_solution
def __UpperCamelCase ( a : Any , a : Optional[Any] , a : int , a : Optional[int] , a : Union[str, Any] ) ->List[Any]:
snake_case = 1
snake_case = first_solution
snake_case = []
snake_case = distance_of_first_solution
snake_case = solution
while count <= iters:
snake_case = find_neighborhood(a , a )
snake_case = 0
snake_case = neighborhood[index_of_best_solution]
snake_case = len(a ) - 1
snake_case = False
while not found:
snake_case = 0
while i < len(a ):
if best_solution[i] != solution[i]:
snake_case = best_solution[i]
snake_case = solution[i]
break
snake_case = i + 1
if [first_exchange_node, second_exchange_node] not in tabu_list and [
second_exchange_node,
first_exchange_node,
] not in tabu_list:
tabu_list.append([first_exchange_node, second_exchange_node] )
snake_case = True
snake_case = best_solution[:-1]
snake_case = neighborhood[index_of_best_solution][best_cost_index]
if cost < best_cost:
snake_case = cost
snake_case = solution
else:
snake_case = index_of_best_solution + 1
snake_case = neighborhood[index_of_best_solution]
if len(a ) >= size:
tabu_list.pop(0 )
snake_case = count + 1
return best_solution_ever, best_cost
def __UpperCamelCase ( a : Union[str, Any]=None ) ->Optional[Any]:
snake_case = generate_neighbours(args.File )
snake_case , snake_case = generate_first_solution(
args.File , a )
snake_case , snake_case = tabu_search(
a , a , a , args.Iterations , args.Size , )
print(f"""Best solution: {best_sol}, with total distance: {best_cost}.""" )
if __name__ == "__main__":
_lowercase = argparse.ArgumentParser(description='Tabu Search')
parser.add_argument(
'-f',
'--File',
type=str,
help='Path to the file containing the data',
required=True,
)
parser.add_argument(
'-i',
'--Iterations',
type=int,
help='How many iterations the algorithm should perform',
required=True,
)
parser.add_argument(
'-s', '--Size', type=int, help='Size of the tabu list', required=True
)
# Pass the arguments to main method
main(parser.parse_args())
| 44
|
'''simple docstring'''
import argparse
import json
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import PoolFormerConfig, PoolFormerForImageClassification, PoolFormerImageProcessor
from transformers.utils import logging
logging.set_verbosity_info()
_lowercase = logging.get_logger(__name__)
def __UpperCamelCase ( a : Dict , a : Optional[int] , a : Dict , a : Dict ) ->Union[str, Any]:
snake_case = original_name.split('''.''' )[0]
snake_case = key.split('''.''' )
snake_case = int(key_list[key_list.index(a ) - 2] )
snake_case = int(key_list[key_list.index(a ) - 1] )
snake_case = orig_block_num - offset
snake_case = key.replace(f"""{orig_block_num}.{layer_num}.{original_name}""" , f"""block.{new_block_num}.{layer_num}.{new_name}""" )
return key
def __UpperCamelCase ( a : Tuple ) ->Dict:
snake_case = OrderedDict()
snake_case , snake_case = 0, 0
for key, value in state_dict.items():
if key.startswith('''network''' ):
snake_case = key.replace('''network''' , '''poolformer.encoder''' )
if "proj" in key:
# Works for the first embedding as well as the internal embedding layers
if key.endswith('''bias''' ) and "patch_embed" not in key:
patch_emb_offset += 1
snake_case = key[: key.find('''proj''' )]
snake_case = key.replace(a , f"""patch_embeddings.{total_embed_found}.""" )
snake_case = key.replace('''proj''' , '''projection''' )
if key.endswith('''bias''' ):
total_embed_found += 1
if "patch_embeddings" in key:
snake_case = '''poolformer.encoder.''' + key
if "mlp.fc1" in key:
snake_case = replace_key_with_offset(a , a , '''mlp.fc1''' , '''output.conv1''' )
if "mlp.fc2" in key:
snake_case = replace_key_with_offset(a , a , '''mlp.fc2''' , '''output.conv2''' )
if "norm1" in key:
snake_case = replace_key_with_offset(a , a , '''norm1''' , '''before_norm''' )
if "norm2" in key:
snake_case = replace_key_with_offset(a , a , '''norm2''' , '''after_norm''' )
if "layer_scale_1" in key:
snake_case = replace_key_with_offset(a , a , '''layer_scale_1''' , '''layer_scale_1''' )
if "layer_scale_2" in key:
snake_case = replace_key_with_offset(a , a , '''layer_scale_2''' , '''layer_scale_2''' )
if "head" in key:
snake_case = key.replace('''head''' , '''classifier''' )
snake_case = value
return new_state_dict
def __UpperCamelCase ( ) ->Optional[int]:
snake_case = '''http://images.cocodataset.org/val2017/000000039769.jpg'''
snake_case = Image.open(requests.get(a , stream=a ).raw )
return image
@torch.no_grad()
def __UpperCamelCase ( a : Dict , a : Optional[Any] , a : Tuple ) ->List[str]:
snake_case = PoolFormerConfig()
# set attributes based on model_name
snake_case = '''huggingface/label-files'''
snake_case = model_name[-3:]
snake_case = 1000
snake_case = '''imagenet-1k-id2label.json'''
snake_case = (1, 1000)
# set config attributes
snake_case = json.load(open(hf_hub_download(a , a , repo_type='''dataset''' ) , '''r''' ) )
snake_case = {int(a ): v for k, v in idalabel.items()}
snake_case = idalabel
snake_case = {v: k for k, v in idalabel.items()}
if size == "s12":
snake_case = [2, 2, 6, 2]
snake_case = [64, 128, 320, 512]
snake_case = 4.0
snake_case = 0.9
elif size == "s24":
snake_case = [4, 4, 12, 4]
snake_case = [64, 128, 320, 512]
snake_case = 4.0
snake_case = 0.9
elif size == "s36":
snake_case = [6, 6, 18, 6]
snake_case = [64, 128, 320, 512]
snake_case = 4.0
snake_case = 1e-6
snake_case = 0.9
elif size == "m36":
snake_case = [6, 6, 18, 6]
snake_case = [96, 192, 384, 768]
snake_case = 4.0
snake_case = 1e-6
snake_case = 0.95
elif size == "m48":
snake_case = [8, 8, 24, 8]
snake_case = [96, 192, 384, 768]
snake_case = 4.0
snake_case = 1e-6
snake_case = 0.95
else:
raise ValueError(f"""Size {size} not supported""" )
# load image processor
snake_case = PoolFormerImageProcessor(crop_pct=a )
# Prepare image
snake_case = prepare_img()
snake_case = image_processor(images=a , return_tensors='''pt''' ).pixel_values
logger.info(f"""Converting model {model_name}...""" )
# load original state dict
snake_case = torch.load(a , map_location=torch.device('''cpu''' ) )
# rename keys
snake_case = rename_keys(a )
# create HuggingFace model and load state dict
snake_case = PoolFormerForImageClassification(a )
model.load_state_dict(a )
model.eval()
# Define image processor
snake_case = PoolFormerImageProcessor(crop_pct=a )
snake_case = image_processor(images=prepare_img() , return_tensors='''pt''' ).pixel_values
# forward pass
snake_case = model(a )
snake_case = outputs.logits
# define expected logit slices for different models
if size == "s12":
snake_case = torch.tensor([-0.3045, -0.6758, -0.4869] )
elif size == "s24":
snake_case = torch.tensor([0.4402, -0.1374, -0.8045] )
elif size == "s36":
snake_case = torch.tensor([-0.6080, -0.5133, -0.5898] )
elif size == "m36":
snake_case = torch.tensor([0.3952, 0.2263, -1.2668] )
elif size == "m48":
snake_case = torch.tensor([0.1167, -0.0656, -0.3423] )
else:
raise ValueError(f"""Size {size} not supported""" )
# verify logits
assert logits.shape == expected_shape
assert torch.allclose(logits[0, :3] , a , atol=1e-2 )
# finally, save model and image processor
logger.info(f"""Saving PyTorch model and image processor to {pytorch_dump_folder_path}...""" )
Path(a ).mkdir(exist_ok=a )
model.save_pretrained(a )
print(f"""Saving image processor to {pytorch_dump_folder_path}""" )
image_processor.save_pretrained(a )
if __name__ == "__main__":
_lowercase = argparse.ArgumentParser()
parser.add_argument(
'--model_name',
default='poolformer_s12',
type=str,
help='Name of the model you\'d like to convert.',
)
parser.add_argument(
'--checkpoint_path', default=None, type=str, help='Path to the original PyTorch checkpoint (.pth file).'
)
parser.add_argument(
'--pytorch_dump_folder_path', default=None, type=str, help='Path to the folder to output PyTorch model.'
)
_lowercase = parser.parse_args()
convert_poolformer_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path)
| 44
| 1
|
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_lowercase = {
'configuration_pegasus_x': ['PEGASUS_X_PRETRAINED_CONFIG_ARCHIVE_MAP', 'PegasusXConfig'],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowercase = [
'PEGASUS_X_PRETRAINED_MODEL_ARCHIVE_LIST',
'PegasusXForConditionalGeneration',
'PegasusXModel',
'PegasusXPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_pegasus_x import PEGASUS_X_PRETRAINED_CONFIG_ARCHIVE_MAP, PegasusXConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_pegasus_x import (
PEGASUS_X_PRETRAINED_MODEL_ARCHIVE_LIST,
PegasusXForConditionalGeneration,
PegasusXModel,
PegasusXPreTrainedModel,
)
else:
import sys
_lowercase = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 44
|
'''simple docstring'''
import argparse
import json
import logging
import os
import sys
from unittest.mock import patch
from transformers.testing_utils import TestCasePlus, get_gpu_count, slow
_lowercase = [
os.path.join(os.path.dirname(__file__), dirname)
for dirname in [
'text-classification',
'language-modeling',
'summarization',
'token-classification',
'question-answering',
]
]
sys.path.extend(SRC_DIRS)
if SRC_DIRS is not None:
import run_clm_flax
import run_flax_glue
import run_flax_ner
import run_mlm_flax
import run_qa
import run_summarization_flax
import run_ta_mlm_flax
logging.basicConfig(level=logging.DEBUG)
_lowercase = logging.getLogger()
def __UpperCamelCase ( ) ->Tuple:
snake_case = argparse.ArgumentParser()
parser.add_argument('''-f''' )
snake_case = parser.parse_args()
return args.f
def __UpperCamelCase ( a : Dict , a : Tuple="eval" ) ->List[Any]:
snake_case = os.path.join(a , f"""{split}_results.json""" )
if os.path.exists(a ):
with open(a , '''r''' ) as f:
return json.load(a )
raise ValueError(f"""can't find {path}""" )
_lowercase = logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
class _lowercase ( __a ):
def UpperCamelCase ( self ) -> List[str]:
snake_case = self.get_auto_remove_tmp_dir()
snake_case = F"""
run_glue.py
--model_name_or_path distilbert-base-uncased
--output_dir {tmp_dir}
--train_file ./tests/fixtures/tests_samples/MRPC/train.csv
--validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv
--per_device_train_batch_size=2
--per_device_eval_batch_size=1
--learning_rate=1e-4
--eval_steps=2
--warmup_steps=2
--seed=42
--max_seq_length=128
""".split()
with patch.object(A__ , '''argv''' , A__ ):
run_flax_glue.main()
snake_case = get_results(A__ )
self.assertGreaterEqual(result['''eval_accuracy'''] , 0.7_5 )
@slow
def UpperCamelCase ( self ) -> List[Any]:
snake_case = self.get_auto_remove_tmp_dir()
snake_case = F"""
run_clm_flax.py
--model_name_or_path distilgpt2
--train_file ./tests/fixtures/sample_text.txt
--validation_file ./tests/fixtures/sample_text.txt
--do_train
--do_eval
--block_size 128
--per_device_train_batch_size 4
--per_device_eval_batch_size 4
--num_train_epochs 2
--logging_steps 2 --eval_steps 2
--output_dir {tmp_dir}
--overwrite_output_dir
""".split()
with patch.object(A__ , '''argv''' , A__ ):
run_clm_flax.main()
snake_case = get_results(A__ )
self.assertLess(result['''eval_perplexity'''] , 1_00 )
@slow
def UpperCamelCase ( self ) -> int:
snake_case = self.get_auto_remove_tmp_dir()
snake_case = F"""
run_summarization.py
--model_name_or_path t5-small
--train_file tests/fixtures/tests_samples/xsum/sample.json
--validation_file tests/fixtures/tests_samples/xsum/sample.json
--test_file tests/fixtures/tests_samples/xsum/sample.json
--output_dir {tmp_dir}
--overwrite_output_dir
--num_train_epochs=3
--warmup_steps=8
--do_train
--do_eval
--do_predict
--learning_rate=2e-4
--per_device_train_batch_size=2
--per_device_eval_batch_size=1
--predict_with_generate
""".split()
with patch.object(A__ , '''argv''' , A__ ):
run_summarization_flax.main()
snake_case = get_results(A__ , split='''test''' )
self.assertGreaterEqual(result['''test_rouge1'''] , 10 )
self.assertGreaterEqual(result['''test_rouge2'''] , 2 )
self.assertGreaterEqual(result['''test_rougeL'''] , 7 )
self.assertGreaterEqual(result['''test_rougeLsum'''] , 7 )
@slow
def UpperCamelCase ( self ) -> Union[str, Any]:
snake_case = self.get_auto_remove_tmp_dir()
snake_case = F"""
run_mlm.py
--model_name_or_path distilroberta-base
--train_file ./tests/fixtures/sample_text.txt
--validation_file ./tests/fixtures/sample_text.txt
--output_dir {tmp_dir}
--overwrite_output_dir
--max_seq_length 128
--per_device_train_batch_size 4
--per_device_eval_batch_size 4
--logging_steps 2 --eval_steps 2
--do_train
--do_eval
--num_train_epochs=1
""".split()
with patch.object(A__ , '''argv''' , A__ ):
run_mlm_flax.main()
snake_case = get_results(A__ )
self.assertLess(result['''eval_perplexity'''] , 42 )
@slow
def UpperCamelCase ( self ) -> Dict:
snake_case = self.get_auto_remove_tmp_dir()
snake_case = F"""
run_t5_mlm_flax.py
--model_name_or_path t5-small
--train_file ./tests/fixtures/sample_text.txt
--validation_file ./tests/fixtures/sample_text.txt
--do_train
--do_eval
--max_seq_length 128
--per_device_train_batch_size 4
--per_device_eval_batch_size 4
--num_train_epochs 2
--logging_steps 2 --eval_steps 2
--output_dir {tmp_dir}
--overwrite_output_dir
""".split()
with patch.object(A__ , '''argv''' , A__ ):
run_ta_mlm_flax.main()
snake_case = get_results(A__ )
self.assertGreaterEqual(result['''eval_accuracy'''] , 0.4_2 )
@slow
def UpperCamelCase ( self ) -> int:
# with so little data distributed training needs more epochs to get the score on par with 0/1 gpu
snake_case = 7 if get_gpu_count() > 1 else 2
snake_case = self.get_auto_remove_tmp_dir()
snake_case = F"""
run_flax_ner.py
--model_name_or_path bert-base-uncased
--train_file tests/fixtures/tests_samples/conll/sample.json
--validation_file tests/fixtures/tests_samples/conll/sample.json
--output_dir {tmp_dir}
--overwrite_output_dir
--do_train
--do_eval
--warmup_steps=2
--learning_rate=2e-4
--logging_steps 2 --eval_steps 2
--per_device_train_batch_size=2
--per_device_eval_batch_size=2
--num_train_epochs={epochs}
--seed 7
""".split()
with patch.object(A__ , '''argv''' , A__ ):
run_flax_ner.main()
snake_case = get_results(A__ )
self.assertGreaterEqual(result['''eval_accuracy'''] , 0.7_5 )
self.assertGreaterEqual(result['''eval_f1'''] , 0.3 )
@slow
def UpperCamelCase ( self ) -> Any:
snake_case = self.get_auto_remove_tmp_dir()
snake_case = F"""
run_qa.py
--model_name_or_path bert-base-uncased
--version_2_with_negative
--train_file tests/fixtures/tests_samples/SQUAD/sample.json
--validation_file tests/fixtures/tests_samples/SQUAD/sample.json
--output_dir {tmp_dir}
--overwrite_output_dir
--num_train_epochs=3
--warmup_steps=2
--do_train
--do_eval
--logging_steps 2 --eval_steps 2
--learning_rate=2e-4
--per_device_train_batch_size=2
--per_device_eval_batch_size=1
""".split()
with patch.object(A__ , '''argv''' , A__ ):
run_qa.main()
snake_case = get_results(A__ )
self.assertGreaterEqual(result['''eval_f1'''] , 30 )
self.assertGreaterEqual(result['''eval_exact'''] , 30 )
| 44
| 1
|
'''simple docstring'''
import argparse
import json
from collections import OrderedDict
from functools import partial
from pathlib import Path
import timm
import torch
from huggingface_hub import hf_hub_download
from transformers import LevitConfig, LevitForImageClassificationWithTeacher, LevitImageProcessor
from transformers.utils import logging
logging.set_verbosity_info()
_lowercase = logging.get_logger()
def __UpperCamelCase ( a : int , a : str , a : LevitConfig , a : Path , a : bool = True ) ->Optional[int]:
print(f"""Converting {name}...""" )
with torch.no_grad():
if hidden_sizes == 128:
if name[-1] == "S":
snake_case = timm.create_model('''levit_128s''' , pretrained=a )
else:
snake_case = timm.create_model('''levit_128''' , pretrained=a )
if hidden_sizes == 192:
snake_case = timm.create_model('''levit_192''' , pretrained=a )
if hidden_sizes == 256:
snake_case = timm.create_model('''levit_256''' , pretrained=a )
if hidden_sizes == 384:
snake_case = timm.create_model('''levit_384''' , pretrained=a )
from_model.eval()
snake_case = LevitForImageClassificationWithTeacher(a ).eval()
snake_case = OrderedDict()
snake_case = from_model.state_dict()
snake_case = list(from_model.state_dict().keys() )
snake_case = list(our_model.state_dict().keys() )
print(len(a ) , len(a ) )
for i in range(len(a ) ):
snake_case = weights[og_keys[i]]
our_model.load_state_dict(a )
snake_case = torch.randn((2, 3, 224, 224) )
snake_case = from_model(a )
snake_case = our_model(a ).logits
assert torch.allclose(a , a ), "The model logits don't match the original one."
snake_case = name
print(a )
if push_to_hub:
our_model.save_pretrained(save_directory / checkpoint_name )
snake_case = LevitImageProcessor()
image_processor.save_pretrained(save_directory / checkpoint_name )
print(f"""Pushed {checkpoint_name}""" )
def __UpperCamelCase ( a : Path , a : str = None , a : bool = True ) ->Union[str, Any]:
snake_case = '''imagenet-1k-id2label.json'''
snake_case = 1000
snake_case = (1, num_labels)
snake_case = '''huggingface/label-files'''
snake_case = num_labels
snake_case = json.load(open(hf_hub_download(a , a , repo_type='''dataset''' ) , '''r''' ) )
snake_case = {int(a ): v for k, v in idalabel.items()}
snake_case = idalabel
snake_case = {v: k for k, v in idalabel.items()}
snake_case = partial(a , num_labels=a , idalabel=a , labelaid=a )
snake_case = {
'''levit-128S''': 128,
'''levit-128''': 128,
'''levit-192''': 192,
'''levit-256''': 256,
'''levit-384''': 384,
}
snake_case = {
'''levit-128S''': ImageNetPreTrainedConfig(
hidden_sizes=[128, 256, 384] , num_attention_heads=[4, 6, 8] , depths=[2, 3, 4] , key_dim=[16, 16, 16] , drop_path_rate=0 , ),
'''levit-128''': ImageNetPreTrainedConfig(
hidden_sizes=[128, 256, 384] , num_attention_heads=[4, 8, 12] , depths=[4, 4, 4] , key_dim=[16, 16, 16] , drop_path_rate=0 , ),
'''levit-192''': ImageNetPreTrainedConfig(
hidden_sizes=[192, 288, 384] , num_attention_heads=[3, 5, 6] , depths=[4, 4, 4] , key_dim=[32, 32, 32] , drop_path_rate=0 , ),
'''levit-256''': ImageNetPreTrainedConfig(
hidden_sizes=[256, 384, 512] , num_attention_heads=[4, 6, 8] , depths=[4, 4, 4] , key_dim=[32, 32, 32] , drop_path_rate=0 , ),
'''levit-384''': ImageNetPreTrainedConfig(
hidden_sizes=[384, 512, 768] , num_attention_heads=[6, 9, 12] , depths=[4, 4, 4] , key_dim=[32, 32, 32] , drop_path_rate=0.1 , ),
}
if model_name:
convert_weight_and_push(
names_to_hidden_sizes[model_name] , a , names_to_config[model_name] , a , a )
else:
for model_name, config in names_to_config.items():
convert_weight_and_push(names_to_hidden_sizes[model_name] , a , a , a , a )
return config, expected_shape
if __name__ == "__main__":
_lowercase = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--model_name',
default=None,
type=str,
help='The name of the model you wish to convert, it must be one of the supported Levit* architecture,',
)
parser.add_argument(
'--pytorch_dump_folder_path',
default='levit-dump-folder/',
type=Path,
required=False,
help='Path to the output PyTorch model directory.',
)
parser.add_argument('--push_to_hub', action='store_true', help='Push model and image processor to the hub')
parser.add_argument(
'--no-push_to_hub',
dest='push_to_hub',
action='store_false',
help='Do not push model and image processor to the hub',
)
_lowercase = parser.parse_args()
_lowercase = args.pytorch_dump_folder_path
pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)
| 44
|
'''simple docstring'''
from typing import Any, Dict, List, Optional, Tuple, Union
import torch
from torch import nn
from torch.utils.data import DistributedSampler, RandomSampler
from transformers import PreTrainedModel, Trainer, logging
from transformers.integrations import is_fairscale_available
from transformers.models.fsmt.configuration_fsmt import FSMTConfig
from transformers.optimization import (
Adafactor,
AdamW,
get_constant_schedule,
get_constant_schedule_with_warmup,
get_cosine_schedule_with_warmup,
get_cosine_with_hard_restarts_schedule_with_warmup,
get_linear_schedule_with_warmup,
get_polynomial_decay_schedule_with_warmup,
)
from transformers.trainer_pt_utils import get_tpu_sampler
from transformers.training_args import ParallelMode
from transformers.utils import is_torch_tpu_available
if is_fairscale_available():
from fairscale.optim import OSS
_lowercase = logging.get_logger(__name__)
_lowercase = {
'linear': get_linear_schedule_with_warmup,
'cosine': get_cosine_schedule_with_warmup,
'cosine_w_restarts': get_cosine_with_hard_restarts_schedule_with_warmup,
'polynomial': get_polynomial_decay_schedule_with_warmup,
'constant': get_constant_schedule,
'constant_w_warmup': get_constant_schedule_with_warmup,
}
class _lowercase ( __a ):
def __init__( self , A__=None , A__=None , *A__ , **A__ ) -> Union[str, Any]:
super().__init__(*A__ , **A__ )
if config is None:
assert isinstance(self.model , A__ ), (
"If no `config` is passed the model to be trained has to be of type `PreTrainedModel`, but is"
F""" {self.model.__class__}"""
)
snake_case = self.model.config
else:
snake_case = config
snake_case = data_args
snake_case = self.config.tgt_vocab_size if isinstance(self.config , A__ ) else self.config.vocab_size
if self.args.label_smoothing != 0 or (self.data_args is not None and self.data_args.ignore_pad_token_for_loss):
assert self.config.pad_token_id is not None, (
"Make sure that `config.pad_token_id` is correcly defined when ignoring `pad_token` for loss"
" calculation or doing label smoothing."
)
if self.config.pad_token_id is None and self.config.eos_token_id is not None:
logger.warning(
F"""The `config.pad_token_id` is `None`. Using `config.eos_token_id` = {self.config.eos_token_id} for"""
''' padding..''' )
if self.args.label_smoothing == 0:
snake_case = torch.nn.CrossEntropyLoss(ignore_index=self.config.pad_token_id )
else:
# dynamically import label_smoothed_nll_loss
from utils import label_smoothed_nll_loss
snake_case = label_smoothed_nll_loss
def UpperCamelCase ( self , A__ ) -> Tuple:
if self.optimizer is None:
snake_case = ['''bias''', '''LayerNorm.weight''']
snake_case = [
{
'''params''': [p for n, p in self.model.named_parameters() if not any(nd in n for nd in no_decay )],
'''weight_decay''': self.args.weight_decay,
},
{
'''params''': [p for n, p in self.model.named_parameters() if any(nd in n for nd in no_decay )],
'''weight_decay''': 0.0,
},
]
snake_case = Adafactor if self.args.adafactor else AdamW
if self.args.adafactor:
snake_case = Adafactor
snake_case = {'''scale_parameter''': False, '''relative_step''': False}
else:
snake_case = AdamW
snake_case = {
'''betas''': (self.args.adam_betaa, self.args.adam_betaa),
'''eps''': self.args.adam_epsilon,
}
snake_case = self.args.learning_rate
if self.sharded_ddp:
snake_case = OSS(
params=A__ , optim=A__ , **A__ , )
else:
snake_case = optimizer_cls(A__ , **A__ )
if self.lr_scheduler is None:
snake_case = self._get_lr_scheduler(A__ )
else: # ignoring --lr_scheduler
logger.warning('''scheduler is passed to `Seq2SeqTrainer`, `--lr_scheduler` arg is ignored.''' )
def UpperCamelCase ( self , A__ ) -> Tuple:
snake_case = arg_to_scheduler[self.args.lr_scheduler]
if self.args.lr_scheduler == "constant":
snake_case = schedule_func(self.optimizer )
elif self.args.lr_scheduler == "constant_w_warmup":
snake_case = schedule_func(self.optimizer , num_warmup_steps=self.args.warmup_steps )
else:
snake_case = schedule_func(
self.optimizer , num_warmup_steps=self.args.warmup_steps , num_training_steps=A__ )
return scheduler
def UpperCamelCase ( self ) -> Optional[torch.utils.data.Sampler]:
if isinstance(self.train_dataset , torch.utils.data.IterableDataset ):
return None
elif is_torch_tpu_available():
return get_tpu_sampler(self.train_dataset )
else:
if self.args.sortish_sampler:
self.train_dataset.make_sortish_sampler(
self.args.per_device_train_batch_size , distributed=(self.args.parallel_mode == ParallelMode.DISTRIBUTED) , )
return (
RandomSampler(self.train_dataset )
if self.args.local_rank == -1
else DistributedSampler(self.train_dataset )
)
def UpperCamelCase ( self , A__ , A__ , A__ ) -> List[Any]:
if self.args.label_smoothing == 0:
if self.data_args is not None and self.data_args.ignore_pad_token_for_loss:
# force training to ignore pad token
snake_case = model(**A__ , use_cache=A__ )[0]
snake_case = self.loss_fn(logits.view(-1 , logits.shape[-1] ) , labels.view(-1 ) )
else:
# compute usual loss via models
snake_case , snake_case = model(**A__ , labels=A__ , use_cache=A__ )[:2]
else:
# compute label smoothed loss
snake_case = model(**A__ , use_cache=A__ )[0]
snake_case = torch.nn.functional.log_softmax(A__ , dim=-1 )
snake_case , snake_case = self.loss_fn(A__ , A__ , self.args.label_smoothing , ignore_index=self.config.pad_token_id )
return loss, logits
def UpperCamelCase ( self , A__ , A__ ) -> Any:
snake_case = inputs.pop('''labels''' )
snake_case , snake_case = self._compute_loss(A__ , A__ , A__ )
return loss
def UpperCamelCase ( self , A__ , A__ , A__ , A__ = None , ) -> Tuple[Optional[float], Optional[torch.Tensor], Optional[torch.Tensor]]:
snake_case = self._prepare_inputs(A__ )
snake_case = {
'''max_length''': self.data_args.val_max_target_length
if self.data_args is not None
else self.config.max_length,
'''num_beams''': self.data_args.eval_beams if self.data_args is not None else self.config.num_beams,
}
if self.args.predict_with_generate and not self.args.prediction_loss_only:
snake_case = self.model.generate(
inputs['''input_ids'''] , attention_mask=inputs['''attention_mask'''] , **A__ , )
# in case the batch is shorter than max length, the output should be padded
if generated_tokens.shape[-1] < gen_kwargs["max_length"]:
snake_case = self._pad_tensors_to_max_len(A__ , gen_kwargs['''max_length'''] )
snake_case = inputs.pop('''labels''' )
with torch.no_grad():
# compute loss on predict data
snake_case , snake_case = self._compute_loss(A__ , A__ , A__ )
snake_case = loss.mean().detach()
if self.args.prediction_loss_only:
return (loss, None, None)
snake_case = generated_tokens if self.args.predict_with_generate else logits
if labels.shape[-1] < gen_kwargs["max_length"]:
snake_case = self._pad_tensors_to_max_len(A__ , gen_kwargs['''max_length'''] )
return (loss, logits, labels)
def UpperCamelCase ( self , A__ , A__ ) -> List[str]:
# If PAD token is not defined at least EOS token has to be defined
snake_case = self.config.pad_token_id if self.config.pad_token_id is not None else self.config.eos_token_id
if pad_token_id is None:
raise ValueError(
'''Make sure that either `config.pad_token_id` or `config.eos_token_id` is defined if tensor has to be'''
F""" padded to `max_length`={max_length}""" )
snake_case = pad_token_id * torch.ones(
(tensor.shape[0], max_length) , dtype=tensor.dtype , device=tensor.device )
snake_case = tensor
return padded_tensor
| 44
| 1
|
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_donut import DonutImageProcessor
_lowercase = logging.get_logger(__name__)
class _lowercase ( __a ):
def __init__( self , *A__ , **A__ ) -> None:
warnings.warn(
'''The class DonutFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'''
''' use DonutImageProcessor instead.''' , A__ , )
super().__init__(*A__ , **A__ )
| 44
|
'''simple docstring'''
import inspect
import re
from hashlib import shaaaa
from typing import Dict, List
from .arrow import arrow
from .audiofolder import audiofolder
from .csv import csv
from .imagefolder import imagefolder
from .json import json
from .pandas import pandas
from .parquet import parquet
from .sql import sql # noqa F401
from .text import text
def __UpperCamelCase ( a : List[str] ) ->str:
snake_case = []
for line in lines:
snake_case = re.sub(R'''#.*''' , '''''' , a ) # remove comments
if line:
filtered_lines.append(a )
snake_case = '''\n'''.join(a )
# Make a hash from all this code
snake_case = full_str.encode('''utf-8''' )
return shaaaa(a ).hexdigest()
# get importable module names and hash for caching
_lowercase = {
'csv': (csv.__name__, _hash_python_lines(inspect.getsource(csv).splitlines())),
'json': (json.__name__, _hash_python_lines(inspect.getsource(json).splitlines())),
'pandas': (pandas.__name__, _hash_python_lines(inspect.getsource(pandas).splitlines())),
'parquet': (parquet.__name__, _hash_python_lines(inspect.getsource(parquet).splitlines())),
'arrow': (arrow.__name__, _hash_python_lines(inspect.getsource(arrow).splitlines())),
'text': (text.__name__, _hash_python_lines(inspect.getsource(text).splitlines())),
'imagefolder': (imagefolder.__name__, _hash_python_lines(inspect.getsource(imagefolder).splitlines())),
'audiofolder': (audiofolder.__name__, _hash_python_lines(inspect.getsource(audiofolder).splitlines())),
}
# Used to infer the module to use based on the data files extensions
_lowercase = {
'.csv': ('csv', {}),
'.tsv': ('csv', {'sep': '\t'}),
'.json': ('json', {}),
'.jsonl': ('json', {}),
'.parquet': ('parquet', {}),
'.arrow': ('arrow', {}),
'.txt': ('text', {}),
}
_EXTENSION_TO_MODULE.update({ext: ('imagefolder', {}) for ext in imagefolder.ImageFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext.upper(): ('imagefolder', {}) for ext in imagefolder.ImageFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext: ('audiofolder', {}) for ext in audiofolder.AudioFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext.upper(): ('audiofolder', {}) for ext in audiofolder.AudioFolder.EXTENSIONS})
_lowercase = {'imagefolder', 'audiofolder'}
# Used to filter data files based on extensions given a module name
_lowercase = {}
for _ext, (_module, _) in _EXTENSION_TO_MODULE.items():
_MODULE_TO_EXTENSIONS.setdefault(_module, []).append(_ext)
_MODULE_TO_EXTENSIONS["imagefolder"].append('.zip')
_MODULE_TO_EXTENSIONS["audiofolder"].append('.zip')
| 44
| 1
|
'''simple docstring'''
from abc import ABC, abstractmethod
from typing import List, Optional
class _lowercase ( __a ):
def __init__( self ) -> List[str]:
# test for the above condition
self.test()
def UpperCamelCase ( self ) -> Dict:
snake_case = 0
snake_case = False
while not completed:
if counter == 1:
self.reset()
snake_case = self.advance()
if not self.does_advance(A__ ):
raise Exception(
'''Custom Constraint is not defined correctly. self.does_advance(self.advance()) must be true.''' )
snake_case , snake_case , snake_case = self.update(A__ )
counter += 1
if counter > 1_00_00:
raise Exception('''update() does not fulfill the constraint.''' )
if self.remaining() != 0:
raise Exception('''Custom Constraint is not defined correctly.''' )
@abstractmethod
def UpperCamelCase ( self ) -> List[str]:
raise NotImplementedError(
F"""{self.__class__} is an abstract class. Only classes inheriting this class can be called.""" )
@abstractmethod
def UpperCamelCase ( self , A__ ) -> int:
raise NotImplementedError(
F"""{self.__class__} is an abstract class. Only classes inheriting this class can be called.""" )
@abstractmethod
def UpperCamelCase ( self , A__ ) -> Tuple:
raise NotImplementedError(
F"""{self.__class__} is an abstract class. Only classes inheriting this class can be called.""" )
@abstractmethod
def UpperCamelCase ( self ) -> Any:
raise NotImplementedError(
F"""{self.__class__} is an abstract class. Only classes inheriting this class can be called.""" )
@abstractmethod
def UpperCamelCase ( self ) -> Tuple:
raise NotImplementedError(
F"""{self.__class__} is an abstract class. Only classes inheriting this class can be called.""" )
@abstractmethod
def UpperCamelCase ( self , A__=False ) -> Optional[Any]:
raise NotImplementedError(
F"""{self.__class__} is an abstract class. Only classes inheriting this class can be called.""" )
class _lowercase ( __a ):
def __init__( self , A__ ) -> Dict:
super(A__ , self ).__init__()
if not isinstance(A__ , A__ ) or len(A__ ) == 0:
raise ValueError(F"""`token_ids` has to be a non-empty list, but is {token_ids}.""" )
if any((not isinstance(A__ , A__ ) or token_id < 0) for token_id in token_ids ):
raise ValueError(F"""Each list in `token_ids` has to be a list of positive integers, but is {token_ids}.""" )
snake_case = token_ids
snake_case = len(self.token_ids )
snake_case = -1 # the index of the currently fulfilled step
snake_case = False
def UpperCamelCase ( self ) -> Any:
if self.completed:
return None
return self.token_ids[self.fulfilled_idx + 1]
def UpperCamelCase ( self , A__ ) -> Optional[int]:
if not isinstance(A__ , A__ ):
raise ValueError(F"""`token_id` has to be an `int`, but is {token_id} of type {type(A__ )}""" )
if self.completed:
return False
return token_id == self.token_ids[self.fulfilled_idx + 1]
def UpperCamelCase ( self , A__ ) -> int:
if not isinstance(A__ , A__ ):
raise ValueError(F"""`token_id` has to be an `int`, but is {token_id} of type {type(A__ )}""" )
snake_case = False
snake_case = False
snake_case = False
if self.does_advance(A__ ):
self.fulfilled_idx += 1
snake_case = True
if self.fulfilled_idx == (self.seqlen - 1):
snake_case = True
snake_case = completed
else:
# failed to make progress.
snake_case = True
self.reset()
return stepped, completed, reset
def UpperCamelCase ( self ) -> int:
snake_case = False
snake_case = 0
def UpperCamelCase ( self ) -> Tuple:
return self.seqlen - (self.fulfilled_idx + 1)
def UpperCamelCase ( self , A__=False ) -> int:
snake_case = PhrasalConstraint(self.token_ids )
if stateful:
snake_case = self.seqlen
snake_case = self.fulfilled_idx
snake_case = self.completed
return new_constraint
class _lowercase :
def __init__( self , A__ , A__=True ) -> Optional[Any]:
snake_case = max([len(A__ ) for one in nested_token_ids] )
snake_case = {}
for token_ids in nested_token_ids:
snake_case = root
for tidx, token_id in enumerate(A__ ):
if token_id not in level:
snake_case = {}
snake_case = level[token_id]
if no_subsets and self.has_subsets(A__ , A__ ):
raise ValueError(
'''Each list in `nested_token_ids` can\'t be a complete subset of another list, but is'''
F""" {nested_token_ids}.""" )
snake_case = root
def UpperCamelCase ( self , A__ ) -> List[Any]:
snake_case = self.trie
for current_token in current_seq:
snake_case = start[current_token]
snake_case = list(start.keys() )
return next_tokens
def UpperCamelCase ( self , A__ ) -> Any:
snake_case = self.next_tokens(A__ )
return len(A__ ) == 0
def UpperCamelCase ( self , A__ ) -> Union[str, Any]:
snake_case = list(root.values() )
if len(A__ ) == 0:
return 1
else:
return sum([self.count_leaves(A__ ) for nn in next_nodes] )
def UpperCamelCase ( self , A__ , A__ ) -> Optional[int]:
snake_case = self.count_leaves(A__ )
return len(A__ ) != leaf_count
class _lowercase ( __a ):
def __init__( self , A__ ) -> str:
super(A__ , self ).__init__()
if not isinstance(A__ , A__ ) or len(A__ ) == 0:
raise ValueError(F"""`nested_token_ids` has to be a non-empty list, but is {nested_token_ids}.""" )
if any(not isinstance(A__ , A__ ) for token_ids in nested_token_ids ):
raise ValueError(F"""`nested_token_ids` has to be a list of lists, but is {nested_token_ids}.""" )
if any(
any((not isinstance(A__ , A__ ) or token_id < 0) for token_id in token_ids )
for token_ids in nested_token_ids ):
raise ValueError(
F"""Each list in `nested_token_ids` has to be a list of positive integers, but is {nested_token_ids}.""" )
snake_case = DisjunctiveTrie(A__ )
snake_case = nested_token_ids
snake_case = self.trie.max_height
snake_case = []
snake_case = False
def UpperCamelCase ( self ) -> List[Any]:
snake_case = self.trie.next_tokens(self.current_seq )
if len(A__ ) == 0:
return None
else:
return token_list
def UpperCamelCase ( self , A__ ) -> List[str]:
if not isinstance(A__ , A__ ):
raise ValueError(F"""`token_id` is supposed to be type `int`, but is {token_id} of type {type(A__ )}""" )
snake_case = self.trie.next_tokens(self.current_seq )
return token_id in next_tokens
def UpperCamelCase ( self , A__ ) -> Union[str, Any]:
if not isinstance(A__ , A__ ):
raise ValueError(F"""`token_id` is supposed to be type `int`, but is {token_id} of type {type(A__ )}""" )
snake_case = False
snake_case = False
snake_case = False
if self.does_advance(A__ ):
self.current_seq.append(A__ )
snake_case = True
else:
snake_case = True
self.reset()
snake_case = self.trie.reached_leaf(self.current_seq )
snake_case = completed
return stepped, completed, reset
def UpperCamelCase ( self ) -> List[Any]:
snake_case = False
snake_case = []
def UpperCamelCase ( self ) -> Tuple:
if self.completed:
# since this can be completed without reaching max height
return 0
else:
return self.seqlen - len(self.current_seq )
def UpperCamelCase ( self , A__=False ) -> Optional[int]:
snake_case = DisjunctiveConstraint(self.token_ids )
if stateful:
snake_case = self.seqlen
snake_case = self.current_seq
snake_case = self.completed
return new_constraint
class _lowercase :
def __init__( self , A__ ) -> Union[str, Any]:
snake_case = constraints
# max # of steps required to fulfill a given constraint
snake_case = max([c.seqlen for c in constraints] )
snake_case = len(A__ )
snake_case = False
self.init_state()
def UpperCamelCase ( self ) -> Optional[Any]:
snake_case = []
snake_case = None
snake_case = [constraint.copy(stateful=A__ ) for constraint in self.constraints]
def UpperCamelCase ( self ) -> Any:
snake_case = 0
if self.inprogress_constraint:
# extra points for having a constraint mid-fulfilled
add += self.max_seqlen - self.inprogress_constraint.remaining()
return (len(self.complete_constraints ) * self.max_seqlen) + add
def UpperCamelCase ( self ) -> Optional[int]:
snake_case = []
if self.inprogress_constraint is None:
for constraint in self.pending_constraints: # "pending" == "unfulfilled yet"
snake_case = constraint.advance()
if isinstance(A__ , A__ ):
token_list.append(A__ )
elif isinstance(A__ , A__ ):
token_list.extend(A__ )
else:
snake_case = self.inprogress_constraint.advance()
if isinstance(A__ , A__ ):
token_list.append(A__ )
elif isinstance(A__ , A__ ):
token_list.extend(A__ )
if len(A__ ) == 0:
return None
else:
return token_list
def UpperCamelCase ( self , A__ ) -> Dict:
self.init_state()
if token_ids is not None:
for token in token_ids:
# completes or steps **one** constraint
snake_case , snake_case = self.add(A__ )
# the entire list of constraints are fulfilled
if self.completed:
break
def UpperCamelCase ( self , A__ ) -> Tuple:
if not isinstance(A__ , A__ ):
raise ValueError(F"""`token_id` should be an `int`, but is `{token_id}`.""" )
snake_case , snake_case = False, False
if self.completed:
snake_case = True
snake_case = False
return complete, stepped
if self.inprogress_constraint is not None:
# In the middle of fulfilling a constraint. If the `token_id` *does* makes an incremental progress to current
# job, simply update the state
snake_case , snake_case , snake_case = self.inprogress_constraint.update(A__ )
if reset:
# 1. If the next token breaks the progress, then we must restart.
# e.g. constraint = "I love pies" and sequence so far is "I love" but `token_id` == "books".
# But that doesn't mean we self.init_state(), since we only reset the state for this particular
# constraint, not the full list of constraints.
self.pending_constraints.append(self.inprogress_constraint.copy(stateful=A__ ) )
snake_case = None
if complete:
# 2. If the next token completes the constraint, move it to completed list, set
# inprogress to None. If there are no pending constraints either, then this full list of constraints
# is complete.
self.complete_constraints.append(self.inprogress_constraint )
snake_case = None
if len(self.pending_constraints ) == 0:
# we're done!
snake_case = True
else:
# Not in the middle of fulfilling a constraint. So does this `token_id` helps us step towards any of our list
# of constraints?
for cidx, pending_constraint in enumerate(self.pending_constraints ):
if pending_constraint.does_advance(A__ ):
snake_case , snake_case , snake_case = pending_constraint.update(A__ )
if not stepped:
raise Exception(
'''`constraint.update(token_id)` is not yielding incremental progress, '''
'''even though `constraint.does_advance(token_id)` is true.''' )
if complete:
self.complete_constraints.append(A__ )
snake_case = None
if not complete and stepped:
snake_case = pending_constraint
if complete or stepped:
# If we made any progress at all, then it's at least not a "pending constraint".
snake_case = (
self.pending_constraints[:cidx] + self.pending_constraints[cidx + 1 :]
)
if len(self.pending_constraints ) == 0 and self.inprogress_constraint is None:
# If there's no longer any pending after this and no inprogress either, then we must be
# complete.
snake_case = True
break # prevent accidentally stepping through multiple constraints with just one token.
return complete, stepped
def UpperCamelCase ( self , A__=True ) -> Union[str, Any]:
snake_case = ConstraintListState(self.constraints ) # we actually never though self.constraints objects
# throughout this process. So it's at initialization state.
if stateful:
snake_case = [
constraint.copy(stateful=A__ ) for constraint in self.complete_constraints
]
if self.inprogress_constraint is not None:
snake_case = self.inprogress_constraint.copy(stateful=A__ )
snake_case = [constraint.copy() for constraint in self.pending_constraints]
return new_state
| 44
|
'''simple docstring'''
_lowercase = {
'Pillow': 'Pillow',
'accelerate': 'accelerate>=0.11.0',
'compel': 'compel==0.1.8',
'black': 'black~=23.1',
'datasets': 'datasets',
'filelock': 'filelock',
'flax': 'flax>=0.4.1',
'hf-doc-builder': 'hf-doc-builder>=0.3.0',
'huggingface-hub': 'huggingface-hub>=0.13.2',
'requests-mock': 'requests-mock==1.10.0',
'importlib_metadata': 'importlib_metadata',
'invisible-watermark': 'invisible-watermark',
'isort': 'isort>=5.5.4',
'jax': 'jax>=0.2.8,!=0.3.2',
'jaxlib': 'jaxlib>=0.1.65',
'Jinja2': 'Jinja2',
'k-diffusion': 'k-diffusion>=0.0.12',
'torchsde': 'torchsde',
'note_seq': 'note_seq',
'librosa': 'librosa',
'numpy': 'numpy',
'omegaconf': 'omegaconf',
'parameterized': 'parameterized',
'protobuf': 'protobuf>=3.20.3,<4',
'pytest': 'pytest',
'pytest-timeout': 'pytest-timeout',
'pytest-xdist': 'pytest-xdist',
'ruff': 'ruff>=0.0.241',
'safetensors': 'safetensors',
'sentencepiece': 'sentencepiece>=0.1.91,!=0.1.92',
'scipy': 'scipy',
'onnx': 'onnx',
'regex': 'regex!=2019.12.17',
'requests': 'requests',
'tensorboard': 'tensorboard',
'torch': 'torch>=1.4',
'torchvision': 'torchvision',
'transformers': 'transformers>=4.25.1',
'urllib3': 'urllib3<=2.0.0',
}
| 44
| 1
|
'''simple docstring'''
def __UpperCamelCase ( a : int ) ->int:
snake_case = 1
for i in range(1 , num + 1 ):
fact *= i
return fact
def __UpperCamelCase ( a : int ) ->int:
snake_case = 0
while number > 0:
snake_case = number % 10
sum_of_digits += last_digit
snake_case = number // 10 # Removing the last_digit from the given number
return sum_of_digits
def __UpperCamelCase ( a : int = 100 ) ->int:
snake_case = factorial(a )
snake_case = split_and_add(a )
return result
if __name__ == "__main__":
print(solution(int(input('Enter the Number: ').strip())))
| 44
|
'''simple docstring'''
import random
import unittest
import torch
from diffusers import IFInpaintingSuperResolutionPipeline
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_INPAINTING_BATCH_PARAMS,
TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin
@skip_mps
class _lowercase ( __a , __a , unittest.TestCase ):
_UpperCAmelCase = IFInpaintingSuperResolutionPipeline
_UpperCAmelCase = TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {'''width''', '''height'''}
_UpperCAmelCase = TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS.union({'''original_image'''} )
_UpperCAmelCase = PipelineTesterMixin.required_optional_params - {'''latents'''}
def UpperCamelCase ( self ) -> int:
return self._get_superresolution_dummy_components()
def UpperCamelCase ( self , A__ , A__=0 ) -> Union[str, Any]:
if str(A__ ).startswith('''mps''' ):
snake_case = torch.manual_seed(A__ )
else:
snake_case = torch.Generator(device=A__ ).manual_seed(A__ )
snake_case = floats_tensor((1, 3, 16, 16) , rng=random.Random(A__ ) ).to(A__ )
snake_case = floats_tensor((1, 3, 32, 32) , rng=random.Random(A__ ) ).to(A__ )
snake_case = floats_tensor((1, 3, 32, 32) , rng=random.Random(A__ ) ).to(A__ )
snake_case = {
'''prompt''': '''A painting of a squirrel eating a burger''',
'''image''': image,
'''original_image''': original_image,
'''mask_image''': mask_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 UpperCamelCase ( self ) -> List[Any]:
self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3 )
def UpperCamelCase ( self ) -> Optional[Any]:
self._test_save_load_optional_components()
@unittest.skipIf(torch_device != '''cuda''' , reason='''float16 requires CUDA''' )
def UpperCamelCase ( self ) -> List[str]:
# Due to non-determinism in save load of the hf-internal-testing/tiny-random-t5 text encoder
super().test_save_load_floataa(expected_max_diff=1e-1 )
def UpperCamelCase ( self ) -> int:
self._test_attention_slicing_forward_pass(expected_max_diff=1e-2 )
def UpperCamelCase ( self ) -> Optional[Any]:
self._test_save_load_local()
def UpperCamelCase ( self ) -> Dict:
self._test_inference_batch_single_identical(
expected_max_diff=1e-2 , )
| 44
| 1
|
'''simple docstring'''
from abc import ABC, abstractmethod
from argparse import ArgumentParser
class _lowercase ( __a ):
@staticmethod
@abstractmethod
def UpperCamelCase ( A__ ) -> Dict:
raise NotImplementedError()
@abstractmethod
def UpperCamelCase ( self ) -> List[str]:
raise NotImplementedError()
| 44
|
'''simple docstring'''
from typing import Dict, List, Optional, Union
import numpy as np
from .feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from .utils import PaddingStrategy, TensorType, is_tf_tensor, is_torch_tensor, logging, to_numpy
_lowercase = logging.get_logger(__name__)
class _lowercase ( __a ):
def __init__( self , A__ , A__ , A__ , **A__ ) -> Union[str, Any]:
snake_case = feature_size
snake_case = sampling_rate
snake_case = padding_value
snake_case = kwargs.pop('''padding_side''' , '''right''' )
snake_case = kwargs.pop('''return_attention_mask''' , A__ )
super().__init__(**A__ )
def UpperCamelCase ( self , A__ , A__ = True , A__ = None , A__ = False , A__ = None , A__ = None , A__ = None , ) -> BatchFeature:
# If we have a list of dicts, let's convert it in a dict of lists
# We do this to allow using this method as a collate_fn function in PyTorch Dataloader
if isinstance(A__ , (list, tuple) ) and isinstance(processed_features[0] , (dict, BatchFeature) ):
snake_case = {
key: [example[key] for example in processed_features] for key in processed_features[0].keys()
}
# The model's main input name, usually `input_values`, has be passed for padding
if self.model_input_names[0] not in processed_features:
raise ValueError(
'''You should supply an instance of `transformers.BatchFeature` or list of `transformers.BatchFeature`'''
F""" to this method that includes {self.model_input_names[0]}, but you provided"""
F""" {list(processed_features.keys() )}""" )
snake_case = processed_features[self.model_input_names[0]]
snake_case = (
return_attention_mask if return_attention_mask is not None else self.return_attention_mask
)
if len(A__ ) == 0:
if return_attention_mask:
snake_case = []
return processed_features
# If we have PyTorch/TF tensors or lists as inputs, we cast them as Numpy arrays
# and rebuild them afterwards if no return_tensors is specified
# Note that we lose the specific device the tensor may be on for PyTorch
snake_case = required_input[0]
if isinstance(A__ , (list, tuple) ):
# first_element might be an empty list/tuple in some edge cases so we grab the first non empty element.
snake_case = 0
while len(required_input[index] ) == 0:
index += 1
if index < len(A__ ):
snake_case = required_input[index][0]
if return_tensors is None:
if is_tf_tensor(A__ ):
snake_case = '''tf'''
elif is_torch_tensor(A__ ):
snake_case = '''pt'''
elif isinstance(A__ , (int, float, list, tuple, np.ndarray) ):
snake_case = '''np'''
else:
raise ValueError(
F"""type of {first_element} unknown: {type(A__ )}. """
'''Should be one of a python, numpy, pytorch or tensorflow object.''' )
for key, value in processed_features.items():
if isinstance(value[0] , (int, float) ):
snake_case = to_numpy(A__ )
else:
snake_case = [to_numpy(A__ ) for v in value]
# Convert padding_strategy in PaddingStrategy
snake_case = self._get_padding_strategies(padding=A__ , max_length=A__ )
snake_case = processed_features[self.model_input_names[0]]
snake_case = len(A__ )
if not all(len(A__ ) == batch_size for v in processed_features.values() ):
raise ValueError('''Some items in the output dictionary have a different batch size than others.''' )
snake_case = []
for i in range(A__ ):
snake_case = {k: v[i] for k, v in processed_features.items()}
# truncation
snake_case = self._truncate(
A__ , max_length=A__ , pad_to_multiple_of=A__ , truncation=A__ , )
truncated_inputs.append(A__ )
if padding_strategy == PaddingStrategy.LONGEST:
# make sure that `max_length` cannot be longer than the longest truncated length
snake_case = max(len(input_slice[self.model_input_names[0]] ) for input_slice in truncated_inputs )
snake_case = PaddingStrategy.MAX_LENGTH
snake_case = {}
for i in range(A__ ):
# padding
snake_case = self._pad(
truncated_inputs[i] , max_length=A__ , padding_strategy=A__ , pad_to_multiple_of=A__ , return_attention_mask=A__ , )
for key, value in outputs.items():
if key not in batch_outputs:
snake_case = []
if value.dtype is np.dtype(np.floataa ):
snake_case = value.astype(np.floataa )
batch_outputs[key].append(A__ )
return BatchFeature(A__ , tensor_type=A__ )
def UpperCamelCase ( self , A__ , A__ = None , A__ = PaddingStrategy.DO_NOT_PAD , A__ = None , A__ = None , ) -> dict:
snake_case = processed_features[self.model_input_names[0]]
if padding_strategy == PaddingStrategy.LONGEST:
snake_case = len(A__ )
if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
snake_case = ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of
snake_case = padding_strategy != PaddingStrategy.DO_NOT_PAD and len(A__ ) < max_length
if return_attention_mask and "attention_mask" not in processed_features:
snake_case = np.ones(len(A__ ) , dtype=np.intaa )
if needs_to_be_padded:
snake_case = max_length - len(A__ )
if self.padding_side == "right":
if return_attention_mask:
snake_case = np.pad(
processed_features['''attention_mask'''] , (0, difference) )
snake_case = ((0, difference), (0, 0)) if self.feature_size > 1 else (0, difference)
snake_case = np.pad(
A__ , A__ , '''constant''' , constant_values=self.padding_value )
elif self.padding_side == "left":
if return_attention_mask:
snake_case = np.pad(
processed_features['''attention_mask'''] , (difference, 0) )
snake_case = ((difference, 0), (0, 0)) if self.feature_size > 1 else (difference, 0)
snake_case = np.pad(
A__ , A__ , '''constant''' , constant_values=self.padding_value )
else:
raise ValueError('''Invalid padding strategy:''' + str(self.padding_side ) )
return processed_features
def UpperCamelCase ( self , A__ , A__ = None , A__ = None , A__ = None , ) -> Union[str, Any]:
if not truncation:
return processed_features
elif truncation and max_length is None:
raise ValueError('''When setting ``truncation=True``, make sure that ``max_length`` is defined.''' )
snake_case = processed_features[self.model_input_names[0]]
# find `max_length` that fits `pad_to_multiple_of`
if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
snake_case = ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of
snake_case = len(A__ ) > max_length
if needs_to_be_truncated:
snake_case = processed_features[self.model_input_names[0]][:max_length]
if "attention_mask" in processed_features:
snake_case = processed_features['''attention_mask'''][:max_length]
return processed_features
def UpperCamelCase ( self , A__=False , A__=None ) -> Union[str, Any]:
# Get padding strategy
if padding is not False:
if padding is True:
snake_case = PaddingStrategy.LONGEST # Default to pad to the longest sequence in the batch
elif not isinstance(A__ , A__ ):
snake_case = PaddingStrategy(A__ )
elif isinstance(A__ , A__ ):
snake_case = padding
else:
snake_case = PaddingStrategy.DO_NOT_PAD
# Set max length if needed
if max_length is None:
if padding_strategy == PaddingStrategy.MAX_LENGTH:
raise ValueError(
F"""When setting ``padding={PaddingStrategy.MAX_LENGTH}``, make sure that max_length is defined""" )
# Test if we have a padding value
if padding_strategy != PaddingStrategy.DO_NOT_PAD and (self.padding_value is None):
raise ValueError(
'''Asking to pad but the feature_extractor does not have a padding value. Please select a value to use'''
''' as `padding_value`. For example: `feature_extractor.padding_value = 0.0`.''' )
return padding_strategy
| 44
| 1
|
'''simple docstring'''
import argparse
import torch
from torch import nn
from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration
def __UpperCamelCase ( a : List[Any] ) ->List[str]:
snake_case = [
'''encoder.version''',
'''decoder.version''',
'''model.encoder.version''',
'''model.decoder.version''',
'''decoder.output_projection.weight''',
'''_float_tensor''',
'''encoder.embed_positions._float_tensor''',
'''decoder.embed_positions._float_tensor''',
]
for k in ignore_keys:
state_dict.pop(a , a )
def __UpperCamelCase ( a : int ) ->Union[str, Any]:
snake_case , snake_case = emb.weight.shape
snake_case = nn.Linear(a , a , bias=a )
snake_case = emb.weight.data
return lin_layer
def __UpperCamelCase ( a : List[str] ) ->List[str]:
snake_case = torch.load(a , map_location='''cpu''' )
snake_case = mam_aaa['''args'''] or mam_aaa['''cfg''']['''model''']
snake_case = mam_aaa['''model''']
remove_ignore_keys_(a )
snake_case = state_dict['''encoder.embed_tokens.weight'''].shape[0]
snake_case = MaMaaaConfig(
vocab_size=a , max_position_embeddings=1024 , encoder_layers=args.encoder_layers , decoder_layers=args.decoder_layers , encoder_attention_heads=args.encoder_attention_heads , decoder_attention_heads=args.decoder_attention_heads , encoder_ffn_dim=args.encoder_ffn_embed_dim , decoder_ffn_dim=args.decoder_ffn_embed_dim , d_model=args.encoder_embed_dim , encoder_layerdrop=args.encoder_layerdrop , decoder_layerdrop=args.decoder_layerdrop , dropout=args.dropout , attention_dropout=args.attention_dropout , activation_dropout=args.activation_dropout , activation_function='''relu''' , )
snake_case = state_dict['''decoder.embed_tokens.weight''']
snake_case = MaMaaaForConditionalGeneration(a )
model.model.load_state_dict(a , strict=a )
snake_case = make_linear_from_emb(model.model.shared )
return model
if __name__ == "__main__":
_lowercase = argparse.ArgumentParser()
# Required parameters
parser.add_argument('fairseq_path', type=str, help='path to a model.pt on local filesystem.')
parser.add_argument('pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
_lowercase = parser.parse_args()
_lowercase = convert_fairseq_mamaaa_checkpoint_from_disk(args.fairseq_pathß)
model.save_pretrained(args.pytorch_dump_folder_path)
| 44
|
'''simple docstring'''
from collections import Counter
from pathlib import Path
from typing import Optional, Tuple
import yaml
class _lowercase ( yaml.SafeLoader ):
def UpperCamelCase ( self , A__ ) -> List[str]:
snake_case = [self.constructed_objects[key_node] for key_node, _ in node.value]
snake_case = [tuple(A__ ) if isinstance(A__ , A__ ) else key for key in keys]
snake_case = Counter(A__ )
snake_case = [key for key in counter if counter[key] > 1]
if duplicate_keys:
raise TypeError(F"""Got duplicate yaml keys: {duplicate_keys}""" )
def UpperCamelCase ( self , A__ , A__=False ) -> List[Any]:
snake_case = super().construct_mapping(A__ , deep=A__ )
self._check_no_duplicates_on_constructed_node(A__ )
return mapping
def __UpperCamelCase ( a : str ) ->Tuple[Optional[str], str]:
snake_case = list(readme_content.splitlines() )
if full_content and full_content[0] == "---" and "---" in full_content[1:]:
snake_case = full_content[1:].index('''---''' ) + 1
snake_case = '''\n'''.join(full_content[1:sep_idx] )
return yamlblock, "\n".join(full_content[sep_idx + 1 :] )
return None, "\n".join(a )
class _lowercase ( __a ):
# class attributes
_UpperCAmelCase = {'''train_eval_index'''} # train-eval-index in the YAML metadata
@classmethod
def UpperCamelCase ( cls , A__ ) -> "DatasetMetadata":
with open(A__ , encoding='''utf-8''' ) as readme_file:
snake_case , snake_case = _split_yaml_from_readme(readme_file.read() )
if yaml_string is not None:
return cls.from_yaml_string(A__ )
else:
return cls()
def UpperCamelCase ( self , A__ ) -> str:
if path.exists():
with open(A__ , encoding='''utf-8''' ) as readme_file:
snake_case = readme_file.read()
else:
snake_case = None
snake_case = self._to_readme(A__ )
with open(A__ , '''w''' , encoding='''utf-8''' ) as readme_file:
readme_file.write(A__ )
def UpperCamelCase ( self , A__ = None ) -> str:
if readme_content is not None:
snake_case , snake_case = _split_yaml_from_readme(A__ )
snake_case = '''---\n''' + self.to_yaml_string() + '''---\n''' + content
else:
snake_case = '''---\n''' + self.to_yaml_string() + '''---\n'''
return full_content
@classmethod
def UpperCamelCase ( cls , A__ ) -> "DatasetMetadata":
snake_case = yaml.load(A__ , Loader=_NoDuplicateSafeLoader ) or {}
# Convert the YAML keys to DatasetMetadata fields
snake_case = {
(key.replace('''-''' , '''_''' ) if key.replace('''-''' , '''_''' ) in cls._FIELDS_WITH_DASHES else key): value
for key, value in metadata_dict.items()
}
return cls(**A__ )
def UpperCamelCase ( self ) -> str:
return yaml.safe_dump(
{
(key.replace('''_''' , '''-''' ) if key in self._FIELDS_WITH_DASHES else key): value
for key, value in self.items()
} , sort_keys=A__ , allow_unicode=A__ , encoding='''utf-8''' , ).decode('''utf-8''' )
_lowercase = {
'image-classification': [],
'translation': [],
'image-segmentation': [],
'fill-mask': [],
'automatic-speech-recognition': [],
'token-classification': [],
'sentence-similarity': [],
'audio-classification': [],
'question-answering': [],
'summarization': [],
'zero-shot-classification': [],
'table-to-text': [],
'feature-extraction': [],
'other': [],
'multiple-choice': [],
'text-classification': [],
'text-to-image': [],
'text2text-generation': [],
'zero-shot-image-classification': [],
'tabular-classification': [],
'tabular-regression': [],
'image-to-image': [],
'tabular-to-text': [],
'unconditional-image-generation': [],
'text-retrieval': [],
'text-to-speech': [],
'object-detection': [],
'audio-to-audio': [],
'text-generation': [],
'conversational': [],
'table-question-answering': [],
'visual-question-answering': [],
'image-to-text': [],
'reinforcement-learning': [],
'voice-activity-detection': [],
'time-series-forecasting': [],
'document-question-answering': [],
}
if __name__ == "__main__":
from argparse import ArgumentParser
_lowercase = ArgumentParser(usage='Validate the yaml metadata block of a README.md file.')
ap.add_argument('readme_filepath')
_lowercase = ap.parse_args()
_lowercase = Path(args.readme_filepath)
_lowercase = DatasetMetadata.from_readme(readme_filepath)
print(dataset_metadata)
dataset_metadata.to_readme(readme_filepath)
| 44
| 1
|
'''simple docstring'''
import logging
import random
import ray
from transformers import RagConfig, RagRetriever, RagTokenizer
from transformers.models.rag.retrieval_rag import CustomHFIndex
_lowercase = logging.getLogger(__name__)
class _lowercase :
def __init__( self ) -> Dict:
snake_case = False
def UpperCamelCase ( self , A__ , A__ , A__ , A__ ) -> int:
if not self.initialized:
snake_case = RagRetriever(
A__ , question_encoder_tokenizer=A__ , generator_tokenizer=A__ , index=A__ , init_retrieval=A__ , )
snake_case = True
def UpperCamelCase ( self ) -> Dict:
self.retriever.index.init_index()
def UpperCamelCase ( self , A__ , A__ ) -> List[Any]:
snake_case , snake_case = self.retriever._main_retrieve(A__ , A__ )
return doc_ids, retrieved_doc_embeds
class _lowercase ( __a ):
def __init__( self , A__ , A__ , A__ , A__ , A__=None ) -> int:
if index is not None and index.is_initialized() and len(A__ ) > 0:
raise ValueError(
'''When using Ray for distributed fine-tuning, '''
'''you\'ll need to provide the paths instead, '''
'''as the dataset and the index are loaded '''
'''separately. More info in examples/rag/use_own_knowledge_dataset.py ''' )
super().__init__(
A__ , question_encoder_tokenizer=A__ , generator_tokenizer=A__ , index=A__ , init_retrieval=A__ , )
snake_case = retrieval_workers
if len(self.retrieval_workers ) > 0:
ray.get(
[
worker.create_rag_retriever.remote(A__ , A__ , A__ , A__ )
for worker in self.retrieval_workers
] )
def UpperCamelCase ( self ) -> Optional[int]:
logger.info('''initializing retrieval''' )
if len(self.retrieval_workers ) > 0:
ray.get([worker.init_retrieval.remote() for worker in self.retrieval_workers] )
else:
# Non-distributed training. Load index into this same process.
self.index.init_index()
def UpperCamelCase ( self , A__ , A__ ) -> List[Any]:
if len(self.retrieval_workers ) > 0:
# Select a random retrieval actor.
snake_case = self.retrieval_workers[random.randint(0 , len(self.retrieval_workers ) - 1 )]
snake_case , snake_case = ray.get(random_worker.retrieve.remote(A__ , A__ ) )
else:
snake_case , snake_case = self._main_retrieve(A__ , A__ )
return retrieved_doc_embeds, doc_ids, self.index.get_doc_dicts(A__ )
@classmethod
def UpperCamelCase ( cls , A__ , A__=None , **A__ ) -> List[str]:
return super(A__ , cls ).get_tokenizers(A__ , A__ , **A__ )
@classmethod
def UpperCamelCase ( cls , A__ , A__ , A__=None , **A__ ) -> Union[str, Any]:
snake_case = kwargs.pop('''config''' , A__ ) or RagConfig.from_pretrained(A__ , **A__ )
snake_case = RagTokenizer.from_pretrained(A__ , config=A__ )
snake_case = rag_tokenizer.question_encoder
snake_case = rag_tokenizer.generator
if indexed_dataset is not None:
snake_case = '''custom'''
snake_case = CustomHFIndex(config.retrieval_vector_size , A__ )
else:
snake_case = cls._build_index(A__ )
return cls(
A__ , question_encoder_tokenizer=A__ , generator_tokenizer=A__ , retrieval_workers=A__ , index=A__ , )
| 44
|
'''simple docstring'''
import json
import os
import re
import unittest
from transformers import CodeGenTokenizer, CodeGenTokenizerFast
from transformers.models.codegen.tokenization_codegen import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class _lowercase ( __a , unittest.TestCase ):
_UpperCAmelCase = CodeGenTokenizer
_UpperCAmelCase = CodeGenTokenizerFast
_UpperCAmelCase = True
_UpperCAmelCase = {'''add_prefix_space''': True}
_UpperCAmelCase = False
def UpperCamelCase ( self ) -> Tuple:
super().setUp()
# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
snake_case = [
'''l''',
'''o''',
'''w''',
'''e''',
'''r''',
'''s''',
'''t''',
'''i''',
'''d''',
'''n''',
'''\u0120''',
'''\u0120l''',
'''\u0120n''',
'''\u0120lo''',
'''\u0120low''',
'''er''',
'''\u0120lowest''',
'''\u0120newer''',
'''\u0120wider''',
'''<unk>''',
'''<|endoftext|>''',
]
snake_case = dict(zip(A__ , range(len(A__ ) ) ) )
snake_case = ['''#version: 0.2''', '''\u0120 l''', '''\u0120l o''', '''\u0120lo w''', '''e r''', '''''']
snake_case = {'''unk_token''': '''<unk>'''}
snake_case = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['''vocab_file'''] )
snake_case = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['''merges_file'''] )
with open(self.vocab_file , '''w''' , encoding='''utf-8''' ) as fp:
fp.write(json.dumps(A__ ) + '''\n''' )
with open(self.merges_file , '''w''' , encoding='''utf-8''' ) as fp:
fp.write('''\n'''.join(A__ ) )
def UpperCamelCase ( self , **A__ ) -> Union[str, Any]:
kwargs.update(self.special_tokens_map )
return CodeGenTokenizer.from_pretrained(self.tmpdirname , **A__ )
def UpperCamelCase ( self , **A__ ) -> Union[str, Any]:
kwargs.update(self.special_tokens_map )
return CodeGenTokenizerFast.from_pretrained(self.tmpdirname , **A__ )
def UpperCamelCase ( self , A__ ) -> Tuple:
snake_case = '''lower newer'''
snake_case = '''lower newer'''
return input_text, output_text
def UpperCamelCase ( self ) -> List[Any]:
snake_case = CodeGenTokenizer(self.vocab_file , self.merges_file , **self.special_tokens_map )
snake_case = '''lower newer'''
snake_case = ['''\u0120low''', '''er''', '''\u0120''', '''n''', '''e''', '''w''', '''er''']
snake_case = tokenizer.tokenize(A__ , add_prefix_space=A__ )
self.assertListEqual(A__ , A__ )
snake_case = tokens + [tokenizer.unk_token]
snake_case = [14, 15, 10, 9, 3, 2, 15, 19]
self.assertListEqual(tokenizer.convert_tokens_to_ids(A__ ) , A__ )
def UpperCamelCase ( self ) -> Optional[int]:
if not self.test_rust_tokenizer:
return
snake_case = self.get_tokenizer()
snake_case = self.get_rust_tokenizer(add_prefix_space=A__ )
snake_case = '''lower newer'''
# Testing tokenization
snake_case = tokenizer.tokenize(A__ , add_prefix_space=A__ )
snake_case = rust_tokenizer.tokenize(A__ )
self.assertListEqual(A__ , A__ )
# Testing conversion to ids without special tokens
snake_case = tokenizer.encode(A__ , add_special_tokens=A__ , add_prefix_space=A__ )
snake_case = rust_tokenizer.encode(A__ , add_special_tokens=A__ )
self.assertListEqual(A__ , A__ )
# Testing conversion to ids with special tokens
snake_case = self.get_rust_tokenizer(add_prefix_space=A__ )
snake_case = tokenizer.encode(A__ , add_prefix_space=A__ )
snake_case = rust_tokenizer.encode(A__ )
self.assertListEqual(A__ , A__ )
# Testing the unknown token
snake_case = tokens + [rust_tokenizer.unk_token]
snake_case = [14, 15, 10, 9, 3, 2, 15, 19]
self.assertListEqual(rust_tokenizer.convert_tokens_to_ids(A__ ) , A__ )
def UpperCamelCase ( self , *A__ , **A__ ) -> List[str]:
# It's very difficult to mix/test pretokenization with byte-level
# And get both CodeGen and Roberta to work at the same time (mostly an issue of adding a space before the string)
pass
def UpperCamelCase ( self , A__=15 ) -> Tuple:
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})""" ):
snake_case = self.rust_tokenizer_class.from_pretrained(A__ , **A__ )
# Simple input
snake_case = '''This is a simple input'''
snake_case = ['''This is a simple input 1''', '''This is a simple input 2''']
snake_case = ('''This is a simple input''', '''This is a pair''')
snake_case = [
('''This is a simple input 1''', '''This is a simple input 2'''),
('''This is a simple pair 1''', '''This is a simple pair 2'''),
]
# Simple input tests
self.assertRaises(A__ , tokenizer_r.encode , A__ , max_length=A__ , padding='''max_length''' )
# Simple input
self.assertRaises(A__ , tokenizer_r.encode_plus , A__ , max_length=A__ , padding='''max_length''' )
# Simple input
self.assertRaises(
A__ , tokenizer_r.batch_encode_plus , A__ , max_length=A__ , padding='''max_length''' , )
# Pair input
self.assertRaises(A__ , tokenizer_r.encode , A__ , max_length=A__ , padding='''max_length''' )
# Pair input
self.assertRaises(A__ , tokenizer_r.encode_plus , A__ , max_length=A__ , padding='''max_length''' )
# Pair input
self.assertRaises(
A__ , tokenizer_r.batch_encode_plus , A__ , max_length=A__ , padding='''max_length''' , )
def UpperCamelCase ( self ) -> Tuple:
snake_case = CodeGenTokenizer.from_pretrained(self.tmpdirname , pad_token='''<pad>''' )
# Simple input
snake_case = '''This is a simple input'''
snake_case = ['''This is a simple input looooooooong''', '''This is a simple input''']
snake_case = ('''This is a simple input''', '''This is a pair''')
snake_case = [
('''This is a simple input loooooong''', '''This is a simple input'''),
('''This is a simple pair loooooong''', '''This is a simple pair'''),
]
snake_case = tokenizer.pad_token_id
snake_case = tokenizer(A__ , padding='''max_length''' , max_length=30 , return_tensors='''np''' )
snake_case = tokenizer(A__ , padding=A__ , truncate=A__ , return_tensors='''np''' )
snake_case = tokenizer(*A__ , padding='''max_length''' , max_length=60 , return_tensors='''np''' )
snake_case = tokenizer(A__ , padding=A__ , truncate=A__ , return_tensors='''np''' )
# s
# test single string max_length padding
self.assertEqual(out_s['''input_ids'''].shape[-1] , 30 )
self.assertTrue(pad_token_id in out_s['''input_ids'''] )
self.assertTrue(0 in out_s['''attention_mask'''] )
# s2
# test automatic padding
self.assertEqual(out_sa['''input_ids'''].shape[-1] , 33 )
# long slice doesn't have padding
self.assertFalse(pad_token_id in out_sa['''input_ids'''][0] )
self.assertFalse(0 in out_sa['''attention_mask'''][0] )
# short slice does have padding
self.assertTrue(pad_token_id in out_sa['''input_ids'''][1] )
self.assertTrue(0 in out_sa['''attention_mask'''][1] )
# p
# test single pair max_length padding
self.assertEqual(out_p['''input_ids'''].shape[-1] , 60 )
self.assertTrue(pad_token_id in out_p['''input_ids'''] )
self.assertTrue(0 in out_p['''attention_mask'''] )
# p2
# test automatic padding pair
self.assertEqual(out_pa['''input_ids'''].shape[-1] , 52 )
# long slice pair doesn't have padding
self.assertFalse(pad_token_id in out_pa['''input_ids'''][0] )
self.assertFalse(0 in out_pa['''attention_mask'''][0] )
# short slice pair does have padding
self.assertTrue(pad_token_id in out_pa['''input_ids'''][1] )
self.assertTrue(0 in out_pa['''attention_mask'''][1] )
def UpperCamelCase ( self ) -> str:
snake_case = '''$$$'''
snake_case = CodeGenTokenizer.from_pretrained(self.tmpdirname , bos_token=A__ , add_bos_token=A__ )
snake_case = '''This is a simple input'''
snake_case = ['''This is a simple input 1''', '''This is a simple input 2''']
snake_case = tokenizer.bos_token_id
snake_case = tokenizer(A__ )
snake_case = tokenizer(A__ )
self.assertEqual(out_s.input_ids[0] , A__ )
self.assertTrue(all(o[0] == bos_token_id for o in out_sa.input_ids ) )
snake_case = tokenizer.decode(out_s.input_ids )
snake_case = tokenizer.batch_decode(out_sa.input_ids )
self.assertEqual(decode_s.split()[0] , A__ )
self.assertTrue(all(d.split()[0] == bos_token for d in decode_sa ) )
@slow
def UpperCamelCase ( self ) -> Any:
snake_case = CodeGenTokenizer.from_pretrained('''Salesforce/codegen-350M-mono''' )
snake_case = '''\nif len_a > len_b:\n result = a\nelse:\n result = b\n\n\n\n#'''
snake_case = '''\nif len_a > len_b: result = a\nelse: result = b'''
snake_case = tokenizer.encode(A__ )
snake_case = ['''^#''', re.escape('''<|endoftext|>''' ), '''^\'\'\'''', '''^"""''', '''\n\n\n''']
snake_case = tokenizer.decode(A__ , truncate_before_pattern=A__ )
self.assertEqual(A__ , A__ )
def UpperCamelCase ( self ) -> Union[str, Any]:
pass
| 44
| 1
|
'''simple docstring'''
import pickle
import numpy as np
from matplotlib import pyplot as plt
class _lowercase :
def __init__( self , A__ , A__ , A__ , A__ , A__ , A__=0.2 , A__=0.2 ) -> int:
snake_case = bp_numa
snake_case = bp_numa
snake_case = bp_numa
snake_case = conva_get[:2]
snake_case = conva_get[2]
snake_case = size_pa
snake_case = rate_w
snake_case = rate_t
snake_case = [
np.mat(-1 * np.random.rand(self.conva[0] , self.conva[0] ) + 0.5 )
for i in range(self.conva[1] )
]
snake_case = np.mat(-1 * np.random.rand(self.num_bpa , self.num_bpa ) + 0.5 )
snake_case = np.mat(-1 * np.random.rand(self.num_bpa , self.num_bpa ) + 0.5 )
snake_case = -2 * np.random.rand(self.conva[1] ) + 1
snake_case = -2 * np.random.rand(self.num_bpa ) + 1
snake_case = -2 * np.random.rand(self.num_bpa ) + 1
def UpperCamelCase ( self , A__ ) -> Tuple:
# save model dict with pickle
snake_case = {
'''num_bp1''': self.num_bpa,
'''num_bp2''': self.num_bpa,
'''num_bp3''': self.num_bpa,
'''conv1''': self.conva,
'''step_conv1''': self.step_conva,
'''size_pooling1''': self.size_poolinga,
'''rate_weight''': self.rate_weight,
'''rate_thre''': self.rate_thre,
'''w_conv1''': self.w_conva,
'''wkj''': self.wkj,
'''vji''': self.vji,
'''thre_conv1''': self.thre_conva,
'''thre_bp2''': self.thre_bpa,
'''thre_bp3''': self.thre_bpa,
}
with open(A__ , '''wb''' ) as f:
pickle.dump(A__ , A__ )
print(F"""Model saved： {save_path}""" )
@classmethod
def UpperCamelCase ( cls , A__ ) -> Optional[Any]:
# read saved model
with open(A__ , '''rb''' ) as f:
snake_case = pickle.load(A__ ) # noqa: S301
snake_case = model_dic.get('''conv1''' )
conv_get.append(model_dic.get('''step_conv1''' ) )
snake_case = model_dic.get('''size_pooling1''' )
snake_case = model_dic.get('''num_bp1''' )
snake_case = model_dic.get('''num_bp2''' )
snake_case = model_dic.get('''num_bp3''' )
snake_case = model_dic.get('''rate_weight''' )
snake_case = model_dic.get('''rate_thre''' )
# create model instance
snake_case = CNN(A__ , A__ , A__ , A__ , A__ , A__ , A__ )
# modify model parameter
snake_case = model_dic.get('''w_conv1''' )
snake_case = model_dic.get('''wkj''' )
snake_case = model_dic.get('''vji''' )
snake_case = model_dic.get('''thre_conv1''' )
snake_case = model_dic.get('''thre_bp2''' )
snake_case = model_dic.get('''thre_bp3''' )
return conv_ins
def UpperCamelCase ( self , A__ ) -> str:
return 1 / (1 + np.exp(-1 * x ))
def UpperCamelCase ( self , A__ ) -> Any:
return round(A__ , 3 )
def UpperCamelCase ( self , A__ , A__ , A__ , A__ , A__ ) -> str:
# convolution process
snake_case = convs[0]
snake_case = convs[1]
snake_case = np.shape(A__ )[0]
# get the data slice of original image data, data_focus
snake_case = []
for i_focus in range(0 , size_data - size_conv + 1 , A__ ):
for j_focus in range(0 , size_data - size_conv + 1 , A__ ):
snake_case = data[
i_focus : i_focus + size_conv, j_focus : j_focus + size_conv
]
data_focus.append(A__ )
# calculate the feature map of every single kernel, and saved as list of matrix
snake_case = []
snake_case = int((size_data - size_conv) / conv_step + 1 )
for i_map in range(A__ ):
snake_case = []
for i_focus in range(len(A__ ) ):
snake_case = (
np.sum(np.multiply(data_focus[i_focus] , w_convs[i_map] ) )
- thre_convs[i_map]
)
featuremap.append(self.sig(A__ ) )
snake_case = np.asmatrix(A__ ).reshape(
A__ , A__ )
data_featuremap.append(A__ )
# expanding the data slice to One dimenssion
snake_case = []
for each_focus in data_focus:
focusa_list.extend(self.Expand_Mat(A__ ) )
snake_case = np.asarray(A__ )
return focus_list, data_featuremap
def UpperCamelCase ( self , A__ , A__ , A__="average_pool" ) -> int:
# pooling process
snake_case = len(featuremaps[0] )
snake_case = int(size_map / size_pooling )
snake_case = []
for i_map in range(len(A__ ) ):
snake_case = featuremaps[i_map]
snake_case = []
for i_focus in range(0 , A__ , A__ ):
for j_focus in range(0 , A__ , A__ ):
snake_case = feature_map[
i_focus : i_focus + size_pooling,
j_focus : j_focus + size_pooling,
]
if pooling_type == "average_pool":
# average pooling
map_pooled.append(np.average(A__ ) )
elif pooling_type == "max_pooling":
# max pooling
map_pooled.append(np.max(A__ ) )
snake_case = np.asmatrix(A__ ).reshape(A__ , A__ )
featuremap_pooled.append(A__ )
return featuremap_pooled
def UpperCamelCase ( self , A__ ) -> Tuple:
# expanding three dimension data to one dimension list
snake_case = []
for i in range(len(A__ ) ):
snake_case = np.shape(data[i] )
snake_case = data[i].reshape(1 , shapes[0] * shapes[1] )
snake_case = data_listed.getA().tolist()[0]
data_expanded.extend(A__ )
snake_case = np.asarray(A__ )
return data_expanded
def UpperCamelCase ( self , A__ ) -> List[str]:
# expanding matrix to one dimension list
snake_case = np.asarray(A__ )
snake_case = np.shape(A__ )
snake_case = data_mat.reshape(1 , shapes[0] * shapes[1] )
return data_expanded
def UpperCamelCase ( self , A__ , A__ , A__ , A__ , A__ ) -> Optional[Any]:
snake_case = []
snake_case = 0
for i_map in range(A__ ):
snake_case = np.ones((size_map, size_map) )
for i in range(0 , A__ , A__ ):
for j in range(0 , A__ , A__ ):
snake_case = pd_pool[
i_pool
]
snake_case = i_pool + 1
snake_case = np.multiply(
A__ , np.multiply(out_map[i_map] , (1 - out_map[i_map]) ) )
pd_all.append(A__ )
return pd_all
def UpperCamelCase ( self , A__ , A__ , A__ , A__ , A__ , A__=bool ) -> List[str]:
# model traning
print('''----------------------Start Training-------------------------''' )
print((''' - - Shape: Train_Data ''', np.shape(A__ )) )
print((''' - - Shape: Teach_Data ''', np.shape(A__ )) )
snake_case = 0
snake_case = []
snake_case = 1_00_00
while rp < n_repeat and mse >= error_accuracy:
snake_case = 0
print(F"""-------------Learning Time {rp}--------------""" )
for p in range(len(A__ ) ):
# print('------------Learning Image: %d--------------'%p)
snake_case = np.asmatrix(datas_train[p] )
snake_case = np.asarray(datas_teach[p] )
snake_case , snake_case = self.convolute(
A__ , self.conva , self.w_conva , self.thre_conva , conv_step=self.step_conva , )
snake_case = self.pooling(A__ , self.size_poolinga )
snake_case = np.shape(A__ )
snake_case = self._expand(A__ )
snake_case = data_bp_input
snake_case = np.dot(A__ , self.vji.T ) - self.thre_bpa
snake_case = self.sig(A__ )
snake_case = np.dot(A__ , self.wkj.T ) - self.thre_bpa
snake_case = self.sig(A__ )
# --------------Model Leaning ------------------------
# calculate error and gradient---------------
snake_case = np.multiply(
(data_teach - bp_outa) , np.multiply(A__ , (1 - bp_outa) ) )
snake_case = np.multiply(
np.dot(A__ , self.wkj ) , np.multiply(A__ , (1 - bp_outa) ) )
snake_case = np.dot(A__ , self.vji )
snake_case = pd_i_all / (self.size_poolinga * self.size_poolinga)
snake_case = pd_conva_pooled.T.getA().tolist()
snake_case = self._calculate_gradient_from_pool(
A__ , A__ , shape_featuremapa[0] , shape_featuremapa[1] , self.size_poolinga , )
# weight and threshold learning process---------
# convolution layer
for k_conv in range(self.conva[1] ):
snake_case = self._expand_mat(pd_conva_all[k_conv] )
snake_case = self.rate_weight * np.dot(A__ , A__ )
snake_case = self.w_conva[k_conv] + delta_w.reshape(
(self.conva[0], self.conva[0]) )
snake_case = (
self.thre_conva[k_conv]
- np.sum(pd_conva_all[k_conv] ) * self.rate_thre
)
# all connected layer
snake_case = self.wkj + pd_k_all.T * bp_outa * self.rate_weight
snake_case = self.vji + pd_j_all.T * bp_outa * self.rate_weight
snake_case = self.thre_bpa - pd_k_all * self.rate_thre
snake_case = self.thre_bpa - pd_j_all * self.rate_thre
# calculate the sum error of all single image
snake_case = np.sum(abs(data_teach - bp_outa ) )
error_count += errors
# print(' ----Teach ',data_teach)
# print(' ----BP_output ',bp_out3)
snake_case = rp + 1
snake_case = error_count / patterns
all_mse.append(A__ )
def draw_error():
snake_case = [error_accuracy for i in range(int(n_repeat * 1.2 ) )]
plt.plot(A__ , '''+-''' )
plt.plot(A__ , '''r--''' )
plt.xlabel('''Learning Times''' )
plt.ylabel('''All_mse''' )
plt.grid(A__ , alpha=0.5 )
plt.show()
print('''------------------Training Complished---------------------''' )
print((''' - - Training epoch: ''', rp, F""" - - Mse: {mse:.6f}""") )
if draw_e:
draw_error()
return mse
def UpperCamelCase ( self , A__ ) -> List[str]:
# model predict
snake_case = []
print('''-------------------Start Testing-------------------------''' )
print((''' - - Shape: Test_Data ''', np.shape(A__ )) )
for p in range(len(A__ ) ):
snake_case = np.asmatrix(datas_test[p] )
snake_case , snake_case = self.convolute(
A__ , self.conva , self.w_conva , self.thre_conva , conv_step=self.step_conva , )
snake_case = self.pooling(A__ , self.size_poolinga )
snake_case = self._expand(A__ )
snake_case = data_bp_input
snake_case = bp_outa * self.vji.T - self.thre_bpa
snake_case = self.sig(A__ )
snake_case = bp_outa * self.wkj.T - self.thre_bpa
snake_case = self.sig(A__ )
produce_out.extend(bp_outa.getA().tolist() )
snake_case = [list(map(self.do_round , A__ ) ) for each in produce_out]
return np.asarray(A__ )
def UpperCamelCase ( self , A__ ) -> Optional[Any]:
# return the data of image after convoluting process so we can check it out
snake_case = np.asmatrix(A__ )
snake_case , snake_case = self.convolute(
A__ , self.conva , self.w_conva , self.thre_conva , conv_step=self.step_conva , )
snake_case = self.pooling(A__ , self.size_poolinga )
return data_conveda, data_pooleda
if __name__ == "__main__":
pass
| 44
|
'''simple docstring'''
from __future__ import annotations
import inspect
import unittest
from transformers import ViTConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import TFViTForImageClassification, TFViTModel
if is_vision_available():
from PIL import Image
from transformers import ViTImageProcessor
class _lowercase :
def __init__( self , A__ , A__=13 , A__=30 , A__=2 , A__=3 , A__=True , A__=True , A__=32 , A__=2 , A__=4 , A__=37 , A__="gelu" , A__=0.1 , A__=0.1 , A__=10 , A__=0.0_2 , A__=3 , A__=None , ) -> List[Any]:
snake_case = parent
snake_case = batch_size
snake_case = image_size
snake_case = patch_size
snake_case = num_channels
snake_case = is_training
snake_case = use_labels
snake_case = hidden_size
snake_case = num_hidden_layers
snake_case = num_attention_heads
snake_case = intermediate_size
snake_case = hidden_act
snake_case = hidden_dropout_prob
snake_case = attention_probs_dropout_prob
snake_case = type_sequence_label_size
snake_case = initializer_range
snake_case = scope
# in ViT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
snake_case = (image_size // patch_size) ** 2
snake_case = num_patches + 1
def UpperCamelCase ( self ) -> int:
snake_case = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
snake_case = None
if self.use_labels:
snake_case = ids_tensor([self.batch_size] , self.type_sequence_label_size )
snake_case = self.get_config()
return config, pixel_values, labels
def UpperCamelCase ( self ) -> int:
return ViTConfig(
image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , 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 , is_decoder=A__ , initializer_range=self.initializer_range , )
def UpperCamelCase ( self , A__ , A__ , A__ ) -> Union[str, Any]:
snake_case = TFViTModel(config=A__ )
snake_case = model(A__ , training=A__ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
# Test with an image with different size than the one specified in config.
snake_case = self.image_size // 2
snake_case = pixel_values[:, :, :image_size, :image_size]
snake_case = model(A__ , interpolate_pos_encoding=A__ , training=A__ )
snake_case = (image_size // self.patch_size) ** 2 + 1
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, seq_length, self.hidden_size) )
def UpperCamelCase ( self , A__ , A__ , A__ ) -> Optional[int]:
snake_case = self.type_sequence_label_size
snake_case = TFViTForImageClassification(A__ )
snake_case = model(A__ , labels=A__ , training=A__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
# Test with an image with different size than the one specified in config.
snake_case = self.image_size // 2
snake_case = pixel_values[:, :, :image_size, :image_size]
snake_case = model(A__ , interpolate_pos_encoding=A__ , training=A__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
# test greyscale images
snake_case = 1
snake_case = TFViTForImageClassification(A__ )
snake_case = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
snake_case = model(A__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
def UpperCamelCase ( self ) -> Union[str, Any]:
snake_case = self.prepare_config_and_inputs()
snake_case , snake_case , snake_case = config_and_inputs
snake_case = {'''pixel_values''': pixel_values}
return config, inputs_dict
@require_tf
class _lowercase ( __a , __a , unittest.TestCase ):
_UpperCAmelCase = (TFViTModel, TFViTForImageClassification) if is_tf_available() else ()
_UpperCAmelCase = (
{'''feature-extraction''': TFViTModel, '''image-classification''': TFViTForImageClassification}
if is_tf_available()
else {}
)
_UpperCAmelCase = False
_UpperCAmelCase = False
_UpperCAmelCase = False
def UpperCamelCase ( self ) -> List[Any]:
snake_case = TFViTModelTester(self )
snake_case = ConfigTester(self , config_class=A__ , has_text_modality=A__ , hidden_size=37 )
def UpperCamelCase ( self ) -> int:
self.config_tester.run_common_tests()
@unittest.skip(reason='''ViT does not use inputs_embeds''' )
def UpperCamelCase ( self ) -> int:
pass
@unittest.skip(reason='''ViT does not use inputs_embeds''' )
def UpperCamelCase ( self ) -> str:
pass
def UpperCamelCase ( self ) -> Union[str, Any]:
snake_case , snake_case = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
snake_case = model_class(A__ )
self.assertIsInstance(model.get_input_embeddings() , (tf.keras.layers.Layer) )
snake_case = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(A__ , tf.keras.layers.Layer ) )
def UpperCamelCase ( self ) -> List[Any]:
snake_case , snake_case = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
snake_case = model_class(A__ )
snake_case = inspect.signature(model.call )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
snake_case = [*signature.parameters.keys()]
snake_case = ['''pixel_values''']
self.assertListEqual(arg_names[:1] , A__ )
def UpperCamelCase ( self ) -> Union[str, Any]:
snake_case = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*A__ )
def UpperCamelCase ( self ) -> Optional[Any]:
snake_case = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*A__ )
@slow
def UpperCamelCase ( self ) -> Any:
snake_case = TFViTModel.from_pretrained('''google/vit-base-patch16-224''' )
self.assertIsNotNone(A__ )
def __UpperCamelCase ( ) ->Any:
snake_case = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
return image
@require_tf
@require_vision
class _lowercase ( unittest.TestCase ):
@cached_property
def UpperCamelCase ( self ) -> Optional[int]:
return ViTImageProcessor.from_pretrained('''google/vit-base-patch16-224''' ) if is_vision_available() else None
@slow
def UpperCamelCase ( self ) -> Dict:
snake_case = TFViTForImageClassification.from_pretrained('''google/vit-base-patch16-224''' )
snake_case = self.default_image_processor
snake_case = prepare_img()
snake_case = image_processor(images=A__ , return_tensors='''tf''' )
# forward pass
snake_case = model(**A__ )
# verify the logits
snake_case = tf.TensorShape((1, 10_00) )
self.assertEqual(outputs.logits.shape , A__ )
snake_case = tf.constant([-0.2_7_4_4, 0.8_2_1_5, -0.0_8_3_6] )
tf.debugging.assert_near(outputs.logits[0, :3] , A__ , atol=1e-4 )
| 44
| 1
|
'''simple docstring'''
import os
from dataclasses import dataclass, field
from io import BytesIO
from typing import TYPE_CHECKING, Any, ClassVar, Dict, Optional, Union
import numpy as np
import pyarrow as pa
from .. import config
from ..download.streaming_download_manager import xopen, xsplitext
from ..table import array_cast
from ..utils.py_utils import no_op_if_value_is_null, string_to_dict
if TYPE_CHECKING:
from .features import FeatureType
_lowercase , _lowercase , _lowercase = False, False, False
@dataclass
class _lowercase :
_UpperCAmelCase = None
_UpperCAmelCase = True
_UpperCAmelCase = True
_UpperCAmelCase = None
# Automatically constructed
_UpperCAmelCase = "dict"
_UpperCAmelCase = pa.struct({'''bytes''': pa.binary(), '''path''': pa.string()} )
_UpperCAmelCase = field(default='''Audio''' , init=__a , repr=__a )
def __call__( self ) -> Optional[Any]:
return self.pa_type
def UpperCamelCase ( self , A__ ) -> dict:
try:
import soundfile as sf # soundfile is a dependency of librosa, needed to decode audio files.
except ImportError as err:
raise ImportError('''To support encoding audio data, please install \'soundfile\'.''' ) from err
if isinstance(A__ , A__ ):
return {"bytes": None, "path": value}
elif isinstance(A__ , A__ ):
return {"bytes": value, "path": None}
elif "array" in value:
# convert the audio array to wav bytes
snake_case = BytesIO()
sf.write(A__ , value['''array'''] , value['''sampling_rate'''] , format='''wav''' )
return {"bytes": buffer.getvalue(), "path": None}
elif value.get('''path''' ) is not None and os.path.isfile(value['''path'''] ):
# we set "bytes": None to not duplicate the data if they're already available locally
if value["path"].endswith('''pcm''' ):
# "PCM" only has raw audio bytes
if value.get('''sampling_rate''' ) is None:
# At least, If you want to convert "PCM-byte" to "WAV-byte", you have to know sampling rate
raise KeyError('''To use PCM files, please specify a \'sampling_rate\' in Audio object''' )
if value.get('''bytes''' ):
# If we already had PCM-byte, we don`t have to make "read file, make bytes" (just use it!)
snake_case = np.frombuffer(value['''bytes'''] , dtype=np.intaa ).astype(np.floataa ) / 3_27_67
else:
snake_case = np.memmap(value['''path'''] , dtype='''h''' , mode='''r''' ).astype(np.floataa ) / 3_27_67
snake_case = BytesIO(bytes() )
sf.write(A__ , A__ , value['''sampling_rate'''] , format='''wav''' )
return {"bytes": buffer.getvalue(), "path": None}
else:
return {"bytes": None, "path": value.get('''path''' )}
elif value.get('''bytes''' ) is not None or value.get('''path''' ) is not None:
# store the audio bytes, and path is used to infer the audio format using the file extension
return {"bytes": value.get('''bytes''' ), "path": value.get('''path''' )}
else:
raise ValueError(
F"""An audio sample should have one of 'path' or 'bytes' but they are missing or None in {value}.""" )
def UpperCamelCase ( self , A__ , A__ = None ) -> dict:
if not self.decode:
raise RuntimeError('''Decoding is disabled for this feature. Please use Audio(decode=True) instead.''' )
snake_case , snake_case = (value['''path'''], BytesIO(value['''bytes'''] )) if value['''bytes'''] is not None else (value['''path'''], None)
if path is None and file is None:
raise ValueError(F"""An audio sample should have one of 'path' or 'bytes' but both are None in {value}.""" )
try:
import librosa
import soundfile as sf
except ImportError as err:
raise ImportError('''To support decoding audio files, please install \'librosa\' and \'soundfile\'.''' ) from err
snake_case = xsplitext(A__ )[1][1:].lower() if path is not None else None
if not config.IS_OPUS_SUPPORTED and audio_format == "opus":
raise RuntimeError(
'''Decoding \'opus\' files requires system library \'libsndfile\'>=1.0.31, '''
'''You can try to update `soundfile` python library: `pip install "soundfile>=0.12.1"`. ''' )
elif not config.IS_MP3_SUPPORTED and audio_format == "mp3":
raise RuntimeError(
'''Decoding \'mp3\' files requires system library \'libsndfile\'>=1.1.0, '''
'''You can try to update `soundfile` python library: `pip install "soundfile>=0.12.1"`. ''' )
if file is None:
snake_case = token_per_repo_id or {}
snake_case = path.split('''::''' )[-1]
try:
snake_case = string_to_dict(A__ , config.HUB_DATASETS_URL )['''repo_id''']
snake_case = token_per_repo_id[repo_id]
except (ValueError, KeyError):
snake_case = None
with xopen(A__ , '''rb''' , use_auth_token=A__ ) as f:
snake_case , snake_case = sf.read(A__ )
else:
snake_case , snake_case = sf.read(A__ )
snake_case = array.T
if self.mono:
snake_case = librosa.to_mono(A__ )
if self.sampling_rate and self.sampling_rate != sampling_rate:
snake_case = librosa.resample(A__ , orig_sr=A__ , target_sr=self.sampling_rate )
snake_case = self.sampling_rate
return {"path": path, "array": array, "sampling_rate": sampling_rate}
def UpperCamelCase ( self ) -> Union["FeatureType", Dict[str, "FeatureType"]]:
from .features import Value
if self.decode:
raise ValueError('''Cannot flatten a decoded Audio feature.''' )
return {
"bytes": Value('''binary''' ),
"path": Value('''string''' ),
}
def UpperCamelCase ( self , A__ ) -> pa.StructArray:
if pa.types.is_string(storage.type ):
snake_case = pa.array([None] * len(A__ ) , type=pa.binary() )
snake_case = pa.StructArray.from_arrays([bytes_array, storage] , ['''bytes''', '''path'''] , mask=storage.is_null() )
elif pa.types.is_binary(storage.type ):
snake_case = pa.array([None] * len(A__ ) , type=pa.string() )
snake_case = pa.StructArray.from_arrays([storage, path_array] , ['''bytes''', '''path'''] , mask=storage.is_null() )
elif pa.types.is_struct(storage.type ) and storage.type.get_all_field_indices('''array''' ):
snake_case = pa.array([Audio().encode_example(A__ ) if x is not None else None for x in storage.to_pylist()] )
elif pa.types.is_struct(storage.type ):
if storage.type.get_field_index('''bytes''' ) >= 0:
snake_case = storage.field('''bytes''' )
else:
snake_case = pa.array([None] * len(A__ ) , type=pa.binary() )
if storage.type.get_field_index('''path''' ) >= 0:
snake_case = storage.field('''path''' )
else:
snake_case = pa.array([None] * len(A__ ) , type=pa.string() )
snake_case = pa.StructArray.from_arrays([bytes_array, path_array] , ['''bytes''', '''path'''] , mask=storage.is_null() )
return array_cast(A__ , self.pa_type )
def UpperCamelCase ( self , A__ ) -> pa.StructArray:
@no_op_if_value_is_null
def path_to_bytes(A__ ):
with xopen(A__ , '''rb''' ) as f:
snake_case = f.read()
return bytes_
snake_case = pa.array(
[
(path_to_bytes(x['''path'''] ) if x['''bytes'''] is None else x['''bytes''']) if x is not None else None
for x in storage.to_pylist()
] , type=pa.binary() , )
snake_case = pa.array(
[os.path.basename(A__ ) if path is not None else None for path in storage.field('''path''' ).to_pylist()] , type=pa.string() , )
snake_case = pa.StructArray.from_arrays([bytes_array, path_array] , ['''bytes''', '''path'''] , mask=bytes_array.is_null() )
return array_cast(A__ , self.pa_type )
| 44
|
'''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
_lowercase = [
{'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 __UpperCamelCase ( a : Dict=True ) ->str:
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=__a ) )
class _lowercase ( __a ):
_UpperCAmelCase = None
_UpperCAmelCase = None
def UpperCamelCase ( self , A__ , A__ ) -> str:
with TemporaryDirectory() as tmp_dir:
snake_case = dataset_module_factory(A__ , cache_dir=A__ )
snake_case = import_main_class(dataset_module.module_path , dataset=A__ )
snake_case = builder_cls(
cache_dir=A__ , config_name=A__ , hash=dataset_module.hash , )
snake_case = '''/'''.join(
[
HF_GCP_BASE_URL,
builder_instance._relative_data_dir(with_hash=A__ ).replace(os.sep , '''/''' ),
config.DATASET_INFO_FILENAME,
] )
snake_case = cached_path(A__ , cache_dir=A__ )
self.assertTrue(os.path.exists(A__ ) )
@pytest.mark.integration
def __UpperCamelCase ( a : List[str] ) ->Any:
snake_case = tmp_path_factory.mktemp('''test_hf_gcp''' ) / '''test_wikipedia_simple'''
snake_case = dataset_module_factory('''wikipedia''' , cache_dir=a )
snake_case = import_main_class(dataset_module.module_path )
snake_case = builder_cls(
cache_dir=a , config_name='''20220301.frr''' , hash=dataset_module.hash , )
# use the HF cloud storage, not the original download_and_prepare that uses apache-beam
snake_case = None
builder_instance.download_and_prepare()
snake_case = builder_instance.as_dataset()
assert ds
@pytest.mark.integration
def __UpperCamelCase ( a : Any ) ->Union[str, Any]:
snake_case = dataset_module_factory('''wikipedia''' , cache_dir=a )
snake_case = import_main_class(dataset_module.module_path , dataset=a )
snake_case = builder_cls(
cache_dir=a , config_name='''20220301.frr''' , hash=dataset_module.hash , )
snake_case = builder_instance.as_streaming_dataset()
assert ds
assert isinstance(a , a )
assert "train" in ds
assert isinstance(ds['''train'''] , a )
assert next(iter(ds['''train'''] ) )
| 44
| 1
|
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
_lowercase = {
'configuration_llama': ['LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'LlamaConfig'],
}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowercase = ['LlamaTokenizer']
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowercase = ['LlamaTokenizerFast']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowercase = [
'LlamaForCausalLM',
'LlamaModel',
'LlamaPreTrainedModel',
'LlamaForSequenceClassification',
]
if TYPE_CHECKING:
from .configuration_llama import LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP, LlamaConfig
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_llama import LlamaTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_llama_fast import LlamaTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_llama import LlamaForCausalLM, LlamaForSequenceClassification, LlamaModel, LlamaPreTrainedModel
else:
import sys
_lowercase = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 44
|
'''simple docstring'''
def __UpperCamelCase ( a : int , a : int ) ->int:
while b:
snake_case , snake_case = b, a % b
return a
def __UpperCamelCase ( a : int , a : int ) ->int:
return a if b == 0 else euclidean_gcd_recursive(a , a % b )
def __UpperCamelCase ( ) ->Optional[Any]:
print(f"""euclidean_gcd(3, 5) = {euclidean_gcd(3 , 5 )}""" )
print(f"""euclidean_gcd(5, 3) = {euclidean_gcd(5 , 3 )}""" )
print(f"""euclidean_gcd(1, 3) = {euclidean_gcd(1 , 3 )}""" )
print(f"""euclidean_gcd(3, 6) = {euclidean_gcd(3 , 6 )}""" )
print(f"""euclidean_gcd(6, 3) = {euclidean_gcd(6 , 3 )}""" )
print(f"""euclidean_gcd_recursive(3, 5) = {euclidean_gcd_recursive(3 , 5 )}""" )
print(f"""euclidean_gcd_recursive(5, 3) = {euclidean_gcd_recursive(5 , 3 )}""" )
print(f"""euclidean_gcd_recursive(1, 3) = {euclidean_gcd_recursive(1 , 3 )}""" )
print(f"""euclidean_gcd_recursive(3, 6) = {euclidean_gcd_recursive(3 , 6 )}""" )
print(f"""euclidean_gcd_recursive(6, 3) = {euclidean_gcd_recursive(6 , 3 )}""" )
if __name__ == "__main__":
main()
| 44
| 1
|
'''simple docstring'''
import math
import unittest
def __UpperCamelCase ( a : int ) ->bool:
assert isinstance(a , a ) 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(a ) + 1 ) , 6 ):
if number % i == 0 or number % (i + 2) == 0:
return False
return True
class _lowercase ( unittest.TestCase ):
def UpperCamelCase ( self ) -> List[Any]:
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 UpperCamelCase ( self ) -> str:
with self.assertRaises(A__ ):
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()
| 44
|
'''simple docstring'''
import argparse
import copy
def __UpperCamelCase ( a : Union[str, Any] ) ->Tuple:
snake_case = {}
with open(a ) as f:
for line in f:
if line.split()[0] not in dict_of_neighbours:
snake_case = []
_list.append([line.split()[1], line.split()[2]] )
snake_case = _list
else:
dict_of_neighbours[line.split()[0]].append(
[line.split()[1], line.split()[2]] )
if line.split()[1] not in dict_of_neighbours:
snake_case = []
_list.append([line.split()[0], line.split()[2]] )
snake_case = _list
else:
dict_of_neighbours[line.split()[1]].append(
[line.split()[0], line.split()[2]] )
return dict_of_neighbours
def __UpperCamelCase ( a : Dict , a : Tuple ) ->int:
with open(a ) as f:
snake_case = f.read(1 )
snake_case = start_node
snake_case = []
snake_case = start_node
snake_case = 0
while visiting not in first_solution:
snake_case = 1_0000
for k in dict_of_neighbours[visiting]:
if int(k[1] ) < int(a ) and k[0] not in first_solution:
snake_case = k[1]
snake_case = k[0]
first_solution.append(a )
snake_case = distance_of_first_solution + int(a )
snake_case = best_node
first_solution.append(a )
snake_case = 0
for k in dict_of_neighbours[first_solution[-2]]:
if k[0] == start_node:
break
position += 1
snake_case = (
distance_of_first_solution
+ int(dict_of_neighbours[first_solution[-2]][position][1] )
- 1_0000
)
return first_solution, distance_of_first_solution
def __UpperCamelCase ( a : Optional[int] , a : str ) ->str:
snake_case = []
for n in solution[1:-1]:
snake_case = solution.index(a )
for kn in solution[1:-1]:
snake_case = solution.index(a )
if n == kn:
continue
snake_case = copy.deepcopy(a )
snake_case = kn
snake_case = n
snake_case = 0
for k in _tmp[:-1]:
snake_case = _tmp[_tmp.index(a ) + 1]
for i in dict_of_neighbours[k]:
if i[0] == next_node:
snake_case = distance + int(i[1] )
_tmp.append(a )
if _tmp not in neighborhood_of_solution:
neighborhood_of_solution.append(_tmp )
snake_case = len(neighborhood_of_solution[0] ) - 1
neighborhood_of_solution.sort(key=lambda a : x[index_of_last_item_in_the_list] )
return neighborhood_of_solution
def __UpperCamelCase ( a : Any , a : Optional[Any] , a : int , a : Optional[int] , a : Union[str, Any] ) ->List[Any]:
snake_case = 1
snake_case = first_solution
snake_case = []
snake_case = distance_of_first_solution
snake_case = solution
while count <= iters:
snake_case = find_neighborhood(a , a )
snake_case = 0
snake_case = neighborhood[index_of_best_solution]
snake_case = len(a ) - 1
snake_case = False
while not found:
snake_case = 0
while i < len(a ):
if best_solution[i] != solution[i]:
snake_case = best_solution[i]
snake_case = solution[i]
break
snake_case = i + 1
if [first_exchange_node, second_exchange_node] not in tabu_list and [
second_exchange_node,
first_exchange_node,
] not in tabu_list:
tabu_list.append([first_exchange_node, second_exchange_node] )
snake_case = True
snake_case = best_solution[:-1]
snake_case = neighborhood[index_of_best_solution][best_cost_index]
if cost < best_cost:
snake_case = cost
snake_case = solution
else:
snake_case = index_of_best_solution + 1
snake_case = neighborhood[index_of_best_solution]
if len(a ) >= size:
tabu_list.pop(0 )
snake_case = count + 1
return best_solution_ever, best_cost
def __UpperCamelCase ( a : Union[str, Any]=None ) ->Optional[Any]:
snake_case = generate_neighbours(args.File )
snake_case , snake_case = generate_first_solution(
args.File , a )
snake_case , snake_case = tabu_search(
a , a , a , args.Iterations , args.Size , )
print(f"""Best solution: {best_sol}, with total distance: {best_cost}.""" )
if __name__ == "__main__":
_lowercase = argparse.ArgumentParser(description='Tabu Search')
parser.add_argument(
'-f',
'--File',
type=str,
help='Path to the file containing the data',
required=True,
)
parser.add_argument(
'-i',
'--Iterations',
type=int,
help='How many iterations the algorithm should perform',
required=True,
)
parser.add_argument(
'-s', '--Size', type=int, help='Size of the tabu list', required=True
)
# Pass the arguments to main method
main(parser.parse_args())
| 44
| 1
|
'''simple docstring'''
def __UpperCamelCase ( a : int = 50 ) ->int:
snake_case = [[0] * 3 for _ in range(length + 1 )]
for row_length in range(length + 1 ):
for tile_length in range(2 , 5 ):
for tile_start in range(row_length - tile_length + 1 ):
different_colour_ways_number[row_length][tile_length - 2] += (
different_colour_ways_number[row_length - tile_start - tile_length][
tile_length - 2
]
+ 1
)
return sum(different_colour_ways_number[length] )
if __name__ == "__main__":
print(f'{solution() = }')
| 44
|
'''simple docstring'''
from ...utils import is_note_seq_available, is_transformers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_torch_and_transformers_objects import * # noqa F403
else:
from .notes_encoder import SpectrogramNotesEncoder
from .continous_encoder import SpectrogramContEncoder
from .pipeline_spectrogram_diffusion import (
SpectrogramContEncoder,
SpectrogramDiffusionPipeline,
TaFilmDecoder,
)
try:
if not (is_transformers_available() and is_torch_available() and is_note_seq_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_transformers_and_torch_and_note_seq_objects import * # noqa F403
else:
from .midi_utils import MidiProcessor
| 44
| 1
|
'''simple docstring'''
import collections
import inspect
import unittest
from transformers import FocalNetConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _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 (
FocalNetBackbone,
FocalNetForImageClassification,
FocalNetForMaskedImageModeling,
FocalNetModel,
)
from transformers.models.focalnet.modeling_focalnet import FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import AutoImageProcessor
class _lowercase :
def __init__( self , A__ , A__=13 , A__=32 , A__=2 , A__=3 , A__=16 , A__=[32, 64, 1_28] , A__=[1, 2, 1] , A__=[2, 2, 4] , A__=2 , A__=2.0 , A__=True , A__=0.0 , A__=0.0 , A__=0.1 , A__="gelu" , A__=False , A__=True , A__=0.0_2 , A__=1e-5 , A__=True , A__=None , A__=True , A__=10 , A__=8 , A__=["stage1", "stage2"] , A__=[1, 2] , ) -> Union[str, Any]:
snake_case = parent
snake_case = batch_size
snake_case = image_size
snake_case = patch_size
snake_case = num_channels
snake_case = embed_dim
snake_case = hidden_sizes
snake_case = depths
snake_case = num_heads
snake_case = window_size
snake_case = mlp_ratio
snake_case = qkv_bias
snake_case = hidden_dropout_prob
snake_case = attention_probs_dropout_prob
snake_case = drop_path_rate
snake_case = hidden_act
snake_case = use_absolute_embeddings
snake_case = patch_norm
snake_case = layer_norm_eps
snake_case = initializer_range
snake_case = is_training
snake_case = scope
snake_case = use_labels
snake_case = type_sequence_label_size
snake_case = encoder_stride
snake_case = out_features
snake_case = out_indices
def UpperCamelCase ( self ) -> Optional[int]:
snake_case = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
snake_case = None
if self.use_labels:
snake_case = ids_tensor([self.batch_size] , self.type_sequence_label_size )
snake_case = self.get_config()
return config, pixel_values, labels
def UpperCamelCase ( self ) -> Optional[Any]:
return FocalNetConfig(
image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , embed_dim=self.embed_dim , hidden_sizes=self.hidden_sizes , depths=self.depths , num_heads=self.num_heads , window_size=self.window_size , mlp_ratio=self.mlp_ratio , qkv_bias=self.qkv_bias , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , drop_path_rate=self.drop_path_rate , hidden_act=self.hidden_act , use_absolute_embeddings=self.use_absolute_embeddings , path_norm=self.patch_norm , layer_norm_eps=self.layer_norm_eps , initializer_range=self.initializer_range , encoder_stride=self.encoder_stride , out_features=self.out_features , out_indices=self.out_indices , )
def UpperCamelCase ( self , A__ , A__ , A__ ) -> str:
snake_case = FocalNetModel(config=A__ )
model.to(A__ )
model.eval()
snake_case = model(A__ )
snake_case = ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths ) - 1))
snake_case = int(config.embed_dim * 2 ** (len(config.depths ) - 1) )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, expected_seq_len, expected_dim) )
def UpperCamelCase ( self , A__ , A__ , A__ ) -> Union[str, Any]:
snake_case = FocalNetBackbone(config=A__ )
model.to(A__ )
model.eval()
snake_case = model(A__ )
# verify feature maps
self.parent.assertEqual(len(result.feature_maps ) , len(config.out_features ) )
self.parent.assertListEqual(list(result.feature_maps[0].shape ) , [self.batch_size, self.image_size, 8, 8] )
# verify channels
self.parent.assertEqual(len(model.channels ) , len(config.out_features ) )
self.parent.assertListEqual(model.channels , config.hidden_sizes[:-1] )
# verify backbone works with out_features=None
snake_case = None
snake_case = FocalNetBackbone(config=A__ )
model.to(A__ )
model.eval()
snake_case = model(A__ )
# verify feature maps
self.parent.assertEqual(len(result.feature_maps ) , 1 )
self.parent.assertListEqual(list(result.feature_maps[0].shape ) , [self.batch_size, self.image_size * 2, 4, 4] )
# verify channels
self.parent.assertEqual(len(model.channels ) , 1 )
self.parent.assertListEqual(model.channels , [config.hidden_sizes[-1]] )
def UpperCamelCase ( self , A__ , A__ , A__ ) -> Dict:
snake_case = FocalNetForMaskedImageModeling(config=A__ )
model.to(A__ )
model.eval()
snake_case = model(A__ )
self.parent.assertEqual(
result.reconstruction.shape , (self.batch_size, self.num_channels, self.image_size, self.image_size) )
# test greyscale images
snake_case = 1
snake_case = FocalNetForMaskedImageModeling(A__ )
model.to(A__ )
model.eval()
snake_case = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
snake_case = model(A__ )
self.parent.assertEqual(result.reconstruction.shape , (self.batch_size, 1, self.image_size, self.image_size) )
def UpperCamelCase ( self , A__ , A__ , A__ ) -> Optional[Any]:
snake_case = self.type_sequence_label_size
snake_case = FocalNetForImageClassification(A__ )
model.to(A__ )
model.eval()
snake_case = model(A__ , labels=A__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
# test greyscale images
snake_case = 1
snake_case = FocalNetForImageClassification(A__ )
model.to(A__ )
model.eval()
snake_case = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
snake_case = model(A__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
def UpperCamelCase ( self ) -> int:
snake_case = self.prepare_config_and_inputs()
snake_case , snake_case , snake_case = config_and_inputs
snake_case = {'''pixel_values''': pixel_values}
return config, inputs_dict
@require_torch
class _lowercase ( __a , __a , unittest.TestCase ):
_UpperCAmelCase = (
(
FocalNetModel,
FocalNetForImageClassification,
FocalNetForMaskedImageModeling,
FocalNetBackbone,
)
if is_torch_available()
else ()
)
_UpperCAmelCase = (
{'''feature-extraction''': FocalNetModel, '''image-classification''': FocalNetForImageClassification}
if is_torch_available()
else {}
)
_UpperCAmelCase = False
_UpperCAmelCase = False
_UpperCAmelCase = False
_UpperCAmelCase = False
_UpperCAmelCase = False
def UpperCamelCase ( self ) -> Dict:
snake_case = FocalNetModelTester(self )
snake_case = ConfigTester(self , config_class=A__ , embed_dim=37 , has_text_modality=A__ )
def UpperCamelCase ( self ) -> Dict:
self.create_and_test_config_common_properties()
self.config_tester.create_and_test_config_to_json_string()
self.config_tester.create_and_test_config_to_json_file()
self.config_tester.create_and_test_config_from_and_save_pretrained()
self.config_tester.create_and_test_config_with_num_labels()
self.config_tester.check_config_can_be_init_without_params()
self.config_tester.check_config_arguments_init()
def UpperCamelCase ( self ) -> List[Any]:
return
def UpperCamelCase ( self ) -> Tuple:
snake_case = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*A__ )
def UpperCamelCase ( self ) -> List[Any]:
snake_case = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_backbone(*A__ )
def UpperCamelCase ( self ) -> Optional[int]:
snake_case = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_image_modeling(*A__ )
def UpperCamelCase ( self ) -> Any:
snake_case = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*A__ )
@unittest.skip(reason='''FocalNet does not use inputs_embeds''' )
def UpperCamelCase ( self ) -> List[Any]:
pass
@unittest.skip(reason='''FocalNet does not use feedforward chunking''' )
def UpperCamelCase ( self ) -> Optional[int]:
pass
def UpperCamelCase ( self ) -> int:
snake_case , snake_case = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes[:-1]:
snake_case = model_class(A__ )
self.assertIsInstance(model.get_input_embeddings() , (nn.Module) )
snake_case = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(A__ , nn.Linear ) )
def UpperCamelCase ( self ) -> List[str]:
snake_case , snake_case = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes[:-1]:
snake_case = model_class(A__ )
snake_case = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
snake_case = [*signature.parameters.keys()]
snake_case = ['''pixel_values''']
self.assertListEqual(arg_names[:1] , A__ )
def UpperCamelCase ( self , A__ , A__ , A__ , A__ ) -> Optional[Any]:
snake_case = model_class(A__ )
model.to(A__ )
model.eval()
with torch.no_grad():
snake_case = model(**self._prepare_for_class(A__ , A__ ) )
snake_case = outputs.hidden_states
snake_case = getattr(
self.model_tester , '''expected_num_hidden_layers''' , len(self.model_tester.depths ) + 1 )
self.assertEqual(len(A__ ) , A__ )
# FocalNet has a different seq_length
snake_case = (
config.patch_size
if isinstance(config.patch_size , collections.abc.Iterable )
else (config.patch_size, config.patch_size)
)
snake_case = (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] , )
snake_case = outputs.reshaped_hidden_states
self.assertEqual(len(A__ ) , A__ )
snake_case , snake_case , snake_case , snake_case = reshaped_hidden_states[0].shape
snake_case = (
reshaped_hidden_states[0].view(A__ , A__ , height * width ).permute(0 , 2 , 1 )
)
self.assertListEqual(
list(reshaped_hidden_states.shape[-2:] ) , [num_patches, self.model_tester.embed_dim] , )
def UpperCamelCase ( self ) -> Optional[int]:
snake_case , snake_case = self.model_tester.prepare_config_and_inputs_for_common()
snake_case = (
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[:-1]:
snake_case = True
self.check_hidden_states_output(A__ , A__ , A__ , A__ )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
snake_case = True
self.check_hidden_states_output(A__ , A__ , A__ , A__ )
def UpperCamelCase ( self ) -> Dict:
snake_case , snake_case = self.model_tester.prepare_config_and_inputs_for_common()
snake_case = 3
snake_case = (
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)
)
snake_case = (
config.patch_size
if isinstance(config.patch_size , collections.abc.Iterable )
else (config.patch_size, config.patch_size)
)
snake_case = image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
snake_case = image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])
for model_class in self.all_model_classes[:-1]:
snake_case = True
self.check_hidden_states_output(A__ , A__ , A__ , (padded_height, padded_width) )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
snake_case = True
self.check_hidden_states_output(A__ , A__ , A__ , (padded_height, padded_width) )
@slow
def UpperCamelCase ( self ) -> Optional[Any]:
for model_name in FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
snake_case = FocalNetModel.from_pretrained(A__ )
self.assertIsNotNone(A__ )
def UpperCamelCase ( self ) -> int:
snake_case , snake_case = self.model_tester.prepare_config_and_inputs_for_common()
snake_case = _config_zero_init(A__ )
for model_class in self.all_model_classes:
snake_case = model_class(config=A__ )
for name, param in model.named_parameters():
if "embeddings" 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 _lowercase ( unittest.TestCase ):
@cached_property
def UpperCamelCase ( self ) -> Optional[int]:
# TODO update organization
return AutoImageProcessor.from_pretrained('''microsoft/focalnet-tiny''' ) if is_vision_available() else None
@slow
def UpperCamelCase ( self ) -> Tuple:
snake_case = FocalNetForImageClassification.from_pretrained('''microsoft/focalnet-tiny''' ).to(A__ )
snake_case = self.default_image_processor
snake_case = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
snake_case = image_processor(images=A__ , return_tensors='''pt''' ).to(A__ )
# forward pass
with torch.no_grad():
snake_case = model(**A__ )
# verify the logits
snake_case = torch.Size((1, 10_00) )
self.assertEqual(outputs.logits.shape , A__ )
snake_case = torch.tensor([0.2_1_6_6, -0.4_3_6_8, 0.2_1_9_1] ).to(A__ )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , A__ , atol=1e-4 ) )
self.assertTrue(outputs.logits.argmax(dim=-1 ).item() , 2_81 )
@require_torch
class _lowercase ( __a , unittest.TestCase ):
_UpperCAmelCase = (FocalNetBackbone,) if is_torch_available() else ()
_UpperCAmelCase = FocalNetConfig
_UpperCAmelCase = False
def UpperCamelCase ( self ) -> Tuple:
snake_case = FocalNetModelTester(self )
| 44
|
'''simple docstring'''
from ...processing_utils import ProcessorMixin
class _lowercase ( __a ):
_UpperCAmelCase = '''WhisperFeatureExtractor'''
_UpperCAmelCase = '''WhisperTokenizer'''
def __init__( self , A__ , A__ ) -> Optional[Any]:
super().__init__(A__ , A__ )
snake_case = self.feature_extractor
snake_case = False
def UpperCamelCase ( self , A__=None , A__=None , A__=True ) -> Union[str, Any]:
return self.tokenizer.get_decoder_prompt_ids(task=A__ , language=A__ , no_timestamps=A__ )
def __call__( self , *A__ , **A__ ) -> Dict:
# For backward compatibility
if self._in_target_context_manager:
return self.current_processor(*A__ , **A__ )
snake_case = kwargs.pop('''audio''' , A__ )
snake_case = kwargs.pop('''sampling_rate''' , A__ )
snake_case = kwargs.pop('''text''' , A__ )
if len(A__ ) > 0:
snake_case = args[0]
snake_case = args[1:]
if audio is None and text is None:
raise ValueError('''You need to specify either an `audio` or `text` input to process.''' )
if audio is not None:
snake_case = self.feature_extractor(A__ , *A__ , sampling_rate=A__ , **A__ )
if text is not None:
snake_case = self.tokenizer(A__ , **A__ )
if text is None:
return inputs
elif audio is None:
return encodings
else:
snake_case = encodings['''input_ids''']
return inputs
def UpperCamelCase ( self , *A__ , **A__ ) -> Optional[Any]:
return self.tokenizer.batch_decode(*A__ , **A__ )
def UpperCamelCase ( self , *A__ , **A__ ) -> str:
return self.tokenizer.decode(*A__ , **A__ )
def UpperCamelCase ( self , A__ , A__="np" ) -> Optional[Any]:
return self.tokenizer.get_prompt_ids(A__ , return_tensors=A__ )
| 44
| 1
|
'''simple docstring'''
from ...processing_utils import ProcessorMixin
class _lowercase ( __a ):
_UpperCAmelCase = '''WhisperFeatureExtractor'''
_UpperCAmelCase = '''WhisperTokenizer'''
def __init__( self , A__ , A__ ) -> Optional[Any]:
super().__init__(A__ , A__ )
snake_case = self.feature_extractor
snake_case = False
def UpperCamelCase ( self , A__=None , A__=None , A__=True ) -> Union[str, Any]:
return self.tokenizer.get_decoder_prompt_ids(task=A__ , language=A__ , no_timestamps=A__ )
def __call__( self , *A__ , **A__ ) -> Dict:
# For backward compatibility
if self._in_target_context_manager:
return self.current_processor(*A__ , **A__ )
snake_case = kwargs.pop('''audio''' , A__ )
snake_case = kwargs.pop('''sampling_rate''' , A__ )
snake_case = kwargs.pop('''text''' , A__ )
if len(A__ ) > 0:
snake_case = args[0]
snake_case = args[1:]
if audio is None and text is None:
raise ValueError('''You need to specify either an `audio` or `text` input to process.''' )
if audio is not None:
snake_case = self.feature_extractor(A__ , *A__ , sampling_rate=A__ , **A__ )
if text is not None:
snake_case = self.tokenizer(A__ , **A__ )
if text is None:
return inputs
elif audio is None:
return encodings
else:
snake_case = encodings['''input_ids''']
return inputs
def UpperCamelCase ( self , *A__ , **A__ ) -> Optional[Any]:
return self.tokenizer.batch_decode(*A__ , **A__ )
def UpperCamelCase ( self , *A__ , **A__ ) -> str:
return self.tokenizer.decode(*A__ , **A__ )
def UpperCamelCase ( self , A__ , A__="np" ) -> Optional[Any]:
return self.tokenizer.get_prompt_ids(A__ , return_tensors=A__ )
| 44
|
'''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 _lowercase ( __a ):
_UpperCAmelCase = '''char'''
_UpperCAmelCase = '''bpe'''
_UpperCAmelCase = '''wp'''
_lowercase = (DecodeType.CHARACTER, DecodeType.BPE, DecodeType.WORDPIECE)
class _lowercase ( __a ):
_UpperCAmelCase = ['''image_processor''', '''char_tokenizer''']
_UpperCAmelCase = '''ViTImageProcessor'''
_UpperCAmelCase = '''MgpstrTokenizer'''
def __init__( self , A__=None , A__=None , **A__ ) -> List[Any]:
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.''' , A__ , )
snake_case = kwargs.pop('''feature_extractor''' )
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`.''' )
snake_case = tokenizer
snake_case = AutoTokenizer.from_pretrained('''gpt2''' )
snake_case = AutoTokenizer.from_pretrained('''bert-base-uncased''' )
super().__init__(A__ , A__ )
def __call__( self , A__=None , A__=None , A__=None , **A__ ) -> List[str]:
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:
snake_case = self.image_processor(A__ , return_tensors=A__ , **A__ )
if text is not None:
snake_case = self.char_tokenizer(A__ , return_tensors=A__ , **A__ )
if text is None:
return inputs
elif images is None:
return encodings
else:
snake_case = encodings['''input_ids''']
return inputs
def UpperCamelCase ( self , A__ ) -> Dict:
snake_case , snake_case , snake_case = sequences
snake_case = char_preds.size(0 )
snake_case , snake_case = self._decode_helper(A__ , '''char''' )
snake_case , snake_case = self._decode_helper(A__ , '''bpe''' )
snake_case , snake_case = self._decode_helper(A__ , '''wp''' )
snake_case = []
snake_case = []
for i in range(A__ ):
snake_case = [char_scores[i], bpe_scores[i], wp_scores[i]]
snake_case = [char_strs[i], bpe_strs[i], wp_strs[i]]
snake_case = scores.index(max(A__ ) )
final_strs.append(strs[max_score_index] )
final_scores.append(scores[max_score_index] )
snake_case = {}
snake_case = final_strs
snake_case = final_scores
snake_case = char_strs
snake_case = bpe_strs
snake_case = wp_strs
return out
def UpperCamelCase ( self , A__ , A__ ) -> Optional[Any]:
if format == DecodeType.CHARACTER:
snake_case = self.char_decode
snake_case = 1
snake_case = '''[s]'''
elif format == DecodeType.BPE:
snake_case = self.bpe_decode
snake_case = 2
snake_case = '''#'''
elif format == DecodeType.WORDPIECE:
snake_case = self.wp_decode
snake_case = 1_02
snake_case = '''[SEP]'''
else:
raise ValueError(F"""Format {format} is not supported.""" )
snake_case , snake_case = [], []
snake_case = pred_logits.size(0 )
snake_case = pred_logits.size(1 )
snake_case , snake_case = pred_logits.topk(1 , dim=-1 , largest=A__ , sorted=A__ )
snake_case = preds_index.view(-1 , A__ )[:, 1:]
snake_case = decoder(A__ )
snake_case , snake_case = torch.nn.functional.softmax(A__ , dim=2 ).max(dim=2 )
snake_case = preds_max_prob[:, 1:]
for index in range(A__ ):
snake_case = preds_str[index].find(A__ )
snake_case = preds_str[index][:pred_eos]
snake_case = preds_index[index].cpu().tolist()
snake_case = pred_index.index(A__ ) if eos_token in pred_index else -1
snake_case = preds_max_prob[index][: pred_eos_index + 1]
snake_case = pred_max_prob.cumprod(dim=0 )[-1] if pred_max_prob.nelement() != 0 else 0.0
dec_strs.append(A__ )
conf_scores.append(A__ )
return dec_strs, conf_scores
def UpperCamelCase ( self , A__ ) -> int:
snake_case = [seq.replace(''' ''' , '''''' ) for seq in self.char_tokenizer.batch_decode(A__ )]
return decode_strs
def UpperCamelCase ( self , A__ ) -> List[str]:
return self.bpe_tokenizer.batch_decode(A__ )
def UpperCamelCase ( self , A__ ) -> Union[str, Any]:
snake_case = [seq.replace(''' ''' , '''''' ) for seq in self.wp_tokenizer.batch_decode(A__ )]
return decode_strs
| 44
| 1
|
'''simple docstring'''
def __UpperCamelCase ( a : int ) ->str:
if number > 0:
raise ValueError('''input must be a negative integer''' )
snake_case = len(bin(a )[3:] )
snake_case = bin(abs(a ) - (1 << binary_number_length) )[3:]
snake_case = (
(
'''1'''
+ '''0''' * (binary_number_length - len(a ))
+ twos_complement_number
)
if number < 0
else '''0'''
)
return "0b" + twos_complement_number
if __name__ == "__main__":
import doctest
doctest.testmod()
| 44
|
'''simple docstring'''
import os
from dataclasses import dataclass, field
from io import BytesIO
from typing import TYPE_CHECKING, Any, ClassVar, Dict, Optional, Union
import numpy as np
import pyarrow as pa
from .. import config
from ..download.streaming_download_manager import xopen, xsplitext
from ..table import array_cast
from ..utils.py_utils import no_op_if_value_is_null, string_to_dict
if TYPE_CHECKING:
from .features import FeatureType
_lowercase , _lowercase , _lowercase = False, False, False
@dataclass
class _lowercase :
_UpperCAmelCase = None
_UpperCAmelCase = True
_UpperCAmelCase = True
_UpperCAmelCase = None
# Automatically constructed
_UpperCAmelCase = "dict"
_UpperCAmelCase = pa.struct({'''bytes''': pa.binary(), '''path''': pa.string()} )
_UpperCAmelCase = field(default='''Audio''' , init=__a , repr=__a )
def __call__( self ) -> Optional[Any]:
return self.pa_type
def UpperCamelCase ( self , A__ ) -> dict:
try:
import soundfile as sf # soundfile is a dependency of librosa, needed to decode audio files.
except ImportError as err:
raise ImportError('''To support encoding audio data, please install \'soundfile\'.''' ) from err
if isinstance(A__ , A__ ):
return {"bytes": None, "path": value}
elif isinstance(A__ , A__ ):
return {"bytes": value, "path": None}
elif "array" in value:
# convert the audio array to wav bytes
snake_case = BytesIO()
sf.write(A__ , value['''array'''] , value['''sampling_rate'''] , format='''wav''' )
return {"bytes": buffer.getvalue(), "path": None}
elif value.get('''path''' ) is not None and os.path.isfile(value['''path'''] ):
# we set "bytes": None to not duplicate the data if they're already available locally
if value["path"].endswith('''pcm''' ):
# "PCM" only has raw audio bytes
if value.get('''sampling_rate''' ) is None:
# At least, If you want to convert "PCM-byte" to "WAV-byte", you have to know sampling rate
raise KeyError('''To use PCM files, please specify a \'sampling_rate\' in Audio object''' )
if value.get('''bytes''' ):
# If we already had PCM-byte, we don`t have to make "read file, make bytes" (just use it!)
snake_case = np.frombuffer(value['''bytes'''] , dtype=np.intaa ).astype(np.floataa ) / 3_27_67
else:
snake_case = np.memmap(value['''path'''] , dtype='''h''' , mode='''r''' ).astype(np.floataa ) / 3_27_67
snake_case = BytesIO(bytes() )
sf.write(A__ , A__ , value['''sampling_rate'''] , format='''wav''' )
return {"bytes": buffer.getvalue(), "path": None}
else:
return {"bytes": None, "path": value.get('''path''' )}
elif value.get('''bytes''' ) is not None or value.get('''path''' ) is not None:
# store the audio bytes, and path is used to infer the audio format using the file extension
return {"bytes": value.get('''bytes''' ), "path": value.get('''path''' )}
else:
raise ValueError(
F"""An audio sample should have one of 'path' or 'bytes' but they are missing or None in {value}.""" )
def UpperCamelCase ( self , A__ , A__ = None ) -> dict:
if not self.decode:
raise RuntimeError('''Decoding is disabled for this feature. Please use Audio(decode=True) instead.''' )
snake_case , snake_case = (value['''path'''], BytesIO(value['''bytes'''] )) if value['''bytes'''] is not None else (value['''path'''], None)
if path is None and file is None:
raise ValueError(F"""An audio sample should have one of 'path' or 'bytes' but both are None in {value}.""" )
try:
import librosa
import soundfile as sf
except ImportError as err:
raise ImportError('''To support decoding audio files, please install \'librosa\' and \'soundfile\'.''' ) from err
snake_case = xsplitext(A__ )[1][1:].lower() if path is not None else None
if not config.IS_OPUS_SUPPORTED and audio_format == "opus":
raise RuntimeError(
'''Decoding \'opus\' files requires system library \'libsndfile\'>=1.0.31, '''
'''You can try to update `soundfile` python library: `pip install "soundfile>=0.12.1"`. ''' )
elif not config.IS_MP3_SUPPORTED and audio_format == "mp3":
raise RuntimeError(
'''Decoding \'mp3\' files requires system library \'libsndfile\'>=1.1.0, '''
'''You can try to update `soundfile` python library: `pip install "soundfile>=0.12.1"`. ''' )
if file is None:
snake_case = token_per_repo_id or {}
snake_case = path.split('''::''' )[-1]
try:
snake_case = string_to_dict(A__ , config.HUB_DATASETS_URL )['''repo_id''']
snake_case = token_per_repo_id[repo_id]
except (ValueError, KeyError):
snake_case = None
with xopen(A__ , '''rb''' , use_auth_token=A__ ) as f:
snake_case , snake_case = sf.read(A__ )
else:
snake_case , snake_case = sf.read(A__ )
snake_case = array.T
if self.mono:
snake_case = librosa.to_mono(A__ )
if self.sampling_rate and self.sampling_rate != sampling_rate:
snake_case = librosa.resample(A__ , orig_sr=A__ , target_sr=self.sampling_rate )
snake_case = self.sampling_rate
return {"path": path, "array": array, "sampling_rate": sampling_rate}
def UpperCamelCase ( self ) -> Union["FeatureType", Dict[str, "FeatureType"]]:
from .features import Value
if self.decode:
raise ValueError('''Cannot flatten a decoded Audio feature.''' )
return {
"bytes": Value('''binary''' ),
"path": Value('''string''' ),
}
def UpperCamelCase ( self , A__ ) -> pa.StructArray:
if pa.types.is_string(storage.type ):
snake_case = pa.array([None] * len(A__ ) , type=pa.binary() )
snake_case = pa.StructArray.from_arrays([bytes_array, storage] , ['''bytes''', '''path'''] , mask=storage.is_null() )
elif pa.types.is_binary(storage.type ):
snake_case = pa.array([None] * len(A__ ) , type=pa.string() )
snake_case = pa.StructArray.from_arrays([storage, path_array] , ['''bytes''', '''path'''] , mask=storage.is_null() )
elif pa.types.is_struct(storage.type ) and storage.type.get_all_field_indices('''array''' ):
snake_case = pa.array([Audio().encode_example(A__ ) if x is not None else None for x in storage.to_pylist()] )
elif pa.types.is_struct(storage.type ):
if storage.type.get_field_index('''bytes''' ) >= 0:
snake_case = storage.field('''bytes''' )
else:
snake_case = pa.array([None] * len(A__ ) , type=pa.binary() )
if storage.type.get_field_index('''path''' ) >= 0:
snake_case = storage.field('''path''' )
else:
snake_case = pa.array([None] * len(A__ ) , type=pa.string() )
snake_case = pa.StructArray.from_arrays([bytes_array, path_array] , ['''bytes''', '''path'''] , mask=storage.is_null() )
return array_cast(A__ , self.pa_type )
def UpperCamelCase ( self , A__ ) -> pa.StructArray:
@no_op_if_value_is_null
def path_to_bytes(A__ ):
with xopen(A__ , '''rb''' ) as f:
snake_case = f.read()
return bytes_
snake_case = pa.array(
[
(path_to_bytes(x['''path'''] ) if x['''bytes'''] is None else x['''bytes''']) if x is not None else None
for x in storage.to_pylist()
] , type=pa.binary() , )
snake_case = pa.array(
[os.path.basename(A__ ) if path is not None else None for path in storage.field('''path''' ).to_pylist()] , type=pa.string() , )
snake_case = pa.StructArray.from_arrays([bytes_array, path_array] , ['''bytes''', '''path'''] , mask=bytes_array.is_null() )
return array_cast(A__ , self.pa_type )
| 44
| 1
|
'''simple docstring'''
import tempfile
import torch
from diffusers import PNDMScheduler
from .test_schedulers import SchedulerCommonTest
class _lowercase ( __a ):
_UpperCAmelCase = (PNDMScheduler,)
_UpperCAmelCase = (('''num_inference_steps''', 50),)
def UpperCamelCase ( self , **A__ ) -> Tuple:
snake_case = {
'''num_train_timesteps''': 10_00,
'''beta_start''': 0.0_0_0_1,
'''beta_end''': 0.0_2,
'''beta_schedule''': '''linear''',
}
config.update(**A__ )
return config
def UpperCamelCase ( self , A__=0 , **A__ ) -> Optional[int]:
snake_case = dict(self.forward_default_kwargs )
snake_case = kwargs.pop('''num_inference_steps''' , A__ )
snake_case = self.dummy_sample
snake_case = 0.1 * sample
snake_case = [residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]
for scheduler_class in self.scheduler_classes:
snake_case = self.get_scheduler_config(**A__ )
snake_case = scheduler_class(**A__ )
scheduler.set_timesteps(A__ )
# copy over dummy past residuals
snake_case = dummy_past_residuals[:]
with tempfile.TemporaryDirectory() as tmpdirname:
scheduler.save_config(A__ )
snake_case = scheduler_class.from_pretrained(A__ )
new_scheduler.set_timesteps(A__ )
# copy over dummy past residuals
snake_case = dummy_past_residuals[:]
snake_case = scheduler.step_prk(A__ , A__ , A__ , **A__ ).prev_sample
snake_case = new_scheduler.step_prk(A__ , A__ , A__ , **A__ ).prev_sample
assert torch.sum(torch.abs(output - new_output ) ) < 1e-5, "Scheduler outputs are not identical"
snake_case = scheduler.step_plms(A__ , A__ , A__ , **A__ ).prev_sample
snake_case = new_scheduler.step_plms(A__ , A__ , A__ , **A__ ).prev_sample
assert torch.sum(torch.abs(output - new_output ) ) < 1e-5, "Scheduler outputs are not identical"
def UpperCamelCase ( self ) -> Dict:
pass
def UpperCamelCase ( self , A__=0 , **A__ ) -> Optional[Any]:
snake_case = dict(self.forward_default_kwargs )
snake_case = kwargs.pop('''num_inference_steps''' , A__ )
snake_case = self.dummy_sample
snake_case = 0.1 * sample
snake_case = [residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]
for scheduler_class in self.scheduler_classes:
snake_case = self.get_scheduler_config()
snake_case = scheduler_class(**A__ )
scheduler.set_timesteps(A__ )
# copy over dummy past residuals (must be after setting timesteps)
snake_case = dummy_past_residuals[:]
with tempfile.TemporaryDirectory() as tmpdirname:
scheduler.save_config(A__ )
snake_case = scheduler_class.from_pretrained(A__ )
# copy over dummy past residuals
new_scheduler.set_timesteps(A__ )
# copy over dummy past residual (must be after setting timesteps)
snake_case = dummy_past_residuals[:]
snake_case = scheduler.step_prk(A__ , A__ , A__ , **A__ ).prev_sample
snake_case = new_scheduler.step_prk(A__ , A__ , A__ , **A__ ).prev_sample
assert torch.sum(torch.abs(output - new_output ) ) < 1e-5, "Scheduler outputs are not identical"
snake_case = scheduler.step_plms(A__ , A__ , A__ , **A__ ).prev_sample
snake_case = new_scheduler.step_plms(A__ , A__ , A__ , **A__ ).prev_sample
assert torch.sum(torch.abs(output - new_output ) ) < 1e-5, "Scheduler outputs are not identical"
def UpperCamelCase ( self , **A__ ) -> Any:
snake_case = self.scheduler_classes[0]
snake_case = self.get_scheduler_config(**A__ )
snake_case = scheduler_class(**A__ )
snake_case = 10
snake_case = self.dummy_model()
snake_case = self.dummy_sample_deter
scheduler.set_timesteps(A__ )
for i, t in enumerate(scheduler.prk_timesteps ):
snake_case = model(A__ , A__ )
snake_case = scheduler.step_prk(A__ , A__ , A__ ).prev_sample
for i, t in enumerate(scheduler.plms_timesteps ):
snake_case = model(A__ , A__ )
snake_case = scheduler.step_plms(A__ , A__ , A__ ).prev_sample
return sample
def UpperCamelCase ( self ) -> Optional[Any]:
snake_case = dict(self.forward_default_kwargs )
snake_case = kwargs.pop('''num_inference_steps''' , A__ )
for scheduler_class in self.scheduler_classes:
snake_case = self.get_scheduler_config()
snake_case = scheduler_class(**A__ )
snake_case = self.dummy_sample
snake_case = 0.1 * sample
if num_inference_steps is not None and hasattr(A__ , '''set_timesteps''' ):
scheduler.set_timesteps(A__ )
elif num_inference_steps is not None and not hasattr(A__ , '''set_timesteps''' ):
snake_case = num_inference_steps
# copy over dummy past residuals (must be done after set_timesteps)
snake_case = [residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]
snake_case = dummy_past_residuals[:]
snake_case = scheduler.step_prk(A__ , 0 , A__ , **A__ ).prev_sample
snake_case = scheduler.step_prk(A__ , 1 , A__ , **A__ ).prev_sample
self.assertEqual(output_a.shape , sample.shape )
self.assertEqual(output_a.shape , output_a.shape )
snake_case = scheduler.step_plms(A__ , 0 , A__ , **A__ ).prev_sample
snake_case = scheduler.step_plms(A__ , 1 , A__ , **A__ ).prev_sample
self.assertEqual(output_a.shape , sample.shape )
self.assertEqual(output_a.shape , output_a.shape )
def UpperCamelCase ( self ) -> Optional[int]:
for timesteps in [1_00, 10_00]:
self.check_over_configs(num_train_timesteps=A__ )
def UpperCamelCase ( self ) -> Optional[int]:
for steps_offset in [0, 1]:
self.check_over_configs(steps_offset=A__ )
snake_case = self.scheduler_classes[0]
snake_case = self.get_scheduler_config(steps_offset=1 )
snake_case = scheduler_class(**A__ )
scheduler.set_timesteps(10 )
assert torch.equal(
scheduler.timesteps , torch.LongTensor(
[9_01, 8_51, 8_51, 8_01, 8_01, 7_51, 7_51, 7_01, 7_01, 6_51, 6_51, 6_01, 6_01, 5_01, 4_01, 3_01, 2_01, 1_01, 1] ) , )
def UpperCamelCase ( self ) -> Any:
for beta_start, beta_end in zip([0.0_0_0_1, 0.0_0_1] , [0.0_0_2, 0.0_2] ):
self.check_over_configs(beta_start=A__ , beta_end=A__ )
def UpperCamelCase ( self ) -> Dict:
for schedule in ["linear", "squaredcos_cap_v2"]:
self.check_over_configs(beta_schedule=A__ )
def UpperCamelCase ( self ) -> Dict:
for prediction_type in ["epsilon", "v_prediction"]:
self.check_over_configs(prediction_type=A__ )
def UpperCamelCase ( self ) -> List[str]:
for t in [1, 5, 10]:
self.check_over_forward(time_step=A__ )
def UpperCamelCase ( self ) -> Dict:
for t, num_inference_steps in zip([1, 5, 10] , [10, 50, 1_00] ):
self.check_over_forward(num_inference_steps=A__ )
def UpperCamelCase ( self ) -> Tuple:
# earlier version of set_timesteps() caused an error indexing alpha's with inference steps as power of 3
snake_case = 27
for scheduler_class in self.scheduler_classes:
snake_case = self.dummy_sample
snake_case = 0.1 * sample
snake_case = self.get_scheduler_config()
snake_case = scheduler_class(**A__ )
scheduler.set_timesteps(A__ )
# before power of 3 fix, would error on first step, so we only need to do two
for i, t in enumerate(scheduler.prk_timesteps[:2] ):
snake_case = scheduler.step_prk(A__ , A__ , A__ ).prev_sample
def UpperCamelCase ( self ) -> Dict:
with self.assertRaises(A__ ):
snake_case = self.scheduler_classes[0]
snake_case = self.get_scheduler_config()
snake_case = scheduler_class(**A__ )
scheduler.step_plms(self.dummy_sample , 1 , self.dummy_sample ).prev_sample
def UpperCamelCase ( self ) -> Dict:
snake_case = self.full_loop()
snake_case = torch.sum(torch.abs(A__ ) )
snake_case = torch.mean(torch.abs(A__ ) )
assert abs(result_sum.item() - 1_9_8.1_3_1_8 ) < 1e-2
assert abs(result_mean.item() - 0.2_5_8_0 ) < 1e-3
def UpperCamelCase ( self ) -> int:
snake_case = self.full_loop(prediction_type='''v_prediction''' )
snake_case = torch.sum(torch.abs(A__ ) )
snake_case = torch.mean(torch.abs(A__ ) )
assert abs(result_sum.item() - 6_7.3_9_8_6 ) < 1e-2
assert abs(result_mean.item() - 0.0_8_7_8 ) < 1e-3
def UpperCamelCase ( self ) -> Dict:
# We specify different beta, so that the first alpha is 0.99
snake_case = self.full_loop(set_alpha_to_one=A__ , beta_start=0.0_1 )
snake_case = torch.sum(torch.abs(A__ ) )
snake_case = torch.mean(torch.abs(A__ ) )
assert abs(result_sum.item() - 2_3_0.0_3_9_9 ) < 1e-2
assert abs(result_mean.item() - 0.2_9_9_5 ) < 1e-3
def UpperCamelCase ( self ) -> List[str]:
# We specify different beta, so that the first alpha is 0.99
snake_case = self.full_loop(set_alpha_to_one=A__ , beta_start=0.0_1 )
snake_case = torch.sum(torch.abs(A__ ) )
snake_case = torch.mean(torch.abs(A__ ) )
assert abs(result_sum.item() - 1_8_6.9_4_8_2 ) < 1e-2
assert abs(result_mean.item() - 0.2_4_3_4 ) < 1e-3
| 44
|
'''simple docstring'''
import hashlib
import unittest
from transformers import MODEL_FOR_DEPTH_ESTIMATION_MAPPING, is_torch_available, is_vision_available
from transformers.pipelines import DepthEstimationPipeline, pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_timm,
require_torch,
require_vision,
slow,
)
from .test_pipelines_common import ANY
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
else:
class _lowercase :
@staticmethod
def UpperCamelCase ( *A__ , **A__ ) -> List[Any]:
pass
def __UpperCamelCase ( a : Image ) ->str:
snake_case = hashlib.mda(image.tobytes() )
return m.hexdigest()
@is_pipeline_test
@require_vision
@require_timm
@require_torch
class _lowercase ( unittest.TestCase ):
_UpperCAmelCase = MODEL_FOR_DEPTH_ESTIMATION_MAPPING
def UpperCamelCase ( self , A__ , A__ , A__ ) -> Union[str, Any]:
snake_case = DepthEstimationPipeline(model=A__ , image_processor=A__ )
return depth_estimator, [
"./tests/fixtures/tests_samples/COCO/000000039769.png",
"./tests/fixtures/tests_samples/COCO/000000039769.png",
]
def UpperCamelCase ( self , A__ , A__ ) -> List[Any]:
snake_case = depth_estimator('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
self.assertEqual({'''predicted_depth''': ANY(torch.Tensor ), '''depth''': ANY(Image.Image )} , A__ )
import datasets
snake_case = datasets.load_dataset('''hf-internal-testing/fixtures_image_utils''' , '''image''' , split='''test''' )
snake_case = depth_estimator(
[
Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' ),
'''http://images.cocodataset.org/val2017/000000039769.jpg''',
# RGBA
dataset[0]['''file'''],
# LA
dataset[1]['''file'''],
# L
dataset[2]['''file'''],
] )
self.assertEqual(
[
{'''predicted_depth''': ANY(torch.Tensor ), '''depth''': ANY(Image.Image )},
{'''predicted_depth''': ANY(torch.Tensor ), '''depth''': ANY(Image.Image )},
{'''predicted_depth''': ANY(torch.Tensor ), '''depth''': ANY(Image.Image )},
{'''predicted_depth''': ANY(torch.Tensor ), '''depth''': ANY(Image.Image )},
{'''predicted_depth''': ANY(torch.Tensor ), '''depth''': ANY(Image.Image )},
] , A__ , )
@require_tf
@unittest.skip('''Depth estimation is not implemented in TF''' )
def UpperCamelCase ( self ) -> Optional[Any]:
pass
@slow
@require_torch
def UpperCamelCase ( self ) -> Dict:
snake_case = '''Intel/dpt-large'''
snake_case = pipeline('''depth-estimation''' , model=A__ )
snake_case = depth_estimator('''http://images.cocodataset.org/val2017/000000039769.jpg''' )
snake_case = hashimage(outputs['''depth'''] )
# This seems flaky.
# self.assertEqual(outputs["depth"], "1a39394e282e9f3b0741a90b9f108977")
self.assertEqual(nested_simplify(outputs['''predicted_depth'''].max().item() ) , 2_9.3_0_4 )
self.assertEqual(nested_simplify(outputs['''predicted_depth'''].min().item() ) , 2.6_6_2 )
@require_torch
def UpperCamelCase ( self ) -> Any:
# This is highly irregular to have no small tests.
self.skipTest('''There is not hf-internal-testing tiny model for either GLPN nor DPT''' )
| 44
| 1
|
'''simple docstring'''
from math import sqrt
def __UpperCamelCase ( a : int ) ->bool:
assert isinstance(a , a ) and (
number >= 0
), "'number' must been an int and positive"
snake_case = True
# 0 and 1 are none primes.
if number <= 1:
snake_case = False
for divisor in range(2 , int(round(sqrt(a ) ) ) + 1 ):
# if 'number' divisible by 'divisor' then sets 'status'
# of false and break up the loop.
if number % divisor == 0:
snake_case = False
break
# precondition
assert isinstance(a , a ), "'status' must been from type bool"
return status
def __UpperCamelCase ( a : str ) ->Any:
assert isinstance(a , a ) and (n > 2), "'N' must been an int and > 2"
# beginList: contains all natural numbers from 2 up to N
snake_case = list(range(2 , n + 1 ) )
snake_case = [] # this list will be returns.
# actual sieve of erathostenes
for i in range(len(a ) ):
for j in range(i + 1 , len(a ) ):
if (begin_list[i] != 0) and (begin_list[j] % begin_list[i] == 0):
snake_case = 0
# filters actual prime numbers.
snake_case = [x for x in begin_list if x != 0]
# precondition
assert isinstance(a , a ), "'ans' must been from type list"
return ans
def __UpperCamelCase ( a : Tuple ) ->Union[str, Any]:
assert isinstance(a , a ) and (n > 2), "'N' must been an int and > 2"
snake_case = []
# iterates over all numbers between 2 up to N+1
# if a number is prime then appends to list 'ans'
for number in range(2 , n + 1 ):
if is_prime(a ):
ans.append(a )
# precondition
assert isinstance(a , a ), "'ans' must been from type list"
return ans
def __UpperCamelCase ( a : Any ) ->int:
assert isinstance(a , a ) and number >= 0, "'number' must been an int and >= 0"
snake_case = [] # this list will be returns of the function.
# potential prime number factors.
snake_case = 2
snake_case = number
if number == 0 or number == 1:
ans.append(a )
# if 'number' not prime then builds the prime factorization of 'number'
elif not is_prime(a ):
while quotient != 1:
if is_prime(a ) and (quotient % factor == 0):
ans.append(a )
quotient /= factor
else:
factor += 1
else:
ans.append(a )
# precondition
assert isinstance(a , a ), "'ans' must been from type list"
return ans
def __UpperCamelCase ( a : Optional[Any] ) ->Tuple:
assert isinstance(a , a ) and (
number >= 0
), "'number' bust been an int and >= 0"
snake_case = 0
# prime factorization of 'number'
snake_case = prime_factorization(a )
snake_case = max(a )
# precondition
assert isinstance(a , a ), "'ans' must been from type int"
return ans
def __UpperCamelCase ( a : Any ) ->Tuple:
assert isinstance(a , a ) and (
number >= 0
), "'number' bust been an int and >= 0"
snake_case = 0
# prime factorization of 'number'
snake_case = prime_factorization(a )
snake_case = min(a )
# precondition
assert isinstance(a , a ), "'ans' must been from type int"
return ans
def __UpperCamelCase ( a : Union[str, Any] ) ->Optional[Any]:
assert isinstance(a , a ), "'number' must been an int"
assert isinstance(number % 2 == 0 , a ), "compare bust been from type bool"
return number % 2 == 0
def __UpperCamelCase ( a : List[str] ) ->Optional[Any]:
assert isinstance(a , a ), "'number' must been an int"
assert isinstance(number % 2 != 0 , a ), "compare bust been from type bool"
return number % 2 != 0
def __UpperCamelCase ( a : List[Any] ) ->Any:
assert (
isinstance(a , a ) and (number > 2) and is_even(a )
), "'number' must been an int, even and > 2"
snake_case = [] # this list will returned
# creates a list of prime numbers between 2 up to 'number'
snake_case = get_prime_numbers(a )
snake_case = len(a )
# run variable for while-loops.
snake_case = 0
snake_case = None
# exit variable. for break up the loops
snake_case = True
while i < len_pn and loop:
snake_case = i + 1
while j < len_pn and loop:
if prime_numbers[i] + prime_numbers[j] == number:
snake_case = False
ans.append(prime_numbers[i] )
ans.append(prime_numbers[j] )
j += 1
i += 1
# precondition
assert (
isinstance(a , a )
and (len(a ) == 2)
and (ans[0] + ans[1] == number)
and is_prime(ans[0] )
and is_prime(ans[1] )
), "'ans' must contains two primes. And sum of elements must been eq 'number'"
return ans
def __UpperCamelCase ( a : Optional[int] , a : Tuple ) ->str:
assert (
isinstance(a , a )
and isinstance(a , a )
and (numbera >= 0)
and (numbera >= 0)
), "'number1' and 'number2' must been positive integer."
snake_case = 0
while numbera != 0:
snake_case = numbera % numbera
snake_case = numbera
snake_case = rest
# precondition
assert isinstance(a , a ) and (
numbera >= 0
), "'number' must been from type int and positive"
return numbera
def __UpperCamelCase ( a : Any , a : str ) ->str:
assert (
isinstance(a , a )
and isinstance(a , a )
and (numbera >= 1)
and (numbera >= 1)
), "'number1' and 'number2' must been positive integer."
snake_case = 1 # actual answer that will be return.
# for kgV (x,1)
if numbera > 1 and numbera > 1:
# builds the prime factorization of 'number1' and 'number2'
snake_case = prime_factorization(a )
snake_case = prime_factorization(a )
elif numbera == 1 or numbera == 1:
snake_case = []
snake_case = []
snake_case = max(a , a )
snake_case = 0
snake_case = 0
snake_case = [] # captured numbers int both 'primeFac1' and 'primeFac2'
# iterates through primeFac1
for n in prime_fac_a:
if n not in done:
if n in prime_fac_a:
snake_case = prime_fac_a.count(a )
snake_case = prime_fac_a.count(a )
for _ in range(max(a , a ) ):
ans *= n
else:
snake_case = prime_fac_a.count(a )
for _ in range(a ):
ans *= n
done.append(a )
# iterates through primeFac2
for n in prime_fac_a:
if n not in done:
snake_case = prime_fac_a.count(a )
for _ in range(a ):
ans *= n
done.append(a )
# precondition
assert isinstance(a , a ) and (
ans >= 0
), "'ans' must been from type int and positive"
return ans
def __UpperCamelCase ( a : List[str] ) ->Optional[Any]:
assert isinstance(a , a ) and (n >= 0), "'number' must been a positive int"
snake_case = 0
snake_case = 2 # this variable holds the answer
while index < n:
index += 1
ans += 1 # counts to the next number
# if ans not prime then
# runs to the next prime number.
while not is_prime(a ):
ans += 1
# precondition
assert isinstance(a , a ) and is_prime(
a ), "'ans' must been a prime number and from type int"
return ans
def __UpperCamelCase ( a : Union[str, Any] , a : int ) ->Union[str, Any]:
assert (
is_prime(a ) and is_prime(a ) and (p_number_a < p_number_a)
), "The arguments must been prime numbers and 'pNumber1' < 'pNumber2'"
snake_case = p_number_a + 1 # jump to the next number
snake_case = [] # this list will be returns.
# if number is not prime then
# fetch the next prime number.
while not is_prime(a ):
number += 1
while number < p_number_a:
ans.append(a )
number += 1
# fetch the next prime number.
while not is_prime(a ):
number += 1
# precondition
assert (
isinstance(a , a )
and ans[0] != p_number_a
and ans[len(a ) - 1] != p_number_a
), "'ans' must been a list without the arguments"
# 'ans' contains not 'pNumber1' and 'pNumber2' !
return ans
def __UpperCamelCase ( a : Any ) ->Dict:
assert isinstance(a , a ) and (n >= 1), "'n' must been int and >= 1"
snake_case = [] # will be returned.
for divisor in range(1 , n + 1 ):
if n % divisor == 0:
ans.append(a )
# precondition
assert ans[0] == 1 and ans[len(a ) - 1] == n, "Error in function getDivisiors(...)"
return ans
def __UpperCamelCase ( a : Union[str, Any] ) ->Optional[int]:
assert isinstance(a , a ) and (
number > 1
), "'number' must been an int and >= 1"
snake_case = get_divisors(a )
# precondition
assert (
isinstance(a , a )
and (divisors[0] == 1)
and (divisors[len(a ) - 1] == number)
), "Error in help-function getDivisiors(...)"
# summed all divisors up to 'number' (exclusive), hence [:-1]
return sum(divisors[:-1] ) == number
def __UpperCamelCase ( a : List[str] , a : int ) ->List[str]:
assert (
isinstance(a , a )
and isinstance(a , a )
and (denominator != 0)
), "The arguments must been from type int and 'denominator' != 0"
# build the greatest common divisor of numerator and denominator.
snake_case = gcd(abs(a ) , abs(a ) )
# precondition
assert (
isinstance(a , a )
and (numerator % gcd_of_fraction == 0)
and (denominator % gcd_of_fraction == 0)
), "Error in function gcd(...,...)"
return (numerator // gcd_of_fraction, denominator // gcd_of_fraction)
def __UpperCamelCase ( a : Any ) ->Any:
assert isinstance(a , a ) and (n >= 0), "'n' must been a int and >= 0"
snake_case = 1 # this will be return.
for factor in range(1 , n + 1 ):
ans *= factor
return ans
def __UpperCamelCase ( a : Tuple ) ->Tuple:
assert isinstance(a , a ) and (n >= 0), "'n' must been an int and >= 0"
snake_case = 0
snake_case = 1
snake_case = 1 # this will be return
for _ in range(n - 1 ):
snake_case = ans
ans += fiba
snake_case = tmp
return ans
| 44
|
'''simple docstring'''
import argparse
import torch
from torch import nn
from transformers import SpeechaTextConfig, SpeechaTextForConditionalGeneration
def __UpperCamelCase ( a : Optional[int] ) ->Dict:
snake_case = [
'''encoder.version''',
'''decoder.version''',
'''model.encoder.version''',
'''model.decoder.version''',
'''decoder.output_projection.weight''',
'''_float_tensor''',
'''encoder.embed_positions._float_tensor''',
'''decoder.embed_positions._float_tensor''',
]
for k in ignore_keys:
state_dict.pop(a , a )
def __UpperCamelCase ( a : Optional[Any] ) ->int:
snake_case = list(s_dict.keys() )
for key in keys:
if "transformer_layers" in key:
snake_case = s_dict.pop(a )
elif "subsample" in key:
snake_case = s_dict.pop(a )
def __UpperCamelCase ( a : Optional[int] ) ->Optional[int]:
snake_case , snake_case = emb.weight.shape
snake_case = nn.Linear(a , a , bias=a )
snake_case = emb.weight.data
return lin_layer
def __UpperCamelCase ( a : Any , a : Tuple ) ->Tuple:
snake_case = torch.load(a , map_location='''cpu''' )
snake_case = mam_aaa['''args''']
snake_case = mam_aaa['''model''']
snake_case = state_dict['''decoder.output_projection.weight''']
remove_ignore_keys_(a )
rename_keys(a )
snake_case = state_dict['''decoder.embed_tokens.weight'''].shape[0]
snake_case = args.share_decoder_input_output_embed
snake_case = [int(a ) for i in args.conv_kernel_sizes.split(''',''' )]
snake_case = SpeechaTextConfig(
vocab_size=a , max_source_positions=args.max_source_positions , max_target_positions=args.max_target_positions , encoder_layers=args.encoder_layers , decoder_layers=args.decoder_layers , encoder_attention_heads=args.encoder_attention_heads , decoder_attention_heads=args.decoder_attention_heads , encoder_ffn_dim=args.encoder_ffn_embed_dim , decoder_ffn_dim=args.decoder_ffn_embed_dim , d_model=args.encoder_embed_dim , dropout=args.dropout , attention_dropout=args.attention_dropout , activation_dropout=args.activation_dropout , activation_function='''relu''' , num_conv_layers=len(a ) , conv_channels=args.conv_channels , conv_kernel_sizes=a , input_feat_per_channel=args.input_feat_per_channel , input_channels=args.input_channels , tie_word_embeddings=a , num_beams=5 , max_length=200 , use_cache=a , decoder_start_token_id=2 , early_stopping=a , )
snake_case = SpeechaTextForConditionalGeneration(a )
snake_case , snake_case = model.model.load_state_dict(a , strict=a )
if len(a ) > 0 and not set(a ) <= {
"encoder.embed_positions.weights",
"decoder.embed_positions.weights",
}:
raise ValueError(
'''Only `encoder.embed_positions.weights` and `decoder.embed_positions.weights` are allowed to be missing,'''
f""" but all the following weights are missing {missing}""" )
if tie_embeds:
snake_case = make_linear_from_emb(model.model.decoder.embed_tokens )
else:
snake_case = lm_head_weights
model.save_pretrained(a )
if __name__ == "__main__":
_lowercase = argparse.ArgumentParser()
# Required parameters
parser.add_argument('--fairseq_path', type=str, help='Path to the fairseq model (.pt) file.')
parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
_lowercase = parser.parse_args()
convert_fairseq_sat_checkpoint_to_tfms(args.fairseq_path, args.pytorch_dump_folder_path)
| 44
| 1
|
'''simple docstring'''
import numpy as np
import skfuzzy as fuzz
if __name__ == "__main__":
# Create universe of discourse in Python using linspace ()
_lowercase = np.linspace(start=0, stop=75, num=75, endpoint=True, retstep=False)
# Create two fuzzy sets by defining any membership function
# (trapmf(), gbellmf(), gaussmf(), etc).
_lowercase = [0, 25, 50]
_lowercase = [25, 50, 75]
_lowercase = fuzz.membership.trimf(X, abca)
_lowercase = fuzz.membership.trimf(X, abca)
# Compute the different operations using inbuilt functions.
_lowercase = np.ones(75)
_lowercase = np.zeros((75,))
# 1. Union = max(µA(x), µB(x))
_lowercase = fuzz.fuzzy_or(X, young, X, middle_aged)[1]
# 2. Intersection = min(µA(x), µB(x))
_lowercase = fuzz.fuzzy_and(X, young, X, middle_aged)[1]
# 3. Complement (A) = (1- min(µA(x))
_lowercase = fuzz.fuzzy_not(young)
# 4. Difference (A/B) = min(µA(x),(1- µB(x)))
_lowercase = fuzz.fuzzy_and(X, young, X, fuzz.fuzzy_not(middle_aged)[1])[1]
# 5. Algebraic Sum = [µA(x) + µB(x) – (µA(x) * µB(x))]
_lowercase = young + middle_aged - (young * middle_aged)
# 6. Algebraic Product = (µA(x) * µB(x))
_lowercase = young * middle_aged
# 7. Bounded Sum = min[1,(µA(x), µB(x))]
_lowercase = fuzz.fuzzy_and(X, one, X, young + middle_aged)[1]
# 8. Bounded difference = min[0,(µA(x), µB(x))]
_lowercase = fuzz.fuzzy_or(X, zero, X, young - middle_aged)[1]
# max-min composition
# max-product composition
# Plot each set A, set B and each operation result using plot() and subplot().
from matplotlib import pyplot as plt
plt.figure()
plt.subplot(4, 3, 1)
plt.plot(X, young)
plt.title('Young')
plt.grid(True)
plt.subplot(4, 3, 2)
plt.plot(X, middle_aged)
plt.title('Middle aged')
plt.grid(True)
plt.subplot(4, 3, 3)
plt.plot(X, union)
plt.title('union')
plt.grid(True)
plt.subplot(4, 3, 4)
plt.plot(X, intersection)
plt.title('intersection')
plt.grid(True)
plt.subplot(4, 3, 5)
plt.plot(X, complement_a)
plt.title('complement_a')
plt.grid(True)
plt.subplot(4, 3, 6)
plt.plot(X, difference)
plt.title('difference a/b')
plt.grid(True)
plt.subplot(4, 3, 7)
plt.plot(X, alg_sum)
plt.title('alg_sum')
plt.grid(True)
plt.subplot(4, 3, 8)
plt.plot(X, alg_product)
plt.title('alg_product')
plt.grid(True)
plt.subplot(4, 3, 9)
plt.plot(X, bdd_sum)
plt.title('bdd_sum')
plt.grid(True)
plt.subplot(4, 3, 10)
plt.plot(X, bdd_difference)
plt.title('bdd_difference')
plt.grid(True)
plt.subplots_adjust(hspace=0.5)
plt.show()
| 44
|
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class _lowercase ( metaclass=__a ):
_UpperCAmelCase = ['''transformers''', '''torch''', '''note_seq''']
def __init__( self , *A__ , **A__ ) -> Union[str, Any]:
requires_backends(self , ['''transformers''', '''torch''', '''note_seq'''] )
@classmethod
def UpperCamelCase ( cls , *A__ , **A__ ) -> Optional[Any]:
requires_backends(cls , ['''transformers''', '''torch''', '''note_seq'''] )
@classmethod
def UpperCamelCase ( cls , *A__ , **A__ ) -> Any:
requires_backends(cls , ['''transformers''', '''torch''', '''note_seq'''] )
| 44
| 1
|
'''simple docstring'''
from __future__ import annotations
from collections import deque
from collections.abc import Iterator
from dataclasses import dataclass
@dataclass
class _lowercase :
_UpperCAmelCase = 42
_UpperCAmelCase = 42
class _lowercase :
def __init__( self , A__ ) -> Union[str, Any]:
snake_case = [[] for _ in range(A__ )]
snake_case = size
def __getitem__( self , A__ ) -> Iterator[Edge]:
return iter(self._graph[vertex] )
@property
def UpperCamelCase ( self ) -> Union[str, Any]:
return self._size
def UpperCamelCase ( self , A__ , A__ , A__ ) -> str:
if weight not in (0, 1):
raise ValueError('''Edge weight must be either 0 or 1.''' )
if to_vertex < 0 or to_vertex >= self.size:
raise ValueError('''Vertex indexes must be in [0; size).''' )
self._graph[from_vertex].append(Edge(A__ , A__ ) )
def UpperCamelCase ( self , A__ , A__ ) -> int | None:
snake_case = deque([start_vertex] )
snake_case = [None] * self.size
snake_case = 0
while queue:
snake_case = queue.popleft()
snake_case = distances[current_vertex]
if current_distance is None:
continue
for edge in self[current_vertex]:
snake_case = current_distance + edge.weight
snake_case = distances[edge.destination_vertex]
if (
isinstance(A__ , A__ )
and new_distance >= dest_vertex_distance
):
continue
snake_case = new_distance
if edge.weight == 0:
queue.appendleft(edge.destination_vertex )
else:
queue.append(edge.destination_vertex )
if distances[finish_vertex] is None:
raise ValueError('''No path from start_vertex to finish_vertex.''' )
return distances[finish_vertex]
if __name__ == "__main__":
import doctest
doctest.testmod()
| 44
|
'''simple docstring'''
from __future__ import annotations
from collections.abc import Iterator
class _lowercase :
def __init__( self , A__ ) -> None:
snake_case = value
snake_case = None
snake_case = None
class _lowercase :
def __init__( self , A__ ) -> None:
snake_case = tree
def UpperCamelCase ( self , A__ ) -> int:
if node is None:
return 0
return node.value + (
self.depth_first_search(node.left ) + self.depth_first_search(node.right )
)
def __iter__( self ) -> Iterator[int]:
yield self.depth_first_search(self.tree )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 44
| 1
|
'''simple docstring'''
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 __UpperCamelCase ( a : str , a : Optional[Any]=False ) ->Any:
try:
snake_case = os.environ[key]
except KeyError:
# KEY isn't set, default to `default`.
snake_case = default
else:
# KEY is set, convert it to True or False.
try:
snake_case = strtobool(a )
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
_lowercase = parse_flag_from_env('RUN_SLOW', default=False)
def __UpperCamelCase ( a : Dict ) ->List[str]:
return unittest.skip('''Test was skipped''' )(a )
def __UpperCamelCase ( a : List[str] ) ->str:
return unittest.skipUnless(_run_slow_tests , '''test is slow''' )(a )
def __UpperCamelCase ( a : List[Any] ) ->Dict:
return unittest.skipUnless(not torch.cuda.is_available() , '''test requires only a CPU''' )(a )
def __UpperCamelCase ( a : Optional[Any] ) ->Dict:
return unittest.skipUnless(torch.cuda.is_available() , '''test requires a GPU''' )(a )
def __UpperCamelCase ( a : Any ) ->Optional[int]:
return unittest.skipUnless(is_xpu_available() , '''test requires a XPU''' )(a )
def __UpperCamelCase ( a : Union[str, Any] ) ->int:
return unittest.skipUnless(is_mps_available() , '''test requires a `mps` backend support in `torch`''' )(a )
def __UpperCamelCase ( a : Optional[Any] ) ->Optional[Any]:
return unittest.skipUnless(
is_transformers_available() and is_datasets_available() , '''test requires the Hugging Face suite''' )(a )
def __UpperCamelCase ( a : Dict ) ->List[str]:
return unittest.skipUnless(is_bnb_available() , '''test requires the bitsandbytes library''' )(a )
def __UpperCamelCase ( a : str ) ->List[Any]:
return unittest.skipUnless(is_tpu_available() , '''test requires TPU''' )(a )
def __UpperCamelCase ( a : int ) ->Any:
return unittest.skipUnless(torch.cuda.device_count() == 1 , '''test requires a GPU''' )(a )
def __UpperCamelCase ( a : Optional[Any] ) ->List[str]:
return unittest.skipUnless(torch.xpu.device_count() == 1 , '''test requires a XPU''' )(a )
def __UpperCamelCase ( a : Any ) ->Dict:
return unittest.skipUnless(torch.cuda.device_count() > 1 , '''test requires multiple GPUs''' )(a )
def __UpperCamelCase ( a : Dict ) ->List[str]:
return unittest.skipUnless(torch.xpu.device_count() > 1 , '''test requires multiple XPUs''' )(a )
def __UpperCamelCase ( a : Tuple ) ->int:
return unittest.skipUnless(is_safetensors_available() , '''test requires safetensors''' )(a )
def __UpperCamelCase ( a : Optional[Any] ) ->Optional[int]:
return unittest.skipUnless(is_deepspeed_available() , '''test requires DeepSpeed''' )(a )
def __UpperCamelCase ( a : List[str] ) ->Optional[Any]:
return unittest.skipUnless(is_torch_version('''>=''' , '''1.12.0''' ) , '''test requires torch version >= 1.12.0''' )(a )
def __UpperCamelCase ( a : int=None , a : Tuple=None ) ->Optional[Any]:
if test_case is None:
return partial(a , version=a )
return unittest.skipUnless(is_torch_version('''>=''' , a ) , f"""test requires torch version >= {version}""" )(a )
def __UpperCamelCase ( a : Tuple ) ->Tuple:
return unittest.skipUnless(is_tensorboard_available() , '''test requires Tensorboard''' )(a )
def __UpperCamelCase ( a : Dict ) ->int:
return unittest.skipUnless(is_wandb_available() , '''test requires wandb''' )(a )
def __UpperCamelCase ( a : Optional[Any] ) ->List[str]:
return unittest.skipUnless(is_comet_ml_available() , '''test requires comet_ml''' )(a )
_lowercase = (
any([is_wandb_available(), is_tensorboard_available()]) and not is_comet_ml_available()
)
def __UpperCamelCase ( a : str ) ->Dict:
return unittest.skipUnless(
_atleast_one_tracker_available , '''test requires at least one tracker to be available and for `comet_ml` to not be installed''' , )(a )
class _lowercase ( unittest.TestCase ):
_UpperCAmelCase = True
@classmethod
def UpperCamelCase ( cls ) -> List[Any]:
snake_case = tempfile.mkdtemp()
@classmethod
def UpperCamelCase ( cls ) -> int:
if os.path.exists(cls.tmpdir ):
shutil.rmtree(cls.tmpdir )
def UpperCamelCase ( self ) -> List[Any]:
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 ):
def UpperCamelCase ( self ) -> int:
super().tearDown()
# Reset the state of the AcceleratorState singleton.
AcceleratorState._reset_state()
PartialState._reset_state()
class _lowercase ( unittest.TestCase ):
def UpperCamelCase ( self , A__ ) -> Optional[Any]:
snake_case = mocks if isinstance(A__ , (tuple, list) ) else [mocks]
for m in self.mocks:
m.start()
self.addCleanup(m.stop )
def __UpperCamelCase ( a : List[Any] ) ->Tuple:
snake_case = AcceleratorState()
snake_case = tensor[None].clone().to(state.device )
snake_case = gather(a ).cpu()
snake_case = tensor[0].cpu()
for i in range(tensors.shape[0] ):
if not torch.equal(tensors[i] , a ):
return False
return True
class _lowercase :
def __init__( self , A__ , A__ , A__ ) -> int:
snake_case = returncode
snake_case = stdout
snake_case = stderr
async def __UpperCamelCase ( a : Tuple , a : int ) ->List[str]:
while True:
snake_case = await stream.readline()
if line:
callback(a )
else:
break
async def __UpperCamelCase ( a : int , a : Optional[Any]=None , a : List[Any]=None , a : Union[str, Any]=None , a : Dict=False , a : Optional[Any]=False ) ->_RunOutput:
if echo:
print('''\nRunning: ''' , ''' '''.join(a ) )
snake_case = await asyncio.create_subprocess_exec(
cmd[0] , *cmd[1:] , stdin=a , stdout=asyncio.subprocess.PIPE , stderr=asyncio.subprocess.PIPE , env=a , )
# 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 = []
snake_case = []
def tee(a : Any , a : Tuple , a : List[Any] , a : List[Any]="" ):
snake_case = line.decode('''utf-8''' ).rstrip()
sink.append(a )
if not quiet:
print(a , a , file=a )
# XXX: the timeout doesn't seem to make any difference here
await asyncio.wait(
[
asyncio.create_task(_read_stream(p.stdout , lambda a : tee(a , a , sys.stdout , label='''stdout:''' ) ) ),
asyncio.create_task(_read_stream(p.stderr , lambda a : tee(a , a , sys.stderr , label='''stderr:''' ) ) ),
] , timeout=a , )
return _RunOutput(await p.wait() , a , a )
def __UpperCamelCase ( a : Optional[int] , a : Dict=None , a : List[Any]=None , a : Tuple=180 , a : Dict=False , a : Optional[int]=True ) ->_RunOutput:
snake_case = asyncio.get_event_loop()
snake_case = loop.run_until_complete(
_stream_subprocess(a , env=a , stdin=a , timeout=a , quiet=a , echo=a ) )
snake_case = ''' '''.join(a )
if result.returncode > 0:
snake_case = '''\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 ( __a ):
pass
def __UpperCamelCase ( a : List[str] , a : Union[str, Any]=False ) ->Union[str, Any]:
try:
snake_case = subprocess.check_output(a , stderr=subprocess.STDOUT )
if return_stdout:
if hasattr(a , '''decode''' ):
snake_case = output.decode('''utf-8''' )
return output
except subprocess.CalledProcessError as e:
raise SubprocessCallException(
f"""Command `{' '.join(a )}` failed with the following error:\n\n{e.output.decode()}""" ) from e
| 44
|
'''simple docstring'''
import argparse
from collections import OrderedDict
from pathlib import Path
import torch
from transformers import (
VisualBertConfig,
VisualBertForMultipleChoice,
VisualBertForPreTraining,
VisualBertForQuestionAnswering,
VisualBertForVisualReasoning,
)
from transformers.utils import logging
logging.set_verbosity_info()
_lowercase = logging.get_logger(__name__)
_lowercase = [
('bert.bert', 'visual_bert'),
('bert.cls', 'cls'),
('bert.classifier', 'cls'),
('token_type_embeddings_visual', 'visual_token_type_embeddings'),
('position_embeddings_visual', 'visual_position_embeddings'),
('projection', 'visual_projection'),
]
_lowercase = [
'nlvr2_coco_pre_trained.th',
'nlvr2_fine_tuned.th',
'nlvr2_pre_trained.th',
'vcr_coco_pre_train.th',
'vcr_fine_tune.th',
'vcr_pre_train.th',
'vqa_coco_pre_trained.th',
'vqa_fine_tuned.th',
'vqa_pre_trained.th',
]
def __UpperCamelCase ( a : List[str] ) ->Optional[int]:
snake_case = torch.load(a , map_location='''cpu''' )
return sd
def __UpperCamelCase ( a : Optional[int] , a : Union[str, Any] , a : int=rename_keys_prefix ) ->Tuple:
snake_case = OrderedDict()
snake_case = torch.arange(config.max_position_embeddings ).expand((1, -1) )
# detector_d = OrderedDict()
for key in d:
if "detector" in key:
# detector_d[key.replace('detector.','')] = d[key]
continue
snake_case = key
for name_pair in rename_keys_prefix:
snake_case = new_key.replace(name_pair[0] , name_pair[1] )
snake_case = d[key]
if key == "bert.cls.predictions.decoder.weight":
# Old bert code didn't have `decoder.bias`, but was added separately
snake_case = new_d['''cls.predictions.bias''']
return new_d
@torch.no_grad()
def __UpperCamelCase ( a : Optional[int] , a : int ) ->Union[str, Any]:
assert (
checkpoint_path.split('''/''' )[-1] in ACCEPTABLE_CHECKPOINTS
), f"""The checkpoint provided must be in {ACCEPTABLE_CHECKPOINTS}."""
# Get Config
if "pre" in checkpoint_path:
snake_case = '''pretraining'''
if "vcr" in checkpoint_path:
snake_case = {'''visual_embedding_dim''': 512}
elif "vqa_advanced" in checkpoint_path:
snake_case = {'''visual_embedding_dim''': 2048}
elif "vqa" in checkpoint_path:
snake_case = {'''visual_embedding_dim''': 2048}
elif "nlvr" in checkpoint_path:
snake_case = {'''visual_embedding_dim''': 1024}
else:
raise NotImplementedError(f"""No implementation found for `{checkpoint_path}`.""" )
else:
if "vcr" in checkpoint_path:
snake_case = {'''visual_embedding_dim''': 512}
snake_case = '''multichoice'''
elif "vqa_advanced" in checkpoint_path:
snake_case = {'''visual_embedding_dim''': 2048}
snake_case = '''vqa_advanced'''
elif "vqa" in checkpoint_path:
snake_case = {'''visual_embedding_dim''': 2048, '''num_labels''': 3129}
snake_case = '''vqa'''
elif "nlvr" in checkpoint_path:
snake_case = {
'''visual_embedding_dim''': 1024,
'''num_labels''': 2,
}
snake_case = '''nlvr'''
snake_case = VisualBertConfig(**a )
# Load State Dict
snake_case = load_state_dict(a )
snake_case = get_new_dict(a , a )
if model_type == "pretraining":
snake_case = VisualBertForPreTraining(a )
elif model_type == "vqa":
snake_case = VisualBertForQuestionAnswering(a )
elif model_type == "nlvr":
snake_case = VisualBertForVisualReasoning(a )
elif model_type == "multichoice":
snake_case = VisualBertForMultipleChoice(a )
model.load_state_dict(a )
# Save Checkpoints
Path(a ).mkdir(exist_ok=a )
model.save_pretrained(a )
if __name__ == "__main__":
_lowercase = argparse.ArgumentParser()
# Required parameters
parser.add_argument('orig_checkpoint_path', type=str, help='A path to .th on local filesystem.')
parser.add_argument('pytorch_dump_folder_path', type=str, help='Path to the output PyTorch model.')
_lowercase = parser.parse_args()
convert_visual_bert_checkpoint(args.orig_checkpoint_path, args.pytorch_dump_folder_path)
| 44
| 1
|
'''simple docstring'''
import math
import sys
def __UpperCamelCase ( a : str ) ->str:
snake_case = ''''''
try:
with open(a , '''rb''' ) as binary_file:
snake_case = binary_file.read()
for dat in data:
snake_case = f"""{dat:08b}"""
result += curr_byte
return result
except OSError:
print('''File not accessible''' )
sys.exit()
def __UpperCamelCase ( a : str ) ->str:
snake_case = {'''0''': '''0''', '''1''': '''1'''}
snake_case , snake_case = '''''', ''''''
snake_case = len(a )
for i in range(len(a ) ):
curr_string += data_bits[i]
if curr_string not in lexicon:
continue
snake_case = lexicon[curr_string]
result += last_match_id
snake_case = last_match_id + '''0'''
if math.loga(a ).is_integer():
snake_case = {}
for curr_key in list(a ):
snake_case = lexicon.pop(a )
snake_case = new_lex
snake_case = last_match_id + '''1'''
index += 1
snake_case = ''''''
return result
def __UpperCamelCase ( a : str , a : str ) ->None:
snake_case = 8
try:
with open(a , '''wb''' ) as opened_file:
snake_case = [
to_write[i : i + byte_length]
for i in range(0 , len(a ) , a )
]
if len(result_byte_array[-1] ) % byte_length == 0:
result_byte_array.append('''10000000''' )
else:
result_byte_array[-1] += "1" + "0" * (
byte_length - len(result_byte_array[-1] ) - 1
)
for elem in result_byte_array[:-1]:
opened_file.write(int(a , 2 ).to_bytes(1 , byteorder='''big''' ) )
except OSError:
print('''File not accessible''' )
sys.exit()
def __UpperCamelCase ( a : str ) ->str:
snake_case = 0
for letter in data_bits:
if letter == "1":
break
counter += 1
snake_case = data_bits[counter:]
snake_case = data_bits[counter + 1 :]
return data_bits
def __UpperCamelCase ( a : str , a : str ) ->None:
snake_case = read_file_binary(a )
snake_case = remove_prefix(a )
snake_case = decompress_data(a )
write_file_binary(a , a )
if __name__ == "__main__":
compress(sys.argv[1], sys.argv[2])
| 44
|
'''simple docstring'''
import argparse
import json
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import PoolFormerConfig, PoolFormerForImageClassification, PoolFormerImageProcessor
from transformers.utils import logging
logging.set_verbosity_info()
_lowercase = logging.get_logger(__name__)
def __UpperCamelCase ( a : Dict , a : Optional[int] , a : Dict , a : Dict ) ->Union[str, Any]:
snake_case = original_name.split('''.''' )[0]
snake_case = key.split('''.''' )
snake_case = int(key_list[key_list.index(a ) - 2] )
snake_case = int(key_list[key_list.index(a ) - 1] )
snake_case = orig_block_num - offset
snake_case = key.replace(f"""{orig_block_num}.{layer_num}.{original_name}""" , f"""block.{new_block_num}.{layer_num}.{new_name}""" )
return key
def __UpperCamelCase ( a : Tuple ) ->Dict:
snake_case = OrderedDict()
snake_case , snake_case = 0, 0
for key, value in state_dict.items():
if key.startswith('''network''' ):
snake_case = key.replace('''network''' , '''poolformer.encoder''' )
if "proj" in key:
# Works for the first embedding as well as the internal embedding layers
if key.endswith('''bias''' ) and "patch_embed" not in key:
patch_emb_offset += 1
snake_case = key[: key.find('''proj''' )]
snake_case = key.replace(a , f"""patch_embeddings.{total_embed_found}.""" )
snake_case = key.replace('''proj''' , '''projection''' )
if key.endswith('''bias''' ):
total_embed_found += 1
if "patch_embeddings" in key:
snake_case = '''poolformer.encoder.''' + key
if "mlp.fc1" in key:
snake_case = replace_key_with_offset(a , a , '''mlp.fc1''' , '''output.conv1''' )
if "mlp.fc2" in key:
snake_case = replace_key_with_offset(a , a , '''mlp.fc2''' , '''output.conv2''' )
if "norm1" in key:
snake_case = replace_key_with_offset(a , a , '''norm1''' , '''before_norm''' )
if "norm2" in key:
snake_case = replace_key_with_offset(a , a , '''norm2''' , '''after_norm''' )
if "layer_scale_1" in key:
snake_case = replace_key_with_offset(a , a , '''layer_scale_1''' , '''layer_scale_1''' )
if "layer_scale_2" in key:
snake_case = replace_key_with_offset(a , a , '''layer_scale_2''' , '''layer_scale_2''' )
if "head" in key:
snake_case = key.replace('''head''' , '''classifier''' )
snake_case = value
return new_state_dict
def __UpperCamelCase ( ) ->Optional[int]:
snake_case = '''http://images.cocodataset.org/val2017/000000039769.jpg'''
snake_case = Image.open(requests.get(a , stream=a ).raw )
return image
@torch.no_grad()
def __UpperCamelCase ( a : Dict , a : Optional[Any] , a : Tuple ) ->List[str]:
snake_case = PoolFormerConfig()
# set attributes based on model_name
snake_case = '''huggingface/label-files'''
snake_case = model_name[-3:]
snake_case = 1000
snake_case = '''imagenet-1k-id2label.json'''
snake_case = (1, 1000)
# set config attributes
snake_case = json.load(open(hf_hub_download(a , a , repo_type='''dataset''' ) , '''r''' ) )
snake_case = {int(a ): v for k, v in idalabel.items()}
snake_case = idalabel
snake_case = {v: k for k, v in idalabel.items()}
if size == "s12":
snake_case = [2, 2, 6, 2]
snake_case = [64, 128, 320, 512]
snake_case = 4.0
snake_case = 0.9
elif size == "s24":
snake_case = [4, 4, 12, 4]
snake_case = [64, 128, 320, 512]
snake_case = 4.0
snake_case = 0.9
elif size == "s36":
snake_case = [6, 6, 18, 6]
snake_case = [64, 128, 320, 512]
snake_case = 4.0
snake_case = 1e-6
snake_case = 0.9
elif size == "m36":
snake_case = [6, 6, 18, 6]
snake_case = [96, 192, 384, 768]
snake_case = 4.0
snake_case = 1e-6
snake_case = 0.95
elif size == "m48":
snake_case = [8, 8, 24, 8]
snake_case = [96, 192, 384, 768]
snake_case = 4.0
snake_case = 1e-6
snake_case = 0.95
else:
raise ValueError(f"""Size {size} not supported""" )
# load image processor
snake_case = PoolFormerImageProcessor(crop_pct=a )
# Prepare image
snake_case = prepare_img()
snake_case = image_processor(images=a , return_tensors='''pt''' ).pixel_values
logger.info(f"""Converting model {model_name}...""" )
# load original state dict
snake_case = torch.load(a , map_location=torch.device('''cpu''' ) )
# rename keys
snake_case = rename_keys(a )
# create HuggingFace model and load state dict
snake_case = PoolFormerForImageClassification(a )
model.load_state_dict(a )
model.eval()
# Define image processor
snake_case = PoolFormerImageProcessor(crop_pct=a )
snake_case = image_processor(images=prepare_img() , return_tensors='''pt''' ).pixel_values
# forward pass
snake_case = model(a )
snake_case = outputs.logits
# define expected logit slices for different models
if size == "s12":
snake_case = torch.tensor([-0.3045, -0.6758, -0.4869] )
elif size == "s24":
snake_case = torch.tensor([0.4402, -0.1374, -0.8045] )
elif size == "s36":
snake_case = torch.tensor([-0.6080, -0.5133, -0.5898] )
elif size == "m36":
snake_case = torch.tensor([0.3952, 0.2263, -1.2668] )
elif size == "m48":
snake_case = torch.tensor([0.1167, -0.0656, -0.3423] )
else:
raise ValueError(f"""Size {size} not supported""" )
# verify logits
assert logits.shape == expected_shape
assert torch.allclose(logits[0, :3] , a , atol=1e-2 )
# finally, save model and image processor
logger.info(f"""Saving PyTorch model and image processor to {pytorch_dump_folder_path}...""" )
Path(a ).mkdir(exist_ok=a )
model.save_pretrained(a )
print(f"""Saving image processor to {pytorch_dump_folder_path}""" )
image_processor.save_pretrained(a )
if __name__ == "__main__":
_lowercase = argparse.ArgumentParser()
parser.add_argument(
'--model_name',
default='poolformer_s12',
type=str,
help='Name of the model you\'d like to convert.',
)
parser.add_argument(
'--checkpoint_path', default=None, type=str, help='Path to the original PyTorch checkpoint (.pth file).'
)
parser.add_argument(
'--pytorch_dump_folder_path', default=None, type=str, help='Path to the folder to output PyTorch model.'
)
_lowercase = parser.parse_args()
convert_poolformer_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path)
| 44
| 1
|
'''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_owlvit': [
'OWLVIT_PRETRAINED_CONFIG_ARCHIVE_MAP',
'OwlViTConfig',
'OwlViTOnnxConfig',
'OwlViTTextConfig',
'OwlViTVisionConfig',
],
'processing_owlvit': ['OwlViTProcessor'],
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowercase = ['OwlViTFeatureExtractor']
_lowercase = ['OwlViTImageProcessor']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowercase = [
'OWLVIT_PRETRAINED_MODEL_ARCHIVE_LIST',
'OwlViTModel',
'OwlViTPreTrainedModel',
'OwlViTTextModel',
'OwlViTVisionModel',
'OwlViTForObjectDetection',
]
if TYPE_CHECKING:
from .configuration_owlvit import (
OWLVIT_PRETRAINED_CONFIG_ARCHIVE_MAP,
OwlViTConfig,
OwlViTOnnxConfig,
OwlViTTextConfig,
OwlViTVisionConfig,
)
from .processing_owlvit import OwlViTProcessor
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_owlvit import OwlViTFeatureExtractor
from .image_processing_owlvit import OwlViTImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_owlvit import (
OWLVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
OwlViTForObjectDetection,
OwlViTModel,
OwlViTPreTrainedModel,
OwlViTTextModel,
OwlViTVisionModel,
)
else:
import sys
_lowercase = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 44
|
'''simple docstring'''
import argparse
import json
import logging
import os
import sys
from unittest.mock import patch
from transformers.testing_utils import TestCasePlus, get_gpu_count, slow
_lowercase = [
os.path.join(os.path.dirname(__file__), dirname)
for dirname in [
'text-classification',
'language-modeling',
'summarization',
'token-classification',
'question-answering',
]
]
sys.path.extend(SRC_DIRS)
if SRC_DIRS is not None:
import run_clm_flax
import run_flax_glue
import run_flax_ner
import run_mlm_flax
import run_qa
import run_summarization_flax
import run_ta_mlm_flax
logging.basicConfig(level=logging.DEBUG)
_lowercase = logging.getLogger()
def __UpperCamelCase ( ) ->Tuple:
snake_case = argparse.ArgumentParser()
parser.add_argument('''-f''' )
snake_case = parser.parse_args()
return args.f
def __UpperCamelCase ( a : Dict , a : Tuple="eval" ) ->List[Any]:
snake_case = os.path.join(a , f"""{split}_results.json""" )
if os.path.exists(a ):
with open(a , '''r''' ) as f:
return json.load(a )
raise ValueError(f"""can't find {path}""" )
_lowercase = logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
class _lowercase ( __a ):
def UpperCamelCase ( self ) -> List[str]:
snake_case = self.get_auto_remove_tmp_dir()
snake_case = F"""
run_glue.py
--model_name_or_path distilbert-base-uncased
--output_dir {tmp_dir}
--train_file ./tests/fixtures/tests_samples/MRPC/train.csv
--validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv
--per_device_train_batch_size=2
--per_device_eval_batch_size=1
--learning_rate=1e-4
--eval_steps=2
--warmup_steps=2
--seed=42
--max_seq_length=128
""".split()
with patch.object(A__ , '''argv''' , A__ ):
run_flax_glue.main()
snake_case = get_results(A__ )
self.assertGreaterEqual(result['''eval_accuracy'''] , 0.7_5 )
@slow
def UpperCamelCase ( self ) -> List[Any]:
snake_case = self.get_auto_remove_tmp_dir()
snake_case = F"""
run_clm_flax.py
--model_name_or_path distilgpt2
--train_file ./tests/fixtures/sample_text.txt
--validation_file ./tests/fixtures/sample_text.txt
--do_train
--do_eval
--block_size 128
--per_device_train_batch_size 4
--per_device_eval_batch_size 4
--num_train_epochs 2
--logging_steps 2 --eval_steps 2
--output_dir {tmp_dir}
--overwrite_output_dir
""".split()
with patch.object(A__ , '''argv''' , A__ ):
run_clm_flax.main()
snake_case = get_results(A__ )
self.assertLess(result['''eval_perplexity'''] , 1_00 )
@slow
def UpperCamelCase ( self ) -> int:
snake_case = self.get_auto_remove_tmp_dir()
snake_case = F"""
run_summarization.py
--model_name_or_path t5-small
--train_file tests/fixtures/tests_samples/xsum/sample.json
--validation_file tests/fixtures/tests_samples/xsum/sample.json
--test_file tests/fixtures/tests_samples/xsum/sample.json
--output_dir {tmp_dir}
--overwrite_output_dir
--num_train_epochs=3
--warmup_steps=8
--do_train
--do_eval
--do_predict
--learning_rate=2e-4
--per_device_train_batch_size=2
--per_device_eval_batch_size=1
--predict_with_generate
""".split()
with patch.object(A__ , '''argv''' , A__ ):
run_summarization_flax.main()
snake_case = get_results(A__ , split='''test''' )
self.assertGreaterEqual(result['''test_rouge1'''] , 10 )
self.assertGreaterEqual(result['''test_rouge2'''] , 2 )
self.assertGreaterEqual(result['''test_rougeL'''] , 7 )
self.assertGreaterEqual(result['''test_rougeLsum'''] , 7 )
@slow
def UpperCamelCase ( self ) -> Union[str, Any]:
snake_case = self.get_auto_remove_tmp_dir()
snake_case = F"""
run_mlm.py
--model_name_or_path distilroberta-base
--train_file ./tests/fixtures/sample_text.txt
--validation_file ./tests/fixtures/sample_text.txt
--output_dir {tmp_dir}
--overwrite_output_dir
--max_seq_length 128
--per_device_train_batch_size 4
--per_device_eval_batch_size 4
--logging_steps 2 --eval_steps 2
--do_train
--do_eval
--num_train_epochs=1
""".split()
with patch.object(A__ , '''argv''' , A__ ):
run_mlm_flax.main()
snake_case = get_results(A__ )
self.assertLess(result['''eval_perplexity'''] , 42 )
@slow
def UpperCamelCase ( self ) -> Dict:
snake_case = self.get_auto_remove_tmp_dir()
snake_case = F"""
run_t5_mlm_flax.py
--model_name_or_path t5-small
--train_file ./tests/fixtures/sample_text.txt
--validation_file ./tests/fixtures/sample_text.txt
--do_train
--do_eval
--max_seq_length 128
--per_device_train_batch_size 4
--per_device_eval_batch_size 4
--num_train_epochs 2
--logging_steps 2 --eval_steps 2
--output_dir {tmp_dir}
--overwrite_output_dir
""".split()
with patch.object(A__ , '''argv''' , A__ ):
run_ta_mlm_flax.main()
snake_case = get_results(A__ )
self.assertGreaterEqual(result['''eval_accuracy'''] , 0.4_2 )
@slow
def UpperCamelCase ( self ) -> int:
# with so little data distributed training needs more epochs to get the score on par with 0/1 gpu
snake_case = 7 if get_gpu_count() > 1 else 2
snake_case = self.get_auto_remove_tmp_dir()
snake_case = F"""
run_flax_ner.py
--model_name_or_path bert-base-uncased
--train_file tests/fixtures/tests_samples/conll/sample.json
--validation_file tests/fixtures/tests_samples/conll/sample.json
--output_dir {tmp_dir}
--overwrite_output_dir
--do_train
--do_eval
--warmup_steps=2
--learning_rate=2e-4
--logging_steps 2 --eval_steps 2
--per_device_train_batch_size=2
--per_device_eval_batch_size=2
--num_train_epochs={epochs}
--seed 7
""".split()
with patch.object(A__ , '''argv''' , A__ ):
run_flax_ner.main()
snake_case = get_results(A__ )
self.assertGreaterEqual(result['''eval_accuracy'''] , 0.7_5 )
self.assertGreaterEqual(result['''eval_f1'''] , 0.3 )
@slow
def UpperCamelCase ( self ) -> Any:
snake_case = self.get_auto_remove_tmp_dir()
snake_case = F"""
run_qa.py
--model_name_or_path bert-base-uncased
--version_2_with_negative
--train_file tests/fixtures/tests_samples/SQUAD/sample.json
--validation_file tests/fixtures/tests_samples/SQUAD/sample.json
--output_dir {tmp_dir}
--overwrite_output_dir
--num_train_epochs=3
--warmup_steps=2
--do_train
--do_eval
--logging_steps 2 --eval_steps 2
--learning_rate=2e-4
--per_device_train_batch_size=2
--per_device_eval_batch_size=1
""".split()
with patch.object(A__ , '''argv''' , A__ ):
run_qa.main()
snake_case = get_results(A__ )
self.assertGreaterEqual(result['''eval_f1'''] , 30 )
self.assertGreaterEqual(result['''eval_exact'''] , 30 )
| 44
| 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 _lowercase ( __a ):
_UpperCAmelCase = '''char'''
_UpperCAmelCase = '''bpe'''
_UpperCAmelCase = '''wp'''
_lowercase = (DecodeType.CHARACTER, DecodeType.BPE, DecodeType.WORDPIECE)
class _lowercase ( __a ):
_UpperCAmelCase = ['''image_processor''', '''char_tokenizer''']
_UpperCAmelCase = '''ViTImageProcessor'''
_UpperCAmelCase = '''MgpstrTokenizer'''
def __init__( self , A__=None , A__=None , **A__ ) -> List[Any]:
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.''' , A__ , )
snake_case = kwargs.pop('''feature_extractor''' )
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`.''' )
snake_case = tokenizer
snake_case = AutoTokenizer.from_pretrained('''gpt2''' )
snake_case = AutoTokenizer.from_pretrained('''bert-base-uncased''' )
super().__init__(A__ , A__ )
def __call__( self , A__=None , A__=None , A__=None , **A__ ) -> List[str]:
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:
snake_case = self.image_processor(A__ , return_tensors=A__ , **A__ )
if text is not None:
snake_case = self.char_tokenizer(A__ , return_tensors=A__ , **A__ )
if text is None:
return inputs
elif images is None:
return encodings
else:
snake_case = encodings['''input_ids''']
return inputs
def UpperCamelCase ( self , A__ ) -> Dict:
snake_case , snake_case , snake_case = sequences
snake_case = char_preds.size(0 )
snake_case , snake_case = self._decode_helper(A__ , '''char''' )
snake_case , snake_case = self._decode_helper(A__ , '''bpe''' )
snake_case , snake_case = self._decode_helper(A__ , '''wp''' )
snake_case = []
snake_case = []
for i in range(A__ ):
snake_case = [char_scores[i], bpe_scores[i], wp_scores[i]]
snake_case = [char_strs[i], bpe_strs[i], wp_strs[i]]
snake_case = scores.index(max(A__ ) )
final_strs.append(strs[max_score_index] )
final_scores.append(scores[max_score_index] )
snake_case = {}
snake_case = final_strs
snake_case = final_scores
snake_case = char_strs
snake_case = bpe_strs
snake_case = wp_strs
return out
def UpperCamelCase ( self , A__ , A__ ) -> Optional[Any]:
if format == DecodeType.CHARACTER:
snake_case = self.char_decode
snake_case = 1
snake_case = '''[s]'''
elif format == DecodeType.BPE:
snake_case = self.bpe_decode
snake_case = 2
snake_case = '''#'''
elif format == DecodeType.WORDPIECE:
snake_case = self.wp_decode
snake_case = 1_02
snake_case = '''[SEP]'''
else:
raise ValueError(F"""Format {format} is not supported.""" )
snake_case , snake_case = [], []
snake_case = pred_logits.size(0 )
snake_case = pred_logits.size(1 )
snake_case , snake_case = pred_logits.topk(1 , dim=-1 , largest=A__ , sorted=A__ )
snake_case = preds_index.view(-1 , A__ )[:, 1:]
snake_case = decoder(A__ )
snake_case , snake_case = torch.nn.functional.softmax(A__ , dim=2 ).max(dim=2 )
snake_case = preds_max_prob[:, 1:]
for index in range(A__ ):
snake_case = preds_str[index].find(A__ )
snake_case = preds_str[index][:pred_eos]
snake_case = preds_index[index].cpu().tolist()
snake_case = pred_index.index(A__ ) if eos_token in pred_index else -1
snake_case = preds_max_prob[index][: pred_eos_index + 1]
snake_case = pred_max_prob.cumprod(dim=0 )[-1] if pred_max_prob.nelement() != 0 else 0.0
dec_strs.append(A__ )
conf_scores.append(A__ )
return dec_strs, conf_scores
def UpperCamelCase ( self , A__ ) -> int:
snake_case = [seq.replace(''' ''' , '''''' ) for seq in self.char_tokenizer.batch_decode(A__ )]
return decode_strs
def UpperCamelCase ( self , A__ ) -> List[str]:
return self.bpe_tokenizer.batch_decode(A__ )
def UpperCamelCase ( self , A__ ) -> Union[str, Any]:
snake_case = [seq.replace(''' ''' , '''''' ) for seq in self.wp_tokenizer.batch_decode(A__ )]
return decode_strs
| 44
|
'''simple docstring'''
from typing import Any, Dict, List, Optional, Tuple, Union
import torch
from torch import nn
from torch.utils.data import DistributedSampler, RandomSampler
from transformers import PreTrainedModel, Trainer, logging
from transformers.integrations import is_fairscale_available
from transformers.models.fsmt.configuration_fsmt import FSMTConfig
from transformers.optimization import (
Adafactor,
AdamW,
get_constant_schedule,
get_constant_schedule_with_warmup,
get_cosine_schedule_with_warmup,
get_cosine_with_hard_restarts_schedule_with_warmup,
get_linear_schedule_with_warmup,
get_polynomial_decay_schedule_with_warmup,
)
from transformers.trainer_pt_utils import get_tpu_sampler
from transformers.training_args import ParallelMode
from transformers.utils import is_torch_tpu_available
if is_fairscale_available():
from fairscale.optim import OSS
_lowercase = logging.get_logger(__name__)
_lowercase = {
'linear': get_linear_schedule_with_warmup,
'cosine': get_cosine_schedule_with_warmup,
'cosine_w_restarts': get_cosine_with_hard_restarts_schedule_with_warmup,
'polynomial': get_polynomial_decay_schedule_with_warmup,
'constant': get_constant_schedule,
'constant_w_warmup': get_constant_schedule_with_warmup,
}
class _lowercase ( __a ):
def __init__( self , A__=None , A__=None , *A__ , **A__ ) -> Union[str, Any]:
super().__init__(*A__ , **A__ )
if config is None:
assert isinstance(self.model , A__ ), (
"If no `config` is passed the model to be trained has to be of type `PreTrainedModel`, but is"
F""" {self.model.__class__}"""
)
snake_case = self.model.config
else:
snake_case = config
snake_case = data_args
snake_case = self.config.tgt_vocab_size if isinstance(self.config , A__ ) else self.config.vocab_size
if self.args.label_smoothing != 0 or (self.data_args is not None and self.data_args.ignore_pad_token_for_loss):
assert self.config.pad_token_id is not None, (
"Make sure that `config.pad_token_id` is correcly defined when ignoring `pad_token` for loss"
" calculation or doing label smoothing."
)
if self.config.pad_token_id is None and self.config.eos_token_id is not None:
logger.warning(
F"""The `config.pad_token_id` is `None`. Using `config.eos_token_id` = {self.config.eos_token_id} for"""
''' padding..''' )
if self.args.label_smoothing == 0:
snake_case = torch.nn.CrossEntropyLoss(ignore_index=self.config.pad_token_id )
else:
# dynamically import label_smoothed_nll_loss
from utils import label_smoothed_nll_loss
snake_case = label_smoothed_nll_loss
def UpperCamelCase ( self , A__ ) -> Tuple:
if self.optimizer is None:
snake_case = ['''bias''', '''LayerNorm.weight''']
snake_case = [
{
'''params''': [p for n, p in self.model.named_parameters() if not any(nd in n for nd in no_decay )],
'''weight_decay''': self.args.weight_decay,
},
{
'''params''': [p for n, p in self.model.named_parameters() if any(nd in n for nd in no_decay )],
'''weight_decay''': 0.0,
},
]
snake_case = Adafactor if self.args.adafactor else AdamW
if self.args.adafactor:
snake_case = Adafactor
snake_case = {'''scale_parameter''': False, '''relative_step''': False}
else:
snake_case = AdamW
snake_case = {
'''betas''': (self.args.adam_betaa, self.args.adam_betaa),
'''eps''': self.args.adam_epsilon,
}
snake_case = self.args.learning_rate
if self.sharded_ddp:
snake_case = OSS(
params=A__ , optim=A__ , **A__ , )
else:
snake_case = optimizer_cls(A__ , **A__ )
if self.lr_scheduler is None:
snake_case = self._get_lr_scheduler(A__ )
else: # ignoring --lr_scheduler
logger.warning('''scheduler is passed to `Seq2SeqTrainer`, `--lr_scheduler` arg is ignored.''' )
def UpperCamelCase ( self , A__ ) -> Tuple:
snake_case = arg_to_scheduler[self.args.lr_scheduler]
if self.args.lr_scheduler == "constant":
snake_case = schedule_func(self.optimizer )
elif self.args.lr_scheduler == "constant_w_warmup":
snake_case = schedule_func(self.optimizer , num_warmup_steps=self.args.warmup_steps )
else:
snake_case = schedule_func(
self.optimizer , num_warmup_steps=self.args.warmup_steps , num_training_steps=A__ )
return scheduler
def UpperCamelCase ( self ) -> Optional[torch.utils.data.Sampler]:
if isinstance(self.train_dataset , torch.utils.data.IterableDataset ):
return None
elif is_torch_tpu_available():
return get_tpu_sampler(self.train_dataset )
else:
if self.args.sortish_sampler:
self.train_dataset.make_sortish_sampler(
self.args.per_device_train_batch_size , distributed=(self.args.parallel_mode == ParallelMode.DISTRIBUTED) , )
return (
RandomSampler(self.train_dataset )
if self.args.local_rank == -1
else DistributedSampler(self.train_dataset )
)
def UpperCamelCase ( self , A__ , A__ , A__ ) -> List[Any]:
if self.args.label_smoothing == 0:
if self.data_args is not None and self.data_args.ignore_pad_token_for_loss:
# force training to ignore pad token
snake_case = model(**A__ , use_cache=A__ )[0]
snake_case = self.loss_fn(logits.view(-1 , logits.shape[-1] ) , labels.view(-1 ) )
else:
# compute usual loss via models
snake_case , snake_case = model(**A__ , labels=A__ , use_cache=A__ )[:2]
else:
# compute label smoothed loss
snake_case = model(**A__ , use_cache=A__ )[0]
snake_case = torch.nn.functional.log_softmax(A__ , dim=-1 )
snake_case , snake_case = self.loss_fn(A__ , A__ , self.args.label_smoothing , ignore_index=self.config.pad_token_id )
return loss, logits
def UpperCamelCase ( self , A__ , A__ ) -> Any:
snake_case = inputs.pop('''labels''' )
snake_case , snake_case = self._compute_loss(A__ , A__ , A__ )
return loss
def UpperCamelCase ( self , A__ , A__ , A__ , A__ = None , ) -> Tuple[Optional[float], Optional[torch.Tensor], Optional[torch.Tensor]]:
snake_case = self._prepare_inputs(A__ )
snake_case = {
'''max_length''': self.data_args.val_max_target_length
if self.data_args is not None
else self.config.max_length,
'''num_beams''': self.data_args.eval_beams if self.data_args is not None else self.config.num_beams,
}
if self.args.predict_with_generate and not self.args.prediction_loss_only:
snake_case = self.model.generate(
inputs['''input_ids'''] , attention_mask=inputs['''attention_mask'''] , **A__ , )
# in case the batch is shorter than max length, the output should be padded
if generated_tokens.shape[-1] < gen_kwargs["max_length"]:
snake_case = self._pad_tensors_to_max_len(A__ , gen_kwargs['''max_length'''] )
snake_case = inputs.pop('''labels''' )
with torch.no_grad():
# compute loss on predict data
snake_case , snake_case = self._compute_loss(A__ , A__ , A__ )
snake_case = loss.mean().detach()
if self.args.prediction_loss_only:
return (loss, None, None)
snake_case = generated_tokens if self.args.predict_with_generate else logits
if labels.shape[-1] < gen_kwargs["max_length"]:
snake_case = self._pad_tensors_to_max_len(A__ , gen_kwargs['''max_length'''] )
return (loss, logits, labels)
def UpperCamelCase ( self , A__ , A__ ) -> List[str]:
# If PAD token is not defined at least EOS token has to be defined
snake_case = self.config.pad_token_id if self.config.pad_token_id is not None else self.config.eos_token_id
if pad_token_id is None:
raise ValueError(
'''Make sure that either `config.pad_token_id` or `config.eos_token_id` is defined if tensor has to be'''
F""" padded to `max_length`={max_length}""" )
snake_case = pad_token_id * torch.ones(
(tensor.shape[0], max_length) , dtype=tensor.dtype , device=tensor.device )
snake_case = tensor
return padded_tensor
| 44
| 1
|
'''simple docstring'''
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
EulerAncestralDiscreteScheduler,
LMSDiscreteScheduler,
PNDMScheduler,
StableDiffusionPanoramaPipeline,
UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps
from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin
enable_full_determinism()
@skip_mps
class _lowercase ( __a , __a , unittest.TestCase ):
_UpperCAmelCase = StableDiffusionPanoramaPipeline
_UpperCAmelCase = TEXT_TO_IMAGE_PARAMS
_UpperCAmelCase = TEXT_TO_IMAGE_BATCH_PARAMS
_UpperCAmelCase = TEXT_TO_IMAGE_IMAGE_PARAMS
_UpperCAmelCase = TEXT_TO_IMAGE_IMAGE_PARAMS
def UpperCamelCase ( self ) -> List[str]:
torch.manual_seed(0 )
snake_case = UNetaDConditionModel(
block_out_channels=(32, 64) , layers_per_block=1 , sample_size=32 , in_channels=4 , out_channels=4 , down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''') , up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''') , cross_attention_dim=32 , )
snake_case = DDIMScheduler()
torch.manual_seed(0 )
snake_case = AutoencoderKL(
block_out_channels=[32, 64] , in_channels=3 , out_channels=3 , down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D'''] , up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D'''] , latent_channels=4 , )
torch.manual_seed(0 )
snake_case = 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=10_00 , )
snake_case = CLIPTextModel(A__ )
snake_case = CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''' )
snake_case = {
'''unet''': unet,
'''scheduler''': scheduler,
'''vae''': vae,
'''text_encoder''': text_encoder,
'''tokenizer''': tokenizer,
'''safety_checker''': None,
'''feature_extractor''': None,
}
return components
def UpperCamelCase ( self , A__ , A__=0 ) -> Optional[Any]:
snake_case = torch.manual_seed(A__ )
snake_case = {
'''prompt''': '''a photo of the dolomites''',
'''generator''': generator,
# Setting height and width to None to prevent OOMs on CPU.
'''height''': None,
'''width''': None,
'''num_inference_steps''': 1,
'''guidance_scale''': 6.0,
'''output_type''': '''numpy''',
}
return inputs
def UpperCamelCase ( self ) -> List[str]:
snake_case = '''cpu''' # ensure determinism for the device-dependent torch.Generator
snake_case = self.get_dummy_components()
snake_case = StableDiffusionPanoramaPipeline(**A__ )
snake_case = sd_pipe.to(A__ )
sd_pipe.set_progress_bar_config(disable=A__ )
snake_case = self.get_dummy_inputs(A__ )
snake_case = sd_pipe(**A__ ).images
snake_case = image[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
snake_case = np.array([0.6_1_8_6, 0.5_3_7_4, 0.4_9_1_5, 0.4_1_3_5, 0.4_1_1_4, 0.4_5_6_3, 0.5_1_2_8, 0.4_9_7_7, 0.4_7_5_7] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
def UpperCamelCase ( self ) -> List[Any]:
super().test_inference_batch_consistent(batch_sizes=[1, 2] )
def UpperCamelCase ( self ) -> Optional[int]:
super().test_inference_batch_single_identical(batch_size=2 , expected_max_diff=3.25e-3 )
def UpperCamelCase ( self ) -> Union[str, Any]:
snake_case = '''cpu''' # ensure determinism for the device-dependent torch.Generator
snake_case = self.get_dummy_components()
snake_case = StableDiffusionPanoramaPipeline(**A__ )
snake_case = sd_pipe.to(A__ )
sd_pipe.set_progress_bar_config(disable=A__ )
snake_case = self.get_dummy_inputs(A__ )
snake_case = '''french fries'''
snake_case = sd_pipe(**A__ , negative_prompt=A__ )
snake_case = output.images
snake_case = image[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
snake_case = np.array([0.6_1_8_7, 0.5_3_7_5, 0.4_9_1_5, 0.4_1_3_6, 0.4_1_1_4, 0.4_5_6_3, 0.5_1_2_8, 0.4_9_7_6, 0.4_7_5_7] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
def UpperCamelCase ( self ) -> str:
snake_case = '''cpu''' # ensure determinism for the device-dependent torch.Generator
snake_case = self.get_dummy_components()
snake_case = StableDiffusionPanoramaPipeline(**A__ )
snake_case = sd_pipe.to(A__ )
sd_pipe.set_progress_bar_config(disable=A__ )
snake_case = self.get_dummy_inputs(A__ )
snake_case = sd_pipe(**A__ , view_batch_size=2 )
snake_case = output.images
snake_case = image[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
snake_case = np.array([0.6_1_8_7, 0.5_3_7_5, 0.4_9_1_5, 0.4_1_3_6, 0.4_1_1_4, 0.4_5_6_3, 0.5_1_2_8, 0.4_9_7_6, 0.4_7_5_7] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
def UpperCamelCase ( self ) -> Union[str, Any]:
snake_case = '''cpu''' # ensure determinism for the device-dependent torch.Generator
snake_case = self.get_dummy_components()
snake_case = EulerAncestralDiscreteScheduler(
beta_start=0.0_0_0_8_5 , beta_end=0.0_1_2 , beta_schedule='''scaled_linear''' )
snake_case = StableDiffusionPanoramaPipeline(**A__ )
snake_case = sd_pipe.to(A__ )
sd_pipe.set_progress_bar_config(disable=A__ )
snake_case = self.get_dummy_inputs(A__ )
snake_case = sd_pipe(**A__ ).images
snake_case = image[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
snake_case = np.array([0.4_0_2_4, 0.6_5_1_0, 0.4_9_0_1, 0.5_3_7_8, 0.5_8_1_3, 0.5_6_2_2, 0.4_7_9_5, 0.4_4_6_7, 0.4_9_5_2] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
def UpperCamelCase ( self ) -> int:
snake_case = '''cpu''' # ensure determinism for the device-dependent torch.Generator
snake_case = self.get_dummy_components()
snake_case = PNDMScheduler(
beta_start=0.0_0_0_8_5 , beta_end=0.0_1_2 , beta_schedule='''scaled_linear''' , skip_prk_steps=A__ )
snake_case = StableDiffusionPanoramaPipeline(**A__ )
snake_case = sd_pipe.to(A__ )
sd_pipe.set_progress_bar_config(disable=A__ )
snake_case = self.get_dummy_inputs(A__ )
snake_case = sd_pipe(**A__ ).images
snake_case = image[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
snake_case = np.array([0.6_3_9_1, 0.6_2_9_1, 0.4_8_6_1, 0.5_1_3_4, 0.5_5_5_2, 0.4_5_7_8, 0.5_0_3_2, 0.5_0_2_3, 0.4_5_3_9] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
@slow
@require_torch_gpu
class _lowercase ( unittest.TestCase ):
def UpperCamelCase ( self ) -> Dict:
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def UpperCamelCase ( self , A__=0 ) -> str:
snake_case = torch.manual_seed(A__ )
snake_case = {
'''prompt''': '''a photo of the dolomites''',
'''generator''': generator,
'''num_inference_steps''': 3,
'''guidance_scale''': 7.5,
'''output_type''': '''numpy''',
}
return inputs
def UpperCamelCase ( self ) -> Any:
snake_case = '''stabilityai/stable-diffusion-2-base'''
snake_case = DDIMScheduler.from_pretrained(A__ , subfolder='''scheduler''' )
snake_case = StableDiffusionPanoramaPipeline.from_pretrained(A__ , scheduler=A__ , safety_checker=A__ )
pipe.to(A__ )
pipe.set_progress_bar_config(disable=A__ )
pipe.enable_attention_slicing()
snake_case = self.get_inputs()
snake_case = pipe(**A__ ).images
snake_case = image[0, -3:, -3:, -1].flatten()
assert image.shape == (1, 5_12, 20_48, 3)
snake_case = np.array(
[
0.3_6_9_6_8_3_9_2,
0.2_7_0_2_5_3_7_2,
0.3_2_4_4_6_7_6_6,
0.2_8_3_7_9_3_8_7,
0.3_6_3_6_3_2_7_4,
0.3_0_7_3_3_3_4_7,
0.2_7_1_0_0_0_2_7,
0.2_7_0_5_4_1_2_5,
0.2_5_5_3_6_0_9_6,
] )
assert np.abs(expected_slice - image_slice ).max() < 1e-2
def UpperCamelCase ( self ) -> Tuple:
snake_case = StableDiffusionPanoramaPipeline.from_pretrained(
'''stabilityai/stable-diffusion-2-base''' , safety_checker=A__ )
snake_case = LMSDiscreteScheduler.from_config(pipe.scheduler.config )
pipe.to(A__ )
pipe.set_progress_bar_config(disable=A__ )
pipe.enable_attention_slicing()
snake_case = self.get_inputs()
snake_case = pipe(**A__ ).images
snake_case = image[0, -3:, -3:, -1].flatten()
assert image.shape == (1, 5_12, 20_48, 3)
snake_case = np.array(
[
[
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
]
] )
assert np.abs(expected_slice - image_slice ).max() < 1e-3
def UpperCamelCase ( self ) -> Union[str, Any]:
snake_case = 0
def callback_fn(A__ , A__ , A__ ) -> None:
snake_case = True
nonlocal number_of_steps
number_of_steps += 1
if step == 1:
snake_case = latents.detach().cpu().numpy()
assert latents.shape == (1, 4, 64, 2_56)
snake_case = latents[0, -3:, -3:, -1]
snake_case = np.array(
[
0.1_8_6_8_1_8_6_9,
0.3_3_9_0_7_8_1_6,
0.5_3_6_1_2_7_6,
0.1_4_4_3_2_8_6_5,
-0.0_2_8_5_6_6_1_1,
-0.7_3_9_4_1_1_2_3,
0.2_3_3_9_7_9_8_7,
0.4_7_3_2_2_6_8_2,
-0.3_7_8_2_3_1_6_4,
] )
assert np.abs(latents_slice.flatten() - expected_slice ).max() < 5e-2
elif step == 2:
snake_case = latents.detach().cpu().numpy()
assert latents.shape == (1, 4, 64, 2_56)
snake_case = latents[0, -3:, -3:, -1]
snake_case = np.array(
[
0.1_8_5_3_9_6_4_5,
0.3_3_9_8_7_2_4_8,
0.5_3_7_8_5_5_9,
0.1_4_4_3_7_1_4_2,
-0.0_2_4_5_5_2_6_1,
-0.7_3_3_8_3_1_7,
0.2_3_9_9_0_7_5_5,
0.4_7_3_5_6_2_7_2,
-0.3_7_8_6_5_0_5,
] )
assert np.abs(latents_slice.flatten() - expected_slice ).max() < 5e-2
snake_case = False
snake_case = '''stabilityai/stable-diffusion-2-base'''
snake_case = DDIMScheduler.from_pretrained(A__ , subfolder='''scheduler''' )
snake_case = StableDiffusionPanoramaPipeline.from_pretrained(A__ , scheduler=A__ , safety_checker=A__ )
snake_case = pipe.to(A__ )
pipe.set_progress_bar_config(disable=A__ )
pipe.enable_attention_slicing()
snake_case = self.get_inputs()
pipe(**A__ , callback=A__ , callback_steps=1 )
assert callback_fn.has_been_called
assert number_of_steps == 3
def UpperCamelCase ( self ) -> Union[str, Any]:
torch.cuda.empty_cache()
torch.cuda.reset_max_memory_allocated()
torch.cuda.reset_peak_memory_stats()
snake_case = '''stabilityai/stable-diffusion-2-base'''
snake_case = DDIMScheduler.from_pretrained(A__ , subfolder='''scheduler''' )
snake_case = StableDiffusionPanoramaPipeline.from_pretrained(A__ , scheduler=A__ , safety_checker=A__ )
snake_case = pipe.to(A__ )
pipe.set_progress_bar_config(disable=A__ )
pipe.enable_attention_slicing(1 )
pipe.enable_sequential_cpu_offload()
snake_case = self.get_inputs()
snake_case = pipe(**A__ )
snake_case = torch.cuda.max_memory_allocated()
# make sure that less than 5.2 GB is allocated
assert mem_bytes < 5.5 * 10**9
| 44
|
'''simple docstring'''
import inspect
import re
from hashlib import shaaaa
from typing import Dict, List
from .arrow import arrow
from .audiofolder import audiofolder
from .csv import csv
from .imagefolder import imagefolder
from .json import json
from .pandas import pandas
from .parquet import parquet
from .sql import sql # noqa F401
from .text import text
def __UpperCamelCase ( a : List[str] ) ->str:
snake_case = []
for line in lines:
snake_case = re.sub(R'''#.*''' , '''''' , a ) # remove comments
if line:
filtered_lines.append(a )
snake_case = '''\n'''.join(a )
# Make a hash from all this code
snake_case = full_str.encode('''utf-8''' )
return shaaaa(a ).hexdigest()
# get importable module names and hash for caching
_lowercase = {
'csv': (csv.__name__, _hash_python_lines(inspect.getsource(csv).splitlines())),
'json': (json.__name__, _hash_python_lines(inspect.getsource(json).splitlines())),
'pandas': (pandas.__name__, _hash_python_lines(inspect.getsource(pandas).splitlines())),
'parquet': (parquet.__name__, _hash_python_lines(inspect.getsource(parquet).splitlines())),
'arrow': (arrow.__name__, _hash_python_lines(inspect.getsource(arrow).splitlines())),
'text': (text.__name__, _hash_python_lines(inspect.getsource(text).splitlines())),
'imagefolder': (imagefolder.__name__, _hash_python_lines(inspect.getsource(imagefolder).splitlines())),
'audiofolder': (audiofolder.__name__, _hash_python_lines(inspect.getsource(audiofolder).splitlines())),
}
# Used to infer the module to use based on the data files extensions
_lowercase = {
'.csv': ('csv', {}),
'.tsv': ('csv', {'sep': '\t'}),
'.json': ('json', {}),
'.jsonl': ('json', {}),
'.parquet': ('parquet', {}),
'.arrow': ('arrow', {}),
'.txt': ('text', {}),
}
_EXTENSION_TO_MODULE.update({ext: ('imagefolder', {}) for ext in imagefolder.ImageFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext.upper(): ('imagefolder', {}) for ext in imagefolder.ImageFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext: ('audiofolder', {}) for ext in audiofolder.AudioFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext.upper(): ('audiofolder', {}) for ext in audiofolder.AudioFolder.EXTENSIONS})
_lowercase = {'imagefolder', 'audiofolder'}
# Used to filter data files based on extensions given a module name
_lowercase = {}
for _ext, (_module, _) in _EXTENSION_TO_MODULE.items():
_MODULE_TO_EXTENSIONS.setdefault(_module, []).append(_ext)
_MODULE_TO_EXTENSIONS["imagefolder"].append('.zip')
_MODULE_TO_EXTENSIONS["audiofolder"].append('.zip')
| 44
| 1
|
'''simple docstring'''
import unittest
from transformers import XLMConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
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 (
XLMForMultipleChoice,
XLMForQuestionAnswering,
XLMForQuestionAnsweringSimple,
XLMForSequenceClassification,
XLMForTokenClassification,
XLMModel,
XLMWithLMHeadModel,
)
from transformers.models.xlm.modeling_xlm import XLM_PRETRAINED_MODEL_ARCHIVE_LIST
class _lowercase :
def __init__( self , A__ , A__=13 , A__=7 , A__=True , A__=True , A__=True , A__=True , A__=True , A__=False , A__=False , A__=False , A__=2 , A__=99 , A__=0 , A__=32 , A__=5 , A__=4 , A__=0.1 , A__=0.1 , A__=5_12 , A__=2 , A__=0.0_2 , A__=2 , A__=4 , A__="last" , A__=True , A__=None , A__=0 , ) -> Optional[Any]:
snake_case = parent
snake_case = batch_size
snake_case = seq_length
snake_case = is_training
snake_case = use_input_lengths
snake_case = use_token_type_ids
snake_case = use_labels
snake_case = gelu_activation
snake_case = sinusoidal_embeddings
snake_case = causal
snake_case = asm
snake_case = n_langs
snake_case = vocab_size
snake_case = n_special
snake_case = hidden_size
snake_case = num_hidden_layers
snake_case = num_attention_heads
snake_case = hidden_dropout_prob
snake_case = attention_probs_dropout_prob
snake_case = max_position_embeddings
snake_case = type_sequence_label_size
snake_case = initializer_range
snake_case = num_labels
snake_case = num_choices
snake_case = summary_type
snake_case = use_proj
snake_case = scope
snake_case = bos_token_id
def UpperCamelCase ( self ) -> Dict:
snake_case = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
snake_case = random_attention_mask([self.batch_size, self.seq_length] )
snake_case = None
if self.use_input_lengths:
snake_case = (
ids_tensor([self.batch_size] , vocab_size=2 ) + self.seq_length - 2
) # small variation of seq_length
snake_case = None
if self.use_token_type_ids:
snake_case = ids_tensor([self.batch_size, self.seq_length] , self.n_langs )
snake_case = None
snake_case = None
snake_case = None
if self.use_labels:
snake_case = ids_tensor([self.batch_size] , self.type_sequence_label_size )
snake_case = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
snake_case = ids_tensor([self.batch_size] , 2 ).float()
snake_case = ids_tensor([self.batch_size] , self.num_choices )
snake_case = self.get_config()
return (
config,
input_ids,
token_type_ids,
input_lengths,
sequence_labels,
token_labels,
is_impossible_labels,
choice_labels,
input_mask,
)
def UpperCamelCase ( self ) -> Any:
return XLMConfig(
vocab_size=self.vocab_size , n_special=self.n_special , emb_dim=self.hidden_size , n_layers=self.num_hidden_layers , n_heads=self.num_attention_heads , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , gelu_activation=self.gelu_activation , sinusoidal_embeddings=self.sinusoidal_embeddings , asm=self.asm , causal=self.causal , n_langs=self.n_langs , max_position_embeddings=self.max_position_embeddings , initializer_range=self.initializer_range , summary_type=self.summary_type , use_proj=self.use_proj , num_labels=self.num_labels , bos_token_id=self.bos_token_id , )
def UpperCamelCase ( self , A__ , A__ , A__ , A__ , A__ , A__ , A__ , A__ , A__ , ) -> str:
snake_case = XLMModel(config=A__ )
model.to(A__ )
model.eval()
snake_case = model(A__ , lengths=A__ , langs=A__ )
snake_case = model(A__ , langs=A__ )
snake_case = model(A__ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def UpperCamelCase ( self , A__ , A__ , A__ , A__ , A__ , A__ , A__ , A__ , A__ , ) -> Union[str, Any]:
snake_case = XLMWithLMHeadModel(A__ )
model.to(A__ )
model.eval()
snake_case = model(A__ , token_type_ids=A__ , labels=A__ )
self.parent.assertEqual(result.loss.shape , () )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def UpperCamelCase ( self , A__ , A__ , A__ , A__ , A__ , A__ , A__ , A__ , A__ , ) -> List[str]:
snake_case = XLMForQuestionAnsweringSimple(A__ )
model.to(A__ )
model.eval()
snake_case = model(A__ )
snake_case = model(A__ , start_positions=A__ , end_positions=A__ )
snake_case = outputs
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 , A__ , A__ , A__ , A__ , A__ , A__ , A__ , A__ , A__ , ) -> Union[str, Any]:
snake_case = XLMForQuestionAnswering(A__ )
model.to(A__ )
model.eval()
snake_case = model(A__ )
snake_case = model(
A__ , start_positions=A__ , end_positions=A__ , cls_index=A__ , is_impossible=A__ , p_mask=A__ , )
snake_case = model(
A__ , start_positions=A__ , end_positions=A__ , cls_index=A__ , is_impossible=A__ , )
((snake_case) , ) = result_with_labels.to_tuple()
snake_case = model(A__ , start_positions=A__ , end_positions=A__ )
((snake_case) , ) = result_with_labels.to_tuple()
self.parent.assertEqual(result_with_labels.loss.shape , () )
self.parent.assertEqual(result.start_top_log_probs.shape , (self.batch_size, model.config.start_n_top) )
self.parent.assertEqual(result.start_top_index.shape , (self.batch_size, model.config.start_n_top) )
self.parent.assertEqual(
result.end_top_log_probs.shape , (self.batch_size, model.config.start_n_top * model.config.end_n_top) )
self.parent.assertEqual(
result.end_top_index.shape , (self.batch_size, model.config.start_n_top * model.config.end_n_top) )
self.parent.assertEqual(result.cls_logits.shape , (self.batch_size,) )
def UpperCamelCase ( self , A__ , A__ , A__ , A__ , A__ , A__ , A__ , A__ , A__ , ) -> int:
snake_case = XLMForSequenceClassification(A__ )
model.to(A__ )
model.eval()
snake_case = model(A__ )
snake_case = model(A__ , labels=A__ )
self.parent.assertEqual(result.loss.shape , () )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
def UpperCamelCase ( self , A__ , A__ , A__ , A__ , A__ , A__ , A__ , A__ , A__ , ) -> Dict:
snake_case = self.num_labels
snake_case = XLMForTokenClassification(A__ )
model.to(A__ )
model.eval()
snake_case = 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 , A__ , A__ , A__ , A__ , A__ , A__ , A__ , A__ , A__ , ) -> Optional[Any]:
snake_case = self.num_choices
snake_case = XLMForMultipleChoice(config=A__ )
model.to(A__ )
model.eval()
snake_case = input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
snake_case = token_type_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
snake_case = input_mask.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
snake_case = model(
A__ , attention_mask=A__ , token_type_ids=A__ , labels=A__ , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def UpperCamelCase ( self ) -> Union[str, Any]:
snake_case = self.prepare_config_and_inputs()
(
(
snake_case
) , (
snake_case
) , (
snake_case
) , (
snake_case
) , (
snake_case
) , (
snake_case
) , (
snake_case
) , (
snake_case
) , (
snake_case
) ,
) = config_and_inputs
snake_case = {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''lengths''': input_lengths}
return config, inputs_dict
@require_torch
class _lowercase ( __a , __a , __a , unittest.TestCase ):
_UpperCAmelCase = (
(
XLMModel,
XLMWithLMHeadModel,
XLMForQuestionAnswering,
XLMForSequenceClassification,
XLMForQuestionAnsweringSimple,
XLMForTokenClassification,
XLMForMultipleChoice,
)
if is_torch_available()
else ()
)
_UpperCAmelCase = (
(XLMWithLMHeadModel,) if is_torch_available() else ()
) # TODO (PVP): Check other models whether language generation is also applicable
_UpperCAmelCase = (
{
'''feature-extraction''': XLMModel,
'''fill-mask''': XLMWithLMHeadModel,
'''question-answering''': XLMForQuestionAnsweringSimple,
'''text-classification''': XLMForSequenceClassification,
'''text-generation''': XLMWithLMHeadModel,
'''token-classification''': XLMForTokenClassification,
'''zero-shot''': XLMForSequenceClassification,
}
if is_torch_available()
else {}
)
def UpperCamelCase ( self , A__ , A__ , A__ , A__ , A__ ) -> Any:
if (
pipeline_test_casse_name == "QAPipelineTests"
and tokenizer_name is not None
and not tokenizer_name.endswith('''Fast''' )
):
# `QAPipelineTests` fails for a few models when the slower tokenizer are used.
# (The slower tokenizers were never used for pipeline tests before the pipeline testing rework)
# TODO: check (and possibly fix) the `QAPipelineTests` with slower tokenizer
return True
return False
def UpperCamelCase ( self , A__ , A__ , A__=False ) -> Optional[int]:
snake_case = super()._prepare_for_class(A__ , A__ , return_labels=A__ )
if return_labels:
if model_class.__name__ == "XLMForQuestionAnswering":
snake_case = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=A__ )
snake_case = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=A__ )
return inputs_dict
def UpperCamelCase ( self ) -> Optional[int]:
snake_case = XLMModelTester(self )
snake_case = ConfigTester(self , config_class=A__ , emb_dim=37 )
def UpperCamelCase ( self ) -> List[Any]:
self.config_tester.run_common_tests()
def UpperCamelCase ( self ) -> Tuple:
snake_case = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_model(*A__ )
def UpperCamelCase ( self ) -> str:
snake_case = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_lm_head(*A__ )
def UpperCamelCase ( self ) -> Tuple:
snake_case = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_simple_qa(*A__ )
def UpperCamelCase ( self ) -> Union[str, Any]:
snake_case = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_qa(*A__ )
def UpperCamelCase ( self ) -> Dict:
snake_case = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_sequence_classif(*A__ )
def UpperCamelCase ( self ) -> List[str]:
snake_case = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_token_classif(*A__ )
def UpperCamelCase ( self ) -> Union[str, Any]:
snake_case = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_for_multiple_choice(*A__ )
def UpperCamelCase ( self , A__ , A__ , A__ , A__ , A__ , A__=False , A__=1 ) -> Any:
self.assertIsInstance(A__ , A__ )
self.assertListEqual(
[isinstance(A__ , A__ ) for iter_attentions in attentions] , [True] * len(A__ ) )
self.assertEqual(len(A__ ) , (max_length - min_length) * num_beam_groups )
for idx, iter_attentions in enumerate(A__ ):
# adds PAD dummy token
snake_case = min_length + idx + 1
snake_case = min_length + idx + 1
snake_case = (
batch_size * num_beam_groups,
config.num_attention_heads,
tgt_len,
src_len,
)
# check attn size
self.assertListEqual(
[layer_attention.shape for layer_attention in iter_attentions] , [expected_shape] * len(A__ ) )
def UpperCamelCase ( self , A__ , A__ , A__ , A__ , A__ , A__=False , A__=1 ) -> Tuple:
self.assertIsInstance(A__ , A__ )
self.assertListEqual(
[isinstance(A__ , A__ ) for iter_hidden_states in hidden_states] , [True] * len(A__ ) , )
self.assertEqual(len(A__ ) , (max_length - min_length) * num_beam_groups )
for idx, iter_hidden_states in enumerate(A__ ):
# adds PAD dummy token
snake_case = min_length + idx + 1
snake_case = (batch_size * num_beam_groups, seq_len, config.hidden_size)
# check hidden size
self.assertListEqual(
[layer_hidden_states.shape for layer_hidden_states in iter_hidden_states] , [expected_shape] * len(A__ ) , )
pass
@slow
def UpperCamelCase ( self ) -> Tuple:
for model_name in XLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
snake_case = XLMModel.from_pretrained(A__ )
self.assertIsNotNone(A__ )
@require_torch
class _lowercase ( unittest.TestCase ):
@slow
def UpperCamelCase ( self ) -> List[str]:
snake_case = XLMWithLMHeadModel.from_pretrained('''xlm-mlm-en-2048''' )
model.to(A__ )
snake_case = torch.tensor([[14, 4_47]] , dtype=torch.long , device=A__ ) # the president
snake_case = [
14,
4_47,
14,
4_47,
14,
4_47,
14,
4_47,
14,
4_47,
14,
4_47,
14,
4_47,
14,
4_47,
14,
4_47,
14,
4_47,
] # the president the president the president the president the president the president the president the president the president the president
# TODO(PVP): this and other input_ids I tried for generation give pretty bad results. Not sure why. Model might just not be made for auto-regressive inference
snake_case = model.generate(A__ , do_sample=A__ )
self.assertListEqual(output_ids[0].cpu().numpy().tolist() , A__ )
| 44
|
'''simple docstring'''
_lowercase = {
'Pillow': 'Pillow',
'accelerate': 'accelerate>=0.11.0',
'compel': 'compel==0.1.8',
'black': 'black~=23.1',
'datasets': 'datasets',
'filelock': 'filelock',
'flax': 'flax>=0.4.1',
'hf-doc-builder': 'hf-doc-builder>=0.3.0',
'huggingface-hub': 'huggingface-hub>=0.13.2',
'requests-mock': 'requests-mock==1.10.0',
'importlib_metadata': 'importlib_metadata',
'invisible-watermark': 'invisible-watermark',
'isort': 'isort>=5.5.4',
'jax': 'jax>=0.2.8,!=0.3.2',
'jaxlib': 'jaxlib>=0.1.65',
'Jinja2': 'Jinja2',
'k-diffusion': 'k-diffusion>=0.0.12',
'torchsde': 'torchsde',
'note_seq': 'note_seq',
'librosa': 'librosa',
'numpy': 'numpy',
'omegaconf': 'omegaconf',
'parameterized': 'parameterized',
'protobuf': 'protobuf>=3.20.3,<4',
'pytest': 'pytest',
'pytest-timeout': 'pytest-timeout',
'pytest-xdist': 'pytest-xdist',
'ruff': 'ruff>=0.0.241',
'safetensors': 'safetensors',
'sentencepiece': 'sentencepiece>=0.1.91,!=0.1.92',
'scipy': 'scipy',
'onnx': 'onnx',
'regex': 'regex!=2019.12.17',
'requests': 'requests',
'tensorboard': 'tensorboard',
'torch': 'torch>=1.4',
'torchvision': 'torchvision',
'transformers': 'transformers>=4.25.1',
'urllib3': 'urllib3<=2.0.0',
}
| 44
| 1
|
'''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 ..models.auto import AutoModelForSeqaSeqLM, AutoTokenizer
from .base import PipelineTool
_lowercase = {
'Acehnese Arabic': 'ace_Arab',
'Acehnese Latin': 'ace_Latn',
'Mesopotamian Arabic': 'acm_Arab',
'Ta\'izzi-Adeni Arabic': 'acq_Arab',
'Tunisian Arabic': 'aeb_Arab',
'Afrikaans': 'afr_Latn',
'South Levantine Arabic': 'ajp_Arab',
'Akan': 'aka_Latn',
'Amharic': 'amh_Ethi',
'North Levantine Arabic': 'apc_Arab',
'Modern Standard Arabic': 'arb_Arab',
'Modern Standard Arabic Romanized': 'arb_Latn',
'Najdi Arabic': 'ars_Arab',
'Moroccan Arabic': 'ary_Arab',
'Egyptian Arabic': 'arz_Arab',
'Assamese': 'asm_Beng',
'Asturian': 'ast_Latn',
'Awadhi': 'awa_Deva',
'Central Aymara': 'ayr_Latn',
'South Azerbaijani': 'azb_Arab',
'North Azerbaijani': 'azj_Latn',
'Bashkir': 'bak_Cyrl',
'Bambara': 'bam_Latn',
'Balinese': 'ban_Latn',
'Belarusian': 'bel_Cyrl',
'Bemba': 'bem_Latn',
'Bengali': 'ben_Beng',
'Bhojpuri': 'bho_Deva',
'Banjar Arabic': 'bjn_Arab',
'Banjar Latin': 'bjn_Latn',
'Standard Tibetan': 'bod_Tibt',
'Bosnian': 'bos_Latn',
'Buginese': 'bug_Latn',
'Bulgarian': 'bul_Cyrl',
'Catalan': 'cat_Latn',
'Cebuano': 'ceb_Latn',
'Czech': 'ces_Latn',
'Chokwe': 'cjk_Latn',
'Central Kurdish': 'ckb_Arab',
'Crimean Tatar': 'crh_Latn',
'Welsh': 'cym_Latn',
'Danish': 'dan_Latn',
'German': 'deu_Latn',
'Southwestern Dinka': 'dik_Latn',
'Dyula': 'dyu_Latn',
'Dzongkha': 'dzo_Tibt',
'Greek': 'ell_Grek',
'English': 'eng_Latn',
'Esperanto': 'epo_Latn',
'Estonian': 'est_Latn',
'Basque': 'eus_Latn',
'Ewe': 'ewe_Latn',
'Faroese': 'fao_Latn',
'Fijian': 'fij_Latn',
'Finnish': 'fin_Latn',
'Fon': 'fon_Latn',
'French': 'fra_Latn',
'Friulian': 'fur_Latn',
'Nigerian Fulfulde': 'fuv_Latn',
'Scottish Gaelic': 'gla_Latn',
'Irish': 'gle_Latn',
'Galician': 'glg_Latn',
'Guarani': 'grn_Latn',
'Gujarati': 'guj_Gujr',
'Haitian Creole': 'hat_Latn',
'Hausa': 'hau_Latn',
'Hebrew': 'heb_Hebr',
'Hindi': 'hin_Deva',
'Chhattisgarhi': 'hne_Deva',
'Croatian': 'hrv_Latn',
'Hungarian': 'hun_Latn',
'Armenian': 'hye_Armn',
'Igbo': 'ibo_Latn',
'Ilocano': 'ilo_Latn',
'Indonesian': 'ind_Latn',
'Icelandic': 'isl_Latn',
'Italian': 'ita_Latn',
'Javanese': 'jav_Latn',
'Japanese': 'jpn_Jpan',
'Kabyle': 'kab_Latn',
'Jingpho': 'kac_Latn',
'Kamba': 'kam_Latn',
'Kannada': 'kan_Knda',
'Kashmiri Arabic': 'kas_Arab',
'Kashmiri Devanagari': 'kas_Deva',
'Georgian': 'kat_Geor',
'Central Kanuri Arabic': 'knc_Arab',
'Central Kanuri Latin': 'knc_Latn',
'Kazakh': 'kaz_Cyrl',
'Kabiyè': 'kbp_Latn',
'Kabuverdianu': 'kea_Latn',
'Khmer': 'khm_Khmr',
'Kikuyu': 'kik_Latn',
'Kinyarwanda': 'kin_Latn',
'Kyrgyz': 'kir_Cyrl',
'Kimbundu': 'kmb_Latn',
'Northern Kurdish': 'kmr_Latn',
'Kikongo': 'kon_Latn',
'Korean': 'kor_Hang',
'Lao': 'lao_Laoo',
'Ligurian': 'lij_Latn',
'Limburgish': 'lim_Latn',
'Lingala': 'lin_Latn',
'Lithuanian': 'lit_Latn',
'Lombard': 'lmo_Latn',
'Latgalian': 'ltg_Latn',
'Luxembourgish': 'ltz_Latn',
'Luba-Kasai': 'lua_Latn',
'Ganda': 'lug_Latn',
'Luo': 'luo_Latn',
'Mizo': 'lus_Latn',
'Standard Latvian': 'lvs_Latn',
'Magahi': 'mag_Deva',
'Maithili': 'mai_Deva',
'Malayalam': 'mal_Mlym',
'Marathi': 'mar_Deva',
'Minangkabau Arabic ': 'min_Arab',
'Minangkabau Latin': 'min_Latn',
'Macedonian': 'mkd_Cyrl',
'Plateau Malagasy': 'plt_Latn',
'Maltese': 'mlt_Latn',
'Meitei Bengali': 'mni_Beng',
'Halh Mongolian': 'khk_Cyrl',
'Mossi': 'mos_Latn',
'Maori': 'mri_Latn',
'Burmese': 'mya_Mymr',
'Dutch': 'nld_Latn',
'Norwegian Nynorsk': 'nno_Latn',
'Norwegian Bokmål': 'nob_Latn',
'Nepali': 'npi_Deva',
'Northern Sotho': 'nso_Latn',
'Nuer': 'nus_Latn',
'Nyanja': 'nya_Latn',
'Occitan': 'oci_Latn',
'West Central Oromo': 'gaz_Latn',
'Odia': 'ory_Orya',
'Pangasinan': 'pag_Latn',
'Eastern Panjabi': 'pan_Guru',
'Papiamento': 'pap_Latn',
'Western Persian': 'pes_Arab',
'Polish': 'pol_Latn',
'Portuguese': 'por_Latn',
'Dari': 'prs_Arab',
'Southern Pashto': 'pbt_Arab',
'Ayacucho Quechua': 'quy_Latn',
'Romanian': 'ron_Latn',
'Rundi': 'run_Latn',
'Russian': 'rus_Cyrl',
'Sango': 'sag_Latn',
'Sanskrit': 'san_Deva',
'Santali': 'sat_Olck',
'Sicilian': 'scn_Latn',
'Shan': 'shn_Mymr',
'Sinhala': 'sin_Sinh',
'Slovak': 'slk_Latn',
'Slovenian': 'slv_Latn',
'Samoan': 'smo_Latn',
'Shona': 'sna_Latn',
'Sindhi': 'snd_Arab',
'Somali': 'som_Latn',
'Southern Sotho': 'sot_Latn',
'Spanish': 'spa_Latn',
'Tosk Albanian': 'als_Latn',
'Sardinian': 'srd_Latn',
'Serbian': 'srp_Cyrl',
'Swati': 'ssw_Latn',
'Sundanese': 'sun_Latn',
'Swedish': 'swe_Latn',
'Swahili': 'swh_Latn',
'Silesian': 'szl_Latn',
'Tamil': 'tam_Taml',
'Tatar': 'tat_Cyrl',
'Telugu': 'tel_Telu',
'Tajik': 'tgk_Cyrl',
'Tagalog': 'tgl_Latn',
'Thai': 'tha_Thai',
'Tigrinya': 'tir_Ethi',
'Tamasheq Latin': 'taq_Latn',
'Tamasheq Tifinagh': 'taq_Tfng',
'Tok Pisin': 'tpi_Latn',
'Tswana': 'tsn_Latn',
'Tsonga': 'tso_Latn',
'Turkmen': 'tuk_Latn',
'Tumbuka': 'tum_Latn',
'Turkish': 'tur_Latn',
'Twi': 'twi_Latn',
'Central Atlas Tamazight': 'tzm_Tfng',
'Uyghur': 'uig_Arab',
'Ukrainian': 'ukr_Cyrl',
'Umbundu': 'umb_Latn',
'Urdu': 'urd_Arab',
'Northern Uzbek': 'uzn_Latn',
'Venetian': 'vec_Latn',
'Vietnamese': 'vie_Latn',
'Waray': 'war_Latn',
'Wolof': 'wol_Latn',
'Xhosa': 'xho_Latn',
'Eastern Yiddish': 'ydd_Hebr',
'Yoruba': 'yor_Latn',
'Yue Chinese': 'yue_Hant',
'Chinese Simplified': 'zho_Hans',
'Chinese Traditional': 'zho_Hant',
'Standard Malay': 'zsm_Latn',
'Zulu': 'zul_Latn',
}
class _lowercase ( __a ):
_UpperCAmelCase = '''facebook/nllb-200-distilled-600M'''
_UpperCAmelCase = (
'''This is a tool that translates text from a language to another. It takes three inputs: `text`, which should '''
'''be the text to translate, `src_lang`, which should be the language of the text to translate and `tgt_lang`, '''
'''which should be the language for the desired ouput language. Both `src_lang` and `tgt_lang` are written in '''
'''plain English, such as \'Romanian\', or \'Albanian\'. It returns the text translated in `tgt_lang`.'''
)
_UpperCAmelCase = '''translator'''
_UpperCAmelCase = AutoTokenizer
_UpperCAmelCase = AutoModelForSeqaSeqLM
_UpperCAmelCase = LANGUAGE_CODES
_UpperCAmelCase = ['''text''', '''text''', '''text''']
_UpperCAmelCase = ['''text''']
def UpperCamelCase ( self , A__ , A__ , A__ ) -> List[str]:
if src_lang not in self.lang_to_code:
raise ValueError(F"""{src_lang} is not a supported language.""" )
if tgt_lang not in self.lang_to_code:
raise ValueError(F"""{tgt_lang} is not a supported language.""" )
snake_case = self.lang_to_code[src_lang]
snake_case = self.lang_to_code[tgt_lang]
return self.pre_processor._build_translation_inputs(
A__ , return_tensors='''pt''' , src_lang=A__ , tgt_lang=A__ )
def UpperCamelCase ( self , A__ ) -> Any:
return self.model.generate(**A__ )
def UpperCamelCase ( self , A__ ) -> List[Any]:
return self.post_processor.decode(outputs[0].tolist() , skip_special_tokens=A__ )
| 44
|
'''simple docstring'''
import random
import unittest
import torch
from diffusers import IFInpaintingSuperResolutionPipeline
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_INPAINTING_BATCH_PARAMS,
TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin
@skip_mps
class _lowercase ( __a , __a , unittest.TestCase ):
_UpperCAmelCase = IFInpaintingSuperResolutionPipeline
_UpperCAmelCase = TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {'''width''', '''height'''}
_UpperCAmelCase = TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS.union({'''original_image'''} )
_UpperCAmelCase = PipelineTesterMixin.required_optional_params - {'''latents'''}
def UpperCamelCase ( self ) -> int:
return self._get_superresolution_dummy_components()
def UpperCamelCase ( self , A__ , A__=0 ) -> Union[str, Any]:
if str(A__ ).startswith('''mps''' ):
snake_case = torch.manual_seed(A__ )
else:
snake_case = torch.Generator(device=A__ ).manual_seed(A__ )
snake_case = floats_tensor((1, 3, 16, 16) , rng=random.Random(A__ ) ).to(A__ )
snake_case = floats_tensor((1, 3, 32, 32) , rng=random.Random(A__ ) ).to(A__ )
snake_case = floats_tensor((1, 3, 32, 32) , rng=random.Random(A__ ) ).to(A__ )
snake_case = {
'''prompt''': '''A painting of a squirrel eating a burger''',
'''image''': image,
'''original_image''': original_image,
'''mask_image''': mask_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 UpperCamelCase ( self ) -> List[Any]:
self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3 )
def UpperCamelCase ( self ) -> Optional[Any]:
self._test_save_load_optional_components()
@unittest.skipIf(torch_device != '''cuda''' , reason='''float16 requires CUDA''' )
def UpperCamelCase ( self ) -> List[str]:
# Due to non-determinism in save load of the hf-internal-testing/tiny-random-t5 text encoder
super().test_save_load_floataa(expected_max_diff=1e-1 )
def UpperCamelCase ( self ) -> int:
self._test_attention_slicing_forward_pass(expected_max_diff=1e-2 )
def UpperCamelCase ( self ) -> Optional[Any]:
self._test_save_load_local()
def UpperCamelCase ( self ) -> Dict:
self._test_inference_batch_single_identical(
expected_max_diff=1e-2 , )
| 44
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|
'''simple docstring'''
_lowercase = range(2, 20 + 1)
_lowercase = [10**k for k in range(ks[-1] + 1)]
_lowercase = {}
def __UpperCamelCase ( a : Dict , a : List[str] , a : Dict , a : Any ) ->List[Any]:
snake_case = sum(a_i[j] for j in range(a , len(a ) ) )
snake_case = sum(a_i[j] * base[j] for j in range(min(len(a ) , a ) ) )
snake_case , snake_case = 0, 0
snake_case = n - i
snake_case = memo.get(a )
if sub_memo is not None:
snake_case = sub_memo.get(a )
if jumps is not None and len(a ) > 0:
# find and make the largest jump without going over
snake_case = -1
for _k in range(len(a ) - 1 , -1 , -1 ):
if jumps[_k][2] <= k and jumps[_k][1] <= max_dn:
snake_case = _k
break
if max_jump >= 0:
snake_case , snake_case , snake_case = jumps[max_jump]
# since the difference between jumps is cached, add c
snake_case = diff + c
for j in range(min(a , len(a ) ) ):
snake_case , snake_case = divmod(a , 10 )
if new_c > 0:
add(a , a , a )
else:
snake_case = []
else:
snake_case = {c: []}
snake_case = sub_memo
if dn >= max_dn or c + diff >= base[k]:
return diff, dn
if k > ks[0]:
while True:
# keep doing smaller jumps
snake_case , snake_case = next_term(a , k - 1 , i + dn , a )
diff += _diff
dn += terms_jumped
if dn >= max_dn or c + diff >= base[k]:
break
else:
# would be too small a jump, just compute sequential terms instead
snake_case , snake_case = compute(a , a , i + dn , a )
diff += _diff
dn += terms_jumped
snake_case = sub_memo[c]
# keep jumps sorted by # of terms skipped
snake_case = 0
while j < len(a ):
if jumps[j][1] > dn:
break
j += 1
# cache the jump for this value digitsum(b) and c
sub_memo[c].insert(a , (diff, dn, k) )
return (diff, dn)
def __UpperCamelCase ( a : List[str] , a : List[str] , a : List[Any] , a : List[str] ) ->List[str]:
if i >= n:
return 0, i
if k > len(a ):
a_i.extend([0 for _ in range(k - len(a ) )] )
# note: a_i -> b * 10^k + c
# ds_b -> digitsum(b)
# ds_c -> digitsum(c)
snake_case = i
snake_case , snake_case , snake_case = 0, 0, 0
for j in range(len(a ) ):
if j >= k:
ds_b += a_i[j]
else:
ds_c += a_i[j]
while i < n:
i += 1
snake_case = ds_c + ds_b
diff += addend
snake_case = 0
for j in range(a ):
snake_case = a_i[j] + addend
snake_case , snake_case = divmod(a , 10 )
ds_c += a_i[j]
if addend > 0:
break
if addend > 0:
add(a , a , a )
return diff, i - start_i
def __UpperCamelCase ( a : Optional[int] , a : Union[str, Any] , a : List[Any] ) ->Any:
for j in range(a , len(a ) ):
snake_case = digits[j] + addend
if s >= 10:
snake_case , snake_case = divmod(a , 10 )
snake_case = addend // 10 + quotient
else:
snake_case = s
snake_case = addend // 10
if addend == 0:
break
while addend > 0:
snake_case , snake_case = divmod(a , 10 )
digits.append(a )
def __UpperCamelCase ( a : int = 10**15 ) ->int:
snake_case = [1]
snake_case = 1
snake_case = 0
while True:
snake_case , snake_case = next_term(a , 20 , i + dn , a )
dn += terms_jumped
if dn == n - i:
break
snake_case = 0
for j in range(len(a ) ):
a_n += digits[j] * 10**j
return a_n
if __name__ == "__main__":
print(f'{solution() = }')
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|
'''simple docstring'''
from typing import Dict, List, Optional, Union
import numpy as np
from .feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from .utils import PaddingStrategy, TensorType, is_tf_tensor, is_torch_tensor, logging, to_numpy
_lowercase = logging.get_logger(__name__)
class _lowercase ( __a ):
def __init__( self , A__ , A__ , A__ , **A__ ) -> Union[str, Any]:
snake_case = feature_size
snake_case = sampling_rate
snake_case = padding_value
snake_case = kwargs.pop('''padding_side''' , '''right''' )
snake_case = kwargs.pop('''return_attention_mask''' , A__ )
super().__init__(**A__ )
def UpperCamelCase ( self , A__ , A__ = True , A__ = None , A__ = False , A__ = None , A__ = None , A__ = None , ) -> BatchFeature:
# If we have a list of dicts, let's convert it in a dict of lists
# We do this to allow using this method as a collate_fn function in PyTorch Dataloader
if isinstance(A__ , (list, tuple) ) and isinstance(processed_features[0] , (dict, BatchFeature) ):
snake_case = {
key: [example[key] for example in processed_features] for key in processed_features[0].keys()
}
# The model's main input name, usually `input_values`, has be passed for padding
if self.model_input_names[0] not in processed_features:
raise ValueError(
'''You should supply an instance of `transformers.BatchFeature` or list of `transformers.BatchFeature`'''
F""" to this method that includes {self.model_input_names[0]}, but you provided"""
F""" {list(processed_features.keys() )}""" )
snake_case = processed_features[self.model_input_names[0]]
snake_case = (
return_attention_mask if return_attention_mask is not None else self.return_attention_mask
)
if len(A__ ) == 0:
if return_attention_mask:
snake_case = []
return processed_features
# If we have PyTorch/TF tensors or lists as inputs, we cast them as Numpy arrays
# and rebuild them afterwards if no return_tensors is specified
# Note that we lose the specific device the tensor may be on for PyTorch
snake_case = required_input[0]
if isinstance(A__ , (list, tuple) ):
# first_element might be an empty list/tuple in some edge cases so we grab the first non empty element.
snake_case = 0
while len(required_input[index] ) == 0:
index += 1
if index < len(A__ ):
snake_case = required_input[index][0]
if return_tensors is None:
if is_tf_tensor(A__ ):
snake_case = '''tf'''
elif is_torch_tensor(A__ ):
snake_case = '''pt'''
elif isinstance(A__ , (int, float, list, tuple, np.ndarray) ):
snake_case = '''np'''
else:
raise ValueError(
F"""type of {first_element} unknown: {type(A__ )}. """
'''Should be one of a python, numpy, pytorch or tensorflow object.''' )
for key, value in processed_features.items():
if isinstance(value[0] , (int, float) ):
snake_case = to_numpy(A__ )
else:
snake_case = [to_numpy(A__ ) for v in value]
# Convert padding_strategy in PaddingStrategy
snake_case = self._get_padding_strategies(padding=A__ , max_length=A__ )
snake_case = processed_features[self.model_input_names[0]]
snake_case = len(A__ )
if not all(len(A__ ) == batch_size for v in processed_features.values() ):
raise ValueError('''Some items in the output dictionary have a different batch size than others.''' )
snake_case = []
for i in range(A__ ):
snake_case = {k: v[i] for k, v in processed_features.items()}
# truncation
snake_case = self._truncate(
A__ , max_length=A__ , pad_to_multiple_of=A__ , truncation=A__ , )
truncated_inputs.append(A__ )
if padding_strategy == PaddingStrategy.LONGEST:
# make sure that `max_length` cannot be longer than the longest truncated length
snake_case = max(len(input_slice[self.model_input_names[0]] ) for input_slice in truncated_inputs )
snake_case = PaddingStrategy.MAX_LENGTH
snake_case = {}
for i in range(A__ ):
# padding
snake_case = self._pad(
truncated_inputs[i] , max_length=A__ , padding_strategy=A__ , pad_to_multiple_of=A__ , return_attention_mask=A__ , )
for key, value in outputs.items():
if key not in batch_outputs:
snake_case = []
if value.dtype is np.dtype(np.floataa ):
snake_case = value.astype(np.floataa )
batch_outputs[key].append(A__ )
return BatchFeature(A__ , tensor_type=A__ )
def UpperCamelCase ( self , A__ , A__ = None , A__ = PaddingStrategy.DO_NOT_PAD , A__ = None , A__ = None , ) -> dict:
snake_case = processed_features[self.model_input_names[0]]
if padding_strategy == PaddingStrategy.LONGEST:
snake_case = len(A__ )
if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
snake_case = ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of
snake_case = padding_strategy != PaddingStrategy.DO_NOT_PAD and len(A__ ) < max_length
if return_attention_mask and "attention_mask" not in processed_features:
snake_case = np.ones(len(A__ ) , dtype=np.intaa )
if needs_to_be_padded:
snake_case = max_length - len(A__ )
if self.padding_side == "right":
if return_attention_mask:
snake_case = np.pad(
processed_features['''attention_mask'''] , (0, difference) )
snake_case = ((0, difference), (0, 0)) if self.feature_size > 1 else (0, difference)
snake_case = np.pad(
A__ , A__ , '''constant''' , constant_values=self.padding_value )
elif self.padding_side == "left":
if return_attention_mask:
snake_case = np.pad(
processed_features['''attention_mask'''] , (difference, 0) )
snake_case = ((difference, 0), (0, 0)) if self.feature_size > 1 else (difference, 0)
snake_case = np.pad(
A__ , A__ , '''constant''' , constant_values=self.padding_value )
else:
raise ValueError('''Invalid padding strategy:''' + str(self.padding_side ) )
return processed_features
def UpperCamelCase ( self , A__ , A__ = None , A__ = None , A__ = None , ) -> Union[str, Any]:
if not truncation:
return processed_features
elif truncation and max_length is None:
raise ValueError('''When setting ``truncation=True``, make sure that ``max_length`` is defined.''' )
snake_case = processed_features[self.model_input_names[0]]
# find `max_length` that fits `pad_to_multiple_of`
if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
snake_case = ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of
snake_case = len(A__ ) > max_length
if needs_to_be_truncated:
snake_case = processed_features[self.model_input_names[0]][:max_length]
if "attention_mask" in processed_features:
snake_case = processed_features['''attention_mask'''][:max_length]
return processed_features
def UpperCamelCase ( self , A__=False , A__=None ) -> Union[str, Any]:
# Get padding strategy
if padding is not False:
if padding is True:
snake_case = PaddingStrategy.LONGEST # Default to pad to the longest sequence in the batch
elif not isinstance(A__ , A__ ):
snake_case = PaddingStrategy(A__ )
elif isinstance(A__ , A__ ):
snake_case = padding
else:
snake_case = PaddingStrategy.DO_NOT_PAD
# Set max length if needed
if max_length is None:
if padding_strategy == PaddingStrategy.MAX_LENGTH:
raise ValueError(
F"""When setting ``padding={PaddingStrategy.MAX_LENGTH}``, make sure that max_length is defined""" )
# Test if we have a padding value
if padding_strategy != PaddingStrategy.DO_NOT_PAD and (self.padding_value is None):
raise ValueError(
'''Asking to pad but the feature_extractor does not have a padding value. Please select a value to use'''
''' as `padding_value`. For example: `feature_extractor.padding_value = 0.0`.''' )
return padding_strategy
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'''simple docstring'''
def __UpperCamelCase ( a : list ) ->list:
if len(a ) < 2:
return collection
def circle_sort_util(a : list , a : int , a : int ) -> bool:
snake_case = False
if low == high:
return swapped
snake_case = low
snake_case = high
while left < right:
if collection[left] > collection[right]:
snake_case , snake_case = (
collection[right],
collection[left],
)
snake_case = True
left += 1
right -= 1
if left == right and collection[left] > collection[right + 1]:
snake_case , snake_case = (
collection[right + 1],
collection[left],
)
snake_case = True
snake_case = low + int((high - low) / 2 )
snake_case = circle_sort_util(a , a , a )
snake_case = circle_sort_util(a , mid + 1 , a )
return swapped or left_swap or right_swap
snake_case = True
while is_not_sorted is True:
snake_case = circle_sort_util(a , 0 , len(a ) - 1 )
return collection
if __name__ == "__main__":
_lowercase = input('Enter numbers separated by a comma:\n').strip()
_lowercase = [int(item) for item in user_input.split(',')]
print(circle_sort(unsorted))
| 44
|
'''simple docstring'''
from collections import Counter
from pathlib import Path
from typing import Optional, Tuple
import yaml
class _lowercase ( yaml.SafeLoader ):
def UpperCamelCase ( self , A__ ) -> List[str]:
snake_case = [self.constructed_objects[key_node] for key_node, _ in node.value]
snake_case = [tuple(A__ ) if isinstance(A__ , A__ ) else key for key in keys]
snake_case = Counter(A__ )
snake_case = [key for key in counter if counter[key] > 1]
if duplicate_keys:
raise TypeError(F"""Got duplicate yaml keys: {duplicate_keys}""" )
def UpperCamelCase ( self , A__ , A__=False ) -> List[Any]:
snake_case = super().construct_mapping(A__ , deep=A__ )
self._check_no_duplicates_on_constructed_node(A__ )
return mapping
def __UpperCamelCase ( a : str ) ->Tuple[Optional[str], str]:
snake_case = list(readme_content.splitlines() )
if full_content and full_content[0] == "---" and "---" in full_content[1:]:
snake_case = full_content[1:].index('''---''' ) + 1
snake_case = '''\n'''.join(full_content[1:sep_idx] )
return yamlblock, "\n".join(full_content[sep_idx + 1 :] )
return None, "\n".join(a )
class _lowercase ( __a ):
# class attributes
_UpperCAmelCase = {'''train_eval_index'''} # train-eval-index in the YAML metadata
@classmethod
def UpperCamelCase ( cls , A__ ) -> "DatasetMetadata":
with open(A__ , encoding='''utf-8''' ) as readme_file:
snake_case , snake_case = _split_yaml_from_readme(readme_file.read() )
if yaml_string is not None:
return cls.from_yaml_string(A__ )
else:
return cls()
def UpperCamelCase ( self , A__ ) -> str:
if path.exists():
with open(A__ , encoding='''utf-8''' ) as readme_file:
snake_case = readme_file.read()
else:
snake_case = None
snake_case = self._to_readme(A__ )
with open(A__ , '''w''' , encoding='''utf-8''' ) as readme_file:
readme_file.write(A__ )
def UpperCamelCase ( self , A__ = None ) -> str:
if readme_content is not None:
snake_case , snake_case = _split_yaml_from_readme(A__ )
snake_case = '''---\n''' + self.to_yaml_string() + '''---\n''' + content
else:
snake_case = '''---\n''' + self.to_yaml_string() + '''---\n'''
return full_content
@classmethod
def UpperCamelCase ( cls , A__ ) -> "DatasetMetadata":
snake_case = yaml.load(A__ , Loader=_NoDuplicateSafeLoader ) or {}
# Convert the YAML keys to DatasetMetadata fields
snake_case = {
(key.replace('''-''' , '''_''' ) if key.replace('''-''' , '''_''' ) in cls._FIELDS_WITH_DASHES else key): value
for key, value in metadata_dict.items()
}
return cls(**A__ )
def UpperCamelCase ( self ) -> str:
return yaml.safe_dump(
{
(key.replace('''_''' , '''-''' ) if key in self._FIELDS_WITH_DASHES else key): value
for key, value in self.items()
} , sort_keys=A__ , allow_unicode=A__ , encoding='''utf-8''' , ).decode('''utf-8''' )
_lowercase = {
'image-classification': [],
'translation': [],
'image-segmentation': [],
'fill-mask': [],
'automatic-speech-recognition': [],
'token-classification': [],
'sentence-similarity': [],
'audio-classification': [],
'question-answering': [],
'summarization': [],
'zero-shot-classification': [],
'table-to-text': [],
'feature-extraction': [],
'other': [],
'multiple-choice': [],
'text-classification': [],
'text-to-image': [],
'text2text-generation': [],
'zero-shot-image-classification': [],
'tabular-classification': [],
'tabular-regression': [],
'image-to-image': [],
'tabular-to-text': [],
'unconditional-image-generation': [],
'text-retrieval': [],
'text-to-speech': [],
'object-detection': [],
'audio-to-audio': [],
'text-generation': [],
'conversational': [],
'table-question-answering': [],
'visual-question-answering': [],
'image-to-text': [],
'reinforcement-learning': [],
'voice-activity-detection': [],
'time-series-forecasting': [],
'document-question-answering': [],
}
if __name__ == "__main__":
from argparse import ArgumentParser
_lowercase = ArgumentParser(usage='Validate the yaml metadata block of a README.md file.')
ap.add_argument('readme_filepath')
_lowercase = ap.parse_args()
_lowercase = Path(args.readme_filepath)
_lowercase = DatasetMetadata.from_readme(readme_filepath)
print(dataset_metadata)
dataset_metadata.to_readme(readme_filepath)
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'''simple docstring'''
from __future__ import annotations
import math
from collections.abc import Callable
def __UpperCamelCase ( a : Callable[[int | float], int | float] , a : int | float , a : int | float , a : int = 100 , ) ->float:
snake_case = x_start
snake_case = fnc(a )
snake_case = 0.0
for _ in range(a ):
# Approximates curve as a sequence of linear lines and sums their length
snake_case = (x_end - x_start) / steps + xa
snake_case = fnc(a )
length += math.hypot(xa - xa , fxa - fxa )
# Increment step
snake_case = xa
snake_case = fxa
return length
if __name__ == "__main__":
def __UpperCamelCase ( a : Optional[int] ) ->str:
return math.sin(10 * x )
print('f(x) = sin(10 * x)')
print('The length of the curve from x = -10 to x = 10 is:')
_lowercase = 10
while i <= 100_000:
print(f'With {i} steps: {line_length(f, -10, 10, i)}')
i *= 10
| 44
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'''simple docstring'''
import json
import os
import re
import unittest
from transformers import CodeGenTokenizer, CodeGenTokenizerFast
from transformers.models.codegen.tokenization_codegen import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class _lowercase ( __a , unittest.TestCase ):
_UpperCAmelCase = CodeGenTokenizer
_UpperCAmelCase = CodeGenTokenizerFast
_UpperCAmelCase = True
_UpperCAmelCase = {'''add_prefix_space''': True}
_UpperCAmelCase = False
def UpperCamelCase ( self ) -> Tuple:
super().setUp()
# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
snake_case = [
'''l''',
'''o''',
'''w''',
'''e''',
'''r''',
'''s''',
'''t''',
'''i''',
'''d''',
'''n''',
'''\u0120''',
'''\u0120l''',
'''\u0120n''',
'''\u0120lo''',
'''\u0120low''',
'''er''',
'''\u0120lowest''',
'''\u0120newer''',
'''\u0120wider''',
'''<unk>''',
'''<|endoftext|>''',
]
snake_case = dict(zip(A__ , range(len(A__ ) ) ) )
snake_case = ['''#version: 0.2''', '''\u0120 l''', '''\u0120l o''', '''\u0120lo w''', '''e r''', '''''']
snake_case = {'''unk_token''': '''<unk>'''}
snake_case = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['''vocab_file'''] )
snake_case = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['''merges_file'''] )
with open(self.vocab_file , '''w''' , encoding='''utf-8''' ) as fp:
fp.write(json.dumps(A__ ) + '''\n''' )
with open(self.merges_file , '''w''' , encoding='''utf-8''' ) as fp:
fp.write('''\n'''.join(A__ ) )
def UpperCamelCase ( self , **A__ ) -> Union[str, Any]:
kwargs.update(self.special_tokens_map )
return CodeGenTokenizer.from_pretrained(self.tmpdirname , **A__ )
def UpperCamelCase ( self , **A__ ) -> Union[str, Any]:
kwargs.update(self.special_tokens_map )
return CodeGenTokenizerFast.from_pretrained(self.tmpdirname , **A__ )
def UpperCamelCase ( self , A__ ) -> Tuple:
snake_case = '''lower newer'''
snake_case = '''lower newer'''
return input_text, output_text
def UpperCamelCase ( self ) -> List[Any]:
snake_case = CodeGenTokenizer(self.vocab_file , self.merges_file , **self.special_tokens_map )
snake_case = '''lower newer'''
snake_case = ['''\u0120low''', '''er''', '''\u0120''', '''n''', '''e''', '''w''', '''er''']
snake_case = tokenizer.tokenize(A__ , add_prefix_space=A__ )
self.assertListEqual(A__ , A__ )
snake_case = tokens + [tokenizer.unk_token]
snake_case = [14, 15, 10, 9, 3, 2, 15, 19]
self.assertListEqual(tokenizer.convert_tokens_to_ids(A__ ) , A__ )
def UpperCamelCase ( self ) -> Optional[int]:
if not self.test_rust_tokenizer:
return
snake_case = self.get_tokenizer()
snake_case = self.get_rust_tokenizer(add_prefix_space=A__ )
snake_case = '''lower newer'''
# Testing tokenization
snake_case = tokenizer.tokenize(A__ , add_prefix_space=A__ )
snake_case = rust_tokenizer.tokenize(A__ )
self.assertListEqual(A__ , A__ )
# Testing conversion to ids without special tokens
snake_case = tokenizer.encode(A__ , add_special_tokens=A__ , add_prefix_space=A__ )
snake_case = rust_tokenizer.encode(A__ , add_special_tokens=A__ )
self.assertListEqual(A__ , A__ )
# Testing conversion to ids with special tokens
snake_case = self.get_rust_tokenizer(add_prefix_space=A__ )
snake_case = tokenizer.encode(A__ , add_prefix_space=A__ )
snake_case = rust_tokenizer.encode(A__ )
self.assertListEqual(A__ , A__ )
# Testing the unknown token
snake_case = tokens + [rust_tokenizer.unk_token]
snake_case = [14, 15, 10, 9, 3, 2, 15, 19]
self.assertListEqual(rust_tokenizer.convert_tokens_to_ids(A__ ) , A__ )
def UpperCamelCase ( self , *A__ , **A__ ) -> List[str]:
# It's very difficult to mix/test pretokenization with byte-level
# And get both CodeGen and Roberta to work at the same time (mostly an issue of adding a space before the string)
pass
def UpperCamelCase ( self , A__=15 ) -> Tuple:
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})""" ):
snake_case = self.rust_tokenizer_class.from_pretrained(A__ , **A__ )
# Simple input
snake_case = '''This is a simple input'''
snake_case = ['''This is a simple input 1''', '''This is a simple input 2''']
snake_case = ('''This is a simple input''', '''This is a pair''')
snake_case = [
('''This is a simple input 1''', '''This is a simple input 2'''),
('''This is a simple pair 1''', '''This is a simple pair 2'''),
]
# Simple input tests
self.assertRaises(A__ , tokenizer_r.encode , A__ , max_length=A__ , padding='''max_length''' )
# Simple input
self.assertRaises(A__ , tokenizer_r.encode_plus , A__ , max_length=A__ , padding='''max_length''' )
# Simple input
self.assertRaises(
A__ , tokenizer_r.batch_encode_plus , A__ , max_length=A__ , padding='''max_length''' , )
# Pair input
self.assertRaises(A__ , tokenizer_r.encode , A__ , max_length=A__ , padding='''max_length''' )
# Pair input
self.assertRaises(A__ , tokenizer_r.encode_plus , A__ , max_length=A__ , padding='''max_length''' )
# Pair input
self.assertRaises(
A__ , tokenizer_r.batch_encode_plus , A__ , max_length=A__ , padding='''max_length''' , )
def UpperCamelCase ( self ) -> Tuple:
snake_case = CodeGenTokenizer.from_pretrained(self.tmpdirname , pad_token='''<pad>''' )
# Simple input
snake_case = '''This is a simple input'''
snake_case = ['''This is a simple input looooooooong''', '''This is a simple input''']
snake_case = ('''This is a simple input''', '''This is a pair''')
snake_case = [
('''This is a simple input loooooong''', '''This is a simple input'''),
('''This is a simple pair loooooong''', '''This is a simple pair'''),
]
snake_case = tokenizer.pad_token_id
snake_case = tokenizer(A__ , padding='''max_length''' , max_length=30 , return_tensors='''np''' )
snake_case = tokenizer(A__ , padding=A__ , truncate=A__ , return_tensors='''np''' )
snake_case = tokenizer(*A__ , padding='''max_length''' , max_length=60 , return_tensors='''np''' )
snake_case = tokenizer(A__ , padding=A__ , truncate=A__ , return_tensors='''np''' )
# s
# test single string max_length padding
self.assertEqual(out_s['''input_ids'''].shape[-1] , 30 )
self.assertTrue(pad_token_id in out_s['''input_ids'''] )
self.assertTrue(0 in out_s['''attention_mask'''] )
# s2
# test automatic padding
self.assertEqual(out_sa['''input_ids'''].shape[-1] , 33 )
# long slice doesn't have padding
self.assertFalse(pad_token_id in out_sa['''input_ids'''][0] )
self.assertFalse(0 in out_sa['''attention_mask'''][0] )
# short slice does have padding
self.assertTrue(pad_token_id in out_sa['''input_ids'''][1] )
self.assertTrue(0 in out_sa['''attention_mask'''][1] )
# p
# test single pair max_length padding
self.assertEqual(out_p['''input_ids'''].shape[-1] , 60 )
self.assertTrue(pad_token_id in out_p['''input_ids'''] )
self.assertTrue(0 in out_p['''attention_mask'''] )
# p2
# test automatic padding pair
self.assertEqual(out_pa['''input_ids'''].shape[-1] , 52 )
# long slice pair doesn't have padding
self.assertFalse(pad_token_id in out_pa['''input_ids'''][0] )
self.assertFalse(0 in out_pa['''attention_mask'''][0] )
# short slice pair does have padding
self.assertTrue(pad_token_id in out_pa['''input_ids'''][1] )
self.assertTrue(0 in out_pa['''attention_mask'''][1] )
def UpperCamelCase ( self ) -> str:
snake_case = '''$$$'''
snake_case = CodeGenTokenizer.from_pretrained(self.tmpdirname , bos_token=A__ , add_bos_token=A__ )
snake_case = '''This is a simple input'''
snake_case = ['''This is a simple input 1''', '''This is a simple input 2''']
snake_case = tokenizer.bos_token_id
snake_case = tokenizer(A__ )
snake_case = tokenizer(A__ )
self.assertEqual(out_s.input_ids[0] , A__ )
self.assertTrue(all(o[0] == bos_token_id for o in out_sa.input_ids ) )
snake_case = tokenizer.decode(out_s.input_ids )
snake_case = tokenizer.batch_decode(out_sa.input_ids )
self.assertEqual(decode_s.split()[0] , A__ )
self.assertTrue(all(d.split()[0] == bos_token for d in decode_sa ) )
@slow
def UpperCamelCase ( self ) -> Any:
snake_case = CodeGenTokenizer.from_pretrained('''Salesforce/codegen-350M-mono''' )
snake_case = '''\nif len_a > len_b:\n result = a\nelse:\n result = b\n\n\n\n#'''
snake_case = '''\nif len_a > len_b: result = a\nelse: result = b'''
snake_case = tokenizer.encode(A__ )
snake_case = ['''^#''', re.escape('''<|endoftext|>''' ), '''^\'\'\'''', '''^"""''', '''\n\n\n''']
snake_case = tokenizer.decode(A__ , truncate_before_pattern=A__ )
self.assertEqual(A__ , A__ )
def UpperCamelCase ( self ) -> Union[str, Any]:
pass
| 44
| 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
_lowercase = [
{'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 __UpperCamelCase ( a : Dict=True ) ->str:
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=__a ) )
class _lowercase ( __a ):
_UpperCAmelCase = None
_UpperCAmelCase = None
def UpperCamelCase ( self , A__ , A__ ) -> str:
with TemporaryDirectory() as tmp_dir:
snake_case = dataset_module_factory(A__ , cache_dir=A__ )
snake_case = import_main_class(dataset_module.module_path , dataset=A__ )
snake_case = builder_cls(
cache_dir=A__ , config_name=A__ , hash=dataset_module.hash , )
snake_case = '''/'''.join(
[
HF_GCP_BASE_URL,
builder_instance._relative_data_dir(with_hash=A__ ).replace(os.sep , '''/''' ),
config.DATASET_INFO_FILENAME,
] )
snake_case = cached_path(A__ , cache_dir=A__ )
self.assertTrue(os.path.exists(A__ ) )
@pytest.mark.integration
def __UpperCamelCase ( a : List[str] ) ->Any:
snake_case = tmp_path_factory.mktemp('''test_hf_gcp''' ) / '''test_wikipedia_simple'''
snake_case = dataset_module_factory('''wikipedia''' , cache_dir=a )
snake_case = import_main_class(dataset_module.module_path )
snake_case = builder_cls(
cache_dir=a , config_name='''20220301.frr''' , hash=dataset_module.hash , )
# use the HF cloud storage, not the original download_and_prepare that uses apache-beam
snake_case = None
builder_instance.download_and_prepare()
snake_case = builder_instance.as_dataset()
assert ds
@pytest.mark.integration
def __UpperCamelCase ( a : Any ) ->Union[str, Any]:
snake_case = dataset_module_factory('''wikipedia''' , cache_dir=a )
snake_case = import_main_class(dataset_module.module_path , dataset=a )
snake_case = builder_cls(
cache_dir=a , config_name='''20220301.frr''' , hash=dataset_module.hash , )
snake_case = builder_instance.as_streaming_dataset()
assert ds
assert isinstance(a , a )
assert "train" in ds
assert isinstance(ds['''train'''] , a )
assert next(iter(ds['''train'''] ) )
| 44
|
'''simple docstring'''
from __future__ import annotations
import inspect
import unittest
from transformers import ViTConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import TFViTForImageClassification, TFViTModel
if is_vision_available():
from PIL import Image
from transformers import ViTImageProcessor
class _lowercase :
def __init__( self , A__ , A__=13 , A__=30 , A__=2 , A__=3 , A__=True , A__=True , A__=32 , A__=2 , A__=4 , A__=37 , A__="gelu" , A__=0.1 , A__=0.1 , A__=10 , A__=0.0_2 , A__=3 , A__=None , ) -> List[Any]:
snake_case = parent
snake_case = batch_size
snake_case = image_size
snake_case = patch_size
snake_case = num_channels
snake_case = is_training
snake_case = use_labels
snake_case = hidden_size
snake_case = num_hidden_layers
snake_case = num_attention_heads
snake_case = intermediate_size
snake_case = hidden_act
snake_case = hidden_dropout_prob
snake_case = attention_probs_dropout_prob
snake_case = type_sequence_label_size
snake_case = initializer_range
snake_case = scope
# in ViT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
snake_case = (image_size // patch_size) ** 2
snake_case = num_patches + 1
def UpperCamelCase ( self ) -> int:
snake_case = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
snake_case = None
if self.use_labels:
snake_case = ids_tensor([self.batch_size] , self.type_sequence_label_size )
snake_case = self.get_config()
return config, pixel_values, labels
def UpperCamelCase ( self ) -> int:
return ViTConfig(
image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , 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 , is_decoder=A__ , initializer_range=self.initializer_range , )
def UpperCamelCase ( self , A__ , A__ , A__ ) -> Union[str, Any]:
snake_case = TFViTModel(config=A__ )
snake_case = model(A__ , training=A__ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
# Test with an image with different size than the one specified in config.
snake_case = self.image_size // 2
snake_case = pixel_values[:, :, :image_size, :image_size]
snake_case = model(A__ , interpolate_pos_encoding=A__ , training=A__ )
snake_case = (image_size // self.patch_size) ** 2 + 1
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, seq_length, self.hidden_size) )
def UpperCamelCase ( self , A__ , A__ , A__ ) -> Optional[int]:
snake_case = self.type_sequence_label_size
snake_case = TFViTForImageClassification(A__ )
snake_case = model(A__ , labels=A__ , training=A__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
# Test with an image with different size than the one specified in config.
snake_case = self.image_size // 2
snake_case = pixel_values[:, :, :image_size, :image_size]
snake_case = model(A__ , interpolate_pos_encoding=A__ , training=A__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
# test greyscale images
snake_case = 1
snake_case = TFViTForImageClassification(A__ )
snake_case = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
snake_case = model(A__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
def UpperCamelCase ( self ) -> Union[str, Any]:
snake_case = self.prepare_config_and_inputs()
snake_case , snake_case , snake_case = config_and_inputs
snake_case = {'''pixel_values''': pixel_values}
return config, inputs_dict
@require_tf
class _lowercase ( __a , __a , unittest.TestCase ):
_UpperCAmelCase = (TFViTModel, TFViTForImageClassification) if is_tf_available() else ()
_UpperCAmelCase = (
{'''feature-extraction''': TFViTModel, '''image-classification''': TFViTForImageClassification}
if is_tf_available()
else {}
)
_UpperCAmelCase = False
_UpperCAmelCase = False
_UpperCAmelCase = False
def UpperCamelCase ( self ) -> List[Any]:
snake_case = TFViTModelTester(self )
snake_case = ConfigTester(self , config_class=A__ , has_text_modality=A__ , hidden_size=37 )
def UpperCamelCase ( self ) -> int:
self.config_tester.run_common_tests()
@unittest.skip(reason='''ViT does not use inputs_embeds''' )
def UpperCamelCase ( self ) -> int:
pass
@unittest.skip(reason='''ViT does not use inputs_embeds''' )
def UpperCamelCase ( self ) -> str:
pass
def UpperCamelCase ( self ) -> Union[str, Any]:
snake_case , snake_case = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
snake_case = model_class(A__ )
self.assertIsInstance(model.get_input_embeddings() , (tf.keras.layers.Layer) )
snake_case = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(A__ , tf.keras.layers.Layer ) )
def UpperCamelCase ( self ) -> List[Any]:
snake_case , snake_case = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
snake_case = model_class(A__ )
snake_case = inspect.signature(model.call )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
snake_case = [*signature.parameters.keys()]
snake_case = ['''pixel_values''']
self.assertListEqual(arg_names[:1] , A__ )
def UpperCamelCase ( self ) -> Union[str, Any]:
snake_case = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*A__ )
def UpperCamelCase ( self ) -> Optional[Any]:
snake_case = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*A__ )
@slow
def UpperCamelCase ( self ) -> Any:
snake_case = TFViTModel.from_pretrained('''google/vit-base-patch16-224''' )
self.assertIsNotNone(A__ )
def __UpperCamelCase ( ) ->Any:
snake_case = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
return image
@require_tf
@require_vision
class _lowercase ( unittest.TestCase ):
@cached_property
def UpperCamelCase ( self ) -> Optional[int]:
return ViTImageProcessor.from_pretrained('''google/vit-base-patch16-224''' ) if is_vision_available() else None
@slow
def UpperCamelCase ( self ) -> Dict:
snake_case = TFViTForImageClassification.from_pretrained('''google/vit-base-patch16-224''' )
snake_case = self.default_image_processor
snake_case = prepare_img()
snake_case = image_processor(images=A__ , return_tensors='''tf''' )
# forward pass
snake_case = model(**A__ )
# verify the logits
snake_case = tf.TensorShape((1, 10_00) )
self.assertEqual(outputs.logits.shape , A__ )
snake_case = tf.constant([-0.2_7_4_4, 0.8_2_1_5, -0.0_8_3_6] )
tf.debugging.assert_near(outputs.logits[0, :3] , A__ , atol=1e-4 )
| 44
| 1
|
'''simple docstring'''
from __future__ import annotations
def __UpperCamelCase ( a : list ) ->float:
if not nums:
raise ValueError('''List is empty''' )
return sum(a ) / len(a )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 44
|
'''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
_lowercase = [
{'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 __UpperCamelCase ( a : Dict=True ) ->str:
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=__a ) )
class _lowercase ( __a ):
_UpperCAmelCase = None
_UpperCAmelCase = None
def UpperCamelCase ( self , A__ , A__ ) -> str:
with TemporaryDirectory() as tmp_dir:
snake_case = dataset_module_factory(A__ , cache_dir=A__ )
snake_case = import_main_class(dataset_module.module_path , dataset=A__ )
snake_case = builder_cls(
cache_dir=A__ , config_name=A__ , hash=dataset_module.hash , )
snake_case = '''/'''.join(
[
HF_GCP_BASE_URL,
builder_instance._relative_data_dir(with_hash=A__ ).replace(os.sep , '''/''' ),
config.DATASET_INFO_FILENAME,
] )
snake_case = cached_path(A__ , cache_dir=A__ )
self.assertTrue(os.path.exists(A__ ) )
@pytest.mark.integration
def __UpperCamelCase ( a : List[str] ) ->Any:
snake_case = tmp_path_factory.mktemp('''test_hf_gcp''' ) / '''test_wikipedia_simple'''
snake_case = dataset_module_factory('''wikipedia''' , cache_dir=a )
snake_case = import_main_class(dataset_module.module_path )
snake_case = builder_cls(
cache_dir=a , config_name='''20220301.frr''' , hash=dataset_module.hash , )
# use the HF cloud storage, not the original download_and_prepare that uses apache-beam
snake_case = None
builder_instance.download_and_prepare()
snake_case = builder_instance.as_dataset()
assert ds
@pytest.mark.integration
def __UpperCamelCase ( a : Any ) ->Union[str, Any]:
snake_case = dataset_module_factory('''wikipedia''' , cache_dir=a )
snake_case = import_main_class(dataset_module.module_path , dataset=a )
snake_case = builder_cls(
cache_dir=a , config_name='''20220301.frr''' , hash=dataset_module.hash , )
snake_case = builder_instance.as_streaming_dataset()
assert ds
assert isinstance(a , a )
assert "train" in ds
assert isinstance(ds['''train'''] , a )
assert next(iter(ds['''train'''] ) )
| 44
| 1
|
'''simple docstring'''
_lowercase = {
'A': ['B', 'C', 'E'],
'B': ['A', 'D', 'E'],
'C': ['A', 'F', 'G'],
'D': ['B'],
'E': ['A', 'B', 'D'],
'F': ['C'],
'G': ['C'],
}
def __UpperCamelCase ( a : dict , a : List[Any] , a : Any ) ->list[str]:
snake_case = set()
# keep track of all the paths to be checked
snake_case = [[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
snake_case = queue.pop(0 )
# get the last node from the path
snake_case = path[-1]
if node not in explored:
snake_case = graph[node]
# go through all neighbour nodes, construct a new path and
# push it into the queue
for neighbour in neighbours:
snake_case = list(a )
new_path.append(a )
queue.append(a )
# return path if neighbour is goal
if neighbour == goal:
return new_path
# mark node as explored
explored.add(a )
# in case there's no path between the 2 nodes
return []
def __UpperCamelCase ( a : dict , a : Tuple , a : str ) ->int:
if not graph or start not in graph or target not in graph:
return -1
if start == target:
return 0
snake_case = [start]
snake_case = set(a )
# Keep tab on distances from `start` node.
snake_case = {start: 0, target: -1}
while queue:
snake_case = queue.pop(0 )
if node == target:
snake_case = (
dist[node] if dist[target] == -1 else min(dist[target] , dist[node] )
)
for adjacent in graph[node]:
if adjacent not in visited:
visited.add(a )
queue.append(a )
snake_case = 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
| 44
|
'''simple docstring'''
def __UpperCamelCase ( a : int , a : int ) ->int:
while b:
snake_case , snake_case = b, a % b
return a
def __UpperCamelCase ( a : int , a : int ) ->int:
return a if b == 0 else euclidean_gcd_recursive(a , a % b )
def __UpperCamelCase ( ) ->Optional[Any]:
print(f"""euclidean_gcd(3, 5) = {euclidean_gcd(3 , 5 )}""" )
print(f"""euclidean_gcd(5, 3) = {euclidean_gcd(5 , 3 )}""" )
print(f"""euclidean_gcd(1, 3) = {euclidean_gcd(1 , 3 )}""" )
print(f"""euclidean_gcd(3, 6) = {euclidean_gcd(3 , 6 )}""" )
print(f"""euclidean_gcd(6, 3) = {euclidean_gcd(6 , 3 )}""" )
print(f"""euclidean_gcd_recursive(3, 5) = {euclidean_gcd_recursive(3 , 5 )}""" )
print(f"""euclidean_gcd_recursive(5, 3) = {euclidean_gcd_recursive(5 , 3 )}""" )
print(f"""euclidean_gcd_recursive(1, 3) = {euclidean_gcd_recursive(1 , 3 )}""" )
print(f"""euclidean_gcd_recursive(3, 6) = {euclidean_gcd_recursive(3 , 6 )}""" )
print(f"""euclidean_gcd_recursive(6, 3) = {euclidean_gcd_recursive(6 , 3 )}""" )
if __name__ == "__main__":
main()
| 44
| 1
|
'''simple docstring'''
import inspect
import os
import unittest
from pathlib import Path
import torch
import accelerate
from accelerate.test_utils import execute_subprocess_async
from accelerate.test_utils.testing import run_command
class _lowercase ( unittest.TestCase ):
_UpperCAmelCase = inspect.getfile(accelerate.test_utils )
_UpperCAmelCase = os.path.sep.join(mod_file.split(os.path.sep )[:-1] + ['''scripts''', '''test_cli.py'''] )
_UpperCAmelCase = ['''accelerate''', '''launch''']
_UpperCAmelCase = Path.home() / '''.cache/huggingface/accelerate'''
_UpperCAmelCase = '''default_config.yaml'''
_UpperCAmelCase = config_folder / config_file
_UpperCAmelCase = config_folder / '''_default_config.yaml'''
_UpperCAmelCase = Path('''tests/test_configs''' )
@classmethod
def UpperCamelCase ( cls ) -> Tuple:
if cls.config_path.is_file():
cls.config_path.rename(cls.changed_path )
@classmethod
def UpperCamelCase ( cls ) -> List[Any]:
if cls.changed_path.is_file():
cls.changed_path.rename(cls.config_path )
def UpperCamelCase ( self ) -> List[Any]:
snake_case = self.base_cmd
if torch.cuda.is_available() and (torch.cuda.device_count() > 1):
cmd += ["--multi_gpu"]
execute_subprocess_async(cmd + [self.test_file_path] , env=os.environ.copy() )
def UpperCamelCase ( self ) -> str:
for config in sorted(self.test_config_path.glob('''**/*.yaml''' ) ):
with self.subTest(config_file=A__ ):
execute_subprocess_async(
self.base_cmd + ['''--config_file''', str(A__ ), self.test_file_path] , env=os.environ.copy() )
def UpperCamelCase ( self ) -> Tuple:
execute_subprocess_async(['''accelerate''', '''test'''] , env=os.environ.copy() )
class _lowercase ( unittest.TestCase ):
_UpperCAmelCase = '''test-tpu'''
_UpperCAmelCase = '''us-central1-a'''
_UpperCAmelCase = '''ls'''
_UpperCAmelCase = ['''accelerate''', '''tpu-config''']
_UpperCAmelCase = '''cd /usr/share'''
_UpperCAmelCase = '''tests/test_samples/test_command_file.sh'''
_UpperCAmelCase = '''Running gcloud compute tpus tpu-vm ssh'''
def UpperCamelCase ( self ) -> List[str]:
snake_case = run_command(
self.cmd
+ ['''--command''', self.command, '''--tpu_zone''', self.tpu_zone, '''--tpu_name''', self.tpu_name, '''--debug'''] , return_stdout=A__ , )
self.assertIn(
F"""{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; ls --worker all""" , A__ , )
def UpperCamelCase ( self ) -> Any:
snake_case = run_command(
self.cmd
+ [
'''--config_file''',
'''tests/test_configs/0_12_0.yaml''',
'''--command''',
self.command,
'''--tpu_zone''',
self.tpu_zone,
'''--tpu_name''',
self.tpu_name,
'''--debug''',
] , return_stdout=A__ , )
self.assertIn(
F"""{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; ls --worker all""" , A__ , )
def UpperCamelCase ( self ) -> Optional[int]:
snake_case = run_command(
self.cmd + ['''--config_file''', '''tests/test_configs/latest.yaml''', '''--debug'''] , return_stdout=A__ )
self.assertIn(
F"""{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; echo \"hello world\"; echo \"this is a second command\" --worker all""" , A__ , )
def UpperCamelCase ( self ) -> Optional[int]:
snake_case = run_command(
self.cmd + ['''--config_file''', '''tests/test_configs/latest.yaml''', '''--command''', self.command, '''--debug'''] , return_stdout=A__ , )
self.assertIn(
F"""{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; ls --worker all""" , A__ , )
def UpperCamelCase ( self ) -> List[str]:
snake_case = run_command(
self.cmd
+ [
'''--config_file''',
'''tests/test_configs/latest.yaml''',
'''--command''',
self.command,
'''--command''',
'''echo "Hello World"''',
'''--debug''',
] , return_stdout=A__ , )
self.assertIn(
F"""{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; ls; echo \"Hello World\" --worker all""" , A__ , )
def UpperCamelCase ( self ) -> Union[str, Any]:
snake_case = run_command(
self.cmd
+ ['''--config_file''', '''tests/test_configs/latest.yaml''', '''--command_file''', self.command_file, '''--debug'''] , return_stdout=A__ , )
self.assertIn(
F"""{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; echo \"hello world\"; echo \"this is a second command\" --worker all""" , A__ , )
def UpperCamelCase ( self ) -> Optional[int]:
snake_case = run_command(
self.cmd
+ [
'''--config_file''',
'''tests/test_configs/0_12_0.yaml''',
'''--command_file''',
self.command_file,
'''--tpu_zone''',
self.tpu_zone,
'''--tpu_name''',
self.tpu_name,
'''--debug''',
] , return_stdout=A__ , )
self.assertIn(
F"""{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; echo \"hello world\"; echo \"this is a second command\" --worker all""" , A__ , )
def UpperCamelCase ( self ) -> Any:
snake_case = run_command(
self.cmd + ['''--config_file''', '''tests/test_configs/latest.yaml''', '''--install_accelerate''', '''--debug'''] , return_stdout=A__ , )
self.assertIn(
F"""{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; pip install accelerate -U; echo \"hello world\"; echo \"this is a second command\" --worker all""" , A__ , )
def UpperCamelCase ( self ) -> str:
snake_case = run_command(
self.cmd
+ [
'''--config_file''',
'''tests/test_configs/latest.yaml''',
'''--install_accelerate''',
'''--accelerate_version''',
'''12.0.0''',
'''--debug''',
] , return_stdout=A__ , )
self.assertIn(
F"""{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; pip install accelerate==12.0.0; echo \"hello world\"; echo \"this is a second command\" --worker all""" , A__ , )
| 44
|
'''simple docstring'''
import argparse
import copy
def __UpperCamelCase ( a : Union[str, Any] ) ->Tuple:
snake_case = {}
with open(a ) as f:
for line in f:
if line.split()[0] not in dict_of_neighbours:
snake_case = []
_list.append([line.split()[1], line.split()[2]] )
snake_case = _list
else:
dict_of_neighbours[line.split()[0]].append(
[line.split()[1], line.split()[2]] )
if line.split()[1] not in dict_of_neighbours:
snake_case = []
_list.append([line.split()[0], line.split()[2]] )
snake_case = _list
else:
dict_of_neighbours[line.split()[1]].append(
[line.split()[0], line.split()[2]] )
return dict_of_neighbours
def __UpperCamelCase ( a : Dict , a : Tuple ) ->int:
with open(a ) as f:
snake_case = f.read(1 )
snake_case = start_node
snake_case = []
snake_case = start_node
snake_case = 0
while visiting not in first_solution:
snake_case = 1_0000
for k in dict_of_neighbours[visiting]:
if int(k[1] ) < int(a ) and k[0] not in first_solution:
snake_case = k[1]
snake_case = k[0]
first_solution.append(a )
snake_case = distance_of_first_solution + int(a )
snake_case = best_node
first_solution.append(a )
snake_case = 0
for k in dict_of_neighbours[first_solution[-2]]:
if k[0] == start_node:
break
position += 1
snake_case = (
distance_of_first_solution
+ int(dict_of_neighbours[first_solution[-2]][position][1] )
- 1_0000
)
return first_solution, distance_of_first_solution
def __UpperCamelCase ( a : Optional[int] , a : str ) ->str:
snake_case = []
for n in solution[1:-1]:
snake_case = solution.index(a )
for kn in solution[1:-1]:
snake_case = solution.index(a )
if n == kn:
continue
snake_case = copy.deepcopy(a )
snake_case = kn
snake_case = n
snake_case = 0
for k in _tmp[:-1]:
snake_case = _tmp[_tmp.index(a ) + 1]
for i in dict_of_neighbours[k]:
if i[0] == next_node:
snake_case = distance + int(i[1] )
_tmp.append(a )
if _tmp not in neighborhood_of_solution:
neighborhood_of_solution.append(_tmp )
snake_case = len(neighborhood_of_solution[0] ) - 1
neighborhood_of_solution.sort(key=lambda a : x[index_of_last_item_in_the_list] )
return neighborhood_of_solution
def __UpperCamelCase ( a : Any , a : Optional[Any] , a : int , a : Optional[int] , a : Union[str, Any] ) ->List[Any]:
snake_case = 1
snake_case = first_solution
snake_case = []
snake_case = distance_of_first_solution
snake_case = solution
while count <= iters:
snake_case = find_neighborhood(a , a )
snake_case = 0
snake_case = neighborhood[index_of_best_solution]
snake_case = len(a ) - 1
snake_case = False
while not found:
snake_case = 0
while i < len(a ):
if best_solution[i] != solution[i]:
snake_case = best_solution[i]
snake_case = solution[i]
break
snake_case = i + 1
if [first_exchange_node, second_exchange_node] not in tabu_list and [
second_exchange_node,
first_exchange_node,
] not in tabu_list:
tabu_list.append([first_exchange_node, second_exchange_node] )
snake_case = True
snake_case = best_solution[:-1]
snake_case = neighborhood[index_of_best_solution][best_cost_index]
if cost < best_cost:
snake_case = cost
snake_case = solution
else:
snake_case = index_of_best_solution + 1
snake_case = neighborhood[index_of_best_solution]
if len(a ) >= size:
tabu_list.pop(0 )
snake_case = count + 1
return best_solution_ever, best_cost
def __UpperCamelCase ( a : Union[str, Any]=None ) ->Optional[Any]:
snake_case = generate_neighbours(args.File )
snake_case , snake_case = generate_first_solution(
args.File , a )
snake_case , snake_case = tabu_search(
a , a , a , args.Iterations , args.Size , )
print(f"""Best solution: {best_sol}, with total distance: {best_cost}.""" )
if __name__ == "__main__":
_lowercase = argparse.ArgumentParser(description='Tabu Search')
parser.add_argument(
'-f',
'--File',
type=str,
help='Path to the file containing the data',
required=True,
)
parser.add_argument(
'-i',
'--Iterations',
type=int,
help='How many iterations the algorithm should perform',
required=True,
)
parser.add_argument(
'-s', '--Size', type=int, help='Size of the tabu list', required=True
)
# Pass the arguments to main method
main(parser.parse_args())
| 44
| 1
|
'''simple docstring'''
import functools
import logging
import os
import sys
import threading
from logging import (
CRITICAL, # NOQA
DEBUG, # NOQA
ERROR, # NOQA
FATAL, # NOQA
INFO, # NOQA
NOTSET, # NOQA
WARN, # NOQA
WARNING, # NOQA
)
from typing import Optional
import huggingface_hub.utils as hf_hub_utils
from tqdm import auto as tqdm_lib
_lowercase = threading.Lock()
_lowercase = None
_lowercase = {
'debug': logging.DEBUG,
'info': logging.INFO,
'warning': logging.WARNING,
'error': logging.ERROR,
'critical': logging.CRITICAL,
}
_lowercase = logging.WARNING
_lowercase = True
def __UpperCamelCase ( ) ->str:
snake_case = os.getenv('''TRANSFORMERS_VERBOSITY''' , a )
if env_level_str:
if env_level_str in log_levels:
return log_levels[env_level_str]
else:
logging.getLogger().warning(
f"""Unknown option TRANSFORMERS_VERBOSITY={env_level_str}, """
f"""has to be one of: { ', '.join(log_levels.keys() ) }""" )
return _default_log_level
def __UpperCamelCase ( ) ->str:
return __name__.split('''.''' )[0]
def __UpperCamelCase ( ) ->logging.Logger:
return logging.getLogger(_get_library_name() )
def __UpperCamelCase ( ) ->None:
global _default_handler
with _lock:
if _default_handler:
# This library has already configured the library root logger.
return
snake_case = logging.StreamHandler() # Set sys.stderr as stream.
snake_case = sys.stderr.flush
# Apply our default configuration to the library root logger.
snake_case = _get_library_root_logger()
library_root_logger.addHandler(_default_handler )
library_root_logger.setLevel(_get_default_logging_level() )
snake_case = False
def __UpperCamelCase ( ) ->None:
global _default_handler
with _lock:
if not _default_handler:
return
snake_case = _get_library_root_logger()
library_root_logger.removeHandler(_default_handler )
library_root_logger.setLevel(logging.NOTSET )
snake_case = None
def __UpperCamelCase ( ) ->str:
return log_levels
def __UpperCamelCase ( a : Optional[str] = None ) ->logging.Logger:
if name is None:
snake_case = _get_library_name()
_configure_library_root_logger()
return logging.getLogger(a )
def __UpperCamelCase ( ) ->int:
_configure_library_root_logger()
return _get_library_root_logger().getEffectiveLevel()
def __UpperCamelCase ( a : int ) ->None:
_configure_library_root_logger()
_get_library_root_logger().setLevel(a )
def __UpperCamelCase ( ) ->Any:
return set_verbosity(a )
def __UpperCamelCase ( ) ->int:
return set_verbosity(a )
def __UpperCamelCase ( ) ->Tuple:
return set_verbosity(a )
def __UpperCamelCase ( ) ->Union[str, Any]:
return set_verbosity(a )
def __UpperCamelCase ( ) ->None:
_configure_library_root_logger()
assert _default_handler is not None
_get_library_root_logger().removeHandler(_default_handler )
def __UpperCamelCase ( ) ->None:
_configure_library_root_logger()
assert _default_handler is not None
_get_library_root_logger().addHandler(_default_handler )
def __UpperCamelCase ( a : logging.Handler ) ->None:
_configure_library_root_logger()
assert handler is not None
_get_library_root_logger().addHandler(a )
def __UpperCamelCase ( a : logging.Handler ) ->None:
_configure_library_root_logger()
assert handler is not None and handler not in _get_library_root_logger().handlers
_get_library_root_logger().removeHandler(a )
def __UpperCamelCase ( ) ->None:
_configure_library_root_logger()
snake_case = False
def __UpperCamelCase ( ) ->None:
_configure_library_root_logger()
snake_case = True
def __UpperCamelCase ( ) ->None:
snake_case = _get_library_root_logger().handlers
for handler in handlers:
snake_case = logging.Formatter('''[%(levelname)s|%(filename)s:%(lineno)s] %(asctime)s >> %(message)s''' )
handler.setFormatter(a )
def __UpperCamelCase ( ) ->None:
snake_case = _get_library_root_logger().handlers
for handler in handlers:
handler.setFormatter(a )
def __UpperCamelCase ( self : List[str] , *a : List[Any] , **a : Union[str, Any] ) ->Optional[Any]:
snake_case = os.getenv('''TRANSFORMERS_NO_ADVISORY_WARNINGS''' , a )
if no_advisory_warnings:
return
self.warning(*a , **a )
_lowercase = warning_advice
@functools.lru_cache(a )
def __UpperCamelCase ( self : Optional[Any] , *a : str , **a : Dict ) ->List[str]:
self.warning(*a , **a )
_lowercase = warning_once
class _lowercase :
def __init__( self , *A__ , **A__ ) -> List[Any]: # pylint: disable=unused-argument
snake_case = args[0] if args else None
def __iter__( self ) -> int:
return iter(self._iterator )
def __getattr__( self , A__ ) -> Union[str, Any]:
def empty_fn(*A__ , **A__ ): # pylint: disable=unused-argument
return
return empty_fn
def __enter__( self ) -> Optional[Any]:
return self
def __exit__( self , A__ , A__ , A__ ) -> Dict:
return
class _lowercase :
def __call__( self , *A__ , **A__ ) -> int:
if _tqdm_active:
return tqdm_lib.tqdm(*A__ , **A__ )
else:
return EmptyTqdm(*A__ , **A__ )
def UpperCamelCase ( self , *A__ , **A__ ) -> List[Any]:
snake_case = None
if _tqdm_active:
return tqdm_lib.tqdm.set_lock(*A__ , **A__ )
def UpperCamelCase ( self ) -> Optional[Any]:
if _tqdm_active:
return tqdm_lib.tqdm.get_lock()
_lowercase = _tqdm_cls()
def __UpperCamelCase ( ) ->bool:
global _tqdm_active
return bool(_tqdm_active )
def __UpperCamelCase ( ) ->List[Any]:
global _tqdm_active
snake_case = True
hf_hub_utils.enable_progress_bars()
def __UpperCamelCase ( ) ->Any:
global _tqdm_active
snake_case = False
hf_hub_utils.disable_progress_bars()
| 44
|
'''simple docstring'''
from ...utils import is_note_seq_available, is_transformers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_torch_and_transformers_objects import * # noqa F403
else:
from .notes_encoder import SpectrogramNotesEncoder
from .continous_encoder import SpectrogramContEncoder
from .pipeline_spectrogram_diffusion import (
SpectrogramContEncoder,
SpectrogramDiffusionPipeline,
TaFilmDecoder,
)
try:
if not (is_transformers_available() and is_torch_available() and is_note_seq_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_transformers_and_torch_and_note_seq_objects import * # noqa F403
else:
from .midi_utils import MidiProcessor
| 44
| 1
|
'''simple docstring'''
from typing import Dict, List, Optional, Union
import numpy as np
from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType
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,
is_valid_image,
to_numpy_array,
valid_images,
)
from ...utils import logging
if is_vision_available():
import PIL
_lowercase = logging.get_logger(__name__)
def __UpperCamelCase ( a : Optional[Any] ) ->List[List[ImageInput]]:
if isinstance(a , (list, tuple) ) and isinstance(videos[0] , (list, tuple) ) and is_valid_image(videos[0][0] ):
return videos
elif isinstance(a , (list, tuple) ) and is_valid_image(videos[0] ):
return [videos]
elif is_valid_image(a ):
return [[videos]]
raise ValueError(f"""Could not make batched video from {videos}""" )
class _lowercase ( __a ):
_UpperCAmelCase = ['''pixel_values''']
def __init__( self , A__ = True , A__ = None , A__ = PILImageResampling.BILINEAR , A__ = True , A__ = None , A__ = True , A__ = 1 / 2_55 , A__ = True , A__ = True , A__ = None , A__ = None , **A__ , ) -> None:
super().__init__(**A__ )
snake_case = size if size is not None else {'''shortest_edge''': 2_56}
snake_case = get_size_dict(A__ , default_to_square=A__ )
snake_case = crop_size if crop_size is not None else {'''height''': 2_24, '''width''': 2_24}
snake_case = get_size_dict(A__ , param_name='''crop_size''' )
snake_case = do_resize
snake_case = size
snake_case = do_center_crop
snake_case = crop_size
snake_case = resample
snake_case = do_rescale
snake_case = rescale_factor
snake_case = offset
snake_case = do_normalize
snake_case = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
snake_case = image_std if image_std is not None else IMAGENET_STANDARD_STD
def UpperCamelCase ( self , A__ , A__ , A__ = PILImageResampling.BILINEAR , A__ = None , **A__ , ) -> np.ndarray:
snake_case = get_size_dict(A__ , default_to_square=A__ )
if "shortest_edge" in size:
snake_case = get_resize_output_image_size(A__ , size['''shortest_edge'''] , default_to_square=A__ )
elif "height" in size and "width" in size:
snake_case = (size['''height'''], size['''width'''])
else:
raise ValueError(F"""Size must have 'height' and 'width' or 'shortest_edge' as keys. Got {size.keys()}""" )
return resize(A__ , size=A__ , resample=A__ , data_format=A__ , **A__ )
def UpperCamelCase ( self , A__ , A__ , A__ = None , **A__ , ) -> np.ndarray:
snake_case = get_size_dict(A__ )
if "height" not in size or "width" not in size:
raise ValueError(F"""Size must have 'height' and 'width' as keys. Got {size.keys()}""" )
return center_crop(A__ , size=(size['''height'''], size['''width''']) , data_format=A__ , **A__ )
def UpperCamelCase ( self , A__ , A__ , A__ = True , A__ = None , **A__ , ) -> List[Any]:
snake_case = image.astype(np.floataa )
if offset:
snake_case = image - (scale / 2)
return rescale(A__ , scale=A__ , data_format=A__ , **A__ )
def UpperCamelCase ( self , A__ , A__ , A__ , A__ = None , **A__ , ) -> np.ndarray:
return normalize(A__ , mean=A__ , std=A__ , data_format=A__ , **A__ )
def UpperCamelCase ( self , A__ , A__ = None , A__ = None , A__ = None , A__ = None , A__ = None , A__ = None , A__ = None , A__ = None , A__ = None , A__ = None , A__ = None , A__ = ChannelDimension.FIRST , ) -> np.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_center_crop and crop_size is None:
raise ValueError('''Crop size must be specified if do_center_crop 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.''' )
if offset and not do_rescale:
raise ValueError('''For offset, do_rescale must also be set to True.''' )
# All transformations expect numpy arrays.
snake_case = to_numpy_array(A__ )
if do_resize:
snake_case = self.resize(image=A__ , size=A__ , resample=A__ )
if do_center_crop:
snake_case = self.center_crop(A__ , size=A__ )
if do_rescale:
snake_case = self.rescale(image=A__ , scale=A__ , offset=A__ )
if do_normalize:
snake_case = self.normalize(image=A__ , mean=A__ , std=A__ )
snake_case = to_channel_dimension_format(A__ , A__ )
return image
def UpperCamelCase ( self , A__ , A__ = None , A__ = None , A__ = None , A__ = None , A__ = None , A__ = None , A__ = None , A__ = None , A__ = None , A__ = None , A__ = None , A__ = None , A__ = ChannelDimension.FIRST , **A__ , ) -> PIL.Image.Image:
snake_case = do_resize if do_resize is not None else self.do_resize
snake_case = resample if resample is not None else self.resample
snake_case = do_center_crop if do_center_crop is not None else self.do_center_crop
snake_case = do_rescale if do_rescale is not None else self.do_rescale
snake_case = rescale_factor if rescale_factor is not None else self.rescale_factor
snake_case = offset if offset is not None else self.offset
snake_case = do_normalize if do_normalize is not None else self.do_normalize
snake_case = image_mean if image_mean is not None else self.image_mean
snake_case = image_std if image_std is not None else self.image_std
snake_case = size if size is not None else self.size
snake_case = get_size_dict(A__ , default_to_square=A__ )
snake_case = crop_size if crop_size is not None else self.crop_size
snake_case = get_size_dict(A__ , param_name='''crop_size''' )
if not valid_images(A__ ):
raise ValueError(
'''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
'''torch.Tensor, tf.Tensor or jax.ndarray.''' )
snake_case = make_batched(A__ )
snake_case = [
[
self._preprocess_image(
image=A__ , do_resize=A__ , size=A__ , resample=A__ , do_center_crop=A__ , crop_size=A__ , do_rescale=A__ , rescale_factor=A__ , offset=A__ , do_normalize=A__ , image_mean=A__ , image_std=A__ , data_format=A__ , )
for img in video
]
for video in videos
]
snake_case = {'''pixel_values''': videos}
return BatchFeature(data=A__ , tensor_type=A__ )
| 44
|
'''simple docstring'''
from ...processing_utils import ProcessorMixin
class _lowercase ( __a ):
_UpperCAmelCase = '''WhisperFeatureExtractor'''
_UpperCAmelCase = '''WhisperTokenizer'''
def __init__( self , A__ , A__ ) -> Optional[Any]:
super().__init__(A__ , A__ )
snake_case = self.feature_extractor
snake_case = False
def UpperCamelCase ( self , A__=None , A__=None , A__=True ) -> Union[str, Any]:
return self.tokenizer.get_decoder_prompt_ids(task=A__ , language=A__ , no_timestamps=A__ )
def __call__( self , *A__ , **A__ ) -> Dict:
# For backward compatibility
if self._in_target_context_manager:
return self.current_processor(*A__ , **A__ )
snake_case = kwargs.pop('''audio''' , A__ )
snake_case = kwargs.pop('''sampling_rate''' , A__ )
snake_case = kwargs.pop('''text''' , A__ )
if len(A__ ) > 0:
snake_case = args[0]
snake_case = args[1:]
if audio is None and text is None:
raise ValueError('''You need to specify either an `audio` or `text` input to process.''' )
if audio is not None:
snake_case = self.feature_extractor(A__ , *A__ , sampling_rate=A__ , **A__ )
if text is not None:
snake_case = self.tokenizer(A__ , **A__ )
if text is None:
return inputs
elif audio is None:
return encodings
else:
snake_case = encodings['''input_ids''']
return inputs
def UpperCamelCase ( self , *A__ , **A__ ) -> Optional[Any]:
return self.tokenizer.batch_decode(*A__ , **A__ )
def UpperCamelCase ( self , *A__ , **A__ ) -> str:
return self.tokenizer.decode(*A__ , **A__ )
def UpperCamelCase ( self , A__ , A__="np" ) -> Optional[Any]:
return self.tokenizer.get_prompt_ids(A__ , return_tensors=A__ )
| 44
| 1
|
'''simple docstring'''
import argparse
import json
import pickle
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import MaskFormerConfig, MaskFormerForInstanceSegmentation, MaskFormerImageProcessor, SwinConfig
from transformers.utils import logging
logging.set_verbosity_info()
_lowercase = logging.get_logger(__name__)
def __UpperCamelCase ( a : str ) ->int:
snake_case = SwinConfig.from_pretrained(
'''microsoft/swin-tiny-patch4-window7-224''' , out_features=['''stage1''', '''stage2''', '''stage3''', '''stage4'''] )
snake_case = MaskFormerConfig(backbone_config=a )
snake_case = '''huggingface/label-files'''
if "ade20k-full" in model_name:
# this should be ok
snake_case = 847
snake_case = '''maskformer-ade20k-full-id2label.json'''
elif "ade" in model_name:
# this should be ok
snake_case = 150
snake_case = '''ade20k-id2label.json'''
elif "coco-stuff" in model_name:
# this should be ok
snake_case = 171
snake_case = '''maskformer-coco-stuff-id2label.json'''
elif "coco" in model_name:
# TODO
snake_case = 133
snake_case = '''coco-panoptic-id2label.json'''
elif "cityscapes" in model_name:
# this should be ok
snake_case = 19
snake_case = '''cityscapes-id2label.json'''
elif "vistas" in model_name:
# this should be ok
snake_case = 65
snake_case = '''mapillary-vistas-id2label.json'''
snake_case = json.load(open(hf_hub_download(a , a , repo_type='''dataset''' ) , '''r''' ) )
snake_case = {int(a ): v for k, v in idalabel.items()}
return config
def __UpperCamelCase ( a : List[str] ) ->Optional[int]:
snake_case = []
# stem
# fmt: off
rename_keys.append(('''backbone.patch_embed.proj.weight''', '''model.pixel_level_module.encoder.model.embeddings.patch_embeddings.projection.weight''') )
rename_keys.append(('''backbone.patch_embed.proj.bias''', '''model.pixel_level_module.encoder.model.embeddings.patch_embeddings.projection.bias''') )
rename_keys.append(('''backbone.patch_embed.norm.weight''', '''model.pixel_level_module.encoder.model.embeddings.norm.weight''') )
rename_keys.append(('''backbone.patch_embed.norm.bias''', '''model.pixel_level_module.encoder.model.embeddings.norm.bias''') )
# stages
for i in range(len(config.backbone_config.depths ) ):
for j in range(config.backbone_config.depths[i] ):
rename_keys.append((f"""backbone.layers.{i}.blocks.{j}.norm1.weight""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_before.weight""") )
rename_keys.append((f"""backbone.layers.{i}.blocks.{j}.norm1.bias""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_before.bias""") )
rename_keys.append((f"""backbone.layers.{i}.blocks.{j}.attn.relative_position_bias_table""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table""") )
rename_keys.append((f"""backbone.layers.{i}.blocks.{j}.attn.relative_position_index""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index""") )
rename_keys.append((f"""backbone.layers.{i}.blocks.{j}.attn.proj.weight""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight""") )
rename_keys.append((f"""backbone.layers.{i}.blocks.{j}.attn.proj.bias""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias""") )
rename_keys.append((f"""backbone.layers.{i}.blocks.{j}.norm2.weight""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_after.weight""") )
rename_keys.append((f"""backbone.layers.{i}.blocks.{j}.norm2.bias""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_after.bias""") )
rename_keys.append((f"""backbone.layers.{i}.blocks.{j}.mlp.fc1.weight""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight""") )
rename_keys.append((f"""backbone.layers.{i}.blocks.{j}.mlp.fc1.bias""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias""") )
rename_keys.append((f"""backbone.layers.{i}.blocks.{j}.mlp.fc2.weight""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.output.dense.weight""") )
rename_keys.append((f"""backbone.layers.{i}.blocks.{j}.mlp.fc2.bias""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.output.dense.bias""") )
if i < 3:
rename_keys.append((f"""backbone.layers.{i}.downsample.reduction.weight""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.reduction.weight""") )
rename_keys.append((f"""backbone.layers.{i}.downsample.norm.weight""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.norm.weight""") )
rename_keys.append((f"""backbone.layers.{i}.downsample.norm.bias""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.norm.bias""") )
rename_keys.append((f"""backbone.norm{i}.weight""", f"""model.pixel_level_module.encoder.hidden_states_norms.{i}.weight""") )
rename_keys.append((f"""backbone.norm{i}.bias""", f"""model.pixel_level_module.encoder.hidden_states_norms.{i}.bias""") )
# FPN
rename_keys.append(('''sem_seg_head.layer_4.weight''', '''model.pixel_level_module.decoder.fpn.stem.0.weight''') )
rename_keys.append(('''sem_seg_head.layer_4.norm.weight''', '''model.pixel_level_module.decoder.fpn.stem.1.weight''') )
rename_keys.append(('''sem_seg_head.layer_4.norm.bias''', '''model.pixel_level_module.decoder.fpn.stem.1.bias''') )
for source_index, target_index in zip(range(3 , 0 , -1 ) , range(0 , 3 ) ):
rename_keys.append((f"""sem_seg_head.adapter_{source_index}.weight""", f"""model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.0.weight""") )
rename_keys.append((f"""sem_seg_head.adapter_{source_index}.norm.weight""", f"""model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.1.weight""") )
rename_keys.append((f"""sem_seg_head.adapter_{source_index}.norm.bias""", f"""model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.1.bias""") )
rename_keys.append((f"""sem_seg_head.layer_{source_index}.weight""", f"""model.pixel_level_module.decoder.fpn.layers.{target_index}.block.0.weight""") )
rename_keys.append((f"""sem_seg_head.layer_{source_index}.norm.weight""", f"""model.pixel_level_module.decoder.fpn.layers.{target_index}.block.1.weight""") )
rename_keys.append((f"""sem_seg_head.layer_{source_index}.norm.bias""", f"""model.pixel_level_module.decoder.fpn.layers.{target_index}.block.1.bias""") )
rename_keys.append(('''sem_seg_head.mask_features.weight''', '''model.pixel_level_module.decoder.mask_projection.weight''') )
rename_keys.append(('''sem_seg_head.mask_features.bias''', '''model.pixel_level_module.decoder.mask_projection.bias''') )
# Transformer decoder
for idx in range(config.decoder_config.decoder_layers ):
# self-attention out projection
rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.out_proj.weight""", f"""model.transformer_module.decoder.layers.{idx}.self_attn.out_proj.weight""") )
rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.out_proj.bias""", f"""model.transformer_module.decoder.layers.{idx}.self_attn.out_proj.bias""") )
# cross-attention out projection
rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.out_proj.weight""", f"""model.transformer_module.decoder.layers.{idx}.encoder_attn.out_proj.weight""") )
rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.out_proj.bias""", f"""model.transformer_module.decoder.layers.{idx}.encoder_attn.out_proj.bias""") )
# MLP 1
rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear1.weight""", f"""model.transformer_module.decoder.layers.{idx}.fc1.weight""") )
rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear1.bias""", f"""model.transformer_module.decoder.layers.{idx}.fc1.bias""") )
# MLP 2
rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear2.weight""", f"""model.transformer_module.decoder.layers.{idx}.fc2.weight""") )
rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear2.bias""", f"""model.transformer_module.decoder.layers.{idx}.fc2.bias""") )
# layernorm 1 (self-attention layernorm)
rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm1.weight""", f"""model.transformer_module.decoder.layers.{idx}.self_attn_layer_norm.weight""") )
rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm1.bias""", f"""model.transformer_module.decoder.layers.{idx}.self_attn_layer_norm.bias""") )
# layernorm 2 (cross-attention layernorm)
rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm2.weight""", f"""model.transformer_module.decoder.layers.{idx}.encoder_attn_layer_norm.weight""") )
rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm2.bias""", f"""model.transformer_module.decoder.layers.{idx}.encoder_attn_layer_norm.bias""") )
# layernorm 3 (final layernorm)
rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm3.weight""", f"""model.transformer_module.decoder.layers.{idx}.final_layer_norm.weight""") )
rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm3.bias""", f"""model.transformer_module.decoder.layers.{idx}.final_layer_norm.bias""") )
rename_keys.append(('''sem_seg_head.predictor.transformer.decoder.norm.weight''', '''model.transformer_module.decoder.layernorm.weight''') )
rename_keys.append(('''sem_seg_head.predictor.transformer.decoder.norm.bias''', '''model.transformer_module.decoder.layernorm.bias''') )
# heads on top
rename_keys.append(('''sem_seg_head.predictor.query_embed.weight''', '''model.transformer_module.queries_embedder.weight''') )
rename_keys.append(('''sem_seg_head.predictor.input_proj.weight''', '''model.transformer_module.input_projection.weight''') )
rename_keys.append(('''sem_seg_head.predictor.input_proj.bias''', '''model.transformer_module.input_projection.bias''') )
rename_keys.append(('''sem_seg_head.predictor.class_embed.weight''', '''class_predictor.weight''') )
rename_keys.append(('''sem_seg_head.predictor.class_embed.bias''', '''class_predictor.bias''') )
for i in range(3 ):
rename_keys.append((f"""sem_seg_head.predictor.mask_embed.layers.{i}.weight""", f"""mask_embedder.{i}.0.weight""") )
rename_keys.append((f"""sem_seg_head.predictor.mask_embed.layers.{i}.bias""", f"""mask_embedder.{i}.0.bias""") )
# fmt: on
return rename_keys
def __UpperCamelCase ( a : Optional[int] , a : List[str] , a : int ) ->Tuple:
snake_case = dct.pop(a )
snake_case = val
def __UpperCamelCase ( a : Optional[int] , a : Tuple ) ->str:
snake_case = [int(backbone_config.embed_dim * 2**i ) for i in range(len(backbone_config.depths ) )]
for i in range(len(backbone_config.depths ) ):
snake_case = num_features[i]
for j in range(backbone_config.depths[i] ):
# fmt: off
# read in weights + bias of input projection layer (in original implementation, this is a single matrix + bias)
snake_case = state_dict.pop(f"""backbone.layers.{i}.blocks.{j}.attn.qkv.weight""" )
snake_case = state_dict.pop(f"""backbone.layers.{i}.blocks.{j}.attn.qkv.bias""" )
# next, add query, keys and values (in that order) to the state dict
snake_case = in_proj_weight[:dim, :]
snake_case = in_proj_bias[: dim]
snake_case = in_proj_weight[
dim : dim * 2, :
]
snake_case = in_proj_bias[
dim : dim * 2
]
snake_case = in_proj_weight[
-dim :, :
]
snake_case = in_proj_bias[-dim :]
# fmt: on
def __UpperCamelCase ( a : List[Any] , a : int ) ->Optional[int]:
# fmt: off
snake_case = config.decoder_config.hidden_size
for idx in range(config.decoder_config.decoder_layers ):
# read in weights + bias of self-attention input projection layer (in the original implementation, this is a single matrix + bias)
snake_case = state_dict.pop(f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_weight""" )
snake_case = state_dict.pop(f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_bias""" )
# next, add query, keys and values (in that order) to the state dict
snake_case = in_proj_weight[: hidden_size, :]
snake_case = in_proj_bias[:config.hidden_size]
snake_case = in_proj_weight[hidden_size : hidden_size * 2, :]
snake_case = in_proj_bias[hidden_size : hidden_size * 2]
snake_case = in_proj_weight[-hidden_size :, :]
snake_case = in_proj_bias[-hidden_size :]
# read in weights + bias of cross-attention input projection layer (in the original implementation, this is a single matrix + bias)
snake_case = state_dict.pop(f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_weight""" )
snake_case = state_dict.pop(f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_bias""" )
# next, add query, keys and values (in that order) to the state dict
snake_case = in_proj_weight[: hidden_size, :]
snake_case = in_proj_bias[:config.hidden_size]
snake_case = in_proj_weight[hidden_size : hidden_size * 2, :]
snake_case = in_proj_bias[hidden_size : hidden_size * 2]
snake_case = in_proj_weight[-hidden_size :, :]
snake_case = in_proj_bias[-hidden_size :]
# fmt: on
def __UpperCamelCase ( ) ->torch.Tensor:
snake_case = '''http://images.cocodataset.org/val2017/000000039769.jpg'''
snake_case = Image.open(requests.get(a , stream=a ).raw )
return im
@torch.no_grad()
def __UpperCamelCase ( a : str , a : str , a : str , a : bool = False ) ->int:
snake_case = get_maskformer_config(a )
# load original state_dict
with open(a , '''rb''' ) as f:
snake_case = pickle.load(a )
snake_case = data['''model''']
# for name, param in state_dict.items():
# print(name, param.shape)
# rename keys
snake_case = create_rename_keys(a )
for src, dest in rename_keys:
rename_key(a , a , a )
read_in_swin_q_k_v(a , config.backbone_config )
read_in_decoder_q_k_v(a , a )
# update to torch tensors
for key, value in state_dict.items():
snake_case = torch.from_numpy(a )
# load 🤗 model
snake_case = MaskFormerForInstanceSegmentation(a )
model.eval()
for name, param in model.named_parameters():
print(a , param.shape )
snake_case , snake_case = model.load_state_dict(a , strict=a )
assert missing_keys == [
"model.pixel_level_module.encoder.model.layernorm.weight",
"model.pixel_level_module.encoder.model.layernorm.bias",
]
assert len(a ) == 0, f"""Unexpected keys: {unexpected_keys}"""
# verify results
snake_case = prepare_img()
if "vistas" in model_name:
snake_case = 65
elif "cityscapes" in model_name:
snake_case = 6_5535
else:
snake_case = 255
snake_case = True if '''ade''' in model_name else False
snake_case = MaskFormerImageProcessor(ignore_index=a , reduce_labels=a )
snake_case = image_processor(a , return_tensors='''pt''' )
snake_case = model(**a )
print('''Logits:''' , outputs.class_queries_logits[0, :3, :3] )
if model_name == "maskformer-swin-tiny-ade":
snake_case = torch.tensor(
[[3.6353, -4.4770, -2.6065], [0.5081, -4.2394, -3.5343], [2.1909, -5.0353, -1.9323]] )
assert torch.allclose(outputs.class_queries_logits[0, :3, :3] , a , atol=1e-4 )
print('''Looks ok!''' )
if pytorch_dump_folder_path is not None:
print(f"""Saving model and image processor to {pytorch_dump_folder_path}""" )
Path(a ).mkdir(exist_ok=a )
model.save_pretrained(a )
image_processor.save_pretrained(a )
if push_to_hub:
print('''Pushing model and image processor to the hub...''' )
model.push_to_hub(f"""nielsr/{model_name}""" )
image_processor.push_to_hub(f"""nielsr/{model_name}""" )
if __name__ == "__main__":
_lowercase = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--model_name',
default='maskformer-swin-tiny-ade',
type=str,
help=('Name of the MaskFormer model you\'d like to convert',),
)
parser.add_argument(
'--checkpoint_path',
default='/Users/nielsrogge/Documents/MaskFormer_checkpoints/MaskFormer-Swin-tiny-ADE20k/model.pkl',
type=str,
help='Path to the original state dict (.pth file).',
)
parser.add_argument(
'--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
)
parser.add_argument(
'--push_to_hub', action='store_true', help='Whether or not to push the converted model to the 🤗 hub.'
)
_lowercase = parser.parse_args()
convert_maskformer_checkpoint(
args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
)
| 44
|
'''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 _lowercase ( __a ):
_UpperCAmelCase = '''char'''
_UpperCAmelCase = '''bpe'''
_UpperCAmelCase = '''wp'''
_lowercase = (DecodeType.CHARACTER, DecodeType.BPE, DecodeType.WORDPIECE)
class _lowercase ( __a ):
_UpperCAmelCase = ['''image_processor''', '''char_tokenizer''']
_UpperCAmelCase = '''ViTImageProcessor'''
_UpperCAmelCase = '''MgpstrTokenizer'''
def __init__( self , A__=None , A__=None , **A__ ) -> List[Any]:
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.''' , A__ , )
snake_case = kwargs.pop('''feature_extractor''' )
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`.''' )
snake_case = tokenizer
snake_case = AutoTokenizer.from_pretrained('''gpt2''' )
snake_case = AutoTokenizer.from_pretrained('''bert-base-uncased''' )
super().__init__(A__ , A__ )
def __call__( self , A__=None , A__=None , A__=None , **A__ ) -> List[str]:
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:
snake_case = self.image_processor(A__ , return_tensors=A__ , **A__ )
if text is not None:
snake_case = self.char_tokenizer(A__ , return_tensors=A__ , **A__ )
if text is None:
return inputs
elif images is None:
return encodings
else:
snake_case = encodings['''input_ids''']
return inputs
def UpperCamelCase ( self , A__ ) -> Dict:
snake_case , snake_case , snake_case = sequences
snake_case = char_preds.size(0 )
snake_case , snake_case = self._decode_helper(A__ , '''char''' )
snake_case , snake_case = self._decode_helper(A__ , '''bpe''' )
snake_case , snake_case = self._decode_helper(A__ , '''wp''' )
snake_case = []
snake_case = []
for i in range(A__ ):
snake_case = [char_scores[i], bpe_scores[i], wp_scores[i]]
snake_case = [char_strs[i], bpe_strs[i], wp_strs[i]]
snake_case = scores.index(max(A__ ) )
final_strs.append(strs[max_score_index] )
final_scores.append(scores[max_score_index] )
snake_case = {}
snake_case = final_strs
snake_case = final_scores
snake_case = char_strs
snake_case = bpe_strs
snake_case = wp_strs
return out
def UpperCamelCase ( self , A__ , A__ ) -> Optional[Any]:
if format == DecodeType.CHARACTER:
snake_case = self.char_decode
snake_case = 1
snake_case = '''[s]'''
elif format == DecodeType.BPE:
snake_case = self.bpe_decode
snake_case = 2
snake_case = '''#'''
elif format == DecodeType.WORDPIECE:
snake_case = self.wp_decode
snake_case = 1_02
snake_case = '''[SEP]'''
else:
raise ValueError(F"""Format {format} is not supported.""" )
snake_case , snake_case = [], []
snake_case = pred_logits.size(0 )
snake_case = pred_logits.size(1 )
snake_case , snake_case = pred_logits.topk(1 , dim=-1 , largest=A__ , sorted=A__ )
snake_case = preds_index.view(-1 , A__ )[:, 1:]
snake_case = decoder(A__ )
snake_case , snake_case = torch.nn.functional.softmax(A__ , dim=2 ).max(dim=2 )
snake_case = preds_max_prob[:, 1:]
for index in range(A__ ):
snake_case = preds_str[index].find(A__ )
snake_case = preds_str[index][:pred_eos]
snake_case = preds_index[index].cpu().tolist()
snake_case = pred_index.index(A__ ) if eos_token in pred_index else -1
snake_case = preds_max_prob[index][: pred_eos_index + 1]
snake_case = pred_max_prob.cumprod(dim=0 )[-1] if pred_max_prob.nelement() != 0 else 0.0
dec_strs.append(A__ )
conf_scores.append(A__ )
return dec_strs, conf_scores
def UpperCamelCase ( self , A__ ) -> int:
snake_case = [seq.replace(''' ''' , '''''' ) for seq in self.char_tokenizer.batch_decode(A__ )]
return decode_strs
def UpperCamelCase ( self , A__ ) -> List[str]:
return self.bpe_tokenizer.batch_decode(A__ )
def UpperCamelCase ( self , A__ ) -> Union[str, Any]:
snake_case = [seq.replace(''' ''' , '''''' ) for seq in self.wp_tokenizer.batch_decode(A__ )]
return decode_strs
| 44
| 1
|
'''simple docstring'''
_lowercase = [
999,
800,
799,
600,
599,
500,
400,
399,
377,
355,
333,
311,
288,
266,
244,
222,
200,
199,
177,
155,
133,
111,
88,
66,
44,
22,
0,
]
_lowercase = [
999,
976,
952,
928,
905,
882,
858,
857,
810,
762,
715,
714,
572,
429,
428,
286,
285,
238,
190,
143,
142,
118,
95,
71,
47,
24,
0,
]
_lowercase = [
999,
988,
977,
966,
955,
944,
933,
922,
911,
900,
899,
879,
859,
840,
820,
800,
799,
766,
733,
700,
699,
650,
600,
599,
500,
499,
400,
399,
350,
300,
299,
266,
233,
200,
199,
179,
159,
140,
120,
100,
99,
88,
77,
66,
55,
44,
33,
22,
11,
0,
]
_lowercase = [
999,
995,
992,
989,
985,
981,
978,
975,
971,
967,
964,
961,
957,
956,
951,
947,
942,
937,
933,
928,
923,
919,
914,
913,
908,
903,
897,
892,
887,
881,
876,
871,
870,
864,
858,
852,
846,
840,
834,
828,
827,
820,
813,
806,
799,
792,
785,
784,
777,
770,
763,
756,
749,
742,
741,
733,
724,
716,
707,
699,
698,
688,
677,
666,
656,
655,
645,
634,
623,
613,
612,
598,
584,
570,
569,
555,
541,
527,
526,
505,
484,
483,
462,
440,
439,
396,
395,
352,
351,
308,
307,
264,
263,
220,
219,
176,
132,
88,
44,
0,
]
_lowercase = [
999,
997,
995,
992,
990,
988,
986,
984,
981,
979,
977,
975,
972,
970,
968,
966,
964,
961,
959,
957,
956,
954,
951,
949,
946,
944,
941,
939,
936,
934,
931,
929,
926,
924,
921,
919,
916,
914,
913,
910,
907,
905,
902,
899,
896,
893,
891,
888,
885,
882,
879,
877,
874,
871,
870,
867,
864,
861,
858,
855,
852,
849,
846,
843,
840,
837,
834,
831,
828,
827,
824,
821,
817,
814,
811,
808,
804,
801,
798,
795,
791,
788,
785,
784,
780,
777,
774,
770,
766,
763,
760,
756,
752,
749,
746,
742,
741,
737,
733,
730,
726,
722,
718,
714,
710,
707,
703,
699,
698,
694,
690,
685,
681,
677,
673,
669,
664,
660,
656,
655,
650,
646,
641,
636,
632,
627,
622,
618,
613,
612,
607,
602,
596,
591,
586,
580,
575,
570,
569,
563,
557,
551,
545,
539,
533,
527,
526,
519,
512,
505,
498,
491,
484,
483,
474,
466,
457,
449,
440,
439,
428,
418,
407,
396,
395,
381,
366,
352,
351,
330,
308,
307,
286,
264,
263,
242,
220,
219,
176,
175,
132,
131,
88,
44,
0,
]
_lowercase = [
999,
991,
982,
974,
966,
958,
950,
941,
933,
925,
916,
908,
900,
899,
874,
850,
825,
800,
799,
700,
600,
500,
400,
300,
200,
100,
0,
]
_lowercase = [
999,
992,
985,
978,
971,
964,
957,
949,
942,
935,
928,
921,
914,
907,
900,
899,
879,
859,
840,
820,
800,
799,
766,
733,
700,
699,
650,
600,
599,
500,
499,
400,
399,
300,
299,
200,
199,
100,
99,
0,
]
_lowercase = [
999,
996,
992,
989,
985,
982,
979,
975,
972,
968,
965,
961,
958,
955,
951,
948,
944,
941,
938,
934,
931,
927,
924,
920,
917,
914,
910,
907,
903,
900,
899,
891,
884,
876,
869,
861,
853,
846,
838,
830,
823,
815,
808,
800,
799,
788,
777,
766,
755,
744,
733,
722,
711,
700,
699,
688,
677,
666,
655,
644,
633,
622,
611,
600,
599,
585,
571,
557,
542,
528,
514,
500,
499,
485,
471,
457,
442,
428,
414,
400,
399,
379,
359,
340,
320,
300,
299,
279,
259,
240,
220,
200,
199,
166,
133,
100,
99,
66,
33,
0,
]
| 44
|
'''simple docstring'''
import os
from dataclasses import dataclass, field
from io import BytesIO
from typing import TYPE_CHECKING, Any, ClassVar, Dict, Optional, Union
import numpy as np
import pyarrow as pa
from .. import config
from ..download.streaming_download_manager import xopen, xsplitext
from ..table import array_cast
from ..utils.py_utils import no_op_if_value_is_null, string_to_dict
if TYPE_CHECKING:
from .features import FeatureType
_lowercase , _lowercase , _lowercase = False, False, False
@dataclass
class _lowercase :
_UpperCAmelCase = None
_UpperCAmelCase = True
_UpperCAmelCase = True
_UpperCAmelCase = None
# Automatically constructed
_UpperCAmelCase = "dict"
_UpperCAmelCase = pa.struct({'''bytes''': pa.binary(), '''path''': pa.string()} )
_UpperCAmelCase = field(default='''Audio''' , init=__a , repr=__a )
def __call__( self ) -> Optional[Any]:
return self.pa_type
def UpperCamelCase ( self , A__ ) -> dict:
try:
import soundfile as sf # soundfile is a dependency of librosa, needed to decode audio files.
except ImportError as err:
raise ImportError('''To support encoding audio data, please install \'soundfile\'.''' ) from err
if isinstance(A__ , A__ ):
return {"bytes": None, "path": value}
elif isinstance(A__ , A__ ):
return {"bytes": value, "path": None}
elif "array" in value:
# convert the audio array to wav bytes
snake_case = BytesIO()
sf.write(A__ , value['''array'''] , value['''sampling_rate'''] , format='''wav''' )
return {"bytes": buffer.getvalue(), "path": None}
elif value.get('''path''' ) is not None and os.path.isfile(value['''path'''] ):
# we set "bytes": None to not duplicate the data if they're already available locally
if value["path"].endswith('''pcm''' ):
# "PCM" only has raw audio bytes
if value.get('''sampling_rate''' ) is None:
# At least, If you want to convert "PCM-byte" to "WAV-byte", you have to know sampling rate
raise KeyError('''To use PCM files, please specify a \'sampling_rate\' in Audio object''' )
if value.get('''bytes''' ):
# If we already had PCM-byte, we don`t have to make "read file, make bytes" (just use it!)
snake_case = np.frombuffer(value['''bytes'''] , dtype=np.intaa ).astype(np.floataa ) / 3_27_67
else:
snake_case = np.memmap(value['''path'''] , dtype='''h''' , mode='''r''' ).astype(np.floataa ) / 3_27_67
snake_case = BytesIO(bytes() )
sf.write(A__ , A__ , value['''sampling_rate'''] , format='''wav''' )
return {"bytes": buffer.getvalue(), "path": None}
else:
return {"bytes": None, "path": value.get('''path''' )}
elif value.get('''bytes''' ) is not None or value.get('''path''' ) is not None:
# store the audio bytes, and path is used to infer the audio format using the file extension
return {"bytes": value.get('''bytes''' ), "path": value.get('''path''' )}
else:
raise ValueError(
F"""An audio sample should have one of 'path' or 'bytes' but they are missing or None in {value}.""" )
def UpperCamelCase ( self , A__ , A__ = None ) -> dict:
if not self.decode:
raise RuntimeError('''Decoding is disabled for this feature. Please use Audio(decode=True) instead.''' )
snake_case , snake_case = (value['''path'''], BytesIO(value['''bytes'''] )) if value['''bytes'''] is not None else (value['''path'''], None)
if path is None and file is None:
raise ValueError(F"""An audio sample should have one of 'path' or 'bytes' but both are None in {value}.""" )
try:
import librosa
import soundfile as sf
except ImportError as err:
raise ImportError('''To support decoding audio files, please install \'librosa\' and \'soundfile\'.''' ) from err
snake_case = xsplitext(A__ )[1][1:].lower() if path is not None else None
if not config.IS_OPUS_SUPPORTED and audio_format == "opus":
raise RuntimeError(
'''Decoding \'opus\' files requires system library \'libsndfile\'>=1.0.31, '''
'''You can try to update `soundfile` python library: `pip install "soundfile>=0.12.1"`. ''' )
elif not config.IS_MP3_SUPPORTED and audio_format == "mp3":
raise RuntimeError(
'''Decoding \'mp3\' files requires system library \'libsndfile\'>=1.1.0, '''
'''You can try to update `soundfile` python library: `pip install "soundfile>=0.12.1"`. ''' )
if file is None:
snake_case = token_per_repo_id or {}
snake_case = path.split('''::''' )[-1]
try:
snake_case = string_to_dict(A__ , config.HUB_DATASETS_URL )['''repo_id''']
snake_case = token_per_repo_id[repo_id]
except (ValueError, KeyError):
snake_case = None
with xopen(A__ , '''rb''' , use_auth_token=A__ ) as f:
snake_case , snake_case = sf.read(A__ )
else:
snake_case , snake_case = sf.read(A__ )
snake_case = array.T
if self.mono:
snake_case = librosa.to_mono(A__ )
if self.sampling_rate and self.sampling_rate != sampling_rate:
snake_case = librosa.resample(A__ , orig_sr=A__ , target_sr=self.sampling_rate )
snake_case = self.sampling_rate
return {"path": path, "array": array, "sampling_rate": sampling_rate}
def UpperCamelCase ( self ) -> Union["FeatureType", Dict[str, "FeatureType"]]:
from .features import Value
if self.decode:
raise ValueError('''Cannot flatten a decoded Audio feature.''' )
return {
"bytes": Value('''binary''' ),
"path": Value('''string''' ),
}
def UpperCamelCase ( self , A__ ) -> pa.StructArray:
if pa.types.is_string(storage.type ):
snake_case = pa.array([None] * len(A__ ) , type=pa.binary() )
snake_case = pa.StructArray.from_arrays([bytes_array, storage] , ['''bytes''', '''path'''] , mask=storage.is_null() )
elif pa.types.is_binary(storage.type ):
snake_case = pa.array([None] * len(A__ ) , type=pa.string() )
snake_case = pa.StructArray.from_arrays([storage, path_array] , ['''bytes''', '''path'''] , mask=storage.is_null() )
elif pa.types.is_struct(storage.type ) and storage.type.get_all_field_indices('''array''' ):
snake_case = pa.array([Audio().encode_example(A__ ) if x is not None else None for x in storage.to_pylist()] )
elif pa.types.is_struct(storage.type ):
if storage.type.get_field_index('''bytes''' ) >= 0:
snake_case = storage.field('''bytes''' )
else:
snake_case = pa.array([None] * len(A__ ) , type=pa.binary() )
if storage.type.get_field_index('''path''' ) >= 0:
snake_case = storage.field('''path''' )
else:
snake_case = pa.array([None] * len(A__ ) , type=pa.string() )
snake_case = pa.StructArray.from_arrays([bytes_array, path_array] , ['''bytes''', '''path'''] , mask=storage.is_null() )
return array_cast(A__ , self.pa_type )
def UpperCamelCase ( self , A__ ) -> pa.StructArray:
@no_op_if_value_is_null
def path_to_bytes(A__ ):
with xopen(A__ , '''rb''' ) as f:
snake_case = f.read()
return bytes_
snake_case = pa.array(
[
(path_to_bytes(x['''path'''] ) if x['''bytes'''] is None else x['''bytes''']) if x is not None else None
for x in storage.to_pylist()
] , type=pa.binary() , )
snake_case = pa.array(
[os.path.basename(A__ ) if path is not None else None for path in storage.field('''path''' ).to_pylist()] , type=pa.string() , )
snake_case = pa.StructArray.from_arrays([bytes_array, path_array] , ['''bytes''', '''path'''] , mask=bytes_array.is_null() )
return array_cast(A__ , self.pa_type )
| 44
| 1
|
'''simple docstring'''
import unittest
from transformers import PegasusConfig, PegasusTokenizer, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor
if is_flax_available():
import os
# The slow tests are often failing with OOM error on GPU
# This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
# but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
_lowercase = 'platform'
import jax
import jax.numpy as jnp
import numpy as np
from transformers import FlaxPegasusForConditionalGeneration, FlaxPegasusModel
@require_flax
class _lowercase :
_UpperCAmelCase = PegasusConfig
_UpperCAmelCase = {}
_UpperCAmelCase = '''gelu'''
def __init__( self , A__ , A__=13 , A__=7 , A__=True , A__=False , A__=99 , A__=32 , A__=5 , A__=4 , A__=37 , A__=0.1 , A__=0.1 , A__=20 , A__=2 , A__=1 , A__=0 , ) -> Optional[int]:
snake_case = parent
snake_case = batch_size
snake_case = seq_length
snake_case = is_training
snake_case = use_labels
snake_case = vocab_size
snake_case = hidden_size
snake_case = num_hidden_layers
snake_case = num_attention_heads
snake_case = intermediate_size
snake_case = hidden_dropout_prob
snake_case = attention_probs_dropout_prob
snake_case = max_position_embeddings
snake_case = eos_token_id
snake_case = pad_token_id
snake_case = bos_token_id
def UpperCamelCase ( self ) -> Optional[Any]:
snake_case = ids_tensor([self.batch_size, self.seq_length - 1] , self.vocab_size ).clip(3 , self.vocab_size )
snake_case = np.expand_dims(np.array([self.eos_token_id] * self.batch_size ) , 1 )
snake_case = np.concatenate([input_ids, eos_tensor] , axis=1 )
snake_case = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
snake_case = 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 , **self.config_updates , )
snake_case = prepare_pegasus_inputs_dict(A__ , A__ , A__ )
return config, inputs_dict
def UpperCamelCase ( self , A__ , A__ , A__ ) -> Dict:
snake_case = 20
snake_case = model_class_name(A__ )
snake_case = model.encode(inputs_dict['''input_ids'''] )
snake_case , snake_case = (
inputs_dict['''decoder_input_ids'''],
inputs_dict['''decoder_attention_mask'''],
)
snake_case = model.init_cache(decoder_input_ids.shape[0] , A__ , A__ )
snake_case = jnp.ones((decoder_input_ids.shape[0], max_decoder_length) , dtype='''i4''' )
snake_case = jnp.broadcast_to(
jnp.arange(decoder_input_ids.shape[-1] - 1 )[None, :] , (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1) , )
snake_case = model.decode(
decoder_input_ids[:, :-1] , A__ , decoder_attention_mask=A__ , past_key_values=A__ , decoder_position_ids=A__ , )
snake_case = jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]] , dtype='''i4''' )
snake_case = model.decode(
decoder_input_ids[:, -1:] , A__ , decoder_attention_mask=A__ , past_key_values=outputs_cache.past_key_values , decoder_position_ids=A__ , )
snake_case = model.decode(A__ , A__ )
snake_case = 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:
snake_case = 20
snake_case = model_class_name(A__ )
snake_case = model.encode(inputs_dict['''input_ids'''] )
snake_case , snake_case = (
inputs_dict['''decoder_input_ids'''],
inputs_dict['''decoder_attention_mask'''],
)
snake_case = jnp.concatenate(
[
decoder_attention_mask,
jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1]) ),
] , axis=-1 , )
snake_case = model.init_cache(decoder_input_ids.shape[0] , A__ , A__ )
snake_case = jnp.broadcast_to(
jnp.arange(decoder_input_ids.shape[-1] - 1 )[None, :] , (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1) , )
snake_case = model.decode(
decoder_input_ids[:, :-1] , A__ , decoder_attention_mask=A__ , past_key_values=A__ , decoder_position_ids=A__ , )
snake_case = jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]] , dtype='''i4''' )
snake_case = model.decode(
decoder_input_ids[:, -1:] , A__ , past_key_values=outputs_cache.past_key_values , decoder_attention_mask=A__ , decoder_position_ids=A__ , )
snake_case = model.decode(A__ , A__ , decoder_attention_mask=A__ )
snake_case = 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 ( a : Union[str, Any] , a : str , a : Union[str, Any] , a : Optional[int]=None , a : int=None , ) ->str:
if attention_mask is None:
snake_case = np.not_equal(a , config.pad_token_id ).astype(np.inta )
if decoder_attention_mask is None:
snake_case = np.concatenate(
[
np.ones(decoder_input_ids[:, :1].shape , dtype=np.inta ),
np.not_equal(decoder_input_ids[:, 1:] , config.pad_token_id ).astype(np.inta ),
] , axis=-1 , )
return {
"input_ids": input_ids,
"decoder_input_ids": decoder_input_ids,
"attention_mask": attention_mask,
"decoder_attention_mask": decoder_attention_mask,
}
@require_flax
class _lowercase ( __a , unittest.TestCase ):
_UpperCAmelCase = (
(
FlaxPegasusForConditionalGeneration,
FlaxPegasusModel,
)
if is_flax_available()
else ()
)
_UpperCAmelCase = (FlaxPegasusForConditionalGeneration,) if is_flax_available() else ()
_UpperCAmelCase = True
_UpperCAmelCase = False
_UpperCAmelCase = False
_UpperCAmelCase = False
def UpperCamelCase ( self ) -> int:
snake_case = FlaxPegasusModelTester(self )
snake_case = ConfigTester(self , config_class=A__ )
def UpperCamelCase ( self ) -> Optional[Any]:
self.config_tester.run_common_tests()
def UpperCamelCase ( self ) -> Dict:
snake_case , snake_case = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
self.model_tester.check_use_cache_forward(A__ , A__ , A__ )
def UpperCamelCase ( self ) -> int:
snake_case , snake_case = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
self.model_tester.check_use_cache_forward_with_attn_mask(A__ , A__ , A__ )
def UpperCamelCase ( self ) -> List[Any]:
snake_case , snake_case = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__ ):
snake_case = self._prepare_for_class(A__ , A__ )
snake_case = model_class(A__ )
@jax.jit
def encode_jitted(A__ , A__=None , **A__ ):
return model.encode(input_ids=A__ , attention_mask=A__ )
with self.subTest('''JIT Enabled''' ):
snake_case = encode_jitted(**A__ ).to_tuple()
with self.subTest('''JIT Disabled''' ):
with jax.disable_jit():
snake_case = encode_jitted(**A__ ).to_tuple()
self.assertEqual(len(A__ ) , len(A__ ) )
for jitted_output, output in zip(A__ , A__ ):
self.assertEqual(jitted_output.shape , output.shape )
def UpperCamelCase ( self ) -> Optional[int]:
snake_case , snake_case = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__ ):
snake_case = model_class(A__ )
snake_case = model.encode(inputs_dict['''input_ids'''] , inputs_dict['''attention_mask'''] )
snake_case = {
'''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=A__ , decoder_attention_mask=A__ , encoder_outputs=A__ , )
with self.subTest('''JIT Enabled''' ):
snake_case = decode_jitted(**A__ ).to_tuple()
with self.subTest('''JIT Disabled''' ):
with jax.disable_jit():
snake_case = decode_jitted(**A__ ).to_tuple()
self.assertEqual(len(A__ ) , len(A__ ) )
for jitted_output, output in zip(A__ , A__ ):
self.assertEqual(jitted_output.shape , output.shape )
@slow
def UpperCamelCase ( self ) -> Optional[int]:
for model_class_name in self.all_model_classes:
snake_case = model_class_name.from_pretrained('''google/pegasus-large''' , from_pt=A__ )
snake_case = np.ones((1, 1) )
snake_case = model(A__ )
self.assertIsNotNone(A__ )
@slow
def UpperCamelCase ( self ) -> Union[str, Any]:
snake_case = FlaxPegasusForConditionalGeneration.from_pretrained('''google/pegasus-xsum''' )
snake_case = PegasusTokenizer.from_pretrained('''google/pegasus-xsum''' )
snake_case = [
''' PG&E stated it scheduled the blackouts in response to forecasts for high winds amid dry conditions. The aim is to reduce the risk of wildfires. Nearly 800 thousand customers were scheduled to be affected by the shutoffs which were expected to last through at least midday tomorrow.''',
''' The London trio are up for best UK act and best album, as well as getting two nominations in the best song category."We got told like this morning \'Oh I think you\'re nominated\'", said Dappy."And I was like \'Oh yeah, which one?\' And now we\'ve got nominated for four awards. I mean, wow!"Bandmate Fazer added: "We thought it\'s best of us to come down and mingle with everyone and say hello to the cameras. And now we find we\'ve got four nominations."The band have two shots at the best song prize, getting the nod for their Tynchy Stryder collaboration Number One, and single Strong Again.Their album Uncle B will also go up against records by the likes of Beyonce and Kanye West.N-Dubz picked up the best newcomer Mobo in 2007, but female member Tulisa said they wouldn\'t be too disappointed if they didn\'t win this time around."At the end of the day we\'re grateful to be where we are in our careers."If it don\'t happen then it don\'t happen - live to fight another day and keep on making albums and hits for the fans."Dappy also revealed they could be performing live several times on the night.The group will be doing Number One and also a possible rendition of the War Child single, I Got Soul.The charity song is a re-working of The Killers\' All These Things That I\'ve Done and is set to feature artists like Chipmunk, Ironik and Pixie Lott.This year\'s Mobos will be held outside of London for the first time, in Glasgow on 30 September.N-Dubz said they were looking forward to performing for their Scottish fans and boasted about their recent shows north of the border."We just done Edinburgh the other day," said Dappy."We smashed up an N-Dubz show over there. We done Aberdeen about three or four months ago - we smashed up that show over there! Everywhere we go we smash it up!" ''',
]
snake_case = [
'''California\'s largest electricity provider has turned off power to hundreds of thousands of customers.''',
'''Pop group N-Dubz have revealed they were surprised to get four nominations for this year\'s Mobo Awards.''',
]
snake_case = tokenizer(A__ , return_tensors='''np''' , truncation=A__ , max_length=5_12 , padding=A__ )
snake_case = model.generate(**A__ , num_beams=2 ).sequences
snake_case = tokenizer.batch_decode(A__ , skip_special_tokens=A__ )
assert tgt_text == decoded
| 44
|
'''simple docstring'''
import hashlib
import unittest
from transformers import MODEL_FOR_DEPTH_ESTIMATION_MAPPING, is_torch_available, is_vision_available
from transformers.pipelines import DepthEstimationPipeline, pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_timm,
require_torch,
require_vision,
slow,
)
from .test_pipelines_common import ANY
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
else:
class _lowercase :
@staticmethod
def UpperCamelCase ( *A__ , **A__ ) -> List[Any]:
pass
def __UpperCamelCase ( a : Image ) ->str:
snake_case = hashlib.mda(image.tobytes() )
return m.hexdigest()
@is_pipeline_test
@require_vision
@require_timm
@require_torch
class _lowercase ( unittest.TestCase ):
_UpperCAmelCase = MODEL_FOR_DEPTH_ESTIMATION_MAPPING
def UpperCamelCase ( self , A__ , A__ , A__ ) -> Union[str, Any]:
snake_case = DepthEstimationPipeline(model=A__ , image_processor=A__ )
return depth_estimator, [
"./tests/fixtures/tests_samples/COCO/000000039769.png",
"./tests/fixtures/tests_samples/COCO/000000039769.png",
]
def UpperCamelCase ( self , A__ , A__ ) -> List[Any]:
snake_case = depth_estimator('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
self.assertEqual({'''predicted_depth''': ANY(torch.Tensor ), '''depth''': ANY(Image.Image )} , A__ )
import datasets
snake_case = datasets.load_dataset('''hf-internal-testing/fixtures_image_utils''' , '''image''' , split='''test''' )
snake_case = depth_estimator(
[
Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' ),
'''http://images.cocodataset.org/val2017/000000039769.jpg''',
# RGBA
dataset[0]['''file'''],
# LA
dataset[1]['''file'''],
# L
dataset[2]['''file'''],
] )
self.assertEqual(
[
{'''predicted_depth''': ANY(torch.Tensor ), '''depth''': ANY(Image.Image )},
{'''predicted_depth''': ANY(torch.Tensor ), '''depth''': ANY(Image.Image )},
{'''predicted_depth''': ANY(torch.Tensor ), '''depth''': ANY(Image.Image )},
{'''predicted_depth''': ANY(torch.Tensor ), '''depth''': ANY(Image.Image )},
{'''predicted_depth''': ANY(torch.Tensor ), '''depth''': ANY(Image.Image )},
] , A__ , )
@require_tf
@unittest.skip('''Depth estimation is not implemented in TF''' )
def UpperCamelCase ( self ) -> Optional[Any]:
pass
@slow
@require_torch
def UpperCamelCase ( self ) -> Dict:
snake_case = '''Intel/dpt-large'''
snake_case = pipeline('''depth-estimation''' , model=A__ )
snake_case = depth_estimator('''http://images.cocodataset.org/val2017/000000039769.jpg''' )
snake_case = hashimage(outputs['''depth'''] )
# This seems flaky.
# self.assertEqual(outputs["depth"], "1a39394e282e9f3b0741a90b9f108977")
self.assertEqual(nested_simplify(outputs['''predicted_depth'''].max().item() ) , 2_9.3_0_4 )
self.assertEqual(nested_simplify(outputs['''predicted_depth'''].min().item() ) , 2.6_6_2 )
@require_torch
def UpperCamelCase ( self ) -> Any:
# This is highly irregular to have no small tests.
self.skipTest('''There is not hf-internal-testing tiny model for either GLPN nor DPT''' )
| 44
| 1
|
'''simple docstring'''
import torch
from transformers import PreTrainedModel, XLMRobertaConfig, XLMRobertaModel
class _lowercase ( __a ):
_UpperCAmelCase = '''M-CLIP'''
def __init__( self , A__=10_24 , A__=7_68 , **A__ ) -> List[Any]:
snake_case = transformerDimSize
snake_case = imageDimSize
super().__init__(**A__ )
class _lowercase ( __a ):
_UpperCAmelCase = MCLIPConfig
def __init__( self , A__ , *A__ , **A__ ) -> List[str]:
super().__init__(A__ , *A__ , **A__ )
snake_case = XLMRobertaModel(A__ )
snake_case = torch.nn.Linear(
in_features=config.transformerDimensions , out_features=config.numDims )
def UpperCamelCase ( self , A__ , A__ ) -> Any:
snake_case = self.transformer(input_ids=A__ , attention_mask=A__ )[0]
snake_case = (embs * attention_mask.unsqueeze(2 )).sum(dim=1 ) / attention_mask.sum(dim=1 )[:, None]
return self.LinearTransformation(A__ ), embs
| 44
|
'''simple docstring'''
import argparse
import torch
from torch import nn
from transformers import SpeechaTextConfig, SpeechaTextForConditionalGeneration
def __UpperCamelCase ( a : Optional[int] ) ->Dict:
snake_case = [
'''encoder.version''',
'''decoder.version''',
'''model.encoder.version''',
'''model.decoder.version''',
'''decoder.output_projection.weight''',
'''_float_tensor''',
'''encoder.embed_positions._float_tensor''',
'''decoder.embed_positions._float_tensor''',
]
for k in ignore_keys:
state_dict.pop(a , a )
def __UpperCamelCase ( a : Optional[Any] ) ->int:
snake_case = list(s_dict.keys() )
for key in keys:
if "transformer_layers" in key:
snake_case = s_dict.pop(a )
elif "subsample" in key:
snake_case = s_dict.pop(a )
def __UpperCamelCase ( a : Optional[int] ) ->Optional[int]:
snake_case , snake_case = emb.weight.shape
snake_case = nn.Linear(a , a , bias=a )
snake_case = emb.weight.data
return lin_layer
def __UpperCamelCase ( a : Any , a : Tuple ) ->Tuple:
snake_case = torch.load(a , map_location='''cpu''' )
snake_case = mam_aaa['''args''']
snake_case = mam_aaa['''model''']
snake_case = state_dict['''decoder.output_projection.weight''']
remove_ignore_keys_(a )
rename_keys(a )
snake_case = state_dict['''decoder.embed_tokens.weight'''].shape[0]
snake_case = args.share_decoder_input_output_embed
snake_case = [int(a ) for i in args.conv_kernel_sizes.split(''',''' )]
snake_case = SpeechaTextConfig(
vocab_size=a , max_source_positions=args.max_source_positions , max_target_positions=args.max_target_positions , encoder_layers=args.encoder_layers , decoder_layers=args.decoder_layers , encoder_attention_heads=args.encoder_attention_heads , decoder_attention_heads=args.decoder_attention_heads , encoder_ffn_dim=args.encoder_ffn_embed_dim , decoder_ffn_dim=args.decoder_ffn_embed_dim , d_model=args.encoder_embed_dim , dropout=args.dropout , attention_dropout=args.attention_dropout , activation_dropout=args.activation_dropout , activation_function='''relu''' , num_conv_layers=len(a ) , conv_channels=args.conv_channels , conv_kernel_sizes=a , input_feat_per_channel=args.input_feat_per_channel , input_channels=args.input_channels , tie_word_embeddings=a , num_beams=5 , max_length=200 , use_cache=a , decoder_start_token_id=2 , early_stopping=a , )
snake_case = SpeechaTextForConditionalGeneration(a )
snake_case , snake_case = model.model.load_state_dict(a , strict=a )
if len(a ) > 0 and not set(a ) <= {
"encoder.embed_positions.weights",
"decoder.embed_positions.weights",
}:
raise ValueError(
'''Only `encoder.embed_positions.weights` and `decoder.embed_positions.weights` are allowed to be missing,'''
f""" but all the following weights are missing {missing}""" )
if tie_embeds:
snake_case = make_linear_from_emb(model.model.decoder.embed_tokens )
else:
snake_case = lm_head_weights
model.save_pretrained(a )
if __name__ == "__main__":
_lowercase = argparse.ArgumentParser()
# Required parameters
parser.add_argument('--fairseq_path', type=str, help='Path to the fairseq model (.pt) file.')
parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
_lowercase = parser.parse_args()
convert_fairseq_sat_checkpoint_to_tfms(args.fairseq_path, args.pytorch_dump_folder_path)
| 44
| 1
|
'''simple docstring'''
# using dfs for finding eulerian path traversal
def __UpperCamelCase ( a : Tuple , a : str , a : Any , a : int=None ) ->Any:
snake_case = (path or []) + [u]
for v in graph[u]:
if visited_edge[u][v] is False:
snake_case , snake_case = True, True
snake_case = dfs(a , a , a , a )
return path
def __UpperCamelCase ( a : Optional[int] , a : Optional[int] ) ->List[Any]:
snake_case = 0
snake_case = -1
for i in range(a ):
if i not in graph.keys():
continue
if len(graph[i] ) % 2 == 1:
odd_degree_nodes += 1
snake_case = i
if odd_degree_nodes == 0:
return 1, odd_node
if odd_degree_nodes == 2:
return 2, odd_node
return 3, odd_node
def __UpperCamelCase ( a : Dict , a : Any ) ->Dict:
snake_case = [[False for _ in range(max_node + 1 )] for _ in range(max_node + 1 )]
snake_case , snake_case = check_circuit_or_path(a , a )
if check == 3:
print('''graph is not Eulerian''' )
print('''no path''' )
return
snake_case = 1
if check == 2:
snake_case = odd_node
print('''graph has a Euler path''' )
if check == 1:
print('''graph has a Euler cycle''' )
snake_case = dfs(a , a , a )
print(a )
def __UpperCamelCase ( ) ->Tuple:
snake_case = {1: [2, 3, 4], 2: [1, 3], 3: [1, 2], 4: [1, 5], 5: [4]}
snake_case = {1: [2, 3, 4, 5], 2: [1, 3], 3: [1, 2], 4: [1, 5], 5: [1, 4]}
snake_case = {1: [2, 3, 4], 2: [1, 3, 4], 3: [1, 2], 4: [1, 2, 5], 5: [4]}
snake_case = {1: [2, 3], 2: [1, 3], 3: [1, 2]}
snake_case = {
1: [],
2: []
# all degree is zero
}
snake_case = 10
check_euler(a , a )
check_euler(a , a )
check_euler(a , a )
check_euler(a , a )
check_euler(a , a )
if __name__ == "__main__":
main()
| 44
|
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class _lowercase ( metaclass=__a ):
_UpperCAmelCase = ['''transformers''', '''torch''', '''note_seq''']
def __init__( self , *A__ , **A__ ) -> Union[str, Any]:
requires_backends(self , ['''transformers''', '''torch''', '''note_seq'''] )
@classmethod
def UpperCamelCase ( cls , *A__ , **A__ ) -> Optional[Any]:
requires_backends(cls , ['''transformers''', '''torch''', '''note_seq'''] )
@classmethod
def UpperCamelCase ( cls , *A__ , **A__ ) -> Any:
requires_backends(cls , ['''transformers''', '''torch''', '''note_seq'''] )
| 44
| 1
|
'''simple docstring'''
from __future__ import annotations
from collections.abc import Iterator
class _lowercase :
def __init__( self , A__ ) -> None:
snake_case = value
snake_case = None
snake_case = None
class _lowercase :
def __init__( self , A__ ) -> None:
snake_case = tree
def UpperCamelCase ( self , A__ ) -> int:
if node is None:
return 0
return node.value + (
self.depth_first_search(node.left ) + self.depth_first_search(node.right )
)
def __iter__( self ) -> Iterator[int]:
yield self.depth_first_search(self.tree )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 44
|
'''simple docstring'''
from __future__ import annotations
from collections.abc import Iterator
class _lowercase :
def __init__( self , A__ ) -> None:
snake_case = value
snake_case = None
snake_case = None
class _lowercase :
def __init__( self , A__ ) -> None:
snake_case = tree
def UpperCamelCase ( self , A__ ) -> int:
if node is None:
return 0
return node.value + (
self.depth_first_search(node.left ) + self.depth_first_search(node.right )
)
def __iter__( self ) -> Iterator[int]:
yield self.depth_first_search(self.tree )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 44
| 1
|
'''simple docstring'''
import dataclasses
import json
import warnings
from dataclasses import dataclass, field
from time import time
from typing import List
from ..utils import logging
_lowercase = logging.get_logger(__name__)
def __UpperCamelCase ( a : List[Any]=None , a : Tuple=None ) ->List[str]:
return field(default_factory=lambda: default , metadata=a )
@dataclass
class _lowercase :
_UpperCAmelCase = list_field(
default=[] , metadata={
'''help''': (
'''Model checkpoints to be provided to the AutoModel classes. Leave blank to benchmark the base version'''
''' of all available models'''
)
} , )
_UpperCAmelCase = list_field(
default=[8] , metadata={'''help''': '''List of batch sizes for which memory and time performance will be evaluated'''} )
_UpperCAmelCase = list_field(
default=[8, 32, 128, 512] , metadata={'''help''': '''List of sequence lengths for which memory and time performance will be evaluated'''} , )
_UpperCAmelCase = field(
default=__a , metadata={'''help''': '''Whether to benchmark inference of model. Inference can be disabled via --no-inference.'''} , )
_UpperCAmelCase = field(
default=__a , metadata={'''help''': '''Whether to run on available cuda devices. Cuda can be disabled via --no-cuda.'''} , )
_UpperCAmelCase = field(
default=__a , metadata={'''help''': '''Whether to run on available tpu devices. TPU can be disabled via --no-tpu.'''} )
_UpperCAmelCase = field(default=__a , metadata={'''help''': '''Use FP16 to accelerate inference.'''} )
_UpperCAmelCase = field(default=__a , metadata={'''help''': '''Benchmark training of model'''} )
_UpperCAmelCase = field(default=__a , metadata={'''help''': '''Verbose memory tracing'''} )
_UpperCAmelCase = field(
default=__a , metadata={'''help''': '''Whether to perform speed measurements. Speed measurements can be disabled via --no-speed.'''} , )
_UpperCAmelCase = field(
default=__a , metadata={
'''help''': '''Whether to perform memory measurements. Memory measurements can be disabled via --no-memory'''
} , )
_UpperCAmelCase = field(default=__a , metadata={'''help''': '''Trace memory line by line'''} )
_UpperCAmelCase = field(default=__a , metadata={'''help''': '''Save result to a CSV file'''} )
_UpperCAmelCase = field(default=__a , metadata={'''help''': '''Save all print statements in a log file'''} )
_UpperCAmelCase = field(default=__a , metadata={'''help''': '''Whether to print environment information'''} )
_UpperCAmelCase = field(
default=__a , metadata={
'''help''': (
'''Whether to use multiprocessing for memory and speed measurement. It is highly recommended to use'''
''' multiprocessing for accurate CPU and GPU memory measurements. This option should only be disabled'''
''' for debugging / testing and on TPU.'''
)
} , )
_UpperCAmelCase = field(
default=F"""inference_time_{round(time() )}.csv""" , metadata={'''help''': '''CSV filename used if saving time results to csv.'''} , )
_UpperCAmelCase = field(
default=F"""inference_memory_{round(time() )}.csv""" , metadata={'''help''': '''CSV filename used if saving memory results to csv.'''} , )
_UpperCAmelCase = field(
default=F"""train_time_{round(time() )}.csv""" , metadata={'''help''': '''CSV filename used if saving time results to csv for training.'''} , )
_UpperCAmelCase = field(
default=F"""train_memory_{round(time() )}.csv""" , metadata={'''help''': '''CSV filename used if saving memory results to csv for training.'''} , )
_UpperCAmelCase = field(
default=F"""env_info_{round(time() )}.csv""" , metadata={'''help''': '''CSV filename used if saving environment information.'''} , )
_UpperCAmelCase = field(
default=F"""log_{round(time() )}.csv""" , metadata={'''help''': '''Log filename used if print statements are saved in log.'''} , )
_UpperCAmelCase = field(default=3 , metadata={'''help''': '''Times an experiment will be run.'''} )
_UpperCAmelCase = field(
default=__a , metadata={
'''help''': (
'''Instead of loading the model as defined in `config.architectures` if exists, just load the pretrain'''
''' model weights.'''
)
} , )
def UpperCamelCase ( self ) -> Any:
warnings.warn(
F"""The class {self.__class__} is deprecated. Hugging Face Benchmarking utils"""
''' are deprecated in general and it is advised to use external Benchmarking libraries '''
''' to benchmark Transformer models.''' , A__ , )
def UpperCamelCase ( self ) -> Tuple:
return json.dumps(dataclasses.asdict(self ) , indent=2 )
@property
def UpperCamelCase ( self ) -> List[str]:
if len(self.models ) <= 0:
raise ValueError(
'''Please make sure you provide at least one model name / model identifier, *e.g.* `--models'''
''' bert-base-cased` or `args.models = [\'bert-base-cased\'].''' )
return self.models
@property
def UpperCamelCase ( self ) -> Optional[int]:
if not self.multi_process:
return False
elif self.is_tpu:
logger.info('''Multiprocessing is currently not possible on TPU.''' )
return False
else:
return True
| 44
|
'''simple docstring'''
import argparse
from collections import OrderedDict
from pathlib import Path
import torch
from transformers import (
VisualBertConfig,
VisualBertForMultipleChoice,
VisualBertForPreTraining,
VisualBertForQuestionAnswering,
VisualBertForVisualReasoning,
)
from transformers.utils import logging
logging.set_verbosity_info()
_lowercase = logging.get_logger(__name__)
_lowercase = [
('bert.bert', 'visual_bert'),
('bert.cls', 'cls'),
('bert.classifier', 'cls'),
('token_type_embeddings_visual', 'visual_token_type_embeddings'),
('position_embeddings_visual', 'visual_position_embeddings'),
('projection', 'visual_projection'),
]
_lowercase = [
'nlvr2_coco_pre_trained.th',
'nlvr2_fine_tuned.th',
'nlvr2_pre_trained.th',
'vcr_coco_pre_train.th',
'vcr_fine_tune.th',
'vcr_pre_train.th',
'vqa_coco_pre_trained.th',
'vqa_fine_tuned.th',
'vqa_pre_trained.th',
]
def __UpperCamelCase ( a : List[str] ) ->Optional[int]:
snake_case = torch.load(a , map_location='''cpu''' )
return sd
def __UpperCamelCase ( a : Optional[int] , a : Union[str, Any] , a : int=rename_keys_prefix ) ->Tuple:
snake_case = OrderedDict()
snake_case = torch.arange(config.max_position_embeddings ).expand((1, -1) )
# detector_d = OrderedDict()
for key in d:
if "detector" in key:
# detector_d[key.replace('detector.','')] = d[key]
continue
snake_case = key
for name_pair in rename_keys_prefix:
snake_case = new_key.replace(name_pair[0] , name_pair[1] )
snake_case = d[key]
if key == "bert.cls.predictions.decoder.weight":
# Old bert code didn't have `decoder.bias`, but was added separately
snake_case = new_d['''cls.predictions.bias''']
return new_d
@torch.no_grad()
def __UpperCamelCase ( a : Optional[int] , a : int ) ->Union[str, Any]:
assert (
checkpoint_path.split('''/''' )[-1] in ACCEPTABLE_CHECKPOINTS
), f"""The checkpoint provided must be in {ACCEPTABLE_CHECKPOINTS}."""
# Get Config
if "pre" in checkpoint_path:
snake_case = '''pretraining'''
if "vcr" in checkpoint_path:
snake_case = {'''visual_embedding_dim''': 512}
elif "vqa_advanced" in checkpoint_path:
snake_case = {'''visual_embedding_dim''': 2048}
elif "vqa" in checkpoint_path:
snake_case = {'''visual_embedding_dim''': 2048}
elif "nlvr" in checkpoint_path:
snake_case = {'''visual_embedding_dim''': 1024}
else:
raise NotImplementedError(f"""No implementation found for `{checkpoint_path}`.""" )
else:
if "vcr" in checkpoint_path:
snake_case = {'''visual_embedding_dim''': 512}
snake_case = '''multichoice'''
elif "vqa_advanced" in checkpoint_path:
snake_case = {'''visual_embedding_dim''': 2048}
snake_case = '''vqa_advanced'''
elif "vqa" in checkpoint_path:
snake_case = {'''visual_embedding_dim''': 2048, '''num_labels''': 3129}
snake_case = '''vqa'''
elif "nlvr" in checkpoint_path:
snake_case = {
'''visual_embedding_dim''': 1024,
'''num_labels''': 2,
}
snake_case = '''nlvr'''
snake_case = VisualBertConfig(**a )
# Load State Dict
snake_case = load_state_dict(a )
snake_case = get_new_dict(a , a )
if model_type == "pretraining":
snake_case = VisualBertForPreTraining(a )
elif model_type == "vqa":
snake_case = VisualBertForQuestionAnswering(a )
elif model_type == "nlvr":
snake_case = VisualBertForVisualReasoning(a )
elif model_type == "multichoice":
snake_case = VisualBertForMultipleChoice(a )
model.load_state_dict(a )
# Save Checkpoints
Path(a ).mkdir(exist_ok=a )
model.save_pretrained(a )
if __name__ == "__main__":
_lowercase = argparse.ArgumentParser()
# Required parameters
parser.add_argument('orig_checkpoint_path', type=str, help='A path to .th on local filesystem.')
parser.add_argument('pytorch_dump_folder_path', type=str, help='Path to the output PyTorch model.')
_lowercase = parser.parse_args()
convert_visual_bert_checkpoint(args.orig_checkpoint_path, args.pytorch_dump_folder_path)
| 44
| 1
|
'''simple docstring'''
import re
import warnings
from contextlib import contextmanager
from ...processing_utils import ProcessorMixin
class _lowercase ( __a ):
_UpperCAmelCase = ['''image_processor''', '''tokenizer''']
_UpperCAmelCase = '''AutoImageProcessor'''
_UpperCAmelCase = '''AutoTokenizer'''
def __init__( self , A__=None , A__=None , **A__ ) -> str:
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.''' , A__ , )
snake_case = kwargs.pop('''feature_extractor''' )
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`.''' )
super().__init__(A__ , A__ )
snake_case = self.image_processor
snake_case = False
def __call__( self , *A__ , **A__ ) -> int:
# For backward compatibility
if self._in_target_context_manager:
return self.current_processor(*A__ , **A__ )
snake_case = kwargs.pop('''images''' , A__ )
snake_case = kwargs.pop('''text''' , A__ )
if len(A__ ) > 0:
snake_case = args[0]
snake_case = args[1:]
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:
snake_case = self.image_processor(A__ , *A__ , **A__ )
if text is not None:
snake_case = self.tokenizer(A__ , **A__ )
if text is None:
return inputs
elif images is None:
return encodings
else:
snake_case = encodings['''input_ids''']
return inputs
def UpperCamelCase ( self , *A__ , **A__ ) -> Tuple:
return self.tokenizer.batch_decode(*A__ , **A__ )
def UpperCamelCase ( self , *A__ , **A__ ) -> List[str]:
return self.tokenizer.decode(*A__ , **A__ )
@contextmanager
def UpperCamelCase ( self ) -> Tuple:
warnings.warn(
'''`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your '''
'''labels by using the argument `text` of the regular `__call__` method (either in the same call as '''
'''your images inputs, or in a separate call.''' )
snake_case = True
snake_case = self.tokenizer
yield
snake_case = self.image_processor
snake_case = False
def UpperCamelCase ( self , A__ , A__=False , A__=None ) -> List[str]:
if added_vocab is None:
snake_case = self.tokenizer.get_added_vocab()
snake_case = {}
while tokens:
snake_case = re.search(R'''<s_(.*?)>''' , A__ , re.IGNORECASE )
if start_token is None:
break
snake_case = start_token.group(1 )
snake_case = re.search(RF"""</s_{key}>""" , A__ , re.IGNORECASE )
snake_case = start_token.group()
if end_token is None:
snake_case = tokens.replace(A__ , '''''' )
else:
snake_case = end_token.group()
snake_case = re.escape(A__ )
snake_case = re.escape(A__ )
snake_case = re.search(F"""{start_token_escaped}(.*?){end_token_escaped}""" , A__ , re.IGNORECASE )
if content is not None:
snake_case = content.group(1 ).strip()
if r"<s_" in content and r"</s_" in content: # non-leaf node
snake_case = self.tokenajson(A__ , is_inner_value=A__ , added_vocab=A__ )
if value:
if len(A__ ) == 1:
snake_case = value[0]
snake_case = value
else: # leaf nodes
snake_case = []
for leaf in content.split(R'''<sep/>''' ):
snake_case = leaf.strip()
if leaf in added_vocab and leaf[0] == "<" and leaf[-2:] == "/>":
snake_case = leaf[1:-2] # for categorical special tokens
output[key].append(A__ )
if len(output[key] ) == 1:
snake_case = output[key][0]
snake_case = tokens[tokens.find(A__ ) + len(A__ ) :].strip()
if tokens[:6] == r"<sep/>": # non-leaf nodes
return [output] + self.tokenajson(tokens[6:] , is_inner_value=A__ , added_vocab=A__ )
if len(A__ ):
return [output] if is_inner_value else output
else:
return [] if is_inner_value else {"text_sequence": tokens}
@property
def UpperCamelCase ( self ) -> Optional[int]:
warnings.warn(
'''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.''' , A__ , )
return self.image_processor_class
@property
def UpperCamelCase ( self ) -> int:
warnings.warn(
'''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.''' , A__ , )
return self.image_processor
| 44
|
'''simple docstring'''
import argparse
import json
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import PoolFormerConfig, PoolFormerForImageClassification, PoolFormerImageProcessor
from transformers.utils import logging
logging.set_verbosity_info()
_lowercase = logging.get_logger(__name__)
def __UpperCamelCase ( a : Dict , a : Optional[int] , a : Dict , a : Dict ) ->Union[str, Any]:
snake_case = original_name.split('''.''' )[0]
snake_case = key.split('''.''' )
snake_case = int(key_list[key_list.index(a ) - 2] )
snake_case = int(key_list[key_list.index(a ) - 1] )
snake_case = orig_block_num - offset
snake_case = key.replace(f"""{orig_block_num}.{layer_num}.{original_name}""" , f"""block.{new_block_num}.{layer_num}.{new_name}""" )
return key
def __UpperCamelCase ( a : Tuple ) ->Dict:
snake_case = OrderedDict()
snake_case , snake_case = 0, 0
for key, value in state_dict.items():
if key.startswith('''network''' ):
snake_case = key.replace('''network''' , '''poolformer.encoder''' )
if "proj" in key:
# Works for the first embedding as well as the internal embedding layers
if key.endswith('''bias''' ) and "patch_embed" not in key:
patch_emb_offset += 1
snake_case = key[: key.find('''proj''' )]
snake_case = key.replace(a , f"""patch_embeddings.{total_embed_found}.""" )
snake_case = key.replace('''proj''' , '''projection''' )
if key.endswith('''bias''' ):
total_embed_found += 1
if "patch_embeddings" in key:
snake_case = '''poolformer.encoder.''' + key
if "mlp.fc1" in key:
snake_case = replace_key_with_offset(a , a , '''mlp.fc1''' , '''output.conv1''' )
if "mlp.fc2" in key:
snake_case = replace_key_with_offset(a , a , '''mlp.fc2''' , '''output.conv2''' )
if "norm1" in key:
snake_case = replace_key_with_offset(a , a , '''norm1''' , '''before_norm''' )
if "norm2" in key:
snake_case = replace_key_with_offset(a , a , '''norm2''' , '''after_norm''' )
if "layer_scale_1" in key:
snake_case = replace_key_with_offset(a , a , '''layer_scale_1''' , '''layer_scale_1''' )
if "layer_scale_2" in key:
snake_case = replace_key_with_offset(a , a , '''layer_scale_2''' , '''layer_scale_2''' )
if "head" in key:
snake_case = key.replace('''head''' , '''classifier''' )
snake_case = value
return new_state_dict
def __UpperCamelCase ( ) ->Optional[int]:
snake_case = '''http://images.cocodataset.org/val2017/000000039769.jpg'''
snake_case = Image.open(requests.get(a , stream=a ).raw )
return image
@torch.no_grad()
def __UpperCamelCase ( a : Dict , a : Optional[Any] , a : Tuple ) ->List[str]:
snake_case = PoolFormerConfig()
# set attributes based on model_name
snake_case = '''huggingface/label-files'''
snake_case = model_name[-3:]
snake_case = 1000
snake_case = '''imagenet-1k-id2label.json'''
snake_case = (1, 1000)
# set config attributes
snake_case = json.load(open(hf_hub_download(a , a , repo_type='''dataset''' ) , '''r''' ) )
snake_case = {int(a ): v for k, v in idalabel.items()}
snake_case = idalabel
snake_case = {v: k for k, v in idalabel.items()}
if size == "s12":
snake_case = [2, 2, 6, 2]
snake_case = [64, 128, 320, 512]
snake_case = 4.0
snake_case = 0.9
elif size == "s24":
snake_case = [4, 4, 12, 4]
snake_case = [64, 128, 320, 512]
snake_case = 4.0
snake_case = 0.9
elif size == "s36":
snake_case = [6, 6, 18, 6]
snake_case = [64, 128, 320, 512]
snake_case = 4.0
snake_case = 1e-6
snake_case = 0.9
elif size == "m36":
snake_case = [6, 6, 18, 6]
snake_case = [96, 192, 384, 768]
snake_case = 4.0
snake_case = 1e-6
snake_case = 0.95
elif size == "m48":
snake_case = [8, 8, 24, 8]
snake_case = [96, 192, 384, 768]
snake_case = 4.0
snake_case = 1e-6
snake_case = 0.95
else:
raise ValueError(f"""Size {size} not supported""" )
# load image processor
snake_case = PoolFormerImageProcessor(crop_pct=a )
# Prepare image
snake_case = prepare_img()
snake_case = image_processor(images=a , return_tensors='''pt''' ).pixel_values
logger.info(f"""Converting model {model_name}...""" )
# load original state dict
snake_case = torch.load(a , map_location=torch.device('''cpu''' ) )
# rename keys
snake_case = rename_keys(a )
# create HuggingFace model and load state dict
snake_case = PoolFormerForImageClassification(a )
model.load_state_dict(a )
model.eval()
# Define image processor
snake_case = PoolFormerImageProcessor(crop_pct=a )
snake_case = image_processor(images=prepare_img() , return_tensors='''pt''' ).pixel_values
# forward pass
snake_case = model(a )
snake_case = outputs.logits
# define expected logit slices for different models
if size == "s12":
snake_case = torch.tensor([-0.3045, -0.6758, -0.4869] )
elif size == "s24":
snake_case = torch.tensor([0.4402, -0.1374, -0.8045] )
elif size == "s36":
snake_case = torch.tensor([-0.6080, -0.5133, -0.5898] )
elif size == "m36":
snake_case = torch.tensor([0.3952, 0.2263, -1.2668] )
elif size == "m48":
snake_case = torch.tensor([0.1167, -0.0656, -0.3423] )
else:
raise ValueError(f"""Size {size} not supported""" )
# verify logits
assert logits.shape == expected_shape
assert torch.allclose(logits[0, :3] , a , atol=1e-2 )
# finally, save model and image processor
logger.info(f"""Saving PyTorch model and image processor to {pytorch_dump_folder_path}...""" )
Path(a ).mkdir(exist_ok=a )
model.save_pretrained(a )
print(f"""Saving image processor to {pytorch_dump_folder_path}""" )
image_processor.save_pretrained(a )
if __name__ == "__main__":
_lowercase = argparse.ArgumentParser()
parser.add_argument(
'--model_name',
default='poolformer_s12',
type=str,
help='Name of the model you\'d like to convert.',
)
parser.add_argument(
'--checkpoint_path', default=None, type=str, help='Path to the original PyTorch checkpoint (.pth file).'
)
parser.add_argument(
'--pytorch_dump_folder_path', default=None, type=str, help='Path to the folder to output PyTorch model.'
)
_lowercase = parser.parse_args()
convert_poolformer_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path)
| 44
| 1
|
'''simple docstring'''
from datetime import datetime
import requests
from bsa import BeautifulSoup
if __name__ == "__main__":
_lowercase = input('Enter image url: ').strip()
print(f'Downloading image from {url} ...')
_lowercase = BeautifulSoup(requests.get(url).content, 'html.parser')
# The image URL is in the content field of the first meta tag with property og:image
_lowercase = soup.find('meta', {'property': 'og:image'})['content']
_lowercase = requests.get(image_url).content
_lowercase = 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}.')
| 44
|
'''simple docstring'''
import argparse
import json
import logging
import os
import sys
from unittest.mock import patch
from transformers.testing_utils import TestCasePlus, get_gpu_count, slow
_lowercase = [
os.path.join(os.path.dirname(__file__), dirname)
for dirname in [
'text-classification',
'language-modeling',
'summarization',
'token-classification',
'question-answering',
]
]
sys.path.extend(SRC_DIRS)
if SRC_DIRS is not None:
import run_clm_flax
import run_flax_glue
import run_flax_ner
import run_mlm_flax
import run_qa
import run_summarization_flax
import run_ta_mlm_flax
logging.basicConfig(level=logging.DEBUG)
_lowercase = logging.getLogger()
def __UpperCamelCase ( ) ->Tuple:
snake_case = argparse.ArgumentParser()
parser.add_argument('''-f''' )
snake_case = parser.parse_args()
return args.f
def __UpperCamelCase ( a : Dict , a : Tuple="eval" ) ->List[Any]:
snake_case = os.path.join(a , f"""{split}_results.json""" )
if os.path.exists(a ):
with open(a , '''r''' ) as f:
return json.load(a )
raise ValueError(f"""can't find {path}""" )
_lowercase = logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
class _lowercase ( __a ):
def UpperCamelCase ( self ) -> List[str]:
snake_case = self.get_auto_remove_tmp_dir()
snake_case = F"""
run_glue.py
--model_name_or_path distilbert-base-uncased
--output_dir {tmp_dir}
--train_file ./tests/fixtures/tests_samples/MRPC/train.csv
--validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv
--per_device_train_batch_size=2
--per_device_eval_batch_size=1
--learning_rate=1e-4
--eval_steps=2
--warmup_steps=2
--seed=42
--max_seq_length=128
""".split()
with patch.object(A__ , '''argv''' , A__ ):
run_flax_glue.main()
snake_case = get_results(A__ )
self.assertGreaterEqual(result['''eval_accuracy'''] , 0.7_5 )
@slow
def UpperCamelCase ( self ) -> List[Any]:
snake_case = self.get_auto_remove_tmp_dir()
snake_case = F"""
run_clm_flax.py
--model_name_or_path distilgpt2
--train_file ./tests/fixtures/sample_text.txt
--validation_file ./tests/fixtures/sample_text.txt
--do_train
--do_eval
--block_size 128
--per_device_train_batch_size 4
--per_device_eval_batch_size 4
--num_train_epochs 2
--logging_steps 2 --eval_steps 2
--output_dir {tmp_dir}
--overwrite_output_dir
""".split()
with patch.object(A__ , '''argv''' , A__ ):
run_clm_flax.main()
snake_case = get_results(A__ )
self.assertLess(result['''eval_perplexity'''] , 1_00 )
@slow
def UpperCamelCase ( self ) -> int:
snake_case = self.get_auto_remove_tmp_dir()
snake_case = F"""
run_summarization.py
--model_name_or_path t5-small
--train_file tests/fixtures/tests_samples/xsum/sample.json
--validation_file tests/fixtures/tests_samples/xsum/sample.json
--test_file tests/fixtures/tests_samples/xsum/sample.json
--output_dir {tmp_dir}
--overwrite_output_dir
--num_train_epochs=3
--warmup_steps=8
--do_train
--do_eval
--do_predict
--learning_rate=2e-4
--per_device_train_batch_size=2
--per_device_eval_batch_size=1
--predict_with_generate
""".split()
with patch.object(A__ , '''argv''' , A__ ):
run_summarization_flax.main()
snake_case = get_results(A__ , split='''test''' )
self.assertGreaterEqual(result['''test_rouge1'''] , 10 )
self.assertGreaterEqual(result['''test_rouge2'''] , 2 )
self.assertGreaterEqual(result['''test_rougeL'''] , 7 )
self.assertGreaterEqual(result['''test_rougeLsum'''] , 7 )
@slow
def UpperCamelCase ( self ) -> Union[str, Any]:
snake_case = self.get_auto_remove_tmp_dir()
snake_case = F"""
run_mlm.py
--model_name_or_path distilroberta-base
--train_file ./tests/fixtures/sample_text.txt
--validation_file ./tests/fixtures/sample_text.txt
--output_dir {tmp_dir}
--overwrite_output_dir
--max_seq_length 128
--per_device_train_batch_size 4
--per_device_eval_batch_size 4
--logging_steps 2 --eval_steps 2
--do_train
--do_eval
--num_train_epochs=1
""".split()
with patch.object(A__ , '''argv''' , A__ ):
run_mlm_flax.main()
snake_case = get_results(A__ )
self.assertLess(result['''eval_perplexity'''] , 42 )
@slow
def UpperCamelCase ( self ) -> Dict:
snake_case = self.get_auto_remove_tmp_dir()
snake_case = F"""
run_t5_mlm_flax.py
--model_name_or_path t5-small
--train_file ./tests/fixtures/sample_text.txt
--validation_file ./tests/fixtures/sample_text.txt
--do_train
--do_eval
--max_seq_length 128
--per_device_train_batch_size 4
--per_device_eval_batch_size 4
--num_train_epochs 2
--logging_steps 2 --eval_steps 2
--output_dir {tmp_dir}
--overwrite_output_dir
""".split()
with patch.object(A__ , '''argv''' , A__ ):
run_ta_mlm_flax.main()
snake_case = get_results(A__ )
self.assertGreaterEqual(result['''eval_accuracy'''] , 0.4_2 )
@slow
def UpperCamelCase ( self ) -> int:
# with so little data distributed training needs more epochs to get the score on par with 0/1 gpu
snake_case = 7 if get_gpu_count() > 1 else 2
snake_case = self.get_auto_remove_tmp_dir()
snake_case = F"""
run_flax_ner.py
--model_name_or_path bert-base-uncased
--train_file tests/fixtures/tests_samples/conll/sample.json
--validation_file tests/fixtures/tests_samples/conll/sample.json
--output_dir {tmp_dir}
--overwrite_output_dir
--do_train
--do_eval
--warmup_steps=2
--learning_rate=2e-4
--logging_steps 2 --eval_steps 2
--per_device_train_batch_size=2
--per_device_eval_batch_size=2
--num_train_epochs={epochs}
--seed 7
""".split()
with patch.object(A__ , '''argv''' , A__ ):
run_flax_ner.main()
snake_case = get_results(A__ )
self.assertGreaterEqual(result['''eval_accuracy'''] , 0.7_5 )
self.assertGreaterEqual(result['''eval_f1'''] , 0.3 )
@slow
def UpperCamelCase ( self ) -> Any:
snake_case = self.get_auto_remove_tmp_dir()
snake_case = F"""
run_qa.py
--model_name_or_path bert-base-uncased
--version_2_with_negative
--train_file tests/fixtures/tests_samples/SQUAD/sample.json
--validation_file tests/fixtures/tests_samples/SQUAD/sample.json
--output_dir {tmp_dir}
--overwrite_output_dir
--num_train_epochs=3
--warmup_steps=2
--do_train
--do_eval
--logging_steps 2 --eval_steps 2
--learning_rate=2e-4
--per_device_train_batch_size=2
--per_device_eval_batch_size=1
""".split()
with patch.object(A__ , '''argv''' , A__ ):
run_qa.main()
snake_case = get_results(A__ )
self.assertGreaterEqual(result['''eval_f1'''] , 30 )
self.assertGreaterEqual(result['''eval_exact'''] , 30 )
| 44
| 1
|
'''simple docstring'''
_lowercase = {
'Pillow': 'Pillow',
'accelerate': 'accelerate>=0.11.0',
'compel': 'compel==0.1.8',
'black': 'black~=23.1',
'datasets': 'datasets',
'filelock': 'filelock',
'flax': 'flax>=0.4.1',
'hf-doc-builder': 'hf-doc-builder>=0.3.0',
'huggingface-hub': 'huggingface-hub>=0.13.2',
'requests-mock': 'requests-mock==1.10.0',
'importlib_metadata': 'importlib_metadata',
'invisible-watermark': 'invisible-watermark',
'isort': 'isort>=5.5.4',
'jax': 'jax>=0.2.8,!=0.3.2',
'jaxlib': 'jaxlib>=0.1.65',
'Jinja2': 'Jinja2',
'k-diffusion': 'k-diffusion>=0.0.12',
'torchsde': 'torchsde',
'note_seq': 'note_seq',
'librosa': 'librosa',
'numpy': 'numpy',
'omegaconf': 'omegaconf',
'parameterized': 'parameterized',
'protobuf': 'protobuf>=3.20.3,<4',
'pytest': 'pytest',
'pytest-timeout': 'pytest-timeout',
'pytest-xdist': 'pytest-xdist',
'ruff': 'ruff>=0.0.241',
'safetensors': 'safetensors',
'sentencepiece': 'sentencepiece>=0.1.91,!=0.1.92',
'scipy': 'scipy',
'onnx': 'onnx',
'regex': 'regex!=2019.12.17',
'requests': 'requests',
'tensorboard': 'tensorboard',
'torch': 'torch>=1.4',
'torchvision': 'torchvision',
'transformers': 'transformers>=4.25.1',
'urllib3': 'urllib3<=2.0.0',
}
| 44
|
'''simple docstring'''
from typing import Any, Dict, List, Optional, Tuple, Union
import torch
from torch import nn
from torch.utils.data import DistributedSampler, RandomSampler
from transformers import PreTrainedModel, Trainer, logging
from transformers.integrations import is_fairscale_available
from transformers.models.fsmt.configuration_fsmt import FSMTConfig
from transformers.optimization import (
Adafactor,
AdamW,
get_constant_schedule,
get_constant_schedule_with_warmup,
get_cosine_schedule_with_warmup,
get_cosine_with_hard_restarts_schedule_with_warmup,
get_linear_schedule_with_warmup,
get_polynomial_decay_schedule_with_warmup,
)
from transformers.trainer_pt_utils import get_tpu_sampler
from transformers.training_args import ParallelMode
from transformers.utils import is_torch_tpu_available
if is_fairscale_available():
from fairscale.optim import OSS
_lowercase = logging.get_logger(__name__)
_lowercase = {
'linear': get_linear_schedule_with_warmup,
'cosine': get_cosine_schedule_with_warmup,
'cosine_w_restarts': get_cosine_with_hard_restarts_schedule_with_warmup,
'polynomial': get_polynomial_decay_schedule_with_warmup,
'constant': get_constant_schedule,
'constant_w_warmup': get_constant_schedule_with_warmup,
}
class _lowercase ( __a ):
def __init__( self , A__=None , A__=None , *A__ , **A__ ) -> Union[str, Any]:
super().__init__(*A__ , **A__ )
if config is None:
assert isinstance(self.model , A__ ), (
"If no `config` is passed the model to be trained has to be of type `PreTrainedModel`, but is"
F""" {self.model.__class__}"""
)
snake_case = self.model.config
else:
snake_case = config
snake_case = data_args
snake_case = self.config.tgt_vocab_size if isinstance(self.config , A__ ) else self.config.vocab_size
if self.args.label_smoothing != 0 or (self.data_args is not None and self.data_args.ignore_pad_token_for_loss):
assert self.config.pad_token_id is not None, (
"Make sure that `config.pad_token_id` is correcly defined when ignoring `pad_token` for loss"
" calculation or doing label smoothing."
)
if self.config.pad_token_id is None and self.config.eos_token_id is not None:
logger.warning(
F"""The `config.pad_token_id` is `None`. Using `config.eos_token_id` = {self.config.eos_token_id} for"""
''' padding..''' )
if self.args.label_smoothing == 0:
snake_case = torch.nn.CrossEntropyLoss(ignore_index=self.config.pad_token_id )
else:
# dynamically import label_smoothed_nll_loss
from utils import label_smoothed_nll_loss
snake_case = label_smoothed_nll_loss
def UpperCamelCase ( self , A__ ) -> Tuple:
if self.optimizer is None:
snake_case = ['''bias''', '''LayerNorm.weight''']
snake_case = [
{
'''params''': [p for n, p in self.model.named_parameters() if not any(nd in n for nd in no_decay )],
'''weight_decay''': self.args.weight_decay,
},
{
'''params''': [p for n, p in self.model.named_parameters() if any(nd in n for nd in no_decay )],
'''weight_decay''': 0.0,
},
]
snake_case = Adafactor if self.args.adafactor else AdamW
if self.args.adafactor:
snake_case = Adafactor
snake_case = {'''scale_parameter''': False, '''relative_step''': False}
else:
snake_case = AdamW
snake_case = {
'''betas''': (self.args.adam_betaa, self.args.adam_betaa),
'''eps''': self.args.adam_epsilon,
}
snake_case = self.args.learning_rate
if self.sharded_ddp:
snake_case = OSS(
params=A__ , optim=A__ , **A__ , )
else:
snake_case = optimizer_cls(A__ , **A__ )
if self.lr_scheduler is None:
snake_case = self._get_lr_scheduler(A__ )
else: # ignoring --lr_scheduler
logger.warning('''scheduler is passed to `Seq2SeqTrainer`, `--lr_scheduler` arg is ignored.''' )
def UpperCamelCase ( self , A__ ) -> Tuple:
snake_case = arg_to_scheduler[self.args.lr_scheduler]
if self.args.lr_scheduler == "constant":
snake_case = schedule_func(self.optimizer )
elif self.args.lr_scheduler == "constant_w_warmup":
snake_case = schedule_func(self.optimizer , num_warmup_steps=self.args.warmup_steps )
else:
snake_case = schedule_func(
self.optimizer , num_warmup_steps=self.args.warmup_steps , num_training_steps=A__ )
return scheduler
def UpperCamelCase ( self ) -> Optional[torch.utils.data.Sampler]:
if isinstance(self.train_dataset , torch.utils.data.IterableDataset ):
return None
elif is_torch_tpu_available():
return get_tpu_sampler(self.train_dataset )
else:
if self.args.sortish_sampler:
self.train_dataset.make_sortish_sampler(
self.args.per_device_train_batch_size , distributed=(self.args.parallel_mode == ParallelMode.DISTRIBUTED) , )
return (
RandomSampler(self.train_dataset )
if self.args.local_rank == -1
else DistributedSampler(self.train_dataset )
)
def UpperCamelCase ( self , A__ , A__ , A__ ) -> List[Any]:
if self.args.label_smoothing == 0:
if self.data_args is not None and self.data_args.ignore_pad_token_for_loss:
# force training to ignore pad token
snake_case = model(**A__ , use_cache=A__ )[0]
snake_case = self.loss_fn(logits.view(-1 , logits.shape[-1] ) , labels.view(-1 ) )
else:
# compute usual loss via models
snake_case , snake_case = model(**A__ , labels=A__ , use_cache=A__ )[:2]
else:
# compute label smoothed loss
snake_case = model(**A__ , use_cache=A__ )[0]
snake_case = torch.nn.functional.log_softmax(A__ , dim=-1 )
snake_case , snake_case = self.loss_fn(A__ , A__ , self.args.label_smoothing , ignore_index=self.config.pad_token_id )
return loss, logits
def UpperCamelCase ( self , A__ , A__ ) -> Any:
snake_case = inputs.pop('''labels''' )
snake_case , snake_case = self._compute_loss(A__ , A__ , A__ )
return loss
def UpperCamelCase ( self , A__ , A__ , A__ , A__ = None , ) -> Tuple[Optional[float], Optional[torch.Tensor], Optional[torch.Tensor]]:
snake_case = self._prepare_inputs(A__ )
snake_case = {
'''max_length''': self.data_args.val_max_target_length
if self.data_args is not None
else self.config.max_length,
'''num_beams''': self.data_args.eval_beams if self.data_args is not None else self.config.num_beams,
}
if self.args.predict_with_generate and not self.args.prediction_loss_only:
snake_case = self.model.generate(
inputs['''input_ids'''] , attention_mask=inputs['''attention_mask'''] , **A__ , )
# in case the batch is shorter than max length, the output should be padded
if generated_tokens.shape[-1] < gen_kwargs["max_length"]:
snake_case = self._pad_tensors_to_max_len(A__ , gen_kwargs['''max_length'''] )
snake_case = inputs.pop('''labels''' )
with torch.no_grad():
# compute loss on predict data
snake_case , snake_case = self._compute_loss(A__ , A__ , A__ )
snake_case = loss.mean().detach()
if self.args.prediction_loss_only:
return (loss, None, None)
snake_case = generated_tokens if self.args.predict_with_generate else logits
if labels.shape[-1] < gen_kwargs["max_length"]:
snake_case = self._pad_tensors_to_max_len(A__ , gen_kwargs['''max_length'''] )
return (loss, logits, labels)
def UpperCamelCase ( self , A__ , A__ ) -> List[str]:
# If PAD token is not defined at least EOS token has to be defined
snake_case = self.config.pad_token_id if self.config.pad_token_id is not None else self.config.eos_token_id
if pad_token_id is None:
raise ValueError(
'''Make sure that either `config.pad_token_id` or `config.eos_token_id` is defined if tensor has to be'''
F""" padded to `max_length`={max_length}""" )
snake_case = pad_token_id * torch.ones(
(tensor.shape[0], max_length) , dtype=tensor.dtype , device=tensor.device )
snake_case = tensor
return padded_tensor
| 44
| 1
|
'''simple docstring'''
import argparse
from collections import OrderedDict
from pathlib import Path
import torch
from transformers import (
VisualBertConfig,
VisualBertForMultipleChoice,
VisualBertForPreTraining,
VisualBertForQuestionAnswering,
VisualBertForVisualReasoning,
)
from transformers.utils import logging
logging.set_verbosity_info()
_lowercase = logging.get_logger(__name__)
_lowercase = [
('bert.bert', 'visual_bert'),
('bert.cls', 'cls'),
('bert.classifier', 'cls'),
('token_type_embeddings_visual', 'visual_token_type_embeddings'),
('position_embeddings_visual', 'visual_position_embeddings'),
('projection', 'visual_projection'),
]
_lowercase = [
'nlvr2_coco_pre_trained.th',
'nlvr2_fine_tuned.th',
'nlvr2_pre_trained.th',
'vcr_coco_pre_train.th',
'vcr_fine_tune.th',
'vcr_pre_train.th',
'vqa_coco_pre_trained.th',
'vqa_fine_tuned.th',
'vqa_pre_trained.th',
]
def __UpperCamelCase ( a : List[str] ) ->Optional[int]:
snake_case = torch.load(a , map_location='''cpu''' )
return sd
def __UpperCamelCase ( a : Optional[int] , a : Union[str, Any] , a : int=rename_keys_prefix ) ->Tuple:
snake_case = OrderedDict()
snake_case = torch.arange(config.max_position_embeddings ).expand((1, -1) )
# detector_d = OrderedDict()
for key in d:
if "detector" in key:
# detector_d[key.replace('detector.','')] = d[key]
continue
snake_case = key
for name_pair in rename_keys_prefix:
snake_case = new_key.replace(name_pair[0] , name_pair[1] )
snake_case = d[key]
if key == "bert.cls.predictions.decoder.weight":
# Old bert code didn't have `decoder.bias`, but was added separately
snake_case = new_d['''cls.predictions.bias''']
return new_d
@torch.no_grad()
def __UpperCamelCase ( a : Optional[int] , a : int ) ->Union[str, Any]:
assert (
checkpoint_path.split('''/''' )[-1] in ACCEPTABLE_CHECKPOINTS
), f"""The checkpoint provided must be in {ACCEPTABLE_CHECKPOINTS}."""
# Get Config
if "pre" in checkpoint_path:
snake_case = '''pretraining'''
if "vcr" in checkpoint_path:
snake_case = {'''visual_embedding_dim''': 512}
elif "vqa_advanced" in checkpoint_path:
snake_case = {'''visual_embedding_dim''': 2048}
elif "vqa" in checkpoint_path:
snake_case = {'''visual_embedding_dim''': 2048}
elif "nlvr" in checkpoint_path:
snake_case = {'''visual_embedding_dim''': 1024}
else:
raise NotImplementedError(f"""No implementation found for `{checkpoint_path}`.""" )
else:
if "vcr" in checkpoint_path:
snake_case = {'''visual_embedding_dim''': 512}
snake_case = '''multichoice'''
elif "vqa_advanced" in checkpoint_path:
snake_case = {'''visual_embedding_dim''': 2048}
snake_case = '''vqa_advanced'''
elif "vqa" in checkpoint_path:
snake_case = {'''visual_embedding_dim''': 2048, '''num_labels''': 3129}
snake_case = '''vqa'''
elif "nlvr" in checkpoint_path:
snake_case = {
'''visual_embedding_dim''': 1024,
'''num_labels''': 2,
}
snake_case = '''nlvr'''
snake_case = VisualBertConfig(**a )
# Load State Dict
snake_case = load_state_dict(a )
snake_case = get_new_dict(a , a )
if model_type == "pretraining":
snake_case = VisualBertForPreTraining(a )
elif model_type == "vqa":
snake_case = VisualBertForQuestionAnswering(a )
elif model_type == "nlvr":
snake_case = VisualBertForVisualReasoning(a )
elif model_type == "multichoice":
snake_case = VisualBertForMultipleChoice(a )
model.load_state_dict(a )
# Save Checkpoints
Path(a ).mkdir(exist_ok=a )
model.save_pretrained(a )
if __name__ == "__main__":
_lowercase = argparse.ArgumentParser()
# Required parameters
parser.add_argument('orig_checkpoint_path', type=str, help='A path to .th on local filesystem.')
parser.add_argument('pytorch_dump_folder_path', type=str, help='Path to the output PyTorch model.')
_lowercase = parser.parse_args()
convert_visual_bert_checkpoint(args.orig_checkpoint_path, args.pytorch_dump_folder_path)
| 44
|
'''simple docstring'''
import inspect
import re
from hashlib import shaaaa
from typing import Dict, List
from .arrow import arrow
from .audiofolder import audiofolder
from .csv import csv
from .imagefolder import imagefolder
from .json import json
from .pandas import pandas
from .parquet import parquet
from .sql import sql # noqa F401
from .text import text
def __UpperCamelCase ( a : List[str] ) ->str:
snake_case = []
for line in lines:
snake_case = re.sub(R'''#.*''' , '''''' , a ) # remove comments
if line:
filtered_lines.append(a )
snake_case = '''\n'''.join(a )
# Make a hash from all this code
snake_case = full_str.encode('''utf-8''' )
return shaaaa(a ).hexdigest()
# get importable module names and hash for caching
_lowercase = {
'csv': (csv.__name__, _hash_python_lines(inspect.getsource(csv).splitlines())),
'json': (json.__name__, _hash_python_lines(inspect.getsource(json).splitlines())),
'pandas': (pandas.__name__, _hash_python_lines(inspect.getsource(pandas).splitlines())),
'parquet': (parquet.__name__, _hash_python_lines(inspect.getsource(parquet).splitlines())),
'arrow': (arrow.__name__, _hash_python_lines(inspect.getsource(arrow).splitlines())),
'text': (text.__name__, _hash_python_lines(inspect.getsource(text).splitlines())),
'imagefolder': (imagefolder.__name__, _hash_python_lines(inspect.getsource(imagefolder).splitlines())),
'audiofolder': (audiofolder.__name__, _hash_python_lines(inspect.getsource(audiofolder).splitlines())),
}
# Used to infer the module to use based on the data files extensions
_lowercase = {
'.csv': ('csv', {}),
'.tsv': ('csv', {'sep': '\t'}),
'.json': ('json', {}),
'.jsonl': ('json', {}),
'.parquet': ('parquet', {}),
'.arrow': ('arrow', {}),
'.txt': ('text', {}),
}
_EXTENSION_TO_MODULE.update({ext: ('imagefolder', {}) for ext in imagefolder.ImageFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext.upper(): ('imagefolder', {}) for ext in imagefolder.ImageFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext: ('audiofolder', {}) for ext in audiofolder.AudioFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext.upper(): ('audiofolder', {}) for ext in audiofolder.AudioFolder.EXTENSIONS})
_lowercase = {'imagefolder', 'audiofolder'}
# Used to filter data files based on extensions given a module name
_lowercase = {}
for _ext, (_module, _) in _EXTENSION_TO_MODULE.items():
_MODULE_TO_EXTENSIONS.setdefault(_module, []).append(_ext)
_MODULE_TO_EXTENSIONS["imagefolder"].append('.zip')
_MODULE_TO_EXTENSIONS["audiofolder"].append('.zip')
| 44
| 1
|
'''simple docstring'''
import unittest
from transformers import AutoTokenizer, NystromformerConfig, 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 (
NystromformerForMaskedLM,
NystromformerForMultipleChoice,
NystromformerForQuestionAnswering,
NystromformerForSequenceClassification,
NystromformerForTokenClassification,
NystromformerModel,
)
from transformers.models.nystromformer.modeling_nystromformer import NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST
class _lowercase :
def __init__( self , A__ , A__=13 , A__=7 , A__=True , A__=True , A__=True , A__=True , A__=99 , A__=32 , A__=5 , A__=4 , A__=37 , A__="gelu" , A__=0.1 , A__=0.1 , A__=5_12 , A__=16 , A__=2 , A__=0.0_2 , A__=3 , A__=4 , A__=None , ) -> Optional[Any]:
snake_case = parent
snake_case = batch_size
snake_case = seq_length
snake_case = is_training
snake_case = use_input_mask
snake_case = use_token_type_ids
snake_case = use_labels
snake_case = vocab_size
snake_case = hidden_size
snake_case = num_hidden_layers
snake_case = num_attention_heads
snake_case = intermediate_size
snake_case = hidden_act
snake_case = hidden_dropout_prob
snake_case = attention_probs_dropout_prob
snake_case = max_position_embeddings
snake_case = type_vocab_size
snake_case = type_sequence_label_size
snake_case = initializer_range
snake_case = num_labels
snake_case = num_choices
snake_case = scope
def UpperCamelCase ( self ) -> Optional[int]:
snake_case = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
snake_case = None
if self.use_input_mask:
snake_case = random_attention_mask([self.batch_size, self.seq_length] )
snake_case = None
if self.use_token_type_ids:
snake_case = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
snake_case = None
snake_case = None
snake_case = None
if self.use_labels:
snake_case = ids_tensor([self.batch_size] , self.type_sequence_label_size )
snake_case = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
snake_case = ids_tensor([self.batch_size] , self.num_choices )
snake_case = self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def UpperCamelCase ( self ) -> str:
return NystromformerConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=A__ , initializer_range=self.initializer_range , )
def UpperCamelCase ( self , A__ , A__ , A__ , A__ , A__ , A__ , A__ ) -> str:
snake_case = NystromformerModel(config=A__ )
model.to(A__ )
model.eval()
snake_case = model(A__ , attention_mask=A__ , token_type_ids=A__ )
snake_case = model(A__ , token_type_ids=A__ )
snake_case = model(A__ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def UpperCamelCase ( self , A__ , A__ , A__ , A__ , A__ , A__ , A__ ) -> Optional[int]:
snake_case = NystromformerForMaskedLM(config=A__ )
model.to(A__ )
model.eval()
snake_case = model(A__ , attention_mask=A__ , token_type_ids=A__ , labels=A__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def UpperCamelCase ( self , A__ , A__ , A__ , A__ , A__ , A__ , A__ ) -> List[Any]:
snake_case = NystromformerForQuestionAnswering(config=A__ )
model.to(A__ )
model.eval()
snake_case = model(
A__ , attention_mask=A__ , token_type_ids=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 , A__ , A__ , A__ , A__ , A__ , A__ , A__ ) -> Dict:
snake_case = self.num_labels
snake_case = NystromformerForSequenceClassification(A__ )
model.to(A__ )
model.eval()
snake_case = model(A__ , attention_mask=A__ , token_type_ids=A__ , labels=A__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def UpperCamelCase ( self , A__ , A__ , A__ , A__ , A__ , A__ , A__ ) -> Any:
snake_case = self.num_labels
snake_case = NystromformerForTokenClassification(config=A__ )
model.to(A__ )
model.eval()
snake_case = model(A__ , attention_mask=A__ , token_type_ids=A__ , labels=A__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def UpperCamelCase ( self , A__ , A__ , A__ , A__ , A__ , A__ , A__ ) -> int:
snake_case = self.num_choices
snake_case = NystromformerForMultipleChoice(config=A__ )
model.to(A__ )
model.eval()
snake_case = input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
snake_case = token_type_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
snake_case = input_mask.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
snake_case = model(
A__ , attention_mask=A__ , token_type_ids=A__ , labels=A__ , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def UpperCamelCase ( self ) -> List[Any]:
snake_case = self.prepare_config_and_inputs()
(
(
snake_case
) , (
snake_case
) , (
snake_case
) , (
snake_case
) , (
snake_case
) , (
snake_case
) , (
snake_case
) ,
) = config_and_inputs
snake_case = {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
return config, inputs_dict
@require_torch
class _lowercase ( __a , __a , unittest.TestCase ):
_UpperCAmelCase = (
(
NystromformerModel,
NystromformerForMaskedLM,
NystromformerForMultipleChoice,
NystromformerForQuestionAnswering,
NystromformerForSequenceClassification,
NystromformerForTokenClassification,
)
if is_torch_available()
else ()
)
_UpperCAmelCase = (
{
'''feature-extraction''': NystromformerModel,
'''fill-mask''': NystromformerForMaskedLM,
'''question-answering''': NystromformerForQuestionAnswering,
'''text-classification''': NystromformerForSequenceClassification,
'''token-classification''': NystromformerForTokenClassification,
'''zero-shot''': NystromformerForSequenceClassification,
}
if is_torch_available()
else {}
)
_UpperCAmelCase = False
_UpperCAmelCase = False
def UpperCamelCase ( self ) -> Union[str, Any]:
snake_case = NystromformerModelTester(self )
snake_case = ConfigTester(self , config_class=A__ , hidden_size=37 )
def UpperCamelCase ( self ) -> Any:
self.config_tester.run_common_tests()
def UpperCamelCase ( self ) -> Union[str, Any]:
snake_case = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*A__ )
def UpperCamelCase ( self ) -> str:
snake_case = self.model_tester.prepare_config_and_inputs()
for type in ["absolute", "relative_key", "relative_key_query"]:
snake_case = type
self.model_tester.create_and_check_model(*A__ )
def UpperCamelCase ( self ) -> List[Any]:
snake_case = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*A__ )
def UpperCamelCase ( self ) -> Optional[Any]:
snake_case = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_multiple_choice(*A__ )
def UpperCamelCase ( self ) -> Tuple:
snake_case = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*A__ )
def UpperCamelCase ( self ) -> Optional[Any]:
snake_case = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*A__ )
def UpperCamelCase ( self ) -> Dict:
snake_case = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*A__ )
@slow
def UpperCamelCase ( self ) -> Tuple:
for model_name in NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
snake_case = NystromformerModel.from_pretrained(A__ )
self.assertIsNotNone(A__ )
@require_torch
class _lowercase ( unittest.TestCase ):
@slow
def UpperCamelCase ( self ) -> List[str]:
snake_case = NystromformerModel.from_pretrained('''uw-madison/nystromformer-512''' )
snake_case = torch.tensor([[0, 1, 2, 3, 4, 5]] )
with torch.no_grad():
snake_case = model(A__ )[0]
snake_case = torch.Size((1, 6, 7_68) )
self.assertEqual(output.shape , A__ )
snake_case = torch.tensor(
[[[-0.4_5_3_2, -0.0_9_3_6, 0.5_1_3_7], [-0.2_6_7_6, 0.0_6_2_8, 0.6_1_8_6], [-0.3_6_2_9, -0.1_7_2_6, 0.4_7_1_6]]] )
self.assertTrue(torch.allclose(output[:, :3, :3] , A__ , atol=1e-4 ) )
@slow
def UpperCamelCase ( self ) -> Union[str, Any]:
snake_case = '''the [MASK] of Belgium is Brussels'''
snake_case = AutoTokenizer.from_pretrained('''uw-madison/nystromformer-512''' )
snake_case = NystromformerForMaskedLM.from_pretrained('''uw-madison/nystromformer-512''' )
snake_case = tokenizer(A__ , return_tensors='''pt''' )
with torch.no_grad():
snake_case = model(encoding.input_ids ).logits
snake_case = token_logits[:, 2, :].argmax(-1 )[0]
self.assertEqual(tokenizer.decode(A__ ) , '''capital''' )
| 44
|
'''simple docstring'''
_lowercase = {
'Pillow': 'Pillow',
'accelerate': 'accelerate>=0.11.0',
'compel': 'compel==0.1.8',
'black': 'black~=23.1',
'datasets': 'datasets',
'filelock': 'filelock',
'flax': 'flax>=0.4.1',
'hf-doc-builder': 'hf-doc-builder>=0.3.0',
'huggingface-hub': 'huggingface-hub>=0.13.2',
'requests-mock': 'requests-mock==1.10.0',
'importlib_metadata': 'importlib_metadata',
'invisible-watermark': 'invisible-watermark',
'isort': 'isort>=5.5.4',
'jax': 'jax>=0.2.8,!=0.3.2',
'jaxlib': 'jaxlib>=0.1.65',
'Jinja2': 'Jinja2',
'k-diffusion': 'k-diffusion>=0.0.12',
'torchsde': 'torchsde',
'note_seq': 'note_seq',
'librosa': 'librosa',
'numpy': 'numpy',
'omegaconf': 'omegaconf',
'parameterized': 'parameterized',
'protobuf': 'protobuf>=3.20.3,<4',
'pytest': 'pytest',
'pytest-timeout': 'pytest-timeout',
'pytest-xdist': 'pytest-xdist',
'ruff': 'ruff>=0.0.241',
'safetensors': 'safetensors',
'sentencepiece': 'sentencepiece>=0.1.91,!=0.1.92',
'scipy': 'scipy',
'onnx': 'onnx',
'regex': 'regex!=2019.12.17',
'requests': 'requests',
'tensorboard': 'tensorboard',
'torch': 'torch>=1.4',
'torchvision': 'torchvision',
'transformers': 'transformers>=4.25.1',
'urllib3': 'urllib3<=2.0.0',
}
| 44
| 1
|
'''simple docstring'''
import os
# Precomputes a list of the 100 first triangular numbers
_lowercase = [int(0.5 * n * (n + 1)) for n in range(1, 101)]
def __UpperCamelCase ( ) ->Any:
snake_case = os.path.dirname(os.path.realpath(a ) )
snake_case = os.path.join(a , '''words.txt''' )
snake_case = ''''''
with open(a ) as f:
snake_case = f.readline()
snake_case = [word.strip('''"''' ) for word in words.strip('''\r\n''' ).split(''',''' )]
snake_case = [
word
for word in [sum(ord(a ) - 64 for x in word ) for word in words]
if word in TRIANGULAR_NUMBERS
]
return len(a )
if __name__ == "__main__":
print(solution())
| 44
|
'''simple docstring'''
import random
import unittest
import torch
from diffusers import IFInpaintingSuperResolutionPipeline
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_INPAINTING_BATCH_PARAMS,
TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin
@skip_mps
class _lowercase ( __a , __a , unittest.TestCase ):
_UpperCAmelCase = IFInpaintingSuperResolutionPipeline
_UpperCAmelCase = TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {'''width''', '''height'''}
_UpperCAmelCase = TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS.union({'''original_image'''} )
_UpperCAmelCase = PipelineTesterMixin.required_optional_params - {'''latents'''}
def UpperCamelCase ( self ) -> int:
return self._get_superresolution_dummy_components()
def UpperCamelCase ( self , A__ , A__=0 ) -> Union[str, Any]:
if str(A__ ).startswith('''mps''' ):
snake_case = torch.manual_seed(A__ )
else:
snake_case = torch.Generator(device=A__ ).manual_seed(A__ )
snake_case = floats_tensor((1, 3, 16, 16) , rng=random.Random(A__ ) ).to(A__ )
snake_case = floats_tensor((1, 3, 32, 32) , rng=random.Random(A__ ) ).to(A__ )
snake_case = floats_tensor((1, 3, 32, 32) , rng=random.Random(A__ ) ).to(A__ )
snake_case = {
'''prompt''': '''A painting of a squirrel eating a burger''',
'''image''': image,
'''original_image''': original_image,
'''mask_image''': mask_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 UpperCamelCase ( self ) -> List[Any]:
self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3 )
def UpperCamelCase ( self ) -> Optional[Any]:
self._test_save_load_optional_components()
@unittest.skipIf(torch_device != '''cuda''' , reason='''float16 requires CUDA''' )
def UpperCamelCase ( self ) -> List[str]:
# Due to non-determinism in save load of the hf-internal-testing/tiny-random-t5 text encoder
super().test_save_load_floataa(expected_max_diff=1e-1 )
def UpperCamelCase ( self ) -> int:
self._test_attention_slicing_forward_pass(expected_max_diff=1e-2 )
def UpperCamelCase ( self ) -> Optional[Any]:
self._test_save_load_local()
def UpperCamelCase ( self ) -> Dict:
self._test_inference_batch_single_identical(
expected_max_diff=1e-2 , )
| 44
| 1
|
'''simple docstring'''
from dataclasses import dataclass, field
from typing import Tuple
from ..utils import cached_property, is_torch_available, is_torch_tpu_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments
if is_torch_available():
import torch
if is_torch_tpu_available(check_device=False):
import torch_xla.core.xla_model as xm
_lowercase = logging.get_logger(__name__)
@dataclass
class _lowercase ( __a ):
_UpperCAmelCase = [
'''no_inference''',
'''no_cuda''',
'''no_tpu''',
'''no_speed''',
'''no_memory''',
'''no_env_print''',
'''no_multi_process''',
]
def __init__( self , **A__ ) -> int:
for deprecated_arg in self.deprecated_args:
if deprecated_arg in kwargs:
snake_case = deprecated_arg[3:]
setattr(self , A__ , not kwargs.pop(A__ ) )
logger.warning(
F"""{deprecated_arg} is depreciated. Please use --no_{positive_arg} or"""
F""" {positive_arg}={kwargs[positive_arg]}""" )
snake_case = kwargs.pop('''torchscript''' , self.torchscript )
snake_case = kwargs.pop('''torch_xla_tpu_print_metrics''' , self.torch_xla_tpu_print_metrics )
snake_case = kwargs.pop('''fp16_opt_level''' , self.fpaa_opt_level )
super().__init__(**A__ )
_UpperCAmelCase = field(default=__a , metadata={'''help''': '''Trace the models using torchscript'''} )
_UpperCAmelCase = field(default=__a , metadata={'''help''': '''Print Xla/PyTorch tpu metrics'''} )
_UpperCAmelCase = field(
default='''O1''' , metadata={
'''help''': (
'''For fp16: Apex AMP optimization level selected in [\'O0\', \'O1\', \'O2\', and \'O3\']. '''
'''See details at https://nvidia.github.io/apex/amp.html'''
)
} , )
@cached_property
def UpperCamelCase ( self ) -> Tuple["torch.device", int]:
requires_backends(self , ['''torch'''] )
logger.info('''PyTorch: setting up devices''' )
if not self.cuda:
snake_case = torch.device('''cpu''' )
snake_case = 0
elif is_torch_tpu_available():
snake_case = xm.xla_device()
snake_case = 0
else:
snake_case = torch.device('''cuda''' if torch.cuda.is_available() else '''cpu''' )
snake_case = torch.cuda.device_count()
return device, n_gpu
@property
def UpperCamelCase ( self ) -> int:
return is_torch_tpu_available() and self.tpu
@property
def UpperCamelCase ( self ) -> int:
requires_backends(self , ['''torch'''] )
# TODO(PVP): currently only single GPU is supported
return torch.cuda.current_device()
@property
def UpperCamelCase ( self ) -> "torch.device":
requires_backends(self , ['''torch'''] )
return self._setup_devices[0]
@property
def UpperCamelCase ( self ) -> List[str]:
requires_backends(self , ['''torch'''] )
return self._setup_devices[1]
@property
def UpperCamelCase ( self ) -> Tuple:
return self.n_gpu > 0
| 44
|
'''simple docstring'''
from typing import Dict, List, Optional, Union
import numpy as np
from .feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from .utils import PaddingStrategy, TensorType, is_tf_tensor, is_torch_tensor, logging, to_numpy
_lowercase = logging.get_logger(__name__)
class _lowercase ( __a ):
def __init__( self , A__ , A__ , A__ , **A__ ) -> Union[str, Any]:
snake_case = feature_size
snake_case = sampling_rate
snake_case = padding_value
snake_case = kwargs.pop('''padding_side''' , '''right''' )
snake_case = kwargs.pop('''return_attention_mask''' , A__ )
super().__init__(**A__ )
def UpperCamelCase ( self , A__ , A__ = True , A__ = None , A__ = False , A__ = None , A__ = None , A__ = None , ) -> BatchFeature:
# If we have a list of dicts, let's convert it in a dict of lists
# We do this to allow using this method as a collate_fn function in PyTorch Dataloader
if isinstance(A__ , (list, tuple) ) and isinstance(processed_features[0] , (dict, BatchFeature) ):
snake_case = {
key: [example[key] for example in processed_features] for key in processed_features[0].keys()
}
# The model's main input name, usually `input_values`, has be passed for padding
if self.model_input_names[0] not in processed_features:
raise ValueError(
'''You should supply an instance of `transformers.BatchFeature` or list of `transformers.BatchFeature`'''
F""" to this method that includes {self.model_input_names[0]}, but you provided"""
F""" {list(processed_features.keys() )}""" )
snake_case = processed_features[self.model_input_names[0]]
snake_case = (
return_attention_mask if return_attention_mask is not None else self.return_attention_mask
)
if len(A__ ) == 0:
if return_attention_mask:
snake_case = []
return processed_features
# If we have PyTorch/TF tensors or lists as inputs, we cast them as Numpy arrays
# and rebuild them afterwards if no return_tensors is specified
# Note that we lose the specific device the tensor may be on for PyTorch
snake_case = required_input[0]
if isinstance(A__ , (list, tuple) ):
# first_element might be an empty list/tuple in some edge cases so we grab the first non empty element.
snake_case = 0
while len(required_input[index] ) == 0:
index += 1
if index < len(A__ ):
snake_case = required_input[index][0]
if return_tensors is None:
if is_tf_tensor(A__ ):
snake_case = '''tf'''
elif is_torch_tensor(A__ ):
snake_case = '''pt'''
elif isinstance(A__ , (int, float, list, tuple, np.ndarray) ):
snake_case = '''np'''
else:
raise ValueError(
F"""type of {first_element} unknown: {type(A__ )}. """
'''Should be one of a python, numpy, pytorch or tensorflow object.''' )
for key, value in processed_features.items():
if isinstance(value[0] , (int, float) ):
snake_case = to_numpy(A__ )
else:
snake_case = [to_numpy(A__ ) for v in value]
# Convert padding_strategy in PaddingStrategy
snake_case = self._get_padding_strategies(padding=A__ , max_length=A__ )
snake_case = processed_features[self.model_input_names[0]]
snake_case = len(A__ )
if not all(len(A__ ) == batch_size for v in processed_features.values() ):
raise ValueError('''Some items in the output dictionary have a different batch size than others.''' )
snake_case = []
for i in range(A__ ):
snake_case = {k: v[i] for k, v in processed_features.items()}
# truncation
snake_case = self._truncate(
A__ , max_length=A__ , pad_to_multiple_of=A__ , truncation=A__ , )
truncated_inputs.append(A__ )
if padding_strategy == PaddingStrategy.LONGEST:
# make sure that `max_length` cannot be longer than the longest truncated length
snake_case = max(len(input_slice[self.model_input_names[0]] ) for input_slice in truncated_inputs )
snake_case = PaddingStrategy.MAX_LENGTH
snake_case = {}
for i in range(A__ ):
# padding
snake_case = self._pad(
truncated_inputs[i] , max_length=A__ , padding_strategy=A__ , pad_to_multiple_of=A__ , return_attention_mask=A__ , )
for key, value in outputs.items():
if key not in batch_outputs:
snake_case = []
if value.dtype is np.dtype(np.floataa ):
snake_case = value.astype(np.floataa )
batch_outputs[key].append(A__ )
return BatchFeature(A__ , tensor_type=A__ )
def UpperCamelCase ( self , A__ , A__ = None , A__ = PaddingStrategy.DO_NOT_PAD , A__ = None , A__ = None , ) -> dict:
snake_case = processed_features[self.model_input_names[0]]
if padding_strategy == PaddingStrategy.LONGEST:
snake_case = len(A__ )
if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
snake_case = ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of
snake_case = padding_strategy != PaddingStrategy.DO_NOT_PAD and len(A__ ) < max_length
if return_attention_mask and "attention_mask" not in processed_features:
snake_case = np.ones(len(A__ ) , dtype=np.intaa )
if needs_to_be_padded:
snake_case = max_length - len(A__ )
if self.padding_side == "right":
if return_attention_mask:
snake_case = np.pad(
processed_features['''attention_mask'''] , (0, difference) )
snake_case = ((0, difference), (0, 0)) if self.feature_size > 1 else (0, difference)
snake_case = np.pad(
A__ , A__ , '''constant''' , constant_values=self.padding_value )
elif self.padding_side == "left":
if return_attention_mask:
snake_case = np.pad(
processed_features['''attention_mask'''] , (difference, 0) )
snake_case = ((difference, 0), (0, 0)) if self.feature_size > 1 else (difference, 0)
snake_case = np.pad(
A__ , A__ , '''constant''' , constant_values=self.padding_value )
else:
raise ValueError('''Invalid padding strategy:''' + str(self.padding_side ) )
return processed_features
def UpperCamelCase ( self , A__ , A__ = None , A__ = None , A__ = None , ) -> Union[str, Any]:
if not truncation:
return processed_features
elif truncation and max_length is None:
raise ValueError('''When setting ``truncation=True``, make sure that ``max_length`` is defined.''' )
snake_case = processed_features[self.model_input_names[0]]
# find `max_length` that fits `pad_to_multiple_of`
if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
snake_case = ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of
snake_case = len(A__ ) > max_length
if needs_to_be_truncated:
snake_case = processed_features[self.model_input_names[0]][:max_length]
if "attention_mask" in processed_features:
snake_case = processed_features['''attention_mask'''][:max_length]
return processed_features
def UpperCamelCase ( self , A__=False , A__=None ) -> Union[str, Any]:
# Get padding strategy
if padding is not False:
if padding is True:
snake_case = PaddingStrategy.LONGEST # Default to pad to the longest sequence in the batch
elif not isinstance(A__ , A__ ):
snake_case = PaddingStrategy(A__ )
elif isinstance(A__ , A__ ):
snake_case = padding
else:
snake_case = PaddingStrategy.DO_NOT_PAD
# Set max length if needed
if max_length is None:
if padding_strategy == PaddingStrategy.MAX_LENGTH:
raise ValueError(
F"""When setting ``padding={PaddingStrategy.MAX_LENGTH}``, make sure that max_length is defined""" )
# Test if we have a padding value
if padding_strategy != PaddingStrategy.DO_NOT_PAD and (self.padding_value is None):
raise ValueError(
'''Asking to pad but the feature_extractor does not have a padding value. Please select a value to use'''
''' as `padding_value`. For example: `feature_extractor.padding_value = 0.0`.''' )
return padding_strategy
| 44
| 1
|
'''simple docstring'''
from __future__ import annotations
def __UpperCamelCase ( a : list[int] , a : list[int] , a : list[int] , a : list[list[str]] , a : int , ) ->None:
snake_case = len(a )
# If row is equal to the size of the board it means there are a queen in each row in
# the current board (possible_board)
if row == n:
# We convert the variable possible_board that looks like this: [1, 3, 0, 2] to
# this: ['. Q . . ', '. . . Q ', 'Q . . . ', '. . Q . ']
boards.append(['''. ''' * i + '''Q ''' + '''. ''' * (n - 1 - i) for i in possible_board] )
return
# We iterate each column in the row to find all possible results in each row
for col in range(a ):
# We apply that we learned previously. First we check that in the current board
# (possible_board) there are not other same value because if there is it means
# that there are a collision in vertical. Then we apply the two formulas we
# learned before:
#
# 45º: y - x = b or 45: row - col = b
# 135º: y + x = b or row + col = b.
#
# And we verify if the results of this two formulas not exist in their variables
# respectively. (diagonal_right_collisions, diagonal_left_collisions)
#
# If any or these are True it means there is a collision so we continue to the
# next value in the for loop.
if (
col in possible_board
or row - col in diagonal_right_collisions
or row + col in diagonal_left_collisions
):
continue
# If it is False we call dfs function again and we update the inputs
depth_first_search(
[*possible_board, col] , [*diagonal_right_collisions, row - col] , [*diagonal_left_collisions, row + col] , a , a , )
def __UpperCamelCase ( a : int ) ->None:
snake_case = []
depth_first_search([] , [] , [] , a , a )
# Print all the boards
for board in boards:
for column in board:
print(a )
print('''''' )
print(len(a ) , '''solutions were found.''' )
if __name__ == "__main__":
import doctest
doctest.testmod()
n_queens_solution(4)
| 44
|
'''simple docstring'''
from collections import Counter
from pathlib import Path
from typing import Optional, Tuple
import yaml
class _lowercase ( yaml.SafeLoader ):
def UpperCamelCase ( self , A__ ) -> List[str]:
snake_case = [self.constructed_objects[key_node] for key_node, _ in node.value]
snake_case = [tuple(A__ ) if isinstance(A__ , A__ ) else key for key in keys]
snake_case = Counter(A__ )
snake_case = [key for key in counter if counter[key] > 1]
if duplicate_keys:
raise TypeError(F"""Got duplicate yaml keys: {duplicate_keys}""" )
def UpperCamelCase ( self , A__ , A__=False ) -> List[Any]:
snake_case = super().construct_mapping(A__ , deep=A__ )
self._check_no_duplicates_on_constructed_node(A__ )
return mapping
def __UpperCamelCase ( a : str ) ->Tuple[Optional[str], str]:
snake_case = list(readme_content.splitlines() )
if full_content and full_content[0] == "---" and "---" in full_content[1:]:
snake_case = full_content[1:].index('''---''' ) + 1
snake_case = '''\n'''.join(full_content[1:sep_idx] )
return yamlblock, "\n".join(full_content[sep_idx + 1 :] )
return None, "\n".join(a )
class _lowercase ( __a ):
# class attributes
_UpperCAmelCase = {'''train_eval_index'''} # train-eval-index in the YAML metadata
@classmethod
def UpperCamelCase ( cls , A__ ) -> "DatasetMetadata":
with open(A__ , encoding='''utf-8''' ) as readme_file:
snake_case , snake_case = _split_yaml_from_readme(readme_file.read() )
if yaml_string is not None:
return cls.from_yaml_string(A__ )
else:
return cls()
def UpperCamelCase ( self , A__ ) -> str:
if path.exists():
with open(A__ , encoding='''utf-8''' ) as readme_file:
snake_case = readme_file.read()
else:
snake_case = None
snake_case = self._to_readme(A__ )
with open(A__ , '''w''' , encoding='''utf-8''' ) as readme_file:
readme_file.write(A__ )
def UpperCamelCase ( self , A__ = None ) -> str:
if readme_content is not None:
snake_case , snake_case = _split_yaml_from_readme(A__ )
snake_case = '''---\n''' + self.to_yaml_string() + '''---\n''' + content
else:
snake_case = '''---\n''' + self.to_yaml_string() + '''---\n'''
return full_content
@classmethod
def UpperCamelCase ( cls , A__ ) -> "DatasetMetadata":
snake_case = yaml.load(A__ , Loader=_NoDuplicateSafeLoader ) or {}
# Convert the YAML keys to DatasetMetadata fields
snake_case = {
(key.replace('''-''' , '''_''' ) if key.replace('''-''' , '''_''' ) in cls._FIELDS_WITH_DASHES else key): value
for key, value in metadata_dict.items()
}
return cls(**A__ )
def UpperCamelCase ( self ) -> str:
return yaml.safe_dump(
{
(key.replace('''_''' , '''-''' ) if key in self._FIELDS_WITH_DASHES else key): value
for key, value in self.items()
} , sort_keys=A__ , allow_unicode=A__ , encoding='''utf-8''' , ).decode('''utf-8''' )
_lowercase = {
'image-classification': [],
'translation': [],
'image-segmentation': [],
'fill-mask': [],
'automatic-speech-recognition': [],
'token-classification': [],
'sentence-similarity': [],
'audio-classification': [],
'question-answering': [],
'summarization': [],
'zero-shot-classification': [],
'table-to-text': [],
'feature-extraction': [],
'other': [],
'multiple-choice': [],
'text-classification': [],
'text-to-image': [],
'text2text-generation': [],
'zero-shot-image-classification': [],
'tabular-classification': [],
'tabular-regression': [],
'image-to-image': [],
'tabular-to-text': [],
'unconditional-image-generation': [],
'text-retrieval': [],
'text-to-speech': [],
'object-detection': [],
'audio-to-audio': [],
'text-generation': [],
'conversational': [],
'table-question-answering': [],
'visual-question-answering': [],
'image-to-text': [],
'reinforcement-learning': [],
'voice-activity-detection': [],
'time-series-forecasting': [],
'document-question-answering': [],
}
if __name__ == "__main__":
from argparse import ArgumentParser
_lowercase = ArgumentParser(usage='Validate the yaml metadata block of a README.md file.')
ap.add_argument('readme_filepath')
_lowercase = ap.parse_args()
_lowercase = Path(args.readme_filepath)
_lowercase = DatasetMetadata.from_readme(readme_filepath)
print(dataset_metadata)
dataset_metadata.to_readme(readme_filepath)
| 44
| 1
|
'''simple docstring'''
def __UpperCamelCase ( a : Union[str, Any] , a : Any ) ->Optional[Any]:
snake_case = ''''''
for i in table:
res += inp[i - 1]
return res
def __UpperCamelCase ( a : str ) ->Tuple:
return data[1:] + data[0]
def __UpperCamelCase ( a : Tuple , a : List[str] ) ->Optional[Any]:
snake_case = ''''''
for i in range(len(a ) ):
if a[i] == b[i]:
res += "0"
else:
res += "1"
return res
def __UpperCamelCase ( a : Optional[Any] , a : Dict ) ->Tuple:
snake_case = int('''0b''' + data[0] + data[-1] , 2 )
snake_case = int('''0b''' + data[1:3] , 2 )
return bin(s[row][col] )[2:]
def __UpperCamelCase ( a : Dict , a : List[Any] , a : Dict , a : Union[str, Any] , a : Union[str, Any] ) ->List[str]:
snake_case = message[:4]
snake_case = message[4:]
snake_case = apply_table(a , a )
snake_case = xor(a , a )
snake_case = apply_sbox(a , temp[:4] ) # noqa: E741
snake_case = apply_sbox(a , temp[4:] )
snake_case = '''0''' * (2 - len(a )) + l # noqa: E741
snake_case = '''0''' * (2 - len(a )) + r
snake_case = apply_table(l + r , a )
snake_case = xor(a , a )
return temp + right
if __name__ == "__main__":
_lowercase = input('Enter 10 bit key: ')
_lowercase = input('Enter 8 bit message: ')
_lowercase = [6, 3, 7, 4, 8, 5, 10, 9]
_lowercase = [3, 5, 2, 7, 4, 10, 1, 9, 8, 6]
_lowercase = [2, 4, 3, 1]
_lowercase = [2, 6, 3, 1, 4, 8, 5, 7]
_lowercase = [4, 1, 3, 5, 7, 2, 8, 6]
_lowercase = [4, 1, 2, 3, 2, 3, 4, 1]
_lowercase = [[1, 0, 3, 2], [3, 2, 1, 0], [0, 2, 1, 3], [3, 1, 3, 2]]
_lowercase = [[0, 1, 2, 3], [2, 0, 1, 3], [3, 0, 1, 0], [2, 1, 0, 3]]
# key generation
_lowercase = apply_table(key, paa_table)
_lowercase = temp[:5]
_lowercase = temp[5:]
_lowercase = left_shift(left)
_lowercase = left_shift(right)
_lowercase = apply_table(left + right, pa_table)
_lowercase = left_shift(left)
_lowercase = left_shift(right)
_lowercase = left_shift(left)
_lowercase = left_shift(right)
_lowercase = apply_table(left + right, pa_table)
# encryption
_lowercase = apply_table(message, IP)
_lowercase = function(expansion, sa, sa, keya, temp)
_lowercase = temp[4:] + temp[:4]
_lowercase = function(expansion, sa, sa, keya, temp)
_lowercase = apply_table(temp, IP_inv)
print('Cipher text is:', CT)
# decryption
_lowercase = apply_table(CT, IP)
_lowercase = function(expansion, sa, sa, keya, temp)
_lowercase = temp[4:] + temp[:4]
_lowercase = function(expansion, sa, sa, keya, temp)
_lowercase = apply_table(temp, IP_inv)
print('Plain text after decypting is:', PT)
| 44
|
'''simple docstring'''
import json
import os
import re
import unittest
from transformers import CodeGenTokenizer, CodeGenTokenizerFast
from transformers.models.codegen.tokenization_codegen import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class _lowercase ( __a , unittest.TestCase ):
_UpperCAmelCase = CodeGenTokenizer
_UpperCAmelCase = CodeGenTokenizerFast
_UpperCAmelCase = True
_UpperCAmelCase = {'''add_prefix_space''': True}
_UpperCAmelCase = False
def UpperCamelCase ( self ) -> Tuple:
super().setUp()
# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
snake_case = [
'''l''',
'''o''',
'''w''',
'''e''',
'''r''',
'''s''',
'''t''',
'''i''',
'''d''',
'''n''',
'''\u0120''',
'''\u0120l''',
'''\u0120n''',
'''\u0120lo''',
'''\u0120low''',
'''er''',
'''\u0120lowest''',
'''\u0120newer''',
'''\u0120wider''',
'''<unk>''',
'''<|endoftext|>''',
]
snake_case = dict(zip(A__ , range(len(A__ ) ) ) )
snake_case = ['''#version: 0.2''', '''\u0120 l''', '''\u0120l o''', '''\u0120lo w''', '''e r''', '''''']
snake_case = {'''unk_token''': '''<unk>'''}
snake_case = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['''vocab_file'''] )
snake_case = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['''merges_file'''] )
with open(self.vocab_file , '''w''' , encoding='''utf-8''' ) as fp:
fp.write(json.dumps(A__ ) + '''\n''' )
with open(self.merges_file , '''w''' , encoding='''utf-8''' ) as fp:
fp.write('''\n'''.join(A__ ) )
def UpperCamelCase ( self , **A__ ) -> Union[str, Any]:
kwargs.update(self.special_tokens_map )
return CodeGenTokenizer.from_pretrained(self.tmpdirname , **A__ )
def UpperCamelCase ( self , **A__ ) -> Union[str, Any]:
kwargs.update(self.special_tokens_map )
return CodeGenTokenizerFast.from_pretrained(self.tmpdirname , **A__ )
def UpperCamelCase ( self , A__ ) -> Tuple:
snake_case = '''lower newer'''
snake_case = '''lower newer'''
return input_text, output_text
def UpperCamelCase ( self ) -> List[Any]:
snake_case = CodeGenTokenizer(self.vocab_file , self.merges_file , **self.special_tokens_map )
snake_case = '''lower newer'''
snake_case = ['''\u0120low''', '''er''', '''\u0120''', '''n''', '''e''', '''w''', '''er''']
snake_case = tokenizer.tokenize(A__ , add_prefix_space=A__ )
self.assertListEqual(A__ , A__ )
snake_case = tokens + [tokenizer.unk_token]
snake_case = [14, 15, 10, 9, 3, 2, 15, 19]
self.assertListEqual(tokenizer.convert_tokens_to_ids(A__ ) , A__ )
def UpperCamelCase ( self ) -> Optional[int]:
if not self.test_rust_tokenizer:
return
snake_case = self.get_tokenizer()
snake_case = self.get_rust_tokenizer(add_prefix_space=A__ )
snake_case = '''lower newer'''
# Testing tokenization
snake_case = tokenizer.tokenize(A__ , add_prefix_space=A__ )
snake_case = rust_tokenizer.tokenize(A__ )
self.assertListEqual(A__ , A__ )
# Testing conversion to ids without special tokens
snake_case = tokenizer.encode(A__ , add_special_tokens=A__ , add_prefix_space=A__ )
snake_case = rust_tokenizer.encode(A__ , add_special_tokens=A__ )
self.assertListEqual(A__ , A__ )
# Testing conversion to ids with special tokens
snake_case = self.get_rust_tokenizer(add_prefix_space=A__ )
snake_case = tokenizer.encode(A__ , add_prefix_space=A__ )
snake_case = rust_tokenizer.encode(A__ )
self.assertListEqual(A__ , A__ )
# Testing the unknown token
snake_case = tokens + [rust_tokenizer.unk_token]
snake_case = [14, 15, 10, 9, 3, 2, 15, 19]
self.assertListEqual(rust_tokenizer.convert_tokens_to_ids(A__ ) , A__ )
def UpperCamelCase ( self , *A__ , **A__ ) -> List[str]:
# It's very difficult to mix/test pretokenization with byte-level
# And get both CodeGen and Roberta to work at the same time (mostly an issue of adding a space before the string)
pass
def UpperCamelCase ( self , A__=15 ) -> Tuple:
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})""" ):
snake_case = self.rust_tokenizer_class.from_pretrained(A__ , **A__ )
# Simple input
snake_case = '''This is a simple input'''
snake_case = ['''This is a simple input 1''', '''This is a simple input 2''']
snake_case = ('''This is a simple input''', '''This is a pair''')
snake_case = [
('''This is a simple input 1''', '''This is a simple input 2'''),
('''This is a simple pair 1''', '''This is a simple pair 2'''),
]
# Simple input tests
self.assertRaises(A__ , tokenizer_r.encode , A__ , max_length=A__ , padding='''max_length''' )
# Simple input
self.assertRaises(A__ , tokenizer_r.encode_plus , A__ , max_length=A__ , padding='''max_length''' )
# Simple input
self.assertRaises(
A__ , tokenizer_r.batch_encode_plus , A__ , max_length=A__ , padding='''max_length''' , )
# Pair input
self.assertRaises(A__ , tokenizer_r.encode , A__ , max_length=A__ , padding='''max_length''' )
# Pair input
self.assertRaises(A__ , tokenizer_r.encode_plus , A__ , max_length=A__ , padding='''max_length''' )
# Pair input
self.assertRaises(
A__ , tokenizer_r.batch_encode_plus , A__ , max_length=A__ , padding='''max_length''' , )
def UpperCamelCase ( self ) -> Tuple:
snake_case = CodeGenTokenizer.from_pretrained(self.tmpdirname , pad_token='''<pad>''' )
# Simple input
snake_case = '''This is a simple input'''
snake_case = ['''This is a simple input looooooooong''', '''This is a simple input''']
snake_case = ('''This is a simple input''', '''This is a pair''')
snake_case = [
('''This is a simple input loooooong''', '''This is a simple input'''),
('''This is a simple pair loooooong''', '''This is a simple pair'''),
]
snake_case = tokenizer.pad_token_id
snake_case = tokenizer(A__ , padding='''max_length''' , max_length=30 , return_tensors='''np''' )
snake_case = tokenizer(A__ , padding=A__ , truncate=A__ , return_tensors='''np''' )
snake_case = tokenizer(*A__ , padding='''max_length''' , max_length=60 , return_tensors='''np''' )
snake_case = tokenizer(A__ , padding=A__ , truncate=A__ , return_tensors='''np''' )
# s
# test single string max_length padding
self.assertEqual(out_s['''input_ids'''].shape[-1] , 30 )
self.assertTrue(pad_token_id in out_s['''input_ids'''] )
self.assertTrue(0 in out_s['''attention_mask'''] )
# s2
# test automatic padding
self.assertEqual(out_sa['''input_ids'''].shape[-1] , 33 )
# long slice doesn't have padding
self.assertFalse(pad_token_id in out_sa['''input_ids'''][0] )
self.assertFalse(0 in out_sa['''attention_mask'''][0] )
# short slice does have padding
self.assertTrue(pad_token_id in out_sa['''input_ids'''][1] )
self.assertTrue(0 in out_sa['''attention_mask'''][1] )
# p
# test single pair max_length padding
self.assertEqual(out_p['''input_ids'''].shape[-1] , 60 )
self.assertTrue(pad_token_id in out_p['''input_ids'''] )
self.assertTrue(0 in out_p['''attention_mask'''] )
# p2
# test automatic padding pair
self.assertEqual(out_pa['''input_ids'''].shape[-1] , 52 )
# long slice pair doesn't have padding
self.assertFalse(pad_token_id in out_pa['''input_ids'''][0] )
self.assertFalse(0 in out_pa['''attention_mask'''][0] )
# short slice pair does have padding
self.assertTrue(pad_token_id in out_pa['''input_ids'''][1] )
self.assertTrue(0 in out_pa['''attention_mask'''][1] )
def UpperCamelCase ( self ) -> str:
snake_case = '''$$$'''
snake_case = CodeGenTokenizer.from_pretrained(self.tmpdirname , bos_token=A__ , add_bos_token=A__ )
snake_case = '''This is a simple input'''
snake_case = ['''This is a simple input 1''', '''This is a simple input 2''']
snake_case = tokenizer.bos_token_id
snake_case = tokenizer(A__ )
snake_case = tokenizer(A__ )
self.assertEqual(out_s.input_ids[0] , A__ )
self.assertTrue(all(o[0] == bos_token_id for o in out_sa.input_ids ) )
snake_case = tokenizer.decode(out_s.input_ids )
snake_case = tokenizer.batch_decode(out_sa.input_ids )
self.assertEqual(decode_s.split()[0] , A__ )
self.assertTrue(all(d.split()[0] == bos_token for d in decode_sa ) )
@slow
def UpperCamelCase ( self ) -> Any:
snake_case = CodeGenTokenizer.from_pretrained('''Salesforce/codegen-350M-mono''' )
snake_case = '''\nif len_a > len_b:\n result = a\nelse:\n result = b\n\n\n\n#'''
snake_case = '''\nif len_a > len_b: result = a\nelse: result = b'''
snake_case = tokenizer.encode(A__ )
snake_case = ['''^#''', re.escape('''<|endoftext|>''' ), '''^\'\'\'''', '''^"""''', '''\n\n\n''']
snake_case = tokenizer.decode(A__ , truncate_before_pattern=A__ )
self.assertEqual(A__ , A__ )
def UpperCamelCase ( self ) -> Union[str, Any]:
pass
| 44
| 1
|
'''simple docstring'''
import inspect
import warnings
from typing import Any, Dict, Optional, Union
from packaging import version
def __UpperCamelCase ( *a : List[str] , a : Optional[Union[Dict, Any]] = None , a : Union[str, Any]=True , a : Dict=2 ) ->Any:
from .. import __version__
snake_case = take_from
snake_case = ()
if not isinstance(args[0] , a ):
snake_case = (args,)
for attribute, version_name, message in args:
if version.parse(version.parse(a ).base_version ) >= version.parse(a ):
raise ValueError(
f"""The deprecation tuple {(attribute, version_name, message)} should be removed since diffusers'"""
f""" version {__version__} is >= {version_name}""" )
snake_case = None
if isinstance(a , a ) and attribute in deprecated_kwargs:
values += (deprecated_kwargs.pop(a ),)
snake_case = f"""The `{attribute}` argument is deprecated and will be removed in version {version_name}."""
elif hasattr(a , a ):
values += (getattr(a , a ),)
snake_case = f"""The `{attribute}` attribute is deprecated and will be removed in version {version_name}."""
elif deprecated_kwargs is None:
snake_case = f"""`{attribute}` is deprecated and will be removed in version {version_name}."""
if warning is not None:
snake_case = warning + ''' ''' if standard_warn else ''''''
warnings.warn(warning + message , a , stacklevel=a )
if isinstance(a , a ) and len(a ) > 0:
snake_case = inspect.getouterframes(inspect.currentframe() )[1]
snake_case = call_frame.filename
snake_case = call_frame.lineno
snake_case = call_frame.function
snake_case , snake_case = next(iter(deprecated_kwargs.items() ) )
raise TypeError(f"""{function} in {filename} line {line_number-1} got an unexpected keyword argument `{key}`""" )
if len(a ) == 0:
return
elif len(a ) == 1:
return values[0]
return values
| 44
|
'''simple docstring'''
from __future__ import annotations
import inspect
import unittest
from transformers import ViTConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import TFViTForImageClassification, TFViTModel
if is_vision_available():
from PIL import Image
from transformers import ViTImageProcessor
class _lowercase :
def __init__( self , A__ , A__=13 , A__=30 , A__=2 , A__=3 , A__=True , A__=True , A__=32 , A__=2 , A__=4 , A__=37 , A__="gelu" , A__=0.1 , A__=0.1 , A__=10 , A__=0.0_2 , A__=3 , A__=None , ) -> List[Any]:
snake_case = parent
snake_case = batch_size
snake_case = image_size
snake_case = patch_size
snake_case = num_channels
snake_case = is_training
snake_case = use_labels
snake_case = hidden_size
snake_case = num_hidden_layers
snake_case = num_attention_heads
snake_case = intermediate_size
snake_case = hidden_act
snake_case = hidden_dropout_prob
snake_case = attention_probs_dropout_prob
snake_case = type_sequence_label_size
snake_case = initializer_range
snake_case = scope
# in ViT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
snake_case = (image_size // patch_size) ** 2
snake_case = num_patches + 1
def UpperCamelCase ( self ) -> int:
snake_case = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
snake_case = None
if self.use_labels:
snake_case = ids_tensor([self.batch_size] , self.type_sequence_label_size )
snake_case = self.get_config()
return config, pixel_values, labels
def UpperCamelCase ( self ) -> int:
return ViTConfig(
image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , 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 , is_decoder=A__ , initializer_range=self.initializer_range , )
def UpperCamelCase ( self , A__ , A__ , A__ ) -> Union[str, Any]:
snake_case = TFViTModel(config=A__ )
snake_case = model(A__ , training=A__ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
# Test with an image with different size than the one specified in config.
snake_case = self.image_size // 2
snake_case = pixel_values[:, :, :image_size, :image_size]
snake_case = model(A__ , interpolate_pos_encoding=A__ , training=A__ )
snake_case = (image_size // self.patch_size) ** 2 + 1
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, seq_length, self.hidden_size) )
def UpperCamelCase ( self , A__ , A__ , A__ ) -> Optional[int]:
snake_case = self.type_sequence_label_size
snake_case = TFViTForImageClassification(A__ )
snake_case = model(A__ , labels=A__ , training=A__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
# Test with an image with different size than the one specified in config.
snake_case = self.image_size // 2
snake_case = pixel_values[:, :, :image_size, :image_size]
snake_case = model(A__ , interpolate_pos_encoding=A__ , training=A__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
# test greyscale images
snake_case = 1
snake_case = TFViTForImageClassification(A__ )
snake_case = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
snake_case = model(A__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
def UpperCamelCase ( self ) -> Union[str, Any]:
snake_case = self.prepare_config_and_inputs()
snake_case , snake_case , snake_case = config_and_inputs
snake_case = {'''pixel_values''': pixel_values}
return config, inputs_dict
@require_tf
class _lowercase ( __a , __a , unittest.TestCase ):
_UpperCAmelCase = (TFViTModel, TFViTForImageClassification) if is_tf_available() else ()
_UpperCAmelCase = (
{'''feature-extraction''': TFViTModel, '''image-classification''': TFViTForImageClassification}
if is_tf_available()
else {}
)
_UpperCAmelCase = False
_UpperCAmelCase = False
_UpperCAmelCase = False
def UpperCamelCase ( self ) -> List[Any]:
snake_case = TFViTModelTester(self )
snake_case = ConfigTester(self , config_class=A__ , has_text_modality=A__ , hidden_size=37 )
def UpperCamelCase ( self ) -> int:
self.config_tester.run_common_tests()
@unittest.skip(reason='''ViT does not use inputs_embeds''' )
def UpperCamelCase ( self ) -> int:
pass
@unittest.skip(reason='''ViT does not use inputs_embeds''' )
def UpperCamelCase ( self ) -> str:
pass
def UpperCamelCase ( self ) -> Union[str, Any]:
snake_case , snake_case = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
snake_case = model_class(A__ )
self.assertIsInstance(model.get_input_embeddings() , (tf.keras.layers.Layer) )
snake_case = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(A__ , tf.keras.layers.Layer ) )
def UpperCamelCase ( self ) -> List[Any]:
snake_case , snake_case = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
snake_case = model_class(A__ )
snake_case = inspect.signature(model.call )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
snake_case = [*signature.parameters.keys()]
snake_case = ['''pixel_values''']
self.assertListEqual(arg_names[:1] , A__ )
def UpperCamelCase ( self ) -> Union[str, Any]:
snake_case = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*A__ )
def UpperCamelCase ( self ) -> Optional[Any]:
snake_case = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*A__ )
@slow
def UpperCamelCase ( self ) -> Any:
snake_case = TFViTModel.from_pretrained('''google/vit-base-patch16-224''' )
self.assertIsNotNone(A__ )
def __UpperCamelCase ( ) ->Any:
snake_case = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
return image
@require_tf
@require_vision
class _lowercase ( unittest.TestCase ):
@cached_property
def UpperCamelCase ( self ) -> Optional[int]:
return ViTImageProcessor.from_pretrained('''google/vit-base-patch16-224''' ) if is_vision_available() else None
@slow
def UpperCamelCase ( self ) -> Dict:
snake_case = TFViTForImageClassification.from_pretrained('''google/vit-base-patch16-224''' )
snake_case = self.default_image_processor
snake_case = prepare_img()
snake_case = image_processor(images=A__ , return_tensors='''tf''' )
# forward pass
snake_case = model(**A__ )
# verify the logits
snake_case = tf.TensorShape((1, 10_00) )
self.assertEqual(outputs.logits.shape , A__ )
snake_case = tf.constant([-0.2_7_4_4, 0.8_2_1_5, -0.0_8_3_6] )
tf.debugging.assert_near(outputs.logits[0, :3] , A__ , atol=1e-4 )
| 44
| 1
|
'''simple docstring'''
_lowercase = '\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'
_lowercase = [{'type': 'code', 'content': INSTALL_CONTENT}]
_lowercase = {
'{processor_class}': 'FakeProcessorClass',
'{model_class}': 'FakeModelClass',
'{object_class}': 'FakeObjectClass',
}
| 44
|
'''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
_lowercase = [
{'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 __UpperCamelCase ( a : Dict=True ) ->str:
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=__a ) )
class _lowercase ( __a ):
_UpperCAmelCase = None
_UpperCAmelCase = None
def UpperCamelCase ( self , A__ , A__ ) -> str:
with TemporaryDirectory() as tmp_dir:
snake_case = dataset_module_factory(A__ , cache_dir=A__ )
snake_case = import_main_class(dataset_module.module_path , dataset=A__ )
snake_case = builder_cls(
cache_dir=A__ , config_name=A__ , hash=dataset_module.hash , )
snake_case = '''/'''.join(
[
HF_GCP_BASE_URL,
builder_instance._relative_data_dir(with_hash=A__ ).replace(os.sep , '''/''' ),
config.DATASET_INFO_FILENAME,
] )
snake_case = cached_path(A__ , cache_dir=A__ )
self.assertTrue(os.path.exists(A__ ) )
@pytest.mark.integration
def __UpperCamelCase ( a : List[str] ) ->Any:
snake_case = tmp_path_factory.mktemp('''test_hf_gcp''' ) / '''test_wikipedia_simple'''
snake_case = dataset_module_factory('''wikipedia''' , cache_dir=a )
snake_case = import_main_class(dataset_module.module_path )
snake_case = builder_cls(
cache_dir=a , config_name='''20220301.frr''' , hash=dataset_module.hash , )
# use the HF cloud storage, not the original download_and_prepare that uses apache-beam
snake_case = None
builder_instance.download_and_prepare()
snake_case = builder_instance.as_dataset()
assert ds
@pytest.mark.integration
def __UpperCamelCase ( a : Any ) ->Union[str, Any]:
snake_case = dataset_module_factory('''wikipedia''' , cache_dir=a )
snake_case = import_main_class(dataset_module.module_path , dataset=a )
snake_case = builder_cls(
cache_dir=a , config_name='''20220301.frr''' , hash=dataset_module.hash , )
snake_case = builder_instance.as_streaming_dataset()
assert ds
assert isinstance(a , a )
assert "train" in ds
assert isinstance(ds['''train'''] , a )
assert next(iter(ds['''train'''] ) )
| 44
| 1
|
'''simple docstring'''
_lowercase = {
"joule": 1.0,
"kilojoule": 1_000,
"megajoule": 1_000_000,
"gigajoule": 1_000_000_000,
"wattsecond": 1.0,
"watthour": 3_600,
"kilowatthour": 3_600_000,
"newtonmeter": 1.0,
"calorie_nutr": 4_186.8,
"kilocalorie_nutr": 4_186_800.00,
"electronvolt": 1.6_0217_6634E-19,
"britishthermalunit_it": 1_055.05_585,
"footpound": 1.35_58_18,
}
def __UpperCamelCase ( a : str , a : str , a : float ) ->float:
if to_type not in ENERGY_CONVERSION or from_type not in ENERGY_CONVERSION:
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()
| 44
|
'''simple docstring'''
def __UpperCamelCase ( a : int , a : int ) ->int:
while b:
snake_case , snake_case = b, a % b
return a
def __UpperCamelCase ( a : int , a : int ) ->int:
return a if b == 0 else euclidean_gcd_recursive(a , a % b )
def __UpperCamelCase ( ) ->Optional[Any]:
print(f"""euclidean_gcd(3, 5) = {euclidean_gcd(3 , 5 )}""" )
print(f"""euclidean_gcd(5, 3) = {euclidean_gcd(5 , 3 )}""" )
print(f"""euclidean_gcd(1, 3) = {euclidean_gcd(1 , 3 )}""" )
print(f"""euclidean_gcd(3, 6) = {euclidean_gcd(3 , 6 )}""" )
print(f"""euclidean_gcd(6, 3) = {euclidean_gcd(6 , 3 )}""" )
print(f"""euclidean_gcd_recursive(3, 5) = {euclidean_gcd_recursive(3 , 5 )}""" )
print(f"""euclidean_gcd_recursive(5, 3) = {euclidean_gcd_recursive(5 , 3 )}""" )
print(f"""euclidean_gcd_recursive(1, 3) = {euclidean_gcd_recursive(1 , 3 )}""" )
print(f"""euclidean_gcd_recursive(3, 6) = {euclidean_gcd_recursive(3 , 6 )}""" )
print(f"""euclidean_gcd_recursive(6, 3) = {euclidean_gcd_recursive(6 , 3 )}""" )
if __name__ == "__main__":
main()
| 44
| 1
|
'''simple docstring'''
import gc
import unittest
from diffusers import FlaxDPMSolverMultistepScheduler, FlaxStableDiffusionPipeline
from diffusers.utils import is_flax_available, slow
from diffusers.utils.testing_utils import require_flax
if is_flax_available():
import jax
import jax.numpy as jnp
from flax.jax_utils import replicate
from flax.training.common_utils import shard
@slow
@require_flax
class _lowercase ( unittest.TestCase ):
def UpperCamelCase ( self ) -> Union[str, Any]:
# clean up the VRAM after each test
super().tearDown()
gc.collect()
def UpperCamelCase ( self ) -> List[str]:
snake_case , snake_case = FlaxStableDiffusionPipeline.from_pretrained(
'''stabilityai/stable-diffusion-2''' , revision='''bf16''' , dtype=jnp.bfloataa , )
snake_case = '''A painting of a squirrel eating a burger'''
snake_case = jax.device_count()
snake_case = num_samples * [prompt]
snake_case = sd_pipe.prepare_inputs(A__ )
snake_case = replicate(A__ )
snake_case = shard(A__ )
snake_case = jax.random.PRNGKey(0 )
snake_case = jax.random.split(A__ , jax.device_count() )
snake_case = sd_pipe(A__ , A__ , A__ , num_inference_steps=25 , jit=A__ )[0]
assert images.shape == (jax.device_count(), 1, 7_68, 7_68, 3)
snake_case = images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:] )
snake_case = images[0, 2_53:2_56, 2_53:2_56, -1]
snake_case = jnp.asarray(jax.device_get(image_slice.flatten() ) )
snake_case = jnp.array([0.4_2_3_8, 0.4_4_1_4, 0.4_3_9_5, 0.4_4_5_3, 0.4_6_2_9, 0.4_5_9_0, 0.4_5_3_1, 0.4_5_5_0_8, 0.4_5_1_2] )
print(F"""output_slice: {output_slice}""" )
assert jnp.abs(output_slice - expected_slice ).max() < 1e-2
def UpperCamelCase ( self ) -> Any:
snake_case = '''stabilityai/stable-diffusion-2'''
snake_case , snake_case = FlaxDPMSolverMultistepScheduler.from_pretrained(A__ , subfolder='''scheduler''' )
snake_case , snake_case = FlaxStableDiffusionPipeline.from_pretrained(
A__ , scheduler=A__ , revision='''bf16''' , dtype=jnp.bfloataa , )
snake_case = scheduler_params
snake_case = '''A painting of a squirrel eating a burger'''
snake_case = jax.device_count()
snake_case = num_samples * [prompt]
snake_case = sd_pipe.prepare_inputs(A__ )
snake_case = replicate(A__ )
snake_case = shard(A__ )
snake_case = jax.random.PRNGKey(0 )
snake_case = jax.random.split(A__ , jax.device_count() )
snake_case = sd_pipe(A__ , A__ , A__ , num_inference_steps=25 , jit=A__ )[0]
assert images.shape == (jax.device_count(), 1, 7_68, 7_68, 3)
snake_case = images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:] )
snake_case = images[0, 2_53:2_56, 2_53:2_56, -1]
snake_case = jnp.asarray(jax.device_get(image_slice.flatten() ) )
snake_case = jnp.array([0.4_3_3_6, 0.4_2_9_6_9, 0.4_4_5_3, 0.4_1_9_9, 0.4_2_9_7, 0.4_5_3_1, 0.4_4_3_4, 0.4_4_3_4, 0.4_2_9_7] )
print(F"""output_slice: {output_slice}""" )
assert jnp.abs(output_slice - expected_slice ).max() < 1e-2
| 44
|
'''simple docstring'''
import argparse
import copy
def __UpperCamelCase ( a : Union[str, Any] ) ->Tuple:
snake_case = {}
with open(a ) as f:
for line in f:
if line.split()[0] not in dict_of_neighbours:
snake_case = []
_list.append([line.split()[1], line.split()[2]] )
snake_case = _list
else:
dict_of_neighbours[line.split()[0]].append(
[line.split()[1], line.split()[2]] )
if line.split()[1] not in dict_of_neighbours:
snake_case = []
_list.append([line.split()[0], line.split()[2]] )
snake_case = _list
else:
dict_of_neighbours[line.split()[1]].append(
[line.split()[0], line.split()[2]] )
return dict_of_neighbours
def __UpperCamelCase ( a : Dict , a : Tuple ) ->int:
with open(a ) as f:
snake_case = f.read(1 )
snake_case = start_node
snake_case = []
snake_case = start_node
snake_case = 0
while visiting not in first_solution:
snake_case = 1_0000
for k in dict_of_neighbours[visiting]:
if int(k[1] ) < int(a ) and k[0] not in first_solution:
snake_case = k[1]
snake_case = k[0]
first_solution.append(a )
snake_case = distance_of_first_solution + int(a )
snake_case = best_node
first_solution.append(a )
snake_case = 0
for k in dict_of_neighbours[first_solution[-2]]:
if k[0] == start_node:
break
position += 1
snake_case = (
distance_of_first_solution
+ int(dict_of_neighbours[first_solution[-2]][position][1] )
- 1_0000
)
return first_solution, distance_of_first_solution
def __UpperCamelCase ( a : Optional[int] , a : str ) ->str:
snake_case = []
for n in solution[1:-1]:
snake_case = solution.index(a )
for kn in solution[1:-1]:
snake_case = solution.index(a )
if n == kn:
continue
snake_case = copy.deepcopy(a )
snake_case = kn
snake_case = n
snake_case = 0
for k in _tmp[:-1]:
snake_case = _tmp[_tmp.index(a ) + 1]
for i in dict_of_neighbours[k]:
if i[0] == next_node:
snake_case = distance + int(i[1] )
_tmp.append(a )
if _tmp not in neighborhood_of_solution:
neighborhood_of_solution.append(_tmp )
snake_case = len(neighborhood_of_solution[0] ) - 1
neighborhood_of_solution.sort(key=lambda a : x[index_of_last_item_in_the_list] )
return neighborhood_of_solution
def __UpperCamelCase ( a : Any , a : Optional[Any] , a : int , a : Optional[int] , a : Union[str, Any] ) ->List[Any]:
snake_case = 1
snake_case = first_solution
snake_case = []
snake_case = distance_of_first_solution
snake_case = solution
while count <= iters:
snake_case = find_neighborhood(a , a )
snake_case = 0
snake_case = neighborhood[index_of_best_solution]
snake_case = len(a ) - 1
snake_case = False
while not found:
snake_case = 0
while i < len(a ):
if best_solution[i] != solution[i]:
snake_case = best_solution[i]
snake_case = solution[i]
break
snake_case = i + 1
if [first_exchange_node, second_exchange_node] not in tabu_list and [
second_exchange_node,
first_exchange_node,
] not in tabu_list:
tabu_list.append([first_exchange_node, second_exchange_node] )
snake_case = True
snake_case = best_solution[:-1]
snake_case = neighborhood[index_of_best_solution][best_cost_index]
if cost < best_cost:
snake_case = cost
snake_case = solution
else:
snake_case = index_of_best_solution + 1
snake_case = neighborhood[index_of_best_solution]
if len(a ) >= size:
tabu_list.pop(0 )
snake_case = count + 1
return best_solution_ever, best_cost
def __UpperCamelCase ( a : Union[str, Any]=None ) ->Optional[Any]:
snake_case = generate_neighbours(args.File )
snake_case , snake_case = generate_first_solution(
args.File , a )
snake_case , snake_case = tabu_search(
a , a , a , args.Iterations , args.Size , )
print(f"""Best solution: {best_sol}, with total distance: {best_cost}.""" )
if __name__ == "__main__":
_lowercase = argparse.ArgumentParser(description='Tabu Search')
parser.add_argument(
'-f',
'--File',
type=str,
help='Path to the file containing the data',
required=True,
)
parser.add_argument(
'-i',
'--Iterations',
type=int,
help='How many iterations the algorithm should perform',
required=True,
)
parser.add_argument(
'-s', '--Size', type=int, help='Size of the tabu list', required=True
)
# Pass the arguments to main method
main(parser.parse_args())
| 44
| 1
|
'''simple docstring'''
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import TensorType, is_torch_available, logging
_lowercase = logging.get_logger(__name__)
_lowercase = {
'Helsinki-NLP/opus-mt-en-de': 'https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/config.json',
# See all Marian models at https://huggingface.co/models?filter=marian
}
class _lowercase ( __a ):
_UpperCAmelCase = '''marian'''
_UpperCAmelCase = ['''past_key_values''']
_UpperCAmelCase = {'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}
def __init__( self , A__=5_81_01 , A__=None , A__=10_24 , A__=12 , A__=40_96 , A__=16 , A__=12 , A__=40_96 , A__=16 , A__=0.0 , A__=0.0 , A__=True , A__=True , A__="gelu" , A__=10_24 , A__=0.1 , A__=0.0 , A__=0.0 , A__=0.0_2 , A__=5_81_00 , A__=False , A__=5_81_00 , A__=0 , A__=0 , A__=True , **A__ , ) -> Tuple:
snake_case = vocab_size
snake_case = decoder_vocab_size or vocab_size
snake_case = max_position_embeddings
snake_case = d_model
snake_case = encoder_ffn_dim
snake_case = encoder_layers
snake_case = encoder_attention_heads
snake_case = decoder_ffn_dim
snake_case = decoder_layers
snake_case = decoder_attention_heads
snake_case = dropout
snake_case = attention_dropout
snake_case = activation_dropout
snake_case = activation_function
snake_case = init_std
snake_case = encoder_layerdrop
snake_case = decoder_layerdrop
snake_case = use_cache
snake_case = encoder_layers
snake_case = scale_embedding # scale factor will be sqrt(d_model) if True
snake_case = share_encoder_decoder_embeddings
super().__init__(
pad_token_id=A__ , eos_token_id=A__ , is_encoder_decoder=A__ , decoder_start_token_id=A__ , forced_eos_token_id=A__ , **A__ , )
class _lowercase ( __a ):
@property
# Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.inputs
def UpperCamelCase ( self ) -> Mapping[str, Mapping[int, str]]:
if self.task in ["default", "seq2seq-lm"]:
snake_case = OrderedDict(
[
('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
] )
if self.use_past:
snake_case = {0: '''batch'''}
snake_case = {0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
else:
snake_case = {0: '''batch''', 1: '''decoder_sequence'''}
snake_case = {0: '''batch''', 1: '''decoder_sequence'''}
if self.use_past:
self.fill_with_past_key_values_(A__ , direction='''inputs''' )
elif self.task == "causal-lm":
# TODO: figure this case out.
snake_case = OrderedDict(
[
('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
] )
if self.use_past:
snake_case , snake_case = self.num_layers
for i in range(A__ ):
snake_case = {0: '''batch''', 2: '''past_sequence + sequence'''}
snake_case = {0: '''batch''', 2: '''past_sequence + sequence'''}
else:
snake_case = OrderedDict(
[
('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
('''decoder_input_ids''', {0: '''batch''', 1: '''decoder_sequence'''}),
('''decoder_attention_mask''', {0: '''batch''', 1: '''decoder_sequence'''}),
] )
return common_inputs
@property
# Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.outputs
def UpperCamelCase ( self ) -> Mapping[str, Mapping[int, str]]:
if self.task in ["default", "seq2seq-lm"]:
snake_case = super().outputs
else:
snake_case = super(A__ , self ).outputs
if self.use_past:
snake_case , snake_case = self.num_layers
for i in range(A__ ):
snake_case = {0: '''batch''', 2: '''past_sequence + sequence'''}
snake_case = {0: '''batch''', 2: '''past_sequence + sequence'''}
return common_outputs
def UpperCamelCase ( self , A__ , A__ = -1 , A__ = -1 , A__ = False , A__ = None , ) -> Mapping[str, Any]:
snake_case = self._generate_dummy_inputs_for_encoder_and_decoder(
A__ , A__ , A__ , A__ , A__ )
# Generate decoder inputs
snake_case = seq_length if not self.use_past else 1
snake_case = self._generate_dummy_inputs_for_encoder_and_decoder(
A__ , A__ , A__ , A__ , A__ )
snake_case = {F"""decoder_{name}""": tensor for name, tensor in decoder_inputs.items()}
snake_case = dict(**A__ , **A__ )
if self.use_past:
if not is_torch_available():
raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.''' )
else:
import torch
snake_case , snake_case = common_inputs['''input_ids'''].shape
snake_case = common_inputs['''decoder_input_ids'''].shape[1]
snake_case , snake_case = self.num_attention_heads
snake_case = (
batch,
num_encoder_attention_heads,
encoder_seq_length,
self._config.hidden_size // num_encoder_attention_heads,
)
snake_case = decoder_seq_length + 3
snake_case = (
batch,
num_decoder_attention_heads,
decoder_past_length,
self._config.hidden_size // num_decoder_attention_heads,
)
snake_case = torch.cat(
[common_inputs['''decoder_attention_mask'''], torch.ones(A__ , A__ )] , dim=1 )
snake_case = []
# If the number of encoder and decoder layers are present in the model configuration, both are considered
snake_case , snake_case = self.num_layers
snake_case = min(A__ , A__ )
snake_case = max(A__ , A__ ) - min_num_layers
snake_case = '''encoder''' if num_encoder_layers > num_decoder_layers else '''decoder'''
for _ in range(A__ ):
common_inputs["past_key_values"].append(
(
torch.zeros(A__ ),
torch.zeros(A__ ),
torch.zeros(A__ ),
torch.zeros(A__ ),
) )
# TODO: test this.
snake_case = encoder_shape if remaining_side_name == '''encoder''' else decoder_shape
for _ in range(A__ , A__ ):
common_inputs["past_key_values"].append((torch.zeros(A__ ), torch.zeros(A__ )) )
return common_inputs
def UpperCamelCase ( self , A__ , A__ = -1 , A__ = -1 , A__ = False , A__ = None , ) -> Mapping[str, Any]:
snake_case = self._generate_dummy_inputs_for_encoder_and_decoder(
A__ , A__ , A__ , A__ , A__ )
if self.use_past:
if not is_torch_available():
raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.''' )
else:
import torch
snake_case , snake_case = common_inputs['''input_ids'''].shape
# Not using the same length for past_key_values
snake_case = seqlen + 2
snake_case , snake_case = self.num_layers
snake_case , snake_case = self.num_attention_heads
snake_case = (
batch,
num_encoder_attention_heads,
past_key_values_length,
self._config.hidden_size // num_encoder_attention_heads,
)
snake_case = common_inputs['''attention_mask'''].dtype
snake_case = torch.cat(
[common_inputs['''attention_mask'''], torch.ones(A__ , A__ , dtype=A__ )] , dim=1 )
snake_case = [
(torch.zeros(A__ ), torch.zeros(A__ )) for _ in range(A__ )
]
return common_inputs
def UpperCamelCase ( self , A__ , A__ = -1 , A__ = -1 , A__ = False , A__ = None , ) -> Mapping[str, Any]:
# Copied from OnnxConfig.generate_dummy_inputs
# Did not use super(OnnxConfigWithPast, self).generate_dummy_inputs for code clarity.
# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
snake_case = compute_effective_axis_dimension(
A__ , fixed_dimension=OnnxConfig.default_fixed_batch , num_token_to_add=0 )
# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
snake_case = tokenizer.num_special_tokens_to_add(A__ )
snake_case = compute_effective_axis_dimension(
A__ , fixed_dimension=OnnxConfig.default_fixed_sequence , num_token_to_add=A__ )
# Generate dummy inputs according to compute batch and sequence
snake_case = [''' '''.join([tokenizer.unk_token] ) * seq_length] * batch_size
snake_case = dict(tokenizer(A__ , return_tensors=A__ ) )
return common_inputs
def UpperCamelCase ( self , A__ , A__ = -1 , A__ = -1 , A__ = False , A__ = None , ) -> Mapping[str, Any]:
if self.task in ["default", "seq2seq-lm"]:
snake_case = self._generate_dummy_inputs_for_default_and_seqaseq_lm(
A__ , batch_size=A__ , seq_length=A__ , is_pair=A__ , framework=A__ )
else:
snake_case = self._generate_dummy_inputs_for_causal_lm(
A__ , batch_size=A__ , seq_length=A__ , is_pair=A__ , framework=A__ )
return common_inputs
def UpperCamelCase ( self , A__ , A__ , A__ , A__ ) -> List[str]:
if self.task in ["default", "seq2seq-lm"]:
snake_case = super()._flatten_past_key_values_(A__ , A__ , A__ , A__ )
else:
snake_case = super(A__ , self )._flatten_past_key_values_(
A__ , A__ , A__ , A__ )
@property
def UpperCamelCase ( self ) -> float:
return 1e-4
| 44
|
'''simple docstring'''
from ...utils import is_note_seq_available, is_transformers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_torch_and_transformers_objects import * # noqa F403
else:
from .notes_encoder import SpectrogramNotesEncoder
from .continous_encoder import SpectrogramContEncoder
from .pipeline_spectrogram_diffusion import (
SpectrogramContEncoder,
SpectrogramDiffusionPipeline,
TaFilmDecoder,
)
try:
if not (is_transformers_available() and is_torch_available() and is_note_seq_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_transformers_and_torch_and_note_seq_objects import * # noqa F403
else:
from .midi_utils import MidiProcessor
| 44
| 1
|
'''simple docstring'''
import math
def __UpperCamelCase ( a : int ) ->int:
if not isinstance(a , a ):
snake_case = f"""Input value of [number={number}] must be an integer"""
raise TypeError(a )
if number < 1:
snake_case = f"""Input value of [number={number}] must be > 0"""
raise ValueError(a )
elif number == 1:
return 3
elif number == 2:
return 5
else:
snake_case = int(math.log(number // 3 , 2 ) ) + 2
snake_case = [3, 5]
snake_case = 2
snake_case = 3
for block in range(1 , a ):
for _ in range(a ):
proth_list.append(2 ** (block + 1) + proth_list[proth_index - 1] )
proth_index += 1
increment *= 2
return proth_list[number - 1]
if __name__ == "__main__":
import doctest
doctest.testmod()
for number in range(11):
_lowercase = 0
try:
_lowercase = proth(number)
except ValueError:
print(f'ValueError: there is no {number}th Proth number')
continue
print(f'The {number}th Proth number: {value}')
| 44
|
'''simple docstring'''
from ...processing_utils import ProcessorMixin
class _lowercase ( __a ):
_UpperCAmelCase = '''WhisperFeatureExtractor'''
_UpperCAmelCase = '''WhisperTokenizer'''
def __init__( self , A__ , A__ ) -> Optional[Any]:
super().__init__(A__ , A__ )
snake_case = self.feature_extractor
snake_case = False
def UpperCamelCase ( self , A__=None , A__=None , A__=True ) -> Union[str, Any]:
return self.tokenizer.get_decoder_prompt_ids(task=A__ , language=A__ , no_timestamps=A__ )
def __call__( self , *A__ , **A__ ) -> Dict:
# For backward compatibility
if self._in_target_context_manager:
return self.current_processor(*A__ , **A__ )
snake_case = kwargs.pop('''audio''' , A__ )
snake_case = kwargs.pop('''sampling_rate''' , A__ )
snake_case = kwargs.pop('''text''' , A__ )
if len(A__ ) > 0:
snake_case = args[0]
snake_case = args[1:]
if audio is None and text is None:
raise ValueError('''You need to specify either an `audio` or `text` input to process.''' )
if audio is not None:
snake_case = self.feature_extractor(A__ , *A__ , sampling_rate=A__ , **A__ )
if text is not None:
snake_case = self.tokenizer(A__ , **A__ )
if text is None:
return inputs
elif audio is None:
return encodings
else:
snake_case = encodings['''input_ids''']
return inputs
def UpperCamelCase ( self , *A__ , **A__ ) -> Optional[Any]:
return self.tokenizer.batch_decode(*A__ , **A__ )
def UpperCamelCase ( self , *A__ , **A__ ) -> str:
return self.tokenizer.decode(*A__ , **A__ )
def UpperCamelCase ( self , A__ , A__="np" ) -> Optional[Any]:
return self.tokenizer.get_prompt_ids(A__ , return_tensors=A__ )
| 44
| 1
|
'''simple docstring'''
import ast
import os
import re
import shutil
import tempfile
import unittest
from unittest import mock
import torch
from accelerate.test_utils.examples import compare_against_test
from accelerate.test_utils.testing import TempDirTestCase, require_trackers, run_command, slow
from accelerate.utils import write_basic_config
# DataLoaders built from `test_samples/MRPC` for quick testing
# Should mock `{script_name}.get_dataloaders` via:
# @mock.patch("{script_name}.get_dataloaders", mocked_dataloaders)
_lowercase = [
'cross_validation.py',
'gradient_accumulation.py',
'local_sgd.py',
'multi_process_metrics.py',
'memory.py',
'automatic_gradient_accumulation.py',
'fsdp_with_peak_mem_tracking.py',
'deepspeed_with_config_support.py',
'megatron_lm_gpt_pretraining.py',
]
class _lowercase ( unittest.TestCase ):
def UpperCamelCase ( self , A__ , A__ , A__ = None , A__ = None ) -> List[str]:
snake_case = None
snake_case = os.path.abspath(os.path.join('''examples''' , '''by_feature''' ) )
snake_case = os.path.abspath('''examples''' )
for item in os.listdir(A__ ):
if item not in EXCLUDE_EXAMPLES:
snake_case = os.path.join(A__ , A__ )
if os.path.isfile(A__ ) and ".py" in item_path:
with self.subTest(
tested_script=A__ , feature_script=A__ , tested_section='''main()''' if parser_only else '''training_function()''' , ):
snake_case = compare_against_test(
os.path.join(A__ , A__ ) , A__ , A__ , A__ )
snake_case = '''\n'''.join(A__ )
if special_strings is not None:
for string in special_strings:
snake_case = diff.replace(A__ , '''''' )
self.assertEqual(A__ , '''''' )
def UpperCamelCase ( self ) -> Optional[int]:
self.one_complete_example('''complete_nlp_example.py''' , A__ )
self.one_complete_example('''complete_nlp_example.py''' , A__ )
def UpperCamelCase ( self ) -> Optional[int]:
snake_case = os.path.abspath(os.path.join('''examples''' , '''cv_example.py''' ) )
snake_case = [
''' ''' * 16 + '''{\n\n''',
''' ''' * 20 + '''"accuracy": eval_metric["accuracy"],\n\n''',
''' ''' * 20 + '''"f1": eval_metric["f1"],\n\n''',
''' ''' * 20 + '''"train_loss": total_loss.item() / len(train_dataloader),\n\n''',
''' ''' * 20 + '''"epoch": epoch,\n\n''',
''' ''' * 16 + '''},\n\n''',
''' ''' * 16 + '''step=epoch,\n''',
''' ''' * 12,
''' ''' * 8 + '''for step, batch in enumerate(active_dataloader):\n''',
]
self.one_complete_example('''complete_cv_example.py''' , A__ , A__ , A__ )
self.one_complete_example('''complete_cv_example.py''' , A__ , A__ , A__ )
@mock.patch.dict(os.environ , {'''TESTING_MOCKED_DATALOADERS''': '''1'''} )
class _lowercase ( __a ):
_UpperCAmelCase = False
@classmethod
def UpperCamelCase ( cls ) -> Union[str, Any]:
super().setUpClass()
snake_case = tempfile.mkdtemp()
snake_case = os.path.join(cls._tmpdir , '''default_config.yml''' )
write_basic_config(save_location=cls.configPath )
snake_case = ['''accelerate''', '''launch''', '''--config_file''', cls.configPath]
@classmethod
def UpperCamelCase ( cls ) -> str:
super().tearDownClass()
shutil.rmtree(cls._tmpdir )
def UpperCamelCase ( self ) -> List[Any]:
snake_case = F"""
examples/by_feature/checkpointing.py
--checkpointing_steps epoch
--output_dir {self.tmpdir}
""".split()
run_command(self._launch_args + testargs )
self.assertTrue(os.path.exists(os.path.join(self.tmpdir , '''epoch_0''' ) ) )
def UpperCamelCase ( self ) -> Any:
snake_case = F"""
examples/by_feature/checkpointing.py
--checkpointing_steps 1
--output_dir {self.tmpdir}
""".split()
snake_case = run_command(self._launch_args + testargs )
self.assertTrue(os.path.exists(os.path.join(self.tmpdir , '''step_2''' ) ) )
def UpperCamelCase ( self ) -> List[str]:
snake_case = F"""
examples/by_feature/checkpointing.py
--resume_from_checkpoint {os.path.join(self.tmpdir , 'epoch_0' )}
""".split()
snake_case = run_command(self._launch_args + testargs , return_stdout=A__ )
self.assertNotIn('''epoch 0:''' , A__ )
self.assertIn('''epoch 1:''' , A__ )
def UpperCamelCase ( self ) -> Optional[Any]:
snake_case = F"""
examples/by_feature/checkpointing.py
--resume_from_checkpoint {os.path.join(self.tmpdir , 'step_2' )}
""".split()
snake_case = run_command(self._launch_args + testargs , return_stdout=A__ )
if torch.cuda.is_available():
snake_case = torch.cuda.device_count()
else:
snake_case = 1
if num_processes > 1:
self.assertNotIn('''epoch 0:''' , A__ )
self.assertIn('''epoch 1:''' , A__ )
else:
self.assertIn('''epoch 0:''' , A__ )
self.assertIn('''epoch 1:''' , A__ )
@slow
def UpperCamelCase ( self ) -> str:
snake_case = '''
examples/by_feature/cross_validation.py
--num_folds 2
'''.split()
with mock.patch.dict(os.environ , {'''TESTING_MOCKED_DATALOADERS''': '''0'''} ):
snake_case = run_command(self._launch_args + testargs , return_stdout=A__ )
snake_case = re.findall('''({.+})''' , A__ )
snake_case = [r for r in results if '''accuracy''' in r][-1]
snake_case = ast.literal_eval(A__ )
self.assertGreaterEqual(results['''accuracy'''] , 0.7_5 )
def UpperCamelCase ( self ) -> List[Any]:
snake_case = ['''examples/by_feature/multi_process_metrics.py''']
run_command(self._launch_args + testargs )
@require_trackers
@mock.patch.dict(os.environ , {'''WANDB_MODE''': '''offline'''} )
def UpperCamelCase ( self ) -> Tuple:
with tempfile.TemporaryDirectory() as tmpdir:
snake_case = F"""
examples/by_feature/tracking.py
--with_tracking
--project_dir {tmpdir}
""".split()
run_command(self._launch_args + testargs )
self.assertTrue(os.path.exists(os.path.join(A__ , '''tracking''' ) ) )
def UpperCamelCase ( self ) -> Dict:
snake_case = ['''examples/by_feature/gradient_accumulation.py''']
run_command(self._launch_args + testargs )
def UpperCamelCase ( self ) -> Union[str, Any]:
snake_case = ['''examples/by_feature/local_sgd.py''']
run_command(self._launch_args + testargs )
| 44
|
'''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 _lowercase ( __a ):
_UpperCAmelCase = '''char'''
_UpperCAmelCase = '''bpe'''
_UpperCAmelCase = '''wp'''
_lowercase = (DecodeType.CHARACTER, DecodeType.BPE, DecodeType.WORDPIECE)
class _lowercase ( __a ):
_UpperCAmelCase = ['''image_processor''', '''char_tokenizer''']
_UpperCAmelCase = '''ViTImageProcessor'''
_UpperCAmelCase = '''MgpstrTokenizer'''
def __init__( self , A__=None , A__=None , **A__ ) -> List[Any]:
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.''' , A__ , )
snake_case = kwargs.pop('''feature_extractor''' )
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`.''' )
snake_case = tokenizer
snake_case = AutoTokenizer.from_pretrained('''gpt2''' )
snake_case = AutoTokenizer.from_pretrained('''bert-base-uncased''' )
super().__init__(A__ , A__ )
def __call__( self , A__=None , A__=None , A__=None , **A__ ) -> List[str]:
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:
snake_case = self.image_processor(A__ , return_tensors=A__ , **A__ )
if text is not None:
snake_case = self.char_tokenizer(A__ , return_tensors=A__ , **A__ )
if text is None:
return inputs
elif images is None:
return encodings
else:
snake_case = encodings['''input_ids''']
return inputs
def UpperCamelCase ( self , A__ ) -> Dict:
snake_case , snake_case , snake_case = sequences
snake_case = char_preds.size(0 )
snake_case , snake_case = self._decode_helper(A__ , '''char''' )
snake_case , snake_case = self._decode_helper(A__ , '''bpe''' )
snake_case , snake_case = self._decode_helper(A__ , '''wp''' )
snake_case = []
snake_case = []
for i in range(A__ ):
snake_case = [char_scores[i], bpe_scores[i], wp_scores[i]]
snake_case = [char_strs[i], bpe_strs[i], wp_strs[i]]
snake_case = scores.index(max(A__ ) )
final_strs.append(strs[max_score_index] )
final_scores.append(scores[max_score_index] )
snake_case = {}
snake_case = final_strs
snake_case = final_scores
snake_case = char_strs
snake_case = bpe_strs
snake_case = wp_strs
return out
def UpperCamelCase ( self , A__ , A__ ) -> Optional[Any]:
if format == DecodeType.CHARACTER:
snake_case = self.char_decode
snake_case = 1
snake_case = '''[s]'''
elif format == DecodeType.BPE:
snake_case = self.bpe_decode
snake_case = 2
snake_case = '''#'''
elif format == DecodeType.WORDPIECE:
snake_case = self.wp_decode
snake_case = 1_02
snake_case = '''[SEP]'''
else:
raise ValueError(F"""Format {format} is not supported.""" )
snake_case , snake_case = [], []
snake_case = pred_logits.size(0 )
snake_case = pred_logits.size(1 )
snake_case , snake_case = pred_logits.topk(1 , dim=-1 , largest=A__ , sorted=A__ )
snake_case = preds_index.view(-1 , A__ )[:, 1:]
snake_case = decoder(A__ )
snake_case , snake_case = torch.nn.functional.softmax(A__ , dim=2 ).max(dim=2 )
snake_case = preds_max_prob[:, 1:]
for index in range(A__ ):
snake_case = preds_str[index].find(A__ )
snake_case = preds_str[index][:pred_eos]
snake_case = preds_index[index].cpu().tolist()
snake_case = pred_index.index(A__ ) if eos_token in pred_index else -1
snake_case = preds_max_prob[index][: pred_eos_index + 1]
snake_case = pred_max_prob.cumprod(dim=0 )[-1] if pred_max_prob.nelement() != 0 else 0.0
dec_strs.append(A__ )
conf_scores.append(A__ )
return dec_strs, conf_scores
def UpperCamelCase ( self , A__ ) -> int:
snake_case = [seq.replace(''' ''' , '''''' ) for seq in self.char_tokenizer.batch_decode(A__ )]
return decode_strs
def UpperCamelCase ( self , A__ ) -> List[str]:
return self.bpe_tokenizer.batch_decode(A__ )
def UpperCamelCase ( self , A__ ) -> Union[str, Any]:
snake_case = [seq.replace(''' ''' , '''''' ) for seq in self.wp_tokenizer.batch_decode(A__ )]
return decode_strs
| 44
| 1
|
'''simple docstring'''
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from tokenizers import processors
from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_nllb import NllbTokenizer
else:
_lowercase = None
_lowercase = logging.get_logger(__name__)
_lowercase = {'vocab_file': 'sentencepiece.bpe.model', 'tokenizer_file': 'tokenizer.json'}
_lowercase = {
'vocab_file': {
'facebook/nllb-200-distilled-600M': (
'https://huggingface.co/facebook/nllb-200-distilled-600M/resolve/main/sentencepiece.bpe.model'
),
},
'tokenizer_file': {
'facebook/nllb-200-distilled-600M': (
'https://huggingface.co/facebook/nllb-200-distilled-600M/resolve/main/tokenizer.json'
),
},
}
_lowercase = {
'facebook/nllb-large-en-ro': 1_024,
'facebook/nllb-200-distilled-600M': 1_024,
}
# fmt: off
_lowercase = ['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 _lowercase ( __a ):
_UpperCAmelCase = VOCAB_FILES_NAMES
_UpperCAmelCase = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
_UpperCAmelCase = PRETRAINED_VOCAB_FILES_MAP
_UpperCAmelCase = ['''input_ids''', '''attention_mask''']
_UpperCAmelCase = NllbTokenizer
_UpperCAmelCase = []
_UpperCAmelCase = []
def __init__( self , A__=None , A__=None , A__="<s>" , A__="</s>" , A__="</s>" , A__="<s>" , A__="<unk>" , A__="<pad>" , A__="<mask>" , A__=None , A__=None , A__=None , A__=False , **A__ , ) -> List[Any]:
# Mask token behave like a normal word, i.e. include the space before it
snake_case = AddedToken(A__ , lstrip=A__ , rstrip=A__ ) if isinstance(A__ , A__ ) else mask_token
snake_case = legacy_behaviour
super().__init__(
vocab_file=A__ , tokenizer_file=A__ , bos_token=A__ , eos_token=A__ , sep_token=A__ , cls_token=A__ , unk_token=A__ , pad_token=A__ , mask_token=A__ , src_lang=A__ , tgt_lang=A__ , additional_special_tokens=A__ , legacy_behaviour=A__ , **A__ , )
snake_case = vocab_file
snake_case = False if not self.vocab_file else True
snake_case = FAIRSEQ_LANGUAGE_CODES.copy()
if additional_special_tokens is not None:
# Only add those special tokens if they are not already there.
_additional_special_tokens.extend(
[t for t in additional_special_tokens if t not in _additional_special_tokens] )
self.add_special_tokens({'''additional_special_tokens''': _additional_special_tokens} )
snake_case = {
lang_code: self.convert_tokens_to_ids(A__ ) for lang_code in FAIRSEQ_LANGUAGE_CODES
}
snake_case = src_lang if src_lang is not None else '''eng_Latn'''
snake_case = self.convert_tokens_to_ids(self._src_lang )
snake_case = tgt_lang
self.set_src_lang_special_tokens(self._src_lang )
@property
def UpperCamelCase ( self ) -> str:
return self._src_lang
@src_lang.setter
def UpperCamelCase ( self , A__ ) -> None:
snake_case = new_src_lang
self.set_src_lang_special_tokens(self._src_lang )
def UpperCamelCase ( self , A__ , A__ = 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 , A__ , A__ = None ) -> List[int]:
snake_case = [self.sep_token_id]
snake_case = [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 , A__ , A__ , A__ , A__ , **A__ ) -> Union[str, 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''' )
snake_case = src_lang
snake_case = self(A__ , add_special_tokens=A__ , return_tensors=A__ , **A__ )
snake_case = self.convert_tokens_to_ids(A__ )
snake_case = tgt_lang_id
return inputs
def UpperCamelCase ( self , A__ , A__ = "eng_Latn" , A__ = None , A__ = "fra_Latn" , **A__ , ) -> BatchEncoding:
snake_case = src_lang
snake_case = tgt_lang
return super().prepare_seqaseq_batch(A__ , A__ , **A__ )
def UpperCamelCase ( self ) -> List[Any]:
return self.set_src_lang_special_tokens(self.src_lang )
def UpperCamelCase ( self ) -> Optional[Any]:
return self.set_tgt_lang_special_tokens(self.tgt_lang )
def UpperCamelCase ( self , A__ ) -> None:
snake_case = self.convert_tokens_to_ids(A__ )
if self.legacy_behaviour:
snake_case = []
snake_case = [self.eos_token_id, self.cur_lang_code]
else:
snake_case = [self.cur_lang_code]
snake_case = [self.eos_token_id]
snake_case = self.convert_ids_to_tokens(self.prefix_tokens )
snake_case = self.convert_ids_to_tokens(self.suffix_tokens )
snake_case = processors.TemplateProcessing(
single=prefix_tokens_str + ['''$A'''] + suffix_tokens_str , pair=prefix_tokens_str + ['''$A''', '''$B'''] + suffix_tokens_str , special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str , self.prefix_tokens + self.suffix_tokens ) ) , )
def UpperCamelCase ( self , A__ ) -> None:
snake_case = self.convert_tokens_to_ids(A__ )
if self.legacy_behaviour:
snake_case = []
snake_case = [self.eos_token_id, self.cur_lang_code]
else:
snake_case = [self.cur_lang_code]
snake_case = [self.eos_token_id]
snake_case = self.convert_ids_to_tokens(self.prefix_tokens )
snake_case = self.convert_ids_to_tokens(self.suffix_tokens )
snake_case = processors.TemplateProcessing(
single=prefix_tokens_str + ['''$A'''] + suffix_tokens_str , pair=prefix_tokens_str + ['''$A''', '''$B'''] + suffix_tokens_str , special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str , self.prefix_tokens + self.suffix_tokens ) ) , )
def UpperCamelCase ( self , A__ , A__ = None ) -> Tuple[str]:
if not self.can_save_slow_tokenizer:
raise ValueError(
'''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '''
'''tokenizer.''' )
if not os.path.isdir(A__ ):
logger.error(F"""Vocabulary path ({save_directory}) should be a directory.""" )
return
snake_case = os.path.join(
A__ , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(A__ ):
copyfile(self.vocab_file , A__ )
return (out_vocab_file,)
| 44
|
'''simple docstring'''
import os
from dataclasses import dataclass, field
from io import BytesIO
from typing import TYPE_CHECKING, Any, ClassVar, Dict, Optional, Union
import numpy as np
import pyarrow as pa
from .. import config
from ..download.streaming_download_manager import xopen, xsplitext
from ..table import array_cast
from ..utils.py_utils import no_op_if_value_is_null, string_to_dict
if TYPE_CHECKING:
from .features import FeatureType
_lowercase , _lowercase , _lowercase = False, False, False
@dataclass
class _lowercase :
_UpperCAmelCase = None
_UpperCAmelCase = True
_UpperCAmelCase = True
_UpperCAmelCase = None
# Automatically constructed
_UpperCAmelCase = "dict"
_UpperCAmelCase = pa.struct({'''bytes''': pa.binary(), '''path''': pa.string()} )
_UpperCAmelCase = field(default='''Audio''' , init=__a , repr=__a )
def __call__( self ) -> Optional[Any]:
return self.pa_type
def UpperCamelCase ( self , A__ ) -> dict:
try:
import soundfile as sf # soundfile is a dependency of librosa, needed to decode audio files.
except ImportError as err:
raise ImportError('''To support encoding audio data, please install \'soundfile\'.''' ) from err
if isinstance(A__ , A__ ):
return {"bytes": None, "path": value}
elif isinstance(A__ , A__ ):
return {"bytes": value, "path": None}
elif "array" in value:
# convert the audio array to wav bytes
snake_case = BytesIO()
sf.write(A__ , value['''array'''] , value['''sampling_rate'''] , format='''wav''' )
return {"bytes": buffer.getvalue(), "path": None}
elif value.get('''path''' ) is not None and os.path.isfile(value['''path'''] ):
# we set "bytes": None to not duplicate the data if they're already available locally
if value["path"].endswith('''pcm''' ):
# "PCM" only has raw audio bytes
if value.get('''sampling_rate''' ) is None:
# At least, If you want to convert "PCM-byte" to "WAV-byte", you have to know sampling rate
raise KeyError('''To use PCM files, please specify a \'sampling_rate\' in Audio object''' )
if value.get('''bytes''' ):
# If we already had PCM-byte, we don`t have to make "read file, make bytes" (just use it!)
snake_case = np.frombuffer(value['''bytes'''] , dtype=np.intaa ).astype(np.floataa ) / 3_27_67
else:
snake_case = np.memmap(value['''path'''] , dtype='''h''' , mode='''r''' ).astype(np.floataa ) / 3_27_67
snake_case = BytesIO(bytes() )
sf.write(A__ , A__ , value['''sampling_rate'''] , format='''wav''' )
return {"bytes": buffer.getvalue(), "path": None}
else:
return {"bytes": None, "path": value.get('''path''' )}
elif value.get('''bytes''' ) is not None or value.get('''path''' ) is not None:
# store the audio bytes, and path is used to infer the audio format using the file extension
return {"bytes": value.get('''bytes''' ), "path": value.get('''path''' )}
else:
raise ValueError(
F"""An audio sample should have one of 'path' or 'bytes' but they are missing or None in {value}.""" )
def UpperCamelCase ( self , A__ , A__ = None ) -> dict:
if not self.decode:
raise RuntimeError('''Decoding is disabled for this feature. Please use Audio(decode=True) instead.''' )
snake_case , snake_case = (value['''path'''], BytesIO(value['''bytes'''] )) if value['''bytes'''] is not None else (value['''path'''], None)
if path is None and file is None:
raise ValueError(F"""An audio sample should have one of 'path' or 'bytes' but both are None in {value}.""" )
try:
import librosa
import soundfile as sf
except ImportError as err:
raise ImportError('''To support decoding audio files, please install \'librosa\' and \'soundfile\'.''' ) from err
snake_case = xsplitext(A__ )[1][1:].lower() if path is not None else None
if not config.IS_OPUS_SUPPORTED and audio_format == "opus":
raise RuntimeError(
'''Decoding \'opus\' files requires system library \'libsndfile\'>=1.0.31, '''
'''You can try to update `soundfile` python library: `pip install "soundfile>=0.12.1"`. ''' )
elif not config.IS_MP3_SUPPORTED and audio_format == "mp3":
raise RuntimeError(
'''Decoding \'mp3\' files requires system library \'libsndfile\'>=1.1.0, '''
'''You can try to update `soundfile` python library: `pip install "soundfile>=0.12.1"`. ''' )
if file is None:
snake_case = token_per_repo_id or {}
snake_case = path.split('''::''' )[-1]
try:
snake_case = string_to_dict(A__ , config.HUB_DATASETS_URL )['''repo_id''']
snake_case = token_per_repo_id[repo_id]
except (ValueError, KeyError):
snake_case = None
with xopen(A__ , '''rb''' , use_auth_token=A__ ) as f:
snake_case , snake_case = sf.read(A__ )
else:
snake_case , snake_case = sf.read(A__ )
snake_case = array.T
if self.mono:
snake_case = librosa.to_mono(A__ )
if self.sampling_rate and self.sampling_rate != sampling_rate:
snake_case = librosa.resample(A__ , orig_sr=A__ , target_sr=self.sampling_rate )
snake_case = self.sampling_rate
return {"path": path, "array": array, "sampling_rate": sampling_rate}
def UpperCamelCase ( self ) -> Union["FeatureType", Dict[str, "FeatureType"]]:
from .features import Value
if self.decode:
raise ValueError('''Cannot flatten a decoded Audio feature.''' )
return {
"bytes": Value('''binary''' ),
"path": Value('''string''' ),
}
def UpperCamelCase ( self , A__ ) -> pa.StructArray:
if pa.types.is_string(storage.type ):
snake_case = pa.array([None] * len(A__ ) , type=pa.binary() )
snake_case = pa.StructArray.from_arrays([bytes_array, storage] , ['''bytes''', '''path'''] , mask=storage.is_null() )
elif pa.types.is_binary(storage.type ):
snake_case = pa.array([None] * len(A__ ) , type=pa.string() )
snake_case = pa.StructArray.from_arrays([storage, path_array] , ['''bytes''', '''path'''] , mask=storage.is_null() )
elif pa.types.is_struct(storage.type ) and storage.type.get_all_field_indices('''array''' ):
snake_case = pa.array([Audio().encode_example(A__ ) if x is not None else None for x in storage.to_pylist()] )
elif pa.types.is_struct(storage.type ):
if storage.type.get_field_index('''bytes''' ) >= 0:
snake_case = storage.field('''bytes''' )
else:
snake_case = pa.array([None] * len(A__ ) , type=pa.binary() )
if storage.type.get_field_index('''path''' ) >= 0:
snake_case = storage.field('''path''' )
else:
snake_case = pa.array([None] * len(A__ ) , type=pa.string() )
snake_case = pa.StructArray.from_arrays([bytes_array, path_array] , ['''bytes''', '''path'''] , mask=storage.is_null() )
return array_cast(A__ , self.pa_type )
def UpperCamelCase ( self , A__ ) -> pa.StructArray:
@no_op_if_value_is_null
def path_to_bytes(A__ ):
with xopen(A__ , '''rb''' ) as f:
snake_case = f.read()
return bytes_
snake_case = pa.array(
[
(path_to_bytes(x['''path'''] ) if x['''bytes'''] is None else x['''bytes''']) if x is not None else None
for x in storage.to_pylist()
] , type=pa.binary() , )
snake_case = pa.array(
[os.path.basename(A__ ) if path is not None else None for path in storage.field('''path''' ).to_pylist()] , type=pa.string() , )
snake_case = pa.StructArray.from_arrays([bytes_array, path_array] , ['''bytes''', '''path'''] , mask=bytes_array.is_null() )
return array_cast(A__ , self.pa_type )
| 44
| 1
|
'''simple docstring'''
import argparse
import json
from pathlib import Path
import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import AutoImageProcessor, SwinvaConfig, SwinvaForImageClassification
def __UpperCamelCase ( a : List[str] ) ->Tuple:
snake_case = SwinvaConfig()
snake_case = swinva_name.split('''_''' )
snake_case = name_split[1]
if "to" in name_split[3]:
snake_case = int(name_split[3][-3:] )
else:
snake_case = int(name_split[3] )
if "to" in name_split[2]:
snake_case = int(name_split[2][-2:] )
else:
snake_case = int(name_split[2][6:] )
if model_size == "tiny":
snake_case = 96
snake_case = (2, 2, 6, 2)
snake_case = (3, 6, 12, 24)
elif model_size == "small":
snake_case = 96
snake_case = (2, 2, 18, 2)
snake_case = (3, 6, 12, 24)
elif model_size == "base":
snake_case = 128
snake_case = (2, 2, 18, 2)
snake_case = (4, 8, 16, 32)
else:
snake_case = 192
snake_case = (2, 2, 18, 2)
snake_case = (6, 12, 24, 48)
if "to" in swinva_name:
snake_case = (12, 12, 12, 6)
if ("22k" in swinva_name) and ("to" not in swinva_name):
snake_case = 2_1841
snake_case = '''huggingface/label-files'''
snake_case = '''imagenet-22k-id2label.json'''
snake_case = json.load(open(hf_hub_download(a , a , repo_type='''dataset''' ) , '''r''' ) )
snake_case = {int(a ): v for k, v in idalabel.items()}
snake_case = idalabel
snake_case = {v: k for k, v in idalabel.items()}
else:
snake_case = 1000
snake_case = '''huggingface/label-files'''
snake_case = '''imagenet-1k-id2label.json'''
snake_case = json.load(open(hf_hub_download(a , a , repo_type='''dataset''' ) , '''r''' ) )
snake_case = {int(a ): v for k, v in idalabel.items()}
snake_case = idalabel
snake_case = {v: k for k, v in idalabel.items()}
snake_case = img_size
snake_case = num_classes
snake_case = embed_dim
snake_case = depths
snake_case = num_heads
snake_case = window_size
return config
def __UpperCamelCase ( a : Dict ) ->int:
if "patch_embed.proj" in name:
snake_case = name.replace('''patch_embed.proj''' , '''embeddings.patch_embeddings.projection''' )
if "patch_embed.norm" in name:
snake_case = name.replace('''patch_embed.norm''' , '''embeddings.norm''' )
if "layers" in name:
snake_case = '''encoder.''' + name
if "attn.proj" in name:
snake_case = name.replace('''attn.proj''' , '''attention.output.dense''' )
if "attn" in name:
snake_case = name.replace('''attn''' , '''attention.self''' )
if "norm1" in name:
snake_case = name.replace('''norm1''' , '''layernorm_before''' )
if "norm2" in name:
snake_case = name.replace('''norm2''' , '''layernorm_after''' )
if "mlp.fc1" in name:
snake_case = name.replace('''mlp.fc1''' , '''intermediate.dense''' )
if "mlp.fc2" in name:
snake_case = name.replace('''mlp.fc2''' , '''output.dense''' )
if "q_bias" in name:
snake_case = name.replace('''q_bias''' , '''query.bias''' )
if "k_bias" in name:
snake_case = name.replace('''k_bias''' , '''key.bias''' )
if "v_bias" in name:
snake_case = name.replace('''v_bias''' , '''value.bias''' )
if "cpb_mlp" in name:
snake_case = name.replace('''cpb_mlp''' , '''continuous_position_bias_mlp''' )
if name == "norm.weight":
snake_case = '''layernorm.weight'''
if name == "norm.bias":
snake_case = '''layernorm.bias'''
if "head" in name:
snake_case = name.replace('''head''' , '''classifier''' )
else:
snake_case = '''swinv2.''' + name
return name
def __UpperCamelCase ( a : Any , a : Union[str, Any] ) ->Dict:
for key in orig_state_dict.copy().keys():
snake_case = orig_state_dict.pop(a )
if "mask" in key:
continue
elif "qkv" in key:
snake_case = key.split('''.''' )
snake_case = int(key_split[1] )
snake_case = int(key_split[3] )
snake_case = model.swinva.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size
if "weight" in key:
snake_case = val[:dim, :]
snake_case = val[dim : dim * 2, :]
snake_case = val[-dim:, :]
else:
snake_case = val[:dim]
snake_case = val[
dim : dim * 2
]
snake_case = val[-dim:]
else:
snake_case = val
return orig_state_dict
def __UpperCamelCase ( a : Optional[int] , a : Union[str, Any] ) ->str:
snake_case = timm.create_model(a , pretrained=a )
timm_model.eval()
snake_case = get_swinva_config(a )
snake_case = SwinvaForImageClassification(a )
model.eval()
snake_case = convert_state_dict(timm_model.state_dict() , a )
model.load_state_dict(a )
snake_case = '''http://images.cocodataset.org/val2017/000000039769.jpg'''
snake_case = AutoImageProcessor.from_pretrained('''microsoft/{}'''.format(swinva_name.replace('''_''' , '''-''' ) ) )
snake_case = Image.open(requests.get(a , stream=a ).raw )
snake_case = image_processor(images=a , return_tensors='''pt''' )
snake_case = timm_model(inputs['''pixel_values'''] )
snake_case = model(**a ).logits
assert torch.allclose(a , a , atol=1e-3 )
print(f"""Saving model {swinva_name} to {pytorch_dump_folder_path}""" )
model.save_pretrained(a )
print(f"""Saving image processor to {pytorch_dump_folder_path}""" )
image_processor.save_pretrained(a )
model.push_to_hub(
repo_path_or_name=Path(a , a ) , organization='''nandwalritik''' , commit_message='''Add model''' , )
if __name__ == "__main__":
_lowercase = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--swinv2_name',
default='swinv2_tiny_patch4_window8_256',
type=str,
help='Name of the Swinv2 timm model you\'d like to convert.',
)
parser.add_argument(
'--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
)
_lowercase = parser.parse_args()
convert_swinva_checkpoint(args.swinva_name, args.pytorch_dump_folder_path)
| 44
|
'''simple docstring'''
import hashlib
import unittest
from transformers import MODEL_FOR_DEPTH_ESTIMATION_MAPPING, is_torch_available, is_vision_available
from transformers.pipelines import DepthEstimationPipeline, pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_timm,
require_torch,
require_vision,
slow,
)
from .test_pipelines_common import ANY
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
else:
class _lowercase :
@staticmethod
def UpperCamelCase ( *A__ , **A__ ) -> List[Any]:
pass
def __UpperCamelCase ( a : Image ) ->str:
snake_case = hashlib.mda(image.tobytes() )
return m.hexdigest()
@is_pipeline_test
@require_vision
@require_timm
@require_torch
class _lowercase ( unittest.TestCase ):
_UpperCAmelCase = MODEL_FOR_DEPTH_ESTIMATION_MAPPING
def UpperCamelCase ( self , A__ , A__ , A__ ) -> Union[str, Any]:
snake_case = DepthEstimationPipeline(model=A__ , image_processor=A__ )
return depth_estimator, [
"./tests/fixtures/tests_samples/COCO/000000039769.png",
"./tests/fixtures/tests_samples/COCO/000000039769.png",
]
def UpperCamelCase ( self , A__ , A__ ) -> List[Any]:
snake_case = depth_estimator('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
self.assertEqual({'''predicted_depth''': ANY(torch.Tensor ), '''depth''': ANY(Image.Image )} , A__ )
import datasets
snake_case = datasets.load_dataset('''hf-internal-testing/fixtures_image_utils''' , '''image''' , split='''test''' )
snake_case = depth_estimator(
[
Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' ),
'''http://images.cocodataset.org/val2017/000000039769.jpg''',
# RGBA
dataset[0]['''file'''],
# LA
dataset[1]['''file'''],
# L
dataset[2]['''file'''],
] )
self.assertEqual(
[
{'''predicted_depth''': ANY(torch.Tensor ), '''depth''': ANY(Image.Image )},
{'''predicted_depth''': ANY(torch.Tensor ), '''depth''': ANY(Image.Image )},
{'''predicted_depth''': ANY(torch.Tensor ), '''depth''': ANY(Image.Image )},
{'''predicted_depth''': ANY(torch.Tensor ), '''depth''': ANY(Image.Image )},
{'''predicted_depth''': ANY(torch.Tensor ), '''depth''': ANY(Image.Image )},
] , A__ , )
@require_tf
@unittest.skip('''Depth estimation is not implemented in TF''' )
def UpperCamelCase ( self ) -> Optional[Any]:
pass
@slow
@require_torch
def UpperCamelCase ( self ) -> Dict:
snake_case = '''Intel/dpt-large'''
snake_case = pipeline('''depth-estimation''' , model=A__ )
snake_case = depth_estimator('''http://images.cocodataset.org/val2017/000000039769.jpg''' )
snake_case = hashimage(outputs['''depth'''] )
# This seems flaky.
# self.assertEqual(outputs["depth"], "1a39394e282e9f3b0741a90b9f108977")
self.assertEqual(nested_simplify(outputs['''predicted_depth'''].max().item() ) , 2_9.3_0_4 )
self.assertEqual(nested_simplify(outputs['''predicted_depth'''].min().item() ) , 2.6_6_2 )
@require_torch
def UpperCamelCase ( self ) -> Any:
# This is highly irregular to have no small tests.
self.skipTest('''There is not hf-internal-testing tiny model for either GLPN nor DPT''' )
| 44
| 1
|
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_lowercase = {
'configuration_timesformer': ['TIMESFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'TimesformerConfig'],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowercase = [
'TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
'TimesformerModel',
'TimesformerForVideoClassification',
'TimesformerPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_timesformer import TIMESFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, TimesformerConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_timesformer import (
TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
TimesformerForVideoClassification,
TimesformerModel,
TimesformerPreTrainedModel,
)
else:
import sys
_lowercase = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 44
|
'''simple docstring'''
import argparse
import torch
from torch import nn
from transformers import SpeechaTextConfig, SpeechaTextForConditionalGeneration
def __UpperCamelCase ( a : Optional[int] ) ->Dict:
snake_case = [
'''encoder.version''',
'''decoder.version''',
'''model.encoder.version''',
'''model.decoder.version''',
'''decoder.output_projection.weight''',
'''_float_tensor''',
'''encoder.embed_positions._float_tensor''',
'''decoder.embed_positions._float_tensor''',
]
for k in ignore_keys:
state_dict.pop(a , a )
def __UpperCamelCase ( a : Optional[Any] ) ->int:
snake_case = list(s_dict.keys() )
for key in keys:
if "transformer_layers" in key:
snake_case = s_dict.pop(a )
elif "subsample" in key:
snake_case = s_dict.pop(a )
def __UpperCamelCase ( a : Optional[int] ) ->Optional[int]:
snake_case , snake_case = emb.weight.shape
snake_case = nn.Linear(a , a , bias=a )
snake_case = emb.weight.data
return lin_layer
def __UpperCamelCase ( a : Any , a : Tuple ) ->Tuple:
snake_case = torch.load(a , map_location='''cpu''' )
snake_case = mam_aaa['''args''']
snake_case = mam_aaa['''model''']
snake_case = state_dict['''decoder.output_projection.weight''']
remove_ignore_keys_(a )
rename_keys(a )
snake_case = state_dict['''decoder.embed_tokens.weight'''].shape[0]
snake_case = args.share_decoder_input_output_embed
snake_case = [int(a ) for i in args.conv_kernel_sizes.split(''',''' )]
snake_case = SpeechaTextConfig(
vocab_size=a , max_source_positions=args.max_source_positions , max_target_positions=args.max_target_positions , encoder_layers=args.encoder_layers , decoder_layers=args.decoder_layers , encoder_attention_heads=args.encoder_attention_heads , decoder_attention_heads=args.decoder_attention_heads , encoder_ffn_dim=args.encoder_ffn_embed_dim , decoder_ffn_dim=args.decoder_ffn_embed_dim , d_model=args.encoder_embed_dim , dropout=args.dropout , attention_dropout=args.attention_dropout , activation_dropout=args.activation_dropout , activation_function='''relu''' , num_conv_layers=len(a ) , conv_channels=args.conv_channels , conv_kernel_sizes=a , input_feat_per_channel=args.input_feat_per_channel , input_channels=args.input_channels , tie_word_embeddings=a , num_beams=5 , max_length=200 , use_cache=a , decoder_start_token_id=2 , early_stopping=a , )
snake_case = SpeechaTextForConditionalGeneration(a )
snake_case , snake_case = model.model.load_state_dict(a , strict=a )
if len(a ) > 0 and not set(a ) <= {
"encoder.embed_positions.weights",
"decoder.embed_positions.weights",
}:
raise ValueError(
'''Only `encoder.embed_positions.weights` and `decoder.embed_positions.weights` are allowed to be missing,'''
f""" but all the following weights are missing {missing}""" )
if tie_embeds:
snake_case = make_linear_from_emb(model.model.decoder.embed_tokens )
else:
snake_case = lm_head_weights
model.save_pretrained(a )
if __name__ == "__main__":
_lowercase = argparse.ArgumentParser()
# Required parameters
parser.add_argument('--fairseq_path', type=str, help='Path to the fairseq model (.pt) file.')
parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
_lowercase = parser.parse_args()
convert_fairseq_sat_checkpoint_to_tfms(args.fairseq_path, args.pytorch_dump_folder_path)
| 44
| 1
|
'''simple docstring'''
from __future__ import annotations
from scipy.special import comb # type: ignore
class _lowercase :
def __init__( self , A__ ) -> List[Any]:
snake_case = list_of_points
# Degree determines the flexibility of the curve.
# Degree = 1 will produce a straight line.
snake_case = len(A__ ) - 1
def UpperCamelCase ( self , A__ ) -> list[float]:
assert 0 <= t <= 1, "Time t must be between 0 and 1."
snake_case = []
for i in range(len(self.list_of_points ) ):
# basis function for each i
output_values.append(
comb(self.degree , A__ ) * ((1 - t) ** (self.degree - i)) * (t**i) )
# the basis must sum up to 1 for it to produce a valid Bezier curve.
assert round(sum(A__ ) , 5 ) == 1
return output_values
def UpperCamelCase ( self , A__ ) -> tuple[float, float]:
assert 0 <= t <= 1, "Time t must be between 0 and 1."
snake_case = self.basis_function(A__ )
snake_case = 0.0
snake_case = 0.0
for i in range(len(self.list_of_points ) ):
# For all points, sum up the product of i-th basis function and i-th point.
x += basis_function[i] * self.list_of_points[i][0]
y += basis_function[i] * self.list_of_points[i][1]
return (x, y)
def UpperCamelCase ( self , A__ = 0.0_1 ) -> str:
from matplotlib import pyplot as plt # type: ignore
snake_case = [] # x coordinates of points to plot
snake_case = [] # y coordinates of points to plot
snake_case = 0.0
while t <= 1:
snake_case = self.bezier_curve_function(A__ )
to_plot_x.append(value[0] )
to_plot_y.append(value[1] )
t += step_size
snake_case = [i[0] for i in self.list_of_points]
snake_case = [i[1] for i in self.list_of_points]
plt.plot(
A__ , A__ , color='''blue''' , label='''Curve of Degree ''' + str(self.degree ) , )
plt.scatter(A__ , A__ , color='''red''' , label='''Control Points''' )
plt.legend()
plt.show()
if __name__ == "__main__":
import doctest
doctest.testmod()
BezierCurve([(1, 2), (3, 5)]).plot_curve() # degree 1
BezierCurve([(0, 0), (5, 5), (5, 0)]).plot_curve() # degree 2
BezierCurve([(0, 0), (5, 5), (5, 0), (2.5, -2.5)]).plot_curve() # degree 3
| 44
|
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class _lowercase ( metaclass=__a ):
_UpperCAmelCase = ['''transformers''', '''torch''', '''note_seq''']
def __init__( self , *A__ , **A__ ) -> Union[str, Any]:
requires_backends(self , ['''transformers''', '''torch''', '''note_seq'''] )
@classmethod
def UpperCamelCase ( cls , *A__ , **A__ ) -> Optional[Any]:
requires_backends(cls , ['''transformers''', '''torch''', '''note_seq'''] )
@classmethod
def UpperCamelCase ( cls , *A__ , **A__ ) -> Any:
requires_backends(cls , ['''transformers''', '''torch''', '''note_seq'''] )
| 44
| 1
|
'''simple docstring'''
def __UpperCamelCase ( a : str , a : str ) ->bool:
snake_case = len(a ) + 1
snake_case = len(a ) + 1
# dp is a 2d matrix where dp[i][j] denotes whether prefix string of
# length i of input_string matches with prefix string of length j of
# given pattern.
# "dp" stands for dynamic programming.
snake_case = [[0 for i in range(a )] for j in range(a )]
# since string of zero length match pattern of zero length
snake_case = 1
# since pattern of zero length will never match with string of non-zero length
for i in range(1 , a ):
snake_case = 0
# since string of zero length will match with pattern where there
# is at least one * alternatively
for j in range(1 , a ):
snake_case = dp[0][j - 2] if pattern[j - 1] == '''*''' else 0
# now using bottom-up approach to find for all remaining lengths
for i in range(1 , a ):
for j in range(1 , a ):
if input_string[i - 1] == pattern[j - 1] or pattern[j - 1] == ".":
snake_case = dp[i - 1][j - 1]
elif pattern[j - 1] == "*":
if dp[i][j - 2] == 1:
snake_case = 1
elif pattern[j - 2] in (input_string[i - 1], "."):
snake_case = dp[i - 1][j]
else:
snake_case = 0
else:
snake_case = 0
return bool(dp[-1][-1] )
if __name__ == "__main__":
import doctest
doctest.testmod()
# inputing the strings
# input_string = input("input a string :")
# pattern = input("input a pattern :")
_lowercase = 'aab'
_lowercase = 'c*a*b'
# using function to check whether given string matches the given pattern
if match_pattern(input_string, pattern):
print(f'{input_string} matches the given pattern {pattern}')
else:
print(f'{input_string} does not match with the given pattern {pattern}')
| 44
|
'''simple docstring'''
from __future__ import annotations
from collections.abc import Iterator
class _lowercase :
def __init__( self , A__ ) -> None:
snake_case = value
snake_case = None
snake_case = None
class _lowercase :
def __init__( self , A__ ) -> None:
snake_case = tree
def UpperCamelCase ( self , A__ ) -> int:
if node is None:
return 0
return node.value + (
self.depth_first_search(node.left ) + self.depth_first_search(node.right )
)
def __iter__( self ) -> Iterator[int]:
yield self.depth_first_search(self.tree )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 44
| 1
|
'''simple docstring'''
import argparse
import datetime
import json
import time
import warnings
from logging import getLogger
from pathlib import Path
from typing import Dict, List
import torch
from tqdm import tqdm
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from utils import calculate_bleu, calculate_rouge, chunks, parse_numeric_n_bool_cl_kwargs, use_task_specific_params
_lowercase = getLogger(__name__)
_lowercase = 'cuda' if torch.cuda.is_available() else 'cpu'
def __UpperCamelCase ( a : List[str] , a : str , a : str , a : int = 8 , a : str = DEFAULT_DEVICE , a : Union[str, Any]=False , a : int="summarization" , a : Optional[int]=None , **a : int , ) ->Dict:
snake_case = Path(a ).open('''w''' , encoding='''utf-8''' )
snake_case = str(a )
snake_case = AutoModelForSeqaSeqLM.from_pretrained(a ).to(a )
if fpaa:
snake_case = model.half()
snake_case = AutoTokenizer.from_pretrained(a )
logger.info(f"""Inferred tokenizer type: {tokenizer.__class__}""" ) # if this is wrong, check config.model_type.
snake_case = time.time()
# update config with task specific params
use_task_specific_params(a , a )
if prefix is None:
snake_case = prefix or getattr(model.config , '''prefix''' , '''''' ) or ''''''
for examples_chunk in tqdm(list(chunks(a , a ) ) ):
snake_case = [prefix + text for text in examples_chunk]
snake_case = tokenizer(a , return_tensors='''pt''' , truncation=a , padding='''longest''' ).to(a )
snake_case = model.generate(
input_ids=batch.input_ids , attention_mask=batch.attention_mask , **a , )
snake_case = tokenizer.batch_decode(a , skip_special_tokens=a , clean_up_tokenization_spaces=a )
for hypothesis in dec:
fout.write(hypothesis + '''\n''' )
fout.flush()
fout.close()
snake_case = int(time.time() - start_time ) # seconds
snake_case = len(a )
return {"n_obs": n_obs, "runtime": runtime, "seconds_per_sample": round(runtime / n_obs , 4 )}
def __UpperCamelCase ( ) ->int:
return datetime.datetime.now().strftime('''%Y-%m-%d %H:%M:%S''' )
def __UpperCamelCase ( a : Optional[Any]=True ) ->Optional[Any]:
snake_case = argparse.ArgumentParser()
parser.add_argument('''model_name''' , type=a , help='''like facebook/bart-large-cnn,t5-base, etc.''' )
parser.add_argument('''input_path''' , type=a , help='''like cnn_dm/test.source''' )
parser.add_argument('''save_path''' , type=a , help='''where to save summaries''' )
parser.add_argument('''--reference_path''' , type=a , required=a , help='''like cnn_dm/test.target''' )
parser.add_argument('''--score_path''' , type=a , required=a , default='''metrics.json''' , help='''where to save metrics''' )
parser.add_argument('''--device''' , type=a , required=a , default=a , help='''cuda, cuda:1, cpu etc.''' )
parser.add_argument(
'''--prefix''' , type=a , required=a , default=a , help='''will be added to the begininng of src examples''' )
parser.add_argument('''--task''' , type=a , default='''summarization''' , help='''used for task_specific_params + metrics''' )
parser.add_argument('''--bs''' , type=a , default=8 , required=a , help='''batch size''' )
parser.add_argument(
'''--n_obs''' , type=a , default=-1 , required=a , help='''How many observations. Defaults to all.''' )
parser.add_argument('''--fp16''' , action='''store_true''' )
parser.add_argument('''--dump-args''' , action='''store_true''' , help='''print the custom hparams with the results''' )
parser.add_argument(
'''--info''' , nargs='''?''' , type=a , const=datetime_now() , help=(
'''use in conjunction w/ --dump-args to print with the results whatever other info you\'d like, e.g.'''
''' lang=en-ru. If no value is passed, the current datetime string will be used.'''
) , )
# Unspecified args like --num_beams=2 --decoder_start_token_id=4 are passed to model.generate
snake_case , snake_case = parser.parse_known_args()
snake_case = parse_numeric_n_bool_cl_kwargs(a )
if parsed_args and verbose:
print(f"""parsed the following generate kwargs: {parsed_args}""" )
snake_case = [''' ''' + x.rstrip() if '''t5''' in args.model_name else x.rstrip() for x in open(args.input_path ).readlines()]
if args.n_obs > 0:
snake_case = examples[: args.n_obs]
Path(args.save_path ).parent.mkdir(exist_ok=a )
if args.reference_path is None and Path(args.score_path ).exists():
warnings.warn(f"""score_path {args.score_path} will be overwritten unless you type ctrl-c.""" )
if args.device == "cpu" and args.fpaa:
# this mix leads to RuntimeError: "threshold_cpu" not implemented for 'Half'
raise ValueError('''Can\'t mix --fp16 and --device cpu''' )
snake_case = generate_summaries_or_translations(
a , args.save_path , args.model_name , batch_size=args.bs , device=args.device , fpaa=args.fpaa , task=args.task , prefix=args.prefix , **a , )
if args.reference_path is None:
return {}
# Compute scores
snake_case = calculate_bleu if '''translation''' in args.task else calculate_rouge
snake_case = [x.rstrip() for x in open(args.save_path ).readlines()]
snake_case = [x.rstrip() for x in open(args.reference_path ).readlines()][: len(a )]
snake_case = score_fn(a , a )
scores.update(a )
if args.dump_args:
scores.update(a )
if args.info:
snake_case = args.info
if verbose:
print(a )
if args.score_path is not None:
json.dump(a , open(args.score_path , '''w''' ) )
return scores
if __name__ == "__main__":
# Usage for MT:
# python run_eval.py MODEL_NAME $DATA_DIR/test.source $save_dir/test_translations.txt --reference_path $DATA_DIR/test.target --score_path $save_dir/test_bleu.json --task translation $@
run_generate(verbose=True)
| 44
|
'''simple docstring'''
import argparse
from collections import OrderedDict
from pathlib import Path
import torch
from transformers import (
VisualBertConfig,
VisualBertForMultipleChoice,
VisualBertForPreTraining,
VisualBertForQuestionAnswering,
VisualBertForVisualReasoning,
)
from transformers.utils import logging
logging.set_verbosity_info()
_lowercase = logging.get_logger(__name__)
_lowercase = [
('bert.bert', 'visual_bert'),
('bert.cls', 'cls'),
('bert.classifier', 'cls'),
('token_type_embeddings_visual', 'visual_token_type_embeddings'),
('position_embeddings_visual', 'visual_position_embeddings'),
('projection', 'visual_projection'),
]
_lowercase = [
'nlvr2_coco_pre_trained.th',
'nlvr2_fine_tuned.th',
'nlvr2_pre_trained.th',
'vcr_coco_pre_train.th',
'vcr_fine_tune.th',
'vcr_pre_train.th',
'vqa_coco_pre_trained.th',
'vqa_fine_tuned.th',
'vqa_pre_trained.th',
]
def __UpperCamelCase ( a : List[str] ) ->Optional[int]:
snake_case = torch.load(a , map_location='''cpu''' )
return sd
def __UpperCamelCase ( a : Optional[int] , a : Union[str, Any] , a : int=rename_keys_prefix ) ->Tuple:
snake_case = OrderedDict()
snake_case = torch.arange(config.max_position_embeddings ).expand((1, -1) )
# detector_d = OrderedDict()
for key in d:
if "detector" in key:
# detector_d[key.replace('detector.','')] = d[key]
continue
snake_case = key
for name_pair in rename_keys_prefix:
snake_case = new_key.replace(name_pair[0] , name_pair[1] )
snake_case = d[key]
if key == "bert.cls.predictions.decoder.weight":
# Old bert code didn't have `decoder.bias`, but was added separately
snake_case = new_d['''cls.predictions.bias''']
return new_d
@torch.no_grad()
def __UpperCamelCase ( a : Optional[int] , a : int ) ->Union[str, Any]:
assert (
checkpoint_path.split('''/''' )[-1] in ACCEPTABLE_CHECKPOINTS
), f"""The checkpoint provided must be in {ACCEPTABLE_CHECKPOINTS}."""
# Get Config
if "pre" in checkpoint_path:
snake_case = '''pretraining'''
if "vcr" in checkpoint_path:
snake_case = {'''visual_embedding_dim''': 512}
elif "vqa_advanced" in checkpoint_path:
snake_case = {'''visual_embedding_dim''': 2048}
elif "vqa" in checkpoint_path:
snake_case = {'''visual_embedding_dim''': 2048}
elif "nlvr" in checkpoint_path:
snake_case = {'''visual_embedding_dim''': 1024}
else:
raise NotImplementedError(f"""No implementation found for `{checkpoint_path}`.""" )
else:
if "vcr" in checkpoint_path:
snake_case = {'''visual_embedding_dim''': 512}
snake_case = '''multichoice'''
elif "vqa_advanced" in checkpoint_path:
snake_case = {'''visual_embedding_dim''': 2048}
snake_case = '''vqa_advanced'''
elif "vqa" in checkpoint_path:
snake_case = {'''visual_embedding_dim''': 2048, '''num_labels''': 3129}
snake_case = '''vqa'''
elif "nlvr" in checkpoint_path:
snake_case = {
'''visual_embedding_dim''': 1024,
'''num_labels''': 2,
}
snake_case = '''nlvr'''
snake_case = VisualBertConfig(**a )
# Load State Dict
snake_case = load_state_dict(a )
snake_case = get_new_dict(a , a )
if model_type == "pretraining":
snake_case = VisualBertForPreTraining(a )
elif model_type == "vqa":
snake_case = VisualBertForQuestionAnswering(a )
elif model_type == "nlvr":
snake_case = VisualBertForVisualReasoning(a )
elif model_type == "multichoice":
snake_case = VisualBertForMultipleChoice(a )
model.load_state_dict(a )
# Save Checkpoints
Path(a ).mkdir(exist_ok=a )
model.save_pretrained(a )
if __name__ == "__main__":
_lowercase = argparse.ArgumentParser()
# Required parameters
parser.add_argument('orig_checkpoint_path', type=str, help='A path to .th on local filesystem.')
parser.add_argument('pytorch_dump_folder_path', type=str, help='Path to the output PyTorch model.')
_lowercase = parser.parse_args()
convert_visual_bert_checkpoint(args.orig_checkpoint_path, args.pytorch_dump_folder_path)
| 44
| 1
|
'''simple docstring'''
import warnings
from contextlib import contextmanager
from ....processing_utils import ProcessorMixin
class _lowercase ( __a ):
_UpperCAmelCase = '''MCTCTFeatureExtractor'''
_UpperCAmelCase = '''AutoTokenizer'''
def __init__( self , A__ , A__ ) -> Union[str, Any]:
super().__init__(A__ , A__ )
snake_case = self.feature_extractor
snake_case = False
def __call__( self , *A__ , **A__ ) -> Tuple:
# For backward compatibility
if self._in_target_context_manager:
return self.current_processor(*A__ , **A__ )
if "raw_speech" in kwargs:
warnings.warn('''Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead.''' )
snake_case = kwargs.pop('''raw_speech''' )
else:
snake_case = kwargs.pop('''audio''' , A__ )
snake_case = kwargs.pop('''sampling_rate''' , A__ )
snake_case = kwargs.pop('''text''' , A__ )
if len(A__ ) > 0:
snake_case = args[0]
snake_case = args[1:]
if audio is None and text is None:
raise ValueError('''You need to specify either an `audio` or `text` input to process.''' )
if audio is not None:
snake_case = self.feature_extractor(A__ , *A__ , sampling_rate=A__ , **A__ )
if text is not None:
snake_case = self.tokenizer(A__ , **A__ )
if text is None:
return inputs
elif audio is None:
return encodings
else:
snake_case = encodings['''input_ids''']
return inputs
def UpperCamelCase ( self , *A__ , **A__ ) -> List[str]:
return self.tokenizer.batch_decode(*A__ , **A__ )
def UpperCamelCase ( self , *A__ , **A__ ) -> Dict:
# For backward compatibility
if self._in_target_context_manager:
return self.current_processor.pad(*A__ , **A__ )
snake_case = kwargs.pop('''input_features''' , A__ )
snake_case = kwargs.pop('''labels''' , A__ )
if len(A__ ) > 0:
snake_case = args[0]
snake_case = args[1:]
if input_features is not None:
snake_case = self.feature_extractor.pad(A__ , *A__ , **A__ )
if labels is not None:
snake_case = self.tokenizer.pad(A__ , **A__ )
if labels is None:
return input_features
elif input_features is None:
return labels
else:
snake_case = labels['''input_ids''']
return input_features
def UpperCamelCase ( self , *A__ , **A__ ) -> Dict:
return self.tokenizer.decode(*A__ , **A__ )
@contextmanager
def UpperCamelCase ( self ) -> Any:
warnings.warn(
'''`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your '''
'''labels by using the argument `text` of the regular `__call__` method (either in the same call as '''
'''your audio inputs, or in a separate call.''' )
snake_case = True
snake_case = self.tokenizer
yield
snake_case = self.feature_extractor
snake_case = False
| 44
|
'''simple docstring'''
import argparse
import json
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import PoolFormerConfig, PoolFormerForImageClassification, PoolFormerImageProcessor
from transformers.utils import logging
logging.set_verbosity_info()
_lowercase = logging.get_logger(__name__)
def __UpperCamelCase ( a : Dict , a : Optional[int] , a : Dict , a : Dict ) ->Union[str, Any]:
snake_case = original_name.split('''.''' )[0]
snake_case = key.split('''.''' )
snake_case = int(key_list[key_list.index(a ) - 2] )
snake_case = int(key_list[key_list.index(a ) - 1] )
snake_case = orig_block_num - offset
snake_case = key.replace(f"""{orig_block_num}.{layer_num}.{original_name}""" , f"""block.{new_block_num}.{layer_num}.{new_name}""" )
return key
def __UpperCamelCase ( a : Tuple ) ->Dict:
snake_case = OrderedDict()
snake_case , snake_case = 0, 0
for key, value in state_dict.items():
if key.startswith('''network''' ):
snake_case = key.replace('''network''' , '''poolformer.encoder''' )
if "proj" in key:
# Works for the first embedding as well as the internal embedding layers
if key.endswith('''bias''' ) and "patch_embed" not in key:
patch_emb_offset += 1
snake_case = key[: key.find('''proj''' )]
snake_case = key.replace(a , f"""patch_embeddings.{total_embed_found}.""" )
snake_case = key.replace('''proj''' , '''projection''' )
if key.endswith('''bias''' ):
total_embed_found += 1
if "patch_embeddings" in key:
snake_case = '''poolformer.encoder.''' + key
if "mlp.fc1" in key:
snake_case = replace_key_with_offset(a , a , '''mlp.fc1''' , '''output.conv1''' )
if "mlp.fc2" in key:
snake_case = replace_key_with_offset(a , a , '''mlp.fc2''' , '''output.conv2''' )
if "norm1" in key:
snake_case = replace_key_with_offset(a , a , '''norm1''' , '''before_norm''' )
if "norm2" in key:
snake_case = replace_key_with_offset(a , a , '''norm2''' , '''after_norm''' )
if "layer_scale_1" in key:
snake_case = replace_key_with_offset(a , a , '''layer_scale_1''' , '''layer_scale_1''' )
if "layer_scale_2" in key:
snake_case = replace_key_with_offset(a , a , '''layer_scale_2''' , '''layer_scale_2''' )
if "head" in key:
snake_case = key.replace('''head''' , '''classifier''' )
snake_case = value
return new_state_dict
def __UpperCamelCase ( ) ->Optional[int]:
snake_case = '''http://images.cocodataset.org/val2017/000000039769.jpg'''
snake_case = Image.open(requests.get(a , stream=a ).raw )
return image
@torch.no_grad()
def __UpperCamelCase ( a : Dict , a : Optional[Any] , a : Tuple ) ->List[str]:
snake_case = PoolFormerConfig()
# set attributes based on model_name
snake_case = '''huggingface/label-files'''
snake_case = model_name[-3:]
snake_case = 1000
snake_case = '''imagenet-1k-id2label.json'''
snake_case = (1, 1000)
# set config attributes
snake_case = json.load(open(hf_hub_download(a , a , repo_type='''dataset''' ) , '''r''' ) )
snake_case = {int(a ): v for k, v in idalabel.items()}
snake_case = idalabel
snake_case = {v: k for k, v in idalabel.items()}
if size == "s12":
snake_case = [2, 2, 6, 2]
snake_case = [64, 128, 320, 512]
snake_case = 4.0
snake_case = 0.9
elif size == "s24":
snake_case = [4, 4, 12, 4]
snake_case = [64, 128, 320, 512]
snake_case = 4.0
snake_case = 0.9
elif size == "s36":
snake_case = [6, 6, 18, 6]
snake_case = [64, 128, 320, 512]
snake_case = 4.0
snake_case = 1e-6
snake_case = 0.9
elif size == "m36":
snake_case = [6, 6, 18, 6]
snake_case = [96, 192, 384, 768]
snake_case = 4.0
snake_case = 1e-6
snake_case = 0.95
elif size == "m48":
snake_case = [8, 8, 24, 8]
snake_case = [96, 192, 384, 768]
snake_case = 4.0
snake_case = 1e-6
snake_case = 0.95
else:
raise ValueError(f"""Size {size} not supported""" )
# load image processor
snake_case = PoolFormerImageProcessor(crop_pct=a )
# Prepare image
snake_case = prepare_img()
snake_case = image_processor(images=a , return_tensors='''pt''' ).pixel_values
logger.info(f"""Converting model {model_name}...""" )
# load original state dict
snake_case = torch.load(a , map_location=torch.device('''cpu''' ) )
# rename keys
snake_case = rename_keys(a )
# create HuggingFace model and load state dict
snake_case = PoolFormerForImageClassification(a )
model.load_state_dict(a )
model.eval()
# Define image processor
snake_case = PoolFormerImageProcessor(crop_pct=a )
snake_case = image_processor(images=prepare_img() , return_tensors='''pt''' ).pixel_values
# forward pass
snake_case = model(a )
snake_case = outputs.logits
# define expected logit slices for different models
if size == "s12":
snake_case = torch.tensor([-0.3045, -0.6758, -0.4869] )
elif size == "s24":
snake_case = torch.tensor([0.4402, -0.1374, -0.8045] )
elif size == "s36":
snake_case = torch.tensor([-0.6080, -0.5133, -0.5898] )
elif size == "m36":
snake_case = torch.tensor([0.3952, 0.2263, -1.2668] )
elif size == "m48":
snake_case = torch.tensor([0.1167, -0.0656, -0.3423] )
else:
raise ValueError(f"""Size {size} not supported""" )
# verify logits
assert logits.shape == expected_shape
assert torch.allclose(logits[0, :3] , a , atol=1e-2 )
# finally, save model and image processor
logger.info(f"""Saving PyTorch model and image processor to {pytorch_dump_folder_path}...""" )
Path(a ).mkdir(exist_ok=a )
model.save_pretrained(a )
print(f"""Saving image processor to {pytorch_dump_folder_path}""" )
image_processor.save_pretrained(a )
if __name__ == "__main__":
_lowercase = argparse.ArgumentParser()
parser.add_argument(
'--model_name',
default='poolformer_s12',
type=str,
help='Name of the model you\'d like to convert.',
)
parser.add_argument(
'--checkpoint_path', default=None, type=str, help='Path to the original PyTorch checkpoint (.pth file).'
)
parser.add_argument(
'--pytorch_dump_folder_path', default=None, type=str, help='Path to the folder to output PyTorch model.'
)
_lowercase = parser.parse_args()
convert_poolformer_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path)
| 44
| 1
|
'''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 _lowercase :
def __init__( self , A__ , A__=13 , A__=7 , A__=True , A__=True , A__=False , A__=True , A__=99 , A__=64 , A__=5 , A__=4 , A__=64 , A__="gelu" , A__=0.1 , A__=0.1 , A__=5_12 , A__=16 , A__=2 , A__=0.0_2 , A__=3 , A__=4 , A__=None , ) -> Any:
snake_case = parent
snake_case = batch_size
snake_case = seq_length
snake_case = is_training
snake_case = use_input_mask
snake_case = use_token_type_ids
snake_case = use_labels
snake_case = vocab_size
snake_case = hidden_size
snake_case = num_hidden_layers
snake_case = num_attention_heads
snake_case = intermediate_size
snake_case = hidden_act
snake_case = hidden_dropout_prob
snake_case = attention_probs_dropout_prob
snake_case = max_position_embeddings
snake_case = type_vocab_size
snake_case = type_sequence_label_size
snake_case = initializer_range
snake_case = num_labels
snake_case = num_choices
snake_case = scope
def UpperCamelCase ( self ) -> List[Any]:
return MPNetConfig.from_pretrained('''microsoft/mpnet-base''' )
def UpperCamelCase ( self ) -> Union[str, Any]:
snake_case = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
snake_case = None
if self.use_input_mask:
snake_case = random_attention_mask([self.batch_size, self.seq_length] )
snake_case = None
snake_case = None
snake_case = None
if self.use_labels:
snake_case = ids_tensor([self.batch_size] , self.type_sequence_label_size )
snake_case = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
snake_case = ids_tensor([self.batch_size] , self.num_choices )
snake_case = self.get_config()
return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
def UpperCamelCase ( self ) -> Dict:
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 , A__ , A__ , A__ , A__ , A__ , A__ ) -> str:
snake_case = MPNetModel(config=A__ )
model.to(A__ )
model.eval()
snake_case = model(A__ , A__ )
snake_case = 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 , A__ , A__ , A__ , A__ , A__ , A__ ) -> str:
snake_case = MPNetForQuestionAnswering(config=A__ )
model.to(A__ )
model.eval()
snake_case = 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 , A__ , A__ , A__ , A__ , A__ , A__ ) -> Dict:
snake_case = self.num_labels
snake_case = MPNetForSequenceClassification(A__ )
model.to(A__ )
model.eval()
snake_case = model(A__ , attention_mask=A__ , labels=A__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def UpperCamelCase ( self , A__ , A__ , A__ , A__ , A__ , A__ ) -> Dict:
snake_case = self.num_choices
snake_case = MPNetForMultipleChoice(config=A__ )
model.to(A__ )
model.eval()
snake_case = input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
snake_case = input_mask.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
snake_case = model(
A__ , attention_mask=A__ , labels=A__ , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def UpperCamelCase ( self , A__ , A__ , A__ , A__ , A__ , A__ ) -> List[Any]:
snake_case = self.num_labels
snake_case = MPNetForTokenClassification(config=A__ )
model.to(A__ )
model.eval()
snake_case = 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 ) -> Optional[Any]:
snake_case = self.prepare_config_and_inputs()
((snake_case) , (snake_case) , (snake_case) , (snake_case) , (snake_case) , (snake_case)) = config_and_inputs
snake_case = {'''input_ids''': input_ids, '''attention_mask''': input_mask}
return config, inputs_dict
@require_torch
class _lowercase ( __a , __a , unittest.TestCase ):
_UpperCAmelCase = (
(
MPNetForMaskedLM,
MPNetForMultipleChoice,
MPNetForQuestionAnswering,
MPNetForSequenceClassification,
MPNetForTokenClassification,
MPNetModel,
)
if is_torch_available()
else ()
)
_UpperCAmelCase = (
{
'''feature-extraction''': MPNetModel,
'''fill-mask''': MPNetForMaskedLM,
'''question-answering''': MPNetForQuestionAnswering,
'''text-classification''': MPNetForSequenceClassification,
'''token-classification''': MPNetForTokenClassification,
'''zero-shot''': MPNetForSequenceClassification,
}
if is_torch_available()
else {}
)
_UpperCAmelCase = False
_UpperCAmelCase = True
def UpperCamelCase ( self ) -> Optional[Any]:
snake_case = MPNetModelTester(self )
snake_case = ConfigTester(self , config_class=A__ , hidden_size=37 )
def UpperCamelCase ( self ) -> Tuple:
self.config_tester.run_common_tests()
def UpperCamelCase ( self ) -> Optional[Any]:
snake_case = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mpnet_model(*A__ )
def UpperCamelCase ( self ) -> Optional[Any]:
snake_case = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mpnet_for_sequence_classification(*A__ )
def UpperCamelCase ( self ) -> Union[str, Any]:
snake_case = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mpnet_for_multiple_choice(*A__ )
def UpperCamelCase ( self ) -> str:
snake_case = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mpnet_for_token_classification(*A__ )
def UpperCamelCase ( self ) -> int:
snake_case = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mpnet_for_question_answering(*A__ )
@require_torch
class _lowercase ( unittest.TestCase ):
@slow
def UpperCamelCase ( self ) -> str:
snake_case = MPNetModel.from_pretrained('''microsoft/mpnet-base''' )
snake_case = torch.tensor([[0, 3_45, 2_32, 3_28, 7_40, 1_40, 16_95, 69, 60_78, 15_88, 2]] )
snake_case = model(A__ )[0]
snake_case = torch.Size((1, 11, 7_68) )
self.assertEqual(output.shape , A__ )
snake_case = 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 ) )
| 44
|
'''simple docstring'''
import argparse
import json
import logging
import os
import sys
from unittest.mock import patch
from transformers.testing_utils import TestCasePlus, get_gpu_count, slow
_lowercase = [
os.path.join(os.path.dirname(__file__), dirname)
for dirname in [
'text-classification',
'language-modeling',
'summarization',
'token-classification',
'question-answering',
]
]
sys.path.extend(SRC_DIRS)
if SRC_DIRS is not None:
import run_clm_flax
import run_flax_glue
import run_flax_ner
import run_mlm_flax
import run_qa
import run_summarization_flax
import run_ta_mlm_flax
logging.basicConfig(level=logging.DEBUG)
_lowercase = logging.getLogger()
def __UpperCamelCase ( ) ->Tuple:
snake_case = argparse.ArgumentParser()
parser.add_argument('''-f''' )
snake_case = parser.parse_args()
return args.f
def __UpperCamelCase ( a : Dict , a : Tuple="eval" ) ->List[Any]:
snake_case = os.path.join(a , f"""{split}_results.json""" )
if os.path.exists(a ):
with open(a , '''r''' ) as f:
return json.load(a )
raise ValueError(f"""can't find {path}""" )
_lowercase = logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
class _lowercase ( __a ):
def UpperCamelCase ( self ) -> List[str]:
snake_case = self.get_auto_remove_tmp_dir()
snake_case = F"""
run_glue.py
--model_name_or_path distilbert-base-uncased
--output_dir {tmp_dir}
--train_file ./tests/fixtures/tests_samples/MRPC/train.csv
--validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv
--per_device_train_batch_size=2
--per_device_eval_batch_size=1
--learning_rate=1e-4
--eval_steps=2
--warmup_steps=2
--seed=42
--max_seq_length=128
""".split()
with patch.object(A__ , '''argv''' , A__ ):
run_flax_glue.main()
snake_case = get_results(A__ )
self.assertGreaterEqual(result['''eval_accuracy'''] , 0.7_5 )
@slow
def UpperCamelCase ( self ) -> List[Any]:
snake_case = self.get_auto_remove_tmp_dir()
snake_case = F"""
run_clm_flax.py
--model_name_or_path distilgpt2
--train_file ./tests/fixtures/sample_text.txt
--validation_file ./tests/fixtures/sample_text.txt
--do_train
--do_eval
--block_size 128
--per_device_train_batch_size 4
--per_device_eval_batch_size 4
--num_train_epochs 2
--logging_steps 2 --eval_steps 2
--output_dir {tmp_dir}
--overwrite_output_dir
""".split()
with patch.object(A__ , '''argv''' , A__ ):
run_clm_flax.main()
snake_case = get_results(A__ )
self.assertLess(result['''eval_perplexity'''] , 1_00 )
@slow
def UpperCamelCase ( self ) -> int:
snake_case = self.get_auto_remove_tmp_dir()
snake_case = F"""
run_summarization.py
--model_name_or_path t5-small
--train_file tests/fixtures/tests_samples/xsum/sample.json
--validation_file tests/fixtures/tests_samples/xsum/sample.json
--test_file tests/fixtures/tests_samples/xsum/sample.json
--output_dir {tmp_dir}
--overwrite_output_dir
--num_train_epochs=3
--warmup_steps=8
--do_train
--do_eval
--do_predict
--learning_rate=2e-4
--per_device_train_batch_size=2
--per_device_eval_batch_size=1
--predict_with_generate
""".split()
with patch.object(A__ , '''argv''' , A__ ):
run_summarization_flax.main()
snake_case = get_results(A__ , split='''test''' )
self.assertGreaterEqual(result['''test_rouge1'''] , 10 )
self.assertGreaterEqual(result['''test_rouge2'''] , 2 )
self.assertGreaterEqual(result['''test_rougeL'''] , 7 )
self.assertGreaterEqual(result['''test_rougeLsum'''] , 7 )
@slow
def UpperCamelCase ( self ) -> Union[str, Any]:
snake_case = self.get_auto_remove_tmp_dir()
snake_case = F"""
run_mlm.py
--model_name_or_path distilroberta-base
--train_file ./tests/fixtures/sample_text.txt
--validation_file ./tests/fixtures/sample_text.txt
--output_dir {tmp_dir}
--overwrite_output_dir
--max_seq_length 128
--per_device_train_batch_size 4
--per_device_eval_batch_size 4
--logging_steps 2 --eval_steps 2
--do_train
--do_eval
--num_train_epochs=1
""".split()
with patch.object(A__ , '''argv''' , A__ ):
run_mlm_flax.main()
snake_case = get_results(A__ )
self.assertLess(result['''eval_perplexity'''] , 42 )
@slow
def UpperCamelCase ( self ) -> Dict:
snake_case = self.get_auto_remove_tmp_dir()
snake_case = F"""
run_t5_mlm_flax.py
--model_name_or_path t5-small
--train_file ./tests/fixtures/sample_text.txt
--validation_file ./tests/fixtures/sample_text.txt
--do_train
--do_eval
--max_seq_length 128
--per_device_train_batch_size 4
--per_device_eval_batch_size 4
--num_train_epochs 2
--logging_steps 2 --eval_steps 2
--output_dir {tmp_dir}
--overwrite_output_dir
""".split()
with patch.object(A__ , '''argv''' , A__ ):
run_ta_mlm_flax.main()
snake_case = get_results(A__ )
self.assertGreaterEqual(result['''eval_accuracy'''] , 0.4_2 )
@slow
def UpperCamelCase ( self ) -> int:
# with so little data distributed training needs more epochs to get the score on par with 0/1 gpu
snake_case = 7 if get_gpu_count() > 1 else 2
snake_case = self.get_auto_remove_tmp_dir()
snake_case = F"""
run_flax_ner.py
--model_name_or_path bert-base-uncased
--train_file tests/fixtures/tests_samples/conll/sample.json
--validation_file tests/fixtures/tests_samples/conll/sample.json
--output_dir {tmp_dir}
--overwrite_output_dir
--do_train
--do_eval
--warmup_steps=2
--learning_rate=2e-4
--logging_steps 2 --eval_steps 2
--per_device_train_batch_size=2
--per_device_eval_batch_size=2
--num_train_epochs={epochs}
--seed 7
""".split()
with patch.object(A__ , '''argv''' , A__ ):
run_flax_ner.main()
snake_case = get_results(A__ )
self.assertGreaterEqual(result['''eval_accuracy'''] , 0.7_5 )
self.assertGreaterEqual(result['''eval_f1'''] , 0.3 )
@slow
def UpperCamelCase ( self ) -> Any:
snake_case = self.get_auto_remove_tmp_dir()
snake_case = F"""
run_qa.py
--model_name_or_path bert-base-uncased
--version_2_with_negative
--train_file tests/fixtures/tests_samples/SQUAD/sample.json
--validation_file tests/fixtures/tests_samples/SQUAD/sample.json
--output_dir {tmp_dir}
--overwrite_output_dir
--num_train_epochs=3
--warmup_steps=2
--do_train
--do_eval
--logging_steps 2 --eval_steps 2
--learning_rate=2e-4
--per_device_train_batch_size=2
--per_device_eval_batch_size=1
""".split()
with patch.object(A__ , '''argv''' , A__ ):
run_qa.main()
snake_case = get_results(A__ )
self.assertGreaterEqual(result['''eval_f1'''] , 30 )
self.assertGreaterEqual(result['''eval_exact'''] , 30 )
| 44
| 1
|
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
_lowercase = {
'configuration_transfo_xl': ['TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP', 'TransfoXLConfig'],
'tokenization_transfo_xl': ['TransfoXLCorpus', 'TransfoXLTokenizer'],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowercase = [
'TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST',
'AdaptiveEmbedding',
'TransfoXLForSequenceClassification',
'TransfoXLLMHeadModel',
'TransfoXLModel',
'TransfoXLPreTrainedModel',
'load_tf_weights_in_transfo_xl',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowercase = [
'TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST',
'TFAdaptiveEmbedding',
'TFTransfoXLForSequenceClassification',
'TFTransfoXLLMHeadModel',
'TFTransfoXLMainLayer',
'TFTransfoXLModel',
'TFTransfoXLPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_transfo_xl import TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP, TransfoXLConfig
from .tokenization_transfo_xl import TransfoXLCorpus, TransfoXLTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_transfo_xl import (
TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
AdaptiveEmbedding,
TransfoXLForSequenceClassification,
TransfoXLLMHeadModel,
TransfoXLModel,
TransfoXLPreTrainedModel,
load_tf_weights_in_transfo_xl,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_transfo_xl import (
TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
TFAdaptiveEmbedding,
TFTransfoXLForSequenceClassification,
TFTransfoXLLMHeadModel,
TFTransfoXLMainLayer,
TFTransfoXLModel,
TFTransfoXLPreTrainedModel,
)
else:
import sys
_lowercase = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 44
|
'''simple docstring'''
from typing import Any, Dict, List, Optional, Tuple, Union
import torch
from torch import nn
from torch.utils.data import DistributedSampler, RandomSampler
from transformers import PreTrainedModel, Trainer, logging
from transformers.integrations import is_fairscale_available
from transformers.models.fsmt.configuration_fsmt import FSMTConfig
from transformers.optimization import (
Adafactor,
AdamW,
get_constant_schedule,
get_constant_schedule_with_warmup,
get_cosine_schedule_with_warmup,
get_cosine_with_hard_restarts_schedule_with_warmup,
get_linear_schedule_with_warmup,
get_polynomial_decay_schedule_with_warmup,
)
from transformers.trainer_pt_utils import get_tpu_sampler
from transformers.training_args import ParallelMode
from transformers.utils import is_torch_tpu_available
if is_fairscale_available():
from fairscale.optim import OSS
_lowercase = logging.get_logger(__name__)
_lowercase = {
'linear': get_linear_schedule_with_warmup,
'cosine': get_cosine_schedule_with_warmup,
'cosine_w_restarts': get_cosine_with_hard_restarts_schedule_with_warmup,
'polynomial': get_polynomial_decay_schedule_with_warmup,
'constant': get_constant_schedule,
'constant_w_warmup': get_constant_schedule_with_warmup,
}
class _lowercase ( __a ):
def __init__( self , A__=None , A__=None , *A__ , **A__ ) -> Union[str, Any]:
super().__init__(*A__ , **A__ )
if config is None:
assert isinstance(self.model , A__ ), (
"If no `config` is passed the model to be trained has to be of type `PreTrainedModel`, but is"
F""" {self.model.__class__}"""
)
snake_case = self.model.config
else:
snake_case = config
snake_case = data_args
snake_case = self.config.tgt_vocab_size if isinstance(self.config , A__ ) else self.config.vocab_size
if self.args.label_smoothing != 0 or (self.data_args is not None and self.data_args.ignore_pad_token_for_loss):
assert self.config.pad_token_id is not None, (
"Make sure that `config.pad_token_id` is correcly defined when ignoring `pad_token` for loss"
" calculation or doing label smoothing."
)
if self.config.pad_token_id is None and self.config.eos_token_id is not None:
logger.warning(
F"""The `config.pad_token_id` is `None`. Using `config.eos_token_id` = {self.config.eos_token_id} for"""
''' padding..''' )
if self.args.label_smoothing == 0:
snake_case = torch.nn.CrossEntropyLoss(ignore_index=self.config.pad_token_id )
else:
# dynamically import label_smoothed_nll_loss
from utils import label_smoothed_nll_loss
snake_case = label_smoothed_nll_loss
def UpperCamelCase ( self , A__ ) -> Tuple:
if self.optimizer is None:
snake_case = ['''bias''', '''LayerNorm.weight''']
snake_case = [
{
'''params''': [p for n, p in self.model.named_parameters() if not any(nd in n for nd in no_decay )],
'''weight_decay''': self.args.weight_decay,
},
{
'''params''': [p for n, p in self.model.named_parameters() if any(nd in n for nd in no_decay )],
'''weight_decay''': 0.0,
},
]
snake_case = Adafactor if self.args.adafactor else AdamW
if self.args.adafactor:
snake_case = Adafactor
snake_case = {'''scale_parameter''': False, '''relative_step''': False}
else:
snake_case = AdamW
snake_case = {
'''betas''': (self.args.adam_betaa, self.args.adam_betaa),
'''eps''': self.args.adam_epsilon,
}
snake_case = self.args.learning_rate
if self.sharded_ddp:
snake_case = OSS(
params=A__ , optim=A__ , **A__ , )
else:
snake_case = optimizer_cls(A__ , **A__ )
if self.lr_scheduler is None:
snake_case = self._get_lr_scheduler(A__ )
else: # ignoring --lr_scheduler
logger.warning('''scheduler is passed to `Seq2SeqTrainer`, `--lr_scheduler` arg is ignored.''' )
def UpperCamelCase ( self , A__ ) -> Tuple:
snake_case = arg_to_scheduler[self.args.lr_scheduler]
if self.args.lr_scheduler == "constant":
snake_case = schedule_func(self.optimizer )
elif self.args.lr_scheduler == "constant_w_warmup":
snake_case = schedule_func(self.optimizer , num_warmup_steps=self.args.warmup_steps )
else:
snake_case = schedule_func(
self.optimizer , num_warmup_steps=self.args.warmup_steps , num_training_steps=A__ )
return scheduler
def UpperCamelCase ( self ) -> Optional[torch.utils.data.Sampler]:
if isinstance(self.train_dataset , torch.utils.data.IterableDataset ):
return None
elif is_torch_tpu_available():
return get_tpu_sampler(self.train_dataset )
else:
if self.args.sortish_sampler:
self.train_dataset.make_sortish_sampler(
self.args.per_device_train_batch_size , distributed=(self.args.parallel_mode == ParallelMode.DISTRIBUTED) , )
return (
RandomSampler(self.train_dataset )
if self.args.local_rank == -1
else DistributedSampler(self.train_dataset )
)
def UpperCamelCase ( self , A__ , A__ , A__ ) -> List[Any]:
if self.args.label_smoothing == 0:
if self.data_args is not None and self.data_args.ignore_pad_token_for_loss:
# force training to ignore pad token
snake_case = model(**A__ , use_cache=A__ )[0]
snake_case = self.loss_fn(logits.view(-1 , logits.shape[-1] ) , labels.view(-1 ) )
else:
# compute usual loss via models
snake_case , snake_case = model(**A__ , labels=A__ , use_cache=A__ )[:2]
else:
# compute label smoothed loss
snake_case = model(**A__ , use_cache=A__ )[0]
snake_case = torch.nn.functional.log_softmax(A__ , dim=-1 )
snake_case , snake_case = self.loss_fn(A__ , A__ , self.args.label_smoothing , ignore_index=self.config.pad_token_id )
return loss, logits
def UpperCamelCase ( self , A__ , A__ ) -> Any:
snake_case = inputs.pop('''labels''' )
snake_case , snake_case = self._compute_loss(A__ , A__ , A__ )
return loss
def UpperCamelCase ( self , A__ , A__ , A__ , A__ = None , ) -> Tuple[Optional[float], Optional[torch.Tensor], Optional[torch.Tensor]]:
snake_case = self._prepare_inputs(A__ )
snake_case = {
'''max_length''': self.data_args.val_max_target_length
if self.data_args is not None
else self.config.max_length,
'''num_beams''': self.data_args.eval_beams if self.data_args is not None else self.config.num_beams,
}
if self.args.predict_with_generate and not self.args.prediction_loss_only:
snake_case = self.model.generate(
inputs['''input_ids'''] , attention_mask=inputs['''attention_mask'''] , **A__ , )
# in case the batch is shorter than max length, the output should be padded
if generated_tokens.shape[-1] < gen_kwargs["max_length"]:
snake_case = self._pad_tensors_to_max_len(A__ , gen_kwargs['''max_length'''] )
snake_case = inputs.pop('''labels''' )
with torch.no_grad():
# compute loss on predict data
snake_case , snake_case = self._compute_loss(A__ , A__ , A__ )
snake_case = loss.mean().detach()
if self.args.prediction_loss_only:
return (loss, None, None)
snake_case = generated_tokens if self.args.predict_with_generate else logits
if labels.shape[-1] < gen_kwargs["max_length"]:
snake_case = self._pad_tensors_to_max_len(A__ , gen_kwargs['''max_length'''] )
return (loss, logits, labels)
def UpperCamelCase ( self , A__ , A__ ) -> List[str]:
# If PAD token is not defined at least EOS token has to be defined
snake_case = self.config.pad_token_id if self.config.pad_token_id is not None else self.config.eos_token_id
if pad_token_id is None:
raise ValueError(
'''Make sure that either `config.pad_token_id` or `config.eos_token_id` is defined if tensor has to be'''
F""" padded to `max_length`={max_length}""" )
snake_case = pad_token_id * torch.ones(
(tensor.shape[0], max_length) , dtype=tensor.dtype , device=tensor.device )
snake_case = tensor
return padded_tensor
| 44
| 1
|
'''simple docstring'''
def __UpperCamelCase ( a : int ) ->bool:
snake_case = (1 + 24 * n) ** 0.5
return ((1 + root) / 6) % 1 == 0
def __UpperCamelCase ( a : int = 5000 ) ->int:
snake_case = [(i * (3 * i - 1)) // 2 for i in range(1 , a )]
for i, pentagonal_i in enumerate(a ):
for j in range(a , len(a ) ):
snake_case = pentagonal_nums[j]
snake_case = pentagonal_i + pentagonal_j
snake_case = pentagonal_j - pentagonal_i
if is_pentagonal(a ) and is_pentagonal(a ):
return b
return -1
if __name__ == "__main__":
print(f'{solution() = }')
| 44
|
'''simple docstring'''
import inspect
import re
from hashlib import shaaaa
from typing import Dict, List
from .arrow import arrow
from .audiofolder import audiofolder
from .csv import csv
from .imagefolder import imagefolder
from .json import json
from .pandas import pandas
from .parquet import parquet
from .sql import sql # noqa F401
from .text import text
def __UpperCamelCase ( a : List[str] ) ->str:
snake_case = []
for line in lines:
snake_case = re.sub(R'''#.*''' , '''''' , a ) # remove comments
if line:
filtered_lines.append(a )
snake_case = '''\n'''.join(a )
# Make a hash from all this code
snake_case = full_str.encode('''utf-8''' )
return shaaaa(a ).hexdigest()
# get importable module names and hash for caching
_lowercase = {
'csv': (csv.__name__, _hash_python_lines(inspect.getsource(csv).splitlines())),
'json': (json.__name__, _hash_python_lines(inspect.getsource(json).splitlines())),
'pandas': (pandas.__name__, _hash_python_lines(inspect.getsource(pandas).splitlines())),
'parquet': (parquet.__name__, _hash_python_lines(inspect.getsource(parquet).splitlines())),
'arrow': (arrow.__name__, _hash_python_lines(inspect.getsource(arrow).splitlines())),
'text': (text.__name__, _hash_python_lines(inspect.getsource(text).splitlines())),
'imagefolder': (imagefolder.__name__, _hash_python_lines(inspect.getsource(imagefolder).splitlines())),
'audiofolder': (audiofolder.__name__, _hash_python_lines(inspect.getsource(audiofolder).splitlines())),
}
# Used to infer the module to use based on the data files extensions
_lowercase = {
'.csv': ('csv', {}),
'.tsv': ('csv', {'sep': '\t'}),
'.json': ('json', {}),
'.jsonl': ('json', {}),
'.parquet': ('parquet', {}),
'.arrow': ('arrow', {}),
'.txt': ('text', {}),
}
_EXTENSION_TO_MODULE.update({ext: ('imagefolder', {}) for ext in imagefolder.ImageFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext.upper(): ('imagefolder', {}) for ext in imagefolder.ImageFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext: ('audiofolder', {}) for ext in audiofolder.AudioFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext.upper(): ('audiofolder', {}) for ext in audiofolder.AudioFolder.EXTENSIONS})
_lowercase = {'imagefolder', 'audiofolder'}
# Used to filter data files based on extensions given a module name
_lowercase = {}
for _ext, (_module, _) in _EXTENSION_TO_MODULE.items():
_MODULE_TO_EXTENSIONS.setdefault(_module, []).append(_ext)
_MODULE_TO_EXTENSIONS["imagefolder"].append('.zip')
_MODULE_TO_EXTENSIONS["audiofolder"].append('.zip')
| 44
| 1
|
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
_lowercase = {'configuration_fnet': ['FNET_PRETRAINED_CONFIG_ARCHIVE_MAP', 'FNetConfig']}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowercase = ['FNetTokenizer']
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowercase = ['FNetTokenizerFast']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowercase = [
'FNET_PRETRAINED_MODEL_ARCHIVE_LIST',
'FNetForMaskedLM',
'FNetForMultipleChoice',
'FNetForNextSentencePrediction',
'FNetForPreTraining',
'FNetForQuestionAnswering',
'FNetForSequenceClassification',
'FNetForTokenClassification',
'FNetLayer',
'FNetModel',
'FNetPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_fnet import FNET_PRETRAINED_CONFIG_ARCHIVE_MAP, FNetConfig
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_fnet import FNetTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_fnet_fast import FNetTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_fnet import (
FNET_PRETRAINED_MODEL_ARCHIVE_LIST,
FNetForMaskedLM,
FNetForMultipleChoice,
FNetForNextSentencePrediction,
FNetForPreTraining,
FNetForQuestionAnswering,
FNetForSequenceClassification,
FNetForTokenClassification,
FNetLayer,
FNetModel,
FNetPreTrainedModel,
)
else:
import sys
_lowercase = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 44
|
'''simple docstring'''
_lowercase = {
'Pillow': 'Pillow',
'accelerate': 'accelerate>=0.11.0',
'compel': 'compel==0.1.8',
'black': 'black~=23.1',
'datasets': 'datasets',
'filelock': 'filelock',
'flax': 'flax>=0.4.1',
'hf-doc-builder': 'hf-doc-builder>=0.3.0',
'huggingface-hub': 'huggingface-hub>=0.13.2',
'requests-mock': 'requests-mock==1.10.0',
'importlib_metadata': 'importlib_metadata',
'invisible-watermark': 'invisible-watermark',
'isort': 'isort>=5.5.4',
'jax': 'jax>=0.2.8,!=0.3.2',
'jaxlib': 'jaxlib>=0.1.65',
'Jinja2': 'Jinja2',
'k-diffusion': 'k-diffusion>=0.0.12',
'torchsde': 'torchsde',
'note_seq': 'note_seq',
'librosa': 'librosa',
'numpy': 'numpy',
'omegaconf': 'omegaconf',
'parameterized': 'parameterized',
'protobuf': 'protobuf>=3.20.3,<4',
'pytest': 'pytest',
'pytest-timeout': 'pytest-timeout',
'pytest-xdist': 'pytest-xdist',
'ruff': 'ruff>=0.0.241',
'safetensors': 'safetensors',
'sentencepiece': 'sentencepiece>=0.1.91,!=0.1.92',
'scipy': 'scipy',
'onnx': 'onnx',
'regex': 'regex!=2019.12.17',
'requests': 'requests',
'tensorboard': 'tensorboard',
'torch': 'torch>=1.4',
'torchvision': 'torchvision',
'transformers': 'transformers>=4.25.1',
'urllib3': 'urllib3<=2.0.0',
}
| 44
| 1
|
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowercase = logging.get_logger(__name__)
_lowercase = {
'EleutherAI/gpt-neox-20b': 'https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/config.json',
# See all GPTNeoX models at https://huggingface.co/models?filter=gpt_neox
}
class _lowercase ( __a ):
_UpperCAmelCase = '''gpt_neox'''
def __init__( self , A__=5_04_32 , A__=61_44 , A__=44 , A__=64 , A__=2_45_76 , A__="gelu" , A__=0.2_5 , A__=1_00_00 , A__=0.0 , A__=0.0 , A__=0.1 , A__=20_48 , A__=0.0_2 , A__=1e-5 , A__=True , A__=0 , A__=2 , A__=False , A__=True , A__=None , **A__ , ) -> List[str]:
super().__init__(bos_token_id=A__ , eos_token_id=A__ , **A__ )
snake_case = vocab_size
snake_case = max_position_embeddings
snake_case = hidden_size
snake_case = num_hidden_layers
snake_case = num_attention_heads
snake_case = intermediate_size
snake_case = hidden_act
snake_case = rotary_pct
snake_case = rotary_emb_base
snake_case = attention_dropout
snake_case = hidden_dropout
snake_case = classifier_dropout
snake_case = initializer_range
snake_case = layer_norm_eps
snake_case = use_cache
snake_case = tie_word_embeddings
snake_case = use_parallel_residual
snake_case = rope_scaling
self._rope_scaling_validation()
if self.hidden_size % self.num_attention_heads != 0:
raise ValueError(
'''The hidden size is not divisble by the number of attention heads! Make sure to update them!''' )
def UpperCamelCase ( self ) -> Dict:
if self.rope_scaling is None:
return
if not isinstance(self.rope_scaling , A__ ) or len(self.rope_scaling ) != 2:
raise ValueError(
'''`rope_scaling` must be a dictionary with with two fields, `name` and `factor`, '''
F"""got {self.rope_scaling}""" )
snake_case = self.rope_scaling.get('''type''' , A__ )
snake_case = self.rope_scaling.get('''factor''' , A__ )
if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
raise ValueError(
F"""`rope_scaling`'s name field must be one of ['linear', 'dynamic'], got {rope_scaling_type}""" )
if rope_scaling_factor is None or not isinstance(A__ , A__ ) or rope_scaling_factor <= 1.0:
raise ValueError(F"""`rope_scaling`'s factor field must be an float > 1, got {rope_scaling_factor}""" )
| 44
|
'''simple docstring'''
import random
import unittest
import torch
from diffusers import IFInpaintingSuperResolutionPipeline
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_INPAINTING_BATCH_PARAMS,
TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin
@skip_mps
class _lowercase ( __a , __a , unittest.TestCase ):
_UpperCAmelCase = IFInpaintingSuperResolutionPipeline
_UpperCAmelCase = TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {'''width''', '''height'''}
_UpperCAmelCase = TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS.union({'''original_image'''} )
_UpperCAmelCase = PipelineTesterMixin.required_optional_params - {'''latents'''}
def UpperCamelCase ( self ) -> int:
return self._get_superresolution_dummy_components()
def UpperCamelCase ( self , A__ , A__=0 ) -> Union[str, Any]:
if str(A__ ).startswith('''mps''' ):
snake_case = torch.manual_seed(A__ )
else:
snake_case = torch.Generator(device=A__ ).manual_seed(A__ )
snake_case = floats_tensor((1, 3, 16, 16) , rng=random.Random(A__ ) ).to(A__ )
snake_case = floats_tensor((1, 3, 32, 32) , rng=random.Random(A__ ) ).to(A__ )
snake_case = floats_tensor((1, 3, 32, 32) , rng=random.Random(A__ ) ).to(A__ )
snake_case = {
'''prompt''': '''A painting of a squirrel eating a burger''',
'''image''': image,
'''original_image''': original_image,
'''mask_image''': mask_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 UpperCamelCase ( self ) -> List[Any]:
self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3 )
def UpperCamelCase ( self ) -> Optional[Any]:
self._test_save_load_optional_components()
@unittest.skipIf(torch_device != '''cuda''' , reason='''float16 requires CUDA''' )
def UpperCamelCase ( self ) -> List[str]:
# Due to non-determinism in save load of the hf-internal-testing/tiny-random-t5 text encoder
super().test_save_load_floataa(expected_max_diff=1e-1 )
def UpperCamelCase ( self ) -> int:
self._test_attention_slicing_forward_pass(expected_max_diff=1e-2 )
def UpperCamelCase ( self ) -> Optional[Any]:
self._test_save_load_local()
def UpperCamelCase ( self ) -> Dict:
self._test_inference_batch_single_identical(
expected_max_diff=1e-2 , )
| 44
| 1
|
'''simple docstring'''
import json
import os
from pathlib import Path
import pytest
from datasets.download.download_config import DownloadConfig
from datasets.download.download_manager import DownloadManager
from datasets.utils.file_utils import hash_url_to_filename
_lowercase = 'http://www.mocksite.com/file1.txt'
_lowercase = '"text": ["foo", "foo"]'
_lowercase = '6d8ce9aa78a471c7477201efbeabd3bb01ac2e7d100a6dc024ba1608361f90a8'
class _lowercase :
_UpperCAmelCase = 200
_UpperCAmelCase = {'''Content-Length''': '''100'''}
_UpperCAmelCase = {}
def UpperCamelCase ( self , **A__ ) -> str:
return [bytes(A__ , '''utf-8''' )]
def __UpperCamelCase ( *a : List[str] , **a : List[str] ) ->List[Any]:
return MockResponse()
@pytest.mark.parametrize('''urls_type''' , [str, list, dict] )
def __UpperCamelCase ( a : Tuple , a : int , a : List[str] ) ->Optional[Any]:
import requests
monkeypatch.setattr(a , '''request''' , a )
snake_case = URL
if issubclass(a , a ):
snake_case = url
elif issubclass(a , a ):
snake_case = [url]
elif issubclass(a , a ):
snake_case = {'''train''': url}
snake_case = '''dummy'''
snake_case = '''downloads'''
snake_case = tmp_path
snake_case = DownloadConfig(
cache_dir=os.path.join(a , a ) , use_etag=a , )
snake_case = DownloadManager(dataset_name=a , download_config=a )
snake_case = dl_manager.download(a )
snake_case = urls
for downloaded_paths in [downloaded_paths]:
if isinstance(a , a ):
snake_case = [downloaded_paths]
snake_case = [urls]
elif isinstance(a , a ):
assert "train" in downloaded_paths.keys()
snake_case = downloaded_paths.values()
snake_case = urls.values()
assert downloaded_paths
for downloaded_path, input_url in zip(a , a ):
assert downloaded_path == dl_manager.downloaded_paths[input_url]
snake_case = Path(a )
snake_case = downloaded_path.parts
assert parts[-1] == HASH
assert parts[-2] == cache_subdir
assert downloaded_path.exists()
snake_case = downloaded_path.read_text()
assert content == CONTENT
snake_case = downloaded_path.with_suffix('''.json''' )
assert metadata_downloaded_path.exists()
snake_case = json.loads(metadata_downloaded_path.read_text() )
assert metadata_content == {"url": URL, "etag": None}
@pytest.mark.parametrize('''paths_type''' , [str, list, dict] )
def __UpperCamelCase ( a : List[str] , a : Optional[Any] , a : Optional[int] ) ->Tuple:
snake_case = str(a )
if issubclass(a , a ):
snake_case = filename
elif issubclass(a , a ):
snake_case = [filename]
elif issubclass(a , a ):
snake_case = {'''train''': filename}
snake_case = '''dummy'''
snake_case = xz_file.parent
snake_case = '''extracted'''
snake_case = DownloadConfig(
cache_dir=a , use_etag=a , )
snake_case = DownloadManager(dataset_name=a , download_config=a )
snake_case = dl_manager.extract(a )
snake_case = paths
for extracted_paths in [extracted_paths]:
if isinstance(a , a ):
snake_case = [extracted_paths]
snake_case = [paths]
elif isinstance(a , a ):
assert "train" in extracted_paths.keys()
snake_case = extracted_paths.values()
snake_case = paths.values()
assert extracted_paths
for extracted_path, input_path in zip(a , a ):
assert extracted_path == dl_manager.extracted_paths[input_path]
snake_case = Path(a )
snake_case = extracted_path.parts
assert parts[-1] == hash_url_to_filename(a , etag=a )
assert parts[-2] == extracted_subdir
assert extracted_path.exists()
snake_case = extracted_path.read_text()
snake_case = text_file.read_text()
assert extracted_file_content == expected_file_content
def __UpperCamelCase ( a : Any , a : Dict ) ->Tuple:
assert path.endswith('''.jsonl''' )
for num_items, line in enumerate(a , start=1 ):
snake_case = json.loads(line.decode('''utf-8''' ) )
assert item.keys() == {"col_1", "col_2", "col_3"}
assert num_items == 4
@pytest.mark.parametrize('''archive_jsonl''' , ['''tar_jsonl_path''', '''zip_jsonl_path'''] )
def __UpperCamelCase ( a : Dict , a : Tuple ) ->List[Any]:
snake_case = request.getfixturevalue(a )
snake_case = DownloadManager()
for num_jsonl, (path, file) in enumerate(dl_manager.iter_archive(a ) , start=1 ):
_test_jsonl(a , a )
assert num_jsonl == 2
@pytest.mark.parametrize('''archive_nested_jsonl''' , ['''tar_nested_jsonl_path''', '''zip_nested_jsonl_path'''] )
def __UpperCamelCase ( a : Tuple , a : Union[str, Any] ) ->Any:
snake_case = request.getfixturevalue(a )
snake_case = DownloadManager()
for num_tar, (path, file) in enumerate(dl_manager.iter_archive(a ) , start=1 ):
for num_jsonl, (subpath, subfile) in enumerate(dl_manager.iter_archive(a ) , start=1 ):
_test_jsonl(a , a )
assert num_tar == 1
assert num_jsonl == 2
def __UpperCamelCase ( a : List[str] ) ->Optional[Any]:
snake_case = DownloadManager()
for num_file, file in enumerate(dl_manager.iter_files(a ) , start=1 ):
assert os.path.basename(a ) == ("test.txt" if num_file == 1 else "train.txt")
assert num_file == 2
| 44
|
'''simple docstring'''
from typing import Dict, List, Optional, Union
import numpy as np
from .feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from .utils import PaddingStrategy, TensorType, is_tf_tensor, is_torch_tensor, logging, to_numpy
_lowercase = logging.get_logger(__name__)
class _lowercase ( __a ):
def __init__( self , A__ , A__ , A__ , **A__ ) -> Union[str, Any]:
snake_case = feature_size
snake_case = sampling_rate
snake_case = padding_value
snake_case = kwargs.pop('''padding_side''' , '''right''' )
snake_case = kwargs.pop('''return_attention_mask''' , A__ )
super().__init__(**A__ )
def UpperCamelCase ( self , A__ , A__ = True , A__ = None , A__ = False , A__ = None , A__ = None , A__ = None , ) -> BatchFeature:
# If we have a list of dicts, let's convert it in a dict of lists
# We do this to allow using this method as a collate_fn function in PyTorch Dataloader
if isinstance(A__ , (list, tuple) ) and isinstance(processed_features[0] , (dict, BatchFeature) ):
snake_case = {
key: [example[key] for example in processed_features] for key in processed_features[0].keys()
}
# The model's main input name, usually `input_values`, has be passed for padding
if self.model_input_names[0] not in processed_features:
raise ValueError(
'''You should supply an instance of `transformers.BatchFeature` or list of `transformers.BatchFeature`'''
F""" to this method that includes {self.model_input_names[0]}, but you provided"""
F""" {list(processed_features.keys() )}""" )
snake_case = processed_features[self.model_input_names[0]]
snake_case = (
return_attention_mask if return_attention_mask is not None else self.return_attention_mask
)
if len(A__ ) == 0:
if return_attention_mask:
snake_case = []
return processed_features
# If we have PyTorch/TF tensors or lists as inputs, we cast them as Numpy arrays
# and rebuild them afterwards if no return_tensors is specified
# Note that we lose the specific device the tensor may be on for PyTorch
snake_case = required_input[0]
if isinstance(A__ , (list, tuple) ):
# first_element might be an empty list/tuple in some edge cases so we grab the first non empty element.
snake_case = 0
while len(required_input[index] ) == 0:
index += 1
if index < len(A__ ):
snake_case = required_input[index][0]
if return_tensors is None:
if is_tf_tensor(A__ ):
snake_case = '''tf'''
elif is_torch_tensor(A__ ):
snake_case = '''pt'''
elif isinstance(A__ , (int, float, list, tuple, np.ndarray) ):
snake_case = '''np'''
else:
raise ValueError(
F"""type of {first_element} unknown: {type(A__ )}. """
'''Should be one of a python, numpy, pytorch or tensorflow object.''' )
for key, value in processed_features.items():
if isinstance(value[0] , (int, float) ):
snake_case = to_numpy(A__ )
else:
snake_case = [to_numpy(A__ ) for v in value]
# Convert padding_strategy in PaddingStrategy
snake_case = self._get_padding_strategies(padding=A__ , max_length=A__ )
snake_case = processed_features[self.model_input_names[0]]
snake_case = len(A__ )
if not all(len(A__ ) == batch_size for v in processed_features.values() ):
raise ValueError('''Some items in the output dictionary have a different batch size than others.''' )
snake_case = []
for i in range(A__ ):
snake_case = {k: v[i] for k, v in processed_features.items()}
# truncation
snake_case = self._truncate(
A__ , max_length=A__ , pad_to_multiple_of=A__ , truncation=A__ , )
truncated_inputs.append(A__ )
if padding_strategy == PaddingStrategy.LONGEST:
# make sure that `max_length` cannot be longer than the longest truncated length
snake_case = max(len(input_slice[self.model_input_names[0]] ) for input_slice in truncated_inputs )
snake_case = PaddingStrategy.MAX_LENGTH
snake_case = {}
for i in range(A__ ):
# padding
snake_case = self._pad(
truncated_inputs[i] , max_length=A__ , padding_strategy=A__ , pad_to_multiple_of=A__ , return_attention_mask=A__ , )
for key, value in outputs.items():
if key not in batch_outputs:
snake_case = []
if value.dtype is np.dtype(np.floataa ):
snake_case = value.astype(np.floataa )
batch_outputs[key].append(A__ )
return BatchFeature(A__ , tensor_type=A__ )
def UpperCamelCase ( self , A__ , A__ = None , A__ = PaddingStrategy.DO_NOT_PAD , A__ = None , A__ = None , ) -> dict:
snake_case = processed_features[self.model_input_names[0]]
if padding_strategy == PaddingStrategy.LONGEST:
snake_case = len(A__ )
if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
snake_case = ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of
snake_case = padding_strategy != PaddingStrategy.DO_NOT_PAD and len(A__ ) < max_length
if return_attention_mask and "attention_mask" not in processed_features:
snake_case = np.ones(len(A__ ) , dtype=np.intaa )
if needs_to_be_padded:
snake_case = max_length - len(A__ )
if self.padding_side == "right":
if return_attention_mask:
snake_case = np.pad(
processed_features['''attention_mask'''] , (0, difference) )
snake_case = ((0, difference), (0, 0)) if self.feature_size > 1 else (0, difference)
snake_case = np.pad(
A__ , A__ , '''constant''' , constant_values=self.padding_value )
elif self.padding_side == "left":
if return_attention_mask:
snake_case = np.pad(
processed_features['''attention_mask'''] , (difference, 0) )
snake_case = ((difference, 0), (0, 0)) if self.feature_size > 1 else (difference, 0)
snake_case = np.pad(
A__ , A__ , '''constant''' , constant_values=self.padding_value )
else:
raise ValueError('''Invalid padding strategy:''' + str(self.padding_side ) )
return processed_features
def UpperCamelCase ( self , A__ , A__ = None , A__ = None , A__ = None , ) -> Union[str, Any]:
if not truncation:
return processed_features
elif truncation and max_length is None:
raise ValueError('''When setting ``truncation=True``, make sure that ``max_length`` is defined.''' )
snake_case = processed_features[self.model_input_names[0]]
# find `max_length` that fits `pad_to_multiple_of`
if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
snake_case = ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of
snake_case = len(A__ ) > max_length
if needs_to_be_truncated:
snake_case = processed_features[self.model_input_names[0]][:max_length]
if "attention_mask" in processed_features:
snake_case = processed_features['''attention_mask'''][:max_length]
return processed_features
def UpperCamelCase ( self , A__=False , A__=None ) -> Union[str, Any]:
# Get padding strategy
if padding is not False:
if padding is True:
snake_case = PaddingStrategy.LONGEST # Default to pad to the longest sequence in the batch
elif not isinstance(A__ , A__ ):
snake_case = PaddingStrategy(A__ )
elif isinstance(A__ , A__ ):
snake_case = padding
else:
snake_case = PaddingStrategy.DO_NOT_PAD
# Set max length if needed
if max_length is None:
if padding_strategy == PaddingStrategy.MAX_LENGTH:
raise ValueError(
F"""When setting ``padding={PaddingStrategy.MAX_LENGTH}``, make sure that max_length is defined""" )
# Test if we have a padding value
if padding_strategy != PaddingStrategy.DO_NOT_PAD and (self.padding_value is None):
raise ValueError(
'''Asking to pad but the feature_extractor does not have a padding value. Please select a value to use'''
''' as `padding_value`. For example: `feature_extractor.padding_value = 0.0`.''' )
return padding_strategy
| 44
| 1
|
'''simple docstring'''
from typing import Dict, List
from nltk.translate import gleu_score
import datasets
from datasets import MetricInfo
_lowercase = '\\n@misc{wu2016googles,\n title={Google\'s Neural Machine Translation System: Bridging the Gap between Human and Machine Translation},\n author={Yonghui Wu and Mike Schuster and Zhifeng Chen and Quoc V. Le and Mohammad Norouzi and Wolfgang Macherey\n and Maxim Krikun and Yuan Cao and Qin Gao and Klaus Macherey and Jeff Klingner and Apurva Shah and Melvin\n Johnson and Xiaobing Liu and Łukasz Kaiser and Stephan Gouws and Yoshikiyo Kato and Taku Kudo and Hideto\n Kazawa and Keith Stevens and George Kurian and Nishant Patil and Wei Wang and Cliff Young and\n Jason Smith and Jason Riesa and Alex Rudnick and Oriol Vinyals and Greg Corrado and Macduff Hughes\n and Jeffrey Dean},\n year={2016},\n eprint={1609.08144},\n archivePrefix={arXiv},\n primaryClass={cs.CL}\n}\n'
_lowercase = '\\nThe BLEU score has some undesirable properties when used for single\nsentences, as it was designed to be a corpus measure. We therefore\nuse a slightly different score for our RL experiments which we call\nthe \'GLEU score\'. For the GLEU score, we record all sub-sequences of\n1, 2, 3 or 4 tokens in output and target sequence (n-grams). We then\ncompute a recall, which is the ratio of the number of matching n-grams\nto the number of total n-grams in the target (ground truth) sequence,\nand a precision, which is the ratio of the number of matching n-grams\nto the number of total n-grams in the generated output sequence. Then\nGLEU score is simply the minimum of recall and precision. This GLEU\nscore\'s range is always between 0 (no matches) and 1 (all match) and\nit is symmetrical when switching output and target. According to\nour experiments, GLEU score correlates quite well with the BLEU\nmetric on a corpus level but does not have its drawbacks for our per\nsentence reward objective.\n'
_lowercase = '\\nComputes corpus-level Google BLEU (GLEU) score of translated segments against one or more references.\nInstead of averaging the sentence level GLEU scores (i.e. macro-average precision), Wu et al. (2016) sum up the matching\ntokens and the max of hypothesis and reference tokens for each sentence, then compute using the aggregate values.\n\nArgs:\n predictions (list of str): list of translations to score.\n Each translation should be tokenized into a list of tokens.\n references (list of list of str): list of lists of references for each translation.\n Each reference should be tokenized into a list of tokens.\n min_len (int): The minimum order of n-gram this function should extract. Defaults to 1.\n max_len (int): The maximum order of n-gram this function should extract. Defaults to 4.\n\nReturns:\n \'google_bleu\': google_bleu score\n\nExamples:\n Example 1:\n >>> hyp1 = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'which\',\n ... \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'always\',\n ... \'disobeys\', \'the\', \'commands\', \'of\', \'the\', \'cat\']\n >>> ref1a = [\'It\', \'is\', \'the\', \'guiding\', \'principle\', \'which\',\n ... \'guarantees\', \'the\', \'rubber\', \'duck\', \'forces\', \'never\',\n ... \'being\', \'under\', \'the\', \'command\', \'of\', \'the\', \'cat\']\n\n >>> hyp2 = [\'he\', \'read\', \'the\', \'book\', \'because\', \'he\', \'was\',\n ... \'interested\', \'in\', \'world\', \'history\']\n >>> ref2a = [\'he\', \'was\', \'interested\', \'in\', \'world\', \'history\',\n ... \'because\', \'he\', \'read\', \'the\', \'book\']\n\n >>> list_of_references = [[ref1a], [ref2a]]\n >>> hypotheses = [hyp1, hyp2]\n >>> google_bleu = datasets.load_metric("google_bleu")\n >>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references)\n >>> print(round(results["google_bleu"], 2))\n 0.44\n\n Example 2:\n >>> hyp1 = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'which\',\n ... \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'always\',\n ... \'disobeys\', \'the\', \'commands\', \'of\', \'the\', \'cat\']\n >>> ref1a = [\'It\', \'is\', \'the\', \'guiding\', \'principle\', \'which\',\n ... \'guarantees\', \'the\', \'rubber\', \'duck\', \'forces\', \'never\',\n ... \'being\', \'under\', \'the\', \'command\', \'of\', \'the\', \'cat\']\n >>> ref1b = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'that\',\n ... \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'will\', \'never\',\n ... \'heed\', \'the\', \'cat\', \'commands\']\n >>> ref1c = [\'It\', \'is\', \'the\', \'practical\', \'guide\', \'for\', \'the\',\n ... \'rubber\', \'duck\', \'army\', \'never\', \'to\', \'heed\', \'the\', \'directions\',\n ... \'of\', \'the\', \'cat\']\n\n >>> hyp2 = [\'he\', \'read\', \'the\', \'book\', \'because\', \'he\', \'was\',\n ... \'interested\', \'in\', \'world\', \'history\']\n >>> ref2a = [\'he\', \'was\', \'interested\', \'in\', \'world\', \'history\',\n ... \'because\', \'he\', \'read\', \'the\', \'book\']\n\n >>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]\n >>> hypotheses = [hyp1, hyp2]\n >>> google_bleu = datasets.load_metric("google_bleu")\n >>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references)\n >>> print(round(results["google_bleu"], 2))\n 0.61\n\n Example 3:\n >>> hyp1 = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'which\',\n ... \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'always\',\n ... \'disobeys\', \'the\', \'commands\', \'of\', \'the\', \'cat\']\n >>> ref1a = [\'It\', \'is\', \'the\', \'guiding\', \'principle\', \'which\',\n ... \'guarantees\', \'the\', \'rubber\', \'duck\', \'forces\', \'never\',\n ... \'being\', \'under\', \'the\', \'command\', \'of\', \'the\', \'cat\']\n >>> ref1b = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'that\',\n ... \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'will\', \'never\',\n ... \'heed\', \'the\', \'cat\', \'commands\']\n >>> ref1c = [\'It\', \'is\', \'the\', \'practical\', \'guide\', \'for\', \'the\',\n ... \'rubber\', \'duck\', \'army\', \'never\', \'to\', \'heed\', \'the\', \'directions\',\n ... \'of\', \'the\', \'cat\']\n\n >>> hyp2 = [\'he\', \'read\', \'the\', \'book\', \'because\', \'he\', \'was\',\n ... \'interested\', \'in\', \'world\', \'history\']\n >>> ref2a = [\'he\', \'was\', \'interested\', \'in\', \'world\', \'history\',\n ... \'because\', \'he\', \'read\', \'the\', \'book\']\n\n >>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]\n >>> hypotheses = [hyp1, hyp2]\n >>> google_bleu = datasets.load_metric("google_bleu")\n >>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references, min_len=2)\n >>> print(round(results["google_bleu"], 2))\n 0.53\n\n Example 4:\n >>> hyp1 = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'which\',\n ... \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'always\',\n ... \'disobeys\', \'the\', \'commands\', \'of\', \'the\', \'cat\']\n >>> ref1a = [\'It\', \'is\', \'the\', \'guiding\', \'principle\', \'which\',\n ... \'guarantees\', \'the\', \'rubber\', \'duck\', \'forces\', \'never\',\n ... \'being\', \'under\', \'the\', \'command\', \'of\', \'the\', \'cat\']\n >>> ref1b = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'that\',\n ... \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'will\', \'never\',\n ... \'heed\', \'the\', \'cat\', \'commands\']\n >>> ref1c = [\'It\', \'is\', \'the\', \'practical\', \'guide\', \'for\', \'the\',\n ... \'rubber\', \'duck\', \'army\', \'never\', \'to\', \'heed\', \'the\', \'directions\',\n ... \'of\', \'the\', \'cat\']\n\n >>> hyp2 = [\'he\', \'read\', \'the\', \'book\', \'because\', \'he\', \'was\',\n ... \'interested\', \'in\', \'world\', \'history\']\n >>> ref2a = [\'he\', \'was\', \'interested\', \'in\', \'world\', \'history\',\n ... \'because\', \'he\', \'read\', \'the\', \'book\']\n\n >>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]\n >>> hypotheses = [hyp1, hyp2]\n >>> google_bleu = datasets.load_metric("google_bleu")\n >>> results = google_bleu.compute(predictions=hypotheses,references=list_of_references, min_len=2, max_len=6)\n >>> print(round(results["google_bleu"], 2))\n 0.4\n'
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class _lowercase ( datasets.Metric ):
def UpperCamelCase ( self ) -> MetricInfo:
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
'''predictions''': datasets.Sequence(datasets.Value('''string''' , id='''token''' ) , id='''sequence''' ),
'''references''': datasets.Sequence(
datasets.Sequence(datasets.Value('''string''' , id='''token''' ) , id='''sequence''' ) , id='''references''' ),
} ) , )
def UpperCamelCase ( self , A__ , A__ , A__ = 1 , A__ = 4 , ) -> Dict[str, float]:
return {
"google_bleu": gleu_score.corpus_gleu(
list_of_references=A__ , hypotheses=A__ , min_len=A__ , max_len=A__ )
}
| 44
|
'''simple docstring'''
from collections import Counter
from pathlib import Path
from typing import Optional, Tuple
import yaml
class _lowercase ( yaml.SafeLoader ):
def UpperCamelCase ( self , A__ ) -> List[str]:
snake_case = [self.constructed_objects[key_node] for key_node, _ in node.value]
snake_case = [tuple(A__ ) if isinstance(A__ , A__ ) else key for key in keys]
snake_case = Counter(A__ )
snake_case = [key for key in counter if counter[key] > 1]
if duplicate_keys:
raise TypeError(F"""Got duplicate yaml keys: {duplicate_keys}""" )
def UpperCamelCase ( self , A__ , A__=False ) -> List[Any]:
snake_case = super().construct_mapping(A__ , deep=A__ )
self._check_no_duplicates_on_constructed_node(A__ )
return mapping
def __UpperCamelCase ( a : str ) ->Tuple[Optional[str], str]:
snake_case = list(readme_content.splitlines() )
if full_content and full_content[0] == "---" and "---" in full_content[1:]:
snake_case = full_content[1:].index('''---''' ) + 1
snake_case = '''\n'''.join(full_content[1:sep_idx] )
return yamlblock, "\n".join(full_content[sep_idx + 1 :] )
return None, "\n".join(a )
class _lowercase ( __a ):
# class attributes
_UpperCAmelCase = {'''train_eval_index'''} # train-eval-index in the YAML metadata
@classmethod
def UpperCamelCase ( cls , A__ ) -> "DatasetMetadata":
with open(A__ , encoding='''utf-8''' ) as readme_file:
snake_case , snake_case = _split_yaml_from_readme(readme_file.read() )
if yaml_string is not None:
return cls.from_yaml_string(A__ )
else:
return cls()
def UpperCamelCase ( self , A__ ) -> str:
if path.exists():
with open(A__ , encoding='''utf-8''' ) as readme_file:
snake_case = readme_file.read()
else:
snake_case = None
snake_case = self._to_readme(A__ )
with open(A__ , '''w''' , encoding='''utf-8''' ) as readme_file:
readme_file.write(A__ )
def UpperCamelCase ( self , A__ = None ) -> str:
if readme_content is not None:
snake_case , snake_case = _split_yaml_from_readme(A__ )
snake_case = '''---\n''' + self.to_yaml_string() + '''---\n''' + content
else:
snake_case = '''---\n''' + self.to_yaml_string() + '''---\n'''
return full_content
@classmethod
def UpperCamelCase ( cls , A__ ) -> "DatasetMetadata":
snake_case = yaml.load(A__ , Loader=_NoDuplicateSafeLoader ) or {}
# Convert the YAML keys to DatasetMetadata fields
snake_case = {
(key.replace('''-''' , '''_''' ) if key.replace('''-''' , '''_''' ) in cls._FIELDS_WITH_DASHES else key): value
for key, value in metadata_dict.items()
}
return cls(**A__ )
def UpperCamelCase ( self ) -> str:
return yaml.safe_dump(
{
(key.replace('''_''' , '''-''' ) if key in self._FIELDS_WITH_DASHES else key): value
for key, value in self.items()
} , sort_keys=A__ , allow_unicode=A__ , encoding='''utf-8''' , ).decode('''utf-8''' )
_lowercase = {
'image-classification': [],
'translation': [],
'image-segmentation': [],
'fill-mask': [],
'automatic-speech-recognition': [],
'token-classification': [],
'sentence-similarity': [],
'audio-classification': [],
'question-answering': [],
'summarization': [],
'zero-shot-classification': [],
'table-to-text': [],
'feature-extraction': [],
'other': [],
'multiple-choice': [],
'text-classification': [],
'text-to-image': [],
'text2text-generation': [],
'zero-shot-image-classification': [],
'tabular-classification': [],
'tabular-regression': [],
'image-to-image': [],
'tabular-to-text': [],
'unconditional-image-generation': [],
'text-retrieval': [],
'text-to-speech': [],
'object-detection': [],
'audio-to-audio': [],
'text-generation': [],
'conversational': [],
'table-question-answering': [],
'visual-question-answering': [],
'image-to-text': [],
'reinforcement-learning': [],
'voice-activity-detection': [],
'time-series-forecasting': [],
'document-question-answering': [],
}
if __name__ == "__main__":
from argparse import ArgumentParser
_lowercase = ArgumentParser(usage='Validate the yaml metadata block of a README.md file.')
ap.add_argument('readme_filepath')
_lowercase = ap.parse_args()
_lowercase = Path(args.readme_filepath)
_lowercase = DatasetMetadata.from_readme(readme_filepath)
print(dataset_metadata)
dataset_metadata.to_readme(readme_filepath)
| 44
| 1
|
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_lowercase = {
'configuration_mobilebert': [
'MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP',
'MobileBertConfig',
'MobileBertOnnxConfig',
],
'tokenization_mobilebert': ['MobileBertTokenizer'],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowercase = ['MobileBertTokenizerFast']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowercase = [
'MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
'MobileBertForMaskedLM',
'MobileBertForMultipleChoice',
'MobileBertForNextSentencePrediction',
'MobileBertForPreTraining',
'MobileBertForQuestionAnswering',
'MobileBertForSequenceClassification',
'MobileBertForTokenClassification',
'MobileBertLayer',
'MobileBertModel',
'MobileBertPreTrainedModel',
'load_tf_weights_in_mobilebert',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowercase = [
'TF_MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
'TFMobileBertForMaskedLM',
'TFMobileBertForMultipleChoice',
'TFMobileBertForNextSentencePrediction',
'TFMobileBertForPreTraining',
'TFMobileBertForQuestionAnswering',
'TFMobileBertForSequenceClassification',
'TFMobileBertForTokenClassification',
'TFMobileBertMainLayer',
'TFMobileBertModel',
'TFMobileBertPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_mobilebert import (
MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
MobileBertConfig,
MobileBertOnnxConfig,
)
from .tokenization_mobilebert import MobileBertTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_mobilebert_fast import MobileBertTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mobilebert import (
MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
MobileBertForMaskedLM,
MobileBertForMultipleChoice,
MobileBertForNextSentencePrediction,
MobileBertForPreTraining,
MobileBertForQuestionAnswering,
MobileBertForSequenceClassification,
MobileBertForTokenClassification,
MobileBertLayer,
MobileBertModel,
MobileBertPreTrainedModel,
load_tf_weights_in_mobilebert,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_mobilebert import (
TF_MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
TFMobileBertForMaskedLM,
TFMobileBertForMultipleChoice,
TFMobileBertForNextSentencePrediction,
TFMobileBertForPreTraining,
TFMobileBertForQuestionAnswering,
TFMobileBertForSequenceClassification,
TFMobileBertForTokenClassification,
TFMobileBertMainLayer,
TFMobileBertModel,
TFMobileBertPreTrainedModel,
)
else:
import sys
_lowercase = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 44
|
'''simple docstring'''
import json
import os
import re
import unittest
from transformers import CodeGenTokenizer, CodeGenTokenizerFast
from transformers.models.codegen.tokenization_codegen import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class _lowercase ( __a , unittest.TestCase ):
_UpperCAmelCase = CodeGenTokenizer
_UpperCAmelCase = CodeGenTokenizerFast
_UpperCAmelCase = True
_UpperCAmelCase = {'''add_prefix_space''': True}
_UpperCAmelCase = False
def UpperCamelCase ( self ) -> Tuple:
super().setUp()
# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
snake_case = [
'''l''',
'''o''',
'''w''',
'''e''',
'''r''',
'''s''',
'''t''',
'''i''',
'''d''',
'''n''',
'''\u0120''',
'''\u0120l''',
'''\u0120n''',
'''\u0120lo''',
'''\u0120low''',
'''er''',
'''\u0120lowest''',
'''\u0120newer''',
'''\u0120wider''',
'''<unk>''',
'''<|endoftext|>''',
]
snake_case = dict(zip(A__ , range(len(A__ ) ) ) )
snake_case = ['''#version: 0.2''', '''\u0120 l''', '''\u0120l o''', '''\u0120lo w''', '''e r''', '''''']
snake_case = {'''unk_token''': '''<unk>'''}
snake_case = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['''vocab_file'''] )
snake_case = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['''merges_file'''] )
with open(self.vocab_file , '''w''' , encoding='''utf-8''' ) as fp:
fp.write(json.dumps(A__ ) + '''\n''' )
with open(self.merges_file , '''w''' , encoding='''utf-8''' ) as fp:
fp.write('''\n'''.join(A__ ) )
def UpperCamelCase ( self , **A__ ) -> Union[str, Any]:
kwargs.update(self.special_tokens_map )
return CodeGenTokenizer.from_pretrained(self.tmpdirname , **A__ )
def UpperCamelCase ( self , **A__ ) -> Union[str, Any]:
kwargs.update(self.special_tokens_map )
return CodeGenTokenizerFast.from_pretrained(self.tmpdirname , **A__ )
def UpperCamelCase ( self , A__ ) -> Tuple:
snake_case = '''lower newer'''
snake_case = '''lower newer'''
return input_text, output_text
def UpperCamelCase ( self ) -> List[Any]:
snake_case = CodeGenTokenizer(self.vocab_file , self.merges_file , **self.special_tokens_map )
snake_case = '''lower newer'''
snake_case = ['''\u0120low''', '''er''', '''\u0120''', '''n''', '''e''', '''w''', '''er''']
snake_case = tokenizer.tokenize(A__ , add_prefix_space=A__ )
self.assertListEqual(A__ , A__ )
snake_case = tokens + [tokenizer.unk_token]
snake_case = [14, 15, 10, 9, 3, 2, 15, 19]
self.assertListEqual(tokenizer.convert_tokens_to_ids(A__ ) , A__ )
def UpperCamelCase ( self ) -> Optional[int]:
if not self.test_rust_tokenizer:
return
snake_case = self.get_tokenizer()
snake_case = self.get_rust_tokenizer(add_prefix_space=A__ )
snake_case = '''lower newer'''
# Testing tokenization
snake_case = tokenizer.tokenize(A__ , add_prefix_space=A__ )
snake_case = rust_tokenizer.tokenize(A__ )
self.assertListEqual(A__ , A__ )
# Testing conversion to ids without special tokens
snake_case = tokenizer.encode(A__ , add_special_tokens=A__ , add_prefix_space=A__ )
snake_case = rust_tokenizer.encode(A__ , add_special_tokens=A__ )
self.assertListEqual(A__ , A__ )
# Testing conversion to ids with special tokens
snake_case = self.get_rust_tokenizer(add_prefix_space=A__ )
snake_case = tokenizer.encode(A__ , add_prefix_space=A__ )
snake_case = rust_tokenizer.encode(A__ )
self.assertListEqual(A__ , A__ )
# Testing the unknown token
snake_case = tokens + [rust_tokenizer.unk_token]
snake_case = [14, 15, 10, 9, 3, 2, 15, 19]
self.assertListEqual(rust_tokenizer.convert_tokens_to_ids(A__ ) , A__ )
def UpperCamelCase ( self , *A__ , **A__ ) -> List[str]:
# It's very difficult to mix/test pretokenization with byte-level
# And get both CodeGen and Roberta to work at the same time (mostly an issue of adding a space before the string)
pass
def UpperCamelCase ( self , A__=15 ) -> Tuple:
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})""" ):
snake_case = self.rust_tokenizer_class.from_pretrained(A__ , **A__ )
# Simple input
snake_case = '''This is a simple input'''
snake_case = ['''This is a simple input 1''', '''This is a simple input 2''']
snake_case = ('''This is a simple input''', '''This is a pair''')
snake_case = [
('''This is a simple input 1''', '''This is a simple input 2'''),
('''This is a simple pair 1''', '''This is a simple pair 2'''),
]
# Simple input tests
self.assertRaises(A__ , tokenizer_r.encode , A__ , max_length=A__ , padding='''max_length''' )
# Simple input
self.assertRaises(A__ , tokenizer_r.encode_plus , A__ , max_length=A__ , padding='''max_length''' )
# Simple input
self.assertRaises(
A__ , tokenizer_r.batch_encode_plus , A__ , max_length=A__ , padding='''max_length''' , )
# Pair input
self.assertRaises(A__ , tokenizer_r.encode , A__ , max_length=A__ , padding='''max_length''' )
# Pair input
self.assertRaises(A__ , tokenizer_r.encode_plus , A__ , max_length=A__ , padding='''max_length''' )
# Pair input
self.assertRaises(
A__ , tokenizer_r.batch_encode_plus , A__ , max_length=A__ , padding='''max_length''' , )
def UpperCamelCase ( self ) -> Tuple:
snake_case = CodeGenTokenizer.from_pretrained(self.tmpdirname , pad_token='''<pad>''' )
# Simple input
snake_case = '''This is a simple input'''
snake_case = ['''This is a simple input looooooooong''', '''This is a simple input''']
snake_case = ('''This is a simple input''', '''This is a pair''')
snake_case = [
('''This is a simple input loooooong''', '''This is a simple input'''),
('''This is a simple pair loooooong''', '''This is a simple pair'''),
]
snake_case = tokenizer.pad_token_id
snake_case = tokenizer(A__ , padding='''max_length''' , max_length=30 , return_tensors='''np''' )
snake_case = tokenizer(A__ , padding=A__ , truncate=A__ , return_tensors='''np''' )
snake_case = tokenizer(*A__ , padding='''max_length''' , max_length=60 , return_tensors='''np''' )
snake_case = tokenizer(A__ , padding=A__ , truncate=A__ , return_tensors='''np''' )
# s
# test single string max_length padding
self.assertEqual(out_s['''input_ids'''].shape[-1] , 30 )
self.assertTrue(pad_token_id in out_s['''input_ids'''] )
self.assertTrue(0 in out_s['''attention_mask'''] )
# s2
# test automatic padding
self.assertEqual(out_sa['''input_ids'''].shape[-1] , 33 )
# long slice doesn't have padding
self.assertFalse(pad_token_id in out_sa['''input_ids'''][0] )
self.assertFalse(0 in out_sa['''attention_mask'''][0] )
# short slice does have padding
self.assertTrue(pad_token_id in out_sa['''input_ids'''][1] )
self.assertTrue(0 in out_sa['''attention_mask'''][1] )
# p
# test single pair max_length padding
self.assertEqual(out_p['''input_ids'''].shape[-1] , 60 )
self.assertTrue(pad_token_id in out_p['''input_ids'''] )
self.assertTrue(0 in out_p['''attention_mask'''] )
# p2
# test automatic padding pair
self.assertEqual(out_pa['''input_ids'''].shape[-1] , 52 )
# long slice pair doesn't have padding
self.assertFalse(pad_token_id in out_pa['''input_ids'''][0] )
self.assertFalse(0 in out_pa['''attention_mask'''][0] )
# short slice pair does have padding
self.assertTrue(pad_token_id in out_pa['''input_ids'''][1] )
self.assertTrue(0 in out_pa['''attention_mask'''][1] )
def UpperCamelCase ( self ) -> str:
snake_case = '''$$$'''
snake_case = CodeGenTokenizer.from_pretrained(self.tmpdirname , bos_token=A__ , add_bos_token=A__ )
snake_case = '''This is a simple input'''
snake_case = ['''This is a simple input 1''', '''This is a simple input 2''']
snake_case = tokenizer.bos_token_id
snake_case = tokenizer(A__ )
snake_case = tokenizer(A__ )
self.assertEqual(out_s.input_ids[0] , A__ )
self.assertTrue(all(o[0] == bos_token_id for o in out_sa.input_ids ) )
snake_case = tokenizer.decode(out_s.input_ids )
snake_case = tokenizer.batch_decode(out_sa.input_ids )
self.assertEqual(decode_s.split()[0] , A__ )
self.assertTrue(all(d.split()[0] == bos_token for d in decode_sa ) )
@slow
def UpperCamelCase ( self ) -> Any:
snake_case = CodeGenTokenizer.from_pretrained('''Salesforce/codegen-350M-mono''' )
snake_case = '''\nif len_a > len_b:\n result = a\nelse:\n result = b\n\n\n\n#'''
snake_case = '''\nif len_a > len_b: result = a\nelse: result = b'''
snake_case = tokenizer.encode(A__ )
snake_case = ['''^#''', re.escape('''<|endoftext|>''' ), '''^\'\'\'''', '''^"""''', '''\n\n\n''']
snake_case = tokenizer.decode(A__ , truncate_before_pattern=A__ )
self.assertEqual(A__ , A__ )
def UpperCamelCase ( self ) -> Union[str, Any]:
pass
| 44
| 1
|
'''simple docstring'''
import argparse
import os
import re
_lowercase = 'src/transformers'
# Pattern that looks at the indentation in a line.
_lowercase = re.compile(R'^(\s*)\S')
# Pattern that matches `"key":" and puts `key` in group 0.
_lowercase = re.compile(R'^\s*"([^"]+)":')
# Pattern that matches `_import_structure["key"]` and puts `key` in group 0.
_lowercase = re.compile(R'^\s*_import_structure\["([^"]+)"\]')
# Pattern that matches `"key",` and puts `key` in group 0.
_lowercase = re.compile(R'^\s*"([^"]+)",\s*$')
# Pattern that matches any `[stuff]` and puts `stuff` in group 0.
_lowercase = re.compile(R'\[([^\]]+)\]')
def __UpperCamelCase ( a : Optional[int] ) ->Any:
snake_case = _re_indent.search(a )
return "" if search is None else search.groups()[0]
def __UpperCamelCase ( a : Union[str, Any] , a : Any="" , a : List[str]=None , a : List[Any]=None ) ->Tuple:
snake_case = 0
snake_case = code.split('''\n''' )
if start_prompt is not None:
while not lines[index].startswith(a ):
index += 1
snake_case = ['''\n'''.join(lines[:index] )]
else:
snake_case = []
# We split into blocks until we get to the `end_prompt` (or the end of the block).
snake_case = [lines[index]]
index += 1
while index < len(a ) and (end_prompt is None or not lines[index].startswith(a )):
if len(lines[index] ) > 0 and get_indent(lines[index] ) == indent_level:
if len(a ) > 0 and get_indent(current_block[-1] ).startswith(indent_level + ''' ''' ):
current_block.append(lines[index] )
blocks.append('''\n'''.join(a ) )
if index < len(a ) - 1:
snake_case = [lines[index + 1]]
index += 1
else:
snake_case = []
else:
blocks.append('''\n'''.join(a ) )
snake_case = [lines[index]]
else:
current_block.append(lines[index] )
index += 1
# Adds current block if it's nonempty.
if len(a ) > 0:
blocks.append('''\n'''.join(a ) )
# Add final block after end_prompt if provided.
if end_prompt is not None and index < len(a ):
blocks.append('''\n'''.join(lines[index:] ) )
return blocks
def __UpperCamelCase ( a : Dict ) ->Dict:
def _inner(a : List[Any] ):
return key(a ).lower().replace('''_''' , '''''' )
return _inner
def __UpperCamelCase ( a : Dict , a : List[str]=None ) ->Optional[int]:
# If no key is provided, we use a noop.
def noop(a : Any ):
return x
if key is None:
snake_case = noop
# Constants are all uppercase, they go first.
snake_case = [obj for obj in objects if key(a ).isupper()]
# Classes are not all uppercase but start with a capital, they go second.
snake_case = [obj for obj in objects if key(a )[0].isupper() and not key(a ).isupper()]
# Functions begin with a lowercase, they go last.
snake_case = [obj for obj in objects if not key(a )[0].isupper()]
snake_case = ignore_underscore(a )
return sorted(a , key=a ) + sorted(a , key=a ) + sorted(a , key=a )
def __UpperCamelCase ( a : int ) ->List[str]:
# This inner function sort imports between [ ].
def _replace(a : List[Any] ):
snake_case = match.groups()[0]
if "," not in imports:
return f"""[{imports}]"""
snake_case = [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 = keys[:-1]
return "[" + ", ".join([f"""\"{k}\"""" for k in sort_objects(a )] ) + "]"
snake_case = import_statement.split('''\n''' )
if len(a ) > 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 = 2 if lines[1].strip() == '''[''' else 1
snake_case = [(i, _re_strip_line.search(a ).groups()[0]) for i, line in enumerate(lines[idx:-idx] )]
snake_case = sort_objects(a , key=lambda a : x[1] )
snake_case = [lines[x[0] + idx] for x in sorted_indices]
return "\n".join(lines[:idx] + sorted_lines + lines[-idx:] )
elif len(a ) == 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 = _re_bracket_content.sub(_replace , lines[1] )
else:
snake_case = [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 = keys[:-1]
snake_case = get_indent(lines[1] ) + ''', '''.join([f"""\"{k}\"""" for k in sort_objects(a )] )
return "\n".join(a )
else:
# Finally we have to deal with imports fitting on one line
snake_case = _re_bracket_content.sub(_replace , a )
return import_statement
def __UpperCamelCase ( a : str , a : int=True ) ->Dict:
with open(a , encoding='''utf-8''' ) as f:
snake_case = f.read()
if "_import_structure" not in code:
return
# Blocks of indent level 0
snake_case = split_code_in_indented_blocks(
a , start_prompt='''_import_structure = {''' , end_prompt='''if TYPE_CHECKING:''' )
# We ignore block 0 (everything untils start_prompt) and the last block (everything after end_prompt).
for block_idx in range(1 , len(a ) - 1 ):
# Check if the block contains some `_import_structure`s thingy to sort.
snake_case = main_blocks[block_idx]
snake_case = block.split('''\n''' )
# Get to the start of the imports.
snake_case = 0
while line_idx < len(a ) and "_import_structure" not in block_lines[line_idx]:
# Skip dummy import blocks
if "import dummy" in block_lines[line_idx]:
snake_case = len(a )
else:
line_idx += 1
if line_idx >= len(a ):
continue
# Ignore beginning and last line: they don't contain anything.
snake_case = '''\n'''.join(block_lines[line_idx:-1] )
snake_case = get_indent(block_lines[1] )
# Slit the internal block into blocks of indent level 1.
snake_case = split_code_in_indented_blocks(a , indent_level=a )
# We have two categories of import key: list or _import_structure[key].append/extend
snake_case = _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 = [(pattern.search(a ).groups()[0] if pattern.search(a ) is not None else None) for b in internal_blocks]
# We only sort the lines with a key.
snake_case = [(i, key) for i, key in enumerate(a ) if key is not None]
snake_case = [x[0] for x in sorted(a , key=lambda a : x[1] )]
# We reorder the blocks by leaving empty lines/comments as they were and reorder the rest.
snake_case = 0
snake_case = []
for i in range(len(a ) ):
if keys[i] is None:
reorderded_blocks.append(internal_blocks[i] )
else:
snake_case = sort_objects_in_import(internal_blocks[sorted_indices[count]] )
reorderded_blocks.append(a )
count += 1
# And we put our main block back together with its first and last line.
snake_case = '''\n'''.join(block_lines[:line_idx] + reorderded_blocks + [block_lines[-1]] )
if code != "\n".join(a ):
if check_only:
return True
else:
print(f"""Overwriting {file}.""" )
with open(a , '''w''' , encoding='''utf-8''' ) as f:
f.write('''\n'''.join(a ) )
def __UpperCamelCase ( a : Union[str, Any]=True ) ->int:
snake_case = []
for root, _, files in os.walk(a ):
if "__init__.py" in files:
snake_case = sort_imports(os.path.join(a , '''__init__.py''' ) , check_only=a )
if result:
snake_case = [os.path.join(a , '''__init__.py''' )]
if len(a ) > 0:
raise ValueError(f"""Would overwrite {len(a )} files, run `make style`.""" )
if __name__ == "__main__":
_lowercase = argparse.ArgumentParser()
parser.add_argument('--check_only', action='store_true', help='Whether to only check or fix style.')
_lowercase = parser.parse_args()
sort_imports_in_all_inits(check_only=args.check_only)
| 44
|
'''simple docstring'''
from __future__ import annotations
import inspect
import unittest
from transformers import ViTConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import TFViTForImageClassification, TFViTModel
if is_vision_available():
from PIL import Image
from transformers import ViTImageProcessor
class _lowercase :
def __init__( self , A__ , A__=13 , A__=30 , A__=2 , A__=3 , A__=True , A__=True , A__=32 , A__=2 , A__=4 , A__=37 , A__="gelu" , A__=0.1 , A__=0.1 , A__=10 , A__=0.0_2 , A__=3 , A__=None , ) -> List[Any]:
snake_case = parent
snake_case = batch_size
snake_case = image_size
snake_case = patch_size
snake_case = num_channels
snake_case = is_training
snake_case = use_labels
snake_case = hidden_size
snake_case = num_hidden_layers
snake_case = num_attention_heads
snake_case = intermediate_size
snake_case = hidden_act
snake_case = hidden_dropout_prob
snake_case = attention_probs_dropout_prob
snake_case = type_sequence_label_size
snake_case = initializer_range
snake_case = scope
# in ViT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
snake_case = (image_size // patch_size) ** 2
snake_case = num_patches + 1
def UpperCamelCase ( self ) -> int:
snake_case = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
snake_case = None
if self.use_labels:
snake_case = ids_tensor([self.batch_size] , self.type_sequence_label_size )
snake_case = self.get_config()
return config, pixel_values, labels
def UpperCamelCase ( self ) -> int:
return ViTConfig(
image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , 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 , is_decoder=A__ , initializer_range=self.initializer_range , )
def UpperCamelCase ( self , A__ , A__ , A__ ) -> Union[str, Any]:
snake_case = TFViTModel(config=A__ )
snake_case = model(A__ , training=A__ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
# Test with an image with different size than the one specified in config.
snake_case = self.image_size // 2
snake_case = pixel_values[:, :, :image_size, :image_size]
snake_case = model(A__ , interpolate_pos_encoding=A__ , training=A__ )
snake_case = (image_size // self.patch_size) ** 2 + 1
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, seq_length, self.hidden_size) )
def UpperCamelCase ( self , A__ , A__ , A__ ) -> Optional[int]:
snake_case = self.type_sequence_label_size
snake_case = TFViTForImageClassification(A__ )
snake_case = model(A__ , labels=A__ , training=A__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
# Test with an image with different size than the one specified in config.
snake_case = self.image_size // 2
snake_case = pixel_values[:, :, :image_size, :image_size]
snake_case = model(A__ , interpolate_pos_encoding=A__ , training=A__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
# test greyscale images
snake_case = 1
snake_case = TFViTForImageClassification(A__ )
snake_case = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
snake_case = model(A__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
def UpperCamelCase ( self ) -> Union[str, Any]:
snake_case = self.prepare_config_and_inputs()
snake_case , snake_case , snake_case = config_and_inputs
snake_case = {'''pixel_values''': pixel_values}
return config, inputs_dict
@require_tf
class _lowercase ( __a , __a , unittest.TestCase ):
_UpperCAmelCase = (TFViTModel, TFViTForImageClassification) if is_tf_available() else ()
_UpperCAmelCase = (
{'''feature-extraction''': TFViTModel, '''image-classification''': TFViTForImageClassification}
if is_tf_available()
else {}
)
_UpperCAmelCase = False
_UpperCAmelCase = False
_UpperCAmelCase = False
def UpperCamelCase ( self ) -> List[Any]:
snake_case = TFViTModelTester(self )
snake_case = ConfigTester(self , config_class=A__ , has_text_modality=A__ , hidden_size=37 )
def UpperCamelCase ( self ) -> int:
self.config_tester.run_common_tests()
@unittest.skip(reason='''ViT does not use inputs_embeds''' )
def UpperCamelCase ( self ) -> int:
pass
@unittest.skip(reason='''ViT does not use inputs_embeds''' )
def UpperCamelCase ( self ) -> str:
pass
def UpperCamelCase ( self ) -> Union[str, Any]:
snake_case , snake_case = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
snake_case = model_class(A__ )
self.assertIsInstance(model.get_input_embeddings() , (tf.keras.layers.Layer) )
snake_case = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(A__ , tf.keras.layers.Layer ) )
def UpperCamelCase ( self ) -> List[Any]:
snake_case , snake_case = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
snake_case = model_class(A__ )
snake_case = inspect.signature(model.call )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
snake_case = [*signature.parameters.keys()]
snake_case = ['''pixel_values''']
self.assertListEqual(arg_names[:1] , A__ )
def UpperCamelCase ( self ) -> Union[str, Any]:
snake_case = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*A__ )
def UpperCamelCase ( self ) -> Optional[Any]:
snake_case = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*A__ )
@slow
def UpperCamelCase ( self ) -> Any:
snake_case = TFViTModel.from_pretrained('''google/vit-base-patch16-224''' )
self.assertIsNotNone(A__ )
def __UpperCamelCase ( ) ->Any:
snake_case = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
return image
@require_tf
@require_vision
class _lowercase ( unittest.TestCase ):
@cached_property
def UpperCamelCase ( self ) -> Optional[int]:
return ViTImageProcessor.from_pretrained('''google/vit-base-patch16-224''' ) if is_vision_available() else None
@slow
def UpperCamelCase ( self ) -> Dict:
snake_case = TFViTForImageClassification.from_pretrained('''google/vit-base-patch16-224''' )
snake_case = self.default_image_processor
snake_case = prepare_img()
snake_case = image_processor(images=A__ , return_tensors='''tf''' )
# forward pass
snake_case = model(**A__ )
# verify the logits
snake_case = tf.TensorShape((1, 10_00) )
self.assertEqual(outputs.logits.shape , A__ )
snake_case = tf.constant([-0.2_7_4_4, 0.8_2_1_5, -0.0_8_3_6] )
tf.debugging.assert_near(outputs.logits[0, :3] , A__ , atol=1e-4 )
| 44
| 1
|
'''simple docstring'''
import math
from datetime import datetime, timedelta
def __UpperCamelCase ( a : int ) ->datetime:
snake_case = year % 19
snake_case = year % 4
snake_case = year % 7
snake_case = math.floor(year / 100 )
snake_case = math.floor((13 + 8 * leap_day_inhibits) / 25 )
snake_case = leap_day_inhibits / 4
snake_case = (
15 - lunar_orbit_correction + leap_day_inhibits - leap_day_reinstall_number
) % 30
snake_case = (4 + leap_day_inhibits - leap_day_reinstall_number) % 7
# days to be added to March 21
snake_case = (19 * metonic_cycle + secular_moon_shift) % 30
# PHM -> Paschal Full Moon
snake_case = (
2 * julian_leap_year
+ 4 * non_leap_year
+ 6 * days_to_add
+ century_starting_point
) % 7
if days_to_add == 29 and days_from_phm_to_sunday == 6:
return datetime(a , 4 , 19 )
elif days_to_add == 28 and days_from_phm_to_sunday == 6:
return datetime(a , 4 , 18 )
else:
return datetime(a , 3 , 22 ) + timedelta(
days=int(days_to_add + days_from_phm_to_sunday ) )
if __name__ == "__main__":
for year in (1_994, 2_000, 2_010, 2_021, 2_023):
_lowercase = 'will be' if year > datetime.now().year else 'was'
print(f'Easter in {year} {tense} {gauss_easter(year)}')
| 44
|
'''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
_lowercase = [
{'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 __UpperCamelCase ( a : Dict=True ) ->str:
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=__a ) )
class _lowercase ( __a ):
_UpperCAmelCase = None
_UpperCAmelCase = None
def UpperCamelCase ( self , A__ , A__ ) -> str:
with TemporaryDirectory() as tmp_dir:
snake_case = dataset_module_factory(A__ , cache_dir=A__ )
snake_case = import_main_class(dataset_module.module_path , dataset=A__ )
snake_case = builder_cls(
cache_dir=A__ , config_name=A__ , hash=dataset_module.hash , )
snake_case = '''/'''.join(
[
HF_GCP_BASE_URL,
builder_instance._relative_data_dir(with_hash=A__ ).replace(os.sep , '''/''' ),
config.DATASET_INFO_FILENAME,
] )
snake_case = cached_path(A__ , cache_dir=A__ )
self.assertTrue(os.path.exists(A__ ) )
@pytest.mark.integration
def __UpperCamelCase ( a : List[str] ) ->Any:
snake_case = tmp_path_factory.mktemp('''test_hf_gcp''' ) / '''test_wikipedia_simple'''
snake_case = dataset_module_factory('''wikipedia''' , cache_dir=a )
snake_case = import_main_class(dataset_module.module_path )
snake_case = builder_cls(
cache_dir=a , config_name='''20220301.frr''' , hash=dataset_module.hash , )
# use the HF cloud storage, not the original download_and_prepare that uses apache-beam
snake_case = None
builder_instance.download_and_prepare()
snake_case = builder_instance.as_dataset()
assert ds
@pytest.mark.integration
def __UpperCamelCase ( a : Any ) ->Union[str, Any]:
snake_case = dataset_module_factory('''wikipedia''' , cache_dir=a )
snake_case = import_main_class(dataset_module.module_path , dataset=a )
snake_case = builder_cls(
cache_dir=a , config_name='''20220301.frr''' , hash=dataset_module.hash , )
snake_case = builder_instance.as_streaming_dataset()
assert ds
assert isinstance(a , a )
assert "train" in ds
assert isinstance(ds['''train'''] , a )
assert next(iter(ds['''train'''] ) )
| 44
| 1
|
'''simple docstring'''
from dataclasses import dataclass, field
from typing import Tuple
from ..utils import cached_property, is_tf_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments
if is_tf_available():
import tensorflow as tf
_lowercase = logging.get_logger(__name__)
@dataclass
class _lowercase ( __a ):
_UpperCAmelCase = [
'''no_inference''',
'''no_cuda''',
'''no_tpu''',
'''no_speed''',
'''no_memory''',
'''no_env_print''',
'''no_multi_process''',
]
def __init__( self , **A__ ) -> Optional[int]:
for deprecated_arg in self.deprecated_args:
if deprecated_arg in kwargs:
snake_case = deprecated_arg[3:]
snake_case = not kwargs.pop(A__ )
logger.warning(
F"""{deprecated_arg} is depreciated. Please use --no-{positive_arg} or"""
F""" {positive_arg}={kwargs[positive_arg]}""" )
snake_case = kwargs.pop('''tpu_name''' , self.tpu_name )
snake_case = kwargs.pop('''device_idx''' , self.device_idx )
snake_case = kwargs.pop('''eager_mode''' , self.eager_mode )
snake_case = kwargs.pop('''use_xla''' , self.use_xla )
super().__init__(**A__ )
_UpperCAmelCase = field(
default=__a , metadata={'''help''': '''Name of TPU'''} , )
_UpperCAmelCase = field(
default=0 , metadata={'''help''': '''CPU / GPU device index. Defaults to 0.'''} , )
_UpperCAmelCase = field(default=__a , metadata={'''help''': '''Benchmark models in eager model.'''} )
_UpperCAmelCase = field(
default=__a , metadata={
'''help''': '''Benchmark models using XLA JIT compilation. Note that `eager_model` has to be set to `False`.'''
} , )
@cached_property
def UpperCamelCase ( self ) -> Tuple["tf.distribute.cluster_resolver.TPUClusterResolver"]:
requires_backends(self , ['''tf'''] )
snake_case = None
if self.tpu:
try:
if self.tpu_name:
snake_case = tf.distribute.cluster_resolver.TPUClusterResolver(self.tpu_name )
else:
snake_case = tf.distribute.cluster_resolver.TPUClusterResolver()
except ValueError:
snake_case = None
return tpu
@cached_property
def UpperCamelCase ( self ) -> Tuple["tf.distribute.Strategy", "tf.distribute.cluster_resolver.TPUClusterResolver"]:
requires_backends(self , ['''tf'''] )
if self.is_tpu:
tf.config.experimental_connect_to_cluster(self._setup_tpu )
tf.tpu.experimental.initialize_tpu_system(self._setup_tpu )
snake_case = tf.distribute.TPUStrategy(self._setup_tpu )
else:
# currently no multi gpu is allowed
if self.is_gpu:
# TODO: Currently only single GPU is supported
tf.config.set_visible_devices(self.gpu_list[self.device_idx] , '''GPU''' )
snake_case = tf.distribute.OneDeviceStrategy(device=F"""/gpu:{self.device_idx}""" )
else:
tf.config.set_visible_devices([] , '''GPU''' ) # disable GPU
snake_case = tf.distribute.OneDeviceStrategy(device=F"""/cpu:{self.device_idx}""" )
return strategy
@property
def UpperCamelCase ( self ) -> bool:
requires_backends(self , ['''tf'''] )
return self._setup_tpu is not None
@property
def UpperCamelCase ( self ) -> "tf.distribute.Strategy":
requires_backends(self , ['''tf'''] )
return self._setup_strategy
@property
def UpperCamelCase ( self ) -> Optional[Any]:
requires_backends(self , ['''tf'''] )
return tf.config.list_physical_devices('''GPU''' )
@property
def UpperCamelCase ( self ) -> int:
requires_backends(self , ['''tf'''] )
if self.cuda:
return len(self.gpu_list )
return 0
@property
def UpperCamelCase ( self ) -> bool:
return self.n_gpu > 0
| 44
|
'''simple docstring'''
def __UpperCamelCase ( a : int , a : int ) ->int:
while b:
snake_case , snake_case = b, a % b
return a
def __UpperCamelCase ( a : int , a : int ) ->int:
return a if b == 0 else euclidean_gcd_recursive(a , a % b )
def __UpperCamelCase ( ) ->Optional[Any]:
print(f"""euclidean_gcd(3, 5) = {euclidean_gcd(3 , 5 )}""" )
print(f"""euclidean_gcd(5, 3) = {euclidean_gcd(5 , 3 )}""" )
print(f"""euclidean_gcd(1, 3) = {euclidean_gcd(1 , 3 )}""" )
print(f"""euclidean_gcd(3, 6) = {euclidean_gcd(3 , 6 )}""" )
print(f"""euclidean_gcd(6, 3) = {euclidean_gcd(6 , 3 )}""" )
print(f"""euclidean_gcd_recursive(3, 5) = {euclidean_gcd_recursive(3 , 5 )}""" )
print(f"""euclidean_gcd_recursive(5, 3) = {euclidean_gcd_recursive(5 , 3 )}""" )
print(f"""euclidean_gcd_recursive(1, 3) = {euclidean_gcd_recursive(1 , 3 )}""" )
print(f"""euclidean_gcd_recursive(3, 6) = {euclidean_gcd_recursive(3 , 6 )}""" )
print(f"""euclidean_gcd_recursive(6, 3) = {euclidean_gcd_recursive(6 , 3 )}""" )
if __name__ == "__main__":
main()
| 44
| 1
|
'''simple docstring'''
import argparse
import json
from pathlib import Path
import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import DeiTImageProcessor, ViTConfig, ViTForImageClassification, ViTImageProcessor, ViTModel
from transformers.utils import logging
logging.set_verbosity_info()
_lowercase = logging.get_logger(__name__)
def __UpperCamelCase ( a : int , a : Tuple=False ) ->str:
snake_case = []
for i in range(config.num_hidden_layers ):
# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
rename_keys.append((f"""blocks.{i}.norm1.weight""", f"""vit.encoder.layer.{i}.layernorm_before.weight""") )
rename_keys.append((f"""blocks.{i}.norm1.bias""", f"""vit.encoder.layer.{i}.layernorm_before.bias""") )
rename_keys.append((f"""blocks.{i}.attn.proj.weight""", f"""vit.encoder.layer.{i}.attention.output.dense.weight""") )
rename_keys.append((f"""blocks.{i}.attn.proj.bias""", f"""vit.encoder.layer.{i}.attention.output.dense.bias""") )
rename_keys.append((f"""blocks.{i}.norm2.weight""", f"""vit.encoder.layer.{i}.layernorm_after.weight""") )
rename_keys.append((f"""blocks.{i}.norm2.bias""", f"""vit.encoder.layer.{i}.layernorm_after.bias""") )
rename_keys.append((f"""blocks.{i}.mlp.fc1.weight""", f"""vit.encoder.layer.{i}.intermediate.dense.weight""") )
rename_keys.append((f"""blocks.{i}.mlp.fc1.bias""", f"""vit.encoder.layer.{i}.intermediate.dense.bias""") )
rename_keys.append((f"""blocks.{i}.mlp.fc2.weight""", f"""vit.encoder.layer.{i}.output.dense.weight""") )
rename_keys.append((f"""blocks.{i}.mlp.fc2.bias""", f"""vit.encoder.layer.{i}.output.dense.bias""") )
# projection layer + position embeddings
rename_keys.extend(
[
('''cls_token''', '''vit.embeddings.cls_token'''),
('''patch_embed.proj.weight''', '''vit.embeddings.patch_embeddings.projection.weight'''),
('''patch_embed.proj.bias''', '''vit.embeddings.patch_embeddings.projection.bias'''),
('''pos_embed''', '''vit.embeddings.position_embeddings'''),
] )
if base_model:
# layernorm + pooler
rename_keys.extend(
[
('''norm.weight''', '''layernorm.weight'''),
('''norm.bias''', '''layernorm.bias'''),
('''pre_logits.fc.weight''', '''pooler.dense.weight'''),
('''pre_logits.fc.bias''', '''pooler.dense.bias'''),
] )
# if just the base model, we should remove "vit" from all keys that start with "vit"
snake_case = [(pair[0], pair[1][4:]) if pair[1].startswith('''vit''' ) else pair for pair in rename_keys]
else:
# layernorm + classification head
rename_keys.extend(
[
('''norm.weight''', '''vit.layernorm.weight'''),
('''norm.bias''', '''vit.layernorm.bias'''),
('''head.weight''', '''classifier.weight'''),
('''head.bias''', '''classifier.bias'''),
] )
return rename_keys
def __UpperCamelCase ( a : str , a : Any , a : Optional[Any]=False ) ->int:
for i in range(config.num_hidden_layers ):
if base_model:
snake_case = ''''''
else:
snake_case = '''vit.'''
# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
snake_case = state_dict.pop(f"""blocks.{i}.attn.qkv.weight""" )
snake_case = state_dict.pop(f"""blocks.{i}.attn.qkv.bias""" )
# next, add query, keys and values (in that order) to the state dict
snake_case = in_proj_weight[
: config.hidden_size, :
]
snake_case = in_proj_bias[: config.hidden_size]
snake_case = in_proj_weight[
config.hidden_size : config.hidden_size * 2, :
]
snake_case = in_proj_bias[
config.hidden_size : config.hidden_size * 2
]
snake_case = in_proj_weight[
-config.hidden_size :, :
]
snake_case = in_proj_bias[-config.hidden_size :]
def __UpperCamelCase ( a : Union[str, Any] ) ->List[str]:
snake_case = ['''head.weight''', '''head.bias''']
for k in ignore_keys:
state_dict.pop(a , a )
def __UpperCamelCase ( a : Optional[Any] , a : Tuple , a : Optional[Any] ) ->Optional[int]:
snake_case = dct.pop(a )
snake_case = val
def __UpperCamelCase ( ) ->List[str]:
snake_case = '''http://images.cocodataset.org/val2017/000000039769.jpg'''
snake_case = Image.open(requests.get(a , stream=a ).raw )
return im
@torch.no_grad()
def __UpperCamelCase ( a : Any , a : Any ) ->Dict:
snake_case = ViTConfig()
snake_case = False
# dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
if vit_name[-5:] == "in21k":
snake_case = True
snake_case = int(vit_name[-12:-10] )
snake_case = int(vit_name[-9:-6] )
else:
snake_case = 1000
snake_case = '''huggingface/label-files'''
snake_case = '''imagenet-1k-id2label.json'''
snake_case = json.load(open(hf_hub_download(a , a , repo_type='''dataset''' ) , '''r''' ) )
snake_case = {int(a ): v for k, v in idalabel.items()}
snake_case = idalabel
snake_case = {v: k for k, v in idalabel.items()}
snake_case = int(vit_name[-6:-4] )
snake_case = int(vit_name[-3:] )
# size of the architecture
if "deit" in vit_name:
if vit_name[9:].startswith('''tiny''' ):
snake_case = 192
snake_case = 768
snake_case = 12
snake_case = 3
elif vit_name[9:].startswith('''small''' ):
snake_case = 384
snake_case = 1536
snake_case = 12
snake_case = 6
else:
pass
else:
if vit_name[4:].startswith('''small''' ):
snake_case = 768
snake_case = 2304
snake_case = 8
snake_case = 8
elif vit_name[4:].startswith('''base''' ):
pass
elif vit_name[4:].startswith('''large''' ):
snake_case = 1024
snake_case = 4096
snake_case = 24
snake_case = 16
elif vit_name[4:].startswith('''huge''' ):
snake_case = 1280
snake_case = 5120
snake_case = 32
snake_case = 16
# load original model from timm
snake_case = timm.create_model(a , pretrained=a )
timm_model.eval()
# load state_dict of original model, remove and rename some keys
snake_case = timm_model.state_dict()
if base_model:
remove_classification_head_(a )
snake_case = create_rename_keys(a , a )
for src, dest in rename_keys:
rename_key(a , a , a )
read_in_q_k_v(a , a , a )
# load HuggingFace model
if vit_name[-5:] == "in21k":
snake_case = ViTModel(a ).eval()
else:
snake_case = ViTForImageClassification(a ).eval()
model.load_state_dict(a )
# Check outputs on an image, prepared by ViTImageProcessor/DeiTImageProcessor
if "deit" in vit_name:
snake_case = DeiTImageProcessor(size=config.image_size )
else:
snake_case = ViTImageProcessor(size=config.image_size )
snake_case = image_processor(images=prepare_img() , return_tensors='''pt''' )
snake_case = encoding['''pixel_values''']
snake_case = model(a )
if base_model:
snake_case = timm_model.forward_features(a )
assert timm_pooled_output.shape == outputs.pooler_output.shape
assert torch.allclose(a , outputs.pooler_output , atol=1e-3 )
else:
snake_case = timm_model(a )
assert timm_logits.shape == outputs.logits.shape
assert torch.allclose(a , outputs.logits , atol=1e-3 )
Path(a ).mkdir(exist_ok=a )
print(f"""Saving model {vit_name} to {pytorch_dump_folder_path}""" )
model.save_pretrained(a )
print(f"""Saving image processor to {pytorch_dump_folder_path}""" )
image_processor.save_pretrained(a )
if __name__ == "__main__":
_lowercase = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--vit_name',
default='vit_base_patch16_224',
type=str,
help='Name of the ViT timm model you\'d like to convert.',
)
parser.add_argument(
'--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
)
_lowercase = parser.parse_args()
convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path)
| 44
|
'''simple docstring'''
import argparse
import copy
def __UpperCamelCase ( a : Union[str, Any] ) ->Tuple:
snake_case = {}
with open(a ) as f:
for line in f:
if line.split()[0] not in dict_of_neighbours:
snake_case = []
_list.append([line.split()[1], line.split()[2]] )
snake_case = _list
else:
dict_of_neighbours[line.split()[0]].append(
[line.split()[1], line.split()[2]] )
if line.split()[1] not in dict_of_neighbours:
snake_case = []
_list.append([line.split()[0], line.split()[2]] )
snake_case = _list
else:
dict_of_neighbours[line.split()[1]].append(
[line.split()[0], line.split()[2]] )
return dict_of_neighbours
def __UpperCamelCase ( a : Dict , a : Tuple ) ->int:
with open(a ) as f:
snake_case = f.read(1 )
snake_case = start_node
snake_case = []
snake_case = start_node
snake_case = 0
while visiting not in first_solution:
snake_case = 1_0000
for k in dict_of_neighbours[visiting]:
if int(k[1] ) < int(a ) and k[0] not in first_solution:
snake_case = k[1]
snake_case = k[0]
first_solution.append(a )
snake_case = distance_of_first_solution + int(a )
snake_case = best_node
first_solution.append(a )
snake_case = 0
for k in dict_of_neighbours[first_solution[-2]]:
if k[0] == start_node:
break
position += 1
snake_case = (
distance_of_first_solution
+ int(dict_of_neighbours[first_solution[-2]][position][1] )
- 1_0000
)
return first_solution, distance_of_first_solution
def __UpperCamelCase ( a : Optional[int] , a : str ) ->str:
snake_case = []
for n in solution[1:-1]:
snake_case = solution.index(a )
for kn in solution[1:-1]:
snake_case = solution.index(a )
if n == kn:
continue
snake_case = copy.deepcopy(a )
snake_case = kn
snake_case = n
snake_case = 0
for k in _tmp[:-1]:
snake_case = _tmp[_tmp.index(a ) + 1]
for i in dict_of_neighbours[k]:
if i[0] == next_node:
snake_case = distance + int(i[1] )
_tmp.append(a )
if _tmp not in neighborhood_of_solution:
neighborhood_of_solution.append(_tmp )
snake_case = len(neighborhood_of_solution[0] ) - 1
neighborhood_of_solution.sort(key=lambda a : x[index_of_last_item_in_the_list] )
return neighborhood_of_solution
def __UpperCamelCase ( a : Any , a : Optional[Any] , a : int , a : Optional[int] , a : Union[str, Any] ) ->List[Any]:
snake_case = 1
snake_case = first_solution
snake_case = []
snake_case = distance_of_first_solution
snake_case = solution
while count <= iters:
snake_case = find_neighborhood(a , a )
snake_case = 0
snake_case = neighborhood[index_of_best_solution]
snake_case = len(a ) - 1
snake_case = False
while not found:
snake_case = 0
while i < len(a ):
if best_solution[i] != solution[i]:
snake_case = best_solution[i]
snake_case = solution[i]
break
snake_case = i + 1
if [first_exchange_node, second_exchange_node] not in tabu_list and [
second_exchange_node,
first_exchange_node,
] not in tabu_list:
tabu_list.append([first_exchange_node, second_exchange_node] )
snake_case = True
snake_case = best_solution[:-1]
snake_case = neighborhood[index_of_best_solution][best_cost_index]
if cost < best_cost:
snake_case = cost
snake_case = solution
else:
snake_case = index_of_best_solution + 1
snake_case = neighborhood[index_of_best_solution]
if len(a ) >= size:
tabu_list.pop(0 )
snake_case = count + 1
return best_solution_ever, best_cost
def __UpperCamelCase ( a : Union[str, Any]=None ) ->Optional[Any]:
snake_case = generate_neighbours(args.File )
snake_case , snake_case = generate_first_solution(
args.File , a )
snake_case , snake_case = tabu_search(
a , a , a , args.Iterations , args.Size , )
print(f"""Best solution: {best_sol}, with total distance: {best_cost}.""" )
if __name__ == "__main__":
_lowercase = argparse.ArgumentParser(description='Tabu Search')
parser.add_argument(
'-f',
'--File',
type=str,
help='Path to the file containing the data',
required=True,
)
parser.add_argument(
'-i',
'--Iterations',
type=int,
help='How many iterations the algorithm should perform',
required=True,
)
parser.add_argument(
'-s', '--Size', type=int, help='Size of the tabu list', required=True
)
# Pass the arguments to main method
main(parser.parse_args())
| 44
| 1
|
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowercase = logging.get_logger(__name__)
_lowercase = {
'studio-ousia/luke-base': 'https://huggingface.co/studio-ousia/luke-base/resolve/main/config.json',
'studio-ousia/luke-large': 'https://huggingface.co/studio-ousia/luke-large/resolve/main/config.json',
}
class _lowercase ( __a ):
_UpperCAmelCase = '''luke'''
def __init__( self , A__=5_02_67 , A__=50_00_00 , A__=7_68 , A__=2_56 , A__=12 , A__=12 , A__=30_72 , A__="gelu" , A__=0.1 , A__=0.1 , A__=5_12 , A__=2 , A__=0.0_2 , A__=1e-12 , A__=True , A__=None , A__=1 , A__=0 , A__=2 , **A__ , ) -> List[Any]:
super().__init__(pad_token_id=A__ , bos_token_id=A__ , eos_token_id=A__ , **A__ )
snake_case = vocab_size
snake_case = entity_vocab_size
snake_case = hidden_size
snake_case = entity_emb_size
snake_case = num_hidden_layers
snake_case = num_attention_heads
snake_case = hidden_act
snake_case = intermediate_size
snake_case = hidden_dropout_prob
snake_case = attention_probs_dropout_prob
snake_case = max_position_embeddings
snake_case = type_vocab_size
snake_case = initializer_range
snake_case = layer_norm_eps
snake_case = use_entity_aware_attention
snake_case = classifier_dropout
| 44
|
'''simple docstring'''
from ...utils import is_note_seq_available, is_transformers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_torch_and_transformers_objects import * # noqa F403
else:
from .notes_encoder import SpectrogramNotesEncoder
from .continous_encoder import SpectrogramContEncoder
from .pipeline_spectrogram_diffusion import (
SpectrogramContEncoder,
SpectrogramDiffusionPipeline,
TaFilmDecoder,
)
try:
if not (is_transformers_available() and is_torch_available() and is_note_seq_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_transformers_and_torch_and_note_seq_objects import * # noqa F403
else:
from .midi_utils import MidiProcessor
| 44
| 1
|
'''simple docstring'''
import gc
import tempfile
import unittest
import numpy as np
import torch
from diffusers import VersatileDiffusionPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device
_lowercase = False
class _lowercase ( unittest.TestCase ):
pass
@nightly
@require_torch_gpu
class _lowercase ( unittest.TestCase ):
def UpperCamelCase ( self ) -> Union[str, Any]:
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def UpperCamelCase ( self ) -> str:
snake_case = VersatileDiffusionPipeline.from_pretrained('''shi-labs/versatile-diffusion''' , torch_dtype=torch.floataa )
pipe.to(A__ )
pipe.set_progress_bar_config(disable=A__ )
snake_case = load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg''' )
snake_case = torch.manual_seed(0 )
snake_case = pipe.dual_guided(
prompt='''first prompt''' , image=A__ , text_to_image_strength=0.7_5 , generator=A__ , guidance_scale=7.5 , num_inference_steps=2 , output_type='''numpy''' , ).images
with tempfile.TemporaryDirectory() as tmpdirname:
pipe.save_pretrained(A__ )
snake_case = VersatileDiffusionPipeline.from_pretrained(A__ , torch_dtype=torch.floataa )
pipe.to(A__ )
pipe.set_progress_bar_config(disable=A__ )
snake_case = generator.manual_seed(0 )
snake_case = pipe.dual_guided(
prompt='''first prompt''' , image=A__ , text_to_image_strength=0.7_5 , generator=A__ , 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 ) -> Optional[int]:
snake_case = VersatileDiffusionPipeline.from_pretrained('''shi-labs/versatile-diffusion''' , torch_dtype=torch.floataa )
pipe.to(A__ )
pipe.set_progress_bar_config(disable=A__ )
snake_case = '''cyberpunk 2077'''
snake_case = load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg''' )
snake_case = torch.manual_seed(0 )
snake_case = pipe.dual_guided(
prompt=A__ , image=A__ , text_to_image_strength=0.7_5 , generator=A__ , guidance_scale=7.5 , num_inference_steps=50 , output_type='''numpy''' , ).images
snake_case = image[0, 2_53:2_56, 2_53:2_56, -1]
assert image.shape == (1, 5_12, 5_12, 3)
snake_case = np.array([0.1_4_4_8, 0.1_6_1_9, 0.1_7_4_1, 0.1_0_8_6, 0.1_1_4_7, 0.1_1_2_8, 0.1_1_9_9, 0.1_1_6_5, 0.1_0_0_1] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-1
snake_case = '''A painting of a squirrel eating a burger '''
snake_case = torch.manual_seed(0 )
snake_case = pipe.text_to_image(
prompt=A__ , generator=A__ , guidance_scale=7.5 , num_inference_steps=50 , output_type='''numpy''' ).images
snake_case = image[0, 2_53:2_56, 2_53:2_56, -1]
assert image.shape == (1, 5_12, 5_12, 3)
snake_case = np.array([0.3_3_6_7, 0.3_1_6_9, 0.2_6_5_6, 0.3_8_7_0, 0.4_7_9_0, 0.3_7_9_6, 0.4_0_0_9, 0.4_8_7_8, 0.4_7_7_8] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-1
snake_case = pipe.image_variation(A__ , generator=A__ , output_type='''numpy''' ).images
snake_case = image[0, 2_53:2_56, 2_53:2_56, -1]
assert image.shape == (1, 5_12, 5_12, 3)
snake_case = np.array([0.3_0_7_6, 0.3_1_2_3, 0.3_2_8_4, 0.3_7_8_2, 0.3_7_7_0, 0.3_8_9_4, 0.4_2_9_7, 0.4_3_3_1, 0.4_4_5_6] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-1
| 44
|
'''simple docstring'''
from ...processing_utils import ProcessorMixin
class _lowercase ( __a ):
_UpperCAmelCase = '''WhisperFeatureExtractor'''
_UpperCAmelCase = '''WhisperTokenizer'''
def __init__( self , A__ , A__ ) -> Optional[Any]:
super().__init__(A__ , A__ )
snake_case = self.feature_extractor
snake_case = False
def UpperCamelCase ( self , A__=None , A__=None , A__=True ) -> Union[str, Any]:
return self.tokenizer.get_decoder_prompt_ids(task=A__ , language=A__ , no_timestamps=A__ )
def __call__( self , *A__ , **A__ ) -> Dict:
# For backward compatibility
if self._in_target_context_manager:
return self.current_processor(*A__ , **A__ )
snake_case = kwargs.pop('''audio''' , A__ )
snake_case = kwargs.pop('''sampling_rate''' , A__ )
snake_case = kwargs.pop('''text''' , A__ )
if len(A__ ) > 0:
snake_case = args[0]
snake_case = args[1:]
if audio is None and text is None:
raise ValueError('''You need to specify either an `audio` or `text` input to process.''' )
if audio is not None:
snake_case = self.feature_extractor(A__ , *A__ , sampling_rate=A__ , **A__ )
if text is not None:
snake_case = self.tokenizer(A__ , **A__ )
if text is None:
return inputs
elif audio is None:
return encodings
else:
snake_case = encodings['''input_ids''']
return inputs
def UpperCamelCase ( self , *A__ , **A__ ) -> Optional[Any]:
return self.tokenizer.batch_decode(*A__ , **A__ )
def UpperCamelCase ( self , *A__ , **A__ ) -> str:
return self.tokenizer.decode(*A__ , **A__ )
def UpperCamelCase ( self , A__ , A__="np" ) -> Optional[Any]:
return self.tokenizer.get_prompt_ids(A__ , return_tensors=A__ )
| 44
| 1
|
'''simple docstring'''
def __UpperCamelCase ( a : int ) ->"list[int]":
if upper_limit < 0:
raise ValueError('''Limit for the Catalan sequence must be ≥ 0''' )
snake_case = [0] * (upper_limit + 1)
# Base case: C(0) = C(1) = 1
snake_case = 1
if upper_limit > 0:
snake_case = 1
# Recurrence relation: C(i) = sum(C(j).C(i-j-1)), from j = 0 to i
for i in range(2 , upper_limit + 1 ):
for j in range(a ):
catalan_list[i] += catalan_list[j] * catalan_list[i - j - 1]
return catalan_list
if __name__ == "__main__":
print('\n********* Catalan Numbers Using Dynamic Programming ************\n')
print('\n*** Enter -1 at any time to quit ***')
print('\nEnter the upper limit (≥ 0) for the Catalan number sequence: ', end='')
try:
while True:
_lowercase = int(input().strip())
if N < 0:
print('\n********* Goodbye!! ************')
break
else:
print(f'The Catalan numbers from 0 through {N} are:')
print(catalan_numbers(N))
print('Try another upper limit for the sequence: ', end='')
except (NameError, ValueError):
print('\n********* Invalid input, goodbye! ************\n')
import doctest
doctest.testmod()
| 44
|
'''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 _lowercase ( __a ):
_UpperCAmelCase = '''char'''
_UpperCAmelCase = '''bpe'''
_UpperCAmelCase = '''wp'''
_lowercase = (DecodeType.CHARACTER, DecodeType.BPE, DecodeType.WORDPIECE)
class _lowercase ( __a ):
_UpperCAmelCase = ['''image_processor''', '''char_tokenizer''']
_UpperCAmelCase = '''ViTImageProcessor'''
_UpperCAmelCase = '''MgpstrTokenizer'''
def __init__( self , A__=None , A__=None , **A__ ) -> List[Any]:
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.''' , A__ , )
snake_case = kwargs.pop('''feature_extractor''' )
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`.''' )
snake_case = tokenizer
snake_case = AutoTokenizer.from_pretrained('''gpt2''' )
snake_case = AutoTokenizer.from_pretrained('''bert-base-uncased''' )
super().__init__(A__ , A__ )
def __call__( self , A__=None , A__=None , A__=None , **A__ ) -> List[str]:
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:
snake_case = self.image_processor(A__ , return_tensors=A__ , **A__ )
if text is not None:
snake_case = self.char_tokenizer(A__ , return_tensors=A__ , **A__ )
if text is None:
return inputs
elif images is None:
return encodings
else:
snake_case = encodings['''input_ids''']
return inputs
def UpperCamelCase ( self , A__ ) -> Dict:
snake_case , snake_case , snake_case = sequences
snake_case = char_preds.size(0 )
snake_case , snake_case = self._decode_helper(A__ , '''char''' )
snake_case , snake_case = self._decode_helper(A__ , '''bpe''' )
snake_case , snake_case = self._decode_helper(A__ , '''wp''' )
snake_case = []
snake_case = []
for i in range(A__ ):
snake_case = [char_scores[i], bpe_scores[i], wp_scores[i]]
snake_case = [char_strs[i], bpe_strs[i], wp_strs[i]]
snake_case = scores.index(max(A__ ) )
final_strs.append(strs[max_score_index] )
final_scores.append(scores[max_score_index] )
snake_case = {}
snake_case = final_strs
snake_case = final_scores
snake_case = char_strs
snake_case = bpe_strs
snake_case = wp_strs
return out
def UpperCamelCase ( self , A__ , A__ ) -> Optional[Any]:
if format == DecodeType.CHARACTER:
snake_case = self.char_decode
snake_case = 1
snake_case = '''[s]'''
elif format == DecodeType.BPE:
snake_case = self.bpe_decode
snake_case = 2
snake_case = '''#'''
elif format == DecodeType.WORDPIECE:
snake_case = self.wp_decode
snake_case = 1_02
snake_case = '''[SEP]'''
else:
raise ValueError(F"""Format {format} is not supported.""" )
snake_case , snake_case = [], []
snake_case = pred_logits.size(0 )
snake_case = pred_logits.size(1 )
snake_case , snake_case = pred_logits.topk(1 , dim=-1 , largest=A__ , sorted=A__ )
snake_case = preds_index.view(-1 , A__ )[:, 1:]
snake_case = decoder(A__ )
snake_case , snake_case = torch.nn.functional.softmax(A__ , dim=2 ).max(dim=2 )
snake_case = preds_max_prob[:, 1:]
for index in range(A__ ):
snake_case = preds_str[index].find(A__ )
snake_case = preds_str[index][:pred_eos]
snake_case = preds_index[index].cpu().tolist()
snake_case = pred_index.index(A__ ) if eos_token in pred_index else -1
snake_case = preds_max_prob[index][: pred_eos_index + 1]
snake_case = pred_max_prob.cumprod(dim=0 )[-1] if pred_max_prob.nelement() != 0 else 0.0
dec_strs.append(A__ )
conf_scores.append(A__ )
return dec_strs, conf_scores
def UpperCamelCase ( self , A__ ) -> int:
snake_case = [seq.replace(''' ''' , '''''' ) for seq in self.char_tokenizer.batch_decode(A__ )]
return decode_strs
def UpperCamelCase ( self , A__ ) -> List[str]:
return self.bpe_tokenizer.batch_decode(A__ )
def UpperCamelCase ( self , A__ ) -> Union[str, Any]:
snake_case = [seq.replace(''' ''' , '''''' ) for seq in self.wp_tokenizer.batch_decode(A__ )]
return decode_strs
| 44
| 1
|
'''simple docstring'''
def __UpperCamelCase ( a : Tuple , a : str , a : List[Any]=False ) ->List[Any]:
if isinstance(a , a ) and isinstance(a , a ):
snake_case = len(set_a.intersection(a ) )
if alternative_union:
snake_case = len(a ) + len(a )
else:
snake_case = len(set_a.union(a ) )
return intersection / union
if isinstance(a , (list, tuple) ) and isinstance(a , (list, tuple) ):
snake_case = [element for element in set_a if element in set_b]
if alternative_union:
snake_case = len(a ) + len(a )
return len(a ) / union
else:
snake_case = set_a + [element for element in set_b if element not in set_a]
return len(a ) / len(a )
return len(a ) / len(a )
return None
if __name__ == "__main__":
_lowercase = {'a', 'b', 'c', 'd', 'e'}
_lowercase = {'c', 'd', 'e', 'f', 'h', 'i'}
print(jaccard_similarity(set_a, set_b))
| 44
|
'''simple docstring'''
import os
from dataclasses import dataclass, field
from io import BytesIO
from typing import TYPE_CHECKING, Any, ClassVar, Dict, Optional, Union
import numpy as np
import pyarrow as pa
from .. import config
from ..download.streaming_download_manager import xopen, xsplitext
from ..table import array_cast
from ..utils.py_utils import no_op_if_value_is_null, string_to_dict
if TYPE_CHECKING:
from .features import FeatureType
_lowercase , _lowercase , _lowercase = False, False, False
@dataclass
class _lowercase :
_UpperCAmelCase = None
_UpperCAmelCase = True
_UpperCAmelCase = True
_UpperCAmelCase = None
# Automatically constructed
_UpperCAmelCase = "dict"
_UpperCAmelCase = pa.struct({'''bytes''': pa.binary(), '''path''': pa.string()} )
_UpperCAmelCase = field(default='''Audio''' , init=__a , repr=__a )
def __call__( self ) -> Optional[Any]:
return self.pa_type
def UpperCamelCase ( self , A__ ) -> dict:
try:
import soundfile as sf # soundfile is a dependency of librosa, needed to decode audio files.
except ImportError as err:
raise ImportError('''To support encoding audio data, please install \'soundfile\'.''' ) from err
if isinstance(A__ , A__ ):
return {"bytes": None, "path": value}
elif isinstance(A__ , A__ ):
return {"bytes": value, "path": None}
elif "array" in value:
# convert the audio array to wav bytes
snake_case = BytesIO()
sf.write(A__ , value['''array'''] , value['''sampling_rate'''] , format='''wav''' )
return {"bytes": buffer.getvalue(), "path": None}
elif value.get('''path''' ) is not None and os.path.isfile(value['''path'''] ):
# we set "bytes": None to not duplicate the data if they're already available locally
if value["path"].endswith('''pcm''' ):
# "PCM" only has raw audio bytes
if value.get('''sampling_rate''' ) is None:
# At least, If you want to convert "PCM-byte" to "WAV-byte", you have to know sampling rate
raise KeyError('''To use PCM files, please specify a \'sampling_rate\' in Audio object''' )
if value.get('''bytes''' ):
# If we already had PCM-byte, we don`t have to make "read file, make bytes" (just use it!)
snake_case = np.frombuffer(value['''bytes'''] , dtype=np.intaa ).astype(np.floataa ) / 3_27_67
else:
snake_case = np.memmap(value['''path'''] , dtype='''h''' , mode='''r''' ).astype(np.floataa ) / 3_27_67
snake_case = BytesIO(bytes() )
sf.write(A__ , A__ , value['''sampling_rate'''] , format='''wav''' )
return {"bytes": buffer.getvalue(), "path": None}
else:
return {"bytes": None, "path": value.get('''path''' )}
elif value.get('''bytes''' ) is not None or value.get('''path''' ) is not None:
# store the audio bytes, and path is used to infer the audio format using the file extension
return {"bytes": value.get('''bytes''' ), "path": value.get('''path''' )}
else:
raise ValueError(
F"""An audio sample should have one of 'path' or 'bytes' but they are missing or None in {value}.""" )
def UpperCamelCase ( self , A__ , A__ = None ) -> dict:
if not self.decode:
raise RuntimeError('''Decoding is disabled for this feature. Please use Audio(decode=True) instead.''' )
snake_case , snake_case = (value['''path'''], BytesIO(value['''bytes'''] )) if value['''bytes'''] is not None else (value['''path'''], None)
if path is None and file is None:
raise ValueError(F"""An audio sample should have one of 'path' or 'bytes' but both are None in {value}.""" )
try:
import librosa
import soundfile as sf
except ImportError as err:
raise ImportError('''To support decoding audio files, please install \'librosa\' and \'soundfile\'.''' ) from err
snake_case = xsplitext(A__ )[1][1:].lower() if path is not None else None
if not config.IS_OPUS_SUPPORTED and audio_format == "opus":
raise RuntimeError(
'''Decoding \'opus\' files requires system library \'libsndfile\'>=1.0.31, '''
'''You can try to update `soundfile` python library: `pip install "soundfile>=0.12.1"`. ''' )
elif not config.IS_MP3_SUPPORTED and audio_format == "mp3":
raise RuntimeError(
'''Decoding \'mp3\' files requires system library \'libsndfile\'>=1.1.0, '''
'''You can try to update `soundfile` python library: `pip install "soundfile>=0.12.1"`. ''' )
if file is None:
snake_case = token_per_repo_id or {}
snake_case = path.split('''::''' )[-1]
try:
snake_case = string_to_dict(A__ , config.HUB_DATASETS_URL )['''repo_id''']
snake_case = token_per_repo_id[repo_id]
except (ValueError, KeyError):
snake_case = None
with xopen(A__ , '''rb''' , use_auth_token=A__ ) as f:
snake_case , snake_case = sf.read(A__ )
else:
snake_case , snake_case = sf.read(A__ )
snake_case = array.T
if self.mono:
snake_case = librosa.to_mono(A__ )
if self.sampling_rate and self.sampling_rate != sampling_rate:
snake_case = librosa.resample(A__ , orig_sr=A__ , target_sr=self.sampling_rate )
snake_case = self.sampling_rate
return {"path": path, "array": array, "sampling_rate": sampling_rate}
def UpperCamelCase ( self ) -> Union["FeatureType", Dict[str, "FeatureType"]]:
from .features import Value
if self.decode:
raise ValueError('''Cannot flatten a decoded Audio feature.''' )
return {
"bytes": Value('''binary''' ),
"path": Value('''string''' ),
}
def UpperCamelCase ( self , A__ ) -> pa.StructArray:
if pa.types.is_string(storage.type ):
snake_case = pa.array([None] * len(A__ ) , type=pa.binary() )
snake_case = pa.StructArray.from_arrays([bytes_array, storage] , ['''bytes''', '''path'''] , mask=storage.is_null() )
elif pa.types.is_binary(storage.type ):
snake_case = pa.array([None] * len(A__ ) , type=pa.string() )
snake_case = pa.StructArray.from_arrays([storage, path_array] , ['''bytes''', '''path'''] , mask=storage.is_null() )
elif pa.types.is_struct(storage.type ) and storage.type.get_all_field_indices('''array''' ):
snake_case = pa.array([Audio().encode_example(A__ ) if x is not None else None for x in storage.to_pylist()] )
elif pa.types.is_struct(storage.type ):
if storage.type.get_field_index('''bytes''' ) >= 0:
snake_case = storage.field('''bytes''' )
else:
snake_case = pa.array([None] * len(A__ ) , type=pa.binary() )
if storage.type.get_field_index('''path''' ) >= 0:
snake_case = storage.field('''path''' )
else:
snake_case = pa.array([None] * len(A__ ) , type=pa.string() )
snake_case = pa.StructArray.from_arrays([bytes_array, path_array] , ['''bytes''', '''path'''] , mask=storage.is_null() )
return array_cast(A__ , self.pa_type )
def UpperCamelCase ( self , A__ ) -> pa.StructArray:
@no_op_if_value_is_null
def path_to_bytes(A__ ):
with xopen(A__ , '''rb''' ) as f:
snake_case = f.read()
return bytes_
snake_case = pa.array(
[
(path_to_bytes(x['''path'''] ) if x['''bytes'''] is None else x['''bytes''']) if x is not None else None
for x in storage.to_pylist()
] , type=pa.binary() , )
snake_case = pa.array(
[os.path.basename(A__ ) if path is not None else None for path in storage.field('''path''' ).to_pylist()] , type=pa.string() , )
snake_case = pa.StructArray.from_arrays([bytes_array, path_array] , ['''bytes''', '''path'''] , mask=bytes_array.is_null() )
return array_cast(A__ , self.pa_type )
| 44
| 1
|
'''simple docstring'''
class _lowercase :
def __init__( self , A__ , A__ , A__ ) -> int:
snake_case = name
snake_case = value
snake_case = weight
def __repr__( self ) -> str:
return F"""{self.__class__.__name__}({self.name}, {self.value}, {self.weight})"""
def UpperCamelCase ( self ) -> Any:
return self.value
def UpperCamelCase ( self ) -> List[str]:
return self.name
def UpperCamelCase ( self ) -> Optional[Any]:
return self.weight
def UpperCamelCase ( self ) -> Optional[int]:
return self.value / self.weight
def __UpperCamelCase ( a : int , a : Optional[int] , a : int ) ->Dict:
snake_case = []
for i in range(len(a ) ):
menu.append(Things(name[i] , value[i] , weight[i] ) )
return menu
def __UpperCamelCase ( a : List[str] , a : Optional[int] , a : Dict ) ->Any:
snake_case = sorted(a , key=a , reverse=a )
snake_case = []
snake_case , snake_case = 0.0, 0.0
for i in range(len(a ) ):
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 __UpperCamelCase ( ) ->Union[str, Any]:
pass
if __name__ == "__main__":
import doctest
doctest.testmod()
| 44
|
'''simple docstring'''
import hashlib
import unittest
from transformers import MODEL_FOR_DEPTH_ESTIMATION_MAPPING, is_torch_available, is_vision_available
from transformers.pipelines import DepthEstimationPipeline, pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_timm,
require_torch,
require_vision,
slow,
)
from .test_pipelines_common import ANY
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
else:
class _lowercase :
@staticmethod
def UpperCamelCase ( *A__ , **A__ ) -> List[Any]:
pass
def __UpperCamelCase ( a : Image ) ->str:
snake_case = hashlib.mda(image.tobytes() )
return m.hexdigest()
@is_pipeline_test
@require_vision
@require_timm
@require_torch
class _lowercase ( unittest.TestCase ):
_UpperCAmelCase = MODEL_FOR_DEPTH_ESTIMATION_MAPPING
def UpperCamelCase ( self , A__ , A__ , A__ ) -> Union[str, Any]:
snake_case = DepthEstimationPipeline(model=A__ , image_processor=A__ )
return depth_estimator, [
"./tests/fixtures/tests_samples/COCO/000000039769.png",
"./tests/fixtures/tests_samples/COCO/000000039769.png",
]
def UpperCamelCase ( self , A__ , A__ ) -> List[Any]:
snake_case = depth_estimator('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
self.assertEqual({'''predicted_depth''': ANY(torch.Tensor ), '''depth''': ANY(Image.Image )} , A__ )
import datasets
snake_case = datasets.load_dataset('''hf-internal-testing/fixtures_image_utils''' , '''image''' , split='''test''' )
snake_case = depth_estimator(
[
Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' ),
'''http://images.cocodataset.org/val2017/000000039769.jpg''',
# RGBA
dataset[0]['''file'''],
# LA
dataset[1]['''file'''],
# L
dataset[2]['''file'''],
] )
self.assertEqual(
[
{'''predicted_depth''': ANY(torch.Tensor ), '''depth''': ANY(Image.Image )},
{'''predicted_depth''': ANY(torch.Tensor ), '''depth''': ANY(Image.Image )},
{'''predicted_depth''': ANY(torch.Tensor ), '''depth''': ANY(Image.Image )},
{'''predicted_depth''': ANY(torch.Tensor ), '''depth''': ANY(Image.Image )},
{'''predicted_depth''': ANY(torch.Tensor ), '''depth''': ANY(Image.Image )},
] , A__ , )
@require_tf
@unittest.skip('''Depth estimation is not implemented in TF''' )
def UpperCamelCase ( self ) -> Optional[Any]:
pass
@slow
@require_torch
def UpperCamelCase ( self ) -> Dict:
snake_case = '''Intel/dpt-large'''
snake_case = pipeline('''depth-estimation''' , model=A__ )
snake_case = depth_estimator('''http://images.cocodataset.org/val2017/000000039769.jpg''' )
snake_case = hashimage(outputs['''depth'''] )
# This seems flaky.
# self.assertEqual(outputs["depth"], "1a39394e282e9f3b0741a90b9f108977")
self.assertEqual(nested_simplify(outputs['''predicted_depth'''].max().item() ) , 2_9.3_0_4 )
self.assertEqual(nested_simplify(outputs['''predicted_depth'''].min().item() ) , 2.6_6_2 )
@require_torch
def UpperCamelCase ( self ) -> Any:
# This is highly irregular to have no small tests.
self.skipTest('''There is not hf-internal-testing tiny model for either GLPN nor DPT''' )
| 44
| 1
|
'''simple docstring'''
import os
import socket
from contextlib import contextmanager
import torch
from ..commands.config.default import write_basic_config # noqa: F401
from ..state import PartialState
from .dataclasses import DistributedType
from .imports import is_deepspeed_available, is_tpu_available
from .transformer_engine import convert_model
from .versions import is_torch_version
if is_deepspeed_available():
from deepspeed import DeepSpeedEngine
if is_tpu_available(check_device=False):
import torch_xla.core.xla_model as xm
def __UpperCamelCase ( a : List[Any] ) ->int:
if is_torch_version('''<''' , '''2.0.0''' ) or not hasattr(a , '''_dynamo''' ):
return False
return isinstance(a , torch._dynamo.eval_frame.OptimizedModule )
def __UpperCamelCase ( a : List[Any] , a : bool = True ) ->Tuple:
snake_case = (torch.nn.parallel.DistributedDataParallel, torch.nn.DataParallel)
snake_case = is_compiled_module(a )
if is_compiled:
snake_case = model
snake_case = model._orig_mod
if is_deepspeed_available():
options += (DeepSpeedEngine,)
while isinstance(a , a ):
snake_case = model.module
if not keep_fpaa_wrapper:
snake_case = getattr(a , '''forward''' )
snake_case = model.__dict__.pop('''_original_forward''' , a )
if original_forward is not None:
while hasattr(a , '''__wrapped__''' ):
snake_case = forward.__wrapped__
if forward == original_forward:
break
snake_case = forward
if getattr(a , '''_converted_to_transformer_engine''' , a ):
convert_model(a , to_transformer_engine=a )
if is_compiled:
snake_case = model
snake_case = compiled_model
return model
def __UpperCamelCase ( ) ->Dict:
PartialState().wait_for_everyone()
def __UpperCamelCase ( a : str , a : Tuple ) ->Optional[int]:
if PartialState().distributed_type == DistributedType.TPU:
xm.save(a , a )
elif PartialState().local_process_index == 0:
torch.save(a , a )
@contextmanager
def __UpperCamelCase ( **a : Optional[int] ) ->List[Any]:
for key, value in kwargs.items():
snake_case = str(a )
yield
for key in kwargs:
if key.upper() in os.environ:
del os.environ[key.upper()]
def __UpperCamelCase ( a : Optional[Any] ) ->List[Any]:
if not hasattr(a , '''__qualname__''' ) and not hasattr(a , '''__name__''' ):
snake_case = getattr(a , '''__class__''' , a )
if hasattr(a , '''__qualname__''' ):
return obj.__qualname__
if hasattr(a , '''__name__''' ):
return obj.__name__
return str(a )
def __UpperCamelCase ( a : List[Any] , a : List[Any] ) ->str:
for key, value in source.items():
if isinstance(a , a ):
snake_case = destination.setdefault(a , {} )
merge_dicts(a , a )
else:
snake_case = value
return destination
def __UpperCamelCase ( a : int = None ) ->bool:
if port is None:
snake_case = 2_9500
with socket.socket(socket.AF_INET , socket.SOCK_STREAM ) as s:
return s.connect_ex(('''localhost''', port) ) == 0
| 44
|
'''simple docstring'''
import argparse
import torch
from torch import nn
from transformers import SpeechaTextConfig, SpeechaTextForConditionalGeneration
def __UpperCamelCase ( a : Optional[int] ) ->Dict:
snake_case = [
'''encoder.version''',
'''decoder.version''',
'''model.encoder.version''',
'''model.decoder.version''',
'''decoder.output_projection.weight''',
'''_float_tensor''',
'''encoder.embed_positions._float_tensor''',
'''decoder.embed_positions._float_tensor''',
]
for k in ignore_keys:
state_dict.pop(a , a )
def __UpperCamelCase ( a : Optional[Any] ) ->int:
snake_case = list(s_dict.keys() )
for key in keys:
if "transformer_layers" in key:
snake_case = s_dict.pop(a )
elif "subsample" in key:
snake_case = s_dict.pop(a )
def __UpperCamelCase ( a : Optional[int] ) ->Optional[int]:
snake_case , snake_case = emb.weight.shape
snake_case = nn.Linear(a , a , bias=a )
snake_case = emb.weight.data
return lin_layer
def __UpperCamelCase ( a : Any , a : Tuple ) ->Tuple:
snake_case = torch.load(a , map_location='''cpu''' )
snake_case = mam_aaa['''args''']
snake_case = mam_aaa['''model''']
snake_case = state_dict['''decoder.output_projection.weight''']
remove_ignore_keys_(a )
rename_keys(a )
snake_case = state_dict['''decoder.embed_tokens.weight'''].shape[0]
snake_case = args.share_decoder_input_output_embed
snake_case = [int(a ) for i in args.conv_kernel_sizes.split(''',''' )]
snake_case = SpeechaTextConfig(
vocab_size=a , max_source_positions=args.max_source_positions , max_target_positions=args.max_target_positions , encoder_layers=args.encoder_layers , decoder_layers=args.decoder_layers , encoder_attention_heads=args.encoder_attention_heads , decoder_attention_heads=args.decoder_attention_heads , encoder_ffn_dim=args.encoder_ffn_embed_dim , decoder_ffn_dim=args.decoder_ffn_embed_dim , d_model=args.encoder_embed_dim , dropout=args.dropout , attention_dropout=args.attention_dropout , activation_dropout=args.activation_dropout , activation_function='''relu''' , num_conv_layers=len(a ) , conv_channels=args.conv_channels , conv_kernel_sizes=a , input_feat_per_channel=args.input_feat_per_channel , input_channels=args.input_channels , tie_word_embeddings=a , num_beams=5 , max_length=200 , use_cache=a , decoder_start_token_id=2 , early_stopping=a , )
snake_case = SpeechaTextForConditionalGeneration(a )
snake_case , snake_case = model.model.load_state_dict(a , strict=a )
if len(a ) > 0 and not set(a ) <= {
"encoder.embed_positions.weights",
"decoder.embed_positions.weights",
}:
raise ValueError(
'''Only `encoder.embed_positions.weights` and `decoder.embed_positions.weights` are allowed to be missing,'''
f""" but all the following weights are missing {missing}""" )
if tie_embeds:
snake_case = make_linear_from_emb(model.model.decoder.embed_tokens )
else:
snake_case = lm_head_weights
model.save_pretrained(a )
if __name__ == "__main__":
_lowercase = argparse.ArgumentParser()
# Required parameters
parser.add_argument('--fairseq_path', type=str, help='Path to the fairseq model (.pt) file.')
parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
_lowercase = parser.parse_args()
convert_fairseq_sat_checkpoint_to_tfms(args.fairseq_path, args.pytorch_dump_folder_path)
| 44
| 1
|
'''simple docstring'''
import hashlib
import unittest
from typing import Dict
import numpy as np
from transformers import (
MODEL_FOR_MASK_GENERATION_MAPPING,
TF_MODEL_FOR_MASK_GENERATION_MAPPING,
is_vision_available,
pipeline,
)
from transformers.pipelines import MaskGenerationPipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_vision,
slow,
)
if is_vision_available():
from PIL import Image
else:
class _lowercase :
@staticmethod
def UpperCamelCase ( *A__ , **A__ ) -> int:
pass
def __UpperCamelCase ( a : Image ) ->str:
snake_case = hashlib.mda(image.tobytes() )
return m.hexdigest()[:10]
def __UpperCamelCase ( a : Image ) ->Dict:
snake_case = np.array(a )
snake_case = npimg.shape
return {"hash": hashimage(a ), "shape": shape}
@is_pipeline_test
@require_vision
@require_torch
class _lowercase ( unittest.TestCase ):
_UpperCAmelCase = dict(
(list(MODEL_FOR_MASK_GENERATION_MAPPING.items() ) if MODEL_FOR_MASK_GENERATION_MAPPING else []) )
_UpperCAmelCase = dict(
(list(TF_MODEL_FOR_MASK_GENERATION_MAPPING.items() ) if TF_MODEL_FOR_MASK_GENERATION_MAPPING else []) )
def UpperCamelCase ( self , A__ , A__ , A__ ) -> int:
snake_case = MaskGenerationPipeline(model=A__ , image_processor=A__ )
return image_segmenter, [
"./tests/fixtures/tests_samples/COCO/000000039769.png",
"./tests/fixtures/tests_samples/COCO/000000039769.png",
]
def UpperCamelCase ( self , A__ , A__ ) -> Any:
pass
@require_tf
@unittest.skip('''Image segmentation not implemented in TF''' )
def UpperCamelCase ( self ) -> Dict:
pass
@slow
@require_torch
def UpperCamelCase ( self ) -> int:
snake_case = pipeline('''mask-generation''' , model='''facebook/sam-vit-huge''' )
snake_case = image_segmenter('''http://images.cocodataset.org/val2017/000000039769.jpg''' , points_per_batch=2_56 )
# Shortening by hashing
snake_case = []
for i, o in enumerate(outputs['''masks'''] ):
new_outupt += [{"mask": mask_to_test_readable(A__ ), "scores": outputs["scores"][i]}]
# fmt: off
self.assertEqual(
nested_simplify(A__ , decimals=4 ) , [
{'''mask''': {'''hash''': '''115ad19f5f''', '''shape''': (4_80, 6_40)}, '''scores''': 1.0_4_4_4},
{'''mask''': {'''hash''': '''6affa964c6''', '''shape''': (4_80, 6_40)}, '''scores''': 1.0_2_1},
{'''mask''': {'''hash''': '''dfe28a0388''', '''shape''': (4_80, 6_40)}, '''scores''': 1.0_1_6_7},
{'''mask''': {'''hash''': '''c0a5f4a318''', '''shape''': (4_80, 6_40)}, '''scores''': 1.0_1_3_2},
{'''mask''': {'''hash''': '''fe8065c197''', '''shape''': (4_80, 6_40)}, '''scores''': 1.0_0_5_3},
{'''mask''': {'''hash''': '''e2d0b7a0b7''', '''shape''': (4_80, 6_40)}, '''scores''': 0.9_9_6_7},
{'''mask''': {'''hash''': '''453c7844bd''', '''shape''': (4_80, 6_40)}, '''scores''': 0.9_9_3},
{'''mask''': {'''hash''': '''3d44f2926d''', '''shape''': (4_80, 6_40)}, '''scores''': 0.9_9_0_9},
{'''mask''': {'''hash''': '''64033ddc3f''', '''shape''': (4_80, 6_40)}, '''scores''': 0.9_8_7_9},
{'''mask''': {'''hash''': '''801064ff79''', '''shape''': (4_80, 6_40)}, '''scores''': 0.9_8_3_4},
{'''mask''': {'''hash''': '''6172f276ef''', '''shape''': (4_80, 6_40)}, '''scores''': 0.9_7_1_6},
{'''mask''': {'''hash''': '''b49e60e084''', '''shape''': (4_80, 6_40)}, '''scores''': 0.9_6_1_2},
{'''mask''': {'''hash''': '''a811e775fd''', '''shape''': (4_80, 6_40)}, '''scores''': 0.9_5_9_9},
{'''mask''': {'''hash''': '''a6a8ebcf4b''', '''shape''': (4_80, 6_40)}, '''scores''': 0.9_5_5_2},
{'''mask''': {'''hash''': '''9d8257e080''', '''shape''': (4_80, 6_40)}, '''scores''': 0.9_5_3_2},
{'''mask''': {'''hash''': '''32de6454a8''', '''shape''': (4_80, 6_40)}, '''scores''': 0.9_5_1_6},
{'''mask''': {'''hash''': '''af3d4af2c8''', '''shape''': (4_80, 6_40)}, '''scores''': 0.9_4_9_9},
{'''mask''': {'''hash''': '''3c6db475fb''', '''shape''': (4_80, 6_40)}, '''scores''': 0.9_4_8_3},
{'''mask''': {'''hash''': '''c290813fb9''', '''shape''': (4_80, 6_40)}, '''scores''': 0.9_4_6_4},
{'''mask''': {'''hash''': '''b6f0b8f606''', '''shape''': (4_80, 6_40)}, '''scores''': 0.9_4_3},
{'''mask''': {'''hash''': '''92ce16bfdf''', '''shape''': (4_80, 6_40)}, '''scores''': 0.9_4_3},
{'''mask''': {'''hash''': '''c749b25868''', '''shape''': (4_80, 6_40)}, '''scores''': 0.9_4_0_8},
{'''mask''': {'''hash''': '''efb6cab859''', '''shape''': (4_80, 6_40)}, '''scores''': 0.9_3_3_5},
{'''mask''': {'''hash''': '''1ff2eafb30''', '''shape''': (4_80, 6_40)}, '''scores''': 0.9_3_2_6},
{'''mask''': {'''hash''': '''788b798e24''', '''shape''': (4_80, 6_40)}, '''scores''': 0.9_2_6_2},
{'''mask''': {'''hash''': '''abea804f0e''', '''shape''': (4_80, 6_40)}, '''scores''': 0.8_9_9_9},
{'''mask''': {'''hash''': '''7b9e8ddb73''', '''shape''': (4_80, 6_40)}, '''scores''': 0.8_9_8_6},
{'''mask''': {'''hash''': '''cd24047c8a''', '''shape''': (4_80, 6_40)}, '''scores''': 0.8_9_8_4},
{'''mask''': {'''hash''': '''6943e6bcbd''', '''shape''': (4_80, 6_40)}, '''scores''': 0.8_8_7_3},
{'''mask''': {'''hash''': '''b5f47c9191''', '''shape''': (4_80, 6_40)}, '''scores''': 0.8_8_7_1}
] , )
# fmt: on
@require_torch
@slow
def UpperCamelCase ( self ) -> Dict:
snake_case = '''facebook/sam-vit-huge'''
snake_case = pipeline('''mask-generation''' , model=A__ )
snake_case = image_segmenter(
'''http://images.cocodataset.org/val2017/000000039769.jpg''' , pred_iou_thresh=1 , points_per_batch=2_56 )
# Shortening by hashing
snake_case = []
for i, o in enumerate(outputs['''masks'''] ):
new_outupt += [{"mask": mask_to_test_readable(A__ ), "scores": outputs["scores"][i]}]
self.assertEqual(
nested_simplify(A__ , decimals=4 ) , [
{'''mask''': {'''hash''': '''115ad19f5f''', '''shape''': (4_80, 6_40)}, '''scores''': 1.0_4_4_4},
{'''mask''': {'''hash''': '''6affa964c6''', '''shape''': (4_80, 6_40)}, '''scores''': 1.0_2_1_0},
{'''mask''': {'''hash''': '''dfe28a0388''', '''shape''': (4_80, 6_40)}, '''scores''': 1.0_1_6_7},
{'''mask''': {'''hash''': '''c0a5f4a318''', '''shape''': (4_80, 6_40)}, '''scores''': 1.0_1_3_2},
{'''mask''': {'''hash''': '''fe8065c197''', '''shape''': (4_80, 6_40)}, '''scores''': 1.0_0_5_3},
] , )
| 44
|
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class _lowercase ( metaclass=__a ):
_UpperCAmelCase = ['''transformers''', '''torch''', '''note_seq''']
def __init__( self , *A__ , **A__ ) -> Union[str, Any]:
requires_backends(self , ['''transformers''', '''torch''', '''note_seq'''] )
@classmethod
def UpperCamelCase ( cls , *A__ , **A__ ) -> Optional[Any]:
requires_backends(cls , ['''transformers''', '''torch''', '''note_seq'''] )
@classmethod
def UpperCamelCase ( cls , *A__ , **A__ ) -> Any:
requires_backends(cls , ['''transformers''', '''torch''', '''note_seq'''] )
| 44
| 1
|
'''simple docstring'''
import math
def __UpperCamelCase ( a : float , a : float ) ->float:
if initial_intensity < 0:
raise ValueError('''The value of intensity cannot be negative''' )
# handling of negative values of initial intensity
if angle < 0 or angle > 360:
raise ValueError('''In Malus Law, the angle is in the range 0-360 degrees''' )
# handling of values out of allowed range
return initial_intensity * (math.cos(math.radians(a ) ) ** 2)
if __name__ == "__main__":
import doctest
doctest.testmod(name='malus_law')
| 44
|
'''simple docstring'''
from __future__ import annotations
from collections.abc import Iterator
class _lowercase :
def __init__( self , A__ ) -> None:
snake_case = value
snake_case = None
snake_case = None
class _lowercase :
def __init__( self , A__ ) -> None:
snake_case = tree
def UpperCamelCase ( self , A__ ) -> int:
if node is None:
return 0
return node.value + (
self.depth_first_search(node.left ) + self.depth_first_search(node.right )
)
def __iter__( self ) -> Iterator[int]:
yield self.depth_first_search(self.tree )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 44
| 1
|
'''simple docstring'''
def __UpperCamelCase ( a : int = 1000 ) ->int:
snake_case = 2**power
snake_case = str(a )
snake_case = list(a )
snake_case = 0
for i in list_num:
sum_of_num += int(a )
return sum_of_num
if __name__ == "__main__":
_lowercase = int(input('Enter the power of 2: ').strip())
print('2 ^ ', power, ' = ', 2**power)
_lowercase = solution(power)
print('Sum of the digits is: ', result)
| 44
|
'''simple docstring'''
import argparse
from collections import OrderedDict
from pathlib import Path
import torch
from transformers import (
VisualBertConfig,
VisualBertForMultipleChoice,
VisualBertForPreTraining,
VisualBertForQuestionAnswering,
VisualBertForVisualReasoning,
)
from transformers.utils import logging
logging.set_verbosity_info()
_lowercase = logging.get_logger(__name__)
_lowercase = [
('bert.bert', 'visual_bert'),
('bert.cls', 'cls'),
('bert.classifier', 'cls'),
('token_type_embeddings_visual', 'visual_token_type_embeddings'),
('position_embeddings_visual', 'visual_position_embeddings'),
('projection', 'visual_projection'),
]
_lowercase = [
'nlvr2_coco_pre_trained.th',
'nlvr2_fine_tuned.th',
'nlvr2_pre_trained.th',
'vcr_coco_pre_train.th',
'vcr_fine_tune.th',
'vcr_pre_train.th',
'vqa_coco_pre_trained.th',
'vqa_fine_tuned.th',
'vqa_pre_trained.th',
]
def __UpperCamelCase ( a : List[str] ) ->Optional[int]:
snake_case = torch.load(a , map_location='''cpu''' )
return sd
def __UpperCamelCase ( a : Optional[int] , a : Union[str, Any] , a : int=rename_keys_prefix ) ->Tuple:
snake_case = OrderedDict()
snake_case = torch.arange(config.max_position_embeddings ).expand((1, -1) )
# detector_d = OrderedDict()
for key in d:
if "detector" in key:
# detector_d[key.replace('detector.','')] = d[key]
continue
snake_case = key
for name_pair in rename_keys_prefix:
snake_case = new_key.replace(name_pair[0] , name_pair[1] )
snake_case = d[key]
if key == "bert.cls.predictions.decoder.weight":
# Old bert code didn't have `decoder.bias`, but was added separately
snake_case = new_d['''cls.predictions.bias''']
return new_d
@torch.no_grad()
def __UpperCamelCase ( a : Optional[int] , a : int ) ->Union[str, Any]:
assert (
checkpoint_path.split('''/''' )[-1] in ACCEPTABLE_CHECKPOINTS
), f"""The checkpoint provided must be in {ACCEPTABLE_CHECKPOINTS}."""
# Get Config
if "pre" in checkpoint_path:
snake_case = '''pretraining'''
if "vcr" in checkpoint_path:
snake_case = {'''visual_embedding_dim''': 512}
elif "vqa_advanced" in checkpoint_path:
snake_case = {'''visual_embedding_dim''': 2048}
elif "vqa" in checkpoint_path:
snake_case = {'''visual_embedding_dim''': 2048}
elif "nlvr" in checkpoint_path:
snake_case = {'''visual_embedding_dim''': 1024}
else:
raise NotImplementedError(f"""No implementation found for `{checkpoint_path}`.""" )
else:
if "vcr" in checkpoint_path:
snake_case = {'''visual_embedding_dim''': 512}
snake_case = '''multichoice'''
elif "vqa_advanced" in checkpoint_path:
snake_case = {'''visual_embedding_dim''': 2048}
snake_case = '''vqa_advanced'''
elif "vqa" in checkpoint_path:
snake_case = {'''visual_embedding_dim''': 2048, '''num_labels''': 3129}
snake_case = '''vqa'''
elif "nlvr" in checkpoint_path:
snake_case = {
'''visual_embedding_dim''': 1024,
'''num_labels''': 2,
}
snake_case = '''nlvr'''
snake_case = VisualBertConfig(**a )
# Load State Dict
snake_case = load_state_dict(a )
snake_case = get_new_dict(a , a )
if model_type == "pretraining":
snake_case = VisualBertForPreTraining(a )
elif model_type == "vqa":
snake_case = VisualBertForQuestionAnswering(a )
elif model_type == "nlvr":
snake_case = VisualBertForVisualReasoning(a )
elif model_type == "multichoice":
snake_case = VisualBertForMultipleChoice(a )
model.load_state_dict(a )
# Save Checkpoints
Path(a ).mkdir(exist_ok=a )
model.save_pretrained(a )
if __name__ == "__main__":
_lowercase = argparse.ArgumentParser()
# Required parameters
parser.add_argument('orig_checkpoint_path', type=str, help='A path to .th on local filesystem.')
parser.add_argument('pytorch_dump_folder_path', type=str, help='Path to the output PyTorch model.')
_lowercase = parser.parse_args()
convert_visual_bert_checkpoint(args.orig_checkpoint_path, args.pytorch_dump_folder_path)
| 44
| 1
|
'''simple docstring'''
from collections import Counter
from pathlib import Path
from typing import Optional, Tuple
import yaml
class _lowercase ( yaml.SafeLoader ):
def UpperCamelCase ( self , A__ ) -> List[str]:
snake_case = [self.constructed_objects[key_node] for key_node, _ in node.value]
snake_case = [tuple(A__ ) if isinstance(A__ , A__ ) else key for key in keys]
snake_case = Counter(A__ )
snake_case = [key for key in counter if counter[key] > 1]
if duplicate_keys:
raise TypeError(F"""Got duplicate yaml keys: {duplicate_keys}""" )
def UpperCamelCase ( self , A__ , A__=False ) -> List[Any]:
snake_case = super().construct_mapping(A__ , deep=A__ )
self._check_no_duplicates_on_constructed_node(A__ )
return mapping
def __UpperCamelCase ( a : str ) ->Tuple[Optional[str], str]:
snake_case = list(readme_content.splitlines() )
if full_content and full_content[0] == "---" and "---" in full_content[1:]:
snake_case = full_content[1:].index('''---''' ) + 1
snake_case = '''\n'''.join(full_content[1:sep_idx] )
return yamlblock, "\n".join(full_content[sep_idx + 1 :] )
return None, "\n".join(a )
class _lowercase ( __a ):
# class attributes
_UpperCAmelCase = {'''train_eval_index'''} # train-eval-index in the YAML metadata
@classmethod
def UpperCamelCase ( cls , A__ ) -> "DatasetMetadata":
with open(A__ , encoding='''utf-8''' ) as readme_file:
snake_case , snake_case = _split_yaml_from_readme(readme_file.read() )
if yaml_string is not None:
return cls.from_yaml_string(A__ )
else:
return cls()
def UpperCamelCase ( self , A__ ) -> str:
if path.exists():
with open(A__ , encoding='''utf-8''' ) as readme_file:
snake_case = readme_file.read()
else:
snake_case = None
snake_case = self._to_readme(A__ )
with open(A__ , '''w''' , encoding='''utf-8''' ) as readme_file:
readme_file.write(A__ )
def UpperCamelCase ( self , A__ = None ) -> str:
if readme_content is not None:
snake_case , snake_case = _split_yaml_from_readme(A__ )
snake_case = '''---\n''' + self.to_yaml_string() + '''---\n''' + content
else:
snake_case = '''---\n''' + self.to_yaml_string() + '''---\n'''
return full_content
@classmethod
def UpperCamelCase ( cls , A__ ) -> "DatasetMetadata":
snake_case = yaml.load(A__ , Loader=_NoDuplicateSafeLoader ) or {}
# Convert the YAML keys to DatasetMetadata fields
snake_case = {
(key.replace('''-''' , '''_''' ) if key.replace('''-''' , '''_''' ) in cls._FIELDS_WITH_DASHES else key): value
for key, value in metadata_dict.items()
}
return cls(**A__ )
def UpperCamelCase ( self ) -> str:
return yaml.safe_dump(
{
(key.replace('''_''' , '''-''' ) if key in self._FIELDS_WITH_DASHES else key): value
for key, value in self.items()
} , sort_keys=A__ , allow_unicode=A__ , encoding='''utf-8''' , ).decode('''utf-8''' )
_lowercase = {
'image-classification': [],
'translation': [],
'image-segmentation': [],
'fill-mask': [],
'automatic-speech-recognition': [],
'token-classification': [],
'sentence-similarity': [],
'audio-classification': [],
'question-answering': [],
'summarization': [],
'zero-shot-classification': [],
'table-to-text': [],
'feature-extraction': [],
'other': [],
'multiple-choice': [],
'text-classification': [],
'text-to-image': [],
'text2text-generation': [],
'zero-shot-image-classification': [],
'tabular-classification': [],
'tabular-regression': [],
'image-to-image': [],
'tabular-to-text': [],
'unconditional-image-generation': [],
'text-retrieval': [],
'text-to-speech': [],
'object-detection': [],
'audio-to-audio': [],
'text-generation': [],
'conversational': [],
'table-question-answering': [],
'visual-question-answering': [],
'image-to-text': [],
'reinforcement-learning': [],
'voice-activity-detection': [],
'time-series-forecasting': [],
'document-question-answering': [],
}
if __name__ == "__main__":
from argparse import ArgumentParser
_lowercase = ArgumentParser(usage='Validate the yaml metadata block of a README.md file.')
ap.add_argument('readme_filepath')
_lowercase = ap.parse_args()
_lowercase = Path(args.readme_filepath)
_lowercase = DatasetMetadata.from_readme(readme_filepath)
print(dataset_metadata)
dataset_metadata.to_readme(readme_filepath)
| 44
|
'''simple docstring'''
import argparse
import json
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import PoolFormerConfig, PoolFormerForImageClassification, PoolFormerImageProcessor
from transformers.utils import logging
logging.set_verbosity_info()
_lowercase = logging.get_logger(__name__)
def __UpperCamelCase ( a : Dict , a : Optional[int] , a : Dict , a : Dict ) ->Union[str, Any]:
snake_case = original_name.split('''.''' )[0]
snake_case = key.split('''.''' )
snake_case = int(key_list[key_list.index(a ) - 2] )
snake_case = int(key_list[key_list.index(a ) - 1] )
snake_case = orig_block_num - offset
snake_case = key.replace(f"""{orig_block_num}.{layer_num}.{original_name}""" , f"""block.{new_block_num}.{layer_num}.{new_name}""" )
return key
def __UpperCamelCase ( a : Tuple ) ->Dict:
snake_case = OrderedDict()
snake_case , snake_case = 0, 0
for key, value in state_dict.items():
if key.startswith('''network''' ):
snake_case = key.replace('''network''' , '''poolformer.encoder''' )
if "proj" in key:
# Works for the first embedding as well as the internal embedding layers
if key.endswith('''bias''' ) and "patch_embed" not in key:
patch_emb_offset += 1
snake_case = key[: key.find('''proj''' )]
snake_case = key.replace(a , f"""patch_embeddings.{total_embed_found}.""" )
snake_case = key.replace('''proj''' , '''projection''' )
if key.endswith('''bias''' ):
total_embed_found += 1
if "patch_embeddings" in key:
snake_case = '''poolformer.encoder.''' + key
if "mlp.fc1" in key:
snake_case = replace_key_with_offset(a , a , '''mlp.fc1''' , '''output.conv1''' )
if "mlp.fc2" in key:
snake_case = replace_key_with_offset(a , a , '''mlp.fc2''' , '''output.conv2''' )
if "norm1" in key:
snake_case = replace_key_with_offset(a , a , '''norm1''' , '''before_norm''' )
if "norm2" in key:
snake_case = replace_key_with_offset(a , a , '''norm2''' , '''after_norm''' )
if "layer_scale_1" in key:
snake_case = replace_key_with_offset(a , a , '''layer_scale_1''' , '''layer_scale_1''' )
if "layer_scale_2" in key:
snake_case = replace_key_with_offset(a , a , '''layer_scale_2''' , '''layer_scale_2''' )
if "head" in key:
snake_case = key.replace('''head''' , '''classifier''' )
snake_case = value
return new_state_dict
def __UpperCamelCase ( ) ->Optional[int]:
snake_case = '''http://images.cocodataset.org/val2017/000000039769.jpg'''
snake_case = Image.open(requests.get(a , stream=a ).raw )
return image
@torch.no_grad()
def __UpperCamelCase ( a : Dict , a : Optional[Any] , a : Tuple ) ->List[str]:
snake_case = PoolFormerConfig()
# set attributes based on model_name
snake_case = '''huggingface/label-files'''
snake_case = model_name[-3:]
snake_case = 1000
snake_case = '''imagenet-1k-id2label.json'''
snake_case = (1, 1000)
# set config attributes
snake_case = json.load(open(hf_hub_download(a , a , repo_type='''dataset''' ) , '''r''' ) )
snake_case = {int(a ): v for k, v in idalabel.items()}
snake_case = idalabel
snake_case = {v: k for k, v in idalabel.items()}
if size == "s12":
snake_case = [2, 2, 6, 2]
snake_case = [64, 128, 320, 512]
snake_case = 4.0
snake_case = 0.9
elif size == "s24":
snake_case = [4, 4, 12, 4]
snake_case = [64, 128, 320, 512]
snake_case = 4.0
snake_case = 0.9
elif size == "s36":
snake_case = [6, 6, 18, 6]
snake_case = [64, 128, 320, 512]
snake_case = 4.0
snake_case = 1e-6
snake_case = 0.9
elif size == "m36":
snake_case = [6, 6, 18, 6]
snake_case = [96, 192, 384, 768]
snake_case = 4.0
snake_case = 1e-6
snake_case = 0.95
elif size == "m48":
snake_case = [8, 8, 24, 8]
snake_case = [96, 192, 384, 768]
snake_case = 4.0
snake_case = 1e-6
snake_case = 0.95
else:
raise ValueError(f"""Size {size} not supported""" )
# load image processor
snake_case = PoolFormerImageProcessor(crop_pct=a )
# Prepare image
snake_case = prepare_img()
snake_case = image_processor(images=a , return_tensors='''pt''' ).pixel_values
logger.info(f"""Converting model {model_name}...""" )
# load original state dict
snake_case = torch.load(a , map_location=torch.device('''cpu''' ) )
# rename keys
snake_case = rename_keys(a )
# create HuggingFace model and load state dict
snake_case = PoolFormerForImageClassification(a )
model.load_state_dict(a )
model.eval()
# Define image processor
snake_case = PoolFormerImageProcessor(crop_pct=a )
snake_case = image_processor(images=prepare_img() , return_tensors='''pt''' ).pixel_values
# forward pass
snake_case = model(a )
snake_case = outputs.logits
# define expected logit slices for different models
if size == "s12":
snake_case = torch.tensor([-0.3045, -0.6758, -0.4869] )
elif size == "s24":
snake_case = torch.tensor([0.4402, -0.1374, -0.8045] )
elif size == "s36":
snake_case = torch.tensor([-0.6080, -0.5133, -0.5898] )
elif size == "m36":
snake_case = torch.tensor([0.3952, 0.2263, -1.2668] )
elif size == "m48":
snake_case = torch.tensor([0.1167, -0.0656, -0.3423] )
else:
raise ValueError(f"""Size {size} not supported""" )
# verify logits
assert logits.shape == expected_shape
assert torch.allclose(logits[0, :3] , a , atol=1e-2 )
# finally, save model and image processor
logger.info(f"""Saving PyTorch model and image processor to {pytorch_dump_folder_path}...""" )
Path(a ).mkdir(exist_ok=a )
model.save_pretrained(a )
print(f"""Saving image processor to {pytorch_dump_folder_path}""" )
image_processor.save_pretrained(a )
if __name__ == "__main__":
_lowercase = argparse.ArgumentParser()
parser.add_argument(
'--model_name',
default='poolformer_s12',
type=str,
help='Name of the model you\'d like to convert.',
)
parser.add_argument(
'--checkpoint_path', default=None, type=str, help='Path to the original PyTorch checkpoint (.pth file).'
)
parser.add_argument(
'--pytorch_dump_folder_path', default=None, type=str, help='Path to the folder to output PyTorch model.'
)
_lowercase = parser.parse_args()
convert_poolformer_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path)
| 44
| 1
|
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowercase = logging.get_logger(__name__)
_lowercase = {
'microsoft/trocr-base-handwritten': (
'https://huggingface.co/microsoft/trocr-base-handwritten/resolve/main/config.json'
),
# See all TrOCR models at https://huggingface.co/models?filter=trocr
}
class _lowercase ( __a ):
_UpperCAmelCase = '''trocr'''
_UpperCAmelCase = ['''past_key_values''']
_UpperCAmelCase = {
'''num_attention_heads''': '''decoder_attention_heads''',
'''hidden_size''': '''d_model''',
'''num_hidden_layers''': '''decoder_layers''',
}
def __init__( self , A__=5_02_65 , A__=10_24 , A__=12 , A__=16 , A__=40_96 , A__="gelu" , A__=5_12 , A__=0.1 , A__=0.0 , A__=0.0 , A__=2 , A__=0.0_2 , A__=0.0 , A__=True , A__=False , A__=True , A__=True , A__=1 , A__=0 , A__=2 , **A__ , ) -> str:
snake_case = vocab_size
snake_case = d_model
snake_case = decoder_layers
snake_case = decoder_attention_heads
snake_case = decoder_ffn_dim
snake_case = activation_function
snake_case = max_position_embeddings
snake_case = dropout
snake_case = attention_dropout
snake_case = activation_dropout
snake_case = init_std
snake_case = decoder_layerdrop
snake_case = use_cache
snake_case = scale_embedding
snake_case = use_learned_position_embeddings
snake_case = layernorm_embedding
super().__init__(
pad_token_id=A__ , bos_token_id=A__ , eos_token_id=A__ , decoder_start_token_id=A__ , **A__ , )
| 44
|
'''simple docstring'''
import argparse
import json
import logging
import os
import sys
from unittest.mock import patch
from transformers.testing_utils import TestCasePlus, get_gpu_count, slow
_lowercase = [
os.path.join(os.path.dirname(__file__), dirname)
for dirname in [
'text-classification',
'language-modeling',
'summarization',
'token-classification',
'question-answering',
]
]
sys.path.extend(SRC_DIRS)
if SRC_DIRS is not None:
import run_clm_flax
import run_flax_glue
import run_flax_ner
import run_mlm_flax
import run_qa
import run_summarization_flax
import run_ta_mlm_flax
logging.basicConfig(level=logging.DEBUG)
_lowercase = logging.getLogger()
def __UpperCamelCase ( ) ->Tuple:
snake_case = argparse.ArgumentParser()
parser.add_argument('''-f''' )
snake_case = parser.parse_args()
return args.f
def __UpperCamelCase ( a : Dict , a : Tuple="eval" ) ->List[Any]:
snake_case = os.path.join(a , f"""{split}_results.json""" )
if os.path.exists(a ):
with open(a , '''r''' ) as f:
return json.load(a )
raise ValueError(f"""can't find {path}""" )
_lowercase = logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
class _lowercase ( __a ):
def UpperCamelCase ( self ) -> List[str]:
snake_case = self.get_auto_remove_tmp_dir()
snake_case = F"""
run_glue.py
--model_name_or_path distilbert-base-uncased
--output_dir {tmp_dir}
--train_file ./tests/fixtures/tests_samples/MRPC/train.csv
--validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv
--per_device_train_batch_size=2
--per_device_eval_batch_size=1
--learning_rate=1e-4
--eval_steps=2
--warmup_steps=2
--seed=42
--max_seq_length=128
""".split()
with patch.object(A__ , '''argv''' , A__ ):
run_flax_glue.main()
snake_case = get_results(A__ )
self.assertGreaterEqual(result['''eval_accuracy'''] , 0.7_5 )
@slow
def UpperCamelCase ( self ) -> List[Any]:
snake_case = self.get_auto_remove_tmp_dir()
snake_case = F"""
run_clm_flax.py
--model_name_or_path distilgpt2
--train_file ./tests/fixtures/sample_text.txt
--validation_file ./tests/fixtures/sample_text.txt
--do_train
--do_eval
--block_size 128
--per_device_train_batch_size 4
--per_device_eval_batch_size 4
--num_train_epochs 2
--logging_steps 2 --eval_steps 2
--output_dir {tmp_dir}
--overwrite_output_dir
""".split()
with patch.object(A__ , '''argv''' , A__ ):
run_clm_flax.main()
snake_case = get_results(A__ )
self.assertLess(result['''eval_perplexity'''] , 1_00 )
@slow
def UpperCamelCase ( self ) -> int:
snake_case = self.get_auto_remove_tmp_dir()
snake_case = F"""
run_summarization.py
--model_name_or_path t5-small
--train_file tests/fixtures/tests_samples/xsum/sample.json
--validation_file tests/fixtures/tests_samples/xsum/sample.json
--test_file tests/fixtures/tests_samples/xsum/sample.json
--output_dir {tmp_dir}
--overwrite_output_dir
--num_train_epochs=3
--warmup_steps=8
--do_train
--do_eval
--do_predict
--learning_rate=2e-4
--per_device_train_batch_size=2
--per_device_eval_batch_size=1
--predict_with_generate
""".split()
with patch.object(A__ , '''argv''' , A__ ):
run_summarization_flax.main()
snake_case = get_results(A__ , split='''test''' )
self.assertGreaterEqual(result['''test_rouge1'''] , 10 )
self.assertGreaterEqual(result['''test_rouge2'''] , 2 )
self.assertGreaterEqual(result['''test_rougeL'''] , 7 )
self.assertGreaterEqual(result['''test_rougeLsum'''] , 7 )
@slow
def UpperCamelCase ( self ) -> Union[str, Any]:
snake_case = self.get_auto_remove_tmp_dir()
snake_case = F"""
run_mlm.py
--model_name_or_path distilroberta-base
--train_file ./tests/fixtures/sample_text.txt
--validation_file ./tests/fixtures/sample_text.txt
--output_dir {tmp_dir}
--overwrite_output_dir
--max_seq_length 128
--per_device_train_batch_size 4
--per_device_eval_batch_size 4
--logging_steps 2 --eval_steps 2
--do_train
--do_eval
--num_train_epochs=1
""".split()
with patch.object(A__ , '''argv''' , A__ ):
run_mlm_flax.main()
snake_case = get_results(A__ )
self.assertLess(result['''eval_perplexity'''] , 42 )
@slow
def UpperCamelCase ( self ) -> Dict:
snake_case = self.get_auto_remove_tmp_dir()
snake_case = F"""
run_t5_mlm_flax.py
--model_name_or_path t5-small
--train_file ./tests/fixtures/sample_text.txt
--validation_file ./tests/fixtures/sample_text.txt
--do_train
--do_eval
--max_seq_length 128
--per_device_train_batch_size 4
--per_device_eval_batch_size 4
--num_train_epochs 2
--logging_steps 2 --eval_steps 2
--output_dir {tmp_dir}
--overwrite_output_dir
""".split()
with patch.object(A__ , '''argv''' , A__ ):
run_ta_mlm_flax.main()
snake_case = get_results(A__ )
self.assertGreaterEqual(result['''eval_accuracy'''] , 0.4_2 )
@slow
def UpperCamelCase ( self ) -> int:
# with so little data distributed training needs more epochs to get the score on par with 0/1 gpu
snake_case = 7 if get_gpu_count() > 1 else 2
snake_case = self.get_auto_remove_tmp_dir()
snake_case = F"""
run_flax_ner.py
--model_name_or_path bert-base-uncased
--train_file tests/fixtures/tests_samples/conll/sample.json
--validation_file tests/fixtures/tests_samples/conll/sample.json
--output_dir {tmp_dir}
--overwrite_output_dir
--do_train
--do_eval
--warmup_steps=2
--learning_rate=2e-4
--logging_steps 2 --eval_steps 2
--per_device_train_batch_size=2
--per_device_eval_batch_size=2
--num_train_epochs={epochs}
--seed 7
""".split()
with patch.object(A__ , '''argv''' , A__ ):
run_flax_ner.main()
snake_case = get_results(A__ )
self.assertGreaterEqual(result['''eval_accuracy'''] , 0.7_5 )
self.assertGreaterEqual(result['''eval_f1'''] , 0.3 )
@slow
def UpperCamelCase ( self ) -> Any:
snake_case = self.get_auto_remove_tmp_dir()
snake_case = F"""
run_qa.py
--model_name_or_path bert-base-uncased
--version_2_with_negative
--train_file tests/fixtures/tests_samples/SQUAD/sample.json
--validation_file tests/fixtures/tests_samples/SQUAD/sample.json
--output_dir {tmp_dir}
--overwrite_output_dir
--num_train_epochs=3
--warmup_steps=2
--do_train
--do_eval
--logging_steps 2 --eval_steps 2
--learning_rate=2e-4
--per_device_train_batch_size=2
--per_device_eval_batch_size=1
""".split()
with patch.object(A__ , '''argv''' , A__ ):
run_qa.main()
snake_case = get_results(A__ )
self.assertGreaterEqual(result['''eval_f1'''] , 30 )
self.assertGreaterEqual(result['''eval_exact'''] , 30 )
| 44
| 1
|
'''simple docstring'''
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow
if is_torch_available():
import torch
from transformers import XLMRobertaModel
@require_sentencepiece
@require_tokenizers
@require_torch
class _lowercase ( unittest.TestCase ):
@slow
def UpperCamelCase ( self ) -> int:
snake_case = XLMRobertaModel.from_pretrained('''xlm-roberta-base''' )
snake_case = torch.tensor([[0, 5_81, 1_02_69, 83, 9_99_42, 1_36, 6_07_42, 23, 70, 8_05_83, 1_82_76, 2]] )
# The dog is cute and lives in the garden house
snake_case = torch.Size((1, 12, 7_68) ) # batch_size, sequence_length, embedding_vector_dim
snake_case = torch.tensor(
[[-0.0_1_0_1, 0.1_2_1_8, -0.0_8_0_3, 0.0_8_0_1, 0.1_3_2_7, 0.0_7_7_6, -0.1_2_1_5, 0.2_3_8_3, 0.3_3_3_8, 0.3_1_0_6, 0.0_3_0_0, 0.0_2_5_2]] )
# xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')
# xlmr.eval()
# expected_output_values_last_dim = xlmr.extract_features(input_ids[0])[:, :, -1]
with torch.no_grad():
snake_case = model(A__ )['''last_hidden_state'''].detach()
self.assertEqual(output.shape , A__ )
# compare the actual values for a slice of last dim
self.assertTrue(torch.allclose(output[:, :, -1] , A__ , atol=1e-3 ) )
@slow
def UpperCamelCase ( self ) -> Optional[int]:
snake_case = XLMRobertaModel.from_pretrained('''xlm-roberta-large''' )
snake_case = torch.tensor([[0, 5_81, 1_02_69, 83, 9_99_42, 1_36, 6_07_42, 23, 70, 8_05_83, 1_82_76, 2]] )
# The dog is cute and lives in the garden house
snake_case = torch.Size((1, 12, 10_24) ) # batch_size, sequence_length, embedding_vector_dim
snake_case = torch.tensor(
[[-0.0_6_9_9, -0.0_3_1_8, 0.0_7_0_5, -0.1_2_4_1, 0.0_9_9_9, -0.0_5_2_0, 0.1_0_0_4, -0.1_8_3_8, -0.4_7_0_4, 0.1_4_3_7, 0.0_8_2_1, 0.0_1_2_6]] )
# xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.large')
# xlmr.eval()
# expected_output_values_last_dim = xlmr.extract_features(input_ids[0])[:, :, -1]
with torch.no_grad():
snake_case = model(A__ )['''last_hidden_state'''].detach()
self.assertEqual(output.shape , A__ )
# compare the actual values for a slice of last dim
self.assertTrue(torch.allclose(output[:, :, -1] , A__ , atol=1e-3 ) )
| 44
|
'''simple docstring'''
from typing import Any, Dict, List, Optional, Tuple, Union
import torch
from torch import nn
from torch.utils.data import DistributedSampler, RandomSampler
from transformers import PreTrainedModel, Trainer, logging
from transformers.integrations import is_fairscale_available
from transformers.models.fsmt.configuration_fsmt import FSMTConfig
from transformers.optimization import (
Adafactor,
AdamW,
get_constant_schedule,
get_constant_schedule_with_warmup,
get_cosine_schedule_with_warmup,
get_cosine_with_hard_restarts_schedule_with_warmup,
get_linear_schedule_with_warmup,
get_polynomial_decay_schedule_with_warmup,
)
from transformers.trainer_pt_utils import get_tpu_sampler
from transformers.training_args import ParallelMode
from transformers.utils import is_torch_tpu_available
if is_fairscale_available():
from fairscale.optim import OSS
_lowercase = logging.get_logger(__name__)
_lowercase = {
'linear': get_linear_schedule_with_warmup,
'cosine': get_cosine_schedule_with_warmup,
'cosine_w_restarts': get_cosine_with_hard_restarts_schedule_with_warmup,
'polynomial': get_polynomial_decay_schedule_with_warmup,
'constant': get_constant_schedule,
'constant_w_warmup': get_constant_schedule_with_warmup,
}
class _lowercase ( __a ):
def __init__( self , A__=None , A__=None , *A__ , **A__ ) -> Union[str, Any]:
super().__init__(*A__ , **A__ )
if config is None:
assert isinstance(self.model , A__ ), (
"If no `config` is passed the model to be trained has to be of type `PreTrainedModel`, but is"
F""" {self.model.__class__}"""
)
snake_case = self.model.config
else:
snake_case = config
snake_case = data_args
snake_case = self.config.tgt_vocab_size if isinstance(self.config , A__ ) else self.config.vocab_size
if self.args.label_smoothing != 0 or (self.data_args is not None and self.data_args.ignore_pad_token_for_loss):
assert self.config.pad_token_id is not None, (
"Make sure that `config.pad_token_id` is correcly defined when ignoring `pad_token` for loss"
" calculation or doing label smoothing."
)
if self.config.pad_token_id is None and self.config.eos_token_id is not None:
logger.warning(
F"""The `config.pad_token_id` is `None`. Using `config.eos_token_id` = {self.config.eos_token_id} for"""
''' padding..''' )
if self.args.label_smoothing == 0:
snake_case = torch.nn.CrossEntropyLoss(ignore_index=self.config.pad_token_id )
else:
# dynamically import label_smoothed_nll_loss
from utils import label_smoothed_nll_loss
snake_case = label_smoothed_nll_loss
def UpperCamelCase ( self , A__ ) -> Tuple:
if self.optimizer is None:
snake_case = ['''bias''', '''LayerNorm.weight''']
snake_case = [
{
'''params''': [p for n, p in self.model.named_parameters() if not any(nd in n for nd in no_decay )],
'''weight_decay''': self.args.weight_decay,
},
{
'''params''': [p for n, p in self.model.named_parameters() if any(nd in n for nd in no_decay )],
'''weight_decay''': 0.0,
},
]
snake_case = Adafactor if self.args.adafactor else AdamW
if self.args.adafactor:
snake_case = Adafactor
snake_case = {'''scale_parameter''': False, '''relative_step''': False}
else:
snake_case = AdamW
snake_case = {
'''betas''': (self.args.adam_betaa, self.args.adam_betaa),
'''eps''': self.args.adam_epsilon,
}
snake_case = self.args.learning_rate
if self.sharded_ddp:
snake_case = OSS(
params=A__ , optim=A__ , **A__ , )
else:
snake_case = optimizer_cls(A__ , **A__ )
if self.lr_scheduler is None:
snake_case = self._get_lr_scheduler(A__ )
else: # ignoring --lr_scheduler
logger.warning('''scheduler is passed to `Seq2SeqTrainer`, `--lr_scheduler` arg is ignored.''' )
def UpperCamelCase ( self , A__ ) -> Tuple:
snake_case = arg_to_scheduler[self.args.lr_scheduler]
if self.args.lr_scheduler == "constant":
snake_case = schedule_func(self.optimizer )
elif self.args.lr_scheduler == "constant_w_warmup":
snake_case = schedule_func(self.optimizer , num_warmup_steps=self.args.warmup_steps )
else:
snake_case = schedule_func(
self.optimizer , num_warmup_steps=self.args.warmup_steps , num_training_steps=A__ )
return scheduler
def UpperCamelCase ( self ) -> Optional[torch.utils.data.Sampler]:
if isinstance(self.train_dataset , torch.utils.data.IterableDataset ):
return None
elif is_torch_tpu_available():
return get_tpu_sampler(self.train_dataset )
else:
if self.args.sortish_sampler:
self.train_dataset.make_sortish_sampler(
self.args.per_device_train_batch_size , distributed=(self.args.parallel_mode == ParallelMode.DISTRIBUTED) , )
return (
RandomSampler(self.train_dataset )
if self.args.local_rank == -1
else DistributedSampler(self.train_dataset )
)
def UpperCamelCase ( self , A__ , A__ , A__ ) -> List[Any]:
if self.args.label_smoothing == 0:
if self.data_args is not None and self.data_args.ignore_pad_token_for_loss:
# force training to ignore pad token
snake_case = model(**A__ , use_cache=A__ )[0]
snake_case = self.loss_fn(logits.view(-1 , logits.shape[-1] ) , labels.view(-1 ) )
else:
# compute usual loss via models
snake_case , snake_case = model(**A__ , labels=A__ , use_cache=A__ )[:2]
else:
# compute label smoothed loss
snake_case = model(**A__ , use_cache=A__ )[0]
snake_case = torch.nn.functional.log_softmax(A__ , dim=-1 )
snake_case , snake_case = self.loss_fn(A__ , A__ , self.args.label_smoothing , ignore_index=self.config.pad_token_id )
return loss, logits
def UpperCamelCase ( self , A__ , A__ ) -> Any:
snake_case = inputs.pop('''labels''' )
snake_case , snake_case = self._compute_loss(A__ , A__ , A__ )
return loss
def UpperCamelCase ( self , A__ , A__ , A__ , A__ = None , ) -> Tuple[Optional[float], Optional[torch.Tensor], Optional[torch.Tensor]]:
snake_case = self._prepare_inputs(A__ )
snake_case = {
'''max_length''': self.data_args.val_max_target_length
if self.data_args is not None
else self.config.max_length,
'''num_beams''': self.data_args.eval_beams if self.data_args is not None else self.config.num_beams,
}
if self.args.predict_with_generate and not self.args.prediction_loss_only:
snake_case = self.model.generate(
inputs['''input_ids'''] , attention_mask=inputs['''attention_mask'''] , **A__ , )
# in case the batch is shorter than max length, the output should be padded
if generated_tokens.shape[-1] < gen_kwargs["max_length"]:
snake_case = self._pad_tensors_to_max_len(A__ , gen_kwargs['''max_length'''] )
snake_case = inputs.pop('''labels''' )
with torch.no_grad():
# compute loss on predict data
snake_case , snake_case = self._compute_loss(A__ , A__ , A__ )
snake_case = loss.mean().detach()
if self.args.prediction_loss_only:
return (loss, None, None)
snake_case = generated_tokens if self.args.predict_with_generate else logits
if labels.shape[-1] < gen_kwargs["max_length"]:
snake_case = self._pad_tensors_to_max_len(A__ , gen_kwargs['''max_length'''] )
return (loss, logits, labels)
def UpperCamelCase ( self , A__ , A__ ) -> List[str]:
# If PAD token is not defined at least EOS token has to be defined
snake_case = self.config.pad_token_id if self.config.pad_token_id is not None else self.config.eos_token_id
if pad_token_id is None:
raise ValueError(
'''Make sure that either `config.pad_token_id` or `config.eos_token_id` is defined if tensor has to be'''
F""" padded to `max_length`={max_length}""" )
snake_case = pad_token_id * torch.ones(
(tensor.shape[0], max_length) , dtype=tensor.dtype , device=tensor.device )
snake_case = tensor
return padded_tensor
| 44
| 1
|
'''simple docstring'''
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
from seqaseq_trainer import SeqaSeqTrainer
from seqaseq_training_args import SeqaSeqTrainingArguments
import transformers
from transformers import (
AutoConfig,
AutoModelForSeqaSeqLM,
AutoTokenizer,
HfArgumentParser,
MBartTokenizer,
MBartTokenizerFast,
set_seed,
)
from transformers.trainer_utils import EvaluationStrategy, is_main_process
from transformers.training_args import ParallelMode
from utils import (
SeqaSeqDataCollator,
SeqaSeqDataset,
assert_all_frozen,
build_compute_metrics_fn,
check_output_dir,
freeze_embeds,
freeze_params,
lmap,
save_json,
use_task_specific_params,
write_txt_file,
)
_lowercase = logging.getLogger(__name__)
@dataclass
class _lowercase :
_UpperCAmelCase = field(
metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''} )
_UpperCAmelCase = field(
default=__a , metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''} )
_UpperCAmelCase = field(
default=__a , metadata={'''help''': '''Pretrained tokenizer name or path if not the same as model_name'''} )
_UpperCAmelCase = field(
default=__a , metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''} , )
_UpperCAmelCase = field(default=__a , metadata={'''help''': '''Whether tp freeze the encoder.'''} )
_UpperCAmelCase = field(default=__a , metadata={'''help''': '''Whether to freeze the embeddings.'''} )
@dataclass
class _lowercase :
_UpperCAmelCase = field(
metadata={'''help''': '''The input data dir. Should contain the .tsv files (or other data files) for the task.'''} )
_UpperCAmelCase = field(
default='''summarization''' , metadata={'''help''': '''Task name, summarization (or summarization_{dataset} for pegasus) or translation'''} , )
_UpperCAmelCase = field(
default=1_024 , metadata={
'''help''': (
'''The maximum total input sequence length after tokenization. Sequences longer '''
'''than this will be truncated, sequences shorter will be padded.'''
)
} , )
_UpperCAmelCase = field(
default=128 , metadata={
'''help''': (
'''The maximum total sequence length for target text after tokenization. Sequences longer '''
'''than this will be truncated, sequences shorter will be padded.'''
)
} , )
_UpperCAmelCase = field(
default=142 , metadata={
'''help''': (
'''The maximum total sequence length for validation target text after tokenization. Sequences longer '''
'''than this will be truncated, sequences shorter will be padded. '''
'''This argument is also used to override the ``max_length`` param of ``model.generate``, which is used '''
'''during ``evaluate`` and ``predict``.'''
)
} , )
_UpperCAmelCase = field(
default=142 , metadata={
'''help''': (
'''The maximum total sequence length for test target text after tokenization. Sequences longer '''
'''than this will be truncated, sequences shorter will be padded.'''
)
} , )
_UpperCAmelCase = field(default=-1 , metadata={'''help''': '''# training examples. -1 means use all.'''} )
_UpperCAmelCase = field(default=-1 , metadata={'''help''': '''# validation examples. -1 means use all.'''} )
_UpperCAmelCase = field(default=-1 , metadata={'''help''': '''# test examples. -1 means use all.'''} )
_UpperCAmelCase = field(default=__a , metadata={'''help''': '''Source language id for translation.'''} )
_UpperCAmelCase = field(default=__a , metadata={'''help''': '''Target language id for translation.'''} )
_UpperCAmelCase = field(default=__a , metadata={'''help''': '''# num_beams to use for evaluation.'''} )
_UpperCAmelCase = field(
default=__a , metadata={'''help''': '''If only pad tokens should be ignored. This assumes that `config.pad_token_id` is defined.'''} , )
def __UpperCamelCase ( a : str , a : Union[str, Any] , a : Optional[Any] ) ->Optional[Any]:
logger.info(f"""***** {split} metrics *****""" )
for key in sorted(metrics.keys() ):
logger.info(f""" {key} = {metrics[key]}""" )
save_json(a , os.path.join(a , f"""{split}_results.json""" ) )
def __UpperCamelCase ( ) ->str:
# 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.
snake_case = HfArgumentParser((ModelArguments, DataTrainingArguments, SeqaSeqTrainingArguments) )
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.
snake_case , snake_case , snake_case = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
else:
snake_case , snake_case , snake_case = parser.parse_args_into_dataclasses()
check_output_dir(a )
# Setup logging
logging.basicConfig(
format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''' , datefmt='''%m/%d/%Y %H:%M:%S''' , level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN , )
logger.warning(
'''Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s''' , training_args.local_rank , training_args.device , training_args.n_gpu , bool(training_args.parallel_mode == ParallelMode.DISTRIBUTED ) , training_args.fpaa , )
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
# Set the verbosity to info of the Transformers logger (on main process only):
if is_main_process(training_args.local_rank ):
transformers.utils.logging.set_verbosity_info()
logger.info('''Training/evaluation parameters %s''' , a )
# Set seed
set_seed(training_args.seed )
# Load pretrained model and tokenizer
#
# Distributed training:
# The .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
snake_case = AutoConfig.from_pretrained(
model_args.config_name if model_args.config_name else model_args.model_name_or_path , cache_dir=model_args.cache_dir , )
snake_case = ('''encoder_layerdrop''', '''decoder_layerdrop''', '''dropout''', '''attention_dropout''')
for p in extra_model_params:
if getattr(a , a , a ):
assert hasattr(a , a ), f"""({config.__class__.__name__}) doesn't have a `{p}` attribute"""
setattr(a , a , getattr(a , a ) )
snake_case = AutoTokenizer.from_pretrained(
model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path , cache_dir=model_args.cache_dir , )
snake_case = AutoModelForSeqaSeqLM.from_pretrained(
model_args.model_name_or_path , from_tf='''.ckpt''' in model_args.model_name_or_path , config=a , cache_dir=model_args.cache_dir , )
# use task specific params
use_task_specific_params(a , data_args.task )
# set num_beams for evaluation
if data_args.eval_beams is None:
snake_case = model.config.num_beams
# set decoder_start_token_id for MBart
if model.config.decoder_start_token_id is None and isinstance(a , (MBartTokenizer, MBartTokenizerFast) ):
assert (
data_args.tgt_lang is not None and data_args.src_lang is not None
), "mBart requires --tgt_lang and --src_lang"
if isinstance(a , a ):
snake_case = tokenizer.lang_code_to_id[data_args.tgt_lang]
else:
snake_case = tokenizer.convert_tokens_to_ids(data_args.tgt_lang )
if model_args.freeze_embeds:
freeze_embeds(a )
if model_args.freeze_encoder:
freeze_params(model.get_encoder() )
assert_all_frozen(model.get_encoder() )
snake_case = SeqaSeqDataset
# Get datasets
snake_case = (
dataset_class(
a , type_path='''train''' , data_dir=data_args.data_dir , n_obs=data_args.n_train , max_target_length=data_args.max_target_length , max_source_length=data_args.max_source_length , prefix=model.config.prefix or '''''' , )
if training_args.do_train
else None
)
snake_case = (
dataset_class(
a , type_path='''val''' , data_dir=data_args.data_dir , n_obs=data_args.n_val , max_target_length=data_args.val_max_target_length , max_source_length=data_args.max_source_length , prefix=model.config.prefix or '''''' , )
if training_args.do_eval or training_args.evaluation_strategy != EvaluationStrategy.NO
else None
)
snake_case = (
dataset_class(
a , type_path='''test''' , data_dir=data_args.data_dir , n_obs=data_args.n_test , max_target_length=data_args.test_max_target_length , max_source_length=data_args.max_source_length , prefix=model.config.prefix or '''''' , )
if training_args.do_predict
else None
)
# Initialize our Trainer
snake_case = (
build_compute_metrics_fn(data_args.task , a ) if training_args.predict_with_generate else None
)
snake_case = SeqaSeqTrainer(
model=a , args=a , data_args=a , train_dataset=a , eval_dataset=a , data_collator=SeqaSeqDataCollator(
a , a , model.config.decoder_start_token_id , training_args.tpu_num_cores ) , compute_metrics=a , tokenizer=a , )
snake_case = {}
# Training
if training_args.do_train:
logger.info('''*** Train ***''' )
snake_case = trainer.train(
model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path ) else None )
snake_case = train_result.metrics
snake_case = data_args.n_train
trainer.save_model() # this also saves the tokenizer
if trainer.is_world_process_zero():
handle_metrics('''train''' , a , training_args.output_dir )
all_metrics.update(a )
# Need to save the state, since Trainer.save_model saves only the tokenizer with the model
trainer.state.save_to_json(os.path.join(training_args.output_dir , '''trainer_state.json''' ) )
# For convenience, we also re-save the tokenizer to the same directory,
# so that you can share your model easily on huggingface.co/models =)
tokenizer.save_pretrained(training_args.output_dir )
# Evaluation
if training_args.do_eval:
logger.info('''*** Evaluate ***''' )
snake_case = trainer.evaluate(metric_key_prefix='''val''' )
snake_case = data_args.n_val
snake_case = round(metrics['''val_loss'''] , 4 )
if trainer.is_world_process_zero():
handle_metrics('''val''' , a , training_args.output_dir )
all_metrics.update(a )
if training_args.do_predict:
logger.info('''*** Predict ***''' )
snake_case = trainer.predict(test_dataset=a , metric_key_prefix='''test''' )
snake_case = test_output.metrics
snake_case = data_args.n_test
if trainer.is_world_process_zero():
snake_case = round(metrics['''test_loss'''] , 4 )
handle_metrics('''test''' , a , training_args.output_dir )
all_metrics.update(a )
if training_args.predict_with_generate:
snake_case = tokenizer.batch_decode(
test_output.predictions , skip_special_tokens=a , clean_up_tokenization_spaces=a )
snake_case = lmap(str.strip , a )
write_txt_file(a , os.path.join(training_args.output_dir , '''test_generations.txt''' ) )
if trainer.is_world_process_zero():
save_json(a , os.path.join(training_args.output_dir , '''all_results.json''' ) )
return all_metrics
def __UpperCamelCase ( a : List[str] ) ->List[str]:
# For xla_spawn (TPUs)
main()
if __name__ == "__main__":
main()
| 44
|
'''simple docstring'''
import inspect
import re
from hashlib import shaaaa
from typing import Dict, List
from .arrow import arrow
from .audiofolder import audiofolder
from .csv import csv
from .imagefolder import imagefolder
from .json import json
from .pandas import pandas
from .parquet import parquet
from .sql import sql # noqa F401
from .text import text
def __UpperCamelCase ( a : List[str] ) ->str:
snake_case = []
for line in lines:
snake_case = re.sub(R'''#.*''' , '''''' , a ) # remove comments
if line:
filtered_lines.append(a )
snake_case = '''\n'''.join(a )
# Make a hash from all this code
snake_case = full_str.encode('''utf-8''' )
return shaaaa(a ).hexdigest()
# get importable module names and hash for caching
_lowercase = {
'csv': (csv.__name__, _hash_python_lines(inspect.getsource(csv).splitlines())),
'json': (json.__name__, _hash_python_lines(inspect.getsource(json).splitlines())),
'pandas': (pandas.__name__, _hash_python_lines(inspect.getsource(pandas).splitlines())),
'parquet': (parquet.__name__, _hash_python_lines(inspect.getsource(parquet).splitlines())),
'arrow': (arrow.__name__, _hash_python_lines(inspect.getsource(arrow).splitlines())),
'text': (text.__name__, _hash_python_lines(inspect.getsource(text).splitlines())),
'imagefolder': (imagefolder.__name__, _hash_python_lines(inspect.getsource(imagefolder).splitlines())),
'audiofolder': (audiofolder.__name__, _hash_python_lines(inspect.getsource(audiofolder).splitlines())),
}
# Used to infer the module to use based on the data files extensions
_lowercase = {
'.csv': ('csv', {}),
'.tsv': ('csv', {'sep': '\t'}),
'.json': ('json', {}),
'.jsonl': ('json', {}),
'.parquet': ('parquet', {}),
'.arrow': ('arrow', {}),
'.txt': ('text', {}),
}
_EXTENSION_TO_MODULE.update({ext: ('imagefolder', {}) for ext in imagefolder.ImageFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext.upper(): ('imagefolder', {}) for ext in imagefolder.ImageFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext: ('audiofolder', {}) for ext in audiofolder.AudioFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext.upper(): ('audiofolder', {}) for ext in audiofolder.AudioFolder.EXTENSIONS})
_lowercase = {'imagefolder', 'audiofolder'}
# Used to filter data files based on extensions given a module name
_lowercase = {}
for _ext, (_module, _) in _EXTENSION_TO_MODULE.items():
_MODULE_TO_EXTENSIONS.setdefault(_module, []).append(_ext)
_MODULE_TO_EXTENSIONS["imagefolder"].append('.zip')
_MODULE_TO_EXTENSIONS["audiofolder"].append('.zip')
| 44
| 1
|
'''simple docstring'''
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Audio, Features, Value
from .base import TaskTemplate
@dataclass(frozen=__a )
class _lowercase ( __a ):
_UpperCAmelCase = field(default='''automatic-speech-recognition''' , metadata={'''include_in_asdict_even_if_is_default''': True} )
_UpperCAmelCase = Features({'''audio''': Audio()} )
_UpperCAmelCase = Features({'''transcription''': Value('''string''' )} )
_UpperCAmelCase = "audio"
_UpperCAmelCase = "transcription"
def UpperCamelCase ( self , A__ ) -> List[Any]:
if self.audio_column not in features:
raise ValueError(F"""Column {self.audio_column} is not present in features.""" )
if not isinstance(features[self.audio_column] , A__ ):
raise ValueError(F"""Column {self.audio_column} is not an Audio type.""" )
snake_case = copy.deepcopy(self )
snake_case = self.input_schema.copy()
snake_case = features[self.audio_column]
snake_case = input_schema
return task_template
@property
def UpperCamelCase ( self ) -> Dict[str, str]:
return {self.audio_column: "audio", self.transcription_column: "transcription"}
| 44
|
'''simple docstring'''
_lowercase = {
'Pillow': 'Pillow',
'accelerate': 'accelerate>=0.11.0',
'compel': 'compel==0.1.8',
'black': 'black~=23.1',
'datasets': 'datasets',
'filelock': 'filelock',
'flax': 'flax>=0.4.1',
'hf-doc-builder': 'hf-doc-builder>=0.3.0',
'huggingface-hub': 'huggingface-hub>=0.13.2',
'requests-mock': 'requests-mock==1.10.0',
'importlib_metadata': 'importlib_metadata',
'invisible-watermark': 'invisible-watermark',
'isort': 'isort>=5.5.4',
'jax': 'jax>=0.2.8,!=0.3.2',
'jaxlib': 'jaxlib>=0.1.65',
'Jinja2': 'Jinja2',
'k-diffusion': 'k-diffusion>=0.0.12',
'torchsde': 'torchsde',
'note_seq': 'note_seq',
'librosa': 'librosa',
'numpy': 'numpy',
'omegaconf': 'omegaconf',
'parameterized': 'parameterized',
'protobuf': 'protobuf>=3.20.3,<4',
'pytest': 'pytest',
'pytest-timeout': 'pytest-timeout',
'pytest-xdist': 'pytest-xdist',
'ruff': 'ruff>=0.0.241',
'safetensors': 'safetensors',
'sentencepiece': 'sentencepiece>=0.1.91,!=0.1.92',
'scipy': 'scipy',
'onnx': 'onnx',
'regex': 'regex!=2019.12.17',
'requests': 'requests',
'tensorboard': 'tensorboard',
'torch': 'torch>=1.4',
'torchvision': 'torchvision',
'transformers': 'transformers>=4.25.1',
'urllib3': 'urllib3<=2.0.0',
}
| 44
| 1
|
'''simple docstring'''
import fire
from utils import calculate_rouge, save_json
def __UpperCamelCase ( a : List[Any] , a : str , a : Optional[Any]=None , **a : int ) ->Any:
snake_case = [x.strip() for x in open(a ).readlines()]
snake_case = [x.strip() for x in open(a ).readlines()][: len(a )]
snake_case = calculate_rouge(a , a , **a )
if save_path is not None:
save_json(a , a , indent=a )
return metrics # these print nicely
if __name__ == "__main__":
fire.Fire(calculate_rouge_path)
| 44
|
'''simple docstring'''
import random
import unittest
import torch
from diffusers import IFInpaintingSuperResolutionPipeline
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_INPAINTING_BATCH_PARAMS,
TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin
@skip_mps
class _lowercase ( __a , __a , unittest.TestCase ):
_UpperCAmelCase = IFInpaintingSuperResolutionPipeline
_UpperCAmelCase = TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {'''width''', '''height'''}
_UpperCAmelCase = TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS.union({'''original_image'''} )
_UpperCAmelCase = PipelineTesterMixin.required_optional_params - {'''latents'''}
def UpperCamelCase ( self ) -> int:
return self._get_superresolution_dummy_components()
def UpperCamelCase ( self , A__ , A__=0 ) -> Union[str, Any]:
if str(A__ ).startswith('''mps''' ):
snake_case = torch.manual_seed(A__ )
else:
snake_case = torch.Generator(device=A__ ).manual_seed(A__ )
snake_case = floats_tensor((1, 3, 16, 16) , rng=random.Random(A__ ) ).to(A__ )
snake_case = floats_tensor((1, 3, 32, 32) , rng=random.Random(A__ ) ).to(A__ )
snake_case = floats_tensor((1, 3, 32, 32) , rng=random.Random(A__ ) ).to(A__ )
snake_case = {
'''prompt''': '''A painting of a squirrel eating a burger''',
'''image''': image,
'''original_image''': original_image,
'''mask_image''': mask_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 UpperCamelCase ( self ) -> List[Any]:
self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3 )
def UpperCamelCase ( self ) -> Optional[Any]:
self._test_save_load_optional_components()
@unittest.skipIf(torch_device != '''cuda''' , reason='''float16 requires CUDA''' )
def UpperCamelCase ( self ) -> List[str]:
# Due to non-determinism in save load of the hf-internal-testing/tiny-random-t5 text encoder
super().test_save_load_floataa(expected_max_diff=1e-1 )
def UpperCamelCase ( self ) -> int:
self._test_attention_slicing_forward_pass(expected_max_diff=1e-2 )
def UpperCamelCase ( self ) -> Optional[Any]:
self._test_save_load_local()
def UpperCamelCase ( self ) -> Dict:
self._test_inference_batch_single_identical(
expected_max_diff=1e-2 , )
| 44
| 1
|
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowercase = logging.get_logger(__name__)
_lowercase = {
'vinvino02/glpn-kitti': 'https://huggingface.co/vinvino02/glpn-kitti/resolve/main/config.json',
# See all GLPN models at https://huggingface.co/models?filter=glpn
}
class _lowercase ( __a ):
_UpperCAmelCase = '''glpn'''
def __init__( self , A__=3 , A__=4 , A__=[2, 2, 2, 2] , A__=[8, 4, 2, 1] , A__=[32, 64, 1_60, 2_56] , A__=[7, 3, 3, 3] , A__=[4, 2, 2, 2] , A__=[1, 2, 5, 8] , A__=[4, 4, 4, 4] , A__="gelu" , A__=0.0 , A__=0.0 , A__=0.0_2 , A__=0.1 , A__=1e-6 , A__=64 , A__=10 , A__=-1 , **A__ , ) -> Union[str, Any]:
super().__init__(**A__ )
snake_case = num_channels
snake_case = num_encoder_blocks
snake_case = depths
snake_case = sr_ratios
snake_case = hidden_sizes
snake_case = patch_sizes
snake_case = strides
snake_case = mlp_ratios
snake_case = num_attention_heads
snake_case = hidden_act
snake_case = hidden_dropout_prob
snake_case = attention_probs_dropout_prob
snake_case = initializer_range
snake_case = drop_path_rate
snake_case = layer_norm_eps
snake_case = decoder_hidden_size
snake_case = max_depth
snake_case = head_in_index
| 44
|
'''simple docstring'''
from typing import Dict, List, Optional, Union
import numpy as np
from .feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from .utils import PaddingStrategy, TensorType, is_tf_tensor, is_torch_tensor, logging, to_numpy
_lowercase = logging.get_logger(__name__)
class _lowercase ( __a ):
def __init__( self , A__ , A__ , A__ , **A__ ) -> Union[str, Any]:
snake_case = feature_size
snake_case = sampling_rate
snake_case = padding_value
snake_case = kwargs.pop('''padding_side''' , '''right''' )
snake_case = kwargs.pop('''return_attention_mask''' , A__ )
super().__init__(**A__ )
def UpperCamelCase ( self , A__ , A__ = True , A__ = None , A__ = False , A__ = None , A__ = None , A__ = None , ) -> BatchFeature:
# If we have a list of dicts, let's convert it in a dict of lists
# We do this to allow using this method as a collate_fn function in PyTorch Dataloader
if isinstance(A__ , (list, tuple) ) and isinstance(processed_features[0] , (dict, BatchFeature) ):
snake_case = {
key: [example[key] for example in processed_features] for key in processed_features[0].keys()
}
# The model's main input name, usually `input_values`, has be passed for padding
if self.model_input_names[0] not in processed_features:
raise ValueError(
'''You should supply an instance of `transformers.BatchFeature` or list of `transformers.BatchFeature`'''
F""" to this method that includes {self.model_input_names[0]}, but you provided"""
F""" {list(processed_features.keys() )}""" )
snake_case = processed_features[self.model_input_names[0]]
snake_case = (
return_attention_mask if return_attention_mask is not None else self.return_attention_mask
)
if len(A__ ) == 0:
if return_attention_mask:
snake_case = []
return processed_features
# If we have PyTorch/TF tensors or lists as inputs, we cast them as Numpy arrays
# and rebuild them afterwards if no return_tensors is specified
# Note that we lose the specific device the tensor may be on for PyTorch
snake_case = required_input[0]
if isinstance(A__ , (list, tuple) ):
# first_element might be an empty list/tuple in some edge cases so we grab the first non empty element.
snake_case = 0
while len(required_input[index] ) == 0:
index += 1
if index < len(A__ ):
snake_case = required_input[index][0]
if return_tensors is None:
if is_tf_tensor(A__ ):
snake_case = '''tf'''
elif is_torch_tensor(A__ ):
snake_case = '''pt'''
elif isinstance(A__ , (int, float, list, tuple, np.ndarray) ):
snake_case = '''np'''
else:
raise ValueError(
F"""type of {first_element} unknown: {type(A__ )}. """
'''Should be one of a python, numpy, pytorch or tensorflow object.''' )
for key, value in processed_features.items():
if isinstance(value[0] , (int, float) ):
snake_case = to_numpy(A__ )
else:
snake_case = [to_numpy(A__ ) for v in value]
# Convert padding_strategy in PaddingStrategy
snake_case = self._get_padding_strategies(padding=A__ , max_length=A__ )
snake_case = processed_features[self.model_input_names[0]]
snake_case = len(A__ )
if not all(len(A__ ) == batch_size for v in processed_features.values() ):
raise ValueError('''Some items in the output dictionary have a different batch size than others.''' )
snake_case = []
for i in range(A__ ):
snake_case = {k: v[i] for k, v in processed_features.items()}
# truncation
snake_case = self._truncate(
A__ , max_length=A__ , pad_to_multiple_of=A__ , truncation=A__ , )
truncated_inputs.append(A__ )
if padding_strategy == PaddingStrategy.LONGEST:
# make sure that `max_length` cannot be longer than the longest truncated length
snake_case = max(len(input_slice[self.model_input_names[0]] ) for input_slice in truncated_inputs )
snake_case = PaddingStrategy.MAX_LENGTH
snake_case = {}
for i in range(A__ ):
# padding
snake_case = self._pad(
truncated_inputs[i] , max_length=A__ , padding_strategy=A__ , pad_to_multiple_of=A__ , return_attention_mask=A__ , )
for key, value in outputs.items():
if key not in batch_outputs:
snake_case = []
if value.dtype is np.dtype(np.floataa ):
snake_case = value.astype(np.floataa )
batch_outputs[key].append(A__ )
return BatchFeature(A__ , tensor_type=A__ )
def UpperCamelCase ( self , A__ , A__ = None , A__ = PaddingStrategy.DO_NOT_PAD , A__ = None , A__ = None , ) -> dict:
snake_case = processed_features[self.model_input_names[0]]
if padding_strategy == PaddingStrategy.LONGEST:
snake_case = len(A__ )
if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
snake_case = ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of
snake_case = padding_strategy != PaddingStrategy.DO_NOT_PAD and len(A__ ) < max_length
if return_attention_mask and "attention_mask" not in processed_features:
snake_case = np.ones(len(A__ ) , dtype=np.intaa )
if needs_to_be_padded:
snake_case = max_length - len(A__ )
if self.padding_side == "right":
if return_attention_mask:
snake_case = np.pad(
processed_features['''attention_mask'''] , (0, difference) )
snake_case = ((0, difference), (0, 0)) if self.feature_size > 1 else (0, difference)
snake_case = np.pad(
A__ , A__ , '''constant''' , constant_values=self.padding_value )
elif self.padding_side == "left":
if return_attention_mask:
snake_case = np.pad(
processed_features['''attention_mask'''] , (difference, 0) )
snake_case = ((difference, 0), (0, 0)) if self.feature_size > 1 else (difference, 0)
snake_case = np.pad(
A__ , A__ , '''constant''' , constant_values=self.padding_value )
else:
raise ValueError('''Invalid padding strategy:''' + str(self.padding_side ) )
return processed_features
def UpperCamelCase ( self , A__ , A__ = None , A__ = None , A__ = None , ) -> Union[str, Any]:
if not truncation:
return processed_features
elif truncation and max_length is None:
raise ValueError('''When setting ``truncation=True``, make sure that ``max_length`` is defined.''' )
snake_case = processed_features[self.model_input_names[0]]
# find `max_length` that fits `pad_to_multiple_of`
if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
snake_case = ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of
snake_case = len(A__ ) > max_length
if needs_to_be_truncated:
snake_case = processed_features[self.model_input_names[0]][:max_length]
if "attention_mask" in processed_features:
snake_case = processed_features['''attention_mask'''][:max_length]
return processed_features
def UpperCamelCase ( self , A__=False , A__=None ) -> Union[str, Any]:
# Get padding strategy
if padding is not False:
if padding is True:
snake_case = PaddingStrategy.LONGEST # Default to pad to the longest sequence in the batch
elif not isinstance(A__ , A__ ):
snake_case = PaddingStrategy(A__ )
elif isinstance(A__ , A__ ):
snake_case = padding
else:
snake_case = PaddingStrategy.DO_NOT_PAD
# Set max length if needed
if max_length is None:
if padding_strategy == PaddingStrategy.MAX_LENGTH:
raise ValueError(
F"""When setting ``padding={PaddingStrategy.MAX_LENGTH}``, make sure that max_length is defined""" )
# Test if we have a padding value
if padding_strategy != PaddingStrategy.DO_NOT_PAD and (self.padding_value is None):
raise ValueError(
'''Asking to pad but the feature_extractor does not have a padding value. Please select a value to use'''
''' as `padding_value`. For example: `feature_extractor.padding_value = 0.0`.''' )
return padding_strategy
| 44
| 1
|
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowercase = logging.get_logger(__name__)
_lowercase = {
'tanreinama/GPTSAN-2.8B-spout_is_uniform': (
'https://huggingface.co/tanreinama/GPTSAN-2.8B-spout_is_uniform/resolve/main/config.json'
),
}
class _lowercase ( __a ):
_UpperCAmelCase = '''gptsan-japanese'''
_UpperCAmelCase = [
'''past_key_values''',
]
_UpperCAmelCase = {
'''hidden_size''': '''d_model''',
'''num_attention_heads''': '''num_heads''',
'''num_hidden_layers''': '''num_layers''',
}
def __init__( self , A__=3_60_00 , A__=12_80 , A__=10_24 , A__=81_92 , A__=40_96 , A__=1_28 , A__=10 , A__=0 , A__=16 , A__=16 , A__=1_28 , A__=0.0 , A__=1e-5 , A__=False , A__=0.0 , A__="float32" , A__=False , A__=False , A__=False , A__=0.0_0_2 , A__=False , A__=True , A__=3_59_98 , A__=3_59_95 , A__=3_59_99 , **A__ , ) -> List[Any]:
snake_case = vocab_size
snake_case = max_position_embeddings
snake_case = d_model
snake_case = d_ff
snake_case = d_ext
snake_case = d_spout
snake_case = num_switch_layers
snake_case = num_ext_layers
snake_case = num_switch_layers + num_ext_layers
snake_case = num_heads
snake_case = num_experts
snake_case = expert_capacity
snake_case = dropout_rate
snake_case = layer_norm_epsilon
snake_case = router_bias
snake_case = router_jitter_noise
snake_case = router_dtype
snake_case = router_ignore_padding_tokens
snake_case = output_hidden_states
snake_case = output_attentions
snake_case = initializer_factor
snake_case = output_router_logits
snake_case = use_cache
super().__init__(
separator_token_id=A__ , pad_token_id=A__ , eos_token_id=A__ , **A__ , )
| 44
|
'''simple docstring'''
from collections import Counter
from pathlib import Path
from typing import Optional, Tuple
import yaml
class _lowercase ( yaml.SafeLoader ):
def UpperCamelCase ( self , A__ ) -> List[str]:
snake_case = [self.constructed_objects[key_node] for key_node, _ in node.value]
snake_case = [tuple(A__ ) if isinstance(A__ , A__ ) else key for key in keys]
snake_case = Counter(A__ )
snake_case = [key for key in counter if counter[key] > 1]
if duplicate_keys:
raise TypeError(F"""Got duplicate yaml keys: {duplicate_keys}""" )
def UpperCamelCase ( self , A__ , A__=False ) -> List[Any]:
snake_case = super().construct_mapping(A__ , deep=A__ )
self._check_no_duplicates_on_constructed_node(A__ )
return mapping
def __UpperCamelCase ( a : str ) ->Tuple[Optional[str], str]:
snake_case = list(readme_content.splitlines() )
if full_content and full_content[0] == "---" and "---" in full_content[1:]:
snake_case = full_content[1:].index('''---''' ) + 1
snake_case = '''\n'''.join(full_content[1:sep_idx] )
return yamlblock, "\n".join(full_content[sep_idx + 1 :] )
return None, "\n".join(a )
class _lowercase ( __a ):
# class attributes
_UpperCAmelCase = {'''train_eval_index'''} # train-eval-index in the YAML metadata
@classmethod
def UpperCamelCase ( cls , A__ ) -> "DatasetMetadata":
with open(A__ , encoding='''utf-8''' ) as readme_file:
snake_case , snake_case = _split_yaml_from_readme(readme_file.read() )
if yaml_string is not None:
return cls.from_yaml_string(A__ )
else:
return cls()
def UpperCamelCase ( self , A__ ) -> str:
if path.exists():
with open(A__ , encoding='''utf-8''' ) as readme_file:
snake_case = readme_file.read()
else:
snake_case = None
snake_case = self._to_readme(A__ )
with open(A__ , '''w''' , encoding='''utf-8''' ) as readme_file:
readme_file.write(A__ )
def UpperCamelCase ( self , A__ = None ) -> str:
if readme_content is not None:
snake_case , snake_case = _split_yaml_from_readme(A__ )
snake_case = '''---\n''' + self.to_yaml_string() + '''---\n''' + content
else:
snake_case = '''---\n''' + self.to_yaml_string() + '''---\n'''
return full_content
@classmethod
def UpperCamelCase ( cls , A__ ) -> "DatasetMetadata":
snake_case = yaml.load(A__ , Loader=_NoDuplicateSafeLoader ) or {}
# Convert the YAML keys to DatasetMetadata fields
snake_case = {
(key.replace('''-''' , '''_''' ) if key.replace('''-''' , '''_''' ) in cls._FIELDS_WITH_DASHES else key): value
for key, value in metadata_dict.items()
}
return cls(**A__ )
def UpperCamelCase ( self ) -> str:
return yaml.safe_dump(
{
(key.replace('''_''' , '''-''' ) if key in self._FIELDS_WITH_DASHES else key): value
for key, value in self.items()
} , sort_keys=A__ , allow_unicode=A__ , encoding='''utf-8''' , ).decode('''utf-8''' )
_lowercase = {
'image-classification': [],
'translation': [],
'image-segmentation': [],
'fill-mask': [],
'automatic-speech-recognition': [],
'token-classification': [],
'sentence-similarity': [],
'audio-classification': [],
'question-answering': [],
'summarization': [],
'zero-shot-classification': [],
'table-to-text': [],
'feature-extraction': [],
'other': [],
'multiple-choice': [],
'text-classification': [],
'text-to-image': [],
'text2text-generation': [],
'zero-shot-image-classification': [],
'tabular-classification': [],
'tabular-regression': [],
'image-to-image': [],
'tabular-to-text': [],
'unconditional-image-generation': [],
'text-retrieval': [],
'text-to-speech': [],
'object-detection': [],
'audio-to-audio': [],
'text-generation': [],
'conversational': [],
'table-question-answering': [],
'visual-question-answering': [],
'image-to-text': [],
'reinforcement-learning': [],
'voice-activity-detection': [],
'time-series-forecasting': [],
'document-question-answering': [],
}
if __name__ == "__main__":
from argparse import ArgumentParser
_lowercase = ArgumentParser(usage='Validate the yaml metadata block of a README.md file.')
ap.add_argument('readme_filepath')
_lowercase = ap.parse_args()
_lowercase = Path(args.readme_filepath)
_lowercase = DatasetMetadata.from_readme(readme_filepath)
print(dataset_metadata)
dataset_metadata.to_readme(readme_filepath)
| 44
| 1
|
'''simple docstring'''
from typing import List, Optional, Union
import numpy as np
from ....audio_utils import mel_filter_bank, optimal_fft_length, spectrogram, window_function
from ....feature_extraction_sequence_utils import SequenceFeatureExtractor
from ....feature_extraction_utils import BatchFeature
from ....file_utils import PaddingStrategy, TensorType
from ....utils import logging
_lowercase = logging.get_logger(__name__)
class _lowercase ( __a ):
_UpperCAmelCase = ['''input_features''', '''attention_mask''']
def __init__( self , A__=80 , A__=1_60_00 , A__=0.0 , A__=10 , A__=25 , A__="hamming_window" , A__=3_2_7_6_8.0 , A__=0.9_7 , A__=1.0 , A__=True , A__=True , A__=False , **A__ , ) -> Union[str, Any]:
super().__init__(feature_size=A__ , sampling_rate=A__ , padding_value=A__ , **A__ )
snake_case = feature_size
snake_case = sampling_rate
snake_case = padding_value
snake_case = hop_length
snake_case = win_length
snake_case = frame_signal_scale
snake_case = preemphasis_coeff
snake_case = mel_floor
snake_case = normalize_means
snake_case = normalize_vars
snake_case = win_function
snake_case = return_attention_mask
snake_case = win_length * sampling_rate // 10_00
snake_case = hop_length * sampling_rate // 10_00
snake_case = optimal_fft_length(self.sample_size )
snake_case = (self.n_fft // 2) + 1
def UpperCamelCase ( self , A__ ) -> np.ndarray:
if self.win_function == "hamming_window":
snake_case = window_function(window_length=self.sample_size , name=self.win_function , periodic=A__ )
else:
snake_case = window_function(window_length=self.sample_size , name=self.win_function )
snake_case = mel_filter_bank(
num_frequency_bins=self.n_freqs , num_mel_filters=self.feature_size , min_frequency=0.0 , max_frequency=self.sampling_rate / 2.0 , sampling_rate=self.sampling_rate , )
snake_case = spectrogram(
one_waveform * self.frame_signal_scale , window=A__ , frame_length=self.sample_size , hop_length=self.sample_stride , fft_length=self.n_fft , center=A__ , preemphasis=self.preemphasis_coeff , mel_filters=A__ , mel_floor=self.mel_floor , log_mel='''log''' , )
return msfc_features.T
def UpperCamelCase ( self , A__ , A__ , A__ ) -> Optional[Any]:
# make sure we normalize float32 arrays
if self.normalize_means:
snake_case = x[:input_length].mean(axis=0 )
snake_case = np.subtract(A__ , A__ )
if self.normalize_vars:
snake_case = x[:input_length].std(axis=0 )
snake_case = np.divide(A__ , A__ )
if input_length < x.shape[0]:
snake_case = padding_value
# make sure array is in float32
snake_case = x.astype(np.floataa )
return x
def UpperCamelCase ( self , A__ , A__ = None ) -> List[np.ndarray]:
snake_case = attention_mask.sum(-1 ) if attention_mask is not None else [x.shape[0] for x in input_features]
return [self._normalize_one(A__ , A__ , self.padding_value ) for x, n in zip(A__ , A__ )]
def __call__( self , A__ , A__ = False , A__ = None , A__ = False , A__ = None , A__ = None , A__ = None , A__ = None , **A__ , ) -> BatchFeature:
if sampling_rate is not None:
if sampling_rate != self.sampling_rate:
raise ValueError(
F"""The model corresponding to this feature extractor: {self} was trained using a sampling rate of"""
F""" {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled with"""
F""" {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.''' )
snake_case = isinstance(A__ , 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}""" )
snake_case = is_batched_numpy or (
isinstance(A__ , (list, tuple) ) and (isinstance(raw_speech[0] , (np.ndarray, tuple, list) ))
)
if is_batched:
snake_case = [np.asarray(A__ , dtype=np.floataa ) for speech in raw_speech]
elif not is_batched and not isinstance(A__ , np.ndarray ):
snake_case = np.asarray(A__ , dtype=np.floataa )
elif isinstance(A__ , np.ndarray ) and raw_speech.dtype is np.dtype(np.floataa ):
snake_case = raw_speech.astype(np.floataa )
# always return batch
if not is_batched:
snake_case = [raw_speech]
# extract fbank features
snake_case = [self._extract_mfsc_features(A__ ) for one_waveform in raw_speech]
# convert into correct format for padding
snake_case = BatchFeature({'''input_features''': features} )
snake_case = self.pad(
A__ , padding=A__ , max_length=A__ , truncation=A__ , pad_to_multiple_of=A__ , return_attention_mask=A__ , **A__ , )
# make sure list is in array format
snake_case = padded_inputs.get('''input_features''' )
if isinstance(input_features[0] , A__ ):
snake_case = [np.asarray(A__ , dtype=np.floataa ) for feature in input_features]
snake_case = padded_inputs.get('''attention_mask''' )
if attention_mask is not None:
snake_case = [np.asarray(A__ , dtype=np.intaa ) for array in attention_mask]
if self.normalize_means or self.normalize_vars:
snake_case = (
np.array(A__ , dtype=np.intaa )
if self._get_padding_strategies(A__ , max_length=A__ ) is not PaddingStrategy.DO_NOT_PAD
and padding
else None
)
snake_case = self.normalize(
padded_inputs['''input_features'''] , attention_mask=A__ )
if return_tensors is not None:
snake_case = padded_inputs.convert_to_tensors(A__ )
return padded_inputs
| 44
|
'''simple docstring'''
import json
import os
import re
import unittest
from transformers import CodeGenTokenizer, CodeGenTokenizerFast
from transformers.models.codegen.tokenization_codegen import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class _lowercase ( __a , unittest.TestCase ):
_UpperCAmelCase = CodeGenTokenizer
_UpperCAmelCase = CodeGenTokenizerFast
_UpperCAmelCase = True
_UpperCAmelCase = {'''add_prefix_space''': True}
_UpperCAmelCase = False
def UpperCamelCase ( self ) -> Tuple:
super().setUp()
# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
snake_case = [
'''l''',
'''o''',
'''w''',
'''e''',
'''r''',
'''s''',
'''t''',
'''i''',
'''d''',
'''n''',
'''\u0120''',
'''\u0120l''',
'''\u0120n''',
'''\u0120lo''',
'''\u0120low''',
'''er''',
'''\u0120lowest''',
'''\u0120newer''',
'''\u0120wider''',
'''<unk>''',
'''<|endoftext|>''',
]
snake_case = dict(zip(A__ , range(len(A__ ) ) ) )
snake_case = ['''#version: 0.2''', '''\u0120 l''', '''\u0120l o''', '''\u0120lo w''', '''e r''', '''''']
snake_case = {'''unk_token''': '''<unk>'''}
snake_case = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['''vocab_file'''] )
snake_case = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['''merges_file'''] )
with open(self.vocab_file , '''w''' , encoding='''utf-8''' ) as fp:
fp.write(json.dumps(A__ ) + '''\n''' )
with open(self.merges_file , '''w''' , encoding='''utf-8''' ) as fp:
fp.write('''\n'''.join(A__ ) )
def UpperCamelCase ( self , **A__ ) -> Union[str, Any]:
kwargs.update(self.special_tokens_map )
return CodeGenTokenizer.from_pretrained(self.tmpdirname , **A__ )
def UpperCamelCase ( self , **A__ ) -> Union[str, Any]:
kwargs.update(self.special_tokens_map )
return CodeGenTokenizerFast.from_pretrained(self.tmpdirname , **A__ )
def UpperCamelCase ( self , A__ ) -> Tuple:
snake_case = '''lower newer'''
snake_case = '''lower newer'''
return input_text, output_text
def UpperCamelCase ( self ) -> List[Any]:
snake_case = CodeGenTokenizer(self.vocab_file , self.merges_file , **self.special_tokens_map )
snake_case = '''lower newer'''
snake_case = ['''\u0120low''', '''er''', '''\u0120''', '''n''', '''e''', '''w''', '''er''']
snake_case = tokenizer.tokenize(A__ , add_prefix_space=A__ )
self.assertListEqual(A__ , A__ )
snake_case = tokens + [tokenizer.unk_token]
snake_case = [14, 15, 10, 9, 3, 2, 15, 19]
self.assertListEqual(tokenizer.convert_tokens_to_ids(A__ ) , A__ )
def UpperCamelCase ( self ) -> Optional[int]:
if not self.test_rust_tokenizer:
return
snake_case = self.get_tokenizer()
snake_case = self.get_rust_tokenizer(add_prefix_space=A__ )
snake_case = '''lower newer'''
# Testing tokenization
snake_case = tokenizer.tokenize(A__ , add_prefix_space=A__ )
snake_case = rust_tokenizer.tokenize(A__ )
self.assertListEqual(A__ , A__ )
# Testing conversion to ids without special tokens
snake_case = tokenizer.encode(A__ , add_special_tokens=A__ , add_prefix_space=A__ )
snake_case = rust_tokenizer.encode(A__ , add_special_tokens=A__ )
self.assertListEqual(A__ , A__ )
# Testing conversion to ids with special tokens
snake_case = self.get_rust_tokenizer(add_prefix_space=A__ )
snake_case = tokenizer.encode(A__ , add_prefix_space=A__ )
snake_case = rust_tokenizer.encode(A__ )
self.assertListEqual(A__ , A__ )
# Testing the unknown token
snake_case = tokens + [rust_tokenizer.unk_token]
snake_case = [14, 15, 10, 9, 3, 2, 15, 19]
self.assertListEqual(rust_tokenizer.convert_tokens_to_ids(A__ ) , A__ )
def UpperCamelCase ( self , *A__ , **A__ ) -> List[str]:
# It's very difficult to mix/test pretokenization with byte-level
# And get both CodeGen and Roberta to work at the same time (mostly an issue of adding a space before the string)
pass
def UpperCamelCase ( self , A__=15 ) -> Tuple:
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})""" ):
snake_case = self.rust_tokenizer_class.from_pretrained(A__ , **A__ )
# Simple input
snake_case = '''This is a simple input'''
snake_case = ['''This is a simple input 1''', '''This is a simple input 2''']
snake_case = ('''This is a simple input''', '''This is a pair''')
snake_case = [
('''This is a simple input 1''', '''This is a simple input 2'''),
('''This is a simple pair 1''', '''This is a simple pair 2'''),
]
# Simple input tests
self.assertRaises(A__ , tokenizer_r.encode , A__ , max_length=A__ , padding='''max_length''' )
# Simple input
self.assertRaises(A__ , tokenizer_r.encode_plus , A__ , max_length=A__ , padding='''max_length''' )
# Simple input
self.assertRaises(
A__ , tokenizer_r.batch_encode_plus , A__ , max_length=A__ , padding='''max_length''' , )
# Pair input
self.assertRaises(A__ , tokenizer_r.encode , A__ , max_length=A__ , padding='''max_length''' )
# Pair input
self.assertRaises(A__ , tokenizer_r.encode_plus , A__ , max_length=A__ , padding='''max_length''' )
# Pair input
self.assertRaises(
A__ , tokenizer_r.batch_encode_plus , A__ , max_length=A__ , padding='''max_length''' , )
def UpperCamelCase ( self ) -> Tuple:
snake_case = CodeGenTokenizer.from_pretrained(self.tmpdirname , pad_token='''<pad>''' )
# Simple input
snake_case = '''This is a simple input'''
snake_case = ['''This is a simple input looooooooong''', '''This is a simple input''']
snake_case = ('''This is a simple input''', '''This is a pair''')
snake_case = [
('''This is a simple input loooooong''', '''This is a simple input'''),
('''This is a simple pair loooooong''', '''This is a simple pair'''),
]
snake_case = tokenizer.pad_token_id
snake_case = tokenizer(A__ , padding='''max_length''' , max_length=30 , return_tensors='''np''' )
snake_case = tokenizer(A__ , padding=A__ , truncate=A__ , return_tensors='''np''' )
snake_case = tokenizer(*A__ , padding='''max_length''' , max_length=60 , return_tensors='''np''' )
snake_case = tokenizer(A__ , padding=A__ , truncate=A__ , return_tensors='''np''' )
# s
# test single string max_length padding
self.assertEqual(out_s['''input_ids'''].shape[-1] , 30 )
self.assertTrue(pad_token_id in out_s['''input_ids'''] )
self.assertTrue(0 in out_s['''attention_mask'''] )
# s2
# test automatic padding
self.assertEqual(out_sa['''input_ids'''].shape[-1] , 33 )
# long slice doesn't have padding
self.assertFalse(pad_token_id in out_sa['''input_ids'''][0] )
self.assertFalse(0 in out_sa['''attention_mask'''][0] )
# short slice does have padding
self.assertTrue(pad_token_id in out_sa['''input_ids'''][1] )
self.assertTrue(0 in out_sa['''attention_mask'''][1] )
# p
# test single pair max_length padding
self.assertEqual(out_p['''input_ids'''].shape[-1] , 60 )
self.assertTrue(pad_token_id in out_p['''input_ids'''] )
self.assertTrue(0 in out_p['''attention_mask'''] )
# p2
# test automatic padding pair
self.assertEqual(out_pa['''input_ids'''].shape[-1] , 52 )
# long slice pair doesn't have padding
self.assertFalse(pad_token_id in out_pa['''input_ids'''][0] )
self.assertFalse(0 in out_pa['''attention_mask'''][0] )
# short slice pair does have padding
self.assertTrue(pad_token_id in out_pa['''input_ids'''][1] )
self.assertTrue(0 in out_pa['''attention_mask'''][1] )
def UpperCamelCase ( self ) -> str:
snake_case = '''$$$'''
snake_case = CodeGenTokenizer.from_pretrained(self.tmpdirname , bos_token=A__ , add_bos_token=A__ )
snake_case = '''This is a simple input'''
snake_case = ['''This is a simple input 1''', '''This is a simple input 2''']
snake_case = tokenizer.bos_token_id
snake_case = tokenizer(A__ )
snake_case = tokenizer(A__ )
self.assertEqual(out_s.input_ids[0] , A__ )
self.assertTrue(all(o[0] == bos_token_id for o in out_sa.input_ids ) )
snake_case = tokenizer.decode(out_s.input_ids )
snake_case = tokenizer.batch_decode(out_sa.input_ids )
self.assertEqual(decode_s.split()[0] , A__ )
self.assertTrue(all(d.split()[0] == bos_token for d in decode_sa ) )
@slow
def UpperCamelCase ( self ) -> Any:
snake_case = CodeGenTokenizer.from_pretrained('''Salesforce/codegen-350M-mono''' )
snake_case = '''\nif len_a > len_b:\n result = a\nelse:\n result = b\n\n\n\n#'''
snake_case = '''\nif len_a > len_b: result = a\nelse: result = b'''
snake_case = tokenizer.encode(A__ )
snake_case = ['''^#''', re.escape('''<|endoftext|>''' ), '''^\'\'\'''', '''^"""''', '''\n\n\n''']
snake_case = tokenizer.decode(A__ , truncate_before_pattern=A__ )
self.assertEqual(A__ , A__ )
def UpperCamelCase ( self ) -> Union[str, Any]:
pass
| 44
| 1
|
'''simple docstring'''
import json
import os
from collections import Counter
import torch
import torchvision
import torchvision.transforms as transforms
from PIL import Image
from torch import nn
from torch.utils.data import Dataset
_lowercase = {1: (1, 1), 2: (2, 1), 3: (3, 1), 4: (2, 2), 5: (5, 1), 6: (3, 2), 7: (7, 1), 8: (4, 2), 9: (3, 3)}
class _lowercase ( nn.Module ):
def __init__( self , A__ ) -> Union[str, Any]:
super().__init__()
snake_case = torchvision.models.resnetaaa(pretrained=A__ )
snake_case = list(model.children() )[:-2]
snake_case = nn.Sequential(*A__ )
snake_case = nn.AdaptiveAvgPoolad(POOLING_BREAKDOWN[args.num_image_embeds] )
def UpperCamelCase ( self , A__ ) -> Optional[int]:
# Bx3x224x224 -> Bx2048x7x7 -> Bx2048xN -> BxNx2048
snake_case = self.pool(self.model(A__ ) )
snake_case = torch.flatten(A__ , start_dim=2 )
snake_case = out.transpose(1 , 2 ).contiguous()
return out # BxNx2048
class _lowercase ( __a ):
def __init__( self , A__ , A__ , A__ , A__ , A__ ) -> Optional[Any]:
snake_case = [json.loads(A__ ) for l in open(A__ )]
snake_case = os.path.dirname(A__ )
snake_case = tokenizer
snake_case = labels
snake_case = len(A__ )
snake_case = max_seq_length
snake_case = transforms
def __len__( self ) -> Union[str, Any]:
return len(self.data )
def __getitem__( self , A__ ) -> Optional[Any]:
snake_case = torch.LongTensor(self.tokenizer.encode(self.data[index]['''text'''] , add_special_tokens=A__ ) )
snake_case , snake_case , snake_case = sentence[0], sentence[1:-1], sentence[-1]
snake_case = sentence[: self.max_seq_length]
snake_case = torch.zeros(self.n_classes )
snake_case = 1
snake_case = Image.open(os.path.join(self.data_dir , self.data[index]['''img'''] ) ).convert('''RGB''' )
snake_case = self.transforms(A__ )
return {
"image_start_token": start_token,
"image_end_token": end_token,
"sentence": sentence,
"image": image,
"label": label,
}
def UpperCamelCase ( self ) -> str:
snake_case = Counter()
for row in self.data:
label_freqs.update(row['''label'''] )
return label_freqs
def __UpperCamelCase ( a : Optional[Any] ) ->str:
snake_case = [len(row['''sentence'''] ) for row in batch]
snake_case , snake_case = len(a ), max(a )
snake_case = torch.zeros(a , a , dtype=torch.long )
snake_case = torch.zeros(a , a , dtype=torch.long )
for i_batch, (input_row, length) in enumerate(zip(a , a ) ):
snake_case = input_row['''sentence''']
snake_case = 1
snake_case = torch.stack([row['''image'''] for row in batch] )
snake_case = torch.stack([row['''label'''] for row in batch] )
snake_case = torch.stack([row['''image_start_token'''] for row in batch] )
snake_case = torch.stack([row['''image_end_token'''] for row in batch] )
return text_tensor, mask_tensor, img_tensor, img_start_token, img_end_token, tgt_tensor
def __UpperCamelCase ( ) ->str:
return [
"Crime",
"Drama",
"Thriller",
"Action",
"Comedy",
"Romance",
"Documentary",
"Short",
"Mystery",
"History",
"Family",
"Adventure",
"Fantasy",
"Sci-Fi",
"Western",
"Horror",
"Sport",
"War",
"Music",
"Musical",
"Animation",
"Biography",
"Film-Noir",
]
def __UpperCamelCase ( ) ->Any:
return transforms.Compose(
[
transforms.Resize(256 ),
transforms.CenterCrop(224 ),
transforms.ToTensor(),
transforms.Normalize(
mean=[0.46777044, 0.44531429, 0.40661017] , std=[0.12221994, 0.12145835, 0.14380469] , ),
] )
| 44
|
'''simple docstring'''
from __future__ import annotations
import inspect
import unittest
from transformers import ViTConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import TFViTForImageClassification, TFViTModel
if is_vision_available():
from PIL import Image
from transformers import ViTImageProcessor
class _lowercase :
def __init__( self , A__ , A__=13 , A__=30 , A__=2 , A__=3 , A__=True , A__=True , A__=32 , A__=2 , A__=4 , A__=37 , A__="gelu" , A__=0.1 , A__=0.1 , A__=10 , A__=0.0_2 , A__=3 , A__=None , ) -> List[Any]:
snake_case = parent
snake_case = batch_size
snake_case = image_size
snake_case = patch_size
snake_case = num_channels
snake_case = is_training
snake_case = use_labels
snake_case = hidden_size
snake_case = num_hidden_layers
snake_case = num_attention_heads
snake_case = intermediate_size
snake_case = hidden_act
snake_case = hidden_dropout_prob
snake_case = attention_probs_dropout_prob
snake_case = type_sequence_label_size
snake_case = initializer_range
snake_case = scope
# in ViT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
snake_case = (image_size // patch_size) ** 2
snake_case = num_patches + 1
def UpperCamelCase ( self ) -> int:
snake_case = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
snake_case = None
if self.use_labels:
snake_case = ids_tensor([self.batch_size] , self.type_sequence_label_size )
snake_case = self.get_config()
return config, pixel_values, labels
def UpperCamelCase ( self ) -> int:
return ViTConfig(
image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , 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 , is_decoder=A__ , initializer_range=self.initializer_range , )
def UpperCamelCase ( self , A__ , A__ , A__ ) -> Union[str, Any]:
snake_case = TFViTModel(config=A__ )
snake_case = model(A__ , training=A__ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
# Test with an image with different size than the one specified in config.
snake_case = self.image_size // 2
snake_case = pixel_values[:, :, :image_size, :image_size]
snake_case = model(A__ , interpolate_pos_encoding=A__ , training=A__ )
snake_case = (image_size // self.patch_size) ** 2 + 1
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, seq_length, self.hidden_size) )
def UpperCamelCase ( self , A__ , A__ , A__ ) -> Optional[int]:
snake_case = self.type_sequence_label_size
snake_case = TFViTForImageClassification(A__ )
snake_case = model(A__ , labels=A__ , training=A__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
# Test with an image with different size than the one specified in config.
snake_case = self.image_size // 2
snake_case = pixel_values[:, :, :image_size, :image_size]
snake_case = model(A__ , interpolate_pos_encoding=A__ , training=A__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
# test greyscale images
snake_case = 1
snake_case = TFViTForImageClassification(A__ )
snake_case = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
snake_case = model(A__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
def UpperCamelCase ( self ) -> Union[str, Any]:
snake_case = self.prepare_config_and_inputs()
snake_case , snake_case , snake_case = config_and_inputs
snake_case = {'''pixel_values''': pixel_values}
return config, inputs_dict
@require_tf
class _lowercase ( __a , __a , unittest.TestCase ):
_UpperCAmelCase = (TFViTModel, TFViTForImageClassification) if is_tf_available() else ()
_UpperCAmelCase = (
{'''feature-extraction''': TFViTModel, '''image-classification''': TFViTForImageClassification}
if is_tf_available()
else {}
)
_UpperCAmelCase = False
_UpperCAmelCase = False
_UpperCAmelCase = False
def UpperCamelCase ( self ) -> List[Any]:
snake_case = TFViTModelTester(self )
snake_case = ConfigTester(self , config_class=A__ , has_text_modality=A__ , hidden_size=37 )
def UpperCamelCase ( self ) -> int:
self.config_tester.run_common_tests()
@unittest.skip(reason='''ViT does not use inputs_embeds''' )
def UpperCamelCase ( self ) -> int:
pass
@unittest.skip(reason='''ViT does not use inputs_embeds''' )
def UpperCamelCase ( self ) -> str:
pass
def UpperCamelCase ( self ) -> Union[str, Any]:
snake_case , snake_case = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
snake_case = model_class(A__ )
self.assertIsInstance(model.get_input_embeddings() , (tf.keras.layers.Layer) )
snake_case = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(A__ , tf.keras.layers.Layer ) )
def UpperCamelCase ( self ) -> List[Any]:
snake_case , snake_case = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
snake_case = model_class(A__ )
snake_case = inspect.signature(model.call )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
snake_case = [*signature.parameters.keys()]
snake_case = ['''pixel_values''']
self.assertListEqual(arg_names[:1] , A__ )
def UpperCamelCase ( self ) -> Union[str, Any]:
snake_case = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*A__ )
def UpperCamelCase ( self ) -> Optional[Any]:
snake_case = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*A__ )
@slow
def UpperCamelCase ( self ) -> Any:
snake_case = TFViTModel.from_pretrained('''google/vit-base-patch16-224''' )
self.assertIsNotNone(A__ )
def __UpperCamelCase ( ) ->Any:
snake_case = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
return image
@require_tf
@require_vision
class _lowercase ( unittest.TestCase ):
@cached_property
def UpperCamelCase ( self ) -> Optional[int]:
return ViTImageProcessor.from_pretrained('''google/vit-base-patch16-224''' ) if is_vision_available() else None
@slow
def UpperCamelCase ( self ) -> Dict:
snake_case = TFViTForImageClassification.from_pretrained('''google/vit-base-patch16-224''' )
snake_case = self.default_image_processor
snake_case = prepare_img()
snake_case = image_processor(images=A__ , return_tensors='''tf''' )
# forward pass
snake_case = model(**A__ )
# verify the logits
snake_case = tf.TensorShape((1, 10_00) )
self.assertEqual(outputs.logits.shape , A__ )
snake_case = tf.constant([-0.2_7_4_4, 0.8_2_1_5, -0.0_8_3_6] )
tf.debugging.assert_near(outputs.logits[0, :3] , A__ , atol=1e-4 )
| 44
| 1
|
'''simple docstring'''
import argparse
import os
# New Code #
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator, DistributedType
from accelerate.utils import find_executable_batch_size
########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to ensure out-of-memory errors never
# interrupt training, and builds off the `nlp_example.py` script.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
# - single CPU or single GPU
# - multi GPUS (using PyTorch distributed mode)
# - (multi) TPUs
# - fp16 (mixed-precision) or fp32 (normal precision)
#
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################
_lowercase = 16
_lowercase = 32
def __UpperCamelCase ( a : Accelerator , a : int = 16 ) ->str:
snake_case = AutoTokenizer.from_pretrained('''bert-base-cased''' )
snake_case = load_dataset('''glue''' , '''mrpc''' )
def tokenize_function(a : Union[str, Any] ):
# max_length=None => use the model max length (it's actually the default)
snake_case = tokenizer(examples['''sentence1'''] , examples['''sentence2'''] , truncation=a , max_length=a )
return outputs
# Apply the method we just defined to all the examples in all the splits of the dataset
# starting with the main process first:
with accelerator.main_process_first():
snake_case = datasets.map(
a , batched=a , remove_columns=['''idx''', '''sentence1''', '''sentence2'''] , )
# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
# transformers library
snake_case = tokenized_datasets.rename_column('''label''' , '''labels''' )
def collate_fn(a : int ):
# On TPU it's best to pad everything to the same length or training will be very slow.
snake_case = 128 if accelerator.distributed_type == DistributedType.TPU else None
# When using mixed precision we want round multiples of 8/16
if accelerator.mixed_precision == "fp8":
snake_case = 16
elif accelerator.mixed_precision != "no":
snake_case = 8
else:
snake_case = None
return tokenizer.pad(
a , padding='''longest''' , max_length=a , pad_to_multiple_of=a , return_tensors='''pt''' , )
# Instantiate dataloaders.
snake_case = DataLoader(
tokenized_datasets['''train'''] , shuffle=a , collate_fn=a , batch_size=a )
snake_case = DataLoader(
tokenized_datasets['''validation'''] , shuffle=a , collate_fn=a , batch_size=a )
return train_dataloader, eval_dataloader
# For testing only
if os.environ.get('TESTING_MOCKED_DATALOADERS', None) == "1":
from accelerate.test_utils.training import mocked_dataloaders
_lowercase = mocked_dataloaders # noqa: F811
def __UpperCamelCase ( a : List[str] , a : int ) ->int:
# For testing only
if os.environ.get('''TESTING_MOCKED_DATALOADERS''' , a ) == "1":
snake_case = 2
# Initialize accelerator
snake_case = Accelerator(cpu=args.cpu , mixed_precision=args.mixed_precision )
# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
snake_case = config['''lr''']
snake_case = int(config['''num_epochs'''] )
snake_case = int(config['''seed'''] )
snake_case = int(config['''batch_size'''] )
snake_case = evaluate.load('''glue''' , '''mrpc''' )
# New Code #
# We now can define an inner training loop function. It should take a batch size as the only parameter,
# and build the dataloaders in there.
# It also gets our decorator
@find_executable_batch_size(starting_batch_size=a )
def inner_training_loop(a : Tuple ):
# And now just move everything below under this function
# We need to bring in the Accelerator object from earlier
nonlocal accelerator
# And reset all of its attributes that could hold onto any memory:
accelerator.free_memory()
# Then we can declare the model, optimizer, and everything else:
set_seed(a )
# Instantiate the model (we build the model here so that the seed also control new weights initialization)
snake_case = AutoModelForSequenceClassification.from_pretrained('''bert-base-cased''' , return_dict=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).
snake_case = model.to(accelerator.device )
# Instantiate optimizer
snake_case = AdamW(params=model.parameters() , lr=a )
snake_case , snake_case = get_dataloaders(a , a )
# Instantiate scheduler
snake_case = get_linear_schedule_with_warmup(
optimizer=a , num_warmup_steps=100 , num_training_steps=(len(a ) * num_epochs) , )
# 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.
snake_case , snake_case , snake_case , snake_case , snake_case = accelerator.prepare(
a , a , a , a , a )
# Now we train the model
for epoch in range(a ):
model.train()
for step, batch in enumerate(a ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
snake_case = model(**a )
snake_case = outputs.loss
accelerator.backward(a )
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
model.eval()
for step, batch in enumerate(a ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
with torch.no_grad():
snake_case = model(**a )
snake_case = outputs.logits.argmax(dim=-1 )
snake_case , snake_case = accelerator.gather_for_metrics((predictions, batch['''labels''']) )
metric.add_batch(
predictions=a , references=a , )
snake_case = metric.compute()
# Use accelerator.print to print only on the main process.
accelerator.print(f"""epoch {epoch}:""" , a )
# New Code #
# And call it at the end with no arguments
# Note: You could also refactor this outside of your training loop function
inner_training_loop()
def __UpperCamelCase ( ) ->Any:
snake_case = argparse.ArgumentParser(description='''Simple example of training script.''' )
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.''' )
snake_case = parser.parse_args()
snake_case = {'''lr''': 2e-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
training_function(a , a )
if __name__ == "__main__":
main()
| 44
|
'''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
_lowercase = [
{'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 __UpperCamelCase ( a : Dict=True ) ->str:
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=__a ) )
class _lowercase ( __a ):
_UpperCAmelCase = None
_UpperCAmelCase = None
def UpperCamelCase ( self , A__ , A__ ) -> str:
with TemporaryDirectory() as tmp_dir:
snake_case = dataset_module_factory(A__ , cache_dir=A__ )
snake_case = import_main_class(dataset_module.module_path , dataset=A__ )
snake_case = builder_cls(
cache_dir=A__ , config_name=A__ , hash=dataset_module.hash , )
snake_case = '''/'''.join(
[
HF_GCP_BASE_URL,
builder_instance._relative_data_dir(with_hash=A__ ).replace(os.sep , '''/''' ),
config.DATASET_INFO_FILENAME,
] )
snake_case = cached_path(A__ , cache_dir=A__ )
self.assertTrue(os.path.exists(A__ ) )
@pytest.mark.integration
def __UpperCamelCase ( a : List[str] ) ->Any:
snake_case = tmp_path_factory.mktemp('''test_hf_gcp''' ) / '''test_wikipedia_simple'''
snake_case = dataset_module_factory('''wikipedia''' , cache_dir=a )
snake_case = import_main_class(dataset_module.module_path )
snake_case = builder_cls(
cache_dir=a , config_name='''20220301.frr''' , hash=dataset_module.hash , )
# use the HF cloud storage, not the original download_and_prepare that uses apache-beam
snake_case = None
builder_instance.download_and_prepare()
snake_case = builder_instance.as_dataset()
assert ds
@pytest.mark.integration
def __UpperCamelCase ( a : Any ) ->Union[str, Any]:
snake_case = dataset_module_factory('''wikipedia''' , cache_dir=a )
snake_case = import_main_class(dataset_module.module_path , dataset=a )
snake_case = builder_cls(
cache_dir=a , config_name='''20220301.frr''' , hash=dataset_module.hash , )
snake_case = builder_instance.as_streaming_dataset()
assert ds
assert isinstance(a , a )
assert "train" in ds
assert isinstance(ds['''train'''] , a )
assert next(iter(ds['''train'''] ) )
| 44
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|
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