






import argparse
import collections
import os
import re
import tempfile

import pandas as pd
from datasets import Dataset
from huggingface_hub import hf_hub_download, upload_folder

from transformers.utils import direct_transformers_import


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/update_metadata.py
lowercase    				=   "src/transformers"


# This is to make sure the transformers module imported is the one in the repo.
lowercase    				=   direct_transformers_import(TRANSFORMERS_PATH)


# Regexes that match TF/Flax/PT model names.
lowercase    				=   re.compile(r"TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)")
lowercase    				=   re.compile(r"Flax(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)")
# Will match any TF or Flax model too so need to be in an else branch afterthe two previous regexes.
lowercase    				=   re.compile(r"(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)")


# Fill this with tuples (pipeline_tag, model_mapping, auto_model)
lowercase    				=   [
    ("pretraining", "MODEL_FOR_PRETRAINING_MAPPING_NAMES", "AutoModelForPreTraining"),
    ("feature-extraction", "MODEL_MAPPING_NAMES", "AutoModel"),
    ("audio-classification", "MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES", "AutoModelForAudioClassification"),
    ("text-generation", "MODEL_FOR_CAUSAL_LM_MAPPING_NAMES", "AutoModelForCausalLM"),
    ("automatic-speech-recognition", "MODEL_FOR_CTC_MAPPING_NAMES", "AutoModelForCTC"),
    ("image-classification", "MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES", "AutoModelForImageClassification"),
    ("image-segmentation", "MODEL_FOR_IMAGE_SEGMENTATION_MAPPING_NAMES", "AutoModelForImageSegmentation"),
    ("fill-mask", "MODEL_FOR_MASKED_LM_MAPPING_NAMES", "AutoModelForMaskedLM"),
    ("object-detection", "MODEL_FOR_OBJECT_DETECTION_MAPPING_NAMES", "AutoModelForObjectDetection"),
    (
        "zero-shot-object-detection",
        "MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING_NAMES",
        "AutoModelForZeroShotObjectDetection",
    ),
    ("question-answering", "MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES", "AutoModelForQuestionAnswering"),
    ("text2text-generation", "MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES", "AutoModelForSeq2SeqLM"),
    ("text-classification", "MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES", "AutoModelForSequenceClassification"),
    ("automatic-speech-recognition", "MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES", "AutoModelForSpeechSeq2Seq"),
    (
        "table-question-answering",
        "MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING_NAMES",
        "AutoModelForTableQuestionAnswering",
    ),
    ("token-classification", "MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES", "AutoModelForTokenClassification"),
    ("multiple-choice", "MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES", "AutoModelForMultipleChoice"),
    (
        "next-sentence-prediction",
        "MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING_NAMES",
        "AutoModelForNextSentencePrediction",
    ),
    (
        "audio-frame-classification",
        "MODEL_FOR_AUDIO_FRAME_CLASSIFICATION_MAPPING_NAMES",
        "AutoModelForAudioFrameClassification",
    ),
    ("audio-xvector", "MODEL_FOR_AUDIO_XVECTOR_MAPPING_NAMES", "AutoModelForAudioXVector"),
    (
        "document-question-answering",
        "MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING_NAMES",
        "AutoModelForDocumentQuestionAnswering",
    ),
    (
        "visual-question-answering",
        "MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING_NAMES",
        "AutoModelForVisualQuestionAnswering",
    ),
    ("image-to-text", "MODEL_FOR_FOR_VISION_2_SEQ_MAPPING_NAMES", "AutoModelForVision2Seq"),
    (
        "zero-shot-image-classification",
        "MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING_NAMES",
        "AutoModelForZeroShotImageClassification",
    ),
    ("depth-estimation", "MODEL_FOR_DEPTH_ESTIMATION_MAPPING_NAMES", "AutoModelForDepthEstimation"),
    ("video-classification", "MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING_NAMES", "AutoModelForVideoClassification"),
    ("mask-generation", "MODEL_FOR_MASK_GENERATION_MAPPING_NAMES", "AutoModelForMaskGeneration"),
]





def 		__UpperCAmelCase     (	a_):
							snake_case_           =				re.finditer('.+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)'				,	a_)
							return [m.group(0) for m in matches]





def 		__UpperCAmelCase     (	):
							snake_case_           =				transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
							snake_case_           =				{
							    config.replace('Config'				,	''): model_type for model_type, config in config_maping_names.items()
							}

							# Dictionaries flagging if each model prefix has a backend in PT/TF/Flax.
							snake_case_           =				collections.defaultdict(a_)
							snake_case_           =				collections.defaultdict(a_)
							snake_case_           =				collections.defaultdict(a_)

							# Let's lookup through all transformers object (once) and find if models are supported by a given backend.
							for attr_name in dir(a_):
														snake_case_           =				None
														if _re_tf_models.match(a_) is not None:
																					snake_case_           =				tf_models
																					snake_case_           =				_re_tf_models.match(a_).groups()[0]
														elif _re_flax_models.match(a_) is not None:
																					snake_case_           =				flax_models
																					snake_case_           =				_re_flax_models.match(a_).groups()[0]
														elif _re_pt_models.match(a_) is not None:
																					snake_case_           =				pt_models
																					snake_case_           =				_re_pt_models.match(a_).groups()[0]

														if lookup_dict is not None:
																					while len(a_) > 0:
																												if attr_name in model_prefix_to_model_type:
																																			snake_case_           =				True
																																			break
																												# Try again after removing the last word in the name
																												snake_case_           =				''.join(camel_case_split(a_)[:-1])

							snake_case_           =				set(list(pt_models.keys()) + list(tf_models.keys()) + list(flax_models.keys()))
							snake_case_           =				list(a_)
							all_models.sort()

							snake_case_           =				{'model_type': all_models}
							snake_case_           =				[pt_models[t] for t in all_models]
							snake_case_           =				[tf_models[t] for t in all_models]
							snake_case_           =				[flax_models[t] for t in all_models]

							# Now let's use the auto-mapping names to make sure
							snake_case_           =				{}
							for t in all_models:
														if t in transformers_module.models.auto.processing_auto.PROCESSOR_MAPPING_NAMES:
																					snake_case_           =				'AutoProcessor'
														elif t in transformers_module.models.auto.tokenization_auto.TOKENIZER_MAPPING_NAMES:
																					snake_case_           =				'AutoTokenizer'
														elif t in transformers_module.models.auto.feature_extraction_auto.FEATURE_EXTRACTOR_MAPPING_NAMES:
																					snake_case_           =				'AutoFeatureExtractor'
														else:
																					# Default to AutoTokenizer if a model has nothing, for backward compatibility.
																					snake_case_           =				'AutoTokenizer'

							snake_case_           =				[processors[t] for t in all_models]

							return pd.DataFrame(a_)





def 		__UpperCAmelCase     (	a_):
							snake_case_           =				[
							    transformers_module.models.auto.modeling_auto,
							    transformers_module.models.auto.modeling_tf_auto,
							    transformers_module.models.auto.modeling_flax_auto,
							]
							for pipeline_tag, model_mapping, auto_class in PIPELINE_TAGS_AND_AUTO_MODELS:
														snake_case_           =				[model_mapping, f'''TF_{model_mapping}''', f'''FLAX_{model_mapping}''']
														snake_case_           =				[auto_class, f'''TF_{auto_class}''', f'''Flax_{auto_class}''']
														# Loop through all three frameworks
														for module, cls, mapping in zip(a_				,	a_				,	a_):
																					# The type of pipeline may not exist in this framework
																					if not hasattr(a_				,	a_):
																												continue
																					# First extract all model_names
																					snake_case_           =				[]
																					for name in getattr(a_				,	a_).values():
																												if isinstance(a_				,	a_):
																																			model_names.append(a_)
																												else:
																																			model_names.extend(list(a_))

            # Add pipeline tag and auto model class for those models
																					table.update({model_name: (pipeline_tag, cls) for model_name in model_names})

							return table





def 		__UpperCAmelCase     (	a_				,	a_):
							snake_case_           =				get_frameworks_table()
							snake_case_           =				Dataset.from_pandas(a_)

							snake_case_           =				hf_hub_download(
							    'huggingface/transformers-metadata'				,	'pipeline_tags.json'				,	repo_type='dataset'				,	token=a_)
							snake_case_           =				Dataset.from_json(a_)
							snake_case_           =				{
							    tags_dataset[i]['model_class']: (tags_dataset[i]['pipeline_tag'], tags_dataset[i]['auto_class'])
							    for i in range(len(a_))
							}
							snake_case_           =				update_pipeline_and_auto_class_table(a_)

							# Sort the model classes to avoid some nondeterministic updates to create false update commits.
							snake_case_           =				sorted(table.keys())
							snake_case_           =				pd.DataFrame(
							    {
							        'model_class': model_classes,
							        'pipeline_tag': [table[m][0] for m in model_classes],
							        'auto_class': [table[m][1] for m in model_classes],
							    })
							snake_case_           =				Dataset.from_pandas(a_)

							with tempfile.TemporaryDirectory() as tmp_dir:
														frameworks_dataset.to_json(os.path.join(a_				,	'frameworks.json'))
														tags_dataset.to_json(os.path.join(a_				,	'pipeline_tags.json'))

														if commit_sha is not None:
																					snake_case_           =				(
																					    f'''Update with commit {commit_sha}\n\nSee: '''
																					    f'''https://github.com/huggingface/transformers/commit/{commit_sha}'''
																					)
														else:
																					snake_case_           =				'Update'

														upload_folder(
														    repo_id='huggingface/transformers-metadata'				,	folder_path=a_				,	repo_type='dataset'				,	token=a_				,	commit_message=a_				,	)





def 		__UpperCAmelCase     (	):
							snake_case_           =				{tag: cls for tag, _, cls in PIPELINE_TAGS_AND_AUTO_MODELS}
							snake_case_           =				transformers_module.pipelines.SUPPORTED_TASKS
							snake_case_           =				[]
							for key in pipeline_tasks:
														if key not in in_table:
																					snake_case_           =				pipeline_tasks[key]['pt']
																					if isinstance(a_				,	(list, tuple)):
																												snake_case_           =				model[0]
																					snake_case_           =				model.__name__
																					if model not in in_table.values():
																												missing.append(a_)

							if len(a_) > 0:
														snake_case_           =				', '.join(a_)
														raise ValueError(
														    'The following pipeline tags are not present in the `PIPELINE_TAGS_AND_AUTO_MODELS` constant inside '
														    f'''`utils/update_metadata.py`: {msg}. Please add them!''')


if __name__ == "__main__":
	lowercase    				=   argparse.ArgumentParser()
	parser.add_argument("--token", type=str, help="The token to use to push to the transformers-metadata dataset.")
	parser.add_argument("--commit_sha", type=str, help="The sha of the commit going with this update.")
	parser.add_argument("--check-only", action="store_true", help="Activate to just check all pipelines are present.")
	lowercase    				=   parser.parse_args()

	if args.check_only:
		check_pipeline_tags()
	else:
		update_metadata(args.token, args.commit_sha)













import os
from argparse import ArgumentParser
from typing import List

import torch.utils.data

from datasets import Dataset, IterableDataset
from datasets.distributed import split_dataset_by_node


lowercase    				=   4
lowercase    				=   3




class 					UpperCamelCase_ (     snake_case_							):



	'''simple docstring'''




	pass






def 		__UpperCAmelCase     (	a_):
							for shard in shards:
														for i in range(a_):
																					yield {"i": i, "shard": shard}





def 		__UpperCAmelCase     (	):
							snake_case_           =				int(os.environ['RANK'])
							snake_case_           =				int(os.environ['WORLD_SIZE'])

							snake_case_           =				ArgumentParser()
							parser.add_argument('--streaming'				,	type=a_)
							parser.add_argument('--local_rank'				,	type=a_)
							parser.add_argument('--num_workers'				,	type=a_				,	default=0)
							snake_case_           =				parser.parse_args()
							snake_case_           =				args.streaming
							snake_case_           =				args.num_workers

							snake_case_           =				{'shards': [f'''shard_{shard_idx}''' for shard_idx in range(a_)]}
							snake_case_           =				IterableDataset.from_generator(a_				,	gen_kwargs=a_)
							if not streaming:
														snake_case_           =				Dataset.from_list(list(a_))

							snake_case_           =				split_dataset_by_node(a_				,	rank=a_				,	world_size=a_)
							snake_case_           =				torch.utils.data.DataLoader(a_				,	num_workers=a_)

							snake_case_           =				NUM_SHARDS * NUM_ITEMS_PER_SHARD
							snake_case_           =				full_size // world_size
							expected_local_size += int(rank < (full_size % world_size))

							snake_case_           =				sum(1 for _ in dataloader)
							if local_size != expected_local_size:
														raise FailedTestError(f'''local_size {local_size} != expected_local_size {expected_local_size}''')


if __name__ == "__main__":
	main()





