


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
__UpperCamelCase    		=       'src/transformers'


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


# Regexes that match TF/Flax/PT model names.
__UpperCamelCase    		=       re.compile(R'TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')
__UpperCamelCase    		=       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.
__UpperCamelCase    		=       re.compile(R'(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')


# Fill this with tuples (pipeline_tag, model_mapping, auto_model)
__UpperCamelCase    		=       [
    ('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   :Any  )->		List[str]:
			UpperCamelCase__					=       re.finditer(".+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)"				,     __lowercase  )
			return [m.group(0  ) for m in matches]
def 			UpperCamelCase_(				)->		Optional[int]:
			UpperCamelCase__					=       transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
			UpperCamelCase__					=       {
			    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.
			UpperCamelCase__					=       collections.defaultdict(__lowercase  )
			UpperCamelCase__					=       collections.defaultdict(__lowercase  )
			UpperCamelCase__					=       collections.defaultdict(__lowercase  )

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

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

			UpperCamelCase__					=       set(list(pt_models.keys()  ) + list(tf_models.keys()  ) + list(flax_models.keys()  )  )
			UpperCamelCase__					=       list(__lowercase  )
			all_models.sort()

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

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

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

			return pd.DataFrame(__lowercase  )
def 			UpperCamelCase_(				_A   :str  )->		Union[str, Any]:
			UpperCamelCase__					=       [
			    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:
						UpperCamelCase__					=       [model_mapping, F'''TF_{model_mapping}''', F'''FLAX_{model_mapping}''']
						UpperCamelCase__					=       [auto_class, F'''TF_{auto_class}''', F'''Flax_{auto_class}''']
						# Loop through all three frameworks
						for module, cls, mapping in zip(__lowercase				,     __lowercase				,     __lowercase  ):
									# The type of pipeline may not exist in this framework
									if not hasattr(__lowercase				,     __lowercase  ):
												continue
									# First extract all model_names
									UpperCamelCase__					=       []
									for name in getattr(__lowercase				,     __lowercase  ).values():
												if isinstance(__lowercase				,     __lowercase  ):
															model_names.append(__lowercase  )
												else:
															model_names.extend(list(__lowercase  )  )

            # 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   :Optional[int]				,     _A   :Union[str, Any]  )->		List[Any]:
			UpperCamelCase__					=       get_frameworks_table()
			UpperCamelCase__					=       Dataset.from_pandas(__lowercase  )

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

			# Sort the model classes to avoid some nondeterministic updates to create false update commits.
			UpperCamelCase__					=       sorted(table.keys()  )
			UpperCamelCase__					=       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],
			    }  )
			UpperCamelCase__					=       Dataset.from_pandas(__lowercase  )

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

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

						upload_folder(
						    repo_id="huggingface/transformers-metadata"				,     folder_path=__lowercase				,     repo_type="dataset"				,     token=__lowercase				,     commit_message=__lowercase				,     )
def 			UpperCamelCase_(				)->		Dict:
			UpperCamelCase__					=       {tag: cls for tag, _, cls in PIPELINE_TAGS_AND_AUTO_MODELS}
			UpperCamelCase__					=       transformers_module.pipelines.SUPPORTED_TASKS
			UpperCamelCase__					=       []
			for key in pipeline_tasks:
						if key not in in_table:
									UpperCamelCase__					=       pipeline_tasks[key]['pt']
									if isinstance(__lowercase				,     (list, tuple)  ):
												UpperCamelCase__					=       model[0]
									UpperCamelCase__					=       model.__name__
									if model not in in_table.values():
												missing.append(__lowercase  )

			if len(__lowercase  ) > 0:
						UpperCamelCase__					=       ', '.join(__lowercase  )
						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__":
						__UpperCamelCase    		=       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.')
						__UpperCamelCase    		=       parser.parse_args()

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






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_speech_available,
    is_torch_available,
)


__UpperCamelCase    		=       {
    'configuration_trocr': ['TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP', 'TrOCRConfig'],
    'processing_trocr': ['TrOCRProcessor'],
}


try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__UpperCamelCase    		=       [
						    'TROCR_PRETRAINED_MODEL_ARCHIVE_LIST',
						    'TrOCRForCausalLM',
						    'TrOCRPreTrainedModel',
						]


if TYPE_CHECKING:
						from .configuration_trocr import TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP, TrOCRConfig
						from .processing_trocr import TrOCRProcessor

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_trocr import TROCR_PRETRAINED_MODEL_ARCHIVE_LIST, TrOCRForCausalLM, TrOCRPreTrainedModel

else:
						import sys

						__UpperCamelCase    		=       _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


