





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		:  Union[str, Any]			       ="""src/transformers"""


# This is to make sure the transformers module imported is the one in the repo.
_lowercase		:  Union[str, Any]			       =direct_transformers_import(TRANSFORMERS_PATH)


# Regexes that match TF/Flax/PT model names.
_lowercase		:  Dict			       =re.compile(r"""TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)""")
_lowercase		:  Tuple			       =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		:  Union[str, Any]			       =re.compile(r"""(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)""")


# Fill this with tuples (pipeline_tag, model_mapping, auto_model)
_lowercase		:  Optional[int]			       =[
    ("""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 		_SCREAMING_SNAKE_CASE     ( lowerCAmelCase__						):
		lowerCamelCase_	:     List[Any] 						=      re.finditer('.+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)'							,lowerCAmelCase__						)
		return [m.group(0						) for m in matches]


def 		_SCREAMING_SNAKE_CASE     ( ):
		lowerCamelCase_	:     Any 						=      transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
		lowerCamelCase_	:     Dict 						=      {
		    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.
		lowerCamelCase_	:     Any 						=      collections.defaultdict(lowerCAmelCase__						)
		lowerCamelCase_	:     int 						=      collections.defaultdict(lowerCAmelCase__						)
		lowerCamelCase_	:     Any 						=      collections.defaultdict(lowerCAmelCase__						)

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

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

		lowerCamelCase_	:     Any 						=      set(list(pt_models.keys()						) + list(tf_models.keys()						) + list(flax_models.keys()						)						)
		lowerCamelCase_	:     List[str] 						=      list(lowerCAmelCase__						)
		all_models.sort()

		lowerCamelCase_	:     Union[str, Any] 						=      {'model_type': all_models}
		lowerCamelCase_	:     int 						=      [pt_models[t] for t in all_models]
		lowerCamelCase_	:     Dict 						=      [tf_models[t] for t in all_models]
		lowerCamelCase_	:     Dict 						=      [flax_models[t] for t in all_models]

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

		lowerCamelCase_	:     Any 						=      [processors[t] for t in all_models]

		return pd.DataFrame(lowerCAmelCase__						)


def 		_SCREAMING_SNAKE_CASE     ( lowerCAmelCase__						):
		lowerCamelCase_	:     int 						=      [
		    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:
				lowerCamelCase_	:     Any 						=      [model_mapping, F"TF_{model_mapping}", F"FLAX_{model_mapping}"]
				lowerCamelCase_	:     Tuple 						=      [auto_class, F"TF_{auto_class}", F"Flax_{auto_class}"]
				# Loop through all three frameworks
				for module, cls, mapping in zip(lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__						):
						# The type of pipeline may not exist in this framework
						if not hasattr(lowerCAmelCase__							,lowerCAmelCase__						):
								continue
						# First extract all model_names
						lowerCamelCase_	:     Any 						=      []
						for name in getattr(lowerCAmelCase__							,lowerCAmelCase__						).values():
								if isinstance(lowerCAmelCase__							,lowerCAmelCase__						):
										model_names.append(lowerCAmelCase__						)
								else:
										model_names.extend(list(lowerCAmelCase__						)						)

            # 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 		_SCREAMING_SNAKE_CASE     ( lowerCAmelCase__							,lowerCAmelCase__						):
		lowerCamelCase_	:     Optional[int] 						=      get_frameworks_table()
		lowerCamelCase_	:     Any 						=      Dataset.from_pandas(lowerCAmelCase__						)

		lowerCamelCase_	:     Union[str, Any] 						=      hf_hub_download(
		    'huggingface/transformers-metadata'							,'pipeline_tags.json'							,repo_type='dataset'							,token=lowerCAmelCase__						)
		lowerCamelCase_	:     Optional[int] 						=      Dataset.from_json(lowerCAmelCase__						)
		lowerCamelCase_	:     List[Any] 						=      {
		    tags_dataset[i]['model_class']: (tags_dataset[i]['pipeline_tag'], tags_dataset[i]['auto_class'])
		    for i in range(len(lowerCAmelCase__						)						)
		}
		lowerCamelCase_	:     str 						=      update_pipeline_and_auto_class_table(lowerCAmelCase__						)

		# Sort the model classes to avoid some nondeterministic updates to create false update commits.
		lowerCamelCase_	:     List[str] 						=      sorted(table.keys()						)
		lowerCamelCase_	:     Optional[Any] 						=      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],
		    }						)
		lowerCamelCase_	:     Optional[Any] 						=      Dataset.from_pandas(lowerCAmelCase__						)

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

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

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


def 		_SCREAMING_SNAKE_CASE     ( ):
		lowerCamelCase_	:     Tuple 						=      {tag: cls for tag, _, cls in PIPELINE_TAGS_AND_AUTO_MODELS}
		lowerCamelCase_	:     Optional[Any] 						=      transformers_module.pipelines.SUPPORTED_TASKS
		lowerCamelCase_	:     int 						=      []
		for key in pipeline_tasks:
				if key not in in_table:
						lowerCamelCase_	:     Tuple 						=      pipeline_tasks[key]['pt']
						if isinstance(lowerCAmelCase__							,(list, tuple)						):
								lowerCamelCase_	:     str 						=      model[0]
						lowerCamelCase_	:     List[str] 						=      model.__name__
						if model not in in_table.values():
								missing.append(lowerCAmelCase__						)

		if len(lowerCAmelCase__						) > 0:
				lowerCamelCase_	:     Union[str, Any] 						=      ', '.join(lowerCAmelCase__						)
				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		:  Dict			       =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		:  Optional[Any]			       =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_flax_available,
    is_tf_available,
    is_torch_available,
)


_lowercase		:  Dict			       ={
    """configuration_resnet""": ["""RESNET_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ResNetConfig""", """ResNetOnnxConfig"""]
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowercase		:  Union[str, Any]			       =[
		    """RESNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
		    """ResNetForImageClassification""",
		    """ResNetModel""",
		    """ResNetPreTrainedModel""",
		    """ResNetBackbone""",
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowercase		:  Union[str, Any]			       =[
		    """TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
		    """TFResNetForImageClassification""",
		    """TFResNetModel""",
		    """TFResNetPreTrainedModel""",
		]

try:
		if not is_flax_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowercase		:  Tuple			       =[
		    """FlaxResNetForImageClassification""",
		    """FlaxResNetModel""",
		    """FlaxResNetPreTrainedModel""",
		]

if TYPE_CHECKING:
		from .configuration_resnet import RESNET_PRETRAINED_CONFIG_ARCHIVE_MAP, ResNetConfig, ResNetOnnxConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_resnet import (
				    RESNET_PRETRAINED_MODEL_ARCHIVE_LIST,
				    ResNetBackbone,
				    ResNetForImageClassification,
				    ResNetModel,
				    ResNetPreTrainedModel,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_resnet import (
				    TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFResNetForImageClassification,
				    TFResNetModel,
				    TFResNetPreTrainedModel,
				)

		try:
				if not is_flax_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_flax_resnet import FlaxResNetForImageClassification, FlaxResNetModel, FlaxResNetPreTrainedModel


else:
		import sys

		_lowercase		:  Union[str, Any]			       =_LazyModule(__name__, globals()["""__file__"""], _import_structure)
