






'''simple docstring'''







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
_A     :      List[Any]   					="src/transformers"


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


# Regexes that match TF/Flax/PT model names.
_A     :      Dict   					=re.compile(r'''TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)''')
_A     :      List[str]   					=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.
_A     :      Any   					=re.compile(r'''(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)''')


# Fill this with tuples (pipeline_tag, model_mapping, auto_model)
_A     :      List[Any]   					=[
    ("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_	(UpperCamelCase				)				->	Dict:
							lowerCamelCase__ :     str	  =	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_	()				->	Dict:
							lowerCamelCase__ :     Optional[Any]	  =	transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
							lowerCamelCase__ :     Any	  =	{
							    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__ :     Union[str, Any]	  =	collections.defaultdict(lowerCamelCase_				)
							lowerCamelCase__ :     Any	  =	collections.defaultdict(lowerCamelCase_				)
							lowerCamelCase__ :     int	  =	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__ :     Union[str, Any]	  =	None
														if _re_tf_models.match(lowerCamelCase_				) is not None:
																					lowerCamelCase__ :     Any	  =	tf_models
																					lowerCamelCase__ :     int	  =	_re_tf_models.match(lowerCamelCase_				).groups()[0]
														elif _re_flax_models.match(lowerCamelCase_				) is not None:
																					lowerCamelCase__ :     List[str]	  =	flax_models
																					lowerCamelCase__ :     Tuple	  =	_re_flax_models.match(lowerCamelCase_				).groups()[0]
														elif _re_pt_models.match(lowerCamelCase_				) is not None:
																					lowerCamelCase__ :     List[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__ :     Tuple	  =	True
																																			break
																												# Try again after removing the last word in the name
																												lowerCamelCase__ :     List[Any]	  =	''.join(camel_case_split(lowerCamelCase_				)[:-1]				)

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

							lowerCamelCase__ :     Tuple	  =	{'model_type': all_models}
							lowerCamelCase__ :     Dict	  =	[pt_models[t] for t in all_models]
							lowerCamelCase__ :     List[Any]	  =	[tf_models[t] for t in all_models]
							lowerCamelCase__ :     Any	  =	[flax_models[t] for t in all_models]

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

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

							return pd.DataFrame(lowerCamelCase_				)





def 			SCREAMING_SNAKE_CASE_	(UpperCamelCase				)				->	Any:
							lowerCamelCase__ :     Union[str, Any]	  =	[
							    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__ :     int	  =	[model_mapping, f'''TF_{model_mapping}''', f'''FLAX_{model_mapping}''']
														lowerCamelCase__ :     List[str]	  =	[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__ :     Union[str, 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_	(UpperCamelCase    ,      UpperCamelCase				)				->	List[Any]:
							lowerCamelCase__ :     Tuple	  =	get_frameworks_table()
							lowerCamelCase__ :     Any	  =	Dataset.from_pandas(lowerCamelCase_				)

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

							# Sort the model classes to avoid some nondeterministic updates to create false update commits.
							lowerCamelCase__ :     int	  =	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__ :     int	  =	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__ :     List[Any]	  =	(
																					    f'''Update with commit {commit_sha}\n\nSee: '''
																					    f'''https://github.com/huggingface/transformers/commit/{commit_sha}'''
																					)
														else:
																					lowerCamelCase__ :     Tuple	  =	'Update'

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





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

							if len(lowerCamelCase_				) > 0:
														lowerCamelCase__ :     Dict	  =	', '.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__":
			_A     :      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.''')
			_A     :      Optional[int]   					=parser.parse_args()

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













'''simple docstring'''







from torch import nn





def 			SCREAMING_SNAKE_CASE_	(UpperCamelCase				)				->	Dict:
							if act_fn in ["swish", "silu"]:
														return nn.SiLU()
							elif act_fn == "mish":
														return nn.Mish()
							elif act_fn == "gelu":
														return nn.GELU()
							else:
														raise ValueError(f'''Unsupported activation function: {act_fn}'''				)

