


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
__snake_case            =   "src/transformers"


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


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


# Fill this with tuples (pipeline_tag, model_mapping, auto_model)
__snake_case            =   [
    ("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 							_lowercase    (		SCREAMING_SNAKE_CASE_   :		Tuple	):





			"""simple docstring"""




			UpperCamelCase         =					re.finditer(""".+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)"""       ,  SCREAMING_SNAKE_CASE_	)
			return [m.group(0	) for m in matches]






def 							_lowercase    (		):





			"""simple docstring"""




			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(SCREAMING_SNAKE_CASE_	)
			UpperCamelCase         =					collections.defaultdict(SCREAMING_SNAKE_CASE_	)
			UpperCamelCase         =					collections.defaultdict(SCREAMING_SNAKE_CASE_	)

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

						if lookup_dict is not None:
									while len(SCREAMING_SNAKE_CASE_	) > 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(SCREAMING_SNAKE_CASE_	)[:-1]	)

			UpperCamelCase         =					set(list(pt_models.keys()	) + list(tf_models.keys()	) + list(flax_models.keys()	)	)
			UpperCamelCase         =					list(SCREAMING_SNAKE_CASE_	)
			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(SCREAMING_SNAKE_CASE_	)






def 							_lowercase    (		SCREAMING_SNAKE_CASE_   :		Union[str, Any]	):





			"""simple docstring"""




			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(SCREAMING_SNAKE_CASE_       ,  SCREAMING_SNAKE_CASE_       ,  SCREAMING_SNAKE_CASE_	):
									# The type of pipeline may not exist in this framework
									if not hasattr(SCREAMING_SNAKE_CASE_       ,  SCREAMING_SNAKE_CASE_	):
												continue
									# First extract all model_names
									UpperCamelCase         =					[]
									for name in getattr(SCREAMING_SNAKE_CASE_       ,  SCREAMING_SNAKE_CASE_	).values():
												if isinstance(SCREAMING_SNAKE_CASE_       ,  SCREAMING_SNAKE_CASE_	):
															model_names.append(SCREAMING_SNAKE_CASE_	)
												else:
															model_names.extend(list(SCREAMING_SNAKE_CASE_	)	)

												# 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 							_lowercase    (		SCREAMING_SNAKE_CASE_   :		Optional[Any]       ,  SCREAMING_SNAKE_CASE_   :		List[Any]	):





			"""simple docstring"""




			UpperCamelCase         =					get_frameworks_table()
			UpperCamelCase         =					Dataset.from_pandas(SCREAMING_SNAKE_CASE_	)

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

			# 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(SCREAMING_SNAKE_CASE_	)

			with tempfile.TemporaryDirectory() as tmp_dir:
						frameworks_dataset.to_json(os.path.join(SCREAMING_SNAKE_CASE_       ,  """frameworks.json"""	)	)
						tags_dataset.to_json(os.path.join(SCREAMING_SNAKE_CASE_       ,  """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=SCREAMING_SNAKE_CASE_       ,  repo_type="""dataset"""       ,  token=SCREAMING_SNAKE_CASE_       ,  commit_message=SCREAMING_SNAKE_CASE_       ,  )






def 							_lowercase    (		):





			"""simple docstring"""




			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(SCREAMING_SNAKE_CASE_       ,  (list, tuple)	):
												UpperCamelCase         =					model[0]
									UpperCamelCase         =					model.__name__
									if model not in in_table.values():
												missing.append(SCREAMING_SNAKE_CASE_	)

			if len(SCREAMING_SNAKE_CASE_	) > 0:
						UpperCamelCase         =					', '.join(SCREAMING_SNAKE_CASE_	)
						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__":
			__snake_case            =   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.")
			__snake_case            =   parser.parse_args()

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








from ..utils import DummyObject, requires_backends




class    UpperCAmelCase					(       metaclass=__snake_case    ):
			lowercase						= ["""keras_nlp"""]




			def __init__(						self							:		List[str]       ,       *__magic_name__							:		str       ,       **__magic_name__							:		int						):







						"""simple docstring"""







						requires_backends(self       ,       ["""keras_nlp"""]						)




