



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
__lowerCamelCase  :				Tuple					=					"src/transformers"


# This is to make sure the transformers module imported is the one in the repo.
__lowerCamelCase  :				Optional[Any]					=					direct_transformers_import(TRANSFORMERS_PATH)


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


# Fill this with tuples (pipeline_tag, model_mapping, auto_model)
__lowerCamelCase  :				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  lowerCamelCase_(lowerCamelCase_     )			->							int:
						UpperCAmelCase								=  re.finditer(".+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)"     , a_     )
						return [m.group(0     ) for m in matches]

def  lowerCamelCase_()			->							List[str]:
						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(a_     )
						UpperCAmelCase								=  collections.defaultdict(a_     )
						UpperCAmelCase								=  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_     ):
												UpperCAmelCase								=  None
												if _re_tf_models.match(a_     ) is not None:
																		UpperCAmelCase								=  tf_models
																		UpperCAmelCase								=  _re_tf_models.match(a_     ).groups()[0]
												elif _re_flax_models.match(a_     ) is not None:
																		UpperCAmelCase								=  flax_models
																		UpperCAmelCase								=  _re_flax_models.match(a_     ).groups()[0]
												elif _re_pt_models.match(a_     ) is not None:
																		UpperCAmelCase								=  pt_models
																		UpperCAmelCase								=  _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:
																														UpperCAmelCase								=  True
																														break
																								# Try again after removing the last word in the name
																								UpperCAmelCase								=  "".join(camel_case_split(a_     )[:-1]     )

						UpperCAmelCase								=  set(list(pt_models.keys()     ) + list(tf_models.keys()     ) + list(flax_models.keys()     )     )
						UpperCAmelCase								=  list(a_     )
						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(a_     )

def  lowerCamelCase_(lowerCamelCase_     )			->							Optional[int]:
						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(a_     , a_     , a_     ):
																		# The type of pipeline may not exist in this framework
																		if not hasattr(a_     , a_     ):
																								continue
																		# First extract all model_names
																		UpperCAmelCase								=  []
																		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  lowerCamelCase_(lowerCamelCase_     , lowerCamelCase_     )			->							str:
						UpperCAmelCase								=  get_frameworks_table()
						UpperCAmelCase								=  Dataset.from_pandas(a_     )

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

						# 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(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:
																		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=a_     , repo_type="dataset"     , token=a_     , commit_message=a_     , )

def  lowerCamelCase_()			->							Any:
						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(a_     , (list, tuple)     ):
																								UpperCAmelCase								=  model[0]
																		UpperCAmelCase								=  model.__name__
																		if model not in in_table.values():
																								missing.append(a_     )

						if len(a_     ) > 0:
												UpperCAmelCase								=  ", ".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__":
		__lowerCamelCase  :				Optional[int]					=					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.")
		__lowerCamelCase  :				Union[str, Any]					=					parser.parse_args()

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











class 					UpperCamelCase_ :



	'''simple docstring'''




	def __init__(			self			,     a   )			->       Tuple:
								snake_case_           =				n
								snake_case_           =				[None] * self.n
								snake_case_           =				0  # index of the first element
								snake_case_           =				0
								snake_case_           =				0




	def __len__(			self   )			->       int:
								return self.size




	def 				_UpperCamelCase     (			self   )			->       bool:
								return self.size == 0




	def 				_UpperCamelCase     (			self   )			->       List[Any]:
								return False if self.is_empty() else self.array[self.front]




	def 				_UpperCamelCase     (			self			,     a   )			->       Dict:
								if self.size >= self.n:
															raise Exception('QUEUE IS FULL'   )

								snake_case_           =				data
								snake_case_           =				(self.rear + 1) % self.n
								self.size += 1
								return self






	def 				_UpperCamelCase     (			self   )			->       List[Any]:
								if self.size == 0:
															raise Exception('UNDERFLOW'   )

								snake_case_           =				self.array[self.front]
								snake_case_           =				None
								snake_case_           =				(self.front + 1) % self.n
								self.size -= 1
								return temp





