



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_							:      List[str]      =  "src/transformers"


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


# Regexes that match TF/Flax/PT model names.
UpperCAmelCase_							:      Optional[int]      =  re.compile(R"TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)")
UpperCAmelCase_							:      Optional[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.
UpperCAmelCase_							:      Tuple      =  re.compile(R"(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)")


# Fill this with tuples (pipeline_tag, model_mapping, auto_model)
UpperCAmelCase_							:      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  UpperCamelCase						(       _A			:				Optional[Any]    )->       str:





						"""simple docstring"""





						A__							=       re.finditer(".+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)"			,							_A    )
						return [m.group(0    ) for m in matches]
def  UpperCamelCase						(       )->       Union[str, Any]:





						"""simple docstring"""





						A__							=       transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
						A__							=       {
						    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.
						A__							=       collections.defaultdict(_A    )
						A__							=       collections.defaultdict(_A    )
						A__							=       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    ):
												A__							=       None
												if _re_tf_models.match(_A    ) is not None:
																		A__							=       tf_models
																		A__							=       _re_tf_models.match(_A    ).groups()[0]
												elif _re_flax_models.match(_A    ) is not None:
																		A__							=       flax_models
																		A__							=       _re_flax_models.match(_A    ).groups()[0]
												elif _re_pt_models.match(_A    ) is not None:
																		A__							=       pt_models
																		A__							=       _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:
																														A__							=       True
																														break
																								# Try again after removing the last word in the name
																								A__							=       "".join(camel_case_split(_A    )[:-1]    )

						A__							=       set(list(pt_models.keys()    ) + list(tf_models.keys()    ) + list(flax_models.keys()    )    )
						A__							=       list(_A    )
						all_models.sort()

						A__							=       {"model_type": all_models}
						A__							=       [pt_models[t] for t in all_models]
						A__							=       [tf_models[t] for t in all_models]
						A__							=       [flax_models[t] for t in all_models]

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

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

						return pd.DataFrame(_A    )
def  UpperCamelCase						(       _A			:				List[str]    )->       Optional[int]:





						"""simple docstring"""





						A__							=       [
						    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:
												A__							=       [model_mapping, f"""TF_{model_mapping}""", f"""FLAX_{model_mapping}"""]
												A__							=       [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
																		A__							=       []
																		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  UpperCamelCase						(       _A			:				Union[str, Any]			,							_A			:				Optional[int]    )->       Dict:





						"""simple docstring"""





						A__							=       get_frameworks_table()
						A__							=       Dataset.from_pandas(_A    )

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

						# Sort the model classes to avoid some nondeterministic updates to create false update commits.
						A__							=       sorted(table.keys()    )
						A__							=       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],
						    }    )
						A__							=       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:
																		A__							=       (
																		    f"""Update with commit {commit_sha}\n\nSee: """
																		    f"""https://github.com/huggingface/transformers/commit/{commit_sha}"""
																		)
												else:
																		A__							=       "Update"

												upload_folder(
												    repo_id="huggingface/transformers-metadata"			,							folder_path=_A			,							repo_type="dataset"			,							token=_A			,							commit_message=_A			,							)
def  UpperCamelCase						(       )->       Tuple:





						"""simple docstring"""





						A__							=       {tag: cls for tag, _, cls in PIPELINE_TAGS_AND_AUTO_MODELS}
						A__							=       transformers_module.pipelines.SUPPORTED_TASKS
						A__							=       []
						for key in pipeline_tasks:
												if key not in in_table:
																		A__							=       pipeline_tasks[key]["pt"]
																		if isinstance(_A			,							(list, tuple)    ):
																								A__							=       model[0]
																		A__							=       model.__name__
																		if model not in in_table.values():
																								missing.append(_A    )

						if len(_A    ) > 0:
												A__							=       ", ".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__":
						UpperCAmelCase_							:      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.")
						UpperCAmelCase_							:      str      =  parser.parse_args()

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










def  UpperCamelCase						(       _A			:				str			,							_A			:				str    )->       str:





						"""simple docstring"""





						A__							=       len(_A    )
						A__							=       len(_A    )
						A__							=       (
						    first_str_length if first_str_length > second_str_length else second_str_length
						)
						A__							=       []
						for char_count in range(_A    ):
												if char_count < first_str_length:
																		output_list.append(first_str[char_count]    )
												if char_count < second_str_length:
																		output_list.append(second_str[char_count]    )
						return "".join(_A    )


if __name__ == "__main__":
						print(alternative_string_arrange("AB", "XYZ"), end=" ")





