

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__	   = """src/transformers"""


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


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


# Fill this with tuples (pipeline_tag, model_mapping, auto_model)
lowerCAmelCase__	   = [
    ("""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__							(	SCREAMING_SNAKE_CASE_:							Tuple      )			->      Dict:




				'''simple docstring'''





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



def 					lowerCAmelCase__							(	)			->      Optional[int]:




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

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



def 					lowerCAmelCase__							(	SCREAMING_SNAKE_CASE_:							int      )			->      Tuple:




				'''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(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
												A__        =			[]
												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 					lowerCAmelCase__							(	SCREAMING_SNAKE_CASE_:							Dict	,       SCREAMING_SNAKE_CASE_:							int      )			->      int:




				'''simple docstring'''





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

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

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



def 					lowerCAmelCase__							(	)			->      int:




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

				if len(SCREAMING_SNAKE_CASE_      ) > 0:
								A__        =			", ".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__":
	lowerCAmelCase__	   = 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__	   = parser.parse_args()

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




import warnings

from ...utils import logging
from .image_processing_clip import CLIPImageProcessor


lowerCAmelCase__	   = logging.get_logger(__name__)

class 					a__						(					snake_case			):
			"""simple docstring"""







			def __init__(    self      ,      *lowercase      ,      **lowercase       )  ->						None:
							'''simple docstring'''






							warnings.warn(
							    "The class CLIPFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"
							    " use CLIPImageProcessor instead."      ,      lowercase      ,      )
							super().__init__(*lowercase      ,      **lowercase       )

