





"""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
UpperCamelCase_									="""src/transformers"""


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


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


# Fill this with tuples (pipeline_tag, model_mapping, auto_model)
UpperCamelCase_									=[
    ("""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 	a_    (							_lowercase       ):
						_UpperCamelCase      : Dict         =   re.finditer('''.+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)'''	,						_lowercase       )
						return [m.group(0       ) for m in matches]





def 	a_    (							):
						_UpperCamelCase      : List[str]         =   transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
						_UpperCamelCase      : str         =   {
						    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      : Any         =   collections.defaultdict(_lowercase       )
						_UpperCamelCase      : Any         =   collections.defaultdict(_lowercase       )
						_UpperCamelCase      : Union[str, Any]         =   collections.defaultdict(_lowercase       )

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

												if lookup_dict is not None:
																		while len(_lowercase       ) > 0:
																								if attr_name in model_prefix_to_model_type:
																														_UpperCamelCase      : int         =   True
																														break
																								# Try again after removing the last word in the name
																								_UpperCamelCase      : List[str]         =   ''''''.join(camel_case_split(_lowercase       )[:-1]       )

						_UpperCamelCase      : List[str]         =   set(list(pt_models.keys()       ) + list(tf_models.keys()       ) + list(flax_models.keys()       )       )
						_UpperCamelCase      : Dict         =   list(_lowercase       )
						all_models.sort()

						_UpperCamelCase      : Union[str, Any]         =   {'''model_type''': all_models}
						_UpperCamelCase      : Optional[Any]         =   [pt_models[t] for t in all_models]
						_UpperCamelCase      : Union[str, Any]         =   [tf_models[t] for t in all_models]
						_UpperCamelCase      : List[Any]         =   [flax_models[t] for t in all_models]

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

						_UpperCamelCase      : List[str]         =   [processors[t] for t in all_models]

						return pd.DataFrame(_lowercase       )





def 	a_    (							_lowercase       ):
						_UpperCamelCase      : List[str]         =   [
						    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      : Optional[Any]         =   [model_mapping, F"""TF_{model_mapping}""", F"""FLAX_{model_mapping}"""]
												_UpperCamelCase      : List[str]         =   [auto_class, F"""TF_{auto_class}""", F"""Flax_{auto_class}"""]
												# Loop through all three frameworks
												for module, cls, mapping in zip(_lowercase	,						_lowercase	,						_lowercase       ):
																		# The type of pipeline may not exist in this framework
																		if not hasattr(_lowercase	,						_lowercase       ):
																								continue
																		# First extract all model_names
																		_UpperCamelCase      : str         =   []
																		for name in getattr(_lowercase	,						_lowercase       ).values():
																								if isinstance(_lowercase	,						_lowercase       ):
																														model_names.append(_lowercase       )
																								else:
																														model_names.extend(list(_lowercase       )       )

            # 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 	a_    (							_lowercase	,						_lowercase       ):
						_UpperCamelCase      : Optional[Any]         =   get_frameworks_table()
						_UpperCamelCase      : List[Any]         =   Dataset.from_pandas(_lowercase       )

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

						# Sort the model classes to avoid some nondeterministic updates to create false update commits.
						_UpperCamelCase      : Dict         =   sorted(table.keys()       )
						_UpperCamelCase      : Optional[int]         =   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      : List[Any]         =   Dataset.from_pandas(_lowercase       )

						with tempfile.TemporaryDirectory() as tmp_dir:
												frameworks_dataset.to_json(os.path.join(_lowercase	,						'''frameworks.json'''       )       )
												tags_dataset.to_json(os.path.join(_lowercase	,						'''pipeline_tags.json'''       )       )

												if commit_sha is not None:
																		_UpperCamelCase      : Tuple         =   (
																		    F"""Update with commit {commit_sha}\n\nSee: """
																		    F"""https://github.com/huggingface/transformers/commit/{commit_sha}"""
																		)
												else:
																		_UpperCamelCase      : List[Any]         =   '''Update'''

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





def 	a_    (							):
						_UpperCamelCase      : Tuple         =   {tag: cls for tag, _, cls in PIPELINE_TAGS_AND_AUTO_MODELS}
						_UpperCamelCase      : int         =   transformers_module.pipelines.SUPPORTED_TASKS
						_UpperCamelCase      : Tuple         =   []
						for key in pipeline_tasks:
												if key not in in_table:
																		_UpperCamelCase      : Dict         =   pipeline_tasks[key]['''pt''']
																		if isinstance(_lowercase	,						(list, tuple)       ):
																								_UpperCamelCase      : Tuple         =   model[0]
																		_UpperCamelCase      : Optional[int]         =   model.__name__
																		if model not in in_table.values():
																								missing.append(_lowercase       )

						if len(_lowercase       ) > 0:
												_UpperCamelCase      : Optional[int]         =   ''', '''.join(_lowercase       )
												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_									=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_									=parser.parse_args()

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










"""simple docstring"""




UpperCamelCase_									=[
    """DownloadConfig""",
    """DownloadManager""",
    """DownloadMode""",
    """StreamingDownloadManager""",
]

from .download_config import DownloadConfig
from .download_manager import DownloadManager, DownloadMode
from .streaming_download_manager import StreamingDownloadManager



