



'''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
a   :			str					  =    """src/transformers"""


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


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


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



def       __lowerCamelCase  (					)      ->			Optional[int]:
							UpperCAmelCase     :		Optional[Any]							     =		transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
							UpperCAmelCase     :		Tuple							     =		{
							    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     :		Union[str, Any]							     =		collections.defaultdict(_lowercase		)
							UpperCAmelCase     :		Optional[Any]							     =		collections.defaultdict(_lowercase		)
							UpperCAmelCase     :		Optional[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     :		Tuple							     =		tf_models
																					UpperCAmelCase     :		Dict							     =		_re_tf_models.match(_lowercase		).groups()[0]
														elif _re_flax_models.match(_lowercase		) is not None:
																					UpperCAmelCase     :		Optional[Any]							     =		flax_models
																					UpperCAmelCase     :		str							     =		_re_flax_models.match(_lowercase		).groups()[0]
														elif _re_pt_models.match(_lowercase		) is not None:
																					UpperCAmelCase     :		Any							     =		pt_models
																					UpperCAmelCase     :		Optional[Any]							     =		_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     :		Optional[int]							     =		True
																																			break
																												# Try again after removing the last word in the name
																												UpperCAmelCase     :		List[Any]							     =		"""""".join(camel_case_split(_lowercase		)[:-1]		)

							UpperCAmelCase     :		Tuple							     =		set(list(pt_models.keys()		) + list(tf_models.keys()		) + list(flax_models.keys()		)		)
							UpperCAmelCase     :		Tuple							     =		list(_lowercase		)
							all_models.sort()

							UpperCAmelCase     :		Optional[int]							     =		{"""model_type""": all_models}
							UpperCAmelCase     :		Optional[Any]							     =		[pt_models[t] for t in all_models]
							UpperCAmelCase     :		List[str]							     =		[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     :		Optional[int]							     =		{}
							for t in all_models:
														if t in transformers_module.models.auto.processing_auto.PROCESSOR_MAPPING_NAMES:
																					UpperCAmelCase     :		Tuple							     =		"""AutoProcessor"""
														elif t in transformers_module.models.auto.tokenization_auto.TOKENIZER_MAPPING_NAMES:
																					UpperCAmelCase     :		Optional[Any]							     =		"""AutoTokenizer"""
														elif t in transformers_module.models.auto.feature_extraction_auto.FEATURE_EXTRACTOR_MAPPING_NAMES:
																					UpperCAmelCase     :		Any							     =		"""AutoFeatureExtractor"""
														else:
																					# Default to AutoTokenizer if a model has nothing, for backward compatibility.
																					UpperCAmelCase     :		Optional[int]							     =		"""AutoTokenizer"""

							UpperCAmelCase     :		List[Any]							     =		[processors[t] for t in all_models]

							return pd.DataFrame(_lowercase		)



def       __lowerCamelCase  (					_lowercase		)      ->			str:
							UpperCAmelCase     :		Any							     =		[
							    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     :		str							     =		[model_mapping, F'''TF_{model_mapping}''', F'''FLAX_{model_mapping}''']
														UpperCAmelCase     :		Optional[Any]							     =		[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     :		Tuple							     =		[]
																					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       __lowerCamelCase  (					_lowercase	,				_lowercase		)      ->			Tuple:
							UpperCAmelCase     :		Tuple							     =		get_frameworks_table()
							UpperCAmelCase     :		str							     =		Dataset.from_pandas(_lowercase		)

							UpperCAmelCase     :		Optional[Any]							     =		hf_hub_download(
							    """huggingface/transformers-metadata"""	,				"""pipeline_tags.json"""	,				repo_type="""dataset"""	,				token=_lowercase		)
							UpperCAmelCase     :		Dict							     =		Dataset.from_json(_lowercase		)
							UpperCAmelCase     :		Optional[int]							     =		{
							    tags_dataset[i]["""model_class"""]: (tags_dataset[i]["""pipeline_tag"""], tags_dataset[i]["""auto_class"""])
							    for i in range(len(_lowercase		)		)
							}
							UpperCAmelCase     :		Dict							     =		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     :		int							     =		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     :		Union[str, Any]							     =		(
																					    F'''Update with commit {commit_sha}\n\nSee: '''
																					    F'''https://github.com/huggingface/transformers/commit/{commit_sha}'''
																					)
														else:
																					UpperCAmelCase     :		Union[str, Any]							     =		"""Update"""

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



def       __lowerCamelCase  (					)      ->			Any:
							UpperCAmelCase     :		Optional[int]							     =		{tag: cls for tag, _, cls in PIPELINE_TAGS_AND_AUTO_MODELS}
							UpperCAmelCase     :		List[str]							     =		transformers_module.pipelines.SUPPORTED_TASKS
							UpperCAmelCase     :		List[str]							     =		[]
							for key in pipeline_tasks:
														if key not in in_table:
																					UpperCAmelCase     :		Tuple							     =		pipeline_tasks[key]["""pt"""]
																					if isinstance(_lowercase	,				(list, tuple)		):
																												UpperCAmelCase     :		List[str]							     =		model[0]
																					UpperCAmelCase     :		List[str]							     =		model.__name__
																					if model not in in_table.values():
																												missing.append(_lowercase		)

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

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






'''simple docstring'''




from operator import delitem, getitem, setitem

import pytest

from data_structures.hashing.hash_map import HashMap



def       __lowerCamelCase  (					_lowercase		)      ->			Optional[Any]:
							return getitem, k



def       __lowerCamelCase  (					_lowercase	,				_lowercase		)      ->			List[str]:
							return setitem, k, v



def       __lowerCamelCase  (					_lowercase		)      ->			int:
							return delitem, k



def       __lowerCamelCase  (					_lowercase	,				_lowercase	,				*_lowercase		)      ->			Optional[Any]:
							try:
														return fun(_lowercase	,				*_lowercase		), None
							except Exception as e:
														return None, e


a   :			List[str]					  =    (
    _set("""key_a""", """val_a"""),
    _set("""key_b""", """val_b"""),
)

a   :			List[Any]					  =    [
    _set("""key_a""", """val_a"""),
    _set("""key_a""", """val_b"""),
]

a   :			int					  =    [
    _set("""key_a""", """val_a"""),
    _set("""key_b""", """val_b"""),
    _del("""key_a"""),
    _del("""key_b"""),
    _set("""key_a""", """val_a"""),
    _del("""key_a"""),
]

a   :			List[Any]					  =    [
    _get("""key_a"""),
    _del("""key_a"""),
    _set("""key_a""", """val_a"""),
    _del("""key_a"""),
    _del("""key_a"""),
    _get("""key_a"""),
]

a   :			Tuple					  =    [
    *[_set(x, x) for x in range(5)],  # guaranteed upsize
]

a   :			Optional[Any]					  =    [
    *[_set(x, x) for x in range(5)],  # guaranteed upsize
    *[_del(x) for x in range(5)],
    _set("""key_a""", """val_b"""),
]



@pytest.mark.parametrize(
    """operations"""	,				(
        pytest.param(_add_items	,				id="""add items"""		),
        pytest.param(_overwrite_items	,				id="""overwrite items"""		),
        pytest.param(_delete_items	,				id="""delete items"""		),
        pytest.param(_access_absent_items	,				id="""access absent items"""		),
        pytest.param(_add_with_resize_up	,				id="""add with resize up"""		),
        pytest.param(_add_with_resize_down	,				id="""add with resize down"""		),
    )	,				)
def       __lowerCamelCase  (					_lowercase		)      ->			Optional[int]:
							UpperCAmelCase     :		List[str]							     =		HashMap(initial_block_size=4		)
							UpperCAmelCase     :		Dict							     =		{}
							for _, (fun, *args) in enumerate(_lowercase		):
														UpperCAmelCase     ,     UpperCAmelCase     :		Union[str, Any]							     =		_run_operation(_lowercase	,				_lowercase	,				*_lowercase		)
														UpperCAmelCase     ,     UpperCAmelCase     :		Any							     =		_run_operation(_lowercase	,				_lowercase	,				*_lowercase		)
														assert my_res == py_res
														assert str(_lowercase		) == str(_lowercase		)
														assert set(_lowercase		) == set(_lowercase		)
														assert len(_lowercase		) == len(_lowercase		)
														assert set(my.items()		) == set(py.items()		)



def       __lowerCamelCase  (					)      ->			List[Any]:
							def is_public(_lowercase		) -> bool:
														return not name.startswith("""_"""		)

							UpperCAmelCase     :		int							     =		{name for name in dir({}		) if is_public(_lowercase		)}
							UpperCAmelCase     :		Any							     =		{name for name in dir(HashMap()		) if is_public(_lowercase		)}

							assert dict_public_names > hash_public_names

