


'''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       _snake_case     (  A )		->					Optional[Any]:
							lowerCAmelCase__      =			re.finditer('''.+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)'''				,					A )
							return [m.group(0 ) for m in matches]


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

							lowerCAmelCase__      =			set(list(pt_models.keys() ) + list(tf_models.keys() ) + list(flax_models.keys() ) )
							lowerCAmelCase__      =			list(A )
							all_models.sort()

							lowerCAmelCase__      =			{'''model_type''': all_models}
							lowerCAmelCase__      =			[pt_models[t] for t in all_models]
							lowerCAmelCase__      =			[tf_models[t] for t in all_models]
							lowerCAmelCase__      =			[flax_models[t] for t in all_models]

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

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

							return pd.DataFrame(A )


def       _snake_case     (  A )		->					List[str]:
							lowerCAmelCase__      =			[
							    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:
														lowerCAmelCase__      =			[model_mapping, F"""TF_{model_mapping}""", F"""FLAX_{model_mapping}"""]
														lowerCAmelCase__      =			[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
																					lowerCAmelCase__      =			[]
																					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       _snake_case     (  A				,					A )		->					Optional[Any]:
							lowerCAmelCase__      =			get_frameworks_table()
							lowerCAmelCase__      =			Dataset.from_pandas(A )

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

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

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


def       _snake_case     (  )		->					Optional[Any]:
							lowerCAmelCase__      =			{tag: cls for tag, _, cls in PIPELINE_TAGS_AND_AUTO_MODELS}
							lowerCAmelCase__      =			transformers_module.pipelines.SUPPORTED_TASKS
							lowerCAmelCase__      =			[]
							for key in pipeline_tasks:
														if key not in in_table:
																					lowerCAmelCase__      =			pipeline_tasks[key]['''pt''']
																					if isinstance(A				,					(list, tuple) ):
																												lowerCAmelCase__      =			model[0]
																					lowerCAmelCase__      =			model.__name__
																					if model not in in_table.values():
																												missing.append(A )

							if len(A ) > 0:
														lowerCAmelCase__      =			''', '''.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					       =							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'''






from math import pi, sqrt, tan


def       _snake_case     (  A )		->					float:
							if side_length < 0:
														raise ValueError('''surface_area_cube() only accepts non-negative values''' )
							return 6 * side_length**2


def       _snake_case     (  A				,					A				,					A )		->					float:
							if length < 0 or breadth < 0 or height < 0:
														raise ValueError('''surface_area_cuboid() only accepts non-negative values''' )
							return 2 * ((length * breadth) + (breadth * height) + (length * height))


def       _snake_case     (  A )		->					float:
							if radius < 0:
														raise ValueError('''surface_area_sphere() only accepts non-negative values''' )
							return 4 * pi * radius**2


def       _snake_case     (  A )		->					float:
							if radius < 0:
														raise ValueError('''surface_area_hemisphere() only accepts non-negative values''' )
							return 3 * pi * radius**2


def       _snake_case     (  A				,					A )		->					float:
							if radius < 0 or height < 0:
														raise ValueError('''surface_area_cone() only accepts non-negative values''' )
							return pi * radius * (radius + (height**2 + radius**2) ** 0.5)


def       _snake_case     (  A				,					A				,					A )		->					float:
							if radius_a < 0 or radius_a < 0 or height < 0:
														raise ValueError(
														    '''surface_area_conical_frustum() only accepts non-negative values''' )
							lowerCAmelCase__      =			(height**2 + (radius_a - radius_a) ** 2) ** 0.5
							return pi * ((slant_height * (radius_a + radius_a)) + radius_a**2 + radius_a**2)


def       _snake_case     (  A				,					A )		->					float:
							if radius < 0 or height < 0:
														raise ValueError('''surface_area_cylinder() only accepts non-negative values''' )
							return 2 * pi * radius * (height + radius)


def       _snake_case     (  A				,					A )		->					float:
							if torus_radius < 0 or tube_radius < 0:
														raise ValueError('''surface_area_torus() only accepts non-negative values''' )
							if torus_radius < tube_radius:
														raise ValueError(
														    '''surface_area_torus() does not support spindle or self intersecting tori''' )
							return 4 * pow(A				,					2 ) * torus_radius * tube_radius


def       _snake_case     (  A				,					A )		->					float:
							if length < 0 or width < 0:
														raise ValueError('''area_rectangle() only accepts non-negative values''' )
							return length * width


def       _snake_case     (  A )		->					float:
							if side_length < 0:
														raise ValueError('''area_square() only accepts non-negative values''' )
							return side_length**2


def       _snake_case     (  A				,					A )		->					float:
							if base < 0 or height < 0:
														raise ValueError('''area_triangle() only accepts non-negative values''' )
							return (base * height) / 2


def       _snake_case     (  A				,					A				,					A )		->					float:
							if sidea < 0 or sidea < 0 or sidea < 0:
														raise ValueError('''area_triangle_three_sides() only accepts non-negative values''' )
							elif sidea + sidea < sidea or sidea + sidea < sidea or sidea + sidea < sidea:
														raise ValueError('''Given three sides do not form a triangle''' )
							lowerCAmelCase__      =			(sidea + sidea + sidea) / 2
							lowerCAmelCase__      =			sqrt(
							    semi_perimeter
							    * (semi_perimeter - sidea)
							    * (semi_perimeter - sidea)
							    * (semi_perimeter - sidea) )
							return area


def       _snake_case     (  A				,					A )		->					float:
							if base < 0 or height < 0:
														raise ValueError('''area_parallelogram() only accepts non-negative values''' )
							return base * height


def       _snake_case     (  A				,					A				,					A )		->					float:
							if basea < 0 or basea < 0 or height < 0:
														raise ValueError('''area_trapezium() only accepts non-negative values''' )
							return 1 / 2 * (basea + basea) * height


def       _snake_case     (  A )		->					float:
							if radius < 0:
														raise ValueError('''area_circle() only accepts non-negative values''' )
							return pi * radius**2


def       _snake_case     (  A				,					A )		->					float:
							if radius_x < 0 or radius_y < 0:
														raise ValueError('''area_ellipse() only accepts non-negative values''' )
							return pi * radius_x * radius_y


def       _snake_case     (  A				,					A )		->					float:
							if diagonal_a < 0 or diagonal_a < 0:
														raise ValueError('''area_rhombus() only accepts non-negative values''' )
							return 1 / 2 * diagonal_a * diagonal_a


def       _snake_case     (  A				,					A )		->					float:
							if not isinstance(A				,					A ) or sides < 3:
														raise ValueError(
														    '''area_reg_polygon() only accepts integers greater than or \
equal to three as number of sides''' )
							elif length < 0:
														raise ValueError(
														    '''area_reg_polygon() only accepts non-negative values as \
length of a side''' )
							return (sides * length**2) / (4 * tan(pi / sides ))
							return (sides * length**2) / (4 * tan(pi / sides ))


if __name__ == "__main__":
		import doctest

		doctest.testmod(verbose=True)  # verbose so we can see methods missing tests

		print('''[DEMO] Areas of various geometric shapes: \n''')
		print(f"""Rectangle: {area_rectangle(10, 20) = }""")
		print(f"""Square: {area_square(10) = }""")
		print(f"""Triangle: {area_triangle(10, 10) = }""")
		print(f"""Triangle: {area_triangle_three_sides(5, 12, 13) = }""")
		print(f"""Parallelogram: {area_parallelogram(10, 20) = }""")
		print(f"""Rhombus: {area_rhombus(10, 20) = }""")
		print(f"""Trapezium: {area_trapezium(10, 20, 30) = }""")
		print(f"""Circle: {area_circle(20) = }""")
		print(f"""Ellipse: {area_ellipse(10, 20) = }""")
		print('''\nSurface Areas of various geometric shapes: \n''')
		print(f"""Cube: {surface_area_cube(20) = }""")
		print(f"""Cuboid: {surface_area_cuboid(10, 20, 30) = }""")
		print(f"""Sphere: {surface_area_sphere(20) = }""")
		print(f"""Hemisphere: {surface_area_hemisphere(20) = }""")
		print(f"""Cone: {surface_area_cone(10, 20) = }""")
		print(f"""Conical Frustum: {surface_area_conical_frustum(10, 20, 30) = }""")
		print(f"""Cylinder: {surface_area_cylinder(10, 20) = }""")
		print(f"""Torus: {surface_area_torus(20, 10) = }""")
		print(f"""Equilateral Triangle: {area_reg_polygon(3, 10) = }""")
		print(f"""Square: {area_reg_polygon(4, 10) = }""")
		print(f"""Reqular Pentagon: {area_reg_polygon(5, 10) = }""")