


'''simple docstring'''






import argparse
import os

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/check_task_guides.py
__A	:	Optional[Any]     =     'src/transformers'
__A	:	List[Any]     =     'docs/source/en/tasks'





def      UpperCAmelCase      (		lowerCamelCase_  :List[str]				,      lowerCamelCase_  :Tuple				,      lowerCamelCase_  :Tuple ):







		'''simple docstring'''
		with open(lowerCamelCase_				,      """r"""				,      encoding="""utf-8"""				,      newline="""\n""" ) as f:
				snake_case_		:							Optional[int]			       =						f.readlines()
		# Find the start prompt.
		snake_case_		:							Optional[int]			       =						0
		while not lines[start_index].startswith(lowerCamelCase_ ):
				start_index += 1
		start_index += 1

		snake_case_		:							Tuple			       =						start_index
		while not lines[end_index].startswith(lowerCamelCase_ ):
				end_index += 1
		end_index -= 1

		while len(lines[start_index] ) <= 1:
				start_index += 1
		while len(lines[end_index] ) <= 1:
				end_index -= 1
		end_index += 1
		return "".join(lines[start_index:end_index] ), start_index, end_index, lines


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

__A	:	Tuple     =     {
    'asr.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_CTC_MAPPING_NAMES,
    'audio_classification.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES,
    'language_modeling.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_CAUSAL_LM_MAPPING_NAMES,
    'image_classification.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES,
    'masked_language_modeling.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_MASKED_LM_MAPPING_NAMES,
    'multiple_choice.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES,
    'object_detection.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_OBJECT_DETECTION_MAPPING_NAMES,
    'question_answering.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES,
    'semantic_segmentation.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING_NAMES,
    'sequence_classification.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES,
    'summarization.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES,
    'token_classification.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES,
    'translation.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES,
    'video_classification.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING_NAMES,
    'document_question_answering.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING_NAMES,
    'monocular_depth_estimation.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_DEPTH_ESTIMATION_MAPPING_NAMES,
}

# This list contains model types used in some task guides that are not in `CONFIG_MAPPING_NAMES` (therefore not in any
# `MODEL_MAPPING_NAMES` or any `MODEL_FOR_XXX_MAPPING_NAMES`).
__A	:	str     =     {
    'summarization.md': ('nllb',),
    'translation.md': ('nllb',),
}





def      UpperCAmelCase      (		lowerCamelCase_  :Any ):







		'''simple docstring'''
		snake_case_		:							List[Any]			       =						TASK_GUIDE_TO_MODELS[task_guide]
		snake_case_		:							str			       =						SPECIAL_TASK_GUIDE_TO_MODEL_TYPES.get(lowerCamelCase_				,      set() )
		snake_case_		:							Tuple			       =						{
		    code: name
		    for code, name in transformers_module.MODEL_NAMES_MAPPING.items()
		    if (code in model_maping_names or code in special_model_types)
		}
		return ", ".join([F'''[{name}](../model_doc/{code})''' for code, name in model_names.items()] ) + "\n"





def      UpperCAmelCase      (		lowerCamelCase_  :Optional[Any]				,      lowerCamelCase_  :Any=False ):







		'''simple docstring'''
		snake_case_       ,      snake_case_       ,      snake_case_       ,      snake_case_		:							Tuple			       =						_find_text_in_file(
		    filename=os.path.join(lowerCamelCase_				,      lowerCamelCase_ )				,      start_prompt="""<!--This tip is automatically generated by `make fix-copies`, do not fill manually!-->"""				,      end_prompt="""<!--End of the generated tip-->"""				,      )

		snake_case_		:							Optional[Any]			       =						get_model_list_for_task(lowerCamelCase_ )

		if current_list != new_list:
				if overwrite:
						with open(os.path.join(lowerCamelCase_				,      lowerCamelCase_ )				,      """w"""				,      encoding="""utf-8"""				,      newline="""\n""" ) as f:
								f.writelines(lines[:start_index] + [new_list] + lines[end_index:] )
				else:
						raise ValueError(
						    F'''The list of models that can be used in the {task_guide} guide needs an update. Run `make fix-copies`'''
						    """ to fix this.""" )


if __name__ == "__main__":
							__A	:	Any     =     argparse.ArgumentParser()
							parser.add_argument('--fix_and_overwrite', action='store_true', help='Whether to fix inconsistencies.')
							__A	:	str     =     parser.parse_args()

							for task_guide in TASK_GUIDE_TO_MODELS.keys():
														check_model_list_for_task(task_guide, args.fix_and_overwrite)



'''simple docstring'''






from __future__ import annotations

import math





def      UpperCAmelCase      (		lowerCamelCase_  :list				,      lowerCamelCase_  :list ):







		'''simple docstring'''
		if len(lowerCamelCase_ ) != 2 or len(a[0] ) != 2 or len(lowerCamelCase_ ) != 2 or len(b[0] ) != 2:
				raise Exception("""Matrices are not 2x2""" )
		snake_case_		:							Tuple			       =						[
		    [a[0][0] * b[0][0] + a[0][1] * b[1][0], a[0][0] * b[0][1] + a[0][1] * b[1][1]],
		    [a[1][0] * b[0][0] + a[1][1] * b[1][0], a[1][0] * b[0][1] + a[1][1] * b[1][1]],
		]
		return new_matrix





def      UpperCAmelCase      (		lowerCamelCase_  :list				,      lowerCamelCase_  :list ):







		'''simple docstring'''
		return [
		    [matrix_a[row][col] + matrix_b[row][col] for col in range(len(matrix_a[row] ) )]
		    for row in range(len(lowerCamelCase_ ) )
		]





def      UpperCAmelCase      (		lowerCamelCase_  :list				,      lowerCamelCase_  :list ):







		'''simple docstring'''
		return [
		    [matrix_a[row][col] - matrix_b[row][col] for col in range(len(matrix_a[row] ) )]
		    for row in range(len(lowerCamelCase_ ) )
		]





def      UpperCAmelCase      (		lowerCamelCase_  :list ):







		'''simple docstring'''
		if len(lowerCamelCase_ ) % 2 != 0 or len(a[0] ) % 2 != 0:
				raise Exception("""Odd matrices are not supported!""" )

		snake_case_		:							Optional[int]			       =						len(lowerCamelCase_ )
		snake_case_		:							List[str]			       =						matrix_length // 2

		snake_case_		:							Union[str, Any]			       =						[[a[i][j] for j in range(lowerCamelCase_				,      lowerCamelCase_ )] for i in range(lowerCamelCase_ )]
		snake_case_		:							Dict			       =						[
		    [a[i][j] for j in range(lowerCamelCase_				,      lowerCamelCase_ )] for i in range(lowerCamelCase_				,      lowerCamelCase_ )
		]

		snake_case_		:							str			       =						[[a[i][j] for j in range(lowerCamelCase_ )] for i in range(lowerCamelCase_ )]
		snake_case_		:							Dict			       =						[[a[i][j] for j in range(lowerCamelCase_ )] for i in range(lowerCamelCase_				,      lowerCamelCase_ )]

		return top_left, top_right, bot_left, bot_right





def      UpperCAmelCase      (		lowerCamelCase_  :list ):







		'''simple docstring'''
		return len(lowerCamelCase_ ), len(matrix[0] )





def      UpperCAmelCase      (		lowerCamelCase_  :list ):







		'''simple docstring'''
		print("""\n""".join(str(lowerCamelCase_ ) for line in matrix ) )





def      UpperCAmelCase      (		lowerCamelCase_  :list				,      lowerCamelCase_  :list ):







		'''simple docstring'''
		if matrix_dimensions(lowerCamelCase_ ) == (2, 2):
				return default_matrix_multiplication(lowerCamelCase_				,      lowerCamelCase_ )

		snake_case_       ,      snake_case_       ,      snake_case_       ,      snake_case_		:							Any			       =						split_matrix(lowerCamelCase_ )
		snake_case_       ,      snake_case_       ,      snake_case_       ,      snake_case_		:							List[Any]			       =						split_matrix(lowerCamelCase_ )

		snake_case_		:							Any			       =						actual_strassen(lowerCamelCase_				,      matrix_subtraction(lowerCamelCase_				,      lowerCamelCase_ ) )
		snake_case_		:							str			       =						actual_strassen(matrix_addition(lowerCamelCase_				,      lowerCamelCase_ )				,      lowerCamelCase_ )
		snake_case_		:							str			       =						actual_strassen(matrix_addition(lowerCamelCase_				,      lowerCamelCase_ )				,      lowerCamelCase_ )
		snake_case_		:							List[str]			       =						actual_strassen(lowerCamelCase_				,      matrix_subtraction(lowerCamelCase_				,      lowerCamelCase_ ) )
		snake_case_		:							Optional[int]			       =						actual_strassen(matrix_addition(lowerCamelCase_				,      lowerCamelCase_ )				,      matrix_addition(lowerCamelCase_				,      lowerCamelCase_ ) )
		snake_case_		:							Dict			       =						actual_strassen(matrix_subtraction(lowerCamelCase_				,      lowerCamelCase_ )				,      matrix_addition(lowerCamelCase_				,      lowerCamelCase_ ) )
		snake_case_		:							int			       =						actual_strassen(matrix_subtraction(lowerCamelCase_				,      lowerCamelCase_ )				,      matrix_addition(lowerCamelCase_				,      lowerCamelCase_ ) )

		snake_case_		:							List[Any]			       =						matrix_addition(matrix_subtraction(matrix_addition(lowerCamelCase_				,      lowerCamelCase_ )				,      lowerCamelCase_ )				,      lowerCamelCase_ )
		snake_case_		:							List[Any]			       =						matrix_addition(lowerCamelCase_				,      lowerCamelCase_ )
		snake_case_		:							str			       =						matrix_addition(lowerCamelCase_				,      lowerCamelCase_ )
		snake_case_		:							Any			       =						matrix_subtraction(matrix_subtraction(matrix_addition(lowerCamelCase_				,      lowerCamelCase_ )				,      lowerCamelCase_ )				,      lowerCamelCase_ )

		# construct the new matrix from our 4 quadrants
		snake_case_		:							Dict			       =						[]
		for i in range(len(lowerCamelCase_ ) ):
				new_matrix.append(top_left[i] + top_right[i] )
		for i in range(len(lowerCamelCase_ ) ):
				new_matrix.append(bot_left[i] + bot_right[i] )
		return new_matrix





def      UpperCAmelCase      (		lowerCamelCase_  :list				,      lowerCamelCase_  :list ):







		'''simple docstring'''
		if matrix_dimensions(lowerCamelCase_ )[1] != matrix_dimensions(lowerCamelCase_ )[0]:
				snake_case_		:							Optional[int]			       =						(
				    """Unable to multiply these matrices, please check the dimensions.\n"""
				    F'''Matrix A: {matrixa}\n'''
				    F'''Matrix B: {matrixa}'''
				)
				raise Exception(lowerCamelCase_ )
		snake_case_		:							Tuple			       =						matrix_dimensions(lowerCamelCase_ )
		snake_case_		:							Union[str, Any]			       =						matrix_dimensions(lowerCamelCase_ )

		if dimensiona[0] == dimensiona[1] and dimensiona[0] == dimensiona[1]:
				return [matrixa, matrixa]

		snake_case_		:							int			       =						max(*lowerCamelCase_				,      *lowerCamelCase_ )
		snake_case_		:							Union[str, Any]			       =						int(math.pow(2				,      math.ceil(math.loga(lowerCamelCase_ ) ) ) )
		snake_case_		:							Dict			       =						matrixa
		snake_case_		:							Optional[int]			       =						matrixa

		# Adding zeros to the matrices so that the arrays dimensions are the same and also
		# power of 2
		for i in range(0				,      lowerCamelCase_ ):
				if i < dimensiona[0]:
						for _ in range(dimensiona[1]				,      lowerCamelCase_ ):
								new_matrixa[i].append(0 )
				else:
						new_matrixa.append([0] * maxim )
				if i < dimensiona[0]:
						for _ in range(dimensiona[1]				,      lowerCamelCase_ ):
								new_matrixa[i].append(0 )
				else:
						new_matrixa.append([0] * maxim )

		snake_case_		:							Dict			       =						actual_strassen(lowerCamelCase_				,      lowerCamelCase_ )

		# Removing the additional zeros
		for i in range(0				,      lowerCamelCase_ ):
				if i < dimensiona[0]:
						for _ in range(dimensiona[1]				,      lowerCamelCase_ ):
								final_matrix[i].pop()
				else:
						final_matrix.pop()
		return final_matrix


if __name__ == "__main__":
							__A	:	Optional[int]     =     [
							    [2, 3, 4, 5],
							    [6, 4, 3, 1],
							    [2, 3, 6, 7],
							    [3, 1, 2, 4],
							    [2, 3, 4, 5],
							    [6, 4, 3, 1],
							    [2, 3, 6, 7],
							    [3, 1, 2, 4],
							    [2, 3, 4, 5],
							    [6, 2, 3, 1],
							]
							__A	:	List[str]     =     [[0, 2, 1, 1], [16, 2, 3, 3], [2, 2, 7, 7], [13, 11, 22, 4]]
							print(strassen(matrixa, matrixa))