
'''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			      =      "src/transformers"
A			      =      "docs/source/en/tasks"





def    UpperCAmelCase							(  UpperCAmelCase__							:							str       ,							UpperCAmelCase__							:							Union[str, Any]       ,							UpperCAmelCase__							:							int):


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

				lowerCamelCase					:			Optional[int]									= start_index
				while not lines[end_index].startswith(__snake_case):
								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			      =      direct_transformers_import(TRANSFORMERS_PATH)

A			      =      {
    "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			      =      {
    "summarization.md": ("nllb",),
    "translation.md": ("nllb",),
}





def    UpperCAmelCase							(  UpperCAmelCase__							:							Tuple):
				lowerCamelCase					:			Union[str, Any]									= TASK_GUIDE_TO_MODELS[task_guide]
				lowerCamelCase					:			Union[str, Any]									= SPECIAL_TASK_GUIDE_TO_MODEL_TYPES.get(__snake_case       ,							set())
				lowerCamelCase					:			str									= {
				    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							(  UpperCAmelCase__							:							int       ,							UpperCAmelCase__							:							List[Any]=False):
				lowerCamelCase					:			Dict									= _find_text_in_file(
				    filename=os.path.join(__snake_case       ,							__snake_case)       ,							start_prompt='<!--This tip is automatically generated by `make fix-copies`, do not fill manually!-->'       ,							end_prompt='<!--End of the generated tip-->'       ,							)

				lowerCamelCase					:			Union[str, Any]									= get_model_list_for_task(__snake_case)

				if current_list != new_list:
								if overwrite:
												with open(os.path.join(__snake_case       ,							__snake_case)       ,							'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			      =      argparse.ArgumentParser()
					parser.add_argument('--fix_and_overwrite', action='store_true', help='Whether to fix inconsistencies.')
					A			      =      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 manim import *




class     __snake_case		(   a__):
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			Optional[int]									= Rectangle(height=0.5,    width=0.5      )
									lowerCamelCase					:			List[Any]									= Rectangle(height=0.46,    width=0.46      ).set_stroke(width=0      )

									lowerCamelCase					:			List[str]									= [mem.copy() for i in range(6      )]
									lowerCamelCase					:			List[Any]									= [mem.copy() for i in range(6      )]
									lowerCamelCase					:			str									= VGroup(*A      ).arrange(A,    buff=0      )
									lowerCamelCase					:			Any									= VGroup(*A      ).arrange(A,    buff=0      )
									lowerCamelCase					:			Dict									= VGroup(A,    A      ).arrange(A,    buff=0      )
									lowerCamelCase					:			str									= Text('CPU',    font_size=24      )
									lowerCamelCase					:			int									= Group(A,    A      ).arrange(A,    buff=0.5,    aligned_edge=A      )
									cpu.move_to([-2.5, -0.5, 0]      )
									self.add(A      )

									lowerCamelCase					:			Optional[int]									= [mem.copy() for i in range(1      )]
									lowerCamelCase					:			Union[str, Any]									= VGroup(*A      ).arrange(A,    buff=0      )
									lowerCamelCase					:			Optional[Any]									= Text('GPU',    font_size=24      )
									lowerCamelCase					:			Tuple									= Group(A,    A      ).arrange(A,    buff=0.5,    aligned_edge=A      )
									gpu.align_to(A,    A      )
									gpu.set_x(gpu.get_x() - 1      )

									self.add(A      )

									lowerCamelCase					:			Optional[int]									= [mem.copy() for i in range(6      )]
									lowerCamelCase					:			Optional[Any]									= VGroup(*A      ).arrange(A,    buff=0      )

									lowerCamelCase					:			Any									= Text('Model',    font_size=24      )
									lowerCamelCase					:			Tuple									= Group(A,    A      ).arrange(A,    buff=0.5,    aligned_edge=A      )
									model.move_to([3, -1.0, 0]      )

									self.play(
									    Create(A,    run_time=1      ),    Create(A,    run_time=1      ),    Create(A,    run_time=1      ),    )

									lowerCamelCase					:			str									= MarkupText(
									    F'''First, an empty model skeleton is loaded\ninto <span fgcolor=\'{YELLOW}\'>memory</span> without using much RAM.''',    font_size=24,    )

									lowerCamelCase					:			Any									= Square(side_length=2.2      )
									key.move_to([-5, 2, 0]      )

									lowerCamelCase					:			Tuple									= MarkupText(
									    F'''<b>Key:</b>\n\n<span fgcolor=\'{YELLOW}\'>●</span> Empty Model''',    font_size=18,    )

									key_text.move_to([-5, 2.4, 0]      )

									step_a.move_to([2, 2, 0]      )
									self.play(Write(A,    run_time=2.5      ),    Write(A      ),    Write(A      )      )

									self.add(A      )

									lowerCamelCase					:			str									= []
									lowerCamelCase					:			Optional[int]									= []
									lowerCamelCase					:			Optional[Any]									= []
									for i, rect in enumerate(A      ):
													lowerCamelCase					:			List[str]									= Rectangle(height=0.46,    width=0.46      ).set_stroke(width=0.0      ).set_fill(A,    opacity=0.7      )
													cpu_target.move_to(A      )
													cpu_target.generate_target()
													lowerCamelCase					:			int									= 0.46 / 4
													lowerCamelCase					:			Optional[int]									= 0.46 / 3

													if i == 0:
																	cpu_target.target.next_to(cpu_left_col_base[0].get_corner(DOWN + LEFT      ),    buff=0.02,    direction=A      )
																	cpu_target.target.set_x(cpu_target.target.get_x() + 0.1      )
													elif i == 3:
																	cpu_target.target.next_to(cpu_targs[0].target,    direction=A,    buff=0.0      )
													else:
																	cpu_target.target.next_to(cpu_targs[i - 1].target,    direction=A,    buff=0.0      )
													cpu_targs.append(A      )

													first_animations.append(rect.animate(run_time=0.5      ).set_stroke(A      )      )
													second_animations.append(MoveToTarget(A,    run_time=1.5      )      )

									self.play(*A      )
									self.play(*A      )

									self.wait()
