"""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
SCREAMING_SNAKE_CASE__:Tuple			=	"src/transformers"
SCREAMING_SNAKE_CASE__:int			=	"docs/source/en/tasks"



def 					_lowerCamelCase(					a ,  a ,  a							):
							with open(lowerCamelCase_ ,  "r" ,  encoding="utf-8" ,  newline="\n"							) as f:
														__a     				=  f.readlines()
							# Find the start prompt.
							__a     				=  0
							while not lines[start_index].startswith(lowerCamelCase_							):
														start_index += 1
							start_index += 1

							__a     				=  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.
SCREAMING_SNAKE_CASE__:Tuple			=	direct_transformers_import(TRANSFORMERS_PATH)

SCREAMING_SNAKE_CASE__:List[Any]			=	{
    "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`).
SCREAMING_SNAKE_CASE__:Any			=	{
    "summarization.md": ("nllb",),
    "translation.md": ("nllb",),
}



def 					_lowerCamelCase(					a							):
							__a     				=  TASK_GUIDE_TO_MODELS[task_guide]
							__a     				=  SPECIAL_TASK_GUIDE_TO_MODEL_TYPES.get(lowerCamelCase_ ,  set()							)
							__a     				=  {
							    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 					_lowerCamelCase(					a ,  a=False							):
							__a     				=  _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-->" ,  )

							__a     				=  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__":
			SCREAMING_SNAKE_CASE__:int			=	argparse.ArgumentParser()
			parser.add_argument("""--fix_and_overwrite""", action="""store_true""", help="""Whether to fix inconsistencies.""")
			SCREAMING_SNAKE_CASE__:Optional[int]			=	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"""



SCREAMING_SNAKE_CASE__:Any			=	{
    """A""": """.-""", """B""": """-...""", """C""": """-.-.""", """D""": """-..""", """E""": """.""", """F""": """..-.""", """G""": """--.""",
    """H""": """....""", """I""": """..""", """J""": """.---""", """K""": """-.-""", """L""": """.-..""", """M""": """--""", """N""": """-.""",
    """O""": """---""", """P""": """.--.""", """Q""": """--.-""", """R""": """.-.""", """S""": """...""", """T""": """-""", """U""": """..-""",
    """V""": """...-""", """W""": """.--""", """X""": """-..-""", """Y""": """-.--""", """Z""": """--..""", """1""": """.----""",
    """2""": """..---""", """3""": """...--""", """4""": """....-""", """5""": """.....""", """6""": """-....""", """7""": """--...""",
    """8""": """---..""", """9""": """----.""", """0""": """-----""", """&""": """.-...""", """@""": """.--.-.""",
    """:""": """---...""", """,""": """--..--""", """.""": """.-.-.-""", """'""": """.----.""", """\"""": """.-..-.""",
    """?""": """..--..""", """/""": """-..-.""", """=""": """-...-""", """+""": """.-.-.""", """-""": """-....-""",
    """(""": """-.--.""", """)""": """-.--.-""", """!""": """-.-.--""", """ """: """/"""
}  # Exclamation mark is not in ITU-R recommendation
# fmt: on
SCREAMING_SNAKE_CASE__:Any			=	{value: key for key, value in MORSE_CODE_DICT.items()}



def 					_lowerCamelCase(					a							):
       return " ".join(MORSE_CODE_DICT[char] for char in message.upper()							)



def 					_lowerCamelCase(					a							):
       return "".join(REVERSE_DICT[char] for char in message.split()							)



def 					_lowerCamelCase(					):
       __a     				=  "Morse code here!"
       print(a							)
       __a     				=  encrypt(a							)
       print(a							)
       __a     				=  decrypt(a							)
       print(a							)


if __name__ == "__main__":
   main()
