



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
UpperCamelCase__											= """src/transformers"""
UpperCamelCase__											= """docs/source/en/tasks"""





def     _UpperCamelCase     (a__   :List[Any]   ,			a__   :Any   ,			a__   :Optional[Any]       ):






							"""simple docstring"""





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

							UpperCamelCase__				     =			start_index
							while not lines[end_index].startswith(a__       ):
														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.
UpperCamelCase__											= direct_transformers_import(TRANSFORMERS_PATH)

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





def     _UpperCamelCase     (a__   :Dict       ):






							"""simple docstring"""





							UpperCamelCase__				     =			TASK_GUIDE_TO_MODELS[task_guide]
							UpperCamelCase__				     =			SPECIAL_TASK_GUIDE_TO_MODEL_TYPES.get(a__   ,			set()       )
							UpperCamelCase__				     =			{
							    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     (a__   :str   ,			a__   :int=False       ):






							"""simple docstring"""





							UpperCamelCase__     ,     UpperCamelCase__     ,     UpperCamelCase__     ,     UpperCamelCase__				     =			_find_text_in_file(
							    filename=os.path.join(a__   ,			a__       )   ,			start_prompt="""<!--This tip is automatically generated by `make fix-copies`, do not fill manually!-->"""   ,			end_prompt="""<!--End of the generated tip-->"""   ,			)

							UpperCamelCase__				     =			get_model_list_for_task(a__       )

							if current_list != new_list:
														if overwrite:
																					with open(os.path.join(a__   ,			a__       )   ,			"""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__":
					UpperCamelCase__											= argparse.ArgumentParser()
					parser.add_argument("--fix_and_overwrite", action="store_true", help="Whether to fix inconsistencies.")
					UpperCamelCase__											= parser.parse_args()

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











UpperCamelCase__											= "ABCDEFGHIJKLMNOPQRSTUVWXYZ"





def     _UpperCamelCase     ():






							"""simple docstring"""





							UpperCamelCase__				     =			input("""Enter message: """       )
							UpperCamelCase__				     =			input("""Enter key [alphanumeric]: """       )
							UpperCamelCase__				     =			input("""Encrypt/Decrypt [e/d]: """       )

							if mode.lower().startswith("""e"""       ):
														UpperCamelCase__				     =			"""encrypt"""
														UpperCamelCase__				     =			encrypt_message(a__   ,			a__       )
							elif mode.lower().startswith("""d"""       ):
														UpperCamelCase__				     =			"""decrypt"""
														UpperCamelCase__				     =			decrypt_message(a__   ,			a__       )

							print(f"""\n{mode.title()}ed message:"""       )
							print(a__       )





def     _UpperCamelCase     (a__   :str   ,			a__   :str       ):






							"""simple docstring"""





							return translate_message(a__   ,			a__   ,			"""encrypt"""       )





def     _UpperCamelCase     (a__   :str   ,			a__   :str       ):






							"""simple docstring"""





							return translate_message(a__   ,			a__   ,			"""decrypt"""       )





def     _UpperCamelCase     (a__   :str   ,			a__   :str   ,			a__   :str       ):






							"""simple docstring"""





							UpperCamelCase__				     =			[]
							UpperCamelCase__				     =			0
							UpperCamelCase__				     =			key.upper()

							for symbol in message:
														UpperCamelCase__				     =			LETTERS.find(symbol.upper()       )
														if num != -1:
																					if mode == "encrypt":
																												num += LETTERS.find(key[key_index]       )
																					elif mode == "decrypt":
																												num -= LETTERS.find(key[key_index]       )

																					num %= len(a__       )

																					if symbol.isupper():
																												translated.append(LETTERS[num]       )
																					elif symbol.islower():
																												translated.append(LETTERS[num].lower()       )

																					key_index += 1
																					if key_index == len(a__       ):
																												UpperCamelCase__				     =			0
														else:
																					translated.append(a__       )
							return "".join(a__       )


if __name__ == "__main__":
					main()




