



"""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_				= """src/transformers"""
SCREAMING_SNAKE_CASE_				= """docs/source/en/tasks"""



def        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	)			->      List[str]:
		with open(SCREAMING_SNAKE_CASE__, "r", encoding="utf-8", newline="\n"	) as f:
				a_     :      Dict 				=     f.readlines()
		# Find the start prompt.
		a_     :      str 				=     0
		while not lines[start_index].startswith(SCREAMING_SNAKE_CASE__	):
				start_index += 1
		start_index += 1

		a_     :      Optional[Any] 				=     start_index
		while not lines[end_index].startswith(SCREAMING_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.
SCREAMING_SNAKE_CASE_				= direct_transformers_import(TRANSFORMERS_PATH)

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



def        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__	)			->      Optional[int]:
		a_     :      List[Any] 				=     TASK_GUIDE_TO_MODELS[task_guide]
		a_     :      str 				=     SPECIAL_TASK_GUIDE_TO_MODEL_TYPES.get(SCREAMING_SNAKE_CASE__, set()	)
		a_     :      int 				=     {
		    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_						(							SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__=False	)			->      int:
		a_     :      List[Any] 				=     _find_text_in_file(
		    filename=os.path.join(SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	), start_prompt="<!--This tip is automatically generated by `make fix-copies`, do not fill manually!-->", end_prompt="<!--End of the generated tip-->", )

		a_     :      Dict 				=     get_model_list_for_task(SCREAMING_SNAKE_CASE__	)

		if current_list != new_list:
				if overwrite:
						with open(os.path.join(SCREAMING_SNAKE_CASE__, SCREAMING_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__":
					SCREAMING_SNAKE_CASE_				= argparse.ArgumentParser()
					parser.add_argument("""--fix_and_overwrite""", action="""store_true""", help="""Whether to fix inconsistencies.""")
					SCREAMING_SNAKE_CASE_				= 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 typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


SCREAMING_SNAKE_CASE_				= {
    """configuration_bigbird_pegasus""": [
        """BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """BigBirdPegasusConfig""",
        """BigBirdPegasusOnnxConfig""",
    ],
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE_				= [
					    """BIGBIRD_PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """BigBirdPegasusForCausalLM""",
					    """BigBirdPegasusForConditionalGeneration""",
					    """BigBirdPegasusForQuestionAnswering""",
					    """BigBirdPegasusForSequenceClassification""",
					    """BigBirdPegasusModel""",
					    """BigBirdPegasusPreTrainedModel""",
					]


if TYPE_CHECKING:
					from .configuration_bigbird_pegasus import (
					    BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP,
					    BigBirdPegasusConfig,
					    BigBirdPegasusOnnxConfig,
					)

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_bigbird_pegasus import (
										    BIGBIRD_PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST,
										    BigBirdPegasusForCausalLM,
										    BigBirdPegasusForConditionalGeneration,
										    BigBirdPegasusForQuestionAnswering,
										    BigBirdPegasusForSequenceClassification,
										    BigBirdPegasusModel,
										    BigBirdPegasusPreTrainedModel,
										)


else:
					import sys

					SCREAMING_SNAKE_CASE_				= _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)