



"""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  :						Any    							=      '''src/transformers'''
__SCREAMING_SNAKE_CASE  :						Optional[int]    							=      '''docs/source/en/tasks'''



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

				_lowerCamelCase        =				start_index
				while not lines[end_index].startswith(lowercase_							):
								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  :						Optional[Any]    							=      direct_transformers_import(TRANSFORMERS_PATH)

__SCREAMING_SNAKE_CASE  :						Union[str, 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  :						List[str]    							=      {
    '''summarization.md''': ('''nllb''',),
    '''translation.md''': ('''nllb''',),
}



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

				_lowerCamelCase        =				get_model_list_for_task(lowercase_							)

				if current_list != new_list:
								if overwrite:
												with open(os.path.join(lowercase_						,   lowercase_							)						,   '''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  :						Optional[int]    							=      argparse.ArgumentParser()
			parser.add_argument('''--fix_and_overwrite''', action='''store_true''', help='''Whether to fix inconsistencies.''')
			__SCREAMING_SNAKE_CASE  :						List[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 typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


__SCREAMING_SNAKE_CASE  :						Optional[Any]    							=      {
    '''configuration_vision_encoder_decoder''': ['''VisionEncoderDecoderConfig''', '''VisionEncoderDecoderOnnxConfig''']
}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__SCREAMING_SNAKE_CASE  :						str    							=      ['''VisionEncoderDecoderModel''']

try:
			if not is_tf_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__SCREAMING_SNAKE_CASE  :						Dict    							=      ['''TFVisionEncoderDecoderModel''']

try:
			if not is_flax_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__SCREAMING_SNAKE_CASE  :						List[Any]    							=      ['''FlaxVisionEncoderDecoderModel''']

if TYPE_CHECKING:
			from .configuration_vision_encoder_decoder import VisionEncoderDecoderConfig, VisionEncoderDecoderOnnxConfig

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_vision_encoder_decoder import VisionEncoderDecoderModel

			try:
						if not is_tf_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tf_vision_encoder_decoder import TFVisionEncoderDecoderModel

			try:
						if not is_flax_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_flax_vision_encoder_decoder import FlaxVisionEncoderDecoderModel

else:
			import sys

			__SCREAMING_SNAKE_CASE  :						Union[str, Any]    							=      _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
