


'''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
lowerCamelCase__    	=     'src/transformers'
lowerCamelCase__    	=     'docs/source/en/tasks'






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

		_UpperCAmelCase		:       Tuple								=      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.
lowerCamelCase__    	=     direct_transformers_import(TRANSFORMERS_PATH)

lowerCamelCase__    	=     {
    '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`).
lowerCamelCase__    	=     {
    'summarization.md': ('nllb',),
    'translation.md': ('nllb',),
}






def  __lowerCAmelCase (__lowerCAmelCase   ):
		_UpperCAmelCase		:       List[Any]								=      TASK_GUIDE_TO_MODELS[task_guide]
		_UpperCAmelCase		:       Optional[Any]								=      SPECIAL_TASK_GUIDE_TO_MODEL_TYPES.get(__lowerCAmelCase      , set()   )
		_UpperCAmelCase		:       Optional[Any]								=      {
		    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 (__lowerCAmelCase      , __lowerCAmelCase=False   ):
		_UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase		:       Union[str, Any]								=      _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-->"      , )

		_UpperCAmelCase		:       List[str]								=      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__":
						lowerCamelCase__    	=     argparse.ArgumentParser()
						parser.add_argument('--fix_and_overwrite', action='store_true', help='Whether to fix inconsistencies.')
						lowerCamelCase__    	=     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 ...utils import (
    OptionalDependencyNotAvailable,
    is_flax_available,
    is_torch_available,
    is_transformers_available,
)


try:
						if not (is_transformers_available() and is_torch_available()):
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						from ...utils.dummy_torch_and_transformers_objects import *  # noqa F403
else:
						from .multicontrolnet import MultiControlNetModel
						from .pipeline_controlnet import StableDiffusionControlNetPipeline
						from .pipeline_controlnet_imgaimg import StableDiffusionControlNetImgaImgPipeline
						from .pipeline_controlnet_inpaint import StableDiffusionControlNetInpaintPipeline


if is_transformers_available() and is_flax_available():
						from .pipeline_flax_controlnet import FlaxStableDiffusionControlNetPipeline



