





"""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
a		:				Optional[int]  			=    '''src/transformers'''
a		:				List[str]  			=    '''docs/source/en/tasks'''






def    _SCREAMING_SNAKE_CASE	(			_lowercase		:					Optional[Any]					, _lowercase		:					int					, _lowercase		:					int      )				->List[Any]:






	'''simple docstring'''






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

	a   :	int			=       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.
a		:				Any  			=    direct_transformers_import(TRANSFORMERS_PATH)

a		:				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`).
a		:				Optional[Any]  			=    {
    '''summarization.md''': ('''nllb''',),
    '''translation.md''': ('''nllb''',),
}






def    _SCREAMING_SNAKE_CASE	(			_lowercase		:					Tuple      )				->Optional[Any]:






	'''simple docstring'''






	a   :	str			=       TASK_GUIDE_TO_MODELS[task_guide]
	a   :	int			=       SPECIAL_TASK_GUIDE_TO_MODEL_TYPES.get(_lowercase					, set()      )
	a   :	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    _SCREAMING_SNAKE_CASE	(			_lowercase		:					Optional[int]					, _lowercase		:					int=False      )				->Union[str, Any]:






	'''simple docstring'''






	a, a, a, a   :	str			=       _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-->"					, )

	a   :	Union[str, Any]			=       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__":
		a		:				Optional[Any]  			=    argparse.ArgumentParser()
		parser.add_argument('''--fix_and_overwrite''', action='''store_true''', help='''Whether to fix inconsistencies.''')
		a		:				Dict  			=    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"""




import unittest

from diffusers.pipelines.pipeline_utils import is_safetensors_compatible


class  __UpperCamelCase       (		unittest.TestCase		):






							def 					__a     (     self   )			->				Optional[Any]:
								a   :	Optional[int]			=       [
								    "safety_checker/pytorch_model.bin",
								    "safety_checker/model.safetensors",
								    "vae/diffusion_pytorch_model.bin",
								    "vae/diffusion_pytorch_model.safetensors",
								    "text_encoder/pytorch_model.bin",
								    "text_encoder/model.safetensors",
								    "unet/diffusion_pytorch_model.bin",
								    "unet/diffusion_pytorch_model.safetensors",
								]
								self.assertTrue(is_safetensors_compatible(lowerCAmelCase__   )   )






							def 					__a     (     self   )			->				Optional[Any]:
								a   :	str			=       [
								    "unet/diffusion_pytorch_model.bin",
								    "unet/diffusion_pytorch_model.safetensors",
								]
								self.assertTrue(is_safetensors_compatible(lowerCAmelCase__   )   )






							def 					__a     (     self   )			->				Dict:
								a   :	List[str]			=       [
								    "safety_checker/pytorch_model.bin",
								    "safety_checker/model.safetensors",
								    "vae/diffusion_pytorch_model.bin",
								    "vae/diffusion_pytorch_model.safetensors",
								    "text_encoder/pytorch_model.bin",
								    "text_encoder/model.safetensors",
								    "unet/diffusion_pytorch_model.bin",
								    # Removed: 'unet/diffusion_pytorch_model.safetensors',
								]
								self.assertFalse(is_safetensors_compatible(lowerCAmelCase__   )   )






							def 					__a     (     self   )			->				List[Any]:
								a   :	Optional[Any]			=       [
								    "text_encoder/pytorch_model.bin",
								    "text_encoder/model.safetensors",
								]
								self.assertTrue(is_safetensors_compatible(lowerCAmelCase__   )   )






							def 					__a     (     self   )			->				Tuple:
								a   :	Tuple			=       [
								    "safety_checker/pytorch_model.bin",
								    "safety_checker/model.safetensors",
								    "vae/diffusion_pytorch_model.bin",
								    "vae/diffusion_pytorch_model.safetensors",
								    "text_encoder/pytorch_model.bin",
								    # Removed: 'text_encoder/model.safetensors',
								    "unet/diffusion_pytorch_model.bin",
								    "unet/diffusion_pytorch_model.safetensors",
								]
								self.assertFalse(is_safetensors_compatible(lowerCAmelCase__   )   )






							def 					__a     (     self   )			->				Dict:
								a   :	Dict			=       [
								    "safety_checker/pytorch_model.fp16.bin",
								    "safety_checker/model.fp16.safetensors",
								    "vae/diffusion_pytorch_model.fp16.bin",
								    "vae/diffusion_pytorch_model.fp16.safetensors",
								    "text_encoder/pytorch_model.fp16.bin",
								    "text_encoder/model.fp16.safetensors",
								    "unet/diffusion_pytorch_model.fp16.bin",
								    "unet/diffusion_pytorch_model.fp16.safetensors",
								]
								a   :	Dict			=       "fp16"
								self.assertTrue(is_safetensors_compatible(lowerCAmelCase__   ,      variant=lowerCAmelCase__   )   )






							def 					__a     (     self   )			->				List[str]:
								a   :	List[Any]			=       [
								    "unet/diffusion_pytorch_model.fp16.bin",
								    "unet/diffusion_pytorch_model.fp16.safetensors",
								]
								a   :	Any			=       "fp16"
								self.assertTrue(is_safetensors_compatible(lowerCAmelCase__   ,      variant=lowerCAmelCase__   )   )






							def 					__a     (     self   )			->				int:
								# pass variant but use the non-variant filenames
								a   :	int			=       [
								    "unet/diffusion_pytorch_model.bin",
								    "unet/diffusion_pytorch_model.safetensors",
								]
								a   :	Tuple			=       "fp16"
								self.assertTrue(is_safetensors_compatible(lowerCAmelCase__   ,      variant=lowerCAmelCase__   )   )






							def 					__a     (     self   )			->				str:
								a   :	str			=       [
								    "safety_checker/pytorch_model.fp16.bin",
								    "safety_checker/model.fp16.safetensors",
								    "vae/diffusion_pytorch_model.fp16.bin",
								    "vae/diffusion_pytorch_model.fp16.safetensors",
								    "text_encoder/pytorch_model.fp16.bin",
								    "text_encoder/model.fp16.safetensors",
								    "unet/diffusion_pytorch_model.fp16.bin",
								    # Removed: 'unet/diffusion_pytorch_model.fp16.safetensors',
								]
								a   :	Any			=       "fp16"
								self.assertFalse(is_safetensors_compatible(lowerCAmelCase__   ,      variant=lowerCAmelCase__   )   )






							def 					__a     (     self   )			->				str:
								a   :	Union[str, Any]			=       [
								    "text_encoder/pytorch_model.fp16.bin",
								    "text_encoder/model.fp16.safetensors",
								]
								a   :	str			=       "fp16"
								self.assertTrue(is_safetensors_compatible(lowerCAmelCase__   ,      variant=lowerCAmelCase__   )   )






							def 					__a     (     self   )			->				List[str]:
								# pass variant but use the non-variant filenames
								a   :	Optional[int]			=       [
								    "text_encoder/pytorch_model.bin",
								    "text_encoder/model.safetensors",
								]
								a   :	str			=       "fp16"
								self.assertTrue(is_safetensors_compatible(lowerCAmelCase__   ,      variant=lowerCAmelCase__   )   )






							def 					__a     (     self   )			->				Optional[Any]:
								a   :	Any			=       [
								    "safety_checker/pytorch_model.fp16.bin",
								    "safety_checker/model.fp16.safetensors",
								    "vae/diffusion_pytorch_model.fp16.bin",
								    "vae/diffusion_pytorch_model.fp16.safetensors",
								    "text_encoder/pytorch_model.fp16.bin",
								    # 'text_encoder/model.fp16.safetensors',
								    "unet/diffusion_pytorch_model.fp16.bin",
								    "unet/diffusion_pytorch_model.fp16.safetensors",
								]
								a   :	Optional[int]			=       "fp16"
								self.assertFalse(is_safetensors_compatible(lowerCAmelCase__   ,      variant=lowerCAmelCase__   )   )


