




'''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 			SCREAMING_SNAKE_CASE_			(							__A    :			int	,					__A    :			Union[str, Any]	,					__A    :			Optional[Any]  )			->			List[str]:
   with open(__A	,					"r"	,					encoding="utf-8"	,					newline="\n"  ) as f:
      _SCREAMING_SNAKE_CASE	=				f.readlines()
   # Find the start prompt.
   _SCREAMING_SNAKE_CASE	=				0
   while not lines[start_index].startswith(__A  ):
      start_index += 1
   start_index += 1

   _SCREAMING_SNAKE_CASE	=				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.
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 			SCREAMING_SNAKE_CASE_			(							__A    :			Optional[Any]  )			->			Optional[Any]:
   _SCREAMING_SNAKE_CASE	=				TASK_GUIDE_TO_MODELS[task_guide]
   _SCREAMING_SNAKE_CASE	=				SPECIAL_TASK_GUIDE_TO_MODEL_TYPES.get(__A	,					set()  )
   _SCREAMING_SNAKE_CASE	=				{
       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_			(							__A    :			Dict	,					__A    :			Optional[Any]=False  )			->			Tuple:
   _SCREAMING_SNAKE_CASE,	_SCREAMING_SNAKE_CASE,	_SCREAMING_SNAKE_CASE,	_SCREAMING_SNAKE_CASE	=				_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-->"	,					)

   _SCREAMING_SNAKE_CASE	=				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__":
 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'''



import unittest

from diffusers.pipelines.pipeline_utils import is_safetensors_compatible







class 		lowercase_   (			unittest.TestCase       ):


   """simple docstring"""

   def 							lowerCAmelCase_      (   self :     List[Any]   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				[
          "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 							lowerCAmelCase_      (   self :     Dict   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				[
          "unet/diffusion_pytorch_model.bin",
          "unet/diffusion_pytorch_model.safetensors",
      ]
      self.assertTrue(is_safetensors_compatible(__lowerCamelCase   )   )



   def 							lowerCAmelCase_      (   self :     Tuple   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				[
          "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 							lowerCAmelCase_      (   self :     List[str]   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				[
          "text_encoder/pytorch_model.bin",
          "text_encoder/model.safetensors",
      ]
      self.assertTrue(is_safetensors_compatible(__lowerCamelCase   )   )



   def 							lowerCAmelCase_      (   self :     int   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				[
          "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 							lowerCAmelCase_      (   self :     Optional[int]   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				[
          "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",
      ]
      _SCREAMING_SNAKE_CASE	=				"fp16"
      self.assertTrue(is_safetensors_compatible(__lowerCamelCase  ,				variant=__lowerCamelCase   )   )



   def 							lowerCAmelCase_      (   self :     Any   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				[
          "unet/diffusion_pytorch_model.fp16.bin",
          "unet/diffusion_pytorch_model.fp16.safetensors",
      ]
      _SCREAMING_SNAKE_CASE	=				"fp16"
      self.assertTrue(is_safetensors_compatible(__lowerCamelCase  ,				variant=__lowerCamelCase   )   )



   def 							lowerCAmelCase_      (   self :     Tuple   ):

      """simple docstring"""
      # pass variant but use the non-variant filenames
      _SCREAMING_SNAKE_CASE	=				[
          "unet/diffusion_pytorch_model.bin",
          "unet/diffusion_pytorch_model.safetensors",
      ]
      _SCREAMING_SNAKE_CASE	=				"fp16"
      self.assertTrue(is_safetensors_compatible(__lowerCamelCase  ,				variant=__lowerCamelCase   )   )



   def 							lowerCAmelCase_      (   self :     int   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				[
          "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',
      ]
      _SCREAMING_SNAKE_CASE	=				"fp16"
      self.assertFalse(is_safetensors_compatible(__lowerCamelCase  ,				variant=__lowerCamelCase   )   )



   def 							lowerCAmelCase_      (   self :     Dict   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				[
          "text_encoder/pytorch_model.fp16.bin",
          "text_encoder/model.fp16.safetensors",
      ]
      _SCREAMING_SNAKE_CASE	=				"fp16"
      self.assertTrue(is_safetensors_compatible(__lowerCamelCase  ,				variant=__lowerCamelCase   )   )



   def 							lowerCAmelCase_      (   self :     str   ):

      """simple docstring"""
      # pass variant but use the non-variant filenames
      _SCREAMING_SNAKE_CASE	=				[
          "text_encoder/pytorch_model.bin",
          "text_encoder/model.safetensors",
      ]
      _SCREAMING_SNAKE_CASE	=				"fp16"
      self.assertTrue(is_safetensors_compatible(__lowerCamelCase  ,				variant=__lowerCamelCase   )   )



   def 							lowerCAmelCase_      (   self :     Optional[Any]   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				[
          "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",
      ]
      _SCREAMING_SNAKE_CASE	=				"fp16"
      self.assertFalse(is_safetensors_compatible(__lowerCamelCase  ,				variant=__lowerCamelCase   )   )

