





__lowerCamelCase :  Optional[int]										=  """
# Installazione di Transformers
! pip install transformers datasets
# Per installare dalla fonte invece dell\'ultima versione rilasciata, commenta il comando sopra e
# rimuovi la modalità commento al comando seguente.
# ! pip install git+https://github.com/huggingface/transformers.git
"""

__lowerCamelCase :  Tuple										=  [{"""type""": """code""", """content""": INSTALL_CONTENT}]
__lowerCamelCase :  Tuple										=  {
    """{processor_class}""": """FakeProcessorClass""",
    """{model_class}""": """FakeModelClass""",
    """{object_class}""": """FakeObjectClass""",
}







from PIL import Image



def 	A_       (  _lowerCAmelCase  )		->				Image:
 UpperCamelCase			,					UpperCamelCase			:      List[Any] 			=     image.size
 UpperCamelCase			:      Union[str, Any] 			=     0
 UpperCamelCase			:      List[str] 			=     image.load()
 for i in range(_lowerCAmelCase  ):
  for j in range(_lowerCAmelCase  ):
   UpperCamelCase			:      List[Any] 			=     pixels[j, i]
   mean += pixel
 mean //= width * height

 for j in range(_lowerCAmelCase  ):
  for i in range(_lowerCAmelCase  ):
   UpperCamelCase			:      Union[str, Any] 			=     255 if pixels[i, j] > mean else 0
 return image


if __name__ == "__main__":
    __lowerCamelCase :  Union[str, Any]										=  mean_threshold(Image.open("""path_to_image""").convert("""L"""))
    image.save("""output_image_path""")
