






"""simple docstring"""



import torch

from ..models.auto import AutoModelForSequenceClassification, AutoTokenizer
from .base import PipelineTool





class 					_UpperCAmelCase       ( _A							):
					SCREAMING_SNAKE_CASE_		:				Union[str, Any]							    =   "facebook/bart-large-mnli"
					SCREAMING_SNAKE_CASE_		:				str							    =   (
					    "This is a tool that classifies an English text using provided labels. It takes two inputs: `text`, which "
					    "should be the text to classify, and `labels`, which should be the list of labels to use for classification. "
					    "It returns the most likely label in the list of provided `labels` for the input text."
					)
					SCREAMING_SNAKE_CASE_		:				Tuple							    =   "text_classifier"
					SCREAMING_SNAKE_CASE_		:				Any							    =   AutoTokenizer
					SCREAMING_SNAKE_CASE_		:				Optional[Any]							    =   AutoModelForSequenceClassification

					SCREAMING_SNAKE_CASE_		:				Any							    =   ["text", ["text"]]
					SCREAMING_SNAKE_CASE_		:				Optional[int]							    =   ["text"]




					def   A  (				self     :						Optional[int]   )  ->  List[Any]:
							super().setup()
							lowercase_ : Any							       =   self.model.config
							lowercase_ : str							       =   -1
							for idx, label in config.idalabel.items():
									if label.lower().startswith('''entail'''   ):
											lowercase_ : Union[str, Any]							       =   int(UpperCAmelCase__   )
							if self.entailment_id == -1:
									raise ValueError('''Could not determine the entailment ID from the model config, please pass it at init.'''   )




					def   A  (				self     :						Optional[Any]    ,						A     :						Union[str, Any]    ,						A     :						Tuple   )  ->  Union[str, Any]:
							lowercase_ : List[Any]							       =   labels
							return self.pre_processor(
							    [text] * len(UpperCAmelCase__   )    ,						[F'''This example is {label}''' for label in labels]    ,						return_tensors='''pt'''    ,						padding='''max_length'''    ,						)






					def   A  (				self     :						Any    ,						A     :						List[str]   )  ->  Dict:
							lowercase_ : Optional[Any]							       =   outputs.logits
							lowercase_ : Any							       =   torch.argmax(logits[:, 2]   ).item()
							return self._labels[label_id]







"""simple docstring"""







from PIL import Image




def    UpperCAmelCase__       (lowerCAmelCase_   ):




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE   ,  __SCREAMING_SNAKE_CASE						   =				image.size
					__SCREAMING_SNAKE_CASE						   =				0
					__SCREAMING_SNAKE_CASE						   =				image.load()
					for i in range(lowerCAmelCase_   ):
										for j in range(lowerCAmelCase_   ):
															__SCREAMING_SNAKE_CASE						   =				pixels[j, i]
															mean += pixel
					mean //= width * height

					for j in range(lowerCAmelCase_   ):
										for i in range(lowerCAmelCase_   ):
															__SCREAMING_SNAKE_CASE						   =				255 if pixels[i, j] > mean else 0
					return image


if __name__ == "__main__":
		a__				:							List[str]						=     mean_threshold(Image.open('''path_to_image''').convert('''L'''))
		image.save('''output_image_path''')
