



from collections import UserDict
from typing import List, Union

from ..utils import (
    add_end_docstrings,
    is_tf_available,
    is_torch_available,
    is_vision_available,
    logging,
    requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
							from PIL import Image

							from ..image_utils import load_image

if is_torch_available():
							from ..models.auto.modeling_auto import MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING

if is_tf_available():
							from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING
							from ..tf_utils import stable_softmax

SCREAMING_SNAKE_CASE__   			=   logging.get_logger(__name__)


@add_end_docstrings(UpperCAmelCase_   )
class   __lowerCAmelCase  (		UpperCAmelCase_   ):
					"""simple docstring"""






					def __init__(  self       :							int					,       **_snake_case       :							Dict       ):






								"""simple docstring"""


								super().__init__(**_snake_case       )

								requires_backends(self					,       'vision'       )
								self.check_model_type(
								    TF_MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING
								    if self.framework == 'tf'
								    else MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING       )



					def __call__(  self       :							Dict					,       _snake_case       :							Union[str, List[str], "Image", List["Image"]]					,       **_snake_case       :							Optional[int]       ):






								"""simple docstring"""


								return super().__call__(_snake_case					,       **_snake_case       )



					def      _a				(  self       :							Tuple					,       **_snake_case       :							Union[str, Any]       ):






								"""simple docstring"""


								A__	  =     {}
								if "candidate_labels" in kwargs:
											A__	  =     kwargs['candidate_labels']
								if "hypothesis_template" in kwargs:
											A__	  =     kwargs['hypothesis_template']

								return preprocess_params, {}, {}



					def      _a				(  self       :							Union[str, Any]					,       _snake_case       :							Union[str, Any]					,       _snake_case       :							int=None					,       _snake_case       :							Optional[Any]="This is a photo of {}."       ):






								"""simple docstring"""


								A__	  =     load_image(_snake_case       )
								A__	  =     self.image_processor(images=[image]					,       return_tensors=self.framework       )
								A__	  =     candidate_labels
								A__	  =     [hypothesis_template.format(_snake_case       ) for x in candidate_labels]
								A__	  =     self.tokenizer(_snake_case					,       return_tensors=self.framework					,       padding=_snake_case       )
								A__	  =     [text_inputs]
								return inputs



					def      _a				(  self       :							Optional[int]					,       _snake_case       :							int       ):






								"""simple docstring"""


								A__	  =     model_inputs.pop('candidate_labels'       )
								A__	  =     model_inputs.pop('text_inputs'       )
								if isinstance(text_inputs[0]					,       _snake_case       ):
											A__	  =     text_inputs[0]
								else:
											# Batching case.
											A__	  =     text_inputs[0][0]

								A__	  =     self.model(**_snake_case					,       **_snake_case       )

								A__	  =     {
								    'candidate_labels': candidate_labels,
								    'logits': outputs.logits_per_image,
								}
								return model_outputs



					def      _a				(  self       :							List[Any]					,       _snake_case       :							List[Any]       ):






								"""simple docstring"""


								A__	  =     model_outputs.pop('candidate_labels'       )
								A__	  =     model_outputs['logits'][0]
								if self.framework == "pt":
											A__	  =     logits.softmax(dim=-1       ).squeeze(-1       )
											A__	  =     probs.tolist()
											if not isinstance(_snake_case					,       _snake_case       ):
														A__	  =     [scores]
								elif self.framework == "tf":
											A__	  =     stable_softmax(_snake_case					,       axis=-1       )
											A__	  =     probs.numpy().tolist()
								else:
											raise ValueError(F'''Unsupported framework: {self.framework}'''       )

								A__	  =     [
								    {'score': score, 'label': candidate_label}
								    for score, candidate_label in sorted(zip(_snake_case					,       _snake_case       )					,       key=lambda _snake_case       : -x[0]       )
								]
								return result











"""simple docstring"""






import os






def      lowercase__ (					)    ->			Optional[Any]:



			'''simple docstring'''
			with open(os.path.dirname(lowerCAmelCase__			) + "/p022_names.txt"			) as file:
						a__  :	Optional[int]		 =			str(file.readlines()[0]			)
						a__  :	Optional[int]		 =			names.replace("\""							,			""			).split(","			)

			names.sort()

			a__  :	int		 =			0
			a__  :	Dict		 =			0

			for i, name in enumerate(lowerCAmelCase__			):
						for letter in name:
									name_score += ord(lowerCAmelCase__			) - 6_4

						total_score += (i + 1) * name_score
						a__  :	Tuple		 =			0
			return total_score


if __name__ == "__main__":
		print(solution())