

'''simple docstring'''

from typing import List, Union

import numpy as np

from ..tokenization_utils import TruncationStrategy
from ..utils import add_end_docstrings, logging
from .base import PIPELINE_INIT_ARGS, ArgumentHandler, ChunkPipeline


A_							    =				logging.get_logger(__name__)





class 				_snake_case			(							_UpperCamelCase    ):
   def      __UpperCamelCase    (			self      :   int  ,SCREAMING_SNAKE_CASE__      :   List[Any]				):



     if isinstance(a_  ,a_				):
       SCREAMING_SNAKE_CASE:Any       =       [label.strip() for label in labels.split(","				) if label.strip()]
     return labels







   def __call__(			self      :   Union[str, Any]  ,SCREAMING_SNAKE_CASE__      :   Tuple  ,SCREAMING_SNAKE_CASE__      :   Optional[Any]  ,SCREAMING_SNAKE_CASE__      :   List[str]				):



     if len(a_				) == 0 or len(a_				) == 0:
       raise ValueError("You must include at least one label and at least one sequence."				)
     if hypothesis_template.format(labels[0]				) == hypothesis_template:
       raise ValueError(
           (
               "The provided hypothesis_template \"{}\" was not able to be formatted with the target labels. "
               "Make sure the passed template includes formatting syntax such as {{}} where the label should go."
           ).format(a_				)				)

     if isinstance(a_  ,a_				):
       SCREAMING_SNAKE_CASE:str       =       [sequences]

     SCREAMING_SNAKE_CASE:Optional[int]       =       []
     for sequence in sequences:
       sequence_pairs.extend([[sequence, hypothesis_template.format(a_				)] for label in labels]				)

     return sequence_pairs, sequences





@add_end_docstrings(_UpperCamelCase    )
class 				_snake_case			(							_UpperCamelCase    ):
   def __init__(			self      :   str  ,SCREAMING_SNAKE_CASE__      :   Optional[Any]=ZeroShotClassificationArgumentHandler()  ,*SCREAMING_SNAKE_CASE__      :   Tuple  ,**SCREAMING_SNAKE_CASE__      :   str				):
     SCREAMING_SNAKE_CASE:List[Any]       =       args_parser
     super().__init__(*a_  ,**a_				)
     if self.entailment_id == -1:
       logger.warning(
           "Failed to determine 'entailment' label id from the label2id mapping in the model config. Setting to "
           "-1. Define a descriptive label2id mapping in the model config to ensure correct outputs."				)
   @property
   def      __UpperCamelCase    (			self      :   Tuple				):



     for label, ind in self.model.config.labelaid.items():
       if label.lower().startswith("entail"				):
         return ind
     return -1
   def      __UpperCamelCase    (			self      :   Optional[int]  ,SCREAMING_SNAKE_CASE__      :   List[Any]  ,SCREAMING_SNAKE_CASE__      :   int=True  ,SCREAMING_SNAKE_CASE__      :   Tuple=True  ,SCREAMING_SNAKE_CASE__      :   Tuple=TruncationStrategy.ONLY_FIRST  ,**SCREAMING_SNAKE_CASE__      :   Dict				):
     SCREAMING_SNAKE_CASE:str       =       self.framework
     if self.tokenizer.pad_token is None:
       # Override for tokenizers not supporting padding
       logger.error(
           "Tokenizer was not supporting padding necessary for zero-shot, attempting to use "
           " `pad_token=eos_token`"				)
       SCREAMING_SNAKE_CASE:List[str]       =       self.tokenizer.eos_token
     try:
       SCREAMING_SNAKE_CASE:List[str]       =       self.tokenizer(
           a_  ,add_special_tokens=a_  ,return_tensors=a_  ,padding=a_  ,truncation=a_  ,)
     except Exception as e:
       if "too short" in str(a_				):
         # tokenizers might yell that we want to truncate
         # to a value that is not even reached by the input.
         # In that case we don't want to truncate.
         # It seems there's not a really better way to catch that
         # exception.

         SCREAMING_SNAKE_CASE:List[str]       =       self.tokenizer(
             a_  ,add_special_tokens=a_  ,return_tensors=a_  ,padding=a_  ,truncation=TruncationStrategy.DO_NOT_TRUNCATE  ,)
       else:
         raise e

     return inputs
   def      __UpperCamelCase    (			self      :   Tuple  ,**SCREAMING_SNAKE_CASE__      :   Tuple				):



     if kwargs.get("multi_class"  ,a_				) is not None:
       SCREAMING_SNAKE_CASE:str       =       kwargs["multi_class"]
       logger.warning(
           "The `multi_class` argument has been deprecated and renamed to `multi_label`. "
           "`multi_class` will be removed in a future version of Transformers."				)
     SCREAMING_SNAKE_CASE:Tuple       =       {}
     if "candidate_labels" in kwargs:
       SCREAMING_SNAKE_CASE:Any       =       self._args_parser._parse_labels(kwargs["candidate_labels"]				)
     if "hypothesis_template" in kwargs:
       SCREAMING_SNAKE_CASE:str       =       kwargs["hypothesis_template"]

     SCREAMING_SNAKE_CASE:Tuple       =       {}
     if "multi_label" in kwargs:
       SCREAMING_SNAKE_CASE:Dict       =       kwargs["multi_label"]
     return preprocess_params, {}, postprocess_params
   def __call__(			self      :   str  ,SCREAMING_SNAKE_CASE__      :   Union[str, List[str]]  ,*SCREAMING_SNAKE_CASE__      :   List[str]  ,**SCREAMING_SNAKE_CASE__      :   List[Any]  ,):



     if len(a_				) == 0:
       pass
     elif len(a_				) == 1 and "candidate_labels" not in kwargs:
       SCREAMING_SNAKE_CASE:Any       =       args[0]
     else:
       raise ValueError(F'''Unable to understand extra arguments {args}'''				)

     return super().__call__(a_  ,**a_				)
   def      __UpperCamelCase    (			self      :   Optional[int]  ,SCREAMING_SNAKE_CASE__      :   Tuple  ,SCREAMING_SNAKE_CASE__      :   Any=None  ,SCREAMING_SNAKE_CASE__      :   Dict="This example is {}."				):
     SCREAMING_SNAKE_CASE:Optional[Any]       =       self._args_parser(a_  ,a_  ,a_				)

     for i, (candidate_label, sequence_pair) in enumerate(zip(a_  ,a_				)				):
       SCREAMING_SNAKE_CASE:Union[str, Any]       =       self._parse_and_tokenize([sequence_pair]				)

       yield {
           "candidate_label": candidate_label,
           "sequence": sequences[0],
           "is_last": i == len(a_				) - 1,
           **model_input,
       }
   def      __UpperCamelCase    (			self      :   Optional[int]  ,SCREAMING_SNAKE_CASE__      :   Optional[Any]				):
     SCREAMING_SNAKE_CASE:Dict       =       inputs["candidate_label"]
     SCREAMING_SNAKE_CASE:Optional[int]       =       inputs["sequence"]
     SCREAMING_SNAKE_CASE:Optional[int]       =       {k: inputs[k] for k in self.tokenizer.model_input_names}
     SCREAMING_SNAKE_CASE:int       =       self.model(**a_				)

     SCREAMING_SNAKE_CASE:Optional[int]       =       {
         "candidate_label": candidate_label,
         "sequence": sequence,
         "is_last": inputs["is_last"],
         **outputs,
     }
     return model_outputs







   def      __UpperCamelCase    (			self      :   Dict  ,SCREAMING_SNAKE_CASE__      :   Any  ,SCREAMING_SNAKE_CASE__      :   List[str]=False				):
     SCREAMING_SNAKE_CASE:int       =       [outputs["candidate_label"] for outputs in model_outputs]
     SCREAMING_SNAKE_CASE:Optional[int]       =       [outputs["sequence"] for outputs in model_outputs]
     SCREAMING_SNAKE_CASE:Union[str, Any]       =       np.concatenate([output["logits"].numpy() for output in model_outputs]				)
     SCREAMING_SNAKE_CASE:List[str]       =       logits.shape[0]
     SCREAMING_SNAKE_CASE:Optional[int]       =       len(a_				)
     SCREAMING_SNAKE_CASE:List[str]       =       N // n
     SCREAMING_SNAKE_CASE:int       =       logits.reshape((num_sequences, n, -1)				)

     if multi_label or len(a_				) == 1:
       # softmax over the entailment vs. contradiction dim for each label independently
       SCREAMING_SNAKE_CASE:str       =       self.entailment_id
       SCREAMING_SNAKE_CASE:str       =       -1 if entailment_id == 0 else 0
       SCREAMING_SNAKE_CASE:str       =       reshaped_outputs[..., [contradiction_id, entailment_id]]
       SCREAMING_SNAKE_CASE:List[Any]       =       np.exp(a_				) / np.exp(a_				).sum(-1  ,keepdims=a_				)
       SCREAMING_SNAKE_CASE:str       =       scores[..., 1]
     else:
       # softmax the "entailment" logits over all candidate labels
       SCREAMING_SNAKE_CASE:str       =       reshaped_outputs[..., self.entailment_id]
       SCREAMING_SNAKE_CASE:Optional[int]       =       np.exp(a_				) / np.exp(a_				).sum(-1  ,keepdims=a_				)

     SCREAMING_SNAKE_CASE:List[str]       =       list(reversed(scores[0].argsort()				)				)
     return {
         "sequence": sequences[0],
         "labels": [candidate_labels[i] for i in top_inds],
         "scores": scores[0, top_inds].tolist(),
     }













"""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())