"""simple docstring"""





import enum
import warnings

from ..tokenization_utils import TruncationStrategy
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_tf_available():
				import tensorflow as tf

				from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING

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

__A     = logging.get_logger(__name__)
class        _snake_case   (    enum.Enum						):
						snake_case__	     =	0
						snake_case__	     =	1

@add_end_docstrings(a__						)
class        _snake_case   (    a__						):
						snake_case__	     =	"generated"






						def __init__(				self					:	Dict							,    *UpperCAmelCase					:	Any							,    **UpperCAmelCase					:	str			):
													super().__init__(*UpperCAmelCase							,    **UpperCAmelCase			)

													self.check_model_type(
													    TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
													    if self.framework == "tf"
													    else MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING			)






						def 					lowerCamelCase__   (				self					:	List[str]							,    UpperCAmelCase					:	List[str]=None							,    UpperCAmelCase					:	List[Any]=None							,    UpperCAmelCase					:	List[str]=None							,    UpperCAmelCase					:	int=None							,    UpperCAmelCase					:	List[Any]=None							,    UpperCAmelCase					:	str=None							,    **UpperCAmelCase					:	List[str]							,    ):
													__lowerCamelCase    :						int						      =   {}
													if truncation is not None:
																				__lowerCamelCase    :						List[Any]						      =   truncation

													__lowerCamelCase    :						Optional[Any]						      =   generate_kwargs

													__lowerCamelCase    :						int						      =   {}
													if return_tensors is not None and return_type is None:
																				__lowerCamelCase    :						int						      =   ReturnType.TENSORS if return_tensors else ReturnType.TEXT
													if return_type is not None:
																				__lowerCamelCase    :						str						      =   return_type

													if clean_up_tokenization_spaces is not None:
																				__lowerCamelCase    :						List[Any]						      =   clean_up_tokenization_spaces

													if stop_sequence is not None:
																				__lowerCamelCase    :						Union[str, Any]						      =   self.tokenizer.encode(UpperCAmelCase							,    add_special_tokens=UpperCAmelCase			)
																				if len(UpperCAmelCase			) > 1:
																											warnings.warn(
																											    "Stopping on a multiple token sequence is not yet supported on transformers. The first token of"
																											    " the stop sequence will be used as the stop sequence string in the interim."			)
																				__lowerCamelCase    :						List[str]						      =   stop_sequence_ids[0]

													return preprocess_params, forward_params, postprocess_params






						def 					lowerCamelCase__   (				self					:	Union[str, Any]							,    UpperCAmelCase					:	Union[str, Any]							,    UpperCAmelCase					:	str							,    UpperCAmelCase					:	str			):
													return True






						def 					lowerCamelCase__   (				self					:	Optional[int]							,    *UpperCAmelCase					:	List[str]							,    UpperCAmelCase					:	int			):
													__lowerCamelCase    :						int						      =   self.model.config.prefix if self.model.config.prefix is not None else ""
													if isinstance(args[0]							,    UpperCAmelCase			):
																				if self.tokenizer.pad_token_id is None:
																											raise ValueError("Please make sure that the tokenizer has a pad_token_id when using a batch input"			)
																				__lowerCamelCase    :						Any						      =   ([prefix + arg for arg in args[0]],)
																				__lowerCamelCase    :						Union[str, Any]						      =   True

													elif isinstance(args[0]							,    UpperCAmelCase			):
																				__lowerCamelCase    :						str						      =   (prefix + args[0],)
																				__lowerCamelCase    :						List[str]						      =   False
													else:
																				raise ValueError(
																				    F""" `args[0]`: {args[0]} have the wrong format. The should be either of type `str` or type `list`"""			)
													__lowerCamelCase    :						Optional[int]						      =   self.tokenizer(*UpperCAmelCase							,    padding=UpperCAmelCase							,    truncation=UpperCAmelCase							,    return_tensors=self.framework			)
													# This is produced by tokenizers but is an invalid generate kwargs
													if "token_type_ids" in inputs:
																				del inputs["token_type_ids"]
													return inputs






						def __call__(				self					:	Any							,    *UpperCAmelCase					:	Optional[Any]							,    **UpperCAmelCase					:	Optional[int]			):
													__lowerCamelCase    :						Union[str, Any]						      =   super().__call__(*UpperCAmelCase							,    **UpperCAmelCase			)
													if (
													    isinstance(args[0]							,    UpperCAmelCase			)
													    and all(isinstance(UpperCAmelCase							,    UpperCAmelCase			) for el in args[0]			)
													    and all(len(UpperCAmelCase			) == 1 for res in result			)
													):
																				return [res[0] for res in result]
													return result






						def 					lowerCamelCase__   (				self					:	Dict							,    UpperCAmelCase					:	Any							,    UpperCAmelCase					:	Any=TruncationStrategy.DO_NOT_TRUNCATE							,    **UpperCAmelCase					:	Tuple			):
													__lowerCamelCase    :						Optional[Any]						      =   self._parse_and_tokenize(UpperCAmelCase							,    truncation=UpperCAmelCase							,    **UpperCAmelCase			)
													return inputs






						def 					lowerCamelCase__   (				self					:	List[Any]							,    UpperCAmelCase					:	List[Any]							,    **UpperCAmelCase					:	int			):






													if self.framework == "pt":
																				__lowerCamelCase      ,     __lowerCamelCase    :						Union[str, Any]						      =   model_inputs["input_ids"].shape
													elif self.framework == "tf":
																				__lowerCamelCase      ,     __lowerCamelCase    :						Optional[Any]						      =   tf.shape(model_inputs["input_ids"]			).numpy()

													__lowerCamelCase    :						Tuple						      =   generate_kwargs.get("min_length"							,    self.model.config.min_length			)
													__lowerCamelCase    :						Tuple						      =   generate_kwargs.get("max_length"							,    self.model.config.max_length			)
													self.check_inputs(UpperCAmelCase							,    generate_kwargs["min_length"]							,    generate_kwargs["max_length"]			)
													__lowerCamelCase    :						Optional[int]						      =   self.model.generate(**UpperCAmelCase							,    **UpperCAmelCase			)
													__lowerCamelCase    :						Optional[Any]						      =   output_ids.shape[0]
													if self.framework == "pt":
																				__lowerCamelCase    :						Optional[Any]						      =   output_ids.reshape(UpperCAmelCase							,    out_b // in_b							,    *output_ids.shape[1:]			)
													elif self.framework == "tf":
																				__lowerCamelCase    :						Optional[int]						      =   tf.reshape(UpperCAmelCase							,    (in_b, out_b // in_b, *output_ids.shape[1:])			)
													return {"output_ids": output_ids}







						def 					lowerCamelCase__   (				self					:	Dict							,    UpperCAmelCase					:	str							,    UpperCAmelCase					:	List[str]=ReturnType.TEXT							,    UpperCAmelCase					:	Any=False			):
													__lowerCamelCase    :						List[str]						      =   []
													for output_ids in model_outputs["output_ids"][0]:
																				if return_type == ReturnType.TENSORS:
																											__lowerCamelCase    :						Union[str, Any]						      =   {F"""{self.return_name}_token_ids""": output_ids}
																				elif return_type == ReturnType.TEXT:
																											__lowerCamelCase    :						int						      =   {
																											    F"""{self.return_name}_text""": self.tokenizer.decode(
																											        UpperCAmelCase							,    skip_special_tokens=UpperCAmelCase							,    clean_up_tokenization_spaces=UpperCAmelCase							,    )
																											}
																				records.append(UpperCAmelCase			)
													return records

@add_end_docstrings(a__						)
class        _snake_case   (    a__						):
						snake_case__	     =	"summary"






						def __call__(				self					:	Optional[int]							,    *UpperCAmelCase					:	Optional[Any]							,    **UpperCAmelCase					:	List[Any]			):
													return super().__call__(*UpperCAmelCase							,    **UpperCAmelCase			)







						def 					lowerCamelCase__   (				self					:	Tuple							,    UpperCAmelCase					:	List[str]							,    UpperCAmelCase					:	Tuple							,    UpperCAmelCase					:	List[Any]			):






													if max_length < min_length:
																				logger.warning(F"""Your min_length={min_length} must be inferior than your max_length={max_length}."""			)

													if input_length < max_length:
																				logger.warning(
																				    F"""Your max_length is set to {max_length}, but your input_length is only {input_length}. Since this is """
																				    "a summarization task, where outputs shorter than the input are typically wanted, you might "
																				    F"""consider decreasing max_length manually, e.g. summarizer(\'...\', max_length={input_length//2})"""			)

@add_end_docstrings(a__						)
class        _snake_case   (    a__						):
						snake_case__	     =	"translation"






						def 					lowerCamelCase__   (				self					:	Any							,    UpperCAmelCase					:	Union[str, Any]							,    UpperCAmelCase					:	Any							,    UpperCAmelCase					:	List[Any]			):






													if input_length > 0.9 * max_length:
																				logger.warning(
																				    F"""Your input_length: {input_length} is bigger than 0.9 * max_length: {max_length}. You might consider """
																				    "increasing your max_length manually, e.g. translator(\'...\', max_length=400)"			)
													return True






						def 					lowerCamelCase__   (				self					:	Dict							,    *UpperCAmelCase					:	Optional[Any]							,    UpperCAmelCase					:	Optional[int]=TruncationStrategy.DO_NOT_TRUNCATE							,    UpperCAmelCase					:	int=None							,    UpperCAmelCase					:	Tuple=None			):






													if getattr(self.tokenizer							,    "_build_translation_inputs"							,    UpperCAmelCase			):
																				return self.tokenizer._build_translation_inputs(
																				    *UpperCAmelCase							,    return_tensors=self.framework							,    truncation=UpperCAmelCase							,    src_lang=UpperCAmelCase							,    tgt_lang=UpperCAmelCase			)
													else:
																				return super()._parse_and_tokenize(*UpperCAmelCase							,    truncation=UpperCAmelCase			)






						def 					lowerCamelCase__   (				self					:	List[Any]							,    UpperCAmelCase					:	List[str]=None							,    UpperCAmelCase					:	int=None							,    **UpperCAmelCase					:	Tuple			):
													__lowerCamelCase      ,     __lowerCamelCase      ,     __lowerCamelCase    :						Optional[Any]						      =   super()._sanitize_parameters(**UpperCAmelCase			)
													if src_lang is not None:
																				__lowerCamelCase    :						Optional[Any]						      =   src_lang
													if tgt_lang is not None:
																				__lowerCamelCase    :						Optional[Any]						      =   tgt_lang
													if src_lang is None and tgt_lang is None:
																				# Backward compatibility, direct arguments use is preferred.
																				__lowerCamelCase    :						Tuple						      =   kwargs.get("task"							,    self.task			)
																				__lowerCamelCase    :						Optional[int]						      =   task.split("_"			)
																				if task and len(UpperCAmelCase			) == 4:
																											# translation, XX, to YY
																											__lowerCamelCase    :						Tuple						      =   items[1]
																											__lowerCamelCase    :						str						      =   items[3]
													return preprocess_params, forward_params, postprocess_params







						def __call__(				self					:	Dict							,    *UpperCAmelCase					:	Any							,    **UpperCAmelCase					:	Union[str, Any]			):
													return super().__call__(*UpperCAmelCase							,    **UpperCAmelCase			)


import os
def      a      (			)    ->      Any:





      """simple docstring"""



      with open(os.path.dirname(A__     ) + '/p022_names.txt'     ) as file:
            _lowercase	       =str(file.readlines()[0]     )
            _lowercase	       =names.replace('"'      ,   ''     ).split(','     )

      names.sort()

      _lowercase	       =0
      _lowercase	       =0

      for i, name in enumerate(A__     ):
            for letter in name:
                  name_score += ord(A__     ) - 64

            total_score += (i + 1) * name_score
            _lowercase	       =0
      return total_score


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


