






'''simple docstring'''





import warnings

from transformers import AutoTokenizer
from transformers.utils import is_torch_available
from transformers.utils.generic import ExplicitEnum

from ...processing_utils import ProcessorMixin


if is_torch_available():
 import torch
class 				lowercase       (							__SCREAMING_SNAKE_CASE      ):


    '''simple docstring'''

    lowerCAmelCase__      				=							"char"
    lowerCAmelCase__      				=							"bpe"
    lowerCAmelCase__      				=							"wp"
__A							:							Dict  							=		(DecodeType.CHARACTER, DecodeType.BPE, DecodeType.WORDPIECE)
class 				lowercase       (							__SCREAMING_SNAKE_CASE      ):


    '''simple docstring'''

    lowerCAmelCase__      				=							["image_processor", "char_tokenizer"]
    lowerCAmelCase__      				=							"ViTImageProcessor"
    lowerCAmelCase__      				=							"MgpstrTokenizer"

    def __init__(					self     :     List[str]				,     __lowerCamelCase     :     Dict=None				,     __lowerCamelCase     :     Tuple=None				,     **__lowerCamelCase     :     Tuple     )							->							Optional[Any]:

     '''simple docstring'''




     lowerCamelCase__  =     None
     if "feature_extractor" in kwargs:
      warnings.warn(
          "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
          " instead."				,     __snake_case				,     )
      lowerCamelCase__  =     kwargs.pop("feature_extractor"     )

     lowerCamelCase__  =     image_processor if image_processor is not None else feature_extractor
     if image_processor is None:
      raise ValueError("You need to specify an `image_processor`."     )
     if tokenizer is None:
      raise ValueError("You need to specify a `tokenizer`."     )

     lowerCamelCase__  =     tokenizer
     lowerCamelCase__  =     AutoTokenizer.from_pretrained("gpt2"     )
     lowerCamelCase__  =     AutoTokenizer.from_pretrained("bert-base-uncased"     )

     super().__init__(__snake_case				,     __snake_case     )

    def __call__(					self     :     Optional[Any]				,     __lowerCamelCase     :     Tuple=None				,     __lowerCamelCase     :     Union[str, Any]=None				,     __lowerCamelCase     :     Dict=None				,     **__lowerCamelCase     :     Tuple     )							->							Union[str, Any]:

     '''simple docstring'''




     if images is None and text is None:
      raise ValueError("You need to specify either an `images` or `text` input to process."     )

     if images is not None:
      lowerCamelCase__  =     self.image_processor(__snake_case				,     return_tensors=__snake_case				,     **__snake_case     )
     if text is not None:
      lowerCamelCase__  =     self.char_tokenizer(__snake_case				,     return_tensors=__snake_case				,     **__snake_case     )

     if text is None:
      return inputs
     elif images is None:
      return encodings
     else:
      lowerCamelCase__  =     encodings['''input_ids''']
      return inputs

    def    a__				(					self     :     Optional[int]				,     __lowerCamelCase     :     Tuple     )							->							Optional[Any]:

     '''simple docstring'''




     lowerCamelCase__  =     sequences
     lowerCamelCase__  =     char_preds.size(0     )

     lowerCamelCase__  =     self._decode_helper(__snake_case				,     "char"     )
     lowerCamelCase__  =     self._decode_helper(__snake_case				,     "bpe"     )
     lowerCamelCase__  =     self._decode_helper(__snake_case				,     "wp"     )

     lowerCamelCase__  =     []
     lowerCamelCase__  =     []
     for i in range(__snake_case     ):
      lowerCamelCase__  =     [char_scores[i], bpe_scores[i], wp_scores[i]]
      lowerCamelCase__  =     [char_strs[i], bpe_strs[i], wp_strs[i]]
      lowerCamelCase__  =     scores.index(max(__snake_case     )     )
      final_strs.append(strs[max_score_index]     )
      final_scores.append(scores[max_score_index]     )

     lowerCamelCase__  =     {}
     lowerCamelCase__  =     final_strs
     lowerCamelCase__  =     final_scores
     lowerCamelCase__  =     char_strs
     lowerCamelCase__  =     bpe_strs
     lowerCamelCase__  =     wp_strs
     return out

    def    a__				(					self     :     List[str]				,     __lowerCamelCase     :     Tuple				,     __lowerCamelCase     :     List[Any]     )							->							Optional[int]:

     '''simple docstring'''




     if format == DecodeType.CHARACTER:
      lowerCamelCase__  =     self.char_decode
      lowerCamelCase__  =     1
      lowerCamelCase__  =     '''[s]'''
     elif format == DecodeType.BPE:
      lowerCamelCase__  =     self.bpe_decode
      lowerCamelCase__  =     2
      lowerCamelCase__  =     '''#'''
     elif format == DecodeType.WORDPIECE:
      lowerCamelCase__  =     self.wp_decode
      lowerCamelCase__  =     102
      lowerCamelCase__  =     '''[SEP]'''
     else:
      raise ValueError(f'''Format {format} is not supported.'''     )

     lowerCamelCase__  =     [], []
     lowerCamelCase__  =     pred_logits.size(0     )
     lowerCamelCase__  =     pred_logits.size(1     )
     lowerCamelCase__  =     pred_logits.topk(1				,     dim=-1				,     largest=__snake_case				,     sorted=__snake_case     )
     lowerCamelCase__  =     preds_index.view(-1				,     __snake_case     )[:, 1:]
     lowerCamelCase__  =     decoder(__snake_case     )
     lowerCamelCase__  =     torch.nn.functional.softmax(__snake_case				,     dim=2     ).max(dim=2     )
     lowerCamelCase__  =     preds_max_prob[:, 1:]

     for index in range(__snake_case     ):
      lowerCamelCase__  =     preds_str[index].find(__snake_case     )
      lowerCamelCase__  =     preds_str[index][:pred_eos]
      lowerCamelCase__  =     preds_index[index].cpu().tolist()
      lowerCamelCase__  =     pred_index.index(__snake_case     ) if eos_token in pred_index else -1
      lowerCamelCase__  =     preds_max_prob[index][: pred_eos_index + 1]
      lowerCamelCase__  =     pred_max_prob.cumprod(dim=0     )[-1] if pred_max_prob.nelement() != 0 else 0.0
      dec_strs.append(__snake_case     )
      conf_scores.append(__snake_case     )

     return dec_strs, conf_scores

    def    a__				(					self     :     Union[str, Any]				,     __lowerCamelCase     :     Dict     )							->							str:

     '''simple docstring'''




     lowerCamelCase__  =     [seq.replace(" "				,     ""     ) for seq in self.char_tokenizer.batch_decode(__snake_case     )]
     return decode_strs

    def    a__				(					self     :     List[Any]				,     __lowerCamelCase     :     Tuple     )							->							Optional[int]:

     '''simple docstring'''




     return self.bpe_tokenizer.batch_decode(__snake_case     )




    def    a__				(					self     :     Optional[Any]				,     __lowerCamelCase     :     str     )							->							int:

     '''simple docstring'''




     lowerCamelCase__  =     [seq.replace(" "				,     ""     ) for seq in self.wp_tokenizer.batch_decode(__snake_case     )]
     return decode_strs













'''simple docstring'''





from decimal import Decimal, getcontext
from math import ceil, factorial






def    lowerCamelCase_     (    lowercase__):

 if not isinstance(lowercase__				,     lowercase__):
  raise TypeError("Undefined for non-integers")
 elif precision < 1:
  raise ValueError("Undefined for non-natural numbers")

 lowerCamelCase__  =     precision
 lowerCamelCase__  =     ceil(precision / 14)
 lowerCamelCase__  =     426880 * Decimal(10005).sqrt()
 lowerCamelCase__  =     1
 lowerCamelCase__  =     13591409
 lowerCamelCase__  =     Decimal(lowercase__)
 for k in range(1				,     lowercase__):
  lowerCamelCase__  =     factorial(6 * k) // (factorial(3 * k) * factorial(lowercase__) ** 3)
  linear_term += 545140134
  exponential_term *= -262537412640768000
  partial_sum += Decimal(multinomial_term * linear_term) / exponential_term
 return str(constant_term / partial_sum)[:-1]


if __name__ == "__main__":
 __A							:							Dict  							=		50
 print(F"""The first {n} digits of pi is: {pi(n)}""")

