


'''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 	__lowerCAmelCase		(							__SCREAMING_SNAKE_CASE    ):


  '''simple docstring'''







  a_  					=							"""char"""
  a_  					=							"""bpe"""
  a_  					=							"""wp"""







__magic_name__			=      (DecodeType.CHARACTER, DecodeType.BPE, DecodeType.WORDPIECE)



class 	__lowerCAmelCase		(							__SCREAMING_SNAKE_CASE    ):


  '''simple docstring'''







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






  def __init__(							self :       Dict	,_a :       Optional[int]=None	,_a :       Dict=None	,**_a :       Optional[int] ):

        '''simple docstring'''




        A_						:  str          =				None
        if "feature_extractor" in kwargs:
              warnings.warn(
                  """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
                  """ instead."""	,_a	,)
              A_						:  Optional[int]          =				kwargs.pop("""feature_extractor""" )

        A_						:  Tuple          =				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`.""" )

        A_						:  Union[str, Any]          =				tokenizer
        A_						:  List[Any]          =				AutoTokenizer.from_pretrained("""gpt2""" )
        A_						:  str          =				AutoTokenizer.from_pretrained("""bert-base-uncased""" )

        super().__init__(_a	,_a )






  def __call__(							self :       Optional[int]	,_a :       str=None	,_a :       int=None	,_a :       int=None	,**_a :       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:
              A_						:  int          =				self.image_processor(_a	,return_tensors=_a	,**_a )
        if text is not None:
              A_						:  Tuple          =				self.char_tokenizer(_a	,return_tensors=_a	,**_a )

        if text is None:
              return inputs
        elif images is None:
              return encodings
        else:
              A_						:  int          =				encodings["""input_ids"""]
              return inputs






  def        _a							(							self :       Optional[Any]	,_a :       Any ):

        '''simple docstring'''




        A_   ,					A_   ,					A_						:  List[str]          =				sequences
        A_						:  List[str]          =				char_preds.size(0 )

        A_   ,					A_						:  Dict          =				self._decode_helper(_a	,"""char""" )
        A_   ,					A_						:  List[str]          =				self._decode_helper(_a	,"""bpe""" )
        A_   ,					A_						:  Union[str, Any]          =				self._decode_helper(_a	,"""wp""" )

        A_						:  str          =				[]
        A_						:  Any          =				[]
        for i in range(_a ):
              A_						:  List[Any]          =				[char_scores[i], bpe_scores[i], wp_scores[i]]
              A_						:  List[Any]          =				[char_strs[i], bpe_strs[i], wp_strs[i]]
              A_						:  Any          =				scores.index(max(_a ) )
              final_strs.append(strs[max_score_index] )
              final_scores.append(scores[max_score_index] )

        A_						:  Optional[int]          =				{}
        A_						:  Dict          =				final_strs
        A_						:  Dict          =				final_scores
        A_						:  List[Any]          =				char_strs
        A_						:  Optional[Any]          =				bpe_strs
        A_						:  Optional[int]          =				wp_strs
        return out






  def        _a							(							self :       List[str]	,_a :       Dict	,_a :       str ):

        '''simple docstring'''




        if format == DecodeType.CHARACTER:
              A_						:  Dict          =				self.char_decode
              A_						:  int          =				1
              A_						:  List[str]          =				"""[s]"""
        elif format == DecodeType.BPE:
              A_						:  Optional[int]          =				self.bpe_decode
              A_						:  List[Any]          =				2
              A_						:  Tuple          =				"""#"""
        elif format == DecodeType.WORDPIECE:
              A_						:  int          =				self.wp_decode
              A_						:  Optional[Any]          =				102
              A_						:  List[str]          =				"""[SEP]"""
        else:
              raise ValueError(f'Format {format} is not supported.' )

        A_   ,					A_						:  Any          =				[], []
        A_						:  Tuple          =				pred_logits.size(0 )
        A_						:  Union[str, Any]          =				pred_logits.size(1 )
        A_   ,					A_						:  List[str]          =				pred_logits.topk(1	,dim=-1	,largest=_a	,sorted=_a )
        A_						:  Optional[int]          =				preds_index.view(-1	,_a )[:, 1:]
        A_						:  Dict          =				decoder(_a )
        A_   ,					A_						:  Tuple          =				torch.nn.functional.softmax(_a	,dim=2 ).max(dim=2 )
        A_						:  str          =				preds_max_prob[:, 1:]

        for index in range(_a ):
              A_						:  List[str]          =				preds_str[index].find(_a )
              A_						:  int          =				preds_str[index][:pred_eos]
              A_						:  Union[str, Any]          =				preds_index[index].cpu().tolist()
              A_						:  Union[str, Any]          =				pred_index.index(_a ) if eos_token in pred_index else -1
              A_						:  Any          =				preds_max_prob[index][: pred_eos_index + 1]
              A_						:  List[str]          =				pred_max_prob.cumprod(dim=0 )[-1] if pred_max_prob.nelement() != 0 else 0.0
              dec_strs.append(_a )
              conf_scores.append(_a )

        return dec_strs, conf_scores






  def        _a							(							self :       Optional[Any]	,_a :       Any ):

        '''simple docstring'''




        A_						:  str          =				[seq.replace(""" """	,"""""" ) for seq in self.char_tokenizer.batch_decode(_a )]
        return decode_strs






  def        _a							(							self :       Union[str, Any]	,_a :       Optional[Any] ):

        '''simple docstring'''




        return self.bpe_tokenizer.batch_decode(_a )






  def        _a							(							self :       int	,_a :       Union[str, Any] ):

        '''simple docstring'''




        A_						:  Optional[Any]          =				[seq.replace(""" """	,"""""" ) for seq in self.wp_tokenizer.batch_decode(_a )]
        return decode_strs




'''simple docstring'''


from sympy import diff, lambdify, symbols
from sympy.functions import *  # noqa: F403




def      lowerCamelCase					(      lowerCamelCase  :      str							,						lowerCamelCase  :      complex							,						lowerCamelCase  :      str = "x"							,						lowerCamelCase  :      float = 10**-10							,						lowerCamelCase  :      int = 1							,						):
      A_						:  int          =				symbols(lowerCamelCase)
      A_						:  List[Any]          =				lambdify(lowerCamelCase							,						lowerCamelCase)
      A_						:  List[str]          =				lambdify(lowerCamelCase							,						diff(lowerCamelCase							,						lowerCamelCase))

      A_						:  str          =				starting_point

      while True:
            if diff_function(lowerCamelCase) != 0:
                  A_						:  int          =				prev_guess - multiplicity * func(lowerCamelCase) / diff_function(
                      lowerCamelCase)
            else:
                  raise ZeroDivisionError("""Could not find root""") from None

            # Precision is checked by comparing the difference of consecutive guesses
            if abs(next_guess - prev_guess) < precision:
                  return next_guess

            A_						:  Union[str, Any]          =				next_guess


# Let's Execute
if __name__ == "__main__":
     # Find root of trigonometric function
     # Find value of pi
     print(f"""The root of sin(x) = 0 is {newton_raphson('sin(x)', 2)}""")

     # Find root of polynomial
     # Find fourth Root of 5
     print(f"""The root of x**4 - 5 = 0 is {newton_raphson('x**4 -5', 0.4 +5j)}""")

     # Find value of e
     print(
         'The root of log(y) - 1 = 0 is ',
         f"""{newton_raphson('log(y) - 1', 2, variable='y')}""",
     )

     # Exponential Roots
     print(
         'The root of exp(x) - 1 = 0 is',
         f"""{newton_raphson('exp(x) - 1', 10, precision=0.0_0_5)}""",
     )

     # Find root of cos(x)
     print(f"""The root of cos(x) = 0 is {newton_raphson('cos(x)', 0)}""")
