






'''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 UpperCAmelCase_					(    __lowerCamelCase				):




      """simple docstring"""




      UpperCamelCase_								=			'char'
      UpperCamelCase_								=			'bpe'
      UpperCamelCase_								=			'wp'





SCREAMING_SNAKE_CASE						=				(DecodeType.CHARACTER, DecodeType.BPE, DecodeType.WORDPIECE)
class UpperCAmelCase_					(    __lowerCamelCase				):




      """simple docstring"""




      UpperCamelCase_								=			['image_processor', 'char_tokenizer']
      UpperCamelCase_								=			'ViTImageProcessor'
      UpperCamelCase_								=			'MgpstrTokenizer'






      def __init__( self			:		Union[str, Any]     ,      UpperCAmelCase			:		Union[str, Any]=None     ,      UpperCAmelCase			:		Any=None     ,      **UpperCAmelCase			:		Union[str, Any]		)  ->  str:


       '''simple docstring'''

       lowercase					:   Union[str, Any]				  =None
       if "feature_extractor" in kwargs:
        warnings.warn(
            '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
            ''' instead.'''     ,      UpperCamelCase_     ,      )
        lowercase					:   str				  =kwargs.pop('''feature_extractor'''		)

       lowercase					:   List[str]				  =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`.'''		)

       lowercase					:   Dict				  =tokenizer
       lowercase					:   str				  =AutoTokenizer.from_pretrained('''gpt2'''		)
       lowercase					:   Optional[Any]				  =AutoTokenizer.from_pretrained('''bert-base-uncased'''		)

       super().__init__(UpperCamelCase_     ,      UpperCamelCase_		)






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


       '''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:
        lowercase					:   Dict				  =self.image_processor(UpperCamelCase_     ,      return_tensors=UpperCamelCase_     ,      **UpperCamelCase_		)
       if text is not None:
        lowercase					:   Tuple				  =self.char_tokenizer(UpperCamelCase_     ,      return_tensors=UpperCamelCase_     ,      **UpperCamelCase_		)

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






      def 				A__      ( self			:		Dict     ,      UpperCAmelCase			:		Any		)  ->  List[Any]:


       '''simple docstring'''

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

       lowercase  ,		lowercase					:   Any				  =self._decode_helper(UpperCamelCase_     ,      '''char'''		)
       lowercase  ,		lowercase					:   Optional[Any]				  =self._decode_helper(UpperCamelCase_     ,      '''bpe'''		)
       lowercase  ,		lowercase					:   Tuple				  =self._decode_helper(UpperCamelCase_     ,      '''wp'''		)

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

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






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


       '''simple docstring'''

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

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

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

       return dec_strs, conf_scores






      def 				A__      ( self			:		Dict     ,      UpperCAmelCase			:		Dict		)  ->  List[str]:


       '''simple docstring'''

       lowercase					:   Any				  =[seq.replace(''' '''     ,      ''''''		) for seq in self.char_tokenizer.batch_decode(UpperCamelCase_		)]
       return decode_strs






      def 				A__      ( self			:		int     ,      UpperCAmelCase			:		Any		)  ->  str:


       '''simple docstring'''

       return self.bpe_tokenizer.batch_decode(UpperCamelCase_		)






      def 				A__      ( self			:		List[Any]     ,      UpperCAmelCase			:		Any		)  ->  Any:


       '''simple docstring'''

       lowercase					:   Optional[int]				  =[seq.replace(''' '''     ,      ''''''		) for seq in self.wp_tokenizer.batch_decode(UpperCamelCase_		)]
       return decode_strs











'''simple docstring'''



import mpmath  # for roots of unity
import numpy as np
class UpperCAmelCase_					:




      """simple docstring"""




      def __init__( self			:		List[Any]     ,      UpperCAmelCase			:		Dict=None     ,      UpperCAmelCase			:		Optional[int]=None		)  ->  Optional[Any]:


       '''simple docstring'''

       lowercase					:   Any				  =list(poly_a or [0]		)[:]
       lowercase					:   Dict				  =list(poly_b or [0]		)[:]

       # Remove leading zero coefficients
       while self.polyA[-1] == 0:
        self.polyA.pop()
       lowercase					:   int				  =len(self.polyA		)

       while self.polyB[-1] == 0:
        self.polyB.pop()
       lowercase					:   List[str]				  =len(self.polyB		)

       # Add 0 to make lengths equal a power of 2
       lowercase					:   Tuple				  =int(
           2 ** np.ceil(np.loga(len(self.polyA		) + len(self.polyB		) - 1		)		)		)

       while len(self.polyA		) < self.c_max_length:
        self.polyA.append(0		)
       while len(self.polyB		) < self.c_max_length:
        self.polyB.append(0		)
       # A complex root used for the fourier transform
       lowercase					:   Optional[int]				  =complex(mpmath.root(x=1     ,      n=self.c_max_length     ,      k=1		)		)

       # The product
       lowercase					:   str				  =self.__multiply()






      def 				A__      ( self			:		Optional[Any]     ,      UpperCAmelCase			:		Union[str, Any]		)  ->  Tuple:


       '''simple docstring'''

       lowercase					:   Tuple				  =[[x] for x in self.polyA] if which == '''A''' else [[x] for x in self.polyB]
       # Corner case
       if len(UpperCAmelCase		) <= 1:
        return dft[0]
       #
       lowercase					:   List[Any]				  =self.c_max_length // 2
       while next_ncol > 0:
        lowercase					:   str				  =[[] for i in range(UpperCAmelCase		)]
        lowercase					:   List[str]				  =self.root**next_ncol

        # First half of next step
        lowercase					:   Union[str, Any]				  =1
        for j in range(self.c_max_length // (next_ncol * 2)		):
         for i in range(UpperCAmelCase		):
          new_dft[i].append(dft[i][j] + current_root * dft[i + next_ncol][j]		)
         current_root *= root
        # Second half of next step
        lowercase					:   Any				  =1
        for j in range(self.c_max_length // (next_ncol * 2)		):
         for i in range(UpperCAmelCase		):
          new_dft[i].append(dft[i][j] - current_root * dft[i + next_ncol][j]		)
         current_root *= root
        # Update
        lowercase					:   Tuple				  =new_dft
        lowercase					:   List[Any]				  =next_ncol // 2
       return dft[0]






      def 				A__      ( self			:		int		)  ->  str:


       '''simple docstring'''

       lowercase					:   List[Any]				  =self.__dft('''A'''		)
       lowercase					:   Union[str, Any]				  =self.__dft('''B'''		)
       lowercase					:   Any				  =[[dft_a[i] * dft_b[i] for i in range(self.c_max_length		)]]
       del dft_a
       del dft_b

       # Corner Case
       if len(inverce_c[0]		) <= 1:
        return inverce_c[0]
       # Inverse DFT
       lowercase					:   Tuple				  =2
       while next_ncol <= self.c_max_length:
        lowercase					:   Tuple				  =[[] for i in range(UpperCAmelCase		)]
        lowercase					:   Tuple				  =self.root ** (next_ncol // 2)
        lowercase					:   Optional[int]				  =1
        # First half of next step
        for j in range(self.c_max_length // next_ncol		):
         for i in range(next_ncol // 2		):
          # Even positions
          new_inverse_c[i].append(
              (
                  inverce_c[i][j]
                  + inverce_c[i][j + self.c_max_length // next_ncol]
              )
              / 2		)
          # Odd positions
          new_inverse_c[i + next_ncol // 2].append(
              (
                  inverce_c[i][j]
                  - inverce_c[i][j + self.c_max_length // next_ncol]
              )
              / (2 * current_root)		)
         current_root *= root
        # Update
        lowercase					:   List[Any]				  =new_inverse_c
        next_ncol *= 2
       # Unpack
       lowercase					:   List[str]				  =[round(x[0].real     ,      8		) + round(x[0].imag     ,      8		) * 1J for x in inverce_c]

       # Remove leading 0's
       while inverce_c[-1] == 0:
        inverce_c.pop()
       return inverce_c






      def __str__( self			:		Dict		)  ->  Optional[Any]:


       '''simple docstring'''

       lowercase					:   Optional[Any]				  ='''A = ''' + ''' + '''.join(
           f'{coef}*x^{i}' for coef, i in enumerate(self.polyA[: self.len_A]		)		)
       lowercase					:   List[str]				  ='''B = ''' + ''' + '''.join(
           f'{coef}*x^{i}' for coef, i in enumerate(self.polyB[: self.len_B]		)		)
       lowercase					:   Optional[Any]				  ='''A*B = ''' + ''' + '''.join(
           f'{coef}*x^{i}' for coef, i in enumerate(self.product		)		)

       return f'{a}\n{b}\n{c}'


# Unit tests
if __name__ == "__main__":
      import doctest

      doctest.testmod()



