






"""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 (     lowerCAmelCase						):
      _a   :     List[Any]								=   '''char'''
      _a   :     Union[str, Any]								=   '''bpe'''
      _a   :     Union[str, Any]								=   '''wp'''



UpperCamelCase_	:				Tuple										= (DecodeType.CHARACTER, DecodeType.BPE, DecodeType.WORDPIECE)


class      _lowercase (     lowerCAmelCase						):
      _a   :     Tuple								=   ['''image_processor''', '''char_tokenizer''']
      _a   :     List[str]								=   '''ViTImageProcessor'''
      _a   :     Tuple								=   '''MgpstrTokenizer'''


      def __init__(    self				:	List[Any]							,   a				:	Optional[int]=None							,   a				:	Dict=None							,   **a				:	Tuple						):





            """simple docstring"""







            __snake_case      :    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.'''							,   a							,   )
                  __snake_case      :    List[str]  			=kwargs.pop('''feature_extractor'''						)

            __snake_case      :    Optional[int]  			=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`.'''						)

            __snake_case      :    Optional[Any]  			=tokenizer
            __snake_case      :    int  			=AutoTokenizer.from_pretrained('''gpt2'''						)
            __snake_case      :    Any  			=AutoTokenizer.from_pretrained('''bert-base-uncased'''						)

            super().__init__(a							,   a						)


      def __call__(    self				:	str							,   a				:	int=None							,   a				:	Dict=None							,   a				:	int=None							,   **a				:	List[str]						):





            """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:
                  __snake_case      :    List[str]  			=self.image_processor(a							,   return_tensors=a							,   **a						)
            if text is not None:
                  __snake_case      :    str  			=self.char_tokenizer(a							,   return_tensors=a							,   **a						)

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


      def _UpperCamelCase						(    self				:	List[Any]							,   a				:	str						):





            """simple docstring"""







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

            __snake_case			,				__snake_case      :    Tuple  			=self._decode_helper(a							,   '''char'''						)
            __snake_case			,				__snake_case      :    List[Any]  			=self._decode_helper(a							,   '''bpe'''						)
            __snake_case			,				__snake_case      :    Optional[int]  			=self._decode_helper(a							,   '''wp'''						)

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

            __snake_case      :    Tuple  			={}
            __snake_case      :    Union[str, Any]  			=final_strs
            __snake_case      :    List[Any]  			=final_scores
            __snake_case      :    List[Any]  			=char_strs
            __snake_case      :    Union[str, Any]  			=bpe_strs
            __snake_case      :    Union[str, Any]  			=wp_strs
            return out


      def _UpperCamelCase						(    self				:	Optional[Any]							,   a				:	Tuple							,   a				:	int						):





            """simple docstring"""







            if format == DecodeType.CHARACTER:
                  __snake_case      :    Any  			=self.char_decode
                  __snake_case      :    Optional[Any]  			=1
                  __snake_case      :    Union[str, Any]  			='''[s]'''
            elif format == DecodeType.BPE:
                  __snake_case      :    Union[str, Any]  			=self.bpe_decode
                  __snake_case      :    Optional[int]  			=2
                  __snake_case      :    Tuple  			='''#'''
            elif format == DecodeType.WORDPIECE:
                  __snake_case      :    Any  			=self.wp_decode
                  __snake_case      :    Dict  			=1_0_2
                  __snake_case      :    str  			='''[SEP]'''
            else:
                  raise ValueError(f'''Format {format} is not supported.'''						)

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

            for index in range(a						):
                  __snake_case      :    Tuple  			=preds_str[index].find(a						)
                  __snake_case      :    Dict  			=preds_str[index][:pred_eos]
                  __snake_case      :    List[Any]  			=preds_index[index].cpu().tolist()
                  __snake_case      :    List[str]  			=pred_index.index(a						) if eos_token in pred_index else -1
                  __snake_case      :    Optional[int]  			=preds_max_prob[index][: pred_eos_index + 1]
                  __snake_case      :    Optional[int]  			=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 _UpperCamelCase						(    self				:	Optional[int]							,   a				:	Any						):





            """simple docstring"""







            __snake_case      :    List[str]  			=[seq.replace(''' '''							,   ''''''						) for seq in self.char_tokenizer.batch_decode(a						)]
            return decode_strs


      def _UpperCamelCase						(    self				:	List[str]							,   a				:	List[str]						):





            """simple docstring"""







            return self.bpe_tokenizer.batch_decode(a						)







      def _UpperCamelCase						(    self				:	Any							,   a				:	Union[str, Any]						):





            """simple docstring"""







            __snake_case      :    str  			=[seq.replace(''' '''							,   ''''''						) for seq in self.wp_tokenizer.batch_decode(a						)]
            return decode_strs










"""simple docstring"""





from __future__ import annotations

import math
from collections import Counter
from string import ascii_lowercase



def     __lowercase					(    a :				str )					->					None:
      __snake_case			,				__snake_case      :    List[Any]  			=analyze_text(a )
      __snake_case      :    Union[str, Any]  			=list(''' ''' + ascii_lowercase )
      # what is our total sum of probabilities.
      __snake_case      :    List[Any]  			=sum(single_char_strings.values() )

      # one length string
      __snake_case      :    str  			=0
      # for each alpha we go in our dict and if it is in it we calculate entropy
      for ch in my_alphas:
            if ch in single_char_strings:
                  __snake_case      :    Union[str, Any]  			=single_char_strings[ch]
                  __snake_case      :    Any  			=my_str / all_sum
                  my_fir_sum += prob * math.loga(a )  # entropy formula.

    # print entropy
      print(f'''{round(-1 * my_fir_sum ):.1f}''' )

      # two len string
      __snake_case      :    Optional[Any]  			=sum(two_char_strings.values() )
      __snake_case      :    Dict  			=0
      # for each alpha (two in size) calculate entropy.
      for cha in my_alphas:
            for cha in my_alphas:
                  __snake_case      :    Tuple  			=cha + cha
                  if sequence in two_char_strings:
                        __snake_case      :    List[Any]  			=two_char_strings[sequence]
                        __snake_case      :    Tuple  			=int(a ) / all_sum
                        my_sec_sum += prob * math.loga(a )

    # print second entropy
      print(f'''{round(-1 * my_sec_sum ):.1f}''' )

      # print the difference between them
      print(f'''{round((-1 * my_sec_sum) - (-1 * my_fir_sum) ):.1f}''' )



def     __lowercase					(    a :				str )					->					tuple[dict, dict]:
      __snake_case      :    List[str]  			=Counter()  # type: ignore
      __snake_case      :    Union[str, Any]  			=Counter()  # type: ignore
      single_char_strings[text[-1]] += 1

      # first case when we have space at start.
      two_char_strings[" " + text[0]] += 1
      for i in range(0       ,		len(a ) - 1 ):
            single_char_strings[text[i]] += 1
            two_char_strings[text[i : i + 2]] += 1
      return single_char_strings, two_char_strings



def     __lowercase					(    )					->					int:
      import doctest

      doctest.testmod()
      # text = (
      #     "Had repulsive dashwoods suspicion sincerity but advantage now him. Remark "
      #     "easily garret nor nay. Civil those mrs enjoy shy fat merry. You greatest "
      #     "jointure saw horrible. He private he on be imagine suppose. Fertile "
      #     "beloved evident through no service elderly is. Blind there if every no so "
      #     "at. Own neglected you preferred way sincerity delivered his attempted. To "
      #     "of message cottage windows do besides against uncivil.  Delightful "
      #     "unreserved impossible few estimating men favourable see entreaties. She "
      #     "propriety immediate was improving. He or entrance humoured likewise "
      #     "moderate. Much nor game son say feel. Fat make met can must form into "
      #     "gate. Me we offending prevailed discovery. "
      # )

      # calculate_prob(text)


if __name__ == "__main__":
    main()


