from dataclasses import dataclass
from typing import Optional, Tuple

import torch
from torch import nn
from transformers import RobertaPreTrainedModel, XLMRobertaConfig, XLMRobertaModel
from transformers.utils import ModelOutput



@dataclass
class     _A   (    __UpperCamelCase							):
  SCREAMING_SNAKE_CASE_	:	Optional[torch.FloatTensor]						=None
  SCREAMING_SNAKE_CASE_	:	torch.FloatTensor						=None
  SCREAMING_SNAKE_CASE_	:	Optional[Tuple[torch.FloatTensor]]						=None
  SCREAMING_SNAKE_CASE_	:	Optional[Tuple[torch.FloatTensor]]						=None



class     _A   (    __UpperCamelCase							):






  def __init__(self   ,				SCREAMING_SNAKE_CASE_=1   ,				SCREAMING_SNAKE_CASE_=0   ,				SCREAMING_SNAKE_CASE_=2   ,				SCREAMING_SNAKE_CASE_=512   ,				SCREAMING_SNAKE_CASE_="cls"   ,				SCREAMING_SNAKE_CASE_=False   ,				SCREAMING_SNAKE_CASE_=True   ,				**SCREAMING_SNAKE_CASE_   ,				)		->      Optional[int]:





       '''simple docstring'''





       super().__init__(pad_token_id=SCREAMING_SNAKE_CASE_   ,				bos_token_id=SCREAMING_SNAKE_CASE_   ,				eos_token_id=SCREAMING_SNAKE_CASE_   ,				**SCREAMING_SNAKE_CASE_	)
       UpperCamelCase__										=      project_dim
       UpperCamelCase__										=      pooler_fn
       UpperCamelCase__										=      learn_encoder
       UpperCamelCase__										=      use_attention_mask



class     _A   (    __UpperCamelCase							):
  SCREAMING_SNAKE_CASE_	:	Dict						=[R"pooler", R"logit_scale"]
  SCREAMING_SNAKE_CASE_	:	Optional[int]						=[R"position_ids", R"predictions.decoder.bias"]
  SCREAMING_SNAKE_CASE_	:	List[str]						="roberta"
  SCREAMING_SNAKE_CASE_	:	Optional[int]						=RobertaSeriesConfig






  def __init__(self   ,				SCREAMING_SNAKE_CASE_	)		->      Dict:





       '''simple docstring'''





       super().__init__(SCREAMING_SNAKE_CASE_	)
       UpperCamelCase__										=      XLMRobertaModel(SCREAMING_SNAKE_CASE_	)
       UpperCamelCase__										=      nn.Linear(config.hidden_size   ,				config.project_dim	)
       UpperCamelCase__										=      getattr(SCREAMING_SNAKE_CASE_   ,				'''has_pre_transformation'''   ,				SCREAMING_SNAKE_CASE_	)
       if self.has_pre_transformation:
            UpperCamelCase__										=      nn.Linear(config.hidden_size   ,				config.project_dim	)
            UpperCamelCase__										=      nn.LayerNorm(config.hidden_size   ,				eps=config.layer_norm_eps	)
       self.post_init()






  def 		_a					(self   ,				SCREAMING_SNAKE_CASE_ = None   ,				SCREAMING_SNAKE_CASE_ = None   ,				SCREAMING_SNAKE_CASE_ = None   ,				SCREAMING_SNAKE_CASE_ = None   ,				SCREAMING_SNAKE_CASE_ = None   ,				SCREAMING_SNAKE_CASE_ = None   ,				SCREAMING_SNAKE_CASE_ = None   ,				SCREAMING_SNAKE_CASE_ = None   ,				SCREAMING_SNAKE_CASE_ = None   ,				SCREAMING_SNAKE_CASE_ = None   ,				SCREAMING_SNAKE_CASE_ = None   ,				)		->      Tuple:





       '''simple docstring'''





       UpperCamelCase__										=      return_dict if return_dict is not None else self.config.use_return_dict

       UpperCamelCase__										=      self.base_model(
           input_ids=SCREAMING_SNAKE_CASE_   ,				attention_mask=SCREAMING_SNAKE_CASE_   ,				token_type_ids=SCREAMING_SNAKE_CASE_   ,				position_ids=SCREAMING_SNAKE_CASE_   ,				head_mask=SCREAMING_SNAKE_CASE_   ,				inputs_embeds=SCREAMING_SNAKE_CASE_   ,				encoder_hidden_states=SCREAMING_SNAKE_CASE_   ,				encoder_attention_mask=SCREAMING_SNAKE_CASE_   ,				output_attentions=SCREAMING_SNAKE_CASE_   ,				output_hidden_states=True if self.has_pre_transformation else output_hidden_states   ,				return_dict=SCREAMING_SNAKE_CASE_   ,				)

       if self.has_pre_transformation:
            UpperCamelCase__										=      outputs['''hidden_states'''][-2]
            UpperCamelCase__										=      self.pre_LN(SCREAMING_SNAKE_CASE_	)
            UpperCamelCase__										=      self.transformation_pre(SCREAMING_SNAKE_CASE_	)

            return TransformationModelOutput(
                projection_state=SCREAMING_SNAKE_CASE_   ,				last_hidden_state=outputs.last_hidden_state   ,				hidden_states=outputs.hidden_states   ,				attentions=outputs.attentions   ,				)
       else:
            UpperCamelCase__										=      self.transformation(outputs.last_hidden_state	)
            return TransformationModelOutput(
                projection_state=SCREAMING_SNAKE_CASE_   ,				last_hidden_state=outputs.last_hidden_state   ,				hidden_states=outputs.hidden_states   ,				attentions=outputs.attentions   ,				)




from __future__ import annotations

import math
from collections import Counter
from string import ascii_lowercase







def  __UpperCamelCase    (						A							):
     UpperCamelCase__						,       UpperCamelCase__										=      analyze_text(A							)
     UpperCamelCase__										=      list(''' ''' + ascii_lowercase							)
     # what is our total sum of probabilities.
     UpperCamelCase__										=      sum(single_char_strings.values()							)

     # one length string
     UpperCamelCase__										=      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:
               UpperCamelCase__										=      single_char_strings[ch]
               UpperCamelCase__										=      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
     UpperCamelCase__										=      sum(two_char_strings.values()							)
     UpperCamelCase__										=      0
     # for each alpha (two in size) calculate entropy.
     for cha in my_alphas:
          for cha in my_alphas:
               UpperCamelCase__										=      cha + cha
               if sequence in two_char_strings:
                    UpperCamelCase__										=      two_char_strings[sequence]
                    UpperCamelCase__										=      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  __UpperCamelCase    (						A							):
     UpperCamelCase__										=      Counter()  # type: ignore
     UpperCamelCase__										=      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  __UpperCamelCase    (						):
     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()



