





'''simple docstring'''






from __future__ import annotations


def 					_a   (__SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE = None  ,      __SCREAMING_SNAKE_CASE = None  ,      __SCREAMING_SNAKE_CASE = False  ,      ):


  """simple docstring"""

  _UpperCamelCase       			=cipher_alphabet or [chr(__SCREAMING_SNAKE_CASE   ) for i in range(97  ,      123   )]

  # If the argument is None or the user provided an empty dictionary
  if not frequencies_dict:
    # Frequencies of letters in the english language (how much they show up)
    _UpperCamelCase       			={
        '''a''': 0.0_8_4_9_7,
        '''b''': 0.0_1_4_9_2,
        '''c''': 0.0_2_2_0_2,
        '''d''': 0.0_4_2_5_3,
        '''e''': 0.1_1_1_6_2,
        '''f''': 0.0_2_2_2_8,
        '''g''': 0.0_2_0_1_5,
        '''h''': 0.0_6_0_9_4,
        '''i''': 0.0_7_5_4_6,
        '''j''': 0.0_0_1_5_3,
        '''k''': 0.0_1_2_9_2,
        '''l''': 0.0_4_0_2_5,
        '''m''': 0.0_2_4_0_6,
        '''n''': 0.0_6_7_4_9,
        '''o''': 0.0_7_5_0_7,
        '''p''': 0.0_1_9_2_9,
        '''q''': 0.0_0_0_9_5,
        '''r''': 0.0_7_5_8_7,
        '''s''': 0.0_6_3_2_7,
        '''t''': 0.0_9_3_5_6,
        '''u''': 0.0_2_7_5_8,
        '''v''': 0.0_0_9_7_8,
        '''w''': 0.0_2_5_6_0,
        '''x''': 0.0_0_1_5_0,
        '''y''': 0.0_1_9_9_4,
        '''z''': 0.0_0_0_7_7,
    }
  else:
    # Custom frequencies dictionary
    _UpperCamelCase       			=frequencies_dict

  if not case_sensitive:
    _UpperCamelCase       			=ciphertext.lower()

  # Chi squared statistic values
  _UpperCamelCase       			={}

  # cycle through all of the shifts
  for shift in range(len(__SCREAMING_SNAKE_CASE   )   ):
    _UpperCamelCase       			=''''''

    # decrypt the message with the shift
    for letter in ciphertext:
      try:
        # Try to index the letter in the alphabet
        _UpperCamelCase       			=(alphabet_letters.index(letter.lower()   ) - shift) % len(
            __SCREAMING_SNAKE_CASE   )
        decrypted_with_shift += (
            alphabet_letters[new_key].upper()
            if case_sensitive and letter.isupper()
            else alphabet_letters[new_key]
        )
      except ValueError:
        # Append the character if it isn't in the alphabet
        decrypted_with_shift += letter

    _UpperCamelCase       			=0.0

    # Loop through each letter in the decoded message with the shift
    for letter in decrypted_with_shift:
      if case_sensitive:
        _UpperCamelCase       			=letter.lower()
        if letter in frequencies:
          # Get the amount of times the letter occurs in the message
          _UpperCamelCase       			=decrypted_with_shift.lower().count(__SCREAMING_SNAKE_CASE   )

          # Get the excepcted amount of times the letter should appear based
          # on letter frequencies
          _UpperCamelCase       			=frequencies[letter] * occurrences

          # Complete the chi squared statistic formula
          _UpperCamelCase       			=((occurrences - expected) ** 2) / expected

          # Add the margin of error to the total chi squared statistic
          chi_squared_statistic += chi_letter_value
      else:
        if letter.lower() in frequencies:
          # Get the amount of times the letter occurs in the message
          _UpperCamelCase       			=decrypted_with_shift.count(__SCREAMING_SNAKE_CASE   )

          # Get the excepcted amount of times the letter should appear based
          # on letter frequencies
          _UpperCamelCase       			=frequencies[letter] * occurrences

          # Complete the chi squared statistic formula
          _UpperCamelCase       			=((occurrences - expected) ** 2) / expected

          # Add the margin of error to the total chi squared statistic
          chi_squared_statistic += chi_letter_value

        # Add the data to the chi_squared_statistic_values dictionary
    _UpperCamelCase       			=(
        chi_squared_statistic,
        decrypted_with_shift,
    )

  # Get the most likely cipher by finding the cipher with the smallest chi squared
  # statistic
  def chi_squared_statistic_values_sorting_key(__SCREAMING_SNAKE_CASE   ) -> tuple[float, str]:
    return chi_squared_statistic_values[key]

  _UpperCamelCase       			=min(
      __SCREAMING_SNAKE_CASE  ,      key=__SCREAMING_SNAKE_CASE  ,      )

  # Get all the data from the most likely cipher (key, decoded message)
  (
      (
      _UpperCamelCase
  )			,     (
      _UpperCamelCase
  )			,     
  )       			=chi_squared_statistic_values[most_likely_cipher]

  # Return the data on the most likely shift
  return (
      most_likely_cipher,
      most_likely_cipher_chi_squared_value,
      decoded_most_likely_cipher,
  )













'''simple docstring'''






from __future__ import absolute_import, division, print_function, unicode_literals

from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss

from transformers import RobertaConfig
from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.roberta.modeling_roberta import (
    ROBERTA_INPUTS_DOCSTRING,
    ROBERTA_START_DOCSTRING,
    RobertaEmbeddings,
)

from .modeling_highway_bert import BertPreTrainedModel, DeeBertModel, HighwayException, entropy







@add_start_docstrings(
    """The RoBERTa Model transformer with early exiting (DeeRoBERTa). """   ,     lowercase_   ,     )
class 		UpperCAmelCase   (				lowercase_):


  """simple docstring"""







  lowerCAmelCase_					=				RobertaConfig
  lowerCAmelCase_					=				"""roberta"""


  def __init__(				self    :					Dict							,	UpperCamelCase__    :					List[str]  )     ->						List[Any]:
    super().__init__(UpperCamelCase__  )

    _UpperCamelCase       			=RobertaEmbeddings(UpperCamelCase__  )
    self.init_weights()







@add_start_docstrings(
    """RoBERTa Model (with early exiting - DeeRoBERTa) with a classifier on top,
    also takes care of multi-layer training. """   ,     lowercase_   ,     )
class 		UpperCAmelCase   (				lowercase_):


  """simple docstring"""







  lowerCAmelCase_					=				RobertaConfig
  lowerCAmelCase_					=				"""roberta"""


  def __init__(				self    :					Tuple							,	UpperCamelCase__    :					Any  )     ->						Dict:
    super().__init__(UpperCamelCase__  )
    _UpperCamelCase       			=config.num_labels
    _UpperCamelCase       			=config.num_hidden_layers

    _UpperCamelCase       			=DeeRobertaModel(UpperCamelCase__  )
    _UpperCamelCase       			=nn.Dropout(config.hidden_dropout_prob  )
    _UpperCamelCase       			=nn.Linear(config.hidden_size							,	self.config.num_labels  )




  @add_start_docstrings_to_model_forward(UpperCamelCase__  )
  def   UpperCamelCase__      (				self    :					List[Any]							,	UpperCamelCase__    :					Optional[int]=None							,	UpperCamelCase__    :					Union[str, Any]=None							,	UpperCamelCase__    :					List[str]=None							,	UpperCamelCase__    :					Optional[Any]=None							,	UpperCamelCase__    :					int=None							,	UpperCamelCase__    :					Union[str, Any]=None							,	UpperCamelCase__    :					Tuple=None							,	UpperCamelCase__    :					Union[str, Any]=-1							,	UpperCamelCase__    :					Dict=False							,	)     ->						Union[str, Any]:
    _UpperCamelCase       			=self.num_layers
    try:
      _UpperCamelCase       			=self.roberta(
          UpperCamelCase__							,	attention_mask=UpperCamelCase__							,	token_type_ids=UpperCamelCase__							,	position_ids=UpperCamelCase__							,	head_mask=UpperCamelCase__							,	inputs_embeds=UpperCamelCase__							,	)

      _UpperCamelCase       			=outputs[1]

      _UpperCamelCase       			=self.dropout(UpperCamelCase__  )
      _UpperCamelCase       			=self.classifier(UpperCamelCase__  )
      _UpperCamelCase       			=(logits,) + outputs[2:]  # add hidden states and attention if they are here
    except HighwayException as e:
      _UpperCamelCase       			=e.message
      _UpperCamelCase       			=e.exit_layer
      _UpperCamelCase       			=outputs[0]

    if not self.training:
      _UpperCamelCase       			=entropy(UpperCamelCase__  )
      _UpperCamelCase       			=[]
      _UpperCamelCase       			=[]
    if labels is not None:
      if self.num_labels == 1:
        #  We are doing regression
        _UpperCamelCase       			=MSELoss()
        _UpperCamelCase       			=loss_fct(logits.view(-1  )							,	labels.view(-1  )  )
      else:
        _UpperCamelCase       			=CrossEntropyLoss()
        _UpperCamelCase       			=loss_fct(logits.view(-1							,	self.num_labels  )							,	labels.view(-1  )  )

      # work with highway exits
      _UpperCamelCase       			=[]
      for highway_exit in outputs[-1]:
        _UpperCamelCase       			=highway_exit[0]
        if not self.training:
          highway_logits_all.append(UpperCamelCase__  )
          highway_entropy.append(highway_exit[2]  )
        if self.num_labels == 1:
          #  We are doing regression
          _UpperCamelCase       			=MSELoss()
          _UpperCamelCase       			=loss_fct(highway_logits.view(-1  )							,	labels.view(-1  )  )
        else:
          _UpperCamelCase       			=CrossEntropyLoss()
          _UpperCamelCase       			=loss_fct(highway_logits.view(-1							,	self.num_labels  )							,	labels.view(-1  )  )
        highway_losses.append(UpperCamelCase__  )

      if train_highway:
        _UpperCamelCase       			=(sum(highway_losses[:-1]  ),) + outputs
        # exclude the final highway, of course
      else:
        _UpperCamelCase       			=(loss,) + outputs
    if not self.training:
      _UpperCamelCase       			=outputs + ((original_entropy, highway_entropy), exit_layer)
      if output_layer >= 0:
        _UpperCamelCase       			=(
            (outputs[0],) + (highway_logits_all[output_layer],) + outputs[2:]
        )  # use the highway of the last layer

    return outputs  # (loss), logits, (hidden_states), (attentions), entropy






