


from __future__ import annotations


def   _SCREAMING_SNAKE_CASE	( SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE = None ,			SCREAMING_SNAKE_CASE = None ,			SCREAMING_SNAKE_CASE = False ,			):
  A_      :				Any		     =					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)
    A_      :				List[Any]		     =					{
        '''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
    A_      :				Optional[int]		     =					frequencies_dict

  if not case_sensitive:
    A_      :				Optional[int]		     =					ciphertext.lower()

  # Chi squared statistic values
  A_      :				dict[int, tuple[float, str]]		     =					{}

  # cycle through all of the shifts
  for shift in range(len(SCREAMING_SNAKE_CASE       )       ):
    A_      :				str		     =					''''''

    # decrypt the message with the shift
    for letter in ciphertext:
      try:
        # Try to index the letter in the alphabet
        A_      :				Optional[Any]		     =					(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

    A_      :				Any		     =					0.0

    # Loop through each letter in the decoded message with the shift
    for letter in decrypted_with_shift:
      if case_sensitive:
        A_      :				List[str]		     =					letter.lower()
        if letter in frequencies:
          # Get the amount of times the letter occurs in the message
          A_      :				Any		     =					decrypted_with_shift.lower().count(SCREAMING_SNAKE_CASE       )

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

          # Complete the chi squared statistic formula
          A_      :				Tuple		     =					((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
          A_      :				Any		     =					decrypted_with_shift.count(SCREAMING_SNAKE_CASE       )

          # Get the excepcted amount of times the letter should appear based
          # on letter frequencies
          A_      :				Optional[int]		     =					frequencies[letter] * occurrences

          # Complete the chi squared statistic formula
          A_      :				Tuple		     =					((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
    A_      :				Tuple		     =					(
        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]

  A_      :				int		     =					min(
      SCREAMING_SNAKE_CASE ,			key=SCREAMING_SNAKE_CASE ,			)

  # Get all the data from the most likely cipher (key, decoded message)
  (
      (
      A_
  )     ,       (
      A_
  )     ,       
  )      :				Optional[Any]		     =					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,
  )








from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging


UpperCamelCase								=	logging.get_logger(__name__)

UpperCamelCase								=	{
    """t5-small""": """https://huggingface.co/t5-small/resolve/main/config.json""",
    """t5-base""": """https://huggingface.co/t5-base/resolve/main/config.json""",
    """t5-large""": """https://huggingface.co/t5-large/resolve/main/config.json""",
    """t5-3b""": """https://huggingface.co/t5-3b/resolve/main/config.json""",
    """t5-11b""": """https://huggingface.co/t5-11b/resolve/main/config.json""",
}






class   _lowerCamelCase (					UpperCamelCase			):




       """simple docstring"""
       snake_case      =    "t5"
       snake_case      =    ["past_key_values"]
       snake_case      =    {"hidden_size": "d_model", "num_attention_heads": "num_heads", "num_hidden_layers": "num_layers"}



       def __init__(      self				,       _SCREAMING_SNAKE_CASE=3_2128				,       _SCREAMING_SNAKE_CASE=512				,       _SCREAMING_SNAKE_CASE=64				,       _SCREAMING_SNAKE_CASE=2048				,       _SCREAMING_SNAKE_CASE=6				,       _SCREAMING_SNAKE_CASE=None				,       _SCREAMING_SNAKE_CASE=8				,       _SCREAMING_SNAKE_CASE=32				,       _SCREAMING_SNAKE_CASE=128				,       _SCREAMING_SNAKE_CASE=0.1				,       _SCREAMING_SNAKE_CASE=1e-6				,       _SCREAMING_SNAKE_CASE=1.0				,       _SCREAMING_SNAKE_CASE="relu"				,       _SCREAMING_SNAKE_CASE=True				,       _SCREAMING_SNAKE_CASE=True				,       _SCREAMING_SNAKE_CASE=0				,       _SCREAMING_SNAKE_CASE=1				,       **_SCREAMING_SNAKE_CASE				,       )->List[Any]:




         '''simple docstring'''





         A_      :				List[Any]		     =					vocab_size
         A_      :				int		     =					d_model
         A_      :				Optional[Any]		     =					d_kv
         A_      :				str		     =					d_ff
         A_      :				int		     =					num_layers
         A_      :				Any		     =					(
             num_decoder_layers if num_decoder_layers is not None else self.num_layers
         )  # default = symmetry
         A_      :				Optional[Any]		     =					num_heads
         A_      :				Union[str, Any]		     =					relative_attention_num_buckets
         A_      :				Dict		     =					relative_attention_max_distance
         A_      :				List[str]		     =					dropout_rate
         A_      :				Dict		     =					layer_norm_epsilon
         A_      :				str		     =					initializer_factor
         A_      :				Dict		     =					feed_forward_proj
         A_      :				int		     =					use_cache

         A_      :				Optional[int]		     =					self.feed_forward_proj.split('''-'''		)
         A_      :				Optional[Any]		     =					act_info[-1]
         A_      :				Optional[Any]		     =					act_info[0] == '''gated'''

         if len(_SCREAMING_SNAKE_CASE		) > 1 and act_info[0] != "gated" or len(_SCREAMING_SNAKE_CASE		) > 2:
           raise ValueError(
               F'''`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer.'''
               '''Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. '''
               '''\'gated-gelu\' or \'relu\''''		)

         # for backwards compatibility
         if feed_forward_proj == "gated-gelu":
           A_      :				Tuple		     =					'''gelu_new'''

         super().__init__(
             pad_token_id=_SCREAMING_SNAKE_CASE				,       eos_token_id=_SCREAMING_SNAKE_CASE				,       is_encoder_decoder=_SCREAMING_SNAKE_CASE				,       **_SCREAMING_SNAKE_CASE				,       )






class   _lowerCamelCase (					UpperCamelCase			):




       """simple docstring"""



       @property
       def        _snake_case       (      self		)->Mapping[str, Mapping[int, str]]:




         '''simple docstring'''





         A_      :				Union[str, Any]		     =					{
             '''input_ids''': {0: '''batch''', 1: '''encoder_sequence'''},
             '''attention_mask''': {0: '''batch''', 1: '''encoder_sequence'''},
         }
         if self.use_past:
           A_      :				List[str]		     =					'''past_encoder_sequence + sequence'''
           A_      :				Optional[int]		     =					{0: '''batch'''}
           A_      :				str		     =					{0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
         else:
           A_      :				Optional[int]		     =					{0: '''batch''', 1: '''decoder_sequence'''}
           A_      :				List[Any]		     =					{0: '''batch''', 1: '''decoder_sequence'''}

         if self.use_past:
           self.fill_with_past_key_values_(_SCREAMING_SNAKE_CASE				,       direction='''inputs'''		)

         return common_inputs







       @property
       def        _snake_case       (      self		)->int:




         '''simple docstring'''





         return 13




