





from __future__ import annotations

def   __snake_case    (       _lowerCAmelCase			:			str					,					_lowerCAmelCase			:			list[str] | None = None					,					_lowerCAmelCase			:			dict[str, float] | None = None					,					_lowerCAmelCase			:			bool = False					,					)			->    tuple[int, float, str]:
   A_       :					Optional[int]        =      cipher_alphabet or [chr(_lowerCAmelCase						) 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_       :					Dict        =      {
          "a": 0.0_84_97,
          "b": 0.0_14_92,
          "c": 0.0_22_02,
          "d": 0.0_42_53,
          "e": 0.1_11_62,
          "f": 0.0_22_28,
          "g": 0.0_20_15,
          "h": 0.0_60_94,
          "i": 0.0_75_46,
          "j": 0.0_01_53,
          "k": 0.0_12_92,
          "l": 0.0_40_25,
          "m": 0.0_24_06,
          "n": 0.0_67_49,
          "o": 0.0_75_07,
          "p": 0.0_19_29,
          "q": 0.0_00_95,
          "r": 0.0_75_87,
          "s": 0.0_63_27,
          "t": 0.0_93_56,
          "u": 0.0_27_58,
          "v": 0.0_09_78,
          "w": 0.0_25_60,
          "x": 0.0_01_50,
          "y": 0.0_19_94,
          "z": 0.0_00_77,
      }
   else:
      # Custom frequencies dictionary
      A_       :					List[Any]        =      frequencies_dict

   if not case_sensitive:
      A_       :					Any        =      ciphertext.lower()

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

   # cycle through all of the shifts
   for shift in range(len(_lowerCAmelCase						)						):
      A_       :					Tuple        =      ""

      # decrypt the message with the shift
      for letter in ciphertext:
         try:
            # Try to index the letter in the alphabet
            A_       :					List[str]        =      (alphabet_letters.index(letter.lower()						) - shift) % len(
                _lowerCAmelCase						)
            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_       :					Union[str, Any]        =      0.0

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

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

               # Complete the chi squared statistic formula
               A_       :					Dict        =      ((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_       :					List[str]        =      decrypted_with_shift.count(_lowerCAmelCase						)

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

               # Complete the chi squared statistic formula
               A_       :					Optional[int]        =      ((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(_lowerCAmelCase			:			int						) -> tuple[float, str]:
      return chi_squared_statistic_values[key]

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

   # Get all the data from the most likely cipher (key, decoded message)
   (
       (
       A_
   )   ,    (
       A_
   )   ,    
   )       :					str        =      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


_lowerCAmelCase				:   Optional[int]						       =						logging.get_logger(__name__)

_lowerCAmelCase				:   List[str]						       =						{
    '''google/umt5-small''': '''https://huggingface.co/google/umt5-small/resolve/main/config.json''',
    # See all umt5 models at https://huggingface.co/models?filter=umt5
}


class        __magic_name__	(				lowerCamelCase__				):





     """simple docstring"""



     __UpperCamelCase            =  '''umt5'''
     __UpperCamelCase            =  ['''past_key_values''']



     def __init__(				self							:int      ,   snake_case							:Optional[Any]=250_112      ,   snake_case							:Optional[int]=512      ,   snake_case							:Any=64      ,   snake_case							:Union[str, Any]=1_024      ,   snake_case							:Tuple=8      ,   snake_case							:Optional[int]=None      ,   snake_case							:Union[str, Any]=6      ,   snake_case							:List[Any]=32      ,   snake_case							:Dict=128      ,   snake_case							:List[str]=0.1      ,   snake_case							:List[Any]=1e-6      ,   snake_case							:Dict=1.0      ,   snake_case							:Union[str, Any]="gated-gelu"      ,   snake_case							:Union[str, Any]=True      ,   snake_case							:Any=True      ,   snake_case							:List[str]="T5Tokenizer"      ,   snake_case							:Union[str, Any]=True      ,   snake_case							:Union[str, Any]=0      ,   snake_case							:List[Any]=1      ,   snake_case							:List[Any]=0      ,   **snake_case							:Any      ,   ):





        '''simple docstring'''




        super().__init__(
            is_encoder_decoder=snake_case      ,   tokenizer_class=snake_case      ,   tie_word_embeddings=snake_case      ,   pad_token_id=snake_case      ,   eos_token_id=snake_case      ,   decoder_start_token_id=snake_case      ,   **snake_case      ,   )
        A_       :					Union[str, Any]        =      vocab_size
        A_       :					Tuple        =      d_model
        A_       :					List[str]        =      d_kv
        A_       :					Union[str, Any]        =      d_ff
        A_       :					Any        =      num_layers
        A_       :					Optional[Any]        =      (
            num_decoder_layers if num_decoder_layers is not None else self.num_layers
        )  # default = symmetry
        A_       :					List[Any]        =      num_heads
        A_       :					List[str]        =      relative_attention_num_buckets
        A_       :					Dict        =      relative_attention_max_distance
        A_       :					Optional[Any]        =      dropout_rate
        A_       :					Any        =      layer_norm_epsilon
        A_       :					List[Any]        =      initializer_factor
        A_       :					Any        =      feed_forward_proj
        A_       :					Optional[Any]        =      use_cache

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

        if len(snake_case	) > 1 and act_info[0] != "gated" or len(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'"	)

        if feed_forward_proj == "gated-gelu":
           A_       :					Optional[Any]        =      "gelu_new"



     @property
     def     SCREAMING_SNAKE_CASE		(				self							:str	):





        '''simple docstring'''




        return self.d_model



     @property
     def     SCREAMING_SNAKE_CASE		(				self							:str	):





        '''simple docstring'''




        return self.num_heads





     @property
     def     SCREAMING_SNAKE_CASE		(				self							:Tuple	):





        '''simple docstring'''




        return self.num_layers




class        __magic_name__	(				lowerCamelCase__				):





     """simple docstring"""



     @property
     # Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs
     def     SCREAMING_SNAKE_CASE		(				self							:Union[str, Any]	):





        '''simple docstring'''




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

        if self.use_past:
           self.fill_with_past_key_values_(snake_case      ,   direction="inputs"	)

        return common_inputs



     @property
     # Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.default_onnx_opset
     def     SCREAMING_SNAKE_CASE		(				self							:Optional[int]	):





        '''simple docstring'''




        return 13





     @property
     def     SCREAMING_SNAKE_CASE		(				self							:List[str]	):





        '''simple docstring'''




        return 5e-4
