




from __future__ import annotations



def       UpperCAmelCase__  (     lowerCamelCase,			lowerCamelCase = None,			lowerCamelCase = None,			lowerCamelCase = False,			):
  lowercase    :Dict      	=						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)
    lowercase    :Any      	=						{
        "a": 0.08_497,
        "b": 0.01_492,
        "c": 0.02_202,
        "d": 0.04_253,
        "e": 0.11_162,
        "f": 0.02_228,
        "g": 0.02_015,
        "h": 0.06_094,
        "i": 0.07_546,
        "j": 0.00_153,
        "k": 0.01_292,
        "l": 0.04_025,
        "m": 0.02_406,
        "n": 0.06_749,
        "o": 0.07_507,
        "p": 0.01_929,
        "q": 0.00_095,
        "r": 0.07_587,
        "s": 0.06_327,
        "t": 0.09_356,
        "u": 0.02_758,
        "v": 0.00_978,
        "w": 0.02_560,
        "x": 0.00_150,
        "y": 0.01_994,
        "z": 0.00_077,
    }
  else:
    # Custom frequencies dictionary
    lowercase    :str      	=						frequencies_dict

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

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

  # cycle through all of the shifts
  for shift in range(len(lowerCamelCase   )   ):
    lowercase    :Dict      	=						""

    # decrypt the message with the shift
    for letter in ciphertext:
      try:
        # Try to index the letter in the alphabet
        lowercase    :Tuple      	=						(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

    lowercase    :Optional[int]      	=						0.0

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

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

          # Complete the chi squared statistic formula
          lowercase    :str      	=						((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
          lowercase    :Dict      	=						decrypted_with_shift.count(lowerCamelCase   )

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

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

  lowercase    :int      	=						min(
      lowerCamelCase,			key=lowerCamelCase,			)

  # Get all the data from the most likely cipher (key, decoded message)
  (
      (
      lowercase
  )	,    (
      lowercase
  )	,    
  )    :Optional[int]      	=						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 collections import OrderedDict
from typing import Any, List, Mapping, Optional

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging


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

_UpperCAmelCase	:						List[Any]			=     {
    "EleutherAI/gpt-j-6B": "https://huggingface.co/EleutherAI/gpt-j-6B/resolve/main/config.json",
    # See all GPT-J models at https://huggingface.co/models?filter=gpt_j
}




class    __lowerCAmelCase       (							lowerCAmelCase):
       _a 					=						'''gptj'''
       _a 					=						{
           '''max_position_embeddings''': '''n_positions''',
           '''hidden_size''': '''n_embd''',
           '''num_attention_heads''': '''n_head''',
           '''num_hidden_layers''': '''n_layer''',
       }






       def __init__(   self:					List[str]       ,					_lowerCAmelCase:					Union[str, Any]=5_04_00       ,					_lowerCAmelCase:					Optional[int]=20_48       ,					_lowerCAmelCase:					Dict=40_96       ,					_lowerCAmelCase:					str=28       ,					_lowerCAmelCase:					Dict=16       ,					_lowerCAmelCase:					str=64       ,					_lowerCAmelCase:					Dict=None       ,					_lowerCAmelCase:					Optional[int]="gelu_new"       ,					_lowerCAmelCase:					Optional[int]=0.0       ,					_lowerCAmelCase:					List[str]=0.0       ,					_lowerCAmelCase:					str=0.0       ,					_lowerCAmelCase:					Any=1e-5       ,					_lowerCAmelCase:					str=0.02       ,					_lowerCAmelCase:					str=True       ,					_lowerCAmelCase:					Any=5_02_56       ,					_lowerCAmelCase:					Union[str, Any]=5_02_56       ,					_lowerCAmelCase:					Optional[int]=False       ,					**_lowerCAmelCase:					int       ,					):
         lowercase    :str      	=						vocab_size
         lowercase    :Union[str, Any]      	=						n_positions
         lowercase    :Optional[int]      	=						n_embd
         lowercase    :int      	=						n_layer
         lowercase    :Tuple      	=						n_head
         lowercase    :Optional[Any]      	=						n_inner
         lowercase    :List[Any]      	=						rotary_dim
         lowercase    :List[Any]      	=						activation_function
         lowercase    :Tuple      	=						resid_pdrop
         lowercase    :Optional[int]      	=						embd_pdrop
         lowercase    :Any      	=						attn_pdrop
         lowercase    :Optional[int]      	=						layer_norm_epsilon
         lowercase    :str      	=						initializer_range
         lowercase    :Optional[int]      	=						use_cache

         lowercase    :Dict      	=						bos_token_id
         lowercase    :List[Any]      	=						eos_token_id

         super().__init__(
             bos_token_id=_lowerCAmelCase       ,					eos_token_id=_lowerCAmelCase       ,					tie_word_embeddings=_lowerCAmelCase       ,					**_lowerCAmelCase				)







class    __lowerCAmelCase       (							lowerCAmelCase):






       def __init__(   self:					Tuple       ,					_lowerCAmelCase:					PretrainedConfig       ,					_lowerCAmelCase:					str = "default"       ,					_lowerCAmelCase:					List[PatchingSpec] = None       ,					_lowerCAmelCase:					bool = False       ,					):
         super().__init__(_lowerCAmelCase       ,					task=_lowerCAmelCase       ,					patching_specs=_lowerCAmelCase       ,					use_past=_lowerCAmelCase				)
         if not getattr(self._config       ,					"pad_token_id"       ,					_lowerCAmelCase				):
           # TODO: how to do that better?
           lowercase    :int      	=						0






       @property
       def 					SCREAMING_SNAKE_CASE    (   self:					int				):
         lowercase    :Optional[int]      	=						OrderedDict({"input_ids": {0: "batch", 1: "sequence"}}				)
         if self.use_past:
           self.fill_with_past_key_values_(_lowerCAmelCase       ,					direction="inputs"				)
           lowercase    :Any      	=						{0: "batch", 1: "past_sequence + sequence"}
         else:
           lowercase    :Optional[int]      	=						{0: "batch", 1: "sequence"}

         return common_inputs






       @property
       def 					SCREAMING_SNAKE_CASE    (   self:					Optional[int]				):
         return self._config.n_layer






       @property
       def 					SCREAMING_SNAKE_CASE    (   self:					List[Any]				):
         return self._config.n_head






       def 					SCREAMING_SNAKE_CASE    (   self:					List[Any]       ,					_lowerCAmelCase:					PreTrainedTokenizer       ,					_lowerCAmelCase:					int = -1       ,					_lowerCAmelCase:					int = -1       ,					_lowerCAmelCase:					bool = False       ,					_lowerCAmelCase:					Optional[TensorType] = None       ,					):
         lowercase    :Union[str, Any]      	=						super(_lowerCAmelCase       ,					self				).generate_dummy_inputs(
             _lowerCAmelCase       ,					batch_size=_lowerCAmelCase       ,					seq_length=_lowerCAmelCase       ,					is_pair=_lowerCAmelCase       ,					framework=_lowerCAmelCase				)

         # We need to order the input in the way they appears in the forward()
         lowercase    :Optional[Any]      	=						OrderedDict({"input_ids": common_inputs["input_ids"]}				)

         # Need to add the past_keys
         if self.use_past:
           if not is_torch_available():
             raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed."				)
           else:
             import torch

             lowercase	,    lowercase    :Optional[int]      	=						common_inputs["input_ids"].shape
             # Not using the same length for past_key_values
             lowercase    :Union[str, Any]      	=						seqlen + 2
             lowercase    :Optional[Any]      	=						(
                 batch,
                 self.num_attention_heads,
                 past_key_values_length,
                 self._config.hidden_size // self.num_attention_heads,
             )
             lowercase    :Optional[int]      	=						[
                 (torch.zeros(_lowerCAmelCase				), torch.zeros(_lowerCAmelCase				)) for _ in range(self.num_layers				)
             ]

         lowercase    :List[Any]      	=						common_inputs["attention_mask"]
         if self.use_past:
           lowercase    :Tuple      	=						ordered_inputs["attention_mask"].dtype
           lowercase    :List[Any]      	=						torch.cat(
               [ordered_inputs["attention_mask"], torch.ones(_lowerCAmelCase       ,					_lowerCAmelCase       ,					dtype=_lowerCAmelCase				)]       ,					dim=1				)

         return ordered_inputs






       @property
       def 					SCREAMING_SNAKE_CASE    (   self:					Tuple				):
         return 13



