





"""simple docstring"""
from __future__ import annotations



def      __lowerCamelCase   (							a_     :		List[Any] ,   a_     :		str = None ,   a_     :		Dict = None ,   a_     :		str = False ,   )			->      Dict:
      __SCREAMING_SNAKE_CASE   :Any						=  cipher_alphabet or [chr(_lowercase							) for i in range(97 ,   1_23							)]

      # 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)
            __SCREAMING_SNAKE_CASE   :int						=  {
                '''a''': 0.0_8497,
                '''b''': 0.0_1492,
                '''c''': 0.0_2202,
                '''d''': 0.0_4253,
                '''e''': 0.1_1162,
                '''f''': 0.0_2228,
                '''g''': 0.0_2015,
                '''h''': 0.0_6094,
                '''i''': 0.0_7546,
                '''j''': 0.0_0153,
                '''k''': 0.0_1292,
                '''l''': 0.0_4025,
                '''m''': 0.0_2406,
                '''n''': 0.0_6749,
                '''o''': 0.0_7507,
                '''p''': 0.0_1929,
                '''q''': 0.0_0095,
                '''r''': 0.0_7587,
                '''s''': 0.0_6327,
                '''t''': 0.0_9356,
                '''u''': 0.0_2758,
                '''v''': 0.0_0978,
                '''w''': 0.0_2560,
                '''x''': 0.0_0150,
                '''y''': 0.0_1994,
                '''z''': 0.0_0077,
            }
      else:
            # Custom frequencies dictionary
            __SCREAMING_SNAKE_CASE   :List[str]						=  frequencies_dict

      if not case_sensitive:
            __SCREAMING_SNAKE_CASE   :str						=  ciphertext.lower()

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

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

            # decrypt the message with the shift
            for letter in ciphertext:
                  try:
                        # Try to index the letter in the alphabet
                        __SCREAMING_SNAKE_CASE   :Dict						=  (alphabet_letters.index(letter.lower()							) - shift) % len(
                            _lowercase							)
                        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

            __SCREAMING_SNAKE_CASE   :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:
                        __SCREAMING_SNAKE_CASE   :Any						=  letter.lower()
                        if letter in frequencies:
                              # Get the amount of times the letter occurs in the message
                              __SCREAMING_SNAKE_CASE   :Optional[int]						=  decrypted_with_shift.lower().count(_lowercase							)

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

                              # Complete the chi squared statistic formula
                              __SCREAMING_SNAKE_CASE   :Optional[int]						=  ((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
                              __SCREAMING_SNAKE_CASE   :List[Any]						=  decrypted_with_shift.count(_lowercase							)

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

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

      __SCREAMING_SNAKE_CASE   :int						=  min(
          _lowercase ,   key=_lowercase ,   )

      # Get all the data from the most likely cipher (key, decoded message)
      (
          __SCREAMING_SNAKE_CASE  
      )   :Tuple						=  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 ...configuration_utils import PretrainedConfig
from ...utils import logging


__UpperCamelCase		:       Any									=     logging.get_logger(__name__)

__UpperCamelCase		:       Optional[int]									=     {
    'facebook/nllb-moe-54B': 'https://huggingface.co/facebook/nllb-moe-54b/resolve/main/config.json',
}





class 					lowercase__						(   UpperCamelCase_):
						UpperCamelCase_     =		"""nllb-moe"""
						UpperCamelCase_     =		["""past_key_values"""]
						UpperCamelCase_     =		{"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}


						def __init__(       self	:				List[str]    ,		UpperCamelCase__	:				List[str]=12_8112    ,		UpperCamelCase__	:				str=1024    ,		UpperCamelCase__	:				Optional[int]=12    ,		UpperCamelCase__	:				Union[str, Any]=4096    ,		UpperCamelCase__	:				Optional[Any]=16    ,		UpperCamelCase__	:				Optional[Any]=12    ,		UpperCamelCase__	:				str=4096    ,		UpperCamelCase__	:				Dict=16    ,		UpperCamelCase__	:				Any=0.05    ,		UpperCamelCase__	:				Any=0.05    ,		UpperCamelCase__	:				Optional[Any]=True    ,		UpperCamelCase__	:				Optional[Any]=True    ,		UpperCamelCase__	:				List[Any]="relu"    ,		UpperCamelCase__	:				Union[str, Any]=1024    ,		UpperCamelCase__	:				Optional[int]=0.1    ,		UpperCamelCase__	:				Any=0.1    ,		UpperCamelCase__	:				Optional[Any]=0.0    ,		UpperCamelCase__	:				str=0.02    ,		UpperCamelCase__	:				Tuple=2    ,		UpperCamelCase__	:				List[str]=True    ,		UpperCamelCase__	:				Dict=False    ,		UpperCamelCase__	:				Any="float32"    ,		UpperCamelCase__	:				List[Any]=False    ,		UpperCamelCase__	:				Tuple=128    ,		UpperCamelCase__	:				Tuple=64    ,		UpperCamelCase__	:				List[str]=4    ,		UpperCamelCase__	:				Union[str, Any]=4    ,		UpperCamelCase__	:				Dict=0.001    ,		UpperCamelCase__	:				Optional[Any]=0.001    ,		UpperCamelCase__	:				Optional[Any]="all"    ,		UpperCamelCase__	:				Tuple=False    ,		UpperCamelCase__	:				List[Any]=False    ,		UpperCamelCase__	:				List[Any]=1.0    ,		UpperCamelCase__	:				str=0.2    ,		UpperCamelCase__	:				List[Any]=1    ,		UpperCamelCase__	:				List[Any]=0    ,		UpperCamelCase__	:				Optional[int]=2    ,		UpperCamelCase__	:				Tuple=False    ,		**UpperCamelCase__	:				Union[str, Any]    ,		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    List[str]							=							vocab_size
												SCREAMING_SNAKE_CASE							:    Dict							=							max_position_embeddings
												SCREAMING_SNAKE_CASE							:    int							=							d_model
												SCREAMING_SNAKE_CASE							:    Any							=							encoder_ffn_dim
												SCREAMING_SNAKE_CASE							:    List[Any]							=							encoder_layers
												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							encoder_attention_heads
												SCREAMING_SNAKE_CASE							:    List[str]							=							decoder_ffn_dim
												SCREAMING_SNAKE_CASE							:    Dict							=							decoder_layers
												SCREAMING_SNAKE_CASE							:    List[Any]							=							decoder_attention_heads
												SCREAMING_SNAKE_CASE							:    List[str]							=							dropout
												SCREAMING_SNAKE_CASE							:    Any							=							attention_dropout
												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							activation_dropout
												SCREAMING_SNAKE_CASE							:    List[Any]							=							activation_function
												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							init_std
												SCREAMING_SNAKE_CASE							:    int							=							encoder_layerdrop
												SCREAMING_SNAKE_CASE							:    List[Any]							=							decoder_layerdrop
												SCREAMING_SNAKE_CASE							:    Any							=							use_cache
												SCREAMING_SNAKE_CASE							:    str							=							encoder_layers
												SCREAMING_SNAKE_CASE							:    List[Any]							=							scale_embedding  # scale factor will be sqrt(d_model) if True
												SCREAMING_SNAKE_CASE							:    List[str]							=							router_z_loss_coef
												SCREAMING_SNAKE_CASE							:    List[str]							=							router_aux_loss_coef
												SCREAMING_SNAKE_CASE							:    int							=							decoder_sparse_step
												SCREAMING_SNAKE_CASE							:    Optional[int]							=							encoder_sparse_step
												SCREAMING_SNAKE_CASE							:    List[str]							=							num_experts
												SCREAMING_SNAKE_CASE							:    int							=							expert_capacity
												SCREAMING_SNAKE_CASE							:    Any							=							router_bias
												if router_dtype not in ["float32", "float16", "bfloat16"]:
																		raise ValueError(f"""`router_dtype` must be one of 'float32', 'float16' or 'bfloat16', got {router_dtype}"""						)
												SCREAMING_SNAKE_CASE							:    str							=							router_dtype

												SCREAMING_SNAKE_CASE							:    List[Any]							=							router_ignore_padding_tokens
												SCREAMING_SNAKE_CASE							:    int							=							batch_prioritized_routing
												SCREAMING_SNAKE_CASE							:    str							=							second_expert_policy
												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							normalize_router_prob_before_dropping
												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							moe_eval_capacity_token_fraction
												SCREAMING_SNAKE_CASE							:    Optional[int]							=							moe_token_dropout
												SCREAMING_SNAKE_CASE							:    Optional[int]							=							output_router_logits
												super().__init__(
												    pad_token_id=UpperCamelCase__    ,		bos_token_id=UpperCamelCase__    ,		eos_token_id=UpperCamelCase__    ,		is_encoder_decoder=UpperCamelCase__    ,		decoder_start_token_id=UpperCamelCase__    ,		**UpperCamelCase__    ,		)





