


'''simple docstring'''
import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging


__magic_name__   :   Any  							=logging.get_logger(__name__)

__magic_name__   :   Optional[Any]  							={}






class 		UpperCamelCase_		(					A   ):





						"""simple docstring"""




						UpperCAmelCase__	:       List[str]         =     '''fsmt_decoder'''
						def __init__( self				:					Optional[int]	,				_lowerCamelCase				:					Dict=0	,				_lowerCamelCase				:					Any=0  )							->					Union[str, Any]:
										super().__init__()
										__magic_name__	  = vocab_size
										__magic_name__	  = bos_token_id






class 		UpperCamelCase_		(					A   ):





						"""simple docstring"""




						UpperCAmelCase__	:       str         =     '''fsmt'''
						UpperCAmelCase__	:       List[str]         =     {'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}
						def __init__( self				:					Union[str, Any]	,				_lowerCamelCase				:					Optional[Any]=["en", "de"]	,				_lowerCamelCase				:					Optional[int]=4_20_24	,				_lowerCamelCase				:					Optional[Any]=4_20_24	,				_lowerCamelCase				:					List[Any]="relu"	,				_lowerCamelCase				:					List[str]=10_24	,				_lowerCamelCase				:					List[Any]=2_00	,				_lowerCamelCase				:					Optional[int]=10_24	,				_lowerCamelCase				:					Union[str, Any]=40_96	,				_lowerCamelCase				:					List[Any]=12	,				_lowerCamelCase				:					Dict=16	,				_lowerCamelCase				:					Optional[int]=0.0	,				_lowerCamelCase				:					Optional[int]=40_96	,				_lowerCamelCase				:					Union[str, Any]=12	,				_lowerCamelCase				:					str=16	,				_lowerCamelCase				:					Union[str, Any]=0.0	,				_lowerCamelCase				:					List[str]=0.0	,				_lowerCamelCase				:					Optional[int]=0.1	,				_lowerCamelCase				:					Any=0.0	,				_lowerCamelCase				:					Any=0.02	,				_lowerCamelCase				:					Tuple=2	,				_lowerCamelCase				:					List[Any]=True	,				_lowerCamelCase				:					str=True	,				_lowerCamelCase				:					Optional[Any]=False	,				_lowerCamelCase				:					Tuple=5	,				_lowerCamelCase				:					Optional[int]=1.0	,				_lowerCamelCase				:					str=False	,				_lowerCamelCase				:					int=True	,				_lowerCamelCase				:					List[str]=1	,				_lowerCamelCase				:					Union[str, Any]=0	,				_lowerCamelCase				:					Union[str, Any]=2	,				_lowerCamelCase				:					Dict=2	,				**_lowerCamelCase				:					str	,				)							->					List[str]:
										__magic_name__	  = langs
										__magic_name__	  = src_vocab_size
										__magic_name__	  = tgt_vocab_size
										__magic_name__	  = d_model  # encoder_embed_dim and decoder_embed_dim

										__magic_name__	  = encoder_ffn_dim
										__magic_name__	  = __magic_name__	  = encoder_layers
										__magic_name__	  = encoder_attention_heads
										__magic_name__	  = encoder_layerdrop
										__magic_name__	  = decoder_layerdrop
										__magic_name__	  = decoder_ffn_dim
										__magic_name__	  = decoder_layers
										__magic_name__	  = decoder_attention_heads
										__magic_name__	  = max_position_embeddings
										__magic_name__	  = init_std  # Normal(0, this parameter)
										__magic_name__	  = activation_function

										__magic_name__	  = DecoderConfig(vocab_size=_lowerCamelCase	,				bos_token_id=_lowerCamelCase  )
										if "decoder" in common_kwargs:
														del common_kwargs["decoder"]

										__magic_name__	  = scale_embedding  # scale factor will be sqrt(d_model) if True

										# 3 Types of Dropout
										__magic_name__	  = attention_dropout
										__magic_name__	  = activation_dropout
										__magic_name__	  = dropout

										__magic_name__	  = use_cache
										super().__init__(
										    pad_token_id=_lowerCamelCase	,				bos_token_id=_lowerCamelCase	,				eos_token_id=_lowerCamelCase	,				decoder_start_token_id=_lowerCamelCase	,				is_encoder_decoder=_lowerCamelCase	,				tie_word_embeddings=_lowerCamelCase	,				forced_eos_token_id=_lowerCamelCase	,				max_length=_lowerCamelCase	,				num_beams=_lowerCamelCase	,				length_penalty=_lowerCamelCase	,				early_stopping=_lowerCamelCase	,				**_lowerCamelCase	,				)

						def 					__A					( self				:					List[Any]  )							->					str:
										__magic_name__	  = copy.deepcopy(self.__dict__  )
										__magic_name__	  = self.decoder.to_dict()
										__magic_name__	  = self.__class__.model_type
										return output







'''simple docstring'''
import numpy as np

__magic_name__   :   str  							=[
    ['a', 'b', 'c', 'd', 'e'],
    ['f', 'g', 'h', 'i', 'k'],
    ['l', 'm', 'n', 'o', 'p'],
    ['q', 'r', 's', 't', 'u'],
    ['v', 'w', 'x', 'y', 'z'],
]






class 		UpperCamelCase_		:





						"""simple docstring"""




						def __init__( self				:					Union[str, Any]  )							->					None:
										__magic_name__	  = np.array(_lowerCamelCase  )
						def 					__A					( self				:					List[str]	,				_lowerCamelCase				:					str  )							->					np.ndarray:
										__magic_name__  ,      __magic_name__	  = np.where(letter == self.SQUARE  )
										__magic_name__	  = np.concatenate([indexa + 1, indexa + 1]  )
										return indexes
						def 					__A					( self				:					Any	,				_lowerCamelCase				:					int	,				_lowerCamelCase				:					int  )							->					str:
										return self.SQUARE[indexa - 1, indexa - 1]
						def 					__A					( self				:					Union[str, Any]	,				_lowerCamelCase				:					str  )							->					str:
										__magic_name__	  = message.lower()
										__magic_name__	  = message.replace("j"	,				"i"  )

										__magic_name__	  = ""
										for letter_index in range(len(_lowerCamelCase  )  ):
														if message[letter_index] != " ":
																		__magic_name__	  = self.letter_to_numbers(message[letter_index]  )
																		__magic_name__	  = encoded_message + str(numbers[0]  ) + str(numbers[1]  )
														elif message[letter_index] == " ":
																		__magic_name__	  = encoded_message + " "

										return encoded_message

						def 					__A					( self				:					Optional[Any]	,				_lowerCamelCase				:					str  )							->					str:
										__magic_name__	  = message.replace(" "	,				"  "  )
										__magic_name__	  = ""
										for numbers_index in range(int(len(_lowerCamelCase  ) / 2  )  ):
														if message[numbers_index * 2] != " ":
																		__magic_name__	  = message[numbers_index * 2]
																		__magic_name__	  = message[numbers_index * 2 + 1]

																		__magic_name__	  = self.numbers_to_letter(int(_lowerCamelCase  )	,				int(_lowerCamelCase  )  )
																		__magic_name__	  = decoded_message + letter
														elif message[numbers_index * 2] == " ":
																		__magic_name__	  = decoded_message + " "

										return decoded_message



