
"""simple docstring"""
import numpy as np

lowercase		:						Dict =					[
    ['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    _snake_case  :






							def __init__(					self						:			int    ):






												"""simple docstring"""



												__a:						Dict		    =					np.array(snake_case__    )






							def 							__lowercase  (					self						:			Optional[Any]						, snake_case__						:			str    ):






												"""simple docstring"""



												__a:						Optional[Any]		    =					np.where(letter == self.SQUARE    )
												__a:						str		    =					np.concatenate([indexa + 1, indexa + 1]    )
												return indexes






							def 							__lowercase  (					self						:			Tuple						, snake_case__						:			int						, snake_case__						:			int    ):






												"""simple docstring"""



												return self.SQUARE[indexa - 1, indexa - 1]






							def 							__lowercase  (					self						:			List[Any]						, snake_case__						:			str    ):






												"""simple docstring"""



												__a:						List[str]		    =					message.lower()
												__a:						Optional[int]		    =					message.replace("""j"""						, """i"""    )

												__a:						Dict		    =					''
												for letter_index in range(len(snake_case__    )    ):
																	if message[letter_index] != " ":
																						__a:						Any		    =					self.letter_to_numbers(message[letter_index]    )
																						__a:						str		    =					encoded_message + str(numbers[0]    ) + str(numbers[1]    )
																	elif message[letter_index] == " ":
																						__a:						Optional[Any]		    =					encoded_message + ' '

												return encoded_message






							def 							__lowercase  (					self						:			Optional[int]						, snake_case__						:			str    ):






												"""simple docstring"""



												__a:						Optional[int]		    =					message.replace(""" """						, """  """    )
												__a:						Optional[int]		    =					''
												for numbers_index in range(int(len(snake_case__    ) / 2    )    ):
																	if message[numbers_index * 2] != " ":
																						__a:						str		    =					message[numbers_index * 2]
																						__a:						List[str]		    =					message[numbers_index * 2 + 1]

																						__a:						List[str]		    =					self.numbers_to_letter(int(snake_case__    )						, int(snake_case__    )    )
																						__a:						Union[str, Any]		    =					decoded_message + letter
																	elif message[numbers_index * 2] == " ":
																						__a:						List[Any]		    =					decoded_message + ' '

												return decoded_message


"""simple docstring"""
import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import (
    TaBlock,
    TaConfig,
    TaLayerNorm,
)

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin




class    _snake_case  (						_lowercase ,				_lowercase ,				_lowercase       ):






							@register_to_config
							def __init__(					self						:			str						, snake_case__						:			int						, snake_case__						:			int						, snake_case__						:			int						, snake_case__						:			float						, snake_case__						:			int						, snake_case__						:			int						, snake_case__						:			int						, snake_case__						:			int						, snake_case__						:			str						, snake_case__						:			bool = False						, ):






												"""simple docstring"""



												super().__init__()

												__a:						Optional[Any]		    =					nn.Linear(snake_case__						, snake_case__						, bias=snake_case__    )

												__a:						Optional[Any]		    =					nn.Embedding(snake_case__						, snake_case__    )
												__a:						Any		    =					False

												__a:						Any		    =					nn.Dropout(p=snake_case__    )

												__a:						List[str]		    =					TaConfig(
												    d_model=snake_case__						, num_heads=snake_case__						, d_kv=snake_case__						, d_ff=snake_case__						, feed_forward_proj=snake_case__						, dropout_rate=snake_case__						, is_decoder=snake_case__						, is_encoder_decoder=snake_case__						, )
												__a:						Optional[int]		    =					nn.ModuleList()
												for lyr_num in range(snake_case__    ):
																	__a:						Union[str, Any]		    =					TaBlock(snake_case__    )
																	self.encoders.append(snake_case__    )

												__a:						List[str]		    =					TaLayerNorm(snake_case__    )
												__a:						Optional[int]		    =					nn.Dropout(p=snake_case__    )






							def 							__lowercase  (					self						:			str						, snake_case__						:			Optional[Any]						, snake_case__						:			int    ):






												"""simple docstring"""



												__a:						str		    =					self.input_proj(snake_case__    )

												# terminal relative positional encodings
												__a:						Optional[int]		    =					encoder_inputs.shape[1]
												__a:						List[Any]		    =					torch.arange(snake_case__						, device=encoder_inputs.device    )

												__a:						Union[str, Any]		    =					encoder_inputs_mask.sum(-1    )
												__a:						str		    =					torch.roll(input_positions.unsqueeze(0    )						, tuple(seq_lens.tolist()    )						, dims=0    )
												x += self.position_encoding(snake_case__    )

												__a:						Any		    =					self.dropout_pre(snake_case__    )

												# inverted the attention mask
												__a:						Any		    =					encoder_inputs.size()
												__a:						str		    =					self.get_extended_attention_mask(snake_case__						, snake_case__    )

												for lyr in self.encoders:
																	__a:						Any		    =					lyr(snake_case__						, snake_case__    )[0]
												__a:						int		    =					self.layer_norm(snake_case__    )

												return self.dropout_post(snake_case__    ), encoder_inputs_mask
