

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 				lowercase       ( lowercase_			,						lowercase_			,						lowercase_    ):




				@register_to_config
				def __init__(      self   ,     snake_case   ,     snake_case   ,     snake_case   ,     snake_case   ,     snake_case   ,     snake_case   ,     snake_case   ,     snake_case   ,     snake_case   ,     snake_case = False   ,     ):
											super().__init__()

											snake_case_           =					nn.Embedding(snake_case   ,     snake_case )

											snake_case_           =					nn.Embedding(snake_case   ,     snake_case )
											snake_case_           =					False

											snake_case_           =					nn.Dropout(p=snake_case )

											snake_case_           =					TaConfig(
											    vocab_size=snake_case   ,     d_model=snake_case   ,     num_heads=snake_case   ,     d_kv=snake_case   ,     d_ff=snake_case   ,     dropout_rate=snake_case   ,     feed_forward_proj=snake_case   ,     is_decoder=snake_case   ,     is_encoder_decoder=snake_case   ,     )

											snake_case_           =					nn.ModuleList()
											for lyr_num in range(snake_case ):
																		snake_case_           =					TaBlock(snake_case )
																		self.encoders.append(snake_case )

											snake_case_           =					TaLayerNorm(snake_case )
											snake_case_           =					nn.Dropout(p=snake_case )




				def 					a							(      self   ,     snake_case   ,     snake_case ):
											snake_case_           =					self.token_embedder(snake_case )

											snake_case_           =					encoder_input_tokens.shape[1]
											snake_case_           =					torch.arange(snake_case   ,     device=encoder_input_tokens.device )
											x += self.position_encoding(snake_case )

											snake_case_           =					self.dropout_pre(snake_case )

											# inverted the attention mask
											snake_case_           =					encoder_input_tokens.size()
											snake_case_           =					self.get_extended_attention_mask(snake_case   ,     snake_case )

											for lyr in self.encoders:
																		snake_case_           =					lyr(snake_case   ,     snake_case )[0]
											snake_case_           =					self.layer_norm(snake_case )

											return self.dropout_post(snake_case ), encoder_inputs_mask







def       __lowerCamelCase			(				UpperCamelCase__     ,							UpperCamelCase__   ):







							'''simple docstring'''






							snake_case_           =					len(UpperCamelCase__   )
							snake_case_           =					len(UpperCamelCase__   )
							snake_case_           =					(
							    first_str_length if first_str_length > second_str_length else second_str_length
							)
							snake_case_           =					[]
							for char_count in range(UpperCamelCase__   ):
														if char_count < first_str_length:
																					output_list.append(first_str[char_count]   )
														if char_count < second_str_length:
																					output_list.append(second_str[char_count]   )
							return "".join(UpperCamelCase__   )


if __name__ == "__main__":
					print(alternative_string_arrange("""AB""", """XYZ"""), end=""" """)




