




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     (_A  ,		_A  ,		_A						):



				@register_to_config
				def __init__(					self					:						Optional[Any]						,		_UpperCAmelCase					:						int						,		_UpperCAmelCase					:						int						,		_UpperCAmelCase					:						int						,		_UpperCAmelCase					:						float						,		_UpperCAmelCase					:						int						,		_UpperCAmelCase					:						int						,		_UpperCAmelCase					:						int						,		_UpperCAmelCase					:						int						,		_UpperCAmelCase					:						str						,		_UpperCAmelCase					:						bool = False						,		)     ->			Optional[int]:





								'''simple docstring'''




								super().__init__()

								_lowerCAmelCase			:			List[str]        =					nn.Linear(__SCREAMING_SNAKE_CASE						,		__SCREAMING_SNAKE_CASE						,		bias=__SCREAMING_SNAKE_CASE					)

								_lowerCAmelCase			:			Tuple        =					nn.Embedding(__SCREAMING_SNAKE_CASE						,		__SCREAMING_SNAKE_CASE					)
								_lowerCAmelCase			:			List[Any]        =					False

								_lowerCAmelCase			:			Any        =					nn.Dropout(p=__SCREAMING_SNAKE_CASE					)

								_lowerCAmelCase			:			str        =					TaConfig(
								    d_model=__SCREAMING_SNAKE_CASE						,		num_heads=__SCREAMING_SNAKE_CASE						,		d_kv=__SCREAMING_SNAKE_CASE						,		d_ff=__SCREAMING_SNAKE_CASE						,		feed_forward_proj=__SCREAMING_SNAKE_CASE						,		dropout_rate=__SCREAMING_SNAKE_CASE						,		is_decoder=__SCREAMING_SNAKE_CASE						,		is_encoder_decoder=__SCREAMING_SNAKE_CASE						,		)
								_lowerCAmelCase			:			Dict        =					nn.ModuleList()
								for lyr_num in range(__SCREAMING_SNAKE_CASE					):
												_lowerCAmelCase			:			Dict        =					TaBlock(__SCREAMING_SNAKE_CASE					)
												self.encoders.append(__SCREAMING_SNAKE_CASE					)

								_lowerCAmelCase			:			int        =					TaLayerNorm(__SCREAMING_SNAKE_CASE					)
								_lowerCAmelCase			:			Union[str, Any]        =					nn.Dropout(p=__SCREAMING_SNAKE_CASE					)



				def 					SCREAMING_SNAKE_CASE				(					self					:						int						,		_UpperCAmelCase					:						Any						,		_UpperCAmelCase					:						str					)     ->			Dict:





								'''simple docstring'''




								_lowerCAmelCase			:			List[str]        =					self.input_proj(__SCREAMING_SNAKE_CASE					)

								# terminal relative positional encodings
								_lowerCAmelCase			:			Any        =					encoder_inputs.shape[1]
								_lowerCAmelCase			:			List[Any]        =					torch.arange(__SCREAMING_SNAKE_CASE						,		device=encoder_inputs.device					)

								_lowerCAmelCase			:			Any        =					encoder_inputs_mask.sum(-1					)
								_lowerCAmelCase			:			int        =					torch.roll(input_positions.unsqueeze(0					)						,		tuple(seq_lens.tolist()					)						,		dims=0					)
								x += self.position_encoding(__SCREAMING_SNAKE_CASE					)

								_lowerCAmelCase			:			str        =					self.dropout_pre(__SCREAMING_SNAKE_CASE					)

								# inverted the attention mask
								_lowerCAmelCase			:			List[Any]        =					encoder_inputs.size()
								_lowerCAmelCase			:			Dict        =					self.get_extended_attention_mask(__SCREAMING_SNAKE_CASE						,		__SCREAMING_SNAKE_CASE					)

								for lyr in self.encoders:
												_lowerCAmelCase			:			int        =					lyr(__SCREAMING_SNAKE_CASE						,		__SCREAMING_SNAKE_CASE					)[0]
								_lowerCAmelCase			:			List[Any]        =					self.layer_norm(__SCREAMING_SNAKE_CASE					)

								return self.dropout_post(__SCREAMING_SNAKE_CASE					), encoder_inputs_mask




"""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 			__UpperCamelCase (   _A     ,		_A     ,		_A						):


		@register_to_config
		def __init__(self :		Optional[Any]     ,     __SCREAMING_SNAKE_CASE :		int     ,     __SCREAMING_SNAKE_CASE :		int     ,     __SCREAMING_SNAKE_CASE :		int     ,     __SCREAMING_SNAKE_CASE :		float     ,     __SCREAMING_SNAKE_CASE :		int     ,     __SCREAMING_SNAKE_CASE :		int     ,     __SCREAMING_SNAKE_CASE :		int     ,     __SCREAMING_SNAKE_CASE :		int     ,     __SCREAMING_SNAKE_CASE :		str     ,     __SCREAMING_SNAKE_CASE :		bool = False     ,     ):
							super().__init__()

							A			=					nn.Linear(__SCREAMING_SNAKE_CASE     ,     __SCREAMING_SNAKE_CASE     ,     bias=__SCREAMING_SNAKE_CASE)

							A			=					nn.Embedding(__SCREAMING_SNAKE_CASE     ,     __SCREAMING_SNAKE_CASE)
							A			=					False

							A			=					nn.Dropout(p=__SCREAMING_SNAKE_CASE)

							A			=					TaConfig(
							    d_model=__SCREAMING_SNAKE_CASE     ,     num_heads=__SCREAMING_SNAKE_CASE     ,     d_kv=__SCREAMING_SNAKE_CASE     ,     d_ff=__SCREAMING_SNAKE_CASE     ,     feed_forward_proj=__SCREAMING_SNAKE_CASE     ,     dropout_rate=__SCREAMING_SNAKE_CASE     ,     is_decoder=__SCREAMING_SNAKE_CASE     ,     is_encoder_decoder=__SCREAMING_SNAKE_CASE     ,     )
							A			=					nn.ModuleList()
							for lyr_num in range(__SCREAMING_SNAKE_CASE):
												A			=					TaBlock(__SCREAMING_SNAKE_CASE)
												self.encoders.append(__SCREAMING_SNAKE_CASE)

							A			=					TaLayerNorm(__SCREAMING_SNAKE_CASE)
							A			=					nn.Dropout(p=__SCREAMING_SNAKE_CASE)


		def 					SCREAMING_SNAKE_CASE__   (self :		int     ,     __SCREAMING_SNAKE_CASE :		Any     ,     __SCREAMING_SNAKE_CASE :		str):
							A			=					self.input_proj(__SCREAMING_SNAKE_CASE)

							# terminal relative positional encodings
							A			=					encoder_inputs.shape[1]
							A			=					torch.arange(__SCREAMING_SNAKE_CASE     ,     device=encoder_inputs.device)

							A			=					encoder_inputs_mask.sum(-1)
							A			=					torch.roll(input_positions.unsqueeze(0)     ,     tuple(seq_lens.tolist())     ,     dims=0)
							x += self.position_encoding(__SCREAMING_SNAKE_CASE)

							A			=					self.dropout_pre(__SCREAMING_SNAKE_CASE)

							# inverted the attention mask
							A			=					encoder_inputs.size()
							A			=					self.get_extended_attention_mask(__SCREAMING_SNAKE_CASE     ,     __SCREAMING_SNAKE_CASE)

							for lyr in self.encoders:
												A			=					lyr(__SCREAMING_SNAKE_CASE     ,     __SCREAMING_SNAKE_CASE)[0]
							A			=					self.layer_norm(__SCREAMING_SNAKE_CASE)

							return self.dropout_post(__SCREAMING_SNAKE_CASE), encoder_inputs_mask





