


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							(      _UpperCAmelCase  ,			_UpperCAmelCase  ,			_UpperCAmelCase):
					@register_to_config
					def __init__(	self					:	int  ,			_lowerCAmelCase					:	int  ,			_lowerCAmelCase					:	int  ,			_lowerCAmelCase					:	int  ,			_lowerCAmelCase					:	float  ,			_lowerCAmelCase					:	int  ,			_lowerCAmelCase					:	int  ,			_lowerCAmelCase					:	int  ,			_lowerCAmelCase					:	int  ,			_lowerCAmelCase					:	str  ,			_lowerCAmelCase					:	bool = False  ,			)       ->				Dict:



											'''simple docstring'''





											super().__init__()

											__magic_name__ 		=			nn.Linear(_lowerCAmelCase  ,			_lowerCAmelCase  ,			bias=_lowerCAmelCase     )

											__magic_name__ 		=			nn.Embedding(_lowerCAmelCase  ,			_lowerCAmelCase     )
											__magic_name__ 		=			False

											__magic_name__ 		=			nn.Dropout(p=_lowerCAmelCase     )

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

											__magic_name__ 		=			TaLayerNorm(_lowerCAmelCase     )
											__magic_name__ 		=			nn.Dropout(p=_lowerCAmelCase     )







					def   a (	self					:	Union[str, Any]  ,			_lowerCAmelCase					:	List[Any]  ,			_lowerCAmelCase					:	Union[str, Any]     )       ->				str:



											'''simple docstring'''





											__magic_name__ 		=			self.input_proj(_lowerCAmelCase     )

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

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

											__magic_name__ 		=			self.dropout_pre(_lowerCAmelCase     )

											# inverted the attention mask
											__magic_name__ 		=			encoder_inputs.size()
											__magic_name__ 		=			self.get_extended_attention_mask(_lowerCAmelCase  ,			_lowerCAmelCase     )

											for lyr in self.encoders:
																	__magic_name__ 		=			lyr(_lowerCAmelCase  ,			_lowerCAmelCase     )[0]
											__magic_name__ 		=			self.layer_norm(_lowerCAmelCase     )

											return self.dropout_post(_lowerCAmelCase     ), encoder_inputs_mask





def 							UpperCamelCase_  (				__snake_case			:						int     )->      bool:




						'''simple docstring'''
						if not isinstance(__snake_case	,			__snake_case     ):
												__magic_name__ 		=			F'''Input value of [number={number}] must be an integer'''
												raise TypeError(__snake_case     )
						if number < 0 or number % 2 == 1:
												return False
						__magic_name__ 		=			int(number**0.5     )
						return number == number_sqrt * (number_sqrt + 1)


if __name__ == "__main__":
					import doctest

					doctest.testmod()

