






"""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[int]	,	lowerCAmelCase_					: int	,	lowerCAmelCase_					: List[str]	,	lowerCAmelCase_					: Optional[Any]	,	lowerCAmelCase_					: Dict	,	lowerCAmelCase_					: Optional[int]	,	lowerCAmelCase_					: Dict	,	lowerCAmelCase_					: Any	,	lowerCAmelCase_					: Tuple	,	lowerCAmelCase_					: int	,	lowerCAmelCase_					: Optional[Any] = False	,	)						->			List[Any]:
				super().__init__()

				UpperCAmelCase_     : int			     =					nn.Embedding(_lowerCamelCase	,	_lowerCamelCase						)

				UpperCAmelCase_     : List[Any]			     =					nn.Embedding(_lowerCamelCase	,	_lowerCamelCase						)
				UpperCAmelCase_     : List[Any]			     =					False

				UpperCAmelCase_     : int			     =					nn.Dropout(p=_lowerCamelCase						)

				UpperCAmelCase_     : Optional[int]			     =					TaConfig(
				    vocab_size=_lowerCamelCase	,	d_model=_lowerCamelCase	,	num_heads=_lowerCamelCase	,	d_kv=_lowerCamelCase	,	d_ff=_lowerCamelCase	,	dropout_rate=_lowerCamelCase	,	feed_forward_proj=_lowerCamelCase	,	is_decoder=_lowerCamelCase	,	is_encoder_decoder=_lowerCamelCase	,	)

				UpperCAmelCase_     : int			     =					nn.ModuleList()
				for lyr_num in range(_lowerCamelCase						):
							UpperCAmelCase_     : Any			     =					TaBlock(_lowerCamelCase						)
							self.encoders.append(_lowerCamelCase						)

				UpperCAmelCase_     : Any			     =					TaLayerNorm(_lowerCamelCase						)
				UpperCAmelCase_     : List[str]			     =					nn.Dropout(p=_lowerCamelCase						)





	def      _SCREAMING_SNAKE_CASE      (      self					: Any	,	lowerCAmelCase_					: Optional[int]	,	lowerCAmelCase_					: Tuple						)						->			str:
				UpperCAmelCase_     : Union[str, Any]			     =					self.token_embedder(_lowerCamelCase						)

				UpperCAmelCase_     : Union[str, Any]			     =					encoder_input_tokens.shape[1]
				UpperCAmelCase_     : Dict			     =					torch.arange(_lowerCamelCase	,	device=encoder_input_tokens.device						)
				x += self.position_encoding(_lowerCamelCase						)

				UpperCAmelCase_     : int			     =					self.dropout_pre(_lowerCamelCase						)

				# inverted the attention mask
				UpperCAmelCase_     : List[str]			     =					encoder_input_tokens.size()
				UpperCAmelCase_     : List[Any]			     =					self.get_extended_attention_mask(_lowerCamelCase	,	_lowerCamelCase						)

				for lyr in self.encoders:
							UpperCAmelCase_     : Any			     =					lyr(_lowerCamelCase	,	_lowerCamelCase						)[0]
				UpperCAmelCase_     : str			     =					self.layer_norm(_lowerCamelCase						)

				return self.dropout_post(_lowerCamelCase						), encoder_inputs_mask











"""simple docstring"""



def 						_SCREAMING_SNAKE_CASE (    __snake_case			:  int = 1_00    ):





  '''simple docstring'''





  lowercase							    =       0
  lowercase							    =       0
  for i in range(1		,					n + 1    ):
    sum_of_squares += i**2
    sum_of_ints += i
  return sum_of_ints**2 - sum_of_squares


if __name__ == "__main__":
    print(F'''{solution() = }''')


