


"""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					(     _UpperCamelCase   , _UpperCamelCase   , _UpperCamelCase):







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





						'''simple docstring'''







						super().__init__()

						_A         =	nn.Linear(a__			,      a__			,      bias=a__						)

						_A         =	nn.Embedding(a__			,      a__						)
						_A         =	False

						_A         =	nn.Dropout(p=a__						)

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

						_A         =	TaLayerNorm(a__						)
						_A         =	nn.Dropout(p=a__						)







		def        a_       (    self					:				Union[str, Any]			,      a__					:				Any			,      a__					:				Optional[Any]						)				->  int:





						'''simple docstring'''







						_A         =	self.input_proj(a__						)

						# terminal relative positional encodings
						_A         =	encoder_inputs.shape[1]
						_A         =	torch.arange(a__			,      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(a__						)

						_A         =	self.dropout_pre(a__						)

						# inverted the attention mask
						_A         =	encoder_inputs.size()
						_A         =	self.get_extended_attention_mask(a__			,      a__						)

						for lyr in self.encoders:
										_A         =	lyr(a__			,      a__						)[0]
						_A         =	self.layer_norm(a__						)

						return self.dropout_post(a__						), encoder_inputs_mask



"""simple docstring"""


def    a__       ( __lowercase = 20      )		->		int:

				try:
								_A         =	int(__lowercase      )
				except (TypeError, ValueError):
								raise TypeError("Parameter n must be int or castable to int."      )
				if n <= 0:
								raise ValueError("Parameter n must be greater than or equal to one."      )
				_A         =	0
				while 1:
								i += n * (n - 1)
								_A         =	0
								for j in range(2   ,	__lowercase      ):
												if i % j != 0:
																_A         =	1
																break
								if nfound == 0:
												if i == 0:
																_A         =	1
												return i
				return None


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