


"""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 (							__snake_case,				__snake_case,				__snake_case				):






		'''simple docstring'''

		@register_to_config
		def __init__(self				,       a_				,       a_				,       a_				,       a_				,       a_				,       a_				,       a_				,       a_				,       a_				,       a_ = False				,       ):





			'''simple docstring'''

			super().__init__()

			__snake_case	:						Optional[int]						=				nn.Embedding(a_				,       a_					)

			__snake_case	:						Union[str, Any]						=				nn.Embedding(a_				,       a_					)
			__snake_case	:						List[str]						=				False

			__snake_case	:						List[Any]						=				nn.Dropout(p=a_					)

			__snake_case	:						Tuple						=				TaConfig(
			    vocab_size=a_				,       d_model=a_				,       num_heads=a_				,       d_kv=a_				,       d_ff=a_				,       dropout_rate=a_				,       feed_forward_proj=a_				,       is_decoder=a_				,       is_encoder_decoder=a_				,       )

			__snake_case	:						Union[str, Any]						=				nn.ModuleList()
			for lyr_num in range(a_					):
				__snake_case	:						Tuple						=				TaBlock(a_					)
				self.encoders.append(a_					)

			__snake_case	:						Optional[int]						=				TaLayerNorm(a_					)
			__snake_case	:						List[str]						=				nn.Dropout(p=a_					)



		def 				SCREAMING_SNAKE_CASE  (self				,       a_				,       a_					):





			'''simple docstring'''

			__snake_case	:						Optional[Any]						=				self.token_embedder(a_					)

			__snake_case	:						Any						=				encoder_input_tokens.shape[1]
			__snake_case	:						str						=				torch.arange(a_				,       device=encoder_input_tokens.device					)
			x += self.position_encoding(a_					)

			__snake_case	:						Any						=				self.dropout_pre(a_					)

			# inverted the attention mask
			__snake_case	:						Dict						=				encoder_input_tokens.size()
			__snake_case	:						Optional[int]						=				self.get_extended_attention_mask(a_				,       a_					)

			for lyr in self.encoders:
				__snake_case	:						Any						=				lyr(a_				,       a_					)[0]
			__snake_case	:						Dict						=				self.layer_norm(a_					)

			return self.dropout_post(a_					), encoder_inputs_mask









"""simple docstring"""
def 		lowercase							(     _snake_case						:      int     ,   _snake_case						:      int     )     ->str:
	"""simple docstring"""





	if a < 0 or b < 0:
		raise ValueError('''the value of both inputs must be positive'''     )
	__snake_case	:						Tuple						=				str(bin(_snake_case     )     )[2:]  # remove the leading "0b"
	__snake_case	:						List[Any]						=				str(bin(_snake_case     )     )[2:]
	__snake_case	:						Any						=				max(len(_snake_case     )     ,   len(_snake_case     )     )
	return "0b" + "".join(
	    str(int('''1''' in (char_a, char_b)     )     )
	    for char_a, char_b in zip(a_binary.zfill(_snake_case     )     ,   b_binary.zfill(_snake_case     )     )     )


if __name__ == "__main__":
			import doctest

			doctest.testmod()





