





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_		(			__lowerCAmelCase						,					__lowerCAmelCase						,					__lowerCAmelCase):







		@register_to_config
		def __init__(		self			,     __lowercase			,     __lowercase			,     __lowercase			,     __lowercase			,     __lowercase			,     __lowercase			,     __lowercase			,     __lowercase			,     __lowercase			,     __lowercase = False			,     )				->   Tuple:
				super().__init__()

				lowerCamelCase   : Optional[Any]								=nn.Embedding(lowerCAmelCase_			,     lowerCAmelCase_   )

				lowerCamelCase   : Optional[int]								=nn.Embedding(lowerCAmelCase_			,     lowerCAmelCase_   )
				lowerCamelCase   : int								=False

				lowerCamelCase   : Dict								=nn.Dropout(p=lowerCAmelCase_   )

				lowerCamelCase   : List[str]								=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_			,     )

				lowerCamelCase   : List[str]								=nn.ModuleList()
				for lyr_num in range(lowerCAmelCase_   ):
						lowerCamelCase   : int								=TaBlock(lowerCAmelCase_   )
						self.encoders.append(lowerCAmelCase_   )

				lowerCamelCase   : Optional[int]								=TaLayerNorm(lowerCAmelCase_   )
				lowerCamelCase   : Union[str, Any]								=nn.Dropout(p=lowerCAmelCase_   )







		def 			__lowercase   (		self			,     __lowercase			,     __lowercase   )				->   List[Any]:
				lowerCamelCase   : List[Any]								=self.token_embedder(lowerCAmelCase_   )

				lowerCamelCase   : str								=encoder_input_tokens.shape[1]
				lowerCamelCase   : int								=torch.arange(lowerCAmelCase_			,     device=encoder_input_tokens.device   )
				x += self.position_encoding(lowerCAmelCase_   )

				lowerCamelCase   : int								=self.dropout_pre(lowerCAmelCase_   )

				# inverted the attention mask
				lowerCamelCase   : Dict								=encoder_input_tokens.size()
				lowerCamelCase   : str								=self.get_extended_attention_mask(lowerCAmelCase_			,     lowerCAmelCase_   )

				for lyr in self.encoders:
						lowerCamelCase   : Union[str, Any]								=lyr(lowerCAmelCase_			,     lowerCAmelCase_   )[0]
				lowerCamelCase   : str								=self.layer_norm(lowerCAmelCase_   )

				return self.dropout_post(lowerCAmelCase_   ), encoder_inputs_mask








from __future__ import annotations


def 					A__	(       SCREAMING_SNAKE_CASE_		)   ->     bool:
		if len(SCREAMING_SNAKE_CASE_		) < 2:
				raise ValueError('''Monogons and Digons are not polygons in the Euclidean space'''		)
		if any(i <= 0 for i in nums		):
				raise ValueError('''All values must be greater than 0'''		)
		lowerCamelCase   : List[Any]								=nums.copy()
		copy_nums.sort()
		return copy_nums[-1] < sum(copy_nums[:-1]		)


if __name__ == "__main__":
				import doctest

				doctest.testmod()

