



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     __lowerCAmelCase					(    A       ,		A       ,		A		):



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



						"""simple docstring"""






						super().__init__()

						_UpperCAmelCase	 =     nn.Embedding(A				,	A)

						_UpperCAmelCase	 =     nn.Embedding(A				,	A)
						_UpperCAmelCase	 =     False

						_UpperCAmelCase	 =     nn.Dropout(p=A)

						_UpperCAmelCase	 =     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				,	)

						_UpperCAmelCase	 =     nn.ModuleList()
						for lyr_num in range(A):
									_UpperCAmelCase	 =     TaBlock(A)
									self.encoders.append(A)

						_UpperCAmelCase	 =     TaLayerNorm(A)
						_UpperCAmelCase	 =     nn.Dropout(p=A)



			def 					_lowerCamelCase (		self     :						Dict				,	A     :						List[Any]				,	A     :						List[str])     ->					Any:



						"""simple docstring"""






						_UpperCAmelCase	 =     self.token_embedder(A)

						_UpperCAmelCase	 =     encoder_input_tokens.shape[1]
						_UpperCAmelCase	 =     torch.arange(A				,	device=encoder_input_tokens.device)
						x += self.position_encoding(A)

						_UpperCAmelCase	 =     self.dropout_pre(A)

						# inverted the attention mask
						_UpperCAmelCase	 =     encoder_input_tokens.size()
						_UpperCAmelCase	 =     self.get_extended_attention_mask(A				,	A)

						for lyr in self.encoders:
									_UpperCAmelCase	 =     lyr(A				,	A)[0]
						_UpperCAmelCase	 =     self.layer_norm(A)

						return self.dropout_post(A), encoder_inputs_mask





import unittest

from knapsack import knapsack as k







class     __lowerCAmelCase					(    unittest.TestCase		):



			def 					_lowerCamelCase (		self     :						Optional[Any])     ->					Any:



						"""simple docstring"""






						_UpperCAmelCase	 =     0
						_UpperCAmelCase	 =     [0]
						_UpperCAmelCase	 =     [0]
						_UpperCAmelCase	 =     len(A)
						self.assertEqual(k.knapsack(A				,	A				,	A				,	A)				,	0)

						_UpperCAmelCase	 =     [60]
						_UpperCAmelCase	 =     [10]
						_UpperCAmelCase	 =     len(A)
						self.assertEqual(k.knapsack(A				,	A				,	A				,	A)				,	0)



			def 					_lowerCamelCase (		self     :						str)     ->					List[str]:



						"""simple docstring"""






						_UpperCAmelCase	 =     3
						_UpperCAmelCase	 =     [1, 2, 3]
						_UpperCAmelCase	 =     [3, 2, 1]
						_UpperCAmelCase	 =     len(A)
						self.assertEqual(k.knapsack(A				,	A				,	A				,	A)				,	5)



			def 					_lowerCamelCase (		self     :						Tuple)     ->					Tuple:



						"""simple docstring"""






						_UpperCAmelCase	 =     50
						_UpperCAmelCase	 =     [60, 1_00, 1_20]
						_UpperCAmelCase	 =     [10, 20, 30]
						_UpperCAmelCase	 =     len(A)
						self.assertEqual(k.knapsack(A				,	A				,	A				,	A)				,	2_20)


if __name__ == "__main__":
							unittest.main()
