"""simple docstring"""







import unittest
from dataclasses import dataclass
from typing import Optional

from transformers.utils import ModelOutput

@dataclass
class 						UpperCamelCase     (       _SCREAMING_SNAKE_CASE		):
				UpperCAmelCase							: Tuple       =						42
				UpperCAmelCase							: str       =						None
				UpperCAmelCase							: Any       =						None







class 						UpperCamelCase     (       unittest.TestCase		):



				def    _lowercase	(self							: Any)       ->      Any:
					__snake_case     :				Tuple 						=					ModelOutputTest(a=30)
					self.assertEqual(x.a      ,    30)
					self.assertIsNone(x.b)
					self.assertIsNone(x.c)
					with self.assertRaises(__A):
						__snake_case     :				List[str] 						=					x.d



				def    _lowercase	(self							: Tuple)       ->      Union[str, Any]:
					__snake_case     :				Any 						=					ModelOutputTest(a=30      ,    b=10)
					self.assertEqual(x[0]      ,    30)
					self.assertEqual(x[1]      ,    10)
					self.assertEqual(x[:2]      ,    (30, 10))
					self.assertEqual(x[:]      ,    (30, 10))

					__snake_case     :				Dict 						=					ModelOutputTest(a=30      ,    c=10)
					self.assertEqual(x[0]      ,    30)
					self.assertEqual(x[1]      ,    10)
					self.assertEqual(x[:2]      ,    (30, 10))
					self.assertEqual(x[:]      ,    (30, 10))



				def    _lowercase	(self							: Union[str, Any])       ->      Optional[Any]:
					__snake_case     :				Optional[int] 						=					ModelOutputTest(a=30      ,    b=10)
					self.assertEqual(x['a']      ,    30)
					self.assertEqual(x['b']      ,    10)
					with self.assertRaises(__A):
						__snake_case     :				Any 						=					x['c']

					__snake_case     :				Optional[int] 						=					ModelOutputTest(a=30      ,    c=10)
					self.assertEqual(x['a']      ,    30)
					self.assertEqual(x['c']      ,    10)
					with self.assertRaises(__A):
						__snake_case     :				int 						=					x['b']



				def    _lowercase	(self							: Optional[Any])       ->      Optional[int]:
					__snake_case     :				int 						=					ModelOutputTest(a=30)
					self.assertEqual(list(x.keys())      ,    ['a'])
					self.assertEqual(list(x.values())      ,    [30])
					self.assertEqual(list(x.items())      ,    [('a', 30)])
					self.assertEqual(list(__A)      ,    ['a'])

					__snake_case     :				Optional[Any] 						=					ModelOutputTest(a=30      ,    b=10)
					self.assertEqual(list(x.keys())      ,    ['a', 'b'])
					self.assertEqual(list(x.values())      ,    [30, 10])
					self.assertEqual(list(x.items())      ,    [('a', 30), ('b', 10)])
					self.assertEqual(list(__A)      ,    ['a', 'b'])

					__snake_case     :				List[str] 						=					ModelOutputTest(a=30      ,    c=10)
					self.assertEqual(list(x.keys())      ,    ['a', 'c'])
					self.assertEqual(list(x.values())      ,    [30, 10])
					self.assertEqual(list(x.items())      ,    [('a', 30), ('c', 10)])
					self.assertEqual(list(__A)      ,    ['a', 'c'])

					with self.assertRaises(__A):
						__snake_case     :				Dict 						=					x.update({'d': 20})
					with self.assertRaises(__A):
						del x["a"]
					with self.assertRaises(__A):
						__snake_case     :				Optional[Any] 						=					x.pop('a')
					with self.assertRaises(__A):
						__snake_case     :				str 						=					x.setdefault('d'      ,    32)



				def    _lowercase	(self							: Optional[Any])       ->      Dict:
					__snake_case     :				Any 						=					ModelOutputTest(a=30)
					__snake_case     :				List[str] 						=					10
					self.assertEqual(x.a      ,    10)
					self.assertEqual(x['a']      ,    10)



				def    _lowercase	(self							: str)       ->      Tuple:
					__snake_case     :				Optional[int] 						=					ModelOutputTest(a=30)
					__snake_case     :				str 						=					10
					self.assertEqual(x.a      ,    10)
					self.assertEqual(x['a']      ,    10)



				def    _lowercase	(self							: List[Any])       ->      Optional[int]:
					__snake_case     :				Optional[Any] 						=					ModelOutputTest({'a': 30, 'b': 10})
					self.assertEqual(list(x.keys())      ,    ['a', 'b'])
					self.assertEqual(x.a      ,    30)
					self.assertEqual(x.b      ,    10)






				def    _lowercase	(self							: Tuple)       ->      List[str]:
					__snake_case     :				Optional[Any] 						=					ModelOutputTest([('a', 30), ('b', 10)])
					self.assertEqual(list(x.keys())      ,    ['a', 'b'])
					self.assertEqual(x.a      ,    30)
					self.assertEqual(x.b      ,    10)

					with self.assertRaises(__A):
						__snake_case     :				List[str] 						=					ModelOutputTest([('a', 30), (10, 10)])

					__snake_case     :				int 						=					ModelOutputTest(a=(30, 30))
					self.assertEqual(list(x.keys())      ,    ['a'])
					self.assertEqual(x.a      ,    (30, 30))









"""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  __A							(			_SCREAMING_SNAKE_CASE,       _SCREAMING_SNAKE_CASE,       _SCREAMING_SNAKE_CASE     ):






 """simple docstring"""




 @register_to_config
 def __init__(						self					,       __A					,       __A					,       __A					,       __A					,       __A					,       __A					,       __A					,       __A					,       __A					,       __A = False					,       )   ->	Optional[Any]:
     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  SCREAMING_SNAKE_CASE				(						self					,       __A					,       __A		)   ->	str:
     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