


'''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						(						UpperCamelCase    ,  UpperCamelCase    ,  UpperCamelCase							):



   '''simple docstring'''






   @register_to_config
   def __init__(	self	:					List[Any]					, lowerCamelCase	:					int					, lowerCamelCase	:					int					, lowerCamelCase	:					int					, lowerCamelCase	:					float					, lowerCamelCase	:					int					, lowerCamelCase	:					int					, lowerCamelCase	:					int					, lowerCamelCase	:					int					, lowerCamelCase	:					str					, lowerCamelCase	:					bool = False					, )->	Dict:
          super().__init__()

          snake_case__					:	Any          =					nn.Linear(lowerCamelCase					, lowerCamelCase					, bias=lowerCamelCase						)

          snake_case__					:	List[str]          =					nn.Embedding(lowerCamelCase					, lowerCamelCase						)
          snake_case__					:	Tuple          =					False

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

          snake_case__					:	Union[str, Any]          =					TaConfig(
              d_model=lowerCamelCase					, num_heads=lowerCamelCase					, d_kv=lowerCamelCase					, d_ff=lowerCamelCase					, feed_forward_proj=lowerCamelCase					, dropout_rate=lowerCamelCase					, is_decoder=lowerCamelCase					, is_encoder_decoder=lowerCamelCase					, )
          snake_case__					:	int          =					nn.ModuleList()
          for lyr_num in range(lowerCamelCase						):
                 snake_case__					:	Dict          =					TaBlock(lowerCamelCase						)
                 self.encoders.append(lowerCamelCase						)

          snake_case__					:	str          =					TaLayerNorm(lowerCamelCase						)
          snake_case__					:	Optional[Any]          =					nn.Dropout(p=lowerCamelCase						)




   def   __lowerCAmelCase						(	self	:					Optional[Any]					, lowerCamelCase	:					List[Any]					, lowerCamelCase	:					List[str]						)->	List[str]:
          snake_case__					:	Union[str, Any]          =					self.input_proj(lowerCamelCase						)

          # terminal relative positional encodings
          snake_case__					:	Optional[int]          =					encoder_inputs.shape[1]
          snake_case__					:	Union[str, Any]          =					torch.arange(lowerCamelCase					, device=encoder_inputs.device						)

          snake_case__					:	Optional[int]          =					encoder_inputs_mask.sum(-1						)
          snake_case__					:	Dict          =					torch.roll(input_positions.unsqueeze(0						)					, tuple(seq_lens.tolist()						)					, dims=0						)
          x += self.position_encoding(lowerCamelCase						)

          snake_case__					:	Tuple          =					self.dropout_pre(lowerCamelCase						)

          # inverted the attention mask
          snake_case__					:	List[Any]          =					encoder_inputs.size()
          snake_case__					:	Tuple          =					self.get_extended_attention_mask(lowerCamelCase					, lowerCamelCase						)

          for lyr in self.encoders:
                 snake_case__					:	Dict          =					lyr(lowerCamelCase					, lowerCamelCase						)[0]
          snake_case__					:	List[str]          =					self.layer_norm(lowerCamelCase						)

          return self.dropout_post(lowerCamelCase						), encoder_inputs_mask








'''simple docstring'''
import unittest
from timeit import timeit



def 				lowerCAmelCase__	(       UpperCAmelCase    ,       UpperCAmelCase     ):






       """simple docstring"""





       snake_case__					:	Any          =					first_num if first_num >= second_num else second_num
       snake_case__					:	Optional[Any]          =					max_num
       while (common_mult % first_num > 0) or (common_mult % second_num > 0):
              common_mult += max_num
       return common_mult



def 				lowerCAmelCase__	(       UpperCAmelCase    ,       UpperCAmelCase     ):






       """simple docstring"""





       return b if a == 0 else greatest_common_divisor(b % a    ,       UpperCAmelCase     )



def 				lowerCAmelCase__	(       UpperCAmelCase    ,       UpperCAmelCase     ):






       """simple docstring"""





       return first_num // greatest_common_divisor(UpperCAmelCase    ,       UpperCAmelCase     ) * second_num



def 				lowerCAmelCase__	(       ):






       """simple docstring"""





       snake_case__					:	Tuple          =					(
           """from __main__ import least_common_multiple_slow, least_common_multiple_fast"""
       )
       print(
           """least_common_multiple_slow():"""    ,       timeit("""least_common_multiple_slow(1000, 999)"""    ,       setup=UpperCAmelCase     )    ,       )
       print(
           """least_common_multiple_fast():"""    ,       timeit("""least_common_multiple_fast(1000, 999)"""    ,       setup=UpperCAmelCase     )    ,       )







class 					_A						(						unittest.TestCase							):



   '''simple docstring'''






   _lowercase    						= (
       (10, 20),
       (13, 15),
       (4, 31),
       (10, 42),
       (43, 34),
       (5, 12),
       (12, 25),
       (10, 25),
       (6, 9),
   )
   _lowercase    						= (20, 195, 124, 210, 1462, 60, 300, 50, 18)




   def   __lowerCAmelCase						(	self	:					Optional[int]						)->	Optional[Any]:
          for i, (first_num, second_num) in enumerate(self.test_inputs						):
                 snake_case__					:	List[Any]          =					least_common_multiple_slow(lowerCamelCase					, lowerCamelCase						)
                 snake_case__					:	Optional[Any]          =					least_common_multiple_fast(lowerCamelCase					, lowerCamelCase						)
                 with self.subTest(i=lowerCamelCase						):
                        self.assertEqual(lowerCamelCase					, self.expected_results[i]						)
                        self.assertEqual(lowerCamelCase					, self.expected_results[i]						)


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




