
"""simple docstring"""




from __future__ import annotations

a		:       int     						=       [-10, -5, 0, 5, 5.1, 11, 13, 21, 3, 4, -21, -10, -5, -1, 0]
a		:       Dict     						=       [-5, 0, 5, 5.1, 11, 13, 21, -1, 4, -1, -10, -5, -1, 0, -1]



def     __magic_name__		(					UpperCamelCase     :		list[float]     )     ->						list[float]:
    a__		=			[]
    a__		=			len(UpperCamelCase     )

    for i in range(UpperCamelCase     ):
        a__		=			-1
        for j in range(i + 1							,		UpperCamelCase     ):
            if arr[i] < arr[j]:
                a__		=			arr[j]
                break
        result.append(UpperCamelCase     )
    return result



def     __magic_name__		(					UpperCamelCase     :		list[float]     )     ->						list[float]:
    a__		=			[]
    for i, outer in enumerate(UpperCamelCase     ):
        a__		=			-1
        for inner in arr[i + 1 :]:
            if outer < inner:
                a__		=			inner
                break
        result.append(UpperCamelCase     )
    return result



def     __magic_name__		(					UpperCamelCase     :		list[float]     )     ->						list[float]:
    a__		=			len(UpperCamelCase     )
    a__		=			[]
    a__		=			[-1] * arr_size

    for index in reversed(range(UpperCamelCase     )     ):
        if stack:
            while stack[-1] <= arr[index]:
                stack.pop()
                if not stack:
                    break
        if stack:
            a__		=			stack[-1]
        stack.append(arr[index]     )
    return result


if __name__ == "__main__":
    from doctest import testmod
    from timeit import timeit

    testmod()
    print(next_greatest_element_slow(arr))
    print(next_greatest_element_fast(arr))
    print(next_greatest_element(arr))

    a		:       Optional[Any]     						=       (
        'from __main__ import arr, next_greatest_element_slow, '
        'next_greatest_element_fast, next_greatest_element'
    )
    print(
        'next_greatest_element_slow():',
        timeit('next_greatest_element_slow(arr)', setup=setup),
    )
    print(
        'next_greatest_element_fast():',
        timeit('next_greatest_element_fast(arr)', setup=setup),
    )
    print(
        '     next_greatest_element():',
        timeit('next_greatest_element(arr)', setup=setup),
    )




"""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     lowercase(_lowercase    ,						_lowercase    ,						_lowercase       ):



     @register_to_config
     def __init__(							self     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE = False     ,				)		->     Union[str, Any]:







         """simple docstring"""



         super().__init__()

         a__		=			nn.Embedding(__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE   )

         a__		=			nn.Embedding(__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE   )
         a__		=			False

         a__		=			nn.Dropout(p=__SCREAMING_SNAKE_CASE   )

         a__		=			TaConfig(
             vocab_size=__SCREAMING_SNAKE_CASE     ,				d_model=__SCREAMING_SNAKE_CASE     ,				num_heads=__SCREAMING_SNAKE_CASE     ,				d_kv=__SCREAMING_SNAKE_CASE     ,				d_ff=__SCREAMING_SNAKE_CASE     ,				dropout_rate=__SCREAMING_SNAKE_CASE     ,				feed_forward_proj=__SCREAMING_SNAKE_CASE     ,				is_decoder=__SCREAMING_SNAKE_CASE     ,				is_encoder_decoder=__SCREAMING_SNAKE_CASE     ,				)

         a__		=			nn.ModuleList()
         for lyr_num in range(__SCREAMING_SNAKE_CASE   ):
             a__		=			TaBlock(__SCREAMING_SNAKE_CASE   )
             self.encoders.append(__SCREAMING_SNAKE_CASE   )

         a__		=			TaLayerNorm(__SCREAMING_SNAKE_CASE   )
         a__		=			nn.Dropout(p=__SCREAMING_SNAKE_CASE   )




     def 			lowercase__  (							self     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE   )		->     List[Any]:







         """simple docstring"""



         a__		=			self.token_embedder(__SCREAMING_SNAKE_CASE   )

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

         a__		=			self.dropout_pre(__SCREAMING_SNAKE_CASE   )

         # inverted the attention mask
         a__		=			encoder_input_tokens.size()
         a__		=			self.get_extended_attention_mask(__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE   )

         for lyr in self.encoders:
             a__		=			lyr(__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE   )[0]
         a__		=			self.layer_norm(__SCREAMING_SNAKE_CASE   )

         return self.dropout_post(__SCREAMING_SNAKE_CASE   ), encoder_inputs_mask


