from __future__ import annotations

from collections.abc import Callable
from typing import Any, Generic, TypeVar

SCREAMING_SNAKE_CASE :	Union[str, Any]							     =		TypeVar("""T""")
class    A_ (       Generic[T] ):

		def __init__(		self :      str     ,							__SCREAMING_SNAKE_CASE :      list[T]     ,							__SCREAMING_SNAKE_CASE :      Callable[[T, T], T]       ):
			__a       =  None

			__a       =  len(__SCREAMING_SNAKE_CASE       )
			__a       =  [any_type for _ in range(self.N       )] + arr
			__a       =  fnc
			self.build()

		def 		_UpperCAmelCase   (		self :      Tuple       ):
			for p in range(self.N - 1     ,							0     ,							-1       ):
				__a       =  self.fn(self.st[p * 2]     ,							self.st[p * 2 + 1]       )

		def 		_UpperCAmelCase   (		self :      Any     ,							__SCREAMING_SNAKE_CASE :      int     ,							__SCREAMING_SNAKE_CASE :      T       ):
			p += self.N
			__a       =  v
			while p > 1:
				__a       =  p // 2
				__a       =  self.fn(self.st[p * 2]     ,							self.st[p * 2 + 1]       )

		def 		_UpperCAmelCase   (		self :      List[Any]     ,							__SCREAMING_SNAKE_CASE :      int     ,							__SCREAMING_SNAKE_CASE :      int       ):  # noqa: E741
			__a       ,     __a       =  l + self.N, r + self.N

			__a       =  None
			while l <= r:
				if l % 2 == 1:
					__a       =  self.st[l] if res is None else self.fn(__SCREAMING_SNAKE_CASE     ,							self.st[l]       )
				if r % 2 == 0:
					__a       =  self.st[r] if res is None else self.fn(__SCREAMING_SNAKE_CASE     ,							self.st[r]       )
				__a       ,     __a       =  (l + 1) // 2, (r - 1) // 2
			return res


if __name__ == "__main__":
		from functools import reduce

		SCREAMING_SNAKE_CASE :	Any							     =		[1, 10, -2, 9, -3, 8, 4, -7, 5, 6, 11, -12]

		SCREAMING_SNAKE_CASE :	Optional[Any]							     =		{
		    0: 7,
		    1: 2,
		    2: 6,
		    3: -14,
		    4: 5,
		    5: 4,
		    6: 7,
		    7: -10,
		    8: 9,
		    9: 10,
		    10: 12,
		    11: 1,
		}

		SCREAMING_SNAKE_CASE :	Tuple							     =		SegmentTree(test_array, min)
		SCREAMING_SNAKE_CASE :	Optional[int]							     =		SegmentTree(test_array, max)
		SCREAMING_SNAKE_CASE :	int							     =		SegmentTree(test_array, lambda a, b: a + b)



		def   __A		(			):



			"""simple docstring"""


			for i in range(len(_A					)					):
				for j in range(_A							,				len(_A					)					):
					__a       =  reduce(_A							,				test_array[i : j + 1]					)
					__a       =  reduce(_A							,				test_array[i : j + 1]					)
					__a       =  reduce(lambda _A							,				_A					: a + b							,				test_array[i : j + 1]					)
					assert min_range == min_segment_tree.query(_A							,				_A					)
					assert max_range == max_segment_tree.query(_A							,				_A					)
					assert sum_range == sum_segment_tree.query(_A							,				_A					)

		test_all_segments()

		for index, value in test_updates.items():
				SCREAMING_SNAKE_CASE :	Dict							     =		value
				min_segment_tree.update(index, value)
				max_segment_tree.update(index, value)
				sum_segment_tree.update(index, value)
				test_all_segments()



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_ (       a_			,       a_			,       a_ ):

  @register_to_config
  def __init__(		self :      List[Any]     ,							__SCREAMING_SNAKE_CASE :      int     ,							__SCREAMING_SNAKE_CASE :      int     ,							__SCREAMING_SNAKE_CASE :      int     ,							__SCREAMING_SNAKE_CASE :      float     ,							__SCREAMING_SNAKE_CASE :      int     ,							__SCREAMING_SNAKE_CASE :      int     ,							__SCREAMING_SNAKE_CASE :      int     ,							__SCREAMING_SNAKE_CASE :      int     ,							__SCREAMING_SNAKE_CASE :      str     ,							__SCREAMING_SNAKE_CASE :      bool = False     ,							):
   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 		_UpperCAmelCase   (		self :      int     ,							__SCREAMING_SNAKE_CASE :      List[str]     ,							__SCREAMING_SNAKE_CASE :      Any       ):
   __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


