





from __future__ import annotations

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

UpperCamelCase_		:				List[str]				=						TypeVar('''T''')


class 			lowercase				(							Generic[T]     ):



						"""simple docstring"""

						def __init__(    self			:		str ,				lowerCamelCase_			:		list[T] ,				lowerCamelCase_			:		Callable[[T, T], T]					):





										'''simple docstring'''







										_snake_case :			Any | T  						=   None

										_snake_case :			int  						=   len(lowerCamelCase_					)
										_snake_case :			list[T]  						=   [any_type for _ in range(self.N					)] + arr
										_snake_case :			int  						=   fnc
										self.build()







						def 		__UpperCAmelCase       (    self			:		Dict					):





										'''simple docstring'''







										for p in range(self.N - 1 ,				0 ,				-1					):
														_snake_case :			Optional[int]  						=   self.fn(self.st[p * 2] ,				self.st[p * 2 + 1]					)







						def 		__UpperCAmelCase       (    self			:		Any ,				lowerCamelCase_			:		int ,				lowerCamelCase_			:		T					):





										'''simple docstring'''







										p += self.N
										_snake_case :			Any  						=   v
										while p > 1:
														_snake_case :			Union[str, Any]  						=   p // 2
														_snake_case :			Any  						=   self.fn(self.st[p * 2] ,				self.st[p * 2 + 1]					)







						def 		__UpperCAmelCase       (    self			:		str ,				lowerCamelCase_			:		int ,				lowerCamelCase_			:		int					):  # noqa: E741





										'''simple docstring'''







										_snake_case :			Union[str, Any]  						=   l + self.N, r + self.N

										_snake_case :			T | None  						=   None
										while l <= r:
														if l % 2 == 1:
																		_snake_case :			int  						=   self.st[l] if res is None else self.fn(lowerCamelCase_ ,				self.st[l]					)
														if r % 2 == 0:
																		_snake_case :			Any  						=   self.st[r] if res is None else self.fn(lowerCamelCase_ ,				self.st[r]					)
														_snake_case :			Dict  						=   (l + 1) // 2, (r - 1) // 2
										return res


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

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

		UpperCamelCase_		:				List[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,
		}

		UpperCamelCase_		:				Optional[Any]				=						SegmentTree(test_array, min)
		UpperCamelCase_		:				Dict				=						SegmentTree(test_array, max)
		UpperCamelCase_		:				Optional[int]				=						SegmentTree(test_array, lambda a, b: a + b)

		def       A__(		):
						for i in range(len(__lowerCAmelCase ) ):
										for j in range(__lowerCAmelCase	,			len(__lowerCAmelCase ) ):
														_snake_case :			Any  						=   reduce(__lowerCAmelCase	,			test_array[i : j + 1] )
														_snake_case :			str  						=   reduce(__lowerCAmelCase	,			test_array[i : j + 1] )
														_snake_case :			Optional[Any]  						=   reduce(lambda __lowerCAmelCase	,			__lowerCAmelCase : a + b	,			test_array[i : j + 1] )
														assert min_range == min_segment_tree.query(__lowerCAmelCase	,			__lowerCAmelCase )
														assert max_range == max_segment_tree.query(__lowerCAmelCase	,			__lowerCAmelCase )
														assert sum_range == sum_segment_tree.query(__lowerCAmelCase	,			__lowerCAmelCase )

		test_all_segments()

		for index, value in test_updates.items():
				UpperCamelCase_		:				Union[str, Any]				=						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 			lowercase				(							a_		,			a_		,			a_     ):



						"""simple docstring"""

						@register_to_config
						def __init__(    self			:		Any ,				lowerCamelCase_			:		int ,				lowerCamelCase_			:		int ,				lowerCamelCase_			:		int ,				lowerCamelCase_			:		float ,				lowerCamelCase_			:		int ,				lowerCamelCase_			:		int ,				lowerCamelCase_			:		int ,				lowerCamelCase_			:		int ,				lowerCamelCase_			:		str ,				lowerCamelCase_			:		bool = False ,				):





										'''simple docstring'''







										super().__init__()

										_snake_case :			Optional[int]  						=   nn.Embedding(lowerCamelCase_ ,				lowerCamelCase_					)

										_snake_case :			List[str]  						=   nn.Embedding(lowerCamelCase_ ,				lowerCamelCase_					)
										_snake_case :			Union[str, Any]  						=   False

										_snake_case :			Tuple  						=   nn.Dropout(p=lowerCamelCase_					)

										_snake_case :			Union[str, Any]  						=   TaConfig(
										    vocab_size=lowerCamelCase_ ,				d_model=lowerCamelCase_ ,				num_heads=lowerCamelCase_ ,				d_kv=lowerCamelCase_ ,				d_ff=lowerCamelCase_ ,				dropout_rate=lowerCamelCase_ ,				feed_forward_proj=lowerCamelCase_ ,				is_decoder=lowerCamelCase_ ,				is_encoder_decoder=lowerCamelCase_ ,				)

										_snake_case :			Union[str, Any]  						=   nn.ModuleList()
										for lyr_num in range(lowerCamelCase_					):
														_snake_case :			Any  						=   TaBlock(lowerCamelCase_					)
														self.encoders.append(lowerCamelCase_					)

										_snake_case :			Tuple  						=   TaLayerNorm(lowerCamelCase_					)
										_snake_case :			List[str]  						=   nn.Dropout(p=lowerCamelCase_					)







						def 		__UpperCAmelCase       (    self			:		Optional[int] ,				lowerCamelCase_			:		int ,				lowerCamelCase_			:		Tuple					):





										'''simple docstring'''







										_snake_case :			Any  						=   self.token_embedder(lowerCamelCase_					)

										_snake_case :			List[Any]  						=   encoder_input_tokens.shape[1]
										_snake_case :			Any  						=   torch.arange(lowerCamelCase_ ,				device=encoder_input_tokens.device					)
										x += self.position_encoding(lowerCamelCase_					)

										_snake_case :			Tuple  						=   self.dropout_pre(lowerCamelCase_					)

										# inverted the attention mask
										_snake_case :			Dict  						=   encoder_input_tokens.size()
										_snake_case :			Optional[int]  						=   self.get_extended_attention_mask(lowerCamelCase_ ,				lowerCamelCase_					)

										for lyr in self.encoders:
														_snake_case :			str  						=   lyr(lowerCamelCase_ ,				lowerCamelCase_					)[0]
										_snake_case :			Any  						=   self.layer_norm(lowerCamelCase_					)

										return self.dropout_post(lowerCamelCase_					), encoder_inputs_mask






