





'''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 				UpperCAmelCase  (    snake_case_							,	snake_case_							,	snake_case_     ):




      @register_to_config
      def __init__(    self    :				Dict    ,    __snake_case    :				int    ,    __snake_case    :				int    ,    __snake_case    :				int    ,    __snake_case    :				float    ,    __snake_case    :				int    ,    __snake_case    :				int    ,    __snake_case    :				int    ,    __snake_case    :				int    ,    __snake_case    :				str    ,    __snake_case    :				bool = False    ,    )       ->   Dict:
            super().__init__()

            _lowerCAmelCase							  =							nn.Linear(__snake_case    ,    __snake_case    ,    bias=__snake_case						)

            _lowerCAmelCase							  =							nn.Embedding(__snake_case    ,    __snake_case						)
            _lowerCAmelCase							  =							False

            _lowerCAmelCase							  =							nn.Dropout(p=__snake_case						)

            _lowerCAmelCase							  =							TaConfig(
                d_model=__snake_case    ,    num_heads=__snake_case    ,    d_kv=__snake_case    ,    d_ff=__snake_case    ,    feed_forward_proj=__snake_case    ,    dropout_rate=__snake_case    ,    is_decoder=__snake_case    ,    is_encoder_decoder=__snake_case    ,    )
            _lowerCAmelCase							  =							nn.ModuleList()
            for lyr_num in range(__snake_case						):
                  _lowerCAmelCase							  =							TaBlock(__snake_case						)
                  self.encoders.append(__snake_case						)

            _lowerCAmelCase							  =							TaLayerNorm(__snake_case						)
            _lowerCAmelCase							  =							nn.Dropout(p=__snake_case						)




      def        lowercase__					(    self    :				Union[str, Any]    ,    __snake_case    :				Optional[int]    ,    __snake_case    :				List[Any]						)       ->   Optional[Any]:
            _lowerCAmelCase							  =							self.input_proj(__snake_case						)

            # terminal relative positional encodings
            _lowerCAmelCase							  =							encoder_inputs.shape[1]
            _lowerCAmelCase							  =							torch.arange(__snake_case    ,    device=encoder_inputs.device						)

            _lowerCAmelCase							  =							encoder_inputs_mask.sum(-1						)
            _lowerCAmelCase							  =							torch.roll(input_positions.unsqueeze(0						)    ,    tuple(seq_lens.tolist()						)    ,    dims=0						)
            x += self.position_encoding(__snake_case						)

            _lowerCAmelCase							  =							self.dropout_pre(__snake_case						)

            # inverted the attention mask
            _lowerCAmelCase							  =							encoder_inputs.size()
            _lowerCAmelCase							  =							self.get_extended_attention_mask(__snake_case    ,    __snake_case						)

            for lyr in self.encoders:
                  _lowerCAmelCase							  =							lyr(__snake_case    ,    __snake_case						)[0]
            _lowerCAmelCase							  =							self.layer_norm(__snake_case						)

            return self.dropout_post(__snake_case						), encoder_inputs_mask













'''simple docstring'''






class 				UpperCAmelCase  :




      def __init__(    self    :				Optional[Any]						)       ->   List[str]:
            _lowerCAmelCase							  =							[0]
            _lowerCAmelCase							  =							0




      def        lowercase__					(    self    :				str    ,    __snake_case    :				int						)       ->   None:
            _lowerCAmelCase							  =							self.__heap[i]
            while i // 2 > 0:
                  if self.__heap[i] > self.__heap[i // 2]:
                        _lowerCAmelCase							  =							self.__heap[i // 2]
                        _lowerCAmelCase							  =							temporary
                  i //= 2




      def        lowercase__					(    self    :				Optional[Any]    ,    __snake_case    :				int						)       ->   None:
            self.__heap.append(__snake_case						)
            self.__size += 1
            self.__swap_up(self.__size						)




      def        lowercase__					(    self    :				str    ,    __snake_case    :				int						)       ->   None:
            while self.__size >= 2 * i:
                  if 2 * i + 1 > self.__size:
                        _lowerCAmelCase							  =							2 * i
                  else:
                        if self.__heap[2 * i] > self.__heap[2 * i + 1]:
                              _lowerCAmelCase							  =							2 * i
                        else:
                              _lowerCAmelCase							  =							2 * i + 1
                  _lowerCAmelCase							  =							self.__heap[i]
                  if self.__heap[i] < self.__heap[bigger_child]:
                        _lowerCAmelCase							  =							self.__heap[bigger_child]
                        _lowerCAmelCase							  =							temporary
                  _lowerCAmelCase							  =							bigger_child




      def        lowercase__					(    self    :				Tuple						)       ->   int:
            _lowerCAmelCase							  =							self.__heap[1]
            _lowerCAmelCase							  =							self.__heap[self.__size]
            self.__size -= 1
            self.__heap.pop()
            self.__swap_down(1						)
            return max_value




      @property
      def        lowercase__					(    self    :				List[Any]						)       ->   str:
            return self.__heap[1:]




      def __len__(    self    :				Optional[Any]						)       ->   str:
            return self.__size


if __name__ == "__main__":
      import doctest

      doctest.testmod()
      # create an instance of BinaryHeap
      A__       :     Tuple          =BinaryHeap()
      binary_heap.insert(6)
      binary_heap.insert(10)
      binary_heap.insert(15)
      binary_heap.insert(12)
      # pop root(max-values because it is max heap)
      print(binary_heap.pop())  # 15
      print(binary_heap.pop())  # 12
      # get the list and size after operations
      print(binary_heap.get_list)
      print(len(binary_heap))






