


'''simple docstring'''







import logging

import torch
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss

from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.albert.modeling_albert import (
    ALBERT_INPUTS_DOCSTRING,
    ALBERT_START_DOCSTRING,
    AlbertModel,
    AlbertPreTrainedModel,
    AlbertTransformer,
)


A   :  List[str]       		=				logging.getLogger(__name__)


class 				__lowerCamelCase				( a_ ):
    """simple docstring"""


    def      A					(						self    :							Any   ,							SCREAMING_SNAKE_CASE    :							Any   ,							SCREAMING_SNAKE_CASE    :							int   ,							SCREAMING_SNAKE_CASE    :							Any=None   ,							SCREAMING_SNAKE_CASE    :							Tuple=None):
           if current_layer == 0:
                  _A  :			Tuple											=      self.embedding_hidden_mapping_in(SCREAMING_SNAKE_CASE)
           else:
                  _A  :			Dict											=      hidden_states[0]

           _A  :			Optional[int]											=      int(self.config.num_hidden_layers / self.config.num_hidden_groups)

           # Index of the hidden group
           _A  :			int											=      int(current_layer / (self.config.num_hidden_layers / self.config.num_hidden_groups))

           _A  :			Union[str, Any]											=      self.albert_layer_groups[group_idx](
               SCREAMING_SNAKE_CASE   ,							SCREAMING_SNAKE_CASE   ,							head_mask[group_idx * layers_per_group : (group_idx + 1) * layers_per_group]   ,							)
           _A  :			Dict											=      layer_group_output[0]

           return (hidden_states,)




@add_start_docstrings(
    "The bare ALBERT Model transformer with PABEE outputting raw hidden-states without any specific head on top."       ,      a_       ,      )
class 				__lowerCamelCase				( a_ ):
    """simple docstring"""


    def __init__(						self    :							Union[str, Any]   ,							SCREAMING_SNAKE_CASE    :							Any):
           super().__init__(SCREAMING_SNAKE_CASE)

           _A  :			Any											=      AlbertTransformerWithPabee(SCREAMING_SNAKE_CASE)

           self.init_weights()
           _A  :			Tuple											=      0
           _A  :			int											=      0
           _A  :			Union[str, Any]											=      0

           _A  :			Union[str, Any]											=      0


    def      A					(						self    :							Optional[int]   ,							SCREAMING_SNAKE_CASE    :							Union[str, Any]):
           _A  :			Any											=      threshold


    def      A					(						self    :							str   ,							SCREAMING_SNAKE_CASE    :							str):
           _A  :			str											=      patience


    def      A					(						self    :							Optional[Any]):
           _A  :			Dict											=      0
           _A  :			int											=      0


    def      A					(						self    :							str):
           _A  :			Tuple											=      self.inference_layers_num / self.inference_instances_num
           _A  :			List[str]											=      (
               F'*** Patience = {self.patience} Avg. Inference Layers = {avg_inf_layers:.2f} Speed Up ='
               F' {1 - avg_inf_layers / self.config.num_hidden_layers:.2f} ***'
           )
           print(SCREAMING_SNAKE_CASE)


    @add_start_docstrings_to_model_forward(SCREAMING_SNAKE_CASE)
    def      A					(						self    :							Optional[Any]   ,							SCREAMING_SNAKE_CASE    :							List[Any]=None   ,							SCREAMING_SNAKE_CASE    :							Any=None   ,							SCREAMING_SNAKE_CASE    :							Dict=None   ,							SCREAMING_SNAKE_CASE    :							List[Any]=None   ,							SCREAMING_SNAKE_CASE    :							Union[str, Any]=None   ,							SCREAMING_SNAKE_CASE    :							Dict=None   ,							SCREAMING_SNAKE_CASE    :							Any=None   ,							SCREAMING_SNAKE_CASE    :							List[str]=None   ,							SCREAMING_SNAKE_CASE    :							Union[str, Any]=False   ,							):

           if input_ids is not None and inputs_embeds is not None:
                  raise ValueError('You cannot specify both input_ids and inputs_embeds at the same time')
           elif input_ids is not None:
                  _A  :			int											=      input_ids.size()
           elif inputs_embeds is not None:
                  _A  :			List[str]											=      inputs_embeds.size()[:-1]
           else:
                  raise ValueError('You have to specify either input_ids or inputs_embeds')

           _A  :			int											=      input_ids.device if input_ids is not None else inputs_embeds.device

           if attention_mask is None:
                  _A  :			int											=      torch.ones(SCREAMING_SNAKE_CASE   ,							device=SCREAMING_SNAKE_CASE)
           if token_type_ids is None:
                  _A  :			Any											=      torch.zeros(SCREAMING_SNAKE_CASE   ,							dtype=torch.long   ,							device=SCREAMING_SNAKE_CASE)

           _A  :			Optional[Any]											=      attention_mask.unsqueeze(1).unsqueeze(2)
           _A  :			Optional[Any]											=      extended_attention_mask.to(dtype=self.dtype)  # fp16 compatibility
           _A  :			Union[str, Any]											=      (1.0 - extended_attention_mask) * -1_0000.0
           _A  :			str											=      self.get_head_mask(SCREAMING_SNAKE_CASE   ,							self.config.num_hidden_layers)

           _A  :			str											=      self.embeddings(
               SCREAMING_SNAKE_CASE   ,							position_ids=SCREAMING_SNAKE_CASE   ,							token_type_ids=SCREAMING_SNAKE_CASE   ,							inputs_embeds=SCREAMING_SNAKE_CASE)
           _A  :			Optional[Any]											=      embedding_output

           if self.training:
                  _A  :			Optional[Any]											=      []
                  for i in range(self.config.num_hidden_layers):
                         _A  :			Optional[int]											=      self.encoder.adaptive_forward(
                             SCREAMING_SNAKE_CASE   ,							current_layer=SCREAMING_SNAKE_CASE   ,							attention_mask=SCREAMING_SNAKE_CASE   ,							head_mask=SCREAMING_SNAKE_CASE   ,							)

                         _A  :			Optional[int]											=      self.pooler_activation(self.pooler(encoder_outputs[0][:, 0]))
                         _A  :			Union[str, Any]											=      output_layers[i](output_dropout(SCREAMING_SNAKE_CASE))
                         res.append(SCREAMING_SNAKE_CASE)
           elif self.patience == 0:  # Use all layers for inference
                  _A  :			str											=      self.encoder(SCREAMING_SNAKE_CASE   ,							SCREAMING_SNAKE_CASE   ,							head_mask=SCREAMING_SNAKE_CASE)
                  _A  :			Optional[int]											=      self.pooler_activation(self.pooler(encoder_outputs[0][:, 0]))
                  _A  :			List[str]											=      [output_layers[self.config.num_hidden_layers - 1](SCREAMING_SNAKE_CASE)]
           else:
                  _A  :			Optional[Any]											=      0
                  _A  :			List[str]											=      None
                  _A  :			Tuple											=      0
                  for i in range(self.config.num_hidden_layers):
                         calculated_layer_num += 1
                         _A  :			Optional[int]											=      self.encoder.adaptive_forward(
                             SCREAMING_SNAKE_CASE   ,							current_layer=SCREAMING_SNAKE_CASE   ,							attention_mask=SCREAMING_SNAKE_CASE   ,							head_mask=SCREAMING_SNAKE_CASE   ,							)

                         _A  :			List[Any]											=      self.pooler_activation(self.pooler(encoder_outputs[0][:, 0]))
                         _A  :			List[Any]											=      output_layers[i](SCREAMING_SNAKE_CASE)
                         if regression:
                                _A  :			List[str]											=      logits.detach()
                                if patient_result is not None:
                                       _A  :			Dict											=      patient_result.detach()
                                if (patient_result is not None) and torch.abs(patient_result - labels) < self.regression_threshold:
                                       patient_counter += 1
                                else:
                                       _A  :			Any											=      0
                         else:
                                _A  :			int											=      logits.detach().argmax(dim=1)
                                if patient_result is not None:
                                       _A  :			Optional[int]											=      patient_result.detach().argmax(dim=1)
                                if (patient_result is not None) and torch.all(labels.eq(SCREAMING_SNAKE_CASE)):
                                       patient_counter += 1
                                else:
                                       _A  :			int											=      0

                         _A  :			Union[str, Any]											=      logits
                         if patient_counter == self.patience:
                                break
                  _A  :			Union[str, Any]											=      [patient_result]
                  self.inference_layers_num += calculated_layer_num
                  self.inference_instances_num += 1

           return res




@add_start_docstrings(
    "Albert Model transformer with PABEE and a sequence classification/regression head on top (a linear layer on top of\n    the pooled output) e.g. for GLUE tasks. "       ,      a_       ,      )
class 				__lowerCamelCase				( a_ ):
    """simple docstring"""


    def __init__(						self    :							Any   ,							SCREAMING_SNAKE_CASE    :							str):
           super().__init__(SCREAMING_SNAKE_CASE)
           _A  :			int											=      config.num_labels

           _A  :			List[str]											=      AlbertModelWithPabee(SCREAMING_SNAKE_CASE)
           _A  :			Optional[Any]											=      nn.Dropout(config.classifier_dropout_prob)
           _A  :			Union[str, Any]											=      nn.ModuleList(
               [nn.Linear(config.hidden_size   ,							self.config.num_labels) for _ in range(config.num_hidden_layers)])

           self.init_weights()


    @add_start_docstrings_to_model_forward(SCREAMING_SNAKE_CASE)
    def      A					(						self    :							str   ,							SCREAMING_SNAKE_CASE    :							Optional[Any]=None   ,							SCREAMING_SNAKE_CASE    :							List[Any]=None   ,							SCREAMING_SNAKE_CASE    :							Optional[int]=None   ,							SCREAMING_SNAKE_CASE    :							List[Any]=None   ,							SCREAMING_SNAKE_CASE    :							Any=None   ,							SCREAMING_SNAKE_CASE    :							Any=None   ,							SCREAMING_SNAKE_CASE    :							List[Any]=None   ,							):
           _A  :			str											=      self.albert(
               input_ids=SCREAMING_SNAKE_CASE   ,							attention_mask=SCREAMING_SNAKE_CASE   ,							token_type_ids=SCREAMING_SNAKE_CASE   ,							position_ids=SCREAMING_SNAKE_CASE   ,							head_mask=SCREAMING_SNAKE_CASE   ,							inputs_embeds=SCREAMING_SNAKE_CASE   ,							output_dropout=self.dropout   ,							output_layers=self.classifiers   ,							regression=self.num_labels == 1   ,							)

           _A  :			List[str]											=      (logits[-1],)

           if labels is not None:
                  _A  :			List[Any]											=      None
                  _A  :			Dict											=      0
                  for ix, logits_item in enumerate(SCREAMING_SNAKE_CASE):
                         if self.num_labels == 1:
                                #  We are doing regression
                                _A  :			List[Any]											=      MSELoss()
                                _A  :			Union[str, Any]											=      loss_fct(logits_item.view(-1)   ,							labels.view(-1))
                         else:
                                _A  :			Union[str, Any]											=      CrossEntropyLoss()
                                _A  :			List[Any]											=      loss_fct(logits_item.view(-1   ,							self.num_labels)   ,							labels.view(-1))
                         if total_loss is None:
                                _A  :			Any											=      loss
                         else:
                                total_loss += loss * (ix + 1)
                         total_weights += ix + 1
                  _A  :			int											=      (total_loss / total_weights,) + outputs

           return outputs










'''simple docstring'''







class 				__lowerCamelCase				:
    """simple docstring"""


    def __init__(						self    :							Optional[Any]):
           _A  :			Optional[Any]											=      [0]
           _A  :			Optional[Any]											=      0


    def      A					(						self    :							List[Any]   ,							SCREAMING_SNAKE_CASE    :							int):
           _A  :			Optional[Any]											=      self.__heap[i]
           while i // 2 > 0:
                  if self.__heap[i] > self.__heap[i // 2]:
                         _A  :			Dict											=      self.__heap[i // 2]
                         _A  :			Optional[Any]											=      temporary
                  i //= 2


    def      A					(						self    :							List[Any]   ,							SCREAMING_SNAKE_CASE    :							int):
           self.__heap.append(SCREAMING_SNAKE_CASE)
           self.__size += 1
           self.__swap_up(self.__size)


    def      A					(						self    :							int   ,							SCREAMING_SNAKE_CASE    :							int):
           while self.__size >= 2 * i:
                  if 2 * i + 1 > self.__size:
                         _A  :			Optional[int]											=      2 * i
                  else:
                         if self.__heap[2 * i] > self.__heap[2 * i + 1]:
                                _A  :			Any											=      2 * i
                         else:
                                _A  :			Optional[Any]											=      2 * i + 1
                  _A  :			List[str]											=      self.__heap[i]
                  if self.__heap[i] < self.__heap[bigger_child]:
                         _A  :			Union[str, Any]											=      self.__heap[bigger_child]
                         _A  :			Optional[int]											=      temporary
                  _A  :			Any											=      bigger_child


    def      A					(						self    :							List[str]):
           _A  :			str											=      self.__heap[1]
           _A  :			Tuple											=      self.__heap[self.__size]
           self.__size -= 1
           self.__heap.pop()
           self.__swap_down(1)
           return max_value


    @property
    def      A					(						self    :							int):
           return self.__heap[1:]


    def __len__(						self    :							List[str]):
           return self.__size


if __name__ == "__main__":
   import doctest

   doctest.testmod()
   # create an instance of BinaryHeap
   A   :  Dict       		=				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))






