






"""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,
)


_UpperCamelCase				:    Tuple     =						logging.getLogger(__name__)






class 						snake_case  (  UpperCAmelCase       ):





    def 				lowerCamelCase__      (							self	:					Optional[Any]				,		A	:					Tuple				,		A	:					Any				,		A	:					Optional[int]=None				,		A	:					str=None			):


      '''simple docstring'''




      if current_layer == 0:
        a     :			Optional[Any]				=   self.embedding_hidden_mapping_in(A			)
      else:
        a     :			Optional[int]				=   hidden_states[0]

      a     :			str				=   int(self.config.num_hidden_layers / self.config.num_hidden_groups			)

      # Index of the hidden group
      a     :			Union[str, Any]				=   int(current_layer / (self.config.num_hidden_layers / self.config.num_hidden_groups)			)

      a     :			Tuple				=   self.albert_layer_groups[group_idx](
          A				,		A				,		head_mask[group_idx * layers_per_group : (group_idx + 1) * layers_per_group]				,		)
      a     :			int				=   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.'''   , UpperCAmelCase   , )
class 						snake_case  (  UpperCAmelCase       ):





    def __init__(							self	:					Optional[int]				,		A	:					Union[str, Any]			):


      '''simple docstring'''




      super().__init__(A			)

      a     :			List[Any]				=   AlbertTransformerWithPabee(A			)

      self.init_weights()
      a     :			Optional[int]				=   0
      a     :			Any				=   0
      a     :			Tuple				=   0

      a     :			Optional[Any]				=   0





    def 				lowerCamelCase__      (							self	:					Union[str, Any]				,		A	:					int			):


      '''simple docstring'''




      a     :			List[str]				=   threshold





    def 				lowerCamelCase__      (							self	:					Union[str, Any]				,		A	:					List[str]			):


      '''simple docstring'''




      a     :			Union[str, Any]				=   patience





    def 				lowerCamelCase__      (							self	:					int			):


      '''simple docstring'''




      a     :			Tuple				=   0
      a     :			Union[str, Any]				=   0





    def 				lowerCamelCase__      (							self	:					Any			):


      '''simple docstring'''




      a     :			Any				=   self.inference_layers_num / self.inference_instances_num
      a     :			Any				=   (
          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(A			)





    @add_start_docstrings_to_model_forward(A			)
    def 				lowerCamelCase__      (							self	:					Optional[int]				,		A	:					int=None				,		A	:					List[str]=None				,		A	:					str=None				,		A	:					Optional[int]=None				,		A	:					List[Any]=None				,		A	:					int=None				,		A	:					int=None				,		A	:					Any=None				,		A	:					Any=False				,		):


      '''simple docstring'''




      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     :			Tuple				=   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     :			Tuple				=   input_ids.device if input_ids is not None else inputs_embeds.device

      if attention_mask is None:
        a     :			List[str]				=   torch.ones(A				,		device=A			)
      if token_type_ids is None:
        a     :			List[str]				=   torch.zeros(A				,		dtype=torch.long				,		device=A			)

      a     :			int				=   attention_mask.unsqueeze(1			).unsqueeze(2			)
      a     :			Optional[int]				=   extended_attention_mask.to(dtype=self.dtype			)  # fp16 compatibility
      a     :			Tuple				=   (1.0 - extended_attention_mask) * -1_00_00.0
      a     :			List[str]				=   self.get_head_mask(A				,		self.config.num_hidden_layers			)

      a     :			List[str]				=   self.embeddings(
          A				,		position_ids=A				,		token_type_ids=A				,		inputs_embeds=A			)
      a     :			str				=   embedding_output

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

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

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

          a     :			List[Any]				=   logits
          if patient_counter == self.patience:
            break
        a     :			int				=   [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
    the pooled output) e.g. for GLUE tasks. '''   , UpperCAmelCase   , )
class 						snake_case  (  UpperCAmelCase       ):





    def __init__(							self	:					List[Any]				,		A	:					Optional[int]			):


      '''simple docstring'''




      super().__init__(A			)
      a     :			Any				=   config.num_labels

      a     :			Dict				=   AlbertModelWithPabee(A			)
      a     :			Optional[int]				=   nn.Dropout(config.classifier_dropout_prob			)
      a     :			List[str]				=   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(A			)
    def 				lowerCamelCase__      (							self	:					List[Any]				,		A	:					List[Any]=None				,		A	:					Dict=None				,		A	:					Union[str, Any]=None				,		A	:					Tuple=None				,		A	:					Tuple=None				,		A	:					Any=None				,		A	:					Tuple=None				,		):


      '''simple docstring'''




      a     :			int				=   self.albert(
          input_ids=A				,		attention_mask=A				,		token_type_ids=A				,		position_ids=A				,		head_mask=A				,		inputs_embeds=A				,		output_dropout=self.dropout				,		output_layers=self.classifiers				,		regression=self.num_labels == 1				,		)

      a     :			Dict				=   (logits[-1],)

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

      return outputs












"""simple docstring"""


def 			snake_case  (A_      :int = 1_0_0_0      ):







  '''simple docstring'''

  a     :			Union[str, Any]				=   0
  a     :			int				=   (n - 1) // 3
  total += ((terms) * (6 + (terms - 1) * 3)) // 2  # total of an A.P.
  a     :			Tuple				=   (n - 1) // 5
  total += ((terms) * (1_0 + (terms - 1) * 5)) // 2
  a     :			Dict				=   (n - 1) // 1_5
  total -= ((terms) * (3_0 + (terms - 1) * 1_5)) // 2
  return total


if __name__ == "__main__":
   print(f'''{solution() = }''')




