code: 
  
  
  """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_			  =    logging.getLogger(__name__)
  class 						__lowerCAmelCase   (			UpperCAmelCase       ):
  
  
  
  
  
  
  
    '''simple docstring'''
  
  
    def 					UpperCamelCase__   (      self:		Optional[Any]				,			UpperCamelCase_:		Any				,			UpperCamelCase_:		List[Any]				,			UpperCamelCase_:		Tuple=None				,			UpperCamelCase_:		Dict=None					):
       if current_layer == 0:
          UpperCamelCase_     =self.embedding_hidden_mapping_in(UpperCamelCase_					)
       else:
          UpperCamelCase_     =hidden_states[0]
  
       UpperCamelCase_     =int(self.config.num_hidden_layers / self.config.num_hidden_groups					)
  
       # Index of the hidden group
       UpperCamelCase_     =int(current_layer / (self.config.num_hidden_layers / self.config.num_hidden_groups)					)
  
       UpperCamelCase_     =self.albert_layer_groups[group_idx](
           UpperCamelCase_				,			UpperCamelCase_				,			head_mask[group_idx * layers_per_group : (group_idx + 1) * layers_per_group]				,			)
       UpperCamelCase_     =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 						__lowerCAmelCase   (			UpperCAmelCase       ):
  
  
  
  
  
  
  
    '''simple docstring'''
  
  
    def __init__(      self:		Tuple				,			UpperCamelCase_:		Union[str, Any]					):
       super().__init__(UpperCamelCase_					)
  
       UpperCamelCase_     =AlbertTransformerWithPabee(UpperCamelCase_					)
  
       self.init_weights()
       UpperCamelCase_     =0
       UpperCamelCase_     =0
       UpperCamelCase_     =0
  
       UpperCamelCase_     =0
    def 					UpperCamelCase__   (      self:		Optional[Any]				,			UpperCamelCase_:		Union[str, Any]					):
       UpperCamelCase_     =threshold
    def 					UpperCamelCase__   (      self:		Any				,			UpperCamelCase_:		str					):
       UpperCamelCase_     =patience
    def 					UpperCamelCase__   (      self:		int					):
       UpperCamelCase_     =0
       UpperCamelCase_     =0
    def 					UpperCamelCase__   (      self:		str					):
       UpperCamelCase_     =self.inference_layers_num / self.inference_instances_num
       UpperCamelCase_     =(
           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(UpperCamelCase_					)
  
  
  
  
  
    @add_start_docstrings_to_model_forward(UpperCamelCase_					)
    def 					UpperCamelCase__   (      self:		int				,			UpperCamelCase_:		List[Any]=None				,			UpperCamelCase_:		List[str]=None				,			UpperCamelCase_:		List[Any]=None				,			UpperCamelCase_:		Dict=None				,			UpperCamelCase_:		Optional[int]=None				,			UpperCamelCase_:		str=None				,			UpperCamelCase_:		str=None				,			UpperCamelCase_:		int=None				,			UpperCamelCase_:		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:
          UpperCamelCase_     =input_ids.size()
       elif inputs_embeds is not None:
          UpperCamelCase_     =inputs_embeds.size()[:-1]
       else:
          raise ValueError("You have to specify either input_ids or inputs_embeds"					)
  
       UpperCamelCase_     =input_ids.device if input_ids is not None else inputs_embeds.device
  
       if attention_mask is None:
          UpperCamelCase_     =torch.ones(UpperCamelCase_				,			device=UpperCamelCase_					)
       if token_type_ids is None:
          UpperCamelCase_     =torch.zeros(UpperCamelCase_				,			dtype=torch.long				,			device=UpperCamelCase_					)
  
       UpperCamelCase_     =attention_mask.unsqueeze(1					).unsqueeze(2					)
       UpperCamelCase_     =extended_attention_mask.to(dtype=self.dtype					)  # fp16 compatibility
       UpperCamelCase_     =(1.0 - extended_attention_mask) * -1_0000.0
       UpperCamelCase_     =self.get_head_mask(UpperCamelCase_				,			self.config.num_hidden_layers					)
  
       UpperCamelCase_     =self.embeddings(
           UpperCamelCase_				,			position_ids=UpperCamelCase_				,			token_type_ids=UpperCamelCase_				,			inputs_embeds=UpperCamelCase_					)
       UpperCamelCase_     =embedding_output
  
       if self.training:
          UpperCamelCase_     =[]
          for i in range(self.config.num_hidden_layers					):
             UpperCamelCase_     =self.encoder.adaptive_forward(
                 UpperCamelCase_				,			current_layer=UpperCamelCase_				,			attention_mask=UpperCamelCase_				,			head_mask=UpperCamelCase_				,			)
  
             UpperCamelCase_     =self.pooler_activation(self.pooler(encoder_outputs[0][:, 0]					)					)
             UpperCamelCase_     =output_layers[i](output_dropout(UpperCamelCase_					)					)
             res.append(UpperCamelCase_					)
       elif self.patience == 0:  # Use all layers for inference
          UpperCamelCase_     =self.encoder(UpperCamelCase_				,			UpperCamelCase_				,			head_mask=UpperCamelCase_					)
          UpperCamelCase_     =self.pooler_activation(self.pooler(encoder_outputs[0][:, 0]					)					)
          UpperCamelCase_     =[output_layers[self.config.num_hidden_layers - 1](UpperCamelCase_					)]
       else:
          UpperCamelCase_     =0
          UpperCamelCase_     =None
          UpperCamelCase_     =0
          for i in range(self.config.num_hidden_layers					):
             calculated_layer_num += 1
             UpperCamelCase_     =self.encoder.adaptive_forward(
                 UpperCamelCase_				,			current_layer=UpperCamelCase_				,			attention_mask=UpperCamelCase_				,			head_mask=UpperCamelCase_				,			)
  
             UpperCamelCase_     =self.pooler_activation(self.pooler(encoder_outputs[0][:, 0]					)					)
             UpperCamelCase_     =output_layers[i](UpperCamelCase_					)
             if regression:
                UpperCamelCase_     =logits.detach()
                if patient_result is not None:
                   UpperCamelCase_     =patient_result.detach()
                if (patient_result is not None) and torch.abs(patient_result - labels					) < self.regression_threshold:
                   patient_counter += 1
                else:
                   UpperCamelCase_     =0
             else:
                UpperCamelCase_     =logits.detach().argmax(dim=1					)
                if patient_result is not None:
                   UpperCamelCase_     =patient_result.detach().argmax(dim=1					)
                if (patient_result is not None) and torch.all(labels.eq(UpperCamelCase_					)					):
                   patient_counter += 1
                else:
                   UpperCamelCase_     =0
  
             UpperCamelCase_     =logits
             if patient_counter == self.patience:
                break
          UpperCamelCase_     =[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. "       ,   UpperCAmelCase       ,   )
  class 						__lowerCAmelCase   (			UpperCAmelCase       ):
  
  
  
  
  
  
  
    '''simple docstring'''
  
  
    def __init__(      self:		str				,			UpperCamelCase_:		Any					):
       super().__init__(UpperCamelCase_					)
       UpperCamelCase_     =config.num_labels
  
       UpperCamelCase_     =AlbertModelWithPabee(UpperCamelCase_					)
       UpperCamelCase_     =nn.Dropout(config.classifier_dropout_prob					)
       UpperCamelCase_     =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(UpperCamelCase_					)
    def 					UpperCamelCase__   (      self:		Optional[int]				,			UpperCamelCase_:		Any=None				,			UpperCamelCase_:		Tuple=None				,			UpperCamelCase_:		Dict=None				,			UpperCamelCase_:		int=None				,			UpperCamelCase_:		List[str]=None				,			UpperCamelCase_:		Optional[Any]=None				,			UpperCamelCase_:		Optional[Any]=None				,			):
       UpperCamelCase_     =self.albert(
           input_ids=UpperCamelCase_				,			attention_mask=UpperCamelCase_				,			token_type_ids=UpperCamelCase_				,			position_ids=UpperCamelCase_				,			head_mask=UpperCamelCase_				,			inputs_embeds=UpperCamelCase_				,			output_dropout=self.dropout				,			output_layers=self.classifiers				,			regression=self.num_labels == 1				,			)
  
       UpperCamelCase_     =(logits[-1],)
  
       if labels is not None:
          UpperCamelCase_     =None
          UpperCamelCase_     =0
          for ix, logits_item in enumerate(UpperCamelCase_					):
             if self.num_labels == 1:
                #  We are doing regression
                UpperCamelCase_     =MSELoss()
                UpperCamelCase_     =loss_fct(logits_item.view(-1					)				,			labels.view(-1					)					)
             else:
                UpperCamelCase_     =CrossEntropyLoss()
                UpperCamelCase_     =loss_fct(logits_item.view(-1				,			self.num_labels					)				,			labels.view(-1					)					)
             if total_loss is None:
                UpperCamelCase_     =loss
             else:
                total_loss += loss * (ix + 1)
             total_weights += ix + 1
          UpperCamelCase_     =(total_loss / total_weights,) + outputs
  
       return outputs
  
style_context: 
  
  
  
  
  
  
  """simple docstring"""
  
  
  
  
  
  
  from __future__ import annotations
  
  from pathlib import Path
  
  
  
  
  def 						_UpperCamelCase			(  A    ,       A			):
     return pointa[0] * pointa[1] - pointa[1] * pointa[0]
  
  
  
  
  def 						_UpperCamelCase			(  A    ,       A    ,       A    ,       A    ,       A    ,       A			):
     UpperCamelCase_     =(xa, ya)
     UpperCamelCase_     =(xa - xa, ya - ya)
     UpperCamelCase_     =(xa - xa, ya - ya)
     UpperCamelCase_     =-vector_product(A    ,       A			) / vector_product(
         A    ,       A			)
     UpperCamelCase_     =+vector_product(A    ,       A			) / vector_product(
         A    ,       A			)
  
     return a > 0 and b > 0 and a + b < 1
  
  
  
  
  def 						_UpperCamelCase			(  A = "p102_triangles.txt"			):
     UpperCamelCase_     =Path(A			).parent.joinpath(A			).read_text(encoding="utf-8"			)
  
     UpperCamelCase_     =[]
     for line in data.strip().split("\n"			):
        triangles.append([int(A			) for number in line.split(","			)]			)
  
     UpperCamelCase_     =0
     UpperCamelCase_     =42
  
     for triangle in triangles:
        ret += contains_origin(*A			)
  
     return ret
  
  
  if __name__ == "__main__":
      print(f'''{solution() = }''')
  