






"""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__       :			Any=						logging.getLogger(__name__)
class  __lowerCamelCase     (	_a	):

   def     SCREAMING_SNAKE_CASE__			(       self			,					snake_case_			,					snake_case_			,					snake_case_=None			,					snake_case_=None   )		->							List[Any]:
       if current_layer == 0:
           UpperCamelCase__								=     self.embedding_hidden_mapping_in(snake_case_   )
       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](
           snake_case_			,					snake_case_			,					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."""				,						_a				,						)
class  __lowerCamelCase     (	_a	):

   def __init__(       self			,					snake_case_   )		->							Any:
       super().__init__(snake_case_   )

       UpperCamelCase__								=     AlbertTransformerWithPabee(snake_case_   )

       self.init_weights()
       UpperCamelCase__								=     0
       UpperCamelCase__								=     0
       UpperCamelCase__								=     0

       UpperCamelCase__								=     0

   def     SCREAMING_SNAKE_CASE__			(       self			,					snake_case_   )		->							Tuple:
       UpperCamelCase__								=     threshold

   def     SCREAMING_SNAKE_CASE__			(       self			,					snake_case_   )		->							Union[str, Any]:
       UpperCamelCase__								=     patience

   def     SCREAMING_SNAKE_CASE__			(       self   )		->							str:
       UpperCamelCase__								=     0
       UpperCamelCase__								=     0

   def     SCREAMING_SNAKE_CASE__			(       self   )		->							List[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(snake_case_   )







   @add_start_docstrings_to_model_forward(snake_case_   )
   def     SCREAMING_SNAKE_CASE__			(       self			,					snake_case_=None			,					snake_case_=None			,					snake_case_=None			,					snake_case_=None			,					snake_case_=None			,					snake_case_=None			,					snake_case_=None			,					snake_case_=None			,					snake_case_=False			,					)		->							Dict:

       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(snake_case_			,					device=snake_case_   )
       if token_type_ids is None:
           UpperCamelCase__								=     torch.zeros(snake_case_			,					dtype=torch.long			,					device=snake_case_   )

       UpperCamelCase__								=     attention_mask.unsqueeze(1   ).unsqueeze(2   )
       UpperCamelCase__								=     extended_attention_mask.to(dtype=self.dtype   )  # fp16 compatibility
       UpperCamelCase__								=     (1.0 - extended_attention_mask) * -10_000.0
       UpperCamelCase__								=     self.get_head_mask(snake_case_			,					self.config.num_hidden_layers   )

       UpperCamelCase__								=     self.embeddings(
           snake_case_			,					position_ids=snake_case_			,					token_type_ids=snake_case_			,					inputs_embeds=snake_case_   )
       UpperCamelCase__								=     embedding_output

       if self.training:
           UpperCamelCase__								=     []
           for i in range(self.config.num_hidden_layers   ):
               UpperCamelCase__								=     self.encoder.adaptive_forward(
                   snake_case_			,					current_layer=snake_case_			,					attention_mask=snake_case_			,					head_mask=snake_case_			,					)

               UpperCamelCase__								=     self.pooler_activation(self.pooler(encoder_outputs[0][:, 0]   )   )
               UpperCamelCase__								=     output_layers[i](output_dropout(snake_case_   )   )
               res.append(snake_case_   )
       elif self.patience == 0:  # Use all layers for inference
           UpperCamelCase__								=     self.encoder(snake_case_			,					snake_case_			,					head_mask=snake_case_   )
           UpperCamelCase__								=     self.pooler_activation(self.pooler(encoder_outputs[0][:, 0]   )   )
           UpperCamelCase__								=     [output_layers[self.config.num_hidden_layers - 1](snake_case_   )]
       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(
                   snake_case_			,					current_layer=snake_case_			,					attention_mask=snake_case_			,					head_mask=snake_case_			,					)

               UpperCamelCase__								=     self.pooler_activation(self.pooler(encoder_outputs[0][:, 0]   )   )
               UpperCamelCase__								=     output_layers[i](snake_case_   )
               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(snake_case_   )   ):
                       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
    the pooled output) e.g. for GLUE tasks. """				,						_a				,						)
class  __lowerCamelCase     (	_a	):

   def __init__(       self			,					snake_case_   )		->							List[Any]:
       super().__init__(snake_case_   )
       UpperCamelCase__								=     config.num_labels

       UpperCamelCase__								=     AlbertModelWithPabee(snake_case_   )
       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(snake_case_   )
   def     SCREAMING_SNAKE_CASE__			(       self			,					snake_case_=None			,					snake_case_=None			,					snake_case_=None			,					snake_case_=None			,					snake_case_=None			,					snake_case_=None			,					snake_case_=None			,					)		->							Union[str, Any]:
       UpperCamelCase__								=     self.albert(
           input_ids=snake_case_			,					attention_mask=snake_case_			,					token_type_ids=snake_case_			,					position_ids=snake_case_			,					head_mask=snake_case_			,					inputs_embeds=snake_case_			,					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(snake_case_   ):
               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












"""simple docstring"""





from math import gcd







def 	lowerCAmelCase_(			SCREAMING_SNAKE_CASE = 1_20_00       )       ->						int:
    """simple docstring"""

    UpperCamelCase__								=     0
    for d in range(max_d + 1       ):
        for n in range(d // 3 + 1			,						(d + 1) // 2       ):
            if gcd(SCREAMING_SNAKE_CASE			,						SCREAMING_SNAKE_CASE       ) == 1:
                fractions_number += 1
    return fractions_number


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




