



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_									=   logging.getLogger(__name__)







class       _snake_case				(     __snake_case ):






   '''simple docstring'''






   def       A__    (    self:     Dict  ,lowerCamelCase_:     Optional[Any]  ,lowerCamelCase_:     List[str]  ,lowerCamelCase_:     Union[str, Any]=None  ,lowerCamelCase_:     List[str]=None							)	->      Optional[Any]:
       if current_layer == 0:
           UpperCAmelCase_     :       str  		=					self.embedding_hidden_mapping_in(lowerCamelCase_							)
       else:
           UpperCAmelCase_     :       Dict  		=					hidden_states[0]

       UpperCAmelCase_     :       Dict  		=					int(self.config.num_hidden_layers / self.config.num_hidden_groups							)

       # Index of the hidden group
       UpperCAmelCase_     :       Optional[Any]  		=					int(current_layer / (self.config.num_hidden_layers / self.config.num_hidden_groups)							)

       UpperCAmelCase_     :       Tuple  		=					self.albert_layer_groups[group_idx](
           lowerCamelCase_  ,lowerCamelCase_  ,head_mask[group_idx * layers_per_group : (group_idx + 1) * layers_per_group]  ,)
       UpperCAmelCase_     :       Tuple  		=					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." ,							__snake_case ,							)
class       _snake_case				(     __snake_case ):






   '''simple docstring'''






   def __init__(    self:     int  ,lowerCamelCase_:     int							)	->      int:
       super().__init__(lowerCamelCase_							)

       UpperCAmelCase_     :       Union[str, Any]  		=					AlbertTransformerWithPabee(lowerCamelCase_							)

       self.init_weights()
       UpperCAmelCase_     :       List[str]  		=					0
       UpperCAmelCase_     :       Tuple  		=					0
       UpperCAmelCase_     :       int  		=					0

       UpperCAmelCase_     :       Union[str, Any]  		=					0


   def       A__    (    self:     Tuple  ,lowerCamelCase_:     Dict							)	->      Optional[Any]:
       UpperCAmelCase_     :       Union[str, Any]  		=					threshold


   def       A__    (    self:     Dict  ,lowerCamelCase_:     Optional[Any]							)	->      Optional[Any]:
       UpperCAmelCase_     :       int  		=					patience


   def       A__    (    self:     List[str]							)	->      List[Any]:
       UpperCAmelCase_     :       List[str]  		=					0
       UpperCAmelCase_     :       Dict  		=					0


   def       A__    (    self:     str							)	->      Any:
       UpperCAmelCase_     :       Optional[int]  		=					self.inference_layers_num / self.inference_instances_num
       UpperCAmelCase_     :       List[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(lowerCamelCase_							)






   @add_start_docstrings_to_model_forward(lowerCamelCase_							)
   def       A__    (    self:     Dict  ,lowerCamelCase_:     Optional[Any]=None  ,lowerCamelCase_:     Dict=None  ,lowerCamelCase_:     Optional[Any]=None  ,lowerCamelCase_:     Union[str, Any]=None  ,lowerCamelCase_:     Any=None  ,lowerCamelCase_:     List[str]=None  ,lowerCamelCase_:     int=None  ,lowerCamelCase_:     List[str]=None  ,lowerCamelCase_:     int=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_     :       Union[str, Any]  		=					input_ids.size()
       elif inputs_embeds is not None:
           UpperCAmelCase_     :       int  		=					inputs_embeds.size()[:-1]
       else:
           raise ValueError("""You have to specify either input_ids or inputs_embeds"""							)

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

       if attention_mask is None:
           UpperCAmelCase_     :       List[Any]  		=					torch.ones(lowerCamelCase_  ,device=lowerCamelCase_							)
       if token_type_ids is None:
           UpperCAmelCase_     :       Any  		=					torch.zeros(lowerCamelCase_  ,dtype=torch.long  ,device=lowerCamelCase_							)

       UpperCAmelCase_     :       Optional[Any]  		=					attention_mask.unsqueeze(1							).unsqueeze(2							)
       UpperCAmelCase_     :       Dict  		=					extended_attention_mask.to(dtype=self.dtype							)  # fp16 compatibility
       UpperCAmelCase_     :       List[str]  		=					(1.0 - extended_attention_mask) * -1_0_0_0_0.0
       UpperCAmelCase_     :       List[Any]  		=					self.get_head_mask(lowerCamelCase_  ,self.config.num_hidden_layers							)

       UpperCAmelCase_     :       Optional[int]  		=					self.embeddings(
           lowerCamelCase_  ,position_ids=lowerCamelCase_  ,token_type_ids=lowerCamelCase_  ,inputs_embeds=lowerCamelCase_							)
       UpperCAmelCase_     :       str  		=					embedding_output

       if self.training:
           UpperCAmelCase_     :       str  		=					[]
           for i in range(self.config.num_hidden_layers							):
               UpperCAmelCase_     :       Tuple  		=					self.encoder.adaptive_forward(
                   lowerCamelCase_  ,current_layer=lowerCamelCase_  ,attention_mask=lowerCamelCase_  ,head_mask=lowerCamelCase_  ,)

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

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

               UpperCAmelCase_     :       Optional[int]  		=					logits
               if patient_counter == self.patience:
                   break
           UpperCAmelCase_     :       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. " ,							__snake_case ,							)
class       _snake_case				(     __snake_case ):






   '''simple docstring'''






   def __init__(    self:     str  ,lowerCamelCase_:     Optional[Any]							)	->      Union[str, Any]:
       super().__init__(lowerCamelCase_							)
       UpperCAmelCase_     :       List[str]  		=					config.num_labels

       UpperCAmelCase_     :       Any  		=					AlbertModelWithPabee(lowerCamelCase_							)
       UpperCAmelCase_     :       Union[str, Any]  		=					nn.Dropout(config.classifier_dropout_prob							)
       UpperCAmelCase_     :       Dict  		=					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(lowerCamelCase_							)
   def       A__    (    self:     List[str]  ,lowerCamelCase_:     Dict=None  ,lowerCamelCase_:     str=None  ,lowerCamelCase_:     Optional[Any]=None  ,lowerCamelCase_:     Optional[Any]=None  ,lowerCamelCase_:     int=None  ,lowerCamelCase_:     List[str]=None  ,lowerCamelCase_:     Any=None  ,)	->      Optional[int]:
       UpperCAmelCase_     :       List[Any]  		=					self.albert(
           input_ids=lowerCamelCase_  ,attention_mask=lowerCamelCase_  ,token_type_ids=lowerCamelCase_  ,position_ids=lowerCamelCase_  ,head_mask=lowerCamelCase_  ,inputs_embeds=lowerCamelCase_  ,output_dropout=self.dropout  ,output_layers=self.classifiers  ,regression=self.num_labels == 1  ,)

       UpperCAmelCase_     :       Any  		=					(logits[-1],)

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

       return outputs





import math







def       lowerCamelCase_  (    _a       :						int    ):



    '''simple docstring'''




    if 1 < number < 4:
        # 2 and 3 are primes
        return True
    elif number < 2 or number % 2 == 0 or number % 3 == 0:
        # Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
        return False

    # All primes number are in format of 6k +/- 1
    for i in range(5   ,		int(math.sqrt(_a    ) + 1    )   ,		6    ):
        if number % i == 0 or number % (i + 2) == 0:
            return False
    return True







def       lowerCamelCase_  (    _a       :						int = 6008_5147_5143    ):



    '''simple docstring'''




    try:
        UpperCAmelCase_     :       str  		=					int(_a    )
    except (TypeError, ValueError):
        raise TypeError("""Parameter n must be int or castable to int."""    )
    if n <= 0:
        raise ValueError("""Parameter n must be greater than or equal to one."""    )
    UpperCAmelCase_     :       Optional[int]  		=					0
    if is_prime(_a    ):
        return n
    while n % 2 == 0:
        n //= 2
    if is_prime(_a    ):
        return n
    for i in range(3   ,		int(math.sqrt(_a    )    ) + 1   ,		2    ):
        if n % i == 0:
            if is_prime(n // i    ):
                UpperCAmelCase_     :       Optional[int]  		=					n // i
                break
            elif is_prime(_a    ):
                UpperCAmelCase_     :       str  		=					i
    return max_number


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