


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[Any]				  =				logging.getLogger(__name__)




class  snake_case__						(	a_					):






     """simple docstring"""


     def        lowercase_       (	self  :  str,			_snake_case  :  int,			_snake_case  :  Tuple,			_snake_case  :  List[Any]=None,			_snake_case  :  Dict=None )       ->Optional[int]:


            if current_layer == 0:
                   snake_case__							:     Tuple            =     self.embedding_hidden_mapping_in(snake_case__ )
            else:
                   snake_case__							:     List[str]            =     hidden_states[0]

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

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

            snake_case__							:     Optional[int]            =     self.albert_layer_groups[group_idx](
                snake_case__,			snake_case__,			head_mask[group_idx * layers_per_group : (group_idx + 1) * layers_per_group],			)
            snake_case__							:     Union[str, Any]            =     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  snake_case__						(	a_					):






     """simple docstring"""


     def __init__(	self  :  List[Any],			_snake_case  :  List[Any] )       ->Any:
            super().__init__(snake_case__ )

            snake_case__							:     Optional[Any]            =     AlbertTransformerWithPabee(snake_case__ )

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

            snake_case__							:     Optional[int]            =     0






     def        lowercase_       (	self  :  Union[str, Any],			_snake_case  :  Union[str, Any] )       ->Any:
            snake_case__							:     Any            =     threshold






     def        lowercase_       (	self  :  Union[str, Any],			_snake_case  :  List[str] )       ->Dict:
            snake_case__							:     Union[str, Any]            =     patience






     def        lowercase_       (	self  :  int )       ->str:
            snake_case__							:     Dict            =     0
            snake_case__							:     List[Any]            =     0






     def        lowercase_       (	self  :  Optional[int] )       ->Union[str, Any]:
            snake_case__							:     Any            =     self.inference_layers_num / self.inference_instances_num
            snake_case__							:     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(snake_case__ )






     @add_start_docstrings_to_model_forward(snake_case__ )
     def        lowercase_       (	self  :  int,			_snake_case  :  Union[str, Any]=None,			_snake_case  :  Tuple=None,			_snake_case  :  Optional[Any]=None,			_snake_case  :  int=None,			_snake_case  :  Tuple=None,			_snake_case  :  Tuple=None,			_snake_case  :  Any=None,			_snake_case  :  Any=None,			_snake_case  :  Dict=False,			)       ->Optional[Any]:


            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:
                   snake_case__							:     List[Any]            =     input_ids.size()
            elif inputs_embeds is not None:
                   snake_case__							:     str            =     inputs_embeds.size()[:-1]
            else:
                   raise ValueError('You have to specify either input_ids or inputs_embeds' )

            snake_case__							:     List[str]            =     input_ids.device if input_ids is not None else inputs_embeds.device

            if attention_mask is None:
                   snake_case__							:     Any            =     torch.ones(snake_case__,			device=snake_case__ )
            if token_type_ids is None:
                   snake_case__							:     Tuple            =     torch.zeros(snake_case__,			dtype=torch.long,			device=snake_case__ )

            snake_case__							:     int            =     attention_mask.unsqueeze(1 ).unsqueeze(2 )
            snake_case__							:     Tuple            =     extended_attention_mask.to(dtype=self.dtype )  # fp16 compatibility
            snake_case__							:     Tuple            =     (1.0 - extended_attention_mask) * -1_0_0_0_0.0
            snake_case__							:     str            =     self.get_head_mask(snake_case__,			self.config.num_hidden_layers )

            snake_case__							:     Tuple            =     self.embeddings(
                snake_case__,			position_ids=snake_case__,			token_type_ids=snake_case__,			inputs_embeds=snake_case__ )
            snake_case__							:     int            =     embedding_output

            if self.training:
                   snake_case__							:     Union[str, Any]            =     []
                   for i in range(self.config.num_hidden_layers ):
                          snake_case__							:     Union[str, Any]            =     self.encoder.adaptive_forward(
                              snake_case__,			current_layer=snake_case__,			attention_mask=snake_case__,			head_mask=snake_case__,			)

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

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

                          snake_case__							:     List[Any]            =     logits
                          if patient_counter == self.patience:
                                 break
                   snake_case__							:     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
    the pooled output) e.g. for GLUE tasks. """    ,					a_    ,					)
class  snake_case__						(	a_					):






     """simple docstring"""


     def __init__(	self  :  Optional[Any],			_snake_case  :  Any )       ->int:
            super().__init__(snake_case__ )
            snake_case__							:     List[Any]            =     config.num_labels

            snake_case__							:     Tuple            =     AlbertModelWithPabee(snake_case__ )
            snake_case__							:     Union[str, Any]            =     nn.Dropout(config.classifier_dropout_prob )
            snake_case__							:     int            =     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        lowercase_       (	self  :  List[str],			_snake_case  :  List[Any]=None,			_snake_case  :  Optional[int]=None,			_snake_case  :  List[Any]=None,			_snake_case  :  str=None,			_snake_case  :  Optional[int]=None,			_snake_case  :  Optional[Any]=None,			_snake_case  :  List[str]=None,			)       ->List[Any]:
            snake_case__							:     Optional[Any]            =     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,			)

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

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

            return outputs











def 	lowercase_							(A			:			Dict				):
       snake_case__							:     Optional[int]            =     len(A				)
       for i in range(length - 1				):
              snake_case__							:     List[str]            =     False
              for j in range(length - 1 - i				):
                     if collection[j] > collection[j + 1]:
                            snake_case__							:     Optional[Any]            =     True
                            snake_case__     ,    snake_case__							:     Dict            =     collection[j + 1], collection[j]
              if not swapped:
                     break  # Stop iteration if the collection is sorted.
       return collection


if __name__ == "__main__":
 import doctest
 import time

 doctest.testmod()

 a_       :Any				  =				input("Enter numbers separated by a comma:").strip()
 a_       :Optional[Any]				  =				[int(item) for item in user_input.split(",")]
 a_       :Optional[int]				  =				time.process_time()
 print(*bubble_sort(unsorted), sep=",")
 print(F"""Processing time: {(time.process_time() - start)%1e9 + 7}""")






