code: 
  
  
  
  """simple docstring"""
  from dataclasses import dataclass
  from enum import Enum
  from typing import List, Optional, Union
  
  import numpy as np
  import PIL
  from PIL import Image
  
  from ...utils import BaseOutput, is_torch_available, is_transformers_available
  @dataclass
  class 							_UpperCAmelCase							(				a   ):
  
  
  
  
  
       '''simple docstring'''
  
  
  
       a__             ={
           '''sld_warmup_steps''': 1_5,
           '''sld_guidance_scale''': 2_0,
           '''sld_threshold''': 0.0,
           '''sld_momentum_scale''': 0.0,
           '''sld_mom_beta''': 0.0,
       }
       a__             ={
           '''sld_warmup_steps''': 1_0,
           '''sld_guidance_scale''': 1_0_0_0,
           '''sld_threshold''': 0.01,
           '''sld_momentum_scale''': 0.3,
           '''sld_mom_beta''': 0.4,
       }
       a__             ={
           '''sld_warmup_steps''': 7,
           '''sld_guidance_scale''': 2_0_0_0,
           '''sld_threshold''': 0.025,
           '''sld_momentum_scale''': 0.5,
           '''sld_mom_beta''': 0.7,
       }
       a__             ={
           '''sld_warmup_steps''': 0,
           '''sld_guidance_scale''': 5_0_0_0,
           '''sld_threshold''': 1.0,
           '''sld_momentum_scale''': 0.5,
           '''sld_mom_beta''': 0.7,
       }
  
  
  
  @dataclass
  class 							_UpperCAmelCase							(				a   ):
  
  
  
  
  
       '''simple docstring'''
  
  
  
       a__             =4_2
       a__             =4_2
       a__             =4_2
       a__             =4_2
  
  
  if is_transformers_available() and is_torch_available():
    from .pipeline_stable_diffusion_safe import StableDiffusionPipelineSafe
    from .safety_checker import SafeStableDiffusionSafetyChecker
  
  
  
style_context: 
  
  
  
  """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,
  )
  
  
  _lowerCAmelCase					:Union[str, Any]  =      logging.getLogger(__name__)
  class 							_UpperCAmelCase							(				a   ):
  
  
  
  
  
       '''simple docstring'''
  
  
  
       def 				__lowerCAmelCase			(      self ,			A ,			A ,			A=None ,			A=None	)							->      Tuple:
           if current_layer == 0:
               _UpperCAmelCase							:						Optional[Any]       							=					self.embedding_hidden_mapping_in(A	)
           else:
               _UpperCAmelCase							:						Union[str, Any]       							=					hidden_states[0]
  
           _UpperCAmelCase							:						Dict       							=					int(self.config.num_hidden_layers / self.config.num_hidden_groups	)
  
           # Index of the hidden group
           _UpperCAmelCase							:						Dict       							=					int(current_layer / (self.config.num_hidden_layers / self.config.num_hidden_groups)	)
  
           _UpperCAmelCase							:						List[Any]       							=					self.albert_layer_groups[group_idx](
               A ,			A ,			head_mask[group_idx * layers_per_group : (group_idx + 1) * layers_per_group] ,			)
           _UpperCAmelCase							:						str       							=					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 							_UpperCAmelCase							(				a   ):
  
  
  
  
  
       '''simple docstring'''
  
  
  
       def __init__(      self ,			A	)							->      str:
           super().__init__(A	)
  
           _UpperCAmelCase							:						Dict       							=					AlbertTransformerWithPabee(A	)
  
           self.init_weights()
           _UpperCAmelCase							:						str       							=					0
           _UpperCAmelCase							:						Union[str, Any]       							=					0
           _UpperCAmelCase							:						Dict       							=					0
  
           _UpperCAmelCase							:						str       							=					0
  
  
  
  
  
       def 				__lowerCAmelCase			(      self ,			A	)							->      Optional[int]:
           _UpperCAmelCase							:						List[Any]       							=					threshold
  
  
  
  
  
       def 				__lowerCAmelCase			(      self ,			A	)							->      Optional[Any]:
           _UpperCAmelCase							:						int       							=					patience
  
  
  
  
  
       def 				__lowerCAmelCase			(      self	)							->      Any:
           _UpperCAmelCase							:						Dict       							=					0
           _UpperCAmelCase							:						List[str]       							=					0
  
  
  
  
  
       def 				__lowerCAmelCase			(      self	)							->      List[Any]:
           _UpperCAmelCase							:						Union[str, Any]       							=					self.inference_layers_num / self.inference_instances_num
           _UpperCAmelCase							:						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(A	)
  
  
  
  
  
       @add_start_docstrings_to_model_forward(A	)
       def 				__lowerCAmelCase			(      self ,			A=None ,			A=None ,			A=None ,			A=None ,			A=None ,			A=None ,			A=None ,			A=None ,			A=False ,			)							->      int:
  
           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							:						Optional[Any]       							=					input_ids.size()
           elif inputs_embeds is not None:
               _UpperCAmelCase							:						Optional[Any]       							=					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							:						Union[str, Any]       							=					torch.ones(A ,			device=A	)
           if token_type_ids is None:
               _UpperCAmelCase							:						Tuple       							=					torch.zeros(A ,			dtype=torch.long ,			device=A	)
  
           _UpperCAmelCase							:						Optional[Any]       							=					attention_mask.unsqueeze(1	).unsqueeze(2	)
           _UpperCAmelCase							:						Union[str, Any]       							=					extended_attention_mask.to(dtype=self.dtype	)  # fp16 compatibility
           _UpperCAmelCase							:						Dict       							=					(1.0 - extended_attention_mask) * -10_000.0
           _UpperCAmelCase							:						Any       							=					self.get_head_mask(A ,			self.config.num_hidden_layers	)
  
           _UpperCAmelCase							:						int       							=					self.embeddings(
               A ,			position_ids=A ,			token_type_ids=A ,			inputs_embeds=A	)
           _UpperCAmelCase							:						Any       							=					embedding_output
  
           if self.training:
               _UpperCAmelCase							:						Optional[Any]       							=					[]
               for i in range(self.config.num_hidden_layers	):
                   _UpperCAmelCase							:						Union[str, Any]       							=					self.encoder.adaptive_forward(
                       A ,			current_layer=A ,			attention_mask=A ,			head_mask=A ,			)
  
                   _UpperCAmelCase							:						List[str]       							=					self.pooler_activation(self.pooler(encoder_outputs[0][:, 0]	)	)
                   _UpperCAmelCase							:						Dict       							=					output_layers[i](output_dropout(A	)	)
                   res.append(A	)
           elif self.patience == 0:  # Use all layers for inference
               _UpperCAmelCase							:						Dict       							=					self.encoder(A ,			A ,			head_mask=A	)
               _UpperCAmelCase							:						Tuple       							=					self.pooler_activation(self.pooler(encoder_outputs[0][:, 0]	)	)
               _UpperCAmelCase							:						Optional[int]       							=					[output_layers[self.config.num_hidden_layers - 1](A	)]
           else:
               _UpperCAmelCase							:						List[Any]       							=					0
               _UpperCAmelCase							:						int       							=					None
               _UpperCAmelCase							:						int       							=					0
               for i in range(self.config.num_hidden_layers	):
                   calculated_layer_num += 1
                   _UpperCAmelCase							:						Tuple       							=					self.encoder.adaptive_forward(
                       A ,			current_layer=A ,			attention_mask=A ,			head_mask=A ,			)
  
                   _UpperCAmelCase							:						Any       							=					self.pooler_activation(self.pooler(encoder_outputs[0][:, 0]	)	)
                   _UpperCAmelCase							:						Optional[Any]       							=					output_layers[i](A	)
                   if regression:
                       _UpperCAmelCase							:						str       							=					logits.detach()
                       if patient_result is not None:
                           _UpperCAmelCase							:						str       							=					patient_result.detach()
                       if (patient_result is not None) and torch.abs(patient_result - labels	) < self.regression_threshold:
                           patient_counter += 1
                       else:
                           _UpperCAmelCase							:						List[Any]       							=					0
                   else:
                       _UpperCAmelCase							:						List[str]       							=					logits.detach().argmax(dim=1	)
                       if patient_result is not None:
                           _UpperCAmelCase							:						Optional[Any]       							=					patient_result.detach().argmax(dim=1	)
                       if (patient_result is not None) and torch.all(labels.eq(A	)	):
                           patient_counter += 1
                       else:
                           _UpperCAmelCase							:						Tuple       							=					0
  
                   _UpperCAmelCase							:						Any       							=					logits
                   if patient_counter == self.patience:
                       break
               _UpperCAmelCase							:						List[str]       							=					[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 							_UpperCAmelCase							(				a   ):
  
  
  
  
  
       '''simple docstring'''
  
  
  
       def __init__(      self ,			A	)							->      Optional[int]:
           super().__init__(A	)
           _UpperCAmelCase							:						List[Any]       							=					config.num_labels
  
           _UpperCAmelCase							:						Optional[int]       							=					AlbertModelWithPabee(A	)
           _UpperCAmelCase							:						List[str]       							=					nn.Dropout(config.classifier_dropout_prob	)
           _UpperCAmelCase							:						Optional[Any]       							=					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 ,			A=None ,			A=None ,			A=None ,			A=None ,			A=None ,			A=None ,			A=None ,			)							->      List[str]:
           _UpperCAmelCase							:						Optional[Any]       							=					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 ,			)
  
           _UpperCAmelCase							:						int       							=					(logits[-1],)
  
           if labels is not None:
               _UpperCAmelCase							:						int       							=					None
               _UpperCAmelCase							:						Tuple       							=					0
               for ix, logits_item in enumerate(A	):
                   if self.num_labels == 1:
                       #  We are doing regression
                       _UpperCAmelCase							:						Dict       							=					MSELoss()
                       _UpperCAmelCase							:						Optional[int]       							=					loss_fct(logits_item.view(-1	) ,			labels.view(-1	)	)
                   else:
                       _UpperCAmelCase							:						List[str]       							=					CrossEntropyLoss()
                       _UpperCAmelCase							:						Any       							=					loss_fct(logits_item.view(-1 ,			self.num_labels	) ,			labels.view(-1	)	)
                   if total_loss is None:
                       _UpperCAmelCase							:						Optional[Any]       							=					loss
                   else:
                       total_loss += loss * (ix + 1)
                   total_weights += ix + 1
               _UpperCAmelCase							:						Any       							=					(total_loss / total_weights,) + outputs
  
           return outputs
  
  
  