code: 
  
  
  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       :       Tuple				   =			logging.getLogger(__name__)
  
  
  
  class 			a      (      UpperCamelCase_   ):
  
  
  
  
  
  
  		def       lowerCAmelCase_  (  self	,     __UpperCamelCase	,     __UpperCamelCase	,     __UpperCamelCase=None	,     __UpperCamelCase=None							)->			Tuple:
  						'''simple docstring'''
  
  
  						if current_layer == 0:
  										A__     :							Dict   				=self.embedding_hidden_mapping_in(__UpperCamelCase							)
  						else:
  										A__     :							List[Any]   				=hidden_states[0]
  
  						A__     :							str   				=int(self.config.num_hidden_layers / self.config.num_hidden_groups							)
  
  						# Index of the hidden group
  						A__     :							Any   				=int(current_layer / (self.config.num_hidden_layers / self.config.num_hidden_groups)							)
  
  						A__     :							Union[str, Any]   				=self.albert_layer_groups[group_idx](
  						    __UpperCamelCase	,     __UpperCamelCase	,     head_mask[group_idx * layers_per_group : (group_idx + 1) * layers_per_group]	,     )
  						A__     :							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."""      ,UpperCamelCase_      ,)
  class 			a      (      UpperCamelCase_   ):
  
  
  
  
  
  
  		def __init__(  self	,     __UpperCamelCase							)->			Tuple:
  						'''simple docstring'''
  
  
  						super().__init__(__UpperCamelCase							)
  
  						A__     :							List[Any]   				=AlbertTransformerWithPabee(__UpperCamelCase							)
  
  						self.init_weights()
  						A__     :							Union[str, Any]   				=0
  						A__     :							Optional[Any]   				=0
  						A__     :							Optional[Any]   				=0
  
  						A__     :							List[str]   				=0
  
  
  
  
  
  
  		def       lowerCAmelCase_  (  self	,     __UpperCamelCase							)->			Union[str, Any]:
  						'''simple docstring'''
  
  
  						A__     :							Dict   				=threshold
  
  
  
  
  
  
  		def       lowerCAmelCase_  (  self	,     __UpperCamelCase							)->			Optional[int]:
  						'''simple docstring'''
  
  
  						A__     :							int   				=patience
  
  
  
  
  
  
  		def       lowerCAmelCase_  (  self							)->			Dict:
  						'''simple docstring'''
  
  
  						A__     :							Dict   				=0
  						A__     :							Any   				=0
  
  
  
  
  
  
  		def       lowerCAmelCase_  (  self							)->			str:
  						'''simple docstring'''
  
  
  						A__     :							Optional[Any]   				=self.inference_layers_num / self.inference_instances_num
  						A__     :							Dict   				=(
  						    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       lowerCAmelCase_  (  self	,     __UpperCamelCase=None	,     __UpperCamelCase=None	,     __UpperCamelCase=None	,     __UpperCamelCase=None	,     __UpperCamelCase=None	,     __UpperCamelCase=None	,     __UpperCamelCase=None	,     __UpperCamelCase=None	,     __UpperCamelCase=False	,     )->			Optional[int]:
  						'''simple docstring'''
  
  
  						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:
  										A__     :							str   				=input_ids.size()
  						elif inputs_embeds is not None:
  										A__     :							Optional[Any]   				=inputs_embeds.size()[:-1]
  						else:
  										raise ValueError('''You have to specify either input_ids or inputs_embeds'''							)
  
  						A__     :							int   				=input_ids.device if input_ids is not None else inputs_embeds.device
  
  						if attention_mask is None:
  										A__     :							Any   				=torch.ones(__UpperCamelCase	,     device=__UpperCamelCase							)
  						if token_type_ids is None:
  										A__     :							Optional[int]   				=torch.zeros(__UpperCamelCase	,     dtype=torch.long	,     device=__UpperCamelCase							)
  
  						A__     :							Tuple   				=attention_mask.unsqueeze(1							).unsqueeze(2							)
  						A__     :							Union[str, Any]   				=extended_attention_mask.to(dtype=self.dtype							)  # fp16 compatibility
  						A__     :							List[str]   				=(1.0 - extended_attention_mask) * -1_0000.0
  						A__     :							Optional[Any]   				=self.get_head_mask(__UpperCamelCase	,     self.config.num_hidden_layers							)
  
  						A__     :							int   				=self.embeddings(
  						    __UpperCamelCase	,     position_ids=__UpperCamelCase	,     token_type_ids=__UpperCamelCase	,     inputs_embeds=__UpperCamelCase							)
  						A__     :							int   				=embedding_output
  
  						if self.training:
  										A__     :							Optional[Any]   				=[]
  										for i in range(self.config.num_hidden_layers							):
  														A__     :							str   				=self.encoder.adaptive_forward(
  														    __UpperCamelCase	,     current_layer=__UpperCamelCase	,     attention_mask=__UpperCamelCase	,     head_mask=__UpperCamelCase	,     )
  
  														A__     :							Tuple   				=self.pooler_activation(self.pooler(encoder_outputs[0][:, 0]							)							)
  														A__     :							Dict   				=output_layers[i](output_dropout(__UpperCamelCase							)							)
  														res.append(__UpperCamelCase							)
  						elif self.patience == 0:  # Use all layers for inference
  										A__     :							List[Any]   				=self.encoder(__UpperCamelCase	,     __UpperCamelCase	,     head_mask=__UpperCamelCase							)
  										A__     :							Tuple   				=self.pooler_activation(self.pooler(encoder_outputs[0][:, 0]							)							)
  										A__     :							Any   				=[output_layers[self.config.num_hidden_layers - 1](__UpperCamelCase							)]
  						else:
  										A__     :							List[str]   				=0
  										A__     :							int   				=None
  										A__     :							Dict   				=0
  										for i in range(self.config.num_hidden_layers							):
  														calculated_layer_num += 1
  														A__     :							Optional[Any]   				=self.encoder.adaptive_forward(
  														    __UpperCamelCase	,     current_layer=__UpperCamelCase	,     attention_mask=__UpperCamelCase	,     head_mask=__UpperCamelCase	,     )
  
  														A__     :							Optional[Any]   				=self.pooler_activation(self.pooler(encoder_outputs[0][:, 0]							)							)
  														A__     :							Optional[int]   				=output_layers[i](__UpperCamelCase							)
  														if regression:
  																		A__     :							Dict   				=logits.detach()
  																		if patient_result is not None:
  																						A__     :							int   				=patient_result.detach()
  																		if (patient_result is not None) and torch.abs(patient_result - labels							) < self.regression_threshold:
  																						patient_counter += 1
  																		else:
  																						A__     :							List[str]   				=0
  														else:
  																		A__     :							Optional[Any]   				=logits.detach().argmax(dim=1							)
  																		if patient_result is not None:
  																						A__     :							Tuple   				=patient_result.detach().argmax(dim=1							)
  																		if (patient_result is not None) and torch.all(labels.eq(__UpperCamelCase							)							):
  																						patient_counter += 1
  																		else:
  																						A__     :							List[Any]   				=0
  
  														A__     :							Union[str, Any]   				=logits
  														if patient_counter == self.patience:
  																		break
  										A__     :							Optional[int]   				=[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. """      ,UpperCamelCase_      ,)
  class 			a      (      UpperCamelCase_   ):
  
  
  
  
  
  
  		def __init__(  self	,     __UpperCamelCase							)->			List[str]:
  						'''simple docstring'''
  
  
  						super().__init__(__UpperCamelCase							)
  						A__     :							Union[str, Any]   				=config.num_labels
  
  						A__     :							Optional[Any]   				=AlbertModelWithPabee(__UpperCamelCase							)
  						A__     :							int   				=nn.Dropout(config.classifier_dropout_prob							)
  						A__     :							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(__UpperCamelCase							)
  		def       lowerCAmelCase_  (  self	,     __UpperCamelCase=None	,     __UpperCamelCase=None	,     __UpperCamelCase=None	,     __UpperCamelCase=None	,     __UpperCamelCase=None	,     __UpperCamelCase=None	,     __UpperCamelCase=None	,     )->			int:
  						'''simple docstring'''
  
  
  						A__     :							Optional[int]   				=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	,     )
  
  						A__     :							Tuple   				=(logits[-1],)
  
  						if labels is not None:
  										A__     :							List[Any]   				=None
  										A__     :							Dict   				=0
  										for ix, logits_item in enumerate(__UpperCamelCase							):
  														if self.num_labels == 1:
  																		#  We are doing regression
  																		A__     :							int   				=MSELoss()
  																		A__     :							Union[str, Any]   				=loss_fct(logits_item.view(-1							)	,     labels.view(-1							)							)
  														else:
  																		A__     :							List[Any]   				=CrossEntropyLoss()
  																		A__     :							Any   				=loss_fct(logits_item.view(-1	,     self.num_labels							)	,     labels.view(-1							)							)
  														if total_loss is None:
  																		A__     :							Optional[Any]   				=loss
  														else:
  																		total_loss += loss * (ix + 1)
  														total_weights += ix + 1
  										A__     :							Optional[int]   				=(total_loss / total_weights,) + outputs
  
  						return outputs
  
  
  
  
style_context: 
  
  
  from typing import TYPE_CHECKING
  
  from ...utils import (
      OptionalDependencyNotAvailable,
      _LazyModule,
      is_torch_available,
  )
  
  
  __lowerCamelCase       :       str				   =			{
      "configuration_align": [
          "ALIGN_PRETRAINED_CONFIG_ARCHIVE_MAP",
          "AlignConfig",
          "AlignTextConfig",
          "AlignVisionConfig",
      ],
      "processing_align": ["AlignProcessor"],
  }
  
  try:
  						if not is_torch_available():
  												raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  						pass
  else:
  						__lowerCamelCase       :       Dict				   =			[
  						    "ALIGN_PRETRAINED_MODEL_ARCHIVE_LIST",
  						    "AlignModel",
  						    "AlignPreTrainedModel",
  						    "AlignTextModel",
  						    "AlignVisionModel",
  						]
  
  if TYPE_CHECKING:
  						from .configuration_align import (
  						    ALIGN_PRETRAINED_CONFIG_ARCHIVE_MAP,
  						    AlignConfig,
  						    AlignTextConfig,
  						    AlignVisionConfig,
  						)
  						from .processing_align import AlignProcessor
  
  						try:
  												if not is_torch_available():
  																		raise OptionalDependencyNotAvailable()
  						except OptionalDependencyNotAvailable:
  												pass
  						else:
  												from .modeling_align import (
  												    ALIGN_PRETRAINED_MODEL_ARCHIVE_LIST,
  												    AlignModel,
  												    AlignPreTrainedModel,
  												    AlignTextModel,
  												    AlignVisionModel,
  												)
  
  else:
  						import sys
  
  						__lowerCamelCase       :       List[str]				   =			_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
  
  
  
  