code: 
  
  
  """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,
  )
  
  
  UpperCAmelCase   =	logging.getLogger(__name__)
  
  
  
  
  class   lowercase__							(				A_							):
  
  
  
  
  
     def 		UpperCamelCase_     (       self			,      SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE=None			,      SCREAMING_SNAKE_CASE=None)     ->     int:
        if current_layer == 0:
           _lowerCamelCase  :				Any      =		self.embedding_hidden_mapping_in(SCREAMING_SNAKE_CASE)
        else:
           _lowerCamelCase  :				Dict      =		hidden_states[0]
  
        _lowerCamelCase  :				List[str]      =		int(self.config.num_hidden_layers / self.config.num_hidden_groups)
  
        # Index of the hidden group
        _lowerCamelCase  :				Optional[int]      =		int(current_layer / (self.config.num_hidden_layers / self.config.num_hidden_groups))
  
        _lowerCamelCase  :				int      =		self.albert_layer_groups[group_idx](
            SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE			,      head_mask[group_idx * layers_per_group : (group_idx + 1) * layers_per_group]			,      )
        _lowerCamelCase  :				List[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   lowercase__							(				A_							):
  
  
  
  
  
     def __init__(       self			,      SCREAMING_SNAKE_CASE)     ->     Dict:
        super().__init__(SCREAMING_SNAKE_CASE)
  
        _lowerCamelCase  :				Dict      =		AlbertTransformerWithPabee(SCREAMING_SNAKE_CASE)
  
        self.init_weights()
        _lowerCamelCase  :				str      =		0
        _lowerCamelCase  :				int      =		0
        _lowerCamelCase  :				str      =		0
  
        _lowerCamelCase  :				Optional[Any]      =		0
  
  
  
  
  
     def 		UpperCamelCase_     (       self			,      SCREAMING_SNAKE_CASE)     ->     Dict:
        _lowerCamelCase  :				Union[str, Any]      =		threshold
  
  
  
  
  
     def 		UpperCamelCase_     (       self			,      SCREAMING_SNAKE_CASE)     ->     str:
        _lowerCamelCase  :				List[str]      =		patience
  
  
  
  
  
     def 		UpperCamelCase_     (       self)     ->     Optional[int]:
        _lowerCamelCase  :				List[str]      =		0
        _lowerCamelCase  :				Optional[int]      =		0
  
  
  
  
  
     def 		UpperCamelCase_     (       self)     ->     Union[str, Any]:
        _lowerCamelCase  :				Any      =		self.inference_layers_num / self.inference_instances_num
        _lowerCamelCase  :				Union[str, 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(SCREAMING_SNAKE_CASE)
  
  
  
  
  
     @add_start_docstrings_to_model_forward(SCREAMING_SNAKE_CASE)
     def 		UpperCamelCase_     (       self			,      SCREAMING_SNAKE_CASE=None			,      SCREAMING_SNAKE_CASE=None			,      SCREAMING_SNAKE_CASE=None			,      SCREAMING_SNAKE_CASE=None			,      SCREAMING_SNAKE_CASE=None			,      SCREAMING_SNAKE_CASE=None			,      SCREAMING_SNAKE_CASE=None			,      SCREAMING_SNAKE_CASE=None			,      SCREAMING_SNAKE_CASE=False			,      )     ->     List[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:
           _lowerCamelCase  :				int      =		input_ids.size()
        elif inputs_embeds is not None:
           _lowerCamelCase  :				List[Any]      =		inputs_embeds.size()[:-1]
        else:
           raise ValueError("""You have to specify either input_ids or inputs_embeds""")
  
        _lowerCamelCase  :				Dict      =		input_ids.device if input_ids is not None else inputs_embeds.device
  
        if attention_mask is None:
           _lowerCamelCase  :				Tuple      =		torch.ones(SCREAMING_SNAKE_CASE			,      device=SCREAMING_SNAKE_CASE)
        if token_type_ids is None:
           _lowerCamelCase  :				Any      =		torch.zeros(SCREAMING_SNAKE_CASE			,      dtype=torch.long			,      device=SCREAMING_SNAKE_CASE)
  
        _lowerCamelCase  :				List[str]      =		attention_mask.unsqueeze(1).unsqueeze(2)
        _lowerCamelCase  :				List[str]      =		extended_attention_mask.to(dtype=self.dtype)  # fp16 compatibility
        _lowerCamelCase  :				Union[str, Any]      =		(1.0 - extended_attention_mask) * -1_00_00.0
        _lowerCamelCase  :				int      =		self.get_head_mask(SCREAMING_SNAKE_CASE			,      self.config.num_hidden_layers)
  
        _lowerCamelCase  :				Optional[int]      =		self.embeddings(
            SCREAMING_SNAKE_CASE			,      position_ids=SCREAMING_SNAKE_CASE			,      token_type_ids=SCREAMING_SNAKE_CASE			,      inputs_embeds=SCREAMING_SNAKE_CASE)
        _lowerCamelCase  :				List[str]      =		embedding_output
  
        if self.training:
           _lowerCamelCase  :				str      =		[]
           for i in range(self.config.num_hidden_layers):
              _lowerCamelCase  :				List[Any]      =		self.encoder.adaptive_forward(
                  SCREAMING_SNAKE_CASE			,      current_layer=SCREAMING_SNAKE_CASE			,      attention_mask=SCREAMING_SNAKE_CASE			,      head_mask=SCREAMING_SNAKE_CASE			,      )
  
              _lowerCamelCase  :				Union[str, Any]      =		self.pooler_activation(self.pooler(encoder_outputs[0][:, 0]))
              _lowerCamelCase  :				List[str]      =		output_layers[i](output_dropout(SCREAMING_SNAKE_CASE))
              res.append(SCREAMING_SNAKE_CASE)
        elif self.patience == 0:  # Use all layers for inference
           _lowerCamelCase  :				str      =		self.encoder(SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE			,      head_mask=SCREAMING_SNAKE_CASE)
           _lowerCamelCase  :				Union[str, Any]      =		self.pooler_activation(self.pooler(encoder_outputs[0][:, 0]))
           _lowerCamelCase  :				Optional[Any]      =		[output_layers[self.config.num_hidden_layers - 1](SCREAMING_SNAKE_CASE)]
        else:
           _lowerCamelCase  :				Dict      =		0
           _lowerCamelCase  :				Tuple      =		None
           _lowerCamelCase  :				Dict      =		0
           for i in range(self.config.num_hidden_layers):
              calculated_layer_num += 1
              _lowerCamelCase  :				str      =		self.encoder.adaptive_forward(
                  SCREAMING_SNAKE_CASE			,      current_layer=SCREAMING_SNAKE_CASE			,      attention_mask=SCREAMING_SNAKE_CASE			,      head_mask=SCREAMING_SNAKE_CASE			,      )
  
              _lowerCamelCase  :				int      =		self.pooler_activation(self.pooler(encoder_outputs[0][:, 0]))
              _lowerCamelCase  :				List[Any]      =		output_layers[i](SCREAMING_SNAKE_CASE)
              if regression:
                 _lowerCamelCase  :				List[str]      =		logits.detach()
                 if patient_result is not None:
                    _lowerCamelCase  :				int      =		patient_result.detach()
                 if (patient_result is not None) and torch.abs(patient_result - labels) < self.regression_threshold:
                    patient_counter += 1
                 else:
                    _lowerCamelCase  :				List[Any]      =		0
              else:
                 _lowerCamelCase  :				List[str]      =		logits.detach().argmax(dim=1)
                 if patient_result is not None:
                    _lowerCamelCase  :				Any      =		patient_result.detach().argmax(dim=1)
                 if (patient_result is not None) and torch.all(labels.eq(SCREAMING_SNAKE_CASE)):
                    patient_counter += 1
                 else:
                    _lowerCamelCase  :				int      =		0
  
              _lowerCamelCase  :				List[str]      =		logits
              if patient_counter == self.patience:
                 break
           _lowerCamelCase  :				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. '''   ,A_   ,)
  class   lowercase__							(				A_							):
  
  
  
  
  
     def __init__(       self			,      SCREAMING_SNAKE_CASE)     ->     Dict:
        super().__init__(SCREAMING_SNAKE_CASE)
        _lowerCamelCase  :				List[Any]      =		config.num_labels
  
        _lowerCamelCase  :				List[str]      =		AlbertModelWithPabee(SCREAMING_SNAKE_CASE)
        _lowerCamelCase  :				str      =		nn.Dropout(config.classifier_dropout_prob)
        _lowerCamelCase  :				List[str]      =		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(SCREAMING_SNAKE_CASE)
     def 		UpperCamelCase_     (       self			,      SCREAMING_SNAKE_CASE=None			,      SCREAMING_SNAKE_CASE=None			,      SCREAMING_SNAKE_CASE=None			,      SCREAMING_SNAKE_CASE=None			,      SCREAMING_SNAKE_CASE=None			,      SCREAMING_SNAKE_CASE=None			,      SCREAMING_SNAKE_CASE=None			,      )     ->     Optional[int]:
        _lowerCamelCase  :				int      =		self.albert(
            input_ids=SCREAMING_SNAKE_CASE			,      attention_mask=SCREAMING_SNAKE_CASE			,      token_type_ids=SCREAMING_SNAKE_CASE			,      position_ids=SCREAMING_SNAKE_CASE			,      head_mask=SCREAMING_SNAKE_CASE			,      inputs_embeds=SCREAMING_SNAKE_CASE			,      output_dropout=self.dropout			,      output_layers=self.classifiers			,      regression=self.num_labels == 1			,      )
  
        _lowerCamelCase  :				str      =		(logits[-1],)
  
        if labels is not None:
           _lowerCamelCase  :				Dict      =		None
           _lowerCamelCase  :				List[Any]      =		0
           for ix, logits_item in enumerate(SCREAMING_SNAKE_CASE):
              if self.num_labels == 1:
                 #  We are doing regression
                 _lowerCamelCase  :				Any      =		MSELoss()
                 _lowerCamelCase  :				Any      =		loss_fct(logits_item.view(-1)			,      labels.view(-1))
              else:
                 _lowerCamelCase  :				int      =		CrossEntropyLoss()
                 _lowerCamelCase  :				List[str]      =		loss_fct(logits_item.view(-1			,      self.num_labels)			,      labels.view(-1))
              if total_loss is None:
                 _lowerCamelCase  :				str      =		loss
              else:
                 total_loss += loss * (ix + 1)
              total_weights += ix + 1
           _lowerCamelCase  :				Optional[int]      =		(total_loss / total_weights,) + outputs
  
        return outputs
  
  
style_context: 
  
  
  """simple docstring"""
  import torch
  import trajectory.utils as utils
  
  from transformers import TrajectoryTransformerModel
  
  
  
  
  class   lowercase__							(				utils.Parser							):
     __UpperCAmelCase       					=				"halfcheetah-medium-expert-v2"
     __UpperCAmelCase       					=				"config.offline"
  
  
  
  
  
  
  
  def 							_snake_case			(      __snake_case       :			Any	,   __snake_case       :			Optional[Any]	,   __snake_case       :			Optional[int]	,   __snake_case       :			List[str]	,   __snake_case       :			Union[str, Any]      ):
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     _lowerCamelCase      ,						_lowerCamelCase  :				int      =		utils.load_model(__snake_case	,   __snake_case	,   __snake_case	,   epoch=__snake_case	,   device=__snake_case      )
     _lowerCamelCase  :				List[str]      =		TrajectoryTransformerModel(gpt.config      )
  
     trajectory_transformer.tok_emb.load_state_dict(gpt.tok_emb.state_dict()      )
     _lowerCamelCase  :				str      =		gpt.pos_emb
     trajectory_transformer.drop.load_state_dict(gpt.drop.state_dict()      )
     trajectory_transformer.ln_f.load_state_dict(gpt.ln_f.state_dict()      )
     trajectory_transformer.head.load_state_dict(gpt.head.state_dict()      )
  
     for i, block in enumerate(gpt.blocks      ):
        trajectory_transformer.blocks[i].lna.load_state_dict(gpt.blocks[i].lna.state_dict()      )
        trajectory_transformer.blocks[i].lna.load_state_dict(gpt.blocks[i].lna.state_dict()      )
        trajectory_transformer.blocks[i].attn.load_state_dict(gpt.blocks[i].attn.state_dict()      )
  
        trajectory_transformer.blocks[i].la.load_state_dict(gpt.blocks[i].mlp[0].state_dict()      )
        trajectory_transformer.blocks[i].act.load_state_dict(gpt.blocks[i].mlp[1].state_dict()      )
        trajectory_transformer.blocks[i].la.load_state_dict(gpt.blocks[i].mlp[2].state_dict()      )
        trajectory_transformer.blocks[i].drop.load_state_dict(gpt.blocks[i].mlp[3].state_dict()      )
  
     torch.save(trajectory_transformer.state_dict()	,   """pytorch_model.bin"""      )
  
  
  if __name__ == "__main__":
   UpperCAmelCase   =	Parser().parse_args("""plan""")
   convert_trajectory_transformer_original_pytorch_checkpoint_to_pytorch(
       args.logbase, args.dataset, args.gpt_loadpath, args.gpt_epoch, args.device
   )
  
  