

import unittest

from transformers import ElectraConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
 import torch

 from transformers import (
     MODEL_FOR_PRETRAINING_MAPPING,
     ElectraForCausalLM,
     ElectraForMaskedLM,
     ElectraForMultipleChoice,
     ElectraForPreTraining,
     ElectraForQuestionAnswering,
     ElectraForSequenceClassification,
     ElectraForTokenClassification,
     ElectraModel,
 )
 from transformers.models.electra.modeling_electra import ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST



class 					lowercase		:



 '''simple docstring'''


 def __init__(self , __a , __a=13 , __a=7 , __a=True , __a=True , __a=True , __a=True , __a=99 , __a=32 , __a=5 , __a=4 , __a=37 , __a="gelu" , __a=0.1 , __a=0.1 , __a=512 , __a=16 , __a=2 , __a=0.02 , __a=3 , __a=4 , __a=None , )				->      Union[str, Any]:






  """simple docstring"""





  UpperCAmelCase__           =       parent
  UpperCAmelCase__           =       batch_size
  UpperCAmelCase__           =       seq_length
  UpperCAmelCase__           =       is_training
  UpperCAmelCase__           =       use_input_mask
  UpperCAmelCase__           =       use_token_type_ids
  UpperCAmelCase__           =       use_labels
  UpperCAmelCase__           =       vocab_size
  UpperCAmelCase__           =       hidden_size
  UpperCAmelCase__           =       num_hidden_layers
  UpperCAmelCase__           =       num_attention_heads
  UpperCAmelCase__           =       intermediate_size
  UpperCAmelCase__           =       hidden_act
  UpperCAmelCase__           =       hidden_dropout_prob
  UpperCAmelCase__           =       attention_probs_dropout_prob
  UpperCAmelCase__           =       max_position_embeddings
  UpperCAmelCase__           =       type_vocab_size
  UpperCAmelCase__           =       type_sequence_label_size
  UpperCAmelCase__           =       initializer_range
  UpperCAmelCase__           =       num_labels
  UpperCAmelCase__           =       num_choices
  UpperCAmelCase__           =       scope




 def 						UpperCamelCase__							(self				)				->      int:






  """simple docstring"""





  UpperCAmelCase__           =       ids_tensor([self.batch_size, self.seq_length] , self.vocab_size				)

  UpperCAmelCase__           =       None
  if self.use_input_mask:
   UpperCAmelCase__           =       random_attention_mask([self.batch_size, self.seq_length]				)

  UpperCAmelCase__           =       None
  if self.use_token_type_ids:
   UpperCAmelCase__           =       ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size				)

  UpperCAmelCase__           =       None
  UpperCAmelCase__           =       None
  UpperCAmelCase__           =       None
  if self.use_labels:
   UpperCAmelCase__           =       ids_tensor([self.batch_size] , self.type_sequence_label_size				)
   UpperCAmelCase__           =       ids_tensor([self.batch_size, self.seq_length] , self.num_labels				)
   UpperCAmelCase__           =       ids_tensor([self.batch_size] , self.num_choices				)
   UpperCAmelCase__           =       ids_tensor([self.batch_size, self.seq_length] , 1				)

  UpperCAmelCase__           =       self.get_config()

  return (
      config,
      input_ids,
      token_type_ids,
      input_mask,
      sequence_labels,
      token_labels,
      choice_labels,
      fake_token_labels,
  )




 def 						UpperCamelCase__							(self				)				->      Dict:






  """simple docstring"""





  return ElectraConfig(
      vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=lowerCamelCase_ , initializer_range=self.initializer_range , )




 def 						UpperCamelCase__							(self				)				->      Dict:






  """simple docstring"""





  (
      (
      UpperCAmelCase__							
  )       ,					(
      UpperCAmelCase__							
  )       ,					(
      UpperCAmelCase__							
  )       ,					(
      UpperCAmelCase__							
  )       ,					(
      UpperCAmelCase__							
  )       ,					(
      UpperCAmelCase__							
  )       ,					(
      UpperCAmelCase__							
  )       ,					(
      UpperCAmelCase__							
  )       ,												
  )           =       self.prepare_config_and_inputs()

  UpperCAmelCase__           =       True
  UpperCAmelCase__           =       floats_tensor([self.batch_size, self.seq_length, self.hidden_size]				)
  UpperCAmelCase__           =       ids_tensor([self.batch_size, self.seq_length] , vocab_size=2				)

  return (
      config,
      input_ids,
      token_type_ids,
      input_mask,
      sequence_labels,
      token_labels,
      choice_labels,
      encoder_hidden_states,
      encoder_attention_mask,
  )




 def 						UpperCamelCase__							(self , __a , __a , __a , __a , __a , __a , __a , __a , )				->      Any:






  """simple docstring"""





  UpperCAmelCase__           =       ElectraModel(config=lowerCamelCase_				)
  model.to(lowerCamelCase_				)
  model.eval()
  UpperCAmelCase__           =       model(lowerCamelCase_ , attention_mask=lowerCamelCase_ , token_type_ids=lowerCamelCase_				)
  UpperCAmelCase__           =       model(lowerCamelCase_ , token_type_ids=lowerCamelCase_				)
  UpperCAmelCase__           =       model(lowerCamelCase_				)
  self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size)				)




 def 						UpperCamelCase__							(self , __a , __a , __a , __a , __a , __a , __a , __a , __a , )				->      Optional[int]:






  """simple docstring"""





  UpperCAmelCase__           =       True
  UpperCAmelCase__           =       ElectraModel(lowerCamelCase_				)
  model.to(lowerCamelCase_				)
  model.eval()
  UpperCAmelCase__           =       model(
      lowerCamelCase_ , attention_mask=lowerCamelCase_ , token_type_ids=lowerCamelCase_ , encoder_hidden_states=lowerCamelCase_ , encoder_attention_mask=lowerCamelCase_ , )
  UpperCAmelCase__           =       model(
      lowerCamelCase_ , attention_mask=lowerCamelCase_ , token_type_ids=lowerCamelCase_ , encoder_hidden_states=lowerCamelCase_ , )
  UpperCAmelCase__           =       model(lowerCamelCase_ , attention_mask=lowerCamelCase_ , token_type_ids=lowerCamelCase_				)
  self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size)				)




 def 						UpperCamelCase__							(self , __a , __a , __a , __a , __a , __a , __a , __a , )				->      Any:






  """simple docstring"""





  UpperCAmelCase__           =       ElectraForMaskedLM(config=lowerCamelCase_				)
  model.to(lowerCamelCase_				)
  model.eval()
  UpperCAmelCase__           =       model(lowerCamelCase_ , attention_mask=lowerCamelCase_ , token_type_ids=lowerCamelCase_ , labels=lowerCamelCase_				)
  self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size)				)




 def 						UpperCamelCase__							(self , __a , __a , __a , __a , __a , __a , __a , __a , __a , )				->      Dict:






  """simple docstring"""





  UpperCAmelCase__           =       ElectraForCausalLM(config=lowerCamelCase_				)
  model.to(lowerCamelCase_				)
  model.eval()
  UpperCAmelCase__           =       model(lowerCamelCase_ , attention_mask=lowerCamelCase_ , token_type_ids=lowerCamelCase_ , labels=lowerCamelCase_				)
  self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size)				)




 def 						UpperCamelCase__							(self , __a , __a , __a , __a , __a , __a , __a , __a , )				->      Optional[int]:






  """simple docstring"""





  UpperCAmelCase__           =       self.num_labels
  UpperCAmelCase__           =       ElectraForTokenClassification(config=lowerCamelCase_				)
  model.to(lowerCamelCase_				)
  model.eval()
  UpperCAmelCase__           =       model(lowerCamelCase_ , attention_mask=lowerCamelCase_ , token_type_ids=lowerCamelCase_ , labels=lowerCamelCase_				)
  self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels)				)




 def 						UpperCamelCase__							(self , __a , __a , __a , __a , __a , __a , __a , __a , )				->      Union[str, Any]:






  """simple docstring"""





  UpperCAmelCase__           =       self.num_labels
  UpperCAmelCase__           =       ElectraForPreTraining(config=lowerCamelCase_				)
  model.to(lowerCamelCase_				)
  model.eval()
  UpperCAmelCase__           =       model(lowerCamelCase_ , attention_mask=lowerCamelCase_ , token_type_ids=lowerCamelCase_ , labels=lowerCamelCase_				)
  self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length)				)




 def 						UpperCamelCase__							(self , __a , __a , __a , __a , __a , __a , __a , __a , )				->      str:






  """simple docstring"""





  UpperCAmelCase__           =       self.num_labels
  UpperCAmelCase__           =       ElectraForSequenceClassification(lowerCamelCase_				)
  model.to(lowerCamelCase_				)
  model.eval()
  UpperCAmelCase__           =       model(lowerCamelCase_ , attention_mask=lowerCamelCase_ , token_type_ids=lowerCamelCase_ , labels=lowerCamelCase_				)
  self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels)				)




 def 						UpperCamelCase__							(self , __a , __a , __a , __a , __a , __a , __a , __a , )				->      Dict:






  """simple docstring"""





  UpperCAmelCase__           =       ElectraForQuestionAnswering(config=lowerCamelCase_				)
  model.to(lowerCamelCase_				)
  model.eval()
  UpperCAmelCase__           =       model(
      lowerCamelCase_ , attention_mask=lowerCamelCase_ , token_type_ids=lowerCamelCase_ , start_positions=lowerCamelCase_ , end_positions=lowerCamelCase_ , )
  self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length)				)
  self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length)				)




 def 						UpperCamelCase__							(self , __a , __a , __a , __a , __a , __a , __a , __a , )				->      Union[str, Any]:






  """simple docstring"""





  UpperCAmelCase__           =       self.num_choices
  UpperCAmelCase__           =       ElectraForMultipleChoice(config=lowerCamelCase_				)
  model.to(lowerCamelCase_				)
  model.eval()
  UpperCAmelCase__           =       input_ids.unsqueeze(1				).expand(-1 , self.num_choices , -1				).contiguous()
  UpperCAmelCase__           =       token_type_ids.unsqueeze(1				).expand(-1 , self.num_choices , -1				).contiguous()
  UpperCAmelCase__           =       input_mask.unsqueeze(1				).expand(-1 , self.num_choices , -1				).contiguous()
  UpperCAmelCase__           =       model(
      lowerCamelCase_ , attention_mask=lowerCamelCase_ , token_type_ids=lowerCamelCase_ , labels=lowerCamelCase_ , )
  self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices)				)




 def 						UpperCamelCase__							(self				)				->      Optional[int]:






  """simple docstring"""





  UpperCAmelCase__           =       self.prepare_config_and_inputs()
  (
      (
      UpperCAmelCase__							
  )       ,					(
      UpperCAmelCase__							
  )       ,					(
      UpperCAmelCase__							
  )       ,					(
      UpperCAmelCase__							
  )       ,					(
      UpperCAmelCase__							
  )       ,					(
      UpperCAmelCase__							
  )       ,					(
      UpperCAmelCase__							
  )       ,					(
      UpperCAmelCase__							
  )       ,												
  )           =       config_and_inputs
  UpperCAmelCase__           =       {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
  return config, inputs_dict







@require_torch
class 					lowercase		( lowerCAmelCase_				,       lowerCAmelCase_				,       unittest.TestCase      ):



 '''simple docstring'''


 __SCREAMING_SNAKE_CASE      							=       (
     (
         ElectraModel,
         ElectraForPreTraining,
         ElectraForMaskedLM,
         ElectraForCausalLM,
         ElectraForMultipleChoice,
         ElectraForTokenClassification,
         ElectraForSequenceClassification,
         ElectraForQuestionAnswering,
     )
     if is_torch_available()
     else ()
 )
 __SCREAMING_SNAKE_CASE      							=       (
     {
         'feature-extraction': ElectraModel,
         'fill-mask': ElectraForMaskedLM,
         'question-answering': ElectraForQuestionAnswering,
         'text-classification': ElectraForSequenceClassification,
         'text-generation': ElectraForCausalLM,
         'token-classification': ElectraForTokenClassification,
         'zero-shot': ElectraForSequenceClassification,
     }
     if is_torch_available()
     else {}
 )
 __SCREAMING_SNAKE_CASE      							=       True




 def 						UpperCamelCase__							(self , __a , __a , __a=False				)				->      Any:






  """simple docstring"""





  UpperCAmelCase__           =       super()._prepare_for_class(lowerCamelCase_ , lowerCamelCase_ , return_labels=lowerCamelCase_				)

  if return_labels:
   if model_class in get_values(lowerCamelCase_				):
    UpperCAmelCase__           =       torch.zeros(
        (self.model_tester.batch_size, self.model_tester.seq_length) , dtype=torch.long , device=lowerCamelCase_				)
  return inputs_dict




 def 						UpperCamelCase__							(self				)				->      List[str]:






  """simple docstring"""





  UpperCAmelCase__           =       ElectraModelTester(self				)
  UpperCAmelCase__           =       ConfigTester(self , config_class=lowerCamelCase_ , hidden_size=37				)




 def 						UpperCamelCase__							(self				)				->      List[Any]:






  """simple docstring"""





  self.config_tester.run_common_tests()




 def 						UpperCamelCase__							(self				)				->      str:






  """simple docstring"""





  UpperCAmelCase__           =       self.model_tester.prepare_config_and_inputs()
  self.model_tester.create_and_check_electra_model(*lowerCamelCase_				)




 def 						UpperCamelCase__							(self				)				->      Optional[Any]:






  """simple docstring"""





  UpperCAmelCase__           =       self.model_tester.prepare_config_and_inputs_for_decoder()
  self.model_tester.create_and_check_electra_model_as_decoder(*lowerCamelCase_				)




 def 						UpperCamelCase__							(self				)				->      Tuple:






  """simple docstring"""





  UpperCAmelCase__           =       self.model_tester.prepare_config_and_inputs()
  for type in ["absolute", "relative_key", "relative_key_query"]:
   UpperCAmelCase__           =       type
   self.model_tester.create_and_check_electra_model(*lowerCamelCase_				)




 def 						UpperCamelCase__							(self				)				->      Tuple:






  """simple docstring"""





  UpperCAmelCase__           =       self.model_tester.prepare_config_and_inputs()
  self.model_tester.create_and_check_electra_for_masked_lm(*lowerCamelCase_				)




 def 						UpperCamelCase__							(self				)				->      int:






  """simple docstring"""





  UpperCAmelCase__           =       self.model_tester.prepare_config_and_inputs()
  self.model_tester.create_and_check_electra_for_token_classification(*lowerCamelCase_				)




 def 						UpperCamelCase__							(self				)				->      Optional[int]:






  """simple docstring"""





  UpperCAmelCase__           =       self.model_tester.prepare_config_and_inputs()
  self.model_tester.create_and_check_electra_for_pretraining(*lowerCamelCase_				)




 def 						UpperCamelCase__							(self				)				->      List[str]:






  """simple docstring"""





  UpperCAmelCase__           =       self.model_tester.prepare_config_and_inputs()
  self.model_tester.create_and_check_electra_for_sequence_classification(*lowerCamelCase_				)




 def 						UpperCamelCase__							(self				)				->      int:






  """simple docstring"""





  UpperCAmelCase__           =       self.model_tester.prepare_config_and_inputs()
  self.model_tester.create_and_check_electra_for_question_answering(*lowerCamelCase_				)




 def 						UpperCamelCase__							(self				)				->      Any:






  """simple docstring"""





  UpperCAmelCase__           =       self.model_tester.prepare_config_and_inputs()
  self.model_tester.create_and_check_electra_for_multiple_choice(*lowerCamelCase_				)




 @slow
 def 						UpperCamelCase__							(self				)				->      Tuple:






  """simple docstring"""





  for model_name in ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
   UpperCAmelCase__           =       ElectraModel.from_pretrained(lowerCamelCase_				)
   self.assertIsNotNone(lowerCamelCase_				)




 def 						UpperCamelCase__							(self				)				->      Optional[Any]:






  """simple docstring"""





  UpperCAmelCase__           =       self.model_tester.prepare_config_and_inputs_for_decoder()
  self.model_tester.create_and_check_electra_for_causal_lm(*lowerCamelCase_				)







@require_torch
class 					lowercase		( unittest.TestCase      ):



 '''simple docstring'''


 @slow
 def 						UpperCamelCase__							(self				)				->      Union[str, Any]:






  """simple docstring"""





  UpperCAmelCase__           =       ElectraModel.from_pretrained('google/electra-small-discriminator'				)
  UpperCAmelCase__           =       torch.tensor([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]]				)
  UpperCAmelCase__           =       torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]				)
  UpperCAmelCase__           =       model(lowerCamelCase_ , attention_mask=lowerCamelCase_				)[0]
  UpperCAmelCase__           =       torch.Size((1, 11, 256)				)
  self.assertEqual(output.shape , lowerCamelCase_				)
  UpperCAmelCase__           =       torch.tensor(
      [[[0.44_71, 0.68_21, -0.32_65], [0.46_27, 0.52_55, -0.36_68], [0.45_32, 0.33_13, -0.43_44]]]				)

  self.assertTrue(torch.allclose(output[:, 1:4, 1:4] , lowerCamelCase_ , atol=1E-4				)				)








import gym
import numpy as np
import torch
from mujoco_py import GlfwContext

from transformers import DecisionTransformerModel


GlfwContext(offscreen=True)  # Create a window to init GLFW.
def 		UpperCamelCase_(    snake_case__:						int						,					snake_case__:						int						,					snake_case__:						Union[str, Any]						,					snake_case__:						Any						,					snake_case__:						Any						,					snake_case__:						Union[str, Any]     ) ->				Any:
 # we don't care about the past rewards in this model

 UpperCAmelCase__           =       states.reshape(1						,					-1						,					model.config.state_dim     )
 UpperCAmelCase__           =       actions.reshape(1						,					-1						,					model.config.act_dim     )
 UpperCAmelCase__           =       returns_to_go.reshape(1						,					-1						,					1     )
 UpperCAmelCase__           =       timesteps.reshape(1						,					-1     )

 if model.config.max_length is not None:
  UpperCAmelCase__           =       states[:, -model.config.max_length :]
  UpperCAmelCase__           =       actions[:, -model.config.max_length :]
  UpperCAmelCase__           =       returns_to_go[:, -model.config.max_length :]
  UpperCAmelCase__           =       timesteps[:, -model.config.max_length :]

  # pad all tokens to sequence length
  UpperCAmelCase__           =       torch.cat(
      [torch.zeros(model.config.max_length - states.shape[1]     ), torch.ones(states.shape[1]     )]     )
  UpperCAmelCase__           =       attention_mask.to(dtype=torch.long						,					device=states.device     ).reshape(1						,					-1     )
  UpperCAmelCase__           =       torch.cat(
      [
          torch.zeros(
              (states.shape[0], model.config.max_length - states.shape[1], model.config.state_dim)						,					device=states.device						,					),
          states,
      ]						,					dim=1						,					).to(dtype=torch.floataa     )
  UpperCAmelCase__           =       torch.cat(
      [
          torch.zeros(
              (actions.shape[0], model.config.max_length - actions.shape[1], model.config.act_dim)						,					device=actions.device						,					),
          actions,
      ]						,					dim=1						,					).to(dtype=torch.floataa     )
  UpperCAmelCase__           =       torch.cat(
      [
          torch.zeros(
              (returns_to_go.shape[0], model.config.max_length - returns_to_go.shape[1], 1)						,					device=returns_to_go.device						,					),
          returns_to_go,
      ]						,					dim=1						,					).to(dtype=torch.floataa     )
  UpperCAmelCase__           =       torch.cat(
      [
          torch.zeros(
              (timesteps.shape[0], model.config.max_length - timesteps.shape[1])						,					device=timesteps.device     ),
          timesteps,
      ]						,					dim=1						,					).to(dtype=torch.long     )
 else:
  UpperCAmelCase__           =       None

 UpperCAmelCase__       ,					UpperCAmelCase__       ,					UpperCAmelCase__           =       model(
     states=snake_case__						,					actions=snake_case__						,					rewards=snake_case__						,					returns_to_go=snake_case__						,					timesteps=snake_case__						,					attention_mask=snake_case__						,					return_dict=snake_case__						,					)

 return action_preds[0, -1]


# build the environment

_UpperCamelCase     =    gym.make('''Hopper-v3''')
_UpperCamelCase     =    env.observation_space.shape[0]
_UpperCamelCase     =    env.action_space.shape[0]
_UpperCamelCase     =    1000
_UpperCamelCase     =    '''cuda'''
_UpperCamelCase     =    1_0_0_0.0  # normalization for rewards/returns
_UpperCamelCase     =    3600 / scale  # evaluation conditioning targets, 3600 is reasonable from the paper LINK
_UpperCamelCase     =    np.array(
    [
        1.3_1_1_2_7_9,
        -0.0_8_4_6_9_5_2_1,
        -0.5_3_8_2_7_1_9,
        -0.0_7_2_0_1_5_7_6,
        0.0_4_9_3_2_3_6_6,
        2.1_0_6_6_8_5_6,
        -0.1_5_0_1_7_3_5_4,
        0.0_0_8_7_8_3_4_5,
        -0.2_8_4_8_1_8_6,
        -0.1_8_5_4_0_0_9_6,
        -0.2_8_4_6_1_2_8_6,
    ]
)
_UpperCamelCase     =    np.array(
    [
        0.1_7_7_9_0_7_5_1,
        0.0_5_4_4_4_6_2_1,
        0.2_1_2_9_7_1_3_9,
        0.1_4_5_3_0_4_1_9,
        0.6_1_2_4_4_4_4,
        0.8_5_1_7_4_4_6_5,
        1.4_5_1_5_2_5_2,
        0.6_7_5_1_6_9_6,
        1.5_3_6_2_3_9,
        1.6_1_6_0_7_4_6,
        5.6_0_7_2_5_3_6,
    ]
)
_UpperCamelCase     =    torch.from_numpy(state_mean).to(device=device)
_UpperCamelCase     =    torch.from_numpy(state_std).to(device=device)

# Create the decision transformer model
_UpperCamelCase     =    DecisionTransformerModel.from_pretrained('''edbeeching/decision-transformer-gym-hopper-medium''')
_UpperCamelCase     =    model.to(device)
model.eval()

for ep in range(10):
 _UpperCamelCase ,       _UpperCamelCase     =    0, 0
 _UpperCamelCase     =    env.reset()
 _UpperCamelCase     =    torch.tensor(TARGET_RETURN, device=device, dtype=torch.floataa).reshape(1, 1)
 _UpperCamelCase     =    torch.from_numpy(state).reshape(1, state_dim).to(device=device, dtype=torch.floataa)
 _UpperCamelCase     =    torch.zeros((0, act_dim), device=device, dtype=torch.floataa)
 _UpperCamelCase     =    torch.zeros(0, device=device, dtype=torch.floataa)

 _UpperCamelCase     =    torch.tensor(0, device=device, dtype=torch.long).reshape(1, 1)
 for t in range(max_ep_len):
  env.render()
  # add padding
  _UpperCamelCase     =    torch.cat([actions, torch.zeros((1, act_dim), device=device)], dim=0)
  _UpperCamelCase     =    torch.cat([rewards, torch.zeros(1, device=device)])

  _UpperCamelCase     =    get_action(
      model,
      (states.to(dtype=torch.floataa) - state_mean) / state_std,
      actions.to(dtype=torch.floataa),
      rewards.to(dtype=torch.floataa),
      target_return.to(dtype=torch.floataa),
      timesteps.to(dtype=torch.long),
  )
  _UpperCamelCase     =    action
  _UpperCamelCase     =    action.detach().cpu().numpy()

  _UpperCamelCase ,       _UpperCamelCase ,       _UpperCamelCase ,       _UpperCamelCase     =    env.step(action)

  _UpperCamelCase     =    torch.from_numpy(state).to(device=device).reshape(1, state_dim)
  _UpperCamelCase     =    torch.cat([states, cur_state], dim=0)
  _UpperCamelCase     =    reward

  _UpperCamelCase     =    target_return[0, -1] - (reward / scale)
  _UpperCamelCase     =    torch.cat([target_return, pred_return.reshape(1, 1)], dim=1)
  _UpperCamelCase     =    torch.cat([timesteps, torch.ones((1, 1), device=device, dtype=torch.long) * (t + 1)], dim=1)

  episode_return += reward
  episode_length += 1

  if done:
   break



