


"""simple docstring"""
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   __A  :





							'''simple docstring'''

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



														"""simple docstring"""



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



							def 		lowerCAmelCase							(					self				:   int							)       ->Union[str, Any]:



														"""simple docstring"""



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

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

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

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

														snake_case_     							=		self.get_config()

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



							def 		lowerCAmelCase							(					self				:   int							)       ->Any:



														"""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=a_		,							initializer_range=self.initializer_range		,							)



							def 		lowerCAmelCase							(					self				:   List[Any]							)       ->Optional[int]:



														"""simple docstring"""



														(
														    snake_case_				
														)     							=		self.prepare_config_and_inputs()

														snake_case_     							=		True
														snake_case_     							=		floats_tensor([self.batch_size, self.seq_length, self.hidden_size]							)
														snake_case_     							=		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 		lowerCAmelCase							(					self				:   int		,							UpperCAmelCase_				:   Tuple		,							UpperCAmelCase_				:   int		,							UpperCAmelCase_				:   Tuple		,							UpperCAmelCase_				:   Optional[Any]		,							UpperCAmelCase_				:   str		,							UpperCAmelCase_				:   Any		,							UpperCAmelCase_				:   int		,							UpperCAmelCase_				:   Dict		,							)       ->str:



														"""simple docstring"""



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



							def 		lowerCAmelCase							(					self				:   Union[str, Any]		,							UpperCAmelCase_				:   Optional[int]		,							UpperCAmelCase_				:   Tuple		,							UpperCAmelCase_				:   List[Any]		,							UpperCAmelCase_				:   List[Any]		,							UpperCAmelCase_				:   Union[str, Any]		,							UpperCAmelCase_				:   Dict		,							UpperCAmelCase_				:   str		,							UpperCAmelCase_				:   Tuple		,							UpperCAmelCase_				:   Dict		,							)       ->Dict:



														"""simple docstring"""



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



							def 		lowerCAmelCase							(					self				:   str		,							UpperCAmelCase_				:   Tuple		,							UpperCAmelCase_				:   List[Any]		,							UpperCAmelCase_				:   Union[str, Any]		,							UpperCAmelCase_				:   List[Any]		,							UpperCAmelCase_				:   Dict		,							UpperCAmelCase_				:   Optional[int]		,							UpperCAmelCase_				:   List[Any]		,							UpperCAmelCase_				:   Dict		,							)       ->List[str]:



														"""simple docstring"""



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



							def 		lowerCAmelCase							(					self				:   str		,							UpperCAmelCase_				:   Dict		,							UpperCAmelCase_				:   Union[str, Any]		,							UpperCAmelCase_				:   str		,							UpperCAmelCase_				:   Optional[int]		,							UpperCAmelCase_				:   List[str]		,							UpperCAmelCase_				:   Tuple		,							UpperCAmelCase_				:   Optional[int]		,							UpperCAmelCase_				:   str		,							UpperCAmelCase_				:   List[Any]		,							)       ->str:



														"""simple docstring"""



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



							def 		lowerCAmelCase							(					self				:   Optional[Any]		,							UpperCAmelCase_				:   Any		,							UpperCAmelCase_				:   Dict		,							UpperCAmelCase_				:   Any		,							UpperCAmelCase_				:   Union[str, Any]		,							UpperCAmelCase_				:   Any		,							UpperCAmelCase_				:   Any		,							UpperCAmelCase_				:   int		,							UpperCAmelCase_				:   str		,							)       ->int:



														"""simple docstring"""



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



							def 		lowerCAmelCase							(					self				:   List[str]		,							UpperCAmelCase_				:   Tuple		,							UpperCAmelCase_				:   List[str]		,							UpperCAmelCase_				:   Dict		,							UpperCAmelCase_				:   int		,							UpperCAmelCase_				:   Tuple		,							UpperCAmelCase_				:   Any		,							UpperCAmelCase_				:   Tuple		,							UpperCAmelCase_				:   int		,							)       ->str:



														"""simple docstring"""



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



							def 		lowerCAmelCase							(					self				:   List[Any]		,							UpperCAmelCase_				:   Optional[Any]		,							UpperCAmelCase_				:   Dict		,							UpperCAmelCase_				:   int		,							UpperCAmelCase_				:   Tuple		,							UpperCAmelCase_				:   Dict		,							UpperCAmelCase_				:   int		,							UpperCAmelCase_				:   Dict		,							UpperCAmelCase_				:   Optional[Any]		,							)       ->Any:



														"""simple docstring"""



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



							def 		lowerCAmelCase							(					self				:   Dict		,							UpperCAmelCase_				:   Tuple		,							UpperCAmelCase_				:   Any		,							UpperCAmelCase_				:   Tuple		,							UpperCAmelCase_				:   Optional[int]		,							UpperCAmelCase_				:   Optional[int]		,							UpperCAmelCase_				:   int		,							UpperCAmelCase_				:   str		,							UpperCAmelCase_				:   Optional[int]		,							)       ->Tuple:



														"""simple docstring"""



														snake_case_     							=		ElectraForQuestionAnswering(config=a_							)
														model.to(a_							)
														model.eval()
														snake_case_     							=		model(
														    a_		,							attention_mask=a_		,							token_type_ids=a_		,							start_positions=a_		,							end_positions=a_		,							)
														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 		lowerCAmelCase							(					self				:   int		,							UpperCAmelCase_				:   Optional[int]		,							UpperCAmelCase_				:   Any		,							UpperCAmelCase_				:   Optional[Any]		,							UpperCAmelCase_				:   str		,							UpperCAmelCase_				:   Dict		,							UpperCAmelCase_				:   Tuple		,							UpperCAmelCase_				:   Optional[int]		,							UpperCAmelCase_				:   str		,							)       ->Optional[int]:



														"""simple docstring"""



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



							def 		lowerCAmelCase							(					self				:   Tuple							)       ->Tuple:



														"""simple docstring"""



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





@require_torch
class   __A  (__snake_case    ,		__snake_case    ,		unittest.TestCase):





							'''simple docstring'''

							__lowercase:		Union[str, Any]				   =     (
							    (
							        ElectraModel,
							        ElectraForPreTraining,
							        ElectraForMaskedLM,
							        ElectraForCausalLM,
							        ElectraForMultipleChoice,
							        ElectraForTokenClassification,
							        ElectraForSequenceClassification,
							        ElectraForQuestionAnswering,
							    )
							    if is_torch_available()
							    else ()
							)
							__lowercase:		Tuple				   =     (
							    {
							        """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 {}
							)
							__lowercase:		Tuple				   =     True



							def 		lowerCAmelCase							(					self				:   Union[str, Any]		,							UpperCAmelCase_				:   Union[str, Any]		,							UpperCAmelCase_				:   List[str]		,							UpperCAmelCase_				:   List[str]=False							)       ->Any:



														"""simple docstring"""



														snake_case_     							=		super()._prepare_for_class(a_		,							a_		,							return_labels=a_							)

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



							def 		lowerCAmelCase							(					self				:   Optional[Any]							)       ->str:



														"""simple docstring"""



														snake_case_     							=		ElectraModelTester(self							)
														snake_case_     							=		ConfigTester(self		,							config_class=a_		,							hidden_size=37							)



							def 		lowerCAmelCase							(					self				:   Any							)       ->Optional[int]:



														"""simple docstring"""



														self.config_tester.run_common_tests()



							def 		lowerCAmelCase							(					self				:   Tuple							)       ->List[Any]:



														"""simple docstring"""



														snake_case_     							=		self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_electra_model(*a_							)



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



														"""simple docstring"""



														snake_case_     							=		self.model_tester.prepare_config_and_inputs_for_decoder()
														self.model_tester.create_and_check_electra_model_as_decoder(*a_							)



							def 		lowerCAmelCase							(					self				:   Union[str, Any]							)       ->Optional[int]:



														"""simple docstring"""



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



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



														"""simple docstring"""



														snake_case_     							=		self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_electra_for_masked_lm(*a_							)



							def 		lowerCAmelCase							(					self				:   int							)       ->Tuple:



														"""simple docstring"""



														snake_case_     							=		self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_electra_for_token_classification(*a_							)



							def 		lowerCAmelCase							(					self				:   Tuple							)       ->List[str]:



														"""simple docstring"""



														snake_case_     							=		self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_electra_for_pretraining(*a_							)



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



														"""simple docstring"""



														snake_case_     							=		self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_electra_for_sequence_classification(*a_							)



							def 		lowerCAmelCase							(					self				:   List[str]							)       ->Tuple:



														"""simple docstring"""



														snake_case_     							=		self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_electra_for_question_answering(*a_							)



							def 		lowerCAmelCase							(					self				:   Optional[Any]							)       ->Tuple:



														"""simple docstring"""



														snake_case_     							=		self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_electra_for_multiple_choice(*a_							)



							@slow
							def 		lowerCAmelCase							(					self				:   int							)       ->Optional[int]:



														"""simple docstring"""



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



							def 		lowerCAmelCase							(					self				:   Dict							)       ->Tuple:



														"""simple docstring"""



														snake_case_     							=		self.model_tester.prepare_config_and_inputs_for_decoder()
														self.model_tester.create_and_check_electra_for_causal_lm(*a_							)





@require_torch
class   __A  (unittest.TestCase):





							'''simple docstring'''

							@slow
							def 		lowerCAmelCase							(					self				:   Optional[int]							)       ->Optional[int]:



														"""simple docstring"""



														snake_case_     							=		ElectraModel.from_pretrained("""google/electra-small-discriminator"""							)
														snake_case_     							=		torch.tensor([[0, 345, 232, 328, 740, 140, 1_695, 69, 6_078, 1_588, 2]]							)
														snake_case_     							=		torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]							)
														snake_case_     							=		model(a_		,							attention_mask=a_							)[0]
														snake_case_     							=		torch.Size((1, 11, 256)							)
														self.assertEqual(output.shape		,							a_							)
														snake_case_     							=		torch.tensor(
														    [[[0.4_471, 0.6_821, -0.3_265], [0.4_627, 0.5_255, -0.3_668], [0.4_532, 0.3_313, -0.4_344]]]							)

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







"""simple docstring"""
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 						lowercase				(	_snake_case		: Tuple	,  _snake_case		: Tuple	,  _snake_case		: Optional[Any]	,  _snake_case		: str	,  _snake_case		: int	,  _snake_case		: List[str]	)	->Optional[int]:
	"""simple docstring"""





	__snake_case      :		Dict         =							states.reshape(1	,  -1	,  model.config.state_dim	)
	__snake_case      :		List[str]         =							actions.reshape(1	,  -1	,  model.config.act_dim	)
	__snake_case      :		Union[str, Any]         =							returns_to_go.reshape(1	,  -1	,  1	)
	__snake_case      :		Any         =							timesteps.reshape(1	,  -1	)

	if model.config.max_length is not None:
		__snake_case      :		Union[str, Any]         =							states[:, -model.config.max_length :]
		__snake_case      :		Any         =							actions[:, -model.config.max_length :]
		__snake_case      :		Tuple         =							returns_to_go[:, -model.config.max_length :]
		__snake_case      :		Union[str, Any]         =							timesteps[:, -model.config.max_length :]

		# pad all tokens to sequence length
		__snake_case      :		Dict         =							torch.cat(
		    [torch.zeros(model.config.max_length - states.shape[1]	), torch.ones(states.shape[1]	)]	)
		__snake_case      :		str         =							attention_mask.to(dtype=torch.long	,  device=states.device	).reshape(1	,  -1	)
		__snake_case      :		Dict         =							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	)
		__snake_case      :		Tuple         =							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	)
		__snake_case      :		str         =							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	)
		__snake_case      :		Union[str, Any]         =							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:
		__snake_case      :		int         =							None

	__snake_case      ,				__snake_case      ,				__snake_case      :		Optional[int]         =							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

SCREAMING_SNAKE_CASE					:       Optional[Any]							   =							gym.make("""Hopper-v3""")
SCREAMING_SNAKE_CASE					:       List[str]							   =							env.observation_space.shape[0]
SCREAMING_SNAKE_CASE					:       Union[str, Any]							   =							env.action_space.shape[0]
SCREAMING_SNAKE_CASE					:       Optional[int]							   =							1000
SCREAMING_SNAKE_CASE					:       Tuple							   =							"""cuda"""
SCREAMING_SNAKE_CASE					:       List[Any]							   =							1_000.0  # normalization for rewards/returns
SCREAMING_SNAKE_CASE					:       Union[str, Any]							   =							3600 / scale  # evaluation conditioning targets, 3600 is reasonable from the paper LINK
SCREAMING_SNAKE_CASE					:       Union[str, Any]							   =							np.array(
    [
        1.311_279,
        -0.08_469_521,
        -0.5_382_719,
        -0.07_201_576,
        0.04_932_366,
        2.1_066_856,
        -0.15_017_354,
        0.00_878_345,
        -0.2_848_186,
        -0.18_540_096,
        -0.28_461_286,
    ]
)
SCREAMING_SNAKE_CASE					:       List[Any]							   =							np.array(
    [
        0.17_790_751,
        0.05_444_621,
        0.21_297_139,
        0.14_530_419,
        0.6_124_444,
        0.85_174_465,
        1.4_515_252,
        0.6_751_696,
        1.536_239,
        1.6_160_746,
        5.6_072_536,
    ]
)
SCREAMING_SNAKE_CASE					:       List[str]							   =							torch.from_numpy(state_mean).to(device=device)
SCREAMING_SNAKE_CASE					:       Union[str, Any]							   =							torch.from_numpy(state_std).to(device=device)

# Create the decision transformer model
SCREAMING_SNAKE_CASE					:       List[Any]							   =							DecisionTransformerModel.from_pretrained("""edbeeching/decision-transformer-gym-hopper-medium""")
SCREAMING_SNAKE_CASE					:       Optional[int]							   =							model.to(device)
model.eval()

for ep in range(10):
			SCREAMING_SNAKE_CASE   ,    SCREAMING_SNAKE_CASE					:       str							   =							0, 0
			SCREAMING_SNAKE_CASE					:       int							   =							env.reset()
			SCREAMING_SNAKE_CASE					:       Optional[int]							   =							torch.tensor(TARGET_RETURN, device=device, dtype=torch.floataa).reshape(1, 1)
			SCREAMING_SNAKE_CASE					:       str							   =							torch.from_numpy(state).reshape(1, state_dim).to(device=device, dtype=torch.floataa)
			SCREAMING_SNAKE_CASE					:       Tuple							   =							torch.zeros((0, act_dim), device=device, dtype=torch.floataa)
			SCREAMING_SNAKE_CASE					:       Optional[int]							   =							torch.zeros(0, device=device, dtype=torch.floataa)

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

						SCREAMING_SNAKE_CASE					:       Dict							   =							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),
						)
						SCREAMING_SNAKE_CASE					:       Optional[int]							   =							action
						SCREAMING_SNAKE_CASE					:       List[Any]							   =							action.detach().cpu().numpy()

						SCREAMING_SNAKE_CASE   ,    SCREAMING_SNAKE_CASE   ,    SCREAMING_SNAKE_CASE   ,    SCREAMING_SNAKE_CASE					:       List[str]							   =							env.step(action)

						SCREAMING_SNAKE_CASE					:       Dict							   =							torch.from_numpy(state).to(device=device).reshape(1, state_dim)
						SCREAMING_SNAKE_CASE					:       int							   =							torch.cat([states, cur_state], dim=0)
						SCREAMING_SNAKE_CASE					:       List[str]							   =							reward

						SCREAMING_SNAKE_CASE					:       Union[str, Any]							   =							target_return[0, -1] - (reward / scale)
						SCREAMING_SNAKE_CASE					:       Dict							   =							torch.cat([target_return, pred_return.reshape(1, 1)], dim=1)
						SCREAMING_SNAKE_CASE					:       List[Any]							   =							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





