



'''simple docstring'''
import inspect
import unittest

from transformers import DecisionTransformerConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
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 DecisionTransformerModel
					from transformers.models.decision_transformer.modeling_decision_transformer import (
					    DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
					)






class 							lowercase__     :





	def __init__(			self :    int			,lowerCamelCase__ :    Union[str, Any]			,lowerCamelCase__ :    Dict=13			,lowerCamelCase__ :    List[str]=7			,lowerCamelCase__ :    Union[str, Any]=6			,lowerCamelCase__ :    str=17			,lowerCamelCase__ :    Dict=23			,lowerCamelCase__ :    List[Any]=11			,lowerCamelCase__ :    Any=True			,):







							'''simple docstring'''
							_UpperCamelCase :		List[Any]			    =       parent
							_UpperCamelCase :		Union[str, Any]			    =       batch_size
							_UpperCamelCase :		Optional[Any]			    =       seq_length
							_UpperCamelCase :		Any			    =       act_dim
							_UpperCamelCase :		Any			    =       state_dim
							_UpperCamelCase :		Any			    =       hidden_size
							_UpperCamelCase :		str			    =       max_length
							_UpperCamelCase :		Tuple			    =       is_training





	def      UpperCamelCase_  (			self :    Union[str, Any]			):







							'''simple docstring'''
							_UpperCamelCase :		List[str]			    =       floats_tensor((self.batch_size, self.seq_length, self.state_dim)			)
							_UpperCamelCase :		Union[str, Any]			    =       floats_tensor((self.batch_size, self.seq_length, self.act_dim)			)
							_UpperCamelCase :		Optional[int]			    =       floats_tensor((self.batch_size, self.seq_length, 1)			)
							_UpperCamelCase :		str			    =       floats_tensor((self.batch_size, self.seq_length, 1)			)
							_UpperCamelCase :		Optional[Any]			    =       ids_tensor((self.batch_size, self.seq_length)			,vocab_size=1000			)
							_UpperCamelCase :		Tuple			    =       random_attention_mask((self.batch_size, self.seq_length)			)

							_UpperCamelCase :		str			    =       self.get_config()

							return (
							    config,
							    states,
							    actions,
							    rewards,
							    returns_to_go,
							    timesteps,
							    attention_mask,
							)





	def      UpperCamelCase_  (			self :    List[str]			):







							'''simple docstring'''
							return DecisionTransformerConfig(
							    batch_size=self.batch_size			,seq_length=self.seq_length			,act_dim=self.act_dim			,state_dim=self.state_dim			,hidden_size=self.hidden_size			,max_length=self.max_length			,)





	def      UpperCamelCase_  (			self :    Dict			,lowerCamelCase__ :    int			,lowerCamelCase__ :    List[Any]			,lowerCamelCase__ :    Dict			,lowerCamelCase__ :    Any			,lowerCamelCase__ :    Dict			,lowerCamelCase__ :    Dict			,lowerCamelCase__ :    List[Any]			,):







							'''simple docstring'''
							_UpperCamelCase :		Dict			    =       DecisionTransformerModel(config=lowerCamelCase__			)
							model.to(lowerCamelCase__			)
							model.eval()
							_UpperCamelCase :		int			    =       model(lowerCamelCase__			,lowerCamelCase__			,lowerCamelCase__			,lowerCamelCase__			,lowerCamelCase__			,lowerCamelCase__			)

							self.parent.assertEqual(result.state_preds.shape			,states.shape			)
							self.parent.assertEqual(result.action_preds.shape			,actions.shape			)
							self.parent.assertEqual(result.return_preds.shape			,returns_to_go.shape			)
							self.parent.assertEqual(
							    result.last_hidden_state.shape			,(self.batch_size, self.seq_length * 3, self.hidden_size)			)  # seq length *3 as there are 3 modelities: states, returns and actions





	def      UpperCamelCase_  (			self :    Any			):







							'''simple docstring'''
							_UpperCamelCase :		List[Any]			    =       self.prepare_config_and_inputs()
							(
							    (
							    _UpperCamelCase
							)    ,      (
							    _UpperCamelCase
							)    ,      (
							    _UpperCamelCase
							)    ,      (
							    _UpperCamelCase
							)    ,      (
							    _UpperCamelCase
							)    ,      (
							    _UpperCamelCase
							)    ,      (
							    _UpperCamelCase
							)    ,      
							) :		Optional[Any]			    =       config_and_inputs
							_UpperCamelCase :		Optional[int]			    =       {
							    'states': states,
							    'actions': actions,
							    'rewards': rewards,
							    'returns_to_go': returns_to_go,
							    'timesteps': timesteps,
							    'attention_mask': attention_mask,
							}
							return config, inputs_dict






@require_torch
class 							lowercase__     (							lowercase						, lowercase						, lowercase						, unittest.TestCase       ):
	lowercase__      =      (DecisionTransformerModel,) if is_torch_available() else ()
	lowercase__      =      ()
	lowercase__      =      {"""feature-extraction""": DecisionTransformerModel} if is_torch_available() else {}

	# Ignoring of a failing test from GenerationTesterMixin, as the model does not use inputs_ids
	lowercase__      =      False

	# Ignoring of a failing tests from ModelTesterMixin, as the model does not implement these features
	lowercase__      =      False
	lowercase__      =      False
	lowercase__      =      False
	lowercase__      =      False
	lowercase__      =      False
	lowercase__      =      False
	lowercase__      =      False
	lowercase__      =      False
	lowercase__      =      False





	def      UpperCamelCase_  (			self :    Tuple			):







							'''simple docstring'''
							_UpperCamelCase :		Union[str, Any]			    =       DecisionTransformerModelTester(self			)
							_UpperCamelCase :		List[str]			    =       ConfigTester(self			,config_class=lowerCamelCase__			,hidden_size=37			)





	def      UpperCamelCase_  (			self :    Tuple			):







							'''simple docstring'''
							self.config_tester.run_common_tests()





	def      UpperCamelCase_  (			self :    Optional[Any]			):







							'''simple docstring'''
							_UpperCamelCase :		Optional[int]			    =       self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*lowerCamelCase__			)





	@slow
	def      UpperCamelCase_  (			self :    List[str]			):







							'''simple docstring'''
							for model_name in DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
													_UpperCamelCase :		Optional[Any]			    =       DecisionTransformerModel.from_pretrained(lowerCamelCase__			)
													self.assertIsNotNone(lowerCamelCase__			)





	def      UpperCamelCase_  (			self :    Optional[int]			):







							'''simple docstring'''
							_UpperCamelCase    ,      _UpperCamelCase :		List[Any]			    =       self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
													_UpperCamelCase :		int			    =       model_class(lowerCamelCase__			)
													_UpperCamelCase :		str			    =       inspect.signature(model.forward			)
													# signature.parameters is an OrderedDict => so arg_names order is deterministic
													_UpperCamelCase :		Dict			    =       [*signature.parameters.keys()]

													_UpperCamelCase :		Optional[Any]			    =       [
													    'states',
													    'actions',
													    'rewards',
													    'returns_to_go',
													    'timesteps',
													    'attention_mask',
													]

													self.assertListEqual(arg_names[: len(lowerCamelCase__			)]			,lowerCamelCase__			)






@require_torch
class 							lowercase__     (							unittest.TestCase       ):





	@slow
	def      UpperCamelCase_  (			self :    str			):







							'''simple docstring'''
							_UpperCamelCase :		List[str]			    =       2  # number of steps of autoregressive prediction we will perform
							_UpperCamelCase :		int			    =       10  # defined by the RL environment, may be normalized
							_UpperCamelCase :		List[str]			    =       DecisionTransformerModel.from_pretrained('edbeeching/decision-transformer-gym-hopper-expert'			)
							_UpperCamelCase :		Any			    =       model.to(lowerCamelCase__			)
							_UpperCamelCase :		str			    =       model.config
							torch.manual_seed(0			)
							_UpperCamelCase :		Union[str, Any]			    =       torch.randn(1			,1			,config.state_dim			).to(device=lowerCamelCase__			,dtype=torch.floataa			)  # env.reset()

							_UpperCamelCase :		Optional[int]			    =       torch.tensor(
							    [[0.2_4_2_7_9_3, -0.2_8_6_9_3_0_7_4, 0.8_7_4_2_6_1_3], [0.6_7_8_1_5_2_7_4, -0.0_8_1_0_1_0_8_5, -0.1_2_9_5_2_1_4_7]]			,device=lowerCamelCase__			)

							_UpperCamelCase :		Optional[Any]			    =       torch.tensor(lowerCamelCase__			,device=lowerCamelCase__			,dtype=torch.floataa			).reshape(1			,1			,1			)
							_UpperCamelCase :		Any			    =       state
							_UpperCamelCase :		Dict			    =       torch.zeros(1			,0			,config.act_dim			,device=lowerCamelCase__			,dtype=torch.floataa			)
							_UpperCamelCase :		Any			    =       torch.zeros(1			,0			,device=lowerCamelCase__			,dtype=torch.floataa			)
							_UpperCamelCase :		Union[str, Any]			    =       torch.tensor(0			,device=lowerCamelCase__			,dtype=torch.long			).reshape(1			,1			)

							for step in range(lowerCamelCase__			):
													_UpperCamelCase :		Optional[Any]			    =       torch.cat([actions, torch.zeros(1			,1			,config.act_dim			,device=lowerCamelCase__			)]			,dim=1			)
													_UpperCamelCase :		str			    =       torch.cat([rewards, torch.zeros(1			,1			,device=lowerCamelCase__			)]			,dim=1			)

													_UpperCamelCase :		Dict			    =       torch.ones(1			,states.shape[1]			).to(dtype=torch.long			,device=states.device			)

													with torch.no_grad():
																			_UpperCamelCase    ,      _UpperCamelCase    ,      _UpperCamelCase :		Union[str, Any]			    =       model(
																			    states=lowerCamelCase__			,actions=lowerCamelCase__			,rewards=lowerCamelCase__			,returns_to_go=lowerCamelCase__			,timesteps=lowerCamelCase__			,attention_mask=lowerCamelCase__			,return_dict=lowerCamelCase__			,)

													self.assertEqual(action_pred.shape			,actions.shape			)
													self.assertTrue(torch.allclose(action_pred[0, -1]			,expected_outputs[step]			,atol=1E-4			)			)
													_UpperCamelCase    ,      _UpperCamelCase    ,      _UpperCamelCase    ,      _UpperCamelCase :		List[Any]			    =       (  # env.step(action)
													    torch.randn(1			,1			,config.state_dim			).to(device=lowerCamelCase__			,dtype=torch.floataa			),
													    1.0,
													    False,
													    {},
													)

													_UpperCamelCase :		Optional[Any]			    =       action_pred[0, -1]
													_UpperCamelCase :		Optional[int]			    =       torch.cat([states, state]			,dim=1			)
													_UpperCamelCase :		Union[str, Any]			    =       returns_to_go[0, -1] - reward
													_UpperCamelCase :		Any			    =       torch.cat([returns_to_go, pred_return.reshape(1			,1			,1			)]			,dim=1			)
													_UpperCamelCase :		List[str]			    =       torch.cat(
													    [timesteps, torch.ones((1, 1)			,device=lowerCamelCase__			,dtype=torch.long			) * (step + 1)]			,dim=1			)







'''simple docstring'''
from abc import ABC, abstractmethod
from argparse import ArgumentParser






class 							lowercase__     (							lowercase       ):





	@staticmethod
	@abstractmethod
	def      UpperCamelCase_  (			lowerCamelCase__ :    ArgumentParser			):







							'''simple docstring'''
							raise NotImplementedError()





	@abstractmethod
	def      UpperCamelCase_  (			self :    Any			):







							'''simple docstring'''
							raise NotImplementedError()


