
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__:



	"""simple docstring"""




	def __init__(			self    :     str			,				SCREAMING_SNAKE_CASE_    :     str			,				SCREAMING_SNAKE_CASE_    :     Tuple=1_3			,				SCREAMING_SNAKE_CASE_    :     Optional[Any]=7			,				SCREAMING_SNAKE_CASE_    :     Dict=6			,				SCREAMING_SNAKE_CASE_    :     int=1_7			,				SCREAMING_SNAKE_CASE_    :     str=2_3			,				SCREAMING_SNAKE_CASE_    :     Dict=1_1			,				SCREAMING_SNAKE_CASE_    :     List[str]=True			,				)			->	Any:
					lowercase_	    =   parent
					lowercase_	    =   batch_size
					lowercase_	    =   seq_length
					lowercase_	    =   act_dim
					lowercase_	    =   state_dim
					lowercase_	    =   hidden_size
					lowercase_	    =   max_length
					lowercase_	    =   is_training
	def 		_lowercase    (			self    :     str			)			->	Optional[Any]:
					lowercase_	    =   floats_tensor((self.batch_size, self.seq_length, self.state_dim)			)
					lowercase_	    =   floats_tensor((self.batch_size, self.seq_length, self.act_dim)			)
					lowercase_	    =   floats_tensor((self.batch_size, self.seq_length, 1)			)
					lowercase_	    =   floats_tensor((self.batch_size, self.seq_length, 1)			)
					lowercase_	    =   ids_tensor((self.batch_size, self.seq_length)			,				vocab_size=1_0_0_0			)
					lowercase_	    =   random_attention_mask((self.batch_size, self.seq_length)			)

					lowercase_	    =   self.get_config()

					return (
					    config,
					    states,
					    actions,
					    rewards,
					    returns_to_go,
					    timesteps,
					    attention_mask,
					)
	def 		_lowercase    (			self    :     int			)			->	List[Any]:
					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 		_lowercase    (			self    :     Optional[Any]			,				SCREAMING_SNAKE_CASE_    :     List[Any]			,				SCREAMING_SNAKE_CASE_    :     str			,				SCREAMING_SNAKE_CASE_    :     List[Any]			,				SCREAMING_SNAKE_CASE_    :     Tuple			,				SCREAMING_SNAKE_CASE_    :     Any			,				SCREAMING_SNAKE_CASE_    :     int			,				SCREAMING_SNAKE_CASE_    :     List[str]			,				)			->	Dict:
					lowercase_	    =   DecisionTransformerModel(config=SCREAMING_SNAKE_CASE_			)
					model.to(SCREAMING_SNAKE_CASE_			)
					model.eval()
					lowercase_	    =   model(SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			)

					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 		_lowercase    (			self    :     Union[str, Any]			)			->	Dict:
					lowercase_	    =   self.prepare_config_and_inputs()
					(
					    (
					    lowercase_
					)    ,      (
					    lowercase_
					)    ,      (
					    lowercase_
					)    ,      (
					    lowercase_
					)    ,      (
					    lowercase_
					)    ,      (
					    lowercase_
					)    ,      (
					    lowercase_
					)    ,      
					)	    =   config_and_inputs
					lowercase_	    =   {
					    '''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__(							UpperCAmelCase					,		UpperCAmelCase					,		UpperCAmelCase					,		unittest.TestCase				):



	"""simple docstring"""




	a     :Dict       	=      (DecisionTransformerModel,) if is_torch_available() else ()
	a     :int       	=      ()
	a     :Optional[Any]       	=      {'feature-extraction': DecisionTransformerModel} if is_torch_available() else {}

	# Ignoring of a failing test from GenerationTesterMixin, as the model does not use inputs_ids
	a     :List[str]       	=      False

	# Ignoring of a failing tests from ModelTesterMixin, as the model does not implement these features
	a     :Optional[int]       	=      False
	a     :Optional[int]       	=      False
	a     :Union[str, Any]       	=      False
	a     :Optional[Any]       	=      False
	a     :Optional[int]       	=      False
	a     :Optional[Any]       	=      False
	a     :Any       	=      False
	a     :Union[str, Any]       	=      False
	a     :Tuple       	=      False
	def 		_lowercase    (			self    :     Union[str, Any]			)			->	Optional[int]:
					lowercase_	    =   DecisionTransformerModelTester(self			)
					lowercase_	    =   ConfigTester(self			,				config_class=SCREAMING_SNAKE_CASE_			,				hidden_size=3_7			)
	def 		_lowercase    (			self    :     Any			)			->	str:
					self.config_tester.run_common_tests()
	def 		_lowercase    (			self    :     Dict			)			->	int:
					lowercase_	    =   self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE_			)
	@slow
	def 		_lowercase    (			self    :     List[str]			)			->	Union[str, Any]:
					for model_name in DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									lowercase_	    =   DecisionTransformerModel.from_pretrained(SCREAMING_SNAKE_CASE_			)
									self.assertIsNotNone(SCREAMING_SNAKE_CASE_			)




	def 		_lowercase    (			self    :     Optional[Any]			)			->	int:
					lowercase_    ,      lowercase_	    =   self.model_tester.prepare_config_and_inputs_for_common()

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

									lowercase_	    =   [
									    '''states''',
									    '''actions''',
									    '''rewards''',
									    '''returns_to_go''',
									    '''timesteps''',
									    '''attention_mask''',
									]

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

@require_torch
class 					lowercase__(							unittest.TestCase				):



	"""simple docstring"""




	@slow
	def 		_lowercase    (			self    :     Optional[Any]			)			->	str:
					lowercase_	    =   2  # number of steps of autoregressive prediction we will perform
					lowercase_	    =   1_0  # defined by the RL environment, may be normalized
					lowercase_	    =   DecisionTransformerModel.from_pretrained('''edbeeching/decision-transformer-gym-hopper-expert'''			)
					lowercase_	    =   model.to(SCREAMING_SNAKE_CASE_			)
					lowercase_	    =   model.config
					torch.manual_seed(0			)
					lowercase_	    =   torch.randn(1			,				1			,				config.state_dim			).to(device=SCREAMING_SNAKE_CASE_			,				dtype=torch.floataa			)  # env.reset()

					lowercase_	    =   torch.tensor(
					    [[0.24_27_93, -0.28_69_30_74, 0.8_74_26_13], [0.67_81_52_74, -0.08_10_10_85, -0.12_95_21_47]]			,				device=SCREAMING_SNAKE_CASE_			)

					lowercase_	    =   torch.tensor(SCREAMING_SNAKE_CASE_			,				device=SCREAMING_SNAKE_CASE_			,				dtype=torch.floataa			).reshape(1			,				1			,				1			)
					lowercase_	    =   state
					lowercase_	    =   torch.zeros(1			,				0			,				config.act_dim			,				device=SCREAMING_SNAKE_CASE_			,				dtype=torch.floataa			)
					lowercase_	    =   torch.zeros(1			,				0			,				device=SCREAMING_SNAKE_CASE_			,				dtype=torch.floataa			)
					lowercase_	    =   torch.tensor(0			,				device=SCREAMING_SNAKE_CASE_			,				dtype=torch.long			).reshape(1			,				1			)

					for step in range(SCREAMING_SNAKE_CASE_			):
									lowercase_	    =   torch.cat([actions, torch.zeros(1			,				1			,				config.act_dim			,				device=SCREAMING_SNAKE_CASE_			)]			,				dim=1			)
									lowercase_	    =   torch.cat([rewards, torch.zeros(1			,				1			,				device=SCREAMING_SNAKE_CASE_			)]			,				dim=1			)

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

									with torch.no_grad():
													lowercase_    ,      lowercase_    ,      lowercase_	    =   model(
													    states=SCREAMING_SNAKE_CASE_			,				actions=SCREAMING_SNAKE_CASE_			,				rewards=SCREAMING_SNAKE_CASE_			,				returns_to_go=SCREAMING_SNAKE_CASE_			,				timesteps=SCREAMING_SNAKE_CASE_			,				attention_mask=SCREAMING_SNAKE_CASE_			,				return_dict=SCREAMING_SNAKE_CASE_			,				)

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

									lowercase_	    =   action_pred[0, -1]
									lowercase_	    =   torch.cat([states, state]			,				dim=1			)
									lowercase_	    =   returns_to_go[0, -1] - reward
									lowercase_	    =   torch.cat([returns_to_go, pred_return.reshape(1			,				1			,				1			)]			,				dim=1			)
									lowercase_	    =   torch.cat(
									    [timesteps, torch.ones((1, 1)			,				device=SCREAMING_SNAKE_CASE_			,				dtype=torch.long			) * (step + 1)]			,				dim=1			)









'''simple docstring'''





from __future__ import annotations




def 							lowerCamelCase	(					_snake_case	:    list[int]							):





    '''simple docstring'''


    return len(set(_snake_case							)							) == len(_snake_case							)


if __name__ == "__main__":
 import doctest

 doctest.testmod()




