


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

													a_      :	int							=						self.get_config()

													return (
													    config,
													    states,
													    actions,
													    rewards,
													    returns_to_go,
													    timesteps,
													    attention_mask,
													)
						def  SCREAMING_SNAKE_CASE							( self       : Tuple  )	->    Union[str, 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  SCREAMING_SNAKE_CASE							( self       : Optional[Any]  ,							SCREAMING_SNAKE_CASE__       : int  ,							SCREAMING_SNAKE_CASE__       : int  ,							SCREAMING_SNAKE_CASE__       : List[Any]  ,							SCREAMING_SNAKE_CASE__       : Dict  ,							SCREAMING_SNAKE_CASE__       : Optional[Any]  ,							SCREAMING_SNAKE_CASE__       : Dict  ,							SCREAMING_SNAKE_CASE__       : Union[str, Any]  ,							)	->    Optional[Any]:
													a_      :	Any							=						DecisionTransformerModel(config=_lowerCAmelCase  )
													model.to(_lowerCAmelCase  )
													model.eval()
													a_      :	Optional[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  SCREAMING_SNAKE_CASE							( self       : int  )	->    str:
													a_      :	Dict							=						self.prepare_config_and_inputs()
													(
													    (
													    a_						
													)							,	(
													    a_						
													)							,	(
													    a_						
													)							,	(
													    a_						
													)							,	(
													    a_						
													)							,	(
													    a_						
													)							,	(
													    a_						
													)							,							
													)      :	Optional[Any]							=						config_and_inputs
													a_      :	List[Any]							=						{
													    '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 				SCREAMING_SNAKE_CASE__						(       _lowerCAmelCase		,					_lowerCAmelCase		,					_lowerCAmelCase		,					unittest.TestCase    ):
						snake_case__       :  Optional[Any]       =		(DecisionTransformerModel,) if is_torch_available() else ()
						snake_case__       :  Optional[int]       =		()
						snake_case__       :  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
						snake_case__       :  Union[str, Any]       =		False

						# Ignoring of a failing tests from ModelTesterMixin, as the model does not implement these features
						snake_case__       :  str       =		False
						snake_case__       :  int       =		False
						snake_case__       :  List[Any]       =		False
						snake_case__       :  Any       =		False
						snake_case__       :  List[Any]       =		False
						snake_case__       :  Any       =		False
						snake_case__       :  Any       =		False
						snake_case__       :  List[Any]       =		False
						snake_case__       :  Optional[int]       =		False
						def  SCREAMING_SNAKE_CASE							( self       : str  )	->    Any:
													a_      :	List[Any]							=						DecisionTransformerModelTester(self  )
													a_      :	Tuple							=						ConfigTester(self  ,							config_class=_lowerCAmelCase  ,							hidden_size=3_7  )
						def  SCREAMING_SNAKE_CASE							( self       : Union[str, Any]  )	->    Optional[int]:
													self.config_tester.run_common_tests()
						def  SCREAMING_SNAKE_CASE							( self       : List[Any]  )	->    Union[str, Any]:
													a_      :	int							=						self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_model(*_lowerCAmelCase  )
						@slow
						def  SCREAMING_SNAKE_CASE							( self       : Union[str, Any]  )	->    Any:







													for model_name in DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																				a_      :	str							=						DecisionTransformerModel.from_pretrained(_lowerCAmelCase  )
																				self.assertIsNotNone(_lowerCAmelCase  )

						def  SCREAMING_SNAKE_CASE							( self       : List[str]  )	->    Union[str, Any]:
													a_							,	a_      :	Union[str, Any]							=						self.model_tester.prepare_config_and_inputs_for_common()

													for model_class in self.all_model_classes:
																				a_      :	Tuple							=						model_class(_lowerCAmelCase  )
																				a_      :	Union[str, Any]							=						inspect.signature(model.forward  )
																				# signature.parameters is an OrderedDict => so arg_names order is deterministic
																				a_      :	Any							=						[*signature.parameters.keys()]

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

																				self.assertListEqual(arg_names[: len(_lowerCAmelCase  )]  ,							_lowerCAmelCase  )
@require_torch
class 				SCREAMING_SNAKE_CASE__						(       unittest.TestCase    ):
						@slow
						def  SCREAMING_SNAKE_CASE							( self       : str  )	->    Union[str, Any]:
													a_      :	Tuple							=						2  # number of steps of autoregressive prediction we will perform
													a_      :	Dict							=						1_0  # defined by the RL environment, may be normalized
													a_      :	Optional[Any]							=						DecisionTransformerModel.from_pretrained('edbeeching/decision-transformer-gym-hopper-expert'  )
													a_      :	Dict							=						model.to(_lowerCAmelCase  )
													a_      :	Optional[int]							=						model.config
													torch.manual_seed(0  )
													a_      :	str							=						torch.randn(1  ,							1  ,							config.state_dim  ).to(device=_lowerCAmelCase  ,							dtype=torch.floataa  )  # env.reset()

													a_      :	Dict							=						torch.tensor(
													    [[0.242793, -0.28693074, 0.8742613], [0.67815274, -0.08101085, -0.12952147]]  ,							device=_lowerCAmelCase  )

													a_      :	int							=						torch.tensor(_lowerCAmelCase  ,							device=_lowerCAmelCase  ,							dtype=torch.floataa  ).reshape(1  ,							1  ,							1  )
													a_      :	Optional[Any]							=						state
													a_      :	int							=						torch.zeros(1  ,							0  ,							config.act_dim  ,							device=_lowerCAmelCase  ,							dtype=torch.floataa  )
													a_      :	Optional[Any]							=						torch.zeros(1  ,							0  ,							device=_lowerCAmelCase  ,							dtype=torch.floataa  )
													a_      :	List[str]							=						torch.tensor(0  ,							device=_lowerCAmelCase  ,							dtype=torch.long  ).reshape(1  ,							1  )

													for step in range(_lowerCAmelCase  ):
																				a_      :	List[str]							=						torch.cat([actions, torch.zeros(1  ,							1  ,							config.act_dim  ,							device=_lowerCAmelCase  )]  ,							dim=1  )
																				a_      :	int							=						torch.cat([rewards, torch.zeros(1  ,							1  ,							device=_lowerCAmelCase  )]  ,							dim=1  )

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

																				with torch.no_grad():
																											a_							,	a_							,	a_      :	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  )  )
																				a_							,	a_							,	a_							,	a_      :	Optional[Any]							=						(  # env.step(action)
																				    torch.randn(1  ,							1  ,							config.state_dim  ).to(device=_lowerCAmelCase  ,							dtype=torch.floataa  ),
																				    1.0,
																				    False,
																				    {},
																				)

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






def  SCREAMING_SNAKE_CASE_		(    __A				:    int						,					__A				:    int						,					__A				:    int       )			->	float:

       """simple docstring"""

       a_      :	Optional[int]							=						(num_of_terms / 2) * (2 * first_term + (num_of_terms - 1) * common_diff)
       # formula for sum of series
       return total







def  SCREAMING_SNAKE_CASE_		(    )			->	Optional[Any]:

       """simple docstring"""

       print(sum_of_series(1						,					1						,					10       )       )


if __name__ == "__main__":
   import doctest

   doctest.testmod()


