





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 ,				lowerCamelCase_			:		Optional[int] ,				lowerCamelCase_			:		Optional[int]=13 ,				lowerCamelCase_			:		Dict=7 ,				lowerCamelCase_			:		str=6 ,				lowerCamelCase_			:		Union[str, Any]=17 ,				lowerCamelCase_			:		Union[str, Any]=23 ,				lowerCamelCase_			:		Tuple=11 ,				lowerCamelCase_			:		Optional[Any]=True ,				):





										'''simple docstring'''







										_snake_case :			List[Any]  						=   parent
										_snake_case :			Union[str, Any]  						=   batch_size
										_snake_case :			int  						=   seq_length
										_snake_case :			Dict  						=   act_dim
										_snake_case :			Union[str, Any]  						=   state_dim
										_snake_case :			Optional[int]  						=   hidden_size
										_snake_case :			List[Any]  						=   max_length
										_snake_case :			Union[str, Any]  						=   is_training







						def 		__UpperCAmelCase       (    self			:		Optional[int]					):





										'''simple docstring'''







										_snake_case :			List[str]  						=   floats_tensor((self.batch_size, self.seq_length, self.state_dim)					)
										_snake_case :			Any  						=   floats_tensor((self.batch_size, self.seq_length, self.act_dim)					)
										_snake_case :			List[Any]  						=   floats_tensor((self.batch_size, self.seq_length, 1)					)
										_snake_case :			Any  						=   floats_tensor((self.batch_size, self.seq_length, 1)					)
										_snake_case :			List[str]  						=   ids_tensor((self.batch_size, self.seq_length) ,				vocab_size=10_00					)
										_snake_case :			int  						=   random_attention_mask((self.batch_size, self.seq_length)					)

										_snake_case :			Union[str, Any]  						=   self.get_config()

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







						def 		__UpperCAmelCase       (    self			:		Optional[Any]					):





										'''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			:		Optional[Any] ,				lowerCamelCase_			:		Union[str, Any] ,				lowerCamelCase_			:		Optional[Any] ,				lowerCamelCase_			:		Optional[Any] ,				lowerCamelCase_			:		int ,				lowerCamelCase_			:		Union[str, Any] ,				lowerCamelCase_			:		List[Any] ,				lowerCamelCase_			:		List[Any] ,				):





										'''simple docstring'''







										_snake_case :			str  						=   DecisionTransformerModel(config=lowerCamelCase_					)
										model.to(lowerCamelCase_					)
										model.eval()
										_snake_case :			str  						=   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			:		Union[str, Any]					):





										'''simple docstring'''







										_snake_case :			int  						=   self.prepare_config_and_inputs()
										(
										    (
										    _snake_case
										)						,			(
										    _snake_case
										)						,			(
										    _snake_case
										)						,			(
										    _snake_case
										)						,			(
										    _snake_case
										)						,			(
										    _snake_case
										)						,			(
										    _snake_case
										)						,			
										) :			Union[str, Any]  						=   config_and_inputs
										_snake_case :			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 			lowercase				(							a_		,			a_		,			a_		,			unittest.TestCase     ):



						"""simple docstring"""

						_UpperCamelCase  :	Any								=	(DecisionTransformerModel,) if is_torch_available() else ()
						_UpperCamelCase  :	Union[str, Any]								=	()
						_UpperCamelCase  :	Optional[int]								=	{"feature-extraction": DecisionTransformerModel} if is_torch_available() else {}

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

						# Ignoring of a failing tests from ModelTesterMixin, as the model does not implement these features
						_UpperCamelCase  :	Dict								=	False
						_UpperCamelCase  :	str								=	False
						_UpperCamelCase  :	Optional[Any]								=	False
						_UpperCamelCase  :	str								=	False
						_UpperCamelCase  :	Optional[int]								=	False
						_UpperCamelCase  :	Tuple								=	False
						_UpperCamelCase  :	str								=	False
						_UpperCamelCase  :	List[Any]								=	False
						_UpperCamelCase  :	List[Any]								=	False







						def 		__UpperCAmelCase       (    self			:		Dict					):





										'''simple docstring'''







										_snake_case :			Union[str, Any]  						=   DecisionTransformerModelTester(self					)
										_snake_case :			Union[str, Any]  						=   ConfigTester(self ,				config_class=lowerCamelCase_ ,				hidden_size=37					)







						def 		__UpperCAmelCase       (    self			:		int					):





										'''simple docstring'''







										self.config_tester.run_common_tests()







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





										'''simple docstring'''







										_snake_case :			Optional[int]  						=   self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*lowerCamelCase_					)







						@slow
						def 		__UpperCAmelCase       (    self			:		Any					):





										'''simple docstring'''







										for model_name in DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
														_snake_case :			Dict  						=   DecisionTransformerModel.from_pretrained(lowerCamelCase_					)
														self.assertIsNotNone(lowerCamelCase_					)







						def 		__UpperCAmelCase       (    self			:		Tuple					):





										'''simple docstring'''







										_snake_case						,			_snake_case :			Optional[int]  						=   self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
														_snake_case :			List[Any]  						=   model_class(lowerCamelCase_					)
														_snake_case :			Dict  						=   inspect.signature(model.forward					)
														# signature.parameters is an OrderedDict => so arg_names order is deterministic
														_snake_case :			Optional[Any]  						=   [*signature.parameters.keys()]

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

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



@require_torch
class 			lowercase				(							unittest.TestCase     ):



						"""simple docstring"""

						@slow
						def 		__UpperCAmelCase       (    self			:		Union[str, Any]					):





										'''simple docstring'''







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

										_snake_case :			int  						=   torch.tensor(
										    [[0.24_2793, -0.2869_3074, 0.874_2613], [0.6781_5274, -0.0810_1085, -0.1295_2147]] ,				device=lowerCamelCase_					)

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

										for step in range(lowerCamelCase_					):
														_snake_case :			int  						=   torch.cat([actions, torch.zeros(1 ,				1 ,				config.act_dim ,				device=lowerCamelCase_					)] ,				dim=1					)
														_snake_case :			Any  						=   torch.cat([rewards, torch.zeros(1 ,				1 ,				device=lowerCamelCase_					)] ,				dim=1					)

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

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

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













from math import factorial

def       A__(		__lowerCAmelCase	,			__lowerCAmelCase	,			__lowerCAmelCase ):
				if successes > trials:
								raise ValueError('successes must be lower or equal to trials' )
				if trials < 0 or successes < 0:
								raise ValueError('the function is defined for non-negative integers' )
				if not isinstance(__lowerCAmelCase	,			__lowerCAmelCase ) or not isinstance(__lowerCAmelCase	,			__lowerCAmelCase ):
								raise ValueError('the function is defined for non-negative integers' )
				if not 0 < prob < 1:
								raise ValueError('prob has to be in range of 1 - 0' )
				_snake_case :			Optional[int]  						=   (prob**successes) * ((1 - prob) ** (trials - successes))
				# Calculate the binomial coefficient: n! / k!(n-k)!
				_snake_case :			List[Any]  						=   float(factorial(__lowerCAmelCase ) )
				coefficient /= factorial(__lowerCAmelCase ) * factorial(trials - successes )
				return probability * coefficient


if __name__ == "__main__":
		from doctest import testmod

		testmod()
		print('''Probability of 2 successes out of 4 trails''')
		print('''with probability of 0.75 is:''', end=''' ''')
		print(binomial_distribution(2, 4, 0.75))






