





'''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 			lowerCamelCase					:


					def __init__(					self	:  List[Any]       ,     __snake_case	:  List[Any]       ,     __snake_case	:  Any=13       ,     __snake_case	:  Tuple=7       ,     __snake_case	:  Optional[int]=6       ,     __snake_case	:  int=17       ,     __snake_case	:  List[Any]=23       ,     __snake_case	:  Optional[Any]=11       ,     __snake_case	:  Dict=True       ,     ):





											'''simple docstring'''





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


					def    SCREAMING_SNAKE_CASE_		(					self	:  List[str]       ):





											'''simple docstring'''





											_snake_case:   Tuple  					=		floats_tensor((self.batch_size, self.seq_length, self.state_dim)       )
											_snake_case:   List[Any]  					=		floats_tensor((self.batch_size, self.seq_length, self.act_dim)       )
											_snake_case:   Dict  					=		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:   Optional[int]  					=		random_attention_mask((self.batch_size, self.seq_length)       )

											_snake_case:   int  					=		self.get_config()

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


					def    SCREAMING_SNAKE_CASE_		(					self	:  int       ):





											'''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    SCREAMING_SNAKE_CASE_		(					self	:  Union[str, Any]       ,     __snake_case	:  str       ,     __snake_case	:  List[Any]       ,     __snake_case	:  Optional[int]       ,     __snake_case	:  Optional[Any]       ,     __snake_case	:  str       ,     __snake_case	:  int       ,     __snake_case	:  List[Any]       ,     ):





											'''simple docstring'''





											_snake_case:   Union[str, Any]  					=		DecisionTransformerModel(config=__snake_case       )
											model.to(__snake_case       )
											model.eval()
											_snake_case:   Optional[Any]  					=		model(__snake_case       ,     __snake_case       ,     __snake_case       ,     __snake_case       ,     __snake_case       ,     __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    SCREAMING_SNAKE_CASE_		(					self	:  Any       ):





											'''simple docstring'''





											_snake_case:   List[Any]  					=		self.prepare_config_and_inputs()
											(
											    _snake_case						
											):   Tuple  					=		config_and_inputs
											_snake_case:   Optional[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 			lowerCamelCase					(						__UpperCAmelCase     ,							__UpperCAmelCase     ,							__UpperCAmelCase     ,							unittest.TestCase  ):
					_SCREAMING_SNAKE_CASE							   =    (DecisionTransformerModel,) if is_torch_available() else ()
					_SCREAMING_SNAKE_CASE							   =    ()
					_SCREAMING_SNAKE_CASE							   =    {"feature-extraction": DecisionTransformerModel} if is_torch_available() else {}

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

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


					def    SCREAMING_SNAKE_CASE_		(					self	:  int       ):





											'''simple docstring'''





											_snake_case:   int  					=		DecisionTransformerModelTester(self       )
											_snake_case:   str  					=		ConfigTester(self       ,     config_class=__snake_case       ,     hidden_size=37       )


					def    SCREAMING_SNAKE_CASE_		(					self	:  Any       ):





											'''simple docstring'''





											self.config_tester.run_common_tests()


					def    SCREAMING_SNAKE_CASE_		(					self	:  Optional[int]       ):





											'''simple docstring'''





											_snake_case:   Any  					=		self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_model(*__snake_case       )


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





											'''simple docstring'''





											for model_name in DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																	_snake_case:   List[str]  					=		DecisionTransformerModel.from_pretrained(__snake_case       )
																	self.assertIsNotNone(__snake_case       )




					def    SCREAMING_SNAKE_CASE_		(					self	:  List[str]       ):





											'''simple docstring'''





											_snake_case:   str  					=		self.model_tester.prepare_config_and_inputs_for_common()

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

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

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







@require_torch
class 			lowerCamelCase					(						unittest.TestCase  ):


					@slow
					def    SCREAMING_SNAKE_CASE_		(					self	:  Optional[int]       ):





											'''simple docstring'''





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

											_snake_case:   Tuple  					=		torch.tensor(
											    [[0.242_793, -0.28_693_074, 0.8_742_613], [0.67_815_274, -0.08_101_085, -0.12_952_147]]       ,     device=__snake_case       )

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

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

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

																	with torch.no_grad():
																							_snake_case:   Tuple  					=		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       ,     )

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

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












'''simple docstring'''






def       lowercase_      (		lowercase__ = 1				,  lowercase__ = 1000						)   ->int:
						_snake_case:   int  					=		1
						_snake_case:   Union[str, Any]  					=		0

						for divide_by_number in range(lowercase__				,  digit + 1						):
												_snake_case:   list[int]  					=		[]
												_snake_case:   int  					=		numerator
												for _ in range(1				,  digit + 1						):
																		if now_divide in has_been_divided:
																								if longest_list_length < len(lowercase__						):
																														_snake_case:   Union[str, Any]  					=		len(lowercase__						)
																														_snake_case:   Tuple  					=		divide_by_number
																		else:
																								has_been_divided.append(lowercase__						)
																								_snake_case:   Union[str, Any]  					=		now_divide * 10 % divide_by_number

						return the_digit


# Tests
if __name__ == "__main__":
		import doctest

		doctest.testmod()





