



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 				_a						:





							'''simple docstring'''

							def __init__(							self ,					__UpperCAmelCase ,					__UpperCAmelCase=13 ,					__UpperCAmelCase=7 ,					__UpperCAmelCase=6 ,					__UpperCAmelCase=17 ,					__UpperCAmelCase=23 ,					__UpperCAmelCase=11 ,					__UpperCAmelCase=True ,					):







											"""simple docstring"""



											a__	:							int      						= parent
											a__	:							Dict      						= batch_size
											a__	:							Dict      						= seq_length
											a__	:							Optional[int]      						= act_dim
											a__	:							Dict      						= state_dim
											a__	:							Tuple      						= hidden_size
											a__	:							List[Any]      						= max_length
											a__	:							int      						= is_training



							def 		_A	(							self				):







											"""simple docstring"""



											a__	:							Any      						= floats_tensor((self.batch_size, self.seq_length, self.state_dim)				)
											a__	:							Any      						= floats_tensor((self.batch_size, self.seq_length, self.act_dim)				)
											a__	:							Tuple      						= floats_tensor((self.batch_size, self.seq_length, 1)				)
											a__	:							Union[str, Any]      						= floats_tensor((self.batch_size, self.seq_length, 1)				)
											a__	:							Union[str, Any]      						= ids_tensor((self.batch_size, self.seq_length) ,					vocab_size=1000				)
											a__	:							int      						= random_attention_mask((self.batch_size, self.seq_length)				)

											a__	:							Any      						= self.get_config()

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



							def 		_A	(							self				):







											"""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 		_A	(							self ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase ,					):







											"""simple docstring"""



											a__	:							Any      						= DecisionTransformerModel(config=__UpperCAmelCase				)
											model.to(__UpperCAmelCase				)
											model.eval()
											a__	:							Any      						= model(__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase				)

											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 		_A	(							self				):







											"""simple docstring"""



											a__	:							Dict      						= self.prepare_config_and_inputs()
											(
											    (
											    a__
											)						,   (
											    a__
											)						,   (
											    a__
											)						,   (
											    a__
											)						,   (
											    a__
											)						,   (
											    a__
											)						,   (
											    a__
											)						,   
											)	:							Optional[int]      						= 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 				_a						(       SCREAMING_SNAKE_CASE       ,  SCREAMING_SNAKE_CASE       ,  SCREAMING_SNAKE_CASE       ,  unittest.TestCase			):





							'''simple docstring'''

							A				:List[Any] =       (DecisionTransformerModel,) if is_torch_available() else ()
							A				:Any =       ()
							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				:Dict =       False

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



							def 		_A	(							self				):







											"""simple docstring"""



											a__	:							int      						= DecisionTransformerModelTester(self				)
											a__	:							Union[str, Any]      						= ConfigTester(self ,					config_class=__UpperCAmelCase ,					hidden_size=37				)



							def 		_A	(							self				):







											"""simple docstring"""



											self.config_tester.run_common_tests()



							def 		_A	(							self				):







											"""simple docstring"""



											a__	:							Optional[int]      						= self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_model(*__UpperCAmelCase				)



							@slow
							def 		_A	(							self				):







											"""simple docstring"""



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




							def 		_A	(							self				):







											"""simple docstring"""



											a__						,   a__	:							Any      						= self.model_tester.prepare_config_and_inputs_for_common()

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

															a__	:							Optional[Any]      						= [
															    "states",
															    "actions",
															    "rewards",
															    "returns_to_go",
															    "timesteps",
															    "attention_mask",
															]

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





@require_torch
class 				_a						(       unittest.TestCase			):





							'''simple docstring'''

							@slow
							def 		_A	(							self				):







											"""simple docstring"""



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

											a__	:							Tuple      						= 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=__UpperCAmelCase				)

											a__	:							List[str]      						= torch.tensor(__UpperCAmelCase ,					device=__UpperCAmelCase ,					dtype=torch.floataa				).reshape(1 ,					1 ,					1				)
											a__	:							str      						= state
											a__	:							Tuple      						= torch.zeros(1 ,					0 ,					config.act_dim ,					device=__UpperCAmelCase ,					dtype=torch.floataa				)
											a__	:							int      						= torch.zeros(1 ,					0 ,					device=__UpperCAmelCase ,					dtype=torch.floataa				)
											a__	:							List[str]      						= torch.tensor(0 ,					device=__UpperCAmelCase ,					dtype=torch.long				).reshape(1 ,					1				)

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

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

															with torch.no_grad():
																			a__						,   a__						,   a__	:							int      						= model(
																			    states=__UpperCAmelCase ,					actions=__UpperCAmelCase ,					rewards=__UpperCAmelCase ,					returns_to_go=__UpperCAmelCase ,					timesteps=__UpperCAmelCase ,					attention_mask=__UpperCAmelCase ,					return_dict=__UpperCAmelCase ,					)

															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__	:							Any      						= (  # env.step(action)
															    torch.randn(1 ,					1 ,					config.state_dim				).to(device=__UpperCAmelCase ,					dtype=torch.floataa				),
															    1.0,
															    False,
															    {},
															)

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







import qiskit






def     SCREAMING_SNAKE_CASE(      __UpperCamelCase = 2 )	->						qiskit.result.counts.Counts:
				a__	:							Any      						= qubits

				# Using Aer's simulator
				a__	:							Dict      						= qiskit.Aer.get_backend("aer_simulator" )

				# Creating a Quantum Circuit acting on the q register
				a__	:							Dict      						= qiskit.QuantumCircuit(__UpperCamelCase	,				__UpperCamelCase )

				# Adding a H gate on qubit 0 (now q0 in superposition)
				circuit.h(0 )

				for i in range(1	,				__UpperCamelCase ):
								# Adding CX (CNOT) gate
								circuit.cx(i - 1	,				__UpperCamelCase )

				# Mapping the quantum measurement to the classical bits
				circuit.measure(list(range(__UpperCamelCase ) )	,				list(range(__UpperCamelCase ) ) )

				# Now measuring any one qubit would affect other qubits to collapse
				# their super position and have same state as the measured one.

				# Executing the circuit on the simulator
				a__	:							Any      						= qiskit.execute(__UpperCamelCase	,				__UpperCamelCase	,				shots=10_00 )

				return job.result().get_counts(__UpperCamelCase )


if __name__ == "__main__":
						print(F'Total count for various states are: {quantum_entanglement(3)}')


