

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 						__magic_name__			:

   '''simple docstring'''




   def __init__(				self,    lowercase_,    lowercase_=13,    lowercase_=7,    lowercase_=6,    lowercase_=17,    lowercase_=23,    lowercase_=11,    lowercase_=True,    )							->    Tuple:






     """simple docstring"""






     a__  =parent
     a__  =batch_size
     a__  =seq_length
     a__  =act_dim
     a__  =state_dim
     a__  =hidden_size
     a__  =max_length
     a__  =is_training



   def  _UpperCAmelCase (				self       )							->    int:






     """simple docstring"""






     a__  =floats_tensor((self.batch_size, self.seq_length, self.state_dim)       )
     a__  =floats_tensor((self.batch_size, self.seq_length, self.act_dim)       )
     a__  =floats_tensor((self.batch_size, self.seq_length, 1)       )
     a__  =floats_tensor((self.batch_size, self.seq_length, 1)       )
     a__  =ids_tensor((self.batch_size, self.seq_length),    vocab_size=1000       )
     a__  =random_attention_mask((self.batch_size, self.seq_length)       )

     a__  =self.get_config()

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



   def  _UpperCAmelCase (				self       )							->    Tuple:






     """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,    lowercase_,    lowercase_,    lowercase_,    lowercase_,    lowercase_,    lowercase_,    lowercase_,    )							->    int:






     """simple docstring"""






     a__  =DecisionTransformerModel(config=lowercase_       )
     model.to(lowercase_       )
     model.eval()
     a__  =model(lowercase_,    lowercase_,    lowercase_,    lowercase_,    lowercase_,    lowercase_       )

     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       )							->    Tuple:






     """simple docstring"""






     a__  =self.prepare_config_and_inputs()
     (
         (
         a__
     ),      (
         a__
     ),      (
         a__
     ),      (
         a__
     ),      (
         a__
     ),      (
         a__
     ),      (
         a__
     ),      
     )  =config_and_inputs
     a__  ={
         '''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 						__magic_name__			(							lowerCamelCase__		,       lowerCamelCase__		,       lowerCamelCase__		,       unittest.TestCase							):

   '''simple docstring'''




   lowerCamelCase__ :	str    =   (DecisionTransformerModel,) if is_torch_available() else ()
   lowerCamelCase__ :	Any    =   ()
   lowerCamelCase__ :	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
   lowerCamelCase__ :	Any    =   False

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



   def  _UpperCAmelCase (				self       )							->    Union[str, Any]:






     """simple docstring"""






     a__  =DecisionTransformerModelTester(self       )
     a__  =ConfigTester(self,    config_class=lowercase_,    hidden_size=37       )



   def  _UpperCAmelCase (				self       )							->    Optional[int]:






     """simple docstring"""






     self.config_tester.run_common_tests()



   def  _UpperCAmelCase (				self       )							->    str:






     """simple docstring"""






     a__  =self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model(*lowercase_       )



   @slow
   def  _UpperCAmelCase (				self       )							->    Dict:






     """simple docstring"""






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



   def  _UpperCAmelCase (				self       )							->    int:






     """simple docstring"""






     a__,      a__  =self.model_tester.prepare_config_and_inputs_for_common()

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

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

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







@require_torch
class 						__magic_name__			(							unittest.TestCase							):

   '''simple docstring'''




   @slow
   def  _UpperCAmelCase (				self       )							->    List[Any]:






     """simple docstring"""






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

     a__  =torch.tensor(
         [[0.242793, -0.28693074, 0.8742613], [0.67815274, -0.08101085, -0.12952147]],    device=lowercase_       )

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

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

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

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

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

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



import math

import numpy as np
import qiskit
from qiskit import Aer, ClassicalRegister, QuantumCircuit, QuantumRegister, execute




def 			UpperCAmelCase__				(			_A      : int = 3							):





  '''simple docstring'''







  if isinstance(_A	,      _A							):
    raise TypeError('''number of qubits must be a integer.'''							)
  if number_of_qubits <= 0:
    raise ValueError('''number of qubits must be > 0.'''							)
  if math.floor(_A							) != number_of_qubits:
    raise ValueError('''number of qubits must be exact integer.'''							)
  if number_of_qubits > 10:
    raise ValueError('''number of qubits too large to simulate(>10).'''							)

  a__  =QuantumRegister(_A	,      '''qr'''							)
  a__  =ClassicalRegister(_A	,      '''cr'''							)

  a__  =QuantumCircuit(_A	,      _A							)

  a__  =number_of_qubits

  for i in range(_A							):
    quantum_circuit.h(number_of_qubits - i - 1							)
    counter -= 1
    for j in range(_A							):
      quantum_circuit.cp(np.pi / 2 ** (counter - j)	,      _A	,      _A							)

  for k in range(number_of_qubits // 2							):
    quantum_circuit.swap(_A	,      number_of_qubits - k - 1							)

  # measure all the qubits
  quantum_circuit.measure(_A	,      _A							)
  # simulate with 10000 shots
  a__  =Aer.get_backend('''qasm_simulator'''							)
  a__  =execute(_A	,      _A	,      shots=1_00_00							)

  return job.result().get_counts(_A							)


if __name__ == "__main__":
       print(
           f"""Total count for quantum fourier transform state is: \
    {quantum_fourier_transform(3)}"""
       )
