code: 
  
  
  
  
  
  
  """simple docstring"""
  import unittest
  from functools import partial
  
  import torch
  
  from accelerate import Accelerator, debug_launcher
  from accelerate.state import AcceleratorState, GradientState
  from accelerate.test_utils import require_cpu, require_huggingface_suite
  from accelerate.utils import GradientAccumulationPlugin
  
  
  def        A	(							__snake_case:   Any=2   ,    __snake_case:   Tuple=True   ,    __snake_case:   Union[str, Any]=False     )						->		int:
  
         """simple docstring"""
  
  
  
  
  
         __magic_name__										=						Accelerator(step_scheduler_with_optimizer=_UpperCamelCase   ,    split_batches=_UpperCamelCase     )
         __magic_name__										=						torch.nn.Linear(2   ,    4     )
         __magic_name__										=						torch.optim.AdamW(model.parameters()   ,    lr=1.0     )
         __magic_name__										=						torch.optim.lr_scheduler.OneCycleLR(_UpperCamelCase   ,    max_lr=0.01   ,    steps_per_epoch=2   ,    epochs=1     )
         __magic_name__							,     __magic_name__							,     __magic_name__										=						accelerator.prepare(_UpperCamelCase   ,    _UpperCamelCase   ,    _UpperCamelCase     )
  
         # Optimizer has stepped
         scheduler.step()
         if step_scheduler_with_optimizer or (num_processes == 1):
                assert (
                    scheduler.scheduler.last_epoch == num_processes
                ), F"""Last Epoch ({scheduler.scheduler.last_epoch}) != Num Processes ({num_processes})"""
         else:
                assert (
                    scheduler.scheduler.last_epoch != num_processes
                ), F"""Last Epoch ({scheduler.scheduler.last_epoch}) == Num Processes ({num_processes})"""
  
  
  def        A	(							__snake_case:   Optional[int]=2   ,    __snake_case:   List[str]=True   ,    __snake_case:   Tuple=False     )						->		Optional[Any]:
  
         """simple docstring"""
  
  
  
  
  
         __magic_name__										=						Accelerator(step_scheduler_with_optimizer=_UpperCamelCase   ,    split_batches=_UpperCamelCase     )
         __magic_name__										=						torch.nn.Linear(2   ,    4     )
         __magic_name__										=						torch.optim.AdamW(model.parameters()   ,    lr=1.0     )
         __magic_name__										=						torch.optim.lr_scheduler.LambdaLR(_UpperCamelCase   ,    lr_lambda=lambda __snake_case     : 1 - n / 1_0     )
         __magic_name__							,     __magic_name__							,     __magic_name__										=						accelerator.prepare(_UpperCamelCase   ,    _UpperCamelCase   ,    _UpperCamelCase     )
  
         # Optimizer has stepped
         __magic_name__										=						False
         scheduler.step()
         __magic_name__										=						1 - (num_processes if (step_scheduler_with_optimizer and not split_batches) else 1) / 1_0
         assert (
             scheduler.get_last_lr()[0] == expected_lr
         ), F"""Wrong lr found at first step, expected {expected_lr}, got {scheduler.get_last_lr()[0]}"""
  
         # Optimizer has not stepped
         __magic_name__										=						True
         scheduler.step()
         if not step_scheduler_with_optimizer:
                __magic_name__										=						1 - 2 / 1_0
         assert (
             scheduler.get_last_lr()[0] == expected_lr
         ), F"""Wrong lr found at second step, expected {expected_lr}, got {scheduler.get_last_lr()[0]}"""
  
  
  def        A	(							__snake_case:   int = 2     )						->		Optional[int]:
  
         """simple docstring"""
  
  
  
  
  
         from transformers import get_linear_schedule_with_warmup
  
         __magic_name__										=						[1, 2, 4]
         for num_steps in steps:
                __magic_name__										=						GradientAccumulationPlugin(num_steps=_UpperCamelCase   ,    adjust_scheduler=num_steps > 1     )
                __magic_name__										=						Accelerator(gradient_accumulation_plugin=_UpperCamelCase     )
                __magic_name__										=						torch.nn.Linear(2   ,    4     )
                __magic_name__										=						torch.optim.AdamW(model.parameters()   ,    lr=1_0.0     )
                __magic_name__										=						get_linear_schedule_with_warmup(optimizer=_UpperCamelCase   ,    num_warmup_steps=0   ,    num_training_steps=2_0     )
  
                __magic_name__							,     __magic_name__							,     __magic_name__										=						accelerator.prepare(_UpperCamelCase   ,    _UpperCamelCase   ,    _UpperCamelCase     )
  
                for i in range(1_0 * num_steps     ):
                       with accelerator.accumulate(_UpperCamelCase     ):
                              optimizer.step()
                              scheduler.step()
  
                       if i == (1_0 * num_steps - 2):
                              assert (
                                  scheduler.get_last_lr()[0] != 0
                              ), F"""Wrong lr found at second-to-last step, expected non-zero, got {scheduler.get_last_lr()[0]}. num_steps: {num_steps}"""
                assert (
                    scheduler.get_last_lr()[0] == 0
                ), F"""Wrong lr found at last step, expected 0, got {scheduler.get_last_lr()[0]}"""
                GradientState._reset_state()
  
  
  
  
  
  
  @require_cpu
  class 					UpperCamelCase__   (							unittest.TestCase):
  
  
  
  
  
  
  
      """simple docstring"""
  
  
  
  
  
  
  
      def 							a__ (		self   :  int   ):
  
  
  
  
  
             '''simple docstring'''
  
             debug_launcher(partial(a_							,						num_processes=1   )							,						num_processes=1   )
             debug_launcher(partial(a_							,						num_processes=1							,						split_batches=a_   )							,						num_processes=1   )
  
  
  
  
  
  
  
      def 							a__ (		self   :  Union[str, Any]   ):
  
  
  
  
  
             '''simple docstring'''
  
             debug_launcher(partial(a_							,						num_processes=1   )							,						num_processes=1   )
             debug_launcher(partial(a_							,						num_processes=1							,						split_batches=a_   )							,						num_processes=1   )
  
  
  
  
  
  
  
      def 							a__ (		self   :  Optional[Any]   ):
  
  
  
  
  
             '''simple docstring'''
  
             debug_launcher(partial(a_							,						num_processes=1							,						step_scheduler_with_optimizer=a_   )							,						num_processes=1   )
  
  
  
  
  
  
  
      def 							a__ (		self   :  List[Any]   ):
  
  
  
  
  
             '''simple docstring'''
  
             debug_launcher(partial(a_							,						num_processes=1							,						step_scheduler_with_optimizer=a_   )							,						num_processes=1   )
  
  
  
  
  
  
  
      def 							a__ (		self   :  Optional[int]   ):
  
  
  
  
  
             '''simple docstring'''
  
             AcceleratorState._reset_state(a_   )
             debug_launcher(a_   )
             debug_launcher(partial(a_							,						num_processes=1							,						split_batches=a_   )							,						num_processes=1   )
  
  
  
  
  
  
  
      def 							a__ (		self   :  Union[str, Any]   ):
  
  
  
  
  
             '''simple docstring'''
  
             AcceleratorState._reset_state(a_   )
             debug_launcher(a_   )
             debug_launcher(partial(a_							,						num_processes=1							,						split_batches=a_   )							,						num_processes=1   )
  
  
  
  
  
  
  
      def 							a__ (		self   :  Optional[Any]   ):
  
  
  
  
  
             '''simple docstring'''
  
             AcceleratorState._reset_state(a_   )
             debug_launcher(partial(a_							,						step_scheduler_with_optimizer=a_   )   )
  
  
  
  
  
  
  
      def 							a__ (		self   :  List[Any]   ):
  
  
  
  
  
             '''simple docstring'''
  
             AcceleratorState._reset_state(a_   )
             debug_launcher(partial(a_							,						step_scheduler_with_optimizer=a_   )   )
  
  
  
  
  
  
  
      @require_huggingface_suite
      def 							a__ (		self   :  Any   ):
  
  
  
  
  
             '''simple docstring'''
  
             AcceleratorState._reset_state(a_   )
             debug_launcher(partial(a_							,						num_processes=1   )   )
             debug_launcher(a_   )
style_context: 
  
  
  
  
  
  
  """simple docstring"""
  import numpy as np
  import qiskit
  
  
  def        A	(							__snake_case:   str   ,    __snake_case:   int     )						->		qiskit.QuantumCircuit:
  
         """simple docstring"""
  
  
  
  
  
         __magic_name__										=						qiskit.QuantumCircuit(num_qubits + 1     )
  
         # First, let's deal with the case in which oracle is balanced
         if case == "balanced":
                # First generate a random number that tells us which CNOTs to
                # wrap in X-gates:
                __magic_name__										=						np.random.randint(1   ,    2**num_qubits     )
                # Next, format 'b' as a binary string of length 'n', padded with zeros:
                __magic_name__										=						format(__snake_case   ,    F"""0{num_qubits}b"""     )
                # Next, we place the first X-gates. Each digit in our binary string
                # corresponds to a qubit, if the digit is 0, we do nothing, if it's 1
                # we apply an X-gate to that qubit:
                for index, bit in enumerate(__snake_case     ):
                       if bit == "1":
                              oracle_qc.x(__snake_case     )
          # Do the controlled-NOT gates for each qubit, using the output qubit
          # as the target:
                for index in range(__snake_case     ):
                       oracle_qc.cx(__snake_case   ,    __snake_case     )
                # Next, place the final X-gates
                for index, bit in enumerate(__snake_case     ):
                       if bit == "1":
                              oracle_qc.x(__snake_case     )
  
      # Case in which oracle is constant
         if case == "constant":
                # First decide what the fixed output of the oracle will be
                # (either always 0 or always 1)
                __magic_name__										=						np.random.randint(2     )
                if output == 1:
                       oracle_qc.x(__snake_case     )
  
         __magic_name__										=						oracle_qc.to_gate()
         __magic_name__										=						'Oracle'  # To show when we display the circuit
         return oracle_gate
  
  
  def        A	(							__snake_case:   qiskit.QuantumCircuit   ,    __snake_case:   int     )						->		qiskit.QuantumCircuit:
  
         """simple docstring"""
  
  
  
  
  
         __magic_name__										=						qiskit.QuantumCircuit(num_qubits + 1   ,    __snake_case     )
         # Set up the output qubit:
         dj_circuit.x(__snake_case     )
         dj_circuit.h(__snake_case     )
         # And set up the input register:
         for qubit in range(__snake_case     ):
                dj_circuit.h(__snake_case     )
         # Let's append the oracle gate to our circuit:
         dj_circuit.append(__snake_case   ,    range(num_qubits + 1     )     )
         # Finally, perform the H-gates again and measure:
         for qubit in range(__snake_case     ):
                dj_circuit.h(__snake_case     )
  
         for i in range(__snake_case     ):
                dj_circuit.measure(__snake_case   ,    __snake_case     )
  
         return dj_circuit
  
  
  def        A	(							__snake_case:   str   ,    __snake_case:   int     )						->		qiskit.result.counts.Counts:
  
         """simple docstring"""
  
  
  
  
  
         __magic_name__										=						qiskit.Aer.get_backend('aer_simulator'     )
  
         __magic_name__										=						dj_oracle(__snake_case   ,    __snake_case     )
         __magic_name__										=						dj_algorithm(__snake_case   ,    __snake_case     )
  
         # Execute the circuit on the simulator
         __magic_name__										=						qiskit.execute(__snake_case   ,    __snake_case   ,    shots=1_0_0_0     )
  
         # Return the histogram data of the results of the experiment.
         return job.result().get_counts(__snake_case     )
  
  
  if __name__ == "__main__":
   print(f"""Deutsch Jozsa - Constant Oracle: {deutsch_jozsa('constant', 3)}""")
   print(f"""Deutsch Jozsa - Balanced Oracle: {deutsch_jozsa('balanced', 3)}""")