code: 
  
  
  
  
  
  
  """simple docstring"""
  import warnings
  from typing import List, Optional, Union
  
  import numpy as np
  import PIL
  import torch
  from PIL import Image
  
  from .configuration_utils import ConfigMixin, register_to_config
  from .utils import CONFIG_NAME, PIL_INTERPOLATION, deprecate
  
  
  
  class 					UpperCamelCase__   (							a_):
  
  
  
  
  
  
  
      """simple docstring"""
      __UpperCAmelCase											=   CONFIG_NAME
  
  
  
  
  
  
  
      @register_to_config
      def __init__(		self   :  Optional[Any]							,						UpperCamelCase_   :  bool = True							,						UpperCamelCase_   :  int = 8							,						UpperCamelCase_   :  str = "lanczos"							,						UpperCamelCase_   :  bool = True							,						UpperCamelCase_   :  bool = False							,						):
  
  
  
  
  
             '''simple docstring'''
  
             super().__init__()
  
  
  
  
  
  
  
      @staticmethod
      def 							a__ (		UpperCamelCase_   :  np.ndarray   ):
  
  
  
  
  
             '''simple docstring'''
  
             if images.ndim == 3:
                    __magic_name__										=						images[None, ...]
             __magic_name__										=						(images * 2_5_5).round().astype('uint8'   )
             if images.shape[-1] == 1:
                    # special case for grayscale (single channel) images
                    __magic_name__										=						[Image.fromarray(image.squeeze()							,						mode='L'   ) for image in images]
             else:
                    __magic_name__										=						[Image.fromarray(UpperCamelCase_   ) for image in images]
  
             return pil_images
  
  
  
  
  
  
  
      @staticmethod
      def 							a__ (		UpperCamelCase_   :  Union[List[PIL.Image.Image], PIL.Image.Image]   ):
  
  
  
  
  
             '''simple docstring'''
  
             if not isinstance(UpperCamelCase_							,						UpperCamelCase_   ):
                    __magic_name__										=						[images]
             __magic_name__										=						[np.array(UpperCamelCase_   ).astype(np.floataa   ) / 255.0 for image in images]
             __magic_name__										=						np.stack(UpperCamelCase_							,						axis=0   )
  
             return images
  
  
  
  
  
  
  
      @staticmethod
      def 							a__ (		UpperCamelCase_   :  np.ndarray   ):
  
  
  
  
  
             '''simple docstring'''
  
             if images.ndim == 3:
                    __magic_name__										=						images[..., None]
  
             __magic_name__										=						torch.from_numpy(images.transpose(0							,						3							,						1							,						2   )   )
             return images
  
  
  
  
  
  
  
      @staticmethod
      def 							a__ (		UpperCamelCase_   :  torch.FloatTensor   ):
  
  
  
  
  
             '''simple docstring'''
  
             __magic_name__										=						images.cpu().permute(0							,						2							,						3							,						1   ).float().numpy()
             return images
  
  
  
  
  
  
  
      @staticmethod
      def 							a__ (		UpperCamelCase_   :  Union[str, Any]   ):
  
  
  
  
  
             '''simple docstring'''
  
             return 2.0 * images - 1.0
  
  
  
  
  
  
  
      @staticmethod
      def 							a__ (		UpperCamelCase_   :  List[Any]   ):
  
  
  
  
  
             '''simple docstring'''
  
             return (images / 2 + 0.5).clamp(0							,						1   )
  
  
  
  
  
  
  
      @staticmethod
      def 							a__ (		UpperCamelCase_   :  PIL.Image.Image   ):
  
  
  
  
  
             '''simple docstring'''
  
             __magic_name__										=						image.convert('RGB'   )
             return image
  
  
  
  
  
  
  
      def 							a__ (		self   :  str							,						UpperCamelCase_   :  PIL.Image.Image							,						UpperCamelCase_   :  Optional[int] = None							,						UpperCamelCase_   :  Optional[int] = None							,						):
  
  
  
  
  
             '''simple docstring'''
  
             if height is None:
                    __magic_name__										=						image.height
             if width is None:
                    __magic_name__										=						image.width
  
             __magic_name__							,     __magic_name__										=						(
                 x - x % self.config.vae_scale_factor for x in (width, height)
             )  # resize to integer multiple of vae_scale_factor
             __magic_name__										=						image.resize((width, height)							,						resample=PIL_INTERPOLATION[self.config.resample]   )
             return image
  
  
  
  
  
  
  
      def 							a__ (		self   :  int							,						UpperCamelCase_   :  Union[torch.FloatTensor, PIL.Image.Image, np.ndarray]							,						UpperCamelCase_   :  Optional[int] = None							,						UpperCamelCase_   :  Optional[int] = None							,						):
  
  
  
  
  
             '''simple docstring'''
  
             __magic_name__										=						(PIL.Image.Image, np.ndarray, torch.Tensor)
             if isinstance(UpperCamelCase_							,						UpperCamelCase_   ):
                    __magic_name__										=						[image]
             elif not (isinstance(UpperCamelCase_							,						UpperCamelCase_   ) and all(isinstance(UpperCamelCase_							,						UpperCamelCase_   ) for i in image   )):
                    raise ValueError(
                        f"""Input is in incorrect format: {[type(UpperCamelCase_   ) for i in image]}. Currently, we only support {", ".join(UpperCamelCase_   )}"""   )
  
             if isinstance(image[0]							,						PIL.Image.Image   ):
                    if self.config.do_convert_rgb:
                           __magic_name__										=						[self.convert_to_rgb(UpperCamelCase_   ) for i in image]
                    if self.config.do_resize:
                           __magic_name__										=						[self.resize(UpperCamelCase_							,						UpperCamelCase_							,						UpperCamelCase_   ) for i in image]
                    __magic_name__										=						self.pil_to_numpy(UpperCamelCase_   )  # to np
                    __magic_name__										=						self.numpy_to_pt(UpperCamelCase_   )  # to pt
  
             elif isinstance(image[0]							,						np.ndarray   ):
                    __magic_name__										=						np.concatenate(UpperCamelCase_							,						axis=0   ) if image[0].ndim == 4 else np.stack(UpperCamelCase_							,						axis=0   )
                    __magic_name__										=						self.numpy_to_pt(UpperCamelCase_   )
                    __magic_name__							,     __magic_name__							,     __magic_name__							,     __magic_name__										=						image.shape
                    if self.config.do_resize and (
                        height % self.config.vae_scale_factor != 0 or width % self.config.vae_scale_factor != 0
                    ):
                           raise ValueError(
                               f"""Currently we only support resizing for PIL image - please resize your numpy array to be divisible by {self.config.vae_scale_factor}"""
                               f"""currently the sizes are {height} and {width}. You can also pass a PIL image instead to use resize option in VAEImageProcessor"""   )
  
             elif isinstance(image[0]							,						torch.Tensor   ):
                    __magic_name__										=						torch.cat(UpperCamelCase_							,						axis=0   ) if image[0].ndim == 4 else torch.stack(UpperCamelCase_							,						axis=0   )
                    __magic_name__							,     __magic_name__							,     __magic_name__							,     __magic_name__										=						image.shape
  
                    # don't need any preprocess if the image is latents
                    if channel == 4:
                           return image
  
                    if self.config.do_resize and (
                        height % self.config.vae_scale_factor != 0 or width % self.config.vae_scale_factor != 0
                    ):
                           raise ValueError(
                               f"""Currently we only support resizing for PIL image - please resize your pytorch tensor to be divisible by {self.config.vae_scale_factor}"""
                               f"""currently the sizes are {height} and {width}. You can also pass a PIL image instead to use resize option in VAEImageProcessor"""   )
  
          # expected range [0,1], normalize to [-1,1]
             __magic_name__										=						self.config.do_normalize
             if image.min() < 0:
                    warnings.warn(
                        'Passing `image` as torch tensor with value range in [-1,1] is deprecated. The expected value range for image tensor is [0,1] '
                        f"""when passing as pytorch tensor or numpy Array. You passed `image` with value range [{image.min()},{image.max()}]"""							,						UpperCamelCase_							,						)
                    __magic_name__										=						False
  
             if do_normalize:
                    __magic_name__										=						self.normalize(UpperCamelCase_   )
  
             return image
  
  
  
  
  
  
  
      def 							a__ (		self   :  Tuple							,						UpperCamelCase_   :  torch.FloatTensor							,						UpperCamelCase_   :  str = "pil"							,						UpperCamelCase_   :  Optional[List[bool]] = None							,						):
  
  
  
  
  
             '''simple docstring'''
  
             if not isinstance(UpperCamelCase_							,						torch.Tensor   ):
                    raise ValueError(
                        f"""Input for postprocessing is in incorrect format: {type(UpperCamelCase_   )}. We only support pytorch tensor"""   )
             if output_type not in ["latent", "pt", "np", "pil"]:
                    __magic_name__										=						(
                        f"""the output_type {output_type} is outdated and has been set to `np`. Please make sure to set it to one of these instead: """
                        '`pil`, `np`, `pt`, `latent`'
                    )
                    deprecate('Unsupported output_type'							,						'1.0.0'							,						UpperCamelCase_							,						standard_warn=UpperCamelCase_   )
                    __magic_name__										=						'np'
  
             if output_type == "latent":
                    return image
  
             if do_denormalize is None:
                    __magic_name__										=						[self.config.do_normalize] * image.shape[0]
  
             __magic_name__										=						torch.stack(
                 [self.denormalize(image[i]   ) if do_denormalize[i] else image[i] for i in range(image.shape[0]   )]   )
  
             if output_type == "pt":
                    return image
  
             __magic_name__										=						self.pt_to_numpy(UpperCamelCase_   )
  
             if output_type == "np":
                    return image
  
             if output_type == "pil":
                    return self.numpy_to_pil(UpperCamelCase_   )
  
  
  
  class 					UpperCamelCase__   (							a_):
  
  
  
  
  
  
  
      """simple docstring"""
      __UpperCAmelCase											=   CONFIG_NAME
  
  
  
  
  
  
  
      @register_to_config
      def __init__(		self   :  List[str]							,						UpperCamelCase_   :  bool = True							,						UpperCamelCase_   :  int = 8							,						UpperCamelCase_   :  str = "lanczos"							,						UpperCamelCase_   :  bool = True							,						):
  
  
  
  
  
             '''simple docstring'''
  
             super().__init__()
  
  
  
  
  
  
  
      @staticmethod
      def 							a__ (		UpperCamelCase_   :  str   ):
  
  
  
  
  
             '''simple docstring'''
  
             if images.ndim == 3:
                    __magic_name__										=						images[None, ...]
             __magic_name__										=						(images * 2_5_5).round().astype('uint8'   )
             if images.shape[-1] == 1:
                    # special case for grayscale (single channel) images
                    __magic_name__										=						[Image.fromarray(image.squeeze()							,						mode='L'   ) for image in images]
             else:
                    __magic_name__										=						[Image.fromarray(image[:, :, :3]   ) for image in images]
  
             return pil_images
  
  
  
  
  
  
  
      @staticmethod
      def 							a__ (		UpperCamelCase_   :  Union[str, Any]   ):
  
  
  
  
  
             '''simple docstring'''
  
             return image[:, :, 1] * 2**8 + image[:, :, 2]
  
  
  
  
  
  
  
      def 							a__ (		self   :  Tuple							,						UpperCamelCase_   :  Tuple   ):
  
  
  
  
  
             '''simple docstring'''
  
             if images.ndim == 3:
                    __magic_name__										=						images[None, ...]
             __magic_name__										=						images[:, :, :, 3:]
             if images.shape[-1] == 6:
                    __magic_name__										=						(images_depth * 2_5_5).round().astype('uint8'   )
                    __magic_name__										=						[
                        Image.fromarray(self.rgblike_to_depthmap(UpperCamelCase_   )							,						mode='I;16'   ) for image_depth in images_depth
                    ]
             elif images.shape[-1] == 4:
                    __magic_name__										=						(images_depth * 65_535.0).astype(np.uintaa   )
                    __magic_name__										=						[Image.fromarray(UpperCamelCase_							,						mode='I;16'   ) for image_depth in images_depth]
             else:
                    raise Exception('Not supported'   )
  
             return pil_images
  
  
  
  
  
  
  
      def 							a__ (		self   :  Tuple							,						UpperCamelCase_   :  torch.FloatTensor							,						UpperCamelCase_   :  str = "pil"							,						UpperCamelCase_   :  Optional[List[bool]] = None							,						):
  
  
  
  
  
             '''simple docstring'''
  
             if not isinstance(UpperCamelCase_							,						torch.Tensor   ):
                    raise ValueError(
                        f"""Input for postprocessing is in incorrect format: {type(UpperCamelCase_   )}. We only support pytorch tensor"""   )
             if output_type not in ["latent", "pt", "np", "pil"]:
                    __magic_name__										=						(
                        f"""the output_type {output_type} is outdated and has been set to `np`. Please make sure to set it to one of these instead: """
                        '`pil`, `np`, `pt`, `latent`'
                    )
                    deprecate('Unsupported output_type'							,						'1.0.0'							,						UpperCamelCase_							,						standard_warn=UpperCamelCase_   )
                    __magic_name__										=						'np'
  
             if do_denormalize is None:
                    __magic_name__										=						[self.config.do_normalize] * image.shape[0]
  
             __magic_name__										=						torch.stack(
                 [self.denormalize(image[i]   ) if do_denormalize[i] else image[i] for i in range(image.shape[0]   )]   )
  
             __magic_name__										=						self.pt_to_numpy(UpperCamelCase_   )
  
             if output_type == "np":
                    if image.shape[-1] == 6:
                           __magic_name__										=						np.stack([self.rgblike_to_depthmap(im[:, :, 3:]   ) for im in image]							,						axis=0   )
                    else:
                           __magic_name__										=						image[:, :, :, 3:]
                    return image[:, :, :, :3], image_depth
  
             if output_type == "pil":
                    return self.numpy_to_pil(UpperCamelCase_   ), self.numpy_to_depth(UpperCamelCase_   )
             else:
                    raise Exception(f"""This type {output_type} is not supported"""   )
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)}""")