
import warnings
from typing import List, Optional, Tuple, Union

import numpy as np
import PIL
import torch

from ...models import UNetaDModel
from ...schedulers import RePaintScheduler
from ...utils import PIL_INTERPOLATION, logging, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


lowerCamelCase__     :		Optional[Any]        = logging.get_logger(__name__)  # pylint: disable=invalid-name

def 		UpperCamelCase						(   lowercase_ )		->						Dict:


  '''simple docstring'''






  warnings.warn(
      """The preprocess method is deprecated and will be removed in a future version. Please"""
      """ use VaeImageProcessor.preprocess instead"""			,			lowercase_			,			)
  if isinstance(lowercase_			,			torch.Tensor ):
    return image
  elif isinstance(lowercase_			,			PIL.Image.Image ):
    lowercase__	: List[str]     		=   [image]

  if isinstance(image[0]			,			PIL.Image.Image ):
    lowercase__		,     lowercase__	: List[str]     		=   image[0].size
    lowercase__		,     lowercase__	: Tuple     		=   (x - x % 8 for x in (w, h))  # resize to integer multiple of 8

    lowercase__	: str     		=   [np.array(i.resize((w, h)			,			resample=PIL_INTERPOLATION["""lanczos"""] ) )[None, :] for i in image]
    lowercase__	: List[str]     		=   np.concatenate(lowercase_			,			axis=0 )
    lowercase__	: Tuple     		=   np.array(lowercase_ ).astype(np.floataa ) / 255.0
    lowercase__	: int     		=   image.transpose(0			,			3			,			1			,			2 )
    lowercase__	: Dict     		=   2.0 * image - 1.0
    lowercase__	: Any     		=   torch.from_numpy(lowercase_ )
  elif isinstance(image[0]			,			torch.Tensor ):
    lowercase__	: Tuple     		=   torch.cat(lowercase_			,			dim=0 )
  return image

def 		UpperCamelCase						(   lowercase_ )		->						Any:


  '''simple docstring'''






  if isinstance(lowercase_			,			torch.Tensor ):
    return mask
  elif isinstance(lowercase_			,			PIL.Image.Image ):
    lowercase__	: int     		=   [mask]

  if isinstance(mask[0]			,			PIL.Image.Image ):
    lowercase__		,     lowercase__	: List[str]     		=   mask[0].size
    lowercase__		,     lowercase__	: int     		=   (x - x % 32 for x in (w, h))  # resize to integer multiple of 32
    lowercase__	: str     		=   [np.array(m.convert("""L""" ).resize((w, h)			,			resample=PIL_INTERPOLATION["""nearest"""] ) )[None, :] for m in mask]
    lowercase__	: str     		=   np.concatenate(lowercase_			,			axis=0 )
    lowercase__	: Any     		=   mask.astype(np.floataa ) / 255.0
    lowercase__	: Any     		=   0
    lowercase__	: Optional[int]     		=   1
    lowercase__	: Optional[int]     		=   torch.from_numpy(lowercase_ )
  elif isinstance(mask[0]			,			torch.Tensor ):
    lowercase__	: Tuple     		=   torch.cat(lowercase_			,			dim=0 )
  return mask



class    _snake_case (						UpperCAmelCase_				):
 __lowerCAmelCase	:  UNetaDModel
 __lowerCAmelCase	:  RePaintScheduler


 def __init__(       self       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   super().__init__()
   self.register_modules(unet=SCREAMING_SNAKE_CASE_       ,	scheduler=SCREAMING_SNAKE_CASE_)



 @torch.no_grad()
 def __call__(       self       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_ = 2_50       ,	SCREAMING_SNAKE_CASE_ = 0.0       ,	SCREAMING_SNAKE_CASE_ = 10       ,	SCREAMING_SNAKE_CASE_ = 10       ,	SCREAMING_SNAKE_CASE_ = None       ,	SCREAMING_SNAKE_CASE_ = "pil"       ,	SCREAMING_SNAKE_CASE_ = True       ,	):


   '''simple docstring'''




   lowercase__	: List[str]     		=   image

   lowercase__	: Union[str, Any]     		=   _preprocess_image(SCREAMING_SNAKE_CASE_)
   lowercase__	: Dict     		=   original_image.to(device=self.device       ,	dtype=self.unet.dtype)
   lowercase__	: str     		=   _preprocess_mask(SCREAMING_SNAKE_CASE_)
   lowercase__	: Optional[int]     		=   mask_image.to(device=self.device       ,	dtype=self.unet.dtype)

   lowercase__	: List[Any]     		=   original_image.shape[0]

   # sample gaussian noise to begin the loop
   if isinstance(SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_) and len(SCREAMING_SNAKE_CASE_) != batch_size:
     raise ValueError(
         f'You have passed a list of generators of length {len(SCREAMING_SNAKE_CASE_)}, but requested an effective batch'
         f' size of {batch_size}. Make sure the batch size matches the length of the generators.')

   lowercase__	: Dict     		=   original_image.shape
   lowercase__	: List[str]     		=   randn_tensor(SCREAMING_SNAKE_CASE_       ,	generator=SCREAMING_SNAKE_CASE_       ,	device=self.device       ,	dtype=self.unet.dtype)

   # set step values
   self.scheduler.set_timesteps(SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	self.device)
   lowercase__	: Dict     		=   eta

   lowercase__	: str     		=   self.scheduler.timesteps[0] + 1
   lowercase__	: List[str]     		=   generator[0] if isinstance(SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_) else generator
   for i, t in enumerate(self.progress_bar(self.scheduler.timesteps)):
     if t < t_last:
       # predict the noise residual
       lowercase__	: List[Any]     		=   self.unet(SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_).sample
       # compute previous image: x_t -> x_t-1
       lowercase__	: Optional[Any]     		=   self.scheduler.step(SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_).prev_sample

     else:
       # compute the reverse: x_t-1 -> x_t
       lowercase__	: Optional[int]     		=   self.scheduler.undo_step(SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_)
     lowercase__	: int     		=   t

   lowercase__	: Union[str, Any]     		=   (image / 2 + 0.5).clamp(0       ,	1)
   lowercase__	: Any     		=   image.cpu().permute(0       ,	2       ,	3       ,	1).numpy()
   if output_type == "pil":
     lowercase__	: Any     		=   self.numpy_to_pil(SCREAMING_SNAKE_CASE_)

   if not return_dict:
     return (image,)

   return ImagePipelineOutput(images=SCREAMING_SNAKE_CASE_)








'''simple docstring'''





import math

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







def snake_case_     (					lowercase__ = 3		):
  if isinstance(lowercase__   ,       lowercase__		):
    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(lowercase__		) != number_of_qubits:
    raise ValueError("number of qubits must be exact integer."		)
  if number_of_qubits > 1_0:
    raise ValueError("number of qubits too large to simulate(>10)."		)

  UpperCAmelCase__      :					Optional[int]				       =							QuantumRegister(lowercase__   ,       "qr"		)
  UpperCAmelCase__      :					List[Any]				       =							ClassicalRegister(lowercase__   ,       "cr"		)

  UpperCAmelCase__      :					List[str]				       =							QuantumCircuit(lowercase__   ,       lowercase__		)

  UpperCAmelCase__      :					Union[str, Any]				       =							number_of_qubits

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

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

  # measure all the qubits
  quantum_circuit.measure(lowercase__   ,       lowercase__		)
  # simulate with 10000 shots
  UpperCAmelCase__      :					Union[str, Any]				       =							Aer.get_backend("qasm_simulator"		)
  UpperCAmelCase__      :					Optional[int]				       =							execute(lowercase__   ,       lowercase__   ,       shots=1_0_0_0_0		)

  return job.result().get_counts(lowercase__		)


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



