
"""simple docstring"""

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

import numpy as np
import PIL
import torch
import torch.utils.checkpoint

from ...models import UNetaDModel, VQModel
from ...schedulers import (
    DDIMScheduler,
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
)
from ...utils import PIL_INTERPOLATION, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput






def 							A		(       snake_case   :Union[str, Any]	)   ->			Any:
 __UpperCamelCase						,			__UpperCamelCase       						= image.size
 __UpperCamelCase						,			__UpperCamelCase       						= (x - x % 3_2 for x in (w, h))  # resize to integer multiple of 32
 __UpperCamelCase       						= image.resize((w, h)       ,      resample=PIL_INTERPOLATION['lanczos']	)
 __UpperCamelCase       						= np.array(snake_case	).astype(np.floataa	) / 255.0
 __UpperCamelCase       						= image[None].transpose(0       ,      3       ,      1       ,      2	)
 __UpperCamelCase       						= torch.from_numpy(snake_case	)
 return 2.0 * image - 1.0





class __lowerCAmelCase	(   __SCREAMING_SNAKE_CASE    ):





     def __init__(			self			,  __UpperCAmelCase			,  __UpperCAmelCase			,  __UpperCAmelCase			,  ):

      '''simple docstring'''




      super().__init__()
      self.register_modules(vqvae=__UpperCAmelCase			,  unet=__UpperCAmelCase			,  scheduler=__UpperCAmelCase   )







     @torch.no_grad()
     def __call__(			self			,  __UpperCAmelCase = None			,  __UpperCAmelCase = 1			,  __UpperCAmelCase = 100			,  __UpperCAmelCase = 0.0			,  __UpperCAmelCase = None			,  __UpperCAmelCase = "pil"			,  __UpperCAmelCase = True			,  ):

      '''simple docstring'''




      if isinstance(__UpperCAmelCase			,  PIL.Image.Image   ):
       __UpperCamelCase       						= 1
      elif isinstance(__UpperCAmelCase			,  torch.Tensor   ):
       __UpperCamelCase       						= image.shape[0]
      else:
       raise ValueError(F'`image` has to be of type `PIL.Image.Image` or `torch.Tensor` but is {type(__UpperCAmelCase   )}'   )

      if isinstance(__UpperCAmelCase			,  PIL.Image.Image   ):
       __UpperCamelCase       						= preprocess(__UpperCAmelCase   )

      __UpperCamelCase						,			__UpperCamelCase       						= image.shape[-2:]

      # in_channels should be 6: 3 for latents, 3 for low resolution image
      __UpperCamelCase       						= (batch_size, self.unet.config.in_channels // 2, height, width)
      __UpperCamelCase       						= next(self.unet.parameters()   ).dtype

      __UpperCamelCase       						= randn_tensor(__UpperCAmelCase			,  generator=__UpperCAmelCase			,  device=self.device			,  dtype=__UpperCAmelCase   )

      __UpperCamelCase       						= image.to(device=self.device			,  dtype=__UpperCAmelCase   )

      # set timesteps and move to the correct device
      self.scheduler.set_timesteps(__UpperCAmelCase			,  device=self.device   )
      __UpperCamelCase       						= self.scheduler.timesteps

      # scale the initial noise by the standard deviation required by the scheduler
      __UpperCamelCase       						= latents * self.scheduler.init_noise_sigma

      # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature.
      # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
      # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
      # and should be between [0, 1]
      __UpperCamelCase       						= 'eta' in set(inspect.signature(self.scheduler.step   ).parameters.keys()   )
      __UpperCamelCase       						= {}
      if accepts_eta:
       __UpperCamelCase       						= eta

      for t in self.progress_bar(__UpperCAmelCase   ):
       # concat latents and low resolution image in the channel dimension.
       __UpperCamelCase       						= torch.cat([latents, image]			,  dim=1   )
       __UpperCamelCase       						= self.scheduler.scale_model_input(__UpperCAmelCase			,  __UpperCAmelCase   )
       # predict the noise residual
       __UpperCamelCase       						= self.unet(__UpperCAmelCase			,  __UpperCAmelCase   ).sample
       # compute the previous noisy sample x_t -> x_t-1
       __UpperCamelCase       						= self.scheduler.step(__UpperCAmelCase			,  __UpperCAmelCase			,  __UpperCAmelCase			,  **__UpperCAmelCase   ).prev_sample

      # decode the image latents with the VQVAE
      __UpperCamelCase       						= self.vqvae.decode(__UpperCAmelCase   ).sample
      __UpperCamelCase       						= torch.clamp(__UpperCAmelCase			,  -1.0			,  1.0   )
      __UpperCamelCase       						= image / 2 + 0.5
      __UpperCamelCase       						= image.cpu().permute(0			,  2			,  3			,  1   ).numpy()

      if output_type == "pil":
       __UpperCamelCase       						= self.numpy_to_pil(__UpperCAmelCase   )

      if not return_dict:
       return (image,)

      return ImagePipelineOutput(images=__UpperCAmelCase   )





"""simple docstring"""

import numpy as np
import qiskit






def 							A		(       snake_case   :int = 8       ,      snake_case   :int | None = None	)   ->			str:
 __UpperCamelCase       						= np.random.default_rng(seed=snake_case	)

 # Roughly 25% of the qubits will contribute to the key.
 # So we take more than we need.
 __UpperCamelCase       						= 6 * key_len
 # Measurement basis for Alice's qubits.
 __UpperCamelCase       						= rng.integers(2       ,      size=snake_case	)
 # The set of states Alice will prepare.
 __UpperCamelCase       						= rng.integers(2       ,      size=snake_case	)
 # Measurement basis for Bob's qubits.
 __UpperCamelCase       						= rng.integers(2       ,      size=snake_case	)

 # Quantum Circuit to simulate BB84
 __UpperCamelCase       						= qiskit.QuantumCircuit(snake_case       ,      name='BB84'	)

 # Alice prepares her qubits according to rules above.
 for index, _ in enumerate(snake_case	):
  if alice_state[index] == 1:
   bbaa_circ.x(snake_case	)
  if alice_basis[index] == 1:
   bbaa_circ.h(snake_case	)
 bbaa_circ.barrier()

 # Bob measures the received qubits according to rules above.
 for index, _ in enumerate(snake_case	):
  if bob_basis[index] == 1:
   bbaa_circ.h(snake_case	)

 bbaa_circ.barrier()
 bbaa_circ.measure_all()

 # Simulate the quantum circuit.
 __UpperCamelCase       						= qiskit.Aer.get_backend('aer_simulator'	)
 # We only need to run one shot because the key is unique.
 # Multiple shots will produce the same key.
 __UpperCamelCase       						= qiskit.execute(snake_case       ,      snake_case       ,      shots=1       ,      seed_simulator=snake_case	)
 # Returns the result of measurement.
 __UpperCamelCase       						= job.result().get_counts(snake_case	).most_frequent()

 # Extracting the generated key from the simulation results.
 # Only keep measurement results where Alice and Bob chose the same basis.
 __UpperCamelCase       						= ''.join(
     [
         result_bit
         for alice_basis_bit, bob_basis_bit, result_bit in zip(
             snake_case       ,      snake_case       ,      snake_case	)
         if alice_basis_bit == bob_basis_bit
     ]	)

 # Get final key. Pad with 0 if too short, otherwise truncate.
 __UpperCamelCase       						= gen_key[:key_len] if len(snake_case	) >= key_len else gen_key.ljust(snake_case       ,      '0'	)
 return key


if __name__ == "__main__":
      print(f'''The generated key is : {bbaa(8, seed=0)}''')
      from doctest import testmod

      testmod()



