


"""simple docstring"""







import os
from glob import glob

import imageio
import torch
import torchvision
import wandb
from img_processing import custom_to_pil, loop_post_process, preprocess, preprocess_vqgan
from loaders import load_vqgan
from PIL import Image
from torch import nn

from transformers import CLIPModel, CLIPTokenizerFast
from utils import get_device, get_timestamp, show_pil






class       __A				:






 """simple docstring"""




 def __init__(		self ,    __A = "cpu" ,    __A = "openai/clip-vit-large-patch14"						)					->     None:
     a 		=device
     a 		=CLIPTokenizerFast.from_pretrained(__A						)
     a 		=[0.48_145_466, 0.4_578_275, 0.40_821_073]
     a 		=[0.26_862_954, 0.26_130_258, 0.27_577_711]
     a 		=torchvision.transforms.Normalize(self.image_mean ,    self.image_std						)
     a 		=torchvision.transforms.Resize(224						)
     a 		=torchvision.transforms.CenterCrop(224						)


 def       SCREAMING_SNAKE_CASE							(		self ,    __A						)					->     Any:
     a 		=self.resize(__A						)
     a 		=self.center_crop(__A						)
     a 		=self.normalize(__A						)
     return images





 def __call__(		self ,    __A=None ,    __A=None ,    **__A						)					->     Optional[Any]:
     a 		=self.tokenizer(text=__A ,    **__A						)
     a 		=self.preprocess_img(__A						)
     a 		={key: value.to(self.device						) for (key, value) in encoding.items()}
     return encoding






class       __A				(       nn.Module			):






 """simple docstring"""




 def __init__(		self ,    __A=10 ,    __A=0.01 ,    __A=None ,    __A=None ,    __A=None ,    __A=None ,    __A=None ,    __A=None ,    __A=False ,    __A=True ,    __A="image" ,    __A=True ,    __A=False ,    __A=False ,    __A=False ,    )					->     None:
     super().__init__()
     a 		=None
     a 		=device if device else get_device()
     if vqgan:
         a 		=vqgan
     else:
         a 		=load_vqgan(self.device ,    conf_path=__A ,    ckpt_path=__A						)
     self.vqgan.eval()
     if clip:
         a 		=clip
     else:
         a 		=CLIPModel.from_pretrained('''openai/clip-vit-base-patch32'''						)
     self.clip.to(self.device						)
     a 		=ProcessorGradientFlow(device=self.device						)

     a 		=iterations
     a 		=lr
     a 		=log
     a 		=make_grid
     a 		=return_val
     a 		=quantize
     a 		=self.vqgan.decoder.z_shape


 def       SCREAMING_SNAKE_CASE							(		self ,    __A=None ,    __A=None ,    __A=5 ,    __A=True						)					->     List[str]:
     a 		=[]
     if output_path is None:
         a 		='''./animation.gif'''
     if input_path is None:
         a 		=self.save_path
     a 		=sorted(glob(input_path + '''/*'''						)						)
     if not len(__A						):
         raise ValueError(
             '''No images found in save path, aborting (did you pass save_intermediate=True to the generate'''
             ''' function?)'''						)
     if len(__A						) == 1:
         print('''Only one image found in save path, (did you pass save_intermediate=True to the generate function?)'''						)
     a 		=total_duration / len(__A						)
     a 		=[frame_duration] * len(__A						)
     if extend_frames:
         a 		=1.5
         a 		=3
     for file_name in paths:
         if file_name.endswith('''.png'''						):
             images.append(imageio.imread(__A						)						)
     imageio.mimsave(__A ,    __A ,    duration=__A						)
     print(f'''gif saved to {output_path}'''						)


 def       SCREAMING_SNAKE_CASE							(		self ,    __A=None ,    __A=None						)					->     Any:
     if not (path or img):
         raise ValueError('''Input either path or tensor'''						)
     if img is not None:
         raise NotImplementedError
     a 		=preprocess(Image.open(__A						) ,    target_image_size=256						).to(self.device						)
     a 		=preprocess_vqgan(__A						)
     a		,      *a 		=self.vqgan.encode(__A						)
     return z


 def       SCREAMING_SNAKE_CASE							(		self ,    __A						)					->     Union[str, Any]:
     a 		=self.latent.detach().requires_grad_()
     a 		=base_latent + transform_vector
     if self.quantize:
         a		,      *a 		=self.vqgan.quantize(__A						)
     else:
         a 		=trans_latent
     return self.vqgan.decode(__A						)


 def       SCREAMING_SNAKE_CASE							(		self ,    __A ,    __A ,    __A=None						)					->     List[str]:
     a 		=self.clip_preprocessor(text=__A ,    images=__A ,    return_tensors='''pt''' ,    padding=__A						)
     a 		=self.clip(**__A						)
     a 		=clip_outputs.logits_per_image
     if weights is not None:
         a 		=similarity_logits * weights
     return similarity_logits.sum()


 def       SCREAMING_SNAKE_CASE							(		self ,    __A ,    __A ,    __A						)					->     Any:
     a 		=self._get_clip_similarity(pos_prompts['''prompts'''] ,    __A ,    weights=(1 / pos_prompts['''weights'''])						)
     if neg_prompts:
         a 		=self._get_clip_similarity(neg_prompts['''prompts'''] ,    __A ,    weights=neg_prompts['''weights''']						)
     else:
         a 		=torch.tensor([1] ,    device=self.device						)
     a 		=-torch.log(__A						) + torch.log(__A						)
     return loss


 def       SCREAMING_SNAKE_CASE							(		self ,    __A ,    __A ,    __A						)					->     Dict:
     a 		=torch.randn_like(self.latent ,    requires_grad=__A ,    device=self.device						)
     a 		=torch.optim.Adam([vector] ,    lr=self.lr						)

     for i in range(self.iterations						):
         optim.zero_grad()
         a 		=self._add_vector(__A						)
         a 		=loop_post_process(__A						)
         a 		=self._get_CLIP_loss(__A ,    __A ,    __A						)
         print('''CLIP loss''' ,    __A						)
         if self.log:
             wandb.log({'''CLIP Loss''': clip_loss}						)
         clip_loss.backward(retain_graph=__A						)
         optim.step()
         if self.return_val == "image":
             yield custom_to_pil(transformed_img[0]						)
         else:
             yield vector


 def       SCREAMING_SNAKE_CASE							(		self ,    __A ,    __A ,    __A						)					->     str:
     wandb.init(reinit=__A ,    project='''face-editor'''						)
     wandb.config.update({'''Positive Prompts''': positive_prompts}						)
     wandb.config.update({'''Negative Prompts''': negative_prompts}						)
     wandb.config.update({'''lr''': self.lr, '''iterations''': self.iterations}						)
     if image_path:
         a 		=Image.open(__A						)
         a 		=image.resize((256, 256)						)
         wandb.log('''Original Image''' ,    wandb.Image(__A						)						)


 def       SCREAMING_SNAKE_CASE							(		self ,    __A						)					->     int:
     if not prompts:
         return []
     a 		=[]
     a 		=[]
     if isinstance(__A ,    __A						):
         a 		=[prompt.strip() for prompt in prompts.split('''|'''						)]
     for prompt in prompts:
         if isinstance(__A ,    (tuple, list)						):
             a 		=prompt[0]
             a 		=float(prompt[1]						)
         elif ":" in prompt:
             a		,      a 		=prompt.split(''':'''						)
             a 		=float(__A						)
         else:
             a 		=prompt
             a 		=1.0
         processed_prompts.append(__A						)
         weights.append(__A						)
     return {
         "prompts": processed_prompts,
         "weights": torch.tensor(__A ,    device=self.device						),
     }





 def       SCREAMING_SNAKE_CASE							(		self ,    __A ,    __A=None ,    __A=None ,    __A=True ,    __A=False ,    __A=True ,    __A=True ,    __A=None ,    )					->     int:
     if image_path:
         a 		=self._get_latent(__A						)
     else:
         a 		=torch.randn(self.latent_dim ,    device=self.device						)
     if self.log:
         self._init_logging(__A ,    __A ,    __A						)

     assert pos_prompts, "You must provide at least one positive prompt."
     a 		=self.process_prompts(__A						)
     a 		=self.process_prompts(__A						)
     if save_final and save_path is None:
         a 		=os.path.join('''./outputs/''' ,    '''_'''.join(pos_prompts['''prompts''']						)						)
     if not os.path.exists(__A						):
         os.makedirs(__A						)
     else:
         a 		=save_path + '''_''' + get_timestamp()
         os.makedirs(__A						)
     a 		=save_path

     a 		=self.vqgan.decode(self.latent						)[0]
     if show_intermediate:
         print('''Original Image'''						)
         show_pil(custom_to_pil(__A						)						)

     a 		=loop_post_process(__A						)
     for iter, transformed_img in enumerate(self._optimize_CLIP(__A ,    __A ,    __A						)						):
         if show_intermediate:
             show_pil(__A						)
         if save_intermediate:
             transformed_img.save(os.path.join(self.save_path ,    f'''iter_{iter:03d}.png'''						)						)
         if self.log:
             wandb.log({'''Image''': wandb.Image(__A						)}						)
     if show_final:
         show_pil(__A						)
     if save_final:
         transformed_img.save(os.path.join(self.save_path ,    f'''iter_{iter:03d}_final.png'''						)						)


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)}"""
       )
