



'''simple docstring'''





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										=     logging.get_logger(__name__)  # pylint: disable=invalid-name
def        _SCREAMING_SNAKE_CASE  (	UpperCamelCase      ):



    """simple docstring"""





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

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

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



    """simple docstring"""





    if isinstance(UpperCamelCase							,   torch.Tensor      ):
        return mask
    elif isinstance(UpperCamelCase							,   PIL.Image.Image      ):
        lowerCAmelCase__			:  Optional[Any]											= [mask]

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






class 	lowerCAmelCase_(    SCREAMING_SNAKE_CASE_					):
  '''simple docstring'''




  __lowercase							:				UNetaDModel
  __lowercase							:				RePaintScheduler




  def __init__(   self  ,__UpperCAmelCase  ,__UpperCAmelCase				)   ->		List[Any]:
      super().__init__()
      self.register_modules(unet=__UpperCAmelCase  ,scheduler=__UpperCAmelCase				)






  @torch.no_grad()
  def __call__(   self  ,__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase = 250  ,__UpperCAmelCase = 0.0  ,__UpperCAmelCase = 10  ,__UpperCAmelCase = 10  ,__UpperCAmelCase = None  ,__UpperCAmelCase = "pil"  ,__UpperCAmelCase = True  ,)   ->		Union[ImagePipelineOutput, Tuple]:
      lowerCAmelCase__			:  Dict											= image

      lowerCAmelCase__			:  List[Any]											= _preprocess_image(__UpperCAmelCase				)
      lowerCAmelCase__			:  int											= original_image.to(device=self.device  ,dtype=self.unet.dtype				)
      lowerCAmelCase__			:  Dict											= _preprocess_mask(__UpperCAmelCase				)
      lowerCAmelCase__			:  List[Any]											= mask_image.to(device=self.device  ,dtype=self.unet.dtype				)

      lowerCAmelCase__			:  Optional[Any]											= original_image.shape[0]

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

      lowerCAmelCase__			:  Optional[int]											= original_image.shape
      lowerCAmelCase__			:  Optional[Any]											= randn_tensor(__UpperCAmelCase  ,generator=__UpperCAmelCase  ,device=self.device  ,dtype=self.unet.dtype				)

      # set step values
      self.scheduler.set_timesteps(__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase  ,self.device				)
      lowerCAmelCase__			:  Union[str, Any]											= eta

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

          else:
              # compute the reverse: x_t-1 -> x_t
              lowerCAmelCase__			:  List[str]											= self.scheduler.undo_step(__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase				)
          lowerCAmelCase__			:  Any											= t

      lowerCAmelCase__			:  int											= (image / 2 + 0.5).clamp(0  ,1				)
      lowerCAmelCase__			:  Optional[Any]											= image.cpu().permute(0  ,2  ,3  ,1				).numpy()
      if output_type == "pil":
          lowerCAmelCase__			:  Optional[Any]											= self.numpy_to_pil(__UpperCAmelCase				)

      if not return_dict:
          return (image,)

      return ImagePipelineOutput(images=__UpperCAmelCase				)











'''simple docstring'''





# Logistic Regression from scratch

# In[62]:

# In[63]:

# importing all the required libraries

import numpy as np
from matplotlib import pyplot as plt
from sklearn import datasets
def        _SCREAMING_SNAKE_CASE  (	UpperCamelCase      ):



    """simple docstring"""





    return 1 / (1 + np.exp(-z      ))
def        _SCREAMING_SNAKE_CASE  (	UpperCamelCase							,   UpperCamelCase      ):



    """simple docstring"""





    return (-y * np.log(UpperCamelCase      ) - (1 - y) * np.log(1 - h      )).mean()
def        _SCREAMING_SNAKE_CASE  (	UpperCamelCase							,   UpperCamelCase							,   UpperCamelCase      ):



    """simple docstring"""





    lowerCAmelCase__			:  Union[str, Any]											= np.dot(UpperCamelCase							,   UpperCamelCase      )
    return np.sum(y * scores - np.log(1 + np.exp(UpperCamelCase      )      )      )
def        _SCREAMING_SNAKE_CASE  (	UpperCamelCase							,   UpperCamelCase							,   UpperCamelCase							,   UpperCamelCase=70000      ):



    """simple docstring"""





    lowerCAmelCase__			:  Union[str, Any]											= np.zeros(x.shape[1]      )

    for iterations in range(UpperCamelCase      ):
        lowerCAmelCase__			:  str											= np.dot(UpperCamelCase							,   UpperCamelCase      )
        lowerCAmelCase__			:  Tuple											= sigmoid_function(UpperCamelCase      )
        lowerCAmelCase__			:  List[str]											= np.dot(x.T							,   h - y      ) / y.size
        lowerCAmelCase__			:  List[Any]											= theta - alpha * gradient  # updating the weights
        lowerCAmelCase__			:  List[Any]											= np.dot(UpperCamelCase							,   UpperCamelCase      )
        lowerCAmelCase__			:  List[Any]											= sigmoid_function(UpperCamelCase      )
        lowerCAmelCase__			:  List[Any]											= cost_function(UpperCamelCase							,   UpperCamelCase      )
        if iterations % 100 == 0:
            print(f"""loss: {j} \t"""      )  # printing the loss after every 100 iterations
    return theta


# In[68]:

if __name__ == "__main__":
 _lowerCAmelCase										=     datasets.load_iris()
 _lowerCAmelCase										=     iris.data[:, :2]
 _lowerCAmelCase										=     (iris.target != 0) * 1

 _lowerCAmelCase										=     0.1
 _lowerCAmelCase										=     logistic_reg(alpha, x, y, max_iterations=7_0000)
 print('''theta: ''', theta)  # printing the theta i.e our weights vector
 def        _SCREAMING_SNAKE_CASE  (	UpperCamelCase      ):



     """simple docstring"""





     return sigmoid_function(
         np.dot(UpperCamelCase							,   UpperCamelCase      )      )  # predicting the value of probability from the logistic regression algorithm

 plt.figure(figsize=(10, 6))
 plt.scatter(x[y == 0][:, 0], x[y == 0][:, 1], color='''b''', label='''0''')
 plt.scatter(x[y == 1][:, 0], x[y == 1][:, 1], color='''r''', label='''1''')
 ((_lowerCAmelCase)						,      (_lowerCAmelCase))										=     (x[:, 0].min(), x[:, 0].max())
 ((_lowerCAmelCase)						,      (_lowerCAmelCase))										=     (x[:, 1].min(), x[:, 1].max())
 ((_lowerCAmelCase)						,      (_lowerCAmelCase))										=     np.meshgrid(np.linspace(xa_min, xa_max), np.linspace(xa_min, xa_max))
 _lowerCAmelCase										=     np.c_[xxa.ravel(), xxa.ravel()]
 _lowerCAmelCase										=     predict_prob(grid).reshape(xxa.shape)
 plt.contour(xxa, xxa, probs, [0.5], linewidths=1, colors='''black''')

 plt.legend()




 plt.show()


