



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


a_								=       logging.get_logger(__name__)  # pylint: disable=invalid-name





def 		a__      (		_UpperCamelCase      :			Union[List, PIL.Image.Image, torch.Tensor]				):
   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    				=     [image]

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

      __lowerCamelCase    				=     [np.array(i.resize((w, h)			,resample=PIL_INTERPOLATION['''lanczos''']				)				)[None, :] for i in image]
      __lowerCamelCase    				=     np.concatenate(_UpperCamelCase			,axis=0				)
      __lowerCamelCase    				=     np.array(_UpperCamelCase				).astype(np.floataa				) / 255.0
      __lowerCamelCase    				=     image.transpose(0			,3			,1			,2				)
      __lowerCamelCase    				=     2.0 * image - 1.0
      __lowerCamelCase    				=     torch.from_numpy(_UpperCamelCase				)
   elif isinstance(image[0]			,torch.Tensor				):
      __lowerCamelCase    				=     torch.cat(_UpperCamelCase			,dim=0				)
   return image





def 		a__      (		_UpperCamelCase      :			Union[List, PIL.Image.Image, torch.Tensor]				):
   if isinstance(_UpperCamelCase			,torch.Tensor				):
      return mask
   elif isinstance(_UpperCamelCase			,PIL.Image.Image				):
      __lowerCamelCase    				=     [mask]

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





class 			__lowerCAmelCase  (						lowerCAmelCase__ ):
 lowerCAmelCase__							       =	42
 lowerCAmelCase__							       =	42




 def __init__(    self						,					__UpperCAmelCase						,					__UpperCAmelCase					):



    '''simple docstring'''
    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						,					):



    '''simple docstring'''
    __lowerCamelCase    				=     image

    __lowerCamelCase    				=     _preprocess_image(__UpperCAmelCase					)
    __lowerCamelCase    				=     original_image.to(device=self.device						,					dtype=self.unet.dtype					)
    __lowerCamelCase    				=     _preprocess_mask(__UpperCAmelCase					)
    __lowerCamelCase    				=     mask_image.to(device=self.device						,					dtype=self.unet.dtype					)

    __lowerCamelCase    				=     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    				=     original_image.shape
    __lowerCamelCase    				=     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    				=     eta

    __lowerCamelCase    				=     self.scheduler.timesteps[0] + 1
    __lowerCamelCase    				=     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    				=     self.unet(__UpperCAmelCase						,					__UpperCAmelCase					).sample
          # compute previous image: x_t -> x_t-1
          __lowerCamelCase    				=     self.scheduler.step(__UpperCAmelCase						,					__UpperCAmelCase						,					__UpperCAmelCase						,					__UpperCAmelCase						,					__UpperCAmelCase						,					__UpperCAmelCase					).prev_sample

       else:
          # compute the reverse: x_t-1 -> x_t
          __lowerCamelCase    				=     self.scheduler.undo_step(__UpperCAmelCase						,					__UpperCAmelCase						,					__UpperCAmelCase					)
       __lowerCamelCase    				=     t

    __lowerCamelCase    				=     (image / 2 + 0.5).clamp(0						,					1					)
    __lowerCamelCase    				=     image.cpu().permute(0						,					2						,					3						,					1					).numpy()
    if output_type == "pil":
       __lowerCamelCase    				=     self.numpy_to_pil(__UpperCAmelCase					)

    if not return_dict:
       return (image,)

    return ImagePipelineOutput(images=__UpperCAmelCase					)











import numpy

# List of input, output pairs
a_								=       (
    ((5, 2, 3), 15),
    ((6, 5, 9), 25),
    ((11, 12, 13), 41),
    ((1, 1, 1), 8),
    ((11, 12, 13), 41),
)
a_								=       (((515, 22, 13), 555), ((61, 35, 49), 150))
a_								=       [2, 4, 1, 5]
a_								=       len(train_data)
a_								=       0.0_09





def 		a__      (		_UpperCamelCase      :			str			,_UpperCamelCase      :			List[Any]="train"				):
   return calculate_hypothesis_value(_UpperCamelCase			,_UpperCamelCase				) - output(
       _UpperCamelCase			,_UpperCamelCase				)





def 		a__      (		_UpperCamelCase      :			Optional[Any]				):
   __lowerCamelCase    				=     0
   for i in range(len(_UpperCamelCase				) - 1				):
      hyp_val += data_input_tuple[i] * parameter_vector[i + 1]
   hyp_val += parameter_vector[0]
   return hyp_val





def 		a__      (		_UpperCamelCase      :			Tuple			,_UpperCamelCase      :			List[Any]				):
   if data_set == "train":
      return train_data[example_no][1]
   elif data_set == "test":
      return test_data[example_no][1]
   return None





def 		a__      (		_UpperCamelCase      :			List[Any]			,_UpperCamelCase      :			Union[str, Any]				):
   if data_set == "train":
      return _hypothesis_value(train_data[example_no][0]				)
   elif data_set == "test":
      return _hypothesis_value(test_data[example_no][0]				)
   return None





def 		a__      (		_UpperCamelCase      :			Dict			,_UpperCamelCase      :			Dict=m				):
   __lowerCamelCase    				=     0
   for i in range(_UpperCamelCase				):
      if index == -1:
         summation_value += _error(_UpperCamelCase				)
      else:
         summation_value += _error(_UpperCamelCase				) * train_data[i][0][index]
   return summation_value





def 		a__      (		_UpperCamelCase      :			Optional[int]				):
   __lowerCamelCase    				=     summation_of_cost_derivative(_UpperCamelCase			,_UpperCamelCase				) / m
   return cost_derivative_value





def 		a__      (		):
   global parameter_vector
   # Tune these values to set a tolerance value for predicted output
   __lowerCamelCase    				=     0.000_002
   __lowerCamelCase    				=     0
   __lowerCamelCase    				=     0
   while True:
      j += 1
      __lowerCamelCase    				=     [0, 0, 0, 0]
      for i in range(0			,len(_UpperCamelCase				)				):
         __lowerCamelCase    				=     get_cost_derivative(i - 1				)
         __lowerCamelCase    				=     (
             parameter_vector[i] - LEARNING_RATE * cost_derivative
         )
      if numpy.allclose(
          _UpperCamelCase			,_UpperCamelCase			,atol=_UpperCamelCase			,rtol=_UpperCamelCase			,):
         break
      __lowerCamelCase    				=     temp_parameter_vector
   print(('''Number of iterations:''', j)				)





def 		a__      (		):
   for i in range(len(_UpperCamelCase				)				):
      print(('''Actual output value:''', output(_UpperCamelCase			,'''test'''				))				)
      print(('''Hypothesis output:''', calculate_hypothesis_value(_UpperCamelCase			,'''test'''				))				)


if __name__ == "__main__":
  run_gradient_descent()
  print("""\nTesting gradient descent for a linear hypothesis function.\n""")
  test_gradient_descent()






