




"""simple docstring"""

from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
       import PIL


a							      =     logging.get_logger(__name__)






def 		_snake_case	(   _snake_case				:   Optional[int]							,    _snake_case				:   str				)       ->   List[Any]:






      '''simple docstring'''


      _A     =  b.T
      _A     =  np.sum(np.square(_snake_case				)							,    axis=1				)
      _A     =  np.sum(np.square(_snake_case				)							,    axis=0				)
      _A     =  np.matmul(_snake_case							,    _snake_case				)
      _A     =  aa[:, None] - 2 * ab + ba[None, :]
      return d






def 		_snake_case	(   _snake_case				:   Optional[Any]							,    _snake_case				:   Union[str, Any]				)       ->   Union[str, Any]:






      '''simple docstring'''


      _A     =  x.reshape(-1							,    3				)
      _A     =  squared_euclidean_distance(_snake_case							,    _snake_case				)
      return np.argmin(_snake_case							,    axis=1				)







class 							lowercase_			(	__lowerCAmelCase				):



 '''simple docstring'''
 UpperCAmelCase       :	Optional[int]     =      ['''pixel_values''']

 def __init__(					self			:			Any       ,				_UpperCAmelCase			:			Optional[Union[List[List[int]], np.ndarray]] = None       ,				_UpperCAmelCase			:			bool = True       ,				_UpperCAmelCase			:			Dict[str, int] = None       ,				_UpperCAmelCase			:			PILImageResampling = PILImageResampling.BILINEAR       ,				_UpperCAmelCase			:			bool = True       ,				_UpperCAmelCase			:			bool = True       ,				**_UpperCAmelCase			:			int       ,				):
       super().__init__(**_UpperCAmelCase						)
       _A     =  size if size is not None else {'height': 256, 'width': 256}
       _A     =  get_size_dict(_UpperCAmelCase						)
       _A     =  np.array(_UpperCAmelCase						) if clusters is not None else None
       _A     =  do_resize
       _A     =  size
       _A     =  resample
       _A     =  do_normalize
       _A     =  do_color_quantize

 def      lowerCAmelCase_				(					self			:			Dict       ,				_UpperCAmelCase			:			np.ndarray       ,				_UpperCAmelCase			:			Dict[str, int]       ,				_UpperCAmelCase			:			PILImageResampling = PILImageResampling.BILINEAR       ,				_UpperCAmelCase			:			Optional[Union[str, ChannelDimension]] = None       ,				**_UpperCAmelCase			:			Tuple       ,				):
       _A     =  get_size_dict(_UpperCAmelCase						)
       if "height" not in size or "width" not in size:
             raise ValueError(F'''Size dictionary must contain both height and width keys. Got {size.keys()}'''						)
       return resize(
           _UpperCAmelCase       ,				size=(size['height'], size['width'])       ,				resample=_UpperCAmelCase       ,				data_format=_UpperCAmelCase       ,				**_UpperCAmelCase						)

 def      lowerCAmelCase_				(					self			:			List[Any]       ,				_UpperCAmelCase			:			np.ndarray       ,				_UpperCAmelCase			:			Optional[Union[str, ChannelDimension]] = None       ,				):
       _A     =  rescale(image=_UpperCAmelCase       ,				scale=1 / 127.5       ,				data_format=_UpperCAmelCase						)
       _A     =  image - 1
       return image



 def      lowerCAmelCase_				(					self			:			Tuple       ,				_UpperCAmelCase			:			ImageInput       ,				_UpperCAmelCase			:			bool = None       ,				_UpperCAmelCase			:			Dict[str, int] = None       ,				_UpperCAmelCase			:			PILImageResampling = None       ,				_UpperCAmelCase			:			bool = None       ,				_UpperCAmelCase			:			Optional[bool] = None       ,				_UpperCAmelCase			:			Optional[Union[List[List[int]], np.ndarray]] = None       ,				_UpperCAmelCase			:			Optional[Union[str, TensorType]] = None       ,				_UpperCAmelCase			:			Optional[Union[str, ChannelDimension]] = ChannelDimension.FIRST       ,				**_UpperCAmelCase			:			List[Any]       ,				):
       _A     =  do_resize if do_resize is not None else self.do_resize
       _A     =  size if size is not None else self.size
       _A     =  get_size_dict(_UpperCAmelCase						)
       _A     =  resample if resample is not None else self.resample
       _A     =  do_normalize if do_normalize is not None else self.do_normalize
       _A     =  do_color_quantize if do_color_quantize is not None else self.do_color_quantize
       _A     =  clusters if clusters is not None else self.clusters
       _A     =  np.array(_UpperCAmelCase						)

       _A     =  make_list_of_images(_UpperCAmelCase						)

       if not valid_images(_UpperCAmelCase						):
             raise ValueError(
                 'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
                 'torch.Tensor, tf.Tensor or jax.ndarray.'						)

       if do_resize and size is None or resample is None:
             raise ValueError('Size and resample must be specified if do_resize is True.'						)

       if do_color_quantize and clusters is None:
             raise ValueError('Clusters must be specified if do_color_quantize is True.'						)

       # All transformations expect numpy arrays.
       _A     =  [to_numpy_array(_UpperCAmelCase						) for image in images]

       if do_resize:
             _A     =  [self.resize(image=_UpperCAmelCase       ,				size=_UpperCAmelCase       ,				resample=_UpperCAmelCase						) for image in images]

       if do_normalize:
             _A     =  [self.normalize(image=_UpperCAmelCase						) for image in images]

       if do_color_quantize:
             _A     =  [to_channel_dimension_format(_UpperCAmelCase       ,				ChannelDimension.LAST						) for image in images]
             # color quantize from (batch_size, height, width, 3) to (batch_size, height, width)
             _A     =  np.array(_UpperCAmelCase						)
             _A     =  color_quantize(_UpperCAmelCase       ,				_UpperCAmelCase						).reshape(images.shape[:-1]						)

             # flatten to (batch_size, height*width)
             _A     =  images.shape[0]
             _A     =  images.reshape(_UpperCAmelCase       ,				-1						)

             # We need to convert back to a list of images to keep consistent behaviour across processors.
             _A     =  list(_UpperCAmelCase						)
       else:
             _A     =  [to_channel_dimension_format(_UpperCAmelCase       ,				_UpperCAmelCase						) for image in images]

       _A     =  {'input_ids': images}
       return BatchFeature(data=_UpperCAmelCase       ,				tensor_type=_UpperCAmelCase						)







"""simple docstring"""

from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import KarrasVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput





class 							lowercase_			(	__lowerCAmelCase				):



 '''simple docstring'''
 UpperCAmelCase       :	UNetaDModel
 UpperCAmelCase       :	KarrasVeScheduler

 def __init__(					self			:			Any       ,				_UpperCAmelCase			:			UNetaDModel       ,				_UpperCAmelCase			:			KarrasVeScheduler						):
       super().__init__()
       self.register_modules(unet=_UpperCAmelCase       ,				scheduler=_UpperCAmelCase						)



 @torch.no_grad()
 def __call__(					self			:			Optional[int]       ,				_UpperCAmelCase			:			int = 1       ,				_UpperCAmelCase			:			int = 50       ,				_UpperCAmelCase			:			Optional[Union[torch.Generator, List[torch.Generator]]] = None       ,				_UpperCAmelCase			:			Optional[str] = "pil"       ,				_UpperCAmelCase			:			bool = True       ,				**_UpperCAmelCase			:			Optional[Any]       ,				):
       _A     =  self.unet.config.sample_size
       _A     =  (batch_size, 3, img_size, img_size)

       _A     =  self.unet

       # sample x_0 ~ N(0, sigma_0^2 * I)
       _A     =  randn_tensor(_UpperCAmelCase       ,				generator=_UpperCAmelCase       ,				device=self.device						) * self.scheduler.init_noise_sigma

       self.scheduler.set_timesteps(_UpperCAmelCase						)

       for t in self.progress_bar(self.scheduler.timesteps						):
             # here sigma_t == t_i from the paper
             _A     =  self.scheduler.schedule[t]
             _A     =  self.scheduler.schedule[t - 1] if t > 0 else 0

             # 1. Select temporarily increased noise level sigma_hat
             # 2. Add new noise to move from sample_i to sample_hat
             _A  ,       _A     =  self.scheduler.add_noise_to_input(_UpperCAmelCase       ,				_UpperCAmelCase       ,				generator=_UpperCAmelCase						)

             # 3. Predict the noise residual given the noise magnitude `sigma_hat`
             # The model inputs and output are adjusted by following eq. (213) in [1].
             _A     =  (sigma_hat / 2) * model((sample_hat + 1) / 2       ,				sigma_hat / 2						).sample

             # 4. Evaluate dx/dt at sigma_hat
             # 5. Take Euler step from sigma to sigma_prev
             _A     =  self.scheduler.step(_UpperCAmelCase       ,				_UpperCAmelCase       ,				_UpperCAmelCase       ,				_UpperCAmelCase						)

             if sigma_prev != 0:
                   # 6. Apply 2nd order correction
                   # The model inputs and output are adjusted by following eq. (213) in [1].
                   _A     =  (sigma_prev / 2) * model((step_output.prev_sample + 1) / 2       ,				sigma_prev / 2						).sample
                   _A     =  self.scheduler.step_correct(
                       _UpperCAmelCase       ,				_UpperCAmelCase       ,				_UpperCAmelCase       ,				_UpperCAmelCase       ,				step_output.prev_sample       ,				step_output['derivative']       ,				)
             _A     =  step_output.prev_sample

       _A     =  (sample / 2 + 0.5).clamp(0       ,				1						)
       _A     =  sample.cpu().permute(0       ,				2       ,				3       ,				1						).numpy()
       if output_type == "pil":
             _A     =  self.numpy_to_pil(_UpperCAmelCase						)

       if not return_dict:
             return (image,)

       return ImagePipelineOutput(images=_UpperCAmelCase						)

