


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 normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


UpperCAmelCase__        =				logging.get_logger(__name__)







class  lowerCamelCase__  (			lowerCAmelCase):
     SCREAMING_SNAKE_CASE__      =     ['''pixel_values''']





     def __init__(self						,	UpperCAmelCase = True						,	UpperCAmelCase = None						,	UpperCAmelCase = PILImageResampling.BILINEAR						,	UpperCAmelCase = True						,	UpperCAmelCase = 1 / 2_5_5						,	UpperCAmelCase = True						,	UpperCAmelCase = None						,	UpperCAmelCase = None						,	**UpperCAmelCase						,	)	->  None:
      super().__init__(**UpperCAmelCase			)
      _lowercase				 =size if size is not None else {'''height''': 2_2_4, '''width''': 2_2_4}
      _lowercase				 =get_size_dict(UpperCAmelCase			)
      _lowercase				 =do_resize
      _lowercase				 =do_rescale
      _lowercase				 =do_normalize
      _lowercase				 =size
      _lowercase				 =resample
      _lowercase				 =rescale_factor
      _lowercase				 =image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
      _lowercase				 =image_std if image_std is not None else IMAGENET_STANDARD_STD





     def 	__A   (self						,	UpperCAmelCase						,	UpperCAmelCase						,	UpperCAmelCase = PILImageResampling.BILINEAR						,	UpperCAmelCase = None						,	**UpperCAmelCase						,	)	->  np.ndarray:
      _lowercase				 =get_size_dict(UpperCAmelCase			)
      if "height" not in size or "width" not in size:
       raise ValueError(f"The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}"			)
      return resize(
          UpperCAmelCase						,	size=(size['''height'''], size['''width'''])						,	resample=UpperCAmelCase						,	data_format=UpperCAmelCase						,	**UpperCAmelCase			)





     def 	__A   (self						,	UpperCAmelCase						,	UpperCAmelCase						,	UpperCAmelCase = None						,	**UpperCAmelCase			)	->  np.ndarray:
      return rescale(UpperCAmelCase						,	scale=UpperCAmelCase						,	data_format=UpperCAmelCase						,	**UpperCAmelCase			)





     def 	__A   (self						,	UpperCAmelCase						,	UpperCAmelCase						,	UpperCAmelCase						,	UpperCAmelCase = None						,	**UpperCAmelCase						,	)	->  np.ndarray:
      return normalize(UpperCAmelCase						,	mean=UpperCAmelCase						,	std=UpperCAmelCase						,	data_format=UpperCAmelCase						,	**UpperCAmelCase			)





     def 	__A   (self						,	UpperCAmelCase						,	UpperCAmelCase = None						,	UpperCAmelCase = None						,	UpperCAmelCase = None						,	UpperCAmelCase = None						,	UpperCAmelCase = None						,	UpperCAmelCase = None						,	UpperCAmelCase = None						,	UpperCAmelCase = None						,	UpperCAmelCase = None						,	UpperCAmelCase = ChannelDimension.FIRST						,	**UpperCAmelCase						,	)	->  List[Any]:
      _lowercase				 =do_resize if do_resize is not None else self.do_resize
      _lowercase				 =do_rescale if do_rescale is not None else self.do_rescale
      _lowercase				 =do_normalize if do_normalize is not None else self.do_normalize
      _lowercase				 =resample if resample is not None else self.resample
      _lowercase				 =rescale_factor if rescale_factor is not None else self.rescale_factor
      _lowercase				 =image_mean if image_mean is not None else self.image_mean
      _lowercase				 =image_std if image_std is not None else self.image_std

      _lowercase				 =size if size is not None else self.size
      _lowercase				 =get_size_dict(UpperCAmelCase			)

      _lowercase				 =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:
       raise ValueError('''Size must be specified if do_resize is True.'''			)

      if do_rescale and rescale_factor is None:
       raise ValueError('''Rescale factor must be specified if do_rescale is True.'''			)

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

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

      if do_rescale:
       _lowercase				 =[self.rescale(image=UpperCAmelCase						,	scale=UpperCAmelCase			) for image in images]

      if do_normalize:
       _lowercase				 =[self.normalize(image=UpperCAmelCase						,	mean=UpperCAmelCase						,	std=UpperCAmelCase			) for image in images]

      _lowercase				 =[to_channel_dimension_format(UpperCAmelCase						,	UpperCAmelCase			) for image in images]

      _lowercase				 ={'''pixel_values''': images}
      return BatchFeature(data=UpperCAmelCase						,	tensor_type=UpperCAmelCase			)





from typing import List, Optional, Tuple, Union

import torch

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







class  lowerCamelCase__  (			lowerCAmelCase):
     SCREAMING_SNAKE_CASE__      =     42
     SCREAMING_SNAKE_CASE__      =     42





     def __init__(self						,	UpperCAmelCase						,	UpperCAmelCase			)	->  Tuple:
      super().__init__()

      _lowercase				 =PNDMScheduler.from_config(scheduler.config			)

      self.register_modules(unet=UpperCAmelCase						,	scheduler=UpperCAmelCase			)





     @torch.no_grad()
     def __call__(self						,	UpperCAmelCase = 1						,	UpperCAmelCase = 5_0						,	UpperCAmelCase = None						,	UpperCAmelCase = "pil"						,	UpperCAmelCase = True						,	**UpperCAmelCase						,	)	->  Union[ImagePipelineOutput, Tuple]:
      _lowercase				 =randn_tensor(
          (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)						,	generator=UpperCAmelCase						,	device=self.device						,	)

      self.scheduler.set_timesteps(UpperCAmelCase			)
      for t in self.progress_bar(self.scheduler.timesteps			):
       _lowercase				 =self.unet(UpperCAmelCase						,	UpperCAmelCase			).sample

       _lowercase				 =self.scheduler.step(UpperCAmelCase						,	UpperCAmelCase						,	UpperCAmelCase			).prev_sample

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

      if not return_dict:
       return (image,)

      return ImagePipelineOutput(images=UpperCAmelCase			)

