'''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 center_crop, 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, is_vision_available, logging


if is_vision_available():
      import PIL


lowercase__     :  Optional[Any]    					=			logging.get_logger(__name__)

class       lowerCamelCase_							(	lowerCAmelCase__  ):






   '''simple docstring'''

   A :     Any				=	["pixel_values"]


   def __init__(  self     ,						__UpperCamelCase = True     ,						__UpperCamelCase = None     ,						__UpperCamelCase = PIL.Image.NEAREST     ,						__UpperCamelCase = False     ,						__UpperCamelCase = None     ,						__UpperCamelCase = 1 / 2_55     ,						__UpperCamelCase = False     ,						__UpperCamelCase = True     ,						__UpperCamelCase = True     ,						__UpperCamelCase = None     ,						__UpperCamelCase = None     ,						__UpperCamelCase = True     ,						**__UpperCamelCase     ,						):
       super().__init__(**_lowerCamelCase )
       UpperCamelCase__      =			size if size is not None else {"""height""": 3_46, """width""": 3_46}
       UpperCamelCase__      =			get_size_dict(_lowerCamelCase )
       UpperCamelCase__      =			crop_size if crop_size is not None else {"""height""": 2_89, """width""": 2_89}
       UpperCamelCase__      =			get_size_dict(_lowerCamelCase     ,						param_name="crop_size" )

       UpperCamelCase__      =			do_resize
       UpperCamelCase__      =			size
       UpperCamelCase__      =			resample
       UpperCamelCase__      =			do_center_crop
       UpperCamelCase__      =			crop_size
       UpperCamelCase__      =			do_rescale
       UpperCamelCase__      =			rescale_factor
       UpperCamelCase__      =			rescale_offset
       UpperCamelCase__      =			do_normalize
       UpperCamelCase__      =			image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
       UpperCamelCase__      =			image_std if image_std is not None else IMAGENET_STANDARD_STD
       UpperCamelCase__      =			include_top


   def 	_UpperCamelCase       (  self     ,						__UpperCamelCase     ,						__UpperCamelCase     ,						__UpperCamelCase = PIL.Image.NEAREST     ,						__UpperCamelCase = None     ,						**__UpperCamelCase     ,						):
       UpperCamelCase__      =			get_size_dict(_lowerCamelCase )
       if "height" not in size or "width" not in size:
           raise ValueError(f'The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}' )
       return resize(
           _lowerCamelCase     ,						size=(size["height"], size["width"])     ,						resample=_lowerCamelCase     ,						data_format=_lowerCamelCase     ,						**_lowerCamelCase )


   def 	_UpperCamelCase       (  self     ,						__UpperCamelCase     ,						__UpperCamelCase     ,						__UpperCamelCase = None     ,						**__UpperCamelCase     ,						):
       UpperCamelCase__      =			get_size_dict(_lowerCamelCase )
       if "height" not in size or "width" not in size:
           raise ValueError(f'The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}' )
       return center_crop(_lowerCamelCase     ,						size=(size["height"], size["width"])     ,						data_format=_lowerCamelCase     ,						**_lowerCamelCase )


   def 	_UpperCamelCase       (  self     ,						__UpperCamelCase     ,						__UpperCamelCase     ,						__UpperCamelCase = True     ,						__UpperCamelCase = None     ,						**__UpperCamelCase     ,						):

       if offset:
           UpperCamelCase__      =			(image - 127.5) * scale
           if data_format is not None:
               UpperCamelCase__      =			to_channel_dimension_format(_lowerCamelCase     ,						_lowerCamelCase )
           UpperCamelCase__      =			rescaled_image.astype(np.floataa )
       else:
           UpperCamelCase__      =			rescale(_lowerCamelCase     ,						scale=_lowerCamelCase     ,						data_format=_lowerCamelCase     ,						**_lowerCamelCase )
       return rescaled_image


   def 	_UpperCamelCase       (  self     ,						__UpperCamelCase     ,						__UpperCamelCase     ,						__UpperCamelCase     ,						__UpperCamelCase = None     ,						**__UpperCamelCase     ,						):
       return normalize(_lowerCamelCase     ,						mean=_lowerCamelCase     ,						std=_lowerCamelCase     ,						data_format=_lowerCamelCase     ,						**_lowerCamelCase )







   def 	_UpperCamelCase       (  self     ,						__UpperCamelCase     ,						__UpperCamelCase = None     ,						__UpperCamelCase = None     ,						__UpperCamelCase=None     ,						__UpperCamelCase = None     ,						__UpperCamelCase = None     ,						__UpperCamelCase = None     ,						__UpperCamelCase = None     ,						__UpperCamelCase = None     ,						__UpperCamelCase = None     ,						__UpperCamelCase = None     ,						__UpperCamelCase = None     ,						__UpperCamelCase = None     ,						__UpperCamelCase = None     ,						__UpperCamelCase = ChannelDimension.FIRST     ,						**__UpperCamelCase     ,						):
       UpperCamelCase__      =			do_resize if do_resize is not None else self.do_resize
       UpperCamelCase__      =			resample if resample is not None else self.resample
       UpperCamelCase__      =			do_center_crop if do_center_crop is not None else self.do_center_crop
       UpperCamelCase__      =			do_rescale if do_rescale is not None else self.do_rescale
       UpperCamelCase__      =			rescale_factor if rescale_factor is not None else self.rescale_factor
       UpperCamelCase__      =			rescale_offset if rescale_offset is not None else self.rescale_offset
       UpperCamelCase__      =			do_normalize if do_normalize is not None else self.do_normalize
       UpperCamelCase__      =			image_mean if image_mean is not None else self.image_mean
       UpperCamelCase__      =			image_std if image_std is not None else self.image_std
       UpperCamelCase__      =			include_top if include_top is not None else self.include_top

       UpperCamelCase__      =			size if size is not None else self.size
       UpperCamelCase__      =			get_size_dict(_lowerCamelCase )
       UpperCamelCase__      =			crop_size if crop_size is not None else self.crop_size
       UpperCamelCase__      =			get_size_dict(_lowerCamelCase     ,						param_name="crop_size" )

       UpperCamelCase__      =			make_list_of_images(_lowerCamelCase )

       if not valid_images(_lowerCamelCase ):
           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_center_crop and crop_size is None:
           raise ValueError("Crop size must be specified if do_center_crop is True." )

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

       if do_normalize and (image_mean is None or image_std is None):
           raise ValueError("Image mean and std must be specified if do_normalize is True." )

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

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

       if do_center_crop:
           UpperCamelCase__      =			[self.center_crop(image=_lowerCamelCase     ,						size=_lowerCamelCase ) for image in images]

       if do_rescale:
           UpperCamelCase__      =			[self.rescale(image=_lowerCamelCase     ,						scale=_lowerCamelCase     ,						offset=_lowerCamelCase ) for image in images]

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

       if include_top:
           UpperCamelCase__      =			[self.normalize(image=_lowerCamelCase     ,						mean=[0, 0, 0]     ,						std=_lowerCamelCase ) for image in images]

       UpperCamelCase__      =			[to_channel_dimension_format(_lowerCamelCase     ,						_lowerCamelCase ) for image in images]

       UpperCamelCase__      =			{"""pixel_values""": images}
       return BatchFeature(data=_lowerCamelCase     ,						tensor_type=_lowerCamelCase )









'''simple docstring'''







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_  ):






   '''simple docstring'''

   A :     UNetaDModel
   A :     PNDMScheduler


   def __init__(  self     ,						__UpperCamelCase     ,						__UpperCamelCase ):
       super().__init__()

       UpperCamelCase__      =			PNDMScheduler.from_config(scheduler.config )

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







   @torch.no_grad()
   def __call__(  self     ,						__UpperCamelCase = 1     ,						__UpperCamelCase = 50     ,						__UpperCamelCase = None     ,						__UpperCamelCase = "pil"     ,						__UpperCamelCase = True     ,						**__UpperCamelCase     ,						):
       UpperCamelCase__      =			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 ):
           UpperCamelCase__      =			self.unet(__UpperCamelCase     ,						__UpperCamelCase ).sample

           UpperCamelCase__      =			self.scheduler.step(__UpperCamelCase     ,						__UpperCamelCase     ,						__UpperCamelCase ).prev_sample

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

       if not return_dict:
           return (image,)

       return ImagePipelineOutput(images=__UpperCamelCase )




