






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 							a_ (						a_		):


							'''simple docstring'''






							__a:	UNetaDModel
							__a:	PNDMScheduler

							def __init__(		self			,			lowercase_			,			lowercase_   )					->					Dict:
														'''simple docstring'''

														super().__init__()

														lowerCAmelCase_										=     PNDMScheduler.from_config(scheduler.config   )

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






							@torch.no_grad()
							def __call__(		self			,			lowercase_ = 1			,			lowercase_ = 5_0			,			lowercase_ = None			,			lowercase_ = "pil"			,			lowercase_ = True			,			**lowercase_			,			)					->					Union[ImagePipelineOutput, Tuple]:
														'''simple docstring'''

														lowerCAmelCase_										=     randn_tensor(
														    (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)			,			generator=lowercase_			,			device=self.device			,			)

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

																					lowerCAmelCase_										=     self.scheduler.step(lowercase_			,			lowercase_			,			lowercase_   ).prev_sample

														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(lowercase_   )

														if not return_dict:
																					return (image,)

														return ImagePipelineOutput(images=lowercase_   )











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_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
		import PIL


lowerCamelCase_						     =  logging.get_logger(__name__)




class 							a_ (						a_		):


							'''simple docstring'''






							__a:	Optional[Any]     =    ['''pixel_values''']

							def __init__(		self			,			lowercase_ = True			,			lowercase_ = None			,			lowercase_ = True			,			lowercase_ = None			,			lowercase_ = PILImageResampling.BICUBIC			,			lowercase_ = True			,			lowercase_ = 1 / 2_5_5			,			lowercase_ = True			,			lowercase_ = None			,			lowercase_ = None			,			**lowercase_			,			)					->					None:
														'''simple docstring'''

														super().__init__(**lowercase_   )
														lowerCAmelCase_										=     crop_size if crop_size is not None else {'height': 2_5_6, 'width': 2_5_6}
														lowerCAmelCase_										=     get_size_dict(lowercase_			,			param_name='crop_size'   )
														lowerCAmelCase_										=     size if size is not None else {'height': 2_2_4, 'width': 2_2_4}
														lowerCAmelCase_										=     get_size_dict(lowercase_   )

														lowerCAmelCase_										=     do_center_crop
														lowerCAmelCase_										=     crop_size
														lowerCAmelCase_										=     do_resize
														lowerCAmelCase_										=     size
														lowerCAmelCase_										=     resample
														lowerCAmelCase_										=     do_rescale
														lowerCAmelCase_										=     rescale_factor
														lowerCAmelCase_										=     do_normalize
														lowerCAmelCase_										=     image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
														lowerCAmelCase_										=     image_std if image_std is not None else IMAGENET_DEFAULT_STD

							def      _lowercase       (		self			,			lowercase_			,			lowercase_			,			lowercase_ = None			,			lowercase_ = None			,			**lowercase_			,			)					->					np.ndarray:
														'''simple docstring'''

														lowerCAmelCase_										=     self.size if size is None else size
														lowerCAmelCase_										=     get_size_dict(lowercase_   )
														lowerCAmelCase_										=     get_size_dict(lowercase_			,			param_name='crop_size'   )

														lowerCAmelCase_						,   lowerCAmelCase_										=     get_image_size(lowercase_   )
														lowerCAmelCase_										=     min(lowercase_			,			lowercase_   )
														lowerCAmelCase_										=     (size['height'] / crop_size['height']) * min_dim
														lowerCAmelCase_										=     (size['width'] / crop_size['width']) * min_dim
														return center_crop(lowercase_			,			size=(cropped_height, cropped_width)			,			data_format=lowercase_			,			**lowercase_   )

							def      _lowercase       (		self			,			lowercase_			,			lowercase_			,			lowercase_ = PIL.Image.BICUBIC			,			lowercase_ = None			,			**lowercase_			,			)					->					np.ndarray:
														'''simple docstring'''

														lowerCAmelCase_										=     get_size_dict(lowercase_   )
														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(
														    lowercase_			,			size=(size['height'], size['width'])			,			resample=lowercase_			,			data_format=lowercase_			,			**lowercase_   )

							def      _lowercase       (		self			,			lowercase_			,			lowercase_			,			lowercase_ = None			,			**lowercase_			,			)					->					Any:
														'''simple docstring'''

														return rescale(lowercase_			,			scale=lowercase_			,			data_format=lowercase_			,			**lowercase_   )

							def      _lowercase       (		self			,			lowercase_			,			lowercase_			,			lowercase_			,			lowercase_ = None			,			**lowercase_			,			)					->					np.ndarray:
														'''simple docstring'''

														return normalize(lowercase_			,			mean=lowercase_			,			std=lowercase_			,			data_format=lowercase_			,			**lowercase_   )






							def      _lowercase       (		self			,			lowercase_			,			lowercase_ = None			,			lowercase_ = None			,			lowercase_ = None			,			lowercase_ = None			,			lowercase_ = None			,			lowercase_ = None			,			lowercase_ = None			,			lowercase_ = None			,			lowercase_ = None			,			lowercase_ = None			,			lowercase_ = None			,			lowercase_ = ChannelDimension.FIRST			,			**lowercase_			,			)					->					PIL.Image.Image:
														'''simple docstring'''

														lowerCAmelCase_										=     do_center_crop if do_center_crop is not None else self.do_center_crop
														lowerCAmelCase_										=     crop_size if crop_size is not None else self.crop_size
														lowerCAmelCase_										=     get_size_dict(lowercase_			,			param_name='crop_size'   )
														lowerCAmelCase_										=     do_resize if do_resize is not None else self.do_resize
														lowerCAmelCase_										=     size if size is not None else self.size
														lowerCAmelCase_										=     get_size_dict(lowercase_   )
														lowerCAmelCase_										=     resample if resample is not None else self.resample
														lowerCAmelCase_										=     do_rescale if do_rescale is not None else self.do_rescale
														lowerCAmelCase_										=     rescale_factor if rescale_factor is not None else self.rescale_factor
														lowerCAmelCase_										=     do_normalize if do_normalize is not None else self.do_normalize
														lowerCAmelCase_										=     image_mean if image_mean is not None else self.image_mean
														lowerCAmelCase_										=     image_std if image_std is not None else self.image_std

														lowerCAmelCase_										=     make_list_of_images(lowercase_   )

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

														if do_center_crop and crop_size is None:
																					raise ValueError('If `do_center_crop` is set to `True`, `crop_size` must be provided.'   )

														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.'   )

														if do_normalize and (image_mean is None or image_std is None):
																					raise ValueError('Image mean and image standard deviation must be specified if do_normalize is True.'   )

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

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

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

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

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

														lowerCAmelCase_										=     [to_channel_dimension_format(lowercase_			,			lowercase_   ) for image in images]

														lowerCAmelCase_										=     {'pixel_values': images}
														return BatchFeature(data=lowercase_			,			tensor_type=lowercase_   )



