



"""simple docstring"""
import unittest

import numpy as np
import torch

from diffusers import PNDMPipeline, PNDMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()
class 				lowerCamelCase  (			unittest.TestCase				):






					'''simple docstring'''





					@property
					def    lowerCAmelCase_				(       self:							str						)   ->	Union[str, Any]:
											torch.manual_seed(0						)
											snake_case_ :str						=			UNetaDModel(
											    block_out_channels=(32, 64)						,     layers_per_block=2						,     sample_size=32						,     in_channels=3						,     out_channels=3						,     down_block_types=("""DownBlock2D""", """AttnDownBlock2D""")						,     up_block_types=("""AttnUpBlock2D""", """UpBlock2D""")						,     )
											return model






					def    lowerCAmelCase_				(       self:							List[str]						)   ->	str:
											snake_case_ :Union[str, Any]						=			self.dummy_uncond_unet
											snake_case_ :Tuple						=			PNDMScheduler()

											snake_case_ :str						=			PNDMPipeline(unet=snake_case						,     scheduler=snake_case						)
											pndm.to(snake_case						)
											pndm.set_progress_bar_config(disable=snake_case						)

											snake_case_ :int						=			torch.manual_seed(0						)
											snake_case_ :List[Any]						=			pndm(generator=snake_case						,     num_inference_steps=20						,     output_type="""numpy"""						).images

											snake_case_ :Optional[Any]						=			torch.manual_seed(0						)
											snake_case_ :Union[str, Any]						=			pndm(generator=snake_case						,     num_inference_steps=20						,     output_type="""numpy"""						,     return_dict=snake_case						)[0]

											snake_case_ :Optional[int]						=			image[0, -3:, -3:, -1]
											snake_case_ :str						=			image_from_tuple[0, -3:, -3:, -1]

											assert image.shape == (1, 32, 32, 3)
											snake_case_ :List[str]						=			np.array([1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0]						)

											assert np.abs(image_slice.flatten() - expected_slice						).max() < 1E-2
											assert np.abs(image_from_tuple_slice.flatten() - expected_slice						).max() < 1E-2

@slow
@require_torch
class 				lowerCamelCase  (			unittest.TestCase				):






					'''simple docstring'''





					def    lowerCAmelCase_				(       self:							List[Any]						)   ->	Union[str, Any]:
											snake_case_ :int						=			"""google/ddpm-cifar10-32"""

											snake_case_ :List[str]						=			UNetaDModel.from_pretrained(snake_case						)
											snake_case_ :Any						=			PNDMScheduler()

											snake_case_ :Tuple						=			PNDMPipeline(unet=snake_case						,     scheduler=snake_case						)
											pndm.to(snake_case						)
											pndm.set_progress_bar_config(disable=snake_case						)
											snake_case_ :str						=			torch.manual_seed(0						)
											snake_case_ :Any						=			pndm(generator=snake_case						,     output_type="""numpy"""						).images

											snake_case_ :Optional[int]						=			image[0, -3:, -3:, -1]

											assert image.shape == (1, 32, 32, 3)
											snake_case_ :Optional[int]						=			np.array([0.1_5_6_4, 0.1_4_6_4_5, 0.1_4_0_6, 0.1_4_7_1_5, 0.1_2_4_2_5, 0.1_4_0_4_5, 0.1_3_1_1_5, 0.1_2_1_7_5, 0.1_2_5]						)

											assert np.abs(image_slice.flatten() - expected_slice						).max() < 1E-2


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,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


UpperCAmelCase	      = logging.get_logger(__name__)


if is_vision_available():
	import PIL







class        A_   (			__lowerCamelCase   ):




	'''simple docstring'''


	_UpperCamelCase     :     str							 =       ["""pixel_values"""]





	def __init__(       self		,    snake_case = True		,    snake_case = None		,    snake_case = PILImageResampling.BICUBIC		,    snake_case = True		,    snake_case = None		,    snake_case = True		,    snake_case = 1 / 255		,    snake_case = True		,    snake_case = None		,    snake_case = None		,    snake_case = True		,    **snake_case		,    ):
		super().__init__(**snake_case       )
		lowercase       =    size if size is not None else {'shortest_edge': 224}
		lowercase       =    get_size_dict(snake_case		,    default_to_square=snake_case       )
		lowercase       =    crop_size if crop_size is not None else {'height': 224, 'width': 224}
		lowercase       =    get_size_dict(snake_case		,    default_to_square=snake_case		,    param_name='crop_size'       )

		lowercase       =    do_resize
		lowercase       =    size
		lowercase       =    resample
		lowercase       =    do_center_crop
		lowercase       =    crop_size
		lowercase       =    do_rescale
		lowercase       =    rescale_factor
		lowercase       =    do_normalize
		lowercase       =    image_mean if image_mean is not None else OPENAI_CLIP_MEAN
		lowercase       =    image_std if image_std is not None else OPENAI_CLIP_STD
		lowercase       =    do_convert_rgb





	def 	SCREAMING_SNAKE_CASE__						(       self		,    snake_case		,    snake_case		,    snake_case = PILImageResampling.BICUBIC		,    snake_case = None		,    **snake_case		,    ):
		lowercase       =    get_size_dict(snake_case		,    default_to_square=snake_case       )
		if "shortest_edge" not in size:
			raise ValueError(F'''The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}'''       )
		lowercase       =    get_resize_output_image_size(snake_case		,    size=size['shortest_edge']		,    default_to_square=snake_case       )
		return resize(snake_case		,    size=snake_case		,    resample=snake_case		,    data_format=snake_case		,    **snake_case       )





	def 	SCREAMING_SNAKE_CASE__						(       self		,    snake_case		,    snake_case		,    snake_case = None		,    **snake_case		,    ):
		lowercase       =    get_size_dict(snake_case       )
		if "height" not in size or "width" not in size:
			raise ValueError(F'''The `size` parameter must contain the keys (height, width). Got {size.keys()}'''       )
		return center_crop(snake_case		,    size=(size['height'], size['width'])		,    data_format=snake_case		,    **snake_case       )





	def 	SCREAMING_SNAKE_CASE__						(       self		,    snake_case		,    snake_case		,    snake_case = None		,    **snake_case		,    ):
		return rescale(snake_case		,    scale=snake_case		,    data_format=snake_case		,    **snake_case       )





	def 	SCREAMING_SNAKE_CASE__						(       self		,    snake_case		,    snake_case		,    snake_case		,    snake_case = None		,    **snake_case		,    ):
		return normalize(snake_case		,    mean=snake_case		,    std=snake_case		,    data_format=snake_case		,    **snake_case       )





	def 	SCREAMING_SNAKE_CASE__						(       self		,    snake_case		,    snake_case = None		,    snake_case = None		,    snake_case = None		,    snake_case = None		,    snake_case = None		,    snake_case = None		,    snake_case = None		,    snake_case = None		,    snake_case = None		,    snake_case = None		,    snake_case = None		,    snake_case = None		,    snake_case = ChannelDimension.FIRST		,    **snake_case		,    ):
		lowercase       =    do_resize if do_resize is not None else self.do_resize
		lowercase       =    size if size is not None else self.size
		lowercase       =    get_size_dict(snake_case		,    param_name='size'		,    default_to_square=snake_case       )
		lowercase       =    resample if resample is not None else self.resample
		lowercase       =    do_center_crop if do_center_crop is not None else self.do_center_crop
		lowercase       =    crop_size if crop_size is not None else self.crop_size
		lowercase       =    get_size_dict(snake_case		,    param_name='crop_size'		,    default_to_square=snake_case       )
		lowercase       =    do_rescale if do_rescale is not None else self.do_rescale
		lowercase       =    rescale_factor if rescale_factor is not None else self.rescale_factor
		lowercase       =    do_normalize if do_normalize is not None else self.do_normalize
		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       =    do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

		lowercase       =    make_list_of_images(snake_case       )

		if not valid_images(snake_case       ):
			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_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.'       )

		# PIL RGBA images are converted to RGB
		if do_convert_rgb:
			lowercase       =    [convert_to_rgb(snake_case       ) for image in images]

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

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

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

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

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

		lowercase       =    [to_channel_dimension_format(snake_case		,    snake_case       ) for image in images]

		lowercase       =    {'pixel_values': images}
		return BatchFeature(data=snake_case		,    tensor_type=snake_case       )

