






'''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,
    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


__lowerCAmelCase       		=      logging.get_logger(__name__)


if is_vision_available():
			import PIL





class 	SCREAMING_SNAKE_CASE					(					SCREAMING_SNAKE_CASE_       ):
			snake_case__  =				["pixel_values"]






			def __init__( self      :			Optional[Any]  ,						__SCREAMING_SNAKE_CASE      :			bool = True  ,						__SCREAMING_SNAKE_CASE      :			Dict[str, int] = None  ,						__SCREAMING_SNAKE_CASE      :			PILImageResampling = PILImageResampling.BICUBIC  ,						__SCREAMING_SNAKE_CASE      :			bool = True  ,						__SCREAMING_SNAKE_CASE      :			Dict[str, int] = None  ,						__SCREAMING_SNAKE_CASE      :			bool = True  ,						__SCREAMING_SNAKE_CASE      :			Union[int, float] = 1 / 255  ,						__SCREAMING_SNAKE_CASE      :			bool = True  ,						__SCREAMING_SNAKE_CASE      :			Optional[Union[float, List[float]]] = None  ,						__SCREAMING_SNAKE_CASE      :			Optional[Union[float, List[float]]] = None  ,						__SCREAMING_SNAKE_CASE      :			bool = True  ,						**__SCREAMING_SNAKE_CASE      :			List[Any]  ,						)			->			None:
							super().__init__(**__SCREAMING_SNAKE_CASE					)
							a_		:  str											=      size if size is not None else {'''shortest_edge''': 224}
							a_		:  Optional[Any]											=      get_size_dict(__SCREAMING_SNAKE_CASE  ,						default_to_square=__SCREAMING_SNAKE_CASE					)
							a_		:  List[Any]											=      crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
							a_		:  Dict											=      get_size_dict(__SCREAMING_SNAKE_CASE  ,						default_to_square=__SCREAMING_SNAKE_CASE  ,						param_name='''crop_size'''					)

							a_		:  int											=      do_resize
							a_		:  int											=      size
							a_		:  Union[str, Any]											=      resample
							a_		:  Any											=      do_center_crop
							a_		:  Optional[Any]											=      crop_size
							a_		:  Any											=      do_rescale
							a_		:  Optional[Any]											=      rescale_factor
							a_		:  Dict											=      do_normalize
							a_		:  int											=      image_mean if image_mean is not None else OPENAI_CLIP_MEAN
							a_		:  Any											=      image_std if image_std is not None else OPENAI_CLIP_STD
							a_		:  int											=      do_convert_rgb






			def 			SCREAMING_SNAKE_CASE      ( self      :			Any  ,						__SCREAMING_SNAKE_CASE      :			np.ndarray  ,						__SCREAMING_SNAKE_CASE      :			Dict[str, int]  ,						__SCREAMING_SNAKE_CASE      :			PILImageResampling = PILImageResampling.BICUBIC  ,						__SCREAMING_SNAKE_CASE      :			Optional[Union[str, ChannelDimension]] = None  ,						**__SCREAMING_SNAKE_CASE      :			Any  ,						)			->			np.ndarray:
							a_		:  Optional[Any]											=      get_size_dict(__SCREAMING_SNAKE_CASE  ,						default_to_square=__SCREAMING_SNAKE_CASE					)
							if "shortest_edge" not in size:
											raise ValueError(f'The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}'					)
							a_		:  Union[str, Any]											=      get_resize_output_image_size(__SCREAMING_SNAKE_CASE  ,						size=size['''shortest_edge''']  ,						default_to_square=__SCREAMING_SNAKE_CASE					)
							return resize(__SCREAMING_SNAKE_CASE  ,						size=__SCREAMING_SNAKE_CASE  ,						resample=__SCREAMING_SNAKE_CASE  ,						data_format=__SCREAMING_SNAKE_CASE  ,						**__SCREAMING_SNAKE_CASE					)






			def 			SCREAMING_SNAKE_CASE      ( self      :			List[Any]  ,						__SCREAMING_SNAKE_CASE      :			np.ndarray  ,						__SCREAMING_SNAKE_CASE      :			Dict[str, int]  ,						__SCREAMING_SNAKE_CASE      :			Optional[Union[str, ChannelDimension]] = None  ,						**__SCREAMING_SNAKE_CASE      :			Any  ,						)			->			np.ndarray:
							a_		:  int											=      get_size_dict(__SCREAMING_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(__SCREAMING_SNAKE_CASE  ,						size=(size['''height'''], size['''width'''])  ,						data_format=__SCREAMING_SNAKE_CASE  ,						**__SCREAMING_SNAKE_CASE					)






			def 			SCREAMING_SNAKE_CASE      ( self      :			List[str]  ,						__SCREAMING_SNAKE_CASE      :			np.ndarray  ,						__SCREAMING_SNAKE_CASE      :			Union[int, float]  ,						__SCREAMING_SNAKE_CASE      :			Optional[Union[str, ChannelDimension]] = None  ,						**__SCREAMING_SNAKE_CASE      :			Tuple  ,						)			->			Tuple:
							return rescale(__SCREAMING_SNAKE_CASE  ,						scale=__SCREAMING_SNAKE_CASE  ,						data_format=__SCREAMING_SNAKE_CASE  ,						**__SCREAMING_SNAKE_CASE					)






			def 			SCREAMING_SNAKE_CASE      ( self      :			Optional[Any]  ,						__SCREAMING_SNAKE_CASE      :			np.ndarray  ,						__SCREAMING_SNAKE_CASE      :			Union[float, List[float]]  ,						__SCREAMING_SNAKE_CASE      :			Union[float, List[float]]  ,						__SCREAMING_SNAKE_CASE      :			Optional[Union[str, ChannelDimension]] = None  ,						**__SCREAMING_SNAKE_CASE      :			str  ,						)			->			np.ndarray:
							return normalize(__SCREAMING_SNAKE_CASE  ,						mean=__SCREAMING_SNAKE_CASE  ,						std=__SCREAMING_SNAKE_CASE  ,						data_format=__SCREAMING_SNAKE_CASE  ,						**__SCREAMING_SNAKE_CASE					)







			def 			SCREAMING_SNAKE_CASE      ( self      :			Any  ,						__SCREAMING_SNAKE_CASE      :			ImageInput  ,						__SCREAMING_SNAKE_CASE      :			bool = None  ,						__SCREAMING_SNAKE_CASE      :			Dict[str, int] = None  ,						__SCREAMING_SNAKE_CASE      :			PILImageResampling = None  ,						__SCREAMING_SNAKE_CASE      :			bool = None  ,						__SCREAMING_SNAKE_CASE      :			int = None  ,						__SCREAMING_SNAKE_CASE      :			bool = None  ,						__SCREAMING_SNAKE_CASE      :			float = None  ,						__SCREAMING_SNAKE_CASE      :			bool = None  ,						__SCREAMING_SNAKE_CASE      :			Optional[Union[float, List[float]]] = None  ,						__SCREAMING_SNAKE_CASE      :			Optional[Union[float, List[float]]] = None  ,						__SCREAMING_SNAKE_CASE      :			bool = None  ,						__SCREAMING_SNAKE_CASE      :			Optional[Union[str, TensorType]] = None  ,						__SCREAMING_SNAKE_CASE      :			Optional[ChannelDimension] = ChannelDimension.FIRST  ,						**__SCREAMING_SNAKE_CASE      :			Any  ,						)			->			PIL.Image.Image:
							a_		:  Tuple											=      do_resize if do_resize is not None else self.do_resize
							a_		:  str											=      size if size is not None else self.size
							a_		:  Tuple											=      get_size_dict(__SCREAMING_SNAKE_CASE  ,						param_name='''size'''  ,						default_to_square=__SCREAMING_SNAKE_CASE					)
							a_		:  Union[str, Any]											=      resample if resample is not None else self.resample
							a_		:  Optional[int]											=      do_center_crop if do_center_crop is not None else self.do_center_crop
							a_		:  Tuple											=      crop_size if crop_size is not None else self.crop_size
							a_		:  Dict											=      get_size_dict(__SCREAMING_SNAKE_CASE  ,						param_name='''crop_size'''  ,						default_to_square=__SCREAMING_SNAKE_CASE					)
							a_		:  Optional[int]											=      do_rescale if do_rescale is not None else self.do_rescale
							a_		:  Union[str, Any]											=      rescale_factor if rescale_factor is not None else self.rescale_factor
							a_		:  str											=      do_normalize if do_normalize is not None else self.do_normalize
							a_		:  Optional[int]											=      image_mean if image_mean is not None else self.image_mean
							a_		:  Any											=      image_std if image_std is not None else self.image_std
							a_		:  Any											=      do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

							a_		:  List[str]											=      make_list_of_images(__SCREAMING_SNAKE_CASE					)

							if not valid_images(__SCREAMING_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:
											a_		:  int											=      [convert_to_rgb(__SCREAMING_SNAKE_CASE					) for image in images]

							# All transformations expect numpy arrays.
							a_		:  List[str]											=      [to_numpy_array(__SCREAMING_SNAKE_CASE					) for image in images]

							if do_resize:
											a_		:  Union[str, Any]											=      [self.resize(image=__SCREAMING_SNAKE_CASE  ,						size=__SCREAMING_SNAKE_CASE  ,						resample=__SCREAMING_SNAKE_CASE					) for image in images]

							if do_center_crop:
											a_		:  Dict											=      [self.center_crop(image=__SCREAMING_SNAKE_CASE  ,						size=__SCREAMING_SNAKE_CASE					) for image in images]

							if do_rescale:
											a_		:  Any											=      [self.rescale(image=__SCREAMING_SNAKE_CASE  ,						scale=__SCREAMING_SNAKE_CASE					) for image in images]

							if do_normalize:
											a_		:  int											=      [self.normalize(image=__SCREAMING_SNAKE_CASE  ,						mean=__SCREAMING_SNAKE_CASE  ,						std=__SCREAMING_SNAKE_CASE					) for image in images]

							a_		:  Dict											=      [to_channel_dimension_format(__SCREAMING_SNAKE_CASE  ,						__SCREAMING_SNAKE_CASE					) for image in images]

							a_		:  Dict											=      {'''pixel_values''': images}
							return BatchFeature(data=__SCREAMING_SNAKE_CASE  ,						tensor_type=__SCREAMING_SNAKE_CASE					)












'''simple docstring'''







import unittest

import numpy as np
import torch

from diffusers import DDIMPipeline, DDIMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device

from ..pipeline_params import UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS, UNCONDITIONAL_IMAGE_GENERATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()





class 	SCREAMING_SNAKE_CASE					(					SCREAMING_SNAKE_CASE_	,   unittest.TestCase       ):
			snake_case__  =				DDIMPipeline
			snake_case__  =				UNCONDITIONAL_IMAGE_GENERATION_PARAMS
			snake_case__  =				PipelineTesterMixin.required_optional_params - {
			    "num_images_per_prompt",
			    "latents",
			    "callback",
			    "callback_steps",
			}
			snake_case__  =				UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS
			snake_case__  =				False






			def 			SCREAMING_SNAKE_CASE      ( self      :			Any					)			->			Optional[Any]:
							torch.manual_seed(0					)
							a_		:  int											=      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''')  ,						)
							a_		:  str											=      DDIMScheduler()
							a_		:  Union[str, Any]											=      {'''unet''': unet, '''scheduler''': scheduler}
							return components






			def 			SCREAMING_SNAKE_CASE      ( self      :			Dict  ,						__SCREAMING_SNAKE_CASE      :			str  ,						__SCREAMING_SNAKE_CASE      :			Tuple=0					)			->			str:
							if str(__SCREAMING_SNAKE_CASE					).startswith('''mps'''					):
											a_		:  Dict											=      torch.manual_seed(__SCREAMING_SNAKE_CASE					)
							else:
											a_		:  Union[str, Any]											=      torch.Generator(device=__SCREAMING_SNAKE_CASE					).manual_seed(__SCREAMING_SNAKE_CASE					)
							a_		:  Union[str, Any]											=      {
							    '''batch_size''': 1,
							    '''generator''': generator,
							    '''num_inference_steps''': 2,
							    '''output_type''': '''numpy''',
							}
							return inputs






			def 			SCREAMING_SNAKE_CASE      ( self      :			Union[str, Any]					)			->			Union[str, Any]:
							a_		:  Dict											=      '''cpu'''

							a_		:  List[Any]											=      self.get_dummy_components()
							a_		:  List[str]											=      self.pipeline_class(**__SCREAMING_SNAKE_CASE					)
							pipe.to(__SCREAMING_SNAKE_CASE					)
							pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE					)

							a_		:  Tuple											=      self.get_dummy_inputs(__SCREAMING_SNAKE_CASE					)
							a_		:  Optional[Any]											=      pipe(**__SCREAMING_SNAKE_CASE					).images
							a_		:  List[str]											=      image[0, -3:, -3:, -1]

							self.assertEqual(image.shape  ,						(1, 32, 32, 3)					)
							a_		:  int											=      np.array(
							    [1.000e00, 5.717e-01, 4.717e-01, 1.000e00, 0.000e00, 1.000e00, 3.000e-04, 0.000e00, 9.000e-04]					)
							a_		:  Union[str, Any]											=      np.abs(image_slice.flatten() - expected_slice					).max()
							self.assertLessEqual(__SCREAMING_SNAKE_CASE  ,						1e-3					)






			def 			SCREAMING_SNAKE_CASE      ( self      :			List[str]					)			->			str:
							super().test_dict_tuple_outputs_equivalent(expected_max_difference=3e-3					)






			def 			SCREAMING_SNAKE_CASE      ( self      :			str					)			->			Optional[int]:
							super().test_save_load_local(expected_max_difference=3e-3					)






			def 			SCREAMING_SNAKE_CASE      ( self      :			Any					)			->			List[str]:
							super().test_save_load_optional_components(expected_max_difference=3e-3					)







			def 			SCREAMING_SNAKE_CASE      ( self      :			str					)			->			Optional[int]:
							super().test_inference_batch_single_identical(expected_max_diff=3e-3					)





@slow
@require_torch_gpu
class 	SCREAMING_SNAKE_CASE					(					unittest.TestCase       ):






			def 			SCREAMING_SNAKE_CASE      ( self      :			str					)			->			Any:
							a_		:  Optional[Any]											=      '''google/ddpm-cifar10-32'''

							a_		:  Optional[Any]											=      UNetaDModel.from_pretrained(__SCREAMING_SNAKE_CASE					)
							a_		:  Dict											=      DDIMScheduler()

							a_		:  List[str]											=      DDIMPipeline(unet=__SCREAMING_SNAKE_CASE  ,						scheduler=__SCREAMING_SNAKE_CASE					)
							ddim.to(__SCREAMING_SNAKE_CASE					)
							ddim.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE					)

							a_		:  Tuple											=      torch.manual_seed(0					)
							a_		:  Tuple											=      ddim(generator=__SCREAMING_SNAKE_CASE  ,						eta=0.0  ,						output_type='''numpy'''					).images

							a_		:  List[Any]											=      image[0, -3:, -3:, -1]

							assert image.shape == (1, 32, 32, 3)
							a_		:  List[str]											=      np.array([0.1723, 0.1617, 0.1600, 0.1626, 0.1497, 0.1513, 0.1505, 0.1442, 0.1453]					)

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







			def 			SCREAMING_SNAKE_CASE      ( self      :			Union[str, Any]					)			->			str:
							a_		:  int											=      '''google/ddpm-ema-bedroom-256'''

							a_		:  str											=      UNetaDModel.from_pretrained(__SCREAMING_SNAKE_CASE					)
							a_		:  Tuple											=      DDIMScheduler.from_pretrained(__SCREAMING_SNAKE_CASE					)

							a_		:  Any											=      DDIMPipeline(unet=__SCREAMING_SNAKE_CASE  ,						scheduler=__SCREAMING_SNAKE_CASE					)
							ddpm.to(__SCREAMING_SNAKE_CASE					)
							ddpm.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE					)

							a_		:  Tuple											=      torch.manual_seed(0					)
							a_		:  List[Any]											=      ddpm(generator=__SCREAMING_SNAKE_CASE  ,						output_type='''numpy'''					).images

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

							assert image.shape == (1, 256, 256, 3)
							a_		:  Optional[Any]											=      np.array([0.0060, 0.0201, 0.0344, 0.0024, 0.0018, 0.0002, 0.0022, 0.0000, 0.0069]					)

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




