

'''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,
    get_resize_output_image_size,
    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 :       Any			   =     logging.get_logger(__name__)







class 						lowerCAmelCase__   (      a					):



 """simple docstring"""



 lowerCAmelCase__  					=      ["pixel_values"]
 def __init__(		self					:       str					,							__SCREAMING_SNAKE_CASE					:       bool = True					,							__SCREAMING_SNAKE_CASE					:       Optional[Dict[str, int]] = None					,							__SCREAMING_SNAKE_CASE					:       PILImageResampling = PILImageResampling.BILINEAR					,							__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					:       Optional[int]					,							)  -> None:


      """simple docstring"""




      super().__init__(**__SCREAMING_SNAKE_CASE			)
      __SCREAMING_SNAKE_CASE     =      size if size is not None else {"""shortest_edge""": 256}
      __SCREAMING_SNAKE_CASE     =      get_size_dict(__SCREAMING_SNAKE_CASE					,							default_to_square=__SCREAMING_SNAKE_CASE			)
      __SCREAMING_SNAKE_CASE     =      crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
      __SCREAMING_SNAKE_CASE     =      get_size_dict(__SCREAMING_SNAKE_CASE			)
      __SCREAMING_SNAKE_CASE     =      do_resize
      __SCREAMING_SNAKE_CASE     =      size
      __SCREAMING_SNAKE_CASE     =      resample
      __SCREAMING_SNAKE_CASE     =      do_center_crop
      __SCREAMING_SNAKE_CASE     =      crop_size
      __SCREAMING_SNAKE_CASE     =      do_rescale
      __SCREAMING_SNAKE_CASE     =      rescale_factor
      __SCREAMING_SNAKE_CASE     =      do_normalize
      __SCREAMING_SNAKE_CASE     =      image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
      __SCREAMING_SNAKE_CASE     =      image_std if image_std is not None else IMAGENET_STANDARD_STD
 def       UpperCAmelCase__		(		self					:       Optional[int]					,							__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					:       List[str]					,							)  -> np.ndarray:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      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()}'			)
      __SCREAMING_SNAKE_CASE     =      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       UpperCAmelCase__		(		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:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      get_size_dict(__SCREAMING_SNAKE_CASE			)
      return center_crop(__SCREAMING_SNAKE_CASE					,							size=(size["""height"""], size["""width"""])					,							data_format=__SCREAMING_SNAKE_CASE					,							**__SCREAMING_SNAKE_CASE			)
 def       UpperCAmelCase__		(		self					:       Any					,							__SCREAMING_SNAKE_CASE					:       np.ndarray					,							__SCREAMING_SNAKE_CASE					:       float					,							__SCREAMING_SNAKE_CASE					:       Optional[Union[str, ChannelDimension]] = None					,							**__SCREAMING_SNAKE_CASE					:       str			)  -> np.ndarray:


      """simple docstring"""




      return rescale(__SCREAMING_SNAKE_CASE					,							scale=__SCREAMING_SNAKE_CASE					,							data_format=__SCREAMING_SNAKE_CASE					,							**__SCREAMING_SNAKE_CASE			)
 def       UpperCAmelCase__		(		self					:       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					:       Dict					,							)  -> np.ndarray:


      """simple docstring"""




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


 def       UpperCAmelCase__		(		self					:       Union[str, Any]					,							__SCREAMING_SNAKE_CASE					:       ImageInput					,							__SCREAMING_SNAKE_CASE					:       Optional[bool] = None					,							__SCREAMING_SNAKE_CASE					:       Dict[str, int] = None					,							__SCREAMING_SNAKE_CASE					:       PILImageResampling = None					,							__SCREAMING_SNAKE_CASE					:       bool = None					,							__SCREAMING_SNAKE_CASE					:       Dict[str, int] = None					,							__SCREAMING_SNAKE_CASE					:       Optional[bool] = None					,							__SCREAMING_SNAKE_CASE					:       Optional[float] = None					,							__SCREAMING_SNAKE_CASE					:       Optional[bool] = None					,							__SCREAMING_SNAKE_CASE					:       Optional[Union[float, List[float]]] = None					,							__SCREAMING_SNAKE_CASE					:       Optional[Union[float, List[float]]] = None					,							__SCREAMING_SNAKE_CASE					:       Optional[Union[str, TensorType]] = None					,							__SCREAMING_SNAKE_CASE					:       Union[str, ChannelDimension] = ChannelDimension.FIRST					,							**__SCREAMING_SNAKE_CASE					:       Dict					,							)  -> str:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      do_resize if do_resize is not None else self.do_resize
      __SCREAMING_SNAKE_CASE     =      size if size is not None else self.size
      __SCREAMING_SNAKE_CASE     =      get_size_dict(__SCREAMING_SNAKE_CASE					,							default_to_square=__SCREAMING_SNAKE_CASE			)
      __SCREAMING_SNAKE_CASE     =      resample if resample is not None else self.resample
      __SCREAMING_SNAKE_CASE     =      do_center_crop if do_center_crop is not None else self.do_center_crop
      __SCREAMING_SNAKE_CASE     =      crop_size if crop_size is not None else self.crop_size
      __SCREAMING_SNAKE_CASE     =      get_size_dict(__SCREAMING_SNAKE_CASE			)
      __SCREAMING_SNAKE_CASE     =      do_rescale if do_rescale is not None else self.do_rescale
      __SCREAMING_SNAKE_CASE     =      rescale_factor if rescale_factor is not None else self.rescale_factor
      __SCREAMING_SNAKE_CASE     =      do_normalize if do_normalize is not None else self.do_normalize
      __SCREAMING_SNAKE_CASE     =      image_mean if image_mean is not None else self.image_mean
      __SCREAMING_SNAKE_CASE     =      image_std if image_std is not None else self.image_std

      __SCREAMING_SNAKE_CASE     =      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."""			)

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

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

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

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

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

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

      __SCREAMING_SNAKE_CASE     =      {"""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 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       UpperCAmelCase__		(		self					:       List[str]			)  -> Optional[int]:


      """simple docstring"""




      torch.manual_seed(0			)
      __SCREAMING_SNAKE_CASE     =      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       UpperCAmelCase__		(		self					:       List[Any]			)  -> List[str]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      self.dummy_uncond_unet
      __SCREAMING_SNAKE_CASE     =      PNDMScheduler()

      __SCREAMING_SNAKE_CASE     =      PNDMPipeline(unet=__SCREAMING_SNAKE_CASE					,							scheduler=__SCREAMING_SNAKE_CASE			)
      pndm.to(__SCREAMING_SNAKE_CASE			)
      pndm.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE			)

      __SCREAMING_SNAKE_CASE     =      torch.manual_seed(0			)
      __SCREAMING_SNAKE_CASE     =      pndm(generator=__SCREAMING_SNAKE_CASE					,							num_inference_steps=20					,							output_type="""numpy"""			).images

      __SCREAMING_SNAKE_CASE     =      torch.manual_seed(0			)
      __SCREAMING_SNAKE_CASE     =      pndm(generator=__SCREAMING_SNAKE_CASE					,							num_inference_steps=20					,							output_type="""numpy"""					,							return_dict=__SCREAMING_SNAKE_CASE			)[0]

      __SCREAMING_SNAKE_CASE     =      image[0, -3:, -3:, -1]
      __SCREAMING_SNAKE_CASE     =      image_from_tuple[0, -3:, -3:, -1]

      assert image.shape == (1, 32, 32, 3)
      __SCREAMING_SNAKE_CASE     =      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       UpperCAmelCase__		(		self					:       Optional[int]			)  -> List[Any]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      """google/ddpm-cifar10-32"""

      __SCREAMING_SNAKE_CASE     =      UNetaDModel.from_pretrained(__SCREAMING_SNAKE_CASE			)
      __SCREAMING_SNAKE_CASE     =      PNDMScheduler()

      __SCREAMING_SNAKE_CASE     =      PNDMPipeline(unet=__SCREAMING_SNAKE_CASE					,							scheduler=__SCREAMING_SNAKE_CASE			)
      pndm.to(__SCREAMING_SNAKE_CASE			)
      pndm.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE			)
      __SCREAMING_SNAKE_CASE     =      torch.manual_seed(0			)
      __SCREAMING_SNAKE_CASE     =      pndm(generator=__SCREAMING_SNAKE_CASE					,							output_type="""numpy"""			).images

      __SCREAMING_SNAKE_CASE     =      image[0, -3:, -3:, -1]

      assert image.shape == (1, 32, 32, 3)
      __SCREAMING_SNAKE_CASE     =      np.array([0.1564, 0.14645, 0.1406, 0.14715, 0.12425, 0.14045, 0.13115, 0.12175, 0.125]			)

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






