
'''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_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_batched,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


__A :  Any    							=				logging.get_logger(__name__)






class        __snake_case	(       _SCREAMING_SNAKE_CASE):







    """simple docstring"""



    lowercase       			= ['pixel_values']

    def __init__(       self   :  Optional[int]   ,						lowerCamelCase   :  bool = True   ,						lowerCamelCase   :  Optional[Dict[str, int]] = None   ,						lowerCamelCase   :  PILImageResampling = PILImageResampling.BICUBIC   ,						lowerCamelCase   :  bool = True   ,						lowerCamelCase   :  bool = True   ,						lowerCamelCase   :  Union[int, float] = 1 / 2_55   ,						lowerCamelCase   :  Dict[str, int] = None   ,						lowerCamelCase   :  bool = True   ,						lowerCamelCase   :  Optional[Union[float, List[float]]] = None   ,						lowerCamelCase   :  Optional[Union[float, List[float]]] = None   ,						**lowerCamelCase   :  Any   ,						)       ->  None:
      super().__init__(**lowerCamelCase		)
      lowerCAmelCase_      :				int      =       size if size is not None else {"""height""": 2_24, """width""": 2_24}
      lowerCAmelCase_      :				int      =       get_size_dict(lowerCamelCase		)
      lowerCAmelCase_      :				Dict      =       crop_size if crop_size is not None else {"""height""": 2_24, """width""": 2_24}
      lowerCAmelCase_      :				Optional[Any]      =       get_size_dict(lowerCamelCase   ,						default_to_square=lowerCamelCase   ,						param_name="""crop_size"""		)

      lowerCAmelCase_      :				str      =       do_resize
      lowerCAmelCase_      :				int      =       do_rescale
      lowerCAmelCase_      :				Any      =       do_normalize
      lowerCAmelCase_      :				List[Any]      =       do_center_crop
      lowerCAmelCase_      :				str      =       crop_size
      lowerCAmelCase_      :				Tuple      =       size
      lowerCAmelCase_      :				Optional[Any]      =       resample
      lowerCAmelCase_      :				str      =       rescale_factor
      lowerCAmelCase_      :				Optional[Any]      =       image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
      lowerCAmelCase_      :				Union[str, Any]      =       image_std if image_std is not None else IMAGENET_DEFAULT_STD

    def __lowercase       (       self   :  Optional[Any]   ,						lowerCamelCase   :  np.ndarray   ,						lowerCamelCase   :  Dict[str, int]   ,						lowerCamelCase   :  PILImageResampling = PILImageResampling.BILINEAR   ,						lowerCamelCase   :  Optional[Union[str, ChannelDimension]] = None   ,						**lowerCamelCase   :  Optional[Any]   ,						)       ->  np.ndarray:
      lowerCAmelCase_      :				Any      =       get_size_dict(lowerCamelCase		)

      if "shortest_edge" in size:
        lowerCAmelCase_      :				Union[str, Any]      =       get_resize_output_image_size(lowerCamelCase   ,						size=size["""shortest_edge"""]   ,						default_to_square=lowerCamelCase		)
        # size = get_resize_output_image_size(image, size["shortest_edge"], size["longest_edge"])
      elif "height" in size and "width" in size:
        lowerCAmelCase_      :				Dict      =       (size["""height"""], size["""width"""])
      else:
        raise ValueError(F'Size must contain \'height\' and \'width\' keys or \'shortest_edge\' key. Got {size.keys()}'		)
      return resize(lowerCamelCase   ,						size=lowerCamelCase   ,						resample=lowerCamelCase   ,						data_format=lowerCamelCase   ,						**lowerCamelCase		)

    def __lowercase       (       self   :  Any   ,						lowerCamelCase   :  np.ndarray   ,						lowerCamelCase   :  Dict[str, int]   ,						lowerCamelCase   :  Optional[Union[str, ChannelDimension]] = None   ,						**lowerCamelCase   :  Union[str, Any]   ,						)       ->  np.ndarray:
      lowerCAmelCase_      :				str      =       get_size_dict(lowerCamelCase		)
      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(lowerCamelCase   ,						size=(size["""height"""], size["""width"""])   ,						data_format=lowerCamelCase   ,						**lowerCamelCase		)

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

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





    def __lowercase       (       self   :  str   ,						lowerCamelCase   :  ImageInput   ,						lowerCamelCase   :  Optional[bool] = None   ,						lowerCamelCase   :  Dict[str, int] = None   ,						lowerCamelCase   :  PILImageResampling = None   ,						lowerCamelCase   :  bool = None   ,						lowerCamelCase   :  int = None   ,						lowerCamelCase   :  Optional[bool] = None   ,						lowerCamelCase   :  Optional[float] = None   ,						lowerCamelCase   :  Optional[bool] = None   ,						lowerCamelCase   :  Optional[Union[float, List[float]]] = None   ,						lowerCamelCase   :  Optional[Union[float, List[float]]] = None   ,						lowerCamelCase   :  Optional[Union[str, TensorType]] = None   ,						lowerCamelCase   :  Union[str, ChannelDimension] = ChannelDimension.FIRST   ,						**lowerCamelCase   :  Optional[int]   ,						)       ->  BatchFeature:
      lowerCAmelCase_      :				List[Any]      =       do_resize if do_resize is not None else self.do_resize
      lowerCAmelCase_      :				List[Any]      =       do_rescale if do_rescale is not None else self.do_rescale
      lowerCAmelCase_      :				Any      =       do_normalize if do_normalize is not None else self.do_normalize
      lowerCAmelCase_      :				Union[str, Any]      =       do_center_crop if do_center_crop is not None else self.do_center_crop
      lowerCAmelCase_      :				Any      =       crop_size if crop_size is not None else self.crop_size
      lowerCAmelCase_      :				Union[str, Any]      =       get_size_dict(lowerCamelCase   ,						param_name="""crop_size"""   ,						default_to_square=lowerCamelCase		)
      lowerCAmelCase_      :				Tuple      =       resample if resample is not None else self.resample
      lowerCAmelCase_      :				Dict      =       rescale_factor if rescale_factor is not None else self.rescale_factor
      lowerCAmelCase_      :				List[str]      =       image_mean if image_mean is not None else self.image_mean
      lowerCAmelCase_      :				List[str]      =       image_std if image_std is not None else self.image_std

      lowerCAmelCase_      :				List[Any]      =       size if size is not None else self.size
      lowerCAmelCase_      :				Dict      =       get_size_dict(lowerCamelCase		)

      if not is_batched(lowerCamelCase		):
        lowerCAmelCase_      :				str      =       [images]

      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:
        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."""		)

      # All transformations expect numpy arrays.
      lowerCAmelCase_      :				Union[str, Any]      =       [to_numpy_array(lowerCamelCase		) for image in images]

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

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

      if do_rescale:
        lowerCAmelCase_      :				Union[str, Any]      =       [self.rescale(image=lowerCamelCase   ,						scale=lowerCamelCase		) for image in images]

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

      lowerCAmelCase_      :				List[str]      =       [to_channel_dimension_format(lowerCamelCase   ,						lowerCamelCase		) for image in images]

      lowerCAmelCase_      :				Union[str, Any]      =       {"""pixel_values""": images}
      return BatchFeature(data=lowerCamelCase   ,						tensor_type=lowerCamelCase		)







'''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        __snake_case	(       unittest.TestCase):







    """simple docstring"""



    @property
    def __lowercase       (       self   :  str		)       ->  List[Any]:
      torch.manual_seed(0		)
      lowerCAmelCase_      :				Optional[Any]      =       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 __lowercase       (       self   :  Tuple		)       ->  Optional[Any]:
      lowerCAmelCase_      :				Optional[int]      =       self.dummy_uncond_unet
      lowerCAmelCase_      :				Tuple      =       PNDMScheduler()

      lowerCAmelCase_      :				List[Any]      =       PNDMPipeline(unet=lowerCamelCase   ,						scheduler=lowerCamelCase		)
      pndm.to(lowerCamelCase		)
      pndm.set_progress_bar_config(disable=lowerCamelCase		)

      lowerCAmelCase_      :				Dict      =       torch.manual_seed(0		)
      lowerCAmelCase_      :				List[Any]      =       pndm(generator=lowerCamelCase   ,						num_inference_steps=20   ,						output_type="""numpy"""		).images

      lowerCAmelCase_      :				str      =       torch.manual_seed(0		)
      lowerCAmelCase_      :				int      =       pndm(generator=lowerCamelCase   ,						num_inference_steps=20   ,						output_type="""numpy"""   ,						return_dict=lowerCamelCase		)[0]

      lowerCAmelCase_      :				Optional[int]      =       image[0, -3:, -3:, -1]
      lowerCAmelCase_      :				Optional[Any]      =       image_from_tuple[0, -3:, -3:, -1]

      assert image.shape == (1, 32, 32, 3)
      lowerCAmelCase_      :				int      =       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        __snake_case	(       unittest.TestCase):







    """simple docstring"""



    def __lowercase       (       self   :  str		)       ->  Tuple:
      lowerCAmelCase_      :				str      =       """google/ddpm-cifar10-32"""

      lowerCAmelCase_      :				Dict      =       UNetaDModel.from_pretrained(lowerCamelCase		)
      lowerCAmelCase_      :				Dict      =       PNDMScheduler()

      lowerCAmelCase_      :				Union[str, Any]      =       PNDMPipeline(unet=lowerCamelCase   ,						scheduler=lowerCamelCase		)
      pndm.to(lowerCamelCase		)
      pndm.set_progress_bar_config(disable=lowerCamelCase		)
      lowerCAmelCase_      :				Any      =       torch.manual_seed(0		)
      lowerCAmelCase_      :				Union[str, Any]      =       pndm(generator=lowerCamelCase   ,						output_type="""numpy"""		).images

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

      assert image.shape == (1, 32, 32, 3)
      lowerCAmelCase_      :				List[Any]      =       np.array([0.1_564, 0.14_645, 0.1_406, 0.14_715, 0.12_425, 0.14_045, 0.13_115, 0.12_175, 0.125]		)

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



