






"""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, 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


_A      :	Union[str, Any]       		=	logging.get_logger(__name__)

class 		a__						(				lowerCAmelCase_						):
   __lowerCAmelCase						=    ["""pixel_values"""]
   def __init__(      self     ,   _a = True     ,   _a = None     ,   _a = True     ,   _a = None     ,   _a = PILImageResampling.BICUBIC     ,   _a = True     ,   _a = 1 / 255     ,   _a = True     ,   _a = None     ,   _a = None     ,   **_a     ,   ):
          super().__init__(**_snake_case    )
          lowercase   :    Optional[int]			=							crop_size if crop_size is not None else {'height': 256, 'width': 256}
          lowercase   :    Dict			=							get_size_dict(_snake_case     ,   param_name="crop_size"    )
          lowercase   :    int			=							size if size is not None else {'height': 224, 'width': 224}
          lowercase   :    List[Any]			=							get_size_dict(_snake_case    )

          lowercase   :    str			=							do_center_crop
          lowercase   :    str			=							crop_size
          lowercase   :    str			=							do_resize
          lowercase   :    Optional[Any]			=							size
          lowercase   :    List[str]			=							resample
          lowercase   :    List[Any]			=							do_rescale
          lowercase   :    int			=							rescale_factor
          lowercase   :    str			=							do_normalize
          lowercase   :    Optional[int]			=							image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
          lowercase   :    Tuple			=							image_std if image_std is not None else IMAGENET_DEFAULT_STD
   def       __magic_name__      (      self     ,   _a     ,   _a     ,   _a = None     ,   _a = None     ,   **_a     ,   ):
          lowercase   :    Tuple			=							self.size if size is None else size
          lowercase   :    Tuple			=							get_size_dict(_snake_case    )
          lowercase   :    Dict			=							get_size_dict(_snake_case     ,   param_name="crop_size"    )

          lowercase   :    str			=							get_image_size(_snake_case    )
          lowercase   :    Optional[int]			=							min(_snake_case     ,   _snake_case    )
          lowercase   :    Tuple			=							(size['height'] / crop_size['height']) * min_dim
          lowercase   :    str			=							(size['width'] / crop_size['width']) * min_dim
          return center_crop(_snake_case     ,   size=(cropped_height, cropped_width)     ,   data_format=_snake_case     ,   **_snake_case    )
   def       __magic_name__      (      self     ,   _a     ,   _a     ,   _a = PIL.Image.BICUBIC     ,   _a = None     ,   **_a     ,   ):
          lowercase   :    Union[str, Any]			=							get_size_dict(_snake_case    )
          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(
              _snake_case     ,   size=(size["height"], size["width"])     ,   resample=_snake_case     ,   data_format=_snake_case     ,   **_snake_case    )
   def       __magic_name__      (      self     ,   _a     ,   _a     ,   _a = None     ,   **_a     ,   ):
          return rescale(_snake_case     ,   scale=_snake_case     ,   data_format=_snake_case     ,   **_snake_case    )
   def       __magic_name__      (      self     ,   _a     ,   _a     ,   _a     ,   _a = None     ,   **_a     ,   ):
          return normalize(_snake_case     ,   mean=_snake_case     ,   std=_snake_case     ,   data_format=_snake_case     ,   **_snake_case    )

   def       __magic_name__      (      self     ,   _a     ,   _a = None     ,   _a = None     ,   _a = None     ,   _a = None     ,   _a = None     ,   _a = None     ,   _a = None     ,   _a = None     ,   _a = None     ,   _a = None     ,   _a = None     ,   _a = ChannelDimension.FIRST     ,   **_a     ,   ):
          lowercase   :    List[Any]			=							do_center_crop if do_center_crop is not None else self.do_center_crop
          lowercase   :    Union[str, Any]			=							crop_size if crop_size is not None else self.crop_size
          lowercase   :    Any			=							get_size_dict(_snake_case     ,   param_name="crop_size"    )
          lowercase   :    Any			=							do_resize if do_resize is not None else self.do_resize
          lowercase   :    int			=							size if size is not None else self.size
          lowercase   :    Any			=							get_size_dict(_snake_case    )
          lowercase   :    str			=							resample if resample is not None else self.resample
          lowercase   :    str			=							do_rescale if do_rescale is not None else self.do_rescale
          lowercase   :    List[Any]			=							rescale_factor if rescale_factor is not None else self.rescale_factor
          lowercase   :    int			=							do_normalize if do_normalize is not None else self.do_normalize
          lowercase   :    Dict			=							image_mean if image_mean is not None else self.image_mean
          lowercase   :    Union[str, Any]			=							image_std if image_std is not None else self.image_std

          lowercase   :    Optional[Any]			=							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_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.
          lowercase   :    Dict			=							[to_numpy_array(_snake_case    ) for image in images]

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

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

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

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

          lowercase   :    Optional[int]			=							[to_channel_dimension_format(_snake_case     ,   _snake_case    ) for image in images]

          lowercase   :    List[str]			=							{'pixel_values': images}
          return BatchFeature(data=_snake_case     ,   tensor_type=_snake_case    )




"""simple docstring"""




import unittest

import numpy as np
import torch

from diffusers import DDPMPipeline, DDPMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device


enable_full_determinism()

class 		a__						(				unittest.TestCase						):
   @property
   def       __magic_name__      (      self    ):
          torch.manual_seed(0    )
          lowercase   :    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")     ,   )
          return model
   def       __magic_name__      (      self    ):
          lowercase   :    int			=							"cpu"
          lowercase   :    Dict			=							self.dummy_uncond_unet
          lowercase   :    List[str]			=							DDPMScheduler()

          lowercase   :    Tuple			=							DDPMPipeline(unet=_a     ,   scheduler=_a    )
          ddpm.to(_a    )
          ddpm.set_progress_bar_config(disable=_a    )

          lowercase   :    Optional[int]			=							torch.Generator(device=_a    ).manual_seed(0    )
          lowercase   :    Optional[int]			=							ddpm(generator=_a     ,   num_inference_steps=2     ,   output_type="numpy"    ).images

          lowercase   :    List[str]			=							torch.Generator(device=_a    ).manual_seed(0    )
          lowercase   :    List[str]			=							ddpm(generator=_a     ,   num_inference_steps=2     ,   output_type="numpy"     ,   return_dict=_a    )[0]

          lowercase   :    List[str]			=							image[0, -3:, -3:, -1]
          lowercase   :    Any			=							image_from_tuple[0, -3:, -3:, -1]

          assert image.shape == (1, 32, 32, 3)
          lowercase   :    List[str]			=							np.array(
              [9.956E-01, 5.785E-01, 4.675E-01, 9.930E-01, 0.0, 1.0_0_0, 1.199E-03, 2.648E-04, 5.101E-04]    )

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

   def       __magic_name__      (      self    ):
          lowercase   :    Any			=							self.dummy_uncond_unet
          lowercase   :    Dict			=							DDPMScheduler(prediction_type="sample"    )

          lowercase   :    List[Any]			=							DDPMPipeline(unet=_a     ,   scheduler=_a    )
          ddpm.to(_a    )
          ddpm.set_progress_bar_config(disable=_a    )

          lowercase   :    Any			=							torch.manual_seed(0    )
          lowercase   :    int			=							ddpm(generator=_a     ,   num_inference_steps=2     ,   output_type="numpy"    ).images

          lowercase   :    List[str]			=							torch.manual_seed(0    )
          lowercase   :    Optional[Any]			=							ddpm(generator=_a     ,   num_inference_steps=2     ,   output_type="numpy"    )[0]

          lowercase   :    Any			=							image[0, -3:, -3:, -1]
          lowercase   :    Dict			=							image_eps[0, -3:, -3:, -1]

          assert image.shape == (1, 32, 32, 3)
          lowercase   :    Tuple			=							1E-2 if torch_device != "mps" else 3E-2
          assert np.abs(image_slice.flatten() - image_eps_slice.flatten()    ).max() < tolerance


@slow
@require_torch_gpu
class 		a__						(				unittest.TestCase						):
   def       __magic_name__      (      self    ):
          lowercase   :    Optional[Any]			=							"google/ddpm-cifar10-32"

          lowercase   :    Dict			=							UNetaDModel.from_pretrained(_a    )
          lowercase   :    str			=							DDPMScheduler.from_pretrained(_a    )

          lowercase   :    int			=							DDPMPipeline(unet=_a     ,   scheduler=_a    )
          ddpm.to(_a    )
          ddpm.set_progress_bar_config(disable=_a    )

          lowercase   :    Dict			=							torch.manual_seed(0    )
          lowercase   :    List[str]			=							ddpm(generator=_a     ,   output_type="numpy"    ).images

          lowercase   :    int			=							image[0, -3:, -3:, -1]

          assert image.shape == (1, 32, 32, 3)
          lowercase   :    Optional[Any]			=							np.array([0.4_2_0_0, 0.3_5_8_8, 0.1_9_3_9, 0.3_8_4_7, 0.3_3_8_2, 0.2_6_4_7, 0.4_1_5_5, 0.3_5_8_2, 0.3_3_8_5]    )
          assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1E-2


