
from typing import Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import get_image_size, pad, rescale, to_channel_dimension_format
from ...image_utils import ChannelDimension, ImageInput, make_list_of_images, to_numpy_array, valid_images
from ...utils import TensorType, logging


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






class   a_    (       lowerCAmelCase__				):



       """simple docstring"""
       __SCREAMING_SNAKE_CASE    : Optional[int]		=   ['''pixel_values''']






       def __init__(						self							,    _lowerCamelCase = True							,    _lowerCamelCase = 1 / 255							,    _lowerCamelCase = True							,    _lowerCamelCase = 8							,    **_lowerCamelCase							,    ) ->List[str]:
           super().__init__(**__lowerCAmelCase					)

           SCREAMING_SNAKE_CASE							:							Tuple								=      do_rescale
           SCREAMING_SNAKE_CASE							:							List[Any]								=      rescale_factor
           SCREAMING_SNAKE_CASE							:							str								=      do_pad
           SCREAMING_SNAKE_CASE							:							Any								=      pad_size






       def      __lowerCAmelCase			(						self							,    _lowerCamelCase							,    _lowerCamelCase							,    _lowerCamelCase = None							,    **_lowerCamelCase					) ->Tuple:
           return rescale(__lowerCAmelCase							,    scale=__lowerCAmelCase							,    data_format=__lowerCAmelCase							,    **__lowerCAmelCase					)






       def      __lowerCAmelCase			(						self							,    _lowerCamelCase							,    _lowerCamelCase							,    _lowerCamelCase = None					) ->Tuple:
           SCREAMING_SNAKE_CASE	,				SCREAMING_SNAKE_CASE							:							List[str]								=      get_image_size(__lowerCAmelCase					)
           SCREAMING_SNAKE_CASE							:							Optional[Any]								=      (old_height // size + 1) * size - old_height
           SCREAMING_SNAKE_CASE							:							Union[str, Any]								=      (old_width // size + 1) * size - old_width

           return pad(__lowerCAmelCase							,    ((0, pad_height), (0, pad_width))							,    mode='''symmetric'''							,    data_format=__lowerCAmelCase					)






       def      __lowerCAmelCase			(						self							,    _lowerCamelCase							,    _lowerCamelCase = None							,    _lowerCamelCase = None							,    _lowerCamelCase = None							,    _lowerCamelCase = None							,    _lowerCamelCase = None							,    _lowerCamelCase = ChannelDimension.FIRST							,    **_lowerCamelCase							,    ) ->Union[str, Any]:
           SCREAMING_SNAKE_CASE							:							Optional[int]								=      do_rescale if do_rescale is not None else self.do_rescale
           SCREAMING_SNAKE_CASE							:							Tuple								=      rescale_factor if rescale_factor is not None else self.rescale_factor
           SCREAMING_SNAKE_CASE							:							Optional[int]								=      do_pad if do_pad is not None else self.do_pad
           SCREAMING_SNAKE_CASE							:							Dict								=      pad_size if pad_size is not None else self.pad_size

           SCREAMING_SNAKE_CASE							:							Dict								=      make_list_of_images(__lowerCAmelCase					)

           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_rescale and rescale_factor is None:
               raise ValueError('''Rescale factor must be specified if do_rescale is True.'''					)

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

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

           if do_pad:
               SCREAMING_SNAKE_CASE							:							str								=      [self.pad(__lowerCAmelCase							,    size=__lowerCAmelCase					) for image in images]

           SCREAMING_SNAKE_CASE							:							Any								=      [to_channel_dimension_format(__lowerCAmelCase							,    __lowerCAmelCase					) for image in images]

           SCREAMING_SNAKE_CASE							:							List[str]								=      {'''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 				a__(					unittest.TestCase	):


			'''simple docstring'''






			@property
			def 							a_       (   self):




					"""simple docstring"""


					torch.manual_seed(0)
					lowerCAmelCase   					=  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 							a_       (   self):




					"""simple docstring"""


					lowerCAmelCase   					=  self.dummy_uncond_unet
					lowerCAmelCase   					=  PNDMScheduler()

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

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

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

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

					assert image.shape == (1, 32, 32, 3)
					lowerCAmelCase   					=  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 				a__(					unittest.TestCase	):


			'''simple docstring'''






			def 							a_       (   self):




					"""simple docstring"""


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

					lowerCAmelCase   					=  UNetaDModel.from_pretrained(__lowerCAmelCase)
					lowerCAmelCase   					=  PNDMScheduler()

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

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

					assert image.shape == (1, 32, 32, 3)
					lowerCAmelCase   					=  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




