

'''simple docstring'''







from typing import Dict, List, Optional, Tuple, 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, is_torch_available, is_torch_tensor, logging


if is_torch_available():
      import torch


__lowerCamelCase									=     logging.get_logger(__name__)


class        A__   (   _snake_case      ):
    lowercase						       =		["pixel_values"]



    def __init__(       self		,					UpperCamelCase__ = True		,					UpperCamelCase__ = None		,					UpperCamelCase__ = PILImageResampling.BILINEAR		,					UpperCamelCase__ = True		,					UpperCamelCase__ = None		,					UpperCamelCase__ = True		,					UpperCamelCase__ = 1 / 255		,					UpperCamelCase__ = True		,					UpperCamelCase__ = None		,					UpperCamelCase__ = None		,					**UpperCamelCase__		,					)    ->   None:





           '''simple docstring'''
           super().__init__(**UpperCamelCase__							)
           A_						       =   size if size is not None else {"""shortest_edge""": 256}
           A_						       =   get_size_dict(UpperCamelCase__		,					default_to_square=UpperCamelCase__							)
           A_						       =   crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
           A_						       =   get_size_dict(UpperCamelCase__		,					param_name="""crop_size"""							)
           A_						       =   do_resize
           A_						       =   size
           A_						       =   resample
           A_						       =   do_center_crop
           A_						       =   crop_size
           A_						       =   do_rescale
           A_						       =   rescale_factor
           A_						       =   do_normalize
           A_						       =   image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
           A_						       =   image_std if image_std is not None else IMAGENET_STANDARD_STD



    def        snake_case_      (       self		,					UpperCamelCase__		,					UpperCamelCase__		,					UpperCamelCase__ = PILImageResampling.BICUBIC		,					UpperCamelCase__ = None		,					**UpperCamelCase__		,					)    ->   np.ndarray:





           '''simple docstring'''
           A_						       =   get_size_dict(UpperCamelCase__		,					default_to_square=UpperCamelCase__							)
           if "shortest_edge" not in size:
                  raise ValueError(f'''The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}'''							)
           A_						       =   get_resize_output_image_size(UpperCamelCase__		,					size=size["""shortest_edge"""]		,					default_to_square=UpperCamelCase__							)
           return resize(UpperCamelCase__		,					size=UpperCamelCase__		,					resample=UpperCamelCase__		,					data_format=UpperCamelCase__		,					**UpperCamelCase__							)



    def        snake_case_      (       self		,					UpperCamelCase__		,					UpperCamelCase__		,					UpperCamelCase__ = None		,					**UpperCamelCase__		,					)    ->   np.ndarray:





           '''simple docstring'''
           A_						       =   get_size_dict(UpperCamelCase__							)
           if "height" not in size or "width" not in size:
                  raise ValueError(f'''The `size` parameter must contain the keys `height` and `width`. Got {size.keys()}'''							)
           return center_crop(UpperCamelCase__		,					size=(size["""height"""], size["""width"""])		,					data_format=UpperCamelCase__		,					**UpperCamelCase__							)



    def        snake_case_      (       self		,					UpperCamelCase__		,					UpperCamelCase__		,					UpperCamelCase__ = None		,					**UpperCamelCase__							)    ->   np.ndarray:





           '''simple docstring'''
           return rescale(UpperCamelCase__		,					scale=UpperCamelCase__		,					data_format=UpperCamelCase__		,					**UpperCamelCase__							)



    def        snake_case_      (       self		,					UpperCamelCase__		,					UpperCamelCase__		,					UpperCamelCase__		,					UpperCamelCase__ = None		,					**UpperCamelCase__		,					)    ->   np.ndarray:





           '''simple docstring'''
           return normalize(UpperCamelCase__		,					mean=UpperCamelCase__		,					std=UpperCamelCase__		,					data_format=UpperCamelCase__		,					**UpperCamelCase__							)



    def        snake_case_      (       self		,					UpperCamelCase__		,					UpperCamelCase__ = None		,					UpperCamelCase__ = None		,					UpperCamelCase__ = None		,					UpperCamelCase__ = None		,					UpperCamelCase__ = None		,					UpperCamelCase__ = None		,					UpperCamelCase__ = None		,					UpperCamelCase__ = None		,					UpperCamelCase__ = None		,					UpperCamelCase__ = None		,					UpperCamelCase__ = None		,					UpperCamelCase__ = ChannelDimension.FIRST		,					**UpperCamelCase__		,					)    ->   str:





           '''simple docstring'''
           A_						       =   do_resize if do_resize is not None else self.do_resize
           A_						       =   size if size is not None else self.size
           A_						       =   get_size_dict(UpperCamelCase__		,					default_to_square=UpperCamelCase__							)
           A_						       =   resample if resample is not None else self.resample
           A_						       =   do_center_crop if do_center_crop is not None else self.do_center_crop
           A_						       =   crop_size if crop_size is not None else self.crop_size
           A_						       =   get_size_dict(UpperCamelCase__		,					param_name="""crop_size"""							)
           A_						       =   do_rescale if do_rescale is not None else self.do_rescale
           A_						       =   rescale_factor if rescale_factor is not None else self.rescale_factor
           A_						       =   do_normalize if do_normalize is not None else self.do_normalize
           A_						       =   image_mean if image_mean is not None else self.image_mean
           A_						       =   image_std if image_std is not None else self.image_std

           A_						       =   make_list_of_images(UpperCamelCase__							)

           if not valid_images(UpperCamelCase__							):
                  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.
           A_						       =   [to_numpy_array(UpperCamelCase__							) for image in images]

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

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

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

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

           A_						       =   [to_channel_dimension_format(UpperCamelCase__		,					UpperCamelCase__							) for image in images]

           A_						       =   {"""pixel_values""": images}
           return BatchFeature(data=UpperCamelCase__		,					tensor_type=UpperCamelCase__							)







    def        snake_case_      (       self		,					UpperCamelCase__		,					UpperCamelCase__ = None							)    ->   List[str]:





           '''simple docstring'''
           A_						       =   outputs.logits

           # Resize logits and compute semantic segmentation maps
           if target_sizes is not None:
                  if len(UpperCamelCase__							) != len(UpperCamelCase__							):
                         raise ValueError(
                             """Make sure that you pass in as many target sizes as the batch dimension of the logits"""							)

                  if is_torch_tensor(UpperCamelCase__							):
                         A_						       =   target_sizes.numpy()

                  A_						       =   []

                  for idx in range(len(UpperCamelCase__							)							):
                         A_						       =   torch.nn.functional.interpolate(
                             logits[idx].unsqueeze(dim=0							)		,					size=target_sizes[idx]		,					mode="""bilinear"""		,					align_corners=UpperCamelCase__							)
                         A_						       =   resized_logits[0].argmax(dim=0							)
                         semantic_segmentation.append(UpperCamelCase__							)
           else:
                  A_						       =   logits.argmax(dim=1							)
                  A_						       =   [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0]							)]

           return semantic_segmentation



'''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      ):



    @property
    def        snake_case_      (       self							)    ->   int:





           '''simple docstring'''
           torch.manual_seed(0							)
           A_						       =   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        snake_case_      (       self							)    ->   str:





           '''simple docstring'''
           A_						       =   self.dummy_uncond_unet
           A_						       =   PNDMScheduler()

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

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

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

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

           assert image.shape == (1, 32, 32, 3)
           A_						       =   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      ):



    def        snake_case_      (       self							)    ->   Tuple:





           '''simple docstring'''
           A_						       =   """google/ddpm-cifar10-32"""

           A_						       =   UNetaDModel.from_pretrained(UpperCamelCase__							)
           A_						       =   PNDMScheduler()

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

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

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