





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  __UpperCamelCase(				self     ):
       '''simple docstring'''




       torch.manual_seed(0     )
       UpperCamelCase			:      str 			=     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     ):
       '''simple docstring'''




       UpperCamelCase			:      Any 			=     self.dummy_uncond_unet
       UpperCamelCase			:      Any 			=     PNDMScheduler()

       UpperCamelCase			:      int 			=     PNDMPipeline(unet=A_ ,							scheduler=A_     )
       pndm.to(A_     )
       pndm.set_progress_bar_config(disable=A_     )

       UpperCamelCase			:      Tuple 			=     torch.manual_seed(0     )
       UpperCamelCase			:      Any 			=     pndm(generator=A_ ,							num_inference_steps=20 ,							output_type="numpy"     ).images

       UpperCamelCase			:      Any 			=     torch.manual_seed(0     )
       UpperCamelCase			:      List[Any] 			=     pndm(generator=A_ ,							num_inference_steps=20 ,							output_type="numpy" ,							return_dict=A_     )[0]

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

       assert image.shape == (1, 32, 32, 3)
       UpperCamelCase			:      Tuple 			=     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  __UpperCamelCase(				self     ):
       '''simple docstring'''




       UpperCamelCase			:      Optional[Any] 			=     "google/ddpm-cifar10-32"

       UpperCamelCase			:      Tuple 			=     UNetaDModel.from_pretrained(A_     )
       UpperCamelCase			:      str 			=     PNDMScheduler()

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

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

       assert image.shape == (1, 32, 32, 3)
       UpperCamelCase			:      int 			=     np.array([0.15_64, 0.1_46_45, 0.14_06, 0.1_47_15, 0.1_24_25, 0.1_40_45, 0.1_31_15, 0.1_21_75, 0.1_25]     )

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







from typing import Callable, Optional

from .. import Features
from ..packaged_modules.generator.generator import Generator
from .abc import AbstractDatasetInputStream



class       A__     ( __snake_case    ):





      def __init__(				self ,							A_ ,							A_ = None ,							A_ = None ,							A_ = False ,							A_ = False ,							A_ = None ,							A_ = None ,							**A_ ,							):
       '''simple docstring'''




       super().__init__(
           features=A_ ,							cache_dir=A_ ,							keep_in_memory=A_ ,							streaming=A_ ,							num_proc=A_ ,							**A_ ,							)
       UpperCamelCase			:      Optional[int] 			=     Generator(
           cache_dir=A_ ,							features=A_ ,							generator=A_ ,							gen_kwargs=A_ ,							**A_ ,							)





      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       if self.streaming:
        UpperCamelCase			:      Optional[Any] 			=     self.builder.as_streaming_dataset(split="train"     )
       # Build regular (map-style) dataset
       else:
        UpperCamelCase			:      Union[str, Any] 			=     None
        UpperCamelCase			:      Union[str, Any] 			=     None
        UpperCamelCase			:      List[Any] 			=     None
        UpperCamelCase			:      List[str] 			=     None

        self.builder.download_and_prepare(
            download_config=A_ ,							download_mode=A_ ,							verification_mode=A_ ,							base_path=A_ ,							num_proc=self.num_proc ,							)
        UpperCamelCase			:      int 			=     self.builder.as_dataset(
            split="train" ,							verification_mode=A_ ,							in_memory=self.keep_in_memory     )
       return dataset
