





import unittest

import numpy as np
import torch

from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()







class     __lowerCAmelCase	(   unittest.TestCase):







      @property
      def        _lowercase  (   self							)       ->						str:



        '''simple docstring'''




        torch.manual_seed(0							)
        a__     : Tuple  =UNetaDModel(
            block_out_channels=(3_2, 6_4)		,				layers_per_block=2		,				sample_size=3_2		,				in_channels=3		,				out_channels=3		,				down_block_types=("DownBlock2D", "AttnDownBlock2D")		,				up_block_types=("AttnUpBlock2D", "UpBlock2D")		,				)
        return model







      def        _lowercase  (   self							)       ->						Tuple:



        '''simple docstring'''




        a__     : Union[str, Any]  =self.dummy_uncond_unet
        a__     : Optional[int]  =ScoreSdeVeScheduler()

        a__     : Optional[Any]  =ScoreSdeVePipeline(unet=lowerCAmelCase__		,				scheduler=lowerCAmelCase__							)
        sde_ve.to(lowerCAmelCase__							)
        sde_ve.set_progress_bar_config(disable=lowerCAmelCase__							)

        a__     : Dict  =torch.manual_seed(0							)
        a__     : Optional[Any]  =sde_ve(num_inference_steps=2		,				output_type="numpy"		,				generator=lowerCAmelCase__							).images

        a__     : Optional[int]  =torch.manual_seed(0							)
        a__     : List[Any]  =sde_ve(num_inference_steps=2		,				output_type="numpy"		,				generator=lowerCAmelCase__		,				return_dict=lowerCAmelCase__							)[
            0
        ]

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

        assert image.shape == (1, 3_2, 3_2, 3)
        a__     : Optional[int]  =np.array([0.0, 1.0, 0.0, 0.0, 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     __lowerCAmelCase	(   unittest.TestCase):







      def        _lowercase  (   self							)       ->						Any:



        '''simple docstring'''




        a__     : Optional[Any]  ="google/ncsnpp-church-256"
        a__     : str  =UNetaDModel.from_pretrained(lowerCAmelCase__							)

        a__     : int  =ScoreSdeVeScheduler.from_pretrained(lowerCAmelCase__							)

        a__     : Optional[int]  =ScoreSdeVePipeline(unet=lowerCAmelCase__		,				scheduler=lowerCAmelCase__							)
        sde_ve.to(lowerCAmelCase__							)
        sde_ve.set_progress_bar_config(disable=lowerCAmelCase__							)

        a__     : int  =torch.manual_seed(0							)
        a__     : Union[str, Any]  =sde_ve(num_inference_steps=1_0		,				output_type="numpy"		,				generator=lowerCAmelCase__							).images

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

        assert image.shape == (1, 2_5_6, 2_5_6, 3)

        a__     : str  =np.array([0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0]							)

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













from typing import Optional

from .. import Features, NamedSplit
from ..packaged_modules.text.text import Text
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader







class     __lowerCAmelCase	(   UpperCamelCase__):







      def __init__(   self		,				lowerCAmelCase__		,				lowerCAmelCase__ = None		,				lowerCAmelCase__ = None		,				lowerCAmelCase__ = None		,				lowerCAmelCase__ = False		,				lowerCAmelCase__ = False		,				lowerCAmelCase__ = None		,				**lowerCAmelCase__		,				)       ->						int:



        '''simple docstring'''




        super().__init__(
            lowerCAmelCase__		,				split=lowerCAmelCase__		,				features=lowerCAmelCase__		,				cache_dir=lowerCAmelCase__		,				keep_in_memory=lowerCAmelCase__		,				streaming=lowerCAmelCase__		,				num_proc=lowerCAmelCase__		,				**lowerCAmelCase__		,				)
        a__     : Tuple  =path_or_paths if isinstance(lowerCAmelCase__		,				lowerCAmelCase__							) else {self.split: path_or_paths}
        a__     : List[str]  =Text(
            cache_dir=lowerCAmelCase__		,				data_files=lowerCAmelCase__		,				features=lowerCAmelCase__		,				**lowerCAmelCase__		,				)







      def        _lowercase  (   self							)       ->						List[Any]:



        '''simple docstring'''




        if self.streaming:
          a__     : str  =self.builder.as_streaming_dataset(split=self.split							)
        # Build regular (map-style) dataset
        else:
          a__     : Dict  =None
          a__     : Optional[Any]  =None
          a__     : Union[str, Any]  =None
          a__     : Tuple  =None

          self.builder.download_and_prepare(
              download_config=lowerCAmelCase__		,				download_mode=lowerCAmelCase__		,				verification_mode=lowerCAmelCase__		,				base_path=lowerCAmelCase__		,				num_proc=self.num_proc		,				)
          a__     : Tuple  =self.builder.as_dataset(
              split=self.split		,				verification_mode=lowerCAmelCase__		,				in_memory=self.keep_in_memory							)
        return dataset






