






'''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   A__						(      self      )  ->      List[str]:



        """simple docstring"""






        torch.manual_seed(0      )
        UpperCamelCase				  =					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      )  ->      Any:



        """simple docstring"""






        UpperCamelCase				  =					self.dummy_uncond_unet
        UpperCamelCase				  =					PNDMScheduler()

        UpperCamelCase				  =					PNDMPipeline(unet=A__			,		scheduler=A__      )
        pndm.to(A__      )
        pndm.set_progress_bar_config(disable=A__      )

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

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

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

        assert image.shape == (1, 32, 32, 3)
        UpperCamelCase				  =					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   A__						(      self      )  ->      List[Any]:



        """simple docstring"""






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

        UpperCamelCase				  =					UNetaDModel.from_pretrained(A__      )
        UpperCamelCase				  =					PNDMScheduler()

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

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

        assert image.shape == (1, 32, 32, 3)
        UpperCamelCase				  =					np.array([0.1_5_6_4, 0.1_4_6_4_5, 0.1_4_0_6, 0.1_4_7_1_5, 0.1_2_4_2_5, 0.1_4_0_4_5, 0.1_3_1_1_5, 0.1_2_1_7_5, 0.1_2_5]      )

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



import os
from collections import namedtuple

import pytest

from datasets import ClassLabel, Features, Sequence, Value
from datasets.commands.test import TestCommand
from datasets.info import DatasetInfo, DatasetInfosDict


A_       : Any         =			namedtuple(
    '_TestCommandArgs',
    [
        'dataset',
        'name',
        'cache_dir',
        'data_dir',
        'all_configs',
        'save_infos',
        'ignore_verifications',
        'force_redownload',
        'clear_cache',
    ],
    defaults=[None, None, None, False, False, False, False, False],
)

def 						UpperCamelCase					(lowercase_: Any    ,      lowercase_: List[str]					)     ->      Optional[int]:
	return (abs(source - target					) / target) < 0.01


@pytest.mark.integration
def 						UpperCamelCase					(lowercase_: str					)     ->      str:
	A__							:    List[str]        =		_TestCommandArgs(dataset=lowercase_    ,      all_configs=lowercase_    ,      save_infos=lowercase_					)
	A__							:    int        =		TestCommand(*lowercase_					)
	test_command.run()
	A__							:    Optional[Any]        =		os.path.join(lowercase_    ,      """README.md"""					)
	assert os.path.exists(lowercase_					)
	A__							:    Dict        =		DatasetInfosDict.from_directory(lowercase_					)
	A__							:    str        =		DatasetInfosDict(
	    {
	        """default""": DatasetInfo(
	            features=Features(
	                {
	                    """tokens""": Sequence(Value("""string"""					)					),
	                    """ner_tags""": Sequence(
	                        ClassLabel(names=["""O""", """B-PER""", """I-PER""", """B-ORG""", """I-ORG""", """B-LOC""", """I-LOC"""]					)					),
	                    """langs""": Sequence(Value("""string"""					)					),
	                    """spans""": Sequence(Value("""string"""					)					),
	                }					)    ,      splits=[
	                {
	                    """name""": """train""",
	                    """num_bytes""": 2351563,
	                    """num_examples""": 10000,
	                },
	                {
	                    """name""": """validation""",
	                    """num_bytes""": 238418,
	                    """num_examples""": 1000,
	                },
	            ]    ,      download_size=3940680    ,      dataset_size=2589981    ,      )
	    }					)
	assert dataset_infos.keys() == expected_dataset_infos.keys()
	for key in DatasetInfo._INCLUDED_INFO_IN_YAML:
		A__   ,   A__							:    Optional[Any]        =		getattr(dataset_infos["""default"""]    ,      lowercase_					), getattr(expected_dataset_infos["""default"""]    ,      lowercase_					)
		if key == "num_bytes":
			assert is_apercent_close(lowercase_    ,      lowercase_					)
		elif key == "splits":
			assert list(lowercase_					) == list(lowercase_					)
			for split in result:
				assert result[split].name == expected[split].name
				assert result[split].num_examples == expected[split].num_examples
				assert is_apercent_close(result[split].num_bytes    ,      expected[split].num_bytes					)
		else:
			result == expected



