





'''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      lowercase	(					self       :    Dict    ):
      torch.manual_seed(0    )
      _UpperCAmelCase     							=   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       :    List[Any]    ):
      _UpperCAmelCase     							=   self.dummy_uncond_unet
      _UpperCAmelCase     							=   PNDMScheduler()

      _UpperCAmelCase     							=   PNDMPipeline(unet=snake_case_	,			scheduler=snake_case_    )
      pndm.to(snake_case_    )
      pndm.set_progress_bar_config(disable=snake_case_    )

      _UpperCAmelCase     							=   torch.manual_seed(0    )
      _UpperCAmelCase     							=   pndm(generator=snake_case_	,			num_inference_steps=2_0	,			output_type="numpy"    ).images

      _UpperCAmelCase     							=   torch.manual_seed(0    )
      _UpperCAmelCase     							=   pndm(generator=snake_case_	,			num_inference_steps=2_0	,			output_type="numpy"	,			return_dict=snake_case_    )[0]

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

      assert image.shape == (1, 3_2, 3_2, 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      lowercase	(					self       :    List[str]    ):
      _UpperCAmelCase     							=   "google/ddpm-cifar10-32"

      _UpperCAmelCase     							=   UNetaDModel.from_pretrained(snake_case_    )
      _UpperCAmelCase     							=   PNDMScheduler()

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

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

      assert image.shape == (1, 3_2, 3_2, 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











'''simple docstring'''




import os

import pytest
import yaml

from datasets.features.features import Features, Value
from datasets.info import DatasetInfo, DatasetInfosDict
@pytest.mark.parametrize(
    "files"			,     [
        ["full:README.md", "dataset_infos.json"],
        ["empty:README.md", "dataset_infos.json"],
        ["dataset_infos.json"],
        ["full:README.md"],
    ]			,     )
def 	UpperCAmelCase_							(					__lowercase							:      Any			,     __lowercase							:      int       )		->       int:


     '''simple docstring'''



     _UpperCAmelCase     							=   tmp_path_factory.mktemp("dset_infos_dir"       )
     if "full:README.md" in files:
          with open(dataset_infos_dir / "README.md"			,     "w"       ) as f:
               f.write("---\ndataset_info:\n  dataset_size: 42\n---"       )
     if "empty:README.md" in files:
          with open(dataset_infos_dir / "README.md"			,     "w"       ) as f:
               f.write(""       )
    # we want to support dataset_infos.json for backward compatibility
     if "dataset_infos.json" in files:
          with open(dataset_infos_dir / "dataset_infos.json"			,     "w"       ) as f:
               f.write("{\"default\": {\"dataset_size\": 42}}"       )
     _UpperCAmelCase     							=   DatasetInfosDict.from_directory(__lowercase       )
     assert dataset_infos
     assert dataset_infos["default"].dataset_size == 42
@pytest.mark.parametrize(
    "dataset_info"			,     [
        DatasetInfo(),
        DatasetInfo(
            description="foo"			,     features=Features({"a": Value("int32"       )}       )			,     builder_name="builder"			,     config_name="config"			,     version="1.0.0"			,     splits=[{"name": "train"}]			,     download_size=42			,     ),
    ]			,     )
def 	UpperCAmelCase_							(					__lowercase							:      Tuple			,     __lowercase							:      DatasetInfo       )		->       Any:


     '''simple docstring'''



     _UpperCAmelCase     							=   str(__lowercase       )
     dataset_info.write_to_directory(__lowercase       )
     _UpperCAmelCase     							=   DatasetInfo.from_directory(__lowercase       )
     assert dataset_info == reloaded
     assert os.path.exists(os.path.join(__lowercase			,     "dataset_info.json"       )       )
def 	UpperCAmelCase_							(					)		->       Optional[Any]:


     '''simple docstring'''



     _UpperCAmelCase     							=   DatasetInfo(
         description="foo"			,     citation="bar"			,     homepage="https://foo.bar"			,     license="CC0"			,     features=Features({"a": Value("int32"       )}       )			,     post_processed={}			,     supervised_keys=()			,     task_templates=[]			,     builder_name="builder"			,     config_name="config"			,     version="1.0.0"			,     splits=[{"name": "train", "num_examples": 42}]			,     download_checksums={}			,     download_size=1337			,     post_processing_size=442			,     dataset_size=1234			,     size_in_bytes=1337 + 442 + 1234			,     )
     _UpperCAmelCase     							=   dataset_info._to_yaml_dict()
     assert sorted(__lowercase       ) == sorted(DatasetInfo._INCLUDED_INFO_IN_YAML       )
     for key in DatasetInfo._INCLUDED_INFO_IN_YAML:
          assert key in dataset_info_yaml_dict
          assert isinstance(dataset_info_yaml_dict[key]			,     (list, dict, int, str)       )
     _UpperCAmelCase     							=   yaml.safe_dump(__lowercase       )
     _UpperCAmelCase     							=   yaml.safe_load(__lowercase       )
     assert dataset_info_yaml_dict == reloaded
def 	UpperCAmelCase_							(					)		->       Optional[int]:


     '''simple docstring'''



     _UpperCAmelCase     							=   DatasetInfo()
     _UpperCAmelCase     							=   dataset_info._to_yaml_dict()
     assert dataset_info_yaml_dict == {}






@pytest.mark.parametrize(
    "dataset_infos_dict"			,     [
        DatasetInfosDict(),
        DatasetInfosDict({"default": DatasetInfo()}       ),
        DatasetInfosDict({"my_config_name": DatasetInfo()}       ),
        DatasetInfosDict(
            {
                "default": DatasetInfo(
                    description="foo"			,     features=Features({"a": Value("int32"       )}       )			,     builder_name="builder"			,     config_name="config"			,     version="1.0.0"			,     splits=[{"name": "train"}]			,     download_size=42			,     )
            }       ),
        DatasetInfosDict(
            {
                "v1": DatasetInfo(dataset_size=42       ),
                "v2": DatasetInfo(dataset_size=1337       ),
            }       ),
    ]			,     )
def 	UpperCAmelCase_							(					__lowercase							:      int			,     __lowercase							:      DatasetInfosDict       )		->       Dict:


     '''simple docstring'''



     _UpperCAmelCase     							=   str(__lowercase       )
     dataset_infos_dict.write_to_directory(__lowercase       )
     _UpperCAmelCase     							=   DatasetInfosDict.from_directory(__lowercase       )

     # the config_name of the dataset_infos_dict take over the attribute
     for config_name, dataset_info in dataset_infos_dict.items():
          _UpperCAmelCase     							=   config_name
          # the yaml representation doesn't include fields like description or citation
          # so we just test that we can recover what we can from the yaml
          _UpperCAmelCase     							=   DatasetInfo._from_yaml_dict(dataset_info._to_yaml_dict()       )
     assert dataset_infos_dict == reloaded

     if dataset_infos_dict:
          assert os.path.exists(os.path.join(__lowercase			,     "README.md"       )       )




