



'''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 							__lowerCamelCase							(  A__       ,		A__       )			->      List[str]:






   """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(A__       )
   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 							__lowerCamelCase							(  A__       ,		A__       )			->      Dict:






   """simple docstring"""
   UpperCamelCase      							=    str(A__       )
   dataset_info.write_to_directory(A__       )
   UpperCamelCase      							=    DatasetInfo.from_directory(A__       )
   assert dataset_info == reloaded
   assert os.path.exists(os.path.join(A__       ,		'dataset_info.json'       )       )







def 							__lowerCamelCase							(  )			->      Tuple:






   """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=1_337       ,		post_processing_size=442       ,		dataset_size=1_234       ,		size_in_bytes=1_337 + 442 + 1_234       ,		)
   UpperCamelCase      							=    dataset_info._to_yaml_dict()
   assert sorted(A__       ) == 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(A__       )
   UpperCamelCase      							=    yaml.safe_load(A__       )
   assert dataset_info_yaml_dict == reloaded







def 							__lowerCamelCase							(  )			->      Any:






   """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=1_337       ),
            }       ),
    ]       ,		)
def 							__lowerCamelCase							(  A__       ,		A__       )			->      Tuple:






   """simple docstring"""
   UpperCamelCase      							=    str(A__       )
   dataset_infos_dict.write_to_directory(A__       )
   UpperCamelCase      							=    DatasetInfosDict.from_directory(A__       )

   # 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(A__       ,		'README.md'       )       )



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 							lowerCAmelCase__   ( unittest.TestCase						):

						@property
						def 			A_					(      self					)       ->   int:


								'''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 							lowerCAmelCase__   ( unittest.TestCase						):

						def 			A_					(      self					)       ->   Optional[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([0.1564, 0.1_4645, 0.1406, 0.1_4715, 0.1_2425, 0.1_4045, 0.1_3115, 0.1_2175, 0.125]					)

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



