



"""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     (		A	:						str     ,				A	:						Optional[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(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 	UpperCAmelCase     (		A	:						str     ,				A	:						DatasetInfo       ):

		'''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 	UpperCAmelCase     (		):

		'''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(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 	UpperCAmelCase     (		):

		'''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     (		A	:						Tuple     ,				A	:						DatasetInfosDict       ):

		'''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'       )       )







"""simple docstring"""





import gc
import unittest

import numpy as np
import torch

from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, DPMSolverMultistepScheduler, TransformeraDModel
from diffusers.utils import is_xformers_available, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS,
    CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()




class 			lowercase__			(      A,					unittest.TestCase    ):
							'''simple docstring'''







							_UpperCAmelCase      				=					DiTPipeline
							_UpperCAmelCase      				=					CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS
							_UpperCAmelCase      				=					PipelineTesterMixin.required_optional_params - {
							    '''latents''',
							    '''num_images_per_prompt''',
							    '''callback''',
							    '''callback_steps''',
							}
							_UpperCAmelCase      				=					CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS
							_UpperCAmelCase      				=					False






							def        lowerCamelCase_   (  self						)			->							str:
									torch.manual_seed(0						)
									_UpperCAmelCase							 =		TransformeraDModel(
									    sample_size=16	,       num_layers=2	,       patch_size=4	,       attention_head_dim=8	,       num_attention_heads=2	,       in_channels=4	,       out_channels=8	,       attention_bias=snake_case	,       activation_fn='gelu-approximate'	,       num_embeds_ada_norm=1000	,       norm_type='ada_norm_zero'	,       norm_elementwise_affine=snake_case	,       )
									_UpperCAmelCase							 =		AutoencoderKL()
									_UpperCAmelCase							 =		DDIMScheduler()
									_UpperCAmelCase							 =		{'transformer': transformer.eval(), 'vae': vae.eval(), 'scheduler': scheduler}
									return components






							def        lowerCamelCase_   (  self	,       snake_case	,       snake_case=0						)			->							Optional[Any]:
									if str(snake_case						).startswith('mps'						):
											_UpperCAmelCase							 =		torch.manual_seed(snake_case						)
									else:
											_UpperCAmelCase							 =		torch.Generator(device=snake_case						).manual_seed(snake_case						)
									_UpperCAmelCase							 =		{
									    'class_labels': [1],
									    'generator': generator,
									    'num_inference_steps': 2,
									    'output_type': 'numpy',
									}
									return inputs






							def        lowerCamelCase_   (  self						)			->							List[Any]:
									_UpperCAmelCase							 =		'cpu'

									_UpperCAmelCase							 =		self.get_dummy_components()
									_UpperCAmelCase							 =		self.pipeline_class(**snake_case						)
									pipe.to(snake_case						)
									pipe.set_progress_bar_config(disable=snake_case						)

									_UpperCAmelCase							 =		self.get_dummy_inputs(snake_case						)
									_UpperCAmelCase							 =		pipe(**snake_case						).images
									_UpperCAmelCase							 =		image[0, -3:, -3:, -1]

									self.assertEqual(image.shape	,       (1, 16, 16, 3)						)
									_UpperCAmelCase							 =		np.array([0.2946, 0.6601, 0.4329, 0.3296, 0.4144, 0.5319, 0.7273, 0.5013, 0.4457]						)
									_UpperCAmelCase							 =		np.abs(image_slice.flatten() - expected_slice						).max()
									self.assertLessEqual(snake_case	,       1E-3						)






							def        lowerCamelCase_   (  self						)			->							Any:
									self._test_inference_batch_single_identical(relax_max_difference=snake_case	,       expected_max_diff=1E-3						)






							@unittest.skipIf(
							    torch_device != 'cuda' or not is_xformers_available()	,       reason='XFormers attention is only available with CUDA and `xformers` installed'	,       )
							def        lowerCamelCase_   (  self						)			->							Optional[int]:
									self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3						)







@require_torch_gpu
@slow
class 			lowercase__			(      unittest.TestCase    ):
							'''simple docstring'''







							def        lowerCamelCase_   (  self						)			->							int:
									super().tearDown()
									gc.collect()
									torch.cuda.empty_cache()






							def        lowerCamelCase_   (  self						)			->							int:
									_UpperCAmelCase							 =		torch.manual_seed(0						)

									_UpperCAmelCase							 =		DiTPipeline.from_pretrained('facebook/DiT-XL-2-256'						)
									pipe.to('cuda'						)

									_UpperCAmelCase							 =		['vase', 'umbrella', 'white shark', 'white wolf']
									_UpperCAmelCase							 =		pipe.get_label_ids(snake_case						)

									_UpperCAmelCase							 =		pipe(snake_case	,       generator=snake_case	,       num_inference_steps=40	,       output_type='np'						).images

									for word, image in zip(snake_case	,       snake_case						):
											_UpperCAmelCase							 =		load_numpy(
											    f'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/dit/{word}.npy'						)
											assert np.abs((expected_image - image).max()						) < 1E-2






							def        lowerCamelCase_   (  self						)			->							int:
									_UpperCAmelCase							 =		DiTPipeline.from_pretrained('facebook/DiT-XL-2-512'						)
									_UpperCAmelCase							 =		DPMSolverMultistepScheduler.from_config(pipe.scheduler.config						)
									pipe.to('cuda'						)

									_UpperCAmelCase							 =		['vase', 'umbrella']
									_UpperCAmelCase							 =		pipe.get_label_ids(snake_case						)

									_UpperCAmelCase							 =		torch.manual_seed(0						)
									_UpperCAmelCase							 =		pipe(snake_case	,       generator=snake_case	,       num_inference_steps=25	,       output_type='np'						).images

									for word, image in zip(snake_case	,       snake_case						):
											_UpperCAmelCase							 =		load_numpy(
											    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
											    f'/dit/{word}_512.npy'						)

											assert np.abs((expected_image - image).max()						) < 1E-1


