






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


__UpperCAmelCase								=						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      lowercase__      (     __snake_case    :							int     ,       __snake_case    :							Union[str, Any]  ):


					'''simple docstring'''


					return (abs(source - target  ) / target) < 0.01






@pytest.mark.integration
def      lowercase__      (     __snake_case    :							Dict  ):


					'''simple docstring'''


					UpperCAmelCase_    :      Tuple            =						_TestCommandArgs(dataset=__snake_case     ,       all_configs=__snake_case     ,       save_infos=__snake_case  )
					UpperCAmelCase_    :      Optional[int]            =						TestCommand(*__snake_case  )
					test_command.run()
					UpperCAmelCase_    :      Optional[int]            =						os.path.join(__snake_case     ,       'README.md'  )
					assert os.path.exists(__snake_case  )
					UpperCAmelCase_    :      str            =						DatasetInfosDict.from_directory(__snake_case  )
					UpperCAmelCase_    :      List[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': 2_351_563,
					                    'num_examples': 10_000,
					                },
					                {
					                    'name': 'validation',
					                    'num_bytes': 238_418,
					                    'num_examples': 1_000,
					                },
					            ]     ,       download_size=3_940_680     ,       dataset_size=2_589_981     ,       )
					    }  )
					assert dataset_infos.keys() == expected_dataset_infos.keys()
					for key in DatasetInfo._INCLUDED_INFO_IN_YAML:
										UpperCAmelCase_       ,  UpperCAmelCase_    :      Optional[Any]            =						getattr(dataset_infos['default']     ,       __snake_case  ), getattr(expected_dataset_infos['default']     ,       __snake_case  )
										if key == "num_bytes":
															assert is_apercent_close(__snake_case     ,       __snake_case  )
										elif key == "splits":
															assert list(__snake_case  ) == list(__snake_case  )
															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














import random
import unittest

import torch

from diffusers import IFImgaImgSuperResolutionPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device

from ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin



@skip_mps
class   lowerCamelCase      (_snake_case ,  _snake_case ,  unittest.TestCase			):







		'''simple docstring'''



		_snake_case    :    Union[str, Any]       						=  IFImgaImgSuperResolutionPipeline
		_snake_case    :    Union[str, Any]       						=  TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'''width''', '''height'''}
		_snake_case    :    List[Any]       						=  TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({'''original_image'''}			)
		_snake_case    :    List[str]       						=  PipelineTesterMixin.required_optional_params - {'''latents'''}



		def 					__UpperCAmelCase			(							self   )	->							Optional[Any]:
							return self._get_superresolution_dummy_components()



		def 					__UpperCAmelCase			(							self				,		_UpperCamelCase				,		_UpperCamelCase=0   )	->							Any:
							if str(_UpperCamelCase   ).startswith('mps'   ):
												UpperCAmelCase_    :      List[Any]            =						torch.manual_seed(_UpperCamelCase   )
							else:
												UpperCAmelCase_    :      int            =						torch.Generator(device=_UpperCamelCase   ).manual_seed(_UpperCamelCase   )

							UpperCAmelCase_    :      List[Any]            =						floats_tensor((1, 3, 3_2, 3_2)				,		rng=random.Random(_UpperCamelCase   )   ).to(_UpperCamelCase   )
							UpperCAmelCase_    :      Dict            =						floats_tensor((1, 3, 1_6, 1_6)				,		rng=random.Random(_UpperCamelCase   )   ).to(_UpperCamelCase   )

							UpperCAmelCase_    :      Tuple            =						{
							    'prompt': 'A painting of a squirrel eating a burger',
							    'image': image,
							    'original_image': original_image,
							    'generator': generator,
							    'num_inference_steps': 2,
							    'output_type': 'numpy',
							}

							return inputs



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



		def 					__UpperCAmelCase			(							self   )	->							Dict:
							self._test_save_load_optional_components()



		@unittest.skipIf(torch_device != 'cuda'				,		reason='float16 requires CUDA'   )
		def 					__UpperCAmelCase			(							self   )	->							str:
							# Due to non-determinism in save load of the hf-internal-testing/tiny-random-t5 text encoder
							super().test_save_load_floataa(expected_max_diff=1E-1   )



		def 					__UpperCAmelCase			(							self   )	->							List[Any]:
							self._test_attention_slicing_forward_pass(expected_max_diff=1E-2   )



		def 					__UpperCAmelCase			(							self   )	->							Union[str, Any]:
							self._test_save_load_local()



		def 					__UpperCAmelCase			(							self   )	->							Dict:
							self._test_inference_batch_single_identical(
							    expected_max_diff=1E-2				,		)






