



'''simple docstring'''






import unittest

import torch

from diffusers import VQModel
from diffusers.utils import floats_tensor, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin


enable_full_determinism()

class        SCREAMING_SNAKE_CASE(							A__							,	A__							,	unittest.TestCase    ):







	"""simple docstring"""






	lowerCamelCase__    				= VQModel
	lowerCamelCase__    				= """sample"""







	@property
	def   A			(		self    :  Union[str, Any]	,    __snake_case    :  int=(32, 32)	)    ->	Any:
								UpperCAmelCase				: Tuple											=	4
								UpperCAmelCase				: Any											=	3

								UpperCAmelCase				: str											=	floats_tensor((batch_size, num_channels) + sizes	).to(__snake_case	)

								return {"sample": image}







	@property
	def   A			(		self    :  str	)    ->	Union[str, Any]:
								return (3, 32, 32)







	@property
	def   A			(		self    :  List[Any]	)    ->	Union[str, Any]:
								return (3, 32, 32)







	def   A			(		self    :  Any	)    ->	Dict:
								UpperCAmelCase				: List[str]											=	{
								    '''block_out_channels''': [32, 64],
								    '''in_channels''': 3,
								    '''out_channels''': 3,
								    '''down_block_types''': ['''DownEncoderBlock2D''', '''DownEncoderBlock2D'''],
								    '''up_block_types''': ['''UpDecoderBlock2D''', '''UpDecoderBlock2D'''],
								    '''latent_channels''': 3,
								}
								UpperCAmelCase				: Any											=	self.dummy_input
								return init_dict, inputs_dict







	def   A			(		self    :  int	)    ->	Dict:
								pass







	def   A			(		self    :  Dict	)    ->	Optional[int]:
								pass







	def   A			(		self    :  str	)    ->	Optional[Any]:
								UpperCAmelCase							,	UpperCAmelCase				: Union[str, Any]											=	VQModel.from_pretrained('''fusing/vqgan-dummy'''	,    output_loading_info=__snake_case	)
								self.assertIsNotNone(__snake_case	)
								self.assertEqual(len(loading_info['''missing_keys''']	)	,    0	)

								model.to(__snake_case	)
								UpperCAmelCase				: Any											=	model(**self.dummy_input	)

								assert image is not None, "Make sure output is not None"







	def   A			(		self    :  List[str]	)    ->	Any:
								UpperCAmelCase				: int											=	VQModel.from_pretrained('''fusing/vqgan-dummy'''	)
								model.to(__snake_case	).eval()

								torch.manual_seed(0	)
								if torch.cuda.is_available():
															torch.cuda.manual_seed_all(0	)

								UpperCAmelCase				: Tuple											=	torch.randn(1	,    model.config.in_channels	,    model.config.sample_size	,    model.config.sample_size	)
								UpperCAmelCase				: Any											=	image.to(__snake_case	)
								with torch.no_grad():
															UpperCAmelCase				: Dict											=	model(__snake_case	).sample

								UpperCAmelCase				: Optional[int]											=	output[0, -1, -3:, -3:].flatten().cpu()
								# fmt: off
								UpperCAmelCase				: Dict											=	torch.tensor([-0.01_53, -0.40_44, -0.18_80, -0.51_61, -0.24_18, -0.40_72, -0.16_12, -0.06_33, -0.01_43]	)
								# fmt: on
								self.assertTrue(torch.allclose(__snake_case	,    __snake_case	,    atol=1E-3	)	)










'''simple docstring'''






import pytest

from datasets.splits import SplitDict, SplitInfo
from datasets.utils.py_utils import asdict


@pytest.mark.parametrize(
    '''split_dict'''     ,   [
        SplitDict(),
        SplitDict({'''train''': SplitInfo(name='''train'''     ,   num_bytes=1337     ,   num_examples=42     ,   dataset_name='''my_dataset'''						)}						),
        SplitDict({'''train''': SplitInfo(name='''train'''     ,   num_bytes=1337     ,   num_examples=42						)}						),
        SplitDict({'''train''': SplitInfo()}						),
    ]     ,   )
def 				snake_case_  (      _lowerCAmelCase						:		SplitDict						)   ->     List[Any]:
							UpperCAmelCase				: str											=	split_dict._to_yaml_list()
							assert len(_lowerCAmelCase						) == len(_lowerCAmelCase						)
							UpperCAmelCase				: Optional[int]											=	SplitDict._from_yaml_list(_lowerCAmelCase						)
							for split_name, split_info in split_dict.items():
														# dataset_name field is deprecated, and is therefore not part of the YAML dump
														UpperCAmelCase				: Optional[Any]											=	None
														# the split name of split_dict takes over the name of the split info object
														UpperCAmelCase				: Dict											=	split_name
							assert split_dict == reloaded




@pytest.mark.parametrize(
    '''split_info'''     ,   [SplitInfo(), SplitInfo(dataset_name=_lowerCAmelCase						), SplitInfo(dataset_name='''my_dataset'''						)]						)
def 				snake_case_  (      _lowerCAmelCase						:		List[Any]						)   ->     Optional[Any]:
							# For backward compatibility, we need asdict(split_dict) to return split info dictrionaries with the "dataset_name"
							# field even if it's deprecated. This way old versionso of `datasets` can still reload dataset_infos.json files
							UpperCAmelCase				: Any											=	asdict(SplitDict({'''train''': split_info}						)						)
							assert "dataset_name" in split_dict_asdict["train"]
							assert split_dict_asdict["train"]["dataset_name"] == split_info.dataset_name





