


'''simple docstring'''



from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


A__		:					Optional[Any]												=  logging.get_logger(__name__)

A__		:					Dict												=  {
    """facebook/convnextv2-tiny-1k-224""": """https://huggingface.co/facebook/convnextv2-tiny-1k-224/resolve/main/config.json""",
}





class    UpperCAmelCase_  (_UpperCAmelCase					,		_UpperCAmelCase  ):





    """simple docstring"""






    lowerCamelCase  :					Union[str, Any]							     =			'convnextv2'







    def __init__(							self			,     SCREAMING_SNAKE_CASE_=3			,     SCREAMING_SNAKE_CASE_=4			,     SCREAMING_SNAKE_CASE_=4			,     SCREAMING_SNAKE_CASE_=None			,     SCREAMING_SNAKE_CASE_=None			,     SCREAMING_SNAKE_CASE_="gelu"			,     SCREAMING_SNAKE_CASE_=0.0_2			,     SCREAMING_SNAKE_CASE_=1E-12			,     SCREAMING_SNAKE_CASE_=0.0			,     SCREAMING_SNAKE_CASE_=2_24			,     SCREAMING_SNAKE_CASE_=None			,     SCREAMING_SNAKE_CASE_=None			,     **SCREAMING_SNAKE_CASE_			,     )				->     Optional[int]:
         super().__init__(**SCREAMING_SNAKE_CASE_	)

         __lowerCamelCase   :	str								=		num_channels
         __lowerCamelCase   :	Any								=		patch_size
         __lowerCamelCase   :	int								=		num_stages
         __lowerCamelCase   :	Optional[int]								=		[96, 1_92, 3_84, 7_68] if hidden_sizes is None else hidden_sizes
         __lowerCamelCase   :	Optional[int]								=		[3, 3, 9, 3] if depths is None else depths
         __lowerCamelCase   :	Optional[int]								=		hidden_act
         __lowerCamelCase   :	Dict								=		initializer_range
         __lowerCamelCase   :	Tuple								=		layer_norm_eps
         __lowerCamelCase   :	List[str]								=		drop_path_rate
         __lowerCamelCase   :	Union[str, Any]								=		image_size
         __lowerCamelCase   :	List[str]								=		['stem'] + [f'stage{idx}' for idx in range(1			,     len(self.depths	) + 1	)]
         __lowerCamelCase				,       __lowerCamelCase   :	Dict								=		get_aligned_output_features_output_indices(
             out_features=SCREAMING_SNAKE_CASE_			,     out_indices=SCREAMING_SNAKE_CASE_			,     stage_names=self.stage_names	)










'''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    UpperCAmelCase_  (_UpperCAmelCase					,		_UpperCAmelCase					,		unittest.TestCase  ):





    """simple docstring"""






    lowerCamelCase  :					Union[str, Any]							     =			VQModel
    lowerCamelCase  :					Union[str, Any]							     =			'sample'







    @property
    def       lowercase_				(							self			,     SCREAMING_SNAKE_CASE_=(32, 32)	)				->     Any:
         __lowerCamelCase   :	Tuple								=		4
         __lowerCamelCase   :	Union[str, Any]								=		3

         __lowerCamelCase   :	List[Any]								=		floats_tensor((batch_size, num_channels) + sizes	).to(SCREAMING_SNAKE_CASE_	)

         return {"sample": image}







    @property
    def       lowercase_				(							self	)				->     Optional[int]:
         return (3, 32, 32)







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







    def       lowercase_				(							self	)				->     Optional[int]:
         __lowerCamelCase   :	Optional[Any]								=		{
             'block_out_channels': [32, 64],
             'in_channels': 3,
             'out_channels': 3,
             'down_block_types': ['DownEncoderBlock2D', 'DownEncoderBlock2D'],
             'up_block_types': ['UpDecoderBlock2D', 'UpDecoderBlock2D'],
             'latent_channels': 3,
         }
         __lowerCamelCase   :	Union[str, Any]								=		self.dummy_input
         return init_dict, inputs_dict







    def       lowercase_				(							self	)				->     str:
         pass







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







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

         model.to(SCREAMING_SNAKE_CASE_	)
         __lowerCamelCase   :	List[Any]								=		model(**self.dummy_input	)

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







    def       lowercase_				(							self	)				->     int:
         __lowerCamelCase   :	List[Any]								=		VQModel.from_pretrained('fusing/vqgan-dummy'	)
         model.to(SCREAMING_SNAKE_CASE_	).eval()

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

         __lowerCamelCase   :	List[str]								=		torch.randn(1			,     model.config.in_channels			,     model.config.sample_size			,     model.config.sample_size	)
         __lowerCamelCase   :	Optional[int]								=		image.to(SCREAMING_SNAKE_CASE_	)
         with torch.no_grad():
              __lowerCamelCase   :	int								=		model(SCREAMING_SNAKE_CASE_	).sample

         __lowerCamelCase   :	List[str]								=		output[0, -1, -3:, -3:].flatten().cpu()
         # fmt: off
         __lowerCamelCase   :	Union[str, Any]								=		torch.tensor([-0.0_1_5_3, -0.4_0_4_4, -0.1_8_8_0, -0.5_1_6_1, -0.2_4_1_8, -0.4_0_7_2, -0.1_6_1_2, -0.0_6_3_3, -0.0_1_4_3]	)
         # fmt: on
         self.assertTrue(torch.allclose(SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_			,     atol=1E-3	)	)






