





import unittest

from diffusers import FlaxAutoencoderKL
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax

from .test_modeling_common_flax import FlaxModelTesterMixin


if is_flax_available():
	import jax






@require_flax
class 							__lowercase    ( lowerCamelCase__	, unittest.TestCase  ):







	'''simple docstring'''

	a      :			Any     =    FlaxAutoencoderKL


	@property
	def   _UpperCAmelCase       (self	)							->							str:







				'''simple docstring'''





				__lowercase			  =							4
				__lowercase			  =							3
				__lowercase			  =							(32, 32)

				__lowercase			  =							jax.random.PRNGKey(0	)
				__lowercase			  =							jax.random.uniform(_lowerCamelCase       ,((batch_size, num_channels) + sizes)	)

				return {"sample": image, "prng_key": prng_key}


	def   _UpperCAmelCase       (self	)							->							Optional[int]:







				'''simple docstring'''





				__lowercase			  =							{
				    "block_out_channels": [32, 64],
				    "in_channels": 3,
				    "out_channels": 3,
				    "down_block_types": ["DownEncoderBlock2D", "DownEncoderBlock2D"],
				    "up_block_types": ["UpDecoderBlock2D", "UpDecoderBlock2D"],
				    "latent_channels": 4,
				}
				__lowercase			  =							self.dummy_input
				return init_dict, inputs_dict












'''simple docstring'''



from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_SCREAMING_SNAKE_CASE   =      {'''configuration_focalnet''': ['''FOCALNET_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''FocalNetConfig''']}


try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_SCREAMING_SNAKE_CASE   =      [
	    '''FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''FocalNetForImageClassification''',
	    '''FocalNetForMaskedImageModeling''',
	    '''FocalNetBackbone''',
	    '''FocalNetModel''',
	    '''FocalNetPreTrainedModel''',
	]

if TYPE_CHECKING:
	from .configuration_focalnet import FOCALNET_PRETRAINED_CONFIG_ARCHIVE_MAP, FocalNetConfig

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_focalnet import (
		    FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST,
		    FocalNetBackbone,
		    FocalNetForImageClassification,
		    FocalNetForMaskedImageModeling,
		    FocalNetModel,
		    FocalNetPreTrainedModel,
		)

else:
	import sys

	_SCREAMING_SNAKE_CASE   =      _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)





