






'''simple docstring'''







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 	SCREAMING_SNAKE_CASE					(					SCREAMING_SNAKE_CASE_	,   unittest.TestCase       ):
			snake_case__  =				FlaxAutoencoderKL






			@property
			def 			SCREAMING_SNAKE_CASE      ( self      :			int					)			->			Optional[int]:
							a_		:  Optional[Any]											=      4
							a_		:  Optional[Any]											=      3
							a_		:  Union[str, Any]											=      (32, 32)

							a_		:  int											=      jax.random.PRNGKey(0					)
							a_		:  Tuple											=      jax.random.uniform(__SCREAMING_SNAKE_CASE  ,						((batch_size, num_channels) + sizes)					)

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







			def 			SCREAMING_SNAKE_CASE      ( self      :			List[str]					)			->			Dict:
							a_		:  Optional[int]											=      {
							    '''block_out_channels''': [32, 64],
							    '''in_channels''': 3,
							    '''out_channels''': 3,
							    '''down_block_types''': ['''DownEncoderBlock2D''', '''DownEncoderBlock2D'''],
							    '''up_block_types''': ['''UpDecoderBlock2D''', '''UpDecoderBlock2D'''],
							    '''latent_channels''': 4,
							}
							a_		:  Dict											=      self.dummy_input
							return init_dict, inputs_dict












'''simple docstring'''







import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import ClassLabel, Features, Image
from .base import TaskTemplate





@dataclass(frozen=SCREAMING_SNAKE_CASE_       )
class 	SCREAMING_SNAKE_CASE					(					SCREAMING_SNAKE_CASE_       ):
			snake_case__  =				field(default="image-classification"	,   metadata={"include_in_asdict_even_if_is_default": True}       )
			snake_case__  =				Features({"image": Image()}       )
			snake_case__  =				Features({"labels": ClassLabel}       )
			snake_case__  =				"image"
			snake_case__  =				"labels"






			def 			SCREAMING_SNAKE_CASE      ( self      :			List[str]  ,						__SCREAMING_SNAKE_CASE      :			List[Any]					)			->			Any:
							if self.label_column not in features:
											raise ValueError(f'Column {self.label_column} is not present in features.'					)
							if not isinstance(features[self.label_column]  ,						__SCREAMING_SNAKE_CASE					):
											raise ValueError(f'Column {self.label_column} is not a ClassLabel.'					)
							a_		:  Optional[int]											=      copy.deepcopy(self					)
							a_		:  int											=      self.label_schema.copy()
							a_		:  Tuple											=      features[self.label_column]
							a_		:  str											=      label_schema
							return task_template







			@property
			def 			SCREAMING_SNAKE_CASE      ( self      :			Tuple					)			->			Dict[str, str]:
							return {
							    self.image_column: "image",
							    self.label_column: "labels",
							}




