

import inspect

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


if is_flax_available():
					import jax

@require_flax
class  a     :


						def 				_UpperCAmelCase (				self		):







										'''simple docstring'''


										_UpperCAmelCase						,							_UpperCAmelCase				:						List[str]       						=			self.prepare_init_args_and_inputs_for_common()

										_UpperCAmelCase				:						Tuple       						=			self.model_class(**A_		)
										_UpperCAmelCase				:						Optional[int]       						=			model.init(inputs_dict["prng_key"]  ,    inputs_dict["sample"]		)
										jax.lax.stop_gradient(A_		)

										_UpperCAmelCase				:						Dict       						=			model.apply(A_  ,    inputs_dict["sample"]		)

										if isinstance(A_  ,    A_		):
														_UpperCAmelCase				:						Any       						=			output.sample

										self.assertIsNotNone(A_		)
										_UpperCAmelCase				:						str       						=			inputs_dict["sample"].shape
										self.assertEqual(output.shape  ,    A_  ,    "Input and output shapes do not match"		)


						def 				_UpperCAmelCase (				self		):







										'''simple docstring'''


										_UpperCAmelCase						,							_UpperCAmelCase				:						Optional[int]       						=			self.prepare_init_args_and_inputs_for_common()

										_UpperCAmelCase				:						List[Any]       						=			16
										_UpperCAmelCase				:						Union[str, Any]       						=			(16, 32)

										_UpperCAmelCase				:						Tuple       						=			self.model_class(**A_		)
										_UpperCAmelCase				:						Dict       						=			model.init(inputs_dict["prng_key"]  ,    inputs_dict["sample"]		)
										jax.lax.stop_gradient(A_		)

										_UpperCAmelCase				:						Tuple       						=			model.apply(A_  ,    inputs_dict["sample"]		)

										if isinstance(A_  ,    A_		):
														_UpperCAmelCase				:						List[Any]       						=			output.sample

										self.assertIsNotNone(A_		)
										_UpperCAmelCase				:						Optional[int]       						=			inputs_dict["sample"].shape
										self.assertEqual(output.shape  ,    A_  ,    "Input and output shapes do not match"		)





						def 				_UpperCAmelCase (				self		):







										'''simple docstring'''


										_UpperCAmelCase				:						List[Any]       						=			"kwargs" in inspect.signature(self.model_class.__init__		).parameters
										_UpperCAmelCase				:						Optional[Any]       						=			len(self.model_class._deprecated_kwargs		) > 0

										if has_kwarg_in_model_class and not has_deprecated_kwarg:
														raise ValueError(
														    f'{self.model_class} has `**kwargs` in its __init__ method but has not defined any deprecated kwargs'
														    " under the `_deprecated_kwargs` class attribute. Make sure to either remove `**kwargs` if there are"
														    " no deprecated arguments or add the deprecated argument with `_deprecated_kwargs ="
														    " [<deprecated_argument>]`"		)

										if not has_kwarg_in_model_class and has_deprecated_kwarg:
														raise ValueError(
														    f'{self.model_class} doesn\'t have `**kwargs` in its __init__ method but has defined deprecated kwargs'
														    " under the `_deprecated_kwargs` class attribute. Make sure to either add the `**kwargs` argument to"
														    f' {self.model_class}.__init__ if there are deprecated arguments or remove the deprecated argument'
														    " from `_deprecated_kwargs = [<deprecated_argument>]`"		)





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


UpperCamelCase__     :					Dict								=			{
    "configuration_donut_swin": ["DONUT_SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP", "DonutSwinConfig"],
    "processing_donut": ["DonutProcessor"],
}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       UpperCamelCase__     :					Optional[int]								=			[
           "DONUT_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST",
           "DonutSwinModel",
           "DonutSwinPreTrainedModel",
       ]

try:
       if not is_vision_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       UpperCamelCase__     :					List[Any]								=			["DonutFeatureExtractor"]
       UpperCamelCase__     :					Dict								=			["DonutImageProcessor"]


if TYPE_CHECKING:
       from .configuration_donut_swin import DONUT_SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP, DonutSwinConfig
       from .processing_donut import DonutProcessor

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_donut_swin import (
                  DONUT_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
                  DonutSwinModel,
                  DonutSwinPreTrainedModel,
              )

       try:
              if not is_vision_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .feature_extraction_donut import DonutFeatureExtractor
              from .image_processing_donut import DonutImageProcessor

else:
       import sys

       UpperCamelCase__     :					Tuple								=			_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


