






"""simple docstring"""



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


_snake_case							:   int	  =       {
    "configuration_sam": [
        "SAM_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "SamConfig",
        "SamMaskDecoderConfig",
        "SamPromptEncoderConfig",
        "SamVisionConfig",
    ],
    "processing_sam": ["SamProcessor"],
}


try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_snake_case							:   Any	  =       [
						    "SAM_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "SamModel",
						    "SamPreTrainedModel",
						]
try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_snake_case							:   Optional[Any]	  =       [
						    "TF_SAM_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "TFSamModel",
						    "TFSamPreTrainedModel",
						]
try:
						if not is_vision_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_snake_case							:   Optional[Any]	  =       ["SamImageProcessor"]


if TYPE_CHECKING:
						from .configuration_sam import (
						    SAM_PRETRAINED_CONFIG_ARCHIVE_MAP,
						    SamConfig,
						    SamMaskDecoderConfig,
						    SamPromptEncoderConfig,
						    SamVisionConfig,
						)
						from .processing_sam import SamProcessor

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_sam import SAM_PRETRAINED_MODEL_ARCHIVE_LIST, SamModel, SamPreTrainedModel

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_sam import TF_SAM_PRETRAINED_MODEL_ARCHIVE_LIST, TFSamModel, TFSamPreTrainedModel

						try:
												if not is_vision_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .image_processing_sam import SamImageProcessor

else:
						import sys

						_snake_case							:   List[str]	  =       _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)







"""simple docstring"""



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 							__lowercase:






						def 		lowerCAmelCase (       self)      ->	str:




											"""simple docstring"""





											lowerCamelCase__					,  lowerCamelCase__					=self.prepare_init_args_and_inputs_for_common()

											lowerCamelCase__					=self.model_class(**_SCREAMING_SNAKE_CASE)
											lowerCamelCase__					=model.init(inputs_dict['prng_key']     ,    inputs_dict['sample'])
											jax.lax.stop_gradient(_SCREAMING_SNAKE_CASE)

											lowerCamelCase__					=model.apply(_SCREAMING_SNAKE_CASE     ,    inputs_dict['sample'])

											if isinstance(_SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE):
																lowerCamelCase__					=output.sample

											self.assertIsNotNone(_SCREAMING_SNAKE_CASE)
											lowerCamelCase__					=inputs_dict['sample'].shape
											self.assertEqual(output.shape     ,    _SCREAMING_SNAKE_CASE     ,    'Input and output shapes do not match')






						def 		lowerCAmelCase (       self)      ->	Optional[Any]:




											"""simple docstring"""





											lowerCamelCase__					,  lowerCamelCase__					=self.prepare_init_args_and_inputs_for_common()

											lowerCamelCase__					=16
											lowerCamelCase__					=(16, 32)

											lowerCamelCase__					=self.model_class(**_SCREAMING_SNAKE_CASE)
											lowerCamelCase__					=model.init(inputs_dict['prng_key']     ,    inputs_dict['sample'])
											jax.lax.stop_gradient(_SCREAMING_SNAKE_CASE)

											lowerCamelCase__					=model.apply(_SCREAMING_SNAKE_CASE     ,    inputs_dict['sample'])

											if isinstance(_SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE):
																lowerCamelCase__					=output.sample

											self.assertIsNotNone(_SCREAMING_SNAKE_CASE)
											lowerCamelCase__					=inputs_dict['sample'].shape
											self.assertEqual(output.shape     ,    _SCREAMING_SNAKE_CASE     ,    'Input and output shapes do not match')






						def 		lowerCAmelCase (       self)      ->	Optional[Any]:




											"""simple docstring"""





											lowerCamelCase__					='kwargs' in inspect.signature(self.model_class.__init__).parameters
											lowerCamelCase__					=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>]`')