




"""simple docstring"""

from typing import TYPE_CHECKING

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


__A							=     {
    "configuration_segformer": ["SEGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "SegformerConfig", "SegformerOnnxConfig"]
}

try:
							if not is_vision_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__A							=     ["SegformerFeatureExtractor"]
							__A							=     ["SegformerImageProcessor"]

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__A							=     [
							    "SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
							    "SegformerDecodeHead",
							    "SegformerForImageClassification",
							    "SegformerForSemanticSegmentation",
							    "SegformerLayer",
							    "SegformerModel",
							    "SegformerPreTrainedModel",
							]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__A							=     [
							    "TF_SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
							    "TFSegformerDecodeHead",
							    "TFSegformerForImageClassification",
							    "TFSegformerForSemanticSegmentation",
							    "TFSegformerModel",
							    "TFSegformerPreTrainedModel",
							]


if TYPE_CHECKING:
							from .configuration_segformer import SEGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, SegformerConfig, SegformerOnnxConfig

							try:
														if not is_vision_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .feature_extraction_segformer import SegformerFeatureExtractor
														from .image_processing_segformer import SegformerImageProcessor

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_segformer import (
														    SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
														    SegformerDecodeHead,
														    SegformerForImageClassification,
														    SegformerForSemanticSegmentation,
														    SegformerLayer,
														    SegformerModel,
														    SegformerPreTrainedModel,
														)
							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_segformer import (
														    TF_SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TFSegformerDecodeHead,
														    TFSegformerForImageClassification,
														    TFSegformerForSemanticSegmentation,
														    TFSegformerModel,
														    TFSegformerPreTrainedModel,
														)

else:
							import sys

							__A							=     _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)







"""simple docstring"""

def 					UpperCamelCase__       (						lowercase__					:       set    ,       lowercase__					:       set				):
		assert isinstance(lowercase__    ,       lowercase__				), F'''The input value of [set_a={set_a}] is not a set'''
		assert isinstance(lowercase__    ,       lowercase__				), F'''The input value of [set_b={set_b}] is not a set'''

		return {a + b for a in set_a for b in set_b}


if __name__ == "__main__":
							from doctest import testmod

							testmod()

