




from typing import TYPE_CHECKING

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


lowercase_     :    Dict      =			{
    'configuration_segformer': ['SEGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'SegformerConfig', 'SegformerOnnxConfig']
}

try:
				if not is_vision_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				lowercase_     :    Union[str, Any]      =			['SegformerFeatureExtractor']
				lowercase_     :    Dict      =			['SegformerImageProcessor']

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				lowercase_     :    Optional[Any]      =			[
				    'SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'SegformerDecodeHead',
				    'SegformerForImageClassification',
				    'SegformerForSemanticSegmentation',
				    'SegformerLayer',
				    'SegformerModel',
				    'SegformerPreTrainedModel',
				]

try:
				if not is_tf_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				lowercase_     :    Dict      =			[
				    '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

				lowercase_     :    Union[str, Any]      =			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)










from __future__ import annotations


def 							__SCREAMING_SNAKE_CASE  (      snake_case_     ,   snake_case_						):

						'''simple docstring'''


						_UpperCAmelCase													=					{}
						for index, val in enumerate(snake_case_						):
												_UpperCAmelCase													=					target - val
												if compl in chk_map:
																		return [chk_map[compl], index]
												_UpperCAmelCase													=					index
						return []


if __name__ == "__main__":
				import doctest

				doctest.testmod()
				print(f"""{two_sum([2, 7, 11, 15], 9) = }""")




