
"""simple docstring"""





from typing import TYPE_CHECKING

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


_a														=		{
    """configuration_convnext""": ["""CONVNEXT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ConvNextConfig""", """ConvNextOnnxConfig"""]
}

try:
				if not is_vision_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_a														=		["""ConvNextFeatureExtractor"""]
				_a														=		["""ConvNextImageProcessor"""]

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_a														=		[
				    """CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST""",
				    """ConvNextForImageClassification""",
				    """ConvNextModel""",
				    """ConvNextPreTrainedModel""",
				    """ConvNextBackbone""",
				]

try:
				if not is_tf_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_a														=		[
				    """TFConvNextForImageClassification""",
				    """TFConvNextModel""",
				    """TFConvNextPreTrainedModel""",
				]

if TYPE_CHECKING:
				from .configuration_convnext import CONVNEXT_PRETRAINED_CONFIG_ARCHIVE_MAP, ConvNextConfig, ConvNextOnnxConfig

				try:
								if not is_vision_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .feature_extraction_convnext import ConvNextFeatureExtractor
								from .image_processing_convnext import ConvNextImageProcessor

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_convnext import (
								    CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
								    ConvNextBackbone,
								    ConvNextForImageClassification,
								    ConvNextModel,
								    ConvNextPreTrainedModel,
								)

				try:
								if not is_tf_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_tf_convnext import TFConvNextForImageClassification, TFConvNextModel, TFConvNextPreTrainedModel


else:
				import sys

				_a														=		_LazyModule(__name__, globals()["""__file__"""], _import_structure)








"""simple docstring"""





from __future__ import annotations



def       lowerCamelCase__		(			__snake_case,     __snake_case,     __snake_case	)							->		int | float:


					"""simple docstring"""





					if len(__snake_case	) == 0:
										raise ValueError('''find_max() arg is an empty sequence'''	)
					if (
					    left >= len(__snake_case	)
					    or left < -len(__snake_case	)
					    or right >= len(__snake_case	)
					    or right < -len(__snake_case	)
					):
										raise IndexError('''list index out of range'''	)
					if left == right:
										return nums[left]
					_UpperCamelCase							 =  (left + right) >> 1  # the middle
					_UpperCamelCase							 =  find_max(__snake_case,     __snake_case,     __snake_case	)  # find max in range[left, mid]
					_UpperCamelCase							 =  find_max(__snake_case,     mid + 1,     __snake_case	)  # find max in range[mid + 1, right]

					return left_max if left_max >= right_max else right_max


if __name__ == "__main__":
				import doctest

				doctest.testmod(verbose=True)






