



from typing import TYPE_CHECKING

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


lowerCAmelCase__		:					Optional[int]							={
    '''configuration_convnext''': ['''CONVNEXT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ConvNextConfig''', '''ConvNextOnnxConfig''']
}

try:
			if not is_vision_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowerCAmelCase__		:					List[Any]							=['''ConvNextFeatureExtractor''']
			lowerCAmelCase__		:					Optional[int]							=['''ConvNextImageProcessor''']

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowerCAmelCase__		:					List[Any]							=[
			    '''CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''ConvNextForImageClassification''',
			    '''ConvNextModel''',
			    '''ConvNextPreTrainedModel''',
			    '''ConvNextBackbone''',
			]

try:
			if not is_tf_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowerCAmelCase__		:					List[Any]							=[
			    '''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

			lowerCAmelCase__		:					List[Any]							=_LazyModule(__name__, globals()['''__file__'''], _import_structure)











from collections import deque

from .hash_table import HashTable

class        UpperCAmelCase_  (       UpperCamelCase_	):

						'''simple docstring'''




						def __init__(   self				,							*_A				,							**_A		):







													'''simple docstring'''

													super().__init__(*_A				,							**_A		)






						def 	_A     (   self				,							_A				,							_A		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= deque([]		) if self.values[key] is None else self.values[key]
													self.values[key].appendleft(_A		)
													__SCREAMING_SNAKE_CASE									= self.values[key]






						def 	_A     (   self		):







													'''simple docstring'''

													return (
													    sum(self.charge_factor - len(_A		) for slot in self.values		)
													    / self.size_table
													    * self.charge_factor
													)






						def 	_A     (   self				,							_A				,							_A=None		):







													'''simple docstring'''

													if not (
													    len(self.values[key]		) == self.charge_factor and self.values.count(_A		) == 0
													):
																				return key
													return super()._collision_resolution(_A				,							_A		)






