from typing import TYPE_CHECKING

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


_SCREAMING_SNAKE_CASE										=						{
    'configuration_convnext': ['CONVNEXT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ConvNextConfig', 'ConvNextOnnxConfig']
}

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

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

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

    _SCREAMING_SNAKE_CASE										=						_LazyModule(__name__, globals()['__file__'], _import_structure)





from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


_SCREAMING_SNAKE_CASE										=						logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE										=						{
    'microsoft/resnet-50': 'https://huggingface.co/microsoft/resnet-50/blob/main/config.json',
}



class 				a							(			__lowerCAmelCase       ,			__lowerCAmelCase       ):







 """simple docstring"""






 lowerCamelCase	:Any    			=		'''resnet'''
 lowerCamelCase	:Any    			=		['''basic''', '''bottleneck''']



 def __init__(					self		,				lowerCAmelCase_=3		,				lowerCAmelCase_=64		,				lowerCAmelCase_=[2_56, 5_12, 10_24, 20_48]		,				lowerCAmelCase_=[3, 4, 6, 3]		,				lowerCAmelCase_="bottleneck"		,				lowerCAmelCase_="relu"		,				lowerCAmelCase_=False		,				lowerCAmelCase_=None		,				lowerCAmelCase_=None		,				**lowerCAmelCase_		,				)  ->					Union[str, Any]:
        super().__init__(**lowerCAmelCase_							)
        if layer_type not in self.layer_types:
               raise ValueError(F'''layer_type={layer_type} is not one of {','.join(self.layer_types							)}'''							)
        _A        =  num_channels
        _A        =  embedding_size
        _A        =  hidden_sizes
        _A        =  depths
        _A        =  layer_type
        _A        =  hidden_act
        _A        =  downsample_in_first_stage
        _A        =  ["""stem"""] + [F'''stage{idx}''' for idx in range(1		,				len(lowerCAmelCase_							) + 1							)]
        _A   ,  _A        =  get_aligned_output_features_output_indices(
            out_features=lowerCAmelCase_		,				out_indices=lowerCAmelCase_		,				stage_names=self.stage_names							)



class 				a							(			__lowerCAmelCase       ):







 """simple docstring"""






 lowerCamelCase	:Optional[Any]    			=		version.parse('''1.11'''       )



 @property
 def      UpperCAmelCase							(					self							)  ->					Mapping[str, Mapping[int, str]]:
        return OrderedDict(
            [
                ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
            ]							)



 @property
 def      UpperCAmelCase							(					self							)  ->					float:
        return 1E-3




