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


lowerCAmelCase__	:  Tuple						=  logging.get_logger(__name__)

lowerCAmelCase__	:  Any						=  {
    '''facebook/convnext-tiny-224''': '''https://huggingface.co/facebook/convnext-tiny-224/resolve/main/config.json''',
    # See all ConvNeXT models at https://huggingface.co/models?filter=convnext
}





class 					__snake_case    (     _lowerCamelCase       ,_lowerCamelCase      ):
					__lowerCamelCase			   =  """convnext"""




					def __init__(  self       ,			__UpperCamelCase=3       ,			__UpperCamelCase=4       ,			__UpperCamelCase=4       ,			__UpperCamelCase=None       ,			__UpperCamelCase=None       ,			__UpperCamelCase="gelu"       ,			__UpperCamelCase=0.0_2       ,			__UpperCamelCase=1E-12       ,			__UpperCamelCase=1E-6       ,			__UpperCamelCase=0.0       ,			__UpperCamelCase=224       ,			__UpperCamelCase=None       ,			__UpperCamelCase=None       ,			**__UpperCamelCase       ,			)	->    str:



									'''simple docstring'''







									super().__init__(**__UpperCamelCase						)

									snake_case__   :      Optional[Any]   				= num_channels
									snake_case__   :      Optional[Any]   				= patch_size
									snake_case__   :      Optional[int]   				= num_stages
									snake_case__   :      Any   				= [96, 192, 384, 768] if hidden_sizes is None else hidden_sizes
									snake_case__   :      Optional[Any]   				= [3, 3, 9, 3] if depths is None else depths
									snake_case__   :      str   				= hidden_act
									snake_case__   :      Optional[int]   				= initializer_range
									snake_case__   :      str   				= layer_norm_eps
									snake_case__   :      List[str]   				= layer_scale_init_value
									snake_case__   :      Dict   				= drop_path_rate
									snake_case__   :      str   				= image_size
									snake_case__   :      List[str]   				= ['stem'] + [F"""stage{idx}""" for idx in range(1       ,			len(self.depths						) + 1						)]
									snake_case__				,    snake_case__   :      Tuple   				= get_aligned_output_features_output_indices(
									    out_features=__UpperCamelCase       ,			out_indices=__UpperCamelCase       ,			stage_names=self.stage_names						)





class 					__snake_case    (     _lowerCamelCase      ):
					__lowerCamelCase			   =  version.parse("""1.11"""      )




					@property
					def      __a     (  self						)	->    Mapping[str, Mapping[int, str]]:



									'''simple docstring'''







									return OrderedDict(
									    [
									        ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
									    ]						)




					@property
					def      __a     (  self						)	->    float:



									'''simple docstring'''







									return 1E-5



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


lowerCAmelCase__	:  int						=  {
    '''configuration_deta''': ['''DETA_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''DetaConfig'''],
}

try:
				if not is_vision_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				lowerCAmelCase__	:  Union[str, Any]						=  ['''DetaImageProcessor''']

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				lowerCAmelCase__	:  Optional[int]						=  [
				    '''DETA_PRETRAINED_MODEL_ARCHIVE_LIST''',
				    '''DetaForObjectDetection''',
				    '''DetaModel''',
				    '''DetaPreTrainedModel''',
				]


if TYPE_CHECKING:
				from .configuration_deta import DETA_PRETRAINED_CONFIG_ARCHIVE_MAP, DetaConfig

				try:
								if not is_vision_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .image_processing_deta import DetaImageProcessor

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_deta import (
								    DETA_PRETRAINED_MODEL_ARCHIVE_LIST,
								    DetaForObjectDetection,
								    DetaModel,
								    DetaPreTrainedModel,
								)

else:
				import sys

				lowerCAmelCase__	:  int						=  _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


