
import copy
from typing import Dict

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import CONFIG_MAPPING
from ..bit import BitConfig


__magic_name__           =					logging.get_logger(__name__)

__magic_name__           =					{
    '''google/vit-hybrid-base-bit-384''': '''https://huggingface.co/vit-hybrid-base-bit-384/resolve/main/config.json''',
    # See all ViT hybrid models at https://huggingface.co/models?filter=vit
}




class    _SCREAMING_SNAKE_CASE					( __UpperCamelCase	):
      _A  :						Dict      		=       'vit-hybrid'
      def __init__(     self						,		lowerCamelCase=None						,		lowerCamelCase=7_68						,		lowerCamelCase=12						,		lowerCamelCase=12						,		lowerCamelCase=30_72						,		lowerCamelCase="gelu"						,		lowerCamelCase=0.0						,		lowerCamelCase=0.0						,		lowerCamelCase=0.0_2						,		lowerCamelCase=1e-12						,		lowerCamelCase=2_24						,		lowerCamelCase=1						,		lowerCamelCase=3						,		lowerCamelCase=[1, 10_24, 24, 24]						,		lowerCamelCase=True						,		**lowerCamelCase						,		):
          super().__init__(**lowerCamelCase	)

          if backbone_config is None:
              logger.info("`backbone_config` is `None`. Initializing the config with a `BiT` backbone."	)
              snake_case__  =			{
                  "global_padding": "same",
                  "layer_type": "bottleneck",
                  "depths": [3, 4, 9],
                  "out_features": ["stage3"],
                  "embedding_dynamic_padding": True,
              }

          if isinstance(lowerCamelCase						,		lowerCamelCase	):
              if "model_type" in backbone_config:
                  snake_case__  =			CONFIG_MAPPING[backbone_config["model_type"]]
              else:
                  logger.info(
                      "`model_type` is not found in `backbone_config`. Use `Bit` as the backbone configuration class."	)
                  snake_case__  =			BitConfig
              snake_case__  =			backbone_config_class(**lowerCamelCase	)

          snake_case__  =			backbone_featmap_shape
          snake_case__  =			backbone_config
          snake_case__  =			hidden_size
          snake_case__  =			num_hidden_layers
          snake_case__  =			num_attention_heads
          snake_case__  =			intermediate_size
          snake_case__  =			hidden_act
          snake_case__  =			hidden_dropout_prob
          snake_case__  =			attention_probs_dropout_prob
          snake_case__  =			initializer_range
          snake_case__  =			layer_norm_eps
          snake_case__  =			image_size
          snake_case__  =			patch_size
          snake_case__  =			num_channels
          snake_case__  =			qkv_bias



      def A_     (     self	):
          snake_case__  =			copy.deepcopy(self.__dict__	)
          snake_case__  =			self.backbone_config.to_dict()
          snake_case__  =			self.__class__.model_type
          return output




# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


__magic_name__           =					{
    '''configuration_convnextv2''': [
        '''CONVNEXTV2_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''ConvNextV2Config''',
    ]
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __magic_name__           =					[
         '''CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST''',
         '''ConvNextV2ForImageClassification''',
         '''ConvNextV2Model''',
         '''ConvNextV2PreTrainedModel''',
         '''ConvNextV2Backbone''',
     ]


if TYPE_CHECKING:
     from .configuration_convnextva import (
         CONVNEXTV2_PRETRAINED_CONFIG_ARCHIVE_MAP,
         ConvNextVaConfig,
     )

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_convnextva import (
              CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST,
              ConvNextVaBackbone,
              ConvNextVaForImageClassification,
              ConvNextVaModel,
              ConvNextVaPreTrainedModel,
          )

else:
     import sys

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



