




from typing import TYPE_CHECKING

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


_lowerCamelCase    				=		{
    'configuration_openai': ['OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'OpenAIGPTConfig'],
    'tokenization_openai': ['OpenAIGPTTokenizer'],
}

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _lowerCamelCase    				=		['OpenAIGPTTokenizerFast']

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _lowerCamelCase    				=		[
     'OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST',
     'OpenAIGPTDoubleHeadsModel',
     'OpenAIGPTForSequenceClassification',
     'OpenAIGPTLMHeadModel',
     'OpenAIGPTModel',
     'OpenAIGPTPreTrainedModel',
     'load_tf_weights_in_openai_gpt',
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _lowerCamelCase    				=		[
     'TF_OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST',
     'TFOpenAIGPTDoubleHeadsModel',
     'TFOpenAIGPTForSequenceClassification',
     'TFOpenAIGPTLMHeadModel',
     'TFOpenAIGPTMainLayer',
     'TFOpenAIGPTModel',
     'TFOpenAIGPTPreTrainedModel',
 ]


if TYPE_CHECKING:
 from .configuration_openai import OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP, OpenAIGPTConfig
 from .tokenization_openai import OpenAIGPTTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_openai_fast import OpenAIGPTTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_openai import (
      OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST,
      OpenAIGPTDoubleHeadsModel,
      OpenAIGPTForSequenceClassification,
      OpenAIGPTLMHeadModel,
      OpenAIGPTModel,
      OpenAIGPTPreTrainedModel,
      load_tf_weights_in_openai_gpt,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_openai import (
      TF_OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFOpenAIGPTDoubleHeadsModel,
      TFOpenAIGPTForSequenceClassification,
      TFOpenAIGPTLMHeadModel,
      TFOpenAIGPTMainLayer,
      TFOpenAIGPTModel,
      TFOpenAIGPTPreTrainedModel,
  )

else:
 import sys

 _lowerCamelCase    				=		_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)











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


_lowerCamelCase    				=		logging.get_logger(__name__)

_lowerCamelCase    				=		{
    '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     a  (     _A    ):

     '''simple docstring'''





     lowerCAmelCase       : Any     	=						'vit-hybrid'
     def __init__(			self     :    str      ,					__snake_case     :    str=None      ,					__snake_case     :    List[Any]=7_68      ,					__snake_case     :    List[Any]=12      ,					__snake_case     :    Dict=12      ,					__snake_case     :    Optional[int]=30_72      ,					__snake_case     :    Optional[int]="gelu"      ,					__snake_case     :    Union[str, Any]=0.0      ,					__snake_case     :    Any=0.0      ,					__snake_case     :    List[Any]=0.02      ,					__snake_case     :    Union[str, Any]=1E-12      ,					__snake_case     :    Optional[int]=2_24      ,					__snake_case     :    Optional[int]=1      ,					__snake_case     :    Tuple=3      ,					__snake_case     :    Union[str, Any]=[1, 10_24, 24, 24]      ,					__snake_case     :    List[Any]=True      ,					**__snake_case     :    Tuple      ,					):
         super().__init__(**__snake_case       )

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

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

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




     def 						lowerCamelCase_     (			self     :    Any       ):
         UpperCAmelCase_											=  copy.deepcopy(self.__dict__       )
         UpperCAmelCase_											=  self.backbone_config.to_dict()
         UpperCAmelCase_											=  self.__class__.model_type
         return output





