
import copy
import os
from typing import Union

from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCAmelCase__		:     Tuple     			=      logging.get_logger(__name__)

UpperCAmelCase__		:     Union[str, Any]     			=      {
    'BridgeTower/bridgetower-base': 'https://huggingface.co/BridgeTower/bridgetower-base/blob/main/config.json',
    'BridgeTower/bridgetower-base-itm-mlm': (
        'https://huggingface.co/BridgeTower/bridgetower-base-itm-mlm/blob/main/config.json'
    ),
}






class UpperCAmelCase   (						SCREAMING_SNAKE_CASE__   ):





   '''simple docstring'''
   __UpperCamelCase		:    Tuple 				=    '''bridgetower_vision_model'''


   def __init__(					self     :							Union[str, Any]			,			lowerCAmelCase_     :							Dict=7_6_8			,			lowerCAmelCase_     :							int=1_2			,			lowerCAmelCase_     :							List[Any]=3			,			lowerCAmelCase_     :							Tuple=1_6			,			lowerCAmelCase_     :							Optional[int]=2_8_8			,			lowerCAmelCase_     :							Optional[Any]=1			,			lowerCAmelCase_     :							Optional[Any]=1e-05			,			lowerCAmelCase_     :							List[Any]=False			,			lowerCAmelCase_     :							Any=True			,			lowerCAmelCase_     :							List[Any]=False			,			**lowerCAmelCase_     :							Tuple			,			):

      """simple docstring"""
      super().__init__(**lowerCAmelCase_     )
      _A:  Any					=       hidden_size
      _A:  Optional[int]					=       num_hidden_layers
      _A:  Union[str, Any]					=       num_channels
      _A:  List[str]					=       patch_size
      _A:  List[str]					=       image_size
      _A:  Tuple					=       initializer_factor
      _A:  List[Any]					=       layer_norm_eps
      _A:  Optional[int]					=       stop_gradient
      _A:  List[Any]					=       share_layernorm
      _A:  List[Any]					=       remove_last_layer


   @classmethod
   def  __magic_name__				(					cls     :							Any			,			lowerCAmelCase_     :							Union[str, os.PathLike]			,			**lowerCAmelCase_     :							Dict     ):

      """simple docstring"""
      _A			,  _A:  Union[str, Any]					=       cls.get_config_dict(lowerCAmelCase_			,			**lowerCAmelCase_     )

      if config_dict.get('''model_type'''     ) == "bridgetower":
         _A:  Any					=       config_dict['''text_config''']

      if "model_type" in config_dict and hasattr(cls			,			'''model_type'''     ) and config_dict["model_type"] != cls.model_type:
         logger.warning(
             F"""You are using a model of type {config_dict['model_type']} to instantiate a model of type """
             F"""{cls.model_type}. This is not supported for all configurations of models and can yield errors."""     )

      return cls.from_dict(lowerCAmelCase_			,			**lowerCAmelCase_     )






class UpperCAmelCase   (						SCREAMING_SNAKE_CASE__   ):





   '''simple docstring'''
   __UpperCamelCase		:    Dict 				=    '''bridgetower_text_model'''


   def __init__(					self     :							Dict			,			lowerCAmelCase_     :							Optional[Any]=5_0_2_6_5			,			lowerCAmelCase_     :							Union[str, Any]=7_6_8			,			lowerCAmelCase_     :							List[str]=1_2			,			lowerCAmelCase_     :							Optional[int]=1_2			,			lowerCAmelCase_     :							str=1			,			lowerCAmelCase_     :							List[Any]=3_0_7_2			,			lowerCAmelCase_     :							Dict="gelu"			,			lowerCAmelCase_     :							Optional[int]=0.1			,			lowerCAmelCase_     :							Optional[Any]=0.1			,			lowerCAmelCase_     :							Optional[int]=5_1_4			,			lowerCAmelCase_     :							Tuple=1			,			lowerCAmelCase_     :							Union[str, Any]=1e-05			,			lowerCAmelCase_     :							Optional[Any]=1			,			lowerCAmelCase_     :							Any=0			,			lowerCAmelCase_     :							int=2			,			lowerCAmelCase_     :							List[str]="absolute"			,			lowerCAmelCase_     :							Optional[Any]=True			,			**lowerCAmelCase_     :							Optional[Any]			,			):

      """simple docstring"""
      super().__init__(**lowerCAmelCase_     )

      _A:  Union[str, Any]					=       vocab_size
      _A:  Optional[Any]					=       hidden_size
      _A:  List[str]					=       num_hidden_layers
      _A:  Tuple					=       num_attention_heads
      _A:  List[str]					=       hidden_act
      _A:  List[str]					=       initializer_factor
      _A:  Union[str, Any]					=       intermediate_size
      _A:  str					=       hidden_dropout_prob
      _A:  List[Any]					=       attention_probs_dropout_prob
      _A:  Tuple					=       max_position_embeddings
      _A:  Union[str, Any]					=       type_vocab_size
      _A:  List[Any]					=       layer_norm_eps
      _A:  Union[str, Any]					=       position_embedding_type
      _A:  Union[str, Any]					=       use_cache
      _A:  List[Any]					=       pad_token_id
      _A:  Optional[int]					=       bos_token_id
      _A:  Dict					=       eos_token_id


   @classmethod
   def  __magic_name__				(					cls     :							Tuple			,			lowerCAmelCase_     :							Union[str, os.PathLike]			,			**lowerCAmelCase_     :							Dict     ):

      """simple docstring"""
      _A			,  _A:  Tuple					=       cls.get_config_dict(lowerCAmelCase_			,			**lowerCAmelCase_     )

      if config_dict.get('''model_type'''     ) == "bridgetower":
         _A:  str					=       config_dict['''text_config''']

      if "model_type" in config_dict and hasattr(cls			,			'''model_type'''     ) and config_dict["model_type"] != cls.model_type:
         logger.warning(
             F"""You are using a model of type {config_dict['model_type']} to instantiate a model of type """
             F"""{cls.model_type}. This is not supported for all configurations of models and can yield errors."""     )

      return cls.from_dict(lowerCAmelCase_			,			**lowerCAmelCase_     )






class UpperCAmelCase   (						SCREAMING_SNAKE_CASE__   ):





   '''simple docstring'''
   __UpperCamelCase		:    Any 				=    '''bridgetower'''


   def __init__(					self     :							Any			,			lowerCAmelCase_     :							Tuple=True			,			lowerCAmelCase_     :							Any="gelu"			,			lowerCAmelCase_     :							Any=7_6_8			,			lowerCAmelCase_     :							Optional[int]=1			,			lowerCAmelCase_     :							List[str]=1e-05			,			lowerCAmelCase_     :							Optional[int]=False			,			lowerCAmelCase_     :							List[Any]="add"			,			lowerCAmelCase_     :							List[str]=1_2			,			lowerCAmelCase_     :							Optional[int]=6			,			lowerCAmelCase_     :							Dict=False			,			lowerCAmelCase_     :							Union[str, Any]=False			,			lowerCAmelCase_     :							str=None			,			lowerCAmelCase_     :							Union[str, Any]=None			,			**lowerCAmelCase_     :							Dict			,			):

      """simple docstring"""
      # TODO: remove this once the Hub files are updated.
      _A:  List[str]					=       kwargs.pop('''text_config_dict'''			,			lowerCAmelCase_     )
      _A:  int					=       kwargs.pop('''vision_config_dict'''			,			lowerCAmelCase_     )

      super().__init__(**lowerCAmelCase_     )
      _A:  int					=       share_cross_modal_transformer_layers
      _A:  Tuple					=       hidden_act
      _A:  Optional[int]					=       hidden_size
      _A:  Optional[int]					=       initializer_factor
      _A:  Optional[int]					=       layer_norm_eps
      _A:  Optional[int]					=       share_link_tower_layers
      _A:  Optional[int]					=       link_tower_type
      _A:  List[str]					=       num_attention_heads
      _A:  Optional[int]					=       num_hidden_layers
      _A:  Dict					=       tie_word_embeddings
      _A:  Optional[Any]					=       init_layernorm_from_vision_encoder

      if text_config is None:
         _A:  Optional[Any]					=       {}
         logger.info('''`text_config` is `None`. Initializing the `BridgeTowerTextConfig` with default values.'''     )

      if vision_config is None:
         _A:  str					=       {}
         logger.info('''`vision_config` is `None`. Initializing the `BridgeTowerVisionConfig` with default values.'''     )

      _A:  Tuple					=       BridgeTowerTextConfig(**lowerCAmelCase_     )
      _A:  Union[str, Any]					=       BridgeTowerVisionConfig(**lowerCAmelCase_     )


   @classmethod
   def  __magic_name__				(					cls     :							int			,			lowerCAmelCase_     :							BridgeTowerTextConfig			,			lowerCAmelCase_     :							BridgeTowerVisionConfig			,			**lowerCAmelCase_     :							str     ):

      """simple docstring"""
      return cls(text_config=text_config.to_dict()			,			vision_config=vision_config.to_dict()			,			**lowerCAmelCase_     )


   def  __magic_name__				(					self     :							int     ):

      """simple docstring"""
      _A:  int					=       copy.deepcopy(self.__dict__     )
      _A:  Optional[int]					=       self.text_config.to_dict()
      _A:  Optional[int]					=       self.vision_config.to_dict()
      _A:  Dict					=       self.__class__.model_type
      return output





from typing import TYPE_CHECKING

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


UpperCAmelCase__		:     Any     			=      {'configuration_swin': ['SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'SwinConfig', 'SwinOnnxConfig']}


try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCAmelCase__		:     Union[str, Any]     			=      [
       'SWIN_PRETRAINED_MODEL_ARCHIVE_LIST',
       'SwinForImageClassification',
       'SwinForMaskedImageModeling',
       'SwinModel',
       'SwinPreTrainedModel',
       'SwinBackbone',
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCAmelCase__		:     str     			=      [
       'TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST',
       'TFSwinForImageClassification',
       'TFSwinForMaskedImageModeling',
       'TFSwinModel',
       'TFSwinPreTrainedModel',
   ]

if TYPE_CHECKING:
   from .configuration_swin import SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP, SwinConfig, SwinOnnxConfig

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_swin import (
          SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
          SwinBackbone,
          SwinForImageClassification,
          SwinForMaskedImageModeling,
          SwinModel,
          SwinPreTrainedModel,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_swin import (
          TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFSwinForImageClassification,
          TFSwinForMaskedImageModeling,
          TFSwinModel,
          TFSwinPreTrainedModel,
      )

else:
   import sys

   UpperCAmelCase__		:     Tuple     			=      _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



