






"""simple docstring"""






from typing import TYPE_CHECKING

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


UpperCAmelCase_   : Dict   			= {"""configuration_glpn""": ["""GLPN_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GLPNConfig"""]}

try:
  if not is_vision_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase_   : int   			= ["""GLPNFeatureExtractor"""]
  UpperCAmelCase_   : Union[str, Any]   			= ["""GLPNImageProcessor"""]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase_   : Optional[int]   			= [
      """GLPN_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """GLPNForDepthEstimation""",
      """GLPNLayer""",
      """GLPNModel""",
      """GLPNPreTrainedModel""",
  ]


if TYPE_CHECKING:
  from .configuration_glpn import GLPN_PRETRAINED_CONFIG_ARCHIVE_MAP, GLPNConfig

  try:
    if not is_vision_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .feature_extraction_glpn import GLPNFeatureExtractor
    from .image_processing_glpn import GLPNImageProcessor

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_glpn import (
        GLPN_PRETRAINED_MODEL_ARCHIVE_LIST,
        GLPNForDepthEstimation,
        GLPNLayer,
        GLPNModel,
        GLPNPreTrainedModel,
    )


else:
  import sys

  UpperCAmelCase_   : str   			= _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




import copy
import os
from typing import Union

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


_lowerCamelCase					   =logging.get_logger(__name__)

_lowerCamelCase					   ={
    """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 							A__		(	__SCREAMING_SNAKE_CASE):
      _UpperCAmelCase				:    Dict           =			"""bridgetower_vision_model"""
      def __init__(      self    ,  __magic_name__=7_6_8    ,  __magic_name__=1_2    ,  __magic_name__=3    ,  __magic_name__=1_6    ,  __magic_name__=2_8_8    ,  __magic_name__=1    ,  __magic_name__=1e-05    ,  __magic_name__=False    ,  __magic_name__=True    ,  __magic_name__=False    ,  **__magic_name__    ,  ):
       super().__init__(**__magic_name__      )
       lowerCamelCase		:	Dict							=      hidden_size
       lowerCamelCase		:	str							=      num_hidden_layers
       lowerCamelCase		:	Optional[int]							=      num_channels
       lowerCamelCase		:	List[str]							=      patch_size
       lowerCamelCase		:	Tuple							=      image_size
       lowerCamelCase		:	Any							=      initializer_factor
       lowerCamelCase		:	Tuple							=      layer_norm_eps
       lowerCamelCase		:	Tuple							=      stop_gradient
       lowerCamelCase		:	Optional[int]							=      share_layernorm
       lowerCamelCase		:	str							=      remove_last_layer






      @classmethod
      def 						UpperCamelCase__							(      cls    ,  __magic_name__    ,  **__magic_name__      ):
       lowerCamelCase			,    lowerCamelCase		:	int							=      cls.get_config_dict(__magic_name__    ,  **__magic_name__      )

       if config_dict.get("""model_type"""      ) == "bridgetower":
        lowerCamelCase		:	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(__magic_name__    ,  **__magic_name__      )





class 							A__		(	__SCREAMING_SNAKE_CASE):
      _UpperCAmelCase				:    Union[str, Any]           =			"""bridgetower_text_model"""
      def __init__(      self    ,  __magic_name__=5_0_2_6_5    ,  __magic_name__=7_6_8    ,  __magic_name__=1_2    ,  __magic_name__=1_2    ,  __magic_name__=1    ,  __magic_name__=3_0_7_2    ,  __magic_name__="gelu"    ,  __magic_name__=0.1    ,  __magic_name__=0.1    ,  __magic_name__=5_1_4    ,  __magic_name__=1    ,  __magic_name__=1e-05    ,  __magic_name__=1    ,  __magic_name__=0    ,  __magic_name__=2    ,  __magic_name__="absolute"    ,  __magic_name__=True    ,  **__magic_name__    ,  ):
       super().__init__(**__magic_name__      )

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






      @classmethod
      def 						UpperCamelCase__							(      cls    ,  __magic_name__    ,  **__magic_name__      ):
       lowerCamelCase			,    lowerCamelCase		:	int							=      cls.get_config_dict(__magic_name__    ,  **__magic_name__      )

       if config_dict.get("""model_type"""      ) == "bridgetower":
        lowerCamelCase		:	Optional[int]							=      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(__magic_name__    ,  **__magic_name__      )





class 							A__		(	__SCREAMING_SNAKE_CASE):
      _UpperCAmelCase				:    Dict           =			"""bridgetower"""
      def __init__(      self    ,  __magic_name__=True    ,  __magic_name__="gelu"    ,  __magic_name__=7_6_8    ,  __magic_name__=1    ,  __magic_name__=1e-05    ,  __magic_name__=False    ,  __magic_name__="add"    ,  __magic_name__=1_2    ,  __magic_name__=6    ,  __magic_name__=False    ,  __magic_name__=False    ,  __magic_name__=None    ,  __magic_name__=None    ,  **__magic_name__    ,  ):
       # TODO: remove this once the Hub files are updated.
       lowerCamelCase		:	int							=      kwargs.pop("""text_config_dict"""    ,  __magic_name__      )
       lowerCamelCase		:	str							=      kwargs.pop("""vision_config_dict"""    ,  __magic_name__      )

       super().__init__(**__magic_name__      )
       lowerCamelCase		:	str							=      share_cross_modal_transformer_layers
       lowerCamelCase		:	Union[str, Any]							=      hidden_act
       lowerCamelCase		:	str							=      hidden_size
       lowerCamelCase		:	Tuple							=      initializer_factor
       lowerCamelCase		:	List[str]							=      layer_norm_eps
       lowerCamelCase		:	int							=      share_link_tower_layers
       lowerCamelCase		:	List[Any]							=      link_tower_type
       lowerCamelCase		:	Tuple							=      num_attention_heads
       lowerCamelCase		:	int							=      num_hidden_layers
       lowerCamelCase		:	Union[str, Any]							=      tie_word_embeddings
       lowerCamelCase		:	Tuple							=      init_layernorm_from_vision_encoder

       if text_config is None:
        lowerCamelCase		:	Any							=      {}
        logger.info("""`text_config` is `None`. Initializing the `BridgeTowerTextConfig` with default values."""      )

       if vision_config is None:
        lowerCamelCase		:	int							=      {}
        logger.info("""`vision_config` is `None`. Initializing the `BridgeTowerVisionConfig` with default values."""      )

       lowerCamelCase		:	Any							=      BridgeTowerTextConfig(**__magic_name__      )
       lowerCamelCase		:	Optional[Any]							=      BridgeTowerVisionConfig(**__magic_name__      )
      @classmethod
      def 						UpperCamelCase__							(      cls    ,  __magic_name__    ,  __magic_name__    ,  **__magic_name__      ):
       return cls(text_config=text_config.to_dict()    ,  vision_config=vision_config.to_dict()    ,  **__magic_name__      )






      def 						UpperCamelCase__							(      self      ):
       lowerCamelCase		:	str							=      copy.deepcopy(self.__dict__      )
       lowerCamelCase		:	int							=      self.text_config.to_dict()
       lowerCamelCase		:	Dict							=      self.vision_config.to_dict()
       lowerCamelCase		:	List[str]							=      self.__class__.model_type
       return output




