




import copy
import os
from typing import Union

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


_snake_case            = logging.get_logger(__name__)

_snake_case            = {
    "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 		lowercase							(     UpperCamelCase__	):
     _a											=		"bridgetower_vision_model"

     def __init__(						self	,  _a=768	,  _a=12	,  _a=3	,  _a=16	,  _a=288	,  _a=1	,  _a=1e-05	,  _a=False	,  _a=True	,  _a=False	,  **_a	,  )		->							List[str]:
           super().__init__(**_a       )
           _A							:  int    =		hidden_size
           _A							:  Optional[int]    =		num_hidden_layers
           _A							:  Tuple    =		num_channels
           _A							:  Optional[Any]    =		patch_size
           _A							:  Optional[Any]    =		image_size
           _A							:  List[Any]    =		initializer_factor
           _A							:  Tuple    =		layer_norm_eps
           _A							:  List[Any]    =		stop_gradient
           _A							:  Dict    =		share_layernorm
           _A							:  Optional[Any]    =		remove_last_layer



     @classmethod
     def       a__			(						cls	,  _a	,  **_a       )		->							"PretrainedConfig":
           _A    ,     _A							:  List[Any]    =		cls.get_config_dict(_a	,  **_a       )

           if config_dict.get("""model_type"""       ) == "bridgetower":
                 _A							:  List[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(_a	,  **_a       )







class 		lowercase							(     UpperCamelCase__	):
     _a											=		"bridgetower_text_model"

     def __init__(						self	,  _a=5_0265	,  _a=768	,  _a=12	,  _a=12	,  _a=1	,  _a=3072	,  _a="gelu"	,  _a=0.1	,  _a=0.1	,  _a=514	,  _a=1	,  _a=1e-05	,  _a=1	,  _a=0	,  _a=2	,  _a="absolute"	,  _a=True	,  **_a	,  )		->							Optional[Any]:
           super().__init__(**_a       )

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



     @classmethod
     def       a__			(						cls	,  _a	,  **_a       )		->							"PretrainedConfig":
           _A    ,     _A							:  Tuple    =		cls.get_config_dict(_a	,  **_a       )

           if config_dict.get("""model_type"""       ) == "bridgetower":
                 _A							:  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(_a	,  **_a       )







class 		lowercase							(     UpperCamelCase__	):
     _a											=		"bridgetower"

     def __init__(						self	,  _a=True	,  _a="gelu"	,  _a=768	,  _a=1	,  _a=1e-05	,  _a=False	,  _a="add"	,  _a=12	,  _a=6	,  _a=False	,  _a=False	,  _a=None	,  _a=None	,  **_a	,  )		->							List[Any]:
           # TODO: remove this once the Hub files are updated.
           _A							:  Optional[Any]    =		kwargs.pop("""text_config_dict"""	,  _a       )
           _A							:  Optional[Any]    =		kwargs.pop("""vision_config_dict"""	,  _a       )

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

           if text_config is None:
                 _A							:  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							:  Optional[Any]    =		BridgeTowerTextConfig(**_a       )
           _A							:  str    =		BridgeTowerVisionConfig(**_a       )

     @classmethod
     def       a__			(						cls	,  _a	,  _a	,  **_a       )		->							Tuple:
           return cls(text_config=text_config.to_dict()	,  vision_config=vision_config.to_dict()	,  **_a       )



     def       a__			(						self       )		->							Optional[Any]:
           _A							:  Optional[int]    =		copy.deepcopy(self.__dict__       )
           _A							:  List[Any]    =		self.text_config.to_dict()
           _A							:  int    =		self.vision_config.to_dict()
           _A							:  int    =		self.__class__.model_type
           return output







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_snake_case            = {
    "configuration_time_series_transformer": [
        "TIME_SERIES_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "TimeSeriesTransformerConfig",
    ],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _snake_case            = [
      "TIME_SERIES_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
      "TimeSeriesTransformerForPrediction",
      "TimeSeriesTransformerModel",
      "TimeSeriesTransformerPreTrainedModel",
  ]


if TYPE_CHECKING:
  from .configuration_time_series_transformer import (
      TIME_SERIES_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
      TimeSeriesTransformerConfig,
  )

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_time_series_transformer import (
        TIME_SERIES_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
        TimeSeriesTransformerForPrediction,
        TimeSeriesTransformerModel,
        TimeSeriesTransformerPreTrainedModel,
    )

else:
  import sys

  _snake_case            = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

