






import copy
import os
from typing import Union

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


__lowerCamelCase :      Tuple										=				logging.get_logger(__name__)

__lowerCamelCase :      Tuple										=				{
    """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 							SCREAMING_SNAKE_CASE__						(    UpperCamelCase_						):





					"""simple docstring"""






					a_     =		"bridgetower_vision_model"



					def __init__( self					:  str							,		__A					:  Tuple=7_6_8							,		__A					:  List[Any]=1_2							,		__A					:  List[Any]=3							,		__A					:  Any=1_6							,		__A					:  str=2_8_8							,		__A					:  Optional[Any]=1							,		__A					:  Any=1e-0_5							,		__A					:  Union[str, Any]=False							,		__A					:  Tuple=True							,		__A					:  Any=False							,		**__A					:  Optional[int]							,		):
						super().__init__(**__A	)
						snake_case__			:				int       =  hidden_size
						snake_case__			:				List[str]       =  num_hidden_layers
						snake_case__			:				Optional[int]       =  num_channels
						snake_case__			:				int       =  patch_size
						snake_case__			:				List[Any]       =  image_size
						snake_case__			:				List[Any]       =  initializer_factor
						snake_case__			:				Any       =  layer_norm_eps
						snake_case__			:				Tuple       =  stop_gradient
						snake_case__			:				str       =  share_layernorm
						snake_case__			:				Union[str, Any]       =  remove_last_layer






					@classmethod
					def 	_lowercase					( cls					:  Any							,		__A					:  Union[str, os.PathLike]							,		**__A					:  Optional[int]	):
						snake_case__,						snake_case__			:				Dict       =  cls.get_config_dict(__A							,		**__A	)

						if config_dict.get("model_type"	) == "bridgetower":
							snake_case__			:				Tuple       =  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 							SCREAMING_SNAKE_CASE__						(    UpperCamelCase_						):





					"""simple docstring"""






					a_     =		"bridgetower_text_model"



					def __init__( self					:  List[Any]							,		__A					:  Union[str, Any]=5_0_2_6_5							,		__A					:  int=7_6_8							,		__A					:  Tuple=1_2							,		__A					:  str=1_2							,		__A					:  List[str]=1							,		__A					:  Optional[Any]=3_0_7_2							,		__A					:  Union[str, Any]="gelu"							,		__A					:  Optional[Any]=0.1							,		__A					:  Union[str, Any]=0.1							,		__A					:  Any=5_1_4							,		__A					:  Optional[Any]=1							,		__A					:  Union[str, Any]=1e-0_5							,		__A					:  Any=1							,		__A					:  List[Any]=0							,		__A					:  List[Any]=2							,		__A					:  Optional[int]="absolute"							,		__A					:  Optional[int]=True							,		**__A					:  Any							,		):
						super().__init__(**__A	)

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






					@classmethod
					def 	_lowercase					( cls					:  List[Any]							,		__A					:  Union[str, os.PathLike]							,		**__A					:  str	):
						snake_case__,						snake_case__			:				Union[str, Any]       =  cls.get_config_dict(__A							,		**__A	)

						if config_dict.get("model_type"	) == "bridgetower":
							snake_case__			:				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 							SCREAMING_SNAKE_CASE__						(    UpperCamelCase_						):





					"""simple docstring"""






					a_     =		"bridgetower"



					def __init__( self					:  Optional[Any]							,		__A					:  str=True							,		__A					:  Optional[Any]="gelu"							,		__A					:  Optional[int]=7_6_8							,		__A					:  Optional[Any]=1							,		__A					:  Dict=1e-0_5							,		__A					:  int=False							,		__A					:  List[str]="add"							,		__A					:  Optional[int]=1_2							,		__A					:  List[str]=6							,		__A					:  Union[str, Any]=False							,		__A					:  int=False							,		__A					:  Dict=None							,		__A					:  List[Any]=None							,		**__A					:  List[str]							,		):
						# TODO: remove this once the Hub files are updated.
						snake_case__			:				Dict       =  kwargs.pop("text_config_dict"							,		__A	)
						snake_case__			:				Any       =  kwargs.pop("vision_config_dict"							,		__A	)

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

						if text_config is None:
							snake_case__			:				Optional[int]       =  {}
							logger.info("`text_config` is `None`. Initializing the `BridgeTowerTextConfig` with default values."	)

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

						snake_case__			:				Optional[int]       =  BridgeTowerTextConfig(**__A	)
						snake_case__			:				Dict       =  BridgeTowerVisionConfig(**__A	)



					@classmethod
					def 	_lowercase					( cls					:  List[str]							,		__A					:  BridgeTowerTextConfig							,		__A					:  BridgeTowerVisionConfig							,		**__A					:  List[Any]	):
						return cls(text_config=text_config.to_dict()							,		vision_config=vision_config.to_dict()							,		**__A	)






					def 	_lowercase					( self					:  int	):
						snake_case__			:				Any       =  copy.deepcopy(self.__dict__	)
						snake_case__			:				Any       =  self.text_config.to_dict()
						snake_case__			:				str       =  self.vision_config.to_dict()
						snake_case__			:				Optional[int]       =  self.__class__.model_type
						return output











from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__lowerCamelCase :      Union[str, Any]										=				{
    """configuration_time_series_transformer""": [
        """TIME_SERIES_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """TimeSeriesTransformerConfig""",
    ],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__lowerCamelCase :      Tuple										=				[
	    """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

	__lowerCamelCase :      Tuple										=				_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



