
"""simple docstring"""



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        _SCREAMING_SNAKE_CASE  (  UpperCAmelCase							):






    '''simple docstring'''



    SCREAMING_SNAKE_CASE_:							Tuple							=					"bridgetower_vision_model"


    def __init__(					self	:			List[str]				,     UpperCAmelCase_	:			int=768				,     UpperCAmelCase_	:			Optional[Any]=12				,     UpperCAmelCase_	:			int=3				,     UpperCAmelCase_	:			Dict=16				,     UpperCAmelCase_	:			Optional[Any]=288				,     UpperCAmelCase_	:			int=1				,     UpperCAmelCase_	:			Tuple=1E-0_5				,     UpperCAmelCase_	:			int=False				,     UpperCAmelCase_	:			Tuple=True				,     UpperCAmelCase_	:			Dict=False				,     **UpperCAmelCase_	:			List[Any]				,     )		->  int:







           """simple docstring"""




           super().__init__(**UpperCAmelCase_  )
           _lowerCAmelCase  	=			hidden_size
           _lowerCAmelCase  	=			num_hidden_layers
           _lowerCAmelCase  	=			num_channels
           _lowerCAmelCase  	=			patch_size
           _lowerCAmelCase  	=			image_size
           _lowerCAmelCase  	=			initializer_factor
           _lowerCAmelCase  	=			layer_norm_eps
           _lowerCAmelCase  	=			stop_gradient
           _lowerCAmelCase  	=			share_layernorm
           _lowerCAmelCase  	=			remove_last_layer







    @classmethod
    def 				__lowerCamelCase	(					cls	:			Tuple				,     UpperCAmelCase_	:			Union[str, os.PathLike]				,     **UpperCAmelCase_	:			Optional[Any]  )		->  "PretrainedConfig":







           """simple docstring"""




           _lowerCAmelCase	,		_lowerCAmelCase  	=			cls.get_config_dict(UpperCAmelCase_				,     **UpperCAmelCase_  )

           if config_dict.get('model_type'  ) == "bridgetower":
                  _lowerCAmelCase  	=			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(UpperCAmelCase_				,     **UpperCAmelCase_  )






class        _SCREAMING_SNAKE_CASE  (  UpperCAmelCase							):






    '''simple docstring'''



    SCREAMING_SNAKE_CASE_:							Tuple							=					"bridgetower_text_model"


    def __init__(					self	:			str				,     UpperCAmelCase_	:			Optional[Any]=50_265				,     UpperCAmelCase_	:			Dict=768				,     UpperCAmelCase_	:			Optional[Any]=12				,     UpperCAmelCase_	:			Dict=12				,     UpperCAmelCase_	:			Tuple=1				,     UpperCAmelCase_	:			str=3_072				,     UpperCAmelCase_	:			Tuple="gelu"				,     UpperCAmelCase_	:			Optional[Any]=0.1				,     UpperCAmelCase_	:			List[Any]=0.1				,     UpperCAmelCase_	:			str=514				,     UpperCAmelCase_	:			List[str]=1				,     UpperCAmelCase_	:			Optional[int]=1E-0_5				,     UpperCAmelCase_	:			Optional[int]=1				,     UpperCAmelCase_	:			List[str]=0				,     UpperCAmelCase_	:			List[str]=2				,     UpperCAmelCase_	:			Dict="absolute"				,     UpperCAmelCase_	:			int=True				,     **UpperCAmelCase_	:			Tuple				,     )		->  Optional[int]:







           """simple docstring"""




           super().__init__(**UpperCAmelCase_  )

           _lowerCAmelCase  	=			vocab_size
           _lowerCAmelCase  	=			hidden_size
           _lowerCAmelCase  	=			num_hidden_layers
           _lowerCAmelCase  	=			num_attention_heads
           _lowerCAmelCase  	=			hidden_act
           _lowerCAmelCase  	=			initializer_factor
           _lowerCAmelCase  	=			intermediate_size
           _lowerCAmelCase  	=			hidden_dropout_prob
           _lowerCAmelCase  	=			attention_probs_dropout_prob
           _lowerCAmelCase  	=			max_position_embeddings
           _lowerCAmelCase  	=			type_vocab_size
           _lowerCAmelCase  	=			layer_norm_eps
           _lowerCAmelCase  	=			position_embedding_type
           _lowerCAmelCase  	=			use_cache
           _lowerCAmelCase  	=			pad_token_id
           _lowerCAmelCase  	=			bos_token_id
           _lowerCAmelCase  	=			eos_token_id







    @classmethod
    def 				__lowerCamelCase	(					cls	:			Union[str, Any]				,     UpperCAmelCase_	:			Union[str, os.PathLike]				,     **UpperCAmelCase_	:			List[str]  )		->  "PretrainedConfig":







           """simple docstring"""




           _lowerCAmelCase	,		_lowerCAmelCase  	=			cls.get_config_dict(UpperCAmelCase_				,     **UpperCAmelCase_  )

           if config_dict.get('model_type'  ) == "bridgetower":
                  _lowerCAmelCase  	=			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(UpperCAmelCase_				,     **UpperCAmelCase_  )






class        _SCREAMING_SNAKE_CASE  (  UpperCAmelCase							):






    '''simple docstring'''



    SCREAMING_SNAKE_CASE_:							Union[str, Any]							=					"bridgetower"


    def __init__(					self	:			Tuple				,     UpperCAmelCase_	:			Tuple=True				,     UpperCAmelCase_	:			Tuple="gelu"				,     UpperCAmelCase_	:			Union[str, Any]=768				,     UpperCAmelCase_	:			List[str]=1				,     UpperCAmelCase_	:			List[str]=1E-0_5				,     UpperCAmelCase_	:			Optional[Any]=False				,     UpperCAmelCase_	:			Optional[int]="add"				,     UpperCAmelCase_	:			List[Any]=12				,     UpperCAmelCase_	:			Any=6				,     UpperCAmelCase_	:			int=False				,     UpperCAmelCase_	:			str=False				,     UpperCAmelCase_	:			Tuple=None				,     UpperCAmelCase_	:			List[str]=None				,     **UpperCAmelCase_	:			List[Any]				,     )		->  Optional[Any]:







           """simple docstring"""




           _lowerCAmelCase  	=			kwargs.pop('text_config_dict'				,     UpperCAmelCase_  )
           _lowerCAmelCase  	=			kwargs.pop('vision_config_dict'				,     UpperCAmelCase_  )

           super().__init__(**UpperCAmelCase_  )
           _lowerCAmelCase  	=			share_cross_modal_transformer_layers
           _lowerCAmelCase  	=			hidden_act
           _lowerCAmelCase  	=			hidden_size
           _lowerCAmelCase  	=			initializer_factor
           _lowerCAmelCase  	=			layer_norm_eps
           _lowerCAmelCase  	=			share_link_tower_layers
           _lowerCAmelCase  	=			link_tower_type
           _lowerCAmelCase  	=			num_attention_heads
           _lowerCAmelCase  	=			num_hidden_layers
           _lowerCAmelCase  	=			tie_word_embeddings
           _lowerCAmelCase  	=			init_layernorm_from_vision_encoder

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

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

           _lowerCAmelCase  	=			BridgeTowerTextConfig(**UpperCAmelCase_  )
           _lowerCAmelCase  	=			BridgeTowerVisionConfig(**UpperCAmelCase_  )


    @classmethod
    def 				__lowerCamelCase	(					cls	:			List[Any]				,     UpperCAmelCase_	:			BridgeTowerTextConfig				,     UpperCAmelCase_	:			BridgeTowerVisionConfig				,     **UpperCAmelCase_	:			List[Any]  )		->  List[str]:







           """simple docstring"""




           return cls(text_config=text_config.to_dict()				,     vision_config=vision_config.to_dict()				,     **UpperCAmelCase_  )







    def 				__lowerCamelCase	(					self	:			Optional[Any]  )		->  List[Any]:







           """simple docstring"""




           _lowerCAmelCase  	=			copy.deepcopy(self.__dict__  )
           _lowerCAmelCase  	=			self.text_config.to_dict()
           _lowerCAmelCase  	=			self.vision_config.to_dict()
           _lowerCAmelCase  	=			self.__class__.model_type
           return output








"""simple docstring"""



from typing import TYPE_CHECKING

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


_snake_case          =						{
    '''configuration_roformer''': ['''ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''RoFormerConfig''', '''RoFormerOnnxConfig'''],
    '''tokenization_roformer''': ['''RoFormerTokenizer'''],
}

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

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_snake_case          =						[
	    '''ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''RoFormerForCausalLM''',
	    '''RoFormerForMaskedLM''',
	    '''RoFormerForMultipleChoice''',
	    '''RoFormerForQuestionAnswering''',
	    '''RoFormerForSequenceClassification''',
	    '''RoFormerForTokenClassification''',
	    '''RoFormerLayer''',
	    '''RoFormerModel''',
	    '''RoFormerPreTrainedModel''',
	    '''load_tf_weights_in_roformer''',
	]


try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_snake_case          =						[
	    '''TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''TFRoFormerForCausalLM''',
	    '''TFRoFormerForMaskedLM''',
	    '''TFRoFormerForMultipleChoice''',
	    '''TFRoFormerForQuestionAnswering''',
	    '''TFRoFormerForSequenceClassification''',
	    '''TFRoFormerForTokenClassification''',
	    '''TFRoFormerLayer''',
	    '''TFRoFormerModel''',
	    '''TFRoFormerPreTrainedModel''',
	]


try:
	if not is_flax_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_snake_case          =						[
	    '''FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''FlaxRoFormerForMaskedLM''',
	    '''FlaxRoFormerForMultipleChoice''',
	    '''FlaxRoFormerForQuestionAnswering''',
	    '''FlaxRoFormerForSequenceClassification''',
	    '''FlaxRoFormerForTokenClassification''',
	    '''FlaxRoFormerModel''',
	    '''FlaxRoFormerPreTrainedModel''',
	]


if TYPE_CHECKING:
	from .configuration_roformer import ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, RoFormerConfig, RoFormerOnnxConfig
	from .tokenization_roformer import RoFormerTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_roformer_fast import RoFormerTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_roformer import (
		    ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
		    RoFormerForCausalLM,
		    RoFormerForMaskedLM,
		    RoFormerForMultipleChoice,
		    RoFormerForQuestionAnswering,
		    RoFormerForSequenceClassification,
		    RoFormerForTokenClassification,
		    RoFormerLayer,
		    RoFormerModel,
		    RoFormerPreTrainedModel,
		    load_tf_weights_in_roformer,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_roformer import (
		    TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFRoFormerForCausalLM,
		    TFRoFormerForMaskedLM,
		    TFRoFormerForMultipleChoice,
		    TFRoFormerForQuestionAnswering,
		    TFRoFormerForSequenceClassification,
		    TFRoFormerForTokenClassification,
		    TFRoFormerLayer,
		    TFRoFormerModel,
		    TFRoFormerPreTrainedModel,
		)

	try:
		if not is_flax_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_flax_roformer import (
		    FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
		    FlaxRoFormerForMaskedLM,
		    FlaxRoFormerForMultipleChoice,
		    FlaxRoFormerForQuestionAnswering,
		    FlaxRoFormerForSequenceClassification,
		    FlaxRoFormerForTokenClassification,
		    FlaxRoFormerModel,
		    FlaxRoFormerPreTrainedModel,
		)


else:
	import sys

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






