




'''simple docstring'''





import copy
import os
from typing import Union

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


UpperCamelCase_						:      int       =						logging.get_logger(__name__)

UpperCamelCase_						:      Optional[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        _a    (						__lowerCAmelCase							):
     SCREAMING_SNAKE_CASE_							:   Optional[Any]       =						"""bridgetower_vision_model"""






     def __init__(			self ,_SCREAMING_SNAKE_CASE=768 ,_SCREAMING_SNAKE_CASE=12 ,_SCREAMING_SNAKE_CASE=3 ,_SCREAMING_SNAKE_CASE=16 ,_SCREAMING_SNAKE_CASE=288 ,_SCREAMING_SNAKE_CASE=1 ,_SCREAMING_SNAKE_CASE=1e-05 ,_SCREAMING_SNAKE_CASE=False ,_SCREAMING_SNAKE_CASE=True ,_SCREAMING_SNAKE_CASE=False ,**_SCREAMING_SNAKE_CASE ,)							->      Dict:
          super().__init__(**_SCREAMING_SNAKE_CASE   )
          _snake_case							=				hidden_size
          _snake_case							=				num_hidden_layers
          _snake_case							=				num_channels
          _snake_case							=				patch_size
          _snake_case							=				image_size
          _snake_case							=				initializer_factor
          _snake_case							=				layer_norm_eps
          _snake_case							=				stop_gradient
          _snake_case							=				share_layernorm
          _snake_case							=				remove_last_layer






     @classmethod
     def     _lowercase     (			cls ,_SCREAMING_SNAKE_CASE ,**_SCREAMING_SNAKE_CASE   )							->      "PretrainedConfig":
          _snake_case							, _snake_case							=				cls.get_config_dict(_SCREAMING_SNAKE_CASE ,**_SCREAMING_SNAKE_CASE   )

          if config_dict.get("model_type"   ) == "bridgetower":
               _snake_case							=				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(_SCREAMING_SNAKE_CASE ,**_SCREAMING_SNAKE_CASE   )







class        _a    (						__lowerCAmelCase							):
     SCREAMING_SNAKE_CASE_							:   Tuple       =						"""bridgetower_text_model"""






     def __init__(			self ,_SCREAMING_SNAKE_CASE=50_265 ,_SCREAMING_SNAKE_CASE=768 ,_SCREAMING_SNAKE_CASE=12 ,_SCREAMING_SNAKE_CASE=12 ,_SCREAMING_SNAKE_CASE=1 ,_SCREAMING_SNAKE_CASE=3_072 ,_SCREAMING_SNAKE_CASE="gelu" ,_SCREAMING_SNAKE_CASE=0.1 ,_SCREAMING_SNAKE_CASE=0.1 ,_SCREAMING_SNAKE_CASE=514 ,_SCREAMING_SNAKE_CASE=1 ,_SCREAMING_SNAKE_CASE=1e-05 ,_SCREAMING_SNAKE_CASE=1 ,_SCREAMING_SNAKE_CASE=0 ,_SCREAMING_SNAKE_CASE=2 ,_SCREAMING_SNAKE_CASE="absolute" ,_SCREAMING_SNAKE_CASE=True ,**_SCREAMING_SNAKE_CASE ,)							->      Any:
          super().__init__(**_SCREAMING_SNAKE_CASE   )

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






     @classmethod
     def     _lowercase     (			cls ,_SCREAMING_SNAKE_CASE ,**_SCREAMING_SNAKE_CASE   )							->      "PretrainedConfig":
          _snake_case							, _snake_case							=				cls.get_config_dict(_SCREAMING_SNAKE_CASE ,**_SCREAMING_SNAKE_CASE   )

          if config_dict.get("model_type"   ) == "bridgetower":
               _snake_case							=				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(_SCREAMING_SNAKE_CASE ,**_SCREAMING_SNAKE_CASE   )







class        _a    (						__lowerCAmelCase							):
     SCREAMING_SNAKE_CASE_							:   Optional[Any]       =						"""bridgetower"""






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

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

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

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

          _snake_case							=				BridgeTowerTextConfig(**_SCREAMING_SNAKE_CASE   )
          _snake_case							=				BridgeTowerVisionConfig(**_SCREAMING_SNAKE_CASE   )






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






     def     _lowercase     (			self   )							->      Optional[int]:
          _snake_case							=				copy.deepcopy(self.__dict__   )
          _snake_case							=				self.text_config.to_dict()
          _snake_case							=				self.vision_config.to_dict()
          _snake_case							=				self.__class__.model_type
          return output








'''simple docstring'''





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCamelCase_						:      Union[str, Any]       =						{'''configuration_plbart''': ['''PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''PLBartConfig''']}

try:
   if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCamelCase_						:      Optional[int]       =						['''PLBartTokenizer''']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCamelCase_						:      int       =						[
       '''PLBART_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''PLBartForCausalLM''',
       '''PLBartForConditionalGeneration''',
       '''PLBartForSequenceClassification''',
       '''PLBartModel''',
       '''PLBartPreTrainedModel''',
   ]


if TYPE_CHECKING:
   from .configuration_plbart import PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP, PLBartConfig

   try:
      if not is_sentencepiece_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_plbart import PLBartTokenizer

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_plbart import (
          PLBART_PRETRAINED_MODEL_ARCHIVE_LIST,
          PLBartForCausalLM,
          PLBartForConditionalGeneration,
          PLBartForSequenceClassification,
          PLBartModel,
          PLBartPreTrainedModel,
      )


else:
   import sys

   UpperCamelCase_						:      str       =						_LazyModule(__name__, globals()['''__file__'''], _import_structure)


