



import copy
import os
from typing import Union

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


UpperCamelCase_										=					logging.get_logger(__name__)

UpperCamelCase_										=					{
    '''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 	_snake_case							(			__a      ):






   '''simple docstring'''






   A__	:      int    =		"bridgetower_vision_model"


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






   @classmethod
   def 	A__  (  cls:			List[Any]   ,lowerCamelCase_:			Union[str, os.PathLike]   ,**lowerCamelCase_:			List[Any]				)     ->	List[str]:
       UpperCAmelCase_			:    Optional[int]   						= cls.get_config_dict(a_   ,**a_				)

       if config_dict.get("""model_type"""				) == "bridgetower":
           UpperCAmelCase_			:    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 	_snake_case							(			__a      ):






   '''simple docstring'''






   A__	:      Optional[int]    =		"bridgetower_text_model"


   def __init__(  self:			Tuple   ,lowerCamelCase_:			Union[str, Any]=50265   ,lowerCamelCase_:			int=768   ,lowerCamelCase_:			Optional[Any]=12   ,lowerCamelCase_:			Dict=12   ,lowerCamelCase_:			int=1   ,lowerCamelCase_:			Any=3072   ,lowerCamelCase_:			Tuple="gelu"   ,lowerCamelCase_:			Union[str, Any]=0.1   ,lowerCamelCase_:			Union[str, Any]=0.1   ,lowerCamelCase_:			str=514   ,lowerCamelCase_:			Dict=1   ,lowerCamelCase_:			str=1e-05   ,lowerCamelCase_:			Union[str, Any]=1   ,lowerCamelCase_:			List[Any]=0   ,lowerCamelCase_:			Optional[int]=2   ,lowerCamelCase_:			str="absolute"   ,lowerCamelCase_:			Union[str, Any]=True   ,**lowerCamelCase_:			int   ,)     ->	Optional[Any]:
       super().__init__(**a_				)

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






   @classmethod
   def 	A__  (  cls:			Dict   ,lowerCamelCase_:			Union[str, os.PathLike]   ,**lowerCamelCase_:			Tuple				)     ->	Union[str, Any]:
       UpperCAmelCase_			:    List[str]   						= cls.get_config_dict(a_   ,**a_				)

       if config_dict.get("""model_type"""				) == "bridgetower":
           UpperCAmelCase_			:    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 	_snake_case							(			__a      ):






   '''simple docstring'''






   A__	:      int    =		"bridgetower"


   def __init__(  self:			Union[str, Any]   ,lowerCamelCase_:			Optional[int]=True   ,lowerCamelCase_:			List[Any]="gelu"   ,lowerCamelCase_:			str=768   ,lowerCamelCase_:			Dict=1   ,lowerCamelCase_:			Optional[int]=1e-05   ,lowerCamelCase_:			List[str]=False   ,lowerCamelCase_:			str="add"   ,lowerCamelCase_:			List[str]=12   ,lowerCamelCase_:			Dict=6   ,lowerCamelCase_:			List[Any]=False   ,lowerCamelCase_:			Optional[int]=False   ,lowerCamelCase_:			int=None   ,lowerCamelCase_:			Optional[Any]=None   ,**lowerCamelCase_:			Any   ,)     ->	Union[str, Any]:
       UpperCAmelCase_			:    Any   						= kwargs.pop("""text_config_dict"""   ,a_				)
       UpperCAmelCase_			:    Dict   						= kwargs.pop("""vision_config_dict"""   ,a_				)

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

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

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

       UpperCAmelCase_			:    int   						= BridgeTowerTextConfig(**a_				)
       UpperCAmelCase_			:    List[str]   						= BridgeTowerVisionConfig(**a_				)


   @classmethod
   def 	A__  (  cls:			Union[str, Any]   ,lowerCamelCase_:			BridgeTowerTextConfig   ,lowerCamelCase_:			BridgeTowerVisionConfig   ,**lowerCamelCase_:			Dict				)     ->	Optional[Any]:
       return cls(text_config=text_config.to_dict()   ,vision_config=vision_config.to_dict()   ,**a_				)






   def 	A__  (  self:			int				)     ->	Tuple:
       UpperCAmelCase_			:    List[str]   						= copy.deepcopy(self.__dict__				)
       UpperCAmelCase_			:    Dict   						= self.text_config.to_dict()
       UpperCAmelCase_			:    List[str]   						= self.vision_config.to_dict()
       UpperCAmelCase_			:    List[str]   						= self.__class__.model_type
       return output






"""simple docstring"""


import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ....tokenization_utils_fast import PreTrainedTokenizerFast
from ....utils import logging
from .tokenization_retribert import RetriBertTokenizer


A_						=				logging.get_logger(__name__)

A_						=				{'''vocab_file''': '''vocab.txt''', '''tokenizer_file''': '''tokenizer.json'''}

A_						=				{
    '''vocab_file''': {
        '''yjernite/retribert-base-uncased''': (
            '''https://huggingface.co/yjernite/retribert-base-uncased/resolve/main/vocab.txt'''
        ),
    },
    '''tokenizer_file''': {
        '''yjernite/retribert-base-uncased''': (
            '''https://huggingface.co/yjernite/retribert-base-uncased/resolve/main/tokenizer.json'''
        ),
    },
}

A_						=				{
    '''yjernite/retribert-base-uncased''': 5_12,
}


A_						=				{
    '''yjernite/retribert-base-uncased''': {'''do_lower_case''': True},
}






class 					lowercase(     __a ):







 '''simple docstring'''





 lowercase__ 					=					VOCAB_FILES_NAMES
 lowercase__ 					=					PRETRAINED_VOCAB_FILES_MAP
 lowercase__ 					=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 lowercase__ 					=					PRETRAINED_INIT_CONFIGURATION
 lowercase__ 					=					RetriBertTokenizer
 lowercase__ 					=					["input_ids", "attention_mask"]


 def __init__(				self:		int,      a_:		int=None,      a_:		Dict=None,      a_:		Any=True,      a_:		int="[UNK]",      a_:		Any="[SEP]",      a_:		List[Any]="[PAD]",      a_:		List[Any]="[CLS]",      a_:		str="[MASK]",      a_:		Dict=True,      a_:		Optional[int]=None,      **a_:		Tuple,      ):




   '''simple docstring'''



   super().__init__(
       a_,      tokenizer_file=a_,      do_lower_case=a_,      unk_token=a_,      sep_token=a_,      pad_token=a_,      cls_token=a_,      mask_token=a_,      tokenize_chinese_chars=a_,      strip_accents=a_,      **a_,      )

   _snake_case							:		List[Any]								=  json.loads(self.backend_tokenizer.normalizer.__getstate__()		)
   if (
       normalizer_state.get("""lowercase""",      a_		) != do_lower_case
       or normalizer_state.get("""strip_accents""",      a_		) != strip_accents
       or normalizer_state.get("""handle_chinese_chars""",      a_		) != tokenize_chinese_chars
   ):
     _snake_case							:		Dict								=  getattr(a_,      normalizer_state.pop("""type"""		)		)
     _snake_case							:		List[Any]								=  do_lower_case
     _snake_case							:		List[str]								=  strip_accents
     _snake_case							:		Tuple								=  tokenize_chinese_chars
     _snake_case							:		Tuple								=  normalizer_class(**a_		)

   _snake_case							:		List[str]								=  do_lower_case


 def  UpperCamelCase_   (				self:		Any,      a_:		str,      a_:		Optional[int]=None		):




   '''simple docstring'''



   _snake_case							:		Optional[Any]								=  [self.cls_token_id] + token_ids_a + [self.sep_token_id]

   if token_ids_a:
     output += token_ids_a + [self.sep_token_id]

   return output


 def  UpperCamelCase_   (				self:		List[str],      a_:		List[int],      a_:		Optional[List[int]] = None		):




   '''simple docstring'''



   _snake_case							:		Union[str, Any]								=  [self.sep_token_id]
   _snake_case							:		List[str]								=  [self.cls_token_id]
   if token_ids_a is None:
     return len(cls + token_ids_a + sep		) * [0]
   return len(cls + token_ids_a + sep		) * [0] + len(token_ids_a + sep		) * [1]


 def  UpperCamelCase_   (				self:		Dict,      a_:		str,      a_:		Optional[str] = None		):




   '''simple docstring'''



   _snake_case							:		Union[str, Any]								=  self._tokenizer.model.save(a_,      name=a_		)
   return tuple(a_		)


