



'''simple docstring'''





import copy
import os
from typing import TYPE_CHECKING, List, Union


if TYPE_CHECKING:
       pass

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


__a          =		logging.get_logger(__name__)

__a          =		{
    "kakaobrain/align-base": "https://huggingface.co/kakaobrain/align-base/resolve/main/config.json",
}







class     UpperCAmelCase_       (       _a	):
    """simple docstring"""







    lowercase					=		"align_text_model"




    def __init__(    self					:     Dict ,     snake_case_					:     Any=30_522 ,     snake_case_					:     Tuple=768 ,     snake_case_					:     Any=12 ,     snake_case_					:     Union[str, Any]=12 ,     snake_case_					:     List[Any]=3_072 ,     snake_case_					:     Dict="gelu" ,     snake_case_					:     Optional[int]=0.1 ,     snake_case_					:     str=0.1 ,     snake_case_					:     List[str]=512 ,     snake_case_					:     str=2 ,     snake_case_					:     Optional[Any]=0.02 ,     snake_case_					:     Optional[Any]=1E-1_2 ,     snake_case_					:     List[Any]=0 ,     snake_case_					:     Union[str, Any]="absolute" ,     snake_case_					:     List[Any]=True ,     **snake_case_					:     List[Any] ,     ):
          super().__init__(**snake_case_						)

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




    @classmethod
    def lowerCamelCase       (    cls					:     Union[str, Any] ,     snake_case_					:     Union[str, os.PathLike] ,     **snake_case_					:     Optional[int]						):
          cls._set_token_in_kwargs(snake_case_						)

          snake_case__						,				snake_case__  :    List[str]    						=      cls.get_config_dict(snake_case_ ,     **snake_case_						)

          # get the text config dict if we are loading from AlignConfig
          if config_dict.get("""model_type"""						) == "align":
                snake_case__  :    Optional[Any]    						=      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(snake_case_ ,     **snake_case_						)







class     UpperCAmelCase_       (       _a	):
    """simple docstring"""







    lowercase					=		"align_vision_model"




    def __init__(    self					:     Union[str, Any] ,     snake_case_					:     int = 3 ,     snake_case_					:     int = 600 ,     snake_case_					:     float = 2.0 ,     snake_case_					:     float = 3.1 ,     snake_case_					:     int = 8 ,     snake_case_					:     List[int] = [3, 3, 5, 3, 5, 5, 3] ,     snake_case_					:     List[int] = [32, 16, 24, 40, 80, 112, 192] ,     snake_case_					:     List[int] = [16, 24, 40, 80, 112, 192, 320] ,     snake_case_					:     List[int] = [] ,     snake_case_					:     List[int] = [1, 2, 2, 2, 1, 2, 1] ,     snake_case_					:     List[int] = [1, 2, 2, 3, 3, 4, 1] ,     snake_case_					:     List[int] = [1, 6, 6, 6, 6, 6, 6] ,     snake_case_					:     float = 0.25 ,     snake_case_					:     str = "swish" ,     snake_case_					:     int = 2_560 ,     snake_case_					:     str = "mean" ,     snake_case_					:     float = 0.02 ,     snake_case_					:     float = 0.001 ,     snake_case_					:     float = 0.99 ,     snake_case_					:     float = 0.2 ,     **snake_case_					:     Optional[int] ,     ):
          super().__init__(**snake_case_						)

          snake_case__  :    int    						=      num_channels
          snake_case__  :    Optional[int]    						=      image_size
          snake_case__  :    int    						=      width_coefficient
          snake_case__  :    str    						=      depth_coefficient
          snake_case__  :    Optional[int]    						=      depth_divisor
          snake_case__  :    Any    						=      kernel_sizes
          snake_case__  :    Optional[int]    						=      in_channels
          snake_case__  :    Optional[Any]    						=      out_channels
          snake_case__  :    Tuple    						=      depthwise_padding
          snake_case__  :    Tuple    						=      strides
          snake_case__  :    str    						=      num_block_repeats
          snake_case__  :    Any    						=      expand_ratios
          snake_case__  :    Any    						=      squeeze_expansion_ratio
          snake_case__  :    Tuple    						=      hidden_act
          snake_case__  :    int    						=      hidden_dim
          snake_case__  :    int    						=      pooling_type
          snake_case__  :    List[Any]    						=      initializer_range
          snake_case__  :    Optional[Any]    						=      batch_norm_eps
          snake_case__  :    Optional[int]    						=      batch_norm_momentum
          snake_case__  :    int    						=      drop_connect_rate
          snake_case__  :    int    						=      sum(snake_case_						) * 4




    @classmethod
    def lowerCamelCase       (    cls					:     str ,     snake_case_					:     Union[str, os.PathLike] ,     **snake_case_					:     List[Any]						):
          cls._set_token_in_kwargs(snake_case_						)

          snake_case__						,				snake_case__  :    Optional[Any]    						=      cls.get_config_dict(snake_case_ ,     **snake_case_						)

          # get the vision config dict if we are loading from AlignConfig
          if config_dict.get("""model_type"""						) == "align":
                snake_case__  :    Optional[Any]    						=      config_dict["""vision_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(snake_case_ ,     **snake_case_						)







class     UpperCAmelCase_       (       _a	):
    """simple docstring"""







    lowercase					=		"align"
    lowercase					=		True




    def __init__(    self					:     int ,     snake_case_					:     List[Any]=None ,     snake_case_					:     List[str]=None ,     snake_case_					:     Tuple=640 ,     snake_case_					:     List[Any]=1.0 ,     snake_case_					:     List[str]=0.02 ,     **snake_case_					:     Any ,     ):
          super().__init__(**snake_case_						)

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

          if vision_config is None:
                snake_case__  :    Optional[Any]    						=      {}
                logger.info("""vision_config is None. Initializing the AlignVisionConfig with default values."""						)

          snake_case__  :    List[str]    						=      AlignTextConfig(**snake_case_						)
          snake_case__  :    Optional[Any]    						=      AlignVisionConfig(**snake_case_						)

          snake_case__  :    Dict    						=      projection_dim
          snake_case__  :    List[str]    						=      temperature_init_value
          snake_case__  :    Tuple    						=      initializer_range




    @classmethod
    def lowerCamelCase       (    cls					:     Union[str, Any] ,     snake_case_					:     AlignTextConfig ,     snake_case_					:     AlignVisionConfig ,     **snake_case_					:     Optional[int]						):
          return cls(text_config=text_config.to_dict() ,     vision_config=vision_config.to_dict() ,     **snake_case_						)




    def lowerCamelCase       (    self					:     List[Any]						):
          snake_case__  :    int    						=      copy.deepcopy(self.__dict__						)
          snake_case__  :    Union[str, Any]    						=      self.text_config.to_dict()
          snake_case__  :    List[str]    						=      self.vision_config.to_dict()
          snake_case__  :    List[str]    						=      self.__class__.model_type
          return output





'''simple docstring'''





import string
from math import logaa





def 						__snake_case(				_lowerCAmelCase     ,   _lowerCAmelCase		)		-> int:
      snake_case__  :    List[str]    						=      document.translate(
          str.maketrans(""""""     ,   """"""     ,   string.punctuation		)		).replace("""\n"""     ,   """"""		)
      snake_case__  :    List[str]    						=      document_without_punctuation.split(""" """		)  # word tokenization
      return len([word for word in tokenize_document if word.lower() == term.lower()]		)





def 						__snake_case(				_lowerCAmelCase     ,   _lowerCAmelCase		)		-> tuple[int, int]:
      snake_case__  :    Dict    						=      corpus.lower().translate(
          str.maketrans(""""""     ,   """"""     ,   string.punctuation		)		)  # strip all punctuation and replace it with ''
      snake_case__  :    Any    						=      corpus_without_punctuation.split("""\n"""		)
      snake_case__  :    int    						=      term.lower()
      return (len([doc for doc in docs if term in doc]		), len(_lowerCAmelCase		))





def 						__snake_case(				_lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase=False		)		-> float:
      if smoothing:
            if n == 0:
                  raise ValueError("""log10(0) is undefined."""		)
            return round(1 + logaa(n / (1 + df)		)     ,   3		)

      if df == 0:
            raise ZeroDivisionError("""df must be > 0"""		)
      elif n == 0:
            raise ValueError("""log10(0) is undefined."""		)
      return round(logaa(n / df		)     ,   3		)





def 						__snake_case(				_lowerCAmelCase     ,   _lowerCAmelCase		)		-> float:
      return round(tf * idf     ,   3		)
