





'''simple docstring'''

import copy
import random

from transformers import CLIPTokenizer







class __a			( _snake_case      ):





   def __init__(       self					:      List[Any]					,*lowerCamelCase					:      Tuple					,**lowerCamelCase					:      List[Any]     ):

      '''simple docstring'''

      super().__init__(*lowerCamelCase					,**lowerCamelCase     )
      __SCREAMING_SNAKE_CASE   				=							{}





   def   UpperCAmelCase__  (       self					:      List[str]					,lowerCamelCase					:      List[Any]					,*lowerCamelCase					:      Any					,**lowerCamelCase					:      Tuple     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							super().add_tokens(lowerCamelCase					,*lowerCamelCase					,**lowerCamelCase     )
      if num_added_tokens == 0:
         raise ValueError(
             f"""The tokenizer already contains the token {placeholder_token}. Please pass a different"""
             """ `placeholder_token` that is not already in the tokenizer."""     )





   def   UpperCAmelCase__  (       self					:      int					,lowerCamelCase					:      Union[str, Any]					,*lowerCamelCase					:      List[str]					,lowerCamelCase					:      Union[str, Any]=1					,**lowerCamelCase					:      Optional[int]     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							[]
      if num_vec_per_token == 1:
         self.try_adding_tokens(lowerCamelCase					,*lowerCamelCase					,**lowerCamelCase     )
         output.append(lowerCamelCase     )
      else:
         __SCREAMING_SNAKE_CASE   				=							[]
         for i in range(lowerCamelCase     ):
            __SCREAMING_SNAKE_CASE   				=							placeholder_token + f"""_{i}"""
            self.try_adding_tokens(lowerCamelCase					,*lowerCamelCase					,**lowerCamelCase     )
            output.append(lowerCamelCase     )
        # handle cases where there is a new placeholder token that contains the current placeholder token but is larger
      for token in self.token_map:
         if token in placeholder_token:
            raise ValueError(
                f"""The tokenizer already has placeholder token {token} that can get confused with"""
                f""" {placeholder_token}keep placeholder tokens independent"""     )
      __SCREAMING_SNAKE_CASE   				=							output





   def   UpperCAmelCase__  (       self					:      int					,lowerCamelCase					:      Tuple					,lowerCamelCase					:      Optional[Any]=False					,lowerCamelCase					:      int=1.0     ):

      '''simple docstring'''

      if isinstance(lowerCamelCase					,lowerCamelCase     ):
         __SCREAMING_SNAKE_CASE   				=							[]
         for i in range(len(lowerCamelCase     )     ):
            output.append(self.replace_placeholder_tokens_in_text(text[i]					,vector_shuffle=lowerCamelCase     )     )
         return output
      for placeholder_token in self.token_map:
         if placeholder_token in text:
            __SCREAMING_SNAKE_CASE   				=							self.token_map[placeholder_token]
            __SCREAMING_SNAKE_CASE   				=							tokens[: 1 + int(len(lowerCamelCase     ) * prop_tokens_to_load     )]
            if vector_shuffle:
               __SCREAMING_SNAKE_CASE   				=							copy.copy(lowerCamelCase     )
               random.shuffle(lowerCamelCase     )
            __SCREAMING_SNAKE_CASE   				=							text.replace(lowerCamelCase					,""" """.join(lowerCamelCase     )     )
      return text





   def __call__(       self					:      Optional[Any]					,lowerCamelCase					:      Any					,*lowerCamelCase					:      List[Any]					,lowerCamelCase					:      List[str]=False					,lowerCamelCase					:      Any=1.0					,**lowerCamelCase					:      Dict     ):

      '''simple docstring'''

      return super().__call__(
          self.replace_placeholder_tokens_in_text(
              lowerCamelCase					,vector_shuffle=lowerCamelCase					,prop_tokens_to_load=lowerCamelCase     )					,*lowerCamelCase					,**lowerCamelCase					,)





   def   UpperCAmelCase__  (       self					:      str					,lowerCamelCase					:      Dict					,*lowerCamelCase					:      Optional[int]					,lowerCamelCase					:      List[Any]=False					,lowerCamelCase					:      Optional[Any]=1.0					,**lowerCamelCase					:      Optional[int]     ):

      '''simple docstring'''

      return super().encode(
          self.replace_placeholder_tokens_in_text(
              lowerCamelCase					,vector_shuffle=lowerCamelCase					,prop_tokens_to_load=lowerCamelCase     )					,*lowerCamelCase					,**lowerCamelCase					,)












'''simple docstring'''

from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


a   	=    {
    "configuration_informer": [
        "INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "InformerConfig",
    ],
}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    a   	=    [
        "INFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
        "InformerForPrediction",
        "InformerModel",
        "InformerPreTrainedModel",
    ]


if TYPE_CHECKING:
    from .configuration_informer import INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, InformerConfig

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_informer import (
            INFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
            InformerForPrediction,
            InformerModel,
            InformerPreTrainedModel,
        )

else:
    import sys

    a   	=    _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





