






import os
import warnings
from pathlib import Path
from typing import Optional, Union

from .dynamic_module_utils import custom_object_save
from .tokenization_utils_base import PreTrainedTokenizerBase
from .utils import PushToHubMixin, copy_func, direct_transformers_import, logging


UpperCamelCase											=      logging.get_logger(__name__)

# Dynamically import the Transformers module to grab the attribute classes of the processor form their names.
UpperCamelCase											=      direct_transformers_import(Path(__file__).parent)


UpperCamelCase											=      {
    '''AutoTokenizer''': '''PreTrainedTokenizerBase''',
    '''AutoFeatureExtractor''': '''FeatureExtractionMixin''',
    '''AutoImageProcessor''': '''ImageProcessingMixin''',
}




class 	lowerCamelCase__				(							UpperCAmelCase ):
      lowerCamelCase_							:   List[str]											=	['feature_extractor', 'tokenizer']
      # Names need to be attr_class for attr in attributes
      lowerCamelCase_							:   Tuple											=	None
      lowerCamelCase_							:   Optional[int]											=	None
      lowerCamelCase_							:   Tuple											=	None






      def __init__(self					:    Tuple		,		*_snake_case					:    Optional[Any]		,		**_snake_case					:    Tuple		)       ->   Tuple:


       """simple docstring"""






       for key in kwargs:
        if key not in self.attributes:
         raise TypeError(f'Unexpected keyword argument {key}.'		)
       for arg, attribute_name in zip(_snake_case		,		self.attributes		):
        if attribute_name in kwargs:
         raise TypeError(f'Got multiple values for argument {attribute_name}.'		)
        else:
         lowerCamelCase_							:    Union[str, Any]							=     arg

       if len(_snake_case		) != len(self.attributes		):
        raise ValueError(
            f'This processor requires {len(self.attributes		)} arguments: {", ".join(self.attributes		)}. Got '
            f'{len(_snake_case		)} arguments instead.'		)

       # Check each arg is of the proper class (this will also catch a user initializing in the wrong order)
       for attribute_name, arg in kwargs.items():
        lowerCamelCase_							:    Optional[Any]							=     getattr(self		,		f'{attribute_name}_class'		)
        # Nothing is ever going to be an instance of "AutoXxx", in that case we check the base class.
        lowerCamelCase_							:    str							=     AUTO_TO_BASE_CLASS_MAPPING.get(_snake_case		,		_snake_case		)
        if isinstance(_snake_case		,		_snake_case		):
         lowerCamelCase_							:    str							=     tuple(getattr(_snake_case		,		_snake_case		) for n in class_name if n is not None		)
        else:
         lowerCamelCase_							:    List[Any]							=     getattr(_snake_case		,		_snake_case		)

        if not isinstance(_snake_case		,		_snake_case		):
         raise ValueError(
             f'Received a {type(_snake_case		).__name__} for argument {attribute_name}, but a {class_name} was expected.'		)

        setattr(self		,		_snake_case		,		_snake_case		)






      def __repr__(self					:    List[str]		)       ->   Any:


       """simple docstring"""






       lowerCamelCase_							:    Optional[Any]							=     [f'- {name}: {repr(getattr(self		,		_snake_case		)		)}' for name in self.attributes]
       lowerCamelCase_							:    Any							=     '\n'.join(_snake_case		)
       return f'{self.__class__.__name__}:\n{attributes_repr}'






      def      UpperCAmelCase_     (self					:    Any		,		_snake_case					:    Tuple		,		_snake_case					:    bool = False		,		**_snake_case					:    Optional[Any]		)       ->   Union[str, Any]:


       """simple docstring"""






       os.makedirs(_snake_case		,		exist_ok=_snake_case		)

       if push_to_hub:
        lowerCamelCase_							:    Tuple							=     kwargs.pop('commit_message'		,		_snake_case		)
        lowerCamelCase_							:    Tuple							=     kwargs.pop('repo_id'		,		save_directory.split(os.path.sep		)[-1]		)
        lowerCamelCase_							:    Optional[Any]							=     self._create_repo(_snake_case		,		**_snake_case		)
        lowerCamelCase_							:    int							=     self._get_files_timestamps(_snake_case		)
       # If we have a custom config, we copy the file defining it in the folder and set the attributes so it can be
       # loaded from the Hub.
       if self._auto_class is not None:
        lowerCamelCase_							:    str							=     [getattr(self		,		_snake_case		) for attribute_name in self.attributes]
        lowerCamelCase_							:    str							=     [(a.init_kwargs if isinstance(_snake_case		,		_snake_case		) else a) for a in attrs]
        custom_object_save(self		,		_snake_case		,		config=_snake_case		)

       for attribute_name in self.attributes:
        lowerCamelCase_							:    Tuple							=     getattr(self		,		_snake_case		)
        # Include the processor class in the attribute config so this processor can then be reloaded with the
        # `AutoProcessor` API.
        if hasattr(_snake_case		,		'_set_processor_class'		):
         attribute._set_processor_class(self.__class__.__name__		)
        attribute.save_pretrained(_snake_case		)

       if self._auto_class is not None:
        # We added an attribute to the init_kwargs of the tokenizers, which needs to be cleaned up.
        for attribute_name in self.attributes:
         lowerCamelCase_							:    Tuple							=     getattr(self		,		_snake_case		)
         if isinstance(_snake_case		,		_snake_case		):
          del attribute.init_kwargs["auto_map"]

       if push_to_hub:
        self._upload_modified_files(
            _snake_case		,		_snake_case		,		_snake_case		,		commit_message=_snake_case		,		token=kwargs.get('use_auth_token'		)		,		)






      @classmethod
      def      UpperCAmelCase_     (cls					:    Tuple		,		_snake_case					:    Union[str, os.PathLike]		,		_snake_case					:    Optional[Union[str, os.PathLike]] = None		,		_snake_case					:    bool = False		,		_snake_case					:    bool = False		,		_snake_case					:    Optional[Union[str, bool]] = None		,		_snake_case					:    str = "main"		,		**_snake_case					:    Union[str, Any]		,		)       ->   Dict:


       """simple docstring"""






       lowerCamelCase_							:    List[Any]							=     cache_dir
       lowerCamelCase_							:    Optional[int]							=     force_download
       lowerCamelCase_							:    Optional[Any]							=     local_files_only
       lowerCamelCase_							:    Dict							=     revision

       lowerCamelCase_							:    Union[str, Any]							=     kwargs.pop('use_auth_token'		,		_snake_case		)
       if use_auth_token is not None:
        warnings.warn(
            'The `use_auth_token` argument is deprecated and will be removed in v5 of Transformers.'		,		_snake_case		)
        if token is not None:
         raise ValueError(
             '`token` and `use_auth_token` are both specified. Please set only the argument `token`.'		)
        lowerCamelCase_							:    int							=     use_auth_token

       if token is not None:
        # change to `token` in a follow-up PR
        lowerCamelCase_							:    Union[str, Any]							=     token

       lowerCamelCase_							:    Optional[int]							=     cls._get_arguments_from_pretrained(_snake_case		,		**_snake_case		)
       return cls(*_snake_case		)






      @classmethod
      def      UpperCAmelCase_     (cls					:    List[str]		,		_snake_case					:    int="AutoProcessor"		)       ->   List[str]:


       """simple docstring"""






       if not isinstance(_snake_case		,		_snake_case		):
        lowerCamelCase_							:    Any							=     auto_class.__name__

       import transformers.models.auto as auto_module

       if not hasattr(_snake_case		,		_snake_case		):
        raise ValueError(f'{auto_class} is not a valid auto class.'		)

       lowerCamelCase_							:    Tuple							=     auto_class






      @classmethod
      def      UpperCAmelCase_     (cls					:    Dict		,		_snake_case					:    List[Any]		,		**_snake_case					:    Optional[Any]		)       ->   Tuple:


       """simple docstring"""






       lowerCamelCase_							:    List[str]							=     []
       for attribute_name in cls.attributes:
        lowerCamelCase_							:    str							=     getattr(cls		,		f'{attribute_name}_class'		)
        if isinstance(_snake_case		,		_snake_case		):
         lowerCamelCase_							:    Tuple							=     tuple(getattr(_snake_case		,		_snake_case		) if n is not None else None for n in class_name		)
         lowerCamelCase_							:    Union[str, Any]							=     kwargs.get('use_fast'		,		_snake_case		)
         if use_fast and classes[1] is not None:
          lowerCamelCase_							:    Any							=     classes[1]
         else:
          lowerCamelCase_							:    Optional[int]							=     classes[0]
        else:
         lowerCamelCase_							:    Optional[Any]							=     getattr(_snake_case		,		_snake_case		)

        args.append(attribute_class.from_pretrained(_snake_case		,		**_snake_case		)		)
       return args






      @property
      def      UpperCAmelCase_     (self					:    List[Any]		)       ->   Optional[int]:


       """simple docstring"""






       lowerCamelCase_							:    Any							=     getattr(self		,		self.attributes[0]		)
       return getattr(_snake_case		,		'model_input_names'		,		_snake_case		)


UpperCamelCase											=      copy_func(ProcessorMixin.push_to_hub)
if ProcessorMixin.push_to_hub.__doc__ is not None:
       UpperCamelCase											=      ProcessorMixin.push_to_hub.__doc__.format(
           object='''processor''', object_class='''AutoProcessor''', object_files='''processor files'''
       )








from dataclasses import dataclass

UpperCamelCase											=      '''0.1'''
UpperCamelCase											=      '''Lucia Harcekova'''




@dataclass
class 	lowerCamelCase__				:
      lowerCamelCase_							:   int
      lowerCamelCase_							:   int
      lowerCamelCase_							:   str






      def __repr__(self					:    Tuple		)       ->   str:


       """simple docstring"""






       return f'({self.offset}, {self.length}, {self.indicator})'




class 	lowerCamelCase__				:






      def __init__(self					:    Optional[int]		,		_snake_case					:    int = 13		,		_snake_case					:    int = 6		)       ->   None:


       """simple docstring"""






       lowerCamelCase_							:    Any							=     window_size
       lowerCamelCase_							:    List[str]							=     lookahead_buffer_size
       lowerCamelCase_							:    Optional[int]							=     self.window_size - self.lookahead_buffer_size






      def      UpperCAmelCase_     (self					:    Tuple		,		_snake_case					:    str		)       ->   list[Token]:


       """simple docstring"""






       lowerCamelCase_							:    Optional[Any]							=     []
       lowerCamelCase_							:    List[str]							=     ''

       # while there are still characters in text to compress
       while text:
        # find the next encoding phrase
        # - triplet with offset, length, indicator (the next encoding character)
        lowerCamelCase_							:    List[Any]							=     self._find_encoding_token(_snake_case		,		_snake_case		)

        # update the search buffer:
        # - add new characters from text into it
        # - check if size exceed the max search buffer size, if so, drop the
        #   oldest elements
        search_buffer += text[: token.length + 1]
        if len(_snake_case		) > self.search_buffer_size:
         lowerCamelCase_							:    int							=     search_buffer[-self.search_buffer_size :]

        # update the text
        lowerCamelCase_							:    str							=     text[token.length + 1 :]

        # append the token to output
        output.append(_snake_case		)

       return output






      def      UpperCAmelCase_     (self					:    Tuple		,		_snake_case					:    list[Token]		)       ->   str:


       """simple docstring"""






       lowerCamelCase_							:    Optional[Any]							=     ''

       for token in tokens:
        for _ in range(token.length		):
         output += output[-token.offset]
        output += token.indicator

       return output






      def      UpperCAmelCase_     (self					:    Dict		,		_snake_case					:    str		,		_snake_case					:    str		)       ->   Token:


       """simple docstring"""






       if not text:
        raise ValueError('We need some text to work with.'		)

       # Initialise result parameters to default values
       lowerCamelCase_		,      lowerCamelCase_							:    List[str]							=     0, 0

       if not search_buffer:
        return Token(_snake_case		,		_snake_case		,		text[length]		)

       for i, character in enumerate(_snake_case		):
        lowerCamelCase_							:    Union[str, Any]							=     len(_snake_case		) - i
        if character == text[0]:
         lowerCamelCase_							:    str							=     self._match_length_from_index(_snake_case		,		_snake_case		,		0		,		_snake_case		)
         # if the found length is bigger than the current or if it's equal,
         # which means it's offset is smaller: update offset and length
         if found_length >= length:
          lowerCamelCase_		,      lowerCamelCase_							:    str							=     found_offset, found_length

       return Token(_snake_case		,		_snake_case		,		text[length]		)






      def      UpperCAmelCase_     (self					:    Any		,		_snake_case					:    str		,		_snake_case					:    str		,		_snake_case					:    int		,		_snake_case					:    int		)       ->   int:


       """simple docstring"""






       if not text or text[text_index] != window[window_index]:
        return 0
       return 1 + self._match_length_from_index(
           _snake_case		,		window + text[text_index]		,		text_index + 1		,		window_index + 1		)


if __name__ == "__main__":
       from doctest import testmod

       testmod()
       # Initialize compressor class
       UpperCamelCase											=      LZaaCompressor(window_size=1_3, lookahead_buffer_size=6)

       # Example
       UpperCamelCase											=      '''cabracadabrarrarrad'''
       UpperCamelCase											=      lzaa_compressor.compress(TEXT)
       print(lzaa_compressor.compress('''ababcbababaa'''))
       UpperCamelCase											=      lzaa_compressor.decompress(compressed_text)
       assert decompressed_text == TEXT, "The LZ77 algorithm returned the invalid result."
