
'''simple docstring'''

from dataclasses import dataclass

__UpperCAmelCase												=   '0.1'
__UpperCAmelCase												=   'Lucia Harcekova'


@dataclass
class 		_a     :







   """simple docstring"""




   A		= 42
   A		= 42
   A		= 42






   def __repr__(       self      ):
    return f"""({self.offset}, {self.length}, {self.indicator})"""



class 		_a     :







   """simple docstring"""




   def __init__(       self    ,__SCREAMING_SNAKE_CASE = 13    ,__SCREAMING_SNAKE_CASE = 6      ):
    SCREAMING_SNAKE_CASE  :  List[Any]								=		window_size
    SCREAMING_SNAKE_CASE  :  Optional[Any]								=		lookahead_buffer_size
    SCREAMING_SNAKE_CASE  :  Union[str, Any]								=		self.window_size - self.lookahead_buffer_size






   def 					__a			(       self    ,__SCREAMING_SNAKE_CASE      ):
    SCREAMING_SNAKE_CASE  :  Tuple								=		[]
    SCREAMING_SNAKE_CASE  :  Dict								=		""""""

    # 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)
     SCREAMING_SNAKE_CASE  :  Optional[Any]								=		self._find_encoding_token(lowerCamelCase_    ,lowerCamelCase_      )

     # 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(lowerCamelCase_      ) > self.search_buffer_size:
      SCREAMING_SNAKE_CASE  :  Dict								=		search_buffer[-self.search_buffer_size :]

     # update the text
     SCREAMING_SNAKE_CASE  :  Union[str, Any]								=		text[token.length + 1 :]

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

    return output






   def 					__a			(       self    ,__SCREAMING_SNAKE_CASE      ):
    SCREAMING_SNAKE_CASE  :  str								=		""""""

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

    return output






   def 					__a			(       self    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE      ):

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

    # Initialise result parameters to default values
    SCREAMING_SNAKE_CASE  :  Optional[Any]								=		0, 0

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

    for i, character in enumerate(lowerCamelCase_      ):
     SCREAMING_SNAKE_CASE  :  Dict								=		len(lowerCamelCase_      ) - i
     if character == text[0]:
      SCREAMING_SNAKE_CASE  :  Union[str, Any]								=		self._match_length_from_index(lowerCamelCase_    ,lowerCamelCase_    ,0    ,lowerCamelCase_      )
      # 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:
       SCREAMING_SNAKE_CASE  :  Optional[Any]								=		found_offset, found_length

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






   def 					__a			(       self    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE      ):
    if not text or text[text_index] != window[window_index]:
     return 0
    return 1 + self._match_length_from_index(
        lowerCamelCase_    ,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=13, 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."






# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
    is_vision_available,
)


__lowerCamelCase   	=  {
    '''configuration_tvlt''': ['''TVLT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''TvltConfig'''],
    '''feature_extraction_tvlt''': ['''TvltFeatureExtractor'''],
    '''processing_tvlt''': ['''TvltProcessor'''],
}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __lowerCamelCase   	=  [
           '''TVLT_PRETRAINED_MODEL_ARCHIVE_LIST''',
           '''TvltModel''',
           '''TvltForPreTraining''',
           '''TvltForAudioVisualClassification''',
           '''TvltPreTrainedModel''',
       ]

try:
       if not is_vision_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __lowerCamelCase   	=  ['''TvltImageProcessor''']


if TYPE_CHECKING:
       from .configuration_tvlt import TVLT_PRETRAINED_CONFIG_ARCHIVE_MAP, TvltConfig
       from .processing_tvlt import TvltProcessor
       from .feature_extraction_tvlt import TvltFeatureExtractor

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tvlt import (
                  TVLT_PRETRAINED_MODEL_ARCHIVE_LIST,
                  TvltForAudioVisualClassification,
                  TvltForPreTraining,
                  TvltModel,
                  TvltPreTrainedModel,
              )

       try:
              if not is_vision_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .image_processing_tvlt import TvltImageProcessor


else:
       import sys

       __lowerCamelCase   	=  _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



