code: 
  
  
  '''simple docstring'''
  
  import json
  import os
  import unittest
  
  from transformers import BartTokenizer, BartTokenizerFast, BatchEncoding
  from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
  from transformers.testing_utils import require_tokenizers, require_torch
  from transformers.utils import cached_property
  
  from ...test_tokenization_common import TokenizerTesterMixin, filter_roberta_detectors
  
  
  
  
  
  
  
  @require_tokenizers
  class  UpperCAmelCase (       SCREAMING_SNAKE_CASE__		,			unittest.TestCase):
   _lowerCamelCase :					List[str]    					=					BartTokenizer
   _lowerCamelCase :					List[Any]    					=					BartTokenizerFast
   _lowerCamelCase :					Any    					=					True
   _lowerCamelCase :					Dict    					=					filter_roberta_detectors
  
   def 		lowercase_						( self :	Dict					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      super().setUp()
      UpperCamelCase__ 						=						[
          "l",
          "o",
          "w",
          "e",
          "r",
          "s",
          "t",
          "i",
          "d",
          "n",
          "\u0120",
          "\u0120l",
          "\u0120n",
          "\u0120lo",
          "\u0120low",
          "er",
          "\u0120lowest",
          "\u0120newer",
          "\u0120wider",
          "<unk>",
      ]
      UpperCamelCase__ 						=						dict(zip(a_,					range(len(a_					)					)					)					)
      UpperCamelCase__ 						=						["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
      UpperCamelCase__ 						=						{"unk_token": "<unk>"}
  
      UpperCamelCase__ 						=						os.path.join(self.tmpdirname,					VOCAB_FILES_NAMES["vocab_file"]					)
      UpperCamelCase__ 						=						os.path.join(self.tmpdirname,					VOCAB_FILES_NAMES["merges_file"]					)
      with open(self.vocab_file,					"w",					encoding="utf-8"					) as fp:
         fp.write(json.dumps(a_					) + "\n"					)
      with open(self.merges_file,					"w",					encoding="utf-8"					) as fp:
         fp.write("\n".join(a_					)					)
  
   def 		lowercase_						( self :	int,					**a_ :	int					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      kwargs.update(self.special_tokens_map					)
      return self.tokenizer_class.from_pretrained(self.tmpdirname,					**a_					)
  
   def 		lowercase_						( self :	List[Any],					**a_ :	List[str]					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      kwargs.update(self.special_tokens_map					)
      return self.rust_tokenizer_class.from_pretrained(self.tmpdirname,					**a_					)
  
   def 		lowercase_						( self :	int,					a_ :	int					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      return "lower newer", "lower newer"
  
   @cached_property
   def 		lowercase_						( self :	Any					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      return BartTokenizer.from_pretrained("facebook/bart-large"					)
  
   @cached_property
   def 		lowercase_						( self :	Tuple					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      return BartTokenizerFast.from_pretrained("facebook/bart-large"					)
  
   @require_torch
   def 		lowercase_						( self :	Optional[int]					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      UpperCamelCase__ 						=						["A long paragraph for summarization.", "Another paragraph for summarization."]
      UpperCamelCase__ 						=						[0, 250, 251, 1_7818, 13, 3_9186, 1938, 4, 2]
  
      for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
         UpperCamelCase__ 						=						tokenizer(a_,					max_length=len(a_					),					padding=a_,					return_tensors="pt"					)
         self.assertIsInstance(a_,					a_					)
  
         self.assertEqual((2, 9),					batch.input_ids.shape					)
         self.assertEqual((2, 9),					batch.attention_mask.shape					)
         UpperCamelCase__ 						=						batch.input_ids.tolist()[0]
         self.assertListEqual(a_,					a_					)
         # Test that special tokens are reset
  
   @require_torch
   def 		lowercase_						( self :	Union[str, Any]					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      UpperCamelCase__ 						=						["A long paragraph for summarization.", "Another paragraph for summarization."]
      for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
         UpperCamelCase__ 						=						tokenizer(a_,					padding=a_,					return_tensors="pt"					)
         # check if input_ids are returned and no labels
         self.assertIn("input_ids",					a_					)
         self.assertIn("attention_mask",					a_					)
         self.assertNotIn("labels",					a_					)
         self.assertNotIn("decoder_attention_mask",					a_					)
  
   @require_torch
   def 		lowercase_						( self :	List[str]					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      UpperCamelCase__ 						=						[
          "Summary of the text.",
          "Another summary.",
      ]
      for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
         UpperCamelCase__ 						=						tokenizer(text_target=a_,					max_length=32,					padding="max_length",					return_tensors="pt"					)
         self.assertEqual(32,					targets["input_ids"].shape[1]					)
  
   @require_torch
   def 		lowercase_						( self :	Tuple					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
         UpperCamelCase__ 						=						tokenizer(
             ["I am a small frog" * 1024, "I am a small frog"],					padding=a_,					truncation=a_,					return_tensors="pt"					)
         self.assertIsInstance(a_,					a_					)
         self.assertEqual(batch.input_ids.shape,					(2, 1024)					)
  
   @require_torch
   def 		lowercase_						( self :	Dict					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      UpperCamelCase__ 						=						["A long paragraph for summarization."]
      UpperCamelCase__ 						=						[
          "Summary of the text.",
      ]
      for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
         UpperCamelCase__ 						=						tokenizer(a_,					return_tensors="pt"					)
         UpperCamelCase__ 						=						tokenizer(text_target=a_,					return_tensors="pt"					)
         UpperCamelCase__ 						=						inputs["input_ids"]
         UpperCamelCase__ 						=						targets["input_ids"]
         self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item()					)
         self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item()					)
         self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item()					)
         self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item()					)
  
   def 		lowercase_						( self :	Optional[int]					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      pass
  
   def 		lowercase_						( self :	Optional[int]					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
         with self.subTest(f'{tokenizer.__class__.__name__} ({pretrained_name})'					):
            UpperCamelCase__ 						=						self.rust_tokenizer_class.from_pretrained(a_,					**a_					)
            UpperCamelCase__ 						=						self.tokenizer_class.from_pretrained(a_,					**a_					)
            UpperCamelCase__ 						=						"A, <mask> AllenNLP sentence."
            UpperCamelCase__ 						=						tokenizer_r.encode_plus(a_,					add_special_tokens=a_,					return_token_type_ids=a_					)
            UpperCamelCase__ 						=						tokenizer_p.encode_plus(a_,					add_special_tokens=a_,					return_token_type_ids=a_					)
  
            # token_type_ids should put 0 everywhere
            self.assertEqual(sum(tokens_r["token_type_ids"]					),					sum(tokens_p["token_type_ids"]					)					)
  
            # attention_mask should put 1 everywhere, so sum over length should be 1
            self.assertEqual(
                sum(tokens_r["attention_mask"]					) / len(tokens_r["attention_mask"]					),					sum(tokens_p["attention_mask"]					) / len(tokens_p["attention_mask"]					),					)
  
            UpperCamelCase__ 						=						tokenizer_r.convert_ids_to_tokens(tokens_r["input_ids"]					)
            UpperCamelCase__ 						=						tokenizer_p.convert_ids_to_tokens(tokens_p["input_ids"]					)
  
            # Rust correctly handles the space before the mask while python doesnt
            self.assertSequenceEqual(tokens_p["input_ids"],					[0, 250, 6, 5_0264, 3823, 487, 2_1992, 3645, 4, 2]					)
            self.assertSequenceEqual(tokens_r["input_ids"],					[0, 250, 6, 5_0264, 3823, 487, 2_1992, 3645, 4, 2]					)
  
            self.assertSequenceEqual(
                a_,					["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"]					)
            self.assertSequenceEqual(
                a_,					["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"]					)
style_context: 
  
  
  '''simple docstring'''
  
  from dataclasses import dataclass
  
  __lowercase:		List[str]							=     "0.1"
  __lowercase:		Union[str, Any]							=     "Lucia Harcekova"
  
  
  
  
  
  
  
  @dataclass
  class  UpperCAmelCase :
   _lowerCamelCase :					int
   _lowerCamelCase :					int
   _lowerCamelCase :					str
  
   def __repr__( self :	List[str]					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      return f'({self.offset}, {self.length}, {self.indicator})'
  
  
  
  
  
  
  
  class  UpperCAmelCase :
  
   def __init__( self :	Optional[int],					a_ :	int = 13,					a_ :	int = 6					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      UpperCamelCase__ 						=						window_size
      UpperCamelCase__ 						=						lookahead_buffer_size
      UpperCamelCase__ 						=						self.window_size - self.lookahead_buffer_size
  
   def 		lowercase_						( self :	Optional[int],					a_ :	str					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      UpperCamelCase__ 						=						[]
      UpperCamelCase__ 						=						""
  
      # 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)
         UpperCamelCase__ 						=						self._find_encoding_token(a_,					a_					)
  
         # 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(a_					) > self.search_buffer_size:
            UpperCamelCase__ 						=						search_buffer[-self.search_buffer_size :]
  
         # update the text
         UpperCamelCase__ 						=						text[token.length + 1 :]
  
         # append the token to output
         output.append(a_					)
  
      return output
  
   def 		lowercase_						( self :	List[Any],					a_ :	list[Token]					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      UpperCamelCase__ 						=						""
  
      for token in tokens:
         for _ in range(token.length					):
            output += output[-token.offset]
         output += token.indicator
  
      return output
  
   def 		lowercase_						( self :	Any,					a_ :	str,					a_ :	str					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      if not text:
         raise ValueError("We need some text to work with."					)
  
      # Initialise result parameters to default values
      UpperCamelCase__						,		UpperCamelCase__ 						=						0, 0
  
      if not search_buffer:
         return Token(a_,					a_,					text[length]					)
  
      for i, character in enumerate(a_					):
         UpperCamelCase__ 						=						len(a_					) - i
         if character == text[0]:
            UpperCamelCase__ 						=						self._match_length_from_index(a_,					a_,					0,					a_					)
            # 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:
               UpperCamelCase__						,		UpperCamelCase__ 						=						found_offset, found_length
  
      return Token(a_,					a_,					text[length]					)
  
   def 		lowercase_						( self :	Optional[int],					a_ :	str,					a_ :	str,					a_ :	int,					a_ :	int					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      if not text or text[text_index] != window[window_index]:
         return 0
      return 1 + self._match_length_from_index(
          a_,					window + text[text_index],					text_index + 1,					window_index + 1					)
  
  
  if __name__ == "__main__":
         from doctest import testmod
  
         testmod()
         # Initialize compressor class
         __lowercase:		Optional[Any]							=     LZaaCompressor(window_size=13, lookahead_buffer_size=6)
  
         # Example
         __lowercase:		Union[str, Any]							=     "cabracadabrarrarrad"
         __lowercase:		Optional[int]							=     lzaa_compressor.compress(TEXT)
         print(lzaa_compressor.compress("ababcbababaa"))
         __lowercase:		Optional[int]							=     lzaa_compressor.decompress(compressed_text)
         assert decompressed_text == TEXT, "The LZ77 algorithm returned the invalid result."