code: 
  
  
  '''simple docstring'''
  
  import json
  import os
  import tempfile
  import unittest
  
  from transformers.modelcard import ModelCard
  
  
  
  
  
  
  
  class  UpperCAmelCase (       unittest.TestCase):
  
   def 		lowercase_						( self :	Tuple					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      UpperCamelCase__ 						=						{
          "model_details": {
              "Organization": "testing",
              "Model date": "today",
              "Model version": "v2.1, Developed by Test Corp in 2019.",
              "Architecture": "Convolutional Neural Network.",
          },
          "metrics": "BLEU and ROUGE-1",
          "evaluation_data": {
              "Datasets": {"BLEU": "My-great-dataset-v1", "ROUGE-1": "My-short-dataset-v2.1"},
              "Preprocessing": "See details on https://arxiv.org/pdf/1810.03993.pdf",
          },
          "training_data": {
              "Dataset": "English Wikipedia dump dated 2018-12-01",
              "Preprocessing": (
                  "Using SentencePiece vocabulary of size 52k tokens. See details on"
                  " https://arxiv.org/pdf/1810.03993.pdf"
              ),
          },
          "quantitative_analyses": {"BLEU": 55.1, "ROUGE-1": 76},
      }
  
   def 		lowercase_						( self :	Optional[Any]					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      UpperCamelCase__ 						=						ModelCard.from_dict(self.inputs_dict					)
      self.assertTrue(hasattr(a_,					"model_details"					)					)
      self.assertTrue(hasattr(a_,					"intended_use"					)					)
      self.assertTrue(hasattr(a_,					"factors"					)					)
      self.assertTrue(hasattr(a_,					"metrics"					)					)
      self.assertTrue(hasattr(a_,					"evaluation_data"					)					)
      self.assertTrue(hasattr(a_,					"training_data"					)					)
      self.assertTrue(hasattr(a_,					"quantitative_analyses"					)					)
      self.assertTrue(hasattr(a_,					"ethical_considerations"					)					)
      self.assertTrue(hasattr(a_,					"caveats_and_recommendations"					)					)
  
   def 		lowercase_						( self :	List[Any]					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      UpperCamelCase__ 						=						ModelCard.from_dict(self.inputs_dict					)
      UpperCamelCase__ 						=						json.loads(modelcard.to_json_string()					)
      for key, value in self.inputs_dict.items():
         self.assertEqual(obj[key],					a_					)
  
   def 		lowercase_						( self :	Optional[Any]					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      UpperCamelCase__ 						=						ModelCard.from_dict(self.inputs_dict					)
  
      with tempfile.TemporaryDirectory() as tmpdirname:
         UpperCamelCase__ 						=						os.path.join(a_,					"modelcard.json"					)
         model_card_first.to_json_file(a_					)
         UpperCamelCase__ 						=						ModelCard.from_json_file(a_					)
  
      self.assertEqual(model_card_second.to_dict(),					model_card_first.to_dict()					)
  
   def 		lowercase_						( self :	Optional[int]					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      UpperCamelCase__ 						=						ModelCard.from_dict(self.inputs_dict					)
  
      with tempfile.TemporaryDirectory() as tmpdirname:
         model_card_first.save_pretrained(a_					)
         UpperCamelCase__ 						=						ModelCard.from_pretrained(a_					)
  
      self.assertEqual(model_card_second.to_dict(),					model_card_first.to_dict()					)
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."