
"""simple docstring"""




import shutil
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile

from transformers import SpeechaTextTokenizer, is_speech_available
from transformers.models.speech_to_text.tokenization_speech_to_text import VOCAB_FILES_NAMES, save_json
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_torch, require_torchaudio
from transformers.utils import FEATURE_EXTRACTOR_NAME

from .test_feature_extraction_speech_to_text import floats_list


if is_speech_available():
	from transformers import SpeechaTextFeatureExtractor, SpeechaTextProcessor


snake_case					  =						get_tests_dir('fixtures/test_sentencepiece.model')




@require_torch
@require_torchaudio
@require_sentencepiece
class 	UpperCamelCase      (			unittest.TestCase ):

				"""simple docstring"""



				def 				A   (							self     )       ->       List[str]:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =      tempfile.mkdtemp()

									SCREAMING_SNAKE_CASE           =      ["<s>", "<pad>", "</s>", "<unk>", "▁This", "▁is", "▁a", "▁t", "est"]
									SCREAMING_SNAKE_CASE           =      dict(zip(UpperCAmelCase_						,      range(len(UpperCAmelCase_     )     )     )     )
									SCREAMING_SNAKE_CASE           =      Path(self.tmpdirname     )
									save_json(UpperCAmelCase_						,      save_dir / VOCAB_FILES_NAMES['vocab_file']     )
									if not (save_dir / VOCAB_FILES_NAMES["spm_file"]).exists():
														copyfile(UpperCAmelCase_						,      save_dir / VOCAB_FILES_NAMES['spm_file']     )

									SCREAMING_SNAKE_CASE           =      SpeechaTextTokenizer.from_pretrained(self.tmpdirname     )
									tokenizer.save_pretrained(self.tmpdirname     )

									SCREAMING_SNAKE_CASE           =      {
									    "feature_size": 24,
									    "num_mel_bins": 24,
									    "padding_value": 0.0,
									    "sampling_rate": 16000,
									    "return_attention_mask": False,
									    "do_normalize": True,
									}
									save_json(UpperCAmelCase_						,      save_dir / FEATURE_EXTRACTOR_NAME     )


				def 				A   (							self						,      **lowercase__     )       ->       List[str]:
									"""simple docstring"""







									return SpeechaTextTokenizer.from_pretrained(self.tmpdirname						,      **UpperCAmelCase_     )


				def 				A   (							self						,      **lowercase__     )       ->       Dict:
									"""simple docstring"""







									return SpeechaTextFeatureExtractor.from_pretrained(self.tmpdirname						,      **UpperCAmelCase_     )


				def 				A   (							self     )       ->       Optional[int]:
									"""simple docstring"""







									shutil.rmtree(self.tmpdirname     )


				def 				A   (							self     )       ->       str:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =      self.get_tokenizer()
									SCREAMING_SNAKE_CASE           =      self.get_feature_extractor()

									SCREAMING_SNAKE_CASE           =      SpeechaTextProcessor(tokenizer=UpperCAmelCase_						,      feature_extractor=UpperCAmelCase_     )

									processor.save_pretrained(self.tmpdirname     )
									SCREAMING_SNAKE_CASE           =      SpeechaTextProcessor.from_pretrained(self.tmpdirname     )

									self.assertEqual(processor.tokenizer.get_vocab()						,      tokenizer.get_vocab()     )
									self.assertIsInstance(processor.tokenizer						,      UpperCAmelCase_     )

									self.assertEqual(processor.feature_extractor.to_json_string()						,      feature_extractor.to_json_string()     )
									self.assertIsInstance(processor.feature_extractor						,      UpperCAmelCase_     )


				def 				A   (							self     )       ->       Tuple:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =      SpeechaTextProcessor(
									    tokenizer=self.get_tokenizer()						,      feature_extractor=self.get_feature_extractor()     )
									processor.save_pretrained(self.tmpdirname     )

									SCREAMING_SNAKE_CASE           =      self.get_tokenizer(bos_token='(BOS)'						,      eos_token='(EOS)'     )
									SCREAMING_SNAKE_CASE           =      self.get_feature_extractor(do_normalize=UpperCAmelCase_						,      padding_value=1.0     )

									SCREAMING_SNAKE_CASE           =      SpeechaTextProcessor.from_pretrained(
									    self.tmpdirname						,      bos_token='(BOS)'						,      eos_token='(EOS)'						,      do_normalize=UpperCAmelCase_						,      padding_value=1.0     )

									self.assertEqual(processor.tokenizer.get_vocab()						,      tokenizer_add_kwargs.get_vocab()     )
									self.assertIsInstance(processor.tokenizer						,      UpperCAmelCase_     )

									self.assertEqual(processor.feature_extractor.to_json_string()						,      feature_extractor_add_kwargs.to_json_string()     )
									self.assertIsInstance(processor.feature_extractor						,      UpperCAmelCase_     )


				def 				A   (							self     )       ->       Any:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =      self.get_feature_extractor()
									SCREAMING_SNAKE_CASE           =      self.get_tokenizer()

									SCREAMING_SNAKE_CASE           =      SpeechaTextProcessor(tokenizer=UpperCAmelCase_						,      feature_extractor=UpperCAmelCase_     )

									SCREAMING_SNAKE_CASE           =      floats_list((3, 1000)     )

									SCREAMING_SNAKE_CASE           =      feature_extractor(UpperCAmelCase_						,      return_tensors='np'     )
									SCREAMING_SNAKE_CASE           =      processor(UpperCAmelCase_						,      return_tensors='np'     )

									for key in input_feat_extract.keys():
														self.assertAlmostEqual(input_feat_extract[key].sum()						,      input_processor[key].sum()						,      delta=1E-2     )


				def 				A   (							self     )       ->       Tuple:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =      self.get_feature_extractor()
									SCREAMING_SNAKE_CASE           =      self.get_tokenizer()

									SCREAMING_SNAKE_CASE           =      SpeechaTextProcessor(tokenizer=UpperCAmelCase_						,      feature_extractor=UpperCAmelCase_     )

									SCREAMING_SNAKE_CASE           =      "This is a test string"

									SCREAMING_SNAKE_CASE           =      processor(text=UpperCAmelCase_     )

									SCREAMING_SNAKE_CASE           =      tokenizer(UpperCAmelCase_     )

									for key in encoded_tok.keys():
														self.assertListEqual(encoded_tok[key]						,      encoded_processor[key]     )


				def 				A   (							self     )       ->       List[Any]:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =      self.get_feature_extractor()
									SCREAMING_SNAKE_CASE           =      self.get_tokenizer()

									SCREAMING_SNAKE_CASE           =      SpeechaTextProcessor(tokenizer=UpperCAmelCase_						,      feature_extractor=UpperCAmelCase_     )

									SCREAMING_SNAKE_CASE           =      [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

									SCREAMING_SNAKE_CASE           =      processor.batch_decode(UpperCAmelCase_     )
									SCREAMING_SNAKE_CASE           =      tokenizer.batch_decode(UpperCAmelCase_     )

									self.assertListEqual(UpperCAmelCase_						,      UpperCAmelCase_     )




				def 				A   (							self     )       ->       Tuple:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =      self.get_feature_extractor()
									SCREAMING_SNAKE_CASE           =      self.get_tokenizer()

									SCREAMING_SNAKE_CASE           =      SpeechaTextProcessor(tokenizer=UpperCAmelCase_						,      feature_extractor=UpperCAmelCase_     )

									self.assertListEqual(
									    processor.model_input_names						,      feature_extractor.model_input_names						,      msg='`processor` and `feature_extractor` model input names do not match'						,      )








"""simple docstring"""





from dataclasses import dataclass

UpperCamelCase					=     """0.1"""
UpperCamelCase					=     """Lucia Harcekova"""







@dataclass
class 						_a  :





   '''simple docstring'''






   UpperCamelCase__     	=							42
   UpperCamelCase__     	=							42
   UpperCamelCase__     	=							42





   def __repr__(       self)			->   str:




          '''simple docstring'''




          return F"""({self.offset}, {self.length}, {self.indicator})"""







class 						_a  :





   '''simple docstring'''






   def __init__(       self					, UpperCAmelCase_ = 13					, UpperCAmelCase_ = 6)			->   None:




          '''simple docstring'''




          lowercase__:							Union[str, Any]         =     window_size
          lowercase__:							Any         =     lookahead_buffer_size
          lowercase__:							Optional[Any]         =     self.window_size - self.lookahead_buffer_size





   def __lowercase							(       self					, UpperCAmelCase_)			->   list[Token]:




          '''simple docstring'''




          lowercase__:							Any         =     []
          lowercase__:							Union[str, Any]         =     ""

          # 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)
                 lowercase__:							Optional[Any]         =     self._find_encoding_token(UpperCAmelCase_					, UpperCAmelCase_)

                 # 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(UpperCAmelCase_) > self.search_buffer_size:
                        lowercase__:							List[Any]         =     search_buffer[-self.search_buffer_size :]

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

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

          return output





   def __lowercase							(       self					, UpperCAmelCase_)			->   str:




          '''simple docstring'''




          lowercase__:							int         =     ""

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

          return output





   def __lowercase							(       self					, UpperCAmelCase_					, UpperCAmelCase_)			->   Token:




          '''simple docstring'''




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

          # Initialise result parameters to default values
          lowercase__    ,      lowercase__:							Optional[int]         =     0, 0

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

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

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





   def __lowercase							(       self					, UpperCAmelCase_					, UpperCAmelCase_					, UpperCAmelCase_					, UpperCAmelCase_)			->   int:




          '''simple docstring'''




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






