






'''simple docstring'''

import math
import sys
def     __lowercase  (_lowercase     )   ->     str:


     """simple docstring"""
     __lowerCamelCase						:       Optional[Any]				   =				""""""
     try:
          with open(_lowercase,						"""rb"""     ) as binary_file:
               __lowerCamelCase						:       str				   =				binary_file.read()
          for dat in data:
               __lowerCamelCase						:       Dict				   =				f"{dat:08b}"
               result += curr_byte
          return result
     except OSError:
          print("""File not accessible"""     )
          sys.exit()
def     __lowercase  (_lowercase     )   ->     str:


     """simple docstring"""
     __lowerCamelCase						:       str				   =				{"""0""": """0""", """1""": """1"""}
     __lowerCamelCase   ,      __lowerCamelCase						:       Optional[int]				   =				"""""", """"""
     __lowerCamelCase						:       Optional[Any]				   =				len(_lowercase     )

     for i in range(len(_lowercase     )     ):
          curr_string += data_bits[i]
          if curr_string not in lexicon:
               continue

          __lowerCamelCase						:       Union[str, Any]				   =				lexicon[curr_string]
          result += last_match_id
          __lowerCamelCase						:       List[Any]				   =				last_match_id + """0"""

          if math.loga(_lowercase     ).is_integer():
               __lowerCamelCase						:       List[Any]				   =				{}
               for curr_key in list(_lowercase     ):
                    __lowerCamelCase						:       Tuple				   =				lexicon.pop(_lowercase     )
               __lowerCamelCase						:       int				   =				new_lex

          __lowerCamelCase						:       int				   =				last_match_id + """1"""
          index += 1
          __lowerCamelCase						:       int				   =				""""""
     return result
def     __lowercase  (_lowercase,						_lowercase     )   ->     None:


     """simple docstring"""
     __lowerCamelCase						:       Union[str, Any]				   =				8
     try:
          with open(_lowercase,						"""wb"""     ) as opened_file:
               __lowerCamelCase						:       Dict				   =				[
                   to_write[i : i + byte_length]
                   for i in range(0,						len(_lowercase     ),						_lowercase     )
               ]

               if len(result_byte_array[-1]     ) % byte_length == 0:
                    result_byte_array.append("""10000000"""     )
               else:
                    result_byte_array[-1] += "1" + "0" * (
                        byte_length - len(result_byte_array[-1]     ) - 1
                    )

               for elem in result_byte_array[:-1]:
                    opened_file.write(int(_lowercase,						2     ).to_bytes(1,						byteorder="""big"""     )     )
     except OSError:
          print("""File not accessible"""     )
          sys.exit()
def     __lowercase  (_lowercase     )   ->     str:


     """simple docstring"""
     __lowerCamelCase						:       Dict				   =				0
     for letter in data_bits:
          if letter == "1":
               break
          counter += 1

     __lowerCamelCase						:       Union[str, Any]				   =				data_bits[counter:]
     __lowerCamelCase						:       Any				   =				data_bits[counter + 1 :]
     return data_bits
def     __lowercase  (_lowercase,						_lowercase     )   ->     None:


     """simple docstring"""
     __lowerCamelCase						:       List[str]				   =				read_file_binary(_lowercase     )
     __lowerCamelCase						:       str				   =				remove_prefix(_lowercase     )
     __lowerCamelCase						:       Union[str, Any]				   =				decompress_data(_lowercase     )
     write_file_binary(_lowercase,						_lowercase     )


if __name__ == "__main__":
   compress(sys.argv[1], sys.argv[2])











'''simple docstring'''

import pickle
import shutil
import tempfile
import unittest

from transformers import SPIECE_UNDERLINE, XLMRobertaTokenizer, XLMRobertaTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


UpperCAmelCase__						:List[str]          =			get_tests_dir("""fixtures/test_sentencepiece.model""")







@require_sentencepiece
@require_tokenizers
class 	SCREAMING_SNAKE_CASE	(      lowerCAmelCase_    ,   unittest.TestCase  ):
       snake_case__    :     Optional[Any]   		=			XLMRobertaTokenizer
       snake_case__    :     str   		=			XLMRobertaTokenizerFast
       snake_case__    :     str   		=			True
       snake_case__    :     int   		=			True




       def 					a_					(					self      : Dict					):


            """simple docstring"""







            super().setUp()

            # We have a SentencePiece fixture for testing
            __lowerCamelCase						:       str				   =				XLMRobertaTokenizer(A__	,    keep_accents=A__					)
            tokenizer.save_pretrained(self.tmpdirname					)




       def 					a_					(					self      : int					):


            """simple docstring"""







            __lowerCamelCase						:       List[Any]				   =				"""<pad>"""
            __lowerCamelCase						:       List[Any]				   =				1

            self.assertEqual(self.get_tokenizer()._convert_token_to_id(A__					)	,    A__					)
            self.assertEqual(self.get_tokenizer()._convert_id_to_token(A__					)	,    A__					)




       def 					a_					(					self      : str					):


            """simple docstring"""







            __lowerCamelCase						:       List[str]				   =				list(self.get_tokenizer().get_vocab().keys()					)

            self.assertEqual(vocab_keys[0]	,    """<s>"""					)
            self.assertEqual(vocab_keys[1]	,    """<pad>"""					)
            self.assertEqual(vocab_keys[-1]	,    """<mask>"""					)
            self.assertEqual(len(A__					)	,    1002					)




       def 					a_					(					self      : List[Any]					):


            """simple docstring"""







            self.assertEqual(self.get_tokenizer().vocab_size	,    1002					)




       def 					a_					(					self      : Tuple					):


            """simple docstring"""







            __lowerCamelCase						:       Any				   =				XLMRobertaTokenizer(A__	,    keep_accents=A__					)

            __lowerCamelCase						:       List[str]				   =				tokenizer.tokenize("""This is a test"""					)
            self.assertListEqual(A__	,    ["""▁This""", """▁is""", """▁a""", """▁t""", """est"""]					)

            self.assertListEqual(
                tokenizer.convert_tokens_to_ids(A__					)	,    [value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]]	,    )

            __lowerCamelCase						:       Any				   =				tokenizer.tokenize("""I was born in 92000, and this is falsé."""					)
            self.assertListEqual(
                A__	,    [
                    SPIECE_UNDERLINE + """I""",
                    SPIECE_UNDERLINE + """was""",
                    SPIECE_UNDERLINE + """b""",
                    """or""",
                    """n""",
                    SPIECE_UNDERLINE + """in""",
                    SPIECE_UNDERLINE + """""",
                    """9""",
                    """2""",
                    """0""",
                    """0""",
                    """0""",
                    """,""",
                    SPIECE_UNDERLINE + """and""",
                    SPIECE_UNDERLINE + """this""",
                    SPIECE_UNDERLINE + """is""",
                    SPIECE_UNDERLINE + """f""",
                    """al""",
                    """s""",
                    """é""",
                    """.""",
                ]	,    )
            __lowerCamelCase						:       List[str]				   =				tokenizer.convert_tokens_to_ids(A__					)
            self.assertListEqual(
                A__	,    [
                    value + tokenizer.fairseq_offset
                    for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
                    #                                       ^ unk: 2 + 1 = 3                  unk: 2 + 1 = 3 ^
                ]	,    )

            __lowerCamelCase						:       Dict				   =				tokenizer.convert_ids_to_tokens(A__					)
            self.assertListEqual(
                A__	,    [
                    SPIECE_UNDERLINE + """I""",
                    SPIECE_UNDERLINE + """was""",
                    SPIECE_UNDERLINE + """b""",
                    """or""",
                    """n""",
                    SPIECE_UNDERLINE + """in""",
                    SPIECE_UNDERLINE + """""",
                    """<unk>""",
                    """2""",
                    """0""",
                    """0""",
                    """0""",
                    """,""",
                    SPIECE_UNDERLINE + """and""",
                    SPIECE_UNDERLINE + """this""",
                    SPIECE_UNDERLINE + """is""",
                    SPIECE_UNDERLINE + """f""",
                    """al""",
                    """s""",
                    """<unk>""",
                    """.""",
                ]	,    )




       def 					a_					(					self      : Any					):


            """simple docstring"""







            if not self.test_slow_tokenizer:
                 # as we don't have a slow version, we can't compare the outputs between slow and fast versions
                 return

            __lowerCamelCase						:       Tuple				   =				(self.rust_tokenizer_class, """hf-internal-testing/tiny-xlm-roberta""", {})
            for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
                 with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"					):
                      __lowerCamelCase						:       List[Any]				   =				self.rust_tokenizer_class.from_pretrained(A__	,    **A__					)
                      __lowerCamelCase						:       Dict				   =				self.tokenizer_class.from_pretrained(A__	,    **A__					)

                      __lowerCamelCase						:       str				   =				tempfile.mkdtemp()

                      __lowerCamelCase						:       Union[str, Any]				   =				tokenizer_r.save_pretrained(A__					)
                      __lowerCamelCase						:       Union[str, Any]				   =				tokenizer_p.save_pretrained(A__					)

                      # Checks it save with the same files + the tokenizer.json file for the fast one
                      self.assertTrue(any("""tokenizer.json""" in f for f in tokenizer_r_files					)					)
                      __lowerCamelCase						:       Any				   =				tuple(f for f in tokenizer_r_files if """tokenizer.json""" not in f					)
                      self.assertSequenceEqual(A__	,    A__					)

                      # Checks everything loads correctly in the same way
                      __lowerCamelCase						:       str				   =				tokenizer_r.from_pretrained(A__					)
                      __lowerCamelCase						:       str				   =				tokenizer_p.from_pretrained(A__					)

                      # Check special tokens are set accordingly on Rust and Python
                      for key in tokenizer_pp.special_tokens_map:
                           self.assertTrue(hasattr(A__	,    A__					)					)
                           # self.assertEqual(getattr(tokenizer_rp, key), getattr(tokenizer_pp, key))
                           # self.assertEqual(getattr(tokenizer_rp, key + "_id"), getattr(tokenizer_pp, key + "_id"))

                      shutil.rmtree(A__					)

                      # Save tokenizer rust, legacy_format=True
                      __lowerCamelCase						:       Optional[Any]				   =				tempfile.mkdtemp()

                      __lowerCamelCase						:       Dict				   =				tokenizer_r.save_pretrained(A__	,    legacy_format=A__					)
                      __lowerCamelCase						:       List[Any]				   =				tokenizer_p.save_pretrained(A__					)

                      # Checks it save with the same files
                      self.assertSequenceEqual(A__	,    A__					)

                      # Checks everything loads correctly in the same way
                      __lowerCamelCase						:       Any				   =				tokenizer_r.from_pretrained(A__					)
                      __lowerCamelCase						:       List[str]				   =				tokenizer_p.from_pretrained(A__					)

                      # Check special tokens are set accordingly on Rust and Python
                      for key in tokenizer_pp.special_tokens_map:
                           self.assertTrue(hasattr(A__	,    A__					)					)

                      shutil.rmtree(A__					)

                      # Save tokenizer rust, legacy_format=False
                      __lowerCamelCase						:       int				   =				tempfile.mkdtemp()

                      __lowerCamelCase						:       Any				   =				tokenizer_r.save_pretrained(A__	,    legacy_format=A__					)
                      __lowerCamelCase						:       List[str]				   =				tokenizer_p.save_pretrained(A__					)

                      # Checks it saved the tokenizer.json file
                      self.assertTrue(any("""tokenizer.json""" in f for f in tokenizer_r_files					)					)

                      # Checks everything loads correctly in the same way
                      __lowerCamelCase						:       List[Any]				   =				tokenizer_r.from_pretrained(A__					)
                      __lowerCamelCase						:       Optional[int]				   =				tokenizer_p.from_pretrained(A__					)

                      # Check special tokens are set accordingly on Rust and Python
                      for key in tokenizer_pp.special_tokens_map:
                           self.assertTrue(hasattr(A__	,    A__					)					)

                      shutil.rmtree(A__					)




       @cached_property
       def 					a_					(					self      : str					):


            """simple docstring"""







            return XLMRobertaTokenizer.from_pretrained("""xlm-roberta-base"""					)




       def 					a_					(					self      : int					):


            """simple docstring"""







            with tempfile.NamedTemporaryFile() as f:
                 shutil.copyfile(A__	,    f.name					)
                 __lowerCamelCase						:       int				   =				XLMRobertaTokenizer(f.name	,    keep_accents=A__					)
                 __lowerCamelCase						:       str				   =				pickle.dumps(A__					)
            pickle.loads(A__					)




       def 					a_					(					self      : Union[str, Any]					):


            """simple docstring"""







            if not self.test_rust_tokenizer:
                 return

            __lowerCamelCase						:       Union[str, Any]				   =				self.get_tokenizer()
            __lowerCamelCase						:       Tuple				   =				self.get_rust_tokenizer()

            __lowerCamelCase						:       Optional[Any]				   =				"""I was born in 92000, and this is falsé."""

            __lowerCamelCase						:       int				   =				tokenizer.tokenize(A__					)
            __lowerCamelCase						:       Optional[Any]				   =				rust_tokenizer.tokenize(A__					)
            self.assertListEqual(A__	,    A__					)

            __lowerCamelCase						:       Any				   =				tokenizer.encode(A__	,    add_special_tokens=A__					)
            __lowerCamelCase						:       Dict				   =				rust_tokenizer.encode(A__	,    add_special_tokens=A__					)
            self.assertListEqual(A__	,    A__					)

            __lowerCamelCase						:       Optional[Any]				   =				self.get_rust_tokenizer()
            __lowerCamelCase						:       Optional[int]				   =				tokenizer.encode(A__					)
            __lowerCamelCase						:       str				   =				rust_tokenizer.encode(A__					)
            self.assertListEqual(A__	,    A__					)




       @slow
       def 					a_					(					self      : Optional[int]					):


            """simple docstring"""







            __lowerCamelCase						:       List[Any]				   =				"""Hello World!"""
            __lowerCamelCase						:       str				   =				[0, 35378, 6661, 38, 2]
            # xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')  # xlmr.large has same tokenizer
            # xlmr.eval()
            # xlmr.encode(symbols)

            self.assertListEqual(A__	,    self.big_tokenizer.encode(A__					)					)




       @slow
       def 					a_					(					self      : Optional[Any]					):


            """simple docstring"""







            __lowerCamelCase						:       Any				   =				(
                """This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) \" [ ] ! : - . Also we will"""
                """ add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth"""
            )
            __lowerCamelCase						:       Optional[Any]				   =				[
                0,
                3293,
                83,
                10,
                4552,
                4989,
                7986,
                678,
                10,
                5915,
                111,
                179459,
                124850,
                4,
                6044,
                237,
                12,
                6,
                5,
                6,
                4,
                6780,
                705,
                15,
                1388,
                44,
                378,
                10114,
                711,
                152,
                20,
                6,
                5,
                22376,
                642,
                1221,
                15190,
                34153,
                450,
                5608,
                959,
                1119,
                57702,
                136,
                186,
                47,
                1098,
                29367,
                47,
                # 4426, # What fairseq tokenizes from "<unk>": "_<"
                # 3678, # What fairseq tokenizes from "<unk>": "unk"
                # 2740, # What fairseq tokenizes from "<unk>": ">"
                3,  # What we tokenize from "<unk>": "<unk>"
                6,  # Residue from the tokenization: an extra sentencepiece underline
                4,
                6044,
                237,
                6284,
                50901,
                528,
                31,
                90,
                34,
                927,
                2,
            ]
            # xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')  # xlmr.large has same tokenizer
            # xlmr.eval()
            # xlmr.encode(symbols)

            self.assertListEqual(A__	,    self.big_tokenizer.encode(A__					)					)




       @slow
       def 					a_					(					self      : Any					):


            """simple docstring"""







            __lowerCamelCase						:       Tuple				   =				{"""input_ids""": [[0, 11062, 82772, 7, 15, 82772, 538, 51529, 237, 17198, 1290, 206, 9, 215175, 1314, 136, 17198, 1290, 206, 9, 56359, 42, 122009, 9, 16466, 16, 87344, 4537, 9, 4717, 78381, 6, 159958, 7, 15, 24480, 618, 4, 527, 22693, 5428, 4, 2777, 24480, 9874, 4, 43523, 594, 4, 803, 18392, 33189, 18, 4, 43523, 24447, 12399, 100, 24955, 83658, 9626, 144057, 15, 839, 22335, 16, 136, 24955, 83658, 83479, 15, 39102, 724, 16, 678, 645, 2789, 1328, 4589, 42, 122009, 115774, 23, 805, 1328, 46876, 7, 136, 53894, 1940, 42227, 41159, 17721, 823, 425, 4, 27512, 98722, 206, 136, 5531, 4970, 919, 17336, 5, 2], [0, 20080, 618, 83, 82775, 47, 479, 9, 1517, 73, 53894, 333, 80581, 110117, 18811, 5256, 1295, 51, 152526, 297, 7986, 390, 124416, 538, 35431, 214, 98, 15044, 25737, 136, 7108, 43701, 23, 756, 135355, 7, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 581, 63773, 119455, 6, 147797, 88203, 7, 645, 70, 21, 3285, 10269, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], """attention_mask""": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
            # fmt: on

            self.tokenizer_integration_test_util(
                expected_encoding=A__	,    model_name="""xlm-roberta-base"""	,    revision="""d9d8a8ea5eb94b1c6654ae9249df7793cd2933d3"""	,    )



