


'''simple docstring'''







import unittest

from transformers import SPIECE_UNDERLINE, ReformerTokenizer, ReformerTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


A__:    Any					    =       get_tests_dir('''fixtures/test_sentencepiece.model''')







@require_sentencepiece
@require_tokenizers
class A__       (						UpperCAmelCase__ ,     unittest.TestCase      ):
      __UpperCamelCase       :  List[str]	       =							ReformerTokenizer
      __UpperCamelCase       :  Any	       =							ReformerTokenizerFast
      __UpperCamelCase       :  str	       =							True
      __UpperCamelCase       :  Dict	       =							False
      __UpperCamelCase       :  Any	       =							True






      def __UpperCAmelCase       ( self			:Any	)       ->					Any:




             '''simple docstring'''
             super().setUp()

             _a    : Union[str, Any]     					=ReformerTokenizer(SCREAMING_SNAKE_CASE     ,						keep_accents=SCREAMING_SNAKE_CASE	)
             tokenizer.save_pretrained(self.tmpdirname	)






      def __UpperCAmelCase       ( self			:Optional[int]	)       ->					Dict:




             '''simple docstring'''
             _a    : List[str]     					="""<s>"""
             _a    : List[str]     					=1

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






      def __UpperCAmelCase       ( self			:Dict	)       ->					Optional[Any]:




             '''simple docstring'''
             _a    : Union[str, Any]     					=list(self.get_tokenizer().get_vocab().keys()	)

             self.assertEqual(vocab_keys[0]     ,						"""<unk>"""	)
             self.assertEqual(vocab_keys[1]     ,						"""<s>"""	)
             self.assertEqual(vocab_keys[-1]     ,						"""j"""	)
             self.assertEqual(len(SCREAMING_SNAKE_CASE	)     ,						1_0_0_0	)






      def __UpperCAmelCase       ( self			:Union[str, Any]	)       ->					Tuple:




             '''simple docstring'''
             self.assertEqual(self.get_tokenizer().vocab_size     ,						1_0_0_0	)






      def __UpperCAmelCase       ( self			:Tuple	)       ->					Optional[Any]:




             '''simple docstring'''
             if not self.test_rust_tokenizer:
                    return

             _a    : Tuple     					=self.get_tokenizer()
             _a    : List[str]     					=self.get_rust_tokenizer()

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

             _a    : Union[str, Any]     					=tokenizer.tokenize(SCREAMING_SNAKE_CASE	)
             _a    : str     					=rust_tokenizer.tokenize(SCREAMING_SNAKE_CASE	)
             self.assertListEqual(SCREAMING_SNAKE_CASE     ,						SCREAMING_SNAKE_CASE	)

             _a    : Dict     					=tokenizer.encode(SCREAMING_SNAKE_CASE     ,						add_special_tokens=SCREAMING_SNAKE_CASE	)
             _a    : Union[str, Any]     					=rust_tokenizer.encode(SCREAMING_SNAKE_CASE     ,						add_special_tokens=SCREAMING_SNAKE_CASE	)
             self.assertListEqual(SCREAMING_SNAKE_CASE     ,						SCREAMING_SNAKE_CASE	)

             _a    : str     					=self.get_rust_tokenizer()
             _a    : int     					=tokenizer.encode(SCREAMING_SNAKE_CASE	)
             _a    : Optional[int]     					=rust_tokenizer.encode(SCREAMING_SNAKE_CASE	)
             self.assertListEqual(SCREAMING_SNAKE_CASE     ,						SCREAMING_SNAKE_CASE	)






      def __UpperCAmelCase       ( self			:int     ,						SCREAMING_SNAKE_CASE			:str=1_5	)       ->					Union[str, Any]:




             '''simple docstring'''
             for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
                    with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"	):
                           _a    : Dict     					=self.rust_tokenizer_class.from_pretrained(SCREAMING_SNAKE_CASE     ,						**SCREAMING_SNAKE_CASE	)

                           # Simple input
                           _a    : List[Any]     					="""This is a simple input"""
                           _a    : Union[str, Any]     					=["""This is a simple input 1""", """This is a simple input 2"""]
                           _a    : Tuple     					=("""This is a simple input""", """This is a pair""")
                           _a    : str     					=[
                               ("""This is a simple input 1""", """This is a simple input 2"""),
                               ("""This is a simple pair 1""", """This is a simple pair 2"""),
                           ]

                           # Simple input tests
                           self.assertRaises(SCREAMING_SNAKE_CASE     ,						tokenizer_r.encode     ,						SCREAMING_SNAKE_CASE     ,						max_length=SCREAMING_SNAKE_CASE     ,						padding="""max_length"""	)

                           # Simple input
                           self.assertRaises(SCREAMING_SNAKE_CASE     ,						tokenizer_r.encode_plus     ,						SCREAMING_SNAKE_CASE     ,						max_length=SCREAMING_SNAKE_CASE     ,						padding="""max_length"""	)

                           # Simple input
                           self.assertRaises(
                               SCREAMING_SNAKE_CASE     ,						tokenizer_r.batch_encode_plus     ,						SCREAMING_SNAKE_CASE     ,						max_length=SCREAMING_SNAKE_CASE     ,						padding="""max_length"""     ,						)

                           # Pair input
                           self.assertRaises(SCREAMING_SNAKE_CASE     ,						tokenizer_r.encode     ,						SCREAMING_SNAKE_CASE     ,						max_length=SCREAMING_SNAKE_CASE     ,						padding="""max_length"""	)

                           # Pair input
                           self.assertRaises(SCREAMING_SNAKE_CASE     ,						tokenizer_r.encode_plus     ,						SCREAMING_SNAKE_CASE     ,						max_length=SCREAMING_SNAKE_CASE     ,						padding="""max_length"""	)

                           # Pair input
                           self.assertRaises(
                               SCREAMING_SNAKE_CASE     ,						tokenizer_r.batch_encode_plus     ,						SCREAMING_SNAKE_CASE     ,						max_length=SCREAMING_SNAKE_CASE     ,						padding="""max_length"""     ,						)






      def __UpperCAmelCase       ( self			:Optional[int]	)       ->					Optional[int]:




             '''simple docstring'''
             pass






      def __UpperCAmelCase       ( self			:Any	)       ->					Union[str, Any]:




             '''simple docstring'''
             _a    : List[str]     					=ReformerTokenizer(SCREAMING_SNAKE_CASE     ,						keep_accents=SCREAMING_SNAKE_CASE	)

             _a    : Union[str, Any]     					=tokenizer.tokenize("""This is a test"""	)
             self.assertListEqual(SCREAMING_SNAKE_CASE     ,						["""▁This""", """▁is""", """▁a""", """▁t""", """est"""]	)

             self.assertListEqual(
                 tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE	)     ,						[2_8_5, 4_6, 1_0, 1_7_0, 3_8_2]     ,						)

             _a    : Tuple     					=tokenizer.tokenize("""I was born in 92000, and this is falsé."""	)
             self.assertListEqual(
                 SCREAMING_SNAKE_CASE     ,						[
                     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""",
                     """.""",
                 ]     ,						)
             _a    : List[Any]     					=tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE	)
             self.assertListEqual(
                 SCREAMING_SNAKE_CASE     ,						[8, 2_1, 8_4, 5_5, 2_4, 1_9, 7, 0, 6_0_2, 3_4_7, 3_4_7, 3_4_7, 3, 1_2, 6_6, 4_6, 7_2, 8_0, 6, 0, 4]     ,						)

             _a    : Union[str, Any]     					=tokenizer.convert_ids_to_tokens(SCREAMING_SNAKE_CASE	)
             self.assertListEqual(
                 SCREAMING_SNAKE_CASE     ,						[
                     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>""",
                     """.""",
                 ]     ,						)






      @cached_property
      def __UpperCAmelCase       ( self			:Optional[int]	)       ->					List[str]:




             '''simple docstring'''
             return ReformerTokenizer.from_pretrained("""google/reformer-crime-and-punishment"""	)






      @slow
      def __UpperCAmelCase       ( self			:Any	)       ->					int:




             '''simple docstring'''
             _a    : Any     					="""Hello World!"""
             _a    : List[str]     					=[1_2_6, 3_2, 2_6_2, 1_5_2, 3_8, 7_2, 2_8_7]

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






      @slow
      def __UpperCAmelCase       ( self			:Optional[Any]	)       ->					List[Any]:




             '''simple docstring'''
             _a    : Tuple     					=(
                 """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"""
             )
             _a    : Optional[Any]     					=[
                 1_0_8,
                 2_6_5,
                 2_4,
                 1_1_1,
                 4,
                 2_5_8,
                 1_5_6,
                 3_5,
                 2_8,
                 2_7_5,
                 3,
                 2_5_9,
                 2_9_7,
                 2_6_0,
                 8_4,
                 4,
                 3_5,
                 1_1_0,
                 4_4,
                 8,
                 2_5_9,
                 9_1,
                 2_6_8,
                 2_1,
                 1_1,
                 2_0_9,
                 2_7_4,
                 1_0_9,
                 2_6_6,
                 2_7_7,
                 1_1_7,
                 8_6,
                 9_3,
                 3_1_5,
                 2_5_8,
                 2_7_8,
                 2_5_8,
                 2_7_7,
                 2_5_8,
                 0,
                 2_5_8,
                 2_8_8,
                 2_5_8,
                 3_1_9,
                 2_5_8,
                 0,
                 2_5_8,
                 0,
                 2_5_8,
                 0,
                 2_5_8,
                 0,
                 2_5_8,
                 2_8_7,
                 2_5_8,
                 3_1_5,
                 2_5_8,
                 2_8_9,
                 2_5_8,
                 2_7_8,
                 9_9,
                 2_6_9,
                 2_6_6,
                 2_6_2,
                 8,
                 2_5_9,
                 2_4_1,
                 4,
                 2_1_7,
                 2_3_0,
                 2_6_8,
                 2_6_6,
                 5_5,
                 1_6_8,
                 1_0_6,
                 7_5,
                 1_9_3,
                 2_6_6,
                 2_2_3,
                 2_7,
                 4_9,
                 2_6,
                 2_8_2,
                 2_5,
                 2_6_4,
                 2_9_9,
                 1_9,
                 2_6,
                 0,
                 2_5_8,
                 2_7_7,
                 1_1_7,
                 8_6,
                 9_3,
                 1_7_6,
                 1_8_3,
                 2_7_0,
                 1_1,
                 2_6_2,
                 4_2,
                 6_1,
                 2_6_5,
             ]

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






      @require_torch
      @slow
      def __UpperCAmelCase       ( self			:str	)       ->					List[str]:




             '''simple docstring'''
             import torch

             from transformers import ReformerConfig, ReformerModel

             # Build sequence
             _a    : Dict     					=list(self.big_tokenizer.get_vocab().keys()	)[:1_0]
             _a    : Dict     					=""" """.join(SCREAMING_SNAKE_CASE	)
             _a    : Optional[int]     					=self.big_tokenizer.encode_plus(SCREAMING_SNAKE_CASE     ,						return_tensors="""pt"""	)
             _a    : Optional[int]     					=self.big_tokenizer.batch_encode_plus([sequence, sequence]     ,						return_tensors="""pt"""	)

             _a    : str     					=ReformerConfig()
             # The input gets padded during training so adjust the axial position encodings from the pretrained model value of (512, 1024)
             _a    : Union[str, Any]     					=encoded_sequence["""input_ids"""].shape
             _a    : List[str]     					=ReformerModel(SCREAMING_SNAKE_CASE	)

             # Reformer has config.vocab_size == tokenizer.vocab_size == len(tokenizer) - 1 = 320; len(tokenizer) is 321 (including a pad token with id 320)
             assert model.get_input_embeddings().weight.shape[0] >= self.big_tokenizer.vocab_size

             with torch.no_grad():
                    model(**SCREAMING_SNAKE_CASE	)
                    model(**SCREAMING_SNAKE_CASE	)






      @slow
      def __UpperCAmelCase       ( self			:int	)       ->					str:




             '''simple docstring'''
             # fmt: off
             _a    : Any     					={"""input_ids""": [[1_0_8, 2_6_5, 2_4, 1_1_1, 4, 2_5_8, 1_5_6, 7, 5_1, 2_7_9, 5_8, 7, 7_6, 2_5, 6_9, 2_7_8], [1_4_0, 2_4_3, 2_6_4, 1_3_4, 1_7, 2_6_7, 7_7, 2_6_3, 2_2, 2_6_2, 2_9_7, 2_5_8, 3_0_4, 1_7_7, 2_7_9, 2_6_6, 1_4, 8_9, 1_3, 3_5, 2_6_1, 2_9_9, 2_7_2, 1_3_7, 2_7_5, 2_7_8]], """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]]}  # noqa: E501
             # fmt: on

             # This tokenizer does not know some characters like ")".
             # That is the reason why we use very simple texts here.
             # Also see https://github.com/huggingface/transformers/pull/11737#issuecomment-850769064
             _a    : str     					=[
                 """This is a very simple sentence.""",
                 """The quick brown fox jumps over the lazy dog.""",
             ]

             self.tokenizer_integration_test_util(
                 expected_encoding=SCREAMING_SNAKE_CASE     ,						model_name="""google/reformer-crime-and-punishment"""     ,						revision="""0e6c3decb8211d49bf881013425dc8b0448b3f5a"""     ,						padding=SCREAMING_SNAKE_CASE     ,						sequences=SCREAMING_SNAKE_CASE     ,						)






'''simple docstring'''







from __future__ import annotations

from typing import TypedDict







class A__       (						UpperCAmelCase__      ):
      __UpperCamelCase       :  str
      __UpperCamelCase       :  int


def 	SCREAMING_SNAKE_CASE_					(  _UpperCAmelCase	:	str  )  ->			list[str]:
       if not isinstance(_UpperCAmelCase    ,_UpperCAmelCase  ):
              raise TypeError("""The parameter s type must be str."""  )

       return [s[i:] + s[:i] for i in range(len(_UpperCAmelCase  )  )]


def 	SCREAMING_SNAKE_CASE_					(  _UpperCAmelCase	:	str  )  ->			BWTTransformDict:
       if not isinstance(_UpperCAmelCase    ,_UpperCAmelCase  ):
              raise TypeError("""The parameter s type must be str."""  )
       if not s:
              raise ValueError("""The parameter s must not be empty."""  )

       _a    : List[Any]     					=all_rotations(_UpperCAmelCase  )
       rotations.sort()  # sort the list of rotations in alphabetically order
       # make a string composed of the last char of each rotation
       _a    : BWTTransformDict     					={
           "bwt_string": "".join([word[-1] for word in rotations]  ),
           "idx_original_string": rotations.index(_UpperCAmelCase  ),
       }
       return response


def 	SCREAMING_SNAKE_CASE_					(  _UpperCAmelCase	:	str    ,_UpperCAmelCase	:	int  )  ->			str:
       if not isinstance(_UpperCAmelCase    ,_UpperCAmelCase  ):
              raise TypeError("""The parameter bwt_string type must be str."""  )
       if not bwt_string:
              raise ValueError("""The parameter bwt_string must not be empty."""  )
       try:
              _a    : List[str]     					=int(_UpperCAmelCase  )
       except ValueError:
              raise TypeError(
                  """The parameter idx_original_string type must be int or passive"""
                  """ of cast to int."""  )
       if idx_original_string < 0:
              raise ValueError("""The parameter idx_original_string must not be lower than 0."""  )
       if idx_original_string >= len(_UpperCAmelCase  ):
              raise ValueError(
                  """The parameter idx_original_string must be lower than""" """ len(bwt_string)."""  )

       _a    : Optional[int]     					=[""""""] * len(_UpperCAmelCase  )
       for _ in range(len(_UpperCAmelCase  )  ):
              for i in range(len(_UpperCAmelCase  )  ):
                     _a    : int     					=bwt_string[i] + ordered_rotations[i]
              ordered_rotations.sort()
       return ordered_rotations[idx_original_string]


if __name__ == "__main__":
   A__:    Any					    =       '''Provide a string that I will generate its BWT transform: '''
   A__:    Union[str, Any]					    =       input(entry_msg).strip()
   A__:    Optional[int]					    =       bwt_transform(s)
   print(
       F"Burrows Wheeler transform for string '{s}' results "
       F"in '{result['bwt_string']}'"
   )
   A__:    Union[str, Any]					    =       reverse_bwt(result['''bwt_string'''], result['''idx_original_string'''])
   print(
       F"Reversing Burrows Wheeler transform for entry '{result['bwt_string']}' "
       F"we get original string '{original_string}'"
   )


