
"""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


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

@require_sentencepiece
@require_tokenizers
class __snake_case		(				lowerCAmelCase ,     unittest.TestCase					):
    _a		:						List[str]=    ReformerTokenizer
    _a		:						Any=    ReformerTokenizerFast
    _a		:						Optional[Any]=    True
    _a		:						Optional[int]=    False
    _a		:						Union[str, Any]=    True
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           super().setUp()

           lowercase							:					int	     =				ReformerTokenizer(snake_case		,keep_accents=snake_case				)
           tokenizer.save_pretrained(self.tmpdirname				)
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           lowercase							:					str	     =				"""<s>"""
           lowercase							:					Any	     =				1

           self.assertEqual(self.get_tokenizer()._convert_token_to_id(snake_case				)		,snake_case				)
           self.assertEqual(self.get_tokenizer()._convert_id_to_token(snake_case				)		,snake_case				)
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           lowercase							:					int	     =				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(snake_case				)		,1000				)
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           self.assertEqual(self.get_tokenizer().vocab_size		,1000				)
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           if not self.test_rust_tokenizer:
                  return

           lowercase							:					Dict	     =				self.get_tokenizer()
           lowercase							:					Dict	     =				self.get_rust_tokenizer()

           lowercase							:					str	     =				"""I was born in 92000, and this is falsé."""

           lowercase							:					List[str]	     =				tokenizer.tokenize(snake_case				)
           lowercase							:					Union[str, Any]	     =				rust_tokenizer.tokenize(snake_case				)
           self.assertListEqual(snake_case		,snake_case				)

           lowercase							:					List[str]	     =				tokenizer.encode(snake_case		,add_special_tokens=snake_case				)
           lowercase							:					List[str]	     =				rust_tokenizer.encode(snake_case		,add_special_tokens=snake_case				)
           self.assertListEqual(snake_case		,snake_case				)

           lowercase							:					Union[str, Any]	     =				self.get_rust_tokenizer()
           lowercase							:					Any	     =				tokenizer.encode(snake_case				)
           lowercase							:					List[Any]	     =				rust_tokenizer.encode(snake_case				)
           self.assertListEqual(snake_case		,snake_case				)
    def 				_SCREAMING_SNAKE_CASE	(  self		,snake_case=15				):



           '''simple docstring'''







           for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
                  with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"				):
                         lowercase							:					Tuple	     =				self.rust_tokenizer_class.from_pretrained(snake_case		,**snake_case				)

                         # Simple input
                         lowercase							:					Dict	     =				"""This is a simple input"""
                         lowercase							:					Any	     =				["""This is a simple input 1""", """This is a simple input 2"""]
                         lowercase							:					str	     =				("""This is a simple input""", """This is a pair""")
                         lowercase							:					List[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(snake_case		,tokenizer_r.encode		,snake_case		,max_length=snake_case		,padding="""max_length"""				)

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

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

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

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

                         # Pair input
                         self.assertRaises(
                             snake_case		,tokenizer_r.batch_encode_plus		,snake_case		,max_length=snake_case		,padding="""max_length"""		,)
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           pass
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           lowercase							:					Any	     =				ReformerTokenizer(snake_case		,keep_accents=snake_case				)

           lowercase							:					Tuple	     =				tokenizer.tokenize("""This is a test"""				)
           self.assertListEqual(snake_case		,["""▁This""", """▁is""", """▁a""", """▁t""", """est"""]				)

           self.assertListEqual(
               tokenizer.convert_tokens_to_ids(snake_case				)		,[285, 46, 10, 170, 382]		,)

           lowercase							:					Optional[Any]	     =				tokenizer.tokenize("""I was born in 92000, and this is falsé."""				)
           self.assertListEqual(
               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""",
                   """.""",
               ]		,)
           lowercase							:					Union[str, Any]	     =				tokenizer.convert_tokens_to_ids(snake_case				)
           self.assertListEqual(
               snake_case		,[8, 21, 84, 55, 24, 19, 7, 0, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 0, 4]		,)

           lowercase							:					List[Any]	     =				tokenizer.convert_ids_to_tokens(snake_case				)
           self.assertListEqual(
               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 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           return ReformerTokenizer.from_pretrained("""google/reformer-crime-and-punishment"""				)
    @slow
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           lowercase							:					int	     =				"""Hello World!"""
           lowercase							:					int	     =				[126, 32, 262, 152, 38, 72, 287]

           self.assertListEqual(snake_case		,self.big_tokenizer.encode(snake_case				)				)
    @slow
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           lowercase							:					List[str]	     =				(
               """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"""
           )
           lowercase							:					str	     =				[
               108,
               265,
               24,
               111,
               4,
               258,
               156,
               35,
               28,
               275,
               3,
               259,
               297,
               260,
               84,
               4,
               35,
               110,
               44,
               8,
               259,
               91,
               268,
               21,
               11,
               209,
               274,
               109,
               266,
               277,
               117,
               86,
               93,
               315,
               258,
               278,
               258,
               277,
               258,
               0,
               258,
               288,
               258,
               319,
               258,
               0,
               258,
               0,
               258,
               0,
               258,
               0,
               258,
               287,
               258,
               315,
               258,
               289,
               258,
               278,
               99,
               269,
               266,
               262,
               8,
               259,
               241,
               4,
               217,
               230,
               268,
               266,
               55,
               168,
               106,
               75,
               193,
               266,
               223,
               27,
               49,
               26,
               282,
               25,
               264,
               299,
               19,
               26,
               0,
               258,
               277,
               117,
               86,
               93,
               176,
               183,
               270,
               11,
               262,
               42,
               61,
               265,
           ]

           self.assertListEqual(snake_case		,self.big_tokenizer.encode(snake_case				)				)
    @require_torch
    @slow
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           import torch

           from transformers import ReformerConfig, ReformerModel

           # Build sequence
           lowercase							:					List[str]	     =				list(self.big_tokenizer.get_vocab().keys()				)[:10]
           lowercase							:					str	     =				""" """.join(snake_case				)
           lowercase							:					Optional[int]	     =				self.big_tokenizer.encode_plus(snake_case		,return_tensors="""pt"""				)
           lowercase							:					Optional[int]	     =				self.big_tokenizer.batch_encode_plus([sequence, sequence]		,return_tensors="""pt"""				)

           lowercase							:					Union[str, Any]	     =				ReformerConfig()
           # The input gets padded during training so adjust the axial position encodings from the pretrained model value of (512, 1024)
           lowercase							:					Optional[Any]	     =				encoded_sequence["""input_ids"""].shape
           lowercase							:					Any	     =				ReformerModel(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(**snake_case				)
                  model(**snake_case				)
    @slow
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           lowercase							:					int	     =				{"""input_ids""": [[108, 265, 24, 111, 4, 258, 156, 7, 51, 279, 58, 7, 76, 25, 69, 278], [140, 243, 264, 134, 17, 267, 77, 263, 22, 262, 297, 258, 304, 177, 279, 266, 14, 89, 13, 35, 261, 299, 272, 137, 275, 278]], """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
           lowercase							:					Any	     =				[
               """This is a very simple sentence.""",
               """The quick brown fox jumps over the lazy dog.""",
           ]

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


from __future__ import annotations

import numpy as np







def 						_snake_case(  SCREAMING_SNAKE_CASE__    )			->   tuple[np.ndarray, np.ndarray]:
       lowercase     ,      lowercase							:					Dict	     =				np.shape(SCREAMING_SNAKE_CASE__    )
       if rows != columns:
              lowercase							:					str	     =				(
                  """'table' has to be of square shaped array but got a """
                  f"{rows}x{columns} array:\n{table}"
              )
              raise ValueError(SCREAMING_SNAKE_CASE__    )

       lowercase							:					Any	     =				np.zeros((rows, columns)    )
       lowercase							:					int	     =				np.zeros((rows, columns)    )
       for i in range(SCREAMING_SNAKE_CASE__    ):
              for j in range(SCREAMING_SNAKE_CASE__    ):
                     lowercase							:					Optional[int]	     =				sum(lower[i][k] * upper[k][j] for k in range(SCREAMING_SNAKE_CASE__    )    )
                     if upper[j][j] == 0:
                            raise ArithmeticError("""No LU decomposition exists"""    )
                     lowercase							:					str	     =				(table[i][j] - total) / upper[j][j]
              lowercase							:					Optional[Any]	     =				1
              for j in range(SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    ):
                     lowercase							:					Any	     =				sum(lower[i][k] * upper[k][j] for k in range(SCREAMING_SNAKE_CASE__    )    )
                     lowercase							:					Tuple	     =				table[i][j] - total
       return lower, upper


if __name__ == "__main__":
  import doctest

  doctest.testmod()
