

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


UpperCAmelCase__ :     Tuple				    =    get_tests_dir("""fixtures/test_sentencepiece.model""")







@require_sentencepiece
@require_tokenizers
class        a__       (   UpperCAmelCase	,			unittest.TestCase       ):







  """simple docstring"""




  UpperCAmelCase__      : Tuple			       =ReformerTokenizer
  UpperCAmelCase__      : Optional[int]			       =ReformerTokenizerFast
  UpperCAmelCase__      : Any			       =True
  UpperCAmelCase__      : List[Any]			       =False
  UpperCAmelCase__      : Tuple			       =True


  def  _lowercase      (			self    : List[str]		)					->Optional[Any]:
        """simple docstring"""







        super().setUp()

        SCREAMING_SNAKE_CASE :      Optional[Any]       =   ReformerTokenizer(UpperCAmelCase__				,				keep_accents=UpperCAmelCase__		)
        tokenizer.save_pretrained(self.tmpdirname		)


  def  _lowercase      (			self    : int		)					->Any:
        """simple docstring"""







        SCREAMING_SNAKE_CASE :      Tuple       =   """<s>"""
        SCREAMING_SNAKE_CASE :      int       =   1

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


  def  _lowercase      (			self    : str		)					->Dict:
        """simple docstring"""







        SCREAMING_SNAKE_CASE :      Dict       =   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(UpperCAmelCase__		)				,				1_0_0_0		)


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







        self.assertEqual(self.get_tokenizer().vocab_size				,				1_0_0_0		)


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







        if not self.test_rust_tokenizer:
              return

        SCREAMING_SNAKE_CASE :      Any       =   self.get_tokenizer()
        SCREAMING_SNAKE_CASE :      int       =   self.get_rust_tokenizer()

        SCREAMING_SNAKE_CASE :      Tuple       =   """I was born in 92000, and this is falsé."""

        SCREAMING_SNAKE_CASE :      str       =   tokenizer.tokenize(UpperCAmelCase__		)
        SCREAMING_SNAKE_CASE :      str       =   rust_tokenizer.tokenize(UpperCAmelCase__		)
        self.assertListEqual(UpperCAmelCase__				,				UpperCAmelCase__		)

        SCREAMING_SNAKE_CASE :      Tuple       =   tokenizer.encode(UpperCAmelCase__				,				add_special_tokens=UpperCAmelCase__		)
        SCREAMING_SNAKE_CASE :      List[str]       =   rust_tokenizer.encode(UpperCAmelCase__				,				add_special_tokens=UpperCAmelCase__		)
        self.assertListEqual(UpperCAmelCase__				,				UpperCAmelCase__		)

        SCREAMING_SNAKE_CASE :      Any       =   self.get_rust_tokenizer()
        SCREAMING_SNAKE_CASE :      List[Any]       =   tokenizer.encode(UpperCAmelCase__		)
        SCREAMING_SNAKE_CASE :      Union[str, Any]       =   rust_tokenizer.encode(UpperCAmelCase__		)
        self.assertListEqual(UpperCAmelCase__				,				UpperCAmelCase__		)


  def  _lowercase      (			self    : Optional[int]				,				UpperCAmelCase__    : str=1_5		)					->List[str]:
        """simple docstring"""







        for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
              with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"		):
                    SCREAMING_SNAKE_CASE :      List[str]       =   self.rust_tokenizer_class.from_pretrained(UpperCAmelCase__				,				**UpperCAmelCase__		)

                    # Simple input
                    SCREAMING_SNAKE_CASE :      Union[str, Any]       =   """This is a simple input"""
                    SCREAMING_SNAKE_CASE :      int       =   ["""This is a simple input 1""", """This is a simple input 2"""]
                    SCREAMING_SNAKE_CASE :      Union[str, Any]       =   ("""This is a simple input""", """This is a pair""")
                    SCREAMING_SNAKE_CASE :      Optional[Any]       =   [
                        ("""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(UpperCAmelCase__				,				tokenizer_r.encode				,				UpperCAmelCase__				,				max_length=UpperCAmelCase__				,				padding="""max_length"""		)

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

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

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

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

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


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







        pass


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







        SCREAMING_SNAKE_CASE :      Tuple       =   ReformerTokenizer(UpperCAmelCase__				,				keep_accents=UpperCAmelCase__		)

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

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

        SCREAMING_SNAKE_CASE :      str       =   tokenizer.tokenize("""I was born in 92000, and this is falsé."""		)
        self.assertListEqual(
            UpperCAmelCase__				,				[
                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""",
                """.""",
            ]				,				)
        SCREAMING_SNAKE_CASE :      int       =   tokenizer.convert_tokens_to_ids(UpperCAmelCase__		)
        self.assertListEqual(
            UpperCAmelCase__				,				[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]				,				)

        SCREAMING_SNAKE_CASE :      str       =   tokenizer.convert_ids_to_tokens(UpperCAmelCase__		)
        self.assertListEqual(
            UpperCAmelCase__				,				[
                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  _lowercase      (			self    : Optional[int]		)					->List[str]:
        """simple docstring"""







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


  @slow
  def  _lowercase      (			self    : int		)					->Any:
        """simple docstring"""







        SCREAMING_SNAKE_CASE :      Optional[int]       =   """Hello World!"""
        SCREAMING_SNAKE_CASE :      Dict       =   [1_2_6, 3_2, 2_6_2, 1_5_2, 3_8, 7_2, 2_8_7]

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


  @slow
  def  _lowercase      (			self    : Dict		)					->str:
        """simple docstring"""







        SCREAMING_SNAKE_CASE :      int       =   (
            """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"""
        )
        SCREAMING_SNAKE_CASE :      int       =   [
            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(UpperCAmelCase__				,				self.big_tokenizer.encode(UpperCAmelCase__		)		)


  @require_torch
  @slow
  def  _lowercase      (			self    : List[str]		)					->Dict:
        """simple docstring"""







        import torch

        from transformers import ReformerConfig, ReformerModel

        # Build sequence
        SCREAMING_SNAKE_CASE :      Union[str, Any]       =   list(self.big_tokenizer.get_vocab().keys()		)[:1_0]
        SCREAMING_SNAKE_CASE :      Tuple       =   """ """.join(UpperCAmelCase__		)
        SCREAMING_SNAKE_CASE :      Tuple       =   self.big_tokenizer.encode_plus(UpperCAmelCase__				,				return_tensors="""pt"""		)
        SCREAMING_SNAKE_CASE :      List[str]       =   self.big_tokenizer.batch_encode_plus([sequence, sequence]				,				return_tensors="""pt"""		)

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

        # 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(**UpperCAmelCase__		)
              model(**UpperCAmelCase__		)




  @slow
  def  _lowercase      (			self    : List[str]		)					->Tuple:
        """simple docstring"""







        SCREAMING_SNAKE_CASE :      Optional[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
        SCREAMING_SNAKE_CASE :      List[str]       =   [
            """This is a very simple sentence.""",
            """The quick brown fox jumps over the lazy dog.""",
        ]

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







from typing import Any

import numpy as np






def 					__lowercase      (     _A			)     ->						bool:
      return np.array_equal(_A						,    matrix.conjugate().T			)






def 					__lowercase      (     _A						,    _A			)     ->						Any:
      SCREAMING_SNAKE_CASE :      Optional[int]       =   v.conjugate().T
      SCREAMING_SNAKE_CASE :      List[Any]       =   v_star.dot(_A			)
      assert isinstance(_A						,    np.ndarray			)
      return (v_star_dot.dot(_A			)) / (v_star.dot(_A			))






def 					__lowercase      (     )     ->						None:
      SCREAMING_SNAKE_CASE :      Any       =   np.array([[2, 2 + 1J, 4], [2 - 1J, 3, 1J], [4, -1J, 1]]			)
      SCREAMING_SNAKE_CASE :      Optional[int]       =   np.array([[1], [2], [3]]			)
      assert is_hermitian(_A			), F"{a} is not hermitian."
      print(rayleigh_quotient(_A						,    _A			)			)

      SCREAMING_SNAKE_CASE :      Any       =   np.array([[1, 2, 4], [2, 3, -1], [4, -1, 1]]			)
      assert is_hermitian(_A			), F"{a} is not hermitian."
      assert rayleigh_quotient(_A						,    _A			) == float(3			)


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  tests()




