




import unittest

from transformers import CamembertTokenizer, CamembertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import is_torch_available

from ...test_tokenization_common import TokenizerTesterMixin


_lowerCamelCase     :		Tuple								=       get_tests_dir("""fixtures/test_sentencepiece.model""")
_lowerCamelCase     :		str								=       get_tests_dir("""fixtures/test_sentencepiece_bpe.model""")

_lowerCamelCase     :		Tuple								=       """pt""" if is_torch_available() else """tf"""


@require_sentencepiece
@require_tokenizers
class     UpperCamelCase_   (			UpperCAmelCase__		, unittest.TestCase					):

    '''simple docstring'''






    UpperCAmelCase__							  =				CamembertTokenizer
    UpperCAmelCase__							  =				CamembertTokenizerFast
    UpperCAmelCase__							  =				True
    UpperCAmelCase__							  =				True


    def 	SCREAMING_SNAKE_CASE							(      self				:     Any)						->Tuple:





           '''simple docstring'''

           super().setUp()

           # We have a SentencePiece fixture for testing
           A__							=   CamembertTokenizer(UpperCAmelCase__)
           tokenizer.save_pretrained(self.tmpdirname)


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





           '''simple docstring'''

           A__							=   '''<pad>'''
           A__							=   1

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


    def 	SCREAMING_SNAKE_CASE							(      self				:     Optional[Any])						->Optional[Any]:





           '''simple docstring'''

           A__							=   list(self.get_tokenizer().get_vocab().keys())

           self.assertEqual(vocab_keys[0] ,			'''<s>NOTUSED''')
           self.assertEqual(vocab_keys[1] ,			'''<pad>''')
           self.assertEqual(vocab_keys[-1] ,			'''<mask>''')
           self.assertEqual(len(UpperCAmelCase__) ,			1_004)


    def 	SCREAMING_SNAKE_CASE							(      self				:     int)						->str:





           '''simple docstring'''

           self.assertEqual(self.get_tokenizer().vocab_size ,			1_005)


    def 	SCREAMING_SNAKE_CASE							(      self				:     Dict)						->str:





           '''simple docstring'''

           A__							=   CamembertTokenizer(UpperCAmelCase__)
           tokenizer.save_pretrained(self.tmpdirname)
           A__							=   CamembertTokenizerFast.from_pretrained(self.tmpdirname)

           A__							=   '''I was born in 92000, and this is falsé.'''

           A__							=   tokenizer.encode(UpperCAmelCase__)
           A__							=   rust_tokenizer.encode(UpperCAmelCase__)
           self.assertListEqual(UpperCAmelCase__ ,			UpperCAmelCase__)

           A__							=   tokenizer.encode(UpperCAmelCase__ ,			add_special_tokens=UpperCAmelCase__)
           A__							=   rust_tokenizer.encode(UpperCAmelCase__ ,			add_special_tokens=UpperCAmelCase__)
           self.assertListEqual(UpperCAmelCase__ ,			UpperCAmelCase__)

           # <unk> tokens are not the same for `rust` than for `slow`.
           # Because spm gives back raw token instead of `unk` in EncodeAsPieces
           # tokens = tokenizer.tokenize(sequence)
           A__							=   tokenizer.convert_ids_to_tokens(UpperCAmelCase__)
           A__							=   rust_tokenizer.tokenize(UpperCAmelCase__)
           self.assertListEqual(UpperCAmelCase__ ,			UpperCAmelCase__)


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





           '''simple docstring'''

           if not self.test_rust_tokenizer:
                  return

           A__							=   self.get_tokenizer()
           A__							=   self.get_rust_tokenizer()

           A__							=   '''I was born in 92000, and this is falsé.'''

           A__							=   tokenizer.tokenize(UpperCAmelCase__)
           A__							=   rust_tokenizer.tokenize(UpperCAmelCase__)
           self.assertListEqual(UpperCAmelCase__ ,			UpperCAmelCase__)

           A__							=   tokenizer.encode(UpperCAmelCase__ ,			add_special_tokens=UpperCAmelCase__)
           A__							=   rust_tokenizer.encode(UpperCAmelCase__ ,			add_special_tokens=UpperCAmelCase__)
           self.assertListEqual(UpperCAmelCase__ ,			UpperCAmelCase__)

           A__							=   self.get_rust_tokenizer()
           A__							=   tokenizer.encode(UpperCAmelCase__)
           A__							=   rust_tokenizer.encode(UpperCAmelCase__)
           self.assertListEqual(UpperCAmelCase__ ,			UpperCAmelCase__)





    @slow
    def 	SCREAMING_SNAKE_CASE							(      self				:     Tuple)						->Tuple:





           '''simple docstring'''

           A__							=   {'''input_ids''': [[5, 54, 7_196, 297, 30, 23, 776, 18, 11, 3_215, 3_705, 8_252, 22, 3_164, 1_181, 2_116, 29, 16, 813, 25, 791, 3_314, 20, 3_446, 38, 27_575, 120, 6, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [5, 468, 17, 11, 9_088, 20, 1_517, 8, 22_804, 18_818, 10, 38, 629, 607, 607, 142, 19, 7_196, 867, 56, 10_326, 24, 2_267, 20, 416, 5_072, 15_612, 233, 734, 7, 2_399, 27, 16, 3_015, 1_649, 7, 24, 20, 4_338, 2_399, 27, 13, 3_400, 14, 13, 6_189, 8, 930, 9, 6]], '''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, 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, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 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

           # camembert is a french model. So we also use french texts.
           A__							=   [
               '''Le transformeur est un modèle d\'apprentissage profond introduit en 2017, '''
               '''utilisé principalement dans le domaine du traitement automatique des langues (TAL).''',
               '''À l\'instar des réseaux de neurones récurrents (RNN), les transformeurs sont conçus '''
               '''pour gérer des données séquentielles, telles que le langage naturel, pour des tâches '''
               '''telles que la traduction et la synthèse de texte.''',
           ]

           self.tokenizer_integration_test_util(
               expected_encoding=UpperCAmelCase__ ,			model_name='''camembert-base''' ,			revision='''3a0641d9a1aeb7e848a74299e7e4c4bca216b4cf''' ,			sequences=UpperCAmelCase__ ,			)








from __future__ import annotations

from typing import Any


class     UpperCamelCase_   :

    '''simple docstring'''






    def __init__(      self				:     str ,			UpperCAmelCase__				:     int ,			UpperCAmelCase__				:     int ,			UpperCAmelCase__				:     float = 0)						->None:





           '''simple docstring'''

           A__			,							A__							=   row, column
           A__							=   [[default_value for c in range(UpperCAmelCase__)] for r in range(UpperCAmelCase__)]


    def __str__(      self				:     List[str])						->str:





           '''simple docstring'''

           A__							=   f"""Matrix consist of {self.row} rows and {self.column} columns\n"""

           # Make string identifier
           A__							=   0
           for row_vector in self.array:
                  for obj in row_vector:
                         A__							=   max(UpperCAmelCase__ ,			len(str(UpperCAmelCase__)))
           A__							=   f"""%{max_element_length}s"""

           # Make string and return
           def single_line(UpperCAmelCase__				:     list[float]) -> str:
                  nonlocal string_format_identifier
                  A__							=   '''['''
                  line += ", ".join(string_format_identifier % (obj,) for obj in row_vector)
                  line += "]"
                  return line

           s += "\n".join(single_line(UpperCAmelCase__) for row_vector in self.array)
           return s


    def __repr__(      self				:     Tuple)						->str:





           '''simple docstring'''

           return str(self)


    def 	SCREAMING_SNAKE_CASE							(      self				:     List[str] ,			UpperCAmelCase__				:     tuple[int, int])						->bool:





           '''simple docstring'''

           if not (isinstance(UpperCAmelCase__ ,			(list, tuple)) and len(UpperCAmelCase__) == 2):
                  return False
           elif not (0 <= loc[0] < self.row and 0 <= loc[1] < self.column):
                  return False
           else:
                  return True


    def __getitem__(      self				:     List[Any] ,			UpperCAmelCase__				:     tuple[int, int])						->Any:





           '''simple docstring'''

           assert self.validate_indicies(UpperCAmelCase__)
           return self.array[loc[0]][loc[1]]


    def __setitem__(      self				:     List[Any] ,			UpperCAmelCase__				:     tuple[int, int] ,			UpperCAmelCase__				:     float)						->None:





           '''simple docstring'''

           assert self.validate_indicies(UpperCAmelCase__)
           A__							=   value


    def __add__(      self				:     Optional[int] ,			UpperCAmelCase__				:     Matrix)						->Matrix:





           '''simple docstring'''

           assert isinstance(UpperCAmelCase__ ,			UpperCAmelCase__)
           assert self.row == another.row and self.column == another.column

           # Add
           A__							=   Matrix(self.row ,			self.column)
           for r in range(self.row):
                  for c in range(self.column):
                         A__							=   self[r, c] + another[r, c]
           return result


    def __neg__(      self				:     str)						->Matrix:





           '''simple docstring'''

           A__							=   Matrix(self.row ,			self.column)
           for r in range(self.row):
                  for c in range(self.column):
                         A__							=   -self[r, c]
           return result


    def __sub__(      self				:     str ,			UpperCAmelCase__				:     Matrix)						->Matrix:





           '''simple docstring'''

           return self + (-another)


    def __mul__(      self				:     Union[str, Any] ,			UpperCAmelCase__				:     int | float | Matrix)						->Matrix:





           '''simple docstring'''

           if isinstance(UpperCAmelCase__ ,			(int, float)):  # Scalar multiplication
                  A__							=   Matrix(self.row ,			self.column)
                  for r in range(self.row):
                         for c in range(self.column):
                                A__							=   self[r, c] * another
                  return result
           elif isinstance(UpperCAmelCase__ ,			UpperCAmelCase__):  # Matrix multiplication
                  assert self.column == another.row
                  A__							=   Matrix(self.row ,			another.column)
                  for r in range(self.row):
                         for c in range(another.column):
                                for i in range(self.column):
                                       result[r, c] += self[r, i] * another[i, c]
                  return result
           else:
                  A__							=   f"""Unsupported type given for another ({type(UpperCAmelCase__)})"""
                  raise TypeError(UpperCAmelCase__)


    def 	SCREAMING_SNAKE_CASE							(      self				:     List[str])						->Matrix:





           '''simple docstring'''

           A__							=   Matrix(self.column ,			self.row)
           for r in range(self.row):
                  for c in range(self.column):
                         A__							=   self[r, c]
           return result





    def 	SCREAMING_SNAKE_CASE							(      self				:     Tuple ,			UpperCAmelCase__				:     Matrix ,			UpperCAmelCase__				:     Matrix)						->Any:





           '''simple docstring'''

           assert isinstance(UpperCAmelCase__ ,			UpperCAmelCase__) and isinstance(UpperCAmelCase__ ,			UpperCAmelCase__)
           assert self.row == self.column == u.row == v.row  # u, v should be column vector
           assert u.column == v.column == 1  # u, v should be column vector

           # Calculate
           A__							=   v.transpose()
           A__							=   (v_t * self * u)[0, 0] + 1
           if numerator_factor == 0:
                  return None  # It's not invertable
           return self - ((self * u) * (v_t * self) * (1.0 / numerator_factor))


# Testing
if __name__ == "__main__":







  def SCREAMING_SNAKE_CASE    (	)       ->	None:



         """simple docstring"""

         A__							=   Matrix(3			,    3			,    0						)
         for i in range(3						):
                A__							=   1
         print(f"""a^(-1) is {ainv}"""						)
         # u, v
         A__							=   Matrix(3			,    1			,    0						)
         A__			,							A__			,							A__							=   1, 2, -3
         A__							=   Matrix(3			,    1			,    0						)
         A__			,							A__			,							A__							=   4, -2, 5
         print(f"""u is {u}"""						)
         print(f"""v is {v}"""						)
         print(f"""uv^T is {u * v.transpose()}"""						)
         # Sherman Morrison
         print(f"""(a + uv^T)^(-1) is {ainv.sherman_morrison(lowercase_			,    lowercase_						)}"""						)







  def SCREAMING_SNAKE_CASE    (	)       ->	None:



         """simple docstring"""

         import doctest

         doctest.testmod()
  testa()


