






import tempfile
import unittest
from pathlib import Path
from shutil import copyfile

from transformers import BatchEncoding, MarianTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import is_sentencepiece_available, is_tf_available, is_torch_available


if is_sentencepiece_available():
     from transformers.models.marian.tokenization_marian import VOCAB_FILES_NAMES, save_json

from ...test_tokenization_common import TokenizerTesterMixin


lowerCamelCase__       		=  get_tests_dir("""fixtures/test_sentencepiece.model""")

lowerCamelCase__       		=  {"""target_lang""": """fi""", """source_lang""": """en"""}
lowerCamelCase__       		=  """>>zh<<"""
lowerCamelCase__       		=  """Helsinki-NLP/"""

if is_torch_available():
     lowerCamelCase__       		=  """pt"""
elif is_tf_available():
     lowerCamelCase__       		=  """tf"""
else:
     lowerCamelCase__       		=  """jax"""




@require_sentencepiece
class       snake_case__ (				lowercase_						,     unittest.TestCase):

       '''simple docstring'''
       lowerCamelCase				:					Union[str, Any]								=			MarianTokenizer
       lowerCamelCase				:					str								=			False
       lowerCamelCase				:					int								=			True



       def        __lowercase    (				self     )			->   int:






            '''simple docstring'''





            super().setUp()
            __snake_case      :Optional[int] 							=    ["""</s>""", """<unk>""", """▁This""", """▁is""", """▁a""", """▁t""", """est""", """\u0120""", """<pad>"""]
            __snake_case      :Union[str, Any] 							=    dict(zip(a__      ,    range(len(a__     )     )     )     )
            __snake_case      :Tuple 							=    Path(self.tmpdirname     )
            save_json(a__      ,    save_dir / VOCAB_FILES_NAMES["""vocab"""]     )
            save_json(a__      ,    save_dir / VOCAB_FILES_NAMES["""tokenizer_config_file"""]     )
            if not (save_dir / VOCAB_FILES_NAMES["source_spm"]).exists():
                 copyfile(a__      ,    save_dir / VOCAB_FILES_NAMES["""source_spm"""]     )
                 copyfile(a__      ,    save_dir / VOCAB_FILES_NAMES["""target_spm"""]     )

            __snake_case      :Union[str, Any] 							=    MarianTokenizer.from_pretrained(self.tmpdirname     )
            tokenizer.save_pretrained(self.tmpdirname     )



       def        __lowercase    (				self      ,    **a__     )			->   MarianTokenizer:






            '''simple docstring'''





            return MarianTokenizer.from_pretrained(self.tmpdirname      ,    **a__     )



       def        __lowercase    (				self      ,    a__     )			->   Dict:






            '''simple docstring'''





            return (
                "This is a test",
                "This is a test",
            )



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






            '''simple docstring'''





            __snake_case      :Optional[Any] 							=    """</s>"""
            __snake_case      :Any 							=    0

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



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






            '''simple docstring'''





            __snake_case      :Any 							=    list(self.get_tokenizer().get_vocab().keys()     )

            self.assertEqual(vocab_keys[0]      ,    """</s>"""     )
            self.assertEqual(vocab_keys[1]      ,    """<unk>"""     )
            self.assertEqual(vocab_keys[-1]      ,    """<pad>"""     )
            self.assertEqual(len(a__     )      ,    9     )



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






            '''simple docstring'''





            self.assertEqual(self.get_tokenizer().vocab_size      ,    9     )



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






            '''simple docstring'''





            __snake_case      :List[str] 							=    MarianTokenizer.from_pretrained(F'''{ORG_NAME}opus-mt-en-de'''     )
            __snake_case      :Optional[Any] 							=    en_de_tokenizer(["""I am a small frog"""]      ,    return_tensors=a__     )
            self.assertIsInstance(a__      ,    a__     )
            __snake_case      :Tuple 							=    [38, 1_21, 14, 6_97, 3_88_48, 0]
            self.assertListEqual(a__      ,    batch.input_ids[0]     )

            __snake_case      :int 							=    tempfile.mkdtemp()
            en_de_tokenizer.save_pretrained(a__     )
            __snake_case      :Optional[Any] 							=    [x.name for x in Path(a__     ).glob("""*"""     )]
            self.assertIn("""source.spm"""      ,    a__     )
            MarianTokenizer.from_pretrained(a__     )



       def        __lowercase    (				self     )			->   List[Any]:






            '''simple docstring'''





            __snake_case      :Optional[int] 							=    self.get_tokenizer()

            __snake_case      :Dict 							=    tok(
                ["""I am a small frog""" * 10_00, """I am a small frog"""]      ,    padding=a__      ,    truncation=a__      ,    return_tensors=a__     )
            self.assertIsInstance(a__      ,    a__     )
            self.assertEqual(batch.input_ids.shape      ,    (2, 5_12)     )



       def        __lowercase    (				self     )			->   List[Any]:






            '''simple docstring'''





            __snake_case      :Optional[Any] 							=    self.get_tokenizer()
            __snake_case      :int 							=    tok(["""I am a tiny frog""", """I am a small frog"""]      ,    padding=a__      ,    return_tensors=a__     )
            self.assertIsInstance(a__      ,    a__     )
            self.assertEqual(batch_smaller.input_ids.shape      ,    (2, 10)     )



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






            '''simple docstring'''





            __snake_case      :Tuple 							=    {"""input_ids""": [[4_34_95, 4_62, 20, 4_21_64, 13_69, 52, 4_64, 1_32, 17_03, 4_92, 13, 74_91, 3_89_99, 6, 8, 4_64, 1_32, 17_03, 4_92, 13, 46_69, 3_78_67, 13, 75_25, 27, 15_93, 9_88, 13, 3_39_72, 70_29, 6, 20, 82_51, 3_83, 2, 2_70, 58_66, 37_88, 2, 23_53, 82_51, 1_23_38, 2, 1_39_58, 3_87, 2, 36_29, 69_53, 1_88, 29_00, 2, 1_39_58, 80_11, 1_15_01, 23, 84_60, 40_73, 3_40_09, 20, 4_35, 1_14_39, 27, 8, 84_60, 40_73, 60_04, 20, 99_88, 3_75, 27, 33, 2_66, 19_45, 10_76, 13_50, 3_78_67, 32_88, 5, 5_77, 10_76, 43_74, 8, 50_82, 5, 2_64_53, 2_57, 5_56, 4_03, 2, 2_42, 1_32, 3_83, 3_16, 4_92, 8, 1_07_67, 6, 3_16, 3_04, 42_39, 3, 0], [1_48, 1_57_22, 19, 18_39, 12, 13_50, 13, 2_23_27, 50_82, 54_18, 4_75_67, 3_59_38, 59, 3_18, 1_95_52, 1_08, 21_83, 54, 1_49_76, 48_35, 32, 5_47, 11_14, 8, 3_15, 24_17, 5, 92, 1_90_88, 3, 0, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00], [36, 63_95, 1_25_70, 3_91_47, 1_15_97, 6, 2_66, 4, 4_54_05, 72_96, 3, 0, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00]], """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, 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], [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]]}  # noqa: E501
            # fmt: on

            self.tokenizer_integration_test_util(
                expected_encoding=a__      ,    model_name="""Helsinki-NLP/opus-mt-en-de"""      ,    revision="""1a8c2263da11e68e50938f97e10cd57820bd504c"""      ,    decode_kwargs={"""use_source_tokenizer""": True}      ,    )



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






            '''simple docstring'''





            __snake_case      :Union[str, Any] 							=    MarianTokenizer.from_pretrained("""hf-internal-testing/test-marian-two-vocabs"""     )

            __snake_case      :Optional[Any] 							=    """Tämä on testi"""
            __snake_case      :List[Any] 							=    """This is a test"""

            __snake_case      :str 							=    [76, 7, 20_47, 2]
            __snake_case      :Optional[int] 							=    [69, 12, 11, 9_40, 2]

            __snake_case      :Dict 							=    tokenizer(a__     ).input_ids
            self.assertListEqual(a__      ,    a__     )

            __snake_case      :List[str] 							=    tokenizer(text_target=a__     ).input_ids
            self.assertListEqual(a__      ,    a__     )

            __snake_case      :Optional[Any] 							=    tokenizer.decode(a__      ,    skip_special_tokens=a__     )
            self.assertEqual(a__      ,    a__     )










import unittest

import numpy as np






def 	UpperCamelCase	(							snake_case__  : np.ndarray						,snake_case__  : np.ndarray						,snake_case__  : np.ndarray						,snake_case__  : np.ndarray | None = None						,):




     '''simple docstring'''






     __snake_case      :Any 							=    np.shape(snake_case__	)
     __snake_case      :List[str] 							=    np.shape(snake_case__	)
     __snake_case      :List[str] 							=    np.shape(snake_case__	)

     if shape_a[0] != shape_b[0]:
          __snake_case      :int 							=    (
              """Expected the same number of rows for A and B. """
              f'''Instead found A of size {shape_a} and B of size {shape_b}'''
          )
          raise ValueError(snake_case__	)

     if shape_b[1] != shape_c[1]:
          __snake_case      :List[str] 							=    (
              """Expected the same number of columns for B and C. """
              f'''Instead found B of size {shape_b} and C of size {shape_c}'''
          )
          raise ValueError(snake_case__	)

     __snake_case      :Any 							=    pseudo_inv
     if a_inv is None:
          try:
               __snake_case      :Optional[Any] 							=    np.linalg.inv(snake_case__	)
          except np.linalg.LinAlgError:
               raise ValueError(
                   """Input matrix A is not invertible. Cannot compute Schur complement."""	)

     return mat_c - mat_b.T @ a_inv @ mat_b




class       snake_case__ (				unittest.TestCase):

       '''simple docstring'''



       def        __lowercase    (				self     )			->   None:






            '''simple docstring'''





            __snake_case      :List[str] 							=    np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]     )
            __snake_case      :Union[str, Any] 							=    np.array([[0, 3], [3, 0], [2, 3]]     )
            __snake_case      :List[Any] 							=    np.array([[2, 1], [6, 3]]     )

            __snake_case      :int 							=    schur_complement(a__      ,    a__      ,    a__     )

            __snake_case      :Union[str, Any] 							=    np.block([[a, b], [b.T, c]]     )

            __snake_case      :Dict 							=    np.linalg.det(a__     )
            __snake_case      :Tuple 							=    np.linalg.det(a__     )
            __snake_case      :List[Any] 							=    np.linalg.det(a__     )

            self.assertAlmostEqual(a__      ,    det_a * det_s     )



       def        __lowercase    (				self     )			->   None:






            '''simple docstring'''





            __snake_case      :List[Any] 							=    np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]     )
            __snake_case      :Union[str, Any] 							=    np.array([[0, 3], [3, 0], [2, 3]]     )
            __snake_case      :Optional[int] 							=    np.array([[2, 1], [6, 3]]     )

            with self.assertRaises(a__     ):
                 schur_complement(a__      ,    a__      ,    a__     )



       def        __lowercase    (				self     )			->   None:






            '''simple docstring'''





            __snake_case      :Optional[Any] 							=    np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]     )
            __snake_case      :List[str] 							=    np.array([[0, 3], [3, 0], [2, 3]]     )
            __snake_case      :List[Any] 							=    np.array([[2, 1, 3], [6, 3, 5]]     )

            with self.assertRaises(a__     ):
                 schur_complement(a__      ,    a__      ,    a__     )


if __name__ == "__main__":
     import doctest

     doctest.testmod()
     unittest.main()


