






import json
import os
import unittest

from transformers import BatchEncoding, MvpTokenizer, MvpTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin, filter_roberta_detectors




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

       '''simple docstring'''
       lowerCamelCase				:					int								=			MvpTokenizer
       lowerCamelCase				:					Union[str, Any]								=			MvpTokenizerFast
       lowerCamelCase				:					List[str]								=			True
       lowerCamelCase				:					Dict								=			filter_roberta_detectors



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






            '''simple docstring'''





            super().setUp()
            __snake_case      :List[Any] 							=    [
                """l""",
                """o""",
                """w""",
                """e""",
                """r""",
                """s""",
                """t""",
                """i""",
                """d""",
                """n""",
                """\u0120""",
                """\u0120l""",
                """\u0120n""",
                """\u0120lo""",
                """\u0120low""",
                """er""",
                """\u0120lowest""",
                """\u0120newer""",
                """\u0120wider""",
                """<unk>""",
            ]
            __snake_case      :int 							=    dict(zip(a__      ,    range(len(a__     )     )     )     )
            __snake_case      :Any 							=    ["""#version: 0.2""", """\u0120 l""", """\u0120l o""", """\u0120lo w""", """e r""", """"""]
            __snake_case      :Optional[Any] 							=    {"""unk_token""": """<unk>"""}

            __snake_case      :Optional[int] 							=    os.path.join(self.tmpdirname      ,    VOCAB_FILES_NAMES["""vocab_file"""]     )
            __snake_case      :Optional[int] 							=    os.path.join(self.tmpdirname      ,    VOCAB_FILES_NAMES["""merges_file"""]     )
            with open(self.vocab_file      ,    """w"""      ,    encoding="""utf-8"""     ) as fp:
                 fp.write(json.dumps(a__     ) + """\n"""     )
            with open(self.merges_file      ,    """w"""      ,    encoding="""utf-8"""     ) as fp:
                 fp.write("""\n""".join(a__     )     )



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






            '''simple docstring'''





            kwargs.update(self.special_tokens_map     )
            return self.tokenizer_class.from_pretrained(self.tmpdirname      ,    **a__     )



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






            '''simple docstring'''





            kwargs.update(self.special_tokens_map     )
            return self.rust_tokenizer_class.from_pretrained(self.tmpdirname      ,    **a__     )



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






            '''simple docstring'''





            return "lower newer", "lower newer"



       @cached_property
       def        __lowercase    (				self     )			->   Any:






            '''simple docstring'''





            return MvpTokenizer.from_pretrained("""RUCAIBox/mvp"""     )



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






            '''simple docstring'''





            return MvpTokenizerFast.from_pretrained("""RUCAIBox/mvp"""     )



       @require_torch
       def        __lowercase    (				self     )			->   int:






            '''simple docstring'''





            __snake_case      :str 							=    ["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
            __snake_case      :Union[str, Any] 							=    [0, 2_50, 2_51, 1_78_18, 13, 3_91_86, 19_38, 4, 2]

            for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                 __snake_case      :str 							=    tokenizer(a__      ,    max_length=len(a__     )      ,    padding=a__      ,    return_tensors="""pt"""     )
                 self.assertIsInstance(a__      ,    a__     )

                 self.assertEqual((2, 9)      ,    batch.input_ids.shape     )
                 self.assertEqual((2, 9)      ,    batch.attention_mask.shape     )
                 __snake_case      :Any 							=    batch.input_ids.tolist()[0]
                 self.assertListEqual(a__      ,    a__     )
                 # Test that special tokens are reset



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






            '''simple docstring'''





            __snake_case      :Optional[Any] 							=    ["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
            for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                 __snake_case      :List[Any] 							=    tokenizer(a__      ,    padding=a__      ,    return_tensors="""pt"""     )
                 # check if input_ids are returned and no labels
                 self.assertIn("""input_ids"""      ,    a__     )
                 self.assertIn("""attention_mask"""      ,    a__     )
                 self.assertNotIn("""labels"""      ,    a__     )
                 self.assertNotIn("""decoder_attention_mask"""      ,    a__     )



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






            '''simple docstring'''





            __snake_case      :str 							=    [
                """Summary of the text.""",
                """Another summary.""",
            ]
            for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                 __snake_case      :Optional[Any] 							=    tokenizer(text_target=a__      ,    max_length=32      ,    padding="""max_length"""      ,    return_tensors="""pt"""     )
                 self.assertEqual(32      ,    targets["""input_ids"""].shape[1]     )



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






            '''simple docstring'''





            for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                 __snake_case      :Optional[Any] 							=    tokenizer(
                     ["""I am a small frog""" * 10_24, """I am a small frog"""]      ,    padding=a__      ,    truncation=a__      ,    return_tensors="""pt"""     )
                 self.assertIsInstance(a__      ,    a__     )
                 self.assertEqual(batch.input_ids.shape      ,    (2, 10_24)     )



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






            '''simple docstring'''





            __snake_case      :List[Any] 							=    ["""A long paragraph for summarization."""]
            __snake_case      :Dict 							=    [
                """Summary of the text.""",
            ]
            for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                 __snake_case      :List[str] 							=    tokenizer(a__      ,    text_target=a__      ,    return_tensors="""pt"""     )
                 __snake_case      :Dict 							=    inputs["""input_ids"""]
                 __snake_case      :Any 							=    inputs["""labels"""]
                 self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item()     )
                 self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item()     )
                 self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item()     )
                 self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item()     )



       def        __lowercase    (				self     )			->   Dict:






            '''simple docstring'''





            pass



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






            '''simple docstring'''





            for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
                 with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''     ):
                      __snake_case      :Tuple 							=    self.rust_tokenizer_class.from_pretrained(a__      ,    **a__     )
                      __snake_case      :Dict 							=    self.tokenizer_class.from_pretrained(a__      ,    **a__     )
                      __snake_case      :Tuple 							=    """A, <mask> AllenNLP sentence."""
                      __snake_case      :List[str] 							=    tokenizer_r.encode_plus(a__      ,    add_special_tokens=a__      ,    return_token_type_ids=a__     )
                      __snake_case      :List[Any] 							=    tokenizer_p.encode_plus(a__      ,    add_special_tokens=a__      ,    return_token_type_ids=a__     )

                      # token_type_ids should put 0 everywhere
                      self.assertEqual(sum(tokens_r["""token_type_ids"""]     )      ,    sum(tokens_p["""token_type_ids"""]     )     )

                      # attention_mask should put 1 everywhere, so sum over length should be 1
                      self.assertEqual(
                          sum(tokens_r["""attention_mask"""]     ) / len(tokens_r["""attention_mask"""]     )      ,    sum(tokens_p["""attention_mask"""]     ) / len(tokens_p["""attention_mask"""]     )      ,    )

                      __snake_case      :Union[str, Any] 							=    tokenizer_r.convert_ids_to_tokens(tokens_r["""input_ids"""]     )
                      __snake_case      :Tuple 							=    tokenizer_p.convert_ids_to_tokens(tokens_p["""input_ids"""]     )

                      # Rust correctly handles the space before the mask while python doesnt
                      self.assertSequenceEqual(tokens_p["""input_ids"""]      ,    [0, 2_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 4, 2]     )
                      self.assertSequenceEqual(tokens_r["""input_ids"""]      ,    [0, 2_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 4, 2]     )

                      self.assertSequenceEqual(
                          a__      ,    ["""<s>""", """A""", """,""", """<mask>""", """ĠAllen""", """N""", """LP""", """Ġsentence""", """.""", """</s>"""]     )
                      self.assertSequenceEqual(
                          a__      ,    ["""<s>""", """A""", """,""", """<mask>""", """ĠAllen""", """N""", """LP""", """Ġsentence""", """.""", """</s>"""]     )










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()


