

'''simple docstring'''



import shutil
import tempfile
import unittest

from transformers import SPIECE_UNDERLINE, BatchEncoding, MBartTokenizer, MBartTokenizerFast, is_torch_available
from transformers.testing_utils import (
    get_tests_dir,
    nested_simplify,
    require_sentencepiece,
    require_tokenizers,
    require_torch,
)

from ...test_tokenization_common import TokenizerTesterMixin


UpperCAmelCase :       Any			   =     get_tests_dir('fixtures/test_sentencepiece.model')


if is_torch_available():
  from transformers.models.mbart.modeling_mbart import shift_tokens_right

UpperCAmelCase :       Optional[Any]			   =     2_5_0_0_0_4
UpperCAmelCase :       Union[str, Any]			   =     2_5_0_0_2_0







@require_sentencepiece
@require_tokenizers
class 						lowerCAmelCase__   (      a ,					unittest.TestCase					):



 """simple docstring"""



 lowerCAmelCase__  					=      MBartTokenizer
 lowerCAmelCase__  					=      MBartTokenizerFast
 lowerCAmelCase__  					=      True
 lowerCAmelCase__  					=      True
 def       UpperCAmelCase__		(		self					:       str			)  -> Any:


      """simple docstring"""




      super().setUp()

      # We have a SentencePiece fixture for testing
      __SCREAMING_SNAKE_CASE     =      MBartTokenizer(__SCREAMING_SNAKE_CASE					,							keep_accents=__SCREAMING_SNAKE_CASE			)
      tokenizer.save_pretrained(self.tmpdirname			)
 def       UpperCAmelCase__		(		self					:       int			)  -> Optional[int]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      MBartTokenizer(__SCREAMING_SNAKE_CASE					,							keep_accents=__SCREAMING_SNAKE_CASE			)

      __SCREAMING_SNAKE_CASE     =      tokenizer.tokenize("""This is a test"""			)
      self.assertListEqual(__SCREAMING_SNAKE_CASE					,							["""▁This""", """▁is""", """▁a""", """▁t""", """est"""]			)

      self.assertListEqual(
          tokenizer.convert_tokens_to_ids(__SCREAMING_SNAKE_CASE			)					,							[value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]]					,							)

      __SCREAMING_SNAKE_CASE     =      tokenizer.tokenize("""I was born in 92000, and this is falsé."""			)
      self.assertListEqual(
          __SCREAMING_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""",
              """.""",
          ]					,							)
      __SCREAMING_SNAKE_CASE     =      tokenizer.convert_tokens_to_ids(__SCREAMING_SNAKE_CASE			)
      self.assertListEqual(
          __SCREAMING_SNAKE_CASE					,							[
              value + tokenizer.fairseq_offset
              for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
              #                                       ^ unk: 2 + 1 = 3                  unk: 2 + 1 = 3 ^
          ]					,							)

      __SCREAMING_SNAKE_CASE     =      tokenizer.convert_ids_to_tokens(__SCREAMING_SNAKE_CASE			)
      self.assertListEqual(
          __SCREAMING_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>""",
              """.""",
          ]					,							)


 def       UpperCAmelCase__		(		self					:       int			)  -> Optional[Any]:


      """simple docstring"""




      if not self.test_slow_tokenizer:
           # as we don't have a slow version, we can't compare the outputs between slow and fast versions
           return

      __SCREAMING_SNAKE_CASE     =      (self.rust_tokenizer_class, """hf-internal-testing/tiny-random-mbart""", {})
      for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
           with self.subTest(f'{tokenizer.__class__.__name__} ({pretrained_name})'			):
                __SCREAMING_SNAKE_CASE     =      self.rust_tokenizer_class.from_pretrained(__SCREAMING_SNAKE_CASE					,							**__SCREAMING_SNAKE_CASE			)
                __SCREAMING_SNAKE_CASE     =      self.tokenizer_class.from_pretrained(__SCREAMING_SNAKE_CASE					,							**__SCREAMING_SNAKE_CASE			)

                __SCREAMING_SNAKE_CASE     =      tempfile.mkdtemp()

                __SCREAMING_SNAKE_CASE     =      tokenizer_r.save_pretrained(__SCREAMING_SNAKE_CASE			)
                __SCREAMING_SNAKE_CASE     =      tokenizer_p.save_pretrained(__SCREAMING_SNAKE_CASE			)

                # Checks it save with the same files + the tokenizer.json file for the fast one
                self.assertTrue(any("""tokenizer.json""" in f for f in tokenizer_r_files			)			)
                __SCREAMING_SNAKE_CASE     =      tuple(f for f in tokenizer_r_files if """tokenizer.json""" not in f			)
                self.assertSequenceEqual(__SCREAMING_SNAKE_CASE					,							__SCREAMING_SNAKE_CASE			)

                # Checks everything loads correctly in the same way
                __SCREAMING_SNAKE_CASE     =      tokenizer_r.from_pretrained(__SCREAMING_SNAKE_CASE			)
                __SCREAMING_SNAKE_CASE     =      tokenizer_p.from_pretrained(__SCREAMING_SNAKE_CASE			)

                # Check special tokens are set accordingly on Rust and Python
                for key in tokenizer_pp.special_tokens_map:
                     self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE					,							__SCREAMING_SNAKE_CASE			)			)
                     # self.assertEqual(getattr(tokenizer_rp, key), getattr(tokenizer_pp, key))
                     # self.assertEqual(getattr(tokenizer_rp, key + "_id"), getattr(tokenizer_pp, key + "_id"))

                shutil.rmtree(__SCREAMING_SNAKE_CASE			)

                # Save tokenizer rust, legacy_format=True
                __SCREAMING_SNAKE_CASE     =      tempfile.mkdtemp()

                __SCREAMING_SNAKE_CASE     =      tokenizer_r.save_pretrained(__SCREAMING_SNAKE_CASE					,							legacy_format=__SCREAMING_SNAKE_CASE			)
                __SCREAMING_SNAKE_CASE     =      tokenizer_p.save_pretrained(__SCREAMING_SNAKE_CASE			)

                # Checks it save with the same files
                self.assertSequenceEqual(__SCREAMING_SNAKE_CASE					,							__SCREAMING_SNAKE_CASE			)

                # Checks everything loads correctly in the same way
                __SCREAMING_SNAKE_CASE     =      tokenizer_r.from_pretrained(__SCREAMING_SNAKE_CASE			)
                __SCREAMING_SNAKE_CASE     =      tokenizer_p.from_pretrained(__SCREAMING_SNAKE_CASE			)

                # Check special tokens are set accordingly on Rust and Python
                for key in tokenizer_pp.special_tokens_map:
                     self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE					,							__SCREAMING_SNAKE_CASE			)			)

                shutil.rmtree(__SCREAMING_SNAKE_CASE			)

                # Save tokenizer rust, legacy_format=False
                __SCREAMING_SNAKE_CASE     =      tempfile.mkdtemp()

                __SCREAMING_SNAKE_CASE     =      tokenizer_r.save_pretrained(__SCREAMING_SNAKE_CASE					,							legacy_format=__SCREAMING_SNAKE_CASE			)
                __SCREAMING_SNAKE_CASE     =      tokenizer_p.save_pretrained(__SCREAMING_SNAKE_CASE			)

                # Checks it saved the tokenizer.json file
                self.assertTrue(any("""tokenizer.json""" in f for f in tokenizer_r_files			)			)

                # Checks everything loads correctly in the same way
                __SCREAMING_SNAKE_CASE     =      tokenizer_r.from_pretrained(__SCREAMING_SNAKE_CASE			)
                __SCREAMING_SNAKE_CASE     =      tokenizer_p.from_pretrained(__SCREAMING_SNAKE_CASE			)

                # Check special tokens are set accordingly on Rust and Python
                for key in tokenizer_pp.special_tokens_map:
                     self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE					,							__SCREAMING_SNAKE_CASE			)			)

                shutil.rmtree(__SCREAMING_SNAKE_CASE			)







@require_torch
@require_sentencepiece
@require_tokenizers
class 						lowerCAmelCase__   (      unittest.TestCase					):



 """simple docstring"""



 lowerCAmelCase__  					=      "facebook/mbart-large-en-ro"
 lowerCAmelCase__  					=      [
     " UN Chief Says There Is No Military Solution in Syria",
     " Secretary-General Ban Ki-moon says his response to Russia's stepped up military support for Syria is that \"there is no military solution\" to the nearly five-year conflict and more weapons will only worsen the violence and misery for millions of people.",
 ]
 lowerCAmelCase__  					=      [
     "Şeful ONU declară că nu există o soluţie militară în Siria",
     "Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al Rusiei"
     " pentru Siria este că \"nu există o soluţie militară\" la conflictul de aproape cinci ani şi că noi arme nu vor"
     " face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.",
 ]
 lowerCAmelCase__  					=      [8274, 127873, 25916, 7, 8622, 2071, 438, 67485, 53, 187895, 23, 51712, 2, EN_CODE]
 @classmethod
 def       UpperCAmelCase__		(		cls					:       List[str]			)  -> List[str]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      MBartTokenizer.from_pretrained(
          cls.checkpoint_name					,							src_lang="""en_XX"""					,							tgt_lang="""ro_RO"""			)
      __SCREAMING_SNAKE_CASE     =      1
      return cls
 def       UpperCAmelCase__		(		self					:       str			)  -> List[Any]:


      """simple docstring"""




      self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["""ar_AR"""]					,							250_001			)
      self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["""en_EN"""]					,							250_004			)
      self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["""ro_RO"""]					,							250_020			)
 def       UpperCAmelCase__		(		self					:       Dict			)  -> List[str]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      self.tokenizer.batch_encode_plus(self.src_text			).input_ids[0]
      self.assertListEqual(self.expected_src_tokens					,							__SCREAMING_SNAKE_CASE			)
 def       UpperCAmelCase__		(		self					:       Optional[Any]			)  -> Tuple:


      """simple docstring"""




      self.assertIn(__SCREAMING_SNAKE_CASE					,							self.tokenizer.all_special_ids			)
      __SCREAMING_SNAKE_CASE     =      [RO_CODE, 884, 9_019, 96, 9, 916, 86_792, 36, 18_743, 15_596, 5, 2]
      __SCREAMING_SNAKE_CASE     =      self.tokenizer.decode(__SCREAMING_SNAKE_CASE					,							skip_special_tokens=__SCREAMING_SNAKE_CASE			)
      __SCREAMING_SNAKE_CASE     =      self.tokenizer.decode(generated_ids[1:]					,							skip_special_tokens=__SCREAMING_SNAKE_CASE			)
      self.assertEqual(__SCREAMING_SNAKE_CASE					,							__SCREAMING_SNAKE_CASE			)
      self.assertNotIn(self.tokenizer.eos_token					,							__SCREAMING_SNAKE_CASE			)
 def       UpperCAmelCase__		(		self					:       Optional[int]			)  -> Optional[Any]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      ["""this is gunna be a long sentence """ * 20]
      assert isinstance(src_text[0]					,							__SCREAMING_SNAKE_CASE			)
      __SCREAMING_SNAKE_CASE     =      10
      __SCREAMING_SNAKE_CASE     =      self.tokenizer(__SCREAMING_SNAKE_CASE					,							max_length=__SCREAMING_SNAKE_CASE					,							truncation=__SCREAMING_SNAKE_CASE			).input_ids[0]
      self.assertEqual(ids[-2]					,							2			)
      self.assertEqual(ids[-1]					,							__SCREAMING_SNAKE_CASE			)
      self.assertEqual(len(__SCREAMING_SNAKE_CASE			)					,							__SCREAMING_SNAKE_CASE			)
 def       UpperCAmelCase__		(		self					:       Union[str, Any]			)  -> Union[str, Any]:


      """simple docstring"""




      self.assertListEqual(self.tokenizer.convert_tokens_to_ids(["""<mask>""", """ar_AR"""]			)					,							[250_026, 250_001]			)
 def       UpperCAmelCase__		(		self					:       Optional[Any]			)  -> int:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      tempfile.mkdtemp()
      __SCREAMING_SNAKE_CASE     =      self.tokenizer.fairseq_tokens_to_ids
      self.tokenizer.save_pretrained(__SCREAMING_SNAKE_CASE			)
      __SCREAMING_SNAKE_CASE     =      MBartTokenizer.from_pretrained(__SCREAMING_SNAKE_CASE			)
      self.assertDictEqual(new_tok.fairseq_tokens_to_ids					,							__SCREAMING_SNAKE_CASE			)
 @require_torch
 def       UpperCAmelCase__		(		self					:       List[Any]			)  -> List[str]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      self.tokenizer(self.src_text					,							text_target=self.tgt_text					,							padding=__SCREAMING_SNAKE_CASE					,							return_tensors="""pt"""			)
      __SCREAMING_SNAKE_CASE     =      shift_tokens_right(batch["""labels"""]					,							self.tokenizer.pad_token_id			)

      # fairseq batch: https://gist.github.com/sshleifer/cba08bc2109361a74ac3760a7e30e4f4
      assert batch.input_ids[1][-2:].tolist() == [2, EN_CODE]
      assert batch.decoder_input_ids[1][0].tolist() == RO_CODE
      assert batch.decoder_input_ids[1][-1] == 2
      assert batch.labels[1][-2:].tolist() == [2, RO_CODE]
 @require_torch
 def       UpperCAmelCase__		(		self					:       Optional[Any]			)  -> Dict:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      self.tokenizer(
          self.src_text					,							text_target=self.tgt_text					,							padding=__SCREAMING_SNAKE_CASE					,							truncation=__SCREAMING_SNAKE_CASE					,							max_length=len(self.expected_src_tokens			)					,							return_tensors="""pt"""					,							)

      __SCREAMING_SNAKE_CASE     =      shift_tokens_right(batch["""labels"""]					,							self.tokenizer.pad_token_id			)

      self.assertIsInstance(__SCREAMING_SNAKE_CASE					,							__SCREAMING_SNAKE_CASE			)

      self.assertEqual((2, 14)					,							batch.input_ids.shape			)
      self.assertEqual((2, 14)					,							batch.attention_mask.shape			)
      __SCREAMING_SNAKE_CASE     =      batch.input_ids.tolist()[0]
      self.assertListEqual(self.expected_src_tokens					,							__SCREAMING_SNAKE_CASE			)
      self.assertEqual(2					,							batch.decoder_input_ids[0, -1]			)  # EOS
      # Test that special tokens are reset
      self.assertEqual(self.tokenizer.prefix_tokens					,							[]			)
      self.assertEqual(self.tokenizer.suffix_tokens					,							[self.tokenizer.eos_token_id, EN_CODE]			)
 def       UpperCAmelCase__		(		self					:       Union[str, Any]			)  -> Optional[Any]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      self.tokenizer(self.src_text					,							padding=__SCREAMING_SNAKE_CASE					,							truncation=__SCREAMING_SNAKE_CASE					,							max_length=3					,							return_tensors="""pt"""			)
      __SCREAMING_SNAKE_CASE     =      self.tokenizer(
          text_target=self.tgt_text					,							padding=__SCREAMING_SNAKE_CASE					,							truncation=__SCREAMING_SNAKE_CASE					,							max_length=10					,							return_tensors="""pt"""			)
      __SCREAMING_SNAKE_CASE     =      targets["""input_ids"""]
      __SCREAMING_SNAKE_CASE     =      shift_tokens_right(__SCREAMING_SNAKE_CASE					,							self.tokenizer.pad_token_id			)

      self.assertEqual(batch.input_ids.shape[1]					,							3			)
      self.assertEqual(batch.decoder_input_ids.shape[1]					,							10			)


 @require_torch
 def       UpperCAmelCase__		(		self					:       Tuple			)  -> Any:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      self.tokenizer._build_translation_inputs(
          """A test"""					,							return_tensors="""pt"""					,							src_lang="""en_XX"""					,							tgt_lang="""ar_AR"""			)

      self.assertEqual(
          nested_simplify(__SCREAMING_SNAKE_CASE			)					,							{
              # A, test, EOS, en_XX
              """input_ids""": [[62, 3_034, 2, 250_004]],
              """attention_mask""": [[1, 1, 1, 1]],
              # ar_AR
              """forced_bos_token_id""": 250_001,
          }					,							)









'''simple docstring'''



import numpy as np







def    a__       (  a__      ,    a__      ,    a__ = 1E-1_2      ,    a__ = 1_00      ,    ):




     """simple docstring"""


     assert np.shape(a__						)[0] == np.shape(a__						)[1]
     # Ensure proper dimensionality.
     assert np.shape(a__						)[0] == np.shape(a__						)[0]
     # Ensure inputs are either both complex or both real
     assert np.iscomplexobj(a__						) == np.iscomplexobj(a__						)
     __SCREAMING_SNAKE_CASE     =      np.iscomplexobj(a__						)
     if is_complex:
          # Ensure complex input_matrix is Hermitian
          assert np.array_equal(a__      ,    input_matrix.conj().T						)

     # Set convergence to False. Will define convergence when we exceed max_iterations
     # or when we have small changes from one iteration to next.

     __SCREAMING_SNAKE_CASE     =      False
     __SCREAMING_SNAKE_CASE     =      0
     __SCREAMING_SNAKE_CASE     =      0
     __SCREAMING_SNAKE_CASE     =      1E1_2

     while not convergence:
          # Multiple matrix by the vector.
          __SCREAMING_SNAKE_CASE     =      np.dot(a__      ,    a__						)
          # Normalize the resulting output vector.
          __SCREAMING_SNAKE_CASE     =      w / np.linalg.norm(a__						)
          # Find rayleigh quotient
          # (faster than usual b/c we know vector is normalized already)
          __SCREAMING_SNAKE_CASE     =      vector.conj().T if is_complex else vector.T
          __SCREAMING_SNAKE_CASE     =      np.dot(a__      ,    np.dot(a__      ,    a__						)						)

          # Check convergence.
          __SCREAMING_SNAKE_CASE     =      np.abs(lambda_ - lambda_previous						) / lambda_
          iterations += 1

          if error <= error_tol or iterations >= max_iterations:
               __SCREAMING_SNAKE_CASE     =      True

          __SCREAMING_SNAKE_CASE     =      lambda_

     if is_complex:
          __SCREAMING_SNAKE_CASE     =      np.real(lambda_						)

     return lambda_, vector







def    a__       (  ):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE     =      np.array([[41, 4, 20], [4, 26, 30], [20, 30, 50]]						)
     __SCREAMING_SNAKE_CASE     =      np.array([41, 4, 20]						)
     __SCREAMING_SNAKE_CASE     =      real_input_matrix.astype(np.complexaaa						)
     __SCREAMING_SNAKE_CASE     =      np.triu(1J * complex_input_matrix      ,    1						)
     complex_input_matrix += imag_matrix
     complex_input_matrix += -1 * imag_matrix.T
     __SCREAMING_SNAKE_CASE     =      np.array([41, 4, 20]						).astype(np.complexaaa						)

     for problem_type in ["real", "complex"]:
          if problem_type == "real":
               __SCREAMING_SNAKE_CASE     =      real_input_matrix
               __SCREAMING_SNAKE_CASE     =      real_vector
          elif problem_type == "complex":
               __SCREAMING_SNAKE_CASE     =      complex_input_matrix
               __SCREAMING_SNAKE_CASE     =      complex_vector

          # Our implementation.
          __SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE     =      power_iteration(a__      ,    a__						)

          # Numpy implementation.

          # Get eigenvalues and eigenvectors using built-in numpy
          # eigh (eigh used for symmetric or hermetian matrices).
          __SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE     =      np.linalg.eigh(a__						)
          # Last eigenvalue is the maximum one.
          __SCREAMING_SNAKE_CASE     =      eigen_values[-1]
          # Last column in this matrix is eigenvector corresponding to largest eigenvalue.
          __SCREAMING_SNAKE_CASE     =      eigen_vectors[:, -1]

          # Check our implementation and numpy gives close answers.
          assert np.abs(eigen_value - eigen_value_max						) <= 1E-6
          # Take absolute values element wise of each eigenvector.
          # as they are only unique to a minus sign.
          assert np.linalg.norm(np.abs(a__						) - np.abs(a__						)						) <= 1E-6


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  test_power_iteration()






