

'''simple docstring'''



import unittest

from transformers import PegasusTokenizer, PegasusTokenizerFast
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 :       Union[str, Any]			   =     get_tests_dir('fixtures/test_sentencepiece_no_bos.model')







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



 """simple docstring"""



 lowerCAmelCase__  					=      PegasusTokenizer
 lowerCAmelCase__  					=      PegasusTokenizerFast
 lowerCAmelCase__  					=      True
 lowerCAmelCase__  					=      True
 def       UpperCAmelCase__		(		self					:       List[str]			)  -> str:


      """simple docstring"""




      super().setUp()

      # We have a SentencePiece fixture for testing
      __SCREAMING_SNAKE_CASE     =      PegasusTokenizer(__SCREAMING_SNAKE_CASE			)
      tokenizer.save_pretrained(self.tmpdirname			)
 @cached_property
 def       UpperCAmelCase__		(		self					:       Any			)  -> str:


      """simple docstring"""




      return PegasusTokenizer.from_pretrained("""google/pegasus-large"""			)
 def       UpperCAmelCase__		(		self					:       Tuple					,							**__SCREAMING_SNAKE_CASE					:       Tuple			)  -> PegasusTokenizer:


      """simple docstring"""




      return PegasusTokenizer.from_pretrained(self.tmpdirname					,							**__SCREAMING_SNAKE_CASE			)
 def       UpperCAmelCase__		(		self					:       List[str]					,							__SCREAMING_SNAKE_CASE					:       Tuple			)  -> Tuple:


      """simple docstring"""




      return ("This is a test", "This is a test")
 def       UpperCAmelCase__		(		self					:       Optional[Any]			)  -> Optional[int]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      """</s>"""
      __SCREAMING_SNAKE_CASE     =      1

      self.assertEqual(self.get_tokenizer()._convert_token_to_id(__SCREAMING_SNAKE_CASE			)					,							__SCREAMING_SNAKE_CASE			)
      self.assertEqual(self.get_tokenizer()._convert_id_to_token(__SCREAMING_SNAKE_CASE			)					,							__SCREAMING_SNAKE_CASE			)
 def       UpperCAmelCase__		(		self					:       Optional[int]			)  -> Tuple:


      """simple docstring"""




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

      self.assertEqual(vocab_keys[0]					,							"""<pad>"""			)
      self.assertEqual(vocab_keys[1]					,							"""</s>"""			)
      self.assertEqual(vocab_keys[-1]					,							"""v"""			)
      self.assertEqual(len(__SCREAMING_SNAKE_CASE			)					,							1_103			)
 def       UpperCAmelCase__		(		self					:       Dict			)  -> Optional[int]:


      """simple docstring"""




      self.assertEqual(self.get_tokenizer().vocab_size					,							1_103			)
 def       UpperCAmelCase__		(		self					:       Optional[Any]			)  -> Tuple:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      self.rust_tokenizer_class.from_pretrained(self.tmpdirname			)
      __SCREAMING_SNAKE_CASE     =      self.tokenizer_class.from_pretrained(self.tmpdirname			)
      __SCREAMING_SNAKE_CASE     =      (
          """Let's see which <unk> is the better <unk_token_11> one <mask_1> It seems like this <mask_2> was important"""
          """ </s> <pad> <pad> <pad>"""
      )
      __SCREAMING_SNAKE_CASE     =      rust_tokenizer([raw_input_str]					,							return_tensors=__SCREAMING_SNAKE_CASE					,							add_special_tokens=__SCREAMING_SNAKE_CASE			).input_ids[0]
      __SCREAMING_SNAKE_CASE     =      py_tokenizer([raw_input_str]					,							return_tensors=__SCREAMING_SNAKE_CASE					,							add_special_tokens=__SCREAMING_SNAKE_CASE			).input_ids[0]
      self.assertListEqual(__SCREAMING_SNAKE_CASE					,							__SCREAMING_SNAKE_CASE			)
 def       UpperCAmelCase__		(		self					:       Any			)  -> Any:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      self._large_tokenizer
      # <mask_1> masks whole sentence while <mask_2> masks single word
      __SCREAMING_SNAKE_CASE     =      """<mask_1> To ensure a <mask_2> flow of bank resolutions."""
      __SCREAMING_SNAKE_CASE     =      [2, 413, 615, 114, 3, 1_971, 113, 1_679, 10_710, 107, 1]
      __SCREAMING_SNAKE_CASE     =      tokenizer([raw_input_str]					,							return_tensors=__SCREAMING_SNAKE_CASE			).input_ids[0]
      self.assertListEqual(__SCREAMING_SNAKE_CASE					,							__SCREAMING_SNAKE_CASE			)
 def       UpperCAmelCase__		(		self					:       Union[str, Any]			)  -> Tuple:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      self._large_tokenizer
      # The tracebacks for the following asserts are **better** without messages or self.assertEqual
      assert tokenizer.vocab_size == 96_103
      assert tokenizer.pad_token_id == 0
      assert tokenizer.eos_token_id == 1
      assert tokenizer.offset == 103
      assert tokenizer.unk_token_id == tokenizer.offset + 2 == 105
      assert tokenizer.unk_token == "<unk>"
      assert tokenizer.model_max_length == 1_024
      __SCREAMING_SNAKE_CASE     =      """To ensure a smooth flow of bank resolutions."""
      __SCREAMING_SNAKE_CASE     =      [413, 615, 114, 2_291, 1_971, 113, 1_679, 10_710, 107, 1]
      __SCREAMING_SNAKE_CASE     =      tokenizer([raw_input_str]					,							return_tensors=__SCREAMING_SNAKE_CASE			).input_ids[0]
      self.assertListEqual(__SCREAMING_SNAKE_CASE					,							__SCREAMING_SNAKE_CASE			)
      assert tokenizer.convert_ids_to_tokens([0, 1, 2, 3]			) == ["<pad>", "</s>", "<mask_1>", "<mask_2>"]
 @require_torch
 def       UpperCAmelCase__		(		self					:       int			)  -> List[str]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      ["""This is going to be way too long.""" * 150, """short example"""]
      __SCREAMING_SNAKE_CASE     =      ["""not super long but more than 5 tokens""", """tiny"""]
      __SCREAMING_SNAKE_CASE     =      self._large_tokenizer(__SCREAMING_SNAKE_CASE					,							padding=__SCREAMING_SNAKE_CASE					,							truncation=__SCREAMING_SNAKE_CASE					,							return_tensors="""pt"""			)
      __SCREAMING_SNAKE_CASE     =      self._large_tokenizer(
          text_target=__SCREAMING_SNAKE_CASE					,							max_length=5					,							padding=__SCREAMING_SNAKE_CASE					,							truncation=__SCREAMING_SNAKE_CASE					,							return_tensors="""pt"""			)

      assert batch.input_ids.shape == (2, 1_024)
      assert batch.attention_mask.shape == (2, 1_024)
      assert targets["input_ids"].shape == (2, 5)
      assert len(__SCREAMING_SNAKE_CASE			) == 2  # input_ids, attention_mask.


 @slow
 def       UpperCAmelCase__		(		self					:       List[str]			)  -> Optional[Any]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      {"""input_ids""": [[38_979, 143, 18_485, 606, 130, 26_669, 87_686, 121, 54_189, 1_129, 111, 26_669, 87_686, 121, 9_114, 14_787, 121, 13_249, 158, 592, 956, 121, 14_621, 31_576, 143, 62_613, 108, 9_688, 930, 43_430, 11_562, 62_613, 304, 108, 11_443, 897, 108, 9_314, 17_415, 63_399, 108, 11_443, 7_614, 18_316, 118, 4_284, 7_148, 12_430, 143, 1_400, 25_703, 158, 111, 4_284, 7_148, 11_772, 143, 21_297, 1_064, 158, 122, 204, 3_506, 1_754, 1_133, 14_787, 1_581, 115, 33_224, 4_482, 111, 1_355, 110, 29_173, 317, 50_833, 108, 20_147, 94_665, 111, 77_198, 107, 1], [110, 62_613, 117, 638, 112, 1_133, 121, 20_098, 1_355, 79_050, 13_872, 135, 1_596, 53_541, 1_352, 141, 13_039, 5_542, 124, 302, 518, 111, 268, 2_956, 115, 149, 4_427, 107, 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], [139, 1_235, 2_799, 18_289, 17_780, 204, 109, 9_474, 1_296, 107, 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]], """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, 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, 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=__SCREAMING_SNAKE_CASE					,							model_name="""google/bigbird-pegasus-large-arxiv"""					,							revision="""ba85d0851d708441f91440d509690f1ab6353415"""					,							)







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



 """simple docstring"""



 lowerCAmelCase__  					=      PegasusTokenizer
 lowerCAmelCase__  					=      PegasusTokenizerFast
 lowerCAmelCase__  					=      True
 lowerCAmelCase__  					=      True
 def       UpperCAmelCase__		(		self					:       Any			)  -> Optional[int]:


      """simple docstring"""




      super().setUp()

      # We have a SentencePiece fixture for testing
      __SCREAMING_SNAKE_CASE     =      PegasusTokenizer(__SCREAMING_SNAKE_CASE					,							offset=0					,							mask_token_sent=__SCREAMING_SNAKE_CASE					,							mask_token="""[MASK]"""			)
      tokenizer.save_pretrained(self.tmpdirname			)
 @cached_property
 def       UpperCAmelCase__		(		self					:       Dict			)  -> Tuple:


      """simple docstring"""




      return PegasusTokenizer.from_pretrained("""google/bigbird-pegasus-large-arxiv"""			)
 def       UpperCAmelCase__		(		self					:       str					,							**__SCREAMING_SNAKE_CASE					:       int			)  -> PegasusTokenizer:


      """simple docstring"""




      return PegasusTokenizer.from_pretrained(self.tmpdirname					,							**__SCREAMING_SNAKE_CASE			)
 def       UpperCAmelCase__		(		self					:       Any					,							__SCREAMING_SNAKE_CASE					:       str			)  -> Optional[int]:


      """simple docstring"""




      return ("This is a test", "This is a test")
 def       UpperCAmelCase__		(		self					:       Tuple			)  -> Optional[int]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      self.rust_tokenizer_class.from_pretrained(self.tmpdirname			)
      __SCREAMING_SNAKE_CASE     =      self.tokenizer_class.from_pretrained(self.tmpdirname			)
      __SCREAMING_SNAKE_CASE     =      (
          """Let's see which <unk> is the better <unk_token> one [MASK] It seems like this [MASK] was important </s>"""
          """ <pad> <pad> <pad>"""
      )
      __SCREAMING_SNAKE_CASE     =      rust_tokenizer([raw_input_str]					,							return_tensors=__SCREAMING_SNAKE_CASE					,							add_special_tokens=__SCREAMING_SNAKE_CASE			).input_ids[0]
      __SCREAMING_SNAKE_CASE     =      py_tokenizer([raw_input_str]					,							return_tensors=__SCREAMING_SNAKE_CASE					,							add_special_tokens=__SCREAMING_SNAKE_CASE			).input_ids[0]
      self.assertListEqual(__SCREAMING_SNAKE_CASE					,							__SCREAMING_SNAKE_CASE			)
 @require_torch
 def       UpperCAmelCase__		(		self					:       int			)  -> Dict:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      ["""This is going to be way too long.""" * 1_000, """short example"""]
      __SCREAMING_SNAKE_CASE     =      ["""not super long but more than 5 tokens""", """tiny"""]
      __SCREAMING_SNAKE_CASE     =      self._large_tokenizer(__SCREAMING_SNAKE_CASE					,							padding=__SCREAMING_SNAKE_CASE					,							truncation=__SCREAMING_SNAKE_CASE					,							return_tensors="""pt"""			)
      __SCREAMING_SNAKE_CASE     =      self._large_tokenizer(
          text_target=__SCREAMING_SNAKE_CASE					,							max_length=5					,							padding=__SCREAMING_SNAKE_CASE					,							truncation=__SCREAMING_SNAKE_CASE					,							return_tensors="""pt"""			)

      assert batch.input_ids.shape == (2, 4_096)
      assert batch.attention_mask.shape == (2, 4_096)
      assert targets["input_ids"].shape == (2, 5)
      assert len(__SCREAMING_SNAKE_CASE			) == 2  # input_ids, attention_mask.


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


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      (
          """This is an example string that is used to test the original TF implementation against the HF"""
          """ implementation"""
      )

      __SCREAMING_SNAKE_CASE     =      self._large_tokenizer(__SCREAMING_SNAKE_CASE			).input_ids

      self.assertListEqual(
          __SCREAMING_SNAKE_CASE					,							[182, 117, 142, 587, 4_211, 120, 117, 263, 112, 804, 109, 856, 25_016, 3_137, 464, 109, 26_955, 3_137, 1]					,							)









'''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()






