



'''simple docstring'''






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


A_							: str						 =	get_tests_dir("""fixtures/test_sentencepiece.model""")

A_							: Optional[Any]						 =	{'target_lang': 'fi', 'source_lang': 'en'}
A_							: Optional[Any]						 =	'>>zh<<'
A_							: int						 =	'Helsinki-NLP/'

if is_torch_available():
   A_							: int						 =	'pt'
elif is_tf_available():
   A_							: List[str]						 =	'tf'
else:
   A_							: Tuple						 =	'jax'



@require_sentencepiece
class 							lowercase       (  __SCREAMING_SNAKE_CASE					,					unittest.TestCase				):


 """simple docstring"""


 UpperCAmelCase							=  MarianTokenizer
 UpperCAmelCase							=  False
 UpperCAmelCase							=  True







 def _snake_case (							self							)		->   Tuple:
        super().setUp()
        _UpperCAmelCase  :						Dict			     = ["""</s>""", """<unk>""", """▁This""", """▁is""", """▁a""", """▁t""", """est""", """\u0120""", """<pad>"""]
        _UpperCAmelCase  :						List[str]			     = dict(zip(a_  ,range(len(a_							)							)							)							)
        _UpperCAmelCase  :						List[str]			     = 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"""]							)

        _UpperCAmelCase  :						Dict			     = MarianTokenizer.from_pretrained(self.tmpdirname							)
        tokenizer.save_pretrained(self.tmpdirname							)







 def _snake_case (							self  ,**a_							)		->   int:
        return MarianTokenizer.from_pretrained(self.tmpdirname  ,**a_							)







 def _snake_case (							self  ,a_							)		->   Tuple:
        return (
            "This is a test",
            "This is a test",
        )







 def _snake_case (							self							)		->   Optional[int]:
        _UpperCAmelCase  :						Optional[Any]			     = """</s>"""
        _UpperCAmelCase  :						Dict			     = 0

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







 def _snake_case (							self							)		->   int:
        _UpperCAmelCase  :						Optional[int]			     = 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 _snake_case (							self							)		->   Union[str, Any]:
        self.assertEqual(self.get_tokenizer().vocab_size  ,9							)







 def _snake_case (							self							)		->   str:
        _UpperCAmelCase  :						str			     = MarianTokenizer.from_pretrained(f'''{ORG_NAME}opus-mt-en-de'''							)
        _UpperCAmelCase  :						Optional[int]			     = en_de_tokenizer(["""I am a small frog"""]  ,return_tensors=a_							)
        self.assertIsInstance(a_  ,a_							)
        _UpperCAmelCase  :						Optional[int]			     = [38, 121, 14, 697, 38_848, 0]
        self.assertListEqual(a_  ,batch.input_ids[0]							)

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







 def _snake_case (							self							)		->   Tuple:
        _UpperCAmelCase  :						Optional[int]			     = self.get_tokenizer()

        _UpperCAmelCase  :						int			     = tok(
            ["""I am a small frog""" * 1_000, """I am a small frog"""]  ,padding=a_  ,truncation=a_  ,return_tensors=a_							)
        self.assertIsInstance(a_  ,a_							)
        self.assertEqual(batch.input_ids.shape  ,(2, 512)							)







 def _snake_case (							self							)		->   str:
        _UpperCAmelCase  :						Dict			     = self.get_tokenizer()
        _UpperCAmelCase  :						Dict			     = 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 _snake_case (							self							)		->   List[Any]:
        # fmt: off
        _UpperCAmelCase  :						Dict			     = {"""input_ids""": [[43_495, 462, 20, 42_164, 1_369, 52, 464, 132, 1_703, 492, 13, 7_491, 38_999, 6, 8, 464, 132, 1_703, 492, 13, 4_669, 37_867, 13, 7_525, 27, 1_593, 988, 13, 33_972, 7_029, 6, 20, 8_251, 383, 2, 270, 5_866, 3_788, 2, 2_353, 8_251, 12_338, 2, 13_958, 387, 2, 3_629, 6_953, 188, 2_900, 2, 13_958, 8_011, 11_501, 23, 8_460, 4_073, 34_009, 20, 435, 11_439, 27, 8, 8_460, 4_073, 6_004, 20, 9_988, 375, 27, 33, 266, 1_945, 1_076, 1_350, 37_867, 3_288, 5, 577, 1_076, 4_374, 8, 5_082, 5, 26_453, 257, 556, 403, 2, 242, 132, 383, 316, 492, 8, 10_767, 6, 316, 304, 4_239, 3, 0], [148, 15_722, 19, 1_839, 12, 1_350, 13, 22_327, 5_082, 5_418, 47_567, 35_938, 59, 318, 19_552, 108, 2_183, 54, 14_976, 4_835, 32, 547, 1_114, 8, 315, 2_417, 5, 92, 19_088, 3, 0, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100], [36, 6_395, 12_570, 39_147, 11_597, 6, 266, 4, 45_405, 7_296, 3, 0, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100]], """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 _snake_case (							self							)		->   Tuple:
        _UpperCAmelCase  :						Union[str, Any]			     = MarianTokenizer.from_pretrained("""hf-internal-testing/test-marian-two-vocabs"""							)

        _UpperCAmelCase  :						int			     = """Tämä on testi"""
        _UpperCAmelCase  :						Union[str, Any]			     = """This is a test"""

        _UpperCAmelCase  :						Optional[Any]			     = [76, 7, 2_047, 2]
        _UpperCAmelCase  :						Tuple			     = [69, 12, 11, 940, 2]

        _UpperCAmelCase  :						Optional[Any]			     = tokenizer(a_							).input_ids
        self.assertListEqual(a_  ,a_							)

        _UpperCAmelCase  :						Union[str, Any]			     = tokenizer(text_target=a_							).input_ids
        self.assertListEqual(a_  ,a_							)

        _UpperCAmelCase  :						Dict			     = tokenizer.decode(a_  ,skip_special_tokens=a_							)
        self.assertEqual(a_  ,a_							)





import numpy as np


def 						UpperCAmelCase			(	a_		,   a_		,   a_ = 1E-12		,   a_ = 1_0_0		,   )		->	tuple[float, np.ndarray]:







				"""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_						)
				__A   			= 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.

				__A   			= False
				__A   			= 0
				__A   			= 0
				__A   			= 1E12

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

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

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

								__A   			= lambda_

				if is_complex:
								__A   			= np.real(lambda_						)

				return lambda_, vector


def 						UpperCAmelCase			(	)		->	None:







				"""simple docstring"""



				__A   			= np.array([[4_1, 4, 2_0], [4, 2_6, 3_0], [2_0, 3_0, 5_0]]						)
				__A   			= np.array([4_1, 4, 2_0]						)
				__A   			= real_input_matrix.astype(np.complexaaa						)
				__A   			= np.triu(1J * complex_input_matrix		,   1						)
				complex_input_matrix += imag_matrix
				complex_input_matrix += -1 * imag_matrix.T
				__A   			= np.array([4_1, 4, 2_0]						).astype(np.complexaaa						)

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

								# Our implementation.
								__A			,							__A   			= power_iteration(a_		,   a_						)

								# Numpy implementation.

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