






"""simple docstring"""



import os
import unittest

from transformers import BatchEncoding
from transformers.models.bert.tokenization_bert import (
    BasicTokenizer,
    WordpieceTokenizer,
    _is_control,
    _is_punctuation,
    _is_whitespace,
)
from transformers.models.prophetnet.tokenization_prophetnet import VOCAB_FILES_NAMES, ProphetNetTokenizer
from transformers.testing_utils import require_torch, slow

from ...test_tokenization_common import TokenizerTesterMixin




class 			lowerCAmelCase__			(    __magic_name__							,						unittest.TestCase   ):
   SCREAMING_SNAKE_CASE_    =ProphetNetTokenizer
   SCREAMING_SNAKE_CASE_    =False
   def       __a							(		self      :	Union[str, Any]							):


        '''simple docstring'''
        super().setUp()

        UpperCAmelCase__						:    List[str]							=							[
            "[UNK]",
            "[CLS]",
            "[SEP]",
            "[PAD]",
            "[MASK]",
            "want",
            "##want",
            "##ed",
            "wa",
            "un",
            "runn",
            "##ing",
            ",",
            "low",
            "lowest",
        ]
        UpperCAmelCase__						:    Dict							=							os.path.join(self.tmpdirname							,			VOCAB_FILES_NAMES["vocab_file"]							)
        with open(self.vocab_file							,			"w"							,			encoding="utf-8"							) as vocab_writer:
             vocab_writer.write("".join([x + "\n" for x in vocab_tokens]							)							)
   def       __a							(		self      :	Tuple							,			snake_case__      :	Any							):


        '''simple docstring'''
        UpperCAmelCase__						:    str							=							"UNwant\u00E9d,running"
        UpperCAmelCase__						:    Optional[Any]							=							"unwanted, running"
        return input_text, output_text
   def       __a							(		self      :	List[str]							):


        '''simple docstring'''
        UpperCAmelCase__						:    Optional[int]							=							self.tokenizer_class(self.vocab_file							)

        UpperCAmelCase__						:    Dict							=							tokenizer.tokenize("UNwant\u00E9d,running"							)
        self.assertListEqual(snake_case__							,			["un", "##want", "##ed", ",", "runn", "##ing"]							)
        self.assertListEqual(tokenizer.convert_tokens_to_ids(snake_case__							)							,			[9, 6, 7, 1_2, 1_0, 1_1]							)
   def       __a							(		self      :	Tuple							):


        '''simple docstring'''
        UpperCAmelCase__						:    int							=							BasicTokenizer()

        self.assertListEqual(tokenizer.tokenize("ah\u535A\u63A8zz"							)							,			["ah", "\u535A", "\u63A8", "zz"]							)
   def       __a							(		self      :	Any							):


        '''simple docstring'''
        UpperCAmelCase__						:    str							=							BasicTokenizer(do_lower_case=snake_case__							)

        self.assertListEqual(
            tokenizer.tokenize(" \tHeLLo!how  \n Are yoU?  "							)							,			["hello", "!", "how", "are", "you", "?"]							)
        self.assertListEqual(tokenizer.tokenize("H\u00E9llo"							)							,			["hello"]							)
   def       __a							(		self      :	Dict							):


        '''simple docstring'''
        UpperCAmelCase__						:    Optional[int]							=							BasicTokenizer(do_lower_case=snake_case__							,			strip_accents=snake_case__							)

        self.assertListEqual(
            tokenizer.tokenize(" \tHäLLo!how  \n Are yoU?  "							)							,			["hällo", "!", "how", "are", "you", "?"]							)
        self.assertListEqual(tokenizer.tokenize("H\u00E9llo"							)							,			["h\u00E9llo"]							)
   def       __a							(		self      :	Tuple							):


        '''simple docstring'''
        UpperCAmelCase__						:    List[Any]							=							BasicTokenizer(do_lower_case=snake_case__							,			strip_accents=snake_case__							)

        self.assertListEqual(
            tokenizer.tokenize(" \tHäLLo!how  \n Are yoU?  "							)							,			["hallo", "!", "how", "are", "you", "?"]							)
        self.assertListEqual(tokenizer.tokenize("H\u00E9llo"							)							,			["hello"]							)
   def       __a							(		self      :	Tuple							):


        '''simple docstring'''
        UpperCAmelCase__						:    Optional[int]							=							BasicTokenizer(do_lower_case=snake_case__							)

        self.assertListEqual(
            tokenizer.tokenize(" \tHäLLo!how  \n Are yoU?  "							)							,			["hallo", "!", "how", "are", "you", "?"]							)
        self.assertListEqual(tokenizer.tokenize("H\u00E9llo"							)							,			["hello"]							)
   def       __a							(		self      :	Optional[int]							):


        '''simple docstring'''
        UpperCAmelCase__						:    Optional[int]							=							BasicTokenizer(do_lower_case=snake_case__							)

        self.assertListEqual(
            tokenizer.tokenize(" \tHeLLo!how  \n Are yoU?  "							)							,			["HeLLo", "!", "how", "Are", "yoU", "?"]							)
   def       __a							(		self      :	Any							):


        '''simple docstring'''
        UpperCAmelCase__						:    List[Any]							=							BasicTokenizer(do_lower_case=snake_case__							,			strip_accents=snake_case__							)

        self.assertListEqual(
            tokenizer.tokenize(" \tHäLLo!how  \n Are yoU?  "							)							,			["HäLLo", "!", "how", "Are", "yoU", "?"]							)
   def       __a							(		self      :	Optional[int]							):


        '''simple docstring'''
        UpperCAmelCase__						:    Union[str, Any]							=							BasicTokenizer(do_lower_case=snake_case__							,			strip_accents=snake_case__							)

        self.assertListEqual(
            tokenizer.tokenize(" \tHäLLo!how  \n Are yoU?  "							)							,			["HaLLo", "!", "how", "Are", "yoU", "?"]							)
   def       __a							(		self      :	Any							):


        '''simple docstring'''
        UpperCAmelCase__						:    Optional[int]							=							BasicTokenizer(do_lower_case=snake_case__							,			never_split=["[UNK]"]							)

        self.assertListEqual(
            tokenizer.tokenize(" \tHeLLo!how  \n Are yoU? [UNK]"							)							,			["HeLLo", "!", "how", "Are", "yoU", "?", "[UNK]"]							)
   def       __a							(		self      :	List[str]							):


        '''simple docstring'''
        UpperCAmelCase__						:    Dict							=							["[UNK]", "[CLS]", "[SEP]", "want", "##want", "##ed", "wa", "un", "runn", "##ing"]

        UpperCAmelCase__						:    Union[str, Any]							=							{}
        for i, token in enumerate(snake_case__							):
             UpperCAmelCase__						:    Dict							=							i
        UpperCAmelCase__						:    Optional[int]							=							WordpieceTokenizer(vocab=snake_case__							,			unk_token="[UNK]"							)

        self.assertListEqual(tokenizer.tokenize(""							)							,			[]							)

        self.assertListEqual(tokenizer.tokenize("unwanted running"							)							,			["un", "##want", "##ed", "runn", "##ing"]							)

        self.assertListEqual(tokenizer.tokenize("unwantedX running"							)							,			["[UNK]", "runn", "##ing"]							)
   @require_torch
   def       __a							(		self      :	Any							):


        '''simple docstring'''
        UpperCAmelCase__						:    Optional[Any]							=							self.tokenizer_class.from_pretrained("microsoft/prophetnet-large-uncased"							)

        UpperCAmelCase__						:    Tuple							=							["A long paragraph for summarization.", "Another paragraph for summarization."]
        UpperCAmelCase__						:    Optional[int]							=							[1_0_3_7, 2_1_4_6, 2_0_4_2_3, 2_0_0_5, 7_6_8_0, 7_8_4_9, 3_9_8_9, 1_0_1_2, 1_0_2]
        UpperCAmelCase__						:    List[Any]							=							tokenizer(snake_case__							,			padding=snake_case__							,			return_tensors="pt"							)
        self.assertIsInstance(snake_case__							,			snake_case__							)
        UpperCAmelCase__						:    Optional[Any]							=							list(batch.input_ids.numpy()[0]							)
        self.assertListEqual(snake_case__							,			snake_case__							)

        self.assertEqual((2, 9)							,			batch.input_ids.shape							)
        self.assertEqual((2, 9)							,			batch.attention_mask.shape							)
   def       __a							(		self      :	Optional[Any]							):


        '''simple docstring'''
        self.assertTrue(_is_whitespace(" "							)							)
        self.assertTrue(_is_whitespace("\t"							)							)
        self.assertTrue(_is_whitespace("\r"							)							)
        self.assertTrue(_is_whitespace("\n"							)							)
        self.assertTrue(_is_whitespace("\u00A0"							)							)

        self.assertFalse(_is_whitespace("A"							)							)
        self.assertFalse(_is_whitespace("-"							)							)
   def       __a							(		self      :	Union[str, Any]							):


        '''simple docstring'''
        self.assertTrue(_is_control("\u0005"							)							)

        self.assertFalse(_is_control("A"							)							)
        self.assertFalse(_is_control(" "							)							)
        self.assertFalse(_is_control("\t"							)							)
        self.assertFalse(_is_control("\r"							)							)
   def       __a							(		self      :	Optional[Any]							):


        '''simple docstring'''
        self.assertTrue(_is_punctuation("-"							)							)
        self.assertTrue(_is_punctuation("$"							)							)
        self.assertTrue(_is_punctuation("`"							)							)
        self.assertTrue(_is_punctuation("."							)							)

        self.assertFalse(_is_punctuation("A"							)							)
        self.assertFalse(_is_punctuation(" "							)							)




   @slow
   def       __a							(		self      :	Any							):


        '''simple docstring'''
        UpperCAmelCase__						:    Dict							=							self.tokenizer_class.from_pretrained("microsoft/prophetnet-large-uncased"							)

        UpperCAmelCase__						:    Tuple							=							tokenizer.encode("sequence builders"							,			add_special_tokens=snake_case__							)
        UpperCAmelCase__						:    List[Any]							=							tokenizer.encode("multi-sequence build"							,			add_special_tokens=snake_case__							)

        UpperCAmelCase__						:    Optional[int]							=							tokenizer.build_inputs_with_special_tokens(snake_case__							)
        UpperCAmelCase__						:    int							=							tokenizer.build_inputs_with_special_tokens(snake_case__							,			snake_case__							)

        assert encoded_sentence == text + [1_0_2]
        assert encoded_pair == text + [1_0_2] + text_a + [1_0_2]










"""simple docstring"""



import numpy as np


def 	SCREAMING_SNAKE_CASE__       (  snake_case       : np.ndarray			,					snake_case       : np.ndarray			,					snake_case       : float = 1E-1_2			,					snake_case       : int = 100			,					)->		tuple[float, np.ndarray]:






     '''simple docstring'''



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

     UpperCAmelCase__						:    Union[str, Any]							=							False
     UpperCAmelCase__						:    List[Any]							=							0
     UpperCAmelCase__						:    Any							=							0
     UpperCAmelCase__						:    List[str]							=							1E1_2

     while not convergence:
          # Multiple matrix by the vector.
          UpperCAmelCase__						:    Optional[Any]							=							np.dot(snake_case			,					snake_case				)
          # Normalize the resulting output vector.
          UpperCAmelCase__						:    Tuple							=							w / np.linalg.norm(snake_case				)
          # Find rayleigh quotient
          # (faster than usual b/c we know vector is normalized already)
          UpperCAmelCase__						:    Any							=							vector.conj().T if is_complex else vector.T
          UpperCAmelCase__						:    List[str]							=							np.dot(snake_case			,					np.dot(snake_case			,					snake_case				)				)

          # Check convergence.
          UpperCAmelCase__						:    Optional[Any]							=							np.abs(lambda_ - lambda_previous				) / lambda_
          iterations += 1

          if error <= error_tol or iterations >= max_iterations:
               UpperCAmelCase__						:    Tuple							=							True

          UpperCAmelCase__						:    Tuple							=							lambda_

     if is_complex:
          UpperCAmelCase__						:    str							=							np.real(lambda_				)

     return lambda_, vector


def 	SCREAMING_SNAKE_CASE__       (  )->		None:






     '''simple docstring'''



     UpperCAmelCase__						:    List[Any]							=							np.array([[41, 4, 20], [4, 26, 30], [20, 30, 50]]				)
     UpperCAmelCase__						:    Dict							=							np.array([41, 4, 20]				)
     UpperCAmelCase__						:    Union[str, Any]							=							real_input_matrix.astype(np.complexaaa				)
     UpperCAmelCase__						:    Optional[int]							=							np.triu(1J * complex_input_matrix			,					1				)
     complex_input_matrix += imag_matrix
     complex_input_matrix += -1 * imag_matrix.T
     UpperCAmelCase__						:    int							=							np.array([41, 4, 20]				).astype(np.complexaaa				)

     for problem_type in ["real", "complex"]:
          if problem_type == "real":
               UpperCAmelCase__						:    List[str]							=							real_input_matrix
               UpperCAmelCase__						:    Any							=							real_vector
          elif problem_type == "complex":
               UpperCAmelCase__						:    str							=							complex_input_matrix
               UpperCAmelCase__						:    List[Any]							=							complex_vector

          # Our implementation.
          UpperCAmelCase__							,				UpperCAmelCase__						:    Optional[int]							=							power_iteration(snake_case			,					snake_case				)

          # Numpy implementation.

          # Get eigenvalues and eigenvectors using built-in numpy
          # eigh (eigh used for symmetric or hermetian matrices).
          UpperCAmelCase__							,				UpperCAmelCase__						:    List[Any]							=							np.linalg.eigh(snake_case				)
          # Last eigenvalue is the maximum one.
          UpperCAmelCase__						:    str							=							eigen_values[-1]
          # Last column in this matrix is eigenvector corresponding to largest eigenvalue.
          UpperCAmelCase__						:    Optional[int]							=							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(snake_case				) - np.abs(snake_case				)				) <= 1E-6


if __name__ == "__main__":
 import doctest

 doctest.testmod()
 test_power_iteration()


