
"""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


_lowercase				 =					get_tests_dir('''fixtures/test_sentencepiece.model''')


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

_lowercase				 =					25_00_04
_lowercase				 =					25_00_20







@require_sentencepiece
@require_tokenizers
class 					lowerCAmelCase_					(   SCREAMING_SNAKE_CASE_		,       unittest.TestCase     ):



						'''simple docstring'''


						_lowerCamelCase:     Optional[int]   =     MBartTokenizer
						_lowerCamelCase:     int   =     MBartTokenizerFast
						_lowerCamelCase:     Dict   =     True
						_lowerCamelCase:     int   =     True


						def     _SCREAMING_SNAKE_CASE   (			self  :   Dict   )  -> Dict:
							super().setUp()

							# We have a SentencePiece fixture for testing
							A     = MBartTokenizer(lowercase_	,keep_accents=lowercase_   )
							tokenizer.save_pretrained(self.tmpdirname   )


						def     _SCREAMING_SNAKE_CASE   (			self  :   Tuple   )  -> List[str]:
							A     = MBartTokenizer(lowercase_	,keep_accents=lowercase_   )

							A     = tokenizer.tokenize('This is a test'   )
							self.assertListEqual(lowercase_	,['▁This', '▁is', '▁a', '▁t', 'est']   )

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

							A     = tokenizer.tokenize('I was born in 92000, and this is falsé.'   )
							self.assertListEqual(
							    lowercase_	,[
							        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',
							        '.',
							    ]	,)
							A     = tokenizer.convert_tokens_to_ids(lowercase_   )
							self.assertListEqual(
							    lowercase_	,[
							        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 ^
							    ]	,)

							A     = tokenizer.convert_ids_to_tokens(lowercase_   )
							self.assertListEqual(
							    lowercase_	,[
							        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     _SCREAMING_SNAKE_CASE   (			self  :   Any   )  -> List[Any]:
							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

							A     = (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})'   ):
									A     = self.rust_tokenizer_class.from_pretrained(lowercase_	,**lowercase_   )
									A     = self.tokenizer_class.from_pretrained(lowercase_	,**lowercase_   )

									A     = tempfile.mkdtemp()

									A     = tokenizer_r.save_pretrained(lowercase_   )
									A     = tokenizer_p.save_pretrained(lowercase_   )

									# 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   )   )
									A     = tuple(f for f in tokenizer_r_files if 'tokenizer.json' not in f   )
									self.assertSequenceEqual(lowercase_	,lowercase_   )

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

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

									shutil.rmtree(lowercase_   )

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

									A     = tokenizer_r.save_pretrained(lowercase_	,legacy_format=lowercase_   )
									A     = tokenizer_p.save_pretrained(lowercase_   )

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

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

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

									shutil.rmtree(lowercase_   )

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

									A     = tokenizer_r.save_pretrained(lowercase_	,legacy_format=lowercase_   )
									A     = tokenizer_p.save_pretrained(lowercase_   )

									# 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
									A     = tokenizer_r.from_pretrained(lowercase_   )
									A     = tokenizer_p.from_pretrained(lowercase_   )

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

									shutil.rmtree(lowercase_   )







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



						'''simple docstring'''


						_lowerCamelCase:     Union[str, Any]   =     "facebook/mbart-large-en-ro"
						_lowerCamelCase:     List[Any]   =     [
						    " 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:     Dict   =     [
						    "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:     List[str]   =     [8274, 127873, 25916, 7, 8622, 2071, 438, 67485, 53, 187895, 23, 51712, 2, EN_CODE]


						@classmethod
						def     _SCREAMING_SNAKE_CASE   (			cls  :   Dict   )  -> Dict:
							A     = MBartTokenizer.from_pretrained(
							    cls.checkpoint_name	,src_lang='en_XX'	,tgt_lang='ro_RO'   )
							A     = 1
							return cls


						def     _SCREAMING_SNAKE_CASE   (			self  :   str   )  -> Optional[int]:
							self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['ar_AR']	,25_0001   )
							self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['en_EN']	,25_0004   )
							self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['ro_RO']	,25_0020   )


						def     _SCREAMING_SNAKE_CASE   (			self  :   List[Any]   )  -> Union[str, Any]:
							A     = self.tokenizer.batch_encode_plus(self.src_text   ).input_ids[0]
							self.assertListEqual(self.expected_src_tokens	,lowercase_   )


						def     _SCREAMING_SNAKE_CASE   (			self  :   int   )  -> str:
							self.assertIn(lowercase_	,self.tokenizer.all_special_ids   )
							A     = [RO_CODE, 884, 9019, 96, 9, 916, 8_6792, 36, 1_8743, 1_5596, 5, 2]
							A     = self.tokenizer.decode(lowercase_	,skip_special_tokens=lowercase_   )
							A     = self.tokenizer.decode(generated_ids[1:]	,skip_special_tokens=lowercase_   )
							self.assertEqual(lowercase_	,lowercase_   )
							self.assertNotIn(self.tokenizer.eos_token	,lowercase_   )


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


						def     _SCREAMING_SNAKE_CASE   (			self  :   Any   )  -> str:
							self.assertListEqual(self.tokenizer.convert_tokens_to_ids(['<mask>', 'ar_AR']   )	,[25_0026, 25_0001]   )


						def     _SCREAMING_SNAKE_CASE   (			self  :   List[str]   )  -> Dict:
							A     = tempfile.mkdtemp()
							A     = self.tokenizer.fairseq_tokens_to_ids
							self.tokenizer.save_pretrained(lowercase_   )
							A     = MBartTokenizer.from_pretrained(lowercase_   )
							self.assertDictEqual(new_tok.fairseq_tokens_to_ids	,lowercase_   )


						@require_torch
						def     _SCREAMING_SNAKE_CASE   (			self  :   Any   )  -> List[str]:
							A     = self.tokenizer(self.src_text	,text_target=self.tgt_text	,padding=lowercase_	,return_tensors='pt'   )
							A     = 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     _SCREAMING_SNAKE_CASE   (			self  :   List[str]   )  -> List[Any]:
							A     = self.tokenizer(
							    self.src_text	,text_target=self.tgt_text	,padding=lowercase_	,truncation=lowercase_	,max_length=len(self.expected_src_tokens   )	,return_tensors='pt'	,)

							A     = shift_tokens_right(batch['labels']	,self.tokenizer.pad_token_id   )

							self.assertIsInstance(lowercase_	,lowercase_   )

							self.assertEqual((2, 14)	,batch.input_ids.shape   )
							self.assertEqual((2, 14)	,batch.attention_mask.shape   )
							A     = batch.input_ids.tolist()[0]
							self.assertListEqual(self.expected_src_tokens	,lowercase_   )
							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     _SCREAMING_SNAKE_CASE   (			self  :   str   )  -> Optional[Any]:
							A     = self.tokenizer(self.src_text	,padding=lowercase_	,truncation=lowercase_	,max_length=3	,return_tensors='pt'   )
							A     = self.tokenizer(
							    text_target=self.tgt_text	,padding=lowercase_	,truncation=lowercase_	,max_length=10	,return_tensors='pt'   )
							A     = targets['input_ids']
							A     = shift_tokens_right(lowercase_	,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     _SCREAMING_SNAKE_CASE   (			self  :   Optional[int]   )  -> Dict:
							A     = self.tokenizer._build_translation_inputs(
							    'A test'	,return_tensors='pt'	,src_lang='en_XX'	,tgt_lang='ar_AR'   )

							self.assertEqual(
							    nested_simplify(lowercase_   )	,{
							        # A, test, EOS, en_XX
							        'input_ids': [[62, 3034, 2, 25_0004]],
							        'attention_mask': [[1, 1, 1, 1]],
							        # ar_AR
							        'forced_bos_token_id': 25_0001,
							    }	,)

"""simple docstring"""

from __future__ import annotations

from collections.abc import Callable

SCREAMING_SNAKE_CASE_            =    list[list[float | int]]




def      lowercase__    (       lowerCAmelCase					:       Matrix			,					lowerCAmelCase					:       Matrix			)			->   Matrix:






    """simple docstring"""




    UpperCAmelCase          =       len(lowerCAmelCase			)
    UpperCAmelCase          =       [[0 for _ in range(size + 1			)] for _ in range(lowerCAmelCase			)]
    UpperCAmelCase          =       42
    UpperCAmelCase          =       42
    UpperCAmelCase          =       42
    UpperCAmelCase          =       42
    UpperCAmelCase          =       42
    UpperCAmelCase          =       42

    for row in range(lowerCAmelCase			):
        for col in range(lowerCAmelCase			):
            UpperCAmelCase          =       matrix[row][col]

        UpperCAmelCase          =       vector[row][0]

    UpperCAmelCase          =       0
    UpperCAmelCase          =       0
    while row < size and col < size:
        # pivoting
        UpperCAmelCase          =       max((abs(augmented[rowa][col]			), rowa) for rowa in range(lowerCAmelCase			,					lowerCAmelCase			)			)[
            1
        ]
        if augmented[pivot_row][col] == 0:
            col += 1
            continue
        else:
            UpperCAmelCase      ,				UpperCAmelCase          =       augmented[pivot_row], augmented[row]

        for rowa in range(row + 1			,					lowerCAmelCase			):
            UpperCAmelCase          =       augmented[rowa][col] / augmented[row][col]
            UpperCAmelCase          =       0
            for cola in range(col + 1			,					size + 1			):
                augmented[rowa][cola] -= augmented[row][cola] * ratio

        row += 1
        col += 1

    # back substitution
    for col in range(1			,					lowerCAmelCase			):
        for row in range(lowerCAmelCase			):
            UpperCAmelCase          =       augmented[row][col] / augmented[col][col]
            for cola in range(lowerCAmelCase			,					size + 1			):
                augmented[row][cola] -= augmented[col][cola] * ratio

    # round to get rid of numbers like 2.000000000000004
    return [
        [round(augmented[row][size] / augmented[row][row]			,					10			)] for row in range(lowerCAmelCase			)
    ]




def      lowercase__    (       lowerCAmelCase					:       list[int]			)			->   Callable[[int], int]:






    """simple docstring"""




    UpperCAmelCase          =       len(lowerCAmelCase			)
    UpperCAmelCase          =       [[0 for _ in range(lowerCAmelCase			)] for _ in range(lowerCAmelCase			)]
    UpperCAmelCase          =       [[0] for _ in range(lowerCAmelCase			)]
    UpperCAmelCase          =       42
    UpperCAmelCase          =       42
    UpperCAmelCase          =       42
    UpperCAmelCase          =       42

    for x_val, y_val in enumerate(lowerCAmelCase			):
        for col in range(lowerCAmelCase			):
            UpperCAmelCase          =       (x_val + 1) ** (size - col - 1)
        UpperCAmelCase          =       y_val

    UpperCAmelCase          =       solve(lowerCAmelCase			,					lowerCAmelCase			)

    def interpolated_func(lowerCAmelCase					:       int			) -> int:
        return sum(
            round(coeffs[x_val][0]			) * (var ** (size - x_val - 1))
            for x_val in range(lowerCAmelCase			)			)

    return interpolated_func




def      lowercase__    (       lowerCAmelCase					:       int			)			->   int:






    """simple docstring"""




    return (
        1
        - variable
        + variable**2
        - variable**3
        + variable**4
        - variable**5
        + variable**6
        - variable**7
        + variable**8
        - variable**9
        + variable**10
    )




def      lowercase__    (       lowerCAmelCase					:       Callable[[int], int] = question_function			,					lowerCAmelCase					:       int = 10			)			->   int:






    """simple docstring"""




    UpperCAmelCase          =       [func(lowerCAmelCase			) for x_val in range(1			,					order + 1			)]

    UpperCAmelCase          =       [
        interpolate(data_points[:max_coeff]			) for max_coeff in range(1			,					order + 1			)
    ]

    UpperCAmelCase          =       0
    UpperCAmelCase          =       42
    UpperCAmelCase          =       42

    for poly in polynomials:
        UpperCAmelCase          =       1
        while func(lowerCAmelCase			) == poly(lowerCAmelCase			):
            x_val += 1

        ret += poly(lowerCAmelCase			)

    return ret


if __name__ == "__main__":
   print(F'{solution() = }')



