

import shutil
import tempfile
import unittest

from transformers import (
    SPIECE_UNDERLINE,
    AddedToken,
    BatchEncoding,
    NllbTokenizer,
    NllbTokenizerFast,
    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


A_ :				Union[str, Any]						 =    get_tests_dir('fixtures/test_sentencepiece.model')


if is_torch_available():
				from transformers.models.mam_aaa.modeling_mam_aaa import shift_tokens_right

A_ :				List[str]						 =    25_6047
A_ :				Union[str, Any]						 =    25_6145



@require_sentencepiece
@require_tokenizers
class 					_a		(__magic_name__	,     unittest.TestCase       ):


					'''simple docstring'''






					UpperCAmelCase__:     Any													= NllbTokenizer
					UpperCAmelCase__:     int													= NllbTokenizerFast
					UpperCAmelCase__:     Union[str, Any]													= True
					UpperCAmelCase__:     Union[str, Any]													= True
					UpperCAmelCase__:     List[str]													= {}






					def 						__A    (				self    ):
						super().setUp()

						# We have a SentencePiece fixture for testing
						A__							:					Tuple       =	NllbTokenizer(A__					, keep_accents=A__    )
						tokenizer.save_pretrained(self.tmpdirname    )






					def 						__A    (				self    ):
						A__							:					Optional[int]       =	NllbTokenizer(A__					, keep_accents=A__    )

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

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

						A__							:					Dict       =	tokenizer.tokenize("""I was born in 92000, and this is falsé."""    )
						self.assertListEqual(
						    A__					, [
						        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__							:					Dict       =	tokenizer.convert_tokens_to_ids(A__    )
						self.assertListEqual(
						    A__					, [
						        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]
						    ]					, )

						A__							:					str       =	tokenizer.convert_ids_to_tokens(A__    )
						self.assertListEqual(
						    A__					, [
						        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 						__A    (				self    ):
						A__							:					int       =	(self.rust_tokenizer_class, """hf-internal-testing/tiny-random-nllb""", {})
						for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
							with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})"""    ):
								A__							:					Tuple       =	self.rust_tokenizer_class.from_pretrained(A__					, **A__    )
								A__							:					List[Any]       =	self.tokenizer_class.from_pretrained(A__					, **A__    )

								A__							:					Optional[int]       =	tempfile.mkdtemp()

								A__							:					str       =	tokenizer_r.save_pretrained(A__    )
								A__							:					str       =	tokenizer_p.save_pretrained(A__    )

								# 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__							:					List[str]       =	tuple(f for f in tokenizer_r_files if """tokenizer.json""" not in f    )
								self.assertSequenceEqual(A__					, A__    )

								# Checks everything loads correctly in the same way
								A__							:					str       =	tokenizer_r.from_pretrained(A__    )
								A__							:					Union[str, Any]       =	tokenizer_p.from_pretrained(A__    )

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

								shutil.rmtree(A__    )

								# Save tokenizer rust, legacy_format=True
								A__							:					str       =	tempfile.mkdtemp()

								A__							:					Optional[Any]       =	tokenizer_r.save_pretrained(A__					, legacy_format=A__    )
								A__							:					Union[str, Any]       =	tokenizer_p.save_pretrained(A__    )

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

								# Checks everything loads correctly in the same way
								A__							:					Optional[int]       =	tokenizer_r.from_pretrained(A__    )
								A__							:					Union[str, Any]       =	tokenizer_p.from_pretrained(A__    )

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

								shutil.rmtree(A__    )

								# Save tokenizer rust, legacy_format=False
								A__							:					Dict       =	tempfile.mkdtemp()

								A__							:					int       =	tokenizer_r.save_pretrained(A__					, legacy_format=A__    )
								A__							:					Optional[int]       =	tokenizer_p.save_pretrained(A__    )

								# 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__							:					int       =	tokenizer_r.from_pretrained(A__    )
								A__							:					str       =	tokenizer_p.from_pretrained(A__    )

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

								shutil.rmtree(A__    )






					@require_torch
					def 						__A    (				self    ):
						if not self.test_seqaseq:
							return

						A__							:					List[Any]       =	self.get_tokenizers()
						for tokenizer in tokenizers:
							with self.subTest(F"""{tokenizer.__class__.__name__}"""    ):
								# Longer text that will definitely require truncation.
								A__							:					Tuple       =	[
								    """ 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.""",
								]
								A__							:					Tuple       =	[
								    """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.""",
								]
								try:
									A__							:					Dict       =	tokenizer.prepare_seqaseq_batch(
									    src_texts=A__					, tgt_texts=A__					, max_length=3					, max_target_length=10					, return_tensors="""pt"""					, src_lang="""eng_Latn"""					, tgt_lang="""ron_Latn"""					, )
								except NotImplementedError:
									return
								self.assertEqual(batch.input_ids.shape[1]					, 3    )
								self.assertEqual(batch.labels.shape[1]					, 10    )
								# max_target_length will default to max_length if not specified
								A__							:					Optional[int]       =	tokenizer.prepare_seqaseq_batch(
								    A__					, tgt_texts=A__					, max_length=3					, return_tensors="""pt"""    )
								self.assertEqual(batch.input_ids.shape[1]					, 3    )
								self.assertEqual(batch.labels.shape[1]					, 3    )

								A__							:					List[Any]       =	tokenizer.prepare_seqaseq_batch(
								    src_texts=A__					, max_length=3					, max_target_length=10					, return_tensors="""pt"""    )
								self.assertEqual(batch_encoder_only.input_ids.shape[1]					, 3    )
								self.assertEqual(batch_encoder_only.attention_mask.shape[1]					, 3    )
								self.assertNotIn("""decoder_input_ids"""					, A__    )






					@unittest.skip("""Unfortunately way too slow to build a BPE with SentencePiece."""    )
					def 						__A    (				self    ):
						pass






					def 						__A    (				self    ):
						for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
							with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})"""    ):
								A__							:					int       =	[AddedToken("""<special>"""					, lstrip=A__    )]

								A__							:					List[str]       =	self.rust_tokenizer_class.from_pretrained(
								    A__					, additional_special_tokens=A__					, **A__    )
								A__							:					Tuple       =	tokenizer_r.encode("""Hey this is a <special> token"""    )

								A__							:					int       =	tokenizer_r.encode("""<special>"""					, add_special_tokens=A__    )[0]

								self.assertTrue(special_token_id in r_output    )

								if self.test_slow_tokenizer:
									A__							:					Dict       =	self.rust_tokenizer_class.from_pretrained(
									    A__					, additional_special_tokens=A__					, **A__					, )
									A__							:					Union[str, Any]       =	self.tokenizer_class.from_pretrained(
									    A__					, additional_special_tokens=A__					, **A__    )

									A__							:					Tuple       =	tokenizer_p.encode("""Hey this is a <special> token"""    )

									A__							:					Any       =	tokenizer_cr.encode("""Hey this is a <special> token"""    )

									self.assertEqual(A__					, A__    )
									self.assertEqual(A__					, A__    )
									self.assertTrue(special_token_id in p_output    )
									self.assertTrue(special_token_id in cr_output    )



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


					'''simple docstring'''






					UpperCAmelCase__:     Optional[Any]													= '''facebook/nllb-200-distilled-600M'''
					UpperCAmelCase__:     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.''',
					]
					UpperCAmelCase__:     Optional[Any]													= [
					    '''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.''',
					]
					UpperCAmelCase__:     Optional[int]													= [
					    25_60_47,
					    1_62_97,
					    13_44_08,
					    81_65,
					    24_80_66,
					    1_47_34,
					    9_50,
					    11_35,
					    10_57_21,
					    35_73,
					    83,
					    2_73_52,
					    1_08,
					    4_94_86,
					    2,
					]






					@classmethod
					def 						__A    (				cls    ):
						A__							:					NllbTokenizer       =	NllbTokenizer.from_pretrained(
						    cls.checkpoint_name					, src_lang="""eng_Latn"""					, tgt_lang="""ron_Latn"""    )
						A__							:					Any       =	1
						return cls






					def 						__A    (				self    ):
						self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["""ace_Arab"""]					, 25_6001    )
						self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["""ace_Latn"""]					, 25_6002    )
						self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["""fra_Latn"""]					, 25_6057    )






					def 						__A    (				self    ):
						A__							:					List[Any]       =	self.tokenizer.batch_encode_plus(self.src_text    ).input_ids[0]
						self.assertListEqual(self.expected_src_tokens					, A__    )






					def 						__A    (				self    ):
						self.assertIn(A__					, self.tokenizer.all_special_ids    )
						# fmt: off
						A__							:					Union[str, Any]       =	[RO_CODE, 4254, 9_8068, 11_2923, 3_9072, 3909, 713, 10_2767, 26, 1_7314, 3_5642, 1_4683, 3_3118, 2022, 6_6987, 2, 25_6047]
						# fmt: on

						A__							:					List[Any]       =	self.tokenizer.decode(A__					, skip_special_tokens=A__    )
						A__							:					Optional[int]       =	self.tokenizer.decode(generated_ids[1:]					, skip_special_tokens=A__    )
						self.assertEqual(A__					, A__    )
						self.assertNotIn(self.tokenizer.eos_token					, A__    )






					def 						__A    (				self    ):
						A__							:					Optional[int]       =	["""this is gunna be a long sentence """ * 20]
						assert isinstance(src_text[0]					, A__    )
						A__							:					int       =	10
						A__							:					List[Any]       =	self.tokenizer(A__					, max_length=A__					, truncation=A__    ).input_ids[0]
						self.assertEqual(ids[-1]					, 2    )
						self.assertEqual(ids[0]					, A__    )
						self.assertEqual(len(A__    )					, A__    )






					def 						__A    (				self    ):
						self.assertListEqual(self.tokenizer.convert_tokens_to_ids(["""<mask>""", """ar_AR"""]    )					, [25_6203, 3]    )






					def 						__A    (				self    ):
						A__							:					int       =	tempfile.mkdtemp()
						A__							:					str       =	self.tokenizer.fairseq_tokens_to_ids
						self.tokenizer.save_pretrained(A__    )
						A__							:					List[str]       =	NllbTokenizer.from_pretrained(A__    )
						self.assertDictEqual(new_tok.fairseq_tokens_to_ids					, A__    )






					@require_torch
					def 						__A    (				self    ):
						A__							:					List[str]       =	self.tokenizer(
						    self.src_text					, text_target=self.tgt_text					, padding=A__					, truncation=A__					, max_length=len(self.expected_src_tokens    )					, return_tensors="""pt"""					, )
						A__							:					Optional[Any]       =	shift_tokens_right(
						    batch["""labels"""]					, self.tokenizer.pad_token_id					, self.tokenizer.lang_code_to_id["""ron_Latn"""]    )

						self.assertIsInstance(A__					, A__    )

						self.assertEqual((2, 15)					, batch.input_ids.shape    )
						self.assertEqual((2, 15)					, batch.attention_mask.shape    )
						A__							:					int       =	batch.input_ids.tolist()[0]
						self.assertListEqual(self.expected_src_tokens					, A__    )
						self.assertEqual(A__					, batch.decoder_input_ids[0, 0]    )  # EOS
						# Test that special tokens are reset
						self.assertEqual(self.tokenizer.prefix_tokens					, [EN_CODE]    )
						self.assertEqual(self.tokenizer.suffix_tokens					, [self.tokenizer.eos_token_id]    )






					def 						__A    (				self    ):
						A__							:					List[str]       =	self.tokenizer(self.src_text					, padding=A__					, truncation=A__					, max_length=3					, return_tensors="""pt"""    )
						A__							:					str       =	self.tokenizer(
						    text_target=self.tgt_text					, padding=A__					, truncation=A__					, max_length=10					, return_tensors="""pt"""    )
						A__							:					Optional[int]       =	targets["""input_ids"""]
						A__							:					Dict       =	shift_tokens_right(
						    A__					, self.tokenizer.pad_token_id					, decoder_start_token_id=self.tokenizer.lang_code_to_id[self.tokenizer.tgt_lang]					, )

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






					@require_torch
					def 						__A    (				self    ):
						A__							:					List[Any]       =	self.tokenizer._build_translation_inputs(
						    """A test"""					, return_tensors="""pt"""					, src_lang="""eng_Latn"""					, tgt_lang="""fra_Latn"""    )

						self.assertEqual(
						    nested_simplify(A__    )					, {
						        # A, test, EOS, en_XX
						        """input_ids""": [[25_6047, 70, 7356, 2]],
						        """attention_mask""": [[1, 1, 1, 1]],
						        # ar_AR
						        """forced_bos_token_id""": 25_6057,
						    }					, )






					@require_torch
					def 						__A    (				self    ):
						A__							:					str       =	True
						A__							:					int       =	self.tokenizer(
						    """UN Chief says there is no military solution in Syria"""					, src_lang="""eng_Latn"""					, tgt_lang="""fra_Latn"""    )
						self.assertEqual(
						    inputs.input_ids					, [1_6297, 13_4408, 2_5653, 6370, 248, 254, 10_3929, 9_4995, 108, 4_9486, 2, 25_6047]    )

						A__							:					Any       =	False
						A__							:					Any       =	self.tokenizer(
						    """UN Chief says there is no military solution in Syria"""					, src_lang="""eng_Latn"""					, tgt_lang="""fra_Latn"""    )
						self.assertEqual(
						    inputs.input_ids					, [25_6047, 1_6297, 13_4408, 2_5653, 6370, 248, 254, 10_3929, 9_4995, 108, 4_9486, 2]    )






from __future__ import annotations

from collections.abc import Callable

A_ :				List[Any]						 =    list[list[float | int]]

def 		UpperCamelCase      (lowercase_:				Matrix					,   lowercase_:				Matrix      )  ->   Matrix:
	A__							:					int       =	len(lowercase_      )
	A__							:					Matrix       =	[[0 for _ in range(size + 1      )] for _ in range(lowercase_      )]
	A__							:					int
	A__							:					int
	A__							:					int
	A__							:					int
	A__							:					int
	A__							:					float

	for row in range(lowercase_      ):
		for col in range(lowercase_      ):
			A__							:					List[str]       =	matrix[row][col]

		A__							:					int       =	vector[row][0]

	A__							:					Optional[int]       =	0
	A__							:					str       =	0
	while row < size and col < size:
		# pivoting
		A__							:					int       =	max((abs(augmented[rowa][col]      ), rowa) for rowa in range(lowercase_					,   lowercase_      )      )[
		    1
		]
		if augmented[pivot_row][col] == 0:
			col += 1
			continue
		else:
			A__     ,     A__							:					Union[str, Any]       =	augmented[pivot_row], augmented[row]

		for rowa in range(row + 1					,   lowercase_      ):
			A__							:					List[Any]       =	augmented[rowa][col] / augmented[row][col]
			A__							:					Dict       =	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					,   lowercase_      ):
		for row in range(lowercase_      ):
			A__							:					List[str]       =	augmented[row][col] / augmented[col][col]
			for cola in range(lowercase_					,   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(lowercase_      )
	]

def 		UpperCamelCase      (lowercase_:				list[int]      )  ->   Callable[[int], int]:
	A__							:					int       =	len(lowercase_      )
	A__							:					Matrix       =	[[0 for _ in range(lowercase_      )] for _ in range(lowercase_      )]
	A__							:					Matrix       =	[[0] for _ in range(lowercase_      )]
	A__							:					Matrix
	A__							:					int
	A__							:					int
	A__							:					int

	for x_val, y_val in enumerate(lowercase_      ):
		for col in range(lowercase_      ):
			A__							:					Dict       =	(x_val + 1) ** (size - col - 1)
		A__							:					Any       =	y_val

	A__							:					Union[str, Any]       =	solve(lowercase_					,   lowercase_      )

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

	return interpolated_func

def 		UpperCamelCase      (lowercase_:				int      )  ->   int:
	return (
	    1
	    - variable
	    + variable**2
	    - variable**3
	    + variable**4
	    - variable**5
	    + variable**6
	    - variable**7
	    + variable**8
	    - variable**9
	    + variable**10
	)

def 		UpperCamelCase      (lowercase_:				Callable[[int], int] = question_function					,   lowercase_:				int = 10      )  ->   int:
	A__							:					list[int]       =	[func(lowercase_      ) for x_val in range(1					,   order + 1      )]

	A__							:					list[Callable[[int], int]]       =	[
	    interpolate(data_points[:max_coeff]      ) for max_coeff in range(1					,   order + 1      )
	]

	A__							:					int       =	0
	A__							:					Callable[[int], int]
	A__							:					int

	for poly in polynomials:
		A__							:					List[str]       =	1
		while func(lowercase_      ) == poly(lowercase_      ):
			x_val += 1

		ret += poly(lowercase_      )

	return ret


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



