




import heapq as hq
import math
from collections.abc import Iterator




class 			UpperCAmelCase_      :




							'''simple docstring'''



							def __init__(					self       :	Dict       ,     a       :	int			)			->					int:
										SCREAMING_SNAKE_CASE           =					str(id_			)
										SCREAMING_SNAKE_CASE           =					None
										SCREAMING_SNAKE_CASE           =					None
										SCREAMING_SNAKE_CASE           =					[]
										SCREAMING_SNAKE_CASE           =					{}  # {vertex:distance}





							def __lt__(					self       :	Tuple       ,     a       :	int			)			->					Optional[Any]:
										return self.key < other.key





							def __repr__(					self       :	Any			)			->					Union[str, Any]:
										return self.id





							def    _UpperCAmelCase     (					self       :	int       ,     a       :	str			)			->					Any:
										self.neighbors.append(a			)







							def    _UpperCAmelCase     (					self       :	List[Any]       ,     a       :	Optional[Any]       ,     a       :	Tuple			)			->					str:
										SCREAMING_SNAKE_CASE           =					weight





def 					lowerCamelCase_    (    SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE   ):
			'''simple docstring'''
			# add the neighbors:
			graph[a - 1].add_neighbor(graph[b - 1]   )
			graph[b - 1].add_neighbor(graph[a - 1]   )
			# add the edges:
			graph[a - 1].add_edge(graph[b - 1]					,	SCREAMING_SNAKE_CASE   )
			graph[b - 1].add_edge(graph[a - 1]					,	SCREAMING_SNAKE_CASE   )
def 					lowerCamelCase_    (    SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE   ):
			'''simple docstring'''
			SCREAMING_SNAKE_CASE           =					[]
			for u in graph:
						SCREAMING_SNAKE_CASE           =					math.inf
						SCREAMING_SNAKE_CASE           =					None
			SCREAMING_SNAKE_CASE           =					0
			SCREAMING_SNAKE_CASE           =					graph[:]
			while q:
						SCREAMING_SNAKE_CASE           =					min(SCREAMING_SNAKE_CASE   )
						q.remove(SCREAMING_SNAKE_CASE   )
						for v in u.neighbors:
									if (v in q) and (u.edges[v.id] < v.key):
												SCREAMING_SNAKE_CASE           =					u
												SCREAMING_SNAKE_CASE           =					u.edges[v.id]
			for i in range(1					,	len(SCREAMING_SNAKE_CASE   )   ):
						a.append((int(graph[i].id   ) + 1, int(graph[i].pi.id   ) + 1)   )
			return a
def 					lowerCamelCase_    (    SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE   ):
			'''simple docstring'''
			for u in graph:
						SCREAMING_SNAKE_CASE           =					math.inf
						SCREAMING_SNAKE_CASE           =					None
			SCREAMING_SNAKE_CASE           =					0

			SCREAMING_SNAKE_CASE           =					list(SCREAMING_SNAKE_CASE   )
			hq.heapify(SCREAMING_SNAKE_CASE   )

			while h:
						SCREAMING_SNAKE_CASE           =					hq.heappop(SCREAMING_SNAKE_CASE   )
						for v in u.neighbors:
									if (v in h) and (u.edges[v.id] < v.key):
												SCREAMING_SNAKE_CASE           =					u
												SCREAMING_SNAKE_CASE           =					u.edges[v.id]
												hq.heapify(SCREAMING_SNAKE_CASE   )

			for i in range(1					,	len(SCREAMING_SNAKE_CASE   )   ):
						yield (int(graph[i].id   ) + 1, int(graph[i].pi.id   ) + 1)
def 					lowerCamelCase_    (    ):
			'''simple docstring'''


if __name__ == "__main__":
		import doctest

		doctest.testmod()












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						:							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						:							Optional[int]							=  2_5_6_0_4_7
__A						:							Dict							=  2_5_6_1_4_5




@require_sentencepiece
@require_tokenizers
class 			UpperCAmelCase_      (   A		,      unittest.TestCase       ):




							'''simple docstring'''



							a__						  =     NllbTokenizer
							a__						  =     NllbTokenizerFast
							a__						  =     True
							a__						  =     True
							a__						  =     {}





							def    _UpperCAmelCase     (					self       :	Any			)			->					int:
										super().setUp()

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





							def    _UpperCAmelCase     (					self       :	int			)			->					str:
										SCREAMING_SNAKE_CASE           =					NllbTokenizer(a       ,     keep_accents=a			)

										SCREAMING_SNAKE_CASE           =					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]]       ,     )

										SCREAMING_SNAKE_CASE           =					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""",
										        """.""",
										    ]       ,     )
										SCREAMING_SNAKE_CASE           =					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]
										    ]       ,     )

										SCREAMING_SNAKE_CASE           =					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    _UpperCAmelCase     (					self       :	Optional[Any]			)			->					Any:
										SCREAMING_SNAKE_CASE           =					(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})"""			):
																SCREAMING_SNAKE_CASE           =					self.rust_tokenizer_class.from_pretrained(a       ,     **a			)
																SCREAMING_SNAKE_CASE           =					self.tokenizer_class.from_pretrained(a       ,     **a			)

																SCREAMING_SNAKE_CASE           =					tempfile.mkdtemp()

																SCREAMING_SNAKE_CASE           =					tokenizer_r.save_pretrained(a			)
																SCREAMING_SNAKE_CASE           =					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			)			)
																SCREAMING_SNAKE_CASE           =					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
																SCREAMING_SNAKE_CASE           =					tokenizer_r.from_pretrained(a			)
																SCREAMING_SNAKE_CASE           =					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
																SCREAMING_SNAKE_CASE           =					tempfile.mkdtemp()

																SCREAMING_SNAKE_CASE           =					tokenizer_r.save_pretrained(a       ,     legacy_format=a			)
																SCREAMING_SNAKE_CASE           =					tokenizer_p.save_pretrained(a			)

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

																# Checks everything loads correctly in the same way
																SCREAMING_SNAKE_CASE           =					tokenizer_r.from_pretrained(a			)
																SCREAMING_SNAKE_CASE           =					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
																SCREAMING_SNAKE_CASE           =					tempfile.mkdtemp()

																SCREAMING_SNAKE_CASE           =					tokenizer_r.save_pretrained(a       ,     legacy_format=a			)
																SCREAMING_SNAKE_CASE           =					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
																SCREAMING_SNAKE_CASE           =					tokenizer_r.from_pretrained(a			)
																SCREAMING_SNAKE_CASE           =					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    _UpperCAmelCase     (					self       :	Dict			)			->					List[str]:
										if not self.test_seqaseq:
													return

										SCREAMING_SNAKE_CASE           =					self.get_tokenizers()
										for tokenizer in tokenizers:
													with self.subTest(f"""{tokenizer.__class__.__name__}"""			):
																# Longer text that will definitely require truncation.
																SCREAMING_SNAKE_CASE           =					[
																    """ 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.""",
																]
																SCREAMING_SNAKE_CASE           =					[
																    """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:
																			SCREAMING_SNAKE_CASE           =					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
																SCREAMING_SNAKE_CASE           =					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			)

																SCREAMING_SNAKE_CASE           =					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    _UpperCAmelCase     (					self       :	int			)			->					Optional[int]:
										pass







							def    _UpperCAmelCase     (					self       :	Optional[int]			)			->					Optional[int]:
										for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
													with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""			):
																SCREAMING_SNAKE_CASE           =					[AddedToken("""<special>"""       ,     lstrip=a			)]

																SCREAMING_SNAKE_CASE           =					self.rust_tokenizer_class.from_pretrained(
																    a       ,     additional_special_tokens=a       ,     **a			)
																SCREAMING_SNAKE_CASE           =					tokenizer_r.encode("""Hey this is a <special> token"""			)

																SCREAMING_SNAKE_CASE           =					tokenizer_r.encode("""<special>"""       ,     add_special_tokens=a			)[0]

																self.assertTrue(special_token_id in r_output			)

																if self.test_slow_tokenizer:
																			SCREAMING_SNAKE_CASE           =					self.rust_tokenizer_class.from_pretrained(
																			    a       ,     additional_special_tokens=a       ,     **a       ,     )
																			SCREAMING_SNAKE_CASE           =					self.tokenizer_class.from_pretrained(
																			    a       ,     additional_special_tokens=a       ,     **a			)

																			SCREAMING_SNAKE_CASE           =					tokenizer_p.encode("""Hey this is a <special> token"""			)

																			SCREAMING_SNAKE_CASE           =					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 			UpperCAmelCase_      (   unittest.TestCase       ):




							'''simple docstring'''



							a__						  =     '''facebook/nllb-200-distilled-600M'''
							a__						  =     [
							    ''' 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__						  =     [
							    '''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.''',
							]
							a__						  =     [
							    2_5_6_0_4_7,
							    1_6_2_9_7,
							    1_3_4_4_0_8,
							    8_1_6_5,
							    2_4_8_0_6_6,
							    1_4_7_3_4,
							    9_5_0,
							    1_1_3_5,
							    1_0_5_7_2_1,
							    3_5_7_3,
							    8_3,
							    2_7_3_5_2,
							    1_0_8,
							    4_9_4_8_6,
							    2,
							]





							@classmethod
							def    _UpperCAmelCase     (					cls       :	List[Any]			)			->					Optional[Any]:
										SCREAMING_SNAKE_CASE           =					NllbTokenizer.from_pretrained(
										    cls.checkpoint_name       ,     src_lang="""eng_Latn"""       ,     tgt_lang="""ron_Latn"""			)
										SCREAMING_SNAKE_CASE           =					1
										return cls





							def    _UpperCAmelCase     (					self       :	Dict			)			->					Dict:
										self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["""ace_Arab"""]       ,     256_001			)
										self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["""ace_Latn"""]       ,     256_002			)
										self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["""fra_Latn"""]       ,     256_057			)





							def    _UpperCAmelCase     (					self       :	Optional[int]			)			->					Optional[Any]:
										SCREAMING_SNAKE_CASE           =					self.tokenizer.batch_encode_plus(self.src_text			).input_ids[0]
										self.assertListEqual(self.expected_src_tokens       ,     a			)





							def    _UpperCAmelCase     (					self       :	Union[str, Any]			)			->					Tuple:
										self.assertIn(a       ,     self.tokenizer.all_special_ids			)
										# fmt: off
										SCREAMING_SNAKE_CASE           =					[RO_CODE, 4_254, 98_068, 112_923, 39_072, 3_909, 713, 102_767, 26, 17_314, 35_642, 14_683, 33_118, 2_022, 66_987, 2, 256_047]
										# fmt: on

										SCREAMING_SNAKE_CASE           =					self.tokenizer.decode(a       ,     skip_special_tokens=a			)
										SCREAMING_SNAKE_CASE           =					self.tokenizer.decode(generated_ids[1:]       ,     skip_special_tokens=a			)
										self.assertEqual(a       ,     a			)
										self.assertNotIn(self.tokenizer.eos_token       ,     a			)





							def    _UpperCAmelCase     (					self       :	Optional[Any]			)			->					List[Any]:
										SCREAMING_SNAKE_CASE           =					["""this is gunna be a long sentence """ * 20]
										assert isinstance(src_text[0]       ,     a			)
										SCREAMING_SNAKE_CASE           =					10
										SCREAMING_SNAKE_CASE           =					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    _UpperCAmelCase     (					self       :	Dict			)			->					Any:
										self.assertListEqual(self.tokenizer.convert_tokens_to_ids(["""<mask>""", """ar_AR"""]			)       ,     [256_203, 3]			)





							def    _UpperCAmelCase     (					self       :	str			)			->					Optional[int]:
										SCREAMING_SNAKE_CASE           =					tempfile.mkdtemp()
										SCREAMING_SNAKE_CASE           =					self.tokenizer.fairseq_tokens_to_ids
										self.tokenizer.save_pretrained(a			)
										SCREAMING_SNAKE_CASE           =					NllbTokenizer.from_pretrained(a			)
										self.assertDictEqual(new_tok.fairseq_tokens_to_ids       ,     a			)





							@require_torch
							def    _UpperCAmelCase     (					self       :	Optional[int]			)			->					Union[str, Any]:
										SCREAMING_SNAKE_CASE           =					self.tokenizer(
										    self.src_text       ,     text_target=self.tgt_text       ,     padding=a       ,     truncation=a       ,     max_length=len(self.expected_src_tokens			)       ,     return_tensors="""pt"""       ,     )
										SCREAMING_SNAKE_CASE           =					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			)
										SCREAMING_SNAKE_CASE           =					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    _UpperCAmelCase     (					self       :	str			)			->					Dict:
										SCREAMING_SNAKE_CASE           =					self.tokenizer(self.src_text       ,     padding=a       ,     truncation=a       ,     max_length=3       ,     return_tensors="""pt"""			)
										SCREAMING_SNAKE_CASE           =					self.tokenizer(
										    text_target=self.tgt_text       ,     padding=a       ,     truncation=a       ,     max_length=10       ,     return_tensors="""pt"""			)
										SCREAMING_SNAKE_CASE           =					targets["""input_ids"""]
										SCREAMING_SNAKE_CASE           =					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    _UpperCAmelCase     (					self       :	int			)			->					Optional[int]:
										SCREAMING_SNAKE_CASE           =					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""": [[256_047, 70, 7_356, 2]],
										        """attention_mask""": [[1, 1, 1, 1]],
										        # ar_AR
										        """forced_bos_token_id""": 256_057,
										    }       ,     )







							@require_torch
							def    _UpperCAmelCase     (					self       :	str			)			->					int:
										SCREAMING_SNAKE_CASE           =					True
										SCREAMING_SNAKE_CASE           =					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       ,     [16_297, 134_408, 25_653, 6_370, 248, 254, 103_929, 94_995, 108, 49_486, 2, 256_047]			)

										SCREAMING_SNAKE_CASE           =					False
										SCREAMING_SNAKE_CASE           =					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       ,     [256_047, 16_297, 134_408, 25_653, 6_370, 248, 254, 103_929, 94_995, 108, 49_486, 2]			)






