




"""simple docstring"""

import shutil
import tempfile
import unittest

from transformers import SPIECE_UNDERLINE, BatchEncoding, MBartaaTokenizer, MBartaaTokenizerFast, is_torch_available
from transformers.testing_utils import (
    get_tests_dir,
    nested_simplify,
    require_sentencepiece,
    require_tokenizers,
    require_torch,
    slow,
)

from ...test_tokenization_common import TokenizerTesterMixin


_lowerCamelCase     :	Any         =				get_tests_dir('fixtures/test_sentencepiece.model')

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

_lowerCamelCase     :	Tuple         =				250004
_lowerCamelCase     :	Optional[Any]         =				250020

@require_sentencepiece
@require_tokenizers
class 				lowercase					(			_SCREAMING_SNAKE_CASE   ,		unittest.TestCase):
				__lowerCAmelCase			:			Tuple     				=      MBartaaTokenizer
				__lowerCAmelCase			:			Dict     				=      MBartaaTokenizerFast
				__lowerCAmelCase			:			Any     				=      True
				__lowerCAmelCase			:			Optional[Any]     				=      True







				def     a_     (							self      :    List[Any]				):



					"""simple docstring"""

					super().setUp()

					# We have a SentencePiece fixture for testing
					A_     : List[str]		      =						MBartaaTokenizer(A_					,   src_lang='''en_XX'''					,   tgt_lang='''ro_RO'''					,   keep_accents=A_				)
					tokenizer.save_pretrained(self.tmpdirname				)







				def     a_     (							self      :    Optional[Any]				):



					"""simple docstring"""

					A_     : Optional[Any]		      =						'''<s>'''
					A_     : Optional[int]		      =						0

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







				def     a_     (							self      :    Any				):



					"""simple docstring"""

					A_     : Tuple		      =						list(self.get_tokenizer().get_vocab().keys()				)

					self.assertEqual(vocab_keys[0]					,   '''<s>'''				)
					self.assertEqual(vocab_keys[1]					,   '''<pad>'''				)
					self.assertEqual(vocab_keys[-1]					,   '''<mask>'''				)
					self.assertEqual(len(A_				)					,   10_54				)







				def     a_     (							self      :    str				):



					"""simple docstring"""

					self.assertEqual(self.get_tokenizer().vocab_size					,   10_54				)







				def     a_     (							self      :    List[str]				):



					"""simple docstring"""

					A_     : Optional[Any]		      =						MBartaaTokenizer(A_					,   src_lang='''en_XX'''					,   tgt_lang='''ro_RO'''					,   keep_accents=A_				)

					A_     : Optional[int]		      =						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 [2_85, 46, 10, 1_70, 3_82]]					,   )

					A_     : List[str]		      =						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_     : List[str]		      =						tokenizer.convert_tokens_to_ids(A_				)
					self.assertListEqual(
					    A_					,   [
					        value + tokenizer.fairseq_offset
					        for value in [8, 21, 84, 55, 24, 19, 7, 2, 6_02, 3_47, 3_47, 3_47, 3, 12, 66, 46, 72, 80, 6, 2, 4]
					    ]					,   )

					A_     : Optional[Any]		      =						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>''', '''.''']					,   )







				@slow
				def     a_     (							self      :    List[str]				):



					"""simple docstring"""

					A_     : Optional[int]		      =						{'''input_ids''': [[25_00_04, 1_10_62, 8_27_72, 7, 15, 8_27_72, 5_38, 5_15_29, 2_37, 1_71_98, 12_90, 2_06, 9, 21_51_75, 13_14, 1_36, 1_71_98, 12_90, 2_06, 9, 5_63_59, 42, 12_20_09, 9, 1_64_66, 16, 8_73_44, 45_37, 9, 47_17, 7_83_81, 6, 15_99_58, 7, 15, 2_44_80, 6_18, 4, 5_27, 2_26_93, 54_28, 4, 27_77, 2_44_80, 98_74, 4, 4_35_23, 5_94, 4, 8_03, 1_83_92, 3_31_89, 18, 4, 4_35_23, 2_44_47, 1_23_99, 1_00, 2_49_55, 8_36_58, 96_26, 14_40_57, 15, 8_39, 2_23_35, 16, 1_36, 2_49_55, 8_36_58, 8_34_79, 15, 3_91_02, 7_24, 16, 6_78, 6_45, 27_89, 13_28, 45_89, 42, 12_20_09, 11_57_74, 23, 8_05, 13_28, 4_68_76, 7, 1_36, 5_38_94, 19_40, 4_22_27, 4_11_59, 1_77_21, 8_23, 4_25, 4, 2_75_12, 9_87_22, 2_06, 1_36, 55_31, 49_70, 9_19, 1_73_36, 5, 2], [25_00_04, 2_00_80, 6_18, 83, 8_27_75, 47, 4_79, 9, 15_17, 73, 5_38_94, 3_33, 8_05_81, 11_01_17, 1_88_11, 52_56, 12_95, 51, 15_25_26, 2_97, 79_86, 3_90, 12_44_16, 5_38, 3_54_31, 2_14, 98, 1_50_44, 2_57_37, 1_36, 71_08, 4_37_01, 23, 7_56, 13_53_55, 7, 5, 2, 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], [25_00_04, 5_81, 6_37_73, 11_94_55, 6, 14_77_97, 8_82_03, 7, 6_45, 70, 21, 32_85, 1_02_69, 5, 2, 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]], '''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, 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], [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, 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='''facebook/mbart-large-50'''					,   revision='''d3913889c59cd5c9e456b269c376325eabad57e2'''					,   )







				def     a_     (							self      :    Tuple				):



					"""simple docstring"""

					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_     : List[Any]		      =						(self.rust_tokenizer_class, '''hf-internal-testing/tiny-random-mbart50''', {})
					for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
						with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})"""				):
							A_     : Any		      =						self.rust_tokenizer_class.from_pretrained(A_					,   **A_				)
							A_     : int		      =						self.tokenizer_class.from_pretrained(A_					,   **A_				)

							A_     : int		      =						tempfile.mkdtemp()

							A_     : Union[str, Any]		      =						tokenizer_r.save_pretrained(A_				)
							A_     : Dict		      =						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_     : Tuple		      =						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_     : List[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_				)				)
								# self.assertEqual(getattr(tokenizer_rp, key), getattr(tokenizer_pp, key))
								# self.assertEqual(getattr(tokenizer_rp, key + "_id"), getattr(tokenizer_pp, key + "_id"))

							shutil.rmtree(A_				)

							# Save tokenizer rust, legacy_format=True
							A_     : List[str]		      =						tempfile.mkdtemp()

							A_     : List[str]		      =						tokenizer_r.save_pretrained(A_					,   legacy_format=A_				)
							A_     : int		      =						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_     : Union[str, Any]		      =						tokenizer_r.from_pretrained(A_				)
							A_     : 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_     : Optional[int]		      =						tempfile.mkdtemp()

							A_     : Any		      =						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_     : Optional[Any]		      =						tokenizer_r.from_pretrained(A_				)
							A_     : List[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
@require_sentencepiece
@require_tokenizers
class 				lowercase					(			unittest.TestCase):
				__lowerCAmelCase			:			Optional[int]     				=      "facebook/mbart-large-50-one-to-many-mmt"
				__lowerCAmelCase			:			List[str]     				=      [
				    " 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			:			Optional[int]     				=      [
				    "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			:			str     				=      [EN_CODE, 8_274, 127_873, 25_916, 7, 8_622, 2_071, 438, 67_485, 53, 187_895, 23, 51_712, 2]







				@classmethod
				def     a_     (							cls      :    int				):



					"""simple docstring"""

					A_     : Any		      =						MBartaaTokenizer.from_pretrained(
					    cls.checkpoint_name					,   src_lang='''en_XX'''					,   tgt_lang='''ro_RO'''				)
					A_     : str		      =						1
					return cls







				def     a_     (							self      :    List[Any]				):



					"""simple docstring"""

					self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['''ar_AR''']					,   25_00_01				)
					self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['''en_EN''']					,   25_00_04				)
					self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['''ro_RO''']					,   25_00_20				)
					self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['''mr_IN''']					,   25_00_38				)







				def     a_     (							self      :    Tuple				):



					"""simple docstring"""

					A_     : str		      =						self.tokenizer.batch_encode_plus(self.src_text				).input_ids[0]
					self.assertListEqual(self.expected_src_tokens					,   A_				)







				def     a_     (							self      :    Optional[Any]				):



					"""simple docstring"""

					self.assertIn(A_					,   self.tokenizer.all_special_ids				)
					A_     : Optional[int]		      =						[RO_CODE, 8_84, 90_19, 96, 9, 9_16, 8_67_92, 36, 1_87_43, 1_55_96, 5, 2]
					A_     : Dict		      =						self.tokenizer.decode(A_					,   skip_special_tokens=A_				)
					A_     : str		      =						self.tokenizer.decode(generated_ids[1:]					,   skip_special_tokens=A_				)
					self.assertEqual(A_					,   A_				)
					self.assertNotIn(self.tokenizer.eos_token					,   A_				)







				def     a_     (							self      :    Optional[int]				):



					"""simple docstring"""

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







				def     a_     (							self      :    Any				):



					"""simple docstring"""

					self.assertListEqual(self.tokenizer.convert_tokens_to_ids(['''<mask>''', '''ar_AR''']				)					,   [25_00_53, 25_00_01]				)







				def     a_     (							self      :    int				):



					"""simple docstring"""

					A_     : List[str]		      =						tempfile.mkdtemp()
					A_     : Optional[Any]		      =						self.tokenizer.fairseq_tokens_to_ids
					self.tokenizer.save_pretrained(A_				)
					A_     : Any		      =						MBartaaTokenizer.from_pretrained(A_				)
					self.assertDictEqual(new_tok.fairseq_tokens_to_ids					,   A_				)







				@require_torch
				def     a_     (							self      :    Union[str, Any]				):



					"""simple docstring"""

					A_     : Optional[Any]		      =						self.tokenizer(self.src_text					,   text_target=self.tgt_text					,   padding=A_					,   return_tensors='''pt'''				)
					A_     : Optional[int]		      =						shift_tokens_right(batch['''labels''']					,   self.tokenizer.pad_token_id				)

					# fairseq batch: https://gist.github.com/sshleifer/cba08bc2109361a74ac3760a7e30e4f4
					assert batch.input_ids[1][0] == EN_CODE
					assert batch.input_ids[1][-1] == 2
					assert batch.labels[1][0] == RO_CODE
					assert batch.labels[1][-1] == 2
					assert batch.decoder_input_ids[1][:2].tolist() == [2, RO_CODE]







				@require_torch
				def     a_     (							self      :    List[str]				):



					"""simple docstring"""

					A_     : Optional[int]		      =						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_     : List[Any]		      =						shift_tokens_right(batch['''labels''']					,   self.tokenizer.pad_token_id				)

					self.assertIsInstance(A_					,   A_				)

					self.assertEqual((2, 14)					,   batch.input_ids.shape				)
					self.assertEqual((2, 14)					,   batch.attention_mask.shape				)
					A_     : List[str]		      =						batch.input_ids.tolist()[0]
					self.assertListEqual(self.expected_src_tokens					,   A_				)
					self.assertEqual(2					,   batch.decoder_input_ids[0, 0]				)  # decoder_start_token_id
					# 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      :    Any				):



					"""simple docstring"""

					A_     : Dict		      =						self.tokenizer(self.src_text					,   padding=A_					,   truncation=A_					,   max_length=3					,   return_tensors='''pt'''				)
					A_     : Optional[Any]		      =						self.tokenizer(
					    text_target=self.tgt_text					,   padding=A_					,   truncation=A_					,   max_length=10					,   return_tensors='''pt'''				)
					A_     : Any		      =						targets['''input_ids''']
					A_     : Dict		      =						shift_tokens_right(A_					,   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     a_     (							self      :    Optional[int]				):



					"""simple docstring"""

					A_     : List[str]		      =						self.tokenizer._build_translation_inputs(
					    '''A test'''					,   return_tensors='''pt'''					,   src_lang='''en_XX'''					,   tgt_lang='''ar_AR'''				)

					self.assertEqual(
					    nested_simplify(A_				)					,   {
					        # en_XX, A, test, EOS
					        '''input_ids''': [[25_00_04, 62, 30_34, 2]],
					        '''attention_mask''': [[1, 1, 1, 1]],
					        # ar_AR
					        '''forced_bos_token_id''': 25_00_01,
					    }					,   )







from __future__ import annotations

import os
from collections.abc import Mapping

_UpperCAmelCase					: Tuple      =       tuple[int, int]


class 				lowercase					:



		def __init__( self				,      A_				,      A_       )  ->					None:
				"""simple docstring"""
				UpperCamelCase		=     vertices
				UpperCamelCase		=     {
				    (min(A_       ), max(A_       )): weight for edge, weight in edges.items()
				}



		def __UpperCamelCase ( self				,      A_				,      A_       )  ->					None:
				"""simple docstring"""
				self.vertices.add(edge[0]       )
				self.vertices.add(edge[1]       )
				UpperCamelCase		=     weight



		def __UpperCamelCase ( self       )  ->					Graph:
				"""simple docstring"""
				UpperCamelCase		=     Graph({min(self.vertices       )}				,      {}       )
				UpperCamelCase		=     42
				UpperCamelCase		=     42
				UpperCamelCase		=     42
				UpperCamelCase		=     42

				while len(subgraph.vertices       ) < len(self.vertices       ):
						UpperCamelCase		=     max(self.edges.values()       ) + 1
						for edge, weight in self.edges.items():
								if (edge[0] in subgraph.vertices) ^ (edge[1] in subgraph.vertices):
										if weight < min_weight:
												UpperCamelCase		=     edge
												UpperCamelCase		=     weight

						subgraph.add_edge(A_				,      A_       )

				return subgraph






def       A       (					lowercase = "p107_network.txt" )   ->  int:
		'''simple docstring'''

		UpperCamelCase		=     os.path.abspath(os.path.dirname(lowercase ) )
		UpperCamelCase		=     os.path.join(lowercase    ,       lowercase )
		UpperCamelCase		=     {}
		UpperCamelCase		=     42
		UpperCamelCase		=     42
		UpperCamelCase		=     42

		with open(lowercase ) as f:
				UpperCamelCase		=     f.read().strip().split('\n' )

		UpperCamelCase		=     [line.split(',' ) for line in data]

		for edgea in range(1    ,       len(lowercase ) ):
				for edgea in range(lowercase ):
						if adjaceny_matrix[edgea][edgea] != "-":
								UpperCamelCase		=     int(adjaceny_matrix[edgea][edgea] )

		UpperCamelCase		=     Graph(set(range(len(lowercase ) ) )    ,       lowercase )

		UpperCamelCase		=     graph.prims_algorithm()

		UpperCamelCase		=     sum(graph.edges.values() )
		UpperCamelCase		=     sum(subgraph.edges.values() )

		return initial_total - optimal_total


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