





"""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


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

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

__magic_name__       :					Tuple						=			2_5_0_0_0_4
__magic_name__       :					Tuple						=			2_5_0_0_2_0






@require_sentencepiece
@require_tokenizers
class 							lowercase__			(   __SCREAMING_SNAKE_CASE					,     unittest.TestCase	):

			"""simple docstring"""
			__lowerCAmelCase     :				Any										=  MBartaaTokenizer
			__lowerCAmelCase     :				str										=  MBartaaTokenizerFast
			__lowerCAmelCase     :				Tuple										=  True
			__lowerCAmelCase     :				Optional[int]										=  True







			def   _a      (      self    ):






									'''simple docstring'''







									super().setUp()

									# We have a SentencePiece fixture for testing
									UpperCamelCase				:		Dict				   =			MBartaaTokenizer(_A						,   src_lang="""en_XX"""						,   tgt_lang="""ro_RO"""						,   keep_accents=_A    )
									tokenizer.save_pretrained(self.tmpdirname    )







			def   _a      (      self    ):






									'''simple docstring'''







									UpperCamelCase				:		Optional[int]				   =			"""<s>"""
									UpperCamelCase				:		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    ):






									'''simple docstring'''







									UpperCamelCase				:		Dict				   =			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    )						,   1_0_5_4    )







			def   _a      (      self    ):






									'''simple docstring'''







									self.assertEqual(self.get_tokenizer().vocab_size						,   1_0_5_4    )







			def   _a      (      self    ):






									'''simple docstring'''







									UpperCamelCase				:		Optional[Any]				   =			MBartaaTokenizer(_A						,   src_lang="""en_XX"""						,   tgt_lang="""ro_RO"""						,   keep_accents=_A    )

									UpperCamelCase				:		List[str]				   =			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_8_5, 4_6, 1_0, 1_7_0, 3_8_2]]						,   )

									UpperCamelCase				:		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""", """."""]						,   )
									UpperCamelCase				:		List[Any]				   =			tokenizer.convert_tokens_to_ids(_A    )
									self.assertListEqual(
									    _A						,   [
									        value + tokenizer.fairseq_offset
									        for value in [8, 2_1, 8_4, 5_5, 2_4, 1_9, 7, 2, 6_0_2, 3_4_7, 3_4_7, 3_4_7, 3, 1_2, 6_6, 4_6, 7_2, 8_0, 6, 2, 4]
									    ]						,   )

									UpperCamelCase				:		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>""", """."""]						,   )







			@slow
			def   _a      (      self    ):






									'''simple docstring'''







									UpperCamelCase				:		Optional[int]				   =			{"""input_ids""": [[2_5_0_0_0_4, 1_1_0_6_2, 8_2_7_7_2, 7, 1_5, 8_2_7_7_2, 5_3_8, 5_1_5_2_9, 2_3_7, 1_7_1_9_8, 1_2_9_0, 2_0_6, 9, 2_1_5_1_7_5, 1_3_1_4, 1_3_6, 1_7_1_9_8, 1_2_9_0, 2_0_6, 9, 5_6_3_5_9, 4_2, 1_2_2_0_0_9, 9, 1_6_4_6_6, 1_6, 8_7_3_4_4, 4_5_3_7, 9, 4_7_1_7, 7_8_3_8_1, 6, 1_5_9_9_5_8, 7, 1_5, 2_4_4_8_0, 6_1_8, 4, 5_2_7, 2_2_6_9_3, 5_4_2_8, 4, 2_7_7_7, 2_4_4_8_0, 9_8_7_4, 4, 4_3_5_2_3, 5_9_4, 4, 8_0_3, 1_8_3_9_2, 3_3_1_8_9, 1_8, 4, 4_3_5_2_3, 2_4_4_4_7, 1_2_3_9_9, 1_0_0, 2_4_9_5_5, 8_3_6_5_8, 9_6_2_6, 1_4_4_0_5_7, 1_5, 8_3_9, 2_2_3_3_5, 1_6, 1_3_6, 2_4_9_5_5, 8_3_6_5_8, 8_3_4_7_9, 1_5, 3_9_1_0_2, 7_2_4, 1_6, 6_7_8, 6_4_5, 2_7_8_9, 1_3_2_8, 4_5_8_9, 4_2, 1_2_2_0_0_9, 1_1_5_7_7_4, 2_3, 8_0_5, 1_3_2_8, 4_6_8_7_6, 7, 1_3_6, 5_3_8_9_4, 1_9_4_0, 4_2_2_2_7, 4_1_1_5_9, 1_7_7_2_1, 8_2_3, 4_2_5, 4, 2_7_5_1_2, 9_8_7_2_2, 2_0_6, 1_3_6, 5_5_3_1, 4_9_7_0, 9_1_9, 1_7_3_3_6, 5, 2], [2_5_0_0_0_4, 2_0_0_8_0, 6_1_8, 8_3, 8_2_7_7_5, 4_7, 4_7_9, 9, 1_5_1_7, 7_3, 5_3_8_9_4, 3_3_3, 8_0_5_8_1, 1_1_0_1_1_7, 1_8_8_1_1, 5_2_5_6, 1_2_9_5, 5_1, 1_5_2_5_2_6, 2_9_7, 7_9_8_6, 3_9_0, 1_2_4_4_1_6, 5_3_8, 3_5_4_3_1, 2_1_4, 9_8, 1_5_0_4_4, 2_5_7_3_7, 1_3_6, 7_1_0_8, 4_3_7_0_1, 2_3, 7_5_6, 1_3_5_3_5_5, 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], [2_5_0_0_0_4, 5_8_1, 6_3_7_7_3, 1_1_9_4_5_5, 6, 1_4_7_7_9_7, 8_8_2_0_3, 7, 6_4_5, 7_0, 2_1, 3_2_8_5, 1_0_2_6_9, 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    ):






									'''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

									UpperCamelCase				:		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})"""    ):
																					UpperCamelCase				:		Optional[Any]				   =			self.rust_tokenizer_class.from_pretrained(_A						,   **_A    )
																					UpperCamelCase				:		Optional[Any]				   =			self.tokenizer_class.from_pretrained(_A						,   **_A    )

																					UpperCamelCase				:		Any				   =			tempfile.mkdtemp()

																					UpperCamelCase				:		List[str]				   =			tokenizer_r.save_pretrained(_A    )
																					UpperCamelCase				:		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    )    )
																					UpperCamelCase				:		Optional[Any]				   =			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
																					UpperCamelCase				:		Any				   =			tokenizer_r.from_pretrained(_A    )
																					UpperCamelCase				:		Tuple				   =			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
																					UpperCamelCase				:		Any				   =			tempfile.mkdtemp()

																					UpperCamelCase				:		Union[str, Any]				   =			tokenizer_r.save_pretrained(_A						,   legacy_format=_A    )
																					UpperCamelCase				:		Optional[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
																					UpperCamelCase				:		Union[str, Any]				   =			tokenizer_r.from_pretrained(_A    )
																					UpperCamelCase				:		Optional[int]				   =			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
																					UpperCamelCase				:		Union[str, Any]				   =			tempfile.mkdtemp()

																					UpperCamelCase				:		List[str]				   =			tokenizer_r.save_pretrained(_A						,   legacy_format=_A    )
																					UpperCamelCase				:		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
																					UpperCamelCase				:		Dict				   =			tokenizer_r.from_pretrained(_A    )
																					UpperCamelCase				:		Dict				   =			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	):

			"""simple docstring"""
			__lowerCAmelCase     :				Any										=  """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     :				Optional[int]										=  [EN_CODE, 8274, 127873, 25916, 7, 8622, 2071, 438, 67485, 53, 187895, 23, 51712, 2]







			@classmethod
			def   _a      (      cls    ):






									'''simple docstring'''







									UpperCamelCase				:		MBartaaTokenizer				   =			MBartaaTokenizer.from_pretrained(
									    cls.checkpoint_name						,   src_lang="""en_XX"""						,   tgt_lang="""ro_RO"""    )
									UpperCamelCase				:		str				   =			1
									return cls







			def   _a      (      self    ):






									'''simple docstring'''







									self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["""ar_AR"""]						,   2_5_0_0_0_1    )
									self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["""en_EN"""]						,   2_5_0_0_0_4    )
									self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["""ro_RO"""]						,   2_5_0_0_2_0    )
									self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["""mr_IN"""]						,   2_5_0_0_3_8    )







			def   _a      (      self    ):






									'''simple docstring'''







									UpperCamelCase				:		Optional[Any]				   =			self.tokenizer.batch_encode_plus(self.src_text    ).input_ids[0]
									self.assertListEqual(self.expected_src_tokens						,   _A    )







			def   _a      (      self    ):






									'''simple docstring'''







									self.assertIn(_A						,   self.tokenizer.all_special_ids    )
									UpperCamelCase				:		Dict				   =			[RO_CODE, 8_8_4, 9_0_1_9, 9_6, 9, 9_1_6, 8_6_7_9_2, 3_6, 1_8_7_4_3, 1_5_5_9_6, 5, 2]
									UpperCamelCase				:		int				   =			self.tokenizer.decode(_A						,   skip_special_tokens=_A    )
									UpperCamelCase				:		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    ):






									'''simple docstring'''







									UpperCamelCase				:		Optional[Any]				   =			["""this is gunna be a long sentence """ * 2_0]
									assert isinstance(src_text[0]						,   _A    )
									UpperCamelCase				:		List[str]				   =			1_0
									UpperCamelCase				:		int				   =			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    ):






									'''simple docstring'''







									self.assertListEqual(self.tokenizer.convert_tokens_to_ids(["""<mask>""", """ar_AR"""]    )						,   [2_5_0_0_5_3, 2_5_0_0_0_1]    )







			def   _a      (      self    ):






									'''simple docstring'''







									UpperCamelCase				:		Dict				   =			tempfile.mkdtemp()
									UpperCamelCase				:		List[str]				   =			self.tokenizer.fairseq_tokens_to_ids
									self.tokenizer.save_pretrained(_A    )
									UpperCamelCase				:		int				   =			MBartaaTokenizer.from_pretrained(_A    )
									self.assertDictEqual(new_tok.fairseq_tokens_to_ids						,   _A    )







			@require_torch
			def   _a      (      self    ):






									'''simple docstring'''







									UpperCamelCase				:		Union[str, Any]				   =			self.tokenizer(self.src_text						,   text_target=self.tgt_text						,   padding=_A						,   return_tensors="""pt"""    )
									UpperCamelCase				:		List[str]				   =			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    ):






									'''simple docstring'''







									UpperCamelCase				:		Tuple				   =			self.tokenizer(
									    self.src_text						,   text_target=self.tgt_text						,   padding=_A						,   truncation=_A						,   max_length=len(self.expected_src_tokens    )						,   return_tensors="""pt"""						,   )
									UpperCamelCase				:		Any				   =			shift_tokens_right(batch["""labels"""]						,   self.tokenizer.pad_token_id    )

									self.assertIsInstance(_A						,   _A    )

									self.assertEqual((2, 1_4)						,   batch.input_ids.shape    )
									self.assertEqual((2, 1_4)						,   batch.attention_mask.shape    )
									UpperCamelCase				:		Tuple				   =			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    ):






									'''simple docstring'''







									UpperCamelCase				:		Union[str, Any]				   =			self.tokenizer(self.src_text						,   padding=_A						,   truncation=_A						,   max_length=3						,   return_tensors="""pt"""    )
									UpperCamelCase				:		Optional[int]				   =			self.tokenizer(
									    text_target=self.tgt_text						,   padding=_A						,   truncation=_A						,   max_length=1_0						,   return_tensors="""pt"""    )
									UpperCamelCase				:		Any				   =			targets["""input_ids"""]
									UpperCamelCase				:		Union[str, Any]				   =			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]						,   1_0    )







			@require_torch
			def   _a      (      self    ):






									'''simple docstring'''







									UpperCamelCase				:		Any				   =			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""": [[2_5_0_0_0_4, 6_2, 3_0_3_4, 2]],
									        """attention_mask""": [[1, 1, 1, 1]],
									        # ar_AR
									        """forced_bos_token_id""": 2_5_0_0_0_1,
									    }						,   )










"""simple docstring"""


from bisect import bisect
from itertools import accumulate

def      UpperCamelCase							(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     ):
						UpperCamelCase				:		Dict				   =			sorted(zip(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     )							,     key=lambda SCREAMING_SNAKE_CASE     : x[0] / x[1]							,     reverse=SCREAMING_SNAKE_CASE     )
						UpperCamelCase   ,						UpperCamelCase				:		int				   =			[i[0] for i in r], [i[1] for i in r]
						UpperCamelCase				:		Optional[Any]				   =			list(accumulate(SCREAMING_SNAKE_CASE     )     )
						UpperCamelCase				:		Optional[Any]				   =			bisect(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     )
						return (
						    0
						    if k == 0
						    else sum(vl[:k]     ) + (w - acc[k - 1]) * (vl[k]) / (wt[k])
						    if k != n
						    else sum(vl[:k]     )
						)


if __name__ == "__main__":
		import doctest

		doctest.testmod()



