



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


_snake_case	=						get_tests_dir("fixtures/test_sentencepiece.model")


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

_snake_case	=						25_0004
_snake_case	=						25_0020



@require_sentencepiece
@require_tokenizers
class 						UpperCAmelCase_					( a__     ,      unittest.TestCase):
							lowerCamelCase__         =	MBartTokenizer
							lowerCamelCase__         =	MBartTokenizerFast
							lowerCamelCase__         =	True
							lowerCamelCase__         =	True







							def      snake_case__   (     self):

														'''simple docstring'''

														super().setUp()

														# We have a SentencePiece fixture for testing
														_lowerCAmelCase  :							Union[str, Any]				     =					MBartTokenizer(lowerCamelCase_,    keep_accents=lowerCamelCase_)
														tokenizer.save_pretrained(self.tmpdirname)







							def      snake_case__   (     self):

														'''simple docstring'''

														_lowerCAmelCase  :							Any				     =					MBartTokenizer(lowerCamelCase_,    keep_accents=lowerCamelCase_)

														_lowerCAmelCase  :							Union[str, Any]				     =					tokenizer.tokenize("This is a test")
														self.assertListEqual(lowerCamelCase_,    ["▁This", "▁is", "▁a", "▁t", "est"])

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

														_lowerCAmelCase  :							Union[str, Any]				     =					tokenizer.tokenize("I was born in 92000, and this is falsé.")
														self.assertListEqual(
														    lowerCamelCase_,    [
														        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",
														        ".",
														    ],    )
														_lowerCAmelCase  :							Any				     =					tokenizer.convert_tokens_to_ids(lowerCamelCase_)
														self.assertListEqual(
														    lowerCamelCase_,    [
														        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 ^
														    ],    )

														_lowerCAmelCase  :							Dict				     =					tokenizer.convert_ids_to_tokens(lowerCamelCase_)
														self.assertListEqual(
														    lowerCamelCase_,    [
														        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      snake_case__   (     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

														_lowerCAmelCase  :							Tuple				     =					(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})"):
																												_lowerCAmelCase  :							str				     =					self.rust_tokenizer_class.from_pretrained(lowerCamelCase_,    **lowerCamelCase_)
																												_lowerCAmelCase  :							Any				     =					self.tokenizer_class.from_pretrained(lowerCamelCase_,    **lowerCamelCase_)

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

																												_lowerCAmelCase  :							Any				     =					tokenizer_r.save_pretrained(lowerCamelCase_)
																												_lowerCAmelCase  :							Optional[int]				     =					tokenizer_p.save_pretrained(lowerCamelCase_)

																												# 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))
																												_lowerCAmelCase  :							Union[str, Any]				     =					tuple(f for f in tokenizer_r_files if "tokenizer.json" not in f)
																												self.assertSequenceEqual(lowerCamelCase_,    lowerCamelCase_)

																												# Checks everything loads correctly in the same way
																												_lowerCAmelCase  :							List[Any]				     =					tokenizer_r.from_pretrained(lowerCamelCase_)
																												_lowerCAmelCase  :							List[Any]				     =					tokenizer_p.from_pretrained(lowerCamelCase_)

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

																												shutil.rmtree(lowerCamelCase_)

																												# Save tokenizer rust, legacy_format=True
																												_lowerCAmelCase  :							Optional[int]				     =					tempfile.mkdtemp()

																												_lowerCAmelCase  :							List[str]				     =					tokenizer_r.save_pretrained(lowerCamelCase_,    legacy_format=lowerCamelCase_)
																												_lowerCAmelCase  :							Union[str, Any]				     =					tokenizer_p.save_pretrained(lowerCamelCase_)

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

																												# Checks everything loads correctly in the same way
																												_lowerCAmelCase  :							str				     =					tokenizer_r.from_pretrained(lowerCamelCase_)
																												_lowerCAmelCase  :							List[str]				     =					tokenizer_p.from_pretrained(lowerCamelCase_)

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

																												shutil.rmtree(lowerCamelCase_)

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

																												_lowerCAmelCase  :							List[str]				     =					tokenizer_r.save_pretrained(lowerCamelCase_,    legacy_format=lowerCamelCase_)
																												_lowerCAmelCase  :							Dict				     =					tokenizer_p.save_pretrained(lowerCamelCase_)

																												# 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
																												_lowerCAmelCase  :							Optional[int]				     =					tokenizer_r.from_pretrained(lowerCamelCase_)
																												_lowerCAmelCase  :							List[str]				     =					tokenizer_p.from_pretrained(lowerCamelCase_)

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

																												shutil.rmtree(lowerCamelCase_)



@require_torch
@require_sentencepiece
@require_tokenizers
class 						UpperCAmelCase_					( unittest.TestCase):
							lowerCamelCase__         =	"facebook/mbart-large-en-ro"
							lowerCamelCase__         =	[
							    " 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__         =	[
							    "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__         =	[8274, 127873, 25916, 7, 8622, 2071, 438, 67485, 53, 187895, 23, 51712, 2, EN_CODE]







							@classmethod
							def      snake_case__   (     cls):

														'''simple docstring'''

														_lowerCAmelCase  :							Optional[Any]				     =					MBartTokenizer.from_pretrained(
														    cls.checkpoint_name,    src_lang="en_XX",    tgt_lang="ro_RO")
														_lowerCAmelCase  :							Optional[int]				     =					1
														return cls







							def      snake_case__   (     self):

														'''simple docstring'''

														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      snake_case__   (     self):

														'''simple docstring'''

														_lowerCAmelCase  :							List[Any]				     =					self.tokenizer.batch_encode_plus(self.src_text).input_ids[0]
														self.assertListEqual(self.expected_src_tokens,    lowerCamelCase_)







							def      snake_case__   (     self):

														'''simple docstring'''

														self.assertIn(lowerCamelCase_,    self.tokenizer.all_special_ids)
														_lowerCAmelCase  :							Union[str, Any]				     =					[RO_CODE, 884, 9019, 96, 9, 916, 8_6792, 36, 1_8743, 1_5596, 5, 2]
														_lowerCAmelCase  :							List[str]				     =					self.tokenizer.decode(lowerCamelCase_,    skip_special_tokens=lowerCamelCase_)
														_lowerCAmelCase  :							Any				     =					self.tokenizer.decode(generated_ids[1:],    skip_special_tokens=lowerCamelCase_)
														self.assertEqual(lowerCamelCase_,    lowerCamelCase_)
														self.assertNotIn(self.tokenizer.eos_token,    lowerCamelCase_)







							def      snake_case__   (     self):

														'''simple docstring'''

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







							def      snake_case__   (     self):

														'''simple docstring'''

														self.assertListEqual(self.tokenizer.convert_tokens_to_ids(["<mask>", "ar_AR"]),    [25_0026, 25_0001])







							def      snake_case__   (     self):

														'''simple docstring'''

														_lowerCAmelCase  :							Any				     =					tempfile.mkdtemp()
														_lowerCAmelCase  :							int				     =					self.tokenizer.fairseq_tokens_to_ids
														self.tokenizer.save_pretrained(lowerCamelCase_)
														_lowerCAmelCase  :							Dict				     =					MBartTokenizer.from_pretrained(lowerCamelCase_)
														self.assertDictEqual(new_tok.fairseq_tokens_to_ids,    lowerCamelCase_)







							@require_torch
							def      snake_case__   (     self):

														'''simple docstring'''

														_lowerCAmelCase  :							Optional[int]				     =					self.tokenizer(self.src_text,    text_target=self.tgt_text,    padding=lowerCamelCase_,    return_tensors="pt")
														_lowerCAmelCase  :							Any				     =					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      snake_case__   (     self):

														'''simple docstring'''

														_lowerCAmelCase  :							Any				     =					self.tokenizer(
														    self.src_text,    text_target=self.tgt_text,    padding=lowerCamelCase_,    truncation=lowerCamelCase_,    max_length=len(self.expected_src_tokens),    return_tensors="pt",    )

														_lowerCAmelCase  :							Optional[int]				     =					shift_tokens_right(batch["labels"],    self.tokenizer.pad_token_id)

														self.assertIsInstance(lowerCamelCase_,    lowerCamelCase_)

														self.assertEqual((2, 14),    batch.input_ids.shape)
														self.assertEqual((2, 14),    batch.attention_mask.shape)
														_lowerCAmelCase  :							Optional[Any]				     =					batch.input_ids.tolist()[0]
														self.assertListEqual(self.expected_src_tokens,    lowerCamelCase_)
														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      snake_case__   (     self):

														'''simple docstring'''

														_lowerCAmelCase  :							Tuple				     =					self.tokenizer(self.src_text,    padding=lowerCamelCase_,    truncation=lowerCamelCase_,    max_length=3,    return_tensors="pt")
														_lowerCAmelCase  :							List[Any]				     =					self.tokenizer(
														    text_target=self.tgt_text,    padding=lowerCamelCase_,    truncation=lowerCamelCase_,    max_length=10,    return_tensors="pt")
														_lowerCAmelCase  :							Any				     =					targets["input_ids"]
														_lowerCAmelCase  :							Any				     =					shift_tokens_right(lowerCamelCase_,    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      snake_case__   (     self):

														'''simple docstring'''

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

														self.assertEqual(
														    nested_simplify(lowerCamelCase_),    {
														        # 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,
														    },    )





def        A       (      _lowerCamelCase       ,				_lowerCamelCase       ,				_lowerCamelCase						):





							'''simple docstring'''




							def count_of_possible_combinations(_lowerCamelCase						) -> int:
														if target < 0:
																					return 0
														if target == 0:
																					return 1
														return sum(count_of_possible_combinations(target - item						) for item in array						)

							return count_of_possible_combinations(_lowerCamelCase						)






def        A       (      _lowerCamelCase       ,				_lowerCamelCase       ,				_lowerCamelCase						):





							'''simple docstring'''




							def count_of_possible_combinations_with_dp_array(
							    _lowerCamelCase       ,				_lowerCamelCase						) -> int:
														if target < 0:
																					return 0
														if target == 0:
																					return 1
														if dp_array[target] != -1:
																					return dp_array[target]
														_lowerCAmelCase  :							Optional[int]				     =					sum(
														    count_of_possible_combinations_with_dp_array(target - item       ,				_lowerCamelCase						)
														    for item in array						)
														_lowerCAmelCase  :							Any				     =					answer
														return answer

							_lowerCAmelCase  :							List[Any]				     =					[-1] * (target + 1)
							return count_of_possible_combinations_with_dp_array(_lowerCamelCase       ,				_lowerCamelCase						)






def        A       (      _lowerCamelCase       ,				_lowerCamelCase       ,				_lowerCamelCase						):





							'''simple docstring'''




							_lowerCAmelCase  :							List[Any]				     =					[0] * (target + 1)
							_lowerCAmelCase  :							List[str]				     =					1

							for i in range(1       ,				target + 1						):
														for j in range(_lowerCamelCase						):
																					if i - array[j] >= 0:
																												dp_array[i] += dp_array[i - array[j]]

							return dp_array[target]


if __name__ == "__main__":
						import doctest

						doctest.testmod()
						_snake_case	=						3
						_snake_case	=						5
						_snake_case	=						[1, 2, 5]
						print(combination_sum_iv(n, array, target))
