

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


A_	:			Optional[int]			=      get_tests_dir('fixtures/test_sentencepiece.model')

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

A_	:			Dict			=      25_0004
A_	:			Tuple			=      25_0020



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


					'''simple docstring'''






					UpperCAmelCase__:       str  							=	MBartaaTokenizer
					UpperCAmelCase__:       Optional[int]  							=	MBartaaTokenizerFast
					UpperCAmelCase__:       str  							=	True
					UpperCAmelCase__:       Optional[Any]  							=	True






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

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






					def   __A      (			self      ):
						A__							:   List[str]					       =		'''<s>'''
						A__							:   Any					       =		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      ):
						A__							:   str					       =		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      )   ,	1054      )






					def   __A      (			self      ):
						self.assertEqual(self.get_tokenizer().vocab_size   ,	1054      )






					def   __A      (			self      ):
						A__							:   int					       =		MBartaaTokenizer(__A   ,	src_lang="""en_XX"""   ,	tgt_lang="""ro_RO"""   ,	keep_accents=__A      )

						A__							:   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 [285, 46, 10, 170, 382]]   ,	)

						A__							:   Any					       =		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__							:   Optional[Any]					       =		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__							:   Union[str, 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      ):
						# fmt: off
						A__							:   List[str]					       =		{'''input_ids''': [[25_0004, 1_1062, 8_2772, 7, 15, 8_2772, 538, 5_1529, 237, 1_7198, 1290, 206, 9, 21_5175, 1314, 136, 1_7198, 1290, 206, 9, 5_6359, 42, 12_2009, 9, 1_6466, 16, 8_7344, 4537, 9, 4717, 7_8381, 6, 15_9958, 7, 15, 2_4480, 618, 4, 527, 2_2693, 5428, 4, 2777, 2_4480, 9874, 4, 4_3523, 594, 4, 803, 1_8392, 3_3189, 18, 4, 4_3523, 2_4447, 1_2399, 100, 2_4955, 8_3658, 9626, 14_4057, 15, 839, 2_2335, 16, 136, 2_4955, 8_3658, 8_3479, 15, 3_9102, 724, 16, 678, 645, 2789, 1328, 4589, 42, 12_2009, 11_5774, 23, 805, 1328, 4_6876, 7, 136, 5_3894, 1940, 4_2227, 4_1159, 1_7721, 823, 425, 4, 2_7512, 9_8722, 206, 136, 5531, 4970, 919, 1_7336, 5, 2], [25_0004, 2_0080, 618, 83, 8_2775, 47, 479, 9, 1517, 73, 5_3894, 333, 8_0581, 11_0117, 1_8811, 5256, 1295, 51, 15_2526, 297, 7986, 390, 12_4416, 538, 3_5431, 214, 98, 1_5044, 2_5737, 136, 7108, 4_3701, 23, 756, 13_5355, 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_0004, 581, 6_3773, 11_9455, 6, 14_7797, 8_8203, 7, 645, 70, 21, 3285, 1_0269, 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      ):
						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__							:   Dict					       =		(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__							:   List[Any]					       =		self.rust_tokenizer_class.from_pretrained(__A   ,	**__A      )
								A__							:   Tuple					       =		self.tokenizer_class.from_pretrained(__A   ,	**__A      )

								A__							:   List[Any]					       =		tempfile.mkdtemp()

								A__							:   Tuple					       =		tokenizer_r.save_pretrained(__A      )
								A__							:   Optional[Any]					       =		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__							:   Dict					       =		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__							:   Tuple					       =		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__							:   Dict					       =		tokenizer_r.save_pretrained(__A   ,	legacy_format=__A      )
								A__							:   Optional[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[Any]					       =		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__							:   Any					       =		tempfile.mkdtemp()

								A__							:   Dict					       =		tokenizer_r.save_pretrained(__A   ,	legacy_format=__A      )
								A__							:   Any					       =		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[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
@require_sentencepiece
@require_tokenizers
class    _a						(unittest.TestCase    ):


					'''simple docstring'''






					UpperCAmelCase__:       str  							=	'''facebook/mbart-large-50-one-to-many-mmt'''
					UpperCAmelCase__:       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__:       Any  							=	[EN_CODE, 82_74, 12_78_73, 2_59_16, 7, 86_22, 20_71, 4_38, 6_74_85, 53, 18_78_95, 23, 5_17_12, 2]






					@classmethod
					def   __A      (			cls      ):
						A__							:   MBartaaTokenizer					       =		MBartaaTokenizer.from_pretrained(
						    cls.checkpoint_name   ,	src_lang="""en_XX"""   ,	tgt_lang="""ro_RO"""      )
						A__							:   int					       =		1
						return cls






					def   __A      (			self      ):
						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      )
						self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["""mr_IN"""]   ,	25_0038      )






					def   __A      (			self      ):
						A__							:   List[str]					       =		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      )
						A__							:   List[Any]					       =		[RO_CODE, 884, 9019, 96, 9, 916, 8_6792, 36, 1_8743, 1_5596, 5, 2]
						A__							:   Optional[Any]					       =		self.tokenizer.decode(__A   ,	skip_special_tokens=__A      )
						A__							:   Tuple					       =		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[Any]					       =		['''this is gunna be a long sentence ''' * 20]
						assert isinstance(src_text[0]   ,	__A      )
						A__							:   Optional[Any]					       =		10
						A__							:   Optional[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      ):
						self.assertListEqual(self.tokenizer.convert_tokens_to_ids(["""<mask>""", """ar_AR"""]      )   ,	[25_0053, 25_0001]      )






					def   __A      (			self      ):
						A__							:   str					       =		tempfile.mkdtemp()
						A__							:   Optional[Any]					       =		self.tokenizer.fairseq_tokens_to_ids
						self.tokenizer.save_pretrained(__A      )
						A__							:   Union[str, Any]					       =		MBartaaTokenizer.from_pretrained(__A      )
						self.assertDictEqual(new_tok.fairseq_tokens_to_ids   ,	__A      )






					@require_torch
					def   __A      (			self      ):
						A__							:   Union[str, Any]					       =		self.tokenizer(self.src_text   ,	text_target=self.tgt_text   ,	padding=__A   ,	return_tensors="""pt"""      )
						A__							:   Union[str, Any]					       =		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      ):
						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__							:   int					       =		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__							:   Optional[Any]					       =		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      ):
						A__							:   List[Any]					       =		self.tokenizer(self.src_text   ,	padding=__A   ,	truncation=__A   ,	max_length=3   ,	return_tensors="""pt"""      )
						A__							:   int					       =		self.tokenizer(
						    text_target=self.tgt_text   ,	padding=__A   ,	truncation=__A   ,	max_length=10   ,	return_tensors="""pt"""      )
						A__							:   str					       =		targets['''input_ids''']
						A__							:   Optional[int]					       =		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      ):
						A__							:   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_0004, 62, 3034, 2]],
						        """attention_mask""": [[1, 1, 1, 1]],
						        # ar_AR
						        """forced_bos_token_id""": 25_0001,
						    }   ,	)






import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
	from transformers import AutoModelForSeqaSeqLM, AutoTokenizer



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


					'''simple docstring'''






					@slow
					def   __A      (			self      ):
						A__							:   Optional[Any]					       =		AutoModelForSeqaSeqLM.from_pretrained("""google/mt5-small"""   ,	return_dict=A__      ).to(A__      )
						A__							:   str					       =		AutoTokenizer.from_pretrained("""google/mt5-small"""      )

						A__							:   int					       =		tokenizer("""Hello there"""   ,	return_tensors="""pt"""      ).input_ids
						A__							:   List[Any]					       =		tokenizer("""Hi I am"""   ,	return_tensors="""pt"""      ).input_ids

						A__							:   Union[str, Any]					       =		model(input_ids.to(A__      )   ,	labels=labels.to(A__      )      ).loss
						A__							:   Union[str, Any]					       =		-(labels.shape[-1] * loss.item())

						A__							:   Any					       =		-8_4.9_1_2_7
						self.assertTrue(abs(mtf_score - EXPECTED_SCORE      ) < 1e-4      )



