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


SCREAMING_SNAKE_CASE		:     str         =	get_tests_dir("fixtures/test_sentencepiece.model")


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

SCREAMING_SNAKE_CASE		:     Any         =	250004
SCREAMING_SNAKE_CASE		:     int         =	250020

@require_sentencepiece
@require_tokenizers
class        UpperCamelCase						(				__a	,	unittest.TestCase				):
						a__		:Any      =			MBartTokenizer
						a__		:Optional[int]      =			MBartTokenizerFast
						a__		:List[Any]      =			True
						a__		:Optional[Any]      =			True





						def        A_							(self						)				->				Optional[int]:
												super().setUp()

												# We have a SentencePiece fixture for testing
												UpperCamelCase_		:							str  =		MBartTokenizer(__UpperCamelCase			,     keep_accents=__UpperCamelCase						)
												tokenizer.save_pretrained(self.tmpdirname						)





						def        A_							(self						)				->				List[Any]:
												UpperCamelCase_		:							Any  =		MBartTokenizer(__UpperCamelCase			,     keep_accents=__UpperCamelCase						)

												UpperCamelCase_		:							Dict  =		tokenizer.tokenize("""This is a test"""						)
												self.assertListEqual(__UpperCamelCase			,     ["""▁This""", """▁is""", """▁a""", """▁t""", """est"""]						)

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

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

												UpperCamelCase_		:							List[Any]  =		tokenizer.convert_ids_to_tokens(__UpperCamelCase						)
												self.assertListEqual(
												    __UpperCamelCase			,     [
												        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        A_							(self						)				->				Union[str, Any]:
												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_		:							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})'''						):
																								UpperCamelCase_		:							Tuple  =		self.rust_tokenizer_class.from_pretrained(__UpperCamelCase			,     **__UpperCamelCase						)
																								UpperCamelCase_		:							List[Any]  =		self.tokenizer_class.from_pretrained(__UpperCamelCase			,     **__UpperCamelCase						)

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

																								UpperCamelCase_		:							int  =		tokenizer_r.save_pretrained(__UpperCamelCase						)
																								UpperCamelCase_		:							int  =		tokenizer_p.save_pretrained(__UpperCamelCase						)

																								# 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_		:							Tuple  =		tuple(f for f in tokenizer_r_files if """tokenizer.json""" not in f						)
																								self.assertSequenceEqual(__UpperCamelCase			,     __UpperCamelCase						)

																								# Checks everything loads correctly in the same way
																								UpperCamelCase_		:							Optional[Any]  =		tokenizer_r.from_pretrained(__UpperCamelCase						)
																								UpperCamelCase_		:							Tuple  =		tokenizer_p.from_pretrained(__UpperCamelCase						)

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

																								shutil.rmtree(__UpperCamelCase						)

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

																								UpperCamelCase_		:							Any  =		tokenizer_r.save_pretrained(__UpperCamelCase			,     legacy_format=__UpperCamelCase						)
																								UpperCamelCase_		:							Optional[int]  =		tokenizer_p.save_pretrained(__UpperCamelCase						)

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

																								# Checks everything loads correctly in the same way
																								UpperCamelCase_		:							Tuple  =		tokenizer_r.from_pretrained(__UpperCamelCase						)
																								UpperCamelCase_		:							str  =		tokenizer_p.from_pretrained(__UpperCamelCase						)

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

																								shutil.rmtree(__UpperCamelCase						)

																								# Save tokenizer rust, legacy_format=False
																								UpperCamelCase_		:							Optional[Any]  =		tempfile.mkdtemp()

																								UpperCamelCase_		:							Any  =		tokenizer_r.save_pretrained(__UpperCamelCase			,     legacy_format=__UpperCamelCase						)
																								UpperCamelCase_		:							List[str]  =		tokenizer_p.save_pretrained(__UpperCamelCase						)

																								# 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_		:							List[str]  =		tokenizer_r.from_pretrained(__UpperCamelCase						)
																								UpperCamelCase_		:							Dict  =		tokenizer_p.from_pretrained(__UpperCamelCase						)

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

																								shutil.rmtree(__UpperCamelCase						)


@require_torch
@require_sentencepiece
@require_tokenizers
class        UpperCamelCase						(				unittest.TestCase				):
						a__		:Optional[Any]      =			'''facebook/mbart-large-en-ro'''
						a__		:Optional[int]      =			[
						    ''' 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__		:List[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.''',
						]
						a__		:Optional[int]      =			[8274, 12_7873, 2_5916, 7, 8622, 2071, 438, 6_7485, 53, 18_7895, 23, 5_1712, 2, EN_CODE]





						@classmethod
						def        A_							(cls						)				->				Tuple:
												UpperCamelCase_		:							MBartTokenizer  =		MBartTokenizer.from_pretrained(
												    cls.checkpoint_name			,     src_lang="""en_XX"""			,     tgt_lang="""ro_RO"""						)
												UpperCamelCase_		:							List[Any]  =		1
												return cls





						def        A_							(self						)				->				int:
												self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["""ar_AR"""]			,     250_001						)
												self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["""en_EN"""]			,     250_004						)
												self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["""ro_RO"""]			,     250_020						)





						def        A_							(self						)				->				Dict:
												UpperCamelCase_		:							List[str]  =		self.tokenizer.batch_encode_plus(self.src_text						).input_ids[0]
												self.assertListEqual(self.expected_src_tokens			,     __UpperCamelCase						)





						def        A_							(self						)				->				Tuple:
												self.assertIn(__UpperCamelCase			,     self.tokenizer.all_special_ids						)
												UpperCamelCase_		:							Dict  =		[RO_CODE, 884, 9_019, 96, 9, 916, 86_792, 36, 18_743, 15_596, 5, 2]
												UpperCamelCase_		:							Optional[int]  =		self.tokenizer.decode(__UpperCamelCase			,     skip_special_tokens=__UpperCamelCase						)
												UpperCamelCase_		:							int  =		self.tokenizer.decode(generated_ids[1:]			,     skip_special_tokens=__UpperCamelCase						)
												self.assertEqual(__UpperCamelCase			,     __UpperCamelCase						)
												self.assertNotIn(self.tokenizer.eos_token			,     __UpperCamelCase						)





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





						def        A_							(self						)				->				int:
												self.assertListEqual(self.tokenizer.convert_tokens_to_ids(["""<mask>""", """ar_AR"""]						)			,     [250_026, 250_001]						)





						def        A_							(self						)				->				Dict:
												UpperCamelCase_		:							Tuple  =		tempfile.mkdtemp()
												UpperCamelCase_		:							int  =		self.tokenizer.fairseq_tokens_to_ids
												self.tokenizer.save_pretrained(__UpperCamelCase						)
												UpperCamelCase_		:							List[str]  =		MBartTokenizer.from_pretrained(__UpperCamelCase						)
												self.assertDictEqual(new_tok.fairseq_tokens_to_ids			,     __UpperCamelCase						)





						@require_torch
						def        A_							(self						)				->				Any:
												UpperCamelCase_		:							str  =		self.tokenizer(self.src_text			,     text_target=self.tgt_text			,     padding=__UpperCamelCase			,     return_tensors="""pt"""						)
												UpperCamelCase_		:							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][-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        A_							(self						)				->				str:
												UpperCamelCase_		:							Optional[Any]  =		self.tokenizer(
												    self.src_text			,     text_target=self.tgt_text			,     padding=__UpperCamelCase			,     truncation=__UpperCamelCase			,     max_length=len(self.expected_src_tokens						)			,     return_tensors="""pt"""			,     )

												UpperCamelCase_		:							Union[str, Any]  =		shift_tokens_right(batch["""labels"""]			,     self.tokenizer.pad_token_id						)

												self.assertIsInstance(__UpperCamelCase			,     __UpperCamelCase						)

												self.assertEqual((2, 14)			,     batch.input_ids.shape						)
												self.assertEqual((2, 14)			,     batch.attention_mask.shape						)
												UpperCamelCase_		:							Tuple  =		batch.input_ids.tolist()[0]
												self.assertListEqual(self.expected_src_tokens			,     __UpperCamelCase						)
												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        A_							(self						)				->				str:
												UpperCamelCase_		:							Union[str, Any]  =		self.tokenizer(self.src_text			,     padding=__UpperCamelCase			,     truncation=__UpperCamelCase			,     max_length=3			,     return_tensors="""pt"""						)
												UpperCamelCase_		:							Any  =		self.tokenizer(
												    text_target=self.tgt_text			,     padding=__UpperCamelCase			,     truncation=__UpperCamelCase			,     max_length=10			,     return_tensors="""pt"""						)
												UpperCamelCase_		:							Tuple  =		targets["""input_ids"""]
												UpperCamelCase_		:							Any  =		shift_tokens_right(__UpperCamelCase			,     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						)				->				str:
												UpperCamelCase_		:							Dict  =		self.tokenizer._build_translation_inputs(
												    """A test"""			,     return_tensors="""pt"""			,     src_lang="""en_XX"""			,     tgt_lang="""ar_AR"""						)

												self.assertEqual(
												    nested_simplify(__UpperCamelCase						)			,     {
												        # A, test, EOS, en_XX
												        """input_ids""": [[62, 3_034, 2, 250_004]],
												        """attention_mask""": [[1, 1, 1, 1]],
												        # ar_AR
												        """forced_bos_token_id""": 250_001,
												    }			,     )







import unittest

from transformers import load_tool
from transformers.utils import is_torch_available


if is_torch_available():
	import torch

from transformers.testing_utils import require_torch

from .test_tools_common import ToolTesterMixin

@require_torch
class        UpperCamelCase						(				unittest.TestCase	,	__a				):





						def        A_							(self						)				->				Tuple:
												UpperCamelCase_		:							Any  =		load_tool("""text-to-speech"""						)
												self.tool.setup()





						def        A_							(self						)				->				Dict:
												# SpeechT5 isn't deterministic
												torch.manual_seed(0						)
												UpperCamelCase_		:							Optional[Any]  =		self.tool("""hey"""						)
												UpperCamelCase_		:							Optional[Any]  =		result.to_raw()
												self.assertTrue(
												    torch.allclose(
												        resulting_tensor[:3]			,     torch.tensor([-0.0_005_966_668_832_115_829, -0.0_003_657_640_190_795_064, -0.00_013_439_502_799_883_485]						)			,     )						)





						def        A_							(self						)				->				Dict:
												# SpeechT5 isn't deterministic
												torch.manual_seed(0						)
												UpperCamelCase_		:							Any  =		self.tool("""hey"""						)
												UpperCamelCase_		:							Tuple  =		result.to_raw()
												self.assertTrue(
												    torch.allclose(
												        resulting_tensor[:3]			,     torch.tensor([-0.0_005_966_668_832_115_829, -0.0_003_657_640_190_795_064, -0.00_013_439_502_799_883_485]						)			,     )						)






