
'''simple docstring'''







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


lowercase					:							int   =      get_tests_dir("fixtures/test_sentencepiece.model")


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

lowercase					:							List[Any]   =      25_0004
lowercase					:							List[Any]   =      25_0020


@require_sentencepiece
@require_tokenizers
class 		__UpperCAmelCase   (      __snake_case		,     unittest.TestCase		):
							__lowercase   	= MBartTokenizer
							__lowercase   	= MBartTokenizerFast
							__lowercase   	= True
							__lowercase   	= True


							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								super().setUp()

								# We have a SentencePiece fixture for testing
								_snake_case     		=	MBartTokenizer(A_					,     keep_accents=A_						)
								tokenizer.save_pretrained(self.tmpdirname						)


							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								_snake_case     		=	MBartTokenizer(A_					,     keep_accents=A_						)

								_snake_case     		=	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]]					,     )

								_snake_case     		=	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',
								        '.',
								    ]					,     )
								_snake_case     		=	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]
								        #                                       ^ unk: 2 + 1 = 3                  unk: 2 + 1 = 3 ^
								    ]					,     )

								_snake_case     		=	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>',
								        '.',
								    ]					,     )




							def 				lowerCamelCase       ( 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

								_snake_case     		=	(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})'						):
										_snake_case     		=	self.rust_tokenizer_class.from_pretrained(A_					,     **A_						)
										_snake_case     		=	self.tokenizer_class.from_pretrained(A_					,     **A_						)

										_snake_case     		=	tempfile.mkdtemp()

										_snake_case     		=	tokenizer_r.save_pretrained(A_						)
										_snake_case     		=	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						)						)
										_snake_case     		=	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
										_snake_case     		=	tokenizer_r.from_pretrained(A_						)
										_snake_case     		=	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
										_snake_case     		=	tempfile.mkdtemp()

										_snake_case     		=	tokenizer_r.save_pretrained(A_					,     legacy_format=A_						)
										_snake_case     		=	tokenizer_p.save_pretrained(A_						)

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

										# Checks everything loads correctly in the same way
										_snake_case     		=	tokenizer_r.from_pretrained(A_						)
										_snake_case     		=	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
										_snake_case     		=	tempfile.mkdtemp()

										_snake_case     		=	tokenizer_r.save_pretrained(A_					,     legacy_format=A_						)
										_snake_case     		=	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
										_snake_case     		=	tokenizer_r.from_pretrained(A_						)
										_snake_case     		=	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 		__UpperCAmelCase   (      unittest.TestCase		):
							__lowercase   	= 'facebook/mbart-large-en-ro'
							__lowercase   	= [
							    ' 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.',
							]
							__lowercase   	= [
							    '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.',
							]
							__lowercase   	= [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, EN_CODE]


							@classmethod
							def 				lowerCamelCase       ( cls						):





								"""simple docstring"""






								_snake_case     		=	MBartTokenizer.from_pretrained(
								    cls.checkpoint_name					,     src_lang='en_XX'					,     tgt_lang='ro_RO'						)
								_snake_case     		=	1
								return cls


							def 				lowerCamelCase       ( self						):





								"""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						)


							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






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


							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






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


							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






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


							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								self.assertListEqual(self.tokenizer.convert_tokens_to_ids(['<mask>', 'ar_AR']						)					,     [25_00_26, 25_00_01]						)


							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								_snake_case     		=	tempfile.mkdtemp()
								_snake_case     		=	self.tokenizer.fairseq_tokens_to_ids
								self.tokenizer.save_pretrained(A_						)
								_snake_case     		=	MBartTokenizer.from_pretrained(A_						)
								self.assertDictEqual(new_tok.fairseq_tokens_to_ids					,     A_						)


							@require_torch
							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								_snake_case     		=	self.tokenizer(self.src_text					,     text_target=self.tgt_text					,     padding=A_					,     return_tensors='pt'						)
								_snake_case     		=	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 				lowerCamelCase       ( self						):





								"""simple docstring"""






								_snake_case     		=	self.tokenizer(
								    self.src_text					,     text_target=self.tgt_text					,     padding=A_					,     truncation=A_					,     max_length=len(self.expected_src_tokens						)					,     return_tensors='pt'					,     )

								_snake_case     		=	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						)
								_snake_case     		=	batch.input_ids.tolist()[0]
								self.assertListEqual(self.expected_src_tokens					,     A_						)
								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 				lowerCamelCase       ( self						):





								"""simple docstring"""






								_snake_case     		=	self.tokenizer(self.src_text					,     padding=A_					,     truncation=A_					,     max_length=3					,     return_tensors='pt'						)
								_snake_case     		=	self.tokenizer(
								    text_target=self.tgt_text					,     padding=A_					,     truncation=A_					,     max_length=10					,     return_tensors='pt'						)
								_snake_case     		=	targets["input_ids"]
								_snake_case     		=	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 				lowerCamelCase       ( self						):





								"""simple docstring"""






								_snake_case     		=	self.tokenizer._build_translation_inputs(
								    'A test'					,     return_tensors='pt'					,     src_lang='en_XX'					,     tgt_lang='ar_AR'						)

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



'''simple docstring'''







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


lowercase					:							str   =      {
    "configuration_nezha": ["NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP", "NezhaConfig"],
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowercase					:							Any   =      [
					    "NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "NezhaForNextSentencePrediction",
					    "NezhaForMaskedLM",
					    "NezhaForPreTraining",
					    "NezhaForMultipleChoice",
					    "NezhaForQuestionAnswering",
					    "NezhaForSequenceClassification",
					    "NezhaForTokenClassification",
					    "NezhaModel",
					    "NezhaPreTrainedModel",
					]


if TYPE_CHECKING:
					from .configuration_nezha import NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP, NezhaConfig

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_nezha import (
										    NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST,
										    NezhaForMaskedLM,
										    NezhaForMultipleChoice,
										    NezhaForNextSentencePrediction,
										    NezhaForPreTraining,
										    NezhaForQuestionAnswering,
										    NezhaForSequenceClassification,
										    NezhaForTokenClassification,
										    NezhaModel,
										    NezhaPreTrainedModel,
										)


else:
					import sys

					lowercase					:							Union[str, Any]   =      _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

