






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


__UpperCamelCase					:		Optional[int]	  =						get_tests_dir("""fixtures/test_sentencepiece.model""")


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

__UpperCamelCase					:		List[str]	  =						250004
__UpperCamelCase					:		str	  =						250020


@require_sentencepiece
@require_tokenizers
class   __SCREAMING_SNAKE_CASE(	a_    ,  unittest.TestCase	):
							_UpperCAmelCase     =       MBartTokenizer
							_UpperCAmelCase     =       MBartTokenizerFast
							_UpperCAmelCase     =       True
							_UpperCAmelCase     =       True
							def lowerCAmelCase_ (     self:      str						)		->		Tuple:
											super().setUp()

											# We have a SentencePiece fixture for testing
											snake_case__									=     MBartTokenizer(UpperCamelCase       ,	keep_accents=UpperCamelCase						)
											tokenizer.save_pretrained(self.tmpdirname						)
							def lowerCAmelCase_ (     self:      int						)		->		Tuple:
											snake_case__									=     MBartTokenizer(UpperCamelCase       ,	keep_accents=UpperCamelCase						)

											snake_case__									=     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 [2_85, 46, 10, 1_70, 3_82]]       ,	)

											snake_case__									=     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',
											        '.',
											    ]       ,	)
											snake_case__									=     tokenizer.convert_tokens_to_ids(UpperCamelCase						)
											self.assertListEqual(
											    UpperCamelCase       ,	[
											        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(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 lowerCAmelCase_ (     self:      Dict						)		->		List[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

											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(UpperCamelCase       ,	**UpperCamelCase						)
																			snake_case__									=     self.tokenizer_class.from_pretrained(UpperCamelCase       ,	**UpperCamelCase						)

																			snake_case__									=     tempfile.mkdtemp()

																			snake_case__									=     tokenizer_r.save_pretrained(UpperCamelCase						)
																			snake_case__									=     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						)						)
																			snake_case__									=     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
																			snake_case__									=     tokenizer_r.from_pretrained(UpperCamelCase						)
																			snake_case__									=     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
																			snake_case__									=     tempfile.mkdtemp()

																			snake_case__									=     tokenizer_r.save_pretrained(UpperCamelCase       ,	legacy_format=UpperCamelCase						)
																			snake_case__									=     tokenizer_p.save_pretrained(UpperCamelCase						)

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

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

																			snake_case__									=     tokenizer_r.save_pretrained(UpperCamelCase       ,	legacy_format=UpperCamelCase						)
																			snake_case__									=     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
																			snake_case__									=     tokenizer_r.from_pretrained(UpperCamelCase						)
																			snake_case__									=     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   __SCREAMING_SNAKE_CASE(	unittest.TestCase	):
							_UpperCAmelCase     =       "facebook/mbart-large-en-ro"
							_UpperCAmelCase     =       [
							    " 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     =       [
							    "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     =       [8_2_7_4, 1_2_7_8_7_3, 2_5_9_1_6, 7, 8_6_2_2, 2_0_7_1, 4_3_8, 6_7_4_8_5, 5_3, 1_8_7_8_9_5, 2_3, 5_1_7_1_2, 2, EN_CODE]
							@classmethod
							def lowerCAmelCase_ (     cls:      Union[str, Any]						)		->		str:
											snake_case__									=     MBartTokenizer.from_pretrained(
											    cls.checkpoint_name       ,	src_lang='en_XX'       ,	tgt_lang='ro_RO'						)
											snake_case__									=     1
											return cls
							def lowerCAmelCase_ (     self:      str						)		->		Tuple:
											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:      Dict						)		->		List[Any]:
											snake_case__									=     self.tokenizer.batch_encode_plus(self.src_text						).input_ids[0]
											self.assertListEqual(self.expected_src_tokens       ,	UpperCamelCase						)
							def lowerCAmelCase_ (     self:      Optional[int]						)		->		int:
											self.assertIn(UpperCamelCase       ,	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(UpperCamelCase       ,	skip_special_tokens=UpperCamelCase						)
											snake_case__									=     self.tokenizer.decode(generated_ids[1:]       ,	skip_special_tokens=UpperCamelCase						)
											self.assertEqual(UpperCamelCase       ,	UpperCamelCase						)
											self.assertNotIn(self.tokenizer.eos_token       ,	UpperCamelCase						)
							def lowerCAmelCase_ (     self:      List[Any]						)		->		List[str]:
											snake_case__									=     ['this is gunna be a long sentence ' * 20]
											assert isinstance(src_text[0]       ,	UpperCamelCase						)
											snake_case__									=     10
											snake_case__									=     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 lowerCAmelCase_ (     self:      List[Any]						)		->		Tuple:
											self.assertListEqual(self.tokenizer.convert_tokens_to_ids(['<mask>', 'ar_AR']						)       ,	[25_00_26, 25_00_01]						)
							def lowerCAmelCase_ (     self:      Union[str, Any]						)		->		Dict:
											snake_case__									=     tempfile.mkdtemp()
											snake_case__									=     self.tokenizer.fairseq_tokens_to_ids
											self.tokenizer.save_pretrained(UpperCamelCase						)
											snake_case__									=     MBartTokenizer.from_pretrained(UpperCamelCase						)
											self.assertDictEqual(new_tok.fairseq_tokens_to_ids       ,	UpperCamelCase						)
							@require_torch
							def lowerCAmelCase_ (     self:      Any						)		->		Optional[int]:
											snake_case__									=     self.tokenizer(self.src_text       ,	text_target=self.tgt_text       ,	padding=UpperCamelCase       ,	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:      List[Any]						)		->		Dict:
											snake_case__									=     self.tokenizer(
											    self.src_text       ,	text_target=self.tgt_text       ,	padding=UpperCamelCase       ,	truncation=UpperCamelCase       ,	max_length=len(self.expected_src_tokens						)       ,	return_tensors='pt'       ,	)

											snake_case__									=     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						)
											snake_case__									=     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 lowerCAmelCase_ (     self:      Dict						)		->		Dict:
											snake_case__									=     self.tokenizer(self.src_text       ,	padding=UpperCamelCase       ,	truncation=UpperCamelCase       ,	max_length=3       ,	return_tensors='pt'						)
											snake_case__									=     self.tokenizer(
											    text_target=self.tgt_text       ,	padding=UpperCamelCase       ,	truncation=UpperCamelCase       ,	max_length=10       ,	return_tensors='pt'						)
											snake_case__									=     targets['input_ids']
											snake_case__									=     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 lowerCAmelCase_ (     self:      List[Any]						)		->		Tuple:
											snake_case__									=     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, 30_34, 2, 25_00_04]],
											        'attention_mask': [[1, 1, 1, 1]],
											        # ar_AR
											        'forced_bos_token_id': 25_00_01,
											    }       ,	)












from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__UpperCamelCase					:		int	  =						{"""configuration_xglm""": ["""XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """XGLMConfig"""]}

try:
							if not is_sentencepiece_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__UpperCamelCase					:		Dict	  =						["""XGLMTokenizer"""]

try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__UpperCamelCase					:		Optional[int]	  =						["""XGLMTokenizerFast"""]

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__UpperCamelCase					:		List[str]	  =						[
							    """XGLM_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """XGLMForCausalLM""",
							    """XGLMModel""",
							    """XGLMPreTrainedModel""",
							]


try:
							if not is_flax_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__UpperCamelCase					:		Any	  =						[
							    """FlaxXGLMForCausalLM""",
							    """FlaxXGLMModel""",
							    """FlaxXGLMPreTrainedModel""",
							]


try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__UpperCamelCase					:		str	  =						[
							    """TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """TFXGLMForCausalLM""",
							    """TFXGLMModel""",
							    """TFXGLMPreTrainedModel""",
							]


if TYPE_CHECKING:
							from .configuration_xglm import XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XGLMConfig

							try:
														if not is_sentencepiece_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_xglm import XGLMTokenizer

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_xglm_fast import XGLMTokenizerFast

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_xglm import XGLM_PRETRAINED_MODEL_ARCHIVE_LIST, XGLMForCausalLM, XGLMModel, XGLMPreTrainedModel

							try:
														if not is_flax_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_flax_xglm import FlaxXGLMForCausalLM, FlaxXGLMModel, FlaxXGLMPreTrainedModel

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_xglm import (
														    TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TFXGLMForCausalLM,
														    TFXGLMModel,
														    TFXGLMPreTrainedModel,
														)


else:
							import sys

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




