



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     :    Any      						=					get_tests_dir("""fixtures/test_sentencepiece.model""")

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

a     :    Dict      						=					2_5_0_0_0_4
a     :    Dict      						=					2_5_0_0_2_0

@require_sentencepiece
@require_tokenizers
class     UpperCamelCase_				(							__magic_name__   ,  unittest.TestCase    ):
	lowercase    =				MBartaaTokenizer
	lowercase    =				MBartaaTokenizerFast
	lowercase    =				True
	lowercase    =				True





	def 				_lowercase(						self					)    ->    int:
								super().setUp()

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





	def 				_lowercase(						self					)    ->    str:
								UpperCAmelCase  :	Tuple							  =	'<s>'
								UpperCAmelCase  :	int							  =	0

								self.assertEqual(self.get_tokenizer()._convert_token_to_id(A					)	,	A					)
								self.assertEqual(self.get_tokenizer()._convert_id_to_token(A					)	,	A					)





	def 				_lowercase(						self					)    ->    List[str]:
								UpperCAmelCase  :	Optional[int]							  =	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 				_lowercase(						self					)    ->    Any:
								self.assertEqual(self.get_tokenizer().vocab_size	,	1054					)





	def 				_lowercase(						self					)    ->    List[Any]:
								UpperCAmelCase  :	Optional[int]							  =	MBartaaTokenizer(A	,	src_lang="""en_XX"""	,	tgt_lang="""ro_RO"""	,	keep_accents=A					)

								UpperCAmelCase  :	Dict							  =	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]]	,	)

								UpperCAmelCase  :	List[str]							  =	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""", """."""]	,	)
								UpperCAmelCase  :	Union[str, 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]
								    ]	,	)

								UpperCAmelCase  :	Tuple							  =	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 				_lowercase(						self					)    ->    Dict:
								UpperCAmelCase  :	str							  =	{'input_ids': [[250004, 11062, 82772, 7, 15, 82772, 538, 51529, 237, 17198, 1290, 206, 9, 215175, 1314, 136, 17198, 1290, 206, 9, 56359, 42, 122009, 9, 16466, 16, 87344, 4537, 9, 4717, 78381, 6, 159958, 7, 15, 24480, 618, 4, 527, 22693, 5428, 4, 2777, 24480, 9874, 4, 43523, 594, 4, 803, 18392, 33189, 18, 4, 43523, 24447, 12399, 100, 24955, 83658, 9626, 144057, 15, 839, 22335, 16, 136, 24955, 83658, 83479, 15, 39102, 724, 16, 678, 645, 2789, 1328, 4589, 42, 122009, 115774, 23, 805, 1328, 46876, 7, 136, 53894, 1940, 42227, 41159, 17721, 823, 425, 4, 27512, 98722, 206, 136, 5531, 4970, 919, 17336, 5, 2], [250004, 20080, 618, 83, 82775, 47, 479, 9, 1517, 73, 53894, 333, 80581, 110117, 18811, 5256, 1295, 51, 152526, 297, 7986, 390, 124416, 538, 35431, 214, 98, 15044, 25737, 136, 7108, 43701, 23, 756, 135355, 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], [250004, 581, 63773, 119455, 6, 147797, 88203, 7, 645, 70, 21, 3285, 10269, 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 				_lowercase(						self					)    ->    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

								UpperCAmelCase  :	Any							  =	(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})'''					):
																						UpperCAmelCase  :	Tuple							  =	self.rust_tokenizer_class.from_pretrained(A	,	**A					)
																						UpperCAmelCase  :	Optional[Any]							  =	self.tokenizer_class.from_pretrained(A	,	**A					)

																						UpperCAmelCase  :	List[str]							  =	tempfile.mkdtemp()

																						UpperCAmelCase  :	int							  =	tokenizer_r.save_pretrained(A					)
																						UpperCAmelCase  :	List[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					)					)
																						UpperCAmelCase  :	Tuple							  =	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
																						UpperCAmelCase  :	Optional[int]							  =	tokenizer_r.from_pretrained(A					)
																						UpperCAmelCase  :	Tuple							  =	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
																						UpperCAmelCase  :	Optional[int]							  =	tempfile.mkdtemp()

																						UpperCAmelCase  :	Tuple							  =	tokenizer_r.save_pretrained(A	,	legacy_format=A					)
																						UpperCAmelCase  :	Tuple							  =	tokenizer_p.save_pretrained(A					)

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

																						# Checks everything loads correctly in the same way
																						UpperCAmelCase  :	Dict							  =	tokenizer_r.from_pretrained(A					)
																						UpperCAmelCase  :	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					)

																						# Save tokenizer rust, legacy_format=False
																						UpperCAmelCase  :	Tuple							  =	tempfile.mkdtemp()

																						UpperCAmelCase  :	Any							  =	tokenizer_r.save_pretrained(A	,	legacy_format=A					)
																						UpperCAmelCase  :	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
																						UpperCAmelCase  :	List[str]							  =	tokenizer_r.from_pretrained(A					)
																						UpperCAmelCase  :	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					)




@require_torch
@require_sentencepiece
@require_tokenizers
class     UpperCamelCase_				(							unittest.TestCase    ):
	lowercase    =				'facebook/mbart-large-50-one-to-many-mmt'
	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    =				[EN_CODE, 8_274, 127_873, 25_916, 7, 8_622, 2_071, 438, 67_485, 53, 187_895, 23, 51_712, 2]





	@classmethod
	def 				_lowercase(						cls					)    ->    Tuple:
								UpperCAmelCase  :	MBartaaTokenizer							  =	MBartaaTokenizer.from_pretrained(
								    cls.checkpoint_name	,	src_lang="""en_XX"""	,	tgt_lang="""ro_RO"""					)
								UpperCAmelCase  :	Any							  =	1
								return cls





	def 				_lowercase(						self					)    ->    List[Any]:
								self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["""ar_AR"""]	,	250001					)
								self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["""en_EN"""]	,	250004					)
								self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["""ro_RO"""]	,	250020					)
								self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["""mr_IN"""]	,	250038					)





	def 				_lowercase(						self					)    ->    Dict:
								UpperCAmelCase  :	Optional[int]							  =	self.tokenizer.batch_encode_plus(self.src_text					).input_ids[0]
								self.assertListEqual(self.expected_src_tokens	,	A					)





	def 				_lowercase(						self					)    ->    List[Any]:
								self.assertIn(A	,	self.tokenizer.all_special_ids					)
								UpperCAmelCase  :	Optional[int]							  =	[RO_CODE, 884, 9019, 96, 9, 916, 86792, 36, 18743, 15596, 5, 2]
								UpperCAmelCase  :	str							  =	self.tokenizer.decode(A	,	skip_special_tokens=A					)
								UpperCAmelCase  :	int							  =	self.tokenizer.decode(generated_ids[1:]	,	skip_special_tokens=A					)
								self.assertEqual(A	,	A					)
								self.assertNotIn(self.tokenizer.eos_token	,	A					)





	def 				_lowercase(						self					)    ->    Optional[Any]:
								UpperCAmelCase  :	Union[str, Any]							  =	['this is gunna be a long sentence ' * 20]
								assert isinstance(src_text[0]	,	A					)
								UpperCAmelCase  :	Any							  =	10
								UpperCAmelCase  :	Dict							  =	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 				_lowercase(						self					)    ->    Tuple:
								self.assertListEqual(self.tokenizer.convert_tokens_to_ids(["""<mask>""", """ar_AR"""]					)	,	[250053, 250001]					)





	def 				_lowercase(						self					)    ->    Tuple:
								UpperCAmelCase  :	List[Any]							  =	tempfile.mkdtemp()
								UpperCAmelCase  :	Optional[Any]							  =	self.tokenizer.fairseq_tokens_to_ids
								self.tokenizer.save_pretrained(A					)
								UpperCAmelCase  :	List[str]							  =	MBartaaTokenizer.from_pretrained(A					)
								self.assertDictEqual(new_tok.fairseq_tokens_to_ids	,	A					)





	@require_torch
	def 				_lowercase(						self					)    ->    str:
								UpperCAmelCase  :	Dict							  =	self.tokenizer(self.src_text	,	text_target=self.tgt_text	,	padding=A	,	return_tensors="""pt"""					)
								UpperCAmelCase  :	int							  =	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 				_lowercase(						self					)    ->    Dict:
								UpperCAmelCase  :	List[Any]							  =	self.tokenizer(
								    self.src_text	,	text_target=self.tgt_text	,	padding=A	,	truncation=A	,	max_length=len(self.expected_src_tokens					)	,	return_tensors="""pt"""	,	)
								UpperCAmelCase  :	Optional[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					)
								UpperCAmelCase  :	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 				_lowercase(						self					)    ->    int:
								UpperCAmelCase  :	Dict							  =	self.tokenizer(self.src_text	,	padding=A	,	truncation=A	,	max_length=3	,	return_tensors="""pt"""					)
								UpperCAmelCase  :	Any							  =	self.tokenizer(
								    text_target=self.tgt_text	,	padding=A	,	truncation=A	,	max_length=10	,	return_tensors="""pt"""					)
								UpperCAmelCase  :	Tuple							  =	targets['input_ids']
								UpperCAmelCase  :	Optional[Any]							  =	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 				_lowercase(						self					)    ->    Any:
								UpperCAmelCase  :	int							  =	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""": [[250004, 62, 3034, 2]],
								        """attention_mask""": [[1, 1, 1, 1]],
								        # ar_AR
								        """forced_bos_token_id""": 250001,
								    }	,	)






'''simple docstring'''




from ...configuration_utils import PretrainedConfig
from ...utils import logging


a     :    str      						=					logging.get_logger(__name__)

a     :    Any      						=					{
    """google/fnet-base""": """https://huggingface.co/google/fnet-base/resolve/main/config.json""",
    """google/fnet-large""": """https://huggingface.co/google/fnet-large/resolve/main/config.json"""
    # See all FNet models at https://huggingface.co/models?filter=fnet
}

class     UpperCamelCase_				(							__magic_name__    ):
	lowercase    =				'fnet'





	def __init__(						self	,	A=32000	,	A=768	,	A=12	,	A=3072	,	A="gelu_new"	,	A=0.1	,	A=512	,	A=4	,	A=0.0_2	,	A=1e-12	,	A=False	,	A=512	,	A=3	,	A=1	,	A=2	,	**A	,	)    ->    int:
								super().__init__(pad_token_id=A	,	bos_token_id=A	,	eos_token_id=A	,	**A					)

								UpperCAmelCase  :	Optional[Any]							  =	vocab_size
								UpperCAmelCase  :	Dict							  =	max_position_embeddings
								UpperCAmelCase  :	List[str]							  =	hidden_size
								UpperCAmelCase  :	int							  =	num_hidden_layers
								UpperCAmelCase  :	Any							  =	intermediate_size
								UpperCAmelCase  :	List[str]							  =	hidden_act
								UpperCAmelCase  :	Tuple							  =	hidden_dropout_prob
								UpperCAmelCase  :	List[str]							  =	initializer_range
								UpperCAmelCase  :	List[Any]							  =	type_vocab_size
								UpperCAmelCase  :	int							  =	layer_norm_eps
								UpperCAmelCase  :	Optional[Any]							  =	use_tpu_fourier_optimizations
								UpperCAmelCase  :	List[Any]							  =	tpu_short_seq_length

