




'''simple docstring'''

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       :						List[str]								=		get_tests_dir('''fixtures/test_sentencepiece.model''')

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

a       :						Optional[Any]								=		250_004
a       :						List[Any]								=		250_020



@require_sentencepiece
@require_tokenizers
class 		SCREAMING_SNAKE_CASE__  (			_UpperCamelCase				,     unittest.TestCase	):
			__SCREAMING_SNAKE_CASE    =			MBartaaTokenizer
			__SCREAMING_SNAKE_CASE    =			MBartaaTokenizerFast
			__SCREAMING_SNAKE_CASE    =			True
			__SCREAMING_SNAKE_CASE    =			True





			def 							A (      self						:    Dict							):



						"""simple docstring"""




						super().setUp()

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





			def 							A (      self						:    Tuple							):



						"""simple docstring"""




						__snake_case			=		"<s>"
						__snake_case			=		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						:    int							):



						"""simple docstring"""




						__snake_case			=		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_							)      ,		1_054							)





			def 							A (      self						:    Tuple							):



						"""simple docstring"""




						self.assertEqual(self.get_tokenizer().vocab_size      ,		1_054							)





			def 							A (      self						:    Any							):



						"""simple docstring"""




						__snake_case			=		MBartaaTokenizer(a_      ,		src_lang="en_XX"      ,		tgt_lang="ro_RO"      ,		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 [285, 46, 10, 170, 382]]      ,		)

						__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, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
						    ]      ,		)

						__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>", "."]      ,		)





			@slow
			def 							A (      self						:    List[str]							):



						"""simple docstring"""




						__snake_case			=		{"input_ids": [[250_004, 11_062, 82_772, 7, 15, 82_772, 538, 51_529, 237, 17_198, 1_290, 206, 9, 215_175, 1_314, 136, 17_198, 1_290, 206, 9, 56_359, 42, 122_009, 9, 16_466, 16, 87_344, 4_537, 9, 4_717, 78_381, 6, 159_958, 7, 15, 24_480, 618, 4, 527, 22_693, 5_428, 4, 2_777, 24_480, 9_874, 4, 43_523, 594, 4, 803, 18_392, 33_189, 18, 4, 43_523, 24_447, 12_399, 100, 24_955, 83_658, 9_626, 144_057, 15, 839, 22_335, 16, 136, 24_955, 83_658, 83_479, 15, 39_102, 724, 16, 678, 645, 2_789, 1_328, 4_589, 42, 122_009, 115_774, 23, 805, 1_328, 46_876, 7, 136, 53_894, 1_940, 42_227, 41_159, 17_721, 823, 425, 4, 27_512, 98_722, 206, 136, 5_531, 4_970, 919, 17_336, 5, 2], [250_004, 20_080, 618, 83, 82_775, 47, 479, 9, 1_517, 73, 53_894, 333, 80_581, 110_117, 18_811, 5_256, 1_295, 51, 152_526, 297, 7_986, 390, 124_416, 538, 35_431, 214, 98, 15_044, 25_737, 136, 7_108, 43_701, 23, 756, 135_355, 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], [250_004, 581, 63_773, 119_455, 6, 147_797, 88_203, 7, 645, 70, 21, 3_285, 10_269, 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						:    Tuple							):



						"""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-mbart50", {})
						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 		SCREAMING_SNAKE_CASE__  (			unittest.TestCase	):
			__SCREAMING_SNAKE_CASE    =			"""facebook/mbart-large-50-one-to-many-mmt"""
			__SCREAMING_SNAKE_CASE    =			[
			    """ 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.""",
			]
			__SCREAMING_SNAKE_CASE    =			[
			    """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.""",
			]
			__SCREAMING_SNAKE_CASE    =			[EN_CODE, 8274, 127873, 25916, 7, 8622, 2071, 438, 67485, 53, 187895, 23, 51712, 2]





			@classmethod
			def 							A (      cls						:    List[Any]							):



						"""simple docstring"""




						__snake_case			=		MBartaaTokenizer.from_pretrained(
						    cls.checkpoint_name      ,		src_lang="en_XX"      ,		tgt_lang="ro_RO"							)
						__snake_case			=		1
						return cls





			def 							A (      self						:    Tuple							):



						"""simple docstring"""




						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							)
						self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["mr_IN"]      ,		250_038							)





			def 							A (      self						:    str							):



						"""simple docstring"""




						__snake_case			=		self.tokenizer.batch_encode_plus(self.src_text							).input_ids[0]
						self.assertListEqual(self.expected_src_tokens      ,		a_							)





			def 							A (      self						:    Dict							):



						"""simple docstring"""




						self.assertIn(a_      ,		self.tokenizer.all_special_ids							)
						__snake_case			=		[RO_CODE, 884, 9_019, 96, 9, 916, 86_792, 36, 18_743, 15_596, 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 							A (      self						:    str							):



						"""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[0]      ,		a_							)
						self.assertEqual(ids[-1]      ,		2							)
						self.assertEqual(len(a_							)      ,		a_							)





			def 							A (      self						:    str							):



						"""simple docstring"""




						self.assertListEqual(self.tokenizer.convert_tokens_to_ids(["<mask>", "ar_AR"]							)      ,		[250_053, 250_001]							)





			def 							A (      self						:    Dict							):



						"""simple docstring"""




						__snake_case			=		tempfile.mkdtemp()
						__snake_case			=		self.tokenizer.fairseq_tokens_to_ids
						self.tokenizer.save_pretrained(a_							)
						__snake_case			=		MBartaaTokenizer.from_pretrained(a_							)
						self.assertDictEqual(new_tok.fairseq_tokens_to_ids      ,		a_							)





			@require_torch
			def 							A (      self						:    Dict							):



						"""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][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						:    int							):



						"""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, 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						:    Dict							):



						"""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 							A (      self						:    Any							):



						"""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_							)      ,		{
						        # en_XX, A, test, EOS
						        "input_ids": [[250_004, 62, 3_034, 2]],
						        "attention_mask": [[1, 1, 1, 1]],
						        # ar_AR
						        "forced_bos_token_id": 250_001,
						    }      ,		)









from typing import TYPE_CHECKING

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


SCREAMING_SNAKE_CASE						:str			=  {'''configuration_unispeech''': ['''UNISPEECH_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''UniSpeechConfig''']}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   SCREAMING_SNAKE_CASE						:List[str]			=  [
       '''UNISPEECH_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''UniSpeechForCTC''',
       '''UniSpeechForPreTraining''',
       '''UniSpeechForSequenceClassification''',
       '''UniSpeechModel''',
       '''UniSpeechPreTrainedModel''',
   ]

if TYPE_CHECKING:
   from .configuration_unispeech import UNISPEECH_PRETRAINED_CONFIG_ARCHIVE_MAP, UniSpeechConfig

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_unispeech import (
          UNISPEECH_PRETRAINED_MODEL_ARCHIVE_LIST,
          UniSpeechForCTC,
          UniSpeechForPreTraining,
          UniSpeechForSequenceClassification,
          UniSpeechModel,
          UniSpeechPreTrainedModel,
      )

else:
   import sys

   SCREAMING_SNAKE_CASE						:List[str]			=  _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




