



import shutil
import tempfile
import unittest

from transformers import (
    SPIECE_UNDERLINE,
    AddedToken,
    BatchEncoding,
    NllbTokenizer,
    NllbTokenizerFast,
    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


_snake_case	=						get_tests_dir("fixtures/test_sentencepiece.model")


if is_torch_available():
						from transformers.models.mam_aaa.modeling_mam_aaa import shift_tokens_right

_snake_case	=						25_6047
_snake_case	=						25_6145



@require_sentencepiece
@require_tokenizers
class 						UpperCAmelCase_					( a     ,      unittest.TestCase):
							lowerCamelCase__         =	NllbTokenizer
							lowerCamelCase__         =	NllbTokenizerFast
							lowerCamelCase__         =	True
							lowerCamelCase__         =	True
							lowerCamelCase__         =	{}







							def      snake_case__   (     self):

														'''simple docstring'''

														super().setUp()

														# We have a SentencePiece fixture for testing
														_lowerCAmelCase  :							Tuple				     =					NllbTokenizer(__a,    keep_accents=__a)
														tokenizer.save_pretrained(self.tmpdirname)







							def      snake_case__   (     self):

														'''simple docstring'''

														_lowerCAmelCase  :							Union[str, Any]				     =					NllbTokenizer(__a,    keep_accents=__a)

														_lowerCAmelCase  :							List[str]				     =					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]],    )

														_lowerCAmelCase  :							Any				     =					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",
														        ".",
														    ],    )
														_lowerCAmelCase  :							Optional[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]
														    ],    )

														_lowerCAmelCase  :							List[str]				     =					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      snake_case__   (     self):

														'''simple docstring'''

														_lowerCAmelCase  :							Dict				     =					(self.rust_tokenizer_class, "hf-internal-testing/tiny-random-nllb", {})
														for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
																					with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"):
																												_lowerCAmelCase  :							Optional[Any]				     =					self.rust_tokenizer_class.from_pretrained(__a,    **__a)
																												_lowerCAmelCase  :							List[str]				     =					self.tokenizer_class.from_pretrained(__a,    **__a)

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

																												_lowerCAmelCase  :							Union[str, Any]				     =					tokenizer_r.save_pretrained(__a)
																												_lowerCAmelCase  :							Tuple				     =					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))
																												_lowerCAmelCase  :							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
																												_lowerCAmelCase  :							Dict				     =					tokenizer_r.from_pretrained(__a)
																												_lowerCAmelCase  :							List[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=True
																												_lowerCAmelCase  :							Union[str, Any]				     =					tempfile.mkdtemp()

																												_lowerCAmelCase  :							List[str]				     =					tokenizer_r.save_pretrained(__a,    legacy_format=__a)
																												_lowerCAmelCase  :							int				     =					tokenizer_p.save_pretrained(__a)

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

																												# Checks everything loads correctly in the same way
																												_lowerCAmelCase  :							Any				     =					tokenizer_r.from_pretrained(__a)
																												_lowerCAmelCase  :							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)

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

																												_lowerCAmelCase  :							Dict				     =					tokenizer_r.save_pretrained(__a,    legacy_format=__a)
																												_lowerCAmelCase  :							Tuple				     =					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
																												_lowerCAmelCase  :							Optional[int]				     =					tokenizer_r.from_pretrained(__a)
																												_lowerCAmelCase  :							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
							def      snake_case__   (     self):

														'''simple docstring'''

														if not self.test_seqaseq:
																					return

														_lowerCAmelCase  :							Optional[int]				     =					self.get_tokenizers()
														for tokenizer in tokenizers:
																					with self.subTest(f"{tokenizer.__class__.__name__}"):
																												# Longer text that will definitely require truncation.
																												_lowerCAmelCase  :							Any				     =					[
																												    " 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.",
																												]
																												_lowerCAmelCase  :							List[str]				     =					[
																												    "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.",
																												]
																												try:
																																			_lowerCAmelCase  :							Union[str, Any]				     =					tokenizer.prepare_seqaseq_batch(
																																			    src_texts=__a,    tgt_texts=__a,    max_length=3,    max_target_length=10,    return_tensors="pt",    src_lang="eng_Latn",    tgt_lang="ron_Latn",    )
																												except NotImplementedError:
																																			return
																												self.assertEqual(batch.input_ids.shape[1],    3)
																												self.assertEqual(batch.labels.shape[1],    10)
																												# max_target_length will default to max_length if not specified
																												_lowerCAmelCase  :							List[Any]				     =					tokenizer.prepare_seqaseq_batch(
																												    __a,    tgt_texts=__a,    max_length=3,    return_tensors="pt")
																												self.assertEqual(batch.input_ids.shape[1],    3)
																												self.assertEqual(batch.labels.shape[1],    3)

																												_lowerCAmelCase  :							List[str]				     =					tokenizer.prepare_seqaseq_batch(
																												    src_texts=__a,    max_length=3,    max_target_length=10,    return_tensors="pt")
																												self.assertEqual(batch_encoder_only.input_ids.shape[1],    3)
																												self.assertEqual(batch_encoder_only.attention_mask.shape[1],    3)
																												self.assertNotIn("decoder_input_ids",    __a)







							@unittest.skip("Unfortunately way too slow to build a BPE with SentencePiece.")
							def      snake_case__   (     self):

														'''simple docstring'''

														pass







							def      snake_case__   (     self):

														'''simple docstring'''

														for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
																					with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"):
																												_lowerCAmelCase  :							Dict				     =					[AddedToken("<special>",    lstrip=__a)]

																												_lowerCAmelCase  :							List[str]				     =					self.rust_tokenizer_class.from_pretrained(
																												    __a,    additional_special_tokens=__a,    **__a)
																												_lowerCAmelCase  :							Optional[int]				     =					tokenizer_r.encode("Hey this is a <special> token")

																												_lowerCAmelCase  :							List[Any]				     =					tokenizer_r.encode("<special>",    add_special_tokens=__a)[0]

																												self.assertTrue(special_token_id in r_output)

																												if self.test_slow_tokenizer:
																																			_lowerCAmelCase  :							Tuple				     =					self.rust_tokenizer_class.from_pretrained(
																																			    __a,    additional_special_tokens=__a,    **__a,    )
																																			_lowerCAmelCase  :							Tuple				     =					self.tokenizer_class.from_pretrained(
																																			    __a,    additional_special_tokens=__a,    **__a)

																																			_lowerCAmelCase  :							Tuple				     =					tokenizer_p.encode("Hey this is a <special> token")

																																			_lowerCAmelCase  :							Union[str, Any]				     =					tokenizer_cr.encode("Hey this is a <special> token")

																																			self.assertEqual(__a,    __a)
																																			self.assertEqual(__a,    __a)
																																			self.assertTrue(special_token_id in p_output)
																																			self.assertTrue(special_token_id in cr_output)



@require_torch
@require_sentencepiece
@require_tokenizers
class 						UpperCAmelCase_					( unittest.TestCase):
							lowerCamelCase__         =	'facebook/nllb-200-distilled-600M'
							lowerCamelCase__         =	[
							    ' 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.',
							]
							lowerCamelCase__         =	[
							    '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.',
							]
							lowerCamelCase__         =	[
							    256047,
							    16297,
							    134408,
							    8165,
							    248066,
							    14734,
							    950,
							    1135,
							    105721,
							    3573,
							    83,
							    27352,
							    108,
							    49486,
							    2,
							]







							@classmethod
							def      snake_case__   (     cls):

														'''simple docstring'''

														_lowerCAmelCase  :							NllbTokenizer				     =					NllbTokenizer.from_pretrained(
														    cls.checkpoint_name,    src_lang="eng_Latn",    tgt_lang="ron_Latn")
														_lowerCAmelCase  :							Union[str, Any]				     =					1
														return cls







							def      snake_case__   (     self):

														'''simple docstring'''

														self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["ace_Arab"],    25_6001)
														self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["ace_Latn"],    25_6002)
														self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["fra_Latn"],    25_6057)







							def      snake_case__   (     self):

														'''simple docstring'''

														_lowerCAmelCase  :							Any				     =					self.tokenizer.batch_encode_plus(self.src_text).input_ids[0]
														self.assertListEqual(self.expected_src_tokens,    __a)







							def      snake_case__   (     self):

														'''simple docstring'''

														self.assertIn(__a,    self.tokenizer.all_special_ids)
														# fmt: off
														_lowerCAmelCase  :							str				     =					[RO_CODE, 4254, 9_8068, 11_2923, 3_9072, 3909, 713, 10_2767, 26, 1_7314, 3_5642, 1_4683, 3_3118, 2022, 6_6987, 2, 25_6047]
														# fmt: on

														_lowerCAmelCase  :							int				     =					self.tokenizer.decode(__a,    skip_special_tokens=__a)
														_lowerCAmelCase  :							Optional[Any]				     =					self.tokenizer.decode(generated_ids[1:],    skip_special_tokens=__a)
														self.assertEqual(__a,    __a)
														self.assertNotIn(self.tokenizer.eos_token,    __a)







							def      snake_case__   (     self):

														'''simple docstring'''

														_lowerCAmelCase  :							List[Any]				     =					["this is gunna be a long sentence " * 20]
														assert isinstance(src_text[0],    __a)
														_lowerCAmelCase  :							str				     =					10
														_lowerCAmelCase  :							Dict				     =					self.tokenizer(__a,    max_length=__a,    truncation=__a).input_ids[0]
														self.assertEqual(ids[-1],    2)
														self.assertEqual(ids[0],    __a)
														self.assertEqual(len(__a),    __a)







							def      snake_case__   (     self):

														'''simple docstring'''

														self.assertListEqual(self.tokenizer.convert_tokens_to_ids(["<mask>", "ar_AR"]),    [25_6203, 3])







							def      snake_case__   (     self):

														'''simple docstring'''

														_lowerCAmelCase  :							Dict				     =					tempfile.mkdtemp()
														_lowerCAmelCase  :							Any				     =					self.tokenizer.fairseq_tokens_to_ids
														self.tokenizer.save_pretrained(__a)
														_lowerCAmelCase  :							List[str]				     =					NllbTokenizer.from_pretrained(__a)
														self.assertDictEqual(new_tok.fairseq_tokens_to_ids,    __a)







							@require_torch
							def      snake_case__   (     self):

														'''simple docstring'''

														_lowerCAmelCase  :							Union[str, 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",    )
														_lowerCAmelCase  :							int				     =					shift_tokens_right(
														    batch["labels"],    self.tokenizer.pad_token_id,    self.tokenizer.lang_code_to_id["ron_Latn"])

														self.assertIsInstance(__a,    __a)

														self.assertEqual((2, 15),    batch.input_ids.shape)
														self.assertEqual((2, 15),    batch.attention_mask.shape)
														_lowerCAmelCase  :							List[Any]				     =					batch.input_ids.tolist()[0]
														self.assertListEqual(self.expected_src_tokens,    __a)
														self.assertEqual(__a,    batch.decoder_input_ids[0, 0])  # EOS
														# 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      snake_case__   (     self):

														'''simple docstring'''

														_lowerCAmelCase  :							Tuple				     =					self.tokenizer(self.src_text,    padding=__a,    truncation=__a,    max_length=3,    return_tensors="pt")
														_lowerCAmelCase  :							Any				     =					self.tokenizer(
														    text_target=self.tgt_text,    padding=__a,    truncation=__a,    max_length=10,    return_tensors="pt")
														_lowerCAmelCase  :							Tuple				     =					targets["input_ids"]
														_lowerCAmelCase  :							Any				     =					shift_tokens_right(
														    __a,    self.tokenizer.pad_token_id,    decoder_start_token_id=self.tokenizer.lang_code_to_id[self.tokenizer.tgt_lang],    )

														self.assertEqual(batch.input_ids.shape[1],    3)
														self.assertEqual(batch.decoder_input_ids.shape[1],    10)







							@require_torch
							def      snake_case__   (     self):

														'''simple docstring'''

														_lowerCAmelCase  :							Optional[Any]				     =					self.tokenizer._build_translation_inputs(
														    "A test",    return_tensors="pt",    src_lang="eng_Latn",    tgt_lang="fra_Latn")

														self.assertEqual(
														    nested_simplify(__a),    {
														        # A, test, EOS, en_XX
														        "input_ids": [[25_6047, 70, 7356, 2]],
														        "attention_mask": [[1, 1, 1, 1]],
														        # ar_AR
														        "forced_bos_token_id": 25_6057,
														    },    )







							@require_torch
							def      snake_case__   (     self):

														'''simple docstring'''

														_lowerCAmelCase  :							int				     =					True
														_lowerCAmelCase  :							List[str]				     =					self.tokenizer(
														    "UN Chief says there is no military solution in Syria",    src_lang="eng_Latn",    tgt_lang="fra_Latn")
														self.assertEqual(
														    inputs.input_ids,    [1_6297, 13_4408, 2_5653, 6370, 248, 254, 10_3929, 9_4995, 108, 4_9486, 2, 25_6047])

														_lowerCAmelCase  :							int				     =					False
														_lowerCAmelCase  :							Tuple				     =					self.tokenizer(
														    "UN Chief says there is no military solution in Syria",    src_lang="eng_Latn",    tgt_lang="fra_Latn")
														self.assertEqual(
														    inputs.input_ids,    [25_6047, 1_6297, 13_4408, 2_5653, 6370, 248, 254, 10_3929, 9_4995, 108, 4_9486, 2])





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


_snake_case	=						logging.get_logger(__name__)

_snake_case	=						{
    "weiweishi/roc-bert-base-zh": "https://huggingface.co/weiweishi/roc-bert-base-zh/resolve/main/config.json",
}



class 						UpperCAmelCase_					( a):
							lowerCamelCase__         =	'roc_bert'







							def __init__(     self,    __a=3_0522,    __a=768,    __a=12,    __a=12,    __a=3072,    __a="gelu",    __a=0.1,    __a=0.1,    __a=512,    __a=2,    __a=0.02,    __a=1E-12,    __a=True,    __a=0,    __a="absolute",    __a=None,    __a=True,    __a=True,    __a=768,    __a=910,    __a=512,    __a=2_4858,    __a=True,    **__a,    ):

														'''simple docstring'''

														_lowerCAmelCase  :							Optional[Any]				     =					vocab_size
														_lowerCAmelCase  :							Optional[Any]				     =					max_position_embeddings
														_lowerCAmelCase  :							Union[str, Any]				     =					hidden_size
														_lowerCAmelCase  :							str				     =					num_hidden_layers
														_lowerCAmelCase  :							List[Any]				     =					num_attention_heads
														_lowerCAmelCase  :							int				     =					intermediate_size
														_lowerCAmelCase  :							Optional[int]				     =					hidden_act
														_lowerCAmelCase  :							Optional[Any]				     =					hidden_dropout_prob
														_lowerCAmelCase  :							Optional[Any]				     =					attention_probs_dropout_prob
														_lowerCAmelCase  :							Union[str, Any]				     =					initializer_range
														_lowerCAmelCase  :							Optional[Any]				     =					type_vocab_size
														_lowerCAmelCase  :							int				     =					layer_norm_eps
														_lowerCAmelCase  :							Union[str, Any]				     =					use_cache
														_lowerCAmelCase  :							Optional[int]				     =					enable_pronunciation
														_lowerCAmelCase  :							Dict				     =					enable_shape
														_lowerCAmelCase  :							Optional[Any]				     =					pronunciation_embed_dim
														_lowerCAmelCase  :							Any				     =					pronunciation_vocab_size
														_lowerCAmelCase  :							List[str]				     =					shape_embed_dim
														_lowerCAmelCase  :							int				     =					shape_vocab_size
														_lowerCAmelCase  :							Optional[int]				     =					concat_input
														_lowerCAmelCase  :							Dict				     =					position_embedding_type
														_lowerCAmelCase  :							Tuple				     =					classifier_dropout
														super().__init__(pad_token_id=__a,    **__a)
