


"""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


__lowerCamelCase      :Any	    =		get_tests_dir('fixtures/test_sentencepiece.model')


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

__lowerCamelCase      :Tuple	    =		250_004
__lowerCamelCase      :Dict	    =		250_020


@require_sentencepiece
@require_tokenizers
class 			A__	(						__lowercase ,     unittest.TestCase):


     """simple docstring"""
     snake_case__	:					int													=MBartTokenizer
     snake_case__	:					Optional[int]													=MBartTokenizerFast
     snake_case__	:					Optional[int]													=True
     snake_case__	:					str													=True

     def 		a__						(	self:						Tuple	)->    Optional[int]:
           super().setUp()

           # We have a SentencePiece fixture for testing
           lowerCamelCase					:					Optional[int]        =	MBartTokenizer(__a     ,		keep_accents=__a	)
           tokenizer.save_pretrained(self.tmpdirname	)

     def 		a__						(	self:						List[str]	)->    Optional[int]:
           lowerCamelCase					:					List[str]        =	MBartTokenizer(__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					:					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""",
                   """.""",
               ]     ,		)
           lowerCamelCase					:					int        =	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]
                   #                                       ^ unk: 2 + 1 = 3                  unk: 2 + 1 = 3 ^
               ]     ,		)

           lowerCamelCase					:					int        =	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 		a__						(	self:						Optional[Any]	)->    Optional[int]:
           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

           lowerCamelCase					:					str        =	(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})'	):
                       lowerCamelCase					:					List[Any]        =	self.rust_tokenizer_class.from_pretrained(__a     ,		**__a	)
                       lowerCamelCase					:					str        =	self.tokenizer_class.from_pretrained(__a     ,		**__a	)

                       lowerCamelCase					:					Optional[int]        =	tempfile.mkdtemp()

                       lowerCamelCase					:					Tuple        =	tokenizer_r.save_pretrained(__a	)
                       lowerCamelCase					:					Optional[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	)	)
                       lowerCamelCase					:					Optional[int]        =	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					:					Optional[int]        =	tokenizer_r.from_pretrained(__a	)
                       lowerCamelCase					:					Optional[int]        =	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
                       lowerCamelCase					:					int        =	tempfile.mkdtemp()

                       lowerCamelCase					:					Union[str, Any]        =	tokenizer_r.save_pretrained(__a     ,		legacy_format=__a	)
                       lowerCamelCase					:					Any        =	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					:					int        =	tokenizer_r.from_pretrained(__a	)
                       lowerCamelCase					:					Optional[int]        =	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					:					Dict        =	tempfile.mkdtemp()

                       lowerCamelCase					:					Any        =	tokenizer_r.save_pretrained(__a     ,		legacy_format=__a	)
                       lowerCamelCase					:					List[str]        =	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					:					Tuple        =	tokenizer_r.from_pretrained(__a	)
                       lowerCamelCase					:					Dict        =	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 			A__	(						unittest.TestCase):


     """simple docstring"""
     snake_case__	:					Dict													='''facebook/mbart-large-en-ro'''
     snake_case__	:					int													=[
         ''' 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.''',
     ]
     snake_case__	:					Optional[int]													=[
         '''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.''',
     ]
     snake_case__	:					Optional[int]													=[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 		a__						(	cls:						int	)->    Union[str, Any]:
           lowerCamelCase					:					MBartTokenizer        =	MBartTokenizer.from_pretrained(
               cls.checkpoint_name     ,		src_lang="""en_XX"""     ,		tgt_lang="""ro_RO"""	)
           lowerCamelCase					:					List[Any]        =	1
           return cls

     def 		a__						(	self:						Tuple	)->    List[str]:
           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	)

     def 		a__						(	self:						Dict	)->    Tuple:
           lowerCamelCase					:					Any        =	self.tokenizer.batch_encode_plus(self.src_text	).input_ids[0]
           self.assertListEqual(self.expected_src_tokens     ,		__a	)

     def 		a__						(	self:						Optional[int]	)->    Optional[Any]:
           self.assertIn(__a     ,		self.tokenizer.all_special_ids	)
           lowerCamelCase					:					Union[str, Any]        =	[RO_CODE, 884, 9_019, 96, 9, 916, 86_792, 36, 18_743, 15_596, 5, 2]
           lowerCamelCase					:					Tuple        =	self.tokenizer.decode(__a     ,		skip_special_tokens=__a	)
           lowerCamelCase					:					Tuple        =	self.tokenizer.decode(generated_ids[1:]     ,		skip_special_tokens=__a	)
           self.assertEqual(__a     ,		__a	)
           self.assertNotIn(self.tokenizer.eos_token     ,		__a	)

     def 		a__						(	self:						List[Any]	)->    Any:
           lowerCamelCase					:					str        =	["""this is gunna be a long sentence """ * 20]
           assert isinstance(src_text[0]     ,		__a	)
           lowerCamelCase					:					Dict        =	10
           lowerCamelCase					:					Union[str, Any]        =	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 		a__						(	self:						str	)->    Any:
           self.assertListEqual(self.tokenizer.convert_tokens_to_ids(["""<mask>""", """ar_AR"""]	)     ,		[250_026, 250_001]	)

     def 		a__						(	self:						Tuple	)->    List[str]:
           lowerCamelCase					:					str        =	tempfile.mkdtemp()
           lowerCamelCase					:					Tuple        =	self.tokenizer.fairseq_tokens_to_ids
           self.tokenizer.save_pretrained(__a	)
           lowerCamelCase					:					Optional[int]        =	MBartTokenizer.from_pretrained(__a	)
           self.assertDictEqual(new_tok.fairseq_tokens_to_ids     ,		__a	)

     @require_torch
     def 		a__						(	self:						Dict	)->    int:
           lowerCamelCase					:					int        =	self.tokenizer(self.src_text     ,		text_target=self.tgt_text     ,		padding=__a     ,		return_tensors="""pt"""	)
           lowerCamelCase					:					Any        =	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 		a__						(	self:						Dict	)->    Dict:
           lowerCamelCase					:					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"""     ,		)

           lowerCamelCase					:					Optional[Any]        =	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	)
           lowerCamelCase					:					Any        =	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 		a__						(	self:						str	)->    Dict:
           lowerCamelCase					:					Any        =	self.tokenizer(self.src_text     ,		padding=__a     ,		truncation=__a     ,		max_length=3     ,		return_tensors="""pt"""	)
           lowerCamelCase					:					int        =	self.tokenizer(
               text_target=self.tgt_text     ,		padding=__a     ,		truncation=__a     ,		max_length=10     ,		return_tensors="""pt"""	)
           lowerCamelCase					:					Optional[int]        =	targets["""input_ids"""]
           lowerCamelCase					:					Optional[int]        =	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	)->    List[Any]:
           lowerCamelCase					:					List[str]        =	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, 3_034, 2, 250_004]],
                   """attention_mask""": [[1, 1, 1, 1]],
                   # ar_AR
                   """forced_bos_token_id""": 250_001,
               }     ,		)








"""simple docstring"""


from typing import TYPE_CHECKING

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


__lowerCamelCase      :Optional[Any]	    =		{
    'configuration_encodec': [
        'ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'EncodecConfig',
    ],
    'feature_extraction_encodec': ['EncodecFeatureExtractor'],
}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __lowerCamelCase      :Union[str, Any]	    =		[
        'ENCODEC_PRETRAINED_MODEL_ARCHIVE_LIST',
        'EncodecModel',
        'EncodecPreTrainedModel',
    ]

if TYPE_CHECKING:
    from .configuration_encodec import (
        ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP,
        EncodecConfig,
    )
    from .feature_extraction_encodec import EncodecFeatureExtractor

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_encodec import (
            ENCODEC_PRETRAINED_MODEL_ARCHIVE_LIST,
            EncodecModel,
            EncodecPreTrainedModel,
        )

else:
    import sys

    __lowerCamelCase      :Dict	    =		_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




