




"""simple docstring"""



import copy
import os
import tempfile
from unittest import TestCase
from unittest.mock import patch

import numpy as np
import pyarrow as pa
import pyarrow.parquet as pq
import pytest

from datasets.arrow_writer import ArrowWriter, OptimizedTypedSequence, ParquetWriter, TypedSequence
from datasets.features import ArrayaD, ClassLabel, Features, Image, Value
from datasets.features.features import ArrayaDExtensionType, cast_to_python_objects
from datasets.keyhash import DuplicatedKeysError, InvalidKeyError

from .utils import require_pil




class      UpperCAmelCase_  (						A_							):




    def 							__magic_name__							(   self						:	List[str]				)				->							Optional[Any]:




     '''simple docstring'''

     A__	=    pa.array(TypedSequence([1, 2, 3]				)				)
     self.assertEqual(arr.type     ,			pa.intaa()				)




    def 							__magic_name__							(   self						:	Dict				)				->							int:




     '''simple docstring'''

     with self.assertRaises(snake_case_				):
      A__	=    pa.array(TypedSequence([1, 2, 3]				)     ,			type=pa.intaa()				)




    def 							__magic_name__							(   self						:	Tuple				)				->							List[Any]:




     '''simple docstring'''

     with self.assertRaises(snake_case_				):
      A__	=    pa.array(TypedSequence([1, 2, 3]     ,			try_type=Value("bool"				)     ,			type=Value("int64"				)				)				)




    def 							__magic_name__							(   self						:	str				)				->							str:




     '''simple docstring'''

     A__	=    pa.array(TypedSequence([1, 2, 3]     ,			type=Value("int32"				)				)				)
     self.assertEqual(arr.type     ,			pa.intaa()				)




    def 							__magic_name__							(   self						:	Any				)				->							Tuple:




     '''simple docstring'''

     with self.assertRaises((TypeError, pa.lib.ArrowInvalid)				):
      A__	=    pa.array(TypedSequence(["foo", "bar"]     ,			type=Value("int64"				)				)				)




    def 							__magic_name__							(   self						:	int				)				->							Tuple:




     '''simple docstring'''

     A__	=    pa.array(TypedSequence([1, 2, 3]     ,			try_type=Value("int32"				)				)				)
     self.assertEqual(arr.type     ,			pa.intaa()				)




    def 							__magic_name__							(   self						:	Any				)				->							Any:




     '''simple docstring'''

     A__	=    pa.array(TypedSequence(["foo", "bar"]     ,			try_type=Value("int64"				)				)				)
     self.assertEqual(arr.type     ,			pa.string()				)




    def 							__magic_name__							(   self						:	int				)				->							str:




     '''simple docstring'''

     A__	=    pa.array(TypedSequence([[[1, 2, 3]]]     ,			type=ArrayaD((1, 3)     ,			"int64"				)				)				)
     self.assertEqual(arr.type     ,			ArrayaDExtensionType((1, 3)     ,			"int64"				)				)




    def 							__magic_name__							(   self						:	int				)				->							Optional[Any]:




     '''simple docstring'''

     with self.assertRaises((TypeError, pa.lib.ArrowInvalid)				):
      A__	=    pa.array(TypedSequence(["foo", "bar"]     ,			type=ArrayaD((1, 3)     ,			"int64"				)				)				)




    def 							__magic_name__							(   self						:	str				)				->							str:




     '''simple docstring'''

     A__	=    pa.array(TypedSequence([[[1, 2, 3]]]     ,			try_type=ArrayaD((1, 3)     ,			"int64"				)				)				)
     self.assertEqual(arr.type     ,			ArrayaDExtensionType((1, 3)     ,			"int64"				)				)




    def 							__magic_name__							(   self						:	Dict				)				->							str:




     '''simple docstring'''

     A__	=    pa.array(TypedSequence(["foo", "bar"]     ,			try_type=ArrayaD((1, 3)     ,			"int64"				)				)				)
     self.assertEqual(arr.type     ,			pa.string()				)




    @require_pil
    def 							__magic_name__							(   self						:	Tuple				)				->							List[Any]:




     '''simple docstring'''

     import PIL.Image

     A__	=    PIL.Image.fromarray(np.arange(10     ,			dtype=np.uinta				).reshape(2     ,			5				)				)
     with patch(
         "datasets.arrow_writer.cast_to_python_objects"     ,			side_effect=snake_case_				) as mock_cast_to_python_objects:
      A__	=    pa.array(TypedSequence([{"path": None, "bytes": b"image_bytes"}, pil_image]     ,			type=Image()				)				)
      A__,				A__	=    mock_cast_to_python_objects.call_args_list[-1]
      self.assertIn("optimize_list_casting"     ,			snake_case_				)
      self.assertFalse(kwargs["optimize_list_casting"]				)







def   _SCREAMING_SNAKE_CASE       (							lowercase_      ,			lowercase_						)						->		int:
 A__	=    pa.BufferReader(lowercase_						) if isinstance(lowercase_      ,			pa.Buffer						) else pa.memory_map(lowercase_						)
 A__	=    pa.ipc.open_stream(lowercase_						)
 A__	=    f.read_all()
 assert len(pa_table.to_batches()						) == expected_num_chunks
 assert pa_table.to_pydict() == {"col_1": ["foo", "bar"], "col_2": [1, 2]}
 del pa_table







@pytest.mark.parametrize("writer_batch_size"      ,			[None, 1, 10]						)
@pytest.mark.parametrize(
    "fields"      ,			[None, {"col_1": pa.string(), "col_2": pa.intaa()}, {"col_1": pa.string(), "col_2": pa.intaa()}]						)
def   _SCREAMING_SNAKE_CASE       (							lowercase_      ,			lowercase_						)						->		Union[str, Any]:
 A__	=    pa.BufferOutputStream()
 A__	=    pa.schema(lowercase_						) if fields else None
 with ArrowWriter(stream=lowercase_      ,			schema=lowercase_      ,			writer_batch_size=lowercase_						) as writer:
  writer.write({"col_1": "foo", "col_2": 1}						)
  writer.write({"col_1": "bar", "col_2": 2}						)
  A__,				A__	=    writer.finalize()
 assert num_examples == 2
 assert num_bytes > 0
 if not fields:
  A__	=    {"col_1": pa.string(), "col_2": pa.intaa()}
 assert writer._schema == pa.schema(lowercase_      ,			metadata=writer._schema.metadata						)
 _check_output(output.getvalue()      ,			expected_num_chunks=num_examples if writer_batch_size == 1 else 1						)







def   _SCREAMING_SNAKE_CASE       (							)						->		Optional[int]:
 A__	=    pa.BufferOutputStream()
 A__	=    Features({"labels": ClassLabel(names=["neg", "pos"]						)}						)
 with ArrowWriter(stream=lowercase_      ,			features=lowercase_						) as writer:
  writer.write({"labels": 0}						)
  writer.write({"labels": 1}						)
  A__,				A__	=    writer.finalize()
 assert num_examples == 2
 assert num_bytes > 0
 assert writer._schema == features.arrow_schema
 assert writer._schema.metadata == features.arrow_schema.metadata
 A__	=    pa.BufferReader(output.getvalue()						)
 A__	=    pa.ipc.open_stream(lowercase_						)
 A__	=    f.read_all()
 A__	=    pa_table.schema
 assert pa_table.num_rows == 2
 assert schema == features.arrow_schema
 assert schema.metadata == features.arrow_schema.metadata
 assert features == Features.from_arrow_schema(lowercase_						)







@pytest.mark.parametrize("writer_batch_size"      ,			[None, 1, 10]						)
def   _SCREAMING_SNAKE_CASE       (							lowercase_						)						->		Any:
 A__	=    pa.BufferOutputStream()
 with ArrowWriter(
     stream=lowercase_      ,			writer_batch_size=lowercase_      ,			hash_salt="split_name"      ,			check_duplicates=lowercase_      ,			) as writer:
  with pytest.raises(lowercase_						):
   writer.write({"col_1": "foo", "col_2": 1}      ,			key=[1, 2]						)
   A__,				A__	=    writer.finalize()







@pytest.mark.parametrize("writer_batch_size"      ,			[None, 2, 10]						)
def   _SCREAMING_SNAKE_CASE       (							lowercase_						)						->		Optional[int]:
 A__	=    pa.BufferOutputStream()
 with ArrowWriter(
     stream=lowercase_      ,			writer_batch_size=lowercase_      ,			hash_salt="split_name"      ,			check_duplicates=lowercase_      ,			) as writer:
  with pytest.raises(lowercase_						):
   writer.write({"col_1": "foo", "col_2": 1}      ,			key=10						)
   writer.write({"col_1": "bar", "col_2": 2}      ,			key=10						)
   A__,				A__	=    writer.finalize()







@pytest.mark.parametrize("writer_batch_size"      ,			[None, 2, 10]						)
def   _SCREAMING_SNAKE_CASE       (							lowercase_						)						->		List[Any]:
 A__	=    pa.BufferOutputStream()
 with ArrowWriter(
     stream=lowercase_      ,			writer_batch_size=lowercase_      ,			hash_salt="split_name"      ,			check_duplicates=lowercase_      ,			) as writer:
  writer.write({"col_1": "foo", "col_2": 1}      ,			key=1						)
  writer.write({"col_1": "bar", "col_2": 2}      ,			key=2						)
  A__,				A__	=    writer.finalize()
 assert num_examples == 2
 assert num_bytes > 0
 _check_output(output.getvalue()      ,			expected_num_chunks=num_examples if writer_batch_size == 1 else 1						)







@pytest.mark.parametrize("writer_batch_size"      ,			[None, 1, 10]						)
@pytest.mark.parametrize(
    "fields"      ,			[None, {"col_1": pa.string(), "col_2": pa.intaa()}, {"col_1": pa.string(), "col_2": pa.intaa()}]						)
def   _SCREAMING_SNAKE_CASE       (							lowercase_      ,			lowercase_						)						->		int:
 A__	=    pa.BufferOutputStream()
 A__	=    pa.schema(lowercase_						) if fields else None
 with ArrowWriter(stream=lowercase_      ,			schema=lowercase_      ,			writer_batch_size=lowercase_						) as writer:
  writer.write_batch({"col_1": ["foo", "bar"], "col_2": [1, 2]}						)
  writer.write_batch({"col_1": [], "col_2": []}						)
  A__,				A__	=    writer.finalize()
 assert num_examples == 2
 assert num_bytes > 0
 if not fields:
  A__	=    {"col_1": pa.string(), "col_2": pa.intaa()}
 assert writer._schema == pa.schema(lowercase_      ,			metadata=writer._schema.metadata						)
 _check_output(output.getvalue()      ,			expected_num_chunks=num_examples if writer_batch_size == 1 else 1						)







@pytest.mark.parametrize("writer_batch_size"      ,			[None, 1, 10]						)
@pytest.mark.parametrize(
    "fields"      ,			[None, {"col_1": pa.string(), "col_2": pa.intaa()}, {"col_1": pa.string(), "col_2": pa.intaa()}]						)
def   _SCREAMING_SNAKE_CASE       (							lowercase_      ,			lowercase_						)						->		Dict:
 A__	=    pa.BufferOutputStream()
 A__	=    pa.schema(lowercase_						) if fields else None
 with ArrowWriter(stream=lowercase_      ,			schema=lowercase_      ,			writer_batch_size=lowercase_						) as writer:
  writer.write_table(pa.Table.from_pydict({"col_1": ["foo", "bar"], "col_2": [1, 2]}						)						)
  A__,				A__	=    writer.finalize()
 assert num_examples == 2
 assert num_bytes > 0
 if not fields:
  A__	=    {"col_1": pa.string(), "col_2": pa.intaa()}
 assert writer._schema == pa.schema(lowercase_      ,			metadata=writer._schema.metadata						)
 _check_output(output.getvalue()      ,			expected_num_chunks=num_examples if writer_batch_size == 1 else 1						)







@pytest.mark.parametrize("writer_batch_size"      ,			[None, 1, 10]						)
@pytest.mark.parametrize(
    "fields"      ,			[None, {"col_1": pa.string(), "col_2": pa.intaa()}, {"col_1": pa.string(), "col_2": pa.intaa()}]						)
def   _SCREAMING_SNAKE_CASE       (							lowercase_      ,			lowercase_						)						->		Union[str, Any]:
 A__	=    pa.BufferOutputStream()
 A__	=    pa.schema(lowercase_						) if fields else None
 with ArrowWriter(stream=lowercase_      ,			schema=lowercase_      ,			writer_batch_size=lowercase_						) as writer:
  writer.write_row(pa.Table.from_pydict({"col_1": ["foo"], "col_2": [1]}						)						)
  writer.write_row(pa.Table.from_pydict({"col_1": ["bar"], "col_2": [2]}						)						)
  A__,				A__	=    writer.finalize()
 assert num_examples == 2
 assert num_bytes > 0
 if not fields:
  A__	=    {"col_1": pa.string(), "col_2": pa.intaa()}
 assert writer._schema == pa.schema(lowercase_      ,			metadata=writer._schema.metadata						)
 _check_output(output.getvalue()      ,			expected_num_chunks=num_examples if writer_batch_size == 1 else 1						)







def   _SCREAMING_SNAKE_CASE       (							)						->		Tuple:
 with tempfile.TemporaryDirectory() as tmp_dir:
  A__	=    {"col_1": pa.string(), "col_2": pa.intaa()}
  A__	=    os.path.join(lowercase_      ,			"test.arrow"						)
  with ArrowWriter(path=lowercase_      ,			schema=pa.schema(lowercase_						)						) as writer:
   writer.write_batch({"col_1": ["foo", "bar"], "col_2": [1, 2]}						)
   A__,				A__	=    writer.finalize()
  assert num_examples == 2
  assert num_bytes > 0
  assert writer._schema == pa.schema(lowercase_      ,			metadata=writer._schema.metadata						)
  _check_output(lowercase_      ,			1						)







def   _SCREAMING_SNAKE_CASE       (							lowercase_						)						->		Dict:
 if pa.types.is_list(lowercase_						):
  return get_base_dtype(arr_type.value_type						)
 else:
  return arr_type







def   _SCREAMING_SNAKE_CASE       (							lowercase_      ,			lowercase_						)						->		List[Any]:
 if isinstance(lst[0]      ,			lowercase_						):
  change_first_primitive_element_in_list(lst[0]      ,			lowercase_						)
 else:
  A__	=    value







@pytest.mark.parametrize("optimized_int_type, expected_dtype"      ,			[(None, pa.intaa()), (Value("int32"						), pa.intaa())]						)
@pytest.mark.parametrize("sequence"      ,			[[1, 2, 3], [[1, 2, 3]], [[[1, 2, 3]]]]						)
def   _SCREAMING_SNAKE_CASE       (							lowercase_      ,			lowercase_      ,			lowercase_						)						->		Union[str, Any]:
 A__	=    pa.array(TypedSequence(lowercase_      ,			optimized_int_type=lowercase_						)						)
 assert get_base_dtype(arr.type						) == expected_dtype







@pytest.mark.parametrize(
    "col, expected_dtype"      ,			[
        ("attention_mask", pa.inta()),
        ("special_tokens_mask", pa.inta()),
        ("token_type_ids", pa.inta()),
        ("input_ids", pa.intaa()),
        ("other", pa.intaa()),
    ]      ,			)
@pytest.mark.parametrize("sequence"      ,			[[1, 2, 3], [[1, 2, 3]], [[[1, 2, 3]]]]						)
def   _SCREAMING_SNAKE_CASE       (							lowercase_      ,			lowercase_      ,			lowercase_						)						->		Optional[Any]:
 # in range
 A__	=    pa.array(OptimizedTypedSequence(lowercase_      ,			col=lowercase_						)						)
 assert get_base_dtype(arr.type						) == expected_dtype

 # not in range
 if col != "other":
  # avoids errors due to in-place modifications
  A__	=    copy.deepcopy(lowercase_						)
  A__	=    np.iinfo(expected_dtype.to_pandas_dtype()						).max + 1
  change_first_primitive_element_in_list(lowercase_      ,			lowercase_						)
  A__	=    pa.array(OptimizedTypedSequence(lowercase_      ,			col=lowercase_						)						)
  assert get_base_dtype(arr.type						) == pa.intaa()







@pytest.mark.parametrize("raise_exception"      ,			[False, True]						)
def   _SCREAMING_SNAKE_CASE       (							lowercase_      ,			lowercase_						)						->		int:
 A__	=    str(tmp_path / "dataset-train.arrow"						)
 try:
  with ArrowWriter(path=lowercase_						) as writer:
   if raise_exception:
    raise pa.lib.ArrowInvalid()
   else:
    writer.stream.close()
 except pa.lib.ArrowInvalid:
  pass
 finally:
  assert writer.stream.closed







def   _SCREAMING_SNAKE_CASE       (							lowercase_						)						->		int:
 A__	=    "mock://dataset-train.arrow"
 with ArrowWriter(path=lowercase_      ,			storage_options=mockfs.storage_options						) as writer:
  assert isinstance(writer._fs      ,			type(lowercase_						)						)
  assert writer._fs.storage_options == mockfs.storage_options
  writer.write({"col_1": "foo", "col_2": 1}						)
  writer.write({"col_1": "bar", "col_2": 2}						)
  A__,				A__	=    writer.finalize()
 assert num_examples == 2
 assert num_bytes > 0
 assert mockfs.exists(lowercase_						)







def   _SCREAMING_SNAKE_CASE       (							)						->		List[Any]:
 A__	=    pa.BufferOutputStream()
 with ParquetWriter(stream=lowercase_						) as writer:
  writer.write({"col_1": "foo", "col_2": 1}						)
  writer.write({"col_1": "bar", "col_2": 2}						)
  A__,				A__	=    writer.finalize()
 assert num_examples == 2
 assert num_bytes > 0
 A__	=    pa.BufferReader(output.getvalue()						)
 A__	=    pq.read_table(lowercase_						)
 assert pa_table.to_pydict() == {"col_1": ["foo", "bar"], "col_2": [1, 2]}







@require_pil
@pytest.mark.parametrize("embed_local_files"      ,			[False, True]						)
def   _SCREAMING_SNAKE_CASE       (							lowercase_      ,			lowercase_						)						->		Tuple:
 import PIL.Image

 A__	=    str(tmp_path / "test_image_rgb.jpg"						)
 PIL.Image.fromarray(np.zeros((5, 5)      ,			dtype=np.uinta						)						).save(lowercase_      ,			format="png"						)
 A__	=    pa.BufferOutputStream()
 with ParquetWriter(
     stream=lowercase_      ,			features=Features({"image": Image()}						)      ,			embed_local_files=lowercase_						) as writer:
  writer.write({"image": image_path}						)
  writer.finalize()
 A__	=    pa.BufferReader(output.getvalue()						)
 A__	=    pq.read_table(lowercase_						)
 A__	=    pa_table.to_pydict()
 if embed_local_files:
  assert isinstance(out["image"][0]["path"]      ,			lowercase_						)
  with open(lowercase_      ,			"rb"						) as f:
   assert out["image"][0]["bytes"] == f.read()
 else:
  assert out["image"][0]["path"] == image_path
  assert out["image"][0]["bytes"] is None







def   _SCREAMING_SNAKE_CASE       (							)						->		str:
 A__	=    pa.schema([pa.field("col_1"      ,			pa.string()      ,			nullable=lowercase_						)]						)
 A__	=    pa.BufferOutputStream()
 with ArrowWriter(stream=lowercase_						) as writer:
  writer._build_writer(inferred_schema=lowercase_						)
  assert writer._schema == pa.schema([pa.field("col_1"      ,			pa.string()						)]						)











"""simple docstring"""



import os
from shutil import copyfile
from typing import List, Optional, Tuple

from tokenizers import processors

from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
      from .tokenization_nllb import NllbTokenizer
else:
      SCREAMING_SNAKE_CASE    =	None


SCREAMING_SNAKE_CASE    =	logging.get_logger(__name__)


SCREAMING_SNAKE_CASE    =	{"vocab_file": "sentencepiece.bpe.model", "tokenizer_file": "tokenizer.json"}

SCREAMING_SNAKE_CASE    =	{
    "vocab_file": {
        "facebook/nllb-200-distilled-600M": (
            "https://huggingface.co/facebook/nllb-200-distilled-600M/resolve/main/sentencepiece.bpe.model"
        ),
    },
    "tokenizer_file": {
        "facebook/nllb-200-distilled-600M": (
            "https://huggingface.co/facebook/nllb-200-distilled-600M/resolve/main/tokenizer.json"
        ),
    },
}

SCREAMING_SNAKE_CASE    =	{
    "facebook/nllb-large-en-ro": 1024,
    "facebook/nllb-200-distilled-600M": 1024,
}

# fmt: off
SCREAMING_SNAKE_CASE    =	["ace_Arab", "ace_Latn", "acm_Arab", "acq_Arab", "aeb_Arab", "afr_Latn", "ajp_Arab", "aka_Latn", "amh_Ethi", "apc_Arab", "arb_Arab", "ars_Arab", "ary_Arab", "arz_Arab", "asm_Beng", "ast_Latn", "awa_Deva", "ayr_Latn", "azb_Arab", "azj_Latn", "bak_Cyrl", "bam_Latn", "ban_Latn", "bel_Cyrl", "bem_Latn", "ben_Beng", "bho_Deva", "bjn_Arab", "bjn_Latn", "bod_Tibt", "bos_Latn", "bug_Latn", "bul_Cyrl", "cat_Latn", "ceb_Latn", "ces_Latn", "cjk_Latn", "ckb_Arab", "crh_Latn", "cym_Latn", "dan_Latn", "deu_Latn", "dik_Latn", "dyu_Latn", "dzo_Tibt", "ell_Grek", "eng_Latn", "epo_Latn", "est_Latn", "eus_Latn", "ewe_Latn", "fao_Latn", "pes_Arab", "fij_Latn", "fin_Latn", "fon_Latn", "fra_Latn", "fur_Latn", "fuv_Latn", "gla_Latn", "gle_Latn", "glg_Latn", "grn_Latn", "guj_Gujr", "hat_Latn", "hau_Latn", "heb_Hebr", "hin_Deva", "hne_Deva", "hrv_Latn", "hun_Latn", "hye_Armn", "ibo_Latn", "ilo_Latn", "ind_Latn", "isl_Latn", "ita_Latn", "jav_Latn", "jpn_Jpan", "kab_Latn", "kac_Latn", "kam_Latn", "kan_Knda", "kas_Arab", "kas_Deva", "kat_Geor", "knc_Arab", "knc_Latn", "kaz_Cyrl", "kbp_Latn", "kea_Latn", "khm_Khmr", "kik_Latn", "kin_Latn", "kir_Cyrl", "kmb_Latn", "kon_Latn", "kor_Hang", "kmr_Latn", "lao_Laoo", "lvs_Latn", "lij_Latn", "lim_Latn", "lin_Latn", "lit_Latn", "lmo_Latn", "ltg_Latn", "ltz_Latn", "lua_Latn", "lug_Latn", "luo_Latn", "lus_Latn", "mag_Deva", "mai_Deva", "mal_Mlym", "mar_Deva", "min_Latn", "mkd_Cyrl", "plt_Latn", "mlt_Latn", "mni_Beng", "khk_Cyrl", "mos_Latn", "mri_Latn", "zsm_Latn", "mya_Mymr", "nld_Latn", "nno_Latn", "nob_Latn", "npi_Deva", "nso_Latn", "nus_Latn", "nya_Latn", "oci_Latn", "gaz_Latn", "ory_Orya", "pag_Latn", "pan_Guru", "pap_Latn", "pol_Latn", "por_Latn", "prs_Arab", "pbt_Arab", "quy_Latn", "ron_Latn", "run_Latn", "rus_Cyrl", "sag_Latn", "san_Deva", "sat_Beng", "scn_Latn", "shn_Mymr", "sin_Sinh", "slk_Latn", "slv_Latn", "smo_Latn", "sna_Latn", "snd_Arab", "som_Latn", "sot_Latn", "spa_Latn", "als_Latn", "srd_Latn", "srp_Cyrl", "ssw_Latn", "sun_Latn", "swe_Latn", "swh_Latn", "szl_Latn", "tam_Taml", "tat_Cyrl", "tel_Telu", "tgk_Cyrl", "tgl_Latn", "tha_Thai", "tir_Ethi", "taq_Latn", "taq_Tfng", "tpi_Latn", "tsn_Latn", "tso_Latn", "tuk_Latn", "tum_Latn", "tur_Latn", "twi_Latn", "tzm_Tfng", "uig_Arab", "ukr_Cyrl", "umb_Latn", "urd_Arab", "uzn_Latn", "vec_Latn", "vie_Latn", "war_Latn", "wol_Latn", "xho_Latn", "ydd_Hebr", "yor_Latn", "yue_Hant", "zho_Hans", "zho_Hant", "zul_Latn"]




class      UpperCAmelCase_  (						A_							):
    lowercase__						 =				VOCAB_FILES_NAMES
    lowercase__						 =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    lowercase__						 =				PRETRAINED_VOCAB_FILES_MAP
    lowercase__						 =				['''input_ids''', '''attention_mask''']
    lowercase__						 =				NllbTokenizer

    lowercase__						 =				[]
    lowercase__						 =				[]




    def __init__(   self						:	int     ,			snake_case_						:	int=None     ,			snake_case_						:	Any=None     ,			snake_case_						:	int="<s>"     ,			snake_case_						:	List[Any]="</s>"     ,			snake_case_						:	Optional[int]="</s>"     ,			snake_case_						:	int="<s>"     ,			snake_case_						:	str="<unk>"     ,			snake_case_						:	str="<pad>"     ,			snake_case_						:	Optional[int]="<mask>"     ,			snake_case_						:	str=None     ,			snake_case_						:	List[Any]=None     ,			snake_case_						:	Tuple=None     ,			snake_case_						:	Optional[int]=False     ,			**snake_case_						:	List[str]     ,			)				->							Tuple:




     '''simple docstring'''

     A__	=    AddedToken(snake_case_     ,			lstrip=snake_case_     ,			rstrip=snake_case_				) if isinstance(snake_case_     ,			snake_case_				) else mask_token
     A__	=    legacy_behaviour
     super().__init__(
         vocab_file=snake_case_     ,			tokenizer_file=snake_case_     ,			bos_token=snake_case_     ,			eos_token=snake_case_     ,			sep_token=snake_case_     ,			cls_token=snake_case_     ,			unk_token=snake_case_     ,			pad_token=snake_case_     ,			mask_token=snake_case_     ,			src_lang=snake_case_     ,			tgt_lang=snake_case_     ,			additional_special_tokens=snake_case_     ,			legacy_behaviour=snake_case_     ,			**snake_case_     ,			)

     A__	=    vocab_file
     A__	=    False if not self.vocab_file else True

     A__	=    FAIRSEQ_LANGUAGE_CODES.copy()

     if additional_special_tokens is not None:
      # Only add those special tokens if they are not already there.
      _additional_special_tokens.extend(
          [t for t in additional_special_tokens if t not in _additional_special_tokens]				)

     self.add_special_tokens({"additional_special_tokens": _additional_special_tokens}				)
     A__	=    {
         lang_code: self.convert_tokens_to_ids(snake_case_				) for lang_code in FAIRSEQ_LANGUAGE_CODES
     }

     A__	=    src_lang if src_lang is not None else "eng_Latn"
     A__	=    self.convert_tokens_to_ids(self._src_lang				)
     A__	=    tgt_lang
     self.set_src_lang_special_tokens(self._src_lang				)




    @property
    def 							__magic_name__							(   self						:	Union[str, Any]				)				->							str:




     '''simple docstring'''

     return self._src_lang




    @src_lang.setter
    def 							__magic_name__							(   self						:	Optional[int]     ,			snake_case_						:	str				)				->							None:




     '''simple docstring'''

     A__	=    new_src_lang
     self.set_src_lang_special_tokens(self._src_lang				)




    def 							__magic_name__							(   self						:	Optional[int]     ,			snake_case_						:	List[int]     ,			snake_case_						:	Optional[List[int]] = None				)				->							List[int]:




     '''simple docstring'''

     if token_ids_a is None:
      return self.prefix_tokens + token_ids_a + self.suffix_tokens
     # We don't expect to process pairs, but leave the pair logic for API consistency
     return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens




    def 							__magic_name__							(   self						:	Any     ,			snake_case_						:	List[int]     ,			snake_case_						:	Optional[List[int]] = None				)				->							List[int]:




     '''simple docstring'''

     A__	=    [self.sep_token_id]
     A__	=    [self.cls_token_id]

     if token_ids_a is None:
      return len(cls + token_ids_a + sep				) * [0]
     return len(cls + token_ids_a + sep + sep + token_ids_a + sep				) * [0]




    def 							__magic_name__							(   self						:	int     ,			snake_case_						:	Tuple     ,			snake_case_						:	str     ,			snake_case_						:	Optional[str]     ,			snake_case_						:	Optional[str]     ,			**snake_case_						:	Tuple				)				->							List[Any]:




     '''simple docstring'''

     if src_lang is None or tgt_lang is None:
      raise ValueError("Translation requires a `src_lang` and a `tgt_lang` for this model"				)
     A__	=    src_lang
     A__	=    self(snake_case_     ,			add_special_tokens=snake_case_     ,			return_tensors=snake_case_     ,			**snake_case_				)
     A__	=    self.convert_tokens_to_ids(snake_case_				)
     A__	=    tgt_lang_id
     return inputs




    def 							__magic_name__							(   self						:	int     ,			snake_case_						:	List[str]     ,			snake_case_						:	str = "eng_Latn"     ,			snake_case_						:	Optional[List[str]] = None     ,			snake_case_						:	str = "fra_Latn"     ,			**snake_case_						:	Dict     ,			)				->							BatchEncoding:




     '''simple docstring'''

     A__	=    src_lang
     A__	=    tgt_lang
     return super().prepare_seqaseq_batch(snake_case_     ,			snake_case_     ,			**snake_case_				)




    def 							__magic_name__							(   self						:	Optional[Any]				)				->							Optional[Any]:




     '''simple docstring'''

     return self.set_src_lang_special_tokens(self.src_lang				)




    def 							__magic_name__							(   self						:	Tuple				)				->							Dict:




     '''simple docstring'''

     return self.set_tgt_lang_special_tokens(self.tgt_lang				)




    def 							__magic_name__							(   self						:	List[Any]     ,			snake_case_						:	Dict				)				->							None:




     '''simple docstring'''

     A__	=    self.convert_tokens_to_ids(snake_case_				)

     if self.legacy_behaviour:
      A__	=    []
      A__	=    [self.eos_token_id, self.cur_lang_code]
     else:
      A__	=    [self.cur_lang_code]
      A__	=    [self.eos_token_id]

     A__	=    self.convert_ids_to_tokens(self.prefix_tokens				)
     A__	=    self.convert_ids_to_tokens(self.suffix_tokens				)

     A__	=    processors.TemplateProcessing(
         single=prefix_tokens_str + ["$A"] + suffix_tokens_str     ,			pair=prefix_tokens_str + ["$A", "$B"] + suffix_tokens_str     ,			special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str     ,			self.prefix_tokens + self.suffix_tokens				)				)     ,			)




    def 							__magic_name__							(   self						:	List[Any]     ,			snake_case_						:	str				)				->							None:




     '''simple docstring'''

     A__	=    self.convert_tokens_to_ids(snake_case_				)
     if self.legacy_behaviour:
      A__	=    []
      A__	=    [self.eos_token_id, self.cur_lang_code]
     else:
      A__	=    [self.cur_lang_code]
      A__	=    [self.eos_token_id]

     A__	=    self.convert_ids_to_tokens(self.prefix_tokens				)
     A__	=    self.convert_ids_to_tokens(self.suffix_tokens				)

     A__	=    processors.TemplateProcessing(
         single=prefix_tokens_str + ["$A"] + suffix_tokens_str     ,			pair=prefix_tokens_str + ["$A", "$B"] + suffix_tokens_str     ,			special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str     ,			self.prefix_tokens + self.suffix_tokens				)				)     ,			)




    def 							__magic_name__							(   self						:	List[str]     ,			snake_case_						:	str     ,			snake_case_						:	Optional[str] = None				)				->							Tuple[str]:




     '''simple docstring'''

     if not self.can_save_slow_tokenizer:
      raise ValueError(
          "Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
          "tokenizer."				)

     if not os.path.isdir(snake_case_				):
      logger.error(F"""Vocabulary path ({save_directory}) should be a directory."""				)
      return
     A__	=    os.path.join(
         snake_case_     ,			(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]				)

     if os.path.abspath(self.vocab_file				) != os.path.abspath(snake_case_				):
      copyfile(self.vocab_file     ,			snake_case_				)

     return (out_vocab_file,)





