






'''simple docstring'''




import os
from dataclasses import dataclass, field
from io import BytesIO
from typing import TYPE_CHECKING, Any, ClassVar, Dict, Optional, Union

import numpy as np
import pyarrow as pa

from .. import config
from ..download.streaming_download_manager import xopen, xsplitext
from ..table import array_cast
from ..utils.py_utils import no_op_if_value_is_null, string_to_dict


if TYPE_CHECKING:
		from .features import FeatureType


__lowerCamelCase							,      __lowerCamelCase							,      __lowerCamelCase   :					List[str]   							=					False, False, False







@dataclass
class    A_	:
							"""simple docstring"""




							a__   		=	None
							a__   		=	True
							a__   		=	True
							a__   		=	None
							# Automatically constructed
							a__   		=	"dict"
							a__   		=	pa.struct({'''bytes''': pa.binary(), '''path''': pa.string()}							)
							a__   		=	field(default='''Audio'''						,					init=lowerCAmelCase__						,					repr=lowerCAmelCase__							)

							def __call__(		self			:int				)				->  Optional[int]:


										'''simple docstring'''




										return self.pa_type

							def 							_A	(		self			:int       ,							lowerCAmelCase__			:int				)				->  dict:


										'''simple docstring'''




										try:
													import soundfile as sf  # soundfile is a dependency of librosa, needed to decode audio files.
										except ImportError as err:
													raise ImportError("To support encoding audio data, please install \'soundfile\'."				) from err
										if isinstance(_lowerCamelCase       ,							_lowerCamelCase				):
													return {"bytes": None, "path": value}
										elif isinstance(_lowerCamelCase       ,							_lowerCamelCase				):
													return {"bytes": value, "path": None}
										elif "array" in value:
													# convert the audio array to wav bytes
													snake_case_				:					Optional[Any]   =       BytesIO()
													sf.write(_lowerCamelCase       ,							value["array"]       ,							value["sampling_rate"]       ,							format="wav"				)
													return {"bytes": buffer.getvalue(), "path": None}
										elif value.get("path"				) is not None and os.path.isfile(value["path"]				):
													# we set "bytes": None to not duplicate the data if they're already available locally
													if value["path"].endswith("pcm"				):
																# "PCM" only has raw audio bytes
																if value.get("sampling_rate"				) is None:
																			# At least, If you want to convert "PCM-byte" to "WAV-byte", you have to know sampling rate
																			raise KeyError("To use PCM files, please specify a \'sampling_rate\' in Audio object"				)
																if value.get("bytes"				):
																			# If we already had PCM-byte, we don`t have to make "read file, make bytes" (just use it!)
																			snake_case_				:					int   =       np.frombuffer(value["bytes"]       ,							dtype=np.intaa				).astype(np.floataa				) / 32_767
																else:
																			snake_case_				:					Dict   =       np.memmap(value["path"]       ,							dtype="h"       ,							mode="r"				).astype(np.floataa				) / 32_767

																snake_case_				:					Optional[int]   =       BytesIO(bytes()				)
																sf.write(_lowerCamelCase       ,							_lowerCamelCase       ,							value["sampling_rate"]       ,							format="wav"				)
																return {"bytes": buffer.getvalue(), "path": None}
													else:
																return {"bytes": None, "path": value.get("path"				)}
										elif value.get("bytes"				) is not None or value.get("path"				) is not None:
													# store the audio bytes, and path is used to infer the audio format using the file extension
													return {"bytes": value.get("bytes"				), "path": value.get("path"				)}
										else:
													raise ValueError(
													    F'''An audio sample should have one of \'path\' or \'bytes\' but they are missing or None in {value}.'''				)

							def 							_A	(		self			:Tuple       ,							lowerCAmelCase__			:Dict       ,							lowerCAmelCase__			:Optional[Any] = None				)				->  dict:


										'''simple docstring'''




										if not self.decode:
													raise RuntimeError("Decoding is disabled for this feature. Please use Audio(decode=True) instead."				)

										snake_case_,	snake_case_				:					Optional[Any]   =       (value["path"], BytesIO(value["bytes"]				)) if value["bytes"] is not None else (value["path"], None)
										if path is None and file is None:
													raise ValueError(F'''An audio sample should have one of \'path\' or \'bytes\' but both are None in {value}.'''				)

										try:
													import librosa
													import soundfile as sf
										except ImportError as err:
													raise ImportError("To support decoding audio files, please install \'librosa\' and \'soundfile\'."				) from err

										snake_case_				:					Tuple   =       xsplitext(_lowerCamelCase				)[1][1:].lower() if path is not None else None
										if not config.IS_OPUS_SUPPORTED and audio_format == "opus":
													raise RuntimeError(
													    "Decoding \'opus\' files requires system library \'libsndfile\'>=1.0.31, "
													    "You can try to update `soundfile` python library: `pip install \"soundfile>=0.12.1\"`. "				)
										elif not config.IS_MP3_SUPPORTED and audio_format == "mp3":
													raise RuntimeError(
													    "Decoding \'mp3\' files requires system library \'libsndfile\'>=1.1.0, "
													    "You can try to update `soundfile` python library: `pip install \"soundfile>=0.12.1\"`. "				)

										if file is None:
													snake_case_				:					Optional[int]   =       token_per_repo_id or {}
													snake_case_				:					List[Any]   =       path.split("::"				)[-1]
													try:
																snake_case_				:					Any   =       string_to_dict(_lowerCamelCase       ,							config.HUB_DATASETS_URL				)["repo_id"]
																snake_case_				:					int   =       token_per_repo_id[repo_id]
													except (ValueError, KeyError):
																snake_case_				:					Tuple   =       None

													with xopen(_lowerCamelCase       ,							"rb"       ,							use_auth_token=_lowerCamelCase				) as f:
																snake_case_,	snake_case_				:					Optional[Any]   =       sf.read(_lowerCamelCase				)

										else:
													snake_case_,	snake_case_				:					Dict   =       sf.read(_lowerCamelCase				)

										snake_case_				:					Tuple   =       array.T
										if self.mono:
													snake_case_				:					Dict   =       librosa.to_mono(_lowerCamelCase				)
										if self.sampling_rate and self.sampling_rate != sampling_rate:
													snake_case_				:					Optional[Any]   =       librosa.resample(_lowerCamelCase       ,							orig_sr=_lowerCamelCase       ,							target_sr=self.sampling_rate				)
													snake_case_				:					Optional[int]   =       self.sampling_rate

										return {"path": path, "array": array, "sampling_rate": sampling_rate}

							def 							_A	(		self			:int				)				->  Union["FeatureType", Dict[str, "FeatureType"]]:


										'''simple docstring'''




										from .features import Value

										if self.decode:
													raise ValueError("Cannot flatten a decoded Audio feature."				)
										return {
										    "bytes": Value("binary"				),
										    "path": Value("string"				),
										}

							def 							_A	(		self			:Tuple       ,							lowerCAmelCase__			:Any				)				->  pa.StructArray:


										'''simple docstring'''




										if pa.types.is_string(storage.type				):
													snake_case_				:					Union[str, Any]   =       pa.array([None] * len(_lowerCamelCase				)       ,							type=pa.binary()				)
													snake_case_				:					Tuple   =       pa.StructArray.from_arrays([bytes_array, storage]       ,							["bytes", "path"]       ,							mask=storage.is_null()				)
										elif pa.types.is_binary(storage.type				):
													snake_case_				:					int   =       pa.array([None] * len(_lowerCamelCase				)       ,							type=pa.string()				)
													snake_case_				:					Optional[int]   =       pa.StructArray.from_arrays([storage, path_array]       ,							["bytes", "path"]       ,							mask=storage.is_null()				)
										elif pa.types.is_struct(storage.type				) and storage.type.get_all_field_indices("array"				):
													snake_case_				:					Optional[Any]   =       pa.array([Audio().encode_example(_lowerCamelCase				) if x is not None else None for x in storage.to_pylist()]				)
										elif pa.types.is_struct(storage.type				):
													if storage.type.get_field_index("bytes"				) >= 0:
																snake_case_				:					Union[str, Any]   =       storage.field("bytes"				)
													else:
																snake_case_				:					Dict   =       pa.array([None] * len(_lowerCamelCase				)       ,							type=pa.binary()				)
													if storage.type.get_field_index("path"				) >= 0:
																snake_case_				:					Optional[int]   =       storage.field("path"				)
													else:
																snake_case_				:					Any   =       pa.array([None] * len(_lowerCamelCase				)       ,							type=pa.string()				)
													snake_case_				:					Dict   =       pa.StructArray.from_arrays([bytes_array, path_array]       ,							["bytes", "path"]       ,							mask=storage.is_null()				)
										return array_cast(_lowerCamelCase       ,							self.pa_type				)






							def 							_A	(		self			:List[Any]       ,							lowerCAmelCase__			:Optional[Any]				)				->  pa.StructArray:


										'''simple docstring'''




										@no_op_if_value_is_null
										def path_to_bytes(lowerCAmelCase__			:Dict				):
													with xopen(_lowerCamelCase       ,							"rb"				) as f:
																snake_case_				:					Optional[int]   =       f.read()
													return bytes_

										snake_case_				:					Any   =       pa.array(
										    [
										        (path_to_bytes(x["path"]				) if x["bytes"] is None else x["bytes"]) if x is not None else None
										        for x in storage.to_pylist()
										    ]       ,							type=pa.binary()       ,							)
										snake_case_				:					Optional[int]   =       pa.array(
										    [os.path.basename(_lowerCamelCase				) if path is not None else None for path in storage.field("path"				).to_pylist()]       ,							type=pa.string()       ,							)
										snake_case_				:					Optional[Any]   =       pa.StructArray.from_arrays([bytes_array, path_array]       ,							["bytes", "path"]       ,							mask=bytes_array.is_null()				)
										return array_cast(_lowerCamelCase       ,							self.pa_type				)












'''simple docstring'''




import tempfile
import unittest
from pathlib import Path
from shutil import copyfile

from transformers import MaMaaaTokenizer, is_torch_available
from transformers.testing_utils import (
    get_tests_dir,
    nested_simplify,
    require_sentencepiece,
    require_tokenizers,
    require_torch,
    slow,
)
from transformers.utils import is_sentencepiece_available


if is_sentencepiece_available():
		from transformers.models.mam_aaa.tokenization_mam_aaa import VOCAB_FILES_NAMES, save_json

from ...test_tokenization_common import TokenizerTesterMixin


if is_sentencepiece_available():
		__lowerCamelCase   :					Optional[Any]   							=					get_tests_dir('''fixtures/test_sentencepiece.model''')


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

__lowerCamelCase   :					str   							=					128022
__lowerCamelCase   :					List[Any]   							=					128028







@require_sentencepiece
class    A_	(a_						,					unittest.TestCase							):
							"""simple docstring"""




							a__   		=	MaMaaaTokenizer
							a__   		=	False
							a__   		=	False
							a__   		=	True

							def 							_A	(		self			:Union[str, Any]				)				->  List[str]:


										'''simple docstring'''




										super().setUp()

										snake_case_				:					int   =       ["</s>", "<unk>", "▁This", "▁is", "▁a", "▁t", "est", "\u0120", "<pad>"]
										snake_case_				:					Any   =       dict(zip(lowerCAmelCase__       ,							range(len(lowerCAmelCase__				)				)				)				)
										snake_case_				:					Optional[int]   =       Path(self.tmpdirname				)
										save_json(lowerCAmelCase__       ,							save_dir / VOCAB_FILES_NAMES["vocab_file"]				)
										if not (save_dir / VOCAB_FILES_NAMES["spm_file"]).exists():
													copyfile(lowerCAmelCase__       ,							save_dir / VOCAB_FILES_NAMES["spm_file"]				)

										snake_case_				:					Union[str, Any]   =       MaMaaaTokenizer.from_pretrained(self.tmpdirname				)
										tokenizer.save_pretrained(self.tmpdirname				)

							def 							_A	(		self			:List[Any]       ,							**lowerCAmelCase__			:List[Any]				)				->  str:


										'''simple docstring'''




										return MaMaaaTokenizer.from_pretrained(self.tmpdirname       ,							**lowerCAmelCase__				)

							def 							_A	(		self			:Optional[int]       ,							lowerCAmelCase__			:Any				)				->  Optional[int]:


										'''simple docstring'''




										return (
										    "This is a test",
										    "This is a test",
										)

							def 							_A	(		self			:List[str]				)				->  Union[str, Any]:


										'''simple docstring'''




										snake_case_				:					str   =       "</s>"
										snake_case_				:					Union[str, Any]   =       0

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

							def 							_A	(		self			:Union[str, Any]				)				->  List[str]:


										'''simple docstring'''




										snake_case_				:					Union[str, Any]   =       self.get_tokenizer()
										snake_case_				:					Any   =       list(tokenizer.get_vocab().keys()				)

										self.assertEqual(vocab_keys[0]       ,							"</s>"				)
										self.assertEqual(vocab_keys[1]       ,							"<unk>"				)
										self.assertEqual(vocab_keys[-1]       ,							"<s>"				)
										self.assertEqual(len(lowerCAmelCase__				)       ,							tokenizer.vocab_size + len(tokenizer.get_added_vocab()				)				)

							@unittest.skip("Skip this test while all models are still to be uploaded."				)
							def 							_A	(		self			:List[Any]				)				->  Union[str, Any]:


										'''simple docstring'''




										pass

							def 							_A	(		self			:Optional[int]				)				->  int:


										'''simple docstring'''




										snake_case_				:					int   =       self.get_tokenizer()

										snake_case_				:					List[str]   =       tokenizer.tokenize("This is a test"				)
										self.assertListEqual(lowerCAmelCase__       ,							["▁This", "▁is", "▁a", "▁t", "est"]				)

										self.assertListEqual(
										    tokenizer.convert_tokens_to_ids(lowerCAmelCase__				)       ,							[2, 3, 4, 5, 6]       ,							)

										snake_case_				:					Any   =       tokenizer.convert_ids_to_tokens([2, 3, 4, 5, 6]				)
										self.assertListEqual(lowerCAmelCase__       ,							["▁This", "▁is", "▁a", "▁t", "est"]				)

										snake_case_				:					Any   =       tokenizer.convert_tokens_to_string(lowerCAmelCase__				)
										self.assertEqual(lowerCAmelCase__       ,							"This is a test"				)






							@slow
							def 							_A	(		self			:Any				)				->  List[Any]:


										'''simple docstring'''




										snake_case_				:					int   =       {"input_ids": [[128_022, 110_108, 397, 11, 38_272, 2_247, 124_811, 285, 18_105, 1_586, 207, 7, 39_534, 4_428, 397, 1_019, 18_105, 1_586, 207, 7, 41_337, 16_786, 241, 7, 20_214, 17, 125_690, 10_398, 7, 44_378, 58_069, 68_342, 7_798, 7_343, 11, 299, 33_310, 4, 158, 37_350, 94_077, 4_569, 299, 33_310, 90, 4, 52_840, 290, 4, 31_270, 112, 299, 682, 4, 52_840, 39_953, 14_079, 193, 52_519, 90_894, 17_894, 120_697, 11, 40_445, 551, 17, 1_019, 52_519, 90_894, 17_756, 963, 11, 40_445, 480, 17, 9_792, 1_120, 5_173, 1_393, 6_240, 16_786, 241, 120_996, 28, 1_245, 1_393, 118_240, 11_123, 1_019, 93_612, 2_691, 10_618, 98_058, 120_409, 1_928, 279, 4, 40_683, 367, 178, 207, 1_019, 103, 103_121, 506, 65_296, 5, 2], [128_022, 21_217, 367, 117, 125_450, 128, 719, 7, 7_308, 40, 93_612, 12_669, 1_116, 16_704, 71, 17_785, 3_699, 15_592, 35, 144, 9_584, 241, 11_943, 713, 950, 799, 2_247, 88_427, 150, 149, 118_813, 120_706, 1_019, 106_906, 81_518, 28, 1_224, 22_799, 397, 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], [128_022, 1_658, 123_311, 5_155, 5_578, 4_722, 279, 14_947, 2_366, 1_120, 1_197, 14, 1_348, 9_232, 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, 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, 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], [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, 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=lowerCAmelCase__       ,							model_name="facebook/m2m100_418M"       ,							revision="c168bae485c864188cf9aa0e4108b0b6934dc91e"       ,							)







@require_torch
@require_sentencepiece
@require_tokenizers
class    A_	(unittest.TestCase							):
							"""simple docstring"""




							a__   		=	'''facebook/m2m100_418M'''
							a__   		=	[
							    '''In my opinion, there are two levels of response from the French government.''',
							    '''NSA Affair Emphasizes Complete Lack of Debate on Intelligence''',
							]
							a__   		=	[
							    '''Selon moi, il y a deux niveaux de réponse de la part du gouvernement français.''',
							    '''L\'affaire NSA souligne l\'absence totale de débat sur le renseignement''',
							]

							# fmt: off
							a__   		=	[EN_CODE, 593, 1949, 115781, 4, 71586, 4234, 60633, 126233, 432, 123808, 15592, 1197, 117132, 120618, 5, 2]

							@classmethod
							def 							_A	(		cls			:str				)				->  int:


										'''simple docstring'''




										snake_case_				:					MaMaaaTokenizer   =       MaMaaaTokenizer.from_pretrained(
										    cls.checkpoint_name       ,							src_lang="en"       ,							tgt_lang="fr"				)
										snake_case_				:					List[str]   =       1
										return cls

							def 							_A	(		self			:Tuple				)				->  Union[str, Any]:


										'''simple docstring'''




										self.assertEqual(self.tokenizer.get_lang_id("ar"				)       ,							128_006				)
										self.assertEqual(self.tokenizer.get_lang_id("en"				)       ,							128_022				)
										self.assertEqual(self.tokenizer.get_lang_id("ro"				)       ,							128_076				)
										self.assertEqual(self.tokenizer.get_lang_id("mr"				)       ,							128_063				)

							def 							_A	(		self			:Optional[int]				)				->  List[str]:


										'''simple docstring'''




										snake_case_				:					Dict   =       self.tokenizer.get_vocab()
										self.assertEqual(len(lowerCAmelCase__				)       ,							self.tokenizer.vocab_size				)
										self.assertEqual(vocab["<unk>"]       ,							3				)
										self.assertIn(self.tokenizer.get_lang_token("en"				)       ,							lowerCAmelCase__				)

							def 							_A	(		self			:Any				)				->  Dict:


										'''simple docstring'''




										snake_case_				:					List[str]   =       "en"
										snake_case_				:					Dict   =       self.tokenizer.batch_encode_plus(self.src_text				).input_ids[0]
										self.assertListEqual(self.expected_src_tokens       ,							lowerCAmelCase__				)

							def 							_A	(		self			:Union[str, Any]				)				->  Dict:


										'''simple docstring'''




										self.assertIn(lowerCAmelCase__       ,							self.tokenizer.all_special_ids				)
										# fmt: off
										snake_case_				:					Dict   =       [FR_CODE, 5_364, 82, 8_642, 4, 294, 47, 8, 14_028, 136, 3_286, 9_706, 6, 90_797, 6, 144_012, 162, 88_128, 30_061, 5, 2]
										# fmt: on
										snake_case_				:					List[str]   =       self.tokenizer.decode(lowerCAmelCase__       ,							skip_special_tokens=lowerCAmelCase__				)
										snake_case_				:					str   =       self.tokenizer.decode(generated_ids[1:]       ,							skip_special_tokens=lowerCAmelCase__				)
										self.assertEqual(lowerCAmelCase__       ,							lowerCAmelCase__				)
										self.assertNotIn(self.tokenizer.eos_token       ,							lowerCAmelCase__				)

							def 							_A	(		self			:Tuple				)				->  Tuple:


										'''simple docstring'''




										snake_case_				:					Union[str, Any]   =       tempfile.mkdtemp()
										snake_case_				:					int   =       self.tokenizer.lang_token_to_id
										self.tokenizer.save_pretrained(lowerCAmelCase__				)
										snake_case_				:					List[str]   =       MaMaaaTokenizer.from_pretrained(lowerCAmelCase__				)
										self.assertDictEqual(new_tok.lang_token_to_id       ,							lowerCAmelCase__				)

							@require_torch
							def 							_A	(		self			:Optional[Any]				)				->  str:


										'''simple docstring'''




										snake_case_				:					Union[str, Any]   =       "en"
										snake_case_				:					Tuple   =       "fr"

										snake_case_				:					Optional[int]   =       self.tokenizer(self.src_text       ,							text_target=self.tgt_text       ,							padding=lowerCAmelCase__       ,							return_tensors="pt"				)

										snake_case_				:					Dict   =       shift_tokens_right(
										    batch["labels"]       ,							self.tokenizer.pad_token_id       ,							self.tokenizer.eos_token_id				)

										for k in batch:
													snake_case_				:					str   =       batch[k].tolist()
										# batch = {k: v.tolist() for k,v in batch.items()}
										# fairseq batch: https://gist.github.com/sshleifer/cba08bc2109361a74ac3760a7e30e4f4
										# batch.decoder_inputs_ids[0][0] ==
										assert batch.input_ids[1][0] == EN_CODE
										assert batch.input_ids[1][-1] == 2
										assert batch.labels[1][0] == FR_CODE
										assert batch.labels[1][-1] == 2
										assert batch.decoder_input_ids[1][:2] == [2, FR_CODE]

							@require_torch
							def 							_A	(		self			:Optional[Any]				)				->  Tuple:


										'''simple docstring'''




										snake_case_				:					List[str]   =       "mr"
										self.assertListEqual(self.tokenizer.prefix_tokens       ,							[self.tokenizer.get_lang_id("mr"				)]				)
										self.assertListEqual(self.tokenizer.suffix_tokens       ,							[self.tokenizer.eos_token_id]				)

										snake_case_				:					int   =       "zh"
										self.assertListEqual(self.tokenizer.prefix_tokens       ,							[self.tokenizer.get_lang_id("zh"				)]				)
										self.assertListEqual(self.tokenizer.suffix_tokens       ,							[self.tokenizer.eos_token_id]				)

							@require_torch
							def 							_A	(		self			:str				)				->  int:


										'''simple docstring'''




										snake_case_				:					Dict   =       "mr"
										self.tokenizer._switch_to_target_mode()
										self.assertListEqual(self.tokenizer.prefix_tokens       ,							[self.tokenizer.get_lang_id("mr"				)]				)
										self.assertListEqual(self.tokenizer.suffix_tokens       ,							[self.tokenizer.eos_token_id]				)
										self.tokenizer._switch_to_input_mode()
										self.assertListEqual(self.tokenizer.prefix_tokens       ,							[self.tokenizer.get_lang_id(self.tokenizer.src_lang				)]				)

										snake_case_				:					Tuple   =       "zh"
										self.tokenizer._switch_to_target_mode()
										self.assertListEqual(self.tokenizer.prefix_tokens       ,							[self.tokenizer.get_lang_id("zh"				)]				)
										self.assertListEqual(self.tokenizer.suffix_tokens       ,							[self.tokenizer.eos_token_id]				)
										self.tokenizer._switch_to_input_mode()
										self.assertListEqual(self.tokenizer.prefix_tokens       ,							[self.tokenizer.get_lang_id(self.tokenizer.src_lang				)]				)






							@require_torch
							def 							_A	(		self			:Optional[Any]				)				->  Optional[int]:


										'''simple docstring'''




										snake_case_				:					Optional[int]   =       self.tokenizer._build_translation_inputs("A test"       ,							return_tensors="pt"       ,							src_lang="en"       ,							tgt_lang="ar"				)

										self.assertEqual(
										    nested_simplify(lowerCAmelCase__				)       ,							{
										        # en_XX, A, test, EOS
										        "input_ids": [[128_022, 58, 4_183, 2]],
										        "attention_mask": [[1, 1, 1, 1]],
										        # ar_AR
										        "forced_bos_token_id": 128_006,
										    }       ,							)




