





"""simple docstring"""




import itertools
import random
import unittest

import numpy as np

from transformers import BatchFeature, SpeechTaFeatureExtractor
from transformers.testing_utils import require_torch
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_torch_available():
		import torch


a		:				List[Any]  			=    random.Random()






def    _SCREAMING_SNAKE_CASE	(			_lowercase		:					List[str]					, _lowercase		:					int=1.0					, _lowercase		:					Optional[int]=None					, _lowercase		:					Union[str, Any]=None      )				->Optional[Any]:






	'''simple docstring'''






	if rng is None:
		a   :	Tuple			=       global_rng

	a   :	Tuple			=       []
	for batch_idx in range(shape[0]      ):
		values.append([]      )
		for _ in range(shape[1]      ):
			values[-1].append(rng.random() * scale      )

	return values


@require_torch
class  __UpperCamelCase       (		unittest.TestCase		):






							def __init__(     self   ,      lowerCAmelCase__   ,      lowerCAmelCase__=7   ,      lowerCAmelCase__=400   ,      lowerCAmelCase__=2000   ,      lowerCAmelCase__=1   ,      lowerCAmelCase__=0.0   ,      lowerCAmelCase__=1_6000   ,      lowerCAmelCase__=True   ,      lowerCAmelCase__=80   ,      lowerCAmelCase__=16   ,      lowerCAmelCase__=64   ,      lowerCAmelCase__="hann_window"   ,      lowerCAmelCase__=80   ,      lowerCAmelCase__=7600   ,      lowerCAmelCase__=1E-10   ,      lowerCAmelCase__=True   ,      )			->				Optional[Any]:
								a   :	int			=       parent
								a   :	Tuple			=       batch_size
								a   :	Dict			=       min_seq_length
								a   :	Any			=       max_seq_length
								a   :	Optional[int]			=       (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
								a   :	Union[str, Any]			=       feature_size
								a   :	Tuple			=       padding_value
								a   :	str			=       sampling_rate
								a   :	Dict			=       do_normalize
								a   :	str			=       num_mel_bins
								a   :	List[str]			=       hop_length
								a   :	str			=       win_length
								a   :	Optional[Any]			=       win_function
								a   :	List[str]			=       fmin
								a   :	Any			=       fmax
								a   :	Optional[int]			=       mel_floor
								a   :	Tuple			=       return_attention_mask






							def 					__a     (     self   )			->				Optional[Any]:
								return {
								    "feature_size": self.feature_size,
								    "padding_value": self.padding_value,
								    "sampling_rate": self.sampling_rate,
								    "do_normalize": self.do_normalize,
								    "num_mel_bins": self.num_mel_bins,
								    "hop_length": self.hop_length,
								    "win_length": self.win_length,
								    "win_function": self.win_function,
								    "fmin": self.fmin,
								    "fmax": self.fmax,
								    "mel_floor": self.mel_floor,
								    "return_attention_mask": self.return_attention_mask,
								}






							def 					__a     (     self   ,      lowerCAmelCase__=False   ,      lowerCAmelCase__=False   )			->				Tuple:
								def _flatten(lowerCAmelCase__   ):
									return list(itertools.chain(*lowerCAmelCase__   )   )

								if equal_length:
									a   :	Union[str, Any]			=       floats_list((self.batch_size, self.max_seq_length)   )
								else:
									# make sure that inputs increase in size
									a   :	str			=       [
									    _flatten(floats_list((x, self.feature_size)   )   )
									    for x in range(self.min_seq_length   ,      self.max_seq_length   ,      self.seq_length_diff   )
									]

								if numpify:
									a   :	Any			=       [np.asarray(lowerCAmelCase__   ) for x in speech_inputs]

								return speech_inputs






							def 					__a     (     self   ,      lowerCAmelCase__=False   ,      lowerCAmelCase__=False   )			->				Dict:
								if equal_length:
									a   :	str			=       [floats_list((self.max_seq_length, self.num_mel_bins)   ) for _ in range(self.batch_size   )]
								else:
									# make sure that inputs increase in size
									a   :	Any			=       [
									    floats_list((x, self.num_mel_bins)   )
									    for x in range(self.min_seq_length   ,      self.max_seq_length   ,      self.seq_length_diff   )
									]

								if numpify:
									a   :	Optional[int]			=       [np.asarray(lowerCAmelCase__   ) for x in speech_inputs]

								return speech_inputs


@require_torch
class  __UpperCamelCase       (		a__	, unittest.TestCase		):
							lowerCamelCase     :      Tuple         =SpeechTaFeatureExtractor






							def 					__a     (     self   )			->				Union[str, Any]:
								a   :	Tuple			=       SpeechTaFeatureExtractionTester(self   )






							def 					__a     (     self   ,      lowerCAmelCase__   )			->				Union[str, Any]:
								self.assertTrue(np.all(np.mean(lowerCAmelCase__   ,      axis=0   ) < 1E-3   )   )
								self.assertTrue(np.all(np.abs(np.var(lowerCAmelCase__   ,      axis=0   ) - 1   ) < 1E-3   )   )






							def 					__a     (     self   )			->				Union[str, Any]:
								# Tests that all call wrap to encode_plus and batch_encode_plus
								a   :	Tuple			=       self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
								# create three inputs of length 800, 1000, and 1200
								a   :	Any			=       [floats_list((1, x)   )[0] for x in range(800   ,      1400   ,      200   )]
								a   :	Any			=       [np.asarray(lowerCAmelCase__   ) for speech_input in speech_inputs]

								# Test not batched input
								a   :	Optional[int]			=       feat_extract(speech_inputs[0]   ,      return_tensors="np"   ).input_values
								a   :	Optional[Any]			=       feat_extract(np_speech_inputs[0]   ,      return_tensors="np"   ).input_values
								self.assertTrue(np.allclose(lowerCAmelCase__   ,      lowerCAmelCase__   ,      atol=1E-3   )   )

								# Test batched
								a   :	int			=       feat_extract(lowerCAmelCase__   ,      return_tensors="np"   ).input_values
								a   :	int			=       feat_extract(lowerCAmelCase__   ,      return_tensors="np"   ).input_values
								for enc_seq_a, enc_seq_a in zip(lowerCAmelCase__   ,      lowerCAmelCase__   ):
									self.assertTrue(np.allclose(lowerCAmelCase__   ,      lowerCAmelCase__   ,      atol=1E-3   )   )






							def 					__a     (     self   )			->				Optional[Any]:
								a   :	List[Any]			=       self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
								a   :	Dict			=       [floats_list((1, x)   )[0] for x in range(800   ,      1400   ,      200   )]

								a   :	int			=       ["longest", "max_length", "do_not_pad"]
								a   :	Tuple			=       [None, 1600, None]
								for max_length, padding in zip(lowerCAmelCase__   ,      lowerCAmelCase__   ):
									a   :	Dict			=       feat_extract(lowerCAmelCase__   ,      padding=lowerCAmelCase__   ,      max_length=lowerCAmelCase__   ,      return_tensors="np"   )
									a   :	List[Any]			=       processed.input_values

									self._check_zero_mean_unit_variance(input_values[0][:800]   )
									self.assertTrue(input_values[0][800:].sum() < 1E-6   )
									self._check_zero_mean_unit_variance(input_values[1][:1000]   )
									self.assertTrue(input_values[0][1000:].sum() < 1E-6   )
									self._check_zero_mean_unit_variance(input_values[2][:1200]   )






							def 					__a     (     self   )			->				str:
								a   :	Tuple			=       self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
								a   :	List[str]			=       range(800   ,      1400   ,      200   )
								a   :	List[str]			=       [floats_list((1, x)   )[0] for x in lengths]

								a   :	Any			=       ["longest", "max_length", "do_not_pad"]
								a   :	Any			=       [None, 1600, None]

								for max_length, padding in zip(lowerCAmelCase__   ,      lowerCAmelCase__   ):
									a   :	List[Any]			=       feat_extract(lowerCAmelCase__   ,      max_length=lowerCAmelCase__   ,      padding=lowerCAmelCase__   )
									a   :	Dict			=       processed.input_values

									self._check_zero_mean_unit_variance(input_values[0][:800]   )
									self._check_zero_mean_unit_variance(input_values[1][:1000]   )
									self._check_zero_mean_unit_variance(input_values[2][:1200]   )






							def 					__a     (     self   )			->				Dict:
								a   :	Any			=       self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
								a   :	int			=       [floats_list((1, x)   )[0] for x in range(800   ,      1400   ,      200   )]
								a   :	Union[str, Any]			=       feat_extract(
								    lowerCAmelCase__   ,      truncation=lowerCAmelCase__   ,      max_length=1000   ,      padding="max_length"   ,      return_tensors="np"   )
								a   :	List[Any]			=       processed.input_values

								self._check_zero_mean_unit_variance(input_values[0, :800]   )
								self._check_zero_mean_unit_variance(input_values[1]   )
								self._check_zero_mean_unit_variance(input_values[2]   )






							def 					__a     (     self   )			->				Dict:
								a   :	Dict			=       self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
								a   :	Tuple			=       [floats_list((1, x)   )[0] for x in range(800   ,      1400   ,      200   )]
								a   :	List[Any]			=       feat_extract(
								    lowerCAmelCase__   ,      truncation=lowerCAmelCase__   ,      max_length=1000   ,      padding="longest"   ,      return_tensors="np"   )
								a   :	Union[str, Any]			=       processed.input_values

								self._check_zero_mean_unit_variance(input_values[0, :800]   )
								self._check_zero_mean_unit_variance(input_values[1, :1000]   )
								self._check_zero_mean_unit_variance(input_values[2]   )

								# make sure that if max_length < longest -> then pad to max_length
								self.assertTrue(input_values.shape == (3, 1000)   )

								a   :	List[Any]			=       [floats_list((1, x)   )[0] for x in range(800   ,      1400   ,      200   )]
								a   :	int			=       feat_extract(
								    lowerCAmelCase__   ,      truncation=lowerCAmelCase__   ,      max_length=2000   ,      padding="longest"   ,      return_tensors="np"   )
								a   :	Dict			=       processed.input_values

								self._check_zero_mean_unit_variance(input_values[0, :800]   )
								self._check_zero_mean_unit_variance(input_values[1, :1000]   )
								self._check_zero_mean_unit_variance(input_values[2]   )

								# make sure that if max_length > longest -> then pad to longest
								self.assertTrue(input_values.shape == (3, 1200)   )






							def 					__a     (     self   )			->				List[str]:
								a   :	Tuple			=       self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
								a   :	Any			=       np.random.rand(100   ).astype(np.floataa   )
								a   :	Optional[Any]			=       np_speech_inputs.tolist()

								for inputs in [py_speech_inputs, np_speech_inputs]:
									a   :	str			=       feature_extractor.pad([{"input_values": inputs}]   ,      return_tensors="np"   )
									self.assertTrue(np_processed.input_values.dtype == np.floataa   )
									a   :	List[str]			=       feature_extractor.pad([{"input_values": inputs}]   ,      return_tensors="pt"   )
									self.assertTrue(pt_processed.input_values.dtype == torch.floataa   )






							def 					__a     (     self   )			->				Tuple:
								# Tests that all call wrap to encode_plus and batch_encode_plus
								a   :	Union[str, Any]			=       self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
								# create three inputs of length 800, 1000, and 1200
								a   :	Optional[Any]			=       [floats_list((1, x)   )[0] for x in range(800   ,      1400   ,      200   )]
								a   :	Tuple			=       [np.asarray(lowerCAmelCase__   ) for speech_input in speech_inputs]

								# Test feature size
								a   :	Union[str, Any]			=       feature_extractor(audio_target=lowerCAmelCase__   ,      padding=lowerCAmelCase__   ,      return_tensors="np"   ).input_values
								self.assertTrue(input_values.ndim == 3   )
								self.assertTrue(input_values.shape[-1] == feature_extractor.num_mel_bins   )

								# Test not batched input
								a   :	Dict			=       feature_extractor(speech_inputs[0]   ,      return_tensors="np"   ).input_values
								a   :	List[Any]			=       feature_extractor(np_speech_inputs[0]   ,      return_tensors="np"   ).input_values
								self.assertTrue(np.allclose(lowerCAmelCase__   ,      lowerCAmelCase__   ,      atol=1E-3   )   )

								# Test batched
								a   :	Optional[int]			=       feature_extractor(lowerCAmelCase__   ,      return_tensors="np"   ).input_values
								a   :	Any			=       feature_extractor(lowerCAmelCase__   ,      return_tensors="np"   ).input_values
								for enc_seq_a, enc_seq_a in zip(lowerCAmelCase__   ,      lowerCAmelCase__   ):
									self.assertTrue(np.allclose(lowerCAmelCase__   ,      lowerCAmelCase__   ,      atol=1E-3   )   )

								# Test 2-D numpy arrays are batched.
								a   :	Optional[Any]			=       [floats_list((1, x)   )[0] for x in (800, 800, 800)]
								a   :	List[Any]			=       np.asarray(lowerCAmelCase__   )
								a   :	str			=       feature_extractor(lowerCAmelCase__   ,      return_tensors="np"   ).input_values
								a   :	str			=       feature_extractor(lowerCAmelCase__   ,      return_tensors="np"   ).input_values
								for enc_seq_a, enc_seq_a in zip(lowerCAmelCase__   ,      lowerCAmelCase__   ):
									self.assertTrue(np.allclose(lowerCAmelCase__   ,      lowerCAmelCase__   ,      atol=1E-3   )   )






							def 					__a     (     self   )			->				str:
								a   :	Optional[Any]			=       self.feat_extract_tester.prepare_inputs_for_target()
								a   :	Any			=       self.feature_extraction_class(**self.feat_extract_dict   )
								a   :	Union[str, Any]			=       feat_extract.model_input_names[0]

								a   :	List[str]			=       BatchFeature({input_name: speech_inputs}   )

								self.assertTrue(all(len(lowerCAmelCase__   ) == len(lowerCAmelCase__   ) for x, y in zip(lowerCAmelCase__   ,      processed_features[input_name]   )   )   )

								a   :	Tuple			=       self.feat_extract_tester.prepare_inputs_for_target(equal_length=lowerCAmelCase__   )
								a   :	List[Any]			=       BatchFeature({input_name: speech_inputs}   ,      tensor_type="np"   )

								a   :	Tuple			=       processed_features[input_name]

								if len(batch_features_input.shape   ) < 3:
									a   :	Dict			=       batch_features_input[:, :, None]

								self.assertTrue(
								    batch_features_input.shape
								    == (self.feat_extract_tester.batch_size, len(speech_inputs[0]   ), self.feat_extract_tester.num_mel_bins)   )






							@require_torch
							def 					__a     (     self   )			->				Tuple:
								a   :	Tuple			=       self.feat_extract_tester.prepare_inputs_for_target(equal_length=lowerCAmelCase__   )
								a   :	Optional[Any]			=       self.feature_extraction_class(**self.feat_extract_dict   )
								a   :	Optional[int]			=       feat_extract.model_input_names[0]

								a   :	List[Any]			=       BatchFeature({input_name: speech_inputs}   ,      tensor_type="pt"   )

								a   :	Tuple			=       processed_features[input_name]

								if len(batch_features_input.shape   ) < 3:
									a   :	List[str]			=       batch_features_input[:, :, None]

								self.assertTrue(
								    batch_features_input.shape
								    == (self.feat_extract_tester.batch_size, len(speech_inputs[0]   ), self.feat_extract_tester.num_mel_bins)   )






							@require_torch
							def 					__a     (     self   )			->				Optional[Any]:
								a   :	Dict			=       self.feature_extraction_class(**self.feat_extract_dict   )
								a   :	Optional[int]			=       self.feat_extract_tester.prepare_inputs_for_target()
								a   :	Optional[Any]			=       feat_extract.model_input_names[0]

								a   :	List[str]			=       BatchFeature({input_name: speech_inputs}   )

								a   :	Tuple			=       feat_extract.num_mel_bins  # hack!

								a   :	List[Any]			=       feat_extract.pad(lowerCAmelCase__   ,      padding="longest"   ,      return_tensors="np"   )[input_name]
								a   :	Any			=       feat_extract.pad(lowerCAmelCase__   ,      padding="longest"   ,      return_tensors="pt"   )[input_name]

								self.assertTrue(abs(input_np.astype(np.floataa   ).sum() - input_pt.numpy().astype(np.floataa   ).sum()   ) < 1E-2   )






							def 					__a     (     self   )			->				Union[str, Any]:
								a   :	Any			=       self.feat_extract_dict
								a   :	Optional[Any]			=       True
								a   :	Union[str, Any]			=       self.feature_extraction_class(**lowerCAmelCase__   )
								a   :	Any			=       self.feat_extract_tester.prepare_inputs_for_target()
								a   :	Dict			=       [len(lowerCAmelCase__   ) for x in speech_inputs]
								a   :	int			=       feat_extract.model_input_names[0]

								a   :	List[Any]			=       BatchFeature({input_name: speech_inputs}   )

								a   :	Union[str, Any]			=       feat_extract.num_mel_bins  # hack!

								a   :	Dict			=       feat_extract.pad(lowerCAmelCase__   ,      padding="longest"   ,      return_tensors="np"   )
								self.assertIn("attention_mask"   ,      lowerCAmelCase__   )
								self.assertListEqual(list(processed.attention_mask.shape   )   ,      list(processed[input_name].shape[:2]   )   )
								self.assertListEqual(processed.attention_mask.sum(-1   ).tolist()   ,      lowerCAmelCase__   )






							def 					__a     (     self   )			->				Union[str, Any]:
								a   :	Tuple			=       self.feat_extract_dict
								a   :	str			=       True
								a   :	Optional[Any]			=       self.feature_extraction_class(**lowerCAmelCase__   )
								a   :	List[Any]			=       self.feat_extract_tester.prepare_inputs_for_target()
								a   :	Dict			=       [len(lowerCAmelCase__   ) for x in speech_inputs]
								a   :	Optional[Any]			=       feat_extract.model_input_names[0]

								a   :	str			=       BatchFeature({input_name: speech_inputs}   )
								a   :	Optional[Any]			=       min(lowerCAmelCase__   )

								a   :	List[Any]			=       feat_extract.num_mel_bins  # hack!

								a   :	Any			=       feat_extract.pad(
								    lowerCAmelCase__   ,      padding="max_length"   ,      max_length=lowerCAmelCase__   ,      truncation=lowerCAmelCase__   ,      return_tensors="np"   )
								self.assertIn("attention_mask"   ,      lowerCAmelCase__   )
								self.assertListEqual(
								    list(processed_pad.attention_mask.shape   )   ,      [processed_pad[input_name].shape[0], max_length]   )
								self.assertListEqual(
								    processed_pad.attention_mask[:, :max_length].sum(-1   ).tolist()   ,      [max_length for x in speech_inputs]   )






							def 					__a     (     self   ,      lowerCAmelCase__   )			->				Optional[int]:
								from datasets import load_dataset

								a   :	Tuple			=       load_dataset("hf-internal-testing/librispeech_asr_dummy"   ,      "clean"   ,      split="validation"   )
								# automatic decoding with librispeech
								a   :	Optional[Any]			=       ds.sort("id"   ).select(range(lowerCAmelCase__   )   )[:num_samples]["audio"]

								return [x["array"] for x in speech_samples]






							def 					__a     (     self   )			->				Union[str, Any]:
								# fmt: off
								a   :	List[Any]			=       torch.tensor(
								    [2.3_804E-03, 2.0_752E-03, 1.9_836E-03, 2.1_057E-03, 1.6_174E-03,
								     3.0_518E-04, 9.1_553E-05, 3.3_569E-04, 9.7_656E-04, 1.8_311E-03,
								     2.0_142E-03, 2.1_057E-03, 1.7_395E-03, 4.5_776E-04, -3.9_673E-04,
								     4.5_776E-04, 1.0_071E-03, 9.1_553E-05, 4.8_828E-04, 1.1_597E-03,
								     7.3_242E-04, 9.4_604E-04, 1.8_005E-03, 1.8_311E-03, 8.8_501E-04,
								     4.2_725E-04, 4.8_828E-04, 7.3_242E-04, 1.0_986E-03, 2.1_057E-03]   )
								# fmt: on

								a   :	List[str]			=       self._load_datasamples(1   )
								a   :	Union[str, Any]			=       SpeechTaFeatureExtractor()
								a   :	str			=       feature_extractor(lowerCAmelCase__   ,      return_tensors="pt"   ).input_values
								self.assertEquals(input_values.shape   ,      (1, 9_3680)   )
								self.assertTrue(torch.allclose(input_values[0, :30]   ,      lowerCAmelCase__   ,      atol=1E-6   )   )






							def 					__a     (     self   )			->				Union[str, Any]:
								# fmt: off
								a   :	Tuple			=       torch.tensor(
								    [-2.6_870, -3.0_104, -3.1_356, -3.5_352, -3.0_044, -3.0_353, -3.4_719, -3.6_777,
								     -3.1_520, -2.9_435, -2.6_553, -2.8_795, -2.9_944, -2.5_921, -3.0_279, -3.0_386,
								     -3.0_864, -3.1_291, -3.2_353, -2.7_444, -2.6_831, -2.7_287, -3.1_761, -3.1_571,
								     -3.2_726, -3.0_582, -3.1_007, -3.4_533, -3.4_695, -3.0_998]   )
								# fmt: on

								a   :	Dict			=       self._load_datasamples(1   )
								a   :	Tuple			=       SpeechTaFeatureExtractor()
								a   :	Optional[int]			=       feature_extractor(audio_target=lowerCAmelCase__   ,      return_tensors="pt"   ).input_values
								self.assertEquals(input_values.shape   ,      (1, 366, 80)   )
								self.assertTrue(torch.allclose(input_values[0, 0, :30]   ,      lowerCAmelCase__   ,      atol=1E-4   )   )









"""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:
		a		:				Tuple  			=    None


a		:				int  			=    logging.get_logger(__name__)


a		:				int  			=    {'''vocab_file''': '''sentencepiece.bpe.model''', '''tokenizer_file''': '''tokenizer.json'''}

a		:				Optional[int]  			=    {
    '''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'''
        ),
    },
}

a		:				int  			=    {
    '''facebook/nllb-large-en-ro''': 1024,
    '''facebook/nllb-200-distilled-600M''': 1024,
}

# fmt: off
a		:				List[Any]  			=    ['''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__		):
							lowerCamelCase     :      Optional[Any]         =VOCAB_FILES_NAMES
							lowerCamelCase     :      str         =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							lowerCamelCase     :      Dict         =PRETRAINED_VOCAB_FILES_MAP
							lowerCamelCase     :      List[Any]         =["""input_ids""", """attention_mask"""]
							lowerCamelCase     :      Union[str, Any]         =NllbTokenizer

							lowerCamelCase     :      List[int]         =[]
							lowerCamelCase     :      List[int]         =[]






							def __init__(     self   ,      lowerCAmelCase__=None   ,      lowerCAmelCase__=None   ,      lowerCAmelCase__="<s>"   ,      lowerCAmelCase__="</s>"   ,      lowerCAmelCase__="</s>"   ,      lowerCAmelCase__="<s>"   ,      lowerCAmelCase__="<unk>"   ,      lowerCAmelCase__="<pad>"   ,      lowerCAmelCase__="<mask>"   ,      lowerCAmelCase__=None   ,      lowerCAmelCase__=None   ,      lowerCAmelCase__=None   ,      lowerCAmelCase__=False   ,      **lowerCAmelCase__   ,      )			->				Optional[Any]:
								# Mask token behave like a normal word, i.e. include the space before it
								a   :	Dict			=       AddedToken(lowerCAmelCase__   ,      lstrip=lowerCAmelCase__   ,      rstrip=lowerCAmelCase__   ) if isinstance(lowerCAmelCase__   ,      lowerCAmelCase__   ) else mask_token
								a   :	Optional[Any]			=       legacy_behaviour
								super().__init__(
								    vocab_file=lowerCAmelCase__   ,      tokenizer_file=lowerCAmelCase__   ,      bos_token=lowerCAmelCase__   ,      eos_token=lowerCAmelCase__   ,      sep_token=lowerCAmelCase__   ,      cls_token=lowerCAmelCase__   ,      unk_token=lowerCAmelCase__   ,      pad_token=lowerCAmelCase__   ,      mask_token=lowerCAmelCase__   ,      src_lang=lowerCAmelCase__   ,      tgt_lang=lowerCAmelCase__   ,      additional_special_tokens=lowerCAmelCase__   ,      legacy_behaviour=lowerCAmelCase__   ,      **lowerCAmelCase__   ,      )

								a   :	int			=       vocab_file
								a   :	Any			=       False if not self.vocab_file else True

								a   :	List[str]			=       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   :	str			=       {
								    lang_code: self.convert_tokens_to_ids(lowerCAmelCase__   ) for lang_code in FAIRSEQ_LANGUAGE_CODES
								}

								a   :	List[Any]			=       src_lang if src_lang is not None else "eng_Latn"
								a   :	str			=       self.convert_tokens_to_ids(self._src_lang   )
								a   :	Any			=       tgt_lang
								self.set_src_lang_special_tokens(self._src_lang   )






							@property
							def 					__a     (     self   )			->				str:
								return self._src_lang






							@src_lang.setter
							def 					__a     (     self   ,      lowerCAmelCase__   )			->				None:
								a   :	List[str]			=       new_src_lang
								self.set_src_lang_special_tokens(self._src_lang   )






							def 					__a     (     self   ,      lowerCAmelCase__   ,      lowerCAmelCase__ = None   )			->				List[int]:
								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 					__a     (     self   ,      lowerCAmelCase__   ,      lowerCAmelCase__ = None   )			->				List[int]:
								a   :	str			=       [self.sep_token_id]
								a   :	Union[str, Any]			=       [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 					__a     (     self   ,      lowerCAmelCase__   ,      lowerCAmelCase__   ,      lowerCAmelCase__   ,      lowerCAmelCase__   ,      **lowerCAmelCase__   )			->				Any:
								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   :	Dict			=       src_lang
								a   :	int			=       self(lowerCAmelCase__   ,      add_special_tokens=lowerCAmelCase__   ,      return_tensors=lowerCAmelCase__   ,      **lowerCAmelCase__   )
								a   :	Dict			=       self.convert_tokens_to_ids(lowerCAmelCase__   )
								a   :	Any			=       tgt_lang_id
								return inputs






							def 					__a     (     self   ,      lowerCAmelCase__   ,      lowerCAmelCase__ = "eng_Latn"   ,      lowerCAmelCase__ = None   ,      lowerCAmelCase__ = "fra_Latn"   ,      **lowerCAmelCase__   ,      )			->				BatchEncoding:
								a   :	Optional[int]			=       src_lang
								a   :	int			=       tgt_lang
								return super().prepare_seqaseq_batch(lowerCAmelCase__   ,      lowerCAmelCase__   ,      **lowerCAmelCase__   )






							def 					__a     (     self   )			->				Tuple:
								return self.set_src_lang_special_tokens(self.src_lang   )






							def 					__a     (     self   )			->				str:
								return self.set_tgt_lang_special_tokens(self.tgt_lang   )






							def 					__a     (     self   ,      lowerCAmelCase__   )			->				None:
								a   :	int			=       self.convert_tokens_to_ids(lowerCAmelCase__   )

								if self.legacy_behaviour:
									a   :	Tuple			=       []
									a   :	List[str]			=       [self.eos_token_id, self.cur_lang_code]
								else:
									a   :	int			=       [self.cur_lang_code]
									a   :	int			=       [self.eos_token_id]

								a   :	Union[str, Any]			=       self.convert_ids_to_tokens(self.prefix_tokens   )
								a   :	Any			=       self.convert_ids_to_tokens(self.suffix_tokens   )

								a   :	Any			=       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 					__a     (     self   ,      lowerCAmelCase__   )			->				None:
								a   :	str			=       self.convert_tokens_to_ids(lowerCAmelCase__   )
								if self.legacy_behaviour:
									a   :	Optional[Any]			=       []
									a   :	int			=       [self.eos_token_id, self.cur_lang_code]
								else:
									a   :	List[Any]			=       [self.cur_lang_code]
									a   :	List[Any]			=       [self.eos_token_id]

								a   :	int			=       self.convert_ids_to_tokens(self.prefix_tokens   )
								a   :	int			=       self.convert_ids_to_tokens(self.suffix_tokens   )

								a   :	Any			=       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 					__a     (     self   ,      lowerCAmelCase__   ,      lowerCAmelCase__ = None   )			->				Tuple[str]:
								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(lowerCAmelCase__   ):
									logger.error(f"""Vocabulary path ({save_directory}) should be a directory."""   )
									return
								a   :	Any			=       os.path.join(
								    lowerCAmelCase__   ,      (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]   )

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

								return (out_vocab_file,)


