'''simple docstring'''




import copy
from typing import Any, Dict, List, Optional, Union

import numpy as np
import torch

from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import TensorType, logging


__SCREAMING_SNAKE_CASE       :     List[str]     =  logging.get_logger(__name__)

class 	lowerCamelCase_	(snake_case__    ):
      '''simple docstring'''

      __UpperCamelCase:  Optional[int]						=						["input_features", "is_longer"]




      def __init__(       self						:				str						,					A						:				int=64						,					A						:				Dict=48000						,					A						:				str=480						,					A						:				List[Any]=10						,					A						:				Optional[Any]=1024						,					A						:				Tuple=0.0						,					A						:				List[Any]=False						,					A						:				float = 0						,					A						:				float = 14000						,					A						:				int = None						,					A						:				str = "fusion"						,					A						:				str = "repeatpad"						,					**A						:				Dict						,					):
             super().__init__(
                 feature_size=A						,					sampling_rate=A						,					padding_value=A						,					return_attention_mask=A						,					**A						,					)
             _UpperCAmelCase     :  Optional[Any]							      =    top_db
             _UpperCAmelCase     :  Dict							      =    truncation
             _UpperCAmelCase     :  List[Any]							      =    padding
             _UpperCAmelCase     :  Optional[Any]							      =    fft_window_size
             _UpperCAmelCase     :  Dict							      =    (fft_window_size >> 1) + 1
             _UpperCAmelCase     :  Any							      =    hop_length
             _UpperCAmelCase     :  Tuple							      =    max_length_s
             _UpperCAmelCase     :  str							      =    max_length_s * sampling_rate
             _UpperCAmelCase     :  Any							      =    sampling_rate
             _UpperCAmelCase     :  Optional[int]							      =    frequency_min
             _UpperCAmelCase     :  str							      =    frequency_max
             _UpperCAmelCase     :  Union[str, Any]							      =    mel_filter_bank(
                 num_frequency_bins=self.nb_frequency_bins						,					num_mel_filters=A						,					min_frequency=A						,					max_frequency=A						,					sampling_rate=A						,					norm=A						,					mel_scale="htk"						,					)
             _UpperCAmelCase     :  Tuple							      =    mel_filter_bank(
                 num_frequency_bins=self.nb_frequency_bins						,					num_mel_filters=A						,					min_frequency=A						,					max_frequency=A						,					sampling_rate=A						,					norm="slaney"						,					mel_scale="slaney"						,					)




      def      _A	(       self						:				List[str]       ):
             _UpperCAmelCase     :  Union[str, Any]							      =    copy.deepcopy(self.__dict__       )
             _UpperCAmelCase     :  Dict							      =    self.__class__.__name__
             if "mel_filters" in output:
                    del output["mel_filters"]
             if "mel_filters_slaney" in output:
                    del output["mel_filters_slaney"]
             return output




      def      _A	(       self						:				Optional[Any]						,					A						:				np.array						,					A						:				Optional[np.array] = None       ):
             _UpperCAmelCase     :  Dict							      =    spectrogram(
                 A						,					window_function(self.fft_window_size						,					"hann"       )						,					frame_length=self.fft_window_size						,					hop_length=self.hop_length						,					power=2.0						,					mel_filters=A						,					log_mel="dB"						,					)
             return log_mel_spectrogram.T




      def      _A	(       self						:				str						,					A						:				str						,					A						:				List[str]						,					A						:				List[Any]       ):
             _UpperCAmelCase     :  List[str]							      =    np.array_split(list(range(0						,					total_frames - chunk_frames + 1       )       )						,					3       )
             if len(ranges[1]       ) == 0:
                    # if the audio is too short, we just use the first chunk
                    _UpperCAmelCase     :  Optional[Any]							      =    [0]
             if len(ranges[2]       ) == 0:
                    # if the audio is too short, we just use the first chunk
                    _UpperCAmelCase     :  Tuple							      =    [0]
             # randomly choose index for each part
             _UpperCAmelCase     :  Dict							      =    np.random.choice(ranges[0]       )
             _UpperCAmelCase     :  str							      =    np.random.choice(ranges[1]       )
             _UpperCAmelCase     :  Tuple							      =    np.random.choice(ranges[2]       )

             _UpperCAmelCase     :  str							      =    mel[idx_front : idx_front + chunk_frames, :]
             _UpperCAmelCase     :  str							      =    mel[idx_middle : idx_middle + chunk_frames, :]
             _UpperCAmelCase     :  List[Any]							      =    mel[idx_back : idx_back + chunk_frames, :]

             _UpperCAmelCase     :  Dict							      =    torch.tensor(mel[None, None, :]       )
             _UpperCAmelCase     :  Optional[Any]							      =    torch.nn.functional.interpolate(
                 A						,					size=[chunk_frames, 64]						,					mode="bilinear"						,					align_corners=A       )
             _UpperCAmelCase     :  List[str]							      =    mel_shrink[0][0].numpy()
             _UpperCAmelCase     :  str							      =    np.stack([mel_shrink, mel_chunk_front, mel_chunk_middle, mel_chunk_back]						,					axis=0       )
             return mel_fusion




      def      _A	(       self						:				List[Any]						,					A						:				np.array						,					A						:				List[str]						,					A						:				Any						,					A						:				Optional[int]       ):
             if waveform.shape[0] > max_length:
                    if truncation == "rand_trunc":
                           _UpperCAmelCase     :  int							      =    True
                           # random crop to max_length (for compatibility) -> this should be handled by self.pad
                           _UpperCAmelCase     :  str							      =    len(A       ) - max_length
                           _UpperCAmelCase     :  str							      =    np.random.randint(0						,					overflow + 1       )
                           _UpperCAmelCase     :  int							      =    waveform[idx : idx + max_length]
                           _UpperCAmelCase     :  Any							      =    self._np_extract_fbank_features(A						,					self.mel_filters_slaney       )[None, :]
                    elif truncation == "fusion":
                           _UpperCAmelCase     :  Tuple							      =    self._np_extract_fbank_features(A						,					self.mel_filters       )
                           _UpperCAmelCase     :  List[str]							      =    max_length // self.hop_length + 1  # the +1 related to how the spectrogram is computed
                           _UpperCAmelCase     :  Optional[Any]							      =    mel.shape[0]
                           if chunk_frames == total_frames:
                                  # there is a corner case where the audio length is larger than max_length but smaller than max_length+hop_length.
                                  # In this case, we just use the whole audio.
                                  _UpperCAmelCase     :  Any							      =    np.stack([mel, mel, mel, mel]						,					axis=0       )
                                  _UpperCAmelCase     :  int							      =    False
                           else:
                                  _UpperCAmelCase     :  Tuple							      =    self._random_mel_fusion(A						,					A						,					A       )
                                  _UpperCAmelCase     :  Any							      =    True
                    else:
                           raise NotImplementedError(F"""data_truncating {truncation} not implemented"""       )

             else:
                    _UpperCAmelCase     :  Optional[Any]							      =    False
                    # only use repeat as a new possible value for padding. you repeat the audio before applying the usual max_length padding
                    if waveform.shape[0] < max_length:
                           if padding == "repeat":
                                  _UpperCAmelCase     :  str							      =    int(max_length / len(A       )       )
                                  _UpperCAmelCase     :  Dict							      =    np.stack(np.tile(A						,					n_repeat + 1       )       )[:max_length]
                           if padding == "repeatpad":
                                  _UpperCAmelCase     :  Dict							      =    int(max_length / len(A       )       )
                                  _UpperCAmelCase     :  List[str]							      =    np.stack(np.tile(A						,					A       )       )
                           _UpperCAmelCase     :  Optional[Any]							      =    np.pad(A						,					(0, max_length - waveform.shape[0])						,					mode="constant"						,					constant_values=0       )

                    if truncation == "fusion":
                           _UpperCAmelCase     :  str							      =    self._np_extract_fbank_features(A						,					self.mel_filters       )
                           _UpperCAmelCase     :  Optional[int]							      =    np.stack([input_mel, input_mel, input_mel, input_mel]						,					axis=0       )
                    else:
                           _UpperCAmelCase     :  List[str]							      =    self._np_extract_fbank_features(A						,					self.mel_filters_slaney       )[None, :]

             return input_mel, longer





      def __call__(       self						:				Union[str, Any]						,					A						:				Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]						,					A						:				str = None						,					A						:				Optional[str] = None						,					A						:				Optional[int] = None						,					A						:				Optional[int] = None						,					A						:				Optional[Union[str, TensorType]] = None						,					**A						:				List[str]						,					):
             _UpperCAmelCase     :  int							      =    truncation if truncation is not None else self.truncation
             _UpperCAmelCase     :  Optional[int]							      =    padding if padding else self.padding

             if sampling_rate is not None:
                    if sampling_rate != self.sampling_rate:
                           raise ValueError(
                               F"""The model corresponding to this feature extractor: {self.__class__.__name__} was trained using a"""
                               F""" sampling rate of {self.sampling_rate}. Please make sure that the provided `raw_speech` input"""
                               F""" was sampled with {self.sampling_rate} and not {sampling_rate}."""       )
             else:
                    logger.warning(
                        "It is strongly recommended to pass the `sampling_rate` argument to this function. "
                        "Failing to do so can result in silent errors that might be hard to debug."       )

             _UpperCAmelCase     :  Any							      =    isinstance(A						,					np.ndarray       ) and len(raw_speech.shape       ) > 1
             if is_batched_numpy and len(raw_speech.shape       ) > 2:
                    raise ValueError(F"""Only mono-channel audio is supported for input to {self}"""       )
             _UpperCAmelCase     :  Optional[Any]							      =    is_batched_numpy or (
                 isinstance(A						,					(list, tuple)       ) and (isinstance(raw_speech[0]						,					(np.ndarray, tuple, list)       ))
             )

             if is_batched:
                    _UpperCAmelCase     :  int							      =    [np.asarray(A						,					dtype=np.floataa       ) for speech in raw_speech]
             elif not is_batched and not isinstance(A						,					np.ndarray       ):
                    _UpperCAmelCase     :  List[str]							      =    np.asarray(A						,					dtype=np.floataa       )
             elif isinstance(A						,					np.ndarray       ) and raw_speech.dtype is np.dtype(np.floataa       ):
                    _UpperCAmelCase     :  Any							      =    raw_speech.astype(np.floataa       )

             # always return batch
             if not is_batched:
                    _UpperCAmelCase     :  List[str]							      =    [np.asarray(A       )]

             # convert to mel spectrogram, truncate and pad if needed.
             _UpperCAmelCase     :  Dict							      =    [
                 self._get_input_mel(A						,					max_length if max_length else self.nb_max_samples						,					A						,					A       )
                 for waveform in raw_speech
             ]

             _UpperCAmelCase     :  int							      =    []
             _UpperCAmelCase     :  Optional[Any]							      =    []
             for mel, longer in padded_inputs:
                    input_mel.append(A       )
                    is_longer.append(A       )

             if truncation == "fusion" and sum(A       ) == 0:
                    # if no audio is longer than 10s, then randomly select one audio to be longer
                    _UpperCAmelCase     :  Union[str, Any]							      =    np.random.randint(0						,					len(A       )       )
                    _UpperCAmelCase     :  Optional[Any]							      =    True

             if isinstance(input_mel[0]						,					A       ):
                    _UpperCAmelCase     :  List[str]							      =    [np.asarray(A						,					dtype=np.floataa       ) for feature in input_mel]

             # is_longer is a list of bool
             _UpperCAmelCase     :  Tuple							      =    [[longer] for longer in is_longer]

             _UpperCAmelCase     :  Optional[Any]							      =    {"input_features": input_mel, "is_longer": is_longer}
             _UpperCAmelCase     :  Tuple							      =    BatchFeature(A       )

             if return_tensors is not None:
                    _UpperCAmelCase     :  List[Any]							      =    input_features.convert_to_tensors(A       )

             return input_features


'''simple docstring'''




import shutil
import tempfile
import unittest

from transformers import (
    SPIECE_UNDERLINE,
    AddedToken,
    BatchEncoding,
    NllbTokenizer,
    NllbTokenizerFast,
    is_torch_available,
)
from transformers.testing_utils import (
    get_tests_dir,
    nested_simplify,
    require_sentencepiece,
    require_tokenizers,
    require_torch,
)

from ...test_tokenization_common import TokenizerTesterMixin


__SCREAMING_SNAKE_CASE       :     Union[str, Any]     =  get_tests_dir("""fixtures/test_sentencepiece.model""")


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

__SCREAMING_SNAKE_CASE       :     Optional[int]     =  256_047
__SCREAMING_SNAKE_CASE       :     Optional[int]     =  256_145

@require_sentencepiece
@require_tokenizers
class 	lowerCamelCase_	(snake_case__							, unittest.TestCase    ):
      '''simple docstring'''

      __UpperCamelCase:  int						=						NllbTokenizer
      __UpperCamelCase:  Tuple						=						NllbTokenizerFast
      __UpperCamelCase:  Union[str, Any]						=						True
      __UpperCamelCase:  Dict						=						True
      __UpperCamelCase:  Optional[Any]						=						{}




      def      _A	(       self						:				Union[str, Any]       ):
             super().setUp()

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




      def      _A	(       self						:				Dict       ):
             _UpperCAmelCase     :  Tuple							      =    NllbTokenizer(A						,					keep_accents=A       )

             _UpperCAmelCase     :  Optional[Any]							      =    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]]						,					)

             _UpperCAmelCase     :  List[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",
                     ".",
                 ]						,					)
             _UpperCAmelCase     :  Optional[Any]							      =    tokenizer.convert_tokens_to_ids(A       )
             self.assertListEqual(
                 A						,					[
                     value + tokenizer.fairseq_offset
                     for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
                 ]						,					)

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

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

                           _UpperCAmelCase     :  Dict							      =    tokenizer_r.save_pretrained(A       )
                           _UpperCAmelCase     :  Dict							      =    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       )       )
                           _UpperCAmelCase     :  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
                           _UpperCAmelCase     :  List[Any]							      =    tokenizer_r.from_pretrained(A       )
                           _UpperCAmelCase     :  List[str]							      =    tokenizer_p.from_pretrained(A       )

                           # Check special tokens are set accordingly on Rust and Python
                           for key in tokenizer_pp.special_tokens_map:
                                  self.assertTrue(hasattr(A						,					A       )       )

                           shutil.rmtree(A       )

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

                           _UpperCAmelCase     :  str							      =    tokenizer_r.save_pretrained(A						,					legacy_format=A       )
                           _UpperCAmelCase     :  str							      =    tokenizer_p.save_pretrained(A       )

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

                           # Checks everything loads correctly in the same way
                           _UpperCAmelCase     :  Optional[int]							      =    tokenizer_r.from_pretrained(A       )
                           _UpperCAmelCase     :  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       )

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

                           _UpperCAmelCase     :  Optional[int]							      =    tokenizer_r.save_pretrained(A						,					legacy_format=A       )
                           _UpperCAmelCase     :  Dict							      =    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
                           _UpperCAmelCase     :  List[Any]							      =    tokenizer_r.from_pretrained(A       )
                           _UpperCAmelCase     :  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       )




      @require_torch
      def      _A	(       self						:				Tuple       ):
             if not self.test_seqaseq:
                    return

             _UpperCAmelCase     :  Union[str, Any]							      =    self.get_tokenizers()
             for tokenizer in tokenizers:
                    with self.subTest(F"""{tokenizer.__class__.__name__}"""       ):
                           # Longer text that will definitely require truncation.
                           _UpperCAmelCase     :  Optional[Any]							      =    [
                               " UN Chief Says There Is No Military Solution in Syria",
                               " Secretary-General Ban Ki-moon says his response to Russia's stepped up military support for"
                               " Syria is that 'there is no military solution' to the nearly five-year conflict and more weapons"
                               " will only worsen the violence and misery for millions of people.",
                           ]
                           _UpperCAmelCase     :  Optional[Any]							      =    [
                               "Şeful ONU declară că nu există o soluţie militară în Siria",
                               "Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al"
                               " Rusiei pentru Siria este că \"nu există o soluţie militară\" la conflictul de aproape cinci ani şi"
                               " că noi arme nu vor face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.",
                           ]
                           try:
                                  _UpperCAmelCase     :  Optional[int]							      =    tokenizer.prepare_seqaseq_batch(
                                      src_texts=A						,					tgt_texts=A						,					max_length=3						,					max_target_length=10						,					return_tensors="pt"						,					src_lang="eng_Latn"						,					tgt_lang="ron_Latn"						,					)
                           except NotImplementedError:
                                  return
                           self.assertEqual(batch.input_ids.shape[1]						,					3       )
                           self.assertEqual(batch.labels.shape[1]						,					10       )
                           # max_target_length will default to max_length if not specified
                           _UpperCAmelCase     :  Tuple							      =    tokenizer.prepare_seqaseq_batch(
                               A						,					tgt_texts=A						,					max_length=3						,					return_tensors="pt"       )
                           self.assertEqual(batch.input_ids.shape[1]						,					3       )
                           self.assertEqual(batch.labels.shape[1]						,					3       )

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




      @unittest.skip("Unfortunately way too slow to build a BPE with SentencePiece."       )
      def      _A	(       self						:				List[Any]       ):
             pass





      def      _A	(       self						:				Union[str, Any]       ):
             for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
                    with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})"""       ):
                           _UpperCAmelCase     :  Any							      =    [AddedToken("<special>"						,					lstrip=A       )]

                           _UpperCAmelCase     :  List[Any]							      =    self.rust_tokenizer_class.from_pretrained(
                               A						,					additional_special_tokens=A						,					**A       )
                           _UpperCAmelCase     :  Dict							      =    tokenizer_r.encode("Hey this is a <special> token"       )

                           _UpperCAmelCase     :  Any							      =    tokenizer_r.encode("<special>"						,					add_special_tokens=A       )[0]

                           self.assertTrue(special_token_id in r_output       )

                           if self.test_slow_tokenizer:
                                  _UpperCAmelCase     :  Dict							      =    self.rust_tokenizer_class.from_pretrained(
                                      A						,					additional_special_tokens=A						,					**A						,					)
                                  _UpperCAmelCase     :  Optional[int]							      =    self.tokenizer_class.from_pretrained(
                                      A						,					additional_special_tokens=A						,					**A       )

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

                                  _UpperCAmelCase     :  Any							      =    tokenizer_cr.encode("Hey this is a <special> token"       )

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

@require_torch
@require_sentencepiece
@require_tokenizers
class 	lowerCamelCase_	(unittest.TestCase    ):
      '''simple docstring'''

      __UpperCamelCase:  Dict						=						"facebook/nllb-200-distilled-600M"
      __UpperCamelCase:  Optional[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.",
      ]
      __UpperCamelCase:  str						=						[
          "Şeful ONU declară că nu există o soluţie militară în Siria",
          "Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al Rusiei"
          " pentru Siria este că \"nu există o soluţie militară\" la conflictul de aproape cinci ani şi că noi arme nu vor"
          " face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.",
      ]
      __UpperCamelCase:  str						=						[
          2_5_6_0_4_7,
          1_6_2_9_7,
          1_3_4_4_0_8,
          8_1_6_5,
          2_4_8_0_6_6,
          1_4_7_3_4,
          9_5_0,
          1_1_3_5,
          1_0_5_7_2_1,
          3_5_7_3,
          8_3,
          2_7_3_5_2,
          1_0_8,
          4_9_4_8_6,
          2,
      ]




      @classmethod
      def      _A	(       cls						:				int       ):
             _UpperCAmelCase     :  NllbTokenizer							      =    NllbTokenizer.from_pretrained(
                 cls.checkpoint_name						,					src_lang="eng_Latn"						,					tgt_lang="ron_Latn"       )
             _UpperCAmelCase     :  Union[str, Any]							      =    1
             return cls




      def      _A	(       self						:				Any       ):
             self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["ace_Arab"]						,					256001       )
             self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["ace_Latn"]						,					256002       )
             self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["fra_Latn"]						,					256057       )




      def      _A	(       self						:				Union[str, Any]       ):
             _UpperCAmelCase     :  Optional[int]							      =    self.tokenizer.batch_encode_plus(self.src_text       ).input_ids[0]
             self.assertListEqual(self.expected_src_tokens						,					A       )




      def      _A	(       self						:				Tuple       ):
             self.assertIn(A						,					self.tokenizer.all_special_ids       )
             # fmt: off
             _UpperCAmelCase     :  List[Any]							      =    [RO_CODE, 4254, 98068, 112923, 39072, 3909, 713, 102767, 26, 17314, 35642, 14683, 33118, 2022, 66987, 2, 256047]
             # fmt: on

             _UpperCAmelCase     :  Tuple							      =    self.tokenizer.decode(A						,					skip_special_tokens=A       )
             _UpperCAmelCase     :  Optional[Any]							      =    self.tokenizer.decode(generated_ids[1:]						,					skip_special_tokens=A       )
             self.assertEqual(A						,					A       )
             self.assertNotIn(self.tokenizer.eos_token						,					A       )




      def      _A	(       self						:				Optional[int]       ):
             _UpperCAmelCase     :  List[Any]							      =    ["this is gunna be a long sentence " * 20]
             assert isinstance(src_text[0]						,					A       )
             _UpperCAmelCase     :  Dict							      =    10
             _UpperCAmelCase     :  Tuple							      =    self.tokenizer(A						,					max_length=A						,					truncation=A       ).input_ids[0]
             self.assertEqual(ids[-1]						,					2       )
             self.assertEqual(ids[0]						,					A       )
             self.assertEqual(len(A       )						,					A       )




      def      _A	(       self						:				Dict       ):
             self.assertListEqual(self.tokenizer.convert_tokens_to_ids(["<mask>", "ar_AR"]       )						,					[256203, 3]       )




      def      _A	(       self						:				Optional[Any]       ):
             _UpperCAmelCase     :  Dict							      =    tempfile.mkdtemp()
             _UpperCAmelCase     :  str							      =    self.tokenizer.fairseq_tokens_to_ids
             self.tokenizer.save_pretrained(A       )
             _UpperCAmelCase     :  Tuple							      =    NllbTokenizer.from_pretrained(A       )
             self.assertDictEqual(new_tok.fairseq_tokens_to_ids						,					A       )




      @require_torch
      def      _A	(       self						:				Dict       ):
             _UpperCAmelCase     :  List[str]							      =    self.tokenizer(
                 self.src_text						,					text_target=self.tgt_text						,					padding=A						,					truncation=A						,					max_length=len(self.expected_src_tokens       )						,					return_tensors="pt"						,					)
             _UpperCAmelCase     :  Tuple							      =    shift_tokens_right(
                 batch["labels"]						,					self.tokenizer.pad_token_id						,					self.tokenizer.lang_code_to_id["ron_Latn"]       )

             self.assertIsInstance(A						,					A       )

             self.assertEqual((2, 15)						,					batch.input_ids.shape       )
             self.assertEqual((2, 15)						,					batch.attention_mask.shape       )
             _UpperCAmelCase     :  Dict							      =    batch.input_ids.tolist()[0]
             self.assertListEqual(self.expected_src_tokens						,					A       )
             self.assertEqual(A						,					batch.decoder_input_ids[0, 0]       )  # EOS
             # Test that special tokens are reset
             self.assertEqual(self.tokenizer.prefix_tokens						,					[EN_CODE]       )
             self.assertEqual(self.tokenizer.suffix_tokens						,					[self.tokenizer.eos_token_id]       )




      def      _A	(       self						:				str       ):
             _UpperCAmelCase     :  Optional[Any]							      =    self.tokenizer(self.src_text						,					padding=A						,					truncation=A						,					max_length=3						,					return_tensors="pt"       )
             _UpperCAmelCase     :  Dict							      =    self.tokenizer(
                 text_target=self.tgt_text						,					padding=A						,					truncation=A						,					max_length=10						,					return_tensors="pt"       )
             _UpperCAmelCase     :  List[Any]							      =    targets["input_ids"]
             _UpperCAmelCase     :  Union[str, Any]							      =    shift_tokens_right(
                 A						,					self.tokenizer.pad_token_id						,					decoder_start_token_id=self.tokenizer.lang_code_to_id[self.tokenizer.tgt_lang]						,					)

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




      @require_torch
      def      _A	(       self						:				List[Any]       ):
             _UpperCAmelCase     :  str							      =    self.tokenizer._build_translation_inputs(
                 "A test"						,					return_tensors="pt"						,					src_lang="eng_Latn"						,					tgt_lang="fra_Latn"       )

             self.assertEqual(
                 nested_simplify(A       )						,					{
                     # A, test, EOS, en_XX
                     "input_ids": [[256047, 70, 7356, 2]],
                     "attention_mask": [[1, 1, 1, 1]],
                     # ar_AR
                     "forced_bos_token_id": 256057,
                 }						,					)





      @require_torch
      def      _A	(       self						:				Any       ):
             _UpperCAmelCase     :  Dict							      =    True
             _UpperCAmelCase     :  Any							      =    self.tokenizer(
                 "UN Chief says there is no military solution in Syria"						,					src_lang="eng_Latn"						,					tgt_lang="fra_Latn"       )
             self.assertEqual(
                 inputs.input_ids						,					[16297, 134408, 25653, 6370, 248, 254, 103929, 94995, 108, 49486, 2, 256047]       )

             _UpperCAmelCase     :  Optional[int]							      =    False
             _UpperCAmelCase     :  str							      =    self.tokenizer(
                 "UN Chief says there is no military solution in Syria"						,					src_lang="eng_Latn"						,					tgt_lang="fra_Latn"       )
             self.assertEqual(
                 inputs.input_ids						,					[256047, 16297, 134408, 25653, 6370, 248, 254, 103929, 94995, 108, 49486, 2]       )

