



"""simple docstring"""





import logging
import sys
from dataclasses import dataclass, field
from typing import Any, Dict, List, Optional, Union

import librosa
import torch
from datasets import DatasetDict, load_dataset
from packaging import version
from torch import nn

from transformers import (
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaForPreTraining,
    is_apex_available,
    trainer_utils,
)
from transformers.models.wavaveca.modeling_wavaveca import _compute_mask_indices


if is_apex_available():
  from apex import amp

if version.parse(version.parse(torch.__version__).base_version) >= version.parse("""1.6"""):
  lowerCamelCase       	=						True
  from torch.cuda.amp import autocast


lowerCamelCase       	=						logging.getLogger(__name__)
@dataclass
class 				lowercase__       :







       '''simple docstring'''


       UpperCamelCase						=					field(
           metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''}				)
       UpperCamelCase						=					field(
           default=SCREAMING_SNAKE_CASE    , metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''}    , )
       UpperCamelCase						=					field(
           default=SCREAMING_SNAKE_CASE    , metadata={'''help''': '''Whether to freeze the feature extractor layers of the model.'''}				)
       UpperCamelCase						=					field(
           default=SCREAMING_SNAKE_CASE    , metadata={'''help''': '''Whether to log verbose messages or not.'''}    , )
       UpperCamelCase						=					field(
           default=2.0    , metadata={'''help''': '''Maximum temperature for gumbel softmax.'''}				)
       UpperCamelCase						=					field(
           default=0.5    , metadata={'''help''': '''Minimum temperature for gumbel softmax.'''}				)
       UpperCamelCase						=					field(
           default=0.9_9_9_9_9_5    , metadata={'''help''': '''Decay of gumbel temperature during training.'''}				)



def        a__     (				lowerCAmelCase__     ,      lowerCAmelCase__				):
      logging.basicConfig(
          format="%(asctime)s - %(levelname)s - %(name)s - %(message)s"     ,      datefmt="%m/%d/%Y %H:%M:%S"     ,      handlers=[logging.StreamHandler(sys.stdout				)]     ,      )
      UpperCAmelCase_      			=							logging.WARNING
      if model_args.verbose_logging:
            UpperCAmelCase_      			=							logging.DEBUG
      elif trainer_utils.is_main_process(training_args.local_rank				):
            UpperCAmelCase_      			=							logging.INFO
      logger.setLevel(lowerCAmelCase__				)
@dataclass
class 				lowercase__       :







       '''simple docstring'''


       UpperCamelCase						=					field(
           default=SCREAMING_SNAKE_CASE    , metadata={'''help''': '''The name of the dataset to use (via the datasets library).'''}				)
       UpperCamelCase						=					field(
           default=SCREAMING_SNAKE_CASE    , metadata={'''help''': '''The configuration name of the dataset to use (via the datasets library).'''}				)
       UpperCamelCase						=					field(
           default='''train'''    , metadata={
               '''help''': '''The name of the training data set split to use (via the datasets library). Defaults to \'train\''''
           }    , )
       UpperCamelCase						=					field(
           default='''validation'''    , metadata={
               '''help''': (
                   '''The name of the validation data set split to use (via the datasets library). Defaults to \'validation\''''
               )
           }    , )
       UpperCamelCase						=					field(
           default='''file'''    , metadata={'''help''': '''Column in the dataset that contains speech file path. Defaults to \'file\''''}    , )
       UpperCamelCase						=					field(
           default=SCREAMING_SNAKE_CASE    , metadata={'''help''': '''Overwrite the cached preprocessed datasets or not.'''}				)
       UpperCamelCase						=					field(
           default=1    , metadata={
               '''help''': '''The percentage of the train set used as validation set in case there\'s no validation split'''
           }    , )
       UpperCamelCase						=					field(
           default=SCREAMING_SNAKE_CASE    , metadata={'''help''': '''The number of processes to use for the preprocessing.'''}    , )
       UpperCamelCase						=					field(
           default=2_0.0    , metadata={'''help''': '''Filter audio files that are longer than `max_duration_in_seconds` seconds'''}				)



@dataclass
class 				lowercase__       :







       '''simple docstring'''


       UpperCamelCase						=					42
       UpperCamelCase						=					42
       UpperCamelCase						=					"longest"
       UpperCamelCase						=					None
       UpperCamelCase						=					None



       def __call__(							self      :  Optional[int]		,    _UpperCAmelCase      :  List[Dict[str, Union[List[int], torch.Tensor]]]   )			->						Dict[str, torch.Tensor]:


             '''simple docstring'''




             UpperCAmelCase_      			=							self.feature_extractor.pad(
                 _UpperCAmelCase		,    max_length=self.max_length		,    padding=self.padding		,    pad_to_multiple_of=self.pad_to_multiple_of		,    return_tensors="pt"		,    )
             UpperCAmelCase_      			=							self.model._get_feat_extract_output_lengths(batch["input_values"].shape[-1]   )

             UpperCAmelCase_      			=							batch["input_values"].shape[0]

             # make sure that no loss is computed on padded inputs
             if batch["attention_mask"] is not None:
                   # compute real output lengths according to convolution formula
                   UpperCAmelCase_      			=							self.model._get_feat_extract_output_lengths(batch["attention_mask"].sum(-1   )   ).to(
                       torch.long   )

                   UpperCAmelCase_      			=							torch.zeros(
                       (batch_size, mask_indices_seq_length)		,    dtype=torch.long		,    device=batch["input_values"].device   )

                   # these two operations makes sure that all values
                   # before the output lengths indices are attended to
                   UpperCAmelCase_      			=							1
                   UpperCAmelCase_      			=							attention_mask.flip([-1]   ).cumsum(-1   ).flip([-1]   ).bool()

             # sample randomly masked indices
             UpperCAmelCase_      			=							_compute_mask_indices(
                 (batch_size, mask_indices_seq_length)		,    self.model.config.mask_time_prob		,    self.model.config.mask_time_length		,    attention_mask=_UpperCAmelCase		,    min_masks=2		,    )

             return batch



class 				lowercase__       (   SCREAMING_SNAKE_CASE				):







       '''simple docstring'''


       def __init__(							self      :  Optional[Any]		,    *_UpperCAmelCase      :  Union[str, Any]		,    _UpperCAmelCase      :  str=1		,    _UpperCAmelCase      :  Optional[int]=0		,    _UpperCAmelCase      :  Union[str, Any]=1.0		,    **_UpperCAmelCase      :  List[str]   )			->						int:


             '''simple docstring'''




             super().__init__(*_UpperCAmelCase		,    **_UpperCAmelCase   )
             UpperCAmelCase_      			=							0
             UpperCAmelCase_      			=							max_gumbel_temp
             UpperCAmelCase_      			=							min_gumbel_temp
             UpperCAmelCase_      			=							gumbel_temp_decay







       def 					lowercase__    (							self      :  Optional[int]		,    _UpperCAmelCase      :  nn.Module		,    _UpperCAmelCase      :  Dict[str, Union[torch.Tensor, Any]]   )			->						torch.Tensor:


             '''simple docstring'''




             model.train()
             UpperCAmelCase_      			=							self._prepare_inputs(_UpperCAmelCase   )

             if self.use_amp:
                   with autocast():
                         UpperCAmelCase_      			=							self.compute_loss(_UpperCAmelCase		,    _UpperCAmelCase   )
             else:
                   UpperCAmelCase_      			=							self.compute_loss(_UpperCAmelCase		,    _UpperCAmelCase   )

             if self.args.n_gpu > 1 or self.deepspeed:
                   if model.module.config.ctc_loss_reduction == "mean":
                         UpperCAmelCase_      			=							loss.mean()
                   elif model.module.config.ctc_loss_reduction == "sum":
                         UpperCAmelCase_      			=							loss.sum() / (inputs["mask_time_indices"]).sum()
                   else:
                         raise ValueError(F"""{model.config.ctc_loss_reduction} is not valid. Choose one of ['mean', 'sum']"""   )

             if self.args.gradient_accumulation_steps > 1:
                   UpperCAmelCase_      			=							loss / self.args.gradient_accumulation_steps

             if self.use_amp:
                   self.scaler.scale(_UpperCAmelCase   ).backward()
             elif self.use_apex:
                   with amp.scale_loss(_UpperCAmelCase		,    self.optimizer   ) as scaled_loss:
                         scaled_loss.backward()
             elif self.deepspeed:
                   self.deepspeed.backward(_UpperCAmelCase   )
             else:
                   loss.backward()

             self.num_update_step += 1
             # make sure gumbel softmax temperature is decayed
             if self.args.n_gpu > 1 or self.deepspeed:
                   model.module.set_gumbel_temperature(
                       max(self.max_gumbel_temp * self.gumbel_temp_decay**self.num_update_step		,    self.min_gumbel_temp   )   )
             else:
                   model.set_gumbel_temperature(
                       max(self.max_gumbel_temp * self.gumbel_temp_decay**self.num_update_step		,    self.min_gumbel_temp   )   )

             return loss.detach()



def        a__     (				):
      # See all possible arguments in src/transformers/training_args.py
      # or by passing the --help flag to this script.
      # We now keep distinct sets of args, for a cleaner separation of concerns.

      UpperCAmelCase_      			=							HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)				)

      UpperCAmelCase_			,   UpperCAmelCase_			,   UpperCAmelCase_      			=							parser.parse_args_into_dataclasses()
      configure_logger(lowerCAmelCase__     ,      lowerCAmelCase__				)

      # Downloading and loading a dataset from the hub.
      UpperCAmelCase_      			=							load_dataset(data_args.dataset_name     ,      data_args.dataset_config_name     ,      cache_dir=model_args.cache_dir				)

      if "validation" not in datasets.keys():
            # make sure only "validation" and "train" keys remain"
            UpperCAmelCase_      			=							DatasetDict()
            UpperCAmelCase_      			=							load_dataset(
                data_args.dataset_name     ,      data_args.dataset_config_name     ,      split=f"""{data_args.train_split_name}[:{data_args.validation_split_percentage}%]"""     ,      cache_dir=model_args.cache_dir     ,      )
            UpperCAmelCase_      			=							load_dataset(
                data_args.dataset_name     ,      data_args.dataset_config_name     ,      split=f"""{data_args.train_split_name}[{data_args.validation_split_percentage}%:]"""     ,      cache_dir=model_args.cache_dir     ,      )
      else:
            # make sure only "validation" and "train" keys remain"
            UpperCAmelCase_      			=							DatasetDict()
            UpperCAmelCase_      			=							load_dataset(
                data_args.dataset_name     ,      data_args.dataset_config_name     ,      split="validation"     ,      cache_dir=model_args.cache_dir     ,      )
            UpperCAmelCase_      			=							load_dataset(
                data_args.dataset_name     ,      data_args.dataset_config_name     ,      split=f"""{data_args.train_split_name}"""     ,      cache_dir=model_args.cache_dir     ,      )

      # only normalized-inputs-training is supported
      UpperCAmelCase_      			=							WavaVecaFeatureExtractor.from_pretrained(
          model_args.model_name_or_path     ,      cache_dir=model_args.cache_dir     ,      do_normalize=lowerCAmelCase__				)

      def prepare_dataset(lowerCAmelCase__				):
            # check that all files have the correct sampling rate
            UpperCAmelCase_			,   UpperCAmelCase_      			=							librosa.load(batch[data_args.speech_file_column]     ,      sr=feature_extractor.sampling_rate				)
            return batch

      # load audio files into numpy arrays
      UpperCAmelCase_      			=							datasets.map(
          lowerCAmelCase__     ,      num_proc=data_args.preprocessing_num_workers     ,      remove_columns=datasets["train"].column_names				)

      # filter audio files that are too long
      UpperCAmelCase_      			=							vectorized_datasets.filter(
          lambda lowerCAmelCase__				: len(data["speech"]				) < int(data_args.max_duration_in_seconds * feature_extractor.sampling_rate				)				)

      def normalize(lowerCAmelCase__				):
            return feature_extractor(batch["speech"]     ,      sampling_rate=feature_extractor.sampling_rate				)

      # normalize and transform to `BatchFeatures`
      UpperCAmelCase_      			=							vectorized_datasets.map(
          lowerCAmelCase__     ,      batched=lowerCAmelCase__     ,      num_proc=data_args.preprocessing_num_workers     ,      load_from_cache_file=not data_args.overwrite_cache     ,      remove_columns=vectorized_datasets["train"].column_names     ,      )

      # pretraining is only supported for "newer" stable layer norm architecture
      # apply_spec_augment has to be True, mask_feature_prob has to be 0.0
      UpperCAmelCase_      			=							WavaVecaConfig.from_pretrained(
          model_args.model_name_or_path     ,      cache_dir=model_args.cache_dir     ,      gradient_checkpointing=training_args.gradient_checkpointing     ,      )

      if not config.do_stable_layer_norm or config.feat_extract_norm != "layer":
            raise ValueError(
                "PreTraining is only supported for ``config.do_stable_layer_norm=True`` and"
                " ``config.feat_extract_norm='layer'"				)

      UpperCAmelCase_      			=							WavaVecaForPreTraining(lowerCAmelCase__				)

      UpperCAmelCase_      			=							DataCollatorForWavaVecaPretraining(model=lowerCAmelCase__     ,      feature_extractor=lowerCAmelCase__				)

      UpperCAmelCase_      			=							WavaVecaPreTrainer(
          model=lowerCAmelCase__     ,      data_collator=lowerCAmelCase__     ,      args=lowerCAmelCase__     ,      train_dataset=vectorized_datasets["train"]     ,      eval_dataset=vectorized_datasets["validation"]     ,      tokenizer=lowerCAmelCase__     ,      max_gumbel_temp=model_args.max_gumbel_temperature     ,      min_gumbel_temp=model_args.min_gumbel_temperature     ,      gumbel_temp_decay=model_args.gumbel_temperature_decay     ,      )
      trainer.train()


if __name__ == "__main__":
  main()









"""simple docstring"""





import shutil
import tempfile
import unittest

from transformers import SPIECE_UNDERLINE, BatchEncoding, MBartTokenizer, MBartTokenizerFast, is_torch_available
from transformers.testing_utils import (
    get_tests_dir,
    nested_simplify,
    require_sentencepiece,
    require_tokenizers,
    require_torch,
)

from ...test_tokenization_common import TokenizerTesterMixin


lowerCamelCase       	=						get_tests_dir("""fixtures/test_sentencepiece.model""")


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

lowerCamelCase       	=						250_004
lowerCamelCase       	=						250_020
@require_sentencepiece
@require_tokenizers
class 				lowercase__       (   SCREAMING_SNAKE_CASE    , unittest.TestCase				):







       '''simple docstring'''


       UpperCamelCase						=					MBartTokenizer
       UpperCamelCase						=					MBartTokenizerFast
       UpperCamelCase						=					True
       UpperCamelCase						=					True



       def 					lowercase__    (							self      :  int   )			->						Union[str, Any]:


             '''simple docstring'''




             super().setUp()

             # We have a SentencePiece fixture for testing
             UpperCAmelCase_      			=							MBartTokenizer(_UpperCAmelCase		,    keep_accents=_UpperCAmelCase   )
             tokenizer.save_pretrained(self.tmpdirname   )



       def 					lowercase__    (							self      :  Any   )			->						int:


             '''simple docstring'''




             UpperCAmelCase_      			=							MBartTokenizer(_UpperCAmelCase		,    keep_accents=_UpperCAmelCase   )

             UpperCAmelCase_      			=							tokenizer.tokenize("This is a test"   )
             self.assertListEqual(_UpperCAmelCase		,    ["▁This", "▁is", "▁a", "▁t", "est"]   )

             self.assertListEqual(
                 tokenizer.convert_tokens_to_ids(_UpperCAmelCase   )		,    [value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]]		,    )

             UpperCAmelCase_      			=							tokenizer.tokenize("I was born in 92000, and this is falsé."   )
             self.assertListEqual(
                 _UpperCAmelCase		,    [
                     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_      			=							tokenizer.convert_tokens_to_ids(_UpperCAmelCase   )
             self.assertListEqual(
                 _UpperCAmelCase		,    [
                     value + tokenizer.fairseq_offset
                     for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
                     #                                       ^ unk: 2 + 1 = 3                  unk: 2 + 1 = 3 ^
                 ]		,    )

             UpperCAmelCase_      			=							tokenizer.convert_ids_to_tokens(_UpperCAmelCase   )
             self.assertListEqual(
                 _UpperCAmelCase		,    [
                     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 					lowercase__    (							self      :  Optional[Any]   )			->						Dict:


             '''simple docstring'''




             if not self.test_slow_tokenizer:
                   # as we don't have a slow version, we can't compare the outputs between slow and fast versions
                   return

             UpperCAmelCase_      			=							(self.rust_tokenizer_class, "hf-internal-testing/tiny-random-mbart", {})
             for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
                   with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})"""   ):
                         UpperCAmelCase_      			=							self.rust_tokenizer_class.from_pretrained(_UpperCAmelCase		,    **_UpperCAmelCase   )
                         UpperCAmelCase_      			=							self.tokenizer_class.from_pretrained(_UpperCAmelCase		,    **_UpperCAmelCase   )

                         UpperCAmelCase_      			=							tempfile.mkdtemp()

                         UpperCAmelCase_      			=							tokenizer_r.save_pretrained(_UpperCAmelCase   )
                         UpperCAmelCase_      			=							tokenizer_p.save_pretrained(_UpperCAmelCase   )

                         # 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_      			=							tuple(f for f in tokenizer_r_files if "tokenizer.json" not in f   )
                         self.assertSequenceEqual(_UpperCAmelCase		,    _UpperCAmelCase   )

                         # Checks everything loads correctly in the same way
                         UpperCAmelCase_      			=							tokenizer_r.from_pretrained(_UpperCAmelCase   )
                         UpperCAmelCase_      			=							tokenizer_p.from_pretrained(_UpperCAmelCase   )

                         # Check special tokens are set accordingly on Rust and Python
                         for key in tokenizer_pp.special_tokens_map:
                               self.assertTrue(hasattr(_UpperCAmelCase		,    _UpperCAmelCase   )   )
                               # self.assertEqual(getattr(tokenizer_rp, key), getattr(tokenizer_pp, key))
                               # self.assertEqual(getattr(tokenizer_rp, key + "_id"), getattr(tokenizer_pp, key + "_id"))

                         shutil.rmtree(_UpperCAmelCase   )

                         # Save tokenizer rust, legacy_format=True
                         UpperCAmelCase_      			=							tempfile.mkdtemp()

                         UpperCAmelCase_      			=							tokenizer_r.save_pretrained(_UpperCAmelCase		,    legacy_format=_UpperCAmelCase   )
                         UpperCAmelCase_      			=							tokenizer_p.save_pretrained(_UpperCAmelCase   )

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

                         # Checks everything loads correctly in the same way
                         UpperCAmelCase_      			=							tokenizer_r.from_pretrained(_UpperCAmelCase   )
                         UpperCAmelCase_      			=							tokenizer_p.from_pretrained(_UpperCAmelCase   )

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

                         shutil.rmtree(_UpperCAmelCase   )

                         # Save tokenizer rust, legacy_format=False
                         UpperCAmelCase_      			=							tempfile.mkdtemp()

                         UpperCAmelCase_      			=							tokenizer_r.save_pretrained(_UpperCAmelCase		,    legacy_format=_UpperCAmelCase   )
                         UpperCAmelCase_      			=							tokenizer_p.save_pretrained(_UpperCAmelCase   )

                         # 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_      			=							tokenizer_r.from_pretrained(_UpperCAmelCase   )
                         UpperCAmelCase_      			=							tokenizer_p.from_pretrained(_UpperCAmelCase   )

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

                         shutil.rmtree(_UpperCAmelCase   )



@require_torch
@require_sentencepiece
@require_tokenizers
class 				lowercase__       (   unittest.TestCase				):







       '''simple docstring'''


       UpperCamelCase						=					'''facebook/mbart-large-en-ro'''
       UpperCamelCase						=					[
           ''' 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						=					[
           '''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						=					[82_74, 12_78_73, 2_59_16, 7, 86_22, 20_71, 4_38, 6_74_85, 53, 18_78_95, 23, 5_17_12, 2, EN_CODE]



       @classmethod
       def 					lowercase__    (							cls      :  Optional[Any]   )			->						List[Any]:


             '''simple docstring'''




             UpperCAmelCase_      			=							MBartTokenizer.from_pretrained(
                 cls.checkpoint_name		,    src_lang="en_XX"		,    tgt_lang="ro_RO"   )
             UpperCAmelCase_      			=							1
             return cls



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


             '''simple docstring'''




             self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["ar_AR"]		,    250001   )
             self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["en_EN"]		,    250004   )
             self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["ro_RO"]		,    250020   )



       def 					lowercase__    (							self      :  Any   )			->						Tuple:


             '''simple docstring'''




             UpperCAmelCase_      			=							self.tokenizer.batch_encode_plus(self.src_text   ).input_ids[0]
             self.assertListEqual(self.expected_src_tokens		,    _UpperCAmelCase   )



       def 					lowercase__    (							self      :  Any   )			->						str:


             '''simple docstring'''




             self.assertIn(_UpperCAmelCase		,    self.tokenizer.all_special_ids   )
             UpperCAmelCase_      			=							[RO_CODE, 884, 9019, 96, 9, 916, 86792, 36, 18743, 15596, 5, 2]
             UpperCAmelCase_      			=							self.tokenizer.decode(_UpperCAmelCase		,    skip_special_tokens=_UpperCAmelCase   )
             UpperCAmelCase_      			=							self.tokenizer.decode(generated_ids[1:]		,    skip_special_tokens=_UpperCAmelCase   )
             self.assertEqual(_UpperCAmelCase		,    _UpperCAmelCase   )
             self.assertNotIn(self.tokenizer.eos_token		,    _UpperCAmelCase   )



       def 					lowercase__    (							self      :  Tuple   )			->						Any:


             '''simple docstring'''




             UpperCAmelCase_      			=							["this is gunna be a long sentence " * 20]
             assert isinstance(src_text[0]		,    _UpperCAmelCase   )
             UpperCAmelCase_      			=							10
             UpperCAmelCase_      			=							self.tokenizer(_UpperCAmelCase		,    max_length=_UpperCAmelCase		,    truncation=_UpperCAmelCase   ).input_ids[0]
             self.assertEqual(ids[-2]		,    2   )
             self.assertEqual(ids[-1]		,    _UpperCAmelCase   )
             self.assertEqual(len(_UpperCAmelCase   )		,    _UpperCAmelCase   )



       def 					lowercase__    (							self      :  int   )			->						int:


             '''simple docstring'''




             self.assertListEqual(self.tokenizer.convert_tokens_to_ids(["<mask>", "ar_AR"]   )		,    [250026, 250001]   )



       def 					lowercase__    (							self      :  Any   )			->						Any:


             '''simple docstring'''




             UpperCAmelCase_      			=							tempfile.mkdtemp()
             UpperCAmelCase_      			=							self.tokenizer.fairseq_tokens_to_ids
             self.tokenizer.save_pretrained(_UpperCAmelCase   )
             UpperCAmelCase_      			=							MBartTokenizer.from_pretrained(_UpperCAmelCase   )
             self.assertDictEqual(new_tok.fairseq_tokens_to_ids		,    _UpperCAmelCase   )



       @require_torch
       def 					lowercase__    (							self      :  List[Any]   )			->						int:


             '''simple docstring'''




             UpperCAmelCase_      			=							self.tokenizer(self.src_text		,    text_target=self.tgt_text		,    padding=_UpperCAmelCase		,    return_tensors="pt"   )
             UpperCAmelCase_      			=							shift_tokens_right(batch["labels"]		,    self.tokenizer.pad_token_id   )

             # fairseq batch: https://gist.github.com/sshleifer/cba08bc2109361a74ac3760a7e30e4f4
             assert batch.input_ids[1][-2:].tolist() == [2, EN_CODE]
             assert batch.decoder_input_ids[1][0].tolist() == RO_CODE
             assert batch.decoder_input_ids[1][-1] == 2
             assert batch.labels[1][-2:].tolist() == [2, RO_CODE]



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


             '''simple docstring'''




             UpperCAmelCase_      			=							self.tokenizer(
                 self.src_text		,    text_target=self.tgt_text		,    padding=_UpperCAmelCase		,    truncation=_UpperCAmelCase		,    max_length=len(self.expected_src_tokens   )		,    return_tensors="pt"		,    )

             UpperCAmelCase_      			=							shift_tokens_right(batch["labels"]		,    self.tokenizer.pad_token_id   )

             self.assertIsInstance(_UpperCAmelCase		,    _UpperCAmelCase   )

             self.assertEqual((2, 14)		,    batch.input_ids.shape   )
             self.assertEqual((2, 14)		,    batch.attention_mask.shape   )
             UpperCAmelCase_      			=							batch.input_ids.tolist()[0]
             self.assertListEqual(self.expected_src_tokens		,    _UpperCAmelCase   )
             self.assertEqual(2		,    batch.decoder_input_ids[0, -1]   )  # EOS
             # Test that special tokens are reset
             self.assertEqual(self.tokenizer.prefix_tokens		,    []   )
             self.assertEqual(self.tokenizer.suffix_tokens		,    [self.tokenizer.eos_token_id, EN_CODE]   )



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


             '''simple docstring'''




             UpperCAmelCase_      			=							self.tokenizer(self.src_text		,    padding=_UpperCAmelCase		,    truncation=_UpperCAmelCase		,    max_length=3		,    return_tensors="pt"   )
             UpperCAmelCase_      			=							self.tokenizer(
                 text_target=self.tgt_text		,    padding=_UpperCAmelCase		,    truncation=_UpperCAmelCase		,    max_length=10		,    return_tensors="pt"   )
             UpperCAmelCase_      			=							targets["input_ids"]
             UpperCAmelCase_      			=							shift_tokens_right(_UpperCAmelCase		,    self.tokenizer.pad_token_id   )

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







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


             '''simple docstring'''




             UpperCAmelCase_      			=							self.tokenizer._build_translation_inputs(
                 "A test"		,    return_tensors="pt"		,    src_lang="en_XX"		,    tgt_lang="ar_AR"   )

             self.assertEqual(
                 nested_simplify(_UpperCAmelCase   )		,    {
                     # A, test, EOS, en_XX
                     "input_ids": [[62, 3034, 2, 250004]],
                     "attention_mask": [[1, 1, 1, 1]],
                     # ar_AR
                     "forced_bos_token_id": 250001,
                 }		,    )




