


import argparse
import json
import os

import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType
from accelerate.utils.deepspeed import DummyOptim, DummyScheduler


SCREAMING_SNAKE_CASE       :		str         =				16
SCREAMING_SNAKE_CASE       :		List[str]         =				32




def     UpperCamelCase    (      _a       ,		_a = 1_6       ,		_a = "bert-base-cased"		)					->							Dict:
     '''simple docstring'''





     lowercase_       :str     =       AutoTokenizer.from_pretrained(lowerCAmelCase__		)
     lowercase_       :Dict     =       load_dataset('''glue'''       ,		'''mrpc'''		)

     def tokenize_function(_a		):
          # max_length=None => use the model max length (it's actually the default)
          lowercase_       :Tuple     =       tokenizer(examples['''sentence1''']       ,		examples['''sentence2''']       ,		truncation=lowerCAmelCase__       ,		max_length=lowerCAmelCase__		)
          return outputs

     # Apply the method we just defined to all the examples in all the splits of the dataset
     lowercase_       :List[str]     =       datasets.map(
         lowerCAmelCase__       ,		batched=lowerCAmelCase__       ,		remove_columns=['''idx''', '''sentence1''', '''sentence2''']       ,		load_from_cache_file=lowerCAmelCase__		)

     # We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
     # transformers library
     lowercase_       :Any     =       tokenized_datasets.rename_column('''label'''       ,		'''labels'''		)

     def collate_fn(_a		):
          # On TPU it's best to pad everything to the same length or training will be very slow.
          if accelerator.distributed_type == DistributedType.TPU:
               return tokenizer.pad(lowerCAmelCase__       ,		padding='''max_length'''       ,		max_length=1_2_8       ,		return_tensors='''pt'''		)
          return tokenizer.pad(lowerCAmelCase__       ,		padding='''longest'''       ,		return_tensors='''pt'''		)

     # Instantiate dataloaders.
     lowercase_       :int     =       DataLoader(
         tokenized_datasets['''train''']       ,		shuffle=lowerCAmelCase__       ,		collate_fn=lowerCAmelCase__       ,		batch_size=lowerCAmelCase__		)
     lowercase_       :Any     =       DataLoader(
         tokenized_datasets['''validation''']       ,		shuffle=lowerCAmelCase__       ,		collate_fn=lowerCAmelCase__       ,		batch_size=lowerCAmelCase__		)

     return train_dataloader, eval_dataloader




def     UpperCamelCase    (      _a       ,		_a		)					->							Any:
     '''simple docstring'''





     lowercase_       :Optional[Any]     =       Accelerator()

     # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
     lowercase_       :Optional[int]     =       config['''lr''']
     lowercase_       :Tuple     =       int(config['''num_epochs''']		)
     lowercase_       :int     =       int(config['''seed''']		)
     lowercase_       :Optional[int]     =       int(config['''batch_size''']		)
     lowercase_       :Union[str, Any]     =       args.model_name_or_path

     set_seed(lowerCAmelCase__		)
     lowercase_    ,      lowercase_       :Any     =       get_dataloaders(lowerCAmelCase__       ,		lowerCAmelCase__       ,		lowerCAmelCase__		)

     # Instantiate the model (we build the model here so that the seed also control new weights initialization)
     lowercase_       :List[str]     =       AutoModelForSequenceClassification.from_pretrained(lowerCAmelCase__       ,		return_dict=lowerCAmelCase__		)

     # Instantiate optimizer
     lowercase_       :Optional[int]     =       (
         AdamW
         if accelerator.state.deepspeed_plugin is None
         or '''optimizer''' not in accelerator.state.deepspeed_plugin.deepspeed_config
         else DummyOptim
     )
     lowercase_       :Optional[Any]     =       optimizer_cls(params=model.parameters()       ,		lr=lowerCAmelCase__		)

     if accelerator.state.deepspeed_plugin is not None:
          lowercase_       :str     =       accelerator.state.deepspeed_plugin.deepspeed_config[
              '''gradient_accumulation_steps'''
          ]
     else:
          lowercase_       :Optional[int]     =       1
     lowercase_       :Dict     =       (len(lowerCAmelCase__		) * num_epochs) // gradient_accumulation_steps

     # Instantiate scheduler
     if (
         accelerator.state.deepspeed_plugin is None
         or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
     ):
          lowercase_       :Any     =       get_linear_schedule_with_warmup(
              optimizer=lowerCAmelCase__       ,		num_warmup_steps=0       ,		num_training_steps=lowerCAmelCase__       ,		)
     else:
          lowercase_       :int     =       DummyScheduler(lowerCAmelCase__       ,		total_num_steps=lowerCAmelCase__       ,		warmup_num_steps=0		)

     # Prepare everything
     # There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
     # prepare method.
     lowercase_    ,      lowercase_    ,      lowercase_    ,      lowercase_    ,      lowercase_       :Tuple     =       accelerator.prepare(
         lowerCAmelCase__       ,		lowerCAmelCase__       ,		lowerCAmelCase__       ,		lowerCAmelCase__       ,		lowerCAmelCase__		)

     # We need to keep track of how many total steps we have iterated over
     lowercase_       :List[Any]     =       0
     # We also need to keep track of the stating epoch so files are named properly
     lowercase_       :int     =       0

     # Now we train the model
     lowercase_       :Optional[Any]     =       evaluate.load('''glue'''       ,		'''mrpc'''		)
     lowercase_       :Dict     =       0
     lowercase_       :Optional[Any]     =       {}
     for epoch in range(lowerCAmelCase__       ,		lowerCAmelCase__		):
          model.train()
          for step, batch in enumerate(lowerCAmelCase__		):
               lowercase_       :Optional[int]     =       model(**lowerCAmelCase__		)
               lowercase_       :Union[str, Any]     =       outputs.loss
               lowercase_       :Any     =       loss / gradient_accumulation_steps
               accelerator.backward(lowerCAmelCase__		)
               if step % gradient_accumulation_steps == 0:
                    optimizer.step()
                    lr_scheduler.step()
                    optimizer.zero_grad()

               overall_step += 1

          model.eval()
          lowercase_       :Tuple     =       0
          for step, batch in enumerate(lowerCAmelCase__		):
               # We could avoid this line since we set the accelerator with `device_placement=True`.
               batch.to(accelerator.device		)
               with torch.no_grad():
                    lowercase_       :Tuple     =       model(**lowerCAmelCase__		)
               lowercase_       :List[str]     =       outputs.logits.argmax(dim=-1		)
               # It is slightly faster to call this once, than multiple times
               lowercase_    ,      lowercase_       :Union[str, Any]     =       accelerator.gather(
                   (predictions, batch['''labels'''])		)  # If we are in a multiprocess environment, the last batch has duplicates
               if accelerator.use_distributed:
                    if step == len(lowerCAmelCase__		) - 1:
                         lowercase_       :str     =       predictions[: len(eval_dataloader.dataset		) - samples_seen]
                         lowercase_       :Any     =       references[: len(eval_dataloader.dataset		) - samples_seen]
                    else:
                         samples_seen += references.shape[0]
               metric.add_batch(
                   predictions=lowerCAmelCase__       ,		references=lowerCAmelCase__       ,		)

          lowercase_       :List[Any]     =       metric.compute()
          # Use accelerator.print to print only on the main process.
          accelerator.print(f"epoch {epoch}:"       ,		lowerCAmelCase__		)
          lowercase_       :Optional[Any]     =       eval_metric['''accuracy''']

          if best_performance < eval_metric["accuracy"]:
               lowercase_       :List[str]     =       eval_metric['''accuracy''']

     if args.performance_lower_bound is not None:
          assert (
              args.performance_lower_bound <= best_performance
          ), f"Best performance metric {best_performance} is lower than the lower bound {args.performance_lower_bound}"

     accelerator.wait_for_everyone()
     if accelerator.is_main_process:
          with open(os.path.join(args.output_dir       ,		'''all_results.json'''		)       ,		'''w'''		) as f:
               json.dump(lowerCAmelCase__       ,		lowerCAmelCase__		)




def     UpperCamelCase    (      )					->							List[Any]:
     '''simple docstring'''





     lowercase_       :List[Any]     =       argparse.ArgumentParser(description='''Simple example of training script tracking peak GPU memory usage.'''		)
     parser.add_argument(
         '''--model_name_or_path'''       ,		type=lowerCAmelCase__       ,		default='''bert-base-cased'''       ,		help='''Path to pretrained model or model identifier from huggingface.co/models.'''       ,		required=lowerCAmelCase__       ,		)
     parser.add_argument(
         '''--output_dir'''       ,		type=lowerCAmelCase__       ,		default='''.'''       ,		help='''Optional save directory where all checkpoint folders will be stored. Default is the current working directory.'''       ,		)
     parser.add_argument(
         '''--performance_lower_bound'''       ,		type=lowerCAmelCase__       ,		default=lowerCAmelCase__       ,		help='''Optional lower bound for the performance metric. If set, the training will throw error when the performance metric drops below this value.'''       ,		)
     parser.add_argument(
         '''--num_epochs'''       ,		type=lowerCAmelCase__       ,		default=3       ,		help='''Number of train epochs.'''       ,		)
     lowercase_       :List[str]     =       parser.parse_args()
     lowercase_       :Tuple     =       {'''lr''': 2E-5, '''num_epochs''': args.num_epochs, '''seed''': 4_2, '''batch_size''': 1_6}
     training_function(lowerCAmelCase__       ,		lowerCAmelCase__		)


if __name__ == "__main__":
   main()









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


SCREAMING_SNAKE_CASE       :		Dict         =				get_tests_dir("fixtures/test_sentencepiece.model")


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

SCREAMING_SNAKE_CASE       :		Any         =				250_004
SCREAMING_SNAKE_CASE       :		Optional[int]         =				250_020






@require_sentencepiece
@require_tokenizers
class      UpperCamelCase					(		lowercase__						,      unittest.TestCase     ):


   '''simple docstring'''







   lowercase					:	Union[str, Any]									=MBartTokenizer
   lowercase					:	Dict									=MBartTokenizerFast
   lowercase					:	Dict									=True
   lowercase					:	List[Any]									=True
   def 		UpperCamelCase  (     self		):
        super().setUp()

        # We have a SentencePiece fixture for testing
        lowercase_       :Optional[Any]     =       MBartTokenizer(UpperCamelCase_   ,							keep_accents=UpperCamelCase_		)
        tokenizer.save_pretrained(self.tmpdirname		)
   def 		UpperCamelCase  (     self		):
        lowercase_       :Optional[Any]     =       MBartTokenizer(UpperCamelCase_   ,							keep_accents=UpperCamelCase_		)

        lowercase_       :Union[str, Any]     =       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]]   ,							)

        lowercase_       :List[str]     =       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''',
                '''.''',
            ]   ,							)
        lowercase_       :Dict     =       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 ^
            ]   ,							)

        lowercase_       :Optional[Any]     =       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 		UpperCamelCase  (     self		):
        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

        lowercase_       :Optional[Any]     =       (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})"		):
                  lowercase_       :Optional[int]     =       self.rust_tokenizer_class.from_pretrained(UpperCamelCase_   ,							**UpperCamelCase_		)
                  lowercase_       :int     =       self.tokenizer_class.from_pretrained(UpperCamelCase_   ,							**UpperCamelCase_		)

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

                  lowercase_       :Union[str, Any]     =       tokenizer_r.save_pretrained(UpperCamelCase_		)
                  lowercase_       :int     =       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		)		)
                  lowercase_       :Union[str, Any]     =       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
                  lowercase_       :str     =       tokenizer_r.from_pretrained(UpperCamelCase_		)
                  lowercase_       :Optional[Any]     =       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
                  lowercase_       :int     =       tempfile.mkdtemp()

                  lowercase_       :Optional[Any]     =       tokenizer_r.save_pretrained(UpperCamelCase_   ,							legacy_format=UpperCamelCase_		)
                  lowercase_       :Any     =       tokenizer_p.save_pretrained(UpperCamelCase_		)

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

                  # Checks everything loads correctly in the same way
                  lowercase_       :Optional[int]     =       tokenizer_r.from_pretrained(UpperCamelCase_		)
                  lowercase_       :Dict     =       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
                  lowercase_       :Tuple     =       tempfile.mkdtemp()

                  lowercase_       :Optional[int]     =       tokenizer_r.save_pretrained(UpperCamelCase_   ,							legacy_format=UpperCamelCase_		)
                  lowercase_       :Tuple     =       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
                  lowercase_       :int     =       tokenizer_r.from_pretrained(UpperCamelCase_		)
                  lowercase_       :Any     =       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      UpperCamelCase					(		unittest.TestCase     ):


   '''simple docstring'''







   lowercase					:	List[Any]									="""facebook/mbart-large-en-ro"""
   lowercase					:	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.""",
   ]
   lowercase					:	Dict									=[
       """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.""",
   ]
   lowercase					:	Tuple									=[8274, 127873, 25916, 7, 8622, 2071, 438, 67485, 53, 187895, 23, 51712, 2, EN_CODE]
   @classmethod
   def 		UpperCamelCase  (     cls		):
        lowercase_       :MBartTokenizer     =       MBartTokenizer.from_pretrained(
            cls.checkpoint_name   ,							src_lang='''en_XX'''   ,							tgt_lang='''ro_RO'''		)
        lowercase_       :Optional[int]     =       1
        return cls
   def 		UpperCamelCase  (     self		):
        self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['''ar_AR''']   ,							25_0001		)
        self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['''en_EN''']   ,							25_0004		)
        self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['''ro_RO''']   ,							25_0020		)
   def 		UpperCamelCase  (     self		):
        lowercase_       :Dict     =       self.tokenizer.batch_encode_plus(self.src_text		).input_ids[0]
        self.assertListEqual(self.expected_src_tokens   ,							UpperCamelCase_		)
   def 		UpperCamelCase  (     self		):
        self.assertIn(UpperCamelCase_   ,							self.tokenizer.all_special_ids		)
        lowercase_       :List[Any]     =       [RO_CODE, 884, 9019, 96, 9, 916, 8_6792, 36, 1_8743, 1_5596, 5, 2]
        lowercase_       :str     =       self.tokenizer.decode(UpperCamelCase_   ,							skip_special_tokens=UpperCamelCase_		)
        lowercase_       :List[str]     =       self.tokenizer.decode(generated_ids[1:]   ,							skip_special_tokens=UpperCamelCase_		)
        self.assertEqual(UpperCamelCase_   ,							UpperCamelCase_		)
        self.assertNotIn(self.tokenizer.eos_token   ,							UpperCamelCase_		)
   def 		UpperCamelCase  (     self		):
        lowercase_       :Dict     =       ['''this is gunna be a long sentence ''' * 20]
        assert isinstance(src_text[0]   ,							UpperCamelCase_		)
        lowercase_       :Dict     =       10
        lowercase_       :Any     =       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 		UpperCamelCase  (     self		):
        self.assertListEqual(self.tokenizer.convert_tokens_to_ids(['''<mask>''', '''ar_AR''']		)   ,							[25_0026, 25_0001]		)
   def 		UpperCamelCase  (     self		):
        lowercase_       :List[Any]     =       tempfile.mkdtemp()
        lowercase_       :List[Any]     =       self.tokenizer.fairseq_tokens_to_ids
        self.tokenizer.save_pretrained(UpperCamelCase_		)
        lowercase_       :Tuple     =       MBartTokenizer.from_pretrained(UpperCamelCase_		)
        self.assertDictEqual(new_tok.fairseq_tokens_to_ids   ,							UpperCamelCase_		)
   @require_torch
   def 		UpperCamelCase  (     self		):
        lowercase_       :str     =       self.tokenizer(self.src_text   ,							text_target=self.tgt_text   ,							padding=UpperCamelCase_   ,							return_tensors='''pt'''		)
        lowercase_       :Union[str, Any]     =       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 		UpperCamelCase  (     self		):
        lowercase_       :Any     =       self.tokenizer(
            self.src_text   ,							text_target=self.tgt_text   ,							padding=UpperCamelCase_   ,							truncation=UpperCamelCase_   ,							max_length=len(self.expected_src_tokens		)   ,							return_tensors='''pt'''   ,							)

        lowercase_       :Tuple     =       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		)
        lowercase_       :List[str]     =       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 		UpperCamelCase  (     self		):
        lowercase_       :Any     =       self.tokenizer(self.src_text   ,							padding=UpperCamelCase_   ,							truncation=UpperCamelCase_   ,							max_length=3   ,							return_tensors='''pt'''		)
        lowercase_       :int     =       self.tokenizer(
            text_target=self.tgt_text   ,							padding=UpperCamelCase_   ,							truncation=UpperCamelCase_   ,							max_length=10   ,							return_tensors='''pt'''		)
        lowercase_       :Optional[int]     =       targets['''input_ids''']
        lowercase_       :List[Any]     =       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 		UpperCamelCase  (     self		):
        lowercase_       :int     =       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, 25_0004]],
                '''attention_mask''': [[1, 1, 1, 1]],
                # ar_AR
                '''forced_bos_token_id''': 25_0001,
            }   ,							)

