




'''simple docstring'''





import inspect
import logging
import os
import random
import shutil
import tempfile
import unittest

import pytest
import torch
from torch import nn
from torch.utils.data import DataLoader, TensorDataset

from accelerate import Accelerator
from accelerate.test_utils import execute_subprocess_async, require_cuda
from accelerate.utils import ProjectConfiguration, set_seed


UpperCamelCase_						:      Optional[int]       =						logging.getLogger(__name__)





def      __a     (       _UpperCamelCase:   List[Any]=2 ,						_UpperCamelCase:   Any=3 ,						_UpperCamelCase:   str=16 ,						_UpperCamelCase:   int = 10 ,						_UpperCamelCase:   int = 2   )						->			Tuple:

     """simple docstring"""




     def get_dataset(_UpperCamelCase:   int   ):
          _snake_case							=				torch.randn(batch_size * n_batches ,						1   )
          return TensorDataset(_UpperCamelCase ,						a * x + b + 0.1 * torch.randn(batch_size * n_batches ,						1   )   )

     _snake_case							=				get_dataset(_UpperCamelCase   )
     _snake_case							=				get_dataset(_UpperCamelCase   )
     _snake_case							=				DataLoader(_UpperCamelCase ,						shuffle=_UpperCamelCase ,						batch_size=_UpperCamelCase ,						num_workers=4   )
     _snake_case							=				DataLoader(_UpperCamelCase ,						shuffle=_UpperCamelCase ,						batch_size=_UpperCamelCase ,						num_workers=4   )
     return (train_dataloader, valid_dataloader)





def      __a     (       _UpperCamelCase:   Union[str, Any] ,						_UpperCamelCase:   Optional[Any] ,						_UpperCamelCase:   Dict ,						_UpperCamelCase:   Tuple ,						_UpperCamelCase:   str ,						_UpperCamelCase:   List[str]=None   )						->			Tuple:

     """simple docstring"""




     _snake_case							=				[]
     for epoch in range(_UpperCamelCase   ):
          # Train quickly
          model.train()
          for batch in dataloader:
               _snake_case							, _snake_case							=				batch
               _snake_case							=				model(_UpperCamelCase   )
               _snake_case							=				torch.nn.functional.mse_loss(_UpperCamelCase ,						_UpperCamelCase   )
               accelerator.backward(_UpperCamelCase   )
               optimizer.step()
               optimizer.zero_grad()
          rands.append(random.random()   )  # Introduce some randomness
          if scheduler is not None:
               scheduler.step()
     return rands

class        _a    (						nn.Module							):






     def __init__(			self   )							->      Union[str, Any]:
          super().__init__()
          _snake_case							=				nn.Parameter(torch.randn(1   )   )
          _snake_case							=				nn.Parameter(torch.randn(1   )   )






     def     _lowercase     (			self ,_SCREAMING_SNAKE_CASE   )							->      Dict:
          return x * self.a + self.b







class        _a    (						unittest.TestCase							):






     def     _lowercase     (			self   )							->      Dict:
          with tempfile.TemporaryDirectory() as tmpdir:
               set_seed(42   )
               _snake_case							=				DummyModel()
               _snake_case							=				torch.optim.Adam(params=model.parameters() ,lr=1e-3   )
               _snake_case							, _snake_case							=				dummy_dataloaders()
               _snake_case							=				ProjectConfiguration(total_limit=1 ,project_dir=_SCREAMING_SNAKE_CASE ,automatic_checkpoint_naming=_SCREAMING_SNAKE_CASE   )
               # Train baseline
               _snake_case							=				Accelerator(project_config=_SCREAMING_SNAKE_CASE   )
               _snake_case							, _snake_case							, _snake_case							, _snake_case							=				accelerator.prepare(
                   _SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   )
               # Save initial
               accelerator.save_state()

               # Save second state
               accelerator.save_state()
               self.assertEqual(len(os.listdir(accelerator.project_dir   )   ) ,1   )






     def     _lowercase     (			self   )							->      int:
          with tempfile.TemporaryDirectory() as tmpdir:
               set_seed(42   )
               _snake_case							=				DummyModel()
               _snake_case							=				torch.optim.Adam(params=model.parameters() ,lr=1e-3   )
               _snake_case							, _snake_case							=				dummy_dataloaders()
               # Train baseline
               _snake_case							=				Accelerator()
               _snake_case							, _snake_case							, _snake_case							, _snake_case							=				accelerator.prepare(
                   _SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   )
               # Save initial
               _snake_case							=				os.path.join(_SCREAMING_SNAKE_CASE ,"initial"   )
               accelerator.save_state(_SCREAMING_SNAKE_CASE   )
               ((_snake_case)							, (_snake_case))							=				model.a.item(), model.b.item()
               _snake_case							=				optimizer.state_dict()
               _snake_case							=				train(3 ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   )
               ((_snake_case)							, (_snake_case))							=				model.a.item(), model.b.item()
               _snake_case							=				optimizer.state_dict()

               # Train partially
               set_seed(42   )
               _snake_case							=				DummyModel()
               _snake_case							=				torch.optim.Adam(params=model.parameters() ,lr=1e-3   )
               _snake_case							, _snake_case							=				dummy_dataloaders()
               _snake_case							=				Accelerator()
               _snake_case							, _snake_case							, _snake_case							, _snake_case							=				accelerator.prepare(
                   _SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   )
               accelerator.load_state(_SCREAMING_SNAKE_CASE   )
               ((_snake_case)							, (_snake_case))							=				model.a.item(), model.b.item()
               _snake_case							=				optimizer.state_dict()
               self.assertEqual(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   )
               self.assertEqual(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   )
               self.assertEqual(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   )

               _snake_case							=				train(2 ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   )
               # Save everything
               _snake_case							=				os.path.join(_SCREAMING_SNAKE_CASE ,"checkpoint"   )
               accelerator.save_state(_SCREAMING_SNAKE_CASE   )

               # Load everything back in and make sure all states work
               accelerator.load_state(_SCREAMING_SNAKE_CASE   )
               test_rands += train(1 ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   )
               ((_snake_case)							, (_snake_case))							=				model.a.item(), model.b.item()
               _snake_case							=				optimizer.state_dict()
               self.assertEqual(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   )
               self.assertEqual(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   )
               self.assertEqual(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   )
               self.assertEqual(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   )






     def     _lowercase     (			self   )							->      Optional[int]:
          with tempfile.TemporaryDirectory() as tmpdir:
               set_seed(42   )
               _snake_case							=				DummyModel()
               _snake_case							=				torch.optim.Adam(params=model.parameters() ,lr=1e-3   )
               _snake_case							, _snake_case							=				dummy_dataloaders()
               _snake_case							=				ProjectConfiguration(automatic_checkpoint_naming=_SCREAMING_SNAKE_CASE   )

               # Train baseline
               _snake_case							=				Accelerator(project_dir=_SCREAMING_SNAKE_CASE ,project_config=_SCREAMING_SNAKE_CASE   )
               _snake_case							, _snake_case							, _snake_case							, _snake_case							=				accelerator.prepare(
                   _SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   )
               # Save initial
               accelerator.save_state()
               ((_snake_case)							, (_snake_case))							=				model.a.item(), model.b.item()
               _snake_case							=				optimizer.state_dict()
               _snake_case							=				train(3 ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   )
               ((_snake_case)							, (_snake_case))							=				model.a.item(), model.b.item()
               _snake_case							=				optimizer.state_dict()

               # Train partially
               set_seed(42   )
               _snake_case							=				DummyModel()
               _snake_case							=				torch.optim.Adam(params=model.parameters() ,lr=1e-3   )
               _snake_case							, _snake_case							=				dummy_dataloaders()
               _snake_case							=				ProjectConfiguration(iteration=1 ,automatic_checkpoint_naming=_SCREAMING_SNAKE_CASE   )
               _snake_case							=				Accelerator(project_dir=_SCREAMING_SNAKE_CASE ,project_config=_SCREAMING_SNAKE_CASE   )
               _snake_case							, _snake_case							, _snake_case							, _snake_case							=				accelerator.prepare(
                   _SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   )
               accelerator.load_state(os.path.join(_SCREAMING_SNAKE_CASE ,"checkpoints" ,"checkpoint_0"   )   )
               ((_snake_case)							, (_snake_case))							=				model.a.item(), model.b.item()
               _snake_case							=				optimizer.state_dict()
               self.assertEqual(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   )
               self.assertEqual(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   )
               self.assertEqual(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   )

               _snake_case							=				train(2 ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   )
               # Save everything
               accelerator.save_state()

               # Load everything back in and make sure all states work
               accelerator.load_state(os.path.join(_SCREAMING_SNAKE_CASE ,"checkpoints" ,"checkpoint_1"   )   )
               test_rands += train(1 ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   )
               ((_snake_case)							, (_snake_case))							=				model.a.item(), model.b.item()
               _snake_case							=				optimizer.state_dict()
               self.assertEqual(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   )
               self.assertEqual(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   )
               self.assertEqual(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   )
               self.assertEqual(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   )






     def     _lowercase     (			self   )							->      Optional[Any]:
          _snake_case							=				torch.tensor([1, 2, 3]   )
          _snake_case							=				torch.tensor([2, 3, 4]   )
          _snake_case							=				DummyModel()
          _snake_case							=				torch.optim.Adam(net.parameters()   )
          _snake_case							=				Accelerator()
          with self.assertRaises(_SCREAMING_SNAKE_CASE   ) as ve:
               accelerator.register_for_checkpointing(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   )
          _snake_case							=				str(ve.exception   )
          self.assertTrue("Item at index 0" in message   )
          self.assertTrue("Item at index 1" in message   )
          self.assertFalse("Item at index 2" in message   )
          self.assertFalse("Item at index 3" in message   )






     def     _lowercase     (			self   )							->      Any:
          with tempfile.TemporaryDirectory() as tmpdir:
               set_seed(42   )
               _snake_case							=				DummyModel()
               _snake_case							=				torch.optim.Adam(params=model.parameters() ,lr=1e-3   )
               _snake_case							=				torch.optim.lr_scheduler.StepLR(_SCREAMING_SNAKE_CASE ,step_size=1 ,gamma=0.9_9   )
               _snake_case							, _snake_case							=				dummy_dataloaders()
               _snake_case							=				ProjectConfiguration(automatic_checkpoint_naming=_SCREAMING_SNAKE_CASE   )
               # Train baseline
               _snake_case							=				Accelerator(project_dir=_SCREAMING_SNAKE_CASE ,project_config=_SCREAMING_SNAKE_CASE   )
               _snake_case							, _snake_case							, _snake_case							, _snake_case							, _snake_case							=				accelerator.prepare(
                   _SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   )
               # Save initial
               accelerator.save_state()
               _snake_case							=				scheduler.state_dict()
               train(3 ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   )
               self.assertNotEqual(_SCREAMING_SNAKE_CASE ,scheduler.state_dict()   )

               # Load everything back in and make sure all states work
               accelerator.load_state(os.path.join(_SCREAMING_SNAKE_CASE ,"checkpoints" ,"checkpoint_0"   )   )
               self.assertEqual(_SCREAMING_SNAKE_CASE ,scheduler.state_dict()   )






     def     _lowercase     (			self   )							->      Tuple:
          with tempfile.TemporaryDirectory() as tmpdir:
               set_seed(42   )
               _snake_case							=				DummyModel()
               _snake_case							=				ProjectConfiguration(automatic_checkpoint_naming=_SCREAMING_SNAKE_CASE ,total_limit=2   )
               # Train baseline
               _snake_case							=				Accelerator(project_dir=_SCREAMING_SNAKE_CASE ,project_config=_SCREAMING_SNAKE_CASE   )
               _snake_case							=				accelerator.prepare(_SCREAMING_SNAKE_CASE   )
               # Save 3 states:
               for _ in range(11   ):
                    accelerator.save_state()
               self.assertTrue(not os.path.exists(os.path.join(_SCREAMING_SNAKE_CASE ,"checkpoints" ,"checkpoint_0"   )   )   )
               self.assertTrue(os.path.exists(os.path.join(_SCREAMING_SNAKE_CASE ,"checkpoints" ,"checkpoint_9"   )   )   )
               self.assertTrue(os.path.exists(os.path.join(_SCREAMING_SNAKE_CASE ,"checkpoints" ,"checkpoint_10"   )   )   )






     @require_cuda
     def     _lowercase     (			self   )							->      Optional[int]:
          _snake_case							=				["torchrun", f"""--nproc_per_node={torch.cuda.device_count()}""", inspect.getfile(self.__class__   )]
          execute_subprocess_async(_SCREAMING_SNAKE_CASE ,env=os.environ.copy()   )


if __name__ == "__main__":
   UpperCamelCase_						:      Dict       =						'''/tmp/accelerate/state_checkpointing'''
   UpperCamelCase_						:      Dict       =						DummyModel()
   UpperCamelCase_						:      Any       =						torch.optim.Adam(params=model.parameters(), lr=1E-3)
   UpperCamelCase_						:      Union[str, Any]       =						torch.optim.lr_scheduler.StepLR(optimizer, step_size=1, gamma=0.9_9)
   UpperCamelCase_     ,     UpperCamelCase_						:      Optional[Any]       =						dummy_dataloaders()
   UpperCamelCase_						:      Dict       =						ProjectConfiguration(automatic_checkpoint_naming=True)
   # Train baseline
   UpperCamelCase_						:      Optional[Any]       =						Accelerator(project_dir=savedir, project_config=project_config, mixed_precision='''no''')
   if accelerator.process_index == 0:
      if os.path.exists(savedir):
         shutil.rmtree(savedir)
      os.makedirs(savedir)
   UpperCamelCase_     ,     UpperCamelCase_     ,     UpperCamelCase_     ,     UpperCamelCase_     ,     UpperCamelCase_						:      Optional[Any]       =						accelerator.prepare(
       model, optimizer, train_dataloader, valid_dataloader, scheduler
   )
   UpperCamelCase_     ,     UpperCamelCase_						:      int       =						accelerator.prepare(model, optimizer)
   train(3, model, train_dataloader, optimizer, accelerator, scheduler)
   # Check that the intial optimizer is loaded on the GPU
   for group in optimizer.param_groups:
      UpperCamelCase_						:      int       =						group['''params'''][0].device
      break
   assert param_device.type == accelerator.device.type
   UpperCamelCase_						:      int       =						model.cpu()
   accelerator.wait_for_everyone()
   accelerator.save_state()
   accelerator.wait_for_everyone()

   # Check CPU state
   accelerator.load_state(os.path.join(savedir, '''checkpoints''', '''checkpoint_0'''), map_location='''cpu''')
   for group in optimizer.param_groups:
      UpperCamelCase_						:      Union[str, Any]       =						group['''params'''][0].device
      break
   assert (
       param_device.type == torch.device('''cpu''').type
   ), F"Loaded optimizer states did not match, expected to be loaded on the CPU but got {param_device}"

   # Check device state
   model.to(accelerator.device)
   accelerator.load_state(os.path.join(savedir, '''checkpoints''', '''checkpoint_0'''), map_location='''on_device''')
   for group in optimizer.param_groups:
      UpperCamelCase_						:      List[Any]       =						group['''params'''][0].device
      break
   assert (
       param_device.type == accelerator.device.type
   ), F"Loaded optimizer states did not match, expected to be loaded on {accelerator.device} but got {param_device}"

   # Check error
   with pytest.raises(TypeError, match='''Unsupported optimizer map location passed'''):
      accelerator.load_state(os.path.join(savedir, '''checkpoints''', '''checkpoint_0'''), map_location='''invalid''')
   accelerator.wait_for_everyone()
   if accelerator.process_index == 0:
      shutil.rmtree(savedir)
   accelerator.wait_for_everyone()








'''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


UpperCamelCase_						:      List[str]       =						get_tests_dir('''fixtures/test_sentencepiece.model''')


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

UpperCamelCase_						:      Optional[Any]       =						250004
UpperCamelCase_						:      Union[str, Any]       =						250020

@require_sentencepiece
@require_tokenizers
class        _a    (						__lowerCAmelCase , unittest.TestCase							):
     SCREAMING_SNAKE_CASE_							:   Optional[int]       =						MBartTokenizer
     SCREAMING_SNAKE_CASE_							:   List[str]       =						MBartTokenizerFast
     SCREAMING_SNAKE_CASE_							:   Union[str, Any]       =						True
     SCREAMING_SNAKE_CASE_							:   List[Any]       =						True






     def     _lowercase     (			self   )							->      Optional[Any]:
          super().setUp()

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






     def     _lowercase     (			self   )							->      Dict:
          _snake_case							=				MBartTokenizer(_SCREAMING_SNAKE_CASE ,keep_accents=_SCREAMING_SNAKE_CASE   )

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

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

          _snake_case							=				tokenizer.tokenize("I was born in 92000, and this is falsé."   )
          self.assertListEqual(
              _SCREAMING_SNAKE_CASE ,[
                  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",
                  ".",
              ] ,)
          _snake_case							=				tokenizer.convert_tokens_to_ids(_SCREAMING_SNAKE_CASE   )
          self.assertListEqual(
              _SCREAMING_SNAKE_CASE ,[
                  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 ^
              ] ,)

          _snake_case							=				tokenizer.convert_ids_to_tokens(_SCREAMING_SNAKE_CASE   )
          self.assertListEqual(
              _SCREAMING_SNAKE_CASE ,[
                  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   )							->      List[str]:
          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

          _snake_case							=				(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})"""   ):
                    _snake_case							=				self.rust_tokenizer_class.from_pretrained(_SCREAMING_SNAKE_CASE ,**_SCREAMING_SNAKE_CASE   )
                    _snake_case							=				self.tokenizer_class.from_pretrained(_SCREAMING_SNAKE_CASE ,**_SCREAMING_SNAKE_CASE   )

                    _snake_case							=				tempfile.mkdtemp()

                    _snake_case							=				tokenizer_r.save_pretrained(_SCREAMING_SNAKE_CASE   )
                    _snake_case							=				tokenizer_p.save_pretrained(_SCREAMING_SNAKE_CASE   )

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

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

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

                    shutil.rmtree(_SCREAMING_SNAKE_CASE   )

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

                    _snake_case							=				tokenizer_r.save_pretrained(_SCREAMING_SNAKE_CASE ,legacy_format=_SCREAMING_SNAKE_CASE   )
                    _snake_case							=				tokenizer_p.save_pretrained(_SCREAMING_SNAKE_CASE   )

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

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

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

                    shutil.rmtree(_SCREAMING_SNAKE_CASE   )

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

                    _snake_case							=				tokenizer_r.save_pretrained(_SCREAMING_SNAKE_CASE ,legacy_format=_SCREAMING_SNAKE_CASE   )
                    _snake_case							=				tokenizer_p.save_pretrained(_SCREAMING_SNAKE_CASE   )

                    # 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
                    _snake_case							=				tokenizer_r.from_pretrained(_SCREAMING_SNAKE_CASE   )
                    _snake_case							=				tokenizer_p.from_pretrained(_SCREAMING_SNAKE_CASE   )

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

                    shutil.rmtree(_SCREAMING_SNAKE_CASE   )







@require_torch
@require_sentencepiece
@require_tokenizers
class        _a    (						unittest.TestCase							):
     SCREAMING_SNAKE_CASE_							:   Optional[Any]       =						"""facebook/mbart-large-en-ro"""
     SCREAMING_SNAKE_CASE_							:   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.""",
     ]
     SCREAMING_SNAKE_CASE_							:   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.""",
     ]
     SCREAMING_SNAKE_CASE_							:   Optional[int]       =						[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   )							->      List[str]:
          _snake_case							=				MBartTokenizer.from_pretrained(
              cls.checkpoint_name ,src_lang="en_XX" ,tgt_lang="ro_RO"   )
          _snake_case							=				1
          return cls






     def     _lowercase     (			self   )							->      Dict:
          self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["ar_AR"] ,250_001   )
          self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["en_EN"] ,250_004   )
          self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["ro_RO"] ,250_020   )






     def     _lowercase     (			self   )							->      Tuple:
          _snake_case							=				self.tokenizer.batch_encode_plus(self.src_text   ).input_ids[0]
          self.assertListEqual(self.expected_src_tokens ,_SCREAMING_SNAKE_CASE   )






     def     _lowercase     (			self   )							->      Optional[int]:
          self.assertIn(_SCREAMING_SNAKE_CASE ,self.tokenizer.all_special_ids   )
          _snake_case							=				[RO_CODE, 884, 9_019, 96, 9, 916, 86_792, 36, 18_743, 15_596, 5, 2]
          _snake_case							=				self.tokenizer.decode(_SCREAMING_SNAKE_CASE ,skip_special_tokens=_SCREAMING_SNAKE_CASE   )
          _snake_case							=				self.tokenizer.decode(generated_ids[1:] ,skip_special_tokens=_SCREAMING_SNAKE_CASE   )
          self.assertEqual(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   )
          self.assertNotIn(self.tokenizer.eos_token ,_SCREAMING_SNAKE_CASE   )






     def     _lowercase     (			self   )							->      List[Any]:
          _snake_case							=				["this is gunna be a long sentence " * 20]
          assert isinstance(src_text[0] ,_SCREAMING_SNAKE_CASE   )
          _snake_case							=				10
          _snake_case							=				self.tokenizer(_SCREAMING_SNAKE_CASE ,max_length=_SCREAMING_SNAKE_CASE ,truncation=_SCREAMING_SNAKE_CASE   ).input_ids[0]
          self.assertEqual(ids[-2] ,2   )
          self.assertEqual(ids[-1] ,_SCREAMING_SNAKE_CASE   )
          self.assertEqual(len(_SCREAMING_SNAKE_CASE   ) ,_SCREAMING_SNAKE_CASE   )






     def     _lowercase     (			self   )							->      Optional[int]:
          self.assertListEqual(self.tokenizer.convert_tokens_to_ids(["<mask>", "ar_AR"]   ) ,[250_026, 250_001]   )






     def     _lowercase     (			self   )							->      str:
          _snake_case							=				tempfile.mkdtemp()
          _snake_case							=				self.tokenizer.fairseq_tokens_to_ids
          self.tokenizer.save_pretrained(_SCREAMING_SNAKE_CASE   )
          _snake_case							=				MBartTokenizer.from_pretrained(_SCREAMING_SNAKE_CASE   )
          self.assertDictEqual(new_tok.fairseq_tokens_to_ids ,_SCREAMING_SNAKE_CASE   )






     @require_torch
     def     _lowercase     (			self   )							->      Dict:
          _snake_case							=				self.tokenizer(self.src_text ,text_target=self.tgt_text ,padding=_SCREAMING_SNAKE_CASE ,return_tensors="pt"   )
          _snake_case							=				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   )							->      Optional[int]:
          _snake_case							=				self.tokenizer(
              self.src_text ,text_target=self.tgt_text ,padding=_SCREAMING_SNAKE_CASE ,truncation=_SCREAMING_SNAKE_CASE ,max_length=len(self.expected_src_tokens   ) ,return_tensors="pt" ,)

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

          self.assertIsInstance(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   )

          self.assertEqual((2, 14) ,batch.input_ids.shape   )
          self.assertEqual((2, 14) ,batch.attention_mask.shape   )
          _snake_case							=				batch.input_ids.tolist()[0]
          self.assertListEqual(self.expected_src_tokens ,_SCREAMING_SNAKE_CASE   )
          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   )							->      str:
          _snake_case							=				self.tokenizer(self.src_text ,padding=_SCREAMING_SNAKE_CASE ,truncation=_SCREAMING_SNAKE_CASE ,max_length=3 ,return_tensors="pt"   )
          _snake_case							=				self.tokenizer(
              text_target=self.tgt_text ,padding=_SCREAMING_SNAKE_CASE ,truncation=_SCREAMING_SNAKE_CASE ,max_length=10 ,return_tensors="pt"   )
          _snake_case							=				targets["input_ids"]
          _snake_case							=				shift_tokens_right(_SCREAMING_SNAKE_CASE ,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   )							->      Any:
          _snake_case							=				self.tokenizer._build_translation_inputs(
              "A test" ,return_tensors="pt" ,src_lang="en_XX" ,tgt_lang="ar_AR"   )

          self.assertEqual(
              nested_simplify(_SCREAMING_SNAKE_CASE   ) ,{
                  # A, test, EOS, en_XX
                  "input_ids": [[62, 3_034, 2, 250_004]],
                  "attention_mask": [[1, 1, 1, 1]],
                  # ar_AR
                  "forced_bos_token_id": 250_001,
              } ,)


