




"""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


a         =      logging.getLogger(__name__)






def 						_snake_case					( _snake_case							: Optional[Any]=2    ,  _snake_case							: Tuple=3    ,  _snake_case							: int=16    ,  _snake_case							: int = 10    ,  _snake_case							: int = 2							)   -> str:






      '''simple docstring'''


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

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






def 						_snake_case					( _snake_case							: Union[str, Any]    ,  _snake_case							: Union[str, Any]    ,  _snake_case							: Union[str, Any]    ,  _snake_case							: int    ,  _snake_case							: str    ,  _snake_case							: Tuple=None							)   -> Dict:






      '''simple docstring'''


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





class     lowercase_	(					nn.Module							):



 '''simple docstring'''

 def __init__(			self	:	List[Any]		):
       super().__init__()
       _A        =       nn.Parameter(torch.randn(1		)		)
       _A        =       nn.Parameter(torch.randn(1		)		)



 def       lowerCAmelCase_							(			self	:	str   ,							_UpperCAmelCase	:	List[str]		):
       return x * self.a + self.b





class     lowercase_	(					unittest.TestCase							):



 '''simple docstring'''

 def       lowerCAmelCase_							(			self	:	Any		):
       with tempfile.TemporaryDirectory() as tmpdir:
             set_seed(42		)
             _A        =       DummyModel()
             _A        =       torch.optim.Adam(params=model.parameters()   ,							lr=1E-3		)
             _A       ,   _A        =       dummy_dataloaders()
             _A        =       ProjectConfiguration(total_limit=1   ,							project_dir=_UpperCAmelCase   ,							automatic_checkpoint_naming=_UpperCAmelCase		)
             # Train baseline
             _A        =       Accelerator(project_config=_UpperCAmelCase		)
             _A       ,   _A       ,   _A       ,   _A        =       accelerator.prepare(
                 _UpperCAmelCase   ,							_UpperCAmelCase   ,							_UpperCAmelCase   ,							_UpperCAmelCase		)
             # Save initial
             accelerator.save_state()

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

 def       lowerCAmelCase_							(			self	:	List[Any]		):
       with tempfile.TemporaryDirectory() as tmpdir:
             set_seed(42		)
             _A        =       DummyModel()
             _A        =       torch.optim.Adam(params=model.parameters()   ,							lr=1E-3		)
             _A       ,   _A        =       dummy_dataloaders()
             # Train baseline
             _A        =       Accelerator()
             _A       ,   _A       ,   _A       ,   _A        =       accelerator.prepare(
                 _UpperCAmelCase   ,							_UpperCAmelCase   ,							_UpperCAmelCase   ,							_UpperCAmelCase		)
             # Save initial
             _A        =       os.path.join(_UpperCAmelCase   ,							'initial'		)
             accelerator.save_state(_UpperCAmelCase		)
             ((_A)       ,   (_A))        =       model.a.item(), model.b.item()
             _A        =       optimizer.state_dict()
             _A        =       train(3   ,							_UpperCAmelCase   ,							_UpperCAmelCase   ,							_UpperCAmelCase   ,							_UpperCAmelCase		)
             ((_A)       ,   (_A))        =       model.a.item(), model.b.item()
             _A        =       optimizer.state_dict()

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

             _A        =       train(2   ,							_UpperCAmelCase   ,							_UpperCAmelCase   ,							_UpperCAmelCase   ,							_UpperCAmelCase		)
             # Save everything
             _A        =       os.path.join(_UpperCAmelCase   ,							'checkpoint'		)
             accelerator.save_state(_UpperCAmelCase		)

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

 def       lowerCAmelCase_							(			self	:	Optional[Any]		):
       with tempfile.TemporaryDirectory() as tmpdir:
             set_seed(42		)
             _A        =       DummyModel()
             _A        =       torch.optim.Adam(params=model.parameters()   ,							lr=1E-3		)
             _A       ,   _A        =       dummy_dataloaders()
             _A        =       ProjectConfiguration(automatic_checkpoint_naming=_UpperCAmelCase		)

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

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

             _A        =       train(2   ,							_UpperCAmelCase   ,							_UpperCAmelCase   ,							_UpperCAmelCase   ,							_UpperCAmelCase		)
             # Save everything
             accelerator.save_state()

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

 def       lowerCAmelCase_							(			self	:	Optional[int]		):
       _A        =       torch.tensor([1, 2, 3]		)
       _A        =       torch.tensor([2, 3, 4]		)
       _A        =       DummyModel()
       _A        =       torch.optim.Adam(net.parameters()		)
       _A        =       Accelerator()
       with self.assertRaises(_UpperCAmelCase		) as ve:
             accelerator.register_for_checkpointing(_UpperCAmelCase   ,							_UpperCAmelCase   ,							_UpperCAmelCase   ,							_UpperCAmelCase		)
       _A        =       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       lowerCAmelCase_							(			self	:	Optional[Any]		):
       with tempfile.TemporaryDirectory() as tmpdir:
             set_seed(42		)
             _A        =       DummyModel()
             _A        =       torch.optim.Adam(params=model.parameters()   ,							lr=1E-3		)
             _A        =       torch.optim.lr_scheduler.StepLR(_UpperCAmelCase   ,							step_size=1   ,							gamma=0.99		)
             _A       ,   _A        =       dummy_dataloaders()
             _A        =       ProjectConfiguration(automatic_checkpoint_naming=_UpperCAmelCase		)
             # Train baseline
             _A        =       Accelerator(project_dir=_UpperCAmelCase   ,							project_config=_UpperCAmelCase		)
             _A       ,   _A       ,   _A       ,   _A       ,   _A        =       accelerator.prepare(
                 _UpperCAmelCase   ,							_UpperCAmelCase   ,							_UpperCAmelCase   ,							_UpperCAmelCase   ,							_UpperCAmelCase		)
             # Save initial
             accelerator.save_state()
             _A        =       scheduler.state_dict()
             train(3   ,							_UpperCAmelCase   ,							_UpperCAmelCase   ,							_UpperCAmelCase   ,							_UpperCAmelCase   ,							_UpperCAmelCase		)
             self.assertNotEqual(_UpperCAmelCase   ,							scheduler.state_dict()		)

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

 def       lowerCAmelCase_							(			self	:	List[Any]		):
       with tempfile.TemporaryDirectory() as tmpdir:
             set_seed(42		)
             _A        =       DummyModel()
             _A        =       ProjectConfiguration(automatic_checkpoint_naming=_UpperCAmelCase   ,							total_limit=2		)
             # Train baseline
             _A        =       Accelerator(project_dir=_UpperCAmelCase   ,							project_config=_UpperCAmelCase		)
             _A        =       accelerator.prepare(_UpperCAmelCase		)
             # Save 3 states:
             for _ in range(11		):
                   accelerator.save_state()
             self.assertTrue(not os.path.exists(os.path.join(_UpperCAmelCase   ,							'checkpoints'   ,							'checkpoint_0'		)		)		)
             self.assertTrue(os.path.exists(os.path.join(_UpperCAmelCase   ,							'checkpoints'   ,							'checkpoint_9'		)		)		)
             self.assertTrue(os.path.exists(os.path.join(_UpperCAmelCase   ,							'checkpoints'   ,							'checkpoint_10'		)		)		)



 @require_cuda
 def       lowerCAmelCase_							(			self	:	Tuple		):
       _A        =       ['torchrun', F'''--nproc_per_node={torch.cuda.device_count()}''', inspect.getfile(self.__class__		)]
       execute_subprocess_async(_UpperCAmelCase   ,							env=os.environ.copy()		)


if __name__ == "__main__":
       a         =      '''/tmp/accelerate/state_checkpointing'''
       a         =      DummyModel()
       a         =      torch.optim.Adam(params=model.parameters(), lr=1e-3)
       a         =      torch.optim.lr_scheduler.StepLR(optimizer, step_size=1, gamma=0.9_9)
       a     ,						a         =      dummy_dataloaders()
       a         =      ProjectConfiguration(automatic_checkpoint_naming=True)
       # Train baseline
       a         =      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)
       a     ,						a     ,						a     ,						a     ,						a         =      accelerator.prepare(
           model, optimizer, train_dataloader, valid_dataloader, scheduler
       )
       a     ,						a         =      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:
              a         =      group['''params'''][0].device
              break
       assert param_device.type == accelerator.device.type
       a         =      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:
              a         =      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:
              a         =      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"""

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

import torch
from torch import nn
from torch.utils.data import DistributedSampler, RandomSampler

from transformers import PreTrainedModel, Trainer, logging
from transformers.integrations import is_fairscale_available
from transformers.models.fsmt.configuration_fsmt import FSMTConfig
from transformers.optimization import (
    Adafactor,
    AdamW,
    get_constant_schedule,
    get_constant_schedule_with_warmup,
    get_cosine_schedule_with_warmup,
    get_cosine_with_hard_restarts_schedule_with_warmup,
    get_linear_schedule_with_warmup,
    get_polynomial_decay_schedule_with_warmup,
)
from transformers.trainer_pt_utils import get_tpu_sampler
from transformers.training_args import ParallelMode
from transformers.utils import is_torch_tpu_available


if is_fairscale_available():
       from fairscale.optim import OSS


a         =      logging.get_logger(__name__)

a         =      {
    '''linear''': get_linear_schedule_with_warmup,
    '''cosine''': get_cosine_schedule_with_warmup,
    '''cosine_w_restarts''': get_cosine_with_hard_restarts_schedule_with_warmup,
    '''polynomial''': get_polynomial_decay_schedule_with_warmup,
    '''constant''': get_constant_schedule,
    '''constant_w_warmup''': get_constant_schedule_with_warmup,
}





class     lowercase_	(					__lowerCAmelCase							):



 '''simple docstring'''

 def __init__(			self	:	Any   ,							_UpperCAmelCase	:	Optional[int]=None   ,							_UpperCAmelCase	:	Tuple=None   ,							*_UpperCAmelCase	:	int   ,							**_UpperCAmelCase	:	Tuple		):
       super().__init__(*_UpperCAmelCase   ,							**_UpperCAmelCase		)

       if config is None:
             assert isinstance(self.model   ,							_UpperCAmelCase		), (
                 "If no `config` is passed the model to be trained has to be of type `PreTrainedModel`, but is"
                 F''' {self.model.__class__}'''
             )
             _A        =       self.model.config
       else:
             _A        =       config

       _A        =       data_args
       _A        =       self.config.tgt_vocab_size if isinstance(self.config   ,							_UpperCAmelCase		) else self.config.vocab_size

       if self.args.label_smoothing != 0 or (self.data_args is not None and self.data_args.ignore_pad_token_for_loss):
             assert self.config.pad_token_id is not None, (
                 "Make sure that `config.pad_token_id` is correcly defined when ignoring `pad_token` for loss"
                 " calculation or doing label smoothing."
             )

       if self.config.pad_token_id is None and self.config.eos_token_id is not None:
             logger.warning(
                 F'''The `config.pad_token_id` is `None`. Using `config.eos_token_id` = {self.config.eos_token_id} for'''
                 ' padding..'		)

       if self.args.label_smoothing == 0:
             _A        =       torch.nn.CrossEntropyLoss(ignore_index=self.config.pad_token_id		)
       else:
             # dynamically import label_smoothed_nll_loss
             from utils import label_smoothed_nll_loss

             _A        =       label_smoothed_nll_loss

 def       lowerCAmelCase_							(			self	:	List[str]   ,							_UpperCAmelCase	:	int		):
       if self.optimizer is None:
             _A        =       ['bias', 'LayerNorm.weight']
             _A        =       [
                 {
                     'params': [p for n, p in self.model.named_parameters() if not any(nd in n for nd in no_decay		)],
                     'weight_decay': self.args.weight_decay,
                 },
                 {
                     'params': [p for n, p in self.model.named_parameters() if any(nd in n for nd in no_decay		)],
                     'weight_decay': 0.0,
                 },
             ]
             _A        =       Adafactor if self.args.adafactor else AdamW
             if self.args.adafactor:
                   _A        =       Adafactor
                   _A        =       {'scale_parameter': False, 'relative_step': False}
             else:
                   _A        =       AdamW
                   _A        =       {
                       'betas': (self.args.adam_betaa, self.args.adam_betaa),
                       'eps': self.args.adam_epsilon,
                   }
             _A        =       self.args.learning_rate
             if self.sharded_ddp:
                   _A        =       OSS(
                       params=_UpperCAmelCase   ,							optim=_UpperCAmelCase   ,							**_UpperCAmelCase   ,							)
             else:
                   _A        =       optimizer_cls(_UpperCAmelCase   ,							**_UpperCAmelCase		)

       if self.lr_scheduler is None:
             _A        =       self._get_lr_scheduler(_UpperCAmelCase		)
       else:  # ignoring --lr_scheduler
             logger.warning('scheduler is passed to `Seq2SeqTrainer`, `--lr_scheduler` arg is ignored.'		)

 def       lowerCAmelCase_							(			self	:	str   ,							_UpperCAmelCase	:	Any		):
       _A        =       arg_to_scheduler[self.args.lr_scheduler]
       if self.args.lr_scheduler == "constant":
             _A        =       schedule_func(self.optimizer		)
       elif self.args.lr_scheduler == "constant_w_warmup":
             _A        =       schedule_func(self.optimizer   ,							num_warmup_steps=self.args.warmup_steps		)
       else:
             _A        =       schedule_func(
                 self.optimizer   ,							num_warmup_steps=self.args.warmup_steps   ,							num_training_steps=_UpperCAmelCase		)
       return scheduler

 def       lowerCAmelCase_							(			self	:	Any		):
       if isinstance(self.train_dataset   ,							torch.utils.data.IterableDataset		):
             return None
       elif is_torch_tpu_available():
             return get_tpu_sampler(self.train_dataset		)
       else:
             if self.args.sortish_sampler:
                   self.train_dataset.make_sortish_sampler(
                       self.args.per_device_train_batch_size   ,							distributed=(self.args.parallel_mode == ParallelMode.DISTRIBUTED)   ,							)

             return (
                 RandomSampler(self.train_dataset		)
                 if self.args.local_rank == -1
                 else DistributedSampler(self.train_dataset		)
             )

 def       lowerCAmelCase_							(			self	:	Optional[int]   ,							_UpperCAmelCase	:	str   ,							_UpperCAmelCase	:	str   ,							_UpperCAmelCase	:	int		):
       if self.args.label_smoothing == 0:
             if self.data_args is not None and self.data_args.ignore_pad_token_for_loss:
                   # force training to ignore pad token
                   _A        =       model(**_UpperCAmelCase   ,							use_cache=_UpperCAmelCase		)[0]
                   _A        =       self.loss_fn(logits.view(-1   ,							logits.shape[-1]		)   ,							labels.view(-1		)		)
             else:
                   # compute usual loss via models
                   _A       ,   _A        =       model(**_UpperCAmelCase   ,							labels=_UpperCAmelCase   ,							use_cache=_UpperCAmelCase		)[:2]
       else:
             # compute label smoothed loss
             _A        =       model(**_UpperCAmelCase   ,							use_cache=_UpperCAmelCase		)[0]
             _A        =       torch.nn.functional.log_softmax(_UpperCAmelCase   ,							dim=-1		)
             _A       ,   _A        =       self.loss_fn(_UpperCAmelCase   ,							_UpperCAmelCase   ,							self.args.label_smoothing   ,							ignore_index=self.config.pad_token_id		)
       return loss, logits

 def       lowerCAmelCase_							(			self	:	Union[str, Any]   ,							_UpperCAmelCase	:	List[str]   ,							_UpperCAmelCase	:	Optional[int]		):
       _A        =       inputs.pop('labels'		)
       _A       ,   _A        =       self._compute_loss(_UpperCAmelCase   ,							_UpperCAmelCase   ,							_UpperCAmelCase		)
       return loss

 def       lowerCAmelCase_							(			self	:	Any   ,							_UpperCAmelCase	:	nn.Module   ,							_UpperCAmelCase	:	Dict[str, Union[torch.Tensor, Any]]   ,							_UpperCAmelCase	:	bool   ,							_UpperCAmelCase	:	Optional[List[str]] = None   ,							):
       _A        =       self._prepare_inputs(_UpperCAmelCase		)

       _A        =       {
           'max_length': self.data_args.val_max_target_length
           if self.data_args is not None
           else self.config.max_length,
           'num_beams': self.data_args.eval_beams if self.data_args is not None else self.config.num_beams,
       }

       if self.args.predict_with_generate and not self.args.prediction_loss_only:
             _A        =       self.model.generate(
                 inputs['input_ids']   ,							attention_mask=inputs['attention_mask']   ,							**_UpperCAmelCase   ,							)
             # in case the batch is shorter than max length, the output should be padded
             if generated_tokens.shape[-1] < gen_kwargs["max_length"]:
                   _A        =       self._pad_tensors_to_max_len(_UpperCAmelCase   ,							gen_kwargs['max_length']		)

       _A        =       inputs.pop('labels'		)
       with torch.no_grad():
             # compute loss on predict data
             _A       ,   _A        =       self._compute_loss(_UpperCAmelCase   ,							_UpperCAmelCase   ,							_UpperCAmelCase		)

       _A        =       loss.mean().detach()
       if self.args.prediction_loss_only:
             return (loss, None, None)

       _A        =       generated_tokens if self.args.predict_with_generate else logits

       if labels.shape[-1] < gen_kwargs["max_length"]:
             _A        =       self._pad_tensors_to_max_len(_UpperCAmelCase   ,							gen_kwargs['max_length']		)

       return (loss, logits, labels)



 def       lowerCAmelCase_							(			self	:	str   ,							_UpperCAmelCase	:	Tuple   ,							_UpperCAmelCase	:	Tuple		):
       # If PAD token is not defined at least EOS token has to be defined
       _A        =       self.config.pad_token_id if self.config.pad_token_id is not None else self.config.eos_token_id

       if pad_token_id is None:
             raise ValueError(
                 'Make sure that either `config.pad_token_id` or `config.eos_token_id` is defined if tensor has to be'
                 F''' padded to `max_length`={max_length}'''		)

       _A        =       pad_token_id * torch.ones(
           (tensor.shape[0], max_length)   ,							dtype=tensor.dtype   ,							device=tensor.device		)
       _A        =       tensor
       return padded_tensor

