'''simple docstring'''




import asyncio
import os
import shutil
import subprocess
import sys
import tempfile
import unittest
from distutils.util import strtobool
from functools import partial
from pathlib import Path
from typing import List, Union
from unittest import mock

import torch

from ..state import AcceleratorState, PartialState
from ..utils import (
    gather,
    is_bnb_available,
    is_comet_ml_available,
    is_datasets_available,
    is_deepspeed_available,
    is_mps_available,
    is_safetensors_available,
    is_tensorboard_available,
    is_torch_version,
    is_tpu_available,
    is_transformers_available,
    is_wandb_available,
    is_xpu_available,
)

def 	UpperCamelCase_							(					_UpperCAmelCase  :      Dict			,	_UpperCAmelCase  :      List[Any]=False    )    ->    int:


       """simple docstring"""







       try:
              _UpperCAmelCase     :  str							      =    os.environ[key]
       except KeyError:
              # KEY isn't set, default to `default`.
              _UpperCAmelCase     :  Optional[Any]							      =    default
       else:
              # KEY is set, convert it to True or False.
              try:
                     _UpperCAmelCase     :  List[str]							      =    strtobool(_UpperCAmelCase    )
              except ValueError:
                     # More values are supported, but let's keep the message simple.
                     raise ValueError(F"""If set, {key} must be yes or no."""    )
       return _value


__SCREAMING_SNAKE_CASE       :     Optional[Any]     =  parse_flag_from_env("""RUN_SLOW""", default=False)

def 	UpperCamelCase_							(					_UpperCAmelCase  :      List[Any]    )    ->    Union[str, Any]:


       """simple docstring"""







       return unittest.skip("Test was skipped"    )(_UpperCAmelCase    )

def 	UpperCamelCase_							(					_UpperCAmelCase  :      str    )    ->    Tuple:


       """simple docstring"""







       return unittest.skipUnless(_run_slow_tests			,	"test is slow"    )(_UpperCAmelCase    )

def 	UpperCamelCase_							(					_UpperCAmelCase  :      Dict    )    ->    List[Any]:


       """simple docstring"""







       return unittest.skipUnless(not torch.cuda.is_available()			,	"test requires only a CPU"    )(_UpperCAmelCase    )

def 	UpperCamelCase_							(					_UpperCAmelCase  :      Any    )    ->    Optional[Any]:


       """simple docstring"""







       return unittest.skipUnless(torch.cuda.is_available()			,	"test requires a GPU"    )(_UpperCAmelCase    )

def 	UpperCamelCase_							(					_UpperCAmelCase  :      Optional[Any]    )    ->    List[str]:


       """simple docstring"""







       return unittest.skipUnless(is_xpu_available()			,	"test requires a XPU"    )(_UpperCAmelCase    )

def 	UpperCamelCase_							(					_UpperCAmelCase  :      List[str]    )    ->    List[Any]:


       """simple docstring"""







       return unittest.skipUnless(is_mps_available()			,	"test requires a `mps` backend support in `torch`"    )(_UpperCAmelCase    )

def 	UpperCamelCase_							(					_UpperCAmelCase  :      Dict    )    ->    Dict:


       """simple docstring"""







       return unittest.skipUnless(
           is_transformers_available() and is_datasets_available()			,	"test requires the Hugging Face suite"    )(_UpperCAmelCase    )

def 	UpperCamelCase_							(					_UpperCAmelCase  :      Union[str, Any]    )    ->    Union[str, Any]:


       """simple docstring"""







       return unittest.skipUnless(is_bnb_available()			,	"test requires the bitsandbytes library"    )(_UpperCAmelCase    )

def 	UpperCamelCase_							(					_UpperCAmelCase  :      Tuple    )    ->    str:


       """simple docstring"""







       return unittest.skipUnless(is_tpu_available()			,	"test requires TPU"    )(_UpperCAmelCase    )

def 	UpperCamelCase_							(					_UpperCAmelCase  :      Dict    )    ->    Tuple:


       """simple docstring"""







       return unittest.skipUnless(torch.cuda.device_count() == 1			,	"test requires a GPU"    )(_UpperCAmelCase    )

def 	UpperCamelCase_							(					_UpperCAmelCase  :      Dict    )    ->    str:


       """simple docstring"""







       return unittest.skipUnless(torch.xpu.device_count() == 1			,	"test requires a XPU"    )(_UpperCAmelCase    )

def 	UpperCamelCase_							(					_UpperCAmelCase  :      List[Any]    )    ->    List[Any]:


       """simple docstring"""







       return unittest.skipUnless(torch.cuda.device_count() > 1			,	"test requires multiple GPUs"    )(_UpperCAmelCase    )

def 	UpperCamelCase_							(					_UpperCAmelCase  :      int    )    ->    Optional[int]:


       """simple docstring"""







       return unittest.skipUnless(torch.xpu.device_count() > 1			,	"test requires multiple XPUs"    )(_UpperCAmelCase    )

def 	UpperCamelCase_							(					_UpperCAmelCase  :      List[Any]    )    ->    List[Any]:


       """simple docstring"""







       return unittest.skipUnless(is_safetensors_available()			,	"test requires safetensors"    )(_UpperCAmelCase    )

def 	UpperCamelCase_							(					_UpperCAmelCase  :      str    )    ->    Optional[Any]:


       """simple docstring"""







       return unittest.skipUnless(is_deepspeed_available()			,	"test requires DeepSpeed"    )(_UpperCAmelCase    )

def 	UpperCamelCase_							(					_UpperCAmelCase  :      int    )    ->    Dict:


       """simple docstring"""







       return unittest.skipUnless(is_torch_version(">="			,	"1.12.0"    )			,	"test requires torch version >= 1.12.0"    )(_UpperCAmelCase    )

def 	UpperCamelCase_							(					_UpperCAmelCase  :      List[Any]=None			,	_UpperCAmelCase  :      Optional[int]=None    )    ->    Dict:


       """simple docstring"""







       if test_case is None:
              return partial(_UpperCAmelCase			,	version=_UpperCAmelCase    )
       return unittest.skipUnless(is_torch_version(">="			,	_UpperCAmelCase    )			,	F"""test requires torch version >= {version}"""    )(_UpperCAmelCase    )

def 	UpperCamelCase_							(					_UpperCAmelCase  :      int    )    ->    List[Any]:


       """simple docstring"""







       return unittest.skipUnless(is_tensorboard_available()			,	"test requires Tensorboard"    )(_UpperCAmelCase    )

def 	UpperCamelCase_							(					_UpperCAmelCase  :      int    )    ->    Optional[int]:


       """simple docstring"""







       return unittest.skipUnless(is_wandb_available()			,	"test requires wandb"    )(_UpperCAmelCase    )

def 	UpperCamelCase_							(					_UpperCAmelCase  :      Union[str, Any]    )    ->    Dict:


       """simple docstring"""







       return unittest.skipUnless(is_comet_ml_available()			,	"test requires comet_ml"    )(_UpperCAmelCase    )


__SCREAMING_SNAKE_CASE       :     str     =  (
    any([is_wandb_available(), is_tensorboard_available()]) and not is_comet_ml_available()
)

def 	UpperCamelCase_							(					_UpperCAmelCase  :      int    )    ->    str:


       """simple docstring"""







       return unittest.skipUnless(
           _atleast_one_tracker_available			,	"test requires at least one tracker to be available and for `comet_ml` to not be installed"			,	)(_UpperCAmelCase    )

class 	lowerCamelCase_	(unittest.TestCase    ):
      '''simple docstring'''

      __UpperCamelCase:  Union[str, Any]						=						True




      @classmethod
      def      _A	(       cls						:				Any       ):
             _UpperCAmelCase     :  Union[str, Any]							      =    tempfile.mkdtemp()




      @classmethod
      def      _A	(       cls						:				Union[str, Any]       ):
             if os.path.exists(cls.tmpdir       ):
                    shutil.rmtree(cls.tmpdir       )





      def      _A	(       self						:				Tuple       ):
             if self.clear_on_setup:
                    for path in Path(self.tmpdir       ).glob("**/*"       ):
                           if path.is_file():
                                  path.unlink()
                           elif path.is_dir():
                                  shutil.rmtree(A       )

class 	lowerCamelCase_	(unittest.TestCase    ):
      '''simple docstring'''

      def      _A	(       self						:				Dict       ):
             super().tearDown()
             # Reset the state of the AcceleratorState singleton.
             AcceleratorState._reset_state()
             PartialState._reset_state()

class 	lowerCamelCase_	(unittest.TestCase    ):
      '''simple docstring'''

      def      _A	(       self						:				Tuple						,					A						:				Union[mock.Mock, List[mock.Mock]]       ):
             _UpperCAmelCase     :  Optional[int]							      =    mocks if isinstance(A						,					(tuple, list)       ) else [mocks]
             for m in self.mocks:
                    m.start()
                    self.addCleanup(m.stop       )

def 	UpperCamelCase_							(					_UpperCAmelCase  :      str    )    ->    str:


       """simple docstring"""







       _UpperCAmelCase     :  Optional[int]							      =    AcceleratorState()
       _UpperCAmelCase     :  Dict							      =    tensor[None].clone().to(state.device    )
       _UpperCAmelCase     :  Union[str, Any]							      =    gather(_UpperCAmelCase    ).cpu()
       _UpperCAmelCase     :  str							      =    tensor[0].cpu()
       for i in range(tensors.shape[0]    ):
              if not torch.equal(tensors[i]			,	_UpperCAmelCase    ):
                     return False
       return True

class 	lowerCamelCase_	:
      '''simple docstring'''

      def __init__(       self						:				Any						,					A						:				int						,					A						:				str						,					A						:				Optional[int]       ):
             _UpperCAmelCase     :  Dict							      =    returncode
             _UpperCAmelCase     :  Optional[int]							      =    stdout
             _UpperCAmelCase     :  str							      =    stderr

async def 	UpperCamelCase_							(					_UpperCAmelCase  :      Any			,	_UpperCAmelCase  :      Any    )    ->    Optional[int]:


       """simple docstring"""







       while True:
              _UpperCAmelCase     :  Union[str, Any]							      =    await stream.readline()
              if line:
                     callback(_UpperCAmelCase    )
              else:
                     break

async def 	UpperCamelCase_							(					_UpperCAmelCase  :      List[str]			,	_UpperCAmelCase  :      Optional[int]=None			,	_UpperCAmelCase  :      Any=None			,	_UpperCAmelCase  :      List[Any]=None			,	_UpperCAmelCase  :      Any=False			,	_UpperCAmelCase  :      Optional[Any]=False    )    ->    _RunOutput:


       """simple docstring"""







       if echo:
              print("\nRunning: "			,	" ".join(_UpperCAmelCase    )    )

       _UpperCAmelCase     :  Optional[int]							      =    await asyncio.create_subprocess_exec(
           cmd[0]			,	*cmd[1:]			,	stdin=_UpperCAmelCase			,	stdout=asyncio.subprocess.PIPE			,	stderr=asyncio.subprocess.PIPE			,	env=_UpperCAmelCase			,	)

       # note: there is a warning for a possible deadlock when using `wait` with huge amounts of data in the pipe
       # https://docs.python.org/3/library/asyncio-subprocess.html#asyncio.asyncio.subprocess.Process.wait
       #
       # If it starts hanging, will need to switch to the following code. The problem is that no data
       # will be seen until it's done and if it hangs for example there will be no debug info.
       # out, err = await p.communicate()
       # return _RunOutput(p.returncode, out, err)

       _UpperCAmelCase     :  Tuple							      =    []
       _UpperCAmelCase     :  Union[str, Any]							      =    []

       def tee(_UpperCAmelCase  :      List[Any]			,	_UpperCAmelCase  :      Union[str, Any]			,	_UpperCAmelCase  :      Union[str, Any]			,	_UpperCAmelCase  :      Optional[int]=""    ):
              _UpperCAmelCase     :  Optional[int]							      =    line.decode("utf-8"    ).rstrip()
              sink.append(_UpperCAmelCase    )
              if not quiet:
                     print(_UpperCAmelCase			,	_UpperCAmelCase			,	file=_UpperCAmelCase    )

    # XXX: the timeout doesn't seem to make any difference here
       await asyncio.wait(
           [
               asyncio.create_task(_read_stream(p.stdout			,	lambda _UpperCAmelCase    : tee(_UpperCAmelCase			,	_UpperCAmelCase			,	sys.stdout			,	label="stdout:"    )    )    ),
               asyncio.create_task(_read_stream(p.stderr			,	lambda _UpperCAmelCase    : tee(_UpperCAmelCase			,	_UpperCAmelCase			,	sys.stderr			,	label="stderr:"    )    )    ),
           ]			,	timeout=_UpperCAmelCase			,	)
       return _RunOutput(await p.wait()			,	_UpperCAmelCase			,	_UpperCAmelCase    )

def 	UpperCamelCase_							(					_UpperCAmelCase  :      Any			,	_UpperCAmelCase  :      Union[str, Any]=None			,	_UpperCAmelCase  :      str=None			,	_UpperCAmelCase  :      List[Any]=180			,	_UpperCAmelCase  :      List[str]=False			,	_UpperCAmelCase  :      List[Any]=True    )    ->    _RunOutput:


       """simple docstring"""







       _UpperCAmelCase     :  str							      =    asyncio.get_event_loop()
       _UpperCAmelCase     :  Dict							      =    loop.run_until_complete(
           _stream_subprocess(_UpperCAmelCase			,	env=_UpperCAmelCase			,	stdin=_UpperCAmelCase			,	timeout=_UpperCAmelCase			,	quiet=_UpperCAmelCase			,	echo=_UpperCAmelCase    )    )

       _UpperCAmelCase     :  Any							      =    " ".join(_UpperCAmelCase    )
       if result.returncode > 0:
              _UpperCAmelCase     :  Union[str, Any]							      =    "\n".join(result.stderr    )
              raise RuntimeError(
                  F"""'{cmd_str}' failed with returncode {result.returncode}\n\n"""
                  F"""The combined stderr from workers follows:\n{stderr}"""    )

       return result

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

      pass

def 	UpperCamelCase_							(					_UpperCAmelCase  :      List[str]			,	_UpperCAmelCase  :      List[Any]=False    )    ->    Optional[Any]:


       """simple docstring"""







       try:
              _UpperCAmelCase     :  Union[str, Any]							      =    subprocess.check_output(_UpperCAmelCase			,	stderr=subprocess.STDOUT    )
              if return_stdout:
                     if hasattr(_UpperCAmelCase			,	"decode"    ):
                            _UpperCAmelCase     :  Optional[Any]							      =    output.decode("utf-8"    )
                     return output
       except subprocess.CalledProcessError as e:
              raise SubprocessCallException(
                  F"""Command `{' '.join(_UpperCAmelCase    )}` failed with the following error:\n\n{e.output.decode()}"""    ) from e


'''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


__SCREAMING_SNAKE_CASE       :     str     =  logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE       :     Union[str, Any]     =  {
    """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 	lowerCamelCase_	(snake_case__    ):
      '''simple docstring'''

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

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

             _UpperCAmelCase     :  List[Any]							      =    data_args
             _UpperCAmelCase     :  str							      =    self.config.tgt_vocab_size if isinstance(self.config						,					A       ) 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:
                    _UpperCAmelCase     :  Optional[Any]							      =    torch.nn.CrossEntropyLoss(ignore_index=self.config.pad_token_id       )
             else:
                    # dynamically import label_smoothed_nll_loss
                    from utils import label_smoothed_nll_loss

                    _UpperCAmelCase     :  Dict							      =    label_smoothed_nll_loss




      def      _A	(       self						:				Tuple						,					A						:				int       ):
             if self.optimizer is None:
                    _UpperCAmelCase     :  Tuple							      =    ["bias", "LayerNorm.weight"]
                    _UpperCAmelCase     :  str							      =    [
                        {
                            "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,
                        },
                    ]
                    _UpperCAmelCase     :  int							      =    Adafactor if self.args.adafactor else AdamW
                    if self.args.adafactor:
                           _UpperCAmelCase     :  List[str]							      =    Adafactor
                           _UpperCAmelCase     :  List[Any]							      =    {"scale_parameter": False, "relative_step": False}
                    else:
                           _UpperCAmelCase     :  List[str]							      =    AdamW
                           _UpperCAmelCase     :  List[str]							      =    {
                               "betas": (self.args.adam_betaa, self.args.adam_betaa),
                               "eps": self.args.adam_epsilon,
                           }
                    _UpperCAmelCase     :  List[Any]							      =    self.args.learning_rate
                    if self.sharded_ddp:
                           _UpperCAmelCase     :  List[Any]							      =    OSS(
                               params=A						,					optim=A						,					**A						,					)
                    else:
                           _UpperCAmelCase     :  Union[str, Any]							      =    optimizer_cls(A						,					**A       )

             if self.lr_scheduler is None:
                    _UpperCAmelCase     :  List[str]							      =    self._get_lr_scheduler(A       )
             else:  # ignoring --lr_scheduler
                    logger.warning("scheduler is passed to `Seq2SeqTrainer`, `--lr_scheduler` arg is ignored."       )




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




      def      _A	(       self						:				Tuple       ):
             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      _A	(       self						:				Any						,					A						:				Union[str, Any]						,					A						:				Union[str, Any]						,					A						:				List[Any]       ):
             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
                           _UpperCAmelCase     :  List[str]							      =    model(**A						,					use_cache=A       )[0]
                           _UpperCAmelCase     :  int							      =    self.loss_fn(logits.view(-1						,					logits.shape[-1]       )						,					labels.view(-1       )       )
                    else:
                           # compute usual loss via models
                           _UpperCAmelCase      ,  _UpperCAmelCase     :  Any							      =    model(**A						,					labels=A						,					use_cache=A       )[:2]
             else:
                    # compute label smoothed loss
                    _UpperCAmelCase     :  Optional[int]							      =    model(**A						,					use_cache=A       )[0]
                    _UpperCAmelCase     :  List[str]							      =    torch.nn.functional.log_softmax(A						,					dim=-1       )
                    _UpperCAmelCase      ,  _UpperCAmelCase     :  Union[str, Any]							      =    self.loss_fn(A						,					A						,					self.args.label_smoothing						,					ignore_index=self.config.pad_token_id       )
             return loss, logits




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




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

             _UpperCAmelCase     :  Dict							      =    {
                 "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:
                    _UpperCAmelCase     :  Dict							      =    self.model.generate(
                        inputs["input_ids"]						,					attention_mask=inputs["attention_mask"]						,					**A						,					)
                    # in case the batch is shorter than max length, the output should be padded
                    if generated_tokens.shape[-1] < gen_kwargs["max_length"]:
                           _UpperCAmelCase     :  int							      =    self._pad_tensors_to_max_len(A						,					gen_kwargs["max_length"]       )

             _UpperCAmelCase     :  Any							      =    inputs.pop("labels"       )
             with torch.no_grad():
                    # compute loss on predict data
                    _UpperCAmelCase      ,  _UpperCAmelCase     :  str							      =    self._compute_loss(A						,					A						,					A       )

             _UpperCAmelCase     :  List[str]							      =    loss.mean().detach()
             if self.args.prediction_loss_only:
                    return (loss, None, None)

             _UpperCAmelCase     :  str							      =    generated_tokens if self.args.predict_with_generate else logits

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

             return (loss, logits, labels)





      def      _A	(       self						:				Dict						,					A						:				int						,					A						:				List[str]       ):
             # If PAD token is not defined at least EOS token has to be defined
             _UpperCAmelCase     :  Union[str, Any]							      =    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}"""       )

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

