


import asyncio
import os
import re
import sys
import tempfile
import unittest
from contextlib import contextmanager
from copy import deepcopy
from distutils.util import strtobool
from enum import Enum
from importlib.util import find_spec
from pathlib import Path
from unittest.mock import patch

import pyarrow as pa
import pytest
import requests
from packaging import version

from datasets import config


if config.PY_VERSION < version.parse('''3.8'''):
						import importlib_metadata
else:
						import importlib.metadata as importlib_metadata






def 							__lowercase     (				__lowerCAmelCase							:       List[str]      ,					__lowerCAmelCase							:       List[str]=False     ):
							try:
														a__    =  os.environ[key]
							except KeyError:
														# KEY isn't set, default to `default`.
														a__    =  default
							else:
														# KEY is set, convert it to True or False.
														try:
																					a__    =  strtobool(__lowerCAmelCase     )
														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


snake_case							: Dict      =   parse_flag_from_env('''RUN_SLOW''', default=False)
snake_case							: str      =   parse_flag_from_env('''RUN_REMOTE''', default=False)
snake_case							: int      =   parse_flag_from_env('''RUN_LOCAL''', default=True)
snake_case							: Dict      =   parse_flag_from_env('''RUN_PACKAGED''', default=True)

# Compression
snake_case							: List[Any]      =   pytest.mark.skipif(not config.LZ4_AVAILABLE, reason='''test requires lz4''')
snake_case							: Union[str, Any]      =   pytest.mark.skipif(not config.PY7ZR_AVAILABLE, reason='''test requires py7zr''')
snake_case							: List[Any]      =   pytest.mark.skipif(not config.ZSTANDARD_AVAILABLE, reason='''test requires zstandard''')

# Audio
snake_case							: List[Any]      =   pytest.mark.skipif(
    # On Windows and OS X, soundfile installs sndfile
    find_spec('''soundfile''') is None or version.parse(importlib_metadata.version('''soundfile''')) < version.parse('''0.12.0'''),
    reason='''test requires sndfile>=0.12.1: \'pip install \"soundfile>=0.12.1\"\'; ''',
)

# Beam
snake_case							: List[str]      =   pytest.mark.skipif(
    not config.BEAM_AVAILABLE or config.DILL_VERSION >= version.parse('''0.3.2'''),
    reason='''test requires apache-beam and a compatible dill version''',
)

# Dill-cloudpickle compatibility
snake_case							: Dict      =   pytest.mark.skipif(
    config.DILL_VERSION <= version.parse('''0.3.2'''),
    reason='''test requires dill>0.3.2 for cloudpickle compatibility''',
)

# Windows
snake_case							: Tuple      =   pytest.mark.skipif(
    sys.platform == '''win32''',
    reason='''test should not be run on Windows''',
)






def 							__lowercase     (				__lowerCAmelCase							:       List[Any]     ):
							try:
														import faiss  # noqa
							except ImportError:
														a__    =  unittest.skip('test requires faiss'     )(__lowerCAmelCase     )
							return test_case






def 							__lowercase     (				__lowerCAmelCase							:       Optional[int]     ):
							try:
														import regex  # noqa
							except ImportError:
														a__    =  unittest.skip('test requires regex'     )(__lowerCAmelCase     )
							return test_case






def 							__lowercase     (				__lowerCAmelCase							:       int     ):
							try:
														import elasticsearch  # noqa
							except ImportError:
														a__    =  unittest.skip('test requires elasticsearch'     )(__lowerCAmelCase     )
							return test_case






def 							__lowercase     (				__lowerCAmelCase							:       int     ):
							try:
														import sqlalchemy  # noqa
							except ImportError:
														a__    =  unittest.skip('test requires sqlalchemy'     )(__lowerCAmelCase     )
							return test_case






def 							__lowercase     (				__lowerCAmelCase							:       Optional[Any]     ):
							if not config.TORCH_AVAILABLE:
														a__    =  unittest.skip('test requires PyTorch'     )(__lowerCAmelCase     )
							return test_case






def 							__lowercase     (				__lowerCAmelCase							:       Tuple     ):
							if not config.TF_AVAILABLE:
														a__    =  unittest.skip('test requires TensorFlow'     )(__lowerCAmelCase     )
							return test_case






def 							__lowercase     (				__lowerCAmelCase							:       Tuple     ):
							if not config.JAX_AVAILABLE:
														a__    =  unittest.skip('test requires JAX'     )(__lowerCAmelCase     )
							return test_case






def 							__lowercase     (				__lowerCAmelCase							:       Union[str, Any]     ):
							if not config.PIL_AVAILABLE:
														a__    =  unittest.skip('test requires Pillow'     )(__lowerCAmelCase     )
							return test_case






def 							__lowercase     (				__lowerCAmelCase							:       Union[str, Any]     ):
							try:
														import transformers  # noqa F401
							except ImportError:
														return unittest.skip('test requires transformers'     )(__lowerCAmelCase     )
							else:
														return test_case






def 							__lowercase     (				__lowerCAmelCase							:       List[Any]     ):
							try:
														import tiktoken  # noqa F401
							except ImportError:
														return unittest.skip('test requires tiktoken'     )(__lowerCAmelCase     )
							else:
														return test_case






def 							__lowercase     (				__lowerCAmelCase							:       Dict     ):
							try:
														import spacy  # noqa F401
							except ImportError:
														return unittest.skip('test requires spacy'     )(__lowerCAmelCase     )
							else:
														return test_case






def 							__lowercase     (				__lowerCAmelCase							:       Any     ):

							def _require_spacy_model(__lowerCAmelCase							:       Any     ):
														try:
																					import spacy  # noqa F401

																					spacy.load(__lowerCAmelCase     )
														except ImportError:
																					return unittest.skip('test requires spacy'     )(__lowerCAmelCase     )
														except OSError:
																					return unittest.skip('test requires spacy model \'{}\''.format(__lowerCAmelCase     )     )(__lowerCAmelCase     )
														else:
																					return test_case

							return _require_spacy_model






def 							__lowercase     (				__lowerCAmelCase							:       Dict     ):
							try:
														import pyspark  # noqa F401
							except ImportError:
														return unittest.skip('test requires pyspark'     )(__lowerCAmelCase     )
							else:
														return test_case






def 							__lowercase     (				__lowerCAmelCase							:       Tuple     ):
							try:
														import joblibspark  # noqa F401
							except ImportError:
														return unittest.skip('test requires joblibspark'     )(__lowerCAmelCase     )
							else:
														return test_case






def 							__lowercase     (				__lowerCAmelCase							:       Dict     ):
							if not _run_slow_tests or _run_slow_tests == 0:
														a__    =  unittest.skip('test is slow'     )(__lowerCAmelCase     )
							return test_case






def 							__lowercase     (				__lowerCAmelCase							:       Any     ):
							if not _run_local_tests or _run_local_tests == 0:
														a__    =  unittest.skip('test is local'     )(__lowerCAmelCase     )
							return test_case






def 							__lowercase     (				__lowerCAmelCase							:       str     ):
							if not _run_packaged_tests or _run_packaged_tests == 0:
														a__    =  unittest.skip('test is packaged'     )(__lowerCAmelCase     )
							return test_case






def 							__lowercase     (				__lowerCAmelCase							:       Tuple     ):
							if not _run_remote_tests or _run_remote_tests == 0:
														a__    =  unittest.skip('test requires remote'     )(__lowerCAmelCase     )
							return test_case






def 							__lowercase     (				*__lowerCAmelCase							:       int     ):
							def decorate(cls							:       Tuple     ):
														for name, fn in cls.__dict__.items():
																					if callable(__lowerCAmelCase     ) and name.startswith('test'     ):
																												for decorator in decorators:
																																			a__    =  decorator(__lowerCAmelCase     )
																												setattr(cls      ,					__lowerCAmelCase      ,					__lowerCAmelCase     )
														return cls

							return decorate







class  snake_case_     (lowerCamelCase_   ):
					pass







class  snake_case_     (lowerCamelCase_   ):
					UpperCAmelCase__      :					Dict					   =    0
					UpperCAmelCase__      :					str					   =    1
					UpperCAmelCase__      :					List[Any]					   =    2






@contextmanager
def 							__lowercase     (				__lowerCAmelCase							:       Optional[int]=OfflineSimulationMode.CONNECTION_FAILS      ,					__lowerCAmelCase							:       str=1E-1_6     ):
							a__    =  requests.Session().request

							def timeout_request(__lowerCAmelCase							:       Tuple      ,					__lowerCAmelCase							:       int      ,					__lowerCAmelCase							:       Tuple      ,					**__lowerCAmelCase							:       List[str]     ):
														# Change the url to an invalid url so that the connection hangs
														a__    =  'https://10.255.255.1'
														if kwargs.get('timeout'     ) is None:
																					raise RequestWouldHangIndefinitelyError(
																					    F'Tried a call to {url} in offline mode with no timeout set. Please set a timeout.'     )
														a__    =  timeout
														try:
																					return online_request(__lowerCAmelCase      ,					__lowerCAmelCase      ,					**__lowerCAmelCase     )
														except Exception as e:
																					# The following changes in the error are just here to make the offline timeout error prettier
																					a__    =  url
																					a__    =  e.args[0]
																					a__    =  (max_retry_error.args[0].replace('10.255.255.1'      ,					F'OfflineMock[{url}]'     ),)
																					a__    =  (max_retry_error,)
																					raise

							def raise_connection_error(__lowerCAmelCase							:       Any      ,					__lowerCAmelCase							:       str      ,					**__lowerCAmelCase							:       List[str]     ):
														raise requests.ConnectionError('Offline mode is enabled.'      ,					request=__lowerCAmelCase     )

							if mode is OfflineSimulationMode.CONNECTION_FAILS:
														with patch('requests.Session.send'      ,					__lowerCAmelCase     ):
																					yield
							elif mode is OfflineSimulationMode.CONNECTION_TIMES_OUT:
														# inspired from https://stackoverflow.com/a/904609
														with patch('requests.Session.request'      ,					__lowerCAmelCase     ):
																					yield
							elif mode is OfflineSimulationMode.HF_DATASETS_OFFLINE_SET_TO_1:
														with patch('datasets.config.HF_DATASETS_OFFLINE'      ,					__lowerCAmelCase     ):
																					yield
							else:
														raise ValueError('Please use a value from the OfflineSimulationMode enum.'     )






@contextmanager
def 							__lowercase     (				*__lowerCAmelCase							:       str      ,					**__lowerCAmelCase							:       Dict     ):
							a__    =  str(Path().resolve()     )
							with tempfile.TemporaryDirectory(*__lowerCAmelCase      ,					**__lowerCAmelCase     ) as tmp_dir:
														try:
																					os.chdir(__lowerCAmelCase     )
																					yield
														finally:
																					os.chdir(__lowerCAmelCase     )






@contextmanager
def 							__lowercase     (				):
							import gc

							gc.collect()
							a__    =  pa.total_allocated_bytes()
							yield
							assert pa.total_allocated_bytes() - previous_allocated_memory > 0, "Arrow memory didn't increase."






@contextmanager
def 							__lowercase     (				):
							import gc

							gc.collect()
							a__    =  pa.total_allocated_bytes()
							yield
							assert pa.total_allocated_bytes() - previous_allocated_memory <= 0, "Arrow memory wasn't expected to increase."






def 							__lowercase     (				__lowerCAmelCase							:       Optional[Any]      ,					__lowerCAmelCase							:       Union[str, Any]     ):
							return deepcopy(__lowerCAmelCase     ).integers(0      ,					1_0_0      ,					1_0     ).tolist() == deepcopy(__lowerCAmelCase     ).integers(0      ,					1_0_0      ,					1_0     ).tolist()






def 							__lowercase     (				__lowerCAmelCase							:       Any     ):
							import decorator
							from requests.exceptions import HTTPError

							def _wrapper(__lowerCAmelCase							:       Any      ,					*__lowerCAmelCase							:       str      ,					**__lowerCAmelCase							:       int     ):
														try:
																					return func(*__lowerCAmelCase      ,					**__lowerCAmelCase     )
														except HTTPError as err:
																					if str(__lowerCAmelCase     ).startswith('500'     ) or str(__lowerCAmelCase     ).startswith('502'     ):
																												pytest.xfail(str(__lowerCAmelCase     )     )
																					raise err

							return decorator.decorator(_wrapper      ,					__lowerCAmelCase     )







class  snake_case_     :
					def __init__(   self     :Tuple    ,__snake_case     :Dict    ,__snake_case     :int    ,__snake_case     :Union[str, Any]   )      ->					Dict:
												a__    =  returncode
												a__    =  stdout
												a__    =  stderr






async def 							__lowercase     (				__lowerCAmelCase							:       Tuple      ,					__lowerCAmelCase							:       Any     ):
							while True:
														a__    =  await stream.readline()
														if line:
																					callback(__lowerCAmelCase     )
														else:
																					break






async def 							__lowercase     (				__lowerCAmelCase							:       str      ,					__lowerCAmelCase							:       str=None      ,					__lowerCAmelCase							:       Union[str, Any]=None      ,					__lowerCAmelCase							:       Optional[int]=None      ,					__lowerCAmelCase							:       int=False      ,					__lowerCAmelCase							:       Dict=False     ):
							if echo:
														print('\nRunning: '      ,					' '.join(__lowerCAmelCase     )     )

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

							# 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)

							a__    =  []
							a__    =  []

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

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






def 							__lowercase     (				__lowerCAmelCase							:       int      ,					__lowerCAmelCase							:       Optional[Any]=None      ,					__lowerCAmelCase							:       Dict=None      ,					__lowerCAmelCase							:       Optional[Any]=1_8_0      ,					__lowerCAmelCase							:       Dict=False      ,					__lowerCAmelCase							:       Tuple=True     ):
							a__    =  asyncio.get_event_loop()
							a__    =  loop.run_until_complete(
							    _stream_subprocess(__lowerCAmelCase      ,					env=__lowerCAmelCase      ,					stdin=__lowerCAmelCase      ,					timeout=__lowerCAmelCase      ,					quiet=__lowerCAmelCase      ,					echo=__lowerCAmelCase     )     )

							a__    =  ' '.join(__lowerCAmelCase     )
							if result.returncode > 0:
														a__    =  '\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}'     )

							# check that the subprocess actually did run and produced some output, should the test rely on
							# the remote side to do the testing
							if not result.stdout and not result.stderr:
														raise RuntimeError(F'\'{cmd_str}\' produced no output.'     )

							return result






def 							__lowercase     (				):
							a__    =  os.environ.get('PYTEST_XDIST_WORKER'      ,					'gw0'     )
							a__    =  re.sub(R'^gw'      ,					''      ,					__lowerCAmelCase      ,					0      ,					re.M     )
							return int(__lowerCAmelCase     )






def 							__lowercase     (				):
							a__    =  2_9_5_0_0
							a__    =  pytest_xdist_worker_id()
							return port + uniq_delta








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


snake_case							: Any      =   logging.get_logger(__name__)

snake_case							: Tuple      =   {
    '''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  snake_case_     (lowerCamelCase_   ):
					def __init__(   self     :str    ,__snake_case     :Dict=None    ,__snake_case     :int=None    ,*__snake_case     :str    ,**__snake_case     :Union[str, Any]   )      ->					Tuple:
												super().__init__(*__snake_case    ,**__snake_case   )

												if config is None:
																			assert isinstance(self.model    ,__snake_case   ), (
																			    "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    ,__snake_case   ) 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     :Optional[Any]    ,__snake_case     :int   )      ->					Tuple:
												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=__snake_case    ,optim=__snake_case    ,**__snake_case    ,)
																			else:
																										a__    =  optimizer_cls(__snake_case    ,**__snake_case   )

												if self.lr_scheduler is None:
																			a__    =  self._get_lr_scheduler(__snake_case   )
												else:  # ignoring --lr_scheduler
																			logger.warning('scheduler is passed to `Seq2SeqTrainer`, `--lr_scheduler` arg is ignored.'   )
					def 				lowerCamelCase__(   self     :Dict    ,__snake_case     :List[str]   )      ->					Union[str, 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=__snake_case   )
												return scheduler
					def 				lowerCamelCase__(   self     :Optional[Any]   )      ->					Optional[torch.utils.data.Sampler]:
												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     :str    ,__snake_case     :Optional[int]    ,__snake_case     :List[Any]    ,__snake_case     :Any   )      ->					Optional[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
																										a__    =  model(**__snake_case    ,use_cache=__snake_case   )[0]
																										a__    =  self.loss_fn(logits.view(-1    ,logits.shape[-1]   )    ,labels.view(-1   )   )
																			else:
																										# compute usual loss via models
																										a__   ,      a__    =  model(**__snake_case    ,labels=__snake_case    ,use_cache=__snake_case   )[:2]
												else:
																			# compute label smoothed loss
																			a__    =  model(**__snake_case    ,use_cache=__snake_case   )[0]
																			a__    =  torch.nn.functional.log_softmax(__snake_case    ,dim=-1   )
																			a__   ,      a__    =  self.loss_fn(__snake_case    ,__snake_case    ,self.args.label_smoothing    ,ignore_index=self.config.pad_token_id   )
												return loss, logits
					def 				lowerCamelCase__(   self     :List[Any]    ,__snake_case     :Dict    ,__snake_case     :Optional[int]   )      ->					Any:
												a__    =  inputs.pop('labels'   )
												a__   ,      a__    =  self._compute_loss(__snake_case    ,__snake_case    ,__snake_case   )
												return loss
					def 				lowerCamelCase__(   self     :Optional[Any]    ,__snake_case     :nn.Module    ,__snake_case     :Dict[str, Union[torch.Tensor, Any]]    ,__snake_case     :bool    ,__snake_case     :Optional[List[str]] = None    ,)      ->					Tuple[Optional[float], Optional[torch.Tensor], Optional[torch.Tensor]]:
												a__    =  self._prepare_inputs(__snake_case   )

												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']    ,**__snake_case    ,)
																			# 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(__snake_case    ,gen_kwargs['max_length']   )

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

												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(__snake_case    ,gen_kwargs['max_length']   )

												return (loss, logits, labels)



					def 				lowerCamelCase__(   self     :List[str]    ,__snake_case     :Optional[Any]    ,__snake_case     :Union[str, Any]   )      ->					int:
												# 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




