




"""simple docstring"""






import argparse
import glob
import logging
import os
import sys
import time
from collections import defaultdict
from pathlib import Path
from typing import Dict, List, Tuple

import numpy as np
import pytorch_lightning as pl
import torch
from callbacks import SeqaSeqLoggingCallback, get_checkpoint_callback, get_early_stopping_callback
from torch import nn
from torch.utils.data import DataLoader

from transformers import MBartTokenizer, TaForConditionalGeneration
from transformers.models.bart.modeling_bart import shift_tokens_right
from utils import (
    ROUGE_KEYS,
    LegacySeqaSeqDataset,
    SeqaSeqDataset,
    assert_all_frozen,
    calculate_bleu,
    calculate_rouge,
    check_output_dir,
    flatten_list,
    freeze_embeds,
    freeze_params,
    get_git_info,
    label_smoothed_nll_loss,
    lmap,
    pickle_save,
    save_git_info,
    save_json,
    use_task_specific_params,
)


# need the parent dir module
sys.path.insert(2, str(Path(__file__).resolve().parents[1]))
from lightning_base import BaseTransformer, add_generic_args, generic_train  # noqa


a  		=		logging.getLogger(__name__)






class lowerCAmelCase__			(  lowercase		):






			'''simple docstring'''



			lowerCamelCase__   =    """summarization"""
			lowerCamelCase__   =    ["""loss"""]
			lowerCamelCase__   =    ROUGE_KEYS
			lowerCamelCase__   =    """rouge2"""
			def __init__(						self						,   lowercase						,   **lowercase						):
							if hparams.sortish_sampler and hparams.gpus > 1:
											_lowerCamelCase  :    Tuple					=					False
							elif hparams.max_tokens_per_batch is not None:
											if hparams.gpus > 1:
															raise NotImplementedError('Dynamic Batch size does not work for multi-gpu training'						)
											if hparams.sortish_sampler:
															raise ValueError('--sortish_sampler and --max_tokens_per_batch may not be used simultaneously'						)

							super().__init__(lowercase						,   num_labels=lowercase						,   mode=self.mode						,   **lowercase						)
							use_task_specific_params(self.model						,   'summarization'						)
							save_git_info(self.hparams.output_dir						)
							_lowerCamelCase  :    Optional[Any]					=					Path(self.output_dir						) / 'metrics.json'
							_lowerCamelCase  :    List[str]					=					Path(self.output_dir						) / 'hparams.pkl'
							pickle_save(self.hparams						,   self.hparams_save_path						)
							_lowerCamelCase  :    List[str]					=					0
							_lowerCamelCase  :    Dict					=					defaultdict(lowercase						)
							_lowerCamelCase  :    Dict					=					self.config.model_type
							_lowerCamelCase  :    str					=					self.config.tgt_vocab_size if self.model_type == 'fsmt' else self.config.vocab_size

							_lowerCamelCase  :    dict					=					{
							    "data_dir": self.hparams.data_dir,
							    "max_source_length": self.hparams.max_source_length,
							    "prefix": self.model.config.prefix or "",
							}
							_lowerCamelCase  :    List[Any]					=					{
							    'train': self.hparams.n_train,
							    'val': self.hparams.n_val,
							    'test': self.hparams.n_test,
							}
							_lowerCamelCase  :    Tuple					=					{k: v if v >= 0 else None for k, v in n_observations_per_split.items()}

							_lowerCamelCase  :    Tuple					=					{
							    'train': self.hparams.max_target_length,
							    'val': self.hparams.val_max_target_length,
							    'test': self.hparams.test_max_target_length,
							}
							assert self.target_lens["train"] <= self.target_lens["val"], F'''target_lens: {self.target_lens}'''
							assert self.target_lens["train"] <= self.target_lens["test"], F'''target_lens: {self.target_lens}'''
							if self.hparams.freeze_embeds:
											freeze_embeds(self.model						)
							if self.hparams.freeze_encoder:
											freeze_params(self.model.get_encoder()						)
											assert_all_frozen(self.model.get_encoder()						)

							_lowerCamelCase  :    Any					=					get_git_info()['repo_sha']
							_lowerCamelCase  :    Optional[Any]					=					hparams.num_workers
							_lowerCamelCase  :    List[Any]					=					None  # default to config
							if self.model.config.decoder_start_token_id is None and isinstance(self.tokenizer						,   lowercase						):
											_lowerCamelCase  :    Optional[int]					=					self.tokenizer.lang_code_to_id[hparams.tgt_lang]
											_lowerCamelCase  :    Optional[int]					=					self.decoder_start_token_id
							_lowerCamelCase  :    Optional[int]					=					(
							    SeqaSeqDataset if hasattr(self.tokenizer						,   'prepare_seq2seq_batch'						) else LegacySeqaSeqDataset
							)
							_lowerCamelCase  :    Dict					=					False
							_lowerCamelCase  :    Optional[int]					=					self.model.config.num_beams if self.hparams.eval_beams is None else self.hparams.eval_beams
							if self.hparams.eval_max_gen_length is not None:
											_lowerCamelCase  :    Optional[int]					=					self.hparams.eval_max_gen_length
							else:
											_lowerCamelCase  :    List[str]					=					self.model.config.max_length
							_lowerCamelCase  :    int					=					self.default_val_metric if self.hparams.val_metric is None else self.hparams.val_metric
			def       A_					(						self						,   lowercase						):
							_lowerCamelCase  :    List[str]					=					{
							    k: self.tokenizer.batch_decode(v.tolist()						) if 'mask' not in k else v.shape for k, v in batch.items()
							}
							save_json(lowercase						,   Path(self.output_dir						) / 'text_batch.json'						)
							save_json({k: v.tolist() for k, v in batch.items()}						,   Path(self.output_dir						) / 'tok_batch.json'						)

							_lowerCamelCase  :    Any					=					True
							return readable_batch
			def       A_					(						self						,   lowercase						,   **lowercase						):
							return self.model(lowercase						,   **lowercase						)
			def       A_					(						self						,   lowercase						):
							_lowerCamelCase  :    List[str]					=					self.tokenizer.batch_decode(
							    lowercase						,   skip_special_tokens=lowercase						,   clean_up_tokenization_spaces=lowercase						)
							return lmap(str.strip						,   lowercase						)
			def       A_					(						self						,   lowercase						):
							_lowerCamelCase  :    Dict					=					self.tokenizer.pad_token_id
							_lowerCamelCase  :    List[Any]					=					batch['input_ids'], batch['attention_mask']
							_lowerCamelCase  :    List[Any]					=					batch['labels']
							if isinstance(self.model						,   lowercase						):
											_lowerCamelCase  :    Optional[Any]					=					self.model._shift_right(lowercase						)
							else:
											_lowerCamelCase  :    int					=					shift_tokens_right(lowercase						,   lowercase						)
							if not self.already_saved_batch:  # This would be slightly better if it only happened on rank zero
											_lowerCamelCase  :    Any					=					decoder_input_ids
											self.save_readable_batch(lowercase						)

							_lowerCamelCase  :    List[Any]					=					self(lowercase						,   attention_mask=lowercase						,   decoder_input_ids=lowercase						,   use_cache=lowercase						)
							_lowerCamelCase  :    List[str]					=					outputs['logits']
							if self.hparams.label_smoothing == 0:
											# Same behavior as modeling_bart.py, besides ignoring pad_token_id
											_lowerCamelCase  :    int					=					nn.CrossEntropyLoss(ignore_index=lowercase						)

											assert lm_logits.shape[-1] == self.vocab_size
											_lowerCamelCase  :    Union[str, Any]					=					ce_loss_fct(lm_logits.view(-1						,   lm_logits.shape[-1]						)						,   tgt_ids.view(-1						)						)
							else:
											_lowerCamelCase  :    int					=					nn.functional.log_softmax(lowercase						,   dim=-1						)
											_lowerCamelCase  :    Union[str, Any]					=					label_smoothed_nll_loss(
											    lowercase						,   lowercase						,   self.hparams.label_smoothing						,   ignore_index=lowercase						)
							return (loss,)
			@property
			def       A_					(						self						):
							return self.tokenizer.pad_token_id
			def       A_					(						self						,   lowercase						,   lowercase						):
							_lowerCamelCase  :    Union[str, Any]					=					self._step(lowercase						)

							_lowerCamelCase  :    Union[str, Any]					=					dict(zip(self.loss_names						,   lowercase						)						)
							# tokens per batch
							_lowerCamelCase  :    Optional[int]					=					batch['input_ids'].ne(self.pad						).sum() + batch['labels'].ne(self.pad						).sum()
							_lowerCamelCase  :    List[str]					=					batch['input_ids'].shape[0]
							_lowerCamelCase  :    List[Any]					=					batch['input_ids'].eq(self.pad						).sum()
							_lowerCamelCase  :    Dict					=					batch['input_ids'].eq(self.pad						).float().mean()
							# TODO(SS): make a wandb summary metric for this
							return {"loss": loss_tensors[0], "log": logs}
			def       A_					(						self						,   lowercase						,   lowercase						):
							return self._generative_step(lowercase						)
			def       A_					(						self						,   lowercase						,   lowercase="val"						):
							self.step_count += 1
							_lowerCamelCase  :    Dict					=					{k: torch.stack([x[k] for x in outputs]						).mean() for k in self.loss_names}
							_lowerCamelCase  :    Optional[Any]					=					losses['loss']
							_lowerCamelCase  :    Optional[Any]					=					{
							    k: np.array([x[k] for x in outputs]						).mean() for k in self.metric_names + ['gen_time', 'gen_len']
							}
							_lowerCamelCase  :    Tuple					=					(
							    generative_metrics[self.val_metric] if self.val_metric in generative_metrics else losses[self.val_metric]
							)
							_lowerCamelCase  :    torch.FloatTensor					=					torch.tensor(lowercase						).type_as(lowercase						)
							generative_metrics.update({k: v.item() for k, v in losses.items()}						)
							losses.update(lowercase						)
							_lowerCamelCase  :    List[Any]					=					{F'''{prefix}_avg_{k}''': x for k, x in losses.items()}
							_lowerCamelCase  :    Optional[Any]					=					self.step_count
							self.metrics[prefix].append(lowercase						)  # callback writes this to self.metrics_save_path
							_lowerCamelCase  :    Union[str, Any]					=					flatten_list([x['preds'] for x in outputs]						)
							return {
							    "log": all_metrics,
							    "preds": preds,
							    F'''{prefix}_loss''': loss,
							    F'''{prefix}_{self.val_metric}''': metric_tensor,
							}
			def       A_					(						self						,   lowercase						,   lowercase						):
							return calculate_rouge(lowercase						,   lowercase						)
			def       A_					(						self						,   lowercase						):
							_lowerCamelCase  :    List[str]					=					time.time()

							# parser.add_argument('--eval_max_gen_length', type=int, default=None, help='never generate more than n tokens')
							_lowerCamelCase  :    Union[str, Any]					=					self.model.generate(
							    batch['input_ids']						,   attention_mask=batch['attention_mask']						,   use_cache=lowercase						,   decoder_start_token_id=self.decoder_start_token_id						,   num_beams=self.eval_beams						,   max_length=self.eval_max_length						,   )
							_lowerCamelCase  :    List[Any]					=					(time.time() - ta) / batch['input_ids'].shape[0]
							_lowerCamelCase  :    List[str]					=					self.ids_to_clean_text(lowercase						)
							_lowerCamelCase  :    List[str]					=					self.ids_to_clean_text(batch['labels']						)
							_lowerCamelCase  :    Union[str, Any]					=					self._step(lowercase						)
							_lowerCamelCase  :    List[Any]					=					dict(zip(self.loss_names						,   lowercase						)						)
							_lowerCamelCase  :    Dict					=					self.calc_generative_metrics(lowercase						,   lowercase						)
							_lowerCamelCase  :    Optional[int]					=					np.mean(lmap(lowercase						,   lowercase						)						)
							base_metrics.update(gen_time=lowercase						,   gen_len=lowercase						,   preds=lowercase						,   target=lowercase						,   **lowercase						)
							return base_metrics
			def       A_					(						self						,   lowercase						,   lowercase						):
							return self._generative_step(lowercase						)
			def       A_					(						self						,   lowercase						):
							return self.validation_epoch_end(lowercase						,   prefix='test'						)
			def       A_					(						self						,   lowercase						):
							_lowerCamelCase  :    Any					=					self.n_obs[type_path]
							_lowerCamelCase  :    Optional[Any]					=					self.target_lens[type_path]
							_lowerCamelCase  :    Dict					=					self.dataset_class(
							    self.tokenizer						,   type_path=lowercase						,   n_obs=lowercase						,   max_target_length=lowercase						,   **self.dataset_kwargs						,   )
							return dataset
			def       A_					(						self						,   lowercase						,   lowercase						,   lowercase = False						):
							_lowerCamelCase  :    Dict					=					self.get_dataset(lowercase						)

							if self.hparams.sortish_sampler and type_path != "test" and type_path != "val":
											_lowerCamelCase  :    Tuple					=					dataset.make_sortish_sampler(lowercase						,   distributed=self.hparams.gpus > 1						)
											return DataLoader(
											    lowercase						,   batch_size=lowercase						,   collate_fn=dataset.collate_fn						,   shuffle=lowercase						,   num_workers=self.num_workers						,   sampler=lowercase						,   )

							elif self.hparams.max_tokens_per_batch is not None and type_path != "test" and type_path != "val":
											_lowerCamelCase  :    Any					=					dataset.make_dynamic_sampler(
											    self.hparams.max_tokens_per_batch						,   distributed=self.hparams.gpus > 1						)
											return DataLoader(
											    lowercase						,   batch_sampler=lowercase						,   collate_fn=dataset.collate_fn						,   num_workers=self.num_workers						,   )
							else:
											return DataLoader(
											    lowercase						,   batch_size=lowercase						,   collate_fn=dataset.collate_fn						,   shuffle=lowercase						,   num_workers=self.num_workers						,   sampler=lowercase						,   )
			def       A_					(						self						):
							_lowerCamelCase  :    int					=					self.get_dataloader('train'						,   batch_size=self.hparams.train_batch_size						,   shuffle=lowercase						)
							return dataloader
			def       A_					(						self						):
							return self.get_dataloader('val'						,   batch_size=self.hparams.eval_batch_size						)
			def       A_					(						self						):
							return self.get_dataloader('test'						,   batch_size=self.hparams.eval_batch_size						)






			@staticmethod
			def       A_					(						lowercase						,   lowercase						):
							BaseTransformer.add_model_specific_args(lowercase						,   lowercase						)
							add_generic_args(lowercase						,   lowercase						)
							parser.add_argument(
							    '--max_source_length'						,   default=1024						,   type=lowercase						,   help=(
							        'The maximum total input sequence length after tokenization. Sequences longer '
							        'than this will be truncated, sequences shorter will be padded.'
							    )						,   )
							parser.add_argument(
							    '--max_target_length'						,   default=56						,   type=lowercase						,   help=(
							        'The maximum total input sequence length after tokenization. Sequences longer '
							        'than this will be truncated, sequences shorter will be padded.'
							    )						,   )
							parser.add_argument(
							    '--val_max_target_length'						,   default=142						,   type=lowercase						,   help=(
							        'The maximum total input sequence length after tokenization. Sequences longer '
							        'than this will be truncated, sequences shorter will be padded.'
							    )						,   )
							parser.add_argument(
							    '--test_max_target_length'						,   default=142						,   type=lowercase						,   help=(
							        'The maximum total input sequence length after tokenization. Sequences longer '
							        'than this will be truncated, sequences shorter will be padded.'
							    )						,   )
							parser.add_argument('--freeze_encoder'						,   action='store_true'						)
							parser.add_argument('--freeze_embeds'						,   action='store_true'						)
							parser.add_argument('--sortish_sampler'						,   action='store_true'						,   default=lowercase						)
							parser.add_argument('--overwrite_output_dir'						,   action='store_true'						,   default=lowercase						)
							parser.add_argument('--max_tokens_per_batch'						,   type=lowercase						,   default=lowercase						)
							parser.add_argument('--logger_name'						,   type=lowercase						,   choices=['default', 'wandb', 'wandb_shared']						,   default='default'						)
							parser.add_argument('--n_train'						,   type=lowercase						,   default=-1						,   required=lowercase						,   help='# examples. -1 means use all.'						)
							parser.add_argument('--n_val'						,   type=lowercase						,   default=500						,   required=lowercase						,   help='# examples. -1 means use all.'						)
							parser.add_argument('--n_test'						,   type=lowercase						,   default=-1						,   required=lowercase						,   help='# examples. -1 means use all.'						)
							parser.add_argument(
							    '--task'						,   type=lowercase						,   default='summarization'						,   required=lowercase						,   help='# examples. -1 means use all.'						)
							parser.add_argument('--label_smoothing'						,   type=lowercase						,   default=0.0						,   required=lowercase						)
							parser.add_argument('--src_lang'						,   type=lowercase						,   default=''						,   required=lowercase						)
							parser.add_argument('--tgt_lang'						,   type=lowercase						,   default=''						,   required=lowercase						)
							parser.add_argument('--eval_beams'						,   type=lowercase						,   default=lowercase						,   required=lowercase						)
							parser.add_argument(
							    '--val_metric'						,   type=lowercase						,   default=lowercase						,   required=lowercase						,   choices=['bleu', 'rouge2', 'loss', None]						)
							parser.add_argument('--eval_max_gen_length'						,   type=lowercase						,   default=lowercase						,   help='never generate more than n tokens'						)
							parser.add_argument('--save_top_k'						,   type=lowercase						,   default=1						,   required=lowercase						,   help='How many checkpoints to save'						)
							parser.add_argument(
							    '--early_stopping_patience'						,   type=lowercase						,   default=-1						,   required=lowercase						,   help=(
							        '-1 means never early stop. early_stopping_patience is measured in validation checks, not epochs. So'
							        ' val_check_interval will effect it.'
							    )						,   )
							return parser






class lowerCAmelCase__			(  lowercase		):






			'''simple docstring'''



			lowerCamelCase__   =    """translation"""
			lowerCamelCase__   =    ["""loss"""]
			lowerCamelCase__   =    ["""bleu"""]
			lowerCamelCase__   =    """bleu"""
			def __init__(						self						,   lowercase						,   **lowercase						):
							super().__init__(lowercase						,   **lowercase						)
							_lowerCamelCase  :    List[str]					=					hparams.src_lang
							_lowerCamelCase  :    Union[str, Any]					=					hparams.tgt_lang






			def       A_					(						self						,   lowercase						,   lowercase						):
							return calculate_bleu(lowercase						,   lowercase						)







def   _snake_case    (       lowercase__							,					lowercase__=None	):
				Path(args.output_dir	).mkdir(exist_ok=lowercase__	)
				check_output_dir(lowercase__							,					expected_items=3	)

				if model is None:
								if "summarization" in args.task:
												_lowerCamelCase  :    SummarizationModule					=					SummarizationModule(lowercase__	)
								else:
												_lowerCamelCase  :    SummarizationModule					=					TranslationModule(lowercase__	)
				_lowerCamelCase  :    Dict					=					Path(args.data_dir	).name
				if (
				    args.logger_name == "default"
				    or args.fast_dev_run
				    or str(args.output_dir	).startswith('/tmp'	)
				    or str(args.output_dir	).startswith('/var'	)
				):
								_lowerCamelCase  :    Union[str, Any]					=					True  # don't pollute wandb logs unnecessarily
				elif args.logger_name == "wandb":
								from pytorch_lightning.loggers import WandbLogger

								_lowerCamelCase  :    int					=					os.environ.get('WANDB_PROJECT'							,					lowercase__	)
								_lowerCamelCase  :    str					=					WandbLogger(name=model.output_dir.name							,					project=lowercase__	)

				elif args.logger_name == "wandb_shared":
								from pytorch_lightning.loggers import WandbLogger

								_lowerCamelCase  :    Tuple					=					WandbLogger(name=model.output_dir.name							,					project=f'''hf_{dataset}'''	)

				if args.early_stopping_patience >= 0:
								_lowerCamelCase  :    Optional[Any]					=					get_early_stopping_callback(model.val_metric							,					args.early_stopping_patience	)
				else:
								_lowerCamelCase  :    Optional[int]					=					False

				_lowerCamelCase  :    Optional[int]					=					args.val_metric == 'loss'
				_lowerCamelCase  :    pl.Trainer					=					generic_train(
				    lowercase__							,					lowercase__							,					logging_callback=SeqaSeqLoggingCallback()							,					checkpoint_callback=get_checkpoint_callback(
				        args.output_dir							,					model.val_metric							,					args.save_top_k							,					lowercase__	)							,					early_stopping_callback=lowercase__							,					logger=lowercase__							,					)
				pickle_save(model.hparams							,					model.output_dir / 'hparams.pkl'	)
				if not args.do_predict:
								return model

				_lowerCamelCase  :    Dict					=					''
				_lowerCamelCase  :    Tuple					=					sorted(glob.glob(os.path.join(args.output_dir							,					'*.ckpt'	)							,					recursive=lowercase__	)	)
				if checkpoints:
								_lowerCamelCase  :    int					=					checkpoints[-1]
								_lowerCamelCase  :    str					=					checkpoints[-1]
				trainer.logger.log_hyperparams(model.hparams	)

				# test() without a model tests using the best checkpoint automatically
				trainer.test()
				return model


if __name__ == "__main__":
			a  		=		argparse.ArgumentParser()
			a  		=		pl.Trainer.add_argparse_args(parser)
			a  		=		SummarizationModule.add_model_specific_args(parser, os.getcwd())

			a  		=		parser.parse_args()

			main(args)





"""simple docstring"""






from typing import Dict

from transformers import EvalPrediction, HfArgumentParser, TrainingArguments, is_torch_available
from transformers.testing_utils import (
    TestCasePlus,
    execute_subprocess_async,
    get_torch_dist_unique_port,
    require_torch_multi_gpu,
    require_torch_neuroncore,
)
from transformers.training_args import ParallelMode
from transformers.utils import logging


lowercase__  		=		logging.get_logger(__name__)


if is_torch_available():
			import torch
			from torch import nn
			from torch.utils.data import Dataset

			from transformers import Trainer






			class lowerCAmelCase__			(  lowercase		):






						'''simple docstring'''



						def __init__(						self						,   lowercase = 101						):
										_lowerCamelCase  :    Optional[int]					=					length
						def __len__(						self						):
										return self.length






						def __getitem__(						self						,   lowercase						):
										return i






			class lowerCAmelCase__			:






						'''simple docstring'''



						def __call__(						self						,   lowercase						):
										return {"input_ids": torch.tensor(lowercase						), "labels": torch.tensor(lowercase						)}






			class lowerCAmelCase__			(  nn.Module		):






						'''simple docstring'''



						def __init__(						self						):
										super().__init__()
										# Add some (unused) params otherwise DDP will complain.
										_lowerCamelCase  :    int					=					nn.Linear(120						,   80						)






						def       A_					(						self						,   lowercase						,   lowercase=None						):
										if labels is not None:
														return torch.tensor(0.0						,   device=input_ids.device						), input_ids
										else:
														return input_ids






class lowerCAmelCase__			(  lowercase		):






			'''simple docstring'''



			@require_torch_neuroncore
			def       A_					(						self						):
							_lowerCamelCase  :    Optional[Any]					=					F'''--nproc_per_node=2
            --master_port={get_torch_dist_unique_port()}
            {self.test_file_dir}/test_trainer_distributed.py
        '''.split()
							_lowerCamelCase  :    int					=					self.get_auto_remove_tmp_dir()
							_lowerCamelCase  :    str					=					F'''--output_dir {output_dir}'''.split()
							_lowerCamelCase  :    Tuple					=					['torchrun'] + distributed_args + args
							execute_subprocess_async(lowercase						,   env=self.get_env()						)
							# successful return here == success - any errors would have caused an error in the sub-call






class lowerCAmelCase__			(  lowercase		):






			'''simple docstring'''



			@require_torch_multi_gpu
			def       A_					(						self						):
							_lowerCamelCase  :    Union[str, Any]					=					F'''--nproc_per_node={torch.cuda.device_count()}
            --master_port={get_torch_dist_unique_port()}
            {self.test_file_dir}/test_trainer_distributed.py
        '''.split()
							_lowerCamelCase  :    List[str]					=					self.get_auto_remove_tmp_dir()
							_lowerCamelCase  :    Any					=					F'''--output_dir {output_dir}'''.split()
							_lowerCamelCase  :    Any					=					['torchrun'] + distributed_args + args
							execute_subprocess_async(lowercase						,   env=self.get_env()						)
							# successful return here == success - any errors would have caused an error in the sub-call


if __name__ == "__main__":
			# The script below is meant to be run under torch.distributed, on a machine with multiple GPUs:
			#
			# PYTHONPATH="src" python -m torch.distributed.run --nproc_per_node 2 --output_dir output_dir ./tests/test_trainer_distributed.py

			lowercase__  		=		HfArgumentParser((TrainingArguments,))
			lowercase__  		=		parser.parse_args_into_dataclasses()[0]

			logger.warning(
			    F"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}, "
			    F"distributed training: {training_args.parallel_mode != ParallelMode.NOT_DISTRIBUTED}"
			)

			# Essentially, what we want to verify in the distributed case is that we get all samples back,
			# in the right order. (this is crucial for prediction for instance)
			for dataset_length in [101, 40, 7]:
						lowercase__  		=		DummyDataset(dataset_length)







						def   _snake_case    (       lowercase__	):
										_lowerCamelCase  :    int					=					list(range(len(lowercase__	)	)	)
										_lowerCamelCase  :    Optional[int]					=					p.predictions.tolist() == sequential and p.label_ids.tolist() == sequential
										if not success and training_args.local_rank == 0:
														logger.warning(
														    'Predictions and/or labels do not match expected results:\n  - predictions: '
														    f'''{p.predictions.tolist()}\n  - labels: {p.label_ids.tolist()}\n  - expected: {sequential}'''	)
										return {"success": success}

						lowercase__  		=		Trainer(
						    model=DummyModel(),
						    args=training_args,
						    data_collator=DummyDataCollator(),
						    eval_dataset=dataset,
						    compute_metrics=compute_metrics,
						)
						lowercase__  		=		trainer.evaluate()
						logger.info(metrics)
						if metrics["eval_success"] is not True:
									logger.error(metrics)
									exit(1)

						lowercase__  		=		trainer.predict(dataset)
						logger.info(p.metrics)
						if p.metrics["test_success"] is not True:
									logger.error(p.metrics)
									exit(1)

						lowercase__  		=		2

						lowercase__  		=		trainer.evaluate()
						logger.info(metrics)
						if metrics["eval_success"] is not True:
									logger.error(metrics)
									exit(1)

						lowercase__  		=		trainer.predict(dataset)
						logger.info(p.metrics)
						if p.metrics["test_success"] is not True:
									logger.error(p.metrics)
									exit(1)
						lowercase__  		=		None