





"""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		:				List[Any]  			=	logging.getLogger(__name__)


class  __UpperCamelCase       (		a__		):
							lowerCamelCase     :      Optional[int]         ="""summarization"""
							lowerCamelCase     :      int         =["""loss"""]
							lowerCamelCase     :      Dict         =ROUGE_KEYS
							lowerCamelCase     :      Optional[Any]         ="""rouge2"""






							def __init__(     self   ,      lowerCAmelCase__   ,      **lowerCAmelCase__   )			->				Tuple:
								if hparams.sortish_sampler and hparams.gpus > 1:
									a   :	Union[str, Any]			=       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__(lowerCAmelCase__   ,      num_labels=lowerCAmelCase__   ,      mode=self.mode   ,      **lowerCAmelCase__   )
								use_task_specific_params(self.model   ,      "summarization"   )
								save_git_info(self.hparams.output_dir   )
								a   :	Optional[Any]			=       Path(self.output_dir   ) / "metrics.json"
								a   :	int			=       Path(self.output_dir   ) / "hparams.pkl"
								pickle_save(self.hparams   ,      self.hparams_save_path   )
								a   :	List[Any]			=       0
								a   :	Union[str, Any]			=       defaultdict(lowerCAmelCase__   )
								a   :	Any			=       self.config.model_type
								a   :	str			=       self.config.tgt_vocab_size if self.model_type == "fsmt" else self.config.vocab_size

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

								a   :	str			=       {
								    "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()   )

								a   :	Optional[Any]			=       get_git_info()["repo_sha"]
								a   :	List[Any]			=       hparams.num_workers
								a   :	Union[str, Any]			=       None  # default to config
								if self.model.config.decoder_start_token_id is None and isinstance(self.tokenizer   ,      lowerCAmelCase__   ):
									a   :	Optional[int]			=       self.tokenizer.lang_code_to_id[hparams.tgt_lang]
									a   :	Optional[Any]			=       self.decoder_start_token_id
								a   :	Union[str, Any]			=       (
								    SeqaSeqDataset if hasattr(self.tokenizer   ,      "prepare_seq2seq_batch"   ) else LegacySeqaSeqDataset
								)
								a   :	Optional[Any]			=       False
								a   :	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:
									a   :	Any			=       self.hparams.eval_max_gen_length
								else:
									a   :	Optional[int]			=       self.model.config.max_length
								a   :	Optional[Any]			=       self.default_val_metric if self.hparams.val_metric is None else self.hparams.val_metric






							def 					__a     (     self   ,      lowerCAmelCase__   )			->				Dict[str, List[str]]:
								a   :	str			=       {
								    k: self.tokenizer.batch_decode(v.tolist()   ) if "mask" not in k else v.shape for k, v in batch.items()
								}
								save_json(lowerCAmelCase__   ,      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"   )

								a   :	Any			=       True
								return readable_batch






							def 					__a     (     self   ,      lowerCAmelCase__   ,      **lowerCAmelCase__   )			->				int:
								return self.model(lowerCAmelCase__   ,      **lowerCAmelCase__   )






							def 					__a     (     self   ,      lowerCAmelCase__   )			->				int:
								a   :	str			=       self.tokenizer.batch_decode(
								    lowerCAmelCase__   ,      skip_special_tokens=lowerCAmelCase__   ,      clean_up_tokenization_spaces=lowerCAmelCase__   )
								return lmap(str.strip   ,      lowerCAmelCase__   )






							def 					__a     (     self   ,      lowerCAmelCase__   )			->				Tuple:
								a   :	List[Any]			=       self.tokenizer.pad_token_id
								a   :	Optional[int]			=       batch["input_ids"], batch["attention_mask"]
								a   :	Tuple			=       batch["labels"]
								if isinstance(self.model   ,      lowerCAmelCase__   ):
									a   :	Optional[int]			=       self.model._shift_right(lowerCAmelCase__   )
								else:
									a   :	Optional[int]			=       shift_tokens_right(lowerCAmelCase__   ,      lowerCAmelCase__   )
								if not self.already_saved_batch:  # This would be slightly better if it only happened on rank zero
									a   :	List[str]			=       decoder_input_ids
									self.save_readable_batch(lowerCAmelCase__   )

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

									assert lm_logits.shape[-1] == self.vocab_size
									a   :	Optional[int]			=       ce_loss_fct(lm_logits.view(-1   ,      lm_logits.shape[-1]   )   ,      tgt_ids.view(-1   )   )
								else:
									a   :	Tuple			=       nn.functional.log_softmax(lowerCAmelCase__   ,      dim=-1   )
									a   :	Any			=       label_smoothed_nll_loss(
									    lowerCAmelCase__   ,      lowerCAmelCase__   ,      self.hparams.label_smoothing   ,      ignore_index=lowerCAmelCase__   )
								return (loss,)






							@property
							def 					__a     (     self   )			->				int:
								return self.tokenizer.pad_token_id






							def 					__a     (     self   ,      lowerCAmelCase__   ,      lowerCAmelCase__   )			->				Dict:
								a   :	Optional[int]			=       self._step(lowerCAmelCase__   )

								a   :	Dict			=       dict(zip(self.loss_names   ,      lowerCAmelCase__   )   )
								# tokens per batch
								a   :	List[Any]			=       batch["input_ids"].ne(self.pad   ).sum() + batch["labels"].ne(self.pad   ).sum()
								a   :	List[Any]			=       batch["input_ids"].shape[0]
								a   :	Any			=       batch["input_ids"].eq(self.pad   ).sum()
								a   :	str			=       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   ,      lowerCAmelCase__   ,      lowerCAmelCase__   )			->				Dict:
								return self._generative_step(lowerCAmelCase__   )






							def 					__a     (     self   ,      lowerCAmelCase__   ,      lowerCAmelCase__="val"   )			->				Dict:
								self.step_count += 1
								a   :	List[str]			=       {k: torch.stack([x[k] for x in outputs]   ).mean() for k in self.loss_names}
								a   :	int			=       losses["loss"]
								a   :	Any			=       {
								    k: np.array([x[k] for x in outputs]   ).mean() for k in self.metric_names + ["gen_time", "gen_len"]
								}
								a   :	Optional[Any]			=       (
								    generative_metrics[self.val_metric] if self.val_metric in generative_metrics else losses[self.val_metric]
								)
								a   :	torch.FloatTensor			=       torch.tensor(lowerCAmelCase__   ).type_as(lowerCAmelCase__   )
								generative_metrics.update({k: v.item() for k, v in losses.items()}   )
								losses.update(lowerCAmelCase__   )
								a   :	Optional[int]			=       {f"""{prefix}_avg_{k}""": x for k, x in losses.items()}
								a   :	List[Any]			=       self.step_count
								self.metrics[prefix].append(lowerCAmelCase__   )  # callback writes this to self.metrics_save_path
								a   :	Optional[int]			=       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   ,      lowerCAmelCase__   ,      lowerCAmelCase__   )			->				Dict:
								return calculate_rouge(lowerCAmelCase__   ,      lowerCAmelCase__   )






							def 					__a     (     self   ,      lowerCAmelCase__   )			->				dict:
								a   :	Any			=       time.time()

								# parser.add_argument('--eval_max_gen_length', type=int, default=None, help='never generate more than n tokens')
								a   :	Optional[int]			=       self.model.generate(
								    batch["input_ids"]   ,      attention_mask=batch["attention_mask"]   ,      use_cache=lowerCAmelCase__   ,      decoder_start_token_id=self.decoder_start_token_id   ,      num_beams=self.eval_beams   ,      max_length=self.eval_max_length   ,      )
								a   :	str			=       (time.time() - ta) / batch["input_ids"].shape[0]
								a   :	List[str]			=       self.ids_to_clean_text(lowerCAmelCase__   )
								a   :	List[str]			=       self.ids_to_clean_text(batch["labels"]   )
								a   :	int			=       self._step(lowerCAmelCase__   )
								a   :	int			=       dict(zip(self.loss_names   ,      lowerCAmelCase__   )   )
								a   :	Dict			=       self.calc_generative_metrics(lowerCAmelCase__   ,      lowerCAmelCase__   )
								a   :	Optional[int]			=       np.mean(lmap(lowerCAmelCase__   ,      lowerCAmelCase__   )   )
								base_metrics.update(gen_time=lowerCAmelCase__   ,      gen_len=lowerCAmelCase__   ,      preds=lowerCAmelCase__   ,      target=lowerCAmelCase__   ,      **lowerCAmelCase__   )
								return base_metrics






							def 					__a     (     self   ,      lowerCAmelCase__   ,      lowerCAmelCase__   )			->				List[Any]:
								return self._generative_step(lowerCAmelCase__   )






							def 					__a     (     self   ,      lowerCAmelCase__   )			->				Optional[Any]:
								return self.validation_epoch_end(lowerCAmelCase__   ,      prefix="test"   )






							def 					__a     (     self   ,      lowerCAmelCase__   )			->				SeqaSeqDataset:
								a   :	Union[str, Any]			=       self.n_obs[type_path]
								a   :	List[str]			=       self.target_lens[type_path]
								a   :	Any			=       self.dataset_class(
								    self.tokenizer   ,      type_path=lowerCAmelCase__   ,      n_obs=lowerCAmelCase__   ,      max_target_length=lowerCAmelCase__   ,      **self.dataset_kwargs   ,      )
								return dataset






							def 					__a     (     self   ,      lowerCAmelCase__   ,      lowerCAmelCase__   ,      lowerCAmelCase__ = False   )			->				DataLoader:
								a   :	Tuple			=       self.get_dataset(lowerCAmelCase__   )

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

								elif self.hparams.max_tokens_per_batch is not None and type_path != "test" and type_path != "val":
									a   :	Union[str, Any]			=       dataset.make_dynamic_sampler(
									    self.hparams.max_tokens_per_batch   ,      distributed=self.hparams.gpus > 1   )
									return DataLoader(
									    lowerCAmelCase__   ,      batch_sampler=lowerCAmelCase__   ,      collate_fn=dataset.collate_fn   ,      num_workers=self.num_workers   ,      )
								else:
									return DataLoader(
									    lowerCAmelCase__   ,      batch_size=lowerCAmelCase__   ,      collate_fn=dataset.collate_fn   ,      shuffle=lowerCAmelCase__   ,      num_workers=self.num_workers   ,      sampler=lowerCAmelCase__   ,      )






							def 					__a     (     self   )			->				DataLoader:
								a   :	List[str]			=       self.get_dataloader("train"   ,      batch_size=self.hparams.train_batch_size   ,      shuffle=lowerCAmelCase__   )
								return dataloader






							def 					__a     (     self   )			->				DataLoader:
								return self.get_dataloader("val"   ,      batch_size=self.hparams.eval_batch_size   )






							def 					__a     (     self   )			->				DataLoader:
								return self.get_dataloader("test"   ,      batch_size=self.hparams.eval_batch_size   )






							@staticmethod
							def 					__a     (     lowerCAmelCase__   ,      lowerCAmelCase__   )			->				Optional[int]:
								BaseTransformer.add_model_specific_args(lowerCAmelCase__   ,      lowerCAmelCase__   )
								add_generic_args(lowerCAmelCase__   ,      lowerCAmelCase__   )
								parser.add_argument(
								    "--max_source_length"   ,      default=1024   ,      type=lowerCAmelCase__   ,      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=lowerCAmelCase__   ,      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=lowerCAmelCase__   ,      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=lowerCAmelCase__   ,      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=lowerCAmelCase__   )
								parser.add_argument("--overwrite_output_dir"   ,      action="store_true"   ,      default=lowerCAmelCase__   )
								parser.add_argument("--max_tokens_per_batch"   ,      type=lowerCAmelCase__   ,      default=lowerCAmelCase__   )
								parser.add_argument("--logger_name"   ,      type=lowerCAmelCase__   ,      choices=["default", "wandb", "wandb_shared"]   ,      default="default"   )
								parser.add_argument("--n_train"   ,      type=lowerCAmelCase__   ,      default=-1   ,      required=lowerCAmelCase__   ,      help="# examples. -1 means use all."   )
								parser.add_argument("--n_val"   ,      type=lowerCAmelCase__   ,      default=500   ,      required=lowerCAmelCase__   ,      help="# examples. -1 means use all."   )
								parser.add_argument("--n_test"   ,      type=lowerCAmelCase__   ,      default=-1   ,      required=lowerCAmelCase__   ,      help="# examples. -1 means use all."   )
								parser.add_argument(
								    "--task"   ,      type=lowerCAmelCase__   ,      default="summarization"   ,      required=lowerCAmelCase__   ,      help="# examples. -1 means use all."   )
								parser.add_argument("--label_smoothing"   ,      type=lowerCAmelCase__   ,      default=0.0   ,      required=lowerCAmelCase__   )
								parser.add_argument("--src_lang"   ,      type=lowerCAmelCase__   ,      default=""   ,      required=lowerCAmelCase__   )
								parser.add_argument("--tgt_lang"   ,      type=lowerCAmelCase__   ,      default=""   ,      required=lowerCAmelCase__   )
								parser.add_argument("--eval_beams"   ,      type=lowerCAmelCase__   ,      default=lowerCAmelCase__   ,      required=lowerCAmelCase__   )
								parser.add_argument(
								    "--val_metric"   ,      type=lowerCAmelCase__   ,      default=lowerCAmelCase__   ,      required=lowerCAmelCase__   ,      choices=["bleu", "rouge2", "loss", None]   )
								parser.add_argument("--eval_max_gen_length"   ,      type=lowerCAmelCase__   ,      default=lowerCAmelCase__   ,      help="never generate more than n tokens"   )
								parser.add_argument("--save_top_k"   ,      type=lowerCAmelCase__   ,      default=1   ,      required=lowerCAmelCase__   ,      help="How many checkpoints to save"   )
								parser.add_argument(
								    "--early_stopping_patience"   ,      type=lowerCAmelCase__   ,      default=-1   ,      required=lowerCAmelCase__   ,      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  __UpperCamelCase       (		a__		):
							lowerCamelCase     :      Tuple         ="""translation"""
							lowerCamelCase     :      Optional[int]         =["""loss"""]
							lowerCamelCase     :      Tuple         =["""bleu"""]
							lowerCamelCase     :      Tuple         ="""bleu"""






							def __init__(     self   ,      lowerCAmelCase__   ,      **lowerCAmelCase__   )			->				Optional[Any]:
								super().__init__(lowerCAmelCase__   ,      **lowerCAmelCase__   )
								a   :	Optional[Any]			=       hparams.src_lang
								a   :	Union[str, Any]			=       hparams.tgt_lang






							def 					__a     (     self   ,      lowerCAmelCase__   ,      lowerCAmelCase__   )			->				dict:
								return calculate_bleu(lowerCAmelCase__   ,      lowerCAmelCase__   )






def    _SCREAMING_SNAKE_CASE	(			_lowercase		:					List[Any]					, _lowercase		:					Tuple=None      )				->SummarizationModule:






	'''simple docstring'''






	Path(args.output_dir      ).mkdir(exist_ok=_lowercase      )
	check_output_dir(_lowercase					, expected_items=3      )

	if model is None:
		if "summarization" in args.task:
			a   :	SummarizationModule			=       SummarizationModule(_lowercase      )
		else:
			a   :	SummarizationModule			=       TranslationModule(_lowercase      )
	a   :	int			=       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"      )
	):
		a   :	Tuple			=       True  # don't pollute wandb logs unnecessarily
	elif args.logger_name == "wandb":
		from pytorch_lightning.loggers import WandbLogger

		a   :	Optional[int]			=       os.environ.get("WANDB_PROJECT"					, _lowercase      )
		a   :	Dict			=       WandbLogger(name=model.output_dir.name					, project=_lowercase      )

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

		a   :	str			=       WandbLogger(name=model.output_dir.name					, project=F"""hf_{dataset}"""      )

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

	a   :	Optional[Any]			=       args.val_metric == "loss"
	a   :	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

	a   :	Optional[Any]			=       ""
	a   :	Dict			=       sorted(glob.glob(os.path.join(args.output_dir					, "*.ckpt"      )					, recursive=_lowercase      )      )
	if checkpoints:
		a   :	Optional[int]			=       checkpoints[-1]
		a   :	int			=       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		:				List[str]  			=	argparse.ArgumentParser()
		a		:				Optional[int]  			=	pl.Trainer.add_argparse_args(parser)
		a		:				int  			=	SummarizationModule.add_model_specific_args(parser, os.getcwd())

		a		:				Union[str, Any]  			=	parser.parse_args()

		main(args)









"""simple docstring"""




# Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import argparse
import os
import subprocess

from packaging.version import Version, parse

from accelerate.commands.config.config_args import default_config_file, load_config_from_file


a		:				Optional[int]  			=    '''Run commands across TPU VMs for initial setup before running `accelerate launch`.'''






def    _SCREAMING_SNAKE_CASE	(			_lowercase		:					Any=None      )				->Optional[Any]:






	'''simple docstring'''






	if subparsers is not None:
		a   :	int			=       subparsers.add_parser("tpu-config"					, description=_description      )
	else:
		a   :	List[Any]			=       argparse.ArgumentParser("Accelerate tpu-config command"					, description=_description      )
	# Core arguments
	a   :	Dict			=       parser.add_argument_group(
	    "Config Arguments"					, "Arguments that can be configured through `accelerate config`."      )
	config_args.add_argument(
	    "--config_file"					, type=_lowercase					, default=_lowercase					, help="Path to the config file to use for accelerate."					, )
	config_args.add_argument(
	    "--tpu_name"					, default=_lowercase					, help="The name of the TPU to use. If not specified, will use the TPU specified in the config file."					, )
	config_args.add_argument(
	    "--tpu_zone"					, default=_lowercase					, help="The zone of the TPU to use. If not specified, will use the zone specified in the config file."					, )
	a   :	Any			=       parser.add_argument_group("TPU Arguments"					, "Arguments for options ran inside the TPU."      )
	pod_args.add_argument(
	    "--use_alpha"					, action="store_true"					, help="Whether to use `gcloud alpha` when running the TPU training script instead of `gcloud`."					, )
	pod_args.add_argument(
	    "--command_file"					, default=_lowercase					, help="The path to the file containing the commands to run on the pod on startup."					, )
	pod_args.add_argument(
	    "--command"					, action="append"					, nargs="+"					, help="A command to run on the pod. Can be passed multiple times."					, )
	pod_args.add_argument(
	    "--install_accelerate"					, action="store_true"					, help="Whether to install accelerate on the pod. Defaults to False."					, )
	pod_args.add_argument(
	    "--accelerate_version"					, default="latest"					, help="The version of accelerate to install on the pod. If not specified, will use the latest pypi version. Specify 'dev' to install from GitHub."					, )
	pod_args.add_argument(
	    "--debug"					, action="store_true"					, help="If set, will print the command that would be run instead of running it."      )

	if subparsers is not None:
		parser.set_defaults(func=_lowercase      )
	return parser






def    _SCREAMING_SNAKE_CASE	(			_lowercase		:					Any      )				->Tuple:






	'''simple docstring'''






	a   :	Union[str, Any]			=       None

	# Get the default from the config file if it exists.
	if args.config_file is not None or os.path.isfile(_lowercase      ):
		a   :	Optional[Any]			=       load_config_from_file(args.config_file      )
		if not args.command_file and defaults.command_file is not None and not args.command:
			a   :	int			=       defaults.command_file
		if not args.command and defaults.commands is not None:
			a   :	Union[str, Any]			=       defaults.commands
		if not args.tpu_name:
			a   :	int			=       defaults.tpu_name
		if not args.tpu_zone:
			a   :	Union[str, Any]			=       defaults.tpu_zone
	if args.accelerate_version == "dev":
		a   :	int			=       "git+https://github.com/huggingface/accelerate.git"
	elif args.accelerate_version == "latest":
		a   :	Optional[Any]			=       "accelerate -U"
	elif isinstance(parse(args.accelerate_version      )					, _lowercase      ):
		a   :	Optional[Any]			=       F"""accelerate=={args.accelerate_version}"""

	if not args.command_file and not args.command:
		raise ValueError("You must specify either a command file or a command to run on the pod."      )

	if args.command_file:
		with open(args.command_file					, "r"      ) as f:
			a   :	int			=       [f.read().splitlines()]

    # To turn list of lists into list of strings
	if isinstance(args.command[0]					, _lowercase      ):
		a   :	Union[str, Any]			=       [line for cmd in args.command for line in cmd]
	# Default to the shared folder and install accelerate
	a   :	Tuple			=       ["cd /usr/share"]
	if args.install_accelerate:
		new_cmd += [F"""pip install {args.accelerate_version}"""]
	new_cmd += args.command
	a   :	List[Any]			=       "; ".join(_lowercase      )

	# Then send it to gcloud
	# Eventually try to use google-api-core to do this instead of subprocess
	a   :	str			=       ["gcloud"]
	if args.use_alpha:
		cmd += ["alpha"]
	cmd += [
	    "compute",
	    "tpus",
	    "tpu-vm",
	    "ssh",
	    args.tpu_name,
	    "--zone",
	    args.tpu_zone,
	    "--command",
	    args.command,
	    "--worker",
	    "all",
	]
	if args.debug:
		print(F"""Running {' '.join(_lowercase      )}"""      )
		return
	subprocess.run(_lowercase      )
	print("Successfully setup pod."      )







def    _SCREAMING_SNAKE_CASE	(			)				->Tuple:






	'''simple docstring'''






	a   :	List[Any]			=       tpu_command_parser()
	a   :	Optional[int]			=       parser.parse_args()

	tpu_command_launcher(_lowercase      )


