


'''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


_UpperCAmelCase											=					logging.getLogger(__name__)
class        lowercase     (							_snake_case ):

		"""simple docstring"""

		_a   			=    'summarization'
		_a   			=    ['loss']
		_a   			=    ROUGE_KEYS
		_a   			=    'rouge2'




		def __init__(		self   ,       UpperCamelCase_   ,       **UpperCamelCase_							):

							'''simple docstring'''


							if hparams.sortish_sampler and hparams.gpus > 1:
												UpperCamelCase__       :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__(UpperCamelCase__   ,       num_labels=UpperCamelCase__   ,       mode=self.mode   ,       **UpperCamelCase__							)
							use_task_specific_params(self.model   ,       '''summarization'''							)
							save_git_info(self.hparams.output_dir							)
							UpperCamelCase__       :Optional[Any]							 =			Path(self.output_dir							) / '''metrics.json'''
							UpperCamelCase__       :Optional[Any]							 =			Path(self.output_dir							) / '''hparams.pkl'''
							pickle_save(self.hparams   ,       self.hparams_save_path							)
							UpperCamelCase__       :Dict							 =			0
							UpperCamelCase__       :int							 =			defaultdict(UpperCamelCase__							)
							UpperCamelCase__       :Dict							 =			self.config.model_type
							UpperCamelCase__       :Any							 =			self.config.tgt_vocab_size if self.model_type == '''fsmt''' else self.config.vocab_size

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

							UpperCamelCase__       :int							 =			{
							    '''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()							)

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




		def    lowerCAmelCase__    (		self   ,       UpperCamelCase_							):

							'''simple docstring'''


							UpperCamelCase__       :Optional[Any]							 =			{
							    k: self.tokenizer.batch_decode(v.tolist()							) if '''mask''' not in k else v.shape for k, v in batch.items()
							}
							save_json(UpperCamelCase__   ,       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'''							)

							UpperCamelCase__       :Union[str, Any]							 =			True
							return readable_batch




		def    lowerCAmelCase__    (		self   ,       UpperCamelCase_   ,       **UpperCamelCase_							):

							'''simple docstring'''


							return self.model(UpperCamelCase__   ,       **UpperCamelCase__							)




		def    lowerCAmelCase__    (		self   ,       UpperCamelCase_							):

							'''simple docstring'''


							UpperCamelCase__       :str							 =			self.tokenizer.batch_decode(
							    UpperCamelCase__   ,       skip_special_tokens=UpperCamelCase__   ,       clean_up_tokenization_spaces=UpperCamelCase__							)
							return lmap(str.strip   ,       UpperCamelCase__							)




		def    lowerCAmelCase__    (		self   ,       UpperCamelCase_							):

							'''simple docstring'''


							UpperCamelCase__       :Optional[Any]							 =			self.tokenizer.pad_token_id
							UpperCamelCase__ ,    UpperCamelCase__       :Optional[Any]							 =			batch['''input_ids'''], batch['''attention_mask''']
							UpperCamelCase__       :Dict							 =			batch['''labels''']
							if isinstance(self.model   ,       UpperCamelCase__							):
												UpperCamelCase__       :Union[str, Any]							 =			self.model._shift_right(UpperCamelCase__							)
							else:
												UpperCamelCase__       :Union[str, Any]							 =			shift_tokens_right(UpperCamelCase__   ,       UpperCamelCase__							)
							if not self.already_saved_batch:  # This would be slightly better if it only happened on rank zero
												UpperCamelCase__       :Tuple							 =			decoder_input_ids
												self.save_readable_batch(UpperCamelCase__							)

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

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




		@property
		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							return self.tokenizer.pad_token_id




		def    lowerCAmelCase__    (		self   ,       UpperCamelCase_   ,       UpperCamelCase_							):

							'''simple docstring'''


							UpperCamelCase__       :Any							 =			self._step(UpperCamelCase__							)

							UpperCamelCase__       :Dict							 =			dict(zip(self.loss_names   ,       UpperCamelCase__							)							)
							# tokens per batch
							UpperCamelCase__       :Any							 =			batch['''input_ids'''].ne(self.pad							).sum() + batch['''labels'''].ne(self.pad							).sum()
							UpperCamelCase__       :List[str]							 =			batch['''input_ids'''].shape[0]
							UpperCamelCase__       :str							 =			batch['''input_ids'''].eq(self.pad							).sum()
							UpperCamelCase__       :List[Any]							 =			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    lowerCAmelCase__    (		self   ,       UpperCamelCase_   ,       UpperCamelCase_							):

							'''simple docstring'''


							return self._generative_step(UpperCamelCase__							)




		def    lowerCAmelCase__    (		self   ,       UpperCamelCase_   ,       UpperCamelCase_="val"							):

							'''simple docstring'''


							self.step_count += 1
							UpperCamelCase__       :Any							 =			{k: torch.stack([x[k] for x in outputs]							).mean() for k in self.loss_names}
							UpperCamelCase__       :Tuple							 =			losses['''loss''']
							UpperCamelCase__       :Optional[Any]							 =			{
							    k: np.array([x[k] for x in outputs]							).mean() for k in self.metric_names + ['''gen_time''', '''gen_len''']
							}
							UpperCamelCase__       :List[Any]							 =			(
							    generative_metrics[self.val_metric] if self.val_metric in generative_metrics else losses[self.val_metric]
							)
							UpperCamelCase__       :str							 =			torch.tensor(UpperCamelCase__							).type_as(UpperCamelCase__							)
							generative_metrics.update({k: v.item() for k, v in losses.items()}							)
							losses.update(UpperCamelCase__							)
							UpperCamelCase__       :Union[str, Any]							 =			{F'''{prefix}_avg_{k}''': x for k, x in losses.items()}
							UpperCamelCase__       :Union[str, Any]							 =			self.step_count
							self.metrics[prefix].append(UpperCamelCase__							)  # callback writes this to self.metrics_save_path
							UpperCamelCase__       :List[str]							 =			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    lowerCAmelCase__    (		self   ,       UpperCamelCase_   ,       UpperCamelCase_							):

							'''simple docstring'''


							return calculate_rouge(UpperCamelCase__   ,       UpperCamelCase__							)




		def    lowerCAmelCase__    (		self   ,       UpperCamelCase_							):

							'''simple docstring'''


							UpperCamelCase__       :List[Any]							 =			time.time()

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




		def    lowerCAmelCase__    (		self   ,       UpperCamelCase_   ,       UpperCamelCase_							):

							'''simple docstring'''


							return self._generative_step(UpperCamelCase__							)




		def    lowerCAmelCase__    (		self   ,       UpperCamelCase_							):

							'''simple docstring'''


							return self.validation_epoch_end(UpperCamelCase__   ,       prefix='''test'''							)




		def    lowerCAmelCase__    (		self   ,       UpperCamelCase_							):

							'''simple docstring'''


							UpperCamelCase__       :Optional[Any]							 =			self.n_obs[type_path]
							UpperCamelCase__       :Union[str, Any]							 =			self.target_lens[type_path]
							UpperCamelCase__       :Tuple							 =			self.dataset_class(
							    self.tokenizer   ,       type_path=UpperCamelCase__   ,       n_obs=UpperCamelCase__   ,       max_target_length=UpperCamelCase__   ,       **self.dataset_kwargs   ,       )
							return dataset




		def    lowerCAmelCase__    (		self   ,       UpperCamelCase_   ,       UpperCamelCase_   ,       UpperCamelCase_ = False							):

							'''simple docstring'''


							UpperCamelCase__       :Dict							 =			self.get_dataset(UpperCamelCase__							)

							if self.hparams.sortish_sampler and type_path != "test" and type_path != "val":
												UpperCamelCase__       :List[str]							 =			dataset.make_sortish_sampler(UpperCamelCase__   ,       distributed=self.hparams.gpus > 1							)
												return DataLoader(
												    UpperCamelCase__   ,       batch_size=UpperCamelCase__   ,       collate_fn=dataset.collate_fn   ,       shuffle=UpperCamelCase__   ,       num_workers=self.num_workers   ,       sampler=UpperCamelCase__   ,       )

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




		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							UpperCamelCase__       :Optional[Any]							 =			self.get_dataloader('''train'''   ,       batch_size=self.hparams.train_batch_size   ,       shuffle=UpperCamelCase__							)
							return dataloader




		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							return self.get_dataloader('''val'''   ,       batch_size=self.hparams.eval_batch_size							)




		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							return self.get_dataloader('''test'''   ,       batch_size=self.hparams.eval_batch_size							)




		@staticmethod
		def    lowerCAmelCase__    (		UpperCamelCase_   ,       UpperCamelCase_							):

							'''simple docstring'''


							BaseTransformer.add_model_specific_args(UpperCamelCase__   ,       UpperCamelCase__							)
							add_generic_args(UpperCamelCase__   ,       UpperCamelCase__							)
							parser.add_argument(
							    '''--max_source_length'''   ,       default=1024   ,       type=UpperCamelCase__   ,       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=UpperCamelCase__   ,       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=UpperCamelCase__   ,       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=UpperCamelCase__   ,       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=UpperCamelCase__							)
							parser.add_argument('''--overwrite_output_dir'''   ,       action='''store_true'''   ,       default=UpperCamelCase__							)
							parser.add_argument('''--max_tokens_per_batch'''   ,       type=UpperCamelCase__   ,       default=UpperCamelCase__							)
							parser.add_argument('''--logger_name'''   ,       type=UpperCamelCase__   ,       choices=['''default''', '''wandb''', '''wandb_shared''']   ,       default='''default'''							)
							parser.add_argument('''--n_train'''   ,       type=UpperCamelCase__   ,       default=-1   ,       required=UpperCamelCase__   ,       help='''# examples. -1 means use all.'''							)
							parser.add_argument('''--n_val'''   ,       type=UpperCamelCase__   ,       default=500   ,       required=UpperCamelCase__   ,       help='''# examples. -1 means use all.'''							)
							parser.add_argument('''--n_test'''   ,       type=UpperCamelCase__   ,       default=-1   ,       required=UpperCamelCase__   ,       help='''# examples. -1 means use all.'''							)
							parser.add_argument(
							    '''--task'''   ,       type=UpperCamelCase__   ,       default='''summarization'''   ,       required=UpperCamelCase__   ,       help='''# examples. -1 means use all.'''							)
							parser.add_argument('''--label_smoothing'''   ,       type=UpperCamelCase__   ,       default=0.0   ,       required=UpperCamelCase__							)
							parser.add_argument('''--src_lang'''   ,       type=UpperCamelCase__   ,       default=''''''   ,       required=UpperCamelCase__							)
							parser.add_argument('''--tgt_lang'''   ,       type=UpperCamelCase__   ,       default=''''''   ,       required=UpperCamelCase__							)
							parser.add_argument('''--eval_beams'''   ,       type=UpperCamelCase__   ,       default=UpperCamelCase__   ,       required=UpperCamelCase__							)
							parser.add_argument(
							    '''--val_metric'''   ,       type=UpperCamelCase__   ,       default=UpperCamelCase__   ,       required=UpperCamelCase__   ,       choices=['''bleu''', '''rouge2''', '''loss''', None]							)
							parser.add_argument('''--eval_max_gen_length'''   ,       type=UpperCamelCase__   ,       default=UpperCamelCase__   ,       help='''never generate more than n tokens'''							)
							parser.add_argument('''--save_top_k'''   ,       type=UpperCamelCase__   ,       default=1   ,       required=UpperCamelCase__   ,       help='''How many checkpoints to save'''							)
							parser.add_argument(
							    '''--early_stopping_patience'''   ,       type=UpperCamelCase__   ,       default=-1   ,       required=UpperCamelCase__   ,       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        lowercase     (							_snake_case ):

		"""simple docstring"""

		_a   			=    'translation'
		_a   			=    ['loss']
		_a   			=    ['bleu']
		_a   			=    'bleu'




		def __init__(		self   ,       UpperCamelCase_   ,       **UpperCamelCase_							):

							'''simple docstring'''


							super().__init__(UpperCamelCase__   ,       **UpperCamelCase__							)
							UpperCamelCase__       :int							 =			hparams.src_lang
							UpperCamelCase__       :Optional[int]							 =			hparams.tgt_lang




		def    lowerCAmelCase__    (		self   ,       UpperCamelCase_   ,       UpperCamelCase_							):

							'''simple docstring'''


							return calculate_bleu(UpperCamelCase__   ,       UpperCamelCase__							)



def       a				(      __a			, __a=None       )  ->     SummarizationModule:



					'''simple docstring'''






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

					if model is None:
										if "summarization" in args.task:
															UpperCamelCase__       :str							 =			SummarizationModule(UpperCAmelCase__       )
										else:
															UpperCamelCase__       :Optional[Any]							 =			TranslationModule(UpperCAmelCase__       )
					UpperCamelCase__       :Optional[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'''       )
					):
										UpperCamelCase__       :List[str]							 =			True  # don't pollute wandb logs unnecessarily
					elif args.logger_name == "wandb":
										from pytorch_lightning.loggers import WandbLogger

										UpperCamelCase__       :List[Any]							 =			os.environ.get('''WANDB_PROJECT'''			, UpperCAmelCase__       )
										UpperCamelCase__       :str							 =			WandbLogger(name=model.output_dir.name			, project=UpperCAmelCase__       )

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

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

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

					UpperCamelCase__       :Optional[int]							 =			args.val_metric == '''loss'''
					UpperCamelCase__       :Union[str, Any]							 =			generic_train(
					    UpperCAmelCase__			, UpperCAmelCase__			, logging_callback=SeqaSeqLoggingCallback()			, checkpoint_callback=get_checkpoint_callback(
					        args.output_dir			, model.val_metric			, args.save_top_k			, UpperCAmelCase__       )			, early_stopping_callback=UpperCAmelCase__			, logger=UpperCAmelCase__			, )
					pickle_save(model.hparams			, model.output_dir / '''hparams.pkl'''       )
					if not args.do_predict:
										return model

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

		_UpperCAmelCase											=					parser.parse_args()

		main(args)



'''simple docstring'''
from argparse import ArgumentParser

from .env import EnvironmentCommand


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



					'''simple docstring'''






					UpperCamelCase__       :Union[str, Any]							 =			ArgumentParser('''Diffusers CLI tool'''			, usage='''diffusers-cli <command> [<args>]'''       )
					UpperCamelCase__       :Union[str, Any]							 =			parser.add_subparsers(help='''diffusers-cli command helpers'''       )

					# Register commands
					EnvironmentCommand.register_subcommand(__a       )

					# Let's go
					UpperCamelCase__       :Optional[int]							 =			parser.parse_args()

					if not hasattr(__a			, '''func'''       ):
										parser.print_help()
										exit(1       )

					# Run
					UpperCamelCase__       :Optional[int]							 =			args.func(__a       )
					service.run()


if __name__ == "__main__":
		main()