'''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


_lowercase			:  int   							=	logging.getLogger(__name__)






class 			lowerCAmelCase__       (  lowerCAmelCase_					):
					lowerCAmelCase_					       =					"""summarization"""
					lowerCAmelCase_					       =					["""loss"""]
					lowerCAmelCase_					       =					ROUGE_KEYS
					lowerCAmelCase_					       =					"""rouge2"""






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


											"""simple docstring"""





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

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

											lowercase_     :					Optional[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()		)

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






					def      _snake_case   (       self    ,							__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											lowercase_     :					str													=	{
											    k: self.tokenizer.batch_decode(v.tolist()		) if '''mask''' not in k else v.shape for k, v in batch.items()
											}
											save_json(__SCREAMING_SNAKE_CASE    ,							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'''		)

											lowercase_     :					Optional[int]													=	True
											return readable_batch






					def      _snake_case   (       self    ,							__SCREAMING_SNAKE_CASE    ,							**__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											return self.model(__SCREAMING_SNAKE_CASE    ,							**__SCREAMING_SNAKE_CASE		)






					def      _snake_case   (       self    ,							__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											lowercase_     :					List[Any]													=	self.tokenizer.batch_decode(
											    __SCREAMING_SNAKE_CASE    ,							skip_special_tokens=__SCREAMING_SNAKE_CASE    ,							clean_up_tokenization_spaces=__SCREAMING_SNAKE_CASE		)
											return lmap(str.strip    ,							__SCREAMING_SNAKE_CASE		)






					def      _snake_case   (       self    ,							__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





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

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

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






					@property
					def      _snake_case   (       self		):


											"""simple docstring"""





											return self.tokenizer.pad_token_id






					def      _snake_case   (       self    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											lowercase_     :					Tuple													=	self._step(__SCREAMING_SNAKE_CASE		)

											lowercase_     :					Optional[Any]													=	dict(zip(self.loss_names    ,							__SCREAMING_SNAKE_CASE		)		)
											# tokens per batch
											lowercase_     :					Optional[Any]													=	batch['''input_ids'''].ne(self.pad		).sum() + batch['''labels'''].ne(self.pad		).sum()
											lowercase_     :					List[str]													=	batch['''input_ids'''].shape[0]
											lowercase_     :					Tuple													=	batch['''input_ids'''].eq(self.pad		).sum()
											lowercase_     :					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      _snake_case   (       self    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											return self._generative_step(__SCREAMING_SNAKE_CASE		)






					def      _snake_case   (       self    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE="val"		):


											"""simple docstring"""





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


											"""simple docstring"""





											return calculate_rouge(__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE		)






					def      _snake_case   (       self    ,							__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											lowercase_     :					Union[str, Any]													=	time.time()

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






					def      _snake_case   (       self    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											return self._generative_step(__SCREAMING_SNAKE_CASE		)






					def      _snake_case   (       self    ,							__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





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






					def      _snake_case   (       self    ,							__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											lowercase_     :					Any													=	self.n_obs[type_path]
											lowercase_     :					Tuple													=	self.target_lens[type_path]
											lowercase_     :					List[Any]													=	self.dataset_class(
											    self.tokenizer    ,							type_path=__SCREAMING_SNAKE_CASE    ,							n_obs=__SCREAMING_SNAKE_CASE    ,							max_target_length=__SCREAMING_SNAKE_CASE    ,							**self.dataset_kwargs    ,							)
											return dataset






					def      _snake_case   (       self    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE = False		):


											"""simple docstring"""





											lowercase_     :					Optional[int]													=	self.get_dataset(__SCREAMING_SNAKE_CASE		)

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

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






					def      _snake_case   (       self		):


											"""simple docstring"""





											lowercase_     :					List[Any]													=	self.get_dataloader('''train'''    ,							batch_size=self.hparams.train_batch_size    ,							shuffle=__SCREAMING_SNAKE_CASE		)
											return dataloader






					def      _snake_case   (       self		):


											"""simple docstring"""





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






					def      _snake_case   (       self		):


											"""simple docstring"""





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






					@staticmethod
					def      _snake_case   (       __SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											BaseTransformer.add_model_specific_args(__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE		)
											add_generic_args(__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE		)
											parser.add_argument(
											    '''--max_source_length'''    ,							default=10_24    ,							type=__SCREAMING_SNAKE_CASE    ,							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=__SCREAMING_SNAKE_CASE    ,							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=1_42    ,							type=__SCREAMING_SNAKE_CASE    ,							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=1_42    ,							type=__SCREAMING_SNAKE_CASE    ,							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=__SCREAMING_SNAKE_CASE		)
											parser.add_argument('''--overwrite_output_dir'''    ,							action='''store_true'''    ,							default=__SCREAMING_SNAKE_CASE		)
											parser.add_argument('''--max_tokens_per_batch'''    ,							type=__SCREAMING_SNAKE_CASE    ,							default=__SCREAMING_SNAKE_CASE		)
											parser.add_argument('''--logger_name'''    ,							type=__SCREAMING_SNAKE_CASE    ,							choices=['''default''', '''wandb''', '''wandb_shared''']    ,							default='''default'''		)
											parser.add_argument('''--n_train'''    ,							type=__SCREAMING_SNAKE_CASE    ,							default=-1    ,							required=__SCREAMING_SNAKE_CASE    ,							help='''# examples. -1 means use all.'''		)
											parser.add_argument('''--n_val'''    ,							type=__SCREAMING_SNAKE_CASE    ,							default=5_00    ,							required=__SCREAMING_SNAKE_CASE    ,							help='''# examples. -1 means use all.'''		)
											parser.add_argument('''--n_test'''    ,							type=__SCREAMING_SNAKE_CASE    ,							default=-1    ,							required=__SCREAMING_SNAKE_CASE    ,							help='''# examples. -1 means use all.'''		)
											parser.add_argument(
											    '''--task'''    ,							type=__SCREAMING_SNAKE_CASE    ,							default='''summarization'''    ,							required=__SCREAMING_SNAKE_CASE    ,							help='''# examples. -1 means use all.'''		)
											parser.add_argument('''--label_smoothing'''    ,							type=__SCREAMING_SNAKE_CASE    ,							default=0.0    ,							required=__SCREAMING_SNAKE_CASE		)
											parser.add_argument('''--src_lang'''    ,							type=__SCREAMING_SNAKE_CASE    ,							default=''''''    ,							required=__SCREAMING_SNAKE_CASE		)
											parser.add_argument('''--tgt_lang'''    ,							type=__SCREAMING_SNAKE_CASE    ,							default=''''''    ,							required=__SCREAMING_SNAKE_CASE		)
											parser.add_argument('''--eval_beams'''    ,							type=__SCREAMING_SNAKE_CASE    ,							default=__SCREAMING_SNAKE_CASE    ,							required=__SCREAMING_SNAKE_CASE		)
											parser.add_argument(
											    '''--val_metric'''    ,							type=__SCREAMING_SNAKE_CASE    ,							default=__SCREAMING_SNAKE_CASE    ,							required=__SCREAMING_SNAKE_CASE    ,							choices=['''bleu''', '''rouge2''', '''loss''', None]		)
											parser.add_argument('''--eval_max_gen_length'''    ,							type=__SCREAMING_SNAKE_CASE    ,							default=__SCREAMING_SNAKE_CASE    ,							help='''never generate more than n tokens'''		)
											parser.add_argument('''--save_top_k'''    ,							type=__SCREAMING_SNAKE_CASE    ,							default=1    ,							required=__SCREAMING_SNAKE_CASE    ,							help='''How many checkpoints to save'''		)
											parser.add_argument(
											    '''--early_stopping_patience'''    ,							type=__SCREAMING_SNAKE_CASE    ,							default=-1    ,							required=__SCREAMING_SNAKE_CASE    ,							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__       (  lowerCAmelCase_					):
					lowerCAmelCase_					       =					"""translation"""
					lowerCAmelCase_					       =					["""loss"""]
					lowerCAmelCase_					       =					["""bleu"""]
					lowerCAmelCase_					       =					"""bleu"""






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


											"""simple docstring"""





											super().__init__(__SCREAMING_SNAKE_CASE    ,							**__SCREAMING_SNAKE_CASE		)
											lowercase_     :					Optional[int]													=	hparams.src_lang
											lowercase_     :					Union[str, Any]													=	hparams.tgt_lang






					def      _snake_case   (       self    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											return calculate_bleu(__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE		)







def  snake_case_		(    __SCREAMING_SNAKE_CASE :			Union[str, Any]				,				__SCREAMING_SNAKE_CASE :			List[str]=None							):



						"""simple docstring"""





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

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

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

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

												lowercase_     :					Optional[int]													=	WandbLogger(name=model.output_dir.name				,				project=F'''hf_{dataset}'''							)

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

						lowercase_     :					List[Any]													=	args.val_metric == '''loss'''
						lowercase_     :					List[Any]													=	generic_train(
						    __SCREAMING_SNAKE_CASE				,				__SCREAMING_SNAKE_CASE				,				logging_callback=SeqaSeqLoggingCallback()				,				checkpoint_callback=get_checkpoint_callback(
						        args.output_dir				,				model.val_metric				,				args.save_top_k				,				__SCREAMING_SNAKE_CASE							)				,				early_stopping_callback=__SCREAMING_SNAKE_CASE				,				logger=__SCREAMING_SNAKE_CASE				,				)
						pickle_save(model.hparams				,				model.output_dir / '''hparams.pkl'''							)
						if not args.do_predict:
												return model

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

							_lowercase			:  Tuple   							=	parser.parse_args()

							main(args)





'''simple docstring'''







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_lowercase			:  List[Any]   							=	{
    "configuration_x_clip": [
        "XCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "XCLIPConfig",
        "XCLIPTextConfig",
        "XCLIPVisionConfig",
    ],
    "processing_x_clip": ["XCLIPProcessor"],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							_lowercase			:  int   							=	[
							    "XCLIP_PRETRAINED_MODEL_ARCHIVE_LIST",
							    "XCLIPModel",
							    "XCLIPPreTrainedModel",
							    "XCLIPTextModel",
							    "XCLIPVisionModel",
							]

if TYPE_CHECKING:
							from .configuration_x_clip import (
							    XCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
							    XCLIPConfig,
							    XCLIPTextConfig,
							    XCLIPVisionConfig,
							)
							from .processing_x_clip import XCLIPProcessor

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_x_clip import (
														    XCLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
														    XCLIPModel,
														    XCLIPPreTrainedModel,
														    XCLIPTextModel,
														    XCLIPVisionModel,
														)

else:
							import sys

							_lowercase			:  List[str]   							=	_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



