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


_lowerCamelCase					:   List[str]							=		logging.getLogger(__name__)


class 						lowercase    (     a     ):
							lowercase__						:       List[str]								=							"""summarization"""
							lowercase__						:       Optional[Any]								=							["""loss"""]
							lowercase__						:       str								=							ROUGE_KEYS
							lowercase__						:       Optional[Any]								=							"""rouge2"""




							def __init__(						self   :					Any	,							_UpperCamelCase   :					str	,							**_UpperCamelCase   :					Optional[Any]      )					->				List[Any]:



												'''simple docstring'''

												if hparams.sortish_sampler and hparams.gpus > 1:
																	SCREAMING_SNAKE_CASE    			=       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      )
												SCREAMING_SNAKE_CASE    			=       Path(self.output_dir      ) / "metrics.json"
												SCREAMING_SNAKE_CASE    			=       Path(self.output_dir      ) / "hparams.pkl"
												pickle_save(self.hparams	,							self.hparams_save_path      )
												SCREAMING_SNAKE_CASE    			=       0
												SCREAMING_SNAKE_CASE    			=       defaultdict(_UpperCamelCase      )
												SCREAMING_SNAKE_CASE    			=       self.config.model_type
												SCREAMING_SNAKE_CASE    			=       self.config.tgt_vocab_size if self.model_type == "fsmt" else self.config.vocab_size

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

												SCREAMING_SNAKE_CASE    			=       {
												    "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()      )

												SCREAMING_SNAKE_CASE    			=       get_git_info()["repo_sha"]
												SCREAMING_SNAKE_CASE    			=       hparams.num_workers
												SCREAMING_SNAKE_CASE    			=       None  # default to config
												if self.model.config.decoder_start_token_id is None and isinstance(self.tokenizer	,							_UpperCamelCase      ):
																	SCREAMING_SNAKE_CASE    			=       self.tokenizer.lang_code_to_id[hparams.tgt_lang]
																	SCREAMING_SNAKE_CASE    			=       self.decoder_start_token_id
												SCREAMING_SNAKE_CASE    			=       (
												    SeqaSeqDataset if hasattr(self.tokenizer	,							"prepare_seq2seq_batch"      ) else LegacySeqaSeqDataset
												)
												SCREAMING_SNAKE_CASE    			=       False
												SCREAMING_SNAKE_CASE    			=       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:
																	SCREAMING_SNAKE_CASE    			=       self.hparams.eval_max_gen_length
												else:
																	SCREAMING_SNAKE_CASE    			=       self.model.config.max_length
												SCREAMING_SNAKE_CASE    			=       self.default_val_metric if self.hparams.val_metric is None else self.hparams.val_metric




							def 	__snake_case(						self   :					str	,							_UpperCamelCase   :					Dict[str, torch.Tensor]      )					->				Dict[str, List[str]]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       {
												    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"      )

												SCREAMING_SNAKE_CASE    			=       True
												return readable_batch




							def 	__snake_case(						self   :					int	,							_UpperCamelCase   :					Dict	,							**_UpperCamelCase   :					List[Any]      )					->				List[str]:



												'''simple docstring'''

												return self.model(_UpperCamelCase	,							**_UpperCamelCase      )




							def 	__snake_case(						self   :					Any	,							_UpperCamelCase   :					List[int]      )					->				str:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       self.tokenizer.batch_decode(
												    _UpperCamelCase	,							skip_special_tokens=_UpperCamelCase	,							clean_up_tokenization_spaces=_UpperCamelCase      )
												return lmap(str.strip	,							_UpperCamelCase      )




							def 	__snake_case(						self   :					List[Any]	,							_UpperCamelCase   :					dict      )					->				Tuple:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       self.tokenizer.pad_token_id
												SCREAMING_SNAKE_CASE			,   SCREAMING_SNAKE_CASE    			=       batch["input_ids"], batch["attention_mask"]
												SCREAMING_SNAKE_CASE    			=       batch["labels"]
												if isinstance(self.model	,							_UpperCamelCase      ):
																	SCREAMING_SNAKE_CASE    			=       self.model._shift_right(_UpperCamelCase      )
												else:
																	SCREAMING_SNAKE_CASE    			=       shift_tokens_right(_UpperCamelCase	,							_UpperCamelCase      )
												if not self.already_saved_batch:  # This would be slightly better if it only happened on rank zero
																	SCREAMING_SNAKE_CASE    			=       decoder_input_ids
																	self.save_readable_batch(_UpperCamelCase      )

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

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




							@property
							def 	__snake_case(						self   :					int      )					->				int:



												'''simple docstring'''

												return self.tokenizer.pad_token_id




							def 	__snake_case(						self   :					List[str]	,							_UpperCamelCase   :					Any	,							_UpperCamelCase   :					Optional[int]      )					->				Dict:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       self._step(_UpperCamelCase      )

												SCREAMING_SNAKE_CASE    			=       dict(zip(self.loss_names	,							_UpperCamelCase      )      )
												# tokens per batch
												SCREAMING_SNAKE_CASE    			=       batch["input_ids"].ne(self.pad      ).sum() + batch["labels"].ne(self.pad      ).sum()
												SCREAMING_SNAKE_CASE    			=       batch["input_ids"].shape[0]
												SCREAMING_SNAKE_CASE    			=       batch["input_ids"].eq(self.pad      ).sum()
												SCREAMING_SNAKE_CASE    			=       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   :					Tuple	,							_UpperCamelCase   :					str	,							_UpperCamelCase   :					int      )					->				Dict:



												'''simple docstring'''

												return self._generative_step(_UpperCamelCase      )




							def 	__snake_case(						self   :					List[Any]	,							_UpperCamelCase   :					Dict	,							_UpperCamelCase   :					Any="val"      )					->				Dict:



												'''simple docstring'''

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



												'''simple docstring'''

												return calculate_rouge(_UpperCamelCase	,							_UpperCamelCase      )




							def 	__snake_case(						self   :					Tuple	,							_UpperCamelCase   :					dict      )					->				dict:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       time.time()

												# parser.add_argument('--eval_max_gen_length', type=int, default=None, help='never generate more than n tokens')
												SCREAMING_SNAKE_CASE    			=       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	,							)
												SCREAMING_SNAKE_CASE    			=       (time.time() - ta) / batch["input_ids"].shape[0]
												SCREAMING_SNAKE_CASE    			=       self.ids_to_clean_text(_UpperCamelCase      )
												SCREAMING_SNAKE_CASE    			=       self.ids_to_clean_text(batch["labels"]      )
												SCREAMING_SNAKE_CASE    			=       self._step(_UpperCamelCase      )
												SCREAMING_SNAKE_CASE    			=       dict(zip(self.loss_names	,							_UpperCamelCase      )      )
												SCREAMING_SNAKE_CASE    			=       self.calc_generative_metrics(_UpperCamelCase	,							_UpperCamelCase      )
												SCREAMING_SNAKE_CASE    			=       np.mean(lmap(_UpperCamelCase	,							_UpperCamelCase      )      )
												base_metrics.update(gen_time=_UpperCamelCase	,							gen_len=_UpperCamelCase	,							preds=_UpperCamelCase	,							target=_UpperCamelCase	,							**_UpperCamelCase      )
												return base_metrics




							def 	__snake_case(						self   :					List[str]	,							_UpperCamelCase   :					str	,							_UpperCamelCase   :					Tuple      )					->				Optional[Any]:



												'''simple docstring'''

												return self._generative_step(_UpperCamelCase      )




							def 	__snake_case(						self   :					Union[str, Any]	,							_UpperCamelCase   :					str      )					->				Dict:



												'''simple docstring'''

												return self.validation_epoch_end(_UpperCamelCase	,							prefix="test"      )




							def 	__snake_case(						self   :					List[Any]	,							_UpperCamelCase   :					List[str]      )					->				SeqaSeqDataset:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       self.n_obs[type_path]
												SCREAMING_SNAKE_CASE    			=       self.target_lens[type_path]
												SCREAMING_SNAKE_CASE    			=       self.dataset_class(
												    self.tokenizer	,							type_path=_UpperCamelCase	,							n_obs=_UpperCamelCase	,							max_target_length=_UpperCamelCase	,							**self.dataset_kwargs	,							)
												return dataset




							def 	__snake_case(						self   :					int	,							_UpperCamelCase   :					str	,							_UpperCamelCase   :					int	,							_UpperCamelCase   :					bool = False      )					->				DataLoader:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       self.get_dataset(_UpperCamelCase      )

												if self.hparams.sortish_sampler and type_path != "test" and type_path != "val":
																	SCREAMING_SNAKE_CASE    			=       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":
																	SCREAMING_SNAKE_CASE    			=       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 	__snake_case(						self   :					Dict      )					->				DataLoader:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       self.get_dataloader("train"	,							batch_size=self.hparams.train_batch_size	,							shuffle=_UpperCamelCase      )
												return dataloader




							def 	__snake_case(						self   :					Any      )					->				DataLoader:



												'''simple docstring'''

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




							def 	__snake_case(						self   :					int      )					->				DataLoader:



												'''simple docstring'''

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





							@staticmethod
							def 	__snake_case(						_UpperCamelCase   :					List[Any]	,							_UpperCamelCase   :					Optional[Any]      )					->				int:



												'''simple docstring'''

												BaseTransformer.add_model_specific_args(_UpperCamelCase	,							_UpperCamelCase      )
												add_generic_args(_UpperCamelCase	,							_UpperCamelCase      )
												parser.add_argument(
												    "--max_source_length"	,							default=1_024	,							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    (     a     ):
							lowercase__						:       int								=							"""translation"""
							lowercase__						:       Tuple								=							["""loss"""]
							lowercase__						:       Tuple								=							["""bleu"""]
							lowercase__						:       List[Any]								=							"""bleu"""




							def __init__(						self   :					List[str]	,							_UpperCamelCase   :					int	,							**_UpperCamelCase   :					List[str]      )					->				Any:



												'''simple docstring'''

												super().__init__(_UpperCamelCase	,							**_UpperCamelCase      )
												SCREAMING_SNAKE_CASE    			=       hparams.src_lang
												SCREAMING_SNAKE_CASE    			=       hparams.tgt_lang





							def 	__snake_case(						self   :					str	,							_UpperCamelCase   :					int	,							_UpperCamelCase   :					List[str]      )					->				dict:



												'''simple docstring'''

												return calculate_bleu(_UpperCamelCase	,							_UpperCamelCase      )





def 	__lowerCamelCase							(UpperCAmelCase__     :							int      , UpperCAmelCase__     :							Tuple=None  ):
					Path(args.output_dir  ).mkdir(exist_ok=UpperCAmelCase__  )
					check_output_dir(UpperCAmelCase__      , expected_items=3  )

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

										SCREAMING_SNAKE_CASE    			=       os.environ.get("WANDB_PROJECT"      , UpperCAmelCase__  )
										SCREAMING_SNAKE_CASE    			=       WandbLogger(name=model.output_dir.name      , project=UpperCAmelCase__  )

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

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

					if args.early_stopping_patience >= 0:
										SCREAMING_SNAKE_CASE    			=       get_early_stopping_callback(model.val_metric      , args.early_stopping_patience  )
					else:
										SCREAMING_SNAKE_CASE    			=       False

					SCREAMING_SNAKE_CASE    			=       args.val_metric == "loss"
					SCREAMING_SNAKE_CASE    			=       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

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

							_lowerCamelCase					:   Any							=		parser.parse_args()

							main(args)





import argparse
import logging
from collections import namedtuple

import torch
from model_bertabs import BertAbsSummarizer
from models.model_builder import AbsSummarizer  # The authors' implementation

from transformers import BertTokenizer


logging.basicConfig(level=logging.INFO)
_lowerCamelCase					:   Optional[int]							=		logging.getLogger(__name__)


_lowerCamelCase					:   Optional[int]							=		'''Hello world! cécé herlolip'''


_lowerCamelCase					:   List[Any]							=		namedtuple(
    '''BertAbsConfig''',
    [
        '''temp_dir''',
        '''large''',
        '''use_bert_emb''',
        '''finetune_bert''',
        '''encoder''',
        '''share_emb''',
        '''max_pos''',
        '''enc_layers''',
        '''enc_hidden_size''',
        '''enc_heads''',
        '''enc_ff_size''',
        '''enc_dropout''',
        '''dec_layers''',
        '''dec_hidden_size''',
        '''dec_heads''',
        '''dec_ff_size''',
        '''dec_dropout''',
    ],
)





def 	__lowerCamelCase							(UpperCAmelCase__     :							str      , UpperCAmelCase__     :							Optional[int]  ):
					SCREAMING_SNAKE_CASE    			=       BertAbsConfig(
					    temp_dir="."      , finetune_bert=UpperCAmelCase__      , large=UpperCAmelCase__      , share_emb=UpperCAmelCase__      , use_bert_emb=UpperCAmelCase__      , encoder="bert"      , max_pos=5_1_2      , enc_layers=6      , enc_hidden_size=5_1_2      , enc_heads=8      , enc_ff_size=5_1_2      , enc_dropout=0.2      , dec_layers=6      , dec_hidden_size=7_6_8      , dec_heads=8      , dec_ff_size=2_0_4_8      , dec_dropout=0.2      , )
					SCREAMING_SNAKE_CASE    			=       torch.load(UpperCAmelCase__      , lambda UpperCAmelCase__      , UpperCAmelCase__  : storage  )
					SCREAMING_SNAKE_CASE    			=       AbsSummarizer(UpperCAmelCase__      , torch.device("cpu"  )      , UpperCAmelCase__  )
					original.eval()

					SCREAMING_SNAKE_CASE    			=       BertAbsSummarizer(UpperCAmelCase__      , torch.device("cpu"  )  )
					new_model.eval()

					# -------------------
					# Convert the weights
					# -------------------

					logging.info("convert the model"  )
					new_model.bert.load_state_dict(original.bert.state_dict()  )
					new_model.decoder.load_state_dict(original.decoder.state_dict()  )
					new_model.generator.load_state_dict(original.generator.state_dict()  )

					# ----------------------------------
					# Make sure the outpus are identical
					# ----------------------------------

					logging.info("Make sure that the models' outputs are identical"  )
					SCREAMING_SNAKE_CASE    			=       BertTokenizer.from_pretrained("bert-base-uncased"  )

					# prepare the model inputs
					SCREAMING_SNAKE_CASE    			=       tokenizer.encode("This is sample éàalj'-."  )
					encoder_input_ids.extend([tokenizer.pad_token_id] * (5_1_2 - len(UpperCAmelCase__  ))  )
					SCREAMING_SNAKE_CASE    			=       torch.tensor(UpperCAmelCase__  ).unsqueeze(0  )
					SCREAMING_SNAKE_CASE    			=       tokenizer.encode("This is sample 3 éàalj'-."  )
					decoder_input_ids.extend([tokenizer.pad_token_id] * (5_1_2 - len(UpperCAmelCase__  ))  )
					SCREAMING_SNAKE_CASE    			=       torch.tensor(UpperCAmelCase__  ).unsqueeze(0  )

					# failsafe to make sure the weights reset does not affect the
					# loaded weights.
					assert torch.max(torch.abs(original.generator[0].weight - new_model.generator[0].weight  )  ) == 0

					# forward pass
					SCREAMING_SNAKE_CASE    			=       encoder_input_ids
					SCREAMING_SNAKE_CASE    			=       decoder_input_ids
					SCREAMING_SNAKE_CASE    			=       SCREAMING_SNAKE_CASE    			=       None
					SCREAMING_SNAKE_CASE    			=       None
					SCREAMING_SNAKE_CASE    			=       SCREAMING_SNAKE_CASE    			=       None
					SCREAMING_SNAKE_CASE    			=       SCREAMING_SNAKE_CASE    			=       None
					SCREAMING_SNAKE_CASE    			=       None

					# The original model does not apply the geneator layer immediatly but rather in
					# the beam search (where it combines softmax + linear layer). Since we already
					# apply the softmax in our generation process we only apply the linear layer here.
					# We make sure that the outputs of the full stack are identical
					SCREAMING_SNAKE_CASE    			=       original(UpperCAmelCase__      , UpperCAmelCase__      , UpperCAmelCase__      , UpperCAmelCase__      , UpperCAmelCase__      , UpperCAmelCase__      , UpperCAmelCase__  )[0]
					SCREAMING_SNAKE_CASE    			=       original.generator(UpperCAmelCase__  )

					SCREAMING_SNAKE_CASE    			=       new_model(
					    UpperCAmelCase__      , UpperCAmelCase__      , UpperCAmelCase__      , UpperCAmelCase__      , UpperCAmelCase__  )[0]
					SCREAMING_SNAKE_CASE    			=       new_model.generator(UpperCAmelCase__  )

					SCREAMING_SNAKE_CASE    			=       torch.max(torch.abs(output_converted_model - output_original_model  )  ).item()
					print("Maximum absolute difference beween weights: {:.2f}".format(UpperCAmelCase__  )  )
					SCREAMING_SNAKE_CASE    			=       torch.max(torch.abs(output_converted_generator - output_original_generator  )  ).item()
					print("Maximum absolute difference beween weights: {:.2f}".format(UpperCAmelCase__  )  )

					SCREAMING_SNAKE_CASE    			=       torch.allclose(UpperCAmelCase__      , UpperCAmelCase__      , atol=1e-3  )
					if are_identical:
										logging.info("all weights are equal up to 1e-3"  )
					else:
										raise ValueError("the weights are different. The new model is likely different from the original one."  )

					# The model has been saved with torch.save(model) and this is bound to the exact
					# directory structure. We save the state_dict instead.
					logging.info("saving the model's state dictionary"  )
					torch.save(
					    new_model.state_dict()      , "./bertabs-finetuned-cnndm-extractive-abstractive-summarization/pytorch_model.bin"  )


if __name__ == "__main__":
							_lowerCamelCase					:   Union[str, Any]							=		argparse.ArgumentParser()
							parser.add_argument(
							    '''--bertabs_checkpoint_path''',
							    default=None,
							    type=str,
							    required=True,
							    help='''Path the official PyTorch dump.''',
							)
							parser.add_argument(
							    '''--pytorch_dump_folder_path''',
							    default=None,
							    type=str,
							    required=True,
							    help='''Path to the output PyTorch model.''',
							)
							_lowerCamelCase					:   Any							=		parser.parse_args()

							convert_bertabs_checkpoints(
							    args.bertabs_checkpoint_path,
							    args.pytorch_dump_folder_path,
							)




