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

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

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


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


_A     =  logging.getLogger(__name__)





class 			_lowerCAmelCase      (			__a ):
				_lowercase    	='''summarization'''
				_lowercase    	=['''loss''']
				_lowercase    	=ROUGE_KEYS
				_lowercase    	='''rouge2'''




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

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

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

							lowerCAmelCase_      			=			get_git_info()["repo_sha"]
							lowerCAmelCase_      			=			hparams.num_workers
							lowerCAmelCase_      			=			None  # default to config
							if self.model.config.decoder_start_token_id is None and isinstance(self.tokenizer       ,       _UpperCamelCase  ):
										lowerCAmelCase_      			=			self.tokenizer.lang_code_to_id[hparams.tgt_lang]
										lowerCAmelCase_      			=			self.decoder_start_token_id
							lowerCAmelCase_      			=			(
							    SeqaSeqDataset if hasattr(self.tokenizer       ,       "prepare_seq2seq_batch"  ) else LegacySeqaSeqDataset
							)
							lowerCAmelCase_      			=			False
							lowerCAmelCase_      			=			self.model.config.num_beams if self.hparams.eval_beams is None else self.hparams.eval_beams
							if self.hparams.eval_max_gen_length is not None:
										lowerCAmelCase_      			=			self.hparams.eval_max_gen_length
							else:
										lowerCAmelCase_      			=			self.model.config.max_length
							lowerCAmelCase_      			=			self.default_val_metric if self.hparams.val_metric is None else self.hparams.val_metric




				def   __a		(      self       ,       _UpperCamelCase  )       ->   Dict[str, List[str]]:
							lowerCAmelCase_      			=			{
							    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"  )

							lowerCAmelCase_      			=			True
							return readable_batch




				def   __a		(      self       ,       _UpperCamelCase       ,       **_UpperCamelCase  )       ->   List[Any]:
							return self.model(_UpperCamelCase       ,       **_UpperCamelCase  )




				def   __a		(      self       ,       _UpperCamelCase  )       ->   Union[str, Any]:
							lowerCAmelCase_      			=			self.tokenizer.batch_decode(
							    _UpperCamelCase       ,       skip_special_tokens=_UpperCamelCase       ,       clean_up_tokenization_spaces=_UpperCamelCase  )
							return lmap(str.strip       ,       _UpperCamelCase  )




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

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

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




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




				def   __a		(      self       ,       _UpperCamelCase       ,       _UpperCamelCase  )       ->   Dict:
							lowerCAmelCase_      			=			self._step(_UpperCamelCase  )

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




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




				def   __a		(      self       ,       _UpperCamelCase  )       ->   dict:
							lowerCAmelCase_      			=			time.time()

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




				def   __a		(      self       ,       _UpperCamelCase       ,       _UpperCamelCase  )       ->   Union[str, Any]:
							return self._generative_step(_UpperCamelCase  )




				def   __a		(      self       ,       _UpperCamelCase  )       ->   Dict:
							return self.validation_epoch_end(_UpperCamelCase       ,       prefix="test"  )




				def   __a		(      self       ,       _UpperCamelCase  )       ->   SeqaSeqDataset:
							lowerCAmelCase_      			=			self.n_obs[type_path]
							lowerCAmelCase_      			=			self.target_lens[type_path]
							lowerCAmelCase_      			=			self.dataset_class(
							    self.tokenizer       ,       type_path=_UpperCamelCase       ,       n_obs=_UpperCamelCase       ,       max_target_length=_UpperCamelCase       ,       **self.dataset_kwargs       ,       )
							return dataset




				def   __a		(      self       ,       _UpperCamelCase       ,       _UpperCamelCase       ,       _UpperCamelCase = False  )       ->   DataLoader:
							lowerCAmelCase_      			=			self.get_dataset(_UpperCamelCase  )

							if self.hparams.sortish_sampler and type_path != "test" and type_path != "val":
										lowerCAmelCase_      			=			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":
										lowerCAmelCase_      			=			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   __a		(      self  )       ->   DataLoader:
							lowerCAmelCase_      			=			self.get_dataloader("train"       ,       batch_size=self.hparams.train_batch_size       ,       shuffle=_UpperCamelCase  )
							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		(      _UpperCamelCase       ,       _UpperCamelCase  )       ->   Tuple:
							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 			_lowerCAmelCase      (			__a ):
				_lowercase    	='''translation'''
				_lowercase    	=['''loss''']
				_lowercase    	=['''bleu''']
				_lowercase    	='''bleu'''




				def __init__(      self       ,       _UpperCamelCase       ,       **_UpperCamelCase  )       ->   List[Any]:
							super().__init__(_UpperCamelCase       ,       **_UpperCamelCase  )
							lowerCAmelCase_      			=			hparams.src_lang
							lowerCAmelCase_      			=			hparams.tgt_lang




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






def      lowerCamelCase__       (       __lowerCAmelCase						:						Tuple					,							__lowerCAmelCase						:						Tuple=None       ):




			"""simple docstring"""


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

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

						lowerCAmelCase_      			=			os.environ.get("WANDB_PROJECT"					,							__lowerCAmelCase       )
						lowerCAmelCase_      			=			WandbLogger(name=model.output_dir.name					,							project=__lowerCAmelCase       )

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

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

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

			lowerCAmelCase_      			=			args.val_metric == "loss"
			lowerCAmelCase_      			=			generic_train(
			    __lowerCAmelCase					,							__lowerCAmelCase					,							logging_callback=SeqaSeqLoggingCallback()					,							checkpoint_callback=get_checkpoint_callback(
			        args.output_dir					,							model.val_metric					,							args.save_top_k					,							__lowerCAmelCase       )					,							early_stopping_callback=__lowerCAmelCase					,							logger=__lowerCAmelCase					,							)
			pickle_save(model.hparams					,							model.output_dir / "hparams.pkl"       )
			if not args.do_predict:
						return model

			lowerCAmelCase_      			=			""
			lowerCAmelCase_      			=			sorted(glob.glob(os.path.join(args.output_dir					,							"*.ckpt"       )					,							recursive=__lowerCAmelCase       )       )
			if checkpoints:
						lowerCAmelCase_      			=			checkpoints[-1]
						lowerCAmelCase_      			=			checkpoints[-1]
			trainer.logger.log_hyperparams(model.hparams       )

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


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

			_A     =  parser.parse_args()

			main(args)





def      lowerCamelCase__       (       __lowerCAmelCase						:						list[list[int]]					,							__lowerCAmelCase						:						int					,							__lowerCAmelCase						:						int					,							__lowerCAmelCase						:						set       ):




			"""simple docstring"""


			lowerCAmelCase_			,						lowerCAmelCase_      			=			len(__lowerCAmelCase       ), len(grid[0]       )
			if (
			    min(__lowerCAmelCase					,							__lowerCAmelCase       ) < 0
			    or row == row_length
			    or col == col_length
			    or (row, col) in visit
			    or grid[row][col] == 1
			):
						return 0
			if row == row_length - 1 and col == col_length - 1:
						return 1

			visit.add((row, col)       )

			lowerCAmelCase_      			=			0
			count += depth_first_search(__lowerCAmelCase					,							row + 1					,							__lowerCAmelCase					,							__lowerCAmelCase       )
			count += depth_first_search(__lowerCAmelCase					,							row - 1					,							__lowerCAmelCase					,							__lowerCAmelCase       )
			count += depth_first_search(__lowerCAmelCase					,							__lowerCAmelCase					,							col + 1					,							__lowerCAmelCase       )
			count += depth_first_search(__lowerCAmelCase					,							__lowerCAmelCase					,							col - 1					,							__lowerCAmelCase       )

			visit.remove((row, col)       )
			return count


if __name__ == "__main__":
			import doctest

			doctest.testmod()



