
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		: Union[str, Any]								=     logging.getLogger(__name__)



class    a_ (			_a    ):
			a	:			str					   =							'summarization'
			a	:			Union[str, Any]					   =							['loss']
			a	:			Dict					   =							ROUGE_KEYS
			a	:			List[Any]					   =							'rouge2'






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




									if hparams.sortish_sampler and hparams.gpus > 1:
															_lowercase					       =					False
									elif hparams.max_tokens_per_batch is not None:
															if hparams.gpus > 1:
																					raise NotImplementedError("""Dynamic Batch size does not work for multi-gpu training"""  )
															if hparams.sortish_sampler:
																					raise ValueError("""--sortish_sampler and --max_tokens_per_batch may not be used simultaneously"""  )

									super().__init__(lowerCAmelCase__				,			num_labels=lowerCAmelCase__				,			mode=self.mode				,			**lowerCAmelCase__  )
									use_task_specific_params(self.model				,			"""summarization"""  )
									save_git_info(self.hparams.output_dir  )
									_lowercase					       =					Path(self.output_dir  ) / """metrics.json"""
									_lowercase					       =					Path(self.output_dir  ) / """hparams.pkl"""
									pickle_save(self.hparams				,			self.hparams_save_path  )
									_lowercase					       =					0
									_lowercase					       =					defaultdict(lowerCAmelCase__  )
									_lowercase					       =					self.config.model_type
									_lowercase					       =					self.config.tgt_vocab_size if self.model_type == """fsmt""" else self.config.vocab_size

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

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






			def    UpperCamelCase_				(      self				,			__UpperCamelCase  ):
									_lowercase					       =					{
									    k: self.tokenizer.batch_decode(v.tolist()  ) if """mask""" not in k else v.shape for k, v in batch.items()
									}
									save_json(lowerCAmelCase__				,			Path(self.output_dir  ) / """text_batch.json"""  )
									save_json({k: v.tolist() for k, v in batch.items()}				,			Path(self.output_dir  ) / """tok_batch.json"""  )

									_lowercase					       =					True
									return readable_batch






			def    UpperCamelCase_				(      self				,			__UpperCamelCase				,			**__UpperCamelCase  ):
									return self.model(lowerCAmelCase__				,			**lowerCAmelCase__  )






			def    UpperCamelCase_				(      self				,			__UpperCamelCase  ):
									_lowercase					       =					self.tokenizer.batch_decode(
									    lowerCAmelCase__				,			skip_special_tokens=lowerCAmelCase__				,			clean_up_tokenization_spaces=lowerCAmelCase__  )
									return lmap(str.strip				,			lowerCAmelCase__  )






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

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

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






			@property
			def    UpperCamelCase_				(      self  ):
									return self.tokenizer.pad_token_id






			def    UpperCamelCase_				(      self				,			__UpperCamelCase				,			__UpperCamelCase  ):
									_lowercase					       =					self._step(lowerCAmelCase__  )

									_lowercase					       =					dict(zip(self.loss_names				,			lowerCAmelCase__  )  )
									# tokens per batch
									_lowercase					       =					batch["""input_ids"""].ne(self.pad  ).sum() + batch["""labels"""].ne(self.pad  ).sum()
									_lowercase					       =					batch["""input_ids"""].shape[0]
									_lowercase					       =					batch["""input_ids"""].eq(self.pad  ).sum()
									_lowercase					       =					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    UpperCamelCase_				(      self				,			__UpperCamelCase				,			__UpperCamelCase  ):
									return self._generative_step(lowerCAmelCase__  )






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






			def    UpperCamelCase_				(      self				,			__UpperCamelCase  ):
									_lowercase					       =					time.time()

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






			def    UpperCamelCase_				(      self				,			__UpperCamelCase				,			__UpperCamelCase  ):
									return self._generative_step(lowerCAmelCase__  )






			def    UpperCamelCase_				(      self				,			__UpperCamelCase  ):
									return self.validation_epoch_end(lowerCAmelCase__				,			prefix="""test"""  )






			def    UpperCamelCase_				(      self				,			__UpperCamelCase  ):
									_lowercase					       =					self.n_obs[type_path]
									_lowercase					       =					self.target_lens[type_path]
									_lowercase					       =					self.dataset_class(
									    self.tokenizer				,			type_path=lowerCAmelCase__				,			n_obs=lowerCAmelCase__				,			max_target_length=lowerCAmelCase__				,			**self.dataset_kwargs				,			)
									return dataset






			def    UpperCamelCase_				(      self				,			__UpperCamelCase				,			__UpperCamelCase				,			__UpperCamelCase = False  ):
									_lowercase					       =					self.get_dataset(lowerCAmelCase__  )

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

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






			def    UpperCamelCase_				(      self  ):
									_lowercase					       =					self.get_dataloader("""train"""				,			batch_size=self.hparams.train_batch_size				,			shuffle=lowerCAmelCase__  )
									return dataloader






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






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






			@staticmethod
			def    UpperCamelCase_				(      __UpperCamelCase				,			__UpperCamelCase  ):
									BaseTransformer.add_model_specific_args(lowerCAmelCase__				,			lowerCAmelCase__  )
									add_generic_args(lowerCAmelCase__				,			lowerCAmelCase__  )
									parser.add_argument(
									    """--max_source_length"""				,			default=1_024				,			type=lowerCAmelCase__				,			help=(
									        """The maximum total input sequence length after tokenization. Sequences longer """
									        """than this will be truncated, sequences shorter will be padded."""
									    )				,			)
									parser.add_argument(
									    """--max_target_length"""				,			default=56				,			type=lowerCAmelCase__				,			help=(
									        """The maximum total input sequence length after tokenization. Sequences longer """
									        """than this will be truncated, sequences shorter will be padded."""
									    )				,			)
									parser.add_argument(
									    """--val_max_target_length"""				,			default=142				,			type=lowerCAmelCase__				,			help=(
									        """The maximum total input sequence length after tokenization. Sequences longer """
									        """than this will be truncated, sequences shorter will be padded."""
									    )				,			)
									parser.add_argument(
									    """--test_max_target_length"""				,			default=142				,			type=lowerCAmelCase__				,			help=(
									        """The maximum total input sequence length after tokenization. Sequences longer """
									        """than this will be truncated, sequences shorter will be padded."""
									    )				,			)
									parser.add_argument("""--freeze_encoder"""				,			action="""store_true"""  )
									parser.add_argument("""--freeze_embeds"""				,			action="""store_true"""  )
									parser.add_argument("""--sortish_sampler"""				,			action="""store_true"""				,			default=lowerCAmelCase__  )
									parser.add_argument("""--overwrite_output_dir"""				,			action="""store_true"""				,			default=lowerCAmelCase__  )
									parser.add_argument("""--max_tokens_per_batch"""				,			type=lowerCAmelCase__				,			default=lowerCAmelCase__  )
									parser.add_argument("""--logger_name"""				,			type=lowerCAmelCase__				,			choices=["""default""", """wandb""", """wandb_shared"""]				,			default="""default"""  )
									parser.add_argument("""--n_train"""				,			type=lowerCAmelCase__				,			default=-1				,			required=lowerCAmelCase__				,			help="""# examples. -1 means use all."""  )
									parser.add_argument("""--n_val"""				,			type=lowerCAmelCase__				,			default=500				,			required=lowerCAmelCase__				,			help="""# examples. -1 means use all."""  )
									parser.add_argument("""--n_test"""				,			type=lowerCAmelCase__				,			default=-1				,			required=lowerCAmelCase__				,			help="""# examples. -1 means use all."""  )
									parser.add_argument(
									    """--task"""				,			type=lowerCAmelCase__				,			default="""summarization"""				,			required=lowerCAmelCase__				,			help="""# examples. -1 means use all."""  )
									parser.add_argument("""--label_smoothing"""				,			type=lowerCAmelCase__				,			default=0.0				,			required=lowerCAmelCase__  )
									parser.add_argument("""--src_lang"""				,			type=lowerCAmelCase__				,			default=""""""				,			required=lowerCAmelCase__  )
									parser.add_argument("""--tgt_lang"""				,			type=lowerCAmelCase__				,			default=""""""				,			required=lowerCAmelCase__  )
									parser.add_argument("""--eval_beams"""				,			type=lowerCAmelCase__				,			default=lowerCAmelCase__				,			required=lowerCAmelCase__  )
									parser.add_argument(
									    """--val_metric"""				,			type=lowerCAmelCase__				,			default=lowerCAmelCase__				,			required=lowerCAmelCase__				,			choices=["""bleu""", """rouge2""", """loss""", None]  )
									parser.add_argument("""--eval_max_gen_length"""				,			type=lowerCAmelCase__				,			default=lowerCAmelCase__				,			help="""never generate more than n tokens"""  )
									parser.add_argument("""--save_top_k"""				,			type=lowerCAmelCase__				,			default=1				,			required=lowerCAmelCase__				,			help="""How many checkpoints to save"""  )
									parser.add_argument(
									    """--early_stopping_patience"""				,			type=lowerCAmelCase__				,			default=-1				,			required=lowerCAmelCase__				,			help=(
									        """-1 means never early stop. early_stopping_patience is measured in validation checks, not epochs. So"""
									        """ val_check_interval will effect it."""
									    )				,			)
									return parser







class    a_ (			_a    ):
			a	:			str					   =							'translation'
			a	:			Optional[Any]					   =							['loss']
			a	:			List[Any]					   =							['bleu']
			a	:			Dict					   =							'bleu'






			def __init__(      self				,			__UpperCamelCase				,			**__UpperCamelCase  ):
									super().__init__(lowerCAmelCase__				,			**lowerCAmelCase__  )
									_lowercase					       =					hparams.src_lang
									_lowercase					       =					hparams.tgt_lang






			def    UpperCamelCase_				(      self				,			__UpperCamelCase				,			__UpperCamelCase  ):
									return calculate_bleu(lowerCAmelCase__				,			lowerCAmelCase__  )







def 	UpperCamelCase__  (     SCREAMING_SNAKE_CASE_	:	Tuple		,   SCREAMING_SNAKE_CASE_	:	List[Any]=None       )  ->     SummarizationModule:
						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					       =					SummarizationModule(SCREAMING_SNAKE_CASE_       )
												else:
																		_lowercase					       =					TranslationModule(SCREAMING_SNAKE_CASE_       )
						_lowercase					       =					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					       =					True  # don't pollute wandb logs unnecessarily
						elif args.logger_name == "wandb":
												from pytorch_lightning.loggers import WandbLogger

												_lowercase					       =					os.environ.get("""WANDB_PROJECT"""		,   SCREAMING_SNAKE_CASE_       )
												_lowercase					       =					WandbLogger(name=model.output_dir.name		,   project=SCREAMING_SNAKE_CASE_       )

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

												_lowercase					       =					WandbLogger(name=model.output_dir.name		,   project=f"""hf_{dataset}"""       )

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

						_lowercase					       =					args.val_metric == """loss"""
						_lowercase					       =					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					       =					""""""
						_lowercase					       =					sorted(glob.glob(os.path.join(args.output_dir		,   """*.ckpt"""       )		,   recursive=SCREAMING_SNAKE_CASE_       )       )
						if checkpoints:
												_lowercase					       =					checkpoints[-1]
												_lowercase					       =					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		: int								=     argparse.ArgumentParser()
		A		: Any								=     pl.Trainer.add_argparse_args(parser)
		A		: int								=     SummarizationModule.add_model_specific_args(parser, os.getcwd())

		A		: int								=     parser.parse_args()

		main(args)






'''simple docstring'''

def    a__							(							lowercase   :	int,   lowercase   :	int,   lowercase   :	int )						->						float:





     """simple docstring"""

     _UpperCamelCase							       =			(num_of_terms / 2) * (2 * first_term + (num_of_terms - 1) * common_diff)
     # formula for sum of series
     return total







def    a__							(							)						->						Tuple:





     """simple docstring"""

     print(sum_of_series(1,   1,   10 ) )


if __name__ == "__main__":
   import doctest

   doctest.testmod()



