



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_		:     Any        =     logging.getLogger(__name__)



class 		_lowercase		(       UpperCAmelCase__	):
   _UpperCAmelCase     	=			'''summarization'''
   _UpperCAmelCase     	=			['''loss''']
   _UpperCAmelCase     	=			ROUGE_KEYS
   _UpperCAmelCase     	=			'''rouge2'''

   def __init__(      self			:       int							,   __lowerCAmelCase			:       Optional[Any]							,   **__lowerCAmelCase			:       List[Any]							)       ->				Tuple:


    """simple docstring"""






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

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

    a						=		{
        "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()							)

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

   def 		A							(      self			:       Tuple							,   __lowerCAmelCase			:       Dict[str, torch.Tensor]							)       ->				Dict[str, List[str]]:


    """simple docstring"""






    a						=		{
        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"							)

    a						=		True
    return readable_batch

   def 		A							(      self			:       str							,   __lowerCAmelCase			:       List[str]							,   **__lowerCAmelCase			:       Optional[Any]							)       ->				List[Any]:


    """simple docstring"""






    return self.model(__lowerCAmelCase							,   **__lowerCAmelCase							)

   def 		A							(      self			:       Dict							,   __lowerCAmelCase			:       List[int]							)       ->				Optional[Any]:


    """simple docstring"""






    a						=		self.tokenizer.batch_decode(
        __lowerCAmelCase							,   skip_special_tokens=__lowerCAmelCase							,   clean_up_tokenization_spaces=__lowerCAmelCase							)
    return lmap(str.strip							,   __lowerCAmelCase							)

   def 		A							(      self			:       int							,   __lowerCAmelCase			:       dict							)       ->				Tuple:


    """simple docstring"""






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

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

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

   @property
   def 		A							(      self			:       str							)       ->				int:


    """simple docstring"""






    return self.tokenizer.pad_token_id

   def 		A							(      self			:       Any							,   __lowerCAmelCase			:       List[str]							,   __lowerCAmelCase			:       int							)       ->				Dict:


    """simple docstring"""






    a						=		self._step(__lowerCAmelCase							)

    a						=		dict(zip(self.loss_names							,   __lowerCAmelCase							)							)
    # tokens per batch
    a						=		batch["input_ids"].ne(self.pad							).sum() + batch["labels"].ne(self.pad							).sum()
    a						=		batch["input_ids"].shape[0]
    a						=		batch["input_ids"].eq(self.pad							).sum()
    a						=		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			:       Union[str, Any]							,   __lowerCAmelCase			:       str							,   __lowerCAmelCase			:       List[str]							)       ->				Dict:


    """simple docstring"""






    return self._generative_step(__lowerCAmelCase							)

   def 		A							(      self			:       str							,   __lowerCAmelCase			:       Optional[int]							,   __lowerCAmelCase			:       str="val"							)       ->				Dict:


    """simple docstring"""






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


    """simple docstring"""






    return calculate_rouge(__lowerCAmelCase							,   __lowerCAmelCase							)

   def 		A							(      self			:       List[str]							,   __lowerCAmelCase			:       dict							)       ->				dict:


    """simple docstring"""






    a						=		time.time()

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

   def 		A							(      self			:       Optional[int]							,   __lowerCAmelCase			:       Any							,   __lowerCAmelCase			:       str							)       ->				Union[str, Any]:


    """simple docstring"""






    return self._generative_step(__lowerCAmelCase							)

   def 		A							(      self			:       str							,   __lowerCAmelCase			:       Optional[int]							)       ->				Optional[Any]:


    """simple docstring"""






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

   def 		A							(      self			:       int							,   __lowerCAmelCase			:       List[Any]							)       ->				SeqaSeqDataset:


    """simple docstring"""






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

   def 		A							(      self			:       str							,   __lowerCAmelCase			:       str							,   __lowerCAmelCase			:       int							,   __lowerCAmelCase			:       bool = False							)       ->				DataLoader:


    """simple docstring"""






    a						=		self.get_dataset(__lowerCAmelCase							)

    if self.hparams.sortish_sampler and type_path != "test" and type_path != "val":
     a						=		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":
     a						=		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 		A							(      self			:       Union[str, Any]							)       ->				DataLoader:


    """simple docstring"""






    a						=		self.get_dataloader("train"							,   batch_size=self.hparams.train_batch_size							,   shuffle=__lowerCAmelCase							)
    return dataloader

   def 		A							(      self			:       Any							)       ->				DataLoader:


    """simple docstring"""






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

   def 		A							(      self			:       List[Any]							)       ->				DataLoader:


    """simple docstring"""






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




   @staticmethod
   def 		A							(      __lowerCAmelCase			:       str							,   __lowerCAmelCase			:       str							)       ->				Tuple:


    """simple docstring"""






    BaseTransformer.add_model_specific_args(__lowerCAmelCase							,   __lowerCAmelCase							)
    add_generic_args(__lowerCAmelCase							,   __lowerCAmelCase							)
    parser.add_argument(
        "--max_source_length"							,   default=1024							,   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 		_lowercase		(       UpperCAmelCase__	):
   _UpperCAmelCase     	=			'''translation'''
   _UpperCAmelCase     	=			['''loss''']
   _UpperCAmelCase     	=			['''bleu''']
   _UpperCAmelCase     	=			'''bleu'''

   def __init__(      self			:       Union[str, Any]							,   __lowerCAmelCase			:       str							,   **__lowerCAmelCase			:       Tuple							)       ->				List[str]:


    """simple docstring"""






    super().__init__(__lowerCAmelCase							,   **__lowerCAmelCase							)
    a						=		hparams.src_lang
    a						=		hparams.tgt_lang




   def 		A							(      self			:       int							,   __lowerCAmelCase			:       List[Any]							,   __lowerCAmelCase			:       Optional[int]							)       ->				dict:


    """simple docstring"""






    return calculate_bleu(__lowerCAmelCase							,   __lowerCAmelCase							)


def 	UpperCAmelCase__      (      UpperCAmelCase__ :Tuple ,					UpperCAmelCase__ :int=None					):






 '''simple docstring'''

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

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

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

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

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

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

 a						=		args.val_metric == "loss"
 a						=		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

 a						=		""
 a						=		sorted(glob.glob(os.path.join(args.output_dir ,					"*.ckpt"					) ,					recursive=UpperCAmelCase__					)					)
 if checkpoints:
  a						=		checkpoints[-1]
  a						=		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_		:     str        =     argparse.ArgumentParser()
   A_		:     Union[str, Any]        =     pl.Trainer.add_argparse_args(parser)
   A_		:     Tuple        =     SummarizationModule.add_model_specific_args(parser, os.getcwd())

   A_		:     Any        =     parser.parse_args()

   main(args)






import argparse

from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging


logging.set_verbosity_info()


def 	UpperCAmelCase__      (      UpperCAmelCase__ :Optional[int] ,					UpperCAmelCase__ :List[str] ,					UpperCAmelCase__ :Any					):






 '''simple docstring'''

 a						=		TaConfig.from_json_file(UpperCAmelCase__					)
 print(F"""Building PyTorch model from configuration: {config}"""					)
 a						=		TaForConditionalGeneration(UpperCAmelCase__					)

 # Load weights from tf checkpoint
 load_tf_weights_in_ta(UpperCAmelCase__ ,					UpperCAmelCase__ ,					UpperCAmelCase__					)

 # Save pytorch-model
 print(F"""Save PyTorch model to {pytorch_dump_path}"""					)
 model.save_pretrained(UpperCAmelCase__					)


if __name__ == "__main__":
     A_		:     Union[str, Any]        =     argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         '''--tf_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
     )
     parser.add_argument(
         '''--config_file''',
         default=None,
         type=str,
         required=True,
         help=(
             '''The config json file corresponding to the pre-trained T5 model. \nThis specifies the model architecture.'''
         ),
     )
     parser.add_argument(
         '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
     )
     A_		:     Tuple        =     parser.parse_args()
     convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)

