



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 (     lowerCamelCase__       ):
				__lowerCamelCase =				'''summarization'''
				__lowerCamelCase =				['''loss''']
				__lowerCamelCase =				ROUGE_KEYS
				__lowerCamelCase =				'''rouge2'''




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



									"""simple docstring"""





									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__(_snake_case						,				num_labels=_snake_case						,				mode=self.mode						,				**_snake_case		)
									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(_snake_case		)
									_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						,				_snake_case		):
														_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 							snake_case   (							self						,				_snake_case		):



									"""simple docstring"""





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

									_lowerCAmelCase						=    True
									return readable_batch




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



									"""simple docstring"""





									return self.model(_snake_case						,				**_snake_case		)




				def 							snake_case   (							self						,				_snake_case		):



									"""simple docstring"""





									_lowerCAmelCase						=    self.tokenizer.batch_decode(
									    _snake_case						,				skip_special_tokens=_snake_case						,				clean_up_tokenization_spaces=_snake_case		)
									return lmap(str.strip						,				_snake_case		)




				def 							snake_case   (							self						,				_snake_case		):



									"""simple docstring"""





									_lowerCAmelCase						=    self.tokenizer.pad_token_id
									_lowerCAmelCase	,	_lowerCAmelCase						=    batch["""input_ids"""], batch["""attention_mask"""]
									_lowerCAmelCase						=    batch["""labels"""]
									if isinstance(self.model						,				_snake_case		):
														_lowerCAmelCase						=    self.model._shift_right(_snake_case		)
									else:
														_lowerCAmelCase						=    shift_tokens_right(_snake_case						,				_snake_case		)
									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(_snake_case		)

									_lowerCAmelCase						=    self(_snake_case						,				attention_mask=_snake_case						,				decoder_input_ids=_snake_case						,				use_cache=_snake_case		)
									_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=_snake_case		)

														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(_snake_case						,				dim=-1		)
														_lowerCAmelCase	,	_lowerCAmelCase						=    label_smoothed_nll_loss(
														    _snake_case						,				_snake_case						,				self.hparams.label_smoothing						,				ignore_index=_snake_case		)
									return (loss,)




				@property
				def 							snake_case   (							self		):



									"""simple docstring"""





									return self.tokenizer.pad_token_id




				def 							snake_case   (							self						,				_snake_case						,				_snake_case		):



									"""simple docstring"""





									_lowerCAmelCase						=    self._step(_snake_case		)

									_lowerCAmelCase						=    dict(zip(self.loss_names						,				_snake_case		)		)
									# 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 							snake_case   (							self						,				_snake_case						,				_snake_case		):



									"""simple docstring"""





									return self._generative_step(_snake_case		)




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



									"""simple docstring"""





									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(_snake_case		).type_as(_snake_case		)
									generative_metrics.update({k: v.item() for k, v in losses.items()}		)
									losses.update(_snake_case		)
									_lowerCAmelCase						=    {F'{prefix}_avg_{k}': x for k, x in losses.items()}
									_lowerCAmelCase						=    self.step_count
									self.metrics[prefix].append(_snake_case		)  # 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 							snake_case   (							self						,				_snake_case						,				_snake_case		):



									"""simple docstring"""





									return calculate_rouge(_snake_case						,				_snake_case		)




				def 							snake_case   (							self						,				_snake_case		):



									"""simple docstring"""





									_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=_snake_case						,				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(_snake_case		)
									_lowerCAmelCase						=    self.ids_to_clean_text(batch["""labels"""]		)
									_lowerCAmelCase						=    self._step(_snake_case		)
									_lowerCAmelCase						=    dict(zip(self.loss_names						,				_snake_case		)		)
									_lowerCAmelCase						=    self.calc_generative_metrics(_snake_case						,				_snake_case		)
									_lowerCAmelCase						=    np.mean(lmap(_snake_case						,				_snake_case		)		)
									base_metrics.update(gen_time=_snake_case						,				gen_len=_snake_case						,				preds=_snake_case						,				target=_snake_case						,				**_snake_case		)
									return base_metrics




				def 							snake_case   (							self						,				_snake_case						,				_snake_case		):



									"""simple docstring"""





									return self._generative_step(_snake_case		)




				def 							snake_case   (							self						,				_snake_case		):



									"""simple docstring"""





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




				def 							snake_case   (							self						,				_snake_case		):



									"""simple docstring"""





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




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



									"""simple docstring"""





									_lowerCAmelCase						=    self.get_dataset(_snake_case		)

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

									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(
														    _snake_case						,				batch_sampler=_snake_case						,				collate_fn=dataset.collate_fn						,				num_workers=self.num_workers						,				)
									else:
														return DataLoader(
														    _snake_case						,				batch_size=_snake_case						,				collate_fn=dataset.collate_fn						,				shuffle=_snake_case						,				num_workers=self.num_workers						,				sampler=_snake_case						,				)




				def 							snake_case   (							self		):



									"""simple docstring"""





									_lowerCAmelCase						=    self.get_dataloader("""train"""						,				batch_size=self.hparams.train_batch_size						,				shuffle=_snake_case		)
									return dataloader




				def 							snake_case   (							self		):



									"""simple docstring"""





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




				def 							snake_case   (							self		):



									"""simple docstring"""





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




				@staticmethod
				def 							snake_case   (							_snake_case						,				_snake_case		):



									"""simple docstring"""





									BaseTransformer.add_model_specific_args(_snake_case						,				_snake_case		)
									add_generic_args(_snake_case						,				_snake_case		)
									parser.add_argument(
									    """--max_source_length"""						,				default=1024						,				type=_snake_case						,				help=(
									        """The maximum total input sequence length after tokenization. Sequences longer """
									        """than this will be truncated, sequences shorter will be padded."""
									    )						,				)
									parser.add_argument(
									    """--max_target_length"""						,				default=56						,				type=_snake_case						,				help=(
									        """The maximum total input sequence length after tokenization. Sequences longer """
									        """than this will be truncated, sequences shorter will be padded."""
									    )						,				)
									parser.add_argument(
									    """--val_max_target_length"""						,				default=142						,				type=_snake_case						,				help=(
									        """The maximum total input sequence length after tokenization. Sequences longer """
									        """than this will be truncated, sequences shorter will be padded."""
									    )						,				)
									parser.add_argument(
									    """--test_max_target_length"""						,				default=142						,				type=_snake_case						,				help=(
									        """The maximum total input sequence length after tokenization. Sequences longer """
									        """than this will be truncated, sequences shorter will be padded."""
									    )						,				)
									parser.add_argument("""--freeze_encoder"""						,				action="""store_true"""		)
									parser.add_argument("""--freeze_embeds"""						,				action="""store_true"""		)
									parser.add_argument("""--sortish_sampler"""						,				action="""store_true"""						,				default=_snake_case		)
									parser.add_argument("""--overwrite_output_dir"""						,				action="""store_true"""						,				default=_snake_case		)
									parser.add_argument("""--max_tokens_per_batch"""						,				type=_snake_case						,				default=_snake_case		)
									parser.add_argument("""--logger_name"""						,				type=_snake_case						,				choices=["""default""", """wandb""", """wandb_shared"""]						,				default="""default"""		)
									parser.add_argument("""--n_train"""						,				type=_snake_case						,				default=-1						,				required=_snake_case						,				help="""# examples. -1 means use all."""		)
									parser.add_argument("""--n_val"""						,				type=_snake_case						,				default=500						,				required=_snake_case						,				help="""# examples. -1 means use all."""		)
									parser.add_argument("""--n_test"""						,				type=_snake_case						,				default=-1						,				required=_snake_case						,				help="""# examples. -1 means use all."""		)
									parser.add_argument(
									    """--task"""						,				type=_snake_case						,				default="""summarization"""						,				required=_snake_case						,				help="""# examples. -1 means use all."""		)
									parser.add_argument("""--label_smoothing"""						,				type=_snake_case						,				default=0.0						,				required=_snake_case		)
									parser.add_argument("""--src_lang"""						,				type=_snake_case						,				default=""""""						,				required=_snake_case		)
									parser.add_argument("""--tgt_lang"""						,				type=_snake_case						,				default=""""""						,				required=_snake_case		)
									parser.add_argument("""--eval_beams"""						,				type=_snake_case						,				default=_snake_case						,				required=_snake_case		)
									parser.add_argument(
									    """--val_metric"""						,				type=_snake_case						,				default=_snake_case						,				required=_snake_case						,				choices=["""bleu""", """rouge2""", """loss""", None]		)
									parser.add_argument("""--eval_max_gen_length"""						,				type=_snake_case						,				default=_snake_case						,				help="""never generate more than n tokens"""		)
									parser.add_argument("""--save_top_k"""						,				type=_snake_case						,				default=1						,				required=_snake_case						,				help="""How many checkpoints to save"""		)
									parser.add_argument(
									    """--early_stopping_patience"""						,				type=_snake_case						,				default=-1						,				required=_snake_case						,				help=(
									        """-1 means never early stop. early_stopping_patience is measured in validation checks, not epochs. So"""
									        """ val_check_interval will effect it."""
									    )						,				)
									return parser





class 					__lowerCAmelCase (     lowerCamelCase__       ):
				__lowerCamelCase =				'''translation'''
				__lowerCamelCase =				['''loss''']
				__lowerCamelCase =				['''bleu''']
				__lowerCamelCase =				'''bleu'''




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



									"""simple docstring"""





									super().__init__(_snake_case						,				**_snake_case		)
									_lowerCAmelCase						=    hparams.src_lang
									_lowerCAmelCase						=    hparams.tgt_lang




				def 							snake_case   (							self						,				_snake_case						,				_snake_case		):



									"""simple docstring"""





									return calculate_bleu(_snake_case						,				_snake_case		)


def 		_UpperCAmelCase				(      snake_case	,						snake_case=None			):





					"""simple docstring"""

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

					if model is None:
										if "summarization" in args.task:
															_lowerCAmelCase						=    SummarizationModule(snake_case			)
										else:
															_lowerCAmelCase						=    TranslationModule(snake_case			)
					_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"""	,						snake_case			)
										_lowerCAmelCase						=    WandbLogger(name=model.output_dir.name	,						project=snake_case			)

					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(
					    snake_case	,						snake_case	,						logging_callback=SeqaSeqLoggingCallback()	,						checkpoint_callback=get_checkpoint_callback(
					        args.output_dir	,						model.val_metric	,						args.save_top_k	,						snake_case			)	,						early_stopping_callback=snake_case	,						logger=snake_case	,						)
					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=snake_case			)			)
					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)








'''simple docstring'''





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


__a          =		{
    "configuration_bloom": ["BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP", "BloomConfig", "BloomOnnxConfig"],
}
try:
       if not is_tokenizers_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __a          =		["BloomTokenizerFast"]

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __a          =		[
           "BLOOM_PRETRAINED_MODEL_ARCHIVE_LIST",
           "BloomForCausalLM",
           "BloomModel",
           "BloomPreTrainedModel",
           "BloomForSequenceClassification",
           "BloomForTokenClassification",
           "BloomForQuestionAnswering",
       ]

if TYPE_CHECKING:
       from .configuration_bloom import BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP, BloomConfig, BloomOnnxConfig

       try:
              if not is_tokenizers_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_bloom_fast import BloomTokenizerFast

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_bloom import (
                  BLOOM_PRETRAINED_MODEL_ARCHIVE_LIST,
                  BloomForCausalLM,
                  BloomForQuestionAnswering,
                  BloomForSequenceClassification,
                  BloomForTokenClassification,
                  BloomModel,
                  BloomPreTrainedModel,
              )

else:
       import sys

       __a          =		_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
