

"""simple docstring"""


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     snake_case	(				__snake_case  ):
				SCREAMING_SNAKE_CASE_   :      Tuple         =  """summarization"""
				SCREAMING_SNAKE_CASE_   :      int         =  ["""loss"""]
				SCREAMING_SNAKE_CASE_   :      str         =  ROUGE_KEYS
				SCREAMING_SNAKE_CASE_   :      List[str]         =  """rouge2"""

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



						'''simple docstring'''


						if hparams.sortish_sampler and hparams.gpus > 1:
								__lowerCAmelCase:       int          =			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:       Optional[int]          =			Path(self.output_dir) / "metrics.json"
						__lowerCAmelCase:       str          =			Path(self.output_dir) / "hparams.pkl"
						pickle_save(self.hparams     ,  self.hparams_save_path)
						__lowerCAmelCase:       Dict          =			0
						__lowerCAmelCase:       Tuple          =			defaultdict(UpperCamelCase__)
						__lowerCAmelCase:       int          =			self.config.model_type
						__lowerCAmelCase:       Tuple          =			self.config.tgt_vocab_size if self.model_type == "fsmt" else self.config.vocab_size

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

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

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



						'''simple docstring'''


						__lowerCAmelCase:       Any          =			{
						    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:       Any          =			True
						return readable_batch

				def 							lowercase_     (	self							:				int     ,  UpperCamelCase__							:				str     ,  **UpperCamelCase__							:				Optional[int])->   int:



						'''simple docstring'''


						return self.model(UpperCamelCase__     ,  **UpperCamelCase__)

				def 							lowercase_     (	self							:				str     ,  UpperCamelCase__							:				List[int])->   List[Any]:



						'''simple docstring'''


						__lowerCAmelCase:       int          =			self.tokenizer.batch_decode(
						    UpperCamelCase__     ,  skip_special_tokens=UpperCamelCase__     ,  clean_up_tokenization_spaces=UpperCamelCase__)
						return lmap(str.strip     ,  UpperCamelCase__)

				def 							lowercase_     (	self							:				Optional[Any]     ,  UpperCamelCase__							:				dict)->   Tuple:



						'''simple docstring'''


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

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

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

				@property
				def 							lowercase_     (	self							:				List[str])->   int:



						'''simple docstring'''


						return self.tokenizer.pad_token_id

				def 							lowercase_     (	self							:				Optional[int]     ,  UpperCamelCase__							:				List[Any]     ,  UpperCamelCase__							:				Dict)->   Dict:



						'''simple docstring'''


						__lowerCAmelCase:       Tuple          =			self._step(UpperCamelCase__)

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



						'''simple docstring'''


						return self._generative_step(UpperCamelCase__)

				def 							lowercase_     (	self							:				Tuple     ,  UpperCamelCase__							:				Any     ,  UpperCamelCase__							:				List[str]="val")->   Dict:



						'''simple docstring'''


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



						'''simple docstring'''


						return calculate_rouge(UpperCamelCase__     ,  UpperCamelCase__)

				def 							lowercase_     (	self							:				int     ,  UpperCamelCase__							:				dict)->   dict:



						'''simple docstring'''


						__lowerCAmelCase:       List[Any]          =			time.time()

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

				def 							lowercase_     (	self							:				List[str]     ,  UpperCamelCase__							:				Tuple     ,  UpperCamelCase__							:				Optional[Any])->   Tuple:



						'''simple docstring'''


						return self._generative_step(UpperCamelCase__)

				def 							lowercase_     (	self							:				Union[str, Any]     ,  UpperCamelCase__							:				List[str])->   int:



						'''simple docstring'''


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

				def 							lowercase_     (	self							:				Union[str, Any]     ,  UpperCamelCase__							:				int)->   SeqaSeqDataset:



						'''simple docstring'''


						__lowerCAmelCase:       List[str]          =			self.n_obs[type_path]
						__lowerCAmelCase:       List[str]          =			self.target_lens[type_path]
						__lowerCAmelCase:       Union[str, Any]          =			self.dataset_class(
						    self.tokenizer     ,  type_path=UpperCamelCase__     ,  n_obs=UpperCamelCase__     ,  max_target_length=UpperCamelCase__     ,  **self.dataset_kwargs     ,  )
						return dataset

				def 							lowercase_     (	self							:				str     ,  UpperCamelCase__							:				str     ,  UpperCamelCase__							:				int     ,  UpperCamelCase__							:				bool = False)->   DataLoader:



						'''simple docstring'''


						__lowerCAmelCase:       List[str]          =			self.get_dataset(UpperCamelCase__)

						if self.hparams.sortish_sampler and type_path != "test" and type_path != "val":
								__lowerCAmelCase:       Optional[Any]          =			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:       List[Any]          =			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 							lowercase_     (	self							:				str)->   DataLoader:



						'''simple docstring'''


						__lowerCAmelCase:       Optional[Any]          =			self.get_dataloader("train"     ,  batch_size=self.hparams.train_batch_size     ,  shuffle=UpperCamelCase__)
						return dataloader

				def 							lowercase_     (	self							:				List[str])->   DataLoader:



						'''simple docstring'''


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

				def 							lowercase_     (	self							:				Optional[Any])->   DataLoader:



						'''simple docstring'''


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




				@staticmethod
				def 							lowercase_     (	UpperCamelCase__							:				str     ,  UpperCamelCase__							:				Tuple)->   int:



						'''simple docstring'''


						BaseTransformer.add_model_specific_args(UpperCamelCase__     ,  UpperCamelCase__)
						add_generic_args(UpperCamelCase__     ,  UpperCamelCase__)
						parser.add_argument(
						    "--max_source_length"     ,  default=1_0_2_4     ,  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=5_6     ,  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=1_4_2     ,  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=1_4_2     ,  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=5_0_0     ,  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     snake_case	(				__snake_case  ):
				SCREAMING_SNAKE_CASE_   :      Optional[Any]         =  """translation"""
				SCREAMING_SNAKE_CASE_   :      List[str]         =  ["""loss"""]
				SCREAMING_SNAKE_CASE_   :      str         =  ["""bleu"""]
				SCREAMING_SNAKE_CASE_   :      List[str]         =  """bleu"""

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



						'''simple docstring'''


						super().__init__(UpperCamelCase__     ,  **UpperCamelCase__)
						__lowerCAmelCase:       Optional[Any]          =			hparams.src_lang
						__lowerCAmelCase:       str          =			hparams.tgt_lang




				def 							lowercase_     (	self							:				str     ,  UpperCamelCase__							:				List[str]     ,  UpperCamelCase__							:				Optional[int])->   dict:



						'''simple docstring'''


						return calculate_bleu(UpperCamelCase__     ,  UpperCamelCase__)






def     a__    (    __SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE=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:
						__lowerCAmelCase:       SummarizationModule          =			SummarizationModule(__SCREAMING_SNAKE_CASE )
				else:
						__lowerCAmelCase:       SummarizationModule          =			TranslationModule(__SCREAMING_SNAKE_CASE )
		__lowerCAmelCase:       Any          =			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:       Optional[int]          =			True  # don't pollute wandb logs unnecessarily
		elif args.logger_name == "wandb":
				from pytorch_lightning.loggers import WandbLogger

				__lowerCAmelCase:       str          =			os.environ.get("WANDB_PROJECT"					, __SCREAMING_SNAKE_CASE )
				__lowerCAmelCase:       int          =			WandbLogger(name=model.output_dir.name					, project=__SCREAMING_SNAKE_CASE )

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

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

		if args.early_stopping_patience >= 0:
				__lowerCAmelCase:       Tuple          =			get_early_stopping_callback(model.val_metric					, args.early_stopping_patience )
		else:
				__lowerCAmelCase:       List[Any]          =			False

		__lowerCAmelCase:       List[str]          =			args.val_metric == "loss"
		__lowerCAmelCase:       pl.Trainer          =			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

		__lowerCAmelCase:       Optional[Any]          =			""
		__lowerCAmelCase:       Optional[int]          =			sorted(glob.glob(os.path.join(args.output_dir					, "*.ckpt" )					, recursive=__SCREAMING_SNAKE_CASE ) )
		if checkpoints:
				__lowerCAmelCase:       List[str]          =			checkpoints[-1]
				__lowerCAmelCase:       List[Any]          =			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"""


import numpy as np

from transformers import BatchFeature
from transformers.testing_utils import require_tf, require_torch

from .test_feature_extraction_common import FeatureExtractionSavingTestMixin





class     snake_case	(				__snake_case  ):
				# to overwrite at feature extractactor specific tests
				SCREAMING_SNAKE_CASE_   :      List[str]         =  None
				SCREAMING_SNAKE_CASE_   :      Tuple         =  None

				@property
				def 							lowercase_     (	self							:				str)->   Any:



						'''simple docstring'''


						return self.feat_extract_tester.prepare_feat_extract_dict()

				def 							lowercase_     (	self							:				Tuple)->   Optional[Any]:



						'''simple docstring'''


						__lowerCAmelCase:       Optional[int]          =			self.feature_extraction_class(**self.feat_extract_dict)
						self.assertTrue(hasattr(UpperCamelCase__     ,  "feature_size"))
						self.assertTrue(hasattr(UpperCamelCase__     ,  "sampling_rate"))
						self.assertTrue(hasattr(UpperCamelCase__     ,  "padding_value"))

				def 							lowercase_     (	self							:				Dict)->   Any:



						'''simple docstring'''


						__lowerCAmelCase:       Tuple          =			self.feat_extract_tester.prepare_inputs_for_common()
						__lowerCAmelCase:       str          =			self.feature_extraction_class(**self.feat_extract_dict)
						__lowerCAmelCase:       int          =			feat_extract.model_input_names[0]

						__lowerCAmelCase:       List[Any]          =			BatchFeature({input_name: speech_inputs})

						self.assertTrue(all(len(UpperCamelCase__) == len(UpperCamelCase__) for x, y in zip(UpperCamelCase__     ,  processed_features[input_name])))

						__lowerCAmelCase:       Tuple          =			self.feat_extract_tester.prepare_inputs_for_common(equal_length=UpperCamelCase__)
						__lowerCAmelCase:       Optional[int]          =			BatchFeature({input_name: speech_inputs}     ,  tensor_type="np")

						__lowerCAmelCase:       Optional[int]          =			processed_features[input_name]

						if len(batch_features_input.shape) < 3:
								__lowerCAmelCase:       Dict          =			batch_features_input[:, :, None]

						self.assertTrue(
						    batch_features_input.shape
						    == (self.feat_extract_tester.batch_size, len(speech_inputs[0]), self.feat_extract_tester.feature_size))

				@require_torch
				def 							lowercase_     (	self							:				Optional[int])->   Optional[Any]:



						'''simple docstring'''


						__lowerCAmelCase:       Any          =			self.feat_extract_tester.prepare_inputs_for_common(equal_length=UpperCamelCase__)
						__lowerCAmelCase:       int          =			self.feature_extraction_class(**self.feat_extract_dict)
						__lowerCAmelCase:       Any          =			feat_extract.model_input_names[0]

						__lowerCAmelCase:       Any          =			BatchFeature({input_name: speech_inputs}     ,  tensor_type="pt")

						__lowerCAmelCase:       str          =			processed_features[input_name]

						if len(batch_features_input.shape) < 3:
								__lowerCAmelCase:       Any          =			batch_features_input[:, :, None]

						self.assertTrue(
						    batch_features_input.shape
						    == (self.feat_extract_tester.batch_size, len(speech_inputs[0]), self.feat_extract_tester.feature_size))

				@require_tf
				def 							lowercase_     (	self							:				str)->   Optional[Any]:



						'''simple docstring'''


						__lowerCAmelCase:       Any          =			self.feat_extract_tester.prepare_inputs_for_common(equal_length=UpperCamelCase__)
						__lowerCAmelCase:       Optional[int]          =			self.feature_extraction_class(**self.feat_extract_dict)
						__lowerCAmelCase:       Any          =			feat_extract.model_input_names[0]

						__lowerCAmelCase:       Optional[int]          =			BatchFeature({input_name: speech_inputs}     ,  tensor_type="tf")

						__lowerCAmelCase:       int          =			processed_features[input_name]

						if len(batch_features_input.shape) < 3:
								__lowerCAmelCase:       Dict          =			batch_features_input[:, :, None]

						self.assertTrue(
						    batch_features_input.shape
						    == (self.feat_extract_tester.batch_size, len(speech_inputs[0]), self.feat_extract_tester.feature_size))

				def 							lowercase_     (	self							:				Union[str, Any]     ,  UpperCamelCase__							:				List[str]=False)->   Optional[int]:



						'''simple docstring'''


						def _inputs_have_equal_length(UpperCamelCase__							:				Tuple):
								__lowerCAmelCase:       Optional[int]          =			len(input[0])
								for input_slice in input[1:]:
										if len(UpperCamelCase__) != length:
												return False
								return True

						def _inputs_are_equal(UpperCamelCase__							:				str     ,  UpperCamelCase__							:				str):
								if len(UpperCamelCase__) != len(UpperCamelCase__):
										return False

								for input_slice_a, input_slice_a in zip(UpperCamelCase__     ,  UpperCamelCase__):
										if not np.allclose(np.asarray(UpperCamelCase__)     ,  np.asarray(UpperCamelCase__)     ,  atol=1e-3):
												return False
								return True

						__lowerCAmelCase:       List[str]          =			self.feature_extraction_class(**self.feat_extract_dict)
						__lowerCAmelCase:       Optional[Any]          =			self.feat_extract_tester.prepare_inputs_for_common(numpify=UpperCamelCase__)
						__lowerCAmelCase:       Tuple          =			feat_extract.model_input_names[0]

						__lowerCAmelCase:       List[Any]          =			BatchFeature({input_name: speech_inputs})

						__lowerCAmelCase:       Optional[int]          =			self.feat_extract_tester.seq_length_diff
						__lowerCAmelCase:       str          =			self.feat_extract_tester.max_seq_length + pad_diff
						__lowerCAmelCase:       Tuple          =			self.feat_extract_tester.min_seq_length
						__lowerCAmelCase:       Union[str, Any]          =			self.feat_extract_tester.batch_size
						__lowerCAmelCase:       Tuple          =			self.feat_extract_tester.feature_size

						# test padding for List[int] + numpy
						__lowerCAmelCase:       Optional[int]          =			feat_extract.pad(UpperCamelCase__     ,  padding=UpperCamelCase__)
						__lowerCAmelCase:       Optional[int]          =			input_a[input_name]

						__lowerCAmelCase:       List[Any]          =			feat_extract.pad(UpperCamelCase__     ,  padding="longest")
						__lowerCAmelCase:       int          =			input_a[input_name]

						__lowerCAmelCase:       Tuple          =			feat_extract.pad(UpperCamelCase__     ,  padding="max_length"     ,  max_length=len(speech_inputs[-1]))
						__lowerCAmelCase:       Optional[Any]          =			input_a[input_name]

						__lowerCAmelCase:       Union[str, Any]          =			feat_extract.pad(UpperCamelCase__     ,  padding="longest"     ,  return_tensors="np")
						__lowerCAmelCase:       Optional[int]          =			input_a[input_name]

						# max_length parameter has to be provided when setting `padding="max_length"`
						with self.assertRaises(UpperCamelCase__):
								feat_extract.pad(UpperCamelCase__     ,  padding="max_length")[input_name]

						__lowerCAmelCase:       Any          =			feat_extract.pad(
						    UpperCamelCase__     ,  padding="max_length"     ,  max_length=UpperCamelCase__     ,  return_tensors="np")
						__lowerCAmelCase:       Optional[Any]          =			input_a[input_name]

						self.assertFalse(_inputs_have_equal_length(UpperCamelCase__))
						self.assertTrue(_inputs_have_equal_length(UpperCamelCase__))
						self.assertTrue(_inputs_have_equal_length(UpperCamelCase__))
						self.assertTrue(_inputs_are_equal(UpperCamelCase__     ,  UpperCamelCase__))
						self.assertTrue(len(input_a[0]) == pad_min_length)
						self.assertTrue(len(input_a[1]) == pad_min_length + pad_diff)
						self.assertTrue(input_a.shape[:2] == (batch_size, len(input_a[0])))
						self.assertTrue(input_a.shape[:2] == (batch_size, pad_max_length))

						if feature_size > 1:
								self.assertTrue(input_a.shape[2] == input_a.shape[2] == feature_size)

						# test padding for `pad_to_multiple_of` for List[int] + numpy
						__lowerCAmelCase:       Dict          =			feat_extract.pad(UpperCamelCase__     ,  pad_to_multiple_of=1_0)
						__lowerCAmelCase:       Tuple          =			input_a[input_name]

						__lowerCAmelCase:       Tuple          =			feat_extract.pad(UpperCamelCase__     ,  padding="longest"     ,  pad_to_multiple_of=1_0)
						__lowerCAmelCase:       List[str]          =			input_a[input_name]

						__lowerCAmelCase:       str          =			feat_extract.pad(
						    UpperCamelCase__     ,  padding="max_length"     ,  pad_to_multiple_of=1_0     ,  max_length=UpperCamelCase__)
						__lowerCAmelCase:       str          =			input_a[input_name]

						__lowerCAmelCase:       Dict          =			feat_extract.pad(
						    UpperCamelCase__     ,  padding="max_length"     ,  pad_to_multiple_of=1_0     ,  max_length=UpperCamelCase__     ,  return_tensors="np"     ,  )
						__lowerCAmelCase:       List[str]          =			input_a[input_name]

						self.assertTrue(all(len(UpperCamelCase__) % 1_0 == 0 for x in input_a))
						self.assertTrue(_inputs_are_equal(UpperCamelCase__     ,  UpperCamelCase__))

						__lowerCAmelCase:       Optional[Any]          =			pad_max_length if pad_max_length % 1_0 == 0 else (pad_max_length // 1_0 + 1) * 1_0
						self.assertTrue(all(len(UpperCamelCase__) == expected_mult_pad_length for x in input_a))
						self.assertEqual(input_a.shape[:2]     ,  (batch_size, expected_mult_pad_length))

						if feature_size > 1:
								self.assertTrue(input_a.shape[2] == feature_size)

						# Check padding value is correct
						__lowerCAmelCase:       Any          =			(np.ones(self.feat_extract_tester.feature_size) * feat_extract.padding_value).sum()
						self.assertTrue(
						    abs(np.asarray(input_a[0])[pad_min_length:].sum() - padding_vector_sum * (pad_max_length - pad_min_length))
						    < 1e-3)
						self.assertTrue(
						    abs(
						        np.asarray(input_a[1])[pad_min_length + pad_diff :].sum()
						        - padding_vector_sum * (pad_max_length - pad_min_length - pad_diff))
						    < 1e-3)
						self.assertTrue(
						    abs(
						        np.asarray(input_a[2])[pad_min_length + 2 * pad_diff :].sum()
						        - padding_vector_sum * (pad_max_length - pad_min_length - 2 * pad_diff))
						    < 1e-3)
						self.assertTrue(
						    abs(input_a[0, pad_min_length:].sum() - padding_vector_sum * (pad_max_length - pad_min_length)) < 1e-3)
						self.assertTrue(
						    abs(input_a[0, pad_min_length:].sum() - padding_vector_sum * (expected_mult_pad_length - pad_min_length))
						    < 1e-3)

				def 							lowercase_     (	self							:				Optional[int]     ,  UpperCamelCase__							:				Optional[int]=False)->   str:



						'''simple docstring'''


						def _inputs_have_equal_length(UpperCamelCase__							:				List[Any]):
								__lowerCAmelCase:       List[str]          =			len(input[0])
								for input_slice in input[1:]:
										if len(UpperCamelCase__) != length:
												return False
								return True

						def _inputs_are_equal(UpperCamelCase__							:				Optional[int]     ,  UpperCamelCase__							:				List[Any]):
								if len(UpperCamelCase__) != len(UpperCamelCase__):
										return False

								for input_slice_a, input_slice_a in zip(UpperCamelCase__     ,  UpperCamelCase__):
										if not np.allclose(np.asarray(UpperCamelCase__)     ,  np.asarray(UpperCamelCase__)     ,  atol=1e-3):
												return False
								return True

						__lowerCAmelCase:       Dict          =			self.feature_extraction_class(**self.feat_extract_dict)
						__lowerCAmelCase:       Any          =			self.feat_extract_tester.prepare_inputs_for_common(numpify=UpperCamelCase__)
						__lowerCAmelCase:       Any          =			feat_extract.model_input_names[0]

						__lowerCAmelCase:       Optional[int]          =			BatchFeature({input_name: speech_inputs})

						# truncate to smallest
						__lowerCAmelCase:       int          =			feat_extract.pad(
						    UpperCamelCase__     ,  padding="max_length"     ,  max_length=len(speech_inputs[0])     ,  truncation=UpperCamelCase__)
						__lowerCAmelCase:       Any          =			input_a[input_name]

						__lowerCAmelCase:       str          =			feat_extract.pad(UpperCamelCase__     ,  padding="max_length"     ,  max_length=len(speech_inputs[0]))
						__lowerCAmelCase:       Tuple          =			input_a[input_name]

						self.assertTrue(_inputs_have_equal_length(UpperCamelCase__))
						self.assertFalse(_inputs_have_equal_length(UpperCamelCase__))

						# truncate to smallest with np
						__lowerCAmelCase:       Optional[int]          =			feat_extract.pad(
						    UpperCamelCase__     ,  padding="max_length"     ,  max_length=len(speech_inputs[0])     ,  return_tensors="np"     ,  truncation=UpperCamelCase__     ,  )
						__lowerCAmelCase:       List[str]          =			input_a[input_name]

						__lowerCAmelCase:       List[str]          =			feat_extract.pad(
						    UpperCamelCase__     ,  padding="max_length"     ,  max_length=len(speech_inputs[0])     ,  return_tensors="np")
						__lowerCAmelCase:       Any          =			input_a[input_name]

						self.assertTrue(_inputs_have_equal_length(UpperCamelCase__))
						self.assertTrue(input_a.shape[1] == len(speech_inputs[0]))

						# since truncation forces padding to be smaller than longest input
						# function can't return `np.ndarray`, but has to return list
						self.assertFalse(_inputs_have_equal_length(UpperCamelCase__))

						# truncate to middle
						__lowerCAmelCase:       Union[str, Any]          =			feat_extract.pad(
						    UpperCamelCase__     ,  padding="max_length"     ,  max_length=len(speech_inputs[1])     ,  truncation=UpperCamelCase__     ,  return_tensors="np"     ,  )
						__lowerCAmelCase:       int          =			input_a[input_name]

						__lowerCAmelCase:       List[Any]          =			feat_extract.pad(
						    UpperCamelCase__     ,  padding="max_length"     ,  max_length=len(speech_inputs[1])     ,  truncation=UpperCamelCase__)
						__lowerCAmelCase:       Dict          =			input_a[input_name]

						__lowerCAmelCase:       List[Any]          =			feat_extract.pad(
						    UpperCamelCase__     ,  padding="max_length"     ,  max_length=len(speech_inputs[1])     ,  return_tensors="np")
						__lowerCAmelCase:       str          =			input_a[input_name]

						self.assertTrue(input_a.shape[1] == len(speech_inputs[1]))
						self.assertTrue(_inputs_have_equal_length(UpperCamelCase__))
						self.assertTrue(_inputs_have_equal_length(UpperCamelCase__))
						self.assertTrue(_inputs_are_equal(UpperCamelCase__     ,  UpperCamelCase__))

						# since truncation forces padding to be smaller than longest input
						# function can't return `np.ndarray`, but has to return list
						self.assertFalse(_inputs_have_equal_length(UpperCamelCase__))
						self.assertTrue(len(input_a[-1]) == len(speech_inputs[-1]))

						# padding has to be max_length when setting `truncation=True`
						with self.assertRaises(UpperCamelCase__):
								feat_extract.pad(UpperCamelCase__     ,  truncation=UpperCamelCase__)[input_name]

						# padding has to be max_length when setting `truncation=True`
						with self.assertRaises(UpperCamelCase__):
								feat_extract.pad(UpperCamelCase__     ,  padding="longest"     ,  truncation=UpperCamelCase__)[input_name]

						# padding has to be max_length when setting `truncation=True`
						with self.assertRaises(UpperCamelCase__):
								feat_extract.pad(UpperCamelCase__     ,  padding="longest"     ,  truncation=UpperCamelCase__)[input_name]

						# max_length parameter has to be provided when setting `truncation=True` and padding="max_length"
						with self.assertRaises(UpperCamelCase__):
								feat_extract.pad(UpperCamelCase__     ,  padding="max_length"     ,  truncation=UpperCamelCase__)[input_name]

						# test truncation for `pad_to_multiple_of` for List[int] + numpy
						__lowerCAmelCase:       Tuple          =			1_2
						__lowerCAmelCase:       Optional[int]          =			feat_extract.pad(
						    UpperCamelCase__     ,  padding="max_length"     ,  max_length=len(speech_inputs[0])     ,  pad_to_multiple_of=UpperCamelCase__     ,  truncation=UpperCamelCase__     ,  )
						__lowerCAmelCase:       Dict          =			input_a[input_name]

						__lowerCAmelCase:       List[str]          =			feat_extract.pad(
						    UpperCamelCase__     ,  padding="max_length"     ,  max_length=len(speech_inputs[0])     ,  pad_to_multiple_of=UpperCamelCase__     ,  )
						__lowerCAmelCase:       str          =			input_a[input_name]

						# retrieve expected_length as multiple of pad_to_multiple_of
						__lowerCAmelCase:       Optional[Any]          =			len(speech_inputs[0])
						if expected_length % pad_to_multiple_of != 0:
								__lowerCAmelCase:       Optional[Any]          =			((len(speech_inputs[0]) // pad_to_multiple_of) + 1) * pad_to_multiple_of

						self.assertTrue(len(input_a[0]) == expected_length)
						self.assertTrue(_inputs_have_equal_length(UpperCamelCase__))
						self.assertFalse(_inputs_have_equal_length(UpperCamelCase__))

				def 							lowercase_     (	self							:				Union[str, Any])->   Optional[Any]:



						'''simple docstring'''


						self._check_padding(numpify=UpperCamelCase__)

				def 							lowercase_     (	self							:				Optional[Any])->   List[str]:



						'''simple docstring'''


						self._check_padding(numpify=UpperCamelCase__)

				def 							lowercase_     (	self							:				List[Any])->   Tuple:



						'''simple docstring'''


						self._check_truncation(numpify=UpperCamelCase__)

				def 							lowercase_     (	self							:				Optional[Any])->   Optional[Any]:



						'''simple docstring'''


						self._check_truncation(numpify=UpperCamelCase__)

				@require_torch
				def 							lowercase_     (	self							:				Dict)->   Dict:



						'''simple docstring'''


						__lowerCAmelCase:       Optional[Any]          =			self.feature_extraction_class(**self.feat_extract_dict)
						__lowerCAmelCase:       Optional[Any]          =			self.feat_extract_tester.prepare_inputs_for_common()
						__lowerCAmelCase:       Any          =			feat_extract.model_input_names[0]

						__lowerCAmelCase:       str          =			BatchFeature({input_name: speech_inputs})

						__lowerCAmelCase:       str          =			feat_extract.pad(UpperCamelCase__     ,  padding="longest"     ,  return_tensors="np")[input_name]
						__lowerCAmelCase:       Dict          =			feat_extract.pad(UpperCamelCase__     ,  padding="longest"     ,  return_tensors="pt")[input_name]

						self.assertTrue(abs(input_np.astype(np.floataa).sum() - input_pt.numpy().astype(np.floataa).sum()) < 1e-2)

				@require_tf
				def 							lowercase_     (	self							:				Tuple)->   Union[str, Any]:



						'''simple docstring'''


						__lowerCAmelCase:       Dict          =			self.feature_extraction_class(**self.feat_extract_dict)
						__lowerCAmelCase:       Tuple          =			self.feat_extract_tester.prepare_inputs_for_common()
						__lowerCAmelCase:       List[Any]          =			feat_extract.model_input_names[0]

						__lowerCAmelCase:       Any          =			BatchFeature({input_name: speech_inputs})

						__lowerCAmelCase:       Dict          =			feat_extract.pad(UpperCamelCase__     ,  padding="longest"     ,  return_tensors="np")[input_name]
						__lowerCAmelCase:       Any          =			feat_extract.pad(UpperCamelCase__     ,  padding="longest"     ,  return_tensors="tf")[input_name]

						self.assertTrue(abs(input_np.astype(np.floataa).sum() - input_tf.numpy().astype(np.floataa).sum()) < 1e-2)

				def 							lowercase_     (	self							:				Optional[int])->   List[Any]:



						'''simple docstring'''


						__lowerCAmelCase:       Any          =			self.feat_extract_dict
						__lowerCAmelCase:       Any          =			True
						__lowerCAmelCase:       str          =			self.feature_extraction_class(**UpperCamelCase__)
						__lowerCAmelCase:       List[Any]          =			self.feat_extract_tester.prepare_inputs_for_common()
						__lowerCAmelCase:       Tuple          =			[len(UpperCamelCase__) for x in speech_inputs]
						__lowerCAmelCase:       Optional[Any]          =			feat_extract.model_input_names[0]

						__lowerCAmelCase:       Optional[int]          =			BatchFeature({input_name: speech_inputs})

						__lowerCAmelCase:       Optional[Any]          =			feat_extract.pad(UpperCamelCase__     ,  padding="longest"     ,  return_tensors="np")
						self.assertIn("attention_mask"     ,  UpperCamelCase__)
						self.assertListEqual(list(processed.attention_mask.shape)     ,  list(processed[input_name].shape[:2]))
						self.assertListEqual(processed.attention_mask.sum(-1).tolist()     ,  UpperCamelCase__)




				def 							lowercase_     (	self							:				List[str])->   Any:



						'''simple docstring'''


						__lowerCAmelCase:       Any          =			self.feat_extract_dict
						__lowerCAmelCase:       str          =			True
						__lowerCAmelCase:       int          =			self.feature_extraction_class(**UpperCamelCase__)
						__lowerCAmelCase:       int          =			self.feat_extract_tester.prepare_inputs_for_common()
						__lowerCAmelCase:       str          =			[len(UpperCamelCase__) for x in speech_inputs]
						__lowerCAmelCase:       Any          =			feat_extract.model_input_names[0]

						__lowerCAmelCase:       Optional[int]          =			BatchFeature({input_name: speech_inputs})
						__lowerCAmelCase:       List[str]          =			min(UpperCamelCase__)

						__lowerCAmelCase:       List[str]          =			feat_extract.pad(
						    UpperCamelCase__     ,  padding="max_length"     ,  max_length=UpperCamelCase__     ,  truncation=UpperCamelCase__     ,  return_tensors="np")
						self.assertIn("attention_mask"     ,  UpperCamelCase__)
						self.assertListEqual(
						    list(processed_pad.attention_mask.shape)     ,  [processed_pad[input_name].shape[0], max_length])
						self.assertListEqual(
						    processed_pad.attention_mask[:, :max_length].sum(-1).tolist()     ,  [max_length for x in speech_inputs])


