

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


__UpperCAmelCase    				=     logging.getLogger(__name__)





class A__    ( a__ ):



 """simple docstring"""







 _lowercase     :					List[str]      =						"summarization"
 _lowercase     :					Optional[Any]      =						["loss"]
 _lowercase     :					List[Any]      =						ROUGE_KEYS
 _lowercase     :					List[str]      =						"rouge2"

 def __init__(		self :	Tuple     ,						A_ :	Optional[Any]     ,						**A_ :	Optional[Any]       ):






      '''simple docstring'''




      if hparams.sortish_sampler and hparams.gpus > 1:
           _lowerCAmelCase   :					Any        =    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       )
      _lowerCAmelCase   :					Optional[Any]        =    Path(self.output_dir       ) / "metrics.json"
      _lowerCAmelCase   :					Tuple        =    Path(self.output_dir       ) / "hparams.pkl"
      pickle_save(self.hparams     ,						self.hparams_save_path       )
      _lowerCAmelCase   :					List[Any]        =    0
      _lowerCAmelCase   :					Any        =    defaultdict(lowerCamelCase_       )
      _lowerCAmelCase   :					List[Any]        =    self.config.model_type
      _lowerCAmelCase   :					List[Any]        =    self.config.tgt_vocab_size if self.model_type == "fsmt" else self.config.vocab_size

      _lowerCAmelCase   :					Union[str, Any]        =    {
          "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   :					List[str]        =    {k: v if v >= 0 else None for k, v in n_observations_per_split.items()}

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

 def    __magic_name__       (		self :	Any     ,						A_ :	Any       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Optional[Any]        =    {
          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"       )

      _lowerCAmelCase   :					str        =    True
      return readable_batch

 def    __magic_name__       (		self :	Optional[Any]     ,						A_ :	Any     ,						**A_ :	int       ):






      '''simple docstring'''




      return self.model(lowerCamelCase_     ,						**lowerCamelCase_       )

 def    __magic_name__       (		self :	List[Any]     ,						A_ :	List[str]       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Dict        =    self.tokenizer.batch_decode(
          lowerCamelCase_     ,						skip_special_tokens=lowerCamelCase_     ,						clean_up_tokenization_spaces=lowerCamelCase_       )
      return lmap(str.strip     ,						lowerCamelCase_       )

 def    __magic_name__       (		self :	Union[str, Any]     ,						A_ :	List[Any]       ):






      '''simple docstring'''




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

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

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

 @property
 def    __magic_name__       (		self :	Optional[int]       ):






      '''simple docstring'''




      return self.tokenizer.pad_token_id

 def    __magic_name__       (		self :	List[str]     ,						A_ :	Union[str, Any]     ,						A_ :	int       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Optional[Any]        =    self._step(lowerCamelCase_       )

      _lowerCAmelCase   :					Any        =    dict(zip(self.loss_names     ,						lowerCamelCase_       )       )
      # tokens per batch
      _lowerCAmelCase   :					Optional[int]        =    batch["input_ids"].ne(self.pad       ).sum() + batch["labels"].ne(self.pad       ).sum()
      _lowerCAmelCase   :					Union[str, Any]        =    batch["input_ids"].shape[0]
      _lowerCAmelCase   :					int        =    batch["input_ids"].eq(self.pad       ).sum()
      _lowerCAmelCase   :					int        =    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    __magic_name__       (		self :	Tuple     ,						A_ :	Any     ,						A_ :	List[Any]       ):






      '''simple docstring'''




      return self._generative_step(lowerCamelCase_       )

 def    __magic_name__       (		self :	str     ,						A_ :	Tuple     ,						A_ :	List[str]="val"       ):






      '''simple docstring'''




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






      '''simple docstring'''




      return calculate_rouge(lowerCamelCase_     ,						lowerCamelCase_       )

 def    __magic_name__       (		self :	Optional[int]     ,						A_ :	List[str]       ):






      '''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   :					List[str]        =    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     ,						)
      _lowerCAmelCase   :					int        =    (time.time() - ta) / batch["input_ids"].shape[0]
      _lowerCAmelCase   :					List[Any]        =    self.ids_to_clean_text(lowerCamelCase_       )
      _lowerCAmelCase   :					Tuple        =    self.ids_to_clean_text(batch["labels"]       )
      _lowerCAmelCase   :					Dict        =    self._step(lowerCamelCase_       )
      _lowerCAmelCase   :					Tuple        =    dict(zip(self.loss_names     ,						lowerCamelCase_       )       )
      _lowerCAmelCase   :					List[Any]        =    self.calc_generative_metrics(lowerCamelCase_     ,						lowerCamelCase_       )
      _lowerCAmelCase   :					List[str]        =    np.mean(lmap(lowerCamelCase_     ,						lowerCamelCase_       )       )
      base_metrics.update(gen_time=lowerCamelCase_     ,						gen_len=lowerCamelCase_     ,						preds=lowerCamelCase_     ,						target=lowerCamelCase_     ,						**lowerCamelCase_       )
      return base_metrics

 def    __magic_name__       (		self :	str     ,						A_ :	Optional[Any]     ,						A_ :	str       ):






      '''simple docstring'''




      return self._generative_step(lowerCamelCase_       )

 def    __magic_name__       (		self :	int     ,						A_ :	Any       ):






      '''simple docstring'''




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

 def    __magic_name__       (		self :	Union[str, Any]     ,						A_ :	Optional[Any]       ):






      '''simple docstring'''




      _lowerCAmelCase   :					List[str]        =    self.n_obs[type_path]
      _lowerCAmelCase   :					Any        =    self.target_lens[type_path]
      _lowerCAmelCase   :					Optional[int]        =    self.dataset_class(
          self.tokenizer     ,						type_path=lowerCamelCase_     ,						n_obs=lowerCamelCase_     ,						max_target_length=lowerCamelCase_     ,						**self.dataset_kwargs     ,						)
      return dataset

 def    __magic_name__       (		self :	str     ,						A_ :	Any     ,						A_ :	List[str]     ,						A_ :	Union[str, Any] = False       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Union[str, Any]        =    self.get_dataset(lowerCamelCase_       )

      if self.hparams.sortish_sampler and type_path != "test" and type_path != "val":
           _lowerCAmelCase   :					str        =    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":
           _lowerCAmelCase   :					Union[str, Any]        =    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    __magic_name__       (		self :	List[Any]       ):






      '''simple docstring'''




      _lowerCAmelCase   :					List[Any]        =    self.get_dataloader("train"     ,						batch_size=self.hparams.train_batch_size     ,						shuffle=lowerCamelCase_       )
      return dataloader

 def    __magic_name__       (		self :	int       ):






      '''simple docstring'''




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

 def    __magic_name__       (		self :	str       ):






      '''simple docstring'''




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


 @staticmethod
 def    __magic_name__       (		A_ :	Optional[Any]     ,						A_ :	Union[str, Any]       ):






      '''simple docstring'''




      BaseTransformer.add_model_specific_args(lowerCamelCase_     ,						lowerCamelCase_       )
      add_generic_args(lowerCamelCase_     ,						lowerCamelCase_       )
      parser.add_argument(
          "--max_source_length"     ,						default=1_0_2_4     ,						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=5_6     ,						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=1_4_2     ,						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=1_4_2     ,						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=5_0_0     ,						required=lowerCamelCase_     ,						help="# examples. -1 means use all."       )
      parser.add_argument("--n_test"     ,						type=lowerCamelCase_     ,						default=-1     ,						required=lowerCamelCase_     ,						help="# examples. -1 means use all."       )
      parser.add_argument(
          "--task"     ,						type=lowerCamelCase_     ,						default="summarization"     ,						required=lowerCamelCase_     ,						help="# examples. -1 means use all."       )
      parser.add_argument("--label_smoothing"     ,						type=lowerCamelCase_     ,						default=0.0     ,						required=lowerCamelCase_       )
      parser.add_argument("--src_lang"     ,						type=lowerCamelCase_     ,						default=""     ,						required=lowerCamelCase_       )
      parser.add_argument("--tgt_lang"     ,						type=lowerCamelCase_     ,						default=""     ,						required=lowerCamelCase_       )
      parser.add_argument("--eval_beams"     ,						type=lowerCamelCase_     ,						default=lowerCamelCase_     ,						required=lowerCamelCase_       )
      parser.add_argument(
          "--val_metric"     ,						type=lowerCamelCase_     ,						default=lowerCamelCase_     ,						required=lowerCamelCase_     ,						choices=["bleu", "rouge2", "loss", None]       )
      parser.add_argument("--eval_max_gen_length"     ,						type=lowerCamelCase_     ,						default=lowerCamelCase_     ,						help="never generate more than n tokens"       )
      parser.add_argument("--save_top_k"     ,						type=lowerCamelCase_     ,						default=1     ,						required=lowerCamelCase_     ,						help="How many checkpoints to save"       )
      parser.add_argument(
          "--early_stopping_patience"     ,						type=lowerCamelCase_     ,						default=-1     ,						required=lowerCamelCase_     ,						help=(
              "-1 means never early stop. early_stopping_patience is measured in validation checks, not epochs. So"
              " val_check_interval will effect it."
          )     ,						)
      return parser





class A__    ( a__ ):



 """simple docstring"""







 _lowercase     :					Optional[int]      =						"translation"
 _lowercase     :					Tuple      =						["loss"]
 _lowercase     :					str      =						["bleu"]
 _lowercase     :					Optional[int]      =						"bleu"

 def __init__(		self :	Optional[int]     ,						A_ :	int     ,						**A_ :	Any       ):






      '''simple docstring'''




      super().__init__(lowerCamelCase_     ,						**lowerCamelCase_       )
      _lowerCAmelCase   :					Union[str, Any]        =    hparams.src_lang
      _lowerCAmelCase   :					Union[str, Any]        =    hparams.tgt_lang


 def    __magic_name__       (		self :	int     ,						A_ :	Union[str, Any]     ,						A_ :	Tuple       ):






      '''simple docstring'''




      return calculate_bleu(lowerCamelCase_     ,						lowerCamelCase_       )







def 	_snake_case		(    SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE=None				)       ->      SummarizationModule:

     """simple docstring"""







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

          _lowerCAmelCase   :					Dict        =    os.environ.get("WANDB_PROJECT"		,							SCREAMING_SNAKE_CASE				)
          _lowerCAmelCase   :					List[Any]        =    WandbLogger(name=model.output_dir.name		,							project=SCREAMING_SNAKE_CASE				)

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

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

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

     _lowerCAmelCase   :					List[str]        =    args.val_metric == "loss"
     _lowerCAmelCase   :					Any        =    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   :					str        =    ""
     _lowerCAmelCase   :					Tuple        =    sorted(glob.glob(os.path.join(args.output_dir		,							"*.ckpt"				)		,							recursive=SCREAMING_SNAKE_CASE				)				)
     if checkpoints:
          _lowerCAmelCase   :					str        =    checkpoints[-1]
          _lowerCAmelCase   :					str        =    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__":
  __UpperCAmelCase    				=     argparse.ArgumentParser()
  __UpperCAmelCase    				=     pl.Trainer.add_argparse_args(parser)
  __UpperCAmelCase    				=     SummarizationModule.add_model_specific_args(parser, os.getcwd())

  __UpperCAmelCase    				=     parser.parse_args()

  main(args)









from typing import Dict, List, Optional, Tuple, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    flip_channel_order,
    get_resize_output_image_size,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_torch_available, is_torch_tensor, is_vision_available, logging


if is_vision_available():
  import PIL

if is_torch_available():
  import torch


__UpperCAmelCase    				=     logging.get_logger(__name__)





class A__    ( A ):



 """simple docstring"""







 _lowercase     :					List[Any]      =						['''pixel_values''']

 def __init__(		self :	Tuple     ,						A_ :	bool = True     ,						A_ :	Dict[str, int] = None     ,						A_ :	PILImageResampling = PILImageResampling.BILINEAR     ,						A_ :	bool = True     ,						A_ :	Union[int, float] = 1 / 2_5_5     ,						A_ :	bool = True     ,						A_ :	Dict[str, int] = None     ,						A_ :	bool = True     ,						**A_ :	List[Any]     ,						):






      '''simple docstring'''




      super().__init__(**A_       )
      _lowerCAmelCase   :					Optional[int]        =    size if size is not None else {"shortest_edge": 2_2_4}
      _lowerCAmelCase   :					Optional[int]        =    get_size_dict(A_     ,						default_to_square=A_       )
      _lowerCAmelCase   :					Any        =    crop_size if crop_size is not None else {"height": 2_5_6, "width": 2_5_6}
      _lowerCAmelCase   :					str        =    get_size_dict(A_     ,						param_name="crop_size"       )

      _lowerCAmelCase   :					Any        =    do_resize
      _lowerCAmelCase   :					Optional[Any]        =    size
      _lowerCAmelCase   :					str        =    resample
      _lowerCAmelCase   :					Optional[Any]        =    do_rescale
      _lowerCAmelCase   :					Dict        =    rescale_factor
      _lowerCAmelCase   :					Any        =    do_center_crop
      _lowerCAmelCase   :					List[Any]        =    crop_size
      _lowerCAmelCase   :					List[Any]        =    do_flip_channel_order

 def    __magic_name__       (		self :	Tuple     ,						A_ :	np.ndarray     ,						A_ :	Dict[str, int]     ,						A_ :	PILImageResampling = PIL.Image.BILINEAR     ,						A_ :	Optional[Union[str, ChannelDimension]] = None     ,						**A_ :	Dict     ,						):






      '''simple docstring'''




      _lowerCAmelCase   :					Any        =    get_size_dict(A_     ,						default_to_square=A_       )
      if "shortest_edge" not in size:
           raise ValueError(F'''The `size` dictionary must contain the key `shortest_edge`. Got {size.keys()}'''       )
      _lowerCAmelCase   :					Union[str, Any]        =    get_resize_output_image_size(A_     ,						size=size["shortest_edge"]     ,						default_to_square=A_       )
      return resize(A_     ,						size=A_     ,						resample=A_     ,						data_format=A_     ,						**A_       )

 def    __magic_name__       (		self :	Union[str, Any]     ,						A_ :	np.ndarray     ,						A_ :	Dict[str, int]     ,						A_ :	Optional[Union[str, ChannelDimension]] = None     ,						**A_ :	List[str]     ,						):






      '''simple docstring'''




      _lowerCAmelCase   :					str        =    get_size_dict(A_       )
      if "height" not in size or "width" not in size:
           raise ValueError(F'''The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}'''       )
      return center_crop(A_     ,						size=(size["height"], size["width"])     ,						data_format=A_     ,						**A_       )

 def    __magic_name__       (		self :	Tuple     ,						A_ :	np.ndarray     ,						A_ :	Union[int, float]     ,						A_ :	Optional[Union[str, ChannelDimension]] = None     ,						**A_ :	Any     ,						):






      '''simple docstring'''




      return rescale(A_     ,						scale=A_     ,						data_format=A_     ,						**A_       )

 def    __magic_name__       (		self :	Optional[Any]     ,						A_ :	np.ndarray     ,						A_ :	Optional[Union[str, ChannelDimension]] = None       ):






      '''simple docstring'''




      return flip_channel_order(A_     ,						data_format=A_       )

 def    __magic_name__       (		self :	List[Any]     ,						A_ :	ImageInput     ,						A_ :	bool = None     ,						A_ :	Dict[str, int] = None     ,						A_ :	PILImageResampling = None     ,						A_ :	bool = None     ,						A_ :	float = None     ,						A_ :	bool = None     ,						A_ :	Dict[str, int] = None     ,						A_ :	bool = None     ,						A_ :	Optional[Union[str, TensorType]] = None     ,						A_ :	ChannelDimension = ChannelDimension.FIRST     ,						**A_ :	Tuple     ,						):






      '''simple docstring'''




      _lowerCAmelCase   :					str        =    do_resize if do_resize is not None else self.do_resize
      _lowerCAmelCase   :					Dict        =    resample if resample is not None else self.resample
      _lowerCAmelCase   :					str        =    do_rescale if do_rescale is not None else self.do_rescale
      _lowerCAmelCase   :					List[Any]        =    rescale_factor if rescale_factor is not None else self.rescale_factor
      _lowerCAmelCase   :					Tuple        =    do_center_crop if do_center_crop is not None else self.do_center_crop
      _lowerCAmelCase   :					str        =    (
          do_flip_channel_order if do_flip_channel_order is not None else self.do_flip_channel_order
      )

      _lowerCAmelCase   :					str        =    size if size is not None else self.size
      _lowerCAmelCase   :					str        =    get_size_dict(A_     ,						default_to_square=A_       )
      _lowerCAmelCase   :					Optional[Any]        =    crop_size if crop_size is not None else self.crop_size
      _lowerCAmelCase   :					List[str]        =    get_size_dict(A_     ,						param_name="crop_size"       )

      _lowerCAmelCase   :					Dict        =    make_list_of_images(A_       )

      if not valid_images(A_       ):
           raise ValueError(
               "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
               "torch.Tensor, tf.Tensor or jax.ndarray."       )

      if do_resize and size is None:
           raise ValueError("Size must be specified if do_resize is True."       )

      if do_rescale and rescale_factor is None:
           raise ValueError("Rescale factor must be specified if do_rescale is True."       )

      if do_center_crop and crop_size is None:
           raise ValueError("Crop size must be specified if do_center_crop is True."       )

      # All transformations expect numpy arrays.
      _lowerCAmelCase   :					Optional[int]        =    [to_numpy_array(A_       ) for image in images]

      if do_resize:
           _lowerCAmelCase   :					Any        =    [self.resize(image=A_     ,						size=A_     ,						resample=A_       ) for image in images]

      if do_center_crop:
           _lowerCAmelCase   :					Tuple        =    [self.center_crop(image=A_     ,						size=A_       ) for image in images]

      if do_rescale:
           _lowerCAmelCase   :					Any        =    [self.rescale(image=A_     ,						scale=A_       ) for image in images]

      # the pretrained checkpoints assume images are BGR, not RGB
      if do_flip_channel_order:
           _lowerCAmelCase   :					Dict        =    [self.flip_channel_order(image=A_       ) for image in images]

      _lowerCAmelCase   :					Optional[Any]        =    [to_channel_dimension_format(A_     ,						A_       ) for image in images]

      _lowerCAmelCase   :					Tuple        =    {"pixel_values": images}
      return BatchFeature(data=A_     ,						tensor_type=A_       )


 def    __magic_name__       (		self :	List[Any]     ,						A_ :	Union[str, Any]     ,						A_ :	List[Tuple] = None       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Union[str, Any]        =    outputs.logits

      # Resize logits and compute semantic segmentation maps
      if target_sizes is not None:
           if len(A_       ) != len(A_       ):
                raise ValueError(
                    "Make sure that you pass in as many target sizes as the batch dimension of the logits"       )

           if is_torch_tensor(A_       ):
                _lowerCAmelCase   :					Dict        =    target_sizes.numpy()

           _lowerCAmelCase   :					List[Any]        =    []

           for idx in range(len(A_       )       ):
                _lowerCAmelCase   :					Tuple        =    torch.nn.functional.interpolate(
                    logits[idx].unsqueeze(dim=0       )     ,						size=target_sizes[idx]     ,						mode="bilinear"     ,						align_corners=A_       )
                _lowerCAmelCase   :					Any        =    resized_logits[0].argmax(dim=0       )
                semantic_segmentation.append(A_       )
      else:
           _lowerCAmelCase   :					Tuple        =    logits.argmax(dim=1       )
           _lowerCAmelCase   :					Tuple        =    [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0]       )]

      return semantic_segmentation






