






import argparse
import glob
import logging
import os
import sys
import time
from collections import defaultdict
from pathlib import Path
from typing import Dict, List, Tuple

import numpy as np
import pytorch_lightning as pl
import torch
from callbacks import SeqaSeqLoggingCallback, get_checkpoint_callback, get_early_stopping_callback
from torch import nn
from torch.utils.data import DataLoader

from transformers import MBartTokenizer, TaForConditionalGeneration
from transformers.models.bart.modeling_bart import shift_tokens_right
from utils import (
    ROUGE_KEYS,
    LegacySeqaSeqDataset,
    SeqaSeqDataset,
    assert_all_frozen,
    calculate_bleu,
    calculate_rouge,
    check_output_dir,
    flatten_list,
    freeze_embeds,
    freeze_params,
    get_git_info,
    label_smoothed_nll_loss,
    lmap,
    pickle_save,
    save_git_info,
    save_json,
    use_task_specific_params,
)


# need the parent dir module
sys.path.insert(2, str(Path(__file__).resolve().parents[1]))
from lightning_base import BaseTransformer, add_generic_args, generic_train  # noqa


a		:       Any			     = logging.getLogger(__name__)
class     a    ( a__   ):



      """simple docstring"""


      a  : int							       =	'''summarization'''
      a  : Tuple							       =	['''loss''']
      a  : List[Any]							       =	ROUGE_KEYS
      a  : Union[str, Any]							       =	'''rouge2'''
      def __init__(      self					:			Union[str, Any]		,      __lowercase					:			int		,      **__lowercase					:			str					)							->      List[str]:




        if hparams.sortish_sampler and hparams.gpus > 1:
          __UpperCAmelCase							:	Union[str, 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__(SCREAMING_SNAKE_CASE__		,      num_labels=SCREAMING_SNAKE_CASE__		,      mode=self.mode		,      **SCREAMING_SNAKE_CASE__					)
        use_task_specific_params(self.model		,      """summarization"""					)
        save_git_info(self.hparams.output_dir					)
        __UpperCAmelCase							:	int           =  Path(self.output_dir					) / """metrics.json"""
        __UpperCAmelCase							:	List[str]           =  Path(self.output_dir					) / """hparams.pkl"""
        pickle_save(self.hparams		,      self.hparams_save_path					)
        __UpperCAmelCase							:	Tuple           =  0
        __UpperCAmelCase							:	Union[str, Any]           =  defaultdict(SCREAMING_SNAKE_CASE__					)
        __UpperCAmelCase							:	Any           =  self.config.model_type
        __UpperCAmelCase							:	int           =  self.config.tgt_vocab_size if self.model_type == """fsmt""" else self.config.vocab_size

        __UpperCAmelCase							:	dict           =  {
            "data_dir": self.hparams.data_dir,
            "max_source_length": self.hparams.max_source_length,
            "prefix": self.model.config.prefix or "",
        }
        __UpperCAmelCase							:	Tuple           =  {
            """train""": self.hparams.n_train,
            """val""": self.hparams.n_val,
            """test""": self.hparams.n_test,
        }
        __UpperCAmelCase							:	Union[str, Any]           =  {k: v if v >= 0 else None for k, v in n_observations_per_split.items()}

        __UpperCAmelCase							:	int           =  {
            """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()					)

        __UpperCAmelCase							:	Optional[Any]           =  get_git_info()["""repo_sha"""]
        __UpperCAmelCase							:	Optional[Any]           =  hparams.num_workers
        __UpperCAmelCase							:	Tuple           =  None  # default to config
        if self.model.config.decoder_start_token_id is None and isinstance(self.tokenizer		,      SCREAMING_SNAKE_CASE__					):
          __UpperCAmelCase							:	Optional[int]           =  self.tokenizer.lang_code_to_id[hparams.tgt_lang]
          __UpperCAmelCase							:	Dict           =  self.decoder_start_token_id
        __UpperCAmelCase							:	Tuple           =  (
            SeqaSeqDataset if hasattr(self.tokenizer		,      """prepare_seq2seq_batch"""					) else LegacySeqaSeqDataset
        )
        __UpperCAmelCase							:	Union[str, Any]           =  False
        __UpperCAmelCase							:	Optional[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:
          __UpperCAmelCase							:	List[str]           =  self.hparams.eval_max_gen_length
        else:
          __UpperCAmelCase							:	int           =  self.model.config.max_length
        __UpperCAmelCase							:	Dict           =  self.default_val_metric if self.hparams.val_metric is None else self.hparams.val_metric
      def 	UpperCAmelCase      (      self					:			str		,      __lowercase					:			List[Any]					)							->      Dict[str, List[str]]:
        __UpperCAmelCase							:	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(SCREAMING_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"""					)

        __UpperCAmelCase							:	int           =  True
        return readable_batch
      def 	UpperCAmelCase      (      self					:			List[Any]		,      __lowercase					:			int		,      **__lowercase					:			Union[str, Any]					)							->      Optional[int]:
        return self.model(SCREAMING_SNAKE_CASE__		,      **SCREAMING_SNAKE_CASE__					)
      def 	UpperCAmelCase      (      self					:			Union[str, Any]		,      __lowercase					:			List[str]					)							->      List[Any]:
        __UpperCAmelCase							:	Any           =  self.tokenizer.batch_decode(
            SCREAMING_SNAKE_CASE__		,      skip_special_tokens=SCREAMING_SNAKE_CASE__		,      clean_up_tokenization_spaces=SCREAMING_SNAKE_CASE__					)
        return lmap(str.strip		,      SCREAMING_SNAKE_CASE__					)
      def 	UpperCAmelCase      (      self					:			Any		,      __lowercase					:			str					)							->      Tuple:
        __UpperCAmelCase							:	Optional[int]           =  self.tokenizer.pad_token_id
        __UpperCAmelCase							:	Union[str, Any]           =  batch["""input_ids"""], batch["""attention_mask"""]
        __UpperCAmelCase							:	Optional[Any]           =  batch["""labels"""]
        if isinstance(self.model		,      SCREAMING_SNAKE_CASE__					):
          __UpperCAmelCase							:	Union[str, Any]           =  self.model._shift_right(SCREAMING_SNAKE_CASE__					)
        else:
          __UpperCAmelCase							:	Union[str, Any]           =  shift_tokens_right(SCREAMING_SNAKE_CASE__		,      SCREAMING_SNAKE_CASE__					)
        if not self.already_saved_batch:  # This would be slightly better if it only happened on rank zero
          __UpperCAmelCase							:	Tuple           =  decoder_input_ids
          self.save_readable_batch(SCREAMING_SNAKE_CASE__					)

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

          assert lm_logits.shape[-1] == self.vocab_size
          __UpperCAmelCase							:	int           =  ce_loss_fct(lm_logits.view(-1		,      lm_logits.shape[-1]					)		,      tgt_ids.view(-1					)					)
        else:
          __UpperCAmelCase							:	Optional[int]           =  nn.functional.log_softmax(SCREAMING_SNAKE_CASE__		,      dim=-1					)
          __UpperCAmelCase							:	int           =  label_smoothed_nll_loss(
              SCREAMING_SNAKE_CASE__		,      SCREAMING_SNAKE_CASE__		,      self.hparams.label_smoothing		,      ignore_index=SCREAMING_SNAKE_CASE__					)
        return (loss,)
      @property
      def 	UpperCAmelCase      (      self					:			List[Any]					)							->      int:
        return self.tokenizer.pad_token_id
      def 	UpperCAmelCase      (      self					:			Optional[int]		,      __lowercase					:			Tuple		,      __lowercase					:			Optional[int]					)							->      Dict:
        __UpperCAmelCase							:	List[Any]           =  self._step(SCREAMING_SNAKE_CASE__					)

        __UpperCAmelCase							:	Dict           =  dict(zip(self.loss_names		,      SCREAMING_SNAKE_CASE__					)					)
        # tokens per batch
        __UpperCAmelCase							:	List[Any]           =  batch["""input_ids"""].ne(self.pad					).sum() + batch["""labels"""].ne(self.pad					).sum()
        __UpperCAmelCase							:	str           =  batch["""input_ids"""].shape[0]
        __UpperCAmelCase							:	List[str]           =  batch["""input_ids"""].eq(self.pad					).sum()
        __UpperCAmelCase							:	List[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 	UpperCAmelCase      (      self					:			Any		,      __lowercase					:			int		,      __lowercase					:			Optional[int]					)							->      Dict:
        return self._generative_step(SCREAMING_SNAKE_CASE__					)
      def 	UpperCAmelCase      (      self					:			str		,      __lowercase					:			Dict		,      __lowercase					:			Tuple="val"					)							->      Dict:
        self.step_count += 1
        __UpperCAmelCase							:	Union[str, Any]           =  {k: torch.stack([x[k] for x in outputs]					).mean() for k in self.loss_names}
        __UpperCAmelCase							:	Tuple           =  losses["""loss"""]
        __UpperCAmelCase							:	List[str]           =  {
            k: np.array([x[k] for x in outputs]					).mean() for k in self.metric_names + ["""gen_time""", """gen_len"""]
        }
        __UpperCAmelCase							:	Tuple           =  (
            generative_metrics[self.val_metric] if self.val_metric in generative_metrics else losses[self.val_metric]
        )
        __UpperCAmelCase							:	torch.FloatTensor           =  torch.tensor(SCREAMING_SNAKE_CASE__					).type_as(SCREAMING_SNAKE_CASE__					)
        generative_metrics.update({k: v.item() for k, v in losses.items()}					)
        losses.update(SCREAMING_SNAKE_CASE__					)
        __UpperCAmelCase							:	str           =  {f"""{prefix}_avg_{k}""": x for k, x in losses.items()}
        __UpperCAmelCase							:	Tuple           =  self.step_count
        self.metrics[prefix].append(SCREAMING_SNAKE_CASE__					)  # callback writes this to self.metrics_save_path
        __UpperCAmelCase							:	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 	UpperCAmelCase      (      self					:			Any		,      __lowercase					:			Tuple		,      __lowercase					:			Any					)							->      Dict:
        return calculate_rouge(SCREAMING_SNAKE_CASE__		,      SCREAMING_SNAKE_CASE__					)
      def 	UpperCAmelCase      (      self					:			int		,      __lowercase					:			Tuple					)							->      dict:
        __UpperCAmelCase							:	Dict           =  time.time()

        # parser.add_argument('--eval_max_gen_length', type=int, default=None, help='never generate more than n tokens')
        __UpperCAmelCase							:	int           =  self.model.generate(
            batch["""input_ids"""]		,      attention_mask=batch["""attention_mask"""]		,      use_cache=SCREAMING_SNAKE_CASE__		,      decoder_start_token_id=self.decoder_start_token_id		,      num_beams=self.eval_beams		,      max_length=self.eval_max_length		,      )
        __UpperCAmelCase							:	Optional[Any]           =  (time.time() - ta) / batch["""input_ids"""].shape[0]
        __UpperCAmelCase							:	List[str]           =  self.ids_to_clean_text(SCREAMING_SNAKE_CASE__					)
        __UpperCAmelCase							:	List[str]           =  self.ids_to_clean_text(batch["""labels"""]					)
        __UpperCAmelCase							:	str           =  self._step(SCREAMING_SNAKE_CASE__					)
        __UpperCAmelCase							:	Optional[Any]           =  dict(zip(self.loss_names		,      SCREAMING_SNAKE_CASE__					)					)
        __UpperCAmelCase							:	Dict           =  self.calc_generative_metrics(SCREAMING_SNAKE_CASE__		,      SCREAMING_SNAKE_CASE__					)
        __UpperCAmelCase							:	int           =  np.mean(lmap(SCREAMING_SNAKE_CASE__		,      SCREAMING_SNAKE_CASE__					)					)
        base_metrics.update(gen_time=SCREAMING_SNAKE_CASE__		,      gen_len=SCREAMING_SNAKE_CASE__		,      preds=SCREAMING_SNAKE_CASE__		,      target=SCREAMING_SNAKE_CASE__		,      **SCREAMING_SNAKE_CASE__					)
        return base_metrics
      def 	UpperCAmelCase      (      self					:			Union[str, Any]		,      __lowercase					:			int		,      __lowercase					:			List[str]					)							->      List[Any]:
        return self._generative_step(SCREAMING_SNAKE_CASE__					)
      def 	UpperCAmelCase      (      self					:			List[str]		,      __lowercase					:			Dict					)							->      Any:
        return self.validation_epoch_end(SCREAMING_SNAKE_CASE__		,      prefix="""test"""					)
      def 	UpperCAmelCase      (      self					:			Union[str, Any]		,      __lowercase					:			Union[str, Any]					)							->      SeqaSeqDataset:
        __UpperCAmelCase							:	Optional[Any]           =  self.n_obs[type_path]
        __UpperCAmelCase							:	Tuple           =  self.target_lens[type_path]
        __UpperCAmelCase							:	Optional[Any]           =  self.dataset_class(
            self.tokenizer		,      type_path=SCREAMING_SNAKE_CASE__		,      n_obs=SCREAMING_SNAKE_CASE__		,      max_target_length=SCREAMING_SNAKE_CASE__		,      **self.dataset_kwargs		,      )
        return dataset
      def 	UpperCAmelCase      (      self					:			Any		,      __lowercase					:			Optional[int]		,      __lowercase					:			List[Any]		,      __lowercase					:			List[str] = False					)							->      DataLoader:
        __UpperCAmelCase							:	List[str]           =  self.get_dataset(SCREAMING_SNAKE_CASE__					)

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

        elif self.hparams.max_tokens_per_batch is not None and type_path != "test" and type_path != "val":
          __UpperCAmelCase							:	List[Any]           =  dataset.make_dynamic_sampler(
              self.hparams.max_tokens_per_batch		,      distributed=self.hparams.gpus > 1					)
          return DataLoader(
              SCREAMING_SNAKE_CASE__		,      batch_sampler=SCREAMING_SNAKE_CASE__		,      collate_fn=dataset.collate_fn		,      num_workers=self.num_workers		,      )
        else:
          return DataLoader(
              SCREAMING_SNAKE_CASE__		,      batch_size=SCREAMING_SNAKE_CASE__		,      collate_fn=dataset.collate_fn		,      shuffle=SCREAMING_SNAKE_CASE__		,      num_workers=self.num_workers		,      sampler=SCREAMING_SNAKE_CASE__		,      )
      def 	UpperCAmelCase      (      self					:			str					)							->      DataLoader:
        __UpperCAmelCase							:	List[str]           =  self.get_dataloader("""train"""		,      batch_size=self.hparams.train_batch_size		,      shuffle=SCREAMING_SNAKE_CASE__					)
        return dataloader
      def 	UpperCAmelCase      (      self					:			Optional[Any]					)							->      DataLoader:
        return self.get_dataloader("""val"""		,      batch_size=self.hparams.eval_batch_size					)
      def 	UpperCAmelCase      (      self					:			Tuple					)							->      DataLoader:
        return self.get_dataloader("""test"""		,      batch_size=self.hparams.eval_batch_size					)




      @staticmethod
      def 	UpperCAmelCase      (      __lowercase					:			Tuple		,      __lowercase					:			Tuple					)							->      Dict:
        BaseTransformer.add_model_specific_args(SCREAMING_SNAKE_CASE__		,      SCREAMING_SNAKE_CASE__					)
        add_generic_args(SCREAMING_SNAKE_CASE__		,      SCREAMING_SNAKE_CASE__					)
        parser.add_argument(
            """--max_source_length"""		,      default=1024		,      type=SCREAMING_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=SCREAMING_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=SCREAMING_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=SCREAMING_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=SCREAMING_SNAKE_CASE__					)
        parser.add_argument("""--overwrite_output_dir"""		,      action="""store_true"""		,      default=SCREAMING_SNAKE_CASE__					)
        parser.add_argument("""--max_tokens_per_batch"""		,      type=SCREAMING_SNAKE_CASE__		,      default=SCREAMING_SNAKE_CASE__					)
        parser.add_argument("""--logger_name"""		,      type=SCREAMING_SNAKE_CASE__		,      choices=["""default""", """wandb""", """wandb_shared"""]		,      default="""default"""					)
        parser.add_argument("""--n_train"""		,      type=SCREAMING_SNAKE_CASE__		,      default=-1		,      required=SCREAMING_SNAKE_CASE__		,      help="""# examples. -1 means use all."""					)
        parser.add_argument("""--n_val"""		,      type=SCREAMING_SNAKE_CASE__		,      default=500		,      required=SCREAMING_SNAKE_CASE__		,      help="""# examples. -1 means use all."""					)
        parser.add_argument("""--n_test"""		,      type=SCREAMING_SNAKE_CASE__		,      default=-1		,      required=SCREAMING_SNAKE_CASE__		,      help="""# examples. -1 means use all."""					)
        parser.add_argument(
            """--task"""		,      type=SCREAMING_SNAKE_CASE__		,      default="""summarization"""		,      required=SCREAMING_SNAKE_CASE__		,      help="""# examples. -1 means use all."""					)
        parser.add_argument("""--label_smoothing"""		,      type=SCREAMING_SNAKE_CASE__		,      default=0.0		,      required=SCREAMING_SNAKE_CASE__					)
        parser.add_argument("""--src_lang"""		,      type=SCREAMING_SNAKE_CASE__		,      default=""""""		,      required=SCREAMING_SNAKE_CASE__					)
        parser.add_argument("""--tgt_lang"""		,      type=SCREAMING_SNAKE_CASE__		,      default=""""""		,      required=SCREAMING_SNAKE_CASE__					)
        parser.add_argument("""--eval_beams"""		,      type=SCREAMING_SNAKE_CASE__		,      default=SCREAMING_SNAKE_CASE__		,      required=SCREAMING_SNAKE_CASE__					)
        parser.add_argument(
            """--val_metric"""		,      type=SCREAMING_SNAKE_CASE__		,      default=SCREAMING_SNAKE_CASE__		,      required=SCREAMING_SNAKE_CASE__		,      choices=["""bleu""", """rouge2""", """loss""", None]					)
        parser.add_argument("""--eval_max_gen_length"""		,      type=SCREAMING_SNAKE_CASE__		,      default=SCREAMING_SNAKE_CASE__		,      help="""never generate more than n tokens"""					)
        parser.add_argument("""--save_top_k"""		,      type=SCREAMING_SNAKE_CASE__		,      default=1		,      required=SCREAMING_SNAKE_CASE__		,      help="""How many checkpoints to save"""					)
        parser.add_argument(
            """--early_stopping_patience"""		,      type=SCREAMING_SNAKE_CASE__		,      default=-1		,      required=SCREAMING_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     a    ( a__   ):



      """simple docstring"""


      a  : Optional[Any]							       =	'''translation'''
      a  : List[Any]							       =	['''loss''']
      a  : Optional[Any]							       =	['''bleu''']
      a  : Dict							       =	'''bleu'''
      def __init__(      self					:			Optional[int]		,      __lowercase					:			int		,      **__lowercase					:			Tuple					)							->      Union[str, Any]:
        super().__init__(SCREAMING_SNAKE_CASE__		,      **SCREAMING_SNAKE_CASE__					)
        __UpperCAmelCase							:	Any           =  hparams.src_lang
        __UpperCAmelCase							:	Optional[Any]           =  hparams.tgt_lang




      def 	UpperCAmelCase      (      self					:			str		,      __lowercase					:			str		,      __lowercase					:			List[str]					)							->      dict:
        return calculate_bleu(SCREAMING_SNAKE_CASE__		,      SCREAMING_SNAKE_CASE__					)






def 				lowerCamelCase__      (    __lowerCamelCase			:     str  ,		__lowerCamelCase			:     str=None		):
  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:
      __UpperCAmelCase							:	SummarizationModule           =  SummarizationModule(_snake_case		)
    else:
      __UpperCAmelCase							:	SummarizationModule           =  TranslationModule(_snake_case		)
  __UpperCAmelCase							:	Tuple           =  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"""		)
  ):
    __UpperCAmelCase							:	int           =  True  # don't pollute wandb logs unnecessarily
  elif args.logger_name == "wandb":
    from pytorch_lightning.loggers import WandbLogger

    __UpperCAmelCase							:	Dict           =  os.environ.get("""WANDB_PROJECT"""  ,		_snake_case		)
    __UpperCAmelCase							:	Optional[int]           =  WandbLogger(name=model.output_dir.name  ,		project=_snake_case		)

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

    __UpperCAmelCase							:	List[Any]           =  WandbLogger(name=model.output_dir.name  ,		project=f"""hf_{dataset}"""		)

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

  __UpperCAmelCase							:	int           =  args.val_metric == """loss"""
  __UpperCAmelCase							:	pl.Trainer           =  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

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

       a		:       List[str]			     = parser.parse_args()

       main(args)




"""simple docstring"""




from typing import List, Union

import numpy as np

from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
					from PIL import Image

					from ..image_utils import load_image

if is_torch_available():
					import torch

					from ..models.auto.modeling_auto import MODEL_FOR_DEPTH_ESTIMATION_MAPPING

UpperCAmelCase__      :    List[str]    							= logging.get_logger(__name__)



@add_end_docstrings(a__  )
class 	lowerCAmelCase_						(a__  ):


				"""simple docstring"""



				def __init__(self   ,       *SCREAMING_SNAKE_CASE__   ,       **SCREAMING_SNAKE_CASE__    )					->		Tuple:



											"""simple docstring"""




											super().__init__(*SCREAMING_SNAKE_CASE__   ,       **SCREAMING_SNAKE_CASE__    )
											requires_backends(self   ,       """vision"""    )
											self.check_model_type(SCREAMING_SNAKE_CASE__    )



				def __call__(self   ,       SCREAMING_SNAKE_CASE__   ,       **SCREAMING_SNAKE_CASE__    )					->		List[str]:



											"""simple docstring"""




											return super().__call__(SCREAMING_SNAKE_CASE__   ,       **SCREAMING_SNAKE_CASE__    )



				def 	__magic_name__    (self   ,       **SCREAMING_SNAKE_CASE__    )					->		Any:



											"""simple docstring"""




											return {}, {}, {}



				def 	__magic_name__    (self   ,       SCREAMING_SNAKE_CASE__    )					->		Union[str, Any]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       List[Any]           =   load_image(SCREAMING_SNAKE_CASE__    )
											SCREAMING_SNAKE_CASE__						:       Any           =   image.size
											SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   self.image_processor(images=SCREAMING_SNAKE_CASE__   ,       return_tensors=self.framework    )
											return model_inputs



				def 	__magic_name__    (self   ,       SCREAMING_SNAKE_CASE__    )					->		int:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       Tuple           =   self.model(**SCREAMING_SNAKE_CASE__    )
											return model_outputs



				def 	__magic_name__    (self   ,       SCREAMING_SNAKE_CASE__    )					->		Optional[int]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       List[Any]           =   model_outputs.predicted_depth
											SCREAMING_SNAKE_CASE__						:       Optional[int]           =   torch.nn.functional.interpolate(
											    predicted_depth.unsqueeze(1    )   ,       size=self.image_size[::-1]   ,       mode="""bicubic"""   ,       align_corners=SCREAMING_SNAKE_CASE__    )
											SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   prediction.squeeze().cpu().numpy()
											SCREAMING_SNAKE_CASE__						:       Any           =   (output * 2_55 / np.max(SCREAMING_SNAKE_CASE__    )).astype("""uint8"""    )
											SCREAMING_SNAKE_CASE__						:       List[str]           =   Image.fromarray(SCREAMING_SNAKE_CASE__    )
											SCREAMING_SNAKE_CASE__						:       str           =   {}
											SCREAMING_SNAKE_CASE__						:       Any           =   predicted_depth
											SCREAMING_SNAKE_CASE__						:       Dict           =   depth
											return output_dict




