






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


lowerCamelCase     :							Optional[Any]    =logging.getLogger(__name__)





class       __a  (   A__	):
 _lowerCAmelCase	:	List[Any]				=			'''summarization'''
 _lowerCAmelCase	:	Optional[Any]				=			['''loss''']
 _lowerCAmelCase	:	Any				=			ROUGE_KEYS
 _lowerCAmelCase	:	Tuple				=			'''rouge2'''







 def __init__(		self :       Optional[int]							,       SCREAMING_SNAKE_CASE :       Tuple							,       **SCREAMING_SNAKE_CASE :       List[str]						):


      '''simple docstring'''






      if hparams.sortish_sampler and hparams.gpus > 1:
           UpperCamelCase__      :       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__      :       Dict	    =  Path(self.output_dir						) / "metrics.json"
      UpperCamelCase__      :       str	    =  Path(self.output_dir						) / "hparams.pkl"
      pickle_save(self.hparams							,       self.hparams_save_path						)
      UpperCamelCase__      :       Tuple	    =  0
      UpperCamelCase__      :       Any	    =  defaultdict(SCREAMING_SNAKE_CASE						)
      UpperCamelCase__      :       int	    =  self.config.model_type
      UpperCamelCase__      :       Tuple	    =  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__      :       Any	    =  {
          "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__      :       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()						)

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







 def  __lowercase (		self :       str							,       SCREAMING_SNAKE_CASE :       Dict[str, torch.Tensor]						):


      '''simple docstring'''






      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  __lowercase (		self :       int							,       SCREAMING_SNAKE_CASE :       List[Any]							,       **SCREAMING_SNAKE_CASE :       Dict						):


      '''simple docstring'''






      return self.model(SCREAMING_SNAKE_CASE							,       **SCREAMING_SNAKE_CASE						)







 def  __lowercase (		self :       Optional[Any]							,       SCREAMING_SNAKE_CASE :       List[int]						):


      '''simple docstring'''






      UpperCamelCase__      :       str	    =  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  __lowercase (		self :       Optional[int]							,       SCREAMING_SNAKE_CASE :       dict						):


      '''simple docstring'''






      UpperCamelCase__      :       int	    =  self.tokenizer.pad_token_id
      UpperCamelCase__    ,					UpperCamelCase__      :       Any	    =  batch["input_ids"], batch["attention_mask"]
      UpperCamelCase__      :       Optional[int]	    =  batch["labels"]
      if isinstance(self.model							,       SCREAMING_SNAKE_CASE						):
           UpperCamelCase__      :       Any	    =  self.model._shift_right(SCREAMING_SNAKE_CASE						)
      else:
           UpperCamelCase__      :       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__      :       List[Any]	    =  decoder_input_ids
           self.save_readable_batch(SCREAMING_SNAKE_CASE						)

      UpperCamelCase__      :       Optional[Any]	    =  self(SCREAMING_SNAKE_CASE							,       attention_mask=SCREAMING_SNAKE_CASE							,       decoder_input_ids=SCREAMING_SNAKE_CASE							,       use_cache=SCREAMING_SNAKE_CASE						)
      UpperCamelCase__      :       Tuple	    =  outputs["logits"]
      if self.hparams.label_smoothing == 0:
           # Same behavior as modeling_bart.py, besides ignoring pad_token_id
           UpperCamelCase__      :       Optional[int]	    =  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__      :       Tuple	    =  nn.functional.log_softmax(SCREAMING_SNAKE_CASE							,       dim=-1						)
           UpperCamelCase__    ,					UpperCamelCase__      :       Tuple	    =  label_smoothed_nll_loss(
               SCREAMING_SNAKE_CASE							,       SCREAMING_SNAKE_CASE							,       self.hparams.label_smoothing							,       ignore_index=SCREAMING_SNAKE_CASE						)
      return (loss,)







 @property
 def  __lowercase (		self :       int						):


      '''simple docstring'''






      return self.tokenizer.pad_token_id







 def  __lowercase (		self :       Dict							,       SCREAMING_SNAKE_CASE :       Tuple							,       SCREAMING_SNAKE_CASE :       Optional[Any]						):


      '''simple docstring'''






      UpperCamelCase__      :       Tuple	    =  self._step(SCREAMING_SNAKE_CASE						)

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


      '''simple docstring'''






      return self._generative_step(SCREAMING_SNAKE_CASE						)







 def  __lowercase (		self :       Dict							,       SCREAMING_SNAKE_CASE :       Tuple							,       SCREAMING_SNAKE_CASE :       Optional[int]="val"						):


      '''simple docstring'''






      self.step_count += 1
      UpperCamelCase__      :       Tuple	    =  {k: torch.stack([x[k] for x in outputs]						).mean() for k in self.loss_names}
      UpperCamelCase__      :       Dict	    =  losses["loss"]
      UpperCamelCase__      :       Optional[int]	    =  {
          k: np.array([x[k] for x in outputs]						).mean() for k in self.metric_names + ["gen_time", "gen_len"]
      }
      UpperCamelCase__      :       Dict	    =  (
          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__      :       List[str]	    =  {F'{prefix}_avg_{k}': x for k, x in losses.items()}
      UpperCamelCase__      :       List[str]	    =  self.step_count
      self.metrics[prefix].append(SCREAMING_SNAKE_CASE						)  # callback writes this to self.metrics_save_path
      UpperCamelCase__      :       List[str]	    =  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 :       Dict							,       SCREAMING_SNAKE_CASE :       Dict							,       SCREAMING_SNAKE_CASE :       Dict						):


      '''simple docstring'''






      return calculate_rouge(SCREAMING_SNAKE_CASE							,       SCREAMING_SNAKE_CASE						)







 def  __lowercase (		self :       int							,       SCREAMING_SNAKE_CASE :       dict						):


      '''simple docstring'''






      UpperCamelCase__      :       List[str]	    =  time.time()

      # parser.add_argument('--eval_max_gen_length', type=int, default=None, help='never generate more than n tokens')
      UpperCamelCase__      :       Dict	    =  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__      :       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__      :       Dict	    =  self._step(SCREAMING_SNAKE_CASE						)
      UpperCamelCase__      :       List[Any]	    =  dict(zip(self.loss_names							,       SCREAMING_SNAKE_CASE						)						)
      UpperCamelCase__      :       Dict	    =  self.calc_generative_metrics(SCREAMING_SNAKE_CASE							,       SCREAMING_SNAKE_CASE						)
      UpperCamelCase__      :       List[str]	    =  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  __lowercase (		self :       int							,       SCREAMING_SNAKE_CASE :       str							,       SCREAMING_SNAKE_CASE :       Any						):


      '''simple docstring'''






      return self._generative_step(SCREAMING_SNAKE_CASE						)







 def  __lowercase (		self :       List[str]							,       SCREAMING_SNAKE_CASE :       List[Any]						):


      '''simple docstring'''






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







 def  __lowercase (		self :       List[str]							,       SCREAMING_SNAKE_CASE :       str						):


      '''simple docstring'''






      UpperCamelCase__      :       Tuple	    =  self.n_obs[type_path]
      UpperCamelCase__      :       Dict	    =  self.target_lens[type_path]
      UpperCamelCase__      :       Tuple	    =  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  __lowercase (		self :       Dict							,       SCREAMING_SNAKE_CASE :       str							,       SCREAMING_SNAKE_CASE :       int							,       SCREAMING_SNAKE_CASE :       bool = False						):


      '''simple docstring'''






      UpperCamelCase__      :       Optional[int]	    =  self.get_dataset(SCREAMING_SNAKE_CASE						)

      if self.hparams.sortish_sampler and type_path != "test" and type_path != "val":
           UpperCamelCase__      :       int	    =  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__      :       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  __lowercase (		self :       Union[str, Any]						):


      '''simple docstring'''






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







 def  __lowercase (		self :       Optional[int]						):


      '''simple docstring'''






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







 def  __lowercase (		self :       Tuple						):


      '''simple docstring'''






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







 @staticmethod
 def  __lowercase (		SCREAMING_SNAKE_CASE :       Tuple							,       SCREAMING_SNAKE_CASE :       Union[str, Any]						):


      '''simple docstring'''






      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=10_24							,       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=1_42							,       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=1_42							,       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=5_00							,       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__	):
 _lowerCAmelCase	:	int				=			'''translation'''
 _lowerCAmelCase	:	Dict				=			['''loss''']
 _lowerCAmelCase	:	Dict				=			['''bleu''']
 _lowerCAmelCase	:	int				=			'''bleu'''







 def __init__(		self :       int							,       SCREAMING_SNAKE_CASE :       int							,       **SCREAMING_SNAKE_CASE :       str						):


      '''simple docstring'''






      super().__init__(SCREAMING_SNAKE_CASE							,       **SCREAMING_SNAKE_CASE						)
      UpperCamelCase__      :       List[str]	    =  hparams.src_lang
      UpperCamelCase__      :       int	    =  hparams.tgt_lang







 def  __lowercase (		self :       Union[str, Any]							,       SCREAMING_SNAKE_CASE :       Optional[Any]							,       SCREAMING_SNAKE_CASE :       List[str]						):


      '''simple docstring'''






      return calculate_bleu(SCREAMING_SNAKE_CASE							,       SCREAMING_SNAKE_CASE						)







def 		SCREAMING_SNAKE_CASE							( __lowerCAmelCase ,   __lowerCAmelCase=None      )			->     SummarizationModule:
     Path(args.output_dir      ).mkdir(exist_ok=__lowerCAmelCase      )
     check_output_dir(__lowerCAmelCase ,   expected_items=3      )

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

          UpperCamelCase__      :       Any	    =  os.environ.get("WANDB_PROJECT" ,   __lowerCAmelCase      )
          UpperCamelCase__      :       Optional[int]	    =  WandbLogger(name=model.output_dir.name ,   project=__lowerCAmelCase      )

     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__      :       Any	    =  get_early_stopping_callback(model.val_metric ,   args.early_stopping_patience      )
     else:
          UpperCamelCase__      :       int	    =  False

     UpperCamelCase__      :       Union[str, Any]	    =  args.val_metric == "loss"
     UpperCamelCase__      :       pl.Trainer	    =  generic_train(
         __lowerCAmelCase ,   __lowerCAmelCase ,   logging_callback=SeqaSeqLoggingCallback() ,   checkpoint_callback=get_checkpoint_callback(
             args.output_dir ,   model.val_metric ,   args.save_top_k ,   __lowerCAmelCase      ) ,   early_stopping_callback=__lowerCAmelCase ,   logger=__lowerCAmelCase ,   )
     pickle_save(model.hparams ,   model.output_dir / "hparams.pkl"      )
     if not args.do_predict:
          return model

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

 lowerCamelCase     :							List[Any]    =parser.parse_args()

 main(args)







from collections import UserDict
from typing import List, Union

from ..utils import (
    add_end_docstrings,
    is_tf_available,
    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():
 from ..models.auto.modeling_auto import MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING

if is_tf_available():
 from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING
 from ..tf_utils import stable_softmax

lowerCamelCase     :							str    =logging.get_logger(__name__)





@add_end_docstrings(A__	)
class       __a  (   A__	):







 def __init__(		self :       List[str]							,       **SCREAMING_SNAKE_CASE :       Tuple						):


      '''simple docstring'''






      super().__init__(**SCREAMING_SNAKE_CASE						)

      requires_backends(self							,       "vision"						)
      self.check_model_type(
          TF_MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING
          if self.framework == "tf"
          else MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING						)







 def __call__(		self :       Dict							,       SCREAMING_SNAKE_CASE :       Union[str, List[str], "Image", List["Image"]]							,       **SCREAMING_SNAKE_CASE :       str						):


      '''simple docstring'''






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







 def  __lowercase (		self :       List[str]							,       **SCREAMING_SNAKE_CASE :       List[str]						):


      '''simple docstring'''






      UpperCamelCase__      :       List[Any]	    =  {}
      if "candidate_labels" in kwargs:
           UpperCamelCase__      :       Optional[Any]	    =  kwargs["candidate_labels"]
      if "hypothesis_template" in kwargs:
           UpperCamelCase__      :       int	    =  kwargs["hypothesis_template"]

      return preprocess_params, {}, {}







 def  __lowercase (		self :       List[Any]							,       SCREAMING_SNAKE_CASE :       List[Any]							,       SCREAMING_SNAKE_CASE :       Tuple=None							,       SCREAMING_SNAKE_CASE :       Optional[int]="This is a photo of {}."						):


      '''simple docstring'''






      UpperCamelCase__      :       Dict	    =  load_image(SCREAMING_SNAKE_CASE						)
      UpperCamelCase__      :       List[Any]	    =  self.image_processor(images=[image]							,       return_tensors=self.framework						)
      UpperCamelCase__      :       Any	    =  candidate_labels
      UpperCamelCase__      :       Dict	    =  [hypothesis_template.format(SCREAMING_SNAKE_CASE						) for x in candidate_labels]
      UpperCamelCase__      :       Optional[Any]	    =  self.tokenizer(SCREAMING_SNAKE_CASE							,       return_tensors=self.framework							,       padding=SCREAMING_SNAKE_CASE						)
      UpperCamelCase__      :       List[Any]	    =  [text_inputs]
      return inputs







 def  __lowercase (		self :       int							,       SCREAMING_SNAKE_CASE :       Union[str, Any]						):


      '''simple docstring'''






      UpperCamelCase__      :       Tuple	    =  model_inputs.pop("candidate_labels"						)
      UpperCamelCase__      :       List[str]	    =  model_inputs.pop("text_inputs"						)
      if isinstance(text_inputs[0]							,       SCREAMING_SNAKE_CASE						):
           UpperCamelCase__      :       Dict	    =  text_inputs[0]
      else:
           # Batching case.
           UpperCamelCase__      :       Union[str, Any]	    =  text_inputs[0][0]

      UpperCamelCase__      :       Any	    =  self.model(**SCREAMING_SNAKE_CASE							,       **SCREAMING_SNAKE_CASE						)

      UpperCamelCase__      :       Dict	    =  {
          "candidate_labels": candidate_labels,
          "logits": outputs.logits_per_image,
      }
      return model_outputs







 def  __lowercase (		self :       Any							,       SCREAMING_SNAKE_CASE :       List[str]						):


      '''simple docstring'''






      UpperCamelCase__      :       Optional[int]	    =  model_outputs.pop("candidate_labels"						)
      UpperCamelCase__      :       int	    =  model_outputs["logits"][0]
      if self.framework == "pt":
           UpperCamelCase__      :       Dict	    =  logits.softmax(dim=-1						).squeeze(-1						)
           UpperCamelCase__      :       Optional[Any]	    =  probs.tolist()
           if not isinstance(SCREAMING_SNAKE_CASE							,       SCREAMING_SNAKE_CASE						):
                UpperCamelCase__      :       List[Any]	    =  [scores]
      elif self.framework == "tf":
           UpperCamelCase__      :       Optional[Any]	    =  stable_softmax(SCREAMING_SNAKE_CASE							,       axis=-1						)
           UpperCamelCase__      :       Optional[int]	    =  probs.numpy().tolist()
      else:
           raise ValueError(F'Unsupported framework: {self.framework}'						)

      UpperCamelCase__      :       Optional[int]	    =  [
          {"score": score, "label": candidate_label}
          for score, candidate_label in sorted(zip(SCREAMING_SNAKE_CASE							,       SCREAMING_SNAKE_CASE						)							,       key=lambda SCREAMING_SNAKE_CASE						: -x[0]						)
      ]
      return result