
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_						     =					logging.getLogger(__name__)




class      __A(    lowerCAmelCase_			):

  """simple docstring"""
  SCREAMING_SNAKE_CASE__         =      """summarization"""
  SCREAMING_SNAKE_CASE__         =      ["""loss"""]
  SCREAMING_SNAKE_CASE__         =      ROUGE_KEYS
  SCREAMING_SNAKE_CASE__         =      """rouge2"""







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

       if hparams.sortish_sampler and hparams.gpus > 1:
            UpperCamelCase__				=    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__				=    Path(self.output_dir						) / """metrics.json"""
       UpperCamelCase__				=    Path(self.output_dir						) / """hparams.pkl"""
       pickle_save(self.hparams	,      self.hparams_save_path						)
       UpperCamelCase__				=    0
       UpperCamelCase__				=    defaultdict(__SCREAMING_SNAKE_CASE						)
       UpperCamelCase__				=    self.config.model_type
       UpperCamelCase__				=    self.config.tgt_vocab_size if self.model_type == """fsmt""" else self.config.vocab_size

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

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







  def 	UpperCAmelCase_ (self	,      SCREAMING_SNAKE_CASE_						):
       UpperCamelCase__				=    {
           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__				=    True
       return readable_batch







  def 	UpperCAmelCase_ (self	,      SCREAMING_SNAKE_CASE_	,      **SCREAMING_SNAKE_CASE_						):
       return self.model(__SCREAMING_SNAKE_CASE	,      **__SCREAMING_SNAKE_CASE						)







  def 	UpperCAmelCase_ (self	,      SCREAMING_SNAKE_CASE_						):
       UpperCamelCase__				=    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	,      SCREAMING_SNAKE_CASE_						):
       UpperCamelCase__				=    self.tokenizer.pad_token_id
       UpperCamelCase__		,	UpperCamelCase__				=    batch["""input_ids"""], batch["""attention_mask"""]
       UpperCamelCase__				=    batch["""labels"""]
       if isinstance(self.model	,      __SCREAMING_SNAKE_CASE						):
            UpperCamelCase__				=    self.model._shift_right(__SCREAMING_SNAKE_CASE						)
       else:
            UpperCamelCase__				=    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__				=    decoder_input_ids
            self.save_readable_batch(__SCREAMING_SNAKE_CASE						)

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

            assert lm_logits.shape[-1] == self.vocab_size
            UpperCamelCase__				=    ce_loss_fct(lm_logits.view(-1	,      lm_logits.shape[-1]						)	,      tgt_ids.view(-1						)						)
       else:
            UpperCamelCase__				=    nn.functional.log_softmax(__SCREAMING_SNAKE_CASE	,      dim=-1						)
            UpperCamelCase__		,	UpperCamelCase__				=    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						):
       return self.tokenizer.pad_token_id







  def 	UpperCAmelCase_ (self	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_						):
       UpperCamelCase__				=    self._step(__SCREAMING_SNAKE_CASE						)

       UpperCamelCase__				=    dict(zip(self.loss_names	,      __SCREAMING_SNAKE_CASE						)						)
       # tokens per batch
       UpperCamelCase__				=    batch["""input_ids"""].ne(self.pad						).sum() + batch["""labels"""].ne(self.pad						).sum()
       UpperCamelCase__				=    batch["""input_ids"""].shape[0]
       UpperCamelCase__				=    batch["""input_ids"""].eq(self.pad						).sum()
       UpperCamelCase__				=    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	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_						):
       return self._generative_step(__SCREAMING_SNAKE_CASE						)







  def 	UpperCAmelCase_ (self	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_="val"						):
       self.step_count += 1
       UpperCamelCase__				=    {k: torch.stack([x[k] for x in outputs]						).mean() for k in self.loss_names}
       UpperCamelCase__				=    losses["""loss"""]
       UpperCamelCase__				=    {
           k: np.array([x[k] for x in outputs]						).mean() for k in self.metric_names + ["""gen_time""", """gen_len"""]
       }
       UpperCamelCase__				=    (
           generative_metrics[self.val_metric] if self.val_metric in generative_metrics else losses[self.val_metric]
       )
       UpperCamelCase__				=    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__				=    {F"{prefix}_avg_{k}": x for k, x in losses.items()}
       UpperCamelCase__				=    self.step_count
       self.metrics[prefix].append(__SCREAMING_SNAKE_CASE						)  # callback writes this to self.metrics_save_path
       UpperCamelCase__				=    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	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_						):
       return calculate_rouge(__SCREAMING_SNAKE_CASE	,      __SCREAMING_SNAKE_CASE						)







  def 	UpperCAmelCase_ (self	,      SCREAMING_SNAKE_CASE_						):
       UpperCamelCase__				=    time.time()

       # parser.add_argument('--eval_max_gen_length', type=int, default=None, help='never generate more than n tokens')
       UpperCamelCase__				=    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__				=    (time.time() - ta) / batch["""input_ids"""].shape[0]
       UpperCamelCase__				=    self.ids_to_clean_text(__SCREAMING_SNAKE_CASE						)
       UpperCamelCase__				=    self.ids_to_clean_text(batch["""labels"""]						)
       UpperCamelCase__				=    self._step(__SCREAMING_SNAKE_CASE						)
       UpperCamelCase__				=    dict(zip(self.loss_names	,      __SCREAMING_SNAKE_CASE						)						)
       UpperCamelCase__				=    self.calc_generative_metrics(__SCREAMING_SNAKE_CASE	,      __SCREAMING_SNAKE_CASE						)
       UpperCamelCase__				=    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	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_						):
       return self._generative_step(__SCREAMING_SNAKE_CASE						)







  def 	UpperCAmelCase_ (self	,      SCREAMING_SNAKE_CASE_						):
       return self.validation_epoch_end(__SCREAMING_SNAKE_CASE	,      prefix="""test"""						)







  def 	UpperCAmelCase_ (self	,      SCREAMING_SNAKE_CASE_						):
       UpperCamelCase__				=    self.n_obs[type_path]
       UpperCamelCase__				=    self.target_lens[type_path]
       UpperCamelCase__				=    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	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_ = False						):
       UpperCamelCase__				=    self.get_dataset(__SCREAMING_SNAKE_CASE						)

       if self.hparams.sortish_sampler and type_path != "test" and type_path != "val":
            UpperCamelCase__				=    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__				=    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						):
       UpperCamelCase__				=    self.get_dataloader("""train"""	,      batch_size=self.hparams.train_batch_size	,      shuffle=__SCREAMING_SNAKE_CASE						)
       return dataloader







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







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







  @staticmethod
  def 	UpperCAmelCase_ (SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_						):
       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(    lowerCAmelCase_			):

  """simple docstring"""
  SCREAMING_SNAKE_CASE__         =      """translation"""
  SCREAMING_SNAKE_CASE__         =      ["""loss"""]
  SCREAMING_SNAKE_CASE__         =      ["""bleu"""]
  SCREAMING_SNAKE_CASE__         =      """bleu"""







  def __init__(self	,      SCREAMING_SNAKE_CASE_	,      **SCREAMING_SNAKE_CASE_						):
       super().__init__(__SCREAMING_SNAKE_CASE	,      **__SCREAMING_SNAKE_CASE						)
       UpperCamelCase__				=    hparams.src_lang
       UpperCamelCase__				=    hparams.tgt_lang







  def 	UpperCAmelCase_ (self	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_						):
       return calculate_bleu(__SCREAMING_SNAKE_CASE	,      __SCREAMING_SNAKE_CASE						)






def 						__magic_name__			(   __a      :	int	,		__a      :	Any=None						):



     '''simple docstring'''




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

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

          UpperCamelCase__				=    os.environ.get("""WANDB_PROJECT"""	,		__a						)
          UpperCamelCase__				=    WandbLogger(name=model.output_dir.name	,		project=__a						)

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

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

     if args.early_stopping_patience >= 0:
          UpperCamelCase__				=    get_early_stopping_callback(model.val_metric	,		args.early_stopping_patience						)
     else:
          UpperCamelCase__				=    False

     UpperCamelCase__				=    args.val_metric == """loss"""
     UpperCamelCase__				=    generic_train(
         __a	,		__a	,		logging_callback=SeqaSeqLoggingCallback()	,		checkpoint_callback=get_checkpoint_callback(
             args.output_dir	,		model.val_metric	,		args.save_top_k	,		__a						)	,		early_stopping_callback=__a	,		logger=__a	,		)
     pickle_save(model.hparams	,		model.output_dir / """hparams.pkl"""						)
     if not args.do_predict:
          return model

     UpperCamelCase__				=    """"""
     UpperCamelCase__				=    sorted(glob.glob(os.path.join(args.output_dir	,		"""*.ckpt"""						)	,		recursive=__a						)						)
     if checkpoints:
          UpperCamelCase__				=    checkpoints[-1]
          UpperCamelCase__				=    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_						     =					argparse.ArgumentParser()
    lowerCamelCase_						     =					pl.Trainer.add_argparse_args(parser)
    lowerCamelCase_						     =					SummarizationModule.add_model_specific_args(parser, os.getcwd())

    lowerCamelCase_						     =					parser.parse_args()

    main(args)







import collections
import os
import re
from pathlib import Path


lowerCamelCase_						     =					'''src/transformers'''


# Matches is_xxx_available()
lowerCamelCase_						     =					re.compile(r'''is\_([a-z_]*)_available()''')
# Catches a one-line _import_struct = {xxx}
lowerCamelCase_						     =					re.compile(r'''^_import_structure\s+=\s+\{([^\}]+)\}''')
# Catches a line with a key-values pattern: "bla": ["foo", "bar"]
lowerCamelCase_						     =					re.compile(r'''\s+"\S*":\s+\[([^\]]*)\]''')
# Catches a line if not is_foo_available
lowerCamelCase_						     =					re.compile(r'''^\s*if\s+not\s+is\_[a-z_]*\_available\(\)''')
# Catches a line _import_struct["bla"].append("foo")
lowerCamelCase_						     =					re.compile(r'''^\s*_import_structure\["\S*"\]\.append\("(\S*)"\)''')
# Catches a line _import_struct["bla"].extend(["foo", "bar"]) or _import_struct["bla"] = ["foo", "bar"]
lowerCamelCase_						     =					re.compile(r'''^\s*_import_structure\[\S*\](?:\.extend\(|\s*=\s+)\[([^\]]*)\]''')
# Catches a line with an object between quotes and a comma:     "MyModel",
lowerCamelCase_						     =					re.compile(r'''^\s+"([^"]+)",''')
# Catches a line with objects between brackets only:    ["foo", "bar"],
lowerCamelCase_						     =					re.compile(r'''^\s+\[([^\]]+)\]''')
# Catches a line with from foo import bar, bla, boo
lowerCamelCase_						     =					re.compile(r'''\s+from\s+\S*\s+import\s+([^\(\s].*)\n''')
# Catches a line with try:
lowerCamelCase_						     =					re.compile(r'''^\s*try:''')
# Catches a line with else:
lowerCamelCase_						     =					re.compile(r'''^\s*else:''')






def 						__magic_name__			(   __a      :	List[Any]						):



     '''simple docstring'''




     if _re_test_backend.search(__a						) is None:
          return None
     UpperCamelCase__				=    [b[0] for b in _re_backend.findall(__a						)]
     backends.sort()
     return "_and_".join(__a						)






def 						__magic_name__			(   __a      :	Dict						):



     '''simple docstring'''




     with open(__a	,		"""r"""	,		encoding="""utf-8"""	,		newline="""\n"""						) as f:
          UpperCamelCase__				=    f.readlines()

     UpperCamelCase__				=    0
     while line_index < len(__a						) and not lines[line_index].startswith("""_import_structure = {"""						):
          line_index += 1

     # If this is a traditional init, just return.
     if line_index >= len(__a						):
          return None

     # First grab the objects without a specific backend in _import_structure
     UpperCamelCase__				=    []
     while not lines[line_index].startswith("""if TYPE_CHECKING"""						) and find_backend(lines[line_index]						) is None:
          UpperCamelCase__				=    lines[line_index]
          # If we have everything on a single line, let's deal with it.
          if _re_one_line_import_struct.search(__a						):
               UpperCamelCase__				=    _re_one_line_import_struct.search(__a						).groups()[0]
               UpperCamelCase__				=    re.findall(R"""\[([^\]]+)\]"""	,		__a						)
               for imp in imports:
                    objects.extend([obj[1:-1] for obj in imp.split(""", """						)]						)
               line_index += 1
               continue
          UpperCamelCase__				=    _re_import_struct_key_value.search(__a						)
          if single_line_import_search is not None:
               UpperCamelCase__				=    [obj[1:-1] for obj in single_line_import_search.groups()[0].split(""", """						) if len(__a						) > 0]
               objects.extend(__a						)
          elif line.startswith(""" """ * 8 + """\""""						):
               objects.append(line[9:-3]						)
          line_index += 1

     UpperCamelCase__				=    {"""none""": objects}
     # Let's continue with backend-specific objects in _import_structure
     while not lines[line_index].startswith("""if TYPE_CHECKING"""						):
          # If the line is an if not is_backend_available, we grab all objects associated.
          UpperCamelCase__				=    find_backend(lines[line_index]						)
          # Check if the backend declaration is inside a try block:
          if _re_try.search(lines[line_index - 1]						) is None:
               UpperCamelCase__				=    None

          if backend is not None:
               line_index += 1

               # Scroll until we hit the else block of try-except-else
               while _re_else.search(lines[line_index]						) is None:
                    line_index += 1

               line_index += 1

               UpperCamelCase__				=    []
               # Until we unindent, add backend objects to the list
               while len(lines[line_index]						) <= 1 or lines[line_index].startswith(""" """ * 4						):
                    UpperCamelCase__				=    lines[line_index]
                    if _re_import_struct_add_one.search(__a						) is not None:
                         objects.append(_re_import_struct_add_one.search(__a						).groups()[0]						)
                    elif _re_import_struct_add_many.search(__a						) is not None:
                         UpperCamelCase__				=    _re_import_struct_add_many.search(__a						).groups()[0].split(""", """						)
                         UpperCamelCase__				=    [obj[1:-1] for obj in imports if len(__a						) > 0]
                         objects.extend(__a						)
                    elif _re_between_brackets.search(__a						) is not None:
                         UpperCamelCase__				=    _re_between_brackets.search(__a						).groups()[0].split(""", """						)
                         UpperCamelCase__				=    [obj[1:-1] for obj in imports if len(__a						) > 0]
                         objects.extend(__a						)
                    elif _re_quote_object.search(__a						) is not None:
                         objects.append(_re_quote_object.search(__a						).groups()[0]						)
                    elif line.startswith(""" """ * 8 + """\""""						):
                         objects.append(line[9:-3]						)
                    elif line.startswith(""" """ * 12 + """\""""						):
                         objects.append(line[13:-3]						)
                    line_index += 1

               UpperCamelCase__				=    objects
          else:
               line_index += 1

    # At this stage we are in the TYPE_CHECKING part, first grab the objects without a specific backend
     UpperCamelCase__				=    []
     while (
         line_index < len(__a						)
         and find_backend(lines[line_index]						) is None
         and not lines[line_index].startswith("""else"""						)
     ):
          UpperCamelCase__				=    lines[line_index]
          UpperCamelCase__				=    _re_import.search(__a						)
          if single_line_import_search is not None:
               objects.extend(single_line_import_search.groups()[0].split(""", """						)						)
          elif line.startswith(""" """ * 8						):
               objects.append(line[8:-2]						)
          line_index += 1

     UpperCamelCase__				=    {"""none""": objects}
     # Let's continue with backend-specific objects
     while line_index < len(__a						):
          # If the line is an if is_backend_available, we grab all objects associated.
          UpperCamelCase__				=    find_backend(lines[line_index]						)
          # Check if the backend declaration is inside a try block:
          if _re_try.search(lines[line_index - 1]						) is None:
               UpperCamelCase__				=    None

          if backend is not None:
               line_index += 1

               # Scroll until we hit the else block of try-except-else
               while _re_else.search(lines[line_index]						) is None:
                    line_index += 1

               line_index += 1

               UpperCamelCase__				=    []
               # Until we unindent, add backend objects to the list
               while len(lines[line_index]						) <= 1 or lines[line_index].startswith(""" """ * 8						):
                    UpperCamelCase__				=    lines[line_index]
                    UpperCamelCase__				=    _re_import.search(__a						)
                    if single_line_import_search is not None:
                         objects.extend(single_line_import_search.groups()[0].split(""", """						)						)
                    elif line.startswith(""" """ * 12						):
                         objects.append(line[12:-2]						)
                    line_index += 1

               UpperCamelCase__				=    objects
          else:
               line_index += 1

     return import_dict_objects, type_hint_objects






def 						__magic_name__			(   __a      :	Dict	,		__a      :	Dict						):



     '''simple docstring'''




     def find_duplicates(__a      :	Optional[Any]						):
          return [k for k, v in collections.Counter(__a						).items() if v > 1]

     if list(import_dict_objects.keys()						) != list(type_hint_objects.keys()						):
          return ["Both sides of the init do not have the same backends!"]

     UpperCamelCase__				=    []
     for key in import_dict_objects.keys():
          UpperCamelCase__				=    find_duplicates(import_dict_objects[key]						)
          if duplicate_imports:
               errors.append(f"Duplicate _import_structure definitions for: {duplicate_imports}"						)
          UpperCamelCase__				=    find_duplicates(type_hint_objects[key]						)
          if duplicate_type_hints:
               errors.append(f"Duplicate TYPE_CHECKING objects for: {duplicate_type_hints}"						)

          if sorted(set(import_dict_objects[key]						)						) != sorted(set(type_hint_objects[key]						)						):
               UpperCamelCase__				=    """base imports""" if key == """none""" else f"{key} backend"
               errors.append(f"Differences for {name}:"						)
               for a in type_hint_objects[key]:
                    if a not in import_dict_objects[key]:
                         errors.append(f"  {a} in TYPE_HINT but not in _import_structure."						)
               for a in import_dict_objects[key]:
                    if a not in type_hint_objects[key]:
                         errors.append(f"  {a} in _import_structure but not in TYPE_HINT."						)
     return errors






def 						__magic_name__			(   ):



     '''simple docstring'''




     UpperCamelCase__				=    []
     for root, _, files in os.walk(__a						):
          if "__init__.py" in files:
               UpperCamelCase__				=    os.path.join(__a	,		"""__init__.py"""						)
               UpperCamelCase__				=    parse_init(__a						)
               if objects is not None:
                    UpperCamelCase__				=    analyze_results(*__a						)
                    if len(__a						) > 0:
                         UpperCamelCase__				=    f"Problem in {fname}, both halves do not define the same objects.\n{errors[0]}"
                         failures.append("""\n""".join(__a						)						)
     if len(__a						) > 0:
          raise ValueError("""\n\n""".join(__a						)						)






def 						__magic_name__			(   ):



     '''simple docstring'''




     UpperCamelCase__				=    []
     for path, directories, files in os.walk(__a						):
          for folder in directories:
               # Ignore private modules
               if folder.startswith("""_"""						):
                    directories.remove(__a						)
                    continue
               # Ignore leftovers from branches (empty folders apart from pycache)
               if len(list((Path(__a						) / folder).glob("""*.py"""						)						)						) == 0:
                    continue
               UpperCamelCase__				=    str((Path(__a						) / folder).relative_to(__a						)						)
               UpperCamelCase__				=    short_path.replace(os.path.sep	,		"""."""						)
               submodules.append(__a						)
          for fname in files:
               if fname == "__init__.py":
                    continue
               UpperCamelCase__				=    str((Path(__a						) / fname).relative_to(__a						)						)
               UpperCamelCase__				=    short_path.replace(""".py"""	,		""""""						).replace(os.path.sep	,		"""."""						)
               if len(submodule.split("""."""						)						) == 1:
                    submodules.append(__a						)
     return submodules


lowerCamelCase_						     =					[
    '''convert_pytorch_checkpoint_to_tf2''',
    '''modeling_flax_pytorch_utils''',
    '''models.esm.openfold_utils''',
]






def 						__magic_name__			(   ):



     '''simple docstring'''




     from transformers.utils import direct_transformers_import

     UpperCamelCase__				=    direct_transformers_import(__a						)

     UpperCamelCase__				=    set(transformers._import_structure.keys()						)
     # This contains all the base keys of the _import_structure object defined in the init, but if the user is missing
     # some optional dependencies, they may not have all of them. Thus we read the init to read all additions and
     # (potentiall re-) add them.
     with open(os.path.join(__a	,		"""__init__.py"""						)	,		"""r"""						) as f:
          UpperCamelCase__				=    f.read()
     import_structure_keys.update(set(re.findall(R"""import_structure\[\"([^\"]*)\"\]"""	,		__a						)						)						)

     UpperCamelCase__				=    [
         module
         for module in get_transformers_submodules()
         if module not in IGNORE_SUBMODULES and module not in import_structure_keys
     ]

     if len(__a						) > 0:
          UpperCamelCase__				=    """\n""".join(f"- {module}" for module in module_not_registered						)
          raise ValueError(
              """The following submodules are not properly registed in the main init of Transformers:\n"""
              f"{list_of_modules}\n"
              """Make sure they appear somewhere in the keys of `_import_structure` with an empty list as value."""						)


if __name__ == "__main__":
    check_all_inits()
    check_submodules()





