





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


snake_case_							=			logging.getLogger(__name__)
class        snake_case_		(			_A):
  lowerCamelCase    :int						=     "summarization"
  lowerCamelCase    :Optional[Any]						=     ["loss"]
  lowerCamelCase    :int						=     ROUGE_KEYS
  lowerCamelCase    :Optional[Any]						=     "rouge2"







  def __init__(		self			,     __lowercase			,     **__lowercase   )				->   Union[str, Any]:
    if hparams.sortish_sampler and hparams.gpus > 1:
      lowerCamelCase   : Any								=False
    elif hparams.max_tokens_per_batch is not None:
      if hparams.gpus > 1:
        raise NotImplementedError('''Dynamic Batch size does not work for multi-gpu training'''   )
      if hparams.sortish_sampler:
        raise ValueError('''--sortish_sampler and --max_tokens_per_batch may not be used simultaneously'''   )

    super().__init__(__lowercase			,     num_labels=__lowercase			,     mode=self.mode			,     **__lowercase   )
    use_task_specific_params(self.model			,     '''summarization'''   )
    save_git_info(self.hparams.output_dir   )
    lowerCamelCase   : int								=Path(self.output_dir   ) / '''metrics.json'''
    lowerCamelCase   : List[Any]								=Path(self.output_dir   ) / '''hparams.pkl'''
    pickle_save(self.hparams			,     self.hparams_save_path   )
    lowerCamelCase   : str								=0
    lowerCamelCase   : List[Any]								=defaultdict(__lowercase   )
    lowerCamelCase   : str								=self.config.model_type
    lowerCamelCase   : List[Any]								=self.config.tgt_vocab_size if self.model_type == '''fsmt''' else self.config.vocab_size

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

    lowerCamelCase   : 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()   )

    lowerCamelCase   : str								=get_git_info()['''repo_sha''']
    lowerCamelCase   : Tuple								=hparams.num_workers
    lowerCamelCase   : Optional[Any]								=None  # default to config
    if self.model.config.decoder_start_token_id is None and isinstance(self.tokenizer			,     __lowercase   ):
      lowerCamelCase   : Any								=self.tokenizer.lang_code_to_id[hparams.tgt_lang]
      lowerCamelCase   : Tuple								=self.decoder_start_token_id
    lowerCamelCase   : Any								=(
        SeqaSeqDataset if hasattr(self.tokenizer			,     '''prepare_seq2seq_batch'''   ) else LegacySeqaSeqDataset
    )
    lowerCamelCase   : Optional[Any]								=False
    lowerCamelCase   : Any								=self.model.config.num_beams if self.hparams.eval_beams is None else self.hparams.eval_beams
    if self.hparams.eval_max_gen_length is not None:
      lowerCamelCase   : Optional[int]								=self.hparams.eval_max_gen_length
    else:
      lowerCamelCase   : Union[str, Any]								=self.model.config.max_length
    lowerCamelCase   : str								=self.default_val_metric if self.hparams.val_metric is None else self.hparams.val_metric







  def 			__lowercase   (		self			,     __lowercase   )				->   Dict[str, List[str]]:
    lowerCamelCase   : Optional[Any]								={
        k: self.tokenizer.batch_decode(v.tolist()   ) if '''mask''' not in k else v.shape for k, v in batch.items()
    }
    save_json(__lowercase			,     Path(self.output_dir   ) / '''text_batch.json'''   )
    save_json({k: v.tolist() for k, v in batch.items()}			,     Path(self.output_dir   ) / '''tok_batch.json'''   )

    lowerCamelCase   : Tuple								=True
    return readable_batch







  def 			__lowercase   (		self			,     __lowercase			,     **__lowercase   )				->   Optional[int]:
    return self.model(__lowercase			,     **__lowercase   )







  def 			__lowercase   (		self			,     __lowercase   )				->   Optional[Any]:
    lowerCamelCase   : Optional[Any]								=self.tokenizer.batch_decode(
        __lowercase			,     skip_special_tokens=__lowercase			,     clean_up_tokenization_spaces=__lowercase   )
    return lmap(str.strip			,     __lowercase   )







  def 			__lowercase   (		self			,     __lowercase   )				->   Tuple:
    lowerCamelCase   : Union[str, Any]								=self.tokenizer.pad_token_id
    lowerCamelCase   : Dict								=batch['''input_ids'''], batch['''attention_mask''']
    lowerCamelCase   : Tuple								=batch['''labels''']
    if isinstance(self.model			,     __lowercase   ):
      lowerCamelCase   : List[Any]								=self.model._shift_right(__lowercase   )
    else:
      lowerCamelCase   : Union[str, Any]								=shift_tokens_right(__lowercase			,     __lowercase   )
    if not self.already_saved_batch:  # This would be slightly better if it only happened on rank zero
      lowerCamelCase   : Optional[int]								=decoder_input_ids
      self.save_readable_batch(__lowercase   )

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

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







  @property
  def 			__lowercase   (		self   )				->   int:
    return self.tokenizer.pad_token_id







  def 			__lowercase   (		self			,     __lowercase			,     __lowercase   )				->   Dict:
    lowerCamelCase   : Optional[Any]								=self._step(__lowercase   )

    lowerCamelCase   : List[Any]								=dict(zip(self.loss_names			,     __lowercase   )   )
    # tokens per batch
    lowerCamelCase   : Dict								=batch['''input_ids'''].ne(self.pad   ).sum() + batch['''labels'''].ne(self.pad   ).sum()
    lowerCamelCase   : List[Any]								=batch['''input_ids'''].shape[0]
    lowerCamelCase   : str								=batch['''input_ids'''].eq(self.pad   ).sum()
    lowerCamelCase   : Optional[int]								=batch['''input_ids'''].eq(self.pad   ).float().mean()
    # TODO(SS): make a wandb summary metric for this
    return {"loss": loss_tensors[0], "log": logs}







  def 			__lowercase   (		self			,     __lowercase			,     __lowercase   )				->   Dict:
    return self._generative_step(__lowercase   )







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







  def 			__lowercase   (		self			,     __lowercase   )				->   dict:
    lowerCamelCase   : List[str]								=time.time()

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







  def 			__lowercase   (		self			,     __lowercase			,     __lowercase   )				->   Union[str, Any]:
    return self._generative_step(__lowercase   )







  def 			__lowercase   (		self			,     __lowercase   )				->   Optional[Any]:
    return self.validation_epoch_end(__lowercase			,     prefix='''test'''   )







  def 			__lowercase   (		self			,     __lowercase   )				->   SeqaSeqDataset:
    lowerCamelCase   : int								=self.n_obs[type_path]
    lowerCamelCase   : Any								=self.target_lens[type_path]
    lowerCamelCase   : Dict								=self.dataset_class(
        self.tokenizer			,     type_path=__lowercase			,     n_obs=__lowercase			,     max_target_length=__lowercase			,     **self.dataset_kwargs			,     )
    return dataset







  def 			__lowercase   (		self			,     __lowercase			,     __lowercase			,     __lowercase = False   )				->   DataLoader:
    lowerCamelCase   : Any								=self.get_dataset(__lowercase   )

    if self.hparams.sortish_sampler and type_path != "test" and type_path != "val":
      lowerCamelCase   : List[Any]								=dataset.make_sortish_sampler(__lowercase			,     distributed=self.hparams.gpus > 1   )
      return DataLoader(
          __lowercase			,     batch_size=__lowercase			,     collate_fn=dataset.collate_fn			,     shuffle=__lowercase			,     num_workers=self.num_workers			,     sampler=__lowercase			,     )

    elif self.hparams.max_tokens_per_batch is not None and type_path != "test" and type_path != "val":
      lowerCamelCase   : Optional[int]								=dataset.make_dynamic_sampler(
          self.hparams.max_tokens_per_batch			,     distributed=self.hparams.gpus > 1   )
      return DataLoader(
          __lowercase			,     batch_sampler=__lowercase			,     collate_fn=dataset.collate_fn			,     num_workers=self.num_workers			,     )
    else:
      return DataLoader(
          __lowercase			,     batch_size=__lowercase			,     collate_fn=dataset.collate_fn			,     shuffle=__lowercase			,     num_workers=self.num_workers			,     sampler=__lowercase			,     )







  def 			__lowercase   (		self   )				->   DataLoader:
    lowerCamelCase   : Any								=self.get_dataloader('''train'''			,     batch_size=self.hparams.train_batch_size			,     shuffle=__lowercase   )
    return dataloader







  def 			__lowercase   (		self   )				->   DataLoader:
    return self.get_dataloader('''val'''			,     batch_size=self.hparams.eval_batch_size   )







  def 			__lowercase   (		self   )				->   DataLoader:
    return self.get_dataloader('''test'''			,     batch_size=self.hparams.eval_batch_size   )







  @staticmethod
  def 			__lowercase   (		__lowercase			,     __lowercase   )				->   Optional[Any]:
    BaseTransformer.add_model_specific_args(__lowercase			,     __lowercase   )
    add_generic_args(__lowercase			,     __lowercase   )
    parser.add_argument(
        '''--max_source_length'''			,     default=1_0_2_4			,     type=__lowercase			,     help=(
            '''The maximum total input sequence length after tokenization. Sequences longer '''
            '''than this will be truncated, sequences shorter will be padded.'''
        )			,     )
    parser.add_argument(
        '''--max_target_length'''			,     default=5_6			,     type=__lowercase			,     help=(
            '''The maximum total input sequence length after tokenization. Sequences longer '''
            '''than this will be truncated, sequences shorter will be padded.'''
        )			,     )
    parser.add_argument(
        '''--val_max_target_length'''			,     default=1_4_2			,     type=__lowercase			,     help=(
            '''The maximum total input sequence length after tokenization. Sequences longer '''
            '''than this will be truncated, sequences shorter will be padded.'''
        )			,     )
    parser.add_argument(
        '''--test_max_target_length'''			,     default=1_4_2			,     type=__lowercase			,     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=__lowercase   )
    parser.add_argument('''--overwrite_output_dir'''			,     action='''store_true'''			,     default=__lowercase   )
    parser.add_argument('''--max_tokens_per_batch'''			,     type=__lowercase			,     default=__lowercase   )
    parser.add_argument('''--logger_name'''			,     type=__lowercase			,     choices=['''default''', '''wandb''', '''wandb_shared''']			,     default='''default'''   )
    parser.add_argument('''--n_train'''			,     type=__lowercase			,     default=-1			,     required=__lowercase			,     help='''# examples. -1 means use all.'''   )
    parser.add_argument('''--n_val'''			,     type=__lowercase			,     default=5_0_0			,     required=__lowercase			,     help='''# examples. -1 means use all.'''   )
    parser.add_argument('''--n_test'''			,     type=__lowercase			,     default=-1			,     required=__lowercase			,     help='''# examples. -1 means use all.'''   )
    parser.add_argument(
        '''--task'''			,     type=__lowercase			,     default='''summarization'''			,     required=__lowercase			,     help='''# examples. -1 means use all.'''   )
    parser.add_argument('''--label_smoothing'''			,     type=__lowercase			,     default=0.0			,     required=__lowercase   )
    parser.add_argument('''--src_lang'''			,     type=__lowercase			,     default=''''''			,     required=__lowercase   )
    parser.add_argument('''--tgt_lang'''			,     type=__lowercase			,     default=''''''			,     required=__lowercase   )
    parser.add_argument('''--eval_beams'''			,     type=__lowercase			,     default=__lowercase			,     required=__lowercase   )
    parser.add_argument(
        '''--val_metric'''			,     type=__lowercase			,     default=__lowercase			,     required=__lowercase			,     choices=['''bleu''', '''rouge2''', '''loss''', None]   )
    parser.add_argument('''--eval_max_gen_length'''			,     type=__lowercase			,     default=__lowercase			,     help='''never generate more than n tokens'''   )
    parser.add_argument('''--save_top_k'''			,     type=__lowercase			,     default=1			,     required=__lowercase			,     help='''How many checkpoints to save'''   )
    parser.add_argument(
        '''--early_stopping_patience'''			,     type=__lowercase			,     default=-1			,     required=__lowercase			,     help=(
            '''-1 means never early stop. early_stopping_patience is measured in validation checks, not epochs. So'''
            ''' val_check_interval will effect it.'''
        )			,     )
    return parser

class        snake_case_		(			_A):
  lowerCamelCase    :Any						=     "translation"
  lowerCamelCase    :List[str]						=     ["loss"]
  lowerCamelCase    :Dict						=     ["bleu"]
  lowerCamelCase    :Optional[Any]						=     "bleu"







  def __init__(		self			,     __lowercase			,     **__lowercase   )				->   List[str]:
    super().__init__(__lowercase			,     **__lowercase   )
    lowerCamelCase   : List[Any]								=hparams.src_lang
    lowerCamelCase   : str								=hparams.tgt_lang







  def 			__lowercase   (		self			,     __lowercase			,     __lowercase   )				->   dict:
    return calculate_bleu(__lowercase			,     __lowercase   )


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

  if model is None:
    if "summarization" in args.task:
      lowerCamelCase   : SummarizationModule								=SummarizationModule(SCREAMING_SNAKE_CASE_		)
    else:
      lowerCamelCase   : SummarizationModule								=TranslationModule(SCREAMING_SNAKE_CASE_		)
  lowerCamelCase   : Union[str, Any]								=Path(args.data_dir		).name
  if (
      args.logger_name == "default"
      or args.fast_dev_run
      or str(args.output_dir		).startswith('''/tmp'''		)
      or str(args.output_dir		).startswith('''/var'''		)
  ):
    lowerCamelCase   : List[Any]								=True  # don't pollute wandb logs unnecessarily
  elif args.logger_name == "wandb":
    from pytorch_lightning.loggers import WandbLogger

    lowerCamelCase   : Any								=os.environ.get('''WANDB_PROJECT'''							,       SCREAMING_SNAKE_CASE_		)
    lowerCamelCase   : List[str]								=WandbLogger(name=model.output_dir.name							,       project=SCREAMING_SNAKE_CASE_		)

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

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

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

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

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

    snake_case_							=			parser.parse_args()

    main(args)








import collections
import json
import math
import os
import re
import time
from fnmatch import fnmatch
from typing import Dict

import requests
from slack_sdk import WebClient


snake_case_							=			WebClient(token=os.environ['''CI_SLACK_BOT_TOKEN'''])


def 					A__	(       SCREAMING_SNAKE_CASE_		)   ->     int:
		lowerCamelCase   : Tuple								=test_results.split(''' '''		)

		lowerCamelCase   : List[str]								=0
		lowerCamelCase   : Optional[Any]								=0

		# When the output is short enough, the output is surrounded by = signs: "== OUTPUT =="
		# When it is too long, those signs are not present.
		lowerCamelCase   : Optional[int]								=expressions[-2] if '''=''' in expressions[-1] else expressions[-1]

		for i, expression in enumerate(SCREAMING_SNAKE_CASE_		):
				if "failed" in expression:
						failed += int(expressions[i - 1]		)
				if "passed" in expression:
						success += int(expressions[i - 1]		)

		return failed, success, time_spent


def 					A__	(       SCREAMING_SNAKE_CASE_		)   ->     int:
		lowerCamelCase   : Union[str, Any]								={}
		lowerCamelCase   : Tuple								=None
		lowerCamelCase   : List[str]								=False
		for line in failures_short_lines.split('''\n'''		):
				if re.search(R'''_ \[doctest\]'''							,       SCREAMING_SNAKE_CASE_		):
						lowerCamelCase   : Optional[int]								=True
						lowerCamelCase   : Any								=line.split(''' '''		)[2]
				elif in_error and not line.split(''' '''		)[0].isdigit():
						lowerCamelCase   : Tuple								=line
						lowerCamelCase   : Optional[Any]								=False

		return failures
class        snake_case_		:







		def __init__(		self			,     __lowercase			,     __lowercase   )				->   Any:
				lowerCamelCase   : Optional[int]								=title

				lowerCamelCase   : Optional[int]								=doc_test_results['''time_spent'''].split(''','''   )[0]
				lowerCamelCase   : List[str]								=doc_test_results['''success''']
				lowerCamelCase   : Tuple								=doc_test_results['''failures''']
				lowerCamelCase   : List[Any]								=self.n_success + self.n_failures

				# Failures and success of the modeling tests
				lowerCamelCase   : Union[str, Any]								=doc_test_results







		@property
		def 			__lowercase   (		self   )				->   str:
				lowerCamelCase   : List[str]								=[self._time_spent]
				lowerCamelCase   : List[str]								=0

				for time in time_spent:
						lowerCamelCase   : List[str]								=time.split(''':'''   )

						# Time can be formatted as xx:xx:xx, as .xx, or as x.xx if the time spent was less than a minute.
						if len(__lowercase   ) == 1:
								lowerCamelCase   : Any								=[0, 0, time_parts[0]]

						lowerCamelCase		,   lowerCamelCase		,   lowerCamelCase   : Optional[int]								=int(time_parts[0]   ), int(time_parts[1]   ), float(time_parts[2]   )
						total_secs += hours * 3_6_0_0 + minutes * 6_0 + seconds

				lowerCamelCase		,   lowerCamelCase		,   lowerCamelCase   : List[str]								=total_secs // 3_6_0_0, (total_secs % 3_6_0_0) // 6_0, total_secs % 6_0
				return F"{int(__lowercase   )}h{int(__lowercase   )}m{int(__lowercase   )}s"







		@property
		def 			__lowercase   (		self   )				->   Dict:
				return {"type": "header", "text": {"type": "plain_text", "text": self.title}}







		@property
		def 			__lowercase   (		self   )				->   Dict:
				return {
				    "type": "section",
				    "text": {
				        "type": "plain_text",
				        "text": F"🌞 There were no failures: all {self.n_tests} tests passed. The suite ran in {self.time}.",
				        "emoji": True,
				    },
				    "accessory": {
				        "type": "button",
				        "text": {"type": "plain_text", "text": "Check Action results", "emoji": True},
				        "url": F"https://github.com/huggingface/transformers/actions/runs/{os.environ['GITHUB_RUN_ID']}",
				    },
				}







		@property
		def 			__lowercase   (		self   )				->   Dict:
				return {
				    "type": "section",
				    "text": {
				        "type": "plain_text",
				        "text": (
				            F"There were {self.n_failures} failures, out of {self.n_tests} tests.\nThe suite ran in"
				            F" {self.time}."
				        ),
				        "emoji": True,
				    },
				    "accessory": {
				        "type": "button",
				        "text": {"type": "plain_text", "text": "Check Action results", "emoji": True},
				        "url": F"https://github.com/huggingface/transformers/actions/runs/{os.environ['GITHUB_RUN_ID']}",
				    },
				}







		@property
		def 			__lowercase   (		self   )				->   Dict:
				lowerCamelCase   : int								=4_0
				lowerCamelCase   : str								={k: v['''failed'''] for k, v in doc_test_results.items() if isinstance(__lowercase			,     __lowercase   )}

				lowerCamelCase   : List[str]								=''''''
				for category, failures in category_failures.items():
						if len(__lowercase   ) == 0:
								continue

						if report != "":
								report += "\n\n"

						report += F"*{category} failures*:".ljust(line_length // 2   ).rjust(line_length // 2   ) + "\n"
						report += "`"
						report += "`\n`".join(__lowercase   )
						report += "`"

				return {
				    "type": "section",
				    "text": {
				        "type": "mrkdwn",
				        "text": F"The following examples had failures:\n\n\n{report}\n",
				    },
				}







		@property
		def 			__lowercase   (		self   )				->   str:
				lowerCamelCase   : Dict								=[self.header]

				if self.n_failures > 0:
						blocks.append(self.failures   )

				if self.n_failures > 0:
						blocks.extend([self.category_failures]   )

				if self.n_failures == 0:
						blocks.append(self.no_failures   )

				return json.dumps(__lowercase   )







		@staticmethod
		def 			__lowercase   (		)				->   Optional[Any]:
				lowerCamelCase   : Tuple								=[
				    {
				        '''type''': '''section''',
				        '''text''': {
				            '''type''': '''plain_text''',
				            '''text''': '''There was an issue running the tests.''',
				        },
				        '''accessory''': {
				            '''type''': '''button''',
				            '''text''': {'''type''': '''plain_text''', '''text''': '''Check Action results''', '''emoji''': True},
				            '''url''': F"https://github.com/huggingface/transformers/actions/runs/{os.environ['GITHUB_RUN_ID']}",
				        },
				    }
				]

				print('''Sending the following payload'''   )
				print(json.dumps({'''blocks''': json.loads(__lowercase   )}   )   )

				client.chat_postMessage(
				    channel=os.environ['''CI_SLACK_CHANNEL_ID_DAILY''']			,     text='''There was an issue running the tests.'''			,     blocks=__lowercase			,     )







		def 			__lowercase   (		self   )				->   Optional[int]:
				print('''Sending the following payload'''   )
				print(json.dumps({'''blocks''': json.loads(self.payload   )}   )   )

				lowerCamelCase   : int								=F"{self.n_failures} failures out of {self.n_tests} tests," if self.n_failures else '''All tests passed.'''

				lowerCamelCase   : Any								=client.chat_postMessage(
				    channel=os.environ['''CI_SLACK_CHANNEL_ID_DAILY''']			,     blocks=self.payload			,     text=__lowercase			,     )







		def 			__lowercase   (		self			,     __lowercase			,     __lowercase			,     __lowercase			,     __lowercase   )				->   Optional[int]:
				lowerCamelCase   : Union[str, Any]								=''''''
				for key, value in failures.items():
						lowerCamelCase   : Any								=value[:2_0_0] + ''' [Truncated]''' if len(__lowercase   ) > 2_5_0 else value
						failures_text += F"*{key}*\n_{value}_\n\n"

				lowerCamelCase   : Union[str, Any]								=job_name
				lowerCamelCase   : List[str]								={'''type''': '''section''', '''text''': {'''type''': '''mrkdwn''', '''text''': text}}

				if job_link is not None:
						lowerCamelCase   : Optional[int]								={
						    '''type''': '''button''',
						    '''text''': {'''type''': '''plain_text''', '''text''': '''GitHub Action job''', '''emoji''': True},
						    '''url''': job_link,
						}

				return [
				    {"type": "header", "text": {"type": "plain_text", "text": title.upper(), "emoji": True}},
				    content,
				    {"type": "section", "text": {"type": "mrkdwn", "text": failures_text}},
				]







		def 			__lowercase   (		self   )				->   Tuple:
				if self.thread_ts is None:
						raise ValueError('''Can only post reply if a post has been made.'''   )

				lowerCamelCase   : str								=self.doc_test_results.pop('''job_link'''   )
				self.doc_test_results.pop('''failures'''   )
				self.doc_test_results.pop('''success'''   )
				self.doc_test_results.pop('''time_spent'''   )

				lowerCamelCase   : Union[str, Any]								=sorted(self.doc_test_results.items()			,     key=lambda __lowercase   : t[0]   )
				for job, job_result in sorted_dict:
						if len(job_result['''failures''']   ):
								lowerCamelCase   : int								=F"*Num failures* :{len(job_result['failed']   )} \n"
								lowerCamelCase   : Optional[Any]								=job_result['''failures''']
								lowerCamelCase   : List[str]								=self.get_reply_blocks(__lowercase			,     __lowercase			,     __lowercase			,     text=__lowercase   )

								print('''Sending the following reply'''   )
								print(json.dumps({'''blocks''': blocks}   )   )

								client.chat_postMessage(
								    channel=os.environ['''CI_SLACK_CHANNEL_ID_DAILY''']			,     text=F"Results for {job}"			,     blocks=__lowercase			,     thread_ts=self.thread_ts['''ts''']			,     )

								time.sleep(1   )


def 					A__	(       )   ->     Union[str, Any]:
		lowerCamelCase   : Optional[Any]								=os.environ['''GITHUB_RUN_ID''']
		lowerCamelCase   : int								=F"https://api.github.com/repos/huggingface/transformers/actions/runs/{run_id}/jobs?per_page=100"
		lowerCamelCase   : Tuple								=requests.get(SCREAMING_SNAKE_CASE_		).json()
		lowerCamelCase   : Union[str, Any]								={}

		try:
				jobs.update({job['''name''']: job['''html_url'''] for job in result['''jobs''']}		)
				lowerCamelCase   : Any								=math.ceil((result['''total_count'''] - 1_0_0) / 1_0_0		)

				for i in range(SCREAMING_SNAKE_CASE_		):
						lowerCamelCase   : Optional[Any]								=requests.get(url + F"&page={i + 2}"		).json()
						jobs.update({job['''name''']: job['''html_url'''] for job in result['''jobs''']}		)

				return jobs
		except Exception as e:
				print('''Unknown error, could not fetch links.'''							,       SCREAMING_SNAKE_CASE_		)

		return {}


def 					A__	(       SCREAMING_SNAKE_CASE_		)   ->     Union[str, Any]:
		lowerCamelCase   : List[str]								={}

		if os.path.exists(SCREAMING_SNAKE_CASE_		):
				lowerCamelCase   : Optional[Any]								=os.listdir(SCREAMING_SNAKE_CASE_		)
				for file in files:
						try:
								with open(os.path.join(SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_		)							,       encoding='''utf-8'''		) as f:
										lowerCamelCase   : List[str]								=f.read()
						except UnicodeDecodeError as e:
								raise ValueError(F"Could not open {os.path.join(SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_		)}."		) from e

		return _artifact


def 					A__	(       )   ->     Any:
		class        snake_case_		:







				def __init__(		self			,     __lowercase   )				->   Optional[Any]:
						lowerCamelCase   : Optional[Any]								=name
						lowerCamelCase   : List[str]								=[]







				def __str__(		self   )				->   Any:
						return self.name







				def 			__lowercase   (		self			,     __lowercase   )				->   Dict:
						self.paths.append({'''name''': self.name, '''path''': path}   )

		lowerCamelCase   : Dict[str, Artifact]								={}

		lowerCamelCase   : List[Any]								=filter(os.path.isdir							,       os.listdir()		)
		for directory in directories:
				lowerCamelCase   : Tuple								=directory
				if artifact_name not in _available_artifacts:
						lowerCamelCase   : Any								=Artifact(SCREAMING_SNAKE_CASE_		)

						_available_artifacts[artifact_name].add_path(SCREAMING_SNAKE_CASE_		)

		return _available_artifacts


if __name__ == "__main__":
				snake_case_							=			get_job_links()
				snake_case_							=			retrieve_available_artifacts()

				snake_case_							=			collections.OrderedDict(
				    [
				        ('''*.py''', '''API Examples'''),
				        ('''*.md''', '''MD Examples'''),
				    ]
				)

				# This dict will contain all the information relative to each doc test category:
				# - failed: list of failed tests
				# - failures: dict in the format 'test': 'error_message'
				snake_case_							=			{
				    v: {
				        '''failed''': [],
				        '''failures''': {},
				    }
				    for v in docs.values()
				}

				# Link to the GitHub Action job
				snake_case_							=			github_actions_job_links.get('''run_doctests''')

				snake_case_							=			available_artifacts['''doc_tests_gpu_test_reports'''].paths[0]
				snake_case_							=			retrieve_artifact(artifact_path['''name'''])
				if "stats" in artifact:
								snake_case_,       snake_case_,       snake_case_							=			handle_test_results(artifact['''stats'''])
								snake_case_							=			failed
								snake_case_							=			success
								snake_case_							=			time_spent[1:-1] + ''', '''

								snake_case_							=			extract_first_line_failure(artifact['''failures_short'''])
								for line in artifact["summary_short"].split('''\n'''):
												if re.search('''FAILED''', line):
																snake_case_							=			line.replace('''FAILED ''', '''''')
																snake_case_							=			line.split()[0].replace('''\n''', '''''')

																if "::" in line:
																				snake_case_,       snake_case_							=			line.split('''::''')
																else:
																				snake_case_,       snake_case_							=			line, line

																for file_regex in docs.keys():
																				if fnmatch(file_path, file_regex):
																								snake_case_							=			docs[file_regex]
																								doc_test_results[category]["failed"].append(test)

																								snake_case_							=			all_failures[test] if test in all_failures else '''N/A'''
																								snake_case_							=			failure
																								break

				snake_case_							=			Message('''🤗 Results of the doc tests.''', doc_test_results)
				message.post()
				message.post_reply()

