

"""simple docstring"""




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

import torch
from torch import nn
from torch.utils.data import DistributedSampler, RandomSampler

from transformers import PreTrainedModel, Trainer, logging
from transformers.integrations import is_fairscale_available
from transformers.models.fsmt.configuration_fsmt import FSMTConfig
from transformers.optimization import (
    Adafactor,
    AdamW,
    get_constant_schedule,
    get_constant_schedule_with_warmup,
    get_cosine_schedule_with_warmup,
    get_cosine_with_hard_restarts_schedule_with_warmup,
    get_linear_schedule_with_warmup,
    get_polynomial_decay_schedule_with_warmup,
)
from transformers.trainer_pt_utils import get_tpu_sampler
from transformers.training_args import ParallelMode
from transformers.utils import is_torch_tpu_available


if is_fairscale_available():
      from fairscale.optim import OSS


__UpperCAmelCase =				logging.get_logger(__name__)

__UpperCAmelCase =				{
    'linear': get_linear_schedule_with_warmup,
    'cosine': get_cosine_schedule_with_warmup,
    'cosine_w_restarts': get_cosine_with_hard_restarts_schedule_with_warmup,
    'polynomial': get_polynomial_decay_schedule_with_warmup,
    'constant': get_constant_schedule,
    'constant_w_warmup': get_constant_schedule_with_warmup,
}







class      __lowercase			(    _UpperCAmelCase					):

  def __init__(  self		:			Tuple					,A		:			Dict=None					,A		:			Dict=None					,*A		:			Dict					,**A		:			int    ):






   '''simple docstring'''
   super().__init__(*lowercase__					,**lowercase__    )

   if config is None:
    assert isinstance(self.model					,lowercase__    ), (
        "If no `config` is passed the model to be trained has to be of type `PreTrainedModel`, but is"
        f" {self.model.__class__}"
    )
    UpperCAmelCase__					:  str					   =					self.model.config
   else:
    UpperCAmelCase__					:  Union[str, Any]					   =					config

   UpperCAmelCase__					:  Any					   =					data_args
   UpperCAmelCase__					:  Optional[Any]					   =					self.config.tgt_vocab_size if isinstance(self.config					,lowercase__    ) else self.config.vocab_size

   if self.args.label_smoothing != 0 or (self.data_args is not None and self.data_args.ignore_pad_token_for_loss):
    assert self.config.pad_token_id is not None, (
        "Make sure that `config.pad_token_id` is correcly defined when ignoring `pad_token` for loss"
        " calculation or doing label smoothing."
    )

   if self.config.pad_token_id is None and self.config.eos_token_id is not None:
    logger.warning(
        f"The `config.pad_token_id` is `None`. Using `config.eos_token_id` = {self.config.eos_token_id} for"
        """ padding.."""    )

   if self.args.label_smoothing == 0:
    UpperCAmelCase__					:  Tuple					   =					torch.nn.CrossEntropyLoss(ignore_index=self.config.pad_token_id    )
   else:
    # dynamically import label_smoothed_nll_loss
    from utils import label_smoothed_nll_loss

    UpperCAmelCase__					:  List[Any]					   =					label_smoothed_nll_loss

  def    __lowercase       (  self		:			Tuple					,A		:			Tuple    ):






   '''simple docstring'''






   if self.optimizer is None:
    UpperCAmelCase__					:  Optional[Any]					   =					["bias", "LayerNorm.weight"]
    UpperCAmelCase__					:  Dict					   =					[
        {
            "params": [p for n, p in self.model.named_parameters() if not any(nd in n for nd in no_decay    )],
            "weight_decay": self.args.weight_decay,
        },
        {
            "params": [p for n, p in self.model.named_parameters() if any(nd in n for nd in no_decay    )],
            "weight_decay": 0.0,
        },
    ]
    UpperCAmelCase__					:  List[str]					   =					Adafactor if self.args.adafactor else AdamW
    if self.args.adafactor:
     UpperCAmelCase__					:  Optional[int]					   =					Adafactor
     UpperCAmelCase__					:  List[Any]					   =					{"scale_parameter": False, "relative_step": False}
    else:
     UpperCAmelCase__					:  int					   =					AdamW
     UpperCAmelCase__					:  Dict					   =					{
         "betas": (self.args.adam_betaa, self.args.adam_betaa),
         "eps": self.args.adam_epsilon,
     }
    UpperCAmelCase__					:  Optional[Any]					   =					self.args.learning_rate
    if self.sharded_ddp:
     UpperCAmelCase__					:  List[str]					   =					OSS(
         params=lowercase__					,optim=lowercase__					,**lowercase__					,)
    else:
     UpperCAmelCase__					:  int					   =					optimizer_cls(lowercase__					,**lowercase__    )

   if self.lr_scheduler is None:
    UpperCAmelCase__					:  List[str]					   =					self._get_lr_scheduler(lowercase__    )
   else:  # ignoring --lr_scheduler
    logger.warning("""scheduler is passed to `Seq2SeqTrainer`, `--lr_scheduler` arg is ignored."""    )

  def    __lowercase       (  self		:			List[str]					,A		:			Dict    ):






   '''simple docstring'''
   UpperCAmelCase__					:  Union[str, Any]					   =					arg_to_scheduler[self.args.lr_scheduler]
   if self.args.lr_scheduler == "constant":
    UpperCAmelCase__					:  Tuple					   =					schedule_func(self.optimizer    )
   elif self.args.lr_scheduler == "constant_w_warmup":
    UpperCAmelCase__					:  str					   =					schedule_func(self.optimizer					,num_warmup_steps=self.args.warmup_steps    )
   else:
    UpperCAmelCase__					:  Dict					   =					schedule_func(
        self.optimizer					,num_warmup_steps=self.args.warmup_steps					,num_training_steps=lowercase__    )
   return scheduler

  def    __lowercase       (  self		:			int    ):






   '''simple docstring'''






   if isinstance(self.train_dataset					,torch.utils.data.IterableDataset    ):
    return None
   elif is_torch_tpu_available():
    return get_tpu_sampler(self.train_dataset    )
   else:
    if self.args.sortish_sampler:
     self.train_dataset.make_sortish_sampler(
         self.args.per_device_train_batch_size					,distributed=(self.args.parallel_mode == ParallelMode.DISTRIBUTED)					,)

    return (
        RandomSampler(self.train_dataset    )
        if self.args.local_rank == -1
        else DistributedSampler(self.train_dataset    )
    )

  def    __lowercase       (  self		:			Optional[Any]					,A		:			Dict					,A		:			str					,A		:			Union[str, Any]    ):






   '''simple docstring'''






   if self.args.label_smoothing == 0:
    if self.data_args is not None and self.data_args.ignore_pad_token_for_loss:
     # force training to ignore pad token
     UpperCAmelCase__					:  List[str]					   =					model(**lowercase__					,use_cache=lowercase__    )[0]
     UpperCAmelCase__					:  Union[str, Any]					   =					self.loss_fn(logits.view(-1					,logits.shape[-1]    )					,labels.view(-1    )    )
    else:
     # compute usual loss via models
     UpperCAmelCase__					:  Optional[Any]					   =					model(**lowercase__					,labels=lowercase__					,use_cache=lowercase__    )[:2]
   else:
    # compute label smoothed loss
    UpperCAmelCase__					:  str					   =					model(**lowercase__					,use_cache=lowercase__    )[0]
    UpperCAmelCase__					:  List[str]					   =					torch.nn.functional.log_softmax(lowercase__					,dim=-1    )
    UpperCAmelCase__					:  Optional[int]					   =					self.loss_fn(lowercase__					,lowercase__					,self.args.label_smoothing					,ignore_index=self.config.pad_token_id    )
   return loss, logits

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






   '''simple docstring'''
   UpperCAmelCase__					:  Dict					   =					inputs.pop("""labels"""    )
   UpperCAmelCase__					:  Any					   =					self._compute_loss(lowercase__					,lowercase__					,lowercase__    )
   return loss

  def    __lowercase       (  self		:			Tuple					,A		:			List[str]					,A		:			Union[str, Any]					,A		:			Union[str, Any]					,A		:			str = None					,):






   '''simple docstring'''
   UpperCAmelCase__					:  Tuple					   =					self._prepare_inputs(lowercase__    )

   UpperCAmelCase__					:  int					   =					{
       "max_length": self.data_args.val_max_target_length
       if self.data_args is not None
       else self.config.max_length,
       "num_beams": self.data_args.eval_beams if self.data_args is not None else self.config.num_beams,
   }

   if self.args.predict_with_generate and not self.args.prediction_loss_only:
    UpperCAmelCase__					:  str					   =					self.model.generate(
        inputs["""input_ids"""]					,attention_mask=inputs["""attention_mask"""]					,**lowercase__					,)
    # in case the batch is shorter than max length, the output should be padded
    if generated_tokens.shape[-1] < gen_kwargs["max_length"]:
     UpperCAmelCase__					:  List[str]					   =					self._pad_tensors_to_max_len(lowercase__					,gen_kwargs["""max_length"""]    )

   UpperCAmelCase__					:  Any					   =					inputs.pop("""labels"""    )
   with torch.no_grad():
    # compute loss on predict data
    UpperCAmelCase__					:  str					   =					self._compute_loss(lowercase__					,lowercase__					,lowercase__    )

   UpperCAmelCase__					:  Union[str, Any]					   =					loss.mean().detach()
   if self.args.prediction_loss_only:
    return (loss, None, None)

   UpperCAmelCase__					:  Any					   =					generated_tokens if self.args.predict_with_generate else logits

   if labels.shape[-1] < gen_kwargs["max_length"]:
    UpperCAmelCase__					:  Optional[int]					   =					self._pad_tensors_to_max_len(lowercase__					,gen_kwargs["""max_length"""]    )

   return (loss, logits, labels)




  def    __lowercase       (  self		:			Dict					,A		:			Any					,A		:			int    ):






   '''simple docstring'''
   UpperCAmelCase__					:  Tuple					   =					self.config.pad_token_id if self.config.pad_token_id is not None else self.config.eos_token_id

   if pad_token_id is None:
    raise ValueError(
        """Make sure that either `config.pad_token_id` or `config.eos_token_id` is defined if tensor has to be"""
        f" padded to `max_length`={max_length}"    )

   UpperCAmelCase__					:  Optional[int]					   =					pad_token_id * torch.ones(
       (tensor.shape[0], max_length)					,dtype=tensor.dtype					,device=tensor.device    )
   UpperCAmelCase__					:  List[str]					   =					tensor
   return padded_tensor








"""simple docstring"""




from datetime import datetime as dt
import os

from github import Github


__UpperCAmelCase =				[
    'good first issue',
    'good second issue',
    'good difficult issue',
    'feature request',
    'new model',
    'wip',
]
def   lowerCAmelCase						(					):
 '''simple docstring'''





 UpperCAmelCase__					:  str					   =					Github(os.environ["""GITHUB_TOKEN"""]      )
 UpperCAmelCase__					:  int					   =					g.get_repo("""huggingface/transformers"""      )
 UpperCAmelCase__					:  Optional[int]					   =					repo.get_issues(state="""open"""      )

 for issue in open_issues:
  UpperCAmelCase__					:  Any					   =					sorted([comment for comment in issue.get_comments()]							,    key=lambda __UpperCamelCase      : i.created_at							,    reverse=__UpperCamelCase      )
  UpperCAmelCase__					:  Union[str, Any]					   =					comments[0] if len(__UpperCamelCase      ) > 0 else None
  if (
      last_comment is not None
      and last_comment.user.login == "github-actions[bot]"
      and (dt.utcnow() - issue.updated_at).days > 7
      and (dt.utcnow() - issue.created_at).days >= 30
      and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels()      )
  ):
   # print(f"Would close issue {issue.number} since it has been 7 days of inactivity since bot mention.")
   issue.edit(state="""closed"""      )
  elif (
      (dt.utcnow() - issue.updated_at).days > 23
      and (dt.utcnow() - issue.created_at).days >= 30
      and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels()      )
  ):
   # print(f"Would add stale comment to {issue.number}")
   issue.create_comment(
       """This issue has been automatically marked as stale because it has not had """
       """recent activity. If you think this still needs to be addressed """
       """please comment on this thread.\n\nPlease note that issues that do not follow the """
       """[contributing guidelines](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md) """
       """are likely to be ignored."""      )


if __name__ == "__main__":
      main()





