



'''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


A_							: int						 =	logging.get_logger(__name__)

A_							: List[Any]						 =	{
    """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       (  _lowerCamelCase				):


 """simple docstring"""


 def __init__(							self  ,a_=None  ,a_=None  ,*a_  ,**a_							)		->   Union[str, Any]:
        super().__init__(*a_  ,**a_							)

        if config is None:
               assert isinstance(self.model  ,a_							), (
                   "If no `config` is passed the model to be trained has to be of type `PreTrainedModel`, but is"
                   f''' {self.model.__class__}'''
               )
               _UpperCAmelCase  :						Dict			     = self.model.config
        else:
               _UpperCAmelCase  :						Tuple			     = config

        _UpperCAmelCase  :						str			     = data_args
        _UpperCAmelCase  :						Dict			     = self.config.tgt_vocab_size if isinstance(self.config  ,a_							) 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  :						str			     = 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  :						str			     = label_smoothed_nll_loss







 def _snake_case (							self  ,a_							)		->   Optional[Any]:
        if self.optimizer is None:
               _UpperCAmelCase  :						Tuple			     = ["""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  :						int			     = Adafactor if self.args.adafactor else AdamW
               if self.args.adafactor:
                      _UpperCAmelCase  :						Optional[Any]			     = Adafactor
                      _UpperCAmelCase  :						Tuple			     = {"""scale_parameter""": False, """relative_step""": False}
               else:
                      _UpperCAmelCase  :						Any			     = AdamW
                      _UpperCAmelCase  :						List[Any]			     = {
                          """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  :						Optional[int]			     = OSS(
                          params=a_  ,optim=a_  ,**a_  ,)
               else:
                      _UpperCAmelCase  :						Optional[Any]			     = optimizer_cls(a_  ,**a_							)

        if self.lr_scheduler is None:
               _UpperCAmelCase  :						Any			     = self._get_lr_scheduler(a_							)
        else:  # ignoring --lr_scheduler
               logger.warning("""scheduler is passed to `Seq2SeqTrainer`, `--lr_scheduler` arg is ignored."""							)







 def _snake_case (							self  ,a_							)		->   List[Any]:
        _UpperCAmelCase  :						Dict			     = arg_to_scheduler[self.args.lr_scheduler]
        if self.args.lr_scheduler == "constant":
               _UpperCAmelCase  :						List[str]			     = schedule_func(self.optimizer							)
        elif self.args.lr_scheduler == "constant_w_warmup":
               _UpperCAmelCase  :						List[Any]			     = schedule_func(self.optimizer  ,num_warmup_steps=self.args.warmup_steps							)
        else:
               _UpperCAmelCase  :						List[str]			     = schedule_func(
                   self.optimizer  ,num_warmup_steps=self.args.warmup_steps  ,num_training_steps=a_							)
        return scheduler







 def _snake_case (							self							)		->   Optional[torch.utils.data.Sampler]:
        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 _snake_case (							self  ,a_  ,a_  ,a_							)		->   Any:
        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  :						int			     = model(**a_  ,use_cache=a_							)[0]
                      _UpperCAmelCase  :						Optional[int]			     = self.loss_fn(logits.view(-1  ,logits.shape[-1]							)  ,labels.view(-1							)							)
               else:
                      # compute usual loss via models
                      _UpperCAmelCase     ,_UpperCAmelCase  :						List[Any]			     = model(**a_  ,labels=a_  ,use_cache=a_							)[:2]
        else:
               # compute label smoothed loss
               _UpperCAmelCase  :						List[str]			     = model(**a_  ,use_cache=a_							)[0]
               _UpperCAmelCase  :						Any			     = torch.nn.functional.log_softmax(a_  ,dim=-1							)
               _UpperCAmelCase     ,_UpperCAmelCase  :						Dict			     = self.loss_fn(a_  ,a_  ,self.args.label_smoothing  ,ignore_index=self.config.pad_token_id							)
        return loss, logits







 def _snake_case (							self  ,a_  ,a_							)		->   Any:
        _UpperCAmelCase  :						Optional[Any]			     = inputs.pop("""labels"""							)
        _UpperCAmelCase     ,_UpperCAmelCase  :						Any			     = self._compute_loss(a_  ,a_  ,a_							)
        return loss







 def _snake_case (							self  ,a_  ,a_  ,a_  ,a_ = None  ,)		->   Tuple[Optional[float], Optional[torch.Tensor], Optional[torch.Tensor]]:
        _UpperCAmelCase  :						List[Any]			     = self._prepare_inputs(a_							)

        _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  :						int			     = self.model.generate(
                   inputs["""input_ids"""]  ,attention_mask=inputs["""attention_mask"""]  ,**a_  ,)
               # in case the batch is shorter than max length, the output should be padded
               if generated_tokens.shape[-1] < gen_kwargs["max_length"]:
                      _UpperCAmelCase  :						Tuple			     = self._pad_tensors_to_max_len(a_  ,gen_kwargs["""max_length"""]							)

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

        _UpperCAmelCase  :						int			     = loss.mean().detach()
        if self.args.prediction_loss_only:
               return (loss, None, None)

        _UpperCAmelCase  :						int			     = generated_tokens if self.args.predict_with_generate else logits

        if labels.shape[-1] < gen_kwargs["max_length"]:
               _UpperCAmelCase  :						Optional[Any]			     = self._pad_tensors_to_max_len(a_  ,gen_kwargs["""max_length"""]							)

        return (loss, logits, labels)







 def _snake_case (							self  ,a_  ,a_							)		->   Tuple:
        # If PAD token is not defined at least EOS token has to be defined
        _UpperCAmelCase  :						Optional[int]			     = 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[Any]			     = 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 numpy import exp, pi, sqrt




def     snake_case_					(     lowerCAmelCase_       ,   lowerCAmelCase_ = 0.0       ,   lowerCAmelCase_ = 1.0		)->		int:





       '''simple docstring'''


       return 1 / sqrt(2 * pi * sigma**2		) * exp(-((x - mu) ** 2) / (2 * sigma**2)		)


if __name__ == "__main__":
   import doctest

   doctest.testmod()



