





"""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


SCREAMING_SNAKE_CASE__					=   logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__					=   {
    "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      ,						lowercase=None      ,						lowercase=None      ,						*lowercase      ,						**lowercase )		->     Optional[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__}'
                  )
                  lowerCAmelCase  						=       self.model.config
            else:
                  lowerCAmelCase  						=       config

            lowerCAmelCase  						=       data_args
            lowerCAmelCase  						=       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:
                  lowerCAmelCase  						=       torch.nn.CrossEntropyLoss(ignore_index=self.config.pad_token_id )
            else:
                  # dynamically import label_smoothed_nll_loss
                  from utils import label_smoothed_nll_loss

                  lowerCAmelCase  						=       label_smoothed_nll_loss





      def     _snake_case			(       self      ,						lowercase )		->     str:





            if self.optimizer is None:
                  lowerCAmelCase  						=       ['''bias''', '''LayerNorm.weight''']
                  lowerCAmelCase  						=       [
                      {
                          '''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,
                      },
                  ]
                  lowerCAmelCase  						=       Adafactor if self.args.adafactor else AdamW
                  if self.args.adafactor:
                        lowerCAmelCase  						=       Adafactor
                        lowerCAmelCase  						=       {'''scale_parameter''': False, '''relative_step''': False}
                  else:
                        lowerCAmelCase  						=       AdamW
                        lowerCAmelCase  						=       {
                            '''betas''': (self.args.adam_betaa, self.args.adam_betaa),
                            '''eps''': self.args.adam_epsilon,
                        }
                  lowerCAmelCase  						=       self.args.learning_rate
                  if self.sharded_ddp:
                        lowerCAmelCase  						=       OSS(
                            params=A_      ,						optim=A_      ,						**A_      ,						)
                  else:
                        lowerCAmelCase  						=       optimizer_cls(A_      ,						**A_ )

            if self.lr_scheduler is None:
                  lowerCAmelCase  						=       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      ,						lowercase )		->     List[str]:
            lowerCAmelCase  						=       arg_to_scheduler[self.args.lr_scheduler]
            if self.args.lr_scheduler == "constant":
                  lowerCAmelCase  						=       schedule_func(self.optimizer )
            elif self.args.lr_scheduler == "constant_w_warmup":
                  lowerCAmelCase  						=       schedule_func(self.optimizer      ,						num_warmup_steps=self.args.warmup_steps )
            else:
                  lowerCAmelCase  						=       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      ,						lowercase      ,						lowercase      ,						lowercase )		->     str:





            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
                        lowerCAmelCase  						=       model(**A_      ,						use_cache=A_ )[0]
                        lowerCAmelCase  						=       self.loss_fn(logits.view(-1      ,						logits.shape[-1] )      ,						labels.view(-1 ) )
                  else:
                        # compute usual loss via models
                        lowerCAmelCase  						=       model(**A_      ,						labels=A_      ,						use_cache=A_ )[:2]
            else:
                  # compute label smoothed loss
                  lowerCAmelCase  						=       model(**A_      ,						use_cache=A_ )[0]
                  lowerCAmelCase  						=       torch.nn.functional.log_softmax(A_      ,						dim=-1 )
                  lowerCAmelCase  						=       self.loss_fn(A_      ,						A_      ,						self.args.label_smoothing      ,						ignore_index=self.config.pad_token_id )
            return loss, logits





      def     _snake_case			(       self      ,						lowercase      ,						lowercase )		->     Union[str, Any]:
            lowerCAmelCase  						=       inputs.pop("""labels""" )
            lowerCAmelCase  						=       self._compute_loss(A_      ,						A_      ,						A_ )
            return loss





      def     _snake_case			(       self      ,						lowercase      ,						lowercase      ,						lowercase      ,						lowercase = None      ,						)		->     Tuple[Optional[float], Optional[torch.Tensor], Optional[torch.Tensor]]:
            lowerCAmelCase  						=       self._prepare_inputs(A_ )

            lowerCAmelCase  						=       {
                '''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:
                  lowerCAmelCase  						=       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"]:
                        lowerCAmelCase  						=       self._pad_tensors_to_max_len(A_      ,						gen_kwargs["""max_length"""] )

            lowerCAmelCase  						=       inputs.pop("""labels""" )
            with torch.no_grad():
                  # compute loss on predict data
                  lowerCAmelCase  						=       self._compute_loss(A_      ,						A_      ,						A_ )

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

            lowerCAmelCase  						=       generated_tokens if self.args.predict_with_generate else logits

            if labels.shape[-1] < gen_kwargs["max_length"]:
                  lowerCAmelCase  						=       self._pad_tensors_to_max_len(A_      ,						gen_kwargs["""max_length"""] )

            return (loss, logits, labels)





      def     _snake_case			(       self      ,						lowercase      ,						lowercase )		->     List[Any]:
            lowerCAmelCase  						=       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}' )

            lowerCAmelCase  						=       pad_token_id * torch.ones(
                (tensor.shape[0], max_length)      ,						dtype=tensor.dtype      ,						device=tensor.device )
            lowerCAmelCase  						=       tensor
            return padded_tensor









def 		_lowercase (     lowercase__   ,	lowercase__						):

      if density <= 0:
            raise ValueError('''Impossible fluid density'''						)
      if bulk_modulus <= 0:
            raise ValueError('''Impossible bulk modulus'''						)

      return (bulk_modulus / density) ** 0.5


if __name__ == "__main__":
 import doctest

 doctest.testmod()


