
"""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__      :	str          =    logging.get_logger(__name__)

A__      :	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 ,			__UpperCamelCase=None ,			__UpperCamelCase=None ,			*__UpperCamelCase ,			**__UpperCamelCase  )->					Optional[int]:
          super().__init__(*__UpperCamelCase ,			**__UpperCamelCase  )

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

          UpperCAmelCase__							: Tuple 							=						data_args
          UpperCAmelCase__							: List[str] 							=						self.config.tgt_vocab_size if isinstance(self.config ,			__UpperCamelCase  ) 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__							: List[Any] 							=						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  lowerCAmelCase__       (      self ,			__UpperCamelCase  )->					Tuple:
          if self.optimizer is None:
               UpperCAmelCase__							: List[Any] 							=						["bias", "LayerNorm.weight"]
               UpperCAmelCase__							: Any 							=						[
                   {
                       "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__							: Dict 							=						Adafactor if self.args.adafactor else AdamW
               if self.args.adafactor:
                    UpperCAmelCase__							: int 							=						Adafactor
                    UpperCAmelCase__							: Optional[int] 							=						{"scale_parameter": False, "relative_step": False}
               else:
                    UpperCAmelCase__							: Tuple 							=						AdamW
                    UpperCAmelCase__							: Any 							=						{
                        "betas": (self.args.adam_betaa, self.args.adam_betaa),
                        "eps": self.args.adam_epsilon,
                    }
               UpperCAmelCase__							: Dict 							=						self.args.learning_rate
               if self.sharded_ddp:
                    UpperCAmelCase__							: Optional[int] 							=						OSS(
                        params=__UpperCamelCase ,			optim=__UpperCamelCase ,			**__UpperCamelCase ,			)
               else:
                    UpperCAmelCase__							: Dict 							=						optimizer_cls(__UpperCamelCase ,			**__UpperCamelCase  )

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



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase  )->					Dict:
          UpperCAmelCase__							: Union[str, Any] 							=						arg_to_scheduler[self.args.lr_scheduler]
          if self.args.lr_scheduler == "constant":
               UpperCAmelCase__							: Dict 							=						schedule_func(self.optimizer  )
          elif self.args.lr_scheduler == "constant_w_warmup":
               UpperCAmelCase__							: Tuple 							=						schedule_func(self.optimizer ,			num_warmup_steps=self.args.warmup_steps  )
          else:
               UpperCAmelCase__							: Optional[Any] 							=						schedule_func(
                   self.optimizer ,			num_warmup_steps=self.args.warmup_steps ,			num_training_steps=__UpperCamelCase  )
          return scheduler



     def  lowerCAmelCase__       (      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  lowerCAmelCase__       (      self ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase  )->					Optional[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__							: List[Any] 							=						model(**__UpperCamelCase ,			use_cache=__UpperCamelCase  )[0]
                    UpperCAmelCase__							: Any 							=						self.loss_fn(logits.view(-1 ,			logits.shape[-1]  ) ,			labels.view(-1  )  )
               else:
                    # compute usual loss via models
                    UpperCAmelCase__							,	UpperCAmelCase__							: List[str] 							=						model(**__UpperCamelCase ,			labels=__UpperCamelCase ,			use_cache=__UpperCamelCase  )[:2]
          else:
               # compute label smoothed loss
               UpperCAmelCase__							: Tuple 							=						model(**__UpperCamelCase ,			use_cache=__UpperCamelCase  )[0]
               UpperCAmelCase__							: Tuple 							=						torch.nn.functional.log_softmax(__UpperCamelCase ,			dim=-1  )
               UpperCAmelCase__							,	UpperCAmelCase__							: Optional[Any] 							=						self.loss_fn(__UpperCamelCase ,			__UpperCamelCase ,			self.args.label_smoothing ,			ignore_index=self.config.pad_token_id  )
          return loss, logits



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase ,			__UpperCamelCase  )->					int:
          UpperCAmelCase__							: Optional[int] 							=						inputs.pop("labels"  )
          UpperCAmelCase__							,	UpperCAmelCase__							: int 							=						self._compute_loss(__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase  )
          return loss



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase = None ,			)->					Tuple[Optional[float], Optional[torch.Tensor], Optional[torch.Tensor]]:
          UpperCAmelCase__							: Union[str, Any] 							=						self._prepare_inputs(__UpperCamelCase  )

          UpperCAmelCase__							: Tuple 							=						{
              "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"] ,			**__UpperCamelCase ,			)
               # in case the batch is shorter than max length, the output should be padded
               if generated_tokens.shape[-1] < gen_kwargs["max_length"]:
                    UpperCAmelCase__							: Dict 							=						self._pad_tensors_to_max_len(__UpperCamelCase ,			gen_kwargs["max_length"]  )

          UpperCAmelCase__							: List[Any] 							=						inputs.pop("labels"  )
          with torch.no_grad():
               # compute loss on predict data
               UpperCAmelCase__							,	UpperCAmelCase__							: Tuple 							=						self._compute_loss(__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase  )

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

          UpperCAmelCase__							: List[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(__UpperCamelCase ,			gen_kwargs["max_length"]  )

          return (loss, logits, labels)



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase ,			__UpperCamelCase  )->					str:
          # 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__							: int 							=						pad_token_id * torch.ones(
              (tensor.shape[0], max_length) ,			dtype=tensor.dtype ,			device=tensor.device  )
          UpperCAmelCase__							: str 							=						tensor
          return padded_tensor





"""simple docstring"""




from typing import Optional, Union

import torch
from torch import nn

from ...configuration_utils import ConfigMixin, register_to_config
from ...models.modeling_utils import ModelMixin







class   _lowercase						(       lowerCAmelCase_	,   lowerCAmelCase_				):


     '''simple docstring'''





     @register_to_config
     def __init__(      self ,			__UpperCamelCase = 7_68 ,			)->					Union[str, Any]:
          super().__init__()

          UpperCAmelCase__							: str 							=						nn.Parameter(torch.zeros(1 ,			__UpperCamelCase  )  )
          UpperCAmelCase__							: Optional[int] 							=						nn.Parameter(torch.ones(1 ,			__UpperCamelCase  )  )



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase = None ,			__UpperCamelCase = None ,			)->					Any:
          UpperCAmelCase__							: Dict 							=						nn.Parameter(self.mean.to(__UpperCamelCase  ).to(__UpperCamelCase  )  )
          UpperCAmelCase__							: Any 							=						nn.Parameter(self.std.to(__UpperCamelCase  ).to(__UpperCamelCase  )  )
          return self



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase  )->					Union[str, Any]:
          UpperCAmelCase__							: Dict 							=						(embeds - self.mean) * 1.0 / self.std
          return embeds



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase  )->					List[Any]:
          UpperCAmelCase__							: Any 							=						(embeds * self.std) + self.mean
          return embeds



