


"""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


__snake_case   			=    logging.get_logger(__name__)

__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 _lowerCAmelCase				(      snake_case_	):







   def __init__(	self						,	UpperCamelCase__=None						,	UpperCamelCase__=None						,	*UpperCamelCase__						,	**UpperCamelCase__						)	->		str:

       '''simple docstring'''




       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__}'
           )
           snake_case    :      Tuple      					=    self.model.config
       else:
           snake_case    :      Tuple      					=    config

       snake_case    :      List[Any]      					=    data_args
       snake_case    :      Optional[int]      					=    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:
           snake_case    :      Dict      					=    torch.nn.CrossEntropyLoss(ignore_index=self.config.pad_token_id						)
       else:
           # dynamically import label_smoothed_nll_loss
           from utils import label_smoothed_nll_loss

           snake_case    :      Union[str, Any]      					=    label_smoothed_nll_loss







   def  lowerCamelCase		(	self						,	UpperCamelCase__						)	->		Any:

       '''simple docstring'''




       if self.optimizer is None:
           snake_case    :      Any      					=    ["bias", "LayerNorm.weight"]
           snake_case    :      str      					=    [
               {
                   "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,
               },
           ]
           snake_case    :      Optional[Any]      					=    Adafactor if self.args.adafactor else AdamW
           if self.args.adafactor:
               snake_case    :      int      					=    Adafactor
               snake_case    :      Optional[int]      					=    {"scale_parameter": False, "relative_step": False}
           else:
               snake_case    :      int      					=    AdamW
               snake_case    :      Optional[Any]      					=    {
                   "betas": (self.args.adam_betaa, self.args.adam_betaa),
                   "eps": self.args.adam_epsilon,
               }
           snake_case    :      Any      					=    self.args.learning_rate
           if self.sharded_ddp:
               snake_case    :      int      					=    OSS(
                   params=UpperCamelCase__						,	optim=UpperCamelCase__						,	**UpperCamelCase__						,	)
           else:
               snake_case    :      str      					=    optimizer_cls(UpperCamelCase__						,	**UpperCamelCase__						)

       if self.lr_scheduler is None:
           snake_case    :      Union[str, Any]      					=    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__						)	->		List[str]:

       '''simple docstring'''




       snake_case    :      Any      					=    arg_to_scheduler[self.args.lr_scheduler]
       if self.args.lr_scheduler == "constant":
           snake_case    :      str      					=    schedule_func(self.optimizer						)
       elif self.args.lr_scheduler == "constant_w_warmup":
           snake_case    :      Dict      					=    schedule_func(self.optimizer						,	num_warmup_steps=self.args.warmup_steps						)
       else:
           snake_case    :      List[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]:

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







   def  lowerCamelCase		(	self						,	UpperCamelCase__						,	UpperCamelCase__						)	->		Any:

       '''simple docstring'''




       snake_case    :      Union[str, Any]      					=    inputs.pop("labels"						)
       snake_case					,snake_case    :      Any      					=    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]]:

       '''simple docstring'''




       snake_case    :      List[Any]      					=    self._prepare_inputs(UpperCamelCase__						)

       snake_case    :      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:
           snake_case    :      Union[str, Any]      					=    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"]:
               snake_case    :      Tuple      					=    self._pad_tensors_to_max_len(UpperCamelCase__						,	gen_kwargs["max_length"]						)

       snake_case    :      Any      					=    inputs.pop("labels"						)
       with torch.no_grad():
           # compute loss on predict data
           snake_case					,snake_case    :      int      					=    self._compute_loss(UpperCamelCase__						,	UpperCamelCase__						,	UpperCamelCase__						)

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

       snake_case    :      Tuple      					=    generated_tokens if self.args.predict_with_generate else logits

       if labels.shape[-1] < gen_kwargs["max_length"]:
           snake_case    :      str      					=    self._pad_tensors_to_max_len(UpperCamelCase__						,	gen_kwargs["max_length"]						)

       return (loss, logits, labels)







   def  lowerCamelCase		(	self						,	UpperCamelCase__						,	UpperCamelCase__						)	->		int:

       '''simple docstring'''




       snake_case    :      List[str]      					=    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}'						)

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









"""simple docstring"""



from typing import Dict

from .base import GenericTensor, Pipeline







class _lowerCAmelCase				(      snake_case_	):







   def  lowerCamelCase		(	self						,	UpperCamelCase__=None						,	UpperCamelCase__=None						,	UpperCamelCase__=None						,	**UpperCamelCase__						)	->		Union[str, Any]:

       '''simple docstring'''




       if tokenize_kwargs is None:
           snake_case    :      Optional[Any]      					=    {}

       if truncation is not None:
           if "truncation" in tokenize_kwargs:
               raise ValueError(
                   "truncation parameter defined twice (given as keyword argument as well as in tokenize_kwargs)"						)
           snake_case    :      List[str]      					=    truncation

       snake_case    :      Union[str, Any]      					=    tokenize_kwargs

       snake_case    :      List[Any]      					=    {}
       if return_tensors is not None:
           snake_case    :      Tuple      					=    return_tensors

       return preprocess_params, {}, postprocess_params







   def  lowerCamelCase		(	self						,	UpperCamelCase__						,	**UpperCamelCase__						)	->		Dict[str, GenericTensor]:

       '''simple docstring'''




       snake_case    :      List[Any]      					=    self.framework
       snake_case    :      str      					=    self.tokenizer(UpperCamelCase__						,	return_tensors=UpperCamelCase__						,	**UpperCamelCase__						)
       return model_inputs







   def  lowerCamelCase		(	self						,	UpperCamelCase__						)	->		Tuple:

       '''simple docstring'''




       snake_case    :      int      					=    self.model(**UpperCamelCase__						)
       return model_outputs







   def  lowerCamelCase		(	self						,	UpperCamelCase__						,	UpperCamelCase__=False						)	->		Union[str, Any]:

       '''simple docstring'''




       if return_tensors:
           return model_outputs[0]
       if self.framework == "pt":
           return model_outputs[0].tolist()
       elif self.framework == "tf":
           return model_outputs[0].numpy().tolist()







   def __call__(	self						,	*UpperCamelCase__						,	**UpperCamelCase__						)	->		Union[str, Any]:

       '''simple docstring'''




       return super().__call__(*UpperCamelCase__						,	**UpperCamelCase__						)





