


'''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


lowerCAmelCase    :     Optional[int]					=						logging.get_logger(__name__)

lowerCAmelCase    :     str					=						{
    "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 	UpperCamelCase__					( _SCREAMING_SNAKE_CASE	):



     """simple docstring"""






     def __init__( self      ,	snake_case__=None      ,	snake_case__=None      ,	*snake_case__      ,	**snake_case__   ):

            '''simple docstring'''

            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    :							Optional[int]   			=       self.model.config
            else:
                   _lowerCAmelCase    :							Optional[Any]   			=       config

            _lowerCAmelCase    :							Optional[Any]   			=       data_args
            _lowerCAmelCase    :							List[Any]   			=       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    :							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

                   _lowerCAmelCase    :							Any   			=       label_smoothed_nll_loss







     def a	( self      ,	snake_case__   ):

            '''simple docstring'''

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

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







     def a	( self      ,	snake_case__   ):

            '''simple docstring'''

            _lowerCAmelCase    :							Dict   			=       arg_to_scheduler[self.args.lr_scheduler]
            if self.args.lr_scheduler == "constant":
                   _lowerCAmelCase    :							Any   			=       schedule_func(self.optimizer   )
            elif self.args.lr_scheduler == "constant_w_warmup":
                   _lowerCAmelCase    :							Optional[int]   			=       schedule_func(self.optimizer      ,	num_warmup_steps=self.args.warmup_steps   )
            else:
                   _lowerCAmelCase    :							str   			=       schedule_func(
                       self.optimizer      ,	num_warmup_steps=self.args.warmup_steps      ,	num_training_steps=A_   )
            return scheduler







     def a	( self   ):

            '''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 a	( self      ,	snake_case__      ,	snake_case__      ,	snake_case__   ):

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







     def a	( self      ,	snake_case__      ,	snake_case__   ):

            '''simple docstring'''

            _lowerCAmelCase    :							Union[str, Any]   			=       inputs.pop('labels'   )
            _lowerCAmelCase			,     _lowerCAmelCase    :							List[str]   			=       self._compute_loss(A_      ,	A_      ,	A_   )
            return loss







     def a	( self      ,	snake_case__      ,	snake_case__      ,	snake_case__      ,	snake_case__ = None      ,	):

            '''simple docstring'''

            _lowerCAmelCase    :							Optional[int]   			=       self._prepare_inputs(A_   )

            _lowerCAmelCase    :							str   			=       {
                '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    :							List[str]   			=       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    :							Dict   			=       self._pad_tensors_to_max_len(A_      ,	gen_kwargs['max_length']   )

            _lowerCAmelCase    :							Optional[int]   			=       inputs.pop('labels'   )
            with torch.no_grad():
                   # compute loss on predict data
                   _lowerCAmelCase			,     _lowerCAmelCase    :							Optional[int]   			=       self._compute_loss(A_      ,	A_      ,	A_   )

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

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

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

            return (loss, logits, labels)







     def a	( self      ,	snake_case__      ,	snake_case__   ):

            '''simple docstring'''

            _lowerCAmelCase    :							Any   			=       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    :							int   			=       pad_token_id * torch.ones(
                (tensor.shape[0], max_length)      ,	dtype=tensor.dtype      ,	device=tensor.device   )
            _lowerCAmelCase    :							Any   			=       tensor
            return padded_tensor





'''simple docstring'''







from .configuration_bert_masked import MaskedBertConfig
from .modeling_bert_masked import (
    MaskedBertForMultipleChoice,
    MaskedBertForQuestionAnswering,
    MaskedBertForSequenceClassification,
    MaskedBertForTokenClassification,
    MaskedBertModel,
)
from .modules import *

