





"""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_       =		logging.get_logger(__name__)

lowerCamelCase_       =		{
    "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  _SCREAMING_SNAKE_CASE(       A  ):




       def __init__(						self		,SCREAMING_SNAKE_CASE__=None		,SCREAMING_SNAKE_CASE__=None		,*SCREAMING_SNAKE_CASE__		,**SCREAMING_SNAKE_CASE__							)      ->				List[str]:





             """simple docstring"""






             super().__init__(*SCREAMING_SNAKE_CASE__		,**SCREAMING_SNAKE_CASE__							)

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

             __SCREAMING_SNAKE_CASE       :str		    =  data_args
             __SCREAMING_SNAKE_CASE       :Tuple		    =  self.config.tgt_vocab_size if isinstance(self.config		,SCREAMING_SNAKE_CASE__							) 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:
                   __SCREAMING_SNAKE_CASE       :Union[str, 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

                   __SCREAMING_SNAKE_CASE       :Any		    =  label_smoothed_nll_loss




       def    _UpperCamelCase					(						self		,SCREAMING_SNAKE_CASE__							)      ->				Tuple:





             """simple docstring"""






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

             if self.lr_scheduler is None:
                   __SCREAMING_SNAKE_CASE       :List[Any]		    =  self._get_lr_scheduler(SCREAMING_SNAKE_CASE__							)
             else:  # ignoring --lr_scheduler
                   logger.warning('''scheduler is passed to `Seq2SeqTrainer`, `--lr_scheduler` arg is ignored.'''							)




       def    _UpperCamelCase					(						self		,SCREAMING_SNAKE_CASE__							)      ->				Dict:





             """simple docstring"""






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




       def    _UpperCamelCase					(						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    _UpperCamelCase					(						self		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__							)      ->				Dict:





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




       def    _UpperCamelCase					(						self		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__							)      ->				str:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  inputs.pop('''labels'''							)
             __SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE       :List[str]		    =  self._compute_loss(SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__							)
             return loss




       def    _UpperCamelCase					(						self		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__ = None		,)      ->				Tuple[Optional[float], Optional[torch.Tensor], Optional[torch.Tensor]]:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :List[Any]		    =  self._prepare_inputs(SCREAMING_SNAKE_CASE__							)

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

             __SCREAMING_SNAKE_CASE       :Dict		    =  inputs.pop('''labels'''							)
             with torch.no_grad():
                   # compute loss on predict data
                   __SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE       :str		    =  self._compute_loss(SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__							)

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

             __SCREAMING_SNAKE_CASE       :List[Any]		    =  generated_tokens if self.args.predict_with_generate else logits

             if labels.shape[-1] < gen_kwargs["max_length"]:
                   __SCREAMING_SNAKE_CASE       :Optional[int]		    =  self._pad_tensors_to_max_len(SCREAMING_SNAKE_CASE__		,gen_kwargs['''max_length''']							)

             return (loss, logits, labels)




       def    _UpperCamelCase					(						self		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__							)      ->				Optional[int]:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :Dict		    =  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}'''							)

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






"""simple docstring"""
from typing import Callable, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCamelCase_       =		logging.get_logger(__name__)

lowerCamelCase_       =		{
    "microsoft/xprophetnet-large-wiki100-cased": (
        "https://huggingface.co/microsoft/xprophetnet-large-wiki100-cased/resolve/main/config.json"
    ),
}






class  _SCREAMING_SNAKE_CASE(       A  ):
       SCREAMING_SNAKE_CASE_       :				Optional[Any]     						=  '''xlm-prophetnet'''
       SCREAMING_SNAKE_CASE_       :				Any     						=  ['''past_key_values''']
       SCREAMING_SNAKE_CASE_       :				Optional[Any]     						=  {
           '''num_attention_heads''': '''num_encoder_attention_heads''',
       }




       def __init__(						self		,SCREAMING_SNAKE_CASE__ = 0.1		,SCREAMING_SNAKE_CASE__ = "gelu"		,SCREAMING_SNAKE_CASE__ = 3_05_22		,SCREAMING_SNAKE_CASE__ = 10_24		,SCREAMING_SNAKE_CASE__ = 40_96		,SCREAMING_SNAKE_CASE__ = 12		,SCREAMING_SNAKE_CASE__ = 16		,SCREAMING_SNAKE_CASE__ = 40_96		,SCREAMING_SNAKE_CASE__ = 12		,SCREAMING_SNAKE_CASE__ = 16		,SCREAMING_SNAKE_CASE__ = 0.1		,SCREAMING_SNAKE_CASE__ = 0.1		,SCREAMING_SNAKE_CASE__ = 5_12		,SCREAMING_SNAKE_CASE__ = 0.0_2		,SCREAMING_SNAKE_CASE__ = True		,SCREAMING_SNAKE_CASE__ = True		,SCREAMING_SNAKE_CASE__ = 0		,SCREAMING_SNAKE_CASE__ = 2		,SCREAMING_SNAKE_CASE__ = 32		,SCREAMING_SNAKE_CASE__ = 1_28		,SCREAMING_SNAKE_CASE__ = False		,SCREAMING_SNAKE_CASE__ = 0.0		,SCREAMING_SNAKE_CASE__ = True		,SCREAMING_SNAKE_CASE__ = 0		,SCREAMING_SNAKE_CASE__ = 1		,SCREAMING_SNAKE_CASE__ = 2		,**SCREAMING_SNAKE_CASE__		,)      ->				Dict:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :int		    =  vocab_size
             __SCREAMING_SNAKE_CASE       :Tuple		    =  hidden_size
             __SCREAMING_SNAKE_CASE       :Optional[int]		    =  encoder_ffn_dim
             __SCREAMING_SNAKE_CASE       :Optional[int]		    =  num_encoder_layers
             __SCREAMING_SNAKE_CASE       :Tuple		    =  num_encoder_attention_heads
             __SCREAMING_SNAKE_CASE       :List[Any]		    =  decoder_ffn_dim
             __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  num_decoder_layers
             __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  num_decoder_attention_heads
             __SCREAMING_SNAKE_CASE       :List[str]		    =  max_position_embeddings
             __SCREAMING_SNAKE_CASE       :Dict		    =  init_std  # Normal(0, this parameter)
             __SCREAMING_SNAKE_CASE       :List[Any]		    =  activation_function

             # parameters for xlmprophetnet
             __SCREAMING_SNAKE_CASE       :Tuple		    =  ngram
             __SCREAMING_SNAKE_CASE       :int		    =  num_buckets
             __SCREAMING_SNAKE_CASE       :Optional[int]		    =  relative_max_distance
             __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  disable_ngram_loss
             __SCREAMING_SNAKE_CASE       :Dict		    =  eps

             # 3 Types of Dropout
             __SCREAMING_SNAKE_CASE       :List[str]		    =  attention_dropout
             __SCREAMING_SNAKE_CASE       :Dict		    =  activation_dropout
             __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  dropout

             __SCREAMING_SNAKE_CASE       :int		    =  use_cache

             super().__init__(
                 pad_token_id=SCREAMING_SNAKE_CASE__		,bos_token_id=SCREAMING_SNAKE_CASE__		,eos_token_id=SCREAMING_SNAKE_CASE__		,is_encoder_decoder=SCREAMING_SNAKE_CASE__		,add_cross_attention=SCREAMING_SNAKE_CASE__		,decoder_start_token_id=SCREAMING_SNAKE_CASE__		,**SCREAMING_SNAKE_CASE__		,)




       @property
       def    _UpperCamelCase					(						self							)      ->				int:





             """simple docstring"""






             return self.num_encoder_layers + self.num_decoder_layers




       @num_hidden_layers.setter
       def    _UpperCamelCase					(						self		,SCREAMING_SNAKE_CASE__							)      ->				Optional[int]:





             """simple docstring"""






             raise NotImplementedError(
                 '''This model does not support the setting of `num_hidden_layers`. Please set `num_encoder_layers` and'''
                 ''' `num_decoder_layers`.'''							)