
'''simple docstring'''







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


__lowerCAmelCase         =    logging.get_logger(__name__)

__lowerCAmelCase         =    {
    """naver-clova-ix/donut-base""": """https://huggingface.co/naver-clova-ix/donut-base/resolve/main/config.json""",
    # See all Donut models at https://huggingface.co/models?filter=donut-swin
}







class UpperCAmelCase__	( lowercase__	):





  """simple docstring"""

  __UpperCAmelCase				:				List[str]   =					'''donut-swin'''

  __UpperCAmelCase				:				Optional[Any]   =					{
      '''num_attention_heads''': '''num_heads''',
      '''num_hidden_layers''': '''num_layers''',
  }


  def __init__(		self      :     Dict							,_a      :     Optional[Any]=224							,_a      :     List[str]=4							,_a      :     Union[str, Any]=3							,_a      :     Any=96							,_a      :     str=[2, 2, 6, 2]							,_a      :     str=[3, 6, 12, 24]							,_a      :     Union[str, Any]=7							,_a      :     int=4.0							,_a      :     Optional[Any]=True							,_a      :     Optional[Any]=0.0							,_a      :     List[Any]=0.0							,_a      :     Dict=0.1							,_a      :     List[Any]="gelu"							,_a      :     str=False							,_a      :     List[Any]=0.02							,_a      :     Dict=1E-5							,**_a      :     List[Any]							,):


     '''simple docstring'''






     super().__init__(**_a       )

     _a				:	Any			=   image_size
     _a				:	Any			=   patch_size
     _a				:	str			=   num_channels
     _a				:	int			=   embed_dim
     _a				:	Union[str, Any]			=   depths
     _a				:	Union[str, Any]			=   len(_a       )
     _a				:	Optional[int]			=   num_heads
     _a				:	Optional[int]			=   window_size
     _a				:	List[str]			=   mlp_ratio
     _a				:	str			=   qkv_bias
     _a				:	int			=   hidden_dropout_prob
     _a				:	Dict			=   attention_probs_dropout_prob
     _a				:	str			=   drop_path_rate
     _a				:	List[str]			=   hidden_act
     _a				:	List[str]			=   use_absolute_embeddings
     _a				:	int			=   layer_norm_eps
     _a				:	List[Any]			=   initializer_range
     # we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
     # this indicates the channel dimension after the last stage of the model
     _a				:	Optional[int]			=   int(embed_dim * 2 ** (len(_a       ) - 1)       )





'''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 UpperCAmelCase__	( lowercase__	):





  """simple docstring"""

  def __init__(		self      :     Optional[int]							,_a      :     Optional[Any]=None							,_a      :     Dict=None							,*_a      :     int							,**_a      :     str       ):


     '''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__}"""
        )
        _a				:	List[Any]			=   self.model.config
     else:
        _a				:	Optional[int]			=   config

     _a				:	List[str]			=   data_args
     _a				:	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:
        _a				:	List[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

        _a				:	Tuple			=   label_smoothed_nll_loss


  def   __lowercase		(		self      :     List[str]							,_a      :     int       ):


     '''simple docstring'''






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

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


  def   __lowercase		(		self      :     List[Any]							,_a      :     List[Any]       ):


     '''simple docstring'''






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


  def   __lowercase		(		self      :     Tuple       ):


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


  def   __lowercase		(		self      :     Optional[int]							,_a      :     Union[str, Any]							,_a      :     List[Any]       ):


     '''simple docstring'''






     _a				:	Optional[int]			=   inputs.pop('labels'       )
     _a,  _a				:	int			=   self._compute_loss(_a							,_a							,_a       )
     return loss


  def   __lowercase		(		self      :     Optional[Any]							,_a      :     nn.Module							,_a      :     Dict[str, Union[torch.Tensor, Any]]							,_a      :     bool							,_a      :     Optional[List[str]] = None							,):


     '''simple docstring'''






     _a				:	int			=   self._prepare_inputs(_a       )

     _a				:	Any			=   {
         '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:
        _a				:	int			=   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"]:
           _a				:	int			=   self._pad_tensors_to_max_len(_a							,gen_kwargs['max_length']       )

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

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

     _a				:	Optional[Any]			=   generated_tokens if self.args.predict_with_generate else logits

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

     return (loss, logits, labels)


  def   __lowercase		(		self      :     str							,_a      :     Tuple							,_a      :     Tuple       ):


     '''simple docstring'''






     _a				:	List[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}"""       )

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



