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


lowercase_     :   Dict 				=				logging.get_logger(__name__)

lowercase_     :   int 				=				{
    "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		(   _lowerCamelCase    ):
    def __init__(  self , lowerCAmelCase=None , lowerCAmelCase=None , *lowerCAmelCase , **lowerCAmelCase   )       ->					List[str]:
     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__}'
      )
      SCREAMING_SNAKE_CASE__:   Tuple=      self.model.config
     else:
      SCREAMING_SNAKE_CASE__:   Optional[int]=      config

     SCREAMING_SNAKE_CASE__:   Any=      data_args
     SCREAMING_SNAKE_CASE__:   int=      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:
      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__:   int=      label_smoothed_nll_loss
    def 						UpperCamelCase_				(  self , lowerCAmelCase   )       ->					Any:
     if self.optimizer is None:
      SCREAMING_SNAKE_CASE__:   Optional[Any]=      ['''bias''', '''LayerNorm.weight''']
      SCREAMING_SNAKE_CASE__:   Optional[int]=      [
          {
              '''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__:   Optional[int]=      Adafactor if self.args.adafactor else AdamW
      if self.args.adafactor:
       SCREAMING_SNAKE_CASE__:   int=      Adafactor
       SCREAMING_SNAKE_CASE__:   Tuple=      {'''scale_parameter''': False, '''relative_step''': False}
      else:
       SCREAMING_SNAKE_CASE__:   List[str]=      AdamW
       SCREAMING_SNAKE_CASE__:   Any=      {
           '''betas''': (self.args.adam_betaa, self.args.adam_betaa),
           '''eps''': self.args.adam_epsilon,
       }
      SCREAMING_SNAKE_CASE__:   int=      self.args.learning_rate
      if self.sharded_ddp:
       SCREAMING_SNAKE_CASE__:   str=      OSS(
           params=A__ , optim=A__ , **A__ , )
      else:
       SCREAMING_SNAKE_CASE__:   str=      optimizer_cls(A__ , **A__   )

     if self.lr_scheduler is None:
      SCREAMING_SNAKE_CASE__:   Tuple=      self._get_lr_scheduler(A__   )
     else:  # ignoring --lr_scheduler
      logger.warning('''scheduler is passed to `Seq2SeqTrainer`, `--lr_scheduler` arg is ignored.'''   )
    def 						UpperCamelCase_				(  self , lowerCAmelCase   )       ->					int:
     SCREAMING_SNAKE_CASE__:   str=      arg_to_scheduler[self.args.lr_scheduler]
     if self.args.lr_scheduler == "constant":
      SCREAMING_SNAKE_CASE__:   Union[str, Any]=      schedule_func(self.optimizer   )
     elif self.args.lr_scheduler == "constant_w_warmup":
      SCREAMING_SNAKE_CASE__:   List[str]=      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=A__   )
     return scheduler
    def 						UpperCamelCase_				(  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 						UpperCamelCase_				(  self , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase   )       ->					Optional[int]:
     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__:   Dict=      model(**A__ , use_cache=A__   )[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__:   str=      model(**A__ , labels=A__ , use_cache=A__   )[:2]
     else:
      # compute label smoothed loss
      SCREAMING_SNAKE_CASE__:   Dict=      model(**A__ , use_cache=A__   )[0]
      SCREAMING_SNAKE_CASE__:   Any=      torch.nn.functional.log_softmax(A__ , dim=-1   )
      SCREAMING_SNAKE_CASE__,	SCREAMING_SNAKE_CASE__:   Any=      self.loss_fn(A__ , A__ , self.args.label_smoothing , ignore_index=self.config.pad_token_id   )
     return loss, logits
    def 						UpperCamelCase_				(  self , lowerCAmelCase , lowerCAmelCase   )       ->					Optional[int]:
     SCREAMING_SNAKE_CASE__:   Tuple=      inputs.pop('''labels'''   )
     SCREAMING_SNAKE_CASE__,	SCREAMING_SNAKE_CASE__:   Dict=      self._compute_loss(A__ , A__ , A__   )
     return loss
    def 						UpperCamelCase_				(  self , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase = None , )       ->					Tuple[Optional[float], Optional[torch.Tensor], Optional[torch.Tensor]]:
     SCREAMING_SNAKE_CASE__:   Optional[int]=      self._prepare_inputs(A__   )

     SCREAMING_SNAKE_CASE__:   Union[str, 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:
      SCREAMING_SNAKE_CASE__:   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"]:
       SCREAMING_SNAKE_CASE__:   List[Any]=      self._pad_tensors_to_max_len(A__ , gen_kwargs['''max_length''']   )

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

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

     SCREAMING_SNAKE_CASE__:   int=      generated_tokens if self.args.predict_with_generate else logits

     if labels.shape[-1] < gen_kwargs["max_length"]:
      SCREAMING_SNAKE_CASE__:   Union[str, Any]=      self._pad_tensors_to_max_len(A__ , gen_kwargs['''max_length''']   )

     return (loss, logits, labels)



    def 						UpperCamelCase_				(  self , lowerCAmelCase , lowerCAmelCase   )       ->					Optional[Any]:
     # If PAD token is not defined at least EOS token has to be defined
     SCREAMING_SNAKE_CASE__:   Union[str, 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}'   )

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


from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowercase_     :   Dict 				=				{
    'configuration_autoformer': [
        'AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'AutoformerConfig',
    ],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase_     :   str 				=				[
          'AUTOFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
          'AutoformerForPrediction',
          'AutoformerModel',
          'AutoformerPreTrainedModel',
      ]


if TYPE_CHECKING:
      from .configuration_autoformer import (
          AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
          AutoformerConfig,
      )

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_autoformer import (
                AUTOFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                AutoformerForPrediction,
                AutoformerModel,
                AutoformerPreTrainedModel,
            )

else:
      import sys

      lowercase_     :   Tuple 				=				_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)

