


'''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


UpperCAmelCase_			:  List[str]										=   logging.get_logger(__name__)

UpperCAmelCase_			:  Optional[Any]										=   {
    '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_					):




 '''simple docstring'''


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

  """simple docstring"""







  super().__init__(*__lowercase   ,   **__lowercase    )

  if config is None:
   assert isinstance(self.model   ,   __lowercase    ), (
       "If no `config` is passed the model to be trained has to be of type `PreTrainedModel`, but is"
       F""" {self.model.__class__}"""
   )
   __A	:		Tuple	  =      self.model.config
  else:
   __A	:		Tuple	  =      config

  __A	:		Any	  =      data_args
  __A	:		Dict	  =      self.config.tgt_vocab_size if isinstance(self.config   ,   __lowercase    ) 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[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

   __A	:		int	  =      label_smoothed_nll_loss


 def 			snake_case__ (   self   ,   __lowercase    ):

  """simple docstring"""







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

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


 def 			snake_case__ (   self   ,   __lowercase    ):

  """simple docstring"""







  __A	:		Dict	  =      arg_to_scheduler[self.args.lr_scheduler]
  if self.args.lr_scheduler == "constant":
   __A	:		Tuple	  =      schedule_func(self.optimizer    )
  elif self.args.lr_scheduler == "constant_w_warmup":
   __A	:		Tuple	  =      schedule_func(self.optimizer   ,   num_warmup_steps=self.args.warmup_steps    )
  else:
   __A	:		str	  =      schedule_func(
       self.optimizer   ,   num_warmup_steps=self.args.warmup_steps   ,   num_training_steps=__lowercase    )
  return scheduler


 def 			snake_case__ (   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 			snake_case__ (   self   ,   __lowercase   ,   __lowercase   ,   __lowercase    ):

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


 def 			snake_case__ (   self   ,   __lowercase   ,   __lowercase    ):

  """simple docstring"""







  __A	:		Union[str, Any]	  =      inputs.pop('labels'    )
  __A  ,__A	:		int	  =      self._compute_loss(__lowercase   ,   __lowercase   ,   __lowercase    )
  return loss


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

  """simple docstring"""







  __A	:		List[str]	  =      self._prepare_inputs(__lowercase    )

  __A	:		List[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']   ,   **__lowercase   ,   )
   # in case the batch is shorter than max length, the output should be padded
   if generated_tokens.shape[-1] < gen_kwargs["max_length"]:
    __A	:		str	  =      self._pad_tensors_to_max_len(__lowercase   ,   gen_kwargs['max_length']    )

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

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

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

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

  return (loss, logits, labels)







 def 			snake_case__ (   self   ,   __lowercase   ,   __lowercase    ):

  """simple docstring"""







  __A	:		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}"""    )

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










'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging


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

UpperCAmelCase_			:  List[str]										=   {
    'google/fnet-base': 'https://huggingface.co/google/fnet-base/resolve/main/config.json',
    'google/fnet-large': 'https://huggingface.co/google/fnet-large/resolve/main/config.json'
    # See all FNet models at https://huggingface.co/models?filter=fnet
}

class     _lowerCamelCase       (     snake_case_					):




 '''simple docstring'''
 __lowercase					:		List[Any]     				=    '''fnet'''


 def __init__(   self   ,   __lowercase=32_000   ,   __lowercase=768   ,   __lowercase=12   ,   __lowercase=3_072   ,   __lowercase="gelu_new"   ,   __lowercase=0.1   ,   __lowercase=512   ,   __lowercase=4   ,   __lowercase=0.0_2   ,   __lowercase=1E-12   ,   __lowercase=False   ,   __lowercase=512   ,   __lowercase=3   ,   __lowercase=1   ,   __lowercase=2   ,   **__lowercase   ,   ):

  """simple docstring"""







  super().__init__(pad_token_id=__lowercase   ,   bos_token_id=__lowercase   ,   eos_token_id=__lowercase   ,   **__lowercase    )

  __A	:		Union[str, Any]	  =      vocab_size
  __A	:		Dict	  =      max_position_embeddings
  __A	:		List[str]	  =      hidden_size
  __A	:		Tuple	  =      num_hidden_layers
  __A	:		Optional[int]	  =      intermediate_size
  __A	:		Dict	  =      hidden_act
  __A	:		List[str]	  =      hidden_dropout_prob
  __A	:		str	  =      initializer_range
  __A	:		Dict	  =      type_vocab_size
  __A	:		int	  =      layer_norm_eps
  __A	:		Tuple	  =      use_tpu_fourier_optimizations
  __A	:		Optional[Any]	  =      tpu_short_seq_length






