


"""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


_snake_case          =							logging.get_logger(__name__)

_snake_case          =							{
    "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   _a      (      SCREAMING_SNAKE_CASE_ ):
    def __init__( self							:					str							,					SCREAMING_SNAKE_CASE__							:					Dict=None							,					SCREAMING_SNAKE_CASE__							:					Optional[Any]=None							,					*SCREAMING_SNAKE_CASE__							:					Dict							,					**SCREAMING_SNAKE_CASE__							:					List[str]				):
          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__}'
                )
                lowerCamelCase__           =		self.model.config
          else:
                lowerCamelCase__           =		config

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

          if self.lr_scheduler is None:
                lowerCamelCase__           =		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							:					Any							,					SCREAMING_SNAKE_CASE__							:					Union[str, Any]				):
          lowerCamelCase__           =		arg_to_scheduler[self.args.lr_scheduler]
          if self.args.lr_scheduler == "constant":
                lowerCamelCase__           =		schedule_func(self.optimizer				)
          elif self.args.lr_scheduler == "constant_w_warmup":
                lowerCamelCase__           =		schedule_func(self.optimizer							,					num_warmup_steps=self.args.warmup_steps				)
          else:
                lowerCamelCase__           =		schedule_func(
                    self.optimizer							,					num_warmup_steps=self.args.warmup_steps							,					num_training_steps=SCREAMING_SNAKE_CASE__				)
          return scheduler
    def      _UpperCamelCase       ( self							:					Any				):
          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							:					str							,					SCREAMING_SNAKE_CASE__							:					Tuple							,					SCREAMING_SNAKE_CASE__							:					List[Any]							,					SCREAMING_SNAKE_CASE__							:					Tuple				):
          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__           =		model(**SCREAMING_SNAKE_CASE__							,					use_cache=SCREAMING_SNAKE_CASE__				)[0]
                      lowerCamelCase__           =		self.loss_fn(logits.view(-1							,					logits.shape[-1]				)							,					labels.view(-1				)				)
                else:
                      # compute usual loss via models
                      lowerCamelCase__    ,  lowerCamelCase__           =		model(**SCREAMING_SNAKE_CASE__							,					labels=SCREAMING_SNAKE_CASE__							,					use_cache=SCREAMING_SNAKE_CASE__				)[:2]
          else:
                # compute label smoothed loss
                lowerCamelCase__           =		model(**SCREAMING_SNAKE_CASE__							,					use_cache=SCREAMING_SNAKE_CASE__				)[0]
                lowerCamelCase__           =		torch.nn.functional.log_softmax(SCREAMING_SNAKE_CASE__							,					dim=-1				)
                lowerCamelCase__    ,  lowerCamelCase__           =		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							:					Tuple							,					SCREAMING_SNAKE_CASE__							:					int							,					SCREAMING_SNAKE_CASE__							:					int				):
          lowerCamelCase__           =		inputs.pop('labels'				)
          lowerCamelCase__    ,  lowerCamelCase__           =		self._compute_loss(SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__				)
          return loss
    def      _UpperCamelCase       ( self							:					str							,					SCREAMING_SNAKE_CASE__							:					nn.Module							,					SCREAMING_SNAKE_CASE__							:					Dict[str, Union[torch.Tensor, Any]]							,					SCREAMING_SNAKE_CASE__							:					bool							,					SCREAMING_SNAKE_CASE__							:					Optional[List[str]] = None							,					):
          lowerCamelCase__           =		self._prepare_inputs(SCREAMING_SNAKE_CASE__				)

          lowerCamelCase__           =		{
              '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__           =		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"]:
                      lowerCamelCase__           =		self._pad_tensors_to_max_len(SCREAMING_SNAKE_CASE__							,					gen_kwargs['max_length']				)

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

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

          lowerCamelCase__           =		generated_tokens if self.args.predict_with_generate else logits

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

          return (loss, logits, labels)


    def      _UpperCamelCase       ( self							:					Optional[Any]							,					SCREAMING_SNAKE_CASE__							:					List[Any]							,					SCREAMING_SNAKE_CASE__							:					Tuple				):
          # If PAD token is not defined at least EOS token has to be defined
          lowerCamelCase__           =		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__           =		pad_token_id * torch.ones(
              (tensor.shape[0], max_length)							,					dtype=tensor.dtype							,					device=tensor.device				)
          lowerCamelCase__           =		tensor
          return padded_tensor







"""simple docstring"""


def   snake_case						(				_a:     int = 4000000							)->		int:
      '''simple docstring'''


      lowerCamelCase__           =		[0, 1]
      lowerCamelCase__           =		0
      while fib[i] <= n:
            fib.append(fib[i] + fib[i + 1]							)
            if fib[i + 2] > n:
                  break
            i += 1
      lowerCamelCase__           =		0
      for j in range(len(_a							) - 1							):
            if fib[j] % 2 == 0:
                  total += fib[j]

      return total


if __name__ == "__main__":
   print(f"""{solution() = }""")



