
'''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__      :		Dict       							=  logging.get_logger(__name__)

UpperCamelCase__      :		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    _a				(_lowerCamelCase):





       """simple docstring"""




       def __init__(							self       ,							A__=None       ,							A__=None       ,							*A__       ,							**A__      )    ->			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      =				self.model.config
          else:
             _SCREAMING_SNAKE_CASE      =				config

          _SCREAMING_SNAKE_CASE      =				data_args
          _SCREAMING_SNAKE_CASE      =				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      =				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      =				label_smoothed_nll_loss






       def        UpperCamelCase				(							self       ,							A__      )    ->			Any:
          if self.optimizer is None:
             _SCREAMING_SNAKE_CASE      =				["""bias""", """LayerNorm.weight"""]
             _SCREAMING_SNAKE_CASE      =				[
                 {
                     """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      =				Adafactor if self.args.adafactor else AdamW
             if self.args.adafactor:
                _SCREAMING_SNAKE_CASE      =				Adafactor
                _SCREAMING_SNAKE_CASE      =				{"""scale_parameter""": False, """relative_step""": False}
             else:
                _SCREAMING_SNAKE_CASE      =				AdamW
                _SCREAMING_SNAKE_CASE      =				{
                    """betas""": (self.args.adam_betaa, self.args.adam_betaa),
                    """eps""": self.args.adam_epsilon,
                }
             _SCREAMING_SNAKE_CASE      =				self.args.learning_rate
             if self.sharded_ddp:
                _SCREAMING_SNAKE_CASE      =				OSS(
                    params=A__       ,							optim=A__       ,							**A__       ,							)
             else:
                _SCREAMING_SNAKE_CASE      =				optimizer_cls(A__       ,							**A__      )

          if self.lr_scheduler is None:
             _SCREAMING_SNAKE_CASE      =				self._get_lr_scheduler(A__      )
          else:  # ignoring --lr_scheduler
             logger.warning("""scheduler is passed to `Seq2SeqTrainer`, `--lr_scheduler` arg is ignored."""      )






       def        UpperCamelCase				(							self       ,							A__      )    ->			int:
          _SCREAMING_SNAKE_CASE      =				arg_to_scheduler[self.args.lr_scheduler]
          if self.args.lr_scheduler == "constant":
             _SCREAMING_SNAKE_CASE      =				schedule_func(self.optimizer      )
          elif self.args.lr_scheduler == "constant_w_warmup":
             _SCREAMING_SNAKE_CASE      =				schedule_func(self.optimizer       ,							num_warmup_steps=self.args.warmup_steps      )
          else:
             _SCREAMING_SNAKE_CASE      =				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       ,							A__       ,							A__       ,							A__      )    ->			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      =				model(**A__       ,							use_cache=A__      )[0]
                _SCREAMING_SNAKE_CASE      =				self.loss_fn(logits.view(-1       ,							logits.shape[-1]      )       ,							labels.view(-1      )      )
             else:
                # compute usual loss via models
                _SCREAMING_SNAKE_CASE ,							_SCREAMING_SNAKE_CASE      =				model(**A__       ,							labels=A__       ,							use_cache=A__      )[:2]
          else:
             # compute label smoothed loss
             _SCREAMING_SNAKE_CASE      =				model(**A__       ,							use_cache=A__      )[0]
             _SCREAMING_SNAKE_CASE      =				torch.nn.functional.log_softmax(A__       ,							dim=-1      )
             _SCREAMING_SNAKE_CASE ,							_SCREAMING_SNAKE_CASE      =				self.loss_fn(A__       ,							A__       ,							self.args.label_smoothing       ,							ignore_index=self.config.pad_token_id      )
          return loss, logits






       def        UpperCamelCase				(							self       ,							A__       ,							A__      )    ->			Optional[int]:
          _SCREAMING_SNAKE_CASE      =				inputs.pop("""labels"""      )
          _SCREAMING_SNAKE_CASE ,							_SCREAMING_SNAKE_CASE      =				self._compute_loss(A__       ,							A__       ,							A__      )
          return loss






       def        UpperCamelCase				(							self       ,							A__       ,							A__       ,							A__       ,							A__ = None       ,							)    ->			Tuple[Optional[float], Optional[torch.Tensor], Optional[torch.Tensor]]:
          _SCREAMING_SNAKE_CASE      =				self._prepare_inputs(A__      )

          _SCREAMING_SNAKE_CASE      =				{
              """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      =				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      =				self._pad_tensors_to_max_len(A__       ,							gen_kwargs["""max_length"""]      )

          _SCREAMING_SNAKE_CASE      =				inputs.pop("""labels"""      )
          with torch.no_grad():
             # compute loss on predict data
             _SCREAMING_SNAKE_CASE ,							_SCREAMING_SNAKE_CASE      =				self._compute_loss(A__       ,							A__       ,							A__      )

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

          _SCREAMING_SNAKE_CASE      =				generated_tokens if self.args.predict_with_generate else logits

          if labels.shape[-1] < gen_kwargs["max_length"]:
             _SCREAMING_SNAKE_CASE      =				self._pad_tensors_to_max_len(A__       ,							gen_kwargs["""max_length"""]      )

          return (loss, logits, labels)






       def        UpperCamelCase				(							self       ,							A__       ,							A__      )    ->			Optional[Any]:
          # If PAD token is not defined at least EOS token has to be defined
          _SCREAMING_SNAKE_CASE      =				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      =				pad_token_id * torch.ones(
              (tensor.shape[0], max_length)       ,							dtype=tensor.dtype       ,							device=tensor.device      )
          _SCREAMING_SNAKE_CASE      =				tensor
          return padded_tensor








'''simple docstring'''



import sys

UpperCamelCase__      :		int       							=  (
    "73167176531330624919225119674426574742355349194934"
    "96983520312774506326239578318016984801869478851843"
    "85861560789112949495459501737958331952853208805511"
    "12540698747158523863050715693290963295227443043557"
    "66896648950445244523161731856403098711121722383113"
    "62229893423380308135336276614282806444486645238749"
    "30358907296290491560440772390713810515859307960866"
    "70172427121883998797908792274921901699720888093776"
    "65727333001053367881220235421809751254540594752243"
    "52584907711670556013604839586446706324415722155397"
    "53697817977846174064955149290862569321978468622482"
    "83972241375657056057490261407972968652414535100474"
    "82166370484403199890008895243450658541227588666881"
    "16427171479924442928230863465674813919123162824586"
    "17866458359124566529476545682848912883142607690042"
    "24219022671055626321111109370544217506941658960408"
    "07198403850962455444362981230987879927244284909188"
    "84580156166097919133875499200524063689912560717606"
    "05886116467109405077541002256983155200055935729725"
    "71636269561882670428252483600823257530420752963450"
)
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_ = N						)     ->     int:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE      =				-sys.maxsize - 1
   for i in range(len(SCREAMING_SNAKE_CASE_						) - 12						):
      _SCREAMING_SNAKE_CASE      =				1
      for j in range(13						):
         product *= int(n[i + j]						)
      if product > largest_product:
         _SCREAMING_SNAKE_CASE      =				product
   return largest_product


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






