
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 math
from collections.abc import Iterator
from itertools import takewhile
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_						)     ->     bool:




   """simple docstring"""

   if 1 < number < 4:
      # 2 and 3 are primes
      return True
   elif number < 2 or number % 2 == 0 or number % 3 == 0:
      # Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
      return False

   # All primes number are in format of 6k +/- 1
   for i in range(5    ,      int(math.sqrt(SCREAMING_SNAKE_CASE_						) + 1						)    ,      6						):
      if number % i == 0 or number % (i + 2) == 0:
         return False
   return True
def    lowerCAmelCase_	(				)     ->     Iterator[int]:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE      =				2
   while True:
      if is_prime(SCREAMING_SNAKE_CASE_						):
         yield num
      num += 1
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_ = 2_00_00_00						)     ->     int:




   """simple docstring"""
   return sum(takewhile(lambda SCREAMING_SNAKE_CASE_						: x < n    ,      prime_generator()						)						)


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






