

"""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													=logging.get_logger(__name__)

UpperCAmelCase													={
    "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__   (				SCREAMING_SNAKE_CASE    ):



    '''simple docstring'''




    def __init__( self ,lowerCamelCase_=None ,lowerCamelCase_=None ,*lowerCamelCase_ ,**lowerCamelCase_    )    ->      Optional[int]:
          super().__init__(*lowerCamelCase_ ,**lowerCamelCase_    )

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

          A				  =		data_args
          A				  =		self.config.tgt_vocab_size if isinstance(self.config ,lowerCamelCase_    ) 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				  =		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				  =		label_smoothed_nll_loss




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

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




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




    def UpperCamelCase__		( self ,lowerCamelCase_ ,lowerCamelCase_    )    ->      Optional[Any]:
          A				  =		inputs.pop("""labels"""    )
          A  ,			A				  =		self._compute_loss(lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_    )
          return loss




    def UpperCamelCase__		( self ,lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ = None ,)    ->      Tuple[Optional[float], Optional[torch.Tensor], Optional[torch.Tensor]]:
          A				  =		self._prepare_inputs(lowerCamelCase_    )

          A				  =		{
              """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				  =		self.model.generate(
                    inputs["""input_ids"""] ,attention_mask=inputs["""attention_mask"""] ,**lowerCamelCase_ ,)
                # in case the batch is shorter than max length, the output should be padded
                if generated_tokens.shape[-1] < gen_kwargs["max_length"]:
                      A				  =		self._pad_tensors_to_max_len(lowerCamelCase_ ,gen_kwargs["""max_length"""]    )

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

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

          A				  =		generated_tokens if self.args.predict_with_generate else logits

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

          return (loss, logits, labels)




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







"""simple docstring"""


from __future__ import annotations



def   _A					(	_a	:						list[float]     ,   _a	:						list[float]				):



      """simple docstring"""






      A				  =		sorted(numsa + numsa				)
      A  ,			A				  =		divmod(len(_a				)     ,   2				)
      if mod == 1:
            return all_numbers[div]
      else:
            return (all_numbers[div] + all_numbers[div - 1]) / 2


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  UpperCAmelCase													=[float(x) for x in input("Enter the elements of first array: ").split()]
  UpperCAmelCase													=[float(x) for x in input("Enter the elements of second array: ").split()]
  print(f"""The median of two arrays is: {median_of_two_arrays(array_a, array_a)}""")




