



class 	_lowercase							:





	"""simple docstring"""




	def __init__(self		,       lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		):
						"""simple docstring"""

						a					     =   name
						a					     =   value
						a					     =   weight




	def __repr__(self		):
						"""simple docstring"""

						return F'''{self.__class__.__name__}({self.name}, {self.value}, {self.weight})'''




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						return self.value




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						return self.name




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						return self.weight




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						return self.value / self.weight






def     a(      A       :    Optional[int] ,     A       :    Optional[Any] ,     A       :    Optional[int]       )		->       List[Any]:
					"""simple docstring"""





					a					     =   []
					for i in range(len(lowerCamelCase_       )       ):
										menu.append(Things(name[i] ,     value[i] ,     weight[i]       )       )
					return menu






def     a(      A       :    Union[str, Any] ,     A       :    Dict ,     A       :    str       )		->       Any:
					"""simple docstring"""





					a					     =   sorted(lowerCamelCase_ ,     key=lowerCamelCase_ ,     reverse=lowerCamelCase_       )
					a					     =   []
					a     ,				a					     =   0.0, 0.0
					for i in range(len(lowerCamelCase_       )       ):
										if (total_cost + items_copy[i].get_weight()) <= max_cost:
															result.append(items_copy[i]       )
															total_cost += items_copy[i].get_weight()
															total_value += items_copy[i].get_value()
					return (result, total_value)






def     a(      )		->       Tuple:
					"""simple docstring"""







if __name__ == "__main__":
				import doctest

				doctest.testmod()








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


A__	:			Tuple			   = logging.get_logger(__name__)

A__	:			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       _UpperCAmelCase					(       A__	):

				"""simple docstring"""




				def __init__(     self						:    Optional[int],  lowerCamelCase						:    int=None,  lowerCamelCase						:    int=None,  *lowerCamelCase						:    List[Any],  **lowerCamelCase						:    Any  ):

								'''simple docstring'''
								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__}"""
												)
												lowercase__    =			self.model.config
								else:
												lowercase__    =			config

								lowercase__    =			data_args
								lowercase__    =			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:
												lowercase__    =			torch.nn.CrossEntropyLoss(ignore_index=self.config.pad_token_id  )
								else:
												# dynamically import label_smoothed_nll_loss
												from utils import label_smoothed_nll_loss

												lowercase__    =			label_smoothed_nll_loss




				def 				lowercase__   (     self						:    List[Any],  lowerCamelCase						:    int  ):

								'''simple docstring'''
								if self.optimizer is None:
												lowercase__    =			['''bias''', '''LayerNorm.weight''']
												lowercase__    =			[
												    {
												        '''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,
												    },
												]
												lowercase__    =			Adafactor if self.args.adafactor else AdamW
												if self.args.adafactor:
																lowercase__    =			Adafactor
																lowercase__    =			{'''scale_parameter''': False, '''relative_step''': False}
												else:
																lowercase__    =			AdamW
																lowercase__    =			{
																    '''betas''': (self.args.adam_betaa, self.args.adam_betaa),
																    '''eps''': self.args.adam_epsilon,
																}
												lowercase__    =			self.args.learning_rate
												if self.sharded_ddp:
																lowercase__    =			OSS(
																    params=lowerCamelCase,  optim=lowerCamelCase,  **lowerCamelCase,  )
												else:
																lowercase__    =			optimizer_cls(lowerCamelCase,  **lowerCamelCase  )

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




				def 				lowercase__   (     self						:    List[str],  lowerCamelCase						:    Optional[int]  ):

								'''simple docstring'''
								lowercase__    =			arg_to_scheduler[self.args.lr_scheduler]
								if self.args.lr_scheduler == "constant":
												lowercase__    =			schedule_func(self.optimizer  )
								elif self.args.lr_scheduler == "constant_w_warmup":
												lowercase__    =			schedule_func(self.optimizer,  num_warmup_steps=self.args.warmup_steps  )
								else:
												lowercase__    =			schedule_func(
												    self.optimizer,  num_warmup_steps=self.args.warmup_steps,  num_training_steps=lowerCamelCase  )
								return scheduler




				def 				lowercase__   (     self						:    List[Any]  ):

								'''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 				lowercase__   (     self						:    Any,  lowerCamelCase						:    List[Any],  lowerCamelCase						:    str,  lowerCamelCase						:    Union[str, Any]  ):

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




				def 				lowercase__   (     self						:    List[str],  lowerCamelCase						:    Optional[Any],  lowerCamelCase						:    Tuple  ):

								'''simple docstring'''
								lowercase__    =			inputs.pop('''labels'''  )
								lowercase__ ,						lowercase__    =			self._compute_loss(lowerCamelCase,  lowerCamelCase,  lowerCamelCase  )
								return loss




				def 				lowercase__   (     self						:    str,  lowerCamelCase						:    nn.Module,  lowerCamelCase						:    Dict[str, Union[torch.Tensor, Any]],  lowerCamelCase						:    bool,  lowerCamelCase						:    Optional[List[str]] = None,  ):

								'''simple docstring'''
								lowercase__    =			self._prepare_inputs(lowerCamelCase  )

								lowercase__    =			{
								    '''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:
												lowercase__    =			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"]:
																lowercase__    =			self._pad_tensors_to_max_len(lowerCamelCase,  gen_kwargs['''max_length''']  )

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

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

								lowercase__    =			generated_tokens if self.args.predict_with_generate else logits

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

								return (loss, logits, labels)




				def 				lowercase__   (     self						:    List[Any],  lowerCamelCase						:    str,  lowerCamelCase						:    Any  ):

								'''simple docstring'''
								# If PAD token is not defined at least EOS token has to be defined
								lowercase__    =			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}"""  )

								lowercase__    =			pad_token_id * torch.ones(
								    (tensor.shape[0], max_length),  dtype=tensor.dtype,  device=tensor.device  )
								lowercase__    =			tensor
								return padded_tensor




