
'''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


lowercase					:							Optional[Any]   =      logging.get_logger(__name__)

lowercase					:							Union[str, Any]   =      {
    "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   (      _lowerCamelCase		):


							def __init__( self					,     lowerCAmelCase_=None					,     lowerCAmelCase_=None					,     *lowerCAmelCase_					,     **lowerCAmelCase_						):





								"""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__}'
									)
									_snake_case     		=	self.model.config
								else:
									_snake_case     		=	config

								_snake_case     		=	data_args
								_snake_case     		=	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:
									_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

									_snake_case     		=	label_smoothed_nll_loss


							def 				lowerCamelCase       ( self					,     lowerCAmelCase_						):





								"""simple docstring"""






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

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


							def 				lowerCamelCase       ( self					,     lowerCAmelCase_						):





								"""simple docstring"""






								_snake_case     		=	arg_to_scheduler[self.args.lr_scheduler]
								if self.args.lr_scheduler == "constant":
									_snake_case     		=	schedule_func(self.optimizer						)
								elif self.args.lr_scheduler == "constant_w_warmup":
									_snake_case     		=	schedule_func(self.optimizer					,     num_warmup_steps=self.args.warmup_steps						)
								else:
									_snake_case     		=	schedule_func(
									    self.optimizer					,     num_warmup_steps=self.args.warmup_steps					,     num_training_steps=lowerCAmelCase_						)
								return scheduler


							def 				lowerCamelCase       ( self						):





								"""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 				lowerCamelCase       ( self					,     lowerCAmelCase_					,     lowerCAmelCase_					,     lowerCAmelCase_						):





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


							def 				lowerCamelCase       ( self					,     lowerCAmelCase_					,     lowerCAmelCase_						):





								"""simple docstring"""






								_snake_case     		=	inputs.pop('labels'						)
								_snake_case		,				_snake_case     		=	self._compute_loss(lowerCAmelCase_					,     lowerCAmelCase_					,     lowerCAmelCase_						)
								return loss


							def 				lowerCamelCase       ( self					,     lowerCAmelCase_					,     lowerCAmelCase_					,     lowerCAmelCase_					,     lowerCAmelCase_ = None					,     ):





								"""simple docstring"""






								_snake_case     		=	self._prepare_inputs(lowerCAmelCase_						)

								_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:
									_snake_case     		=	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"]:
										_snake_case     		=	self._pad_tensors_to_max_len(lowerCAmelCase_					,     gen_kwargs['max_length']						)

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

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

								_snake_case     		=	generated_tokens if self.args.predict_with_generate else logits

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

								return (loss, logits, labels)




							def 				lowerCamelCase       ( self					,     lowerCAmelCase_					,     lowerCAmelCase_						):





								"""simple docstring"""






								_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}'						)

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



'''simple docstring'''







import math







def        SCREAMING_SNAKE_CASE__			(     __A    ,       __A			) ->			float:
	if (
	    not isinstance(__A    ,       (int, float)			)
	    or power_factor < -1
	    or power_factor > 1
	):
		raise ValueError('power_factor must be a valid float value between -1 and 1.'			)
	return apparent_power * power_factor







def        SCREAMING_SNAKE_CASE__			(     __A    ,       __A			) ->			float:
	if (
	    not isinstance(__A    ,       (int, float)			)
	    or power_factor < -1
	    or power_factor > 1
	):
		raise ValueError('power_factor must be a valid float value between -1 and 1.'			)
	return apparent_power * math.sqrt(1 - power_factor**2			)


if __name__ == "__main__":
					import doctest

					doctest.testmod()

