





"""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 		SCREAMING_SNAKE_CASE_				(     snake_case__ ):
				"""simple docstring"""




				def __init__(  self	:List[str]    ,     __lowercase	:Dict=None    ,     __lowercase	:Union[str, Any]=None    ,     *__lowercase	:Tuple    ,     **__lowercase	:List[str]      ):
											super().__init__(*__lowercase    ,     **__lowercase      )

											if config is None:
																		assert isinstance(self.model    ,     __lowercase      ), (
																		    "If no `config` is passed the model to be trained has to be of type `PreTrainedModel`, but is"
																		    f' {self.model.__class__}'
																		)
																		__lowerCamelCase   :   Optional[Any]       =self.model.config
											else:
																		__lowerCamelCase   :   Union[str, Any]       =config

											__lowerCamelCase   :   Dict       =data_args
											__lowerCamelCase   :   List[str]       =self.config.tgt_vocab_size if isinstance(self.config    ,     __lowercase      ) 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:
																		__lowerCamelCase   :   Optional[Any]       =torch.nn.CrossEntropyLoss(ignore_index=self.config.pad_token_id      )
											else:
																		# dynamically import label_smoothed_nll_loss
																		from utils import label_smoothed_nll_loss

																		__lowerCamelCase   :   int       =label_smoothed_nll_loss




				def        __lowercase	(  self	:Any    ,     __lowercase	:int      ):
											if self.optimizer is None:
																		__lowerCamelCase   :   Union[str, Any]       =['''bias''', '''LayerNorm.weight''']
																		__lowerCamelCase   :   int       =[
																		    {
																		        '''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,
																		    },
																		]
																		__lowerCamelCase   :   Optional[Any]       =Adafactor if self.args.adafactor else AdamW
																		if self.args.adafactor:
																									__lowerCamelCase   :   Tuple       =Adafactor
																									__lowerCamelCase   :   Optional[int]       ={'''scale_parameter''': False, '''relative_step''': False}
																		else:
																									__lowerCamelCase   :   Any       =AdamW
																									__lowerCamelCase   :   List[Any]       ={
																									    '''betas''': (self.args.adam_betaa, self.args.adam_betaa),
																									    '''eps''': self.args.adam_epsilon,
																									}
																		__lowerCamelCase   :   Optional[int]       =self.args.learning_rate
																		if self.sharded_ddp:
																									__lowerCamelCase   :   List[str]       =OSS(
																									    params=__lowercase    ,     optim=__lowercase    ,     **__lowercase    ,     )
																		else:
																									__lowerCamelCase   :   str       =optimizer_cls(__lowercase    ,     **__lowercase      )

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




				def        __lowercase	(  self	:Any    ,     __lowercase	:str      ):
											__lowerCamelCase   :   Union[str, Any]       =arg_to_scheduler[self.args.lr_scheduler]
											if self.args.lr_scheduler == "constant":
																		__lowerCamelCase   :   Any       =schedule_func(self.optimizer      )
											elif self.args.lr_scheduler == "constant_w_warmup":
																		__lowerCamelCase   :   str       =schedule_func(self.optimizer    ,     num_warmup_steps=self.args.warmup_steps      )
											else:
																		__lowerCamelCase   :   Optional[int]       =schedule_func(
																		    self.optimizer    ,     num_warmup_steps=self.args.warmup_steps    ,     num_training_steps=__lowercase      )
											return scheduler




				def        __lowercase	(  self	:Tuple      ):
											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	:List[Any]    ,     __lowercase	:int    ,     __lowercase	:Any    ,     __lowercase	: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
																									__lowerCamelCase   :   List[Any]       =model(**__lowercase    ,     use_cache=__lowercase      )[0]
																									__lowerCamelCase   :   Any       =self.loss_fn(logits.view(-1    ,     logits.shape[-1]      )    ,     labels.view(-1      )      )
																		else:
																									# compute usual loss via models
																									__lowerCamelCase     ,    __lowerCamelCase   :   Optional[Any]       =model(**__lowercase    ,     labels=__lowercase    ,     use_cache=__lowercase      )[:2]
											else:
																		# compute label smoothed loss
																		__lowerCamelCase   :   Optional[int]       =model(**__lowercase    ,     use_cache=__lowercase      )[0]
																		__lowerCamelCase   :   int       =torch.nn.functional.log_softmax(__lowercase    ,     dim=-1      )
																		__lowerCamelCase     ,    __lowerCamelCase   :   Dict       =self.loss_fn(__lowercase    ,     __lowercase    ,     self.args.label_smoothing    ,     ignore_index=self.config.pad_token_id      )
											return loss, logits




				def        __lowercase	(  self	:int    ,     __lowercase	:Tuple    ,     __lowercase	:List[str]      ):
											__lowerCamelCase   :   Tuple       =inputs.pop('''labels'''      )
											__lowerCamelCase     ,    __lowerCamelCase   :   Tuple       =self._compute_loss(__lowercase    ,     __lowercase    ,     __lowercase      )
											return loss




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

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

											__lowerCamelCase   :   List[Any]       =inputs.pop('''labels'''      )
											with torch.no_grad():
																		# compute loss on predict data
																		__lowerCamelCase     ,    __lowerCamelCase   :   Optional[int]       =self._compute_loss(__lowercase    ,     __lowercase    ,     __lowercase      )

											__lowerCamelCase   :   List[str]       =loss.mean().detach()
											if self.args.prediction_loss_only:
																		return (loss, None, None)

											__lowerCamelCase   :   Optional[int]       =generated_tokens if self.args.predict_with_generate else logits

											if labels.shape[-1] < gen_kwargs["max_length"]:
																		__lowerCamelCase   :   Union[str, Any]       =self._pad_tensors_to_max_len(__lowercase    ,     gen_kwargs['''max_length''']      )

											return (loss, logits, labels)




				def        __lowercase	(  self	:List[str]    ,     __lowercase	:List[str]    ,     __lowercase	:Optional[Any]      ):
											# If PAD token is not defined at least EOS token has to be defined
											__lowerCamelCase   :   List[str]       =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}'      )

											__lowerCamelCase   :   List[str]       =pad_token_id * torch.ones(
											    (tensor.shape[0], max_length)    ,     dtype=tensor.dtype    ,     device=tensor.device      )
											__lowerCamelCase   :   List[str]       =tensor
											return padded_tensor











"""simple docstring"""





import numpy as np



def 					lowerCAmelCase_      (   SCREAMING_SNAKE_CASE							:				np.array ):


							'''simple docstring'''







							return 1 / (1 + np.exp(-vector ))


if __name__ == "__main__":
							import doctest

							doctest.testmod()




