





'''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_					= logging.get_logger(__name__)

lowercase_					= {
    """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_     (				snake_case_       ):

    '''simple docstring'''


    def __init__(		self ,		A=None ,		A=None ,		*A ,		**A       )		->   Optional[int]:
        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 			snake_case_(		self ,		A       )		->   int:
        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 			snake_case_(		self ,		A       )		->   Optional[Any]:
        _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 			snake_case_(		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 			snake_case_(		self ,		A ,		A ,		A       )		->   List[str]:
        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 			snake_case_(		self ,		A ,		A       )		->   Union[str, Any]:
        _SCREAMING_SNAKE_CASE									=			inputs.pop("""labels"""       )
        _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE									=			self._compute_loss(A ,		A ,		A       )
        return loss


    def 			snake_case_(		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 			snake_case_(		self ,		A ,		A       )		->   int:
        # 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'''

from string import ascii_lowercase, ascii_uppercase





def lowerCamelCase						(     __lowerCamelCase :    str    )						->str:
    if not sentence:
        return ""
    _SCREAMING_SNAKE_CASE									=			dict(zip(__lowerCamelCase					,       __lowerCamelCase    )    )
    return lower_to_upper.get(sentence[0]					,       sentence[0]    ) + sentence[1:]


if __name__ == "__main__":
      from doctest import testmod

      testmod()



