


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


__snake_case   							=						logging.get_logger(__name__)

__snake_case   							=						{
    '''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			(_a						):







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






            '''simple docstring'''




            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__}'''
                 )
                 __UpperCamelCase =			self.model.config
            else:
                 __UpperCamelCase =			config

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

                 __UpperCamelCase =			label_smoothed_nll_loss







       def 		snake_case_	(			self: Dict,A_: int					):






            '''simple docstring'''




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

            if self.lr_scheduler is None:
                 __UpperCamelCase =			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: Any,A_: Optional[Any]					):






            '''simple docstring'''




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







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







       def 		snake_case_	(			self: Tuple,A_: List[Any],A_: List[Any]					):






            '''simple docstring'''




            __UpperCamelCase =			inputs.pop('labels'					)
            __UpperCamelCase,    __UpperCamelCase =			self._compute_loss(A_,A_,A_					)
            return loss







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






            '''simple docstring'''




            __UpperCamelCase =			self._prepare_inputs(A_					)

            __UpperCamelCase =			{
                '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:
                 __UpperCamelCase =			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"]:
                      __UpperCamelCase =			self._pad_tensors_to_max_len(A_,gen_kwargs['max_length']					)

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

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

            __UpperCamelCase =			generated_tokens if self.args.predict_with_generate else logits

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

            return (loss, logits, labels)







       def 		snake_case_	(			self: List[Any],A_: Tuple,A_: Optional[Any]					):






            '''simple docstring'''




            __UpperCamelCase =			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}'''					)

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






import string






def 		_A		(		_lowercase )					-> None:


     """simple docstring"""


     for key in range(len(string.ascii_uppercase ) ):
          __UpperCamelCase =			''
          for symbol in message:
               if symbol in string.ascii_uppercase:
                    __UpperCamelCase =			string.ascii_uppercase.find(_lowercase )
                    __UpperCamelCase =			num - key
                    if num < 0:
                         __UpperCamelCase =			num + len(string.ascii_uppercase )
                    __UpperCamelCase =			translated + string.ascii_uppercase[num]
               else:
                    __UpperCamelCase =			translated + symbol
          print(f'''Decryption using Key #{key}: {translated}''' )






def 		_A		(		)					-> None:


     """simple docstring"""


     __UpperCamelCase =			input('Encrypted message: ' )
     __UpperCamelCase =			message.upper()
     decrypt(_lowercase )


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    main()


