'''simple docstring'''


def  _lowerCamelCase   (				lowerCamelCase_ :							str       ):



     """simple docstring"""





     if n_term == "":
          return []
     UpperCAmelCase_							:       list       =				[]
     for temp in range(int(lowerCamelCase_       )       ):
          series.append(F'''1/{temp + 1}''' if series else '1'       )
     return series


if __name__ == "__main__":
 snake_case__					:      int				      =  input('''Enter the last number (nth term) of the Harmonic Series''')
 print('''Formula of Harmonic Series => 1+1/2+1/3 ..... 1/n''')
 print(harmonic_series(nth_term))







'''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


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

snake_case__					:      List[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 					__SCREAMING_SNAKE_CASE						(	lowerCamelCase_	):







  '''simple docstring'''






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




       '''simple docstring'''

       super().__init__(*snake_case_				,		**snake_case_						)

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

       UpperCAmelCase_							:       Optional[Any]       =				data_args
       UpperCAmelCase_							:       Dict       =				self.config.tgt_vocab_size if isinstance(self.config				,		snake_case_						) 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_							:       Dict       =				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_							:       Union[str, Any]       =				label_smoothed_nll_loss


  def _UpperCamelCase					(			self				,		snake_case_						):




       '''simple docstring'''

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

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


  def _UpperCamelCase					(			self				,		snake_case_						):




       '''simple docstring'''

       UpperCAmelCase_							:       int       =				arg_to_scheduler[self.args.lr_scheduler]
       if self.args.lr_scheduler == "constant":
            UpperCAmelCase_							:       List[Any]       =				schedule_func(self.optimizer						)
       elif self.args.lr_scheduler == "constant_w_warmup":
            UpperCAmelCase_							:       Optional[Any]       =				schedule_func(self.optimizer				,		num_warmup_steps=self.args.warmup_steps						)
       else:
            UpperCAmelCase_							:       int       =				schedule_func(
                self.optimizer				,		num_warmup_steps=self.args.warmup_steps				,		num_training_steps=snake_case_						)
       return scheduler


  def _UpperCamelCase					(			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 _UpperCamelCase					(			self				,		snake_case_				,		snake_case_				,		snake_case_						):




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


  def _UpperCamelCase					(			self				,		snake_case_				,		snake_case_						):




       '''simple docstring'''

       UpperCAmelCase_							:       Union[str, Any]       =				inputs.pop('labels'						)
       UpperCAmelCase_       ,      UpperCAmelCase_							:       Dict       =				self._compute_loss(snake_case_				,		snake_case_				,		snake_case_						)
       return loss


  def _UpperCamelCase					(			self				,		snake_case_				,		snake_case_				,		snake_case_				,		snake_case_ = None				,		):




       '''simple docstring'''

       UpperCAmelCase_							:       Optional[Any]       =				self._prepare_inputs(snake_case_						)

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

       UpperCAmelCase_							:       List[str]       =				inputs.pop('labels'						)
       with torch.no_grad():
            # compute loss on predict data
            UpperCAmelCase_       ,      UpperCAmelCase_							:       List[Any]       =				self._compute_loss(snake_case_				,		snake_case_				,		snake_case_						)

       UpperCAmelCase_							:       Optional[Any]       =				loss.mean().detach()
       if self.args.prediction_loss_only:
            return (loss, None, None)

       UpperCAmelCase_							:       str       =				generated_tokens if self.args.predict_with_generate else logits

       if labels.shape[-1] < gen_kwargs["max_length"]:
            UpperCAmelCase_							:       List[Any]       =				self._pad_tensors_to_max_len(snake_case_				,		gen_kwargs['max_length']						)

       return (loss, logits, labels)






  def _UpperCamelCase					(			self				,		snake_case_				,		snake_case_						):




       '''simple docstring'''

       UpperCAmelCase_							:       Any       =				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_							:       Tuple       =				pad_token_id * torch.ones(
           (tensor.shape[0], max_length)				,		dtype=tensor.dtype				,		device=tensor.device						)
       UpperCAmelCase_							:       Dict       =				tensor
       return padded_tensor






