






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


SCREAMING_SNAKE_CASE__		:							Optional[int] 				=				logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__		:							Optional[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		):
          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__}"
               )
               UpperCAmelCase__      :  Union[str, Any]				      =			self.model.config
          else:
               UpperCAmelCase__      :  Optional[Any]				      =			config

          UpperCAmelCase__      :  Dict				      =			data_args
          UpperCAmelCase__      :  Dict				      =			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:
               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__      :  List[str]				      =			label_smoothed_nll_loss




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

          if self.lr_scheduler is None:
               UpperCAmelCase__      :  str				      =			self._get_lr_scheduler(_lowerCAmelCase		)
          else:  # ignoring --lr_scheduler
               logger.warning("""scheduler is passed to `Seq2SeqTrainer`, `--lr_scheduler` arg is ignored."""		)




     def   __UpperCAmelCase  (				self	,						_lowerCAmelCase		):
          UpperCAmelCase__      :  Dict				      =			arg_to_scheduler[self.args.lr_scheduler]
          if self.args.lr_scheduler == "constant":
               UpperCAmelCase__      :  str				      =			schedule_func(self.optimizer		)
          elif self.args.lr_scheduler == "constant_w_warmup":
               UpperCAmelCase__      :  List[Any]				      =			schedule_func(self.optimizer	,						num_warmup_steps=self.args.warmup_steps		)
          else:
               UpperCAmelCase__      :  List[Any]				      =			schedule_func(
                   self.optimizer	,						num_warmup_steps=self.args.warmup_steps	,						num_training_steps=_lowerCAmelCase		)
          return scheduler




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




     def   __UpperCAmelCase  (				self	,						_lowerCAmelCase	,						_lowerCAmelCase		):
          UpperCAmelCase__      :  Optional[int]				      =			inputs.pop("""labels"""		)
          UpperCAmelCase__      ,		UpperCAmelCase__      :  Tuple				      =			self._compute_loss(_lowerCAmelCase	,						_lowerCAmelCase	,						_lowerCAmelCase		)
          return loss




     def   __UpperCAmelCase  (				self	,						_lowerCAmelCase	,						_lowerCAmelCase	,						_lowerCAmelCase	,						_lowerCAmelCase = None	,						):
          UpperCAmelCase__      :  Any				      =			self._prepare_inputs(_lowerCAmelCase		)

          UpperCAmelCase__      :  int				      =			{
              """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__      :  Any				      =			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"]:
                    UpperCAmelCase__      :  str				      =			self._pad_tensors_to_max_len(_lowerCAmelCase	,						gen_kwargs["""max_length"""]		)

          UpperCAmelCase__      :  Union[str, Any]				      =			inputs.pop("""labels"""		)
          with torch.no_grad():
               # compute loss on predict data
               UpperCAmelCase__      ,		UpperCAmelCase__      :  List[Any]				      =			self._compute_loss(_lowerCAmelCase	,						_lowerCAmelCase	,						_lowerCAmelCase		)

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

          UpperCAmelCase__      :  Optional[Any]				      =			generated_tokens if self.args.predict_with_generate else logits

          if labels.shape[-1] < gen_kwargs["max_length"]:
               UpperCAmelCase__      :  Any				      =			self._pad_tensors_to_max_len(_lowerCAmelCase	,						gen_kwargs["""max_length"""]		)

          return (loss, logits, labels)




     def   __UpperCAmelCase  (				self	,						_lowerCAmelCase	,						_lowerCAmelCase		):
          # If PAD token is not defined at least EOS token has to be defined
          UpperCAmelCase__      :  Optional[int]				      =			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__      :  Optional[Any]				      =			pad_token_id * torch.ones(
              (tensor.shape[0], max_length)	,						dtype=tensor.dtype	,						device=tensor.device		)
          UpperCAmelCase__      :  List[str]				      =			tensor
          return padded_tensor












'''simple docstring'''


def 						lowerCamelCase__   (				a							):
						__snake_case						 =   int(a							)
						if n_element < 1:
												__snake_case						 =   ValueError('a should be a positive number'							)
												raise my_error

						__snake_case						 =   [1]
						__snake_case ,       __snake_case ,       __snake_case						 =   (0, 0, 0)
						__snake_case						 =   1
						while index < n_element:
												while hamming_list[i] * 2 <= hamming_list[-1]:
																		i += 1
												while hamming_list[j] * 3 <= hamming_list[-1]:
																		j += 1
												while hamming_list[k] * 5 <= hamming_list[-1]:
																		k += 1
												hamming_list.append(
												    min(hamming_list[i] * 2    ,	hamming_list[j] * 3    ,	hamming_list[k] * 5							)							)
												index += 1
						return hamming_list


if __name__ == "__main__":
	_lowercase  =			input("""Enter the last number (nth term) of the Hamming Number Series: """)
	print("""Formula of Hamming Number Series => 2^i * 3^j * 5^k""")
	_lowercase  =			hamming(int(n))
	print("""-----------------------------------------------------""")
	print(f'''The list with nth numbers is: {hamming_numbers}''')
	print("""-----------------------------------------------------""")

