


"""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 					__lowerCAmelCase					(   __SCREAMING_SNAKE_CASE       ):


		'''simple docstring'''



		def __init__(  self							, _a=None							, _a=None							, *_a							, **_a      ):
				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__}'''
						)
						__a           =			self.model.config
				else:
						__a           =			config

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

						__a           =			label_smoothed_nll_loss

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

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

		def 			__UpperCAmelCase       (  self							, _a      ):
				__a           =			arg_to_scheduler[self.args.lr_scheduler]
				if self.args.lr_scheduler == "constant":
						__a           =			schedule_func(self.optimizer      )
				elif self.args.lr_scheduler == "constant_w_warmup":
						__a           =			schedule_func(self.optimizer							, num_warmup_steps=self.args.warmup_steps      )
				else:
						__a           =			schedule_func(
						    self.optimizer							, num_warmup_steps=self.args.warmup_steps							, num_training_steps=_a      )
				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							, _a							, _a							, _a      ):
				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
								__a           =			model(**_a							, use_cache=_a      )[0]
								__a           =			self.loss_fn(logits.view(-1							, logits.shape[-1]      )							, labels.view(-1      )      )
						else:
								# compute usual loss via models
								__a      ,     __a           =			model(**_a							, labels=_a							, use_cache=_a      )[:2]
				else:
						# compute label smoothed loss
						__a           =			model(**_a							, use_cache=_a      )[0]
						__a           =			torch.nn.functional.log_softmax(_a							, dim=-1      )
						__a      ,     __a           =			self.loss_fn(_a							, _a							, self.args.label_smoothing							, ignore_index=self.config.pad_token_id      )
				return loss, logits

		def 			__UpperCAmelCase       (  self							, _a							, _a      ):
				__a           =			inputs.pop('''labels'''      )
				__a      ,     __a           =			self._compute_loss(_a							, _a							, _a      )
				return loss

		def 			__UpperCAmelCase       (  self							, _a							, _a							, _a							, _a = None							, ):
				__a           =			self._prepare_inputs(_a      )

				__a           =			{
				    '''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:
						__a           =			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"]:
								__a           =			self._pad_tensors_to_max_len(_a							, gen_kwargs['''max_length''']      )

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

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

				__a           =			generated_tokens if self.args.predict_with_generate else logits

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

				return (loss, logits, labels)





		def 			__UpperCAmelCase       (  self							, _a							, _a      ):
				# If PAD token is not defined at least EOS token has to be defined
				__a           =			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}'''      )

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










"""simple docstring"""




import math

def        lowercase							(		lowerCAmelCase__ :     list    ,					lowerCAmelCase__ :     int			) ->      int:
		__a           =			len(lowerCAmelCase__			)
		__a           =			int(math.floor(math.sqrt(lowerCAmelCase__			)			)			)
		__a           =			0
		while arr[min(lowerCAmelCase__    ,					lowerCAmelCase__			) - 1] < x:
				__a           =			step
				step += int(math.floor(math.sqrt(lowerCAmelCase__			)			)			)
				if prev >= n:
						return -1

		while arr[prev] < x:
				__a           =			prev + 1
				if prev == min(lowerCAmelCase__    ,					lowerCAmelCase__			):
						return -1
		if arr[prev] == x:
				return prev
		return -1


if __name__ == "__main__":
				lowercase_			     =  input("Enter numbers separated by a comma:\n").strip()
				lowercase_			     =  [int(item) for item in user_input.split(",")]
				lowercase_			     =  int(input("Enter the number to be searched:\n"))
				lowercase_			     =  jump_search(arr, x)
				if res == -1:
								print("Number not found!")
				else:
								print(F'''Number {x} is at index {res}''')






