





'''simple docstring'''
import math
def 				lowercase__   (   __lowercase						:  list				,  __lowercase						:  int = 0				,  __lowercase						:  int = 0     ) ->      list:



       """simple docstring"""






       __UpperCamelCase       =  end or len(__lowercase     )
       for i in range(__lowercase				,  __lowercase     ):
              __UpperCamelCase       =  i
              __UpperCamelCase       =  array[i]
              while temp_index != start and temp_index_value < array[temp_index - 1]:
                     __UpperCamelCase       =  array[temp_index - 1]
                     temp_index -= 1
              __UpperCamelCase       =  temp_index_value
       return array
def 				lowercase__   (   __lowercase						:  list				,  __lowercase						:  int				,  __lowercase						:  int     ) ->      None:  # Max Heap



       """simple docstring"""






       __UpperCamelCase       =  index
       __UpperCamelCase       =  2 * index + 1  # Left Node
       __UpperCamelCase       =  2 * index + 2  # Right Node

       if left_index < heap_size and array[largest] < array[left_index]:
              __UpperCamelCase       =  left_index

       if right_index < heap_size and array[largest] < array[right_index]:
              __UpperCamelCase       =  right_index

       if largest != index:
              __UpperCamelCase       ,						__UpperCamelCase       =  array[largest], array[index]
              heapify(__lowercase				,  __lowercase				,  __lowercase     )
def 				lowercase__   (   __lowercase						:  list     ) ->      list:



       """simple docstring"""






       __UpperCamelCase       =  len(__lowercase     )

       for i in range(n // 2				,  -1				,  -1     ):
              heapify(__lowercase				,  __lowercase				,  __lowercase     )

       for i in range(n - 1				,  0				,  -1     ):
              __UpperCamelCase       ,						__UpperCamelCase       =  array[0], array[i]
              heapify(__lowercase				,  0				,  __lowercase     )

       return array
def 				lowercase__   (   __lowercase						:  list				,  __lowercase						:  int				,  __lowercase						:  int				,  __lowercase						:  int     ) ->      int:



       """simple docstring"""






       if (array[first_index] > array[middle_index]) != (
           array[first_index] > array[last_index]
       ):
              return array[first_index]
       elif (array[middle_index] > array[first_index]) != (
           array[middle_index] > array[last_index]
       ):
              return array[middle_index]
       else:
              return array[last_index]
def 				lowercase__   (   __lowercase						:  list				,  __lowercase						:  int				,  __lowercase						:  int				,  __lowercase						:  int     ) ->      int:



       """simple docstring"""






       __UpperCamelCase       =  low
       __UpperCamelCase       =  high
       while True:
              while array[i] < pivot:
                     i += 1
              j -= 1
              while pivot < array[j]:
                     j -= 1
              if i >= j:
                     return i
              __UpperCamelCase       ,						__UpperCamelCase       =  array[j], array[i]
              i += 1
def 				lowercase__   (   __lowercase						:  list     ) ->      list:



       """simple docstring"""






       if len(__lowercase     ) == 0:
              return array
       __UpperCamelCase       =  2 * math.ceil(math.loga(len(__lowercase     )     )     )
       __UpperCamelCase       =  16
       return intro_sort(__lowercase				,  0				,  len(__lowercase     )				,  __lowercase				,  __lowercase     )
def 				lowercase__   (   __lowercase						:  list				,  __lowercase						:  int				,  __lowercase						:  int				,  __lowercase						:  int				,  __lowercase						:  int     ) ->      list:



       """simple docstring"""






       while end - start > size_threshold:
              if max_depth == 0:
                     return heap_sort(__lowercase     )
              max_depth -= 1
              __UpperCamelCase       =  median_of_a(__lowercase				,  __lowercase				,  start + ((end - start) // 2) + 1				,  end - 1     )
              __UpperCamelCase       =  partition(__lowercase				,  __lowercase				,  __lowercase				,  __lowercase     )
              intro_sort(__lowercase				,  __lowercase				,  __lowercase				,  __lowercase				,  __lowercase     )
              __UpperCamelCase       =  p
       return insertion_sort(__lowercase				,  __lowercase				,  __lowercase     )


if __name__ == "__main__":
    import doctest

    doctest.testmod()

    a__							:							Dict =input('''Enter numbers separated by a comma : ''').strip()
    a__							:							Optional[int] =[float(item) for item in user_input.split(''',''')]
    print(sort(unsorted))







'''simple docstring'''
import logging

import torch
from accelerate import Accelerator
from arguments import EvaluationArguments
from datasets import load_dataset
from torch.utils.data import IterableDataset
from torch.utils.data.dataloader import DataLoader

from transformers import AutoModelForCausalLM, AutoTokenizer, HfArgumentParser, set_seed

class      snake_case     (    __lowerCamelCase       ):




 """simple docstring"""



 def __init__(       self       :							Any							,	__A       :							Dict							,	__A       :							str							,	__A       :							List[Any]=1_0_2_4							,	__A       :							Tuple=1_0_2_4							,	__A       :							str=3.6	):
        __UpperCamelCase       =  tokenizer
        __UpperCamelCase       =  tokenizer.bos_token_id
        __UpperCamelCase       =  dataset
        __UpperCamelCase       =  seq_length
        __UpperCamelCase       =  seq_length * chars_per_token * num_of_sequences






 def __iter__(       self       :							Any	):
        __UpperCamelCase       =  iter(self.dataset	)
        __UpperCamelCase       =  True
        while more_examples:
               __UpperCamelCase       ,						__UpperCamelCase       =  [], 0
               while True:
                      if buffer_len >= self.input_characters:
                             break
                      try:
                             buffer.append(next(__A	)['content']	)
                             buffer_len += len(buffer[-1]	)
                      except StopIteration:
                             __UpperCamelCase       =  False
                             break
               __UpperCamelCase       =  tokenizer(__A							,	truncation=__A	)['input_ids']
               __UpperCamelCase       =  []
               for tokenized_input in tokenized_inputs:
                      all_token_ids.extend(tokenized_input + [self.concat_token_id]	)
               for i in range(0							,	len(__A	)							,	self.seq_length	):
                      __UpperCamelCase       =  all_token_ids[i : i + self.seq_length]
                      if len(__A	) == self.seq_length:
                             yield torch.tensor(__A	)






def 				lowercase__   (   __lowercase						:  Optional[Any]     ) ->      Union[str, Any]:



       """simple docstring"""






       __UpperCamelCase       =  {'streaming': True}
       __UpperCamelCase       =  load_dataset(args.dataset_name				,  split='train'				,  **__lowercase     )
       __UpperCamelCase       =  ConstantLengthDataset(__lowercase				,  __lowercase				,  seq_length=args.seq_length     )
       __UpperCamelCase       =  DataLoader(__lowercase				,  batch_size=args.batch_size     )
       return eval_dataloader
def 				lowercase__   (   __lowercase						:  Tuple     ) ->      Optional[Any]:



       """simple docstring"""






       model.eval()
       __UpperCamelCase       =  []
       for step, batch in enumerate(__lowercase     ):
              with torch.no_grad():
                     __UpperCamelCase       =  model(__lowercase				,  labels=__lowercase     )
              __UpperCamelCase       =  outputs.loss.repeat(args.batch_size     )
              losses.append(accelerator.gather(__lowercase     )     )

              if args.max_eval_steps > 0 and step >= args.max_eval_steps:
                     break
       __UpperCamelCase       =  torch.mean(torch.cat(__lowercase     )     )
       try:
              __UpperCamelCase       =  torch.exp(__lowercase     )
       except OverflowError:
              __UpperCamelCase       =  float('inf'     )
       return loss.item(), perplexity.item()


# Setup Accelerator
a__							:							int =Accelerator()

# Parse configuration
a__							:							Dict =HfArgumentParser(EvaluationArguments)
a__							:							Union[str, Any] =parser.parse_args()
set_seed(args.seed)

# Logging
a__							:							List[Any] =logging.getLogger(__name__)
logging.basicConfig(
    format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''', datefmt='''%m/%d/%Y %H:%M:%S''', level=logging.INFO
)

# Load model and tokenizer
a__							:							Union[str, Any] =AutoModelForCausalLM.from_pretrained(args.model_ckpt)
a__							:							List[Any] =AutoTokenizer.from_pretrained(args.model_ckpt)

# Load dataset and dataloader
a__							:							Union[str, Any] =create_dataloader(args)

# Prepare everything with our `accelerator`.
a__ , a__							:							List[str] =accelerator.prepare(model, eval_dataloader)

# Evaluate and save the last checkpoint
logger.info('''Evaluating and saving model after training''')
a__ , a__							:							Any =evaluate(args)


logger.info(f'loss/eval: {eval_loss}, perplexity: {perplexity}')
