






'''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 		UpperCamelCase__    (SCREAMING_SNAKE_CASE_			):




		'''simple docstring'''





		def __init__(      self   ,_lowerCAmelCase   ,_lowerCAmelCase   ,_lowerCAmelCase=10_24   ,_lowerCAmelCase=10_24   ,_lowerCAmelCase=3.6       ):
								lowerCamelCase__         =  tokenizer
								lowerCamelCase__         =  tokenizer.bos_token_id
								lowerCamelCase__         =  dataset
								lowerCamelCase__         =  seq_length
								lowerCamelCase__         =  seq_length * chars_per_token * num_of_sequences






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



def 						A__			(				__lowerCAmelCase						):
						lowerCamelCase__         =  {'''streaming''': True}
						lowerCamelCase__         =  load_dataset(args.dataset_name   ,    split="""train"""   ,    **__UpperCamelCase						)
						lowerCamelCase__         =  ConstantLengthDataset(__UpperCamelCase   ,    __UpperCamelCase   ,    seq_length=args.seq_length						)
						lowerCamelCase__         =  DataLoader(__UpperCamelCase   ,    batch_size=args.batch_size						)
						return eval_dataloader
def 						A__			(				__lowerCAmelCase						):
						model.eval()
						lowerCamelCase__         =  []
						for step, batch in enumerate(__UpperCamelCase						):
												with torch.no_grad():
																		lowerCamelCase__         =  model(__UpperCamelCase   ,    labels=__UpperCamelCase						)
												lowerCamelCase__         =  outputs.loss.repeat(args.batch_size						)
												losses.append(accelerator.gather(__UpperCamelCase						)						)

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


# Setup Accelerator
UpperCamelCase    :      List[Any]  			=		Accelerator()

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

# Logging
UpperCamelCase    :      Dict  			=		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
UpperCamelCase    :      Optional[int]  			=		AutoModelForCausalLM.from_pretrained(args.model_ckpt)
UpperCamelCase    :      List[str]  			=		AutoTokenizer.from_pretrained(args.model_ckpt)

# Load dataset and dataloader
UpperCamelCase    :      Optional[Any]  			=		create_dataloader(args)

# Prepare everything with our `accelerator`.
UpperCamelCase    :      Optional[Any]  			=		accelerator.prepare(model, eval_dataloader)

# Evaluate and save the last checkpoint
logger.info('Evaluating and saving model after training')
UpperCamelCase    :      Optional[int]  			=		evaluate(args)





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









'''simple docstring'''







UpperCamelCase    :      Tuple  			=		[sum(int(c, 10) ** 2 for c in i.__str__()) for i in range(10_00_00)]
def 						A__			(				__lowerCAmelCase		:			int						):
						lowerCamelCase__         =  0
						while number:
												# Increased Speed Slightly by checking every 5 digits together.
												sum_of_digits_squared += DIGITS_SQUARED[number % 10_0000]
												number //= 10_0000

						return sum_of_digits_squared


# There are 2 Chains made,
# One ends with 89 with the chain member 58 being the one which when declared first,
# there will be the least number of iterations for all the members to be checked.

# The other one ends with 1 and has only one element 1.

# So 58 and 1 are chosen to be declared at the starting.

# Changed dictionary to an array to quicken the solution
UpperCamelCase    :      list[bool | None]  			=		[None] * 10_00_00_00
UpperCamelCase    :      Tuple  			=		True
UpperCamelCase    :      Optional[int]  			=		False
def 						A__			(				__lowerCAmelCase		:			int						):

						if CHAINS[number - 1] is not None:
												return CHAINS[number - 1]  # type: ignore

						lowerCamelCase__         =  chain(next_number(__lowerCAmelCase						)						)
						lowerCamelCase__         =  number_chain

						while number < 1000_0000:
												lowerCamelCase__         =  number_chain
												number *= 10

						return number_chain
def 						A__			(				__lowerCAmelCase		:			int = 1000_0000						):
						for i in range(1   ,    __lowerCAmelCase						):
												if CHAINS[i] is None:
																		chain(i + 1						)

						return CHAINS[:number].count(__lowerCAmelCase						)


if __name__ == "__main__":
							import doctest

							doctest.testmod()
							print(F'{solution() = }')

