


'''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				(		lowerCAmelCase_					):




	def __init__(   self						,				__a						,				__a						,				__a=1024						,				__a=1024						,				__a=3.6							):

						'''simple docstring'''





						__a			:       List[str]					    =  tokenizer
						__a			:       List[Any]					    =  tokenizer.bos_token_id
						__a			:       Tuple					    =  dataset
						__a			:       Optional[int]					    =  seq_length
						__a			:       Optional[int]					    =  seq_length * chars_per_token * num_of_sequences





	def __iter__(   self							):

						'''simple docstring'''





						__a			:       Optional[Any]					    =  iter(self.dataset							)
						__a			:       Optional[int]					    =  True
						while more_examples:
											__a    ,				__a			:       Optional[int]					    =  [], 0
											while True:
																if buffer_len >= self.input_characters:
																					break
																try:
																					buffer.append(next(__a							)['content']							)
																					buffer_len += len(buffer[-1]							)
																except StopIteration:
																					__a			:       str					    =  False
																					break
											__a			:       Optional[int]					    =  tokenizer(__a						,				truncation=__a							)['input_ids']
											__a			:       str					    =  []
											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							):
																__a			:       int					    =  all_token_ids[i : i + self.seq_length]
																if len(__a							) == self.seq_length:
																					yield torch.tensor(__a							)






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   int   ):
					__a			:       List[Any]					    =  {'streaming': True}
					__a			:       int					    =  load_dataset(args.dataset_name						,      split='train'						,      **_SCREAMING_SNAKE_CASE   )
					__a			:       Union[str, Any]					    =  ConstantLengthDataset(_SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE						,      seq_length=args.seq_length   )
					__a			:       Optional[Any]					    =  DataLoader(_SCREAMING_SNAKE_CASE						,      batch_size=args.batch_size   )
					return eval_dataloader






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   Optional[int]   ):
					model.eval()
					__a			:       Any					    =  []
					for step, batch in enumerate(_SCREAMING_SNAKE_CASE   ):
										with torch.no_grad():
															__a			:       str					    =  model(_SCREAMING_SNAKE_CASE						,      labels=_SCREAMING_SNAKE_CASE   )
										__a			:       int					    =  outputs.loss.repeat(args.batch_size   )
										losses.append(accelerator.gather(_SCREAMING_SNAKE_CASE   )   )

										if args.max_eval_steps > 0 and step >= args.max_eval_steps:
															break
					__a			:       Union[str, Any]					    =  torch.mean(torch.cat(_SCREAMING_SNAKE_CASE   )   )
					try:
										__a			:       int					    =  torch.exp(_SCREAMING_SNAKE_CASE   )
					except OverflowError:
										__a			:       str					    =  float('inf'   )
					return loss.item(), perplexity.item()


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

# Parse configuration
__lowercase	:						Optional[int]       =	HfArgumentParser(EvaluationArguments)
__lowercase	:						int       =	parser.parse_args()
set_seed(args.seed)

# Logging
__lowercase	:						str       =	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
__lowercase	:						Any       =	AutoModelForCausalLM.from_pretrained(args.model_ckpt)
__lowercase	:						Dict       =	AutoTokenizer.from_pretrained(args.model_ckpt)

# Load dataset and dataloader
__lowercase	:						List[str]       =	create_dataloader(args)

# Prepare everything with our `accelerator`.
__lowercase       ,      __lowercase	:						Any       =	accelerator.prepare(model, eval_dataloader)

# Evaluate and save the last checkpoint
logger.info('Evaluating and saving model after training')
__lowercase       ,      __lowercase	:						List[str]       =	evaluate(args)




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







'''simple docstring'''



from __future__ import annotations

from fractions import Fraction
from math import gcd, sqrt






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   int   ):
					__a			:       int					    =  int(number**0.5   )
					return number == sq * sq






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   int						,      _SCREAMING_SNAKE_CASE      :   int						,      _SCREAMING_SNAKE_CASE      :   int						,      _SCREAMING_SNAKE_CASE      :   int						,      _SCREAMING_SNAKE_CASE      :   int						,      _SCREAMING_SNAKE_CASE      :   int   ):
					__a			:       int					    =  x_num * y_den * z_den + y_num * x_den * z_den + z_num * x_den * y_den
					__a			:       int					    =  x_den * y_den * z_den
					__a			:       int					    =  gcd(_SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE   )
					top //= hcf
					bottom //= hcf
					return top, bottom






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   int = 35   ):
					__a			:       set					    =  set()
					__a			:       int
					__a			:       Fraction					    =  Fraction(0   )
					__a			:       tuple[int, int]

					for x_num in range(1						,      order + 1   ):
										for x_den in range(x_num + 1						,      order + 1   ):
															for y_num in range(1						,      order + 1   ):
																				for y_den in range(y_num + 1						,      order + 1   ):
																									# n=1
																									__a			:       Union[str, Any]					    =  x_num * y_den + x_den * y_num
																									__a			:       Optional[Any]					    =  x_den * y_den
																									__a			:       int					    =  gcd(_SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE   )
																									z_num //= hcf
																									z_den //= hcf
																									if 0 < z_num < z_den <= order:
																														__a			:       Any					    =  add_three(
																														    _SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE   )
																														unique_s.add(_SCREAMING_SNAKE_CASE   )

																									# n=2
																									__a			:       Optional[int]					    =  (
																									    x_num * x_num * y_den * y_den + x_den * x_den * y_num * y_num
																									)
																									__a			:       Union[str, Any]					    =  x_den * x_den * y_den * y_den
																									if is_sq(_SCREAMING_SNAKE_CASE   ) and is_sq(_SCREAMING_SNAKE_CASE   ):
																														__a			:       List[Any]					    =  int(sqrt(_SCREAMING_SNAKE_CASE   )   )
																														__a			:       Any					    =  int(sqrt(_SCREAMING_SNAKE_CASE   )   )
																														__a			:       Optional[int]					    =  gcd(_SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE   )
																														z_num //= hcf
																														z_den //= hcf
																														if 0 < z_num < z_den <= order:
																																			__a			:       List[Any]					    =  add_three(
																																			    _SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE   )
																																			unique_s.add(_SCREAMING_SNAKE_CASE   )

                    # n=-1
																									__a			:       int					    =  x_num * y_num
																									__a			:       Optional[Any]					    =  x_den * y_num + x_num * y_den
																									__a			:       Tuple					    =  gcd(_SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE   )
																									z_num //= hcf
																									z_den //= hcf
																									if 0 < z_num < z_den <= order:
																														__a			:       Any					    =  add_three(
																														    _SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE   )
																														unique_s.add(_SCREAMING_SNAKE_CASE   )

																									# n=2
																									__a			:       List[Any]					    =  x_num * x_num * y_num * y_num
																									__a			:       List[Any]					    =  (
																									    x_den * x_den * y_num * y_num + x_num * x_num * y_den * y_den
																									)
																									if is_sq(_SCREAMING_SNAKE_CASE   ) and is_sq(_SCREAMING_SNAKE_CASE   ):
																														__a			:       Optional[Any]					    =  int(sqrt(_SCREAMING_SNAKE_CASE   )   )
																														__a			:       Union[str, Any]					    =  int(sqrt(_SCREAMING_SNAKE_CASE   )   )
																														__a			:       int					    =  gcd(_SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE   )
																														z_num //= hcf
																														z_den //= hcf
																														if 0 < z_num < z_den <= order:
																																			__a			:       List[str]					    =  add_three(
																																			    _SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE   )
																																			unique_s.add(_SCREAMING_SNAKE_CASE   )

					for num, den in unique_s:
										total += Fraction(_SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE   )

					return total.denominator + total.numerator


if __name__ == "__main__":
	print(f'''{solution() = }''')



