


"""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 (							__snake_case				):






		'''simple docstring'''

		def __init__(self				,       a_				,       a_				,       a_=10_24				,       a_=10_24				,       a_=3.6					):





			'''simple docstring'''

			__snake_case	:						Tuple						=				tokenizer
			__snake_case	:						List[Any]						=				tokenizer.bos_token_id
			__snake_case	:						str						=				dataset
			__snake_case	:						Union[str, Any]						=				seq_length
			__snake_case	:						Union[str, Any]						=				seq_length * chars_per_token * num_of_sequences



		def __iter__(self					):





			'''simple docstring'''

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






def 		lowercase							(     _snake_case						:      List[str]     )     ->Optional[Any]:
	"""simple docstring"""





	__snake_case	:						List[str]						=				{'''streaming''': True}
	__snake_case	:						List[Any]						=				load_dataset(args.dataset_name     ,   split='''train'''     ,   **_snake_case     )
	__snake_case	:						Dict						=				ConstantLengthDataset(_snake_case     ,   _snake_case     ,   seq_length=args.seq_length     )
	__snake_case	:						List[str]						=				DataLoader(_snake_case     ,   batch_size=args.batch_size     )
	return eval_dataloader






def 		lowercase							(     _snake_case						:      Union[str, Any]     )     ->Union[str, Any]:
	"""simple docstring"""





	model.eval()
	__snake_case	:						List[Any]						=				[]
	for step, batch in enumerate(_snake_case     ):
		with torch.no_grad():
			__snake_case	:						str						=				model(_snake_case     ,   labels=_snake_case     )
		__snake_case	:						Dict						=				outputs.loss.repeat(args.batch_size     )
		losses.append(accelerator.gather(_snake_case     )     )

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


# Setup Accelerator
SCREAMING_SNAKE_CASE :    Tuple									=				Accelerator()

# Parse configuration
SCREAMING_SNAKE_CASE :    Dict									=				HfArgumentParser(EvaluationArguments)
SCREAMING_SNAKE_CASE :    Any									=				parser.parse_args()
set_seed(args.seed)

# Logging
SCREAMING_SNAKE_CASE :    int									=				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
SCREAMING_SNAKE_CASE :    Any									=				AutoModelForCausalLM.from_pretrained(args.model_ckpt)
SCREAMING_SNAKE_CASE :    str									=				AutoTokenizer.from_pretrained(args.model_ckpt)

# Load dataset and dataloader
SCREAMING_SNAKE_CASE :    Optional[int]									=				create_dataloader(args)

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

# Evaluate and save the last checkpoint
logger.info("""Evaluating and saving model after training""")
SCREAMING_SNAKE_CASE					,  SCREAMING_SNAKE_CASE :    Union[str, Any]									=				evaluate(args)

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








import string

def 						UpperCamelCase					(lowercase_: str					)     ->      None:
	for key in range(len(string.ascii_uppercase					)					):
		A__							:    Dict        =		""""""
		for symbol in message:
			if symbol in string.ascii_uppercase:
				A__							:    Dict        =		string.ascii_uppercase.find(lowercase_					)
				A__							:    Optional[int]        =		num - key
				if num < 0:
					A__							:    Optional[int]        =		num + len(string.ascii_uppercase					)
				A__							:    Any        =		translated + string.ascii_uppercase[num]
			else:
				A__							:    Optional[Any]        =		translated + symbol
		print(f"""Decryption using Key #{key}: {translated}"""					)

def 						UpperCamelCase					()     ->      None:
	A__							:    Optional[Any]        =		input("""Encrypted message: """					)
	A__							:    Optional[Any]        =		message.upper()
	decrypt(lowercase_					)


if __name__ == "__main__":
	import doctest

	doctest.testmod()
	main()



