

'''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		(__SCREAMING_SNAKE_CASE):






			def __init__(  self			,_snake_case			,_snake_case			,_snake_case=10_24			,_snake_case=10_24			,_snake_case=3.6     ):
				UpperCAmelCase_     :      Optional[int]						=  tokenizer
				UpperCAmelCase_     :      str						=  tokenizer.bos_token_id
				UpperCAmelCase_     :      List[Any]						=  dataset
				UpperCAmelCase_     :      Optional[int]						=  seq_length
				UpperCAmelCase_     :      str						=  seq_length * chars_per_token * num_of_sequences






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




def  a__       (    _SCREAMING_SNAKE_CASE   :       int				)     ->					Dict:


	"""simple docstring"""





	UpperCAmelCase_     :      Any						=  {"streaming": True}
	UpperCAmelCase_     :      Optional[int]						=  load_dataset(args.dataset_name	,				split="train"	,				**_SCREAMING_SNAKE_CASE				)
	UpperCAmelCase_     :      Any						=  ConstantLengthDataset(_SCREAMING_SNAKE_CASE	,				_SCREAMING_SNAKE_CASE	,				seq_length=args.seq_length				)
	UpperCAmelCase_     :      str						=  DataLoader(_SCREAMING_SNAKE_CASE	,				batch_size=args.batch_size				)
	return eval_dataloader


def  a__       (    _SCREAMING_SNAKE_CASE   :       Optional[Any]				)     ->					Union[str, Any]:


	"""simple docstring"""





	model.eval()
	UpperCAmelCase_     :      str						=  []
	for step, batch in enumerate(_SCREAMING_SNAKE_CASE				):
		with torch.no_grad():
			UpperCAmelCase_     :      Tuple						=  model(_SCREAMING_SNAKE_CASE	,				labels=_SCREAMING_SNAKE_CASE				)
		UpperCAmelCase_     :      str						=  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
	UpperCAmelCase_     :      Dict						=  torch.mean(torch.cat(_SCREAMING_SNAKE_CASE				)				)
	try:
		UpperCAmelCase_     :      int						=  torch.exp(_SCREAMING_SNAKE_CASE				)
	except OverflowError:
		UpperCAmelCase_     :      int						=  float("inf"				)
	return loss.item(), perplexity.item()


# Setup Accelerator
_lowerCamelCase      					=     Accelerator()

# Parse configuration
_lowerCamelCase      					=     HfArgumentParser(EvaluationArguments)
_lowerCamelCase      					=     parser.parse_args()
set_seed(args.seed)

# Logging
_lowerCamelCase      					=     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
_lowerCamelCase      					=     AutoModelForCausalLM.from_pretrained(args.model_ckpt)
_lowerCamelCase      					=     AutoTokenizer.from_pretrained(args.model_ckpt)

# Load dataset and dataloader
_lowerCamelCase      					=     create_dataloader(args)

# Prepare everything with our `accelerator`.
_lowerCamelCase					,      _lowerCamelCase      					=     accelerator.prepare(model, eval_dataloader)

# Evaluate and save the last checkpoint
logger.info("""Evaluating and saving model after training""")
_lowerCamelCase					,      _lowerCamelCase      					=     evaluate(args)





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











# We ignore warnings about stepping the scheduler since we step it ourselves during gradient accumulation

import warnings

from .state import AcceleratorState, GradientState


warnings.filterwarnings("ignore", category=UserWarning, module="torch.optim.lr_scheduler")

class 							__SCREAMING_SNAKE_CASE       :






							def __init__(    self	,			__lowerCAmelCase	,			__lowerCAmelCase	,			__lowerCAmelCase = True	,			__lowerCAmelCase = False	):
														UpperCamelCase__					  =      scheduler
														UpperCamelCase__					  =      optimizers if isinstance(__lowerCAmelCase	,			(list, tuple)	) else [optimizers]
														UpperCamelCase__					  =      split_batches
														UpperCamelCase__					  =      step_with_optimizer
														UpperCamelCase__					  =      GradientState()






							def 					_lowerCamelCase		(    self	,			*__lowerCAmelCase	,			**__lowerCAmelCase	):
														if not self.step_with_optimizer:
																					# No link between scheduler and optimizer -> just step
																					self.scheduler.step(*__lowerCAmelCase	,			**__lowerCAmelCase	)
																					return

														# Otherwise, first make sure the optimizer was stepped.
														if not self.gradient_state.sync_gradients:
																					if self.gradient_state.adjust_scheduler:
																												self.scheduler._step_count += 1
																					return

														for opt in self.optimizers:
																					if opt.step_was_skipped:
																												return
														if self.split_batches:
																					# Split batches -> the training dataloader batch size is not changed so one step per training step
																					self.scheduler.step(*__lowerCAmelCase	,			**__lowerCAmelCase	)
														else:
																					# Otherwise the training dataloader batch size was multiplied by `num_processes`, so we need to do
																					# num_processes steps per training step
																					UpperCamelCase__					  =      AcceleratorState().num_processes
																					for _ in range(__lowerCAmelCase	):
																												# Special case when using OneCycle and `drop_last` was not used
																												if hasattr(self.scheduler	,			"""total_steps"""	):
																																			if self.scheduler._step_count <= self.scheduler.total_steps:
																																										self.scheduler.step(*__lowerCAmelCase	,			**__lowerCAmelCase	)
																												else:
																																			self.scheduler.step(*__lowerCAmelCase	,			**__lowerCAmelCase	)






							def 					_lowerCamelCase		(    self	):
														return self.scheduler.get_last_lr()






							def 					_lowerCamelCase		(    self	):
														return self.scheduler.state_dict()






							def 					_lowerCamelCase		(    self	,			__lowerCAmelCase	):
														self.scheduler.load_state_dict(__lowerCAmelCase	)






							def 					_lowerCamelCase		(    self	):
														return self.scheduler.get_lr()







							def 					_lowerCamelCase		(    self	,			*__lowerCAmelCase	,			**__lowerCAmelCase	):
														return self.scheduler.print_lr(*__lowerCAmelCase	,			**__lowerCAmelCase	)




