






'''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__      (					UpperCamelCase):




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





					def __iter__(  self  :		int      )    ->							List[str]:
								UpperCAmelCase_      :       str          =       iter(self.dataset      )
								UpperCAmelCase_      :       int          =       True
								while more_examples:
											UpperCAmelCase_      :       Any          =       [], 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_      :       List[str]          =       False
																	break
											UpperCAmelCase_      :       Union[str, Any]          =       tokenizer(snake_case__				, truncation=snake_case__      )["input_ids"]
											UpperCAmelCase_      :       Tuple          =       []
											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_      :       Optional[int]          =       all_token_ids[i : i + self.seq_length]
														if len(snake_case__      ) == self.seq_length:
																	yield torch.tensor(snake_case__      )






def __UpperCAmelCase   (					A					:			Tuple		)    ->   Dict:
			UpperCAmelCase_      :       List[str]          =       {"streaming": True}
			UpperCAmelCase_      :       Tuple          =       load_dataset(args.dataset_name			,       split='''train'''			,       **A		)
			UpperCAmelCase_      :       str          =       ConstantLengthDataset(A			,       A			,       seq_length=args.seq_length		)
			UpperCAmelCase_      :       int          =       DataLoader(A			,       batch_size=args.batch_size		)
			return eval_dataloader
def __UpperCAmelCase   (					A					:			Optional[Any]		)    ->   str:
			model.eval()
			UpperCAmelCase_      :       Tuple          =       []
			for step, batch in enumerate(A		):
						with torch.no_grad():
									UpperCAmelCase_      :       Dict          =       model(A			,       labels=A		)
						UpperCAmelCase_      :       Union[str, Any]          =       outputs.loss.repeat(args.batch_size		)
						losses.append(accelerator.gather(A		)		)

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


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

# Parse configuration
_UpperCamelCase						:						int  =					HfArgumentParser(EvaluationArguments)
_UpperCamelCase						:						List[str]  =					parser.parse_args()
set_seed(args.seed)

# Logging
_UpperCamelCase						:						Union[str, 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
_UpperCamelCase						:						Dict  =					AutoModelForCausalLM.from_pretrained(args.model_ckpt)
_UpperCamelCase						:						Any  =					AutoTokenizer.from_pretrained(args.model_ckpt)

# Load dataset and dataloader
_UpperCamelCase						:						str  =					create_dataloader(args)

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

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







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










"""simple docstring"""



from ...configuration_utils import PretrainedConfig
from ...utils import logging


_lowerCAmelCase	:	Any				=      logging.get_logger(__name__)

_lowerCAmelCase	:	Union[str, Any]				=      {
    # See all MEGATRON_BERT models at https://huggingface.co/models?filter=bert
}




class 			lowerCAmelCase__			(    __magic_name__   ):
   SCREAMING_SNAKE_CASE_    ='''megatron-bert'''
   def __init__(		self      :	Tuple							,			snake_case__      :	List[Any]=2_9_0_5_6							,			snake_case__      :	Any=1_0_2_4							,			snake_case__      :	Tuple=2_4							,			snake_case__      :	Optional[Any]=1_6							,			snake_case__      :	List[str]=4_0_9_6							,			snake_case__      :	Union[str, Any]="gelu"							,			snake_case__      :	int=0.1							,			snake_case__      :	Dict=0.1							,			snake_case__      :	str=5_1_2							,			snake_case__      :	Optional[Any]=2							,			snake_case__      :	Any=0.02							,			snake_case__      :	Optional[Any]=1e-12							,			snake_case__      :	str=0							,			snake_case__      :	Optional[Any]="absolute"							,			snake_case__      :	Optional[Any]=True							,			**snake_case__      :	Any							,			):


        '''simple docstring'''
        super().__init__(pad_token_id=snake_case__							,			**snake_case__							)

        UpperCAmelCase__						:    Union[str, Any]							=							vocab_size
        UpperCAmelCase__						:    Union[str, Any]							=							hidden_size
        UpperCAmelCase__						:    Optional[Any]							=							num_hidden_layers
        UpperCAmelCase__						:    Tuple							=							num_attention_heads
        UpperCAmelCase__						:    List[Any]							=							hidden_act
        UpperCAmelCase__						:    Optional[int]							=							intermediate_size
        UpperCAmelCase__						:    Dict							=							hidden_dropout_prob
        UpperCAmelCase__						:    str							=							attention_probs_dropout_prob
        UpperCAmelCase__						:    str							=							max_position_embeddings
        UpperCAmelCase__						:    List[str]							=							type_vocab_size
        UpperCAmelCase__						:    Tuple							=							initializer_range
        UpperCAmelCase__						:    str							=							layer_norm_eps
        UpperCAmelCase__						:    List[Any]							=							position_embedding_type
        UpperCAmelCase__						:    Dict							=							use_cache


