
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 						a   (				__magic_name__    ):




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




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




def      A (       A_							:					Tuple       ):
						snake_case       :     List[str] =    {'''streaming''': True}
						snake_case       :     Any =    load_dataset(args.dataset_name    ,							split='''train'''    ,							**A_       )
						snake_case       :     Optional[int] =    ConstantLengthDataset(A_    ,							A_    ,							seq_length=args.seq_length       )
						snake_case       :     List[str] =    DataLoader(A_    ,							batch_size=args.batch_size       )
						return eval_dataloader

def      A (       A_							:					Any       ):
						model.eval()
						snake_case       :     Dict =    []
						for step, batch in enumerate(A_       ):
												with torch.no_grad():
																		snake_case       :     int =    model(A_    ,							labels=A_       )
												snake_case       :     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
						snake_case       :     Tuple =    torch.mean(torch.cat(A_       )       )
						try:
												snake_case       :     int =    torch.exp(A_       )
						except OverflowError:
												snake_case       :     Dict =    float('''inf'''       )
						return loss.item(), perplexity.item()


# Setup Accelerator
UpperCAmelCase    					=   Accelerator()

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

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

# Load dataset and dataloader
UpperCAmelCase    					=   create_dataloader(args)

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

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





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



'''simple docstring'''





import pytest

from datasets import inspect_metric, list_metrics, load_metric

@pytest.fixture
def      A (       A_							:					str       ):
						monkeypatch.setattr('''datasets.utils.deprecation_utils._emitted_deprecation_warnings'''    ,							set()       )

@pytest.fixture
def      A (       A_							:					Optional[Any]       ):

						class 						a   :




										def __init__(							self	:				List[str],    SCREAMING_SNAKE_CASE_	:				Optional[Any]      ):
																snake_case       :     List[str] =    metric_id




						class 						a   :
										_snake_case		    = [MetricMock(__magic_name__    ) for metric_id in ['''accuracy''', '''mse''', '''precision''', '''codeparrot/apps_metric''']]




										def 	__snake_case							(							self	:				str      ):
																return self._metrics




						monkeypatch.setattr('''datasets.inspect.huggingface_hub'''    ,							HfhMock()       )





@pytest.mark.parametrize(
    '''func, args'''    ,							[(load_metric, ('''metrics/mse''',)), (list_metrics, ()), (inspect_metric, ('''metrics/mse''', '''tmp_path'''))]       )
def      A (       A_							:					str    ,							A_							:					Dict    ,							A_							:					int    ,							A_							:					int    ,							A_							:					str       ):
						if "tmp_path" in args:
												snake_case       :     List[str] =    tuple(arg if arg != '''tmp_path''' else tmp_path for arg in args       )
						with pytest.warns(A_    ,							match='''https://huggingface.co/docs/evaluate'''       ):
												func(*A_       )

