
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        lowerCAmelCase__      (   __lowercase	):

						def __init__(			self       ,							a       ,							a       ,							a=10_24       ,							a=10_24       ,							a=3.6				)					->      Union[str, Any]:


								'''simple docstring'''

								_UpperCamelCase						    =	tokenizer
								_UpperCamelCase						    =	tokenizer.bos_token_id
								_UpperCamelCase						    =	dataset
								_UpperCamelCase						    =	seq_length
								_UpperCamelCase						    =	seq_length * chars_per_token * num_of_sequences







						def __iter__(			self				)					->      Union[str, Any]:


								'''simple docstring'''

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




def 							__A(lowerCAmelCase )		->				int:

		"""simple docstring"""






		_UpperCamelCase						    =	{"""streaming""": True}
		_UpperCamelCase						    =	load_dataset(args.dataset_name						,  split="""train"""						,  **lowerCAmelCase )
		_UpperCamelCase						    =	ConstantLengthDataset(lowerCAmelCase						,  lowerCAmelCase						,  seq_length=args.seq_length )
		_UpperCamelCase						    =	DataLoader(lowerCAmelCase						,  batch_size=args.batch_size )
		return eval_dataloader


def 							__A(lowerCAmelCase )		->				Optional[int]:

		"""simple docstring"""






		model.eval()
		_UpperCamelCase						    =	[]
		for step, batch in enumerate(lowerCAmelCase ):
				with torch.no_grad():
						_UpperCamelCase						    =	model(lowerCAmelCase						,  labels=lowerCAmelCase )
				_UpperCamelCase						    =	outputs.loss.repeat(args.batch_size )
				losses.append(accelerator.gather(lowerCAmelCase ) )

				if args.max_eval_steps > 0 and step >= args.max_eval_steps:
						break
		_UpperCamelCase						    =	torch.mean(torch.cat(lowerCAmelCase ) )
		try:
				_UpperCamelCase						    =	torch.exp(lowerCAmelCase )
		except OverflowError:
				_UpperCamelCase						    =	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}""")





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


lowerCamelCase__	=							{"configuration_yolos": ["YOLOS_PRETRAINED_CONFIG_ARCHIVE_MAP", "YolosConfig", "YolosOnnxConfig"]}

try:
		if not is_vision_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowerCamelCase__	=							["YolosFeatureExtractor"]
		lowerCamelCase__	=							["YolosImageProcessor"]

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowerCamelCase__	=							[
		    "YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST",
		    "YolosForObjectDetection",
		    "YolosModel",
		    "YolosPreTrainedModel",
		]


if TYPE_CHECKING:
		from .configuration_yolos import YOLOS_PRETRAINED_CONFIG_ARCHIVE_MAP, YolosConfig, YolosOnnxConfig

		try:
				if not is_vision_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .feature_extraction_yolos import YolosFeatureExtractor
				from .image_processing_yolos import YolosImageProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_yolos import (
				    YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST,
				    YolosForObjectDetection,
				    YolosModel,
				    YolosPreTrainedModel,
				)


else:
		import sys

		lowerCamelCase__	=							_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



