


'''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__(       SCREAMING_SNAKE_CASE__	):




				def __init__(		self      :			Optional[int]				,   lowerCAmelCase      :			Any				,   lowerCAmelCase      :			int				,   lowerCAmelCase      :			Union[str, Any]=1024				,   lowerCAmelCase      :			str=1024				,   lowerCAmelCase      :			Optional[int]=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      :			int				)->					Tuple:

										"""simple docstring"""




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







def 				lowerCamelCase__				(	A       :      Any					):




						'''simple docstring'''






						UpperCAmelCase					    =     {'streaming': True}
						UpperCAmelCase					    =     load_dataset(args.dataset_name				,       split='''train'''				,       **_UpperCAmelCase					)
						UpperCAmelCase					    =     ConstantLengthDataset(_UpperCAmelCase				,       _UpperCAmelCase				,       seq_length=args.seq_length					)
						UpperCAmelCase					    =     DataLoader(_UpperCAmelCase				,       batch_size=args.batch_size					)
						return eval_dataloader


def 				lowerCamelCase__				(	A       :      Tuple					):




						'''simple docstring'''






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

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


# Setup Accelerator
_lowercase      :      Optional[int]            =					Accelerator()

# Parse configuration
_lowercase      :      Optional[Any]            =					HfArgumentParser(EvaluationArguments)
_lowercase      :      Optional[int]            =					parser.parse_args()
set_seed(args.seed)

# Logging
_lowercase      :      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
_lowercase      :      Optional[Any]            =					AutoModelForCausalLM.from_pretrained(args.model_ckpt)
_lowercase      :      str            =					AutoTokenizer.from_pretrained(args.model_ckpt)

# Load dataset and dataloader
_lowercase      :      Union[str, Any]            =					create_dataloader(args)

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

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




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






'''simple docstring'''



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_lowercase      :      List[Any]            =					{
    """configuration_x_clip""": [
        """XCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """XCLIPConfig""",
        """XCLIPTextConfig""",
        """XCLIPVisionConfig""",
    ],
    """processing_x_clip""": ["""XCLIPProcessor"""],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_lowercase      :      List[str]            =					[
	    """XCLIP_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """XCLIPModel""",
	    """XCLIPPreTrainedModel""",
	    """XCLIPTextModel""",
	    """XCLIPVisionModel""",
	]

if TYPE_CHECKING:
	from .configuration_x_clip import (
	    XCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    XCLIPConfig,
	    XCLIPTextConfig,
	    XCLIPVisionConfig,
	)
	from .processing_x_clip import XCLIPProcessor

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_x_clip import (
		    XCLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
		    XCLIPModel,
		    XCLIPPreTrainedModel,
		    XCLIPTextModel,
		    XCLIPVisionModel,
		)

else:
	import sys

	_lowercase      :      List[Any]            =					_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)





