




"""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 lowerCAmelCase__	(     lowercase						):






			'''simple docstring'''



			def __init__(  self  ,      lowercase  ,      lowercase  ,      lowercase=1024  ,      lowercase=1024  ,      lowercase=3.6  ):
							_lowerCamelCase     :			Union[str, Any]    =    tokenizer
							_lowerCamelCase     :			Any    =    tokenizer.bos_token_id
							_lowerCamelCase     :			Optional[int]    =    dataset
							_lowerCamelCase     :			Optional[int]    =    seq_length
							_lowerCamelCase     :			Union[str, Any]    =    seq_length * chars_per_token * num_of_sequences






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







def      _snake_case			(				lowercase__		):
				_lowerCamelCase     :			List[Any]    =    {'streaming': True}
				_lowerCamelCase     :			str    =    load_dataset(args.dataset_name							,    split='train'							,    **lowercase__		)
				_lowerCamelCase     :			Dict    =    ConstantLengthDataset(lowercase__							,    lowercase__							,    seq_length=args.seq_length		)
				_lowerCamelCase     :			str    =    DataLoader(lowercase__							,    batch_size=args.batch_size		)
				return eval_dataloader







def      _snake_case			(				lowercase__		):
				model.eval()
				_lowerCamelCase     :			Tuple    =    []
				for step, batch in enumerate(lowercase__		):
								with torch.no_grad():
												_lowerCamelCase     :			str    =    model(lowercase__							,    labels=lowercase__		)
								_lowerCamelCase     :			Optional[Any]    =    outputs.loss.repeat(args.batch_size		)
								losses.append(accelerator.gather(lowercase__		)		)

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


# Setup Accelerator
lowercase__     						=						Accelerator()

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

# Logging
lowercase__     						=						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__     						=						AutoModelForCausalLM.from_pretrained(args.model_ckpt)
lowercase__     						=						AutoTokenizer.from_pretrained(args.model_ckpt)

# Load dataset and dataloader
lowercase__     						=						create_dataloader(args)

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

# Evaluate and save the last checkpoint
logger.info("""Evaluating and saving model after training""")
lowercase__						,							lowercase__     						=						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__     						=						{"""configuration_ibert""": ["""IBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """IBertConfig""", """IBertOnnxConfig"""]}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowercase__     						=						[
			    """IBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
			    """IBertForMaskedLM""",
			    """IBertForMultipleChoice""",
			    """IBertForQuestionAnswering""",
			    """IBertForSequenceClassification""",
			    """IBertForTokenClassification""",
			    """IBertModel""",
			    """IBertPreTrainedModel""",
			]

if TYPE_CHECKING:
			from .configuration_ibert import IBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, IBertConfig, IBertOnnxConfig

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_ibert import (
						    IBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
						    IBertForMaskedLM,
						    IBertForMultipleChoice,
						    IBertForQuestionAnswering,
						    IBertForSequenceClassification,
						    IBertForTokenClassification,
						    IBertModel,
						    IBertPreTrainedModel,
						)

else:
			import sys

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