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__							(       __snake_case		):



			def __init__(						self				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase=1_0_2_4				,						UpperCAmelCase=1_0_2_4				,						UpperCAmelCase=3.6   )			->   Optional[Any]:
								__a		 =       tokenizer
								__a		 =       tokenizer.bos_token_id
								__a		 =       dataset
								__a		 =       seq_length
								__a		 =       seq_length * chars_per_token * num_of_sequences







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






def 							lowerCAmelCase(     __lowerCamelCase						):
					__a		 =       {'''streaming''': True}
					__a		 =       load_dataset(args.dataset_name				,    split='train'				,    **UpperCamelCase__						)
					__a		 =       ConstantLengthDataset(UpperCamelCase__				,    UpperCamelCase__				,    seq_length=args.seq_length						)
					__a		 =       DataLoader(UpperCamelCase__				,    batch_size=args.batch_size						)
					return eval_dataloader
def 							lowerCAmelCase(     __lowerCamelCase						):
					model.eval()
					__a		 =       []
					for step, batch in enumerate(UpperCamelCase__						):
										with torch.no_grad():
															__a		 =       model(UpperCamelCase__				,    labels=UpperCamelCase__						)
										__a		 =       outputs.loss.repeat(args.batch_size						)
										losses.append(accelerator.gather(UpperCamelCase__						)						)

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


# Setup Accelerator
lowerCamelCase_    :		str							=	Accelerator()

# Parse configuration
lowerCamelCase_    :		List[Any]							=	HfArgumentParser(EvaluationArguments)
lowerCamelCase_    :		Any							=	parser.parse_args()
set_seed(args.seed)

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

# Load dataset and dataloader
lowerCamelCase_    :		Optional[Any]							=	create_dataloader(args)

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

# Evaluate and save the last checkpoint
logger.info("""Evaluating and saving model after training""")
lowerCamelCase_    :		List[Any]							=	evaluate(args)



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






from typing import TYPE_CHECKING

from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable


lowerCamelCase_    :		Union[str, Any]							=	{"""configuration_gpt_neox""": ["""GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPTNeoXConfig"""]}

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowerCamelCase_    :		Union[str, Any]							=	["""GPTNeoXTokenizerFast"""]

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowerCamelCase_    :		List[str]							=	[
	    """GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """GPTNeoXForCausalLM""",
	    """GPTNeoXForQuestionAnswering""",
	    """GPTNeoXForSequenceClassification""",
	    """GPTNeoXForTokenClassification""",
	    """GPTNeoXLayer""",
	    """GPTNeoXModel""",
	    """GPTNeoXPreTrainedModel""",
	]


if TYPE_CHECKING:
	from .configuration_gpt_neox import GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoXConfig

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_gpt_neox_fast import GPTNeoXTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_gpt_neox import (
		    GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST,
		    GPTNeoXForCausalLM,
		    GPTNeoXForQuestionAnswering,
		    GPTNeoXForSequenceClassification,
		    GPTNeoXForTokenClassification,
		    GPTNeoXLayer,
		    GPTNeoXModel,
		    GPTNeoXPreTrainedModel,
		)


else:
	import sys

	lowerCamelCase_    :		Dict							=	_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




