


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(  _a   ):


					def __init__(						self	:		Optional[Any]     ,    SCREAMING_SNAKE_CASE	:		str     ,    SCREAMING_SNAKE_CASE	:		str     ,    SCREAMING_SNAKE_CASE	:		Any=1_0_2_4     ,    SCREAMING_SNAKE_CASE	:		Optional[int]=1_0_2_4     ,    SCREAMING_SNAKE_CASE	:		List[str]=3.6						)			->	List[Any]:




											'''simple docstring'''

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






					def __iter__(						self	:		int						)			->	Any:




											'''simple docstring'''

											__snake_case				=		iter(self.dataset						)
											__snake_case				=		True
											while more_examples:
																	__snake_case		,					__snake_case				=		[], 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				=		False
																													break
																	__snake_case				=		tokenizer(SCREAMING_SNAKE_CASE     ,    truncation=SCREAMING_SNAKE_CASE						)["input_ids"]
																	__snake_case				=		[]
																	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				=		all_token_ids[i : i + self.seq_length]
																							if len(SCREAMING_SNAKE_CASE						) == self.seq_length:
																													yield torch.tensor(SCREAMING_SNAKE_CASE						)





def 	_lowerCAmelCase       (    _lowerCAmelCase )						->	List[Any]:


						'''simple docstring'''






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





def 	_lowerCAmelCase       (    _lowerCAmelCase )						->	Optional[Any]:


						'''simple docstring'''






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

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


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

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

# Logging
A							:					Optional[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
A							:					Tuple     						=     AutoModelForCausalLM.from_pretrained(args.model_ckpt)
A							:					List[str]     						=     AutoTokenizer.from_pretrained(args.model_ckpt)

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

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

# Evaluate and save the last checkpoint
logger.info('Evaluating and saving model after training')
A						,  A							:					Optional[int]     						=     evaluate(args)


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









import os
from typing import Dict, List, Tuple, TypeVar, Union


A							:					List[Any]     						=     TypeVar('T')

A							:					Dict     						=     Union[List[T], Tuple[T, ...]]
A							:					Any     						=     Union[T, List[T], Dict[str, T]]
A							:					Optional[int]     						=     Union[str, bytes, os.PathLike]





