




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__(					__lowerCamelCase):
      def __init__(				self: str    ,       UpperCamelCase_: int    ,       UpperCamelCase_: List[Any]    ,       UpperCamelCase_: str=10_24    ,       UpperCamelCase_: int=10_24    ,       UpperCamelCase_: Any=3.6   ):
             __lowerCamelCase   =						tokenizer
             __lowerCamelCase   =						tokenizer.bos_token_id
             __lowerCamelCase   =						dataset
             __lowerCamelCase   =						seq_length
             __lowerCamelCase   =						seq_length * chars_per_token * num_of_sequences




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




def 					lowerCamelCase__       (						A__       :					Tuple	):





       '''simple docstring'''



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




def 					lowerCamelCase__       (						A__       :					List[str]	):





       '''simple docstring'''



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

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


# Setup Accelerator
UpperCAmelCase_									=	Accelerator()

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

# Logging
UpperCAmelCase_									=	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
UpperCAmelCase_									=	AutoModelForCausalLM.from_pretrained(args.model_ckpt)
UpperCAmelCase_									=	AutoTokenizer.from_pretrained(args.model_ckpt)

# Load dataset and dataloader
UpperCAmelCase_									=	create_dataloader(args)

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

# Evaluate and save the last checkpoint
logger.info('Evaluating and saving model after training')
UpperCAmelCase_				,			UpperCAmelCase_									=	evaluate(args)




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







def 					lowerCamelCase__       (						A__       :					list	):





       '''simple docstring'''



       __lowerCamelCase   =						len(A__	)
       for _ in range(A__	):
              for i in range(_ % 2  ,			arr_size - 1  ,			2	):
                     if arr[i + 1] < arr[i]:
                            __lowerCamelCase,   __lowerCamelCase   =						arr[i + 1], arr[i]

       return arr


if __name__ == "__main__":
       UpperCAmelCase_									=	list(range(10, 0, -1))
       print(f"""Original: {arr}. Sorted: {odd_even_transposition(arr)}""")

