






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  SCREAMING_SNAKE_CASE			(			lowerCAmelCase					):
     '''simple docstring'''


     def __init__(				self       :	Dict					, UpperCAmelCase_       :	Optional[int]					, UpperCAmelCase_       :	Tuple					, UpperCAmelCase_       :	Optional[Any]=1024					, UpperCAmelCase_       :	Optional[Any]=1024					, UpperCAmelCase_       :	Tuple=3.6      ):
       SCREAMING_SNAKE_CASE       :      Dict							=    tokenizer
       SCREAMING_SNAKE_CASE       :      List[str]							=    tokenizer.bos_token_id
       SCREAMING_SNAKE_CASE       :      str							=    dataset
       SCREAMING_SNAKE_CASE       :      List[Any]							=    seq_length
       SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    seq_length * chars_per_token * num_of_sequences






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





def   lowerCamelCase__			(     lowercase				):

  """simple docstring"""






  SCREAMING_SNAKE_CASE       :      List[str]							=    {"streaming": True}
  SCREAMING_SNAKE_CASE       :      int							=    load_dataset(args.dataset_name						,     split="train"						,     **lowercase				)
  SCREAMING_SNAKE_CASE       :      Dict							=    ConstantLengthDataset(lowercase						,     lowercase						,     seq_length=args.seq_length				)
  SCREAMING_SNAKE_CASE       :      List[str]							=    DataLoader(lowercase						,     batch_size=args.batch_size				)
  return eval_dataloader


def   lowerCamelCase__			(     lowercase				):

  """simple docstring"""






  model.eval()
  SCREAMING_SNAKE_CASE       :      int							=    []
  for step, batch in enumerate(lowercase				):
    with torch.no_grad():
      SCREAMING_SNAKE_CASE       :      Optional[Any]							=    model(lowercase						,     labels=lowercase				)
    SCREAMING_SNAKE_CASE       :      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
  SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    torch.mean(torch.cat(lowercase				)				)
  try:
    SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    torch.exp(lowercase				)
  except OverflowError:
    SCREAMING_SNAKE_CASE       :      Dict							=    float("inf"				)
  return loss.item(), perplexity.item()


# Setup Accelerator
snake_case    				=		Accelerator()

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

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

# Load dataset and dataloader
snake_case    				=		create_dataloader(args)

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

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

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














def   lowerCamelCase__			(     lowercase						,     lowercase				):

  """simple docstring"""






  return numa ^ numa < 0


if __name__ == "__main__":
   import doctest

   doctest.testmod()






