



"""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    lowercase__     (					__snake_case   ):







       '''simple docstring'''


       def __init__(							self    :						Optional[int]	,       _UpperCAmelCase    :						List[str]	,       _UpperCAmelCase    :						Optional[int]	,       _UpperCAmelCase    :						List[Any]=1024	,       _UpperCAmelCase    :						str=1024	,       _UpperCAmelCase    :						Dict=3.6					)				->		Dict:


             '''simple docstring'''




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







       def __iter__(							self    :						Tuple					)				->		Union[str, Any]:


             '''simple docstring'''




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



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

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


# Setup Accelerator
lowerCamelCase      =   Accelerator()

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

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

# Load dataset and dataloader
lowerCamelCase      =   create_dataloader(args)

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

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


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









"""simple docstring"""





lowerCamelCase      =		{
    "joule": 1.0,
    "kilojoule": 1_000,
    "megajoule": 1_000_000,
    "gigajoule": 1_000_000_000,
    "wattsecond": 1.0,
    "watthour": 3_600,
    "kilowatthour": 3_600_000,
    "newtonmeter": 1.0,
    "calorie_nutr": 4_186.8,
    "kilocalorie_nutr": 4_186_800.00,
    "electronvolt": 1.6_0_2_1_7_6_6_3_4e-1_9,
    "britishthermalunit_it": 1_055.05_585,
    "footpound": 1.355_818,
}
def      a__			(   lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__   ):
      if to_type not in ENERGY_CONVERSION or from_type not in ENERGY_CONVERSION:
            UpperCAmelCase_    				=							(
                f"""Incorrect 'from_type' or 'to_type' value: {from_type!r}, {to_type!r}\n"""
                f"""Valid values are: {', '.join(lowerCAmelCase__   )}"""
            )
            raise ValueError(lowerCAmelCase__   )
      return value * ENERGY_CONVERSION[from_type] / ENERGY_CONVERSION[to_type]


if __name__ == "__main__":
  import doctest

  doctest.testmod()




