


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(						__a   ):
       def __init__(					self,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase=10_24,      lowerCamelCase=10_24,      lowerCamelCase=3.6)       ->   List[Any]:




           """simple docstring"""

           _lowercase       :							List[str]      		=		tokenizer
           _lowercase       :							str      		=		tokenizer.bos_token_id
           _lowercase       :							List[str]      		=		dataset
           _lowercase       :							Union[str, Any]      		=		seq_length
           _lowercase       :							List[Any]      		=		seq_length * chars_per_token * num_of_sequences




       def __iter__(					self)       ->   str:




           """simple docstring"""

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



def 				UpperCamelCase_(      lowerCamelCase_ )    ->						str:
    _lowercase       :							Dict      		=		{"""streaming""": True}
    _lowercase       :							Dict      		=		load_dataset(args.dataset_name							, split='train'							, **snake_case__ )
    _lowercase       :							Optional[int]      		=		ConstantLengthDataset(snake_case__							, snake_case__							, seq_length=args.seq_length )
    _lowercase       :							Optional[Any]      		=		DataLoader(snake_case__							, batch_size=args.batch_size )
    return eval_dataloader



def 				UpperCamelCase_(      lowerCamelCase_ )    ->						Optional[int]:
    model.eval()
    _lowercase       :							Any      		=		[]
    for step, batch in enumerate(snake_case__ ):
        with torch.no_grad():
            _lowercase       :							Tuple      		=		model(snake_case__							, labels=snake_case__ )
        _lowercase       :							List[Any]      		=		outputs.loss.repeat(args.batch_size )
        losses.append(accelerator.gather(snake_case__ ) )

        if args.max_eval_steps > 0 and step >= args.max_eval_steps:
            break
    _lowercase       :							Tuple      		=		torch.mean(torch.cat(snake_case__ ) )
    try:
        _lowercase       :							Union[str, Any]      		=		torch.exp(snake_case__ )
    except OverflowError:
        _lowercase       :							List[Any]      		=		float('inf' )
    return loss.item(), perplexity.item()


# Setup Accelerator
SCREAMING_SNAKE_CASE       :      Tuple												= Accelerator()

# Parse configuration
SCREAMING_SNAKE_CASE       :      Any												= HfArgumentParser(EvaluationArguments)
SCREAMING_SNAKE_CASE       :      Dict												= parser.parse_args()
set_seed(args.seed)

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

# Load dataset and dataloader
SCREAMING_SNAKE_CASE       :      List[str]												= create_dataloader(args)

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

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

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








"""simple docstring"""


from .integrations import (
    is_optuna_available,
    is_ray_available,
    is_sigopt_available,
    is_wandb_available,
    run_hp_search_optuna,
    run_hp_search_ray,
    run_hp_search_sigopt,
    run_hp_search_wandb,
)
from .trainer_utils import (
    HPSearchBackend,
    default_hp_space_optuna,
    default_hp_space_ray,
    default_hp_space_sigopt,
    default_hp_space_wandb,
)
from .utils import logging


A_						=				logging.get_logger(__name__)






class 					lowercase:







 '''simple docstring'''





 lowercase__ 					=					42
 lowercase__ 					=					None


 @staticmethod
 def  UpperCamelCase_   (				):




   '''simple docstring'''



   raise NotImplementedError


 def  UpperCamelCase_   (				self:		Tuple,      a_:		int,      a_:		int,      a_:		str,      **a_:		Dict		):




   '''simple docstring'''



   raise NotImplementedError


 def  UpperCamelCase_   (				self:		Union[str, Any],      a_:		List[str]		):




   '''simple docstring'''



   raise NotImplementedError


 def  UpperCamelCase_   (				self:		Union[str, Any]		):




   '''simple docstring'''



   if not self.is_available():
     raise RuntimeError(
         f"You picked the {self.name} backend, but it is not installed. Run {self.pip_install()}."		)


 @classmethod
 def  UpperCamelCase_   (				cls:		Tuple		):




   '''simple docstring'''



   return f"`pip install {cls.pip_package or cls.name}`"







class 					lowercase(     __a ):







 '''simple docstring'''





 lowercase__ 					=					"optuna"


 @staticmethod
 def  UpperCamelCase_   (				):




   '''simple docstring'''



   return is_optuna_available()


 def  UpperCamelCase_   (				self:		Union[str, Any],      a_:		List[Any],      a_:		int,      a_:		str,      **a_:		List[str]		):




   '''simple docstring'''



   return run_hp_search_optuna(a_,      a_,      a_,      **a_		)


 def  UpperCamelCase_   (				self:		Optional[Any],      a_:		Any		):




   '''simple docstring'''



   return default_hp_space_optuna(a_		)







class 					lowercase(     __a ):







 '''simple docstring'''





 lowercase__ 					=					"ray"
 lowercase__ 					=					"'ray[tune]'"


 @staticmethod
 def  UpperCamelCase_   (				):




   '''simple docstring'''



   return is_ray_available()


 def  UpperCamelCase_   (				self:		int,      a_:		Optional[Any],      a_:		int,      a_:		str,      **a_:		List[Any]		):




   '''simple docstring'''



   return run_hp_search_ray(a_,      a_,      a_,      **a_		)


 def  UpperCamelCase_   (				self:		str,      a_:		Tuple		):




   '''simple docstring'''



   return default_hp_space_ray(a_		)







class 					lowercase(     __a ):







 '''simple docstring'''





 lowercase__ 					=					"sigopt"


 @staticmethod
 def  UpperCamelCase_   (				):




   '''simple docstring'''



   return is_sigopt_available()


 def  UpperCamelCase_   (				self:		Dict,      a_:		str,      a_:		int,      a_:		str,      **a_:		int		):




   '''simple docstring'''



   return run_hp_search_sigopt(a_,      a_,      a_,      **a_		)


 def  UpperCamelCase_   (				self:		str,      a_:		List[str]		):




   '''simple docstring'''



   return default_hp_space_sigopt(a_		)







class 					lowercase(     __a ):







 '''simple docstring'''





 lowercase__ 					=					"wandb"


 @staticmethod
 def  UpperCamelCase_   (				):




   '''simple docstring'''



   return is_wandb_available()


 def  UpperCamelCase_   (				self:		Optional[Any],      a_:		str,      a_:		int,      a_:		str,      **a_:		Union[str, Any]		):




   '''simple docstring'''



   return run_hp_search_wandb(a_,      a_,      a_,      **a_		)


 def  UpperCamelCase_   (				self:		str,      a_:		Any		):




   '''simple docstring'''



   return default_hp_space_wandb(a_		)







A_						=				{
    HPSearchBackend(backend.name): backend for backend in [OptunaBackend, RayTuneBackend, SigOptBackend, WandbBackend]
}


def 					UpperCAmelCase__       ():
  """simple docstring"""

  _snake_case							:		Optional[int]								=  [backend for backend in ALL_HYPERPARAMETER_SEARCH_BACKENDS.values() if backend.is_available()]
  if len(snake_case__    ) > 0:
    _snake_case							:		Any								=  available_backends[0].name
    if len(snake_case__    ) > 1:
      logger.info(
          F"{len(snake_case__    )} hyperparameter search backends available. Using {name} as the default."    )
    return name
  raise RuntimeError(
      """No hyperparameter search backend available.\n"""
      + """\n""".join(
          F" - To install {backend.name} run {backend.pip_install()}"
          for backend in ALL_HYPERPARAMETER_SEARCH_BACKENDS.values()    )    )


