




"""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(     __a ):







 '''simple docstring'''





 def __init__(				self:		Tuple,      a_:		Union[str, Any],      a_:		Tuple,      a_:		Optional[Any]=1_024,      a_:		Union[str, Any]=1_024,      a_:		Any=3.6		):




   '''simple docstring'''



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


 def __iter__(				self:		Dict		):




   '''simple docstring'''



   _snake_case							:		Any								=  iter(self.dataset		)
   _snake_case							:		Tuple								=  True
   while more_examples:
     _snake_case		,					_snake_case							:		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:
         _snake_case							:		List[Any]								=  False
         break
     _snake_case							:		Any								=  tokenizer(a_,      truncation=a_		)["""input_ids"""]
     _snake_case							:		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		):
       _snake_case							:		int								=  all_token_ids[i : i + self.seq_length]
       if len(a_		) == self.seq_length:
         yield torch.tensor(a_		)







def 					UpperCAmelCase__       (snake_case__							:					Optional[int]    ):
  """simple docstring"""

  _snake_case							:		Dict								=  {"""streaming""": True}
  _snake_case							:		Dict								=  load_dataset(args.dataset_name		,   split="""train"""		,   **snake_case__    )
  _snake_case							:		Optional[int]								=  ConstantLengthDataset(snake_case__		,   snake_case__		,   seq_length=args.seq_length    )
  _snake_case							:		Optional[Any]								=  DataLoader(snake_case__		,   batch_size=args.batch_size    )
  return eval_dataloader


def 					UpperCAmelCase__       (snake_case__							:					List[str]    ):
  """simple docstring"""

  model.eval()
  _snake_case							:		Any								=  []
  for step, batch in enumerate(snake_case__    ):
    with torch.no_grad():
      _snake_case							:		Tuple								=  model(snake_case__		,   labels=snake_case__    )
    _snake_case							:		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
  _snake_case							:		Tuple								=  torch.mean(torch.cat(snake_case__    )    )
  try:
    _snake_case							:		Union[str, Any]								=  torch.exp(snake_case__    )
  except OverflowError:
    _snake_case							:		List[Any]								=  float("""inf"""    )
  return loss.item(), perplexity.item()


# Setup Accelerator
A_						=				Accelerator()

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

# Logging
A_						=				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_						=				AutoModelForCausalLM.from_pretrained(args.model_ckpt)
A_						=				AutoTokenizer.from_pretrained(args.model_ckpt)

# Load dataset and dataloader
A_						=				create_dataloader(args)

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

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


