




'''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


_snake_case				:				List[Any]       							=		logging.get_logger(__name__)

class       A   :
							lowercase_          =    42
							lowercase_          =    None







							@staticmethod
							def        __lowerCAmelCase      (  )      ->							List[Any]:


														"""simple docstring"""


														raise NotImplementedError







							def        __lowerCAmelCase      (  self	:  List[Any]							,      lowerCAmelCase_	:  List[str]							,      lowerCAmelCase_	:  int							,      lowerCAmelCase_	:  str							,      **lowerCAmelCase_	:  Tuple )      ->							Optional[int]:


														"""simple docstring"""


														raise NotImplementedError







							def        __lowerCAmelCase      (  self	:  Union[str, Any]							,      lowerCAmelCase_	:  List[Any] )      ->							int:


														"""simple docstring"""


														raise NotImplementedError







							def        __lowerCAmelCase      (  self	:  Tuple )      ->							Optional[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        __lowerCAmelCase      (  cls	:  Optional[Any] )      ->							Optional[Any]:


														"""simple docstring"""


														return F'`pip install {cls.pip_package or cls.name}`'



class       A   (		_a   ):
							lowercase_          =    'optuna'







							@staticmethod
							def        __lowerCAmelCase      (  )      ->							Union[str, Any]:


														"""simple docstring"""


														return is_optuna_available()







							def        __lowerCAmelCase      (  self	:  Union[str, Any]							,      lowerCAmelCase_	:  Union[str, Any]							,      lowerCAmelCase_	:  int							,      lowerCAmelCase_	:  str							,      **lowerCAmelCase_	:  str )      ->							List[Any]:


														"""simple docstring"""


														return run_hp_search_optuna(lowerCAmelCase_							,      lowerCAmelCase_							,      lowerCAmelCase_							,      **lowerCAmelCase_ )







							def        __lowerCAmelCase      (  self	:  Tuple							,      lowerCAmelCase_	:  str )      ->							int:


														"""simple docstring"""


														return default_hp_space_optuna(lowerCAmelCase_ )



class       A   (		_a   ):
							lowercase_          =    'ray'
							lowercase_          =    '\'ray[tune]\''







							@staticmethod
							def        __lowerCAmelCase      (  )      ->							Any:


														"""simple docstring"""


														return is_ray_available()







							def        __lowerCAmelCase      (  self	:  Optional[Any]							,      lowerCAmelCase_	:  List[str]							,      lowerCAmelCase_	:  int							,      lowerCAmelCase_	:  str							,      **lowerCAmelCase_	:  str )      ->							Tuple:


														"""simple docstring"""


														return run_hp_search_ray(lowerCAmelCase_							,      lowerCAmelCase_							,      lowerCAmelCase_							,      **lowerCAmelCase_ )







							def        __lowerCAmelCase      (  self	:  Union[str, Any]							,      lowerCAmelCase_	:  Tuple )      ->							List[Any]:


														"""simple docstring"""


														return default_hp_space_ray(lowerCAmelCase_ )



class       A   (		_a   ):
							lowercase_          =    'sigopt'







							@staticmethod
							def        __lowerCAmelCase      (  )      ->							List[Any]:


														"""simple docstring"""


														return is_sigopt_available()







							def        __lowerCAmelCase      (  self	:  Tuple							,      lowerCAmelCase_	:  Tuple							,      lowerCAmelCase_	:  int							,      lowerCAmelCase_	:  str							,      **lowerCAmelCase_	:  Union[str, Any] )      ->							str:


														"""simple docstring"""


														return run_hp_search_sigopt(lowerCAmelCase_							,      lowerCAmelCase_							,      lowerCAmelCase_							,      **lowerCAmelCase_ )







							def        __lowerCAmelCase      (  self	:  List[str]							,      lowerCAmelCase_	:  str )      ->							str:


														"""simple docstring"""


														return default_hp_space_sigopt(lowerCAmelCase_ )



class       A   (		_a   ):
							lowercase_          =    'wandb'







							@staticmethod
							def        __lowerCAmelCase      (  )      ->							int:


														"""simple docstring"""


														return is_wandb_available()







							def        __lowerCAmelCase      (  self	:  List[str]							,      lowerCAmelCase_	:  Union[str, Any]							,      lowerCAmelCase_	:  int							,      lowerCAmelCase_	:  str							,      **lowerCAmelCase_	:  Optional[int] )      ->							Tuple:


														"""simple docstring"""


														return run_hp_search_wandb(lowerCAmelCase_							,      lowerCAmelCase_							,      lowerCAmelCase_							,      **lowerCAmelCase_ )







							def        __lowerCAmelCase      (  self	:  Optional[Any]							,      lowerCAmelCase_	:  str )      ->							List[Any]:


														"""simple docstring"""


														return default_hp_space_wandb(lowerCAmelCase_ )



_snake_case				:				Any       							=		{
    HPSearchBackend(backend.name): backend for backend in [OptunaBackend, RayTuneBackend, SigOptBackend, WandbBackend]
}
def 		snake_case_   ():





							'''simple docstring'''


							_a     					=    [backend for backend in ALL_HYPERPARAMETER_SEARCH_BACKENDS.values() if backend.is_available()]
							if len(UpperCamelCase				) > 0:
														_a     					=    available_backends[0].name
														if len(UpperCamelCase				) > 1:
																					logger.info(
																					    f'{len(UpperCamelCase				)} 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()				)				)







'''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       A   (		_a   ):







							def __init__(  self	:  Optional[int]							,      lowerCAmelCase_	:  Optional[Any]							,      lowerCAmelCase_	:  Union[str, Any]							,      lowerCAmelCase_	:  List[str]=10_24							,      lowerCAmelCase_	:  Optional[Any]=10_24							,      lowerCAmelCase_	:  Tuple=3.6 )      ->							List[Any]:


														"""simple docstring"""


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







							def __iter__(  self	:  Any )      ->							int:


														"""simple docstring"""


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



def 		snake_case_   (UpperCamelCase	:  int				):





							'''simple docstring'''


							_a     					=    {'''streaming''': True}
							_a     					=    load_dataset(args.dataset_name			,						split='''train'''			,						**UpperCamelCase				)
							_a     					=    ConstantLengthDataset(UpperCamelCase			,						UpperCamelCase			,						seq_length=args.seq_length				)
							_a     					=    DataLoader(UpperCamelCase			,						batch_size=args.batch_size				)
							return eval_dataloader
def 		snake_case_   (UpperCamelCase	:  int				):





							'''simple docstring'''


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

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


# Setup Accelerator
_snake_case				:				List[str]       							=		Accelerator()

# Parse configuration
_snake_case				:				List[str]       							=		HfArgumentParser(EvaluationArguments)
_snake_case				:				Optional[int]       							=		parser.parse_args()
set_seed(args.seed)

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

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

# Prepare everything with our `accelerator`.
_snake_case							,    _snake_case				:				Optional[int]       							=		accelerator.prepare(model, eval_dataloader)

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

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

