"""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   UpperCAmelCase_			(					_a):


      def __init__(     self	,    a	,    a	,    a=1_0_2_4	,    a=1_0_2_4	,    a=3.6							) ->      Any:
             lowercase__    :	List[Any]     					=			tokenizer
             lowercase__    :	Dict     					=			tokenizer.bos_token_id
             lowercase__    :	Optional[Any]     					=			dataset
             lowercase__    :	str     					=			seq_length
             lowercase__    :	int     					=			seq_length * chars_per_token * num_of_sequences


      def __iter__(     self							) ->      List[str]:
             lowercase__    :	List[Any]     					=			iter(self.dataset							)
             lowercase__    :	Tuple     					=			True
             while more_examples:
                    lowercase__					,   lowercase__    :	Optional[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__    :	Optional[int]     					=			tokenizer(a	,    truncation=a							)['input_ids']
                    lowercase__    :	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__    :	Optional[int]     					=			all_token_ids[i : i + self.seq_length]
                           if len(a							) == self.seq_length:
                                  yield torch.tensor(a							)





def        a_  (   _lowerCAmelCase						:				Optional[Any]  ):





       '''simple docstring'''

       lowercase__    :	int     					=			{'streaming': True}
       lowercase__    :	List[str]     					=			load_dataset(args.dataset_name     ,				split='train'     ,				**_lowerCAmelCase  )
       lowercase__    :	Any     					=			ConstantLengthDataset(_lowerCAmelCase     ,				_lowerCAmelCase     ,				seq_length=args.seq_length  )
       lowercase__    :	Union[str, Any]     					=			DataLoader(_lowerCAmelCase     ,				batch_size=args.batch_size  )
       return eval_dataloader





def        a_  (   _lowerCAmelCase						:				Union[str, Any]  ):





       '''simple docstring'''

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

              if args.max_eval_steps > 0 and step >= args.max_eval_steps:
                     break
       lowercase__    :	int     					=			torch.mean(torch.cat(_lowerCAmelCase  )  )
       try:
              lowercase__    :	Optional[Any]     					=			torch.exp(_lowerCAmelCase  )
       except OverflowError:
              lowercase__    :	Tuple     					=			float('inf'  )
       return loss.item(), perplexity.item()


# Setup Accelerator
_UpperCamelCase     :		Optional[int]											=					Accelerator()

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

# Logging
_UpperCamelCase     :		List[str]											=					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
_UpperCamelCase     :		Dict											=					AutoModelForCausalLM.from_pretrained(args.model_ckpt)
_UpperCamelCase     :		Tuple											=					AutoTokenizer.from_pretrained(args.model_ckpt)

# Load dataset and dataloader
_UpperCamelCase     :		Union[str, Any]											=					create_dataloader(args)

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

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






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


"""simple docstring"""





import numpy as np
from sklearn.datasets import fetch_california_housing
from sklearn.metrics import mean_absolute_error, mean_squared_error
from sklearn.model_selection import train_test_split
from xgboost import XGBRegressor





def        a_  (   _lowerCAmelCase						:				dict  ):





       '''simple docstring'''

       return (data["data"], data["target"])





def        a_  (   _lowerCAmelCase						:				np.ndarray     ,				_lowerCAmelCase						:				np.ndarray     ,				_lowerCAmelCase						:				np.ndarray  ):





       '''simple docstring'''

       lowercase__    :	Any     					=			XGBRegressor(verbosity=0     ,				random_state=42  )
       xgb.fit(_lowerCAmelCase     ,				_lowerCAmelCase  )
       # Predict target for test data
       lowercase__    :	str     					=			xgb.predict(_lowerCAmelCase  )
       lowercase__    :	Union[str, Any]     					=			predictions.reshape(len(_lowerCAmelCase  )     ,				1  )
       return predictions





def        a_  (   ):





       '''simple docstring'''

       lowercase__    :	Optional[Any]     					=			fetch_california_housing()
       lowercase__					,   lowercase__    :	str     					=			data_handling(_lowerCAmelCase  )
       lowercase__					,   lowercase__					,   lowercase__					,   lowercase__    :	str     					=			train_test_split(
           _lowerCAmelCase     ,				_lowerCAmelCase     ,				test_size=0.2_5     ,				random_state=1  )
       lowercase__    :	Any     					=			xgboost(_lowerCAmelCase     ,				_lowerCAmelCase     ,				_lowerCAmelCase  )
       # Error printing
       print(f"""Mean Absolute Error : {mean_absolute_error(_lowerCAmelCase     ,				_lowerCAmelCase  )}"""  )
       print(f"""Mean Square Error  : {mean_squared_error(_lowerCAmelCase     ,				_lowerCAmelCase  )}"""  )


if __name__ == "__main__":
       import doctest

       doctest.testmod(verbose=True)
       main()

