




"""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"""
def 						__a   (	_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE = False   )	->str:

  if not isinstance(_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   ):
    a__:     Optional[int]										=	F'Expected string as input, found {type(_SCREAMING_SNAKE_CASE   )}'
    raise ValueError(_SCREAMING_SNAKE_CASE   )
  if not isinstance(_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   ):
    a__:     List[str]										=	F'Expected boolean as use_pascal parameter, found {type(_SCREAMING_SNAKE_CASE   )}'
    raise ValueError(_SCREAMING_SNAKE_CASE   )

  a__:     int										=	input_str.split('_'   )

  a__:     List[str]										=	0 if use_pascal else 1

  a__:     List[str]										=	words[start_index:]

  a__:     List[str]										=	[word[0].upper() + word[1:] for word in words_to_capitalize]

  a__:     List[str]										=	'' if use_pascal else words[0]

  return "".join([initial_word, *capitalized_words]   )


if __name__ == "__main__":
 from doctest import testmod

 testmod()


