




"""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:	int,   a_:	int,   a_:	int,   a_:	Any=1_024,   a_:	List[Any]=1_024,   a_:	Optional[Any]=3.6	):




   '''simple docstring'''



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


 def __iter__(       self:	Optional[int]	):




   '''simple docstring'''



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







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

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


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

  model.eval()
  _snake_case							:	int			=						[]
  for step, batch in enumerate(snake_case__					):
    with torch.no_grad():
      _snake_case							:	int			=						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							:	List[str]			=						torch.mean(torch.cat(snake_case__					)					)
  try:
    _snake_case							:	Tuple			=						torch.exp(snake_case__					)
  except OverflowError:
    _snake_case							:	Dict			=						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 typing import Any


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

  if not input_list:
    return []
  _snake_case							:	List[Any]			=						[input_list.count(snake_case__					) for value in input_list]
  _snake_case							:	Optional[int]			=						max(snake_case__					)  # Gets the maximum count in the input list.
  # Gets values of modes
  return sorted({input_list[i] for i, value in enumerate(snake_case__					) if value == y}					)


if __name__ == "__main__":
     import doctest

     doctest.testmod()


