
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				(					_UpperCamelCase):
    def __init__(    self						,    snake_case_						,    snake_case_						,    snake_case_=10_24						,    snake_case_=10_24						,    snake_case_=3.6     ):
      _snake_case     :						Union[str, Any]						=   tokenizer
      _snake_case     :						str						=   tokenizer.bos_token_id
      _snake_case     :						List[Any]						=   dataset
      _snake_case     :						Union[str, Any]						=   seq_length
      _snake_case     :						List[str]						=   seq_length * chars_per_token * num_of_sequences


    def __iter__(    self     ):
      _snake_case     :						int						=   iter(self.dataset     )
      _snake_case     :						List[Any]						=   True
      while more_examples:
        _snake_case     :						Tuple						=   [], 0
        while True:
          if buffer_len >= self.input_characters:
            break
          try:
            buffer.append(next(_UpperCAmelCase     )["content"]     )
            buffer_len += len(buffer[-1]     )
          except StopIteration:
            _snake_case     :						Union[str, Any]						=   False
            break
        _snake_case     :						Any						=   tokenizer(_UpperCAmelCase						,    truncation=_UpperCAmelCase     )['''input_ids''']
        _snake_case     :						Dict						=   []
        for tokenized_input in tokenized_inputs:
          all_token_ids.extend(tokenized_input + [self.concat_token_id]     )
        for i in range(0						,    len(_UpperCAmelCase     )						,    self.seq_length     ):
          _snake_case     :						Union[str, Any]						=   all_token_ids[i : i + self.seq_length]
          if len(_UpperCAmelCase     ) == self.seq_length:
            yield torch.tensor(_UpperCAmelCase     )


def 				a__   (   a   :				Tuple		):

  """simple docstring"""







  _snake_case     :						int						=   {'''streaming''': True}
  _snake_case     :						int						=   load_dataset(args.dataset_name       ,     split="train"       ,     **a_		)
  _snake_case     :						int						=   ConstantLengthDataset(a_       ,     a_       ,     seq_length=args.seq_length		)
  _snake_case     :						Optional[int]						=   DataLoader(a_       ,     batch_size=args.batch_size		)
  return eval_dataloader
def 				a__   (   a   :				Optional[int]		):

  """simple docstring"""







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

    if args.max_eval_steps > 0 and step >= args.max_eval_steps:
      break
  _snake_case     :						Union[str, Any]						=   torch.mean(torch.cat(a_		)		)
  try:
    _snake_case     :						Tuple						=   torch.exp(a_		)
  except OverflowError:
    _snake_case     :						Dict						=   float("inf"		)
  return loss.item(), perplexity.item()


# Setup Accelerator
_a      :			Union[str, Any]					=							Accelerator()

# Parse configuration
_a      :			Union[str, Any]					=							HfArgumentParser(EvaluationArguments)
_a      :			int					=							parser.parse_args()
set_seed(args.seed)

# Logging
_a      :			Dict					=							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      :			Optional[int]					=							AutoModelForCausalLM.from_pretrained(args.model_ckpt)
_a      :			Union[str, Any]					=							AutoTokenizer.from_pretrained(args.model_ckpt)

# Load dataset and dataloader
_a      :			Optional[Any]					=							create_dataloader(args)

# Prepare everything with our `accelerator`.
_a,			_a      :			List[Any]					=							accelerator.prepare(model, eval_dataloader)

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



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



"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


_a      :			Optional[int]					=							logging.get_logger(__name__)

_a      :			List[str]					=							{
    """facebook/convnextv2-tiny-1k-224""": """https://huggingface.co/facebook/convnextv2-tiny-1k-224/resolve/main/config.json""",
}






class      _UpperCAmelCase				(					_snake_case		,					_snake_case):
    __lowercase							: List[Any]  =					"""convnextv2"""
    def __init__(    self						,    snake_case_=3						,    snake_case_=4						,    snake_case_=4						,    snake_case_=None						,    snake_case_=None						,    snake_case_="gelu"						,    snake_case_=0.02						,    snake_case_=1E-12						,    snake_case_=0.0						,    snake_case_=2_24						,    snake_case_=None						,    snake_case_=None						,    **snake_case_						,    ):
      super().__init__(**snake_case_     )

      _snake_case     :						Tuple						=   num_channels
      _snake_case     :						Optional[int]						=   patch_size
      _snake_case     :						Tuple						=   num_stages
      _snake_case     :						int						=   [96, 1_92, 3_84, 7_68] if hidden_sizes is None else hidden_sizes
      _snake_case     :						str						=   [3, 3, 9, 3] if depths is None else depths
      _snake_case     :						int						=   hidden_act
      _snake_case     :						Tuple						=   initializer_range
      _snake_case     :						Union[str, Any]						=   layer_norm_eps
      _snake_case     :						Optional[int]						=   drop_path_rate
      _snake_case     :						Union[str, Any]						=   image_size
      _snake_case     :						List[Any]						=   ["stem"] + [F'stage{idx}' for idx in range(1						,    len(self.depths     ) + 1     )]
      _snake_case			,					_snake_case     :						Dict						=   get_aligned_output_features_output_indices(
          out_features=snake_case_						,    out_indices=snake_case_						,    stage_names=self.stage_names     )

