





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__     ( __snake_case    ):





      def __init__(				self ,							A_ ,							A_ ,							A_=1024 ,							A_=1024 ,							A_=3.6     ):
       '''simple docstring'''




       UpperCamelCase			:      List[Any] 			=     tokenizer
       UpperCamelCase			:      Optional[int] 			=     tokenizer.bos_token_id
       UpperCamelCase			:      Optional[int] 			=     dataset
       UpperCamelCase			:      Optional[int] 			=     seq_length
       UpperCamelCase			:      List[str] 			=     seq_length * chars_per_token * num_of_sequences





      def __iter__(				self     ):
       '''simple docstring'''




       UpperCamelCase			:      List[str] 			=     iter(self.dataset     )
       UpperCamelCase			:      Union[str, Any] 			=     True
       while more_examples:
        UpperCamelCase			:      Union[str, Any] 			=     [], 0
        while True:
         if buffer_len >= self.input_characters:
          break
         try:
          buffer.append(next(A_     )["content"]     )
          buffer_len += len(buffer[-1]     )
         except StopIteration:
          UpperCamelCase			:      Optional[int] 			=     False
          break
        UpperCamelCase			:      Dict 			=     tokenizer(A_ ,							truncation=A_     )["input_ids"]
        UpperCamelCase			:      List[str] 			=     []
        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     ):
         UpperCamelCase			:      Optional[Any] 			=     all_token_ids[i : i + self.seq_length]
         if len(A_     ) == self.seq_length:
          yield torch.tensor(A_     )



def 	A_       (  _lowerCAmelCase  )		->				Optional[int]:
 UpperCamelCase			:      Union[str, Any] 			=     {"streaming": True}
 UpperCamelCase			:      Optional[Any] 			=     load_dataset(args.dataset_name							,     split="train"							,     **_lowerCAmelCase  )
 UpperCamelCase			:      List[str] 			=     ConstantLengthDataset(_lowerCAmelCase							,     _lowerCAmelCase							,     seq_length=args.seq_length  )
 UpperCamelCase			:      List[str] 			=     DataLoader(_lowerCAmelCase							,     batch_size=args.batch_size  )
 return eval_dataloader



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

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


# Setup Accelerator
__lowerCamelCase :  Tuple										=  Accelerator()

# Parse configuration
__lowerCamelCase :  Tuple										=  HfArgumentParser(EvaluationArguments)
__lowerCamelCase :  List[str]										=  parser.parse_args()
set_seed(args.seed)

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

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

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

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


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







from typing import List, Optional

from tokenizers import ByteLevelBPETokenizer

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot_small import BlenderbotSmallTokenizer


__lowerCamelCase :  Dict										=  logging.get_logger(__name__)

__lowerCamelCase :  Union[str, Any]										=  {
    """vocab_file""": """vocab.json""",
    """merges_file""": """merges.txt""",
    """tokenizer_config_file""": """tokenizer_config.json""",
}

__lowerCamelCase :  Dict										=  {
    """vocab_file""": {
        """facebook/blenderbot_small-90M""": """https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/vocab.json"""
    },
    """merges_file""": {
        """facebook/blenderbot_small-90M""": """https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/merges.txt"""
    },
    """tokenizer_config_file""": {
        """facebook/blenderbot_small-90M""": (
            """https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/tokenizer_config.json"""
        )
    },
}

__lowerCamelCase :  Tuple										=  {
    """facebook/blenderbot_small-90M""": 512,
}



class       A__     ( __snake_case    ):
      _UpperCAmelCase			:Union[str, Any]							=     VOCAB_FILES_NAMES
      _UpperCAmelCase			:Dict							=     PRETRAINED_VOCAB_FILES_MAP
      _UpperCAmelCase			:List[Any]							=     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      _UpperCAmelCase			:Optional[Any]							=     BlenderbotSmallTokenizer





      def __init__(				self ,							A_=None ,							A_=None ,							A_="<|endoftext|>" ,							A_="<|endoftext|>" ,							A_="<|endoftext|>" ,							A_=False ,							A_=True ,							**A_ ,							):
       '''simple docstring'''




       super().__init__(
           ByteLevelBPETokenizer(
               vocab=A_ ,							merges=A_ ,							add_prefix_space=A_ ,							trim_offsets=A_ ,							) ,							bos_token=A_ ,							eos_token=A_ ,							unk_token=A_ ,							**A_ ,							)
       UpperCamelCase			:      Union[str, Any] 			=     add_prefix_space





      def  __UpperCamelCase(				self ,							A_ ,							A_=None     ):
       '''simple docstring'''




       UpperCamelCase			:      Dict 			=     [self.bos_token_id] + token_ids_a + [self.eos_token_id]
       if token_ids_a is None:
        return output

       return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]





      def  __UpperCamelCase(				self ,							A_ ,							A_ = None     ):
       '''simple docstring'''




       UpperCamelCase			:      Tuple 			=     [self.sep_token_id]
       UpperCamelCase			:      int 			=     [self.cls_token_id]

       if token_ids_a is None:
        return len(cls + token_ids_a + sep     ) * [0]
       return len(cls + token_ids_a + sep + sep + token_ids_a + sep     ) * [0]
