"""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 					_A		(			lowerCAmelCase      ):

      def __init__(		self						,			__lowerCAmelCase						,			__lowerCAmelCase						,			__lowerCAmelCase=1024						,			__lowerCAmelCase=1024						,			__lowerCAmelCase=3.6		):







            """simple docstring"""



            lowercase								=      tokenizer
            lowercase								=      tokenizer.bos_token_id
            lowercase								=      dataset
            lowercase								=      seq_length
            lowercase								=      seq_length * chars_per_token * num_of_sequences







      def __iter__(		self		):







            """simple docstring"""



            lowercase								=      iter(self.dataset		)
            lowercase								=      True
            while more_examples:
                  lowercase				,				lowercase								=      [], 0
                  while True:
                        if buffer_len >= self.input_characters:
                              break
                        try:
                              buffer.append(next(__lowerCAmelCase		)["""content"""]		)
                              buffer_len += len(buffer[-1]		)
                        except StopIteration:
                              lowercase								=      False
                              break
                  lowercase								=      tokenizer(__lowerCAmelCase						,			truncation=__lowerCAmelCase		)["""input_ids"""]
                  lowercase								=      []
                  for tokenized_input in tokenized_inputs:
                        all_token_ids.extend(tokenized_input + [self.concat_token_id]		)
                  for i in range(0						,			len(__lowerCAmelCase		)						,			self.seq_length		):
                        lowercase								=      all_token_ids[i : i + self.seq_length]
                        if len(__lowerCAmelCase		) == self.seq_length:
                              yield torch.tensor(__lowerCAmelCase		)






def      UpperCAmelCase__			(							lowerCAmelCase__	:Tuple						)					->       Optional[int]:
      '''simple docstring'''


      lowercase								=      {"""streaming""": True}
      lowercase								=      load_dataset(args.dataset_name   ,						split="""train"""   ,						**lowerCAmelCase__						)
      lowercase								=      ConstantLengthDataset(lowerCAmelCase__   ,						lowerCAmelCase__   ,						seq_length=args.seq_length						)
      lowercase								=      DataLoader(lowerCAmelCase__   ,						batch_size=args.batch_size						)
      return eval_dataloader


def      UpperCAmelCase__			(							lowerCAmelCase__	:List[Any]						)					->       int:
      '''simple docstring'''


      model.eval()
      lowercase								=      []
      for step, batch in enumerate(lowerCAmelCase__						):
            with torch.no_grad():
                  lowercase								=      model(lowerCAmelCase__   ,						labels=lowerCAmelCase__						)
            lowercase								=      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								=      torch.mean(torch.cat(lowerCAmelCase__						)						)
      try:
            lowercase								=      torch.exp(lowerCAmelCase__						)
      except OverflowError:
            lowercase								=      float("""inf"""						)
      return loss.item(), perplexity.item()


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

# Parse configuration
__lowerCAmelCase       :			Any   	=HfArgumentParser(EvaluationArguments)
__lowerCAmelCase       :			Any   	=parser.parse_args()
set_seed(args.seed)

# Logging
__lowerCAmelCase       :			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       :			int   	=AutoModelForCausalLM.from_pretrained(args.model_ckpt)
__lowerCAmelCase       :			Any   	=AutoTokenizer.from_pretrained(args.model_ckpt)

# Load dataset and dataloader
__lowerCAmelCase       :			Tuple   	=create_dataloader(args)

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

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






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




"""simple docstring"""






from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import Features, Sequence, Value
from .base import TaskTemplate





@dataclass(frozen=lowerCAmelCase      )
class 					_A		(			lowerCAmelCase      ):
      # `task` is not a ClassVar since we want it to be part of the `asdict` output for JSON serialization
      snake_case__     :  str				    =      field(default='question-answering-extractive'    ,       metadata={'include_in_asdict_even_if_is_default': True}      )
      snake_case__     :  ClassVar[Features]				    =      Features({'question': Value('string'      ), 'context': Value('string'      )}      )
      snake_case__     :  ClassVar[Features]				    =      Features(
          {
              'answers': Sequence(
                  {
                      'text': Value('string'      ),
                      'answer_start': Value('int32'      ),
                  }      )
          }      )
      snake_case__     :  str				    =      "question"
      snake_case__     :  str				    =      "context"
      snake_case__     :  str				    =      "answers"

      @property
      def 							A__			(		self		):







            """simple docstring"""



            return {self.question_column: "question", self.context_column: "context", self.answers_column: "answers"}



