





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  ):







  def __init__(	self    ,						_lowercase    ,						_lowercase    ,						_lowercase=1_024    ,						_lowercase=1_024    ,						_lowercase=3.6						):
       lowerCAmelCase_					:       Union[str, Any]    =		tokenizer
       lowerCAmelCase_					:       Optional[Any]    =		tokenizer.bos_token_id
       lowerCAmelCase_					:       int    =		dataset
       lowerCAmelCase_					:       Optional[int]    =		seq_length
       lowerCAmelCase_					:       Optional[Any]    =		seq_length * chars_per_token * num_of_sequences







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






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






def 							_lowerCAmelCase (  _a   :					Any			)	->			Tuple:
     model.eval()
     lowerCAmelCase_					:       Any    =		[]
     for step, batch in enumerate(_a			):
          with torch.no_grad():
               lowerCAmelCase_					:       Union[str, Any]    =		model(_a					,   labels=_a			)
          lowerCAmelCase_					:       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
     lowerCAmelCase_					:       List[Any]    =		torch.mean(torch.cat(_a			)			)
     try:
          lowerCAmelCase_					:       int    =		torch.exp(_a			)
     except OverflowError:
          lowerCAmelCase_					:       Optional[Any]    =		float("""inf"""			)
     return loss.item(), perplexity.item()


# Setup Accelerator
UpperCAmelCase_					:   Any 							=						Accelerator()

# Parse configuration
UpperCAmelCase_					:   int 							=						HfArgumentParser(EvaluationArguments)
UpperCAmelCase_					:   List[Any] 							=						parser.parse_args()
set_seed(args.seed)

# Logging
UpperCAmelCase_					:   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
UpperCAmelCase_					:   Dict 							=						AutoModelForCausalLM.from_pretrained(args.model_ckpt)
UpperCAmelCase_					:   List[str] 							=						AutoTokenizer.from_pretrained(args.model_ckpt)

# Load dataset and dataloader
UpperCAmelCase_					:   Any 							=						create_dataloader(args)

# Prepare everything with our `accelerator`.
UpperCAmelCase_							,						UpperCAmelCase_					:   Optional[int] 							=						accelerator.prepare(model, eval_dataloader)

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







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







import enum
import os
from hashlib import shaaaa
from typing import Optional

from .. import config
from .logging import get_logger


UpperCAmelCase_					:   int 							=						get_logger(__name__)





class      lowercase__    (							enum.Enum  ):
  __UpperCamelCase	     =  """all_checks"""
  __UpperCamelCase	     =  """basic_checks"""
  __UpperCamelCase	     =  """no_checks"""





class      lowercase__    (							__A  ):
  pass





class      lowercase__    (							__A  ):
  pass





class      lowercase__    (							__A  ):
  pass





class      lowercase__    (							__A  ):
  pass






def 							_lowerCAmelCase (  _a   :					Optional[dict]					,   _a   :					dict					,   _a   :					str=None			)	->			Optional[Any]:
     if expected_checksums is None:
          logger.info("""Unable to verify checksums."""			)
          return
     if len(set(_a			) - set(_a			)			) > 0:
          raise ExpectedMoreDownloadedFiles(str(set(_a			) - set(_a			)			)			)
     if len(set(_a			) - set(_a			)			) > 0:
          raise UnexpectedDownloadedFile(str(set(_a			) - set(_a			)			)			)
     lowerCAmelCase_					:       Union[str, Any]    =		[url for url in expected_checksums if expected_checksums[url] != recorded_checksums[url]]
     lowerCAmelCase_					:       Optional[Any]    =		""" for """ + verification_name if verification_name is not None else """"""
     if len(_a			) > 0:
          raise NonMatchingChecksumError(
              F'Checksums didn\'t match{for_verification_name}:\n'
              F'{bad_urls}\n'
              """Set `verification_mode='no_checks'` to skip checksums verification and ignore this error"""			)
     logger.info("""All the checksums matched successfully""" + for_verification_name			)





class      lowercase__    (							__A  ):
  pass





class      lowercase__    (							__A  ):
  pass





class      lowercase__    (							__A  ):
  pass





class      lowercase__    (							__A  ):
  pass






def 							_lowerCAmelCase (  _a   :					Optional[dict]					,   _a   :					dict			)	->			str:
     if expected_splits is None:
          logger.info("""Unable to verify splits sizes."""			)
          return
     if len(set(_a			) - set(_a			)			) > 0:
          raise ExpectedMoreSplits(str(set(_a			) - set(_a			)			)			)
     if len(set(_a			) - set(_a			)			) > 0:
          raise UnexpectedSplits(str(set(_a			) - set(_a			)			)			)
     lowerCAmelCase_					:       Optional[int]    =		[
         {"""expected""": expected_splits[name], """recorded""": recorded_splits[name]}
         for name in expected_splits
         if expected_splits[name].num_examples != recorded_splits[name].num_examples
     ]
     if len(_a			) > 0:
          raise NonMatchingSplitsSizesError(str(_a			)			)
     logger.info("""All the splits matched successfully."""			)






def 							_lowerCAmelCase (  _a   :					str					,   _a   :					bool = True			)	->			dict:
     if record_checksum:
          lowerCAmelCase_					:       int    =		shaaaa()
          with open(_a					,   """rb"""			) as f:
               for chunk in iter(lambda: f.read(1 << 20			)					,   B""""""			):
                    m.update(_a			)
               lowerCAmelCase_					:       List[str]    =		m.hexdigest()
     else:
          lowerCAmelCase_					:       Union[str, Any]    =		None
     return {"num_bytes": os.path.getsize(_a			), "checksum": checksum}







def 							_lowerCAmelCase (  _a   :					Tuple			)	->			Optional[int]:
     if dataset_size and config.IN_MEMORY_MAX_SIZE:
          return dataset_size < config.IN_MEMORY_MAX_SIZE
     else:
          return False
