


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

from .. import config
from .logging import get_logger


__a     =			get_logger(__name__)






class   __lowercase				(  enum.Enum  ):
      UpperCamelCase								=       '''all_checks'''
      UpperCamelCase								=       '''basic_checks'''
      UpperCamelCase								=       '''no_checks'''






class   __lowercase				(  __snake_case  ):
      pass






class   __lowercase				(  __snake_case  ):
      pass






class   __lowercase				(  __snake_case  ):
      pass






class   __lowercase				(  __snake_case  ):
      pass

def   _UpperCamelCase				(						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_=None   )						->Optional[Any]:
   if expected_checksums is None:
      logger.info("""Unable to verify checksums."""   )
      return
   if len(set(lowerCAmelCase_   ) - set(lowerCAmelCase_   )   ) > 0:
      raise ExpectedMoreDownloadedFiles(str(set(lowerCAmelCase_   ) - set(lowerCAmelCase_   )   )   )
   if len(set(lowerCAmelCase_   ) - set(lowerCAmelCase_   )   ) > 0:
      raise UnexpectedDownloadedFile(str(set(lowerCAmelCase_   ) - set(lowerCAmelCase_   )   )   )
   UpperCAmelCase							=     [url for url in expected_checksums if expected_checksums[url] != recorded_checksums[url]]
   UpperCAmelCase							=     """ for """ + verification_name if verification_name is not None else """"""
   if len(lowerCAmelCase_   ) > 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				(  __snake_case  ):
      pass






class   __lowercase				(  __snake_case  ):
      pass






class   __lowercase				(  __snake_case  ):
      pass






class   __lowercase				(  __snake_case  ):
      pass

def   _UpperCamelCase				(						lowerCAmelCase_  ,						lowerCAmelCase_   )						->Tuple:
   if expected_splits is None:
      logger.info("""Unable to verify splits sizes."""   )
      return
   if len(set(lowerCAmelCase_   ) - set(lowerCAmelCase_   )   ) > 0:
      raise ExpectedMoreSplits(str(set(lowerCAmelCase_   ) - set(lowerCAmelCase_   )   )   )
   if len(set(lowerCAmelCase_   ) - set(lowerCAmelCase_   )   ) > 0:
      raise UnexpectedSplits(str(set(lowerCAmelCase_   ) - set(lowerCAmelCase_   )   )   )
   UpperCAmelCase							=     [
       {"""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(lowerCAmelCase_   ) > 0:
      raise NonMatchingSplitsSizesError(str(lowerCAmelCase_   )   )
   logger.info("""All the splits matched successfully."""   )
def   _UpperCamelCase				(						lowerCAmelCase_  ,						lowerCAmelCase_ = True   )						->dict:
   if record_checksum:
      UpperCAmelCase							=     shaaaa()
      with open(lowerCAmelCase_  ,						"""rb"""   ) as f:
         for chunk in iter(lambda: f.read(1 << 2_0   )  ,						b""""""   ):
            m.update(lowerCAmelCase_   )
         UpperCAmelCase							=     m.hexdigest()
   else:
      UpperCAmelCase							=     None
   return {"num_bytes": os.path.getsize(lowerCAmelCase_   ), "checksum": checksum}







def   _UpperCamelCase				(						lowerCAmelCase_   )						->Tuple:
   if dataset_size and config.IN_MEMORY_MAX_SIZE:
      return dataset_size < config.IN_MEMORY_MAX_SIZE
   else:
      return False







import pickle
import unittest

import torch

from accelerate import Accelerator
from accelerate.state import AcceleratorState
from accelerate.test_utils import require_cpu






@require_cpu
class   __lowercase				(  unittest.TestCase  ):





      def        _lowercase				(		self						:					Any       )    ->   Union[str, Any]:






         """simple docstring"""







         UpperCAmelCase							=     torch.nn.Linear(1_0					,       1_0       )
         UpperCAmelCase							=     torch.optim.SGD(model.parameters()					,       0.1       )
         UpperCAmelCase							=     Accelerator()
         UpperCAmelCase							=     accelerator.prepare(__lowerCamelCase       )
         try:
            pickle.loads(pickle.dumps(__lowerCamelCase       )       )
         except Exception as e:
            self.fail(F"""Accelerated optimizer pickling failed with {e}"""       )
         AcceleratorState._reset_state()



