






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

from .. import config
from .logging import get_logger


UpperCAmelCase__							  =						get_logger(__name__)






class 						lowercase_   (      enum.Enum  ):






      '''simple docstring'''




      __snake_case	    =    '''all_checks'''
      __snake_case	    =    '''basic_checks'''
      __snake_case	    =    '''no_checks'''






class 						lowercase_   (      lowercase  ):






      '''simple docstring'''










class 						lowercase_   (      lowercase  ):






      '''simple docstring'''










class 						lowercase_   (      lowercase  ):






      '''simple docstring'''










class 						lowercase_   (      lowercase  ):






      '''simple docstring'''




def 		_a       ( a					:Optional[dict]      ,				a					:dict      ,				a					:Optional[int]=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					)					)					)
 a						    = [url for url in expected_checksums if expected_checksums[url] != recorded_checksums[url]]
 a						    = ''' 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_   (      lowercase  ):






      '''simple docstring'''










class 						lowercase_   (      lowercase  ):






      '''simple docstring'''










class 						lowercase_   (      lowercase  ):






      '''simple docstring'''










class 						lowercase_   (      lowercase  ):






      '''simple docstring'''




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

def 		_a       ( a					:Tuple					)				->     Dict:
 if dataset_size and config.IN_MEMORY_MAX_SIZE:
  return dataset_size < config.IN_MEMORY_MAX_SIZE
 else:
  return False










from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


UpperCAmelCase__							  =						{
    "configuration_biogpt": ["BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP", "BioGptConfig"],
    "tokenization_biogpt": ["BioGptTokenizer"],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCAmelCase__							  =						[
         "BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST",
         "BioGptForCausalLM",
         "BioGptForTokenClassification",
         "BioGptForSequenceClassification",
         "BioGptModel",
         "BioGptPreTrainedModel",
     ]


if TYPE_CHECKING:
     from .configuration_biogpt import BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP, BioGptConfig
     from .tokenization_biogpt import BioGptTokenizer

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_biogpt import (
              BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST,
              BioGptForCausalLM,
              BioGptForSequenceClassification,
              BioGptForTokenClassification,
              BioGptModel,
              BioGptPreTrainedModel,
          )


else:
     import sys

     UpperCAmelCase__							  =						_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


