




"""simple docstring"""

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


a         =      logging.get_logger(__name__)

a         =      {
    "google/bit-50": "https://huggingface.co/google/bit-50/resolve/main/config.json",
}





class     lowercase_	(					UpperCamelCase_      ,     UpperCamelCase_							):



 '''simple docstring'''
 UpperCAmelCase   :					Tuple				  =     """bit"""
 UpperCAmelCase   :					Dict				  =     ["""preactivation""", """bottleneck"""]
 UpperCAmelCase   :					Dict				  =     ["""SAME""", """VALID"""]

 def __init__(			self	:	int   ,							_UpperCAmelCase	:	List[str]=3   ,							_UpperCAmelCase	:	Optional[int]=64   ,							_UpperCAmelCase	:	Union[str, Any]=[256, 512, 1_024, 2_048]   ,							_UpperCAmelCase	:	Tuple=[3, 4, 6, 3]   ,							_UpperCAmelCase	:	Union[str, Any]="preactivation"   ,							_UpperCAmelCase	:	Dict="relu"   ,							_UpperCAmelCase	:	Optional[Any]=None   ,							_UpperCAmelCase	:	str=32   ,							_UpperCAmelCase	:	Tuple=0.0   ,							_UpperCAmelCase	:	List[str]=False   ,							_UpperCAmelCase	:	Optional[Any]=32   ,							_UpperCAmelCase	:	int=1   ,							_UpperCAmelCase	:	Dict=None   ,							_UpperCAmelCase	:	Optional[int]=None   ,							**_UpperCAmelCase	:	Union[str, Any]   ,							):
       super().__init__(**_a		)
       if layer_type not in self.layer_types:
             raise ValueError(F'''layer_type={layer_type} is not one of {",".join(self.layer_types		)}'''		)
       if global_padding is not None:
             if global_padding.upper() in self.supported_padding:
                   _A        =       global_padding.upper()
             else:
                   raise ValueError(F'''Padding strategy {global_padding} not supported'''		)
       _A        =       num_channels
       _A        =       embedding_size
       _A        =       hidden_sizes
       _A        =       depths
       _A        =       layer_type
       _A        =       hidden_act
       _A        =       global_padding
       _A        =       num_groups
       _A        =       drop_path_rate
       _A        =       embedding_dynamic_padding
       _A        =       output_stride
       _A        =       width_factor

       _A        =       ["""stem"""] + [F'''stage{idx}''' for idx in range(1   ,							len(_a		) + 1		)]
       _A        =       get_aligned_output_features_output_indices(
           out_features=_a   ,							out_indices=_a   ,							stage_names=self.stage_names		)







"""simple docstring"""

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							):



 '''simple docstring'''
 UpperCAmelCase   :					Optional[int]				  =     '''all_checks'''
 UpperCAmelCase   :					List[Any]				  =     '''basic_checks'''
 UpperCAmelCase   :					Any				  =     '''no_checks'''





class     lowercase_	(					__lowerCAmelCase							):



 '''simple docstring'''





class     lowercase_	(					__lowerCAmelCase							):



 '''simple docstring'''





class     lowercase_	(					__lowerCAmelCase							):



 '''simple docstring'''





class     lowercase_	(					__lowerCAmelCase							):



 '''simple docstring'''






def 						_snake_case					( _snake_case							: Optional[dict]    ,  _snake_case							: dict    ,  _snake_case							: Dict=None							)   -> Dict:






      '''simple docstring'''


      if expected_checksums is None:
            logger.info('Unable to verify checksums.'							)
            return
      if len(set(_snake_case							) - set(_snake_case							)							) > 0:
            raise ExpectedMoreDownloadedFiles(str(set(_snake_case							) - set(_snake_case							)							)							)
      if len(set(_snake_case							) - set(_snake_case							)							) > 0:
            raise UnexpectedDownloadedFile(str(set(_snake_case							) - set(_snake_case							)							)							)
      _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(_snake_case							) > 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_	(					__lowerCAmelCase							):



 '''simple docstring'''





class     lowercase_	(					__lowerCAmelCase							):



 '''simple docstring'''





class     lowercase_	(					__lowerCAmelCase							):



 '''simple docstring'''





class     lowercase_	(					__lowerCAmelCase							):



 '''simple docstring'''






def 						_snake_case					( _snake_case							: Optional[dict]    ,  _snake_case							: dict							)   -> List[str]:






      '''simple docstring'''


      if expected_splits is None:
            logger.info('Unable to verify splits sizes.'							)
            return
      if len(set(_snake_case							) - set(_snake_case							)							) > 0:
            raise ExpectedMoreSplits(str(set(_snake_case							) - set(_snake_case							)							)							)
      if len(set(_snake_case							) - set(_snake_case							)							) > 0:
            raise UnexpectedSplits(str(set(_snake_case							) - set(_snake_case							)							)							)
      _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(_snake_case							) > 0:
            raise NonMatchingSplitsSizesError(str(_snake_case							)							)
      logger.info('All the splits matched successfully.'							)






def 						_snake_case					( _snake_case							: str    ,  _snake_case							: bool = True							)   -> dict:






      '''simple docstring'''


      if record_checksum:
            _A        =       shaaaa()
            with open(_snake_case    ,  'rb'							) as f:
                  for chunk in iter(lambda: f.read(1 << 20							)    ,  B''							):
                        m.update(_snake_case							)
                  _A        =       m.hexdigest()
      else:
            _A        =       None
      return {"num_bytes": os.path.getsize(_snake_case							), "checksum": checksum}







def 						_snake_case					( _snake_case							: int							)   -> int:






      '''simple docstring'''


      if dataset_size and config.IN_MEMORY_MAX_SIZE:
            return dataset_size < config.IN_MEMORY_MAX_SIZE
      else:
            return False

