




"""simple docstring"""

from typing import Dict, List, Optional, Type

from .. import config
from ..utils import logging
from .formatting import (
    ArrowFormatter,
    CustomFormatter,
    Formatter,
    PandasFormatter,
    PythonFormatter,
    TensorFormatter,
    format_table,
    query_table,
)
from .np_formatter import NumpyFormatter


a         =      logging.get_logger(__name__)

a         =      {}
a         =      {}
a         =      {}






def 						_snake_case					( _snake_case							: str    ,  _snake_case							: str    ,  _snake_case							: Tuple = None    ,  )   -> List[str]:
      _A        =       aliases if aliases is not None else []
      if format_type in _FORMAT_TYPES:
            logger.warning(
                F'''Overwriting format type \'{format_type}\' ({_FORMAT_TYPES[format_type].__name__} -> {formatter_cls.__name__})'''							)
      _A        =       formatter_cls
      for alias in set(aliases + [format_type]							):
            if alias in _FORMAT_TYPES_ALIASES:
                  logger.warning(
                      F'''Overwriting format type alias \'{alias}\' ({_FORMAT_TYPES_ALIASES[alias]} -> {format_type})'''							)
            _A        =       format_type






def 						_snake_case					( _snake_case							: Optional[Any]    ,  _snake_case							: str    ,  _snake_case							: Tuple = None							)   -> int:
      _A        =       aliases if aliases is not None else []
      for alias in set(aliases + [format_type]							):
            _A        =       unavailable_error


# Here we define all the available formatting functions that can be used by `Dataset.set_format`
_register_formatter(PythonFormatter, None, aliases=['''python'''])
_register_formatter(ArrowFormatter, '''arrow''', aliases=['''pa''', '''pyarrow'''])
_register_formatter(NumpyFormatter, '''numpy''', aliases=['''np'''])
_register_formatter(PandasFormatter, '''pandas''', aliases=['''pd'''])
_register_formatter(CustomFormatter, '''custom''')

if config.TORCH_AVAILABLE:
       from .torch_formatter import TorchFormatter

       _register_formatter(TorchFormatter, '''torch''', aliases=['''pt''', '''pytorch'''])
else:
       a         =      ValueError('''PyTorch needs to be installed to be able to return PyTorch tensors.''')
       _register_unavailable_formatter(_torch_error, '''torch''', aliases=['''pt''', '''pytorch'''])

if config.TF_AVAILABLE:
       from .tf_formatter import TFFormatter

       _register_formatter(TFFormatter, '''tensorflow''', aliases=['''tf'''])
else:
       a         =      ValueError('''Tensorflow needs to be installed to be able to return Tensorflow tensors.''')
       _register_unavailable_formatter(_tf_error, '''tensorflow''', aliases=['''tf'''])

if config.JAX_AVAILABLE:
       from .jax_formatter import JaxFormatter

       _register_formatter(JaxFormatter, '''jax''', aliases=[])
else:
       a         =      ValueError('''JAX needs to be installed to be able to return JAX arrays.''')
       _register_unavailable_formatter(_jax_error, '''jax''', aliases=[])






def 						_snake_case					( _snake_case							: Optional[Any]							)   -> Optional[str]:





      if format_type in _FORMAT_TYPES_ALIASES:
            return _FORMAT_TYPES_ALIASES[format_type]
      else:
            return format_type







def 						_snake_case					( _snake_case							: List[str]    ,  **_snake_case							: Union[str, Any]							)   -> Formatter:
      _A        =       get_format_type_from_alias(__snake_case							)
      if format_type in _FORMAT_TYPES:
            return _FORMAT_TYPES[format_type](**__snake_case							)
      if format_type in _FORMAT_TYPES_ALIASES_UNAVAILABLE:
            raise _FORMAT_TYPES_ALIASES_UNAVAILABLE[format_type]
      else:
            raise ValueError(
                F'''Return type should be None or selected in {list(type for type in _FORMAT_TYPES.keys() if type != None							)}, but got \'{format_type}\''''							)







"""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

