





'''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


UpperCAmelCase					:		Any		=							logging.get_logger(__name__)

UpperCAmelCase					:		Dict[Optional[str], Type[Formatter]]		=							{}
UpperCAmelCase					:		Dict[Optional[str], str]		=							{}
UpperCAmelCase					:		Dict[Optional[str], Exception]		=							{}




def    _a    (							lowerCAmelCase_			,      lowerCAmelCase_			,      lowerCAmelCase_ = None			,      ):





      """simple docstring"""



      _snake_case		:	Tuple					     =     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__})'''	)
      _snake_case		:	Tuple					     =     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})'''	)
            _snake_case		:	Optional[Any]					     =     format_type




def    _a    (							lowerCAmelCase_			,      lowerCAmelCase_			,      lowerCAmelCase_ = None	):





      """simple docstring"""



      _snake_case		:	Tuple					     =     aliases if aliases is not None else []
      for alias in set(aliases + [format_type]	):
            _snake_case		:	Dict					     =     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:
   UpperCAmelCase					:		Tuple		=							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:
   UpperCAmelCase					:		Dict		=							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:
   UpperCAmelCase					:		Optional[int]		=							ValueError('JAX needs to be installed to be able to return JAX arrays.')
   _register_unavailable_formatter(_jax_error, 'jax', aliases=[])




def    _a    (							lowerCAmelCase_	):





      """simple docstring"""



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




def    _a    (							lowerCAmelCase_			,      **lowerCAmelCase_	):





      """simple docstring"""



      _snake_case		:	Dict					     =     get_format_type_from_alias(lowerCAmelCase_	)
      if format_type in _FORMAT_TYPES:
            return _FORMAT_TYPES[format_type](**lowerCAmelCase_	)
      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'''

from random import randint
from tempfile import TemporaryFile

import numpy as np




def    _a    (							lowerCAmelCase_			,      lowerCAmelCase_			,      lowerCAmelCase_	):





      """simple docstring"""



      _snake_case		:	List[Any]					     =     0
      if start < end:
            _snake_case		:	List[Any]					     =     randint(lowerCAmelCase_			,      lowerCAmelCase_	)
            _snake_case		:	Any					     =     a[end]
            _snake_case		:	List[str]					     =     a[pivot]
            _snake_case		:	Optional[int]					     =     temp

            _snake_case     ,							_snake_case		:	List[Any]					     =     _in_place_partition(lowerCAmelCase_			,      lowerCAmelCase_			,      lowerCAmelCase_	)
            count += _in_place_quick_sort(lowerCAmelCase_			,      lowerCAmelCase_			,      p - 1	)
            count += _in_place_quick_sort(lowerCAmelCase_			,      p + 1			,      lowerCAmelCase_	)
      return count




def    _a    (							lowerCAmelCase_			,      lowerCAmelCase_			,      lowerCAmelCase_	):





      """simple docstring"""



      _snake_case		:	Optional[Any]					     =     0
      _snake_case		:	Optional[int]					     =     randint(lowerCAmelCase_			,      lowerCAmelCase_	)
      _snake_case		:	Tuple					     =     a[end]
      _snake_case		:	Optional[Any]					     =     a[pivot]
      _snake_case		:	Union[str, Any]					     =     temp
      _snake_case		:	Union[str, Any]					     =     start - 1
      for index in range(lowerCAmelCase_			,      lowerCAmelCase_	):
            count += 1
            if a[index] < a[end]:  # check if current val is less than pivot value
                  _snake_case		:	Optional[int]					     =     new_pivot_index + 1
                  _snake_case		:	Optional[Any]					     =     a[new_pivot_index]
                  _snake_case		:	Tuple					     =     a[index]
                  _snake_case		:	str					     =     temp

      _snake_case		:	Any					     =     a[new_pivot_index + 1]
      _snake_case		:	str					     =     a[end]
      _snake_case		:	Optional[int]					     =     temp
      return new_pivot_index + 1, count


UpperCAmelCase					:		Dict		=							TemporaryFile()
UpperCAmelCase					:		Dict		=							1_0_0  # 1000 elements are to be sorted


UpperCAmelCase,       UpperCAmelCase					:		str		=							0, 1  # mean and standard deviation
UpperCAmelCase					:		Optional[Any]		=							np.random.normal(mu, sigma, p)
np.save(outfile, X)
print('The array is')
print(X)


outfile.seek(0)  # using the same array
UpperCAmelCase					:		int		=							np.load(outfile)
UpperCAmelCase					:		Optional[int]		=							len(M) - 1
UpperCAmelCase					:		str		=							_in_place_quick_sort(M, 0, r)

print(
    'No of Comparisons for 100 elements selected from a standard normal distribution'
    'is :'
)
print(z)


