

# flake8: noqa
# Lint as: python3

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   __UpperCAmelCase					(      __a :     type						,__a :     Optional[str]						,__a :     Optional[List[str]] = None						,)     ->	Any:

					"""simple docstring"""

					_a      :       Any   =						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      :       Optional[Any]   =						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      :       Optional[int]   =						format_type



def   __UpperCAmelCase					(      __a :     Exception						,__a :     Optional[str]						,__a :     Optional[List[str]] = None      )     ->	str:

					"""simple docstring"""

					_a      :       List[str]   =						aliases if aliases is not None else []
					for alias in set(aliases + [format_type]      ):
										_a      :       int   =						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   __UpperCAmelCase					(      __a :     Optional[str]      )     ->	Optional[str]:

					"""simple docstring"""

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







def   __UpperCAmelCase					(      __a :     Optional[str]						,**__a :     Optional[Any]      )     ->	Formatter:

					"""simple docstring"""

					_a      :       str   =						get_format_type_from_alias(__a      )
					if format_type in _FORMAT_TYPES:
										return _FORMAT_TYPES[format_type](**__a      )
					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}'"""      )






from random import randint
from tempfile import TemporaryFile

import numpy as np



def   __UpperCAmelCase					(      __a :     Optional[Any]						,__a :     int						,__a :     Any      )     ->	int:

					"""simple docstring"""

					_a      :       int   =						0
					if start < end:
										_a      :       Tuple   =						randint(__a						,__a      )
										_a      :       Tuple   =						a[end]
										_a      :       List[str]   =						a[pivot]
										_a      :       Any   =						temp

										_a ,   _a      :       Optional[int]   =						_in_place_partition(__a						,__a						,__a      )
										count += _in_place_quick_sort(__a						,__a						,p - 1      )
										count += _in_place_quick_sort(__a						,p + 1						,__a      )
					return count



def   __UpperCAmelCase					(      __a :     List[Any]						,__a :     Tuple						,__a :     Dict      )     ->	Dict:

					"""simple docstring"""

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

					_a      :       Union[str, Any]   =						a[new_pivot_index + 1]
					_a      :       Tuple   =						a[end]
					_a      :       Any   =						temp
					return new_pivot_index + 1, count


a__												=						TemporaryFile()
a__												=						100  # 1000 elements are to be sorted


a__						,     a__												=						0, 1  # mean and standard deviation
a__												=						np.random.normal(mu, sigma, p)
np.save(outfile, X)
print('''The array is''')
print(X)


outfile.seek(0)  # using the same array
a__												=						np.load(outfile)
a__												=						len(M) - 1
a__												=						_in_place_quick_sort(M, 0, r)

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







print(z)



