



from random import randint
from tempfile import TemporaryFile

import numpy as np






def        A       (      _lowerCamelCase       ,				_lowerCamelCase       ,				_lowerCamelCase						):





       '''simple docstring'''




       _lowerCAmelCase  :							Dict				     =					0
       if start < end:
              _lowerCAmelCase  :							int				     =					randint(_lowerCamelCase       ,				_lowerCamelCase						)
              _lowerCAmelCase  :							List[Any]				     =					a[end]
              _lowerCAmelCase  :							List[str]				     =					a[pivot]
              _lowerCAmelCase  :							Dict				     =					temp

              _lowerCAmelCase  :							str				     =					_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'''




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

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


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


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


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

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



print(z)





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


_snake_case	=						logging.get_logger(__name__)

_snake_case	=						{
    "microsoft/focalnet-tiny": "https://huggingface.co/microsoft/focalnet-tiny/resolve/main/config.json",
}



class 						UpperCAmelCase_					( a     ,      a):
							lowerCamelCase__         =	'focalnet'







							def __init__(     self,    __a=224,    __a=4,    __a=3,    __a=96,    __a=False,    __a=[192, 384, 768, 768],    __a=[2, 2, 6, 2],    __a=[2, 2, 2, 2],    __a=[3, 3, 3, 3],    __a="gelu",    __a=4.0,    __a=0.0,    __a=0.1,    __a=False,    __a=1E-4,    __a=False,    __a=False,    __a=False,    __a=0.02,    __a=1E-5,    __a=32,    __a=None,    __a=None,    **__a,    ):

														'''simple docstring'''

														super().__init__(**__a)

														_lowerCAmelCase  :							str				     =					image_size
														_lowerCAmelCase  :							List[str]				     =					patch_size
														_lowerCAmelCase  :							List[Any]				     =					num_channels
														_lowerCAmelCase  :							Tuple				     =					embed_dim
														_lowerCAmelCase  :							List[Any]				     =					use_conv_embed
														_lowerCAmelCase  :							Any				     =					hidden_sizes
														_lowerCAmelCase  :							Tuple				     =					depths
														_lowerCAmelCase  :							Dict				     =					focal_levels
														_lowerCAmelCase  :							Optional[Any]				     =					focal_windows
														_lowerCAmelCase  :							str				     =					hidden_act
														_lowerCAmelCase  :							Union[str, Any]				     =					mlp_ratio
														_lowerCAmelCase  :							Any				     =					hidden_dropout_prob
														_lowerCAmelCase  :							Dict				     =					drop_path_rate
														_lowerCAmelCase  :							str				     =					use_layerscale
														_lowerCAmelCase  :							str				     =					layerscale_value
														_lowerCAmelCase  :							Union[str, Any]				     =					use_post_layernorm
														_lowerCAmelCase  :							Optional[int]				     =					use_post_layernorm_in_modulation
														_lowerCAmelCase  :							str				     =					normalize_modulator
														_lowerCAmelCase  :							Any				     =					initializer_range
														_lowerCAmelCase  :							Union[str, Any]				     =					layer_norm_eps
														_lowerCAmelCase  :							Any				     =					encoder_stride
														_lowerCAmelCase  :							List[str]				     =					["stem"] + [f"stage{idx}" for idx in range(1,    len(self.depths) + 1)]
														_lowerCAmelCase     ,					_lowerCAmelCase  :							List[str]				     =					get_aligned_output_features_output_indices(
														    out_features=__a,    out_indices=__a,    stage_names=self.stage_names)
