



"""simple docstring"""





from random import randint
from tempfile import TemporaryFile

import numpy as np
def      a__			(   lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__   ):
      UpperCAmelCase_    				=							0
      if start < end:
            UpperCAmelCase_    				=							randint(lowerCAmelCase__			,    lowerCAmelCase__   )
            UpperCAmelCase_    				=							a[end]
            UpperCAmelCase_    				=							a[pivot]
            UpperCAmelCase_    				=							temp

            UpperCAmelCase_				,					UpperCAmelCase_    				=							_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__   ):
      UpperCAmelCase_    				=							0
      UpperCAmelCase_    				=							randint(lowerCAmelCase__			,    lowerCAmelCase__   )
      UpperCAmelCase_    				=							a[end]
      UpperCAmelCase_    				=							a[pivot]
      UpperCAmelCase_    				=							temp
      UpperCAmelCase_    				=							start - 1
      for index in range(lowerCAmelCase__			,    lowerCAmelCase__   ):
            count += 1
            if a[index] < a[end]:  # check if current val is less than pivot value
                  UpperCAmelCase_    				=							new_pivot_index + 1
                  UpperCAmelCase_    				=							a[new_pivot_index]
                  UpperCAmelCase_    				=							a[index]
                  UpperCAmelCase_    				=							temp

      UpperCAmelCase_    				=							a[new_pivot_index + 1]
      UpperCAmelCase_    				=							a[end]
      UpperCAmelCase_    				=							temp
      return new_pivot_index + 1, count


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


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


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

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


print(z)









"""simple docstring"""





from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowerCamelCase      =		logging.get_logger(__name__)

lowerCamelCase      =		{
    """YituTech/conv-bert-base""": """https://huggingface.co/YituTech/conv-bert-base/resolve/main/config.json""",
    """YituTech/conv-bert-medium-small""": (
        """https://huggingface.co/YituTech/conv-bert-medium-small/resolve/main/config.json"""
    ),
    """YituTech/conv-bert-small""": """https://huggingface.co/YituTech/conv-bert-small/resolve/main/config.json""",
    # See all ConvBERT models at https://huggingface.co/models?filter=convbert
}
class    lowercase__     (					SCREAMING_SNAKE_CASE   ):







       '''simple docstring'''


       UpperCamelCase		=  '''convbert'''



       def __init__(							self    :						Any	,       _UpperCAmelCase    :						Optional[int]=30522	,       _UpperCAmelCase    :						Tuple=768	,       _UpperCAmelCase    :						Optional[int]=12	,       _UpperCAmelCase    :						Dict=12	,       _UpperCAmelCase    :						Any=3072	,       _UpperCAmelCase    :						Dict="gelu"	,       _UpperCAmelCase    :						Optional[int]=0.1	,       _UpperCAmelCase    :						str=0.1	,       _UpperCAmelCase    :						Tuple=512	,       _UpperCAmelCase    :						Optional[Any]=2	,       _UpperCAmelCase    :						Union[str, Any]=0.02	,       _UpperCAmelCase    :						Dict=1e-12	,       _UpperCAmelCase    :						Optional[int]=1	,       _UpperCAmelCase    :						int=0	,       _UpperCAmelCase    :						Optional[Any]=2	,       _UpperCAmelCase    :						List[Any]=768	,       _UpperCAmelCase    :						Optional[int]=2	,       _UpperCAmelCase    :						str=9	,       _UpperCAmelCase    :						List[Any]=1	,       _UpperCAmelCase    :						Optional[Any]=None	,       **_UpperCAmelCase    :						str	,       )				->		List[Any]:


             '''simple docstring'''




             super().__init__(
                 pad_token_id=_UpperCAmelCase	,       bos_token_id=_UpperCAmelCase	,       eos_token_id=_UpperCAmelCase	,       **_UpperCAmelCase	,       )

             UpperCAmelCase_    				=							vocab_size
             UpperCAmelCase_    				=							hidden_size
             UpperCAmelCase_    				=							num_hidden_layers
             UpperCAmelCase_    				=							num_attention_heads
             UpperCAmelCase_    				=							intermediate_size
             UpperCAmelCase_    				=							hidden_act
             UpperCAmelCase_    				=							hidden_dropout_prob
             UpperCAmelCase_    				=							attention_probs_dropout_prob
             UpperCAmelCase_    				=							max_position_embeddings
             UpperCAmelCase_    				=							type_vocab_size
             UpperCAmelCase_    				=							initializer_range
             UpperCAmelCase_    				=							layer_norm_eps
             UpperCAmelCase_    				=							embedding_size
             UpperCAmelCase_    				=							head_ratio
             UpperCAmelCase_    				=							conv_kernel_size
             UpperCAmelCase_    				=							num_groups
             UpperCAmelCase_    				=							classifier_dropout



class    lowercase__     (					SCREAMING_SNAKE_CASE   ):







       '''simple docstring'''


       @property
       def   lowercase__       (							self    :						Dict					)				->		Mapping[str, Mapping[int, str]]:


             '''simple docstring'''




             if self.task == "multiple-choice":
                   UpperCAmelCase_    				=							{0: "batch", 1: "choice", 2: "sequence"}
             else:
                   UpperCAmelCase_    				=							{0: "batch", 1: "sequence"}
             return OrderedDict(
                 [
                     ("input_ids", dynamic_axis),
                     ("attention_mask", dynamic_axis),
                     ("token_type_ids", dynamic_axis),
                 ]					)




