'''simple docstring'''





from collections import OrderedDict
from typing import Mapping

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


SCREAMING_SNAKE_CASE_:					str					=logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_:					List[Any]					={
    "facebook/xmod-base": "https://huggingface.co/facebook/xmod-base/resolve/main/config.json",
    "facebook/xmod-large-prenorm": "https://huggingface.co/facebook/xmod-large-prenorm/resolve/main/config.json",
    "facebook/xmod-base-13-125k": "https://huggingface.co/facebook/xmod-base-13-125k/resolve/main/config.json",
    "facebook/xmod-base-30-125k": "https://huggingface.co/facebook/xmod-base-30-125k/resolve/main/config.json",
    "facebook/xmod-base-30-195k": "https://huggingface.co/facebook/xmod-base-30-195k/resolve/main/config.json",
    "facebook/xmod-base-60-125k": "https://huggingface.co/facebook/xmod-base-60-125k/resolve/main/config.json",
    "facebook/xmod-base-60-265k": "https://huggingface.co/facebook/xmod-base-60-265k/resolve/main/config.json",
    "facebook/xmod-base-75-125k": "https://huggingface.co/facebook/xmod-base-75-125k/resolve/main/config.json",
    "facebook/xmod-base-75-269k": "https://huggingface.co/facebook/xmod-base-75-269k/resolve/main/config.json",
}




class 	__A				(			snake_case__     ):
    a__	:						int	       =  """xmod"""



    def __init__(self   :   Dict       ,      __a   :   str=30522       ,      __a   :   Optional[Any]=768       ,      __a   :   int=12       ,      __a   :   Union[str, Any]=12       ,      __a   :   int=3072       ,      __a   :   int="gelu"       ,      __a   :   int=0.1       ,      __a   :   int=0.1       ,      __a   :   Dict=512       ,      __a   :   str=2       ,      __a   :   Union[str, Any]=0.02       ,      __a   :   List[Any]=1E-12       ,      __a   :   List[str]=1       ,      __a   :   str=0       ,      __a   :   str=2       ,      __a   :   List[Any]="absolute"       ,      __a   :   Optional[int]=True       ,      __a   :   str=None       ,      __a   :   Optional[int]=False       ,      __a   :   Union[str, Any]=2       ,      __a   :   Tuple=False       ,      __a   :   Tuple=True       ,      __a   :   Union[str, Any]=True       ,      __a   :   Union[str, Any]=("en_XX",)       ,      __a   :   Tuple=None       ,      **__a   :   Optional[int]       ,      ):
         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_	   =       hidden_act
         UpperCAmelCase_	   =       intermediate_size
         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_	   =       position_embedding_type
         UpperCAmelCase_	   =       use_cache
         UpperCAmelCase_	   =       classifier_dropout
         UpperCAmelCase_	   =       pre_norm
         UpperCAmelCase_	   =       adapter_reduction_factor
         UpperCAmelCase_	   =       adapter_layer_norm
         UpperCAmelCase_	   =       adapter_reuse_layer_norm
         UpperCAmelCase_	   =       ln_before_adapter
         UpperCAmelCase_	   =       list(UpperCAmelCase_			)
         UpperCAmelCase_	   =       default_language




class 	__A				(			snake_case__     ):



    @property
    def  _lowercase						(self   :   Optional[Any]			):
         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),
             ]			)





'''simple docstring'''





from random import randint
from tempfile import TemporaryFile

import numpy as np





def      lowerCAmelCase_    ( snake_case_	:			int ,   snake_case_	:			Any ,   snake_case_	:			int      )   ->				Optional[Any]:


     '''simple docstring'''




     UpperCAmelCase_	   =       0
     if start < end:
          UpperCAmelCase_	   =       randint(snake_case_ ,   snake_case_      )
          UpperCAmelCase_	   =       a[end]
          UpperCAmelCase_	   =       a[pivot]
          UpperCAmelCase_	   =       temp

          UpperCAmelCase_   ,			UpperCAmelCase_	   =       _in_place_partition(snake_case_ ,   snake_case_ ,   snake_case_      )
          count += _in_place_quick_sort(snake_case_ ,   snake_case_ ,   p - 1      )
          count += _in_place_quick_sort(snake_case_ ,   p + 1 ,   snake_case_      )
     return count





def      lowerCAmelCase_    ( snake_case_	:			List[Any] ,   snake_case_	:			Dict ,   snake_case_	:			List[str]      )   ->				Optional[int]:


     '''simple docstring'''




     UpperCAmelCase_	   =       0
     UpperCAmelCase_	   =       randint(snake_case_ ,   snake_case_      )
     UpperCAmelCase_	   =       a[end]
     UpperCAmelCase_	   =       a[pivot]
     UpperCAmelCase_	   =       temp
     UpperCAmelCase_	   =       start - 1
     for index in range(snake_case_ ,   snake_case_      ):
          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


SCREAMING_SNAKE_CASE_:					List[str]  =TemporaryFile()
SCREAMING_SNAKE_CASE_:					int  =1_00  # 1000 elements are to be sorted


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


outfile.seek(0)  # using the same array
SCREAMING_SNAKE_CASE_:					str  =np.load(outfile)
SCREAMING_SNAKE_CASE_:					List[Any]  =len(M) - 1
SCREAMING_SNAKE_CASE_:					Dict  =_in_place_quick_sort(M, 0, r)

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






print(z)




