


from random import randint
from tempfile import TemporaryFile

import numpy as np
def 				UpperCAmelCase_					(							__snake_case     ,						__snake_case     ,						__snake_case						)     ->			Dict:







 """simple docstring"""


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

  _lowercase      ,    _lowercase							      =_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 				UpperCAmelCase_					(							__snake_case     ,						__snake_case     ,						__snake_case						)     ->			Union[str, Any]:







 """simple docstring"""


 _lowercase							      =0
 _lowercase							      =randint(__snake_case     ,						__snake_case						)
 _lowercase							      =a[end]
 _lowercase							      =a[pivot]
 _lowercase							      =temp
 _lowercase							      =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
   _lowercase							      =new_pivot_index + 1
   _lowercase							      =a[new_pivot_index]
   _lowercase							      =a[index]
   _lowercase							      =temp

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


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


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


outfile.seek(0)  # using the same array
UpperCAmelCase__   	=							np.load(outfile)
UpperCAmelCase__   	=							len(M) - 1
UpperCAmelCase__   	=							_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 ...configuration_utils import PretrainedConfig


lowerCAmelCase_   						=   {
    'google/tapas-base-finetuned-sqa': (
        'https://huggingface.co/google/tapas-base-finetuned-sqa/resolve/main/config.json'
    ),
    'google/tapas-base-finetuned-wtq': (
        'https://huggingface.co/google/tapas-base-finetuned-wtq/resolve/main/config.json'
    ),
    'google/tapas-base-finetuned-wikisql-supervised': (
        'https://huggingface.co/google/tapas-base-finetuned-wikisql-supervised/resolve/main/config.json'
    ),
    'google/tapas-base-finetuned-tabfact': (
        'https://huggingface.co/google/tapas-base-finetuned-tabfact/resolve/main/config.json'
    ),
}
class        __A       (	A_    ):







		'''simple docstring'''





		lowerCAmelCase			:					str   	=				"tapas"







		def __init__(	self						:  List[Any]			,_snake_case						:  Dict=30_522			,_snake_case						:  Union[str, Any]=768			,_snake_case						:  int=12			,_snake_case						:  Union[str, Any]=12			,_snake_case						:  Union[str, Any]=3_072			,_snake_case						:  List[Any]="gelu"			,_snake_case						:  Optional[int]=0.1			,_snake_case						:  Tuple=0.1			,_snake_case						:  List[Any]=1_024			,_snake_case						:  Any=[3, 256, 256, 2, 256, 256, 10]			,_snake_case						:  List[Any]=0.02			,_snake_case						:  Union[str, Any]=1e-12			,_snake_case						:  str=0			,_snake_case						:  Any=10.0			,_snake_case						:  int=0			,_snake_case						:  Optional[Any]=1.0			,_snake_case						:  List[str]=None			,_snake_case						:  Tuple=1.0			,_snake_case						:  Tuple=False			,_snake_case						:  List[Any]=None			,_snake_case						:  int=1.0			,_snake_case						:  List[Any]=1.0			,_snake_case						:  Optional[int]=False			,_snake_case						:  Optional[int]=False			,_snake_case						:  Optional[int]="ratio"			,_snake_case						:  Any=None			,_snake_case						:  Union[str, Any]=None			,_snake_case						:  List[str]=64			,_snake_case						:  Optional[Any]=32			,_snake_case						:  Optional[Any]=False			,_snake_case						:  Optional[int]=True			,_snake_case						:  Dict=False			,_snake_case						:  Tuple=False			,_snake_case						:  int=True			,_snake_case						:  List[str]=False			,_snake_case						:  Dict=None			,_snake_case						:  Optional[int]=None			,**_snake_case						:  int			,)					->		List[Any]:






				"""simple docstring"""



				super().__init__(pad_token_id=_snake_case			,**_snake_case						)

				# BERT hyperparameters (with updated max_position_embeddings and type_vocab_sizes)
				lowercase__   :    Optional[int]				=			vocab_size
				lowercase__   :    List[str]				=			hidden_size
				lowercase__   :    Any				=			num_hidden_layers
				lowercase__   :    Optional[Any]				=			num_attention_heads
				lowercase__   :    Optional[int]				=			hidden_act
				lowercase__   :    List[Any]				=			intermediate_size
				lowercase__   :    List[Any]				=			hidden_dropout_prob
				lowercase__   :    Dict				=			attention_probs_dropout_prob
				lowercase__   :    str				=			max_position_embeddings
				lowercase__   :    Dict				=			type_vocab_sizes
				lowercase__   :    Optional[Any]				=			initializer_range
				lowercase__   :    Dict				=			layer_norm_eps

				# Fine-tuning task hyperparameters
				lowercase__   :    Any				=			positive_label_weight
				lowercase__   :    int				=			num_aggregation_labels
				lowercase__   :    List[str]				=			aggregation_loss_weight
				lowercase__   :    Optional[int]				=			use_answer_as_supervision
				lowercase__   :    Optional[Any]				=			answer_loss_importance
				lowercase__   :    Union[str, Any]				=			use_normalized_answer_loss
				lowercase__   :    str				=			huber_loss_delta
				lowercase__   :    str				=			temperature
				lowercase__   :    int				=			aggregation_temperature
				lowercase__   :    List[Any]				=			use_gumbel_for_cells
				lowercase__   :    Tuple				=			use_gumbel_for_aggregation
				lowercase__   :    Union[str, Any]				=			average_approximation_function
				lowercase__   :    Union[str, Any]				=			cell_selection_preference
				lowercase__   :    Any				=			answer_loss_cutoff
				lowercase__   :    List[Any]				=			max_num_rows
				lowercase__   :    str				=			max_num_columns
				lowercase__   :    int				=			average_logits_per_cell
				lowercase__   :    str				=			select_one_column
				lowercase__   :    str				=			allow_empty_column_selection
				lowercase__   :    Any				=			init_cell_selection_weights_to_zero
				lowercase__   :    Optional[int]				=			reset_position_index_per_cell
				lowercase__   :    Union[str, Any]				=			disable_per_token_loss

				# Aggregation hyperparameters
				lowercase__   :    Optional[Any]				=			aggregation_labels
				lowercase__   :    List[Any]				=			no_aggregation_label_index

				if isinstance(self.aggregation_labels			,_snake_case						):
						lowercase__   :    Union[str, Any]				=			{int(_snake_case						): v for k, v in aggregation_labels.items()}
