



"""simple docstring"""

from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union

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


if TYPE_CHECKING:
			from ... import FeatureExtractionMixin, PreTrainedTokenizerBase, TensorType


a_			   =					logging.get_logger(__name__)

a_			   =					{
    '''microsoft/deberta-v2-xlarge''': '''https://huggingface.co/microsoft/deberta-v2-xlarge/resolve/main/config.json''',
    '''microsoft/deberta-v2-xxlarge''': '''https://huggingface.co/microsoft/deberta-v2-xxlarge/resolve/main/config.json''',
    '''microsoft/deberta-v2-xlarge-mnli''': (
        '''https://huggingface.co/microsoft/deberta-v2-xlarge-mnli/resolve/main/config.json'''
    ),
    '''microsoft/deberta-v2-xxlarge-mnli''': (
        '''https://huggingface.co/microsoft/deberta-v2-xxlarge-mnli/resolve/main/config.json'''
    ),
}







class    __lowercase     (					_UpperCAmelCase):







						"""simple docstring"""







						_A		:							Dict							       =		"""deberta-v2"""







						def __init__(self   ,		lowercase__=12_81_00   ,		lowercase__=15_36   ,		lowercase__=24   ,		lowercase__=24   ,		lowercase__=61_44   ,		lowercase__="gelu"   ,		lowercase__=0.1   ,		lowercase__=0.1   ,		lowercase__=5_12   ,		lowercase__=0   ,		lowercase__=0.02   ,		lowercase__=1e-7   ,		lowercase__=False   ,		lowercase__=-1   ,		lowercase__=0   ,		lowercase__=True   ,		lowercase__=None   ,		lowercase__=0   ,		lowercase__="gelu"   ,		**lowercase__   ,		):
											super().__init__(**lowercase__       )

											snake_case_					:		Union[str, Any] 					=					hidden_size
											snake_case_					:		str 					=					num_hidden_layers
											snake_case_					:		Tuple 					=					num_attention_heads
											snake_case_					:		Dict 					=					intermediate_size
											snake_case_					:		Optional[int] 					=					hidden_act
											snake_case_					:		Union[str, Any] 					=					hidden_dropout_prob
											snake_case_					:		Any 					=					attention_probs_dropout_prob
											snake_case_					:		List[Any] 					=					max_position_embeddings
											snake_case_					:		Union[str, Any] 					=					type_vocab_size
											snake_case_					:		Union[str, Any] 					=					initializer_range
											snake_case_					:		List[Any] 					=					relative_attention
											snake_case_					:		Dict 					=					max_relative_positions
											snake_case_					:		Optional[int] 					=					pad_token_id
											snake_case_					:		List[str] 					=					position_biased_input

											# Backwards compatibility
											if type(lowercase__       ) == str:
																snake_case_					:		Union[str, Any] 					=					[x.strip() for x in pos_att_type.lower().split("""|"""       )]

											snake_case_					:		Optional[int] 					=					pos_att_type
											snake_case_					:		List[str] 					=					vocab_size
											snake_case_					:		Tuple 					=					layer_norm_eps

											snake_case_					:		List[Any] 					=					kwargs.get("""pooler_hidden_size"""   ,		lowercase__       )
											snake_case_					:		List[str] 					=					pooler_dropout
											snake_case_					:		int 					=					pooler_hidden_act







class    __lowercase     (					_UpperCAmelCase):







						"""simple docstring"""







						@property
						def 						__UpperCamelCase							(self       ):
											if self.task == "multiple-choice":
																snake_case_					:		List[Any] 					=					{0: """batch""", 1: """choice""", 2: """sequence"""}
											else:
																snake_case_					:		int 					=					{0: """batch""", 1: """sequence"""}
											if self._config.type_vocab_size > 0:
																return OrderedDict(
																    [("""input_ids""", dynamic_axis), ("""attention_mask""", dynamic_axis), ("""token_type_ids""", dynamic_axis)]       )
											else:
																return OrderedDict([("""input_ids""", dynamic_axis), ("""attention_mask""", dynamic_axis)]       )







						@property
						def 						__UpperCamelCase							(self       ):
											return 12







						def 						__UpperCamelCase							(self   ,		lowercase__   ,		lowercase__ = -1   ,		lowercase__ = -1   ,		lowercase__ = -1   ,		lowercase__ = False   ,		lowercase__ = None   ,		lowercase__ = 3   ,		lowercase__ = 40   ,		lowercase__ = 40   ,		lowercase__ = None   ,		):
											snake_case_					:		str 					=					super().generate_dummy_inputs(preprocessor=lowercase__   ,		framework=lowercase__       )
											if self._config.type_vocab_size == 0 and "token_type_ids" in dummy_inputs:
																del dummy_inputs["token_type_ids"]
											return dummy_inputs









"""simple docstring"""

from random import randint
from tempfile import TemporaryFile

import numpy as np







def 							SCREAMING_SNAKE_CASE__      (		SCREAMING_SNAKE_CASE__ :  List[Any]				,    SCREAMING_SNAKE_CASE__ :  Dict				,    SCREAMING_SNAKE_CASE__ :  Optional[int]			):



					"""simple docstring"""





					snake_case_					:		Union[str, Any] 					=					0
					if start < end:
										snake_case_					:		Union[str, Any] 					=					randint(SCREAMING_SNAKE_CASE__				,    SCREAMING_SNAKE_CASE__			)
										snake_case_					:		List[Any] 					=					a[end]
										snake_case_					:		Dict 					=					a[pivot]
										snake_case_					:		Any 					=					temp

										snake_case_					,			snake_case_					:		Dict 					=					_in_place_partition(SCREAMING_SNAKE_CASE__				,    SCREAMING_SNAKE_CASE__				,    SCREAMING_SNAKE_CASE__			)
										count += _in_place_quick_sort(SCREAMING_SNAKE_CASE__				,    SCREAMING_SNAKE_CASE__				,    p - 1			)
										count += _in_place_quick_sort(SCREAMING_SNAKE_CASE__				,    p + 1				,    SCREAMING_SNAKE_CASE__			)
					return count







def 							SCREAMING_SNAKE_CASE__      (		SCREAMING_SNAKE_CASE__ :  int				,    SCREAMING_SNAKE_CASE__ :  List[Any]				,    SCREAMING_SNAKE_CASE__ :  Dict			):



					"""simple docstring"""





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

					snake_case_					:		Union[str, Any] 					=					a[new_pivot_index + 1]
					snake_case_					:		Union[str, Any] 					=					a[end]
					snake_case_					:		Union[str, 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)




