





def      _A       (		SCREAMING_SNAKE_CASE     :   Optional[int]       ,       SCREAMING_SNAKE_CASE     :   Optional[int]       ,       SCREAMING_SNAKE_CASE     :   int						):





  """simple docstring"""



  if n == 0:
    return 1

  elif n % 2 == 1:
    return (binary_exponentiation(SCREAMING_SNAKE_CASE       ,       n - 1       ,       SCREAMING_SNAKE_CASE						) * a) % mod

  else:
    a__	:					List[str]										=binary_exponentiation(SCREAMING_SNAKE_CASE       ,       n / 2       ,       SCREAMING_SNAKE_CASE						)
    return (b * b) % mod


# a prime number
UpperCAmelCase	:	Any		     =       701

UpperCAmelCase	:	List[Any]		     =       1000000000
UpperCAmelCase	:	Any		     =       10

# using binary exponentiation function, O(log(p)):
print((a / b) % p == (a * binary_exponentiation(b, p - 2, p)) % p)





print((a / b) % p == (a * b ** (p - 2)) % p)














from collections import OrderedDict
from typing import Mapping

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


_a      	=  logging.get_logger(__name__)

_a      	=  {
    '''distilbert-base-uncased''': '''https://huggingface.co/distilbert-base-uncased/resolve/main/config.json''',
    '''distilbert-base-uncased-distilled-squad''': (
        '''https://huggingface.co/distilbert-base-uncased-distilled-squad/resolve/main/config.json'''
    ),
    '''distilbert-base-cased''': '''https://huggingface.co/distilbert-base-cased/resolve/main/config.json''',
    '''distilbert-base-cased-distilled-squad''': (
        '''https://huggingface.co/distilbert-base-cased-distilled-squad/resolve/main/config.json'''
    ),
    '''distilbert-base-german-cased''': '''https://huggingface.co/distilbert-base-german-cased/resolve/main/config.json''',
    '''distilbert-base-multilingual-cased''': (
        '''https://huggingface.co/distilbert-base-multilingual-cased/resolve/main/config.json'''
    ),
    '''distilbert-base-uncased-finetuned-sst-2-english''': (
        '''https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english/resolve/main/config.json'''
    ),
}





class __lowerCamelCase (      snake_case__):





	"""simple docstring"""






	UpperCamelCase__								=      "distilbert"
	UpperCamelCase__								=      {
	    "hidden_size": "dim",
	    "num_attention_heads": "n_heads",
	    "num_hidden_layers": "n_layers",
	}

	def __init__(							self       ,    UpperCAmelCase=3_0522       ,    UpperCAmelCase=512       ,    UpperCAmelCase=False       ,    UpperCAmelCase=6       ,    UpperCAmelCase=12       ,    UpperCAmelCase=768       ,    UpperCAmelCase=4 * 768       ,    UpperCAmelCase=0.1       ,    UpperCAmelCase=0.1       ,    UpperCAmelCase="gelu"       ,    UpperCAmelCase=0.02       ,    UpperCAmelCase=0.1       ,    UpperCAmelCase=0.2       ,    UpperCAmelCase=0       ,    **UpperCAmelCase       ,    ):
							"""simple docstring"""







							_UpperCAmelCase									=			vocab_size
							_UpperCAmelCase									=			max_position_embeddings
							_UpperCAmelCase									=			sinusoidal_pos_embds
							_UpperCAmelCase									=			n_layers
							_UpperCAmelCase									=			n_heads
							_UpperCAmelCase									=			dim
							_UpperCAmelCase									=			hidden_dim
							_UpperCAmelCase									=			dropout
							_UpperCAmelCase									=			attention_dropout
							_UpperCAmelCase									=			activation
							_UpperCAmelCase									=			initializer_range
							_UpperCAmelCase									=			qa_dropout
							_UpperCAmelCase									=			seq_classif_dropout
							super().__init__(**UpperCAmelCase       ,    pad_token_id=UpperCAmelCase    )





class __lowerCamelCase (      snake_case__):





	"""simple docstring"""






	@property
	def        UpperCamelCase    (							self    ):
							"""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),
							    ]    )
