


"""simple docstring"""


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_											=     {
    "squeezebert/squeezebert-uncased": (
        "https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/config.json"
    ),
    "squeezebert/squeezebert-mnli": "https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/config.json",
    "squeezebert/squeezebert-mnli-headless": (
        "https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/config.json"
    ),
}
class snake_case   (	_UpperCamelCase):
		__UpperCamelCase							     =      SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP
		__UpperCamelCase							     =      'squeezebert'







		def __init__(       self   :							Optional[int]     , a__   :							Tuple=3_05_22     , a__   :							Tuple=7_68     , a__   :							str=12     , a__   :							Any=12     , a__   :							List[Any]=30_72     , a__   :							List[str]="gelu"     , a__   :							str=0.1     , a__   :							Any=0.1     , a__   :							str=5_12     , a__   :							Any=2     , a__   :							Union[str, Any]=0.0_2     , a__   :							Optional[Any]=1E-1_2     , a__   :							Dict=0     , a__   :							Dict=7_68     , a__   :							int=4     , a__   :							List[Any]=4     , a__   :							str=4     , a__   :							Any=1     , a__   :							Tuple=4     , a__   :							Tuple=4     , **a__   :							Union[str, Any]     , )							->    Any:





						'''simple docstring'''







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

						_A       	=      vocab_size
						_A       	=      hidden_size
						_A       	=      num_hidden_layers
						_A       	=      num_attention_heads
						_A       	=      hidden_act
						_A       	=      intermediate_size
						_A       	=      hidden_dropout_prob
						_A       	=      attention_probs_dropout_prob
						_A       	=      max_position_embeddings
						_A       	=      type_vocab_size
						_A       	=      initializer_range
						_A       	=      layer_norm_eps
						_A       	=      embedding_size
						_A       	=      q_groups
						_A       	=      k_groups
						_A       	=      v_groups
						_A       	=      post_attention_groups
						_A       	=      intermediate_groups
						_A       	=      output_groups





class snake_case   (	_UpperCamelCase):







		@property
		def 			a_			(       self   :							Union[str, Any]		)							->    Mapping[str, Mapping[int, str]]:





						'''simple docstring'''







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



"""simple docstring"""


from math import factorial






def   a__			(					__lowercase  ,      __lowercase )						->						List[Any]:
				return factorial(__lowercase ) / (factorial(__lowercase ) * factorial(n - r ))






def   a__			(					)						->						List[str]:
				_A       	=      0

				for i in range(1  ,      101 ):
								for j in range(1  ,      i + 1 ):
												if combinations(__lowercase  ,      __lowercase ) > 1E6:
																total += 1
				return total


if __name__ == "__main__":
		print(solution())