



from collections import OrderedDict
from typing import Mapping

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


lowerCamelCase__			= logging.get_logger(__name__)

lowerCamelCase__			= {
    "google/bigbird-roberta-base": "https://huggingface.co/google/bigbird-roberta-base/resolve/main/config.json",
    "google/bigbird-roberta-large": "https://huggingface.co/google/bigbird-roberta-large/resolve/main/config.json",
    "google/bigbird-base-trivia-itc": "https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/config.json",
    # See all BigBird models at https://huggingface.co/models?filter=big_bird
}






class    SCREAMING_SNAKE_CASE			(							_lowerCamelCase					):
							__lowerCamelCase	:	Optional[int]    ='big_bird'





							def __init__(   self		:		Dict      ,				__lowercase		:		Dict=50358      ,				__lowercase		:		int=768      ,				__lowercase		:		Dict=12      ,				__lowercase		:		int=12      ,				__lowercase		:		Union[str, Any]=3072      ,				__lowercase		:		Optional[int]="gelu_new"      ,				__lowercase		:		List[Any]=0.1      ,				__lowercase		:		Union[str, Any]=0.1      ,				__lowercase		:		Optional[Any]=4096      ,				__lowercase		:		str=2      ,				__lowercase		:		List[str]=0.02      ,				__lowercase		:		str=1E-12      ,				__lowercase		:		Optional[int]=True      ,				__lowercase		:		Tuple=0      ,				__lowercase		:		int=1      ,				__lowercase		:		str=2      ,				__lowercase		:		Union[str, Any]=66      ,				__lowercase		:		int="block_sparse"      ,				__lowercase		:		Union[str, Any]=True      ,				__lowercase		:		List[Any]=False      ,				__lowercase		:		Any=64      ,				__lowercase		:		Union[str, Any]=3      ,				__lowercase		:		Tuple=None      ,				**__lowercase		:		int      ,				):

											'''simple docstring'''
											super().__init__(
											    pad_token_id=A__      ,				bos_token_id=A__      ,				eos_token_id=A__      ,				sep_token_id=A__      ,				**A__      ,				)

											__a       =		vocab_size
											__a       =		max_position_embeddings
											__a       =		hidden_size
											__a       =		num_hidden_layers
											__a       =		num_attention_heads
											__a       =		intermediate_size
											__a       =		hidden_act
											__a       =		hidden_dropout_prob
											__a       =		attention_probs_dropout_prob
											__a       =		initializer_range
											__a       =		type_vocab_size
											__a       =		layer_norm_eps
											__a       =		use_cache

											__a       =		rescale_embeddings
											__a       =		attention_type
											__a       =		use_bias
											__a       =		block_size
											__a       =		num_random_blocks
											__a       =		classifier_dropout






class    SCREAMING_SNAKE_CASE			(							_lowerCamelCase					):





							@property
							def 						UpperCamelCase_					(   self		:		List[Any]	):

											'''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),
											    ]	)





def   lowerCAmelCase__						(							_SCREAMING_SNAKE_CASE       :			int = 3				,     _SCREAMING_SNAKE_CASE       :			int = 7				,     _SCREAMING_SNAKE_CASE       :			int = 100_0000    ):
				"""simple docstring"""







				__a       =		0
				__a       =		1

				for current_denominator in range(1				,     limit + 1    ):
								__a       =		current_denominator * numerator // denominator
								if current_denominator % denominator == 0:
												current_numerator -= 1
								if current_numerator * max_denominator > current_denominator * max_numerator:
												__a       =		current_numerator
												__a       =		current_denominator
				return max_numerator


if __name__ == "__main__":
						print(solution(numerator=3, denominator=7, limit=100_0000))
