






from collections import OrderedDict
from typing import Mapping

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


__lowerCamelCase      :    Union[str, Any]   		=   logging.get_logger(__name__)

__lowerCamelCase      :    Dict   		=   {
    """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__		(       UpperCamelCase_		):





					"""simple docstring"""






					a_			=	"big_bird"



					def __init__(      self  :						Union[str, Any]							,	__A  :						int=5_0_3_5_8							,	__A  :						List[Any]=7_6_8							,	__A  :						List[str]=1_2							,	__A  :						int=1_2							,	__A  :						int=3_0_7_2							,	__A  :						Dict="gelu_new"							,	__A  :						Union[str, Any]=0.1							,	__A  :						Tuple=0.1							,	__A  :						Any=4_0_9_6							,	__A  :						Union[str, Any]=2							,	__A  :						Tuple=0.0_2							,	__A  :						Union[str, Any]=1e-1_2							,	__A  :						Tuple=True							,	__A  :						Union[str, Any]=0							,	__A  :						List[str]=1							,	__A  :						Any=2							,	__A  :						Dict=6_6							,	__A  :						Union[str, Any]="block_sparse"							,	__A  :						int=True							,	__A  :						str=False							,	__A  :						str=6_4							,	__A  :						List[str]=3							,	__A  :						int=None							,	**__A  :						Optional[Any]							,	):
						super().__init__(
						    pad_token_id=__A							,	bos_token_id=__A							,	eos_token_id=__A							,	sep_token_id=__A							,	**__A							,	)

						snake_case__				:   Optional[Any]     =						vocab_size
						snake_case__				:   Optional[int]     =						max_position_embeddings
						snake_case__				:   Union[str, Any]     =						hidden_size
						snake_case__				:   int     =						num_hidden_layers
						snake_case__				:   List[str]     =						num_attention_heads
						snake_case__				:   Optional[Any]     =						intermediate_size
						snake_case__				:   Dict     =						hidden_act
						snake_case__				:   List[str]     =						hidden_dropout_prob
						snake_case__				:   Any     =						attention_probs_dropout_prob
						snake_case__				:   Union[str, Any]     =						initializer_range
						snake_case__				:   int     =						type_vocab_size
						snake_case__				:   List[Any]     =						layer_norm_eps
						snake_case__				:   int     =						use_cache

						snake_case__				:   Any     =						rescale_embeddings
						snake_case__				:   Union[str, Any]     =						attention_type
						snake_case__				:   Any     =						use_bias
						snake_case__				:   Union[str, Any]     =						block_size
						snake_case__				:   Optional[Any]     =						num_random_blocks
						snake_case__				:   int     =						classifier_dropout



class 							SCREAMING_SNAKE_CASE__		(       UpperCamelCase_		):





					"""simple docstring"""






					@property
					def  _lowercase  (      self  :						Union[str, Any] ):
						if self.task == "multiple-choice":
							snake_case__				:   Optional[Any]     =						{0: "batch", 1: "choice", 2: "sequence"}
						else:
							snake_case__				:   List[str]     =						{0: "batch", 1: "sequence"}
						return OrderedDict(
						    [
						        ("input_ids", dynamic_axis),
						        ("attention_mask", dynamic_axis),
						    ] )











from __future__ import annotations







def   SCREAMING_SNAKE_CASE       (				snake_case_      :							int     ):
	snake_case__				:   str     =						[True] * limit
	snake_case__				:   str     =						False
	snake_case__				:   str     =						False
	snake_case__				:   str     =						True

	for i in range(3  ,						int(limit**0.5 + 1     )  ,						2     ):
		snake_case__				:   Optional[Any]     =						i * 2
		while index < limit:
			snake_case__				:   Union[str, Any]     =						False
			snake_case__				:   Any     =						index + i

	snake_case__				:   Optional[Any]     =						[2]

	for i in range(3  ,						snake_case_  ,						2     ):
		if is_prime[i]:
			primes.append(snake_case_     )

	return primes







def   SCREAMING_SNAKE_CASE       (				snake_case_      :							int = 1000000     ):
	snake_case__				:   Optional[int]     =						prime_sieve(snake_case_     )
	snake_case__				:   List[Any]     =						0
	snake_case__				:   List[str]     =						0

	for i in range(len(snake_case_     )     ):
		for j in range(i + length  ,						len(snake_case_     )     ):
			snake_case__				:   Dict     =						sum(primes[i:j]     )
			if sol >= ceiling:
				break

			if sol in primes:
				snake_case__				:   Tuple     =						j - i
				snake_case__				:   str     =						sol

	return largest


if __name__ == "__main__":
	print(f"{solution() = }")



