



"""simple docstring"""







import sys
import webbrowser

import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent

if __name__ == "__main__":
							print('Googling.....')
							lowerCAmelCase_   						=   'https://www.google.com/search?q=' + ' '.join(sys.argv[1:])
							lowerCAmelCase_   						=   requests.get(url, headers={'UserAgent': UserAgent().random})
							# res.raise_for_status()
							with open('project1a.html', 'wb') as out_file:  # only for knowing the class
														for data in res.iter_content(10_000):
																					out_file.write(data)
							lowerCAmelCase_   						=   BeautifulSoup(res.text, 'html.parser')
							lowerCAmelCase_   						=   list(soup.select('.eZt8xd'))[:5]

							print(len(links))
							for link in links:
														if link.text == "Maps":
																					webbrowser.open(link.get('href'))
														else:
																					webbrowser.open(F'''https://google.com{link.get("href")}''')







'''simple docstring'''







from collections import OrderedDict
from typing import Mapping

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


lowerCAmelCase_					:    int      	=			logging.get_logger(__name__)

lowerCAmelCase_					:    Tuple      	=			{
    '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_      ):






				"""simple docstring"""







				__a            ='big_bird'




				def __init__(  self     :					Optional[int]					,				__a     :					Dict=5_03_58					,				__a     :					str=7_68					,				__a     :					List[Any]=12					,				__a     :					List[str]=12					,				__a     :					Union[str, Any]=30_72					,				__a     :					str="gelu_new"					,				__a     :					Dict=0.1					,				__a     :					Union[str, Any]=0.1					,				__a     :					Any=40_96					,				__a     :					int=2					,				__a     :					Tuple=0.02					,				__a     :					List[Any]=1e-1_2					,				__a     :					int=True					,				__a     :					List[str]=0					,				__a     :					Tuple=1					,				__a     :					Optional[Any]=2					,				__a     :					Tuple=66					,				__a     :					str="block_sparse"					,				__a     :					Tuple=True					,				__a     :					Optional[int]=False					,				__a     :					str=64					,				__a     :					Tuple=3					,				__a     :					Any=None					,				**__a     :					Dict					,				):
								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_      ):






				"""simple docstring"""







				@property
				def   UpperCamelCase__   (  self     :					Optional[int]					):
								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),
								    ]					)
