


"""simple docstring"""


import unittest

import numpy as np

from transformers import RobertaConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
    from transformers.models.roberta.modeling_flax_roberta import (
        FlaxRobertaForCausalLM,
        FlaxRobertaForMaskedLM,
        FlaxRobertaForMultipleChoice,
        FlaxRobertaForQuestionAnswering,
        FlaxRobertaForSequenceClassification,
        FlaxRobertaForTokenClassification,
        FlaxRobertaModel,
    )


class       A__						(    unittest.TestCase):


     """simple docstring"""

     def __init__(						self:    Dict  ,		__a:    List[Any]  ,		__a:    Tuple=13  ,		__a:    Optional[Any]=7  ,		__a:    Any=True  ,		__a:    Union[str, Any]=True  ,		__a:    str=True  ,		__a:    List[str]=True  ,		__a:    Optional[Any]=99  ,		__a:    str=32  ,		__a:    Optional[Any]=5  ,		__a:    Optional[Any]=4  ,		__a:    Any=37  ,		__a:    Dict="gelu"  ,		__a:    Dict=0.1  ,		__a:    Optional[int]=0.1  ,		__a:    Optional[int]=512  ,		__a:    str=16  ,		__a:    Optional[Any]=2  ,		__a:    Dict=0.02  ,		__a:    str=4  ,		)->     Tuple:
           lowerCamelCase       :       Dict   =						parent
           lowerCamelCase       :       List[str]   =						batch_size
           lowerCamelCase       :       Dict   =						seq_length
           lowerCamelCase       :       Any   =						is_training
           lowerCamelCase       :       Union[str, Any]   =						use_attention_mask
           lowerCamelCase       :       Optional[Any]   =						use_token_type_ids
           lowerCamelCase       :       List[Any]   =						use_labels
           lowerCamelCase       :       List[Any]   =						vocab_size
           lowerCamelCase       :       Dict   =						hidden_size
           lowerCamelCase       :       Any   =						num_hidden_layers
           lowerCamelCase       :       List[str]   =						num_attention_heads
           lowerCamelCase       :       Union[str, Any]   =						intermediate_size
           lowerCamelCase       :       int   =						hidden_act
           lowerCamelCase       :       Optional[int]   =						hidden_dropout_prob
           lowerCamelCase       :       str   =						attention_probs_dropout_prob
           lowerCamelCase       :       Tuple   =						max_position_embeddings
           lowerCamelCase       :       Any   =						type_vocab_size
           lowerCamelCase       :       str   =						type_sequence_label_size
           lowerCamelCase       :       str   =						initializer_range
           lowerCamelCase       :       Union[str, Any]   =						num_choices

     def 		a__    (						self:    Any						)->     Tuple:
           lowerCamelCase       :       Union[str, Any]   =						ids_tensor([self.batch_size, self.seq_length]  ,		self.vocab_size						)

           lowerCamelCase       :       str   =						None
           if self.use_attention_mask:
                 lowerCamelCase       :       Union[str, Any]   =						random_attention_mask([self.batch_size, self.seq_length]						)

           lowerCamelCase       :       Optional[int]   =						None
           if self.use_token_type_ids:
                 lowerCamelCase       :       List[Any]   =						ids_tensor([self.batch_size, self.seq_length]  ,		self.type_vocab_size						)

           lowerCamelCase       :       Any   =						RobertaConfig(
               vocab_size=self.vocab_size  ,		hidden_size=self.hidden_size  ,		num_hidden_layers=self.num_hidden_layers  ,		num_attention_heads=self.num_attention_heads  ,		intermediate_size=self.intermediate_size  ,		hidden_act=self.hidden_act  ,		hidden_dropout_prob=self.hidden_dropout_prob  ,		attention_probs_dropout_prob=self.attention_probs_dropout_prob  ,		max_position_embeddings=self.max_position_embeddings  ,		type_vocab_size=self.type_vocab_size  ,		is_decoder=lowerCAmelCase__  ,		initializer_range=self.initializer_range  ,		)

           return config, input_ids, token_type_ids, attention_mask

     def 		a__    (						self:    str						)->     List[Any]:
           lowerCamelCase       :       str   =						self.prepare_config_and_inputs()
           lowerCamelCase       :       str   =						config_and_inputs
           lowerCamelCase       :       List[str]   =						{'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': attention_mask}
           return config, inputs_dict







     def 		a__    (						self:    List[str]						)->     Union[str, Any]:
           lowerCamelCase       :       List[str]   =						self.prepare_config_and_inputs()
           lowerCamelCase       :       Optional[Any]   =						config_and_inputs

           lowerCamelCase       :       str   =						True
           lowerCamelCase       :       Dict   =						floats_tensor([self.batch_size, self.seq_length, self.hidden_size]						)
           lowerCamelCase       :       int   =						ids_tensor([self.batch_size, self.seq_length]  ,		vocab_size=2						)

           return (
               config,
               input_ids,
               token_type_ids,
               encoder_hidden_states,
               encoder_attention_mask,
           )


@require_flax
class       A__						(    __SCREAMING_SNAKE_CASE			,  unittest.TestCase):


     """simple docstring"""
     snake_case__						:       Union[str, Any]				    =True

     snake_case__						:       Union[str, Any]				    =(
         (
             FlaxRobertaModel,
             FlaxRobertaForCausalLM,
             FlaxRobertaForMaskedLM,
             FlaxRobertaForSequenceClassification,
             FlaxRobertaForTokenClassification,
             FlaxRobertaForMultipleChoice,
             FlaxRobertaForQuestionAnswering,
         )
         if is_flax_available()
         else ()
     )

     def 		a__    (						self:    str						)->     Any:
           lowerCamelCase       :       Any   =						FlaxRobertaModelTester(self						)







     @slow
     def 		a__    (						self:    Tuple						)->     List[str]:




           for model_class_name in self.all_model_classes:
                 lowerCamelCase       :       List[str]   =						model_class_name.from_pretrained("""roberta-base"""  ,		from_pt=lowerCAmelCase__						)
                 lowerCamelCase       :       str   =						model(np.ones((1, 1)						)						)
                 self.assertIsNotNone(lowerCAmelCase__						)








import webbrowser
from sys import argv
from urllib.parse import parse_qs, quote

import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent

if __name__ == "__main__":
				lowerCAmelCase__			:      Optional[int]								='%20'.join(argv[1:]) if len(argv) > 1 else quote(str(input('Search: ')))

				print('Googling.....')

				lowerCAmelCase__			:      Optional[int]								=F"""https://www.google.com/search?q={query}&num=100"""

				lowerCAmelCase__			:      Union[str, Any]								=requests.get(
				    url,
				    headers={'User-Agent': str(UserAgent().random)},
				)

				try:
								lowerCAmelCase__			:      str								=(
								    BeautifulSoup(res.text, 'html.parser')
								    .find('div', attrs={'class': 'yuRUbf'})
								    .find('a')
								    .get('href')
								)

				except AttributeError:
								lowerCAmelCase__			:      Optional[Any]								=parse_qs(
								    BeautifulSoup(res.text, 'html.parser')
								    .find('div', attrs={'class': 'kCrYT'})
								    .find('a')
								    .get('href')
								)['url'][0]

				webbrowser.open(link)





