



"""simple docstring"""







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__":
     lowercase__		:    str				   =     """%20""".join(argv[1:]) if len(argv) > 1 else quote(str(input("""Search: """)))

     print("""Googling.....""")

     lowercase__		:    Optional[Any]				   =     f'https://www.google.com/search?q={query}&num=100'

     lowercase__		:    Optional[Any]				   =     requests.get(
         url,
         headers={"""User-Agent""": str(UserAgent().random)},
     )

     try:
          lowercase__		:    Tuple				   =     (
              BeautifulSoup(res.text, """html.parser""")
              .find("""div""", attrs={"""class""": """yuRUbf"""})
              .find("""a""")
              .get("""href""")
          )

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

     webbrowser.open(link)










"""simple docstring"""







import unittest

from transformers import AlbertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
     import torch

     from transformers import (
         MODEL_FOR_PRETRAINING_MAPPING,
         AlbertForMaskedLM,
         AlbertForMultipleChoice,
         AlbertForPreTraining,
         AlbertForQuestionAnswering,
         AlbertForSequenceClassification,
         AlbertForTokenClassification,
         AlbertModel,
     )
     from transformers.models.albert.modeling_albert import ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST







class 				UpperCamelCase__       :




      """simple docstring"""




      def __init__(				self    :				Optional[Any]   ,      SCREAMING_SNAKE_CASE_    :				List[Any]   ,      SCREAMING_SNAKE_CASE_    :				Dict=1_3   ,      SCREAMING_SNAKE_CASE_    :				List[Any]=7   ,      SCREAMING_SNAKE_CASE_    :				Dict=True   ,      SCREAMING_SNAKE_CASE_    :				Optional[Any]=True   ,      SCREAMING_SNAKE_CASE_    :				Optional[Any]=True   ,      SCREAMING_SNAKE_CASE_    :				str=True   ,      SCREAMING_SNAKE_CASE_    :				List[str]=9_9   ,      SCREAMING_SNAKE_CASE_    :				int=1_6   ,      SCREAMING_SNAKE_CASE_    :				List[str]=3_6   ,      SCREAMING_SNAKE_CASE_    :				List[Any]=6   ,      SCREAMING_SNAKE_CASE_    :				Tuple=6   ,      SCREAMING_SNAKE_CASE_    :				List[Any]=6   ,      SCREAMING_SNAKE_CASE_    :				Union[str, Any]=3_7   ,      SCREAMING_SNAKE_CASE_    :				Tuple="gelu"   ,      SCREAMING_SNAKE_CASE_    :				Union[str, Any]=0.1   ,      SCREAMING_SNAKE_CASE_    :				int=0.1   ,      SCREAMING_SNAKE_CASE_    :				Optional[Any]=5_1_2   ,      SCREAMING_SNAKE_CASE_    :				List[str]=1_6   ,      SCREAMING_SNAKE_CASE_    :				List[str]=2   ,      SCREAMING_SNAKE_CASE_    :				List[Any]=0.02   ,      SCREAMING_SNAKE_CASE_    :				Dict=3   ,      SCREAMING_SNAKE_CASE_    :				int=4   ,      SCREAMING_SNAKE_CASE_    :				Tuple=None   ,      ):
        lowerCAmelCase_       :    Any								=			parent
        lowerCAmelCase_       :    Optional[int]								=			batch_size
        lowerCAmelCase_       :    Dict								=			seq_length
        lowerCAmelCase_       :    Tuple								=			is_training
        lowerCAmelCase_       :    str								=			use_input_mask
        lowerCAmelCase_       :    Union[str, Any]								=			use_token_type_ids
        lowerCAmelCase_       :    Tuple								=			use_labels
        lowerCAmelCase_       :    Optional[int]								=			vocab_size
        lowerCAmelCase_       :    Any								=			embedding_size
        lowerCAmelCase_       :    Optional[Any]								=			hidden_size
        lowerCAmelCase_       :    str								=			num_hidden_layers
        lowerCAmelCase_       :    Optional[Any]								=			num_hidden_groups
        lowerCAmelCase_       :    Dict								=			num_attention_heads
        lowerCAmelCase_       :    Optional[Any]								=			intermediate_size
        lowerCAmelCase_       :    Any								=			hidden_act
        lowerCAmelCase_       :    Union[str, Any]								=			hidden_dropout_prob
        lowerCAmelCase_       :    int								=			attention_probs_dropout_prob
        lowerCAmelCase_       :    int								=			max_position_embeddings
        lowerCAmelCase_       :    List[Any]								=			type_vocab_size
        lowerCAmelCase_       :    Any								=			type_sequence_label_size
        lowerCAmelCase_       :    Optional[int]								=			initializer_range
        lowerCAmelCase_       :    Tuple								=			num_labels
        lowerCAmelCase_       :    Dict								=			num_choices
        lowerCAmelCase_       :    Tuple								=			scope



      def 					SCREAMING_SNAKE_CASE__			(				self    :				Union[str, Any]	):
        lowerCAmelCase_       :    List[str]								=			ids_tensor([self.batch_size, self.seq_length]   ,      self.vocab_size	)

        lowerCAmelCase_       :    str								=			None
        if self.use_input_mask:
          lowerCAmelCase_       :    int								=			random_attention_mask([self.batch_size, self.seq_length]	)

        lowerCAmelCase_       :    List[Any]								=			None
        if self.use_token_type_ids:
          lowerCAmelCase_       :    str								=			ids_tensor([self.batch_size, self.seq_length]   ,      self.type_vocab_size	)

        lowerCAmelCase_       :    List[str]								=			None
        lowerCAmelCase_       :    Union[str, Any]								=			None
        lowerCAmelCase_       :    str								=			None
        if self.use_labels:
          lowerCAmelCase_       :    Any								=			ids_tensor([self.batch_size]   ,      self.type_sequence_label_size	)
          lowerCAmelCase_       :    List[str]								=			ids_tensor([self.batch_size, self.seq_length]   ,      self.num_labels	)
          lowerCAmelCase_       :    Dict								=			ids_tensor([self.batch_size]   ,      self.num_choices	)

        lowerCAmelCase_       :    List[Any]								=			self.get_config()

        return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels



      def 					SCREAMING_SNAKE_CASE__			(				self    :				Dict	):
        return AlbertConfig(
            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   ,      initializer_range=self.initializer_range   ,      num_hidden_groups=self.num_hidden_groups   ,      )



      def 					SCREAMING_SNAKE_CASE__			(				self    :				Tuple   ,      SCREAMING_SNAKE_CASE_    :				Any   ,      SCREAMING_SNAKE_CASE_    :				str   ,      SCREAMING_SNAKE_CASE_    :				Dict   ,      SCREAMING_SNAKE_CASE_    :				str   ,      SCREAMING_SNAKE_CASE_    :				Dict   ,      SCREAMING_SNAKE_CASE_    :				Optional[int]   ,      SCREAMING_SNAKE_CASE_    :				str	):
        lowerCAmelCase_       :    Union[str, Any]								=			AlbertModel(config=SCREAMING_SNAKE_CASE_	)
        model.to(SCREAMING_SNAKE_CASE_	)
        model.eval()
        lowerCAmelCase_       :    List[str]								=			model(SCREAMING_SNAKE_CASE_   ,      attention_mask=SCREAMING_SNAKE_CASE_   ,      token_type_ids=SCREAMING_SNAKE_CASE_	)
        lowerCAmelCase_       :    List[Any]								=			model(SCREAMING_SNAKE_CASE_   ,      token_type_ids=SCREAMING_SNAKE_CASE_	)
        lowerCAmelCase_       :    int								=			model(SCREAMING_SNAKE_CASE_	)
        self.parent.assertEqual(result.last_hidden_state.shape   ,      (self.batch_size, self.seq_length, self.hidden_size)	)
        self.parent.assertEqual(result.pooler_output.shape   ,      (self.batch_size, self.hidden_size)	)



      def 					SCREAMING_SNAKE_CASE__			(				self    :				int   ,      SCREAMING_SNAKE_CASE_    :				Dict   ,      SCREAMING_SNAKE_CASE_    :				Tuple   ,      SCREAMING_SNAKE_CASE_    :				List[Any]   ,      SCREAMING_SNAKE_CASE_    :				Optional[int]   ,      SCREAMING_SNAKE_CASE_    :				List[Any]   ,      SCREAMING_SNAKE_CASE_    :				List[Any]   ,      SCREAMING_SNAKE_CASE_    :				Tuple	):
        lowerCAmelCase_       :    Optional[Any]								=			AlbertForPreTraining(config=SCREAMING_SNAKE_CASE_	)
        model.to(SCREAMING_SNAKE_CASE_	)
        model.eval()
        lowerCAmelCase_       :    Optional[Any]								=			model(
            SCREAMING_SNAKE_CASE_   ,      attention_mask=SCREAMING_SNAKE_CASE_   ,      token_type_ids=SCREAMING_SNAKE_CASE_   ,      labels=SCREAMING_SNAKE_CASE_   ,      sentence_order_label=SCREAMING_SNAKE_CASE_   ,      )
        self.parent.assertEqual(result.prediction_logits.shape   ,      (self.batch_size, self.seq_length, self.vocab_size)	)
        self.parent.assertEqual(result.sop_logits.shape   ,      (self.batch_size, config.num_labels)	)



      def 					SCREAMING_SNAKE_CASE__			(				self    :				Dict   ,      SCREAMING_SNAKE_CASE_    :				Tuple   ,      SCREAMING_SNAKE_CASE_    :				Union[str, Any]   ,      SCREAMING_SNAKE_CASE_    :				Dict   ,      SCREAMING_SNAKE_CASE_    :				Union[str, Any]   ,      SCREAMING_SNAKE_CASE_    :				Optional[Any]   ,      SCREAMING_SNAKE_CASE_    :				Any   ,      SCREAMING_SNAKE_CASE_    :				Dict	):
        lowerCAmelCase_       :    str								=			AlbertForMaskedLM(config=SCREAMING_SNAKE_CASE_	)
        model.to(SCREAMING_SNAKE_CASE_	)
        model.eval()
        lowerCAmelCase_       :    str								=			model(SCREAMING_SNAKE_CASE_   ,      attention_mask=SCREAMING_SNAKE_CASE_   ,      token_type_ids=SCREAMING_SNAKE_CASE_   ,      labels=SCREAMING_SNAKE_CASE_	)
        self.parent.assertEqual(result.logits.shape   ,      (self.batch_size, self.seq_length, self.vocab_size)	)



      def 					SCREAMING_SNAKE_CASE__			(				self    :				str   ,      SCREAMING_SNAKE_CASE_    :				Dict   ,      SCREAMING_SNAKE_CASE_    :				int   ,      SCREAMING_SNAKE_CASE_    :				Optional[Any]   ,      SCREAMING_SNAKE_CASE_    :				int   ,      SCREAMING_SNAKE_CASE_    :				Optional[Any]   ,      SCREAMING_SNAKE_CASE_    :				Optional[int]   ,      SCREAMING_SNAKE_CASE_    :				List[Any]	):
        lowerCAmelCase_       :    List[str]								=			AlbertForQuestionAnswering(config=SCREAMING_SNAKE_CASE_	)
        model.to(SCREAMING_SNAKE_CASE_	)
        model.eval()
        lowerCAmelCase_       :    Any								=			model(
            SCREAMING_SNAKE_CASE_   ,      attention_mask=SCREAMING_SNAKE_CASE_   ,      token_type_ids=SCREAMING_SNAKE_CASE_   ,      start_positions=SCREAMING_SNAKE_CASE_   ,      end_positions=SCREAMING_SNAKE_CASE_   ,      )
        self.parent.assertEqual(result.start_logits.shape   ,      (self.batch_size, self.seq_length)	)
        self.parent.assertEqual(result.end_logits.shape   ,      (self.batch_size, self.seq_length)	)



      def 					SCREAMING_SNAKE_CASE__			(				self    :				Dict   ,      SCREAMING_SNAKE_CASE_    :				Optional[int]   ,      SCREAMING_SNAKE_CASE_    :				Optional[int]   ,      SCREAMING_SNAKE_CASE_    :				Optional[Any]   ,      SCREAMING_SNAKE_CASE_    :				List[Any]   ,      SCREAMING_SNAKE_CASE_    :				List[Any]   ,      SCREAMING_SNAKE_CASE_    :				Union[str, Any]   ,      SCREAMING_SNAKE_CASE_    :				Dict	):
        lowerCAmelCase_       :    Union[str, Any]								=			self.num_labels
        lowerCAmelCase_       :    Union[str, Any]								=			AlbertForSequenceClassification(SCREAMING_SNAKE_CASE_	)
        model.to(SCREAMING_SNAKE_CASE_	)
        model.eval()
        lowerCAmelCase_       :    Union[str, Any]								=			model(SCREAMING_SNAKE_CASE_   ,      attention_mask=SCREAMING_SNAKE_CASE_   ,      token_type_ids=SCREAMING_SNAKE_CASE_   ,      labels=SCREAMING_SNAKE_CASE_	)
        self.parent.assertEqual(result.logits.shape   ,      (self.batch_size, self.num_labels)	)



      def 					SCREAMING_SNAKE_CASE__			(				self    :				Any   ,      SCREAMING_SNAKE_CASE_    :				Optional[int]   ,      SCREAMING_SNAKE_CASE_    :				Dict   ,      SCREAMING_SNAKE_CASE_    :				Dict   ,      SCREAMING_SNAKE_CASE_    :				Dict   ,      SCREAMING_SNAKE_CASE_    :				Dict   ,      SCREAMING_SNAKE_CASE_    :				Optional[Any]   ,      SCREAMING_SNAKE_CASE_    :				List[str]	):
        lowerCAmelCase_       :    List[str]								=			self.num_labels
        lowerCAmelCase_       :    List[Any]								=			AlbertForTokenClassification(config=SCREAMING_SNAKE_CASE_	)
        model.to(SCREAMING_SNAKE_CASE_	)
        model.eval()
        lowerCAmelCase_       :    str								=			model(SCREAMING_SNAKE_CASE_   ,      attention_mask=SCREAMING_SNAKE_CASE_   ,      token_type_ids=SCREAMING_SNAKE_CASE_   ,      labels=SCREAMING_SNAKE_CASE_	)
        self.parent.assertEqual(result.logits.shape   ,      (self.batch_size, self.seq_length, self.num_labels)	)



      def 					SCREAMING_SNAKE_CASE__			(				self    :				List[Any]   ,      SCREAMING_SNAKE_CASE_    :				str   ,      SCREAMING_SNAKE_CASE_    :				Optional[Any]   ,      SCREAMING_SNAKE_CASE_    :				Union[str, Any]   ,      SCREAMING_SNAKE_CASE_    :				Dict   ,      SCREAMING_SNAKE_CASE_    :				Any   ,      SCREAMING_SNAKE_CASE_    :				str   ,      SCREAMING_SNAKE_CASE_    :				List[str]	):
        lowerCAmelCase_       :    Optional[Any]								=			self.num_choices
        lowerCAmelCase_       :    int								=			AlbertForMultipleChoice(config=SCREAMING_SNAKE_CASE_	)
        model.to(SCREAMING_SNAKE_CASE_	)
        model.eval()
        lowerCAmelCase_       :    Optional[int]								=			input_ids.unsqueeze(1	).expand(-1   ,      self.num_choices   ,      -1	).contiguous()
        lowerCAmelCase_       :    List[Any]								=			token_type_ids.unsqueeze(1	).expand(-1   ,      self.num_choices   ,      -1	).contiguous()
        lowerCAmelCase_       :    List[Any]								=			input_mask.unsqueeze(1	).expand(-1   ,      self.num_choices   ,      -1	).contiguous()
        lowerCAmelCase_       :    List[Any]								=			model(
            SCREAMING_SNAKE_CASE_   ,      attention_mask=SCREAMING_SNAKE_CASE_   ,      token_type_ids=SCREAMING_SNAKE_CASE_   ,      labels=SCREAMING_SNAKE_CASE_   ,      )
        self.parent.assertEqual(result.logits.shape   ,      (self.batch_size, self.num_choices)	)





      def 					SCREAMING_SNAKE_CASE__			(				self    :				List[str]	):
        lowerCAmelCase_       :    Optional[int]								=			self.prepare_config_and_inputs()
        (
            (
            lowerCAmelCase_
        )						,(
            lowerCAmelCase_
        )						,(
            lowerCAmelCase_
        )						,(
            lowerCAmelCase_
        )						,(
            lowerCAmelCase_
        )						,(
            lowerCAmelCase_
        )						,(
            lowerCAmelCase_
        )						,
        )       :    Optional[int]								=			config_and_inputs
        lowerCAmelCase_       :    Dict								=			{'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
        return config, inputs_dict







@require_torch
class 				UpperCamelCase__       (     lowercase_, lowercase_, unittest.TestCase  ):




      """simple docstring"""




      _SCREAMING_SNAKE_CASE      =					(
          (
              AlbertModel,
              AlbertForPreTraining,
              AlbertForMaskedLM,
              AlbertForMultipleChoice,
              AlbertForSequenceClassification,
              AlbertForTokenClassification,
              AlbertForQuestionAnswering,
          )
          if is_torch_available()
          else ()
      )
      _SCREAMING_SNAKE_CASE      =					(
          {
              """feature-extraction""": AlbertModel,
              """fill-mask""": AlbertForMaskedLM,
              """question-answering""": AlbertForQuestionAnswering,
              """text-classification""": AlbertForSequenceClassification,
              """token-classification""": AlbertForTokenClassification,
              """zero-shot""": AlbertForSequenceClassification,
          }
          if is_torch_available()
          else {}
      )
      _SCREAMING_SNAKE_CASE      =					True



      def 					SCREAMING_SNAKE_CASE__			(				self    :				Optional[int]   ,      SCREAMING_SNAKE_CASE_    :				Dict   ,      SCREAMING_SNAKE_CASE_    :				List[str]   ,      SCREAMING_SNAKE_CASE_    :				str=False	):
        lowerCAmelCase_       :    List[str]								=			super()._prepare_for_class(SCREAMING_SNAKE_CASE_   ,      SCREAMING_SNAKE_CASE_   ,      return_labels=SCREAMING_SNAKE_CASE_	)

        if return_labels:
          if model_class in get_values(SCREAMING_SNAKE_CASE_	):
            lowerCAmelCase_       :    List[str]								=			torch.zeros(
                (self.model_tester.batch_size, self.model_tester.seq_length)   ,      dtype=torch.long   ,      device=SCREAMING_SNAKE_CASE_	)
            lowerCAmelCase_       :    Any								=			torch.zeros(
                self.model_tester.batch_size   ,      dtype=torch.long   ,      device=SCREAMING_SNAKE_CASE_	)
        return inputs_dict



      def 					SCREAMING_SNAKE_CASE__			(				self    :				List[Any]	):
        lowerCAmelCase_       :    str								=			AlbertModelTester(self	)
        lowerCAmelCase_       :    Optional[int]								=			ConfigTester(self   ,      config_class=SCREAMING_SNAKE_CASE_   ,      hidden_size=3_7	)



      def 					SCREAMING_SNAKE_CASE__			(				self    :				Optional[int]	):
        self.config_tester.run_common_tests()



      def 					SCREAMING_SNAKE_CASE__			(				self    :				Tuple	):
        lowerCAmelCase_       :    List[Any]								=			self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE_	)



      def 					SCREAMING_SNAKE_CASE__			(				self    :				Optional[Any]	):
        lowerCAmelCase_       :    Any								=			self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_pretraining(*SCREAMING_SNAKE_CASE_	)



      def 					SCREAMING_SNAKE_CASE__			(				self    :				Tuple	):
        lowerCAmelCase_       :    Tuple								=			self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_masked_lm(*SCREAMING_SNAKE_CASE_	)



      def 					SCREAMING_SNAKE_CASE__			(				self    :				List[Any]	):
        lowerCAmelCase_       :    List[Any]								=			self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_multiple_choice(*SCREAMING_SNAKE_CASE_	)



      def 					SCREAMING_SNAKE_CASE__			(				self    :				List[Any]	):
        lowerCAmelCase_       :    str								=			self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_question_answering(*SCREAMING_SNAKE_CASE_	)



      def 					SCREAMING_SNAKE_CASE__			(				self    :				str	):
        lowerCAmelCase_       :    Optional[int]								=			self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_sequence_classification(*SCREAMING_SNAKE_CASE_	)



      def 					SCREAMING_SNAKE_CASE__			(				self    :				Optional[Any]	):
        lowerCAmelCase_       :    Tuple								=			self.model_tester.prepare_config_and_inputs()
        for type in ["absolute", "relative_key", "relative_key_query"]:
          lowerCAmelCase_       :    int								=			type
          self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE_	)





      @slow
      def 					SCREAMING_SNAKE_CASE__			(				self    :				Optional[Any]	):
        for model_name in ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
          lowerCAmelCase_       :    Optional[Any]								=			AlbertModel.from_pretrained(SCREAMING_SNAKE_CASE_	)
          self.assertIsNotNone(SCREAMING_SNAKE_CASE_	)







@require_torch
class 				UpperCamelCase__       (     unittest.TestCase  ):




      """simple docstring"""




      @slow
      def 					SCREAMING_SNAKE_CASE__			(				self    :				List[str]	):
        lowerCAmelCase_       :    Any								=			AlbertModel.from_pretrained('albert-base-v2'	)
        lowerCAmelCase_       :    Tuple								=			torch.tensor([[0, 3_4_5, 2_3_2, 3_2_8, 7_4_0, 1_4_0, 1_6_9_5, 6_9, 6_0_7_8, 1_5_8_8, 2]]	)
        lowerCAmelCase_       :    Tuple								=			torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]	)
        with torch.no_grad():
          lowerCAmelCase_       :    List[Any]								=			model(SCREAMING_SNAKE_CASE_   ,      attention_mask=SCREAMING_SNAKE_CASE_	)[0]
        lowerCAmelCase_       :    str								=			torch.Size((1, 1_1, 7_6_8)	)
        self.assertEqual(output.shape   ,      SCREAMING_SNAKE_CASE_	)
        lowerCAmelCase_       :    int								=			torch.tensor(
            [[[-0.65_13, 1.50_35, -0.27_66], [-0.65_15, 1.50_46, -0.27_80], [-0.65_12, 1.50_49, -0.27_84]]]	)

        self.assertTrue(torch.allclose(output[:, 1:4, 1:4]   ,      SCREAMING_SNAKE_CASE_   ,      atol=1E-4	)	)





