





from __future__ import annotations

import unittest

from transformers import is_tf_available, is_torch_available
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, SMALL_MODEL_IDENTIFIER, is_pt_tf_cross_test, slow


if is_tf_available():
  from transformers import (
      AutoConfig,
      BertConfig,
      GPTaConfig,
      TaConfig,
      TFAutoModel,
      TFAutoModelForCausalLM,
      TFAutoModelForMaskedLM,
      TFAutoModelForPreTraining,
      TFAutoModelForQuestionAnswering,
      TFAutoModelForSeqaSeqLM,
      TFAutoModelForSequenceClassification,
      TFAutoModelWithLMHead,
      TFBertForMaskedLM,
      TFBertForPreTraining,
      TFBertForQuestionAnswering,
      TFBertForSequenceClassification,
      TFBertModel,
      TFGPTaLMHeadModel,
      TFRobertaForMaskedLM,
      TFTaForConditionalGeneration,
  )
  from transformers.models.bert.modeling_tf_bert import TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST
  from transformers.models.gpta.modeling_tf_gpta import TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST
  from transformers.models.ta.modeling_tf_ta import TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST

if is_torch_available():
  from transformers import (
      AutoModel,
      AutoModelForCausalLM,
      AutoModelForMaskedLM,
      AutoModelForPreTraining,
      AutoModelForQuestionAnswering,
      AutoModelForSeqaSeqLM,
      AutoModelForSequenceClassification,
      AutoModelWithLMHead,
      BertForMaskedLM,
      BertForPreTraining,
      BertForQuestionAnswering,
      BertForSequenceClassification,
      BertModel,
      GPTaLMHeadModel,
      RobertaForMaskedLM,
      TaForConditionalGeneration,
  )


@is_pt_tf_cross_test
class 						lowerCamelCase       (unittest.TestCase      ):







       """simple docstring"""




       @slow
       def  A_     ( self     :					str						)				->			Tuple:





              """simple docstring"""
              # for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              for model_name in ["bert-base-uncased"]:
                     SCREAMING_SNAKE_CASE__       :      Dict     =						AutoConfig.from_pretrained(__A						)
                     self.assertIsNotNone(__A						)
                     self.assertIsInstance(__A,			__A						)

                     SCREAMING_SNAKE_CASE__       :      str     =						TFAutoModel.from_pretrained(__A,			from_pt=__A						)
                     self.assertIsNotNone(__A						)
                     self.assertIsInstance(__A,			__A						)

                     SCREAMING_SNAKE_CASE__       :      int     =						AutoModel.from_pretrained(__A,			from_tf=__A						)
                     self.assertIsNotNone(__A						)
                     self.assertIsInstance(__A,			__A						)






       @slow
       def  A_     ( self     :					Dict						)				->			int:





              """simple docstring"""
              # for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              for model_name in ["bert-base-uncased"]:
                     SCREAMING_SNAKE_CASE__       :      List[str]     =						AutoConfig.from_pretrained(__A						)
                     self.assertIsNotNone(__A						)
                     self.assertIsInstance(__A,			__A						)

                     SCREAMING_SNAKE_CASE__       :      Optional[int]     =						TFAutoModelForPreTraining.from_pretrained(__A,			from_pt=__A						)
                     self.assertIsNotNone(__A						)
                     self.assertIsInstance(__A,			__A						)

                     SCREAMING_SNAKE_CASE__       :      Optional[int]     =						AutoModelForPreTraining.from_pretrained(__A,			from_tf=__A						)
                     self.assertIsNotNone(__A						)
                     self.assertIsInstance(__A,			__A						)






       @slow
       def  A_     ( self     :					str						)				->			Union[str, Any]:





              """simple docstring"""
              for model_name in TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                     SCREAMING_SNAKE_CASE__       :      List[Any]     =						AutoConfig.from_pretrained(__A						)
                     self.assertIsNotNone(__A						)
                     self.assertIsInstance(__A,			__A						)

                     SCREAMING_SNAKE_CASE__       :      List[Any]     =						TFAutoModelForCausalLM.from_pretrained(__A,			from_pt=__A						)
                     SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						TFAutoModelForCausalLM.from_pretrained(
                         __A,			output_loading_info=__A,			from_pt=__A						)
                     self.assertIsNotNone(__A						)
                     self.assertIsInstance(__A,			__A						)

                     SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						AutoModelForCausalLM.from_pretrained(__A,			from_tf=__A						)
                     SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__       :      List[str]     =						AutoModelForCausalLM.from_pretrained(
                         __A,			output_loading_info=__A,			from_tf=__A						)
                     self.assertIsNotNone(__A						)
                     self.assertIsInstance(__A,			__A						)






       @slow
       def  A_     ( self     :					List[str]						)				->			Union[str, Any]:





              """simple docstring"""
              for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                     SCREAMING_SNAKE_CASE__       :      Optional[int]     =						AutoConfig.from_pretrained(__A						)
                     self.assertIsNotNone(__A						)
                     self.assertIsInstance(__A,			__A						)

                     SCREAMING_SNAKE_CASE__       :      Tuple     =						TFAutoModelWithLMHead.from_pretrained(__A,			from_pt=__A						)
                     self.assertIsNotNone(__A						)
                     self.assertIsInstance(__A,			__A						)

                     SCREAMING_SNAKE_CASE__       :      List[str]     =						AutoModelWithLMHead.from_pretrained(__A,			from_tf=__A						)
                     self.assertIsNotNone(__A						)
                     self.assertIsInstance(__A,			__A						)






       @slow
       def  A_     ( self     :					Union[str, Any]						)				->			Dict:





              """simple docstring"""
              for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                     SCREAMING_SNAKE_CASE__       :      List[Any]     =						AutoConfig.from_pretrained(__A						)
                     self.assertIsNotNone(__A						)
                     self.assertIsInstance(__A,			__A						)

                     SCREAMING_SNAKE_CASE__       :      Optional[int]     =						TFAutoModelForMaskedLM.from_pretrained(__A,			from_pt=__A						)
                     SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__       :      str     =						TFAutoModelForMaskedLM.from_pretrained(
                         __A,			output_loading_info=__A,			from_pt=__A						)
                     self.assertIsNotNone(__A						)
                     self.assertIsInstance(__A,			__A						)

                     SCREAMING_SNAKE_CASE__       :      Optional[int]     =						AutoModelForMaskedLM.from_pretrained(__A,			from_tf=__A						)
                     SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__       :      str     =						AutoModelForMaskedLM.from_pretrained(
                         __A,			output_loading_info=__A,			from_tf=__A						)
                     self.assertIsNotNone(__A						)
                     self.assertIsInstance(__A,			__A						)






       @slow
       def  A_     ( self     :					Tuple						)				->			Tuple:





              """simple docstring"""
              for model_name in TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                     SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						AutoConfig.from_pretrained(__A						)
                     self.assertIsNotNone(__A						)
                     self.assertIsInstance(__A,			__A						)

                     SCREAMING_SNAKE_CASE__       :      Optional[int]     =						TFAutoModelForSeqaSeqLM.from_pretrained(__A,			from_pt=__A						)
                     SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__       :      Any     =						TFAutoModelForSeqaSeqLM.from_pretrained(
                         __A,			output_loading_info=__A,			from_pt=__A						)
                     self.assertIsNotNone(__A						)
                     self.assertIsInstance(__A,			__A						)

                     SCREAMING_SNAKE_CASE__       :      List[str]     =						AutoModelForSeqaSeqLM.from_pretrained(__A,			from_tf=__A						)
                     SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						AutoModelForSeqaSeqLM.from_pretrained(
                         __A,			output_loading_info=__A,			from_tf=__A						)
                     self.assertIsNotNone(__A						)
                     self.assertIsInstance(__A,			__A						)






       @slow
       def  A_     ( self     :					List[str]						)				->			Any:





              """simple docstring"""
              # for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              for model_name in ["bert-base-uncased"]:
                     SCREAMING_SNAKE_CASE__       :      Dict     =						AutoConfig.from_pretrained(__A						)
                     self.assertIsNotNone(__A						)
                     self.assertIsInstance(__A,			__A						)

                     SCREAMING_SNAKE_CASE__       :      Any     =						TFAutoModelForSequenceClassification.from_pretrained(__A,			from_pt=__A						)
                     self.assertIsNotNone(__A						)
                     self.assertIsInstance(__A,			__A						)

                     SCREAMING_SNAKE_CASE__       :      int     =						AutoModelForSequenceClassification.from_pretrained(__A,			from_tf=__A						)
                     self.assertIsNotNone(__A						)
                     self.assertIsInstance(__A,			__A						)






       @slow
       def  A_     ( self     :					Union[str, Any]						)				->			Dict:





              """simple docstring"""
              # for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              for model_name in ["bert-base-uncased"]:
                     SCREAMING_SNAKE_CASE__       :      Tuple     =						AutoConfig.from_pretrained(__A						)
                     self.assertIsNotNone(__A						)
                     self.assertIsInstance(__A,			__A						)

                     SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						TFAutoModelForQuestionAnswering.from_pretrained(__A,			from_pt=__A						)
                     self.assertIsNotNone(__A						)
                     self.assertIsInstance(__A,			__A						)

                     SCREAMING_SNAKE_CASE__       :      int     =						AutoModelForQuestionAnswering.from_pretrained(__A,			from_tf=__A						)
                     self.assertIsNotNone(__A						)
                     self.assertIsInstance(__A,			__A						)






       def  A_     ( self     :					Optional[int]						)				->			Union[str, Any]:





              """simple docstring"""
              SCREAMING_SNAKE_CASE__       :      List[str]     =						TFAutoModelWithLMHead.from_pretrained(__A,			from_pt=__A						)
              self.assertIsInstance(__A,			__A						)
              self.assertEqual(model.num_parameters(),			1_4_4_1_0						)
              self.assertEqual(model.num_parameters(only_trainable=__A						),			1_4_4_1_0						)

              SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						AutoModelWithLMHead.from_pretrained(__A,			from_tf=__A						)
              self.assertIsInstance(__A,			__A						)
              self.assertEqual(model.num_parameters(),			1_4_4_1_0						)
              self.assertEqual(model.num_parameters(only_trainable=__A						),			1_4_4_1_0						)






       def  A_     ( self     :					str						)				->			List[str]:





              """simple docstring"""
              SCREAMING_SNAKE_CASE__       :      Dict     =						TFAutoModelWithLMHead.from_pretrained(__A,			from_pt=__A						)
              self.assertIsInstance(__A,			__A						)
              self.assertEqual(model.num_parameters(),			1_4_4_1_0						)
              self.assertEqual(model.num_parameters(only_trainable=__A						),			1_4_4_1_0						)

              SCREAMING_SNAKE_CASE__       :      Tuple     =						AutoModelWithLMHead.from_pretrained(__A,			from_tf=__A						)
              self.assertIsInstance(__A,			__A						)
              self.assertEqual(model.num_parameters(),			1_4_4_1_0						)
              self.assertEqual(model.num_parameters(only_trainable=__A						),			1_4_4_1_0						)













import requests
from bsa import BeautifulSoup




def _a (  SCREAMING_SNAKE_CASE__      :  str			,				SCREAMING_SNAKE_CASE__      :  dict       )	->    str:

							'''simple docstring'''






							SCREAMING_SNAKE_CASE__       :      Any     =						BeautifulSoup(requests.get(SCREAMING_SNAKE_CASE__			,				params=SCREAMING_SNAKE_CASE__       ).content			,				"html.parser"       )
							SCREAMING_SNAKE_CASE__       :      str     =						soup.find("div"			,				attrs={"class": "gs_ri"}       )
							SCREAMING_SNAKE_CASE__       :      int     =						div.find("div"			,				attrs={"class": "gs_fl"}       ).find_all("a"       )
							return anchors[2].get_text()


if __name__ == "__main__":
		_lowerCamelCase :    Any  							= {
		    '''title''': (
		        '''Precisely geometry controlled microsupercapacitors for ultrahigh areal '''
		        '''capacitance, volumetric capacitance, and energy density'''
		    ),
		    '''journal''': '''Chem. Mater.''',
		    '''volume''': 3_0,
		    '''pages''': '''3979-3990''',
		    '''year''': 2_0_1_8,
		    '''hl''': '''en''',
		}
		print(get_citation('''https://scholar.google.com/scholar_lookup''', params=params))






