





'''simple docstring'''





# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowerCAmelCase_	:		int			=     {
    """configuration_xmod""": [
        """XMOD_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """XmodConfig""",
        """XmodOnnxConfig""",
    ],
}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowerCAmelCase_	:		List[Any]			=     [
			    """XMOD_PRETRAINED_MODEL_ARCHIVE_LIST""",
			    """XmodForCausalLM""",
			    """XmodForMaskedLM""",
			    """XmodForMultipleChoice""",
			    """XmodForQuestionAnswering""",
			    """XmodForSequenceClassification""",
			    """XmodForTokenClassification""",
			    """XmodModel""",
			    """XmodPreTrainedModel""",
			]

if TYPE_CHECKING:
			from .configuration_xmod import XMOD_PRETRAINED_CONFIG_ARCHIVE_MAP, XmodConfig, XmodOnnxConfig

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_xmod import (
						    XMOD_PRETRAINED_MODEL_ARCHIVE_LIST,
						    XmodForCausalLM,
						    XmodForMaskedLM,
						    XmodForMultipleChoice,
						    XmodForQuestionAnswering,
						    XmodForSequenceClassification,
						    XmodForTokenClassification,
						    XmodModel,
						    XmodPreTrainedModel,
						)

else:
			import sys

			lowerCAmelCase_	:		str			=     _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



'''simple docstring'''





import requests
from bsa import BeautifulSoup
def 						__A							(     UpperCAmelCase     ,UpperCAmelCase					)     ->			str:




						'''simple docstring'''
						_UpperCamelCase			:			Dict   			=						BeautifulSoup(requests.get(UpperCAmelCase     ,params=UpperCAmelCase					).content     ,"html.parser"					)
						_UpperCamelCase			:			Union[str, Any]   			=						soup.find("div"     ,attrs={"class": "gs_ri"}					)
						_UpperCamelCase			:			Optional[Any]   			=						div.find("div"     ,attrs={"class": "gs_fl"}					).find_all("a"					)
						return anchors[2].get_text()


if __name__ == "__main__":
			lowerCAmelCase_	:		Optional[int]			=     {
			    """title""": (
			        """Precisely geometry controlled microsupercapacitors for ultrahigh areal """
			        """capacitance, volumetric capacitance, and energy density"""
			    ),
			    """journal""": """Chem. Mater.""",
			    """volume""": 30,
			    """pages""": """3979-3990""",
			    """year""": 2018,
			    """hl""": """en""",
			}
			print(get_citation("""https://scholar.google.com/scholar_lookup""", params=params))


