




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


UpperCamelCase__					    =    {
    """configuration_perceiver""": ["""PERCEIVER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """PerceiverConfig""", """PerceiverOnnxConfig"""],
    """tokenization_perceiver""": ["""PerceiverTokenizer"""],
}

try:
					if not is_vision_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCamelCase__					    =    ["""PerceiverFeatureExtractor"""]
					UpperCamelCase__					    =    ["""PerceiverImageProcessor"""]

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCamelCase__					    =    [
					    """PERCEIVER_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """PerceiverForImageClassificationConvProcessing""",
					    """PerceiverForImageClassificationFourier""",
					    """PerceiverForImageClassificationLearned""",
					    """PerceiverForMaskedLM""",
					    """PerceiverForMultimodalAutoencoding""",
					    """PerceiverForOpticalFlow""",
					    """PerceiverForSequenceClassification""",
					    """PerceiverLayer""",
					    """PerceiverModel""",
					    """PerceiverPreTrainedModel""",
					]


if TYPE_CHECKING:
					from .configuration_perceiver import PERCEIVER_PRETRAINED_CONFIG_ARCHIVE_MAP, PerceiverConfig, PerceiverOnnxConfig
					from .tokenization_perceiver import PerceiverTokenizer

					try:
										if not is_vision_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .feature_extraction_perceiver import PerceiverFeatureExtractor
										from .image_processing_perceiver import PerceiverImageProcessor

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_perceiver import (
										    PERCEIVER_PRETRAINED_MODEL_ARCHIVE_LIST,
										    PerceiverForImageClassificationConvProcessing,
										    PerceiverForImageClassificationFourier,
										    PerceiverForImageClassificationLearned,
										    PerceiverForMaskedLM,
										    PerceiverForMultimodalAutoencoding,
										    PerceiverForOpticalFlow,
										    PerceiverForSequenceClassification,
										    PerceiverLayer,
										    PerceiverModel,
										    PerceiverPreTrainedModel,
										)

else:
					import sys

					UpperCamelCase__					    =    _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)










import requests
from bsa import BeautifulSoup







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


if __name__ == "__main__":
					UpperCamelCase__					    =    {
					    """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))




