






import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding


class    _lowerCAmelCase			(		lowercase_      ):



				"""simple docstring"""



				_lowercase				:   str      =  ['''image_processor''', '''tokenizer''']
				_lowercase				:   Optional[int]      =  '''ViTImageProcessor'''
				_lowercase				:   str      =  ('''CLIPTokenizer''', '''CLIPTokenizerFast''')



				def __init__(      self    :    Any			,		UpperCamelCase__    :    List[Any]=None			,		UpperCamelCase__    :    Tuple=None			,		**UpperCamelCase__    :    Optional[int]):






											'''simple docstring'''







											snake_case__	  =       None
											if "feature_extractor" in kwargs:
																		warnings.warn(
																		    """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
																		    """ instead."""			,		UpperCamelCase__			,		)
																		snake_case__	  =       kwargs.pop("""feature_extractor""")

											snake_case__	  =       image_processor if image_processor is not None else feature_extractor
											if image_processor is None:
																		raise ValueError("""You need to specify an `image_processor`.""")
											if tokenizer is None:
																		raise ValueError("""You need to specify a `tokenizer`.""")

											super().__init__(UpperCamelCase__			,		UpperCamelCase__)



				def __call__(      self    :    Union[str, Any]			,		UpperCamelCase__    :    str=None			,		UpperCamelCase__    :    Union[str, Any]=None			,		UpperCamelCase__    :    List[Any]=None			,		UpperCamelCase__    :    int=None			,		**UpperCamelCase__    :    Tuple):






											'''simple docstring'''







											if text is None and visual_prompt is None and images is None:
																		raise ValueError("""You have to specify either text, visual prompt or images.""")

											if text is not None and visual_prompt is not None:
																		raise ValueError("""You have to specify exactly one type of prompt. Either text or visual prompt.""")

											if text is not None:
																		snake_case__	  =       self.tokenizer(UpperCamelCase__			,		return_tensors=UpperCamelCase__			,		**UpperCamelCase__)

											if visual_prompt is not None:
																		snake_case__	  =       self.image_processor(UpperCamelCase__			,		return_tensors=UpperCamelCase__			,		**UpperCamelCase__)

											if images is not None:
																		snake_case__	  =       self.image_processor(UpperCamelCase__			,		return_tensors=UpperCamelCase__			,		**UpperCamelCase__)

											if visual_prompt is not None and images is not None:
																		snake_case__	  =       {
																		    """pixel_values""": image_features.pixel_values,
																		    """conditional_pixel_values""": prompt_features.pixel_values,
																		}
																		return encoding
											elif text is not None and images is not None:
																		snake_case__	  =       image_features.pixel_values
																		return encoding
											elif text is not None:
																		return encoding
											elif visual_prompt is not None:
																		snake_case__	  =       {
																		    """conditional_pixel_values""": prompt_features.pixel_values,
																		}
																		return encoding
											else:
																		return BatchEncoding(data=dict(**UpperCamelCase__)			,		tensor_type=UpperCamelCase__)



				def 					__magic_name__							(      self    :    str			,		*UpperCamelCase__    :    Tuple			,		**UpperCamelCase__    :    Optional[Any]):






											'''simple docstring'''







											return self.tokenizer.batch_decode(*UpperCamelCase__			,		**UpperCamelCase__)



				def 					__magic_name__							(      self    :    Tuple			,		*UpperCamelCase__    :    List[Any]			,		**UpperCamelCase__    :    List[str]):






											'''simple docstring'''







											return self.tokenizer.decode(*UpperCamelCase__			,		**UpperCamelCase__)



				@property
				def 					__magic_name__							(      self    :    int):






											'''simple docstring'''







											warnings.warn(
											    """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""			,		UpperCamelCase__			,		)
											return self.image_processor_class



				@property
				def 					__magic_name__							(      self    :    Optional[Any]):






											'''simple docstring'''







											warnings.warn(
											    """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."""			,		UpperCamelCase__			,		)
											return self.image_processor








import requests
from bsa import BeautifulSoup

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


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