"""simple docstring"""







import warnings

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


class       _lowercase		( __lowerCamelCase	):
				lowerCamelCase__							=							['image_processor', 'tokenizer']
				lowerCamelCase__							=							'AutoImageProcessor'
				lowerCamelCase__							=							'AutoTokenizer'

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


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

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

										super().__init__(_lowercase							,     _lowercase  )
										UpperCAmelCase__       						=		self.image_processor

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


										'''simple docstring'''

										if text is None and images is None:
																raise ValueError("""You have to specify either text or images. Both cannot be none."""  )

										if text is not None:
																UpperCAmelCase__       						=		self.tokenizer(_lowercase							,     return_tensors=_lowercase							,     **_lowercase  )

										if images is not None:
																UpperCAmelCase__       						=		self.image_processor(_lowercase							,     return_tensors=_lowercase							,     **_lowercase  )

										if text is not None and images is not None:
																UpperCAmelCase__       						=		image_features.pixel_values
																return encoding
										elif text is not None:
																return encoding
										else:
																return BatchEncoding(data=dict(**_lowercase  )							,     tensor_type=_lowercase  )

				def 							A			(  self							:     Dict							,     *_lowercase							:     Optional[Any]							,     **_lowercase							:     Dict  ):


										'''simple docstring'''
										return self.tokenizer.batch_decode(*_lowercase							,     **_lowercase  )

				def 							A			(  self							:     Dict							,     *_lowercase							:     Optional[int]							,     **_lowercase							:     Tuple  ):


										'''simple docstring'''
										return self.tokenizer.decode(*_lowercase							,     **_lowercase  )

				@property
				def 							A			(  self							:     Any  ):


										'''simple docstring'''
										UpperCAmelCase__       						=		self.tokenizer.model_input_names
										UpperCAmelCase__       						=		self.image_processor.model_input_names
										return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names  )  )

				@property
				def 							A			(  self							:     Any  ):


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

				@property
				def 							A			(  self							:     str  ):


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


"""simple docstring"""







import shutil

import requests


def 	a__   (		UpperCAmelCase__							:      str		,      UpperCAmelCase__							:      bool = False		,      UpperCAmelCase__							:      str = "."      )->						dict:
						UpperCAmelCase__       						=		"""https://api.nasa.gov/planetary/apod"""
						return requests.get(UpperCAmelCase__		,      params={"""api_key""": api_key}      ).json()


def 	a__   (		UpperCAmelCase__							:      str		,      UpperCAmelCase__							:      str = "."      )->						dict:
						UpperCAmelCase__       						=		get_apod_data(UpperCAmelCase__      )
						UpperCAmelCase__       						=		apod_data["""url"""]
						UpperCAmelCase__       						=		img_url.split("""/"""      )[-1]
						UpperCAmelCase__       						=		requests.get(UpperCAmelCase__		,      stream=UpperCAmelCase__      )

						with open(f'{path}/{img_name}'		,      """wb+"""      ) as img_file:
												shutil.copyfileobj(response.raw		,      UpperCAmelCase__      )
						del response
						return apod_data


def 	a__   (		UpperCAmelCase__							:      str      )->						dict:
						UpperCAmelCase__       						=		"""https://images-api.nasa.gov/search"""
						return requests.get(UpperCAmelCase__		,      params={"""q""": query}      ).json()


if __name__ == "__main__":
						print(save_apod("""YOUR API KEY"""))
						UpperCamelCase_							:							Optional[Any]				 =		get_archive_data("""apollo 2011""")["""collection"""]["""items"""]
						print(apollo_aaaa_items[0]["""data"""][0]["""description"""])

