
"""simple docstring"""




from ctypes import c_float, sizeof
from enum import Enum
from typing import TYPE_CHECKING, Optional, Union


if TYPE_CHECKING:
    from .. import AutoFeatureExtractor, AutoProcessor, AutoTokenizer  # tests_ignore







class 						lowercase(SCREAMING_SNAKE_CASE__					):
     __snake_case:  Any  				=    c_float



     @property
     def  lowercase__     (       self      )		->    List[Any]:







         """simple docstring"""



         return sizeof(self.value      )




def 						__magic_name__		(				UpperCamelCase    :		int       ,		UpperCamelCase    :		int       ,		UpperCamelCase    :		int = 0    )    ->	int:

    '''simple docstring'''





    if dimension <= 0:
        a__    = fixed_dimension

    dimension -= num_token_to_add
    return dimension



def 						__magic_name__		(				UpperCamelCase    :		int       ,		UpperCamelCase    :		ParameterFormat    )    ->	int:

    '''simple docstring'''





    return num_parameters * dtype.size



def 						__magic_name__		(				UpperCamelCase    :		str    )    ->	Optional[Union["AutoTokenizer", "AutoFeatureExtractor", "AutoProcessor"]]:

    '''simple docstring'''





    from .. import AutoFeatureExtractor, AutoProcessor, AutoTokenizer  # tests_ignore

    try:
        return AutoProcessor.from_pretrained(_UpperCamelCase    )
    except (ValueError, OSError, KeyError):
        a__  ,    a__    = None, None
        try:
            a__    = AutoTokenizer.from_pretrained(_UpperCamelCase    )
        except (OSError, KeyError):
            pass
        try:
            a__    = AutoFeatureExtractor.from_pretrained(_UpperCamelCase    )
        except (OSError, KeyError):
            pass

        if tokenizer is not None and feature_extractor is not None:
            raise ValueError(
                f'Couldn\'t auto-detect preprocessor for {model_name}. Found both a tokenizer and a feature extractor.'    )
        elif tokenizer is None and feature_extractor is None:
            return None
        elif tokenizer is not None:
            return tokenizer
        else:
            return feature_extractor




"""simple docstring"""




import shutil

import requests



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



def 						__magic_name__		(				UpperCamelCase    :		str       ,		UpperCamelCase    :		str = "."    )    ->	dict:
    a__    = get_apod_data(UpperCamelCase    )
    a__    = apod_data['url']
    a__    = img_url.split('/'    )[-1]
    a__    = 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 						__magic_name__		(				UpperCamelCase    :		str    )    ->	dict:
    a__    = 'https://images-api.nasa.gov/search'
    return requests.get(UpperCamelCase       ,		params={'q': query}    ).json()


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


