import shutil

import requests





def 	lowerCAmelCase_			(					__lowerCAmelCase		,       __lowerCAmelCase = False		,       __lowerCAmelCase = "."    )->	dict:






      '''simple docstring'''





      UpperCAmelCase     :			List[str]     	='''https://api.nasa.gov/planetary/apod'''
      return requests.get(__lowerCAmelCase		,       params={'''api_key''': api_key}    ).json()





def 	lowerCAmelCase_			(					__lowerCAmelCase		,       __lowerCAmelCase = "."    )->	dict:






      '''simple docstring'''





      UpperCAmelCase     :			Tuple     	=get_apod_data(__lowerCAmelCase    )
      UpperCAmelCase     :			Dict     	=apod_data['''url''']
      UpperCAmelCase     :			Any     	=img_url.split('''/'''    )[-1]
      UpperCAmelCase     :			Union[str, Any]     	=requests.get(__lowerCAmelCase		,       stream=__lowerCAmelCase    )

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





def 	lowerCAmelCase_			(					__lowerCAmelCase    )->	dict:






      '''simple docstring'''





      UpperCAmelCase     :			Any     	='''https://images-api.nasa.gov/search'''
      return requests.get(__lowerCAmelCase		,       params={'''q''': query}    ).json()


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

from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


__snake_case         =     {'''configuration_gptj''': ['''GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GPTJConfig''', '''GPTJOnnxConfig''']}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __snake_case         =     [
         '''GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST''',
         '''GPTJForCausalLM''',
         '''GPTJForQuestionAnswering''',
         '''GPTJForSequenceClassification''',
         '''GPTJModel''',
         '''GPTJPreTrainedModel''',
     ]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __snake_case         =     [
         '''TFGPTJForCausalLM''',
         '''TFGPTJForQuestionAnswering''',
         '''TFGPTJForSequenceClassification''',
         '''TFGPTJModel''',
         '''TFGPTJPreTrainedModel''',
     ]

try:
     if not is_flax_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __snake_case         =     [
         '''FlaxGPTJForCausalLM''',
         '''FlaxGPTJModel''',
         '''FlaxGPTJPreTrainedModel''',
     ]


if TYPE_CHECKING:
     from .configuration_gptj import GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTJConfig, GPTJOnnxConfig

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_gptj import (
              GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST,
              GPTJForCausalLM,
              GPTJForQuestionAnswering,
              GPTJForSequenceClassification,
              GPTJModel,
              GPTJPreTrainedModel,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_gptj import (
              TFGPTJForCausalLM,
              TFGPTJForQuestionAnswering,
              TFGPTJForSequenceClassification,
              TFGPTJModel,
              TFGPTJPreTrainedModel,
          )

     try:
          if not is_flax_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_flax_gptj import FlaxGPTJForCausalLM, FlaxGPTJModel, FlaxGPTJPreTrainedModel

else:
     import sys

     __snake_case         =     _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
