


'''simple docstring'''
import shutil

import requests

def 				lowerCamelCase_   (   __UpperCamelCase				:					str       ,						__UpperCamelCase				:					bool = False       ,						__UpperCamelCase				:					str = "."		)				->  dict:







     """simple docstring"""
     _A				=       'https://api.nasa.gov/planetary/apod'
     return requests.get(__UpperCamelCase       ,						params={'api_key': api_key}		).json()

def 				lowerCamelCase_   (   __UpperCamelCase				:					str       ,						__UpperCamelCase				:					str = "."		)				->  dict:







     """simple docstring"""
     _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 				lowerCamelCase_   (   __UpperCamelCase				:					str		)				->  dict:







     """simple docstring"""
     _A				=       'https://images-api.nasa.gov/search'
     return requests.get(__UpperCamelCase       ,						params={'q': query}		).json()


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









'''simple docstring'''
from typing import TYPE_CHECKING

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


lowerCAmelCase  =				{
    """configuration_segformer""": ["""SEGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """SegformerConfig""", """SegformerOnnxConfig"""]
}

try:
    if not is_vision_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCAmelCase  =				["""SegformerFeatureExtractor"""]
    lowerCAmelCase  =				["""SegformerImageProcessor"""]

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCAmelCase  =				[
        """SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
        """SegformerDecodeHead""",
        """SegformerForImageClassification""",
        """SegformerForSemanticSegmentation""",
        """SegformerLayer""",
        """SegformerModel""",
        """SegformerPreTrainedModel""",
    ]

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCAmelCase  =				[
        """TF_SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
        """TFSegformerDecodeHead""",
        """TFSegformerForImageClassification""",
        """TFSegformerForSemanticSegmentation""",
        """TFSegformerModel""",
        """TFSegformerPreTrainedModel""",
    ]


if TYPE_CHECKING:
    from .configuration_segformer import SEGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, SegformerConfig, SegformerOnnxConfig

    try:
        if not is_vision_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .feature_extraction_segformer import SegformerFeatureExtractor
        from .image_processing_segformer import SegformerImageProcessor

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_segformer import (
            SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
            SegformerDecodeHead,
            SegformerForImageClassification,
            SegformerForSemanticSegmentation,
            SegformerLayer,
            SegformerModel,
            SegformerPreTrainedModel,
        )
    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_segformer import (
            TF_SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
            TFSegformerDecodeHead,
            TFSegformerForImageClassification,
            TFSegformerForSemanticSegmentation,
            TFSegformerModel,
            TFSegformerPreTrainedModel,
        )

else:
    import sys

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





