


from typing import TYPE_CHECKING

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


_lowercase       :      Union[str, Any]	=	{"configuration_deit": ["DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "DeiTConfig", "DeiTOnnxConfig"]}

try:
    if not is_vision_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _lowercase       :      Union[str, Any]	=	["DeiTFeatureExtractor"]
    _lowercase       :      Dict	=	["DeiTImageProcessor"]

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _lowercase       :      Optional[int]	=	[
        "DEIT_PRETRAINED_MODEL_ARCHIVE_LIST",
        "DeiTForImageClassification",
        "DeiTForImageClassificationWithTeacher",
        "DeiTForMaskedImageModeling",
        "DeiTModel",
        "DeiTPreTrainedModel",
    ]

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _lowercase       :      Optional[Any]	=	[
        "TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST",
        "TFDeiTForImageClassification",
        "TFDeiTForImageClassificationWithTeacher",
        "TFDeiTForMaskedImageModeling",
        "TFDeiTModel",
        "TFDeiTPreTrainedModel",
    ]


if TYPE_CHECKING:
    from .configuration_deit import DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP, DeiTConfig, DeiTOnnxConfig

    try:
        if not is_vision_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .feature_extraction_deit import DeiTFeatureExtractor
        from .image_processing_deit import DeiTImageProcessor

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_deit import (
            DEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
            DeiTForImageClassification,
            DeiTForImageClassificationWithTeacher,
            DeiTForMaskedImageModeling,
            DeiTModel,
            DeiTPreTrainedModel,
        )

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_deit import (
            TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
            TFDeiTForImageClassification,
            TFDeiTForImageClassificationWithTeacher,
            TFDeiTForMaskedImageModeling,
            TFDeiTModel,
            TFDeiTPreTrainedModel,
        )


else:
    import sys

    _lowercase       :      Tuple	=	_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)







from __future__ import annotations

import requests





def   _lowerCAmelCase				(   UpperCamelCase__:     str						) ->   dict:





	"""simple docstring"""







	A								=				f'https://hacker-news.firebaseio.com/v0/item/{story_id}.json?print=pretty'
	return requests.get(UpperCamelCase__						).json()





def   _lowerCAmelCase				(   UpperCamelCase__:     int = 10						) ->   list[dict]:





	"""simple docstring"""







	A								=				"""https://hacker-news.firebaseio.com/v0/topstories.json?print=pretty"""
	A								=				requests.get(UpperCamelCase__						).json()[:max_stories]
	return [get_hackernews_story(UpperCamelCase__						) for story_id in story_ids]





def   _lowerCAmelCase				(   UpperCamelCase__:     int = 10						) ->   str:





	"""simple docstring"""







	A								=				hackernews_top_stories(UpperCamelCase__						)
	return "\n".join("""* [{title}]({url})""".format(**UpperCamelCase__						) for story in stories						)


if __name__ == "__main__":
				print(hackernews_top_stories_as_markdown())



