





'''simple docstring'''

from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


_A				:    Union[str, Any]     						={'''configuration_reformer''': ['''REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ReformerConfig''']}

try:
		if not is_sentencepiece_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_A				:    Dict     						=['''ReformerTokenizer''']

try:
		if not is_tokenizers_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_A				:    Union[str, Any]     						=['''ReformerTokenizerFast''']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_A				:    str     						=[
		    '''REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''ReformerAttention''',
		    '''ReformerForMaskedLM''',
		    '''ReformerForQuestionAnswering''',
		    '''ReformerForSequenceClassification''',
		    '''ReformerLayer''',
		    '''ReformerModel''',
		    '''ReformerModelWithLMHead''',
		    '''ReformerPreTrainedModel''',
		]


if TYPE_CHECKING:
		from .configuration_reformer import REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, ReformerConfig

		try:
				if not is_sentencepiece_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_reformer import ReformerTokenizer

		try:
				if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_reformer_fast import ReformerTokenizerFast

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_reformer import (
				    REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
				    ReformerAttention,
				    ReformerForMaskedLM,
				    ReformerForQuestionAnswering,
				    ReformerForSequenceClassification,
				    ReformerLayer,
				    ReformerModel,
				    ReformerModelWithLMHead,
				    ReformerPreTrainedModel,
				)

else:
		import sys

		_A				:    Union[str, Any]     						=_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)





'''simple docstring'''

from __future__ import annotations

import requests







def 						__UpperCamelCase				(							_lowercase					) ->			dict:
	_lowercase					:       Optional[int]       =		f'''https://hacker-news.firebaseio.com/v0/item/{story_id}.json?print=pretty'''
	return requests.get(_lowercase					).json()







def 						__UpperCamelCase				(							_lowercase = 10					) ->			list[dict]:
	_lowercase					:       Union[str, Any]       =		'https://hacker-news.firebaseio.com/v0/topstories.json?print=pretty'
	_lowercase					:       Optional[Any]       =		requests.get(_lowercase					).json()[:max_stories]
	return [get_hackernews_story(_lowercase					) for story_id in story_ids]







def 						__UpperCamelCase				(							_lowercase = 10					) ->			str:
	_lowercase					:       Tuple       =		hackernews_top_stories(_lowercase					)
	return "\n".join('* [{title}]({url})'.format(**_lowercase					) for story in stories					)


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