





'''simple docstring'''

import inspect
import os
import unittest
from dataclasses import dataclass

import torch

from accelerate import Accelerator, DistributedDataParallelKwargs, GradScalerKwargs
from accelerate.state import AcceleratorState
from accelerate.test_utils import execute_subprocess_async, require_cuda, require_multi_gpu
from accelerate.utils import KwargsHandler



@dataclass
class 		lowerCamelCase__      (						_SCREAMING_SNAKE_CASE     ):




					'''simple docstring'''



					A_						    =						0
					A_						    =						False
					A_						    =						3.0






class 		lowerCamelCase__      (						unittest.TestCase     ):




					'''simple docstring'''



					def 						__UpperCAmelCase     (  self				:	Union[str, Any]					)  ->       Optional[Any]:



						'''simple docstring'''





						self.assertDictEqual(MockClass().to_kwargs()     ,  {}					)
						self.assertDictEqual(MockClass(a=2					).to_kwargs()     ,  {'a': 2}					)
						self.assertDictEqual(MockClass(a=2     ,  b=A_					).to_kwargs()     ,  {'a': 2, 'b': True}					)
						self.assertDictEqual(MockClass(a=2     ,  c=2.25					).to_kwargs()     ,  {'a': 2, 'c': 2.25}					)




					@require_cuda
					def 						__UpperCAmelCase     (  self				:	Optional[Any]					)  ->       int:



						'''simple docstring'''





						_lowercase					:       Optional[int]       =		GradScalerKwargs(init_scale=1024     ,  growth_factor=2					)
						AcceleratorState._reset_state()
						_lowercase					:       Tuple       =		Accelerator(mixed_precision='fp16'     ,  kwargs_handlers=[scaler_handler]					)
						print(accelerator.use_fpaa					)
						_lowercase					:       Union[str, Any]       =		accelerator.scaler

						# Check the kwargs have been applied
						self.assertEqual(scaler._init_scale     ,  10_24.0					)
						self.assertEqual(scaler._growth_factor     ,  2.0					)

						# Check the other values are at the default
						self.assertEqual(scaler._backoff_factor     ,  0.5					)
						self.assertEqual(scaler._growth_interval     ,  2000					)
						self.assertEqual(scaler._enabled     ,  A_					)




					@require_multi_gpu
					def 						__UpperCAmelCase     (  self				:	Optional[int]					)  ->       Optional[int]:



						'''simple docstring'''





						_lowercase					:       List[str]       =		['torchrun', F'''--nproc_per_node={torch.cuda.device_count()}''', inspect.getfile(self.__class__					)]
						execute_subprocess_async(A_     ,  env=os.environ.copy()					)


if __name__ == "__main__":
		_A				:    Optional[Any]     						=DistributedDataParallelKwargs(bucket_cap_mb=1_5, find_unused_parameters=True)
		_A				:    Optional[Any]     						=Accelerator(kwargs_handlers=[ddp_scaler])
		_A				:    List[str]     						=torch.nn.Linear(1_0_0, 2_0_0)
		_A				:    Optional[Any]     						=accelerator.prepare(model)

		# Check the values changed in kwargs
		_A				:    Dict     						=""
		_A				:    Any     						=model.bucket_bytes_cap // (1_0_2_4 * 1_0_2_4)
		if observed_bucket_cap_map != 1_5:
				error_msg += F"Kwargs badly passed, should have `15` but found {observed_bucket_cap_map}.\n"
		if model.find_unused_parameters is not True:
				error_msg += F"Kwargs badly passed, should have `True` but found {model.find_unused_parameters}.\n"

		# Check the values of the defaults
		if model.dim != 0:
				error_msg += F"Default value not respected, should have `0` but found {model.dim}.\n"
		if model.broadcast_buffers is not True:
				error_msg += F"Default value not respected, should have `True` but found {model.broadcast_buffers}.\n"
		if model.gradient_as_bucket_view is not False:
				error_msg += F"Default value not respected, should have `False` but found {model.gradient_as_bucket_view}.\n"

		# Raise error at the end to make sure we don't stop at the first failure.
		if len(error_msg) > 0:
				raise ValueError(error_msg)





'''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())
