



'''simple docstring'''

import unittest

import torch
from torch import nn

from accelerate.test_utils import require_cuda
from accelerate.utils.memory import find_executable_batch_size, release_memory







def 						__UpperCamelCase				(							) ->			Tuple:
	raise RuntimeError('CUDA out of memory.'					)



class 		lowerCamelCase__      (						nn.Module     ):




	'''simple docstring'''



	def __init__(  self				:	List[str]					)  ->       Union[str, Any]:



		'''simple docstring'''





		super().__init__()
		_lowercase					:       List[Any]       =		nn.Linear(3     ,  4					)
		_lowercase					:       Dict       =		nn.BatchNormad(4					)
		_lowercase					:       Optional[Any]       =		nn.Linear(4     ,  5					)




	def 						__UpperCAmelCase     (  self				:	List[Any]     ,  UpperCamelCase_				:	Optional[int]					)  ->       List[Any]:



		'''simple docstring'''





		return self.lineara(self.batchnorm(self.lineara(_a					)					)					)






class 		lowerCamelCase__      (						unittest.TestCase     ):




	'''simple docstring'''



	def 						__UpperCAmelCase     (  self				:	Tuple					)  ->       int:



		'''simple docstring'''





		_lowercase					:       Any       =		[]

		@find_executable_batch_size(starting_batch_size=128					)
		def mock_training_loop_function(UpperCamelCase_				:	int					):
			nonlocal batch_sizes
			batch_sizes.append(_a					)
			if batch_size != 8:
				raise_fake_out_of_memory()

		mock_training_loop_function()
		self.assertListEqual(_a     ,  [128, 64, 32, 16, 8]					)




	def 						__UpperCAmelCase     (  self				:	Optional[Any]					)  ->       Tuple:



		'''simple docstring'''





		_lowercase					:       Optional[int]       =		[]

		@find_executable_batch_size(starting_batch_size=128					)
		def mock_training_loop_function(UpperCamelCase_				:	Union[str, Any]     ,  UpperCamelCase_				:	List[str]					):
			nonlocal batch_sizes
			batch_sizes.append(_a					)
			if batch_size != 8:
				raise_fake_out_of_memory()
			return batch_size, arga

		_lowercase  ,    _lowercase					:       Optional[Any]       =		mock_training_loop_function('hello'					)
		self.assertListEqual(_a     ,  [128, 64, 32, 16, 8]					)
		self.assertListEqual([bs, arga]     ,  [8, 'hello']					)




	def 						__UpperCAmelCase     (  self				:	Tuple					)  ->       Any:



		'''simple docstring'''





		@find_executable_batch_size(starting_batch_size=0					)
		def mock_training_loop_function(UpperCamelCase_				:	Dict					):
			pass

		with self.assertRaises(_a					) as cm:
			mock_training_loop_function()
			self.assertIn('No executable batch size found, reached zero.'     ,  cm.exception.args[0]					)




	def 						__UpperCAmelCase     (  self				:	List[str]					)  ->       int:



		'''simple docstring'''





		@find_executable_batch_size(starting_batch_size=16					)
		def mock_training_loop_function(UpperCamelCase_				:	Any					):
			if batch_size > 0:
				raise_fake_out_of_memory()
			pass

		with self.assertRaises(_a					) as cm:
			mock_training_loop_function()
			self.assertIn('No executable batch size found, reached zero.'     ,  cm.exception.args[0]					)




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



		'''simple docstring'''





		@find_executable_batch_size(starting_batch_size=128					)
		def mock_training_loop_function(UpperCamelCase_				:	Union[str, Any]     ,  UpperCamelCase_				:	Tuple     ,  UpperCamelCase_				:	Optional[int]					):
			if batch_size != 8:
				raise raise_fake_out_of_memory()

		with self.assertRaises(_a					) as cm:
			mock_training_loop_function(128     ,  'hello'     ,  'world'					)
			self.assertIn('Batch size was passed into `f`'     ,  cm.exception.args[0]					)
			self.assertIn('`f(arg1=\'hello\', arg2=\'world\')'     ,  cm.exception.args[0]					)




	def 						__UpperCAmelCase     (  self				:	Any					)  ->       Dict:



		'''simple docstring'''





		@find_executable_batch_size(starting_batch_size=16					)
		def mock_training_loop_function(UpperCamelCase_				:	Tuple					):
			raise ValueError('Oops, we had an error!'					)

		with self.assertRaises(_a					) as cm:
			mock_training_loop_function()
			self.assertIn('Oops, we had an error!'     ,  cm.exception.args[0]					)




	@require_cuda
	def 						__UpperCAmelCase     (  self				:	Dict					)  ->       str:



		'''simple docstring'''





		_lowercase					:       Any       =		torch.cuda.memory_allocated()
		_lowercase					:       Optional[Any]       =		ModelForTest()
		model.cuda()
		self.assertGreater(torch.cuda.memory_allocated()     ,  _a					)
		_lowercase					:       Optional[Any]       =		release_memory(_a					)
		self.assertEqual(torch.cuda.memory_allocated()     ,  _a					)





'''simple docstring'''

def 						__UpperCamelCase				(							_lowercase,				_lowercase					) ->			list:
	_lowercase					:       List[str]       =		word.split()

	def justify(_lowercase,				_lowercase,				_lowercase					) -> str:
		_lowercase					:       Dict       =		max_width - width
		_lowercase					:       Tuple       =		len(_lowercase					)
		if len(_lowercase					) == 1:
			# if there is only word in line
			# just insert overall_spaces_count for the remainder of line
			return line[0] + " " * overall_spaces_count
		else:
			_lowercase					:       Tuple       =		words_count - 1
			# num_spaces_between_words_list[i] : tells you to insert
			# num_spaces_between_words_list[i] spaces
			# after word on line[i]
			_lowercase					:       str       =		spaces_to_insert_between_words * [
			    overall_spaces_count // spaces_to_insert_between_words
			]
			_lowercase					:       Optional[int]       =		(
			    overall_spaces_count % spaces_to_insert_between_words
			)
			# distribute spaces via round robin to the left words
			for i in range(_lowercase					):
				num_spaces_between_words_list[i] += 1
			_lowercase					:       Union[str, Any]       =		[]
			for i in range(_lowercase					):
				# add the word
				aligned_words_list.append(line[i]					)
				# add the spaces to insert
				aligned_words_list.append(num_spaces_between_words_list[i] * ' '					)
			# just add the last word to the sentence
			aligned_words_list.append(line[-1]					)
			# join the aligned words list to form a justified line
			return "".join(_lowercase					)

	_lowercase					:       str       =		[]
	_lowercase					:       list[str]       =		[]
	_lowercase					:       Union[str, Any]       =		0
	for word in words:
		if width + len(_lowercase					) + len(_lowercase					) <= max_width:
			# keep adding words until we can fill out max_width
			# width = sum of length of all words (without overall_spaces_count)
			# len(word) = length of current word
			# len(line) = number of overall_spaces_count to insert between words
			line.append(_lowercase					)
			width += len(_lowercase					)
		else:
			# justify the line and add it to result
			answer.append(justify(_lowercase,				_lowercase,				_lowercase					)					)
			# reset new line and new width
			_lowercase  ,    _lowercase					:       Optional[Any]       =		[word], len(_lowercase					)
	_lowercase					:       Optional[int]       =		max_width - width - len(_lowercase					)
	answer.append(' '.join(_lowercase					) + (remaining_spaces + 1) * ' '					)
	return answer


if __name__ == "__main__":
		from doctest import testmod

		testmod()
