
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    a      ( )     ->Any:




			'''simple docstring'''


			raise RuntimeError('''CUDA out of memory.'''	)





class     lowerCamelCase      (       nn.Module					):







	def __init__(     self      :Dict			)		->      Tuple:







				"""simple docstring"""






				super().__init__()
				SCREAMING_SNAKE_CASE     						= nn.Linear(3   ,     4			)
				SCREAMING_SNAKE_CASE     						= nn.BatchNormad(4			)
				SCREAMING_SNAKE_CASE     						= nn.Linear(4   ,     5			)







	def  snake_case__     (     self      :int   ,     lowercase      :int			)		->      str:







				"""simple docstring"""






				return self.lineara(self.batchnorm(self.lineara(lowercase			)			)			)





class     lowerCamelCase      (       unittest.TestCase					):







	def  snake_case__     (     self      :Dict			)		->      Optional[int]:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= []

				@find_executable_batch_size(starting_batch_size=1_2_8			)
				def mock_training_loop_function(lowercase      :Union[str, Any]			):
							nonlocal batch_sizes
							batch_sizes.append(lowercase			)
							if batch_size != 8:
										raise_fake_out_of_memory()

				mock_training_loop_function()
				self.assertListEqual(lowercase   ,     [1_2_8, 6_4, 3_2, 1_6, 8]			)







	def  snake_case__     (     self      :Dict			)		->      Union[str, Any]:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= []

				@find_executable_batch_size(starting_batch_size=1_2_8			)
				def mock_training_loop_function(lowercase      :int   ,     lowercase      :int			):
							nonlocal batch_sizes
							batch_sizes.append(lowercase			)
							if batch_size != 8:
										raise_fake_out_of_memory()
							return batch_size, arga

				SCREAMING_SNAKE_CASE						,							SCREAMING_SNAKE_CASE     						= mock_training_loop_function('''hello'''			)
				self.assertListEqual(lowercase   ,     [1_2_8, 6_4, 3_2, 1_6, 8]			)
				self.assertListEqual([bs, arga]   ,     [8, '''hello''']			)







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







				"""simple docstring"""






				@find_executable_batch_size(starting_batch_size=0			)
				def mock_training_loop_function(lowercase      :List[Any]			):
							pass

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







	def  snake_case__     (     self      :Any			)		->      int:







				"""simple docstring"""






				@find_executable_batch_size(starting_batch_size=1_6			)
				def mock_training_loop_function(lowercase      :Dict			):
							if batch_size > 0:
										raise_fake_out_of_memory()
							pass

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







	def  snake_case__     (     self      :int			)		->      Optional[Any]:







				"""simple docstring"""






				@find_executable_batch_size(starting_batch_size=1_2_8			)
				def mock_training_loop_function(lowercase      :Tuple   ,     lowercase      :List[str]   ,     lowercase      :Optional[int]			):
							if batch_size != 8:
										raise raise_fake_out_of_memory()

				with self.assertRaises(lowercase			) as cm:
							mock_training_loop_function(1_2_8   ,     '''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  snake_case__     (     self      :List[Any]			)		->      Dict:







				"""simple docstring"""






				@find_executable_batch_size(starting_batch_size=1_6			)
				def mock_training_loop_function(lowercase      :int			):
							raise ValueError('''Oops, we had an error!'''			)

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







	@require_cuda
	def  snake_case__     (     self      :Tuple			)		->      Optional[int]:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= torch.cuda.memory_allocated()
				SCREAMING_SNAKE_CASE     						= ModelForTest()
				model.cuda()
				self.assertGreater(torch.cuda.memory_allocated()   ,     lowercase			)
				SCREAMING_SNAKE_CASE     						= release_memory(lowercase			)
				self.assertEqual(torch.cuda.memory_allocated()   ,     lowercase			)

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


__lowerCAmelCase					   =       {
    'configuration_bloom': ['BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP', 'BloomConfig', 'BloomOnnxConfig'],
}
try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__lowerCAmelCase					   =       ['BloomTokenizerFast']

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__lowerCAmelCase					   =       [
							    'BLOOM_PRETRAINED_MODEL_ARCHIVE_LIST',
							    'BloomForCausalLM',
							    'BloomModel',
							    'BloomPreTrainedModel',
							    'BloomForSequenceClassification',
							    'BloomForTokenClassification',
							    'BloomForQuestionAnswering',
							]

if TYPE_CHECKING:
							from .configuration_bloom import BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP, BloomConfig, BloomOnnxConfig

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_bloom_fast import BloomTokenizerFast

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_bloom import (
														    BLOOM_PRETRAINED_MODEL_ARCHIVE_LIST,
														    BloomForCausalLM,
														    BloomForQuestionAnswering,
														    BloomForSequenceClassification,
														    BloomForTokenClassification,
														    BloomModel,
														    BloomPreTrainedModel,
														)

else:
							import sys

							__lowerCAmelCase					   =       _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)