





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






class 					lowercase__ (			nn.Module       ):

   '''simple docstring'''


   def __init__(					self							)				->   int:






       """simple docstring"""
       super().__init__()
       UpperCamelCase__							:       Tuple      =   nn.Linear(3,	4							)
       UpperCamelCase__							:       Tuple      =   nn.BatchNormad(4							)
       UpperCamelCase__							:       Tuple      =   nn.Linear(4,	5							)




   def    UpperCamelCase__						(					self,	__magic_name__							)				->   int:






       """simple docstring"""
       return self.lineara(self.batchnorm(self.lineara(__magic_name__							)							)							)






class 					lowercase__ (			unittest.TestCase       ):

   '''simple docstring'''


   def    UpperCamelCase__						(					self							)				->   List[Any]:






       """simple docstring"""
       UpperCamelCase__							:       List[Any]      =   []

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

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




   def    UpperCamelCase__						(					self							)				->   Any:






       """simple docstring"""
       UpperCamelCase__							:       Optional[int]      =   []

       @find_executable_batch_size(starting_batch_size=128							)
       def mock_training_loop_function(__magic_name__,	__magic_name__							):
           nonlocal batch_sizes
           batch_sizes.append(__magic_name__							)
           if batch_size != 8:
               raise_fake_out_of_memory()
           return batch_size, arga

       UpperCamelCase__     ,UpperCamelCase__							:       Dict      =   mock_training_loop_function('''hello'''							)
       self.assertListEqual(__magic_name__,	[128, 64, 32, 16, 8]							)
       self.assertListEqual([bs, arga],	[8, '''hello''']							)




   def    UpperCamelCase__						(					self							)				->   List[str]:






       """simple docstring"""
       @find_executable_batch_size(starting_batch_size=0							)
       def mock_training_loop_function(__magic_name__							):
           pass

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




   def    UpperCamelCase__						(					self							)				->   Optional[int]:






       """simple docstring"""
       @find_executable_batch_size(starting_batch_size=16							)
       def mock_training_loop_function(__magic_name__							):
           if batch_size > 0:
               raise_fake_out_of_memory()
           pass

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




   def    UpperCamelCase__						(					self							)				->   str:






       """simple docstring"""
       @find_executable_batch_size(starting_batch_size=128							)
       def mock_training_loop_function(__magic_name__,	__magic_name__,	__magic_name__							):
           if batch_size != 8:
               raise raise_fake_out_of_memory()

       with self.assertRaises(__magic_name__							) 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							)				->   Optional[Any]:






       """simple docstring"""
       @find_executable_batch_size(starting_batch_size=16							)
       def mock_training_loop_function(__magic_name__							):
           raise ValueError('''Oops, we had an error!'''							)

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




   @require_cuda
   def    UpperCamelCase__						(					self							)				->   Dict:






       """simple docstring"""
       UpperCamelCase__							:       Dict      =   torch.cuda.memory_allocated()
       UpperCamelCase__							:       int      =   ModelForTest()
       model.cuda()
       self.assertGreater(torch.cuda.memory_allocated(),	__magic_name__							)
       UpperCamelCase__							:       Optional[int]      =   release_memory(__magic_name__							)
       self.assertEqual(torch.cuda.memory_allocated(),	__magic_name__							)








from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCAmelCase_  							=				{
    'configuration_informer': [
        'INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'InformerConfig',
    ],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase_  							=				[
      'INFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
      'InformerForPrediction',
      'InformerModel',
      'InformerPreTrainedModel',
  ]


if TYPE_CHECKING:
  from .configuration_informer import INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, InformerConfig

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_informer import (
        INFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
        InformerForPrediction,
        InformerModel,
        InformerPreTrainedModel,
    )

else:
  import sys

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

