






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__						( )						->      List[Any]:





    """simple docstring"""

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


class       __lowercase      (	nn.Module  ):







     '''simple docstring'''




     def __init__(				self       :			Optional[int]				):


         """simple docstring"""



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





     def 		lowerCAmelCase_     (				self       :			Tuple ,	UpperCamelCase_       :			str				):


         """simple docstring"""



         return self.lineara(self.batchnorm(self.lineara(UpperCamelCase_				)				)				)


class       __lowercase      (	unittest.TestCase  ):







     '''simple docstring'''




     def 		lowerCAmelCase_     (				self       :			Tuple				):


         """simple docstring"""



         __A         =	[]

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

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




     def 		lowerCAmelCase_     (				self       :			Union[str, Any]				):


         """simple docstring"""



         __A         =	[]

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

         __A    ,	__A         =	mock_training_loop_function("""hello"""				)
         self.assertListEqual(UpperCamelCase_ ,	[128, 64, 32, 16, 8]				)
         self.assertListEqual([bs, arga] ,	[8, """hello"""]				)




     def 		lowerCAmelCase_     (				self       :			List[Any]				):


         """simple docstring"""



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

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




     def 		lowerCAmelCase_     (				self       :			Union[str, Any]				):


         """simple docstring"""



         @find_executable_batch_size(starting_batch_size=16				)
         def mock_training_loop_function(UpperCamelCase_       :			Optional[int]				):
             if batch_size > 0:
                 raise_fake_out_of_memory()
             pass

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




     def 		lowerCAmelCase_     (				self       :			List[Any]				):


         """simple docstring"""



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

         with self.assertRaises(UpperCamelCase_				) 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 		lowerCAmelCase_     (				self       :			Optional[int]				):


         """simple docstring"""



         @find_executable_batch_size(starting_batch_size=16				)
         def mock_training_loop_function(UpperCamelCase_       :			List[str]				):
             raise ValueError("""Oops, we had an error!"""				)

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





     @require_cuda
     def 		lowerCAmelCase_     (				self       :			Any				):


         """simple docstring"""



         __A         =	torch.cuda.memory_allocated()
         __A         =	ModelForTest()
         model.cuda()
         self.assertGreater(torch.cuda.memory_allocated() ,	UpperCamelCase_				)
         __A         =	release_memory(UpperCamelCase_				)
         self.assertEqual(torch.cuda.memory_allocated() ,	UpperCamelCase_				)












from typing import TYPE_CHECKING

from ...utils import _LazyModule


__a      :       Optional[int]				  =   {"tokenization_bertweet": ["BertweetTokenizer"]}


if TYPE_CHECKING:
  from .tokenization_bertweet import BertweetTokenizer

else:
  import sys

  __a      :       int				  =   _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)




