"""simple docstring"""



__magic_name__     					=       """
# Installazione di Transformers
! pip install transformers datasets
# Per installare dalla fonte invece dell'ultima versione rilasciata, commenta il comando sopra e
# rimuovi la modalità commento al comando seguente.
# ! pip install git+https://github.com/huggingface/transformers.git
"""

__magic_name__     					=       [{"""type""": """code""", """content""": INSTALL_CONTENT}]
__magic_name__     					=       {
    """{processor_class}""": """FakeProcessorClass""",
    """{model_class}""": """FakeModelClass""",
    """{object_class}""": """FakeObjectClass""",
}




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						(   )		->						List[Any]:


  '''simple docstring'''






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



class    _snake_case (						nn.Module				):


 def __init__(       self):


   '''simple docstring'''




   super().__init__()
   lowercase__	: Optional[Any]     		=   nn.Linear(3       ,	4)
   lowercase__	: Union[str, Any]     		=   nn.BatchNormad(4)
   lowercase__	: str     		=   nn.Linear(4       ,	5)



 def   lowercase__  (       self       ,	SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   return self.lineara(self.batchnorm(self.lineara(SCREAMING_SNAKE_CASE_)))



class    _snake_case (						unittest.TestCase				):


 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: List[str]     		=   []

   @find_executable_batch_size(starting_batch_size=1_28)
   def mock_training_loop_function(SCREAMING_SNAKE_CASE_):
     nonlocal batch_sizes
     batch_sizes.append(SCREAMING_SNAKE_CASE_)
     if batch_size != 8:
       raise_fake_out_of_memory()

   mock_training_loop_function()
   self.assertListEqual(SCREAMING_SNAKE_CASE_       ,	[1_28, 64, 32, 16, 8])


 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: int     		=   []

   @find_executable_batch_size(starting_batch_size=1_28)
   def mock_training_loop_function(SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_):
     nonlocal batch_sizes
     batch_sizes.append(SCREAMING_SNAKE_CASE_)
     if batch_size != 8:
       raise_fake_out_of_memory()
     return batch_size, arga

   lowercase__		,     lowercase__	: int     		=   mock_training_loop_function("""hello""")
   self.assertListEqual(SCREAMING_SNAKE_CASE_       ,	[1_28, 64, 32, 16, 8])
   self.assertListEqual([bs, arga]       ,	[8, """hello"""])


 def   lowercase__  (       self):


   '''simple docstring'''




   @find_executable_batch_size(starting_batch_size=0)
   def mock_training_loop_function(SCREAMING_SNAKE_CASE_):
     pass

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


 def   lowercase__  (       self):


   '''simple docstring'''




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

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


 def   lowercase__  (       self):


   '''simple docstring'''




   @find_executable_batch_size(starting_batch_size=1_28)
   def mock_training_loop_function(SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_):
     if batch_size != 8:
       raise raise_fake_out_of_memory()

   with self.assertRaises(SCREAMING_SNAKE_CASE_) as cm:
     mock_training_loop_function(1_28       ,	"""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   lowercase__  (       self):


   '''simple docstring'''




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

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



 @require_cuda
 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: str     		=   torch.cuda.memory_allocated()
   lowercase__	: str     		=   ModelForTest()
   model.cuda()
   self.assertGreater(torch.cuda.memory_allocated()       ,	SCREAMING_SNAKE_CASE_)
   lowercase__	: Tuple     		=   release_memory(SCREAMING_SNAKE_CASE_)
   self.assertEqual(torch.cuda.memory_allocated()       ,	SCREAMING_SNAKE_CASE_)






