





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__    (							)      ->				Dict:
   raise RuntimeError('''CUDA out of memory.'''       )

class 		SCREAMING_SNAKE_CASE__				(       nn.Module  ):


      def __init__(self       :      List[Any]      ):





         """simple docstring"""



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


      def    a (self       :      Optional[Any]				,			a__       :      List[str]      ):





         """simple docstring"""



         return self.lineara(self.batchnorm(self.lineara(a__      )      )      )






class 		SCREAMING_SNAKE_CASE__				(       unittest.TestCase  ):


      def    a (self       :      Any      ):





         """simple docstring"""



         __snake_case							       =   []

         @find_executable_batch_size(starting_batch_size=128      )
         def mock_training_loop_function(a__       :      Any      ):
            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    a (self       :      Tuple      ):





         """simple docstring"""



         __snake_case							       =   []

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

         __snake_case       ,	__snake_case							       =   mock_training_loop_function('''hello'''      )
         self.assertListEqual(a__				,			[128, 64, 32, 16, 8]      )
         self.assertListEqual([bs, arga]				,			[8, '''hello''']      )


      def    a (self       :      List[Any]      ):





         """simple docstring"""



         @find_executable_batch_size(starting_batch_size=0      )
         def mock_training_loop_function(a__       :      Optional[int]      ):
            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    a (self       :      Tuple      ):





         """simple docstring"""



         @find_executable_batch_size(starting_batch_size=16      )
         def mock_training_loop_function(a__       :      str      ):
            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    a (self       :      int      ):





         """simple docstring"""



         @find_executable_batch_size(starting_batch_size=128      )
         def mock_training_loop_function(a__       :      int				,			a__       :      Tuple				,			a__       :      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    a (self       :      Optional[Any]      ):





         """simple docstring"""



         @find_executable_batch_size(starting_batch_size=16      )
         def mock_training_loop_function(a__       :      Optional[Any]      ):
            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    a (self       :      Union[str, Any]      ):





         """simple docstring"""



         __snake_case							       =   torch.cuda.memory_allocated()
         __snake_case							       =   ModelForTest()
         model.cuda()
         self.assertGreater(torch.cuda.memory_allocated()				,			a__      )
         __snake_case							       =   release_memory(a__      )
         self.assertEqual(torch.cuda.memory_allocated()				,			a__      )





"""simple docstring"""




__UpperCamelCase						=      frozenset(
    [
        '''prompt''',
        '''height''',
        '''width''',
        '''guidance_scale''',
        '''negative_prompt''',
        '''prompt_embeds''',
        '''negative_prompt_embeds''',
        '''cross_attention_kwargs''',
    ]
)

__UpperCamelCase						=      frozenset(['''prompt''', '''negative_prompt'''])

__UpperCamelCase						=      frozenset([])

__UpperCamelCase						=      frozenset(['''image'''])

__UpperCamelCase						=      frozenset(
    [
        '''image''',
        '''height''',
        '''width''',
        '''guidance_scale''',
    ]
)

__UpperCamelCase						=      frozenset(['''image'''])

__UpperCamelCase						=      frozenset(
    [
        '''prompt''',
        '''image''',
        '''height''',
        '''width''',
        '''guidance_scale''',
        '''negative_prompt''',
        '''prompt_embeds''',
        '''negative_prompt_embeds''',
    ]
)

__UpperCamelCase						=      frozenset(['''prompt''', '''image''', '''negative_prompt'''])

__UpperCamelCase						=      frozenset(
    [
        # Text guided image variation with an image mask
        '''prompt''',
        '''image''',
        '''mask_image''',
        '''height''',
        '''width''',
        '''guidance_scale''',
        '''negative_prompt''',
        '''prompt_embeds''',
        '''negative_prompt_embeds''',
    ]
)

__UpperCamelCase						=      frozenset(['''prompt''', '''image''', '''mask_image''', '''negative_prompt'''])

__UpperCamelCase						=      frozenset(
    [
        # image variation with an image mask
        '''image''',
        '''mask_image''',
        '''height''',
        '''width''',
        '''guidance_scale''',
    ]
)

__UpperCamelCase						=      frozenset(['''image''', '''mask_image'''])

__UpperCamelCase						=      frozenset(
    [
        '''example_image''',
        '''image''',
        '''mask_image''',
        '''height''',
        '''width''',
        '''guidance_scale''',
    ]
)

__UpperCamelCase						=      frozenset(['''example_image''', '''image''', '''mask_image'''])

__UpperCamelCase						=      frozenset(['''class_labels'''])

__UpperCamelCase						=      frozenset(['''class_labels'''])

__UpperCamelCase						=      frozenset(['''batch_size'''])

__UpperCamelCase						=      frozenset([])

__UpperCamelCase						=      frozenset(['''batch_size'''])

__UpperCamelCase						=      frozenset([])

__UpperCamelCase						=      frozenset(
    [
        '''prompt''',
        '''audio_length_in_s''',
        '''guidance_scale''',
        '''negative_prompt''',
        '''prompt_embeds''',
        '''negative_prompt_embeds''',
        '''cross_attention_kwargs''',
    ]
)

__UpperCamelCase						=      frozenset(['''prompt''', '''negative_prompt'''])
__UpperCamelCase						=      frozenset(['''input_tokens'''])

__UpperCamelCase						=      frozenset(['''input_tokens'''])


