

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__							(		)	->					Optional[Any]:




				'''simple docstring'''





				raise RuntimeError("CUDA out of memory."					)

class     a__  (    nn.Module	):
			"""simple docstring"""







			def __init__(			self				)     ->  Dict:
							'''simple docstring'''






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


			def    UpperCamelCase						(			self					,					lowercase				)     ->  int:
							'''simple docstring'''






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

class     a__  (    unittest.TestCase	):
			"""simple docstring"""







			def    UpperCamelCase						(			self				)     ->  Optional[int]:
							'''simple docstring'''






							A__						=	[]

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

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


			def    UpperCamelCase						(			self				)     ->  Union[str, Any]:
							'''simple docstring'''






							A__						=	[]

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

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


			def    UpperCamelCase						(			self				)     ->  Optional[Any]:
							'''simple docstring'''






							@find_executable_batch_size(starting_batch_size=0				)
							def mock_training_loop_function(lowercase				):
											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    UpperCamelCase						(			self				)     ->  List[str]:
							'''simple docstring'''






							@find_executable_batch_size(starting_batch_size=16				)
							def mock_training_loop_function(lowercase				):
											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    UpperCamelCase						(			self				)     ->  List[str]:
							'''simple docstring'''






							@find_executable_batch_size(starting_batch_size=128				)
							def mock_training_loop_function(lowercase					,					lowercase					,					lowercase				):
											if batch_size != 8:
															raise raise_fake_out_of_memory()

							with self.assertRaises(lowercase				) 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				)     ->  Dict:
							'''simple docstring'''






							@find_executable_batch_size(starting_batch_size=16				)
							def mock_training_loop_function(lowercase				):
											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    UpperCamelCase						(			self				)     ->  Tuple:
							'''simple docstring'''






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








import flax.linen as nn
import jax
import jax.numpy as jnp

class    lowerCAmelCase							(					nn.Module			):
 UpperCAmelCase__													=		42
 UpperCAmelCase__													=		jnp.floataa
 def      A_	(      self  :      Any	)			->	Any:
     lowerCamelCase__    :      str					 =		nn.Conv(
         self.out_channels			,     kernel_size=(3, 3)			,     strides=(1, 1)			,     padding=((1, 1), (1, 1))			,     dtype=self.dtype			,     )







 def __call__(      self  :      int			,     UpperCAmelCase  :      Dict	)			->	Optional[Any]:
     lowerCamelCase__ ,							lowerCamelCase__ ,							lowerCamelCase__ ,							lowerCamelCase__    :      str					 =		hidden_states.shape
     lowerCamelCase__    :      Union[str, Any]					 =		jax.image.resize(
         UpperCAmelCase			,     shape=(batch, height * 2, width * 2, channels)			,     method='nearest'			,     )
     lowerCamelCase__    :      Optional[Any]					 =		self.conv(UpperCAmelCase	)
     return hidden_states


class    lowerCAmelCase							(					nn.Module			):
 UpperCAmelCase__													=		42
 UpperCAmelCase__													=		jnp.floataa
 def      A_	(      self  :      List[str]	)			->	int:
     lowerCamelCase__    :      Tuple					 =		nn.Conv(
         self.out_channels			,     kernel_size=(3, 3)			,     strides=(2, 2)			,     padding=((1, 1), (1, 1))			,     dtype=self.dtype			,     )







 def __call__(      self  :      str			,     UpperCAmelCase  :      Union[str, Any]	)			->	Optional[Any]:
     # pad = ((0, 0), (0, 1), (0, 1), (0, 0))  # pad height and width dim
     # hidden_states = jnp.pad(hidden_states, pad_width=pad)
     lowerCamelCase__    :      Optional[Any]					 =		self.conv(UpperCAmelCase	)
     return hidden_states


class    lowerCAmelCase							(					nn.Module			):
 UpperCAmelCase__													=		42
 UpperCAmelCase__													=		None
 UpperCAmelCase__													=		0.0
 UpperCAmelCase__													=		None
 UpperCAmelCase__													=		jnp.floataa
 def      A_	(      self  :      List[str]	)			->	Union[str, Any]:
     lowerCamelCase__    :      Optional[Any]					 =		self.in_channels if self.out_channels is None else self.out_channels

     lowerCamelCase__    :      Tuple					 =		nn.GroupNorm(num_groups=32			,     epsilon=1e-5	)
     lowerCamelCase__    :      int					 =		nn.Conv(
         UpperCAmelCase			,     kernel_size=(3, 3)			,     strides=(1, 1)			,     padding=((1, 1), (1, 1))			,     dtype=self.dtype			,     )

     lowerCamelCase__    :      Union[str, Any]					 =		nn.Dense(UpperCAmelCase			,     dtype=self.dtype	)

     lowerCamelCase__    :      Union[str, Any]					 =		nn.GroupNorm(num_groups=32			,     epsilon=1e-5	)
     lowerCamelCase__    :      List[Any]					 =		nn.Dropout(self.dropout_prob	)
     lowerCamelCase__    :      Tuple					 =		nn.Conv(
         UpperCAmelCase			,     kernel_size=(3, 3)			,     strides=(1, 1)			,     padding=((1, 1), (1, 1))			,     dtype=self.dtype			,     )

     lowerCamelCase__    :      Optional[Any]					 =		self.in_channels != out_channels if self.use_nin_shortcut is None else self.use_nin_shortcut

     lowerCamelCase__    :      Union[str, Any]					 =		None
     if use_nin_shortcut:
         lowerCamelCase__    :      Dict					 =		nn.Conv(
             UpperCAmelCase			,     kernel_size=(1, 1)			,     strides=(1, 1)			,     padding='VALID'			,     dtype=self.dtype			,     )







 def __call__(      self  :      Optional[Any]			,     UpperCAmelCase  :      str			,     UpperCAmelCase  :      Union[str, Any]			,     UpperCAmelCase  :      int=True	)			->	Optional[int]:
     lowerCamelCase__    :      Union[str, Any]					 =		hidden_states
     lowerCamelCase__    :      List[Any]					 =		self.norma(UpperCAmelCase	)
     lowerCamelCase__    :      List[Any]					 =		nn.swish(UpperCAmelCase	)
     lowerCamelCase__    :      Any					 =		self.conva(UpperCAmelCase	)

     lowerCamelCase__    :      Optional[Any]					 =		self.time_emb_proj(nn.swish(UpperCAmelCase	)	)
     lowerCamelCase__    :      List[str]					 =		jnp.expand_dims(jnp.expand_dims(UpperCAmelCase			,     1	)			,     1	)
     lowerCamelCase__    :      List[str]					 =		hidden_states + temb

     lowerCamelCase__    :      Optional[Any]					 =		self.norma(UpperCAmelCase	)
     lowerCamelCase__    :      List[str]					 =		nn.swish(UpperCAmelCase	)
     lowerCamelCase__    :      Optional[int]					 =		self.dropout(UpperCAmelCase			,     UpperCAmelCase	)
     lowerCamelCase__    :      str					 =		self.conva(UpperCAmelCase	)

     if self.conv_shortcut is not None:
         lowerCamelCase__    :      Dict					 =		self.conv_shortcut(UpperCAmelCase	)

     return hidden_states + residual





