



'''simple docstring'''




import importlib
import math
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Tuple, Union

import flax
import jax.numpy as jnp

from ..utils import BaseOutput


a   :			Any					  =    """scheduler_config.json"""

class 			UpperCamelCase_ (							__magic_name__			):
	lowercase          = 1
	lowercase          = 2
	lowercase          = 3
	lowercase          = 4
	lowercase          = 5




@dataclass
class 			UpperCamelCase_ (							__magic_name__			):
	lowercase          = 42




class 			UpperCamelCase_ :
	lowercase          = SCHEDULER_CONFIG_NAME
	lowercase          = ['dtype']
	lowercase          = []
	lowercase          = True





	@classmethod
	def  _lowercase(  cls		,		A = None		,		A = None		,		A=False		,		**A		,		)			->			List[str]:
								UpperCAmelCase     ,     UpperCAmelCase     :		Optional[int]							     =		cls.load_config(
								    pretrained_model_name_or_path=A		,		subfolder=A		,		return_unused_kwargs=A		,		**A		,		)
								UpperCAmelCase     ,     UpperCAmelCase     :		str							     =		cls.from_config(A		,		return_unused_kwargs=A		,		**A    )

								if hasattr(A		,		"""create_state"""    ) and getattr(A		,		"""has_state"""		,		A    ):
															UpperCAmelCase     :		List[str]							     =		scheduler.create_state()

								if return_unused_kwargs:
															return scheduler, state, unused_kwargs

								return scheduler, state





	def  _lowercase(  self		,		A		,		A = False		,		**A    )			->			int:
								self.save_config(save_directory=A		,		push_to_hub=A		,		**A    )





	@property
	def  _lowercase(  self    )			->			Union[str, Any]:
								return self._get_compatibles()






	@classmethod
	def  _lowercase(  cls    )			->			int:
								UpperCAmelCase     :		Union[str, Any]							     =		list(set([cls.__name__] + cls._compatibles    )    )
								UpperCAmelCase     :		str							     =		importlib.import_module(__name__.split("""."""    )[0]    )
								UpperCAmelCase     :		Optional[int]							     =		[
								    getattr(A		,		A    ) for c in compatible_classes_str if hasattr(A		,		A    )
								]
								return compatible_classes




def       __lowerCamelCase  (					_lowercase	,				_lowercase		)      ->			jnp.ndarray:
							assert len(_lowercase		) >= x.ndim
							return jnp.broadcast_to(x.reshape(x.shape + (1,) * (len(_lowercase		) - x.ndim)		)	,				_lowercase		)



def       __lowerCamelCase  (					_lowercase	,				_lowercase=0.999	,				_lowercase=jnp.floataa		)      ->			jnp.ndarray:

							def alpha_bar(_lowercase		):
														return math.cos((time_step + 0.008) / 1.008 * math.pi / 2		) ** 2

							UpperCAmelCase     :		Tuple							     =		[]
							for i in range(_lowercase		):
														UpperCAmelCase     :		int							     =		i / num_diffusion_timesteps
														UpperCAmelCase     :		Optional[int]							     =		(i + 1) / num_diffusion_timesteps
														betas.append(min(1 - alpha_bar(_lowercase		) / alpha_bar(_lowercase		)	,				_lowercase		)		)
							return jnp.array(_lowercase	,				dtype=_lowercase		)

@flax.struct.dataclass
class 			UpperCamelCase_ :
	lowercase          = 42
	lowercase          = 42
	lowercase          = 42





	@classmethod
	def  _lowercase(  cls		,		A    )			->			List[str]:
								UpperCAmelCase     :		Dict							     =		scheduler.config

								if config.trained_betas is not None:
															UpperCAmelCase     :		Union[str, Any]							     =		jnp.asarray(config.trained_betas		,		dtype=scheduler.dtype    )
								elif config.beta_schedule == "linear":
															UpperCAmelCase     :		Tuple							     =		jnp.linspace(config.beta_start		,		config.beta_end		,		config.num_train_timesteps		,		dtype=scheduler.dtype    )
								elif config.beta_schedule == "scaled_linear":
															# this schedule is very specific to the latent diffusion model.
															UpperCAmelCase     :		Tuple							     =		(
															    jnp.linspace(
															        config.beta_start**0.5		,		config.beta_end**0.5		,		config.num_train_timesteps		,		dtype=scheduler.dtype    )
															    ** 2
															)
								elif config.beta_schedule == "squaredcos_cap_v2":
															# Glide cosine schedule
															UpperCAmelCase     :		List[str]							     =		betas_for_alpha_bar(config.num_train_timesteps		,		dtype=scheduler.dtype    )
								else:
															raise NotImplementedError(
															    f'''beta_schedule {config.beta_schedule} is not implemented for scheduler {scheduler.__class__.__name__}'''    )

								UpperCAmelCase     :		Optional[int]							     =		1.0 - betas

								UpperCAmelCase     :		List[str]							     =		jnp.cumprod(A		,		axis=0    )

								return cls(
								    alphas=A		,		betas=A		,		alphas_cumprod=A		,		)




def       __lowerCamelCase  (					_lowercase	,				_lowercase	,				_lowercase	,				_lowercase		)      ->			List[Any]:
							UpperCAmelCase     :		List[str]							     =		state.alphas_cumprod

							UpperCAmelCase     :		Optional[Any]							     =		alphas_cumprod[timesteps] ** 0.5
							UpperCAmelCase     :		Optional[Any]							     =		sqrt_alpha_prod.flatten()
							UpperCAmelCase     :		Tuple							     =		broadcast_to_shape_from_left(_lowercase	,				original_samples.shape		)

							UpperCAmelCase     :		Any							     =		(1 - alphas_cumprod[timesteps]) ** 0.5
							UpperCAmelCase     :		List[Any]							     =		sqrt_one_minus_alpha_prod.flatten()
							UpperCAmelCase     :		Dict							     =		broadcast_to_shape_from_left(_lowercase	,				original_samples.shape		)

							return sqrt_alpha_prod, sqrt_one_minus_alpha_prod



def       __lowerCamelCase  (					_lowercase	,				_lowercase	,				_lowercase	,				_lowercase		)      ->			Optional[int]:
							UpperCAmelCase     ,     UpperCAmelCase     :		Optional[Any]							     =		get_sqrt_alpha_prod(_lowercase	,				_lowercase	,				_lowercase	,				_lowercase		)
							UpperCAmelCase     :		Tuple							     =		sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
							return noisy_samples



def       __lowerCamelCase  (					_lowercase	,				_lowercase	,				_lowercase	,				_lowercase		)      ->			Optional[int]:
							UpperCAmelCase     ,     UpperCAmelCase     :		str							     =		get_sqrt_alpha_prod(_lowercase	,				_lowercase	,				_lowercase	,				_lowercase		)
							UpperCAmelCase     :		Any							     =		sqrt_alpha_prod * noise - sqrt_one_minus_alpha_prod * sample
							return velocity






'''simple docstring'''




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  (					)      ->			Any:
							raise RuntimeError("""CUDA out of memory."""		)

class 			UpperCamelCase_ (							nn.Module			):





	def __init__(  self    )			->			Any:
								super().__init__()
								UpperCAmelCase     :		Tuple							     =		nn.Linear(3		,		4    )
								UpperCAmelCase     :		Tuple							     =		nn.BatchNormad(4    )
								UpperCAmelCase     :		int							     =		nn.Linear(4		,		5    )






	def  _lowercase(  self		,		A    )			->			Any:
								return self.lineara(self.batchnorm(self.lineara(A    )    )    )




class 			UpperCamelCase_ (							unittest.TestCase			):





	def  _lowercase(  self    )			->			Optional[Any]:
								UpperCAmelCase     :		Optional[int]							     =		[]

								@find_executable_batch_size(starting_batch_size=128    )
								def mock_training_loop_function(A    ):
															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  _lowercase(  self    )			->			Any:
								UpperCAmelCase     :		Optional[Any]							     =		[]

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

								UpperCAmelCase     ,     UpperCAmelCase     :		Optional[int]							     =		mock_training_loop_function("""hello"""    )
								self.assertListEqual(A		,		[128, 64, 32, 16, 8]    )
								self.assertListEqual([bs, arga]		,		[8, """hello"""]    )





	def  _lowercase(  self    )			->			Any:
								@find_executable_batch_size(starting_batch_size=0    )
								def mock_training_loop_function(A    ):
															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  _lowercase(  self    )			->			Optional[int]:
								@find_executable_batch_size(starting_batch_size=16    )
								def mock_training_loop_function(A    ):
															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  _lowercase(  self    )			->			Optional[Any]:
								@find_executable_batch_size(starting_batch_size=128    )
								def mock_training_loop_function(A		,		A		,		A    ):
															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  _lowercase(  self    )			->			int:
								@find_executable_batch_size(starting_batch_size=16    )
								def mock_training_loop_function(A    ):
															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  _lowercase(  self    )			->			Optional[int]:
								UpperCAmelCase     :		Optional[Any]							     =		torch.cuda.memory_allocated()
								UpperCAmelCase     :		List[str]							     =		ModelForTest()
								model.cuda()
								self.assertGreater(torch.cuda.memory_allocated()		,		A    )
								UpperCAmelCase     :		Tuple							     =		release_memory(A    )
								self.assertEqual(torch.cuda.memory_allocated()		,		A    )

