






import math
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, logging, randn_tensor
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin


SCREAMING_SNAKE_CASE__    :    str								=					logging.get_logger(__name__)  # pylint: disable=invalid-name



@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->EulerDiscrete
class 			a_					(    SCREAMING_SNAKE_CASE__    ):
	A						       =   42
	A						       =   None




def lowercase						(     SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE=0.999					,						SCREAMING_SNAKE_CASE="cosine"					,						) ->       Dict:


			'''simple docstring'''







			if alpha_transform_type == "cosine":

						def alpha_bar_fn(SCREAMING_SNAKE_CASE   ):
									return math.cos((t + 0.008) / 1.008 * math.pi / 2   ) ** 2

			elif alpha_transform_type == "exp":

						def alpha_bar_fn(SCREAMING_SNAKE_CASE   ):
									return math.exp(t * -12.0   )

			else:
						raise ValueError(F'Unsupported alpha_tranform_type: {alpha_transform_type}'   )

			SCREAMING_SNAKE_CASE_			=	[]
			for i in range(SCREAMING_SNAKE_CASE   ):
						SCREAMING_SNAKE_CASE_			=	i / num_diffusion_timesteps
						SCREAMING_SNAKE_CASE_			=	(i + 1) / num_diffusion_timesteps
						betas.append(min(1 - alpha_bar_fn(SCREAMING_SNAKE_CASE   ) / alpha_bar_fn(SCREAMING_SNAKE_CASE   )					,						SCREAMING_SNAKE_CASE   )   )
			return torch.tensor(SCREAMING_SNAKE_CASE					,						dtype=torch.floataa   )



class 			a_					(    SCREAMING_SNAKE_CASE__     ,						SCREAMING_SNAKE_CASE__    ):
	A						       =   [e.name for e in KarrasDiffusionSchedulers]
	A						       =   1





	@register_to_config
	def __init__(       self			,    SCREAMING_SNAKE_CASE = 1000			,    SCREAMING_SNAKE_CASE = 0.0_0_0_1			,    SCREAMING_SNAKE_CASE = 0.0_2			,    SCREAMING_SNAKE_CASE = "linear"			,    SCREAMING_SNAKE_CASE = None			,    SCREAMING_SNAKE_CASE = "epsilon"			,    SCREAMING_SNAKE_CASE = "linear"			,    SCREAMING_SNAKE_CASE = False			,    SCREAMING_SNAKE_CASE = "linspace"			,    SCREAMING_SNAKE_CASE = 0			,    )     ->				Dict:




				"""simple docstring"""



				if trained_betas is not None:
							SCREAMING_SNAKE_CASE_			=	torch.tensor(SCREAMING_SNAKE_CASE			,    dtype=torch.floataa			)
				elif beta_schedule == "linear":
							SCREAMING_SNAKE_CASE_			=	torch.linspace(SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			,    dtype=torch.floataa			)
				elif beta_schedule == "scaled_linear":
							# this schedule is very specific to the latent diffusion model.
							SCREAMING_SNAKE_CASE_			=	(
							    torch.linspace(beta_start**0.5			,    beta_end**0.5			,    SCREAMING_SNAKE_CASE			,    dtype=torch.floataa			) ** 2
							)
				elif beta_schedule == "squaredcos_cap_v2":
							# Glide cosine schedule
							SCREAMING_SNAKE_CASE_			=	betas_for_alpha_bar(SCREAMING_SNAKE_CASE			)
				else:
							raise NotImplementedError(f'{beta_schedule} does is not implemented for {self.__class__}'			)

				SCREAMING_SNAKE_CASE_			=	1.0 - self.betas
				SCREAMING_SNAKE_CASE_			=	torch.cumprod(self.alphas			,    dim=0			)

				SCREAMING_SNAKE_CASE_			=	np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5			)
				SCREAMING_SNAKE_CASE_			=	np.concatenate([sigmas[::-1], [0.0]]			).astype(np.floataa			)
				SCREAMING_SNAKE_CASE_			=	torch.from_numpy(SCREAMING_SNAKE_CASE			)

				# setable values
				SCREAMING_SNAKE_CASE_			=	None
				SCREAMING_SNAKE_CASE_			=	np.linspace(0			,    num_train_timesteps - 1			,    SCREAMING_SNAKE_CASE			,    dtype=SCREAMING_SNAKE_CASE			)[::-1].copy()
				SCREAMING_SNAKE_CASE_			=	torch.from_numpy(SCREAMING_SNAKE_CASE			)
				SCREAMING_SNAKE_CASE_			=	False
				SCREAMING_SNAKE_CASE_			=	use_karras_sigmas





	@property
	def 	A_(       self			)     ->				Optional[int]:




				"""simple docstring"""



				if self.config.timestep_spacing in ["linspace", "trailing"]:
							return self.sigmas.max()

				return (self.sigmas.max() ** 2 + 1) ** 0.5





	def 	A_(       self			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			)     ->				torch.FloatTensor:




				"""simple docstring"""



				if isinstance(SCREAMING_SNAKE_CASE			,    torch.Tensor			):
							SCREAMING_SNAKE_CASE_			=	timestep.to(self.timesteps.device			)
				SCREAMING_SNAKE_CASE_			=	(self.timesteps == timestep).nonzero().item()
				SCREAMING_SNAKE_CASE_			=	self.sigmas[step_index]

				SCREAMING_SNAKE_CASE_			=	sample / ((sigma**2 + 1) ** 0.5)

				SCREAMING_SNAKE_CASE_			=	True
				return sample





	def 	A_(       self			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE = None			)     ->				Dict:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_			=	num_inference_steps

				# "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
				if self.config.timestep_spacing == "linspace":
							SCREAMING_SNAKE_CASE_			=	np.linspace(0			,    self.config.num_train_timesteps - 1			,    SCREAMING_SNAKE_CASE			,    dtype=SCREAMING_SNAKE_CASE			)[
							    ::-1
							].copy()
				elif self.config.timestep_spacing == "leading":
							SCREAMING_SNAKE_CASE_			=	self.config.num_train_timesteps // self.num_inference_steps
							# creates integer timesteps by multiplying by ratio
							# casting to int to avoid issues when num_inference_step is power of 3
							SCREAMING_SNAKE_CASE_			=	(np.arange(0			,    SCREAMING_SNAKE_CASE			) * step_ratio).round()[::-1].copy().astype(SCREAMING_SNAKE_CASE			)
							timesteps += self.config.steps_offset
				elif self.config.timestep_spacing == "trailing":
							SCREAMING_SNAKE_CASE_			=	self.config.num_train_timesteps / self.num_inference_steps
							# creates integer timesteps by multiplying by ratio
							# casting to int to avoid issues when num_inference_step is power of 3
							SCREAMING_SNAKE_CASE_			=	(np.arange(self.config.num_train_timesteps			,    0			,    -step_ratio			)).round().copy().astype(SCREAMING_SNAKE_CASE			)
							timesteps -= 1
				else:
							raise ValueError(
							    f'{self.config.timestep_spacing} is not supported. Please make sure to choose one of \'linspace\', \'leading\' or \'trailing\'.'			)

				SCREAMING_SNAKE_CASE_			=	np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5			)
				SCREAMING_SNAKE_CASE_			=	np.log(SCREAMING_SNAKE_CASE			)

				if self.config.interpolation_type == "linear":
							SCREAMING_SNAKE_CASE_			=	np.interp(SCREAMING_SNAKE_CASE			,    np.arange(0			,    len(SCREAMING_SNAKE_CASE			)			)			,    SCREAMING_SNAKE_CASE			)
				elif self.config.interpolation_type == "log_linear":
							SCREAMING_SNAKE_CASE_			=	torch.linspace(np.log(sigmas[-1]			)			,    np.log(sigmas[0]			)			,    num_inference_steps + 1			).exp()
				else:
							raise ValueError(
							    f'{self.config.interpolation_type} is not implemented. Please specify interpolation_type to either'
							    ' \'linear\' or \'log_linear\''			)

				if self.use_karras_sigmas:
							SCREAMING_SNAKE_CASE_			=	self._convert_to_karras(in_sigmas=SCREAMING_SNAKE_CASE			,    num_inference_steps=self.num_inference_steps			)
							SCREAMING_SNAKE_CASE_			=	np.array([self._sigma_to_t(SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			) for sigma in sigmas]			)

				SCREAMING_SNAKE_CASE_			=	np.concatenate([sigmas, [0.0]]			).astype(np.floataa			)
				SCREAMING_SNAKE_CASE_			=	torch.from_numpy(SCREAMING_SNAKE_CASE			).to(device=SCREAMING_SNAKE_CASE			)
				if str(SCREAMING_SNAKE_CASE			).startswith('mps'			):
							# mps does not support float64
							SCREAMING_SNAKE_CASE_			=	torch.from_numpy(SCREAMING_SNAKE_CASE			).to(SCREAMING_SNAKE_CASE			,    dtype=torch.floataa			)
				else:
							SCREAMING_SNAKE_CASE_			=	torch.from_numpy(SCREAMING_SNAKE_CASE			).to(device=SCREAMING_SNAKE_CASE			)





	def 	A_(       self			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			)     ->				List[Any]:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_			=	np.log(SCREAMING_SNAKE_CASE			)

				# get distribution
				SCREAMING_SNAKE_CASE_			=	log_sigma - log_sigmas[:, np.newaxis]

				# get sigmas range
				SCREAMING_SNAKE_CASE_			=	np.cumsum((dists >= 0)			,    axis=0			).argmax(axis=0			).clip(max=log_sigmas.shape[0] - 2			)
				SCREAMING_SNAKE_CASE_			=	low_idx + 1

				SCREAMING_SNAKE_CASE_			=	log_sigmas[low_idx]
				SCREAMING_SNAKE_CASE_			=	log_sigmas[high_idx]

				# interpolate sigmas
				SCREAMING_SNAKE_CASE_			=	(low - log_sigma) / (low - high)
				SCREAMING_SNAKE_CASE_			=	np.clip(SCREAMING_SNAKE_CASE			,    0			,    1			)

				# transform interpolation to time range
				SCREAMING_SNAKE_CASE_			=	(1 - w) * low_idx + w * high_idx
				SCREAMING_SNAKE_CASE_			=	t.reshape(sigma.shape			)
				return t





	def 	A_(       self			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			)     ->				torch.FloatTensor:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_			=	in_sigmas[-1].item()
				SCREAMING_SNAKE_CASE_			=	in_sigmas[0].item()

				SCREAMING_SNAKE_CASE_			=	7.0  # 7.0 is the value used in the paper
				SCREAMING_SNAKE_CASE_			=	np.linspace(0			,    1			,    SCREAMING_SNAKE_CASE			)
				SCREAMING_SNAKE_CASE_			=	sigma_min ** (1 / rho)
				SCREAMING_SNAKE_CASE_			=	sigma_max ** (1 / rho)
				SCREAMING_SNAKE_CASE_			=	(max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
				return sigmas





	def 	A_(       self			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE = 0.0			,    SCREAMING_SNAKE_CASE = 0.0			,    SCREAMING_SNAKE_CASE = float('inf'			)			,    SCREAMING_SNAKE_CASE = 1.0			,    SCREAMING_SNAKE_CASE = None			,    SCREAMING_SNAKE_CASE = True			,    )     ->				Union[EulerDiscreteSchedulerOutput, Tuple]:




				"""simple docstring"""



				if (
				    isinstance(SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			)
				    or isinstance(SCREAMING_SNAKE_CASE			,    torch.IntTensor			)
				    or isinstance(SCREAMING_SNAKE_CASE			,    torch.LongTensor			)
				):
							raise ValueError(
							    (
							        'Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to'
							        ' `EulerDiscreteScheduler.step()` is not supported. Make sure to pass'
							        ' one of the `scheduler.timesteps` as a timestep.'
							    )			,    )

				if not self.is_scale_input_called:
							logger.warning(
							    'The `scale_model_input` function should be called before `step` to ensure correct denoising. '
							    'See `StableDiffusionPipeline` for a usage example.'			)

				if isinstance(SCREAMING_SNAKE_CASE			,    torch.Tensor			):
							SCREAMING_SNAKE_CASE_			=	timestep.to(self.timesteps.device			)

				SCREAMING_SNAKE_CASE_			=	(self.timesteps == timestep).nonzero().item()
				SCREAMING_SNAKE_CASE_			=	self.sigmas[step_index]

				SCREAMING_SNAKE_CASE_			=	min(s_churn / (len(self.sigmas			) - 1)			,    2**0.5 - 1			) if s_tmin <= sigma <= s_tmax else 0.0

				SCREAMING_SNAKE_CASE_			=	randn_tensor(
				    model_output.shape			,    dtype=model_output.dtype			,    device=model_output.device			,    generator=SCREAMING_SNAKE_CASE			)

				SCREAMING_SNAKE_CASE_			=	noise * s_noise
				SCREAMING_SNAKE_CASE_			=	sigma * (gamma + 1)

				if gamma > 0:
							SCREAMING_SNAKE_CASE_			=	sample + eps * (sigma_hat**2 - sigma**2) ** 0.5

				# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
				# NOTE: "original_sample" should not be an expected prediction_type but is left in for
				# backwards compatibility
				if self.config.prediction_type == "original_sample" or self.config.prediction_type == "sample":
							SCREAMING_SNAKE_CASE_			=	model_output
				elif self.config.prediction_type == "epsilon":
							SCREAMING_SNAKE_CASE_			=	sample - sigma_hat * model_output
				elif self.config.prediction_type == "v_prediction":
							# * c_out + input * c_skip
							SCREAMING_SNAKE_CASE_			=	model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
				else:
							raise ValueError(
							    f'prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`'			)

				# 2. Convert to an ODE derivative
				SCREAMING_SNAKE_CASE_			=	(sample - pred_original_sample) / sigma_hat

				SCREAMING_SNAKE_CASE_			=	self.sigmas[step_index + 1] - sigma_hat

				SCREAMING_SNAKE_CASE_			=	sample + derivative * dt

				if not return_dict:
							return (prev_sample,)

				return EulerDiscreteSchedulerOutput(prev_sample=SCREAMING_SNAKE_CASE			,    pred_original_sample=SCREAMING_SNAKE_CASE			)





	def 	A_(       self			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			,    )     ->				torch.FloatTensor:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_			=	self.sigmas.to(device=original_samples.device			,    dtype=original_samples.dtype			)
				if original_samples.device.type == "mps" and torch.is_floating_point(SCREAMING_SNAKE_CASE			):
							# mps does not support float64
							SCREAMING_SNAKE_CASE_			=	self.timesteps.to(original_samples.device			,    dtype=torch.floataa			)
							SCREAMING_SNAKE_CASE_			=	timesteps.to(original_samples.device			,    dtype=torch.floataa			)
				else:
							SCREAMING_SNAKE_CASE_			=	self.timesteps.to(original_samples.device			)
							SCREAMING_SNAKE_CASE_			=	timesteps.to(original_samples.device			)

				SCREAMING_SNAKE_CASE_			=	[(schedule_timesteps == t).nonzero().item() for t in timesteps]

				SCREAMING_SNAKE_CASE_			=	sigmas[step_indices].flatten()
				while len(sigma.shape			) < len(original_samples.shape			):
							SCREAMING_SNAKE_CASE_			=	sigma.unsqueeze(-1			)

				SCREAMING_SNAKE_CASE_			=	original_samples + noise * sigma
				return noisy_samples





	def __len__(       self			)     ->				Any:




				"""simple docstring"""



				return self.config.num_train_timesteps






import os
import pickle
import unittest
from collections import UserDict, namedtuple

import torch

from accelerate.test_utils.testing import require_cuda, require_torch_min_version
from accelerate.test_utils.training import RegressionModel
from accelerate.utils import (
    convert_outputs_to_fpaa,
    extract_model_from_parallel,
    find_device,
    listify,
    patch_environment,
    recursively_apply,
    send_to_device,
)


SCREAMING_SNAKE_CASE__    :    Optional[Any]								=					namedtuple("ExampleNamedTuple", "a b c")



class 			a_					(    unittest.TestCase    ):





	def 	A_(       self			)     ->				List[str]:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_			=	torch.randn(5			,    2			)
				SCREAMING_SNAKE_CASE_			=	torch.device('cuda'			) if torch.cuda.is_available() else torch.device('cpu'			)

				SCREAMING_SNAKE_CASE_			=	send_to_device(SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			)
				self.assertTrue(torch.equal(resulta.cpu()			,    SCREAMING_SNAKE_CASE			)			)

				SCREAMING_SNAKE_CASE_			=	send_to_device((tensor, [tensor, tensor], 1)			,    SCREAMING_SNAKE_CASE			)
				self.assertIsInstance(SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			)
				self.assertTrue(torch.equal(resulta[0].cpu()			,    SCREAMING_SNAKE_CASE			)			)
				self.assertIsInstance(resulta[1]			,    SCREAMING_SNAKE_CASE			)
				self.assertTrue(torch.equal(resulta[1][0].cpu()			,    SCREAMING_SNAKE_CASE			)			)
				self.assertTrue(torch.equal(resulta[1][1].cpu()			,    SCREAMING_SNAKE_CASE			)			)
				self.assertEqual(resulta[2]			,    1			)

				SCREAMING_SNAKE_CASE_			=	send_to_device({'a': tensor, 'b': [tensor, tensor], 'c': 1}			,    SCREAMING_SNAKE_CASE			)
				self.assertIsInstance(SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			)
				self.assertTrue(torch.equal(resulta['a'].cpu()			,    SCREAMING_SNAKE_CASE			)			)
				self.assertIsInstance(resulta['b']			,    SCREAMING_SNAKE_CASE			)
				self.assertTrue(torch.equal(resulta['b'][0].cpu()			,    SCREAMING_SNAKE_CASE			)			)
				self.assertTrue(torch.equal(resulta['b'][1].cpu()			,    SCREAMING_SNAKE_CASE			)			)
				self.assertEqual(resulta['c']			,    1			)

				SCREAMING_SNAKE_CASE_			=	send_to_device(ExampleNamedTuple(a=SCREAMING_SNAKE_CASE			,    b=[tensor, tensor]			,    c=1			)			,    SCREAMING_SNAKE_CASE			)
				self.assertIsInstance(SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			)
				self.assertTrue(torch.equal(resulta.a.cpu()			,    SCREAMING_SNAKE_CASE			)			)
				self.assertIsInstance(resulta.b			,    SCREAMING_SNAKE_CASE			)
				self.assertTrue(torch.equal(resulta.b[0].cpu()			,    SCREAMING_SNAKE_CASE			)			)
				self.assertTrue(torch.equal(resulta.b[1].cpu()			,    SCREAMING_SNAKE_CASE			)			)
				self.assertEqual(resulta.c			,    1			)

				SCREAMING_SNAKE_CASE_			=	send_to_device(UserDict({'a': tensor, 'b': [tensor, tensor], 'c': 1}			)			,    SCREAMING_SNAKE_CASE			)
				self.assertIsInstance(SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			)
				self.assertTrue(torch.equal(resulta['a'].cpu()			,    SCREAMING_SNAKE_CASE			)			)
				self.assertIsInstance(resulta['b']			,    SCREAMING_SNAKE_CASE			)
				self.assertTrue(torch.equal(resulta['b'][0].cpu()			,    SCREAMING_SNAKE_CASE			)			)
				self.assertTrue(torch.equal(resulta['b'][1].cpu()			,    SCREAMING_SNAKE_CASE			)			)
				self.assertEqual(resulta['c']			,    1			)





	def 	A_(       self			)     ->				int:




				"""simple docstring"""



				with self.assertRaises(SCREAMING_SNAKE_CASE			) as cm:
							SCREAMING_SNAKE_CASE_			=	recursively_apply(torch.tensor			,    (torch.tensor(1			), 1)			,    error_on_other_type=SCREAMING_SNAKE_CASE			)
				self.assertEqual(
				    str(cm.exception			)			,    'Unsupported types (<class \'int\'>) passed to `tensor`. Only nested list/tuple/dicts of objects that are valid for `is_torch_tensor` should be passed.'			,    )





	def 	A_(       self			)     ->				int:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_			=	torch.tensor([1, 2, 3, 4, 5]			)
				self.assertEqual(listify(SCREAMING_SNAKE_CASE			)			,    [1, 2, 3, 4, 5]			)

				SCREAMING_SNAKE_CASE_			=	torch.tensor([[1, 2, 3, 4, 5], [6, 7, 8, 9, 10]]			)
				self.assertEqual(listify(SCREAMING_SNAKE_CASE			)			,    [[1, 2, 3, 4, 5], [6, 7, 8, 9, 10]]			)

				SCREAMING_SNAKE_CASE_			=	torch.tensor([[[1, 2, 3, 4, 5], [6, 7, 8, 9, 10]], [[11, 12, 13, 14, 15], [16, 17, 18, 19, 20]]]			)
				self.assertEqual(
				    listify(SCREAMING_SNAKE_CASE			)			,    [[[1, 2, 3, 4, 5], [6, 7, 8, 9, 10]], [[11, 12, 13, 14, 15], [16, 17, 18, 19, 20]]]			)





	def 	A_(       self			)     ->				Tuple:




				"""simple docstring"""



				with patch_environment(aa=1			,    BB=2			):
							self.assertEqual(os.environ.get('AA'			)			,    '1'			)
							self.assertEqual(os.environ.get('BB'			)			,    '2'			)

				self.assertNotIn('AA'			,    os.environ			)
				self.assertNotIn('BB'			,    os.environ			)





	def 	A_(       self			)     ->				Dict:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_			=	RegressionModel()
				SCREAMING_SNAKE_CASE_			=	model.forward
				SCREAMING_SNAKE_CASE_			=	convert_outputs_to_fpaa(model.forward			)
				SCREAMING_SNAKE_CASE_			=	extract_model_from_parallel(SCREAMING_SNAKE_CASE			,    keep_fpaa_wrapper=SCREAMING_SNAKE_CASE			)
				SCREAMING_SNAKE_CASE_			=	pickle.dumps(SCREAMING_SNAKE_CASE			)





	@require_cuda
	def 	A_(       self			)     ->				int:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_			=	RegressionModel()
				SCREAMING_SNAKE_CASE_			=	model.forward
				SCREAMING_SNAKE_CASE_			=	torch.cuda.amp.autocast(dtype=torch.floataa			)(model.forward			)
				SCREAMING_SNAKE_CASE_			=	convert_outputs_to_fpaa(model.forward			)
				SCREAMING_SNAKE_CASE_			=	extract_model_from_parallel(SCREAMING_SNAKE_CASE			,    keep_fpaa_wrapper=SCREAMING_SNAKE_CASE			)
				SCREAMING_SNAKE_CASE_			=	pickle.dumps(SCREAMING_SNAKE_CASE			)





	@require_cuda
	@require_torch_min_version(version='2.0'			)
	def 	A_(       self			)     ->				Optional[Any]:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_			=	RegressionModel()
				SCREAMING_SNAKE_CASE_			=	model.forward
				SCREAMING_SNAKE_CASE_			=	torch.cuda.amp.autocast(dtype=torch.floataa			)(model.forward			)
				SCREAMING_SNAKE_CASE_			=	convert_outputs_to_fpaa(model.forward			)
				SCREAMING_SNAKE_CASE_			=	torch.compile(model.forward			,    backend='inductor'			)
				SCREAMING_SNAKE_CASE_			=	torch.randn(4			,    10			).cuda()
				SCREAMING_SNAKE_CASE_			=	model(SCREAMING_SNAKE_CASE			)





	def 	A_(       self			)     ->				List[str]:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_			=	RegressionModel()
				# could also do a test with DistributedDataParallel, but difficult to run on CPU or single GPU
				SCREAMING_SNAKE_CASE_			=	torch.nn.parallel.DataParallel(SCREAMING_SNAKE_CASE			)
				SCREAMING_SNAKE_CASE_			=	extract_model_from_parallel(SCREAMING_SNAKE_CASE			)

				self.assertEqual(SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			)





	@require_torch_min_version(version='2.0'			)
	def 	A_(       self			)     ->				int:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_			=	RegressionModel()
				SCREAMING_SNAKE_CASE_			=	torch.compile(SCREAMING_SNAKE_CASE			)

				# could also do a test with DistributedDataParallel, but difficult to run on CPU or single GPU
				SCREAMING_SNAKE_CASE_			=	torch.nn.parallel.DataParallel(SCREAMING_SNAKE_CASE			)
				SCREAMING_SNAKE_CASE_			=	torch.compile(SCREAMING_SNAKE_CASE			)
				SCREAMING_SNAKE_CASE_			=	extract_model_from_parallel(SCREAMING_SNAKE_CASE			)

				self.assertEqual(compiled_model._orig_mod			,    compiled_model_unwrapped._orig_mod			)





	def 	A_(       self			)     ->				Optional[Any]:




				"""simple docstring"""



				self.assertEqual(find_device([1, 'a', torch.tensor([1, 2, 3]			)]			)			,    torch.device('cpu'			)			)
				self.assertEqual(find_device({'a': 1, 'b': torch.tensor([1, 2, 3]			)}			)			,    torch.device('cpu'			)			)
				self.assertIsNone(find_device([1, 'a']			)			)
