'''simple docstring'''


import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput





def       lowerCAmelCase					(		UpperCamelCase__				:   Optional[Any]    ,					UpperCamelCase__				:   str=0.9_99    ,					UpperCamelCase__				:   Dict="cosine"    ,					):


	"""simple docstring"""
	if alpha_transform_type == "cosine":

		def alpha_bar_fn(UpperCamelCase__				:   Union[str, Any]       ):
			return math.cos((t + 0.0_08) / 1.0_08 * math.pi / 2       ) ** 2

	elif alpha_transform_type == "exp":

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

	else:
		raise ValueError(f"""Unsupported alpha_tranform_type: {alpha_transform_type}"""       )

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






class 						A   (   UpperCAmelCase       ,	UpperCAmelCase   ):
							a_        =      [e.name for e in KarrasDiffusionSchedulers]
							a_        =      2





							@register_to_config
							def __init__(      self				:							Tuple     ,		__a				:							int = 1_0_0_0     ,		__a				:							float = 0.0_0_0_8_5     ,		__a				:							float = 0.0_1_2     ,		__a				:							str = "linear"     ,		__a				:							Optional[Union[np.ndarray, List[float]]] = None     ,		__a				:							str = "epsilon"     ,		__a				:							Optional[bool] = False     ,		__a				:							Optional[bool] = False     ,		__a				:							float = 1.0     ,		__a				:							str = "linspace"     ,		__a				:							int = 0     ,		)							->	str:
								if trained_betas is not None:
									__UpperCAmelCase							=			torch.tensor(__a     ,		dtype=torch.floataa	)
								elif beta_schedule == "linear":
									__UpperCAmelCase							=			torch.linspace(__a     ,		__a     ,		__a     ,		dtype=torch.floataa	)
								elif beta_schedule == "scaled_linear":
									# this schedule is very specific to the latent diffusion model.
									__UpperCAmelCase							=			(
									    torch.linspace(beta_start**0.5     ,		beta_end**0.5     ,		__a     ,		dtype=torch.floataa	) ** 2
									)
								elif beta_schedule == "squaredcos_cap_v2":
									# Glide cosine schedule
									__UpperCAmelCase							=			betas_for_alpha_bar(__a     ,		alpha_transform_type='''cosine'''	)
								elif beta_schedule == "exp":
									__UpperCAmelCase							=			betas_for_alpha_bar(__a     ,		alpha_transform_type='''exp'''	)
								else:
									raise NotImplementedError(f"""{beta_schedule} does is not implemented for {self.__class__}"""	)

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

								#  set all values
								self.set_timesteps(__a     ,		__a     ,		__a	)
								__UpperCAmelCase							=			use_karras_sigmas





							def       snake_case__						(      self				:							Optional[int]     ,		__a				:							int     ,		__a				:							List[Any]=None	)							->	Any:
								if schedule_timesteps is None:
									__UpperCAmelCase							=			self.timesteps

								__UpperCAmelCase							=			(schedule_timesteps == timestep).nonzero()

								# The sigma index that is taken for the **very** first `step`
								# is always the second index (or the last index if there is only 1)
								# This way we can ensure we don't accidentally skip a sigma in
								# case we start in the middle of the denoising schedule (e.g. for image-to-image)
								if len(self._index_counter	) == 0:
									__UpperCAmelCase							=			1 if len(__a	) > 1 else 0
								else:
									__UpperCAmelCase							=			timestep.cpu().item() if torch.is_tensor(__a	) else timestep
									__UpperCAmelCase							=			self._index_counter[timestep_int]

								return indices[pos].item()





							@property
							def       snake_case__						(      self				:							List[str]	)							->	Tuple:
								# standard deviation of the initial noise distribution
								if self.config.timestep_spacing in ["linspace", "trailing"]:
									return self.sigmas.max()

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





							def       snake_case__						(      self				:							int     ,		__a				:							torch.FloatTensor     ,		__a				:							Union[float, torch.FloatTensor]     ,		)							->	torch.FloatTensor:
								__UpperCAmelCase							=			self.index_for_timestep(__a	)

								__UpperCAmelCase							=			self.sigmas[step_index]
								__UpperCAmelCase							=			sample / ((sigma**2 + 1) ** 0.5)
								return sample





							def       snake_case__						(      self				:							str     ,		__a				:							int     ,		__a				:							Union[str, torch.device] = None     ,		__a				:							Optional[int] = None     ,		)							->	Union[str, Any]:
								__UpperCAmelCase							=			num_inference_steps

								__UpperCAmelCase							=			num_train_timesteps or self.config.num_train_timesteps

								# "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
								if self.config.timestep_spacing == "linspace":
									__UpperCAmelCase							=			np.linspace(0     ,		num_train_timesteps - 1     ,		__a     ,		dtype=__a	)[::-1].copy()
								elif self.config.timestep_spacing == "leading":
									__UpperCAmelCase							=			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
									__UpperCAmelCase							=			(np.arange(0     ,		__a	) * step_ratio).round()[::-1].copy().astype(__a	)
									timesteps += self.config.steps_offset
								elif self.config.timestep_spacing == "trailing":
									__UpperCAmelCase							=			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
									__UpperCAmelCase							=			(np.arange(__a     ,		0     ,		-step_ratio	)).round().copy().astype(__a	)
									timesteps -= 1
								else:
									raise ValueError(
									    f"""{self.config.timestep_spacing} is not supported. Please make sure to choose one of 'linspace', 'leading' or 'trailing'."""	)

								__UpperCAmelCase							=			np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5	)
								__UpperCAmelCase							=			np.log(__a	)
								__UpperCAmelCase							=			np.interp(__a     ,		np.arange(0     ,		len(__a	)	)     ,		__a	)

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

								__UpperCAmelCase							=			np.concatenate([sigmas, [0.0]]	).astype(np.floataa	)
								__UpperCAmelCase							=			torch.from_numpy(__a	).to(device=__a	)
								__UpperCAmelCase							=			torch.cat([sigmas[:1], sigmas[1:-1].repeat_interleave(2	), sigmas[-1:]]	)

								__UpperCAmelCase							=			torch.from_numpy(__a	)
								__UpperCAmelCase							=			torch.cat([timesteps[:1], timesteps[1:].repeat_interleave(2	)]	)

								if str(__a	).startswith('''mps'''	):
									# mps does not support float64
									__UpperCAmelCase							=			timesteps.to(__a     ,		dtype=torch.floataa	)
								else:
									__UpperCAmelCase							=			timesteps.to(device=__a	)

								# empty dt and derivative
								__UpperCAmelCase							=			None
								__UpperCAmelCase							=			None

								# for exp beta schedules, such as the one for `pipeline_shap_e.py`
								# we need an index counter
								__UpperCAmelCase							=			defaultdict(__a	)





							def       snake_case__						(      self				:							Tuple     ,		__a				:							Optional[int]     ,		__a				:							Optional[Any]	)							->	List[str]:
								# get log sigma
								__UpperCAmelCase							=			np.log(__a	)

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

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

								__UpperCAmelCase							=			log_sigmas[low_idx]
								__UpperCAmelCase							=			log_sigmas[high_idx]

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

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





							def       snake_case__						(      self				:							List[str]     ,		__a				:							torch.FloatTensor     ,		__a				:							int	)							->	torch.FloatTensor:
								__UpperCAmelCase							=			in_sigmas[-1].item()
								__UpperCAmelCase							=			in_sigmas[0].item()

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





							@property
							def       snake_case__						(      self				:							List[Any]	)							->	List[Any]:
								return self.dt is None





							def       snake_case__						(      self				:							str     ,		__a				:							Union[torch.FloatTensor, np.ndarray]     ,		__a				:							Union[float, torch.FloatTensor]     ,		__a				:							Union[torch.FloatTensor, np.ndarray]     ,		__a				:							bool = True     ,		)							->	Union[SchedulerOutput, Tuple]:
								__UpperCAmelCase							=			self.index_for_timestep(__a	)

								# advance index counter by 1
								__UpperCAmelCase							=			timestep.cpu().item() if torch.is_tensor(__a	) else timestep
								self._index_counter[timestep_int] += 1

								if self.state_in_first_order:
									__UpperCAmelCase							=			self.sigmas[step_index]
									__UpperCAmelCase							=			self.sigmas[step_index + 1]
								else:
									# 2nd order / Heun's method
									__UpperCAmelCase							=			self.sigmas[step_index - 1]
									__UpperCAmelCase							=			self.sigmas[step_index]

								# currently only gamma=0 is supported. This usually works best anyways.
								# We can support gamma in the future but then need to scale the timestep before
								# passing it to the model which requires a change in API
								__UpperCAmelCase							=			0
								__UpperCAmelCase							=			sigma * (gamma + 1)  # Note: sigma_hat == sigma for now

								# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
								if self.config.prediction_type == "epsilon":
									__UpperCAmelCase							=			sigma_hat if self.state_in_first_order else sigma_next
									__UpperCAmelCase							=			sample - sigma_input * model_output
								elif self.config.prediction_type == "v_prediction":
									__UpperCAmelCase							=			sigma_hat if self.state_in_first_order else sigma_next
									__UpperCAmelCase							=			model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
									    sample / (sigma_input**2 + 1)
									)
								elif self.config.prediction_type == "sample":
									__UpperCAmelCase							=			model_output
								else:
									raise ValueError(
									    f"""prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`"""	)

								if self.config.clip_sample:
									__UpperCAmelCase							=			pred_original_sample.clamp(
									    -self.config.clip_sample_range     ,		self.config.clip_sample_range	)

								if self.state_in_first_order:
									# 2. Convert to an ODE derivative for 1st order
									__UpperCAmelCase							=			(sample - pred_original_sample) / sigma_hat
									# 3. delta timestep
									__UpperCAmelCase							=			sigma_next - sigma_hat

									# store for 2nd order step
									__UpperCAmelCase							=			derivative
									__UpperCAmelCase							=			dt
									__UpperCAmelCase							=			sample
								else:
									# 2. 2nd order / Heun's method
									__UpperCAmelCase							=			(sample - pred_original_sample) / sigma_next
									__UpperCAmelCase							=			(self.prev_derivative + derivative) / 2

									# 3. take prev timestep & sample
									__UpperCAmelCase							=			self.dt
									__UpperCAmelCase							=			self.sample

									# free dt and derivative
									# Note, this puts the scheduler in "first order mode"
									__UpperCAmelCase							=			None
									__UpperCAmelCase							=			None
									__UpperCAmelCase							=			None

								__UpperCAmelCase							=			sample + derivative * dt

								if not return_dict:
									return (prev_sample,)

								return SchedulerOutput(prev_sample=__a	)





							def       snake_case__						(      self				:							Optional[int]     ,		__a				:							torch.FloatTensor     ,		__a				:							torch.FloatTensor     ,		__a				:							torch.FloatTensor     ,		)							->	torch.FloatTensor:
								# Make sure sigmas and timesteps have the same device and dtype as original_samples
								__UpperCAmelCase							=			self.sigmas.to(device=original_samples.device     ,		dtype=original_samples.dtype	)
								if original_samples.device.type == "mps" and torch.is_floating_point(__a	):
									# mps does not support float64
									__UpperCAmelCase							=			self.timesteps.to(original_samples.device     ,		dtype=torch.floataa	)
									__UpperCAmelCase							=			timesteps.to(original_samples.device     ,		dtype=torch.floataa	)
								else:
									__UpperCAmelCase							=			self.timesteps.to(original_samples.device	)
									__UpperCAmelCase							=			timesteps.to(original_samples.device	)

								__UpperCAmelCase							=			[self.index_for_timestep(__a     ,		__a	) for t in timesteps]

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

								__UpperCAmelCase							=			original_samples + noise * sigma
								return noisy_samples






							def __len__(      self				:							Union[str, Any]	)							->	Tuple:
								return self.config.num_train_timesteps

'''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					(		):


	"""simple docstring"""
	raise RuntimeError('''CUDA out of memory.'''       )






class 						A   (   nn.Module   ):





							def __init__(      self				:							Optional[Any]	)							->	int:
								super().__init__()
								__UpperCAmelCase							=			nn.Linear(3     ,		4	)
								__UpperCAmelCase							=			nn.BatchNormad(4	)
								__UpperCAmelCase							=			nn.Linear(4     ,		5	)






							def       snake_case__						(      self				:							List[str]     ,		__a				:							Optional[int]	)							->	Optional[int]:
								return self.lineara(self.batchnorm(self.lineara(__a	)	)	)






class 						A   (   unittest.TestCase   ):





							def       snake_case__						(      self				:							Optional[int]	)							->	Any:
								__UpperCAmelCase							=			[]

								@find_executable_batch_size(starting_batch_size=1_2_8	)
								def mock_training_loop_function(__a				:							Union[str, Any]	):
									nonlocal batch_sizes
									batch_sizes.append(__a	)
									if batch_size != 8:
										raise_fake_out_of_memory()

								mock_training_loop_function()
								self.assertListEqual(__a     ,		[1_2_8, 6_4, 3_2, 1_6, 8]	)





							def       snake_case__						(      self				:							str	)							->	int:
								__UpperCAmelCase							=			[]

								@find_executable_batch_size(starting_batch_size=1_2_8	)
								def mock_training_loop_function(__a				:							str     ,		__a				:							Optional[int]	):
									nonlocal batch_sizes
									batch_sizes.append(__a	)
									if batch_size != 8:
										raise_fake_out_of_memory()
									return batch_size, arga

								__UpperCAmelCase				,			__UpperCAmelCase							=			mock_training_loop_function('''hello'''	)
								self.assertListEqual(__a     ,		[1_2_8, 6_4, 3_2, 1_6, 8]	)
								self.assertListEqual([bs, arga]     ,		[8, '''hello''']	)





							def       snake_case__						(      self				:							Any	)							->	int:
								@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       snake_case__						(      self				:							Any	)							->	List[Any]:
								@find_executable_batch_size(starting_batch_size=1_6	)
								def mock_training_loop_function(__a				:							Dict	):
									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       snake_case__						(      self				:							List[Any]	)							->	List[str]:
								@find_executable_batch_size(starting_batch_size=1_2_8	)
								def mock_training_loop_function(__a				:							str     ,		__a				:							Union[str, Any]     ,		__a				:							int	):
									if batch_size != 8:
										raise raise_fake_out_of_memory()

								with self.assertRaises(__a	) as cm:
									mock_training_loop_function(1_2_8     ,		'''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       snake_case__						(      self				:							Tuple	)							->	Optional[Any]:
								@find_executable_batch_size(starting_batch_size=1_6	)
								def mock_training_loop_function(__a				:							Tuple	):
									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       snake_case__						(      self				:							Any	)							->	List[Any]:
								__UpperCAmelCase							=			torch.cuda.memory_allocated()
								__UpperCAmelCase							=			ModelForTest()
								model.cuda()
								self.assertGreater(torch.cuda.memory_allocated()     ,		__a	)
								__UpperCAmelCase							=			release_memory(__a	)
								self.assertEqual(torch.cuda.memory_allocated()     ,		__a	)
