



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

import numpy as np
import torch

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





@dataclass
class 							lowerCAmelCase_		(		lowercase_     ):
		SCREAMING_SNAKE_CASE_       :			torch.FloatTensor
		SCREAMING_SNAKE_CASE_       :			torch.FloatTensor


def 	_A	(   _UpperCamelCase      ,					_UpperCamelCase=0.999      ,					_UpperCamelCase="cosine"      ,					):
				if alpha_transform_type == "cosine":

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

				elif alpha_transform_type == "exp":

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

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

				_UpperCAmelCase						:  int     					=	[]
				for i in range(_UpperCamelCase			):
								_UpperCAmelCase						:  Union[str, Any]     					=	i / num_diffusion_timesteps
								_UpperCAmelCase						:  Optional[Any]     					=	(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 							lowerCAmelCase_		(		lowercase_							,		lowercase_     ):
		SCREAMING_SNAKE_CASE_       :			Optional[int]					       =	1







		@register_to_config
		def __init__(    self  :   Any       ,		UpperCAmelCase_  :   int = 1000       ,		UpperCAmelCase_  :   float = 0.0_001       ,		UpperCAmelCase_  :   float = 0.02       ,		UpperCAmelCase_  :   str = "linear"       ,		UpperCAmelCase_  :   float = 0.0       ,		UpperCAmelCase_  :   Optional[np.ndarray] = None       ,		UpperCAmelCase_  :   bool = True       ,		)       ->		Union[str, Any]:


						'''simple docstring'''







						if trained_betas is not None:
										_UpperCAmelCase						:  Tuple     					=	torch.from_numpy(UpperCAmelCase_					)
						elif beta_schedule == "linear":
										_UpperCAmelCase						:  Tuple     					=	torch.linspace(UpperCAmelCase_       ,		UpperCAmelCase_       ,		UpperCAmelCase_       ,		dtype=torch.floataa					)
						elif beta_schedule == "scaled_linear":
										# this schedule is very specific to the latent diffusion model.
										_UpperCAmelCase						:  Any     					=	(
										    torch.linspace(beta_start**0.5       ,		beta_end**0.5       ,		UpperCAmelCase_       ,		dtype=torch.floataa					) ** 2
										)
						elif beta_schedule == "squaredcos_cap_v2":
										# Glide cosine schedule
										_UpperCAmelCase						:  int     					=	betas_for_alpha_bar(UpperCAmelCase_					)
						elif beta_schedule == "sigmoid":
										# GeoDiff sigmoid schedule
										_UpperCAmelCase						:  Optional[Any]     					=	torch.linspace(-6       ,		6       ,		UpperCAmelCase_					)
										_UpperCAmelCase						:  Tuple     					=	torch.sigmoid(UpperCAmelCase_					) * (beta_end - beta_start) + beta_start
						else:
										raise NotImplementedError(F'''{beta_schedule} does is not implemented for {self.__class__}'''					)

						_UpperCAmelCase						:  str     					=	1.0 - self.betas
						_UpperCAmelCase						:  str     					=	torch.cumprod(self.alphas       ,		dim=0					)
						_UpperCAmelCase						:  List[str]     					=	torch.tensor(1.0					)

						_UpperCAmelCase						:  Optional[int]     					=	torch.tensor(1.0					)

						# standard deviation of the initial noise distribution
						_UpperCAmelCase						:  int     					=	1.0

						# setable values
						_UpperCAmelCase						:  Optional[int]     					=	None
						_UpperCAmelCase						:  Tuple     					=	torch.from_numpy(np.arange(0       ,		UpperCAmelCase_					)[::-1].copy()					)

						_UpperCAmelCase						:  int     					=	eta







		def 					a_  (    self  :   Optional[int]       ,		UpperCAmelCase_  :   torch.FloatTensor       ,		UpperCAmelCase_  :   Optional[int] = None					)       ->		torch.FloatTensor:


						'''simple docstring'''







						return sample







		def 					a_  (    self  :   Optional[int]       ,		UpperCAmelCase_  :   int       ,		UpperCAmelCase_  :   int = 10       ,		UpperCAmelCase_  :   int = 10       ,		UpperCAmelCase_  :   Union[str, torch.device] = None       ,		)       ->		Optional[Any]:


						'''simple docstring'''







						_UpperCAmelCase						:  Optional[int]     					=	min(self.config.num_train_timesteps       ,		UpperCAmelCase_					)
						_UpperCAmelCase						:  List[Any]     					=	num_inference_steps

						_UpperCAmelCase						:  int     					=	[]

						_UpperCAmelCase						:  Union[str, Any]     					=	{}
						for j in range(0       ,		num_inference_steps - jump_length       ,		UpperCAmelCase_					):
										_UpperCAmelCase						:  Any     					=	jump_n_sample - 1

						_UpperCAmelCase						:  Optional[Any]     					=	num_inference_steps
						while t >= 1:
										_UpperCAmelCase						:  Dict     					=	t - 1
										timesteps.append(UpperCAmelCase_					)

										if jumps.get(UpperCAmelCase_       ,		0					) > 0:
														_UpperCAmelCase						:  Union[str, Any]     					=	jumps[t] - 1
														for _ in range(UpperCAmelCase_					):
																		_UpperCAmelCase						:  str     					=	t + 1
																		timesteps.append(UpperCAmelCase_					)

						_UpperCAmelCase						:  Dict     					=	np.array(UpperCAmelCase_					) * (self.config.num_train_timesteps // self.num_inference_steps)
						_UpperCAmelCase						:  Any     					=	torch.from_numpy(UpperCAmelCase_					).to(UpperCAmelCase_					)







		def 					a_  (    self  :   Optional[Any]       ,		UpperCAmelCase_  :   str					)       ->		Optional[int]:


						'''simple docstring'''







						_UpperCAmelCase						:  Any     					=	t - self.config.num_train_timesteps // self.num_inference_steps

						_UpperCAmelCase						:  Tuple     					=	self.alphas_cumprod[t]
						_UpperCAmelCase						:  int     					=	self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
						_UpperCAmelCase						:  Any     					=	1 - alpha_prod_t
						_UpperCAmelCase						:  List[str]     					=	1 - alpha_prod_t_prev

						# For t > 0, compute predicted variance βt (see formula (6) and (7) from
						# https://arxiv.org/pdf/2006.11239.pdf) and sample from it to get
						# previous sample x_{t-1} ~ N(pred_prev_sample, variance) == add
						# variance to pred_sample
						# Is equivalent to formula (16) in https://arxiv.org/pdf/2010.02502.pdf
						# without eta.
						# variance = (1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * self.betas[t]
						_UpperCAmelCase						:  Union[str, Any]     					=	(beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev)

						return variance







		def 					a_  (    self  :   Optional[Any]       ,		UpperCAmelCase_  :   torch.FloatTensor       ,		UpperCAmelCase_  :   int       ,		UpperCAmelCase_  :   torch.FloatTensor       ,		UpperCAmelCase_  :   torch.FloatTensor       ,		UpperCAmelCase_  :   torch.FloatTensor       ,		UpperCAmelCase_  :   Optional[torch.Generator] = None       ,		UpperCAmelCase_  :   bool = True       ,		)       ->		Union[RePaintSchedulerOutput, Tuple]:


						'''simple docstring'''







						_UpperCAmelCase						:  int     					=	timestep
						_UpperCAmelCase						:  int     					=	timestep - self.config.num_train_timesteps // self.num_inference_steps

						# 1. compute alphas, betas
						_UpperCAmelCase						:  Tuple     					=	self.alphas_cumprod[t]
						_UpperCAmelCase						:  Optional[Any]     					=	self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
						_UpperCAmelCase						:  Dict     					=	1 - alpha_prod_t

						# 2. compute predicted original sample from predicted noise also called
						# "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
						_UpperCAmelCase						:  Optional[int]     					=	(sample - beta_prod_t**0.5 * model_output) / alpha_prod_t**0.5

						# 3. Clip "predicted x_0"
						if self.config.clip_sample:
										_UpperCAmelCase						:  Tuple     					=	torch.clamp(UpperCAmelCase_       ,		-1       ,		1					)

						# We choose to follow RePaint Algorithm 1 to get x_{t-1}, however we
						# substitute formula (7) in the algorithm coming from DDPM paper
						# (formula (4) Algorithm 2 - Sampling) with formula (12) from DDIM paper.
						# DDIM schedule gives the same results as DDPM with eta = 1.0
						# Noise is being reused in 7. and 8., but no impact on quality has
						# been observed.

						# 5. Add noise
						_UpperCAmelCase						:  Optional[int]     					=	model_output.device
						_UpperCAmelCase						:  Tuple     					=	randn_tensor(model_output.shape       ,		generator=UpperCAmelCase_       ,		device=UpperCAmelCase_       ,		dtype=model_output.dtype					)
						_UpperCAmelCase						:  Optional[int]     					=	self.eta * self._get_variance(UpperCAmelCase_					) ** 0.5

						_UpperCAmelCase						:  List[Any]     					=	0
						if t > 0 and self.eta > 0:
										_UpperCAmelCase						:  List[str]     					=	std_dev_t * noise

						# 6. compute "direction pointing to x_t" of formula (12)
						# from https://arxiv.org/pdf/2010.02502.pdf
						_UpperCAmelCase						:  Union[str, Any]     					=	(1 - alpha_prod_t_prev - std_dev_t**2) ** 0.5 * model_output

						# 7. compute x_{t-1} of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
						_UpperCAmelCase						:  List[str]     					=	alpha_prod_t_prev**0.5 * pred_original_sample + pred_sample_direction + variance

						# 8. Algorithm 1 Line 5 https://arxiv.org/pdf/2201.09865.pdf
						_UpperCAmelCase						:  Optional[int]     					=	(alpha_prod_t_prev**0.5) * original_image + ((1 - alpha_prod_t_prev) ** 0.5) * noise

						# 9. Algorithm 1 Line 8 https://arxiv.org/pdf/2201.09865.pdf
						_UpperCAmelCase						:  Dict     					=	mask * prev_known_part + (1.0 - mask) * prev_unknown_part

						if not return_dict:
										return (
										    pred_prev_sample,
										    pred_original_sample,
										)

						return RePaintSchedulerOutput(prev_sample=UpperCAmelCase_       ,		pred_original_sample=UpperCAmelCase_					)







		def 					a_  (    self  :   Tuple       ,		UpperCAmelCase_  :   Any       ,		UpperCAmelCase_  :   List[str]       ,		UpperCAmelCase_  :   Optional[int]=None					)       ->		Optional[Any]:


						'''simple docstring'''







						_UpperCAmelCase						:  Dict     					=	self.config.num_train_timesteps // self.num_inference_steps

						for i in range(UpperCAmelCase_					):
										_UpperCAmelCase						:  Optional[int]     					=	self.betas[timestep + i]
										if sample.device.type == "mps":
														# randn does not work reproducibly on mps
														_UpperCAmelCase						:  Dict     					=	randn_tensor(sample.shape       ,		dtype=sample.dtype       ,		generator=UpperCAmelCase_					)
														_UpperCAmelCase						:  int     					=	noise.to(sample.device					)
										else:
														_UpperCAmelCase						:  int     					=	randn_tensor(sample.shape       ,		generator=UpperCAmelCase_       ,		device=sample.device       ,		dtype=sample.dtype					)

										# 10. Algorithm 1 Line 10 https://arxiv.org/pdf/2201.09865.pdf
										_UpperCAmelCase						:  List[Any]     					=	(1 - beta) ** 0.5 * sample + beta**0.5 * noise

						return sample







		def 					a_  (    self  :   List[Any]       ,		UpperCAmelCase_  :   torch.FloatTensor       ,		UpperCAmelCase_  :   torch.FloatTensor       ,		UpperCAmelCase_  :   torch.IntTensor       ,		)       ->		torch.FloatTensor:


						'''simple docstring'''







						raise NotImplementedError('''Use `DDPMScheduler.add_noise()` to train for sampling with RePaint.'''					)







		def __len__(    self  :   Dict					)       ->		Optional[int]:


						'''simple docstring'''







						return self.config.num_train_timesteps





import importlib.metadata
import sys

from transformers.testing_utils import TestCasePlus
from transformers.utils.versions import require_version, require_version_core


UpperCAmelCase__			:   str      			=    importlib.metadata.version('numpy')
UpperCAmelCase__			:   Optional[int]      			=    '.'.join([str(x) for x in sys.version_info[:3]])





class 							lowerCAmelCase_		(		lowercase_     ):







		def 					a_  (    self  :   Tuple					)       ->		Tuple:


						'''simple docstring'''







						require_version_core('''numpy<1000.4.5'''					)
						require_version_core('''numpy<1000.4'''					)
						require_version_core('''numpy<1000'''					)

						# le
						require_version_core('''numpy<=1000.4.5'''					)
						require_version_core(F'''numpy<={numpy_ver}'''					)

						# eq
						require_version_core(F'''numpy=={numpy_ver}'''					)

						# ne
						require_version_core('''numpy!=1000.4.5'''					)

						# ge
						require_version_core('''numpy>=1.0'''					)
						require_version_core('''numpy>=1.0.0'''					)
						require_version_core(F'''numpy>={numpy_ver}'''					)

						# gt
						require_version_core('''numpy>1.0.0'''					)

						# mix
						require_version_core('''numpy>1.0.0,<1000'''					)

						# requirement w/o version
						require_version_core('''numpy'''					)

						# unmet requirements due to version conflict
						for req in ["numpy==1.0.0", "numpy>=1000.0.0", F'''numpy<{numpy_ver}''']:
										try:
														require_version_core(UpperCAmelCase_					)
										except ImportError as e:
														self.assertIn(F'''{req} is required'''       ,		str(UpperCAmelCase_					)					)
														self.assertIn('''but found'''       ,		str(UpperCAmelCase_					)					)

        # unmet requirements due to missing module
						for req in ["numpipypie>1", "numpipypie2"]:
										try:
														require_version_core(UpperCAmelCase_					)
										except importlib.metadata.PackageNotFoundError as e:
														self.assertIn(F'''The \'{req}\' distribution was not found and is required by this application'''       ,		str(UpperCAmelCase_					)					)
														self.assertIn('''Try: pip install transformers -U'''       ,		str(UpperCAmelCase_					)					)

        # bogus requirements formats:
        # 1. whole thing
						for req in ["numpy??1.0.0", "numpy1.0.0"]:
										try:
														require_version_core(UpperCAmelCase_					)
										except ValueError as e:
														self.assertIn('''requirement needs to be in the pip package format'''       ,		str(UpperCAmelCase_					)					)
        # 2. only operators
						for req in ["numpy=1.0.0", "numpy == 1.00", "numpy<>1.0.0", "numpy><1.00", "numpy>>1.0.0"]:
										try:
														require_version_core(UpperCAmelCase_					)
										except ValueError as e:
														self.assertIn('''need one of '''       ,		str(UpperCAmelCase_					)					)







		def 					a_  (    self  :   Tuple					)       ->		List[Any]:


						'''simple docstring'''







						require_version('''python>=3.6.0'''					)

						# not matching requirements
						for req in ["python>9.9.9", "python<3.0.0"]:
										try:
														require_version_core(UpperCAmelCase_					)
										except ImportError as e:
														self.assertIn(F'''{req} is required'''       ,		str(UpperCAmelCase_					)					)
														self.assertIn(F'''but found python=={python_ver}'''       ,		str(UpperCAmelCase_					)					)
