






'''simple docstring'''
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    _snake_case   (				snake_case					):

					"""simple docstring"""





					_UpperCamelCase		 =      42
					_UpperCamelCase		 =      42



def 							a	(   _UpperCAmelCase					,  _UpperCAmelCase=0.9_9_9					,  _UpperCAmelCase="cosine"					,  )				->				str:






					"""simple docstring"""







					if alpha_transform_type == "cosine":

										def alpha_bar_fn(_UpperCAmelCase ):
															return math.cos((t + 0.0_0_8) / 1.0_0_8 * math.pi / 2 ) ** 2

					elif alpha_transform_type == "exp":

										def alpha_bar_fn(_UpperCAmelCase ):
															return math.exp(t * -1_2.0 )

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

					a_									=   []
					for i in range(_UpperCAmelCase ):
										a_									=   i / num_diffusion_timesteps
										a_									=   (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    _snake_case   (				snake_case     ,					snake_case					):

					"""simple docstring"""





					_UpperCamelCase		 =      1




					@register_to_config
					def __init__(     self   , UpperCAmelCase__ = 1000   , UpperCAmelCase__ = 0.0_0_0_1   , UpperCAmelCase__ = 0.0_2   , UpperCAmelCase__ = "linear"   , UpperCAmelCase__ = 0.0   , UpperCAmelCase__ = None   , UpperCAmelCase__ = True   , )      ->	str:
										if trained_betas is not None:
															a_									=   torch.from_numpy(UpperCAmelCase__     )
										elif beta_schedule == "linear":
															a_									=   torch.linspace(UpperCAmelCase__   , UpperCAmelCase__   , UpperCAmelCase__   , dtype=torch.floataa     )
										elif beta_schedule == "scaled_linear":
															# this schedule is very specific to the latent diffusion model.
															a_									=   (
															    torch.linspace(beta_start**0.5   , beta_end**0.5   , UpperCAmelCase__   , dtype=torch.floataa     ) ** 2
															)
										elif beta_schedule == "squaredcos_cap_v2":
															# Glide cosine schedule
															a_									=   betas_for_alpha_bar(UpperCAmelCase__     )
										elif beta_schedule == "sigmoid":
															# GeoDiff sigmoid schedule
															a_									=   torch.linspace(-6   , 6   , UpperCAmelCase__     )
															a_									=   torch.sigmoid(UpperCAmelCase__     ) * (beta_end - beta_start) + beta_start
										else:
															raise NotImplementedError(F'''{beta_schedule} does is not implemented for {self.__class__}'''     )

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

										a_									=   torch.tensor(1.0     )

										# standard deviation of the initial noise distribution
										a_									=   1.0

										# setable values
										a_									=   None
										a_									=   torch.from_numpy(np.arange(0   , UpperCAmelCase__     )[::-1].copy()     )

										a_									=   eta




					def  __SCREAMING_SNAKE_CASE (     self   , UpperCAmelCase__   , UpperCAmelCase__ = None     )      ->	torch.FloatTensor:
										return sample




					def  __SCREAMING_SNAKE_CASE (     self   , UpperCAmelCase__   , UpperCAmelCase__ = 10   , UpperCAmelCase__ = 10   , UpperCAmelCase__ = None   , )      ->	Tuple:
										a_									=   min(self.config.num_train_timesteps   , UpperCAmelCase__     )
										a_									=   num_inference_steps

										a_									=   []

										a_									=   {}
										for j in range(0   , num_inference_steps - jump_length   , UpperCAmelCase__     ):
															a_									=   jump_n_sample - 1

										a_									=   num_inference_steps
										while t >= 1:
															a_									=   t - 1
															timesteps.append(UpperCAmelCase__     )

															if jumps.get(UpperCAmelCase__   , 0     ) > 0:
																				a_									=   jumps[t] - 1
																				for _ in range(UpperCAmelCase__     ):
																									a_									=   t + 1
																									timesteps.append(UpperCAmelCase__     )

										a_									=   np.array(UpperCAmelCase__     ) * (self.config.num_train_timesteps // self.num_inference_steps)
										a_									=   torch.from_numpy(UpperCAmelCase__     ).to(UpperCAmelCase__     )




					def  __SCREAMING_SNAKE_CASE (     self   , UpperCAmelCase__     )      ->	Union[str, Any]:
										a_									=   t - self.config.num_train_timesteps // self.num_inference_steps

										a_									=   self.alphas_cumprod[t]
										a_									=   self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
										a_									=   1 - alpha_prod_t
										a_									=   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]
										a_									=   (beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev)

										return variance




					def  __SCREAMING_SNAKE_CASE (     self   , UpperCAmelCase__   , UpperCAmelCase__   , UpperCAmelCase__   , UpperCAmelCase__   , UpperCAmelCase__   , UpperCAmelCase__ = None   , UpperCAmelCase__ = True   , )      ->	Union[RePaintSchedulerOutput, Tuple]:
										a_									=   timestep
										a_									=   timestep - self.config.num_train_timesteps // self.num_inference_steps

										# 1. compute alphas, betas
										a_									=   self.alphas_cumprod[t]
										a_									=   self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
										a_									=   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
										a_									=   (sample - beta_prod_t**0.5 * model_output) / alpha_prod_t**0.5

										# 3. Clip "predicted x_0"
										if self.config.clip_sample:
															a_									=   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
										a_									=   model_output.device
										a_									=   randn_tensor(model_output.shape   , generator=UpperCAmelCase__   , device=UpperCAmelCase__   , dtype=model_output.dtype     )
										a_									=   self.eta * self._get_variance(UpperCAmelCase__     ) ** 0.5

										a_									=   0
										if t > 0 and self.eta > 0:
															a_									=   std_dev_t * noise

										# 6. compute "direction pointing to x_t" of formula (12)
										# from https://arxiv.org/pdf/2010.02502.pdf
										a_									=   (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
										a_									=   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
										a_									=   (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
										a_									=   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  __SCREAMING_SNAKE_CASE (     self   , UpperCAmelCase__   , UpperCAmelCase__   , UpperCAmelCase__=None     )      ->	int:
										a_									=   self.config.num_train_timesteps // self.num_inference_steps

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

															# 10. Algorithm 1 Line 10 https://arxiv.org/pdf/2201.09865.pdf
															a_									=   (1 - beta) ** 0.5 * sample + beta**0.5 * noise

										return sample




					def  __SCREAMING_SNAKE_CASE (     self   , UpperCAmelCase__   , UpperCAmelCase__   , UpperCAmelCase__   , )      ->	torch.FloatTensor:
										raise NotImplementedError('Use `DDPMScheduler.add_noise()` to train for sampling with RePaint.'     )




					def __len__(     self     )      ->	Tuple:
										return self.config.num_train_timesteps









'''simple docstring'''
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
						import torch

if is_vision_available():
						from PIL import Image

						from transformers import ConvNextImageProcessor


class    _snake_case   (				unittest.TestCase					):

					"""simple docstring"""





					def __init__(     self   , UpperCAmelCase__   , UpperCAmelCase__=7   , UpperCAmelCase__=3   , UpperCAmelCase__=18   , UpperCAmelCase__=30   , UpperCAmelCase__=400   , UpperCAmelCase__=True   , UpperCAmelCase__=None   , UpperCAmelCase__=0.8_7_5   , UpperCAmelCase__=True   , UpperCAmelCase__=[0.5, 0.5, 0.5]   , UpperCAmelCase__=[0.5, 0.5, 0.5]   , )      ->	Optional[int]:
										a_									=   size if size is not None else {'shortest_edge': 20}
										a_									=   parent
										a_									=   batch_size
										a_									=   num_channels
										a_									=   image_size
										a_									=   min_resolution
										a_									=   max_resolution
										a_									=   do_resize
										a_									=   size
										a_									=   crop_pct
										a_									=   do_normalize
										a_									=   image_mean
										a_									=   image_std




					def  __SCREAMING_SNAKE_CASE (     self     )      ->	List[str]:
										return {
										    "image_mean": self.image_mean,
										    "image_std": self.image_std,
										    "do_normalize": self.do_normalize,
										    "do_resize": self.do_resize,
										    "size": self.size,
										    "crop_pct": self.crop_pct,
										}



@require_torch
@require_vision
class    _snake_case   (				snake_case     ,					unittest.TestCase					):

					"""simple docstring"""





					_UpperCamelCase		 =      ConvNextImageProcessor if is_vision_available() else None




					def  __SCREAMING_SNAKE_CASE (     self     )      ->	Tuple:
										a_									=   ConvNextImageProcessingTester(self     )




					@property
					def  __SCREAMING_SNAKE_CASE (     self     )      ->	Dict:
										return self.image_processor_tester.prepare_image_processor_dict()




					def  __SCREAMING_SNAKE_CASE (     self     )      ->	Any:
										a_									=   self.image_processing_class(**self.image_processor_dict     )
										self.assertTrue(hasattr(UpperCAmelCase__   , 'do_resize'     )     )
										self.assertTrue(hasattr(UpperCAmelCase__   , 'size'     )     )
										self.assertTrue(hasattr(UpperCAmelCase__   , 'crop_pct'     )     )
										self.assertTrue(hasattr(UpperCAmelCase__   , 'do_normalize'     )     )
										self.assertTrue(hasattr(UpperCAmelCase__   , 'image_mean'     )     )
										self.assertTrue(hasattr(UpperCAmelCase__   , 'image_std'     )     )




					def  __SCREAMING_SNAKE_CASE (     self     )      ->	Tuple:
										a_									=   self.image_processing_class.from_dict(self.image_processor_dict     )
										self.assertEqual(image_processor.size   , {'shortest_edge': 20}     )

										a_									=   self.image_processing_class.from_dict(self.image_processor_dict   , size=42     )
										self.assertEqual(image_processor.size   , {'shortest_edge': 42}     )




					def  __SCREAMING_SNAKE_CASE (     self     )      ->	Optional[Any]:
										pass




					def  __SCREAMING_SNAKE_CASE (     self     )      ->	Tuple:
										# Initialize image_processing
										a_									=   self.image_processing_class(**self.image_processor_dict     )
										# create random PIL images
										a_									=   prepare_image_inputs(self.image_processor_tester   , equal_resolution=UpperCAmelCase__     )
										for image in image_inputs:
															self.assertIsInstance(UpperCAmelCase__   , Image.Image     )

										# Test not batched input
										a_									=   image_processing(image_inputs[0]   , return_tensors='pt'     ).pixel_values
										self.assertEqual(
										    encoded_images.shape   , (
										        1,
										        self.image_processor_tester.num_channels,
										        self.image_processor_tester.size['shortest_edge'],
										        self.image_processor_tester.size['shortest_edge'],
										    )   , )

										# Test batched
										a_									=   image_processing(UpperCAmelCase__   , return_tensors='pt'     ).pixel_values
										self.assertEqual(
										    encoded_images.shape   , (
										        self.image_processor_tester.batch_size,
										        self.image_processor_tester.num_channels,
										        self.image_processor_tester.size['shortest_edge'],
										        self.image_processor_tester.size['shortest_edge'],
										    )   , )




					def  __SCREAMING_SNAKE_CASE (     self     )      ->	int:
										# Initialize image_processing
										a_									=   self.image_processing_class(**self.image_processor_dict     )
										# create random numpy tensors
										a_									=   prepare_image_inputs(self.image_processor_tester   , equal_resolution=UpperCAmelCase__   , numpify=UpperCAmelCase__     )
										for image in image_inputs:
															self.assertIsInstance(UpperCAmelCase__   , np.ndarray     )

										# Test not batched input
										a_									=   image_processing(image_inputs[0]   , return_tensors='pt'     ).pixel_values
										self.assertEqual(
										    encoded_images.shape   , (
										        1,
										        self.image_processor_tester.num_channels,
										        self.image_processor_tester.size['shortest_edge'],
										        self.image_processor_tester.size['shortest_edge'],
										    )   , )

										# Test batched
										a_									=   image_processing(UpperCAmelCase__   , return_tensors='pt'     ).pixel_values
										self.assertEqual(
										    encoded_images.shape   , (
										        self.image_processor_tester.batch_size,
										        self.image_processor_tester.num_channels,
										        self.image_processor_tester.size['shortest_edge'],
										        self.image_processor_tester.size['shortest_edge'],
										    )   , )




					def  __SCREAMING_SNAKE_CASE (     self     )      ->	int:
										# Initialize image_processing
										a_									=   self.image_processing_class(**self.image_processor_dict     )
										# create random PyTorch tensors
										a_									=   prepare_image_inputs(self.image_processor_tester   , equal_resolution=UpperCAmelCase__   , torchify=UpperCAmelCase__     )
										for image in image_inputs:
															self.assertIsInstance(UpperCAmelCase__   , torch.Tensor     )

										# Test not batched input
										a_									=   image_processing(image_inputs[0]   , return_tensors='pt'     ).pixel_values
										self.assertEqual(
										    encoded_images.shape   , (
										        1,
										        self.image_processor_tester.num_channels,
										        self.image_processor_tester.size['shortest_edge'],
										        self.image_processor_tester.size['shortest_edge'],
										    )   , )

										# Test batched
										a_									=   image_processing(UpperCAmelCase__   , return_tensors='pt'     ).pixel_values
										self.assertEqual(
										    encoded_images.shape   , (
										        self.image_processor_tester.batch_size,
										        self.image_processor_tester.num_channels,
										        self.image_processor_tester.size['shortest_edge'],
										        self.image_processor_tester.size['shortest_edge'],
										    )   , )

