
from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


UpperCamelCase_  =	logging.get_logger(__name__)

class     _SCREAMING_SNAKE_CASE							(		snake_case							):
				lowerCamelCase_       =     ['pixel_values']





				def __init__(		self :		List[Any]					,      snake_case_ :		bool = True					,      snake_case_ :		Optional[Dict[str, int]] = None					,      snake_case_ :		PILImageResampling = PILImageResampling.BILINEAR					,      snake_case_ :		bool = True					,      snake_case_ :		Union[int, float] = 1 / 255					,      snake_case_ :		bool = True					,      snake_case_ :		Optional[Union[float, List[float]]] = None					,      snake_case_ :		Optional[Union[float, List[float]]] = None					,      **snake_case_ :		Optional[int]					,      ):


								"""simple docstring"""

								super().__init__(**snake_case_							)
								A     :  int   =				size if size is not None else {'''height''': 224, '''width''': 224}
								A     :  str   =				get_size_dict(snake_case_							)
								A     :  Any   =				do_resize
								A     :  Union[str, Any]   =				do_rescale
								A     :  str   =				do_normalize
								A     :  int   =				size
								A     :  int   =				resample
								A     :  List[str]   =				rescale_factor
								A     :  str   =				image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
								A     :  str   =				image_std if image_std is not None else IMAGENET_STANDARD_STD





				def        _UpperCAmelCase						(		self :		Dict					,      snake_case_ :		np.ndarray					,      snake_case_ :		Dict[str, int]					,      snake_case_ :		PILImageResampling = PILImageResampling.BILINEAR					,      snake_case_ :		Optional[Union[str, ChannelDimension]] = None					,      **snake_case_ :		str					,      ):


								"""simple docstring"""

								A     :  List[str]   =				get_size_dict(snake_case_							)
								if "height" not in size or "width" not in size:
												raise ValueError(f"""The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}"""							)
								return resize(
								    snake_case_					,      size=(size['''height'''], size['''width'''])					,      resample=snake_case_					,      data_format=snake_case_					,      **snake_case_							)





				def        _UpperCAmelCase						(		self :		Dict					,      snake_case_ :		np.ndarray					,      snake_case_ :		float					,      snake_case_ :		Optional[Union[str, ChannelDimension]] = None					,      **snake_case_ :		List[Any]							):


								"""simple docstring"""

								return rescale(snake_case_					,      scale=snake_case_					,      data_format=snake_case_					,      **snake_case_							)





				def        _UpperCAmelCase						(		self :		Optional[Any]					,      snake_case_ :		np.ndarray					,      snake_case_ :		Union[float, List[float]]					,      snake_case_ :		Union[float, List[float]]					,      snake_case_ :		Optional[Union[str, ChannelDimension]] = None					,      **snake_case_ :		List[str]					,      ):


								"""simple docstring"""

								return normalize(snake_case_					,      mean=snake_case_					,      std=snake_case_					,      data_format=snake_case_					,      **snake_case_							)






				def        _UpperCAmelCase						(		self :		Union[str, Any]					,      snake_case_ :		ImageInput					,      snake_case_ :		Optional[bool] = None					,      snake_case_ :		Dict[str, int] = None					,      snake_case_ :		PILImageResampling = None					,      snake_case_ :		Optional[bool] = None					,      snake_case_ :		Optional[float] = None					,      snake_case_ :		Optional[bool] = None					,      snake_case_ :		Optional[Union[float, List[float]]] = None					,      snake_case_ :		Optional[Union[float, List[float]]] = None					,      snake_case_ :		Optional[Union[str, TensorType]] = None					,      snake_case_ :		Union[str, ChannelDimension] = ChannelDimension.FIRST					,      **snake_case_ :		Optional[Any]					,      ):


								"""simple docstring"""

								A     :  int   =				do_resize if do_resize is not None else self.do_resize
								A     :  Union[str, Any]   =				do_rescale if do_rescale is not None else self.do_rescale
								A     :  Optional[Any]   =				do_normalize if do_normalize is not None else self.do_normalize
								A     :  Tuple   =				resample if resample is not None else self.resample
								A     :  int   =				rescale_factor if rescale_factor is not None else self.rescale_factor
								A     :  str   =				image_mean if image_mean is not None else self.image_mean
								A     :  Any   =				image_std if image_std is not None else self.image_std

								A     :  str   =				size if size is not None else self.size
								A     :  List[Any]   =				get_size_dict(snake_case_							)

								A     :  List[str]   =				make_list_of_images(snake_case_							)

								if not valid_images(snake_case_							):
												raise ValueError(
												    '''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
												    '''torch.Tensor, tf.Tensor or jax.ndarray.'''							)

								if do_resize and size is None:
												raise ValueError('''Size must be specified if do_resize is True.'''							)

								if do_rescale and rescale_factor is None:
												raise ValueError('''Rescale factor must be specified if do_rescale is True.'''							)

								# All transformations expect numpy arrays.
								A     :  Union[str, Any]   =				[to_numpy_array(snake_case_							) for image in images]

								if do_resize:
												A     :  Any   =				[self.resize(image=snake_case_					,      size=snake_case_					,      resample=snake_case_							) for image in images]

								if do_rescale:
												A     :  List[str]   =				[self.rescale(image=snake_case_					,      scale=snake_case_							) for image in images]

								if do_normalize:
												A     :  Any   =				[self.normalize(image=snake_case_					,      mean=snake_case_					,      std=snake_case_							) for image in images]

								A     :  Tuple   =				[to_channel_dimension_format(snake_case_					,      snake_case_							) for image in images]

								A     :  List[Any]   =				{'''pixel_values''': images}
								return BatchFeature(data=snake_case_					,      tensor_type=snake_case_							)

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     _SCREAMING_SNAKE_CASE							(		snake_case							):
				lowerCamelCase_       =     42
				lowerCamelCase_       =     42





def 							_lowerCamelCase			(					lowerCamelCase_:  str				,	lowerCamelCase_:  int=0.9_9_9				,	lowerCamelCase_:  Optional[Any]="cosine"				,	):



				'''simple docstring'''







				if alpha_transform_type == "cosine":

								def alpha_bar_fn(lowerCamelCase_:  Optional[Any]		):
												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(lowerCamelCase_:  Optional[Any]		):
												return math.exp(t * -1_2.0		)

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

				A     :  Dict   =				[]
				for i in range(lowerCamelCase_		):
								A     :  Dict   =				i / num_diffusion_timesteps
								A     :  List[str]   =				(i + 1) / num_diffusion_timesteps
								betas.append(min(1 - alpha_bar_fn(lowerCamelCase_		) / alpha_bar_fn(lowerCamelCase_		)				,	lowerCamelCase_		)		)
				return torch.tensor(lowerCamelCase_				,	dtype=torch.floataa		)






class     _SCREAMING_SNAKE_CASE							(		snake_case,    snake_case							):
				lowerCamelCase_       =     1





				@register_to_config
				def __init__(		self :		Optional[Any]					,      snake_case_ :		int = 1000					,      snake_case_ :		float = 0.00_01					,      snake_case_ :		float = 0.02					,      snake_case_ :		str = "linear"					,      snake_case_ :		float = 0.0					,      snake_case_ :		Optional[np.ndarray] = None					,      snake_case_ :		bool = True					,      ):


								"""simple docstring"""

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

								A     :  Any   =				1.0 - self.betas
								A     :  int   =				torch.cumprod(self.alphas					,      dim=0							)
								A     :  Any   =				torch.tensor(1.0							)

								A     :  Dict   =				torch.tensor(1.0							)

								# standard deviation of the initial noise distribution
								A     :  Optional[Any]   =				1.0

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

								A     :  Any   =				eta





				def        _UpperCAmelCase						(		self :		List[Any]					,      snake_case_ :		torch.FloatTensor					,      snake_case_ :		Optional[int] = None							):


								"""simple docstring"""

								return sample





				def        _UpperCAmelCase						(		self :		List[Any]					,      snake_case_ :		int					,      snake_case_ :		int = 10					,      snake_case_ :		int = 10					,      snake_case_ :		Union[str, torch.device] = None					,      ):


								"""simple docstring"""

								A     :  List[Any]   =				min(self.config.num_train_timesteps					,      snake_case_							)
								A     :  int   =				num_inference_steps

								A     :  List[Any]   =				[]

								A     :  Optional[Any]   =				{}
								for j in range(0					,      num_inference_steps - jump_length					,      snake_case_							):
												A     :  List[Any]   =				jump_n_sample - 1

								A     :  Optional[int]   =				num_inference_steps
								while t >= 1:
												A     :  Optional[Any]   =				t - 1
												timesteps.append(snake_case_							)

												if jumps.get(snake_case_					,      0							) > 0:
																A     :  Optional[int]   =				jumps[t] - 1
																for _ in range(snake_case_							):
																				A     :  Dict   =				t + 1
																				timesteps.append(snake_case_							)

								A     :  Optional[Any]   =				np.array(snake_case_							) * (self.config.num_train_timesteps // self.num_inference_steps)
								A     :  Optional[Any]   =				torch.from_numpy(snake_case_							).to(snake_case_							)





				def        _UpperCAmelCase						(		self :		Optional[int]					,      snake_case_ :		Optional[int]							):


								"""simple docstring"""

								A     :  str   =				t - self.config.num_train_timesteps // self.num_inference_steps

								A     :  str   =				self.alphas_cumprod[t]
								A     :  List[Any]   =				self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
								A     :  Dict   =				1 - alpha_prod_t
								A     :  Any   =				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     :  int   =				(beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev)

								return variance





				def        _UpperCAmelCase						(		self :		List[str]					,      snake_case_ :		torch.FloatTensor					,      snake_case_ :		int					,      snake_case_ :		torch.FloatTensor					,      snake_case_ :		torch.FloatTensor					,      snake_case_ :		torch.FloatTensor					,      snake_case_ :		Optional[torch.Generator] = None					,      snake_case_ :		bool = True					,      ):


								"""simple docstring"""

								A     :  Any   =				timestep
								A     :  List[str]   =				timestep - self.config.num_train_timesteps // self.num_inference_steps

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

								# 3. Clip "predicted x_0"
								if self.config.clip_sample:
												A     :  Optional[Any]   =				torch.clamp(snake_case_					,      -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     :  Dict   =				model_output.device
								A     :  str   =				randn_tensor(model_output.shape					,      generator=snake_case_					,      device=snake_case_					,      dtype=model_output.dtype							)
								A     :  Tuple   =				self.eta * self._get_variance(snake_case_							) ** 0.5

								A     :  List[str]   =				0
								if t > 0 and self.eta > 0:
												A     :  Optional[int]   =				std_dev_t * noise

								# 6. compute "direction pointing to x_t" of formula (12)
								# from https://arxiv.org/pdf/2010.02502.pdf
								A     :  List[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
								A     :  Dict   =				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     :  Any   =				(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     :  Optional[int]   =				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=snake_case_					,      pred_original_sample=snake_case_							)





				def        _UpperCAmelCase						(		self :		Tuple					,      snake_case_ :		Any					,      snake_case_ :		Tuple					,      snake_case_ :		List[str]=None							):


								"""simple docstring"""

								A     :  List[str]   =				self.config.num_train_timesteps // self.num_inference_steps

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

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

								return sample





				def        _UpperCAmelCase						(		self :		List[str]					,      snake_case_ :		torch.FloatTensor					,      snake_case_ :		torch.FloatTensor					,      snake_case_ :		torch.IntTensor					,      ):


								"""simple docstring"""

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






				def __len__(		self :		Optional[Any]							):


								"""simple docstring"""

								return self.config.num_train_timesteps