





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  __A   (   lowercase_			):

			'''simple docstring'''







			lowerCAmelCase_												=						42
			lowerCAmelCase_												=						42





def lowerCAmelCase__(__snake_case	,__snake_case=0.9_9_9	,__snake_case="cosine"	,)    ->				str:







	'''simple docstring'''







	if alpha_transform_type == "cosine":

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

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

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






class  __A   (   lowercase_      ,    lowercase_			):

			'''simple docstring'''







			lowerCAmelCase_												=						1





			@register_to_config
			def __init__(   self	,  __lowerCAmelCase = 1_0_0_0	,  __lowerCAmelCase = 0.0001	,  __lowerCAmelCase = 0.02	,  __lowerCAmelCase = "linear"	,  __lowerCAmelCase = 0.0	,  __lowerCAmelCase = None	,  __lowerCAmelCase = True	,  ):



				'''simple docstring'''







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

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

				lowerCamelCase__        =		torch.tensor(1.0							)

				# standard deviation of the initial noise distribution
				lowerCamelCase__        =		1.0

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

				lowerCamelCase__        =		eta





			def 		__lowerCamelCase    (   self	,  __lowerCAmelCase	,  __lowerCAmelCase = None							):



				'''simple docstring'''







				return sample





			def 		__lowerCamelCase    (   self	,  __lowerCAmelCase	,  __lowerCAmelCase = 1_0	,  __lowerCAmelCase = 1_0	,  __lowerCAmelCase = None	,  ):



				'''simple docstring'''







				lowerCamelCase__        =		min(self.config.num_train_timesteps	,  __UpperCamelCase							)
				lowerCamelCase__        =		num_inference_steps

				lowerCamelCase__        =		[]

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

				lowerCamelCase__        =		num_inference_steps
				while t >= 1:
					lowerCamelCase__        =		t - 1
					timesteps.append(__UpperCamelCase							)

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

				lowerCamelCase__        =		np.array(__UpperCamelCase							) * (self.config.num_train_timesteps // self.num_inference_steps)
				lowerCamelCase__        =		torch.from_numpy(__UpperCamelCase							).to(__UpperCamelCase							)





			def 		__lowerCamelCase    (   self	,  __lowerCAmelCase							):



				'''simple docstring'''







				lowerCamelCase__        =		t - self.config.num_train_timesteps // self.num_inference_steps

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

				return variance





			def 		__lowerCamelCase    (   self	,  __lowerCAmelCase	,  __lowerCAmelCase	,  __lowerCAmelCase	,  __lowerCAmelCase	,  __lowerCAmelCase	,  __lowerCAmelCase = None	,  __lowerCAmelCase = True	,  ):



				'''simple docstring'''







				lowerCamelCase__        =		timestep
				lowerCamelCase__        =		timestep - self.config.num_train_timesteps // self.num_inference_steps

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

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

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

				# 6. compute "direction pointing to x_t" of formula (12)
				# from https://arxiv.org/pdf/2010.02502.pdf
				lowerCamelCase__        =		(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
				lowerCamelCase__        =		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
				lowerCamelCase__        =		(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
				lowerCamelCase__        =		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 		__lowerCamelCase    (   self	,  __lowerCAmelCase	,  __lowerCAmelCase	,  __lowerCAmelCase=None							):



				'''simple docstring'''







				lowerCamelCase__        =		self.config.num_train_timesteps // self.num_inference_steps

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

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

				return sample





			def 		__lowerCamelCase    (   self	,  __lowerCAmelCase	,  __lowerCAmelCase	,  __lowerCAmelCase	,  ):



				'''simple docstring'''







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





			def __len__(   self							):



				'''simple docstring'''







				return self.config.num_train_timesteps













import warnings

from .generation import FlaxGenerationMixin






class  __A   (   lowerCAmelCase			):

			'''simple docstring'''







			warnings.warn(
			    """Importing `FlaxGenerationMixin` from `src/transformers/generation_flax_utils.py` is deprecated and will """
			    """be removed in Transformers v5. Import as `from transformers import FlaxGenerationMixin` instead."""      ,    lowerCAmelCase      ,    )



