'''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
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->UnCLIP
class 				a__(					lowerCAmelCase__	):


			'''simple docstring'''






			UpperCAmelCase_			:     torch.FloatTensor
			UpperCAmelCase_			:     Optional[torch.FloatTensor]      =       None







def 				snake_case__							(			_A:					str      ,   _A:					str=0.999      ,   _A:					Union[str, Any]="cosine"      ,   )    ->	int:

		'''simple docstring'''


		if alpha_transform_type == "cosine":

				def alpha_bar_fn(_A:					Optional[Any] ):
						return math.cos((t + 0.008) / 1.008 * math.pi / 2 ) ** 2

		elif alpha_transform_type == "exp":

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

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

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






class 				a__(					lowerCAmelCase__ ,						lowerCAmelCase__	):


			'''simple docstring'''






			@register_to_config
			def __init__(   self       ,    __lowerCAmelCase = 1000       ,    __lowerCAmelCase = "fixed_small_log"       ,    __lowerCAmelCase = True       ,    __lowerCAmelCase = 1.0       ,    __lowerCAmelCase = "epsilon"       ,    __lowerCAmelCase = "squaredcos_cap_v2"       ,    ):




					"""simple docstring"""


					if beta_schedule != "squaredcos_cap_v2":
							raise ValueError("""UnCLIPScheduler only supports `beta_schedule`: 'squaredcos_cap_v2'""")

					lowerCAmelCase   					=  betas_for_alpha_bar(__lowerCAmelCase)

					lowerCAmelCase   					=  1.0 - self.betas
					lowerCAmelCase   					=  torch.cumprod(self.alphas       ,    dim=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       ,    __lowerCAmelCase)[::-1].copy())

					lowerCAmelCase   					=  variance_type



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




					"""simple docstring"""


					return sample



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




					"""simple docstring"""


					lowerCAmelCase   					=  num_inference_steps
					lowerCAmelCase   					=  (self.config.num_train_timesteps - 1) / (self.num_inference_steps - 1)
					lowerCAmelCase   					=  (np.arange(0       ,    __lowerCAmelCase) * step_ratio).round()[::-1].copy().astype(np.intaa)
					lowerCAmelCase   					=  torch.from_numpy(__lowerCAmelCase).to(__lowerCAmelCase)



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




					"""simple docstring"""


					if prev_timestep is None:
							lowerCAmelCase   					=  t - 1

					lowerCAmelCase   					=  self.alphas_cumprod[t]
					lowerCAmelCase   					=  self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
					lowerCAmelCase   					=  1 - alpha_prod_t
					lowerCAmelCase   					=  1 - alpha_prod_t_prev

					if prev_timestep == t - 1:
							lowerCAmelCase   					=  self.betas[t]
					else:
							lowerCAmelCase   					=  1 - alpha_prod_t / 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
					lowerCAmelCase   					=  beta_prod_t_prev / beta_prod_t * beta

					if variance_type is None:
							lowerCAmelCase   					=  self.config.variance_type

					# hacks - were probably added for training stability
					if variance_type == "fixed_small_log":
							lowerCAmelCase   					=  torch.log(torch.clamp(__lowerCAmelCase       ,    min=1E-2_0))
							lowerCAmelCase   					=  torch.exp(0.5 * variance)
					elif variance_type == "learned_range":
							# NOTE difference with DDPM scheduler
							lowerCAmelCase   					=  variance.log()
							lowerCAmelCase   					=  beta.log()

							lowerCAmelCase   					=  (predicted_variance + 1) / 2
							lowerCAmelCase   					=  frac * max_log + (1 - frac) * min_log

					return variance



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




					"""simple docstring"""


					lowerCAmelCase   					=  timestep

					if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type == "learned_range":
							lowerCAmelCase					,     lowerCAmelCase   					=  torch.split(__lowerCAmelCase       ,    sample.shape[1]       ,    dim=1)
					else:
							lowerCAmelCase   					=  None

					# 1. compute alphas, betas
					if prev_timestep is None:
							lowerCAmelCase   					=  t - 1

					lowerCAmelCase   					=  self.alphas_cumprod[t]
					lowerCAmelCase   					=  self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
					lowerCAmelCase   					=  1 - alpha_prod_t
					lowerCAmelCase   					=  1 - alpha_prod_t_prev

					if prev_timestep == t - 1:
							lowerCAmelCase   					=  self.betas[t]
							lowerCAmelCase   					=  self.alphas[t]
					else:
							lowerCAmelCase   					=  1 - alpha_prod_t / alpha_prod_t_prev
							lowerCAmelCase   					=  1 - beta

					# 2. compute predicted original sample from predicted noise also called
					# "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
					if self.config.prediction_type == "epsilon":
							lowerCAmelCase   					=  (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
					elif self.config.prediction_type == "sample":
							lowerCAmelCase   					=  model_output
					else:
							raise ValueError(
							    f"prediction_type given as {self.config.prediction_type} must be one of `epsilon` or `sample`"
							    """ for the UnCLIPScheduler.""")

					# 3. Clip "predicted x_0"
					if self.config.clip_sample:
							lowerCAmelCase   					=  torch.clamp(
							    __lowerCAmelCase       ,    -self.config.clip_sample_range       ,    self.config.clip_sample_range)

					# 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
					# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
					lowerCAmelCase   					=  (alpha_prod_t_prev ** 0.5 * beta) / beta_prod_t
					lowerCAmelCase   					=  alpha ** 0.5 * beta_prod_t_prev / beta_prod_t

					# 5. Compute predicted previous sample µ_t
					# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
					lowerCAmelCase   					=  pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

					# 6. Add noise
					lowerCAmelCase   					=  0
					if t > 0:
							lowerCAmelCase   					=  randn_tensor(
							    model_output.shape       ,    dtype=model_output.dtype       ,    generator=__lowerCAmelCase       ,    device=model_output.device)

							lowerCAmelCase   					=  self._get_variance(
							    __lowerCAmelCase       ,    predicted_variance=__lowerCAmelCase       ,    prev_timestep=__lowerCAmelCase       ,    )

							if self.variance_type == "fixed_small_log":
									lowerCAmelCase   					=  variance
							elif self.variance_type == "learned_range":
									lowerCAmelCase   					=  (0.5 * variance).exp()
							else:
									raise ValueError(
									    f"variance_type given as {self.variance_type} must be one of `fixed_small_log` or `learned_range`"
									    """ for the UnCLIPScheduler.""")

							lowerCAmelCase   					=  variance * variance_noise

					lowerCAmelCase   					=  pred_prev_sample + variance

					if not return_dict:
							return (pred_prev_sample,)

					return UnCLIPSchedulerOutput(prev_sample=__lowerCAmelCase       ,    pred_original_sample=__lowerCAmelCase)







			def 							a_       (   self       ,    __lowerCAmelCase       ,    __lowerCAmelCase       ,    __lowerCAmelCase       ,    ):




					"""simple docstring"""


					lowerCAmelCase   					=  self.alphas_cumprod.to(device=original_samples.device       ,    dtype=original_samples.dtype)
					lowerCAmelCase   					=  timesteps.to(original_samples.device)

					lowerCAmelCase   					=  alphas_cumprod[timesteps] ** 0.5
					lowerCAmelCase   					=  sqrt_alpha_prod.flatten()
					while len(sqrt_alpha_prod.shape) < len(original_samples.shape):
							lowerCAmelCase   					=  sqrt_alpha_prod.unsqueeze(-1)

					lowerCAmelCase   					=  (1 - alphas_cumprod[timesteps]) ** 0.5
					lowerCAmelCase   					=  sqrt_one_minus_alpha_prod.flatten()
					while len(sqrt_one_minus_alpha_prod.shape) < len(original_samples.shape):
							lowerCAmelCase   					=  sqrt_one_minus_alpha_prod.unsqueeze(-1)

					lowerCAmelCase   					=  sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
					return noisy_samples





'''simple docstring'''




import math

import flax.linen as nn
import jax.numpy as jnp






def 				snake_case__							(			_A:					jnp.ndarray      ,   _A:					int      ,   _A:					float = 1      ,   _A:					float = 1      ,   _A:					float = 1.0e4      ,   _A:					bool = False      ,   _A:					float = 1.0      ,   )    ->	jnp.ndarray:

		'''simple docstring'''


		assert timesteps.ndim == 1, "Timesteps should be a 1d-array"
		assert embedding_dim % 2 == 0, f"Embedding dimension {embedding_dim} should be even"
		lowerCAmelCase   					=  float(embedding_dim // 2 )
		lowerCAmelCase   					=  math.log(max_timescale / min_timescale ) / (num_timescales - freq_shift)
		lowerCAmelCase   					=  min_timescale * jnp.exp(jnp.arange(_A      ,   dtype=jnp.floataa ) * -log_timescale_increment )
		lowerCAmelCase   					=  jnp.expand_dims(_A      ,   1 ) * jnp.expand_dims(_A      ,   0 )

		# scale embeddings
		lowerCAmelCase   					=  scale * emb

		if flip_sin_to_cos:
				lowerCAmelCase   					=  jnp.concatenate([jnp.cos(_A ), jnp.sin(_A )]      ,   axis=1 )
		else:
				lowerCAmelCase   					=  jnp.concatenate([jnp.sin(_A ), jnp.cos(_A )]      ,   axis=1 )
		lowerCAmelCase   					=  jnp.reshape(_A      ,   [jnp.shape(_A )[0], embedding_dim] )
		return signal






class 				a__(					nn.Module	):


			'''simple docstring'''






			UpperCAmelCase_			:     int      =       3_2
			UpperCAmelCase_			:     jnp.dtype      =       jnp.floataa



			@nn.compact
			def __call__(   self       ,    __lowerCAmelCase):




					"""simple docstring"""


					lowerCAmelCase   					=  nn.Dense(self.time_embed_dim       ,    dtype=self.dtype       ,    name="""linear_1""")(__lowerCAmelCase)
					lowerCAmelCase   					=  nn.silu(__lowerCAmelCase)
					lowerCAmelCase   					=  nn.Dense(self.time_embed_dim       ,    dtype=self.dtype       ,    name="""linear_2""")(__lowerCAmelCase)
					return temb







class 				a__(					nn.Module	):


			'''simple docstring'''






			UpperCAmelCase_			:     int      =       3_2
			UpperCAmelCase_			:     bool      =       False
			UpperCAmelCase_			:     float      =       1



			@nn.compact
			def __call__(   self       ,    __lowerCAmelCase):




					"""simple docstring"""


					return get_sinusoidal_embeddings(
					    __lowerCAmelCase       ,    embedding_dim=self.dim       ,    flip_sin_to_cos=self.flip_sin_to_cos       ,    freq_shift=self.freq_shift)




