






# DISCLAIMER: This file is strongly influenced by https://github.com/ermongroup/ddim

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

import flax
import jax
import jax.numpy as jnp

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import (
    CommonSchedulerState,
    FlaxKarrasDiffusionSchedulers,
    FlaxSchedulerMixin,
    FlaxSchedulerOutput,
    add_noise_common,
    get_velocity_common,
)



@flax.struct.dataclass
class  __lowerCAmelCase     :
 lowerCAmelCase__       					=  4_2

 # setable values
 lowerCAmelCase__       					=  4_2
 lowerCAmelCase__       					=  4_2
 lowerCAmelCase__       					=  None




 @classmethod
 def        lowerCamelCase      (						cls	,				__UpperCAmelCase	,				__UpperCAmelCase	,				__UpperCAmelCase				):



    '''simple docstring'''
    return cls(common=__UpperCAmelCase	,				init_noise_sigma=__UpperCAmelCase	,				timesteps=__UpperCAmelCase				)



@dataclass
class  __lowerCAmelCase     (	lowerCAmelCase__   ):
 lowerCAmelCase__       					=  4_2



class  __lowerCAmelCase     (	lowerCAmelCase__  ,	lowerCAmelCase__   ):
 lowerCAmelCase__       					=  [e.name for e in FlaxKarrasDiffusionSchedulers]

 lowerCAmelCase__       					=  4_2




 @property
 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    return True




 @register_to_config
 def __init__(						self	,				__UpperCAmelCase = 1000	,				__UpperCAmelCase = 0.0_001	,				__UpperCAmelCase = 0.02	,				__UpperCAmelCase = "linear"	,				__UpperCAmelCase = None	,				__UpperCAmelCase = "fixed_small"	,				__UpperCAmelCase = True	,				__UpperCAmelCase = "epsilon"	,				__UpperCAmelCase = jnp.floataa	,				):



    '''simple docstring'''
    __lowerCamelCase													=    dtype




 def        lowerCamelCase      (						self	,				__UpperCAmelCase = None				):



    '''simple docstring'''
    if common is None:
       __lowerCamelCase													=    CommonSchedulerState.create(self				)

    # standard deviation of the initial noise distribution
    __lowerCamelCase													=    jnp.array(1.0	,				dtype=self.dtype				)

    __lowerCamelCase													=    jnp.arange(0	,				self.config.num_train_timesteps				).round()[::-1]

    return DDPMSchedulerState.create(
        common=__UpperCAmelCase	,				init_noise_sigma=__UpperCAmelCase	,				timesteps=__UpperCAmelCase	,				)




 def        lowerCamelCase      (						self	,				__UpperCAmelCase	,				__UpperCAmelCase	,				__UpperCAmelCase = None				):



    '''simple docstring'''
    return sample




 def        lowerCamelCase      (						self	,				__UpperCAmelCase	,				__UpperCAmelCase	,				__UpperCAmelCase = ()				):



    '''simple docstring'''
    __lowerCamelCase													=    self.config.num_train_timesteps // num_inference_steps
    # creates integer timesteps by multiplying by ratio
    # rounding to avoid issues when num_inference_step is power of 3
    __lowerCamelCase													=    (jnp.arange(0	,				__UpperCAmelCase				) * step_ratio).round()[::-1]

    return state.replace(
        num_inference_steps=__UpperCAmelCase	,				timesteps=__UpperCAmelCase	,				)




 def        lowerCamelCase      (						self	,				__UpperCAmelCase	,				__UpperCAmelCase	,				__UpperCAmelCase=None	,				__UpperCAmelCase=None				):



    '''simple docstring'''
    __lowerCamelCase													=    state.common.alphas_cumprod[t]
    __lowerCamelCase													=    jnp.where(t > 0	,				state.common.alphas_cumprod[t - 1]	,				jnp.array(1.0	,				dtype=self.dtype				)				)

    # 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													=    (1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * state.common.betas[t]

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

    # hacks - were probably added for training stability
    if variance_type == "fixed_small":
       __lowerCamelCase													=    jnp.clip(__UpperCAmelCase	,				a_min=1E-2_0				)
    # for rl-diffuser https://arxiv.org/abs/2205.09991
    elif variance_type == "fixed_small_log":
       __lowerCamelCase													=    jnp.log(jnp.clip(__UpperCAmelCase	,				a_min=1E-2_0				)				)
    elif variance_type == "fixed_large":
       __lowerCamelCase													=    state.common.betas[t]
    elif variance_type == "fixed_large_log":
       # Glide max_log
       __lowerCamelCase													=    jnp.log(state.common.betas[t]				)
    elif variance_type == "learned":
       return predicted_variance
    elif variance_type == "learned_range":
       __lowerCamelCase													=    variance
       __lowerCamelCase													=    state.common.betas[t]
       __lowerCamelCase													=    (predicted_variance + 1) / 2
       __lowerCamelCase													=    frac * max_log + (1 - frac) * min_log

    return variance




 def        lowerCamelCase      (						self	,				__UpperCAmelCase	,				__UpperCAmelCase	,				__UpperCAmelCase	,				__UpperCAmelCase	,				__UpperCAmelCase = None	,				__UpperCAmelCase = True	,				):



    '''simple docstring'''
    __lowerCamelCase													=    timestep

    if key is None:
       __lowerCamelCase													=    jax.random.PRNGKey(0				)

    if model_output.shape[1] == sample.shape[1] * 2 and self.config.variance_type in ["learned", "learned_range"]:
       __lowerCamelCase							,__lowerCamelCase													=    jnp.split(__UpperCAmelCase	,				sample.shape[1]	,				axis=1				)
    else:
       __lowerCamelCase													=    None

    # 1. compute alphas, betas
    __lowerCamelCase													=    state.common.alphas_cumprod[t]
    __lowerCamelCase													=    jnp.where(t > 0	,				state.common.alphas_cumprod[t - 1]	,				jnp.array(1.0	,				dtype=self.dtype				)				)
    __lowerCamelCase													=    1 - alpha_prod_t
    __lowerCamelCase													=    1 - alpha_prod_t_prev

    # 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
    elif self.config.prediction_type == "v_prediction":
       __lowerCamelCase													=    (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
    else:
       raise ValueError(
           F"""prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample` """
           ''' for the FlaxDDPMScheduler.'''				)

    # 3. Clip "predicted x_0"
    if self.config.clip_sample:
       __lowerCamelCase													=    jnp.clip(__UpperCAmelCase	,				-1	,				1				)

    # 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 * state.common.betas[t]) / beta_prod_t
    __lowerCamelCase													=    state.common.alphas[t] ** 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
    def random_variance():
       __lowerCamelCase													=    jax.random.split(__UpperCAmelCase	,				num=1				)
       __lowerCamelCase													=    jax.random.normal(__UpperCAmelCase	,				shape=model_output.shape	,				dtype=self.dtype				)
       return (self._get_variance(__UpperCAmelCase	,				__UpperCAmelCase	,				predicted_variance=__UpperCAmelCase				) ** 0.5) * noise

    __lowerCamelCase													=    jnp.where(t > 0	,				random_variance()	,				jnp.zeros(model_output.shape	,				dtype=self.dtype				)				)

    __lowerCamelCase													=    pred_prev_sample + variance

    if not return_dict:
       return (pred_prev_sample, state)

    return FlaxDDPMSchedulerOutput(prev_sample=__UpperCAmelCase	,				state=__UpperCAmelCase				)




 def        lowerCamelCase      (						self	,				__UpperCAmelCase	,				__UpperCAmelCase	,				__UpperCAmelCase	,				__UpperCAmelCase	,				):



    '''simple docstring'''
    return add_noise_common(state.common	,				__UpperCAmelCase	,				__UpperCAmelCase	,				__UpperCAmelCase				)




 def        lowerCamelCase      (						self	,				__UpperCAmelCase	,				__UpperCAmelCase	,				__UpperCAmelCase	,				__UpperCAmelCase	,				):



    '''simple docstring'''
    return get_velocity_common(state.common	,				__UpperCAmelCase	,				__UpperCAmelCase	,				__UpperCAmelCase				)




 def __len__(						self				):



    '''simple docstring'''
    return self.config.num_train_timesteps











import torch

from diffusers import StableDiffusionPipeline


a_			=					"""path-to-your-trained-model"""
a_			=					StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.floataa).to("""cuda""")

a_			=					"""A photo of sks dog in a bucket"""
a_			=					pipe(prompt, num_inference_steps=50, guidance_scale=7.5).images[0]

image.save("""dog-bucket.png""")






