
"""simple docstring"""




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

import flax
import jax.numpy as jnp
from scipy import integrate

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







@flax.struct.dataclass
class 						lowercase:
     __snake_case:  CommonSchedulerState

     # setable values
     __snake_case:  jnp.ndarray
     __snake_case:  jnp.ndarray
     __snake_case:  jnp.ndarray
     __snake_case:  Optional[int]  				=    None

     # running values
     __snake_case:  Optional[jnp.ndarray]  				=    None



     @classmethod
     def  lowercase__     (       cls    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE      )		->    Dict:







         """simple docstring"""



         return cls(common=__SCREAMING_SNAKE_CASE    ,							init_noise_sigma=__SCREAMING_SNAKE_CASE    ,							timesteps=__SCREAMING_SNAKE_CASE    ,							sigmas=__SCREAMING_SNAKE_CASE      )







@dataclass
class 						lowercase(_lowercase					):
     __snake_case:  LMSDiscreteSchedulerState







class 						lowercase(_lowercase						,	_lowercase					):
     __snake_case:  Any  				=    [e.name for e in FlaxKarrasDiffusionSchedulers]

     __snake_case:  jnp.dtype



     @property
     def  lowercase__     (       self      )		->    List[str]:







         """simple docstring"""



         return True



     @register_to_config
     def __init__(       self    ,							__SCREAMING_SNAKE_CASE = 1_0_0_0    ,							__SCREAMING_SNAKE_CASE = 0.00_01    ,							__SCREAMING_SNAKE_CASE = 0.02    ,							__SCREAMING_SNAKE_CASE = "linear"    ,							__SCREAMING_SNAKE_CASE = None    ,							__SCREAMING_SNAKE_CASE = "epsilon"    ,							__SCREAMING_SNAKE_CASE = jnp.floataa    ,							)		->    Dict:







         """simple docstring"""



         a__    = dtype



     def  lowercase__     (       self    ,							__SCREAMING_SNAKE_CASE = None      )		->    LMSDiscreteSchedulerState:







         """simple docstring"""



         if common is None:
             a__    = CommonSchedulerState.create(self      )

         a__    = jnp.arange(0    ,							self.config.num_train_timesteps      ).round()[::-1]
         a__    = ((1 - common.alphas_cumprod) / common.alphas_cumprod) ** 0.5

         # standard deviation of the initial noise distribution
         a__    = sigmas.max()

         return LMSDiscreteSchedulerState.create(
             common=__SCREAMING_SNAKE_CASE    ,							init_noise_sigma=__SCREAMING_SNAKE_CASE    ,							timesteps=__SCREAMING_SNAKE_CASE    ,							sigmas=__SCREAMING_SNAKE_CASE    ,							)



     def  lowercase__     (       self    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE      )		->    jnp.ndarray:







         """simple docstring"""



         ((a__)  ,    )    = jnp.where(state.timesteps == timestep    ,							size=1      )
         a__    = step_index[0]

         a__    = state.sigmas[step_index]
         a__    = sample / ((sigma**2 + 1) ** 0.5)
         return sample



     def  lowercase__     (       self    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE      )		->    Optional[int]:







         """simple docstring"""



         def lms_derivative(__SCREAMING_SNAKE_CASE      ):
             a__    = 1.0
             for k in range(__SCREAMING_SNAKE_CASE      ):
                 if current_order == k:
                     continue
                 prod *= (tau - state.sigmas[t - k]) / (state.sigmas[t - current_order] - state.sigmas[t - k])
             return prod

         a__    = integrate.quad(__SCREAMING_SNAKE_CASE    ,							state.sigmas[t]    ,							state.sigmas[t + 1]    ,							epsrel=1e-4      )[0]

         return integrated_coeff



     def  lowercase__     (       self    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE = ()      )		->    LMSDiscreteSchedulerState:







         """simple docstring"""



         a__    = jnp.linspace(self.config.num_train_timesteps - 1    ,							0    ,							__SCREAMING_SNAKE_CASE    ,							dtype=self.dtype      )

         a__    = jnp.floor(__SCREAMING_SNAKE_CASE      ).astype(jnp.intaa      )
         a__    = jnp.ceil(__SCREAMING_SNAKE_CASE      ).astype(jnp.intaa      )

         a__    = jnp.mod(__SCREAMING_SNAKE_CASE    ,							1.0      )

         a__    = ((1 - state.common.alphas_cumprod) / state.common.alphas_cumprod) ** 0.5
         a__    = (1 - frac) * sigmas[low_idx] + frac * sigmas[high_idx]
         a__    = jnp.concatenate([sigmas, jnp.array([0.0]    ,							dtype=self.dtype      )]      )

         a__    = timesteps.astype(jnp.intaa      )

         # initial running values
         a__    = jnp.zeros((0,) + shape    ,							dtype=self.dtype      )

         return state.replace(
             timesteps=__SCREAMING_SNAKE_CASE    ,							sigmas=__SCREAMING_SNAKE_CASE    ,							num_inference_steps=__SCREAMING_SNAKE_CASE    ,							derivatives=__SCREAMING_SNAKE_CASE    ,							)



     def  lowercase__     (       self    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE = 4    ,							__SCREAMING_SNAKE_CASE = True    ,							)		->    Union[FlaxLMSSchedulerOutput, Tuple]:







         """simple docstring"""



         if state.num_inference_steps is None:
             raise ValueError(
                 'Number of inference steps is \'None\', you need to run \'set_timesteps\' after creating the scheduler'      )

         a__    = state.sigmas[timestep]

         # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
         if self.config.prediction_type == "epsilon":
             a__    = sample - sigma * model_output
         elif self.config.prediction_type == "v_prediction":
             # * c_out + input * c_skip
             a__    = model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
         else:
             raise ValueError(
                 f'prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`'      )

         # 2. Convert to an ODE derivative
         a__    = (sample - pred_original_sample) / sigma
         a__    = state.replace(derivatives=jnp.append(state.derivatives    ,							__SCREAMING_SNAKE_CASE      )      )
         if len(state.derivatives      ) > order:
             a__    = state.replace(derivatives=jnp.delete(state.derivatives    ,							0      )      )

         # 3. Compute linear multistep coefficients
         a__    = min(timestep + 1    ,							__SCREAMING_SNAKE_CASE      )
         a__    = [self.get_lms_coefficient(__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE      ) for curr_order in range(__SCREAMING_SNAKE_CASE      )]

         # 4. Compute previous sample based on the derivatives path
         a__    = sample + sum(
             coeff * derivative for coeff, derivative in zip(__SCREAMING_SNAKE_CASE    ,							reversed(state.derivatives      )      )      )

         if not return_dict:
             return (prev_sample, state)

         return FlaxLMSSchedulerOutput(prev_sample=__SCREAMING_SNAKE_CASE    ,							state=__SCREAMING_SNAKE_CASE      )



     def  lowercase__     (       self    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							)		->    jnp.ndarray:







         """simple docstring"""



         a__    = state.sigmas[timesteps].flatten()
         a__    = broadcast_to_shape_from_left(__SCREAMING_SNAKE_CASE    ,							noise.shape      )

         a__    = original_samples + noise * sigma

         return noisy_samples




     def __len__(       self      )		->    Any:







         """simple docstring"""



         return self.config.num_train_timesteps




"""simple docstring"""




import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionDualGuidedPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device


a		:					str    	=					False







@nightly
@require_torch_gpu
class 						lowercase(unittest.TestCase					):



     def  lowercase__     (       self      )		->    Any:







         """simple docstring"""



         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()



     def  lowercase__     (       self      )		->    Optional[int]:







         """simple docstring"""



         a__    = VersatileDiffusionDualGuidedPipeline.from_pretrained('shi-labs/versatile-diffusion'      )
         # remove text_unet
         pipe.remove_unused_weights()
         pipe.to(__SCREAMING_SNAKE_CASE      )
         pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE      )

         a__    = load_image(
             'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'      )

         a__    = torch.manual_seed(0      )
         a__    = pipe(
             prompt='first prompt'    ,							image=__SCREAMING_SNAKE_CASE    ,							text_to_image_strength=0.75    ,							generator=__SCREAMING_SNAKE_CASE    ,							guidance_scale=7.5    ,							num_inference_steps=2    ,							output_type='numpy'    ,							).images

         with tempfile.TemporaryDirectory() as tmpdirname:
             pipe.save_pretrained(__SCREAMING_SNAKE_CASE      )
             a__    = VersatileDiffusionDualGuidedPipeline.from_pretrained(__SCREAMING_SNAKE_CASE      )

         pipe.to(__SCREAMING_SNAKE_CASE      )
         pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE      )

         a__    = generator.manual_seed(0      )
         a__    = pipe(
             prompt='first prompt'    ,							image=__SCREAMING_SNAKE_CASE    ,							text_to_image_strength=0.75    ,							generator=__SCREAMING_SNAKE_CASE    ,							guidance_scale=7.5    ,							num_inference_steps=2    ,							output_type='numpy'    ,							).images

         assert np.abs(image - new_image      ).sum() < 1e-5, "Models don't have the same forward pass"




     def  lowercase__     (       self      )		->    Union[str, Any]:







         """simple docstring"""



         a__    = VersatileDiffusionDualGuidedPipeline.from_pretrained('shi-labs/versatile-diffusion'      )
         pipe.remove_unused_weights()
         pipe.to(__SCREAMING_SNAKE_CASE      )
         pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE      )

         a__    = 'cyberpunk 2077'
         a__    = load_image(
             'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'      )
         a__    = torch.manual_seed(0      )
         a__    = pipe(
             prompt=__SCREAMING_SNAKE_CASE    ,							image=__SCREAMING_SNAKE_CASE    ,							text_to_image_strength=0.75    ,							generator=__SCREAMING_SNAKE_CASE    ,							guidance_scale=7.5    ,							num_inference_steps=5_0    ,							output_type='numpy'    ,							).images

         a__    = image[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

         assert image.shape == (1, 5_1_2, 5_1_2, 3)
         a__    = np.array([0.07_87, 0.08_49, 0.08_26, 0.08_12, 0.08_07, 0.07_95, 0.08_18, 0.07_98, 0.07_79]      )

         assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1e-2


