



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

import flax
import jax.numpy as jnp
from jax import random

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .scheduling_utils_flax import FlaxSchedulerMixin





@flax.struct.dataclass
class 		UpperCAmelCase						:
   # setable values
   SCREAMING_SNAKE_CASE__							=     None
   SCREAMING_SNAKE_CASE__							=     None
   SCREAMING_SNAKE_CASE__							=     None  # sigma(t_i)





   @classmethod
   def 	__lowerCAmelCase							(	cls			):
         return cls()






@dataclass
class 		UpperCAmelCase						(						snake_case_				):
   SCREAMING_SNAKE_CASE__							=     42
   SCREAMING_SNAKE_CASE__							=     42
   SCREAMING_SNAKE_CASE__							=     42






class 		UpperCAmelCase						(						snake_case_       ,snake_case_				):





   @property
   def 	__lowerCAmelCase							(	self			):
         return True





   @register_to_config
   def __init__(	self       ,     _lowerCAmelCase = 0.02       ,     _lowerCAmelCase = 100       ,     _lowerCAmelCase = 1.007       ,     _lowerCAmelCase = 80       ,     _lowerCAmelCase = 0.05       ,     _lowerCAmelCase = 50       ,     ):
         pass





   def 	__lowerCAmelCase							(	self			):
         return KarrasVeSchedulerState.create()





   def 	__lowerCAmelCase							(	self       ,     _lowerCAmelCase       ,     _lowerCAmelCase       ,     _lowerCAmelCase = ()			):
         _lowerCAmelCase 			=  jnp.arange(0       ,     _lowerCAmelCase			)[::-1].copy()
         _lowerCAmelCase 			=  [
             (
                 self.config.sigma_max**2
                 * (self.config.sigma_min**2 / self.config.sigma_max**2) ** (i / (num_inference_steps - 1))
             )
             for i in timesteps
         ]

         return state.replace(
             num_inference_steps=_lowerCAmelCase       ,     schedule=jnp.array(_lowerCAmelCase       ,     dtype=jnp.floataa			)       ,     timesteps=_lowerCAmelCase       ,     )





   def 	__lowerCAmelCase							(	self       ,     _lowerCAmelCase       ,     _lowerCAmelCase       ,     _lowerCAmelCase       ,     _lowerCAmelCase       ,     ):
         if self.config.s_min <= sigma <= self.config.s_max:
               _lowerCAmelCase 			=  min(self.config.s_churn / state.num_inference_steps       ,     2**0.5 - 1			)
         else:
               _lowerCAmelCase 			=  0

         # sample eps ~ N(0, S_noise^2 * I)
         _lowerCAmelCase 			=  random.split(_lowerCAmelCase       ,     num=1			)
         _lowerCAmelCase 			=  self.config.s_noise * random.normal(key=_lowerCAmelCase       ,     shape=sample.shape			)
         _lowerCAmelCase 			=  sigma + gamma * sigma
         _lowerCAmelCase 			=  sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)

         return sample_hat, sigma_hat





   def 	__lowerCAmelCase							(	self       ,     _lowerCAmelCase       ,     _lowerCAmelCase       ,     _lowerCAmelCase       ,     _lowerCAmelCase       ,     _lowerCAmelCase       ,     _lowerCAmelCase = True       ,     ):
         _lowerCAmelCase 			=  sample_hat + sigma_hat * model_output
         _lowerCAmelCase 			=  (sample_hat - pred_original_sample) / sigma_hat
         _lowerCAmelCase 			=  sample_hat + (sigma_prev - sigma_hat) * derivative

         if not return_dict:
               return (sample_prev, derivative, state)

         return FlaxKarrasVeOutput(prev_sample=_lowerCAmelCase       ,     derivative=_lowerCAmelCase       ,     state=_lowerCAmelCase			)





   def 	__lowerCAmelCase							(	self       ,     _lowerCAmelCase       ,     _lowerCAmelCase       ,     _lowerCAmelCase       ,     _lowerCAmelCase       ,     _lowerCAmelCase       ,     _lowerCAmelCase       ,     _lowerCAmelCase       ,     _lowerCAmelCase = True       ,     ):
         _lowerCAmelCase 			=  sample_prev + sigma_prev * model_output
         _lowerCAmelCase 			=  (sample_prev - pred_original_sample) / sigma_prev
         _lowerCAmelCase 			=  sample_hat + (sigma_prev - sigma_hat) * (0.5 * derivative + 0.5 * derivative_corr)

         if not return_dict:
               return (sample_prev, derivative, state)

         return FlaxKarrasVeOutput(prev_sample=_lowerCAmelCase       ,     derivative=_lowerCAmelCase       ,     state=_lowerCAmelCase			)





   def 	__lowerCAmelCase							(	self       ,     _lowerCAmelCase       ,     _lowerCAmelCase       ,     _lowerCAmelCase       ,     _lowerCAmelCase			):
         raise NotImplementedError()




from ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
    if not (is_transformers_available() and is_torch_available()):
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    from ...utils.dummy_torch_and_transformers_objects import ShapEPipeline
else:
    from .camera import create_pan_cameras
    from .pipeline_shap_e import ShapEPipeline
    from .pipeline_shap_e_img2img import ShapEImgaImgPipeline
    from .renderer import (
        BoundingBoxVolume,
        ImportanceRaySampler,
        MLPNeRFModelOutput,
        MLPNeRSTFModel,
        ShapEParamsProjModel,
        ShapERenderer,
        StratifiedRaySampler,
        VoidNeRFModel,
    )