"""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  a__     :
   snake_case_				=   42

   # setable values
   snake_case_				=   42
   snake_case_				=   42
   snake_case_				=   42
   snake_case_				=   None

   # running values
   snake_case_				=   None


   @classmethod
   def 						snake_case__						(  cls,					_UpperCAmelCase,					_UpperCAmelCase,					_UpperCAmelCase,					_UpperCAmelCase	):
        '''simple docstring'''



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






@dataclass
class  a__     ( _a						):
   snake_case_				=   42






class  a__     ( _a       ,       _a						):
   snake_case_				=   [e.name for e in FlaxKarrasDiffusionSchedulers]

   snake_case_				=   42


   @property
   def 						snake_case__						(  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 = "epsilon",					_UpperCAmelCase = jnp.floataa,					):
        '''simple docstring'''



        lowercase__								=				dtype


   def 						snake_case__						(  self,					_UpperCAmelCase = None	):
        '''simple docstring'''



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

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

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

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


   def 						snake_case__						(  self,					_UpperCAmelCase,					_UpperCAmelCase,					_UpperCAmelCase	):
        '''simple docstring'''



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

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


   def 						snake_case__						(  self,					_UpperCAmelCase,					_UpperCAmelCase,					_UpperCAmelCase,					_UpperCAmelCase	):
        '''simple docstring'''



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

        lowercase__								=				integrate.quad(_UpperCAmelCase,					state.sigmas[t],					state.sigmas[t + 1],					epsrel=1E-4	)[0]

        return integrated_coeff


   def 						snake_case__						(  self,					_UpperCAmelCase,					_UpperCAmelCase,					_UpperCAmelCase = ()	):
        '''simple docstring'''



        lowercase__								=				jnp.linspace(self.config.num_train_timesteps - 1,					0,					_UpperCAmelCase,					dtype=self.dtype	)

        lowercase__								=				jnp.floor(_UpperCAmelCase	).astype(jnp.intaa	)
        lowercase__								=				jnp.ceil(_UpperCAmelCase	).astype(jnp.intaa	)

        lowercase__								=				jnp.mod(_UpperCAmelCase,					1.0	)

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

        lowercase__								=				timesteps.astype(jnp.intaa	)

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

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


   def 						snake_case__						(  self,					_UpperCAmelCase,					_UpperCAmelCase,					_UpperCAmelCase,					_UpperCAmelCase,					_UpperCAmelCase = 4,					_UpperCAmelCase = True,					):
        '''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"	)

        lowercase__								=				state.sigmas[timestep]

        # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
        if self.config.prediction_type == "epsilon":
             lowercase__								=				sample - sigma * model_output
        elif self.config.prediction_type == "v_prediction":
             # * c_out + input * c_skip
             lowercase__								=				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
        lowercase__								=				(sample - pred_original_sample) / sigma
        lowercase__								=				state.replace(derivatives=jnp.append(state.derivatives,					_UpperCAmelCase	)	)
        if len(state.derivatives	) > order:
             lowercase__								=				state.replace(derivatives=jnp.delete(state.derivatives,					0	)	)

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

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

        if not return_dict:
             return (prev_sample, state)

        return FlaxLMSSchedulerOutput(prev_sample=_UpperCAmelCase,					state=_UpperCAmelCase	)


   def 						snake_case__						(  self,					_UpperCAmelCase,					_UpperCAmelCase,					_UpperCAmelCase,					_UpperCAmelCase,					):
        '''simple docstring'''



        lowercase__								=				state.sigmas[timesteps].flatten()
        lowercase__								=				broadcast_to_shape_from_left(_UpperCAmelCase,					noise.shape	)

        lowercase__								=				original_samples + noise * sigma

        return noisy_samples


   def __len__(  self	):
        '''simple docstring'''



        return self.config.num_train_timesteps





"""simple docstring"""


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


lowerCAmelCase_:       Optional[int]			     =       {"configuration_vit_hybrid": ["VIT_HYBRID_PRETRAINED_CONFIG_ARCHIVE_MAP", "ViTHybridConfig"]}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCAmelCase_:       Any			     =       [
     "VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST",
     "ViTHybridForImageClassification",
     "ViTHybridModel",
     "ViTHybridPreTrainedModel",
 ]

try:
 if not is_vision_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCAmelCase_:       Optional[Any]			     =       ["ViTHybridImageProcessor"]


if TYPE_CHECKING:
 from .configuration_vit_hybrid import VIT_HYBRID_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTHybridConfig

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_vit_hybrid import (
      VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST,
      ViTHybridForImageClassification,
      ViTHybridModel,
      ViTHybridPreTrainedModel,
  )

 try:
  if not is_vision_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .image_processing_vit_hybrid import ViTHybridImageProcessor


else:
 import sys

 lowerCAmelCase_:       List[Any]			     =       _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)




