
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_							:
 A__    :					CommonSchedulerState

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

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

 @classmethod
 def lowerCamelCase_					(   cls  ,						__UpperCamelCase  ,						__UpperCamelCase  ,						__UpperCamelCase  ,						__UpperCamelCase  ):




        """simple docstring"""


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



@dataclass
class 		lowercase_							(							__SCREAMING_SNAKE_CASE					):
 A__    :					LMSDiscreteSchedulerState



class 		lowercase_							(							__SCREAMING_SNAKE_CASE				,						__SCREAMING_SNAKE_CASE					):
 A__    :					Any    =     [e.name for e in FlaxKarrasDiffusionSchedulers]

 A__    :					jnp.dtype

 @property
 def lowerCamelCase_					(   self  ):




        """simple docstring"""


        return True

 @register_to_config
 def __init__(   self  ,						__UpperCamelCase = 1_0_0_0  ,						__UpperCamelCase = 0.0_001  ,						__UpperCamelCase = 0.02  ,						__UpperCamelCase = "linear"  ,						__UpperCamelCase = None  ,						__UpperCamelCase = "epsilon"  ,						__UpperCamelCase = jnp.floataa  ,						):




        """simple docstring"""


        UpperCamelCase_											=							dtype

 def lowerCamelCase_					(   self  ,						__UpperCamelCase = None  ):




        """simple docstring"""


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

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

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

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

 def lowerCamelCase_					(   self  ,						__UpperCamelCase  ,						__UpperCamelCase  ,						__UpperCamelCase  ):




        """simple docstring"""


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

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

 def lowerCamelCase_					(   self  ,						__UpperCamelCase  ,						__UpperCamelCase  ,						__UpperCamelCase  ,						__UpperCamelCase  ):




        """simple docstring"""


        def lms_derivative(__UpperCamelCase  ):
               UpperCamelCase_											=							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

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

        return integrated_coeff

 def lowerCamelCase_					(   self  ,						__UpperCamelCase  ,						__UpperCamelCase  ,						__UpperCamelCase = ()  ):




        """simple docstring"""


        UpperCamelCase_											=							jnp.linspace(self.config.num_train_timesteps - 1  ,						0  ,						__UpperCamelCase  ,						dtype=self.dtype  )

        UpperCamelCase_											=							jnp.floor(__UpperCamelCase  ).astype(jnp.intaa  )
        UpperCamelCase_											=							jnp.ceil(__UpperCamelCase  ).astype(jnp.intaa  )

        UpperCamelCase_											=							jnp.mod(__UpperCamelCase  ,						1.0  )

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

        UpperCamelCase_											=							timesteps.astype(jnp.intaa  )

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

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

 def lowerCamelCase_					(   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"""  )

        UpperCamelCase_											=							state.sigmas[timestep]

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

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

        # 4. Compute previous sample based on the derivatives path
        UpperCamelCase_											=							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 lowerCamelCase_					(   self  ,						__UpperCamelCase  ,						__UpperCamelCase  ,						__UpperCamelCase  ,						__UpperCamelCase  ,						):




        """simple docstring"""


        UpperCamelCase_											=							state.sigmas[timesteps].flatten()
        UpperCamelCase_											=							broadcast_to_shape_from_left(__UpperCamelCase  ,						noise.shape  )

        UpperCamelCase_											=							original_samples + noise * sigma

        return noisy_samples

 def __len__(   self  ):




        """simple docstring"""


        return self.config.num_train_timesteps





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_A							=       {'''configuration_dinat''': ['''DINAT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''DinatConfig''']}


try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      _A							=       [
          '''DINAT_PRETRAINED_MODEL_ARCHIVE_LIST''',
          '''DinatForImageClassification''',
          '''DinatModel''',
          '''DinatPreTrainedModel''',
          '''DinatBackbone''',
      ]

if TYPE_CHECKING:
      from .configuration_dinat import DINAT_PRETRAINED_CONFIG_ARCHIVE_MAP, DinatConfig

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_dinat import (
                DINAT_PRETRAINED_MODEL_ARCHIVE_LIST,
                DinatBackbone,
                DinatForImageClassification,
                DinatModel,
                DinatPreTrainedModel,
            )

else:
      import sys

      _A							=       _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



