






'''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					:



      """simple docstring"""


      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 			UpperCAmelCase   (   cls      :    str	,   __lowercase      :    str	,   __lowercase      :    Any	,   __lowercase      :    Optional[int]	,   __lowercase      :    int      )							->   Dict:
        return cls(common=__lowercase	,   init_noise_sigma=__lowercase	,   timesteps=__lowercase	,   sigmas=__lowercase      )


@dataclass
class 					a					(				A_     ):



      """simple docstring"""


      a						:				LMSDiscreteSchedulerState


class 					a					(				A_							, A_     ):



      """simple docstring"""


      a						:				List[str]							 =			[e.name for e in FlaxKarrasDiffusionSchedulers]

      a						:				jnp.dtype
      @property
      def 			UpperCAmelCase   (   self      :    Optional[Any]      )							->   List[str]:
        return True
      @register_to_config
      def __init__(   self      :    Any	,   __lowercase      :    int = 1000	,   __lowercase      :    List[str] = 0.0_001	,   __lowercase      :    Optional[Any] = 0.02	,   __lowercase      :    str = "linear"	,   __lowercase      :    Optional[Any] = None	,   __lowercase      :    List[str] = "epsilon"	,   __lowercase      :    Optional[Any] = jnp.floataa	,   )							->   List[Any]:
        __UpperCAmelCase							:			List[Any]     =						dtype
      def 			UpperCAmelCase   (   self      :    Dict	,   __lowercase      :    str = None      )							->   Union[str, Any]:
        if common is None:
          __UpperCAmelCase							:			int     =						CommonSchedulerState.create(self      )

        __UpperCAmelCase							:			Dict     =						jnp.arange(0	,   self.config.num_train_timesteps      ).round()[::-1]
        __UpperCAmelCase							:			Union[str, Any]     =						((1 - common.alphas_cumprod) / common.alphas_cumprod) ** 0.5

        # standard deviation of the initial noise distribution
        __UpperCAmelCase							:			int     =						sigmas.max()

        return LMSDiscreteSchedulerState.create(
            common=__lowercase	,   init_noise_sigma=__lowercase	,   timesteps=__lowercase	,   sigmas=__lowercase	,   )
      def 			UpperCAmelCase   (   self      :    List[Any]	,   __lowercase      :    Dict	,   __lowercase      :    Optional[Any]	,   __lowercase      :    Optional[Any]      )							->   Any:
        ((__UpperCAmelCase							)   ,											)							:			Optional[int]     =						jnp.where(state.timesteps == timestep	,   size=1      )
        __UpperCAmelCase							:			int     =						step_index[0]

        __UpperCAmelCase							:			Tuple     =						state.sigmas[step_index]
        __UpperCAmelCase							:			int     =						sample / ((sigma**2 + 1) ** 0.5)
        return sample
      def 			UpperCAmelCase   (   self      :    Dict	,   __lowercase      :    Tuple	,   __lowercase      :    Tuple	,   __lowercase      :    str	,   __lowercase      :    Optional[int]      )							->   Union[str, Any]:

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

        __UpperCAmelCase							:			str     =						integrate.quad(__lowercase	,   state.sigmas[t]	,   state.sigmas[t + 1]	,   epsrel=1e-4      )[0]

        return integrated_coeff
      def 			UpperCAmelCase   (   self      :    Optional[int]	,   __lowercase      :    int	,   __lowercase      :    Optional[int]	,   __lowercase      :    Any = ()      )							->   Optional[int]:
        __UpperCAmelCase							:			Union[str, Any]     =						jnp.linspace(self.config.num_train_timesteps - 1	,   0	,   __lowercase	,   dtype=self.dtype      )

        __UpperCAmelCase							:			Optional[int]     =						jnp.floor(__lowercase      ).astype(jnp.intaa      )
        __UpperCAmelCase							:			Any     =						jnp.ceil(__lowercase      ).astype(jnp.intaa      )

        __UpperCAmelCase							:			Optional[Any]     =						jnp.mod(__lowercase	,   1.0      )

        __UpperCAmelCase							:			Union[str, Any]     =						((1 - state.common.alphas_cumprod) / state.common.alphas_cumprod) ** 0.5
        __UpperCAmelCase							:			Tuple     =						(1 - frac) * sigmas[low_idx] + frac * sigmas[high_idx]
        __UpperCAmelCase							:			Optional[int]     =						jnp.concatenate([sigmas, jnp.array([0.0]	,   dtype=self.dtype      )]      )

        __UpperCAmelCase							:			List[Any]     =						timesteps.astype(jnp.intaa      )

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

        return state.replace(
            timesteps=__lowercase	,   sigmas=__lowercase	,   num_inference_steps=__lowercase	,   derivatives=__lowercase	,   )
      def 			UpperCAmelCase   (   self      :    Optional[int]	,   __lowercase      :    str	,   __lowercase      :    int	,   __lowercase      :    Dict	,   __lowercase      :    Any	,   __lowercase      :    Optional[Any] = 4	,   __lowercase      :    str = True	,   )							->   str:
        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							:			Any     =						state.sigmas[timestep]

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

        # 3. Compute linear multistep coefficients
        __UpperCAmelCase							:			Dict     =						min(timestep + 1	,   __lowercase      )
        __UpperCAmelCase							:			List[Any]     =						[self.get_lms_coefficient(__lowercase	,   __lowercase	,   __lowercase	,   __lowercase      ) for curr_order in range(__lowercase      )]

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

        if not return_dict:
          return (prev_sample, state)

        return FlaxLMSSchedulerOutput(prev_sample=__lowercase	,   state=__lowercase      )
      def 			UpperCAmelCase   (   self      :    int	,   __lowercase      :    Tuple	,   __lowercase      :    Union[str, Any]	,   __lowercase      :    Optional[Any]	,   __lowercase      :    List[Any]	,   )							->   int:
        __UpperCAmelCase							:			str     =						state.sigmas[timesteps].flatten()
        __UpperCAmelCase							:			Dict     =						broadcast_to_shape_from_left(__lowercase	,   noise.shape      )

        __UpperCAmelCase							:			Optional[int]     =						original_samples + noise * sigma

        return noisy_samples




      def __len__(   self      :    Union[str, Any]      )							->   List[Any]:
        return self.config.num_train_timesteps










from typing import TYPE_CHECKING

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


a	: Union[str, Any]      =				{
    "configuration_mobilenet_v1": [
        "MOBILENET_V1_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "MobileNetV1Config",
        "MobileNetV1OnnxConfig",
    ],
}

try:
       if not is_vision_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a	: int      =				["MobileNetV1FeatureExtractor"]
       a	: int      =				["MobileNetV1ImageProcessor"]

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a	: int      =				[
           "MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST",
           "MobileNetV1ForImageClassification",
           "MobileNetV1Model",
           "MobileNetV1PreTrainedModel",
           "load_tf_weights_in_mobilenet_v1",
       ]


if TYPE_CHECKING:
       from .configuration_mobilenet_va import (
           MOBILENET_V1_PRETRAINED_CONFIG_ARCHIVE_MAP,
           MobileNetVaConfig,
           MobileNetVaOnnxConfig,
       )

       try:
              if not is_vision_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .feature_extraction_mobilenet_va import MobileNetVaFeatureExtractor
              from .image_processing_mobilenet_va import MobileNetVaImageProcessor

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_mobilenet_va import (
                  MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST,
                  MobileNetVaForImageClassification,
                  MobileNetVaModel,
                  MobileNetVaPreTrainedModel,
                  load_tf_weights_in_mobilenet_va,
              )


else:
       import sys

       a	: str      =				_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


