





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:
     __A							=							42

     # setable values
     __A							=							42
     __A							=							42
     __A							=							42
     __A							=							None

     # running values
     __A							=							None

     @classmethod
     def 						_UpperCamelCase  (   cls,							A,							A,							A,							A   ):





            """simple docstring"""

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






@dataclass
class 			__A(    __UpperCAmelCase			):
     __A							=							42






class 			__A(    __UpperCAmelCase    ,      __UpperCAmelCase			):
     __A							=							[e.name for e in FlaxKarrasDiffusionSchedulers]

     __A							=							42

     @property
     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

            return True

     @register_to_config
     def __init__(   self,							A = 1000,							A = 0.0_001,							A = 0.02,							A = "linear",							A = None,							A = "epsilon",							A = jnp.floataa,							):





            """simple docstring"""

            _UpperCamelCase														=    dtype

     def 						_UpperCamelCase  (   self,							A = 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=A,							init_noise_sigma=A,							timesteps=A,							sigmas=A,							)

     def 						_UpperCamelCase  (   self,							A,							A,							A   ):





            """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 						_UpperCamelCase  (   self,							A,							A,							A,							A   ):





            """simple docstring"""

            def lms_derivative(A   ):
                   _UpperCamelCase														=    1.0
                   for k in range(A   ):
                          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(A,							state.sigmas[t],							state.sigmas[t + 1],							epsrel=1E-4   )[0]

            return integrated_coeff

     def 						_UpperCamelCase  (   self,							A,							A,							A = ()   ):





            """simple docstring"""

            _UpperCamelCase														=    jnp.linspace(self.config.num_train_timesteps - 1,							0,							A,							dtype=self.dtype   )

            _UpperCamelCase														=    jnp.floor(A   ).astype(jnp.intaa   )
            _UpperCamelCase														=    jnp.ceil(A   ).astype(jnp.intaa   )

            _UpperCamelCase														=    jnp.mod(A,							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=A,							sigmas=A,							num_inference_steps=A,							derivatives=A,							)

     def 						_UpperCamelCase  (   self,							A,							A,							A,							A,							A = 4,							A = 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,							A   )   )
            if len(state.derivatives   ) > order:
                   _UpperCamelCase														=    state.replace(derivatives=jnp.delete(state.derivatives,							0   )   )

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

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

            if not return_dict:
                   return (prev_sample, state)

            return FlaxLMSSchedulerOutput(prev_sample=A,							state=A   )

     def 						_UpperCamelCase  (   self,							A,							A,							A,							A,							):





            """simple docstring"""

            _UpperCamelCase														=    state.sigmas[timesteps].flatten()
            _UpperCamelCase														=    broadcast_to_shape_from_left(A,							noise.shape   )

            _UpperCamelCase														=    original_samples + noise * sigma

            return noisy_samples



     def __len__(   self   ):





            """simple docstring"""

            return self.config.num_train_timesteps













import warnings

from ...utils import logging
from .image_processing_mobilenet_va import MobileNetVaImageProcessor


lowercase     :			str     						=	logging.get_logger(__name__)

class 			__A(    __UpperCAmelCase			):

     def __init__(   self,							*A,							**A   ):





            """simple docstring"""

            warnings.warn(
                '''The class MobileNetV2FeatureExtractor is deprecated and will be removed in version 5 of Transformers.'''
                ''' Please use MobileNetV2ImageProcessor instead.''',							A,							)
            super().__init__(*A,							**A   )






