





"""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 					_UpperCAmelCase    :



      '''simple docstring'''
      lowercase_ :					CommonSchedulerState

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

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

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

        """simple docstring"""

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


@dataclass
class 					_UpperCAmelCase    (  UpperCAmelCase__						):



      '''simple docstring'''
      lowercase_ :					LMSDiscreteSchedulerState


class 					_UpperCAmelCase    (  UpperCAmelCase__     ,  UpperCAmelCase__						):



      '''simple docstring'''
      lowercase_ :					Optional[Any]             =  [e.name for e in FlaxKarrasDiffusionSchedulers]

      lowercase_ :					jnp.dtype

      @property
      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        return True

      @register_to_config
      def __init__(	self   , snake_case_ = 1_0_0_0   , snake_case_ = 0.00_01   , snake_case_ = 0.02   , snake_case_ = "linear"   , snake_case_ = None   , snake_case_ = "epsilon"   , snake_case_ = jnp.floataa   , ):

        """simple docstring"""

        A_    :			Union[str, Any]									=    dtype

      def    lowerCamelCase_     (	self   , snake_case_ = None    ):

        """simple docstring"""

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

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

        # standard deviation of the initial noise distribution
        A_    :			str									=    sigmas.max()

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

      def    lowerCamelCase_     (	self   , snake_case_   , snake_case_   , snake_case_    ):

        """simple docstring"""

        ((A_)		,			)    :			Optional[Any]									=    jnp.where(state.timesteps == timestep   , size=1    )
        A_    :			Any									=    step_index[0]

        A_    :			Union[str, Any]									=    state.sigmas[step_index]
        A_    :			Tuple									=    sample / ((sigma**2 + 1) ** 0.5)
        return sample

      def    lowerCamelCase_     (	self   , snake_case_   , snake_case_   , snake_case_   , snake_case_    ):

        """simple docstring"""

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

        A_    :			List[str]									=    integrate.quad(snake_case_   , state.sigmas[t]   , state.sigmas[t + 1]   , epsrel=1E-4    )[0]

        return integrated_coeff

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

        """simple docstring"""

        A_    :			Optional[Any]									=    jnp.linspace(self.config.num_train_timesteps - 1   , 0   , snake_case_   , dtype=self.dtype    )

        A_    :			List[str]									=    jnp.floor(snake_case_    ).astype(jnp.intaa    )
        A_    :			str									=    jnp.ceil(snake_case_    ).astype(jnp.intaa    )

        A_    :			Any									=    jnp.mod(snake_case_   , 1.0    )

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

        A_    :			str									=    timesteps.astype(jnp.intaa    )

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

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

      def    lowerCamelCase_     (	self   , snake_case_   , snake_case_   , snake_case_   , snake_case_   , snake_case_ = 4   , snake_case_ = 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'    )

        A_    :			List[str]									=    state.sigmas[timestep]

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

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

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

        if not return_dict:
          return (prev_sample, state)

        return FlaxLMSSchedulerOutput(prev_sample=snake_case_   , state=snake_case_    )

      def    lowerCamelCase_     (	self   , snake_case_   , snake_case_   , snake_case_   , snake_case_   , ):

        """simple docstring"""

        A_    :			Tuple									=    state.sigmas[timesteps].flatten()
        A_    :			Optional[Any]									=    broadcast_to_shape_from_left(snake_case_   , noise.shape    )

        A_    :			List[str]									=    original_samples + noise * sigma

        return noisy_samples

      def __len__(	self    ):

        """simple docstring"""

        return self.config.num_train_timesteps






"""simple docstring"""




import warnings

from ...utils import logging
from .image_processing_detr import DetrImageProcessor


lowerCamelCase_	:		List[str]         =   logging.get_logger(__name__)

class 					_UpperCAmelCase    (  UpperCAmelCase__						):



      '''simple docstring'''

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

        """simple docstring"""

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