





'''simple docstring'''


# DISCLAIMER: This code is strongly influenced by https://github.com/pesser/pytorch_diffusion
# and https://github.com/hojonathanho/diffusion

from dataclasses import dataclass
from typing import Optional, Tuple, Union

import flax
import jax.numpy as jnp

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import (
    CommonSchedulerState,
    FlaxKarrasDiffusionSchedulers,
    FlaxSchedulerMixin,
    FlaxSchedulerOutput,
    add_noise_common,
    get_velocity_common,
)



@flax.struct.dataclass
class 				__UpperCAmelCase	:







		'''simple docstring'''







		__lowerCAmelCase								=     42
		__lowerCAmelCase								=     42

		# setable values
		__lowerCAmelCase								=     42
		__lowerCAmelCase								=     42
		__lowerCAmelCase								=     None







		@classmethod
		def 							A  (cls    :   int   ,   _lowerCAmelCase    :   CommonSchedulerState   ,   _lowerCAmelCase    :   jnp.ndarray   ,   _lowerCAmelCase    :   jnp.ndarray   ,   _lowerCAmelCase    :   jnp.ndarray   ,   ):
			return cls(
			    common=_lowerCAmelCase   ,   final_alpha_cumprod=_lowerCAmelCase   ,   init_noise_sigma=_lowerCAmelCase   ,   timesteps=_lowerCAmelCase   ,   )






@dataclass
class 				__UpperCAmelCase	(							A__    ):







		'''simple docstring'''







		__lowerCAmelCase								=     42






class 				__UpperCAmelCase	(							A__		,       A__    ):







		'''simple docstring'''







		__lowerCAmelCase								=     [e.name for e in FlaxKarrasDiffusionSchedulers]

		__lowerCAmelCase								=     42







		@property
		def 							A  (self    :   Optional[Any]   ):
			return True







		@register_to_config
		def __init__(self    :   List[str]   ,   _lowerCAmelCase    :   int = 1000   ,   _lowerCAmelCase    :   float = 0.0_001   ,   _lowerCAmelCase    :   float = 0.02   ,   _lowerCAmelCase    :   str = "linear"   ,   _lowerCAmelCase    :   Optional[jnp.ndarray] = None   ,   _lowerCAmelCase    :   bool = True   ,   _lowerCAmelCase    :   int = 0   ,   _lowerCAmelCase    :   str = "epsilon"   ,   _lowerCAmelCase    :   jnp.dtype = jnp.floataa   ,   ):
			A					=   dtype







		def 							A  (self    :   str   ,   _lowerCAmelCase    :   Optional[CommonSchedulerState] = None   ):
			if common is None:
				A					=   CommonSchedulerState.create(self   )

			# At every step in ddim, we are looking into the previous alphas_cumprod
			# For the final step, there is no previous alphas_cumprod because we are already at 0
			# `set_alpha_to_one` decides whether we set this parameter simply to one or
			# whether we use the final alpha of the "non-previous" one.
			A					=   (
			    jnp.array(1.0   ,   dtype=self.dtype   ) if self.config.set_alpha_to_one else common.alphas_cumprod[0]
			)

			# standard deviation of the initial noise distribution
			A					=   jnp.array(1.0   ,   dtype=self.dtype   )

			A					=   jnp.arange(0   ,   self.config.num_train_timesteps   ).round()[::-1]

			return DDIMSchedulerState.create(
			    common=_lowerCAmelCase   ,   final_alpha_cumprod=_lowerCAmelCase   ,   init_noise_sigma=_lowerCAmelCase   ,   timesteps=_lowerCAmelCase   ,   )







		def 							A  (self    :   int   ,   _lowerCAmelCase    :   DDIMSchedulerState   ,   _lowerCAmelCase    :   jnp.ndarray   ,   _lowerCAmelCase    :   Optional[int] = None   ):
			return sample







		def 							A  (self    :   List[str]   ,   _lowerCAmelCase    :   DDIMSchedulerState   ,   _lowerCAmelCase    :   int   ,   _lowerCAmelCase    :   Tuple = ()   ):
			A					=   self.config.num_train_timesteps // num_inference_steps
			# creates integer timesteps by multiplying by ratio
			# rounding to avoid issues when num_inference_step is power of 3
			A					=   (jnp.arange(0   ,   _lowerCAmelCase   ) * step_ratio).round()[::-1] + self.config.steps_offset

			return state.replace(
			    num_inference_steps=_lowerCAmelCase   ,   timesteps=_lowerCAmelCase   ,   )







		def 							A  (self    :   Optional[int]   ,   _lowerCAmelCase    :   DDIMSchedulerState   ,   _lowerCAmelCase    :   List[str]   ,   _lowerCAmelCase    :   Dict   ):
			A					=   state.common.alphas_cumprod[timestep]
			A					=   jnp.where(
			    prev_timestep >= 0   ,   state.common.alphas_cumprod[prev_timestep]   ,   state.final_alpha_cumprod   )
			A					=   1 - alpha_prod_t
			A					=   1 - alpha_prod_t_prev

			A					=   (beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev)

			return variance







		def 							A  (self    :   Any   ,   _lowerCAmelCase    :   DDIMSchedulerState   ,   _lowerCAmelCase    :   jnp.ndarray   ,   _lowerCAmelCase    :   int   ,   _lowerCAmelCase    :   jnp.ndarray   ,   _lowerCAmelCase    :   float = 0.0   ,   _lowerCAmelCase    :   bool = True   ,   ):
			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"""   )

			# See formulas (12) and (16) of DDIM paper https://arxiv.org/pdf/2010.02502.pdf
			# Ideally, read DDIM paper in-detail understanding

			# Notation (<variable name> -> <name in paper>
			# - pred_noise_t -> e_theta(x_t, t)
			# - pred_original_sample -> f_theta(x_t, t) or x_0
			# - std_dev_t -> sigma_t
			# - eta -> η
			# - pred_sample_direction -> "direction pointing to x_t"
			# - pred_prev_sample -> "x_t-1"

			# 1. get previous step value (=t-1)
			A					=   timestep - self.config.num_train_timesteps // state.num_inference_steps

			A					=   state.common.alphas_cumprod
			A					=   state.final_alpha_cumprod

			# 2. compute alphas, betas
			A					=   alphas_cumprod[timestep]
			A					=   jnp.where(prev_timestep >= 0   ,   alphas_cumprod[prev_timestep]   ,   _lowerCAmelCase   )

			A					=   1 - alpha_prod_t

			# 3. compute predicted original sample from predicted noise also called
			# "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
			if self.config.prediction_type == "epsilon":
				A					=   (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
				A					=   model_output
			elif self.config.prediction_type == "sample":
				A					=   model_output
				A					=   (sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5
			elif self.config.prediction_type == "v_prediction":
				A					=   (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
				A					=   (alpha_prod_t**0.5) * model_output + (beta_prod_t**0.5) * sample
			else:
				raise ValueError(
				    F"""prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`, or"""
				    """ `v_prediction`"""   )

			# 4. compute variance: "sigma_t(η)" -> see formula (16)
			# σ_t = sqrt((1 − α_t−1)/(1 − α_t)) * sqrt(1 − α_t/α_t−1)
			A					=   self._get_variance(_lowerCAmelCase   ,   _lowerCAmelCase   ,   _lowerCAmelCase   )
			A					=   eta * variance ** 0.5

			# 5. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
			A					=   (1 - alpha_prod_t_prev - std_dev_t**2) ** 0.5 * pred_epsilon

			# 6. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
			A					=   alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction

			if not return_dict:
				return (prev_sample, state)

			return FlaxDDIMSchedulerOutput(prev_sample=_lowerCAmelCase   ,   state=_lowerCAmelCase   )







		def 							A  (self    :   Dict   ,   _lowerCAmelCase    :   DDIMSchedulerState   ,   _lowerCAmelCase    :   jnp.ndarray   ,   _lowerCAmelCase    :   jnp.ndarray   ,   _lowerCAmelCase    :   jnp.ndarray   ,   ):
			return add_noise_common(state.common   ,   _lowerCAmelCase   ,   _lowerCAmelCase   ,   _lowerCAmelCase   )







		def 							A  (self    :   Any   ,   _lowerCAmelCase    :   DDIMSchedulerState   ,   _lowerCAmelCase    :   jnp.ndarray   ,   _lowerCAmelCase    :   jnp.ndarray   ,   _lowerCAmelCase    :   jnp.ndarray   ,   ):
			return get_velocity_common(state.common   ,   _lowerCAmelCase   ,   _lowerCAmelCase   ,   _lowerCAmelCase   )







		def __len__(self    :   List[str]   ):
			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					:						Dict					     =							{
    'configuration_donut_swin': ['DONUT_SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'DonutSwinConfig'],
    'processing_donut': ['DonutProcessor'],
}

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_lowerCamelCase					:						List[str]					     =							[
				    'DONUT_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'DonutSwinModel',
				    'DonutSwinPreTrainedModel',
				]

try:
				if not is_vision_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_lowerCamelCase					:						Any					     =							['DonutFeatureExtractor']
				_lowerCamelCase					:						Union[str, Any]					     =							['DonutImageProcessor']


if TYPE_CHECKING:
				from .configuration_donut_swin import DONUT_SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP, DonutSwinConfig
				from .processing_donut import DonutProcessor

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_donut_swin import (
								    DONUT_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
								    DonutSwinModel,
								    DonutSwinPreTrainedModel,
								)

				try:
								if not is_vision_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .feature_extraction_donut import DonutFeatureExtractor
								from .image_processing_donut import DonutImageProcessor

else:
				import sys

				_lowerCamelCase					:						Optional[int]					     =							_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)

