






"""simple docstring"""



# DISCLAIMER: This file is strongly influenced by https://github.com/ermongroup/ddim

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

import flax
import jax
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 			lowerCAmelCase__			:
   SCREAMING_SNAKE_CASE_    =42

   # setable values
   SCREAMING_SNAKE_CASE_    =42
   SCREAMING_SNAKE_CASE_    =42
   SCREAMING_SNAKE_CASE_    =None
   @classmethod
   def       __a							(		cls      :	Optional[int]							,			snake_case__      :	Optional[Any]							,			snake_case__      :	Dict							,			snake_case__      :	int							):


        '''simple docstring'''
        return cls(common=snake_case__							,			init_noise_sigma=snake_case__							,			timesteps=snake_case__							)




@dataclass
class 			lowerCAmelCase__			(    lowerCamelCase_   ):
   SCREAMING_SNAKE_CASE_    =42




class 			lowerCAmelCase__			(    lowerCamelCase_							,						lowerCamelCase_   ):
   SCREAMING_SNAKE_CASE_    =[e.name for e in FlaxKarrasDiffusionSchedulers]

   SCREAMING_SNAKE_CASE_    =42
   @property
   def       __a							(		self      :	Optional[Any]							):


        '''simple docstring'''
        return True
   @register_to_config
   def __init__(		self      :	int							,			snake_case__      :	Optional[Any] = 1_0_0_0							,			snake_case__      :	int = 0.0001							,			snake_case__      :	Optional[int] = 0.02							,			snake_case__      :	List[str] = "linear"							,			snake_case__      :	Union[str, Any] = None							,			snake_case__      :	Optional[Any] = "fixed_small"							,			snake_case__      :	Optional[int] = True							,			snake_case__      :	Optional[int] = "epsilon"							,			snake_case__      :	List[str] = jnp.floataa							,			):


        '''simple docstring'''
        UpperCAmelCase__						:    Any							=							dtype
   def       __a							(		self      :	Dict							,			snake_case__      :	str = None							):


        '''simple docstring'''
        if common is None:
             UpperCAmelCase__						:    Optional[Any]							=							CommonSchedulerState.create(self							)

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

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

        return DDPMSchedulerState.create(
            common=snake_case__							,			init_noise_sigma=snake_case__							,			timesteps=snake_case__							,			)
   def       __a							(		self      :	Any							,			snake_case__      :	Dict							,			snake_case__      :	List[str]							,			snake_case__      :	Union[str, Any] = None							):


        '''simple docstring'''
        return sample
   def       __a							(		self      :	List[str]							,			snake_case__      :	Union[str, Any]							,			snake_case__      :	Optional[int]							,			snake_case__      :	Union[str, Any] = ()							):


        '''simple docstring'''
        UpperCAmelCase__						:    Optional[int]							=							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
        UpperCAmelCase__						:    Optional[int]							=							(jnp.arange(0							,			snake_case__							) * step_ratio).round()[::-1]

        return state.replace(
            num_inference_steps=snake_case__							,			timesteps=snake_case__							,			)
   def       __a							(		self      :	Any							,			snake_case__      :	Optional[int]							,			snake_case__      :	Optional[Any]							,			snake_case__      :	int=None							,			snake_case__      :	int=None							):


        '''simple docstring'''
        UpperCAmelCase__						:    List[str]							=							state.common.alphas_cumprod[t]
        UpperCAmelCase__						:    int							=							jnp.where(t > 0							,			state.common.alphas_cumprod[t - 1]							,			jnp.array(1.0							,			dtype=self.dtype							)							)

        # For t > 0, compute predicted variance βt (see formula (6) and (7) from https://arxiv.org/pdf/2006.11239.pdf)
        # and sample from it to get previous sample
        # x_{t-1} ~ N(pred_prev_sample, variance) == add variance to pred_sample
        UpperCAmelCase__						:    Dict							=							(1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * state.common.betas[t]

        if variance_type is None:
             UpperCAmelCase__						:    int							=							self.config.variance_type

        # hacks - were probably added for training stability
        if variance_type == "fixed_small":
             UpperCAmelCase__						:    List[Any]							=							jnp.clip(snake_case__							,			a_min=1e-20							)
        # for rl-diffuser https://arxiv.org/abs/2205.09991
        elif variance_type == "fixed_small_log":
             UpperCAmelCase__						:    str							=							jnp.log(jnp.clip(snake_case__							,			a_min=1e-20							)							)
        elif variance_type == "fixed_large":
             UpperCAmelCase__						:    Any							=							state.common.betas[t]
        elif variance_type == "fixed_large_log":
             # Glide max_log
             UpperCAmelCase__						:    Union[str, Any]							=							jnp.log(state.common.betas[t]							)
        elif variance_type == "learned":
             return predicted_variance
        elif variance_type == "learned_range":
             UpperCAmelCase__						:    Union[str, Any]							=							variance
             UpperCAmelCase__						:    Tuple							=							state.common.betas[t]
             UpperCAmelCase__						:    Optional[Any]							=							(predicted_variance + 1) / 2
             UpperCAmelCase__						:    List[Any]							=							frac * max_log + (1 - frac) * min_log

        return variance
   def       __a							(		self      :	str							,			snake_case__      :	Any							,			snake_case__      :	str							,			snake_case__      :	str							,			snake_case__      :	Optional[int]							,			snake_case__      :	Union[str, Any] = None							,			snake_case__      :	str = True							,			):


        '''simple docstring'''
        UpperCAmelCase__						:    int							=							timestep

        if key is None:
             UpperCAmelCase__						:    int							=							jax.random.PRNGKey(0							)

        if model_output.shape[1] == sample.shape[1] * 2 and self.config.variance_type in ["learned", "learned_range"]:
             UpperCAmelCase__						:    Any							=							jnp.split(snake_case__							,			sample.shape[1]							,			axis=1							)
        else:
             UpperCAmelCase__						:    List[str]							=							None

        # 1. compute alphas, betas
        UpperCAmelCase__						:    List[Any]							=							state.common.alphas_cumprod[t]
        UpperCAmelCase__						:    int							=							jnp.where(t > 0							,			state.common.alphas_cumprod[t - 1]							,			jnp.array(1.0							,			dtype=self.dtype							)							)
        UpperCAmelCase__						:    List[str]							=							1 - alpha_prod_t
        UpperCAmelCase__						:    int							=							1 - alpha_prod_t_prev

        # 2. compute predicted original sample from predicted noise also called
        # "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
        if self.config.prediction_type == "epsilon":
             UpperCAmelCase__						:    List[Any]							=							(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
        elif self.config.prediction_type == "sample":
             UpperCAmelCase__						:    int							=							model_output
        elif self.config.prediction_type == "v_prediction":
             UpperCAmelCase__						:    Optional[int]							=							(alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
        else:
             raise ValueError(
                 f'prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample` '
                 " for the FlaxDDPMScheduler."							)

        # 3. Clip "predicted x_0"
        if self.config.clip_sample:
             UpperCAmelCase__						:    Tuple							=							jnp.clip(snake_case__							,			-1							,			1							)

        # 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
        # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
        UpperCAmelCase__						:    int							=							(alpha_prod_t_prev ** 0.5 * state.common.betas[t]) / beta_prod_t
        UpperCAmelCase__						:    Tuple							=							state.common.alphas[t] ** 0.5 * beta_prod_t_prev / beta_prod_t

        # 5. Compute predicted previous sample µ_t
        # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
        UpperCAmelCase__						:    Optional[Any]							=							pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

        # 6. Add noise
        def random_variance():
             UpperCAmelCase__						:    int							=							jax.random.split(snake_case__							,			num=1							)
             UpperCAmelCase__						:    List[str]							=							jax.random.normal(snake_case__							,			shape=model_output.shape							,			dtype=self.dtype							)
             return (self._get_variance(snake_case__							,			snake_case__							,			predicted_variance=snake_case__							) ** 0.5) * noise

        UpperCAmelCase__						:    Optional[int]							=							jnp.where(t > 0							,			random_variance()							,			jnp.zeros(model_output.shape							,			dtype=self.dtype							)							)

        UpperCAmelCase__						:    str							=							pred_prev_sample + variance

        if not return_dict:
             return (pred_prev_sample, state)

        return FlaxDDPMSchedulerOutput(prev_sample=snake_case__							,			state=snake_case__							)
   def       __a							(		self      :	str							,			snake_case__      :	str							,			snake_case__      :	Optional[int]							,			snake_case__      :	Any							,			snake_case__      :	List[Any]							,			):


        '''simple docstring'''
        return add_noise_common(state.common							,			snake_case__							,			snake_case__							,			snake_case__							)
   def       __a							(		self      :	Union[str, Any]							,			snake_case__      :	str							,			snake_case__      :	str							,			snake_case__      :	List[str]							,			snake_case__      :	Any							,			):


        '''simple docstring'''
        return get_velocity_common(state.common							,			snake_case__							,			snake_case__							,			snake_case__							)




   def __len__(		self      :	str							):


        '''simple docstring'''
        return self.config.num_train_timesteps








import warnings

from ...utils import logging
from .image_processing_poolformer import PoolFormerImageProcessor


UpperCamelCase__ :   str						=							logging.get_logger(__name__)







class 				lowerCAmelCase_       (					lowerCamelCase_							):






				def __init__(			self      ,*snake_case__      ,**snake_case__ ):
							warnings.warn(
							    'The class PoolFormerFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'
							    ' Please use PoolFormerImageProcessor instead.'      ,snake_case__      ,)
							super().__init__(*snake_case__      ,**snake_case__ )



