

# 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 					_a		:


					'''simple docstring'''






					UpperCAmelCase__:     CommonSchedulerState

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






					@classmethod
					def 						__A    (				cls					, A__					, A__					, A__    ):
						return cls(common=A__					, init_noise_sigma=A__					, timesteps=A__    )



@dataclass
class 					_a		(__magic_name__       ):


					'''simple docstring'''






					UpperCAmelCase__:     DDPMSchedulerState



class 					_a		(__magic_name__	,     __magic_name__       ):


					'''simple docstring'''






					UpperCAmelCase__:     int													= [e.name for e in FlaxKarrasDiffusionSchedulers]

					UpperCAmelCase__:     jnp.dtype






					@property
					def 						__A    (				self    ):
						return True






					@register_to_config
					def __init__(				self					, A__ = 1000					, A__ = 0.0_0_0_1					, A__ = 0.0_2					, A__ = "linear"					, A__ = None					, A__ = "fixed_small"					, A__ = True					, A__ = "epsilon"					, A__ = jnp.floataa					, ):
						A__							:					Union[str, Any]       =	dtype






					def 						__A    (				self					, A__ = None    ):
						if common is None:
							A__							:					List[str]       =	CommonSchedulerState.create(self    )

						# standard deviation of the initial noise distribution
						A__							:					Optional[Any]       =	jnp.array(1.0					, dtype=self.dtype    )

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

						return DDPMSchedulerState.create(
						    common=A__					, init_noise_sigma=A__					, timesteps=A__					, )






					def 						__A    (				self					, A__					, A__					, A__ = None    ):
						return sample






					def 						__A    (				self					, A__					, A__					, A__ = ()    ):
						A__							:					Tuple       =	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__							:					str       =	(jnp.arange(0					, A__    ) * step_ratio).round()[::-1]

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






					def 						__A    (				self					, A__					, A__					, A__=None					, A__=None    ):
						A__							:					Dict       =	state.common.alphas_cumprod[t]
						A__							:					List[Any]       =	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
						A__							:					Optional[int]       =	(1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * state.common.betas[t]

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

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

						return variance






					def 						__A    (				self					, A__					, A__					, A__					, A__					, A__ = None					, A__ = True					, ):
						A__							:					List[str]       =	timestep

						if key is None:
							A__							:					Optional[int]       =	jax.random.PRNGKey(0    )

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

						# 1. compute alphas, betas
						A__							:					Dict       =	state.common.alphas_cumprod[t]
						A__							:					Dict       =	jnp.where(t > 0					, state.common.alphas_cumprod[t - 1]					, jnp.array(1.0					, dtype=self.dtype    )    )
						A__							:					List[Any]       =	1 - alpha_prod_t
						A__							:					List[str]       =	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":
							A__							:					Union[str, Any]       =	(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
						elif self.config.prediction_type == "sample":
							A__							:					List[Any]       =	model_output
						elif self.config.prediction_type == "v_prediction":
							A__							:					Optional[Any]       =	(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:
							A__							:					List[str]       =	jnp.clip(A__					, -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
						A__							:					Optional[int]       =	(alpha_prod_t_prev ** 0.5 * state.common.betas[t]) / beta_prod_t
						A__							:					int       =	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
						A__							:					Dict       =	pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

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

						A__							:					int       =	jnp.where(t > 0					, random_variance()					, jnp.zeros(model_output.shape					, dtype=self.dtype    )    )

						A__							:					Optional[Any]       =	pred_prev_sample + variance

						if not return_dict:
							return (pred_prev_sample, state)

						return FlaxDDPMSchedulerOutput(prev_sample=A__					, state=A__    )






					def 						__A    (				self					, A__					, A__					, A__					, A__					, ):
						return add_noise_common(state.common					, A__					, A__					, A__    )






					def 						__A    (				self					, A__					, A__					, A__					, A__					, ):
						return get_velocity_common(state.common					, A__					, A__					, A__    )






					def __len__(				self    ):
						return self.config.num_train_timesteps






import warnings

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


A_ :				Union[str, Any]						 =    logging.get_logger(__name__)



class 					_a		(__magic_name__       ):


					'''simple docstring'''






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



