






"""simple docstring"""



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

import flax
import jax.numpy as jnp
from jax import random

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .scheduling_utils_flax import FlaxSchedulerMixin







@flax.struct.dataclass
class        lowercase__      :
			# setable values
			_UpperCAmelCase		:Optional[int]             =			None
			_UpperCAmelCase		:Optional[jnp.ndarray]             =			None
			_UpperCAmelCase		:Optional[jnp.ndarray]             =			None  # sigma(t_i)


			@classmethod
			def      UpperCAmelCase__						(				cls					:  Any ):
										return cls()







@dataclass
class        lowercase__      (							snake_case__       ):
			_UpperCAmelCase		:jnp.ndarray
			_UpperCAmelCase		:jnp.ndarray
			_UpperCAmelCase		:KarrasVeSchedulerState







class        lowercase__      (							snake_case__,       snake_case__       ):


			@property
			def      UpperCAmelCase__						(				self					:  Dict ):
										return True


			@register_to_config
			def __init__(				self					:  Dict       ,	snake_case__					:  float = 0.02       ,	snake_case__					:  float = 100       ,	snake_case__					:  float = 1.007       ,	snake_case__					:  float = 80       ,	snake_case__					:  float = 0.05       ,	snake_case__					:  float = 50       ,	):
										pass


			def      UpperCAmelCase__						(				self					:  Optional[int] ):
										return KarrasVeSchedulerState.create()


			def      UpperCAmelCase__						(				self					:  Optional[Any]       ,	snake_case__					:  KarrasVeSchedulerState       ,	snake_case__					:  int       ,	snake_case__					:  Tuple = () ):
										lowerCamelCase_	:							str									=jnp.arange(0       ,	snake_case__ )[::-1].copy()
										lowerCamelCase_	:							str									=[
										    (
										        self.config.sigma_max**2
										        * (self.config.sigma_min**2 / self.config.sigma_max**2) ** (i / (num_inference_steps - 1))
										    )
										    for i in timesteps
										]

										return state.replace(
										    num_inference_steps=snake_case__       ,	schedule=jnp.array(snake_case__       ,	dtype=jnp.floataa )       ,	timesteps=snake_case__       ,	)


			def      UpperCAmelCase__						(				self					:  Optional[int]       ,	snake_case__					:  KarrasVeSchedulerState       ,	snake_case__					:  jnp.ndarray       ,	snake_case__					:  float       ,	snake_case__					:  random.KeyArray       ,	):
										if self.config.s_min <= sigma <= self.config.s_max:
																	lowerCamelCase_	:							List[Any]									=min(self.config.s_churn / state.num_inference_steps       ,	2**0.5 - 1 )
										else:
																	lowerCamelCase_	:							Tuple									=0

										# sample eps ~ N(0, S_noise^2 * I)
										lowerCamelCase_	:							int									=random.split(snake_case__       ,	num=1 )
										lowerCamelCase_	:							List[str]									=self.config.s_noise * random.normal(key=snake_case__       ,	shape=sample.shape )
										lowerCamelCase_	:							Optional[Any]									=sigma + gamma * sigma
										lowerCamelCase_	:							Tuple									=sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)

										return sample_hat, sigma_hat


			def      UpperCAmelCase__						(				self					:  List[str]       ,	snake_case__					:  KarrasVeSchedulerState       ,	snake_case__					:  jnp.ndarray       ,	snake_case__					:  float       ,	snake_case__					:  float       ,	snake_case__					:  jnp.ndarray       ,	snake_case__					:  bool = True       ,	):
										lowerCamelCase_	:							Optional[Any]									=sample_hat + sigma_hat * model_output
										lowerCamelCase_	:							Dict									=(sample_hat - pred_original_sample) / sigma_hat
										lowerCamelCase_	:							Dict									=sample_hat + (sigma_prev - sigma_hat) * derivative

										if not return_dict:
																	return (sample_prev, derivative, state)

										return FlaxKarrasVeOutput(prev_sample=snake_case__       ,	derivative=snake_case__       ,	state=snake_case__ )


			def      UpperCAmelCase__						(				self					:  Optional[Any]       ,	snake_case__					:  KarrasVeSchedulerState       ,	snake_case__					:  jnp.ndarray       ,	snake_case__					:  float       ,	snake_case__					:  float       ,	snake_case__					:  jnp.ndarray       ,	snake_case__					:  jnp.ndarray       ,	snake_case__					:  jnp.ndarray       ,	snake_case__					:  bool = True       ,	):
										lowerCamelCase_	:							List[Any]									=sample_prev + sigma_prev * model_output
										lowerCamelCase_	:							Dict									=(sample_prev - pred_original_sample) / sigma_prev
										lowerCamelCase_	:							Union[str, Any]									=sample_hat + (sigma_prev - sigma_hat) * (0.5 * derivative + 0.5 * derivative_corr)

										if not return_dict:
																	return (sample_prev, derivative, state)

										return FlaxKarrasVeOutput(prev_sample=snake_case__       ,	derivative=snake_case__       ,	state=snake_case__ )


			def      UpperCAmelCase__						(				self					:  Union[str, Any]       ,	snake_case__					:  KarrasVeSchedulerState       ,	snake_case__					:  Optional[Any]       ,	snake_case__					:  List[str]       ,	snake_case__					:  Tuple ):
										raise NotImplementedError()











"""simple docstring"""



import warnings

from ...utils import logging
from .image_processing_perceiver import PerceiverImageProcessor


A__	:    str			 = logging.get_logger(__name__)







class        lowercase__      (							snake_case__       ):


			def __init__(				self					:  Optional[Any]       ,	*snake_case__					:  int       ,	**snake_case__					:  Any ):
										warnings.warn(
										    "The class PerceiverFeatureExtractor is deprecated and will be removed in version 5 of Transformers."
										    " Please use PerceiverImageProcessor instead."       ,	snake_case__       ,	)
										super().__init__(*snake_case__       ,	**snake_case__ )



