

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 					a__						:
			"""simple docstring"""







			__lowerCamelCase        =       None
			__lowerCamelCase        =       None
			__lowerCamelCase        =       None  # sigma(t_i)


			@classmethod
			def      UpperCamelCase		(    cls       )  ->						Optional[Any]:
							'''simple docstring'''






							return cls()

@dataclass
class 					a__						(					snake_case			):
			"""simple docstring"""







			__lowerCamelCase        =       42
			__lowerCamelCase        =       42
			__lowerCamelCase        =       42

class 					a__						(					snake_case							,	snake_case			):
			"""simple docstring"""







			@property
			def      UpperCamelCase		(    self       )  ->						Union[str, Any]:
							'''simple docstring'''






							return True


			@register_to_config
			def __init__(    self      ,      lowercase = 0.02      ,      lowercase = 100      ,      lowercase = 1.007      ,      lowercase = 80      ,      lowercase = 0.05      ,      lowercase = 50      ,      )  ->						Any:
							'''simple docstring'''






							pass


			def      UpperCamelCase		(    self       )  ->						str:
							'''simple docstring'''






							return KarrasVeSchedulerState.create()


			def      UpperCamelCase		(    self      ,      lowercase      ,      lowercase      ,      lowercase = ()       )  ->						KarrasVeSchedulerState:
							'''simple docstring'''






							A__        =			jnp.arange(0      ,      lowercase       )[::-1].copy()
							A__        =			[
							    (
							        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=lowercase      ,      schedule=jnp.array(lowercase      ,      dtype=jnp.floataa       )      ,      timesteps=lowercase      ,      )


			def      UpperCamelCase		(    self      ,      lowercase      ,      lowercase      ,      lowercase      ,      lowercase      ,      )  ->						Tuple[jnp.ndarray, float]:
							'''simple docstring'''






							if self.config.s_min <= sigma <= self.config.s_max:
											A__        =			min(self.config.s_churn / state.num_inference_steps      ,      2**0.5 - 1       )
							else:
											A__        =			0

							# sample eps ~ N(0, S_noise^2 * I)
							A__        =			random.split(lowercase      ,      num=1       )
							A__        =			self.config.s_noise * random.normal(key=lowercase      ,      shape=sample.shape       )
							A__        =			sigma + gamma * sigma
							A__        =			sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)

							return sample_hat, sigma_hat


			def      UpperCamelCase		(    self      ,      lowercase      ,      lowercase      ,      lowercase      ,      lowercase      ,      lowercase      ,      lowercase = True      ,      )  ->						Union[FlaxKarrasVeOutput, Tuple]:
							'''simple docstring'''






							A__        =			sample_hat + sigma_hat * model_output
							A__        =			(sample_hat - pred_original_sample) / sigma_hat
							A__        =			sample_hat + (sigma_prev - sigma_hat) * derivative

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

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


			def      UpperCamelCase		(    self      ,      lowercase      ,      lowercase      ,      lowercase      ,      lowercase      ,      lowercase      ,      lowercase      ,      lowercase      ,      lowercase = True      ,      )  ->						Union[FlaxKarrasVeOutput, Tuple]:
							'''simple docstring'''






							A__        =			sample_prev + sigma_prev * model_output
							A__        =			(sample_prev - pred_original_sample) / sigma_prev
							A__        =			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=lowercase      ,      derivative=lowercase      ,      state=lowercase       )


			def      UpperCamelCase		(    self      ,      lowercase      ,      lowercase      ,      lowercase      ,      lowercase       )  ->						Optional[Any]:
							'''simple docstring'''






							raise NotImplementedError()




import warnings

from ...utils import logging
from .image_processing_clip import CLIPImageProcessor


lowerCAmelCase__	   = logging.get_logger(__name__)

class 					a__						(					snake_case			):
			"""simple docstring"""







			def __init__(    self      ,      *lowercase      ,      **lowercase       )  ->						None:
							'''simple docstring'''






							warnings.warn(
							    "The class CLIPFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"
							    " use CLIPImageProcessor instead."      ,      lowercase      ,      )
							super().__init__(*lowercase      ,      **lowercase       )

