


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'''


      __snake_case 	=       None
      __snake_case 	=       None
      __snake_case 	=       None  # sigma(t_i)




      @classmethod
      def 					_snake_case     (    cls:	Tuple    ):
       return cls()



@dataclass
class 	A_ (	__UpperCamelCase   ):




      '''simple docstring'''


      __snake_case 	=       42
      __snake_case 	=       42
      __snake_case 	=       42



class 	A_ (	__UpperCamelCase ,				__UpperCamelCase   ):




      '''simple docstring'''


      @property
      def 					_snake_case     (    self:	Tuple    ):
       return True




      @register_to_config
      def __init__(    self:	Union[str, Any]						,				a:	float = 0.0_2						,				a:	float = 100						,				a:	float = 1.0_0_7						,				a:	float = 80						,				a:	float = 0.0_5						,				a:	float = 50						,				):
       pass




      def 					_snake_case     (    self:	Tuple    ):
       return KarrasVeSchedulerState.create()




      def 					_snake_case     (    self:	int						,				a:	KarrasVeSchedulerState						,				a:	int						,				a:	Tuple = ()    ):
       __lowerCamelCase		:							str		 =						jnp.arange(0						,				a    )[::-1].copy()
       __lowerCamelCase		:							Optional[Any]		 =						[
           (
               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=a						,				schedule=jnp.array(a						,				dtype=jnp.floataa    )						,				timesteps=a						,				)




      def 					_snake_case     (    self:	List[str]						,				a:	KarrasVeSchedulerState						,				a:	jnp.ndarray						,				a:	float						,				a:	random.KeyArray						,				):
       if self.config.s_min <= sigma <= self.config.s_max:
        __lowerCamelCase		:							Union[str, Any]		 =						min(self.config.s_churn / state.num_inference_steps						,				2**0.5 - 1    )
       else:
        __lowerCamelCase		:							Optional[Any]		 =						0

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

       return sample_hat, sigma_hat




      def 					_snake_case     (    self:	Optional[int]						,				a:	KarrasVeSchedulerState						,				a:	jnp.ndarray						,				a:	float						,				a:	float						,				a:	jnp.ndarray						,				a:	bool = True						,				):
       __lowerCamelCase		:							str		 =						sample_hat + sigma_hat * model_output
       __lowerCamelCase		:							Tuple		 =						(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=a						,				derivative=a						,				state=a    )




      def 					_snake_case     (    self:	List[Any]						,				a:	KarrasVeSchedulerState						,				a:	jnp.ndarray						,				a:	float						,				a:	float						,				a:	jnp.ndarray						,				a:	jnp.ndarray						,				a:	jnp.ndarray						,				a:	bool = True						,				):
       __lowerCamelCase		:							Dict		 =						sample_prev + sigma_prev * model_output
       __lowerCamelCase		:							int		 =						(sample_prev - pred_original_sample) / sigma_prev
       __lowerCamelCase		:							Tuple		 =						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=a						,				derivative=a						,				state=a    )




      def 					_snake_case     (    self:	Union[str, Any]						,				a:	KarrasVeSchedulerState						,				a:	int						,				a:	Tuple						,				a:	str    ):
       raise NotImplementedError()





import warnings

from .generation import TFGenerationMixin


class 	A_ (	__UpperCamelCase   ):




      '''simple docstring'''


      warnings.warn(
          """Importing `TFGenerationMixin` from `src/transformers/generation_tf_utils.py` is deprecated and will """
          """be removed in Transformers v5. Import as `from transformers import TFGenerationMixin` instead.""" ,				__UpperCamelCase ,				)

