





"""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      __SCREAMING_SNAKE_CASE    :
  SCREAMING_SNAKE_CASE__							:Optional[Any]	     =					None
  SCREAMING_SNAKE_CASE__							:Optional[Any]	     =					None
  SCREAMING_SNAKE_CASE__							:Optional[int]	     =					None  # sigma(t_i)

  @classmethod
  def    __SCREAMING_SNAKE_CASE				(		cls   :       Optional[int]	)		->				List[str]:
        return cls()





@dataclass
class      __SCREAMING_SNAKE_CASE    (						__A   ):
  SCREAMING_SNAKE_CASE__							:Any	     =					42
  SCREAMING_SNAKE_CASE__							:List[Any]	     =					42
  SCREAMING_SNAKE_CASE__							:str	     =					42





class      __SCREAMING_SNAKE_CASE    (						__A		,  __A   ):

  @property
  def    __SCREAMING_SNAKE_CASE				(		self   :       List[str]	)		->				Tuple:
        return True

  @register_to_config
  def __init__(		self   :       Any					,   __a   :       Any = 0.02					,   __a   :       Dict = 100					,   __a   :       Tuple = 1.0_07					,   __a   :       Optional[int] = 80					,   __a   :       Dict = 0.05					,   __a   :       int = 50					,   )		->				Optional[int]:
        pass

  def    __SCREAMING_SNAKE_CASE				(		self   :       List[Any]	)		->				List[str]:
        return KarrasVeSchedulerState.create()

  def    __SCREAMING_SNAKE_CASE				(		self   :       Tuple					,   __a   :       Tuple					,   __a   :       int					,   __a   :       List[Any] = ()	)		->				List[str]:
        _UpperCamelCase      :	Optional[Any]						=       jnp.arange(0					,   __a	)[::-1].copy()
        _UpperCamelCase      :	Union[str, 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    __SCREAMING_SNAKE_CASE				(		self   :       str					,   __a   :       Tuple					,   __a   :       List[Any]					,   __a   :       Optional[Any]					,   __a   :       Optional[int]					,   )		->				Tuple:





        if self.config.s_min <= sigma <= self.config.s_max:
              _UpperCamelCase      :	Optional[Any]						=       min(self.config.s_churn / state.num_inference_steps					,   2**0.5 - 1	)
        else:
              _UpperCamelCase      :	int						=       0

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

        return sample_hat, sigma_hat

  def    __SCREAMING_SNAKE_CASE				(		self   :       str					,   __a   :       Dict					,   __a   :       List[str]					,   __a   :       Dict					,   __a   :       List[Any]					,   __a   :       Any					,   __a   :       List[Any] = True					,   )		->				Tuple:
        _UpperCamelCase      :	List[str]						=       sample_hat + sigma_hat * model_output
        _UpperCamelCase      :	str						=       (sample_hat - pred_original_sample) / sigma_hat
        _UpperCamelCase      :	Tuple						=       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    __SCREAMING_SNAKE_CASE				(		self   :       List[Any]					,   __a   :       Optional[Any]					,   __a   :       Dict					,   __a   :       Optional[Any]					,   __a   :       int					,   __a   :       List[str]					,   __a   :       List[Any]					,   __a   :       Union[str, Any]					,   __a   :       List[Any] = True					,   )		->				Dict:
        _UpperCamelCase      :	Any						=       sample_prev + sigma_prev * model_output
        _UpperCamelCase      :	Optional[int]						=       (sample_prev - pred_original_sample) / sigma_prev
        _UpperCamelCase      :	List[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=__a					,   derivative=__a					,   state=__a	)


  def    __SCREAMING_SNAKE_CASE				(		self   :       int					,   __a   :       Any					,   __a   :       Tuple					,   __a   :       List[Any]					,   __a   :       Any	)		->				List[Any]:
        raise NotImplementedError()







"""simple docstring"""


lowerCamelCase__				  =   "\n# Installazione di Transformers\n! pip install transformers datasets\n# Per installare dalla fonte invece dell'ultima versione rilasciata, commenta il comando sopra e\n# rimuovi la modalità commento al comando seguente.\n# ! pip install git+https://github.com/huggingface/transformers.git\n"

lowerCamelCase__				  =   [{"type": "code", "content": INSTALL_CONTENT}]
lowerCamelCase__				  =   {
    "{processor_class}": "FakeProcessorClass",
    "{model_class}": "FakeModelClass",
    "{object_class}": "FakeObjectClass",
}
