




"""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_	:



 '''simple docstring'''
 # setable values
 UpperCAmelCase   :					Optional[int]				  =     None
 UpperCAmelCase   :					Optional[jnp.ndarray]				  =     None
 UpperCAmelCase   :					Optional[jnp.ndarray]				  =     None  # sigma(t_i)

 @classmethod
 def       lowerCAmelCase_							(			cls	:	str		):
       return cls()





@dataclass
class     lowercase_	(					__lowerCAmelCase							):



 '''simple docstring'''
 UpperCAmelCase   :					jnp.ndarray
 UpperCAmelCase   :					jnp.ndarray
 UpperCAmelCase   :					KarrasVeSchedulerState





class     lowercase_	(					__lowerCAmelCase      ,     __lowerCAmelCase							):



 '''simple docstring'''

 @property
 def       lowerCAmelCase_							(			self	:	Any		):
       return True

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

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

 def       lowerCAmelCase_							(			self	:	int   ,							_UpperCAmelCase	:	KarrasVeSchedulerState   ,							_UpperCAmelCase	:	int   ,							_UpperCAmelCase	:	Tuple = ()		):
       _A        =       jnp.arange(0   ,							_UpperCAmelCase		)[::-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=_UpperCAmelCase   ,							schedule=jnp.array(_UpperCAmelCase   ,							dtype=jnp.floataa		)   ,							timesteps=_UpperCAmelCase   ,							)

 def       lowerCAmelCase_							(			self	:	Optional[Any]   ,							_UpperCAmelCase	:	KarrasVeSchedulerState   ,							_UpperCAmelCase	:	jnp.ndarray   ,							_UpperCAmelCase	:	float   ,							_UpperCAmelCase	:	random.KeyArray   ,							):
       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(_UpperCAmelCase   ,							num=1		)
       _A        =       self.config.s_noise * random.normal(key=_UpperCAmelCase   ,							shape=sample.shape		)
       _A        =       sigma + gamma * sigma
       _A        =       sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)

       return sample_hat, sigma_hat

 def       lowerCAmelCase_							(			self	:	str   ,							_UpperCAmelCase	:	KarrasVeSchedulerState   ,							_UpperCAmelCase	:	jnp.ndarray   ,							_UpperCAmelCase	:	float   ,							_UpperCAmelCase	:	float   ,							_UpperCAmelCase	:	jnp.ndarray   ,							_UpperCAmelCase	:	bool = True   ,							):
       _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=_UpperCAmelCase   ,							derivative=_UpperCAmelCase   ,							state=_UpperCAmelCase		)

 def       lowerCAmelCase_							(			self	:	Optional[Any]   ,							_UpperCAmelCase	:	KarrasVeSchedulerState   ,							_UpperCAmelCase	:	jnp.ndarray   ,							_UpperCAmelCase	:	float   ,							_UpperCAmelCase	:	float   ,							_UpperCAmelCase	:	jnp.ndarray   ,							_UpperCAmelCase	:	jnp.ndarray   ,							_UpperCAmelCase	:	jnp.ndarray   ,							_UpperCAmelCase	:	bool = True   ,							):
       _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=_UpperCAmelCase   ,							derivative=_UpperCAmelCase   ,							state=_UpperCAmelCase		)



 def       lowerCAmelCase_							(			self	:	Dict   ,							_UpperCAmelCase	:	KarrasVeSchedulerState   ,							_UpperCAmelCase	:	List[str]   ,							_UpperCAmelCase	:	Optional[int]   ,							_UpperCAmelCase	:	List[Any]		):
       raise NotImplementedError()







"""simple docstring"""

import logging
from dataclasses import dataclass, field
from pathlib import Path
from typing import Optional, Union

from .generation.configuration_utils import GenerationConfig
from .training_args import TrainingArguments
from .utils import add_start_docstrings


a         =      logging.getLogger(__name__)





@dataclass
@add_start_docstrings(TrainingArguments.__doc__							)
class     lowercase_	(					__lowerCAmelCase							):



 '''simple docstring'''
 UpperCAmelCase   :					bool				  =     field(default=__lowerCAmelCase      ,     metadata={'''help''': '''Whether to use SortishSampler or not.'''}							)
 UpperCAmelCase   :					bool				  =     field(
     default=__lowerCAmelCase      ,     metadata={'''help''': '''Whether to use generate to calculate generative metrics (ROUGE, BLEU).'''}							)
 UpperCAmelCase   :					Optional[int]				  =     field(
     default=__lowerCAmelCase      ,     metadata={
         '''help''': (
             '''The `max_length` to use on each evaluation loop when `predict_with_generate=True`. Will default '''
             '''to the `max_length` value of the model configuration.'''
         )
     }      ,     )
 UpperCAmelCase   :					Optional[int]				  =     field(
     default=__lowerCAmelCase      ,     metadata={
         '''help''': (
             '''The `num_beams` to use on each evaluation loop when `predict_with_generate=True`. Will default '''
             '''to the `num_beams` value of the model configuration.'''
         )
     }      ,     )
 UpperCAmelCase   :					Optional[Union[str, Path, GenerationConfig]]				  =     field(
     default=__lowerCAmelCase      ,     metadata={
         '''help''': '''Model id, file path or url pointing to a GenerationConfig json file, to use during prediction.'''
     }      ,     )

 def       lowerCAmelCase_							(			self	:	int		):
       _A        =       super().to_dict()
       for k, v in d.items():
             if isinstance(_UpperCAmelCase   ,							_UpperCAmelCase		):
                   _A        =       v.to_dict()
       return d

