


'''simple docstring'''
import math
from enum import Enum
from typing import Optional, Union

from torch.optim import Optimizer
from torch.optim.lr_scheduler import LambdaLR

from .utils import logging


UpperCamelCase__ =			logging.get_logger(__name__)


class        _UpperCAmelCase						(		snake_case			):
    __lowerCamelCase:   Tuple		       =    'linear'
    __lowerCamelCase:   Any		       =    'cosine'
    __lowerCamelCase:   Optional[Any]		       =    'cosine_with_restarts'
    __lowerCamelCase:   Tuple		       =    'polynomial'
    __lowerCamelCase:   int		       =    'constant'
    __lowerCamelCase:   Optional[Any]		       =    'constant_with_warmup'
    __lowerCamelCase:   List[str]		       =    'piecewise_constant'




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase						,		_UpperCamelCase = -1							):


   """simple docstring"""




   return LambdaLR(_UpperCamelCase						,		lambda _UpperCamelCase							: 1						,		last_epoch=_UpperCamelCase							)




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase = -1							):


   """simple docstring"""




   def lr_lambda(_UpperCamelCase							):
      if current_step < num_warmup_steps:
         return float(_UpperCamelCase							) / float(max(1.0						,		_UpperCamelCase							)							)
      return 1.0

   return LambdaLR(_UpperCamelCase						,		_UpperCamelCase						,		last_epoch=_UpperCamelCase							)




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase = -1							):


   """simple docstring"""




   lowercase_	:    List[Any]           =     {}
   lowercase_	:    Dict           =     step_rules.split(","							)
   for rule_str in rule_list[:-1]:
      lowercase_	:    Any           =     rule_str.split(":"							)
      lowercase_	:    List[Any]           =     int(_UpperCamelCase							)
      lowercase_	:    int           =     float(_UpperCamelCase							)
      lowercase_	:    Optional[int]           =     value
   lowercase_	:    Union[str, Any]           =     float(rule_list[-1]							)

   def create_rules_function(_UpperCamelCase						,		_UpperCamelCase							):
      def rule_func(_UpperCamelCase							) -> float:
         lowercase_	:    Optional[Any]           =     sorted(rules_dict.keys()							)
         for i, sorted_step in enumerate(_UpperCamelCase							):
            if steps < sorted_step:
               return rules_dict[sorted_steps[i]]
         return last_lr_multiple

      return rule_func

   lowercase_	:    Optional[int]           =     create_rules_function(_UpperCamelCase						,		_UpperCamelCase							)

   return LambdaLR(_UpperCamelCase						,		_UpperCamelCase						,		last_epoch=_UpperCamelCase							)




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase=-1							):


   """simple docstring"""




   def lr_lambda(_UpperCamelCase							):
      if current_step < num_warmup_steps:
         return float(_UpperCamelCase							) / float(max(1						,		_UpperCamelCase							)							)
      return max(
          0.0						,		float(num_training_steps - current_step							) / float(max(1						,		num_training_steps - num_warmup_steps							)							)							)

   return LambdaLR(_UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase							)




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase = 0.5						,		_UpperCamelCase = -1							):


   """simple docstring"""




   def lr_lambda(_UpperCamelCase							):
      if current_step < num_warmup_steps:
         return float(_UpperCamelCase							) / float(max(1						,		_UpperCamelCase							)							)
      lowercase_	:    List[str]           =     float(current_step - num_warmup_steps							) / float(max(1						,		num_training_steps - num_warmup_steps							)							)
      return max(0.0						,		0.5 * (1.0 + math.cos(math.pi * float(_UpperCamelCase							) * 2.0 * progress							))							)

   return LambdaLR(_UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase							)




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase = 1						,		_UpperCamelCase = -1							):


   """simple docstring"""




   def lr_lambda(_UpperCamelCase							):
      if current_step < num_warmup_steps:
         return float(_UpperCamelCase							) / float(max(1						,		_UpperCamelCase							)							)
      lowercase_	:    Dict           =     float(current_step - num_warmup_steps							) / float(max(1						,		num_training_steps - num_warmup_steps							)							)
      if progress >= 1.0:
         return 0.0
      return max(0.0						,		0.5 * (1.0 + math.cos(math.pi * ((float(_UpperCamelCase							) * progress) % 1.0)							))							)

   return LambdaLR(_UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase							)




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase=1e-7						,		_UpperCamelCase=1.0						,		_UpperCamelCase=-1							):


   """simple docstring"""




   lowercase_	:    Dict           =     optimizer.defaults["lr"]
   if not (lr_init > lr_end):
      raise ValueError(F"""lr_end ({lr_end}) must be be smaller than initial lr ({lr_init})"""							)

   def lr_lambda(_UpperCamelCase							):
      if current_step < num_warmup_steps:
         return float(_UpperCamelCase							) / float(max(1						,		_UpperCamelCase							)							)
      elif current_step > num_training_steps:
         return lr_end / lr_init  # as LambdaLR multiplies by lr_init
      else:
         lowercase_	:    int           =     lr_init - lr_end
         lowercase_	:    Optional[int]           =     num_training_steps - num_warmup_steps
         lowercase_	:    Optional[Any]           =     1 - (current_step - num_warmup_steps) / decay_steps
         lowercase_	:    List[Any]           =     lr_range * pct_remaining**power + lr_end
         return decay / lr_init  # as LambdaLR multiplies by lr_init

   return LambdaLR(_UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase							)


UpperCamelCase__ =			{
    SchedulerType.LINEAR: get_linear_schedule_with_warmup,
    SchedulerType.COSINE: get_cosine_schedule_with_warmup,
    SchedulerType.COSINE_WITH_RESTARTS: get_cosine_with_hard_restarts_schedule_with_warmup,
    SchedulerType.POLYNOMIAL: get_polynomial_decay_schedule_with_warmup,
    SchedulerType.CONSTANT: get_constant_schedule,
    SchedulerType.CONSTANT_WITH_WARMUP: get_constant_schedule_with_warmup,
    SchedulerType.PIECEWISE_CONSTANT: get_piecewise_constant_schedule,
}





def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase = None						,		_UpperCamelCase = None						,		_UpperCamelCase = None						,		_UpperCamelCase = 1						,		_UpperCamelCase = 1.0						,		_UpperCamelCase = -1						,		):


   """simple docstring"""




   lowercase_	:    Any           =     SchedulerType(_UpperCamelCase							)
   lowercase_	:    List[Any]           =     TYPE_TO_SCHEDULER_FUNCTION[name]
   if name == SchedulerType.CONSTANT:
      return schedule_func(_UpperCamelCase						,		last_epoch=_UpperCamelCase							)

   if name == SchedulerType.PIECEWISE_CONSTANT:
      return schedule_func(_UpperCamelCase						,		step_rules=_UpperCamelCase						,		last_epoch=_UpperCamelCase							)

   # All other schedulers require `num_warmup_steps`
   if num_warmup_steps is None:
      raise ValueError(F"""{name} requires `num_warmup_steps`, please provide that argument."""							)

   if name == SchedulerType.CONSTANT_WITH_WARMUP:
      return schedule_func(_UpperCamelCase						,		num_warmup_steps=_UpperCamelCase						,		last_epoch=_UpperCamelCase							)

   # All other schedulers require `num_training_steps`
   if num_training_steps is None:
      raise ValueError(F"""{name} requires `num_training_steps`, please provide that argument."""							)

   if name == SchedulerType.COSINE_WITH_RESTARTS:
      return schedule_func(
          _UpperCamelCase						,		num_warmup_steps=_UpperCamelCase						,		num_training_steps=_UpperCamelCase						,		num_cycles=_UpperCamelCase						,		last_epoch=_UpperCamelCase						,		)

   if name == SchedulerType.POLYNOMIAL:
      return schedule_func(
          _UpperCamelCase						,		num_warmup_steps=_UpperCamelCase						,		num_training_steps=_UpperCamelCase						,		power=_UpperCamelCase						,		last_epoch=_UpperCamelCase						,		)

   return schedule_func(
       _UpperCamelCase						,		num_warmup_steps=_UpperCamelCase						,		num_training_steps=_UpperCamelCase						,		last_epoch=_UpperCamelCase							)












'''simple docstring'''
import importlib
import math
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Tuple, Union

import flax
import jax.numpy as jnp

from ..utils import BaseOutput


UpperCamelCase__ =			'scheduler_config.json'


class        _UpperCAmelCase						(		snake_case			):
    __lowerCamelCase:   int		       =    1
    __lowerCamelCase:   List[Any]		       =    2
    __lowerCamelCase:   Optional[Any]		       =    3
    __lowerCamelCase:   int		       =    4
    __lowerCamelCase:   Optional[int]		       =    5


@dataclass
class        _UpperCAmelCase						(		snake_case			):
    __lowerCamelCase:   jnp.ndarray


class        _UpperCAmelCase						:
    __lowerCamelCase:   List[str]		       =    SCHEDULER_CONFIG_NAME
    __lowerCamelCase:   Optional[int]		       =    ['dtype']
    __lowerCamelCase:   int		       =    []
    __lowerCamelCase:   Dict		       =    True




    @classmethod
    def 				lowerCAmelCase__				(			cls  :       Tuple  ,     a  :       Dict[str, Any] = None  ,     a  :       Optional[str] = None  ,     a  :       Union[str, Any]=False  ,     **a  :       Union[str, Any]  ,     ):






       '''simple docstring'''






       lowercase_    ,		lowercase_	:    Any           =     cls.load_config(
           pretrained_model_name_or_path=a  ,     subfolder=a  ,     return_unused_kwargs=a  ,     **a  ,     )
       lowercase_    ,		lowercase_	:    Union[str, Any]           =     cls.from_config(a  ,     return_unused_kwargs=a  ,     **a			)

       if hasattr(a  ,     "create_state"			) and getattr(a  ,     "has_state"  ,     a			):
          lowercase_	:    Tuple           =     scheduler.create_state()

       if return_unused_kwargs:
          return scheduler, state, unused_kwargs

       return scheduler, state




    def 				lowerCAmelCase__				(			self  :       int  ,     a  :       Union[str, os.PathLike]  ,     a  :       bool = False  ,     **a  :       int			):






       '''simple docstring'''






       self.save_config(save_directory=a  ,     push_to_hub=a  ,     **a			)




    @property
    def 				lowerCAmelCase__				(			self  :       Tuple			):






       '''simple docstring'''






       return self._get_compatibles()





    @classmethod
    def 				lowerCAmelCase__				(			cls  :       Dict			):






       '''simple docstring'''






       lowercase_	:    str           =     list(set([cls.__name__] + cls._compatibles			)			)
       lowercase_	:    str           =     importlib.import_module(__name__.split("."			)[0]			)
       lowercase_	:    Optional[Any]           =     [
           getattr(a  ,     a			) for c in compatible_classes_str if hasattr(a  ,     a			)
       ]
       return compatible_classes




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase						,		_UpperCamelCase							):


   """simple docstring"""




   assert len(_UpperCamelCase							) >= x.ndim
   return jnp.broadcast_to(x.reshape(x.shape + (1,) * (len(_UpperCamelCase							) - x.ndim)							)						,		_UpperCamelCase							)




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase						,		_UpperCamelCase=0.999						,		_UpperCamelCase=jnp.floataa							):


   """simple docstring"""




   def alpha_bar(_UpperCamelCase							):
      return math.cos((time_step + 0.008) / 1.008 * math.pi / 2							) ** 2

   lowercase_	:    int           =     []
   for i in range(_UpperCamelCase							):
      lowercase_	:    Union[str, Any]           =     i / num_diffusion_timesteps
      lowercase_	:    Dict           =     (i + 1) / num_diffusion_timesteps
      betas.append(min(1 - alpha_bar(_UpperCamelCase							) / alpha_bar(_UpperCamelCase							)						,		_UpperCamelCase							)							)
   return jnp.array(_UpperCamelCase						,		dtype=_UpperCamelCase							)


@flax.struct.dataclass
class        _UpperCAmelCase						:
    __lowerCamelCase:   jnp.ndarray
    __lowerCamelCase:   jnp.ndarray
    __lowerCamelCase:   jnp.ndarray




    @classmethod
    def 				lowerCAmelCase__				(			cls  :       Tuple  ,     a  :       Optional[int]			):






       '''simple docstring'''






       lowercase_	:    Any           =     scheduler.config

       if config.trained_betas is not None:
          lowercase_	:    Union[str, Any]           =     jnp.asarray(config.trained_betas  ,     dtype=scheduler.dtype			)
       elif config.beta_schedule == "linear":
          lowercase_	:    List[Any]           =     jnp.linspace(config.beta_start  ,     config.beta_end  ,     config.num_train_timesteps  ,     dtype=scheduler.dtype			)
       elif config.beta_schedule == "scaled_linear":
          # this schedule is very specific to the latent diffusion model.
          lowercase_	:    Tuple           =     (
              jnp.linspace(
                  config.beta_start**0.5  ,     config.beta_end**0.5  ,     config.num_train_timesteps  ,     dtype=scheduler.dtype			)
              ** 2
          )
       elif config.beta_schedule == "squaredcos_cap_v2":
          # Glide cosine schedule
          lowercase_	:    Union[str, Any]           =     betas_for_alpha_bar(config.num_train_timesteps  ,     dtype=scheduler.dtype			)
       else:
          raise NotImplementedError(
              f"""beta_schedule {config.beta_schedule} is not implemented for scheduler {scheduler.__class__.__name__}"""			)

       lowercase_	:    str           =     1.0 - betas

       lowercase_	:    Dict           =     jnp.cumprod(a  ,     axis=0			)

       return cls(
           alphas=a  ,     betas=a  ,     alphas_cumprod=a  ,     )




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase							):


   """simple docstring"""




   lowercase_	:    List[str]           =     state.alphas_cumprod

   lowercase_	:    Optional[Any]           =     alphas_cumprod[timesteps] ** 0.5
   lowercase_	:    int           =     sqrt_alpha_prod.flatten()
   lowercase_	:    Union[str, Any]           =     broadcast_to_shape_from_left(_UpperCamelCase						,		original_samples.shape							)

   lowercase_	:    Optional[int]           =     (1 - alphas_cumprod[timesteps]) ** 0.5
   lowercase_	:    Union[str, Any]           =     sqrt_one_minus_alpha_prod.flatten()
   lowercase_	:    Any           =     broadcast_to_shape_from_left(_UpperCamelCase						,		original_samples.shape							)

   return sqrt_alpha_prod, sqrt_one_minus_alpha_prod




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase							):


   """simple docstring"""




   lowercase_    ,		lowercase_	:    int           =     get_sqrt_alpha_prod(_UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase							)
   lowercase_	:    str           =     sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
   return noisy_samples





def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase							):


   """simple docstring"""




   lowercase_    ,		lowercase_	:    int           =     get_sqrt_alpha_prod(_UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase							)
   lowercase_	:    Any           =     sqrt_alpha_prod * noise - sqrt_one_minus_alpha_prod * sample
   return velocity




