



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 		UpperCAmelCase						:
   # setable values
   SCREAMING_SNAKE_CASE__							=     None
   SCREAMING_SNAKE_CASE__							=     None
   SCREAMING_SNAKE_CASE__							=     None  # sigma(t_i)





   @classmethod
   def 	__lowerCAmelCase							(	cls			):
         return cls()






@dataclass
class 		UpperCAmelCase						(						__UpperCAmelCase				):
   SCREAMING_SNAKE_CASE__							=     4_2
   SCREAMING_SNAKE_CASE__							=     4_2
   SCREAMING_SNAKE_CASE__							=     4_2






class 		UpperCAmelCase						(						__UpperCAmelCase       ,__UpperCAmelCase				):





   @property
   def 	__lowerCAmelCase							(	self			):
         return True





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





   def 	__lowerCAmelCase							(	self			):
         return KarrasVeSchedulerState.create()





   def 	__lowerCAmelCase							(	self       ,     _lowerCAmelCase       ,     _lowerCAmelCase       ,     _lowerCAmelCase = ()			):
         _lowerCAmelCase 			=  jnp.arange(0       ,     lowerCAmelCase_			)[::-1].copy()
         _lowerCAmelCase 			=  [
             (
                 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=lowerCAmelCase_       ,     schedule=jnp.array(lowerCAmelCase_       ,     dtype=jnp.floataa			)       ,     timesteps=lowerCAmelCase_       ,     )





   def 	__lowerCAmelCase							(	self       ,     _lowerCAmelCase       ,     _lowerCAmelCase       ,     _lowerCAmelCase       ,     _lowerCAmelCase       ,     ):







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

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

         return sample_hat, sigma_hat





   def 	__lowerCAmelCase							(	self       ,     _lowerCAmelCase       ,     _lowerCAmelCase       ,     _lowerCAmelCase       ,     _lowerCAmelCase       ,     _lowerCAmelCase       ,     _lowerCAmelCase = True       ,     ):
         _lowerCAmelCase 			=  sample_hat + sigma_hat * model_output
         _lowerCAmelCase 			=  (sample_hat - pred_original_sample) / sigma_hat
         _lowerCAmelCase 			=  sample_hat + (sigma_prev - sigma_hat) * derivative

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

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





   def 	__lowerCAmelCase							(	self       ,     _lowerCAmelCase       ,     _lowerCAmelCase       ,     _lowerCAmelCase       ,     _lowerCAmelCase       ,     _lowerCAmelCase       ,     _lowerCAmelCase       ,     _lowerCAmelCase       ,     _lowerCAmelCase = True       ,     ):
         _lowerCAmelCase 			=  sample_prev + sigma_prev * model_output
         _lowerCAmelCase 			=  (sample_prev - pred_original_sample) / sigma_prev
         _lowerCAmelCase 			=  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=lowerCAmelCase_       ,     derivative=lowerCAmelCase_       ,     state=lowerCAmelCase_			)





   def 	__lowerCAmelCase							(	self       ,     _lowerCAmelCase       ,     _lowerCAmelCase       ,     _lowerCAmelCase       ,     _lowerCAmelCase			):
         raise NotImplementedError()











import functools
import gc
import inspect

import torch

from .imports import is_npu_available, is_xpu_available
def 							UpperCAmelCase__		(				*_SCREAMING_SNAKE_CASE   :	Tuple				)->List[Any]:
      if not isinstance(_SCREAMING_SNAKE_CASE     ,				_SCREAMING_SNAKE_CASE				):
            _lowerCAmelCase 			=  list(_SCREAMING_SNAKE_CASE				)
      for i in range(len(_SCREAMING_SNAKE_CASE				)				):
            _lowerCAmelCase 			=  None
      gc.collect()
      if is_xpu_available():
            torch.xpu.empty_cache()
      elif is_npu_available():
            torch.npu.empty_cache()
      else:
            torch.cuda.empty_cache()
      return objects
def 							UpperCAmelCase__		(				_SCREAMING_SNAKE_CASE   :	Exception				)->bool:
      _lowerCAmelCase 			=  [
          '''CUDA out of memory.''',  # CUDA OOM
          '''cuDNN error: CUDNN_STATUS_NOT_SUPPORTED.''',  # CUDNN SNAFU
          '''DefaultCPUAllocator: can\'t allocate memory''',  # CPU OOM
      ]
      if isinstance(_SCREAMING_SNAKE_CASE     ,				_SCREAMING_SNAKE_CASE				) and len(exception.args				) == 1:
            return any(err in exception.args[0] for err in _statements				)
      return False


def 							UpperCAmelCase__		(				_SCREAMING_SNAKE_CASE   :	callable = None     ,				_SCREAMING_SNAKE_CASE   :	int = 1_2_8				)->Optional[int]:
      if function is None:
            return functools.partial(_SCREAMING_SNAKE_CASE     ,				starting_batch_size=_SCREAMING_SNAKE_CASE				)

      _lowerCAmelCase 			=  starting_batch_size

      def decorator(*_SCREAMING_SNAKE_CASE   :	Optional[int]     ,				**_SCREAMING_SNAKE_CASE   :	Optional[Any]				):
            nonlocal batch_size
            gc.collect()
            if is_xpu_available():
                  torch.xpu.empty_cache()
            elif is_npu_available():
                  torch.npu.empty_cache()
            else:
                  torch.cuda.empty_cache()
            _lowerCAmelCase 			=  list(inspect.signature(_SCREAMING_SNAKE_CASE				).parameters.keys()				)
            # Guard against user error
            if len(_SCREAMING_SNAKE_CASE				) < (len(_SCREAMING_SNAKE_CASE				) + 1):
                  _lowerCAmelCase 			=  ''', '''.join([f'''{arg}={value}''' for arg, value in zip(params[1:]     ,				args[1:]				)]				)
                  raise TypeError(
                      f'''Batch size was passed into `{function.__name__}` as the first argument when called.'''
                      f'''Remove this as the decorator already does so: `{function.__name__}({arg_str})`'''				)
            while True:
                  if batch_size == 0:
                        raise RuntimeError('''No executable batch size found, reached zero.'''				)
                  try:
                        return function(_SCREAMING_SNAKE_CASE     ,				*_SCREAMING_SNAKE_CASE     ,				**_SCREAMING_SNAKE_CASE				)
                  except Exception as e:
                        if should_reduce_batch_size(_SCREAMING_SNAKE_CASE				):
                              gc.collect()
                              if is_xpu_available():
                                    torch.xpu.empty_cache()
                              elif is_npu_available():
                                    torch.npu.empty_cache()
                              else:
                                    torch.cuda.empty_cache()
                              batch_size //= 2
                        else:
                              raise

      return decorator