
'''simple docstring'''





import random
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
      import torch

if is_vision_available():
      import PIL

      from transformers import FlavaImageProcessor
      from transformers.image_utils import PILImageResampling
      from transformers.models.flava.image_processing_flava import (
          FLAVA_CODEBOOK_MEAN,
          FLAVA_CODEBOOK_STD,
          FLAVA_IMAGE_MEAN,
          FLAVA_IMAGE_STD,
      )
else:
      SCREAMING_SNAKE_CASE    =					SCREAMING_SNAKE_CASE    =					SCREAMING_SNAKE_CASE    =					SCREAMING_SNAKE_CASE    =					None






class        UpperCAmelCase_       (						unittest.TestCase							):

 '''simple docstring'''







 def __init__(				self		:     List[Any] ,							snake_case__		:     Optional[int] ,							snake_case__		:     List[str]=7 ,							snake_case__		:     Union[str, Any]=3 ,							snake_case__		:     int=30 ,							snake_case__		:     Optional[int]=4_00 ,							snake_case__		:     Any=True ,							snake_case__		:     Optional[Any]=None ,							snake_case__		:     List[Any]=True ,							snake_case__		:     Dict=None ,							snake_case__		:     Tuple=None ,							snake_case__		:     Union[str, Any]=True ,							snake_case__		:     Tuple=1 / 2_55 ,							snake_case__		:     Any=True ,							snake_case__		:     Optional[Any]=FLAVA_IMAGE_MEAN ,							snake_case__		:     Union[str, Any]=FLAVA_IMAGE_STD ,							snake_case__		:     Optional[int]=14 ,							snake_case__		:     Any=75 ,							snake_case__		:     Dict=None ,							snake_case__		:     Optional[int]=16 ,							snake_case__		:     Tuple=0.3 ,							snake_case__		:     int=None ,							snake_case__		:     str=True ,							snake_case__		:     Tuple=None ,							snake_case__		:     Union[str, Any]=None ,							snake_case__		:     Dict=True ,							snake_case__		:     Union[str, Any]=None ,							snake_case__		:     List[Any]=True ,							snake_case__		:     Dict=True ,							snake_case__		:     Tuple=FLAVA_CODEBOOK_MEAN ,							snake_case__		:     Optional[Any]=FLAVA_CODEBOOK_STD ,							):



   '''simple docstring'''




   UpperCAmelCase__			:				List[Any]												=       size if size is not None else {"height": 2_24, "width": 2_24}
   UpperCAmelCase__			:				Dict												=       crop_size if crop_size is not None else {"height": 2_24, "width": 2_24}
   UpperCAmelCase__			:				Optional[int]												=       codebook_size if codebook_size is not None else {"height": 1_12, "width": 1_12}
   UpperCAmelCase__			:				List[str]												=       codebook_crop_size if codebook_crop_size is not None else {"height": 1_12, "width": 1_12}

   UpperCAmelCase__			:				Dict												=       parent
   UpperCAmelCase__			:				Optional[Any]												=       batch_size
   UpperCAmelCase__			:				Dict												=       num_channels
   UpperCAmelCase__			:				Optional[int]												=       do_resize
   UpperCAmelCase__			:				Any												=       do_rescale
   UpperCAmelCase__			:				Tuple												=       rescale_factor
   UpperCAmelCase__			:				Any												=       min_resolution
   UpperCAmelCase__			:				List[Any]												=       max_resolution
   UpperCAmelCase__			:				int												=       size
   UpperCAmelCase__			:				List[Any]												=       resample if resample is not None else PILImageResampling.BICUBIC
   UpperCAmelCase__			:				Optional[int]												=       do_normalize
   UpperCAmelCase__			:				List[str]												=       image_mean
   UpperCAmelCase__			:				Dict												=       image_std
   UpperCAmelCase__			:				List[Any]												=       do_center_crop
   UpperCAmelCase__			:				List[str]												=       crop_size

   UpperCAmelCase__			:				Union[str, Any]												=       input_size_patches
   UpperCAmelCase__			:				List[str]												=       total_mask_patches
   UpperCAmelCase__			:				Dict												=       mask_group_max_patches
   UpperCAmelCase__			:				int												=       mask_group_min_patches
   UpperCAmelCase__			:				Dict												=       mask_group_min_aspect_ratio
   UpperCAmelCase__			:				int												=       mask_group_max_aspect_ratio

   UpperCAmelCase__			:				Optional[int]												=       codebook_do_resize
   UpperCAmelCase__			:				List[Any]												=       codebook_size
   UpperCAmelCase__			:				Optional[Any]												=       codebook_resample if codebook_resample is not None else PILImageResampling.LANCZOS
   UpperCAmelCase__			:				Tuple												=       codebook_do_center_crop
   UpperCAmelCase__			:				List[str]												=       codebook_crop_size
   UpperCAmelCase__			:				List[str]												=       codebook_do_map_pixels
   UpperCAmelCase__			:				Optional[int]												=       codebook_do_normalize
   UpperCAmelCase__			:				Optional[Any]												=       codebook_image_mean
   UpperCAmelCase__			:				Union[str, Any]												=       codebook_image_std







 def        UpperCamelCase     (				self		:     int   ):



   '''simple docstring'''




   return {
       "image_mean": self.image_mean,
       "image_std": self.image_std,
       "do_normalize": self.do_normalize,
       "do_resize": self.do_resize,
       "size": self.size,
       "resample": self.resample,
       "do_rescale": self.do_rescale,
       "rescale_factor": self.rescale_factor,
       "do_center_crop": self.do_center_crop,
       "crop_size": self.crop_size,
       "input_size_patches": self.input_size_patches,
       "total_mask_patches": self.total_mask_patches,
       "mask_group_max_patches": self.mask_group_max_patches,
       "mask_group_min_patches": self.mask_group_min_patches,
       "mask_group_min_aspect_ratio": self.mask_group_min_aspect_ratio,
       "mask_group_max_aspect_ratio": self.mask_group_min_aspect_ratio,
       "codebook_do_resize": self.codebook_do_resize,
       "codebook_size": self.codebook_size,
       "codebook_resample": self.codebook_resample,
       "codebook_do_center_crop": self.codebook_do_center_crop,
       "codebook_crop_size": self.codebook_crop_size,
       "codebook_do_map_pixels": self.codebook_do_map_pixels,
       "codebook_do_normalize": self.codebook_do_normalize,
       "codebook_image_mean": self.codebook_image_mean,
       "codebook_image_std": self.codebook_image_std,
   }







 def        UpperCamelCase     (				self		:     Optional[int]   ):



   '''simple docstring'''




   return (self.size["height"], self.size["width"])







 def        UpperCamelCase     (				self		:     Dict   ):



   '''simple docstring'''




   return (
       (self.input_size_patches, self.input_size_patches)
       if not isinstance(self.input_size_patches ,							snake_case__   )
       else self.input_size_patches
   )







 def        UpperCamelCase     (				self		:     int   ):



   '''simple docstring'''




   return (self.codebook_size["height"], self.codebook_size["width"])






@require_torch
@require_vision
class        UpperCAmelCase_       (						A				,							unittest.TestCase							):

 '''simple docstring'''
 lowercase_				:						Dict				=       FlavaImageProcessor if is_vision_available() else None
 lowercase_				:						Tuple				=       None







 def        UpperCamelCase     (				self		:     List[str]   ):



   '''simple docstring'''




   UpperCAmelCase__			:				Any												=       FlavaImageProcessingTester(self   )







 @property
 def        UpperCamelCase     (				self		:     Union[str, Any]   ):



   '''simple docstring'''




   return self.image_processor_tester.prepare_image_processor_dict()







 def        UpperCamelCase     (				self		:     Optional[int]   ):



   '''simple docstring'''




   UpperCAmelCase__			:				Dict												=       self.image_processing_class(**self.image_processor_dict   )
   self.assertTrue(hasattr(snake_case__ ,							"image_mean"   )   )
   self.assertTrue(hasattr(snake_case__ ,							"image_std"   )   )
   self.assertTrue(hasattr(snake_case__ ,							"do_normalize"   )   )
   self.assertTrue(hasattr(snake_case__ ,							"do_resize"   )   )
   self.assertTrue(hasattr(snake_case__ ,							"resample"   )   )
   self.assertTrue(hasattr(snake_case__ ,							"crop_size"   )   )
   self.assertTrue(hasattr(snake_case__ ,							"do_center_crop"   )   )
   self.assertTrue(hasattr(snake_case__ ,							"do_rescale"   )   )
   self.assertTrue(hasattr(snake_case__ ,							"rescale_factor"   )   )
   self.assertTrue(hasattr(snake_case__ ,							"masking_generator"   )   )
   self.assertTrue(hasattr(snake_case__ ,							"codebook_do_resize"   )   )
   self.assertTrue(hasattr(snake_case__ ,							"codebook_size"   )   )
   self.assertTrue(hasattr(snake_case__ ,							"codebook_resample"   )   )
   self.assertTrue(hasattr(snake_case__ ,							"codebook_do_center_crop"   )   )
   self.assertTrue(hasattr(snake_case__ ,							"codebook_crop_size"   )   )
   self.assertTrue(hasattr(snake_case__ ,							"codebook_do_map_pixels"   )   )
   self.assertTrue(hasattr(snake_case__ ,							"codebook_do_normalize"   )   )
   self.assertTrue(hasattr(snake_case__ ,							"codebook_image_mean"   )   )
   self.assertTrue(hasattr(snake_case__ ,							"codebook_image_std"   )   )







 def        UpperCamelCase     (				self		:     List[Any]   ):



   '''simple docstring'''




   UpperCAmelCase__			:				int												=       self.image_processing_class.from_dict(self.image_processor_dict   )
   self.assertEqual(image_processor.size ,							{"height": 2_24, "width": 2_24}   )
   self.assertEqual(image_processor.crop_size ,							{"height": 2_24, "width": 2_24}   )
   self.assertEqual(image_processor.codebook_size ,							{"height": 1_12, "width": 1_12}   )
   self.assertEqual(image_processor.codebook_crop_size ,							{"height": 1_12, "width": 1_12}   )

   UpperCAmelCase__			:				Optional[int]												=       self.image_processing_class.from_dict(
       self.image_processor_dict ,							size=42 ,							crop_size=84 ,							codebook_size=33 ,							codebook_crop_size=66   )
   self.assertEqual(image_processor.size ,							{"height": 42, "width": 42}   )
   self.assertEqual(image_processor.crop_size ,							{"height": 84, "width": 84}   )
   self.assertEqual(image_processor.codebook_size ,							{"height": 33, "width": 33}   )
   self.assertEqual(image_processor.codebook_crop_size ,							{"height": 66, "width": 66}   )







 def        UpperCamelCase     (				self		:     int   ):



   '''simple docstring'''




   pass







 def        UpperCamelCase     (				self		:     List[str]   ):



   '''simple docstring'''




   UpperCAmelCase__			:				Dict												=       self.image_processing_class(**self.image_processor_dict   )
   # create random PIL images
   UpperCAmelCase__			:				Optional[int]												=       prepare_image_inputs(self.image_processor_tester ,							equal_resolution=snake_case__   )
   for image in image_inputs:
     self.assertIsInstance(snake_case__ ,							PIL.Image.Image   )

   # Test not batched input
   UpperCAmelCase__			:				List[Any]												=       image_processing(image_inputs[0] ,							return_tensors="pt"   )

   # Test no bool masked pos
   self.assertFalse("bool_masked_pos" in encoded_images   )

   UpperCAmelCase__					, UpperCAmelCase__			:				List[str]												=       self.image_processor_tester.get_expected_image_size()

   self.assertEqual(
       encoded_images.pixel_values.shape ,							(1, self.image_processor_tester.num_channels, expected_height, expected_width) ,							)

   # Test batched
   UpperCAmelCase__			:				str												=       image_processing(snake_case__ ,							return_tensors="pt"   )
   UpperCAmelCase__					, UpperCAmelCase__			:				List[str]												=       self.image_processor_tester.get_expected_image_size()

   # Test no bool masked pos
   self.assertFalse("bool_masked_pos" in encoded_images   )

   self.assertEqual(
       encoded_images.pixel_values.shape ,							(
           self.image_processor_tester.batch_size,
           self.image_processor_tester.num_channels,
           expected_height,
           expected_width,
       ) ,							)







 def        UpperCamelCase     (				self		:     Tuple ,							snake_case__		:     Union[str, Any] ,							snake_case__		:     Optional[int]   ):



   '''simple docstring'''




   UpperCAmelCase__			:				Optional[int]												=       self.image_processing_class(**self.image_processor_dict   )
   # create random tensors
   UpperCAmelCase__			:				Any												=       prepare_image_inputs(self.image_processor_tester ,							equal_resolution=snake_case__ ,							**snake_case__   )
   for image in image_inputs:
     self.assertIsInstance(snake_case__ ,							snake_case__   )

   # Test not batched input
   UpperCAmelCase__			:				Any												=       image_processing(image_inputs[0] ,							return_tensors="pt"   )

   UpperCAmelCase__					, UpperCAmelCase__			:				Optional[int]												=       self.image_processor_tester.get_expected_image_size()
   self.assertEqual(
       encoded_images.pixel_values.shape ,							(1, self.image_processor_tester.num_channels, expected_height, expected_width) ,							)

   UpperCAmelCase__			:				int												=       image_processing(snake_case__ ,							return_image_mask=snake_case__ ,							return_tensors="pt"   )

   UpperCAmelCase__					, UpperCAmelCase__			:				Optional[Any]												=       self.image_processor_tester.get_expected_image_size()
   self.assertEqual(
       encoded_images.pixel_values.shape ,							(
           self.image_processor_tester.batch_size,
           self.image_processor_tester.num_channels,
           expected_height,
           expected_width,
       ) ,							)

   UpperCAmelCase__					, UpperCAmelCase__			:				int												=       self.image_processor_tester.get_expected_mask_size()
   self.assertEqual(
       encoded_images.bool_masked_pos.shape ,							(
           self.image_processor_tester.batch_size,
           expected_height,
           expected_width,
       ) ,							)

   # Test batched
   UpperCAmelCase__			:				Optional[Any]												=       image_processing(snake_case__ ,							return_tensors="pt"   ).pixel_values

   UpperCAmelCase__					, UpperCAmelCase__			:				Optional[int]												=       self.image_processor_tester.get_expected_image_size()
   self.assertEqual(
       encoded_images.shape ,							(
           self.image_processor_tester.batch_size,
           self.image_processor_tester.num_channels,
           expected_height,
           expected_width,
       ) ,							)

   # Test masking
   UpperCAmelCase__			:				Tuple												=       image_processing(snake_case__ ,							return_image_mask=snake_case__ ,							return_tensors="pt"   )

   UpperCAmelCase__					, UpperCAmelCase__			:				Optional[Any]												=       self.image_processor_tester.get_expected_image_size()
   self.assertEqual(
       encoded_images.pixel_values.shape ,							(
           self.image_processor_tester.batch_size,
           self.image_processor_tester.num_channels,
           expected_height,
           expected_width,
       ) ,							)

   UpperCAmelCase__					, UpperCAmelCase__			:				List[str]												=       self.image_processor_tester.get_expected_mask_size()
   self.assertEqual(
       encoded_images.bool_masked_pos.shape ,							(
           self.image_processor_tester.batch_size,
           expected_height,
           expected_width,
       ) ,							)







 def        UpperCamelCase     (				self		:     Tuple   ):



   '''simple docstring'''




   self._test_call_framework(np.ndarray ,							prepare_kwargs={"numpify": True}   )







 def        UpperCamelCase     (				self		:     List[Any]   ):



   '''simple docstring'''




   self._test_call_framework(torch.Tensor ,							prepare_kwargs={"torchify": True}   )







 def        UpperCamelCase     (				self		:     List[str]   ):



   '''simple docstring'''




   random.seed(12_34   )
   UpperCAmelCase__			:				Dict												=       self.image_processing_class(**self.image_processor_dict   )
   UpperCAmelCase__			:				List[Any]												=       prepare_image_inputs(self.image_processor_tester ,							equal_resolution=snake_case__ ,							torchify=snake_case__   )

   # Test not batched input
   UpperCAmelCase__			:				Any												=       image_processing(image_inputs[0] ,							return_image_mask=snake_case__ ,							return_tensors="pt"   )
   self.assertEqual(encoded_images.bool_masked_pos.sum().item() ,							75   )







 def        UpperCamelCase     (				self		:     Optional[int]   ):



   '''simple docstring'''




   UpperCAmelCase__			:				str												=       self.image_processing_class(**self.image_processor_dict   )
   # create random PIL images
   UpperCAmelCase__			:				Any												=       prepare_image_inputs(self.image_processor_tester ,							equal_resolution=snake_case__   )
   for image in image_inputs:
     self.assertIsInstance(snake_case__ ,							PIL.Image.Image   )

   # Test not batched input
   UpperCAmelCase__			:				List[str]												=       image_processing(image_inputs[0] ,							return_codebook_pixels=snake_case__ ,							return_tensors="pt"   )
   UpperCAmelCase__					, UpperCAmelCase__			:				Optional[Any]												=       self.image_processor_tester.get_expected_codebook_image_size()
   self.assertEqual(
       encoded_images.codebook_pixel_values.shape ,							(1, self.image_processor_tester.num_channels, expected_height, expected_width) ,							)

   # Test batched
   UpperCAmelCase__			:				Union[str, Any]												=       image_processing(snake_case__ ,							return_codebook_pixels=snake_case__ ,							return_tensors="pt"   )
   UpperCAmelCase__					, UpperCAmelCase__			:				Tuple												=       self.image_processor_tester.get_expected_codebook_image_size()
   self.assertEqual(
       encoded_images.codebook_pixel_values.shape ,							(
           self.image_processor_tester.batch_size,
           self.image_processor_tester.num_channels,
           expected_height,
           expected_width,
       ) ,							)





'''simple docstring'''





# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch

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 .scheduling_utils_flax import FlaxSchedulerMixin, FlaxSchedulerOutput, broadcast_to_shape_from_left






@flax.struct.dataclass
class        UpperCAmelCase_       :

 '''simple docstring'''
 # setable values
 lowercase_				:						Optional[jnp.ndarray]				=       None
 lowercase_				:						Optional[jnp.ndarray]				=       None
 lowercase_				:						Optional[jnp.ndarray]				=       None







 @classmethod
 def        UpperCamelCase     (				cls		:     List[str]   ):



   '''simple docstring'''




   return cls()






@dataclass
class        UpperCAmelCase_       (						A							):

 '''simple docstring'''
 lowercase_				:						ScoreSdeVeSchedulerState
 lowercase_				:						jnp.ndarray
 lowercase_				:						Optional[jnp.ndarray]				=       None






class        UpperCAmelCase_       (						A				,							A							):

 '''simple docstring'''







 @property
 def        UpperCamelCase     (				self		:     List[Any]   ):



   '''simple docstring'''




   return True







 @register_to_config
 def __init__(				self		:     Optional[Any] ,							snake_case__		:     int = 20_00 ,							snake_case__		:     float = 0.15 ,							snake_case__		:     float = 0.01 ,							snake_case__		:     float = 1348.0 ,							snake_case__		:     float = 1e-5 ,							snake_case__		:     int = 1 ,							):



   '''simple docstring'''




   pass







 def        UpperCamelCase     (				self		:     Optional[int]   ):



   '''simple docstring'''




   UpperCAmelCase__			:				Union[str, Any]												=       ScoreSdeVeSchedulerState.create()
   return self.set_sigmas(
       snake_case__ ,							self.config.num_train_timesteps ,							self.config.sigma_min ,							self.config.sigma_max ,							self.config.sampling_eps ,							)







 def        UpperCamelCase     (				self		:     List[Any] ,							snake_case__		:     ScoreSdeVeSchedulerState ,							snake_case__		:     int ,							snake_case__		:     Tuple = () ,							snake_case__		:     float = None   ):



   '''simple docstring'''




   UpperCAmelCase__			:				str												=       sampling_eps if sampling_eps is not None else self.config.sampling_eps

   UpperCAmelCase__			:				Optional[int]												=       jnp.linspace(1 ,							snake_case__ ,							snake_case__   )
   return state.replace(timesteps=snake_case__   )







 def        UpperCamelCase     (				self		:     str ,							snake_case__		:     ScoreSdeVeSchedulerState ,							snake_case__		:     int ,							snake_case__		:     float = None ,							snake_case__		:     float = None ,							snake_case__		:     float = None ,							):



   '''simple docstring'''




   UpperCAmelCase__			:				int												=       sigma_min if sigma_min is not None else self.config.sigma_min
   UpperCAmelCase__			:				Dict												=       sigma_max if sigma_max is not None else self.config.sigma_max
   UpperCAmelCase__			:				Any												=       sampling_eps if sampling_eps is not None else self.config.sampling_eps
   if state.timesteps is None:
     UpperCAmelCase__			:				Optional[int]												=       self.set_timesteps(snake_case__ ,							snake_case__ ,							snake_case__   )

   UpperCAmelCase__			:				int												=       jnp.exp(jnp.linspace(jnp.log(snake_case__   ) ,							jnp.log(snake_case__   ) ,							snake_case__   )   )
   UpperCAmelCase__			:				Optional[int]												=       jnp.array([sigma_min * (sigma_max / sigma_min) ** t for t in state.timesteps]   )

   return state.replace(discrete_sigmas=snake_case__ ,							sigmas=snake_case__   )







 def        UpperCamelCase     (				self		:     List[str] ,							snake_case__		:     str ,							snake_case__		:     Tuple ,							snake_case__		:     Dict   ):



   '''simple docstring'''




   return jnp.where(timesteps == 0 ,							jnp.zeros_like(snake_case__   ) ,							state.discrete_sigmas[timesteps - 1]   )







 def        UpperCamelCase     (				self		:     Optional[int] ,							snake_case__		:     ScoreSdeVeSchedulerState ,							snake_case__		:     jnp.ndarray ,							snake_case__		:     int ,							snake_case__		:     jnp.ndarray ,							snake_case__		:     random.KeyArray ,							snake_case__		:     bool = True ,							):



   '''simple docstring'''




   if state.timesteps is None:
     raise ValueError(
         "`state.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"   )

   UpperCAmelCase__			:				str												=       timestep * jnp.ones(
       sample.shape[0] ,							)
   UpperCAmelCase__			:				int												=       (timestep * (len(state.timesteps   ) - 1)).long()

   UpperCAmelCase__			:				Optional[int]												=       state.discrete_sigmas[timesteps]
   UpperCAmelCase__			:				str												=       self.get_adjacent_sigma(snake_case__ ,							snake_case__ ,							snake_case__   )
   UpperCAmelCase__			:				Optional[Any]												=       jnp.zeros_like(snake_case__   )
   UpperCAmelCase__			:				List[Any]												=       (sigma**2 - adjacent_sigma**2) ** 0.5

   # equation 6 in the paper: the model_output modeled by the network is grad_x log pt(x)
   # also equation 47 shows the analog from SDE models to ancestral sampling methods
   UpperCAmelCase__			:				Optional[Any]												=       diffusion.flatten()
   UpperCAmelCase__			:				List[str]												=       broadcast_to_shape_from_left(snake_case__ ,							sample.shape   )
   UpperCAmelCase__			:				Optional[Any]												=       drift - diffusion**2 * model_output

   #  equation 6: sample noise for the diffusion term of
   UpperCAmelCase__			:				List[str]												=       random.split(snake_case__ ,							num=1   )
   UpperCAmelCase__			:				Dict												=       random.normal(key=snake_case__ ,							shape=sample.shape   )
   UpperCAmelCase__			:				Optional[int]												=       sample - drift  # subtract because `dt` is a small negative timestep
   # TODO is the variable diffusion the correct scaling term for the noise?
   UpperCAmelCase__			:				int												=       prev_sample_mean + diffusion * noise  # add impact of diffusion field g

   if not return_dict:
     return (prev_sample, prev_sample_mean, state)

   return FlaxSdeVeOutput(prev_sample=snake_case__ ,							prev_sample_mean=snake_case__ ,							state=snake_case__   )







 def        UpperCamelCase     (				self		:     Any ,							snake_case__		:     ScoreSdeVeSchedulerState ,							snake_case__		:     jnp.ndarray ,							snake_case__		:     jnp.ndarray ,							snake_case__		:     random.KeyArray ,							snake_case__		:     bool = True ,							):



   '''simple docstring'''




   if state.timesteps is None:
     raise ValueError(
         "`state.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"   )

   # For small batch sizes, the paper "suggest replacing norm(z) with sqrt(d), where d is the dim. of z"
   # sample noise for correction
   UpperCAmelCase__			:				str												=       random.split(snake_case__ ,							num=1   )
   UpperCAmelCase__			:				Tuple												=       random.normal(key=snake_case__ ,							shape=sample.shape   )

   # compute step size from the model_output, the noise, and the snr
   UpperCAmelCase__			:				Union[str, Any]												=       jnp.linalg.norm(snake_case__   )
   UpperCAmelCase__			:				List[str]												=       jnp.linalg.norm(snake_case__   )
   UpperCAmelCase__			:				Optional[Any]												=       (self.config.snr * noise_norm / grad_norm) ** 2 * 2
   UpperCAmelCase__			:				str												=       step_size * jnp.ones(sample.shape[0]   )

   # compute corrected sample: model_output term and noise term
   UpperCAmelCase__			:				Union[str, Any]												=       step_size.flatten()
   UpperCAmelCase__			:				Optional[int]												=       broadcast_to_shape_from_left(snake_case__ ,							sample.shape   )
   UpperCAmelCase__			:				Tuple												=       sample + step_size * model_output
   UpperCAmelCase__			:				Any												=       prev_sample_mean + ((step_size * 2) ** 0.5) * noise

   if not return_dict:
     return (prev_sample, state)

   return FlaxSdeVeOutput(prev_sample=snake_case__ ,							state=snake_case__   )







 def __len__(				self		:     List[str]   ):



   '''simple docstring'''




   return self.config.num_train_timesteps



