
'''simple docstring'''





import os
import pickle
import tempfile
import unittest
from typing import Callable, Optional

import numpy as np

from transformers import (
    BatchEncoding,
    BertTokenizer,
    BertTokenizerFast,
    PreTrainedTokenizer,
    PreTrainedTokenizerFast,
    TensorType,
    TokenSpan,
    is_tokenizers_available,
)
from transformers.models.gpta.tokenization_gpta import GPTaTokenizer
from transformers.testing_utils import CaptureStderr, require_flax, require_tf, require_tokenizers, require_torch, slow


if is_tokenizers_available():
      from tokenizers import Tokenizer
      from tokenizers.models import WordPiece






class        UpperCAmelCase_       (						unittest.TestCase							):

 '''simple docstring'''







 def        UpperCamelCase     (				self		:     Optional[Any] ,							snake_case__		:     List[Any]   ):



   '''simple docstring'''




   UpperCAmelCase__			:				int												=       list(tokenizer_class.max_model_input_sizes.keys()   )
   for model_name in sa_models[:1]:
     UpperCAmelCase__			:				Dict												=       tokenizer_class.from_pretrained(snake_case__   )
     self.assertIsNotNone(snake_case__   )
     self.assertIsInstance(snake_case__ ,							snake_case__   )
     self.assertIsInstance(snake_case__ ,							snake_case__   )

     for special_tok in tokenizer.all_special_tokens:
       self.assertIsInstance(snake_case__ ,							snake_case__   )
       UpperCAmelCase__			:				List[Any]												=       tokenizer.convert_tokens_to_ids(snake_case__   )
       self.assertIsInstance(snake_case__ ,							snake_case__   )







 def        UpperCamelCase     (				self		:     Union[str, Any] ,							snake_case__		:     BatchEncoding ,							snake_case__		:     Optional[Callable] = None   ):



   '''simple docstring'''




   UpperCAmelCase__			:				List[str]												=       pickle.dumps(snake_case__   )
   self.assertIsNotNone(snake_case__   )

   UpperCAmelCase__			:				Optional[Any]												=       pickle.loads(snake_case__   )

   # Ensure is_fast is correctly restored
   self.assertEqual(be_restored.is_fast ,							be_original.is_fast   )

   # Ensure encodings are potentially correctly restored
   if be_original.is_fast:
     self.assertIsNotNone(be_restored.encodings   )
   else:
     self.assertIsNone(be_restored.encodings   )

   # Ensure the keys are the same
   for original_v, restored_v in zip(be_original.values() ,							be_restored.values()   ):
     if equal_op:
       self.assertTrue(equal_op(snake_case__ ,							snake_case__   )   )
     else:
       self.assertEqual(snake_case__ ,							snake_case__   )







 @slow
 def        UpperCamelCase     (				self		:     Dict   ):



   '''simple docstring'''




   self.check_tokenizer_from_pretrained(snake_case__   )







 def        UpperCamelCase     (				self		:     Optional[Any]   ):



   '''simple docstring'''




   self.assertEqual(TensorType("tf"   ) ,							TensorType.TENSORFLOW   )
   self.assertEqual(TensorType("pt"   ) ,							TensorType.PYTORCH   )
   self.assertEqual(TensorType("np"   ) ,							TensorType.NUMPY   )







 @require_tokenizers
 def        UpperCamelCase     (				self		:     Optional[Any]   ):



   '''simple docstring'''




   import numpy as np

   UpperCAmelCase__			:				Optional[Any]												=       BertTokenizer.from_pretrained("bert-base-cased"   )
   UpperCAmelCase__			:				Tuple												=       BertTokenizerFast.from_pretrained("bert-base-cased"   )

   # Python no tensor
   with self.subTest("BatchEncoding (Python, return_tensors=None)"   ):
     self.assert_dump_and_restore(tokenizer_p("Small example to encode"   )   )

   with self.subTest("BatchEncoding (Python, return_tensors=NUMPY)"   ):
     self.assert_dump_and_restore(
         tokenizer_p("Small example to encode" ,							return_tensors=TensorType.NUMPY   ) ,							np.array_equal   )

   with self.subTest("BatchEncoding (Rust, return_tensors=None)"   ):
     self.assert_dump_and_restore(tokenizer_r("Small example to encode"   )   )

   with self.subTest("BatchEncoding (Rust, return_tensors=NUMPY)"   ):
     self.assert_dump_and_restore(
         tokenizer_r("Small example to encode" ,							return_tensors=TensorType.NUMPY   ) ,							np.array_equal   )







 @require_tf
 @require_tokenizers
 def        UpperCamelCase     (				self		:     List[Any]   ):



   '''simple docstring'''




   import tensorflow as tf

   def tf_array_equals(snake_case__		:     int ,							snake_case__		:     str   ):
     return tf.reduce_all(tf.equal(snake_case__ ,							snake_case__   )   )

   UpperCAmelCase__			:				Dict												=       BertTokenizer.from_pretrained("bert-base-cased"   )
   UpperCAmelCase__			:				Optional[Any]												=       BertTokenizerFast.from_pretrained("bert-base-cased"   )

   with self.subTest("BatchEncoding (Python, return_tensors=TENSORFLOW)"   ):
     self.assert_dump_and_restore(
         tokenizer_p("Small example to encode" ,							return_tensors=TensorType.TENSORFLOW   ) ,							snake_case__   )

   with self.subTest("BatchEncoding (Rust, return_tensors=TENSORFLOW)"   ):
     self.assert_dump_and_restore(
         tokenizer_r("Small example to encode" ,							return_tensors=TensorType.TENSORFLOW   ) ,							snake_case__   )







 @require_torch
 @require_tokenizers
 def        UpperCamelCase     (				self		:     Dict   ):



   '''simple docstring'''




   import torch

   UpperCAmelCase__			:				Any												=       BertTokenizer.from_pretrained("bert-base-cased"   )
   UpperCAmelCase__			:				str												=       BertTokenizerFast.from_pretrained("bert-base-cased"   )

   with self.subTest("BatchEncoding (Python, return_tensors=PYTORCH)"   ):
     self.assert_dump_and_restore(
         tokenizer_p("Small example to encode" ,							return_tensors=TensorType.PYTORCH   ) ,							torch.equal   )

   with self.subTest("BatchEncoding (Rust, return_tensors=PYTORCH)"   ):
     self.assert_dump_and_restore(
         tokenizer_r("Small example to encode" ,							return_tensors=TensorType.PYTORCH   ) ,							torch.equal   )







 @require_tokenizers
 def        UpperCamelCase     (				self		:     Dict   ):



   '''simple docstring'''




   UpperCAmelCase__			:				List[Any]												=       BertTokenizer.from_pretrained("bert-base-cased"   )
   UpperCAmelCase__			:				int												=       BertTokenizerFast.from_pretrained("bert-base-cased"   )

   with self.subTest("Python Tokenizer"   ):
     self.assertFalse(tokenizer_p("Small example to_encode"   ).is_fast   )

   with self.subTest("Rust Tokenizer"   ):
     self.assertTrue(tokenizer_r("Small example to_encode"   ).is_fast   )







 @require_tokenizers
 def        UpperCamelCase     (				self		:     List[str]   ):



   '''simple docstring'''




   UpperCAmelCase__			:				Optional[Any]												=       BertTokenizerFast.from_pretrained("bert-base-cased"   )
   UpperCAmelCase__			:				Optional[Any]												=       tokenizer_r(["Test", "\xad", "test"] ,							is_split_into_words=snake_case__   )

   self.assertEqual(encoded.word_to_tokens(0   ) ,							TokenSpan(start=1 ,							end=2   )   )
   self.assertEqual(encoded.word_to_tokens(1   ) ,							snake_case__   )
   self.assertEqual(encoded.word_to_tokens(2   ) ,							TokenSpan(start=2 ,							end=3   )   )







 def        UpperCamelCase     (				self		:     str   ):



   '''simple docstring'''




   UpperCAmelCase__			:				Tuple												=       BatchEncoding({"inputs": [[1, 2, 3], [4, 5, 6]], "labels": [0, 1]}   )
   UpperCAmelCase__			:				Dict												=       batch.convert_to_tensors(tensor_type="np"   )
   self.assertEqual(tensor_batch["inputs"].shape ,							(2, 3)   )
   self.assertEqual(tensor_batch["labels"].shape ,							(2,)   )
   # test converting the converted
   with CaptureStderr() as cs:
     UpperCAmelCase__			:				List[Any]												=       batch.convert_to_tensors(tensor_type="np"   )
   self.assertFalse(len(cs.err   ) ,							msg=F"""should have no warning, but got {cs.err}"""   )

   UpperCAmelCase__			:				Tuple												=       BatchEncoding({"inputs": [1, 2, 3], "labels": 0}   )
   UpperCAmelCase__			:				str												=       batch.convert_to_tensors(tensor_type="np" ,							prepend_batch_axis=snake_case__   )
   self.assertEqual(tensor_batch["inputs"].shape ,							(1, 3)   )
   self.assertEqual(tensor_batch["labels"].shape ,							(1,)   )







 @require_torch
 def        UpperCamelCase     (				self		:     Optional[int]   ):



   '''simple docstring'''




   UpperCAmelCase__			:				Any												=       BatchEncoding({"inputs": [[1, 2, 3], [4, 5, 6]], "labels": [0, 1]}   )
   UpperCAmelCase__			:				Union[str, Any]												=       batch.convert_to_tensors(tensor_type="pt"   )
   self.assertEqual(tensor_batch["inputs"].shape ,							(2, 3)   )
   self.assertEqual(tensor_batch["labels"].shape ,							(2,)   )
   # test converting the converted
   with CaptureStderr() as cs:
     UpperCAmelCase__			:				List[Any]												=       batch.convert_to_tensors(tensor_type="pt"   )
   self.assertFalse(len(cs.err   ) ,							msg=F"""should have no warning, but got {cs.err}"""   )

   UpperCAmelCase__			:				int												=       BatchEncoding({"inputs": [1, 2, 3], "labels": 0}   )
   UpperCAmelCase__			:				Optional[Any]												=       batch.convert_to_tensors(tensor_type="pt" ,							prepend_batch_axis=snake_case__   )
   self.assertEqual(tensor_batch["inputs"].shape ,							(1, 3)   )
   self.assertEqual(tensor_batch["labels"].shape ,							(1,)   )







 @require_tf
 def        UpperCamelCase     (				self		:     List[str]   ):



   '''simple docstring'''




   UpperCAmelCase__			:				Any												=       BatchEncoding({"inputs": [[1, 2, 3], [4, 5, 6]], "labels": [0, 1]}   )
   UpperCAmelCase__			:				Optional[Any]												=       batch.convert_to_tensors(tensor_type="tf"   )
   self.assertEqual(tensor_batch["inputs"].shape ,							(2, 3)   )
   self.assertEqual(tensor_batch["labels"].shape ,							(2,)   )
   # test converting the converted
   with CaptureStderr() as cs:
     UpperCAmelCase__			:				Any												=       batch.convert_to_tensors(tensor_type="tf"   )
   self.assertFalse(len(cs.err   ) ,							msg=F"""should have no warning, but got {cs.err}"""   )

   UpperCAmelCase__			:				List[str]												=       BatchEncoding({"inputs": [1, 2, 3], "labels": 0}   )
   UpperCAmelCase__			:				Union[str, Any]												=       batch.convert_to_tensors(tensor_type="tf" ,							prepend_batch_axis=snake_case__   )
   self.assertEqual(tensor_batch["inputs"].shape ,							(1, 3)   )
   self.assertEqual(tensor_batch["labels"].shape ,							(1,)   )







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



   '''simple docstring'''




   UpperCAmelCase__			:				Optional[int]												=       BatchEncoding({"inputs": [[1, 2, 3], [4, 5, 6]], "labels": [0, 1]}   )
   UpperCAmelCase__			:				Optional[int]												=       batch.convert_to_tensors(tensor_type="jax"   )
   self.assertEqual(tensor_batch["inputs"].shape ,							(2, 3)   )
   self.assertEqual(tensor_batch["labels"].shape ,							(2,)   )
   # test converting the converted
   with CaptureStderr() as cs:
     UpperCAmelCase__			:				Tuple												=       batch.convert_to_tensors(tensor_type="jax"   )
   self.assertFalse(len(cs.err   ) ,							msg=F"""should have no warning, but got {cs.err}"""   )

   UpperCAmelCase__			:				Optional[Any]												=       BatchEncoding({"inputs": [1, 2, 3], "labels": 0}   )
   UpperCAmelCase__			:				Optional[Any]												=       batch.convert_to_tensors(tensor_type="jax" ,							prepend_batch_axis=snake_case__   )
   self.assertEqual(tensor_batch["inputs"].shape ,							(1, 3)   )
   self.assertEqual(tensor_batch["labels"].shape ,							(1,)   )







 def        UpperCamelCase     (				self		:     Tuple   ):



   '''simple docstring'''




   UpperCAmelCase__			:				List[Any]												=       [{"input_ids": np.array([0, 1, 2]   )}, {"input_ids": np.array([0, 1, 2, 3]   )}]
   UpperCAmelCase__			:				str												=       BertTokenizer.from_pretrained("bert-base-cased"   )

   UpperCAmelCase__			:				Optional[Any]												=       tokenizer.pad(snake_case__ ,							padding=snake_case__   )
   self.assertTrue(isinstance(batch["input_ids"] ,							np.ndarray   )   )
   self.assertEqual(batch["input_ids"].tolist() ,							[[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]]   )
   UpperCAmelCase__			:				int												=       tokenizer.pad(snake_case__ ,							padding=snake_case__ ,							return_tensors="np"   )
   self.assertTrue(isinstance(batch["input_ids"] ,							np.ndarray   )   )
   self.assertEqual(batch["input_ids"].tolist() ,							[[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]]   )







 @require_torch
 def        UpperCamelCase     (				self		:     Dict   ):



   '''simple docstring'''




   import torch

   UpperCAmelCase__			:				Optional[int]												=       [{"input_ids": torch.tensor([0, 1, 2]   )}, {"input_ids": torch.tensor([0, 1, 2, 3]   )}]
   UpperCAmelCase__			:				Dict												=       BertTokenizer.from_pretrained("bert-base-cased"   )

   UpperCAmelCase__			:				Any												=       tokenizer.pad(snake_case__ ,							padding=snake_case__   )
   self.assertTrue(isinstance(batch["input_ids"] ,							torch.Tensor   )   )
   self.assertEqual(batch["input_ids"].tolist() ,							[[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]]   )
   UpperCAmelCase__			:				str												=       tokenizer.pad(snake_case__ ,							padding=snake_case__ ,							return_tensors="pt"   )
   self.assertTrue(isinstance(batch["input_ids"] ,							torch.Tensor   )   )
   self.assertEqual(batch["input_ids"].tolist() ,							[[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]]   )







 @require_tf
 def        UpperCamelCase     (				self		:     int   ):



   '''simple docstring'''




   import tensorflow as tf

   UpperCAmelCase__			:				Any												=       [{"input_ids": tf.constant([0, 1, 2]   )}, {"input_ids": tf.constant([0, 1, 2, 3]   )}]
   UpperCAmelCase__			:				List[str]												=       BertTokenizer.from_pretrained("bert-base-cased"   )

   UpperCAmelCase__			:				Optional[Any]												=       tokenizer.pad(snake_case__ ,							padding=snake_case__   )
   self.assertTrue(isinstance(batch["input_ids"] ,							tf.Tensor   )   )
   self.assertEqual(batch["input_ids"].numpy().tolist() ,							[[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]]   )
   UpperCAmelCase__			:				Union[str, Any]												=       tokenizer.pad(snake_case__ ,							padding=snake_case__ ,							return_tensors="tf"   )
   self.assertTrue(isinstance(batch["input_ids"] ,							tf.Tensor   )   )
   self.assertEqual(batch["input_ids"].numpy().tolist() ,							[[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]]   )







 @require_tokenizers
 def        UpperCamelCase     (				self		:     int   ):



   '''simple docstring'''




   UpperCAmelCase__			:				Tuple												=       Tokenizer(WordPiece(unk_token="[UNK]"   )   )
   PreTrainedTokenizerFast(tokenizer_object=snake_case__   )







 @require_tokenizers
 def        UpperCamelCase     (				self		:     Tuple   ):



   '''simple docstring'''




   UpperCAmelCase__			:				Any												=       Tokenizer(WordPiece(unk_token="[UNK]"   )   )
   with tempfile.TemporaryDirectory() as tmpdirname:
     bert_tokenizer.save(os.path.join(snake_case__ ,							"tokenizer.json"   )   )
     PreTrainedTokenizerFast(tokenizer_file=os.path.join(snake_case__ ,							"tokenizer.json"   )   )





'''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



