import tempfile
import unittest

import numpy as np

import transformers
from transformers import GPTaConfig, GPTaTokenizer, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, slow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
 import jax
 import jax.numpy as jnp

 from transformers.modeling_flax_pytorch_utils import (
     convert_pytorch_state_dict_to_flax,
     load_flax_weights_in_pytorch_model,
 )
 from transformers.models.gpta.modeling_flax_gpta import FlaxGPTaLMHeadModel, FlaxGPTaModel

if is_torch_available():
 import torch




class  __lowerCAmelCase	:



 '''simple docstring'''

 def __init__(      self							:		int							,      lowerCamelCase							:		Union[str, Any]							,      lowerCamelCase							:		Dict=1_4							,      lowerCamelCase							:		Dict=7							,      lowerCamelCase							:		str=True							,      lowerCamelCase							:		List[Any]=True							,      lowerCamelCase							:		Any=False							,      lowerCamelCase							:		int=True							,      lowerCamelCase							:		List[str]=9_9							,      lowerCamelCase							:		Dict=3_2							,      lowerCamelCase							:		Any=5							,      lowerCamelCase							:		List[Any]=4							,      lowerCamelCase							:		Tuple=3_7							,      lowerCamelCase							:		Tuple="gelu"							,      lowerCamelCase							:		int=0.1							,      lowerCamelCase							:		List[str]=0.1							,      lowerCamelCase							:		int=5_1_2							,      lowerCamelCase							:		Union[str, Any]=0.0_2							,      )			->				Optional[int]:
        _UpperCamelCase  :							Any	    =	parent
        _UpperCamelCase  :							List[Any]	    =	batch_size
        _UpperCamelCase  :							List[str]	    =	seq_length
        _UpperCamelCase  :							Any	    =	is_training
        _UpperCamelCase  :							Optional[int]	    =	use_input_mask
        _UpperCamelCase  :							int	    =	use_token_type_ids
        _UpperCamelCase  :							Any	    =	use_labels
        _UpperCamelCase  :							str	    =	vocab_size
        _UpperCamelCase  :							Any	    =	hidden_size
        _UpperCamelCase  :							Any	    =	num_hidden_layers
        _UpperCamelCase  :							int	    =	num_attention_heads
        _UpperCamelCase  :							Dict	    =	intermediate_size
        _UpperCamelCase  :							Any	    =	hidden_act
        _UpperCamelCase  :							Optional[int]	    =	hidden_dropout_prob
        _UpperCamelCase  :							Any	    =	attention_probs_dropout_prob
        _UpperCamelCase  :							Tuple	    =	max_position_embeddings
        _UpperCamelCase  :							Tuple	    =	initializer_range
        _UpperCamelCase  :							Union[str, Any]	    =	None
        _UpperCamelCase  :							Dict	    =	vocab_size - 1
        _UpperCamelCase  :							Optional[int]	    =	vocab_size - 1
        _UpperCamelCase  :							Optional[Any]	    =	vocab_size - 1

 def        __snake_case       (      self							:		List[Any]	)			->				Any:
        _UpperCamelCase  :							Dict	    =	ids_tensor([self.batch_size, self.seq_length]							,      self.vocab_size	)

        _UpperCamelCase  :							Optional[int]	    =	None
        if self.use_input_mask:
               _UpperCamelCase  :							Union[str, Any]	    =	random_attention_mask([self.batch_size, self.seq_length]	)

        _UpperCamelCase  :							str	    =	GPTaConfig(
            vocab_size=self.vocab_size							,      n_embd=self.hidden_size							,      n_layer=self.num_hidden_layers							,      n_head=self.num_attention_heads							,      n_positions=self.max_position_embeddings							,      use_cache=lowerCamelCase							,      bos_token_id=self.bos_token_id							,      eos_token_id=self.eos_token_id							,      pad_token_id=self.pad_token_id							,      )

        return (config, input_ids, input_mask)

 def        __snake_case       (      self							:		int	)			->				Union[str, Any]:
        _UpperCamelCase  :							Any	    =	self.prepare_config_and_inputs()
        _UpperCamelCase    ,   _UpperCamelCase    ,   _UpperCamelCase  :							Optional[int]	    =	config_and_inputs
        _UpperCamelCase  :							Optional[int]	    =	{'input_ids': input_ids, 'attention_mask': attention_mask}
        return config, inputs_dict

 def        __snake_case       (      self							:		Optional[int]	)			->				List[str]:
        _UpperCamelCase    ,   _UpperCamelCase    ,   _UpperCamelCase  :							int	    =	self.prepare_config_and_inputs()

        _UpperCamelCase  :							List[str]	    =	floats_tensor([self.batch_size, self.seq_length, self.hidden_size]	)
        _UpperCamelCase  :							str	    =	ids_tensor([self.batch_size, self.seq_length]							,      vocab_size=2	)

        return (
            config,
            input_ids,
            attention_mask,
            encoder_hidden_states,
            encoder_attention_mask,
        )

 def        __snake_case       (      self							:		str							,      lowerCamelCase							:		Union[str, Any]							,      lowerCamelCase							:		str							,      lowerCamelCase							:		List[Any]							,      lowerCamelCase							:		Optional[int]	)			->				Tuple:
        _UpperCamelCase  :							str	    =	2_0
        _UpperCamelCase  :							Any	    =	model_class_name(lowerCamelCase	)

        _UpperCamelCase  :							List[str]	    =	model.init_cache(input_ids.shape[0]							,      lowerCamelCase	)
        _UpperCamelCase  :							List[Any]	    =	jnp.ones((input_ids.shape[0], max_decoder_length)							,      dtype='i4'	)

        _UpperCamelCase  :							Optional[Any]	    =	jnp.broadcast_to(
            jnp.arange(input_ids.shape[-1] - 1	)[None, :]							,      (input_ids.shape[0], input_ids.shape[-1] - 1)	)
        _UpperCamelCase  :							Any	    =	model(
            input_ids[:, :-1]							,      attention_mask=lowerCamelCase							,      past_key_values=lowerCamelCase							,      position_ids=lowerCamelCase							,      )

        _UpperCamelCase  :							str	    =	jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]							,      dtype='i4'	)
        _UpperCamelCase  :							List[str]	    =	model(
            input_ids[:, -1:]							,      attention_mask=lowerCamelCase							,      past_key_values=outputs_cache.past_key_values							,      position_ids=lowerCamelCase							,      )

        _UpperCamelCase  :							Dict	    =	model(lowerCamelCase	)

        _UpperCamelCase  :							int	    =	np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])	)	)
        self.parent.assertTrue(diff < 1e-3							,      msg=f"Max diff is {diff}"	)

 def        __snake_case       (      self							:		Optional[int]							,      lowerCamelCase							:		Optional[Any]							,      lowerCamelCase							:		List[Any]							,      lowerCamelCase							:		int							,      lowerCamelCase							:		List[Any]	)			->				List[Any]:
        _UpperCamelCase  :							Dict	    =	2_0
        _UpperCamelCase  :							Optional[Any]	    =	model_class_name(lowerCamelCase	)

        _UpperCamelCase  :							Optional[Any]	    =	jnp.concatenate(
            [attention_mask, jnp.zeros((attention_mask.shape[0], max_decoder_length - attention_mask.shape[1])	)]							,      axis=-1							,      )

        _UpperCamelCase  :							Optional[Any]	    =	model.init_cache(input_ids.shape[0]							,      lowerCamelCase	)
        _UpperCamelCase  :							Tuple	    =	jnp.broadcast_to(
            jnp.arange(input_ids.shape[-1] - 1	)[None, :]							,      (input_ids.shape[0], input_ids.shape[-1] - 1)	)

        _UpperCamelCase  :							str	    =	model(
            input_ids[:, :-1]							,      attention_mask=lowerCamelCase							,      past_key_values=lowerCamelCase							,      position_ids=lowerCamelCase							,      )
        _UpperCamelCase  :							Optional[int]	    =	jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]							,      dtype='i4'	)
        _UpperCamelCase  :							Tuple	    =	model(
            input_ids[:, -1:]							,      past_key_values=outputs_cache.past_key_values							,      attention_mask=lowerCamelCase							,      position_ids=lowerCamelCase							,      )

        _UpperCamelCase  :							Tuple	    =	model(lowerCamelCase							,      attention_mask=lowerCamelCase	)

        _UpperCamelCase  :							List[Any]	    =	np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])	)	)
        self.parent.assertTrue(diff < 1e-3							,      msg=f"Max diff is {diff}"	)







@require_flax
class  __lowerCAmelCase	(							a_,						a_,						unittest.TestCase    ):



 '''simple docstring'''
 _lowerCamelCase    :						Tuple					     =			(FlaxGPTaModel, FlaxGPTaLMHeadModel) if is_flax_available() else ()
 _lowerCamelCase    :						List[str]					     =			(FlaxGPTaLMHeadModel,) if is_flax_available() else ()

 def        __snake_case       (      self							:		Dict	)			->				Union[str, Any]:
        _UpperCamelCase  :							List[str]	    =	FlaxGPTaModelTester(self	)

 def        __snake_case       (      self							:		Optional[Any]	)			->				Tuple:
        for model_class_name in self.all_model_classes:
               _UpperCamelCase    ,   _UpperCamelCase    ,   _UpperCamelCase  :							Union[str, Any]	    =	self.model_tester.prepare_config_and_inputs()
               self.model_tester.check_use_cache_forward(lowerCamelCase							,      lowerCamelCase							,      lowerCamelCase							,      lowerCamelCase	)

 def        __snake_case       (      self							:		Union[str, Any]	)			->				List[Any]:
        for model_class_name in self.all_model_classes:
               _UpperCamelCase    ,   _UpperCamelCase    ,   _UpperCamelCase  :							Union[str, Any]	    =	self.model_tester.prepare_config_and_inputs()
               self.model_tester.check_use_cache_forward_with_attn_mask(
                   lowerCamelCase							,      lowerCamelCase							,      lowerCamelCase							,      lowerCamelCase	)

 @slow
 def        __snake_case       (      self							:		List[str]	)			->				int:
        _UpperCamelCase  :							Any	    =	GPTaTokenizer.from_pretrained('gpt2'							,      pad_token='</s>'							,      padding_side='left'	)
        _UpperCamelCase  :							Tuple	    =	tokenizer(['Hello this is a long string', 'Hey']							,      return_tensors='np'							,      padding=lowerCamelCase							,      truncation=lowerCamelCase	)

        _UpperCamelCase  :							str	    =	FlaxGPTaLMHeadModel.from_pretrained('gpt2'	)
        _UpperCamelCase  :							Dict	    =	False
        _UpperCamelCase  :							Tuple	    =	model.config.eos_token_id

        _UpperCamelCase  :							Any	    =	jax.jit(model.generate	)

        _UpperCamelCase  :							Any	    =	jit_generate(inputs['input_ids']							,      attention_mask=inputs['attention_mask']	).sequences

        _UpperCamelCase  :							List[Any]	    =	tokenizer.batch_decode(lowerCamelCase							,      skip_special_tokens=lowerCamelCase	)

        _UpperCamelCase  :							Dict	    =	[
            'Hello this is a long string of words. I\'m going to start with the first one.\n',
            'Hey, I\'m not sure if I\'m going to be able to do',
        ]

        self.assertListEqual(lowerCamelCase							,      lowerCamelCase	)

 @is_pt_flax_cross_test
 def        __snake_case       (      self							:		int	)			->				Optional[int]:
        _UpperCamelCase    ,   _UpperCamelCase  :							Optional[Any]	    =	self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
               with self.subTest(model_class.__name__	):
                      # prepare inputs
                      _UpperCamelCase  :							Dict	    =	self._prepare_for_class(lowerCamelCase							,      lowerCamelCase	)
                      _UpperCamelCase  :							Union[str, Any]	    =	{k: torch.tensor(v.tolist()	) for k, v in prepared_inputs_dict.items()}

                      # load corresponding PyTorch class
                      _UpperCamelCase  :							Tuple	    =	model_class.__name__[4:]  # Skip the "Flax" at the beginning
                      _UpperCamelCase  :							Tuple	    =	getattr(lowerCamelCase							,      lowerCamelCase	)

                      _UpperCamelCase    ,   _UpperCamelCase  :							List[str]	    =	pt_inputs['input_ids'].shape
                      _UpperCamelCase  :							Optional[Any]	    =	np.random.randint(0							,      seq_length - 1							,      size=(batch_size,)	)
                      for batch_idx, start_index in enumerate(lowerCamelCase	):
                             _UpperCamelCase  :							Any	    =	0
                             _UpperCamelCase  :							Optional[Any]	    =	1
                             _UpperCamelCase  :							Optional[int]	    =	0
                             _UpperCamelCase  :							Optional[Any]	    =	1
                      _UpperCamelCase  :							int	    =	pt_model_class(lowerCamelCase	).eval()
                      _UpperCamelCase  :							Optional[int]	    =	model_class(lowerCamelCase							,      dtype=jnp.floataa	)

                      _UpperCamelCase  :							List[str]	    =	convert_pytorch_state_dict_to_flax(pt_model.state_dict()							,      lowerCamelCase	)
                      _UpperCamelCase  :							Tuple	    =	fx_state

                      with torch.no_grad():
                             _UpperCamelCase  :							Optional[int]	    =	pt_model(**lowerCamelCase	).to_tuple()

                      _UpperCamelCase  :							Any	    =	fx_model(**lowerCamelCase	).to_tuple()
                      self.assertEqual(len(lowerCamelCase	)							,      len(lowerCamelCase	)							,      'Output lengths differ between Flax and PyTorch'	)
                      for fx_output, pt_output in zip(lowerCamelCase							,      lowerCamelCase	):
                             self.assert_almost_equals(fx_output[:, -1]							,      pt_output[:, -1].numpy()							,      4e-2	)

                      with tempfile.TemporaryDirectory() as tmpdirname:
                             pt_model.save_pretrained(lowerCamelCase	)
                             _UpperCamelCase  :							Optional[Any]	    =	model_class.from_pretrained(lowerCamelCase							,      from_pt=lowerCamelCase	)

                      _UpperCamelCase  :							List[Any]	    =	fx_model_loaded(**lowerCamelCase	).to_tuple()
                      self.assertEqual(
                          len(lowerCamelCase	)							,      len(lowerCamelCase	)							,      'Output lengths differ between Flax and PyTorch'	)
                      for fx_output_loaded, pt_output in zip(lowerCamelCase							,      lowerCamelCase	):
                             self.assert_almost_equals(fx_output_loaded[:, -1]							,      pt_output[:, -1].numpy()							,      4e-2	)

 @is_pt_flax_cross_test
 def        __snake_case       (      self							:		Optional[Any]	)			->				int:
        _UpperCamelCase    ,   _UpperCamelCase  :							Any	    =	self.model_tester.prepare_config_and_inputs_for_common()
        for model_class in self.all_model_classes:
               with self.subTest(model_class.__name__	):
                      # prepare inputs
                      _UpperCamelCase  :							Optional[int]	    =	self._prepare_for_class(lowerCamelCase							,      lowerCamelCase	)
                      _UpperCamelCase  :							int	    =	{k: torch.tensor(v.tolist()	) for k, v in prepared_inputs_dict.items()}

                      # load corresponding PyTorch class
                      _UpperCamelCase  :							str	    =	model_class.__name__[4:]  # Skip the "Flax" at the beginning
                      _UpperCamelCase  :							Union[str, Any]	    =	getattr(lowerCamelCase							,      lowerCamelCase	)

                      _UpperCamelCase  :							Optional[Any]	    =	pt_model_class(lowerCamelCase	).eval()
                      _UpperCamelCase  :							str	    =	model_class(lowerCamelCase							,      dtype=jnp.floataa	)

                      _UpperCamelCase  :							Tuple	    =	load_flax_weights_in_pytorch_model(lowerCamelCase							,      fx_model.params	)
                      _UpperCamelCase    ,   _UpperCamelCase  :							List[Any]	    =	pt_inputs['input_ids'].shape
                      _UpperCamelCase  :							List[Any]	    =	np.random.randint(0							,      seq_length - 1							,      size=(batch_size,)	)
                      for batch_idx, start_index in enumerate(lowerCamelCase	):
                             _UpperCamelCase  :							Optional[int]	    =	0
                             _UpperCamelCase  :							Union[str, Any]	    =	1
                             _UpperCamelCase  :							Dict	    =	0
                             _UpperCamelCase  :							Union[str, Any]	    =	1

                      # make sure weights are tied in PyTorch
                      pt_model.tie_weights()

                      with torch.no_grad():
                             _UpperCamelCase  :							Optional[int]	    =	pt_model(**lowerCamelCase	).to_tuple()

                      _UpperCamelCase  :							Any	    =	fx_model(**lowerCamelCase	).to_tuple()
                      self.assertEqual(len(lowerCamelCase	)							,      len(lowerCamelCase	)							,      'Output lengths differ between Flax and PyTorch'	)
                      for fx_output, pt_output in zip(lowerCamelCase							,      lowerCamelCase	):
                             self.assert_almost_equals(fx_output[:, -1]							,      pt_output[:, -1].numpy()							,      4e-2	)

                      with tempfile.TemporaryDirectory() as tmpdirname:
                             fx_model.save_pretrained(lowerCamelCase	)
                             _UpperCamelCase  :							Dict	    =	pt_model_class.from_pretrained(lowerCamelCase							,      from_flax=lowerCamelCase	)

                      with torch.no_grad():
                             _UpperCamelCase  :							Tuple	    =	pt_model_loaded(**lowerCamelCase	).to_tuple()

                      self.assertEqual(
                          len(lowerCamelCase	)							,      len(lowerCamelCase	)							,      'Output lengths differ between Flax and PyTorch'	)
                      for fx_output, pt_output in zip(lowerCamelCase							,      lowerCamelCase	):
                             self.assert_almost_equals(fx_output[:, -1]							,      pt_output[:, -1].numpy()							,      4e-2	)

 @slow
 def        __snake_case       (      self							:		Union[str, Any]	)			->				Dict:
        for model_class_name in self.all_model_classes:
               _UpperCamelCase  :							List[Any]	    =	model_class_name.from_pretrained('gpt2'							,      from_pt=lowerCamelCase	)
               _UpperCamelCase  :							Tuple	    =	model(np.ones((1, 1)	)	)
               self.assertIsNotNone(lowerCamelCase	)







"""simple docstring"""






from dataclasses import dataclass
from typing import Optional, Tuple, Union

import flax
import jax.numpy as jnp
from scipy import integrate

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import (
    CommonSchedulerState,
    FlaxKarrasDiffusionSchedulers,
    FlaxSchedulerMixin,
    FlaxSchedulerOutput,
    broadcast_to_shape_from_left,
)


@flax.struct.dataclass
class 	A_       :





  """simple docstring"""

  lowercase		:	CommonSchedulerState

  # setable values
  lowercase		:	jnp.ndarray
  lowercase		:	jnp.ndarray
  lowercase		:	jnp.ndarray
  lowercase		:	Optional[int]   =						None

  # running values
  lowercase		:	Optional[jnp.ndarray]   =						None
  @classmethod
  def 							lowercase_     (						cls  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase						)				->					Tuple:
     return cls(common=__UpperCAmelCase  ,					init_noise_sigma=__UpperCAmelCase  ,					timesteps=__UpperCAmelCase  ,					sigmas=__UpperCAmelCase						)



@dataclass
class 	A_       (     _UpperCAmelCase   ):





  """simple docstring"""

  lowercase		:	LMSDiscreteSchedulerState



class 	A_       (     _UpperCAmelCase      ,  _UpperCAmelCase   ):





  """simple docstring"""

  lowercase		:	int   =						[e.name for e in FlaxKarrasDiffusionSchedulers]

  lowercase		:	jnp.dtype
  @property
  def 							lowercase_     (						self						)				->					Union[str, Any]:
     return True
  @register_to_config
  def __init__(						self  ,					__UpperCAmelCase = 10_00  ,					__UpperCAmelCase = 0.0001  ,					__UpperCAmelCase = 0.02  ,					__UpperCAmelCase = "linear"  ,					__UpperCAmelCase = None  ,					__UpperCAmelCase = "epsilon"  ,					__UpperCAmelCase = jnp.floataa  ,					)				->					Union[str, Any]:
     a   :	Dict						=    dtype
  def 							lowercase_     (						self  ,					__UpperCAmelCase = None						)				->					LMSDiscreteSchedulerState:
     if common is None:
        a   :	Dict						=    CommonSchedulerState.create(self						)

     a   :	List[Any]						=    jnp.arange(0  ,					self.config.num_train_timesteps						).round()[::-1]
     a   :	Dict						=    ((1 - common.alphas_cumprod) / common.alphas_cumprod) ** 0.5

     # standard deviation of the initial noise distribution
     a   :	Union[str, Any]						=    sigmas.max()

     return LMSDiscreteSchedulerState.create(
         common=__UpperCAmelCase  ,					init_noise_sigma=__UpperCAmelCase  ,					timesteps=__UpperCAmelCase  ,					sigmas=__UpperCAmelCase  ,					)
  def 							lowercase_     (						self  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase						)				->					jnp.ndarray:
     ((a)	, )   :	Optional[int]						=    jnp.where(state.timesteps == timestep  ,					size=1						)
     a   :	Optional[Any]						=    step_index[0]

     a   :	Union[str, Any]						=    state.sigmas[step_index]
     a   :	Optional[int]						=    sample / ((sigma**2 + 1) ** 0.5)
     return sample
  def 							lowercase_     (						self  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase						)				->					Optional[int]:

     def lms_derivative(__UpperCAmelCase						):
        a   :	Optional[Any]						=    1.0
        for k in range(__UpperCAmelCase						):
           if current_order == k:
              continue
           prod *= (tau - state.sigmas[t - k]) / (state.sigmas[t - current_order] - state.sigmas[t - k])
        return prod

     a   :	Union[str, Any]						=    integrate.quad(__UpperCAmelCase  ,					state.sigmas[t]  ,					state.sigmas[t + 1]  ,					epsrel=1E-4						)[0]

     return integrated_coeff
  def 							lowercase_     (						self  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase = ()						)				->					LMSDiscreteSchedulerState:
     a   :	Optional[int]						=    jnp.linspace(self.config.num_train_timesteps - 1  ,					0  ,					__UpperCAmelCase  ,					dtype=self.dtype						)

     a   :	Dict						=    jnp.floor(__UpperCAmelCase						).astype(jnp.intaa						)
     a   :	str						=    jnp.ceil(__UpperCAmelCase						).astype(jnp.intaa						)

     a   :	List[Any]						=    jnp.mod(__UpperCAmelCase  ,					1.0						)

     a   :	Optional[int]						=    ((1 - state.common.alphas_cumprod) / state.common.alphas_cumprod) ** 0.5
     a   :	int						=    (1 - frac) * sigmas[low_idx] + frac * sigmas[high_idx]
     a   :	Optional[int]						=    jnp.concatenate([sigmas, jnp.array([0.0]  ,					dtype=self.dtype						)]						)

     a   :	Any						=    timesteps.astype(jnp.intaa						)

     # initial running values
     a   :	Optional[int]						=    jnp.zeros((0,) + shape  ,					dtype=self.dtype						)

     return state.replace(
         timesteps=__UpperCAmelCase  ,					sigmas=__UpperCAmelCase  ,					num_inference_steps=__UpperCAmelCase  ,					derivatives=__UpperCAmelCase  ,					)
  def 							lowercase_     (						self  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase = 4  ,					__UpperCAmelCase = True  ,					)				->					Union[FlaxLMSSchedulerOutput, Tuple]:
     if state.num_inference_steps is None:
        raise ValueError(
            'Number of inference steps is \'None\', you need to run \'set_timesteps\' after creating the scheduler'						)

     a   :	Dict						=    state.sigmas[timestep]

     # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
     if self.config.prediction_type == "epsilon":
        a   :	Any						=    sample - sigma * model_output
     elif self.config.prediction_type == "v_prediction":
        # * c_out + input * c_skip
        a   :	str						=    model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
     else:
        raise ValueError(
            f'prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`'						)

     # 2. Convert to an ODE derivative
     a   :	str						=    (sample - pred_original_sample) / sigma
     a   :	int						=    state.replace(derivatives=jnp.append(state.derivatives  ,					__UpperCAmelCase						)						)
     if len(state.derivatives						) > order:
        a   :	Any						=    state.replace(derivatives=jnp.delete(state.derivatives  ,					0						)						)

     # 3. Compute linear multistep coefficients
     a   :	Any						=    min(timestep + 1  ,					__UpperCAmelCase						)
     a   :	Dict						=    [self.get_lms_coefficient(__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase						) for curr_order in range(__UpperCAmelCase						)]

     # 4. Compute previous sample based on the derivatives path
     a   :	int						=    sample + sum(
         coeff * derivative for coeff, derivative in zip(__UpperCAmelCase  ,					reversed(state.derivatives						)						)						)

     if not return_dict:
        return (prev_sample, state)

     return FlaxLMSSchedulerOutput(prev_sample=__UpperCAmelCase  ,					state=__UpperCAmelCase						)
  def 							lowercase_     (						self  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					)				->					jnp.ndarray:
     a   :	Dict						=    state.sigmas[timesteps].flatten()
     a   :	Union[str, Any]						=    broadcast_to_shape_from_left(__UpperCAmelCase  ,					noise.shape						)

     a   :	List[Any]						=    original_samples + noise * sigma

     return noisy_samples






  def __len__(						self						)				->					List[Any]:
     return self.config.num_train_timesteps





