




import tempfile
import unittest

import numpy as np

import transformers
from transformers import GPTaTokenizer, GPTNeoConfig, 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, 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.gpt_neo.modeling_flax_gpt_neo import FlaxGPTNeoForCausalLM, FlaxGPTNeoModel

if is_torch_available():
 import torch

class        _lowerCamelCase  :

      """simple docstring"""

      def __init__(   self    :       List[str]       ,				snake_case    :       Any       ,				snake_case    :       Union[str, Any]=14       ,				snake_case    :       Optional[Any]=7       ,				snake_case    :       Tuple=True       ,				snake_case    :       int=True       ,				snake_case    :       Dict=False       ,				snake_case    :       List[Any]=True       ,				snake_case    :       Optional[int]=99       ,				snake_case    :       Tuple=32       ,				snake_case    :       Any=4       ,				snake_case    :       List[Any]=4       ,				snake_case    :       int=[[["global", "local"], 2]]       ,				snake_case    :       Optional[Any]=37       ,				snake_case    :       Optional[Any]="gelu"       ,				snake_case    :       Dict=0.1       ,				snake_case    :       List[Any]=0.1       ,				snake_case    :       Union[str, Any]=512       ,				snake_case    :       int=7       ,				snake_case    :       Any=0.02       ,				):
          __UpperCamelCase     						=  parent
          __UpperCamelCase     						=  batch_size
          __UpperCamelCase     						=  seq_length
          __UpperCamelCase     						=  is_training
          __UpperCamelCase     						=  use_input_mask
          __UpperCamelCase     						=  use_token_type_ids
          __UpperCamelCase     						=  use_labels
          __UpperCamelCase     						=  vocab_size
          __UpperCamelCase     						=  hidden_size
          __UpperCamelCase     						=  num_hidden_layers
          __UpperCamelCase     						=  num_attention_heads
          __UpperCamelCase     						=  attention_types
          __UpperCamelCase     						=  intermediate_size
          __UpperCamelCase     						=  hidden_act
          __UpperCamelCase     						=  hidden_dropout_prob
          __UpperCamelCase     						=  attention_probs_dropout_prob
          __UpperCamelCase     						=  max_position_embeddings
          __UpperCamelCase     						=  window_size
          __UpperCamelCase     						=  initializer_range
          __UpperCamelCase     						=  None
          __UpperCamelCase     						=  vocab_size - 1
          __UpperCamelCase     						=  vocab_size - 1
          __UpperCamelCase     						=  vocab_size - 1







      def 					snake_case		(   self    :       Union[str, Any] ):
          __UpperCamelCase     						=  ids_tensor([self.batch_size, self.seq_length]       ,				self.vocab_size )

          __UpperCamelCase     						=  None
          if self.use_input_mask:
              __UpperCamelCase     						=  random_attention_mask([self.batch_size, self.seq_length] )

          __UpperCamelCase     						=  GPTNeoConfig(
              vocab_size=self.vocab_size       ,				hidden_size=self.hidden_size       ,				num_layers=self.num_hidden_layers       ,				num_heads=self.num_attention_heads       ,				max_position_embeddings=self.max_position_embeddings       ,				use_cache=snake_case       ,				bos_token_id=self.bos_token_id       ,				eos_token_id=self.eos_token_id       ,				pad_token_id=self.pad_token_id       ,				window_size=self.window_size       ,				attention_types=self.attention_types       ,				)

          return (config, input_ids, input_mask)







      def 					snake_case		(   self    :       Tuple ):
          __UpperCamelCase     						=  self.prepare_config_and_inputs()
          __UpperCamelCase						,					__UpperCamelCase						,					__UpperCamelCase     						=  config_and_inputs
          __UpperCamelCase     						=  {'''input_ids''': input_ids, '''attention_mask''': attention_mask}
          return config, inputs_dict







      def 					snake_case		(   self    :       List[Any]       ,				snake_case    :       int       ,				snake_case    :       Union[str, Any]       ,				snake_case    :       Optional[int]       ,				snake_case    :       Optional[int] ):
          __UpperCamelCase     						=  20
          __UpperCamelCase     						=  model_class_name(snake_case )

          __UpperCamelCase     						=  model.init_cache(input_ids.shape[0]       ,				snake_case )
          __UpperCamelCase     						=  jnp.ones((input_ids.shape[0], max_decoder_length)       ,				dtype='''i4''' )

          __UpperCamelCase     						=  jnp.broadcast_to(
              jnp.arange(input_ids.shape[-1] - 1 )[None, :]       ,				(input_ids.shape[0], input_ids.shape[-1] - 1) )
          __UpperCamelCase     						=  model(
              input_ids[:, :-1]       ,				attention_mask=snake_case       ,				past_key_values=snake_case       ,				position_ids=snake_case       ,				)

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

          __UpperCamelCase     						=  model(snake_case )

          __UpperCamelCase     						=  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[Any]       ,				snake_case    :       Dict       ,				snake_case    :       Dict       ,				snake_case    :       Optional[Any]       ,				snake_case    :       Tuple ):
          __UpperCamelCase     						=  20
          __UpperCamelCase     						=  model_class_name(snake_case )

          __UpperCamelCase     						=  jnp.concatenate(
              [attention_mask, jnp.zeros((attention_mask.shape[0], max_decoder_length - attention_mask.shape[1]) )]       ,				axis=-1       ,				)

          __UpperCamelCase     						=  model.init_cache(input_ids.shape[0]       ,				snake_case )
          __UpperCamelCase     						=  jnp.broadcast_to(
              jnp.arange(input_ids.shape[-1] - 1 )[None, :]       ,				(input_ids.shape[0], input_ids.shape[-1] - 1) )

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

          __UpperCamelCase     						=  model(snake_case       ,				attention_mask=snake_case )

          __UpperCamelCase     						=  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  (	_SCREAMING_SNAKE_CASE							,	_SCREAMING_SNAKE_CASE							,	unittest.TestCase      ):

      """simple docstring"""

      lowerCAmelCase__	:       Dict			=    (FlaxGPTNeoModel, FlaxGPTNeoForCausalLM) if is_flax_available() else ()
      lowerCAmelCase__	:       Any			=    (FlaxGPTNeoForCausalLM,) if is_flax_available() else ()







      def 					snake_case		(   self    :       Dict ):
          __UpperCamelCase     						=  FlaxGPTNeoModelTester(self )







      def 					snake_case		(   self    :       int ):
          for model_class_name in self.all_model_classes:
              __UpperCamelCase						,					__UpperCamelCase						,					__UpperCamelCase     						=  self.model_tester.prepare_config_and_inputs()
              self.model_tester.check_use_cache_forward(snake_case       ,				snake_case       ,				snake_case       ,				snake_case )







      def 					snake_case		(   self    :       List[str] ):
          for model_class_name in self.all_model_classes:
              __UpperCamelCase						,					__UpperCamelCase						,					__UpperCamelCase     						=  self.model_tester.prepare_config_and_inputs()
              self.model_tester.check_use_cache_forward_with_attn_mask(
                  snake_case       ,				snake_case       ,				snake_case       ,				snake_case )







      @slow
      def 					snake_case		(   self    :       Union[str, Any] ):
          __UpperCamelCase     						=  GPTaTokenizer.from_pretrained('''gpt2'''       ,				pad_token='''<|endoftext|>'''       ,				padding_side='''left''' )
          __UpperCamelCase     						=  tokenizer(['''Hello this is a long string''', '''Hey''']       ,				return_tensors='''np'''       ,				padding=snake_case       ,				truncation=snake_case )

          __UpperCamelCase     						=  FlaxGPTNeoForCausalLM.from_pretrained('''EleutherAI/gpt-neo-125M''' )
          __UpperCamelCase     						=  False
          __UpperCamelCase     						=  model.config.eos_token_id

          __UpperCamelCase     						=  jax.jit(model.generate )

          __UpperCamelCase     						=  jit_generate(
              inputs['''input_ids''']       ,				attention_mask=inputs['''attention_mask''']       ,				pad_token_id=tokenizer.pad_token_id ).sequences

          __UpperCamelCase     						=  tokenizer.batch_decode(snake_case       ,				skip_special_tokens=snake_case )

          __UpperCamelCase     						=  [
              '''Hello this is a long string of text.\n\nI\'m trying to get the text of the''',
              '''Hey, I\'m a little late to the party. I\'m going to''',
          ]

          self.assertListEqual(snake_case       ,				snake_case )







      @is_pt_flax_cross_test
      def 					snake_case		(   self    :       Dict ):
          __UpperCamelCase						,					__UpperCamelCase     						=  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     						=  self._prepare_for_class(snake_case       ,				snake_case )
                  __UpperCamelCase     						=  {k: torch.tensor(v.tolist() ) for k, v in prepared_inputs_dict.items()}

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

                  __UpperCamelCase						,					__UpperCamelCase     						=  pt_inputs['''input_ids'''].shape
                  __UpperCamelCase     						=  np.random.randint(0       ,				seq_length - 1       ,				size=(batch_size,) )
                  for batch_idx, start_index in enumerate(snake_case ):
                      __UpperCamelCase     						=  0
                      __UpperCamelCase     						=  1
                      __UpperCamelCase     						=  0
                      __UpperCamelCase     						=  1
                  __UpperCamelCase     						=  pt_model_class(snake_case ).eval()
                  __UpperCamelCase     						=  model_class(snake_case       ,				dtype=jnp.floataa )

                  __UpperCamelCase     						=  convert_pytorch_state_dict_to_flax(pt_model.state_dict()       ,				snake_case )
                  __UpperCamelCase     						=  fx_state

                  with torch.no_grad():
                      __UpperCamelCase     						=  pt_model(**snake_case ).to_tuple()

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

                  with tempfile.TemporaryDirectory() as tmpdirname:
                      pt_model.save_pretrained(snake_case )
                      __UpperCamelCase     						=  model_class.from_pretrained(snake_case       ,				from_pt=snake_case )

                  __UpperCamelCase     						=  fx_model_loaded(**snake_case ).to_tuple()
                  self.assertEqual(
                      len(snake_case )       ,				len(snake_case )       ,				'''Output lengths differ between Flax and PyTorch''' )
                  for fx_output_loaded, pt_output in zip(snake_case       ,				snake_case ):
                      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] ):
          __UpperCamelCase						,					__UpperCamelCase     						=  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     						=  self._prepare_for_class(snake_case       ,				snake_case )
                  __UpperCamelCase     						=  {k: torch.tensor(v.tolist() ) for k, v in prepared_inputs_dict.items()}

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

                  __UpperCamelCase     						=  pt_model_class(snake_case ).eval()
                  __UpperCamelCase     						=  model_class(snake_case       ,				dtype=jnp.floataa )

                  __UpperCamelCase     						=  load_flax_weights_in_pytorch_model(snake_case       ,				fx_model.params )
                  __UpperCamelCase						,					__UpperCamelCase     						=  pt_inputs['''input_ids'''].shape
                  __UpperCamelCase     						=  np.random.randint(0       ,				seq_length - 1       ,				size=(batch_size,) )
                  for batch_idx, start_index in enumerate(snake_case ):
                      __UpperCamelCase     						=  0
                      __UpperCamelCase     						=  1
                      __UpperCamelCase     						=  0
                      __UpperCamelCase     						=  1

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

                  with torch.no_grad():
                      __UpperCamelCase     						=  pt_model(**snake_case ).to_tuple()

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

                  with tempfile.TemporaryDirectory() as tmpdirname:
                      fx_model.save_pretrained(snake_case )
                      __UpperCamelCase     						=  pt_model_class.from_pretrained(snake_case       ,				from_flax=snake_case )

                  with torch.no_grad():
                      __UpperCamelCase     						=  pt_model_loaded(**snake_case ).to_tuple()

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







      @slow
      def 					snake_case		(   self    :       Tuple ):
          for model_class_name in self.all_model_classes:
              __UpperCamelCase     						=  model_class_name.from_pretrained('''EleutherAI/gpt-neo-125M''' )
              __UpperCamelCase     						=  model(np.ones((1, 1) ) )
              self.assertIsNotNone(snake_case )












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

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, logging, randn_tensor
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin


a_      						=   logging.get_logger(__name__)  # pylint: disable=invalid-name

@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->EulerAncestralDiscrete
class        _lowerCamelCase  (	_SCREAMING_SNAKE_CASE      ):

      """simple docstring"""

      lowerCAmelCase__	:       torch.FloatTensor
      lowerCAmelCase__	:       Optional[torch.FloatTensor]			=    None







def     __SCREAMING_SNAKE_CASE      (     lowercase_    ,   lowercase_=0.999    ,   lowercase_="cosine"    ,   )				->		Dict:

    """simple docstring"""


    if alpha_transform_type == "cosine":

        def alpha_bar_fn(lowercase_					):
            return math.cos((t + 0.008) / 1.008 * math.pi / 2					) ** 2

    elif alpha_transform_type == "exp":

        def alpha_bar_fn(lowercase_					):
            return math.exp(t * -12.0					)

    else:
        raise ValueError(F"Unsupported alpha_tranform_type: {alpha_transform_type}"					)

    __UpperCamelCase     						=  []
    for i in range(lowercase_					):
        __UpperCamelCase     						=  i / num_diffusion_timesteps
        __UpperCamelCase     						=  (i + 1) / num_diffusion_timesteps
        betas.append(min(1 - alpha_bar_fn(lowercase_					) / alpha_bar_fn(lowercase_					)    ,   lowercase_					)					)
    return torch.tensor(lowercase_    ,   dtype=torch.floataa					)


class        _lowerCamelCase  (	_SCREAMING_SNAKE_CASE							,	_SCREAMING_SNAKE_CASE      ):

      """simple docstring"""

      lowerCAmelCase__	:       Tuple			=    [e.name for e in KarrasDiffusionSchedulers]
      lowerCAmelCase__	:       List[Any]			=    1







      @register_to_config
      def __init__(   self    :       Tuple       ,				snake_case    :       int = 1000       ,				snake_case    :       float = 0.00_01       ,				snake_case    :       float = 0.02       ,				snake_case    :       str = "linear"       ,				snake_case    :       Optional[Union[np.ndarray, List[float]]] = None       ,				snake_case    :       str = "epsilon"       ,				snake_case    :       str = "linspace"       ,				snake_case    :       int = 0       ,				):
          if trained_betas is not None:
              __UpperCamelCase     						=  torch.tensor(snake_case       ,				dtype=torch.floataa )
          elif beta_schedule == "linear":
              __UpperCamelCase     						=  torch.linspace(snake_case       ,				snake_case       ,				snake_case       ,				dtype=torch.floataa )
          elif beta_schedule == "scaled_linear":
              # this schedule is very specific to the latent diffusion model.
              __UpperCamelCase     						=  (
                  torch.linspace(beta_start**0.5       ,				beta_end**0.5       ,				snake_case       ,				dtype=torch.floataa ) ** 2
              )
          elif beta_schedule == "squaredcos_cap_v2":
              # Glide cosine schedule
              __UpperCamelCase     						=  betas_for_alpha_bar(snake_case )
          else:
              raise NotImplementedError(F"{beta_schedule} does is not implemented for {self.__class__}" )

          __UpperCamelCase     						=  1.0 - self.betas
          __UpperCamelCase     						=  torch.cumprod(self.alphas       ,				dim=0 )

          __UpperCamelCase     						=  np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5 )
          __UpperCamelCase     						=  np.concatenate([sigmas[::-1], [0.0]] ).astype(np.floataa )
          __UpperCamelCase     						=  torch.from_numpy(snake_case )

          # setable values
          __UpperCamelCase     						=  None
          __UpperCamelCase     						=  np.linspace(0       ,				num_train_timesteps - 1       ,				snake_case       ,				dtype=snake_case )[::-1].copy()
          __UpperCamelCase     						=  torch.from_numpy(snake_case )
          __UpperCamelCase     						=  False







      @property
      def 					snake_case		(   self    :       Tuple ):
          # standard deviation of the initial noise distribution
          if self.config.timestep_spacing in ["linspace", "trailing"]:
              return self.sigmas.max()

          return (self.sigmas.max() ** 2 + 1) ** 0.5







      def 					snake_case		(   self    :       int       ,				snake_case    :       torch.FloatTensor       ,				snake_case    :       Union[float, torch.FloatTensor] ):
          if isinstance(snake_case       ,				torch.Tensor ):
              __UpperCamelCase     						=  timestep.to(self.timesteps.device )
          __UpperCamelCase     						=  (self.timesteps == timestep).nonzero().item()
          __UpperCamelCase     						=  self.sigmas[step_index]
          __UpperCamelCase     						=  sample / ((sigma**2 + 1) ** 0.5)
          __UpperCamelCase     						=  True
          return sample







      def 					snake_case		(   self    :       Any       ,				snake_case    :       int       ,				snake_case    :       Union[str, torch.device] = None ):
          __UpperCamelCase     						=  num_inference_steps

          # "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
          if self.config.timestep_spacing == "linspace":
              __UpperCamelCase     						=  np.linspace(0       ,				self.config.num_train_timesteps - 1       ,				snake_case       ,				dtype=snake_case )[
                  ::-1
              ].copy()
          elif self.config.timestep_spacing == "leading":
              __UpperCamelCase     						=  self.config.num_train_timesteps // self.num_inference_steps
              # creates integer timesteps by multiplying by ratio
              # casting to int to avoid issues when num_inference_step is power of 3
              __UpperCamelCase     						=  (np.arange(0       ,				snake_case ) * step_ratio).round()[::-1].copy().astype(snake_case )
              timesteps += self.config.steps_offset
          elif self.config.timestep_spacing == "trailing":
              __UpperCamelCase     						=  self.config.num_train_timesteps / self.num_inference_steps
              # creates integer timesteps by multiplying by ratio
              # casting to int to avoid issues when num_inference_step is power of 3
              __UpperCamelCase     						=  (np.arange(self.config.num_train_timesteps       ,				0       ,				-step_ratio )).round().copy().astype(snake_case )
              timesteps -= 1
          else:
              raise ValueError(
                  F"{self.config.timestep_spacing} is not supported. Please make sure to choose one of 'linspace', 'leading' or 'trailing'." )

          __UpperCamelCase     						=  np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5 )
          __UpperCamelCase     						=  np.interp(snake_case       ,				np.arange(0       ,				len(snake_case ) )       ,				snake_case )
          __UpperCamelCase     						=  np.concatenate([sigmas, [0.0]] ).astype(np.floataa )
          __UpperCamelCase     						=  torch.from_numpy(snake_case ).to(device=snake_case )
          if str(snake_case ).startswith('''mps''' ):
              # mps does not support float64
              __UpperCamelCase     						=  torch.from_numpy(snake_case ).to(snake_case       ,				dtype=torch.floataa )
          else:
              __UpperCamelCase     						=  torch.from_numpy(snake_case ).to(device=snake_case )







      def 					snake_case		(   self    :       int       ,				snake_case    :       torch.FloatTensor       ,				snake_case    :       Union[float, torch.FloatTensor]       ,				snake_case    :       torch.FloatTensor       ,				snake_case    :       Optional[torch.Generator] = None       ,				snake_case    :       bool = True       ,				):

          if (
              isinstance(snake_case       ,				snake_case )
              or isinstance(snake_case       ,				torch.IntTensor )
              or isinstance(snake_case       ,				torch.LongTensor )
          ):
              raise ValueError(
                  (
                      '''Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to'''
                      ''' `EulerDiscreteScheduler.step()` is not supported. Make sure to pass'''
                      ''' one of the `scheduler.timesteps` as a timestep.'''
                  )       ,				)

          if not self.is_scale_input_called:
              logger.warning(
                  '''The `scale_model_input` function should be called before `step` to ensure correct denoising. '''
                  '''See `StableDiffusionPipeline` for a usage example.''' )

          if isinstance(snake_case       ,				torch.Tensor ):
              __UpperCamelCase     						=  timestep.to(self.timesteps.device )

          __UpperCamelCase     						=  (self.timesteps == timestep).nonzero().item()
          __UpperCamelCase     						=  self.sigmas[step_index]

          # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
          if self.config.prediction_type == "epsilon":
              __UpperCamelCase     						=  sample - sigma * model_output
          elif self.config.prediction_type == "v_prediction":
              # * c_out + input * c_skip
              __UpperCamelCase     						=  model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
          elif self.config.prediction_type == "sample":
              raise NotImplementedError('''prediction_type not implemented yet: sample''' )
          else:
              raise ValueError(
                  F"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`" )

          __UpperCamelCase     						=  self.sigmas[step_index]
          __UpperCamelCase     						=  self.sigmas[step_index + 1]
          __UpperCamelCase     						=  (sigma_to**2 * (sigma_from**2 - sigma_to**2) / sigma_from**2) ** 0.5
          __UpperCamelCase     						=  (sigma_to**2 - sigma_up**2) ** 0.5

          # 2. Convert to an ODE derivative
          __UpperCamelCase     						=  (sample - pred_original_sample) / sigma

          __UpperCamelCase     						=  sigma_down - sigma

          __UpperCamelCase     						=  sample + derivative * dt

          __UpperCamelCase     						=  model_output.device
          __UpperCamelCase     						=  randn_tensor(model_output.shape       ,				dtype=model_output.dtype       ,				device=snake_case       ,				generator=snake_case )

          __UpperCamelCase     						=  prev_sample + noise * sigma_up

          if not return_dict:
              return (prev_sample,)

          return EulerAncestralDiscreteSchedulerOutput(
              prev_sample=snake_case       ,				pred_original_sample=snake_case )







      def 					snake_case		(   self    :       List[Any]       ,				snake_case    :       torch.FloatTensor       ,				snake_case    :       torch.FloatTensor       ,				snake_case    :       torch.FloatTensor       ,				):
          # Make sure sigmas and timesteps have the same device and dtype as original_samples
          __UpperCamelCase     						=  self.sigmas.to(device=original_samples.device       ,				dtype=original_samples.dtype )
          if original_samples.device.type == "mps" and torch.is_floating_point(snake_case ):
              # mps does not support float64
              __UpperCamelCase     						=  self.timesteps.to(original_samples.device       ,				dtype=torch.floataa )
              __UpperCamelCase     						=  timesteps.to(original_samples.device       ,				dtype=torch.floataa )
          else:
              __UpperCamelCase     						=  self.timesteps.to(original_samples.device )
              __UpperCamelCase     						=  timesteps.to(original_samples.device )

          __UpperCamelCase     						=  [(schedule_timesteps == t).nonzero().item() for t in timesteps]

          __UpperCamelCase     						=  sigmas[step_indices].flatten()
          while len(sigma.shape ) < len(original_samples.shape ):
              __UpperCamelCase     						=  sigma.unsqueeze(-1 )

          __UpperCamelCase     						=  original_samples + noise * sigma
          return noisy_samples







      def __len__(   self    :       Optional[Any] ):
          return self.config.num_train_timesteps






