

'''simple docstring'''






import copy
import inspect
import unittest

import numpy as np
from huggingface_hub import hf_hub_download

from transformers import VideoMAEConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
 import torch
 from torch import nn

 from transformers import (
     MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING,
     VideoMAEForPreTraining,
     VideoMAEForVideoClassification,
     VideoMAEModel,
 )
 from transformers.models.videomae.modeling_videomae import VIDEOMAE_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
 from transformers import VideoMAEImageProcessor






class 			lowercase_	:




  """simple docstring"""




  def __init__(      self   :     str					,lowercase__   :     int					,lowercase__   :     List[str]=1_3					,lowercase__   :     List[str]=1_0					,lowercase__   :     int=3					,lowercase__   :     Tuple=2					,lowercase__   :     Union[str, Any]=2					,lowercase__   :     List[str]=2					,lowercase__   :     List[Any]=True					,lowercase__   :     Any=True					,lowercase__   :     Optional[int]=3_2					,lowercase__   :     List[str]=5					,lowercase__   :     Tuple=4					,lowercase__   :     str=3_7					,lowercase__   :     List[Any]="gelu"					,lowercase__   :     Dict=0.1					,lowercase__   :     Any=0.1					,lowercase__   :     str=1_0					,lowercase__   :     Any=0.0_2					,lowercase__   :     Tuple=0.9					,lowercase__   :     Tuple=None					,):
   __lowercase							  = parent
   __lowercase							  = batch_size
   __lowercase							  = image_size
   __lowercase							  = num_channels
   __lowercase							  = patch_size
   __lowercase							  = tubelet_size
   __lowercase							  = num_frames
   __lowercase							  = is_training
   __lowercase							  = use_labels
   __lowercase							  = hidden_size
   __lowercase							  = num_hidden_layers
   __lowercase							  = num_attention_heads
   __lowercase							  = intermediate_size
   __lowercase							  = hidden_act
   __lowercase							  = hidden_dropout_prob
   __lowercase							  = attention_probs_dropout_prob
   __lowercase							  = type_sequence_label_size
   __lowercase							  = initializer_range
   __lowercase							  = mask_ratio
   __lowercase							  = scope

   # in VideoMAE, the number of tokens equals num_frames/tubelet_size * num_patches per frame
   __lowercase							  = (image_size // patch_size) ** 2
   __lowercase							  = (num_frames // tubelet_size) * self.num_patches_per_frame

   # use this variable to define bool_masked_pos
   __lowercase							  = int(mask_ratio * self.seq_length			)






  def 					SCREAMING_SNAKE_CASE      (      self   :     Any			):
   __lowercase							  = floats_tensor(
       [self.batch_size, self.num_frames, self.num_channels, self.image_size, self.image_size]			)

   __lowercase							  = None
   if self.use_labels:
    __lowercase							  = ids_tensor([self.batch_size]					,self.type_sequence_label_size			)

   __lowercase							  = self.get_config()

   return config, pixel_values, labels






  def 					SCREAMING_SNAKE_CASE      (      self   :     List[str]			):
   return VideoMAEConfig(
       image_size=self.image_size					,patch_size=self.patch_size					,num_channels=self.num_channels					,num_frames=self.num_frames					,tubelet_size=self.tubelet_size					,hidden_size=self.hidden_size					,num_hidden_layers=self.num_hidden_layers					,num_attention_heads=self.num_attention_heads					,intermediate_size=self.intermediate_size					,hidden_act=self.hidden_act					,hidden_dropout_prob=self.hidden_dropout_prob					,attention_probs_dropout_prob=self.attention_probs_dropout_prob					,is_decoder=lowercase__					,initializer_range=self.initializer_range					,)






  def 					SCREAMING_SNAKE_CASE      (      self   :     int					,lowercase__   :     int					,lowercase__   :     Any					,lowercase__   :     Optional[Any]			):
   __lowercase							  = VideoMAEModel(config=lowercase__			)
   model.to(lowercase__			)
   model.eval()
   __lowercase							  = model(lowercase__			)
   self.parent.assertEqual(result.last_hidden_state.shape					,(self.batch_size, self.seq_length, self.hidden_size)			)






  def 					SCREAMING_SNAKE_CASE      (      self   :     Dict					,lowercase__   :     Optional[Any]					,lowercase__   :     Any					,lowercase__   :     Any			):
   __lowercase							  = VideoMAEForPreTraining(lowercase__			)
   model.to(lowercase__			)
   model.eval()
   # important: each video needs to have the same number of masked patches
   # hence we define a single mask, which we then repeat for each example in the batch
   __lowercase							  = torch.ones((self.num_masks,)			)
   __lowercase							  = torch.cat([mask, torch.zeros(self.seq_length - mask.size(0			)			)]			)
   __lowercase							  = mask.expand(self.batch_size					,-1			).bool()

   __lowercase							  = model(lowercase__					,lowercase__			)
   # model only returns predictions for masked patches
   __lowercase							  = mask.sum().item()
   __lowercase							  = 3 * self.tubelet_size * self.patch_size**2
   self.parent.assertEqual(result.logits.shape					,(self.batch_size, num_masked_patches, decoder_num_labels)			)






  def 					SCREAMING_SNAKE_CASE      (      self   :     Tuple			):
   __lowercase							  = self.prepare_config_and_inputs()
   __lowercase  ,	__lowercase  ,	__lowercase							  = config_and_inputs
   __lowercase							  = {'''pixel_values''': pixel_values}
   return config, inputs_dict






@require_torch
class 			lowercase_	(lowerCamelCase__				,       lowerCamelCase__				,       unittest.TestCase      ):




  """simple docstring"""




  SCREAMING_SNAKE_CASE    :  Optional[Any]							=			(
      (VideoMAEModel, VideoMAEForPreTraining, VideoMAEForVideoClassification) if is_torch_available() else ()
  )
  SCREAMING_SNAKE_CASE    :  int							=			(
      {'feature-extraction': VideoMAEModel, 'video-classification': VideoMAEForVideoClassification}
      if is_torch_available()
      else {}
  )

  SCREAMING_SNAKE_CASE    :  str							=			False
  SCREAMING_SNAKE_CASE    :  Any							=			False
  SCREAMING_SNAKE_CASE    :  str							=			False
  SCREAMING_SNAKE_CASE    :  Union[str, Any]							=			False






  def 					SCREAMING_SNAKE_CASE      (      self   :     Any			):
   __lowercase							  = VideoMAEModelTester(self			)
   __lowercase							  = ConfigTester(self					,config_class=lowercase__					,has_text_modality=lowercase__					,hidden_size=3_7			)






  def 					SCREAMING_SNAKE_CASE      (      self   :     int					,lowercase__   :     Tuple					,lowercase__   :     List[str]					,lowercase__   :     List[str]=False			):
   __lowercase							  = copy.deepcopy(lowercase__			)

   if model_class == VideoMAEForPreTraining:
    # important: each video needs to have the same number of masked patches
    # hence we define a single mask, which we then repeat for each example in the batch
    __lowercase							  = torch.ones((self.model_tester.num_masks,)			)
    __lowercase							  = torch.cat([mask, torch.zeros(self.model_tester.seq_length - mask.size(0			)			)]			)
    __lowercase							  = mask.expand(self.model_tester.batch_size					,-1			).bool()
    __lowercase							  = bool_masked_pos.to(lowercase__			)

   if return_labels:
    if model_class in [
        *get_values(lowercase__			),
    ]:
     __lowercase							  = torch.zeros(
         self.model_tester.batch_size					,dtype=torch.long					,device=lowercase__			)

   return inputs_dict






  def 					SCREAMING_SNAKE_CASE      (      self   :     str			):
   self.config_tester.run_common_tests()






  @unittest.skip(reason='''VideoMAE does not use inputs_embeds'''			)
  def 					SCREAMING_SNAKE_CASE      (      self   :     Optional[Any]			):
   pass






  def 					SCREAMING_SNAKE_CASE      (      self   :     str			):
   __lowercase  ,	__lowercase							  = self.model_tester.prepare_config_and_inputs_for_common()

   for model_class in self.all_model_classes:
    __lowercase							  = model_class(lowercase__			)
    self.assertIsInstance(model.get_input_embeddings()					,(nn.Module)			)
    __lowercase							  = model.get_output_embeddings()
    self.assertTrue(x is None or isinstance(lowercase__					,nn.Linear			)			)






  def 					SCREAMING_SNAKE_CASE      (      self   :     Optional[int]			):
   __lowercase  ,	__lowercase							  = self.model_tester.prepare_config_and_inputs_for_common()

   for model_class in self.all_model_classes:
    __lowercase							  = model_class(lowercase__			)
    __lowercase							  = inspect.signature(model.forward			)
    # signature.parameters is an OrderedDict => so arg_names order is deterministic
    __lowercase							  = [*signature.parameters.keys()]

    __lowercase							  = ['''pixel_values''']
    self.assertListEqual(arg_names[:1]					,lowercase__			)






  def 					SCREAMING_SNAKE_CASE      (      self   :     Optional[Any]			):
   __lowercase							  = self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_model(*lowercase__			)






  def 					SCREAMING_SNAKE_CASE      (      self   :     str			):
   __lowercase							  = self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_for_pretraining(*lowercase__			)






  @slow
  def 					SCREAMING_SNAKE_CASE      (      self   :     Any			):
   for model_name in VIDEOMAE_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
    __lowercase							  = VideoMAEModel.from_pretrained(lowercase__			)
    self.assertIsNotNone(lowercase__			)






  def 					SCREAMING_SNAKE_CASE      (      self   :     Optional[int]			):
   if not self.has_attentions:
    pass

   else:
    __lowercase  ,	__lowercase							  = self.model_tester.prepare_config_and_inputs_for_common()
    __lowercase							  = True

    for model_class in self.all_model_classes:
     __lowercase							  = self.model_tester.seq_length - self.model_tester.num_masks
     __lowercase							  = (
         num_visible_patches if model_class == VideoMAEForPreTraining else self.model_tester.seq_length
     )

     __lowercase							  = True
     __lowercase							  = False
     __lowercase							  = True
     __lowercase							  = model_class(lowercase__			)
     model.to(lowercase__			)
     model.eval()
     with torch.no_grad():
      __lowercase							  = model(**self._prepare_for_class(lowercase__					,lowercase__			)			)
     __lowercase							  = outputs.attentions
     self.assertEqual(len(lowercase__			)					,self.model_tester.num_hidden_layers			)

     # check that output_attentions also work using config
     del inputs_dict["output_attentions"]
     __lowercase							  = True
     __lowercase							  = model_class(lowercase__			)
     model.to(lowercase__			)
     model.eval()
     with torch.no_grad():
      __lowercase							  = model(**self._prepare_for_class(lowercase__					,lowercase__			)			)
     __lowercase							  = outputs.attentions
     self.assertEqual(len(lowercase__			)					,self.model_tester.num_hidden_layers			)

     self.assertListEqual(
         list(attentions[0].shape[-3:]			)					,[self.model_tester.num_attention_heads, seq_len, seq_len]					,)
     __lowercase							  = len(lowercase__			)

     # Check attention is always last and order is fine
     __lowercase							  = True
     __lowercase							  = True
     __lowercase							  = model_class(lowercase__			)
     model.to(lowercase__			)
     model.eval()
     with torch.no_grad():
      __lowercase							  = model(**self._prepare_for_class(lowercase__					,lowercase__			)			)

     self.assertEqual(out_len + 1					,len(lowercase__			)			)

     __lowercase							  = outputs.attentions

     self.assertEqual(len(lowercase__			)					,self.model_tester.num_hidden_layers			)
     self.assertListEqual(
         list(self_attentions[0].shape[-3:]			)					,[self.model_tester.num_attention_heads, seq_len, seq_len]					,)






  def 					SCREAMING_SNAKE_CASE      (      self   :     Dict			):
   def check_hidden_states_output(lowercase__   :     List[str]					,lowercase__   :     int					,lowercase__   :     int			):
    __lowercase							  = model_class(lowercase__			)
    model.to(lowercase__			)
    model.eval()

    with torch.no_grad():
     __lowercase							  = model(**self._prepare_for_class(lowercase__					,lowercase__			)			)

    __lowercase							  = outputs.hidden_states
    __lowercase							  = self.model_tester.num_hidden_layers + 1
    self.assertEqual(len(lowercase__			)					,lowercase__			)

    __lowercase							  = self.model_tester.seq_length - self.model_tester.num_masks
    __lowercase							  = num_visible_patches if model_class == VideoMAEForPreTraining else self.model_tester.seq_length

    self.assertListEqual(
        list(hidden_states[0].shape[-2:]			)					,[seq_length, self.model_tester.hidden_size]					,)

   __lowercase  ,	__lowercase							  = self.model_tester.prepare_config_and_inputs_for_common()

   for model_class in self.all_model_classes:
    __lowercase							  = True
    check_hidden_states_output(lowercase__					,lowercase__					,lowercase__			)

    # check that output_hidden_states also work using config
    del inputs_dict["output_hidden_states"]
    __lowercase							  = True

    check_hidden_states_output(lowercase__					,lowercase__					,lowercase__			)






  @unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.'''			)
  def 					SCREAMING_SNAKE_CASE      (      self   :     Tuple			):
   pass


def 		_A     (    ):






 """simple docstring"""



 __lowercase							  = hf_hub_download(
     repo_id='''hf-internal-testing/spaghetti-video'''     ,				filename='''eating_spaghetti.npy'''     ,				repo_type='''dataset'''      )
 __lowercase							  = np.load(A__      )
 return list(A__      )






@require_torch
@require_vision
class 			lowercase_	(unittest.TestCase      ):




  """simple docstring"""




  @cached_property
  def 					SCREAMING_SNAKE_CASE      (      self   :     List[str]			):
   # logits were tested with a different mean and std, so we use the same here
   return (
       VideoMAEImageProcessor(image_mean=[0.5, 0.5, 0.5]					,image_std=[0.5, 0.5, 0.5]			)
       if is_vision_available()
       else None
   )






  @slow
  def 					SCREAMING_SNAKE_CASE      (      self   :     Optional[Any]			):
   __lowercase							  = VideoMAEForVideoClassification.from_pretrained('''MCG-NJU/videomae-base-finetuned-kinetics'''			).to(
       lowercase__			)

   __lowercase							  = self.default_image_processor
   __lowercase							  = prepare_video()
   __lowercase							  = image_processor(lowercase__					,return_tensors='''pt'''			).to(lowercase__			)

   # forward pass
   with torch.no_grad():
    __lowercase							  = model(**lowercase__			)

   # verify the logits
   __lowercase							  = torch.Size((1, 4_0_0)			)
   self.assertEqual(outputs.logits.shape					,lowercase__			)

   __lowercase							  = torch.tensor([0.3_6_6_9, -0.0_6_8_8, -0.2_4_2_1]			).to(lowercase__			)

   self.assertTrue(torch.allclose(outputs.logits[0, :3]					,lowercase__					,atol=1e-4			)			)






  @slow
  def 					SCREAMING_SNAKE_CASE      (      self   :     Tuple			):
   __lowercase							  = VideoMAEForPreTraining.from_pretrained('''MCG-NJU/videomae-base-short'''			).to(lowercase__			)

   __lowercase							  = self.default_image_processor
   __lowercase							  = prepare_video()
   __lowercase							  = image_processor(lowercase__					,return_tensors='''pt'''			).to(lowercase__			)

   # add boolean mask, indicating which patches to mask
   __lowercase							  = hf_hub_download(repo_id='''hf-internal-testing/bool-masked-pos'''					,filename='''bool_masked_pos.pt'''			)
   __lowercase							  = torch.load(lowercase__			)

   # forward pass
   with torch.no_grad():
    __lowercase							  = model(**lowercase__			)

   # verify the logits
   __lowercase							  = torch.Size([1, 1_4_0_8, 1_5_3_6]			)
   __lowercase							  = torch.tensor(
       [[0.7_9_9_4, 0.9_6_1_2, 0.8_5_0_8], [0.7_4_0_1, 0.8_9_5_8, 0.8_3_0_2], [0.5_8_6_2, 0.7_4_6_8, 0.7_3_2_5]]					,device=lowercase__			)
   self.assertEqual(outputs.logits.shape					,lowercase__			)
   self.assertTrue(torch.allclose(outputs.logits[0, :3, :3]					,lowercase__					,atol=1e-4			)			)

   # verify the loss (`config.norm_pix_loss` = `True`)
   __lowercase							  = torch.tensor([0.5_1_4_2]					,device=lowercase__			)
   self.assertTrue(torch.allclose(outputs.loss					,lowercase__					,atol=1e-4			)			)

   # verify the loss (`config.norm_pix_loss` = `False`)
   __lowercase							  = VideoMAEForPreTraining.from_pretrained('''MCG-NJU/videomae-base-short'''					,norm_pix_loss=lowercase__			).to(
       lowercase__			)

   with torch.no_grad():
    __lowercase							  = model(**lowercase__			)

   __lowercase							  = torch.tensor(torch.tensor([0.6_4_6_9]			)					,device=lowercase__			)
   self.assertTrue(torch.allclose(outputs.loss					,lowercase__					,atol=1e-4			)			)






import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput
def 		UpperCamelCase	(   snake_case__       :    Dict       ,     snake_case__       :    Any=0.999       ,     snake_case__       :    List[Any]="cosine"       ,     )	->     Optional[int]:
   if alpha_transform_type == "cosine":

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

   elif alpha_transform_type == "exp":

      def alpha_bar_fn(snake_case__       :    Optional[Any]	):
         return math.exp(t * -12.0	)

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

   UpperCamelCase    :  List[Any]							=	[]
   for i in range(snake_case__	):
      UpperCamelCase    :  Optional[Any]							=	i / num_diffusion_timesteps
      UpperCamelCase    :  Union[str, Any]							=	(i + 1) / num_diffusion_timesteps
      betas.append(min(1 - alpha_bar_fn(snake_case__	) / alpha_bar_fn(snake_case__	)       ,     snake_case__	)	)
   return torch.tensor(snake_case__       ,     dtype=torch.floataa	)



class 				lowerCAmelCase_		(  a__       ,    a__							):
       UpperCAmelCase__				:					List[Any]        =  [e.name for e in KarrasDiffusionSchedulers]
       UpperCAmelCase__				:					List[str]        =  2
       @register_to_config
       def __init__(			self,			SCREAMING_SNAKE_CASE_ = 1000,			SCREAMING_SNAKE_CASE_ = 0.0_00_85,			SCREAMING_SNAKE_CASE_ = 0.0_12,			SCREAMING_SNAKE_CASE_ = "linear",			SCREAMING_SNAKE_CASE_ = None,			SCREAMING_SNAKE_CASE_ = "epsilon",			SCREAMING_SNAKE_CASE_ = "linspace",			SCREAMING_SNAKE_CASE_ = 0,			)     ->		List[str]:
          if trained_betas is not None:
             UpperCamelCase    :  Union[str, Any]							=	torch.tensor(SCREAMING_SNAKE_CASE_,			dtype=torch.floataa       )
          elif beta_schedule == "linear":
             UpperCamelCase    :  List[Any]							=	torch.linspace(SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_,			dtype=torch.floataa       )
          elif beta_schedule == "scaled_linear":
             # this schedule is very specific to the latent diffusion model.
             UpperCamelCase    :  int							=	(
                 torch.linspace(beta_start**0.5,			beta_end**0.5,			SCREAMING_SNAKE_CASE_,			dtype=torch.floataa       ) ** 2
             )
          elif beta_schedule == "squaredcos_cap_v2":
             # Glide cosine schedule
             UpperCamelCase    :  Optional[int]							=	betas_for_alpha_bar(SCREAMING_SNAKE_CASE_       )
          else:
             raise NotImplementedError(F"""{beta_schedule} does is not implemented for {self.__class__}"""       )

          UpperCamelCase    :  Optional[int]							=	1.0 - self.betas
          UpperCamelCase    :  int							=	torch.cumprod(self.alphas,			dim=0       )

          #  set all values
          self.set_timesteps(SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_       )
       def 					snake_case_       (			self,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_=None       )     ->		str:
          if schedule_timesteps is None:
             UpperCamelCase    :  Union[str, Any]							=	self.timesteps

          UpperCamelCase    :  Dict							=	(schedule_timesteps == timestep).nonzero()

          # The sigma index that is taken for the **very** first `step`
          # is always the second index (or the last index if there is only 1)
          # This way we can ensure we don't accidentally skip a sigma in
          # case we start in the middle of the denoising schedule (e.g. for image-to-image)
          if len(self._index_counter       ) == 0:
             UpperCamelCase    :  Optional[int]							=	1 if len(SCREAMING_SNAKE_CASE_       ) > 1 else 0
          else:
             UpperCamelCase    :  str							=	timestep.cpu().item() if torch.is_tensor(SCREAMING_SNAKE_CASE_       ) else timestep
             UpperCamelCase    :  int							=	self._index_counter[timestep_int]

          return indices[pos].item()
       @property
       def 					snake_case_       (			self       )     ->		List[Any]:
          # 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,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_,			)     ->		torch.FloatTensor:
          UpperCamelCase    :  Optional[int]							=	self.index_for_timestep(SCREAMING_SNAKE_CASE_       )

          if self.state_in_first_order:
             UpperCamelCase    :  Dict							=	self.sigmas[step_index]
          else:
             UpperCamelCase    :  int							=	self.sigmas_interpol[step_index]

          UpperCamelCase    :  Any							=	sample / ((sigma**2 + 1) ** 0.5)
          return sample
       def 					snake_case_       (			self,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_ = None,			SCREAMING_SNAKE_CASE_ = None,			)     ->		Optional[int]:
          UpperCamelCase    :  Dict							=	num_inference_steps

          UpperCamelCase    :  List[Any]							=	num_train_timesteps or self.config.num_train_timesteps

          # "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
          if self.config.timestep_spacing == "linspace":
             UpperCamelCase    :  int							=	np.linspace(0,			num_train_timesteps - 1,			SCREAMING_SNAKE_CASE_,			dtype=SCREAMING_SNAKE_CASE_       )[::-1].copy()
          elif self.config.timestep_spacing == "leading":
             UpperCamelCase    :  List[Any]							=	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    :  Optional[Any]							=	(np.arange(0,			SCREAMING_SNAKE_CASE_       ) * step_ratio).round()[::-1].copy().astype(SCREAMING_SNAKE_CASE_       )
             timesteps += self.config.steps_offset
          elif self.config.timestep_spacing == "trailing":
             UpperCamelCase    :  Optional[int]							=	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    :  Any							=	(np.arange(SCREAMING_SNAKE_CASE_,			0,			-step_ratio       )).round().copy().astype(SCREAMING_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    :  Tuple							=	np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5       )
          UpperCamelCase    :  Optional[int]							=	torch.from_numpy(np.log(SCREAMING_SNAKE_CASE_       )       ).to(SCREAMING_SNAKE_CASE_       )

          UpperCamelCase    :  Optional[int]							=	np.interp(SCREAMING_SNAKE_CASE_,			np.arange(0,			len(SCREAMING_SNAKE_CASE_       )       ),			SCREAMING_SNAKE_CASE_       )
          UpperCamelCase    :  Dict							=	np.concatenate([sigmas, [0.0]]       ).astype(np.floataa       )
          UpperCamelCase    :  Optional[int]							=	torch.from_numpy(SCREAMING_SNAKE_CASE_       ).to(device=SCREAMING_SNAKE_CASE_       )

          # interpolate sigmas
          UpperCamelCase    :  Union[str, Any]							=	sigmas.log().lerp(sigmas.roll(1       ).log(),			0.5       ).exp()

          UpperCamelCase    :  Any							=	torch.cat([sigmas[:1], sigmas[1:].repeat_interleave(2       ), sigmas[-1:]]       )
          UpperCamelCase    :  Optional[Any]							=	torch.cat(
              [sigmas_interpol[:1], sigmas_interpol[1:].repeat_interleave(2       ), sigmas_interpol[-1:]]       )

          if str(SCREAMING_SNAKE_CASE_       ).startswith('mps'       ):
             # mps does not support float64
             UpperCamelCase    :  Any							=	torch.from_numpy(SCREAMING_SNAKE_CASE_       ).to(SCREAMING_SNAKE_CASE_,			dtype=torch.floataa       )
          else:
             UpperCamelCase    :  Dict							=	torch.from_numpy(SCREAMING_SNAKE_CASE_       ).to(SCREAMING_SNAKE_CASE_       )

          # interpolate timesteps
          UpperCamelCase    :  int							=	self.sigma_to_t(SCREAMING_SNAKE_CASE_       ).to(SCREAMING_SNAKE_CASE_,			dtype=timesteps.dtype       )
          UpperCamelCase    :  List[str]							=	torch.stack((timesteps_interpol[1:-1, None], timesteps[1:, None]),			dim=-1       ).flatten()

          UpperCamelCase    :  Optional[Any]							=	torch.cat([timesteps[:1], interleaved_timesteps]       )

          UpperCamelCase    :  Optional[Any]							=	None

          # for exp beta schedules, such as the one for `pipeline_shap_e.py`
          # we need an index counter
          UpperCamelCase    :  Dict							=	defaultdict(SCREAMING_SNAKE_CASE_       )
       def 					snake_case_       (			self,			SCREAMING_SNAKE_CASE_       )     ->		Union[str, Any]:
          # get log sigma
          UpperCamelCase    :  List[Any]							=	sigma.log()

          # get distribution
          UpperCamelCase    :  Optional[int]							=	log_sigma - self.log_sigmas[:, None]

          # get sigmas range
          UpperCamelCase    :  Optional[int]							=	dists.ge(0       ).cumsum(dim=0       ).argmax(dim=0       ).clamp(max=self.log_sigmas.shape[0] - 2       )
          UpperCamelCase    :  Tuple							=	low_idx + 1

          UpperCamelCase    :  List[str]							=	self.log_sigmas[low_idx]
          UpperCamelCase    :  Optional[Any]							=	self.log_sigmas[high_idx]

          # interpolate sigmas
          UpperCamelCase    :  int							=	(low - log_sigma) / (low - high)
          UpperCamelCase    :  Tuple							=	w.clamp(0,			1       )

          # transform interpolation to time range
          UpperCamelCase    :  List[str]							=	(1 - w) * low_idx + w * high_idx
          UpperCamelCase    :  Dict							=	t.view(sigma.shape       )
          return t
       @property
       def 					snake_case_       (			self       )     ->		Optional[int]:
          return self.sample is None
       def 					snake_case_       (			self,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_ = True,			)     ->		Union[SchedulerOutput, Tuple]:
          UpperCamelCase    :  str							=	self.index_for_timestep(SCREAMING_SNAKE_CASE_       )

          # advance index counter by 1
          UpperCamelCase    :  Optional[int]							=	timestep.cpu().item() if torch.is_tensor(SCREAMING_SNAKE_CASE_       ) else timestep
          self._index_counter[timestep_int] += 1

          if self.state_in_first_order:
             UpperCamelCase    :  Tuple							=	self.sigmas[step_index]
             UpperCamelCase    :  Dict							=	self.sigmas_interpol[step_index + 1]
             UpperCamelCase    :  Optional[int]							=	self.sigmas[step_index + 1]
          else:
             # 2nd order / KDPM2's method
             UpperCamelCase    :  str							=	self.sigmas[step_index - 1]
             UpperCamelCase    :  Dict							=	self.sigmas_interpol[step_index]
             UpperCamelCase    :  Any							=	self.sigmas[step_index]

          # currently only gamma=0 is supported. This usually works best anyways.
          # We can support gamma in the future but then need to scale the timestep before
          # passing it to the model which requires a change in API
          UpperCamelCase    :  Dict							=	0
          UpperCamelCase    :  Any							=	sigma * (gamma + 1)  # Note: sigma_hat == sigma for now

          # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
          if self.config.prediction_type == "epsilon":
             UpperCamelCase    :  Any							=	sigma_hat if self.state_in_first_order else sigma_interpol
             UpperCamelCase    :  List[Any]							=	sample - sigma_input * model_output
          elif self.config.prediction_type == "v_prediction":
             UpperCamelCase    :  Union[str, Any]							=	sigma_hat if self.state_in_first_order else sigma_interpol
             UpperCamelCase    :  Optional[int]							=	model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
                 sample / (sigma_input**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`"""       )

          if self.state_in_first_order:
             # 2. Convert to an ODE derivative for 1st order
             UpperCamelCase    :  int							=	(sample - pred_original_sample) / sigma_hat
             # 3. delta timestep
             UpperCamelCase    :  Any							=	sigma_interpol - sigma_hat

             # store for 2nd order step
             UpperCamelCase    :  Tuple							=	sample
          else:
             # DPM-Solver-2
             # 2. Convert to an ODE derivative for 2nd order
             UpperCamelCase    :  Union[str, Any]							=	(sample - pred_original_sample) / sigma_interpol

             # 3. delta timestep
             UpperCamelCase    :  Dict							=	sigma_next - sigma_hat

             UpperCamelCase    :  Any							=	self.sample
             UpperCamelCase    :  str							=	None

          UpperCamelCase    :  Any							=	sample + derivative * dt

          if not return_dict:
             return (prev_sample,)

          return SchedulerOutput(prev_sample=SCREAMING_SNAKE_CASE_       )
       def 					snake_case_       (			self,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_,			)     ->		torch.FloatTensor:
          # Make sure sigmas and timesteps have the same device and dtype as original_samples
          UpperCamelCase    :  Optional[Any]							=	self.sigmas.to(device=original_samples.device,			dtype=original_samples.dtype       )
          if original_samples.device.type == "mps" and torch.is_floating_point(SCREAMING_SNAKE_CASE_       ):
             # mps does not support float64
             UpperCamelCase    :  List[str]							=	self.timesteps.to(original_samples.device,			dtype=torch.floataa       )
             UpperCamelCase    :  str							=	timesteps.to(original_samples.device,			dtype=torch.floataa       )
          else:
             UpperCamelCase    :  Dict							=	self.timesteps.to(original_samples.device       )
             UpperCamelCase    :  int							=	timesteps.to(original_samples.device       )

          UpperCamelCase    :  str							=	[self.index_for_timestep(SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_       ) for t in timesteps]

          UpperCamelCase    :  List[Any]							=	sigmas[step_indices].flatten()
          while len(sigma.shape       ) < len(original_samples.shape       ):
             UpperCamelCase    :  int							=	sigma.unsqueeze(-1       )

          UpperCamelCase    :  Any							=	original_samples + noise * sigma
          return noisy_samples






       def __len__(			self       )     ->		Optional[int]:
          return self.config.num_train_timesteps



