
"""simple docstring"""




import copy
import inspect
import unittest

import numpy as np
from huggingface_hub import hf_hub_download

from transformers import VivitConfig
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


if is_torch_available():
    import torch
    from torch import nn

    from transformers import MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING, VivitForVideoClassification, VivitModel
    from transformers.models.vivit.modeling_vivit import VIVIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
    from transformers import VivitImageProcessor







class 						lowercase:



     def __init__(       self    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE=2    ,							__SCREAMING_SNAKE_CASE=True    ,							__SCREAMING_SNAKE_CASE=True    ,							__SCREAMING_SNAKE_CASE=1_0    ,							__SCREAMING_SNAKE_CASE=1_0    ,							__SCREAMING_SNAKE_CASE=8    ,							__SCREAMING_SNAKE_CASE=[2, 4, 4]    ,							__SCREAMING_SNAKE_CASE=3    ,							__SCREAMING_SNAKE_CASE=7_6_8    ,							__SCREAMING_SNAKE_CASE=5    ,							__SCREAMING_SNAKE_CASE=4    ,							__SCREAMING_SNAKE_CASE=3_7    ,							__SCREAMING_SNAKE_CASE="gelu_fast"    ,							__SCREAMING_SNAKE_CASE=0.0    ,							__SCREAMING_SNAKE_CASE=0.0    ,							__SCREAMING_SNAKE_CASE=0.02    ,							__SCREAMING_SNAKE_CASE=1e-06    ,							__SCREAMING_SNAKE_CASE=True    ,							__SCREAMING_SNAKE_CASE=None    ,							)		->    Dict:







         """simple docstring"""



         a__    = parent
         a__    = batch_size
         a__    = is_training
         a__    = use_labels
         a__    = num_labels
         a__    = image_size
         a__    = num_frames
         a__    = tubelet_size
         a__    = num_channels
         a__    = hidden_size
         a__    = num_hidden_layers
         a__    = num_attention_heads
         a__    = intermediate_size
         a__    = hidden_act
         a__    = hidden_dropout_prob
         a__    = attention_probs_dropout_prob
         a__    = initializer_range
         a__    = layer_norm_eps
         a__    = qkv_bias
         a__    = scope

         a__    = (
             (self.image_size // self.tubelet_size[2])
             * (self.image_size // self.tubelet_size[1])
             * (self.num_frames // self.tubelet_size[0])
         ) + 1  # CLS token



     def  lowercase__     (       self      )		->    List[str]:







         """simple docstring"""



         a__    = floats_tensor(
             [self.batch_size, self.num_frames, self.num_channels, self.image_size, self.image_size]      )

         a__    = None
         if self.use_labels:
             a__    = ids_tensor([self.batch_size]    ,							self.num_labels      )

         a__    = self.get_config()

         return config, pixel_values, labels



     def  lowercase__     (       self      )		->    Tuple:







         """simple docstring"""



         a__    = VivitConfig(
             num_frames=self.num_frames    ,							image_size=self.image_size    ,							tubelet_size=self.tubelet_size    ,							num_channels=self.num_channels    ,							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    ,							initializer_range=self.initializer_range    ,							layer_norm_eps=self.layer_norm_eps    ,							qkv_bias=self.qkv_bias    ,							)
         a__    = self.num_labels
         return config



     def  lowercase__     (       self    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE      )		->    int:







         """simple docstring"""



         a__    = VivitModel(config=__SCREAMING_SNAKE_CASE      )
         model.to(__SCREAMING_SNAKE_CASE      )
         model.eval()
         a__    = model(__SCREAMING_SNAKE_CASE      )
         self.parent.assertEqual(result.last_hidden_state.shape    ,							(self.batch_size, self.seq_length, self.hidden_size)      )



     def  lowercase__     (       self    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE      )		->    Dict:







         """simple docstring"""



         a__    = VivitForVideoClassification(__SCREAMING_SNAKE_CASE      )
         model.to(__SCREAMING_SNAKE_CASE      )
         model.eval()

         a__    = model(__SCREAMING_SNAKE_CASE      )

         # verify the logits shape
         a__    = torch.Size((self.batch_size, self.num_labels)      )
         self.parent.assertEqual(result.logits.shape    ,							__SCREAMING_SNAKE_CASE      )




     def  lowercase__     (       self      )		->    Optional[Any]:







         """simple docstring"""



         a__    = self.prepare_config_and_inputs()
         a__  ,    a__  ,    a__    = config_and_inputs
         a__    = {'pixel_values': pixel_values}
         return config, inputs_dict







@require_torch
class 						lowercase(_lowercase						,	unittest.TestCase					):
     __snake_case:  int  				=    (VivitModel, VivitForVideoClassification) if is_torch_available() else ()

     __snake_case:  int  				=    False
     __snake_case:  List[str]  				=    False
     __snake_case:  Optional[Any]  				=    False
     __snake_case:  str  				=    False



     def  lowercase__     (       self      )		->    str:







         """simple docstring"""



         a__    = VivitModelTester(self      )
         a__    = ConfigTester(self    ,							config_class=__SCREAMING_SNAKE_CASE    ,							has_text_modality=__SCREAMING_SNAKE_CASE    ,							hidden_size=3_7      )



     def  lowercase__     (       self    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE=False      )		->    str:







         """simple docstring"""



         a__    = copy.deepcopy(__SCREAMING_SNAKE_CASE      )

         if return_labels:
             if model_class in get_values(__SCREAMING_SNAKE_CASE      ):
                 a__    = torch.zeros(
                     self.model_tester.batch_size    ,							dtype=torch.long    ,							device=__SCREAMING_SNAKE_CASE      )

         return inputs_dict



     def  lowercase__     (       self      )		->    Optional[int]:







         """simple docstring"""



         self.config_tester.run_common_tests()



     @unittest.skip(reason='Vivit does not use inputs_embeds'      )
     def  lowercase__     (       self      )		->    Optional[int]:







         """simple docstring"""



         pass



     def  lowercase__     (       self      )		->    List[str]:







         """simple docstring"""



         a__  ,    a__    = self.model_tester.prepare_config_and_inputs_for_common()

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



     def  lowercase__     (       self      )		->    Optional[int]:







         """simple docstring"""



         a__  ,    a__    = self.model_tester.prepare_config_and_inputs_for_common()

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

             a__    = ['pixel_values', 'head_mask']
             self.assertListEqual(arg_names[:2]    ,							__SCREAMING_SNAKE_CASE      )



     def  lowercase__     (       self      )		->    Optional[Any]:







         """simple docstring"""



         a__    = self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*__SCREAMING_SNAKE_CASE      )



     def  lowercase__     (       self      )		->    Optional[int]:







         """simple docstring"""



         a__    = self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_video_classification(*__SCREAMING_SNAKE_CASE      )



     @slow
     def  lowercase__     (       self      )		->    List[Any]:







         """simple docstring"""



         for model_name in VIVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             a__    = VivitModel.from_pretrained(__SCREAMING_SNAKE_CASE      )
             self.assertIsNotNone(__SCREAMING_SNAKE_CASE      )



     def  lowercase__     (       self      )		->    List[str]:







         """simple docstring"""



         a__  ,    a__    = self.model_tester.prepare_config_and_inputs_for_common()
         a__    = True

         for model_class in self.all_model_classes:
             a__    = self.model_tester.seq_length

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

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

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

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

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

             a__    = outputs.attentions

             self.assertEqual(len(__SCREAMING_SNAKE_CASE      )    ,							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  lowercase__     (       self      )		->    List[Any]:







         """simple docstring"""



         def check_hidden_states_output(__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE      ):
             a__    = model_class(__SCREAMING_SNAKE_CASE      )
             model.to(__SCREAMING_SNAKE_CASE      )
             model.eval()

             with torch.no_grad():
                 a__    = model(**self._prepare_for_class(__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE      )      )

             a__    = outputs.hidden_states
             a__    = self.model_tester.num_hidden_layers + 1
             self.assertEqual(len(__SCREAMING_SNAKE_CASE      )    ,							__SCREAMING_SNAKE_CASE      )

             a__    = self.model_tester.seq_length

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

         a__  ,    a__    = self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
             a__    = True
             check_hidden_states_output(__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE      )

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

             check_hidden_states_output(__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE      )




     @unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.'      )
     def  lowercase__     (       self      )		->    int:







         """simple docstring"""



         pass




def 						__magic_name__		(				)    ->	Dict:
    a__    = hf_hub_download(
        repo_id='hf-internal-testing/spaghetti-video'       ,		filename='eating_spaghetti_32_frames.npy'       ,		repo_type='dataset'    )
    a__    = np.load(UpperCamelCase    )
    return list(UpperCamelCase    )







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



     @cached_property
     def  lowercase__     (       self      )		->    str:







         """simple docstring"""



         return VivitImageProcessor() if is_vision_available() else None




     @slow
     def  lowercase__     (       self      )		->    Optional[int]:







         """simple docstring"""



         a__    = VivitForVideoClassification.from_pretrained('google/vivit-b-16x2-kinetics400'      ).to(__SCREAMING_SNAKE_CASE      )

         a__    = self.default_image_processor
         a__    = prepare_video()
         a__    = image_processor(__SCREAMING_SNAKE_CASE    ,							return_tensors='pt'      ).to(__SCREAMING_SNAKE_CASE      )

         # forward pass
         with torch.no_grad():
             a__    = model(**__SCREAMING_SNAKE_CASE      )

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

         # taken from original model
         a__    = torch.tensor([-1.05_43, 2.07_64, -0.21_04, 0.44_39, -0.96_58]      ).to(__SCREAMING_SNAKE_CASE      )

         self.assertTrue(torch.allclose(outputs.logits[0, :5]    ,							__SCREAMING_SNAKE_CASE    ,							atol=1e-4      )      )




"""simple docstring"""




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 SchedulerMixin


a		:					List[Any]    	=					logging.get_logger(__name__)  # pylint: disable=invalid-name







@dataclass
class 						lowercase(_lowercase					):
     __snake_case:  torch.FloatTensor







class 						lowercase(_lowercase						,	_lowercase					):
     __snake_case:  List[Any]  				=    1



     @register_to_config
     def __init__(       self    ,							__SCREAMING_SNAKE_CASE = 4_0    ,							__SCREAMING_SNAKE_CASE = 0.0_02    ,							__SCREAMING_SNAKE_CASE = 80.0    ,							__SCREAMING_SNAKE_CASE = 0.5    ,							__SCREAMING_SNAKE_CASE = 1.0    ,							__SCREAMING_SNAKE_CASE = 7.0    ,							__SCREAMING_SNAKE_CASE = True    ,							)		->    Dict:







         """simple docstring"""



         a__    = sigma_max

         a__    = np.linspace(0    ,							1    ,							__SCREAMING_SNAKE_CASE      )
         a__    = self._convert_to_karras(__SCREAMING_SNAKE_CASE      )
         a__    = self.sigma_to_t(__SCREAMING_SNAKE_CASE      )

         # setable values
         a__    = None
         a__    = torch.from_numpy(__SCREAMING_SNAKE_CASE      )
         a__    = torch.from_numpy(__SCREAMING_SNAKE_CASE      )
         a__    = False
         a__    = False



     def  lowercase__     (       self    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE=None      )		->    Union[str, Any]:







         """simple docstring"""



         if schedule_timesteps is None:
             a__    = self.timesteps

         a__    = (schedule_timesteps == timestep).nonzero()
         return indices.item()



     def  lowercase__     (       self    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE      )		->    torch.FloatTensor:







         """simple docstring"""



         if isinstance(__SCREAMING_SNAKE_CASE    ,							torch.Tensor      ):
             a__    = timestep.to(self.timesteps.device      )
         a__    = self.index_for_timestep(__SCREAMING_SNAKE_CASE      )
         a__    = self.sigmas[step_idx]

         a__    = sample / ((sigma**2 + self.config.sigma_data**2) ** 0.5)

         a__    = True
         return sample



     def  lowercase__     (       self    ,							__SCREAMING_SNAKE_CASE      )		->    int:







         """simple docstring"""



         if not isinstance(__SCREAMING_SNAKE_CASE    ,							np.ndarray      ):
             a__    = np.array(__SCREAMING_SNAKE_CASE    ,							dtype=np.floataa      )

         a__    = 1_0_0_0 * 0.25 * np.log(sigmas + 1e-44      )

         return timesteps



     def  lowercase__     (       self    ,							__SCREAMING_SNAKE_CASE = None    ,							__SCREAMING_SNAKE_CASE = None    ,							__SCREAMING_SNAKE_CASE = None    ,							)		->    Optional[int]:







         """simple docstring"""



         if num_inference_steps is None and timesteps is None:
             raise ValueError('Exactly one of `num_inference_steps` or `timesteps` must be supplied.'      )

         if num_inference_steps is not None and timesteps is not None:
             raise ValueError('Can only pass one of `num_inference_steps` or `timesteps`.'      )

         # Follow DDPMScheduler custom timesteps logic
         if timesteps is not None:
             for i in range(1    ,							len(__SCREAMING_SNAKE_CASE      )      ):
                 if timesteps[i] >= timesteps[i - 1]:
                     raise ValueError('`timesteps` must be in descending order.'      )

             if timesteps[0] >= self.config.num_train_timesteps:
                 raise ValueError(
                     f'`timesteps` must start before `self.config.train_timesteps`:'
                     f' {self.config.num_train_timesteps}.'      )

             a__    = np.array(__SCREAMING_SNAKE_CASE    ,							dtype=np.intaa      )
             a__    = True
         else:
             if num_inference_steps > self.config.num_train_timesteps:
                 raise ValueError(
                     f'`num_inference_steps`: {num_inference_steps} cannot be larger than `self.config.train_timesteps`:'
                     f' {self.config.num_train_timesteps} as the unet model trained with this scheduler can only handle'
                     f' maximal {self.config.num_train_timesteps} timesteps.'      )

             a__    = num_inference_steps

             a__    = self.config.num_train_timesteps // self.num_inference_steps
             a__    = (np.arange(0    ,							__SCREAMING_SNAKE_CASE      ) * step_ratio).round()[::-1].copy().astype(np.intaa      )
             a__    = False

         # Map timesteps to Karras sigmas directly for multistep sampling
         # See https://github.com/openai/consistency_models/blob/main/cm/karras_diffusion.py#L675
         a__    = self.config.num_train_timesteps
         a__    = timesteps[::-1].copy()
         a__    = ramp / (num_train_timesteps - 1)
         a__    = self._convert_to_karras(__SCREAMING_SNAKE_CASE      )
         a__    = self.sigma_to_t(__SCREAMING_SNAKE_CASE      )

         a__    = np.concatenate([sigmas, [self.sigma_min]]      ).astype(np.floataa      )
         a__    = torch.from_numpy(__SCREAMING_SNAKE_CASE      ).to(device=__SCREAMING_SNAKE_CASE      )

         if str(__SCREAMING_SNAKE_CASE      ).startswith('mps'      ):
             # mps does not support float64
             a__    = torch.from_numpy(__SCREAMING_SNAKE_CASE      ).to(__SCREAMING_SNAKE_CASE    ,							dtype=torch.floataa      )
         else:
             a__    = torch.from_numpy(__SCREAMING_SNAKE_CASE      ).to(device=__SCREAMING_SNAKE_CASE      )



     def  lowercase__     (       self    ,							__SCREAMING_SNAKE_CASE      )		->    Optional[int]:







         """simple docstring"""



         a__    = self.config.sigma_min
         a__    = self.config.sigma_max

         a__    = self.config.rho
         a__    = sigma_min ** (1 / rho)
         a__    = sigma_max ** (1 / rho)
         a__    = (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
         return sigmas



     def  lowercase__     (       self    ,							__SCREAMING_SNAKE_CASE      )		->    Union[str, Any]:







         """simple docstring"""



         a__    = self.config.sigma_data

         a__    = sigma_data**2 / (sigma**2 + sigma_data**2)
         a__    = sigma * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
         return c_skip, c_out



     def  lowercase__     (       self    ,							__SCREAMING_SNAKE_CASE      )		->    List[str]:







         """simple docstring"""



         a__    = self.config.sigma_min
         a__    = self.config.sigma_data

         a__    = sigma_data**2 / ((sigma - sigma_min) ** 2 + sigma_data**2)
         a__    = (sigma - sigma_min) * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
         return c_skip, c_out



     def  lowercase__     (       self    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE = None    ,							__SCREAMING_SNAKE_CASE = True    ,							)		->    Union[CMStochasticIterativeSchedulerOutput, Tuple]:







         """simple docstring"""



         if (
             isinstance(__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE      )
             or isinstance(__SCREAMING_SNAKE_CASE    ,							torch.IntTensor      )
             or isinstance(__SCREAMING_SNAKE_CASE    ,							torch.LongTensor      )
         ):
             raise ValueError(
                 (
                     'Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to'
                     f' `{self.__class__}.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(__SCREAMING_SNAKE_CASE    ,							torch.Tensor      ):
             a__    = timestep.to(self.timesteps.device      )

         a__    = self.config.sigma_min
         a__    = self.config.sigma_max

         a__    = self.index_for_timestep(__SCREAMING_SNAKE_CASE      )

         # sigma_next corresponds to next_t in original implementation
         a__    = self.sigmas[step_index]
         if step_index + 1 < self.config.num_train_timesteps:
             a__    = self.sigmas[step_index + 1]
         else:
             # Set sigma_next to sigma_min
             a__    = self.sigmas[-1]

         # Get scalings for boundary conditions
         a__  ,    a__    = self.get_scalings_for_boundary_condition(__SCREAMING_SNAKE_CASE      )

         # 1. Denoise model output using boundary conditions
         a__    = c_out * model_output + c_skip * sample
         if self.config.clip_denoised:
             a__    = denoised.clamp(-1    ,							1      )

         # 2. Sample z ~ N(0, s_noise^2 * I)
         # Noise is not used for onestep sampling.
         if len(self.timesteps      ) > 1:
             a__    = randn_tensor(
                 model_output.shape    ,							dtype=model_output.dtype    ,							device=model_output.device    ,							generator=__SCREAMING_SNAKE_CASE      )
         else:
             a__    = torch.zeros_like(__SCREAMING_SNAKE_CASE      )
         a__    = noise * self.config.s_noise

         a__    = sigma_next.clamp(min=__SCREAMING_SNAKE_CASE    ,							max=__SCREAMING_SNAKE_CASE      )

         # 3. Return noisy sample
         # tau = sigma_hat, eps = sigma_min
         a__    = denoised + z * (sigma_hat**2 - sigma_min**2) ** 0.5

         if not return_dict:
             return (prev_sample,)

         return CMStochasticIterativeSchedulerOutput(prev_sample=__SCREAMING_SNAKE_CASE      )



     def  lowercase__     (       self    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							)		->    torch.FloatTensor:







         """simple docstring"""



         a__    = 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
             a__    = self.timesteps.to(original_samples.device    ,							dtype=torch.floataa      )
             a__    = timesteps.to(original_samples.device    ,							dtype=torch.floataa      )
         else:
             a__    = self.timesteps.to(original_samples.device      )
             a__    = timesteps.to(original_samples.device      )

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

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

         a__    = original_samples + noise * sigma
         return noisy_samples




     def __len__(       self      )		->    str:







         """simple docstring"""



         return self.config.num_train_timesteps


