



'''simple docstring'''






from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import TensorType, is_torch_available, logging


A_							:				int						=			logging.get_logger(__name__)

A_							:				List[Any]						=			{
    """facebook/mbart-large-cc25""": """https://huggingface.co/facebook/mbart-large-cc25/resolve/main/config.json""",
    # See all MBART models at https://huggingface.co/models?filter=mbart
}



class 							lowercase (						_lowerCamelCase       ):


 """simple docstring"""


 UpperCAmelCase					=						"""mbart"""
 UpperCAmelCase					=						["""past_key_values"""]
 UpperCAmelCase					=						{"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}







 def __init__(							self						,a_=50_265						,a_=1_024						,a_=12						,a_=4_096						,a_=16						,a_=12						,a_=4_096						,a_=16						,a_=0.0						,a_=0.0						,a_=True						,a_=True						,a_="gelu"						,a_=1_024						,a_=0.1						,a_=0.0						,a_=0.0						,a_=0.02						,a_=0.0						,a_=False						,a_=1						,a_=0						,a_=2						,a_=2						,**a_						,)	->     List[str]:
        _UpperCAmelCase						:		Tuple      =				vocab_size
        _UpperCAmelCase						:		Tuple      =				max_position_embeddings
        _UpperCAmelCase						:		Any      =				d_model
        _UpperCAmelCase						:		str      =				encoder_ffn_dim
        _UpperCAmelCase						:		List[Any]      =				encoder_layers
        _UpperCAmelCase						:		List[str]      =				encoder_attention_heads
        _UpperCAmelCase						:		List[str]      =				decoder_ffn_dim
        _UpperCAmelCase						:		int      =				decoder_layers
        _UpperCAmelCase						:		str      =				decoder_attention_heads
        _UpperCAmelCase						:		int      =				dropout
        _UpperCAmelCase						:		Optional[Any]      =				attention_dropout
        _UpperCAmelCase						:		List[str]      =				activation_dropout
        _UpperCAmelCase						:		str      =				activation_function
        _UpperCAmelCase						:		int      =				init_std
        _UpperCAmelCase						:		Any      =				encoder_layerdrop
        _UpperCAmelCase						:		Optional[int]      =				decoder_layerdrop
        _UpperCAmelCase						:		int      =				classifier_dropout
        _UpperCAmelCase						:		str      =				use_cache
        _UpperCAmelCase						:		List[str]      =				encoder_layers
        _UpperCAmelCase						:		List[Any]      =				scale_embedding  # scale factor will be sqrt(d_model) if True
        super().__init__(
            pad_token_id=a_						,bos_token_id=a_						,eos_token_id=a_						,is_encoder_decoder=a_						,forced_eos_token_id=a_						,**a_						,)







class 							lowercase (						_lowerCamelCase       ):


 """simple docstring"""


 @property
 def _snake_case				(							self							)	->     Mapping[str, Mapping[int, str]]:
        if self.task in ["default", "seq2seq-lm"]:
               _UpperCAmelCase						:		List[str]      =				OrderedDict(
                   [
                       ("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
                       ("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
                   ]							)

               if self.use_past:
                      _UpperCAmelCase						:		str      =				{0: """batch"""}
                      _UpperCAmelCase						:		Union[str, Any]      =				{0: """batch""", 1: """past_decoder_sequence + sequence"""}
               else:
                      _UpperCAmelCase						:		Any      =				{0: """batch""", 1: """decoder_sequence"""}
                      _UpperCAmelCase						:		Any      =				{0: """batch""", 1: """decoder_sequence"""}

               if self.use_past:
                      self.fill_with_past_key_values_(a_						,direction="""inputs"""							)
        elif self.task == "causal-lm":
               # TODO: figure this case out.
               _UpperCAmelCase						:		List[str]      =				OrderedDict(
                   [
                       ("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
                       ("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
                   ]							)
               if self.use_past:
                      _UpperCAmelCase						:		Union[str, Any]      =				self.num_layers
                      for i in range(a_							):
                             _UpperCAmelCase						:		int      =				{0: """batch""", 2: """past_sequence + sequence"""}
                             _UpperCAmelCase						:		int      =				{0: """batch""", 2: """past_sequence + sequence"""}
        else:
               _UpperCAmelCase						:		Optional[int]      =				OrderedDict(
                   [
                       ("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
                       ("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
                       ("""decoder_input_ids""", {0: """batch""", 1: """decoder_sequence"""}),
                       ("""decoder_attention_mask""", {0: """batch""", 1: """decoder_sequence"""}),
                   ]							)

        return common_inputs







 @property
 def _snake_case				(							self							)	->     Mapping[str, Mapping[int, str]]:
        if self.task in ["default", "seq2seq-lm"]:
               _UpperCAmelCase						:		Any      =				super().outputs
        else:
               _UpperCAmelCase						:		Any      =				super(a_						,self							).outputs
               if self.use_past:
                      _UpperCAmelCase						:		int      =				self.num_layers
                      for i in range(a_							):
                             _UpperCAmelCase						:		Union[str, Any]      =				{0: """batch""", 2: """past_sequence + sequence"""}
                             _UpperCAmelCase						:		Union[str, Any]      =				{0: """batch""", 2: """past_sequence + sequence"""}
        return common_outputs







 def _snake_case				(							self						,a_						,a_ = -1						,a_ = -1						,a_ = False						,a_ = None						,)	->     Mapping[str, Any]:
        _UpperCAmelCase						:		str      =				self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
            a_						,a_						,a_						,a_						,a_							)

        # Generate decoder inputs
        _UpperCAmelCase						:		List[Any]      =				seq_length if not self.use_past else 1
        _UpperCAmelCase						:		int      =				self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
            a_						,a_						,a_						,a_						,a_							)
        _UpperCAmelCase						:		List[Any]      =				{f'''decoder_{name}''': tensor for name, tensor in decoder_inputs.items()}
        _UpperCAmelCase						:		int      =				dict(**a_						,**a_							)

        if self.use_past:
               if not is_torch_available():
                      raise ValueError("""Cannot generate dummy past_keys inputs without PyTorch installed."""							)
               else:
                      import torch
               _UpperCAmelCase						:		Dict      =				common_inputs["""input_ids"""].shape
               _UpperCAmelCase						:		Optional[Any]      =				common_inputs["""decoder_input_ids"""].shape[1]
               _UpperCAmelCase						:		Dict      =				self.num_attention_heads
               _UpperCAmelCase						:		Dict      =				(
                   batch,
                   num_encoder_attention_heads,
                   encoder_seq_length,
                   self._config.hidden_size // num_encoder_attention_heads,
               )
               _UpperCAmelCase						:		List[str]      =				decoder_seq_length + 3
               _UpperCAmelCase						:		Optional[int]      =				(
                   batch,
                   num_decoder_attention_heads,
                   decoder_past_length,
                   self._config.hidden_size // num_decoder_attention_heads,
               )

               _UpperCAmelCase						:		List[Any]      =				torch.cat(
                   [common_inputs["""decoder_attention_mask"""], torch.ones(a_						,a_							)]						,dim=1							)

               _UpperCAmelCase						:		Dict      =				[]
               # If the number of encoder and decoder layers are present in the model configuration, both are considered
               _UpperCAmelCase						:		Any      =				self.num_layers
               _UpperCAmelCase						:		Optional[Any]      =				min(a_						,a_							)
               _UpperCAmelCase						:		int      =				max(a_						,a_							) - min_num_layers
               _UpperCAmelCase						:		List[Any]      =				"""encoder""" if num_encoder_layers > num_decoder_layers else """decoder"""

               for _ in range(a_							):
                      common_inputs["past_key_values"].append(
                          (
                              torch.zeros(a_							),
                              torch.zeros(a_							),
                              torch.zeros(a_							),
                              torch.zeros(a_							),
                          )							)
               # TODO: test this.
               _UpperCAmelCase						:		int      =				encoder_shape if remaining_side_name == """encoder""" else decoder_shape
               for _ in range(a_						,a_							):
                      common_inputs["past_key_values"].append((torch.zeros(a_							), torch.zeros(a_							))							)
        return common_inputs







 def _snake_case				(							self						,a_						,a_ = -1						,a_ = -1						,a_ = False						,a_ = None						,)	->     Mapping[str, Any]:
        _UpperCAmelCase						:		Union[str, Any]      =				self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
            a_						,a_						,a_						,a_						,a_							)

        if self.use_past:
               if not is_torch_available():
                      raise ValueError("""Cannot generate dummy past_keys inputs without PyTorch installed."""							)
               else:
                      import torch
               _UpperCAmelCase						:		Tuple      =				common_inputs["""input_ids"""].shape
               # Not using the same length for past_key_values
               _UpperCAmelCase						:		List[Any]      =				seqlen + 2
               _UpperCAmelCase						:		Optional[int]      =				self.num_layers
               _UpperCAmelCase						:		List[str]      =				self.num_attention_heads
               _UpperCAmelCase						:		List[str]      =				(
                   batch,
                   num_encoder_attention_heads,
                   past_key_values_length,
                   self._config.hidden_size // num_encoder_attention_heads,
               )

               _UpperCAmelCase						:		Tuple      =				common_inputs["""attention_mask"""].dtype
               _UpperCAmelCase						:		Optional[int]      =				torch.cat(
                   [common_inputs["""attention_mask"""], torch.ones(a_						,a_						,dtype=a_							)]						,dim=1							)
               _UpperCAmelCase						:		Tuple      =				[
                   (torch.zeros(a_							), torch.zeros(a_							)) for _ in range(a_							)
               ]
        return common_inputs







 def _snake_case				(							self						,a_						,a_ = -1						,a_ = -1						,a_ = False						,a_ = None						,)	->     Mapping[str, Any]:
        # Copied from OnnxConfig.generate_dummy_inputs
        # Did not use super(OnnxConfigWithPast, self).generate_dummy_inputs for code clarity.
        # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
        _UpperCAmelCase						:		Optional[Any]      =				compute_effective_axis_dimension(
            a_						,fixed_dimension=OnnxConfig.default_fixed_batch						,num_token_to_add=0							)

        # If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
        _UpperCAmelCase						:		Optional[int]      =				tokenizer.num_special_tokens_to_add(a_							)
        _UpperCAmelCase						:		Tuple      =				compute_effective_axis_dimension(
            a_						,fixed_dimension=OnnxConfig.default_fixed_sequence						,num_token_to_add=a_							)

        # Generate dummy inputs according to compute batch and sequence
        _UpperCAmelCase						:		Tuple      =				[""" """.join([tokenizer.unk_token]							) * seq_length] * batch_size
        _UpperCAmelCase						:		Union[str, Any]      =				dict(tokenizer(a_						,return_tensors=a_							)							)
        return common_inputs







 def _snake_case				(							self						,a_						,a_ = -1						,a_ = -1						,a_ = False						,a_ = None						,)	->     Mapping[str, Any]:
        if self.task in ["default", "seq2seq-lm"]:
               _UpperCAmelCase						:		Union[str, Any]      =				self._generate_dummy_inputs_for_default_and_seqaseq_lm(
                   a_						,batch_size=a_						,seq_length=a_						,is_pair=a_						,framework=a_							)

        elif self.task == "causal-lm":
               _UpperCAmelCase						:		Union[str, Any]      =				self._generate_dummy_inputs_for_causal_lm(
                   a_						,batch_size=a_						,seq_length=a_						,is_pair=a_						,framework=a_							)
        else:
               _UpperCAmelCase						:		str      =				self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
                   a_						,batch_size=a_						,seq_length=a_						,is_pair=a_						,framework=a_							)

        return common_inputs







 def _snake_case				(							self						,a_						,a_						,a_						,a_							)	->     List[str]:
        if self.task in ["default", "seq2seq-lm"]:
               _UpperCAmelCase						:		Tuple      =				super()._flatten_past_key_values_(a_						,a_						,a_						,a_							)
        else:
               _UpperCAmelCase						:		List[Any]      =				super(a_						,self							)._flatten_past_key_values_(
                   a_						,a_						,a_						,a_							)








'''simple docstring'''






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



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


 """simple docstring"""


 UpperCAmelCase					=						42
 UpperCAmelCase					=						None







def 					snake_case_      (  lowerCAmelCase_ ,     lowerCAmelCase_=0.9_9_9 ,     lowerCAmelCase_="cosine" ,     )->	Any:





       '''simple docstring'''


       if alpha_transform_type == "cosine":

              def alpha_bar_fn(lowerCAmelCase_	):
                     return math.cos((t + 0.0_0_8) / 1.0_0_8 * math.pi / 2	) ** 2

       elif alpha_transform_type == "exp":

              def alpha_bar_fn(lowerCAmelCase_	):
                     return math.exp(t * -1_2.0	)

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

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



class 							lowercase (						_lowerCamelCase      ,      _lowerCamelCase       ):


 """simple docstring"""


 UpperCAmelCase					=						[e.name for e in KarrasDiffusionSchedulers]
 UpperCAmelCase					=						1







 @register_to_config
 def __init__(							self						,a_ = 1_000						,a_ = 0.0001						,a_ = 0.02						,a_ = "linear"						,a_ = None						,a_ = "epsilon"						,a_ = "linspace"						,a_ = 0						,)	->     Optional[Any]:
        if trained_betas is not None:
               _UpperCAmelCase						:		List[str]      =				torch.tensor(a_						,dtype=torch.floataa							)
        elif beta_schedule == "linear":
               _UpperCAmelCase						:		int      =				torch.linspace(a_						,a_						,a_						,dtype=torch.floataa							)
        elif beta_schedule == "scaled_linear":
               # this schedule is very specific to the latent diffusion model.
               _UpperCAmelCase						:		Optional[int]      =				(
                   torch.linspace(beta_start**0.5						,beta_end**0.5						,a_						,dtype=torch.floataa							) ** 2
               )
        elif beta_schedule == "squaredcos_cap_v2":
               # Glide cosine schedule
               _UpperCAmelCase						:		int      =				betas_for_alpha_bar(a_							)
        else:
               raise NotImplementedError(f'''{beta_schedule} does is not implemented for {self.__class__}'''							)

        _UpperCAmelCase						:		Tuple      =				1.0 - self.betas
        _UpperCAmelCase						:		Tuple      =				torch.cumprod(self.alphas						,dim=0							)

        _UpperCAmelCase						:		List[str]      =				np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5							)
        _UpperCAmelCase						:		List[Any]      =				np.concatenate([sigmas[::-1], [0.0]]							).astype(np.floataa							)
        _UpperCAmelCase						:		Optional[Any]      =				torch.from_numpy(a_							)

        # setable values
        _UpperCAmelCase						:		Tuple      =				None
        _UpperCAmelCase						:		int      =				np.linspace(0						,num_train_timesteps - 1						,a_						,dtype=a_							)[::-1].copy()
        _UpperCAmelCase						:		Dict      =				torch.from_numpy(a_							)
        _UpperCAmelCase						:		Optional[int]      =				False







 @property
 def _snake_case				(							self							)	->     List[str]:
        # 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						,a_						,a_							)	->     torch.FloatTensor:
        if isinstance(a_						,torch.Tensor							):
               _UpperCAmelCase						:		Optional[int]      =				timestep.to(self.timesteps.device							)
        _UpperCAmelCase						:		Any      =				(self.timesteps == timestep).nonzero().item()
        _UpperCAmelCase						:		Dict      =				self.sigmas[step_index]
        _UpperCAmelCase						:		List[str]      =				sample / ((sigma**2 + 1) ** 0.5)
        _UpperCAmelCase						:		str      =				True
        return sample







 def _snake_case				(							self						,a_						,a_ = None							)	->     Optional[Any]:
        _UpperCAmelCase						:		str      =				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						:		Optional[int]      =				np.linspace(0						,self.config.num_train_timesteps - 1						,a_						,dtype=a_							)[
                   ::-1
               ].copy()
        elif self.config.timestep_spacing == "leading":
               _UpperCAmelCase						:		str      =				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						:		int      =				(np.arange(0						,a_							) * step_ratio).round()[::-1].copy().astype(a_							)
               timesteps += self.config.steps_offset
        elif self.config.timestep_spacing == "trailing":
               _UpperCAmelCase						:		List[str]      =				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						:		Union[str, Any]      =				(np.arange(self.config.num_train_timesteps						,0						,-step_ratio							)).round().copy().astype(a_							)
               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						:		Optional[int]      =				np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5							)
        _UpperCAmelCase						:		Optional[int]      =				np.interp(a_						,np.arange(0						,len(a_							)							)						,a_							)
        _UpperCAmelCase						:		Any      =				np.concatenate([sigmas, [0.0]]							).astype(np.floataa							)
        _UpperCAmelCase						:		Optional[Any]      =				torch.from_numpy(a_							).to(device=a_							)
        if str(a_							).startswith("""mps"""							):
               # mps does not support float64
               _UpperCAmelCase						:		Optional[Any]      =				torch.from_numpy(a_							).to(a_						,dtype=torch.floataa							)
        else:
               _UpperCAmelCase						:		Union[str, Any]      =				torch.from_numpy(a_							).to(device=a_							)







 def _snake_case				(							self						,a_						,a_						,a_						,a_ = None						,a_ = True						,)	->     Union[EulerAncestralDiscreteSchedulerOutput, Tuple]:

        if (
            isinstance(a_						,a_							)
            or isinstance(a_						,torch.IntTensor							)
            or isinstance(a_						,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(a_						,torch.Tensor							):
               _UpperCAmelCase						:		Union[str, Any]      =				timestep.to(self.timesteps.device							)

        _UpperCAmelCase						:		List[str]      =				(self.timesteps == timestep).nonzero().item()
        _UpperCAmelCase						:		Any      =				self.sigmas[step_index]

        # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
        if self.config.prediction_type == "epsilon":
               _UpperCAmelCase						:		Optional[int]      =				sample - sigma * model_output
        elif self.config.prediction_type == "v_prediction":
               # * c_out + input * c_skip
               _UpperCAmelCase						:		Union[str, Any]      =				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						:		List[Any]      =				self.sigmas[step_index]
        _UpperCAmelCase						:		List[Any]      =				self.sigmas[step_index + 1]
        _UpperCAmelCase						:		List[str]      =				(sigma_to**2 * (sigma_from**2 - sigma_to**2) / sigma_from**2) ** 0.5
        _UpperCAmelCase						:		Any      =				(sigma_to**2 - sigma_up**2) ** 0.5

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

        _UpperCAmelCase						:		List[str]      =				sigma_down - sigma

        _UpperCAmelCase						:		List[str]      =				sample + derivative * dt

        _UpperCAmelCase						:		Optional[int]      =				model_output.device
        _UpperCAmelCase						:		Union[str, Any]      =				randn_tensor(model_output.shape						,dtype=model_output.dtype						,device=a_						,generator=a_							)

        _UpperCAmelCase						:		Optional[int]      =				prev_sample + noise * sigma_up

        if not return_dict:
               return (prev_sample,)

        return EulerAncestralDiscreteSchedulerOutput(
            prev_sample=a_						,pred_original_sample=a_							)







 def _snake_case				(							self						,a_						,a_						,a_						,)	->     torch.FloatTensor:
        # Make sure sigmas and timesteps have the same device and dtype as original_samples
        _UpperCAmelCase						:		Dict      =				self.sigmas.to(device=original_samples.device						,dtype=original_samples.dtype							)
        if original_samples.device.type == "mps" and torch.is_floating_point(a_							):
               # mps does not support float64
               _UpperCAmelCase						:		Tuple      =				self.timesteps.to(original_samples.device						,dtype=torch.floataa							)
               _UpperCAmelCase						:		Optional[Any]      =				timesteps.to(original_samples.device						,dtype=torch.floataa							)
        else:
               _UpperCAmelCase						:		Optional[Any]      =				self.timesteps.to(original_samples.device							)
               _UpperCAmelCase						:		int      =				timesteps.to(original_samples.device							)

        _UpperCAmelCase						:		Any      =				[(schedule_timesteps == t).nonzero().item() for t in timesteps]

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

        _UpperCAmelCase						:		int      =				original_samples + noise * sigma
        return noisy_samples







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



