





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

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


_a									=				logging.get_logger(__name__)

_a									=				{
    "facebook/blenderbot-3B": "https://huggingface.co/facebook/blenderbot-3B/resolve/main/config.json",
    # See all Blenderbot models at https://huggingface.co/models?filter=blenderbot
}






class  __A   (   lowerCAmelCase			):

			'''simple docstring'''







			lowerCAmelCase_												=						"""blenderbot"""
			lowerCAmelCase_												=						["""past_key_values"""]
			lowerCAmelCase_												=						{"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}





			def __init__(   self	,  __lowerCAmelCase=8_0_0_8	,  __lowerCAmelCase=1_2_8	,  __lowerCAmelCase=2	,  __lowerCAmelCase=1_0_2_4_0	,  __lowerCAmelCase=3_2	,  __lowerCAmelCase=2_4	,  __lowerCAmelCase=1_0_2_4_0	,  __lowerCAmelCase=3_2	,  __lowerCAmelCase=0.0	,  __lowerCAmelCase=0.0	,  __lowerCAmelCase=True	,  __lowerCAmelCase=True	,  __lowerCAmelCase="gelu"	,  __lowerCAmelCase=2_5_6_0	,  __lowerCAmelCase=0.1	,  __lowerCAmelCase=0.0	,  __lowerCAmelCase=0.0	,  __lowerCAmelCase=0.02	,  __lowerCAmelCase=1	,  __lowerCAmelCase=False	,  __lowerCAmelCase=0	,  __lowerCAmelCase=1	,  __lowerCAmelCase=2	,  __lowerCAmelCase=3	,  __lowerCAmelCase=2	,  **__lowerCAmelCase	,  ):



				'''simple docstring'''







				lowerCamelCase__        =		vocab_size
				lowerCamelCase__        =		max_position_embeddings
				lowerCamelCase__        =		d_model
				lowerCamelCase__        =		encoder_ffn_dim
				lowerCamelCase__        =		encoder_layers
				lowerCamelCase__        =		encoder_attention_heads
				lowerCamelCase__        =		decoder_ffn_dim
				lowerCamelCase__        =		decoder_layers
				lowerCamelCase__        =		decoder_attention_heads
				lowerCamelCase__        =		dropout
				lowerCamelCase__        =		attention_dropout
				lowerCamelCase__        =		activation_dropout
				lowerCamelCase__        =		activation_function
				lowerCamelCase__        =		init_std
				lowerCamelCase__        =		encoder_layerdrop
				lowerCamelCase__        =		decoder_layerdrop
				lowerCamelCase__        =		use_cache
				lowerCamelCase__        =		encoder_layers
				lowerCamelCase__        =		scale_embedding  # scale factor will be sqrt(d_model) if True

				super().__init__(
				    pad_token_id=__lowerCAmelCase	,  bos_token_id=__lowerCAmelCase	,  eos_token_id=__lowerCAmelCase	,  is_encoder_decoder=__lowerCAmelCase	,  decoder_start_token_id=__lowerCAmelCase	,  encoder_no_repeat_ngram_size=__lowerCAmelCase	,  forced_eos_token_id=__lowerCAmelCase	,  **__lowerCAmelCase	,  )






class  __A   (   lowerCAmelCase			):

			'''simple docstring'''







			@property
			def 		__lowerCamelCase    (   self							):



				'''simple docstring'''







				if self.task in ["default", "seq2seq-lm"]:
					lowerCamelCase__        =		OrderedDict(
					    [
					        ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
					        ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
					    ]							)
					if self.use_past:
						lowerCamelCase__        =		{0: '''batch'''}
						lowerCamelCase__        =		{0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
					else:
						lowerCamelCase__        =		{0: '''batch''', 1: '''decoder_sequence'''}
						lowerCamelCase__        =		{0: '''batch''', 1: '''decoder_sequence'''}
					if self.use_past:
						self.fill_with_past_key_values_(__lowerCAmelCase	,  direction='''inputs'''							)
				elif self.task == "causal-lm":
					lowerCamelCase__        =		OrderedDict(
					    [
					        ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
					        ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
					    ]							)
					if self.use_past:
						lowerCamelCase__    ,     lowerCamelCase__        =		self.num_layers
						for i in range(__lowerCAmelCase							):
							lowerCamelCase__        =		{0: '''batch''', 2: '''past_sequence + sequence'''}
							lowerCamelCase__        =		{0: '''batch''', 2: '''past_sequence + sequence'''}
				else:
					lowerCamelCase__        =		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
			# Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.outputs
			def 		__lowerCamelCase    (   self							):



				'''simple docstring'''







				if self.task in ["default", "seq2seq-lm"]:
					lowerCamelCase__        =		super().outputs
				else:
					lowerCamelCase__        =		super(__lowerCAmelCase	,  self							).outputs
					if self.use_past:
						lowerCamelCase__    ,     lowerCamelCase__        =		self.num_layers
						for i in range(__lowerCAmelCase							):
							lowerCamelCase__        =		{0: '''batch''', 2: '''past_sequence + sequence'''}
							lowerCamelCase__        =		{0: '''batch''', 2: '''past_sequence + sequence'''}
				return common_outputs





			def 		__lowerCamelCase    (   self	,  __lowerCAmelCase	,  __lowerCAmelCase = -1	,  __lowerCAmelCase = -1	,  __lowerCAmelCase = False	,  __lowerCAmelCase = None	,  ):



				'''simple docstring'''







				lowerCamelCase__        =		self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
				    __lowerCAmelCase	,  __lowerCAmelCase	,  __lowerCAmelCase	,  __lowerCAmelCase	,  __lowerCAmelCase							)
				# Generate decoder inputs
				lowerCamelCase__        =		seq_length if not self.use_past else 1
				lowerCamelCase__        =		self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
				    __lowerCAmelCase	,  __lowerCAmelCase	,  __lowerCAmelCase	,  __lowerCAmelCase	,  __lowerCAmelCase							)
				lowerCamelCase__        =		{F'decoder_{name}': tensor for name, tensor in decoder_inputs.items()}
				lowerCamelCase__        =		dict(**__lowerCAmelCase	,  **__lowerCAmelCase							)

				if self.use_past:
					if not is_torch_available():
						raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.'''							)
					else:
						import torch
					lowerCamelCase__    ,     lowerCamelCase__        =		common_inputs['''input_ids'''].shape
					lowerCamelCase__        =		common_inputs['''decoder_input_ids'''].shape[1]
					lowerCamelCase__    ,     lowerCamelCase__        =		self.num_attention_heads
					lowerCamelCase__        =		(
					    batch,
					    num_encoder_attention_heads,
					    encoder_seq_length,
					    self._config.hidden_size // num_encoder_attention_heads,
					)
					lowerCamelCase__        =		decoder_seq_length
					lowerCamelCase__        =		(
					    batch,
					    num_decoder_attention_heads,
					    decoder_past_length,
					    self._config.hidden_size // num_decoder_attention_heads,
					)
					lowerCamelCase__        =		torch.cat(
					    [common_inputs['''decoder_attention_mask'''], torch.ones(__lowerCAmelCase	,  __lowerCAmelCase							)]	,  dim=1							)
					lowerCamelCase__        =		[]
					lowerCamelCase__    ,     lowerCamelCase__        =		self.num_layers

					for _ in range(__lowerCAmelCase							):
						common_inputs["past_key_values"].append(
						    (
						        torch.zeros(__lowerCAmelCase							),
						        torch.zeros(__lowerCAmelCase							),
						        torch.zeros(__lowerCAmelCase							),
						        torch.zeros(__lowerCAmelCase							),
						    )							)
				return common_inputs





			def 		__lowerCamelCase    (   self	,  __lowerCAmelCase	,  __lowerCAmelCase = -1	,  __lowerCAmelCase = -1	,  __lowerCAmelCase = False	,  __lowerCAmelCase = None	,  ):



				'''simple docstring'''







				lowerCamelCase__        =		self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
				    __lowerCAmelCase	,  __lowerCAmelCase	,  __lowerCAmelCase	,  __lowerCAmelCase	,  __lowerCAmelCase							)

				if self.use_past:
					if not is_torch_available():
						raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.'''							)
					else:
						import torch
					lowerCamelCase__    ,     lowerCamelCase__        =		common_inputs['''input_ids'''].shape
					lowerCamelCase__        =		seqlen
					lowerCamelCase__    ,     lowerCamelCase__        =		self.num_layers
					lowerCamelCase__    ,     lowerCamelCase__        =		self.num_attention_heads
					lowerCamelCase__        =		(
					    batch,
					    num_encoder_attention_heads,
					    past_key_values_length,
					    self._config.hidden_size // num_encoder_attention_heads,
					)
					lowerCamelCase__        =		common_inputs['''attention_mask'''].dtype
					lowerCamelCase__        =		torch.cat(
					    [common_inputs['''attention_mask'''], torch.ones(__lowerCAmelCase	,  __lowerCAmelCase	,  dtype=__lowerCAmelCase							)]	,  dim=1							)
					lowerCamelCase__        =		[
					    (torch.zeros(__lowerCAmelCase							), torch.zeros(__lowerCAmelCase							)) for _ in range(__lowerCAmelCase							)
					]
				return common_inputs





			def 		__lowerCamelCase    (   self	,  __lowerCAmelCase	,  __lowerCAmelCase = -1	,  __lowerCAmelCase = -1	,  __lowerCAmelCase = False	,  __lowerCAmelCase = None	,  ):



				'''simple docstring'''







				lowerCamelCase__        =		compute_effective_axis_dimension(
				    __lowerCAmelCase	,  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
				lowerCamelCase__        =		tokenizer.num_special_tokens_to_add(__lowerCAmelCase							)
				lowerCamelCase__        =		compute_effective_axis_dimension(
				    __lowerCAmelCase	,  fixed_dimension=OnnxConfig.default_fixed_sequence	,  num_token_to_add=__lowerCAmelCase							)

				# Generate dummy inputs according to compute batch and sequence
				lowerCamelCase__        =		[''' '''.join([tokenizer.unk_token]							) * seq_length] * batch_size
				lowerCamelCase__        =		dict(tokenizer(__lowerCAmelCase	,  return_tensors=__lowerCAmelCase							)							)
				return common_inputs





			def 		__lowerCamelCase    (   self	,  __lowerCAmelCase	,  __lowerCAmelCase = -1	,  __lowerCAmelCase = -1	,  __lowerCAmelCase = False	,  __lowerCAmelCase = None	,  ):



				'''simple docstring'''







				if self.task in ["default", "seq2seq-lm"]:
					lowerCamelCase__        =		self._generate_dummy_inputs_for_default_and_seqaseq_lm(
					    __lowerCAmelCase	,  batch_size=__lowerCAmelCase	,  seq_length=__lowerCAmelCase	,  is_pair=__lowerCAmelCase	,  framework=__lowerCAmelCase							)

				elif self.task == "causal-lm":
					lowerCamelCase__        =		self._generate_dummy_inputs_for_causal_lm(
					    __lowerCAmelCase	,  batch_size=__lowerCAmelCase	,  seq_length=__lowerCAmelCase	,  is_pair=__lowerCAmelCase	,  framework=__lowerCAmelCase							)
				else:
					lowerCamelCase__        =		self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
					    __lowerCAmelCase	,  batch_size=__lowerCAmelCase	,  seq_length=__lowerCAmelCase	,  is_pair=__lowerCAmelCase	,  framework=__lowerCAmelCase							)

				return common_inputs





			def 		__lowerCamelCase    (   self	,  __lowerCAmelCase	,  __lowerCAmelCase	,  __lowerCAmelCase	,  __lowerCAmelCase							):



				'''simple docstring'''







				if self.task in ["default", "seq2seq-lm"]:
					lowerCamelCase__        =		super()._flatten_past_key_values_(__lowerCAmelCase	,  __lowerCAmelCase	,  __lowerCAmelCase	,  __lowerCAmelCase							)
				else:
					lowerCamelCase__        =		super(__lowerCAmelCase	,  self							)._flatten_past_key_values_(
					    __lowerCAmelCase	,  __lowerCAmelCase	,  __lowerCAmelCase	,  __lowerCAmelCase							)





			def 		__lowerCamelCase    (   self	,  __lowerCAmelCase	,  __lowerCAmelCase							):



				'''simple docstring'''







				if direction not in ["inputs", "outputs"]:
					raise ValueError(F'direction must either be "inputs" or "outputs", but {direction} was given'							)

				lowerCamelCase__        =		'''past_key_values''' if direction == '''inputs''' else '''present'''
				lowerCamelCase__    ,     lowerCamelCase__        =		self.num_layers

				lowerCamelCase__        =		'''past_encoder_sequence'''
				lowerCamelCase__        =		'''past_decoder_sequence''' if direction == '''inputs''' else '''past_decoder_sequence + sequence'''

				for i in range(__lowerCAmelCase							):
					lowerCamelCase__        =		{0: '''batch''', 2: decoder_sequence}
					lowerCamelCase__        =		{0: '''batch''', 2: decoder_sequence}
					lowerCamelCase__        =		{0: '''batch''', 2: encoder_sequence}
					lowerCamelCase__        =		{0: '''batch''', 2: encoder_sequence}










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






@dataclass
class  __A   (   lowerCAmelCase			):

			'''simple docstring'''







			lowerCAmelCase_												=						42






class  __A   (   lowerCAmelCase      ,    lowerCAmelCase			):

			'''simple docstring'''







			lowerCAmelCase_												=						1





			@register_to_config
			def __init__(   self	,  __lowerCAmelCase = 4_0	,  __lowerCAmelCase = 0.002	,  __lowerCAmelCase = 80.0	,  __lowerCAmelCase = 0.5	,  __lowerCAmelCase = 1.0	,  __lowerCAmelCase = 7.0	,  __lowerCAmelCase = True	,  ):



				'''simple docstring'''







				lowerCamelCase__        =		sigma_max

				lowerCamelCase__        =		np.linspace(0	,  1	,  __lowerCAmelCase							)
				lowerCamelCase__        =		self._convert_to_karras(__lowerCAmelCase							)
				lowerCamelCase__        =		self.sigma_to_t(__lowerCAmelCase							)

				# setable values
				lowerCamelCase__        =		None
				lowerCamelCase__        =		torch.from_numpy(__lowerCAmelCase							)
				lowerCamelCase__        =		torch.from_numpy(__lowerCAmelCase							)
				lowerCamelCase__        =		False
				lowerCamelCase__        =		False





			def 		__lowerCamelCase    (   self	,  __lowerCAmelCase	,  __lowerCAmelCase=None							):



				'''simple docstring'''







				if schedule_timesteps is None:
					lowerCamelCase__        =		self.timesteps

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





			def 		__lowerCamelCase    (   self	,  __lowerCAmelCase	,  __lowerCAmelCase							):



				'''simple docstring'''







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

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

				lowerCamelCase__        =		True
				return sample





			def 		__lowerCamelCase    (   self	,  __lowerCAmelCase							):



				'''simple docstring'''







				if not isinstance(__lowerCAmelCase	,  np.ndarray							):
					lowerCamelCase__        =		np.array(__lowerCAmelCase	,  dtype=np.floataa							)

				lowerCamelCase__        =		1_0_0_0 * 0.25 * np.log(sigmas + 1E-44							)

				return timesteps





			def 		__lowerCamelCase    (   self	,  __lowerCAmelCase = None	,  __lowerCAmelCase = None	,  __lowerCAmelCase = None	,  ):



				'''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(__lowerCAmelCase							)							):
						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}.'							)

					lowerCamelCase__        =		np.array(__lowerCAmelCase	,  dtype=np.intaa							)
					lowerCamelCase__        =		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.'							)

					lowerCamelCase__        =		num_inference_steps

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

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

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

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





			def 		__lowerCamelCase    (   self	,  __lowerCAmelCase							):



				'''simple docstring'''







				lowerCamelCase__        =		self.config.sigma_min
				lowerCamelCase__        =		self.config.sigma_max

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





			def 		__lowerCamelCase    (   self	,  __lowerCAmelCase							):



				'''simple docstring'''







				lowerCamelCase__        =		self.config.sigma_data

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





			def 		__lowerCamelCase    (   self	,  __lowerCAmelCase							):



				'''simple docstring'''







				lowerCamelCase__        =		self.config.sigma_min
				lowerCamelCase__        =		self.config.sigma_data

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





			def 		__lowerCamelCase    (   self	,  __lowerCAmelCase	,  __lowerCAmelCase	,  __lowerCAmelCase	,  __lowerCAmelCase = None	,  __lowerCAmelCase = True	,  ):



				'''simple docstring'''







				if (
				    isinstance(__lowerCAmelCase	,  __lowerCAmelCase							)
				    or isinstance(__lowerCAmelCase	,  torch.IntTensor							)
				    or isinstance(__lowerCAmelCase	,  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(__lowerCAmelCase	,  torch.Tensor							):
					lowerCamelCase__        =		timestep.to(self.timesteps.device							)

				lowerCamelCase__        =		self.config.sigma_min
				lowerCamelCase__        =		self.config.sigma_max

				lowerCamelCase__        =		self.index_for_timestep(__lowerCAmelCase							)

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

				# Get scalings for boundary conditions
				lowerCamelCase__    ,     lowerCamelCase__        =		self.get_scalings_for_boundary_condition(__lowerCAmelCase							)

				# 1. Denoise model output using boundary conditions
				lowerCamelCase__        =		c_out * model_output + c_skip * sample
				if self.config.clip_denoised:
					lowerCamelCase__        =		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:
					lowerCamelCase__        =		randn_tensor(
					    model_output.shape	,  dtype=model_output.dtype	,  device=model_output.device	,  generator=__lowerCAmelCase							)
				else:
					lowerCamelCase__        =		torch.zeros_like(__lowerCAmelCase							)
				lowerCamelCase__        =		noise * self.config.s_noise

				lowerCamelCase__        =		sigma_next.clamp(min=__lowerCAmelCase	,  max=__lowerCAmelCase							)

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

				if not return_dict:
					return (prev_sample,)

				return CMStochasticIterativeSchedulerOutput(prev_sample=__lowerCAmelCase							)





			def 		__lowerCamelCase    (   self	,  __lowerCAmelCase	,  __lowerCAmelCase	,  __lowerCAmelCase	,  ):



				'''simple docstring'''







				lowerCamelCase__        =		self.sigmas.to(device=original_samples.device	,  dtype=original_samples.dtype							)
				if original_samples.device.type == "mps" and torch.is_floating_point(__lowerCAmelCase							):
					# mps does not support float64
					lowerCamelCase__        =		self.timesteps.to(original_samples.device	,  dtype=torch.floataa							)
					lowerCamelCase__        =		timesteps.to(original_samples.device	,  dtype=torch.floataa							)
				else:
					lowerCamelCase__        =		self.timesteps.to(original_samples.device							)
					lowerCamelCase__        =		timesteps.to(original_samples.device							)

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

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

				lowerCamelCase__        =		original_samples + noise * sigma
				return noisy_samples





			def __len__(   self							):



				'''simple docstring'''







				return self.config.num_train_timesteps



