import argparse
import json
import os
import re
from collections import OrderedDict
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from pprint import pprint
from typing import Dict, List, Tuple

import torch
import torch.nn as nn
from classy_vision.models.regnet import RegNet, RegNetParams
from huggingface_hub import cached_download, hf_hub_url
from torch import Tensor
from vissl.models.model_helpers import get_trunk_forward_outputs

from transformers import AutoImageProcessor, RegNetConfig, RegNetForImageClassification, RegNetModel
from transformers.modeling_utils import PreTrainedModel
from transformers.utils import logging


logging.set_verbosity_info()
lowerCAmelCase      :				Union[str, Any]     			=logging.get_logger()




@dataclass
class 						__snake_case						:





      '''simple docstring'''






      _snake_case     		=							42
      _snake_case     		=							field(default_factory=lowerCAmelCase__		)
      _snake_case     		=							field(default_factory=lowerCAmelCase__		)
      _snake_case     		=							field(default_factory=lowerCAmelCase__		)



      def 						_SCREAMING_SNAKE_CASE     (       self  :					Optional[Any]     ,	_UpperCamelCase  :					Tuple     ,	_UpperCamelCase  :					Tensor     ,	_UpperCamelCase  :					Tensor     ,	_UpperCamelCase  :					str)					->List[Any]:


          """simple docstring"""


          _lowerCamelCase  :		List[str]			    =    len(list(m.modules())) == 1 or isinstance(_lowercase     ,	nn.Convad) or isinstance(_lowercase     ,	nn.BatchNormad)
          if has_not_submodules:
              self.traced.append(_lowercase)
              _lowerCamelCase  :		Optional[int]			    =    m



      def __call__(       self  :					Optional[Any]     ,	_UpperCamelCase  :					Tensor)					->List[Any]:


          """simple docstring"""


          for name, m in self.module.named_modules():
              self.handles.append(m.register_forward_hook(partial(self._forward_hook     ,	name=_lowercase)))
          self.module(_lowercase)
          [x.remove() for x in self.handles]
          return self




      @property
      def 						_SCREAMING_SNAKE_CASE     (       self  :					Dict)					->List[Any]:


          """simple docstring"""


          return {k: v for k, v in self.nameamodule.items() if len(list(v.state_dict().keys())) > 0}





class 						__snake_case						(			nn.Module		):





      '''simple docstring'''






      def __init__(       self  :					Optional[int]     ,	_UpperCamelCase  :					nn.Module)					->str:


          """simple docstring"""


          super().__init__()

          _lowerCamelCase  :		int			    =    []
          # - get the stem
          feature_blocks.append(("""conv1""", model.stem))
          # - get all the feature blocks
          for k, v in model.trunk_output.named_children():
              assert k.startswith("""block"""), F"""Unexpected layer name {k}"""
              _lowerCamelCase  :		Optional[Any]			    =    len(_lowercase) + 1
              feature_blocks.append((F"""res{block_index}""", v))

          _lowerCamelCase  :		str			    =    nn.ModuleDict(_lowercase)




      def 						_SCREAMING_SNAKE_CASE     (       self  :					List[str]     ,	_UpperCamelCase  :					Tensor)					->Optional[int]:


          """simple docstring"""


          return get_trunk_forward_outputs(
              _lowercase     ,	out_feat_keys=_lowercase     ,	feature_blocks=self._feature_blocks     ,	)





class 						__snake_case						(			lowerCAmelCase__		):





      '''simple docstring'''






      def 						_SCREAMING_SNAKE_CASE     (       self  :					Dict)					->Dict:


          """simple docstring"""


          return [(8, 2, 2, 8, 1.0), (8, 2, 7, 8, 1.0), (8, 2, 17, 8, 1.0), (8, 2, 1, 8, 1.0)]







def     A__	(       __A						):







    '''simple docstring'''
    _lowerCamelCase  :		List[Any]			    =    RegNetConfig(depths=[2, 7, 17, 1] ,			hidden_sizes=[8, 8, 8, 8] ,			groups_width=8						)
    if "in1k" in model_name:
        _lowerCamelCase  :		Dict			    =    RegNetForImageClassification(UpperCamelCase__						)
    else:
        _lowerCamelCase  :		Optional[Any]			    =    RegNetModel(UpperCamelCase__						)
    # create from model (with small weights)
    _lowerCamelCase  :		Tuple			    =    FakeRegNetVisslWrapper(
        RegNet(FakeRegNetParams(depth=27 ,			group_width=1_010 ,			w_a=1_744 ,			w_a=620.83 ,			w_m=2.52						)						)						)

    with torch.no_grad():
        _lowerCamelCase  :		Union[str, Any]			    =    from_model.eval()
        _lowerCamelCase  :		Optional[int]			    =    our_model.eval()

        _lowerCamelCase  :		Optional[int]			    =    torch.randn((1, 3, 32, 32)						)
        # trace both
        _lowerCamelCase  :		Union[str, Any]			    =    Tracker(UpperCamelCase__						)
        _lowerCamelCase  :		Tuple			    =    dest_tracker(UpperCamelCase__						).parametrized

        pprint(dest_tracker.nameamodule						)
        _lowerCamelCase  :		Optional[Any]			    =    Tracker(UpperCamelCase__						)
        _lowerCamelCase  :		List[Any]			    =    src_tracker(UpperCamelCase__						).parametrized

    # convert the keys -> module dict to keys -> params
    def to_params_dict(__A						):
        _lowerCamelCase  :		Union[str, Any]			    =    OrderedDict()
        for name, module in dict_with_modules.items():
            for param_name, param in module.state_dict().items():
                _lowerCamelCase  :		int			    =    param
        return params_dict

    _lowerCamelCase  :		Dict			    =    {}

    _lowerCamelCase  :		Optional[Any]			    =    to_params_dict(UpperCamelCase__						)
    _lowerCamelCase  :		Dict			    =    to_params_dict(UpperCamelCase__						)

    for (src_key, src_param), (dest_key, dest_param) in zip(src_state_dict.items() ,			dst_state_dict.items()						):
        _lowerCamelCase  :		Dict			    =    dest_key
        logger.info(F"""{src_key} -> {dest_key}"""						)
    # if "in1k" was in the model_name it means it must have a classification head (was finetuned)
    if "in1k" in model_name:
        _lowerCamelCase  :		Optional[Any]			    =    """classifier.1.weight"""
        _lowerCamelCase  :		Tuple			    =    """classifier.1.bias"""

    return from_to_ours_keys







def     A__	(       __A ,			__A = None ,			__A = True						):







    '''simple docstring'''
    _lowerCamelCase  :		Tuple			    =    """imagenet-1k-id2label.json"""
    _lowerCamelCase  :		Optional[int]			    =    1_000

    _lowerCamelCase  :		Optional[Any]			    =    """huggingface/label-files"""
    _lowerCamelCase  :		List[str]			    =    num_labels
    _lowerCamelCase  :		Optional[int]			    =    json.load(open(cached_download(hf_hub_url(UpperCamelCase__ ,			UpperCamelCase__ ,			repo_type="""dataset"""						)						) ,			"""r"""						)						)
    _lowerCamelCase  :		Optional[Any]			    =    {int(UpperCamelCase__						): v for k, v in idalabel.items()}

    _lowerCamelCase  :		Dict			    =    idalabel
    _lowerCamelCase  :		Dict			    =    {v: k for k, v in idalabel.items()}

    _lowerCamelCase  :		List[str]			    =    partial(UpperCamelCase__ ,			num_labels=UpperCamelCase__ ,			idalabel=UpperCamelCase__ ,			labelaid=UpperCamelCase__						)

    _lowerCamelCase  :		Tuple			    =    {
        """regnet-y-10b-seer""": ImageNetPreTrainedConfig(
            depths=[2, 7, 17, 1] ,			hidden_sizes=[2_020, 4_040, 11_110, 28_280] ,			groups_width=1_010						),
        # finetuned on imagenet
        """regnet-y-10b-seer-in1k""": ImageNetPreTrainedConfig(
            depths=[2, 7, 17, 1] ,			hidden_sizes=[2_020, 4_040, 11_110, 28_280] ,			groups_width=1_010						),
    }

    # add seer weights logic
    def load_using_classy_vision(__A						) -> Tuple[Dict, Dict]:
        _lowerCamelCase  :		Union[str, Any]			    =    torch.hub.load_state_dict_from_url(UpperCamelCase__ ,			model_dir=str(UpperCamelCase__						) ,			map_location="""cpu"""						)
        # check if we have a head, if yes add it
        _lowerCamelCase  :		List[str]			    =    files["""classy_state_dict"""]["""base_model"""]["""model"""]
        return model_state_dict["trunk"], model_state_dict["heads"]

    _lowerCamelCase  :		Optional[Any]			    =    {
        """regnet-y-10b-seer""": partial(
            UpperCamelCase__ ,			"""https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet10B/model_iteration124500_conso.torch""" ,			),
        """regnet-y-10b-seer-in1k""": partial(
            UpperCamelCase__ ,			"""https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_10b_finetuned_in1k_model_phase28_conso.torch""" ,			),
    }

    _lowerCamelCase  :		List[Any]			    =    get_from_to_our_keys(UpperCamelCase__						)

    if not (save_directory / F"""{model_name}.pth""").exists():
        logger.info("""Loading original state_dict."""						)
        _lowerCamelCase    ,				_lowerCamelCase  :		int			    =    names_to_from_model[model_name]()
        _lowerCamelCase  :		Tuple			    =    from_state_dict_trunk
        if "in1k" in model_name:
            # add the head
            _lowerCamelCase  :		Optional[Any]			    =    {**from_state_dict_trunk, **from_state_dict_head}
        logger.info("""Done!"""						)

        _lowerCamelCase  :		str			    =    {}

        _lowerCamelCase  :		int			    =    list(from_state_dict.keys()						)
        _lowerCamelCase  :		Dict			    =    r"""\.block.-part."""
        # this is "interesting", so the original checkpoints have `block[0,1]-part` in each key name, we remove it
        for key in from_state_dict.keys():
            # remove the weird "block[0,1]-part" from the key
            _lowerCamelCase  :		Union[str, Any]			    =    re.sub(UpperCamelCase__ ,			"""""" ,			UpperCamelCase__						)
            # now src_key from the model checkpoints is the one we got from the original model after tracing, so use it to get the correct destination key
            _lowerCamelCase  :		Union[str, Any]			    =    from_to_ours_keys[src_key]
            # store the parameter with our key
            _lowerCamelCase  :		Tuple			    =    from_state_dict[key]
            not_used_keys.remove(UpperCamelCase__						)
        # check that all keys have been updated
        assert len(UpperCamelCase__						) == 0, F"""Some keys where not used {','.join(UpperCamelCase__						)}"""

        logger.info(F"""The following keys were not used: {','.join(UpperCamelCase__						)}"""						)

        # save our state dict to disk
        torch.save(UpperCamelCase__ ,			save_directory / F"""{model_name}.pth"""						)

        del converted_state_dict
    else:
        logger.info("""The state_dict was already stored on disk."""						)
    if push_to_hub:
        logger.info(F"""Token is {os.environ['HF_TOKEN']}"""						)
        logger.info("""Loading our model."""						)
        # create our model
        _lowerCamelCase  :		List[str]			    =    names_to_config[model_name]
        _lowerCamelCase  :		Optional[Any]			    =    RegNetModel
        if "in1k" in model_name:
            _lowerCamelCase  :		Dict			    =    RegNetForImageClassification
        _lowerCamelCase  :		Optional[Any]			    =    our_model_func(UpperCamelCase__						)
        # place our model to the meta device (so remove all the weights)
        our_model.to(torch.device("""meta"""						)						)
        logger.info("""Loading state_dict in our model."""						)
        # load state dict
        _lowerCamelCase  :		Optional[Any]			    =    our_model.state_dict().keys()
        PreTrainedModel._load_pretrained_model_low_mem(
            UpperCamelCase__ ,			UpperCamelCase__ ,			[save_directory / F"""{model_name}.pth"""]						)
        logger.info("""Finally, pushing!"""						)
        # push it to hub
        our_model.push_to_hub(
            repo_path_or_name=save_directory / model_name ,			commit_message="""Add model""" ,			output_dir=save_directory / model_name ,			)
        _lowerCamelCase  :		Any			    =    384
        # we can use the convnext one
        _lowerCamelCase  :		Dict			    =    AutoImageProcessor.from_pretrained("""facebook/convnext-base-224-22k-1k""" ,			size=UpperCamelCase__						)
        image_processor.push_to_hub(
            repo_path_or_name=save_directory / model_name ,			commit_message="""Add image processor""" ,			output_dir=save_directory / model_name ,			)


if __name__ == "__main__":
    lowerCAmelCase      :				Any     			=argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        "--model_name",
        default=None,
        type=str,
        help=(
            "The name of the model you wish to convert, it must be one of the supported regnet* architecture,"
            " currently: regnetx-*, regnety-*. If `None`, all of them will the converted."
        ),
    )
    parser.add_argument(
        "--pytorch_dump_folder_path",
        default=None,
        type=Path,
        required=True,
        help="Path to the output PyTorch model directory.",
    )
    parser.add_argument(
        "--push_to_hub",
        default=True,
        type=bool,
        required=False,
        help="If True, push model and image processor to the hub.",
    )

    lowerCAmelCase      :				Tuple     			=parser.parse_args()

    lowerCAmelCase      :				Path     			=args.pytorch_dump_folder_path
    pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
    convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)










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						:		Union[str, Any]          =						logging.get_logger(__name__)  # pylint: disable=invalid-name

@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->EulerDiscrete
class 		lowercase_       (	lowerCAmelCase__				):
					__UpperCamelCase     	= 42
					__UpperCamelCase     	= None





def      UpperCAmelCase  (						UpperCamelCase__						,			UpperCamelCase__=0.999						,			UpperCamelCase__="cosine"						,			)     ->     Optional[int]:







				'''simple docstring'''


				if alpha_transform_type == "cosine":

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

				elif alpha_transform_type == "exp":

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

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

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

class 		lowercase_       (	lowerCAmelCase__						,							lowerCAmelCase__				):
					__UpperCamelCase     	= [e.name for e in KarrasDiffusionSchedulers]
					__UpperCamelCase     	= 1




					@register_to_config
					def __init__(   self:		Any,     _lowercase:		int = 1000,     _lowercase:		float = 0.0_001,     _lowercase:		float = 0.02,     _lowercase:		str = "linear",     _lowercase:		Optional[Union[np.ndarray, List[float]]] = None,     _lowercase:		str = "epsilon",     _lowercase:		str = "linear",     _lowercase:		Optional[bool] = False,     _lowercase:		str = "linspace",     _lowercase:		int = 0,     ):


									'''simple docstring'''


									if trained_betas is not None:
													__lowerCAmelCase					=  torch.tensor(_lowercase,     dtype=torch.floataa)
									elif beta_schedule == "linear":
													__lowerCAmelCase					=  torch.linspace(_lowercase,     _lowercase,     _lowercase,     dtype=torch.floataa)
									elif beta_schedule == "scaled_linear":
													# this schedule is very specific to the latent diffusion model.
													__lowerCAmelCase					=  (
													    torch.linspace(beta_start**0.5,     beta_end**0.5,     _lowercase,     dtype=torch.floataa) ** 2
													)
									elif beta_schedule == "squaredcos_cap_v2":
													# Glide cosine schedule
													__lowerCAmelCase					=  betas_for_alpha_bar(_lowercase)
									else:
													raise NotImplementedError(f'''{beta_schedule} does is not implemented for {self.__class__}''')

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

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

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




					@property
					def     _lowercase						(   self:		Optional[Any]):


									'''simple docstring'''


									if self.config.timestep_spacing in ["linspace", "trailing"]:
													return self.sigmas.max()

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




					def     _lowercase						(   self:		Tuple,     _lowercase:		torch.FloatTensor,     _lowercase:		Union[float, torch.FloatTensor]):


									'''simple docstring'''


									if isinstance(_lowercase,     torch.Tensor):
													__lowerCAmelCase					=  timestep.to(self.timesteps.device)
									__lowerCAmelCase					=  (self.timesteps == timestep).nonzero().item()
									__lowerCAmelCase					=  self.sigmas[step_index]

									__lowerCAmelCase					=  sample / ((sigma**2 + 1) ** 0.5)

									__lowerCAmelCase					=  True
									return sample




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


									'''simple docstring'''


									__lowerCAmelCase					=  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":
													__lowerCAmelCase					=  np.linspace(0,     self.config.num_train_timesteps - 1,     _lowercase,     dtype=_lowercase)[
													    ::-1
													].copy()
									elif self.config.timestep_spacing == "leading":
													__lowerCAmelCase					=  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
													__lowerCAmelCase					=  (np.arange(0,     _lowercase) * step_ratio).round()[::-1].copy().astype(_lowercase)
													timesteps += self.config.steps_offset
									elif self.config.timestep_spacing == "trailing":
													__lowerCAmelCase					=  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
													__lowerCAmelCase					=  (np.arange(self.config.num_train_timesteps,     0,     -step_ratio)).round().copy().astype(_lowercase)
													timesteps -= 1
									else:
													raise ValueError(
													    f'''{self.config.timestep_spacing} is not supported. Please make sure to choose one of \'linspace\', \'leading\' or \'trailing\'.''')

									__lowerCAmelCase					=  np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5)
									__lowerCAmelCase					=  np.log(_lowercase)

									if self.config.interpolation_type == "linear":
													__lowerCAmelCase					=  np.interp(_lowercase,     np.arange(0,     len(_lowercase)),     _lowercase)
									elif self.config.interpolation_type == "log_linear":
													__lowerCAmelCase					=  torch.linspace(np.log(sigmas[-1]),     np.log(sigmas[0]),     num_inference_steps + 1).exp()
									else:
													raise ValueError(
													    f'''{self.config.interpolation_type} is not implemented. Please specify interpolation_type to either'''
													    """ 'linear' or 'log_linear'""")

									if self.use_karras_sigmas:
													__lowerCAmelCase					=  self._convert_to_karras(in_sigmas=_lowercase,     num_inference_steps=self.num_inference_steps)
													__lowerCAmelCase					=  np.array([self._sigma_to_t(_lowercase,     _lowercase) for sigma in sigmas])

									__lowerCAmelCase					=  np.concatenate([sigmas, [0.0]]).astype(np.floataa)
									__lowerCAmelCase					=  torch.from_numpy(_lowercase).to(device=_lowercase)
									if str(_lowercase).startswith("""mps"""):
													# mps does not support float64
													__lowerCAmelCase					=  torch.from_numpy(_lowercase).to(_lowercase,     dtype=torch.floataa)
									else:
													__lowerCAmelCase					=  torch.from_numpy(_lowercase).to(device=_lowercase)




					def     _lowercase						(   self:		List[Any],     _lowercase:		Optional[int],     _lowercase:		Tuple):


									'''simple docstring'''


									__lowerCAmelCase					=  np.log(_lowercase)

									# get distribution
									__lowerCAmelCase					=  log_sigma - log_sigmas[:, np.newaxis]

									# get sigmas range
									__lowerCAmelCase					=  np.cumsum((dists >= 0),     axis=0).argmax(axis=0).clip(max=log_sigmas.shape[0] - 2)
									__lowerCAmelCase					=  low_idx + 1

									__lowerCAmelCase					=  log_sigmas[low_idx]
									__lowerCAmelCase					=  log_sigmas[high_idx]

									# interpolate sigmas
									__lowerCAmelCase					=  (low - log_sigma) / (low - high)
									__lowerCAmelCase					=  np.clip(_lowercase,     0,     1)

									# transform interpolation to time range
									__lowerCAmelCase					=  (1 - w) * low_idx + w * high_idx
									__lowerCAmelCase					=  t.reshape(sigma.shape)
									return t




					def     _lowercase						(   self:		Union[str, Any],     _lowercase:		torch.FloatTensor,     _lowercase:		Union[str, Any]):


									'''simple docstring'''


									__lowerCAmelCase					=  in_sigmas[-1].item()
									__lowerCAmelCase					=  in_sigmas[0].item()

									__lowerCAmelCase					=  7.0  # 7.0 is the value used in the paper
									__lowerCAmelCase					=  np.linspace(0,     1,     _lowercase)
									__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     _lowercase						(   self:		Optional[int],     _lowercase:		torch.FloatTensor,     _lowercase:		Union[float, torch.FloatTensor],     _lowercase:		torch.FloatTensor,     _lowercase:		float = 0.0,     _lowercase:		float = 0.0,     _lowercase:		float = float("""inf"""),     _lowercase:		float = 1.0,     _lowercase:		Optional[torch.Generator] = None,     _lowercase:		bool = True,     ):


									'''simple docstring'''


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

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

									__lowerCAmelCase					=  min(s_churn / (len(self.sigmas) - 1),     2**0.5 - 1) if s_tmin <= sigma <= s_tmax else 0.0

									__lowerCAmelCase					=  randn_tensor(
									    model_output.shape,     dtype=model_output.dtype,     device=model_output.device,     generator=_lowercase)

									__lowerCAmelCase					=  noise * s_noise
									__lowerCAmelCase					=  sigma * (gamma + 1)

									if gamma > 0:
													__lowerCAmelCase					=  sample + eps * (sigma_hat**2 - sigma**2) ** 0.5

									# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
									# NOTE: "original_sample" should not be an expected prediction_type but is left in for
									# backwards compatibility
									if self.config.prediction_type == "original_sample" or self.config.prediction_type == "sample":
													__lowerCAmelCase					=  model_output
									elif self.config.prediction_type == "epsilon":
													__lowerCAmelCase					=  sample - sigma_hat * model_output
									elif self.config.prediction_type == "v_prediction":
													# * c_out + input * c_skip
													__lowerCAmelCase					=  model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
									else:
													raise ValueError(
													    f'''prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`''')

									# 2. Convert to an ODE derivative
									__lowerCAmelCase					=  (sample - pred_original_sample) / sigma_hat

									__lowerCAmelCase					=  self.sigmas[step_index + 1] - sigma_hat

									__lowerCAmelCase					=  sample + derivative * dt

									if not return_dict:
													return (prev_sample,)

									return EulerDiscreteSchedulerOutput(prev_sample=_lowercase,     pred_original_sample=_lowercase)




					def     _lowercase						(   self:		Union[str, Any],     _lowercase:		torch.FloatTensor,     _lowercase:		torch.FloatTensor,     _lowercase:		torch.FloatTensor,     ):


									'''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(_lowercase):
													# 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:		str):


									'''simple docstring'''


									return self.config.num_train_timesteps





