





'''simple docstring'''




import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import Callable, Dict, List, Tuple

import timm
import torch
import torch.nn as nn
from classy_vision.models.regnet import RegNet, RegNetParams, RegNetYaagf, RegNetYaagf, RegNetYaaagf
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.utils import logging


logging.set_verbosity_info()
_SCREAMING_SNAKE_CASE							     =			logging.get_logger()
@dataclass
class 			_lowerCAmelCase		:






     """simple docstring"""






     snake_case_						      =   42
     snake_case_						      =   field(default_factory=A__	)
     snake_case_						      =   field(default_factory=A__	)



     def      lowerCAmelCase			(   self							:			List[Any]   ,       __snake_case							:			Optional[Any]   ,       __snake_case							:			Tensor   ,       __snake_case							:			Tensor						)->	Dict:
        snake_case 						=   len(list(m.modules()						)						) == 1 or isinstance(__snake_case   ,       nn.Convad						) or isinstance(__snake_case   ,       nn.BatchNormad						)
        if has_not_submodules:
           self.traced.append(__snake_case						)



     def __call__(   self							:			int   ,       __snake_case							:			Tensor						)->	Optional[int]:
        for m in self.module.modules():
           self.handles.append(m.register_forward_hook(self._forward_hook						)						)
        self.module(__snake_case						)
        [x.remove() for x in self.handles]
        return self



     @property
     def      lowerCAmelCase			(   self							:			Tuple						)->	int:
        # check the len of the state_dict keys to see if we have learnable params
        return list(filter(lambda __snake_case						: len(list(x.state_dict().keys()						)						) > 0   ,       self.traced						)						)




@dataclass
class 			_lowerCAmelCase		:






     """simple docstring"""






     snake_case_						      =   42
     snake_case_						      =   42
     snake_case_						      =   1
     snake_case_						      =   field(default_factory=A__	)
     snake_case_						      =   field(default_factory=A__	)
     snake_case_						      =   True



     def __call__(   self							:			Optional[int]   ,       __snake_case							:			Tensor						)->	Tuple:
        snake_case 						=   Tracker(self.dest						)(__snake_case						).parametrized
        snake_case 						=   Tracker(self.src						)(__snake_case						).parametrized

        snake_case 						=   list(filter(lambda __snake_case						: type(__snake_case						) not in self.src_skip   ,       __snake_case						)						)
        snake_case 						=   list(filter(lambda __snake_case						: type(__snake_case						) not in self.dest_skip   ,       __snake_case						)						)

        if len(__snake_case						) != len(__snake_case						) and self.raise_if_mismatch:
           raise Exception(
               f'''Numbers of operations are different. Source module has {len(__snake_case						)} operations while'''
               f''' destination module has {len(__snake_case						)}.'''						)

        for dest_m, src_m in zip(__snake_case   ,       __snake_case						):
           dest_m.load_state_dict(src_m.state_dict()						)
           if self.verbose == 1:
              print(f'''Transfered from={src_m} to={dest_m}'''						)




class 			_lowerCAmelCase		(			nn.Module	):






     """simple docstring"""






     def __init__(   self							:			Optional[Any]   ,       __snake_case							:			nn.Module						)->	str:
        super().__init__()

        snake_case 						=   []
        # - 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}'''
           snake_case 						=   len(__snake_case						) + 1
           feature_blocks.append((f'''res{block_index}''', v)						)

        snake_case 						=   nn.ModuleDict(__snake_case						)



     def      lowerCAmelCase			(   self							:			List[str]   ,       __snake_case							:			Tensor						)->	Dict:
        return get_trunk_forward_outputs(
            __snake_case   ,       out_feat_keys=__snake_case   ,       feature_blocks=self._feature_blocks   ,       )




class 			_lowerCAmelCase		(			A__	):






     """simple docstring"""






     def      lowerCAmelCase			(   self							:			List[Any]   ,       __snake_case							:			str						)->	str:
        snake_case 						=   x.split("""-"""						)
        return x_split[0] + x_split[1] + "_" + "".join(x_split[2:]						)



     def __getitem__(   self							:			Optional[int]   ,       __snake_case							:			str						)->	Callable[[], Tuple[nn.Module, Dict]]:
        # default to timm!
        if x not in self:
           snake_case 						=   self.convert_name_to_timm(__snake_case						)
           snake_case 						=   partial(lambda: (timm.create_model(__snake_case   ,       pretrained=__snake_case						).eval(), None)						)

        else:
           snake_case 						=   super().__getitem__(__snake_case						)

        return val




class 			_lowerCAmelCase		(			A__	):






     """simple docstring"""






     def __getitem__(   self							:			int   ,       __snake_case							:			str						)->	Callable[[], nn.Module]:
        if "seer" in x and "in1k" not in x:
           snake_case 						=   RegNetModel
        else:
           snake_case 						=   RegNetForImageClassification
        return val





def 	__lowerCamelCase			(      __lowerCAmelCase       :							List[Any]      ,    __lowerCAmelCase       :							Any      ,    __lowerCAmelCase       :							List[Tuple[str, str]]		)		->  str:
   for from_key, to_key in keys:
      snake_case 						=   from_state_dict[from_key].clone()
      print(F'''Copied key={from_key} to={to_key}'''		)
   return to_state_dict





def 	__lowerCamelCase			(      __lowerCAmelCase       :							str      ,    __lowerCAmelCase       :							Callable[[], nn.Module]      ,    __lowerCAmelCase       :							Callable[[], nn.Module]      ,    __lowerCAmelCase       :							RegNetConfig      ,    __lowerCAmelCase       :							Path      ,    __lowerCAmelCase       :							bool = True      ,    )		->  Union[str, Any]:
   print(F'''Converting {name}...'''		)
   with torch.no_grad():
      snake_case						,    snake_case 						=   from_model_func()
      snake_case 						=   our_model_func(__lowerCAmelCase		).eval()
      snake_case 						=   ModuleTransfer(src=__lowerCAmelCase      ,    dest=__lowerCAmelCase      ,    raise_if_mismatch=__lowerCAmelCase		)
      snake_case 						=   torch.randn((1, 3, 2_24, 2_24)		)
      module_transfer(__lowerCAmelCase		)

   if from_state_dict is not None:
      snake_case 						=   []
      # for seer - in1k finetuned we have to manually copy the head
      if "seer" in name and "in1k" in name:
         snake_case 						=   [("""0.clf.0.weight""", """classifier.1.weight"""), ("""0.clf.0.bias""", """classifier.1.bias""")]
      snake_case 						=   manually_copy_vissl_head(__lowerCAmelCase      ,    our_model.state_dict()      ,    __lowerCAmelCase		)
      our_model.load_state_dict(__lowerCAmelCase		)

   snake_case 						=   our_model(__lowerCAmelCase      ,    output_hidden_states=__lowerCAmelCase		)
   snake_case 						=   (
       our_outputs.logits if isinstance(__lowerCAmelCase      ,    __lowerCAmelCase		) else our_outputs.last_hidden_state
   )

   snake_case 						=   from_model(__lowerCAmelCase		)
   snake_case 						=   from_output[-1] if type(__lowerCAmelCase		) is list else from_output

   # now since I don't want to use any config files, vissl seer model doesn't actually have an head, so let's just check the last hidden state
   if "seer" in name and "in1k" in name:
      snake_case 						=   our_outputs.hidden_states[-1]

   assert torch.allclose(__lowerCAmelCase      ,    __lowerCAmelCase		), "The model logits don't match the original one."

   if push_to_hub:
      our_model.push_to_hub(
          repo_path_or_name=save_directory / name      ,    commit_message="""Add model"""      ,    use_temp_dir=__lowerCAmelCase      ,    )

      snake_case 						=   2_24 if """seer""" not in name else 3_84
      # we can use the convnext one
      snake_case 						=   AutoImageProcessor.from_pretrained("""facebook/convnext-base-224-22k-1k"""      ,    size=__lowerCAmelCase		)
      image_processor.push_to_hub(
          repo_path_or_name=save_directory / name      ,    commit_message="""Add image processor"""      ,    use_temp_dir=__lowerCAmelCase      ,    )

      print(F'''Pushed {name}'''		)





def 	__lowerCamelCase			(      __lowerCAmelCase       :							Path      ,    __lowerCAmelCase       :							str = None      ,    __lowerCAmelCase       :							bool = True		)		->  Optional[int]:
   snake_case 						=   """imagenet-1k-id2label.json"""
   snake_case 						=   10_00
   snake_case 						=   (1, num_labels)

   snake_case 						=   """huggingface/label-files"""
   snake_case 						=   num_labels
   snake_case 						=   json.load(open(cached_download(hf_hub_url(__lowerCAmelCase      ,    __lowerCAmelCase      ,    repo_type="""dataset"""		)		)      ,    """r"""		)		)
   snake_case 						=   {int(__lowerCAmelCase		): v for k, v in idalabel.items()}

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

   snake_case 						=   partial(__lowerCAmelCase      ,    num_labels=__lowerCAmelCase      ,    idalabel=__lowerCAmelCase      ,    labelaid=__lowerCAmelCase		)

   snake_case 						=   {
       """regnet-x-002""": ImageNetPreTrainedConfig(
           depths=[1, 1, 4, 7]      ,    hidden_sizes=[24, 56, 1_52, 3_68]      ,    groups_width=8      ,    layer_type="""x"""		),
       """regnet-x-004""": ImageNetPreTrainedConfig(
           depths=[1, 2, 7, 12]      ,    hidden_sizes=[32, 64, 1_60, 3_84]      ,    groups_width=16      ,    layer_type="""x"""		),
       """regnet-x-006""": ImageNetPreTrainedConfig(
           depths=[1, 3, 5, 7]      ,    hidden_sizes=[48, 96, 2_40, 5_28]      ,    groups_width=24      ,    layer_type="""x"""		),
       """regnet-x-008""": ImageNetPreTrainedConfig(
           depths=[1, 3, 7, 5]      ,    hidden_sizes=[64, 1_28, 2_88, 6_72]      ,    groups_width=16      ,    layer_type="""x"""		),
       """regnet-x-016""": ImageNetPreTrainedConfig(
           depths=[2, 4, 10, 2]      ,    hidden_sizes=[72, 1_68, 4_08, 9_12]      ,    groups_width=24      ,    layer_type="""x"""		),
       """regnet-x-032""": ImageNetPreTrainedConfig(
           depths=[2, 6, 15, 2]      ,    hidden_sizes=[96, 1_92, 4_32, 10_08]      ,    groups_width=48      ,    layer_type="""x"""		),
       """regnet-x-040""": ImageNetPreTrainedConfig(
           depths=[2, 5, 14, 2]      ,    hidden_sizes=[80, 2_40, 5_60, 13_60]      ,    groups_width=40      ,    layer_type="""x"""		),
       """regnet-x-064""": ImageNetPreTrainedConfig(
           depths=[2, 4, 10, 1]      ,    hidden_sizes=[1_68, 3_92, 7_84, 16_24]      ,    groups_width=56      ,    layer_type="""x"""		),
       """regnet-x-080""": ImageNetPreTrainedConfig(
           depths=[2, 5, 15, 1]      ,    hidden_sizes=[80, 2_40, 7_20, 19_20]      ,    groups_width=1_20      ,    layer_type="""x"""		),
       """regnet-x-120""": ImageNetPreTrainedConfig(
           depths=[2, 5, 11, 1]      ,    hidden_sizes=[2_24, 4_48, 8_96, 22_40]      ,    groups_width=1_12      ,    layer_type="""x"""		),
       """regnet-x-160""": ImageNetPreTrainedConfig(
           depths=[2, 6, 13, 1]      ,    hidden_sizes=[2_56, 5_12, 8_96, 20_48]      ,    groups_width=1_28      ,    layer_type="""x"""		),
       """regnet-x-320""": ImageNetPreTrainedConfig(
           depths=[2, 7, 13, 1]      ,    hidden_sizes=[3_36, 6_72, 13_44, 25_20]      ,    groups_width=1_68      ,    layer_type="""x"""		),
       # y variant
       """regnet-y-002""": ImageNetPreTrainedConfig(depths=[1, 1, 4, 7]      ,    hidden_sizes=[24, 56, 1_52, 3_68]      ,    groups_width=8		),
       """regnet-y-004""": ImageNetPreTrainedConfig(
           depths=[1, 3, 6, 6]      ,    hidden_sizes=[48, 1_04, 2_08, 4_40]      ,    groups_width=8		),
       """regnet-y-006""": ImageNetPreTrainedConfig(
           depths=[1, 3, 7, 4]      ,    hidden_sizes=[48, 1_12, 2_56, 6_08]      ,    groups_width=16		),
       """regnet-y-008""": ImageNetPreTrainedConfig(
           depths=[1, 3, 8, 2]      ,    hidden_sizes=[64, 1_28, 3_20, 7_68]      ,    groups_width=16		),
       """regnet-y-016""": ImageNetPreTrainedConfig(
           depths=[2, 6, 17, 2]      ,    hidden_sizes=[48, 1_20, 3_36, 8_88]      ,    groups_width=24		),
       """regnet-y-032""": ImageNetPreTrainedConfig(
           depths=[2, 5, 13, 1]      ,    hidden_sizes=[72, 2_16, 5_76, 15_12]      ,    groups_width=24		),
       """regnet-y-040""": ImageNetPreTrainedConfig(
           depths=[2, 6, 12, 2]      ,    hidden_sizes=[1_28, 1_92, 5_12, 10_88]      ,    groups_width=64		),
       """regnet-y-064""": ImageNetPreTrainedConfig(
           depths=[2, 7, 14, 2]      ,    hidden_sizes=[1_44, 2_88, 5_76, 12_96]      ,    groups_width=72		),
       """regnet-y-080""": ImageNetPreTrainedConfig(
           depths=[2, 4, 10, 1]      ,    hidden_sizes=[1_68, 4_48, 8_96, 20_16]      ,    groups_width=56		),
       """regnet-y-120""": ImageNetPreTrainedConfig(
           depths=[2, 5, 11, 1]      ,    hidden_sizes=[2_24, 4_48, 8_96, 22_40]      ,    groups_width=1_12		),
       """regnet-y-160""": ImageNetPreTrainedConfig(
           depths=[2, 4, 11, 1]      ,    hidden_sizes=[2_24, 4_48, 12_32, 30_24]      ,    groups_width=1_12		),
       """regnet-y-320""": ImageNetPreTrainedConfig(
           depths=[2, 5, 12, 1]      ,    hidden_sizes=[2_32, 6_96, 13_92, 37_12]      ,    groups_width=2_32		),
       # models created by SEER -> https://arxiv.org/abs/2202.08360
       """regnet-y-320-seer""": RegNetConfig(depths=[2, 5, 12, 1]      ,    hidden_sizes=[2_32, 6_96, 13_92, 37_12]      ,    groups_width=2_32		),
       """regnet-y-640-seer""": RegNetConfig(depths=[2, 5, 12, 1]      ,    hidden_sizes=[3_28, 9_84, 19_68, 49_20]      ,    groups_width=3_28		),
       """regnet-y-1280-seer""": RegNetConfig(
           depths=[2, 7, 17, 1]      ,    hidden_sizes=[5_28, 10_56, 29_04, 73_92]      ,    groups_width=2_64		),
       """regnet-y-2560-seer""": RegNetConfig(
           depths=[3, 7, 16, 1]      ,    hidden_sizes=[6_40, 16_96, 25_44, 50_88]      ,    groups_width=6_40		),
       """regnet-y-10b-seer""": ImageNetPreTrainedConfig(
           depths=[2, 7, 17, 1]      ,    hidden_sizes=[20_20, 40_40, 1_11_10, 2_82_80]      ,    groups_width=10_10		),
       # finetuned on imagenet
       """regnet-y-320-seer-in1k""": ImageNetPreTrainedConfig(
           depths=[2, 5, 12, 1]      ,    hidden_sizes=[2_32, 6_96, 13_92, 37_12]      ,    groups_width=2_32		),
       """regnet-y-640-seer-in1k""": ImageNetPreTrainedConfig(
           depths=[2, 5, 12, 1]      ,    hidden_sizes=[3_28, 9_84, 19_68, 49_20]      ,    groups_width=3_28		),
       """regnet-y-1280-seer-in1k""": ImageNetPreTrainedConfig(
           depths=[2, 7, 17, 1]      ,    hidden_sizes=[5_28, 10_56, 29_04, 73_92]      ,    groups_width=2_64		),
       """regnet-y-2560-seer-in1k""": ImageNetPreTrainedConfig(
           depths=[3, 7, 16, 1]      ,    hidden_sizes=[6_40, 16_96, 25_44, 50_88]      ,    groups_width=6_40		),
       """regnet-y-10b-seer-in1k""": ImageNetPreTrainedConfig(
           depths=[2, 7, 17, 1]      ,    hidden_sizes=[20_20, 40_40, 1_11_10, 2_82_80]      ,    groups_width=10_10		),
   }

   snake_case 						=   NameToOurModelFuncMap()
   snake_case 						=   NameToFromModelFuncMap()
   # add seer weights logic

   def load_using_classy_vision(__lowerCAmelCase       :							str      ,    __lowerCAmelCase       :							Callable[[], nn.Module]		) -> Tuple[nn.Module, Dict]:
      snake_case 						=   torch.hub.load_state_dict_from_url(__lowerCAmelCase      ,    model_dir=str(__lowerCAmelCase		)      ,    map_location="""cpu"""		)
      snake_case 						=   model_func()
      # check if we have a head, if yes add it
      snake_case 						=   files["""classy_state_dict"""]["""base_model"""]["""model"""]
      snake_case 						=   model_state_dict["""trunk"""]
      model.load_state_dict(__lowerCAmelCase		)
      return model.eval(), model_state_dict["heads"]

   # pretrained
   snake_case 						=   partial(
       __lowerCAmelCase      ,    """https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet32d/seer_regnet32gf_model_iteration244000.torch"""      ,    lambda: FakeRegNetVisslWrapper(RegNetYaagf()		)      ,    )

   snake_case 						=   partial(
       __lowerCAmelCase      ,    """https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet64/seer_regnet64gf_model_final_checkpoint_phase0.torch"""      ,    lambda: FakeRegNetVisslWrapper(RegNetYaagf()		)      ,    )

   snake_case 						=   partial(
       __lowerCAmelCase      ,    """https://dl.fbaipublicfiles.com/vissl/model_zoo/swav_ig1b_regnet128Gf_cnstant_bs32_node16_sinkhorn10_proto16k_syncBN64_warmup8k/model_final_checkpoint_phase0.torch"""      ,    lambda: FakeRegNetVisslWrapper(RegNetYaaagf()		)      ,    )

   snake_case 						=   partial(
       __lowerCAmelCase      ,    """https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet10B/model_iteration124500_conso.torch"""      ,    lambda: FakeRegNetVisslWrapper(
           RegNet(RegNetParams(depth=27      ,    group_width=10_10      ,    w_a=17_44      ,    w_a=620.83      ,    w_m=2.52		)		)		)      ,    )

   # IN1K finetuned
   snake_case 						=   partial(
       __lowerCAmelCase      ,    """https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet32_finetuned_in1k_model_final_checkpoint_phase78.torch"""      ,    lambda: FakeRegNetVisslWrapper(RegNetYaagf()		)      ,    )

   snake_case 						=   partial(
       __lowerCAmelCase      ,    """https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet64_finetuned_in1k_model_final_checkpoint_phase78.torch"""      ,    lambda: FakeRegNetVisslWrapper(RegNetYaagf()		)      ,    )

   snake_case 						=   partial(
       __lowerCAmelCase      ,    """https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet128_finetuned_in1k_model_final_checkpoint_phase78.torch"""      ,    lambda: FakeRegNetVisslWrapper(RegNetYaaagf()		)      ,    )

   snake_case 						=   partial(
       __lowerCAmelCase      ,    """https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_10b_finetuned_in1k_model_phase28_conso.torch"""      ,    lambda: FakeRegNetVisslWrapper(
           RegNet(RegNetParams(depth=27      ,    group_width=10_10      ,    w_a=17_44      ,    w_a=620.83      ,    w_m=2.52		)		)		)      ,    )

   if model_name:
      convert_weight_and_push(
          __lowerCAmelCase      ,    names_to_from_model_map[model_name]      ,    names_to_ours_model_map[model_name]      ,    names_to_config[model_name]      ,    __lowerCAmelCase      ,    __lowerCAmelCase      ,    )
   else:
      for model_name, config in names_to_config.items():
         convert_weight_and_push(
             __lowerCAmelCase      ,    names_to_from_model_map[model_name]      ,    names_to_ours_model_map[model_name]      ,    __lowerCAmelCase      ,    __lowerCAmelCase      ,    __lowerCAmelCase      ,    )
   return config, expected_shape


if __name__ == "__main__":
 _SCREAMING_SNAKE_CASE							     =			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.",
 )

 _SCREAMING_SNAKE_CASE							     =			parser.parse_args()

 _SCREAMING_SNAKE_CASE							     =			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)








'''simple docstring'''




# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch

import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union

import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin, SchedulerOutput
@dataclass
class 			_lowerCAmelCase		(			A__	):






     """simple docstring"""






     snake_case_						      =   42
     snake_case_						      =   42




class 			_lowerCAmelCase		(			A__							,     A__	):






     """simple docstring"""






     snake_case_						      =   1



     @register_to_config
     def __init__(   self							:			str   ,       __snake_case							:			int = 20_00   ,       __snake_case							:			float = 0.15   ,       __snake_case							:			float = 0.01   ,       __snake_case							:			float = 13_48.0   ,       __snake_case							:			float = 1e-5   ,       __snake_case							:			int = 1   ,       )->	str:
        # standard deviation of the initial noise distribution
        snake_case 						=   sigma_max

        # setable values
        snake_case 						=   None

        self.set_sigmas(__snake_case   ,       __snake_case   ,       __snake_case   ,       __snake_case						)



     def      lowerCAmelCase			(   self							:			List[Any]   ,       __snake_case							:			torch.FloatTensor   ,       __snake_case							:			Optional[int] = None						)->	torch.FloatTensor:
        return sample



     def      lowerCAmelCase			(   self							:			List[str]   ,       __snake_case							:			int   ,       __snake_case							:			float = None   ,       __snake_case							:			Union[str, torch.device] = None						)->	Optional[Any]:
        snake_case 						=   sampling_eps if sampling_eps is not None else self.config.sampling_eps

        snake_case 						=   torch.linspace(1   ,       __snake_case   ,       __snake_case   ,       device=__snake_case						)



     def      lowerCAmelCase			(   self							:			Union[str, Any]   ,       __snake_case							:			int   ,       __snake_case							:			float = None   ,       __snake_case							:			float = None   ,       __snake_case							:			float = None						)->	str:
        snake_case 						=   sigma_min if sigma_min is not None else self.config.sigma_min
        snake_case 						=   sigma_max if sigma_max is not None else self.config.sigma_max
        snake_case 						=   sampling_eps if sampling_eps is not None else self.config.sampling_eps
        if self.timesteps is None:
           self.set_timesteps(__snake_case   ,       __snake_case						)

        snake_case 						=   sigma_min * (sigma_max / sigma_min) ** (self.timesteps / sampling_eps)
        snake_case 						=   torch.exp(torch.linspace(math.log(__snake_case						)   ,       math.log(__snake_case						)   ,       __snake_case						)						)
        snake_case 						=   torch.tensor([sigma_min * (sigma_max / sigma_min) ** t for t in self.timesteps]						)



     def      lowerCAmelCase			(   self							:			str   ,       __snake_case							:			List[str]   ,       __snake_case							:			str						)->	Optional[int]:
        return torch.where(
            timesteps == 0   ,       torch.zeros_like(t.to(timesteps.device						)						)   ,       self.discrete_sigmas[timesteps - 1].to(timesteps.device						)   ,       )



     def      lowerCAmelCase			(   self							:			int   ,       __snake_case							:			torch.FloatTensor   ,       __snake_case							:			int   ,       __snake_case							:			torch.FloatTensor   ,       __snake_case							:			Optional[torch.Generator] = None   ,       __snake_case							:			bool = True   ,       )->	Union[SdeVeOutput, Tuple]:
        if self.timesteps is None:
           raise ValueError(
               """`self.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"""						)

        snake_case 						=   timestep * torch.ones(
            sample.shape[0]   ,       device=sample.device						)  # torch.repeat_interleave(timestep, sample.shape[0])
        snake_case 						=   (timestep * (len(self.timesteps						) - 1)).long()

        # mps requires indices to be in the same device, so we use cpu as is the default with cuda
        snake_case 						=   timesteps.to(self.discrete_sigmas.device						)

        snake_case 						=   self.discrete_sigmas[timesteps].to(sample.device						)
        snake_case 						=   self.get_adjacent_sigma(__snake_case   ,       __snake_case						).to(sample.device						)
        snake_case 						=   torch.zeros_like(__snake_case						)
        snake_case 						=   (sigma**2 - adjacent_sigma**2) ** 0.5

        # equation 6 in the paper: the model_output modeled by the network is grad_x log pt(x)
        # also equation 47 shows the analog from SDE models to ancestral sampling methods
        snake_case 						=   diffusion.flatten()
        while len(diffusion.shape						) < len(sample.shape						):
           snake_case 						=   diffusion.unsqueeze(-1						)
        snake_case 						=   drift - diffusion**2 * model_output

        #  equation 6: sample noise for the diffusion term of
        snake_case 						=   randn_tensor(
            sample.shape   ,       layout=sample.layout   ,       generator=__snake_case   ,       device=sample.device   ,       dtype=sample.dtype						)
        snake_case 						=   sample - drift  # subtract because `dt` is a small negative timestep
        # TODO is the variable diffusion the correct scaling term for the noise?
        snake_case 						=   prev_sample_mean + diffusion * noise  # add impact of diffusion field g

        if not return_dict:
           return (prev_sample, prev_sample_mean)

        return SdeVeOutput(prev_sample=__snake_case   ,       prev_sample_mean=__snake_case						)



     def      lowerCAmelCase			(   self							:			Optional[Any]   ,       __snake_case							:			torch.FloatTensor   ,       __snake_case							:			torch.FloatTensor   ,       __snake_case							:			Optional[torch.Generator] = None   ,       __snake_case							:			bool = True   ,       )->	Union[SchedulerOutput, Tuple]:
        if self.timesteps is None:
           raise ValueError(
               """`self.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"""						)

        # For small batch sizes, the paper "suggest replacing norm(z) with sqrt(d), where d is the dim. of z"
        # sample noise for correction
        snake_case 						=   randn_tensor(sample.shape   ,       layout=sample.layout   ,       generator=__snake_case						).to(sample.device						)

        # compute step size from the model_output, the noise, and the snr
        snake_case 						=   torch.norm(model_output.reshape(model_output.shape[0]   ,       -1						)   ,       dim=-1						).mean()
        snake_case 						=   torch.norm(noise.reshape(noise.shape[0]   ,       -1						)   ,       dim=-1						).mean()
        snake_case 						=   (self.config.snr * noise_norm / grad_norm) ** 2 * 2
        snake_case 						=   step_size * torch.ones(sample.shape[0]						).to(sample.device						)
        # self.repeat_scalar(step_size, sample.shape[0])

        # compute corrected sample: model_output term and noise term
        snake_case 						=   step_size.flatten()
        while len(step_size.shape						) < len(sample.shape						):
           snake_case 						=   step_size.unsqueeze(-1						)
        snake_case 						=   sample + step_size * model_output
        snake_case 						=   prev_sample_mean + ((step_size * 2) ** 0.5) * noise

        if not return_dict:
           return (prev_sample,)

        return SchedulerOutput(prev_sample=__snake_case						)



     def      lowerCAmelCase			(   self							:			str   ,       __snake_case							:			torch.FloatTensor   ,       __snake_case							:			torch.FloatTensor   ,       __snake_case							:			torch.FloatTensor   ,       )->	torch.FloatTensor:
        # Make sure sigmas and timesteps have the same device and dtype as original_samples
        snake_case 						=   timesteps.to(original_samples.device						)
        snake_case 						=   self.discrete_sigmas.to(original_samples.device						)[timesteps]
        snake_case 						=   (
            noise * sigmas[:, None, None, None]
            if noise is not None
            else torch.randn_like(__snake_case						) * sigmas[:, None, None, None]
        )
        snake_case 						=   noise + original_samples
        return noisy_samples



     def __len__(   self							:			str						)->	Any:
        return self.config.num_train_timesteps

