






'''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()
_A			:    List[Any]					  =logging.get_logger()

@dataclass
class _lowercase			:
							a          = 42
							a          = field(default_factory=_lowercase	)
							a          = field(default_factory=_lowercase	)




							def 	lowerCamelCase_       (							self:       Dict    ,							UpperCamelCase__:       List[Any]    ,							UpperCamelCase__:       Tensor    ,							UpperCamelCase__:       Tensor  ):
														lowerCamelCase__      :			List[str] 						= len(list(m.modules()  )  ) == 1 or isinstance(UpperCamelCase__    ,							nn.Convad  ) or isinstance(UpperCamelCase__    ,							nn.BatchNormad  )
														if has_not_submodules:
																					self.traced.append(UpperCamelCase__  )




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




							@property
							def 	lowerCamelCase_       (							self:       List[Any]  ):
														# check the len of the state_dict keys to see if we have learnable params
														return list(filter(lambda UpperCamelCase__  : len(list(x.state_dict().keys()  )  ) > 0    ,							self.traced  )  )



@dataclass
class _lowercase			:
							a          = 42
							a          = 42
							a          = 1
							a          = field(default_factory=_lowercase	)
							a          = field(default_factory=_lowercase	)
							a          = True




							def __call__(							self:       Union[str, Any]    ,							UpperCamelCase__:       Tensor  ):
														lowerCamelCase__      :			Dict 						= Tracker(self.dest  )(UpperCamelCase__  ).parametrized
														lowerCamelCase__      :			str 						= Tracker(self.src  )(UpperCamelCase__  ).parametrized

														lowerCamelCase__      :			Any 						= list(filter(lambda UpperCamelCase__  : type(UpperCamelCase__  ) not in self.src_skip    ,							UpperCamelCase__  )  )
														lowerCamelCase__      :			Optional[int] 						= list(filter(lambda UpperCamelCase__  : type(UpperCamelCase__  ) not in self.dest_skip    ,							UpperCamelCase__  )  )

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

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



class _lowercase			(       nn.Module	):




							def __init__(							self:       Optional[Any]    ,							UpperCamelCase__:       nn.Module  ):
														super().__init__()

														lowerCamelCase__      :			List[Tuple[str, nn.Module]] 						= []
														# - 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__      :			Union[str, Any] 						= len(UpperCamelCase__  ) + 1
																					feature_blocks.append((F'''res{block_index}''', v)  )

														lowerCamelCase__      :			str 						= nn.ModuleDict(UpperCamelCase__  )




							def 	lowerCamelCase_       (							self:       List[Any]    ,							UpperCamelCase__:       Tensor  ):
														return get_trunk_forward_outputs(
														    UpperCamelCase__    ,							out_feat_keys=UpperCamelCase__    ,							feature_blocks=self._feature_blocks    ,							)



class _lowercase			(       _lowercase	):




							def 	lowerCamelCase_       (							self:       Any    ,							UpperCamelCase__:       str  ):
														lowerCamelCase__      :			Optional[int] 						= x.split("""-"""  )
														return x_split[0] + x_split[1] + "_" + "".join(x_split[2:]  )




							def __getitem__(							self:       Tuple    ,							UpperCamelCase__:       str  ):
														# default to timm!
														if x not in self:
																					lowerCamelCase__      :			Dict 						= self.convert_name_to_timm(UpperCamelCase__  )
																					lowerCamelCase__      :			List[Any] 						= partial(lambda: (timm.create_model(UpperCamelCase__    ,							pretrained=UpperCamelCase__  ).eval(), None)  )

														else:
																					lowerCamelCase__      :			Dict 						= super().__getitem__(UpperCamelCase__  )

														return val



class _lowercase			(       _lowercase	):




							def __getitem__(							self:       int    ,							UpperCamelCase__:       str  ):
														if "seer" in x and "in1k" not in x:
																					lowerCamelCase__      :			Optional[Any] 						= RegNetModel
														else:
																					lowerCamelCase__      :			Optional[int] 						= RegNetForImageClassification
														return val





def 				SCREAMING_SNAKE_CASE_ (UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase   )   -> List[Any]:
							for from_key, to_key in keys:
														lowerCamelCase__      :			Union[str, Any] 						= from_state_dict[from_key].clone()
														print(f'''Copied key={from_key} to={to_key}'''   )
							return to_state_dict





def 				SCREAMING_SNAKE_CASE_ (UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase = True       ,    )   -> Dict:
							print(f'''Converting {name}...'''   )
							with torch.no_grad():
														lowerCamelCase__						,	lowerCamelCase__      :			Union[str, Any] 						= from_model_func()
														lowerCamelCase__      :			Tuple 						= our_model_func(UpperCamelCase   ).eval()
														lowerCamelCase__      :			List[str] 						= ModuleTransfer(src=UpperCamelCase       ,    dest=UpperCamelCase       ,    raise_if_mismatch=UpperCamelCase   )
														lowerCamelCase__      :			Union[str, Any] 						= torch.randn((1, 3, 224, 224)   )
														module_transfer(UpperCamelCase   )

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

							lowerCamelCase__      :			int 						= our_model(UpperCamelCase       ,    output_hidden_states=UpperCamelCase   )
							lowerCamelCase__      :			List[str] 						= (
							    our_outputs.logits if isinstance(UpperCamelCase       ,    UpperCamelCase   ) else our_outputs.last_hidden_state
							)

							lowerCamelCase__      :			List[Any] 						= from_model(UpperCamelCase   )
							lowerCamelCase__      :			Optional[Any] 						= from_output[-1] if type(UpperCamelCase   ) 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:
														lowerCamelCase__      :			Union[str, Any] 						= our_outputs.hidden_states[-1]

							assert torch.allclose(UpperCamelCase       ,    UpperCamelCase   ), "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=UpperCamelCase       ,    )

														lowerCamelCase__      :			str 						= 224 if """seer""" not in name else 384
														# we can use the convnext one
														lowerCamelCase__      :			Any 						= AutoImageProcessor.from_pretrained("""facebook/convnext-base-224-22k-1k"""       ,    size=UpperCamelCase   )
														image_processor.push_to_hub(
														    repo_path_or_name=save_directory / name       ,    commit_message="""Add image processor"""       ,    use_temp_dir=UpperCamelCase       ,    )

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





def 				SCREAMING_SNAKE_CASE_ (UpperCamelCase       ,    UpperCamelCase = None       ,    UpperCamelCase = True   )   -> Optional[int]:
							lowerCamelCase__      :			Dict 						= """imagenet-1k-id2label.json"""
							lowerCamelCase__      :			Dict 						= 1000
							lowerCamelCase__      :			Any 						= (1, num_labels)

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

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

							lowerCamelCase__      :			int 						= partial(UpperCamelCase       ,    num_labels=UpperCamelCase       ,    idalabel=UpperCamelCase       ,    labelaid=UpperCamelCase   )

							lowerCamelCase__      :			Any 						= {
							    """regnet-x-002""": ImageNetPreTrainedConfig(
							        depths=[1, 1, 4, 7]       ,    hidden_sizes=[24, 56, 152, 368]       ,    groups_width=8       ,    layer_type="""x"""   ),
							    """regnet-x-004""": ImageNetPreTrainedConfig(
							        depths=[1, 2, 7, 12]       ,    hidden_sizes=[32, 64, 160, 384]       ,    groups_width=16       ,    layer_type="""x"""   ),
							    """regnet-x-006""": ImageNetPreTrainedConfig(
							        depths=[1, 3, 5, 7]       ,    hidden_sizes=[48, 96, 240, 528]       ,    groups_width=24       ,    layer_type="""x"""   ),
							    """regnet-x-008""": ImageNetPreTrainedConfig(
							        depths=[1, 3, 7, 5]       ,    hidden_sizes=[64, 128, 288, 672]       ,    groups_width=16       ,    layer_type="""x"""   ),
							    """regnet-x-016""": ImageNetPreTrainedConfig(
							        depths=[2, 4, 10, 2]       ,    hidden_sizes=[72, 168, 408, 912]       ,    groups_width=24       ,    layer_type="""x"""   ),
							    """regnet-x-032""": ImageNetPreTrainedConfig(
							        depths=[2, 6, 15, 2]       ,    hidden_sizes=[96, 192, 432, 1008]       ,    groups_width=48       ,    layer_type="""x"""   ),
							    """regnet-x-040""": ImageNetPreTrainedConfig(
							        depths=[2, 5, 14, 2]       ,    hidden_sizes=[80, 240, 560, 1360]       ,    groups_width=40       ,    layer_type="""x"""   ),
							    """regnet-x-064""": ImageNetPreTrainedConfig(
							        depths=[2, 4, 10, 1]       ,    hidden_sizes=[168, 392, 784, 1624]       ,    groups_width=56       ,    layer_type="""x"""   ),
							    """regnet-x-080""": ImageNetPreTrainedConfig(
							        depths=[2, 5, 15, 1]       ,    hidden_sizes=[80, 240, 720, 1920]       ,    groups_width=120       ,    layer_type="""x"""   ),
							    """regnet-x-120""": ImageNetPreTrainedConfig(
							        depths=[2, 5, 11, 1]       ,    hidden_sizes=[224, 448, 896, 2240]       ,    groups_width=112       ,    layer_type="""x"""   ),
							    """regnet-x-160""": ImageNetPreTrainedConfig(
							        depths=[2, 6, 13, 1]       ,    hidden_sizes=[256, 512, 896, 2048]       ,    groups_width=128       ,    layer_type="""x"""   ),
							    """regnet-x-320""": ImageNetPreTrainedConfig(
							        depths=[2, 7, 13, 1]       ,    hidden_sizes=[336, 672, 1344, 2520]       ,    groups_width=168       ,    layer_type="""x"""   ),
							    # y variant
							    """regnet-y-002""": ImageNetPreTrainedConfig(depths=[1, 1, 4, 7]       ,    hidden_sizes=[24, 56, 152, 368]       ,    groups_width=8   ),
							    """regnet-y-004""": ImageNetPreTrainedConfig(
							        depths=[1, 3, 6, 6]       ,    hidden_sizes=[48, 104, 208, 440]       ,    groups_width=8   ),
							    """regnet-y-006""": ImageNetPreTrainedConfig(
							        depths=[1, 3, 7, 4]       ,    hidden_sizes=[48, 112, 256, 608]       ,    groups_width=16   ),
							    """regnet-y-008""": ImageNetPreTrainedConfig(
							        depths=[1, 3, 8, 2]       ,    hidden_sizes=[64, 128, 320, 768]       ,    groups_width=16   ),
							    """regnet-y-016""": ImageNetPreTrainedConfig(
							        depths=[2, 6, 17, 2]       ,    hidden_sizes=[48, 120, 336, 888]       ,    groups_width=24   ),
							    """regnet-y-032""": ImageNetPreTrainedConfig(
							        depths=[2, 5, 13, 1]       ,    hidden_sizes=[72, 216, 576, 1512]       ,    groups_width=24   ),
							    """regnet-y-040""": ImageNetPreTrainedConfig(
							        depths=[2, 6, 12, 2]       ,    hidden_sizes=[128, 192, 512, 1088]       ,    groups_width=64   ),
							    """regnet-y-064""": ImageNetPreTrainedConfig(
							        depths=[2, 7, 14, 2]       ,    hidden_sizes=[144, 288, 576, 1296]       ,    groups_width=72   ),
							    """regnet-y-080""": ImageNetPreTrainedConfig(
							        depths=[2, 4, 10, 1]       ,    hidden_sizes=[168, 448, 896, 2016]       ,    groups_width=56   ),
							    """regnet-y-120""": ImageNetPreTrainedConfig(
							        depths=[2, 5, 11, 1]       ,    hidden_sizes=[224, 448, 896, 2240]       ,    groups_width=112   ),
							    """regnet-y-160""": ImageNetPreTrainedConfig(
							        depths=[2, 4, 11, 1]       ,    hidden_sizes=[224, 448, 1232, 3024]       ,    groups_width=112   ),
							    """regnet-y-320""": ImageNetPreTrainedConfig(
							        depths=[2, 5, 12, 1]       ,    hidden_sizes=[232, 696, 1392, 3712]       ,    groups_width=232   ),
							    # models created by SEER -> https://arxiv.org/abs/2202.08360
							    """regnet-y-320-seer""": RegNetConfig(depths=[2, 5, 12, 1]       ,    hidden_sizes=[232, 696, 1392, 3712]       ,    groups_width=232   ),
							    """regnet-y-640-seer""": RegNetConfig(depths=[2, 5, 12, 1]       ,    hidden_sizes=[328, 984, 1968, 4920]       ,    groups_width=328   ),
							    """regnet-y-1280-seer""": RegNetConfig(
							        depths=[2, 7, 17, 1]       ,    hidden_sizes=[528, 1056, 2904, 7392]       ,    groups_width=264   ),
							    """regnet-y-2560-seer""": RegNetConfig(
							        depths=[3, 7, 16, 1]       ,    hidden_sizes=[640, 1696, 2544, 5088]       ,    groups_width=640   ),
							    """regnet-y-10b-seer""": ImageNetPreTrainedConfig(
							        depths=[2, 7, 17, 1]       ,    hidden_sizes=[2020, 4040, 11110, 28280]       ,    groups_width=1010   ),
							    # finetuned on imagenet
							    """regnet-y-320-seer-in1k""": ImageNetPreTrainedConfig(
							        depths=[2, 5, 12, 1]       ,    hidden_sizes=[232, 696, 1392, 3712]       ,    groups_width=232   ),
							    """regnet-y-640-seer-in1k""": ImageNetPreTrainedConfig(
							        depths=[2, 5, 12, 1]       ,    hidden_sizes=[328, 984, 1968, 4920]       ,    groups_width=328   ),
							    """regnet-y-1280-seer-in1k""": ImageNetPreTrainedConfig(
							        depths=[2, 7, 17, 1]       ,    hidden_sizes=[528, 1056, 2904, 7392]       ,    groups_width=264   ),
							    """regnet-y-2560-seer-in1k""": ImageNetPreTrainedConfig(
							        depths=[3, 7, 16, 1]       ,    hidden_sizes=[640, 1696, 2544, 5088]       ,    groups_width=640   ),
							    """regnet-y-10b-seer-in1k""": ImageNetPreTrainedConfig(
							        depths=[2, 7, 17, 1]       ,    hidden_sizes=[2020, 4040, 11110, 28280]       ,    groups_width=1010   ),
							}

							lowerCamelCase__      :			List[Any] 						= NameToOurModelFuncMap()
							lowerCamelCase__      :			Any 						= NameToFromModelFuncMap()
							# add seer weights logic

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

							# pretrained
							lowerCamelCase__      :			Union[str, Any] 						= partial(
							    UpperCamelCase       ,    """https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet32d/seer_regnet32gf_model_iteration244000.torch"""       ,    lambda: FakeRegNetVisslWrapper(RegNetYaagf()   )       ,    )

							lowerCamelCase__      :			List[Any] 						= partial(
							    UpperCamelCase       ,    """https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet64/seer_regnet64gf_model_final_checkpoint_phase0.torch"""       ,    lambda: FakeRegNetVisslWrapper(RegNetYaagf()   )       ,    )

							lowerCamelCase__      :			Optional[int] 						= partial(
							    UpperCamelCase       ,    """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()   )       ,    )

							lowerCamelCase__      :			str 						= partial(
							    UpperCamelCase       ,    """https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet10B/model_iteration124500_conso.torch"""       ,    lambda: FakeRegNetVisslWrapper(
							        RegNet(RegNetParams(depth=27       ,    group_width=1010       ,    w_a=1744       ,    w_a=620.83       ,    w_m=2.52   )   )   )       ,    )

							# IN1K finetuned
							lowerCamelCase__      :			List[str] 						= partial(
							    UpperCamelCase       ,    """https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet32_finetuned_in1k_model_final_checkpoint_phase78.torch"""       ,    lambda: FakeRegNetVisslWrapper(RegNetYaagf()   )       ,    )

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

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

							lowerCamelCase__      :			Union[str, Any] 						= partial(
							    UpperCamelCase       ,    """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=1010       ,    w_a=1744       ,    w_a=620.83       ,    w_m=2.52   )   )   )       ,    )

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


if __name__ == "__main__":
			_A			:    List[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.''',
			)

			_A			:    Optional[Any]					  =parser.parse_args()

			_A			:    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)









'''simple docstring'''







import unittest

import torch

from diffusers import DDIMScheduler, DDPMScheduler, UNetaDModel
from diffusers.training_utils import set_seed
from diffusers.utils.testing_utils import slow


_A			:    Union[str, Any]					  =False

class _lowercase			(       unittest.TestCase	):




							def 	lowerCamelCase_       (							self:       Dict    ,							UpperCamelCase__:       str=32  ):
														set_seed(0  )
														lowerCamelCase__      :			Optional[int] 						= UNetaDModel(sample_size=UpperCamelCase__    ,							in_channels=3    ,							out_channels=3  )
														lowerCamelCase__      :			List[Any] 						= torch.optim.SGD(model.parameters()    ,							lr=0.0_001  )
														return model, optimizer




							@slow
							def 	lowerCamelCase_       (							self:       List[str]  ):
														lowerCamelCase__      :			Optional[Any] 						= """cpu"""  # ensure full determinism without setting the CUBLAS_WORKSPACE_CONFIG env variable
														lowerCamelCase__      :			List[Any] 						= DDPMScheduler(
														    num_train_timesteps=1_000    ,							beta_start=0.0_001    ,							beta_end=0.02    ,							beta_schedule="""linear"""    ,							clip_sample=UpperCamelCase__    ,							)
														lowerCamelCase__      :			Any 						= DDIMScheduler(
														    num_train_timesteps=1_000    ,							beta_start=0.0_001    ,							beta_end=0.02    ,							beta_schedule="""linear"""    ,							clip_sample=UpperCamelCase__    ,							)

														assert ddpm_scheduler.config.num_train_timesteps == ddim_scheduler.config.num_train_timesteps

														# shared batches for DDPM and DDIM
														set_seed(0  )
														lowerCamelCase__      :			str 						= [torch.randn((4, 3, 32, 32)  ).clip(-1    ,							1  ).to(UpperCamelCase__  ) for _ in range(4  )]
														lowerCamelCase__      :			Tuple 						= [torch.randn((4, 3, 32, 32)  ).to(UpperCamelCase__  ) for _ in range(4  )]
														lowerCamelCase__      :			Tuple 						= [torch.randint(0    ,							1_000    ,							(4,)  ).long().to(UpperCamelCase__  ) for _ in range(4  )]

														# train with a DDPM scheduler
														lowerCamelCase__						,	lowerCamelCase__      :			Any 						= self.get_model_optimizer(resolution=32  )
														model.train().to(UpperCamelCase__  )
														for i in range(4  ):
																					optimizer.zero_grad()
																					lowerCamelCase__      :			str 						= ddpm_scheduler.add_noise(clean_images[i]    ,							noise[i]    ,							timesteps[i]  )
																					lowerCamelCase__      :			str 						= model(UpperCamelCase__    ,							timesteps[i]  ).sample
																					lowerCamelCase__      :			Tuple 						= torch.nn.functional.mse_loss(UpperCamelCase__    ,							noise[i]  )
																					loss.backward()
																					optimizer.step()
														del model, optimizer

														# recreate the model and optimizer, and retry with DDIM
														lowerCamelCase__						,	lowerCamelCase__      :			Optional[Any] 						= self.get_model_optimizer(resolution=32  )
														model.train().to(UpperCamelCase__  )
														for i in range(4  ):
																					optimizer.zero_grad()
																					lowerCamelCase__      :			Optional[Any] 						= ddim_scheduler.add_noise(clean_images[i]    ,							noise[i]    ,							timesteps[i]  )
																					lowerCamelCase__      :			Dict 						= model(UpperCamelCase__    ,							timesteps[i]  ).sample
																					lowerCamelCase__      :			Union[str, Any] 						= torch.nn.functional.mse_loss(UpperCamelCase__    ,							noise[i]  )
																					loss.backward()
																					optimizer.step()
														del model, optimizer

														self.assertTrue(torch.allclose(UpperCamelCase__    ,							UpperCamelCase__    ,							atol=1e-5  )  )
														self.assertTrue(torch.allclose(UpperCamelCase__    ,							UpperCamelCase__    ,							atol=1e-5  )  )

