code: 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__			    =				logging.get_logger()
  
  
  
  
  @dataclass
  class 					UpperCamelCase	:
  					__UpperCamelCase						=   42
  					__UpperCamelCase						=   field(default_factory=lowerCAmelCase_      )
  					__UpperCamelCase						=   field(default_factory=lowerCAmelCase_      )
  					__UpperCamelCase						=   field(default_factory=lowerCAmelCase_      )
  
  
  
  
  					def 							UpperCamelCase_						( self :					Union[str, Any]      ,_lowerCAmelCase :					str      ,_lowerCAmelCase :					int      ,_lowerCAmelCase :					Any      ,_lowerCAmelCase :					Optional[int]						):
  
  
  							"""simple docstring"""
  
  
  
  
  							__snake_case		       =     len(list(m.modules()						)						) == 1 or isinstance(__a      ,nn.Convad						) or isinstance(__a      ,nn.BatchNormad						)
  							if has_not_submodules:
  									self.traced.append(__a						)
  									__snake_case		       =     m
  
  
  
  
  					def __call__( self :					List[str]      ,_lowerCAmelCase :					Tuple						):
  
  
  							"""simple docstring"""
  
  
  
  
  							for name, m in self.module.named_modules():
  									self.handles.append(m.register_forward_hook(partial(self._forward_hook      ,name=__a						)						)						)
  							self.module(__a						)
  							[x.remove() for x in self.handles]
  							return self
  
  
  
  
  
  
  
  					@property
  					def 							UpperCamelCase_						( self :					int						):
  
  
  							"""simple docstring"""
  
  
  
  
  							return {k: v for k, v in self.nameamodule.items() if len(list(v.state_dict().keys()						)						) > 0}
  
  
  
  
  class 					UpperCamelCase	(     nn.Module      ):
  
  
  
  
  					def __init__( self :					str      ,_lowerCAmelCase :					List[str]						):
  
  
  							"""simple docstring"""
  
  
  
  
  							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(__a						) + 1
  									feature_blocks.append((F"""res{block_index}""", v)						)
  
  							__snake_case		       =     nn.ModuleDict(__a						)
  
  
  
  
  
  
  
  					def 							UpperCamelCase_						( self :					str      ,_lowerCAmelCase :					Tuple						):
  
  
  							"""simple docstring"""
  
  
  
  
  							return get_trunk_forward_outputs(
  							    __a      ,out_feat_keys=__a      ,feature_blocks=self._feature_blocks      ,)
  
  
  
  
  class 					UpperCamelCase	(     lowerCAmelCase_      ):
  
  
  
  
  					def 							UpperCamelCase_						( self :					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 							_lowerCamelCase(				__snake_case	)  ->   Any:
  		__snake_case		       =     RegNetConfig(depths=[2, 7, 17, 1]						,						hidden_sizes=[8, 8, 8, 8]						,						groups_width=8	)
  		if "in1k" in model_name:
  				__snake_case		       =     RegNetForImageClassification(_SCREAMING_SNAKE_CASE	)
  		else:
  				__snake_case		       =     RegNetModel(_SCREAMING_SNAKE_CASE	)
  		# create from model (with small weights)
  		__snake_case		       =     FakeRegNetVisslWrapper(
  		    RegNet(FakeRegNetParams(depth=27						,						group_width=1010						,						w_a=1744						,						w_a=620.83						,						w_m=2.5_2	)	)	)
  
  		with torch.no_grad():
  				__snake_case		       =     from_model.eval()
  				__snake_case		       =     our_model.eval()
  
  				__snake_case		       =     torch.randn((1, 3, 32, 32)	)
  				# trace both
  				__snake_case		       =     Tracker(_SCREAMING_SNAKE_CASE	)
  				__snake_case		       =     dest_tracker(_SCREAMING_SNAKE_CASE	).parametrized
  
  				pprint(dest_tracker.nameamodule	)
  				__snake_case		       =     Tracker(_SCREAMING_SNAKE_CASE	)
  				__snake_case		       =     src_tracker(_SCREAMING_SNAKE_CASE	).parametrized
  
  		# convert the keys -> module dict to keys -> params
  		def to_params_dict(__snake_case	):
  				__snake_case		       =     OrderedDict()
  				for name, module in dict_with_modules.items():
  						for param_name, param in module.state_dict().items():
  								__snake_case		       =     param
  				return params_dict
  
  		__snake_case		       =     {}
  
  		__snake_case		       =     to_params_dict(_SCREAMING_SNAKE_CASE	)
  		__snake_case		       =     to_params_dict(_SCREAMING_SNAKE_CASE	)
  
  		for (src_key, src_param), (dest_key, dest_param) in zip(src_state_dict.items()						,						dst_state_dict.items()	):
  				__snake_case		       =     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:
  				__snake_case		       =     'classifier.1.weight'
  				__snake_case		       =     'classifier.1.bias'
  
  		return from_to_ours_keys
  
  
  
  def 							_lowerCamelCase(				__snake_case						,						__snake_case = None						,						__snake_case = True	)  ->   str:
  		__snake_case		       =     'imagenet-1k-id2label.json'
  		__snake_case		       =     1000
  
  		__snake_case		       =     'huggingface/label-files'
  		__snake_case		       =     num_labels
  		__snake_case		       =     json.load(open(cached_download(hf_hub_url(_SCREAMING_SNAKE_CASE						,						_SCREAMING_SNAKE_CASE						,						repo_type="dataset"	)	)						,						"r"	)	)
  		__snake_case		       =     {int(_SCREAMING_SNAKE_CASE	): v for k, v in idalabel.items()}
  
  		__snake_case		       =     idalabel
  		__snake_case		       =     {v: k for k, v in idalabel.items()}
  
  		__snake_case		       =     partial(_SCREAMING_SNAKE_CASE						,						num_labels=_SCREAMING_SNAKE_CASE						,						idalabel=_SCREAMING_SNAKE_CASE						,						labelaid=_SCREAMING_SNAKE_CASE	)
  
  		__snake_case		       =     {
  		    'regnet-y-10b-seer': ImageNetPreTrainedConfig(
  		        depths=[2, 7, 17, 1]						,						hidden_sizes=[2020, 4040, 1_1110, 2_8280]						,						groups_width=1010	),
  		    # finetuned on imagenet
  		    'regnet-y-10b-seer-in1k': ImageNetPreTrainedConfig(
  		        depths=[2, 7, 17, 1]						,						hidden_sizes=[2020, 4040, 1_1110, 2_8280]						,						groups_width=1010	),
  		}
  
  		# add seer weights logic
  		def load_using_classy_vision(__snake_case	) -> Tuple[Dict, Dict]:
  				__snake_case		       =     torch.hub.load_state_dict_from_url(_SCREAMING_SNAKE_CASE						,						model_dir=str(_SCREAMING_SNAKE_CASE	)						,						map_location="cpu"	)
  				# check if we have a head, if yes add it
  				__snake_case		       =     files['classy_state_dict']['base_model']['model']
  				return model_state_dict["trunk"], model_state_dict["heads"]
  
  		__snake_case		       =     {
  		    'regnet-y-10b-seer': partial(
  		        _SCREAMING_SNAKE_CASE						,						"https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet10B/model_iteration124500_conso.torch"						,						),
  		    'regnet-y-10b-seer-in1k': partial(
  		        _SCREAMING_SNAKE_CASE						,						"https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_10b_finetuned_in1k_model_phase28_conso.torch"						,						),
  		}
  
  		__snake_case		       =     get_from_to_our_keys(_SCREAMING_SNAKE_CASE	)
  
  		if not (save_directory / f"""{model_name}.pth""").exists():
  				logger.info("Loading original state_dict."	)
  				__snake_case		       =     names_to_from_model[model_name]()
  				__snake_case		       =     from_state_dict_trunk
  				if "in1k" in model_name:
  						# add the head
  						__snake_case		       =     {**from_state_dict_trunk, **from_state_dict_head}
  				logger.info("Done!"	)
  
  				__snake_case		       =     {}
  
  				__snake_case		       =     list(from_state_dict.keys()	)
  				__snake_case		       =     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
  						__snake_case		       =     re.sub(_SCREAMING_SNAKE_CASE						,						""						,						_SCREAMING_SNAKE_CASE	)
  						# 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
  						__snake_case		       =     from_to_ours_keys[src_key]
  						# store the parameter with our key
  						__snake_case		       =     from_state_dict[key]
  						not_used_keys.remove(_SCREAMING_SNAKE_CASE	)
  				# check that all keys have been updated
  				assert len(_SCREAMING_SNAKE_CASE	) == 0, f"""Some keys where not used {",".join(_SCREAMING_SNAKE_CASE	)}"""
  
  				logger.info(f"""The following keys were not used: {",".join(_SCREAMING_SNAKE_CASE	)}"""	)
  
  				# save our state dict to disk
  				torch.save(_SCREAMING_SNAKE_CASE						,						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
  				__snake_case		       =     names_to_config[model_name]
  				__snake_case		       =     RegNetModel
  				if "in1k" in model_name:
  						__snake_case		       =     RegNetForImageClassification
  				__snake_case		       =     our_model_func(_SCREAMING_SNAKE_CASE	)
  				# 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
  				__snake_case		       =     our_model.state_dict().keys()
  				PreTrainedModel._load_pretrained_model_low_mem(
  				    _SCREAMING_SNAKE_CASE						,						_SCREAMING_SNAKE_CASE						,						[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						,						)
  				__snake_case		       =     384
  				# we can use the convnext one
  				__snake_case		       =     AutoImageProcessor.from_pretrained("facebook/convnext-base-224-22k-1k"						,						size=_SCREAMING_SNAKE_CASE	)
  				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__			    =				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__			    =				parser.parse_args()
  
  		lowerCamelCase__			    =				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)
  
  
  
  
  
  
style_context: import gc
  import random
  import unittest
  
  import numpy as np
  import torch
  from PIL import Image
  from transformers import CLIPImageProcessor, CLIPVisionConfig
  
  from diffusers import AutoencoderKL, PaintByExamplePipeline, PNDMScheduler, UNetaDConditionModel
  from diffusers.pipelines.paint_by_example import PaintByExampleImageEncoder
  from diffusers.utils import floats_tensor, load_image, slow, torch_device
  from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
  
  from ..pipeline_params import IMAGE_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, IMAGE_GUIDED_IMAGE_INPAINTING_PARAMS
  from ..test_pipelines_common import PipelineTesterMixin
  
  
  enable_full_determinism()
  
  
  
  
  class 					UpperCamelCase	(     snake_case__			,      unittest.TestCase      ):
  					__UpperCamelCase						=   PaintByExamplePipeline
  					__UpperCamelCase						=   IMAGE_GUIDED_IMAGE_INPAINTING_PARAMS
  					__UpperCamelCase						=   IMAGE_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
  					__UpperCamelCase						=   frozenset([]      )  # TO_DO: update the image_prams once refactored VaeImageProcessor.preprocess
  
  
  
  
  					def 							UpperCamelCase_						( self :					Tuple						):
  
  
  							"""simple docstring"""
  
  
  
  
  							torch.manual_seed(0						)
  							__snake_case		       =     UNetaDConditionModel(
  							    block_out_channels=(32, 64)      ,layers_per_block=2      ,sample_size=32      ,in_channels=9      ,out_channels=4      ,down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")      ,up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")      ,cross_attention_dim=32      ,)
  							__snake_case		       =     PNDMScheduler(skip_prk_steps=_lowerCAmelCase						)
  							torch.manual_seed(0						)
  							__snake_case		       =     AutoencoderKL(
  							    block_out_channels=[32, 64]      ,in_channels=3      ,out_channels=3      ,down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"]      ,up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"]      ,latent_channels=4      ,)
  							torch.manual_seed(0						)
  							__snake_case		       =     CLIPVisionConfig(
  							    hidden_size=32      ,projection_dim=32      ,intermediate_size=37      ,layer_norm_eps=1E-05      ,num_attention_heads=4      ,num_hidden_layers=5      ,image_size=32      ,patch_size=4      ,)
  							__snake_case		       =     PaintByExampleImageEncoder(_lowerCAmelCase      ,proj_size=32						)
  							__snake_case		       =     CLIPImageProcessor(crop_size=32      ,size=32						)
  
  							__snake_case		       =     {
  							    "unet": unet,
  							    "scheduler": scheduler,
  							    "vae": vae,
  							    "image_encoder": image_encoder,
  							    "safety_checker": None,
  							    "feature_extractor": feature_extractor,
  							}
  							return components
  
  
  
  
  					def 							UpperCamelCase_						( self :					Union[str, Any]      ,_lowerCAmelCase :					Any						):
  
  
  							"""simple docstring"""
  
  
  
  
  							__snake_case		       =     np.array(image.convert("RGB"						)						)
  							__snake_case		       =     image[None].transpose(0      ,3      ,1      ,2						)
  							__snake_case		       =     torch.from_numpy(_lowerCAmelCase						).to(dtype=torch.floataa						) / 1_2_7.5 - 1.0
  							return image
  
  
  
  
  					def 							UpperCamelCase_						( self :					int      ,_lowerCAmelCase :					Any="cpu"      ,_lowerCAmelCase :					List[str]=0						):
  
  
  							"""simple docstring"""
  
  
  
  
  							__snake_case		       =     floats_tensor((1, 3, 32, 32)      ,rng=random.Random(_lowerCAmelCase						)						).to(_lowerCAmelCase						)
  							__snake_case		       =     image.cpu().permute(0      ,2      ,3      ,1						)[0]
  							__snake_case		       =     Image.fromarray(np.uinta(_lowerCAmelCase						)						).convert("RGB"						).resize((64, 64)						)
  							__snake_case		       =     Image.fromarray(np.uinta(image + 4						)						).convert("RGB"						).resize((64, 64)						)
  							__snake_case		       =     Image.fromarray(np.uinta(_lowerCAmelCase						)						).convert("RGB"						).resize((32, 32)						)
  
  							if str(_lowerCAmelCase						).startswith("mps"						):
  									__snake_case		       =     torch.manual_seed(_lowerCAmelCase						)
  							else:
  									__snake_case		       =     torch.Generator(device=_lowerCAmelCase						).manual_seed(_lowerCAmelCase						)
  							__snake_case		       =     {
  							    "example_image": example_image,
  							    "image": init_image,
  							    "mask_image": mask_image,
  							    "generator": generator,
  							    "num_inference_steps": 2,
  							    "guidance_scale": 6.0,
  							    "output_type": "numpy",
  							}
  							return inputs
  
  
  
  
  					def 							UpperCamelCase_						( self :					int						):
  
  
  							"""simple docstring"""
  
  
  
  
  							__snake_case		       =     self.get_dummy_components()
  
  							# make sure here that pndm scheduler skips prk
  							__snake_case		       =     PaintByExamplePipeline(**_lowerCAmelCase						)
  							__snake_case		       =     pipe.to("cpu"						)
  							pipe.set_progress_bar_config(disable=_lowerCAmelCase						)
  
  							__snake_case		       =     self.get_dummy_inputs()
  							__snake_case		       =     pipe(**_lowerCAmelCase						)
  							__snake_case		       =     output.images
  
  							__snake_case		       =     image[0, -3:, -3:, -1]
  
  							assert image.shape == (1, 64, 64, 3)
  							__snake_case		       =     np.array([0.4_6_8_6, 0.5_6_8_7, 0.4_0_0_7, 0.5_2_1_8, 0.5_7_4_1, 0.4_4_8_2, 0.4_9_4_0, 0.4_6_2_9, 0.4_5_0_3]						)
  
  							assert np.abs(image_slice.flatten() - expected_slice						).max() < 1E-2
  
  
  
  
  					def 							UpperCamelCase_						( self :					int						):
  
  
  							"""simple docstring"""
  
  
  
  
  							__snake_case		       =     "cpu"
  							__snake_case		       =     self.get_dummy_inputs()
  							inputs.pop("mask_image"						)
  							__snake_case		       =     self.convert_to_pt(inputs.pop("image"						)						)
  							__snake_case		       =     image.clamp(0      ,1						) / 2
  
  							# make sure here that pndm scheduler skips prk
  							__snake_case		       =     PaintByExamplePipeline(**self.get_dummy_components()						)
  							__snake_case		       =     pipe.to(_lowerCAmelCase						)
  							pipe.set_progress_bar_config(disable=_lowerCAmelCase						)
  
  							__snake_case		       =     pipe(image=_lowerCAmelCase      ,mask_image=mask_image[:, 0]      ,**_lowerCAmelCase						)
  							__snake_case		       =     output.images
  
  							__snake_case		       =     image.cpu().permute(0      ,2      ,3      ,1						)[0]
  							__snake_case		       =     mask_image.cpu().permute(0      ,2      ,3      ,1						)[0]
  
  							__snake_case		       =     Image.fromarray(np.uinta(_lowerCAmelCase						)						).convert("RGB"						)
  							__snake_case		       =     Image.fromarray(np.uinta(_lowerCAmelCase						)						).convert("RGB"						)
  
  							__snake_case		       =     pipe(**self.get_dummy_inputs()						)
  							__snake_case		       =     output.images
  
  							assert out_a.shape == (1, 64, 64, 3)
  							assert np.abs(out_a.flatten() - out_a.flatten()						).max() < 5E-2
  
  
  
  
  
  
  
  					def 							UpperCamelCase_						( self :					Union[str, Any]						):
  
  
  							"""simple docstring"""
  
  
  
  
  							super().test_inference_batch_single_identical(expected_max_diff=3E-3						)
  
  
  
  
  @slow
  @require_torch_gpu
  class 					UpperCamelCase	(     unittest.TestCase      ):
  
  
  
  
  					def 							UpperCamelCase_						( self :					Any						):
  
  
  							"""simple docstring"""
  
  
  
  
  							super().tearDown()
  							gc.collect()
  							torch.cuda.empty_cache()
  
  
  
  
  
  
  
  					def 							UpperCamelCase_						( self :					Dict						):
  
  
  							"""simple docstring"""
  
  
  
  
  							__snake_case		       =     load_image(
  							    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
  							    "/paint_by_example/dog_in_bucket.png"						)
  							__snake_case		       =     load_image(
  							    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
  							    "/paint_by_example/mask.png"						)
  							__snake_case		       =     load_image(
  							    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
  							    "/paint_by_example/panda.jpg"						)
  
  							__snake_case		       =     PaintByExamplePipeline.from_pretrained("Fantasy-Studio/Paint-by-Example"						)
  							__snake_case		       =     pipe.to(_lowerCAmelCase						)
  							pipe.set_progress_bar_config(disable=_lowerCAmelCase						)
  
  							__snake_case		       =     torch.manual_seed(321						)
  							__snake_case		       =     pipe(
  							    image=_lowerCAmelCase      ,mask_image=_lowerCAmelCase      ,example_image=_lowerCAmelCase      ,generator=_lowerCAmelCase      ,guidance_scale=5.0      ,num_inference_steps=50      ,output_type="np"      ,)
  
  							__snake_case		       =     output.images
  
  							__snake_case		       =     image[0, -3:, -3:, -1]
  
  							assert image.shape == (1, 512, 512, 3)
  							__snake_case		       =     np.array([0.4_8_3_4, 0.4_8_1_1, 0.4_8_7_4, 0.5_1_2_2, 0.5_0_8_1, 0.5_1_4_4, 0.5_2_9_1, 0.5_2_9_0, 0.5_3_7_4]						)
  
  							assert np.abs(image_slice.flatten() - expected_slice						).max() < 1E-2
  
  
  
  
  
  