



'''simple docstring'''

import argparse
import json
from collections import OrderedDict
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    SegformerConfig,
    SegformerForImageClassification,
    SegformerForSemanticSegmentation,
    SegformerImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
__lowerCAmelCase									=   logging.get_logger(__name__)



def      __lowerCamelCase     (      lowerCAmelCase_						,			lowerCAmelCase_=False  )			->      Union[str, Any]:
		_a     :      List[str]        =							OrderedDict()
		for key, value in state_dict.items():
				if encoder_only and not key.startswith('head'  ):
						_a     :      int        =							'segformer.encoder.' + key
				if key.startswith('backbone'  ):
						_a     :      int        =							key.replace('backbone'						,			'segformer.encoder'  )
				if "patch_embed" in key:
						# replace for example patch_embed1 by patch_embeddings.0
						_a     :      List[Any]        =							key[key.find('patch_embed'  ) + len('patch_embed'  )]
						_a     :      Union[str, Any]        =							key.replace(f"""patch_embed{idx}"""						,			f"""patch_embeddings.{int(lowerCAmelCase_  )-1}"""  )
				if "norm" in key:
						_a     :      int        =							key.replace('norm'						,			'layer_norm'  )
				if "segformer.encoder.layer_norm" in key:
						# replace for example layer_norm1 by layer_norm.0
						_a     :      List[Any]        =							key[key.find('segformer.encoder.layer_norm'  ) + len('segformer.encoder.layer_norm'  )]
						_a     :      str        =							key.replace(f"""layer_norm{idx}"""						,			f"""layer_norm.{int(lowerCAmelCase_  )-1}"""  )
				if "layer_norm1" in key:
						_a     :      str        =							key.replace('layer_norm1'						,			'layer_norm_1'  )
				if "layer_norm2" in key:
						_a     :      Optional[int]        =							key.replace('layer_norm2'						,			'layer_norm_2'  )
				if "block" in key:
						# replace for example block1 by block.0
						_a     :      List[Any]        =							key[key.find('block'  ) + len('block'  )]
						_a     :      List[Any]        =							key.replace(f"""block{idx}"""						,			f"""block.{int(lowerCAmelCase_  )-1}"""  )
				if "attn.q" in key:
						_a     :      List[Any]        =							key.replace('attn.q'						,			'attention.self.query'  )
				if "attn.proj" in key:
						_a     :      str        =							key.replace('attn.proj'						,			'attention.output.dense'  )
				if "attn" in key:
						_a     :      Any        =							key.replace('attn'						,			'attention.self'  )
				if "fc1" in key:
						_a     :      Union[str, Any]        =							key.replace('fc1'						,			'dense1'  )
				if "fc2" in key:
						_a     :      Tuple        =							key.replace('fc2'						,			'dense2'  )
				if "linear_pred" in key:
						_a     :      Optional[int]        =							key.replace('linear_pred'						,			'classifier'  )
				if "linear_fuse" in key:
						_a     :      List[str]        =							key.replace('linear_fuse.conv'						,			'linear_fuse'  )
						_a     :      List[str]        =							key.replace('linear_fuse.bn'						,			'batch_norm'  )
				if "linear_c" in key:
						# replace for example linear_c4 by linear_c.3
						_a     :      Tuple        =							key[key.find('linear_c'  ) + len('linear_c'  )]
						_a     :      Union[str, Any]        =							key.replace(f"""linear_c{idx}"""						,			f"""linear_c.{int(lowerCAmelCase_  )-1}"""  )
				if key.startswith('head'  ):
						_a     :      Any        =							key.replace('head'						,			'classifier'  )
				_a     :      List[str]        =							value

		return new_state_dict



def      __lowerCamelCase     (      lowerCAmelCase_						,			lowerCAmelCase_  )			->      Dict:
		# for each of the encoder blocks:
		for i in range(config.num_encoder_blocks  ):
				for j in range(config.depths[i]  ):
						# read in weights + bias of keys and values (which is a single matrix in the original implementation)
						_a     :      Dict        =							state_dict.pop(f"""segformer.encoder.block.{i}.{j}.attention.self.kv.weight"""  )
						_a     :      Any        =							state_dict.pop(f"""segformer.encoder.block.{i}.{j}.attention.self.kv.bias"""  )
						# next, add keys and values (in that order) to the state dict
						_a     :      Any        =							kv_weight[
						    : config.hidden_sizes[i], :
						]
						_a     :      Optional[Any]        =							kv_bias[: config.hidden_sizes[i]]
						_a     :      List[Any]        =							kv_weight[
						    config.hidden_sizes[i] :, :
						]
						_a     :      Union[str, Any]        =							kv_bias[
						    config.hidden_sizes[i] :
						]



def      __lowerCamelCase     (      )			->      Tuple:
		_a     :      List[Any]        =							'http://images.cocodataset.org/val2017/000000039769.jpg'
		_a     :      Any        =							Image.open(requests.get(lowerCAmelCase_						,			stream=lowerCAmelCase_  ).raw  )

		return image



@torch.no_grad()
def      __lowerCamelCase     (      lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_  )			->      List[str]:
		_a     :      str        =							SegformerConfig()
		_a     :      Optional[int]        =							False

		# set attributes based on model_name
		_a     :      Optional[int]        =							'huggingface/label-files'
		if "segformer" in model_name:
				_a     :      List[str]        =							model_name[len('segformer.'  ) : len('segformer.'  ) + 2]
				if "ade" in model_name:
						_a     :      List[str]        =							150
						_a     :      Dict        =							'ade20k-id2label.json'
						_a     :      Optional[Any]        =							(1, 150, 128, 128)
				elif "city" in model_name:
						_a     :      Dict        =							19
						_a     :      Union[str, Any]        =							'cityscapes-id2label.json'
						_a     :      Dict        =							(1, 19, 128, 128)
				else:
						raise ValueError(f"""Model {model_name} not supported"""  )
		elif "mit" in model_name:
				_a     :      List[str]        =							True
				_a     :      Dict        =							model_name[4:6]
				_a     :      List[Any]        =							1000
				_a     :      Any        =							'imagenet-1k-id2label.json'
				_a     :      Union[str, Any]        =							(1, 1000)
		else:
				raise ValueError(f"""Model {model_name} not supported"""  )

		# set config attributes
		_a     :      List[str]        =							json.load(open(hf_hub_download(lowerCAmelCase_						,			lowerCAmelCase_						,			repo_type='dataset'  )						,			'r'  )  )
		_a     :      Optional[Any]        =							{int(lowerCAmelCase_  ): v for k, v in idalabel.items()}
		_a     :      Any        =							idalabel
		_a     :      List[Any]        =							{v: k for k, v in idalabel.items()}
		if size == "b0":
				pass
		elif size == "b1":
				_a     :      Tuple        =							[64, 128, 320, 512]
				_a     :      Any        =							256
		elif size == "b2":
				_a     :      int        =							[64, 128, 320, 512]
				_a     :      int        =							768
				_a     :      Tuple        =							[3, 4, 6, 3]
		elif size == "b3":
				_a     :      List[str]        =							[64, 128, 320, 512]
				_a     :      List[Any]        =							768
				_a     :      Tuple        =							[3, 4, 18, 3]
		elif size == "b4":
				_a     :      List[Any]        =							[64, 128, 320, 512]
				_a     :      List[Any]        =							768
				_a     :      str        =							[3, 8, 27, 3]
		elif size == "b5":
				_a     :      Optional[int]        =							[64, 128, 320, 512]
				_a     :      Dict        =							768
				_a     :      Tuple        =							[3, 6, 40, 3]
		else:
				raise ValueError(f"""Size {size} not supported"""  )

		# load image processor (only resize + normalize)
		_a     :      List[Any]        =							SegformerImageProcessor(
		    image_scale=(512, 512)						,			keep_ratio=lowerCAmelCase_						,			align=lowerCAmelCase_						,			do_random_crop=lowerCAmelCase_  )

		# prepare image
		_a     :      List[Any]        =							prepare_img()
		_a     :      Optional[Any]        =							image_processor(images=lowerCAmelCase_						,			return_tensors='pt'  ).pixel_values

		logger.info(f"""Converting model {model_name}..."""  )

		# load original state dict
		if encoder_only:
				_a     :      List[Any]        =							torch.load(lowerCAmelCase_						,			map_location=torch.device('cpu'  )  )
		else:
				_a     :      Optional[Any]        =							torch.load(lowerCAmelCase_						,			map_location=torch.device('cpu'  )  )['state_dict']

		# rename keys
		_a     :      Union[str, Any]        =							rename_keys(lowerCAmelCase_						,			encoder_only=lowerCAmelCase_  )
		if not encoder_only:
				del state_dict["decode_head.conv_seg.weight"]
				del state_dict["decode_head.conv_seg.bias"]

		# key and value matrices need special treatment
		read_in_k_v(lowerCAmelCase_						,			lowerCAmelCase_  )

		# create HuggingFace model and load state dict
		if encoder_only:
				_a     :      Tuple        =							False
				_a     :      List[Any]        =							SegformerForImageClassification(lowerCAmelCase_  )
		else:
				_a     :      Union[str, Any]        =							SegformerForSemanticSegmentation(lowerCAmelCase_  )
		model.load_state_dict(lowerCAmelCase_  )
		model.eval()

		# forward pass
		_a     :      Union[str, Any]        =							model(lowerCAmelCase_  )
		_a     :      List[Any]        =							outputs.logits

		# set expected_slice based on model name
		# ADE20k checkpoints
		if model_name == "segformer.b0.512x512.ade.160k":
				_a     :      List[str]        =							torch.tensor(
				    [
				        [[-4.6_310, -5.5_232, -6.2_356], [-5.1_921, -6.1_444, -6.5_996], [-5.4_424, -6.2_790, -6.7_574]],
				        [[-12.1_391, -13.3_122, -13.9_554], [-12.8_732, -13.9_352, -14.3_563], [-12.9_438, -13.8_226, -14.2_513]],
				        [[-12.5_134, -13.4_686, -14.4_915], [-12.8_669, -14.4_343, -14.7_758], [-13.2_523, -14.5_819, -15.0_694]],
				    ]  )
		elif model_name == "segformer.b1.512x512.ade.160k":
				_a     :      Union[str, Any]        =							torch.tensor(
				    [
				        [[-7.5_820, -8.7_231, -8.3_215], [-8.0_600, -10.3_529, -10.0_304], [-7.5_208, -9.4_103, -9.6_239]],
				        [[-12.6_918, -13.8_994, -13.7_137], [-13.3_196, -15.7_523, -15.4_789], [-12.9_343, -14.8_757, -14.9_689]],
				        [[-11.1_911, -11.9_421, -11.3_243], [-11.3_342, -13.6_839, -13.3_581], [-10.3_909, -12.1_832, -12.4_858]],
				    ]  )
		elif model_name == "segformer.b2.512x512.ade.160k":
				_a     :      str        =							torch.tensor(
				    [
				        [[-11.8_173, -14.3_850, -16.3_128], [-14.5_648, -16.5_804, -18.6_568], [-14.7_223, -15.7_387, -18.4_218]],
				        [[-15.7_290, -17.9_171, -19.4_423], [-18.3_105, -19.9_448, -21.4_661], [-17.9_296, -18.6_497, -20.7_910]],
				        [[-15.0_783, -17.0_336, -18.2_789], [-16.8_771, -18.6_870, -20.1_612], [-16.2_454, -17.1_426, -19.5_055]],
				    ]  )
		elif model_name == "segformer.b3.512x512.ade.160k":
				_a     :      Tuple        =							torch.tensor(
				    [
				        [[-9.0_878, -10.2_081, -10.1_891], [-9.3_144, -10.7_941, -10.9_843], [-9.2_294, -10.3_855, -10.5_704]],
				        [[-12.2_316, -13.9_068, -13.6_102], [-12.9_161, -14.3_702, -14.3_235], [-12.5_233, -13.7_174, -13.7_932]],
				        [[-14.6_275, -15.2_490, -14.9_727], [-14.3_400, -15.9_687, -16.2_827], [-14.1_484, -15.4_033, -15.8_937]],
				    ]  )
		elif model_name == "segformer.b4.512x512.ade.160k":
				_a     :      List[Any]        =							torch.tensor(
				    [
				        [[-12.3_144, -13.2_447, -14.0_802], [-13.3_614, -14.5_816, -15.6_117], [-13.3_340, -14.4_433, -16.2_219]],
				        [[-19.2_781, -20.4_128, -20.7_506], [-20.6_153, -21.6_566, -22.0_998], [-19.9_800, -21.0_430, -22.1_494]],
				        [[-18.8_739, -19.7_804, -21.1_834], [-20.1_233, -21.6_765, -23.2_944], [-20.0_315, -21.2_641, -23.6_944]],
				    ]  )
		elif model_name == "segformer.b5.640x640.ade.160k":
				_a     :      int        =							torch.tensor(
				    [
				        [[-9.5_524, -12.0_835, -11.7_348], [-10.5_229, -13.6_446, -14.5_662], [-9.5_842, -12.8_851, -13.9_414]],
				        [[-15.3_432, -17.5_323, -17.0_818], [-16.3_330, -18.9_255, -19.2_101], [-15.1_340, -17.7_848, -18.3_971]],
				        [[-12.6_072, -14.9_486, -14.6_631], [-13.7_629, -17.0_907, -17.7_745], [-12.7_899, -16.1_695, -17.1_671]],
				    ]  )
		# Cityscapes checkpoints
		elif model_name == "segformer.b0.1024x1024.city.160k":
				_a     :      str        =							torch.tensor(
				    [
				        [[-11.9_295, -13.4_057, -14.8_106], [-13.3_431, -14.8_179, -15.3_781], [-14.2_836, -15.5_942, -16.1_588]],
				        [[-11.4_906, -12.8_067, -13.6_564], [-13.1_189, -14.0_500, -14.1_543], [-13.8_748, -14.5_136, -14.8_789]],
				        [[0.5_374, 0.1_067, -0.4_742], [0.1_141, -0.2_255, -0.7_099], [-0.3_000, -0.5_924, -1.3_105]],
				    ]  )
		elif model_name == "segformer.b0.512x1024.city.160k":
				_a     :      Tuple        =							torch.tensor(
				    [
				        [[-7.8_217, -9.8_767, -10.1_717], [-9.4_438, -10.9_058, -11.4_047], [-9.7_939, -12.3_495, -12.1_079]],
				        [[-7.1_514, -9.5_336, -10.0_860], [-9.7_776, -11.6_822, -11.8_439], [-10.1_411, -12.7_655, -12.8_972]],
				        [[0.3_021, 0.0_805, -0.2_310], [-0.0_328, -0.1_605, -0.2_714], [-0.1_408, -0.5_477, -0.6_976]],
				    ]  )
		elif model_name == "segformer.b0.640x1280.city.160k":
				_a     :      Any        =							torch.tensor(
				    [
				        [
				            [-1.1_372E01, -1.2_787E01, -1.3_477E01],
				            [-1.2_536E01, -1.4_194E01, -1.4_409E01],
				            [-1.3_217E01, -1.4_888E01, -1.5_327E01],
				        ],
				        [
				            [-1.4_791E01, -1.7_122E01, -1.8_277E01],
				            [-1.7_163E01, -1.9_192E01, -1.9_533E01],
				            [-1.7_897E01, -1.9_991E01, -2.0_315E01],
				        ],
				        [
				            [7.6_723E-01, 4.1_921E-01, -7.7_878E-02],
				            [4.7_772E-01, 9.5_557E-03, -2.8_082E-01],
				            [3.6_032E-01, -2.4_826E-01, -5.1_168E-01],
				        ],
				    ]  )
		elif model_name == "segformer.b0.768x768.city.160k":
				_a     :      Union[str, Any]        =							torch.tensor(
				    [
				        [[-9.4_959, -11.3_087, -11.7_479], [-11.0_025, -12.6_540, -12.3_319], [-11.4_064, -13.0_487, -12.9_905]],
				        [[-9.8_905, -11.3_084, -12.0_854], [-11.1_726, -12.7_698, -12.9_583], [-11.5_985, -13.3_278, -14.1_774]],
				        [[0.2_213, 0.0_192, -0.2_466], [-0.1_731, -0.4_213, -0.4_874], [-0.3_126, -0.6_541, -1.1_389]],
				    ]  )
		elif model_name == "segformer.b1.1024x1024.city.160k":
				_a     :      Dict        =							torch.tensor(
				    [
				        [[-13.5_748, -13.9_111, -12.6_500], [-14.3_500, -15.3_683, -14.2_328], [-14.7_532, -16.0_424, -15.6_087]],
				        [[-17.1_651, -15.8_725, -12.9_653], [-17.2_580, -17.3_718, -14.8_223], [-16.6_058, -16.8_783, -16.7_452]],
				        [[-3.6_456, -3.0_209, -1.4_203], [-3.0_797, -3.1_959, -2.0_000], [-1.8_757, -1.9_217, -1.6_997]],
				    ]  )
		elif model_name == "segformer.b2.1024x1024.city.160k":
				_a     :      Any        =							torch.tensor(
				    [
				        [[-16.0_976, -16.4_856, -17.3_962], [-16.6_234, -19.0_342, -19.7_685], [-16.0_900, -18.0_661, -19.1_180]],
				        [[-18.4_750, -18.8_488, -19.5_074], [-19.4_030, -22.1_570, -22.5_977], [-19.1_191, -20.8_486, -22.3_783]],
				        [[-4.5_178, -5.5_037, -6.5_109], [-5.0_884, -7.2_174, -8.0_334], [-4.4_156, -5.8_117, -7.2_970]],
				    ]  )
		elif model_name == "segformer.b3.1024x1024.city.160k":
				_a     :      Any        =							torch.tensor(
				    [
				        [[-14.2_081, -14.4_732, -14.1_977], [-14.5_867, -16.4_423, -16.6_356], [-13.4_441, -14.9_685, -16.8_696]],
				        [[-14.4_576, -14.7_073, -15.0_451], [-15.0_816, -17.6_237, -17.9_873], [-14.4_213, -16.0_199, -18.5_992]],
				        [[-4.7_349, -4.9_588, -5.0_966], [-4.3_210, -6.9_325, -7.2_591], [-3.4_312, -4.7_484, -7.1_917]],
				    ]  )
		elif model_name == "segformer.b4.1024x1024.city.160k":
				_a     :      Dict        =							torch.tensor(
				    [
				        [[-11.7_737, -11.9_526, -11.3_273], [-13.6_692, -14.4_574, -13.8_878], [-13.8_937, -14.6_924, -15.9_345]],
				        [[-14.6_706, -14.5_330, -14.1_306], [-16.1_502, -16.8_180, -16.4_269], [-16.8_338, -17.8_939, -20.1_746]],
				        [[1.0_491, 0.8_289, 1.0_310], [1.1_044, 0.5_219, 0.8_055], [1.0_899, 0.6_926, 0.5_590]],
				    ]  )
		elif model_name == "segformer.b5.1024x1024.city.160k":
				_a     :      str        =							torch.tensor(
				    [
				        [[-12.5_641, -13.4_777, -13.0_684], [-13.9_587, -15.8_983, -16.6_557], [-13.3_109, -15.7_350, -16.3_141]],
				        [[-14.7_074, -15.4_352, -14.5_944], [-16.6_353, -18.1_663, -18.6_120], [-15.1_702, -18.0_329, -18.1_547]],
				        [[-1.7_990, -2.0_951, -1.7_784], [-2.6_397, -3.8_245, -3.9_686], [-1.5_264, -2.8_126, -2.9_316]],
				    ]  )
		else:
				_a     :      int        =							logits.argmax(-1  ).item()
				print('Predicted class:'						,			model.config.idalabel[predicted_class_idx]  )

		# verify logits
		if not encoder_only:
				assert logits.shape == expected_shape
				assert torch.allclose(logits[0, :3, :3, :3]						,			lowerCAmelCase_						,			atol=1E-2  )

		# finally, save model and image processor
		logger.info(f"""Saving PyTorch model and image processor to {pytorch_dump_folder_path}..."""  )
		Path(lowerCAmelCase_  ).mkdir(exist_ok=lowerCAmelCase_  )
		model.save_pretrained(lowerCAmelCase_  )
		image_processor.save_pretrained(lowerCAmelCase_  )


if __name__ == "__main__":
							__lowerCAmelCase									=   argparse.ArgumentParser()

							parser.add_argument(
							    '''--model_name''',
							    default='''segformer.b0.512x512.ade.160k''',
							    type=str,
							    help='''Name of the model you\'d like to convert.''',
							)
							parser.add_argument(
							    '''--checkpoint_path''', default=None, type=str, help='''Path to the original PyTorch checkpoint (.pth file).'''
							)
							parser.add_argument(
							    '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the folder to output PyTorch model.'''
							)
							__lowerCAmelCase									=   parser.parse_args()
							convert_segformer_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path)









'''simple docstring'''

import argparse
import json

import torch

from diffusers import DDPMScheduler, LDMPipeline, UNetaDModel, VQModel



def      __lowerCamelCase     (      lowerCAmelCase_						,			lowerCAmelCase_=1  )			->      Dict:
		if n_shave_prefix_segments >= 0:
				return ".".join(path.split('.'  )[n_shave_prefix_segments:]  )
		else:
				return ".".join(path.split('.'  )[:n_shave_prefix_segments]  )



def      __lowerCamelCase     (      lowerCAmelCase_						,			lowerCAmelCase_=0  )			->      Tuple:
		_a     :      Any        =							[]
		for old_item in old_list:
				_a     :      Union[str, Any]        =							old_item.replace('in_layers.0'						,			'norm1'  )
				_a     :      Optional[int]        =							new_item.replace('in_layers.2'						,			'conv1'  )

				_a     :      str        =							new_item.replace('out_layers.0'						,			'norm2'  )
				_a     :      List[str]        =							new_item.replace('out_layers.3'						,			'conv2'  )

				_a     :      str        =							new_item.replace('emb_layers.1'						,			'time_emb_proj'  )
				_a     :      Tuple        =							new_item.replace('skip_connection'						,			'conv_shortcut'  )

				_a     :      Any        =							shave_segments(lowerCAmelCase_						,			n_shave_prefix_segments=lowerCAmelCase_  )

				mapping.append({'old': old_item, 'new': new_item}  )

		return mapping



def      __lowerCamelCase     (      lowerCAmelCase_						,			lowerCAmelCase_=0  )			->      Any:
		_a     :      List[str]        =							[]
		for old_item in old_list:
				_a     :      List[Any]        =							old_item

				_a     :      Optional[int]        =							new_item.replace('norm.weight'						,			'group_norm.weight'  )
				_a     :      Optional[Any]        =							new_item.replace('norm.bias'						,			'group_norm.bias'  )

				_a     :      Any        =							new_item.replace('proj_out.weight'						,			'proj_attn.weight'  )
				_a     :      Optional[Any]        =							new_item.replace('proj_out.bias'						,			'proj_attn.bias'  )

				_a     :      Optional[int]        =							shave_segments(lowerCAmelCase_						,			n_shave_prefix_segments=lowerCAmelCase_  )

				mapping.append({'old': old_item, 'new': new_item}  )

		return mapping



def      __lowerCamelCase     (      lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_=None						,			lowerCAmelCase_=None						,			lowerCAmelCase_=None  )			->      Any:
		assert isinstance(lowerCAmelCase_						,			lowerCAmelCase_  ), "Paths should be a list of dicts containing 'old' and 'new' keys."

		# Splits the attention layers into three variables.
		if attention_paths_to_split is not None:
				for path, path_map in attention_paths_to_split.items():
						_a     :      Optional[Any]        =							old_checkpoint[path]
						_a     :      Optional[Any]        =							old_tensor.shape[0] // 3

						_a     :      Any        =							(-1, channels) if len(old_tensor.shape  ) == 3 else (-1)

						_a     :      int        =							old_tensor.shape[0] // config['num_head_channels'] // 3

						_a     :      str        =							old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:]  )
						_a       ,					_a       ,					_a     :      Tuple        =							old_tensor.split(channels // num_heads						,			dim=1  )

						_a     :      Dict        =							query.reshape(lowerCAmelCase_  )
						_a     :      str        =							key.reshape(lowerCAmelCase_  )
						_a     :      Optional[int]        =							value.reshape(lowerCAmelCase_  )

		for path in paths:
				_a     :      Dict        =							path['new']

				# These have already been assigned
				if attention_paths_to_split is not None and new_path in attention_paths_to_split:
						continue

				# Global renaming happens here
				_a     :      Any        =							new_path.replace('middle_block.0'						,			'mid_block.resnets.0'  )
				_a     :      str        =							new_path.replace('middle_block.1'						,			'mid_block.attentions.0'  )
				_a     :      Union[str, Any]        =							new_path.replace('middle_block.2'						,			'mid_block.resnets.1'  )

				if additional_replacements is not None:
						for replacement in additional_replacements:
								_a     :      int        =							new_path.replace(replacement['old']						,			replacement['new']  )

        # proj_attn.weight has to be converted from conv 1D to linear
				if "proj_attn.weight" in new_path:
						_a     :      List[str]        =							old_checkpoint[path['old']][:, :, 0]
				else:
						_a     :      Dict        =							old_checkpoint[path['old']]



def      __lowerCamelCase     (      lowerCAmelCase_						,			lowerCAmelCase_  )			->      List[Any]:
		_a     :      Optional[int]        =							{}

		_a     :      Dict        =							checkpoint['time_embed.0.weight']
		_a     :      Tuple        =							checkpoint['time_embed.0.bias']
		_a     :      Union[str, Any]        =							checkpoint['time_embed.2.weight']
		_a     :      List[str]        =							checkpoint['time_embed.2.bias']

		_a     :      List[str]        =							checkpoint['input_blocks.0.0.weight']
		_a     :      Union[str, Any]        =							checkpoint['input_blocks.0.0.bias']

		_a     :      Optional[int]        =							checkpoint['out.0.weight']
		_a     :      int        =							checkpoint['out.0.bias']
		_a     :      List[str]        =							checkpoint['out.2.weight']
		_a     :      Optional[int]        =							checkpoint['out.2.bias']

		# Retrieves the keys for the input blocks only
		_a     :      Optional[int]        =							len({'.'.join(layer.split('.'  )[:2]  ) for layer in checkpoint if 'input_blocks' in layer}  )
		_a     :      Dict        =							{
		    layer_id: [key for key in checkpoint if f"""input_blocks.{layer_id}""" in key]
		    for layer_id in range(lowerCAmelCase_  )
		}

		# Retrieves the keys for the middle blocks only
		_a     :      List[Any]        =							len({'.'.join(layer.split('.'  )[:2]  ) for layer in checkpoint if 'middle_block' in layer}  )
		_a     :      Union[str, Any]        =							{
		    layer_id: [key for key in checkpoint if f"""middle_block.{layer_id}""" in key]
		    for layer_id in range(lowerCAmelCase_  )
		}

		# Retrieves the keys for the output blocks only
		_a     :      Optional[int]        =							len({'.'.join(layer.split('.'  )[:2]  ) for layer in checkpoint if 'output_blocks' in layer}  )
		_a     :      str        =							{
		    layer_id: [key for key in checkpoint if f"""output_blocks.{layer_id}""" in key]
		    for layer_id in range(lowerCAmelCase_  )
		}

		for i in range(1						,			lowerCAmelCase_  ):
				_a     :      List[Any]        =							(i - 1) // (config['num_res_blocks'] + 1)
				_a     :      Optional[int]        =							(i - 1) % (config['num_res_blocks'] + 1)

				_a     :      Optional[int]        =							[key for key in input_blocks[i] if f"""input_blocks.{i}.0""" in key]
				_a     :      Optional[Any]        =							[key for key in input_blocks[i] if f"""input_blocks.{i}.1""" in key]

				if f"""input_blocks.{i}.0.op.weight""" in checkpoint:
						_a     :      List[Any]        =							checkpoint[
						    f"""input_blocks.{i}.0.op.weight"""
						]
						_a     :      Union[str, Any]        =							checkpoint[
						    f"""input_blocks.{i}.0.op.bias"""
						]
						continue

				_a     :      Any        =							renew_resnet_paths(lowerCAmelCase_  )
				_a     :      List[str]        =							{'old': f"""input_blocks.{i}.0""", 'new': f"""down_blocks.{block_id}.resnets.{layer_in_block_id}"""}
				_a     :      Optional[Any]        =							{'old': 'resnets.2.op', 'new': 'downsamplers.0.op'}
				assign_to_checkpoint(
				    lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			additional_replacements=[meta_path, resnet_op]						,			config=lowerCAmelCase_  )

				if len(lowerCAmelCase_  ):
						_a     :      List[str]        =							renew_attention_paths(lowerCAmelCase_  )
						_a     :      List[Any]        =							{
						    'old': f"""input_blocks.{i}.1""",
						    'new': f"""down_blocks.{block_id}.attentions.{layer_in_block_id}""",
						}
						_a     :      Optional[Any]        =							{
						    f"""input_blocks.{i}.1.qkv.bias""": {
						        'key': f"""down_blocks.{block_id}.attentions.{layer_in_block_id}.key.bias""",
						        'query': f"""down_blocks.{block_id}.attentions.{layer_in_block_id}.query.bias""",
						        'value': f"""down_blocks.{block_id}.attentions.{layer_in_block_id}.value.bias""",
						    },
						    f"""input_blocks.{i}.1.qkv.weight""": {
						        'key': f"""down_blocks.{block_id}.attentions.{layer_in_block_id}.key.weight""",
						        'query': f"""down_blocks.{block_id}.attentions.{layer_in_block_id}.query.weight""",
						        'value': f"""down_blocks.{block_id}.attentions.{layer_in_block_id}.value.weight""",
						    },
						}
						assign_to_checkpoint(
						    lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			additional_replacements=[meta_path]						,			attention_paths_to_split=lowerCAmelCase_						,			config=lowerCAmelCase_						,			)

		_a     :      str        =							middle_blocks[0]
		_a     :      Tuple        =							middle_blocks[1]
		_a     :      Any        =							middle_blocks[2]

		_a     :      List[Any]        =							renew_resnet_paths(lowerCAmelCase_  )
		assign_to_checkpoint(lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			config=lowerCAmelCase_  )

		_a     :      Any        =							renew_resnet_paths(lowerCAmelCase_  )
		assign_to_checkpoint(lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			config=lowerCAmelCase_  )

		_a     :      int        =							renew_attention_paths(lowerCAmelCase_  )
		_a     :      int        =							{
		    'middle_block.1.qkv.bias': {
		        'key': 'mid_block.attentions.0.key.bias',
		        'query': 'mid_block.attentions.0.query.bias',
		        'value': 'mid_block.attentions.0.value.bias',
		    },
		    'middle_block.1.qkv.weight': {
		        'key': 'mid_block.attentions.0.key.weight',
		        'query': 'mid_block.attentions.0.query.weight',
		        'value': 'mid_block.attentions.0.value.weight',
		    },
		}
		assign_to_checkpoint(
		    lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			attention_paths_to_split=lowerCAmelCase_						,			config=lowerCAmelCase_  )

		for i in range(lowerCAmelCase_  ):
				_a     :      List[str]        =							i // (config['num_res_blocks'] + 1)
				_a     :      Any        =							i % (config['num_res_blocks'] + 1)
				_a     :      Union[str, Any]        =							[shave_segments(lowerCAmelCase_						,			2  ) for name in output_blocks[i]]
				_a     :      Optional[Any]        =							{}

				for layer in output_block_layers:
						_a       ,					_a     :      str        =							layer.split('.'  )[0], shave_segments(lowerCAmelCase_						,			1  )
						if layer_id in output_block_list:
								output_block_list[layer_id].append(lowerCAmelCase_  )
						else:
								_a     :      str        =							[layer_name]

				if len(lowerCAmelCase_  ) > 1:
						_a     :      str        =							[key for key in output_blocks[i] if f"""output_blocks.{i}.0""" in key]
						_a     :      Optional[Any]        =							[key for key in output_blocks[i] if f"""output_blocks.{i}.1""" in key]

						_a     :      Dict        =							renew_resnet_paths(lowerCAmelCase_  )
						_a     :      str        =							renew_resnet_paths(lowerCAmelCase_  )

						_a     :      Optional[int]        =							{'old': f"""output_blocks.{i}.0""", 'new': f"""up_blocks.{block_id}.resnets.{layer_in_block_id}"""}
						assign_to_checkpoint(lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			additional_replacements=[meta_path]						,			config=lowerCAmelCase_  )

						if ["conv.weight", "conv.bias"] in output_block_list.values():
								_a     :      List[Any]        =							list(output_block_list.values()  ).index(['conv.weight', 'conv.bias']  )
								_a     :      Tuple        =							checkpoint[
								    f"""output_blocks.{i}.{index}.conv.weight"""
								]
								_a     :      List[str]        =							checkpoint[
								    f"""output_blocks.{i}.{index}.conv.bias"""
								]

								# Clear attentions as they have been attributed above.
								if len(lowerCAmelCase_  ) == 2:
										_a     :      Union[str, Any]        =							[]

						if len(lowerCAmelCase_  ):
								_a     :      Tuple        =							renew_attention_paths(lowerCAmelCase_  )
								_a     :      str        =							{
								    'old': f"""output_blocks.{i}.1""",
								    'new': f"""up_blocks.{block_id}.attentions.{layer_in_block_id}""",
								}
								_a     :      List[Any]        =							{
								    f"""output_blocks.{i}.1.qkv.bias""": {
								        'key': f"""up_blocks.{block_id}.attentions.{layer_in_block_id}.key.bias""",
								        'query': f"""up_blocks.{block_id}.attentions.{layer_in_block_id}.query.bias""",
								        'value': f"""up_blocks.{block_id}.attentions.{layer_in_block_id}.value.bias""",
								    },
								    f"""output_blocks.{i}.1.qkv.weight""": {
								        'key': f"""up_blocks.{block_id}.attentions.{layer_in_block_id}.key.weight""",
								        'query': f"""up_blocks.{block_id}.attentions.{layer_in_block_id}.query.weight""",
								        'value': f"""up_blocks.{block_id}.attentions.{layer_in_block_id}.value.weight""",
								    },
								}
								assign_to_checkpoint(
								    lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			additional_replacements=[meta_path]						,			attention_paths_to_split=to_split if any('qkv' in key for key in attentions  ) else None						,			config=lowerCAmelCase_						,			)
				else:
						_a     :      List[Any]        =							renew_resnet_paths(lowerCAmelCase_						,			n_shave_prefix_segments=1  )
						for path in resnet_0_paths:
								_a     :      int        =							'.'.join(['output_blocks', str(lowerCAmelCase_  ), path['old']]  )
								_a     :      Union[str, Any]        =							'.'.join(['up_blocks', str(lowerCAmelCase_  ), 'resnets', str(lowerCAmelCase_  ), path['new']]  )

								_a     :      Union[str, Any]        =							checkpoint[old_path]

		return new_checkpoint


if __name__ == "__main__":
							__lowerCAmelCase									=   argparse.ArgumentParser()

							parser.add_argument(
							    '''--checkpoint_path''', default=None, type=str, required=True, help='''Path to the checkpoint to convert.'''
							)

							parser.add_argument(
							    '''--config_file''',
							    default=None,
							    type=str,
							    required=True,
							    help='''The config json file corresponding to the architecture.''',
							)

							parser.add_argument('''--dump_path''', default=None, type=str, required=True, help='''Path to the output model.''')

							__lowerCAmelCase									=   parser.parse_args()

							__lowerCAmelCase									=   torch.load(args.checkpoint_path)

							with open(args.config_file) as f:
														__lowerCAmelCase									=   json.loads(f.read())

							__lowerCAmelCase									=   convert_ldm_checkpoint(checkpoint, config)

							if "ldm" in config:
														del config["ldm"]

							__lowerCAmelCase									=   UNetaDModel(**config)
							model.load_state_dict(converted_checkpoint)

							try:
														__lowerCAmelCase									=   DDPMScheduler.from_config('''/'''.join(args.checkpoint_path.split('''/''')[:-1]))
														__lowerCAmelCase									=   VQModel.from_pretrained('''/'''.join(args.checkpoint_path.split('''/''')[:-1]))

														__lowerCAmelCase									=   LDMPipeline(unet=model, scheduler=scheduler, vae=vqvae)
														pipe.save_pretrained(args.dump_path)
							except:  # noqa: E722
														model.save_pretrained(args.dump_path)




