





import argparse

import torch

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_from_original_stable_diffusion_ckpt


if __name__ == "__main__":
					A       :   Any					= argparse.ArgumentParser()

					parser.add_argument(
					    '''--checkpoint_path''', default=None, type=str, required=True, help='''Path to the checkpoint to convert.'''
					)
					# !wget https://raw.githubusercontent.com/CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml
					parser.add_argument(
					    '''--original_config_file''',
					    default=None,
					    type=str,
					    help='''The YAML config file corresponding to the original architecture.''',
					)
					parser.add_argument(
					    '''--num_in_channels''',
					    default=None,
					    type=int,
					    help='''The number of input channels. If `None` number of input channels will be automatically inferred.''',
					)
					parser.add_argument(
					    '''--scheduler_type''',
					    default='''pndm''',
					    type=str,
					    help='''Type of scheduler to use. Should be one of [\'pndm\', \'lms\', \'ddim\', \'euler\', \'euler-ancestral\', \'dpm\']''',
					)
					parser.add_argument(
					    '''--pipeline_type''',
					    default=None,
					    type=str,
					    help=(
					        '''The pipeline type. One of \'FrozenOpenCLIPEmbedder\', \'FrozenCLIPEmbedder\', \'PaintByExample\''''
					        '''. If `None` pipeline will be automatically inferred.'''
					    ),
					)
					parser.add_argument(
					    '''--image_size''',
					    default=None,
					    type=int,
					    help=(
					        '''The image size that the model was trained on. Use 512 for Stable Diffusion v1.X and Stable Siffusion v2'''
					        ''' Base. Use 768 for Stable Diffusion v2.'''
					    ),
					)
					parser.add_argument(
					    '''--prediction_type''',
					    default=None,
					    type=str,
					    help=(
					        '''The prediction type that the model was trained on. Use \'epsilon\' for Stable Diffusion v1.X and Stable'''
					        ''' Diffusion v2 Base. Use \'v_prediction\' for Stable Diffusion v2.'''
					    ),
					)
					parser.add_argument(
					    '''--extract_ema''',
					    action='''store_true''',
					    help=(
					        '''Only relevant for checkpoints that have both EMA and non-EMA weights. Whether to extract the EMA weights'''
					        ''' or not. Defaults to `False`. Add `--extract_ema` to extract the EMA weights. EMA weights usually yield'''
					        ''' higher quality images for inference. Non-EMA weights are usually better to continue fine-tuning.'''
					    ),
					)
					parser.add_argument(
					    '''--upcast_attention''',
					    action='''store_true''',
					    help=(
					        '''Whether the attention computation should always be upcasted. This is necessary when running stable'''
					        ''' diffusion 2.1.'''
					    ),
					)
					parser.add_argument(
					    '''--from_safetensors''',
					    action='''store_true''',
					    help='''If `--checkpoint_path` is in `safetensors` format, load checkpoint with safetensors instead of PyTorch.''',
					)
					parser.add_argument(
					    '''--to_safetensors''',
					    action='''store_true''',
					    help='''Whether to store pipeline in safetensors format or not.''',
					)
					parser.add_argument('''--dump_path''', default=None, type=str, required=True, help='''Path to the output model.''')
					parser.add_argument('''--device''', type=str, help='''Device to use (e.g. cpu, cuda:0, cuda:1, etc.)''')
					parser.add_argument(
					    '''--stable_unclip''',
					    type=str,
					    default=None,
					    required=False,
					    help='''Set if this is a stable unCLIP model. One of \'txt2img\' or \'img2img\'.''',
					)
					parser.add_argument(
					    '''--stable_unclip_prior''',
					    type=str,
					    default=None,
					    required=False,
					    help='''Set if this is a stable unCLIP txt2img model. Selects which prior to use. If `--stable_unclip` is set to `txt2img`, the karlo prior (https://huggingface.co/kakaobrain/karlo-v1-alpha/tree/main/prior) is selected by default.''',
					)
					parser.add_argument(
					    '''--clip_stats_path''',
					    type=str,
					    help='''Path to the clip stats file. Only required if the stable unclip model\'s config specifies `model.params.noise_aug_config.params.clip_stats_path`.''',
					    required=False,
					)
					parser.add_argument(
					    '''--controlnet''', action='''store_true''', default=None, help='''Set flag if this is a controlnet checkpoint.'''
					)
					parser.add_argument('''--half''', action='''store_true''', help='''Save weights in half precision.''')
					parser.add_argument(
					    '''--vae_path''',
					    type=str,
					    default=None,
					    required=False,
					    help='''Set to a path, hub id to an already converted vae to not convert it again.''',
					)
					A       :   Union[str, Any]					= parser.parse_args()

					A       :   List[Any]					= download_from_original_stable_diffusion_ckpt(
					    checkpoint_path=args.checkpoint_path,
					    original_config_file=args.original_config_file,
					    image_size=args.image_size,
					    prediction_type=args.prediction_type,
					    model_type=args.pipeline_type,
					    extract_ema=args.extract_ema,
					    scheduler_type=args.scheduler_type,
					    num_in_channels=args.num_in_channels,
					    upcast_attention=args.upcast_attention,
					    from_safetensors=args.from_safetensors,
					    device=args.device,
					    stable_unclip=args.stable_unclip,
					    stable_unclip_prior=args.stable_unclip_prior,
					    clip_stats_path=args.clip_stats_path,
					    controlnet=args.controlnet,
					    vae_path=args.vae_path,
					)

					if args.half:
										pipe.to(torch_dtype=torch.floataa)

					if args.controlnet:
										# only save the controlnet model
										pipe.controlnet.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)
					else:
										pipe.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)










import argparse
import json

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

from transformers import ConvNextConfig, SegformerImageProcessor, UperNetConfig, UperNetForSemanticSegmentation







def   __lowerCamelCase   (				__a  :int       )						->							int:






		"""simple docstring"""

		A__     =  3_8_4
		if "tiny" in model_name:
				A__     =  [3, 3, 9, 3]
				A__     =  [9_6, 1_9_2, 3_8_4, 7_6_8]
		if "small" in model_name:
				A__     =  [3, 3, 2_7, 3]
				A__     =  [9_6, 1_9_2, 3_8_4, 7_6_8]
		if "base" in model_name:
				A__     =  [3, 3, 2_7, 3]
				A__     =  [1_2_8, 2_5_6, 5_1_2, 1_0_2_4]
				A__     =  5_1_2
		if "large" in model_name:
				A__     =  [3, 3, 2_7, 3]
				A__     =  [1_9_2, 3_8_4, 7_6_8, 1_5_3_6]
				A__     =  7_6_8
		if "xlarge" in model_name:
				A__     =  [3, 3, 2_7, 3]
				A__     =  [2_5_6, 5_1_2, 1_0_2_4, 2_0_4_8]
				A__     =  1_0_2_4

		# set label information
		A__     =  1_5_0
		A__     =  """huggingface/label-files"""
		A__     =  """ade20k-id2label.json"""
		A__     =  json.load(open(hf_hub_download(__a       ,	__a       ,	repo_type="""dataset"""       )       ,	"""r"""       )       )
		A__     =  {int(__a       ): v for k, v in idalabel.items()}
		A__     =  {v: k for k, v in idalabel.items()}

		A__     =  ConvNextConfig(
		    depths=__a       ,	hidden_sizes=__a       ,	out_features=["""stage1""", """stage2""", """stage3""", """stage4"""]       )
		A__     =  UperNetConfig(
		    backbone_config=__a       ,	auxiliary_in_channels=__a       ,	num_labels=__a       ,	idalabel=__a       ,	labelaid=__a       ,	)

		return config







def   __lowerCamelCase   (				__a  :Union[str, Any]       )						->							List[Any]:






		"""simple docstring"""

		A__     =  []

		# fmt: off
		# stem
		rename_keys.append(("""backbone.downsample_layers.0.0.weight""", """backbone.embeddings.patch_embeddings.weight""")       )
		rename_keys.append(("""backbone.downsample_layers.0.0.bias""", """backbone.embeddings.patch_embeddings.bias""")       )
		rename_keys.append(("""backbone.downsample_layers.0.1.weight""", """backbone.embeddings.layernorm.weight""")       )
		rename_keys.append(("""backbone.downsample_layers.0.1.bias""", """backbone.embeddings.layernorm.bias""")       )
		# stages
		for i in range(len(config.backbone_config.depths       )       ):
				for j in range(config.backbone_config.depths[i]       ):
						rename_keys.append((F'backbone.stages.{i}.{j}.gamma', F'backbone.encoder.stages.{i}.layers.{j}.layer_scale_parameter')       )
						rename_keys.append((F'backbone.stages.{i}.{j}.depthwise_conv.weight', F'backbone.encoder.stages.{i}.layers.{j}.dwconv.weight')       )
						rename_keys.append((F'backbone.stages.{i}.{j}.depthwise_conv.bias', F'backbone.encoder.stages.{i}.layers.{j}.dwconv.bias')       )
						rename_keys.append((F'backbone.stages.{i}.{j}.norm.weight', F'backbone.encoder.stages.{i}.layers.{j}.layernorm.weight')       )
						rename_keys.append((F'backbone.stages.{i}.{j}.norm.bias', F'backbone.encoder.stages.{i}.layers.{j}.layernorm.bias')       )
						rename_keys.append((F'backbone.stages.{i}.{j}.pointwise_conv1.weight', F'backbone.encoder.stages.{i}.layers.{j}.pwconv1.weight')       )
						rename_keys.append((F'backbone.stages.{i}.{j}.pointwise_conv1.bias', F'backbone.encoder.stages.{i}.layers.{j}.pwconv1.bias')       )
						rename_keys.append((F'backbone.stages.{i}.{j}.pointwise_conv2.weight', F'backbone.encoder.stages.{i}.layers.{j}.pwconv2.weight')       )
						rename_keys.append((F'backbone.stages.{i}.{j}.pointwise_conv2.bias', F'backbone.encoder.stages.{i}.layers.{j}.pwconv2.bias')       )
				if i > 0:
						rename_keys.append((F'backbone.downsample_layers.{i}.0.weight', F'backbone.encoder.stages.{i}.downsampling_layer.0.weight')       )
						rename_keys.append((F'backbone.downsample_layers.{i}.0.bias', F'backbone.encoder.stages.{i}.downsampling_layer.0.bias')       )
						rename_keys.append((F'backbone.downsample_layers.{i}.1.weight', F'backbone.encoder.stages.{i}.downsampling_layer.1.weight')       )
						rename_keys.append((F'backbone.downsample_layers.{i}.1.bias', F'backbone.encoder.stages.{i}.downsampling_layer.1.bias')       )

				rename_keys.append((F'backbone.norm{i}.weight', F'backbone.hidden_states_norms.stage{i+1}.weight')       )
				rename_keys.append((F'backbone.norm{i}.bias', F'backbone.hidden_states_norms.stage{i+1}.bias')       )

		# decode head
		rename_keys.extend(
		    [
		        ("""decode_head.conv_seg.weight""", """decode_head.classifier.weight"""),
		        ("""decode_head.conv_seg.bias""", """decode_head.classifier.bias"""),
		        ("""auxiliary_head.conv_seg.weight""", """auxiliary_head.classifier.weight"""),
		        ("""auxiliary_head.conv_seg.bias""", """auxiliary_head.classifier.bias"""),
		    ]       )
		# fmt: on

		return rename_keys







def   __lowerCamelCase   (				__a  :Union[str, Any]       ,	__a  :List[str]       ,	__a  :str       )						->							str:






		"""simple docstring"""

		A__     =  dct.pop(__a       )
		A__     =  val







def   __lowerCamelCase   (				__a  :Any       ,	__a  :int       ,	__a  :Any       )						->							List[Any]:






		"""simple docstring"""

		A__     =  {
		    """upernet-convnext-tiny""": """https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_tiny_fp16_512x512_160k_ade20k/upernet_convnext_tiny_fp16_512x512_160k_ade20k_20220227_124553-cad485de.pth""",
		    """upernet-convnext-small""": """https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_small_fp16_512x512_160k_ade20k/upernet_convnext_small_fp16_512x512_160k_ade20k_20220227_131208-1b1e394f.pth""",
		    """upernet-convnext-base""": """https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_base_fp16_512x512_160k_ade20k/upernet_convnext_base_fp16_512x512_160k_ade20k_20220227_181227-02a24fc6.pth""",
		    """upernet-convnext-large""": """https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_large_fp16_640x640_160k_ade20k/upernet_convnext_large_fp16_640x640_160k_ade20k_20220226_040532-e57aa54d.pth""",
		    """upernet-convnext-xlarge""": """https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_xlarge_fp16_640x640_160k_ade20k/upernet_convnext_xlarge_fp16_640x640_160k_ade20k_20220226_080344-95fc38c2.pth""",
		}
		A__     =  model_name_to_url[model_name]
		A__     =  torch.hub.load_state_dict_from_url(__a       ,	map_location="""cpu"""       )["""state_dict"""]

		A__     =  get_upernet_config(__a       )
		A__     =  UperNetForSemanticSegmentation(__a       )
		model.eval()

		# replace "bn" => "batch_norm"
		for key in state_dict.copy().keys():
				A__     =  state_dict.pop(__a       )
				if "bn" in key:
						A__     =  key.replace("""bn"""       ,	"""batch_norm"""       )
				A__     =  val

		# rename keys
		A__     =  create_rename_keys(__a       )
		for src, dest in rename_keys:
				rename_key(__a       ,	__a       ,	__a       )

		model.load_state_dict(__a       )

		# verify on image
		A__     =  """https://huggingface.co/datasets/hf-internal-testing/fixtures_ade20k/resolve/main/ADE_val_00000001.jpg"""
		A__     =  Image.open(requests.get(__a       ,	stream=__a       ).raw       ).convert("""RGB"""       )

		A__     =  SegformerImageProcessor()
		A__     =  processor(__a       ,	return_tensors="""pt"""       ).pixel_values

		with torch.no_grad():
				A__     =  model(__a       )

		if model_name == "upernet-convnext-tiny":
				A__     =  torch.tensor(
				    [[-8.8110, -8.8110, -8.6521], [-8.8110, -8.8110, -8.6521], [-8.7746, -8.7746, -8.6130]]       )
		elif model_name == "upernet-convnext-small":
				A__     =  torch.tensor(
				    [[-8.8236, -8.8236, -8.6771], [-8.8236, -8.8236, -8.6771], [-8.7638, -8.7638, -8.6240]]       )
		elif model_name == "upernet-convnext-base":
				A__     =  torch.tensor(
				    [[-8.8558, -8.8558, -8.6905], [-8.8558, -8.8558, -8.6905], [-8.7669, -8.7669, -8.6021]]       )
		elif model_name == "upernet-convnext-large":
				A__     =  torch.tensor(
				    [[-8.6660, -8.6660, -8.6210], [-8.6660, -8.6660, -8.6210], [-8.6310, -8.6310, -8.5964]]       )
		elif model_name == "upernet-convnext-xlarge":
				A__     =  torch.tensor(
				    [[-8.4980, -8.4980, -8.3977], [-8.4980, -8.4980, -8.3977], [-8.4379, -8.4379, -8.3412]]       )
		print("""Logits:"""       ,	outputs.logits[0, 0, :3, :3]       )
		assert torch.allclose(outputs.logits[0, 0, :3, :3]       ,	__a       ,	atol=1E-4       )
		print("""Looks ok!"""       )

		if pytorch_dump_folder_path is not None:
				print(F'Saving model {model_name} to {pytorch_dump_folder_path}'       )
				model.save_pretrained(__a       )
				print(F'Saving processor to {pytorch_dump_folder_path}'       )
				processor.save_pretrained(__a       )

		if push_to_hub:
				print(F'Pushing model and processor for {model_name} to hub'       )
				model.push_to_hub(F'openmmlab/{model_name}'       )
				processor.push_to_hub(F'openmmlab/{model_name}'       )


if __name__ == "__main__":
					A       :   Optional[int]					= argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    '''--model_name''',
					    default='''upernet-convnext-tiny''',
					    type=str,
					    choices=[F'''upernet-convnext-{size}''' for size in ['''tiny''', '''small''', '''base''', '''large''', '''xlarge''']],
					    help='''Name of the ConvNext UperNet model you\'d like to convert.''',
					)
					parser.add_argument(
					    '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
					)
					parser.add_argument(
					    '''--push_to_hub''', action='''store_true''', help='''Whether or not to push the converted model to the 🤗 hub.'''
					)

					A       :   str					= parser.parse_args()
					convert_upernet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)



