

import argparse
import os.path as osp
import re

import torch
from safetensors.torch import load_file, save_file


# =================#
# UNet Conversion #
# =================#

a_						     =    [
    # (stable-diffusion, HF Diffusers)
    ('time_embed.0.weight', 'time_embedding.linear_1.weight'),
    ('time_embed.0.bias', 'time_embedding.linear_1.bias'),
    ('time_embed.2.weight', 'time_embedding.linear_2.weight'),
    ('time_embed.2.bias', 'time_embedding.linear_2.bias'),
    ('input_blocks.0.0.weight', 'conv_in.weight'),
    ('input_blocks.0.0.bias', 'conv_in.bias'),
    ('out.0.weight', 'conv_norm_out.weight'),
    ('out.0.bias', 'conv_norm_out.bias'),
    ('out.2.weight', 'conv_out.weight'),
    ('out.2.bias', 'conv_out.bias'),
]

a_						     =    [
    # (stable-diffusion, HF Diffusers)
    ('in_layers.0', 'norm1'),
    ('in_layers.2', 'conv1'),
    ('out_layers.0', 'norm2'),
    ('out_layers.3', 'conv2'),
    ('emb_layers.1', 'time_emb_proj'),
    ('skip_connection', 'conv_shortcut'),
]

a_						     =    []
# hardcoded number of downblocks and resnets/attentions...
# would need smarter logic for other networks.
for i in range(4):
			# loop over downblocks/upblocks

			for j in range(2):
						# loop over resnets/attentions for downblocks
						a_						     =    F'''down_blocks.{i}.resnets.{j}.'''
						a_						     =    F'''input_blocks.{3*i + j + 1}.0.'''
						unet_conversion_map_layer.append((sd_down_res_prefix, hf_down_res_prefix))

						if i < 3:
									# no attention layers in down_blocks.3
									a_						     =    F'''down_blocks.{i}.attentions.{j}.'''
									a_						     =    F'''input_blocks.{3*i + j + 1}.1.'''
									unet_conversion_map_layer.append((sd_down_atn_prefix, hf_down_atn_prefix))

			for j in range(3):
						# loop over resnets/attentions for upblocks
						a_						     =    F'''up_blocks.{i}.resnets.{j}.'''
						a_						     =    F'''output_blocks.{3*i + j}.0.'''
						unet_conversion_map_layer.append((sd_up_res_prefix, hf_up_res_prefix))

						if i > 0:
									# no attention layers in up_blocks.0
									a_						     =    F'''up_blocks.{i}.attentions.{j}.'''
									a_						     =    F'''output_blocks.{3*i + j}.1.'''
									unet_conversion_map_layer.append((sd_up_atn_prefix, hf_up_atn_prefix))

			if i < 3:
						# no downsample in down_blocks.3
						a_						     =    F'''down_blocks.{i}.downsamplers.0.conv.'''
						a_						     =    F'''input_blocks.{3*(i+1)}.0.op.'''
						unet_conversion_map_layer.append((sd_downsample_prefix, hf_downsample_prefix))

						# no upsample in up_blocks.3
						a_						     =    F'''up_blocks.{i}.upsamplers.0.'''
						a_						     =    F'''output_blocks.{3*i + 2}.{1 if i == 0 else 2}.'''
						unet_conversion_map_layer.append((sd_upsample_prefix, hf_upsample_prefix))

a_						     =    'mid_block.attentions.0.'
a_						     =    'middle_block.1.'
unet_conversion_map_layer.append((sd_mid_atn_prefix, hf_mid_atn_prefix))

for j in range(2):
			a_						     =    F'''mid_block.resnets.{j}.'''
			a_						     =    F'''middle_block.{2*j}.'''
			unet_conversion_map_layer.append((sd_mid_res_prefix, hf_mid_res_prefix))

def  lowerCamelCase__ (					_a):
	# buyer beware: this is a *brittle* function,
	# and correct output requires that all of these pieces interact in
	# the exact order in which I have arranged them.
	SCREAMING_SNAKE_CASE   :      str				     =       {k: k for k in unet_state_dict.keys()}
	for sd_name, hf_name in unet_conversion_map:
		SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       sd_name
	for k, v in mapping.items():
		if "resnets" in k:
			for sd_part, hf_part in unet_conversion_map_resnet:
				SCREAMING_SNAKE_CASE   :      List[Any]				     =       v.replace(_a							,			_a)
			SCREAMING_SNAKE_CASE   :      Tuple				     =       v
	for k, v in mapping.items():
		for sd_part, hf_part in unet_conversion_map_layer:
			SCREAMING_SNAKE_CASE   :      List[str]				     =       v.replace(_a							,			_a)
		SCREAMING_SNAKE_CASE   :      int				     =       v
	SCREAMING_SNAKE_CASE   :      Tuple				     =       {v: unet_state_dict[k] for k, v in mapping.items()}
	return new_state_dict


# ================#
# VAE Conversion #
# ================#

a_						     =    [
    # (stable-diffusion, HF Diffusers)
    ('nin_shortcut', 'conv_shortcut'),
    ('norm_out', 'conv_norm_out'),
    ('mid.attn_1.', 'mid_block.attentions.0.'),
]

for i in range(4):
			# down_blocks have two resnets
			for j in range(2):
						a_						     =    F'''encoder.down_blocks.{i}.resnets.{j}.'''
						a_						     =    F'''encoder.down.{i}.block.{j}.'''
						vae_conversion_map.append((sd_down_prefix, hf_down_prefix))

			if i < 3:
						a_						     =    F'''down_blocks.{i}.downsamplers.0.'''
						a_						     =    F'''down.{i}.downsample.'''
						vae_conversion_map.append((sd_downsample_prefix, hf_downsample_prefix))

						a_						     =    F'''up_blocks.{i}.upsamplers.0.'''
						a_						     =    F'''up.{3-i}.upsample.'''
						vae_conversion_map.append((sd_upsample_prefix, hf_upsample_prefix))

			# up_blocks have three resnets
			# also, up blocks in hf are numbered in reverse from sd
			for j in range(3):
						a_						     =    F'''decoder.up_blocks.{i}.resnets.{j}.'''
						a_						     =    F'''decoder.up.{3-i}.block.{j}.'''
						vae_conversion_map.append((sd_up_prefix, hf_up_prefix))

# this part accounts for mid blocks in both the encoder and the decoder
for i in range(2):
			a_						     =    F'''mid_block.resnets.{i}.'''
			a_						     =    F'''mid.block_{i+1}.'''
			vae_conversion_map.append((sd_mid_res_prefix, hf_mid_res_prefix))


a_						     =    [
    # (stable-diffusion, HF Diffusers)
    ('norm.', 'group_norm.'),
    ('q.', 'query.'),
    ('k.', 'key.'),
    ('v.', 'value.'),
    ('proj_out.', 'proj_attn.'),
]

def  lowerCamelCase__ (					_a):
	# convert HF linear weights to SD conv2d weights
	return w.reshape(*w.shape							,			1							,			1)

def  lowerCamelCase__ (					_a):
	SCREAMING_SNAKE_CASE   :      Dict				     =       {k: k for k in vae_state_dict.keys()}
	for k, v in mapping.items():
		for sd_part, hf_part in vae_conversion_map:
			SCREAMING_SNAKE_CASE   :      str				     =       v.replace(_a							,			_a)
		SCREAMING_SNAKE_CASE   :      int				     =       v
	for k, v in mapping.items():
		if "attentions" in k:
			for sd_part, hf_part in vae_conversion_map_attn:
				SCREAMING_SNAKE_CASE   :      List[str]				     =       v.replace(_a							,			_a)
			SCREAMING_SNAKE_CASE   :      int				     =       v
	SCREAMING_SNAKE_CASE   :      Dict				     =       {v: vae_state_dict[k] for k, v in mapping.items()}
	SCREAMING_SNAKE_CASE   :      int				     =       ["q", "k", "v", "proj_out"]
	for k, v in new_state_dict.items():
		for weight_name in weights_to_convert:
			if f"mid.attn_1.{weight_name}.weight" in k:
				print(f"Reshaping {k} for SD format")
				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       reshape_weight_for_sd(_a)
	return new_state_dict


# =========================#
# Text Encoder Conversion #
# =========================#


a_						     =    [
    # (stable-diffusion, HF Diffusers)
    ('resblocks.', 'text_model.encoder.layers.'),
    ('ln_1', 'layer_norm1'),
    ('ln_2', 'layer_norm2'),
    ('.c_fc.', '.fc1.'),
    ('.c_proj.', '.fc2.'),
    ('.attn', '.self_attn'),
    ('ln_final.', 'transformer.text_model.final_layer_norm.'),
    ('token_embedding.weight', 'transformer.text_model.embeddings.token_embedding.weight'),
    ('positional_embedding', 'transformer.text_model.embeddings.position_embedding.weight'),
]
a_						     =    {re.escape(x[1]): x[0] for x in textenc_conversion_lst}
a_						     =    re.compile('|'.join(protected.keys()))

# Ordering is from https://github.com/pytorch/pytorch/blob/master/test/cpp/api/modules.cpp
a_						     =    {'q': 0, 'k': 1, 'v': 2}

def  lowerCamelCase__ (					_a):
	SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       {}
	SCREAMING_SNAKE_CASE   :      List[str]				     =       {}
	SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       {}
	for k, v in text_enc_dict.items():
		if (
		    k.endswith(".self_attn.q_proj.weight")
		    or k.endswith(".self_attn.k_proj.weight")
		    or k.endswith(".self_attn.v_proj.weight")
		):
			SCREAMING_SNAKE_CASE   :      str				     =       k[: -len(".q_proj.weight")]
			SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       k[-len("q_proj.weight")]
			if k_pre not in capture_qkv_weight:
				SCREAMING_SNAKE_CASE   :      int				     =       [None, None, None]
			SCREAMING_SNAKE_CASE   :      List[str]				     =       v
			continue

		if (
		    k.endswith(".self_attn.q_proj.bias")
		    or k.endswith(".self_attn.k_proj.bias")
		    or k.endswith(".self_attn.v_proj.bias")
		):
			SCREAMING_SNAKE_CASE   :      Optional[int]				     =       k[: -len(".q_proj.bias")]
			SCREAMING_SNAKE_CASE   :      str				     =       k[-len("q_proj.bias")]
			if k_pre not in capture_qkv_bias:
				SCREAMING_SNAKE_CASE   :      List[Any]				     =       [None, None, None]
			SCREAMING_SNAKE_CASE   :      List[Any]				     =       v
			continue

		SCREAMING_SNAKE_CASE   :      Optional[int]				     =       textenc_pattern.sub(lambda _a: protected[re.escape(m.group(0))]							,			_a)
		SCREAMING_SNAKE_CASE   :      Optional[int]				     =       v

	for k_pre, tensors in capture_qkv_weight.items():
		if None in tensors:
			raise Exception("CORRUPTED MODEL: one of the q-k-v values for the text encoder was missing")
		SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       textenc_pattern.sub(lambda _a: protected[re.escape(m.group(0))]							,			_a)
		SCREAMING_SNAKE_CASE   :      List[Any]				     =       torch.cat(_a)

	for k_pre, tensors in capture_qkv_bias.items():
		if None in tensors:
			raise Exception("CORRUPTED MODEL: one of the q-k-v values for the text encoder was missing")
		SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       textenc_pattern.sub(lambda _a: protected[re.escape(m.group(0))]							,			_a)
		SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       torch.cat(_a)

	return new_state_dict

def  lowerCamelCase__ (					_a):
	return text_enc_dict


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

			parser.add_argument('--model_path', default=None, type=str, required=True, help='Path to the model to convert.')
			parser.add_argument('--checkpoint_path', default=None, type=str, required=True, help='Path to the output model.')
			parser.add_argument('--half', action='store_true', help='Save weights in half precision.')
			parser.add_argument(
			    '--use_safetensors', action='store_true', help='Save weights use safetensors, default is ckpt.'
			)

			a_						     =    parser.parse_args()

			assert args.model_path is not None, "Must provide a model path!"

			assert args.checkpoint_path is not None, "Must provide a checkpoint path!"

			# Path for safetensors
			a_						     =    osp.join(args.model_path, 'unet', 'diffusion_pytorch_model.safetensors')
			a_						     =    osp.join(args.model_path, 'vae', 'diffusion_pytorch_model.safetensors')
			a_						     =    osp.join(args.model_path, 'text_encoder', 'model.safetensors')

			# Load models from safetensors if it exists, if it doesn't pytorch
			if osp.exists(unet_path):
						a_						     =    load_file(unet_path, device='cpu')
			else:
						a_						     =    osp.join(args.model_path, 'unet', 'diffusion_pytorch_model.bin')
						a_						     =    torch.load(unet_path, map_location='cpu')

			if osp.exists(vae_path):
						a_						     =    load_file(vae_path, device='cpu')
			else:
						a_						     =    osp.join(args.model_path, 'vae', 'diffusion_pytorch_model.bin')
						a_						     =    torch.load(vae_path, map_location='cpu')

			if osp.exists(text_enc_path):
						a_						     =    load_file(text_enc_path, device='cpu')
			else:
						a_						     =    osp.join(args.model_path, 'text_encoder', 'pytorch_model.bin')
						a_						     =    torch.load(text_enc_path, map_location='cpu')

			# Convert the UNet model
			a_						     =    convert_unet_state_dict(unet_state_dict)
			a_						     =    {'model.diffusion_model.' + k: v for k, v in unet_state_dict.items()}

			# Convert the VAE model
			a_						     =    convert_vae_state_dict(vae_state_dict)
			a_						     =    {'first_stage_model.' + k: v for k, v in vae_state_dict.items()}

			# Easiest way to identify v2.0 model seems to be that the text encoder (OpenCLIP) is deeper
			a_						     =    'text_model.encoder.layers.22.layer_norm2.bias' in text_enc_dict

			if is_vaa_model:
						# Need to add the tag 'transformer' in advance so we can knock it out from the final layer-norm
						a_						     =    {'transformer.' + k: v for k, v in text_enc_dict.items()}
						a_						     =    convert_text_enc_state_dict_vaa(text_enc_dict)
						a_						     =    {'cond_stage_model.model.' + k: v for k, v in text_enc_dict.items()}
			else:
						a_						     =    convert_text_enc_state_dict(text_enc_dict)
						a_						     =    {'cond_stage_model.transformer.' + k: v for k, v in text_enc_dict.items()}

			# Put together new checkpoint
			a_						     =    {**unet_state_dict, **vae_state_dict, **text_enc_dict}
			if args.half:
						a_						     =    {k: v.half() for k, v in state_dict.items()}

			if args.use_safetensors:
						save_file(state_dict, args.checkpoint_path)
			else:
						a_						     =    {'state_dict': state_dict}
						torch.save(state_dict, args.checkpoint_path)


import argparse
import json

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

from transformers import SegformerImageProcessor, SwinConfig, UperNetConfig, UperNetForSemanticSegmentation

def  lowerCamelCase__ (					_a):
	SCREAMING_SNAKE_CASE   :      Tuple				     =       384
	SCREAMING_SNAKE_CASE   :      Dict				     =       7
	if "tiny" in model_name:
		SCREAMING_SNAKE_CASE   :      List[Any]				     =       96
		SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       (2, 2, 6, 2)
		SCREAMING_SNAKE_CASE   :      Dict				     =       (3, 6, 12, 24)
	elif "small" in model_name:
		SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       96
		SCREAMING_SNAKE_CASE   :      Dict				     =       (2, 2, 18, 2)
		SCREAMING_SNAKE_CASE   :      Dict				     =       (3, 6, 12, 24)
	elif "base" in model_name:
		SCREAMING_SNAKE_CASE   :      Optional[int]				     =       128
		SCREAMING_SNAKE_CASE   :      Tuple				     =       (2, 2, 18, 2)
		SCREAMING_SNAKE_CASE   :      str				     =       (4, 8, 16, 32)
		SCREAMING_SNAKE_CASE   :      Any				     =       12
		SCREAMING_SNAKE_CASE   :      int				     =       512
	elif "large" in model_name:
		SCREAMING_SNAKE_CASE   :      Any				     =       192
		SCREAMING_SNAKE_CASE   :      List[Any]				     =       (2, 2, 18, 2)
		SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       (6, 12, 24, 48)
		SCREAMING_SNAKE_CASE   :      Any				     =       12
		SCREAMING_SNAKE_CASE   :      Any				     =       768

	# set label information
	SCREAMING_SNAKE_CASE   :      Optional[int]				     =       150
	SCREAMING_SNAKE_CASE   :      int				     =       "huggingface/label-files"
	SCREAMING_SNAKE_CASE   :      Dict				     =       "ade20k-id2label.json"
	SCREAMING_SNAKE_CASE   :      List[Any]				     =       json.load(open(hf_hub_download(_a							,			_a							,			repo_type="dataset")							,			"r"))
	SCREAMING_SNAKE_CASE   :      List[Any]				     =       {int(_a): v for k, v in idalabel.items()}
	SCREAMING_SNAKE_CASE   :      int				     =       {v: k for k, v in idalabel.items()}

	SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       SwinConfig(
	    embed_dim=_a							,			depths=_a							,			num_heads=_a							,			window_size=_a							,			out_features=["stage1", "stage2", "stage3", "stage4"]							,			)
	SCREAMING_SNAKE_CASE   :      List[str]				     =       UperNetConfig(
	    backbone_config=_a							,			auxiliary_in_channels=_a							,			num_labels=_a							,			idalabel=_a							,			labelaid=_a							,			)

	return config

def  lowerCamelCase__ (					_a):
	SCREAMING_SNAKE_CASE   :      List[Any]				     =       []

	# fmt: off
	# stem
	rename_keys.append(("backbone.patch_embed.projection.weight", "backbone.embeddings.patch_embeddings.projection.weight"))
	rename_keys.append(("backbone.patch_embed.projection.bias", "backbone.embeddings.patch_embeddings.projection.bias"))
	rename_keys.append(("backbone.patch_embed.norm.weight", "backbone.embeddings.norm.weight"))
	rename_keys.append(("backbone.patch_embed.norm.bias", "backbone.embeddings.norm.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}.blocks.{j}.norm1.weight", f"backbone.encoder.layers.{i}.blocks.{j}.layernorm_before.weight"))
			rename_keys.append((f"backbone.stages.{i}.blocks.{j}.norm1.bias", f"backbone.encoder.layers.{i}.blocks.{j}.layernorm_before.bias"))
			rename_keys.append((f"backbone.stages.{i}.blocks.{j}.attn.w_msa.relative_position_bias_table", f"backbone.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table"))
			rename_keys.append((f"backbone.stages.{i}.blocks.{j}.attn.w_msa.relative_position_index", f"backbone.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index"))
			rename_keys.append((f"backbone.stages.{i}.blocks.{j}.attn.w_msa.proj.weight", f"backbone.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight"))
			rename_keys.append((f"backbone.stages.{i}.blocks.{j}.attn.w_msa.proj.bias", f"backbone.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias"))
			rename_keys.append((f"backbone.stages.{i}.blocks.{j}.norm2.weight", f"backbone.encoder.layers.{i}.blocks.{j}.layernorm_after.weight"))
			rename_keys.append((f"backbone.stages.{i}.blocks.{j}.norm2.bias", f"backbone.encoder.layers.{i}.blocks.{j}.layernorm_after.bias"))
			rename_keys.append((f"backbone.stages.{i}.blocks.{j}.ffn.layers.0.0.weight", f"backbone.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight"))
			rename_keys.append((f"backbone.stages.{i}.blocks.{j}.ffn.layers.0.0.bias", f"backbone.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias"))
			rename_keys.append((f"backbone.stages.{i}.blocks.{j}.ffn.layers.1.weight", f"backbone.encoder.layers.{i}.blocks.{j}.output.dense.weight"))
			rename_keys.append((f"backbone.stages.{i}.blocks.{j}.ffn.layers.1.bias", f"backbone.encoder.layers.{i}.blocks.{j}.output.dense.bias"))

		if i < 3:
			rename_keys.append((f"backbone.stages.{i}.downsample.reduction.weight", f"backbone.encoder.layers.{i}.downsample.reduction.weight"))
			rename_keys.append((f"backbone.stages.{i}.downsample.norm.weight", f"backbone.encoder.layers.{i}.downsample.norm.weight"))
			rename_keys.append((f"backbone.stages.{i}.downsample.norm.bias", f"backbone.encoder.layers.{i}.downsample.norm.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							,			_a							,			_a):
	SCREAMING_SNAKE_CASE   :      Any				     =       dct.pop(_a)
	SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       val

def  lowerCamelCase__ (					_a							,			_a):
	SCREAMING_SNAKE_CASE   :      Optional[int]				     =       [int(backbone_config.embed_dim * 2**i) for i in range(len(backbone_config.depths))]
	for i in range(len(backbone_config.depths)):
		SCREAMING_SNAKE_CASE   :      Tuple				     =       num_features[i]
		for j in range(backbone_config.depths[i]):
			# fmt: off
			# read in weights + bias of input projection layer (in original implementation, this is a single matrix + bias)
			SCREAMING_SNAKE_CASE   :      int				     =       state_dict.pop(f"backbone.stages.{i}.blocks.{j}.attn.w_msa.qkv.weight")
			SCREAMING_SNAKE_CASE   :      List[str]				     =       state_dict.pop(f"backbone.stages.{i}.blocks.{j}.attn.w_msa.qkv.bias")
			# next, add query, keys and values (in that order) to the state dict
			SCREAMING_SNAKE_CASE   :      Optional[int]				     =       in_proj_weight[:dim, :]
			SCREAMING_SNAKE_CASE   :      int				     =       in_proj_bias[: dim]
			SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       in_proj_weight[
			    dim : dim * 2, :
			]
			SCREAMING_SNAKE_CASE   :      str				     =       in_proj_bias[
			    dim : dim * 2
			]
			SCREAMING_SNAKE_CASE   :      List[str]				     =       in_proj_weight[
			    -dim :, :
			]
			SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       in_proj_bias[-dim :]
			# fmt: on

def  lowerCamelCase__ (					_a):
	SCREAMING_SNAKE_CASE     ,SCREAMING_SNAKE_CASE   :      List[Any]				     =       x.shape
	SCREAMING_SNAKE_CASE   :      List[str]				     =       x.reshape(_a							,			4							,			in_channel // 4)
	SCREAMING_SNAKE_CASE   :      str				     =       x[:, [0, 2, 1, 3], :].transpose(1							,			2).reshape(_a							,			_a)
	return x

def  lowerCamelCase__ (					_a):
	SCREAMING_SNAKE_CASE     ,SCREAMING_SNAKE_CASE   :      Any				     =       x.shape
	SCREAMING_SNAKE_CASE   :      Tuple				     =       x.reshape(_a							,			in_channel // 4							,			4)
	SCREAMING_SNAKE_CASE   :      Optional[int]				     =       x[:, :, [0, 2, 1, 3]].transpose(1							,			2).reshape(_a							,			_a)

	return x

def  lowerCamelCase__ (					_a):
	SCREAMING_SNAKE_CASE   :      str				     =       x.shape[0]
	SCREAMING_SNAKE_CASE   :      int				     =       x.reshape(4							,			in_channel // 4)
	SCREAMING_SNAKE_CASE   :      int				     =       x[[0, 2, 1, 3], :].transpose(0							,			1).reshape(_a)
	return x

def  lowerCamelCase__ (					_a):
	SCREAMING_SNAKE_CASE   :      Any				     =       x.shape[0]
	SCREAMING_SNAKE_CASE   :      Dict				     =       x.reshape(in_channel // 4							,			4)
	SCREAMING_SNAKE_CASE   :      Dict				     =       x[:, [0, 2, 1, 3]].transpose(0							,			1).reshape(_a)
	return x

def  lowerCamelCase__ (					_a							,			_a							,			_a):
	SCREAMING_SNAKE_CASE   :      Any				     =       {
	    "upernet-swin-tiny": "https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_tiny_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K/upernet_swin_tiny_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K_20210531_112542-e380ad3e.pth",
	    "upernet-swin-small": "https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_small_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K/upernet_swin_small_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K_20210526_192015-ee2fff1c.pth",
	    "upernet-swin-base": "https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_base_patch4_window12_512x512_160k_ade20k_pretrain_384x384_22K/upernet_swin_base_patch4_window12_512x512_160k_ade20k_pretrain_384x384_22K_20210531_125459-429057bf.pth",
	    "upernet-swin-large": "https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_large_patch4_window12_512x512_pretrain_384x384_22K_160k_ade20k/upernet_swin_large_patch4_window12_512x512_pretrain_384x384_22K_160k_ade20k_20220318_091743-9ba68901.pth",
	}
	SCREAMING_SNAKE_CASE   :      Optional[int]				     =       model_name_to_url[model_name]
	SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       torch.hub.load_state_dict_from_url(_a							,			map_location="cpu"							,			file_name=_a)[
	    "state_dict"
	]

	for name, param in state_dict.items():
		print(_a							,			param.shape)

	SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       get_upernet_config(_a)
	SCREAMING_SNAKE_CASE   :      Dict				     =       UperNetForSemanticSegmentation(_a)
	model.eval()

	# replace "bn" => "batch_norm"
	for key in state_dict.copy().keys():
		SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       state_dict.pop(_a)
		if "bn" in key:
			SCREAMING_SNAKE_CASE   :      Any				     =       key.replace("bn"							,			"batch_norm")
		SCREAMING_SNAKE_CASE   :      List[Any]				     =       val

	# rename keys
	SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       create_rename_keys(_a)
	for src, dest in rename_keys:
		rename_key(_a							,			_a							,			_a)
	read_in_q_k_v(_a							,			config.backbone_config)

	# fix downsample parameters
	for key, value in state_dict.items():
		if "downsample" in key:
			if "reduction" in key:
				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       reverse_correct_unfold_reduction_order(_a)
			if "norm" in key:
				SCREAMING_SNAKE_CASE   :      str				     =       reverse_correct_unfold_norm_order(_a)

	model.load_state_dict(_a)

	# verify on image
	SCREAMING_SNAKE_CASE   :      Optional[int]				     =       "https://huggingface.co/datasets/hf-internal-testing/fixtures_ade20k/resolve/main/ADE_val_00000001.jpg"
	SCREAMING_SNAKE_CASE   :      List[Any]				     =       Image.open(requests.get(_a							,			stream=_a).raw).convert("RGB")

	SCREAMING_SNAKE_CASE   :      Tuple				     =       SegformerImageProcessor()
	SCREAMING_SNAKE_CASE   :      Dict				     =       processor(_a							,			return_tensors="pt").pixel_values

	with torch.no_grad():
		SCREAMING_SNAKE_CASE   :      str				     =       model(_a)
		SCREAMING_SNAKE_CASE   :      List[Any]				     =       outputs.logits

	print(logits.shape)
	print("First values of logits:"							,			logits[0, 0, :3, :3])
	# assert values
	if model_name == "upernet-swin-tiny":
		SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       torch.tensor(
		    [[-7.5958, -7.5958, -7.4302], [-7.5958, -7.5958, -7.4302], [-7.4797, -7.4797, -7.3068]])
	elif model_name == "upernet-swin-small":
		SCREAMING_SNAKE_CASE   :      Tuple				     =       torch.tensor(
		    [[-7.1921, -7.1921, -6.9532], [-7.1921, -7.1921, -6.9532], [-7.0908, -7.0908, -6.8534]])
	elif model_name == "upernet-swin-base":
		SCREAMING_SNAKE_CASE   :      str				     =       torch.tensor(
		    [[-6.5851, -6.5851, -6.4330], [-6.5851, -6.5851, -6.4330], [-6.4763, -6.4763, -6.3254]])
	elif model_name == "upernet-swin-large":
		SCREAMING_SNAKE_CASE   :      Any				     =       torch.tensor(
		    [[-7.5297, -7.5297, -7.3802], [-7.5297, -7.5297, -7.3802], [-7.4044, -7.4044, -7.2586]])
	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_						     =    argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    '--model_name',
			    default='upernet-swin-tiny',
			    type=str,
			    choices=[F'''upernet-swin-{size}''' for size in ['tiny', 'small', 'base', 'large']],
			    help='Name of the Swin + 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_						     =    parser.parse_args()
			convert_upernet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)