
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 							_lowercase							(     UpperCamelCase_		)		->		List[Any]:
					'''simple docstring'''


					SCREAMING_SNAKE_CASE__													= 384
					SCREAMING_SNAKE_CASE__													= 7
					if "tiny" in model_name:
										SCREAMING_SNAKE_CASE__													= 96
										SCREAMING_SNAKE_CASE__													= (2, 2, 6, 2)
										SCREAMING_SNAKE_CASE__													= (3, 6, 12, 24)
					elif "small" in model_name:
										SCREAMING_SNAKE_CASE__													= 96
										SCREAMING_SNAKE_CASE__													= (2, 2, 18, 2)
										SCREAMING_SNAKE_CASE__													= (3, 6, 12, 24)
					elif "base" in model_name:
										SCREAMING_SNAKE_CASE__													= 128
										SCREAMING_SNAKE_CASE__													= (2, 2, 18, 2)
										SCREAMING_SNAKE_CASE__													= (4, 8, 16, 32)
										SCREAMING_SNAKE_CASE__													= 12
										SCREAMING_SNAKE_CASE__													= 512
					elif "large" in model_name:
										SCREAMING_SNAKE_CASE__													= 192
										SCREAMING_SNAKE_CASE__													= (2, 2, 18, 2)
										SCREAMING_SNAKE_CASE__													= (6, 12, 24, 48)
										SCREAMING_SNAKE_CASE__													= 12
										SCREAMING_SNAKE_CASE__													= 768

					# set label information
					SCREAMING_SNAKE_CASE__													= 150
					SCREAMING_SNAKE_CASE__													= 'huggingface/label-files'
					SCREAMING_SNAKE_CASE__													= 'ade20k-id2label.json'
					SCREAMING_SNAKE_CASE__													= json.load(open(hf_hub_download(UpperCamelCase_	,	UpperCamelCase_	,	repo_type='dataset'		)	,	'r'		)		)
					SCREAMING_SNAKE_CASE__													= {int(UpperCamelCase_		): v for k, v in idalabel.items()}
					SCREAMING_SNAKE_CASE__													= {v: k for k, v in idalabel.items()}

					SCREAMING_SNAKE_CASE__													= SwinConfig(
					    embed_dim=UpperCamelCase_	,	depths=UpperCamelCase_	,	num_heads=UpperCamelCase_	,	window_size=UpperCamelCase_	,	out_features=['stage1', 'stage2', 'stage3', 'stage4']	,	)
					SCREAMING_SNAKE_CASE__													= UperNetConfig(
					    backbone_config=UpperCamelCase_	,	auxiliary_in_channels=UpperCamelCase_	,	num_labels=UpperCamelCase_	,	idalabel=UpperCamelCase_	,	labelaid=UpperCamelCase_	,	)

					return config






def 							_lowercase							(     UpperCamelCase_		)		->		int:
					'''simple docstring'''


					SCREAMING_SNAKE_CASE__													= []

					# 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 							_lowercase							(     UpperCamelCase_	,	UpperCamelCase_	,	UpperCamelCase_		)		->		Optional[Any]:
					'''simple docstring'''


					SCREAMING_SNAKE_CASE__													= dct.pop(UpperCamelCase_		)
					SCREAMING_SNAKE_CASE__													= val






def 							_lowercase							(     UpperCamelCase_	,	UpperCamelCase_		)		->		Optional[int]:
					'''simple docstring'''


					SCREAMING_SNAKE_CASE__													= [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__													= 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__													= state_dict.pop(F'backbone.stages.{i}.blocks.{j}.attn.w_msa.qkv.weight'		)
															SCREAMING_SNAKE_CASE__													= 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__													= in_proj_weight[:dim, :]
															SCREAMING_SNAKE_CASE__													= in_proj_bias[: dim]
															SCREAMING_SNAKE_CASE__													= in_proj_weight[
															    dim : dim * 2, :
															]
															SCREAMING_SNAKE_CASE__													= in_proj_bias[
															    dim : dim * 2
															]
															SCREAMING_SNAKE_CASE__													= in_proj_weight[
															    -dim :, :
															]
															SCREAMING_SNAKE_CASE__													= in_proj_bias[-dim :]
															# fmt: on






def 							_lowercase							(     UpperCamelCase_		)		->		Tuple:
					'''simple docstring'''


					SCREAMING_SNAKE_CASE__						, SCREAMING_SNAKE_CASE__													= x.shape
					SCREAMING_SNAKE_CASE__													= x.reshape(UpperCamelCase_	,	4	,	in_channel // 4		)
					SCREAMING_SNAKE_CASE__													= x[:, [0, 2, 1, 3], :].transpose(1	,	2		).reshape(UpperCamelCase_	,	UpperCamelCase_		)
					return x






def 							_lowercase							(     UpperCamelCase_		)		->		str:
					'''simple docstring'''


					SCREAMING_SNAKE_CASE__						, SCREAMING_SNAKE_CASE__													= x.shape
					SCREAMING_SNAKE_CASE__													= x.reshape(UpperCamelCase_	,	in_channel // 4	,	4		)
					SCREAMING_SNAKE_CASE__													= x[:, :, [0, 2, 1, 3]].transpose(1	,	2		).reshape(UpperCamelCase_	,	UpperCamelCase_		)

					return x






def 							_lowercase							(     UpperCamelCase_		)		->		str:
					'''simple docstring'''


					SCREAMING_SNAKE_CASE__													= x.shape[0]
					SCREAMING_SNAKE_CASE__													= x.reshape(4	,	in_channel // 4		)
					SCREAMING_SNAKE_CASE__													= x[[0, 2, 1, 3], :].transpose(0	,	1		).reshape(UpperCamelCase_		)
					return x






def 							_lowercase							(     UpperCamelCase_		)		->		Dict:
					'''simple docstring'''


					SCREAMING_SNAKE_CASE__													= x.shape[0]
					SCREAMING_SNAKE_CASE__													= x.reshape(in_channel // 4	,	4		)
					SCREAMING_SNAKE_CASE__													= x[:, [0, 2, 1, 3]].transpose(0	,	1		).reshape(UpperCamelCase_		)
					return x






def 							_lowercase							(     UpperCamelCase_	,	UpperCamelCase_	,	UpperCamelCase_		)		->		Any:
					'''simple docstring'''


					SCREAMING_SNAKE_CASE__													= {
					    '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__													= model_name_to_url[model_name]
					SCREAMING_SNAKE_CASE__													= torch.hub.load_state_dict_from_url(UpperCamelCase_	,	map_location='cpu'	,	file_name=UpperCamelCase_		)[
					    'state_dict'
					]

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

					SCREAMING_SNAKE_CASE__													= get_upernet_config(UpperCamelCase_		)
					SCREAMING_SNAKE_CASE__													= UperNetForSemanticSegmentation(UpperCamelCase_		)
					model.eval()

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

					# rename keys
					SCREAMING_SNAKE_CASE__													= create_rename_keys(UpperCamelCase_		)
					for src, dest in rename_keys:
										rename_key(UpperCamelCase_	,	UpperCamelCase_	,	UpperCamelCase_		)
					read_in_q_k_v(UpperCamelCase_	,	config.backbone_config		)

					# fix downsample parameters
					for key, value in state_dict.items():
										if "downsample" in key:
															if "reduction" in key:
																				SCREAMING_SNAKE_CASE__													= reverse_correct_unfold_reduction_order(UpperCamelCase_		)
															if "norm" in key:
																				SCREAMING_SNAKE_CASE__													= reverse_correct_unfold_norm_order(UpperCamelCase_		)

					model.load_state_dict(UpperCamelCase_		)

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

					SCREAMING_SNAKE_CASE__													= SegformerImageProcessor()
					SCREAMING_SNAKE_CASE__													= processor(UpperCamelCase_	,	return_tensors='pt'		).pixel_values

					with torch.no_grad():
										SCREAMING_SNAKE_CASE__													= model(UpperCamelCase_		)
										SCREAMING_SNAKE_CASE__													= 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__													= torch.tensor(
										    [[-7.5_958, -7.5_958, -7.4_302], [-7.5_958, -7.5_958, -7.4_302], [-7.4_797, -7.4_797, -7.3_068]]		)
					elif model_name == "upernet-swin-small":
										SCREAMING_SNAKE_CASE__													= torch.tensor(
										    [[-7.1_921, -7.1_921, -6.9_532], [-7.1_921, -7.1_921, -6.9_532], [-7.0_908, -7.0_908, -6.8_534]]		)
					elif model_name == "upernet-swin-base":
										SCREAMING_SNAKE_CASE__													= torch.tensor(
										    [[-6.5_851, -6.5_851, -6.4_330], [-6.5_851, -6.5_851, -6.4_330], [-6.4_763, -6.4_763, -6.3_254]]		)
					elif model_name == "upernet-swin-large":
										SCREAMING_SNAKE_CASE__													= torch.tensor(
										    [[-7.5_297, -7.5_297, -7.3_802], [-7.5_297, -7.5_297, -7.3_802], [-7.4_044, -7.4_044, -7.2_586]]		)
					print('Logits:'	,	outputs.logits[0, 0, :3, :3]		)
					assert torch.allclose(outputs.logits[0, 0, :3, :3]	,	UpperCamelCase_	,	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(UpperCamelCase_		)
										print(F'Saving processor to {pytorch_dump_folder_path}'		)
										processor.save_pretrained(UpperCamelCase_		)

					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__":
				__snake_case								=  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."""
				)

				__snake_case								=  parser.parse_args()
				convert_upernet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)







def 							_lowercase							(     )		->		Optional[int]:
					'''simple docstring'''


					SCREAMING_SNAKE_CASE__													= []
					SCREAMING_SNAKE_CASE__													= 1

					while len(UpperCamelCase_		) < 1e6:
										constant.append(str(UpperCamelCase_		)		)
										i += 1

					SCREAMING_SNAKE_CASE__													= ''.join(UpperCamelCase_		)

					return (
					    int(constant[0]		)
					    * int(constant[9]		)
					    * int(constant[99]		)
					    * int(constant[999]		)
					    * int(constant[9999]		)
					    * int(constant[99999]		)
					    * int(constant[999999]		)
					)


if __name__ == "__main__":
				print(solution())





