






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       (			snake_case			):







				"""simple docstring"""






				__magic_name__		:int   =   3_8_4
				__magic_name__		:Tuple   =   7
				if "tiny" in model_name:
								__magic_name__		:Dict   =   9_6
								__magic_name__		:List[str]   =   (2, 2, 6, 2)
								__magic_name__		:List[str]   =   (3, 6, 1_2, 2_4)
				elif "small" in model_name:
								__magic_name__		:Dict   =   9_6
								__magic_name__		:Dict   =   (2, 2, 1_8, 2)
								__magic_name__		:Any   =   (3, 6, 1_2, 2_4)
				elif "base" in model_name:
								__magic_name__		:Any   =   1_2_8
								__magic_name__		:Dict   =   (2, 2, 1_8, 2)
								__magic_name__		:Optional[int]   =   (4, 8, 1_6, 3_2)
								__magic_name__		:List[Any]   =   1_2
								__magic_name__		:Union[str, Any]   =   5_1_2
				elif "large" in model_name:
								__magic_name__		:List[str]   =   1_9_2
								__magic_name__		:int   =   (2, 2, 1_8, 2)
								__magic_name__		:Tuple   =   (6, 1_2, 2_4, 4_8)
								__magic_name__		:Union[str, Any]   =   1_2
								__magic_name__		:Tuple   =   7_6_8

				# set label information
				__magic_name__		:Tuple   =   1_5_0
				__magic_name__		:Union[str, Any]   =   '''huggingface/label-files'''
				__magic_name__		:List[Any]   =   '''ade20k-id2label.json'''
				__magic_name__		:Tuple   =   json.load(open(hf_hub_download(snake_case,	snake_case,	repo_type='''dataset'''			),	'''r'''			)			)
				__magic_name__		:Tuple   =   {int(snake_case			): v for k, v in idalabel.items()}
				__magic_name__		:Dict   =   {v: k for k, v in idalabel.items()}

				__magic_name__		:Any   =   SwinConfig(
				    embed_dim=snake_case,	depths=snake_case,	num_heads=snake_case,	window_size=snake_case,	out_features=['''stage1''', '''stage2''', '''stage3''', '''stage4'''],	)
				__magic_name__		:Any   =   UperNetConfig(
				    backbone_config=snake_case,	auxiliary_in_channels=snake_case,	num_labels=snake_case,	idalabel=snake_case,	labelaid=snake_case,	)

				return config

def   __lowercase       (			snake_case			):







				"""simple docstring"""






				__magic_name__		:List[str]   =   []

				# 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       (			snake_case,	snake_case,	snake_case			):







				"""simple docstring"""






				__magic_name__		:Optional[int]   =   dct.pop(snake_case			)
				__magic_name__		:Optional[int]   =   val

def   __lowercase       (			snake_case,	snake_case			):







				"""simple docstring"""






				__magic_name__		:Any   =   [int(backbone_config.embed_dim * 2**i			) for i in range(len(backbone_config.depths			)			)]
				for i in range(len(backbone_config.depths			)			):
								__magic_name__		:List[str]   =   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)
												__magic_name__		:Dict   =   state_dict.pop(f'''backbone.stages.{i}.blocks.{j}.attn.w_msa.qkv.weight'''			)
												__magic_name__		:Dict   =   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
												__magic_name__		:Optional[int]   =   in_proj_weight[:dim, :]
												__magic_name__		:Optional[Any]   =   in_proj_bias[: dim]
												__magic_name__		:int   =   in_proj_weight[
												    dim : dim * 2, :
												]
												__magic_name__		:List[str]   =   in_proj_bias[
												    dim : dim * 2
												]
												__magic_name__		:Dict   =   in_proj_weight[
												    -dim :, :
												]
												__magic_name__		:Any   =   in_proj_bias[-dim :]
												# fmt: on

def   __lowercase       (			snake_case			):







				"""simple docstring"""






				__magic_name__  ,					__magic_name__		:Tuple   =   x.shape
				__magic_name__		:int   =   x.reshape(snake_case,	4,	in_channel // 4			)
				__magic_name__		:str   =   x[:, [0, 2, 1, 3], :].transpose(1,	2			).reshape(snake_case,	snake_case			)
				return x

def   __lowercase       (			snake_case			):







				"""simple docstring"""






				__magic_name__  ,					__magic_name__		:List[Any]   =   x.shape
				__magic_name__		:Optional[int]   =   x.reshape(snake_case,	in_channel // 4,	4			)
				__magic_name__		:Tuple   =   x[:, :, [0, 2, 1, 3]].transpose(1,	2			).reshape(snake_case,	snake_case			)

				return x

def   __lowercase       (			snake_case			):







				"""simple docstring"""






				__magic_name__		:Dict   =   x.shape[0]
				__magic_name__		:Dict   =   x.reshape(4,	in_channel // 4			)
				__magic_name__		:List[str]   =   x[[0, 2, 1, 3], :].transpose(0,	1			).reshape(snake_case			)
				return x

def   __lowercase       (			snake_case			):







				"""simple docstring"""






				__magic_name__		:List[str]   =   x.shape[0]
				__magic_name__		:Dict   =   x.reshape(in_channel // 4,	4			)
				__magic_name__		:Optional[Any]   =   x[:, [0, 2, 1, 3]].transpose(0,	1			).reshape(snake_case			)
				return x

def   __lowercase       (			snake_case,	snake_case,	snake_case			):







				"""simple docstring"""






				__magic_name__		:Tuple   =   {
				    '''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''',
				}
				__magic_name__		:Dict   =   model_name_to_url[model_name]
				__magic_name__		:Dict   =   torch.hub.load_state_dict_from_url(snake_case,	map_location='''cpu''',	file_name=snake_case			)[
				    '''state_dict'''
				]

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

				__magic_name__		:Any   =   get_upernet_config(snake_case			)
				__magic_name__		:str   =   UperNetForSemanticSegmentation(snake_case			)
				model.eval()

				# replace "bn" => "batch_norm"
				for key in state_dict.copy().keys():
								__magic_name__		:Optional[Any]   =   state_dict.pop(snake_case			)
								if "bn" in key:
												__magic_name__		:Tuple   =   key.replace('''bn''',	'''batch_norm'''			)
								__magic_name__		:List[str]   =   val

				# rename keys
				__magic_name__		:Union[str, Any]   =   create_rename_keys(snake_case			)
				for src, dest in rename_keys:
								rename_key(snake_case,	snake_case,	snake_case			)
				read_in_q_k_v(snake_case,	config.backbone_config			)

				# fix downsample parameters
				for key, value in state_dict.items():
								if "downsample" in key:
												if "reduction" in key:
																__magic_name__		:Dict   =   reverse_correct_unfold_reduction_order(snake_case			)
												if "norm" in key:
																__magic_name__		:Any   =   reverse_correct_unfold_norm_order(snake_case			)

				model.load_state_dict(snake_case			)

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

				__magic_name__		:List[Any]   =   SegformerImageProcessor()
				__magic_name__		:str   =   processor(snake_case,	return_tensors='''pt'''			).pixel_values

				with torch.no_grad():
								__magic_name__		:List[Any]   =   model(snake_case			)
								__magic_name__		: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":
								__magic_name__		:List[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":
								__magic_name__		:List[Any]   =   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":
								__magic_name__		:Dict   =   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":
								__magic_name__		:Union[str, 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],	snake_case,	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(snake_case			)
								print(f'''Saving processor to {pytorch_dump_folder_path}'''			)
								processor.save_pretrained(snake_case			)

				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__":
					SCREAMING_SNAKE_CASE__   :      Union[str, Any]				=		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."""
					)

					SCREAMING_SNAKE_CASE__   :      Optional[Any]				=		parser.parse_args()
					convert_upernet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)














from __future__ import annotations

def   __lowercase       (			snake_case,	snake_case			):







				"""simple docstring"""






				print(f'''Vertex\tShortest Distance from vertex {src}'''			)
				for i, d in enumerate(snake_case			):
								print(f'''{i}\t\t{d}'''			)

def   __lowercase       (			snake_case,	snake_case,	snake_case			):







				"""simple docstring"""






				for j in range(snake_case			):
								__magic_name__  ,					__magic_name__  ,					__magic_name__		:Tuple   =   (graph[j][k] for k in ['''src''', '''dst''', '''weight'''])
								if distance[u] != float('''inf'''			) and distance[u] + w < distance[v]:
												return True
				return False

def   __lowercase       (			snake_case,	snake_case,	snake_case,	snake_case			):







				"""simple docstring"""






				__magic_name__		:List[Any]   =   [float('''inf'''			)] * vertex_count
				__magic_name__		:Tuple   =   0.0

				for _ in range(vertex_count - 1			):
								for j in range(snake_case			):
												__magic_name__  ,					__magic_name__  ,					__magic_name__		:Dict   =   (graph[j][k] for k in ['''src''', '''dst''', '''weight'''])

												if distance[u] != float('''inf'''			) and distance[u] + w < distance[v]:
																__magic_name__		:Tuple   =   distance[u] + w

				__magic_name__		:Tuple   =   check_negative_cycle(snake_case,	snake_case,	snake_case			)
				if negative_cycle_exists:
								raise Exception('''Negative cycle found'''			)

				return distance


if __name__ == "__main__":
					import doctest

					doctest.testmod()

					SCREAMING_SNAKE_CASE__   :      Tuple				=		int(input("""Enter number of vertices: """).strip())
					SCREAMING_SNAKE_CASE__   :      Any				=		int(input("""Enter number of edges: """).strip())

					SCREAMING_SNAKE_CASE__   :      list[dict[str, int]]				=		[{} for _ in range(E)]

					for i in range(E):
										print("""Edge """, i + 1)
										SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__   :      Dict				=		(
										    int(x)
										    for x in input("""Enter source, destination, weight: """).strip().split(""" """)
										)
										SCREAMING_SNAKE_CASE__   :      Dict				=		{"""src""": src, """dst""": dest, """weight""": weight}

					SCREAMING_SNAKE_CASE__   :      List[Any]				=		int(input("""\nEnter shortest path source:""").strip())
					SCREAMING_SNAKE_CASE__   :      List[str]				=		bellman_ford(graph, V, E, source)
					print_distance(shortest_distance, 0)






