
"""simple docstring"""







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

	# set label information
	__lowerCAmelCase    :		Union[str, Any]		    =   150
	__lowerCAmelCase    :		Any		    =   'huggingface/label-files'
	__lowerCAmelCase    :		str		    =   'ade20k-id2label.json'
	__lowerCAmelCase    :		Any		    =   json.load(open(hf_hub_download(_UpperCamelCase ,						_UpperCamelCase ,						repo_type='dataset'	) ,						'r'	)	)
	__lowerCAmelCase    :		Tuple		    =   {int(_UpperCamelCase	): v for k, v in idalabel.items()}
	__lowerCAmelCase    :		Any		    =   {v: k for k, v in idalabel.items()}

	__lowerCAmelCase    :		int		    =   SwinConfig(
	    embed_dim=_UpperCamelCase ,						depths=_UpperCamelCase ,						num_heads=_UpperCamelCase ,						window_size=_UpperCamelCase ,						out_features=['stage1', 'stage2', 'stage3', 'stage4'] ,						)
	__lowerCAmelCase    :		Dict		    =   UperNetConfig(
	    backbone_config=_UpperCamelCase ,						auxiliary_in_channels=_UpperCamelCase ,						num_labels=_UpperCamelCase ,						idalabel=_UpperCamelCase ,						labelaid=_UpperCamelCase ,						)

	return config
def 					__lowerCAmelCase       (_UpperCamelCase	):
	__lowerCAmelCase    :		Dict		    =   []

	# 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       (_UpperCamelCase ,						_UpperCamelCase ,						_UpperCamelCase	):
	__lowerCAmelCase    :		Tuple		    =   dct.pop(_UpperCamelCase	)
	__lowerCAmelCase    :		Optional[Any]		    =   val
def 					__lowerCAmelCase       (_UpperCamelCase ,						_UpperCamelCase	):
	__lowerCAmelCase    :		str		    =   [int(backbone_config.embed_dim * 2**i	) for i in range(len(backbone_config.depths	)	)]
	for i in range(len(backbone_config.depths	)	):
		__lowerCAmelCase    :		int		    =   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)
			__lowerCAmelCase    :		Dict		    =   state_dict.pop(F"backbone.stages.{i}.blocks.{j}.attn.w_msa.qkv.weight"	)
			__lowerCAmelCase    :		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
			__lowerCAmelCase    :		Any		    =   in_proj_weight[:dim, :]
			__lowerCAmelCase    :		Optional[Any]		    =   in_proj_bias[: dim]
			__lowerCAmelCase    :		List[Any]		    =   in_proj_weight[
			    dim : dim * 2, :
			]
			__lowerCAmelCase    :		List[str]		    =   in_proj_bias[
			    dim : dim * 2
			]
			__lowerCAmelCase    :		List[str]		    =   in_proj_weight[
			    -dim :, :
			]
			__lowerCAmelCase    :		List[str]		    =   in_proj_bias[-dim :]
			# fmt: on
def 					__lowerCAmelCase       (_UpperCamelCase	):
	__lowerCAmelCase    ,     __lowerCAmelCase    :		Dict		    =   x.shape
	__lowerCAmelCase    :		Tuple		    =   x.reshape(_UpperCamelCase ,						4 ,						in_channel // 4	)
	__lowerCAmelCase    :		Union[str, Any]		    =   x[:, [0, 2, 1, 3], :].transpose(1 ,						2	).reshape(_UpperCamelCase ,						_UpperCamelCase	)
	return x
def 					__lowerCAmelCase       (_UpperCamelCase	):
	__lowerCAmelCase    ,     __lowerCAmelCase    :		Union[str, Any]		    =   x.shape
	__lowerCAmelCase    :		List[str]		    =   x.reshape(_UpperCamelCase ,						in_channel // 4 ,						4	)
	__lowerCAmelCase    :		List[Any]		    =   x[:, :, [0, 2, 1, 3]].transpose(1 ,						2	).reshape(_UpperCamelCase ,						_UpperCamelCase	)

	return x
def 					__lowerCAmelCase       (_UpperCamelCase	):
	__lowerCAmelCase    :		str		    =   x.shape[0]
	__lowerCAmelCase    :		Optional[Any]		    =   x.reshape(4 ,						in_channel // 4	)
	__lowerCAmelCase    :		List[str]		    =   x[[0, 2, 1, 3], :].transpose(0 ,						1	).reshape(_UpperCamelCase	)
	return x
def 					__lowerCAmelCase       (_UpperCamelCase	):
	__lowerCAmelCase    :		Tuple		    =   x.shape[0]
	__lowerCAmelCase    :		Optional[int]		    =   x.reshape(in_channel // 4 ,						4	)
	__lowerCAmelCase    :		List[Any]		    =   x[:, [0, 2, 1, 3]].transpose(0 ,						1	).reshape(_UpperCamelCase	)
	return x
def 					__lowerCAmelCase       (_UpperCamelCase ,						_UpperCamelCase ,						_UpperCamelCase	):
	__lowerCAmelCase    :		List[str]		    =   {
	    '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',
	}
	__lowerCAmelCase    :		Dict		    =   model_name_to_url[model_name]
	__lowerCAmelCase    :		Union[str, Any]		    =   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	)

	__lowerCAmelCase    :		Any		    =   get_upernet_config(_UpperCamelCase	)
	__lowerCAmelCase    :		List[str]		    =   UperNetForSemanticSegmentation(_UpperCamelCase	)
	model.eval()

	# replace "bn" => "batch_norm"
	for key in state_dict.copy().keys():
		__lowerCAmelCase    :		Dict		    =   state_dict.pop(_UpperCamelCase	)
		if "bn" in key:
			__lowerCAmelCase    :		Tuple		    =   key.replace('bn' ,						'batch_norm'	)
		__lowerCAmelCase    :		Tuple		    =   val

	# rename keys
	__lowerCAmelCase    :		Any		    =   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:
				__lowerCAmelCase    :		Tuple		    =   reverse_correct_unfold_reduction_order(_UpperCamelCase	)
			if "norm" in key:
				__lowerCAmelCase    :		str		    =   reverse_correct_unfold_norm_order(_UpperCamelCase	)

	model.load_state_dict(_UpperCamelCase	)

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

	__lowerCAmelCase    :		Any		    =   SegformerImageProcessor()
	__lowerCAmelCase    :		Optional[int]		    =   processor(_UpperCamelCase ,						return_tensors='pt'	).pixel_values

	with torch.no_grad():
		__lowerCAmelCase    :		List[str]		    =   model(_UpperCamelCase	)
		__lowerCAmelCase    :		Dict		    =   outputs.logits

	print(logits.shape	)
	print('First values of logits:' ,						logits[0, 0, :3, :3]	)
	# assert values
	if model_name == "upernet-swin-tiny":
		__lowerCAmelCase    :		str		    =   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":
		__lowerCAmelCase    :		Dict		    =   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":
		__lowerCAmelCase    :		Optional[Any]		    =   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":
		__lowerCAmelCase    :		int		    =   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] ,						_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__":
							lowerCamelCase__           =	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."""
							)

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

"""simple docstring"""







from __future__ import annotations

from collections import Counter
from random import random




class 				A__ :

		def __init__(   self				):
			__lowerCAmelCase    :		Any		    =   {}

		def 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE				):
			__lowerCAmelCase    :		Dict		    =   {}

		def 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				):
			if nodea not in self.connections:
				self.add_node(_SCREAMING_SNAKE_CASE				)
			if nodea not in self.connections:
				self.add_node(_SCREAMING_SNAKE_CASE				)
			__lowerCAmelCase    :		Optional[Any]		    =   probability

		def 							__lowerCamelCase							(   self				):
			return list(self.connections				)







		def 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE				):
			__lowerCAmelCase    :		List[str]		    =   0
			__lowerCAmelCase    :		List[Any]		    =   random()

			for dest in self.connections[node]:
				current_probability += self.connections[node][dest]
				if current_probability > random_value:
					return dest
			return ""



def 					__lowerCAmelCase       (_UpperCamelCase ,						_UpperCamelCase ,						_UpperCamelCase	):
	__lowerCAmelCase    :		Optional[Any]		    =   MarkovChainGraphUndirectedUnweighted()

	for nodea, nodea, probability in transitions:
		graph.add_transition_probability(_UpperCamelCase ,						_UpperCamelCase ,						_UpperCamelCase	)

	__lowerCAmelCase    :		str		    =   Counter(graph.get_nodes()	)
	__lowerCAmelCase    :		Tuple		    =   start

	for _ in range(_UpperCamelCase	):
		__lowerCAmelCase    :		int		    =   graph.transition(_UpperCamelCase	)
		visited[node] += 1

	return visited


if __name__ == "__main__":
							import doctest

							doctest.testmod()