



"""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    _SCREAMING_SNAKE_CASE	(			_lowercase		:					Tuple      )				->int:






	'''simple docstring'''






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

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

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

	return config






def    _SCREAMING_SNAKE_CASE	(			_lowercase		:					List[Any]      )				->List[str]:






	'''simple docstring'''






	a   :	Optional[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    _SCREAMING_SNAKE_CASE	(			_lowercase		:					Optional[int]					, _lowercase		:					Optional[Any]					, _lowercase		:					Union[str, Any]      )				->Dict:






	'''simple docstring'''






	a   :	str			=       dct.pop(snake_case_      )
	a   :	Union[str, Any]			=       val






def    _SCREAMING_SNAKE_CASE	(			_lowercase		:					List[Any]					, _lowercase		:					Optional[Any]      )				->Tuple:






	'''simple docstring'''






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






def    _SCREAMING_SNAKE_CASE	(			_lowercase		:					Union[str, Any]      )				->Union[str, Any]:






	'''simple docstring'''






	a   :	Optional[int]			=       x.shape
	a   :	Optional[Any]			=       x.reshape(snake_case_					, 4					, in_channel // 4      )
	a   :	Tuple			=       x[:, [0, 2, 1, 3], :].transpose(1					, 2      ).reshape(snake_case_					, snake_case_      )
	return x






def    _SCREAMING_SNAKE_CASE	(			_lowercase		:					Optional[int]      )				->List[Any]:






	'''simple docstring'''






	a   :	Optional[Any]			=       x.shape
	a   :	str			=       x.reshape(snake_case_					, in_channel // 4					, 4      )
	a   :	Any			=       x[:, :, [0, 2, 1, 3]].transpose(1					, 2      ).reshape(snake_case_					, snake_case_      )

	return x






def    _SCREAMING_SNAKE_CASE	(			_lowercase		:					Optional[Any]      )				->List[Any]:






	'''simple docstring'''






	a   :	Any			=       x.shape[0]
	a   :	List[Any]			=       x.reshape(4					, in_channel // 4      )
	a   :	Dict			=       x[[0, 2, 1, 3], :].transpose(0					, 1      ).reshape(snake_case_      )
	return x






def    _SCREAMING_SNAKE_CASE	(			_lowercase		:					str      )				->Union[str, Any]:






	'''simple docstring'''






	a   :	List[str]			=       x.shape[0]
	a   :	Union[str, Any]			=       x.reshape(in_channel // 4					, 4      )
	a   :	str			=       x[:, [0, 2, 1, 3]].transpose(0					, 1      ).reshape(snake_case_      )
	return x






def    _SCREAMING_SNAKE_CASE	(			_lowercase		:					Any					, _lowercase		:					List[str]					, _lowercase		:					List[Any]      )				->List[str]:






	'''simple docstring'''






	a   :	Dict			=       {
	    """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""",
	}
	a   :	Optional[int]			=       model_name_to_url[model_name]
	a   :	int			=       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      )

	a   :	Tuple			=       get_upernet_config(snake_case_      )
	a   :	Optional[Any]			=       UperNetForSemanticSegmentation(snake_case_      )
	model.eval()

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

	# rename keys
	a   :	Tuple			=       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:
				a   :	Union[str, Any]			=       reverse_correct_unfold_reduction_order(snake_case_      )
			if "norm" in key:
				a   :	str			=       reverse_correct_unfold_norm_order(snake_case_      )

	model.load_state_dict(snake_case_      )

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

	a   :	int			=       SegformerImageProcessor()
	a   :	List[Any]			=       processor(snake_case_					, return_tensors="pt"      ).pixel_values

	with torch.no_grad():
		a   :	Any			=       model(snake_case_      )
		a   :	str			=       outputs.logits

	print(logits.shape      )
	print("First values of logits:"					, logits[0, 0, :3, :3]      )
	# assert values
	if model_name == "upernet-swin-tiny":
		a   :	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":
		a   :	Optional[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":
		a   :	Union[str, 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":
		a   :	List[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__":
		a		:				Tuple  			=    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		:				Any  			=    parser.parse_args()
		convert_upernet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)









"""simple docstring"""




import flax.linen as nn
import jax
import jax.numpy as jnp


class  __UpperCamelCase       (		nn.Module		):
							lowerCamelCase     :      int
							lowerCamelCase     :      jnp.dtype         =jnp.floataa






							def 					__a     (     self   )			->				Tuple:
								a   :	str			=       nn.Conv(
								    self.out_channels   ,      kernel_size=(3, 3)   ,      strides=(1, 1)   ,      padding=((1, 1), (1, 1))   ,      dtype=self.dtype   ,      )






							def __call__(     self   ,      lowerCAmelCase__   )			->				Optional[Any]:
								a, a, a, a   :	List[str]			=       hidden_states.shape
								a   :	List[Any]			=       jax.image.resize(
								    lowerCAmelCase__   ,      shape=(batch, height * 2, width * 2, channels)   ,      method="nearest"   ,      )
								a   :	List[str]			=       self.conv(lowerCAmelCase__   )
								return hidden_states


class  __UpperCamelCase       (		nn.Module		):
							lowerCamelCase     :      int
							lowerCamelCase     :      jnp.dtype         =jnp.floataa






							def 					__a     (     self   )			->				Dict:
								a   :	Optional[Any]			=       nn.Conv(
								    self.out_channels   ,      kernel_size=(3, 3)   ,      strides=(2, 2)   ,      padding=((1, 1), (1, 1))   ,      dtype=self.dtype   ,      )






							def __call__(     self   ,      lowerCAmelCase__   )			->				Tuple:
								# pad = ((0, 0), (0, 1), (0, 1), (0, 0))  # pad height and width dim
								# hidden_states = jnp.pad(hidden_states, pad_width=pad)
								a   :	Tuple			=       self.conv(lowerCAmelCase__   )
								return hidden_states


class  __UpperCamelCase       (		nn.Module		):
							lowerCamelCase     :      int
							lowerCamelCase     :      int         =None
							lowerCamelCase     :      float         =0.0
							lowerCamelCase     :      bool         =None
							lowerCamelCase     :      jnp.dtype         =jnp.floataa






							def 					__a     (     self   )			->				int:
								a   :	Dict			=       self.in_channels if self.out_channels is None else self.out_channels

								a   :	Union[str, Any]			=       nn.GroupNorm(num_groups=32   ,      epsilon=1E-5   )
								a   :	List[Any]			=       nn.Conv(
								    lowerCAmelCase__   ,      kernel_size=(3, 3)   ,      strides=(1, 1)   ,      padding=((1, 1), (1, 1))   ,      dtype=self.dtype   ,      )

								a   :	List[Any]			=       nn.Dense(lowerCAmelCase__   ,      dtype=self.dtype   )

								a   :	Union[str, Any]			=       nn.GroupNorm(num_groups=32   ,      epsilon=1E-5   )
								a   :	Optional[int]			=       nn.Dropout(self.dropout_prob   )
								a   :	Dict			=       nn.Conv(
								    lowerCAmelCase__   ,      kernel_size=(3, 3)   ,      strides=(1, 1)   ,      padding=((1, 1), (1, 1))   ,      dtype=self.dtype   ,      )

								a   :	Union[str, Any]			=       self.in_channels != out_channels if self.use_nin_shortcut is None else self.use_nin_shortcut

								a   :	List[str]			=       None
								if use_nin_shortcut:
									a   :	Optional[Any]			=       nn.Conv(
									    lowerCAmelCase__   ,      kernel_size=(1, 1)   ,      strides=(1, 1)   ,      padding="VALID"   ,      dtype=self.dtype   ,      )






							def __call__(     self   ,      lowerCAmelCase__   ,      lowerCAmelCase__   ,      lowerCAmelCase__=True   )			->				str:
								a   :	int			=       hidden_states
								a   :	Tuple			=       self.norma(lowerCAmelCase__   )
								a   :	Any			=       nn.swish(lowerCAmelCase__   )
								a   :	int			=       self.conva(lowerCAmelCase__   )

								a   :	int			=       self.time_emb_proj(nn.swish(lowerCAmelCase__   )   )
								a   :	Tuple			=       jnp.expand_dims(jnp.expand_dims(lowerCAmelCase__   ,      1   )   ,      1   )
								a   :	Dict			=       hidden_states + temb

								a   :	str			=       self.norma(lowerCAmelCase__   )
								a   :	List[Any]			=       nn.swish(lowerCAmelCase__   )
								a   :	List[str]			=       self.dropout(lowerCAmelCase__   ,      lowerCAmelCase__   )
								a   :	List[str]			=       self.conva(lowerCAmelCase__   )

								if self.conv_shortcut is not None:
									a   :	Tuple			=       self.conv_shortcut(lowerCAmelCase__   )

								return hidden_states + residual


