






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_		(lowercase__						:       List[Any]       )		->    List[str]:


				'''simple docstring'''




				lowerCAmelCase__      			=						3_84
				lowerCAmelCase__      			=						7
				if "tiny" in model_name:
								lowerCAmelCase__      			=						96
								lowerCAmelCase__      			=						(2, 2, 6, 2)
								lowerCAmelCase__      			=						(3, 6, 12, 24)
				elif "small" in model_name:
								lowerCAmelCase__      			=						96
								lowerCAmelCase__      			=						(2, 2, 18, 2)
								lowerCAmelCase__      			=						(3, 6, 12, 24)
				elif "base" in model_name:
								lowerCAmelCase__      			=						1_28
								lowerCAmelCase__      			=						(2, 2, 18, 2)
								lowerCAmelCase__      			=						(4, 8, 16, 32)
								lowerCAmelCase__      			=						12
								lowerCAmelCase__      			=						5_12
				elif "large" in model_name:
								lowerCAmelCase__      			=						1_92
								lowerCAmelCase__      			=						(2, 2, 18, 2)
								lowerCAmelCase__      			=						(6, 12, 24, 48)
								lowerCAmelCase__      			=						12
								lowerCAmelCase__      			=						7_68

				# set label information
				lowerCAmelCase__      			=						1_50
				lowerCAmelCase__      			=						'huggingface/label-files'
				lowerCAmelCase__      			=						'ade20k-id2label.json'
				lowerCAmelCase__      			=						json.load(open(hf_hub_download(lowerCAmelCase__     ,					lowerCAmelCase__     ,					repo_type='''dataset'''       )     ,					'''r'''       )       )
				lowerCAmelCase__      			=						{int(lowerCAmelCase__       ): v for k, v in idalabel.items()}
				lowerCAmelCase__      			=						{v: k for k, v in idalabel.items()}

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

				return config





def 	lowerCAmelCase_		(lowercase__						:       str       )		->    Optional[int]:


				'''simple docstring'''




				lowerCAmelCase__      			=						[]

				# 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_		(lowercase__						:       str     ,					lowercase__						:       List[Any]     ,					lowercase__						:       Dict       )		->    Tuple:


				'''simple docstring'''




				lowerCAmelCase__      			=						dct.pop(lowerCAmelCase__       )
				lowerCAmelCase__      			=						val





def 	lowerCAmelCase_		(lowercase__						:       Optional[Any]     ,					lowercase__						:       List[Any]       )		->    str:


				'''simple docstring'''




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





def 	lowerCAmelCase_		(lowercase__						:       Tuple       )		->    Dict:


				'''simple docstring'''




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





def 	lowerCAmelCase_		(lowercase__						:       str       )		->    Optional[int]:


				'''simple docstring'''




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

				return x





def 	lowerCAmelCase_		(lowercase__						:       List[str]       )		->    Tuple:


				'''simple docstring'''




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





def 	lowerCAmelCase_		(lowercase__						:       Any       )		->    Tuple:


				'''simple docstring'''




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





def 	lowerCAmelCase_		(lowercase__						:       str     ,					lowercase__						:       Dict     ,					lowercase__						:       List[str]       )		->    Tuple:


				'''simple docstring'''




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

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

				lowerCAmelCase__      			=						get_upernet_config(lowerCAmelCase__       )
				lowerCAmelCase__      			=						UperNetForSemanticSegmentation(lowerCAmelCase__       )
				model.eval()

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

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

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

				model.load_state_dict(lowerCAmelCase__       )

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

				lowerCAmelCase__      			=						SegformerImageProcessor()
				lowerCAmelCase__      			=						processor(lowerCAmelCase__     ,					return_tensors='''pt'''       ).pixel_values

				with torch.no_grad():
								lowerCAmelCase__      			=						model(lowerCAmelCase__       )
								lowerCAmelCase__      			=						outputs.logits

				print(logits.shape       )
				print('''First values of logits:'''     ,					logits[0, 0, :3, :3]       )
				# assert values
				if model_name == "upernet-swin-tiny":
								lowerCAmelCase__      			=						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__      			=						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__      			=						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__      			=						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]     ,					lowerCAmelCase__     ,					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(lowerCAmelCase__       )
								print(f'Saving processor to {pytorch_dump_folder_path}'       )
								processor.save_pretrained(lowerCAmelCase__       )

				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__":
			_UpperCAmelCase			:		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."
			)

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













from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


_UpperCAmelCase			:		List[Any]   				=    {
    "configuration_mobilenet_v2": [
        "MOBILENET_V2_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "MobileNetV2Config",
        "MobileNetV2OnnxConfig",
    ],
}

try:
			if not is_vision_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_UpperCAmelCase			:		str   				=    ["MobileNetV2FeatureExtractor"]
			_UpperCAmelCase			:		Union[str, Any]   				=    ["MobileNetV2ImageProcessor"]


try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_UpperCAmelCase			:		Any   				=    [
			    "MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST",
			    "MobileNetV2ForImageClassification",
			    "MobileNetV2ForSemanticSegmentation",
			    "MobileNetV2Model",
			    "MobileNetV2PreTrainedModel",
			    "load_tf_weights_in_mobilenet_v2",
			]


if TYPE_CHECKING:
			from .configuration_mobilenet_va import (
			    MOBILENET_V2_PRETRAINED_CONFIG_ARCHIVE_MAP,
			    MobileNetVaConfig,
			    MobileNetVaOnnxConfig,
			)

			try:
						if not is_vision_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .feature_extraction_mobilenet_va import MobileNetVaFeatureExtractor
						from .image_processing_mobilenet_va import MobileNetVaImageProcessor

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_mobilenet_va import (
						    MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
						    MobileNetVaForImageClassification,
						    MobileNetVaForSemanticSegmentation,
						    MobileNetVaModel,
						    MobileNetVaPreTrainedModel,
						    load_tf_weights_in_mobilenet_va,
						)


else:
			import sys

			_UpperCAmelCase			:		Dict   				=    _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





