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

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

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

					return config
def 							lowerCAmelCase(     __lowerCamelCase						):
					__a		 =       []

					# 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(     __lowerCamelCase				,    __lowerCamelCase				,    __lowerCamelCase						):
					__a		 =       dct.pop(_a						)
					__a		 =       val
def 							lowerCAmelCase(     __lowerCamelCase				,    __lowerCamelCase						):
					__a		 =       [int(backbone_config.embed_dim * 2**i						) for i in range(len(backbone_config.depths						)						)]
					for i in range(len(backbone_config.depths						)						):
										__a		 =       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		 =       state_dict.pop(f'''backbone.stages.{i}.blocks.{j}.attn.w_msa.qkv.weight'''						)
															__a		 =       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		 =       in_proj_weight[:dim, :]
															__a		 =       in_proj_bias[: dim]
															__a		 =       in_proj_weight[
															    dim : dim * 2, :
															]
															__a		 =       in_proj_bias[
															    dim : dim * 2
															]
															__a		 =       in_proj_weight[
															    -dim :, :
															]
															__a		 =       in_proj_bias[-dim :]
															# fmt: on
def 							lowerCAmelCase(     __lowerCamelCase						):
					__a		 =       x.shape
					__a		 =       x.reshape(_a				,    4				,    in_channel // 4						)
					__a		 =       x[:, [0, 2, 1, 3], :].transpose(1				,    2						).reshape(_a				,    _a						)
					return x
def 							lowerCAmelCase(     __lowerCamelCase						):
					__a		 =       x.shape
					__a		 =       x.reshape(_a				,    in_channel // 4				,    4						)
					__a		 =       x[:, :, [0, 2, 1, 3]].transpose(1				,    2						).reshape(_a				,    _a						)

					return x
def 							lowerCAmelCase(     __lowerCamelCase						):
					__a		 =       x.shape[0]
					__a		 =       x.reshape(4				,    in_channel // 4						)
					__a		 =       x[[0, 2, 1, 3], :].transpose(0				,    1						).reshape(_a						)
					return x
def 							lowerCAmelCase(     __lowerCamelCase						):
					__a		 =       x.shape[0]
					__a		 =       x.reshape(in_channel // 4				,    4						)
					__a		 =       x[:, [0, 2, 1, 3]].transpose(0				,    1						).reshape(_a						)
					return x
def 							lowerCAmelCase(     __lowerCamelCase				,    __lowerCamelCase				,    __lowerCamelCase						):
					__a		 =       {
					    """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		 =       model_name_to_url[model_name]
					__a		 =       torch.hub.load_state_dict_from_url(_a				,    map_location='cpu'				,    file_name=_a						)[
					    """state_dict"""
					]

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

					__a		 =       get_upernet_config(_a						)
					__a		 =       UperNetForSemanticSegmentation(_a						)
					model.eval()

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

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

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

					model.load_state_dict(_a						)

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

					__a		 =       SegformerImageProcessor()
					__a		 =       processor(_a				,    return_tensors='pt'						).pixel_values

					with torch.no_grad():
										__a		 =       model(_a						)
										__a		 =       outputs.logits

					print(logits.shape						)
					print('First values of logits:'				,    logits[0, 0, :3, :3]						)
					# assert values
					if model_name == "upernet-swin-tiny":
										__a		 =       torch.tensor(
										    [[-7.59_58, -7.59_58, -7.43_02], [-7.59_58, -7.59_58, -7.43_02], [-7.47_97, -7.47_97, -7.30_68]]						)
					elif model_name == "upernet-swin-small":
										__a		 =       torch.tensor(
										    [[-7.19_21, -7.19_21, -6.95_32], [-7.19_21, -7.19_21, -6.95_32], [-7.09_08, -7.09_08, -6.85_34]]						)
					elif model_name == "upernet-swin-base":
										__a		 =       torch.tensor(
										    [[-6.58_51, -6.58_51, -6.43_30], [-6.58_51, -6.58_51, -6.43_30], [-6.47_63, -6.47_63, -6.32_54]]						)
					elif model_name == "upernet-swin-large":
										__a		 =       torch.tensor(
										    [[-7.52_97, -7.52_97, -7.38_02], [-7.52_97, -7.52_97, -7.38_02], [-7.40_44, -7.40_44, -7.25_86]]						)
					print('Logits:'				,    outputs.logits[0, 0, :3, :3]						)
					assert torch.allclose(outputs.logits[0, 0, :3, :3]				,    _a				,    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(_a						)
										print(f'''Saving processor to {pytorch_dump_folder_path}'''						)
										processor.save_pretrained(_a						)

					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_    :		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."""
	)

	lowerCamelCase_    :		int							=	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_tf_available, is_torch_available


lowerCamelCase_    :		Dict							=	{"""configuration_swin""": ["""SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP""", """SwinConfig""", """SwinOnnxConfig"""]}


try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowerCamelCase_    :		int							=	[
	    """SWIN_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """SwinForImageClassification""",
	    """SwinForMaskedImageModeling""",
	    """SwinModel""",
	    """SwinPreTrainedModel""",
	    """SwinBackbone""",
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowerCamelCase_    :		Any							=	[
	    """TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """TFSwinForImageClassification""",
	    """TFSwinForMaskedImageModeling""",
	    """TFSwinModel""",
	    """TFSwinPreTrainedModel""",
	]

if TYPE_CHECKING:
	from .configuration_swin import SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP, SwinConfig, SwinOnnxConfig

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_swin import (
		    SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
		    SwinBackbone,
		    SwinForImageClassification,
		    SwinForMaskedImageModeling,
		    SwinModel,
		    SwinPreTrainedModel,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_swin import (
		    TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFSwinForImageClassification,
		    TFSwinForMaskedImageModeling,
		    TFSwinModel,
		    TFSwinPreTrainedModel,
		)

else:
	import sys

	lowerCamelCase_    :		List[Any]							=	_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




