



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

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

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

						return config






def A__       (	UpperCAmelCase_				):
						_UpperCamelCase   :						List[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 A__       (	UpperCAmelCase_    ,    UpperCAmelCase_    ,    UpperCAmelCase_				):
						_UpperCamelCase   :						Union[str, Any]												=  dct.pop(UpperCAmelCase_				)
						_UpperCamelCase   :						Dict												=  val






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






def A__       (	UpperCAmelCase_				):
						_UpperCamelCase							,     _UpperCamelCase   :						Optional[int]												=  x.shape
						_UpperCamelCase   :						Tuple												=  x.reshape(UpperCAmelCase_    ,    4    ,    in_channel // 4				)
						_UpperCamelCase   :						Optional[int]												=  x[:, [0, 2, 1, 3], :].transpose(1    ,    2				).reshape(UpperCAmelCase_    ,    UpperCAmelCase_				)
						return x






def A__       (	UpperCAmelCase_				):
						_UpperCamelCase							,     _UpperCamelCase   :						Optional[int]												=  x.shape
						_UpperCamelCase   :						List[str]												=  x.reshape(UpperCAmelCase_    ,    in_channel // 4    ,    4				)
						_UpperCamelCase   :						Union[str, Any]												=  x[:, :, [0, 2, 1, 3]].transpose(1    ,    2				).reshape(UpperCAmelCase_    ,    UpperCAmelCase_				)

						return x






def A__       (	UpperCAmelCase_				):
						_UpperCamelCase   :						Tuple												=  x.shape[0]
						_UpperCamelCase   :						int												=  x.reshape(4    ,    in_channel // 4				)
						_UpperCamelCase   :						Dict												=  x[[0, 2, 1, 3], :].transpose(0    ,    1				).reshape(UpperCAmelCase_				)
						return x






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






def A__       (	UpperCAmelCase_    ,    UpperCAmelCase_    ,    UpperCAmelCase_				):
						_UpperCamelCase   :						Optional[Any]												=  {
						    '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',
						}
						_UpperCamelCase   :						Tuple												=  model_name_to_url[model_name]
						_UpperCamelCase   :						str												=  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				)

						_UpperCamelCase   :						Tuple												=  get_upernet_config(UpperCAmelCase_				)
						_UpperCamelCase   :						List[Any]												=  UperNetForSemanticSegmentation(UpperCAmelCase_				)
						model.eval()

						# replace "bn" => "batch_norm"
						for key in state_dict.copy().keys():
												_UpperCamelCase   :						Optional[int]												=  state_dict.pop(UpperCAmelCase_				)
												if "bn" in key:
																		_UpperCamelCase   :						Optional[int]												=  key.replace('bn'    ,    'batch_norm'				)
												_UpperCamelCase   :						List[str]												=  val

						# rename keys
						_UpperCamelCase   :						Tuple												=  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:
																								_UpperCamelCase   :						Any												=  reverse_correct_unfold_reduction_order(UpperCAmelCase_				)
																		if "norm" in key:
																								_UpperCamelCase   :						Optional[Any]												=  reverse_correct_unfold_norm_order(UpperCAmelCase_				)

						model.load_state_dict(UpperCAmelCase_				)

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

						_UpperCamelCase   :						Tuple												=  SegformerImageProcessor()
						_UpperCamelCase   :						Union[str, Any]												=  processor(UpperCAmelCase_    ,    return_tensors='pt'				).pixel_values

						with torch.no_grad():
												_UpperCamelCase   :						List[str]												=  model(UpperCAmelCase_				)
												_UpperCamelCase   :						Optional[Any]												=  outputs.logits

						print(logits.shape				)
						print('First values of logits:'    ,    logits[0, 0, :3, :3]				)
						# assert values
						if model_name == "upernet-swin-tiny":
												_UpperCamelCase   :						Any												=  torch.tensor(
												    [[-7.5_958, -7.5_958, -7.4_302], [-7.5_958, -7.5_958, -7.4_302], [-7.4_797, -7.4_797, -7.3_068]]				)
						elif model_name == "upernet-swin-small":
												_UpperCamelCase   :						Dict												=  torch.tensor(
												    [[-7.1_921, -7.1_921, -6.9_532], [-7.1_921, -7.1_921, -6.9_532], [-7.0_908, -7.0_908, -6.8_534]]				)
						elif model_name == "upernet-swin-base":
												_UpperCamelCase   :						List[Any]												=  torch.tensor(
												    [[-6.5_851, -6.5_851, -6.4_330], [-6.5_851, -6.5_851, -6.4_330], [-6.4_763, -6.4_763, -6.3_254]]				)
						elif model_name == "upernet-swin-large":
												_UpperCamelCase   :						str												=  torch.tensor(
												    [[-7.5_297, -7.5_297, -7.3_802], [-7.5_297, -7.5_297, -7.3_802], [-7.4_044, -7.4_044, -7.2_586]]				)
						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__":
					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.'
					)

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







'''simple docstring'''
import os
from pathlib import Path






def A__       (	):
						from torch.utils.cpp_extension import load

						_UpperCamelCase   :						Optional[Any]												=  Path(UpperCAmelCase_				).resolve().parent.parent.parent / 'kernels' / 'deformable_detr'
						_UpperCamelCase   :						Tuple												=  [
						    root / filename
						    for filename in [
						        'vision.cpp',
						        os.path.join('cpu'    ,    'ms_deform_attn_cpu.cpp'				),
						        os.path.join('cuda'    ,    'ms_deform_attn_cuda.cu'				),
						    ]
						]

						load(
						    'MultiScaleDeformableAttention'    ,    UpperCAmelCase_    ,    with_cuda=UpperCAmelCase_    ,    extra_include_paths=[str(UpperCAmelCase_				)]    ,    extra_cflags=['-DWITH_CUDA=1']    ,    extra_cuda_cflags=[
						        '-DCUDA_HAS_FP16=1',
						        '-D__CUDA_NO_HALF_OPERATORS__',
						        '-D__CUDA_NO_HALF_CONVERSIONS__',
						        '-D__CUDA_NO_HALF2_OPERATORS__',
						    ]    ,    )

						import MultiScaleDeformableAttention as MSDA

						return MSDA


