



from __future__ import annotations

from math import pi, sqrt
def  UpperCamelCase						(       _A			:				float			,							_A			:				float    )->       tuple:





						"""simple docstring"""





						if inductance <= 0:
												raise ValueError("Inductance cannot be 0 or negative"    )

						elif capacitance <= 0:
												raise ValueError("Capacitance cannot be 0 or negative"    )

						else:
												return (
												    "Resonant frequency",
												    float(1 / (2 * pi * (sqrt(inductance * capacitance    )))    ),
												)


if __name__ == "__main__":
						import doctest

						doctest.testmod()










import argparse
import json
import pickle
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import MaskFormerConfig, MaskFormerForInstanceSegmentation, MaskFormerImageProcessor, SwinConfig
from transformers.utils import logging


logging.set_verbosity_info()
UpperCAmelCase_							:      str      =  logging.get_logger(__name__)
def  UpperCamelCase						(       _A			:				str    )->       List[str]:





						"""simple docstring"""





						A__							=       SwinConfig.from_pretrained(
						    "microsoft/swin-tiny-patch4-window7-224"			,							out_features=["stage1", "stage2", "stage3", "stage4"]    )
						A__							=       MaskFormerConfig(backbone_config=_A    )

						A__							=       "huggingface/label-files"
						if "ade20k-full" in model_name:
												# this should be ok
												A__							=       847
												A__							=       "maskformer-ade20k-full-id2label.json"
						elif "ade" in model_name:
												# this should be ok
												A__							=       150
												A__							=       "ade20k-id2label.json"
						elif "coco-stuff" in model_name:
												# this should be ok
												A__							=       171
												A__							=       "maskformer-coco-stuff-id2label.json"
						elif "coco" in model_name:
												# TODO
												A__							=       133
												A__							=       "coco-panoptic-id2label.json"
						elif "cityscapes" in model_name:
												# this should be ok
												A__							=       19
												A__							=       "cityscapes-id2label.json"
						elif "vistas" in model_name:
												# this should be ok
												A__							=       65
												A__							=       "mapillary-vistas-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()}

						return config
def  UpperCamelCase						(       _A			:				Tuple    )->       Union[str, Any]:





						"""simple docstring"""





						A__							=       []
						# stem
						# fmt: off
						rename_keys.append(("backbone.patch_embed.proj.weight", "model.pixel_level_module.encoder.model.embeddings.patch_embeddings.projection.weight")    )
						rename_keys.append(("backbone.patch_embed.proj.bias", "model.pixel_level_module.encoder.model.embeddings.patch_embeddings.projection.bias")    )
						rename_keys.append(("backbone.patch_embed.norm.weight", "model.pixel_level_module.encoder.model.embeddings.norm.weight")    )
						rename_keys.append(("backbone.patch_embed.norm.bias", "model.pixel_level_module.encoder.model.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.layers.{i}.blocks.{j}.norm1.weight""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_before.weight""")    )
																		rename_keys.append((f"""backbone.layers.{i}.blocks.{j}.norm1.bias""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_before.bias""")    )
																		rename_keys.append((f"""backbone.layers.{i}.blocks.{j}.attn.relative_position_bias_table""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table""")    )
																		rename_keys.append((f"""backbone.layers.{i}.blocks.{j}.attn.relative_position_index""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index""")    )
																		rename_keys.append((f"""backbone.layers.{i}.blocks.{j}.attn.proj.weight""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight""")    )
																		rename_keys.append((f"""backbone.layers.{i}.blocks.{j}.attn.proj.bias""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias""")    )
																		rename_keys.append((f"""backbone.layers.{i}.blocks.{j}.norm2.weight""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_after.weight""")    )
																		rename_keys.append((f"""backbone.layers.{i}.blocks.{j}.norm2.bias""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_after.bias""")    )
																		rename_keys.append((f"""backbone.layers.{i}.blocks.{j}.mlp.fc1.weight""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight""")    )
																		rename_keys.append((f"""backbone.layers.{i}.blocks.{j}.mlp.fc1.bias""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias""")    )
																		rename_keys.append((f"""backbone.layers.{i}.blocks.{j}.mlp.fc2.weight""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.output.dense.weight""")    )
																		rename_keys.append((f"""backbone.layers.{i}.blocks.{j}.mlp.fc2.bias""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.output.dense.bias""")    )

												if i < 3:
																		rename_keys.append((f"""backbone.layers.{i}.downsample.reduction.weight""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.reduction.weight""")    )
																		rename_keys.append((f"""backbone.layers.{i}.downsample.norm.weight""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.norm.weight""")    )
																		rename_keys.append((f"""backbone.layers.{i}.downsample.norm.bias""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.norm.bias""")    )
												rename_keys.append((f"""backbone.norm{i}.weight""", f"""model.pixel_level_module.encoder.hidden_states_norms.{i}.weight""")    )
												rename_keys.append((f"""backbone.norm{i}.bias""", f"""model.pixel_level_module.encoder.hidden_states_norms.{i}.bias""")    )

						# FPN
						rename_keys.append(("sem_seg_head.layer_4.weight", "model.pixel_level_module.decoder.fpn.stem.0.weight")    )
						rename_keys.append(("sem_seg_head.layer_4.norm.weight", "model.pixel_level_module.decoder.fpn.stem.1.weight")    )
						rename_keys.append(("sem_seg_head.layer_4.norm.bias", "model.pixel_level_module.decoder.fpn.stem.1.bias")    )
						for source_index, target_index in zip(range(3			,							0			,							-1    )			,							range(0			,							3    )    ):
												rename_keys.append((f"""sem_seg_head.adapter_{source_index}.weight""", f"""model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.0.weight""")    )
												rename_keys.append((f"""sem_seg_head.adapter_{source_index}.norm.weight""", f"""model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.1.weight""")    )
												rename_keys.append((f"""sem_seg_head.adapter_{source_index}.norm.bias""", f"""model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.1.bias""")    )
												rename_keys.append((f"""sem_seg_head.layer_{source_index}.weight""", f"""model.pixel_level_module.decoder.fpn.layers.{target_index}.block.0.weight""")    )
												rename_keys.append((f"""sem_seg_head.layer_{source_index}.norm.weight""", f"""model.pixel_level_module.decoder.fpn.layers.{target_index}.block.1.weight""")    )
												rename_keys.append((f"""sem_seg_head.layer_{source_index}.norm.bias""", f"""model.pixel_level_module.decoder.fpn.layers.{target_index}.block.1.bias""")    )
						rename_keys.append(("sem_seg_head.mask_features.weight", "model.pixel_level_module.decoder.mask_projection.weight")    )
						rename_keys.append(("sem_seg_head.mask_features.bias", "model.pixel_level_module.decoder.mask_projection.bias")    )

						# Transformer decoder
						for idx in range(config.decoder_config.decoder_layers    ):
												# self-attention out projection
												rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.out_proj.weight""", f"""model.transformer_module.decoder.layers.{idx}.self_attn.out_proj.weight""")    )
												rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.out_proj.bias""", f"""model.transformer_module.decoder.layers.{idx}.self_attn.out_proj.bias""")    )
												# cross-attention out projection
												rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.out_proj.weight""", f"""model.transformer_module.decoder.layers.{idx}.encoder_attn.out_proj.weight""")    )
												rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.out_proj.bias""", f"""model.transformer_module.decoder.layers.{idx}.encoder_attn.out_proj.bias""")    )
												# MLP 1
												rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear1.weight""", f"""model.transformer_module.decoder.layers.{idx}.fc1.weight""")    )
												rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear1.bias""", f"""model.transformer_module.decoder.layers.{idx}.fc1.bias""")    )
												# MLP 2
												rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear2.weight""", f"""model.transformer_module.decoder.layers.{idx}.fc2.weight""")    )
												rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear2.bias""", f"""model.transformer_module.decoder.layers.{idx}.fc2.bias""")    )
												# layernorm 1 (self-attention layernorm)
												rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm1.weight""", f"""model.transformer_module.decoder.layers.{idx}.self_attn_layer_norm.weight""")    )
												rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm1.bias""", f"""model.transformer_module.decoder.layers.{idx}.self_attn_layer_norm.bias""")    )
												# layernorm 2 (cross-attention layernorm)
												rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm2.weight""", f"""model.transformer_module.decoder.layers.{idx}.encoder_attn_layer_norm.weight""")    )
												rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm2.bias""", f"""model.transformer_module.decoder.layers.{idx}.encoder_attn_layer_norm.bias""")    )
												# layernorm 3 (final layernorm)
												rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm3.weight""", f"""model.transformer_module.decoder.layers.{idx}.final_layer_norm.weight""")    )
												rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm3.bias""", f"""model.transformer_module.decoder.layers.{idx}.final_layer_norm.bias""")    )

						rename_keys.append(("sem_seg_head.predictor.transformer.decoder.norm.weight", "model.transformer_module.decoder.layernorm.weight")    )
						rename_keys.append(("sem_seg_head.predictor.transformer.decoder.norm.bias", "model.transformer_module.decoder.layernorm.bias")    )

						# heads on top
						rename_keys.append(("sem_seg_head.predictor.query_embed.weight", "model.transformer_module.queries_embedder.weight")    )

						rename_keys.append(("sem_seg_head.predictor.input_proj.weight", "model.transformer_module.input_projection.weight")    )
						rename_keys.append(("sem_seg_head.predictor.input_proj.bias", "model.transformer_module.input_projection.bias")    )

						rename_keys.append(("sem_seg_head.predictor.class_embed.weight", "class_predictor.weight")    )
						rename_keys.append(("sem_seg_head.predictor.class_embed.bias", "class_predictor.bias")    )

						for i in range(3    ):
												rename_keys.append((f"""sem_seg_head.predictor.mask_embed.layers.{i}.weight""", f"""mask_embedder.{i}.0.weight""")    )
												rename_keys.append((f"""sem_seg_head.predictor.mask_embed.layers.{i}.bias""", f"""mask_embedder.{i}.0.bias""")    )
						# fmt: on

						return rename_keys
def  UpperCamelCase						(       _A			:				int			,							_A			:				List[str]			,							_A			:				List[str]    )->       Any:





						"""simple docstring"""





						A__							=       dct.pop(_A    )
						A__							=       val
def  UpperCamelCase						(       _A			:				Tuple			,							_A			:				List[str]    )->       Dict:





						"""simple docstring"""





						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.layers.{i}.blocks.{j}.attn.qkv.weight"""    )
																		A__							=       state_dict.pop(f"""backbone.layers.{i}.blocks.{j}.attn.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  UpperCamelCase						(       _A			:				Tuple			,							_A			:				Optional[int]    )->       Union[str, Any]:





						"""simple docstring"""





						A__							=       config.decoder_config.hidden_size
						for idx in range(config.decoder_config.decoder_layers    ):
												# read in weights + bias of self-attention input projection layer (in the original implementation, this is a single matrix + bias)
												A__							=       state_dict.pop(f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_weight"""    )
												A__							=       state_dict.pop(f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_bias"""    )
												# next, add query, keys and values (in that order) to the state dict
												A__							=       in_proj_weight[: hidden_size, :]
												A__							=       in_proj_bias[:config.hidden_size]
												A__							=       in_proj_weight[hidden_size : hidden_size * 2, :]
												A__							=       in_proj_bias[hidden_size : hidden_size * 2]
												A__							=       in_proj_weight[-hidden_size :, :]
												A__							=       in_proj_bias[-hidden_size :]
												# read in weights + bias of cross-attention input projection layer (in the original implementation, this is a single matrix + bias)
												A__							=       state_dict.pop(f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_weight"""    )
												A__							=       state_dict.pop(f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_bias"""    )
												# next, add query, keys and values (in that order) to the state dict
												A__							=       in_proj_weight[: hidden_size, :]
												A__							=       in_proj_bias[:config.hidden_size]
												A__							=       in_proj_weight[hidden_size : hidden_size * 2, :]
												A__							=       in_proj_bias[hidden_size : hidden_size * 2]
												A__							=       in_proj_weight[-hidden_size :, :]
												A__							=       in_proj_bias[-hidden_size :]
						# fmt: on
def  UpperCamelCase						(       )->       torch.Tensor:





						"""simple docstring"""





						A__							=       "http://images.cocodataset.org/val2017/000000039769.jpg"
						A__							=       Image.open(requests.get(_A			,							stream=_A    ).raw    )
						return im
@torch.no_grad()
def  UpperCamelCase						(       _A			:				str			,							_A			:				str			,							_A			:				str			,							_A			:				bool = False    )->       Tuple:





						"""simple docstring"""





						A__							=       get_maskformer_config(_A    )

						# load original state_dict
						with open(_A			,							"rb"    ) as f:
												A__							=       pickle.load(_A    )
						A__							=       data["model"]

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

						# rename keys
						A__							=       create_rename_keys(_A    )
						for src, dest in rename_keys:
												rename_key(_A			,							_A			,							_A    )
						read_in_swin_q_k_v(_A			,							config.backbone_config    )
						read_in_decoder_q_k_v(_A			,							_A    )

						# update to torch tensors
						for key, value in state_dict.items():
												A__							=       torch.from_numpy(_A    )

						# load 🤗 model
						A__							=       MaskFormerForInstanceSegmentation(_A    )
						model.eval()

						for name, param in model.named_parameters():
												print(_A			,							param.shape    )

						A__						,       A__							=       model.load_state_dict(_A			,							strict=_A    )
						assert missing_keys == [
						    "model.pixel_level_module.encoder.model.layernorm.weight",
						    "model.pixel_level_module.encoder.model.layernorm.bias",
						]
						assert len(_A    ) == 0, f"""Unexpected keys: {unexpected_keys}"""

						# verify results
						A__							=       prepare_img()
						if "vistas" in model_name:
												A__							=       65
						elif "cityscapes" in model_name:
												A__							=       65535
						else:
												A__							=       255
						A__							=       True if "ade" in model_name else False
						A__							=       MaskFormerImageProcessor(ignore_index=_A			,							reduce_labels=_A    )

						A__							=       image_processor(_A			,							return_tensors="pt"    )

						A__							=       model(**_A    )

						print("Logits:"			,							outputs.class_queries_logits[0, :3, :3]    )

						if model_name == "maskformer-swin-tiny-ade":
												A__							=       torch.tensor(
												    [[3.6353, -4.4770, -2.6065], [0.5081, -4.2394, -3.5343], [2.1909, -5.0353, -1.9323]]    )
						assert torch.allclose(outputs.class_queries_logits[0, :3, :3]			,							_A			,							atol=1E-4    )
						print("Looks ok!"    )

						if pytorch_dump_folder_path is not None:
												print(f"""Saving model and image processor to {pytorch_dump_folder_path}"""    )
												Path(_A    ).mkdir(exist_ok=_A    )
												model.save_pretrained(_A    )
												image_processor.save_pretrained(_A    )

						if push_to_hub:
												print("Pushing model and image processor to the hub..."    )
												model.push_to_hub(f"""nielsr/{model_name}"""    )
												image_processor.push_to_hub(f"""nielsr/{model_name}"""    )


if __name__ == "__main__":
						UpperCAmelCase_							:      str      =  argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    "--model_name",
						    default="maskformer-swin-tiny-ade",
						    type=str,
						    help=("Name of the MaskFormer model you'd like to convert",),
						)
						parser.add_argument(
						    "--checkpoint_path",
						    default="/Users/nielsrogge/Documents/MaskFormer_checkpoints/MaskFormer-Swin-tiny-ADE20k/model.pkl",
						    type=str,
						    help="Path to the original state dict (.pth file).",
						)
						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_							:      Tuple      =  parser.parse_args()
						convert_maskformer_checkpoint(
						    args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
						)





