



'''simple docstring'''





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()
SCREAMING_SNAKE_CASE__          =  logging.get_logger(__name__)




def 							lowerCamelCase	(					_snake_case	:    str							):





    '''simple docstring'''


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

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

    lowercase__									=    json.load(open(hf_hub_download(__A  ,__A  ,repo_type="dataset"							)  ,"r"							)							)
    lowercase__									=    {int(__A							): v for k, v in idalabel.items()}

    return config




def 							lowerCamelCase	(					_snake_case	:    str							):





    '''simple docstring'''


    lowercase__									=    []
    # 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 							lowerCamelCase	(					_snake_case	:    Tuple  ,_snake_case	:    str  ,_snake_case	:    int							):





    '''simple docstring'''


    lowercase__									=    dct.pop(__A							)
    lowercase__									=    val




def 							lowerCamelCase	(					_snake_case	:    Optional[Any]  ,_snake_case	:    int							):





    '''simple docstring'''


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




def 							lowerCamelCase	(					_snake_case	:    Union[str, Any]  ,_snake_case	:    str							):





    '''simple docstring'''


    lowercase__									=    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)
        lowercase__									=    state_dict.pop(f'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_weight'''							)
        lowercase__									=    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
        lowercase__									=    in_proj_weight[: hidden_size, :]
        lowercase__									=    in_proj_bias[:config.hidden_size]
        lowercase__									=    in_proj_weight[hidden_size : hidden_size * 2, :]
        lowercase__									=    in_proj_bias[hidden_size : hidden_size * 2]
        lowercase__									=    in_proj_weight[-hidden_size :, :]
        lowercase__									=    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)
        lowercase__									=    state_dict.pop(f'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_weight'''							)
        lowercase__									=    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
        lowercase__									=    in_proj_weight[: hidden_size, :]
        lowercase__									=    in_proj_bias[:config.hidden_size]
        lowercase__									=    in_proj_weight[hidden_size : hidden_size * 2, :]
        lowercase__									=    in_proj_bias[hidden_size : hidden_size * 2]
        lowercase__									=    in_proj_weight[-hidden_size :, :]
        lowercase__									=    in_proj_bias[-hidden_size :]
    # fmt: on




def 							lowerCamelCase	(					):





    '''simple docstring'''


    lowercase__									=    "http://images.cocodataset.org/val2017/000000039769.jpg"
    lowercase__									=    Image.open(requests.get(__A  ,stream=__A							).raw							)
    return im




@torch.no_grad()
def 							lowerCamelCase	(					_snake_case	:    str  ,_snake_case	:    str  ,_snake_case	:    str  ,_snake_case	:    bool = False							):





    '''simple docstring'''


    lowercase__									=    get_maskformer_config(__A							)

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

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

    # rename keys
    lowercase__									=    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():
        lowercase__									=    torch.from_numpy(__A							)

    # load 🤗 model
    lowercase__									=    MaskFormerForInstanceSegmentation(__A							)
    model.eval()

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

    lowercase__							,							lowercase__									=    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
    lowercase__									=    prepare_img()
    if "vistas" in model_name:
        lowercase__									=    65
    elif "cityscapes" in model_name:
        lowercase__									=    65_535
    else:
        lowercase__									=    255
    lowercase__									=    True if "ade" in model_name else False
    lowercase__									=    MaskFormerImageProcessor(ignore_index=__A  ,reduce_labels=__A							)

    lowercase__									=    image_processor(__A  ,return_tensors="pt"							)

    lowercase__									=    model(**__A							)

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

    if model_name == "maskformer-swin-tiny-ade":
        lowercase__									=    torch.tensor(
            [[3.6_353, -4.4_770, -2.6_065], [0.5_081, -4.2_394, -3.5_343], [2.1_909, -5.0_353, -1.9_323]]							)
    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__":
 SCREAMING_SNAKE_CASE__          =  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."
 )

 SCREAMING_SNAKE_CASE__          =  parser.parse_args()
 convert_maskformer_checkpoint(
     args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
 )







import gc
import threading
import time

import psutil
import torch





class        lowerCAmelCase__  :



       '''simple docstring'''







       def __init__(							self     ):







             '''simple docstring'''



             __A    =psutil.Process()
             __A    =False

       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             __A    =-1

             while True:
                   __A    =max(self.process.memory_info().rss ,   self.cpu_memory_peak     )

                   # can't sleep or will not catch the peak right (this comment is here on purpose)
                   if not self.peak_monitoring:
                         break

       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             __A    =True
             __A    =threading.Thread(target=self.peak_monitor     )
             __A    =True
             self.thread.start()


       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             __A    =False
             self.thread.join()
             return self.cpu_memory_peak

_lowerCamelCase      :					Union[str, Any]					    =      PeakCPUMemory()





def      A__					(			)					->Dict:
      # Time
      __A    ={'''time''': time.time()}

      gc.collect()
      torch.cuda.empty_cache()

      # CPU mem
      __A    =psutil.Process().memory_info().rss
      cpu_peak_tracker.start()

      # GPU mem
      for i in range(torch.cuda.device_count()			):
            __A    =torch.cuda.memory_allocated(__A			)
      torch.cuda.reset_peak_memory_stats()

      return measures





def      A__					(			__A  :						Any			)					->Any:
      # Time
      __A    ={'''time''': time.time() - start_measures['''time''']}

      gc.collect()
      torch.cuda.empty_cache()

      # CPU mem
      __A    =(psutil.Process().memory_info().rss - start_measures['''cpu''']) / 2**20
      __A    =(cpu_peak_tracker.stop() - start_measures['''cpu''']) / 2**20

      # GPU mem
      for i in range(torch.cuda.device_count()			):
            __A    =(torch.cuda.memory_allocated(__A			) - start_measures[str(__A			)]) / 2**20
            __A    =(torch.cuda.max_memory_allocated(__A			) - start_measures[str(__A			)]) / 2**20

      return measures





def      A__					(			__A  :						Union[str, Any]     ,				__A  :						Any			)					->Union[str, Any]:
      print(F'''{description}:'''			)
      print(F'''- Time: {measures['time']:.2f}s'''			)
      for i in range(torch.cuda.device_count()			):
            print(F'''- GPU {i} allocated: {measures[str(__A			)]:.2f}MiB'''			)
            __A    =measures[F'''{i}-peak''']
            print(F'''- GPU {i} peak: {peak:.2f}MiB'''			)
      print(F'''- CPU RAM allocated: {measures['cpu']:.2f}MiB'''			)
      print(F'''- CPU RAM peak: {measures['cpu-peak']:.2f}MiB'''			)


