



'''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  :						List[Any]       ):





    '''simple docstring'''


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

    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(lowerCamelCase_     ,   lowerCamelCase_     ,   repo_type="dataset"       )     ,   "r"       )       )
    lowercase__		       =						{int(lowerCamelCase_       ): v for k, v in idalabel.items()}

    return config




def        lowerCamelCase  (				_snake_case  :						Tuple       ):





    '''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  :						int     ,   _snake_case  :						int     ,   _snake_case  :						Optional[Any]       ):





    '''simple docstring'''


    lowercase__		       =						dct.pop(lowerCamelCase_       )
    lowercase__		       =						val




def        lowerCamelCase  (				_snake_case  :						str     ,   _snake_case  :						Dict       ):





    '''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  :						int     ,   _snake_case  :						Any       ):





    '''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(lowerCamelCase_     ,   stream=lowerCamelCase_       ).raw       )
    return im




@torch.no_grad()
def        lowerCamelCase  (				_snake_case  :						List[Any]     ,   _snake_case  :						Any     ,   _snake_case  :						int     ,   _snake_case  :						List[str] = False       ):





    '''simple docstring'''


    lowercase__		       =						get_maskformer_config(lowerCamelCase_       )

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

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

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

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

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

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

    lowercase__		       =						model.load_state_dict(lowerCamelCase_     ,   strict=lowerCamelCase_       )
    assert missing_keys == [
        "model.pixel_level_module.encoder.model.layernorm.weight",
        "model.pixel_level_module.encoder.model.layernorm.bias",
    ]
    assert len(lowerCamelCase_       ) == 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=lowerCamelCase_     ,   reduce_labels=lowerCamelCase_       )

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

    lowercase__		       =						model(**lowerCamelCase_       )

    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]     ,   lowerCamelCase_     ,   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(lowerCamelCase_       ).mkdir(exist_ok=lowerCamelCase_       )
        model.save_pretrained(lowerCamelCase_       )
        image_processor.save_pretrained(lowerCamelCase_       )

    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
 )









'''simple docstring'''





def        lowerCamelCase  (				_snake_case  :						int     ,_snake_case  :						int       ):





    '''simple docstring'''


    return x if y == 0 else greatest_common_divisor(_snake_case     ,x % y       )




def        lowerCamelCase  (				_snake_case  :						int     ,_snake_case  :						int       ):





    '''simple docstring'''


    return (x * y) // greatest_common_divisor(_snake_case     ,_snake_case       )




def        lowerCamelCase  (				_snake_case  :						int = 20       ):





    '''simple docstring'''


    lowercase__		       =						1
    for i in range(1     ,n + 1       ):
        lowercase__		       =						lcm(_snake_case     ,_snake_case       )
    return g


if __name__ == "__main__":
 print(f'''{solution() = }''')




