



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()
__snake_case						:Optional[int]						=					logging.get_logger(__name__)





def 					__snake_case     (      _UpperCAmelCase    ):
    __a							=    SwinConfig.from_pretrained(
        '''microsoft/swin-tiny-patch4-window7-224'''       ,			out_features=['''stage1''', '''stage2''', '''stage3''', '''stage4''']    )
    __a							=    MaskFormerConfig(backbone_config=_UpperCAmelCase    )

    __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(_UpperCAmelCase       ,			_UpperCAmelCase       ,			repo_type='''dataset'''    )       ,			'''r'''    )    )
    __a							=    {int(_UpperCAmelCase    ): v for k, v in idalabel.items()}

    return config





def 					__snake_case     (      _UpperCAmelCase    ):
    __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 					__snake_case     (      _UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase    ):
    __a							=    dct.pop(_UpperCAmelCase    )
    __a							=    val





def 					__snake_case     (      _UpperCAmelCase       ,			_UpperCAmelCase    ):
    __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 					__snake_case     (      _UpperCAmelCase       ,			_UpperCAmelCase    ):
    # fmt: off
    __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 					__snake_case     (      ):
    __a							=    '''http://images.cocodataset.org/val2017/000000039769.jpg'''
    __a							=    Image.open(requests.get(_UpperCAmelCase       ,			stream=_UpperCAmelCase    ).raw    )
    return im





@torch.no_grad()
def 					__snake_case     (      _UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase = False    ):
    __a							=    get_maskformer_config(_UpperCAmelCase    )

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

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

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

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

    # load 🤗 model
    __a							=    MaskFormerForInstanceSegmentation(_UpperCAmelCase    )
    model.eval()

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

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

    __a							=    image_processor(_UpperCAmelCase       ,			return_tensors='''pt'''    )

    __a							=    model(**_UpperCAmelCase    )

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

    if model_name == "maskformer-swin-tiny-ade":
        __a							=    torch.tensor(
            [[3.63_53, -4.47_70, -2.60_65], [0.50_81, -4.23_94, -3.53_43], [2.19_09, -5.03_53, -1.93_23]]    )
    assert torch.allclose(outputs.class_queries_logits[0, :3, :3]       ,			_UpperCAmelCase       ,			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(_UpperCAmelCase    ).mkdir(exist_ok=_UpperCAmelCase    )
        model.save_pretrained(_UpperCAmelCase    )
        image_processor.save_pretrained(_UpperCAmelCase    )

    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__":
 __snake_case						:Optional[Any]						=					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.'''
 )

 __snake_case						:str						=					parser.parse_args()
 convert_maskformer_checkpoint(
     args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
 )










def 					__snake_case     (      _UpperCAmelCase = 1000000    ):
    __a							=    limit + 1
    __a							=    [0] * limit
    for first_term in range(1       ,			_UpperCAmelCase    ):
        for n in range(_UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase    ):
            __a							=    first_term + n / first_term
            if common_difference % 4:  # d must be divisble by 4
                continue
            else:
                common_difference /= 4
                if (
                    first_term > common_difference
                    and first_term < 4 * common_difference
                ):  # since x,y,z are positive integers
                    frequency[n] += 1  # so z>0 and a>d ,also 4d<a

    __a							=    sum(1 for x in frequency[1:limit] if x == 10    )

    return count


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





