'''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()
__UpperCAmelCase												=logging.get_logger(__name__)






def __lowerCAmelCase			(							UpperCamelCase__    )     ->   Dict:
    __lowerCamelCase          =  SwinConfig.from_pretrained(
        '''microsoft/swin-tiny-patch4-window7-224'''    ,  out_features=['''stage1''', '''stage2''', '''stage3''', '''stage4''']    )
    __lowerCamelCase          =  MaskFormerConfig(backbone_config=UpperCamelCase__    )

    __lowerCamelCase          =  '''huggingface/label-files'''
    if "ade20k-full" in model_name:
        # this should be ok
        __lowerCamelCase          =  8_47
        __lowerCamelCase          =  '''maskformer-ade20k-full-id2label.json'''
    elif "ade" in model_name:
        # this should be ok
        __lowerCamelCase          =  1_50
        __lowerCamelCase          =  '''ade20k-id2label.json'''
    elif "coco-stuff" in model_name:
        # this should be ok
        __lowerCamelCase          =  1_71
        __lowerCamelCase          =  '''maskformer-coco-stuff-id2label.json'''
    elif "coco" in model_name:
        # TODO
        __lowerCamelCase          =  1_33
        __lowerCamelCase          =  '''coco-panoptic-id2label.json'''
    elif "cityscapes" in model_name:
        # this should be ok
        __lowerCamelCase          =  19
        __lowerCamelCase          =  '''cityscapes-id2label.json'''
    elif "vistas" in model_name:
        # this should be ok
        __lowerCamelCase          =  65
        __lowerCamelCase          =  '''mapillary-vistas-id2label.json'''

    __lowerCamelCase          =  json.load(open(hf_hub_download(UpperCamelCase__    ,  UpperCamelCase__    ,  repo_type='''dataset'''    )    ,  '''r'''    )    )
    __lowerCamelCase          =  {int(UpperCamelCase__    ): v for k, v in idalabel.items()}

    return config






def __lowerCAmelCase			(							UpperCamelCase__    )     ->   List[str]:
    __lowerCamelCase          =  []
    # 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			(							UpperCamelCase__    ,  UpperCamelCase__    ,  UpperCamelCase__    )     ->   List[Any]:
    __lowerCamelCase          =  dct.pop(UpperCamelCase__    )
    __lowerCamelCase          =  val






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






def __lowerCAmelCase			(							UpperCamelCase__    ,  UpperCamelCase__    )     ->   List[str]:
    # fmt: off
    __lowerCamelCase          =  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)
        __lowerCamelCase          =  state_dict.pop(f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_weight"""    )
        __lowerCamelCase          =  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
        __lowerCamelCase          =  in_proj_weight[: hidden_size, :]
        __lowerCamelCase          =  in_proj_bias[:config.hidden_size]
        __lowerCamelCase          =  in_proj_weight[hidden_size : hidden_size * 2, :]
        __lowerCamelCase          =  in_proj_bias[hidden_size : hidden_size * 2]
        __lowerCamelCase          =  in_proj_weight[-hidden_size :, :]
        __lowerCamelCase          =  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)
        __lowerCamelCase          =  state_dict.pop(f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_weight"""    )
        __lowerCamelCase          =  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
        __lowerCamelCase          =  in_proj_weight[: hidden_size, :]
        __lowerCamelCase          =  in_proj_bias[:config.hidden_size]
        __lowerCamelCase          =  in_proj_weight[hidden_size : hidden_size * 2, :]
        __lowerCamelCase          =  in_proj_bias[hidden_size : hidden_size * 2]
        __lowerCamelCase          =  in_proj_weight[-hidden_size :, :]
        __lowerCamelCase          =  in_proj_bias[-hidden_size :]
    # fmt: on






def __lowerCAmelCase			(							)     ->   torch.Tensor:
    __lowerCamelCase          =  '''http://images.cocodataset.org/val2017/000000039769.jpg'''
    __lowerCamelCase          =  Image.open(requests.get(UpperCamelCase__    ,  stream=UpperCamelCase__    ).raw    )
    return im






@torch.no_grad()
def __lowerCAmelCase			(							UpperCamelCase__    ,  UpperCamelCase__    ,  UpperCamelCase__    ,  UpperCamelCase__ = False    )     ->   str:
    __lowerCamelCase          =  get_maskformer_config(UpperCamelCase__    )

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

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

    # rename keys
    __lowerCamelCase          =  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():
        __lowerCamelCase          =  torch.from_numpy(UpperCamelCase__    )

    # load 🤗 model
    __lowerCamelCase          =  MaskFormerForInstanceSegmentation(UpperCamelCase__    )
    model.eval()

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

    __lowerCamelCase     ,  __lowerCamelCase          =  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
    __lowerCamelCase          =  prepare_img()
    if "vistas" in model_name:
        __lowerCamelCase          =  65
    elif "cityscapes" in model_name:
        __lowerCamelCase          =  6_55_35
    else:
        __lowerCamelCase          =  2_55
    __lowerCamelCase          =  True if '''ade''' in model_name else False
    __lowerCamelCase          =  MaskFormerImageProcessor(ignore_index=UpperCamelCase__    ,  reduce_labels=UpperCamelCase__    )

    __lowerCamelCase          =  image_processor(UpperCamelCase__    ,  return_tensors='''pt'''    )

    __lowerCamelCase          =  model(**UpperCamelCase__    )

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

    if model_name == "maskformer-swin-tiny-ade":
        __lowerCamelCase          =  torch.tensor(
            [[3.6_3_5_3, -4.4_7_7_0, -2.6_0_6_5], [0.5_0_8_1, -4.2_3_9_4, -3.5_3_4_3], [2.1_9_0_9, -5.0_3_5_3, -1.9_3_2_3]]    )
    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__":
  __UpperCAmelCase												=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												=parser.parse_args()
  convert_maskformer_checkpoint(
      args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
  )

'''simple docstring'''




from __future__ import annotations

from typing import Any






def __lowerCAmelCase			(							UpperCamelCase__    )     ->   None:
    create_state_space_tree(UpperCamelCase__    ,  []    ,  0    )






def __lowerCAmelCase			(							UpperCamelCase__    ,  UpperCamelCase__    ,  UpperCamelCase__    )     ->   None:

    if index == len(UpperCamelCase__    ):
        print(UpperCamelCase__    )
        return

    create_state_space_tree(UpperCamelCase__    ,  UpperCamelCase__    ,  index + 1    )
    current_subsequence.append(sequence[index]    )
    create_state_space_tree(UpperCamelCase__    ,  UpperCamelCase__    ,  index + 1    )
    current_subsequence.pop()


if __name__ == "__main__":
  __UpperCAmelCase												=[3, 1, 2, 4]
  generate_all_subsequences(seq)

  seq.clear()
  seq.extend(["A", "B", "C"])
  generate_all_subsequences(seq)
