





'''simple docstring'''

import argparse
import json

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

from transformers import SegformerImageProcessor, SwinConfig, UperNetConfig, UperNetForSemanticSegmentation





def 						UpperCamelCase				(							_lowerCamelCase					:					Union[str, Any]       ):
    A__				=			3_84
    A__				=			7
    if "tiny" in model_name:
        A__				=			96
        A__				=			(2, 2, 6, 2)
        A__				=			(3, 6, 12, 24)
    elif "small" in model_name:
        A__				=			96
        A__				=			(2, 2, 18, 2)
        A__				=			(3, 6, 12, 24)
    elif "base" in model_name:
        A__				=			1_28
        A__				=			(2, 2, 18, 2)
        A__				=			(4, 8, 16, 32)
        A__				=			12
        A__				=			5_12
    elif "large" in model_name:
        A__				=			1_92
        A__				=			(2, 2, 18, 2)
        A__				=			(6, 12, 24, 48)
        A__				=			12
        A__				=			7_68

    # set label information
    A__				=			1_50
    A__				=			"huggingface/label-files"
    A__				=			"ade20k-id2label.json"
    A__				=			json.load(open(hf_hub_download(_lowerCamelCase						,       _lowerCamelCase						,       repo_type="dataset"       )						,       "r"       )       )
    A__				=			{int(_lowerCamelCase       ): v for k, v in idalabel.items()}
    A__				=			{v: k for k, v in idalabel.items()}

    A__				=			SwinConfig(
        embed_dim=_lowerCamelCase						,       depths=_lowerCamelCase						,       num_heads=_lowerCamelCase						,       window_size=_lowerCamelCase						,       out_features=["stage1", "stage2", "stage3", "stage4"]						,       )
    A__				=			UperNetConfig(
        backbone_config=_lowerCamelCase						,       auxiliary_in_channels=_lowerCamelCase						,       num_labels=_lowerCamelCase						,       idalabel=_lowerCamelCase						,       labelaid=_lowerCamelCase						,       )

    return config





def 						UpperCamelCase				(							_lowerCamelCase					:					Tuple       ):
    A__				=			[]

    # fmt: off
    # stem
    rename_keys.append(("backbone.patch_embed.projection.weight", "backbone.embeddings.patch_embeddings.projection.weight")       )
    rename_keys.append(("backbone.patch_embed.projection.bias", "backbone.embeddings.patch_embeddings.projection.bias")       )
    rename_keys.append(("backbone.patch_embed.norm.weight", "backbone.embeddings.norm.weight")       )
    rename_keys.append(("backbone.patch_embed.norm.bias", "backbone.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.stages.{i}.blocks.{j}.norm1.weight", F"backbone.encoder.layers.{i}.blocks.{j}.layernorm_before.weight")       )
            rename_keys.append((F"backbone.stages.{i}.blocks.{j}.norm1.bias", F"backbone.encoder.layers.{i}.blocks.{j}.layernorm_before.bias")       )
            rename_keys.append((F"backbone.stages.{i}.blocks.{j}.attn.w_msa.relative_position_bias_table", F"backbone.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table")       )
            rename_keys.append((F"backbone.stages.{i}.blocks.{j}.attn.w_msa.relative_position_index", F"backbone.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index")       )
            rename_keys.append((F"backbone.stages.{i}.blocks.{j}.attn.w_msa.proj.weight", F"backbone.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight")       )
            rename_keys.append((F"backbone.stages.{i}.blocks.{j}.attn.w_msa.proj.bias", F"backbone.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias")       )
            rename_keys.append((F"backbone.stages.{i}.blocks.{j}.norm2.weight", F"backbone.encoder.layers.{i}.blocks.{j}.layernorm_after.weight")       )
            rename_keys.append((F"backbone.stages.{i}.blocks.{j}.norm2.bias", F"backbone.encoder.layers.{i}.blocks.{j}.layernorm_after.bias")       )
            rename_keys.append((F"backbone.stages.{i}.blocks.{j}.ffn.layers.0.0.weight", F"backbone.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight")       )
            rename_keys.append((F"backbone.stages.{i}.blocks.{j}.ffn.layers.0.0.bias", F"backbone.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias")       )
            rename_keys.append((F"backbone.stages.{i}.blocks.{j}.ffn.layers.1.weight", F"backbone.encoder.layers.{i}.blocks.{j}.output.dense.weight")       )
            rename_keys.append((F"backbone.stages.{i}.blocks.{j}.ffn.layers.1.bias", F"backbone.encoder.layers.{i}.blocks.{j}.output.dense.bias")       )

        if i < 3:
            rename_keys.append((F"backbone.stages.{i}.downsample.reduction.weight", F"backbone.encoder.layers.{i}.downsample.reduction.weight")       )
            rename_keys.append((F"backbone.stages.{i}.downsample.norm.weight", F"backbone.encoder.layers.{i}.downsample.norm.weight")       )
            rename_keys.append((F"backbone.stages.{i}.downsample.norm.bias", F"backbone.encoder.layers.{i}.downsample.norm.bias")       )
        rename_keys.append((F"backbone.norm{i}.weight", F"backbone.hidden_states_norms.stage{i+1}.weight")       )
        rename_keys.append((F"backbone.norm{i}.bias", F"backbone.hidden_states_norms.stage{i+1}.bias")       )

    # decode head
    rename_keys.extend(
        [
            ("decode_head.conv_seg.weight", "decode_head.classifier.weight"),
            ("decode_head.conv_seg.bias", "decode_head.classifier.bias"),
            ("auxiliary_head.conv_seg.weight", "auxiliary_head.classifier.weight"),
            ("auxiliary_head.conv_seg.bias", "auxiliary_head.classifier.bias"),
        ]       )
    # fmt: on

    return rename_keys





def 						UpperCamelCase				(							_lowerCamelCase					:					Dict						,       _lowerCamelCase					:					List[Any]						,       _lowerCamelCase					:					List[Any]       ):
    A__				=			dct.pop(_lowerCamelCase       )
    A__				=			val





def 						UpperCamelCase				(							_lowerCamelCase					:					Dict						,       _lowerCamelCase					:					str       ):
    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.stages.{i}.blocks.{j}.attn.w_msa.qkv.weight"       )
            A__				=			state_dict.pop(F"backbone.stages.{i}.blocks.{j}.attn.w_msa.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				(							_lowerCamelCase					:					Optional[Any]       ):
    A__,	A__				=			x.shape
    A__				=			x.reshape(_lowerCamelCase						,       4						,       in_channel // 4       )
    A__				=			x[:, [0, 2, 1, 3], :].transpose(1						,       2       ).reshape(_lowerCamelCase						,       _lowerCamelCase       )
    return x





def 						UpperCamelCase				(							_lowerCamelCase					:					Any       ):
    A__,	A__				=			x.shape
    A__				=			x.reshape(_lowerCamelCase						,       in_channel // 4						,       4       )
    A__				=			x[:, :, [0, 2, 1, 3]].transpose(1						,       2       ).reshape(_lowerCamelCase						,       _lowerCamelCase       )

    return x





def 						UpperCamelCase				(							_lowerCamelCase					:					Optional[int]       ):
    A__				=			x.shape[0]
    A__				=			x.reshape(4						,       in_channel // 4       )
    A__				=			x[[0, 2, 1, 3], :].transpose(0						,       1       ).reshape(_lowerCamelCase       )
    return x





def 						UpperCamelCase				(							_lowerCamelCase					:					Optional[Any]       ):
    A__				=			x.shape[0]
    A__				=			x.reshape(in_channel // 4						,       4       )
    A__				=			x[:, [0, 2, 1, 3]].transpose(0						,       1       ).reshape(_lowerCamelCase       )
    return x





def 						UpperCamelCase				(							_lowerCamelCase					:					List[str]						,       _lowerCamelCase					:					List[Any]						,       _lowerCamelCase					:					List[str]       ):
    A__				=			{
        "upernet-swin-tiny": "https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_tiny_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K/upernet_swin_tiny_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K_20210531_112542-e380ad3e.pth",
        "upernet-swin-small": "https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_small_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K/upernet_swin_small_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K_20210526_192015-ee2fff1c.pth",
        "upernet-swin-base": "https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_base_patch4_window12_512x512_160k_ade20k_pretrain_384x384_22K/upernet_swin_base_patch4_window12_512x512_160k_ade20k_pretrain_384x384_22K_20210531_125459-429057bf.pth",
        "upernet-swin-large": "https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_large_patch4_window12_512x512_pretrain_384x384_22K_160k_ade20k/upernet_swin_large_patch4_window12_512x512_pretrain_384x384_22K_160k_ade20k_20220318_091743-9ba68901.pth",
    }
    A__				=			model_name_to_url[model_name]
    A__				=			torch.hub.load_state_dict_from_url(_lowerCamelCase						,       map_location="cpu"						,       file_name=_lowerCamelCase       )[
        "state_dict"
    ]

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

    A__				=			get_upernet_config(_lowerCamelCase       )
    A__				=			UperNetForSemanticSegmentation(_lowerCamelCase       )
    model.eval()

    # replace "bn" => "batch_norm"
    for key in state_dict.copy().keys():
        A__				=			state_dict.pop(_lowerCamelCase       )
        if "bn" in key:
            A__				=			key.replace("bn"						,       "batch_norm"       )
        A__				=			val

    # rename keys
    A__				=			create_rename_keys(_lowerCamelCase       )
    for src, dest in rename_keys:
        rename_key(_lowerCamelCase						,       _lowerCamelCase						,       _lowerCamelCase       )
    read_in_q_k_v(_lowerCamelCase						,       config.backbone_config       )

    # fix downsample parameters
    for key, value in state_dict.items():
        if "downsample" in key:
            if "reduction" in key:
                A__				=			reverse_correct_unfold_reduction_order(_lowerCamelCase       )
            if "norm" in key:
                A__				=			reverse_correct_unfold_norm_order(_lowerCamelCase       )

    model.load_state_dict(_lowerCamelCase       )

    # verify on image
    A__				=			"https://huggingface.co/datasets/hf-internal-testing/fixtures_ade20k/resolve/main/ADE_val_00000001.jpg"
    A__				=			Image.open(requests.get(_lowerCamelCase						,       stream=_lowerCamelCase       ).raw       ).convert("RGB"       )

    A__				=			SegformerImageProcessor()
    A__				=			processor(_lowerCamelCase						,       return_tensors="pt"       ).pixel_values

    with torch.no_grad():
        A__				=			model(_lowerCamelCase       )
        A__				=			outputs.logits

    print(logits.shape       )
    print("First values of logits:"						,       logits[0, 0, :3, :3]       )
    # assert values
    if model_name == "upernet-swin-tiny":
        A__				=			torch.tensor(
            [[-7.5_9_5_8, -7.5_9_5_8, -7.4_3_0_2], [-7.5_9_5_8, -7.5_9_5_8, -7.4_3_0_2], [-7.4_7_9_7, -7.4_7_9_7, -7.3_0_6_8]]       )
    elif model_name == "upernet-swin-small":
        A__				=			torch.tensor(
            [[-7.1_9_2_1, -7.1_9_2_1, -6.9_5_3_2], [-7.1_9_2_1, -7.1_9_2_1, -6.9_5_3_2], [-7.0_9_0_8, -7.0_9_0_8, -6.8_5_3_4]]       )
    elif model_name == "upernet-swin-base":
        A__				=			torch.tensor(
            [[-6.5_8_5_1, -6.5_8_5_1, -6.4_3_3_0], [-6.5_8_5_1, -6.5_8_5_1, -6.4_3_3_0], [-6.4_7_6_3, -6.4_7_6_3, -6.3_2_5_4]]       )
    elif model_name == "upernet-swin-large":
        A__				=			torch.tensor(
            [[-7.5_2_9_7, -7.5_2_9_7, -7.3_8_0_2], [-7.5_2_9_7, -7.5_2_9_7, -7.3_8_0_2], [-7.4_0_4_4, -7.4_0_4_4, -7.2_5_8_6]]       )
    print("Logits:"						,       outputs.logits[0, 0, :3, :3]       )
    assert torch.allclose(outputs.logits[0, 0, :3, :3]						,       _lowerCamelCase						,       atol=1e-4       )
    print("Looks ok!"       )

    if pytorch_dump_folder_path is not None:
        print(F"Saving model {model_name} to {pytorch_dump_folder_path}"       )
        model.save_pretrained(_lowerCamelCase       )
        print(F"Saving processor to {pytorch_dump_folder_path}"       )
        processor.save_pretrained(_lowerCamelCase       )

    if push_to_hub:
        print(F"Pushing model and processor for {model_name} to hub"       )
        model.push_to_hub(F"openmmlab/{model_name}"       )
        processor.push_to_hub(F"openmmlab/{model_name}"       )


if __name__ == "__main__":
  __lowerCAmelCase							:	Optional[Any]			=argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      "--model_name",
      default="upernet-swin-tiny",
      type=str,
      choices=[f"""upernet-swin-{size}""" for size in ["tiny", "small", "base", "large"]],
      help="Name of the Swin + UperNet model you'd like to convert.",
  )
  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."
  )

  __lowerCAmelCase							:	List[str]			=parser.parse_args()
  convert_upernet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)










'''simple docstring'''

import functools





def 						UpperCamelCase				(							_lowerCamelCase					:					str						,       _lowerCamelCase					:					str       ):
    A__				=			len(_lowerCamelCase       )
    A__				=			len(_lowerCamelCase       )

    @functools.cache
    def min_distance(_lowerCamelCase					:					int						,       _lowerCamelCase					:					int       ) -> int:
        # if first word index is overflow - delete all from the second word
        if indexa >= len_worda:
            return len_worda - indexa
        # if second word index is overflow - delete all from the first word
        if indexa >= len_worda:
            return len_worda - indexa
        A__				=			int(worda[indexa] != worda[indexa]       )  # current letters not identical
        return min(
            1 + min_distance(indexa + 1						,       _lowerCamelCase       )						,       1 + min_distance(_lowerCamelCase						,       indexa + 1       )						,       diff + min_distance(indexa + 1						,       indexa + 1       )						,       )

    return min_distance(0						,       0       )


if __name__ == "__main__":
  import doctest

  doctest.testmod()



