
'''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 						lowerCAmelCase_							(  snake_case__		):




    '''simple docstring'''



    A			:  Tuple   =		384
    A			:  Any   =		7
    if "tiny" in model_name:
        A			:  str   =		96
        A			:  List[str]   =		(2, 2, 6, 2)
        A			:  Any   =		(3, 6, 12, 24)
    elif "small" in model_name:
        A			:  List[str]   =		96
        A			:  Union[str, Any]   =		(2, 2, 18, 2)
        A			:  Union[str, Any]   =		(3, 6, 12, 24)
    elif "base" in model_name:
        A			:  str   =		128
        A			:  Tuple   =		(2, 2, 18, 2)
        A			:  Optional[int]   =		(4, 8, 16, 32)
        A			:  List[str]   =		12
        A			:  Dict   =		512
    elif "large" in model_name:
        A			:  Any   =		192
        A			:  Optional[Any]   =		(2, 2, 18, 2)
        A			:  Tuple   =		(6, 12, 24, 48)
        A			:  str   =		12
        A			:  Union[str, Any]   =		768

    # set label information
    A			:  Tuple   =		150
    A			:  Tuple   =		'''huggingface/label-files'''
    A			:  List[str]   =		'''ade20k-id2label.json'''
    A			:  Any   =		json.load(open(hf_hub_download(snake_case__						,							snake_case__						,							repo_type='''dataset'''		)						,							'''r'''		)		)
    A			:  Optional[int]   =		{int(snake_case__		): v for k, v in idalabel.items()}
    A			:  Tuple   =		{v: k for k, v in idalabel.items()}

    A			:  List[Any]   =		SwinConfig(
        embed_dim=snake_case__						,							depths=snake_case__						,							num_heads=snake_case__						,							window_size=snake_case__						,							out_features=['''stage1''', '''stage2''', '''stage3''', '''stage4''']						,							)
    A			:  Union[str, Any]   =		UperNetConfig(
        backbone_config=snake_case__						,							auxiliary_in_channels=snake_case__						,							num_labels=snake_case__						,							idalabel=snake_case__						,							labelaid=snake_case__						,							)

    return config







def 						lowerCAmelCase_							(  snake_case__		):




    '''simple docstring'''



    A			:  Dict   =		[]

    # 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 						lowerCAmelCase_							(  snake_case__						,							snake_case__						,							snake_case__		):




    '''simple docstring'''



    A			:  Optional[int]   =		dct.pop(snake_case__		)
    A			:  Any   =		val







def 						lowerCAmelCase_							(  snake_case__						,							snake_case__		):




    '''simple docstring'''



    A			:  int   =		[int(backbone_config.embed_dim * 2**i		) for i in range(len(backbone_config.depths		)		)]
    for i in range(len(backbone_config.depths		)		):
        A			:  Optional[Any]   =		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			:  Optional[Any]   =		state_dict.pop(F'backbone.stages.{i}.blocks.{j}.attn.w_msa.qkv.weight'		)
            A			:  Any   =		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			:  str   =		in_proj_weight[:dim, :]
            A			:  List[Any]   =		in_proj_bias[: dim]
            A			:  List[str]   =		in_proj_weight[
                dim : dim * 2, :
            ]
            A			:  Any   =		in_proj_bias[
                dim : dim * 2
            ]
            A			:  List[Any]   =		in_proj_weight[
                -dim :, :
            ]
            A			:  int   =		in_proj_bias[-dim :]
            # fmt: on







def 						lowerCAmelCase_							(  snake_case__		):




    '''simple docstring'''



    A,    A			:  int   =		x.shape
    A			:  Any   =		x.reshape(snake_case__						,							4						,							in_channel // 4		)
    A			:  Dict   =		x[:, [0, 2, 1, 3], :].transpose(1						,							2		).reshape(snake_case__						,							snake_case__		)
    return x







def 						lowerCAmelCase_							(  snake_case__		):




    '''simple docstring'''



    A,    A			:  List[Any]   =		x.shape
    A			:  List[str]   =		x.reshape(snake_case__						,							in_channel // 4						,							4		)
    A			:  Optional[int]   =		x[:, :, [0, 2, 1, 3]].transpose(1						,							2		).reshape(snake_case__						,							snake_case__		)

    return x







def 						lowerCAmelCase_							(  snake_case__		):




    '''simple docstring'''



    A			:  str   =		x.shape[0]
    A			:  Any   =		x.reshape(4						,							in_channel // 4		)
    A			:  Union[str, Any]   =		x[[0, 2, 1, 3], :].transpose(0						,							1		).reshape(snake_case__		)
    return x







def 						lowerCAmelCase_							(  snake_case__		):




    '''simple docstring'''



    A			:  Optional[int]   =		x.shape[0]
    A			:  Optional[Any]   =		x.reshape(in_channel // 4						,							4		)
    A			:  Union[str, Any]   =		x[:, [0, 2, 1, 3]].transpose(0						,							1		).reshape(snake_case__		)
    return x







def 						lowerCAmelCase_							(  snake_case__						,							snake_case__						,							snake_case__		):




    '''simple docstring'''



    A			:  str   =		{
        '''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			:  Union[str, Any]   =		model_name_to_url[model_name]
    A			:  str   =		torch.hub.load_state_dict_from_url(snake_case__						,							map_location='''cpu'''						,							file_name=snake_case__		)[
        '''state_dict'''
    ]

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

    A			:  Dict   =		get_upernet_config(snake_case__		)
    A			:  Optional[int]   =		UperNetForSemanticSegmentation(snake_case__		)
    model.eval()

    # replace "bn" => "batch_norm"
    for key in state_dict.copy().keys():
        A			:  Dict   =		state_dict.pop(snake_case__		)
        if "bn" in key:
            A			:  Union[str, Any]   =		key.replace('''bn'''						,							'''batch_norm'''		)
        A			:  Optional[Any]   =		val

    # rename keys
    A			:  Optional[Any]   =		create_rename_keys(snake_case__		)
    for src, dest in rename_keys:
        rename_key(snake_case__						,							snake_case__						,							snake_case__		)
    read_in_q_k_v(snake_case__						,							config.backbone_config		)

    # fix downsample parameters
    for key, value in state_dict.items():
        if "downsample" in key:
            if "reduction" in key:
                A			:  Any   =		reverse_correct_unfold_reduction_order(snake_case__		)
            if "norm" in key:
                A			:  List[Any]   =		reverse_correct_unfold_norm_order(snake_case__		)

    model.load_state_dict(snake_case__		)

    # verify on image
    A			:  Any   =		'''https://huggingface.co/datasets/hf-internal-testing/fixtures_ade20k/resolve/main/ADE_val_00000001.jpg'''
    A			:  Union[str, Any]   =		Image.open(requests.get(snake_case__						,							stream=snake_case__		).raw		).convert('''RGB'''		)

    A			:  int   =		SegformerImageProcessor()
    A			:  List[str]   =		processor(snake_case__						,							return_tensors='''pt'''		).pixel_values

    with torch.no_grad():
        A			:  Dict   =		model(snake_case__		)
        A			:  Any   =		outputs.logits

    print(logits.shape		)
    print('''First values of logits:'''						,							logits[0, 0, :3, :3]		)
    # assert values
    if model_name == "upernet-swin-tiny":
        A			:  Dict   =		torch.tensor(
            [[-7.59_58, -7.59_58, -7.43_02], [-7.59_58, -7.59_58, -7.43_02], [-7.47_97, -7.47_97, -7.30_68]]		)
    elif model_name == "upernet-swin-small":
        A			:  List[Any]   =		torch.tensor(
            [[-7.19_21, -7.19_21, -6.95_32], [-7.19_21, -7.19_21, -6.95_32], [-7.09_08, -7.09_08, -6.85_34]]		)
    elif model_name == "upernet-swin-base":
        A			:  str   =		torch.tensor(
            [[-6.58_51, -6.58_51, -6.43_30], [-6.58_51, -6.58_51, -6.43_30], [-6.47_63, -6.47_63, -6.32_54]]		)
    elif model_name == "upernet-swin-large":
        A			:  Optional[Any]   =		torch.tensor(
            [[-7.52_97, -7.52_97, -7.38_02], [-7.52_97, -7.52_97, -7.38_02], [-7.40_44, -7.40_44, -7.25_86]]		)
    print('''Logits:'''						,							outputs.logits[0, 0, :3, :3]		)
    assert torch.allclose(outputs.logits[0, 0, :3, :3]						,							snake_case__						,							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(snake_case__		)
        print(F'Saving processor to {pytorch_dump_folder_path}'		)
        processor.save_pretrained(snake_case__		)

    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__":
      lowercase     :							List[str]       						=     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.'
      )

      lowercase     :							Dict       						=     parser.parse_args()
      convert_upernet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)






'''simple docstring'''




import random







def 						lowerCAmelCase_							(  snake_case__						,							snake_case__		):




    '''simple docstring'''



    A,    A,    A			:  Any   =		[], [], []
    for element in data:
        if element < pivot:
            less.append(snake_case__		)
        elif element > pivot:
            greater.append(snake_case__		)
        else:
            equal.append(snake_case__		)
    return less, equal, greater







def 						lowerCAmelCase_							(  snake_case__						,							snake_case__		):




    '''simple docstring'''



    if index >= len(snake_case__		) or index < 0:
        return None

    A			:  List[Any]   =		items[random.randint(0						,							len(snake_case__		) - 1		)]
    A			:  Optional[int]   =		0
    A,    A,    A			:  Dict   =		_partition(snake_case__						,							snake_case__		)
    A			:  int   =		len(snake_case__		)
    A			:  List[Any]   =		len(snake_case__		)

    # index is the pivot
    if m <= index < m + count:
        return pivot
    # must be in smaller
    elif m > index:
        return quick_select(snake_case__						,							snake_case__		)
    # must be in larger
    else:
        return quick_select(snake_case__						,							index - (m + count)		)




