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 						A__  (					__A	):







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

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

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

    return config







def 						A__  (					__A	):







    '''simple docstring'''
    _lowerCamelCase     :		Any 						=						[]

    # 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 						A__  (					__A   , __A   , __A	):







    '''simple docstring'''
    _lowerCamelCase     :		Optional[int] 						=						dct.pop(_lowercase	)
    _lowerCamelCase     :		List[str] 						=						val







def 						A__  (					__A   , __A	):







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







def 						A__  (					__A	):







    '''simple docstring'''
    _lowerCamelCase     :		Dict 						=						x.shape
    _lowerCamelCase     :		Dict 						=						x.reshape(_lowercase   , 4   , in_channel // 4	)
    _lowerCamelCase     :		Optional[Any] 						=						x[:, [0, 2, 1, 3], :].transpose(1   , 2	).reshape(_lowercase   , _lowercase	)
    return x







def 						A__  (					__A	):







    '''simple docstring'''
    _lowerCamelCase     :		Optional[int] 						=						x.shape
    _lowerCamelCase     :		Optional[Any] 						=						x.reshape(_lowercase   , in_channel // 4   , 4	)
    _lowerCamelCase     :		str 						=						x[:, :, [0, 2, 1, 3]].transpose(1   , 2	).reshape(_lowercase   , _lowercase	)

    return x







def 						A__  (					__A	):







    '''simple docstring'''
    _lowerCamelCase     :		str 						=						x.shape[0]
    _lowerCamelCase     :		List[Any] 						=						x.reshape(4   , in_channel // 4	)
    _lowerCamelCase     :		Union[str, Any] 						=						x[[0, 2, 1, 3], :].transpose(0   , 1	).reshape(_lowercase	)
    return x







def 						A__  (					__A	):







    '''simple docstring'''
    _lowerCamelCase     :		Dict 						=						x.shape[0]
    _lowerCamelCase     :		Any 						=						x.reshape(in_channel // 4   , 4	)
    _lowerCamelCase     :		List[str] 						=						x[:, [0, 2, 1, 3]].transpose(0   , 1	).reshape(_lowercase	)
    return x







def 						A__  (					__A   , __A   , __A	):







    '''simple docstring'''
    _lowerCamelCase     :		Dict 						=						{
        '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',
    }
    _lowerCamelCase     :		Dict 						=						model_name_to_url[model_name]
    _lowerCamelCase     :		Any 						=						torch.hub.load_state_dict_from_url(_lowercase   , map_location="""cpu"""   , file_name=_lowercase	)[
        'state_dict'
    ]

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

    _lowerCamelCase     :		str 						=						get_upernet_config(_lowercase	)
    _lowerCamelCase     :		List[Any] 						=						UperNetForSemanticSegmentation(_lowercase	)
    model.eval()

    # replace "bn" => "batch_norm"
    for key in state_dict.copy().keys():
        _lowerCamelCase     :		Tuple 						=						state_dict.pop(_lowercase	)
        if "bn" in key:
            _lowerCamelCase     :		str 						=						key.replace("""bn"""   , """batch_norm"""	)
        _lowerCamelCase     :		Union[str, Any] 						=						val

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

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

    model.load_state_dict(_lowercase	)

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

    _lowerCamelCase     :		str 						=						SegformerImageProcessor()
    _lowerCamelCase     :		Optional[int] 						=						processor(_lowercase   , return_tensors="""pt"""	).pixel_values

    with torch.no_grad():
        _lowerCamelCase     :		str 						=						model(_lowercase	)
        _lowerCamelCase     :		Tuple 						=						outputs.logits

    print(logits.shape	)
    print("""First values of logits:"""   , logits[0, 0, :3, :3]	)
    # assert values
    if model_name == "upernet-swin-tiny":
        _lowerCamelCase     :		Dict 						=						torch.tensor(
            [[-7.5_958, -7.5_958, -7.4_302], [-7.5_958, -7.5_958, -7.4_302], [-7.4_797, -7.4_797, -7.3_068]]	)
    elif model_name == "upernet-swin-small":
        _lowerCamelCase     :		Union[str, Any] 						=						torch.tensor(
            [[-7.1_921, -7.1_921, -6.9_532], [-7.1_921, -7.1_921, -6.9_532], [-7.0_908, -7.0_908, -6.8_534]]	)
    elif model_name == "upernet-swin-base":
        _lowerCamelCase     :		int 						=						torch.tensor(
            [[-6.5_851, -6.5_851, -6.4_330], [-6.5_851, -6.5_851, -6.4_330], [-6.4_763, -6.4_763, -6.3_254]]	)
    elif model_name == "upernet-swin-large":
        _lowerCamelCase     :		Any 						=						torch.tensor(
            [[-7.5_297, -7.5_297, -7.3_802], [-7.5_297, -7.5_297, -7.3_802], [-7.4_044, -7.4_044, -7.2_586]]	)
    print("""Logits:"""   , outputs.logits[0, 0, :3, :3]	)
    assert torch.allclose(outputs.logits[0, 0, :3, :3]   , _lowercase   , 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(_lowercase	)
        print(F"""Saving processor to {pytorch_dump_folder_path}"""	)
        processor.save_pretrained(_lowercase	)

    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			:							Optional[Any]     =			parser.parse_args()
    convert_upernet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)





lowerCAmelCase			:							Tuple     =0  # The first color of the flag.
lowerCAmelCase			:							Union[str, Any]     =1  # The second color of the flag.
lowerCAmelCase			:							Any     =2  # The third color of the flag.
lowerCAmelCase			:							List[str]     =(red, white, blue)







def 						A__  (					__A	):







    '''simple docstring'''
    if not sequence:
        return []
    if len(__A	) == 1:
        return list(__A	)
    _lowerCamelCase     :		int 						=						0
    _lowerCamelCase     :		Dict 						=						len(__A	) - 1
    _lowerCamelCase     :		str 						=						0
    while mid <= high:
        if sequence[mid] == colors[0]:
            _lowerCamelCase						,							_lowerCamelCase     :		Tuple 						=						sequence[mid], sequence[low]
            low += 1
            mid += 1
        elif sequence[mid] == colors[1]:
            mid += 1
        elif sequence[mid] == colors[2]:
            _lowerCamelCase						,							_lowerCamelCase     :		str 						=						sequence[high], sequence[mid]
            high -= 1
        else:
            _lowerCamelCase     :		int 						=						F"""The elements inside the sequence must contains only {colors} values"""
            raise ValueError(__A	)
    return sequence


if __name__ == "__main__":
    import doctest

    doctest.testmod()

    lowerCAmelCase			:							List[str]     =input("Enter numbers separated by commas:\n").strip()
    lowerCAmelCase			:							Dict     =[int(item.strip()) for item in user_input.split(",")]
    print(F"""{dutch_national_flag_sort(unsorted)}""")




