
"""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     lowercase__						(					lowerCAmelCase				:					int ) ->      Optional[Any]:






    """simple docstring"""




    UpperCAmelCase									=					384
    UpperCAmelCase									=					7
    if "tiny" in model_name:
        UpperCAmelCase									=					96
        UpperCAmelCase									=					(2, 2, 6, 2)
        UpperCAmelCase									=					(3, 6, 12, 24)
    elif "small" in model_name:
        UpperCAmelCase									=					96
        UpperCAmelCase									=					(2, 2, 18, 2)
        UpperCAmelCase									=					(3, 6, 12, 24)
    elif "base" in model_name:
        UpperCAmelCase									=					128
        UpperCAmelCase									=					(2, 2, 18, 2)
        UpperCAmelCase									=					(4, 8, 16, 32)
        UpperCAmelCase									=					12
        UpperCAmelCase									=					512
    elif "large" in model_name:
        UpperCAmelCase									=					192
        UpperCAmelCase									=					(2, 2, 18, 2)
        UpperCAmelCase									=					(6, 12, 24, 48)
        UpperCAmelCase									=					12
        UpperCAmelCase									=					768

    # set label information
    UpperCAmelCase									=					150
    UpperCAmelCase									=					'huggingface/label-files'
    UpperCAmelCase									=					'ade20k-id2label.json'
    UpperCAmelCase									=					json.load(open(hf_hub_download(lowerCAmelCase ,				lowerCAmelCase ,				repo_type='dataset' ) ,				'r' ) )
    UpperCAmelCase									=					{int(lowerCAmelCase ): v for k, v in idalabel.items()}
    UpperCAmelCase									=					{v: k for k, v in idalabel.items()}

    UpperCAmelCase									=					SwinConfig(
        embed_dim=lowerCAmelCase ,				depths=lowerCAmelCase ,				num_heads=lowerCAmelCase ,				window_size=lowerCAmelCase ,				out_features=['stage1', 'stage2', 'stage3', 'stage4'] ,				)
    UpperCAmelCase									=					UperNetConfig(
        backbone_config=lowerCAmelCase ,				auxiliary_in_channels=lowerCAmelCase ,				num_labels=lowerCAmelCase ,				idalabel=lowerCAmelCase ,				labelaid=lowerCAmelCase ,				)

    return config




def     lowercase__						(					lowerCAmelCase				:					List[Any] ) ->      Optional[Any]:






    """simple docstring"""




    UpperCAmelCase									=					[]

    # 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     lowercase__						(					lowerCAmelCase				:					Dict ,				lowerCAmelCase				:					Optional[int] ,				lowerCAmelCase				:					List[str] ) ->      str:






    """simple docstring"""




    UpperCAmelCase									=					dct.pop(lowerCAmelCase )
    UpperCAmelCase									=					val




def     lowercase__						(					lowerCAmelCase				:					Any ,				lowerCAmelCase				:					List[Any] ) ->      Tuple:






    """simple docstring"""




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




def     lowercase__						(					lowerCAmelCase				:					Any ) ->      List[Any]:






    """simple docstring"""




    UpperCAmelCase			,							UpperCAmelCase									=					x.shape
    UpperCAmelCase									=					x.reshape(lowerCAmelCase ,				4 ,				in_channel // 4 )
    UpperCAmelCase									=					x[:, [0, 2, 1, 3], :].transpose(1 ,				2 ).reshape(lowerCAmelCase ,				lowerCAmelCase )
    return x




def     lowercase__						(					lowerCAmelCase				:					str ) ->      Union[str, Any]:






    """simple docstring"""




    UpperCAmelCase			,							UpperCAmelCase									=					x.shape
    UpperCAmelCase									=					x.reshape(lowerCAmelCase ,				in_channel // 4 ,				4 )
    UpperCAmelCase									=					x[:, :, [0, 2, 1, 3]].transpose(1 ,				2 ).reshape(lowerCAmelCase ,				lowerCAmelCase )

    return x




def     lowercase__						(					lowerCAmelCase				:					Dict ) ->      Optional[Any]:






    """simple docstring"""




    UpperCAmelCase									=					x.shape[0]
    UpperCAmelCase									=					x.reshape(4 ,				in_channel // 4 )
    UpperCAmelCase									=					x[[0, 2, 1, 3], :].transpose(0 ,				1 ).reshape(lowerCAmelCase )
    return x




def     lowercase__						(					lowerCAmelCase				:					List[Any] ) ->      Dict:






    """simple docstring"""




    UpperCAmelCase									=					x.shape[0]
    UpperCAmelCase									=					x.reshape(in_channel // 4 ,				4 )
    UpperCAmelCase									=					x[:, [0, 2, 1, 3]].transpose(0 ,				1 ).reshape(lowerCAmelCase )
    return x




def     lowercase__						(					lowerCAmelCase				:					str ,				lowerCAmelCase				:					int ,				lowerCAmelCase				:					Tuple ) ->      List[str]:






    """simple docstring"""




    UpperCAmelCase									=					{
        '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',
    }
    UpperCAmelCase									=					model_name_to_url[model_name]
    UpperCAmelCase									=					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 )

    UpperCAmelCase									=					get_upernet_config(lowerCAmelCase )
    UpperCAmelCase									=					UperNetForSemanticSegmentation(lowerCAmelCase )
    model.eval()

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

    # rename keys
    UpperCAmelCase									=					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:
                UpperCAmelCase									=					reverse_correct_unfold_reduction_order(lowerCAmelCase )
            if "norm" in key:
                UpperCAmelCase									=					reverse_correct_unfold_norm_order(lowerCAmelCase )

    model.load_state_dict(lowerCAmelCase )

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

    UpperCAmelCase									=					SegformerImageProcessor()
    UpperCAmelCase									=					processor(lowerCAmelCase ,				return_tensors='pt' ).pixel_values

    with torch.no_grad():
        UpperCAmelCase									=					model(lowerCAmelCase )
        UpperCAmelCase									=					outputs.logits

    print(logits.shape )
    print('First values of logits:' ,				logits[0, 0, :3, :3] )
    # assert values
    if model_name == "upernet-swin-tiny":
        UpperCAmelCase									=					torch.tensor(
            [[-7.5958, -7.5958, -7.4302], [-7.5958, -7.5958, -7.4302], [-7.4797, -7.4797, -7.3068]] )
    elif model_name == "upernet-swin-small":
        UpperCAmelCase									=					torch.tensor(
            [[-7.1921, -7.1921, -6.9532], [-7.1921, -7.1921, -6.9532], [-7.0908, -7.0908, -6.8534]] )
    elif model_name == "upernet-swin-base":
        UpperCAmelCase									=					torch.tensor(
            [[-6.5851, -6.5851, -6.4330], [-6.5851, -6.5851, -6.4330], [-6.4763, -6.4763, -6.3254]] )
    elif model_name == "upernet-swin-large":
        UpperCAmelCase									=					torch.tensor(
            [[-7.5297, -7.5297, -7.3802], [-7.5297, -7.5297, -7.3802], [-7.4044, -7.4044, -7.2586]] )
    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__":
   SCREAMING_SNAKE_CASE_    					=						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.'''
   )

   SCREAMING_SNAKE_CASE_    					=						parser.parse_args()
   convert_upernet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)





"""simple docstring"""

from __future__ import annotations

import numpy as np




def     lowercase__						(					lowerCAmelCase				:					list[float] ) ->      Dict:






    """simple docstring"""




    return np.maximum(0 ,				lowerCAmelCase )


if __name__ == "__main__":
   print(np.array(relu([-1, 0, 5])))  # --> [0, 0, 5]



