



import argparse
import json

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

from transformers import ConvNextConfig, SegformerImageProcessor, UperNetConfig, UperNetForSemanticSegmentation





def   __snake_case			(    _UpperCAmelCase       ):
    __a		 =       384
    if "tiny" in model_name:
        __a		 =       [3, 3, 9, 3]
        __a		 =       [96, 192, 384, 768]
    if "small" in model_name:
        __a		 =       [3, 3, 27, 3]
        __a		 =       [96, 192, 384, 768]
    if "base" in model_name:
        __a		 =       [3, 3, 27, 3]
        __a		 =       [128, 256, 512, 1024]
        __a		 =       512
    if "large" in model_name:
        __a		 =       [3, 3, 27, 3]
        __a		 =       [192, 384, 768, 1536]
        __a		 =       768
    if "xlarge" in model_name:
        __a		 =       [3, 3, 27, 3]
        __a		 =       [256, 512, 1024, 2048]
        __a		 =       1024

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

    __a		 =       ConvNextConfig(
        depths=snake_case__     ,				hidden_sizes=snake_case__     ,				out_features=['''stage1''', '''stage2''', '''stage3''', '''stage4''']       )
    __a		 =       UperNetConfig(
        backbone_config=snake_case__     ,				auxiliary_in_channels=snake_case__     ,				num_labels=snake_case__     ,				idalabel=snake_case__     ,				labelaid=snake_case__     ,				)

    return config





def   __snake_case			(    _UpperCAmelCase       ):
    __a		 =       []

    # fmt: off
    # stem
    rename_keys.append(('''backbone.downsample_layers.0.0.weight''', '''backbone.embeddings.patch_embeddings.weight''')       )
    rename_keys.append(('''backbone.downsample_layers.0.0.bias''', '''backbone.embeddings.patch_embeddings.bias''')       )
    rename_keys.append(('''backbone.downsample_layers.0.1.weight''', '''backbone.embeddings.layernorm.weight''')       )
    rename_keys.append(('''backbone.downsample_layers.0.1.bias''', '''backbone.embeddings.layernorm.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}.{j}.gamma', f'backbone.encoder.stages.{i}.layers.{j}.layer_scale_parameter')       )
            rename_keys.append((f'backbone.stages.{i}.{j}.depthwise_conv.weight', f'backbone.encoder.stages.{i}.layers.{j}.dwconv.weight')       )
            rename_keys.append((f'backbone.stages.{i}.{j}.depthwise_conv.bias', f'backbone.encoder.stages.{i}.layers.{j}.dwconv.bias')       )
            rename_keys.append((f'backbone.stages.{i}.{j}.norm.weight', f'backbone.encoder.stages.{i}.layers.{j}.layernorm.weight')       )
            rename_keys.append((f'backbone.stages.{i}.{j}.norm.bias', f'backbone.encoder.stages.{i}.layers.{j}.layernorm.bias')       )
            rename_keys.append((f'backbone.stages.{i}.{j}.pointwise_conv1.weight', f'backbone.encoder.stages.{i}.layers.{j}.pwconv1.weight')       )
            rename_keys.append((f'backbone.stages.{i}.{j}.pointwise_conv1.bias', f'backbone.encoder.stages.{i}.layers.{j}.pwconv1.bias')       )
            rename_keys.append((f'backbone.stages.{i}.{j}.pointwise_conv2.weight', f'backbone.encoder.stages.{i}.layers.{j}.pwconv2.weight')       )
            rename_keys.append((f'backbone.stages.{i}.{j}.pointwise_conv2.bias', f'backbone.encoder.stages.{i}.layers.{j}.pwconv2.bias')       )
        if i > 0:
            rename_keys.append((f'backbone.downsample_layers.{i}.0.weight', f'backbone.encoder.stages.{i}.downsampling_layer.0.weight')       )
            rename_keys.append((f'backbone.downsample_layers.{i}.0.bias', f'backbone.encoder.stages.{i}.downsampling_layer.0.bias')       )
            rename_keys.append((f'backbone.downsample_layers.{i}.1.weight', f'backbone.encoder.stages.{i}.downsampling_layer.1.weight')       )
            rename_keys.append((f'backbone.downsample_layers.{i}.1.bias', f'backbone.encoder.stages.{i}.downsampling_layer.1.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   __snake_case			(    _UpperCAmelCase     ,				_UpperCAmelCase     ,				_UpperCAmelCase       ):
    __a		 =       dct.pop(snake_case__       )
    __a		 =       val





def   __snake_case			(    _UpperCAmelCase     ,				_UpperCAmelCase     ,				_UpperCAmelCase       ):
    __a		 =       {
        '''upernet-convnext-tiny''': '''https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_tiny_fp16_512x512_160k_ade20k/upernet_convnext_tiny_fp16_512x512_160k_ade20k_20220227_124553-cad485de.pth''',
        '''upernet-convnext-small''': '''https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_small_fp16_512x512_160k_ade20k/upernet_convnext_small_fp16_512x512_160k_ade20k_20220227_131208-1b1e394f.pth''',
        '''upernet-convnext-base''': '''https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_base_fp16_512x512_160k_ade20k/upernet_convnext_base_fp16_512x512_160k_ade20k_20220227_181227-02a24fc6.pth''',
        '''upernet-convnext-large''': '''https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_large_fp16_640x640_160k_ade20k/upernet_convnext_large_fp16_640x640_160k_ade20k_20220226_040532-e57aa54d.pth''',
        '''upernet-convnext-xlarge''': '''https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_xlarge_fp16_640x640_160k_ade20k/upernet_convnext_xlarge_fp16_640x640_160k_ade20k_20220226_080344-95fc38c2.pth''',
    }
    __a		 =       model_name_to_url[model_name]
    __a		 =       torch.hub.load_state_dict_from_url(snake_case__     ,				map_location='''cpu'''       )['''state_dict''']

    __a		 =       get_upernet_config(snake_case__       )
    __a		 =       UperNetForSemanticSegmentation(snake_case__       )
    model.eval()

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

    # rename keys
    __a		 =       create_rename_keys(snake_case__       )
    for src, dest in rename_keys:
        rename_key(snake_case__     ,				snake_case__     ,				snake_case__       )

    model.load_state_dict(snake_case__       )

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

    __a		 =       SegformerImageProcessor()
    __a		 =       processor(snake_case__     ,				return_tensors='''pt'''       ).pixel_values

    with torch.no_grad():
        __a		 =       model(snake_case__       )

    if model_name == "upernet-convnext-tiny":
        __a		 =       torch.tensor(
            [[-8.81_10, -8.81_10, -8.65_21], [-8.81_10, -8.81_10, -8.65_21], [-8.77_46, -8.77_46, -8.61_30]]       )
    elif model_name == "upernet-convnext-small":
        __a		 =       torch.tensor(
            [[-8.82_36, -8.82_36, -8.67_71], [-8.82_36, -8.82_36, -8.67_71], [-8.76_38, -8.76_38, -8.62_40]]       )
    elif model_name == "upernet-convnext-base":
        __a		 =       torch.tensor(
            [[-8.85_58, -8.85_58, -8.69_05], [-8.85_58, -8.85_58, -8.69_05], [-8.76_69, -8.76_69, -8.60_21]]       )
    elif model_name == "upernet-convnext-large":
        __a		 =       torch.tensor(
            [[-8.66_60, -8.66_60, -8.62_10], [-8.66_60, -8.66_60, -8.62_10], [-8.63_10, -8.63_10, -8.59_64]]       )
    elif model_name == "upernet-convnext-xlarge":
        __a		 =       torch.tensor(
            [[-8.49_80, -8.49_80, -8.39_77], [-8.49_80, -8.49_80, -8.39_77], [-8.43_79, -8.43_79, -8.34_12]]       )
    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__":
 __snake_case		:Any   	=  argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     '''--model_name''',
     default='''upernet-convnext-tiny''',
     type=str,
     choices=[f'upernet-convnext-{size}' for size in ['''tiny''', '''small''', '''base''', '''large''', '''xlarge''']],
     help='''Name of the ConvNext 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.'''
 )

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





def   __snake_case			(    _UpperCAmelCase       ):
    __a		 =       ''''''
    for ch in key:
        if ch == " " or ch not in key_no_dups and ch.isalpha():
            key_no_dups += ch
    return key_no_dups





def   __snake_case			(    _UpperCAmelCase       ):
    __a		 =       [chr(i + 65       ) for i in range(26       )]
    # Remove duplicate characters from key
    __a		 =       remove_duplicates(key.upper()       )
    __a		 =       len(_UpperCAmelCase       )
    # First fill cipher with key characters
    __a		 =       {alphabet[i]: char for i, char in enumerate(_UpperCAmelCase       )}
    # Then map remaining characters in alphabet to
    # the alphabet from the beginning
    for i in range(len(_UpperCAmelCase       )     ,				26       ):
        __a		 =       alphabet[i - offset]
        # Ensure we are not mapping letters to letters previously mapped
        while char in key:
            offset -= 1
            __a		 =       alphabet[i - offset]
        __a		 =       char
    return cipher_alphabet





def   __snake_case			(    _UpperCAmelCase     ,				_UpperCAmelCase       ):
    return "".join(cipher_map.get(_UpperCAmelCase     ,				_UpperCAmelCase       ) for ch in message.upper()       )





def   __snake_case			(    _UpperCAmelCase     ,				_UpperCAmelCase       ):
    __a		 =       {v: k for k, v in cipher_map.items()}
    return "".join(rev_cipher_map.get(_UpperCAmelCase     ,				_UpperCAmelCase       ) for ch in message.upper()       )





def   __snake_case			(    ):
    __a		 =       input('''Enter message to encode or decode: '''       ).strip()
    __a		 =       input('''Enter keyword: '''       ).strip()
    __a		 =       input('''Encipher or decipher? E/D:'''       ).strip()[0].lower()
    try:
        __a		 =       {'''e''': encipher, '''d''': decipher}[option]
    except KeyError:
        raise KeyError('''invalid input option'''       )
    __a		 =       create_cipher_map(_UpperCAmelCase       )
    print(func(_UpperCAmelCase     ,				_UpperCAmelCase       )       )


if __name__ == "__main__":
 import doctest

 doctest.testmod()
 main()





