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 lowerCamelCase						(     SCREAMING_SNAKE_CASE			):
    '''simple docstring'''







    __UpperCamelCase  :List[Any]    						=	384
    if "tiny" in model_name:
        __UpperCamelCase  :Tuple    						=	[3, 3, 9, 3]
        __UpperCamelCase  :List[str]    						=	[96, 192, 384, 768]
    if "small" in model_name:
        __UpperCamelCase  :Optional[Any]    						=	[3, 3, 27, 3]
        __UpperCamelCase  :List[str]    						=	[96, 192, 384, 768]
    if "base" in model_name:
        __UpperCamelCase  :int    						=	[3, 3, 27, 3]
        __UpperCamelCase  :int    						=	[128, 256, 512, 1_024]
        __UpperCamelCase  :Dict    						=	512
    if "large" in model_name:
        __UpperCamelCase  :Union[str, Any]    						=	[3, 3, 27, 3]
        __UpperCamelCase  :List[Any]    						=	[192, 384, 768, 1_536]
        __UpperCamelCase  :Any    						=	768
    if "xlarge" in model_name:
        __UpperCamelCase  :List[Any]    						=	[3, 3, 27, 3]
        __UpperCamelCase  :Optional[Any]    						=	[256, 512, 1_024, 2_048]
        __UpperCamelCase  :Tuple    						=	1_024

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

    __UpperCamelCase  :Any    						=	ConvNextConfig(
        depths=SCREAMING_SNAKE_CASE						,    hidden_sizes=SCREAMING_SNAKE_CASE						,    out_features=['''stage1''', '''stage2''', '''stage3''', '''stage4''']			)
    __UpperCamelCase  :Any    						=	UperNetConfig(
        backbone_config=SCREAMING_SNAKE_CASE						,    auxiliary_in_channels=SCREAMING_SNAKE_CASE						,    num_labels=SCREAMING_SNAKE_CASE						,    idalabel=SCREAMING_SNAKE_CASE						,    labelaid=SCREAMING_SNAKE_CASE						,    )

    return config



def lowerCamelCase						(     SCREAMING_SNAKE_CASE			):
    '''simple docstring'''







    __UpperCamelCase  :str    						=	[]

    # 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 lowerCamelCase						(     SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE			):
    '''simple docstring'''







    __UpperCamelCase  :int    						=	dct.pop(SCREAMING_SNAKE_CASE			)
    __UpperCamelCase  :Optional[Any]    						=	val



def lowerCamelCase						(     SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE			):
    '''simple docstring'''







    __UpperCamelCase  :Tuple    						=	{
        '''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''',
    }
    __UpperCamelCase  :Optional[int]    						=	model_name_to_url[model_name]
    __UpperCamelCase  :List[Any]    						=	torch.hub.load_state_dict_from_url(SCREAMING_SNAKE_CASE						,    map_location='''cpu'''			)['''state_dict''']

    __UpperCamelCase  :List[Any]    						=	get_upernet_config(SCREAMING_SNAKE_CASE			)
    __UpperCamelCase  :List[Any]    						=	UperNetForSemanticSegmentation(SCREAMING_SNAKE_CASE			)
    model.eval()

    # replace "bn" => "batch_norm"
    for key in state_dict.copy().keys():
        __UpperCamelCase  :List[Any]    						=	state_dict.pop(SCREAMING_SNAKE_CASE			)
        if "bn" in key:
            __UpperCamelCase  :List[Any]    						=	key.replace('''bn'''						,    '''batch_norm'''			)
        __UpperCamelCase  :Dict    						=	val

    # rename keys
    __UpperCamelCase  :Any    						=	create_rename_keys(SCREAMING_SNAKE_CASE			)
    for src, dest in rename_keys:
        rename_key(SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE			)

    model.load_state_dict(SCREAMING_SNAKE_CASE			)

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

    __UpperCamelCase  :Dict    						=	SegformerImageProcessor()
    __UpperCamelCase  :Optional[Any]    						=	processor(SCREAMING_SNAKE_CASE						,    return_tensors='''pt'''			).pixel_values

    with torch.no_grad():
        __UpperCamelCase  :Dict    						=	model(SCREAMING_SNAKE_CASE			)

    if model_name == "upernet-convnext-tiny":
        __UpperCamelCase  :Union[str, Any]    						=	torch.tensor(
            [[-8.8_110, -8.8_110, -8.6_521], [-8.8_110, -8.8_110, -8.6_521], [-8.7_746, -8.7_746, -8.6_130]]			)
    elif model_name == "upernet-convnext-small":
        __UpperCamelCase  :str    						=	torch.tensor(
            [[-8.8_236, -8.8_236, -8.6_771], [-8.8_236, -8.8_236, -8.6_771], [-8.7_638, -8.7_638, -8.6_240]]			)
    elif model_name == "upernet-convnext-base":
        __UpperCamelCase  :Optional[Any]    						=	torch.tensor(
            [[-8.8_558, -8.8_558, -8.6_905], [-8.8_558, -8.8_558, -8.6_905], [-8.7_669, -8.7_669, -8.6_021]]			)
    elif model_name == "upernet-convnext-large":
        __UpperCamelCase  :List[str]    						=	torch.tensor(
            [[-8.6_660, -8.6_660, -8.6_210], [-8.6_660, -8.6_660, -8.6_210], [-8.6_310, -8.6_310, -8.5_964]]			)
    elif model_name == "upernet-convnext-xlarge":
        __UpperCamelCase  :Any    						=	torch.tensor(
            [[-8.4_980, -8.4_980, -8.3_977], [-8.4_980, -8.4_980, -8.3_977], [-8.4_379, -8.4_379, -8.3_412]]			)
    print('''Logits:'''						,    outputs.logits[0, 0, :3, :3]			)
    assert torch.allclose(outputs.logits[0, 0, :3, :3]						,    SCREAMING_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(SCREAMING_SNAKE_CASE			)
        print(f"""Saving processor to {pytorch_dump_folder_path}"""			)
        processor.save_pretrained(SCREAMING_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											=      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.'''
    )

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






import numpy as np
from cva import COLOR_BGR2GRAY, cvtColor, imread
from numpy import array, uinta
from PIL import Image

from digital_image_processing import change_contrast as cc
from digital_image_processing import convert_to_negative as cn
from digital_image_processing import sepia as sp
from digital_image_processing.dithering import burkes as bs
from digital_image_processing.edge_detection import canny
from digital_image_processing.filters import convolve as conv
from digital_image_processing.filters import gaussian_filter as gg
from digital_image_processing.filters import local_binary_pattern as lbp
from digital_image_processing.filters import median_filter as med
from digital_image_processing.filters import sobel_filter as sob
from digital_image_processing.resize import resize as rs

__lowercase											=      imread(r'''digital_image_processing/image_data/lena_small.jpg''')
__lowercase											=      cvtColor(img, COLOR_BGR2GRAY)



def lowerCamelCase						(     ):
    '''simple docstring'''







    __UpperCamelCase  :Optional[Any]    						=	cn.convert_to_negative(SCREAMING_SNAKE_CASE			)
    # assert negative_img array for at least one True
    assert negative_img.any()



def lowerCamelCase						(     ):
    '''simple docstring'''







    with Image.open('''digital_image_processing/image_data/lena_small.jpg'''			) as img:
        # Work around assertion for response
        assert str(cc.change_contrast(SCREAMING_SNAKE_CASE						,    110			)			).startswith(
            '''<PIL.Image.Image image mode=RGB size=100x100 at'''			)



def lowerCamelCase						(     ):
    '''simple docstring'''







    __UpperCamelCase  :List[Any]    						=	canny.gen_gaussian_kernel(9						,    sigma=1.4			)
    # Assert ambiguous array
    assert resp.all()



def lowerCamelCase						(     ):
    '''simple docstring'''







    __UpperCamelCase  :List[str]    						=	imread('''digital_image_processing/image_data/lena_small.jpg'''						,    0			)
    # assert ambiguous array for all == True
    assert canny_img.all()
    __UpperCamelCase  :str    						=	canny.canny(SCREAMING_SNAKE_CASE			)
    # assert canny array for at least one True
    assert canny_array.any()



def lowerCamelCase						(     ):
    '''simple docstring'''







    assert gg.gaussian_filter(SCREAMING_SNAKE_CASE						,    5						,    sigma=0.9			).all()



def lowerCamelCase						(     ):
    '''simple docstring'''







    __UpperCamelCase  :List[str]    						=	array([[0.25, 0.5, 0.25], [0.5, -3, 0.5], [0.25, 0.5, 0.25]]			)
    __UpperCamelCase  :Optional[Any]    						=	conv.img_convolve(SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE			).astype(SCREAMING_SNAKE_CASE			)
    assert res.any()



def lowerCamelCase						(     ):
    '''simple docstring'''







    assert med.median_filter(SCREAMING_SNAKE_CASE						,    3			).any()



def lowerCamelCase						(     ):
    '''simple docstring'''







    __UpperCamelCase						, __UpperCamelCase  :Dict    						=	sob.sobel_filter(SCREAMING_SNAKE_CASE			)
    assert grad.any() and theta.any()



def lowerCamelCase						(     ):
    '''simple docstring'''







    __UpperCamelCase  :Dict    						=	sp.make_sepia(SCREAMING_SNAKE_CASE						,    20			)
    assert sepia.all()



def lowerCamelCase						(     SCREAMING_SNAKE_CASE = "digital_image_processing/image_data/lena_small.jpg"			):
    '''simple docstring'''







    __UpperCamelCase  :Dict    						=	bs.Burkes(imread(SCREAMING_SNAKE_CASE						,    1			)						,    120			)
    burkes.process()
    assert burkes.output_img.any()



def lowerCamelCase						(     SCREAMING_SNAKE_CASE = "digital_image_processing/image_data/lena_small.jpg"						,    ):
    '''simple docstring'''







    __UpperCamelCase  :List[str]    						=	rs.NearestNeighbour(imread(SCREAMING_SNAKE_CASE						,    1			)						,    400						,    200			)
    nn.process()
    assert nn.output.any()



def lowerCamelCase						(     ):
    '''simple docstring'''







    __UpperCamelCase  :Union[str, Any]    						=	'''digital_image_processing/image_data/lena.jpg'''

    # Reading the image and converting it to grayscale.
    __UpperCamelCase  :Optional[int]    						=	imread(SCREAMING_SNAKE_CASE						,    0			)

    # Test for get_neighbors_pixel function() return not None
    __UpperCamelCase  :Optional[int]    						=	0
    __UpperCamelCase  :List[Any]    						=	0
    __UpperCamelCase  :Dict    						=	image[x_coordinate][y_coordinate]

    __UpperCamelCase  :int    						=	lbp.get_neighbors_pixel(
        SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE			)

    assert neighbors_pixels is not None

    # Test for local_binary_pattern function()
    # Create a numpy array as the same height and width of read image
    __UpperCamelCase  :Tuple    						=	np.zeros((image.shape[0], image.shape[1])			)

    # Iterating through the image and calculating the local binary pattern value
    # for each pixel.
    for i in range(0						,    image.shape[0]			):
        for j in range(0						,    image.shape[1]			):
            __UpperCamelCase  :Optional[int]    						=	lbp.local_binary_value(SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE			)

    assert lbp_image.any()





