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 						A__  (					__A	):







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

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

    _lowerCamelCase     :		str 						=						ConvNextConfig(
        depths=__A   , hidden_sizes=__A   , out_features=["""stage1""", """stage2""", """stage3""", """stage4"""]	)
    _lowerCamelCase     :		int 						=						UperNetConfig(
        backbone_config=__A   , auxiliary_in_channels=__A   , num_labels=__A   , idalabel=__A   , labelaid=__A   , )

    return config







def 						A__  (					__A	):







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

    # 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 						A__  (					__A   , __A   , __A	):







    '''simple docstring'''
    _lowerCamelCase     :		Any 						=						dct.pop(__A	)
    _lowerCamelCase     :		Optional[int] 						=						val







def 						A__  (					__A   , __A   , __A	):







    '''simple docstring'''
    _lowerCamelCase     :		Any 						=						{
        """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""",
    }
    _lowerCamelCase     :		Tuple 						=						model_name_to_url[model_name]
    _lowerCamelCase     :		Optional[int] 						=						torch.hub.load_state_dict_from_url(__A   , map_location="""cpu"""	)["""state_dict"""]

    _lowerCamelCase     :		Optional[int] 						=						get_upernet_config(__A	)
    _lowerCamelCase     :		Any 						=						UperNetForSemanticSegmentation(__A	)
    model.eval()

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

    # rename keys
    _lowerCamelCase     :		str 						=						create_rename_keys(__A	)
    for src, dest in rename_keys:
        rename_key(__A   , __A   , __A	)

    model.load_state_dict(__A	)

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

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

    with torch.no_grad():
        _lowerCamelCase     :		Optional[Any] 						=						model(__A	)

    if model_name == "upernet-convnext-tiny":
        _lowerCamelCase     :		Optional[int] 						=						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":
        _lowerCamelCase     :		Any 						=						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":
        _lowerCamelCase     :		Dict 						=						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":
        _lowerCamelCase     :		Optional[int] 						=						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":
        _lowerCamelCase     :		Tuple 						=						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]   , __A   , 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(__A	)
        print(F"""Saving processor to {pytorch_dump_folder_path}"""	)
        processor.save_pretrained(__A	)

    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			:							str     =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."
    )

    lowerCAmelCase			:							Tuple     =parser.parse_args()
    convert_upernet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)





import argparse
from pathlib import Path

import torch
from packaging import version
from torch.onnx import export

from diffusers import AutoencoderKL


lowerCAmelCase			:							Tuple     =version.parse(version.parse(torch.__version__).base_version) < version.parse("1.11")







def 						A__  (					__A   , __A   , __A   , __A   , __A   , __A   , __A   , __A=False   , ):







    '''simple docstring'''
    output_path.parent.mkdir(parents=__A   , exist_ok=__A	)
    # PyTorch deprecated the `enable_onnx_checker` and `use_external_data_format` arguments in v1.11,
    # so we check the torch version for backwards compatibility
    if is_torch_less_than_1_11:
        export(
            __A   , __A   , f=output_path.as_posix()   , input_names=__A   , output_names=__A   , dynamic_axes=__A   , do_constant_folding=__A   , use_external_data_format=__A   , enable_onnx_checker=__A   , opset_version=__A   , )
    else:
        export(
            __A   , __A   , f=output_path.as_posix()   , input_names=__A   , output_names=__A   , dynamic_axes=__A   , do_constant_folding=__A   , opset_version=__A   , )







@torch.no_grad()
def 						A__  (					__A   , __A   , __A   , __A = False	):







    '''simple docstring'''
    _lowerCamelCase     :		Tuple 						=						torch.floataa if fpaa else torch.floataa
    if fpaa and torch.cuda.is_available():
        _lowerCamelCase     :		str 						=						"""cuda"""
    elif fpaa and not torch.cuda.is_available():
        raise ValueError("""`float16` model export is only supported on GPUs with CUDA"""	)
    else:
        _lowerCamelCase     :		List[str] 						=						"""cpu"""
    _lowerCamelCase     :		Dict 						=						Path(__A	)

    # VAE DECODER
    _lowerCamelCase     :		Optional[Any] 						=						AutoencoderKL.from_pretrained(model_path + """/vae"""	)
    _lowerCamelCase     :		List[str] 						=						vae_decoder.config.latent_channels
    # forward only through the decoder part
    _lowerCamelCase     :		Tuple 						=						vae_decoder.decode
    onnx_export(
        __A   , model_args=(
            torch.randn(1   , __A   , 25   , 25	).to(device=__A   , dtype=__A	),
            False,
        )   , output_path=output_path / """vae_decoder""" / """model.onnx"""   , ordered_input_names=["""latent_sample""", """return_dict"""]   , output_names=["""sample"""]   , dynamic_axes={
            """latent_sample""": {0: """batch""", 1: """channels""", 2: """height""", 3: """width"""},
        }   , opset=__A   , )
    del vae_decoder


if __name__ == "__main__":
    lowerCAmelCase			:							Optional[int]     =argparse.ArgumentParser()

    parser.add_argument(
        "--model_path",
        type=str,
        required=True,
        help="Path to the `diffusers` checkpoint to convert (either a local directory or on the Hub).",
    )

    parser.add_argument("--output_path", type=str, required=True, help="Path to the output model.")
    parser.add_argument(
        "--opset",
        default=14,
        type=int,
        help="The version of the ONNX operator set to use.",
    )
    parser.add_argument("--fp16", action="store_true", default=False, help="Export the models in `float16` mode")

    lowerCAmelCase			:							Optional[Any]     =parser.parse_args()
    print(args.output_path)
    convert_models(args.model_path, args.output_path, args.opset, args.fpaa)
    print("SD: Done: ONNX")




