



'''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        lowerCamelCase  (				_snake_case  :						List[str]       ):





    '''simple docstring'''


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

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

    lowercase__		       =						SwinConfig(
        embed_dim=_snake_case     ,depths=_snake_case     ,num_heads=_snake_case     ,window_size=_snake_case     ,out_features=["stage1", "stage2", "stage3", "stage4"]     ,)
    lowercase__		       =						UperNetConfig(
        backbone_config=_snake_case     ,auxiliary_in_channels=_snake_case     ,num_labels=_snake_case     ,idalabel=_snake_case     ,labelaid=_snake_case     ,)

    return config




def        lowerCamelCase  (				_snake_case  :						List[Any]       ):





    '''simple docstring'''


    lowercase__		       =						[]

    # 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        lowerCamelCase  (				_snake_case  :						Union[str, Any]     ,_snake_case  :						str     ,_snake_case  :						List[str]       ):





    '''simple docstring'''


    lowercase__		       =						dct.pop(_snake_case       )
    lowercase__		       =						val




def        lowerCamelCase  (				_snake_case  :						Any     ,_snake_case  :						List[Any]       ):





    '''simple docstring'''


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




def        lowerCamelCase  (				_snake_case  :						Optional[int]       ):





    '''simple docstring'''


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




def        lowerCamelCase  (				_snake_case  :						List[str]       ):





    '''simple docstring'''


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

    return x




def        lowerCamelCase  (				_snake_case  :						int       ):





    '''simple docstring'''


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




def        lowerCamelCase  (				_snake_case  :						Tuple       ):





    '''simple docstring'''


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




def        lowerCamelCase  (				_snake_case  :						Optional[Any]     ,_snake_case  :						Union[str, Any]     ,_snake_case  :						Optional[int]       ):





    '''simple docstring'''


    lowercase__		       =						{
        "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",
    }
    lowercase__		       =						model_name_to_url[model_name]
    lowercase__		       =						torch.hub.load_state_dict_from_url(_snake_case     ,map_location="cpu"     ,file_name=_snake_case       )[
        "state_dict"
    ]

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

    lowercase__		       =						get_upernet_config(_snake_case       )
    lowercase__		       =						UperNetForSemanticSegmentation(_snake_case       )
    model.eval()

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

    # rename keys
    lowercase__		       =						create_rename_keys(_snake_case       )
    for src, dest in rename_keys:
        rename_key(_snake_case     ,_snake_case     ,_snake_case       )
    read_in_q_k_v(_snake_case     ,config.backbone_config       )

    # fix downsample parameters
    for key, value in state_dict.items():
        if "downsample" in key:
            if "reduction" in key:
                lowercase__		       =						reverse_correct_unfold_reduction_order(_snake_case       )
            if "norm" in key:
                lowercase__		       =						reverse_correct_unfold_norm_order(_snake_case       )

    model.load_state_dict(_snake_case       )

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

    lowercase__		       =						SegformerImageProcessor()
    lowercase__		       =						processor(_snake_case     ,return_tensors="pt"       ).pixel_values

    with torch.no_grad():
        lowercase__		       =						model(_snake_case       )
        lowercase__		       =						outputs.logits

    print(logits.shape       )
    print("First values of logits:"     ,logits[0, 0, :3, :3]       )
    # assert values
    if model_name == "upernet-swin-tiny":
        lowercase__		       =						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":
        lowercase__		       =						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":
        lowercase__		       =						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":
        lowercase__		       =						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]     ,_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__":
 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'''





import os
import time

import numpy as np
import onnxruntime as ort


SCREAMING_SNAKE_CASE__      					=     "1"
SCREAMING_SNAKE_CASE__      					=     "0"
SCREAMING_SNAKE_CASE__      					=     "1"

SCREAMING_SNAKE_CASE__      					=     ort.SessionOptions()
SCREAMING_SNAKE_CASE__      					=     ort.GraphOptimizationLevel.ORT_DISABLE_ALL
print("Create inference session...")
SCREAMING_SNAKE_CASE__      					=     ["TensorrtExecutionProvider", "CUDAExecutionProvider"]
SCREAMING_SNAKE_CASE__      					=     ort.InferenceSession("model.onnx", sess_options=sess_opt, providers=execution_provider)
SCREAMING_SNAKE_CASE__      					=     ort.RunOptions()

SCREAMING_SNAKE_CASE__      					=     128
SCREAMING_SNAKE_CASE__      					=     1
SCREAMING_SNAKE_CASE__      					=     np.ones((batch, sequence), dtype=np.intaa)
SCREAMING_SNAKE_CASE__      					=     np.ones((batch, sequence), dtype=np.intaa)
SCREAMING_SNAKE_CASE__      					=     np.ones((batch, sequence), dtype=np.intaa)

print("Warm up phase...")
sess.run(
    None,
    {
        sess.get_inputs()[0].name: input_ids,
        sess.get_inputs()[1].name: attention_mask,
        sess.get_inputs()[2].name: token_type_ids,
    },
    run_options=run_opt,
)

print("Start inference...")
SCREAMING_SNAKE_CASE__      					=     time.time()
SCREAMING_SNAKE_CASE__      					=     2000
SCREAMING_SNAKE_CASE__      					=     {}
for iter in range(max_iters):
 SCREAMING_SNAKE_CASE__      					=     sess.run(
     None,
     {
         sess.get_inputs()[0].name: input_ids,
         sess.get_inputs()[1].name: attention_mask,
         sess.get_inputs()[2].name: token_type_ids,
     },
     run_options=run_opt,
 )
print("Average Inference Time = {:.3f} ms".format((time.time() - start_time) * 1000 / max_iters))




