




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       _SCREAMING_SNAKE_CASE      (							lowercase     :	Tuple					):


      '''simple docstring'''



      lowerCamelCase_							=   3_84
      lowerCamelCase_							=   7
      if "tiny" in model_name:
            lowerCamelCase_							=   96
            lowerCamelCase_							=   (2, 2, 6, 2)
            lowerCamelCase_							=   (3, 6, 12, 24)
      elif "small" in model_name:
            lowerCamelCase_							=   96
            lowerCamelCase_							=   (2, 2, 18, 2)
            lowerCamelCase_							=   (3, 6, 12, 24)
      elif "base" in model_name:
            lowerCamelCase_							=   1_28
            lowerCamelCase_							=   (2, 2, 18, 2)
            lowerCamelCase_							=   (4, 8, 16, 32)
            lowerCamelCase_							=   12
            lowerCamelCase_							=   5_12
      elif "large" in model_name:
            lowerCamelCase_							=   1_92
            lowerCamelCase_							=   (2, 2, 18, 2)
            lowerCamelCase_							=   (6, 12, 24, 48)
            lowerCamelCase_							=   12
            lowerCamelCase_							=   7_68

      # set label information
      lowerCamelCase_							=   1_50
      lowerCamelCase_							=   'huggingface/label-files'
      lowerCamelCase_							=   'ade20k-id2label.json'
      lowerCamelCase_							=   json.load(open(hf_hub_download(lowercase     ,							lowercase     ,							repo_type='dataset'					)     ,							'r'					)					)
      lowerCamelCase_							=   {int(lowercase					): v for k, v in idalabel.items()}
      lowerCamelCase_							=   {v: k for k, v in idalabel.items()}

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

      return config

def       _SCREAMING_SNAKE_CASE      (							lowercase     :	Union[str, Any]					):


      '''simple docstring'''



      lowerCamelCase_							=   []

      # 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       _SCREAMING_SNAKE_CASE      (							lowercase     :	List[Any]     ,							lowercase     :	int     ,							lowercase     :	str					):


      '''simple docstring'''



      lowerCamelCase_							=   dct.pop(lowercase					)
      lowerCamelCase_							=   val

def       _SCREAMING_SNAKE_CASE      (							lowercase     :	List[Any]     ,							lowercase     :	Optional[int]					):


      '''simple docstring'''



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

def       _SCREAMING_SNAKE_CASE      (							lowercase     :	Dict					):


      '''simple docstring'''



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

def       _SCREAMING_SNAKE_CASE      (							lowercase     :	Dict					):


      '''simple docstring'''



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

      return x

def       _SCREAMING_SNAKE_CASE      (							lowercase     :	Optional[Any]					):


      '''simple docstring'''



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

def       _SCREAMING_SNAKE_CASE      (							lowercase     :	Optional[Any]					):


      '''simple docstring'''



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

def       _SCREAMING_SNAKE_CASE      (							lowercase     :	Optional[Any]     ,							lowercase     :	Any     ,							lowercase     :	Any					):


      '''simple docstring'''



      lowerCamelCase_							=   {
          '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',
      }
      lowerCamelCase_							=   model_name_to_url[model_name]
      lowerCamelCase_							=   torch.hub.load_state_dict_from_url(lowercase     ,							map_location='cpu'     ,							file_name=lowercase					)[
          'state_dict'
      ]

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

      lowerCamelCase_							=   get_upernet_config(lowercase					)
      lowerCamelCase_							=   UperNetForSemanticSegmentation(lowercase					)
      model.eval()

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

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

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

      model.load_state_dict(lowercase					)

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

      lowerCamelCase_							=   SegformerImageProcessor()
      lowerCamelCase_							=   processor(lowercase     ,							return_tensors='pt'					).pixel_values

      with torch.no_grad():
            lowerCamelCase_							=   model(lowercase					)
            lowerCamelCase_							=   outputs.logits

      print(logits.shape					)
      print('First values of logits:'     ,							logits[0, 0, :3, :3]					)
      # assert values
      if model_name == "upernet-swin-tiny":
            lowerCamelCase_							=   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":
            lowerCamelCase_							=   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":
            lowerCamelCase_							=   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":
            lowerCamelCase_							=   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]     ,							lowercase     ,							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(lowercase					)
            print(f"""Saving processor to {pytorch_dump_folder_path}"""					)
            processor.save_pretrained(lowercase					)

      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-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."
 )

 lowerCamelCase    :		List[Any]			   =       parser.parse_args()
 convert_upernet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)









import os
import time

import numpy as np
import onnxruntime as ort


lowerCamelCase    :		int			   =       "1"
lowerCamelCase    :		int			   =       "0"
lowerCamelCase    :		Union[str, Any]			   =       "1"

lowerCamelCase    :		List[Any]			   =       ort.SessionOptions()
lowerCamelCase    :		Optional[Any]			   =       ort.GraphOptimizationLevel.ORT_DISABLE_ALL
print("Create inference session...")
lowerCamelCase    :		Union[str, Any]			   =       ["TensorrtExecutionProvider", "CUDAExecutionProvider"]
lowerCamelCase    :		Tuple			   =       ort.InferenceSession("model.onnx", sess_options=sess_opt, providers=execution_provider)
lowerCamelCase    :		List[Any]			   =       ort.RunOptions()

lowerCamelCase    :		List[str]			   =       128
lowerCamelCase    :		List[Any]			   =       1
lowerCamelCase    :		Union[str, Any]			   =       np.ones((batch, sequence), dtype=np.intaa)
lowerCamelCase    :		Dict			   =       np.ones((batch, sequence), dtype=np.intaa)
lowerCamelCase    :		Optional[Any]			   =       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...")
lowerCamelCase    :		int			   =       time.time()
lowerCamelCase    :		Dict			   =       2_000
lowerCamelCase    :		Any			   =       {}
for iter in range(max_iters):
 lowerCamelCase    :		Union[str, Any]			   =       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) * 1_000 / max_iters))



