import argparse
import json
from pathlib import Path

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

from transformers import (
    MobileViTConfig,
    MobileViTForImageClassification,
    MobileViTForSemanticSegmentation,
    MobileViTImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
a_							=      logging.get_logger(__name__)




def       __lowerCAmelCase      (						A_   : Tuple					)    ->  Union[str, Any]:
  __UpperCAmelCase						    =       MobileViTConfig()

  # size of the architecture
  if "mobilevit_s" in mobilevit_name:
    __UpperCAmelCase						    =       [1_44, 1_92, 2_40]
    __UpperCAmelCase						    =       [16, 32, 64, 96, 1_28, 1_60, 6_40]
  elif "mobilevit_xs" in mobilevit_name:
    __UpperCAmelCase						    =       [96, 1_20, 1_44]
    __UpperCAmelCase						    =       [16, 32, 48, 64, 80, 96, 3_84]
  elif "mobilevit_xxs" in mobilevit_name:
    __UpperCAmelCase						    =       [64, 80, 96]
    __UpperCAmelCase						    =       [16, 16, 24, 48, 64, 80, 3_20]
    __UpperCAmelCase						    =       0.05
    __UpperCAmelCase						    =       2.0

  if mobilevit_name.startswith("deeplabv3_"					):
    __UpperCAmelCase						    =       5_12
    __UpperCAmelCase						    =       16
    __UpperCAmelCase						    =       21
    __UpperCAmelCase						    =       "pascal-voc-id2label.json"
  else:
    __UpperCAmelCase						    =       10_00
    __UpperCAmelCase						    =       "imagenet-1k-id2label.json"

  __UpperCAmelCase						    =       "huggingface/label-files"
  __UpperCAmelCase						    =       json.load(open(hf_hub_download(A_						,	A_						,	repo_type="dataset"					)						,	"r"					)					)
  __UpperCAmelCase						    =       {int(A_					): v for k, v in idalabel.items()}
  __UpperCAmelCase						    =       idalabel
  __UpperCAmelCase						    =       {v: k for k, v in idalabel.items()}

  return config




def       __lowerCAmelCase      (						A_   : Any						,	A_   : Optional[int]=False					)    ->  Tuple:
  for i in range(1						,	6					):
    if F'''layer_{i}.''' in name:
      __UpperCAmelCase						    =       name.replace(F'''layer_{i}.'''						,	F'''encoder.layer.{i - 1}.'''					)

  if "conv_1." in name:
    __UpperCAmelCase						    =       name.replace("conv_1."						,	"conv_stem."					)
  if ".block." in name:
    __UpperCAmelCase						    =       name.replace(".block."						,	"."					)

  if "exp_1x1" in name:
    __UpperCAmelCase						    =       name.replace("exp_1x1"						,	"expand_1x1"					)
  if "red_1x1" in name:
    __UpperCAmelCase						    =       name.replace("red_1x1"						,	"reduce_1x1"					)
  if ".local_rep.conv_3x3." in name:
    __UpperCAmelCase						    =       name.replace(".local_rep.conv_3x3."						,	".conv_kxk."					)
  if ".local_rep.conv_1x1." in name:
    __UpperCAmelCase						    =       name.replace(".local_rep.conv_1x1."						,	".conv_1x1."					)
  if ".norm." in name:
    __UpperCAmelCase						    =       name.replace(".norm."						,	".normalization."					)
  if ".conv." in name:
    __UpperCAmelCase						    =       name.replace(".conv."						,	".convolution."					)
  if ".conv_proj." in name:
    __UpperCAmelCase						    =       name.replace(".conv_proj."						,	".conv_projection."					)

  for i in range(0						,	2					):
    for j in range(0						,	4					):
      if F'''.{i}.{j}.''' in name:
        __UpperCAmelCase						    =       name.replace(F'''.{i}.{j}.'''						,	F'''.{i}.layer.{j}.'''					)

  for i in range(2						,	6					):
    for j in range(0						,	4					):
      if F'''.{i}.{j}.''' in name:
        __UpperCAmelCase						    =       name.replace(F'''.{i}.{j}.'''						,	F'''.{i}.'''					)
        if "expand_1x1" in name:
          __UpperCAmelCase						    =       name.replace("expand_1x1"						,	"downsampling_layer.expand_1x1"					)
        if "conv_3x3" in name:
          __UpperCAmelCase						    =       name.replace("conv_3x3"						,	"downsampling_layer.conv_3x3"					)
        if "reduce_1x1" in name:
          __UpperCAmelCase						    =       name.replace("reduce_1x1"						,	"downsampling_layer.reduce_1x1"					)

  for i in range(2						,	5					):
    if F'''.global_rep.{i}.weight''' in name:
      __UpperCAmelCase						    =       name.replace(F'''.global_rep.{i}.weight'''						,	".layernorm.weight"					)
    if F'''.global_rep.{i}.bias''' in name:
      __UpperCAmelCase						    =       name.replace(F'''.global_rep.{i}.bias'''						,	".layernorm.bias"					)

  if ".global_rep." in name:
    __UpperCAmelCase						    =       name.replace(".global_rep."						,	".transformer."					)
  if ".pre_norm_mha.0." in name:
    __UpperCAmelCase						    =       name.replace(".pre_norm_mha.0."						,	".layernorm_before."					)
  if ".pre_norm_mha.1.out_proj." in name:
    __UpperCAmelCase						    =       name.replace(".pre_norm_mha.1.out_proj."						,	".attention.output.dense."					)
  if ".pre_norm_ffn.0." in name:
    __UpperCAmelCase						    =       name.replace(".pre_norm_ffn.0."						,	".layernorm_after."					)
  if ".pre_norm_ffn.1." in name:
    __UpperCAmelCase						    =       name.replace(".pre_norm_ffn.1."						,	".intermediate.dense."					)
  if ".pre_norm_ffn.4." in name:
    __UpperCAmelCase						    =       name.replace(".pre_norm_ffn.4."						,	".output.dense."					)
  if ".transformer." in name:
    __UpperCAmelCase						    =       name.replace(".transformer."						,	".transformer.layer."					)

  if ".aspp_layer." in name:
    __UpperCAmelCase						    =       name.replace(".aspp_layer."						,	"."					)
  if ".aspp_pool." in name:
    __UpperCAmelCase						    =       name.replace(".aspp_pool."						,	"."					)
  if "seg_head." in name:
    __UpperCAmelCase						    =       name.replace("seg_head."						,	"segmentation_head."					)
  if "segmentation_head.classifier.classifier." in name:
    __UpperCAmelCase						    =       name.replace("segmentation_head.classifier.classifier."						,	"segmentation_head.classifier."					)

  if "classifier.fc." in name:
    __UpperCAmelCase						    =       name.replace("classifier.fc."						,	"classifier."					)
  elif (not base_model) and ("segmentation_head." not in name):
    __UpperCAmelCase						    =       "mobilevit." + name

  return name




def       __lowerCAmelCase      (						A_   : Any						,	A_   : Union[str, Any]						,	A_   : Optional[Any]=False					)    ->  str:
  if base_model:
    __UpperCAmelCase						    =       ""
  else:
    __UpperCAmelCase						    =       "mobilevit."

  for key in orig_state_dict.copy().keys():
    __UpperCAmelCase						    =       orig_state_dict.pop(A_					)

    if key[:8] == "encoder.":
      __UpperCAmelCase						    =       key[8:]

    if "qkv" in key:
      __UpperCAmelCase						    =       key.split("."					)
      __UpperCAmelCase						    =       int(key_split[0][6:]					) - 1
      __UpperCAmelCase						    =       int(key_split[3]					)
      __UpperCAmelCase						    =       model.get_submodule(F'''{model_prefix}encoder.layer.{layer_num}'''					)
      __UpperCAmelCase						    =       layer.transformer.layer[transformer_num].attention.attention.all_head_size
      __UpperCAmelCase						    =       (
          F'''{model_prefix}encoder.layer.{layer_num}.transformer.layer.{transformer_num}.attention.attention.'''
      )
      if "weight" in key:
        __UpperCAmelCase						    =       val[:dim, :]
        __UpperCAmelCase						    =       val[dim : dim * 2, :]
        __UpperCAmelCase						    =       val[-dim:, :]
      else:
        __UpperCAmelCase						    =       val[:dim]
        __UpperCAmelCase						    =       val[dim : dim * 2]
        __UpperCAmelCase						    =       val[-dim:]
    else:
      __UpperCAmelCase						    =       val

  return orig_state_dict




def       __lowerCAmelCase      (						)    ->  Dict:
  __UpperCAmelCase						    =       "http://images.cocodataset.org/val2017/000000039769.jpg"
  __UpperCAmelCase						    =       Image.open(requests.get(A_						,	stream=A_					).raw					)
  return im




@torch.no_grad()
def       __lowerCAmelCase      (						A_   : Dict						,	A_   : List[Any]						,	A_   : Dict						,	A_   : Optional[Any]=False					)    ->  Union[str, Any]:
  __UpperCAmelCase						    =       get_mobilevit_config(A_					)

  # load original state_dict
  __UpperCAmelCase						    =       torch.load(A_						,	map_location="cpu"					)

  # load 🤗 model
  if mobilevit_name.startswith("deeplabv3_"					):
    __UpperCAmelCase						    =       MobileViTForSemanticSegmentation(A_					).eval()
  else:
    __UpperCAmelCase						    =       MobileViTForImageClassification(A_					).eval()

  __UpperCAmelCase						    =       convert_state_dict(A_						,	A_					)
  model.load_state_dict(A_					)

  # Check outputs on an image, prepared by MobileViTImageProcessor
  __UpperCAmelCase						    =       MobileViTImageProcessor(crop_size=config.image_size						,	size=config.image_size + 32					)
  __UpperCAmelCase						    =       image_processor(images=prepare_img()						,	return_tensors="pt"					)
  __UpperCAmelCase						    =       model(**A_					)
  __UpperCAmelCase						    =       outputs.logits

  if mobilevit_name.startswith("deeplabv3_"					):
    assert logits.shape == (1, 21, 32, 32)

    if mobilevit_name == "deeplabv3_mobilevit_s":
      __UpperCAmelCase						    =       torch.tensor(
          [
              [[6.20_65, 6.12_92, 6.20_70], [6.10_79, 6.12_54, 6.17_47], [6.00_42, 6.10_71, 6.10_34]],
              [[-6.92_53, -6.86_53, -7.03_98], [-7.32_18, -7.39_83, -7.36_70], [-7.19_61, -7.24_82, -7.15_69]],
              [[-4.47_23, -4.43_48, -4.37_69], [-5.36_29, -5.46_32, -5.45_98], [-5.15_87, -5.34_02, -5.50_59]],
          ]					)
    elif mobilevit_name == "deeplabv3_mobilevit_xs":
      __UpperCAmelCase						    =       torch.tensor(
          [
              [[5.44_49, 5.57_33, 5.63_14], [5.18_15, 5.39_30, 5.59_63], [5.16_56, 5.43_33, 5.48_53]],
              [[-9.44_23, -9.77_66, -9.67_14], [-9.15_81, -9.57_20, -9.55_19], [-9.10_06, -9.64_58, -9.57_03]],
              [[-7.77_21, -7.37_16, -7.15_83], [-8.45_99, -8.06_24, -7.79_44], [-8.41_72, -7.83_66, -7.50_25]],
          ]					)
    elif mobilevit_name == "deeplabv3_mobilevit_xxs":
      __UpperCAmelCase						    =       torch.tensor(
          [
              [[6.98_11, 6.97_43, 7.31_23], [7.17_77, 7.19_31, 7.39_38], [7.56_33, 7.80_50, 7.89_01]],
              [[-10.55_36, -10.23_32, -10.29_24], [-10.23_36, -9.86_24, -9.59_64], [-10.88_40, -10.81_58, -10.66_59]],
              [[-3.49_38, -3.06_31, -2.86_20], [-3.42_05, -2.81_35, -2.68_75], [-3.41_79, -2.79_45, -2.87_50]],
          ]					)
    else:
      raise ValueError(F'''Unknown mobilevit_name: {mobilevit_name}'''					)

    assert torch.allclose(logits[0, :3, :3, :3]						,	A_						,	atol=1e-4					)
  else:
    assert logits.shape == (1, 10_00)

    if mobilevit_name == "mobilevit_s":
      __UpperCAmelCase						    =       torch.tensor([-0.98_66, 0.23_92, -1.12_41]					)
    elif mobilevit_name == "mobilevit_xs":
      __UpperCAmelCase						    =       torch.tensor([-2.47_61, -0.93_99, -1.95_87]					)
    elif mobilevit_name == "mobilevit_xxs":
      __UpperCAmelCase						    =       torch.tensor([-1.93_64, -1.23_27, -0.46_53]					)
    else:
      raise ValueError(F'''Unknown mobilevit_name: {mobilevit_name}'''					)

    assert torch.allclose(logits[0, :3]						,	A_						,	atol=1e-4					)

  Path(A_					).mkdir(exist_ok=A_					)
  print(F'''Saving model {mobilevit_name} to {pytorch_dump_folder_path}'''					)
  model.save_pretrained(A_					)
  print(F'''Saving image processor to {pytorch_dump_folder_path}'''					)
  image_processor.save_pretrained(A_					)

  if push_to_hub:
    __UpperCAmelCase						    =       {
        "mobilevit_s": "mobilevit-small",
        "mobilevit_xs": "mobilevit-x-small",
        "mobilevit_xxs": "mobilevit-xx-small",
        "deeplabv3_mobilevit_s": "deeplabv3-mobilevit-small",
        "deeplabv3_mobilevit_xs": "deeplabv3-mobilevit-x-small",
        "deeplabv3_mobilevit_xxs": "deeplabv3-mobilevit-xx-small",
    }

    print("Pushing to the hub..."					)
    __UpperCAmelCase						    =       model_mapping[mobilevit_name]
    image_processor.push_to_hub(A_						,	organization="apple"					)
    model.push_to_hub(A_						,	organization="apple"					)


if __name__ == "__main__":
       a_							=      argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           """--mobilevit_name""",
           default="""mobilevit_s""",
           type=str,
           help=(
               """Name of the MobileViT model you'd like to convert. Should be one of 'mobilevit_s', 'mobilevit_xs',"""
               """ 'mobilevit_xxs', 'deeplabv3_mobilevit_s', 'deeplabv3_mobilevit_xs', 'deeplabv3_mobilevit_xxs'."""
           ),
       )
       parser.add_argument(
           """--checkpoint_path""", required=True, type=str, help="""Path to the original state dict (.pt file)."""
       )
       parser.add_argument(
           """--pytorch_dump_folder_path""", required=True, 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."""
       )

       a_							=      parser.parse_args()
       convert_movilevit_checkpoint(
           args.mobilevit_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
       )




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

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

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

  return config




def       __lowerCAmelCase      (						A_   : Optional[int]					)    ->  Optional[Any]:
  __UpperCAmelCase						    =       []

  # 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      (						A_   : List[Any]						,	A_   : List[str]						,	A_   : Optional[Any]					)    ->  List[str]:
  __UpperCAmelCase						    =       dct.pop(A_					)
  __UpperCAmelCase						    =       val




def       __lowerCAmelCase      (						A_   : Tuple						,	A_   : Dict					)    ->  Tuple:
  __UpperCAmelCase						    =       [int(backbone_config.embed_dim * 2**i					) for i in range(len(backbone_config.depths					)					)]
  for i in range(len(backbone_config.depths					)					):
    __UpperCAmelCase						    =       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)
      __UpperCAmelCase						    =       state_dict.pop(F'''backbone.stages.{i}.blocks.{j}.attn.w_msa.qkv.weight'''					)
      __UpperCAmelCase						    =       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
      __UpperCAmelCase						    =       in_proj_weight[:dim, :]
      __UpperCAmelCase						    =       in_proj_bias[: dim]
      __UpperCAmelCase						    =       in_proj_weight[
          dim : dim * 2, :
      ]
      __UpperCAmelCase						    =       in_proj_bias[
          dim : dim * 2
      ]
      __UpperCAmelCase						    =       in_proj_weight[
          -dim :, :
      ]
      __UpperCAmelCase						    =       in_proj_bias[-dim :]
      # fmt: on




def       __lowerCAmelCase      (						A_   : List[Any]					)    ->  int:
  __UpperCAmelCase    ,		__UpperCAmelCase						    =       x.shape
  __UpperCAmelCase						    =       x.reshape(A_						,	4						,	in_channel // 4					)
  __UpperCAmelCase						    =       x[:, [0, 2, 1, 3], :].transpose(1						,	2					).reshape(A_						,	A_					)
  return x




def       __lowerCAmelCase      (						A_   : Tuple					)    ->  int:
  __UpperCAmelCase    ,		__UpperCAmelCase						    =       x.shape
  __UpperCAmelCase						    =       x.reshape(A_						,	in_channel // 4						,	4					)
  __UpperCAmelCase						    =       x[:, :, [0, 2, 1, 3]].transpose(1						,	2					).reshape(A_						,	A_					)

  return x




def       __lowerCAmelCase      (						A_   : Union[str, Any]					)    ->  str:
  __UpperCAmelCase						    =       x.shape[0]
  __UpperCAmelCase						    =       x.reshape(4						,	in_channel // 4					)
  __UpperCAmelCase						    =       x[[0, 2, 1, 3], :].transpose(0						,	1					).reshape(A_					)
  return x




def       __lowerCAmelCase      (						A_   : Optional[int]					)    ->  str:
  __UpperCAmelCase						    =       x.shape[0]
  __UpperCAmelCase						    =       x.reshape(in_channel // 4						,	4					)
  __UpperCAmelCase						    =       x[:, [0, 2, 1, 3]].transpose(0						,	1					).reshape(A_					)
  return x




def       __lowerCAmelCase      (						A_   : Any						,	A_   : str						,	A_   : Any					)    ->  Dict:
  __UpperCAmelCase						    =       {
      "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",
  }
  __UpperCAmelCase						    =       model_name_to_url[model_name]
  __UpperCAmelCase						    =       torch.hub.load_state_dict_from_url(A_						,	map_location="cpu"						,	file_name=A_					)[
      "state_dict"
  ]

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

  __UpperCAmelCase						    =       get_upernet_config(A_					)
  __UpperCAmelCase						    =       UperNetForSemanticSegmentation(A_					)
  model.eval()

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

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

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

  model.load_state_dict(A_					)

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

  __UpperCAmelCase						    =       SegformerImageProcessor()
  __UpperCAmelCase						    =       processor(A_						,	return_tensors="pt"					).pixel_values

  with torch.no_grad():
    __UpperCAmelCase						    =       model(A_					)
    __UpperCAmelCase						    =       outputs.logits

  print(logits.shape					)
  print("First values of logits:"						,	logits[0, 0, :3, :3]					)
  # assert values
  if model_name == "upernet-swin-tiny":
    __UpperCAmelCase						    =       torch.tensor(
        [[-7.59_58, -7.59_58, -7.43_02], [-7.59_58, -7.59_58, -7.43_02], [-7.47_97, -7.47_97, -7.30_68]]					)
  elif model_name == "upernet-swin-small":
    __UpperCAmelCase						    =       torch.tensor(
        [[-7.19_21, -7.19_21, -6.95_32], [-7.19_21, -7.19_21, -6.95_32], [-7.09_08, -7.09_08, -6.85_34]]					)
  elif model_name == "upernet-swin-base":
    __UpperCAmelCase						    =       torch.tensor(
        [[-6.58_51, -6.58_51, -6.43_30], [-6.58_51, -6.58_51, -6.43_30], [-6.47_63, -6.47_63, -6.32_54]]					)
  elif model_name == "upernet-swin-large":
    __UpperCAmelCase						    =       torch.tensor(
        [[-7.52_97, -7.52_97, -7.38_02], [-7.52_97, -7.52_97, -7.38_02], [-7.40_44, -7.40_44, -7.25_86]]					)
  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__":
       a_							=      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."""
       )

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



