



"""simple docstring"""





import argparse
import collections
import json
from pathlib import Path

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

from transformers import (
    MobileViTImageProcessor,
    MobileViTVaConfig,
    MobileViTVaForImageClassification,
    MobileViTVaForSemanticSegmentation,
)
from transformers.utils import logging


logging.set_verbosity_info()
lowerCamelCase            =		logging.get_logger(__name__)
def      a__			(   lowerCAmelCase__   ):
      print("Loading config file..."   )

      def flatten_yaml_as_dict(lowerCAmelCase__			,    lowerCAmelCase__=""			,    lowerCAmelCase__="."   ):
            UpperCAmelCase_    				=							[]
            for k, v in d.items():
                  UpperCAmelCase_    				=							parent_key + sep + k if parent_key else k
                  if isinstance(lowercase_			,    collections.abc.MutableMapping   ):
                        items.extend(flatten_yaml_as_dict(lowercase_			,    lowercase_			,    sep=lowercase_   ).items()   )
                  else:
                        items.append((new_key, v)   )
            return dict(lowercase_   )

      UpperCAmelCase_    				=							argparse.Namespace()
      with open(lowercase_			,    "r"   ) as yaml_file:
            try:
                  UpperCAmelCase_    				=							yaml.load(lowercase_			,    Loader=yaml.FullLoader   )

                  UpperCAmelCase_    				=							flatten_yaml_as_dict(lowercase_   )
                  for k, v in flat_cfg.items():
                        setattr(lowercase_			,    lowercase_			,    lowercase_   )
            except yaml.YAMLError as exc:
                  logger.error("Error while loading config file: {}. Error message: {}".format(lowercase_			,    str(lowercase_   )   )   )
      return config
def      a__			(   lowerCAmelCase__			,    lowerCAmelCase__   ):
      UpperCAmelCase_    				=							MobileViTVaConfig()

      UpperCAmelCase_    				=							False

      # dataset
      if task_name.startswith("imagenet1k_"   ):
            UpperCAmelCase_    				=							1000
            if int(task_name.strip().split("_"   )[-1]   ) == 384:
                  UpperCAmelCase_    				=							384
            else:
                  UpperCAmelCase_    				=							256
            UpperCAmelCase_    				=							"imagenet-1k-id2label.json"
      elif task_name.startswith("imagenet21k_to_1k_"   ):
            UpperCAmelCase_    				=							21000
            if int(task_name.strip().split("_"   )[-1]   ) == 384:
                  UpperCAmelCase_    				=							384
            else:
                  UpperCAmelCase_    				=							256
            UpperCAmelCase_    				=							"imagenet-22k-id2label.json"
      elif task_name.startswith("ade20k_"   ):
            UpperCAmelCase_    				=							151
            UpperCAmelCase_    				=							512
            UpperCAmelCase_    				=							"ade20k-id2label.json"
            UpperCAmelCase_    				=							True
      elif task_name.startswith("voc_"   ):
            UpperCAmelCase_    				=							21
            UpperCAmelCase_    				=							512
            UpperCAmelCase_    				=							"pascal-voc-id2label.json"
            UpperCAmelCase_    				=							True

      # orig_config
      UpperCAmelCase_    				=							load_orig_config_file(lowercase_   )
      assert getattr(lowercase_			,    "model.classification.name"			,    -1   ) == "mobilevit_v2", "Invalid model"
      UpperCAmelCase_    				=							getattr(lowercase_			,    "model.classification.mitv2.width_multiplier"			,    1.0   )
      assert (
          getattr(lowercase_			,    "model.classification.mitv2.attn_norm_layer"			,    -1   ) == "layer_norm_2d"
      ), "Norm layers other than layer_norm_2d is not supported"
      UpperCAmelCase_    				=							getattr(lowercase_			,    "model.classification.activation.name"			,    "swish"   )
      # config.image_size == getattr(orig_config,  'sampler.bs.crop_size_width', 256)

      if is_segmentation_model:
            UpperCAmelCase_    				=							getattr(lowercase_			,    "model.segmentation.output_stride"			,    16   )
            if "_deeplabv3" in task_name:
                  UpperCAmelCase_    				=							getattr(lowercase_			,    "model.segmentation.deeplabv3.aspp_rates"			,    [12, 24, 36]   )
                  UpperCAmelCase_    				=							getattr(lowercase_			,    "model.segmentation.deeplabv3.aspp_out_channels"			,    512   )
                  UpperCAmelCase_    				=							getattr(lowercase_			,    "model.segmentation.deeplabv3.aspp_dropout"			,    0.1   )

    # id2label
      UpperCAmelCase_    				=							"huggingface/label-files"
      UpperCAmelCase_    				=							json.load(open(hf_hub_download(lowercase_			,    lowercase_			,    repo_type="dataset"   )			,    "r"   )   )
      UpperCAmelCase_    				=							{int(lowercase_   ): v for k, v in idalabel.items()}
      UpperCAmelCase_    				=							idalabel
      UpperCAmelCase_    				=							{v: k for k, v in idalabel.items()}

      return config
def      a__			(   lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__   ):
      UpperCAmelCase_    				=							dct.pop(lowercase_   )
      UpperCAmelCase_    				=							val
def      a__			(   lowerCAmelCase__			,    lowerCAmelCase__=False   ):

      if base_model:
            UpperCAmelCase_    				=							""
      else:
            UpperCAmelCase_    				=							"mobilevitv2."

      UpperCAmelCase_    				=							[]
      for k in state_dict.keys():
            if k[:8] == "encoder.":
                  UpperCAmelCase_    				=							k[8:]
            else:
                  UpperCAmelCase_    				=							k

            if ".block." in k:
                  UpperCAmelCase_    				=							k_new.replace(".block."			,    "."   )
            if ".conv." in k:
                  UpperCAmelCase_    				=							k_new.replace(".conv."			,    ".convolution."   )
            if ".norm." in k:
                  UpperCAmelCase_    				=							k_new.replace(".norm."			,    ".normalization."   )

            if "conv_1." in k:
                  UpperCAmelCase_    				=							k_new.replace("conv_1."			,    f"""{model_prefix}conv_stem."""   )
            for i in [1, 2]:
                  if f"""layer_{i}.""" in k:
                        UpperCAmelCase_    				=							k_new.replace(f"""layer_{i}."""			,    f"""{model_prefix}encoder.layer.{i-1}.layer."""   )
            if ".exp_1x1." in k:
                  UpperCAmelCase_    				=							k_new.replace(".exp_1x1."			,    ".expand_1x1."   )
            if ".red_1x1." in k:
                  UpperCAmelCase_    				=							k_new.replace(".red_1x1."			,    ".reduce_1x1."   )

            for i in [3, 4, 5]:
                  if f"""layer_{i}.0.""" in k:
                        UpperCAmelCase_    				=							k_new.replace(f"""layer_{i}.0."""			,    f"""{model_prefix}encoder.layer.{i-1}.downsampling_layer."""   )
                  if f"""layer_{i}.1.local_rep.0.""" in k:
                        UpperCAmelCase_    				=							k_new.replace(f"""layer_{i}.1.local_rep.0."""			,    f"""{model_prefix}encoder.layer.{i-1}.conv_kxk."""   )
                  if f"""layer_{i}.1.local_rep.1.""" in k:
                        UpperCAmelCase_    				=							k_new.replace(f"""layer_{i}.1.local_rep.1."""			,    f"""{model_prefix}encoder.layer.{i-1}.conv_1x1."""   )

            for i in [3, 4, 5]:
                  if i == 3:
                        UpperCAmelCase_    				=							[0, 1]
                  elif i == 4:
                        UpperCAmelCase_    				=							[0, 1, 2, 3]
                  elif i == 5:
                        UpperCAmelCase_    				=							[0, 1, 2]

                  for j in j_in:
                        if f"""layer_{i}.1.global_rep.{j}.""" in k:
                              UpperCAmelCase_    				=							k_new.replace(
                                  f"""layer_{i}.1.global_rep.{j}."""			,    f"""{model_prefix}encoder.layer.{i-1}.transformer.layer.{j}."""   )
                  if f"""layer_{i}.1.global_rep.{j+1}.""" in k:
                        UpperCAmelCase_    				=							k_new.replace(
                            f"""layer_{i}.1.global_rep.{j+1}."""			,    f"""{model_prefix}encoder.layer.{i-1}.layernorm."""   )

                  if f"""layer_{i}.1.conv_proj.""" in k:
                        UpperCAmelCase_    				=							k_new.replace(f"""layer_{i}.1.conv_proj."""			,    f"""{model_prefix}encoder.layer.{i-1}.conv_projection."""   )

            if "pre_norm_attn.0." in k:
                  UpperCAmelCase_    				=							k_new.replace("pre_norm_attn.0."			,    "layernorm_before."   )
            if "pre_norm_attn.1." in k:
                  UpperCAmelCase_    				=							k_new.replace("pre_norm_attn.1."			,    "attention."   )
            if "pre_norm_ffn.0." in k:
                  UpperCAmelCase_    				=							k_new.replace("pre_norm_ffn.0."			,    "layernorm_after."   )
            if "pre_norm_ffn.1." in k:
                  UpperCAmelCase_    				=							k_new.replace("pre_norm_ffn.1."			,    "ffn.conv1."   )
            if "pre_norm_ffn.3." in k:
                  UpperCAmelCase_    				=							k_new.replace("pre_norm_ffn.3."			,    "ffn.conv2."   )

            if "classifier.1." in k:
                  UpperCAmelCase_    				=							k_new.replace("classifier.1."			,    "classifier."   )

            if "seg_head." in k:
                  UpperCAmelCase_    				=							k_new.replace("seg_head."			,    "segmentation_head."   )
            if ".aspp_layer." in k:
                  UpperCAmelCase_    				=							k_new.replace(".aspp_layer."			,    "."   )
            if ".aspp_pool." in k:
                  UpperCAmelCase_    				=							k_new.replace(".aspp_pool."			,    "."   )

            rename_keys.append((k, k_new)   )
      return rename_keys
def      a__			(   lowerCAmelCase__   ):
      UpperCAmelCase_    				=							[]
      for k in state_dict.keys():
            if k.startswith("seg_head.aux_head."   ):
                  keys_to_ignore.append(lowercase_   )
      for k in keys_to_ignore:
            state_dict.pop(lowercase_			,    lowercase_   )
def      a__			(   ):
      UpperCAmelCase_    				=							"http://images.cocodataset.org/val2017/000000039769.jpg"
      # url = "https://cdn.britannica.com/86/141086-050-9D7C75EE/Gulfstream-G450-business-jet-passengers.jpg"
      UpperCAmelCase_    				=							Image.open(requests.get(lowercase_			,    stream=lowercase_   ).raw   )
      return im
@torch.no_grad()
def      a__			(   lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__   ):
      UpperCAmelCase_    				=							get_mobilevitva_config(lowercase_			,    lowercase_   )

      # load original state_dict
      UpperCAmelCase_    				=							torch.load(lowercase_			,    map_location="cpu"   )

      # load huggingface model
      if task_name.startswith("ade20k_"   ) or task_name.startswith("voc_"   ):
            UpperCAmelCase_    				=							MobileViTVaForSemanticSegmentation(lowercase_   ).eval()
            UpperCAmelCase_    				=							False
      else:
            UpperCAmelCase_    				=							MobileViTVaForImageClassification(lowercase_   ).eval()
            UpperCAmelCase_    				=							False

      # remove and rename some keys of load the original model
      UpperCAmelCase_    				=							checkpoint
      remove_unused_keys(lowercase_   )
      UpperCAmelCase_    				=							create_rename_keys(lowercase_			,    base_model=lowercase_   )
      for rename_key_src, rename_key_dest in rename_keys:
            rename_key(lowercase_			,    lowercase_			,    lowercase_   )

      # load modified state_dict
      model.load_state_dict(lowercase_   )

      # 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(**lowercase_   )

      # verify classification model
      if task_name.startswith("imagenet"   ):
            UpperCAmelCase_    				=							outputs.logits
            UpperCAmelCase_    				=							logits.argmax(-1   ).item()
            print("Predicted class:"			,    model.config.idalabel[predicted_class_idx]   )
            if task_name.startswith("imagenet1k_256"   ) and config.width_multiplier == 1.0:
                  # expected_logits for base variant
                  UpperCAmelCase_    				=							torch.tensor([-1.6336e00, -7.3204e-02, -5.1883e-01]   )
                  assert torch.allclose(logits[0, :3]			,    lowercase_			,    atol=1e-4   )

      Path(lowercase_   ).mkdir(exist_ok=lowercase_   )
      print(f"""Saving model {task_name} to {pytorch_dump_folder_path}"""   )
      model.save_pretrained(lowercase_   )
      print(f"""Saving image processor to {pytorch_dump_folder_path}"""   )
      image_processor.save_pretrained(lowercase_   )


if __name__ == "__main__":
  lowerCamelCase            =		argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      """--task""",
      default="""imagenet1k_256""",
      type=str,
      help=(
          """Name of the task for which the MobileViTV2 model you'd like to convert is trained on . """
          """\n                Classification (ImageNet-1k)\n                    - MobileViTV2 (256x256) : imagenet1k_256\n                    - MobileViTV2 (Trained on 256x256 and Finetuned on 384x384) : imagenet1k_384\n                    - MobileViTV2 (Trained on ImageNet-21k and Finetuned on ImageNet-1k 256x256) :\n                      imagenet21k_to_1k_256\n                    - MobileViTV2 (Trained on ImageNet-21k, Finetuned on ImageNet-1k 256x256, and Finetuned on\n                      ImageNet-1k 384x384) : imagenet21k_to_1k_384\n                Segmentation\n                    - ADE20K Dataset : ade20k_deeplabv3\n                    - Pascal VOC 2012 Dataset: voc_deeplabv3\n            """
      ),
      choices=[
          """imagenet1k_256""",
          """imagenet1k_384""",
          """imagenet21k_to_1k_256""",
          """imagenet21k_to_1k_384""",
          """ade20k_deeplabv3""",
          """voc_deeplabv3""",
      ],
  )

  parser.add_argument(
      """--orig_checkpoint_path""", required=True, type=str, help="""Path to the original state dict (.pt file)."""
  )
  parser.add_argument("""--orig_config_path""", required=True, type=str, help="""Path to the original config file.""")
  parser.add_argument(
      """--pytorch_dump_folder_path""", required=True, type=str, help="""Path to the output PyTorch model directory."""
  )

  lowerCamelCase            =		parser.parse_args()
  convert_mobilevitva_checkpoint(
      args.task, args.orig_checkpoint_path, args.orig_config_path, args.pytorch_dump_folder_path
  )









"""simple docstring"""





def      a__			(   lowerCAmelCase__   ):
      if not head:
            return True
      # split the list to two parts
      UpperCAmelCase_				,					UpperCAmelCase_    				=							head.next, head
      while fast and fast.next:
            UpperCAmelCase_    				=							fast.next.next
            UpperCAmelCase_    				=							slow.next
      UpperCAmelCase_    				=							slow.next
      UpperCAmelCase_    				=							None  # Don't forget here! But forget still works!
      # reverse the second part
      UpperCAmelCase_    				=							None
      while second:
            UpperCAmelCase_    				=							second.next
            UpperCAmelCase_    				=							node
            UpperCAmelCase_    				=							second
            UpperCAmelCase_    				=							nxt
      # compare two parts
      # second part has the same or one less node
      while node:
            if node.val != head.val:
                  return False
            UpperCAmelCase_    				=							node.next
            UpperCAmelCase_    				=							head.next
      return True
def      a__			(   lowerCAmelCase__   ):
      if not head or not head.next:
            return True

      # 1. Get the midpoint (slow)
      UpperCAmelCase_    				=							UpperCAmelCase_    				=							UpperCAmelCase_    				=							head
      while fast and fast.next:
            UpperCAmelCase_				,					UpperCAmelCase_    				=							fast.next.next, slow.next

      # 2. Push the second half into the stack
      UpperCAmelCase_    				=							[slow.val]
      while slow.next:
            UpperCAmelCase_    				=							slow.next
            stack.append(slow.val   )

      # 3. Comparison
      while stack:
            if stack.pop() != cur.val:
                  return False
            UpperCAmelCase_    				=							cur.next

      return True


def      a__			(   lowerCAmelCase__   ):
      if not head or not head.next:
            return True
      UpperCAmelCase_    				=							{}
      UpperCAmelCase_    				=							0
      while head:
            if head.val in d:
                  d[head.val].append(lowerCAmelCase__   )
            else:
                  UpperCAmelCase_    				=							[pos]
            UpperCAmelCase_    				=							head.next
            pos += 1
      UpperCAmelCase_    				=							pos - 1
      UpperCAmelCase_    				=							0
      for v in d.values():
            if len(lowerCAmelCase__   ) % 2 != 0:
                  middle += 1
            else:
                  UpperCAmelCase_    				=							0
                  for i in range(0			,    len(lowerCAmelCase__   )   ):
                        if v[i] + v[len(lowerCAmelCase__   ) - 1 - step] != checksum:
                              return False
                        step += 1
            if middle > 1:
                  return False
      return True




