





"""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        lowercase__     (       lowercase_      )       ->		List[Any]:




      """simple docstring"""

      print("Loading config file..."      )

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

      _UpperCamelCase   :						Dict    =							argparse.Namespace()
      with open(__UpperCamelCase		,"r"      ) as yaml_file:
            try:
                  _UpperCamelCase   :						Dict    =							yaml.load(__UpperCamelCase		,Loader=yaml.FullLoader      )

                  _UpperCamelCase   :						List[Any]    =							flatten_yaml_as_dict(__UpperCamelCase      )
                  for k, v in flat_cfg.items():
                        setattr(__UpperCamelCase		,__UpperCamelCase		,__UpperCamelCase      )
            except yaml.YAMLError as exc:
                  logger.error("Error while loading config file: {}. Error message: {}".format(__UpperCamelCase		,str(__UpperCamelCase      )      )      )
      return config




def        lowercase__     (       lowercase_		,lowercase_      )       ->		Optional[Any]:




      """simple docstring"""

      _UpperCamelCase   :						str    =							MobileViTVaConfig()

      _UpperCamelCase   :						Dict    =							False

      # dataset
      if task_name.startswith("imagenet1k_"      ):
            _UpperCamelCase   :						Optional[int]    =							1_000
            if int(task_name.strip().split("_"      )[-1]      ) == 384:
                  _UpperCamelCase   :						Any    =							384
            else:
                  _UpperCamelCase   :						Dict    =							256
            _UpperCamelCase   :						List[str]    =							"imagenet-1k-id2label.json"
      elif task_name.startswith("imagenet21k_to_1k_"      ):
            _UpperCamelCase   :						Tuple    =							21_000
            if int(task_name.strip().split("_"      )[-1]      ) == 384:
                  _UpperCamelCase   :						Optional[int]    =							384
            else:
                  _UpperCamelCase   :						Tuple    =							256
            _UpperCamelCase   :						Union[str, Any]    =							"imagenet-22k-id2label.json"
      elif task_name.startswith("ade20k_"      ):
            _UpperCamelCase   :						Optional[int]    =							151
            _UpperCamelCase   :						Tuple    =							512
            _UpperCamelCase   :						List[Any]    =							"ade20k-id2label.json"
            _UpperCamelCase   :						Tuple    =							True
      elif task_name.startswith("voc_"      ):
            _UpperCamelCase   :						Optional[Any]    =							21
            _UpperCamelCase   :						Dict    =							512
            _UpperCamelCase   :						Union[str, Any]    =							"pascal-voc-id2label.json"
            _UpperCamelCase   :						Tuple    =							True

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

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

    # id2label
      _UpperCamelCase   :						int    =							"huggingface/label-files"
      _UpperCamelCase   :						Union[str, Any]    =							json.load(open(hf_hub_download(__UpperCamelCase		,__UpperCamelCase		,repo_type="dataset"      )		,"r"      )      )
      _UpperCamelCase   :						Tuple    =							{int(__UpperCamelCase      ): v for k, v in idalabel.items()}
      _UpperCamelCase   :						Dict    =							idalabel
      _UpperCamelCase   :						Dict    =							{v: k for k, v in idalabel.items()}

      return config




def        lowercase__     (       lowercase_		,lowercase_		,lowercase_      )       ->		List[str]:




      """simple docstring"""

      _UpperCamelCase   :						List[Any]    =							dct.pop(__UpperCamelCase      )
      _UpperCamelCase   :						Any    =							val




def        lowercase__     (       lowercase_		,lowercase_=False      )       ->		Dict:




      """simple docstring"""

      if base_model:
            _UpperCamelCase   :						Any    =							""
      else:
            _UpperCamelCase   :						int    =							"mobilevitv2."

      _UpperCamelCase   :						Optional[int]    =							[]
      for k in state_dict.keys():
            if k[:8] == "encoder.":
                  _UpperCamelCase   :						Tuple    =							k[8:]
            else:
                  _UpperCamelCase   :						List[str]    =							k

            if ".block." in k:
                  _UpperCamelCase   :						Union[str, Any]    =							k_new.replace(".block."		,"."      )
            if ".conv." in k:
                  _UpperCamelCase   :						List[str]    =							k_new.replace(".conv."		,".convolution."      )
            if ".norm." in k:
                  _UpperCamelCase   :						str    =							k_new.replace(".norm."		,".normalization."      )

            if "conv_1." in k:
                  _UpperCamelCase   :						str    =							k_new.replace("conv_1."		,F'''{model_prefix}conv_stem.'''      )
            for i in [1, 2]:
                  if F'''layer_{i}.''' in k:
                        _UpperCamelCase   :						str    =							k_new.replace(F'''layer_{i}.'''		,F'''{model_prefix}encoder.layer.{i-1}.layer.'''      )
            if ".exp_1x1." in k:
                  _UpperCamelCase   :						int    =							k_new.replace(".exp_1x1."		,".expand_1x1."      )
            if ".red_1x1." in k:
                  _UpperCamelCase   :						Any    =							k_new.replace(".red_1x1."		,".reduce_1x1."      )

            for i in [3, 4, 5]:
                  if F'''layer_{i}.0.''' in k:
                        _UpperCamelCase   :						str    =							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   :						int    =							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   :						List[Any]    =							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   :						Tuple    =							[0, 1]
                  elif i == 4:
                        _UpperCamelCase   :						int    =							[0, 1, 2, 3]
                  elif i == 5:
                        _UpperCamelCase   :						Dict    =							[0, 1, 2]

                  for j in j_in:
                        if F'''layer_{i}.1.global_rep.{j}.''' in k:
                              _UpperCamelCase   :						Union[str, Any]    =							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   :						int    =							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   :						Optional[Any]    =							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   :						Tuple    =							k_new.replace("pre_norm_attn.0."		,"layernorm_before."      )
            if "pre_norm_attn.1." in k:
                  _UpperCamelCase   :						List[str]    =							k_new.replace("pre_norm_attn.1."		,"attention."      )
            if "pre_norm_ffn.0." in k:
                  _UpperCamelCase   :						Union[str, Any]    =							k_new.replace("pre_norm_ffn.0."		,"layernorm_after."      )
            if "pre_norm_ffn.1." in k:
                  _UpperCamelCase   :						str    =							k_new.replace("pre_norm_ffn.1."		,"ffn.conv1."      )
            if "pre_norm_ffn.3." in k:
                  _UpperCamelCase   :						Tuple    =							k_new.replace("pre_norm_ffn.3."		,"ffn.conv2."      )

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

            if "seg_head." in k:
                  _UpperCamelCase   :						Any    =							k_new.replace("seg_head."		,"segmentation_head."      )
            if ".aspp_layer." in k:
                  _UpperCamelCase   :						Dict    =							k_new.replace(".aspp_layer."		,"."      )
            if ".aspp_pool." in k:
                  _UpperCamelCase   :						str    =							k_new.replace(".aspp_pool."		,"."      )

            rename_keys.append((k, k_new)      )
      return rename_keys




def        lowercase__     (       lowercase_      )       ->		Tuple:




      """simple docstring"""

      _UpperCamelCase   :						List[str]    =							[]
      for k in state_dict.keys():
            if k.startswith("seg_head.aux_head."      ):
                  keys_to_ignore.append(__UpperCamelCase      )
      for k in keys_to_ignore:
            state_dict.pop(__UpperCamelCase		,__UpperCamelCase      )




def        lowercase__     (       )       ->		str:




      """simple docstring"""

      _UpperCamelCase   :						Union[str, Any]    =							"http://images.cocodataset.org/val2017/000000039769.jpg"
      # url = "https://cdn.britannica.com/86/141086-050-9D7C75EE/Gulfstream-G450-business-jet-passengers.jpg"
      _UpperCamelCase   :						Optional[int]    =							Image.open(requests.get(__UpperCamelCase		,stream=__UpperCamelCase      ).raw      )
      return im




@torch.no_grad()
def        lowercase__     (       lowercase_		,lowercase_		,lowercase_		,lowercase_      )       ->		Optional[Any]:




      """simple docstring"""

      _UpperCamelCase   :						Union[str, Any]    =							get_mobilevitva_config(__UpperCamelCase		,__UpperCamelCase      )

      # load original state_dict
      _UpperCamelCase   :						Dict    =							torch.load(__UpperCamelCase		,map_location="cpu"      )

      # load huggingface model
      if task_name.startswith("ade20k_"      ) or task_name.startswith("voc_"      ):
            _UpperCamelCase   :						Optional[int]    =							MobileViTVaForSemanticSegmentation(__UpperCamelCase      ).eval()
            _UpperCamelCase   :						List[Any]    =							False
      else:
            _UpperCamelCase   :						List[Any]    =							MobileViTVaForImageClassification(__UpperCamelCase      ).eval()
            _UpperCamelCase   :						List[str]    =							False

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

      # load modified state_dict
      model.load_state_dict(__UpperCamelCase      )

      # Check outputs on an image, prepared by MobileViTImageProcessor
      _UpperCamelCase   :						Tuple    =							MobileViTImageProcessor(crop_size=config.image_size		,size=config.image_size + 32      )
      _UpperCamelCase   :						str    =							image_processor(images=prepare_img()		,return_tensors="pt"      )
      _UpperCamelCase   :						Union[str, Any]    =							model(**__UpperCamelCase      )

      # verify classification model
      if task_name.startswith("imagenet"      ):
            _UpperCamelCase   :						Optional[int]    =							outputs.logits
            _UpperCamelCase   :						Any    =							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   :						Optional[Any]    =							torch.tensor([-1.6336e00, -7.3204e-02, -5.1883e-01]      )
                  assert torch.allclose(logits[0, :3]		,__UpperCamelCase		,atol=1e-4      )

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


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
  )





def A    (							__UpperCamelCase			,						__UpperCamelCase       )    ->   Optional[int]:
			A__	  =     0
			A__	  =     len(__UpperCamelCase       ) - 1

			while left <= right:
						# avoid divided by 0 during interpolation
						if sorted_collection[left] == sorted_collection[right]:
									if sorted_collection[left] == item:
												return left
									else:
												return None

						A__	  =     left + ((item - sorted_collection[left]) * (right - left)) // (
						    sorted_collection[right] - sorted_collection[left]
						)

						# out of range check
						if point < 0 or point >= len(__UpperCamelCase       ):
									return None

						A__	  =     sorted_collection[point]
						if current_item == item:
									return point
						else:
									if point < left:
												A__	  =     left
												A__	  =     point
									elif point > right:
												A__	  =     right
												A__	  =     point
									else:
												if item < current_item:
															A__	  =     point - 1
												else:
															A__	  =     point + 1
			return None


def A    (							__UpperCamelCase			,						__UpperCamelCase			,						__UpperCamelCase			,						__UpperCamelCase       )    ->   int:

			# avoid divided by 0 during interpolation
			if sorted_collection[left] == sorted_collection[right]:
						if sorted_collection[left] == item:
									return left
						else:
									return None

			A__	  =     left + ((item - sorted_collection[left]) * (right - left)) // (
			    sorted_collection[right] - sorted_collection[left]
			)

			# out of range check
			if point < 0 or point >= len(__UpperCamelCase       ):
						return None

			if sorted_collection[point] == item:
						return point
			elif point < left:
						return interpolation_search_by_recursion(__UpperCamelCase			,						__UpperCamelCase			,						__UpperCamelCase			,						__UpperCamelCase       )
			elif point > right:
						return interpolation_search_by_recursion(__UpperCamelCase			,						__UpperCamelCase			,						__UpperCamelCase			,						__UpperCamelCase       )
			else:
						if sorted_collection[point] > item:
									return interpolation_search_by_recursion(
									    __UpperCamelCase			,						__UpperCamelCase			,						__UpperCamelCase			,						point - 1       )
						else:
									return interpolation_search_by_recursion(
									    __UpperCamelCase			,						__UpperCamelCase			,						point + 1			,						__UpperCamelCase       )


def A    (							__UpperCamelCase       )    ->   List[str]:
			if collection != sorted(__UpperCamelCase       ):
						raise ValueError('Collection must be ascending sorted'       )
			return True


if __name__ == "__main__":
							import sys
							SCREAMING_SNAKE_CASE__   			=   0
							if debug == 1:
														SCREAMING_SNAKE_CASE__   			=   [1_0, 3_0, 4_0, 4_5, 5_0, 6_6, 7_7, 9_3]
														try:
																					__assert_sorted(collection)
														except ValueError:
																					sys.exit('''Sequence must be ascending sorted to apply interpolation search''')
														SCREAMING_SNAKE_CASE__   			=   6_7

							SCREAMING_SNAKE_CASE__   			=   interpolation_search(collection, target)
							if result is not None:
														print(f'{target} found at positions: {result}')
							else:
														print('''Not found''')




