


"""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()
A     :   Union[str, Any]			    =			logging.get_logger(__name__)


def _lowerCamelCase	(		_UpperCamelCase      ):

			'''simple docstring'''




			print("Loading config file..."      )

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

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

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


def _lowerCamelCase	(		_UpperCamelCase    , _UpperCamelCase      ):

			'''simple docstring'''




			__lowerCAmelCase				    =			MobileViTVaConfig()

			__lowerCAmelCase				    =			False

			# dataset
			if task_name.startswith("imagenet1k_"      ):
						__lowerCAmelCase				    =			1000
						if int(task_name.strip().split("_"      )[-1]      ) == 384:
									__lowerCAmelCase				    =			384
						else:
									__lowerCAmelCase				    =			256
						__lowerCAmelCase				    =			"imagenet-1k-id2label.json"
			elif task_name.startswith("imagenet21k_to_1k_"      ):
						__lowerCAmelCase				    =			2_1000
						if int(task_name.strip().split("_"      )[-1]      ) == 384:
									__lowerCAmelCase				    =			384
						else:
									__lowerCAmelCase				    =			256
						__lowerCAmelCase				    =			"imagenet-22k-id2label.json"
			elif task_name.startswith("ade20k_"      ):
						__lowerCAmelCase				    =			151
						__lowerCAmelCase				    =			512
						__lowerCAmelCase				    =			"ade20k-id2label.json"
						__lowerCAmelCase				    =			True
			elif task_name.startswith("voc_"      ):
						__lowerCAmelCase				    =			21
						__lowerCAmelCase				    =			512
						__lowerCAmelCase				    =			"pascal-voc-id2label.json"
						__lowerCAmelCase				    =			True

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

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

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

			return config


def _lowerCamelCase	(		_UpperCamelCase    , _UpperCamelCase    , _UpperCamelCase      ):

			'''simple docstring'''




			__lowerCAmelCase				    =			dct.pop(snake_case_      )
			__lowerCAmelCase				    =			val


def _lowerCamelCase	(		_UpperCamelCase    , _UpperCamelCase=False      ):

			'''simple docstring'''




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

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

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

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

						for i in [3, 4, 5]:
									if f"layer_{i}.0." in k:
												__lowerCAmelCase				    =			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:
												__lowerCAmelCase				    =			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:
												__lowerCAmelCase				    =			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:
												__lowerCAmelCase				    =			[0, 1]
									elif i == 4:
												__lowerCAmelCase				    =			[0, 1, 2, 3]
									elif i == 5:
												__lowerCAmelCase				    =			[0, 1, 2]

									for j in j_in:
												if f"layer_{i}.1.global_rep.{j}." in k:
															__lowerCAmelCase				    =			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:
												__lowerCAmelCase				    =			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:
												__lowerCAmelCase				    =			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:
									__lowerCAmelCase				    =			k_new.replace("pre_norm_attn.0."    , "layernorm_before."      )
						if "pre_norm_attn.1." in k:
									__lowerCAmelCase				    =			k_new.replace("pre_norm_attn.1."    , "attention."      )
						if "pre_norm_ffn.0." in k:
									__lowerCAmelCase				    =			k_new.replace("pre_norm_ffn.0."    , "layernorm_after."      )
						if "pre_norm_ffn.1." in k:
									__lowerCAmelCase				    =			k_new.replace("pre_norm_ffn.1."    , "ffn.conv1."      )
						if "pre_norm_ffn.3." in k:
									__lowerCAmelCase				    =			k_new.replace("pre_norm_ffn.3."    , "ffn.conv2."      )

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

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

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


def _lowerCamelCase	(		_UpperCamelCase      ):

			'''simple docstring'''




			__lowerCAmelCase				    =			[]
			for k in state_dict.keys():
						if k.startswith("seg_head.aux_head."      ):
									keys_to_ignore.append(snake_case_      )
			for k in keys_to_ignore:
						state_dict.pop(snake_case_    , snake_case_      )


def _lowerCamelCase	(		):

			'''simple docstring'''




			__lowerCAmelCase				    =			"http://images.cocodataset.org/val2017/000000039769.jpg"
			# url = "https://cdn.britannica.com/86/141086-050-9D7C75EE/Gulfstream-G450-business-jet-passengers.jpg"
			__lowerCAmelCase				    =			Image.open(requests.get(snake_case_    , stream=snake_case_      ).raw      )
			return im


@torch.no_grad()
def _lowerCamelCase	(		_UpperCamelCase    , _UpperCamelCase    , _UpperCamelCase    , _UpperCamelCase      ):

			'''simple docstring'''




			__lowerCAmelCase				    =			get_mobilevitva_config(snake_case_    , snake_case_      )

			# load original state_dict
			__lowerCAmelCase				    =			torch.load(snake_case_    , map_location="cpu"      )

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

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

			# load modified state_dict
			model.load_state_dict(snake_case_      )

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

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

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


if __name__ == "__main__":
	A     :   Dict			    =			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."
	)

	A     :   Optional[Any]			    =			parser.parse_args()
	convert_mobilevitva_checkpoint(
	    args.task, args.orig_checkpoint_path, args.orig_config_path, args.pytorch_dump_folder_path
	)











from __future__ import annotations






def lowerCamelCase__    (							snake_case_	:  list[int]       ,      snake_case_	:  int       )      ->				list[list[int]]:
   __snake_case							       =   []
   __snake_case							       =   []
   __snake_case							       =   0
   __snake_case							       =   sum(snake_case_       )
   create_state_space_tree(snake_case_       ,      snake_case_       ,      snake_case_       ,      snake_case_       ,      snake_case_       ,      snake_case_       )
   return result






def lowerCamelCase__    (							snake_case_	:  list[int]       ,      snake_case_	:  int       ,      snake_case_	:  int       ,      snake_case_	:  list[int]       ,      snake_case_	:  list[list[int]]       ,      snake_case_	:  int       ,      )      ->				None:
   if sum(snake_case_       ) > max_sum or (remaining_nums_sum + sum(snake_case_       )) < max_sum:
      return
   if sum(snake_case_       ) == max_sum:
      result.append(snake_case_       )
      return
   for index in range(snake_case_       ,      len(snake_case_       )       ):
      create_state_space_tree(
          snake_case_       ,      snake_case_       ,      index + 1       ,      [*path, nums[index]]       ,      snake_case_       ,      remaining_nums_sum - nums[index]       ,      )
snake_case_				=			[3, 34, 4, 12, 5, 2]
snake_case_				=			9
snake_case_				=			generate_sum_of_subsets_soln(nums, max_sum)







print(*result)

