





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()
_lowercase		:  List[Any]			       =logging.get_logger(__name__)


def 		_SCREAMING_SNAKE_CASE     ( lowerCAmelCase__						):
		print('Loading config file...'						)

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

		lowerCamelCase_	:     Union[str, Any] 						=      argparse.Namespace()
		with open(lowerCAmelCase__							,'r'						) as yaml_file:
				try:
						lowerCamelCase_	:     int 						=      yaml.load(lowerCAmelCase__							,Loader=yaml.FullLoader						)

						lowerCamelCase_	:     Optional[int] 						=      flatten_yaml_as_dict(lowerCAmelCase__						)
						for k, v in flat_cfg.items():
								setattr(lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__						)
				except yaml.YAMLError as exc:
						logger.error('Error while loading config file: {}. Error message: {}'.format(lowerCAmelCase__							,str(lowerCAmelCase__						)						)						)
		return config


def 		_SCREAMING_SNAKE_CASE     ( lowerCAmelCase__							,lowerCAmelCase__						):
		lowerCamelCase_	:     List[Any] 						=      MobileViTVaConfig()

		lowerCamelCase_	:     str 						=      False

		# dataset
		if task_name.startswith('imagenet1k_'						):
				lowerCamelCase_	:     Optional[int] 						=      10_00
				if int(task_name.strip().split('_'						)[-1]						) == 3_84:
						lowerCamelCase_	:     Dict 						=      3_84
				else:
						lowerCamelCase_	:     Any 						=      2_56
				lowerCamelCase_	:     Optional[int] 						=      'imagenet-1k-id2label.json'
		elif task_name.startswith('imagenet21k_to_1k_'						):
				lowerCamelCase_	:     List[str] 						=      2_10_00
				if int(task_name.strip().split('_'						)[-1]						) == 3_84:
						lowerCamelCase_	:     Optional[Any] 						=      3_84
				else:
						lowerCamelCase_	:     int 						=      2_56
				lowerCamelCase_	:     int 						=      'imagenet-22k-id2label.json'
		elif task_name.startswith('ade20k_'						):
				lowerCamelCase_	:     Union[str, Any] 						=      1_51
				lowerCamelCase_	:     Any 						=      5_12
				lowerCamelCase_	:     Dict 						=      'ade20k-id2label.json'
				lowerCamelCase_	:     List[Any] 						=      True
		elif task_name.startswith('voc_'						):
				lowerCamelCase_	:     Optional[int] 						=      21
				lowerCamelCase_	:     List[Any] 						=      5_12
				lowerCamelCase_	:     Any 						=      'pascal-voc-id2label.json'
				lowerCamelCase_	:     Tuple 						=      True

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

		if is_segmentation_model:
				lowerCamelCase_	:     Any 						=      getattr(lowerCAmelCase__							,'model.segmentation.output_stride'							,16						)
				if "_deeplabv3" in task_name:
						lowerCamelCase_	:     List[Any] 						=      getattr(lowerCAmelCase__							,'model.segmentation.deeplabv3.aspp_rates'							,[12, 24, 36]						)
						lowerCamelCase_	:     str 						=      getattr(lowerCAmelCase__							,'model.segmentation.deeplabv3.aspp_out_channels'							,5_12						)
						lowerCamelCase_	:     str 						=      getattr(lowerCAmelCase__							,'model.segmentation.deeplabv3.aspp_dropout'							,0.1						)

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

		return config


def 		_SCREAMING_SNAKE_CASE     ( lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__						):
		lowerCamelCase_	:     Any 						=      dct.pop(lowerCAmelCase__						)
		lowerCamelCase_	:     Any 						=      val


def 		_SCREAMING_SNAKE_CASE     ( lowerCAmelCase__							,lowerCAmelCase__=False						):
		if base_model:
				lowerCamelCase_	:     int 						=      ''
		else:
				lowerCamelCase_	:     int 						=      'mobilevitv2.'

		lowerCamelCase_	:     Union[str, Any] 						=      []
		for k in state_dict.keys():
				if k[:8] == "encoder.":
						lowerCamelCase_	:     Tuple 						=      k[8:]
				else:
						lowerCamelCase_	:     List[Any] 						=      k

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

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

				for i in [3, 4, 5]:
						if F"layer_{i}.0." in k:
								lowerCamelCase_	:     List[Any] 						=      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_	:     Any 						=      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_	:     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:
								lowerCamelCase_	:     Any 						=      [0, 1]
						elif i == 4:
								lowerCamelCase_	:     Optional[Any] 						=      [0, 1, 2, 3]
						elif i == 5:
								lowerCamelCase_	:     List[str] 						=      [0, 1, 2]

						for j in j_in:
								if F"layer_{i}.1.global_rep.{j}." in k:
										lowerCamelCase_	:     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:
								lowerCamelCase_	:     Dict 						=      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_	:     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:
						lowerCamelCase_	:     Tuple 						=      k_new.replace('pre_norm_attn.0.'							,'layernorm_before.'						)
				if "pre_norm_attn.1." in k:
						lowerCamelCase_	:     Any 						=      k_new.replace('pre_norm_attn.1.'							,'attention.'						)
				if "pre_norm_ffn.0." in k:
						lowerCamelCase_	:     Any 						=      k_new.replace('pre_norm_ffn.0.'							,'layernorm_after.'						)
				if "pre_norm_ffn.1." in k:
						lowerCamelCase_	:     Any 						=      k_new.replace('pre_norm_ffn.1.'							,'ffn.conv1.'						)
				if "pre_norm_ffn.3." in k:
						lowerCamelCase_	:     Dict 						=      k_new.replace('pre_norm_ffn.3.'							,'ffn.conv2.'						)

				if "classifier.1." in k:
						lowerCamelCase_	:     Any 						=      k_new.replace('classifier.1.'							,'classifier.'						)

				if "seg_head." in k:
						lowerCamelCase_	:     List[Any] 						=      k_new.replace('seg_head.'							,'segmentation_head.'						)
				if ".aspp_layer." in k:
						lowerCamelCase_	:     int 						=      k_new.replace('.aspp_layer.'							,'.'						)
				if ".aspp_pool." in k:
						lowerCamelCase_	:     Optional[int] 						=      k_new.replace('.aspp_pool.'							,'.'						)

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


def 		_SCREAMING_SNAKE_CASE     ( lowerCAmelCase__						):
		lowerCamelCase_	:     Optional[int] 						=      []
		for k in state_dict.keys():
				if k.startswith('seg_head.aux_head.'						):
						keys_to_ignore.append(lowerCAmelCase__						)
		for k in keys_to_ignore:
				state_dict.pop(lowerCAmelCase__							,lowerCAmelCase__						)


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


@torch.no_grad()
def 		_SCREAMING_SNAKE_CASE     ( lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__						):
		lowerCamelCase_	:     str 						=      get_mobilevitva_config(lowerCAmelCase__							,lowerCAmelCase__						)

		# load original state_dict
		lowerCamelCase_	:     Union[str, Any] 						=      torch.load(lowerCAmelCase__							,map_location='cpu'						)

		# load huggingface model
		if task_name.startswith('ade20k_'						) or task_name.startswith('voc_'						):
				lowerCamelCase_	:     str 						=      MobileViTVaForSemanticSegmentation(lowerCAmelCase__						).eval()
				lowerCamelCase_	:     Optional[int] 						=      False
		else:
				lowerCamelCase_	:     List[str] 						=      MobileViTVaForImageClassification(lowerCAmelCase__						).eval()
				lowerCamelCase_	:     Optional[int] 						=      False

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

		# load modified state_dict
		model.load_state_dict(lowerCAmelCase__						)

		# Check outputs on an image, prepared by MobileViTImageProcessor
		lowerCamelCase_	:     Dict 						=      MobileViTImageProcessor(crop_size=config.image_size							,size=config.image_size + 32						)
		lowerCamelCase_	:     List[str] 						=      image_processor(images=prepare_img()							,return_tensors='pt'						)
		lowerCamelCase_	:     List[Any] 						=      model(**lowerCAmelCase__						)

		# verify classification model
		if task_name.startswith('imagenet'						):
				lowerCamelCase_	:     Dict 						=      outputs.logits
				lowerCamelCase_	:     int 						=      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_	:     int 						=      torch.tensor([-1.6_336e00, -7.3_204e-02, -5.1_883e-01]						)
						assert torch.allclose(logits[0, :3]							,lowerCAmelCase__							,atol=1e-4						)

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


if __name__ == "__main__":
		_lowercase		:  Optional[Any]			       =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 . """
		        """
                Classification (ImageNet-1k)
                    - MobileViTV2 (256x256) : imagenet1k_256
                    - MobileViTV2 (Trained on 256x256 and Finetuned on 384x384) : imagenet1k_384
                    - MobileViTV2 (Trained on ImageNet-21k and Finetuned on ImageNet-1k 256x256) :
                      imagenet21k_to_1k_256
                    - MobileViTV2 (Trained on ImageNet-21k, Finetuned on ImageNet-1k 256x256, and Finetuned on
                      ImageNet-1k 384x384) : imagenet21k_to_1k_384
                Segmentation
                    - ADE20K Dataset : ade20k_deeplabv3
                    - Pascal VOC 2012 Dataset: voc_deeplabv3
            """
		    ),
		    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."""
		)

		_lowercase		:  Any			       =parser.parse_args()
		convert_mobilevitva_checkpoint(
		    args.task, args.orig_checkpoint_path, args.orig_config_path, args.pytorch_dump_folder_path
		)







_lowercase		:  Any			       =[0, 2, 4, 6, 8]
_lowercase		:  List[Any]			       =[1, 3, 5, 7, 9]


def 		_SCREAMING_SNAKE_CASE     ( lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__						):
		if remaining_length == 0:
				if digits[0] == 0 or digits[-1] == 0:
						return 0

				for i in range(length // 2 - 1							,-1							,-1						):
						remainder += digits[i] + digits[length - i - 1]

						if remainder % 2 == 0:
								return 0

						remainder //= 10

				return 1

		if remaining_length == 1:
				if remainder % 2 == 0:
						return 0

				lowerCamelCase_	:     Dict 						=      0
				for digit in range(10						):
						lowerCamelCase_	:     List[Any] 						=      digit
						result += reversible_numbers(
						    0							,(remainder + 2 * digit) // 10							,lowerCAmelCase__							,lowerCAmelCase__						)
				return result

		lowerCamelCase_	:     List[str] 						=      0
		for digita in range(10						):
				lowerCamelCase_	:     Tuple 						=      digita

				if (remainder + digita) % 2 == 0:
						lowerCamelCase_	:     Dict 						=      ODD_DIGITS
				else:
						lowerCamelCase_	:     str 						=      EVEN_DIGITS

				for digita in other_parity_digits:
						lowerCamelCase_	:     Optional[int] 						=      digita
						result += reversible_numbers(
						    remaining_length - 2							,(remainder + digita + digita) // 10							,lowerCAmelCase__							,lowerCAmelCase__							,)
		return result


def 		_SCREAMING_SNAKE_CASE     ( lowerCAmelCase__ = 9						):
		lowerCamelCase_	:     int 						=      0
		for length in range(1							,max_power + 1						):
				result += reversible_numbers(lowerCAmelCase__							,0							,[0] * length							,lowerCAmelCase__						)
		return result


if __name__ == "__main__":
		print(F'''{solution() = }''')
