


'''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  __lowerCAmelCase (__lowerCAmelCase   ):
		print("Loading config file..."   )

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

		_UpperCAmelCase		:       Optional[int]								=      argparse.Namespace()
		with open(a_      , "r"   ) as yaml_file:
				try:
						_UpperCAmelCase		:       Tuple								=      yaml.load(a_      , Loader=yaml.FullLoader   )

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






def  __lowerCAmelCase (__lowerCAmelCase      , __lowerCAmelCase   ):
		_UpperCAmelCase		:       Union[str, Any]								=      MobileViTVaConfig()

		_UpperCAmelCase		:       Tuple								=      False

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

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

		if is_segmentation_model:
				_UpperCAmelCase		:       str								=      getattr(a_      , "model.segmentation.output_stride"      , 16   )
				if "_deeplabv3" in task_name:
						_UpperCAmelCase		:       int								=      getattr(a_      , "model.segmentation.deeplabv3.aspp_rates"      , [12, 24, 36]   )
						_UpperCAmelCase		:       Optional[int]								=      getattr(a_      , "model.segmentation.deeplabv3.aspp_out_channels"      , 512   )
						_UpperCAmelCase		:       List[Any]								=      getattr(a_      , "model.segmentation.deeplabv3.aspp_dropout"      , 0.1   )

    # id2label
		_UpperCAmelCase		:       List[Any]								=      '''huggingface/label-files'''
		_UpperCAmelCase		:       Any								=      json.load(open(hf_hub_download(a_      , a_      , repo_type="dataset"   )      , "r"   )   )
		_UpperCAmelCase		:       str								=      {int(a_   ): v for k, v in idalabel.items()}
		_UpperCAmelCase		:       int								=      idalabel
		_UpperCAmelCase		:       List[Any]								=      {v: k for k, v in idalabel.items()}

		return config






def  __lowerCAmelCase (__lowerCAmelCase      , __lowerCAmelCase      , __lowerCAmelCase   ):
		_UpperCAmelCase		:       Tuple								=      dct.pop(a_   )
		_UpperCAmelCase		:       int								=      val






def  __lowerCAmelCase (__lowerCAmelCase      , __lowerCAmelCase=False   ):
		if base_model:
				_UpperCAmelCase		:       List[str]								=      ''''''
		else:
				_UpperCAmelCase		:       Union[str, Any]								=      '''mobilevitv2.'''

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

				if ".block." in k:
						_UpperCAmelCase		:       int								=      k_new.replace(".block."      , "."   )
				if ".conv." in k:
						_UpperCAmelCase		:       int								=      k_new.replace(".conv."      , ".convolution."   )
				if ".norm." in k:
						_UpperCAmelCase		:       Optional[int]								=      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		:       Union[str, Any]								=      k_new.replace(F"""layer_{i}."""      , F"""{model_prefix}encoder.layer.{i-1}.layer."""   )
				if ".exp_1x1." in k:
						_UpperCAmelCase		:       List[str]								=      k_new.replace(".exp_1x1."      , ".expand_1x1."   )
				if ".red_1x1." in k:
						_UpperCAmelCase		:       str								=      k_new.replace(".red_1x1."      , ".reduce_1x1."   )

				for i in [3, 4, 5]:
						if F"""layer_{i}.0.""" in k:
								_UpperCAmelCase		:       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:
								_UpperCAmelCase		:       Tuple								=      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		:       Optional[int]								=      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		:       int								=      [0, 1]
						elif i == 4:
								_UpperCAmelCase		:       int								=      [0, 1, 2, 3]
						elif i == 5:
								_UpperCAmelCase		:       Union[str, Any]								=      [0, 1, 2]

						for j in j_in:
								if F"""layer_{i}.1.global_rep.{j}.""" in k:
										_UpperCAmelCase		:       Dict								=      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		:       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:
								_UpperCAmelCase		:       int								=      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		:       Dict								=      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		:       List[Any]								=      k_new.replace("pre_norm_ffn.0."      , "layernorm_after."   )
				if "pre_norm_ffn.1." in k:
						_UpperCAmelCase		:       Optional[Any]								=      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		:       Any								=      k_new.replace("classifier.1."      , "classifier."   )

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

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






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






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






@torch.no_grad()
def  __lowerCAmelCase (__lowerCAmelCase      , __lowerCAmelCase      , __lowerCAmelCase      , __lowerCAmelCase   ):
		_UpperCAmelCase		:       List[str]								=      get_mobilevitva_config(a_      , a_   )

		# load original state_dict
		_UpperCAmelCase		:       Optional[int]								=      torch.load(a_      , map_location="cpu"   )

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

		# remove and rename some keys of load the original model
		_UpperCAmelCase		:       List[str]								=      checkpoint
		remove_unused_keys(a_   )
		_UpperCAmelCase		:       Optional[int]								=      create_rename_keys(a_      , base_model=a_   )
		for rename_key_src, rename_key_dest in rename_keys:
				rename_key(a_      , a_      , a_   )

		# load modified state_dict
		model.load_state_dict(a_   )

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

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

		Path(a_   ).mkdir(exist_ok=a_   )
		print(F"""Saving model {task_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 __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  __lowerCAmelCase ():
		_UpperCAmelCase		:       str								=      0
		for i in range(1      , 1_001   ):
				total += i**i
		return str(__lowerCAmelCase   )[-10:]


if __name__ == "__main__":
						print(solution())



