





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


def        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase )			->     str:
    print('Loading config file...' )

    def flatten_yaml_as_dict(_UpperCAmelCase						,	_UpperCAmelCase=""						,	_UpperCAmelCase="." ):
        lowerCamelCase__    :      List[Any]					 =		[]
        for k, v in d.items():
            lowerCamelCase__    :      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 )

    lowerCamelCase__    :      Optional[Any]					 =		argparse.Namespace()
    with open(_UpperCAmelCase						,	'r' ) as yaml_file:
        try:
            lowerCamelCase__    :      Dict					 =		yaml.load(_UpperCAmelCase						,	Loader=yaml.FullLoader )

            lowerCamelCase__    :      Optional[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        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase						,	_UpperCAmelCase )			->     str:
    lowerCamelCase__    :      List[Any]					 =		MobileViTVaConfig()

    lowerCamelCase__    :      Any					 =		False

    # dataset
    if task_name.startswith('imagenet1k_' ):
        lowerCamelCase__    :      Dict					 =		1000
        if int(task_name.strip().split('_' )[-1] ) == 384:
            lowerCamelCase__    :      Optional[int]					 =		384
        else:
            lowerCamelCase__    :      Optional[int]					 =		256
        lowerCamelCase__    :      Tuple					 =		'imagenet-1k-id2label.json'
    elif task_name.startswith('imagenet21k_to_1k_' ):
        lowerCamelCase__    :      Any					 =		2_1000
        if int(task_name.strip().split('_' )[-1] ) == 384:
            lowerCamelCase__    :      List[Any]					 =		384
        else:
            lowerCamelCase__    :      Union[str, Any]					 =		256
        lowerCamelCase__    :      int					 =		'imagenet-22k-id2label.json'
    elif task_name.startswith('ade20k_' ):
        lowerCamelCase__    :      List[str]					 =		151
        lowerCamelCase__    :      List[str]					 =		512
        lowerCamelCase__    :      Tuple					 =		'ade20k-id2label.json'
        lowerCamelCase__    :      List[Any]					 =		True
    elif task_name.startswith('voc_' ):
        lowerCamelCase__    :      str					 =		21
        lowerCamelCase__    :      Optional[Any]					 =		512
        lowerCamelCase__    :      Union[str, Any]					 =		'pascal-voc-id2label.json'
        lowerCamelCase__    :      Union[str, Any]					 =		True

    # orig_config
    lowerCamelCase__    :      Tuple					 =		load_orig_config_file(_UpperCAmelCase )
    assert getattr(_UpperCAmelCase						,	'model.classification.name'						,	-1 ) == "mobilevit_v2", "Invalid model"
    lowerCamelCase__    :      Tuple					 =		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"
    lowerCamelCase__    :      str					 =		getattr(_UpperCAmelCase						,	'model.classification.activation.name'						,	'swish' )
    # config.image_size == getattr(orig_config,  'sampler.bs.crop_size_width', 256)

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

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

    return config


def        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase )			->     int:
    lowerCamelCase__    :      Union[str, Any]					 =		dct.pop(_UpperCAmelCase )
    lowerCamelCase__    :      Optional[int]					 =		val


def        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase						,	_UpperCAmelCase=False )			->     str:
    if base_model:
        lowerCamelCase__    :      Optional[Any]					 =		''
    else:
        lowerCamelCase__    :      Tuple					 =		'mobilevitv2.'

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

        if ".block." in k:
            lowerCamelCase__    :      str					 =		k_new.replace('.block.'						,	'.' )
        if ".conv." in k:
            lowerCamelCase__    :      List[str]					 =		k_new.replace('.conv.'						,	'.convolution.' )
        if ".norm." in k:
            lowerCamelCase__    :      Optional[int]					 =		k_new.replace('.norm.'						,	'.normalization.' )

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

        for i in [3, 4, 5]:
            if F"""layer_{i}.0.""" in k:
                lowerCamelCase__    :      Dict					 =		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__    :      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:
                lowerCamelCase__    :      Dict					 =		[0, 1]
            elif i == 4:
                lowerCamelCase__    :      Optional[int]					 =		[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__    :      List[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__    :      Optional[Any]					 =		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__    :      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__    :      str					 =		k_new.replace('pre_norm_attn.0.'						,	'layernorm_before.' )
        if "pre_norm_attn.1." in k:
            lowerCamelCase__    :      Optional[int]					 =		k_new.replace('pre_norm_attn.1.'						,	'attention.' )
        if "pre_norm_ffn.0." in k:
            lowerCamelCase__    :      Union[str, Any]					 =		k_new.replace('pre_norm_ffn.0.'						,	'layernorm_after.' )
        if "pre_norm_ffn.1." in k:
            lowerCamelCase__    :      Dict					 =		k_new.replace('pre_norm_ffn.1.'						,	'ffn.conv1.' )
        if "pre_norm_ffn.3." in k:
            lowerCamelCase__    :      int					 =		k_new.replace('pre_norm_ffn.3.'						,	'ffn.conv2.' )

        if "classifier.1." in k:
            lowerCamelCase__    :      int					 =		k_new.replace('classifier.1.'						,	'classifier.' )

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

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


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


@torch.no_grad()
def        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase )			->     Tuple:
    lowerCamelCase__    :      Optional[Any]					 =		get_mobilevitva_config(_UpperCAmelCase						,	_UpperCAmelCase )

    # load original state_dict
    lowerCamelCase__    :      int					 =		torch.load(_UpperCAmelCase						,	map_location='cpu' )

    # load huggingface model
    if task_name.startswith('ade20k_' ) or task_name.startswith('voc_' ):
        lowerCamelCase__    :      Tuple					 =		MobileViTVaForSemanticSegmentation(_UpperCAmelCase ).eval()
        lowerCamelCase__    :      Dict					 =		False
    else:
        lowerCamelCase__    :      Union[str, Any]					 =		MobileViTVaForImageClassification(_UpperCAmelCase ).eval()
        lowerCamelCase__    :      List[Any]					 =		False

    # remove and rename some keys of load the original model
    lowerCamelCase__    :      List[Any]					 =		checkpoint
    remove_unused_keys(_UpperCAmelCase )
    lowerCamelCase__    :      Any					 =		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
    lowerCamelCase__    :      Optional[Any]					 =		MobileViTImageProcessor(crop_size=config.image_size						,	size=config.image_size + 32 )
    lowerCamelCase__    :      Optional[Any]					 =		image_processor(images=prepare_img()						,	return_tensors='pt' )
    lowerCamelCase__    :      str					 =		model(**_UpperCAmelCase )

    # verify classification model
    if task_name.startswith('imagenet' ):
        lowerCamelCase__    :      Any					 =		outputs.logits
        lowerCamelCase__    :      str					 =		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__    :      Union[str, Any]					 =		torch.tensor([-1.6_336e00, -7.3_204e-02, -5.1_883e-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__":
    _UpperCAmelCase				:       Optional[int]	   =				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."""
    )

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












def        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase						,	_UpperCAmelCase )			->     bool:
    lowerCamelCase__    :      List[str]					 =		len(_UpperCAmelCase )
    lowerCamelCase__    :      str					 =		[[False] * (required_sum + 1) for _ in range(arr_len + 1 )]

    # for each arr value, a sum of zero(0) can be formed by not taking any element
    # hence True/1
    for i in range(arr_len + 1 ):
        lowerCamelCase__    :      Tuple					 =		True

    # sum is not zero and set is empty then false
    for i in range(1						,	required_sum + 1 ):
        lowerCamelCase__    :      Dict					 =		False

    for i in range(1						,	arr_len + 1 ):
        for j in range(1						,	required_sum + 1 ):
            if arr[i - 1] > j:
                lowerCamelCase__    :      str					 =		subset[i - 1][j]
            if arr[i - 1] <= j:
                lowerCamelCase__    :      Dict					 =		subset[i - 1][j] or subset[i - 1][j - arr[i - 1]]

    return subset[arr_len][required_sum]


if __name__ == "__main__":
    import doctest

    doctest.testmod()





