





import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    MobileViTConfig,
    MobileViTForImageClassification,
    MobileViTForSemanticSegmentation,
    MobileViTImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
A__:  int        =     logging.get_logger(__name__)



def 							lowerCAmelCase_      (				A_):
    UpperCamelCase__:     Any     							=			MobileViTConfig()

    # size of the architecture
    if "mobilevit_s" in mobilevit_name:
        UpperCamelCase__:     Tuple     							=			[1_44, 1_92, 2_40]
        UpperCamelCase__:     Optional[Any]     							=			[16, 32, 64, 96, 1_28, 1_60, 6_40]
    elif "mobilevit_xs" in mobilevit_name:
        UpperCamelCase__:     Optional[Any]     							=			[96, 1_20, 1_44]
        UpperCamelCase__:     Tuple     							=			[16, 32, 48, 64, 80, 96, 3_84]
    elif "mobilevit_xxs" in mobilevit_name:
        UpperCamelCase__:     List[Any]     							=			[64, 80, 96]
        UpperCamelCase__:     List[Any]     							=			[16, 16, 24, 48, 64, 80, 3_20]
        UpperCamelCase__:     List[str]     							=			0.05
        UpperCamelCase__:     int     							=			2.0

    if mobilevit_name.startswith("deeplabv3_"):
        UpperCamelCase__:     int     							=			5_12
        UpperCamelCase__:     List[str]     							=			16
        UpperCamelCase__:     Tuple     							=			21
        UpperCamelCase__:     str     							=			"pascal-voc-id2label.json"
    else:
        UpperCamelCase__:     List[Any]     							=			10_00
        UpperCamelCase__:     str     							=			"imagenet-1k-id2label.json"

    UpperCamelCase__:     List[str]     							=			"huggingface/label-files"
    UpperCamelCase__:     Union[str, Any]     							=			json.load(open(hf_hub_download(a__  ,a__  ,repo_type="dataset")  ,"r"))
    UpperCamelCase__:     Optional[Any]     							=			{int(a__): v for k, v in idalabel.items()}
    UpperCamelCase__:     Tuple     							=			idalabel
    UpperCamelCase__:     Optional[Any]     							=			{v: k for k, v in idalabel.items()}

    return config



def 							lowerCAmelCase_      (				A_  ,A_=False):
    for i in range(1  ,6):
        if F"layer_{i}." in name:
            UpperCamelCase__:     Optional[int]     							=			name.replace(F"layer_{i}."  ,F"encoder.layer.{i - 1}.")

    if "conv_1." in name:
        UpperCamelCase__:     Dict     							=			name.replace("conv_1."  ,"conv_stem.")
    if ".block." in name:
        UpperCamelCase__:     Optional[int]     							=			name.replace(".block."  ,".")

    if "exp_1x1" in name:
        UpperCamelCase__:     List[str]     							=			name.replace("exp_1x1"  ,"expand_1x1")
    if "red_1x1" in name:
        UpperCamelCase__:     Optional[int]     							=			name.replace("red_1x1"  ,"reduce_1x1")
    if ".local_rep.conv_3x3." in name:
        UpperCamelCase__:     int     							=			name.replace(".local_rep.conv_3x3."  ,".conv_kxk.")
    if ".local_rep.conv_1x1." in name:
        UpperCamelCase__:     str     							=			name.replace(".local_rep.conv_1x1."  ,".conv_1x1.")
    if ".norm." in name:
        UpperCamelCase__:     List[str]     							=			name.replace(".norm."  ,".normalization.")
    if ".conv." in name:
        UpperCamelCase__:     Dict     							=			name.replace(".conv."  ,".convolution.")
    if ".conv_proj." in name:
        UpperCamelCase__:     List[str]     							=			name.replace(".conv_proj."  ,".conv_projection.")

    for i in range(0  ,2):
        for j in range(0  ,4):
            if F".{i}.{j}." in name:
                UpperCamelCase__:     Optional[int]     							=			name.replace(F".{i}.{j}."  ,F".{i}.layer.{j}.")

    for i in range(2  ,6):
        for j in range(0  ,4):
            if F".{i}.{j}." in name:
                UpperCamelCase__:     Tuple     							=			name.replace(F".{i}.{j}."  ,F".{i}.")
                if "expand_1x1" in name:
                    UpperCamelCase__:     List[Any]     							=			name.replace("expand_1x1"  ,"downsampling_layer.expand_1x1")
                if "conv_3x3" in name:
                    UpperCamelCase__:     Union[str, Any]     							=			name.replace("conv_3x3"  ,"downsampling_layer.conv_3x3")
                if "reduce_1x1" in name:
                    UpperCamelCase__:     str     							=			name.replace("reduce_1x1"  ,"downsampling_layer.reduce_1x1")

    for i in range(2  ,5):
        if F".global_rep.{i}.weight" in name:
            UpperCamelCase__:     Any     							=			name.replace(F".global_rep.{i}.weight"  ,".layernorm.weight")
        if F".global_rep.{i}.bias" in name:
            UpperCamelCase__:     str     							=			name.replace(F".global_rep.{i}.bias"  ,".layernorm.bias")

    if ".global_rep." in name:
        UpperCamelCase__:     int     							=			name.replace(".global_rep."  ,".transformer.")
    if ".pre_norm_mha.0." in name:
        UpperCamelCase__:     Optional[int]     							=			name.replace(".pre_norm_mha.0."  ,".layernorm_before.")
    if ".pre_norm_mha.1.out_proj." in name:
        UpperCamelCase__:     Optional[Any]     							=			name.replace(".pre_norm_mha.1.out_proj."  ,".attention.output.dense.")
    if ".pre_norm_ffn.0." in name:
        UpperCamelCase__:     Optional[int]     							=			name.replace(".pre_norm_ffn.0."  ,".layernorm_after.")
    if ".pre_norm_ffn.1." in name:
        UpperCamelCase__:     List[str]     							=			name.replace(".pre_norm_ffn.1."  ,".intermediate.dense.")
    if ".pre_norm_ffn.4." in name:
        UpperCamelCase__:     int     							=			name.replace(".pre_norm_ffn.4."  ,".output.dense.")
    if ".transformer." in name:
        UpperCamelCase__:     List[Any]     							=			name.replace(".transformer."  ,".transformer.layer.")

    if ".aspp_layer." in name:
        UpperCamelCase__:     Union[str, Any]     							=			name.replace(".aspp_layer."  ,".")
    if ".aspp_pool." in name:
        UpperCamelCase__:     int     							=			name.replace(".aspp_pool."  ,".")
    if "seg_head." in name:
        UpperCamelCase__:     int     							=			name.replace("seg_head."  ,"segmentation_head.")
    if "segmentation_head.classifier.classifier." in name:
        UpperCamelCase__:     str     							=			name.replace("segmentation_head.classifier.classifier."  ,"segmentation_head.classifier.")

    if "classifier.fc." in name:
        UpperCamelCase__:     Optional[Any]     							=			name.replace("classifier.fc."  ,"classifier.")
    elif (not base_model) and ("segmentation_head." not in name):
        UpperCamelCase__:     List[Any]     							=			"mobilevit." + name

    return name



def 							lowerCAmelCase_      (				A_  ,A_  ,A_=False):
    if base_model:
        UpperCamelCase__:     int     							=			""
    else:
        UpperCamelCase__:     Union[str, Any]     							=			"mobilevit."

    for key in orig_state_dict.copy().keys():
        UpperCamelCase__:     int     							=			orig_state_dict.pop(a__)

        if key[:8] == "encoder.":
            UpperCamelCase__:     Tuple     							=			key[8:]

        if "qkv" in key:
            UpperCamelCase__:     Dict     							=			key.split(".")
            UpperCamelCase__:     Tuple     							=			int(key_split[0][6:]) - 1
            UpperCamelCase__:     Any     							=			int(key_split[3])
            UpperCamelCase__:     List[Any]     							=			model.get_submodule(F"{model_prefix}encoder.layer.{layer_num}")
            UpperCamelCase__:     List[Any]     							=			layer.transformer.layer[transformer_num].attention.attention.all_head_size
            UpperCamelCase__:     List[Any]     							=			(
                F"{model_prefix}encoder.layer.{layer_num}.transformer.layer.{transformer_num}.attention.attention."
            )
            if "weight" in key:
                UpperCamelCase__:     Optional[int]     							=			val[:dim, :]
                UpperCamelCase__:     Tuple     							=			val[dim : dim * 2, :]
                UpperCamelCase__:     List[str]     							=			val[-dim:, :]
            else:
                UpperCamelCase__:     int     							=			val[:dim]
                UpperCamelCase__:     Dict     							=			val[dim : dim * 2]
                UpperCamelCase__:     Dict     							=			val[-dim:]
        else:
            UpperCamelCase__:     str     							=			val

    return orig_state_dict



def 							lowerCAmelCase_      (				):
    UpperCamelCase__:     Dict     							=			"http://images.cocodataset.org/val2017/000000039769.jpg"
    UpperCamelCase__:     str     							=			Image.open(requests.get(a__  ,stream=a__).raw)
    return im



@torch.no_grad()
def 							lowerCAmelCase_      (				A_  ,A_  ,A_  ,A_=False):
    UpperCamelCase__:     Union[str, Any]     							=			get_mobilevit_config(a__)

    # load original state_dict
    UpperCamelCase__:     int     							=			torch.load(a__  ,map_location="cpu")

    # load 🤗 model
    if mobilevit_name.startswith("deeplabv3_"):
        UpperCamelCase__:     str     							=			MobileViTForSemanticSegmentation(a__).eval()
    else:
        UpperCamelCase__:     Optional[Any]     							=			MobileViTForImageClassification(a__).eval()

    UpperCamelCase__:     Any     							=			convert_state_dict(a__  ,a__)
    model.load_state_dict(a__)

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

    if mobilevit_name.startswith("deeplabv3_"):
        assert logits.shape == (1, 21, 32, 32)

        if mobilevit_name == "deeplabv3_mobilevit_s":
            UpperCamelCase__:     Any     							=			torch.tensor(
                [
                    [[6.2065, 6.1292, 6.2070], [6.1079, 6.1254, 6.1747], [6.0042, 6.1071, 6.1034]],
                    [[-6.9253, -6.8653, -7.0398], [-7.3218, -7.3983, -7.3670], [-7.1961, -7.2482, -7.1569]],
                    [[-4.4723, -4.4348, -4.3769], [-5.3629, -5.4632, -5.4598], [-5.1587, -5.3402, -5.5059]],
                ])
        elif mobilevit_name == "deeplabv3_mobilevit_xs":
            UpperCamelCase__:     int     							=			torch.tensor(
                [
                    [[5.4449, 5.5733, 5.6314], [5.1815, 5.3930, 5.5963], [5.1656, 5.4333, 5.4853]],
                    [[-9.4423, -9.7766, -9.6714], [-9.1581, -9.5720, -9.5519], [-9.1006, -9.6458, -9.5703]],
                    [[-7.7721, -7.3716, -7.1583], [-8.4599, -8.0624, -7.7944], [-8.4172, -7.8366, -7.5025]],
                ])
        elif mobilevit_name == "deeplabv3_mobilevit_xxs":
            UpperCamelCase__:     int     							=			torch.tensor(
                [
                    [[6.9811, 6.9743, 7.3123], [7.1777, 7.1931, 7.3938], [7.5633, 7.8050, 7.8901]],
                    [[-10.5536, -10.2332, -10.2924], [-10.2336, -9.8624, -9.5964], [-10.8840, -10.8158, -10.6659]],
                    [[-3.4938, -3.0631, -2.8620], [-3.4205, -2.8135, -2.6875], [-3.4179, -2.7945, -2.8750]],
                ])
        else:
            raise ValueError(F"Unknown mobilevit_name: {mobilevit_name}")

        assert torch.allclose(logits[0, :3, :3, :3]  ,a__  ,atol=1e-4)
    else:
        assert logits.shape == (1, 10_00)

        if mobilevit_name == "mobilevit_s":
            UpperCamelCase__:     Optional[Any]     							=			torch.tensor([-0.9866, 0.2392, -1.1241])
        elif mobilevit_name == "mobilevit_xs":
            UpperCamelCase__:     Union[str, Any]     							=			torch.tensor([-2.4761, -0.9399, -1.9587])
        elif mobilevit_name == "mobilevit_xxs":
            UpperCamelCase__:     str     							=			torch.tensor([-1.9364, -1.2327, -0.4653])
        else:
            raise ValueError(F"Unknown mobilevit_name: {mobilevit_name}")

        assert torch.allclose(logits[0, :3]  ,a__  ,atol=1e-4)

    Path(a__).mkdir(exist_ok=a__)
    print(F"Saving model {mobilevit_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 push_to_hub:
        UpperCamelCase__:     Optional[int]     							=			{
            "mobilevit_s": "mobilevit-small",
            "mobilevit_xs": "mobilevit-x-small",
            "mobilevit_xxs": "mobilevit-xx-small",
            "deeplabv3_mobilevit_s": "deeplabv3-mobilevit-small",
            "deeplabv3_mobilevit_xs": "deeplabv3-mobilevit-x-small",
            "deeplabv3_mobilevit_xxs": "deeplabv3-mobilevit-xx-small",
        }

        print("Pushing to the hub...")
        UpperCamelCase__:     Dict     							=			model_mapping[mobilevit_name]
        image_processor.push_to_hub(a__  ,organization="apple")
        model.push_to_hub(a__  ,organization="apple")


if __name__ == "__main__":
   A__:  Dict        =     argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       '''--mobilevit_name''',
       default='''mobilevit_s''',
       type=str,
       help=(
           '''Name of the MobileViT model you\'d like to convert. Should be one of \'mobilevit_s\', \'mobilevit_xs\','''
           ''' \'mobilevit_xxs\', \'deeplabv3_mobilevit_s\', \'deeplabv3_mobilevit_xs\', \'deeplabv3_mobilevit_xxs\'.'''
       ),
   )
   parser.add_argument(
       '''--checkpoint_path''', required=True, type=str, help='''Path to the original state dict (.pt file).'''
   )
   parser.add_argument(
       '''--pytorch_dump_folder_path''', required=True, type=str, help='''Path to the output PyTorch model directory.'''
   )
   parser.add_argument(
       '''--push_to_hub''', action='''store_true''', help='''Whether or not to push the converted model to the 🤗 hub.'''
   )

   A__:  Optional[int]        =     parser.parse_args()
   convert_movilevit_checkpoint(
       args.mobilevit_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
   )



def     __lowerCAmelCase			(			a__						,    a__  )       ->  float:

				def get_matched_characters(a__						,    a__  ) -> str:
								__a										=    []
								__a										=    min(len(_stra  )						,    len(_stra  )  ) // 2
								for i, l in enumerate(_stra  ):
												__a										=    int(max(0						,    i - limit  )  )
												__a										=    int(min(i + limit + 1						,    len(_stra  )  )  )
												if l in _stra[left:right]:
																matched.append(a__  )
																__a										=    F"""{_stra[0:_stra.index(a__  )]} {_stra[_stra.index(a__  ) + 1:]}"""

								return "".join(a__  )

				# matching characters
				__a										=    get_matched_characters(a__						,    a__  )
				__a										=    get_matched_characters(a__						,    a__  )
				__a										=    len(a__  )

				# transposition
				__a										=    (
				    len([(ca, ca) for ca, ca in zip(a__						,    a__  ) if ca != ca]  ) // 2
				)

				if not match_count:
								__a										=    0.0
				else:
								__a										=    (
								    1
								    / 3
								    * (
								        match_count / len(a__  )
								        + match_count / len(a__  )
								        + (match_count - transpositions) / match_count
								    )
								)

				# common prefix up to 4 characters
				__a										=    0
				for ca, ca in zip(stra[:4]						,    stra[:4]  ):
								if ca == ca:
												prefix_len += 1
								else:
												break

				return jaro + 0.1 * prefix_len * (1 - jaro)


if __name__ == "__main__":
					import doctest

					doctest.testmod()
					print(jaro_winkler('hello', 'world'))