





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()
_UpperCAmelCase				:       List[str]	   =				logging.get_logger(__name__)


def        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase )			->     Optional[int]:
    lowerCamelCase__    :      Dict					 =		MobileViTConfig()

    # size of the architecture
    if "mobilevit_s" in mobilevit_name:
        lowerCamelCase__    :      List[Any]					 =		[144, 192, 240]
        lowerCamelCase__    :      Optional[Any]					 =		[16, 32, 64, 96, 128, 160, 640]
    elif "mobilevit_xs" in mobilevit_name:
        lowerCamelCase__    :      Dict					 =		[96, 120, 144]
        lowerCamelCase__    :      int					 =		[16, 32, 48, 64, 80, 96, 384]
    elif "mobilevit_xxs" in mobilevit_name:
        lowerCamelCase__    :      Tuple					 =		[64, 80, 96]
        lowerCamelCase__    :      List[str]					 =		[16, 16, 24, 48, 64, 80, 320]
        lowerCamelCase__    :      Union[str, Any]					 =		0.05
        lowerCamelCase__    :      Tuple					 =		2.0

    if mobilevit_name.startswith('deeplabv3_' ):
        lowerCamelCase__    :      Union[str, Any]					 =		512
        lowerCamelCase__    :      Optional[Any]					 =		16
        lowerCamelCase__    :      Dict					 =		21
        lowerCamelCase__    :      int					 =		'pascal-voc-id2label.json'
    else:
        lowerCamelCase__    :      List[Any]					 =		1000
        lowerCamelCase__    :      int					 =		'imagenet-1k-id2label.json'

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

    return config


def        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase						,	_UpperCAmelCase=False )			->     List[str]:
    for i in range(1						,	6 ):
        if F"""layer_{i}.""" in name:
            lowerCamelCase__    :      str					 =		name.replace(F"""layer_{i}."""						,	F"""encoder.layer.{i - 1}.""" )

    if "conv_1." in name:
        lowerCamelCase__    :      Any					 =		name.replace('conv_1.'						,	'conv_stem.' )
    if ".block." in name:
        lowerCamelCase__    :      Any					 =		name.replace('.block.'						,	'.' )

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

    for i in range(0						,	2 ):
        for j in range(0						,	4 ):
            if F""".{i}.{j}.""" in name:
                lowerCamelCase__    :      Any					 =		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:
                lowerCamelCase__    :      Optional[int]					 =		name.replace(F""".{i}.{j}."""						,	F""".{i}.""" )
                if "expand_1x1" in name:
                    lowerCamelCase__    :      int					 =		name.replace('expand_1x1'						,	'downsampling_layer.expand_1x1' )
                if "conv_3x3" in name:
                    lowerCamelCase__    :      Union[str, Any]					 =		name.replace('conv_3x3'						,	'downsampling_layer.conv_3x3' )
                if "reduce_1x1" in name:
                    lowerCamelCase__    :      Union[str, Any]					 =		name.replace('reduce_1x1'						,	'downsampling_layer.reduce_1x1' )

    for i in range(2						,	5 ):
        if F""".global_rep.{i}.weight""" in name:
            lowerCamelCase__    :      Optional[int]					 =		name.replace(F""".global_rep.{i}.weight"""						,	'.layernorm.weight' )
        if F""".global_rep.{i}.bias""" in name:
            lowerCamelCase__    :      List[Any]					 =		name.replace(F""".global_rep.{i}.bias"""						,	'.layernorm.bias' )

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

    if ".aspp_layer." in name:
        lowerCamelCase__    :      Union[str, Any]					 =		name.replace('.aspp_layer.'						,	'.' )
    if ".aspp_pool." in name:
        lowerCamelCase__    :      Tuple					 =		name.replace('.aspp_pool.'						,	'.' )
    if "seg_head." in name:
        lowerCamelCase__    :      Union[str, Any]					 =		name.replace('seg_head.'						,	'segmentation_head.' )
    if "segmentation_head.classifier.classifier." in name:
        lowerCamelCase__    :      Dict					 =		name.replace('segmentation_head.classifier.classifier.'						,	'segmentation_head.classifier.' )

    if "classifier.fc." in name:
        lowerCamelCase__    :      Optional[int]					 =		name.replace('classifier.fc.'						,	'classifier.' )
    elif (not base_model) and ("segmentation_head." not in name):
        lowerCamelCase__    :      Tuple					 =		'mobilevit.' + name

    return name


def        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase=False )			->     Tuple:
    if base_model:
        lowerCamelCase__    :      Any					 =		''
    else:
        lowerCamelCase__    :      Any					 =		'mobilevit.'

    for key in orig_state_dict.copy().keys():
        lowerCamelCase__    :      Any					 =		orig_state_dict.pop(_UpperCAmelCase )

        if key[:8] == "encoder.":
            lowerCamelCase__    :      Union[str, Any]					 =		key[8:]

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

    return orig_state_dict


def        SCREAMING_SNAKE_CASE      (       )			->     List[Any]:
    lowerCamelCase__    :      Union[str, Any]					 =		'http://images.cocodataset.org/val2017/000000039769.jpg'
    lowerCamelCase__    :      List[str]					 =		Image.open(requests.get(_UpperCAmelCase						,	stream=_UpperCAmelCase ).raw )
    return im


@torch.no_grad()
def        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase=False )			->     str:
    lowerCamelCase__    :      str					 =		get_mobilevit_config(_UpperCAmelCase )

    # load original state_dict
    lowerCamelCase__    :      List[str]					 =		torch.load(_UpperCAmelCase						,	map_location='cpu' )

    # load 🤗 model
    if mobilevit_name.startswith('deeplabv3_' ):
        lowerCamelCase__    :      Optional[Any]					 =		MobileViTForSemanticSegmentation(_UpperCAmelCase ).eval()
    else:
        lowerCamelCase__    :      Optional[int]					 =		MobileViTForImageClassification(_UpperCAmelCase ).eval()

    lowerCamelCase__    :      Optional[int]					 =		convert_state_dict(_UpperCAmelCase						,	_UpperCAmelCase )
    model.load_state_dict(_UpperCAmelCase )

    # Check outputs on an image, prepared by MobileViTImageProcessor
    lowerCamelCase__    :      Any					 =		MobileViTImageProcessor(crop_size=config.image_size						,	size=config.image_size + 32 )
    lowerCamelCase__    :      int					 =		image_processor(images=prepare_img()						,	return_tensors='pt' )
    lowerCamelCase__    :      Union[str, Any]					 =		model(**_UpperCAmelCase )
    lowerCamelCase__    :      int					 =		outputs.logits

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

        if mobilevit_name == "deeplabv3_mobilevit_s":
            lowerCamelCase__    :      Any					 =		torch.tensor(
                [
                    [[6.2_065, 6.1_292, 6.2_070], [6.1_079, 6.1_254, 6.1_747], [6.0_042, 6.1_071, 6.1_034]],
                    [[-6.9_253, -6.8_653, -7.0_398], [-7.3_218, -7.3_983, -7.3_670], [-7.1_961, -7.2_482, -7.1_569]],
                    [[-4.4_723, -4.4_348, -4.3_769], [-5.3_629, -5.4_632, -5.4_598], [-5.1_587, -5.3_402, -5.5_059]],
                ] )
        elif mobilevit_name == "deeplabv3_mobilevit_xs":
            lowerCamelCase__    :      Tuple					 =		torch.tensor(
                [
                    [[5.4_449, 5.5_733, 5.6_314], [5.1_815, 5.3_930, 5.5_963], [5.1_656, 5.4_333, 5.4_853]],
                    [[-9.4_423, -9.7_766, -9.6_714], [-9.1_581, -9.5_720, -9.5_519], [-9.1_006, -9.6_458, -9.5_703]],
                    [[-7.7_721, -7.3_716, -7.1_583], [-8.4_599, -8.0_624, -7.7_944], [-8.4_172, -7.8_366, -7.5_025]],
                ] )
        elif mobilevit_name == "deeplabv3_mobilevit_xxs":
            lowerCamelCase__    :      Optional[int]					 =		torch.tensor(
                [
                    [[6.9_811, 6.9_743, 7.3_123], [7.1_777, 7.1_931, 7.3_938], [7.5_633, 7.8_050, 7.8_901]],
                    [[-10.5_536, -10.2_332, -10.2_924], [-10.2_336, -9.8_624, -9.5_964], [-10.8_840, -10.8_158, -10.6_659]],
                    [[-3.4_938, -3.0_631, -2.8_620], [-3.4_205, -2.8_135, -2.6_875], [-3.4_179, -2.7_945, -2.8_750]],
                ] )
        else:
            raise ValueError(F"""Unknown mobilevit_name: {mobilevit_name}""" )

        assert torch.allclose(logits[0, :3, :3, :3]						,	_UpperCAmelCase						,	atol=1e-4 )
    else:
        assert logits.shape == (1, 1000)

        if mobilevit_name == "mobilevit_s":
            lowerCamelCase__    :      Optional[int]					 =		torch.tensor([-0.9_866, 0.2_392, -1.1_241] )
        elif mobilevit_name == "mobilevit_xs":
            lowerCamelCase__    :      Tuple					 =		torch.tensor([-2.4_761, -0.9_399, -1.9_587] )
        elif mobilevit_name == "mobilevit_xxs":
            lowerCamelCase__    :      str					 =		torch.tensor([-1.9_364, -1.2_327, -0.4_653] )
        else:
            raise ValueError(F"""Unknown mobilevit_name: {mobilevit_name}""" )

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

    Path(_UpperCAmelCase ).mkdir(exist_ok=_UpperCAmelCase )
    print(F"""Saving model {mobilevit_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 push_to_hub:
        lowerCamelCase__    :      Union[str, Any]					 =		{
            '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...' )
        lowerCamelCase__    :      Tuple					 =		model_mapping[mobilevit_name]
        image_processor.push_to_hub(_UpperCAmelCase						,	organization='apple' )
        model.push_to_hub(_UpperCAmelCase						,	organization='apple' )


if __name__ == "__main__":
    _UpperCAmelCase				:       Union[str, Any]	   =				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."""
    )

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












from __future__ import annotations

import math


def        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase )			->     bool:

    if 1 < number < 4:
        # 2 and 3 are primes
        return True
    elif number < 2 or number % 2 == 0 or number % 3 == 0:
        # Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
        return False

    # All primes number are in format of 6k +/- 1
    for i in range(5						,	int(math.sqrt(_UpperCAmelCase ) + 1 )						,	6 ):
        if number % i == 0 or number % (i + 2) == 0:
            return False
    return True


_UpperCAmelCase				:       Any	   =				[num for num in range(3, 10_00_01, 2) if not is_prime(num)]


def        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase )			->     list[int]:
    if not isinstance(_UpperCAmelCase						,	_UpperCAmelCase ):
        raise ValueError('n must be an integer' )
    if n <= 0:
        raise ValueError('n must be >= 0' )

    lowerCamelCase__    :      int					 =		[]
    for num in range(len(_UpperCAmelCase ) ):
        lowerCamelCase__    :      Union[str, Any]					 =		0
        while 2 * i * i <= odd_composites[num]:
            lowerCamelCase__    :      Dict					 =		odd_composites[num] - 2 * i * i
            if is_prime(_UpperCAmelCase ):
                break
            i += 1
        else:
            list_nums.append(odd_composites[num] )
            if len(_UpperCAmelCase ) == n:
                return list_nums

    return []


def        SCREAMING_SNAKE_CASE      (       )			->     int:
    return compute_nums(1 )[0]


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





