






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__			:    Any         =				logging.get_logger(__name__)






def 						a    (		lowerCamelCase_       ):






    '''simple docstring'''
    lowercase__     				=    MobileViTConfig()

    # size of the architecture
    if "mobilevit_s" in mobilevit_name:
        lowercase__     				=    [144, 192, 240]
        lowercase__     				=    [16, 32, 64, 96, 128, 160, 640]
    elif "mobilevit_xs" in mobilevit_name:
        lowercase__     				=    [96, 120, 144]
        lowercase__     				=    [16, 32, 48, 64, 80, 96, 384]
    elif "mobilevit_xxs" in mobilevit_name:
        lowercase__     				=    [64, 80, 96]
        lowercase__     				=    [16, 16, 24, 48, 64, 80, 320]
        lowercase__     				=    0.05
        lowercase__     				=    2.0

    if mobilevit_name.startswith('''deeplabv3_'''       ):
        lowercase__     				=    512
        lowercase__     				=    16
        lowercase__     				=    21
        lowercase__     				=    """pascal-voc-id2label.json"""
    else:
        lowercase__     				=    1000
        lowercase__     				=    """imagenet-1k-id2label.json"""

    lowercase__     				=    """huggingface/label-files"""
    lowercase__     				=    json.load(open(hf_hub_download(lowerCamelCase_	,     lowerCamelCase_	,     repo_type='''dataset'''       )	,     '''r'''       )       )
    lowercase__     				=    {int(lowerCamelCase_       ): v for k, v in idalabel.items()}
    lowercase__     				=    idalabel
    lowercase__     				=    {v: k for k, v in idalabel.items()}

    return config






def 						a    (		lowerCamelCase_	,     lowerCamelCase_=False       ):






    '''simple docstring'''
    for i in range(1	,     6       ):
        if F"""layer_{i}.""" in name:
            lowercase__     				=    name.replace(F"""layer_{i}."""	,     F"""encoder.layer.{i - 1}."""       )

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

    if "exp_1x1" in name:
        lowercase__     				=    name.replace('''exp_1x1'''	,     '''expand_1x1'''       )
    if "red_1x1" in name:
        lowercase__     				=    name.replace('''red_1x1'''	,     '''reduce_1x1'''       )
    if ".local_rep.conv_3x3." in name:
        lowercase__     				=    name.replace('''.local_rep.conv_3x3.'''	,     '''.conv_kxk.'''       )
    if ".local_rep.conv_1x1." in name:
        lowercase__     				=    name.replace('''.local_rep.conv_1x1.'''	,     '''.conv_1x1.'''       )
    if ".norm." in name:
        lowercase__     				=    name.replace('''.norm.'''	,     '''.normalization.'''       )
    if ".conv." in name:
        lowercase__     				=    name.replace('''.conv.'''	,     '''.convolution.'''       )
    if ".conv_proj." in name:
        lowercase__     				=    name.replace('''.conv_proj.'''	,     '''.conv_projection.'''       )

    for i in range(0	,     2       ):
        for j in range(0	,     4       ):
            if F""".{i}.{j}.""" in name:
                lowercase__     				=    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:
                lowercase__     				=    name.replace(F""".{i}.{j}."""	,     F""".{i}."""       )
                if "expand_1x1" in name:
                    lowercase__     				=    name.replace('''expand_1x1'''	,     '''downsampling_layer.expand_1x1'''       )
                if "conv_3x3" in name:
                    lowercase__     				=    name.replace('''conv_3x3'''	,     '''downsampling_layer.conv_3x3'''       )
                if "reduce_1x1" in name:
                    lowercase__     				=    name.replace('''reduce_1x1'''	,     '''downsampling_layer.reduce_1x1'''       )

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

    if ".global_rep." in name:
        lowercase__     				=    name.replace('''.global_rep.'''	,     '''.transformer.'''       )
    if ".pre_norm_mha.0." in name:
        lowercase__     				=    name.replace('''.pre_norm_mha.0.'''	,     '''.layernorm_before.'''       )
    if ".pre_norm_mha.1.out_proj." in name:
        lowercase__     				=    name.replace('''.pre_norm_mha.1.out_proj.'''	,     '''.attention.output.dense.'''       )
    if ".pre_norm_ffn.0." in name:
        lowercase__     				=    name.replace('''.pre_norm_ffn.0.'''	,     '''.layernorm_after.'''       )
    if ".pre_norm_ffn.1." in name:
        lowercase__     				=    name.replace('''.pre_norm_ffn.1.'''	,     '''.intermediate.dense.'''       )
    if ".pre_norm_ffn.4." in name:
        lowercase__     				=    name.replace('''.pre_norm_ffn.4.'''	,     '''.output.dense.'''       )
    if ".transformer." in name:
        lowercase__     				=    name.replace('''.transformer.'''	,     '''.transformer.layer.'''       )

    if ".aspp_layer." in name:
        lowercase__     				=    name.replace('''.aspp_layer.'''	,     '''.'''       )
    if ".aspp_pool." in name:
        lowercase__     				=    name.replace('''.aspp_pool.'''	,     '''.'''       )
    if "seg_head." in name:
        lowercase__     				=    name.replace('''seg_head.'''	,     '''segmentation_head.'''       )
    if "segmentation_head.classifier.classifier." in name:
        lowercase__     				=    name.replace('''segmentation_head.classifier.classifier.'''	,     '''segmentation_head.classifier.'''       )

    if "classifier.fc." in name:
        lowercase__     				=    name.replace('''classifier.fc.'''	,     '''classifier.'''       )
    elif (not base_model) and ("segmentation_head." not in name):
        lowercase__     				=    """mobilevit.""" + name

    return name






def 						a    (		lowerCamelCase_	,     lowerCamelCase_	,     lowerCamelCase_=False       ):






    '''simple docstring'''
    if base_model:
        lowercase__     				=    """"""
    else:
        lowercase__     				=    """mobilevit."""

    for key in orig_state_dict.copy().keys():
        lowercase__     				=    orig_state_dict.pop(lowerCamelCase_       )

        if key[:8] == "encoder.":
            lowercase__     				=    key[8:]

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

    return orig_state_dict






def 						a    (		):






    '''simple docstring'''
    lowercase__     				=    """http://images.cocodataset.org/val2017/000000039769.jpg"""
    lowercase__     				=    Image.open(requests.get(lowerCamelCase_	,     stream=lowerCamelCase_       ).raw       )
    return im






@torch.no_grad()
def 						a    (		lowerCamelCase_	,     lowerCamelCase_	,     lowerCamelCase_	,     lowerCamelCase_=False       ):






    '''simple docstring'''
    lowercase__     				=    get_mobilevit_config(lowerCamelCase_       )

    # load original state_dict
    lowercase__     				=    torch.load(lowerCamelCase_	,     map_location='''cpu'''       )

    # load 🤗 model
    if mobilevit_name.startswith('''deeplabv3_'''       ):
        lowercase__     				=    MobileViTForSemanticSegmentation(lowerCamelCase_       ).eval()
    else:
        lowercase__     				=    MobileViTForImageClassification(lowerCamelCase_       ).eval()

    lowercase__     				=    convert_state_dict(lowerCamelCase_	,     lowerCamelCase_       )
    model.load_state_dict(lowerCamelCase_       )

    # Check outputs on an image, prepared by MobileViTImageProcessor
    lowercase__     				=    MobileViTImageProcessor(crop_size=config.image_size	,     size=config.image_size + 32       )
    lowercase__     				=    image_processor(images=prepare_img()	,     return_tensors='''pt'''       )
    lowercase__     				=    model(**lowerCamelCase_       )
    lowercase__     				=    outputs.logits

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

        if mobilevit_name == "deeplabv3_mobilevit_s":
            lowercase__     				=    torch.tensor(
                [
                    [[6.20_65, 6.12_92, 6.20_70], [6.10_79, 6.12_54, 6.17_47], [6.00_42, 6.10_71, 6.10_34]],
                    [[-6.92_53, -6.86_53, -7.03_98], [-7.32_18, -7.39_83, -7.36_70], [-7.19_61, -7.24_82, -7.15_69]],
                    [[-4.47_23, -4.43_48, -4.37_69], [-5.36_29, -5.46_32, -5.45_98], [-5.15_87, -5.34_02, -5.50_59]],
                ]       )
        elif mobilevit_name == "deeplabv3_mobilevit_xs":
            lowercase__     				=    torch.tensor(
                [
                    [[5.44_49, 5.57_33, 5.63_14], [5.18_15, 5.39_30, 5.59_63], [5.16_56, 5.43_33, 5.48_53]],
                    [[-9.44_23, -9.77_66, -9.67_14], [-9.15_81, -9.57_20, -9.55_19], [-9.10_06, -9.64_58, -9.57_03]],
                    [[-7.77_21, -7.37_16, -7.15_83], [-8.45_99, -8.06_24, -7.79_44], [-8.41_72, -7.83_66, -7.50_25]],
                ]       )
        elif mobilevit_name == "deeplabv3_mobilevit_xxs":
            lowercase__     				=    torch.tensor(
                [
                    [[6.98_11, 6.97_43, 7.31_23], [7.17_77, 7.19_31, 7.39_38], [7.56_33, 7.80_50, 7.89_01]],
                    [[-10.55_36, -10.23_32, -10.29_24], [-10.23_36, -9.86_24, -9.59_64], [-10.88_40, -10.81_58, -10.66_59]],
                    [[-3.49_38, -3.06_31, -2.86_20], [-3.42_05, -2.81_35, -2.68_75], [-3.41_79, -2.79_45, -2.87_50]],
                ]       )
        else:
            raise ValueError(F"""Unknown mobilevit_name: {mobilevit_name}"""       )

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

        if mobilevit_name == "mobilevit_s":
            lowercase__     				=    torch.tensor([-0.98_66, 0.23_92, -1.12_41]       )
        elif mobilevit_name == "mobilevit_xs":
            lowercase__     				=    torch.tensor([-2.47_61, -0.93_99, -1.95_87]       )
        elif mobilevit_name == "mobilevit_xxs":
            lowercase__     				=    torch.tensor([-1.93_64, -1.23_27, -0.46_53]       )
        else:
            raise ValueError(F"""Unknown mobilevit_name: {mobilevit_name}"""       )

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

    Path(lowerCamelCase_       ).mkdir(exist_ok=lowerCamelCase_       )
    print(F"""Saving model {mobilevit_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 push_to_hub:
        lowercase__     				=    {
            """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...'''       )
        lowercase__     				=    model_mapping[mobilevit_name]
        image_processor.push_to_hub(lowerCamelCase_	,     organization='''apple'''       )
        model.push_to_hub(lowerCamelCase_	,     organization='''apple'''       )


if __name__ == "__main__":
       A__			:    int         =				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__			:    Tuple         =				parser.parse_args()
       convert_movilevit_checkpoint(
           args.mobilevit_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
       )












def 						a    (		lowerCamelCase_	,     lowerCamelCase_	,     lowerCamelCase_       ):






				'''simple docstring'''

				if exponent == 1:
								return base
				if exponent % 2 == 0:
								lowercase__     				=    _modexpt(lowerCamelCase_	,     exponent // 2	,     lowerCamelCase_       ) % modulo_value
								return (x * x) % modulo_value
				else:
								return (base * _modexpt(lowerCamelCase_	,     exponent - 1	,     lowerCamelCase_       )) % modulo_value






def 						a    (		lowerCamelCase_ = 1777	,     lowerCamelCase_ = 1855	,     lowerCamelCase_ = 8       ):






				'''simple docstring'''
				lowercase__     				=    base
				for _ in range(1	,     lowerCamelCase_       ):
								lowercase__     				=    _modexpt(lowerCamelCase_	,     lowerCamelCase_	,     10**digits       )

				return result


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




