
"""simple docstring"""




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



def     __magic_name__		(					UpperCamelCase     :		Optional[int]     )     ->						Optional[int]:
    a__		=			MobileViTConfig()

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

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

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

    return config



def     __magic_name__		(					UpperCamelCase     :		Optional[int]							,		UpperCamelCase     :		str=False     )     ->						Dict:
    for i in range(1							,		6     ):
        if f'layer_{i}.' in name:
            a__		=			name.replace(f'layer_{i}.'							,		f'encoder.layer.{i - 1}.'     )

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

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

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

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

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

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

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

    return name



def     __magic_name__		(					UpperCamelCase     :		Optional[int]							,		UpperCamelCase     :		Dict							,		UpperCamelCase     :		str=False     )     ->						str:
    if base_model:
        a__		=			''
    else:
        a__		=			'mobilevit.'

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

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

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

    return orig_state_dict



def     __magic_name__		(					)     ->						int:
    a__		=			'http://images.cocodataset.org/val2017/000000039769.jpg'
    a__		=			Image.open(requests.get(UpperCamelCase							,		stream=UpperCamelCase     ).raw     )
    return im



@torch.no_grad()
def     __magic_name__		(					UpperCamelCase     :		int							,		UpperCamelCase     :		str							,		UpperCamelCase     :		str							,		UpperCamelCase     :		Tuple=False     )     ->						Union[str, Any]:
    a__		=			get_mobilevit_config(UpperCamelCase     )

    # load original state_dict
    a__		=			torch.load(UpperCamelCase							,		map_location='cpu'     )

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

    a__		=			convert_state_dict(UpperCamelCase							,		UpperCamelCase     )
    model.load_state_dict(UpperCamelCase     )

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

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

        if mobilevit_name == "deeplabv3_mobilevit_s":
            a__		=			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":
            a__		=			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":
            a__		=			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]							,		UpperCamelCase							,		atol=1E-4     )
    else:
        assert logits.shape == (1, 1000)

        if mobilevit_name == "mobilevit_s":
            a__		=			torch.tensor([-0.9866, 0.2392, -1.1241]     )
        elif mobilevit_name == "mobilevit_xs":
            a__		=			torch.tensor([-2.4761, -0.9399, -1.9587]     )
        elif mobilevit_name == "mobilevit_xxs":
            a__		=			torch.tensor([-1.9364, -1.2327, -0.4653]     )
        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:
        a__		=			{
            '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...'     )
        a__		=			model_mapping[mobilevit_name]
        image_processor.push_to_hub(UpperCamelCase							,		organization='apple'     )
        model.push_to_hub(UpperCamelCase							,		organization='apple'     )


if __name__ == "__main__":
    a		:       Optional[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.'
    )

    a		:       List[Any]     						=       parser.parse_args()
    convert_movilevit_checkpoint(
        args.mobilevit_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
    )




"""simple docstring"""




from itertools import product



def     __magic_name__		(					UpperCamelCase     :		int							,		UpperCamelCase     :		int     )     ->						list[int]:
    a__		=			sides_number
    a__		=			max_face_number * dice_number
    a__		=			[0] * (max_total + 1)

    a__		=			1
    a__		=			range(UpperCamelCase							,		max_face_number + 1     )
    for dice_numbers in product(UpperCamelCase							,		repeat=UpperCamelCase     ):
        a__		=			sum(UpperCamelCase     )
        totals_frequencies[total] += 1

    return totals_frequencies



def     __magic_name__		(					)     ->						float:
    a__		=			total_frequency_distribution(
        sides_number=4							,		dice_number=9     )
    a__		=			total_frequency_distribution(
        sides_number=6							,		dice_number=6     )

    a__		=			0
    a__		=			9
    a__		=			4 * 9
    a__		=			6
    for peter_total in range(UpperCamelCase							,		max_peter_total + 1     ):
        peter_wins_count += peter_totals_frequencies[peter_total] * sum(
            colin_totals_frequencies[min_colin_total:peter_total]     )

    a__		=			(4**9) * (6**6)
    a__		=			peter_wins_count / total_games_number

    a__		=			round(UpperCamelCase							,		ndigits=7     )

    return rounded_peter_win_probability


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


