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






def 							A						(    snake_case				:str		)				->     Optional[int]:
	__UpperCamelCase      =	MobileViTConfig()

	# size of the architecture
	if "mobilevit_s" in mobilevit_name:
		__UpperCamelCase      =	[1_4_4, 1_9_2, 2_4_0]
		__UpperCamelCase      =	[1_6, 3_2, 6_4, 9_6, 1_2_8, 1_6_0, 6_4_0]
	elif "mobilevit_xs" in mobilevit_name:
		__UpperCamelCase      =	[9_6, 1_2_0, 1_4_4]
		__UpperCamelCase      =	[1_6, 3_2, 4_8, 6_4, 8_0, 9_6, 3_8_4]
	elif "mobilevit_xxs" in mobilevit_name:
		__UpperCamelCase      =	[6_4, 8_0, 9_6]
		__UpperCamelCase      =	[1_6, 1_6, 2_4, 4_8, 6_4, 8_0, 3_2_0]
		__UpperCamelCase      =	0.05
		__UpperCamelCase      =	2.0

	if mobilevit_name.startswith('deeplabv3_'		):
		__UpperCamelCase      =	5_1_2
		__UpperCamelCase      =	1_6
		__UpperCamelCase      =	2_1
		__UpperCamelCase      =	'pascal-voc-id2label.json'
	else:
		__UpperCamelCase      =	1_0_0_0
		__UpperCamelCase      =	'imagenet-1k-id2label.json'

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

	return config






def 							A						(    snake_case				:List[str]    , snake_case				:Optional[int]=False		)				->     Any:
	for i in range(1    , 6		):
		if f'layer_{i}.' in name:
			__UpperCamelCase      =	name.replace(f'layer_{i}.'    , f'encoder.layer.{i - 1}.'		)

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

	if "exp_1x1" in name:
		__UpperCamelCase      =	name.replace('exp_1x1'    , 'expand_1x1'		)
	if "red_1x1" in name:
		__UpperCamelCase      =	name.replace('red_1x1'    , 'reduce_1x1'		)
	if ".local_rep.conv_3x3." in name:
		__UpperCamelCase      =	name.replace('.local_rep.conv_3x3.'    , '.conv_kxk.'		)
	if ".local_rep.conv_1x1." in name:
		__UpperCamelCase      =	name.replace('.local_rep.conv_1x1.'    , '.conv_1x1.'		)
	if ".norm." in name:
		__UpperCamelCase      =	name.replace('.norm.'    , '.normalization.'		)
	if ".conv." in name:
		__UpperCamelCase      =	name.replace('.conv.'    , '.convolution.'		)
	if ".conv_proj." in name:
		__UpperCamelCase      =	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      =	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      =	name.replace(f'.{i}.{j}.'    , f'.{i}.'		)
				if "expand_1x1" in name:
					__UpperCamelCase      =	name.replace('expand_1x1'    , 'downsampling_layer.expand_1x1'		)
				if "conv_3x3" in name:
					__UpperCamelCase      =	name.replace('conv_3x3'    , 'downsampling_layer.conv_3x3'		)
				if "reduce_1x1" in name:
					__UpperCamelCase      =	name.replace('reduce_1x1'    , 'downsampling_layer.reduce_1x1'		)

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

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

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

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

	return name






def 							A						(    snake_case				:Any    , snake_case				:Union[str, Any]    , snake_case				:Dict=False		)				->     Optional[Any]:
	if base_model:
		__UpperCamelCase      =	''
	else:
		__UpperCamelCase      =	'mobilevit.'

	for key in orig_state_dict.copy().keys():
		__UpperCamelCase      =	orig_state_dict.pop(snake_case		)

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

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

	return orig_state_dict






def 							A						(    )				->     str:
	__UpperCamelCase      =	'http://images.cocodataset.org/val2017/000000039769.jpg'
	__UpperCamelCase      =	Image.open(requests.get(snake_case    , stream=snake_case		).raw		)
	return im






@torch.no_grad()
def 							A						(    snake_case				:List[Any]    , snake_case				:Union[str, Any]    , snake_case				:Dict    , snake_case				:int=False		)				->     Optional[int]:
	__UpperCamelCase      =	get_mobilevit_config(snake_case		)

	# load original state_dict
	__UpperCamelCase      =	torch.load(snake_case    , map_location='cpu'		)

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

	__UpperCamelCase      =	convert_state_dict(snake_case    , snake_case		)
	model.load_state_dict(snake_case		)

	# Check outputs on an image, prepared by MobileViTImageProcessor
	__UpperCamelCase      =	MobileViTImageProcessor(crop_size=config.image_size    , size=config.image_size + 3_2		)
	__UpperCamelCase      =	image_processor(images=prepare_img()    , return_tensors='pt'		)
	__UpperCamelCase      =	model(**snake_case		)
	__UpperCamelCase      =	outputs.logits

	if mobilevit_name.startswith('deeplabv3_'		):
		assert logits.shape == (1, 2_1, 3_2, 3_2)

		if mobilevit_name == "deeplabv3_mobilevit_s":
			__UpperCamelCase      =	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":
			__UpperCamelCase      =	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":
			__UpperCamelCase      =	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]],
			        [[-1_0.5_5_3_6, -1_0.2_3_3_2, -1_0.2_9_2_4], [-1_0.2_3_3_6, -9.8_624, -9.5_964], [-1_0.8_8_4_0, -1_0.8_1_5_8, -1_0.6_6_5_9]],
			        [[-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]    , snake_case    , atol=1e-4		)
	else:
		assert logits.shape == (1, 1_0_0_0)

		if mobilevit_name == "mobilevit_s":
			__UpperCamelCase      =	torch.tensor([-0.9_866, 0.2_392, -1.1_241]		)
		elif mobilevit_name == "mobilevit_xs":
			__UpperCamelCase      =	torch.tensor([-2.4_761, -0.9_399, -1.9_587]		)
		elif mobilevit_name == "mobilevit_xxs":
			__UpperCamelCase      =	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]    , snake_case    , atol=1e-4		)

	Path(snake_case		).mkdir(exist_ok=snake_case		)
	print(f'Saving model {mobilevit_name} to {pytorch_dump_folder_path}'		)
	model.save_pretrained(snake_case		)
	print(f'Saving image processor to {pytorch_dump_folder_path}'		)
	image_processor.save_pretrained(snake_case		)

	if push_to_hub:
		__UpperCamelCase      =	{
		    '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      =	model_mapping[mobilevit_name]
		image_processor.push_to_hub(snake_case    , organization='apple'		)
		model.push_to_hub(snake_case    , organization='apple'		)


if __name__ == "__main__":
						UpperCamelCase     :    List[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     :    Union[str, 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 math import factorial

UpperCamelCase     :    List[Any]       =			{str(d): factorial(d) for d in range(1_0)}






def 							A						(    snake_case				:int		)				->     int:
 return sum(DIGIT_FACTORIAL[d] for d in str(snake_case		)		)






def 							A						(    )				->     int:
 __UpperCamelCase      =	7 * factorial(9		) + 1
 return sum(i for i in range(3    , snake_case		) if sum_of_digit_factorial(snake_case		) == i		)


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



