





'''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   : Optional[Any]             =			logging.get_logger(__name__)





def       lowercase_      (		lowercase__						)   ->List[Any]:
						_snake_case:   Union[str, Any]  					=		MobileViTConfig()

						# size of the architecture
						if "mobilevit_s" in mobilevit_name:
												_snake_case:   Optional[Any]  					=		[144, 192, 240]
												_snake_case:   str  					=		[16, 32, 64, 96, 128, 160, 640]
						elif "mobilevit_xs" in mobilevit_name:
												_snake_case:   Any  					=		[96, 120, 144]
												_snake_case:   int  					=		[16, 32, 48, 64, 80, 96, 384]
						elif "mobilevit_xxs" in mobilevit_name:
												_snake_case:   Optional[Any]  					=		[64, 80, 96]
												_snake_case:   List[str]  					=		[16, 16, 24, 48, 64, 80, 320]
												_snake_case:   Optional[int]  					=		0.0_5
												_snake_case:   List[Any]  					=		2.0

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

						_snake_case:   Dict  					=		'huggingface/label-files'
						_snake_case:   Dict  					=		json.load(open(hf_hub_download(lowercase__				,  lowercase__				,  repo_type='dataset'						)				,  'r'						)						)
						_snake_case:   Optional[int]  					=		{int(lowercase__						): v for k, v in idalabel.items()}
						_snake_case:   List[str]  					=		idalabel
						_snake_case:   Tuple  					=		{v: k for k, v in idalabel.items()}

						return config





def       lowercase_      (		lowercase__				,  lowercase__=False						)   ->str:
						for i in range(1				,  6						):
												if F'''layer_{i}.''' in name:
																		_snake_case:   Union[str, Any]  					=		name.replace(F'''layer_{i}.'''				,  F'''encoder.layer.{i - 1}.'''						)

						if "conv_1." in name:
												_snake_case:   List[Any]  					=		name.replace('conv_1.'				,  'conv_stem.'						)
						if ".block." in name:
												_snake_case:   List[str]  					=		name.replace('.block.'				,  '.'						)

						if "exp_1x1" in name:
												_snake_case:   int  					=		name.replace('exp_1x1'				,  'expand_1x1'						)
						if "red_1x1" in name:
												_snake_case:   List[str]  					=		name.replace('red_1x1'				,  'reduce_1x1'						)
						if ".local_rep.conv_3x3." in name:
												_snake_case:   List[str]  					=		name.replace('.local_rep.conv_3x3.'				,  '.conv_kxk.'						)
						if ".local_rep.conv_1x1." in name:
												_snake_case:   Dict  					=		name.replace('.local_rep.conv_1x1.'				,  '.conv_1x1.'						)
						if ".norm." in name:
												_snake_case:   Tuple  					=		name.replace('.norm.'				,  '.normalization.'						)
						if ".conv." in name:
												_snake_case:   Union[str, Any]  					=		name.replace('.conv.'				,  '.convolution.'						)
						if ".conv_proj." in name:
												_snake_case:   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:
																								_snake_case:   Dict  					=		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:
																								_snake_case:   int  					=		name.replace(F'''.{i}.{j}.'''				,  F'''.{i}.'''						)
																								if "expand_1x1" in name:
																														_snake_case:   Any  					=		name.replace('expand_1x1'				,  'downsampling_layer.expand_1x1'						)
																								if "conv_3x3" in name:
																														_snake_case:   str  					=		name.replace('conv_3x3'				,  'downsampling_layer.conv_3x3'						)
																								if "reduce_1x1" in name:
																														_snake_case:   Dict  					=		name.replace('reduce_1x1'				,  'downsampling_layer.reduce_1x1'						)

						for i in range(2				,  5						):
												if F'''.global_rep.{i}.weight''' in name:
																		_snake_case:   Union[str, Any]  					=		name.replace(F'''.global_rep.{i}.weight'''				,  '.layernorm.weight'						)
												if F'''.global_rep.{i}.bias''' in name:
																		_snake_case:   Any  					=		name.replace(F'''.global_rep.{i}.bias'''				,  '.layernorm.bias'						)

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

						if ".aspp_layer." in name:
												_snake_case:   List[str]  					=		name.replace('.aspp_layer.'				,  '.'						)
						if ".aspp_pool." in name:
												_snake_case:   Optional[int]  					=		name.replace('.aspp_pool.'				,  '.'						)
						if "seg_head." in name:
												_snake_case:   Dict  					=		name.replace('seg_head.'				,  'segmentation_head.'						)
						if "segmentation_head.classifier.classifier." in name:
												_snake_case:   List[Any]  					=		name.replace('segmentation_head.classifier.classifier.'				,  'segmentation_head.classifier.'						)

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

						return name





def       lowercase_      (		lowercase__				,  lowercase__				,  lowercase__=False						)   ->str:
						if base_model:
												_snake_case:   Dict  					=		''
						else:
												_snake_case:   List[str]  					=		'mobilevit.'

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

												if key[:8] == "encoder.":
																		_snake_case:   Dict  					=		key[8:]

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

						return orig_state_dict





def       lowercase_      (		)   ->List[str]:
						_snake_case:   str  					=		'http://images.cocodataset.org/val2017/000000039769.jpg'
						_snake_case:   Tuple  					=		Image.open(requests.get(lowercase__				,  stream=lowercase__						).raw						)
						return im





@torch.no_grad()
def       lowercase_      (		lowercase__				,  lowercase__				,  lowercase__				,  lowercase__=False						)   ->Optional[Any]:
						_snake_case:   int  					=		get_mobilevit_config(lowercase__						)

						# load original state_dict
						_snake_case:   Any  					=		torch.load(lowercase__				,  map_location='cpu'						)

						# load 🤗 model
						if mobilevit_name.startswith('deeplabv3_'						):
												_snake_case:   Any  					=		MobileViTForSemanticSegmentation(lowercase__						).eval()
						else:
												_snake_case:   List[str]  					=		MobileViTForImageClassification(lowercase__						).eval()

						_snake_case:   Tuple  					=		convert_state_dict(lowercase__				,  lowercase__						)
						model.load_state_dict(lowercase__						)

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

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

												if mobilevit_name == "deeplabv3_mobilevit_s":
																		_snake_case:   Optional[Any]  					=		torch.tensor(
																		    [
																		        [[6.2_0_6_5, 6.1_2_9_2, 6.2_0_7_0], [6.1_0_7_9, 6.1_2_5_4, 6.1_7_4_7], [6.0_0_4_2, 6.1_0_7_1, 6.1_0_3_4]],
																		        [[-6.9_2_5_3, -6.8_6_5_3, -7.0_3_9_8], [-7.3_2_1_8, -7.3_9_8_3, -7.3_6_7_0], [-7.1_9_6_1, -7.2_4_8_2, -7.1_5_6_9]],
																		        [[-4.4_7_2_3, -4.4_3_4_8, -4.3_7_6_9], [-5.3_6_2_9, -5.4_6_3_2, -5.4_5_9_8], [-5.1_5_8_7, -5.3_4_0_2, -5.5_0_5_9]],
																		    ]						)
												elif mobilevit_name == "deeplabv3_mobilevit_xs":
																		_snake_case:   Optional[Any]  					=		torch.tensor(
																		    [
																		        [[5.4_4_4_9, 5.5_7_3_3, 5.6_3_1_4], [5.1_8_1_5, 5.3_9_3_0, 5.5_9_6_3], [5.1_6_5_6, 5.4_3_3_3, 5.4_8_5_3]],
																		        [[-9.4_4_2_3, -9.7_7_6_6, -9.6_7_1_4], [-9.1_5_8_1, -9.5_7_2_0, -9.5_5_1_9], [-9.1_0_0_6, -9.6_4_5_8, -9.5_7_0_3]],
																		        [[-7.7_7_2_1, -7.3_7_1_6, -7.1_5_8_3], [-8.4_5_9_9, -8.0_6_2_4, -7.7_9_4_4], [-8.4_1_7_2, -7.8_3_6_6, -7.5_0_2_5]],
																		    ]						)
												elif mobilevit_name == "deeplabv3_mobilevit_xxs":
																		_snake_case:   Optional[int]  					=		torch.tensor(
																		    [
																		        [[6.9_8_1_1, 6.9_7_4_3, 7.3_1_2_3], [7.1_7_7_7, 7.1_9_3_1, 7.3_9_3_8], [7.5_6_3_3, 7.8_0_5_0, 7.8_9_0_1]],
																		        [[-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_6_2_4, -9.5_9_6_4], [-1_0.8_8_4_0, -1_0.8_1_5_8, -1_0.6_6_5_9]],
																		        [[-3.4_9_3_8, -3.0_6_3_1, -2.8_6_2_0], [-3.4_2_0_5, -2.8_1_3_5, -2.6_8_7_5], [-3.4_1_7_9, -2.7_9_4_5, -2.8_7_5_0]],
																		    ]						)
												else:
																		raise ValueError(F'''Unknown mobilevit_name: {mobilevit_name}'''						)

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

												if mobilevit_name == "mobilevit_s":
																		_snake_case:   str  					=		torch.tensor([-0.9_8_6_6, 0.2_3_9_2, -1.1_2_4_1]						)
												elif mobilevit_name == "mobilevit_xs":
																		_snake_case:   Optional[int]  					=		torch.tensor([-2.4_7_6_1, -0.9_3_9_9, -1.9_5_8_7]						)
												elif mobilevit_name == "mobilevit_xxs":
																		_snake_case:   List[str]  					=		torch.tensor([-1.9_3_6_4, -1.2_3_2_7, -0.4_6_5_3]						)
												else:
																		raise ValueError(F'''Unknown mobilevit_name: {mobilevit_name}'''						)

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

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

						if push_to_hub:
												_snake_case:   str  					=		{
												    '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...'						)
												_snake_case:   Any  					=		model_mapping[mobilevit_name]
												image_processor.push_to_hub(lowercase__				,  organization='apple'						)
												model.push_to_hub(lowercase__				,  organization='apple'						)


if __name__ == "__main__":
		A   : 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[str]             =			parser.parse_args()
		convert_movilevit_checkpoint(
		    args.mobilevit_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
		)












'''simple docstring'''






from ..utils import DummyObject, requires_backends







class 			lowerCamelCase					(						metaclass=__UpperCAmelCase  ):
					_SCREAMING_SNAKE_CASE							   =    ["torch", "scipy"]


					def __init__(					self	:  Tuple       ,     *__snake_case	:  List[Any]       ,     **__snake_case	:  List[Any]       ):





											'''simple docstring'''





											requires_backends(self       ,     ['torch', 'scipy']       )


					@classmethod
					def    SCREAMING_SNAKE_CASE_		(					cls	:  Optional[int]       ,     *__snake_case	:  Optional[Any]       ,     **__snake_case	:  Tuple       ):





											'''simple docstring'''





											requires_backends(cls       ,     ['torch', 'scipy']       )




					@classmethod
					def    SCREAMING_SNAKE_CASE_		(					cls	:  List[Any]       ,     *__snake_case	:  Optional[Any]       ,     **__snake_case	:  str       ):





											'''simple docstring'''





											requires_backends(cls       ,     ['torch', 'scipy']       )





