




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()
SCREAMING_SNAKE_CASE__     :     str		    =       logging.get_logger(__name__)

def lowercase    (  SCREAMING_SNAKE_CASE )			->							Tuple:


			'''simple docstring'''







			SCREAMING_SNAKE_CASE_								=						MobileViTConfig()

			# size of the architecture
			if "mobilevit_s" in mobilevit_name:
						SCREAMING_SNAKE_CASE_								=						[1_44, 1_92, 2_40]
						SCREAMING_SNAKE_CASE_								=						[16, 32, 64, 96, 1_28, 1_60, 6_40]
			elif "mobilevit_xs" in mobilevit_name:
						SCREAMING_SNAKE_CASE_								=						[96, 1_20, 1_44]
						SCREAMING_SNAKE_CASE_								=						[16, 32, 48, 64, 80, 96, 3_84]
			elif "mobilevit_xxs" in mobilevit_name:
						SCREAMING_SNAKE_CASE_								=						[64, 80, 96]
						SCREAMING_SNAKE_CASE_								=						[16, 16, 24, 48, 64, 80, 3_20]
						SCREAMING_SNAKE_CASE_								=						0.05
						SCREAMING_SNAKE_CASE_								=						2.0

			if mobilevit_name.startswith('deeplabv3_' ):
						SCREAMING_SNAKE_CASE_								=						5_12
						SCREAMING_SNAKE_CASE_								=						16
						SCREAMING_SNAKE_CASE_								=						21
						SCREAMING_SNAKE_CASE_								=						'pascal-voc-id2label.json'
			else:
						SCREAMING_SNAKE_CASE_								=						10_00
						SCREAMING_SNAKE_CASE_								=						'imagenet-1k-id2label.json'

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

			return config

def lowercase    (  SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE=False )			->							List[str]:


			'''simple docstring'''







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

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

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

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

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

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

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

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

			return name

def lowercase    (  SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE=False )			->							str:


			'''simple docstring'''







			if base_model:
						SCREAMING_SNAKE_CASE_								=						''
			else:
						SCREAMING_SNAKE_CASE_								=						'mobilevit.'

			for key in orig_state_dict.copy().keys():
						SCREAMING_SNAKE_CASE_								=						orig_state_dict.pop(SCREAMING_SNAKE_CASE )

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

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

			return orig_state_dict

def lowercase    (  )			->							Tuple:


			'''simple docstring'''







			SCREAMING_SNAKE_CASE_								=						'http://images.cocodataset.org/val2017/000000039769.jpg'
			SCREAMING_SNAKE_CASE_								=						Image.open(requests.get(SCREAMING_SNAKE_CASE       ,    stream=SCREAMING_SNAKE_CASE ).raw )
			return im

@torch.no_grad()
def lowercase    (  SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE=False )			->							List[Any]:


			'''simple docstring'''







			SCREAMING_SNAKE_CASE_								=						get_mobilevit_config(SCREAMING_SNAKE_CASE )

			# load original state_dict
			SCREAMING_SNAKE_CASE_								=						torch.load(SCREAMING_SNAKE_CASE       ,    map_location='cpu' )

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

			SCREAMING_SNAKE_CASE_								=						convert_state_dict(SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE )
			model.load_state_dict(SCREAMING_SNAKE_CASE )

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

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

						if mobilevit_name == "deeplabv3_mobilevit_s":
									SCREAMING_SNAKE_CASE_								=						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":
									SCREAMING_SNAKE_CASE_								=						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":
									SCREAMING_SNAKE_CASE_								=						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]       ,    SCREAMING_SNAKE_CASE       ,    atol=1E-4 )
			else:
						assert logits.shape == (1, 10_00)

						if mobilevit_name == "mobilevit_s":
									SCREAMING_SNAKE_CASE_								=						torch.tensor([-0.9866, 0.2392, -1.1241] )
						elif mobilevit_name == "mobilevit_xs":
									SCREAMING_SNAKE_CASE_								=						torch.tensor([-2.4761, -0.9399, -1.9587] )
						elif mobilevit_name == "mobilevit_xxs":
									SCREAMING_SNAKE_CASE_								=						torch.tensor([-1.9364, -1.2327, -0.4653] )
						else:
									raise ValueError(F'Unknown mobilevit_name: {mobilevit_name}' )

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

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

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


if __name__ == "__main__":
				SCREAMING_SNAKE_CASE__     :     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."
				)

				SCREAMING_SNAKE_CASE__     :     Dict		    =       parser.parse_args()
				convert_movilevit_checkpoint(
				    args.mobilevit_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
				)






from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


SCREAMING_SNAKE_CASE__     :     Optional[int]		    =       logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__     :     str		    =       {
    "google/mobilenet_v2_1.4_224": "https://huggingface.co/google/mobilenet_v2_1.4_224/resolve/main/config.json",
    "google/mobilenet_v2_1.0_224": "https://huggingface.co/google/mobilenet_v2_1.0_224/resolve/main/config.json",
    "google/mobilenet_v2_0.75_160": "https://huggingface.co/google/mobilenet_v2_0.75_160/resolve/main/config.json",
    "google/mobilenet_v2_0.35_96": "https://huggingface.co/google/mobilenet_v2_0.35_96/resolve/main/config.json",
    # See all MobileNetV2 models at https://huggingface.co/models?filter=mobilenet_v2
}



class       a_       (     SCREAMING_SNAKE_CASE__     ):
	A    							= '''mobilenet_v2'''





	def __init__(							self      ,     SCREAMING_SNAKE_CASE=3      ,     SCREAMING_SNAKE_CASE=224      ,     SCREAMING_SNAKE_CASE=1.0      ,     SCREAMING_SNAKE_CASE=8      ,     SCREAMING_SNAKE_CASE=8      ,     SCREAMING_SNAKE_CASE=6      ,     SCREAMING_SNAKE_CASE=32      ,     SCREAMING_SNAKE_CASE=True      ,     SCREAMING_SNAKE_CASE=True      ,     SCREAMING_SNAKE_CASE="relu6"      ,     SCREAMING_SNAKE_CASE=True      ,     SCREAMING_SNAKE_CASE=0.8      ,     SCREAMING_SNAKE_CASE=0.0_2      ,     SCREAMING_SNAKE_CASE=0.0_0_1      ,     SCREAMING_SNAKE_CASE=255      ,     **SCREAMING_SNAKE_CASE      ,     )  ->					Dict:




				"""simple docstring"""



				super().__init__(**SCREAMING_SNAKE_CASE      )

				if depth_multiplier <= 0:
							raise ValueError('depth_multiplier must be greater than zero.'      )

				SCREAMING_SNAKE_CASE_								=						num_channels
				SCREAMING_SNAKE_CASE_								=						image_size
				SCREAMING_SNAKE_CASE_								=						depth_multiplier
				SCREAMING_SNAKE_CASE_								=						depth_divisible_by
				SCREAMING_SNAKE_CASE_								=						min_depth
				SCREAMING_SNAKE_CASE_								=						expand_ratio
				SCREAMING_SNAKE_CASE_								=						output_stride
				SCREAMING_SNAKE_CASE_								=						first_layer_is_expansion
				SCREAMING_SNAKE_CASE_								=						finegrained_output
				SCREAMING_SNAKE_CASE_								=						hidden_act
				SCREAMING_SNAKE_CASE_								=						tf_padding
				SCREAMING_SNAKE_CASE_								=						classifier_dropout_prob
				SCREAMING_SNAKE_CASE_								=						initializer_range
				SCREAMING_SNAKE_CASE_								=						layer_norm_eps
				SCREAMING_SNAKE_CASE_								=						semantic_loss_ignore_index




class       a_       (     SCREAMING_SNAKE_CASE__     ):
	A    							= version.parse('''1.11'''     )





	@property
	def 						A_(							self      )  ->					Mapping[str, Mapping[int, str]]:




				"""simple docstring"""



				return OrderedDict([('pixel_values', {0: 'batch'})]      )





	@property
	def 						A_(							self      )  ->					Mapping[str, Mapping[int, str]]:




				"""simple docstring"""



				if self.task == "image-classification":
							return OrderedDict([('logits', {0: 'batch'})]      )
				else:
							return OrderedDict([('last_hidden_state', {0: 'batch'}), ('pooler_output', {0: 'batch'})]      )





	@property
	def 						A_(							self      )  ->					float:




				"""simple docstring"""



				return 1e-4
