



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()
_lowerCamelCase    :		List[Any]		      =    logging.get_logger(__name__)

def        __a			(   __lowerCAmelCase   )     ->     str:
					SCREAMING_SNAKE_CASE							:     Any						=				MobileViTConfig()

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

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

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

					return config

def        __a			(   __lowerCAmelCase      ,	__lowerCAmelCase=False   )     ->     int:
					for i in range(1      ,	6   ):
										if F'''layer_{i}.''' in name:
															SCREAMING_SNAKE_CASE							:     List[Any]						=				name.replace(F'''layer_{i}.'''      ,	F'''encoder.layer.{i - 1}.'''   )

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

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

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

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

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

					return name

def        __a			(   __lowerCAmelCase      ,	__lowerCAmelCase      ,	__lowerCAmelCase=False   )     ->     Any:
					if base_model:
										SCREAMING_SNAKE_CASE							:     Optional[int]						=				''
					else:
										SCREAMING_SNAKE_CASE							:     str						=				'mobilevit.'

					for key in orig_state_dict.copy().keys():
										SCREAMING_SNAKE_CASE							:     List[Any]						=				orig_state_dict.pop(__lowerCAmelCase   )

										if key[:8] == "encoder.":
															SCREAMING_SNAKE_CASE							:     Any						=				key[8:]

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

					return orig_state_dict

def        __a			(   )     ->     int:
					SCREAMING_SNAKE_CASE							:     Tuple						=				'http://images.cocodataset.org/val2017/000000039769.jpg'
					SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				Image.open(requests.get(__lowerCAmelCase      ,	stream=__lowerCAmelCase   ).raw   )
					return im

@torch.no_grad()
def        __a			(   __lowerCAmelCase      ,	__lowerCAmelCase      ,	__lowerCAmelCase      ,	__lowerCAmelCase=False   )     ->     Optional[int]:
					SCREAMING_SNAKE_CASE							:     Optional[Any]						=				get_mobilevit_config(__lowerCAmelCase   )

					# load original state_dict
					SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				torch.load(__lowerCAmelCase      ,	map_location='cpu'   )

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

					SCREAMING_SNAKE_CASE							:     List[str]						=				convert_state_dict(__lowerCAmelCase      ,	__lowerCAmelCase   )
					model.load_state_dict(__lowerCAmelCase   )

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

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

										if mobilevit_name == "deeplabv3_mobilevit_s":
															SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				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":
															SCREAMING_SNAKE_CASE							:     Tuple						=				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":
															SCREAMING_SNAKE_CASE							:     Tuple						=				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]],
															        [[-10.5_536, -10.2_332, -10.2_924], [-10.2_336, -9.8_624, -9.5_964], [-10.8_840, -10.8_158, -10.6_659]],
															        [[-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]      ,	__lowerCAmelCase      ,	atol=1E-4   )
					else:
										assert logits.shape == (1, 1000)

										if mobilevit_name == "mobilevit_s":
															SCREAMING_SNAKE_CASE							:     int						=				torch.tensor([-0.9_866, 0.2_392, -1.1_241]   )
										elif mobilevit_name == "mobilevit_xs":
															SCREAMING_SNAKE_CASE							:     Optional[int]						=				torch.tensor([-2.4_761, -0.9_399, -1.9_587]   )
										elif mobilevit_name == "mobilevit_xxs":
															SCREAMING_SNAKE_CASE							:     List[str]						=				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]      ,	__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:
										SCREAMING_SNAKE_CASE							:     List[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...'   )
										SCREAMING_SNAKE_CASE							:     Optional[Any]						=				model_mapping[mobilevit_name]
										image_processor.push_to_hub(__lowerCAmelCase      ,	organization='apple'   )
										model.push_to_hub(__lowerCAmelCase      ,	organization='apple'   )


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

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




def        __a			(   __lowerCAmelCase   )     ->     List[str]:
					stooge(__lowerCAmelCase      ,	0      ,	len(__lowerCAmelCase   ) - 1   )
					return arr

def        __a			(   __lowerCAmelCase      ,	__lowerCAmelCase      ,	__lowerCAmelCase   )     ->     int:
					if i >= h:
										return

					# If first element is smaller than the last then swap them
					if arr[i] > arr[h]:
										SCREAMING_SNAKE_CASE					,  SCREAMING_SNAKE_CASE							:     Any						=				arr[h], arr[i]

					# If there are more than 2 elements in the array
					if h - i + 1 > 2:
										SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				(int)((h - i + 1) / 3   )

										# Recursively sort first 2/3 elements
										stooge(__lowerCAmelCase      ,	__lowerCAmelCase      ,	(h - t)   )

										# Recursively sort last 2/3 elements
										stooge(__lowerCAmelCase      ,	i + t      ,	(__lowerCAmelCase)   )

										# Recursively sort first 2/3 elements
										stooge(__lowerCAmelCase      ,	__lowerCAmelCase      ,	(h - t)   )


if __name__ == "__main__":
			_lowerCamelCase    :		List[str]		      =    input("""Enter numbers separated by a comma:\n""").strip()
			_lowerCamelCase    :		List[Any]		      =    [int(item) for item in user_input.split(""",""")]
			print(stooge_sort(unsorted))