



'''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()
__lowerCAmelCase						      =       logging.get_logger(__name__)



def 					__lowerCamelCase					(				lowerCAmelCase_  )      ->				int:
		_a					:				Dict				=							MobileViTConfig()

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

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

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

		return config



def 					__lowerCamelCase					(				lowerCAmelCase_						,      lowerCAmelCase_=False  )      ->				List[Any]:
		for i in range(1						,      6  ):
				if f"""layer_{i}.""" in name:
						_a					:				str				=							name.replace(f"""layer_{i}."""						,      f"""encoder.layer.{i - 1}."""  )

		if "conv_1." in name:
				_a					:				Tuple				=							name.replace('conv_1.'						,      'conv_stem.'  )
		if ".block." in name:
				_a					:				Optional[int]				=							name.replace('.block.'						,      '.'  )

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

		for i in range(2						,      5  ):
				if f""".global_rep.{i}.weight""" in name:
						_a					:				List[Any]				=							name.replace(f""".global_rep.{i}.weight"""						,      '.layernorm.weight'  )
				if f""".global_rep.{i}.bias""" in name:
						_a					:				Dict				=							name.replace(f""".global_rep.{i}.bias"""						,      '.layernorm.bias'  )

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

		if ".aspp_layer." in name:
				_a					:				Tuple				=							name.replace('.aspp_layer.'						,      '.'  )
		if ".aspp_pool." in name:
				_a					:				Any				=							name.replace('.aspp_pool.'						,      '.'  )
		if "seg_head." in name:
				_a					:				Union[str, Any]				=							name.replace('seg_head.'						,      'segmentation_head.'  )
		if "segmentation_head.classifier.classifier." in name:
				_a					:				str				=							name.replace('segmentation_head.classifier.classifier.'						,      'segmentation_head.classifier.'  )

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

		return name



def 					__lowerCamelCase					(				lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_=False  )      ->				Dict:
		if base_model:
				_a					:				str				=							''
		else:
				_a					:				List[Any]				=							'mobilevit.'

		for key in orig_state_dict.copy().keys():
				_a					:				int				=							orig_state_dict.pop(lowerCAmelCase_  )

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

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

		return orig_state_dict



def 					__lowerCamelCase					(				)      ->				str:
		_a					:				List[str]				=							'http://images.cocodataset.org/val2017/000000039769.jpg'
		_a					:				int				=							Image.open(requests.get(lowerCAmelCase_						,      stream=lowerCAmelCase_  ).raw  )
		return im



@torch.no_grad()
def 					__lowerCamelCase					(				lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_=False  )      ->				Any:
		_a					:				Optional[int]				=							get_mobilevit_config(lowerCAmelCase_  )

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

		# load 🤗 model
		if mobilevit_name.startswith('deeplabv3_'  ):
				_a					:				Union[str, Any]				=							MobileViTForSemanticSegmentation(lowerCAmelCase_  ).eval()
		else:
				_a					:				Tuple				=							MobileViTForImageClassification(lowerCAmelCase_  ).eval()

		_a					:				int				=							convert_state_dict(lowerCAmelCase_						,      lowerCAmelCase_  )
		model.load_state_dict(lowerCAmelCase_  )

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

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

				if mobilevit_name == "deeplabv3_mobilevit_s":
						_a					:				Tuple				=							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":
						_a					:				Optional[Any]				=							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":
						_a					:				Union[str, Any]				=							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]],
						        [[-10.5536, -10.2332, -10.2924], [-10.2336, -9.8_624, -9.5_964], [-10.8840, -10.8158, -10.6659]],
						        [[-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":
						_a					:				Any				=							torch.tensor([-0.9_866, 0.2_392, -1.1_241]  )
				elif mobilevit_name == "mobilevit_xs":
						_a					:				Optional[Any]				=							torch.tensor([-2.4_761, -0.9_399, -1.9_587]  )
				elif mobilevit_name == "mobilevit_xxs":
						_a					:				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:
				_a					:				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...'  )
				_a					:				Optional[int]				=							model_mapping[mobilevit_name]
				image_processor.push_to_hub(lowerCAmelCase_						,      organization='apple'  )
				model.push_to_hub(lowerCAmelCase_						,      organization='apple'  )


if __name__ == "__main__":
							__lowerCAmelCase						      =       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						      =       parser.parse_args()
							convert_movilevit_checkpoint(
							    args.mobilevit_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
							)









'''simple docstring'''

def 					__lowerCamelCase					(				lowerCAmelCase_  )      ->				list:
		if any(not isinstance(lowerCAmelCase_						,      lowerCAmelCase_  ) or x < 0 for x in sequence  ):
				raise TypeError('Sequence must be list of non-negative integers'  )
		for _ in range(len(lowerCAmelCase_  )  ):
				for i, (rod_upper, rod_lower) in enumerate(zip(lowerCAmelCase_						,      sequence[1:]  )  ):
						if rod_upper > rod_lower:
								sequence[i] -= rod_upper - rod_lower
								sequence[i + 1] += rod_upper - rod_lower
		return sequence


if __name__ == "__main__":
							assert bead_sort([5, 4, 3, 2, 1]) == [1, 2, 3, 4, 5]
							assert bead_sort([7, 9, 4, 3, 5]) == [3, 4, 5, 7, 9]




