



from __future__ import annotations

import math



class 							lowerCAmelCase_						:
       def __init__(     self,     SCREAMING_SNAKE_CASE_			)	->						None:
          UpperCamelCase       :    str		      =      size
          # approximate the overall size of segment tree with given value
          UpperCamelCase       :    Dict		      =      [0 for i in range(0,     4 * size			)]
          # create array to store lazy update
          UpperCamelCase       :    Dict		      =      [0 for i in range(0,     4 * size			)]
          UpperCamelCase       :    List[str]		      =      [0 for i in range(0,     4 * size			)]  # flag for lazy update
       def    snake_case_			(     self,     SCREAMING_SNAKE_CASE_			)	->						int:
          return idx * 2
       def    snake_case_			(     self,     SCREAMING_SNAKE_CASE_			)	->						int:
          return idx * 2 + 1
       def    snake_case_			(     self,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_			)	->						None:
          if left_element == right_element:
             UpperCamelCase       :    Dict		      =      a[left_element - 1]
          else:
             UpperCamelCase       :    str		      =      (left_element + right_element) // 2
             self.build(self.left(SCREAMING_SNAKE_CASE_			),     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_			)
             self.build(self.right(SCREAMING_SNAKE_CASE_			),     mid + 1,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_			)
             UpperCamelCase       :    Any		      =      max(
                 self.segment_tree[self.left(SCREAMING_SNAKE_CASE_			)],     self.segment_tree[self.right(SCREAMING_SNAKE_CASE_			)]			)
       def    snake_case_			(     self,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_			)	->						bool:
          if self.flag[idx] is True:
             UpperCamelCase       :    Union[str, Any]		      =      self.lazy[idx]
             UpperCamelCase       :    str		      =      False
             if left_element != right_element:
                UpperCamelCase       :    int		      =      self.lazy[idx]
                UpperCamelCase       :    Tuple		      =      self.lazy[idx]
                UpperCamelCase       :    Optional[Any]		      =      True
                UpperCamelCase       :    Tuple		      =      True

          if right_element < a or left_element > b:
             return True
          if left_element >= a and right_element <= b:
             UpperCamelCase       :    List[str]		      =      val
             if left_element != right_element:
                UpperCamelCase       :    Optional[int]		      =      val
                UpperCamelCase       :    Union[str, Any]		      =      val
                UpperCamelCase       :    Optional[int]		      =      True
                UpperCamelCase       :    List[Any]		      =      True
             return True
          UpperCamelCase       :    Union[str, Any]		      =      (left_element + right_element) // 2
          self.update(self.left(SCREAMING_SNAKE_CASE_			),     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_			)
          self.update(self.right(SCREAMING_SNAKE_CASE_			),     mid + 1,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_			)
          UpperCamelCase       :    Union[str, Any]		      =      max(
              self.segment_tree[self.left(SCREAMING_SNAKE_CASE_			)],     self.segment_tree[self.right(SCREAMING_SNAKE_CASE_			)]			)
          return True
       def    snake_case_			(     self,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_			)	->						int | float:
          if self.flag[idx] is True:
             UpperCamelCase       :    str		      =      self.lazy[idx]
             UpperCamelCase       :    Union[str, Any]		      =      False
             if left_element != right_element:
                UpperCamelCase       :    Optional[Any]		      =      self.lazy[idx]
                UpperCamelCase       :    Optional[int]		      =      self.lazy[idx]
                UpperCamelCase       :    Union[str, Any]		      =      True
                UpperCamelCase       :    int		      =      True
          if right_element < a or left_element > b:
             return -math.inf
          if left_element >= a and right_element <= b:
             return self.segment_tree[idx]
          UpperCamelCase       :    Any		      =      (left_element + right_element) // 2
          UpperCamelCase       :    int		      =      self.query(self.left(SCREAMING_SNAKE_CASE_			),     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_			)
          UpperCamelCase       :    Optional[int]		      =      self.query(self.right(SCREAMING_SNAKE_CASE_			),     mid + 1,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_			)
          return max(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_			)






       def __str__(     self			)	->						str:
          return str([self.query(1,     1,     self.size,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_			) for i in range(1,     self.size + 1			)]			)


if __name__ == "__main__":
    __UpperCAmelCase			  =			[1, 2, -4, 7, 3, -5, 6, 11, -20, 9, 14, 15, 5, 2, -8]
    __UpperCAmelCase			  =			15
    __UpperCAmelCase			  =			SegmentTree(size)
    segt.build(1, 1, size, A)
    print(segt.query(1, 1, size, 4, 6))
    print(segt.query(1, 1, size, 7, 11))
    print(segt.query(1, 1, size, 7, 12))
    segt.update(1, 1, size, 1, 3, 111)
    print(segt.query(1, 1, size, 1, 15))
    segt.update(1, 1, size, 7, 8, 235)
    print(segt)




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()
_lowercase:	Dict       			=		logging.get_logger(__name__)






def _lowerCamelCase  (		snake_case  ):
							_lowerCAmelCase							=							MobileViTConfig()

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

							if mobilevit_name.startswith('deeplabv3_'  ):
														_lowerCAmelCase							=							512
														_lowerCAmelCase							=							16
														_lowerCAmelCase							=							21
														_lowerCAmelCase							=							'pascal-voc-id2label.json'
							else:
														_lowerCAmelCase							=							1_000
														_lowerCAmelCase							=							'imagenet-1k-id2label.json'

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

							return config






def _lowerCamelCase  (		snake_case     , snake_case=False  ):
							for i in range(1     , 6  ):
														if F'layer_{i}.' in name:
																					_lowerCAmelCase							=							name.replace(F'layer_{i}.'     , F'encoder.layer.{i - 1}.'  )

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

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

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

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

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

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

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

							return name






def _lowerCamelCase  (		snake_case     , snake_case     , snake_case=False  ):
							if base_model:
														_lowerCAmelCase							=							''
							else:
														_lowerCAmelCase							=							'mobilevit.'

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

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

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

							return orig_state_dict






def _lowerCamelCase  (		):
							_lowerCAmelCase							=							'http://images.cocodataset.org/val2017/000000039769.jpg'
							_lowerCAmelCase							=							Image.open(requests.get(snake_case     , stream=snake_case  ).raw  )
							return im






@torch.no_grad()
def _lowerCamelCase  (		snake_case     , snake_case     , snake_case     , snake_case=False  ):
							_lowerCAmelCase							=							get_mobilevit_config(snake_case  )

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

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

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

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

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

														if mobilevit_name == "deeplabv3_mobilevit_s":
																					_lowerCAmelCase							=							torch.tensor(
																					    [
																					        [[6.20_65, 6.12_92, 6.20_70], [6.10_79, 6.12_54, 6.17_47], [6.00_42, 6.10_71, 6.10_34]],
																					        [[-6.92_53, -6.86_53, -7.03_98], [-7.32_18, -7.39_83, -7.36_70], [-7.19_61, -7.24_82, -7.15_69]],
																					        [[-4.47_23, -4.43_48, -4.37_69], [-5.36_29, -5.46_32, -5.45_98], [-5.15_87, -5.34_02, -5.50_59]],
																					    ]  )
														elif mobilevit_name == "deeplabv3_mobilevit_xs":
																					_lowerCAmelCase							=							torch.tensor(
																					    [
																					        [[5.44_49, 5.57_33, 5.63_14], [5.18_15, 5.39_30, 5.59_63], [5.16_56, 5.43_33, 5.48_53]],
																					        [[-9.44_23, -9.77_66, -9.67_14], [-9.15_81, -9.57_20, -9.55_19], [-9.10_06, -9.64_58, -9.57_03]],
																					        [[-7.77_21, -7.37_16, -7.15_83], [-8.45_99, -8.06_24, -7.79_44], [-8.41_72, -7.83_66, -7.50_25]],
																					    ]  )
														elif mobilevit_name == "deeplabv3_mobilevit_xxs":
																					_lowerCAmelCase							=							torch.tensor(
																					    [
																					        [[6.98_11, 6.97_43, 7.31_23], [7.17_77, 7.19_31, 7.39_38], [7.56_33, 7.80_50, 7.89_01]],
																					        [[-10.55_36, -10.23_32, -10.29_24], [-10.23_36, -9.86_24, -9.59_64], [-10.88_40, -10.81_58, -10.66_59]],
																					        [[-3.49_38, -3.06_31, -2.86_20], [-3.42_05, -2.81_35, -2.68_75], [-3.41_79, -2.79_45, -2.87_50]],
																					    ]  )
														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_000)

														if mobilevit_name == "mobilevit_s":
																					_lowerCAmelCase							=							torch.tensor([-0.98_66, 0.23_92, -1.12_41]  )
														elif mobilevit_name == "mobilevit_xs":
																					_lowerCAmelCase							=							torch.tensor([-2.47_61, -0.93_99, -1.95_87]  )
														elif mobilevit_name == "mobilevit_xxs":
																					_lowerCAmelCase							=							torch.tensor([-1.93_64, -1.23_27, -0.46_53]  )
														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:
														_lowerCAmelCase							=							{
														    '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...'  )
														_lowerCAmelCase							=							model_mapping[mobilevit_name]
														image_processor.push_to_hub(snake_case     , organization='apple'  )
														model.push_to_hub(snake_case     , organization='apple'  )


if __name__ == "__main__":
				_lowercase:	Union[str, 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.'''
				)

				_lowercase:	List[str]       			=		parser.parse_args()
				convert_movilevit_checkpoint(
				    args.mobilevit_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
				)






