



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__      :				Tuple   	=			logging.get_logger(__name__)

def   _lowerCAmelCase   (						A__	):
						lowercase__     =  MobileViTConfig()

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

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

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

						return config

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

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

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

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

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

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

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

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

						return name

def   _lowerCAmelCase   (						A__						,  A__						,  A__=False	):
						if base_model:
												lowercase__     =  """"""
						else:
												lowercase__     =  """mobilevit."""

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

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

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

						return orig_state_dict

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

@torch.no_grad()
def   _lowerCAmelCase   (						A__						,  A__						,  A__						,  A__=False	):
						lowercase__     =  get_mobilevit_config(UpperCamelCase__	)

						# load original state_dict
						lowercase__     =  torch.load(UpperCamelCase__						,  map_location='cpu'	)

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

						lowercase__     =  convert_state_dict(UpperCamelCase__						,  UpperCamelCase__	)
						model.load_state_dict(UpperCamelCase__	)

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

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

												if mobilevit_name == "deeplabv3_mobilevit_s":
																		lowercase__     =  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":
																		lowercase__     =  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":
																		lowercase__     =  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]],
																		        [[-10.5_536, -10.2_332, -10.2_924], [-10.2_336, -9.86_24, -9.59_64], [-10.8_840, -10.8_158, -10.6_659]],
																		        [[-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]						,  UpperCamelCase__						,  atol=1E-4	)
						else:
												assert logits.shape == (1, 1_000)

												if mobilevit_name == "mobilevit_s":
																		lowercase__     =  torch.tensor([-0.98_66, 0.23_92, -1.12_41]	)
												elif mobilevit_name == "mobilevit_xs":
																		lowercase__     =  torch.tensor([-2.47_61, -0.93_99, -1.95_87]	)
												elif mobilevit_name == "mobilevit_xxs":
																		lowercase__     =  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]						,  UpperCamelCase__						,  atol=1E-4	)

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

						if push_to_hub:
												lowercase__     =  {
												    """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...'	)
												lowercase__     =  model_mapping[mobilevit_name]
												image_processor.push_to_hub(UpperCamelCase__						,  organization='apple'	)
												model.push_to_hub(UpperCamelCase__						,  organization='apple'	)


if __name__ == "__main__":
							a__      :				str   	=			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__      :				int   	=			parser.parse_args()
							convert_movilevit_checkpoint(
							    args.mobilevit_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
							)





import cva
import numpy as np



class       UpperCAmelCase__:

   '''simple docstring'''




   def __init__(						self  :			Union[str, Any]			,				lowerCAmelCase  :			float			,				lowerCAmelCase  :			int)	->				Dict:




         """simple docstring"""

         if k in (0.04, 0.06):
               lowercase__     =  k
               lowercase__     =  window_size
         else:
               raise ValueError('invalid k value')


   def __str__(						self  :			Tuple)	->				str:




         """simple docstring"""

         return str(self.k)



   def 			UpperCAmelCase		(						self  :			Union[str, Any]			,				lowerCAmelCase  :			str)	->				tuple[cva.Mat, list[list[int]]]:




         """simple docstring"""

         lowercase__     =  cva.imread(lowerCAmelCase			,				0)
         lowercase__,	lowercase__     =  img.shape
         lowercase__     =  []
         lowercase__     =  img.copy()
         lowercase__     =  cva.cvtColor(lowerCAmelCase			,				cva.COLOR_GRAY2RGB)
         lowercase__,	lowercase__     =  np.gradient(lowerCAmelCase)
         lowercase__     =  dx**2
         lowercase__     =  dy**2
         lowercase__     =  dx * dy
         lowercase__     =  0.04
         lowercase__     =  self.window_size // 2
         for y in range(lowerCAmelCase			,				h - offset):
               for x in range(lowerCAmelCase			,				w - offset):
                     lowercase__     =  ixx[
                         y - offset : y + offset + 1, x - offset : x + offset + 1
                     ].sum()
                     lowercase__     =  iyy[
                         y - offset : y + offset + 1, x - offset : x + offset + 1
                     ].sum()
                     lowercase__     =  ixy[
                         y - offset : y + offset + 1, x - offset : x + offset + 1
                     ].sum()

                     lowercase__     =  (wxx * wyy) - (wxy**2)
                     lowercase__     =  wxx + wyy
                     lowercase__     =  det - k * (trace**2)
                     # Can change the value
                     if r > 0.5:
                           corner_list.append([x, y, r])
                           color_img.itemset((y, x, 0)			,				0)
                           color_img.itemset((y, x, 1)			,				0)
                           color_img.itemset((y, x, 2)			,				2_55)
         return color_img, corner_list


if __name__ == "__main__":
       a__      :				Dict   	=			HarrisCorner(0.0_4, 3)
       a__	,		a__      :				Dict   	=			edge_detect.detect("path_to_image")
       cva.imwrite("detect.png", color_img)
