

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



def 			__a		(       lowerCAmelCase__ :    Dict      ):
     a__   :      Optional[int]     =						MobileViTConfig()

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

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

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

     return config



def 			__a		(       lowerCAmelCase__ :    Optional[Any]     ,  lowerCAmelCase__ :    Tuple=False      ):




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

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

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

     for i in range(2     ,  5      ):
          if F'.global_rep.{i}.weight' in name:
               a__   :      Optional[int]     =						name.replace(F'.global_rep.{i}.weight'     ,  '''.layernorm.weight'''      )
          if F'.global_rep.{i}.bias' in name:
               a__   :      str     =						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__   :      List[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__   :      int     =						name.replace('''.pre_norm_ffn.0.'''     ,  '''.layernorm_after.'''      )
     if ".pre_norm_ffn.1." in name:
          a__   :      str     =						name.replace('''.pre_norm_ffn.1.'''     ,  '''.intermediate.dense.'''      )
     if ".pre_norm_ffn.4." in name:
          a__   :      Optional[int]     =						name.replace('''.pre_norm_ffn.4.'''     ,  '''.output.dense.'''      )
     if ".transformer." in name:
          a__   :      int     =						name.replace('''.transformer.'''     ,  '''.transformer.layer.'''      )

     if ".aspp_layer." in name:
          a__   :      str     =						name.replace('''.aspp_layer.'''     ,  '''.'''      )
     if ".aspp_pool." in name:
          a__   :      Any     =						name.replace('''.aspp_pool.'''     ,  '''.'''      )
     if "seg_head." in name:
          a__   :      List[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__   :      List[str]     =						name.replace('''classifier.fc.'''     ,  '''classifier.'''      )
     elif (not base_model) and ("segmentation_head." not in name):
          a__   :      List[str]     =						'''mobilevit.''' + name

     return name



def 			__a		(       lowerCAmelCase__ :    int     ,  lowerCAmelCase__ :    List[Any]     ,  lowerCAmelCase__ :    str=False      ):




     if base_model:
          a__   :      int     =						''''''
     else:
          a__   :      Any     =						'''mobilevit.'''

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

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

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

     return orig_state_dict



def 			__a		(       ):
     a__   :      List[str]     =						'''http://images.cocodataset.org/val2017/000000039769.jpg'''
     a__   :      List[Any]     =						Image.open(requests.get(_UpperCamelCase     ,  stream=_UpperCamelCase      ).raw      )
     return im



@torch.no_grad()
def 			__a		(       lowerCAmelCase__ :    Union[str, Any]     ,  lowerCAmelCase__ :    Union[str, Any]     ,  lowerCAmelCase__ :    List[Any]     ,  lowerCAmelCase__ :    Tuple=False      ):
     a__   :      int     =						get_mobilevit_config(_UpperCamelCase      )

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

     # load 🤗 model
     if mobilevit_name.startswith('''deeplabv3_'''      ):
          a__   :      Optional[Any]     =						MobileViTForSemanticSegmentation(_UpperCamelCase      ).eval()
     else:
          a__   :      int     =						MobileViTForImageClassification(_UpperCamelCase      ).eval()

     a__   :      int     =						convert_state_dict(_UpperCamelCase     ,  _UpperCamelCase      )
     model.load_state_dict(_UpperCamelCase      )

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

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

          if mobilevit_name == "deeplabv3_mobilevit_s":
               a__   :      int     =						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":
               a__   :      Tuple     =						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":
               a__   :      List[Any]     =						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]     ,  _UpperCamelCase     ,  atol=1E-4      )
     else:
          assert logits.shape == (1, 1000)

          if mobilevit_name == "mobilevit_s":
               a__   :      str     =						torch.tensor([-0.9866, 0.2392, -1.1241]      )
          elif mobilevit_name == "mobilevit_xs":
               a__   :      str     =						torch.tensor([-2.4761, -0.9399, -1.9587]      )
          elif mobilevit_name == "mobilevit_xxs":
               a__   :      Optional[Any]     =						torch.tensor([-1.9364, -1.2327, -0.4653]      )
          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:
          a__   :      int     =						{
              '''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__   :      int     =						model_mapping[mobilevit_name]
          image_processor.push_to_hub(_UpperCamelCase     ,  organization='''apple'''      )
          model.push_to_hub(_UpperCamelCase     ,  organization='''apple'''      )


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




"""simple docstring"""


from __future__ import annotations

import math

import numpy as np
from numpy.linalg import norm


def _lowerCamelCase      (						_UpperCamelCase       ,					_UpperCamelCase ):

			'''simple docstring'''




			return math.sqrt(sum(pow(a - b       ,					2 ) for a, b in zip(_UpperCamelCase       ,					_UpperCamelCase ) ) )


def _lowerCamelCase      (						_UpperCamelCase       ,					_UpperCamelCase ):

			'''simple docstring'''




			if dataset.ndim != value_array.ndim:
						__lowerCAmelCase							       =     (
						    "Wrong input data's dimensions... "
						    f"dataset : {dataset.ndim}, value_array : {value_array.ndim}"
						)
						raise ValueError(_UpperCamelCase )

			try:
						if dataset.shape[1] != value_array.shape[1]:
									__lowerCAmelCase							       =     (
									    "Wrong input data's shape... "
									    f"dataset : {dataset.shape[1]}, value_array : {value_array.shape[1]}"
									)
									raise ValueError(_UpperCamelCase )
			except IndexError:
						if dataset.ndim != value_array.ndim:
									raise TypeError("Wrong shape" )

			if dataset.dtype != value_array.dtype:
						__lowerCAmelCase							       =     (
						    "Input data have different datatype... "
						    f"dataset : {dataset.dtype}, value_array : {value_array.dtype}"
						)
						raise TypeError(_UpperCamelCase )

			__lowerCAmelCase							       =     []

			for value in value_array:
						__lowerCAmelCase							       =     euclidean(_UpperCamelCase       ,					dataset[0] )
						__lowerCAmelCase							       =     dataset[0].tolist()

						for dataset_value in dataset[1:]:
									__lowerCAmelCase							       =     euclidean(_UpperCamelCase       ,					_UpperCamelCase )

									if dist > temp_dist:
												__lowerCAmelCase							       =     temp_dist
												__lowerCAmelCase							       =     dataset_value.tolist()

						answer.append([vector, dist] )

			return answer


def _lowerCamelCase      (						_UpperCamelCase       ,					_UpperCamelCase ):

			'''simple docstring'''




			return np.dot(_UpperCamelCase       ,					_UpperCamelCase ) / (norm(_UpperCamelCase ) * norm(_UpperCamelCase ))


if __name__ == "__main__":
	import doctest

	doctest.testmod()




