


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



def      UpperCAmelCase__   (      UpperCAmelCase_       :							List[Any]				)	->						Union[str, Any]:
     __lowerCamelCase  :      Optional[int]														=						MobileViTConfig()

     # size of the architecture
     if "mobilevit_s" in mobilevit_name:
          __lowerCamelCase  :      Any														=						[1_44, 1_92, 2_40]
          __lowerCamelCase  :      str														=						[16, 32, 64, 96, 1_28, 1_60, 6_40]
     elif "mobilevit_xs" in mobilevit_name:
          __lowerCamelCase  :      int														=						[96, 1_20, 1_44]
          __lowerCamelCase  :      Tuple														=						[16, 32, 48, 64, 80, 96, 3_84]
     elif "mobilevit_xxs" in mobilevit_name:
          __lowerCamelCase  :      Union[str, Any]														=						[64, 80, 96]
          __lowerCamelCase  :      List[str]														=						[16, 16, 24, 48, 64, 80, 3_20]
          __lowerCamelCase  :      Union[str, Any]														=						0.05
          __lowerCamelCase  :      List[str]														=						2.0

     if mobilevit_name.startswith('deeplabv3_'				):
          __lowerCamelCase  :      Union[str, Any]														=						5_12
          __lowerCamelCase  :      Any														=						16
          __lowerCamelCase  :      Union[str, Any]														=						21
          __lowerCamelCase  :      Tuple														=						'pascal-voc-id2label.json'
     else:
          __lowerCamelCase  :      Dict														=						10_00
          __lowerCamelCase  :      Union[str, Any]														=						'imagenet-1k-id2label.json'

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

     return config



def      UpperCAmelCase__   (      UpperCAmelCase_       :							Any ,       UpperCAmelCase_       :							str=False				)	->						Optional[int]:
     for i in range(1 ,       6				):
          if F'layer_{i}.' in name:
               __lowerCamelCase  :      Optional[Any]														=						name.replace(F'layer_{i}.' ,       F'encoder.layer.{i - 1}.'				)

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

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

     for i in range(2 ,       5				):
          if F'.global_rep.{i}.weight' in name:
               __lowerCamelCase  :      List[Any]														=						name.replace(F'.global_rep.{i}.weight' ,       '.layernorm.weight'				)
          if F'.global_rep.{i}.bias' in name:
               __lowerCamelCase  :      Tuple														=						name.replace(F'.global_rep.{i}.bias' ,       '.layernorm.bias'				)

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

     if ".aspp_layer." in name:
          __lowerCamelCase  :      int														=						name.replace('.aspp_layer.' ,       '.'				)
     if ".aspp_pool." in name:
          __lowerCamelCase  :      Any														=						name.replace('.aspp_pool.' ,       '.'				)
     if "seg_head." in name:
          __lowerCamelCase  :      str														=						name.replace('seg_head.' ,       'segmentation_head.'				)
     if "segmentation_head.classifier.classifier." in name:
          __lowerCamelCase  :      Any														=						name.replace('segmentation_head.classifier.classifier.' ,       'segmentation_head.classifier.'				)

     if "classifier.fc." in name:
          __lowerCamelCase  :      Optional[Any]														=						name.replace('classifier.fc.' ,       'classifier.'				)
     elif (not base_model) and ("segmentation_head." not in name):
          __lowerCamelCase  :      Tuple														=						'mobilevit.' + name

     return name



def      UpperCAmelCase__   (      UpperCAmelCase_       :							List[Any] ,       UpperCAmelCase_       :							List[Any] ,       UpperCAmelCase_       :							Any=False				)	->						Union[str, Any]:
     if base_model:
          __lowerCamelCase  :      Optional[int]														=						''
     else:
          __lowerCamelCase  :      Union[str, Any]														=						'mobilevit.'

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

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

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

     return orig_state_dict



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



@torch.no_grad()
def      UpperCAmelCase__   (      UpperCAmelCase_       :							Dict ,       UpperCAmelCase_       :							Dict ,       UpperCAmelCase_       :							Union[str, Any] ,       UpperCAmelCase_       :							Tuple=False				)	->						List[Any]:
     __lowerCamelCase  :      Tuple														=						get_mobilevit_config(UpperCAmelCase_				)

     # load original state_dict
     __lowerCamelCase  :      Dict														=						torch.load(UpperCAmelCase_ ,       map_location='cpu'				)

     # load 🤗 model
     if mobilevit_name.startswith('deeplabv3_'				):
          __lowerCamelCase  :      int														=						MobileViTForSemanticSegmentation(UpperCAmelCase_				).eval()
     else:
          __lowerCamelCase  :      Any														=						MobileViTForImageClassification(UpperCAmelCase_				).eval()

     __lowerCamelCase  :      Any														=						convert_state_dict(UpperCAmelCase_ ,       UpperCAmelCase_				)
     model.load_state_dict(UpperCAmelCase_				)

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

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

          if mobilevit_name == "deeplabv3_mobilevit_s":
               __lowerCamelCase  :      List[str]														=						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":
               __lowerCamelCase  :      str														=						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":
               __lowerCamelCase  :      int														=						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] ,       UpperCAmelCase_ ,       atol=1e-4				)
     else:
          assert logits.shape == (1, 10_00)

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


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

      A__						:   Tuple				=    parser.parse_args()
      convert_movilevit_checkpoint(
          args.mobilevit_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
      )








'''simple docstring'''
from string import ascii_lowercase, ascii_uppercase







def 		A_(     A						:		str):
							if not sentence:
														return ""
							UpperCamelCase   			=     dict(zip(A		,     A))
							return lower_to_upper.get(sentence[0]		,     sentence[0]) + sentence[1:]


if __name__ == "__main__":
						from doctest import testmod

						testmod()




