



"""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 			snake_case_    ( A_   :						Any		):


       '''simple docstring'''





       _lowerCamelCase		:							List[str]            =					MobileViTConfig()

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

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

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

       return config






def 			snake_case_    ( A_   :						Tuple,							A_   :						Union[str, Any]=False		):


       '''simple docstring'''





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

       if "conv_1." in name:
              _lowerCamelCase		:							Union[str, Any]            =					name.replace('''conv_1.''',							'''conv_stem.'''		)
       if ".block." in name:
              _lowerCamelCase		:							Union[str, Any]            =					name.replace('''.block.''',							'''.'''		)

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

       for i in range(2,							5		):
              if F'''.global_rep.{i}.weight''' in name:
                     _lowerCamelCase		:							Optional[Any]            =					name.replace(F'''.global_rep.{i}.weight''',							'''.layernorm.weight'''		)
              if F'''.global_rep.{i}.bias''' in name:
                     _lowerCamelCase		:							Union[str, Any]            =					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		:							Dict            =					name.replace('''.pre_norm_mha.0.''',							'''.layernorm_before.'''		)
       if ".pre_norm_mha.1.out_proj." in name:
              _lowerCamelCase		:							Optional[int]            =					name.replace('''.pre_norm_mha.1.out_proj.''',							'''.attention.output.dense.'''		)
       if ".pre_norm_ffn.0." in name:
              _lowerCamelCase		:							int            =					name.replace('''.pre_norm_ffn.0.''',							'''.layernorm_after.'''		)
       if ".pre_norm_ffn.1." in name:
              _lowerCamelCase		:							Tuple            =					name.replace('''.pre_norm_ffn.1.''',							'''.intermediate.dense.'''		)
       if ".pre_norm_ffn.4." in name:
              _lowerCamelCase		:							Optional[Any]            =					name.replace('''.pre_norm_ffn.4.''',							'''.output.dense.'''		)
       if ".transformer." in name:
              _lowerCamelCase		:							List[str]            =					name.replace('''.transformer.''',							'''.transformer.layer.'''		)

       if ".aspp_layer." in name:
              _lowerCamelCase		:							Optional[Any]            =					name.replace('''.aspp_layer.''',							'''.'''		)
       if ".aspp_pool." in name:
              _lowerCamelCase		:							Tuple            =					name.replace('''.aspp_pool.''',							'''.'''		)
       if "seg_head." in name:
              _lowerCamelCase		:							Dict            =					name.replace('''seg_head.''',							'''segmentation_head.'''		)
       if "segmentation_head.classifier.classifier." in name:
              _lowerCamelCase		:							Dict            =					name.replace('''segmentation_head.classifier.classifier.''',							'''segmentation_head.classifier.'''		)

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

       return name






def 			snake_case_    ( A_   :						Union[str, Any],							A_   :						str,							A_   :						Union[str, Any]=False		):


       '''simple docstring'''





       if base_model:
              _lowerCamelCase		:							Union[str, Any]            =					''''''
       else:
              _lowerCamelCase		:							Tuple            =					'''mobilevit.'''

       for key in orig_state_dict.copy().keys():
              _lowerCamelCase		:							Optional[int]            =					orig_state_dict.pop(A_		)

              if key[:8] == "encoder.":
                     _lowerCamelCase		:							List[str]            =					key[8:]

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

       return orig_state_dict






def 			snake_case_    ( ):


       '''simple docstring'''





       _lowerCamelCase		:							List[str]            =					'''http://images.cocodataset.org/val2017/000000039769.jpg'''
       _lowerCamelCase		:							str            =					Image.open(requests.get(A_,							stream=A_		).raw		)
       return im






@torch.no_grad()
def 			snake_case_    ( A_   :						Any,							A_   :						Tuple,							A_   :						Tuple,							A_   :						Any=False		):


       '''simple docstring'''





       _lowerCamelCase		:							Any            =					get_mobilevit_config(A_		)

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

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

       _lowerCamelCase		:							List[str]            =					convert_state_dict(A_,							A_		)
       model.load_state_dict(A_		)

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

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

              if mobilevit_name == "deeplabv3_mobilevit_s":
                     _lowerCamelCase		:							List[Any]            =					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":
                     _lowerCamelCase		:							List[str]            =					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":
                     _lowerCamelCase		:							str            =					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],							A_,							atol=1E-4		)
       else:
              assert logits.shape == (1, 10_00)

              if mobilevit_name == "mobilevit_s":
                     _lowerCamelCase		:							Dict            =					torch.tensor([-0.9866, 0.2392, -1.1241]		)
              elif mobilevit_name == "mobilevit_xs":
                     _lowerCamelCase		:							Optional[int]            =					torch.tensor([-2.4761, -0.9399, -1.9587]		)
              elif mobilevit_name == "mobilevit_xxs":
                     _lowerCamelCase		:							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],							A_,							atol=1E-4		)

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

       if push_to_hub:
              _lowerCamelCase		:							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		:							Tuple            =					model_mapping[mobilevit_name]
              image_processor.push_to_hub(A_,							organization='''apple'''		)
              model.push_to_hub(A_,							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
   )







import socket

def      __lowercase(						):


      """simple docstring"""



      lowerCamelCase             =		socket.socket(socket.AF_INET , socket.SOCK_STREAM			)
      lowerCamelCase             =		socket.gethostname()
      lowerCamelCase             =		12312

      sock.connect((host, port)			)
      sock.send(B"Hello server!"			)

      with open("Received_file" , "wb"			) as out_file:
            print("File opened"			)
            print("Receiving data..."			)
            while True:
                  lowerCamelCase             =		sock.recv(1024			)
                  if not data:
                        break
                  out_file.write(UpperCAmelCase__			)

      print("Successfully received the file"			)
      sock.close()
      print("Connection closed"			)


if __name__ == "__main__":
     main()