
'''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()
UpperCamelCase__      :		Dict       							=  logging.get_logger(__name__)
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_						)     ->     List[str]:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE      =				MobileViTConfig()

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

   if mobilevit_name.startswith("""deeplabv3_"""						):
      _SCREAMING_SNAKE_CASE      =				5_12
      _SCREAMING_SNAKE_CASE      =				16
      _SCREAMING_SNAKE_CASE      =				21
      _SCREAMING_SNAKE_CASE      =				"""pascal-voc-id2label.json"""
   else:
      _SCREAMING_SNAKE_CASE      =				10_00
      _SCREAMING_SNAKE_CASE      =				"""imagenet-1k-id2label.json"""

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

   return config
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_=False						)     ->     Any:




   """simple docstring"""






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

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

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

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

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

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

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

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

   return name
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_=False						)     ->     str:




   """simple docstring"""






   if base_model:
      _SCREAMING_SNAKE_CASE      =				""""""
   else:
      _SCREAMING_SNAKE_CASE      =				"""mobilevit."""

   for key in orig_state_dict.copy().keys():
      _SCREAMING_SNAKE_CASE      =				orig_state_dict.pop(__UpperCamelCase						)

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

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

   return orig_state_dict
def    lowerCAmelCase_	(				)     ->     int:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE      =				"""http://images.cocodataset.org/val2017/000000039769.jpg"""
   _SCREAMING_SNAKE_CASE      =				Image.open(requests.get(__UpperCamelCase    ,      stream=__UpperCamelCase						).raw						)
   return im
@torch.no_grad()
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_=False						)     ->     str:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE      =				get_mobilevit_config(__UpperCamelCase						)

   # load original state_dict
   _SCREAMING_SNAKE_CASE      =				torch.load(__UpperCamelCase    ,      map_location="""cpu"""						)

   # load 🤗 model
   if mobilevit_name.startswith("""deeplabv3_"""						):
      _SCREAMING_SNAKE_CASE      =				MobileViTForSemanticSegmentation(__UpperCamelCase						).eval()
   else:
      _SCREAMING_SNAKE_CASE      =				MobileViTForImageClassification(__UpperCamelCase						).eval()

   _SCREAMING_SNAKE_CASE      =				convert_state_dict(__UpperCamelCase    ,      __UpperCamelCase						)
   model.load_state_dict(__UpperCamelCase						)

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

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

      if mobilevit_name == "deeplabv3_mobilevit_s":
         _SCREAMING_SNAKE_CASE      =				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":
         _SCREAMING_SNAKE_CASE      =				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":
         _SCREAMING_SNAKE_CASE      =				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, 10_00)

      if mobilevit_name == "mobilevit_s":
         _SCREAMING_SNAKE_CASE      =				torch.tensor([-0.9866, 0.2392, -1.1241]						)
      elif mobilevit_name == "mobilevit_xs":
         _SCREAMING_SNAKE_CASE      =				torch.tensor([-2.4761, -0.9399, -1.9587]						)
      elif mobilevit_name == "mobilevit_xxs":
         _SCREAMING_SNAKE_CASE      =				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:
      _SCREAMING_SNAKE_CASE      =				{
          """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..."""						)
      _SCREAMING_SNAKE_CASE      =				model_mapping[mobilevit_name]
      image_processor.push_to_hub(__UpperCamelCase    ,      organization="""apple"""						)
      model.push_to_hub(__UpperCamelCase    ,      organization="""apple"""						)


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

     UpperCamelCase__      :		str       							=  parser.parse_args()
     convert_movilevit_checkpoint(
         args.mobilevit_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
     )

'''simple docstring'''



def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_						)     ->     int:




   """simple docstring"""

   if not isinstance(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						):
      raise ValueError("""multiplicative_persistence() only accepts integral values"""						)
   if num < 0:
      raise ValueError("""multiplicative_persistence() does not accept negative values"""						)

   _SCREAMING_SNAKE_CASE      =				0
   _SCREAMING_SNAKE_CASE      =				str(SCREAMING_SNAKE_CASE_						)

   while len(SCREAMING_SNAKE_CASE_						) != 1:
      _SCREAMING_SNAKE_CASE      =				[int(SCREAMING_SNAKE_CASE_						) for i in num_string]

      _SCREAMING_SNAKE_CASE      =				1
      for i in range(0    ,      len(SCREAMING_SNAKE_CASE_						)						):
         total *= numbers[i]

      _SCREAMING_SNAKE_CASE      =				str(SCREAMING_SNAKE_CASE_						)

      steps += 1
   return steps
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_						)     ->     int:




   """simple docstring"""

   if not isinstance(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						):
      raise ValueError("""additive_persistence() only accepts integral values"""						)
   if num < 0:
      raise ValueError("""additive_persistence() does not accept negative values"""						)

   _SCREAMING_SNAKE_CASE      =				0
   _SCREAMING_SNAKE_CASE      =				str(SCREAMING_SNAKE_CASE_						)

   while len(SCREAMING_SNAKE_CASE_						) != 1:
      _SCREAMING_SNAKE_CASE      =				[int(SCREAMING_SNAKE_CASE_						) for i in num_string]

      _SCREAMING_SNAKE_CASE      =				0
      for i in range(0    ,      len(SCREAMING_SNAKE_CASE_						)						):
         total += numbers[i]

      _SCREAMING_SNAKE_CASE      =				str(SCREAMING_SNAKE_CASE_						)

      steps += 1
   return steps


if __name__ == "__main__":
     import doctest

     doctest.testmod()






