





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()
snake_case_           =     logging.get_logger(__name__)






def lowerCamelCase__       (				snake_case_      :    List[Any]			) ->							Union[str, Any]:
   __snake_case							=  MobileViTConfig()

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

   if mobilevit_name.startswith('''deeplabv3_'''			):
      __snake_case							=  512
      __snake_case							=  16
      __snake_case							=  21
      __snake_case							=  'pascal-voc-id2label.json'
   else:
      __snake_case							=  1000
      __snake_case							=  'imagenet-1k-id2label.json'

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

   return config






def lowerCamelCase__       (				snake_case_      :    Any			, snake_case_      :    str=False			) ->							Optional[int]:
   for i in range(1			, 6			):
      if f"""layer_{i}.""" in name:
         __snake_case							=  name.replace(f"""layer_{i}."""			, f"""encoder.layer.{i - 1}."""			)

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

   if "exp_1x1" in name:
      __snake_case							=  name.replace('''exp_1x1'''			, '''expand_1x1'''			)
   if "red_1x1" in name:
      __snake_case							=  name.replace('''red_1x1'''			, '''reduce_1x1'''			)
   if ".local_rep.conv_3x3." in name:
      __snake_case							=  name.replace('''.local_rep.conv_3x3.'''			, '''.conv_kxk.'''			)
   if ".local_rep.conv_1x1." in name:
      __snake_case							=  name.replace('''.local_rep.conv_1x1.'''			, '''.conv_1x1.'''			)
   if ".norm." in name:
      __snake_case							=  name.replace('''.norm.'''			, '''.normalization.'''			)
   if ".conv." in name:
      __snake_case							=  name.replace('''.conv.'''			, '''.convolution.'''			)
   if ".conv_proj." in name:
      __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:
            __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:
            __snake_case							=  name.replace(f""".{i}.{j}."""			, f""".{i}."""			)
            if "expand_1x1" in name:
               __snake_case							=  name.replace('''expand_1x1'''			, '''downsampling_layer.expand_1x1'''			)
            if "conv_3x3" in name:
               __snake_case							=  name.replace('''conv_3x3'''			, '''downsampling_layer.conv_3x3'''			)
            if "reduce_1x1" in name:
               __snake_case							=  name.replace('''reduce_1x1'''			, '''downsampling_layer.reduce_1x1'''			)

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

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

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

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

   return name






def lowerCamelCase__       (				snake_case_      :    List[Any]			, snake_case_      :    List[Any]			, snake_case_      :    Any=False			) ->							Union[str, Any]:
   if base_model:
      __snake_case							=  ''
   else:
      __snake_case							=  'mobilevit.'

   for key in orig_state_dict.copy().keys():
      __snake_case							=  orig_state_dict.pop(UpperCAmelCase_			)

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

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

   return orig_state_dict






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






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

   # load original state_dict
   __snake_case							=  torch.load(UpperCAmelCase_			, map_location='''cpu'''			)

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

   __snake_case							=  convert_state_dict(UpperCAmelCase_			, UpperCAmelCase_			)
   model.load_state_dict(UpperCAmelCase_			)

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

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

      if mobilevit_name == "deeplabv3_mobilevit_s":
         __snake_case							=  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":
         __snake_case							=  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":
         __snake_case							=  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, 1000)

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


if __name__ == "__main__":
    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.'
    )

    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'''



import argparse


A__						:   Optional[Any]				=    """docs/source/_static/js/custom.js"""



def      UpperCAmelCase__   (      UpperCAmelCase_       :							Optional[int]				)	->						int:
     with open(UpperCAmelCase_ ,       encoding='utf-8' ,       newline='\n'				) as f:
          __lowerCamelCase  :      Dict														=						f.readlines()
     __lowerCamelCase  :      Tuple														=						0

     # First let's put the right version
     while not lines[index].startswith('const stableVersion ='				):
          index += 1
     __lowerCamelCase  :      Dict														=						F'const stableVersion = "v{version}"\n'

     # Then update the dictionary
     while not lines[index].startswith('const versionMapping = {'				):
          index += 1

     # We go until the end
     while not lines[index].startswith('}'				):
          index += 1
     # We add the new version at the end
     lines[index - 1] += F'    "v{version}": "v{version}",\n'

     with open(UpperCAmelCase_ ,       'w' ,       encoding='utf-8' ,       newline='\n'				) as f:
          f.writelines(UpperCAmelCase_				)


if __name__ == "__main__":
      A__						:   str				=    argparse.ArgumentParser()
      parser.add_argument("""--version""", help="""Release version.""")
      A__						:   Any				=    parser.parse_args()
      update_custom_js(args.version)






