






'''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 					__lowercase						(			__lowercase       )       ->						Union[str, Any]:

      '''simple docstring'''


      _A			     =	MobileViTConfig()

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

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

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

      return config

def 					__lowercase						(			__lowercase   ,  __lowercase=False       )       ->						int:

      '''simple docstring'''


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

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

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

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

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

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

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

      return name

def 					__lowercase						(			__lowercase   ,  __lowercase   ,  __lowercase=False       )       ->						Union[str, Any]:

      '''simple docstring'''


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

      for key in orig_state_dict.copy().keys():
            _A			     =	orig_state_dict.pop(__lowercase       )

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

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

      return orig_state_dict

def 					__lowercase						(			)       ->						Optional[Any]:

      '''simple docstring'''


      _A			     =	"http://images.cocodataset.org/val2017/000000039769.jpg"
      _A			     =	Image.open(requests.get(__lowercase   ,  stream=__lowercase       ).raw       )
      return im

@torch.no_grad()
def 					__lowercase						(			__lowercase   ,  __lowercase   ,  __lowercase   ,  __lowercase=False       )       ->						List[str]:

      '''simple docstring'''


      _A			     =	get_mobilevit_config(__lowercase       )

      # load original state_dict
      _A			     =	torch.load(__lowercase   ,  map_location="cpu"       )

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

      _A			     =	convert_state_dict(__lowercase   ,  __lowercase       )
      model.load_state_dict(__lowercase       )

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

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

            if mobilevit_name == "deeplabv3_mobilevit_s":
                  _A			     =	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			     =	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			     =	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]   ,  __lowercase   ,  atol=1e-4       )
      else:
            assert logits.shape == (1, 1000)

            if mobilevit_name == "mobilevit_s":
                  _A			     =	torch.tensor([-0.9866, 0.2392, -1.1241]       )
            elif mobilevit_name == "mobilevit_xs":
                  _A			     =	torch.tensor([-2.4761, -0.9399, -1.9587]       )
            elif mobilevit_name == "mobilevit_xxs":
                  _A			     =	torch.tensor([-1.9364, -1.2327, -0.4653]       )
            else:
                  raise ValueError(F'''Unknown mobilevit_name: {mobilevit_name}'''       )

            assert torch.allclose(logits[0, :3]   ,  __lowercase   ,  atol=1e-4       )

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

      if push_to_hub:
            _A			     =	{
                "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			     =	model_mapping[mobilevit_name]
            image_processor.push_to_hub(__lowercase   ,  organization="apple"       )
            model.push_to_hub(__lowercase   ,  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
    )









from __future__ import annotations
def 							_UpperCAmelCase				(UpperCamelCase__     :  list[int]				,							UpperCamelCase__     :  list[int]				,							UpperCamelCase__     :  int						):
       _A      :       Dict				    = list(range(len(UpperCamelCase__						)						)						)
       _A      :       Any				    = [v / w for v, w in zip(UpperCamelCase__				,							UpperCamelCase__						)]
       index.sort(key=lambda UpperCamelCase__						: ratio[i]				,							reverse=UpperCamelCase__						)

       _A      :       float				    = 0
       _A      :       list[float]				    = [0] * len(UpperCamelCase__						)
       for i in index:
              if weight[i] <= capacity:
                     _A      :       Union[str, Any]				    = 1
                     max_value += value[i]
                     capacity -= weight[i]
              else:
                     _A      :       Optional[Any]				    = capacity / weight[i]
                     max_value += value[i] * capacity / weight[i]
                     break

       return max_value, fractions


if __name__ == "__main__":
    import doctest

    doctest.testmod()
