





"""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 					__lowerCamelCase       (							a_					:		Union[str, Any]							)      ->				int:
      __SCREAMING_SNAKE_CASE       :int		    =  MobileViTConfig()

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

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

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

      return config



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

      if "conv_1." in name:
            __SCREAMING_SNAKE_CASE       :Tuple		    =  name.replace('''conv_1.'''			,       '''conv_stem.'''							)
      if ".block." in name:
            __SCREAMING_SNAKE_CASE       :Any		    =  name.replace('''.block.'''			,       '''.'''							)

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

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

      if ".aspp_layer." in name:
            __SCREAMING_SNAKE_CASE       :Dict		    =  name.replace('''.aspp_layer.'''			,       '''.'''							)
      if ".aspp_pool." in name:
            __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  name.replace('''.aspp_pool.'''			,       '''.'''							)
      if "seg_head." in name:
            __SCREAMING_SNAKE_CASE       :List[str]		    =  name.replace('''seg_head.'''			,       '''segmentation_head.'''							)
      if "segmentation_head.classifier.classifier." in name:
            __SCREAMING_SNAKE_CASE       :Tuple		    =  name.replace('''segmentation_head.classifier.classifier.'''			,       '''segmentation_head.classifier.'''							)

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

      return name



def 					__lowerCamelCase       (							a_					:		Tuple			,       a_					:		str			,       a_					:		int=False							)      ->				Optional[int]:
      if base_model:
            __SCREAMING_SNAKE_CASE       :str		    =  ''''''
      else:
            __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  '''mobilevit.'''

      for key in orig_state_dict.copy().keys():
            __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  orig_state_dict.pop(a_							)

            if key[:8] == "encoder.":
                  __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  key[8:]

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

      return orig_state_dict



def 					__lowerCamelCase       (							)      ->				Optional[int]:
      __SCREAMING_SNAKE_CASE       :Dict		    =  '''http://images.cocodataset.org/val2017/000000039769.jpg'''
      __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  Image.open(requests.get(a_			,       stream=a_							).raw							)
      return im



@torch.no_grad()
def 					__lowerCamelCase       (							a_					:		Any			,       a_					:		str			,       a_					:		Optional[Any]			,       a_					:		str=False							)      ->				Any:
      __SCREAMING_SNAKE_CASE       :Dict		    =  get_mobilevit_config(a_							)

      # load original state_dict
      __SCREAMING_SNAKE_CASE       :Dict		    =  torch.load(a_			,       map_location='''cpu'''							)

      # load 🤗 model
      if mobilevit_name.startswith('''deeplabv3_'''							):
            __SCREAMING_SNAKE_CASE       :Dict		    =  MobileViTForSemanticSegmentation(a_							).eval()
      else:
            __SCREAMING_SNAKE_CASE       :Any		    =  MobileViTForImageClassification(a_							).eval()

      __SCREAMING_SNAKE_CASE       :Any		    =  convert_state_dict(a_			,       a_							)
      model.load_state_dict(a_							)

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

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

            if mobilevit_name == "deeplabv3_mobilevit_s":
                  __SCREAMING_SNAKE_CASE       :Union[str, 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":
                  __SCREAMING_SNAKE_CASE       :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":
                  __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  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":
                  __SCREAMING_SNAKE_CASE       :List[str]		    =  torch.tensor([-0.9866, 0.2392, -1.1241]							)
            elif mobilevit_name == "mobilevit_xs":
                  __SCREAMING_SNAKE_CASE       :List[str]		    =  torch.tensor([-2.4761, -0.9399, -1.9587]							)
            elif mobilevit_name == "mobilevit_xxs":
                  __SCREAMING_SNAKE_CASE       :Optional[int]		    =  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:
            __SCREAMING_SNAKE_CASE       :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...'''							)
            __SCREAMING_SNAKE_CASE       :List[str]		    =  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
  )






"""simple docstring"""
from __future__ import annotations

import math



def 					__lowerCamelCase       (							a_					:		int			,       a_					:		int			,       a_					:		bool			,       a_					:		list[int]			,       a_					:		float							)      ->				int:

      if depth < 0:
            raise ValueError('''Depth cannot be less than 0'''							)

      if len(a_							) == 0:
            raise ValueError('''Scores cannot be empty'''							)

      if depth == height:
            return scores[node_index]

      if is_max:
            return max(
                minimax(depth + 1			,       node_index * 2			,       a_			,       a_			,       a_							)			,       minimax(depth + 1			,       node_index * 2 + 1			,       a_			,       a_			,       a_							)			,       )

      return min(
          minimax(depth + 1			,       node_index * 2			,       a_			,       a_			,       a_							)			,       minimax(depth + 1			,       node_index * 2 + 1			,       a_			,       a_			,       a_							)			,       )



def 					__lowerCamelCase       (							)      ->				None:
      __SCREAMING_SNAKE_CASE       :Dict		    =  [90, 23, 6, 33, 21, 65, 1_23, 3_44_23]
      __SCREAMING_SNAKE_CASE       :Optional[int]		    =  math.log(len(a_							)			,       2							)
      print('''Optimal value : '''			,       end=''''''							)
      print(minimax(0			,       0			,       a_			,       a_			,       a_							)							)


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  main()