'''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												=logging.get_logger(__name__)






def __lowerCAmelCase			(							UpperCamelCase__    )     ->   List[Any]:
   __lowerCamelCase          =  MobileViTConfig()

   # size of the architecture
   if "mobilevit_s" in mobilevit_name:
      __lowerCamelCase          =  [1_44, 1_92, 2_40]
      __lowerCamelCase          =  [16, 32, 64, 96, 1_28, 1_60, 6_40]
   elif "mobilevit_xs" in mobilevit_name:
      __lowerCamelCase          =  [96, 1_20, 1_44]
      __lowerCamelCase          =  [16, 32, 48, 64, 80, 96, 3_84]
   elif "mobilevit_xxs" in mobilevit_name:
      __lowerCamelCase          =  [64, 80, 96]
      __lowerCamelCase          =  [16, 16, 24, 48, 64, 80, 3_20]
      __lowerCamelCase          =  0.0_5
      __lowerCamelCase          =  2.0

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

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

   return config






def __lowerCAmelCase			(							UpperCamelCase__    ,  UpperCamelCase__=False    )     ->   List[Any]:
   for i in range(1    ,  6    ):
      if f"""layer_{i}.""" in name:
         __lowerCamelCase          =  name.replace(f"""layer_{i}."""    ,  f"""encoder.layer.{i - 1}."""    )

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

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

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

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

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

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

   return name






def __lowerCAmelCase			(							UpperCamelCase__    ,  UpperCamelCase__    ,  UpperCamelCase__=False    )     ->   str:
   if base_model:
      __lowerCamelCase          =  ''''''
   else:
      __lowerCamelCase          =  '''mobilevit.'''

   for key in orig_state_dict.copy().keys():
      __lowerCamelCase          =  orig_state_dict.pop(a_    )

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

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

   return orig_state_dict






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






@torch.no_grad()
def __lowerCAmelCase			(							UpperCamelCase__    ,  UpperCamelCase__    ,  UpperCamelCase__    ,  UpperCamelCase__=False    )     ->   List[Any]:
   __lowerCamelCase          =  get_mobilevit_config(a_    )

   # load original state_dict
   __lowerCamelCase          =  torch.load(a_    ,  map_location='''cpu'''    )

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

   __lowerCamelCase          =  convert_state_dict(a_    ,  a_    )
   model.load_state_dict(a_    )

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

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

      if mobilevit_name == "deeplabv3_mobilevit_s":
         __lowerCamelCase          =  torch.tensor(
             [
                 [[6.2_0_6_5, 6.1_2_9_2, 6.2_0_7_0], [6.1_0_7_9, 6.1_2_5_4, 6.1_7_4_7], [6.0_0_4_2, 6.1_0_7_1, 6.1_0_3_4]],
                 [[-6.9_2_5_3, -6.8_6_5_3, -7.0_3_9_8], [-7.3_2_1_8, -7.3_9_8_3, -7.3_6_7_0], [-7.1_9_6_1, -7.2_4_8_2, -7.1_5_6_9]],
                 [[-4.4_7_2_3, -4.4_3_4_8, -4.3_7_6_9], [-5.3_6_2_9, -5.4_6_3_2, -5.4_5_9_8], [-5.1_5_8_7, -5.3_4_0_2, -5.5_0_5_9]],
             ]    )
      elif mobilevit_name == "deeplabv3_mobilevit_xs":
         __lowerCamelCase          =  torch.tensor(
             [
                 [[5.4_4_4_9, 5.5_7_3_3, 5.6_3_1_4], [5.1_8_1_5, 5.3_9_3_0, 5.5_9_6_3], [5.1_6_5_6, 5.4_3_3_3, 5.4_8_5_3]],
                 [[-9.4_4_2_3, -9.7_7_6_6, -9.6_7_1_4], [-9.1_5_8_1, -9.5_7_2_0, -9.5_5_1_9], [-9.1_0_0_6, -9.6_4_5_8, -9.5_7_0_3]],
                 [[-7.7_7_2_1, -7.3_7_1_6, -7.1_5_8_3], [-8.4_5_9_9, -8.0_6_2_4, -7.7_9_4_4], [-8.4_1_7_2, -7.8_3_6_6, -7.5_0_2_5]],
             ]    )
      elif mobilevit_name == "deeplabv3_mobilevit_xxs":
         __lowerCamelCase          =  torch.tensor(
             [
                 [[6.9_8_1_1, 6.9_7_4_3, 7.3_1_2_3], [7.1_7_7_7, 7.1_9_3_1, 7.3_9_3_8], [7.5_6_3_3, 7.8_0_5_0, 7.8_9_0_1]],
                 [[-1_0.5_5_3_6, -1_0.2_3_3_2, -1_0.2_9_2_4], [-1_0.2_3_3_6, -9.8_6_2_4, -9.5_9_6_4], [-1_0.8_8_4_0, -1_0.8_1_5_8, -1_0.6_6_5_9]],
                 [[-3.4_9_3_8, -3.0_6_3_1, -2.8_6_2_0], [-3.4_2_0_5, -2.8_1_3_5, -2.6_8_7_5], [-3.4_1_7_9, -2.7_9_4_5, -2.8_7_5_0]],
             ]    )
      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          =  torch.tensor([-0.9_8_6_6, 0.2_3_9_2, -1.1_2_4_1]    )
      elif mobilevit_name == "mobilevit_xs":
         __lowerCamelCase          =  torch.tensor([-2.4_7_6_1, -0.9_3_9_9, -1.9_5_8_7]    )
      elif mobilevit_name == "mobilevit_xxs":
         __lowerCamelCase          =  torch.tensor([-1.9_3_6_4, -1.2_3_2_7, -0.4_6_5_3]    )
      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          =  {
          '''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          =  model_mapping[mobilevit_name]
      image_processor.push_to_hub(a_    ,  organization='''apple'''    )
      model.push_to_hub(a_    ,  organization='''apple'''    )


if __name__ == "__main__":
  __UpperCAmelCase												=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												=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






def __lowerCAmelCase			(							UpperCamelCase__    ,  UpperCamelCase__    )     ->   list[list[int]]:
    __lowerCamelCase          =  []
    create_all_state(1    ,  UpperCamelCase__    ,  UpperCamelCase__    ,  []    ,  UpperCamelCase__    )
    return result






def __lowerCAmelCase			(							UpperCamelCase__    ,  UpperCamelCase__    ,  UpperCamelCase__    ,  UpperCamelCase__    ,  UpperCamelCase__    ,  )     ->   None:
    if level == 0:
        total_list.append(current_list[:]    )
        return

    for i in range(UpperCamelCase__    ,  total_number - level + 2    ):
        current_list.append(UpperCamelCase__    )
        create_all_state(i + 1    ,  UpperCamelCase__    ,  level - 1    ,  UpperCamelCase__    ,  UpperCamelCase__    )
        current_list.pop()






def __lowerCAmelCase			(							UpperCamelCase__    )     ->   None:
    for i in total_list:
        print(*UpperCamelCase__    )


if __name__ == "__main__":
  __UpperCAmelCase												=4
  __UpperCAmelCase												=2
  __UpperCAmelCase												=generate_all_combinations(n, k)
  print_all_state(total_list)
