





import argparse
import collections
import json
from pathlib import Path

import requests
import torch
import yaml
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    MobileViTImageProcessor,
    MobileViTVaConfig,
    MobileViTVaForImageClassification,
    MobileViTVaForSemanticSegmentation,
)
from transformers.utils import logging


logging.set_verbosity_info()
snake_case_				=			logging.get_logger(__name__)






def lowerCamelCase__    (							snake_case_	:  Tuple       )      ->				str:
   print('''Loading config file...'''       )

   def flatten_yaml_as_dict(snake_case_	:  Optional[int]       ,      snake_case_	:  Optional[int]=""       ,      snake_case_	:  Tuple="."       ):
      __snake_case							       =   []
      for k, v in d.items():
         __snake_case							       =   parent_key + sep + k if parent_key else k
         if isinstance(snake_case_       ,      collections.abc.MutableMapping       ):
            items.extend(flatten_yaml_as_dict(snake_case_       ,      snake_case_       ,      sep=snake_case_       ).items()       )
         else:
            items.append((new_key, v)       )
      return dict(snake_case_       )

   __snake_case							       =   argparse.Namespace()
   with open(snake_case_       ,      '''r'''       ) as yaml_file:
      try:
         __snake_case							       =   yaml.load(snake_case_       ,      Loader=yaml.FullLoader       )

         __snake_case							       =   flatten_yaml_as_dict(snake_case_       )
         for k, v in flat_cfg.items():
            setattr(snake_case_       ,      snake_case_       ,      snake_case_       )
      except yaml.YAMLError as exc:
         logger.error('''Error while loading config file: {}. Error message: {}'''.format(snake_case_       ,      str(snake_case_       )       )       )
   return config






def lowerCamelCase__    (							snake_case_	:  str       ,      snake_case_	:  Tuple       )      ->				Dict:
   __snake_case							       =   MobileViTVaConfig()

   __snake_case							       =   False

   # dataset
   if task_name.startswith('''imagenet1k_'''       ):
      __snake_case							       =   1000
      if int(task_name.strip().split('''_'''       )[-1]       ) == 384:
         __snake_case							       =   384
      else:
         __snake_case							       =   256
      __snake_case							       =   '''imagenet-1k-id2label.json'''
   elif task_name.startswith('''imagenet21k_to_1k_'''       ):
      __snake_case							       =   2_1000
      if int(task_name.strip().split('''_'''       )[-1]       ) == 384:
         __snake_case							       =   384
      else:
         __snake_case							       =   256
      __snake_case							       =   '''imagenet-22k-id2label.json'''
   elif task_name.startswith('''ade20k_'''       ):
      __snake_case							       =   151
      __snake_case							       =   512
      __snake_case							       =   '''ade20k-id2label.json'''
      __snake_case							       =   True
   elif task_name.startswith('''voc_'''       ):
      __snake_case							       =   21
      __snake_case							       =   512
      __snake_case							       =   '''pascal-voc-id2label.json'''
      __snake_case							       =   True

   # orig_config
   __snake_case							       =   load_orig_config_file(snake_case_       )
   assert getattr(snake_case_       ,      '''model.classification.name'''       ,      -1       ) == "mobilevit_v2", "Invalid model"
   __snake_case							       =   getattr(snake_case_       ,      '''model.classification.mitv2.width_multiplier'''       ,      1.0       )
   assert (
       getattr(snake_case_       ,      '''model.classification.mitv2.attn_norm_layer'''       ,      -1       ) == "layer_norm_2d"
   ), "Norm layers other than layer_norm_2d is not supported"
   __snake_case							       =   getattr(snake_case_       ,      '''model.classification.activation.name'''       ,      '''swish'''       )
   # config.image_size == getattr(orig_config,  'sampler.bs.crop_size_width', 256)

   if is_segmentation_model:
      __snake_case							       =   getattr(snake_case_       ,      '''model.segmentation.output_stride'''       ,      16       )
      if "_deeplabv3" in task_name:
         __snake_case							       =   getattr(snake_case_       ,      '''model.segmentation.deeplabv3.aspp_rates'''       ,      [12, 24, 36]       )
         __snake_case							       =   getattr(snake_case_       ,      '''model.segmentation.deeplabv3.aspp_out_channels'''       ,      512       )
         __snake_case							       =   getattr(snake_case_       ,      '''model.segmentation.deeplabv3.aspp_dropout'''       ,      0.1       )

    # id2label
   __snake_case							       =   '''huggingface/label-files'''
   __snake_case							       =   json.load(open(hf_hub_download(snake_case_       ,      snake_case_       ,      repo_type='''dataset'''       )       ,      '''r'''       )       )
   __snake_case							       =   {int(snake_case_       ): 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_	:  Tuple       ,      snake_case_	:  List[str]       ,      snake_case_	:  List[Any]       )      ->				Optional[int]:
   __snake_case							       =   dct.pop(snake_case_       )
   __snake_case							       =   val






def lowerCamelCase__    (							snake_case_	:  Optional[int]       ,      snake_case_	:  Tuple=False       )      ->				int:
   if base_model:
      __snake_case							       =   ''''''
   else:
      __snake_case							       =   '''mobilevitv2.'''

   __snake_case							       =   []
   for k in state_dict.keys():
      if k[:8] == "encoder.":
         __snake_case							       =   k[8:]
      else:
         __snake_case							       =   k

      if ".block." in k:
         __snake_case							       =   k_new.replace('''.block.'''       ,      '''.'''       )
      if ".conv." in k:
         __snake_case							       =   k_new.replace('''.conv.'''       ,      '''.convolution.'''       )
      if ".norm." in k:
         __snake_case							       =   k_new.replace('''.norm.'''       ,      '''.normalization.'''       )

      if "conv_1." in k:
         __snake_case							       =   k_new.replace('''conv_1.'''       ,      f"""{model_prefix}conv_stem."""       )
      for i in [1, 2]:
         if f"""layer_{i}.""" in k:
            __snake_case							       =   k_new.replace(f"""layer_{i}."""       ,      f"""{model_prefix}encoder.layer.{i-1}.layer."""       )
      if ".exp_1x1." in k:
         __snake_case							       =   k_new.replace('''.exp_1x1.'''       ,      '''.expand_1x1.'''       )
      if ".red_1x1." in k:
         __snake_case							       =   k_new.replace('''.red_1x1.'''       ,      '''.reduce_1x1.'''       )

      for i in [3, 4, 5]:
         if f"""layer_{i}.0.""" in k:
            __snake_case							       =   k_new.replace(f"""layer_{i}.0."""       ,      f"""{model_prefix}encoder.layer.{i-1}.downsampling_layer."""       )
         if f"""layer_{i}.1.local_rep.0.""" in k:
            __snake_case							       =   k_new.replace(f"""layer_{i}.1.local_rep.0."""       ,      f"""{model_prefix}encoder.layer.{i-1}.conv_kxk."""       )
         if f"""layer_{i}.1.local_rep.1.""" in k:
            __snake_case							       =   k_new.replace(f"""layer_{i}.1.local_rep.1."""       ,      f"""{model_prefix}encoder.layer.{i-1}.conv_1x1."""       )

      for i in [3, 4, 5]:
         if i == 3:
            __snake_case							       =   [0, 1]
         elif i == 4:
            __snake_case							       =   [0, 1, 2, 3]
         elif i == 5:
            __snake_case							       =   [0, 1, 2]

         for j in j_in:
            if f"""layer_{i}.1.global_rep.{j}.""" in k:
               __snake_case							       =   k_new.replace(
                   f"""layer_{i}.1.global_rep.{j}."""       ,      f"""{model_prefix}encoder.layer.{i-1}.transformer.layer.{j}."""       )
         if f"""layer_{i}.1.global_rep.{j+1}.""" in k:
            __snake_case							       =   k_new.replace(
                f"""layer_{i}.1.global_rep.{j+1}."""       ,      f"""{model_prefix}encoder.layer.{i-1}.layernorm."""       )

         if f"""layer_{i}.1.conv_proj.""" in k:
            __snake_case							       =   k_new.replace(f"""layer_{i}.1.conv_proj."""       ,      f"""{model_prefix}encoder.layer.{i-1}.conv_projection."""       )

      if "pre_norm_attn.0." in k:
         __snake_case							       =   k_new.replace('''pre_norm_attn.0.'''       ,      '''layernorm_before.'''       )
      if "pre_norm_attn.1." in k:
         __snake_case							       =   k_new.replace('''pre_norm_attn.1.'''       ,      '''attention.'''       )
      if "pre_norm_ffn.0." in k:
         __snake_case							       =   k_new.replace('''pre_norm_ffn.0.'''       ,      '''layernorm_after.'''       )
      if "pre_norm_ffn.1." in k:
         __snake_case							       =   k_new.replace('''pre_norm_ffn.1.'''       ,      '''ffn.conv1.'''       )
      if "pre_norm_ffn.3." in k:
         __snake_case							       =   k_new.replace('''pre_norm_ffn.3.'''       ,      '''ffn.conv2.'''       )

      if "classifier.1." in k:
         __snake_case							       =   k_new.replace('''classifier.1.'''       ,      '''classifier.'''       )

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

      rename_keys.append((k, k_new)       )
   return rename_keys






def lowerCamelCase__    (							snake_case_	:  Tuple       )      ->				List[str]:
   __snake_case							       =   []
   for k in state_dict.keys():
      if k.startswith('''seg_head.aux_head.'''       ):
         keys_to_ignore.append(snake_case_       )
   for k in keys_to_ignore:
      state_dict.pop(snake_case_       ,      snake_case_       )






def lowerCamelCase__    (							)      ->				str:
   __snake_case							       =   '''http://images.cocodataset.org/val2017/000000039769.jpg'''
   # url = "https://cdn.britannica.com/86/141086-050-9D7C75EE/Gulfstream-G450-business-jet-passengers.jpg"
   __snake_case							       =   Image.open(requests.get(snake_case_       ,      stream=snake_case_       ).raw       )
   return im






@torch.no_grad()
def lowerCamelCase__    (							snake_case_	:  Any       ,      snake_case_	:  List[Any]       ,      snake_case_	:  Any       ,      snake_case_	:  int       )      ->				int:
   __snake_case							       =   get_mobilevitva_config(snake_case_       ,      snake_case_       )

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

   # load huggingface model
   if task_name.startswith('''ade20k_'''       ) or task_name.startswith('''voc_'''       ):
      __snake_case							       =   MobileViTVaForSemanticSegmentation(snake_case_       ).eval()
      __snake_case							       =   False
   else:
      __snake_case							       =   MobileViTVaForImageClassification(snake_case_       ).eval()
      __snake_case							       =   False

   # remove and rename some keys of load the original model
   __snake_case							       =   checkpoint
   remove_unused_keys(snake_case_       )
   __snake_case							       =   create_rename_keys(snake_case_       ,      base_model=snake_case_       )
   for rename_key_src, rename_key_dest in rename_keys:
      rename_key(snake_case_       ,      snake_case_       ,      snake_case_       )

   # load modified state_dict
   model.load_state_dict(snake_case_       )

   # 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(**snake_case_       )

   # verify classification model
   if task_name.startswith('''imagenet'''       ):
      __snake_case							       =   outputs.logits
      __snake_case							       =   logits.argmax(-1       ).item()
      print('''Predicted class:'''       ,      model.config.idalabel[predicted_class_idx]       )
      if task_name.startswith('''imagenet1k_256'''       ) and config.width_multiplier == 1.0:
         # expected_logits for base variant
         __snake_case							       =   torch.tensor([-1.6336e00, -7.3204e-02, -5.1883e-01]       )
         assert torch.allclose(logits[0, :3]       ,      snake_case_       ,      atol=1e-4       )

   Path(snake_case_       ).mkdir(exist_ok=snake_case_       )
   print(f"""Saving model {task_name} to {pytorch_dump_folder_path}"""       )
   model.save_pretrained(snake_case_       )
   print(f"""Saving image processor to {pytorch_dump_folder_path}"""       )
   image_processor.save_pretrained(snake_case_       )


if __name__ == "__main__":
    snake_case_				=			argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        '--task',
        default='imagenet1k_256',
        type=str,
        help=(
            'Name of the task for which the MobileViTV2 model you\'d like to convert is trained on . '
            '\n                Classification (ImageNet-1k)\n                    - MobileViTV2 (256x256) : imagenet1k_256\n                    - MobileViTV2 (Trained on 256x256 and Finetuned on 384x384) : imagenet1k_384\n                    - MobileViTV2 (Trained on ImageNet-21k and Finetuned on ImageNet-1k 256x256) :\n                      imagenet21k_to_1k_256\n                    - MobileViTV2 (Trained on ImageNet-21k, Finetuned on ImageNet-1k 256x256, and Finetuned on\n                      ImageNet-1k 384x384) : imagenet21k_to_1k_384\n                Segmentation\n                    - ADE20K Dataset : ade20k_deeplabv3\n                    - Pascal VOC 2012 Dataset: voc_deeplabv3\n            '
        ),
        choices=[
            'imagenet1k_256',
            'imagenet1k_384',
            'imagenet21k_to_1k_256',
            'imagenet21k_to_1k_384',
            'ade20k_deeplabv3',
            'voc_deeplabv3',
        ],
    )

    parser.add_argument(
        '--orig_checkpoint_path', required=True, type=str, help='Path to the original state dict (.pt file).'
    )
    parser.add_argument('--orig_config_path', required=True, type=str, help='Path to the original config file.')
    parser.add_argument(
        '--pytorch_dump_folder_path', required=True, type=str, help='Path to the output PyTorch model directory.'
    )

    snake_case_				=			parser.parse_args()
    convert_mobilevitva_checkpoint(
        args.task, args.orig_checkpoint_path, args.orig_config_path, args.pytorch_dump_folder_path
    )








from datasets.utils.patching import _PatchedModuleObj, patch_submodule

from . import _test_patching






def lowerCamelCase__    (							)      ->				Any:
   import os as original_os
   from os import path as original_path
   from os import rename as original_rename
   from os.path import dirname as original_dirname
   from os.path import join as original_join

   assert _test_patching.os is original_os
   assert _test_patching.path is original_path
   assert _test_patching.join is original_join

   assert _test_patching.renamed_os is original_os
   assert _test_patching.renamed_path is original_path
   assert _test_patching.renamed_join is original_join

   __snake_case							       =   '''__test_patch_submodule_mock__'''
   with patch_submodule(_test_patching       ,      '''os.path.join'''       ,      snake_case_       ):
      # Every way to access os.path.join must be patched, and the rest must stay untouched

      # check os.path.join
      assert isinstance(_test_patching.os       ,      _PatchedModuleObj       )
      assert isinstance(_test_patching.os.path       ,      _PatchedModuleObj       )
      assert _test_patching.os.path.join is mock

      # check path.join
      assert isinstance(_test_patching.path       ,      _PatchedModuleObj       )
      assert _test_patching.path.join is mock

      # check join
      assert _test_patching.join is mock

      # check that the other attributes are untouched
      assert _test_patching.os.rename is original_rename
      assert _test_patching.path.dirname is original_dirname
      assert _test_patching.os.path.dirname is original_dirname

      # Even renamed modules or objects must be patched

      # check renamed_os.path.join
      assert isinstance(_test_patching.renamed_os       ,      _PatchedModuleObj       )
      assert isinstance(_test_patching.renamed_os.path       ,      _PatchedModuleObj       )
      assert _test_patching.renamed_os.path.join is mock

      # check renamed_path.join
      assert isinstance(_test_patching.renamed_path       ,      _PatchedModuleObj       )
      assert _test_patching.renamed_path.join is mock

      # check renamed_join
      assert _test_patching.renamed_join is mock

      # check that the other attributes are untouched
      assert _test_patching.renamed_os.rename is original_rename
      assert _test_patching.renamed_path.dirname is original_dirname
      assert _test_patching.renamed_os.path.dirname is original_dirname

   # check that everthing is back to normal when the patch is over

   assert _test_patching.os is original_os
   assert _test_patching.path is original_path
   assert _test_patching.join is original_join

   assert _test_patching.renamed_os is original_os
   assert _test_patching.renamed_path is original_path
   assert _test_patching.renamed_join is original_join






def lowerCamelCase__    (							)      ->				Any:
   assert _test_patching.open is open

   __snake_case							       =   '''__test_patch_submodule_builtin_mock__'''
   # _test_patching has "open" in its globals
   assert _test_patching.open is open
   with patch_submodule(_test_patching       ,      '''open'''       ,      snake_case_       ):
      assert _test_patching.open is mock

   # check that everthing is back to normal when the patch is over

   assert _test_patching.open is open






def lowerCamelCase__    (							)      ->				List[str]:
   # pandas.read_csv is not present in _test_patching
   __snake_case							       =   '''__test_patch_submodule_missing_mock__'''
   with patch_submodule(_test_patching       ,      '''pandas.read_csv'''       ,      snake_case_       ):
      pass






def lowerCamelCase__    (							)      ->				Union[str, Any]:
   # builtin should always be mocked even if they're not in the globals
   # in case they're loaded at one point
   __snake_case							       =   '''__test_patch_submodule_missing_builtin_mock__'''
   # _test_patching doesn't have "len" in its globals
   assert getattr(_test_patching       ,      '''len'''       ,      snake_case_       ) is None
   with patch_submodule(_test_patching       ,      '''len'''       ,      snake_case_       ):
      assert _test_patching.len is mock
   assert _test_patching.len is len






def lowerCamelCase__    (							)      ->				Union[str, Any]:
   __snake_case							       =   '''__test_patch_submodule_start_and_stop_mock__'''
   __snake_case							       =   patch_submodule(_test_patching       ,      '''open'''       ,      snake_case_       )
   assert _test_patching.open is open
   patch.start()
   assert _test_patching.open is mock
   patch.stop()
   assert _test_patching.open is open






def lowerCamelCase__    (							)      ->				Optional[int]:
   from os import rename as original_rename
   from os.path import dirname as original_dirname
   from os.path import join as original_join

   __snake_case							       =   '''__test_patch_submodule_successive_join__'''
   __snake_case							       =   '''__test_patch_submodule_successive_dirname__'''
   __snake_case							       =   '''__test_patch_submodule_successive_rename__'''
   assert _test_patching.os.path.join is original_join
   assert _test_patching.os.path.dirname is original_dirname
   assert _test_patching.os.rename is original_rename

   with patch_submodule(_test_patching       ,      '''os.path.join'''       ,      snake_case_       ):
      with patch_submodule(_test_patching       ,      '''os.rename'''       ,      snake_case_       ):
         with patch_submodule(_test_patching       ,      '''os.path.dirname'''       ,      snake_case_       ):
            assert _test_patching.os.path.join is mock_join
            assert _test_patching.os.path.dirname is mock_dirname
            assert _test_patching.os.rename is mock_rename

    # try another order
   with patch_submodule(_test_patching       ,      '''os.rename'''       ,      snake_case_       ):
      with patch_submodule(_test_patching       ,      '''os.path.join'''       ,      snake_case_       ):
         with patch_submodule(_test_patching       ,      '''os.path.dirname'''       ,      snake_case_       ):
            assert _test_patching.os.path.join is mock_join
            assert _test_patching.os.path.dirname is mock_dirname
            assert _test_patching.os.rename is mock_rename

   assert _test_patching.os.path.join is original_join
   assert _test_patching.os.path.dirname is original_dirname
   assert _test_patching.os.rename is original_rename







def lowerCamelCase__    (							)      ->				Tuple:
   __snake_case							       =   '''__test_patch_submodule_doesnt_exist_mock__'''
   with patch_submodule(_test_patching       ,      '''__module_that_doesn_exist__.__attribute_that_doesn_exist__'''       ,      snake_case_       ):
      pass
   with patch_submodule(_test_patching       ,      '''os.__attribute_that_doesn_exist__'''       ,      snake_case_       ):
      pass

