

"""simple docstring"""






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()
__lowerCamelCase										=		logging.get_logger(__name__)





def 	lowercase      (   __UpperCamelCase						) ->      int:
 print('''Loading config file...'''						)

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

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

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





def 	lowercase      (   __UpperCamelCase      ,   __UpperCamelCase						) ->      Optional[Any]:
 __magic_name__     =      MobileViTVaConfig()

 __magic_name__     =      False

 # dataset
 if task_name.startswith('''imagenet1k_'''						):
  __magic_name__     =      1000
  if int(task_name.strip().split('''_'''						)[-1]						) == 384:
   __magic_name__     =      384
  else:
   __magic_name__     =      256
  __magic_name__     =      '''imagenet-1k-id2label.json'''
 elif task_name.startswith('''imagenet21k_to_1k_'''						):
  __magic_name__     =      21000
  if int(task_name.strip().split('''_'''						)[-1]						) == 384:
   __magic_name__     =      384
  else:
   __magic_name__     =      256
  __magic_name__     =      '''imagenet-22k-id2label.json'''
 elif task_name.startswith('''ade20k_'''						):
  __magic_name__     =      151
  __magic_name__     =      512
  __magic_name__     =      '''ade20k-id2label.json'''
  __magic_name__     =      True
 elif task_name.startswith('''voc_'''						):
  __magic_name__     =      21
  __magic_name__     =      512
  __magic_name__     =      '''pascal-voc-id2label.json'''
  __magic_name__     =      True

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

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

    # id2label
 __magic_name__     =      '''huggingface/label-files'''
 __magic_name__     =      json.load(open(hf_hub_download(__UpperCamelCase      ,   __UpperCamelCase      ,   repo_type='''dataset'''						)      ,   '''r'''						)						)
 __magic_name__     =      {int(__UpperCamelCase						): v for k, v in idalabel.items()}
 __magic_name__     =      idalabel
 __magic_name__     =      {v: k for k, v in idalabel.items()}

 return config





def 	lowercase      (   __UpperCamelCase      ,   __UpperCamelCase      ,   __UpperCamelCase						) ->      Tuple:
 __magic_name__     =      dct.pop(__UpperCamelCase						)
 __magic_name__     =      val





def 	lowercase      (   __UpperCamelCase      ,   __UpperCamelCase=False						) ->      int:
 if base_model:
  __magic_name__     =      ''''''
 else:
  __magic_name__     =      '''mobilevitv2.'''

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

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

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

  for i in [3, 4, 5]:
   if f'''layer_{i}.0.''' in k:
    __magic_name__     =      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:
    __magic_name__     =      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:
    __magic_name__     =      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:
    __magic_name__     =      [0, 1]
   elif i == 4:
    __magic_name__     =      [0, 1, 2, 3]
   elif i == 5:
    __magic_name__     =      [0, 1, 2]

   for j in j_in:
    if f'''layer_{i}.1.global_rep.{j}.''' in k:
     __magic_name__     =      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:
    __magic_name__     =      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:
    __magic_name__     =      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:
   __magic_name__     =      k_new.replace('''pre_norm_attn.0.'''      ,   '''layernorm_before.'''						)
  if "pre_norm_attn.1." in k:
   __magic_name__     =      k_new.replace('''pre_norm_attn.1.'''      ,   '''attention.'''						)
  if "pre_norm_ffn.0." in k:
   __magic_name__     =      k_new.replace('''pre_norm_ffn.0.'''      ,   '''layernorm_after.'''						)
  if "pre_norm_ffn.1." in k:
   __magic_name__     =      k_new.replace('''pre_norm_ffn.1.'''      ,   '''ffn.conv1.'''						)
  if "pre_norm_ffn.3." in k:
   __magic_name__     =      k_new.replace('''pre_norm_ffn.3.'''      ,   '''ffn.conv2.'''						)

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

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

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





def 	lowercase      (   __UpperCamelCase						) ->      Tuple:
 __magic_name__     =      []
 for k in state_dict.keys():
  if k.startswith('''seg_head.aux_head.'''						):
   keys_to_ignore.append(__UpperCamelCase						)
 for k in keys_to_ignore:
  state_dict.pop(__UpperCamelCase      ,   __UpperCamelCase						)





def 	lowercase      (   ) ->      Optional[int]:
 __magic_name__     =      '''http://images.cocodataset.org/val2017/000000039769.jpg'''
 # url = "https://cdn.britannica.com/86/141086-050-9D7C75EE/Gulfstream-G450-business-jet-passengers.jpg"
 __magic_name__     =      Image.open(requests.get(__UpperCamelCase      ,   stream=__UpperCamelCase						).raw						)
 return im





@torch.no_grad()
def 	lowercase      (   __UpperCamelCase      ,   __UpperCamelCase      ,   __UpperCamelCase      ,   __UpperCamelCase						) ->      str:
 __magic_name__     =      get_mobilevitva_config(__UpperCamelCase      ,   __UpperCamelCase						)

 # load original state_dict
 __magic_name__     =      torch.load(__UpperCamelCase      ,   map_location='''cpu'''						)

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

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

 # load modified state_dict
 model.load_state_dict(__UpperCamelCase						)

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

 # verify classification model
 if task_name.startswith('''imagenet'''						):
  __magic_name__     =      outputs.logits
  __magic_name__     =      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
   __magic_name__     =      torch.tensor([-1.6336e00, -7.3204e-02, -5.1883e-01]						)
   assert torch.allclose(logits[0, :3]      ,   __UpperCamelCase      ,   atol=1e-4						)

 Path(__UpperCamelCase						).mkdir(exist_ok=__UpperCamelCase						)
 print(f'''Saving model {task_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 __name__ == "__main__":
 __lowerCamelCase										=		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."
 )

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




"""simple docstring"""






import os

import pytest
import yaml

from datasets.features.features import Features, Value
from datasets.info import DatasetInfo, DatasetInfosDict





@pytest.mark.parametrize(
    '''files'''      ,   [
        ['''full:README.md''', '''dataset_infos.json'''],
        ['''empty:README.md''', '''dataset_infos.json'''],
        ['''dataset_infos.json'''],
        ['''full:README.md'''],
    ]      ,   )
def 	lowercase      (   __UpperCamelCase      ,   __UpperCamelCase						) ->      List[str]:
 __magic_name__     =      tmp_path_factory.mktemp('''dset_infos_dir'''						)
 if "full:README.md" in files:
  with open(dataset_infos_dir / '''README.md'''      ,   '''w'''						) as f:
   f.write('''---\ndataset_info:\n  dataset_size: 42\n---'''						)
 if "empty:README.md" in files:
  with open(dataset_infos_dir / '''README.md'''      ,   '''w'''						) as f:
   f.write(''''''						)
    # we want to support dataset_infos.json for backward compatibility
 if "dataset_infos.json" in files:
  with open(dataset_infos_dir / '''dataset_infos.json'''      ,   '''w'''						) as f:
   f.write('''{"default": {"dataset_size": 42}}'''						)
 __magic_name__     =      DatasetInfosDict.from_directory(__UpperCamelCase						)
 assert dataset_infos
 assert dataset_infos["default"].dataset_size == 42





@pytest.mark.parametrize(
    '''dataset_info'''      ,   [
        DatasetInfo(),
        DatasetInfo(
            description='''foo'''      ,   features=Features({'''a''': Value('''int32'''						)}						)      ,   builder_name='''builder'''      ,   config_name='''config'''      ,   version='''1.0.0'''      ,   splits=[{'''name''': '''train'''}]      ,   download_size=42      ,   ),
    ]      ,   )
def 	lowercase      (   __UpperCamelCase      ,   __UpperCamelCase						) ->      Tuple:
 __magic_name__     =      str(__UpperCamelCase						)
 dataset_info.write_to_directory(__UpperCamelCase						)
 __magic_name__     =      DatasetInfo.from_directory(__UpperCamelCase						)
 assert dataset_info == reloaded
 assert os.path.exists(os.path.join(__UpperCamelCase      ,   '''dataset_info.json'''						)						)





def 	lowercase      (   ) ->      Any:
 __magic_name__     =      DatasetInfo(
     description='''foo'''      ,   citation='''bar'''      ,   homepage='''https://foo.bar'''      ,   license='''CC0'''      ,   features=Features({'''a''': Value('''int32'''						)}						)      ,   post_processed={}      ,   supervised_keys=()      ,   task_templates=[]      ,   builder_name='''builder'''      ,   config_name='''config'''      ,   version='''1.0.0'''      ,   splits=[{'''name''': '''train''', '''num_examples''': 42}]      ,   download_checksums={}      ,   download_size=1337      ,   post_processing_size=442      ,   dataset_size=1234      ,   size_in_bytes=1337 + 442 + 1234      ,   )
 __magic_name__     =      dataset_info._to_yaml_dict()
 assert sorted(__UpperCamelCase						) == sorted(DatasetInfo._INCLUDED_INFO_IN_YAML						)
 for key in DatasetInfo._INCLUDED_INFO_IN_YAML:
  assert key in dataset_info_yaml_dict
  assert isinstance(dataset_info_yaml_dict[key]      ,   (list, dict, int, str)						)
 __magic_name__     =      yaml.safe_dump(__UpperCamelCase						)
 __magic_name__     =      yaml.safe_load(__UpperCamelCase						)
 assert dataset_info_yaml_dict == reloaded





def 	lowercase      (   ) ->      str:
 __magic_name__     =      DatasetInfo()
 __magic_name__     =      dataset_info._to_yaml_dict()
 assert dataset_info_yaml_dict == {}





@pytest.mark.parametrize(
    '''dataset_infos_dict'''      ,   [
        DatasetInfosDict(),
        DatasetInfosDict({'''default''': DatasetInfo()}						),
        DatasetInfosDict({'''my_config_name''': DatasetInfo()}						),
        DatasetInfosDict(
            {
                '''default''': DatasetInfo(
                    description='''foo'''      ,   features=Features({'''a''': Value('''int32'''						)}						)      ,   builder_name='''builder'''      ,   config_name='''config'''      ,   version='''1.0.0'''      ,   splits=[{'''name''': '''train'''}]      ,   download_size=42      ,   )
            }						),
        DatasetInfosDict(
            {
                '''v1''': DatasetInfo(dataset_size=42						),
                '''v2''': DatasetInfo(dataset_size=1337						),
            }						),
    ]      ,   )
def 	lowercase      (   __UpperCamelCase      ,   __UpperCamelCase						) ->      str:
 __magic_name__     =      str(__UpperCamelCase						)
 dataset_infos_dict.write_to_directory(__UpperCamelCase						)
 __magic_name__     =      DatasetInfosDict.from_directory(__UpperCamelCase						)

 # the config_name of the dataset_infos_dict take over the attribute
 for config_name, dataset_info in dataset_infos_dict.items():
  __magic_name__     =      config_name
  # the yaml representation doesn't include fields like description or citation
  # so we just test that we can recover what we can from the yaml
  __magic_name__     =      DatasetInfo._from_yaml_dict(dataset_info._to_yaml_dict()						)
 assert dataset_infos_dict == reloaded

 if dataset_infos_dict:
  assert os.path.exists(os.path.join(__UpperCamelCase      ,   '''README.md'''						)						)

