





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()
a__: List[str]							   =						logging.get_logger(__name__)


def       UpperCamelCase__(			UpperCamelCase__     :			Dict    )->str:
       print('''Loading config file...'''    )

       def flatten_yaml_as_dict(UpperCamelCase__     :			Optional[int]					,	UpperCamelCase__     :			Dict=""					,	UpperCamelCase__     :			Optional[int]="."    ):
              A__		   =		[]
              for k, v in d.items():
                     A__		   =		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__    )

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

                     A__		   =		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       UpperCamelCase__(			UpperCamelCase__     :			str					,	UpperCamelCase__     :			Optional[Any]    )->Union[str, Any]:
       A__		   =		MobileViTVaConfig()

       A__		   =		False

       # dataset
       if task_name.startswith('''imagenet1k_'''    ):
              A__		   =		10_00
              if int(task_name.strip().split('''_'''    )[-1]    ) == 3_84:
                     A__		   =		3_84
              else:
                     A__		   =		2_56
              A__		   =		'''imagenet-1k-id2label.json'''
       elif task_name.startswith('''imagenet21k_to_1k_'''    ):
              A__		   =		2_10_00
              if int(task_name.strip().split('''_'''    )[-1]    ) == 3_84:
                     A__		   =		3_84
              else:
                     A__		   =		2_56
              A__		   =		'''imagenet-22k-id2label.json'''
       elif task_name.startswith('''ade20k_'''    ):
              A__		   =		1_51
              A__		   =		5_12
              A__		   =		'''ade20k-id2label.json'''
              A__		   =		True
       elif task_name.startswith('''voc_'''    ):
              A__		   =		21
              A__		   =		5_12
              A__		   =		'''pascal-voc-id2label.json'''
              A__		   =		True

       # orig_config
       A__		   =		load_orig_config_file(UpperCamelCase__    )
       assert getattr(UpperCamelCase__					,	'''model.classification.name'''					,	-1    ) == "mobilevit_v2", "Invalid model"
       A__		   =		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"
       A__		   =		getattr(UpperCamelCase__					,	'''model.classification.activation.name'''					,	'''swish'''    )
       # config.image_size == getattr(orig_config,  'sampler.bs.crop_size_width', 256)

       if is_segmentation_model:
              A__		   =		getattr(UpperCamelCase__					,	'''model.segmentation.output_stride'''					,	16    )
              if "_deeplabv3" in task_name:
                     A__		   =		getattr(UpperCamelCase__					,	'''model.segmentation.deeplabv3.aspp_rates'''					,	[12, 24, 36]    )
                     A__		   =		getattr(UpperCamelCase__					,	'''model.segmentation.deeplabv3.aspp_out_channels'''					,	5_12    )
                     A__		   =		getattr(UpperCamelCase__					,	'''model.segmentation.deeplabv3.aspp_dropout'''					,	0.1    )

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

       return config


def       UpperCamelCase__(			UpperCamelCase__     :			Dict					,	UpperCamelCase__     :			str					,	UpperCamelCase__     :			str    )->Dict:
       A__		   =		dct.pop(UpperCamelCase__    )
       A__		   =		val


def       UpperCamelCase__(			UpperCamelCase__     :			List[str]					,	UpperCamelCase__     :			Any=False    )->List[Any]:
       if base_model:
              A__		   =		''''''
       else:
              A__		   =		'''mobilevitv2.'''

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

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

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

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

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

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

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

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


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


@torch.no_grad()
def       UpperCamelCase__(			UpperCamelCase__     :			Optional[Any]					,	UpperCamelCase__     :			Optional[Any]					,	UpperCamelCase__     :			Union[str, Any]					,	UpperCamelCase__     :			Union[str, Any]    )->Any:
       A__		   =		get_mobilevitva_config(UpperCamelCase__					,	UpperCamelCase__    )

       # load original state_dict
       A__		   =		torch.load(UpperCamelCase__					,	map_location='''cpu'''    )

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

       # remove and rename some keys of load the original model
       A__		   =		checkpoint
       remove_unused_keys(UpperCamelCase__    )
       A__		   =		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
       A__		   =		MobileViTImageProcessor(crop_size=config.image_size					,	size=config.image_size + 32    )
       A__		   =		image_processor(images=prepare_img()					,	return_tensors='''pt'''    )
       A__		   =		model(**UpperCamelCase__    )

       # verify classification model
       if task_name.startswith('''imagenet'''    ):
              A__		   =		outputs.logits
              A__		   =		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
                     A__		   =		torch.tensor([-1.6_3_3_6e0_0, -7.3_2_0_4e-0_2, -5.1_8_8_3e-0_1]    )
                     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__":
       a__: List[str]							   =						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.'
       )

       a__: Optional[Any]							   =						parser.parse_args()
       convert_mobilevitva_checkpoint(
           args.task, args.orig_checkpoint_path, args.orig_config_path, args.pytorch_dump_folder_path
       )












import pytest

import datasets


# Import fixture modules as plugins
a__: Dict							   =						['tests.fixtures.files', 'tests.fixtures.hub', 'tests.fixtures.fsspec']


def       UpperCamelCase__(			UpperCamelCase__     :			List[Any]					,	UpperCamelCase__     :			Tuple    )->List[str]:
       # Mark tests as "unit" by default if not marked as "integration" (or already marked as "unit")
       for item in items:
              if any(marker in item.keywords for marker in ['''integration''', '''unit''']    ):
                     continue
              item.add_marker(pytest.mark.unit    )


def       UpperCamelCase__(			UpperCamelCase__     :			Dict    )->List[Any]:
       config.addinivalue_line('''markers'''					,	'''torchaudio_latest: mark test to run with torchaudio>=0.12'''    )


@pytest.fixture(autouse=UpperCamelCase__    )
def       UpperCamelCase__(			UpperCamelCase__     :			Optional[int]					,	UpperCamelCase__     :			int    )->Union[str, Any]:
       # test_hf_cache_home = tmp_path_factory.mktemp("cache")  # TODO: why a cache dir per test function does not work?
       A__		   =		tmp_path_factory.getbasetemp() / '''cache'''
       A__		   =		test_hf_cache_home / '''datasets'''
       A__		   =		test_hf_cache_home / '''metrics'''
       A__		   =		test_hf_cache_home / '''modules'''
       monkeypatch.setattr('''datasets.config.HF_DATASETS_CACHE'''					,	str(UpperCamelCase__    )    )
       monkeypatch.setattr('''datasets.config.HF_METRICS_CACHE'''					,	str(UpperCamelCase__    )    )
       monkeypatch.setattr('''datasets.config.HF_MODULES_CACHE'''					,	str(UpperCamelCase__    )    )
       A__		   =		test_hf_datasets_cache / '''downloads'''
       monkeypatch.setattr('''datasets.config.DOWNLOADED_DATASETS_PATH'''					,	str(UpperCamelCase__    )    )
       A__		   =		test_hf_datasets_cache / '''downloads''' / '''extracted'''
       monkeypatch.setattr('''datasets.config.EXTRACTED_DATASETS_PATH'''					,	str(UpperCamelCase__    )    )


@pytest.fixture(autouse=UpperCamelCase__					,	scope='''session'''    )
def       UpperCamelCase__(			)->int:
       datasets.disable_progress_bar()


@pytest.fixture(autouse=UpperCamelCase__    )
def       UpperCamelCase__(			UpperCamelCase__     :			str    )->Tuple:
       # don't take tests into account when counting downloads
       monkeypatch.setattr('''datasets.config.HF_UPDATE_DOWNLOAD_COUNTS'''					,	UpperCamelCase__    )


@pytest.fixture
def       UpperCamelCase__(			UpperCamelCase__     :			List[Any]    )->Optional[Any]:
       # Required to suppress RemovedIn20Warning when feature(s) are not compatible with SQLAlchemy 2.0
       # To be removed once SQLAlchemy 2.0 supported
       monkeypatch.setattr('''sqlalchemy.util.deprecations.SILENCE_UBER_WARNING'''					,	UpperCamelCase__    )





