


"""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      :Tuple	    =		logging.get_logger(__name__)




def   snake_case		(     UpperCamelCase__       :						Dict    )   ->      Any:
      print("""Loading config file..."""    )

      def flatten_yaml_as_dict(UpperCamelCase__       :						Optional[int]			,				UpperCamelCase__       :						List[str]=""			,				UpperCamelCase__       :						List[str]="."    ):
            lowerCamelCase					:					List[Any]        =	[]
            for k, v in d.items():
                  lowerCamelCase					:					int        =	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__    )

      lowerCamelCase					:					List[str]        =	argparse.Namespace()
      with open(UpperCamelCase__			,				"""r"""    ) as yaml_file:
            try:
                  lowerCamelCase					:					str        =	yaml.load(UpperCamelCase__			,				Loader=yaml.FullLoader    )

                  lowerCamelCase					:					Optional[int]        =	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   snake_case		(     UpperCamelCase__       :						List[Any]			,				UpperCamelCase__       :						Tuple    )   ->      Optional[Any]:
      lowerCamelCase					:					Optional[int]        =	MobileViTVaConfig()

      lowerCamelCase					:					Union[str, Any]        =	False

      # dataset
      if task_name.startswith("""imagenet1k_"""    ):
            lowerCamelCase					:					List[Any]        =	1000
            if int(task_name.strip().split("""_"""    )[-1]    ) == 384:
                  lowerCamelCase					:					Any        =	384
            else:
                  lowerCamelCase					:					str        =	256
            lowerCamelCase					:					Tuple        =	"""imagenet-1k-id2label.json"""
      elif task_name.startswith("""imagenet21k_to_1k_"""    ):
            lowerCamelCase					:					Optional[Any]        =	21000
            if int(task_name.strip().split("""_"""    )[-1]    ) == 384:
                  lowerCamelCase					:					Dict        =	384
            else:
                  lowerCamelCase					:					Tuple        =	256
            lowerCamelCase					:					Dict        =	"""imagenet-22k-id2label.json"""
      elif task_name.startswith("""ade20k_"""    ):
            lowerCamelCase					:					List[str]        =	151
            lowerCamelCase					:					Dict        =	512
            lowerCamelCase					:					Optional[Any]        =	"""ade20k-id2label.json"""
            lowerCamelCase					:					Tuple        =	True
      elif task_name.startswith("""voc_"""    ):
            lowerCamelCase					:					List[Any]        =	21
            lowerCamelCase					:					Union[str, Any]        =	512
            lowerCamelCase					:					str        =	"""pascal-voc-id2label.json"""
            lowerCamelCase					:					str        =	True

      # orig_config
      lowerCamelCase					:					Optional[Any]        =	load_orig_config_file(UpperCamelCase__    )
      assert getattr(UpperCamelCase__			,				"""model.classification.name"""			,				-1    ) == "mobilevit_v2", "Invalid model"
      lowerCamelCase					:					str        =	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"
      lowerCamelCase					:					str        =	getattr(UpperCamelCase__			,				"""model.classification.activation.name"""			,				"""swish"""    )
      # config.image_size == getattr(orig_config,  'sampler.bs.crop_size_width', 256)

      if is_segmentation_model:
            lowerCamelCase					:					Tuple        =	getattr(UpperCamelCase__			,				"""model.segmentation.output_stride"""			,				16    )
            if "_deeplabv3" in task_name:
                  lowerCamelCase					:					int        =	getattr(UpperCamelCase__			,				"""model.segmentation.deeplabv3.aspp_rates"""			,				[12, 24, 36]    )
                  lowerCamelCase					:					Any        =	getattr(UpperCamelCase__			,				"""model.segmentation.deeplabv3.aspp_out_channels"""			,				512    )
                  lowerCamelCase					:					str        =	getattr(UpperCamelCase__			,				"""model.segmentation.deeplabv3.aspp_dropout"""			,				0.1    )

    # id2label
      lowerCamelCase					:					Optional[Any]        =	"""huggingface/label-files"""
      lowerCamelCase					:					Optional[Any]        =	json.load(open(hf_hub_download(UpperCamelCase__			,				UpperCamelCase__			,				repo_type="""dataset"""    )			,				"""r"""    )    )
      lowerCamelCase					:					Dict        =	{int(UpperCamelCase__    ): v for k, v in idalabel.items()}
      lowerCamelCase					:					Union[str, Any]        =	idalabel
      lowerCamelCase					:					Optional[Any]        =	{v: k for k, v in idalabel.items()}

      return config




def   snake_case		(     UpperCamelCase__       :						Dict			,				UpperCamelCase__       :						Optional[int]			,				UpperCamelCase__       :						int    )   ->      Tuple:
      lowerCamelCase					:					Any        =	dct.pop(UpperCamelCase__    )
      lowerCamelCase					:					List[str]        =	val




def   snake_case		(     UpperCamelCase__       :						List[Any]			,				UpperCamelCase__       :						Optional[Any]=False    )   ->      int:
      if base_model:
            lowerCamelCase					:					Any        =	""""""
      else:
            lowerCamelCase					:					Dict        =	"""mobilevitv2."""

      lowerCamelCase					:					List[str]        =	[]
      for k in state_dict.keys():
            if k[:8] == "encoder.":
                  lowerCamelCase					:					List[Any]        =	k[8:]
            else:
                  lowerCamelCase					:					Optional[Any]        =	k

            if ".block." in k:
                  lowerCamelCase					:					Dict        =	k_new.replace(""".block."""			,				"""."""    )
            if ".conv." in k:
                  lowerCamelCase					:					int        =	k_new.replace(""".conv."""			,				""".convolution."""    )
            if ".norm." in k:
                  lowerCamelCase					:					Union[str, Any]        =	k_new.replace(""".norm."""			,				""".normalization."""    )

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

            for i in [3, 4, 5]:
                  if F'layer_{i}.0.' in k:
                        lowerCamelCase					:					List[str]        =	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:
                        lowerCamelCase					:					Dict        =	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:
                        lowerCamelCase					:					List[str]        =	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:
                        lowerCamelCase					:					Optional[int]        =	[0, 1]
                  elif i == 4:
                        lowerCamelCase					:					Dict        =	[0, 1, 2, 3]
                  elif i == 5:
                        lowerCamelCase					:					Dict        =	[0, 1, 2]

                  for j in j_in:
                        if F'layer_{i}.1.global_rep.{j}.' in k:
                              lowerCamelCase					:					int        =	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:
                        lowerCamelCase					:					Optional[Any]        =	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:
                        lowerCamelCase					:					Dict        =	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:
                  lowerCamelCase					:					str        =	k_new.replace("""pre_norm_attn.0."""			,				"""layernorm_before."""    )
            if "pre_norm_attn.1." in k:
                  lowerCamelCase					:					Optional[Any]        =	k_new.replace("""pre_norm_attn.1."""			,				"""attention."""    )
            if "pre_norm_ffn.0." in k:
                  lowerCamelCase					:					List[Any]        =	k_new.replace("""pre_norm_ffn.0."""			,				"""layernorm_after."""    )
            if "pre_norm_ffn.1." in k:
                  lowerCamelCase					:					List[str]        =	k_new.replace("""pre_norm_ffn.1."""			,				"""ffn.conv1."""    )
            if "pre_norm_ffn.3." in k:
                  lowerCamelCase					:					Optional[int]        =	k_new.replace("""pre_norm_ffn.3."""			,				"""ffn.conv2."""    )

            if "classifier.1." in k:
                  lowerCamelCase					:					Optional[int]        =	k_new.replace("""classifier.1."""			,				"""classifier."""    )

            if "seg_head." in k:
                  lowerCamelCase					:					Dict        =	k_new.replace("""seg_head."""			,				"""segmentation_head."""    )
            if ".aspp_layer." in k:
                  lowerCamelCase					:					str        =	k_new.replace(""".aspp_layer."""			,				"""."""    )
            if ".aspp_pool." in k:
                  lowerCamelCase					:					Any        =	k_new.replace(""".aspp_pool."""			,				"""."""    )

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




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




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

      # load original state_dict
      lowerCamelCase					:					Any        =	torch.load(UpperCamelCase__			,				map_location="""cpu"""    )

      # load huggingface model
      if task_name.startswith("""ade20k_"""    ) or task_name.startswith("""voc_"""    ):
            lowerCamelCase					:					List[str]        =	MobileViTVaForSemanticSegmentation(UpperCamelCase__    ).eval()
            lowerCamelCase					:					List[Any]        =	False
      else:
            lowerCamelCase					:					int        =	MobileViTVaForImageClassification(UpperCamelCase__    ).eval()
            lowerCamelCase					:					Dict        =	False

      # remove and rename some keys of load the original model
      lowerCamelCase					:					List[str]        =	checkpoint
      remove_unused_keys(UpperCamelCase__    )
      lowerCamelCase					:					List[Any]        =	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
      lowerCamelCase					:					Optional[Any]        =	MobileViTImageProcessor(crop_size=config.image_size			,				size=config.image_size + 32    )
      lowerCamelCase					:					Union[str, Any]        =	image_processor(images=prepare_img()			,				return_tensors="""pt"""    )
      lowerCamelCase					:					Tuple        =	model(**UpperCamelCase__    )

      # verify classification model
      if task_name.startswith("""imagenet"""    ):
            lowerCamelCase					:					Tuple        =	outputs.logits
            lowerCamelCase					:					int        =	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
                  lowerCamelCase					:					Optional[Any]        =	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      :List[Any]	    =		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      :Any	    =		parser.parse_args()
    convert_mobilevitva_checkpoint(
        args.task, args.orig_checkpoint_path, args.orig_config_path, args.pytorch_dump_folder_path
    )








"""simple docstring"""


import unittest

from knapsack import greedy_knapsack as kp


class 			A__	(						unittest.TestCase):


     """simple docstring"""

     def 		a__						(	self:						Optional[int]	)->    Union[str, Any]:
           lowerCamelCase					:					Tuple        =	[10, 20, 30, 40, 50, 60]
           lowerCamelCase					:					Union[str, Any]        =	[2, 4, 6, 8, 10, 12]
           lowerCamelCase					:					Union[str, Any]        =	100
           self.assertEqual(kp.calc_profit(__a     ,		__a     ,		__a	)     ,		210	)

     def 		a__						(	self:						str	)->    str:
           self.assertRaisesRegex(__a     ,		"""max_weight must greater than zero."""	)

     def 		a__						(	self:						str	)->    List[Any]:
           self.assertRaisesRegex(__a     ,		"""Weight can not be negative."""	)

     def 		a__						(	self:						Any	)->    Dict:
           self.assertRaisesRegex(__a     ,		"""Profit can not be negative."""	)

     def 		a__						(	self:						Optional[Any]	)->    List[Any]:
           self.assertRaisesRegex(__a     ,		"""max_weight must greater than zero."""	)







     def 		a__						(	self:						Optional[Any]	)->    Tuple:
           self.assertRaisesRegex(
               __a     ,		"""The length of profit and weight must be same."""	)


if __name__ == "__main__":
    unittest.main()




