

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      A__					(			__A  :						Union[str, Any]			)					->Optional[Any]:
      print('''Loading config file...'''			)

      def flatten_yaml_as_dict(__A  :						List[Any]     ,				__A  :						str=""     ,				__A  :						Union[str, Any]="."			):
            __A    =[]
            for k, v in d.items():
                  __A    =parent_key + sep + k if parent_key else k
                  if isinstance(_lowercase     ,				collections.abc.MutableMapping			):
                        items.extend(flatten_yaml_as_dict(_lowercase     ,				_lowercase     ,				sep=_lowercase			).items()			)
                  else:
                        items.append((new_key, v)			)
            return dict(_lowercase			)

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

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





def      A__					(			__A  :						Optional[Any]     ,				__A  :						Tuple			)					->List[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(_lowercase			)
      assert getattr(_lowercase     ,				'''model.classification.name'''     ,				-1			) == "mobilevit_v2", "Invalid model"
      __A    =getattr(_lowercase     ,				'''model.classification.mitv2.width_multiplier'''     ,				1.0			)
      assert (
          getattr(_lowercase     ,				'''model.classification.mitv2.attn_norm_layer'''     ,				-1			) == "layer_norm_2d"
      ), "Norm layers other than layer_norm_2d is not supported"
      __A    =getattr(_lowercase     ,				'''model.classification.activation.name'''     ,				'''swish'''			)
      # config.image_size == getattr(orig_config,  'sampler.bs.crop_size_width', 256)

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

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

      return config





def      A__					(			__A  :						str     ,				__A  :						Any     ,				__A  :						List[Any]			)					->Any:
      __A    =dct.pop(_lowercase			)
      __A    =val





def      A__					(			__A  :						Optional[Any]     ,				__A  :						Dict=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      A__					(			__A  :						Any			)					->List[Any]:
      __A    =[]
      for k in state_dict.keys():
            if k.startswith('''seg_head.aux_head.'''			):
                  keys_to_ignore.append(_lowercase			)
      for k in keys_to_ignore:
            state_dict.pop(_lowercase     ,				_lowercase			)





def      A__					(			)					->List[Any]:
      __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(_lowercase     ,				stream=_lowercase			).raw			)
      return im





@torch.no_grad()
def      A__					(			__A  :						str     ,				__A  :						Any     ,				__A  :						List[Any]     ,				__A  :						Tuple			)					->Any:
      __A    =get_mobilevitva_config(_lowercase     ,				_lowercase			)

      # load original state_dict
      __A    =torch.load(_lowercase     ,				map_location='''cpu'''			)

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

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

      # load modified state_dict
      model.load_state_dict(_lowercase			)

      # 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(**_lowercase			)

      # 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([-1.6336e00, -7.3204e-02, -5.1883e-01]			)
                  assert torch.allclose(logits[0, :3]     ,				_lowercase     ,				atol=1e-4			)

      Path(_lowercase			).mkdir(exist_ok=_lowercase			)
      print(F'''Saving model {task_name} to {pytorch_dump_folder_path}'''			)
      model.save_pretrained(_lowercase			)
      print(F'''Saving image processor to {pytorch_dump_folder_path}'''			)
      image_processor.save_pretrained(_lowercase			)


if __name__ == "__main__":
   _lowerCamelCase      :					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 . '''
           '''
                Classification (ImageNet-1k)
                    - MobileViTV2 (256x256) : imagenet1k_256
                    - MobileViTV2 (Trained on 256x256 and Finetuned on 384x384) : imagenet1k_384
                    - MobileViTV2 (Trained on ImageNet-21k and Finetuned on ImageNet-1k 256x256) :
                      imagenet21k_to_1k_256
                    - MobileViTV2 (Trained on ImageNet-21k, Finetuned on ImageNet-1k 256x256, and Finetuned on
                      ImageNet-1k 384x384) : imagenet21k_to_1k_384
                Segmentation
                    - ADE20K Dataset : ade20k_deeplabv3
                    - Pascal VOC 2012 Dataset: voc_deeplabv3
            '''
       ),
       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      :					Optional[int]					    =      parser.parse_args()
   convert_mobilevitva_checkpoint(
       args.task, args.orig_checkpoint_path, args.orig_config_path, args.pytorch_dump_folder_path
   )









def       SCREAMING_SNAKE_CASE__  (       _lowercase     : int   ,    _lowercase     : int			)   ->							Optional[int]:




		'''simple docstring'''
		if b == 0:
				return 1
		if (b % 2) == 0:
				return actual_power(_lowercase   ,    int(b / 2			)			) * actual_power(_lowercase   ,    int(b / 2			)			)
		else:
				return a * actual_power(_lowercase   ,    int(b / 2			)			) * actual_power(_lowercase   ,    int(b / 2			)			)


def       SCREAMING_SNAKE_CASE__  (       _lowercase     : int   ,    _lowercase     : int			)   ->							float:




		'''simple docstring'''
		if b < 0:
				return 1 / actual_power(_lowercase   ,    _lowercase			)
		return actual_power(_lowercase   ,    _lowercase			)


if __name__ == "__main__":
			print(power(-2, -3))


