"""simple docstring"""







from __future__ import annotations

from collections.abc import Callable

__SCREAMING_SNAKE_CASE    	=list[list[float | int]]



def 			lowercase__(       __SCREAMING_SNAKE_CASE							:     Matrix     ,			__SCREAMING_SNAKE_CASE							:     Matrix      ):
     lowercase_	:				int													=	len(__SCREAMING_SNAKE_CASE      )
     lowercase_	:				Matrix													=	[[0 for _ in range(size + 1      )] for _ in range(__SCREAMING_SNAKE_CASE      )]
     lowercase_	:				int
     lowercase_	:				int
     lowercase_	:				int
     lowercase_	:				int
     lowercase_	:				int
     lowercase_	:				float

     for row in range(__SCREAMING_SNAKE_CASE      ):
          for col in range(__SCREAMING_SNAKE_CASE      ):
               lowercase_	:				Any													=	matrix[row][col]

          lowercase_	:				Optional[int]													=	vector[row][0]

     lowercase_	:				List[Any]													=	0
     lowercase_	:				str													=	0
     while row < size and col < size:
          # pivoting
          lowercase_	:				Tuple													=	max((abs(augmented[rowa][col]      ), rowa) for rowa in range(__SCREAMING_SNAKE_CASE     ,			__SCREAMING_SNAKE_CASE      )      )[
              1
          ]
          if augmented[pivot_row][col] == 0:
               col += 1
               continue
          else:
               lowercase_							,  lowercase_	:				Optional[int]													=	augmented[pivot_row], augmented[row]

          for rowa in range(row + 1     ,			__SCREAMING_SNAKE_CASE      ):
               lowercase_	:				Any													=	augmented[rowa][col] / augmented[row][col]
               lowercase_	:				Optional[Any]													=	0
               for cola in range(col + 1     ,			size + 1      ):
                    augmented[rowa][cola] -= augmented[row][cola] * ratio

          row += 1
          col += 1

     # back substitution
     for col in range(1     ,			__SCREAMING_SNAKE_CASE      ):
          for row in range(__SCREAMING_SNAKE_CASE      ):
               lowercase_	:				List[str]													=	augmented[row][col] / augmented[col][col]
               for cola in range(__SCREAMING_SNAKE_CASE     ,			size + 1      ):
                    augmented[row][cola] -= augmented[col][cola] * ratio

    # round to get rid of numbers like 2.000000000000004
     return [
         [round(augmented[row][size] / augmented[row][row]     ,			10      )] for row in range(__SCREAMING_SNAKE_CASE      )
     ]



def 			lowercase__(       __SCREAMING_SNAKE_CASE							:     list[int]      ):
     lowercase_	:				int													=	len(__SCREAMING_SNAKE_CASE      )
     lowercase_	:				Matrix													=	[[0 for _ in range(__SCREAMING_SNAKE_CASE      )] for _ in range(__SCREAMING_SNAKE_CASE      )]
     lowercase_	:				Matrix													=	[[0] for _ in range(__SCREAMING_SNAKE_CASE      )]
     lowercase_	:				Matrix
     lowercase_	:				int
     lowercase_	:				int
     lowercase_	:				int

     for x_val, y_val in enumerate(__SCREAMING_SNAKE_CASE      ):
          for col in range(__SCREAMING_SNAKE_CASE      ):
               lowercase_	:				Any													=	(x_val + 1) ** (size - col - 1)
          lowercase_	:				Union[str, Any]													=	y_val

     lowercase_	:				Dict													=	solve(__SCREAMING_SNAKE_CASE     ,			__SCREAMING_SNAKE_CASE      )

     def interpolated_func(__SCREAMING_SNAKE_CASE							:     int      ) -> int:
          return sum(
              round(coeffs[x_val][0]      ) * (var ** (size - x_val - 1))
              for x_val in range(__SCREAMING_SNAKE_CASE      )      )

     return interpolated_func



def 			lowercase__(       __SCREAMING_SNAKE_CASE							:     int      ):
     return (
         1
         - variable
         + variable**2
         - variable**3
         + variable**4
         - variable**5
         + variable**6
         - variable**7
         + variable**8
         - variable**9
         + variable**10
     )



def 			lowercase__(       __SCREAMING_SNAKE_CASE							:     Callable[[int], int] = question_function     ,			__SCREAMING_SNAKE_CASE							:     int = 10      ):
     lowercase_	:				list[int]													=	[func(__SCREAMING_SNAKE_CASE      ) for x_val in range(1     ,			order + 1      )]

     lowercase_	:				list[Callable[[int], int]]													=	[
         interpolate(data_points[:max_coeff]      ) for max_coeff in range(1     ,			order + 1      )
     ]

     lowercase_	:				int													=	0
     lowercase_	:				Callable[[int], int]
     lowercase_	:				int

     for poly in polynomials:
          lowercase_	:				Optional[Any]													=	1
          while func(__SCREAMING_SNAKE_CASE      ) == poly(__SCREAMING_SNAKE_CASE      ):
               x_val += 1

          ret += poly(__SCREAMING_SNAKE_CASE      )

     return ret


if __name__ == "__main__":
       print(F"{solution() = }")


"""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()
__SCREAMING_SNAKE_CASE    	=logging.get_logger(__name__)



def 			lowercase__(       __SCREAMING_SNAKE_CASE							:     Union[str, Any]      ):
     print('Loading config file...'      )

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

     lowercase_	:				Optional[Any]													=	argparse.Namespace()
     with open(__SCREAMING_SNAKE_CASE     ,			'r'      ) as yaml_file:
          try:
               lowercase_	:				str													=	yaml.load(__SCREAMING_SNAKE_CASE     ,			Loader=yaml.FullLoader      )

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



def 			lowercase__(       __SCREAMING_SNAKE_CASE							:     int     ,			__SCREAMING_SNAKE_CASE							:     List[str]      ):
     lowercase_	:				int													=	MobileViTVaConfig()

     lowercase_	:				List[str]													=	False

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

     # orig_config
     lowercase_	:				Optional[int]													=	load_orig_config_file(__SCREAMING_SNAKE_CASE      )
     assert getattr(__SCREAMING_SNAKE_CASE     ,			'model.classification.name'     ,			-1      ) == "mobilevit_v2", "Invalid model"
     lowercase_	:				Optional[Any]													=	getattr(__SCREAMING_SNAKE_CASE     ,			'model.classification.mitv2.width_multiplier'     ,			1.0      )
     assert (
         getattr(__SCREAMING_SNAKE_CASE     ,			'model.classification.mitv2.attn_norm_layer'     ,			-1      ) == "layer_norm_2d"
     ), "Norm layers other than layer_norm_2d is not supported"
     lowercase_	:				Any													=	getattr(__SCREAMING_SNAKE_CASE     ,			'model.classification.activation.name'     ,			'swish'      )
     # config.image_size == getattr(orig_config,  'sampler.bs.crop_size_width', 256)

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

    # id2label
     lowercase_	:				Optional[Any]													=	'huggingface/label-files'
     lowercase_	:				List[Any]													=	json.load(open(hf_hub_download(__SCREAMING_SNAKE_CASE     ,			__SCREAMING_SNAKE_CASE     ,			repo_type='dataset'      )     ,			'r'      )      )
     lowercase_	:				List[str]													=	{int(__SCREAMING_SNAKE_CASE      ): v for k, v in idalabel.items()}
     lowercase_	:				int													=	idalabel
     lowercase_	:				List[Any]													=	{v: k for k, v in idalabel.items()}

     return config



def 			lowercase__(       __SCREAMING_SNAKE_CASE							:     List[Any]     ,			__SCREAMING_SNAKE_CASE							:     List[Any]     ,			__SCREAMING_SNAKE_CASE							:     str      ):
     lowercase_	:				List[Any]													=	dct.pop(__SCREAMING_SNAKE_CASE      )
     lowercase_	:				Optional[Any]													=	val



def 			lowercase__(       __SCREAMING_SNAKE_CASE							:     str     ,			__SCREAMING_SNAKE_CASE							:     Optional[Any]=False      ):
     if base_model:
          lowercase_	:				int													=	''
     else:
          lowercase_	:				str													=	'mobilevitv2.'

     lowercase_	:				Dict													=	[]
     for k in state_dict.keys():
          if k[:8] == "encoder.":
               lowercase_	:				Dict													=	k[8:]
          else:
               lowercase_	:				Union[str, Any]													=	k

          if ".block." in k:
               lowercase_	:				List[str]													=	k_new.replace('.block.'     ,			'.'      )
          if ".conv." in k:
               lowercase_	:				List[Any]													=	k_new.replace('.conv.'     ,			'.convolution.'      )
          if ".norm." in k:
               lowercase_	:				str													=	k_new.replace('.norm.'     ,			'.normalization.'      )

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

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

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

          if "classifier.1." in k:
               lowercase_	:				Union[str, Any]													=	k_new.replace('classifier.1.'     ,			'classifier.'      )

          if "seg_head." in k:
               lowercase_	:				Optional[int]													=	k_new.replace('seg_head.'     ,			'segmentation_head.'      )
          if ".aspp_layer." in k:
               lowercase_	:				Dict													=	k_new.replace('.aspp_layer.'     ,			'.'      )
          if ".aspp_pool." in k:
               lowercase_	:				Dict													=	k_new.replace('.aspp_pool.'     ,			'.'      )

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



def 			lowercase__(       __SCREAMING_SNAKE_CASE							:     Any      ):
     lowercase_	:				str													=	[]
     for k in state_dict.keys():
          if k.startswith('seg_head.aux_head.'      ):
               keys_to_ignore.append(__SCREAMING_SNAKE_CASE      )
     for k in keys_to_ignore:
          state_dict.pop(__SCREAMING_SNAKE_CASE     ,			__SCREAMING_SNAKE_CASE      )



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



@torch.no_grad()
def 			lowercase__(       __SCREAMING_SNAKE_CASE							:     Optional[Any]     ,			__SCREAMING_SNAKE_CASE							:     int     ,			__SCREAMING_SNAKE_CASE							:     Optional[int]     ,			__SCREAMING_SNAKE_CASE							:     Optional[int]      ):
     lowercase_	:				Tuple													=	get_mobilevitva_config(__SCREAMING_SNAKE_CASE     ,			__SCREAMING_SNAKE_CASE      )

     # load original state_dict
     lowercase_	:				Tuple													=	torch.load(__SCREAMING_SNAKE_CASE     ,			map_location='cpu'      )

     # load huggingface model
     if task_name.startswith('ade20k_'      ) or task_name.startswith('voc_'      ):
          lowercase_	:				Tuple													=	MobileViTVaForSemanticSegmentation(__SCREAMING_SNAKE_CASE      ).eval()
          lowercase_	:				Optional[int]													=	False
     else:
          lowercase_	:				Any													=	MobileViTVaForImageClassification(__SCREAMING_SNAKE_CASE      ).eval()
          lowercase_	:				int													=	False

     # remove and rename some keys of load the original model
     lowercase_	:				Any													=	checkpoint
     remove_unused_keys(__SCREAMING_SNAKE_CASE      )
     lowercase_	:				Union[str, Any]													=	create_rename_keys(__SCREAMING_SNAKE_CASE     ,			base_model=__SCREAMING_SNAKE_CASE      )
     for rename_key_src, rename_key_dest in rename_keys:
          rename_key(__SCREAMING_SNAKE_CASE     ,			__SCREAMING_SNAKE_CASE     ,			__SCREAMING_SNAKE_CASE      )

     # load modified state_dict
     model.load_state_dict(__SCREAMING_SNAKE_CASE      )

     # Check outputs on an image, prepared by MobileViTImageProcessor
     lowercase_	:				Union[str, Any]													=	MobileViTImageProcessor(crop_size=config.image_size     ,			size=config.image_size + 32      )
     lowercase_	:				Any													=	image_processor(images=prepare_img()     ,			return_tensors='pt'      )
     lowercase_	:				Optional[int]													=	model(**__SCREAMING_SNAKE_CASE      )

     # verify classification model
     if task_name.startswith('imagenet'      ):
          lowercase_	:				List[str]													=	outputs.logits
          lowercase_	:				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
               lowercase_	:				Optional[int]													=	torch.tensor([-1.6_336E00, -7.3_204E-02, -5.1_883E-01]      )
               assert torch.allclose(logits[0, :3]     ,			__SCREAMING_SNAKE_CASE     ,			atol=1E-4      )

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


if __name__ == "__main__":
       __SCREAMING_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."
       )

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

