





'''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()
a__							:							List[Any] =logging.get_logger(__name__)
def 				lowercase__   (   __lowercase						:  Dict     ) ->      Optional[int]:



       """simple docstring"""






       print('Loading config file...'     )

       def flatten_yaml_as_dict(__lowercase						:  List[Any]				,  __lowercase						:  int=""				,  __lowercase						:  Optional[int]="."     ):
              __UpperCamelCase       =  []
              for k, v in d.items():
                     __UpperCamelCase       =  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     )

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

                     __UpperCamelCase       =  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 				lowercase__   (   __lowercase						:  Union[str, Any]				,  __lowercase						:  List[str]     ) ->      Optional[int]:



       """simple docstring"""






       __UpperCamelCase       =  MobileViTVaConfig()

       __UpperCamelCase       =  False

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

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

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

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

       return config
def 				lowercase__   (   __lowercase						:  Union[str, Any]				,  __lowercase						:  Union[str, Any]				,  __lowercase						:  List[str]     ) ->      Any:



       """simple docstring"""






       __UpperCamelCase       =  dct.pop(__lowercase     )
       __UpperCamelCase       =  val
def 				lowercase__   (   __lowercase						:  Optional[int]				,  __lowercase						:  str=False     ) ->      Union[str, Any]:



       """simple docstring"""






       if base_model:
              __UpperCamelCase       =  ''
       else:
              __UpperCamelCase       =  'mobilevitv2.'

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

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

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

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

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

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

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

              rename_keys.append((k, k_new)     )
       return rename_keys
def 				lowercase__   (   __lowercase						:  Dict     ) ->      str:



       """simple docstring"""






       __UpperCamelCase       =  []
       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 				lowercase__   (   ) ->      Union[str, Any]:



       """simple docstring"""






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



       """simple docstring"""






       __UpperCamelCase       =  get_mobilevitva_config(__lowercase				,  __lowercase     )

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

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

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

       # verify classification model
       if task_name.startswith('imagenet'     ):
              __UpperCamelCase       =  outputs.logits
              __UpperCamelCase       =  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
                     __UpperCamelCase       =  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__":
    a__							:							Optional[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 . '''
            '''
                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.'''
    )

    a__							:							Union[str, 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 numpy as np
from numpy import ndarray
from scipy.optimize import Bounds, LinearConstraint, minimize



def   a_						(					lowerCamelCase       ):
				return np.dot(lowerCamelCase					,			lowerCamelCase       )
class 				snake_case       :




					"""simple docstring"""

					def __init__(				self   :	int    ,*,
					    lowerCamelCase__   :	float = np.inf    ,lowerCamelCase__   :	str = "linear"    ,lowerCamelCase__   :	float = 0.0    ,):
									UpperCAmelCase__             =							regularization
									UpperCAmelCase__             =							gamma
									if kernel == "linear":
													UpperCAmelCase__             =							self.__linear
									elif kernel == "rbf":
													if self.gamma == 0:
																	raise ValueError('rbf kernel requires gamma'			)
													if not isinstance(self.gamma    ,(float, int)			):
																	raise ValueError('gamma must be float or int'			)
													if not self.gamma > 0:
																	raise ValueError('gamma must be > 0'			)
													UpperCAmelCase__             =							self.__rbf
													# in the future, there could be a default value like in sklearn
													# sklear: def_gamma = 1/(n_features * X.var()) (wiki)
													# previously it was 1/(n_features)
									else:
													UpperCAmelCase__             =							f'''Unknown kernel: {kernel}'''
													raise ValueError(lowerCamelCase__			)







					def 		__lowerCAmelCase						(				self   :	List[Any]    ,lowerCamelCase__   :	ndarray    ,lowerCamelCase__   :	ndarray			):
									return np.dot(lowerCamelCase__    ,lowerCamelCase__			)







					def 		__lowerCAmelCase						(				self   :	Any    ,lowerCamelCase__   :	ndarray    ,lowerCamelCase__   :	ndarray			):
									return np.exp(-(self.gamma * norm_squared(vectora - vectora			))			)







					def 		__lowerCAmelCase						(				self   :	Tuple    ,lowerCamelCase__   :	list[ndarray]    ,lowerCamelCase__   :	ndarray			):
									UpperCAmelCase__             =							observations
									UpperCAmelCase__             =							classes

									# using Wolfe's Dual to calculate w.
									# Primal problem: minimize 1/2*norm_squared(w)
									#   constraint: yn(w . xn + b) >= 1
									#
									# With l a vector
									# Dual problem: maximize sum_n(ln) -
									#       1/2 * sum_n(sum_m(ln*lm*yn*ym*xn . xm))
									#   constraint: self.C >= ln >= 0
									#           and sum_n(ln*yn) = 0
									# Then we get w using w = sum_n(ln*yn*xn)
									# At the end we can get b ~= mean(yn - w . xn)
									#
									# Since we use kernels, we only need l_star to calculate b
									# and to classify observations

									((UpperCAmelCase__)      ,	)             =							np.shape(lowerCamelCase__			)

									def to_minimize(lowerCamelCase__   :	ndarray			) -> float:
													UpperCAmelCase__             =							0
													((UpperCAmelCase__)      ,	)             =							np.shape(lowerCamelCase__			)
													for i in range(lowerCamelCase__			):
																	for j in range(lowerCamelCase__			):
																					s += (
																					    candidate[i]
																					    * candidate[j]
																					    * classes[i]
																					    * classes[j]
																					    * self.kernel(observations[i]    ,observations[j]			)
																					)
													return 1 / 2 * s - sum(lowerCamelCase__			)

									UpperCAmelCase__             =							LinearConstraint(lowerCamelCase__    ,0    ,0			)
									UpperCAmelCase__             =							Bounds(0    ,self.regularization			)

									UpperCAmelCase__             =							minimize(
									    lowerCamelCase__    ,np.ones(lowerCamelCase__			)    ,bounds=lowerCamelCase__    ,constraints=[ly_contraint]			).x
									UpperCAmelCase__             =							l_star

									# calculating mean offset of separation plane to points
									UpperCAmelCase__             =							0
									for i in range(lowerCamelCase__			):
													for j in range(lowerCamelCase__			):
																	s += classes[i] - classes[i] * self.optimum[i] * self.kernel(
																	    observations[i]    ,observations[j]			)
									UpperCAmelCase__             =							s / n







					def 		__lowerCAmelCase						(				self   :	int    ,lowerCamelCase__   :	ndarray			):
									UpperCAmelCase__             =							sum(
									    self.optimum[n]
									    * self.classes[n]
									    * self.kernel(self.observations[n]    ,lowerCamelCase__			)
									    for n in range(len(self.classes			)			)			)
									return 1 if s + self.offset >= 0 else -1


if __name__ == "__main__":
						import doctest

						doctest.testmod()



