




'''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_      :		Optional[Any]							      =       logging.get_logger(__name__)





def      a_							(      __snake_case							:			int   )							->						Optional[int]:



       """simple docstring"""
       print('''Loading config file...'''   )

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

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

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





def      a_							(      __snake_case							:			Tuple   ,       __snake_case							:			List[str]   )							->						Tuple:



       """simple docstring"""
       lowerCamelCase_							=MobileViTVaConfig()

       lowerCamelCase_							=False

       # dataset
       if task_name.startswith('''imagenet1k_'''   ):
              lowerCamelCase_							=1000
              if int(task_name.strip().split('''_'''   )[-1]   ) == 384:
                     lowerCamelCase_							=384
              else:
                     lowerCamelCase_							=256
              lowerCamelCase_							='''imagenet-1k-id2label.json'''
       elif task_name.startswith('''imagenet21k_to_1k_'''   ):
              lowerCamelCase_							=2_1000
              if int(task_name.strip().split('''_'''   )[-1]   ) == 384:
                     lowerCamelCase_							=384
              else:
                     lowerCamelCase_							=256
              lowerCamelCase_							='''imagenet-22k-id2label.json'''
       elif task_name.startswith('''ade20k_'''   ):
              lowerCamelCase_							=151
              lowerCamelCase_							=512
              lowerCamelCase_							='''ade20k-id2label.json'''
              lowerCamelCase_							=True
       elif task_name.startswith('''voc_'''   ):
              lowerCamelCase_							=21
              lowerCamelCase_							=512
              lowerCamelCase_							='''pascal-voc-id2label.json'''
              lowerCamelCase_							=True

       # orig_config
       lowerCamelCase_							=load_orig_config_file(__snake_case   )
       assert getattr(__snake_case   ,       '''model.classification.name'''   ,       -1   ) == "mobilevit_v2", "Invalid model"
       lowerCamelCase_							=getattr(__snake_case   ,       '''model.classification.mitv2.width_multiplier'''   ,       1.0   )
       assert (
           getattr(__snake_case   ,       '''model.classification.mitv2.attn_norm_layer'''   ,       -1   ) == "layer_norm_2d"
       ), "Norm layers other than layer_norm_2d is not supported"
       lowerCamelCase_							=getattr(__snake_case   ,       '''model.classification.activation.name'''   ,       '''swish'''   )
       # config.image_size == getattr(orig_config,  'sampler.bs.crop_size_width', 256)

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

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

       return config





def      a_							(      __snake_case							:			Optional[Any]   ,       __snake_case							:			Optional[int]   ,       __snake_case							:			List[str]   )							->						Optional[int]:



       """simple docstring"""
       lowerCamelCase_							=dct.pop(__snake_case   )
       lowerCamelCase_							=val





def      a_							(      __snake_case							:			List[str]   ,       __snake_case							:			Optional[int]=False   )							->						Optional[int]:



       """simple docstring"""
       if base_model:
              lowerCamelCase_							=''''''
       else:
              lowerCamelCase_							='''mobilevitv2.'''

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

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

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

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

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

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

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

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





def      a_							(      __snake_case							:			Any   )							->						Dict:



       """simple docstring"""
       lowerCamelCase_							=[]
       for k in state_dict.keys():
              if k.startswith('''seg_head.aux_head.'''   ):
                     keys_to_ignore.append(__snake_case   )
       for k in keys_to_ignore:
              state_dict.pop(__snake_case   ,       __snake_case   )





def      a_							(      )							->						Optional[Any]:



       """simple docstring"""
       lowerCamelCase_							='''http://images.cocodataset.org/val2017/000000039769.jpg'''
       # url = "https://cdn.britannica.com/86/141086-050-9D7C75EE/Gulfstream-G450-business-jet-passengers.jpg"
       lowerCamelCase_							=Image.open(requests.get(__snake_case   ,       stream=__snake_case   ).raw   )
       return im





@torch.no_grad()
def      a_							(      __snake_case							:			Dict   ,       __snake_case							:			str   ,       __snake_case							:			Optional[int]   ,       __snake_case							:			Union[str, Any]   )							->						str:



       """simple docstring"""
       lowerCamelCase_							=get_mobilevitva_config(__snake_case   ,       __snake_case   )

       # load original state_dict
       lowerCamelCase_							=torch.load(__snake_case   ,       map_location='''cpu'''   )

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

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

       # load modified state_dict
       model.load_state_dict(__snake_case   )

       # Check outputs on an image, prepared by MobileViTImageProcessor
       lowerCamelCase_							=MobileViTImageProcessor(crop_size=config.image_size   ,       size=config.image_size + 32   )
       lowerCamelCase_							=image_processor(images=prepare_img()   ,       return_tensors='''pt'''   )
       lowerCamelCase_							=model(**__snake_case   )

       # verify classification model
       if task_name.startswith('''imagenet'''   ):
              lowerCamelCase_							=outputs.logits
              lowerCamelCase_							=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_							=torch.tensor([-1.63_36e00, -7.32_04e-02, -5.18_83e-01]   )
                     assert torch.allclose(logits[0, :3]   ,       __snake_case   ,       atol=1e-4   )

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


if __name__ == "__main__":
     a_      :		Union[str, 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_      :		Dict							      =       parser.parse_args()
     convert_mobilevitva_checkpoint(
         args.task, args.orig_checkpoint_path, args.orig_config_path, args.pytorch_dump_folder_path
     )












import numpy as np
from sklearn.datasets import fetch_california_housing
from sklearn.metrics import mean_absolute_error, mean_squared_error
from sklearn.model_selection import train_test_split
from xgboost import XGBRegressor





def 						__UpperCAmelCase			(     a_):
							return (data["data"], data["target"])





def 						__UpperCAmelCase			(     a_							,     a_							,     a_):
							snake_case_				=							XGBRegressor(verbosity=0							,     random_state=42)
							xgb.fit(a_							,     a_)
							# Predict target for test data
							snake_case_				=							xgb.predict(a_)
							snake_case_				=							predictions.reshape(len(a_)							,     1)
							return predictions





def 						__UpperCAmelCase			(     ):
							snake_case_				=							fetch_california_housing()
							snake_case_			,		snake_case_				=							data_handling(a_)
							snake_case_			,		snake_case_			,		snake_case_			,		snake_case_				=							train_test_split(
							    a_							,     a_							,     test_size=0.25							,     random_state=1)
							snake_case_				=							xgboost(a_							,     a_							,     a_)
							# Error printing
							print(f'''Mean Absolute Error : {mean_absolute_error(a_							,     a_)}''')
							print(f'''Mean Square Error  : {mean_squared_error(a_							,     a_)}''')


if __name__ == "__main__":
	import doctest

	doctest.testmod(verbose=True)
	main()





