


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      =   logging.get_logger(__name__)





def __UpperCAmelCase       (		__A			)						->					Any:



       '''simple docstring'''





       print("Loading config file..."			)

       def flatten_yaml_as_dict(__A ,						__A="" ,						__A="."			):
              UpperCAmelCase__										=     []
              for k, v in d.items():
                     UpperCAmelCase__										=     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__			)

       UpperCAmelCase__										=     argparse.Namespace()
       with open(UpperCamelCase__ ,						"r"			) as yaml_file:
              try:
                     UpperCAmelCase__										=     yaml.load(UpperCamelCase__ ,						Loader=yaml.FullLoader			)

                     UpperCAmelCase__										=     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 __UpperCAmelCase       (		__A ,						__A			)						->					Optional[Any]:



       '''simple docstring'''





       UpperCAmelCase__										=     MobileViTVaConfig()

       UpperCAmelCase__										=     False

       # dataset
       if task_name.startswith("imagenet1k_"			):
              UpperCAmelCase__										=     1_0_0_0
              if int(task_name.strip().split("_"			)[-1]			) == 3_8_4:
                     UpperCAmelCase__										=     3_8_4
              else:
                     UpperCAmelCase__										=     2_5_6
              UpperCAmelCase__										=     """imagenet-1k-id2label.json"""
       elif task_name.startswith("imagenet21k_to_1k_"			):
              UpperCAmelCase__										=     2_1_0_0_0
              if int(task_name.strip().split("_"			)[-1]			) == 3_8_4:
                     UpperCAmelCase__										=     3_8_4
              else:
                     UpperCAmelCase__										=     2_5_6
              UpperCAmelCase__										=     """imagenet-22k-id2label.json"""
       elif task_name.startswith("ade20k_"			):
              UpperCAmelCase__										=     1_5_1
              UpperCAmelCase__										=     5_1_2
              UpperCAmelCase__										=     """ade20k-id2label.json"""
              UpperCAmelCase__										=     True
       elif task_name.startswith("voc_"			):
              UpperCAmelCase__										=     2_1
              UpperCAmelCase__										=     5_1_2
              UpperCAmelCase__										=     """pascal-voc-id2label.json"""
              UpperCAmelCase__										=     True

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

       if is_segmentation_model:
              UpperCAmelCase__										=     getattr(UpperCamelCase__ ,						"model.segmentation.output_stride" ,						1_6			)
              if "_deeplabv3" in task_name:
                     UpperCAmelCase__										=     getattr(UpperCamelCase__ ,						"model.segmentation.deeplabv3.aspp_rates" ,						[1_2, 2_4, 3_6]			)
                     UpperCAmelCase__										=     getattr(UpperCamelCase__ ,						"model.segmentation.deeplabv3.aspp_out_channels" ,						5_1_2			)
                     UpperCAmelCase__										=     getattr(UpperCamelCase__ ,						"model.segmentation.deeplabv3.aspp_dropout" ,						0.1			)

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

       return config





def __UpperCAmelCase       (		__A ,						__A ,						__A			)						->					Tuple:



       '''simple docstring'''





       UpperCAmelCase__										=     dct.pop(UpperCamelCase__			)
       UpperCAmelCase__										=     val





def __UpperCAmelCase       (		__A ,						__A=False			)						->					int:



       '''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 __UpperCAmelCase       (		__A			)						->					Optional[Any]:



       '''simple docstring'''





       UpperCAmelCase__										=     []
       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 __UpperCAmelCase       (		)						->					int:



       '''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(UpperCamelCase__ ,						stream=UpperCamelCase__			).raw			)
       return im





@torch.no_grad()
def __UpperCAmelCase       (		__A ,						__A ,						__A ,						__A			)						->					int:



       '''simple docstring'''





       UpperCAmelCase__										=     get_mobilevitva_config(UpperCamelCase__ ,						UpperCamelCase__			)

       # load original state_dict
       UpperCAmelCase__										=     torch.load(UpperCamelCase__ ,						map_location="cpu"			)

       # load huggingface model
       if task_name.startswith("ade20k_"			) or task_name.startswith("voc_"			):
              UpperCAmelCase__										=     MobileViTVaForSemanticSegmentation(UpperCamelCase__			).eval()
              UpperCAmelCase__										=     False
       else:
              UpperCAmelCase__										=     MobileViTVaForImageClassification(UpperCamelCase__			).eval()
              UpperCAmelCase__										=     False

       # remove and rename some keys of load the original model
       UpperCAmelCase__										=     checkpoint
       remove_unused_keys(UpperCamelCase__			)
       UpperCAmelCase__										=     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
       UpperCAmelCase__										=     MobileViTImageProcessor(crop_size=config.image_size ,						size=config.image_size + 3_2			)
       UpperCAmelCase__										=     image_processor(images=prepare_img() ,						return_tensors="pt"			)
       UpperCAmelCase__										=     model(**UpperCamelCase__			)

       # 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] ,						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__":
    A      =   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."
    )

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









def __UpperCAmelCase       (		__A			)						->					list[int]:



       '''simple docstring'''





       UpperCAmelCase__										=     len(__A			)
       for i in range(__A			):
              for j in range(i + 1 ,						__A			):
                     if numbers[j] < numbers[i]:
                            UpperCAmelCase__						,			UpperCAmelCase__										=     numbers[j], numbers[i]
       return numbers


if __name__ == "__main__":
    A      = input("Enter numbers separated by a comma:\n").strip()
    A      = [int(item) for item in user_input.split(",")]
    print(exchange_sort(unsorted))





