





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[str]  	=	logging.get_logger(__name__)



def 				lowerCAmelCase_ (							A_):
    print("Loading config file...")

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

    UpperCamelCase__:	Any					=     argparse.Namespace()
    with open(A_    ,"r") as yaml_file:
        try:
            UpperCamelCase__:	List[Any]					=     yaml.load(A_    ,Loader=yaml.FullLoader)

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



def 				lowerCAmelCase_ (							A_    ,A_):
    UpperCamelCase__:	Dict					=     MobileViTVaConfig()

    UpperCamelCase__:	List[str]					=     False

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

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

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

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

    return config



def 				lowerCAmelCase_ (							A_    ,A_    ,A_):
    UpperCamelCase__:	Optional[Any]					=     dct.pop(A_)
    UpperCamelCase__:	str					=     val



def 				lowerCAmelCase_ (							A_    ,A_=False):
    if base_model:
        UpperCamelCase__:	str					=     ""
    else:
        UpperCamelCase__:	str					=     "mobilevitv2."

    UpperCamelCase__:	List[str]					=     []
    for k in state_dict.keys():
        if k[:8] == "encoder.":
            UpperCamelCase__:	Optional[int]					=     k[8:]
        else:
            UpperCamelCase__:	Optional[Any]					=     k

        if ".block." in k:
            UpperCamelCase__:	int					=     k_new.replace(".block."    ,".")
        if ".conv." in k:
            UpperCamelCase__:	Union[str, Any]					=     k_new.replace(".conv."    ,".convolution.")
        if ".norm." in k:
            UpperCamelCase__:	Dict					=     k_new.replace(".norm."    ,".normalization.")

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

        for i in [3, 4, 5]:
            if F"layer_{i}.0." in k:
                UpperCamelCase__:	List[str]					=     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__:	Union[str, 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:
                UpperCamelCase__:	Optional[int]					=     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__:	str					=     [0, 1]
            elif i == 4:
                UpperCamelCase__:	Dict					=     [0, 1, 2, 3]
            elif i == 5:
                UpperCamelCase__:	Tuple					=     [0, 1, 2]

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

        if "classifier.1." in k:
            UpperCamelCase__:	Optional[Any]					=     k_new.replace("classifier.1."    ,"classifier.")

        if "seg_head." in k:
            UpperCamelCase__:	List[str]					=     k_new.replace("seg_head."    ,"segmentation_head.")
        if ".aspp_layer." in k:
            UpperCamelCase__:	Dict					=     k_new.replace(".aspp_layer."    ,".")
        if ".aspp_pool." in k:
            UpperCamelCase__:	Any					=     k_new.replace(".aspp_pool."    ,".")

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



def 				lowerCAmelCase_ (							A_):
    UpperCamelCase__:	List[str]					=     []
    for k in state_dict.keys():
        if k.startswith("seg_head.aux_head."):
            keys_to_ignore.append(A_)
    for k in keys_to_ignore:
        state_dict.pop(A_    ,A_)



def 				lowerCAmelCase_ (							):
    UpperCamelCase__:	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"
    UpperCamelCase__:	int					=     Image.open(requests.get(A_    ,stream=A_).raw)
    return im



@torch.no_grad()
def 				lowerCAmelCase_ (							A_    ,A_    ,A_    ,A_):
    UpperCamelCase__:	Dict					=     get_mobilevitva_config(A_    ,A_)

    # load original state_dict
    UpperCamelCase__:	Any					=     torch.load(A_    ,map_location="cpu")

    # load huggingface model
    if task_name.startswith("ade20k_") or task_name.startswith("voc_"):
        UpperCamelCase__:	Tuple					=     MobileViTVaForSemanticSegmentation(A_).eval()
        UpperCamelCase__:	Optional[int]					=     False
    else:
        UpperCamelCase__:	Dict					=     MobileViTVaForImageClassification(A_).eval()
        UpperCamelCase__:	Optional[int]					=     False

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

    # load modified state_dict
    model.load_state_dict(A_)

    # Check outputs on an image, prepared by MobileViTImageProcessor
    UpperCamelCase__:	List[str]					=     MobileViTImageProcessor(crop_size=config.image_size    ,size=config.image_size + 32)
    UpperCamelCase__:	List[Any]					=     image_processor(images=prepare_img()    ,return_tensors="pt")
    UpperCamelCase__:	Tuple					=     model(**A_)

    # verify classification model
    if task_name.startswith("imagenet"):
        UpperCamelCase__:	str					=     outputs.logits
        UpperCamelCase__:	List[str]					=     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__:	Optional[int]					=     torch.tensor([-1.6_336e00, -7.3_204e-02, -5.1_883e-01])
            assert torch.allclose(logits[0, :3]    ,A_    ,atol=1e-4)

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


if __name__ == "__main__":
   A__:    Tuple  	=	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__:    Tuple  	=	parser.parse_args()
   convert_mobilevitva_checkpoint(
       args.task, args.orig_checkpoint_path, args.orig_config_path, args.pytorch_dump_folder_path
   )








import math



def 				lowerCAmelCase_ (							A_    ,A_):
    UpperCamelCase__:	Dict					=     len(A_)
    UpperCamelCase__:	Optional[Any]					=     int(math.floor(math.sqrt(A_)))
    UpperCamelCase__:	Union[str, Any]					=     0
    while arr[min(A_    ,A_) - 1] < x:
        UpperCamelCase__:	Any					=     step
        step += int(math.floor(math.sqrt(A_)))
        if prev >= n:
            return -1

    while arr[prev] < x:
        UpperCamelCase__:	Dict					=     prev + 1
        if prev == min(A_    ,A_):
            return -1
    if arr[prev] == x:
        return prev
    return -1


if __name__ == "__main__":
   A__:    Tuple  	=	input('''Enter numbers separated by a comma:\n''').strip()
   A__:    List[Any]  	=	[int(item) for item in user_input.split(''',''')]
   A__:    int  	=	int(input('''Enter the number to be searched:\n'''))
   A__:    Any  	=	jump_search(arr, x)
   if res == -1:
      print('''Number not found!''')
   else:
      print(f"Number {x} is at index {res}")

