



'''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()
snake_case_ :	Optional[int]         =  logging.get_logger(__name__)






def A__    (     UpperCAmelCase_							):
						print('Loading config file...'							)

						def flatten_yaml_as_dict(UpperCAmelCase_  ,  UpperCAmelCase_=""  ,  UpperCAmelCase_="."							):
												_UpperCamelCase					:		int 				=   []
												for k, v in d.items():
																		_UpperCamelCase					:		Optional[int] 				=   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					:		Tuple 				=   argparse.Namespace()
						with open(UpperCAmelCase_  ,  'r'							) as yaml_file:
												try:
																		_UpperCamelCase					:		int 				=   yaml.load(UpperCAmelCase_  ,  Loader=yaml.FullLoader							)

																		_UpperCamelCase					:		Optional[int] 				=   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 A__    (     UpperCAmelCase_  ,  UpperCAmelCase_							):
						_UpperCamelCase					:		Dict 				=   MobileViTVaConfig()

						_UpperCamelCase					:		Union[str, Any] 				=   False

						# dataset
						if task_name.startswith('imagenet1k_'							):
												_UpperCamelCase					:		Dict 				=   1_0_0_0
												if int(task_name.strip().split('_'							)[-1]							) == 3_8_4:
																		_UpperCamelCase					:		List[str] 				=   3_8_4
												else:
																		_UpperCamelCase					:		Union[str, Any] 				=   2_5_6
												_UpperCamelCase					:		Union[str, Any] 				=   'imagenet-1k-id2label.json'
						elif task_name.startswith('imagenet21k_to_1k_'							):
												_UpperCamelCase					:		Optional[int] 				=   2_1_0_0_0
												if int(task_name.strip().split('_'							)[-1]							) == 3_8_4:
																		_UpperCamelCase					:		Tuple 				=   3_8_4
												else:
																		_UpperCamelCase					:		Dict 				=   2_5_6
												_UpperCamelCase					:		Any 				=   'imagenet-22k-id2label.json'
						elif task_name.startswith('ade20k_'							):
												_UpperCamelCase					:		Optional[Any] 				=   1_5_1
												_UpperCamelCase					:		Any 				=   5_1_2
												_UpperCamelCase					:		Optional[Any] 				=   'ade20k-id2label.json'
												_UpperCamelCase					:		Optional[int] 				=   True
						elif task_name.startswith('voc_'							):
												_UpperCamelCase					:		Any 				=   2_1
												_UpperCamelCase					:		List[str] 				=   5_1_2
												_UpperCamelCase					:		Union[str, Any] 				=   'pascal-voc-id2label.json'
												_UpperCamelCase					:		Optional[Any] 				=   True

						# orig_config
						_UpperCamelCase					:		Dict 				=   load_orig_config_file(UpperCAmelCase_							)
						assert getattr(UpperCAmelCase_  ,  'model.classification.name'  ,  -1							) == "mobilevit_v2", "Invalid model"
						_UpperCamelCase					:		Dict 				=   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					:		Tuple 				=   getattr(UpperCAmelCase_  ,  'model.classification.activation.name'  ,  'swish'							)
						# config.image_size == getattr(orig_config,  'sampler.bs.crop_size_width', 256)

						if is_segmentation_model:
												_UpperCamelCase					:		List[str] 				=   getattr(UpperCAmelCase_  ,  'model.segmentation.output_stride'  ,  1_6							)
												if "_deeplabv3" in task_name:
																		_UpperCamelCase					:		Optional[Any] 				=   getattr(UpperCAmelCase_  ,  'model.segmentation.deeplabv3.aspp_rates'  ,  [1_2, 2_4, 3_6]							)
																		_UpperCamelCase					:		str 				=   getattr(UpperCAmelCase_  ,  'model.segmentation.deeplabv3.aspp_out_channels'  ,  5_1_2							)
																		_UpperCamelCase					:		List[str] 				=   getattr(UpperCAmelCase_  ,  'model.segmentation.deeplabv3.aspp_dropout'  ,  0.1							)

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

						return config






def A__    (     UpperCAmelCase_  ,  UpperCAmelCase_  ,  UpperCAmelCase_							):
						_UpperCamelCase					:		List[Any] 				=   dct.pop(UpperCAmelCase_							)
						_UpperCamelCase					:		Union[str, Any] 				=   val






def A__    (     UpperCAmelCase_  ,  UpperCAmelCase_=False							):
						if base_model:
												_UpperCamelCase					:		Optional[int] 				=   ''
						else:
												_UpperCamelCase					:		Union[str, Any] 				=   'mobilevitv2.'

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

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

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

												for i in [3, 4, 5]:
																		if f'layer_{i}.0.' in k:
																								_UpperCamelCase					:		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:
																								_UpperCamelCase					:		str 				=   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[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:
																								_UpperCamelCase					:		List[Any] 				=   [0, 1]
																		elif i == 4:
																								_UpperCamelCase					:		Any 				=   [0, 1, 2, 3]
																		elif i == 5:
																								_UpperCamelCase					:		List[Any] 				=   [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					:		Tuple 				=   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					:		List[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:
																		_UpperCamelCase					:		str 				=   k_new.replace('pre_norm_attn.0.'  ,  'layernorm_before.'							)
												if "pre_norm_attn.1." in k:
																		_UpperCamelCase					:		Optional[int] 				=   k_new.replace('pre_norm_attn.1.'  ,  'attention.'							)
												if "pre_norm_ffn.0." in k:
																		_UpperCamelCase					:		Tuple 				=   k_new.replace('pre_norm_ffn.0.'  ,  'layernorm_after.'							)
												if "pre_norm_ffn.1." in k:
																		_UpperCamelCase					:		Any 				=   k_new.replace('pre_norm_ffn.1.'  ,  'ffn.conv1.'							)
												if "pre_norm_ffn.3." in k:
																		_UpperCamelCase					:		Any 				=   k_new.replace('pre_norm_ffn.3.'  ,  'ffn.conv2.'							)

												if "classifier.1." in k:
																		_UpperCamelCase					:		Optional[int] 				=   k_new.replace('classifier.1.'  ,  'classifier.'							)

												if "seg_head." in k:
																		_UpperCamelCase					:		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					:		Union[str, Any] 				=   k_new.replace('.aspp_pool.'  ,  '.'							)

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






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






@torch.no_grad()
def A__    (     UpperCAmelCase_  ,  UpperCAmelCase_  ,  UpperCAmelCase_  ,  UpperCAmelCase_							):
						_UpperCamelCase					:		Union[str, Any] 				=   get_mobilevitva_config(UpperCAmelCase_  ,  UpperCAmelCase_							)

						# load original state_dict
						_UpperCamelCase					:		Union[str, Any] 				=   torch.load(UpperCAmelCase_  ,  map_location='cpu'							)

						# load huggingface model
						if task_name.startswith('ade20k_'							) or task_name.startswith('voc_'							):
												_UpperCamelCase					:		Optional[int] 				=   MobileViTVaForSemanticSegmentation(UpperCAmelCase_							).eval()
												_UpperCamelCase					:		Optional[int] 				=   False
						else:
												_UpperCamelCase					:		Union[str, Any] 				=   MobileViTVaForImageClassification(UpperCAmelCase_							).eval()
												_UpperCamelCase					:		str 				=   False

						# remove and rename some keys of load the original model
						_UpperCamelCase					:		Tuple 				=   checkpoint
						remove_unused_keys(UpperCAmelCase_							)
						_UpperCamelCase					:		Tuple 				=   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					:		Union[str, Any] 				=   MobileViTImageProcessor(crop_size=config.image_size  ,  size=config.image_size + 3_2							)
						_UpperCamelCase					:		Any 				=   image_processor(images=prepare_img()  ,  return_tensors='pt'							)
						_UpperCamelCase					:		Optional[Any] 				=   model(**UpperCAmelCase_							)

						# verify classification model
						if task_name.startswith('imagenet'							):
												_UpperCamelCase					:		List[str] 				=   outputs.logits
												_UpperCamelCase					:		List[Any] 				=   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					:		List[str] 				=   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__":
					snake_case_ :	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 . '
					        '\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.'
					)

					snake_case_ :	int         =  parser.parse_args()
					convert_mobilevitva_checkpoint(
					    args.task, args.orig_checkpoint_path, args.orig_config_path, args.pytorch_dump_folder_path
					)






"""simple docstring"""




import math

def        lowercase							(		lowerCAmelCase__ :     list    ,					lowerCAmelCase__ :     int			) ->      int:
		__a           =			len(lowerCAmelCase__			)
		__a           =			int(math.floor(math.sqrt(lowerCAmelCase__			)			)			)
		__a           =			0
		while arr[min(lowerCAmelCase__    ,					lowerCAmelCase__			) - 1] < x:
				__a           =			step
				step += int(math.floor(math.sqrt(lowerCAmelCase__			)			)			)
				if prev >= n:
						return -1

		while arr[prev] < x:
				__a           =			prev + 1
				if prev == min(lowerCAmelCase__    ,					lowerCAmelCase__			):
						return -1
		if arr[prev] == x:
				return prev
		return -1


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






