






'''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_							=    logging.get_logger(__name__)


def        _a  (     UpperCamelCase_					:				Optional[int]   ) ->   Optional[int]:
						"""simple docstring"""







						print("Loading config file..."   )

						def flatten_yaml_as_dict(UpperCamelCase_					:				Any    ,   UpperCamelCase_					:				str=""    ,   UpperCamelCase_					:				str="."   ):
												lowerCAmelCase__       =						[]
												for k, v in d.items():
																		lowerCAmelCase__       =						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_   )

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

																		lowerCAmelCase__       =						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_					:				str    ,   UpperCamelCase_					:				Any   ) ->   int:
						"""simple docstring"""







						lowerCAmelCase__       =						MobileViTVaConfig()

						lowerCAmelCase__       =						False

						# dataset
						if task_name.startswith("imagenet1k_"   ):
												lowerCAmelCase__       =						1_000
												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__       =						21_000
												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(UpperCamelCase_   )
						assert getattr(UpperCamelCase_    ,   "model.classification.name"    ,   -1   ) == "mobilevit_v2", "Invalid model"
						lowerCAmelCase__       =						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"
						lowerCAmelCase__       =						getattr(UpperCamelCase_    ,   "model.classification.activation.name"    ,   "swish"   )
						# config.image_size == getattr(orig_config,  'sampler.bs.crop_size_width', 256)

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

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

						return config


def        _a  (     UpperCamelCase_					:				Optional[Any]    ,   UpperCamelCase_					:				Any    ,   UpperCamelCase_					:				Optional[int]   ) ->   Tuple:
						"""simple docstring"""







						lowerCAmelCase__       =						dct.pop(UpperCamelCase_   )
						lowerCAmelCase__       =						val


def        _a  (     UpperCamelCase_					:				Optional[int]    ,   UpperCamelCase_					:				Dict=False   ) ->   Tuple:
						"""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  (     UpperCamelCase_					:				int   ) ->   int:
						"""simple docstring"""







						lowerCAmelCase__       =						[]
						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  (     ) ->   Dict:
						"""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(UpperCamelCase_    ,   stream=UpperCamelCase_   ).raw   )
						return im


@torch.no_grad()
def        _a  (     UpperCamelCase_					:				int    ,   UpperCamelCase_					:				int    ,   UpperCamelCase_					:				Dict    ,   UpperCamelCase_					:				Optional[Any]   ) ->   Tuple:
						"""simple docstring"""







						lowerCAmelCase__       =						get_mobilevitva_config(UpperCamelCase_    ,   UpperCamelCase_   )

						# load original state_dict
						lowerCAmelCase__       =						torch.load(UpperCamelCase_    ,   map_location="cpu"   )

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

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

						# 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([-1.6_3_3_6e0_0, -7.3_2_0_4e-0_2, -5.1_8_8_3e-0_1]   )
																		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 . '''
	        '''
                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_							=    parser.parse_args()
	convert_mobilevitva_checkpoint(
	    args.task, args.orig_checkpoint_path, args.orig_config_path, args.pytorch_dump_folder_path
	)














import random


def        _a  (     UpperCamelCase_					:				int   ) ->   bool:
						"""simple docstring"""







						lowerCAmelCase__       =						num - 1
						lowerCAmelCase__       =						0

						while s % 2 == 0:
												lowerCAmelCase__       =						s // 2
												t += 1

						for _ in range(5   ):
												lowerCAmelCase__       =						random.randrange(2    ,   num - 1   )
												lowerCAmelCase__       =						pow(UpperCamelCase_    ,   UpperCamelCase_    ,   UpperCamelCase_   )
												if v != 1:
																		lowerCAmelCase__       =						0
																		while v != (num - 1):
																								if i == t - 1:
																														return False
																								else:
																														lowerCAmelCase__       =						i + 1
																														lowerCAmelCase__       =						(v**2) % num
						return True


def        _a  (     UpperCamelCase_					:				int   ) ->   bool:
						"""simple docstring"""







						if num < 2:
												return False

						lowerCAmelCase__       =						[
						    2,
						    3,
						    5,
						    7,
						    11,
						    13,
						    17,
						    19,
						    23,
						    29,
						    31,
						    37,
						    41,
						    43,
						    47,
						    53,
						    59,
						    61,
						    67,
						    71,
						    73,
						    79,
						    83,
						    89,
						    97,
						    101,
						    103,
						    107,
						    109,
						    113,
						    127,
						    131,
						    137,
						    139,
						    149,
						    151,
						    157,
						    163,
						    167,
						    173,
						    179,
						    181,
						    191,
						    193,
						    197,
						    199,
						    211,
						    223,
						    227,
						    229,
						    233,
						    239,
						    241,
						    251,
						    257,
						    263,
						    269,
						    271,
						    277,
						    281,
						    283,
						    293,
						    307,
						    311,
						    313,
						    317,
						    331,
						    337,
						    347,
						    349,
						    353,
						    359,
						    367,
						    373,
						    379,
						    383,
						    389,
						    397,
						    401,
						    409,
						    419,
						    421,
						    431,
						    433,
						    439,
						    443,
						    449,
						    457,
						    461,
						    463,
						    467,
						    479,
						    487,
						    491,
						    499,
						    503,
						    509,
						    521,
						    523,
						    541,
						    547,
						    557,
						    563,
						    569,
						    571,
						    577,
						    587,
						    593,
						    599,
						    601,
						    607,
						    613,
						    617,
						    619,
						    631,
						    641,
						    643,
						    647,
						    653,
						    659,
						    661,
						    673,
						    677,
						    683,
						    691,
						    701,
						    709,
						    719,
						    727,
						    733,
						    739,
						    743,
						    751,
						    757,
						    761,
						    769,
						    773,
						    787,
						    797,
						    809,
						    811,
						    821,
						    823,
						    827,
						    829,
						    839,
						    853,
						    857,
						    859,
						    863,
						    877,
						    881,
						    883,
						    887,
						    907,
						    911,
						    919,
						    929,
						    937,
						    941,
						    947,
						    953,
						    967,
						    971,
						    977,
						    983,
						    991,
						    997,
						]

						if num in low_primes:
												return True

						for prime in low_primes:
												if (num % prime) == 0:
																		return False

						return rabin_miller(UpperCamelCase_   )


def        _a  (     UpperCamelCase_					:				int = 1_024   ) ->   int:
						"""simple docstring"""







						while True:
												lowerCAmelCase__       =						random.randrange(2 ** (keysize - 1)    ,   2 ** (keysize)   )
												if is_prime_low_num(UpperCamelCase_   ):
																		return num


if __name__ == "__main__":
	a_							=    generate_large_prime()
	print(('''Prime number:''', num))
	print(('''is_prime_low_num:''', is_prime_low_num(num)))






