



"""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()
__lowerCAmelCase       :					int      					=					logging.get_logger(__name__)
def 	__lowerCAmelCase						(		__UpperCamelCase      :			Optional[Any]    ):


						'''simple docstring'''






						print("""Loading config file..."""    )

						def flatten_yaml_as_dict(__UpperCamelCase      :			Tuple					, __UpperCamelCase      :			Dict=""					, __UpperCamelCase      :			List[Any]="."    ):
												snake_case_   : Optional[Any]						=			[]
												for k, v in d.items():
																		snake_case_   : List[Any]						=			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    )

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

																		snake_case_   : Union[str, Any]						=			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 	__lowerCAmelCase						(		__UpperCamelCase      :			Union[str, Any]					, __UpperCamelCase      :			List[Any]    ):


						'''simple docstring'''






						snake_case_   : List[str]						=			MobileViTVaConfig()

						snake_case_   : str						=			False

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

						# orig_config
						snake_case_   : Optional[int]						=			load_orig_config_file(__UpperCamelCase    )
						assert getattr(__UpperCamelCase					, """model.classification.name"""					, -1    ) == "mobilevit_v2", "Invalid model"
						snake_case_   : int						=			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"
						snake_case_   : List[str]						=			getattr(__UpperCamelCase					, """model.classification.activation.name"""					, """swish"""    )
						# config.image_size == getattr(orig_config,  'sampler.bs.crop_size_width', 256)

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

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

						return config
def 	__lowerCAmelCase						(		__UpperCamelCase      :			Dict					, __UpperCamelCase      :			str					, __UpperCamelCase      :			int    ):


						'''simple docstring'''






						snake_case_   : int						=			dct.pop(__UpperCamelCase    )
						snake_case_   : Dict						=			val
def 	__lowerCAmelCase						(		__UpperCamelCase      :			List[str]					, __UpperCamelCase      :			int=False    ):


						'''simple docstring'''






						if base_model:
												snake_case_   : Tuple						=			""""""
						else:
												snake_case_   : Union[str, Any]						=			"""mobilevitv2."""

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

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

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

												for i in [3, 4, 5]:
																		if F'layer_{i}.0.' in k:
																								snake_case_   : 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:
																								snake_case_   : List[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:
																								snake_case_   : List[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:
																								snake_case_   : Dict						=			[0, 1]
																		elif i == 4:
																								snake_case_   : str						=			[0, 1, 2, 3]
																		elif i == 5:
																								snake_case_   : Union[str, Any]						=			[0, 1, 2]

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

												if "classifier.1." in k:
																		snake_case_   : Tuple						=			k_new.replace("""classifier.1."""					, """classifier."""    )

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

												rename_keys.append((k, k_new)    )
						return rename_keys
def 	__lowerCAmelCase						(		__UpperCamelCase      :			List[Any]    ):


						'''simple docstring'''






						snake_case_   : Dict						=			[]
						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 	__lowerCAmelCase						(		):


						'''simple docstring'''






						snake_case_   : 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"
						snake_case_   : int						=			Image.open(requests.get(__UpperCamelCase					, stream=__UpperCamelCase    ).raw    )
						return im
@torch.no_grad()
def 	__lowerCAmelCase						(		__UpperCamelCase      :			str					, __UpperCamelCase      :			Dict					, __UpperCamelCase      :			Union[str, Any]					, __UpperCamelCase      :			List[str]    ):


						'''simple docstring'''






						snake_case_   : Dict						=			get_mobilevitva_config(__UpperCamelCase					, __UpperCamelCase    )

						# load original state_dict
						snake_case_   : Tuple						=			torch.load(__UpperCamelCase					, map_location="""cpu"""    )

						# load huggingface model
						if task_name.startswith("""ade20k_"""    ) or task_name.startswith("""voc_"""    ):
												snake_case_   : Optional[int]						=			MobileViTVaForSemanticSegmentation(__UpperCamelCase    ).eval()
												snake_case_   : str						=			False
						else:
												snake_case_   : Optional[int]						=			MobileViTVaForImageClassification(__UpperCamelCase    ).eval()
												snake_case_   : str						=			False

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

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

					__lowerCAmelCase       :					Tuple      					=					parser.parse_args()
					convert_mobilevitva_checkpoint(
					    args.task, args.orig_checkpoint_path, args.orig_config_path, args.pytorch_dump_folder_path
					)






"""simple docstring"""





def 	__lowerCAmelCase						(		__UpperCamelCase      :			int					, __UpperCamelCase      :			bool = False    ):


						'''simple docstring'''






						if n == 2:
												return True
						if not n % 2 or n < 2:
												return False
						if n > 5 and n % 1_0 not in (1, 3, 7, 9):  # can quickly check last digit
												return False
						if n > 3_3_1_7_0_4_4_0_6_4_6_7_9_8_8_7_3_8_5_9_6_1_9_8_1 and not allow_probable:
												raise ValueError(
												    """Warning: upper bound of deterministic test is exceeded. """
												    """Pass allow_probable=True to allow probabilistic test. """
												    """A return value of True indicates a probable prime."""    )
						# array bounds provided by analysis
						snake_case_   : List[Any]						=			[
						    2_0_4_7,
						    1_3_7_3_6_5_3,
						    2_5_3_2_6_0_0_1,
						    3_2_1_5_0_3_1_7_5_1,
						    2_1_5_2_3_0_2_8_9_8_7_4_7,
						    3_4_7_4_7_4_9_6_6_0_3_8_3,
						    3_4_1_5_5_0_0_7_1_7_2_8_3_2_1,
						    1,
						    3_8_2_5_1_2_3_0_5_6_5_4_6_4_1_3_0_5_1,
						    1,
						    1,
						    3_1_8_6_6_5_8_5_7_8_3_4_0_3_1_1_5_1_1_6_7_4_6_1,
						    3_3_1_7_0_4_4_0_6_4_6_7_9_8_8_7_3_8_5_9_6_1_9_8_1,
						]

						snake_case_   : Dict						=			[2, 3, 5, 7, 1_1, 1_3, 1_7, 1_9, 2_3, 2_9, 3_1, 3_7, 4_1]
						for idx, _p in enumerate(__UpperCamelCase					, 1    ):
												if n < _p:
																		# then we have our last prime to check
																		snake_case_   : Optional[int]						=			primes[:idx]
																		break
						snake_case_			,		snake_case_   : Tuple						=			n - 1, 0
						# break up n -1 into a power of 2 (s) and
						# remaining odd component
						# essentially, solve for d * 2 ** s == n - 1
						while d % 2 == 0:
												d //= 2
												s += 1
						for prime in plist:
												snake_case_   : List[str]						=			False
												for r in range(__UpperCamelCase    ):
																		snake_case_   : int						=			pow(__UpperCamelCase					, d * 2**r					, __UpperCamelCase    )
																		# see article for analysis explanation for m
																		if (r == 0 and m == 1) or ((m + 1) % n == 0):
																								snake_case_   : Optional[Any]						=			True
																								# this loop will not determine compositeness
																								break
												if pr:
																		continue
												# if pr is False, then the above loop never evaluated to true,
												# and the n MUST be composite
												return False
						return True
def 	__lowerCAmelCase						(		):


						'''simple docstring'''






						assert not miller_rabin(5_6_1    )
						assert miller_rabin(5_6_3    )
						# 2047

						assert not miller_rabin(8_3_8_2_0_1    )
						assert miller_rabin(8_3_8_2_0_7    )
						# 1_373_653

						assert not miller_rabin(1_7_3_1_6_0_0_1    )
						assert miller_rabin(1_7_3_1_6_0_1_7    )
						# 25_326_001

						assert not miller_rabin(3_0_7_8_3_8_6_6_4_1    )
						assert miller_rabin(3_0_7_8_3_8_6_6_5_3    )
						# 3_215_031_751

						assert not miller_rabin(1_7_1_3_0_4_5_5_7_4_8_0_1    )
						assert miller_rabin(1_7_1_3_0_4_5_5_7_4_8_1_9    )
						# 2_152_302_898_747

						assert not miller_rabin(2_7_7_9_7_9_9_7_2_8_3_0_7    )
						assert miller_rabin(2_7_7_9_7_9_9_7_2_8_3_2_7    )
						# 3_474_749_660_383

						assert not miller_rabin(1_1_3_8_5_0_0_2_3_9_0_9_4_4_1    )
						assert miller_rabin(1_1_3_8_5_0_0_2_3_9_0_9_5_2_7    )
						# 341_550_071_728_321

						assert not miller_rabin(1_2_7_5_0_4_1_0_1_8_8_4_8_8_0_4_3_5_1    )
						assert miller_rabin(1_2_7_5_0_4_1_0_1_8_8_4_8_8_0_4_3_9_1    )
						# 3_825_123_056_546_413_051

						assert not miller_rabin(7_9_6_6_6_4_6_4_4_5_8_5_0_7_7_8_7_7_9_1_8_6_7    )
						assert miller_rabin(7_9_6_6_6_4_6_4_4_5_8_5_0_7_7_8_7_7_9_1_9_5_1    )
						# 318_665_857_834_031_151_167_461

						assert not miller_rabin(5_5_2_8_4_0_6_7_7_4_4_6_6_4_7_8_9_7_6_6_0_3_3_3    )
						assert miller_rabin(5_5_2_8_4_0_6_7_7_4_4_6_6_4_7_8_9_7_6_6_0_3_5_9    )
						# 3_317_044_064_679_887_385_961_981
						# upper limit for probabilistic test


if __name__ == "__main__":
					test_miller_rabin()

