




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	:       Optional[Any]     =	logging.get_logger(__name__)




def     __magic_name__       (				A	:      Tuple     ):






			'''simple docstring'''







			print("Loading config file..."     )

			def flatten_yaml_as_dict(A	:      Optional[int],						A	:      Dict="",						A	:      List[str]="."     ):
						a					=    []
						for k, v in d.items():
									a					=    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     )

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

									a					=    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     __magic_name__       (				A	:      Union[str, Any],						A	:      List[Any]     ):






			'''simple docstring'''







			a					=    MobileViTVaConfig()

			a					=    False

			# dataset
			if task_name.startswith("imagenet1k_"     ):
						a					=    1000
						if int(task_name.strip().split("_"     )[-1]     ) == 384:
									a					=    384
						else:
									a					=    256
						a					=    "imagenet-1k-id2label.json"
			elif task_name.startswith("imagenet21k_to_1k_"     ):
						a					=    21000
						if int(task_name.strip().split("_"     )[-1]     ) == 384:
									a					=    384
						else:
									a					=    256
						a					=    "imagenet-22k-id2label.json"
			elif task_name.startswith("ade20k_"     ):
						a					=    151
						a					=    512
						a					=    "ade20k-id2label.json"
						a					=    True
			elif task_name.startswith("voc_"     ):
						a					=    21
						a					=    512
						a					=    "pascal-voc-id2label.json"
						a					=    True

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

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

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

			return config




def     __magic_name__       (				A	:      int,						A	:      List[Any],						A	:      Union[str, Any]     ):






			'''simple docstring'''







			a					=    dct.pop(A     )
			a					=    val




def     __magic_name__       (				A	:      List[str],						A	:      int=False     ):






			'''simple docstring'''







			if base_model:
						a					=    ""
			else:
						a					=    "mobilevitv2."

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

						if ".block." in k:
									a					=    k_new.replace(".block.",						"."     )
						if ".conv." in k:
									a					=    k_new.replace(".conv.",						".convolution."     )
						if ".norm." in k:
									a					=    k_new.replace(".norm.",						".normalization."     )

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

						for i in [3, 4, 5]:
									if F"""layer_{i}.0.""" in k:
												a					=    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:
												a					=    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:
												a					=    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:
												a					=    [0, 1]
									elif i == 4:
												a					=    [0, 1, 2, 3]
									elif i == 5:
												a					=    [0, 1, 2]

									for j in j_in:
												if F"""layer_{i}.1.global_rep.{j}.""" in k:
															a					=    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:
												a					=    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:
												a					=    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:
									a					=    k_new.replace("pre_norm_attn.0.",						"layernorm_before."     )
						if "pre_norm_attn.1." in k:
									a					=    k_new.replace("pre_norm_attn.1.",						"attention."     )
						if "pre_norm_ffn.0." in k:
									a					=    k_new.replace("pre_norm_ffn.0.",						"layernorm_after."     )
						if "pre_norm_ffn.1." in k:
									a					=    k_new.replace("pre_norm_ffn.1.",						"ffn.conv1."     )
						if "pre_norm_ffn.3." in k:
									a					=    k_new.replace("pre_norm_ffn.3.",						"ffn.conv2."     )

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

						if "seg_head." in k:
									a					=    k_new.replace("seg_head.",						"segmentation_head."     )
						if ".aspp_layer." in k:
									a					=    k_new.replace(".aspp_layer.",						"."     )
						if ".aspp_pool." in k:
									a					=    k_new.replace(".aspp_pool.",						"."     )

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




def     __magic_name__       (				A	:      Tuple     ):






			'''simple docstring'''







			a					=    []
			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     __magic_name__       (				):






			'''simple docstring'''







			a					=    "http://images.cocodataset.org/val2017/000000039769.jpg"
			# url = "https://cdn.britannica.com/86/141086-050-9D7C75EE/Gulfstream-G450-business-jet-passengers.jpg"
			a					=    Image.open(requests.get(A,						stream=A     ).raw     )
			return im




@torch.no_grad()
def     __magic_name__       (				A	:      int,						A	:      List[str],						A	:      Dict,						A	:      Dict     ):






			'''simple docstring'''







			a					=    get_mobilevitva_config(A,						A     )

			# load original state_dict
			a					=    torch.load(A,						map_location="cpu"     )

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

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

			# verify classification model
			if task_name.startswith("imagenet"     ):
						a					=    outputs.logits
						a					=    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
									a					=    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__":
		__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 . '
		        '\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.'
		)

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












__lowerCAmelCase	:       List[Any]     =	{str(digit): digit**5 for digit in range(10)}




def     __magic_name__       (				A	:      int     ):






			'''simple docstring'''







			return sum(DIGITS_FIFTH_POWER[digit] for digit in str(A     )     )




def     __magic_name__       (				):






			'''simple docstring'''







			return sum(
			    number
			    for number in range(1000,						1000000     )
			    if number == digits_fifth_powers_sum(A     )     )


if __name__ == "__main__":
		print(solution())






