






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
	)














def        _a  (     UpperCamelCase_					:				list    ,   UpperCamelCase_					:				list   ) ->   float:
						"""simple docstring"""







						_validate_point(UpperCamelCase_   )
						_validate_point(UpperCamelCase_   )
						if len(UpperCamelCase_   ) != len(UpperCamelCase_   ):
												raise ValueError("Both points must be in the same n-dimensional space"   )

						return float(sum(abs(a - b   ) for a, b in zip(UpperCamelCase_    ,   UpperCamelCase_   )   )   )


def        _a  (     UpperCamelCase_					:				list[float]   ) ->   None:
						"""simple docstring"""







						if point:
												if isinstance(UpperCamelCase_    ,   UpperCamelCase_   ):
																		for item in point:
																								if not isinstance(UpperCamelCase_    ,   (int, float)   ):
																														lowerCAmelCase__       =						(
																														    "Expected a list of numbers as input, found "
																														    F"{type(UpperCamelCase_   ).__name__}"
																														)
																														raise TypeError(UpperCamelCase_   )
												else:
																		lowerCAmelCase__       =						F"Expected a list of numbers as input, found {type(UpperCamelCase_   ).__name__}"
																		raise TypeError(UpperCamelCase_   )
						else:
												raise ValueError("Missing an input"   )


def        _a  (     UpperCamelCase_					:				list    ,   UpperCamelCase_					:				list   ) ->   float:
						"""simple docstring"""







						_validate_point(UpperCamelCase_   )
						_validate_point(UpperCamelCase_   )
						if len(UpperCamelCase_   ) != len(UpperCamelCase_   ):
												raise ValueError("Both points must be in the same n-dimensional space"   )

						return float(sum(abs(x - y   ) for x, y in zip(UpperCamelCase_    ,   UpperCamelCase_   )   )   )


if __name__ == "__main__":
	import doctest

	doctest.testmod()






