

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        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__      )  ->					Optional[int]:
	print('Loading config file...'      )

	def flatten_yaml_as_dict(lowerCamelCase__     ,			lowerCamelCase__=""     ,			lowerCamelCase__="."      ):
		__lowerCamelCase    :    Dict 	=			[]
		for k, v in d.items():
			__lowerCamelCase    :    Optional[Any] 	=			parent_key + sep + k if parent_key else k
			if isinstance(lowerCamelCase__     ,			collections.abc.MutableMapping      ):
				items.extend(flatten_yaml_as_dict(lowerCamelCase__     ,			lowerCamelCase__     ,			sep=lowerCamelCase__      ).items()      )
			else:
				items.append((new_key, v)      )
		return dict(lowerCamelCase__      )

	__lowerCamelCase    :    Dict 	=			argparse.Namespace()
	with open(lowerCamelCase__     ,			'r'      ) as yaml_file:
		try:
			__lowerCamelCase    :    Optional[int] 	=			yaml.load(lowerCamelCase__     ,			Loader=yaml.FullLoader      )

			__lowerCamelCase    :    str 	=			flatten_yaml_as_dict(lowerCamelCase__      )
			for k, v in flat_cfg.items():
				setattr(lowerCamelCase__     ,			lowerCamelCase__     ,			lowerCamelCase__      )
		except yaml.YAMLError as exc:
			logger.error('Error while loading config file: {}. Error message: {}'.format(lowerCamelCase__     ,			str(lowerCamelCase__      )      )      )
	return config






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__     ,			lowerCamelCase__      )  ->					str:
	__lowerCamelCase    :    List[str] 	=			MobileViTVaConfig()

	__lowerCamelCase    :    str 	=			False

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

	# orig_config
	__lowerCamelCase    :    List[Any] 	=			load_orig_config_file(lowerCamelCase__      )
	assert getattr(lowerCamelCase__     ,			'model.classification.name'     ,			-1      ) == "mobilevit_v2", "Invalid model"
	__lowerCamelCase    :    Optional[Any] 	=			getattr(lowerCamelCase__     ,			'model.classification.mitv2.width_multiplier'     ,			1.0      )
	assert (
	    getattr(lowerCamelCase__     ,			'model.classification.mitv2.attn_norm_layer'     ,			-1      ) == "layer_norm_2d"
	), "Norm layers other than layer_norm_2d is not supported"
	__lowerCamelCase    :    Any 	=			getattr(lowerCamelCase__     ,			'model.classification.activation.name'     ,			'swish'      )
	# config.image_size == getattr(orig_config,  'sampler.bs.crop_size_width', 256)

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

    # id2label
	__lowerCamelCase    :    int 	=			'huggingface/label-files'
	__lowerCamelCase    :    Any 	=			json.load(open(hf_hub_download(lowerCamelCase__     ,			lowerCamelCase__     ,			repo_type='dataset'      )     ,			'r'      )      )
	__lowerCamelCase    :    List[Any] 	=			{int(lowerCamelCase__      ): v for k, v in idalabel.items()}
	__lowerCamelCase    :    Union[str, Any] 	=			idalabel
	__lowerCamelCase    :    Tuple 	=			{v: k for k, v in idalabel.items()}

	return config






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__     ,			lowerCamelCase__     ,			lowerCamelCase__      )  ->					Optional[Any]:
	__lowerCamelCase    :    Any 	=			dct.pop(lowerCamelCase__      )
	__lowerCamelCase    :    Union[str, Any] 	=			val






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__     ,			lowerCamelCase__=False      )  ->					Optional[int]:
	if base_model:
		__lowerCamelCase    :    Tuple 	=			''
	else:
		__lowerCamelCase    :    Dict 	=			'mobilevitv2.'

	__lowerCamelCase    :    Tuple 	=			[]
	for k in state_dict.keys():
		if k[:8] == "encoder.":
			__lowerCamelCase    :    List[str] 	=			k[8:]
		else:
			__lowerCamelCase    :    Tuple 	=			k

		if ".block." in k:
			__lowerCamelCase    :    Any 	=			k_new.replace('.block.'     ,			'.'      )
		if ".conv." in k:
			__lowerCamelCase    :    Any 	=			k_new.replace('.conv.'     ,			'.convolution.'      )
		if ".norm." in k:
			__lowerCamelCase    :    Tuple 	=			k_new.replace('.norm.'     ,			'.normalization.'      )

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

		for i in [3, 4, 5]:
			if F"layer_{i}.0." in k:
				__lowerCamelCase    :    Any 	=			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    :    Any 	=			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    :    str 	=			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    :    Optional[Any] 	=			[0, 1]
			elif i == 4:
				__lowerCamelCase    :    Tuple 	=			[0, 1, 2, 3]
			elif i == 5:
				__lowerCamelCase    :    Any 	=			[0, 1, 2]

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

		if "classifier.1." in k:
			__lowerCamelCase    :    List[Any] 	=			k_new.replace('classifier.1.'     ,			'classifier.'      )

		if "seg_head." in k:
			__lowerCamelCase    :    Optional[Any] 	=			k_new.replace('seg_head.'     ,			'segmentation_head.'      )
		if ".aspp_layer." in k:
			__lowerCamelCase    :    Any 	=			k_new.replace('.aspp_layer.'     ,			'.'      )
		if ".aspp_pool." in k:
			__lowerCamelCase    :    Dict 	=			k_new.replace('.aspp_pool.'     ,			'.'      )

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






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__      )  ->					List[str]:
	__lowerCamelCase    :    Union[str, Any] 	=			[]
	for k in state_dict.keys():
		if k.startswith('seg_head.aux_head.'      ):
			keys_to_ignore.append(lowerCamelCase__      )
	for k in keys_to_ignore:
		state_dict.pop(lowerCamelCase__     ,			lowerCamelCase__      )






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






@torch.no_grad()
def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__     ,			lowerCamelCase__     ,			lowerCamelCase__     ,			lowerCamelCase__      )  ->					Dict:
	__lowerCamelCase    :    int 	=			get_mobilevitva_config(lowerCamelCase__     ,			lowerCamelCase__      )

	# load original state_dict
	__lowerCamelCase    :    Optional[int] 	=			torch.load(lowerCamelCase__     ,			map_location='cpu'      )

	# load huggingface model
	if task_name.startswith('ade20k_'      ) or task_name.startswith('voc_'      ):
		__lowerCamelCase    :    str 	=			MobileViTVaForSemanticSegmentation(lowerCamelCase__      ).eval()
		__lowerCamelCase    :    List[str] 	=			False
	else:
		__lowerCamelCase    :    List[str] 	=			MobileViTVaForImageClassification(lowerCamelCase__      ).eval()
		__lowerCamelCase    :    int 	=			False

	# remove and rename some keys of load the original model
	__lowerCamelCase    :    Optional[int] 	=			checkpoint
	remove_unused_keys(lowerCamelCase__      )
	__lowerCamelCase    :    str 	=			create_rename_keys(lowerCamelCase__     ,			base_model=lowerCamelCase__      )
	for rename_key_src, rename_key_dest in rename_keys:
		rename_key(lowerCamelCase__     ,			lowerCamelCase__     ,			lowerCamelCase__      )

	# load modified state_dict
	model.load_state_dict(lowerCamelCase__      )

	# Check outputs on an image, prepared by MobileViTImageProcessor
	__lowerCamelCase    :    Tuple 	=			MobileViTImageProcessor(crop_size=config.image_size     ,			size=config.image_size + 3_2      )
	__lowerCamelCase    :    Dict 	=			image_processor(images=prepare_img()     ,			return_tensors='pt'      )
	__lowerCamelCase    :    Tuple 	=			model(**lowerCamelCase__      )

	# verify classification model
	if task_name.startswith('imagenet'      ):
		__lowerCamelCase    :    Any 	=			outputs.logits
		__lowerCamelCase    :    Optional[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
			__lowerCamelCase    :    int 	=			torch.tensor([-1.6336e00, -7.3204e-02, -5.1883e-01]      )
			assert torch.allclose(logits[0, :3]     ,			lowerCamelCase__     ,			atol=1e-4      )

	Path(lowerCamelCase__      ).mkdir(exist_ok=lowerCamelCase__      )
	print(F"Saving model {task_name} to {pytorch_dump_folder_path}"      )
	model.save_pretrained(lowerCamelCase__      )
	print(F"Saving image processor to {pytorch_dump_folder_path}"      )
	image_processor.save_pretrained(lowerCamelCase__      )


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
					)







from __future__ import annotations

from collections.abc import Callable
from typing import Generic, TypeVar

a		=TypeVar("""T""")
a		=TypeVar("""U""")






class 	A_		(	Generic[T, U]    ):
							def __init__(		self		:      Tuple       ,SCREAMING_SNAKE_CASE__		:      T | None       ,SCREAMING_SNAKE_CASE__		:      U | None):
								__lowerCamelCase    :    Optional[Any] 	=			key
								__lowerCamelCase    :    Tuple 	=			val
								__lowerCamelCase    :    DoubleLinkedListNode[T, U] | None 	=			None
								__lowerCamelCase    :    DoubleLinkedListNode[T, U] | None 	=			None


							def __repr__(		self		:      Optional[int]):
								return (
								    F"Node: key: {self.key}, val: {self.val}, "
								    F"has next: {bool(self.next)}, has prev: {bool(self.prev)}"
								)






class 	A_		(	Generic[T, U]    ):
							def __init__(		self		:      List[Any]):
								__lowerCamelCase    :    DoubleLinkedListNode[T, U] 	=			DoubleLinkedListNode(SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__)
								__lowerCamelCase    :    DoubleLinkedListNode[T, U] 	=			DoubleLinkedListNode(SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__)
								__lowerCamelCase	,			__lowerCamelCase    :    List[Any] 	=			self.rear, self.head
							def __repr__(		self		:      Optional[int]):
								__lowerCamelCase    :    int 	=			['DoubleLinkedList']
								__lowerCamelCase    :    Tuple 	=			self.head
								while node.next is not None:
									rep.append(str(SCREAMING_SNAKE_CASE__))
									__lowerCamelCase    :    Union[str, Any] 	=			node.next
								rep.append(str(self.rear))
								return ",\n    ".join(SCREAMING_SNAKE_CASE__)
							def    lowerCAmelCase					(		self		:      Optional[Any]       ,SCREAMING_SNAKE_CASE__		:      DoubleLinkedListNode[T, U]):
								__lowerCamelCase    :    str 	=			self.rear.prev

								# All nodes other than self.head are guaranteed to have non-None previous
								assert previous is not None

								__lowerCamelCase    :    Any 	=			node
								__lowerCamelCase    :    str 	=			previous
								__lowerCamelCase    :    str 	=			node
								__lowerCamelCase    :    List[str] 	=			self.rear


							def    lowerCAmelCase					(		self		:      int       ,SCREAMING_SNAKE_CASE__		:      DoubleLinkedListNode[T, U]):

								if node.prev is None or node.next is None:
									return None

								__lowerCamelCase    :    Union[str, Any] 	=			node.next
								__lowerCamelCase    :    Dict 	=			node.prev
								__lowerCamelCase    :    Optional[int] 	=			None
								__lowerCamelCase    :    List[str] 	=			None
								return node






class 	A_		(	Generic[T, U]    ):
							_UpperCAmelCase   :  dict[Callable[[T], U], LRUCache[T, U]]										=				{}
							def __init__(		self		:      str       ,SCREAMING_SNAKE_CASE__		:      int):
								__lowerCamelCase    :    DoubleLinkedList[T, U] 	=			DoubleLinkedList()
								__lowerCamelCase    :    List[Any] 	=			capacity
								__lowerCamelCase    :    Optional[int] 	=			0
								__lowerCamelCase    :    int 	=			0
								__lowerCamelCase    :    Optional[Any] 	=			0
								__lowerCamelCase    :    dict[T, DoubleLinkedListNode[T, U]] 	=			{}
							def __repr__(		self		:      Optional[Any]):
								return (
								    F"CacheInfo(hits={self.hits}, misses={self.miss}, "
								    F"capacity={self.capacity}, current size={self.num_keys})"
								)
							def __contains__(		self		:      List[str]       ,SCREAMING_SNAKE_CASE__		:      T):
								return key in self.cache
							def    lowerCAmelCase					(		self		:      Union[str, Any]       ,SCREAMING_SNAKE_CASE__		:      T):
								# Note: pythonic interface would throw KeyError rather than return None

								if key in self.cache:
									self.hits += 1
									__lowerCamelCase    :    DoubleLinkedListNode[T, U] 	=			self.cache[key]
									__lowerCamelCase    :    List[str] 	=			self.list.remove(self.cache[key])
									assert node == value_node

									# node is guaranteed not None because it is in self.cache
									assert node is not None
									self.list.add(SCREAMING_SNAKE_CASE__)
									return node.val
								self.miss += 1
								return None
							def    lowerCAmelCase					(		self		:      Dict       ,SCREAMING_SNAKE_CASE__		:      T       ,SCREAMING_SNAKE_CASE__		:      U):

								if key not in self.cache:
									if self.num_keys >= self.capacity:
										# delete first node (oldest) when over capacity
										__lowerCamelCase    :    Optional[Any] 	=			self.list.head.next

										# guaranteed to have a non-None first node when num_keys > 0
										# explain to type checker via assertions
										assert first_node is not None
										assert first_node.key is not None
										assert (
										    self.list.remove(SCREAMING_SNAKE_CASE__) is not None
										)  # node guaranteed to be in list assert node.key is not None

										del self.cache[first_node.key]
										self.num_keys -= 1
									__lowerCamelCase    :    List[Any] 	=			DoubleLinkedListNode(SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__)
									self.list.add(self.cache[key])
									self.num_keys += 1

								else:
									# bump node to the end of the list, update value
									__lowerCamelCase    :    List[str] 	=			self.list.remove(self.cache[key])
									assert node is not None  # node guaranteed to be in list
									__lowerCamelCase    :    List[Any] 	=			value
									self.list.add(SCREAMING_SNAKE_CASE__)


							@classmethod
							def    lowerCAmelCase					(		cls		:      Optional[int]       ,SCREAMING_SNAKE_CASE__		:      int = 1_2_8):

								def cache_decorator_inner(SCREAMING_SNAKE_CASE__		:      Callable[[T], U]) -> Callable[..., U]:
									def cache_decorator_wrapper(*SCREAMING_SNAKE_CASE__		:      T) -> U:
										if func not in cls.decorator_function_to_instance_map:
											__lowerCamelCase    :    str 	=			LRUCache(SCREAMING_SNAKE_CASE__)

										__lowerCamelCase    :    Dict 	=			cls.decorator_function_to_instance_map[func].get(args[0])
										if result is None:
											__lowerCamelCase    :    str 	=			func(*SCREAMING_SNAKE_CASE__)
											cls.decorator_function_to_instance_map[func].put(args[0]       ,SCREAMING_SNAKE_CASE__)
										return result

									def cache_info() -> LRUCache[T, U]:
										return cls.decorator_function_to_instance_map[func]

									setattr(SCREAMING_SNAKE_CASE__       ,'cache_info'       ,SCREAMING_SNAKE_CASE__)  # noqa: B010

									return cache_decorator_wrapper

								return cache_decorator_inner


if __name__ == "__main__":
					import doctest

					doctest.testmod()




