
'''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()
_UpperCAmelCase :   Tuple			    =	logging.get_logger(__name__)







def     snake_case__						(   UpperCamelCase						)			->   Optional[Any]:
		print('''Loading config file...'''						)

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

		_UpperCamelCase							:			str     =   argparse.Namespace()
		with open(snake_case__      ,'''r'''						) as yaml_file:
				try:
						_UpperCamelCase							:			str     =   yaml.load(snake_case__      ,Loader=yaml.FullLoader						)

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







def     snake_case__						(   UpperCamelCase      ,UpperCamelCase						)			->   Tuple:
		_UpperCamelCase							:			Union[str, Any]     =   MobileViTVaConfig()

		_UpperCamelCase							:			Dict     =   False

		# dataset
		if task_name.startswith('''imagenet1k_'''						):
				_UpperCamelCase							:			List[str]     =   10_00
				if int(task_name.strip().split('''_'''						)[-1]						) == 3_84:
						_UpperCamelCase							:			int     =   3_84
				else:
						_UpperCamelCase							:			List[Any]     =   2_56
				_UpperCamelCase							:			Union[str, Any]     =   """imagenet-1k-id2label.json"""
		elif task_name.startswith('''imagenet21k_to_1k_'''						):
				_UpperCamelCase							:			Optional[int]     =   2_10_00
				if int(task_name.strip().split('''_'''						)[-1]						) == 3_84:
						_UpperCamelCase							:			Tuple     =   3_84
				else:
						_UpperCamelCase							:			Any     =   2_56
				_UpperCamelCase							:			Any     =   """imagenet-22k-id2label.json"""
		elif task_name.startswith('''ade20k_'''						):
				_UpperCamelCase							:			Tuple     =   1_51
				_UpperCamelCase							:			int     =   5_12
				_UpperCamelCase							:			int     =   """ade20k-id2label.json"""
				_UpperCamelCase							:			Optional[int]     =   True
		elif task_name.startswith('''voc_'''						):
				_UpperCamelCase							:			Dict     =   21
				_UpperCamelCase							:			Any     =   5_12
				_UpperCamelCase							:			Tuple     =   """pascal-voc-id2label.json"""
				_UpperCamelCase							:			Tuple     =   True

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

		if is_segmentation_model:
				_UpperCamelCase							:			int     =   getattr(snake_case__      ,'''model.segmentation.output_stride'''      ,16						)
				if "_deeplabv3" in task_name:
						_UpperCamelCase							:			List[str]     =   getattr(snake_case__      ,'''model.segmentation.deeplabv3.aspp_rates'''      ,[12, 24, 36]						)
						_UpperCamelCase							:			Union[str, Any]     =   getattr(snake_case__      ,'''model.segmentation.deeplabv3.aspp_out_channels'''      ,5_12						)
						_UpperCamelCase							:			List[Any]     =   getattr(snake_case__      ,'''model.segmentation.deeplabv3.aspp_dropout'''      ,0.1						)

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

		return config







def     snake_case__						(   UpperCamelCase      ,UpperCamelCase      ,UpperCamelCase						)			->   Union[str, Any]:
		_UpperCamelCase							:			Tuple     =   dct.pop(snake_case__						)
		_UpperCamelCase							:			Dict     =   val







def     snake_case__						(   UpperCamelCase      ,UpperCamelCase=False						)			->   Union[str, Any]:






		if base_model:
				_UpperCamelCase							:			int     =   """"""
		else:
				_UpperCamelCase							:			str     =   """mobilevitv2."""

		_UpperCamelCase							:			Any     =   []
		for k in state_dict.keys():
				if k[:8] == "encoder.":
						_UpperCamelCase							:			Union[str, Any]     =   k[8:]
				else:
						_UpperCamelCase							:			Optional[Any]     =   k

				if ".block." in k:
						_UpperCamelCase							:			List[str]     =   k_new.replace('''.block.'''      ,'''.'''						)
				if ".conv." in k:
						_UpperCamelCase							:			str     =   k_new.replace('''.conv.'''      ,'''.convolution.'''						)
				if ".norm." in k:
						_UpperCamelCase							:			Tuple     =   k_new.replace('''.norm.'''      ,'''.normalization.'''						)

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

				for i in [3, 4, 5]:
						if f'''layer_{i}.0.''' in k:
								_UpperCamelCase							:			int     =   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:
								_UpperCamelCase							:			int     =   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:
								_UpperCamelCase							:			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:
								_UpperCamelCase							:			Tuple     =   [0, 1]
						elif i == 4:
								_UpperCamelCase							:			List[Any]     =   [0, 1, 2, 3]
						elif i == 5:
								_UpperCamelCase							:			int     =   [0, 1, 2]

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

				if "classifier.1." in k:
						_UpperCamelCase							:			Any     =   k_new.replace('''classifier.1.'''      ,'''classifier.'''						)

				if "seg_head." in k:
						_UpperCamelCase							:			Union[str, Any]     =   k_new.replace('''seg_head.'''      ,'''segmentation_head.'''						)
				if ".aspp_layer." in k:
						_UpperCamelCase							:			List[str]     =   k_new.replace('''.aspp_layer.'''      ,'''.'''						)
				if ".aspp_pool." in k:
						_UpperCamelCase							:			Optional[int]     =   k_new.replace('''.aspp_pool.'''      ,'''.'''						)

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







def     snake_case__						(   UpperCamelCase						)			->   Any:
		_UpperCamelCase							:			Any     =   []
		for k in state_dict.keys():
				if k.startswith('''seg_head.aux_head.'''						):
						keys_to_ignore.append(snake_case__						)
		for k in keys_to_ignore:
				state_dict.pop(snake_case__      ,snake_case__						)







def     snake_case__						(   )			->   Tuple:
		_UpperCamelCase							:			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"
		_UpperCamelCase							:			str     =   Image.open(requests.get(snake_case__      ,stream=snake_case__						).raw						)
		return im







@torch.no_grad()
def     snake_case__						(   UpperCamelCase      ,UpperCamelCase      ,UpperCamelCase      ,UpperCamelCase						)			->   Optional[int]:
		_UpperCamelCase							:			int     =   get_mobilevitva_config(snake_case__      ,snake_case__						)

		# load original state_dict
		_UpperCamelCase							:			Dict     =   torch.load(snake_case__      ,map_location='''cpu'''						)

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

		# remove and rename some keys of load the original model
		_UpperCamelCase							:			int     =   checkpoint
		remove_unused_keys(snake_case__						)
		_UpperCamelCase							:			int     =   create_rename_keys(snake_case__      ,base_model=snake_case__						)
		for rename_key_src, rename_key_dest in rename_keys:
				rename_key(snake_case__      ,snake_case__      ,snake_case__						)

		# load modified state_dict
		model.load_state_dict(snake_case__						)

		# Check outputs on an image, prepared by MobileViTImageProcessor
		_UpperCamelCase							:			List[Any]     =   MobileViTImageProcessor(crop_size=config.image_size      ,size=config.image_size + 32						)
		_UpperCamelCase							:			Union[str, Any]     =   image_processor(images=prepare_img()      ,return_tensors='''pt'''						)
		_UpperCamelCase							:			Tuple     =   model(**snake_case__						)

		# verify classification model
		if task_name.startswith('''imagenet'''						):
				_UpperCamelCase							:			List[str]     =   outputs.logits
				_UpperCamelCase							:			Tuple     =   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
						_UpperCamelCase							:			Optional[int]     =   torch.tensor([-1.6_336e00, -7.3_204e-02, -5.1_883e-01]						)
						assert torch.allclose(logits[0, :3]      ,snake_case__      ,atol=1e-4						)

		Path(snake_case__						).mkdir(exist_ok=snake_case__						)
		print(f'''Saving model {task_name} to {pytorch_dump_folder_path}'''						)
		model.save_pretrained(snake_case__						)
		print(f'''Saving image processor to {pytorch_dump_folder_path}'''						)
		image_processor.save_pretrained(snake_case__						)


if __name__ == "__main__":
	_UpperCAmelCase :   Tuple			    =	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."""
	)

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













from collections.abc import Callable




class       snake_case__ :

       '''simple docstring'''



       def __init__(				self      ,    a__ = None     )			->   None:






            '''simple docstring'''





            __snake_case      :list 							=    []
            # Stores indexes of each item for supporting updates and deletion.
            __snake_case      :dict 							=    {}
            # Stores current size of heap.
            __snake_case      :List[Any] 							=    0
            # Stores function used to evaluate the score of an item on which basis ordering
            # will be done.
            __snake_case      :Optional[Any] 							=    key or (lambda a__     : x)



       def        __lowercase    (				self      ,    a__     )			->   int | None:






            '''simple docstring'''





            return int((i - 1) / 2     ) if i > 0 else None



       def        __lowercase    (				self      ,    a__     )			->   int | None:






            '''simple docstring'''





            __snake_case      :int 							=    int(2 * i + 1     )
            return left if 0 < left < self.size else None



       def        __lowercase    (				self      ,    a__     )			->   int | None:






            '''simple docstring'''





            __snake_case      :Optional[int] 							=    int(2 * i + 2     )
            return right if 0 < right < self.size else None



       def        __lowercase    (				self      ,    a__      ,    a__     )			->   None:






            '''simple docstring'''





            __snake_case							,			__snake_case      :Dict 							=    (
                self.pos_map[self.arr[j][0]],
                self.pos_map[self.arr[i][0]],
            )
            # Then swap the items in the list.
            __snake_case							,			__snake_case      :Tuple 							=    self.arr[j], self.arr[i]



       def        __lowercase    (				self      ,    a__      ,    a__     )			->   bool:






            '''simple docstring'''





            return self.arr[i][1] < self.arr[j][1]



       def        __lowercase    (				self      ,    a__     )			->   int:






            '''simple docstring'''





            __snake_case      :int 							=    self._left(a__     )
            __snake_case      :Optional[int] 							=    self._right(a__     )
            __snake_case      :List[Any] 							=    i

            if left is not None and not self._cmp(a__      ,    a__     ):
                 __snake_case      :int 							=    left
            if right is not None and not self._cmp(a__      ,    a__     ):
                 __snake_case      :Tuple 							=    right

            return valid_parent



       def        __lowercase    (				self      ,    a__     )			->   None:






            '''simple docstring'''





            __snake_case      :int 							=    self._parent(a__     )
            while parent is not None and not self._cmp(a__      ,    a__     ):
                 self._swap(a__      ,    a__     )
                 __snake_case							,			__snake_case      :str 							=    parent, self._parent(a__     )



       def        __lowercase    (				self      ,    a__     )			->   None:






            '''simple docstring'''





            __snake_case      :List[Any] 							=    self._get_valid_parent(a__     )
            while valid_parent != index:
                 self._swap(a__      ,    a__     )
                 __snake_case							,			__snake_case      :Optional[int] 							=    valid_parent, self._get_valid_parent(a__     )



       def        __lowercase    (				self      ,    a__      ,    a__     )			->   None:






            '''simple docstring'''





            if item not in self.pos_map:
                 return
            __snake_case      :List[Any] 							=    self.pos_map[item]
            __snake_case      :Optional[Any] 							=    [item, self.key(a__     )]
            # Make sure heap is right in both up and down direction.
            # Ideally only one of them will make any change.
            self._heapify_up(a__     )
            self._heapify_down(a__     )



       def        __lowercase    (				self      ,    a__     )			->   None:






            '''simple docstring'''





            if item not in self.pos_map:
                 return
            __snake_case      :Dict 							=    self.pos_map[item]
            del self.pos_map[item]
            __snake_case      :Dict 							=    self.arr[self.size - 1]
            __snake_case      :List[str] 							=    index
            self.size -= 1
            # Make sure heap is right in both up and down direction. Ideally only one
            # of them will make any change- so no performance loss in calling both.
            if self.size > index:
                 self._heapify_up(a__     )
                 self._heapify_down(a__     )



       def        __lowercase    (				self      ,    a__      ,    a__     )			->   None:






            '''simple docstring'''





            __snake_case      :Optional[int] 							=    len(self.arr     )
            if arr_len == self.size:
                 self.arr.append([item, self.key(a__     )]     )
            else:
                 __snake_case      :Optional[Any] 							=    [item, self.key(a__     )]
            __snake_case      :str 							=    self.size
            self.size += 1
            self._heapify_up(self.size - 1     )



       def        __lowercase    (				self     )			->   tuple | None:






            '''simple docstring'''





            return self.arr[0] if self.size else None



       def        __lowercase    (				self     )			->   tuple | None:






            '''simple docstring'''





            __snake_case      :Optional[Any] 							=    self.get_top()
            if top_item_tuple:
                 self.delete_item(top_item_tuple[0]     )
            return top_item_tuple






def 	UpperCamelCase	(							):




     '''simple docstring'''








if __name__ == "__main__":
     import doctest

     doctest.testmod()


