import argparse
import json

import torch

from diffusers import DDPMScheduler, LDMPipeline, UNetaDModel, VQModel





def 				UpperCAmelCase						(    lowercase							,			lowercase=1							):







				"""simple docstring"""



				if n_shave_prefix_segments >= 0:
								return ".".join(path.split('''.'''							)[n_shave_prefix_segments:]							)
				else:
								return ".".join(path.split('''.'''							)[:n_shave_prefix_segments]							)





def 				UpperCAmelCase						(    lowercase							,			lowercase=0							):







				"""simple docstring"""



				__lowercase 			=							[]
				for old_item in old_list:
								__lowercase 			=							old_item.replace('''in_layers.0'''							,			'''norm1'''							)
								__lowercase 			=							new_item.replace('''in_layers.2'''							,			'''conv1'''							)

								__lowercase 			=							new_item.replace('''out_layers.0'''							,			'''norm2'''							)
								__lowercase 			=							new_item.replace('''out_layers.3'''							,			'''conv2'''							)

								__lowercase 			=							new_item.replace('''emb_layers.1'''							,			'''time_emb_proj'''							)
								__lowercase 			=							new_item.replace('''skip_connection'''							,			'''conv_shortcut'''							)

								__lowercase 			=							shave_segments(__SCREAMING_SNAKE_CASE							,			n_shave_prefix_segments=__SCREAMING_SNAKE_CASE							)

								mapping.append({'''old''': old_item, '''new''': new_item}							)

				return mapping





def 				UpperCAmelCase						(    lowercase							,			lowercase=0							):







				"""simple docstring"""



				__lowercase 			=							[]
				for old_item in old_list:
								__lowercase 			=							old_item

								__lowercase 			=							new_item.replace('''norm.weight'''							,			'''group_norm.weight'''							)
								__lowercase 			=							new_item.replace('''norm.bias'''							,			'''group_norm.bias'''							)

								__lowercase 			=							new_item.replace('''proj_out.weight'''							,			'''proj_attn.weight'''							)
								__lowercase 			=							new_item.replace('''proj_out.bias'''							,			'''proj_attn.bias'''							)

								__lowercase 			=							shave_segments(__SCREAMING_SNAKE_CASE							,			n_shave_prefix_segments=__SCREAMING_SNAKE_CASE							)

								mapping.append({'''old''': old_item, '''new''': new_item}							)

				return mapping





def 				UpperCAmelCase						(    lowercase							,			lowercase							,			lowercase							,			lowercase=None							,			lowercase=None							,			lowercase=None							):







				"""simple docstring"""



				assert isinstance(__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE							), "Paths should be a list of dicts containing 'old' and 'new' keys."

				# Splits the attention layers into three variables.
				if attention_paths_to_split is not None:
								for path, path_map in attention_paths_to_split.items():
												__lowercase 			=							old_checkpoint[path]
												__lowercase 			=							old_tensor.shape[0] // 3

												__lowercase 			=							(-1, channels) if len(old_tensor.shape							) == 3 else (-1)

												__lowercase 			=							old_tensor.shape[0] // config['num_head_channels'] // 3

												__lowercase 			=							old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:]							)
												__lowercase 			=							old_tensor.split(channels // num_heads							,			dim=1							)

												__lowercase 			=							query.reshape(__SCREAMING_SNAKE_CASE							)
												__lowercase 			=							key.reshape(__SCREAMING_SNAKE_CASE							)
												__lowercase 			=							value.reshape(__SCREAMING_SNAKE_CASE							)

				for path in paths:
								__lowercase 			=							path['new']

								# These have already been assigned
								if attention_paths_to_split is not None and new_path in attention_paths_to_split:
												continue

								# Global renaming happens here
								__lowercase 			=							new_path.replace('''middle_block.0'''							,			'''mid_block.resnets.0'''							)
								__lowercase 			=							new_path.replace('''middle_block.1'''							,			'''mid_block.attentions.0'''							)
								__lowercase 			=							new_path.replace('''middle_block.2'''							,			'''mid_block.resnets.1'''							)

								if additional_replacements is not None:
												for replacement in additional_replacements:
																__lowercase 			=							new_path.replace(replacement['''old''']							,			replacement['''new''']							)

        # proj_attn.weight has to be converted from conv 1D to linear
								if "proj_attn.weight" in new_path:
												__lowercase 			=							old_checkpoint[path['old']][:, :, 0]
								else:
												__lowercase 			=							old_checkpoint[path['old']]





def 				UpperCAmelCase						(    lowercase							,			lowercase							):







				"""simple docstring"""



				__lowercase 			=							{}

				__lowercase 			=							checkpoint['time_embed.0.weight']
				__lowercase 			=							checkpoint['time_embed.0.bias']
				__lowercase 			=							checkpoint['time_embed.2.weight']
				__lowercase 			=							checkpoint['time_embed.2.bias']

				__lowercase 			=							checkpoint['input_blocks.0.0.weight']
				__lowercase 			=							checkpoint['input_blocks.0.0.bias']

				__lowercase 			=							checkpoint['out.0.weight']
				__lowercase 			=							checkpoint['out.0.bias']
				__lowercase 			=							checkpoint['out.2.weight']
				__lowercase 			=							checkpoint['out.2.bias']

				# Retrieves the keys for the input blocks only
				__lowercase 			=							len({'''.'''.join(layer.split('''.'''							)[:2]							) for layer in checkpoint if '''input_blocks''' in layer}							)
				__lowercase 			=							{
				    layer_id: [key for key in checkpoint if F"input_blocks.{layer_id}" in key]
				    for layer_id in range(__SCREAMING_SNAKE_CASE							)
				}

				# Retrieves the keys for the middle blocks only
				__lowercase 			=							len({'''.'''.join(layer.split('''.'''							)[:2]							) for layer in checkpoint if '''middle_block''' in layer}							)
				__lowercase 			=							{
				    layer_id: [key for key in checkpoint if F"middle_block.{layer_id}" in key]
				    for layer_id in range(__SCREAMING_SNAKE_CASE							)
				}

				# Retrieves the keys for the output blocks only
				__lowercase 			=							len({'''.'''.join(layer.split('''.'''							)[:2]							) for layer in checkpoint if '''output_blocks''' in layer}							)
				__lowercase 			=							{
				    layer_id: [key for key in checkpoint if F"output_blocks.{layer_id}" in key]
				    for layer_id in range(__SCREAMING_SNAKE_CASE							)
				}

				for i in range(1							,			__SCREAMING_SNAKE_CASE							):
								__lowercase 			=							(i - 1) // (config['num_res_blocks'] + 1)
								__lowercase 			=							(i - 1) % (config['num_res_blocks'] + 1)

								__lowercase 			=							[key for key in input_blocks[i] if F"input_blocks.{i}.0" in key]
								__lowercase 			=							[key for key in input_blocks[i] if F"input_blocks.{i}.1" in key]

								if F"input_blocks.{i}.0.op.weight" in checkpoint:
												__lowercase 			=							checkpoint[
												    F"input_blocks.{i}.0.op.weight"
												]
												__lowercase 			=							checkpoint[
												    F"input_blocks.{i}.0.op.bias"
												]
												continue

								__lowercase 			=							renew_resnet_paths(__SCREAMING_SNAKE_CASE							)
								__lowercase 			=							{'old': F"input_blocks.{i}.0", 'new': F"down_blocks.{block_id}.resnets.{layer_in_block_id}"}
								__lowercase 			=							{'old': 'resnets.2.op', 'new': 'downsamplers.0.op'}
								assign_to_checkpoint(
								    __SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE							,			additional_replacements=[meta_path, resnet_op]							,			config=__SCREAMING_SNAKE_CASE							)

								if len(__SCREAMING_SNAKE_CASE							):
												__lowercase 			=							renew_attention_paths(__SCREAMING_SNAKE_CASE							)
												__lowercase 			=							{
												    'old': F"input_blocks.{i}.1",
												    'new': F"down_blocks.{block_id}.attentions.{layer_in_block_id}",
												}
												__lowercase 			=							{
												    F"input_blocks.{i}.1.qkv.bias": {
												        'key': F"down_blocks.{block_id}.attentions.{layer_in_block_id}.key.bias",
												        'query': F"down_blocks.{block_id}.attentions.{layer_in_block_id}.query.bias",
												        'value': F"down_blocks.{block_id}.attentions.{layer_in_block_id}.value.bias",
												    },
												    F"input_blocks.{i}.1.qkv.weight": {
												        'key': F"down_blocks.{block_id}.attentions.{layer_in_block_id}.key.weight",
												        'query': F"down_blocks.{block_id}.attentions.{layer_in_block_id}.query.weight",
												        'value': F"down_blocks.{block_id}.attentions.{layer_in_block_id}.value.weight",
												    },
												}
												assign_to_checkpoint(
												    __SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE							,			additional_replacements=[meta_path]							,			attention_paths_to_split=__SCREAMING_SNAKE_CASE							,			config=__SCREAMING_SNAKE_CASE							,			)

				__lowercase 			=							middle_blocks[0]
				__lowercase 			=							middle_blocks[1]
				__lowercase 			=							middle_blocks[2]

				__lowercase 			=							renew_resnet_paths(__SCREAMING_SNAKE_CASE							)
				assign_to_checkpoint(__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE							,			config=__SCREAMING_SNAKE_CASE							)

				__lowercase 			=							renew_resnet_paths(__SCREAMING_SNAKE_CASE							)
				assign_to_checkpoint(__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE							,			config=__SCREAMING_SNAKE_CASE							)

				__lowercase 			=							renew_attention_paths(__SCREAMING_SNAKE_CASE							)
				__lowercase 			=							{
				    'middle_block.1.qkv.bias': {
				        'key': 'mid_block.attentions.0.key.bias',
				        'query': 'mid_block.attentions.0.query.bias',
				        'value': 'mid_block.attentions.0.value.bias',
				    },
				    'middle_block.1.qkv.weight': {
				        'key': 'mid_block.attentions.0.key.weight',
				        'query': 'mid_block.attentions.0.query.weight',
				        'value': 'mid_block.attentions.0.value.weight',
				    },
				}
				assign_to_checkpoint(
				    __SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE							,			attention_paths_to_split=__SCREAMING_SNAKE_CASE							,			config=__SCREAMING_SNAKE_CASE							)

				for i in range(__SCREAMING_SNAKE_CASE							):
								__lowercase 			=							i // (config['num_res_blocks'] + 1)
								__lowercase 			=							i % (config['num_res_blocks'] + 1)
								__lowercase 			=							[shave_segments(__SCREAMING_SNAKE_CASE							,			2							) for name in output_blocks[i]]
								__lowercase 			=							{}

								for layer in output_block_layers:
												__lowercase 			=							layer.split('''.'''							)[0], shave_segments(__SCREAMING_SNAKE_CASE							,			1							)
												if layer_id in output_block_list:
																output_block_list[layer_id].append(__SCREAMING_SNAKE_CASE							)
												else:
																__lowercase 			=							[layer_name]

								if len(__SCREAMING_SNAKE_CASE							) > 1:
												__lowercase 			=							[key for key in output_blocks[i] if F"output_blocks.{i}.0" in key]
												__lowercase 			=							[key for key in output_blocks[i] if F"output_blocks.{i}.1" in key]

												__lowercase 			=							renew_resnet_paths(__SCREAMING_SNAKE_CASE							)
												__lowercase 			=							renew_resnet_paths(__SCREAMING_SNAKE_CASE							)

												__lowercase 			=							{'old': F"output_blocks.{i}.0", 'new': F"up_blocks.{block_id}.resnets.{layer_in_block_id}"}
												assign_to_checkpoint(__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE							,			additional_replacements=[meta_path]							,			config=__SCREAMING_SNAKE_CASE							)

												if ["conv.weight", "conv.bias"] in output_block_list.values():
																__lowercase 			=							list(output_block_list.values()							).index(['''conv.weight''', '''conv.bias''']							)
																__lowercase 			=							checkpoint[
																    F"output_blocks.{i}.{index}.conv.weight"
																]
																__lowercase 			=							checkpoint[
																    F"output_blocks.{i}.{index}.conv.bias"
																]

																# Clear attentions as they have been attributed above.
																if len(__SCREAMING_SNAKE_CASE							) == 2:
																				__lowercase 			=							[]

												if len(__SCREAMING_SNAKE_CASE							):
																__lowercase 			=							renew_attention_paths(__SCREAMING_SNAKE_CASE							)
																__lowercase 			=							{
																    'old': F"output_blocks.{i}.1",
																    'new': F"up_blocks.{block_id}.attentions.{layer_in_block_id}",
																}
																__lowercase 			=							{
																    F"output_blocks.{i}.1.qkv.bias": {
																        'key': F"up_blocks.{block_id}.attentions.{layer_in_block_id}.key.bias",
																        'query': F"up_blocks.{block_id}.attentions.{layer_in_block_id}.query.bias",
																        'value': F"up_blocks.{block_id}.attentions.{layer_in_block_id}.value.bias",
																    },
																    F"output_blocks.{i}.1.qkv.weight": {
																        'key': F"up_blocks.{block_id}.attentions.{layer_in_block_id}.key.weight",
																        'query': F"up_blocks.{block_id}.attentions.{layer_in_block_id}.query.weight",
																        'value': F"up_blocks.{block_id}.attentions.{layer_in_block_id}.value.weight",
																    },
																}
																assign_to_checkpoint(
																    __SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE							,			additional_replacements=[meta_path]							,			attention_paths_to_split=to_split if any('''qkv''' in key for key in attentions							) else None							,			config=__SCREAMING_SNAKE_CASE							,			)
								else:
												__lowercase 			=							renew_resnet_paths(__SCREAMING_SNAKE_CASE							,			n_shave_prefix_segments=1							)
												for path in resnet_0_paths:
																__lowercase 			=							'.'.join(['''output_blocks''', str(__SCREAMING_SNAKE_CASE							), path['''old''']]							)
																__lowercase 			=							'.'.join(['''up_blocks''', str(__SCREAMING_SNAKE_CASE							), '''resnets''', str(__SCREAMING_SNAKE_CASE							), path['''new''']]							)

																__lowercase 			=							checkpoint[old_path]

				return new_checkpoint


if __name__ == "__main__":
		__a  :     List[str]       =  argparse.ArgumentParser()

		parser.add_argument(
		    """--checkpoint_path""", default=None, type=str, required=True, help="""Path to the checkpoint to convert."""
		)

		parser.add_argument(
		    """--config_file""",
		    default=None,
		    type=str,
		    required=True,
		    help="""The config json file corresponding to the architecture.""",
		)

		parser.add_argument("""--dump_path""", default=None, type=str, required=True, help="""Path to the output model.""")

		__a  :     Union[str, Any]       =  parser.parse_args()

		__a  :     int       =  torch.load(args.checkpoint_path)

		with open(args.config_file) as f:
				__a  :     Any       =  json.loads(f.read())

		__a  :     Optional[int]       =  convert_ldm_checkpoint(checkpoint, config)

		if "ldm" in config:
				del config["ldm"]

		__a  :     Any       =  UNetaDModel(**config)
		model.load_state_dict(converted_checkpoint)

		try:
				__a  :     Optional[Any]       =  DDPMScheduler.from_config("""/""".join(args.checkpoint_path.split("""/""")[:-1]))
				__a  :     str       =  VQModel.from_pretrained("""/""".join(args.checkpoint_path.split("""/""")[:-1]))

				__a  :     List[Any]       =  LDMPipeline(unet=model, scheduler=scheduler, vae=vqvae)
				pipe.save_pretrained(args.dump_path)
		except:  # noqa: E722
				model.save_pretrained(args.dump_path)
"""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()
__SCREAMING_SNAKE_CASE    	=logging.get_logger(__name__)



def 			lowercase__(       __SCREAMING_SNAKE_CASE							:     Union[str, Any]      ):
     print('Loading config file...'      )

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

     lowercase_	:				Optional[Any]													=	argparse.Namespace()
     with open(__SCREAMING_SNAKE_CASE     ,			'r'      ) as yaml_file:
          try:
               lowercase_	:				str													=	yaml.load(__SCREAMING_SNAKE_CASE     ,			Loader=yaml.FullLoader      )

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



def 			lowercase__(       __SCREAMING_SNAKE_CASE							:     int     ,			__SCREAMING_SNAKE_CASE							:     List[str]      ):
     lowercase_	:				int													=	MobileViTVaConfig()

     lowercase_	:				List[str]													=	False

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

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

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

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

     return config



def 			lowercase__(       __SCREAMING_SNAKE_CASE							:     List[Any]     ,			__SCREAMING_SNAKE_CASE							:     List[Any]     ,			__SCREAMING_SNAKE_CASE							:     str      ):
     lowercase_	:				List[Any]													=	dct.pop(__SCREAMING_SNAKE_CASE      )
     lowercase_	:				Optional[Any]													=	val



def 			lowercase__(       __SCREAMING_SNAKE_CASE							:     str     ,			__SCREAMING_SNAKE_CASE							:     Optional[Any]=False      ):
     if base_model:
          lowercase_	:				int													=	''
     else:
          lowercase_	:				str													=	'mobilevitv2.'

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

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

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

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

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

          if "classifier.1." in k:
               lowercase_	:				Union[str, Any]													=	k_new.replace('classifier.1.'     ,			'classifier.'      )

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

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



def 			lowercase__(       __SCREAMING_SNAKE_CASE							:     Any      ):
     lowercase_	:				str													=	[]
     for k in state_dict.keys():
          if k.startswith('seg_head.aux_head.'      ):
               keys_to_ignore.append(__SCREAMING_SNAKE_CASE      )
     for k in keys_to_ignore:
          state_dict.pop(__SCREAMING_SNAKE_CASE     ,			__SCREAMING_SNAKE_CASE      )



def 			lowercase__(       ):
     lowercase_	:				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"
     lowercase_	:				Any													=	Image.open(requests.get(__SCREAMING_SNAKE_CASE     ,			stream=__SCREAMING_SNAKE_CASE      ).raw      )
     return im



@torch.no_grad()
def 			lowercase__(       __SCREAMING_SNAKE_CASE							:     Optional[Any]     ,			__SCREAMING_SNAKE_CASE							:     int     ,			__SCREAMING_SNAKE_CASE							:     Optional[int]     ,			__SCREAMING_SNAKE_CASE							:     Optional[int]      ):
     lowercase_	:				Tuple													=	get_mobilevitva_config(__SCREAMING_SNAKE_CASE     ,			__SCREAMING_SNAKE_CASE      )

     # load original state_dict
     lowercase_	:				Tuple													=	torch.load(__SCREAMING_SNAKE_CASE     ,			map_location='cpu'      )

     # load huggingface model
     if task_name.startswith('ade20k_'      ) or task_name.startswith('voc_'      ):
          lowercase_	:				Tuple													=	MobileViTVaForSemanticSegmentation(__SCREAMING_SNAKE_CASE      ).eval()
          lowercase_	:				Optional[int]													=	False
     else:
          lowercase_	:				Any													=	MobileViTVaForImageClassification(__SCREAMING_SNAKE_CASE      ).eval()
          lowercase_	:				int													=	False

     # remove and rename some keys of load the original model
     lowercase_	:				Any													=	checkpoint
     remove_unused_keys(__SCREAMING_SNAKE_CASE      )
     lowercase_	:				Union[str, Any]													=	create_rename_keys(__SCREAMING_SNAKE_CASE     ,			base_model=__SCREAMING_SNAKE_CASE      )
     for rename_key_src, rename_key_dest in rename_keys:
          rename_key(__SCREAMING_SNAKE_CASE     ,			__SCREAMING_SNAKE_CASE     ,			__SCREAMING_SNAKE_CASE      )

     # load modified state_dict
     model.load_state_dict(__SCREAMING_SNAKE_CASE      )

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

     # verify classification model
     if task_name.startswith('imagenet'      ):
          lowercase_	:				List[str]													=	outputs.logits
          lowercase_	:				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
               lowercase_	:				Optional[int]													=	torch.tensor([-1.6_336E00, -7.3_204E-02, -5.1_883E-01]      )
               assert torch.allclose(logits[0, :3]     ,			__SCREAMING_SNAKE_CASE     ,			atol=1E-4      )

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


if __name__ == "__main__":
       __SCREAMING_SNAKE_CASE    	=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."
       )

       __SCREAMING_SNAKE_CASE    	=parser.parse_args()
       convert_mobilevitva_checkpoint(
           args.task, args.orig_checkpoint_path, args.orig_config_path, args.pytorch_dump_folder_path
       )

