



"""simple docstring"""






import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    MobileViTConfig,
    MobileViTForImageClassification,
    MobileViTForSemanticSegmentation,
    MobileViTImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
__snake_case				:  str =       logging.get_logger(__name__)


def      _lowercase							(    __snake_case      )		->  Optional[int]:
			__lowerCAmelCase :    Dict				=			MobileViTConfig()

			# size of the architecture
			if "mobilevit_s" in mobilevit_name:
						__lowerCAmelCase :    Dict				=			[144, 192, 240]
						__lowerCAmelCase :    str				=			[16, 32, 64, 96, 128, 160, 640]
			elif "mobilevit_xs" in mobilevit_name:
						__lowerCAmelCase :    List[Any]				=			[96, 120, 144]
						__lowerCAmelCase :    str				=			[16, 32, 48, 64, 80, 96, 384]
			elif "mobilevit_xxs" in mobilevit_name:
						__lowerCAmelCase :    Dict				=			[64, 80, 96]
						__lowerCAmelCase :    Tuple				=			[16, 16, 24, 48, 64, 80, 320]
						__lowerCAmelCase :    Optional[int]				=			0.05
						__lowerCAmelCase :    Optional[int]				=			2.0

			if mobilevit_name.startswith("deeplabv3_"      ):
						__lowerCAmelCase :    str				=			512
						__lowerCAmelCase :    Optional[int]				=			16
						__lowerCAmelCase :    List[Any]				=			21
						__lowerCAmelCase :    int				=			"pascal-voc-id2label.json"
			else:
						__lowerCAmelCase :    Optional[Any]				=			1_000
						__lowerCAmelCase :    Tuple				=			"imagenet-1k-id2label.json"

			__lowerCAmelCase :    Union[str, Any]				=			"huggingface/label-files"
			__lowerCAmelCase :    Optional[int]				=			json.load(open(hf_hub_download(__snake_case   ,__snake_case   ,repo_type="dataset"      )   ,"r"      )      )
			__lowerCAmelCase :    Optional[int]				=			{int(__snake_case      ): v for k, v in idalabel.items()}
			__lowerCAmelCase :    Tuple				=			idalabel
			__lowerCAmelCase :    Optional[Any]				=			{v: k for k, v in idalabel.items()}

			return config


def      _lowercase							(    __snake_case   ,__snake_case=False      )		->  Union[str, Any]:
			for i in range(1   ,6      ):
						if F"""layer_{i}.""" in name:
									__lowerCAmelCase :    str				=			name.replace(F"""layer_{i}."""   ,F"""encoder.layer.{i - 1}."""      )

			if "conv_1." in name:
						__lowerCAmelCase :    List[str]				=			name.replace("conv_1."   ,"conv_stem."      )
			if ".block." in name:
						__lowerCAmelCase :    Optional[int]				=			name.replace(".block."   ,"."      )

			if "exp_1x1" in name:
						__lowerCAmelCase :    Optional[Any]				=			name.replace("exp_1x1"   ,"expand_1x1"      )
			if "red_1x1" in name:
						__lowerCAmelCase :    Dict				=			name.replace("red_1x1"   ,"reduce_1x1"      )
			if ".local_rep.conv_3x3." in name:
						__lowerCAmelCase :    List[Any]				=			name.replace(".local_rep.conv_3x3."   ,".conv_kxk."      )
			if ".local_rep.conv_1x1." in name:
						__lowerCAmelCase :    Tuple				=			name.replace(".local_rep.conv_1x1."   ,".conv_1x1."      )
			if ".norm." in name:
						__lowerCAmelCase :    Tuple				=			name.replace(".norm."   ,".normalization."      )
			if ".conv." in name:
						__lowerCAmelCase :    List[str]				=			name.replace(".conv."   ,".convolution."      )
			if ".conv_proj." in name:
						__lowerCAmelCase :    Union[str, Any]				=			name.replace(".conv_proj."   ,".conv_projection."      )

			for i in range(0   ,2      ):
						for j in range(0   ,4      ):
									if F""".{i}.{j}.""" in name:
												__lowerCAmelCase :    Any				=			name.replace(F""".{i}.{j}."""   ,F""".{i}.layer.{j}."""      )

			for i in range(2   ,6      ):
						for j in range(0   ,4      ):
									if F""".{i}.{j}.""" in name:
												__lowerCAmelCase :    str				=			name.replace(F""".{i}.{j}."""   ,F""".{i}."""      )
												if "expand_1x1" in name:
															__lowerCAmelCase :    Any				=			name.replace("expand_1x1"   ,"downsampling_layer.expand_1x1"      )
												if "conv_3x3" in name:
															__lowerCAmelCase :    Optional[Any]				=			name.replace("conv_3x3"   ,"downsampling_layer.conv_3x3"      )
												if "reduce_1x1" in name:
															__lowerCAmelCase :    Tuple				=			name.replace("reduce_1x1"   ,"downsampling_layer.reduce_1x1"      )

			for i in range(2   ,5      ):
						if F""".global_rep.{i}.weight""" in name:
									__lowerCAmelCase :    int				=			name.replace(F""".global_rep.{i}.weight"""   ,".layernorm.weight"      )
						if F""".global_rep.{i}.bias""" in name:
									__lowerCAmelCase :    str				=			name.replace(F""".global_rep.{i}.bias"""   ,".layernorm.bias"      )

			if ".global_rep." in name:
						__lowerCAmelCase :    Any				=			name.replace(".global_rep."   ,".transformer."      )
			if ".pre_norm_mha.0." in name:
						__lowerCAmelCase :    Tuple				=			name.replace(".pre_norm_mha.0."   ,".layernorm_before."      )
			if ".pre_norm_mha.1.out_proj." in name:
						__lowerCAmelCase :    Tuple				=			name.replace(".pre_norm_mha.1.out_proj."   ,".attention.output.dense."      )
			if ".pre_norm_ffn.0." in name:
						__lowerCAmelCase :    Union[str, Any]				=			name.replace(".pre_norm_ffn.0."   ,".layernorm_after."      )
			if ".pre_norm_ffn.1." in name:
						__lowerCAmelCase :    Optional[Any]				=			name.replace(".pre_norm_ffn.1."   ,".intermediate.dense."      )
			if ".pre_norm_ffn.4." in name:
						__lowerCAmelCase :    str				=			name.replace(".pre_norm_ffn.4."   ,".output.dense."      )
			if ".transformer." in name:
						__lowerCAmelCase :    Dict				=			name.replace(".transformer."   ,".transformer.layer."      )

			if ".aspp_layer." in name:
						__lowerCAmelCase :    Any				=			name.replace(".aspp_layer."   ,"."      )
			if ".aspp_pool." in name:
						__lowerCAmelCase :    str				=			name.replace(".aspp_pool."   ,"."      )
			if "seg_head." in name:
						__lowerCAmelCase :    Dict				=			name.replace("seg_head."   ,"segmentation_head."      )
			if "segmentation_head.classifier.classifier." in name:
						__lowerCAmelCase :    Tuple				=			name.replace("segmentation_head.classifier.classifier."   ,"segmentation_head.classifier."      )

			if "classifier.fc." in name:
						__lowerCAmelCase :    List[Any]				=			name.replace("classifier.fc."   ,"classifier."      )
			elif (not base_model) and ("segmentation_head." not in name):
						__lowerCAmelCase :    List[str]				=			"mobilevit." + name

			return name


def      _lowercase							(    __snake_case   ,__snake_case   ,__snake_case=False      )		->  Tuple:
			if base_model:
						__lowerCAmelCase :    Union[str, Any]				=			""
			else:
						__lowerCAmelCase :    Dict				=			"mobilevit."

			for key in orig_state_dict.copy().keys():
						__lowerCAmelCase :    List[str]				=			orig_state_dict.pop(__snake_case      )

						if key[:8] == "encoder.":
									__lowerCAmelCase :    str				=			key[8:]

						if "qkv" in key:
									__lowerCAmelCase :    List[str]				=			key.split("."      )
									__lowerCAmelCase :    Any				=			int(key_split[0][6:]      ) - 1
									__lowerCAmelCase :    int				=			int(key_split[3]      )
									__lowerCAmelCase :    Any				=			model.get_submodule(F"""{model_prefix}encoder.layer.{layer_num}"""      )
									__lowerCAmelCase :    List[Any]				=			layer.transformer.layer[transformer_num].attention.attention.all_head_size
									__lowerCAmelCase :    Dict				=			(
									    F"""{model_prefix}encoder.layer.{layer_num}.transformer.layer.{transformer_num}.attention.attention."""
									)
									if "weight" in key:
												__lowerCAmelCase :    str				=			val[:dim, :]
												__lowerCAmelCase :    List[str]				=			val[dim : dim * 2, :]
												__lowerCAmelCase :    str				=			val[-dim:, :]
									else:
												__lowerCAmelCase :    Any				=			val[:dim]
												__lowerCAmelCase :    str				=			val[dim : dim * 2]
												__lowerCAmelCase :    List[Any]				=			val[-dim:]
						else:
									__lowerCAmelCase :    Optional[Any]				=			val

			return orig_state_dict


def      _lowercase							(    )		->  int:
			__lowerCAmelCase :    Union[str, Any]				=			"http://images.cocodataset.org/val2017/000000039769.jpg"
			__lowerCAmelCase :    List[str]				=			Image.open(requests.get(__snake_case   ,stream=__snake_case      ).raw      )
			return im


@torch.no_grad()
def      _lowercase							(    __snake_case   ,__snake_case   ,__snake_case   ,__snake_case=False      )		->  Tuple:
			__lowerCAmelCase :    Union[str, Any]				=			get_mobilevit_config(__snake_case      )

			# load original state_dict
			__lowerCAmelCase :    List[Any]				=			torch.load(__snake_case   ,map_location="cpu"      )

			# load 🤗 model
			if mobilevit_name.startswith("deeplabv3_"      ):
						__lowerCAmelCase :    Dict				=			MobileViTForSemanticSegmentation(__snake_case      ).eval()
			else:
						__lowerCAmelCase :    Optional[int]				=			MobileViTForImageClassification(__snake_case      ).eval()

			__lowerCAmelCase :    Optional[Any]				=			convert_state_dict(__snake_case   ,__snake_case      )
			model.load_state_dict(__snake_case      )

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

			if mobilevit_name.startswith("deeplabv3_"      ):
						assert logits.shape == (1, 21, 32, 32)

						if mobilevit_name == "deeplabv3_mobilevit_s":
									__lowerCAmelCase :    Optional[Any]				=			torch.tensor(
									    [
									        [[6.2065, 6.1292, 6.2070], [6.1079, 6.1254, 6.1747], [6.0042, 6.1071, 6.1034]],
									        [[-6.9253, -6.8653, -7.0398], [-7.3218, -7.3983, -7.3670], [-7.1961, -7.2482, -7.1569]],
									        [[-4.4723, -4.4348, -4.3769], [-5.3629, -5.4632, -5.4598], [-5.1587, -5.3402, -5.5059]],
									    ]      )
						elif mobilevit_name == "deeplabv3_mobilevit_xs":
									__lowerCAmelCase :    Any				=			torch.tensor(
									    [
									        [[5.4449, 5.5733, 5.6314], [5.1815, 5.3930, 5.5963], [5.1656, 5.4333, 5.4853]],
									        [[-9.4423, -9.7766, -9.6714], [-9.1581, -9.5720, -9.5519], [-9.1006, -9.6458, -9.5703]],
									        [[-7.7721, -7.3716, -7.1583], [-8.4599, -8.0624, -7.7944], [-8.4172, -7.8366, -7.5025]],
									    ]      )
						elif mobilevit_name == "deeplabv3_mobilevit_xxs":
									__lowerCAmelCase :    Any				=			torch.tensor(
									    [
									        [[6.9811, 6.9743, 7.3123], [7.1777, 7.1931, 7.3938], [7.5633, 7.8050, 7.8901]],
									        [[-10.5536, -10.2332, -10.2924], [-10.2336, -9.8624, -9.5964], [-10.8840, -10.8158, -10.6659]],
									        [[-3.4938, -3.0631, -2.8620], [-3.4205, -2.8135, -2.6875], [-3.4179, -2.7945, -2.8750]],
									    ]      )
						else:
									raise ValueError(F"""Unknown mobilevit_name: {mobilevit_name}"""      )

						assert torch.allclose(logits[0, :3, :3, :3]   ,__snake_case   ,atol=1e-4      )
			else:
						assert logits.shape == (1, 1_000)

						if mobilevit_name == "mobilevit_s":
									__lowerCAmelCase :    List[Any]				=			torch.tensor([-0.9866, 0.2392, -1.1241]      )
						elif mobilevit_name == "mobilevit_xs":
									__lowerCAmelCase :    Any				=			torch.tensor([-2.4761, -0.9399, -1.9587]      )
						elif mobilevit_name == "mobilevit_xxs":
									__lowerCAmelCase :    List[Any]				=			torch.tensor([-1.9364, -1.2327, -0.4653]      )
						else:
									raise ValueError(F"""Unknown mobilevit_name: {mobilevit_name}"""      )

						assert torch.allclose(logits[0, :3]   ,__snake_case   ,atol=1e-4      )

			Path(__snake_case      ).mkdir(exist_ok=__snake_case      )
			print(F"""Saving model {mobilevit_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 push_to_hub:
						__lowerCAmelCase :    Tuple				=			{
						    "mobilevit_s": "mobilevit-small",
						    "mobilevit_xs": "mobilevit-x-small",
						    "mobilevit_xxs": "mobilevit-xx-small",
						    "deeplabv3_mobilevit_s": "deeplabv3-mobilevit-small",
						    "deeplabv3_mobilevit_xs": "deeplabv3-mobilevit-x-small",
						    "deeplabv3_mobilevit_xxs": "deeplabv3-mobilevit-xx-small",
						}

						print("Pushing to the hub..."      )
						__lowerCAmelCase :    str				=			model_mapping[mobilevit_name]
						image_processor.push_to_hub(__snake_case   ,organization="apple"      )
						model.push_to_hub(__snake_case   ,organization="apple"      )


if __name__ == "__main__":
	__snake_case				:  List[str] =       argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    '--mobilevit_name',
	    default='mobilevit_s',
	    type=str,
	    help=(
	        'Name of the MobileViT model you\'d like to convert. Should be one of \'mobilevit_s\', \'mobilevit_xs\','
	        ' \'mobilevit_xxs\', \'deeplabv3_mobilevit_s\', \'deeplabv3_mobilevit_xs\', \'deeplabv3_mobilevit_xxs\'.'
	    ),
	)
	parser.add_argument(
	    '--checkpoint_path', required=True, type=str, help='Path to the original state dict (.pt file).'
	)
	parser.add_argument(
	    '--pytorch_dump_folder_path', required=True, type=str, help='Path to the output PyTorch model directory.'
	)
	parser.add_argument(
	    '--push_to_hub', action='store_true', help='Whether or not to push the converted model to the 🤗 hub.'
	)

	__snake_case				:  Tuple =       parser.parse_args()
	convert_movilevit_checkpoint(
	    args.mobilevit_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
	)




"""simple docstring"""






from ...configuration_utils import PretrainedConfig
from ...utils import logging


__snake_case				:  Dict =       logging.get_logger(__name__)

__snake_case				:  int =       {
    'uclanlp/visualbert-vqa': 'https://huggingface.co/uclanlp/visualbert-vqa/resolve/main/config.json',
    'uclanlp/visualbert-vqa-pre': 'https://huggingface.co/uclanlp/visualbert-vqa-pre/resolve/main/config.json',
    'uclanlp/visualbert-vqa-coco-pre': (
        'https://huggingface.co/uclanlp/visualbert-vqa-coco-pre/resolve/main/config.json'
    ),
    'uclanlp/visualbert-vcr': 'https://huggingface.co/uclanlp/visualbert-vcr/resolve/main/config.json',
    'uclanlp/visualbert-vcr-pre': 'https://huggingface.co/uclanlp/visualbert-vcr-pre/resolve/main/config.json',
    'uclanlp/visualbert-vcr-coco-pre': (
        'https://huggingface.co/uclanlp/visualbert-vcr-coco-pre/resolve/main/config.json'
    ),
    'uclanlp/visualbert-nlvr2': 'https://huggingface.co/uclanlp/visualbert-nlvr2/resolve/main/config.json',
    'uclanlp/visualbert-nlvr2-pre': 'https://huggingface.co/uclanlp/visualbert-nlvr2-pre/resolve/main/config.json',
    'uclanlp/visualbert-nlvr2-coco-pre': (
        'https://huggingface.co/uclanlp/visualbert-nlvr2-coco-pre/resolve/main/config.json'
    )
    # See all VisualBERT models at https://huggingface.co/models?filter=visual_bert
}



class      A__  (    __SCREAMING_SNAKE_CASE				):




	'''simple docstring'''





	SCREAMING_SNAKE_CASE        =      'visual_bert'

	def __init__(	self:						int			,     _SCREAMING_SNAKE_CASE:						Dict=3_0522			,     _SCREAMING_SNAKE_CASE:						Union[str, Any]=768			,     _SCREAMING_SNAKE_CASE:						List[str]=512			,     _SCREAMING_SNAKE_CASE:						Optional[int]=12			,     _SCREAMING_SNAKE_CASE:						List[str]=12			,     _SCREAMING_SNAKE_CASE:						Tuple=3072			,     _SCREAMING_SNAKE_CASE:						Union[str, Any]="gelu"			,     _SCREAMING_SNAKE_CASE:						Tuple=0.1			,     _SCREAMING_SNAKE_CASE:						str=0.1			,     _SCREAMING_SNAKE_CASE:						List[Any]=512			,     _SCREAMING_SNAKE_CASE:						Any=2			,     _SCREAMING_SNAKE_CASE:						Optional[int]=0.02			,     _SCREAMING_SNAKE_CASE:						Union[str, Any]=1e-12			,     _SCREAMING_SNAKE_CASE:						List[str]=False			,     _SCREAMING_SNAKE_CASE:						int=True			,     _SCREAMING_SNAKE_CASE:						str=1			,     _SCREAMING_SNAKE_CASE:						Tuple=0			,     _SCREAMING_SNAKE_CASE:						Any=2			,     **_SCREAMING_SNAKE_CASE:						List[str]			,     )							->			str:

				"""simple docstring"""

				super().__init__(pad_token_id=_SCREAMING_SNAKE_CASE			,     bos_token_id=_SCREAMING_SNAKE_CASE			,     eos_token_id=_SCREAMING_SNAKE_CASE			,     **_SCREAMING_SNAKE_CASE)

				__lowerCAmelCase :    str				=			vocab_size
				__lowerCAmelCase :    Tuple				=			max_position_embeddings
				__lowerCAmelCase :    Any				=			hidden_size
				__lowerCAmelCase :    Union[str, Any]				=			visual_embedding_dim
				__lowerCAmelCase :    List[str]				=			num_hidden_layers
				__lowerCAmelCase :    Optional[int]				=			num_attention_heads
				__lowerCAmelCase :    str				=			intermediate_size
				__lowerCAmelCase :    int				=			hidden_act
				__lowerCAmelCase :    Union[str, Any]				=			hidden_dropout_prob
				__lowerCAmelCase :    int				=			attention_probs_dropout_prob
				__lowerCAmelCase :    List[Any]				=			initializer_range
				__lowerCAmelCase :    Dict				=			type_vocab_size
				__lowerCAmelCase :    Optional[int]				=			layer_norm_eps
				__lowerCAmelCase :    Union[str, Any]				=			bypass_transformer
				__lowerCAmelCase :    List[str]				=			special_visual_initialize