

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()
_a:    Tuple 							=			logging.get_logger(__name__)



def       __lowerCAmelCase      (						A    ):
	UpperCAmelCase_       				=			MobileViTConfig()

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

	if mobilevit_name.startswith("deeplabv3_"    ):
		UpperCAmelCase_       				=			512
		UpperCAmelCase_       				=			16
		UpperCAmelCase_       				=			21
		UpperCAmelCase_       				=			"""pascal-voc-id2label.json"""
	else:
		UpperCAmelCase_       				=			1000
		UpperCAmelCase_       				=			"""imagenet-1k-id2label.json"""

	UpperCAmelCase_       				=			"""huggingface/label-files"""
	UpperCAmelCase_       				=			json.load(open(hf_hub_download(UpperCamelCase__       ,     UpperCamelCase__       ,     repo_type="dataset"    )       ,     "r"    )    )
	UpperCAmelCase_       				=			{int(UpperCamelCase__    ): v for k, v in idalabel.items()}
	UpperCAmelCase_       				=			idalabel
	UpperCAmelCase_       				=			{v: k for k, v in idalabel.items()}

	return config



def       __lowerCAmelCase      (						A       ,     A=False    ):
	for i in range(1       ,     6    ):
		if F"layer_{i}." in name:
			UpperCAmelCase_       				=			name.replace(F"layer_{i}."       ,     F"encoder.layer.{i - 1}."    )

	if "conv_1." in name:
		UpperCAmelCase_       				=			name.replace("conv_1."       ,     "conv_stem."    )
	if ".block." in name:
		UpperCAmelCase_       				=			name.replace(".block."       ,     "."    )

	if "exp_1x1" in name:
		UpperCAmelCase_       				=			name.replace("exp_1x1"       ,     "expand_1x1"    )
	if "red_1x1" in name:
		UpperCAmelCase_       				=			name.replace("red_1x1"       ,     "reduce_1x1"    )
	if ".local_rep.conv_3x3." in name:
		UpperCAmelCase_       				=			name.replace(".local_rep.conv_3x3."       ,     ".conv_kxk."    )
	if ".local_rep.conv_1x1." in name:
		UpperCAmelCase_       				=			name.replace(".local_rep.conv_1x1."       ,     ".conv_1x1."    )
	if ".norm." in name:
		UpperCAmelCase_       				=			name.replace(".norm."       ,     ".normalization."    )
	if ".conv." in name:
		UpperCAmelCase_       				=			name.replace(".conv."       ,     ".convolution."    )
	if ".conv_proj." in name:
		UpperCAmelCase_       				=			name.replace(".conv_proj."       ,     ".conv_projection."    )

	for i in range(0       ,     2    ):
		for j in range(0       ,     4    ):
			if F".{i}.{j}." in name:
				UpperCAmelCase_       				=			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:
				UpperCAmelCase_       				=			name.replace(F".{i}.{j}."       ,     F".{i}."    )
				if "expand_1x1" in name:
					UpperCAmelCase_       				=			name.replace("expand_1x1"       ,     "downsampling_layer.expand_1x1"    )
				if "conv_3x3" in name:
					UpperCAmelCase_       				=			name.replace("conv_3x3"       ,     "downsampling_layer.conv_3x3"    )
				if "reduce_1x1" in name:
					UpperCAmelCase_       				=			name.replace("reduce_1x1"       ,     "downsampling_layer.reduce_1x1"    )

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

	if ".global_rep." in name:
		UpperCAmelCase_       				=			name.replace(".global_rep."       ,     ".transformer."    )
	if ".pre_norm_mha.0." in name:
		UpperCAmelCase_       				=			name.replace(".pre_norm_mha.0."       ,     ".layernorm_before."    )
	if ".pre_norm_mha.1.out_proj." in name:
		UpperCAmelCase_       				=			name.replace(".pre_norm_mha.1.out_proj."       ,     ".attention.output.dense."    )
	if ".pre_norm_ffn.0." in name:
		UpperCAmelCase_       				=			name.replace(".pre_norm_ffn.0."       ,     ".layernorm_after."    )
	if ".pre_norm_ffn.1." in name:
		UpperCAmelCase_       				=			name.replace(".pre_norm_ffn.1."       ,     ".intermediate.dense."    )
	if ".pre_norm_ffn.4." in name:
		UpperCAmelCase_       				=			name.replace(".pre_norm_ffn.4."       ,     ".output.dense."    )
	if ".transformer." in name:
		UpperCAmelCase_       				=			name.replace(".transformer."       ,     ".transformer.layer."    )

	if ".aspp_layer." in name:
		UpperCAmelCase_       				=			name.replace(".aspp_layer."       ,     "."    )
	if ".aspp_pool." in name:
		UpperCAmelCase_       				=			name.replace(".aspp_pool."       ,     "."    )
	if "seg_head." in name:
		UpperCAmelCase_       				=			name.replace("seg_head."       ,     "segmentation_head."    )
	if "segmentation_head.classifier.classifier." in name:
		UpperCAmelCase_       				=			name.replace("segmentation_head.classifier.classifier."       ,     "segmentation_head.classifier."    )

	if "classifier.fc." in name:
		UpperCAmelCase_       				=			name.replace("classifier.fc."       ,     "classifier."    )
	elif (not base_model) and ("segmentation_head." not in name):
		UpperCAmelCase_       				=			"""mobilevit.""" + name

	return name



def       __lowerCAmelCase      (						A       ,     A       ,     A=False    ):
	if base_model:
		UpperCAmelCase_       				=			""""""
	else:
		UpperCAmelCase_       				=			"""mobilevit."""

	for key in orig_state_dict.copy().keys():
		UpperCAmelCase_       				=			orig_state_dict.pop(UpperCamelCase__    )

		if key[:8] == "encoder.":
			UpperCAmelCase_       				=			key[8:]

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

	return orig_state_dict



def       __lowerCAmelCase      (						):
	UpperCAmelCase_       				=			"""http://images.cocodataset.org/val2017/000000039769.jpg"""
	UpperCAmelCase_       				=			Image.open(requests.get(UpperCamelCase__       ,     stream=UpperCamelCase__    ).raw    )
	return im



@torch.no_grad()
def       __lowerCAmelCase      (						A       ,     A       ,     A       ,     A=False    ):
	UpperCAmelCase_       				=			get_mobilevit_config(UpperCamelCase__    )

	# load original state_dict
	UpperCAmelCase_       				=			torch.load(UpperCamelCase__       ,     map_location="cpu"    )

	# load 🤗 model
	if mobilevit_name.startswith("deeplabv3_"    ):
		UpperCAmelCase_       				=			MobileViTForSemanticSegmentation(UpperCamelCase__    ).eval()
	else:
		UpperCAmelCase_       				=			MobileViTForImageClassification(UpperCamelCase__    ).eval()

	UpperCAmelCase_       				=			convert_state_dict(UpperCamelCase__       ,     UpperCamelCase__    )
	model.load_state_dict(UpperCamelCase__    )

	# Check outputs on an image, prepared by MobileViTImageProcessor
	UpperCAmelCase_       				=			MobileViTImageProcessor(crop_size=config.image_size       ,     size=config.image_size + 32    )
	UpperCAmelCase_       				=			image_processor(images=prepare_img()       ,     return_tensors="pt"    )
	UpperCAmelCase_       				=			model(**UpperCamelCase__    )
	UpperCAmelCase_       				=			outputs.logits

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

		if mobilevit_name == "deeplabv3_mobilevit_s":
			UpperCAmelCase_       				=			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":
			UpperCAmelCase_       				=			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":
			UpperCAmelCase_       				=			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]       ,     UpperCamelCase__       ,     atol=1E-4    )
	else:
		assert logits.shape == (1, 1000)

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

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

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

	if push_to_hub:
		UpperCAmelCase_       				=			{
		    """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..."    )
		UpperCAmelCase_       				=			model_mapping[mobilevit_name]
		image_processor.push_to_hub(UpperCamelCase__       ,     organization="apple"    )
		model.push_to_hub(UpperCamelCase__       ,     organization="apple"    )


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

				_a:    int 							=			parser.parse_args()
				convert_movilevit_checkpoint(
				    args.mobilevit_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
				)







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


_a:    List[str] 							=			logging.get_logger(__name__)

_a:    Any 							=			{
    """sayakpaul/vit-msn-base""": """https://huggingface.co/sayakpaul/vit-msn-base/resolve/main/config.json""",
    # See all ViT MSN models at https://huggingface.co/models?filter=vit_msn
}

class 						__UpperCamelCase    (					lowercase						):
	SCREAMING_SNAKE_CASE__    				=     'vit_msn'




	def __init__(		self	:						Union[str, Any]		,	lowerCAmelCase	:						Optional[int]=768		,	lowerCAmelCase	:						Dict=12		,	lowerCAmelCase	:						str=12		,	lowerCAmelCase	:						List[str]=3_072		,	lowerCAmelCase	:						Tuple="gelu"		,	lowerCAmelCase	:						int=0.0		,	lowerCAmelCase	:						Optional[int]=0.0		,	lowerCAmelCase	:						Union[str, Any]=0.02		,	lowerCAmelCase	:						str=1e-06		,	lowerCAmelCase	:						Dict=224		,	lowerCAmelCase	:						str=16		,	lowerCAmelCase	:						List[Any]=3		,	lowerCAmelCase	:						Union[str, Any]=True		,	**lowerCAmelCase	:						List[str]		,	):


		'''simple docstring'''







		super().__init__(**lowerCAmelCase							)

		UpperCAmelCase_       				=			hidden_size
		UpperCAmelCase_       				=			num_hidden_layers
		UpperCAmelCase_       				=			num_attention_heads
		UpperCAmelCase_       				=			intermediate_size
		UpperCAmelCase_       				=			hidden_act
		UpperCAmelCase_       				=			hidden_dropout_prob
		UpperCAmelCase_       				=			attention_probs_dropout_prob
		UpperCAmelCase_       				=			initializer_range
		UpperCAmelCase_       				=			layer_norm_eps
		UpperCAmelCase_       				=			image_size
		UpperCAmelCase_       				=			patch_size
		UpperCAmelCase_       				=			num_channels
		UpperCAmelCase_       				=			qkv_bias