





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()
_SCREAMING_SNAKE_CASE      	=		logging.get_logger(__name__)


def 							SCREAMING_SNAKE_CASE__						(    __a   ):
	snake_case_		:			Dict											=	MobileViTConfig()

	# size of the architecture
	if "mobilevit_s" in mobilevit_name:
		snake_case_		:			List[Any]											=	[1_44, 1_92, 2_40]
		snake_case_		:			List[str]											=	[16, 32, 64, 96, 1_28, 1_60, 6_40]
	elif "mobilevit_xs" in mobilevit_name:
		snake_case_		:			Dict											=	[96, 1_20, 1_44]
		snake_case_		:			Union[str, Any]											=	[16, 32, 48, 64, 80, 96, 3_84]
	elif "mobilevit_xxs" in mobilevit_name:
		snake_case_		:			str											=	[64, 80, 96]
		snake_case_		:			Union[str, Any]											=	[16, 16, 24, 48, 64, 80, 3_20]
		snake_case_		:			Dict											=	0.05
		snake_case_		:			List[Any]											=	2.0

	if mobilevit_name.startswith('deeplabv3_'   ):
		snake_case_		:			List[str]											=	5_12
		snake_case_		:			Dict											=	16
		snake_case_		:			Optional[Any]											=	21
		snake_case_		:			Optional[int]											=	'pascal-voc-id2label.json'
	else:
		snake_case_		:			Optional[Any]											=	10_00
		snake_case_		:			Any											=	'imagenet-1k-id2label.json'

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

	return config


def 							SCREAMING_SNAKE_CASE__						(    __a   , __a=False   ):
	for i in range(1   , 6   ):
		if f"""layer_{i}.""" in name:
			snake_case_		:			List[Any]											=	name.replace(f"""layer_{i}."""   , f"""encoder.layer.{i - 1}."""   )

	if "conv_1." in name:
		snake_case_		:			str											=	name.replace('conv_1.'   , 'conv_stem.'   )
	if ".block." in name:
		snake_case_		:			Tuple											=	name.replace('.block.'   , '.'   )

	if "exp_1x1" in name:
		snake_case_		:			int											=	name.replace('exp_1x1'   , 'expand_1x1'   )
	if "red_1x1" in name:
		snake_case_		:			List[Any]											=	name.replace('red_1x1'   , 'reduce_1x1'   )
	if ".local_rep.conv_3x3." in name:
		snake_case_		:			Optional[int]											=	name.replace('.local_rep.conv_3x3.'   , '.conv_kxk.'   )
	if ".local_rep.conv_1x1." in name:
		snake_case_		:			int											=	name.replace('.local_rep.conv_1x1.'   , '.conv_1x1.'   )
	if ".norm." in name:
		snake_case_		:			List[str]											=	name.replace('.norm.'   , '.normalization.'   )
	if ".conv." in name:
		snake_case_		:			str											=	name.replace('.conv.'   , '.convolution.'   )
	if ".conv_proj." in name:
		snake_case_		:			Optional[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:
				snake_case_		:			str											=	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:
				snake_case_		:			Tuple											=	name.replace(f""".{i}.{j}."""   , f""".{i}."""   )
				if "expand_1x1" in name:
					snake_case_		:			Any											=	name.replace('expand_1x1'   , 'downsampling_layer.expand_1x1'   )
				if "conv_3x3" in name:
					snake_case_		:			List[str]											=	name.replace('conv_3x3'   , 'downsampling_layer.conv_3x3'   )
				if "reduce_1x1" in name:
					snake_case_		:			Tuple											=	name.replace('reduce_1x1'   , 'downsampling_layer.reduce_1x1'   )

	for i in range(2   , 5   ):
		if f""".global_rep.{i}.weight""" in name:
			snake_case_		:			Optional[int]											=	name.replace(f""".global_rep.{i}.weight"""   , '.layernorm.weight'   )
		if f""".global_rep.{i}.bias""" in name:
			snake_case_		:			Any											=	name.replace(f""".global_rep.{i}.bias"""   , '.layernorm.bias'   )

	if ".global_rep." in name:
		snake_case_		:			int											=	name.replace('.global_rep.'   , '.transformer.'   )
	if ".pre_norm_mha.0." in name:
		snake_case_		:			str											=	name.replace('.pre_norm_mha.0.'   , '.layernorm_before.'   )
	if ".pre_norm_mha.1.out_proj." in name:
		snake_case_		:			Any											=	name.replace('.pre_norm_mha.1.out_proj.'   , '.attention.output.dense.'   )
	if ".pre_norm_ffn.0." in name:
		snake_case_		:			Any											=	name.replace('.pre_norm_ffn.0.'   , '.layernorm_after.'   )
	if ".pre_norm_ffn.1." in name:
		snake_case_		:			Dict											=	name.replace('.pre_norm_ffn.1.'   , '.intermediate.dense.'   )
	if ".pre_norm_ffn.4." in name:
		snake_case_		:			List[str]											=	name.replace('.pre_norm_ffn.4.'   , '.output.dense.'   )
	if ".transformer." in name:
		snake_case_		:			int											=	name.replace('.transformer.'   , '.transformer.layer.'   )

	if ".aspp_layer." in name:
		snake_case_		:			str											=	name.replace('.aspp_layer.'   , '.'   )
	if ".aspp_pool." in name:
		snake_case_		:			Optional[Any]											=	name.replace('.aspp_pool.'   , '.'   )
	if "seg_head." in name:
		snake_case_		:			Optional[Any]											=	name.replace('seg_head.'   , 'segmentation_head.'   )
	if "segmentation_head.classifier.classifier." in name:
		snake_case_		:			List[str]											=	name.replace('segmentation_head.classifier.classifier.'   , 'segmentation_head.classifier.'   )

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

	return name


def 							SCREAMING_SNAKE_CASE__						(    __a   , __a   , __a=False   ):
	if base_model:
		snake_case_		:			Any											=	''
	else:
		snake_case_		:			Optional[int]											=	'mobilevit.'

	for key in orig_state_dict.copy().keys():
		snake_case_		:			Dict											=	orig_state_dict.pop(__a   )

		if key[:8] == "encoder.":
			snake_case_		:			Any											=	key[8:]

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

	return orig_state_dict


def 							SCREAMING_SNAKE_CASE__						(    ):
	snake_case_		:			Optional[Any]											=	'http://images.cocodataset.org/val2017/000000039769.jpg'
	snake_case_		:			Any											=	Image.open(requests.get(__a   , stream=__a   ).raw   )
	return im


@torch.no_grad()
def 							SCREAMING_SNAKE_CASE__						(    __a   , __a   , __a   , __a=False   ):
	snake_case_		:			Dict											=	get_mobilevit_config(__a   )

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

	# load 🤗 model
	if mobilevit_name.startswith('deeplabv3_'   ):
		snake_case_		:			Tuple											=	MobileViTForSemanticSegmentation(__a   ).eval()
	else:
		snake_case_		:			List[Any]											=	MobileViTForImageClassification(__a   ).eval()

	snake_case_		:			Optional[Any]											=	convert_state_dict(__a   , __a   )
	model.load_state_dict(__a   )

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

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

		if mobilevit_name == "deeplabv3_mobilevit_s":
			snake_case_		:			str											=	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":
			snake_case_		:			Dict											=	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":
			snake_case_		:			Optional[int]											=	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]   , __a   , atol=1E-4   )
	else:
		assert logits.shape == (1, 10_00)

		if mobilevit_name == "mobilevit_s":
			snake_case_		:			Union[str, Any]											=	torch.tensor([-0.9866, 0.2392, -1.1241]   )
		elif mobilevit_name == "mobilevit_xs":
			snake_case_		:			str											=	torch.tensor([-2.4761, -0.9399, -1.9587]   )
		elif mobilevit_name == "mobilevit_xxs":
			snake_case_		:			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]   , __a   , atol=1E-4   )

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

	if push_to_hub:
		snake_case_		:			Union[str, Any]											=	{
		    '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...'   )
		snake_case_		:			Dict											=	model_mapping[mobilevit_name]
		image_processor.push_to_hub(__a   , organization='apple'   )
		model.push_to_hub(__a   , organization='apple'   )


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

						_SCREAMING_SNAKE_CASE      	=		parser.parse_args()
						convert_movilevit_checkpoint(
						    args.mobilevit_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
						)








from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


_SCREAMING_SNAKE_CASE      	=		{"""configuration_swin""": ["""SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP""", """SwinConfig""", """SwinOnnxConfig"""]}


try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_SCREAMING_SNAKE_CASE      	=		[
						    """SWIN_PRETRAINED_MODEL_ARCHIVE_LIST""",
						    """SwinForImageClassification""",
						    """SwinForMaskedImageModeling""",
						    """SwinModel""",
						    """SwinPreTrainedModel""",
						    """SwinBackbone""",
						]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_SCREAMING_SNAKE_CASE      	=		[
						    """TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST""",
						    """TFSwinForImageClassification""",
						    """TFSwinForMaskedImageModeling""",
						    """TFSwinModel""",
						    """TFSwinPreTrainedModel""",
						]

if TYPE_CHECKING:
						from .configuration_swin import SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP, SwinConfig, SwinOnnxConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_swin import (
												    SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
												    SwinBackbone,
												    SwinForImageClassification,
												    SwinForMaskedImageModeling,
												    SwinModel,
												    SwinPreTrainedModel,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_swin import (
												    TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TFSwinForImageClassification,
												    TFSwinForMaskedImageModeling,
												    TFSwinModel,
												    TFSwinPreTrainedModel,
												)

else:
						import sys

						_SCREAMING_SNAKE_CASE      	=		_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

