
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()
UpperCamelCase_   	=	logging.get_logger(__name__)






def 		_UpperCAmelCase   (	UpperCamelCase:     List[Any]  ):






	"""simple docstring"""


	__lowerCAmelCase         =							MobileViTConfig()

	# size of the architecture
	if "mobilevit_s" in mobilevit_name:
		__lowerCAmelCase         =							[1_4_4, 1_9_2, 2_4_0]
		__lowerCAmelCase         =							[1_6, 3_2, 6_4, 9_6, 1_2_8, 1_6_0, 6_4_0]
	elif "mobilevit_xs" in mobilevit_name:
		__lowerCAmelCase         =							[9_6, 1_2_0, 1_4_4]
		__lowerCAmelCase         =							[1_6, 3_2, 4_8, 6_4, 8_0, 9_6, 3_8_4]
	elif "mobilevit_xxs" in mobilevit_name:
		__lowerCAmelCase         =							[6_4, 8_0, 9_6]
		__lowerCAmelCase         =							[1_6, 1_6, 2_4, 4_8, 6_4, 8_0, 3_2_0]
		__lowerCAmelCase         =							0.05
		__lowerCAmelCase         =							2.0

	if mobilevit_name.startswith("deeplabv3_"  ):
		__lowerCAmelCase         =							5_1_2
		__lowerCAmelCase         =							1_6
		__lowerCAmelCase         =							2_1
		__lowerCAmelCase         =							"pascal-voc-id2label.json"
	else:
		__lowerCAmelCase         =							1_0_0_0
		__lowerCAmelCase         =							"imagenet-1k-id2label.json"

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

	return config






def 		_UpperCAmelCase   (	UpperCamelCase:     Union[str, Any]			,							UpperCamelCase:     str=False  ):






	"""simple docstring"""


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

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

	if "exp_1x1" in name:
		__lowerCAmelCase         =							name.replace("exp_1x1"			,							"expand_1x1"  )
	if "red_1x1" in name:
		__lowerCAmelCase         =							name.replace("red_1x1"			,							"reduce_1x1"  )
	if ".local_rep.conv_3x3." in name:
		__lowerCAmelCase         =							name.replace(".local_rep.conv_3x3."			,							".conv_kxk."  )
	if ".local_rep.conv_1x1." in name:
		__lowerCAmelCase         =							name.replace(".local_rep.conv_1x1."			,							".conv_1x1."  )
	if ".norm." in name:
		__lowerCAmelCase         =							name.replace(".norm."			,							".normalization."  )
	if ".conv." in name:
		__lowerCAmelCase         =							name.replace(".conv."			,							".convolution."  )
	if ".conv_proj." in name:
		__lowerCAmelCase         =							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         =							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         =							name.replace(F".{i}.{j}."			,							F".{i}."  )
				if "expand_1x1" in name:
					__lowerCAmelCase         =							name.replace("expand_1x1"			,							"downsampling_layer.expand_1x1"  )
				if "conv_3x3" in name:
					__lowerCAmelCase         =							name.replace("conv_3x3"			,							"downsampling_layer.conv_3x3"  )
				if "reduce_1x1" in name:
					__lowerCAmelCase         =							name.replace("reduce_1x1"			,							"downsampling_layer.reduce_1x1"  )

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

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

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

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

	return name






def 		_UpperCAmelCase   (	UpperCamelCase:     Union[str, Any]			,							UpperCamelCase:     Any			,							UpperCamelCase:     Dict=False  ):






	"""simple docstring"""


	if base_model:
		__lowerCAmelCase         =							""
	else:
		__lowerCAmelCase         =							"mobilevit."

	for key in orig_state_dict.copy().keys():
		__lowerCAmelCase         =							orig_state_dict.pop(UpperCamelCase  )

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

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

	return orig_state_dict






def 		_UpperCAmelCase   (	):






	"""simple docstring"""


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






@torch.no_grad()
def 		_UpperCAmelCase   (	UpperCamelCase:     int			,							UpperCamelCase:     Dict			,							UpperCamelCase:     List[Any]			,							UpperCamelCase:     Any=False  ):






	"""simple docstring"""


	__lowerCAmelCase         =							get_mobilevit_config(UpperCamelCase  )

	# load original state_dict
	__lowerCAmelCase         =							torch.load(UpperCamelCase			,							map_location="cpu"  )

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

	__lowerCAmelCase         =							convert_state_dict(UpperCamelCase			,							UpperCamelCase  )
	model.load_state_dict(UpperCamelCase  )

	# Check outputs on an image, prepared by MobileViTImageProcessor
	__lowerCAmelCase         =							MobileViTImageProcessor(crop_size=config.image_size			,							size=config.image_size + 3_2  )
	__lowerCAmelCase         =							image_processor(images=prepare_img()			,							return_tensors="pt"  )
	__lowerCAmelCase         =							model(**UpperCamelCase  )
	__lowerCAmelCase         =							outputs.logits

	if mobilevit_name.startswith("deeplabv3_"  ):
		assert logits.shape == (1, 2_1, 3_2, 3_2)

		if mobilevit_name == "deeplabv3_mobilevit_s":
			__lowerCAmelCase         =							torch.tensor(
			    [
			        [[6.2_065, 6.1_292, 6.2_070], [6.1_079, 6.1_254, 6.1_747], [6.0_042, 6.1_071, 6.1_034]],
			        [[-6.9_253, -6.8_653, -7.0_398], [-7.3_218, -7.3_983, -7.3_670], [-7.1_961, -7.2_482, -7.1_569]],
			        [[-4.4_723, -4.4_348, -4.3_769], [-5.3_629, -5.4_632, -5.4_598], [-5.1_587, -5.3_402, -5.5_059]],
			    ]  )
		elif mobilevit_name == "deeplabv3_mobilevit_xs":
			__lowerCAmelCase         =							torch.tensor(
			    [
			        [[5.4_449, 5.5_733, 5.6_314], [5.1_815, 5.3_930, 5.5_963], [5.1_656, 5.4_333, 5.4_853]],
			        [[-9.4_423, -9.7_766, -9.6_714], [-9.1_581, -9.5_720, -9.5_519], [-9.1_006, -9.6_458, -9.5_703]],
			        [[-7.7_721, -7.3_716, -7.1_583], [-8.4_599, -8.0_624, -7.7_944], [-8.4_172, -7.8_366, -7.5_025]],
			    ]  )
		elif mobilevit_name == "deeplabv3_mobilevit_xxs":
			__lowerCAmelCase         =							torch.tensor(
			    [
			        [[6.9_811, 6.9_743, 7.3_123], [7.1_777, 7.1_931, 7.3_938], [7.5_633, 7.8_050, 7.8_901]],
			        [[-10.5_536, -10.2_332, -10.2_924], [-10.2_336, -9.8_624, -9.5_964], [-10.8_840, -10.8_158, -10.6_659]],
			        [[-3.4_938, -3.0_631, -2.8_620], [-3.4_205, -2.8_135, -2.6_875], [-3.4_179, -2.7_945, -2.8_750]],
			    ]  )
		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, 1_0_0_0)

		if mobilevit_name == "mobilevit_s":
			__lowerCAmelCase         =							torch.tensor([-0.9_866, 0.2_392, -1.1_241]  )
		elif mobilevit_name == "mobilevit_xs":
			__lowerCAmelCase         =							torch.tensor([-2.4_761, -0.9_399, -1.9_587]  )
		elif mobilevit_name == "mobilevit_xxs":
			__lowerCAmelCase         =							torch.tensor([-1.9_364, -1.2_327, -0.4_653]  )
		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:
		__lowerCAmelCase         =							{
		    "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         =							model_mapping[mobilevit_name]
		image_processor.push_to_hub(UpperCamelCase			,							organization="apple"  )
		model.push_to_hub(UpperCamelCase			,							organization="apple"  )


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

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








import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionImageVariationPipeline
from diffusers.utils.testing_utils import load_image, require_torch_gpu, slow, torch_device


UpperCamelCase_   	=	False

class       a		(   unittest.TestCase					):
							pass

@slow
@require_torch_gpu
class       a		(   unittest.TestCase					):







							def       UpperCAmelCase__					(						self  :  Dict ):
								"""simple docstring"""







								__lowerCAmelCase         =							VersatileDiffusionImageVariationPipeline.from_pretrained("shi-labs/versatile-diffusion" )
								pipe.to(snake_case__ )
								pipe.set_progress_bar_config(disable=snake_case__ )

								__lowerCAmelCase         =							load_image(
								    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg" )
								__lowerCAmelCase         =							torch.manual_seed(0 )
								__lowerCAmelCase         =							pipe(
								    image=snake_case__      ,				generator=snake_case__      ,				guidance_scale=7.5      ,				num_inference_steps=50      ,				output_type="numpy"      ,				).images

								__lowerCAmelCase         =							image[0, 253:256, 253:256, -1]

								assert image.shape == (1, 512, 512, 3)
								__lowerCAmelCase         =							np.array([0.0_4_4_1, 0.0_4_6_9, 0.0_5_0_7, 0.0_5_7_5, 0.0_6_3_2, 0.0_6_5_0, 0.0_8_6_5, 0.0_9_0_9, 0.0_9_4_5] )

								assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2






