

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







def       A_				(   _UpperCAmelCase   ):
							SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	MobileViTConfig()

							# size of the architecture
							if "mobilevit_s" in mobilevit_name:
														SCREAMING_SNAKE_CASE_:			str  		=	[1_44, 1_92, 2_40]
														SCREAMING_SNAKE_CASE_:			Any  		=	[16, 32, 64, 96, 1_28, 1_60, 6_40]
							elif "mobilevit_xs" in mobilevit_name:
														SCREAMING_SNAKE_CASE_:			Tuple  		=	[96, 1_20, 1_44]
														SCREAMING_SNAKE_CASE_:			Any  		=	[16, 32, 48, 64, 80, 96, 3_84]
							elif "mobilevit_xxs" in mobilevit_name:
														SCREAMING_SNAKE_CASE_:			Optional[int]  		=	[64, 80, 96]
														SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	[16, 16, 24, 48, 64, 80, 3_20]
														SCREAMING_SNAKE_CASE_:			Any  		=	0.0_5
														SCREAMING_SNAKE_CASE_:			Tuple  		=	2.0

							if mobilevit_name.startswith("deeplabv3_"   ):
														SCREAMING_SNAKE_CASE_:			List[str]  		=	5_12
														SCREAMING_SNAKE_CASE_:			Tuple  		=	16
														SCREAMING_SNAKE_CASE_:			int  		=	21
														SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	"pascal-voc-id2label.json"
							else:
														SCREAMING_SNAKE_CASE_:			int  		=	10_00
														SCREAMING_SNAKE_CASE_:			int  		=	"imagenet-1k-id2label.json"

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

							return config







def       A_				(   _UpperCAmelCase    ,     _UpperCAmelCase=False   ):
							for i in range(1    ,     6   ):
														if f"layer_{i}." in name:
																					SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	name.replace(f"layer_{i}."    ,     f"encoder.layer.{i - 1}."   )

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

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

							for i in range(2    ,     5   ):
														if f".global_rep.{i}.weight" in name:
																					SCREAMING_SNAKE_CASE_:			Any  		=	name.replace(f".global_rep.{i}.weight"    ,     ".layernorm.weight"   )
														if f".global_rep.{i}.bias" in name:
																					SCREAMING_SNAKE_CASE_:			str  		=	name.replace(f".global_rep.{i}.bias"    ,     ".layernorm.bias"   )

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

							if ".aspp_layer." in name:
														SCREAMING_SNAKE_CASE_:			int  		=	name.replace(".aspp_layer."    ,     "."   )
							if ".aspp_pool." in name:
														SCREAMING_SNAKE_CASE_:			Tuple  		=	name.replace(".aspp_pool."    ,     "."   )
							if "seg_head." in name:
														SCREAMING_SNAKE_CASE_:			str  		=	name.replace("seg_head."    ,     "segmentation_head."   )
							if "segmentation_head.classifier.classifier." in name:
														SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	name.replace("segmentation_head.classifier.classifier."    ,     "segmentation_head.classifier."   )

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

							return name







def       A_				(   _UpperCAmelCase    ,     _UpperCAmelCase    ,     _UpperCAmelCase=False   ):
							if base_model:
														SCREAMING_SNAKE_CASE_:			Tuple  		=	""
							else:
														SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	"mobilevit."

							for key in orig_state_dict.copy().keys():
														SCREAMING_SNAKE_CASE_:			int  		=	orig_state_dict.pop(_UpperCAmelCase   )

														if key[:8] == "encoder.":
																					SCREAMING_SNAKE_CASE_:			Tuple  		=	key[8:]

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

							return orig_state_dict







def       A_				(   ):
							SCREAMING_SNAKE_CASE_:			Tuple  		=	"http://images.cocodataset.org/val2017/000000039769.jpg"
							SCREAMING_SNAKE_CASE_:			Dict  		=	Image.open(requests.get(_UpperCAmelCase    ,     stream=_UpperCAmelCase   ).raw   )
							return im







@torch.no_grad()
def       A_				(   _UpperCAmelCase    ,     _UpperCAmelCase    ,     _UpperCAmelCase    ,     _UpperCAmelCase=False   ):
							SCREAMING_SNAKE_CASE_:			Tuple  		=	get_mobilevit_config(_UpperCAmelCase   )

							# load original state_dict
							SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	torch.load(_UpperCAmelCase    ,     map_location="cpu"   )

							# load 🤗 model
							if mobilevit_name.startswith("deeplabv3_"   ):
														SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	MobileViTForSemanticSegmentation(_UpperCAmelCase   ).eval()
							else:
														SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	MobileViTForImageClassification(_UpperCAmelCase   ).eval()

							SCREAMING_SNAKE_CASE_:			str  		=	convert_state_dict(_UpperCAmelCase    ,     _UpperCAmelCase   )
							model.load_state_dict(_UpperCAmelCase   )

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

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

														if mobilevit_name == "deeplabv3_mobilevit_s":
																					SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	torch.tensor(
																					    [
																					        [[6.2_0_6_5, 6.1_2_9_2, 6.2_0_7_0], [6.1_0_7_9, 6.1_2_5_4, 6.1_7_4_7], [6.0_0_4_2, 6.1_0_7_1, 6.1_0_3_4]],
																					        [[-6.9_2_5_3, -6.8_6_5_3, -7.0_3_9_8], [-7.3_2_1_8, -7.3_9_8_3, -7.3_6_7_0], [-7.1_9_6_1, -7.2_4_8_2, -7.1_5_6_9]],
																					        [[-4.4_7_2_3, -4.4_3_4_8, -4.3_7_6_9], [-5.3_6_2_9, -5.4_6_3_2, -5.4_5_9_8], [-5.1_5_8_7, -5.3_4_0_2, -5.5_0_5_9]],
																					    ]   )
														elif mobilevit_name == "deeplabv3_mobilevit_xs":
																					SCREAMING_SNAKE_CASE_:			str  		=	torch.tensor(
																					    [
																					        [[5.4_4_4_9, 5.5_7_3_3, 5.6_3_1_4], [5.1_8_1_5, 5.3_9_3_0, 5.5_9_6_3], [5.1_6_5_6, 5.4_3_3_3, 5.4_8_5_3]],
																					        [[-9.4_4_2_3, -9.7_7_6_6, -9.6_7_1_4], [-9.1_5_8_1, -9.5_7_2_0, -9.5_5_1_9], [-9.1_0_0_6, -9.6_4_5_8, -9.5_7_0_3]],
																					        [[-7.7_7_2_1, -7.3_7_1_6, -7.1_5_8_3], [-8.4_5_9_9, -8.0_6_2_4, -7.7_9_4_4], [-8.4_1_7_2, -7.8_3_6_6, -7.5_0_2_5]],
																					    ]   )
														elif mobilevit_name == "deeplabv3_mobilevit_xxs":
																					SCREAMING_SNAKE_CASE_:			Optional[int]  		=	torch.tensor(
																					    [
																					        [[6.9_8_1_1, 6.9_7_4_3, 7.3_1_2_3], [7.1_7_7_7, 7.1_9_3_1, 7.3_9_3_8], [7.5_6_3_3, 7.8_0_5_0, 7.8_9_0_1]],
																					        [[-10.55_36, -10.23_32, -10.29_24], [-10.23_36, -9.8_6_2_4, -9.5_9_6_4], [-10.88_40, -10.81_58, -10.66_59]],
																					        [[-3.4_9_3_8, -3.0_6_3_1, -2.8_6_2_0], [-3.4_2_0_5, -2.8_1_3_5, -2.6_8_7_5], [-3.4_1_7_9, -2.7_9_4_5, -2.8_7_5_0]],
																					    ]   )
														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, 10_00)

														if mobilevit_name == "mobilevit_s":
																					SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	torch.tensor([-0.9_8_6_6, 0.2_3_9_2, -1.1_2_4_1]   )
														elif mobilevit_name == "mobilevit_xs":
																					SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	torch.tensor([-2.4_7_6_1, -0.9_3_9_9, -1.9_5_8_7]   )
														elif mobilevit_name == "mobilevit_xxs":
																					SCREAMING_SNAKE_CASE_:			str  		=	torch.tensor([-1.9_3_6_4, -1.2_3_2_7, -0.4_6_5_3]   )
														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:
														SCREAMING_SNAKE_CASE_:			List[str]  		=	{
														    "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..."   )
														SCREAMING_SNAKE_CASE_:			Optional[int]  		=	model_mapping[mobilevit_name]
														image_processor.push_to_hub(_UpperCAmelCase    ,     organization="apple"   )
														model.push_to_hub(_UpperCAmelCase    ,     organization="apple"   )


if __name__ == "__main__":
						lowerCAmelCase							:  Dict            =						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."""
						)

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









import torch

from diffusers import StableDiffusionPipeline


lowerCAmelCase							:  Any            =						"""path-to-your-trained-model"""
lowerCAmelCase							:  int            =						StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.floataa).to("""cuda""")

lowerCAmelCase							:  Union[str, Any]            =						"""A photo of sks dog in a bucket"""
lowerCAmelCase							:  Any            =						pipe(prompt, num_inference_steps=50, guidance_scale=7.5).images[0]

image.save("""dog-bucket.png""")






