

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				:    Optional[int]        =		logging.get_logger(__name__)







def      A_ (       _UpperCAmelCase       ):
						SCREAMING_SNAKE_CASE_:					str    						=			MobileViTConfig()

						# size of the architecture
						if "mobilevit_s" in mobilevit_name:
												SCREAMING_SNAKE_CASE_:					Dict    						=			[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_:					str    						=			[96, 1_20, 1_44]
												SCREAMING_SNAKE_CASE_:					Tuple    						=			[16, 32, 48, 64, 80, 96, 3_84]
						elif "mobilevit_xxs" in mobilevit_name:
												SCREAMING_SNAKE_CASE_:					List[str]    						=			[64, 80, 96]
												SCREAMING_SNAKE_CASE_:					Union[str, Any]    						=			[16, 16, 24, 48, 64, 80, 3_20]
												SCREAMING_SNAKE_CASE_:					str    						=			0.0_5
												SCREAMING_SNAKE_CASE_:					Any    						=			2.0

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

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

						if "exp_1x1" in name:
												SCREAMING_SNAKE_CASE_:					int    						=			name.replace("exp_1x1"							,      "expand_1x1"       )
						if "red_1x1" in name:
												SCREAMING_SNAKE_CASE_:					Union[str, Any]    						=			name.replace("red_1x1"							,      "reduce_1x1"       )
						if ".local_rep.conv_3x3." in name:
												SCREAMING_SNAKE_CASE_:					Optional[Any]    						=			name.replace(".local_rep.conv_3x3."							,      ".conv_kxk."       )
						if ".local_rep.conv_1x1." in name:
												SCREAMING_SNAKE_CASE_:					Optional[Any]    						=			name.replace(".local_rep.conv_1x1."							,      ".conv_1x1."       )
						if ".norm." in name:
												SCREAMING_SNAKE_CASE_:					Union[str, Any]    						=			name.replace(".norm."							,      ".normalization."       )
						if ".conv." in name:
												SCREAMING_SNAKE_CASE_:					Optional[int]    						=			name.replace(".conv."							,      ".convolution."       )
						if ".conv_proj." in name:
												SCREAMING_SNAKE_CASE_:					List[str]    						=			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_:					Optional[int]    						=			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_:					Tuple    						=			name.replace(f".{i}.{j}."							,      f".{i}."       )
																								if "expand_1x1" in name:
																														SCREAMING_SNAKE_CASE_:					int    						=			name.replace("expand_1x1"							,      "downsampling_layer.expand_1x1"       )
																								if "conv_3x3" in name:
																														SCREAMING_SNAKE_CASE_:					List[str]    						=			name.replace("conv_3x3"							,      "downsampling_layer.conv_3x3"       )
																								if "reduce_1x1" in name:
																														SCREAMING_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:
																		SCREAMING_SNAKE_CASE_:					Optional[Any]    						=			name.replace(f".global_rep.{i}.weight"							,      ".layernorm.weight"       )
												if f".global_rep.{i}.bias" in name:
																		SCREAMING_SNAKE_CASE_:					List[str]    						=			name.replace(f".global_rep.{i}.bias"							,      ".layernorm.bias"       )

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

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

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

						return name







def      A_ (       _UpperCAmelCase							,      _UpperCAmelCase							,      _UpperCAmelCase=False       ):





						if base_model:
												SCREAMING_SNAKE_CASE_:					Optional[Any]    						=			""
						else:
												SCREAMING_SNAKE_CASE_:					Any    						=			"mobilevit."

						for key in orig_state_dict.copy().keys():
												SCREAMING_SNAKE_CASE_:					Optional[int]    						=			orig_state_dict.pop(_A       )

												if key[:8] == "encoder.":
																		SCREAMING_SNAKE_CASE_:					Optional[int]    						=			key[8:]

												if "qkv" in key:
																		SCREAMING_SNAKE_CASE_:					Tuple    						=			key.split("."       )
																		SCREAMING_SNAKE_CASE_:					Optional[Any]    						=			int(key_split[0][6:]       ) - 1
																		SCREAMING_SNAKE_CASE_:					int    						=			int(key_split[3]       )
																		SCREAMING_SNAKE_CASE_:					Optional[Any]    						=			model.get_submodule(f"{model_prefix}encoder.layer.{layer_num}"       )
																		SCREAMING_SNAKE_CASE_:					Optional[Any]    						=			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_:					Optional[Any]    						=			val[:dim, :]
																								SCREAMING_SNAKE_CASE_:					str    						=			val[dim : dim * 2, :]
																								SCREAMING_SNAKE_CASE_:					Any    						=			val[-dim:, :]
																		else:
																								SCREAMING_SNAKE_CASE_:					Any    						=			val[:dim]
																								SCREAMING_SNAKE_CASE_:					Union[str, Any]    						=			val[dim : dim * 2]
																								SCREAMING_SNAKE_CASE_:					int    						=			val[-dim:]
												else:
																		SCREAMING_SNAKE_CASE_:					Tuple    						=			val

						return orig_state_dict







def      A_ (       ):
						SCREAMING_SNAKE_CASE_:					List[Any]    						=			"http://images.cocodataset.org/val2017/000000039769.jpg"
						SCREAMING_SNAKE_CASE_:					str    						=			Image.open(requests.get(_A							,      stream=_A       ).raw       )
						return im







@torch.no_grad()
def      A_ (       _UpperCAmelCase							,      _UpperCAmelCase							,      _UpperCAmelCase							,      _UpperCAmelCase=False       ):
						SCREAMING_SNAKE_CASE_:					List[Any]    						=			get_mobilevit_config(_A       )

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

						# load 🤗 model
						if mobilevit_name.startswith("deeplabv3_"       ):
												SCREAMING_SNAKE_CASE_:					Dict    						=			MobileViTForSemanticSegmentation(_A       ).eval()
						else:
												SCREAMING_SNAKE_CASE_:					Any    						=			MobileViTForImageClassification(_A       ).eval()

						SCREAMING_SNAKE_CASE_:					Union[str, Any]    						=			convert_state_dict(_A							,      _A       )
						model.load_state_dict(_A       )

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

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

												if mobilevit_name == "deeplabv3_mobilevit_s":
																		SCREAMING_SNAKE_CASE_:					str    						=			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_:					Optional[Any]    						=			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_:					Tuple    						=			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]],
																		        [[-1_0.5_5_3_6, -1_0.2_3_3_2, -1_0.2_9_2_4], [-1_0.2_3_3_6, -9.8_6_2_4, -9.5_9_6_4], [-1_0.8_8_4_0, -1_0.8_1_5_8, -1_0.6_6_5_9]],
																		        [[-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]							,      _A							,      atol=1e-4       )
						else:
												assert logits.shape == (1, 10_00)

												if mobilevit_name == "mobilevit_s":
																		SCREAMING_SNAKE_CASE_:					Tuple    						=			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_:					List[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_:					Union[str, Any]    						=			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]							,      _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:
												SCREAMING_SNAKE_CASE_:					Dict    						=			{
												    "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_:					Any    						=			model_mapping[mobilevit_name]
												image_processor.push_to_hub(_A							,      organization="apple"       )
												model.push_to_hub(_A							,      organization="apple"       )


if __name__ == "__main__":
						lowerCAmelCase				:    Any        =		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				:    List[str]        =		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 ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available, is_vision_available
from ...utils import OptionalDependencyNotAvailable


lowercase__							:	Union[str, Any]											=		{"configuration_dpt": ["DPT_PRETRAINED_CONFIG_ARCHIVE_MAP", "DPTConfig"]}

try:
		if not is_vision_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase__							:	Tuple											=		["DPTFeatureExtractor"]
		lowercase__							:	Optional[Any]											=		["DPTImageProcessor"]

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase__							:	Dict											=		[
		    "DPT_PRETRAINED_MODEL_ARCHIVE_LIST",
		    "DPTForDepthEstimation",
		    "DPTForSemanticSegmentation",
		    "DPTModel",
		    "DPTPreTrainedModel",
		]


if TYPE_CHECKING:
		from .configuration_dpt import DPT_PRETRAINED_CONFIG_ARCHIVE_MAP, DPTConfig

		try:
				if not is_vision_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .feature_extraction_dpt import DPTFeatureExtractor
				from .image_processing_dpt import DPTImageProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_dpt import (
				    DPT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    DPTForDepthEstimation,
				    DPTForSemanticSegmentation,
				    DPTModel,
				    DPTPreTrainedModel,
				)


else:
		import sys

		lowercase__							:	List[Any]											=		_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
