

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__	   = logging.get_logger(__name__)



def 					lowerCAmelCase__							(	SCREAMING_SNAKE_CASE_:							Optional[int]      )			->      Dict:




				'''simple docstring'''





				A__        =			MobileViTConfig()

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

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

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

				return config



def 					lowerCAmelCase__							(	SCREAMING_SNAKE_CASE_:							str	,       SCREAMING_SNAKE_CASE_:							List[Any]=False      )			->      str:




				'''simple docstring'''





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

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

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

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

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

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

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

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

				return name



def 					lowerCAmelCase__							(	SCREAMING_SNAKE_CASE_:							Any	,       SCREAMING_SNAKE_CASE_:							List[str]	,       SCREAMING_SNAKE_CASE_:							List[str]=False      )			->      Union[str, Any]:




				'''simple docstring'''





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

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

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

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

				return orig_state_dict



def 					lowerCAmelCase__							(	)			->      Any:




				'''simple docstring'''





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



@torch.no_grad()
def 					lowerCAmelCase__							(	SCREAMING_SNAKE_CASE_:							Union[str, Any]	,       SCREAMING_SNAKE_CASE_:							str	,       SCREAMING_SNAKE_CASE_:							Optional[int]	,       SCREAMING_SNAKE_CASE_:							List[Any]=False      )			->      int:




				'''simple docstring'''





				A__        =			get_mobilevit_config(SCREAMING_SNAKE_CASE_      )

				# load original state_dict
				A__        =			torch.load(SCREAMING_SNAKE_CASE_	,       map_location="cpu"      )

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

				A__        =			convert_state_dict(SCREAMING_SNAKE_CASE_	,       SCREAMING_SNAKE_CASE_      )
				model.load_state_dict(SCREAMING_SNAKE_CASE_      )

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

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

								if mobilevit_name == "deeplabv3_mobilevit_s":
												A__        =			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":
												A__        =			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":
												A__        =			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]	,       SCREAMING_SNAKE_CASE_	,       atol=1e-4      )
				else:
								assert logits.shape == (1, 1_0_0_0)

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

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

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

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


if __name__ == "__main__":
	lowerCAmelCase__	   = 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__	   = parser.parse_args()
	convert_movilevit_checkpoint(
	    args.mobilevit_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
	)




import warnings

from ...utils import logging
from .image_processing_clip import CLIPImageProcessor


lowerCAmelCase__	   = logging.get_logger(__name__)

class 					a__						(					snake_case			):
			"""simple docstring"""







			def __init__(    self      ,      *lowercase      ,      **lowercase       )  ->						None:
							'''simple docstring'''






							warnings.warn(
							    "The class CLIPFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"
							    " use CLIPImageProcessor instead."      ,      lowercase      ,      )
							super().__init__(*lowercase      ,      **lowercase       )

