

import argparse
import collections
import json
from pathlib import Path

import requests
import torch
import yaml
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    MobileViTImageProcessor,
    MobileViTVaConfig,
    MobileViTVaForImageClassification,
    MobileViTVaForSemanticSegmentation,
)
from transformers.utils import logging


logging.set_verbosity_info()
lowerCAmelCase__	   = logging.get_logger(__name__)



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




				'''simple docstring'''





				print("Loading config file..."      )

				def flatten_yaml_as_dict(SCREAMING_SNAKE_CASE_:							Union[str, Any]	,       SCREAMING_SNAKE_CASE_:							Any=""	,       SCREAMING_SNAKE_CASE_:							List[Any]="."      ):
								A__        =			[]
								for k, v in d.items():
												A__        =			parent_key + sep + k if parent_key else k
												if isinstance(SCREAMING_SNAKE_CASE_	,       collections.abc.MutableMapping      ):
																items.extend(flatten_yaml_as_dict(SCREAMING_SNAKE_CASE_	,       SCREAMING_SNAKE_CASE_	,       sep=SCREAMING_SNAKE_CASE_      ).items()      )
												else:
																items.append((new_key, v)      )
								return dict(SCREAMING_SNAKE_CASE_      )

				A__        =			argparse.Namespace()
				with open(SCREAMING_SNAKE_CASE_	,       "r"      ) as yaml_file:
								try:
												A__        =			yaml.load(SCREAMING_SNAKE_CASE_	,       Loader=yaml.FullLoader      )

												A__        =			flatten_yaml_as_dict(SCREAMING_SNAKE_CASE_      )
												for k, v in flat_cfg.items():
																setattr(SCREAMING_SNAKE_CASE_	,       SCREAMING_SNAKE_CASE_	,       SCREAMING_SNAKE_CASE_      )
								except yaml.YAMLError as exc:
												logger.error("Error while loading config file: {}. Error message: {}".format(SCREAMING_SNAKE_CASE_	,       str(SCREAMING_SNAKE_CASE_      )      )      )
				return config



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




				'''simple docstring'''





				A__        =			MobileViTVaConfig()

				A__        =			False

				# dataset
				if task_name.startswith("imagenet1k_"      ):
								A__        =			1_0_0_0
								if int(task_name.strip().split("_"      )[-1]      ) == 3_8_4:
												A__        =			3_8_4
								else:
												A__        =			2_5_6
								A__        =			"imagenet-1k-id2label.json"
				elif task_name.startswith("imagenet21k_to_1k_"      ):
								A__        =			2_1_0_0_0
								if int(task_name.strip().split("_"      )[-1]      ) == 3_8_4:
												A__        =			3_8_4
								else:
												A__        =			2_5_6
								A__        =			"imagenet-22k-id2label.json"
				elif task_name.startswith("ade20k_"      ):
								A__        =			1_5_1
								A__        =			5_1_2
								A__        =			"ade20k-id2label.json"
								A__        =			True
				elif task_name.startswith("voc_"      ):
								A__        =			2_1
								A__        =			5_1_2
								A__        =			"pascal-voc-id2label.json"
								A__        =			True

				# orig_config
				A__        =			load_orig_config_file(SCREAMING_SNAKE_CASE_      )
				assert getattr(SCREAMING_SNAKE_CASE_	,       "model.classification.name"	,       -1      ) == "mobilevit_v2", "Invalid model"
				A__        =			getattr(SCREAMING_SNAKE_CASE_	,       "model.classification.mitv2.width_multiplier"	,       1.0      )
				assert (
				    getattr(SCREAMING_SNAKE_CASE_	,       "model.classification.mitv2.attn_norm_layer"	,       -1      ) == "layer_norm_2d"
				), "Norm layers other than layer_norm_2d is not supported"
				A__        =			getattr(SCREAMING_SNAKE_CASE_	,       "model.classification.activation.name"	,       "swish"      )
				# config.image_size == getattr(orig_config,  'sampler.bs.crop_size_width', 256)

				if is_segmentation_model:
								A__        =			getattr(SCREAMING_SNAKE_CASE_	,       "model.segmentation.output_stride"	,       1_6      )
								if "_deeplabv3" in task_name:
												A__        =			getattr(SCREAMING_SNAKE_CASE_	,       "model.segmentation.deeplabv3.aspp_rates"	,       [1_2, 2_4, 3_6]      )
												A__        =			getattr(SCREAMING_SNAKE_CASE_	,       "model.segmentation.deeplabv3.aspp_out_channels"	,       5_1_2      )
												A__        =			getattr(SCREAMING_SNAKE_CASE_	,       "model.segmentation.deeplabv3.aspp_dropout"	,       0.1      )

    # id2label
				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_:							Tuple	,       SCREAMING_SNAKE_CASE_:							int	,       SCREAMING_SNAKE_CASE_:							List[str]      )			->      Optional[Any]:




				'''simple docstring'''





				A__        =			dct.pop(SCREAMING_SNAKE_CASE_      )
				A__        =			val



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




				'''simple docstring'''





				if base_model:
								A__        =			""
				else:
								A__        =			"mobilevitv2."

				A__        =			[]
				for k in state_dict.keys():
								if k[:8] == "encoder.":
												A__        =			k[8:]
								else:
												A__        =			k

								if ".block." in k:
												A__        =			k_new.replace(".block."	,       "."      )
								if ".conv." in k:
												A__        =			k_new.replace(".conv."	,       ".convolution."      )
								if ".norm." in k:
												A__        =			k_new.replace(".norm."	,       ".normalization."      )

								if "conv_1." in k:
												A__        =			k_new.replace("conv_1."	,       F'{model_prefix}conv_stem.'      )
								for i in [1, 2]:
												if F'layer_{i}.' in k:
																A__        =			k_new.replace(F'layer_{i}.'	,       F'{model_prefix}encoder.layer.{i-1}.layer.'      )
								if ".exp_1x1." in k:
												A__        =			k_new.replace(".exp_1x1."	,       ".expand_1x1."      )
								if ".red_1x1." in k:
												A__        =			k_new.replace(".red_1x1."	,       ".reduce_1x1."      )

								for i in [3, 4, 5]:
												if F'layer_{i}.0.' in k:
																A__        =			k_new.replace(F'layer_{i}.0.'	,       F'{model_prefix}encoder.layer.{i-1}.downsampling_layer.'      )
												if F'layer_{i}.1.local_rep.0.' in k:
																A__        =			k_new.replace(F'layer_{i}.1.local_rep.0.'	,       F'{model_prefix}encoder.layer.{i-1}.conv_kxk.'      )
												if F'layer_{i}.1.local_rep.1.' in k:
																A__        =			k_new.replace(F'layer_{i}.1.local_rep.1.'	,       F'{model_prefix}encoder.layer.{i-1}.conv_1x1.'      )

								for i in [3, 4, 5]:
												if i == 3:
																A__        =			[0, 1]
												elif i == 4:
																A__        =			[0, 1, 2, 3]
												elif i == 5:
																A__        =			[0, 1, 2]

												for j in j_in:
																if F'layer_{i}.1.global_rep.{j}.' in k:
																				A__        =			k_new.replace(
																				    F'layer_{i}.1.global_rep.{j}.'	,       F'{model_prefix}encoder.layer.{i-1}.transformer.layer.{j}.'      )
												if F'layer_{i}.1.global_rep.{j+1}.' in k:
																A__        =			k_new.replace(
																    F'layer_{i}.1.global_rep.{j+1}.'	,       F'{model_prefix}encoder.layer.{i-1}.layernorm.'      )

												if F'layer_{i}.1.conv_proj.' in k:
																A__        =			k_new.replace(F'layer_{i}.1.conv_proj.'	,       F'{model_prefix}encoder.layer.{i-1}.conv_projection.'      )

								if "pre_norm_attn.0." in k:
												A__        =			k_new.replace("pre_norm_attn.0."	,       "layernorm_before."      )
								if "pre_norm_attn.1." in k:
												A__        =			k_new.replace("pre_norm_attn.1."	,       "attention."      )
								if "pre_norm_ffn.0." in k:
												A__        =			k_new.replace("pre_norm_ffn.0."	,       "layernorm_after."      )
								if "pre_norm_ffn.1." in k:
												A__        =			k_new.replace("pre_norm_ffn.1."	,       "ffn.conv1."      )
								if "pre_norm_ffn.3." in k:
												A__        =			k_new.replace("pre_norm_ffn.3."	,       "ffn.conv2."      )

								if "classifier.1." in k:
												A__        =			k_new.replace("classifier.1."	,       "classifier."      )

								if "seg_head." in k:
												A__        =			k_new.replace("seg_head."	,       "segmentation_head."      )
								if ".aspp_layer." in k:
												A__        =			k_new.replace(".aspp_layer."	,       "."      )
								if ".aspp_pool." in k:
												A__        =			k_new.replace(".aspp_pool."	,       "."      )

								rename_keys.append((k, k_new)      )
				return rename_keys



def 					lowerCAmelCase__							(	SCREAMING_SNAKE_CASE_:							Dict      )			->      str:




				'''simple docstring'''





				A__        =			[]
				for k in state_dict.keys():
								if k.startswith("seg_head.aux_head."      ):
												keys_to_ignore.append(SCREAMING_SNAKE_CASE_      )
				for k in keys_to_ignore:
								state_dict.pop(SCREAMING_SNAKE_CASE_	,       SCREAMING_SNAKE_CASE_      )



def 					lowerCAmelCase__							(	)			->      Optional[Any]:




				'''simple docstring'''





				A__        =			"http://images.cocodataset.org/val2017/000000039769.jpg"
				# url = "https://cdn.britannica.com/86/141086-050-9D7C75EE/Gulfstream-G450-business-jet-passengers.jpg"
				A__        =			Image.open(requests.get(SCREAMING_SNAKE_CASE_	,       stream=SCREAMING_SNAKE_CASE_      ).raw      )
				return im



@torch.no_grad()
def 					lowerCAmelCase__							(	SCREAMING_SNAKE_CASE_:							int	,       SCREAMING_SNAKE_CASE_:							List[Any]	,       SCREAMING_SNAKE_CASE_:							Any	,       SCREAMING_SNAKE_CASE_:							List[Any]      )			->      Any:




				'''simple docstring'''





				A__        =			get_mobilevitva_config(SCREAMING_SNAKE_CASE_	,       SCREAMING_SNAKE_CASE_      )

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

				# load huggingface model
				if task_name.startswith("ade20k_"      ) or task_name.startswith("voc_"      ):
								A__        =			MobileViTVaForSemanticSegmentation(SCREAMING_SNAKE_CASE_      ).eval()
								A__        =			False
				else:
								A__        =			MobileViTVaForImageClassification(SCREAMING_SNAKE_CASE_      ).eval()
								A__        =			False

				# remove and rename some keys of load the original model
				A__        =			checkpoint
				remove_unused_keys(SCREAMING_SNAKE_CASE_      )
				A__        =			create_rename_keys(SCREAMING_SNAKE_CASE_	,       base_model=SCREAMING_SNAKE_CASE_      )
				for rename_key_src, rename_key_dest in rename_keys:
								rename_key(SCREAMING_SNAKE_CASE_	,       SCREAMING_SNAKE_CASE_	,       SCREAMING_SNAKE_CASE_      )

				# load modified state_dict
				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_      )

				# verify classification model
				if task_name.startswith("imagenet"      ):
								A__        =			outputs.logits
								A__        =			logits.argmax(-1      ).item()
								print("Predicted class:"	,       model.config.idalabel[predicted_class_idx]      )
								if task_name.startswith("imagenet1k_256"      ) and config.width_multiplier == 1.0:
												# expected_logits for base variant
												A__        =			torch.tensor([-1.6_336e00, -7.3_204e-02, -5.1_883e-01]      )
												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 {task_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 __name__ == "__main__":
	lowerCAmelCase__	   = argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    """--task""",
	    default="""imagenet1k_256""",
	    type=str,
	    help=(
	        """Name of the task for which the MobileViTV2 model you'd like to convert is trained on . """
	        """
                Classification (ImageNet-1k)
                    - MobileViTV2 (256x256) : imagenet1k_256
                    - MobileViTV2 (Trained on 256x256 and Finetuned on 384x384) : imagenet1k_384
                    - MobileViTV2 (Trained on ImageNet-21k and Finetuned on ImageNet-1k 256x256) :
                      imagenet21k_to_1k_256
                    - MobileViTV2 (Trained on ImageNet-21k, Finetuned on ImageNet-1k 256x256, and Finetuned on
                      ImageNet-1k 384x384) : imagenet21k_to_1k_384
                Segmentation
                    - ADE20K Dataset : ade20k_deeplabv3
                    - Pascal VOC 2012 Dataset: voc_deeplabv3
            """
	    ),
	    choices=[
	        """imagenet1k_256""",
	        """imagenet1k_384""",
	        """imagenet21k_to_1k_256""",
	        """imagenet21k_to_1k_384""",
	        """ade20k_deeplabv3""",
	        """voc_deeplabv3""",
	    ],
	)

	parser.add_argument(
	    """--orig_checkpoint_path""", required=True, type=str, help="""Path to the original state dict (.pt file)."""
	)
	parser.add_argument("""--orig_config_path""", required=True, type=str, help="""Path to the original config file.""")
	parser.add_argument(
	    """--pytorch_dump_folder_path""", required=True, type=str, help="""Path to the output PyTorch model directory."""
	)

	lowerCAmelCase__	   = parser.parse_args()
	convert_mobilevitva_checkpoint(
	    args.task, args.orig_checkpoint_path, args.orig_config_path, args.pytorch_dump_folder_path
	)




from typing import Union

import fire
import torch
from tqdm import tqdm



def 					lowerCAmelCase__							(	SCREAMING_SNAKE_CASE_:							str	,       SCREAMING_SNAKE_CASE_:							str = "cpu"	,       SCREAMING_SNAKE_CASE_:							Union[str, None] = None      )			->      None:




				'''simple docstring'''





				A__        =			torch.load(SCREAMING_SNAKE_CASE_	,       map_location=SCREAMING_SNAKE_CASE_      )
				for k, v in tqdm(state_dict.items()      ):
								if not isinstance(SCREAMING_SNAKE_CASE_	,       torch.Tensor      ):
												raise TypeError("FP16 conversion only works on paths that are saved state dicts, like pytorch_model.bin"      )
								A__        =			v.half()
				if save_path is None:  # overwrite src_path
								A__        =			src_path
				torch.save(SCREAMING_SNAKE_CASE_	,       SCREAMING_SNAKE_CASE_      )


if __name__ == "__main__":
	fire.Fire(convert)

