


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()
SCREAMING_SNAKE_CASE__     					=			logging.get_logger(__name__)


def     lowercase	(    a ):



		'''simple docstring'''




		print("Loading config file..." )

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

		SCREAMING_SNAKE_CASE_				:List[str]				     =						argparse.Namespace()
		with open(a     , "r" ) as yaml_file:
				try:
						SCREAMING_SNAKE_CASE_				:Optional[int]				     =						yaml.load(a     , Loader=yaml.FullLoader )

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


def     lowercase	(    a     , a ):



		'''simple docstring'''




		SCREAMING_SNAKE_CASE_				:List[str]				     =						MobileViTVaConfig()

		SCREAMING_SNAKE_CASE_				:List[Any]				     =						False

		# dataset
		if task_name.startswith("imagenet1k_" ):
				SCREAMING_SNAKE_CASE_				:List[Any]				     =						1000
				if int(task_name.strip().split("_" )[-1] ) == 384:
						SCREAMING_SNAKE_CASE_				:List[Any]				     =						384
				else:
						SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						256
				SCREAMING_SNAKE_CASE_				:Optional[int]				     =						"imagenet-1k-id2label.json"
		elif task_name.startswith("imagenet21k_to_1k_" ):
				SCREAMING_SNAKE_CASE_				:Any				     =						2_1000
				if int(task_name.strip().split("_" )[-1] ) == 384:
						SCREAMING_SNAKE_CASE_				:int				     =						384
				else:
						SCREAMING_SNAKE_CASE_				:str				     =						256
				SCREAMING_SNAKE_CASE_				:str				     =						"imagenet-22k-id2label.json"
		elif task_name.startswith("ade20k_" ):
				SCREAMING_SNAKE_CASE_				:Optional[int]				     =						151
				SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						512
				SCREAMING_SNAKE_CASE_				:List[str]				     =						"ade20k-id2label.json"
				SCREAMING_SNAKE_CASE_				:List[Any]				     =						True
		elif task_name.startswith("voc_" ):
				SCREAMING_SNAKE_CASE_				:Optional[int]				     =						21
				SCREAMING_SNAKE_CASE_				:Tuple				     =						512
				SCREAMING_SNAKE_CASE_				:List[Any]				     =						"pascal-voc-id2label.json"
				SCREAMING_SNAKE_CASE_				:Any				     =						True

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

		if is_segmentation_model:
				SCREAMING_SNAKE_CASE_				:str				     =						getattr(a     , "model.segmentation.output_stride"     , 16 )
				if "_deeplabv3" in task_name:
						SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						getattr(a     , "model.segmentation.deeplabv3.aspp_rates"     , [12, 24, 36] )
						SCREAMING_SNAKE_CASE_				:Optional[int]				     =						getattr(a     , "model.segmentation.deeplabv3.aspp_out_channels"     , 512 )
						SCREAMING_SNAKE_CASE_				:str				     =						getattr(a     , "model.segmentation.deeplabv3.aspp_dropout"     , 0.1 )

    # id2label
		SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						"huggingface/label-files"
		SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						json.load(open(hf_hub_download(a     , a     , repo_type="dataset" )     , "r" ) )
		SCREAMING_SNAKE_CASE_				:Optional[int]				     =						{int(a ): v for k, v in idalabel.items()}
		SCREAMING_SNAKE_CASE_				:Dict				     =						idalabel
		SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						{v: k for k, v in idalabel.items()}

		return config


def     lowercase	(    a     , a     , a ):



		'''simple docstring'''




		SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						dct.pop(a )
		SCREAMING_SNAKE_CASE_				:int				     =						val


def     lowercase	(    a     , a=False ):



		'''simple docstring'''




		if base_model:
				SCREAMING_SNAKE_CASE_				:str				     =						""
		else:
				SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						"mobilevitv2."

		SCREAMING_SNAKE_CASE_				:List[str]				     =						[]
		for k in state_dict.keys():
				if k[:8] == "encoder.":
						SCREAMING_SNAKE_CASE_				:Tuple				     =						k[8:]
				else:
						SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						k

				if ".block." in k:
						SCREAMING_SNAKE_CASE_				:Any				     =						k_new.replace(".block."     , "." )
				if ".conv." in k:
						SCREAMING_SNAKE_CASE_				:List[str]				     =						k_new.replace(".conv."     , ".convolution." )
				if ".norm." in k:
						SCREAMING_SNAKE_CASE_				:Tuple				     =						k_new.replace(".norm."     , ".normalization." )

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

				for i in [3, 4, 5]:
						if F"layer_{i}.0." in k:
								SCREAMING_SNAKE_CASE_				:str				     =						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:
								SCREAMING_SNAKE_CASE_				:Any				     =						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:
								SCREAMING_SNAKE_CASE_				:str				     =						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:
								SCREAMING_SNAKE_CASE_				:str				     =						[0, 1]
						elif i == 4:
								SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						[0, 1, 2, 3]
						elif i == 5:
								SCREAMING_SNAKE_CASE_				:str				     =						[0, 1, 2]

						for j in j_in:
								if F"layer_{i}.1.global_rep.{j}." in k:
										SCREAMING_SNAKE_CASE_				:Dict				     =						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:
								SCREAMING_SNAKE_CASE_				:int				     =						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:
								SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						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:
						SCREAMING_SNAKE_CASE_				:int				     =						k_new.replace("pre_norm_attn.0."     , "layernorm_before." )
				if "pre_norm_attn.1." in k:
						SCREAMING_SNAKE_CASE_				:List[Any]				     =						k_new.replace("pre_norm_attn.1."     , "attention." )
				if "pre_norm_ffn.0." in k:
						SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						k_new.replace("pre_norm_ffn.0."     , "layernorm_after." )
				if "pre_norm_ffn.1." in k:
						SCREAMING_SNAKE_CASE_				:Dict				     =						k_new.replace("pre_norm_ffn.1."     , "ffn.conv1." )
				if "pre_norm_ffn.3." in k:
						SCREAMING_SNAKE_CASE_				:Optional[int]				     =						k_new.replace("pre_norm_ffn.3."     , "ffn.conv2." )

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

				if "seg_head." in k:
						SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						k_new.replace("seg_head."     , "segmentation_head." )
				if ".aspp_layer." in k:
						SCREAMING_SNAKE_CASE_				:Tuple				     =						k_new.replace(".aspp_layer."     , "." )
				if ".aspp_pool." in k:
						SCREAMING_SNAKE_CASE_				:Dict				     =						k_new.replace(".aspp_pool."     , "." )

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


def     lowercase	(    a ):



		'''simple docstring'''




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


def     lowercase	(    ):



		'''simple docstring'''




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


@torch.no_grad()
def     lowercase	(    a     , a     , a     , a ):



		'''simple docstring'''




		SCREAMING_SNAKE_CASE_				:str				     =						get_mobilevitva_config(a     , a )

		# load original state_dict
		SCREAMING_SNAKE_CASE_				:Dict				     =						torch.load(a     , map_location="cpu" )

		# load huggingface model
		if task_name.startswith("ade20k_" ) or task_name.startswith("voc_" ):
				SCREAMING_SNAKE_CASE_				:List[Any]				     =						MobileViTVaForSemanticSegmentation(a ).eval()
				SCREAMING_SNAKE_CASE_				:List[str]				     =						False
		else:
				SCREAMING_SNAKE_CASE_				:List[str]				     =						MobileViTVaForImageClassification(a ).eval()
				SCREAMING_SNAKE_CASE_				:Optional[int]				     =						False

		# remove and rename some keys of load the original model
		SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						checkpoint
		remove_unused_keys(a )
		SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						create_rename_keys(a     , base_model=a )
		for rename_key_src, rename_key_dest in rename_keys:
				rename_key(a     , a     , a )

		# load modified state_dict
		model.load_state_dict(a )

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

		# verify classification model
		if task_name.startswith("imagenet" ):
				SCREAMING_SNAKE_CASE_				:List[Any]				     =						outputs.logits
				SCREAMING_SNAKE_CASE_				:Any				     =						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
						SCREAMING_SNAKE_CASE_				:Optional[int]				     =						torch.tensor([-1.6_336e00, -7.3_204e-02, -5.1_883e-01] )
						assert torch.allclose(logits[0, :3]     , a     , atol=1e-4 )

		Path(a ).mkdir(exist_ok=a )
		print(F"Saving model {task_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 __name__ == "__main__":
		SCREAMING_SNAKE_CASE__     					=			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 . "
		        "\n                Classification (ImageNet-1k)\n                    - MobileViTV2 (256x256) : imagenet1k_256\n                    - MobileViTV2 (Trained on 256x256 and Finetuned on 384x384) : imagenet1k_384\n                    - MobileViTV2 (Trained on ImageNet-21k and Finetuned on ImageNet-1k 256x256) :\n                      imagenet21k_to_1k_256\n                    - MobileViTV2 (Trained on ImageNet-21k, Finetuned on ImageNet-1k 256x256, and Finetuned on\n                      ImageNet-1k 384x384) : imagenet21k_to_1k_384\n                Segmentation\n                    - ADE20K Dataset : ade20k_deeplabv3\n                    - Pascal VOC 2012 Dataset: voc_deeplabv3\n            "
		    ),
		    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."
		)

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










import logging
import os
from typing import List, TextIO, Union

from conllu import parse_incr
from utils_ner import InputExample, Split, TokenClassificationTask


SCREAMING_SNAKE_CASE__     					=			logging.getLogger(__name__)



class 						_UpperCAmelCase      (     lowercase							):






		def __init__(						self				:     Optional[int]   ,  UpperCAmelCase				:     Any=-1):
				# in NER datasets, the last column is usually reserved for NER label
				SCREAMING_SNAKE_CASE_				:Tuple				     =						label_idx






		def _snake_case		(						self				:     Tuple   ,  UpperCAmelCase				:     Any   ,  UpperCAmelCase				:     Union[Split, str]):
				if isinstance(UpperCAmelCase   ,  UpperCAmelCase):
						SCREAMING_SNAKE_CASE_				:List[Any]				     =						mode.value
				SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						os.path.join(UpperCAmelCase   ,  F"{mode}.txt")
				SCREAMING_SNAKE_CASE_				:Tuple				     =						1
				SCREAMING_SNAKE_CASE_				:str				     =						[]
				with open(UpperCAmelCase   ,  encoding="utf-8") as f:
						SCREAMING_SNAKE_CASE_				:Tuple				     =						[]
						SCREAMING_SNAKE_CASE_				:int				     =						[]
						for line in f:
								if line.startswith("-DOCSTART-") or line == "" or line == "\n":
										if words:
												examples.append(InputExample(guid=F"{mode}-{guid_index}"   ,  words=UpperCAmelCase   ,  labels=UpperCAmelCase))
												guid_index += 1
												SCREAMING_SNAKE_CASE_				:Tuple				     =						[]
												SCREAMING_SNAKE_CASE_				:Any				     =						[]
								else:
										SCREAMING_SNAKE_CASE_				:int				     =						line.split(" ")
										words.append(splits[0])
										if len(UpperCAmelCase) > 1:
												labels.append(splits[self.label_idx].replace("\n"   ,  ""))
										else:
												# Examples could have no label for mode = "test"
												labels.append("O")
						if words:
								examples.append(InputExample(guid=F"{mode}-{guid_index}"   ,  words=UpperCAmelCase   ,  labels=UpperCAmelCase))
				return examples






		def _snake_case		(						self				:     List[Any]   ,  UpperCAmelCase				:     TextIO   ,  UpperCAmelCase				:     TextIO   ,  UpperCAmelCase				:     List):
				SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						0
				for line in test_input_reader:
						if line.startswith("-DOCSTART-") or line == "" or line == "\n":
								writer.write(UpperCAmelCase)
								if not preds_list[example_id]:
										example_id += 1
						elif preds_list[example_id]:
								SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						line.split()[0] + " " + preds_list[example_id].pop(0) + "\n"
								writer.write(UpperCAmelCase)
						else:
								logger.warning("Maximum sequence length exceeded: No prediction for '%s'."   ,  line.split()[0])







		def _snake_case		(						self				:     List[str]   ,  UpperCAmelCase				:     str):
				if path:
						with open(UpperCAmelCase   ,  "r") as f:
								SCREAMING_SNAKE_CASE_				:Any				     =						f.read().splitlines()
						if "O" not in labels:
								SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						["O"] + labels
						return labels
				else:
						return ["O", "B-MISC", "I-MISC", "B-PER", "I-PER", "B-ORG", "I-ORG", "B-LOC", "I-LOC"]




class 						_UpperCAmelCase      (     lowercase							):






		def __init__(						self				:     Dict):
				# in CONLL2003 dataset chunk column is second-to-last
				super().__init__(label_idx=-2)







		def _snake_case		(						self				:     Union[str, Any]   ,  UpperCAmelCase				:     str):
				if path:
						with open(UpperCAmelCase   ,  "r") as f:
								SCREAMING_SNAKE_CASE_				:Optional[int]				     =						f.read().splitlines()
						if "O" not in labels:
								SCREAMING_SNAKE_CASE_				:Dict				     =						["O"] + labels
						return labels
				else:
						return [
						    "O",
						    "B-ADVP",
						    "B-INTJ",
						    "B-LST",
						    "B-PRT",
						    "B-NP",
						    "B-SBAR",
						    "B-VP",
						    "B-ADJP",
						    "B-CONJP",
						    "B-PP",
						    "I-ADVP",
						    "I-INTJ",
						    "I-LST",
						    "I-PRT",
						    "I-NP",
						    "I-SBAR",
						    "I-VP",
						    "I-ADJP",
						    "I-CONJP",
						    "I-PP",
						]




class 						_UpperCAmelCase      (     lowercase							):






		def _snake_case		(						self				:     int   ,  UpperCAmelCase				:     List[Any]   ,  UpperCAmelCase				:     Union[Split, str]):
				if isinstance(UpperCAmelCase   ,  UpperCAmelCase):
						SCREAMING_SNAKE_CASE_				:List[str]				     =						mode.value
				SCREAMING_SNAKE_CASE_				:List[str]				     =						os.path.join(UpperCAmelCase   ,  F"{mode}.txt")
				SCREAMING_SNAKE_CASE_				:Dict				     =						1
				SCREAMING_SNAKE_CASE_				:List[str]				     =						[]

				with open(UpperCAmelCase   ,  encoding="utf-8") as f:
						for sentence in parse_incr(UpperCAmelCase):
								SCREAMING_SNAKE_CASE_				:List[str]				     =						[]
								SCREAMING_SNAKE_CASE_				:int				     =						[]
								for token in sentence:
										words.append(token["form"])
										labels.append(token["upos"])
								assert len(UpperCAmelCase) == len(UpperCAmelCase)
								if words:
										examples.append(InputExample(guid=F"{mode}-{guid_index}"   ,  words=UpperCAmelCase   ,  labels=UpperCAmelCase))
										guid_index += 1
				return examples






		def _snake_case		(						self				:     List[Any]   ,  UpperCAmelCase				:     TextIO   ,  UpperCAmelCase				:     TextIO   ,  UpperCAmelCase				:     List):
				SCREAMING_SNAKE_CASE_				:List[str]				     =						0
				for sentence in parse_incr(UpperCAmelCase):
						SCREAMING_SNAKE_CASE_				:str				     =						preds_list[example_id]
						SCREAMING_SNAKE_CASE_				:List[Any]				     =						""
						for token in sentence:
								out += F"{token['form']} ({token['upos']}|{s_p.pop(0)}) "
						out += "\n"
						writer.write(UpperCAmelCase)
						example_id += 1







		def _snake_case		(						self				:     Tuple   ,  UpperCAmelCase				:     str):
				if path:
						with open(UpperCAmelCase   ,  "r") as f:
								return f.read().splitlines()
				else:
						return [
						    "ADJ",
						    "ADP",
						    "ADV",
						    "AUX",
						    "CCONJ",
						    "DET",
						    "INTJ",
						    "NOUN",
						    "NUM",
						    "PART",
						    "PRON",
						    "PROPN",
						    "PUNCT",
						    "SCONJ",
						    "SYM",
						    "VERB",
						    "X",
						]






