






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      :    int   		=   logging.get_logger(__name__)







def   SCREAMING_SNAKE_CASE       (				snake_case_      :							Tuple     ):
	print("Loading config file..."     )

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

	snake_case__				:   int     =						argparse.Namespace()
	with open(snake_case_  ,						"r"     ) as yaml_file:
		try:
			snake_case__				:   Dict     =						yaml.load(snake_case_  ,						Loader=yaml.FullLoader     )

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







def   SCREAMING_SNAKE_CASE       (				snake_case_      :							Any  ,						snake_case_      :							str     ):
	snake_case__				:   List[Any]     =						MobileViTVaConfig()

	snake_case__				:   Optional[int]     =						False

	# dataset
	if task_name.startswith("imagenet1k_"     ):
		snake_case__				:   List[Any]     =						1000
		if int(task_name.strip().split("_"     )[-1]     ) == 384:
			snake_case__				:   Any     =						384
		else:
			snake_case__				:   int     =						256
		snake_case__				:   Union[str, Any]     =						"imagenet-1k-id2label.json"
	elif task_name.startswith("imagenet21k_to_1k_"     ):
		snake_case__				:   str     =						21000
		if int(task_name.strip().split("_"     )[-1]     ) == 384:
			snake_case__				:   Union[str, Any]     =						384
		else:
			snake_case__				:   str     =						256
		snake_case__				:   List[Any]     =						"imagenet-22k-id2label.json"
	elif task_name.startswith("ade20k_"     ):
		snake_case__				:   Tuple     =						151
		snake_case__				:   Union[str, Any]     =						512
		snake_case__				:   Optional[int]     =						"ade20k-id2label.json"
		snake_case__				:   int     =						True
	elif task_name.startswith("voc_"     ):
		snake_case__				:   List[Any]     =						21
		snake_case__				:   Union[str, Any]     =						512
		snake_case__				:   int     =						"pascal-voc-id2label.json"
		snake_case__				:   Dict     =						True

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

	if is_segmentation_model:
		snake_case__				:   Any     =						getattr(snake_case_  ,						"model.segmentation.output_stride"  ,						16     )
		if "_deeplabv3" in task_name:
			snake_case__				:   List[str]     =						getattr(snake_case_  ,						"model.segmentation.deeplabv3.aspp_rates"  ,						[12, 24, 36]     )
			snake_case__				:   List[Any]     =						getattr(snake_case_  ,						"model.segmentation.deeplabv3.aspp_out_channels"  ,						512     )
			snake_case__				:   Union[str, Any]     =						getattr(snake_case_  ,						"model.segmentation.deeplabv3.aspp_dropout"  ,						0.1     )

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

	return config







def   SCREAMING_SNAKE_CASE       (				snake_case_      :							Union[str, Any]  ,						snake_case_      :							Optional[Any]  ,						snake_case_      :							List[Any]     ):
	snake_case__				:   int     =						dct.pop(snake_case_     )
	snake_case__				:   List[str]     =						val







def   SCREAMING_SNAKE_CASE       (				snake_case_      :							Optional[int]  ,						snake_case_      :							str=False     ):
	if base_model:
		snake_case__				:   Optional[int]     =						""
	else:
		snake_case__				:   Optional[int]     =						"mobilevitv2."

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

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

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

		for i in [3, 4, 5]:
			if F'''layer_{i}.0.''' in k:
				snake_case__				:   int     =						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:
				snake_case__				:   Optional[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:
				snake_case__				:   Dict     =						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:
				snake_case__				:   int     =						[0, 1]
			elif i == 4:
				snake_case__				:   List[str]     =						[0, 1, 2, 3]
			elif i == 5:
				snake_case__				:   Optional[Any]     =						[0, 1, 2]

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

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

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

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







def   SCREAMING_SNAKE_CASE       (				snake_case_      :							Tuple     ):
	snake_case__				:   Optional[int]     =						[]
	for k in state_dict.keys():
		if k.startswith("seg_head.aux_head."     ):
			keys_to_ignore.append(snake_case_     )
	for k in keys_to_ignore:
		state_dict.pop(snake_case_  ,						snake_case_     )







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







@torch.no_grad()
def   SCREAMING_SNAKE_CASE       (				snake_case_      :							Dict  ,						snake_case_      :							int  ,						snake_case_      :							Tuple  ,						snake_case_      :							Dict     ):
	snake_case__				:   List[Any]     =						get_mobilevitva_config(snake_case_  ,						snake_case_     )

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

	# load huggingface model
	if task_name.startswith("ade20k_"     ) or task_name.startswith("voc_"     ):
		snake_case__				:   Union[str, Any]     =						MobileViTVaForSemanticSegmentation(snake_case_     ).eval()
		snake_case__				:   List[str]     =						False
	else:
		snake_case__				:   int     =						MobileViTVaForImageClassification(snake_case_     ).eval()
		snake_case__				:   Tuple     =						False

	# remove and rename some keys of load the original model
	snake_case__				:   List[str]     =						checkpoint
	remove_unused_keys(snake_case_     )
	snake_case__				:   List[str]     =						create_rename_keys(snake_case_  ,						base_model=snake_case_     )
	for rename_key_src, rename_key_dest in rename_keys:
		rename_key(snake_case_  ,						snake_case_  ,						snake_case_     )

	# load modified state_dict
	model.load_state_dict(snake_case_     )

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

	# verify classification model
	if task_name.startswith("imagenet"     ):
		snake_case__				:   List[Any]     =						outputs.logits
		snake_case__				:   int     =						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
			snake_case__				:   Union[str, Any]     =						torch.tensor([-1.6336E00, -7.3204E-02, -5.1883E-01]     )
			assert torch.allclose(logits[0, :3]  ,						snake_case_  ,						atol=1E-4     )

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


if __name__ == "__main__":
	__lowerCamelCase      :    str   		=   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      :    Optional[Any]   		=   parser.parse_args()
	convert_mobilevitva_checkpoint(
	    args.task, args.orig_checkpoint_path, args.orig_config_path, args.pytorch_dump_folder_path
	)











from __future__ import annotations







def   SCREAMING_SNAKE_CASE       (				snake_case_      :							int     ):
	snake_case__				:   str     =						[True] * limit
	snake_case__				:   str     =						False
	snake_case__				:   str     =						False
	snake_case__				:   str     =						True

	for i in range(3  ,						int(limit**0.5 + 1     )  ,						2     ):
		snake_case__				:   Optional[Any]     =						i * 2
		while index < limit:
			snake_case__				:   Union[str, Any]     =						False
			snake_case__				:   Any     =						index + i

	snake_case__				:   Optional[Any]     =						[2]

	for i in range(3  ,						snake_case_  ,						2     ):
		if is_prime[i]:
			primes.append(snake_case_     )

	return primes







def   SCREAMING_SNAKE_CASE       (				snake_case_      :							int = 1000000     ):
	snake_case__				:   Optional[int]     =						prime_sieve(snake_case_     )
	snake_case__				:   List[Any]     =						0
	snake_case__				:   List[str]     =						0

	for i in range(len(snake_case_     )     ):
		for j in range(i + length  ,						len(snake_case_     )     ):
			snake_case__				:   Dict     =						sum(primes[i:j]     )
			if sol >= ceiling:
				break

			if sol in primes:
				snake_case__				:   Tuple     =						j - i
				snake_case__				:   str     =						sol

	return largest


if __name__ == "__main__":
	print(f"{solution() = }")



