


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(	lowercase__	)						->	int:

		'''simple docstring'''







		print('Loading config file...'	)

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

		__lowercase=			argparse.Namespace()
		with open(lowercase__   , 'r'	) as yaml_file:
				try:
						__lowercase=			yaml.load(lowercase__   , Loader=yaml.FullLoader	)

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







def        _lowerCamelCase(	lowercase__   , lowercase__	)						->	str:

		'''simple docstring'''







		__lowercase=			MobileViTVaConfig()

		__lowercase=			False

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

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

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

    # id2label
		__lowercase=			'huggingface/label-files'
		__lowercase=			json.load(open(hf_hub_download(lowercase__   , lowercase__   , repo_type='dataset'	)   , 'r'	)	)
		__lowercase=			{int(lowercase__	): v for k, v in idalabel.items()}
		__lowercase=			idalabel
		__lowercase=			{v: k for k, v in idalabel.items()}

		return config







def        _lowerCamelCase(	lowercase__   , lowercase__   , lowercase__	)						->	str:

		'''simple docstring'''







		__lowercase=			dct.pop(lowercase__	)
		__lowercase=			val







def        _lowerCamelCase(	lowercase__   , lowercase__=False	)						->	Optional[Any]:

		'''simple docstring'''







		if base_model:
				__lowercase=			''
		else:
				__lowercase=			'mobilevitv2.'

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

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

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

				for i in [3, 4, 5]:
						if F'layer_{i}.0.' in k:
								__lowercase=			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:
								__lowercase=			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:
								__lowercase=			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:
								__lowercase=			[0, 1]
						elif i == 4:
								__lowercase=			[0, 1, 2, 3]
						elif i == 5:
								__lowercase=			[0, 1, 2]

						for j in j_in:
								if F'layer_{i}.1.global_rep.{j}.' in k:
										__lowercase=			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:
								__lowercase=			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:
								__lowercase=			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:
						__lowercase=			k_new.replace('pre_norm_attn.0.'   , 'layernorm_before.'	)
				if "pre_norm_attn.1." in k:
						__lowercase=			k_new.replace('pre_norm_attn.1.'   , 'attention.'	)
				if "pre_norm_ffn.0." in k:
						__lowercase=			k_new.replace('pre_norm_ffn.0.'   , 'layernorm_after.'	)
				if "pre_norm_ffn.1." in k:
						__lowercase=			k_new.replace('pre_norm_ffn.1.'   , 'ffn.conv1.'	)
				if "pre_norm_ffn.3." in k:
						__lowercase=			k_new.replace('pre_norm_ffn.3.'   , 'ffn.conv2.'	)

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

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

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







def        _lowerCamelCase(	lowercase__	)						->	Tuple:

		'''simple docstring'''







		__lowercase=			[]
		for k in state_dict.keys():
				if k.startswith('seg_head.aux_head.'	):
						keys_to_ignore.append(lowercase__	)
		for k in keys_to_ignore:
				state_dict.pop(lowercase__   , lowercase__	)







def        _lowerCamelCase(	)						->	Optional[int]:

		'''simple docstring'''







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







@torch.no_grad()
def        _lowerCamelCase(	lowercase__   , lowercase__   , lowercase__   , lowercase__	)						->	Any:

		'''simple docstring'''







		__lowercase=			get_mobilevitva_config(lowercase__   , lowercase__	)

		# load original state_dict
		__lowercase=			torch.load(lowercase__   , map_location='cpu'	)

		# load huggingface model
		if task_name.startswith('ade20k_'	) or task_name.startswith('voc_'	):
				__lowercase=			MobileViTVaForSemanticSegmentation(lowercase__	).eval()
				__lowercase=			False
		else:
				__lowercase=			MobileViTVaForImageClassification(lowercase__	).eval()
				__lowercase=			False

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

		# load modified state_dict
		model.load_state_dict(lowercase__	)

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

		# verify classification model
		if task_name.startswith('imagenet'	):
				__lowercase=			outputs.logits
				__lowercase=			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
						__lowercase=			torch.tensor([-1.6_336E00, -7.3_204E-02, -5.1_883E-01]	)
						assert torch.allclose(logits[0, :3]   , lowercase__   , atol=1E-4	)

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


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 collections.abc import Sequence







def        _lowerCamelCase(	lowercase__   , lowercase__ = False	)						->	float:

		'''simple docstring'''







		if not arr:
				return 0

		__lowercase=			0 if allow_empty_subarrays else float('-inf'	)
		__lowercase=			0.0
		for num in arr:
				__lowercase=			max(0 if allow_empty_subarrays else num   , curr_sum + num	)
				__lowercase=			max(lowercase__   , lowercase__	)

		return max_sum


if __name__ == "__main__":
	from doctest import testmod

	testmod()

	lowerCAmelCase           =		[-2, 1, -3, 4, -1, 2, 1, -5, 4]
	print(F'{max_subarray_sum(nums) = }')






