'''simple docstring'''







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()
UpperCamelCase_      	=	logging.get_logger(__name__)


def    lowerCamelCase       (			UpperCAmelCase__				:  Tuple		)			->	List[Any]:


							'''simple docstring'''






							print('Loading config file...'		)

							def flatten_yaml_as_dict(UpperCAmelCase__				:  Optional[Any]				,						UpperCAmelCase__				:  Optional[Any]=""				,						UpperCAmelCase__				:  Optional[Any]="."		):
														SCREAMING_SNAKE_CASE__     :Dict						    = []
														for k, v in d.items():
																					SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = 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_		)

							SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = argparse.Namespace()
							with open(SCREAMING_SNAKE_CASE_				,						'r'		) as yaml_file:
														try:
																					SCREAMING_SNAKE_CASE__     :List[Any]						    = yaml.load(SCREAMING_SNAKE_CASE_				,						Loader=yaml.FullLoader		)

																					SCREAMING_SNAKE_CASE__     :Any						    = 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       (			UpperCAmelCase__				:  Optional[Any]				,						UpperCAmelCase__				:  Optional[int]		)			->	List[Any]:


							'''simple docstring'''






							SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = MobileViTVaConfig()

							SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = False

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

							# orig_config
							SCREAMING_SNAKE_CASE__     :Optional[Any]						    = load_orig_config_file(SCREAMING_SNAKE_CASE_		)
							assert getattr(SCREAMING_SNAKE_CASE_				,						'model.classification.name'				,						-1		) == "mobilevit_v2", "Invalid model"
							SCREAMING_SNAKE_CASE__     :List[str]						    = 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"
							SCREAMING_SNAKE_CASE__     :Any						    = 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:
														SCREAMING_SNAKE_CASE__     :str						    = getattr(SCREAMING_SNAKE_CASE_				,						'model.segmentation.output_stride'				,						1_6		)
														if "_deeplabv3" in task_name:
																					SCREAMING_SNAKE_CASE__     :int						    = getattr(SCREAMING_SNAKE_CASE_				,						'model.segmentation.deeplabv3.aspp_rates'				,						[1_2, 2_4, 3_6]		)
																					SCREAMING_SNAKE_CASE__     :Optional[Any]						    = getattr(SCREAMING_SNAKE_CASE_				,						'model.segmentation.deeplabv3.aspp_out_channels'				,						5_1_2		)
																					SCREAMING_SNAKE_CASE__     :List[str]						    = getattr(SCREAMING_SNAKE_CASE_				,						'model.segmentation.deeplabv3.aspp_dropout'				,						0.1		)

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

							return config


def    lowerCamelCase       (			UpperCAmelCase__				:  int				,						UpperCAmelCase__				:  int				,						UpperCAmelCase__				:  Optional[Any]		)			->	Optional[Any]:


							'''simple docstring'''






							SCREAMING_SNAKE_CASE__     :Optional[int]						    = dct.pop(SCREAMING_SNAKE_CASE_		)
							SCREAMING_SNAKE_CASE__     :str						    = val


def    lowerCamelCase       (			UpperCAmelCase__				:  str				,						UpperCAmelCase__				:  Tuple=False		)			->	Dict:


							'''simple docstring'''






							if base_model:
														SCREAMING_SNAKE_CASE__     :Optional[Any]						    = ''
							else:
														SCREAMING_SNAKE_CASE__     :int						    = 'mobilevitv2.'

							SCREAMING_SNAKE_CASE__     :str						    = []
							for k in state_dict.keys():
														if k[:8] == "encoder.":
																					SCREAMING_SNAKE_CASE__     :Optional[int]						    = k[8:]
														else:
																					SCREAMING_SNAKE_CASE__     :Dict						    = k

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

														if "conv_1." in k:
																					SCREAMING_SNAKE_CASE__     :Any						    = 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[Any]						    = k_new.replace(F'''layer_{i}.'''				,						F'''{model_prefix}encoder.layer.{i-1}.layer.'''		)
														if ".exp_1x1." in k:
																					SCREAMING_SNAKE_CASE__     :Optional[Any]						    = k_new.replace('.exp_1x1.'				,						'.expand_1x1.'		)
														if ".red_1x1." in k:
																					SCREAMING_SNAKE_CASE__     :str						    = k_new.replace('.red_1x1.'				,						'.reduce_1x1.'		)

														for i in [3, 4, 5]:
																					if F'''layer_{i}.0.''' in k:
																												SCREAMING_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:
																												SCREAMING_SNAKE_CASE__     :Tuple						    = 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__     :int						    = 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__     :Tuple						    = [0, 1]
																					elif i == 4:
																												SCREAMING_SNAKE_CASE__     :Optional[int]						    = [0, 1, 2, 3]
																					elif i == 5:
																												SCREAMING_SNAKE_CASE__     :Dict						    = [0, 1, 2]

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

														if "classifier.1." in k:
																					SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = k_new.replace('classifier.1.'				,						'classifier.'		)

														if "seg_head." in k:
																					SCREAMING_SNAKE_CASE__     :Union[str, 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__     :List[str]						    = k_new.replace('.aspp_pool.'				,						'.'		)

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


def    lowerCamelCase       (			UpperCAmelCase__				:  str		)			->	List[str]:


							'''simple docstring'''






							SCREAMING_SNAKE_CASE__     :Optional[Any]						    = []
							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       (			)			->	str:


							'''simple docstring'''






							SCREAMING_SNAKE_CASE__     :List[Any]						    = '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__     :List[str]						    = Image.open(requests.get(SCREAMING_SNAKE_CASE_				,						stream=SCREAMING_SNAKE_CASE_		).raw		)
							return im


@torch.no_grad()
def    lowerCamelCase       (			UpperCAmelCase__				:  Optional[int]				,						UpperCAmelCase__				:  Optional[int]				,						UpperCAmelCase__				:  Union[str, Any]				,						UpperCAmelCase__				:  int		)			->	List[Any]:


							'''simple docstring'''






							SCREAMING_SNAKE_CASE__     :int						    = get_mobilevitva_config(SCREAMING_SNAKE_CASE_				,						SCREAMING_SNAKE_CASE_		)

							# load original state_dict
							SCREAMING_SNAKE_CASE__     :Optional[Any]						    = torch.load(SCREAMING_SNAKE_CASE_				,						map_location='cpu'		)

							# load huggingface model
							if task_name.startswith('ade20k_'		) or task_name.startswith('voc_'		):
														SCREAMING_SNAKE_CASE__     :Any						    = MobileViTVaForSemanticSegmentation(SCREAMING_SNAKE_CASE_		).eval()
														SCREAMING_SNAKE_CASE__     :List[Any]						    = False
							else:
														SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = MobileViTVaForImageClassification(SCREAMING_SNAKE_CASE_		).eval()
														SCREAMING_SNAKE_CASE__     :List[Any]						    = False

							# remove and rename some keys of load the original model
							SCREAMING_SNAKE_CASE__     :Optional[int]						    = checkpoint
							remove_unused_keys(SCREAMING_SNAKE_CASE_		)
							SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = 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
							SCREAMING_SNAKE_CASE__     :Optional[Any]						    = MobileViTImageProcessor(crop_size=config.image_size				,						size=config.image_size + 3_2		)
							SCREAMING_SNAKE_CASE__     :Tuple						    = image_processor(images=prepare_img()				,						return_tensors='pt'		)
							SCREAMING_SNAKE_CASE__     :Tuple						    = model(**SCREAMING_SNAKE_CASE_		)

							# verify classification model
							if task_name.startswith('imagenet'		):
														SCREAMING_SNAKE_CASE__     :List[Any]						    = outputs.logits
														SCREAMING_SNAKE_CASE__     :List[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__     :str						    = 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__":
		UpperCamelCase_      	=	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.'''
		)

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







'''simple docstring'''


def   A_      (       SCREAMING_SNAKE_CASE_						)			->bool:
  if num < 0:
    return False

  lowercase_     =       num
  lowercase_     =       0
  while num > 0:
    lowercase_     =       rev_num * 10 + (num % 10)
    num //= 10

  return num_copy == rev_num


if __name__ == "__main__":
       import doctest

       doctest.testmod()

