





'''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()
lowercase_		:				Optional[int]				=						logging.get_logger(__name__)

def       A__(		__lowerCAmelCase ):
				print('Loading config file...' )

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

				_snake_case :			Dict  						=   argparse.Namespace()
				with open(__lowerCAmelCase	,			'r' ) as yaml_file:
								try:
												_snake_case :			Tuple  						=   yaml.load(__lowerCAmelCase	,			Loader=yaml.FullLoader )

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

def       A__(		__lowerCAmelCase	,			__lowerCAmelCase ):
				_snake_case :			Dict  						=   MobileViTVaConfig()

				_snake_case :			List[str]  						=   False

				# dataset
				if task_name.startswith('imagenet1k_' ):
								_snake_case :			int  						=   10_00
								if int(task_name.strip().split('_' )[-1] ) == 3_84:
												_snake_case :			Optional[int]  						=   3_84
								else:
												_snake_case :			Dict  						=   2_56
								_snake_case :			List[Any]  						=   'imagenet-1k-id2label.json'
				elif task_name.startswith('imagenet21k_to_1k_' ):
								_snake_case :			int  						=   2_10_00
								if int(task_name.strip().split('_' )[-1] ) == 3_84:
												_snake_case :			Optional[int]  						=   3_84
								else:
												_snake_case :			Tuple  						=   2_56
								_snake_case :			int  						=   'imagenet-22k-id2label.json'
				elif task_name.startswith('ade20k_' ):
								_snake_case :			int  						=   1_51
								_snake_case :			int  						=   5_12
								_snake_case :			Tuple  						=   'ade20k-id2label.json'
								_snake_case :			List[str]  						=   True
				elif task_name.startswith('voc_' ):
								_snake_case :			Union[str, Any]  						=   21
								_snake_case :			str  						=   5_12
								_snake_case :			int  						=   'pascal-voc-id2label.json'
								_snake_case :			Union[str, Any]  						=   True

				# orig_config
				_snake_case :			Tuple  						=   load_orig_config_file(__lowerCAmelCase )
				assert getattr(__lowerCAmelCase	,			'model.classification.name'	,			-1 ) == "mobilevit_v2", "Invalid model"
				_snake_case :			int  						=   getattr(__lowerCAmelCase	,			'model.classification.mitv2.width_multiplier'	,			1.0 )
				assert (
				    getattr(__lowerCAmelCase	,			'model.classification.mitv2.attn_norm_layer'	,			-1 ) == "layer_norm_2d"
				), "Norm layers other than layer_norm_2d is not supported"
				_snake_case :			str  						=   getattr(__lowerCAmelCase	,			'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(__lowerCAmelCase	,			'model.segmentation.output_stride'	,			16 )
								if "_deeplabv3" in task_name:
												_snake_case :			Optional[int]  						=   getattr(__lowerCAmelCase	,			'model.segmentation.deeplabv3.aspp_rates'	,			[12, 24, 36] )
												_snake_case :			Dict  						=   getattr(__lowerCAmelCase	,			'model.segmentation.deeplabv3.aspp_out_channels'	,			5_12 )
												_snake_case :			Optional[int]  						=   getattr(__lowerCAmelCase	,			'model.segmentation.deeplabv3.aspp_dropout'	,			0.1 )

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

				return config

def       A__(		__lowerCAmelCase	,			__lowerCAmelCase	,			__lowerCAmelCase ):
				_snake_case :			Any  						=   dct.pop(__lowerCAmelCase )
				_snake_case :			int  						=   val

def       A__(		__lowerCAmelCase	,			__lowerCAmelCase=False ):
				if base_model:
								_snake_case :			Optional[int]  						=   ''
				else:
								_snake_case :			List[Any]  						=   'mobilevitv2.'

				_snake_case :			List[Any]  						=   []
				for k in state_dict.keys():
								if k[:8] == "encoder.":
												_snake_case :			List[Any]  						=   k[8:]
								else:
												_snake_case :			Optional[Any]  						=   k

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

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

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

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

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

def       A__(		__lowerCAmelCase ):
				_snake_case :			Any  						=   []
				for k in state_dict.keys():
								if k.startswith('seg_head.aux_head.' ):
												keys_to_ignore.append(__lowerCAmelCase )
				for k in keys_to_ignore:
								state_dict.pop(__lowerCAmelCase	,			__lowerCAmelCase )

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

@torch.no_grad()
def       A__(		__lowerCAmelCase	,			__lowerCAmelCase	,			__lowerCAmelCase	,			__lowerCAmelCase ):
				_snake_case :			List[str]  						=   get_mobilevitva_config(__lowerCAmelCase	,			__lowerCAmelCase )

				# load original state_dict
				_snake_case :			Optional[int]  						=   torch.load(__lowerCAmelCase	,			map_location='cpu' )

				# load huggingface model
				if task_name.startswith('ade20k_' ) or task_name.startswith('voc_' ):
								_snake_case :			Union[str, Any]  						=   MobileViTVaForSemanticSegmentation(__lowerCAmelCase ).eval()
								_snake_case :			Tuple  						=   False
				else:
								_snake_case :			Any  						=   MobileViTVaForImageClassification(__lowerCAmelCase ).eval()
								_snake_case :			Optional[Any]  						=   False

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

				# load modified state_dict
				model.load_state_dict(__lowerCAmelCase )

				# 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 :			Optional[Any]  						=   image_processor(images=prepare_img()	,			return_tensors='pt' )
				_snake_case :			Tuple  						=   model(**__lowerCAmelCase )

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

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


if __name__ == "__main__":
		lowercase_		:				Tuple				=						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.'''
		)

		lowercase_		:				Optional[Any]				=						parser.parse_args()
		convert_mobilevitva_checkpoint(
		    args.task, args.orig_checkpoint_path, args.orig_config_path, args.pytorch_dump_folder_path
		)













import argparse

import torch

from transformers import BertConfig, BertForPreTraining, load_tf_weights_in_bert
from transformers.utils import logging


logging.set_verbosity_info()

def       A__(		__lowerCAmelCase	,			__lowerCAmelCase	,			__lowerCAmelCase ):
				# Initialise PyTorch model
				_snake_case :			Optional[int]  						=   BertConfig.from_json_file(__lowerCAmelCase )
				print(F'''Building PyTorch model from configuration: {config}''' )
				_snake_case :			List[str]  						=   BertForPreTraining(__lowerCAmelCase )

				# Load weights from tf checkpoint
				load_tf_weights_in_bert(__lowerCAmelCase	,			__lowerCAmelCase	,			__lowerCAmelCase )

				# Save pytorch-model
				print(F'''Save PyTorch model to {pytorch_dump_path}''' )
				torch.save(model.state_dict()	,			__lowerCAmelCase )


if __name__ == "__main__":
		lowercase_		:				Any				=						argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '''--tf_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
		)
		parser.add_argument(
		    '''--bert_config_file''',
		    default=None,
		    type=str,
		    required=True,
		    help=(
		        '''The config json file corresponding to the pre-trained BERT model. \n'''
		        '''This specifies the model architecture.'''
		    ),
		)
		parser.add_argument(
		    '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
		)
		lowercase_		:				List[str]				=						parser.parse_args()
		convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)






