


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






def 							__lowercase   ( __lowerCAmelCase							:				List[Any]   ):
							print('Loading config file...'   )

							def flatten_yaml_as_dict(__lowerCAmelCase							:				Tuple					,    __lowerCAmelCase							:				int=""					,    __lowerCAmelCase							:				Optional[int]="."   ):
														a__             =     []
														for k, v in d.items():
																					a__             =     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   )

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

																					a__             =     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 							__lowercase   ( __lowerCAmelCase							:				Dict					,    __lowerCAmelCase							:				Dict   ):
							a__             =     MobileViTVaConfig()

							a__             =     False

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

							# orig_config
							a__             =     load_orig_config_file(__lowerCAmelCase   )
							assert getattr(__lowerCAmelCase					,    'model.classification.name'					,    -1   ) == "mobilevit_v2", "Invalid model"
							a__             =     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"
							a__             =     getattr(__lowerCAmelCase					,    'model.classification.activation.name'					,    'swish'   )
							# config.image_size == getattr(orig_config,  'sampler.bs.crop_size_width', 256)

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

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

							return config






def 							__lowercase   ( __lowerCAmelCase							:				Dict					,    __lowerCAmelCase							:				Dict					,    __lowerCAmelCase							:				int   ):
							a__             =     dct.pop(__lowerCAmelCase   )
							a__             =     val






def 							__lowercase   ( __lowerCAmelCase							:				Optional[Any]					,    __lowerCAmelCase							:				Dict=False   ):




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

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

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

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

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

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

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

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

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






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






@torch.no_grad()
def 							__lowercase   ( __lowerCAmelCase							:				List[str]					,    __lowerCAmelCase							:				Any					,    __lowerCAmelCase							:				int					,    __lowerCAmelCase							:				List[Any]   ):
							a__             =     get_mobilevitva_config(__lowerCAmelCase					,    __lowerCAmelCase   )

							# load original state_dict
							a__             =     torch.load(__lowerCAmelCase					,    map_location='cpu'   )

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

							# remove and rename some keys of load the original model
							a__             =     checkpoint
							remove_unused_keys(__lowerCAmelCase   )
							a__             =     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
							a__             =     MobileViTImageProcessor(crop_size=config.image_size					,    size=config.image_size + 3_2   )
							a__             =     image_processor(images=prepare_img()					,    return_tensors='pt'   )
							a__             =     model(**__lowerCAmelCase   )

							# verify classification model
							if task_name.startswith('imagenet'   ):
														a__             =     outputs.logits
														a__             =     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
																					a__             =     torch.tensor([-1.6_3_3_6E0_0, -7.3_2_0_4E-0_2, -5.1_8_8_3E-0_1]   )
																					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__":
						snake_case							:       Any     			=      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.'''
						)

						snake_case							:       Dict     			=      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 huggingface_hub import hf_hub_download

from transformers import AutoTokenizer, RobertaPreLayerNormConfig, RobertaPreLayerNormForMaskedLM
from transformers.utils import logging


logging.set_verbosity_info()
lowerCAmelCase       =							logging.get_logger(__name__)
def 			_a (      SCREAMING_SNAKE_CASE						,				SCREAMING_SNAKE_CASE     ):


		"""simple docstring"""




		lowercase__            =      RobertaPreLayerNormConfig.from_pretrained(
		    SCREAMING_SNAKE_CASE						,				architectures=['''RobertaPreLayerNormForMaskedLM''']     )

		# convert state_dict
		lowercase__            =      torch.load(hf_hub_download(repo_id=SCREAMING_SNAKE_CASE						,				filename='''pytorch_model.bin'''     )     )
		lowercase__            =      {}
		for tensor_key, tensor_value in original_state_dict.items():
				# The transformer implementation gives the model a unique name, rather than overwiriting 'roberta'
				if tensor_key.startswith('''roberta.'''     ):
						lowercase__            =      '''roberta_prelayernorm.''' + tensor_key[len('''roberta.'''     ) :]

				# The original implementation contains weights which are not used, remove them from the state_dict
				if tensor_key.endswith('''.self.LayerNorm.weight'''     ) or tensor_key.endswith('''.self.LayerNorm.bias'''     ):
						continue

				lowercase__            =      tensor_value

		lowercase__            =      RobertaPreLayerNormForMaskedLM.from_pretrained(
		    pretrained_model_name_or_path=SCREAMING_SNAKE_CASE						,				config=SCREAMING_SNAKE_CASE						,				state_dict=SCREAMING_SNAKE_CASE     )
		model.save_pretrained(SCREAMING_SNAKE_CASE     )

		# convert tokenizer
		lowercase__            =      AutoTokenizer.from_pretrained(SCREAMING_SNAKE_CASE     )
		tokenizer.save_pretrained(SCREAMING_SNAKE_CASE     )


if __name__ == "__main__":
			lowerCAmelCase       =							argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    '--checkpoint-repo',
			    default=None,
			    type=str,
			    required=True,
			    help='Path the official PyTorch dump, e.g. \'andreasmadsen/efficient_mlm_m0.40\'.',
			)
			parser.add_argument(
			    '--pytorch_dump_folder_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
			)
			lowerCAmelCase       =							parser.parse_args()
			convert_roberta_prelayernorm_checkpoint_to_pytorch(args.checkpoint_repo, args.pytorch_dump_folder_path)


