






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 : Tuple						   =       logging.get_logger(__name__)
def       snake_case_		(     lowerCAmelCase_       :						Dict      ):
							print("""Loading config file..."""      )

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

							__lowercase      :							List[str]				=     argparse.Namespace()
							with open(UpperCamelCase__       ,       """r"""      ) as yaml_file:
														try:
																					__lowercase      :							str				=     yaml.load(UpperCamelCase__       ,       Loader=yaml.FullLoader      )

																					__lowercase      :							Optional[int]				=     flatten_yaml_as_dict(UpperCamelCase__      )
																					for k, v in flat_cfg.items():
																												setattr(UpperCamelCase__       ,       UpperCamelCase__       ,       UpperCamelCase__      )
														except yaml.YAMLError as exc:
																					logger.error("""Error while loading config file: {}. Error message: {}""".format(UpperCamelCase__       ,       str(UpperCamelCase__      )      )      )
							return config
def       snake_case_		(     lowerCAmelCase_       :						List[Any]       ,       lowerCAmelCase_       :						Tuple      ):
							__lowercase      :							Optional[int]				=     MobileViTVaConfig()

							__lowercase      :							Union[str, Any]				=     False

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

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

							if is_segmentation_model:
														__lowercase      :							Tuple				=     getattr(UpperCamelCase__       ,       """model.segmentation.output_stride"""       ,       16      )
														if "_deeplabv3" in task_name:
																					__lowercase      :							int				=     getattr(UpperCamelCase__       ,       """model.segmentation.deeplabv3.aspp_rates"""       ,       [12, 24, 36]      )
																					__lowercase      :							Any				=     getattr(UpperCamelCase__       ,       """model.segmentation.deeplabv3.aspp_out_channels"""       ,       512      )
																					__lowercase      :							str				=     getattr(UpperCamelCase__       ,       """model.segmentation.deeplabv3.aspp_dropout"""       ,       0.1      )

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

							return config
def       snake_case_		(     lowerCAmelCase_       :						Dict       ,       lowerCAmelCase_       :						Optional[int]       ,       lowerCAmelCase_       :						int      ):
							__lowercase      :							Any				=     dct.pop(UpperCamelCase__      )
							__lowercase      :							List[str]				=     val
def       snake_case_		(     lowerCAmelCase_       :						List[Any]       ,       lowerCAmelCase_       :						Optional[Any]=False      ):
							if base_model:
														__lowercase      :							Any				=     """"""
							else:
														__lowercase      :							Dict				=     """mobilevitv2."""

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

														if ".block." in k:
																					__lowercase      :							Dict				=     k_new.replace(""".block."""       ,       """."""      )
														if ".conv." in k:
																					__lowercase      :							int				=     k_new.replace(""".conv."""       ,       """.convolution."""      )
														if ".norm." in k:
																					__lowercase      :							Union[str, Any]				=     k_new.replace(""".norm."""       ,       """.normalization."""      )

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

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

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

														if "classifier.1." in k:
																					__lowercase      :							Optional[int]				=     k_new.replace("""classifier.1."""       ,       """classifier."""      )

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

														rename_keys.append((k, k_new)      )
							return rename_keys
def       snake_case_		(     lowerCAmelCase_       :						Tuple      ):
							__lowercase      :							List[str]				=     []
							for k in state_dict.keys():
														if k.startswith("""seg_head.aux_head."""      ):
																					keys_to_ignore.append(UpperCamelCase__      )
							for k in keys_to_ignore:
														state_dict.pop(UpperCamelCase__       ,       UpperCamelCase__      )
def       snake_case_		(     ):
							__lowercase      :							List[str]				=     """http://images.cocodataset.org/val2017/000000039769.jpg"""
							# url = "https://cdn.britannica.com/86/141086-050-9D7C75EE/Gulfstream-G450-business-jet-passengers.jpg"
							__lowercase      :							List[Any]				=     Image.open(requests.get(UpperCamelCase__       ,       stream=UpperCamelCase__      ).raw      )
							return im
@torch.no_grad()
def       snake_case_		(     lowerCAmelCase_       :						Union[str, Any]       ,       lowerCAmelCase_       :						Optional[Any]       ,       lowerCAmelCase_       :						Union[str, Any]       ,       lowerCAmelCase_       :						Dict      ):
							__lowercase      :							Any				=     get_mobilevitva_config(UpperCamelCase__       ,       UpperCamelCase__      )

							# load original state_dict
							__lowercase      :							Any				=     torch.load(UpperCamelCase__       ,       map_location="""cpu"""      )

							# load huggingface model
							if task_name.startswith("""ade20k_"""      ) or task_name.startswith("""voc_"""      ):
														__lowercase      :							List[str]				=     MobileViTVaForSemanticSegmentation(UpperCamelCase__      ).eval()
														__lowercase      :							List[Any]				=     False
							else:
														__lowercase      :							int				=     MobileViTVaForImageClassification(UpperCamelCase__      ).eval()
														__lowercase      :							Dict				=     False

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

							# load modified state_dict
							model.load_state_dict(UpperCamelCase__      )

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

							# verify classification model
							if task_name.startswith("""imagenet"""      ):
														__lowercase      :							Tuple				=     outputs.logits
														__lowercase      :							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
																					__lowercase      :							Optional[Any]				=     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]       ,       UpperCamelCase__       ,       atol=1e-4      )

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


if __name__ == "__main__":
	lowerCamelCase : List[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.'''
	)

	lowerCamelCase : Any						   =       parser.parse_args()
	convert_mobilevitva_checkpoint(
	    args.task, args.orig_checkpoint_path, args.orig_config_path, args.pytorch_dump_folder_path
	)













from torch import nn

class 		lowerCAmelCase     ( nn.Module			):

					'''simple docstring'''

					def __init__(  self				:	Tuple		,     __a				:	int		,     __a				:	Optional[int]							)			->  Union[str, Any]:


												"""simple docstring"""

												super().__init__()
												__lowercase      :							int				=     class_size
												__lowercase      :							int				=     embed_size
												# self.mlp1 = nn.Linear(embed_size, embed_size)
												# self.mlp2 = (nn.Linear(embed_size, class_size))
												__lowercase      :							str				=     nn.Linear(__a		,     __a							)



					def       lowerCAmelCase				(  self				:	Tuple		,     __a				:	int							)			->  Tuple:


												"""simple docstring"""

												__lowercase      :							str				=     self.mlp(__a							)
												return logits