



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()
a_   :Union[str, Any]        =      logging.get_logger(__name__)
def        lowercase_							(A : Dict      ):
       print('Loading config file...'      )

       def flatten_yaml_as_dict(A : int		,					A : str=""		,					A : Dict="."      ):
              snake_case__							:	List[Any]   	=	[]
              for k, v in d.items():
                     snake_case__							:	Tuple   	=	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      )

       snake_case__							:	Optional[Any]   	=	argparse.Namespace()
       with open(_UpperCAmelCase		,					'r'      ) as yaml_file:
              try:
                     snake_case__							:	Dict   	=	yaml.load(_UpperCAmelCase		,					Loader=yaml.FullLoader      )

                     snake_case__							:	Optional[Any]   	=	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        lowercase_							(A : List[str]		,					A : Tuple      ):
       snake_case__							:	List[Any]   	=	MobileViTVaConfig()

       snake_case__							:	Any   	=	False

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

       # orig_config
       snake_case__							:	Tuple   	=	load_orig_config_file(_UpperCAmelCase      )
       assert getattr(_UpperCAmelCase		,					'model.classification.name'		,					-1      ) == "mobilevit_v2", "Invalid model"
       snake_case__							:	Tuple   	=	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"
       snake_case__							:	str   	=	getattr(_UpperCAmelCase		,					'model.classification.activation.name'		,					'swish'      )
       # config.image_size == getattr(orig_config,  'sampler.bs.crop_size_width', 256)

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

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

       return config
def        lowercase_							(A : Tuple		,					A : Union[str, Any]		,					A : str      ):
       snake_case__							:	Union[str, Any]   	=	dct.pop(_UpperCAmelCase      )
       snake_case__							:	Optional[int]   	=	val
def        lowercase_							(A : Dict		,					A : List[Any]=False      ):
       if base_model:
              snake_case__							:	Optional[Any]   	=	''
       else:
              snake_case__							:	Tuple   	=	'mobilevitv2.'

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

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

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

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

                     for j in j_in:
                            if F'''layer_{i}.1.global_rep.{j}.''' in k:
                                   snake_case__							:	List[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__							:	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:
                            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__							:	str   	=	k_new.replace('pre_norm_attn.0.'		,					'layernorm_before.'      )
              if "pre_norm_attn.1." in k:
                     snake_case__							:	Optional[int]   	=	k_new.replace('pre_norm_attn.1.'		,					'attention.'      )
              if "pre_norm_ffn.0." in k:
                     snake_case__							:	Union[str, Any]   	=	k_new.replace('pre_norm_ffn.0.'		,					'layernorm_after.'      )
              if "pre_norm_ffn.1." in k:
                     snake_case__							:	Dict   	=	k_new.replace('pre_norm_ffn.1.'		,					'ffn.conv1.'      )
              if "pre_norm_ffn.3." in k:
                     snake_case__							:	int   	=	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__							:	Optional[int]   	=	k_new.replace('seg_head.'		,					'segmentation_head.'      )
              if ".aspp_layer." in k:
                     snake_case__							:	Tuple   	=	k_new.replace('.aspp_layer.'		,					'.'      )
              if ".aspp_pool." in k:
                     snake_case__							:	List[str]   	=	k_new.replace('.aspp_pool.'		,					'.'      )

              rename_keys.append((k, k_new)      )
       return rename_keys
def        lowercase_							(A : Optional[Any]      ):
       snake_case__							:	Any   	=	[]
       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        lowercase_							():
       snake_case__							:	int   	=	'http://images.cocodataset.org/val2017/000000039769.jpg'
       # url = "https://cdn.britannica.com/86/141086-050-9D7C75EE/Gulfstream-G450-business-jet-passengers.jpg"
       snake_case__							:	Optional[Any]   	=	Image.open(requests.get(_UpperCAmelCase		,					stream=_UpperCAmelCase      ).raw      )
       return im
@torch.no_grad()
def        lowercase_							(A : Tuple		,					A : Union[str, Any]		,					A : Dict		,					A : Any      ):
       snake_case__							:	Optional[Any]   	=	get_mobilevitva_config(_UpperCAmelCase		,					_UpperCAmelCase      )

       # load original state_dict
       snake_case__							:	int   	=	torch.load(_UpperCAmelCase		,					map_location='cpu'      )

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

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

       # verify classification model
       if task_name.startswith('imagenet'      ):
              snake_case__							:	Any   	=	outputs.logits
              snake_case__							:	str   	=	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__							:	Union[str, Any]   	=	torch.tensor([-1.6_336e00, -7.3_204e-02, -5.1_883e-01]      )
                     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__":
 a_   :Optional[int]        =      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."
 )

 a_   :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 torch import nn

from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration


def        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase )			->     str:
    lowerCamelCase__    :      Optional[int]					 =		[
        'encoder.version',
        'decoder.version',
        'model.encoder.version',
        'model.decoder.version',
        'decoder.output_projection.weight',
        '_float_tensor',
        'encoder.embed_positions._float_tensor',
        'decoder.embed_positions._float_tensor',
    ]
    for k in ignore_keys:
        state_dict.pop(_UpperCAmelCase						,	_UpperCAmelCase )


def        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase )			->     List[Any]:
    lowerCamelCase__ ,							lowerCamelCase__    :      List[str]					 =		emb.weight.shape
    lowerCamelCase__    :      Tuple					 =		nn.Linear(_UpperCAmelCase						,	_UpperCAmelCase						,	bias=_UpperCAmelCase )
    lowerCamelCase__    :      Dict					 =		emb.weight.data
    return lin_layer


def        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase )			->     str:
    lowerCamelCase__    :      Tuple					 =		torch.load(_UpperCAmelCase						,	map_location='cpu' )
    lowerCamelCase__    :      List[str]					 =		mam_aaa['args'] or mam_aaa['cfg']['model']
    lowerCamelCase__    :      Optional[int]					 =		mam_aaa['model']
    remove_ignore_keys_(_UpperCAmelCase )
    lowerCamelCase__    :      str					 =		state_dict['encoder.embed_tokens.weight'].shape[0]

    lowerCamelCase__    :      Union[str, Any]					 =		MaMaaaConfig(
        vocab_size=_UpperCAmelCase						,	max_position_embeddings=1024						,	encoder_layers=args.encoder_layers						,	decoder_layers=args.decoder_layers						,	encoder_attention_heads=args.encoder_attention_heads						,	decoder_attention_heads=args.decoder_attention_heads						,	encoder_ffn_dim=args.encoder_ffn_embed_dim						,	decoder_ffn_dim=args.decoder_ffn_embed_dim						,	d_model=args.encoder_embed_dim						,	encoder_layerdrop=args.encoder_layerdrop						,	decoder_layerdrop=args.decoder_layerdrop						,	dropout=args.dropout						,	attention_dropout=args.attention_dropout						,	activation_dropout=args.activation_dropout						,	activation_function='relu'						,	)

    lowerCamelCase__    :      Optional[Any]					 =		state_dict['decoder.embed_tokens.weight']
    lowerCamelCase__    :      Union[str, Any]					 =		MaMaaaForConditionalGeneration(_UpperCAmelCase )
    model.model.load_state_dict(_UpperCAmelCase						,	strict=_UpperCAmelCase )
    lowerCamelCase__    :      List[str]					 =		make_linear_from_emb(model.model.shared )

    return model


if __name__ == "__main__":
    _UpperCAmelCase				:       Tuple	   =				argparse.ArgumentParser()
    # Required parameters
    parser.add_argument("""fairseq_path""", type=str, help="""path to a model.pt on local filesystem.""")
    parser.add_argument("""pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
    _UpperCAmelCase				:       str	   =				parser.parse_args()
    _UpperCAmelCase				:       Optional[Any]	   =				convert_fairseq_mamaaa_checkpoint_from_disk(args.fairseq_pathß)
    model.save_pretrained(args.pytorch_dump_folder_path)





