


import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    MobileViTConfig,
    MobileViTForImageClassification,
    MobileViTForSemanticSegmentation,
    MobileViTImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
snake_case							:	Dict					      =				logging.get_logger(__name__)






def 							__lowercase       (			__lowerCAmelCase							:    str       ):
       a__					=					MobileViTConfig()

       # size of the architecture
       if "mobilevit_s" in mobilevit_name:
              a__					=					[1_4_4, 1_9_2, 2_4_0]
              a__					=					[1_6, 3_2, 6_4, 9_6, 1_2_8, 1_6_0, 6_4_0]
       elif "mobilevit_xs" in mobilevit_name:
              a__					=					[9_6, 1_2_0, 1_4_4]
              a__					=					[1_6, 3_2, 4_8, 6_4, 8_0, 9_6, 3_8_4]
       elif "mobilevit_xxs" in mobilevit_name:
              a__					=					[6_4, 8_0, 9_6]
              a__					=					[1_6, 1_6, 2_4, 4_8, 6_4, 8_0, 3_2_0]
              a__					=					0.05
              a__					=					2.0

       if mobilevit_name.startswith('deeplabv3_'       ):
              a__					=					5_1_2
              a__					=					1_6
              a__					=					2_1
              a__					=					'pascal-voc-id2label.json'
       else:
              a__					=					1_0_0_0
              a__					=					'imagenet-1k-id2label.json'

       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							:    List[str] ,   __lowerCAmelCase							:    Optional[int]=False       ):
       for i in range(1 ,   6       ):
              if F'layer_{i}.' in name:
                     a__					=					name.replace(F'layer_{i}.' ,   F'encoder.layer.{i - 1}.'       )

       if "conv_1." in name:
              a__					=					name.replace('conv_1.' ,   'conv_stem.'       )
       if ".block." in name:
              a__					=					name.replace('.block.' ,   '.'       )

       if "exp_1x1" in name:
              a__					=					name.replace('exp_1x1' ,   'expand_1x1'       )
       if "red_1x1" in name:
              a__					=					name.replace('red_1x1' ,   'reduce_1x1'       )
       if ".local_rep.conv_3x3." in name:
              a__					=					name.replace('.local_rep.conv_3x3.' ,   '.conv_kxk.'       )
       if ".local_rep.conv_1x1." in name:
              a__					=					name.replace('.local_rep.conv_1x1.' ,   '.conv_1x1.'       )
       if ".norm." in name:
              a__					=					name.replace('.norm.' ,   '.normalization.'       )
       if ".conv." in name:
              a__					=					name.replace('.conv.' ,   '.convolution.'       )
       if ".conv_proj." in name:
              a__					=					name.replace('.conv_proj.' ,   '.conv_projection.'       )

       for i in range(0 ,   2       ):
              for j in range(0 ,   4       ):
                     if F'.{i}.{j}.' in name:
                            a__					=					name.replace(F'.{i}.{j}.' ,   F'.{i}.layer.{j}.'       )

       for i in range(2 ,   6       ):
              for j in range(0 ,   4       ):
                     if F'.{i}.{j}.' in name:
                            a__					=					name.replace(F'.{i}.{j}.' ,   F'.{i}.'       )
                            if "expand_1x1" in name:
                                   a__					=					name.replace('expand_1x1' ,   'downsampling_layer.expand_1x1'       )
                            if "conv_3x3" in name:
                                   a__					=					name.replace('conv_3x3' ,   'downsampling_layer.conv_3x3'       )
                            if "reduce_1x1" in name:
                                   a__					=					name.replace('reduce_1x1' ,   'downsampling_layer.reduce_1x1'       )

       for i in range(2 ,   5       ):
              if F'.global_rep.{i}.weight' in name:
                     a__					=					name.replace(F'.global_rep.{i}.weight' ,   '.layernorm.weight'       )
              if F'.global_rep.{i}.bias' in name:
                     a__					=					name.replace(F'.global_rep.{i}.bias' ,   '.layernorm.bias'       )

       if ".global_rep." in name:
              a__					=					name.replace('.global_rep.' ,   '.transformer.'       )
       if ".pre_norm_mha.0." in name:
              a__					=					name.replace('.pre_norm_mha.0.' ,   '.layernorm_before.'       )
       if ".pre_norm_mha.1.out_proj." in name:
              a__					=					name.replace('.pre_norm_mha.1.out_proj.' ,   '.attention.output.dense.'       )
       if ".pre_norm_ffn.0." in name:
              a__					=					name.replace('.pre_norm_ffn.0.' ,   '.layernorm_after.'       )
       if ".pre_norm_ffn.1." in name:
              a__					=					name.replace('.pre_norm_ffn.1.' ,   '.intermediate.dense.'       )
       if ".pre_norm_ffn.4." in name:
              a__					=					name.replace('.pre_norm_ffn.4.' ,   '.output.dense.'       )
       if ".transformer." in name:
              a__					=					name.replace('.transformer.' ,   '.transformer.layer.'       )

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

       if "classifier.fc." in name:
              a__					=					name.replace('classifier.fc.' ,   'classifier.'       )
       elif (not base_model) and ("segmentation_head." not in name):
              a__					=					'mobilevit.' + name

       return name






def 							__lowercase       (			__lowerCAmelCase							:    Any ,   __lowerCAmelCase							:    Union[str, Any] ,   __lowerCAmelCase							:    Dict=False       ):
       if base_model:
              a__					=					''
       else:
              a__					=					'mobilevit.'

       for key in orig_state_dict.copy().keys():
              a__					=					orig_state_dict.pop(__lowerCAmelCase       )

              if key[:8] == "encoder.":
                     a__					=					key[8:]

              if "qkv" in key:
                     a__					=					key.split('.'       )
                     a__					=					int(key_split[0][6:]       ) - 1
                     a__					=					int(key_split[3]       )
                     a__					=					model.get_submodule(F'{model_prefix}encoder.layer.{layer_num}'       )
                     a__					=					layer.transformer.layer[transformer_num].attention.attention.all_head_size
                     a__					=					(
                         F'{model_prefix}encoder.layer.{layer_num}.transformer.layer.{transformer_num}.attention.attention.'
                     )
                     if "weight" in key:
                            a__					=					val[:dim, :]
                            a__					=					val[dim : dim * 2, :]
                            a__					=					val[-dim:, :]
                     else:
                            a__					=					val[:dim]
                            a__					=					val[dim : dim * 2]
                            a__					=					val[-dim:]
              else:
                     a__					=					val

       return orig_state_dict






def 							__lowercase       (			):
       a__					=					'http://images.cocodataset.org/val2017/000000039769.jpg'
       a__					=					Image.open(requests.get(__lowerCAmelCase ,   stream=__lowerCAmelCase       ).raw       )
       return im






@torch.no_grad()
def 							__lowercase       (			__lowerCAmelCase							:    List[Any] ,   __lowerCAmelCase							:    Union[str, Any] ,   __lowerCAmelCase							:    Dict ,   __lowerCAmelCase							:    int=False       ):
       a__					=					get_mobilevit_config(__lowerCAmelCase       )

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

       # load 🤗 model
       if mobilevit_name.startswith('deeplabv3_'       ):
              a__					=					MobileViTForSemanticSegmentation(__lowerCAmelCase       ).eval()
       else:
              a__					=					MobileViTForImageClassification(__lowerCAmelCase       ).eval()

       a__					=					convert_state_dict(__lowerCAmelCase ,   __lowerCAmelCase       )
       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       )
       a__					=					outputs.logits

       if mobilevit_name.startswith('deeplabv3_'       ):
              assert logits.shape == (1, 2_1, 3_2, 3_2)

              if mobilevit_name == "deeplabv3_mobilevit_s":
                     a__					=					torch.tensor(
                         [
                             [[6.2_065, 6.1_292, 6.2_070], [6.1_079, 6.1_254, 6.1_747], [6.0_042, 6.1_071, 6.1_034]],
                             [[-6.9_253, -6.8_653, -7.0_398], [-7.3_218, -7.3_983, -7.3_670], [-7.1_961, -7.2_482, -7.1_569]],
                             [[-4.4_723, -4.4_348, -4.3_769], [-5.3_629, -5.4_632, -5.4_598], [-5.1_587, -5.3_402, -5.5_059]],
                         ]       )
              elif mobilevit_name == "deeplabv3_mobilevit_xs":
                     a__					=					torch.tensor(
                         [
                             [[5.4_449, 5.5_733, 5.6_314], [5.1_815, 5.3_930, 5.5_963], [5.1_656, 5.4_333, 5.4_853]],
                             [[-9.4_423, -9.7_766, -9.6_714], [-9.1_581, -9.5_720, -9.5_519], [-9.1_006, -9.6_458, -9.5_703]],
                             [[-7.7_721, -7.3_716, -7.1_583], [-8.4_599, -8.0_624, -7.7_944], [-8.4_172, -7.8_366, -7.5_025]],
                         ]       )
              elif mobilevit_name == "deeplabv3_mobilevit_xxs":
                     a__					=					torch.tensor(
                         [
                             [[6.9_811, 6.9_743, 7.3_123], [7.1_777, 7.1_931, 7.3_938], [7.5_633, 7.8_050, 7.8_901]],
                             [[-1_0.5_5_3_6, -1_0.2_3_3_2, -1_0.2_9_2_4], [-1_0.2_3_3_6, -9.8_624, -9.5_964], [-1_0.8_8_4_0, -1_0.8_1_5_8, -1_0.6_6_5_9]],
                             [[-3.4_938, -3.0_631, -2.8_620], [-3.4_205, -2.8_135, -2.6_875], [-3.4_179, -2.7_945, -2.8_750]],
                         ]       )
              else:
                     raise ValueError(F'Unknown mobilevit_name: {mobilevit_name}'       )

              assert torch.allclose(logits[0, :3, :3, :3] ,   __lowerCAmelCase ,   atol=1E-4       )
       else:
              assert logits.shape == (1, 1_0_0_0)

              if mobilevit_name == "mobilevit_s":
                     a__					=					torch.tensor([-0.9_866, 0.2_392, -1.1_241]       )
              elif mobilevit_name == "mobilevit_xs":
                     a__					=					torch.tensor([-2.4_761, -0.9_399, -1.9_587]       )
              elif mobilevit_name == "mobilevit_xxs":
                     a__					=					torch.tensor([-1.9_364, -1.2_327, -0.4_653]       )
              else:
                     raise ValueError(F'Unknown mobilevit_name: {mobilevit_name}'       )

              assert torch.allclose(logits[0, :3] ,   __lowerCAmelCase ,   atol=1E-4       )

       Path(__lowerCAmelCase       ).mkdir(exist_ok=__lowerCAmelCase       )
       print(F'Saving model {mobilevit_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 push_to_hub:
              a__					=					{
                  'mobilevit_s': 'mobilevit-small',
                  'mobilevit_xs': 'mobilevit-x-small',
                  'mobilevit_xxs': 'mobilevit-xx-small',
                  'deeplabv3_mobilevit_s': 'deeplabv3-mobilevit-small',
                  'deeplabv3_mobilevit_xs': 'deeplabv3-mobilevit-x-small',
                  'deeplabv3_mobilevit_xxs': 'deeplabv3-mobilevit-xx-small',
              }

              print('Pushing to the hub...'       )
              a__					=					model_mapping[mobilevit_name]
              image_processor.push_to_hub(__lowerCAmelCase ,   organization='apple'       )
              model.push_to_hub(__lowerCAmelCase ,   organization='apple'       )


if __name__ == "__main__":
      snake_case							:	Dict					      =				argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          '''--mobilevit_name''',
          default='''mobilevit_s''',
          type=str,
          help=(
              '''Name of the MobileViT model you\'d like to convert. Should be one of \'mobilevit_s\', \'mobilevit_xs\','''
              ''' \'mobilevit_xxs\', \'deeplabv3_mobilevit_s\', \'deeplabv3_mobilevit_xs\', \'deeplabv3_mobilevit_xxs\'.'''
          ),
      )
      parser.add_argument(
          '''--checkpoint_path''', required=True, type=str, help='''Path to the original state dict (.pt file).'''
      )
      parser.add_argument(
          '''--pytorch_dump_folder_path''', required=True, type=str, help='''Path to the output PyTorch model directory.'''
      )
      parser.add_argument(
          '''--push_to_hub''', action='''store_true''', help='''Whether or not to push the converted model to the 🤗 hub.'''
      )

      snake_case							:	List[str]					      =				parser.parse_args()
      convert_movilevit_checkpoint(
          args.mobilevit_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
      )








from typing import List

from ...configuration_utils import PretrainedConfig
from ...utils import logging


snake_case							:	Tuple					      =				logging.get_logger(__name__)

snake_case							:	List[Any]					      =				{
    '''snap-research/efficientformer-l1-300''': (
        '''https://huggingface.co/snap-research/efficientformer-l1-300/resolve/main/config.json'''
    ),
}







class 	snake_case_       (lowerCamelCase_				):
					UpperCAmelCase__ :   int   				=      '''efficientformer'''
					def __init__(				self       :List[str]      ,__snake_case       :List[int] = [3, 2, 6, 4]      ,__snake_case       :List[int] = [48, 96, 2_24, 4_48]      ,__snake_case       :List[bool] = [True, True, True, True]      ,__snake_case       :int = 4_48      ,__snake_case       :int = 32      ,__snake_case       :int = 4      ,__snake_case       :int = 7      ,__snake_case       :int = 5      ,__snake_case       :int = 8      ,__snake_case       :int = 4      ,__snake_case       :float = 0.0      ,__snake_case       :int = 16      ,__snake_case       :int = 3      ,__snake_case       :int = 3      ,__snake_case       :int = 3      ,__snake_case       :int = 2      ,__snake_case       :int = 1      ,__snake_case       :float = 0.0      ,__snake_case       :int = 1      ,__snake_case       :bool = True      ,__snake_case       :bool = True      ,__snake_case       :float = 1E-5      ,__snake_case       :str = "gelu"      ,__snake_case       :float = 0.02      ,__snake_case       :float = 1E-12      ,__snake_case       :int = 2_24      ,__snake_case       :float = 1E-05      ,**__snake_case       :Dict      ,) ->   None:
												super().__init__(**__snake_case				)

												a__					=					hidden_act
												a__					=					hidden_dropout_prob
												a__					=					hidden_sizes
												a__					=					num_hidden_layers
												a__					=					num_attention_heads
												a__					=					initializer_range
												a__					=					layer_norm_eps
												a__					=					patch_size
												a__					=					num_channels
												a__					=					depths
												a__					=					mlp_expansion_ratio
												a__					=					downsamples
												a__					=					dim
												a__					=					key_dim
												a__					=					attention_ratio
												a__					=					resolution
												a__					=					pool_size
												a__					=					downsample_patch_size
												a__					=					downsample_stride
												a__					=					downsample_pad
												a__					=					drop_path_rate
												a__					=					num_metaad_blocks
												a__					=					distillation
												a__					=					use_layer_scale
												a__					=					layer_scale_init_value
												a__					=					image_size
												a__					=					batch_norm_eps




