






'''simple docstring'''

import argparse
from collections import OrderedDict
from pathlib import Path

import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from torchvision.transforms import functional as F

from transformers import DetrImageProcessor, TableTransformerConfig, TableTransformerForObjectDetection
from transformers.utils import logging


logging.set_verbosity_info()
UpperCamelCase      					=logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
UpperCamelCase      					=[]
for i in range(6):
      # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
      rename_keys.append(
          (f"transformer.encoder.layers.{i}.self_attn.out_proj.weight", f"encoder.layers.{i}.self_attn.out_proj.weight")
      )
      rename_keys.append(
          (f"transformer.encoder.layers.{i}.self_attn.out_proj.bias", f"encoder.layers.{i}.self_attn.out_proj.bias")
      )
      rename_keys.append((f"transformer.encoder.layers.{i}.linear1.weight", f"encoder.layers.{i}.fc1.weight"))
      rename_keys.append((f"transformer.encoder.layers.{i}.linear1.bias", f"encoder.layers.{i}.fc1.bias"))
      rename_keys.append((f"transformer.encoder.layers.{i}.linear2.weight", f"encoder.layers.{i}.fc2.weight"))
      rename_keys.append((f"transformer.encoder.layers.{i}.linear2.bias", f"encoder.layers.{i}.fc2.bias"))
      rename_keys.append(
          (f"transformer.encoder.layers.{i}.norm1.weight", f"encoder.layers.{i}.self_attn_layer_norm.weight")
      )
      rename_keys.append((f"transformer.encoder.layers.{i}.norm1.bias", f"encoder.layers.{i}.self_attn_layer_norm.bias"))
      rename_keys.append((f"transformer.encoder.layers.{i}.norm2.weight", f"encoder.layers.{i}.final_layer_norm.weight"))
      rename_keys.append((f"transformer.encoder.layers.{i}.norm2.bias", f"encoder.layers.{i}.final_layer_norm.bias"))
      # decoder layers: 2 times output projection, 2 feedforward neural networks and 3 layernorms
      rename_keys.append(
          (f"transformer.decoder.layers.{i}.self_attn.out_proj.weight", f"decoder.layers.{i}.self_attn.out_proj.weight")
      )
      rename_keys.append(
          (f"transformer.decoder.layers.{i}.self_attn.out_proj.bias", f"decoder.layers.{i}.self_attn.out_proj.bias")
      )
      rename_keys.append(
          (
              f"transformer.decoder.layers.{i}.multihead_attn.out_proj.weight",
              f"decoder.layers.{i}.encoder_attn.out_proj.weight",
          )
      )
      rename_keys.append(
          (
              f"transformer.decoder.layers.{i}.multihead_attn.out_proj.bias",
              f"decoder.layers.{i}.encoder_attn.out_proj.bias",
          )
      )
      rename_keys.append((f"transformer.decoder.layers.{i}.linear1.weight", f"decoder.layers.{i}.fc1.weight"))
      rename_keys.append((f"transformer.decoder.layers.{i}.linear1.bias", f"decoder.layers.{i}.fc1.bias"))
      rename_keys.append((f"transformer.decoder.layers.{i}.linear2.weight", f"decoder.layers.{i}.fc2.weight"))
      rename_keys.append((f"transformer.decoder.layers.{i}.linear2.bias", f"decoder.layers.{i}.fc2.bias"))
      rename_keys.append(
          (f"transformer.decoder.layers.{i}.norm1.weight", f"decoder.layers.{i}.self_attn_layer_norm.weight")
      )
      rename_keys.append((f"transformer.decoder.layers.{i}.norm1.bias", f"decoder.layers.{i}.self_attn_layer_norm.bias"))
      rename_keys.append(
          (f"transformer.decoder.layers.{i}.norm2.weight", f"decoder.layers.{i}.encoder_attn_layer_norm.weight")
      )
      rename_keys.append(
          (f"transformer.decoder.layers.{i}.norm2.bias", f"decoder.layers.{i}.encoder_attn_layer_norm.bias")
      )
      rename_keys.append((f"transformer.decoder.layers.{i}.norm3.weight", f"decoder.layers.{i}.final_layer_norm.weight"))
      rename_keys.append((f"transformer.decoder.layers.{i}.norm3.bias", f"decoder.layers.{i}.final_layer_norm.bias"))

# convolutional projection + query embeddings + layernorm of encoder + layernorm of decoder + class and bounding box heads
rename_keys.extend(
    [
        ("input_proj.weight", "input_projection.weight"),
        ("input_proj.bias", "input_projection.bias"),
        ("query_embed.weight", "query_position_embeddings.weight"),
        ("transformer.encoder.norm.weight", "encoder.layernorm.weight"),
        ("transformer.encoder.norm.bias", "encoder.layernorm.bias"),
        ("transformer.decoder.norm.weight", "decoder.layernorm.weight"),
        ("transformer.decoder.norm.bias", "decoder.layernorm.bias"),
        ("class_embed.weight", "class_labels_classifier.weight"),
        ("class_embed.bias", "class_labels_classifier.bias"),
        ("bbox_embed.layers.0.weight", "bbox_predictor.layers.0.weight"),
        ("bbox_embed.layers.0.bias", "bbox_predictor.layers.0.bias"),
        ("bbox_embed.layers.1.weight", "bbox_predictor.layers.1.weight"),
        ("bbox_embed.layers.1.bias", "bbox_predictor.layers.1.bias"),
        ("bbox_embed.layers.2.weight", "bbox_predictor.layers.2.weight"),
        ("bbox_embed.layers.2.bias", "bbox_predictor.layers.2.bias"),
    ]
)



def 				snake_case       (  a_				:			List[str]     , a_				:			Dict     , a_				:			Dict						)							->					int:





      """simple docstring"""

      UpperCamelCase_					: List[Any]            =				state_dict.pop(a_						)
      UpperCamelCase_					: Dict            =				val



def 				snake_case       (  a_				:			List[str]						)							->					int:





      """simple docstring"""

      UpperCamelCase_					: List[str]            =				OrderedDict()
      for key, value in state_dict.items():
            if "backbone.0.body" in key:
                  UpperCamelCase_					: List[str]            =				key.replace("""backbone.0.body"""     , """backbone.conv_encoder.model"""						)
                  UpperCamelCase_					: Optional[int]            =				value
            else:
                  UpperCamelCase_					: int            =				value

      return new_state_dict



def 				snake_case       (  a_				:			str						)							->					Tuple:





      """simple docstring"""

      UpperCamelCase_					: Union[str, Any]            =				""""""

      # first: transformer encoder
      for i in range(6						):
            # read in weights + bias of input projection layer (in PyTorch's MultiHeadAttention, this is a single matrix + bias)
            UpperCamelCase_					: int            =				state_dict.pop(f"{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight"						)
            UpperCamelCase_					: Tuple            =				state_dict.pop(f"{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_bias"						)
            # next, add query, keys and values (in that order) to the state dict
            UpperCamelCase_					: List[Any]            =				in_proj_weight[:256, :]
            UpperCamelCase_					: List[str]            =				in_proj_bias[:256]
            UpperCamelCase_					: Union[str, Any]            =				in_proj_weight[256:512, :]
            UpperCamelCase_					: int            =				in_proj_bias[256:512]
            UpperCamelCase_					: Tuple            =				in_proj_weight[-256:, :]
            UpperCamelCase_					: List[Any]            =				in_proj_bias[-256:]
      # next: transformer decoder (which is a bit more complex because it also includes cross-attention)
      for i in range(6						):
            # read in weights + bias of input projection layer of self-attention
            UpperCamelCase_					: Union[str, Any]            =				state_dict.pop(f"{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight"						)
            UpperCamelCase_					: List[Any]            =				state_dict.pop(f"{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_bias"						)
            # next, add query, keys and values (in that order) to the state dict
            UpperCamelCase_					: Union[str, Any]            =				in_proj_weight[:256, :]
            UpperCamelCase_					: List[str]            =				in_proj_bias[:256]
            UpperCamelCase_					: Optional[Any]            =				in_proj_weight[256:512, :]
            UpperCamelCase_					: Any            =				in_proj_bias[256:512]
            UpperCamelCase_					: List[Any]            =				in_proj_weight[-256:, :]
            UpperCamelCase_					: int            =				in_proj_bias[-256:]
            # read in weights + bias of input projection layer of cross-attention
            UpperCamelCase_					: Any            =				state_dict.pop(
                f"{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight"						)
            UpperCamelCase_					: Dict            =				state_dict.pop(f"{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_bias"						)
            # next, add query, keys and values (in that order) of cross-attention to the state dict
            UpperCamelCase_					: Optional[int]            =				in_proj_weight_cross_attn[:256, :]
            UpperCamelCase_					: Dict            =				in_proj_bias_cross_attn[:256]
            UpperCamelCase_					: Dict            =				in_proj_weight_cross_attn[256:512, :]
            UpperCamelCase_					: Union[str, Any]            =				in_proj_bias_cross_attn[256:512]
            UpperCamelCase_					: Dict            =				in_proj_weight_cross_attn[-256:, :]
            UpperCamelCase_					: Union[str, Any]            =				in_proj_bias_cross_attn[-256:]



def 				snake_case       (  a_				:			str     , a_				:			str						)							->					List[Any]:





      """simple docstring"""

      UpperCamelCase_     ,      UpperCamelCase_					: Tuple            =				image.size
      UpperCamelCase_					: str            =				max(a_     , a_						)
      UpperCamelCase_					: Dict            =				800 if """detection""" in checkpoint_url else 1_000
      UpperCamelCase_					: List[Any]            =				target_max_size / current_max_size
      UpperCamelCase_					: Dict            =				image.resize((int(round(scale * width						)						), int(round(scale * height						)						))						)

      return resized_image



def 				snake_case       (  a_				:			Optional[int]						)							->					Tuple:





      """simple docstring"""

      UpperCamelCase_					: Optional[Any]            =				F.to_tensor(a_						)
      UpperCamelCase_					: Union[str, Any]            =				F.normalize(a_     , mean=[0.485, 0.456, 0.406]     , std=[0.229, 0.224, 0.225]						)
      return image



@torch.no_grad()
def 				snake_case       (  a_				:			Union[str, Any]     , a_				:			str     , a_				:			List[Any]						)							->					Any:





      """simple docstring"""

      logger.info("""Converting model..."""						)

      # load original state dict
      UpperCamelCase_					: Optional[Any]            =				torch.hub.load_state_dict_from_url(a_     , map_location="""cpu"""						)
      # rename keys
      for src, dest in rename_keys:
            rename_key(a_     , a_     , a_						)
      UpperCamelCase_					: Optional[Any]            =				rename_backbone_keys(a_						)
      # query, key and value matrices need special treatment
      read_in_q_k_v(a_						)
      # important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
      UpperCamelCase_					: Optional[int]            =				"""model."""
      for key in state_dict.copy().keys():
            if not key.startswith("""class_labels_classifier"""						) and not key.startswith("""bbox_predictor"""						):
                  UpperCamelCase_					: List[str]            =				state_dict.pop(a_						)
                  UpperCamelCase_					: str            =				val
    # create HuggingFace model and load state dict
      UpperCamelCase_					: Optional[Any]            =				TableTransformerConfig(
          backbone="""resnet18"""     , mask_loss_coefficient=1     , dice_loss_coefficient=1     , ce_loss_coefficient=1     , bbox_loss_coefficient=5     , giou_loss_coefficient=2     , eos_coefficient=0.4     , class_cost=1     , bbox_cost=5     , giou_cost=2     , )

      if "detection" in checkpoint_url:
            UpperCamelCase_					: Optional[Any]            =				15
            UpperCamelCase_					: Tuple            =				2
            UpperCamelCase_					: Tuple            =				{0: """table""", 1: """table rotated"""}
            UpperCamelCase_					: Any            =				idalabel
            UpperCamelCase_					: Tuple            =				{v: k for k, v in idalabel.items()}
      else:
            UpperCamelCase_					: str            =				125
            UpperCamelCase_					: Optional[Any]            =				6
            UpperCamelCase_					: str            =				{
                0: """table""",
                1: """table column""",
                2: """table row""",
                3: """table column header""",
                4: """table projected row header""",
                5: """table spanning cell""",
            }
            UpperCamelCase_					: List[str]            =				idalabel
            UpperCamelCase_					: Optional[Any]            =				{v: k for k, v in idalabel.items()}

      UpperCamelCase_					: str            =				DetrImageProcessor(
          format="""coco_detection"""     , max_size=800 if """detection""" in checkpoint_url else 1_000						)
      UpperCamelCase_					: str            =				TableTransformerForObjectDetection(a_						)
      model.load_state_dict(a_						)
      model.eval()

      # verify our conversion
      UpperCamelCase_					: List[Any]            =				"""example_pdf.png""" if """detection""" in checkpoint_url else """example_table.png"""
      UpperCamelCase_					: Any            =				hf_hub_download(repo_id="""nielsr/example-pdf"""     , repo_type="""dataset"""     , filename=a_						)
      UpperCamelCase_					: Optional[int]            =				Image.open(a_						).convert("""RGB"""						)
      UpperCamelCase_					: Optional[Any]            =				normalize(resize(a_     , a_						)						).unsqueeze(0						)

      UpperCamelCase_					: str            =				model(a_						)

      if "detection" in checkpoint_url:
            UpperCamelCase_					: Optional[Any]            =				(1, 15, 3)
            UpperCamelCase_					: List[Any]            =				torch.tensor(
                [[-6.7_897, -16.9_985, 6.7_937], [-8.0_186, -22.2_192, 6.9_677], [-7.3_117, -21.0_708, 7.4_055]]						)
            UpperCamelCase_					: Any            =				torch.tensor([[0.4_867, 0.1_767, 0.6_732], [0.6_718, 0.4_479, 0.3_830], [0.4_716, 0.1_760, 0.6_364]]						)

      else:
            UpperCamelCase_					: Dict            =				(1, 125, 7)
            UpperCamelCase_					: str            =				torch.tensor(
                [[-18.1_430, -8.3_214, 4.8_274], [-18.4_685, -7.1_361, -4.2_667], [-26.3_693, -9.3_429, -4.9_962]]						)
            UpperCamelCase_					: Tuple            =				torch.tensor([[0.4_983, 0.5_595, 0.9_440], [0.4_916, 0.6_315, 0.5_954], [0.6_108, 0.8_637, 0.1_135]]						)

      assert outputs.logits.shape == expected_shape
      assert torch.allclose(outputs.logits[0, :3, :3]     , a_     , atol=1e-4						)
      assert torch.allclose(outputs.pred_boxes[0, :3, :3]     , a_     , atol=1e-4						)
      print("""Looks ok!"""						)

      if pytorch_dump_folder_path is not None:
            # Save model and image processor
            logger.info(f"Saving PyTorch model and image processor to {pytorch_dump_folder_path}..."						)
            Path(a_						).mkdir(exist_ok=a_						)
            model.save_pretrained(a_						)
            image_processor.save_pretrained(a_						)

      if push_to_hub:
            # Push model to HF hub
            logger.info("""Pushing model to the hub..."""						)
            UpperCamelCase_					: Optional[int]            =				(
                """microsoft/table-transformer-detection"""
                if """detection""" in checkpoint_url
                else """microsoft/table-transformer-structure-recognition"""
            )
            model.push_to_hub(a_						)
            image_processor.push_to_hub(a_						)


if __name__ == "__main__":
      UpperCamelCase      					=argparse.ArgumentParser()

      parser.add_argument(
          "--checkpoint_url",
          default="https://pubtables1m.blob.core.windows.net/model/pubtables1m_detection_detr_r18.pth",
          type=str,
          choices=[
              "https://pubtables1m.blob.core.windows.net/model/pubtables1m_detection_detr_r18.pth",
              "https://pubtables1m.blob.core.windows.net/model/pubtables1m_structure_detr_r18.pth",
          ],
          help="URL of the Table Transformer checkpoint you\'d like to convert.",
      )
      parser.add_argument(
          "--pytorch_dump_folder_path", default=None, type=str, help="Path to the folder to output PyTorch model."
      )
      parser.add_argument(
          "--push_to_hub", action="store_true", help="Whether or not to push the converted model to the 🤗 hub."
      )
      UpperCamelCase      					=parser.parse_args()
      convert_table_transformer_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)







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()
_lowercase:	Dict       			=		logging.get_logger(__name__)






def _lowerCamelCase  (		snake_case  ):
							_lowerCAmelCase							=							MobileViTConfig()

							# size of the architecture
							if "mobilevit_s" in mobilevit_name:
														_lowerCAmelCase							=							[144, 192, 240]
														_lowerCAmelCase							=							[16, 32, 64, 96, 128, 160, 640]
							elif "mobilevit_xs" in mobilevit_name:
														_lowerCAmelCase							=							[96, 120, 144]
														_lowerCAmelCase							=							[16, 32, 48, 64, 80, 96, 384]
							elif "mobilevit_xxs" in mobilevit_name:
														_lowerCAmelCase							=							[64, 80, 96]
														_lowerCAmelCase							=							[16, 16, 24, 48, 64, 80, 320]
														_lowerCAmelCase							=							0.05
														_lowerCAmelCase							=							2.0

							if mobilevit_name.startswith('deeplabv3_'  ):
														_lowerCAmelCase							=							512
														_lowerCAmelCase							=							16
														_lowerCAmelCase							=							21
														_lowerCAmelCase							=							'pascal-voc-id2label.json'
							else:
														_lowerCAmelCase							=							1_000
														_lowerCAmelCase							=							'imagenet-1k-id2label.json'

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

							return config






def _lowerCamelCase  (		snake_case     , snake_case=False  ):
							for i in range(1     , 6  ):
														if F'layer_{i}.' in name:
																					_lowerCAmelCase							=							name.replace(F'layer_{i}.'     , F'encoder.layer.{i - 1}.'  )

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

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

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

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

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

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

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

							return name






def _lowerCamelCase  (		snake_case     , snake_case     , snake_case=False  ):
							if base_model:
														_lowerCAmelCase							=							''
							else:
														_lowerCAmelCase							=							'mobilevit.'

							for key in orig_state_dict.copy().keys():
														_lowerCAmelCase							=							orig_state_dict.pop(snake_case  )

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

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

							return orig_state_dict






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






@torch.no_grad()
def _lowerCamelCase  (		snake_case     , snake_case     , snake_case     , snake_case=False  ):
							_lowerCAmelCase							=							get_mobilevit_config(snake_case  )

							# load original state_dict
							_lowerCAmelCase							=							torch.load(snake_case     , map_location='cpu'  )

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

							_lowerCAmelCase							=							convert_state_dict(snake_case     , snake_case  )
							model.load_state_dict(snake_case  )

							# Check outputs on an image, prepared by MobileViTImageProcessor
							_lowerCAmelCase							=							MobileViTImageProcessor(crop_size=config.image_size     , size=config.image_size + 32  )
							_lowerCAmelCase							=							image_processor(images=prepare_img()     , return_tensors='pt'  )
							_lowerCAmelCase							=							model(**snake_case  )
							_lowerCAmelCase							=							outputs.logits

							if mobilevit_name.startswith('deeplabv3_'  ):
														assert logits.shape == (1, 21, 32, 32)

														if mobilevit_name == "deeplabv3_mobilevit_s":
																					_lowerCAmelCase							=							torch.tensor(
																					    [
																					        [[6.20_65, 6.12_92, 6.20_70], [6.10_79, 6.12_54, 6.17_47], [6.00_42, 6.10_71, 6.10_34]],
																					        [[-6.92_53, -6.86_53, -7.03_98], [-7.32_18, -7.39_83, -7.36_70], [-7.19_61, -7.24_82, -7.15_69]],
																					        [[-4.47_23, -4.43_48, -4.37_69], [-5.36_29, -5.46_32, -5.45_98], [-5.15_87, -5.34_02, -5.50_59]],
																					    ]  )
														elif mobilevit_name == "deeplabv3_mobilevit_xs":
																					_lowerCAmelCase							=							torch.tensor(
																					    [
																					        [[5.44_49, 5.57_33, 5.63_14], [5.18_15, 5.39_30, 5.59_63], [5.16_56, 5.43_33, 5.48_53]],
																					        [[-9.44_23, -9.77_66, -9.67_14], [-9.15_81, -9.57_20, -9.55_19], [-9.10_06, -9.64_58, -9.57_03]],
																					        [[-7.77_21, -7.37_16, -7.15_83], [-8.45_99, -8.06_24, -7.79_44], [-8.41_72, -7.83_66, -7.50_25]],
																					    ]  )
														elif mobilevit_name == "deeplabv3_mobilevit_xxs":
																					_lowerCAmelCase							=							torch.tensor(
																					    [
																					        [[6.98_11, 6.97_43, 7.31_23], [7.17_77, 7.19_31, 7.39_38], [7.56_33, 7.80_50, 7.89_01]],
																					        [[-10.55_36, -10.23_32, -10.29_24], [-10.23_36, -9.86_24, -9.59_64], [-10.88_40, -10.81_58, -10.66_59]],
																					        [[-3.49_38, -3.06_31, -2.86_20], [-3.42_05, -2.81_35, -2.68_75], [-3.41_79, -2.79_45, -2.87_50]],
																					    ]  )
														else:
																					raise ValueError(F'Unknown mobilevit_name: {mobilevit_name}'  )

														assert torch.allclose(logits[0, :3, :3, :3]     , snake_case     , atol=1E-4  )
							else:
														assert logits.shape == (1, 1_000)

														if mobilevit_name == "mobilevit_s":
																					_lowerCAmelCase							=							torch.tensor([-0.98_66, 0.23_92, -1.12_41]  )
														elif mobilevit_name == "mobilevit_xs":
																					_lowerCAmelCase							=							torch.tensor([-2.47_61, -0.93_99, -1.95_87]  )
														elif mobilevit_name == "mobilevit_xxs":
																					_lowerCAmelCase							=							torch.tensor([-1.93_64, -1.23_27, -0.46_53]  )
														else:
																					raise ValueError(F'Unknown mobilevit_name: {mobilevit_name}'  )

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

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

							if push_to_hub:
														_lowerCAmelCase							=							{
														    '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...'  )
														_lowerCAmelCase							=							model_mapping[mobilevit_name]
														image_processor.push_to_hub(snake_case     , organization='apple'  )
														model.push_to_hub(snake_case     , organization='apple'  )


if __name__ == "__main__":
				_lowercase:	Union[str, Any]       			=		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.'''
				)

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






