


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()
UpperCAmelCase__   	=							logging.get_logger(__name__)
def 				UpperCAmelCase_					(							__snake_case						)     ->			Optional[Any]:







 """simple docstring"""


 _lowercase							      =MobileViTConfig()

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

 if mobilevit_name.startswith('''deeplabv3_'''						):
  _lowercase							      =512
  _lowercase							      =16
  _lowercase							      =21
  _lowercase							      ='''pascal-voc-id2label.json'''
 else:
  _lowercase							      =1000
  _lowercase							      ='''imagenet-1k-id2label.json'''

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

 return config
def 				UpperCAmelCase_					(							__snake_case     ,						__snake_case=False						)     ->			Tuple:







 """simple docstring"""


 for i in range(1     ,						6						):
  if F"layer_{i}." in name:
   _lowercase							      =name.replace(F"layer_{i}."     ,						F"encoder.layer.{i - 1}."						)

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

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

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

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

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

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

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

 return name
def 				UpperCAmelCase_					(							__snake_case     ,						__snake_case     ,						__snake_case=False						)     ->			Optional[Any]:







 """simple docstring"""


 if base_model:
  _lowercase							      =''''''
 else:
  _lowercase							      ='''mobilevit.'''

 for key in orig_state_dict.copy().keys():
  _lowercase							      =orig_state_dict.pop(__snake_case						)

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

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

 return orig_state_dict
def 				UpperCAmelCase_					(							)     ->			Union[str, Any]:







 """simple docstring"""


 _lowercase							      ='''http://images.cocodataset.org/val2017/000000039769.jpg'''
 _lowercase							      =Image.open(requests.get(__snake_case     ,						stream=__snake_case						).raw						)
 return im
@torch.no_grad()
def 				UpperCAmelCase_					(							__snake_case     ,						__snake_case     ,						__snake_case     ,						__snake_case=False						)     ->			int:







 """simple docstring"""


 _lowercase							      =get_mobilevit_config(__snake_case						)

 # load original state_dict
 _lowercase							      =torch.load(__snake_case     ,						map_location='''cpu'''						)

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

 _lowercase							      =convert_state_dict(__snake_case     ,						__snake_case						)
 model.load_state_dict(__snake_case						)

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

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

  if mobilevit_name == "deeplabv3_mobilevit_s":
   _lowercase							      =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":
   _lowercase							      =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":
   _lowercase							      =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, 1000)

  if mobilevit_name == "mobilevit_s":
   _lowercase							      =torch.tensor([-0.98_66, 0.23_92, -1.12_41]						)
  elif mobilevit_name == "mobilevit_xs":
   _lowercase							      =torch.tensor([-2.47_61, -0.93_99, -1.95_87]						)
  elif mobilevit_name == "mobilevit_xxs":
   _lowercase							      =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:
  _lowercase							      ={
      '''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...'''						)
  _lowercase							      =model_mapping[mobilevit_name]
  image_processor.push_to_hub(__snake_case     ,						organization='''apple'''						)
  model.push_to_hub(__snake_case     ,						organization='''apple'''						)


if __name__ == "__main__":
  UpperCAmelCase__   	=							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.'''
  )

  UpperCAmelCase__   	=							parser.parse_args()
  convert_movilevit_checkpoint(
      args.mobilevit_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
  )








import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
     import torch

if is_vision_available():
     from PIL import Image

     from transformers import MobileViTImageProcessor







class 				__lowerCAmelCase       (    unittest.TestCase):







      def __init__(    self		,      lowerCAmelCase__		,      lowerCAmelCase__=7		,      lowerCAmelCase__=3		,      lowerCAmelCase__=1_8		,      lowerCAmelCase__=3_0		,      lowerCAmelCase__=4_0_0		,      lowerCAmelCase__=True		,      lowerCAmelCase__=None		,      lowerCAmelCase__=True		,      lowerCAmelCase__=None		,      lowerCAmelCase__=True		,      )   ->						Optional[Any]:



        '''simple docstring'''




        a__	:					Union[str, Any]										=size if size is not None else {"shortest_edge": 2_0}
        a__	:					List[str]										=crop_size if crop_size is not None else {"height": 1_8, "width": 1_8}
        a__	:					Tuple										=parent
        a__	:					Union[str, Any]										=batch_size
        a__	:					List[str]										=num_channels
        a__	:					List[Any]										=image_size
        a__	:					str										=min_resolution
        a__	:					Optional[int]										=max_resolution
        a__	:					Tuple										=do_resize
        a__	:					Union[str, Any]										=size
        a__	:					List[Any]										=do_center_crop
        a__	:					List[str]										=crop_size
        a__	:					Optional[int]										=do_flip_channel_order







      def    _lowercase  (    self							)   ->						Optional[int]:



        '''simple docstring'''




        return {
            "do_resize": self.do_resize,
            "size": self.size,
            "do_center_crop": self.do_center_crop,
            "crop_size": self.crop_size,
            "do_flip_channel_order": self.do_flip_channel_order,
        }







@require_torch
@require_vision
class 				__lowerCAmelCase       (    UpperCamelCase__						,				unittest.TestCase):
      _lowercase				:					int						=			MobileViTImageProcessor if is_vision_available() else None







      def    _lowercase  (    self							)   ->						Optional[Any]:



        '''simple docstring'''




        a__	:					Tuple										=MobileViTImageProcessingTester(self							)







      @property
      def    _lowercase  (    self							)   ->						List[str]:



        '''simple docstring'''




        return self.image_processor_tester.prepare_image_processor_dict()







      def    _lowercase  (    self							)   ->						List[str]:



        '''simple docstring'''




        a__	:					str										=self.image_processing_class(**self.image_processor_dict							)
        self.assertTrue(hasattr(lowerCAmelCase__		,      "do_resize"							)							)
        self.assertTrue(hasattr(lowerCAmelCase__		,      "size"							)							)
        self.assertTrue(hasattr(lowerCAmelCase__		,      "do_center_crop"							)							)
        self.assertTrue(hasattr(lowerCAmelCase__		,      "center_crop"							)							)
        self.assertTrue(hasattr(lowerCAmelCase__		,      "do_flip_channel_order"							)							)







      def    _lowercase  (    self							)   ->						Optional[int]:



        '''simple docstring'''




        a__	:					List[Any]										=self.image_processing_class.from_dict(self.image_processor_dict							)
        self.assertEqual(image_processor.size		,      {"shortest_edge": 2_0}							)
        self.assertEqual(image_processor.crop_size		,      {"height": 1_8, "width": 1_8}							)

        a__	:					Union[str, Any]										=self.image_processing_class.from_dict(self.image_processor_dict		,      size=4_2		,      crop_size=8_4							)
        self.assertEqual(image_processor.size		,      {"shortest_edge": 4_2}							)
        self.assertEqual(image_processor.crop_size		,      {"height": 8_4, "width": 8_4}							)







      def    _lowercase  (    self							)   ->						Optional[Any]:



        '''simple docstring'''




        pass







      def    _lowercase  (    self							)   ->						Tuple:



        '''simple docstring'''




        a__	:					List[str]										=self.image_processing_class(**self.image_processor_dict							)
        # create random PIL images
        a__	:					Any										=prepare_image_inputs(self.image_processor_tester		,      equal_resolution=lowerCAmelCase__							)
        for image in image_inputs:
          self.assertIsInstance(lowerCAmelCase__		,      Image.Image							)

        # Test not batched input
        a__	:					Tuple										=image_processing(image_inputs[0]		,      return_tensors="pt"							).pixel_values
        self.assertEqual(
            encoded_images.shape		,      (
                1,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size["height"],
                self.image_processor_tester.crop_size["width"],
            )		,      )

        # Test batched
        a__	:					List[Any]										=image_processing(lowerCAmelCase__		,      return_tensors="pt"							).pixel_values
        self.assertEqual(
            encoded_images.shape		,      (
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size["height"],
                self.image_processor_tester.crop_size["width"],
            )		,      )







      def    _lowercase  (    self							)   ->						Any:



        '''simple docstring'''




        a__	:					Optional[Any]										=self.image_processing_class(**self.image_processor_dict							)
        # create random numpy tensors
        a__	:					List[Any]										=prepare_image_inputs(self.image_processor_tester		,      equal_resolution=lowerCAmelCase__		,      numpify=lowerCAmelCase__							)
        for image in image_inputs:
          self.assertIsInstance(lowerCAmelCase__		,      np.ndarray							)

        # Test not batched input
        a__	:					Tuple										=image_processing(image_inputs[0]		,      return_tensors="pt"							).pixel_values
        self.assertEqual(
            encoded_images.shape		,      (
                1,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size["height"],
                self.image_processor_tester.crop_size["width"],
            )		,      )

        # Test batched
        a__	:					int										=image_processing(lowerCAmelCase__		,      return_tensors="pt"							).pixel_values
        self.assertEqual(
            encoded_images.shape		,      (
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size["height"],
                self.image_processor_tester.crop_size["width"],
            )		,      )







      def    _lowercase  (    self							)   ->						Optional[Any]:



        '''simple docstring'''




        a__	:					int										=self.image_processing_class(**self.image_processor_dict							)
        # create random PyTorch tensors
        a__	:					Tuple										=prepare_image_inputs(self.image_processor_tester		,      equal_resolution=lowerCAmelCase__		,      torchify=lowerCAmelCase__							)
        for image in image_inputs:
          self.assertIsInstance(lowerCAmelCase__		,      torch.Tensor							)

        # Test not batched input
        a__	:					List[str]										=image_processing(image_inputs[0]		,      return_tensors="pt"							).pixel_values
        self.assertEqual(
            encoded_images.shape		,      (
                1,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size["height"],
                self.image_processor_tester.crop_size["width"],
            )		,      )

        # Test batched
        a__	:					List[str]										=image_processing(lowerCAmelCase__		,      return_tensors="pt"							).pixel_values
        self.assertEqual(
            encoded_images.shape		,      (
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size["height"],
                self.image_processor_tester.crop_size["width"],
            )		,      )






