
"""simple docstring"""





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()
SCREAMING_SNAKE_CASE_												= logging.get_logger(__name__)







def   A__ ( A__		)		->			Tuple:





   '''simple docstring'''




   _UpperCAmelCase         =      MobileViTConfig()

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

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

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

   return config







def   A__ ( A__      ,      A__=False		)		->			Any:





   '''simple docstring'''




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

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

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

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

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

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

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

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

   return name







def   A__ ( A__      ,      A__      ,      A__=False		)		->			str:





   '''simple docstring'''




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

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

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

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

   return orig_state_dict







def   A__ ( )		->			List[Any]:





   '''simple docstring'''




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







@torch.no_grad()
def   A__ ( A__      ,      A__      ,      A__      ,      A__=False		)		->			Optional[int]:





   '''simple docstring'''




   _UpperCAmelCase         =      get_mobilevit_config(_UpperCAmelCase		)

   # load original state_dict
   _UpperCAmelCase         =      torch.load(_UpperCAmelCase      ,      map_location="cpu"		)

   # load 🤗 model
   if mobilevit_name.startswith("deeplabv3_"		):
      _UpperCAmelCase         =      MobileViTForSemanticSegmentation(_UpperCAmelCase		).eval()
   else:
      _UpperCAmelCase         =      MobileViTForImageClassification(_UpperCAmelCase		).eval()

   _UpperCAmelCase         =      convert_state_dict(_UpperCAmelCase      ,      _UpperCAmelCase		)
   model.load_state_dict(_UpperCAmelCase		)

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

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

      if mobilevit_name == "deeplabv3_mobilevit_s":
         _UpperCAmelCase         =      torch.tensor(
             [
                 [[6.2065, 6.1292, 6.2070], [6.1079, 6.1254, 6.1747], [6.0042, 6.1071, 6.1034]],
                 [[-6.9253, -6.8653, -7.0398], [-7.3218, -7.3983, -7.3670], [-7.1961, -7.2482, -7.1569]],
                 [[-4.4723, -4.4348, -4.3769], [-5.3629, -5.4632, -5.4598], [-5.1587, -5.3402, -5.5059]],
             ]		)
      elif mobilevit_name == "deeplabv3_mobilevit_xs":
         _UpperCAmelCase         =      torch.tensor(
             [
                 [[5.4449, 5.5733, 5.6314], [5.1815, 5.3930, 5.5963], [5.1656, 5.4333, 5.4853]],
                 [[-9.4423, -9.7766, -9.6714], [-9.1581, -9.5720, -9.5519], [-9.1006, -9.6458, -9.5703]],
                 [[-7.7721, -7.3716, -7.1583], [-8.4599, -8.0624, -7.7944], [-8.4172, -7.8366, -7.5025]],
             ]		)
      elif mobilevit_name == "deeplabv3_mobilevit_xxs":
         _UpperCAmelCase         =      torch.tensor(
             [
                 [[6.9811, 6.9743, 7.3123], [7.1777, 7.1931, 7.3938], [7.5633, 7.8050, 7.8901]],
                 [[-10.5536, -10.2332, -10.2924], [-10.2336, -9.8624, -9.5964], [-10.8840, -10.8158, -10.6659]],
                 [[-3.4938, -3.0631, -2.8620], [-3.4205, -2.8135, -2.6875], [-3.4179, -2.7945, -2.8750]],
             ]		)
      else:
         raise ValueError(F"""Unknown mobilevit_name: {mobilevit_name}"""		)

      assert torch.allclose(logits[0, :3, :3, :3]      ,      _UpperCAmelCase      ,      atol=1E-4		)
   else:
      assert logits.shape == (1, 1000)

      if mobilevit_name == "mobilevit_s":
         _UpperCAmelCase         =      torch.tensor([-0.9866, 0.2392, -1.1241]		)
      elif mobilevit_name == "mobilevit_xs":
         _UpperCAmelCase         =      torch.tensor([-2.4761, -0.9399, -1.9587]		)
      elif mobilevit_name == "mobilevit_xxs":
         _UpperCAmelCase         =      torch.tensor([-1.9364, -1.2327, -0.4653]		)
      else:
         raise ValueError(F"""Unknown mobilevit_name: {mobilevit_name}"""		)

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

   Path(_UpperCAmelCase		).mkdir(exist_ok=_UpperCAmelCase		)
   print(F"""Saving model {mobilevit_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 push_to_hub:
      _UpperCAmelCase         =      {
          '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..."		)
      _UpperCAmelCase         =      model_mapping[mobilevit_name]
      image_processor.push_to_hub(_UpperCAmelCase      ,      organization="apple"		)
      model.push_to_hub(_UpperCAmelCase      ,      organization="apple"		)


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

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






'''simple docstring'''





import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding



class  snake_case__						(    SCREAMING_SNAKE_CASE_				):
 A__				= ['''image_processor''', '''tokenizer''']
 A__				= '''CLIPImageProcessor'''
 A__				= ('''CLIPTokenizer''', '''CLIPTokenizerFast''')
 def __init__(   self						:							Any      ,     __a						:							str=None      ,     __a						:							List[Any]=None      ,     **__a						:							List[str]  )      ->   Optional[Any]:







       '''simple docstring'''
       __snake_case :   Optional[Any]													=    None
       if "feature_extractor" in kwargs:
             warnings.warn(
                 'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
                 ' instead.'      ,     __a      ,     )
             __snake_case :   List[str]													=    kwargs.pop('feature_extractor'  )

       __snake_case :   List[Any]													=    image_processor if image_processor is not None else feature_extractor
       if image_processor is None:
             raise ValueError('You need to specify an `image_processor`.'  )
       if tokenizer is None:
             raise ValueError('You need to specify a `tokenizer`.'  )

       super().__init__(__a      ,     __a  )
 def __call__(   self						:							List[Any]      ,     __a						:							Optional[int]=None      ,     __a						:							Optional[int]=None      ,     __a						:							Union[str, Any]=None      ,     **__a						:							Union[str, Any]  )      ->   List[Any]:







       '''simple docstring'''

       if text is None and images is None:
             raise ValueError('You have to specify either text or images. Both cannot be none.'  )

       if text is not None:
             __snake_case :   Any													=    self.tokenizer(__a      ,     return_tensors=__a      ,     **__a  )

       if images is not None:
             __snake_case :   str													=    self.image_processor(__a      ,     return_tensors=__a      ,     **__a  )

       if text is not None and images is not None:
             __snake_case :   Optional[Any]													=    image_features.pixel_values
             return encoding
       elif text is not None:
             return encoding
       else:
             return BatchEncoding(data=dict(**__a  )      ,     tensor_type=__a  )
 def A_   (   self						:							List[Any]      ,     *__a						:							Dict      ,     **__a						:							Dict  )      ->   Union[str, Any]:







       '''simple docstring'''
       return self.tokenizer.batch_decode(*__a      ,     **__a  )
 def A_   (   self						:							str      ,     *__a						:							Tuple      ,     **__a						:							List[str]  )      ->   int:







       '''simple docstring'''
       return self.tokenizer.decode(*__a      ,     **__a  )
 @property
 def A_   (   self						:							Union[str, Any]  )      ->   Optional[Any]:







       '''simple docstring'''
       __snake_case :   Tuple													=    self.tokenizer.model_input_names
       __snake_case :   Tuple													=    self.image_processor.model_input_names
       return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names  )  )
 @property
 def A_   (   self						:							Any  )      ->   Any:







       '''simple docstring'''
       warnings.warn(
           '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'      ,     __a      ,     )
       return self.image_processor_class





 @property
 def A_   (   self						:							Optional[int]  )      ->   Optional[Any]:







       '''simple docstring'''
       warnings.warn(
           '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'      ,     __a      ,     )
       return self.image_processor



