





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()
_A					:    Tuple   =  logging.get_logger(__name__)



def        _a						(    UpperCAmelCase     )				->				str:







   """simple docstring"""
   lowerCamelCase__   :						Tuple					   =							MobileViTConfig()

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

   if mobilevit_name.startswith('''deeplabv3_'''     ):
      lowerCamelCase__   :						Dict					   =							512
      lowerCamelCase__   :						List[Any]					   =							16
      lowerCamelCase__   :						List[str]					   =							21
      lowerCamelCase__   :						Dict					   =							'''pascal-voc-id2label.json'''
   else:
      lowerCamelCase__   :						Dict					   =							1000
      lowerCamelCase__   :						Optional[Any]					   =							'''imagenet-1k-id2label.json'''

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

   return config



def        _a						(    UpperCAmelCase			,  UpperCAmelCase=False     )				->				int:







   """simple docstring"""
   for i in range(1			,  6     ):
      if f"layer_{i}." in name:
         lowerCamelCase__   :						Optional[int]					   =							name.replace(f"layer_{i}."			,  f"encoder.layer.{i - 1}."     )

   if "conv_1." in name:
      lowerCamelCase__   :						str					   =							name.replace('''conv_1.'''			,  '''conv_stem.'''     )
   if ".block." in name:
      lowerCamelCase__   :						Optional[Any]					   =							name.replace('''.block.'''			,  '''.'''     )

   if "exp_1x1" in name:
      lowerCamelCase__   :						int					   =							name.replace('''exp_1x1'''			,  '''expand_1x1'''     )
   if "red_1x1" in name:
      lowerCamelCase__   :						int					   =							name.replace('''red_1x1'''			,  '''reduce_1x1'''     )
   if ".local_rep.conv_3x3." in name:
      lowerCamelCase__   :						Optional[int]					   =							name.replace('''.local_rep.conv_3x3.'''			,  '''.conv_kxk.'''     )
   if ".local_rep.conv_1x1." in name:
      lowerCamelCase__   :						Tuple					   =							name.replace('''.local_rep.conv_1x1.'''			,  '''.conv_1x1.'''     )
   if ".norm." in name:
      lowerCamelCase__   :						Union[str, Any]					   =							name.replace('''.norm.'''			,  '''.normalization.'''     )
   if ".conv." in name:
      lowerCamelCase__   :						int					   =							name.replace('''.conv.'''			,  '''.convolution.'''     )
   if ".conv_proj." in name:
      lowerCamelCase__   :						Union[str, Any]					   =							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__   :						Union[str, Any]					   =							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__   :						Union[str, Any]					   =							name.replace(f".{i}.{j}."			,  f".{i}."     )
            if "expand_1x1" in name:
               lowerCamelCase__   :						int					   =							name.replace('''expand_1x1'''			,  '''downsampling_layer.expand_1x1'''     )
            if "conv_3x3" in name:
               lowerCamelCase__   :						Optional[Any]					   =							name.replace('''conv_3x3'''			,  '''downsampling_layer.conv_3x3'''     )
            if "reduce_1x1" in name:
               lowerCamelCase__   :						List[Any]					   =							name.replace('''reduce_1x1'''			,  '''downsampling_layer.reduce_1x1'''     )

   for i in range(2			,  5     ):
      if f".global_rep.{i}.weight" in name:
         lowerCamelCase__   :						Optional[int]					   =							name.replace(f".global_rep.{i}.weight"			,  '''.layernorm.weight'''     )
      if f".global_rep.{i}.bias" in name:
         lowerCamelCase__   :						Dict					   =							name.replace(f".global_rep.{i}.bias"			,  '''.layernorm.bias'''     )

   if ".global_rep." in name:
      lowerCamelCase__   :						Optional[int]					   =							name.replace('''.global_rep.'''			,  '''.transformer.'''     )
   if ".pre_norm_mha.0." in name:
      lowerCamelCase__   :						Any					   =							name.replace('''.pre_norm_mha.0.'''			,  '''.layernorm_before.'''     )
   if ".pre_norm_mha.1.out_proj." in name:
      lowerCamelCase__   :						List[Any]					   =							name.replace('''.pre_norm_mha.1.out_proj.'''			,  '''.attention.output.dense.'''     )
   if ".pre_norm_ffn.0." in name:
      lowerCamelCase__   :						List[Any]					   =							name.replace('''.pre_norm_ffn.0.'''			,  '''.layernorm_after.'''     )
   if ".pre_norm_ffn.1." in name:
      lowerCamelCase__   :						Dict					   =							name.replace('''.pre_norm_ffn.1.'''			,  '''.intermediate.dense.'''     )
   if ".pre_norm_ffn.4." in name:
      lowerCamelCase__   :						List[str]					   =							name.replace('''.pre_norm_ffn.4.'''			,  '''.output.dense.'''     )
   if ".transformer." in name:
      lowerCamelCase__   :						List[str]					   =							name.replace('''.transformer.'''			,  '''.transformer.layer.'''     )

   if ".aspp_layer." in name:
      lowerCamelCase__   :						Optional[Any]					   =							name.replace('''.aspp_layer.'''			,  '''.'''     )
   if ".aspp_pool." in name:
      lowerCamelCase__   :						Union[str, Any]					   =							name.replace('''.aspp_pool.'''			,  '''.'''     )
   if "seg_head." in name:
      lowerCamelCase__   :						Tuple					   =							name.replace('''seg_head.'''			,  '''segmentation_head.'''     )
   if "segmentation_head.classifier.classifier." in name:
      lowerCamelCase__   :						Dict					   =							name.replace('''segmentation_head.classifier.classifier.'''			,  '''segmentation_head.classifier.'''     )

   if "classifier.fc." in name:
      lowerCamelCase__   :						Union[str, Any]					   =							name.replace('''classifier.fc.'''			,  '''classifier.'''     )
   elif (not base_model) and ("segmentation_head." not in name):
      lowerCamelCase__   :						List[Any]					   =							'''mobilevit.''' + name

   return name



def        _a						(    UpperCAmelCase			,  UpperCAmelCase			,  UpperCAmelCase=False     )				->				Tuple:







   """simple docstring"""
   if base_model:
      lowerCamelCase__   :						str					   =							''''''
   else:
      lowerCamelCase__   :						Dict					   =							'''mobilevit.'''

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

      if key[:8] == "encoder.":
         lowerCamelCase__   :						Union[str, Any]					   =							key[8:]

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

   return orig_state_dict



def        _a						(    )				->				Dict:







   """simple docstring"""
   lowerCamelCase__   :						Any					   =							'''http://images.cocodataset.org/val2017/000000039769.jpg'''
   lowerCamelCase__   :						Tuple					   =							Image.open(requests.get(_lowercase			,  stream=_lowercase     ).raw     )
   return im



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







   """simple docstring"""
   lowerCamelCase__   :						Optional[int]					   =							get_mobilevit_config(_lowercase     )

   # load original state_dict
   lowerCamelCase__   :						Tuple					   =							torch.load(_lowercase			,  map_location='''cpu'''     )

   # load 🤗 model
   if mobilevit_name.startswith('''deeplabv3_'''     ):
      lowerCamelCase__   :						List[Any]					   =							MobileViTForSemanticSegmentation(_lowercase     ).eval()
   else:
      lowerCamelCase__   :						Any					   =							MobileViTForImageClassification(_lowercase     ).eval()

   lowerCamelCase__   :						Optional[int]					   =							convert_state_dict(_lowercase			,  _lowercase     )
   model.load_state_dict(_lowercase     )

   # Check outputs on an image, prepared by MobileViTImageProcessor
   lowerCamelCase__   :						str					   =							MobileViTImageProcessor(crop_size=config.image_size			,  size=config.image_size + 32     )
   lowerCamelCase__   :						List[Any]					   =							image_processor(images=prepare_img()			,  return_tensors='''pt'''     )
   lowerCamelCase__   :						Any					   =							model(**_lowercase     )
   lowerCamelCase__   :						List[Any]					   =							outputs.logits

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

      if mobilevit_name == "deeplabv3_mobilevit_s":
         lowerCamelCase__   :						Union[str, Any]					   =							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__   :						int					   =							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__   :						Union[str, Any]					   =							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]			,  _lowercase			,  atol=1E-4     )
   else:
      assert logits.shape == (1, 1000)

      if mobilevit_name == "mobilevit_s":
         lowerCamelCase__   :						Any					   =							torch.tensor([-0.98_66, 0.23_92, -1.12_41]     )
      elif mobilevit_name == "mobilevit_xs":
         lowerCamelCase__   :						List[str]					   =							torch.tensor([-2.47_61, -0.93_99, -1.95_87]     )
      elif mobilevit_name == "mobilevit_xxs":
         lowerCamelCase__   :						Optional[Any]					   =							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]			,  _lowercase			,  atol=1E-4     )

   Path(_lowercase     ).mkdir(exist_ok=_lowercase     )
   print(f"Saving model {mobilevit_name} to {pytorch_dump_folder_path}"     )
   model.save_pretrained(_lowercase     )
   print(f"Saving image processor to {pytorch_dump_folder_path}"     )
   image_processor.save_pretrained(_lowercase     )

   if push_to_hub:
      lowerCamelCase__   :						Optional[Any]					   =							{
          '''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__   :						int					   =							model_mapping[mobilevit_name]
      image_processor.push_to_hub(_lowercase			,  organization='''apple'''     )
      model.push_to_hub(_lowercase			,  organization='''apple'''     )


if __name__ == "__main__":
       _A					:    str   =  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.'
       )

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




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


__UpperCamelCase		:       int									=     {'configuration_yolos': ['YOLOS_PRETRAINED_CONFIG_ARCHIVE_MAP', 'YolosConfig', 'YolosOnnxConfig']}

try:
					if not is_vision_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__UpperCamelCase		:       int									=     ['YolosFeatureExtractor']
					__UpperCamelCase		:       Union[str, Any]									=     ['YolosImageProcessor']

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__UpperCamelCase		:       str									=     [
					    'YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'YolosForObjectDetection',
					    'YolosModel',
					    'YolosPreTrainedModel',
					]


if TYPE_CHECKING:
					from .configuration_yolos import YOLOS_PRETRAINED_CONFIG_ARCHIVE_MAP, YolosConfig, YolosOnnxConfig

					try:
										if not is_vision_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .feature_extraction_yolos import YolosFeatureExtractor
										from .image_processing_yolos import YolosImageProcessor

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_yolos import (
										    YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST,
										    YolosForObjectDetection,
										    YolosModel,
										    YolosPreTrainedModel,
										)


else:
					import sys

					__UpperCamelCase		:       int									=     _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





