"""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()
__lowerCAmelCase					: Tuple        =logging.get_logger(__name__)


def     UpperCAmelCase__ (       lowerCAmelCase__  :Tuple	)				->					Optional[Any]:
      '''simple docstring'''


      lowercase														=			MobileViTConfig()

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

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

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

      return config


def     UpperCAmelCase__ (       lowerCAmelCase__  :Union[str, Any]      ,	lowerCAmelCase__  :Tuple=False	)				->					str:
      '''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__ (       lowerCAmelCase__  :str      ,	lowerCAmelCase__  :List[Any]      ,	lowerCAmelCase__  :str=False	)				->					List[str]:
      '''simple docstring'''


      if base_model:
            lowercase														=			""""""
      else:
            lowercase														=			"""mobilevit."""

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

            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__ (       )				->					Optional[int]:
      '''simple docstring'''


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


@torch.no_grad()
def     UpperCAmelCase__ (       lowerCAmelCase__  :Dict      ,	lowerCAmelCase__  :Dict      ,	lowerCAmelCase__  :str      ,	lowerCAmelCase__  :Any=False	)				->					int:
      '''simple docstring'''


      lowercase														=			get_mobilevit_config(lowerCAmelCase__	)

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

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

      lowercase														=			convert_state_dict(lowerCAmelCase__      ,	lowerCAmelCase__	)
      model.load_state_dict(lowerCAmelCase__	)

      # Check outputs on an image, prepared by MobileViTImageProcessor
      lowercase														=			MobileViTImageProcessor(crop_size=config.image_size      ,	size=config.image_size + 3_2	)
      lowercase														=			image_processor(images=prepare_img()      ,	return_tensors="""pt"""	)
      lowercase														=			model(**lowerCAmelCase__	)
      lowercase														=			outputs.logits

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

            if mobilevit_name == "deeplabv3_mobilevit_s":
                  lowercase														=			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":
                  lowercase														=			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":
                  lowercase														=			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]],
                          [[-10.5_536, -10.2_332, -10.2_924], [-10.2_336, -9.8_624, -9.5_964], [-10.8_840, -10.8_158, -10.6_659]],
                          [[-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":
                  lowercase														=			torch.tensor([-0.9_866, 0.2_392, -1.1_241]	)
            elif mobilevit_name == "mobilevit_xs":
                  lowercase														=			torch.tensor([-2.4_761, -0.9_399, -1.9_587]	)
            elif mobilevit_name == "mobilevit_xxs":
                  lowercase														=			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:
            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(lowerCAmelCase__      ,	organization="""apple"""	)
            model.push_to_hub(lowerCAmelCase__      ,	organization="""apple"""	)


if __name__ == "__main__":
   __lowerCAmelCase					: Optional[int]        =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."""
   )

   __lowerCAmelCase					: int        =parser.parse_args()
   convert_movilevit_checkpoint(
       args.mobilevit_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
   )




"""simple docstring"""






import argparse
import json
from collections import OrderedDict
from pathlib import Path

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

from transformers import PoolFormerConfig, PoolFormerForImageClassification, PoolFormerImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
__lowerCAmelCase					: Optional[Any]        =logging.get_logger(__name__)


def     UpperCAmelCase__ (       lowerCAmelCase__  :Tuple      ,	lowerCAmelCase__  :Dict      ,	lowerCAmelCase__  :str      ,	lowerCAmelCase__  :Optional[int]	)				->					Any:
      '''simple docstring'''


      lowercase														=			original_name.split("""."""	)[0]
      lowercase														=			key.split("""."""	)
      lowercase														=			int(key_list[key_list.index(lowerCAmelCase__	) - 2]	)
      lowercase														=			int(key_list[key_list.index(lowerCAmelCase__	) - 1]	)
      lowercase														=			orig_block_num - offset

      lowercase														=			key.replace(f'{orig_block_num}.{layer_num}.{original_name}'      ,	f'block.{new_block_num}.{layer_num}.{new_name}'	)
      return key


def     UpperCAmelCase__ (       lowerCAmelCase__  :Optional[Any]	)				->					Union[str, Any]:
      '''simple docstring'''


      lowercase														=			OrderedDict()
      lowercase							,							lowercase														=			0, 0
      for key, value in state_dict.items():
            if key.startswith("""network"""	):
                  lowercase														=			key.replace("""network"""      ,	"""poolformer.encoder"""	)
            if "proj" in key:
                  # Works for the first embedding as well as the internal embedding layers
                  if key.endswith("""bias"""	) and "patch_embed" not in key:
                        patch_emb_offset += 1
                  lowercase														=			key[: key.find("""proj"""	)]
                  lowercase														=			key.replace(lowerCAmelCase__      ,	f'patch_embeddings.{total_embed_found}.'	)
                  lowercase														=			key.replace("""proj"""      ,	"""projection"""	)
                  if key.endswith("""bias"""	):
                        total_embed_found += 1
            if "patch_embeddings" in key:
                  lowercase														=			"""poolformer.encoder.""" + key
            if "mlp.fc1" in key:
                  lowercase														=			replace_key_with_offset(lowerCAmelCase__      ,	lowerCAmelCase__      ,	"""mlp.fc1"""      ,	"""output.conv1"""	)
            if "mlp.fc2" in key:
                  lowercase														=			replace_key_with_offset(lowerCAmelCase__      ,	lowerCAmelCase__      ,	"""mlp.fc2"""      ,	"""output.conv2"""	)
            if "norm1" in key:
                  lowercase														=			replace_key_with_offset(lowerCAmelCase__      ,	lowerCAmelCase__      ,	"""norm1"""      ,	"""before_norm"""	)
            if "norm2" in key:
                  lowercase														=			replace_key_with_offset(lowerCAmelCase__      ,	lowerCAmelCase__      ,	"""norm2"""      ,	"""after_norm"""	)
            if "layer_scale_1" in key:
                  lowercase														=			replace_key_with_offset(lowerCAmelCase__      ,	lowerCAmelCase__      ,	"""layer_scale_1"""      ,	"""layer_scale_1"""	)
            if "layer_scale_2" in key:
                  lowercase														=			replace_key_with_offset(lowerCAmelCase__      ,	lowerCAmelCase__      ,	"""layer_scale_2"""      ,	"""layer_scale_2"""	)
            if "head" in key:
                  lowercase														=			key.replace("""head"""      ,	"""classifier"""	)
            lowercase														=			value
      return new_state_dict


def     UpperCAmelCase__ (       )				->					Any:
      '''simple docstring'''


      lowercase														=			"""http://images.cocodataset.org/val2017/000000039769.jpg"""
      lowercase														=			Image.open(requests.get(lowerCAmelCase__      ,	stream=lowerCAmelCase__	).raw	)

      return image


@torch.no_grad()
def     UpperCAmelCase__ (       lowerCAmelCase__  :Any      ,	lowerCAmelCase__  :List[str]      ,	lowerCAmelCase__  :Tuple	)				->					Any:
      '''simple docstring'''


      lowercase														=			PoolFormerConfig()

      # set attributes based on model_name
      lowercase														=			"""huggingface/label-files"""
      lowercase														=			model_name[-3:]
      lowercase														=			1_0_0_0
      lowercase														=			"""imagenet-1k-id2label.json"""
      lowercase														=			(1, 1_0_0_0)

      # set config attributes
      lowercase														=			json.load(open(hf_hub_download(lowerCAmelCase__      ,	lowerCAmelCase__      ,	repo_type="""dataset"""	)      ,	"""r"""	)	)
      lowercase														=			{int(lowerCAmelCase__	): v for k, v in idalabel.items()}
      lowercase														=			idalabel
      lowercase														=			{v: k for k, v in idalabel.items()}
      if size == "s12":
            lowercase														=			[2, 2, 6, 2]
            lowercase														=			[6_4, 1_2_8, 3_2_0, 5_1_2]
            lowercase														=			4.0
            lowercase														=			0.9
      elif size == "s24":
            lowercase														=			[4, 4, 1_2, 4]
            lowercase														=			[6_4, 1_2_8, 3_2_0, 5_1_2]
            lowercase														=			4.0
            lowercase														=			0.9
      elif size == "s36":
            lowercase														=			[6, 6, 1_8, 6]
            lowercase														=			[6_4, 1_2_8, 3_2_0, 5_1_2]
            lowercase														=			4.0
            lowercase														=			1e-6
            lowercase														=			0.9
      elif size == "m36":
            lowercase														=			[6, 6, 1_8, 6]
            lowercase														=			[9_6, 1_9_2, 3_8_4, 7_6_8]
            lowercase														=			4.0
            lowercase														=			1e-6
            lowercase														=			0.95
      elif size == "m48":
            lowercase														=			[8, 8, 2_4, 8]
            lowercase														=			[9_6, 1_9_2, 3_8_4, 7_6_8]
            lowercase														=			4.0
            lowercase														=			1e-6
            lowercase														=			0.95
      else:
            raise ValueError(f'Size {size} not supported'	)

      # load image processor
      lowercase														=			PoolFormerImageProcessor(crop_pct=lowerCAmelCase__	)

      # Prepare image
      lowercase														=			prepare_img()
      lowercase														=			image_processor(images=lowerCAmelCase__      ,	return_tensors="""pt"""	).pixel_values

      logger.info(f'Converting model {model_name}...'	)

      # load original state dict
      lowercase														=			torch.load(lowerCAmelCase__      ,	map_location=torch.device("""cpu"""	)	)

      # rename keys
      lowercase														=			rename_keys(lowerCAmelCase__	)

      # create HuggingFace model and load state dict
      lowercase														=			PoolFormerForImageClassification(lowerCAmelCase__	)
      model.load_state_dict(lowerCAmelCase__	)
      model.eval()

      # Define image processor
      lowercase														=			PoolFormerImageProcessor(crop_pct=lowerCAmelCase__	)
      lowercase														=			image_processor(images=prepare_img()      ,	return_tensors="""pt"""	).pixel_values

      # forward pass
      lowercase														=			model(lowerCAmelCase__	)
      lowercase														=			outputs.logits

      # define expected logit slices for different models
      if size == "s12":
            lowercase														=			torch.tensor([-0.3_045, -0.6_758, -0.4_869]	)
      elif size == "s24":
            lowercase														=			torch.tensor([0.4_402, -0.1_374, -0.8_045]	)
      elif size == "s36":
            lowercase														=			torch.tensor([-0.6_080, -0.5_133, -0.5_898]	)
      elif size == "m36":
            lowercase														=			torch.tensor([0.3_952, 0.2_263, -1.2_668]	)
      elif size == "m48":
            lowercase														=			torch.tensor([0.1_167, -0.0_656, -0.3_423]	)
      else:
            raise ValueError(f'Size {size} not supported'	)

      # verify logits
      assert logits.shape == expected_shape
      assert torch.allclose(logits[0, :3]      ,	lowerCAmelCase__      ,	atol=1e-2	)

      # finally, save model and image processor
      logger.info(f'Saving PyTorch model and image processor to {pytorch_dump_folder_path}...'	)
      Path(lowerCAmelCase__	).mkdir(exist_ok=lowerCAmelCase__	)
      model.save_pretrained(lowerCAmelCase__	)
      print(f'Saving image processor to {pytorch_dump_folder_path}'	)
      image_processor.save_pretrained(lowerCAmelCase__	)


if __name__ == "__main__":
       __lowerCAmelCase					: List[Any]        =argparse.ArgumentParser()

       parser.add_argument(
           """--model_name""",
           default="""poolformer_s12""",
           type=str,
           help="""Name of the model you'd like to convert.""",
       )
       parser.add_argument(
           """--checkpoint_path""", default=None, type=str, help="""Path to the original PyTorch checkpoint (.pth file)."""
       )
       parser.add_argument(
           """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the folder to output PyTorch model."""
       )
       __lowerCAmelCase					: int        =parser.parse_args()
       convert_poolformer_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path)



