





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      __lowercase     (   _SCREAMING_SNAKE_CASE			)				->     Dict:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE	=		MobileViTConfig()

      # size of the architecture
      if "mobilevit_s" in mobilevit_name:
            SCREAMING_SNAKE_CASE	=		[1_44, 1_92, 2_40]
            SCREAMING_SNAKE_CASE	=		[16, 32, 64, 96, 1_28, 1_60, 6_40]
      elif "mobilevit_xs" in mobilevit_name:
            SCREAMING_SNAKE_CASE	=		[96, 1_20, 1_44]
            SCREAMING_SNAKE_CASE	=		[16, 32, 48, 64, 80, 96, 3_84]
      elif "mobilevit_xxs" in mobilevit_name:
            SCREAMING_SNAKE_CASE	=		[64, 80, 96]
            SCREAMING_SNAKE_CASE	=		[16, 16, 24, 48, 64, 80, 3_20]
            SCREAMING_SNAKE_CASE	=		0.05
            SCREAMING_SNAKE_CASE	=		2.0

      if mobilevit_name.startswith("""deeplabv3_"""			):
            SCREAMING_SNAKE_CASE	=		5_12
            SCREAMING_SNAKE_CASE	=		16
            SCREAMING_SNAKE_CASE	=		21
            SCREAMING_SNAKE_CASE	=		"""pascal-voc-id2label.json"""
      else:
            SCREAMING_SNAKE_CASE	=		10_00
            SCREAMING_SNAKE_CASE	=		"""imagenet-1k-id2label.json"""

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

      return config



def      __lowercase     (   _SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE=False			)				->     Tuple:


      '''simple docstring'''


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

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

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

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

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

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

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

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

      return name



def      __lowercase     (   _SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE=False			)				->     Tuple:


      '''simple docstring'''


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

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

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

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

      return orig_state_dict



def      __lowercase     (   )				->     Dict:


      '''simple docstring'''


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



@torch.no_grad()
def      __lowercase     (   _SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE=False			)				->     List[Any]:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE	=		get_mobilevit_config(__UpperCAmelCase			)

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

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

      SCREAMING_SNAKE_CASE	=		convert_state_dict(__UpperCAmelCase				,		__UpperCAmelCase			)
      model.load_state_dict(__UpperCAmelCase			)

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

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

            if mobilevit_name == "deeplabv3_mobilevit_s":
                  SCREAMING_SNAKE_CASE	=		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":
                  SCREAMING_SNAKE_CASE	=		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":
                  SCREAMING_SNAKE_CASE	=		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]],
                          [[-10.55_36, -10.23_32, -10.29_24], [-10.23_36, -9.8_624, -9.5_964], [-10.88_40, -10.81_58, -10.66_59]],
                          [[-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]				,		__UpperCAmelCase				,		atol=1E-4			)
      else:
            assert logits.shape == (1, 10_00)

            if mobilevit_name == "mobilevit_s":
                  SCREAMING_SNAKE_CASE	=		torch.tensor([-0.9_866, 0.2_392, -1.1_241]			)
            elif mobilevit_name == "mobilevit_xs":
                  SCREAMING_SNAKE_CASE	=		torch.tensor([-2.4_761, -0.9_399, -1.9_587]			)
            elif mobilevit_name == "mobilevit_xxs":
                  SCREAMING_SNAKE_CASE	=		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]				,		__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:
            SCREAMING_SNAKE_CASE	=		{
                """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..."""			)
            SCREAMING_SNAKE_CASE	=		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
      )









def      __lowercase     (   _SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE			)				->     float:


      '''simple docstring'''


      return price * (1 + tax_rate)


if __name__ == "__main__":
      print(F'''{price_plus_tax(1_0_0, 0.25) = }''')
      print(F'''{price_plus_tax(125.50, 0.05) = }''')


