
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()
lowercase  : Union[str, Any]						   =							logging.get_logger(__name__)







def 						_snake_case(  SCREAMING_SNAKE_CASE__    )			->   Optional[int]:
       lowercase							:					Optional[Any]	     =				MobileViTConfig()

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

       if mobilevit_name.startswith("""deeplabv3_"""    ):
              lowercase							:					Optional[Any]	     =				512
              lowercase							:					Union[str, Any]	     =				16
              lowercase							:					List[str]	     =				21
              lowercase							:					Union[str, Any]	     =				"""pascal-voc-id2label.json"""
       else:
              lowercase							:					Union[str, Any]	     =				1_000
              lowercase							:					Optional[int]	     =				"""imagenet-1k-id2label.json"""

       lowercase							:					Tuple	     =				"""huggingface/label-files"""
       lowercase							:					Dict	     =				json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     repo_type="""dataset"""    ) ,     """r"""    )    )
       lowercase							:					List[Any]	     =				{int(SCREAMING_SNAKE_CASE__    ): v for k, v in idalabel.items()}
       lowercase							:					Tuple	     =				idalabel
       lowercase							:					List[str]	     =				{v: k for k, v in idalabel.items()}

       return config







def 						_snake_case(  SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__=False    )			->   List[Any]:
       for i in range(1 ,     6    ):
              if f"layer_{i}." in name:
                     lowercase							:					Optional[Any]	     =				name.replace(f"layer_{i}." ,     f"encoder.layer.{i - 1}."    )

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

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

       for i in range(2 ,     5    ):
              if f".global_rep.{i}.weight" in name:
                     lowercase							:					Union[str, Any]	     =				name.replace(f".global_rep.{i}.weight" ,     """.layernorm.weight"""    )
              if f".global_rep.{i}.bias" in name:
                     lowercase							:					Union[str, Any]	     =				name.replace(f".global_rep.{i}.bias" ,     """.layernorm.bias"""    )

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

       if ".aspp_layer." in name:
              lowercase							:					List[str]	     =				name.replace(""".aspp_layer.""" ,     """."""    )
       if ".aspp_pool." in name:
              lowercase							:					int	     =				name.replace(""".aspp_pool.""" ,     """."""    )
       if "seg_head." in name:
              lowercase							:					str	     =				name.replace("""seg_head.""" ,     """segmentation_head."""    )
       if "segmentation_head.classifier.classifier." in name:
              lowercase							:					Optional[int]	     =				name.replace("""segmentation_head.classifier.classifier.""" ,     """segmentation_head.classifier."""    )

       if "classifier.fc." in name:
              lowercase							:					Optional[int]	     =				name.replace("""classifier.fc.""" ,     """classifier."""    )
       elif (not base_model) and ("segmentation_head." not in name):
              lowercase							:					Dict	     =				"""mobilevit.""" + name

       return name







def 						_snake_case(  SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__=False    )			->   int:
       if base_model:
              lowercase							:					Union[str, Any]	     =				""""""
       else:
              lowercase							:					Optional[Any]	     =				"""mobilevit."""

       for key in orig_state_dict.copy().keys():
              lowercase							:					Optional[int]	     =				orig_state_dict.pop(SCREAMING_SNAKE_CASE__    )

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

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

       return orig_state_dict







def 						_snake_case(  )			->   Union[str, Any]:
       lowercase							:					List[str]	     =				"""http://images.cocodataset.org/val2017/000000039769.jpg"""
       lowercase							:					Tuple	     =				Image.open(requests.get(SCREAMING_SNAKE_CASE__ ,     stream=SCREAMING_SNAKE_CASE__    ).raw    )
       return im







@torch.no_grad()
def 						_snake_case(  SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__=False    )			->   Any:
       lowercase							:					str	     =				get_mobilevit_config(SCREAMING_SNAKE_CASE__    )

       # load original state_dict
       lowercase							:					List[str]	     =				torch.load(SCREAMING_SNAKE_CASE__ ,     map_location="""cpu"""    )

       # load 🤗 model
       if mobilevit_name.startswith("""deeplabv3_"""    ):
              lowercase							:					Dict	     =				MobileViTForSemanticSegmentation(SCREAMING_SNAKE_CASE__    ).eval()
       else:
              lowercase							:					List[str]	     =				MobileViTForImageClassification(SCREAMING_SNAKE_CASE__    ).eval()

       lowercase							:					Tuple	     =				convert_state_dict(SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    )
       model.load_state_dict(SCREAMING_SNAKE_CASE__    )

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

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

              if mobilevit_name == "deeplabv3_mobilevit_s":
                     lowercase							:					Any	     =				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":
                     lowercase							:					Tuple	     =				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":
                     lowercase							:					List[str]	     =				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] ,     SCREAMING_SNAKE_CASE__ ,     atol=1e-4    )
       else:
              assert logits.shape == (1, 1_000)

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

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

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

       if push_to_hub:
              lowercase							:					Union[str, 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..."""    )
              lowercase							:					Dict	     =				model_mapping[mobilevit_name]
              image_processor.push_to_hub(SCREAMING_SNAKE_CASE__ ,     organization="""apple"""    )
              model.push_to_hub(SCREAMING_SNAKE_CASE__ ,     organization="""apple"""    )


if __name__ == "__main__":
  lowercase  : List[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."""
  )

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


from __future__ import annotations

from itertools import permutations
from random import randint
from timeit import repeat







def 						_snake_case(  )			->   tuple[list[int], int]:
       lowercase							:					List[Any]	     =				[randint(-1_000 ,     1_000    ) for i in range(10    )]
       lowercase							:					Tuple	     =				randint(-5_000 ,     5_000    )
       return (arr, r)


lowercase  : List[Any]						   =							make_dataset()







def 						_snake_case(  SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    )			->   tuple[int, ...]:
       for triplet in permutations(SCREAMING_SNAKE_CASE__ ,     3    ):
              if sum(SCREAMING_SNAKE_CASE__    ) == target:
                     return tuple(sorted(SCREAMING_SNAKE_CASE__    )    )
       return (0, 0, 0)







def 						_snake_case(  SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    )			->   tuple[int, int, int]:
       arr.sort()
       lowercase							:					Optional[int]	     =				len(SCREAMING_SNAKE_CASE__    )
       for i in range(n - 1    ):
              lowercase     ,      lowercase							:					Optional[Any]	     =				i + 1, n - 1
              while left < right:
                     if arr[i] + arr[left] + arr[right] == target:
                            return (arr[i], arr[left], arr[right])
                     elif arr[i] + arr[left] + arr[right] < target:
                            left += 1
                     elif arr[i] + arr[left] + arr[right] > target:
                            right -= 1
       return (0, 0, 0)







def 						_snake_case(  )			->   tuple[float, float]:
       lowercase							:					Dict	     =				"""
from __main__ import dataset, triplet_sum1, triplet_sum2
"""
       lowercase							:					Tuple	     =				"""
triplet_sum1(*dataset)
"""
       lowercase							:					int	     =				"""
triplet_sum2(*dataset)
"""
       lowercase							:					str	     =				repeat(setup=SCREAMING_SNAKE_CASE__ ,     stmt=SCREAMING_SNAKE_CASE__ ,     repeat=5 ,     number=10_000    )
       lowercase							:					Dict	     =				repeat(setup=SCREAMING_SNAKE_CASE__ ,     stmt=SCREAMING_SNAKE_CASE__ ,     repeat=5 ,     number=10_000    )
       return (min(SCREAMING_SNAKE_CASE__    ), min(SCREAMING_SNAKE_CASE__    ))


if __name__ == "__main__":
  from doctest import testmod

  testmod()
  lowercase  : Union[str, Any]						   =							solution_times()
  print(F'''The time for naive implementation is {times[0]}.''')
  print(F'''The time for optimized implementation is {times[1]}.''')
