


"""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()
__snake_case    					=					logging.get_logger(__name__)


def    _lowerCamelCase   (     lowerCamelCase__	:							Any  ):
 lowercase__       :       int				=						MobileViTConfig()

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

 if mobilevit_name.startswith("""deeplabv3_"""  ):
  lowercase__       :       Union[str, Any]				=						5_12
  lowercase__       :       Optional[int]				=						16
  lowercase__       :       Optional[Any]				=						21
  lowercase__       :       int				=						"""pascal-voc-id2label.json"""
 else:
  lowercase__       :       Optional[int]				=						10_00
  lowercase__       :       Optional[Any]				=						"""imagenet-1k-id2label.json"""

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

 return config


def    _lowerCamelCase   (     lowerCamelCase__	:							Union[str, Any]       ,		lowerCamelCase__	:							int=False  ):
 for i in range(1       ,		6  ):
  if f'''layer_{i}.''' in name:
   lowercase__       :       Union[str, Any]				=						name.replace(f'''layer_{i}.'''       ,		f'''encoder.layer.{i - 1}.'''  )

 if "conv_1." in name:
  lowercase__       :       List[Any]				=						name.replace("""conv_1."""       ,		"""conv_stem."""  )
 if ".block." in name:
  lowercase__       :       Dict				=						name.replace(""".block."""       ,		"""."""  )

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

 for i in range(2       ,		5  ):
  if f'''.global_rep.{i}.weight''' in name:
   lowercase__       :       Tuple				=						name.replace(f'''.global_rep.{i}.weight'''       ,		""".layernorm.weight"""  )
  if f'''.global_rep.{i}.bias''' in name:
   lowercase__       :       str				=						name.replace(f'''.global_rep.{i}.bias'''       ,		""".layernorm.bias"""  )

 if ".global_rep." in name:
  lowercase__       :       Any				=						name.replace(""".global_rep."""       ,		""".transformer."""  )
 if ".pre_norm_mha.0." in name:
  lowercase__       :       List[Any]				=						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__       :       Dict				=						name.replace(""".pre_norm_ffn.1."""       ,		""".intermediate.dense."""  )
 if ".pre_norm_ffn.4." in name:
  lowercase__       :       List[Any]				=						name.replace(""".pre_norm_ffn.4."""       ,		""".output.dense."""  )
 if ".transformer." in name:
  lowercase__       :       Any				=						name.replace(""".transformer."""       ,		""".transformer.layer."""  )

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

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

 return name


def    _lowerCamelCase   (     lowerCamelCase__	:							Dict       ,		lowerCamelCase__	:							Tuple       ,		lowerCamelCase__	:							Optional[int]=False  ):
 if base_model:
  lowercase__       :       Union[str, Any]				=						""""""
 else:
  lowercase__       :       Optional[int]				=						"""mobilevit."""

 for key in orig_state_dict.copy().keys():
  lowercase__       :       Optional[Any]				=						orig_state_dict.pop(lowerCamelCase__  )

  if key[:8] == "encoder.":
   lowercase__       :       int				=						key[8:]

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

 return orig_state_dict


def    _lowerCamelCase   (     ):
 lowercase__       :       Optional[int]				=						"""http://images.cocodataset.org/val2017/000000039769.jpg"""
 lowercase__       :       Any				=						Image.open(requests.get(lowerCamelCase__       ,		stream=lowerCamelCase__  ).raw  )
 return im


@torch.no_grad()
def    _lowerCamelCase   (     lowerCamelCase__	:							Dict       ,		lowerCamelCase__	:							Tuple       ,		lowerCamelCase__	:							Optional[int]       ,		lowerCamelCase__	:							Dict=False  ):
 lowercase__       :       List[str]				=						get_mobilevit_config(lowerCamelCase__  )

 # load original state_dict
 lowercase__       :       Optional[Any]				=						torch.load(lowerCamelCase__       ,		map_location="""cpu"""  )

 # load 🤗 model
 if mobilevit_name.startswith("""deeplabv3_"""  ):
  lowercase__       :       Tuple				=						MobileViTForSemanticSegmentation(lowerCamelCase__  ).eval()
 else:
  lowercase__       :       Union[str, Any]				=						MobileViTForImageClassification(lowerCamelCase__  ).eval()

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

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

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

  if mobilevit_name == "deeplabv3_mobilevit_s":
   lowercase__       :       Optional[int]				=						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__       :       Any				=						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__       :       Union[str, Any]				=						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]       ,		lowerCamelCase__       ,		atol=1E-4  )
 else:
  assert logits.shape == (1, 10_00)

  if mobilevit_name == "mobilevit_s":
   lowercase__       :       Any				=						torch.tensor([-0.9866, 0.2392, -1.1241]  )
  elif mobilevit_name == "mobilevit_xs":
   lowercase__       :       List[Any]				=						torch.tensor([-2.4761, -0.9399, -1.9587]  )
  elif mobilevit_name == "mobilevit_xxs":
   lowercase__       :       List[str]				=						torch.tensor([-1.9364, -1.2327, -0.4653]  )
  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__       :       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__       :       Any				=						model_mapping[mobilevit_name]
  image_processor.push_to_hub(lowerCamelCase__       ,		organization="""apple"""  )
  model.push_to_hub(lowerCamelCase__       ,		organization="""apple"""  )


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

   __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 random


def    _lowerCamelCase   (     lowerCamelCase__	:							Tuple       ,		lowerCamelCase__	:							Dict       ,		lowerCamelCase__	:							str  ):
 lowercase__       :       List[Any]				=						a[left_index]
 lowercase__       :       List[Any]				=						left_index + 1
 for j in range(left_index + 1       ,		lowerCamelCase__  ):
  if a[j] < pivot:
   lowercase__	,       lowercase__       :       Dict				=						a[i], a[j]
   i += 1
 lowercase__	,       lowercase__       :       Any				=						a[i - 1], a[left_index]
 return i - 1


def    _lowerCamelCase   (     lowerCamelCase__	:							int       ,		lowerCamelCase__	:							Tuple       ,		lowerCamelCase__	:							Optional[int]  ):
 if left < right:
  lowercase__       :       str				=						random.randint(lowerCamelCase__       ,		right - 1  )
  lowercase__	,       lowercase__       :       Any				=						(
      a[left],
      a[pivot],
  )  # switches the pivot with the left most bound
  lowercase__       :       str				=						partition(lowerCamelCase__       ,		lowerCamelCase__       ,		lowerCamelCase__  )
  quick_sort_random(
      lowerCamelCase__       ,		lowerCamelCase__       ,		lowerCamelCase__  )  # recursive quicksort to the left of the pivot point
  quick_sort_random(
      lowerCamelCase__       ,		pivot_index + 1       ,		lowerCamelCase__  )  # recursive quicksort to the right of the pivot point


def    _lowerCamelCase   (     ):
 lowercase__       :       List[Any]				=						input("""Enter numbers separated by a comma:\n"""  ).strip()
 lowercase__       :       int				=						[int(lowerCamelCase__  ) for item in user_input.split(""","""  )]

 quick_sort_random(lowerCamelCase__       ,		0       ,		len(lowerCamelCase__  )  )

 print(lowerCamelCase__  )


if __name__ == "__main__":
   main()