


'''simple docstring'''






from __future__ import annotations

import time
from collections.abc import Sequence
from random import randint

from matplotlib import pyplot as plt







def     UpperCamelCase_      (   A__ :       Sequence[float]							,   A__ :       int							,   A__ :       int ):
  '''simple docstring'''





  if not arr:
    return None, None, 0
  if low == high:
    return low, high, arr[low]

  lowerCAmelCase_      :				Any      =       (low + high) // 2
  lowerCAmelCase_,							lowerCAmelCase_,							lowerCAmelCase_      :				Union[str, Any]      =       max_subarray(A__							,   A__							,   A__ )
  lowerCAmelCase_,							lowerCAmelCase_,							lowerCAmelCase_      :				Optional[Any]      =       max_subarray(A__							,   mid + 1							,   A__ )
  lowerCAmelCase_,							lowerCAmelCase_,							lowerCAmelCase_      :				int      =       max_cross_sum(A__							,   A__							,   A__							,   A__ )
  if left_sum >= right_sum and left_sum >= cross_sum:
    return left_low, left_high, left_sum
  elif right_sum >= left_sum and right_sum >= cross_sum:
    return right_low, right_high, right_sum
  return cross_left, cross_right, cross_sum







def     UpperCamelCase_      (   A__ :       Sequence[float]							,   A__ :       int							,   A__ :       int							,   A__ :       int ):
  '''simple docstring'''





  lowerCAmelCase_,							lowerCAmelCase_      :				Any      =       float("""-inf""" ), -1
  lowerCAmelCase_,							lowerCAmelCase_      :				Any      =       float("""-inf""" ), -1

  lowerCAmelCase_      :				int | float      =       0
  for i in range(A__							,   low - 1							,   -1 ):
    summ += arr[i]
    if summ > left_sum:
      lowerCAmelCase_      :				Any      =       summ
      lowerCAmelCase_      :				str      =       i

  lowerCAmelCase_      :				List[str]      =       0
  for i in range(mid + 1							,   high + 1 ):
    summ += arr[i]
    if summ > right_sum:
      lowerCAmelCase_      :				List[Any]      =       summ
      lowerCAmelCase_      :				Tuple      =       i

  return max_left, max_right, (left_sum + right_sum)







def     UpperCamelCase_      (   A__ :       int ):
  '''simple docstring'''





  lowerCAmelCase_      :				Optional[int]      =       [randint(1							,   A__ ) for _ in range(A__ )]
  lowerCAmelCase_      :				List[str]      =       time.time()
  max_subarray(A__							,   0							,   input_size - 1 )
  lowerCAmelCase_      :				str      =       time.time()
  return end - start







def     UpperCamelCase_      (   ):
  '''simple docstring'''





  lowerCAmelCase_      :				str      =       [10, 1_00, 10_00, 1_00_00, 5_00_00, 10_00_00, 20_00_00, 30_00_00, 40_00_00, 50_00_00]
  lowerCAmelCase_      :				Tuple      =       [time_max_subarray(A__ ) for input_size in input_sizes]
  print("""No of Inputs\t\tTime Taken""" )
  for input_size, runtime in zip(A__							,   A__ ):
    print(A__							,   """\t\t"""							,   A__ )
  plt.plot(A__							,   A__ )
  plt.xlabel("""Number of Inputs""" )
  plt.ylabel("""Time taken in seconds""" )
  plt.show()


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

  testmod()







'''simple docstring'''






import argparse
import collections
import json
from pathlib import Path

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

from transformers import (
    MobileViTImageProcessor,
    MobileViTVaConfig,
    MobileViTVaForImageClassification,
    MobileViTVaForSemanticSegmentation,
)
from transformers.utils import logging


logging.set_verbosity_info()
__A :  List[str]    							=				logging.get_logger(__name__)







def     UpperCamelCase_      (   A__ :       List[str] ):
  '''simple docstring'''





  print("""Loading config file...""" )

  def flatten_yaml_as_dict(A__ :       int							,   A__ :       str=""							,   A__ :       Dict="." ):
    lowerCAmelCase_      :				int      =       []
    for k, v in d.items():
      lowerCAmelCase_      :				Any      =       parent_key + sep + k if parent_key else k
      if isinstance(A__							,   collections.abc.MutableMapping ):
        items.extend(flatten_yaml_as_dict(A__							,   A__							,   sep=A__ ).items() )
      else:
        items.append((new_key, v) )
    return dict(A__ )

  lowerCAmelCase_      :				Optional[int]      =       argparse.Namespace()
  with open(A__							,   """r""" ) as yaml_file:
    try:
      lowerCAmelCase_      :				Any      =       yaml.load(A__							,   Loader=yaml.FullLoader )

      lowerCAmelCase_      :				Any      =       flatten_yaml_as_dict(A__ )
      for k, v in flat_cfg.items():
        setattr(A__							,   A__							,   A__ )
    except yaml.YAMLError as exc:
      logger.error("""Error while loading config file: {}. Error message: {}""".format(A__							,   str(A__ ) ) )
  return config







def     UpperCamelCase_      (   A__ :       Optional[int]							,   A__ :       List[Any] ):
  '''simple docstring'''





  lowerCAmelCase_      :				List[str]      =       MobileViTVaConfig()

  lowerCAmelCase_      :				Any      =       False

  # dataset
  if task_name.startswith("""imagenet1k_""" ):
    lowerCAmelCase_      :				Tuple      =       10_00
    if int(task_name.strip().split("""_""" )[-1] ) == 3_84:
      lowerCAmelCase_      :				List[str]      =       3_84
    else:
      lowerCAmelCase_      :				Optional[int]      =       2_56
    lowerCAmelCase_      :				int      =       """imagenet-1k-id2label.json"""
  elif task_name.startswith("""imagenet21k_to_1k_""" ):
    lowerCAmelCase_      :				int      =       2_10_00
    if int(task_name.strip().split("""_""" )[-1] ) == 3_84:
      lowerCAmelCase_      :				int      =       3_84
    else:
      lowerCAmelCase_      :				int      =       2_56
    lowerCAmelCase_      :				Optional[Any]      =       """imagenet-22k-id2label.json"""
  elif task_name.startswith("""ade20k_""" ):
    lowerCAmelCase_      :				Dict      =       1_51
    lowerCAmelCase_      :				Any      =       5_12
    lowerCAmelCase_      :				int      =       """ade20k-id2label.json"""
    lowerCAmelCase_      :				Union[str, Any]      =       True
  elif task_name.startswith("""voc_""" ):
    lowerCAmelCase_      :				Any      =       21
    lowerCAmelCase_      :				List[str]      =       5_12
    lowerCAmelCase_      :				Union[str, Any]      =       """pascal-voc-id2label.json"""
    lowerCAmelCase_      :				int      =       True

  # orig_config
  lowerCAmelCase_      :				Optional[int]      =       load_orig_config_file(A__ )
  assert getattr(A__							,   """model.classification.name"""							,   -1 ) == "mobilevit_v2", "Invalid model"
  lowerCAmelCase_      :				Union[str, Any]      =       getattr(A__							,   """model.classification.mitv2.width_multiplier"""							,   1.0 )
  assert (
      getattr(A__							,   """model.classification.mitv2.attn_norm_layer"""							,   -1 ) == "layer_norm_2d"
  ), "Norm layers other than layer_norm_2d is not supported"
  lowerCAmelCase_      :				List[Any]      =       getattr(A__							,   """model.classification.activation.name"""							,   """swish""" )
  # config.image_size == getattr(orig_config,  'sampler.bs.crop_size_width', 256)

  if is_segmentation_model:
    lowerCAmelCase_      :				List[str]      =       getattr(A__							,   """model.segmentation.output_stride"""							,   16 )
    if "_deeplabv3" in task_name:
      lowerCAmelCase_      :				Optional[int]      =       getattr(A__							,   """model.segmentation.deeplabv3.aspp_rates"""							,   [12, 24, 36] )
      lowerCAmelCase_      :				Optional[Any]      =       getattr(A__							,   """model.segmentation.deeplabv3.aspp_out_channels"""							,   5_12 )
      lowerCAmelCase_      :				Optional[int]      =       getattr(A__							,   """model.segmentation.deeplabv3.aspp_dropout"""							,   0.1 )

    # id2label
  lowerCAmelCase_      :				Any      =       """huggingface/label-files"""
  lowerCAmelCase_      :				Any      =       json.load(open(hf_hub_download(A__							,   A__							,   repo_type="""dataset""" )							,   """r""" ) )
  lowerCAmelCase_      :				Any      =       {int(A__ ): v for k, v in idalabel.items()}
  lowerCAmelCase_      :				Optional[int]      =       idalabel
  lowerCAmelCase_      :				Tuple      =       {v: k for k, v in idalabel.items()}

  return config







def     UpperCamelCase_      (   A__ :       int							,   A__ :       Dict							,   A__ :       Tuple ):
  '''simple docstring'''





  lowerCAmelCase_      :				List[Any]      =       dct.pop(A__ )
  lowerCAmelCase_      :				Optional[int]      =       val







def     UpperCamelCase_      (   A__ :       int							,   A__ :       int=False ):
  '''simple docstring'''





  if base_model:
    lowerCAmelCase_      :				List[Any]      =       """"""
  else:
    lowerCAmelCase_      :				Any      =       """mobilevitv2."""

  lowerCAmelCase_      :				int      =       []
  for k in state_dict.keys():
    if k[:8] == "encoder.":
      lowerCAmelCase_      :				List[Any]      =       k[8:]
    else:
      lowerCAmelCase_      :				Optional[Any]      =       k

    if ".block." in k:
      lowerCAmelCase_      :				Dict      =       k_new.replace(""".block."""							,   """.""" )
    if ".conv." in k:
      lowerCAmelCase_      :				Any      =       k_new.replace(""".conv."""							,   """.convolution.""" )
    if ".norm." in k:
      lowerCAmelCase_      :				List[Any]      =       k_new.replace(""".norm."""							,   """.normalization.""" )

    if "conv_1." in k:
      lowerCAmelCase_      :				Any      =       k_new.replace("""conv_1."""							,   f'{model_prefix}conv_stem.' )
    for i in [1, 2]:
      if f'layer_{i}.' in k:
        lowerCAmelCase_      :				Optional[Any]      =       k_new.replace(f'layer_{i}.'							,   f'{model_prefix}encoder.layer.{i-1}.layer.' )
    if ".exp_1x1." in k:
      lowerCAmelCase_      :				Optional[int]      =       k_new.replace(""".exp_1x1."""							,   """.expand_1x1.""" )
    if ".red_1x1." in k:
      lowerCAmelCase_      :				List[str]      =       k_new.replace(""".red_1x1."""							,   """.reduce_1x1.""" )

    for i in [3, 4, 5]:
      if f'layer_{i}.0.' in k:
        lowerCAmelCase_      :				Optional[int]      =       k_new.replace(f'layer_{i}.0.'							,   f'{model_prefix}encoder.layer.{i-1}.downsampling_layer.' )
      if f'layer_{i}.1.local_rep.0.' in k:
        lowerCAmelCase_      :				str      =       k_new.replace(f'layer_{i}.1.local_rep.0.'							,   f'{model_prefix}encoder.layer.{i-1}.conv_kxk.' )
      if f'layer_{i}.1.local_rep.1.' in k:
        lowerCAmelCase_      :				Optional[Any]      =       k_new.replace(f'layer_{i}.1.local_rep.1.'							,   f'{model_prefix}encoder.layer.{i-1}.conv_1x1.' )

    for i in [3, 4, 5]:
      if i == 3:
        lowerCAmelCase_      :				Optional[Any]      =       [0, 1]
      elif i == 4:
        lowerCAmelCase_      :				Any      =       [0, 1, 2, 3]
      elif i == 5:
        lowerCAmelCase_      :				Any      =       [0, 1, 2]

      for j in j_in:
        if f'layer_{i}.1.global_rep.{j}.' in k:
          lowerCAmelCase_      :				Optional[int]      =       k_new.replace(
              f'layer_{i}.1.global_rep.{j}.'							,   f'{model_prefix}encoder.layer.{i-1}.transformer.layer.{j}.' )
      if f'layer_{i}.1.global_rep.{j+1}.' in k:
        lowerCAmelCase_      :				Optional[Any]      =       k_new.replace(
            f'layer_{i}.1.global_rep.{j+1}.'							,   f'{model_prefix}encoder.layer.{i-1}.layernorm.' )

      if f'layer_{i}.1.conv_proj.' in k:
        lowerCAmelCase_      :				str      =       k_new.replace(f'layer_{i}.1.conv_proj.'							,   f'{model_prefix}encoder.layer.{i-1}.conv_projection.' )

    if "pre_norm_attn.0." in k:
      lowerCAmelCase_      :				Dict      =       k_new.replace("""pre_norm_attn.0."""							,   """layernorm_before.""" )
    if "pre_norm_attn.1." in k:
      lowerCAmelCase_      :				Optional[int]      =       k_new.replace("""pre_norm_attn.1."""							,   """attention.""" )
    if "pre_norm_ffn.0." in k:
      lowerCAmelCase_      :				Any      =       k_new.replace("""pre_norm_ffn.0."""							,   """layernorm_after.""" )
    if "pre_norm_ffn.1." in k:
      lowerCAmelCase_      :				int      =       k_new.replace("""pre_norm_ffn.1."""							,   """ffn.conv1.""" )
    if "pre_norm_ffn.3." in k:
      lowerCAmelCase_      :				List[Any]      =       k_new.replace("""pre_norm_ffn.3."""							,   """ffn.conv2.""" )

    if "classifier.1." in k:
      lowerCAmelCase_      :				Optional[int]      =       k_new.replace("""classifier.1."""							,   """classifier.""" )

    if "seg_head." in k:
      lowerCAmelCase_      :				Optional[Any]      =       k_new.replace("""seg_head."""							,   """segmentation_head.""" )
    if ".aspp_layer." in k:
      lowerCAmelCase_      :				Dict      =       k_new.replace(""".aspp_layer."""							,   """.""" )
    if ".aspp_pool." in k:
      lowerCAmelCase_      :				List[Any]      =       k_new.replace(""".aspp_pool."""							,   """.""" )

    rename_keys.append((k, k_new) )
  return rename_keys







def     UpperCamelCase_      (   A__ :       Tuple ):
  '''simple docstring'''





  lowerCAmelCase_      :				Tuple      =       []
  for k in state_dict.keys():
    if k.startswith("""seg_head.aux_head.""" ):
      keys_to_ignore.append(A__ )
  for k in keys_to_ignore:
    state_dict.pop(A__							,   A__ )







def     UpperCamelCase_      (   ):
  '''simple docstring'''





  lowerCAmelCase_      :				int      =       """http://images.cocodataset.org/val2017/000000039769.jpg"""
  # url = "https://cdn.britannica.com/86/141086-050-9D7C75EE/Gulfstream-G450-business-jet-passengers.jpg"
  lowerCAmelCase_      :				Optional[Any]      =       Image.open(requests.get(A__							,   stream=A__ ).raw )
  return im







@torch.no_grad()
def     UpperCamelCase_      (   A__ :       Dict							,   A__ :       Union[str, Any]							,   A__ :       str							,   A__ :       str ):
  '''simple docstring'''





  lowerCAmelCase_      :				int      =       get_mobilevitva_config(A__							,   A__ )

  # load original state_dict
  lowerCAmelCase_      :				int      =       torch.load(A__							,   map_location="""cpu""" )

  # load huggingface model
  if task_name.startswith("""ade20k_""" ) or task_name.startswith("""voc_""" ):
    lowerCAmelCase_      :				Union[str, Any]      =       MobileViTVaForSemanticSegmentation(A__ ).eval()
    lowerCAmelCase_      :				Union[str, Any]      =       False
  else:
    lowerCAmelCase_      :				List[Any]      =       MobileViTVaForImageClassification(A__ ).eval()
    lowerCAmelCase_      :				Optional[int]      =       False

  # remove and rename some keys of load the original model
  lowerCAmelCase_      :				Tuple      =       checkpoint
  remove_unused_keys(A__ )
  lowerCAmelCase_      :				List[Any]      =       create_rename_keys(A__							,   base_model=A__ )
  for rename_key_src, rename_key_dest in rename_keys:
    rename_key(A__							,   A__							,   A__ )

  # load modified state_dict
  model.load_state_dict(A__ )

  # Check outputs on an image, prepared by MobileViTImageProcessor
  lowerCAmelCase_      :				Dict      =       MobileViTImageProcessor(crop_size=config.image_size							,   size=config.image_size + 32 )
  lowerCAmelCase_      :				Dict      =       image_processor(images=prepare_img()							,   return_tensors="""pt""" )
  lowerCAmelCase_      :				int      =       model(**A__ )

  # verify classification model
  if task_name.startswith("""imagenet""" ):
    lowerCAmelCase_      :				Optional[Any]      =       outputs.logits
    lowerCAmelCase_      :				Tuple      =       logits.argmax(-1 ).item()
    print("""Predicted class:"""							,   model.config.idalabel[predicted_class_idx] )
    if task_name.startswith("""imagenet1k_256""" ) and config.width_multiplier == 1.0:
      # expected_logits for base variant
      lowerCAmelCase_      :				int      =       torch.tensor([-1.63_36E00, -7.32_04E-02, -5.18_83E-01] )
      assert torch.allclose(logits[0, :3]							,   A__							,   atol=1E-4 )

  Path(A__ ).mkdir(exist_ok=A__ )
  print(f'Saving model {task_name} to {pytorch_dump_folder_path}' )
  model.save_pretrained(A__ )
  print(f'Saving image processor to {pytorch_dump_folder_path}' )
  image_processor.save_pretrained(A__ )


if __name__ == "__main__":
  __A :  Dict    							=				argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      "--task",
      default="imagenet1k_256",
      type=str,
      help=(
          "Name of the task for which the MobileViTV2 model you'd like to convert is trained on . "
          "\n                Classification (ImageNet-1k)\n                    - MobileViTV2 (256x256) : imagenet1k_256\n                    - MobileViTV2 (Trained on 256x256 and Finetuned on 384x384) : imagenet1k_384\n                    - MobileViTV2 (Trained on ImageNet-21k and Finetuned on ImageNet-1k 256x256) :\n                      imagenet21k_to_1k_256\n                    - MobileViTV2 (Trained on ImageNet-21k, Finetuned on ImageNet-1k 256x256, and Finetuned on\n                      ImageNet-1k 384x384) : imagenet21k_to_1k_384\n                Segmentation\n                    - ADE20K Dataset : ade20k_deeplabv3\n                    - Pascal VOC 2012 Dataset: voc_deeplabv3\n            "
      ),
      choices=[
          "imagenet1k_256",
          "imagenet1k_384",
          "imagenet21k_to_1k_256",
          "imagenet21k_to_1k_384",
          "ade20k_deeplabv3",
          "voc_deeplabv3",
      ],
  )

  parser.add_argument(
      "--orig_checkpoint_path", required=True, type=str, help="Path to the original state dict (.pt file)."
  )
  parser.add_argument("--orig_config_path", required=True, type=str, help="Path to the original config file.")
  parser.add_argument(
      "--pytorch_dump_folder_path", required=True, type=str, help="Path to the output PyTorch model directory."
  )

  __A :  Union[str, Any]    							=				parser.parse_args()
  convert_mobilevitva_checkpoint(
      args.task, args.orig_checkpoint_path, args.orig_config_path, args.pytorch_dump_folder_path
  )



