


'''simple docstring'''






from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union

import numpy as np
import PIL
from PIL import Image

from ...utils import BaseOutput, is_torch_available, is_transformers_available






@dataclass
class        __snake_case	(       _SCREAMING_SNAKE_CASE):







    """simple docstring"""



    lowercase       			= 42
    lowercase       			= 42


if is_transformers_available() and is_torch_available():
  from .pipeline_semantic_stable_diffusion import SemanticStableDiffusionPipeline







'''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
  )



