



'''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()
UpperCamelCase__  :			Optional[Any]          =					logging.get_logger(__name__)



def   UpperCAmelCase			(  a_						)					->    Dict:



   """simple docstring"""






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

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

   A_   :						Optional[Any]		= argparse.Namespace()
   with open(_UpperCAmelCase	,						"""r"""						) as yaml_file:
      try:
         A_   :						Dict		= yaml.load(_UpperCAmelCase	,						Loader=yaml.FullLoader						)

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



def   UpperCAmelCase			(  a_	,						a_						)					->    List[str]:



   """simple docstring"""






   A_   :						Union[str, Any]		= MobileViTVaConfig()

   A_   :						Any		= False

   # dataset
   if task_name.startswith("""imagenet1k_"""						):
      A_   :						Optional[int]		= 1_0_0_0
      if int(task_name.strip().split("""_"""						)[-1]						) == 3_8_4:
         A_   :						Optional[Any]		= 3_8_4
      else:
         A_   :						List[Any]		= 2_5_6
      A_   :						Union[str, Any]		= '''imagenet-1k-id2label.json'''
   elif task_name.startswith("""imagenet21k_to_1k_"""						):
      A_   :						Any		= 2_1_0_0_0
      if int(task_name.strip().split("""_"""						)[-1]						) == 3_8_4:
         A_   :						Dict		= 3_8_4
      else:
         A_   :						Tuple		= 2_5_6
      A_   :						str		= '''imagenet-22k-id2label.json'''
   elif task_name.startswith("""ade20k_"""						):
      A_   :						str		= 1_5_1
      A_   :						Dict		= 5_1_2
      A_   :						Any		= '''ade20k-id2label.json'''
      A_   :						str		= True
   elif task_name.startswith("""voc_"""						):
      A_   :						Optional[int]		= 2_1
      A_   :						int		= 5_1_2
      A_   :						List[str]		= '''pascal-voc-id2label.json'''
      A_   :						Optional[Any]		= True

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

   if is_segmentation_model:
      A_   :						Tuple		= getattr(_UpperCAmelCase	,						"""model.segmentation.output_stride"""	,						1_6						)
      if "_deeplabv3" in task_name:
         A_   :						Dict		= getattr(_UpperCAmelCase	,						"""model.segmentation.deeplabv3.aspp_rates"""	,						[1_2, 2_4, 3_6]						)
         A_   :						Dict		= getattr(_UpperCAmelCase	,						"""model.segmentation.deeplabv3.aspp_out_channels"""	,						5_1_2						)
         A_   :						Union[str, Any]		= getattr(_UpperCAmelCase	,						"""model.segmentation.deeplabv3.aspp_dropout"""	,						0.1						)

    # id2label
   A_   :						str		= '''huggingface/label-files'''
   A_   :						int		= json.load(open(hf_hub_download(_UpperCAmelCase	,						_UpperCAmelCase	,						repo_type="""dataset"""						)	,						"""r"""						)						)
   A_   :						List[str]		= {int(_UpperCAmelCase						): v for k, v in idalabel.items()}
   A_   :						str		= idalabel
   A_   :						Union[str, Any]		= {v: k for k, v in idalabel.items()}

   return config



def   UpperCAmelCase			(  a_	,						a_	,						a_						)					->    Optional[int]:



   """simple docstring"""






   A_   :						List[str]		= dct.pop(_UpperCAmelCase						)
   A_   :						int		= val



def   UpperCAmelCase			(  a_	,						a_=False						)					->    Dict:



   """simple docstring"""






   if base_model:
      A_   :						Dict		= ''''''
   else:
      A_   :						Optional[int]		= '''mobilevitv2.'''

   A_   :						Optional[Any]		= []
   for k in state_dict.keys():
      if k[:8] == "encoder.":
         A_   :						Dict		= k[8:]
      else:
         A_   :						Dict		= k

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

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

      for i in [3, 4, 5]:
         if F"layer_{i}.0." in k:
            A_   :						List[str]		= 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:
            A_   :						Any		= 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:
            A_   :						List[str]		= 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:
            A_   :						Union[str, Any]		= [0, 1]
         elif i == 4:
            A_   :						int		= [0, 1, 2, 3]
         elif i == 5:
            A_   :						List[Any]		= [0, 1, 2]

         for j in j_in:
            if F"layer_{i}.1.global_rep.{j}." in k:
               A_   :						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:
            A_   :						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:
            A_   :						int		= 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:
         A_   :						List[Any]		= k_new.replace("""pre_norm_attn.0."""	,						"""layernorm_before."""						)
      if "pre_norm_attn.1." in k:
         A_   :						List[str]		= k_new.replace("""pre_norm_attn.1."""	,						"""attention."""						)
      if "pre_norm_ffn.0." in k:
         A_   :						List[Any]		= k_new.replace("""pre_norm_ffn.0."""	,						"""layernorm_after."""						)
      if "pre_norm_ffn.1." in k:
         A_   :						Union[str, Any]		= k_new.replace("""pre_norm_ffn.1."""	,						"""ffn.conv1."""						)
      if "pre_norm_ffn.3." in k:
         A_   :						Any		= k_new.replace("""pre_norm_ffn.3."""	,						"""ffn.conv2."""						)

      if "classifier.1." in k:
         A_   :						Dict		= k_new.replace("""classifier.1."""	,						"""classifier."""						)

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

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



def   UpperCAmelCase			(  a_						)					->    List[Any]:



   """simple docstring"""






   A_   :						int		= []
   for k in state_dict.keys():
      if k.startswith("""seg_head.aux_head."""						):
         keys_to_ignore.append(_UpperCAmelCase						)
   for k in keys_to_ignore:
      state_dict.pop(_UpperCAmelCase	,						_UpperCAmelCase						)



def   UpperCAmelCase			(  )					->    str:



   """simple docstring"""






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



@torch.no_grad()
def   UpperCAmelCase			(  a_	,						a_	,						a_	,						a_						)					->    Any:



   """simple docstring"""






   A_   :						Tuple		= get_mobilevitva_config(_UpperCAmelCase	,						_UpperCAmelCase						)

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

   # load huggingface model
   if task_name.startswith("""ade20k_"""						) or task_name.startswith("""voc_"""						):
      A_   :						Dict		= MobileViTVaForSemanticSegmentation(_UpperCAmelCase						).eval()
      A_   :						List[str]		= False
   else:
      A_   :						Optional[Any]		= MobileViTVaForImageClassification(_UpperCAmelCase						).eval()
      A_   :						Optional[Any]		= False

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

   # load modified state_dict
   model.load_state_dict(_UpperCAmelCase						)

   # Check outputs on an image, prepared by MobileViTImageProcessor
   A_   :						Optional[int]		= MobileViTImageProcessor(crop_size=config.image_size	,						size=config.image_size + 3_2						)
   A_   :						Union[str, Any]		= image_processor(images=prepare_img()	,						return_tensors="""pt"""						)
   A_   :						List[str]		= model(**_UpperCAmelCase						)

   # verify classification model
   if task_name.startswith("""imagenet"""						):
      A_   :						Optional[Any]		= outputs.logits
      A_   :						int		= 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
         A_   :						Tuple		= torch.tensor([-1.6336E00, -7.3204E-02, -5.1883E-01]						)
         assert torch.allclose(logits[0, :3]	,						_UpperCAmelCase	,						atol=1E-4						)

   Path(_UpperCAmelCase						).mkdir(exist_ok=_UpperCAmelCase						)
   print(F"Saving model {task_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 __name__ == "__main__":
  UpperCamelCase__  :			int          =					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.'
  )

  UpperCamelCase__  :			int          =					parser.parse_args()
  convert_mobilevitva_checkpoint(
      args.task, args.orig_checkpoint_path, args.orig_config_path, args.pytorch_dump_folder_path
  )





from ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
				if not (is_transformers_available() and is_torch_available()):
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				from ...utils.dummy_torch_and_transformers_objects import KandinskyPipeline, KandinskyPriorPipeline
else:
				from .pipeline_kandinsky import KandinskyPipeline
				from .pipeline_kandinsky_imgaimg import KandinskyImgaImgPipeline
				from .pipeline_kandinsky_inpaint import KandinskyInpaintPipeline
				from .pipeline_kandinsky_prior import KandinskyPriorPipeline, KandinskyPriorPipelineOutput
				from .text_encoder import MultilingualCLIP





