





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()
_lowerCamelCase			       =      logging.get_logger(__name__)




def    __UpperCAmelCase(      lowercase_      ):
       print('''Loading config file...'''      )

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

       _lowerCamelCase							:     int	=		argparse.Namespace()
       with open(lowercase_ ,    '''r'''      ) as yaml_file:
              try:
                     _lowerCamelCase							:     List[str]	=		yaml.load(lowercase_ ,    Loader=yaml.FullLoader      )

                     _lowerCamelCase							:     List[str]	=		flatten_yaml_as_dict(lowercase_      )
                     for k, v in flat_cfg.items():
                            setattr(lowercase_ ,    lowercase_ ,    lowercase_      )
              except yaml.YAMLError as exc:
                     logger.error('''Error while loading config file: {}. Error message: {}'''.format(lowercase_ ,    str(lowercase_      )      )      )
       return config




def    __UpperCAmelCase(      lowercase_ ,    lowercase_      ):
       _lowerCamelCase							:     Optional[Any]	=		MobileViTVaConfig()

       _lowerCamelCase							:     str	=		False

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

       # orig_config
       _lowerCamelCase							:     Tuple	=		load_orig_config_file(lowercase_      )
       assert getattr(lowercase_ ,    '''model.classification.name''' ,    -1      ) == "mobilevit_v2", "Invalid model"
       _lowerCamelCase							:     str	=		getattr(lowercase_ ,    '''model.classification.mitv2.width_multiplier''' ,    1.0      )
       assert (
           getattr(lowercase_ ,    '''model.classification.mitv2.attn_norm_layer''' ,    -1      ) == "layer_norm_2d"
       ), "Norm layers other than layer_norm_2d is not supported"
       _lowerCamelCase							:     Tuple	=		getattr(lowercase_ ,    '''model.classification.activation.name''' ,    '''swish'''      )
       # config.image_size == getattr(orig_config,  'sampler.bs.crop_size_width', 256)

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

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

       return config




def    __UpperCAmelCase(      lowercase_ ,    lowercase_ ,    lowercase_      ):
       _lowerCamelCase							:     Any	=		dct.pop(lowercase_      )
       _lowerCamelCase							:     Optional[int]	=		val




def    __UpperCAmelCase(      lowercase_ ,    lowercase_=False      ):
       if base_model:
              _lowerCamelCase							:     Optional[int]	=		''''''
       else:
              _lowerCamelCase							:     Any	=		'''mobilevitv2.'''

       _lowerCamelCase							:     Union[str, Any]	=		[]
       for k in state_dict.keys():
              if k[:8] == "encoder.":
                     _lowerCamelCase							:     int	=		k[8:]
              else:
                     _lowerCamelCase							:     List[Any]	=		k

              if ".block." in k:
                     _lowerCamelCase							:     str	=		k_new.replace('''.block.''' ,    '''.'''      )
              if ".conv." in k:
                     _lowerCamelCase							:     List[str]	=		k_new.replace('''.conv.''' ,    '''.convolution.'''      )
              if ".norm." in k:
                     _lowerCamelCase							:     int	=		k_new.replace('''.norm.''' ,    '''.normalization.'''      )

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

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

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

              if "classifier.1." in k:
                     _lowerCamelCase							:     Tuple	=		k_new.replace('''classifier.1.''' ,    '''classifier.'''      )

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

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




def    __UpperCAmelCase(      lowercase_      ):
       _lowerCamelCase							:     Union[str, Any]	=		[]
       for k in state_dict.keys():
              if k.startswith('''seg_head.aux_head.'''      ):
                     keys_to_ignore.append(lowercase_      )
       for k in keys_to_ignore:
              state_dict.pop(lowercase_ ,    lowercase_      )




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




@torch.no_grad()
def    __UpperCAmelCase(      lowercase_ ,    lowercase_ ,    lowercase_ ,    lowercase_      ):
       _lowerCamelCase							:     Optional[int]	=		get_mobilevitva_config(lowercase_ ,    lowercase_      )

       # load original state_dict
       _lowerCamelCase							:     Dict	=		torch.load(lowercase_ ,    map_location='''cpu'''      )

       # load huggingface model
       if task_name.startswith('''ade20k_'''      ) or task_name.startswith('''voc_'''      ):
              _lowerCamelCase							:     Tuple	=		MobileViTVaForSemanticSegmentation(lowercase_      ).eval()
              _lowerCamelCase							:     Union[str, Any]	=		False
       else:
              _lowerCamelCase							:     List[str]	=		MobileViTVaForImageClassification(lowercase_      ).eval()
              _lowerCamelCase							:     List[Any]	=		False

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

       # load modified state_dict
       model.load_state_dict(lowercase_      )

       # Check outputs on an image, prepared by MobileViTImageProcessor
       _lowerCamelCase							:     str	=		MobileViTImageProcessor(crop_size=config.image_size ,    size=config.image_size + 32      )
       _lowerCamelCase							:     List[Any]	=		image_processor(images=prepare_img() ,    return_tensors='''pt'''      )
       _lowerCamelCase							:     Dict	=		model(**lowercase_      )

       # verify classification model
       if task_name.startswith('''imagenet'''      ):
              _lowerCamelCase							:     Union[str, Any]	=		outputs.logits
              _lowerCamelCase							:     Union[str, Any]	=		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							:     Tuple	=		torch.tensor([-1.6_336e00, -7.3_204e-02, -5.1_883e-01]      )
                     assert torch.allclose(logits[0, :3] ,    lowercase_ ,    atol=1e-4      )

       Path(lowercase_      ).mkdir(exist_ok=lowercase_      )
       print(F"""Saving model {task_name} to {pytorch_dump_folder_path}"""      )
       model.save_pretrained(lowercase_      )
       print(F"""Saving image processor to {pytorch_dump_folder_path}"""      )
       image_processor.save_pretrained(lowercase_      )


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

    _lowerCamelCase			       =      parser.parse_args()
    convert_mobilevitva_checkpoint(
        args.task, args.orig_checkpoint_path, args.orig_config_path, args.pytorch_dump_folder_path
    )








import warnings

from ...utils import logging
from .image_processing_donut import DonutImageProcessor


_lowerCamelCase			       =      logging.get_logger(__name__)







class 			__A       (      lowerCamelCase__  ):




      """simple docstring"""







      def __init__(		self      ,							*a__      ,							**a__):



             """simple docstring"""





             warnings.warn(
                 '''The class DonutFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'''
                 ''' use DonutImageProcessor instead.'''      ,							a__      ,							)
             super().__init__(*a__      ,							**a__)

