


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()
_SCREAMING_SNAKE_CASE      :			Optional[int]      				=   logging.get_logger(__name__)
def       UpperCAmelCase__						(UpperCamelCase_    ):



     """simple docstring"""






     print('''Loading config file...'''    )

     def flatten_yaml_as_dict(UpperCamelCase_							,UpperCamelCase_=""							,UpperCamelCase_="."    ):
          snake_case					=    []
          for k, v in d.items():
               snake_case					=    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_    )

     snake_case					=    argparse.Namespace()
     with open(UpperCamelCase_							,'''r'''    ) as yaml_file:
          try:
               snake_case					=    yaml.load(UpperCamelCase_							,Loader=yaml.FullLoader    )

               snake_case					=    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__						(UpperCamelCase_							,UpperCamelCase_    ):



     """simple docstring"""






     snake_case					=    MobileViTVaConfig()

     snake_case					=    False

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

     # orig_config
     snake_case					=    load_orig_config_file(UpperCamelCase_    )
     assert getattr(UpperCamelCase_							,'''model.classification.name'''							,-1    ) == "mobilevit_v2", "Invalid model"
     snake_case					=    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"
     snake_case					=    getattr(UpperCamelCase_							,'''model.classification.activation.name'''							,'''swish'''    )
     # config.image_size == getattr(orig_config,  'sampler.bs.crop_size_width', 256)

     if is_segmentation_model:
          snake_case					=    getattr(UpperCamelCase_							,'''model.segmentation.output_stride'''							,16    )
          if "_deeplabv3" in task_name:
               snake_case					=    getattr(UpperCamelCase_							,'''model.segmentation.deeplabv3.aspp_rates'''							,[12, 24, 36]    )
               snake_case					=    getattr(UpperCamelCase_							,'''model.segmentation.deeplabv3.aspp_out_channels'''							,5_12    )
               snake_case					=    getattr(UpperCamelCase_							,'''model.segmentation.deeplabv3.aspp_dropout'''							,0.1    )

    # id2label
     snake_case					=    '''huggingface/label-files'''
     snake_case					=    json.load(open(hf_hub_download(UpperCamelCase_							,UpperCamelCase_							,repo_type='''dataset'''    )							,'''r'''    )    )
     snake_case					=    {int(UpperCamelCase_    ): v for k, v in idalabel.items()}
     snake_case					=    idalabel
     snake_case					=    {v: k for k, v in idalabel.items()}

     return config
def       UpperCAmelCase__						(UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_    ):



     """simple docstring"""






     snake_case					=    dct.pop(UpperCamelCase_    )
     snake_case					=    val
def       UpperCAmelCase__						(UpperCamelCase_							,UpperCamelCase_=False    ):



     """simple docstring"""






     if base_model:
          snake_case					=    ''''''
     else:
          snake_case					=    '''mobilevitv2.'''

     snake_case					=    []
     for k in state_dict.keys():
          if k[:8] == "encoder.":
               snake_case					=    k[8:]
          else:
               snake_case					=    k

          if ".block." in k:
               snake_case					=    k_new.replace('''.block.'''							,'''.'''    )
          if ".conv." in k:
               snake_case					=    k_new.replace('''.conv.'''							,'''.convolution.'''    )
          if ".norm." in k:
               snake_case					=    k_new.replace('''.norm.'''							,'''.normalization.'''    )

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

          for i in [3, 4, 5]:
               if F'''layer_{i}.0.''' in k:
                    snake_case					=    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:
                    snake_case					=    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:
                    snake_case					=    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:
                    snake_case					=    [0, 1]
               elif i == 4:
                    snake_case					=    [0, 1, 2, 3]
               elif i == 5:
                    snake_case					=    [0, 1, 2]

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

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

          if "seg_head." in k:
               snake_case					=    k_new.replace('''seg_head.'''							,'''segmentation_head.'''    )
          if ".aspp_layer." in k:
               snake_case					=    k_new.replace('''.aspp_layer.'''							,'''.'''    )
          if ".aspp_pool." in k:
               snake_case					=    k_new.replace('''.aspp_pool.'''							,'''.'''    )

          rename_keys.append((k, k_new)    )
     return rename_keys
def       UpperCAmelCase__						(UpperCamelCase_    ):



     """simple docstring"""






     snake_case					=    []
     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__						():



     """simple docstring"""






     snake_case					=    '''http://images.cocodataset.org/val2017/000000039769.jpg'''
     # url = "https://cdn.britannica.com/86/141086-050-9D7C75EE/Gulfstream-G450-business-jet-passengers.jpg"
     snake_case					=    Image.open(requests.get(UpperCamelCase_							,stream=UpperCamelCase_    ).raw    )
     return im
@torch.no_grad()
def       UpperCAmelCase__						(UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_    ):



     """simple docstring"""






     snake_case					=    get_mobilevitva_config(UpperCamelCase_							,UpperCamelCase_    )

     # load original state_dict
     snake_case					=    torch.load(UpperCamelCase_							,map_location='''cpu'''    )

     # load huggingface model
     if task_name.startswith('''ade20k_'''    ) or task_name.startswith('''voc_'''    ):
          snake_case					=    MobileViTVaForSemanticSegmentation(UpperCamelCase_    ).eval()
          snake_case					=    False
     else:
          snake_case					=    MobileViTVaForImageClassification(UpperCamelCase_    ).eval()
          snake_case					=    False

     # remove and rename some keys of load the original model
     snake_case					=    checkpoint
     remove_unused_keys(UpperCamelCase_    )
     snake_case					=    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
     snake_case					=    MobileViTImageProcessor(crop_size=config.image_size							,size=config.image_size + 32    )
     snake_case					=    image_processor(images=prepare_img()							,return_tensors='''pt'''    )
     snake_case					=    model(**UpperCamelCase_    )

     # verify classification model
     if task_name.startswith('''imagenet'''    ):
          snake_case					=    outputs.logits
          snake_case					=    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
               snake_case					=    torch.tensor([-1.6_3_3_6e0_0, -7.3_2_0_4e-0_2, -5.1_8_8_3e-0_1]    )
               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__":
 _SCREAMING_SNAKE_CASE      :			Optional[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."
 )

 _SCREAMING_SNAKE_CASE      :			Optional[int]      				=   parser.parse_args()
 convert_mobilevitva_checkpoint(
     args.task, args.orig_checkpoint_path, args.orig_config_path, args.pytorch_dump_folder_path
 )








import copy
from collections import OrderedDict
from typing import Dict, Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


_SCREAMING_SNAKE_CASE      :			Dict      				=   logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE      :			Any      				=   {
    "facebook/detr-resnet-50": "https://huggingface.co/facebook/detr-resnet-50/resolve/main/config.json",
    # See all DETR models at https://huggingface.co/models?filter=detr
}

class   A__ (   snake_case__     ):







    """simple docstring"""

    __magic_name__        =		'detr'
    __magic_name__        =		['past_key_values']
    __magic_name__        =		{
        'hidden_size': 'd_model',
        'num_attention_heads': 'encoder_attention_heads',
    }
    def __init__(	self      , __snake_case=True      , __snake_case=None      , __snake_case=3      , __snake_case=1_0_0      , __snake_case=6      , __snake_case=2_0_4_8      , __snake_case=8      , __snake_case=6      , __snake_case=2_0_4_8      , __snake_case=8      , __snake_case=0.0      , __snake_case=0.0      , __snake_case=True      , __snake_case="relu"      , __snake_case=2_5_6      , __snake_case=0.1      , __snake_case=0.0      , __snake_case=0.0      , __snake_case=0.02      , __snake_case=1.0      , __snake_case=False      , __snake_case="sine"      , __snake_case="resnet50"      , __snake_case=True      , __snake_case=False      , __snake_case=1      , __snake_case=5      , __snake_case=2      , __snake_case=1      , __snake_case=1      , __snake_case=5      , __snake_case=2      , __snake_case=0.1      , **__snake_case      , ):
         if backbone_config is not None and use_timm_backbone:
              raise ValueError('''You can\'t specify both `backbone_config` and `use_timm_backbone`.'''   )

         if not use_timm_backbone:
              if backbone_config is None:
                   logger.info('''`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.'''   )
                   snake_case					=    CONFIG_MAPPING['''resnet'''](out_features=['''stage4''']   )
              elif isinstance(__snake_case      , __snake_case   ):
                   snake_case					=    backbone_config.get('''model_type'''   )
                   snake_case					=    CONFIG_MAPPING[backbone_model_type]
                   snake_case					=    config_class.from_dict(__snake_case   )
              # set timm attributes to None
              snake_case			,  snake_case			,  snake_case					=    None, None, None

         snake_case					=    use_timm_backbone
         snake_case					=    backbone_config
         snake_case					=    num_channels
         snake_case					=    num_queries
         snake_case					=    d_model
         snake_case					=    encoder_ffn_dim
         snake_case					=    encoder_layers
         snake_case					=    encoder_attention_heads
         snake_case					=    decoder_ffn_dim
         snake_case					=    decoder_layers
         snake_case					=    decoder_attention_heads
         snake_case					=    dropout
         snake_case					=    attention_dropout
         snake_case					=    activation_dropout
         snake_case					=    activation_function
         snake_case					=    init_std
         snake_case					=    init_xavier_std
         snake_case					=    encoder_layerdrop
         snake_case					=    decoder_layerdrop
         snake_case					=    encoder_layers
         snake_case					=    auxiliary_loss
         snake_case					=    position_embedding_type
         snake_case					=    backbone
         snake_case					=    use_pretrained_backbone
         snake_case					=    dilation
         # Hungarian matcher
         snake_case					=    class_cost
         snake_case					=    bbox_cost
         snake_case					=    giou_cost
         # Loss coefficients
         snake_case					=    mask_loss_coefficient
         snake_case					=    dice_loss_coefficient
         snake_case					=    bbox_loss_coefficient
         snake_case					=    giou_loss_coefficient
         snake_case					=    eos_coefficient
         super().__init__(is_encoder_decoder=__snake_case      , **__snake_case   )
    @property
    def 		a_      (	self   ):
         return self.encoder_attention_heads
    @property
    def 		a_      (	self   ):
         return self.d_model
    @classmethod
    def 		a_      (	cls      , __snake_case      , **__snake_case   ):
         return cls(backbone_config=__snake_case      , **__snake_case   )




    def 		a_      (	self   ):
         snake_case					=    copy.deepcopy(self.__dict__   )
         if output["backbone_config"] is not None:
              snake_case					=    self.backbone_config.to_dict()
         snake_case					=    self.__class__.model_type
         return output






class   A__ (   snake_case__     ):







    """simple docstring"""

    __magic_name__        =		version.parse('1.11'     )
    @property
    def 		a_      (	self   ):
         return OrderedDict(
             [
                 ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
                 ('''pixel_mask''', {0: '''batch'''}),
             ]   )
    @property
    def 		a_      (	self   ):
         return 1E-5




    @property
    def 		a_      (	self   ):
         return 1_2




