

'''simple docstring'''
import argparse
import json
from pathlib import Path

import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform

from transformers import (
    BitConfig,
    ViTHybridConfig,
    ViTHybridForImageClassification,
    ViTHybridImageProcessor,
    ViTHybridModel,
)
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


logging.set_verbosity_info()
lowerCamelCase :      List[Any]					 =     logging.get_logger(__name__)


def        _lowerCAmelCase					(						_UpperCamelCase						:     str							, _UpperCamelCase						:     List[str]=False				)      ->			Optional[Any]:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE							    =[]

     # fmt: off
     # stem:
     rename_keys.append(('cls_token', 'vit.embeddings.cls_token')				)
     rename_keys.append(('pos_embed', 'vit.embeddings.position_embeddings')				)

     rename_keys.append(('patch_embed.proj.weight', 'vit.embeddings.patch_embeddings.projection.weight')				)
     rename_keys.append(('patch_embed.proj.bias', 'vit.embeddings.patch_embeddings.projection.bias')				)

     # backbone
     rename_keys.append(('patch_embed.backbone.stem.conv.weight', 'vit.embeddings.patch_embeddings.backbone.bit.embedder.convolution.weight')				)
     rename_keys.append(('patch_embed.backbone.stem.norm.weight', 'vit.embeddings.patch_embeddings.backbone.bit.embedder.norm.weight')				)
     rename_keys.append(('patch_embed.backbone.stem.norm.bias', 'vit.embeddings.patch_embeddings.backbone.bit.embedder.norm.bias')				)

     for stage_idx in range(len(config.backbone_config.depths				)				):
          for layer_idx in range(config.backbone_config.depths[stage_idx]				):
               rename_keys.append((f"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv1.weight", f"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv1.weight")				)
               rename_keys.append((f"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm1.weight", f"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm1.weight")				)
               rename_keys.append((f"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm1.bias", f"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm1.bias")				)
               rename_keys.append((f"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv2.weight", f"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv2.weight")				)
               rename_keys.append((f"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm2.weight", f"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm2.weight")				)
               rename_keys.append((f"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm2.bias", f"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm2.bias")				)
               rename_keys.append((f"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv3.weight", f"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv3.weight")				)
               rename_keys.append((f"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm3.weight", f"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm3.weight")				)
               rename_keys.append((f"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm3.bias", f"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm3.bias")				)

          rename_keys.append((f"patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.conv.weight", f"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.conv.weight")				)
          rename_keys.append((f"patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.norm.weight", f"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.norm.weight")				)
          rename_keys.append((f"patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.norm.bias", f"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.norm.bias")				)

     # transformer encoder
     for i in range(config.num_hidden_layers				):
          # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
          rename_keys.append((f"blocks.{i}.norm1.weight", f"vit.encoder.layer.{i}.layernorm_before.weight")				)
          rename_keys.append((f"blocks.{i}.norm1.bias", f"vit.encoder.layer.{i}.layernorm_before.bias")				)
          rename_keys.append((f"blocks.{i}.attn.proj.weight", f"vit.encoder.layer.{i}.attention.output.dense.weight")				)
          rename_keys.append((f"blocks.{i}.attn.proj.bias", f"vit.encoder.layer.{i}.attention.output.dense.bias")				)
          rename_keys.append((f"blocks.{i}.norm2.weight", f"vit.encoder.layer.{i}.layernorm_after.weight")				)
          rename_keys.append((f"blocks.{i}.norm2.bias", f"vit.encoder.layer.{i}.layernorm_after.bias")				)
          rename_keys.append((f"blocks.{i}.mlp.fc1.weight", f"vit.encoder.layer.{i}.intermediate.dense.weight")				)
          rename_keys.append((f"blocks.{i}.mlp.fc1.bias", f"vit.encoder.layer.{i}.intermediate.dense.bias")				)
          rename_keys.append((f"blocks.{i}.mlp.fc2.weight", f"vit.encoder.layer.{i}.output.dense.weight")				)
          rename_keys.append((f"blocks.{i}.mlp.fc2.bias", f"vit.encoder.layer.{i}.output.dense.bias")				)

     if base_model:
          # layernorm + pooler
          rename_keys.extend(
              [
                  ('norm.weight', 'layernorm.weight'),
                  ('norm.bias', 'layernorm.bias'),
                  ('pre_logits.fc.weight', 'pooler.dense.weight'),
                  ('pre_logits.fc.bias', 'pooler.dense.bias'),
              ]				)

          # if just the base model, we should remove "vit" from all keys that start with "vit"
          _SCREAMING_SNAKE_CASE							    =[(pair[0], pair[1][4:]) if pair[1].startswith('vit'				) else pair for pair in rename_keys]
     else:
          # layernorm + classification head
          rename_keys.extend(
              [
                  ('norm.weight', 'vit.layernorm.weight'),
                  ('norm.bias', 'vit.layernorm.bias'),
                  ('head.weight', 'classifier.weight'),
                  ('head.bias', 'classifier.bias'),
              ]				)
     # fmt: on

     return rename_keys


def        _lowerCAmelCase					(						_UpperCamelCase						:     Tuple							, _UpperCamelCase						:     Any							, _UpperCamelCase						:     List[str]=False				)      ->			int:
     """simple docstring"""




     for i in range(config.num_hidden_layers				):
          if base_model:
               _SCREAMING_SNAKE_CASE							    =''
          else:
               _SCREAMING_SNAKE_CASE							    ='vit.'
          # read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
          _SCREAMING_SNAKE_CASE							    =state_dict.pop(f"blocks.{i}.attn.qkv.weight"				)
          _SCREAMING_SNAKE_CASE							    =state_dict.pop(f"blocks.{i}.attn.qkv.bias"				)
          # next, add query, keys and values (in that order) to the state dict
          _SCREAMING_SNAKE_CASE							    =in_proj_weight[
              : config.hidden_size, :
          ]
          _SCREAMING_SNAKE_CASE							    =in_proj_bias[: config.hidden_size]
          _SCREAMING_SNAKE_CASE							    =in_proj_weight[
              config.hidden_size : config.hidden_size * 2, :
          ]
          _SCREAMING_SNAKE_CASE							    =in_proj_bias[
              config.hidden_size : config.hidden_size * 2
          ]
          _SCREAMING_SNAKE_CASE							    =in_proj_weight[
              -config.hidden_size :, :
          ]
          _SCREAMING_SNAKE_CASE							    =in_proj_bias[-config.hidden_size :]


def        _lowerCAmelCase					(						_UpperCamelCase						:     Tuple				)      ->			List[Any]:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE							    =['head.weight', 'head.bias']
     for k in ignore_keys:
          state_dict.pop(_UpperCamelCase							, _UpperCamelCase				)


def        _lowerCAmelCase					(						_UpperCamelCase						:     Optional[Any]							, _UpperCamelCase						:     Union[str, Any]							, _UpperCamelCase						:     str				)      ->			List[Any]:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE							    =dct.pop(_UpperCamelCase				)
     _SCREAMING_SNAKE_CASE							    =val


def        _lowerCAmelCase					(						)      ->			Optional[Any]:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE							    ='http://images.cocodataset.org/val2017/000000039769.jpg'
     _SCREAMING_SNAKE_CASE							    =Image.open(requests.get(_UpperCamelCase							, stream=_UpperCamelCase				).raw				)
     return im


@torch.no_grad()
def        _lowerCAmelCase					(						_UpperCamelCase						:     int							, _UpperCamelCase						:     Dict							, _UpperCamelCase						:     Dict=False				)      ->			List[str]:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE							    =BitConfig(
         global_padding='same'							, layer_type='bottleneck'							, depths=(3, 4, 9)							, out_features=['stage3']							, embedding_dynamic_padding=_UpperCamelCase							, )
     _SCREAMING_SNAKE_CASE							    =ViTHybridConfig(backbone_config=_UpperCamelCase							, image_size=3_84							, num_labels=10_00				)
     _SCREAMING_SNAKE_CASE							    =False

     # load original model from timm
     _SCREAMING_SNAKE_CASE							    =timm.create_model(_UpperCamelCase							, pretrained=_UpperCamelCase				)
     timm_model.eval()

     # load state_dict of original model, remove and rename some keys
     _SCREAMING_SNAKE_CASE							    =timm_model.state_dict()
     if base_model:
          remove_classification_head_(_UpperCamelCase				)
     _SCREAMING_SNAKE_CASE							    =create_rename_keys(_UpperCamelCase							, _UpperCamelCase				)
     for src, dest in rename_keys:
          rename_key(_UpperCamelCase							, _UpperCamelCase							, _UpperCamelCase				)
     read_in_q_k_v(_UpperCamelCase							, _UpperCamelCase							, _UpperCamelCase				)

     _SCREAMING_SNAKE_CASE							    ='huggingface/label-files'
     _SCREAMING_SNAKE_CASE							    ='imagenet-1k-id2label.json'
     _SCREAMING_SNAKE_CASE							    =json.load(open(hf_hub_download(_UpperCamelCase							, _UpperCamelCase							, repo_type='dataset'				)							, 'r'				)				)
     _SCREAMING_SNAKE_CASE							    ={int(_UpperCamelCase				): v for k, v in idalabel.items()}
     _SCREAMING_SNAKE_CASE							    =idalabel
     _SCREAMING_SNAKE_CASE							    ={v: k for k, v in idalabel.items()}

     # load HuggingFace model
     if vit_name[-5:] == "in21k":
          _SCREAMING_SNAKE_CASE							    =ViTHybridModel(_UpperCamelCase				).eval()
     else:
          _SCREAMING_SNAKE_CASE							    =ViTHybridForImageClassification(_UpperCamelCase				).eval()
     model.load_state_dict(_UpperCamelCase				)

     # create image processor
     _SCREAMING_SNAKE_CASE							    =create_transform(**resolve_data_config({}							, model=_UpperCamelCase				)				)
     _SCREAMING_SNAKE_CASE							    =transform.transforms

     _SCREAMING_SNAKE_CASE							    ={
         'bilinear': PILImageResampling.BILINEAR,
         'bicubic': PILImageResampling.BICUBIC,
         'nearest': PILImageResampling.NEAREST,
     }

     _SCREAMING_SNAKE_CASE							    =ViTHybridImageProcessor(
         do_resize=_UpperCamelCase							, size={'shortest_edge': timm_transforms[0].size}							, resample=pillow_resamplings[timm_transforms[0].interpolation.value]							, do_center_crop=_UpperCamelCase							, crop_size={'height': timm_transforms[1].size[0], 'width': timm_transforms[1].size[1]}							, do_normalize=_UpperCamelCase							, image_mean=timm_transforms[-1].mean.tolist()							, image_std=timm_transforms[-1].std.tolist()							, )

     _SCREAMING_SNAKE_CASE							    =prepare_img()
     _SCREAMING_SNAKE_CASE							    =transform(_UpperCamelCase				).unsqueeze(0				)
     _SCREAMING_SNAKE_CASE							    =processor(_UpperCamelCase							, return_tensors='pt'				).pixel_values

     # verify pixel values
     assert torch.allclose(_UpperCamelCase							, _UpperCamelCase				)

     # verify logits
     with torch.no_grad():
          _SCREAMING_SNAKE_CASE							    =model(_UpperCamelCase				)
          _SCREAMING_SNAKE_CASE							    =outputs.logits

     print('Predicted class:'							, logits.argmax(-1				).item()				)
     if base_model:
          _SCREAMING_SNAKE_CASE							    =timm_model.forward_features(_UpperCamelCase				)
          assert timm_pooled_output.shape == outputs.pooler_output.shape
          assert torch.allclose(_UpperCamelCase							, outputs.pooler_output							, atol=1E-3				)
     else:
          _SCREAMING_SNAKE_CASE							    =timm_model(_UpperCamelCase				)
          assert timm_logits.shape == outputs.logits.shape
          assert torch.allclose(_UpperCamelCase							, outputs.logits							, atol=1E-3				)
     print('Looks ok!'				)

     if pytorch_dump_folder_path is not None:
          Path(_UpperCamelCase				).mkdir(exist_ok=_UpperCamelCase				)
          print(f"Saving model {vit_name} to {pytorch_dump_folder_path}"				)
          model.save_pretrained(_UpperCamelCase				)
          print(f"Saving processor to {pytorch_dump_folder_path}"				)
          processor.save_pretrained(_UpperCamelCase				)

     if push_to_hub:
          print(f"Pushing model and processor to the hub {vit_name}"				)
          model.push_to_hub(f"ybelkada/{vit_name}"				)
          processor.push_to_hub(f"ybelkada/{vit_name}"				)


if __name__ == "__main__":
     lowerCamelCase :      Optional[int]					 =     argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         "--vit_name",
         default="vit_base_r50_s16_384",
         type=str,
         help="Name of the hybrid ViT timm model you'd like to convert.",
     )
     parser.add_argument(
         "--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
     )
     parser.add_argument(
         "--push_to_hub", action="store_true", help="Whether to upload the model to the HuggingFace hub."
     )

     lowerCamelCase :      Tuple					 =     parser.parse_args()
     convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path, args.push_to_hub)



'''simple docstring'''
import copy

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


lowerCamelCase :      Dict					 =     logging.get_logger(__name__)

lowerCamelCase :      List[Any]					 =     {
    "ut/deta": "https://huggingface.co/ut/deta/resolve/main/config.json",
}







class 							A__    (       A__  ):
  A__		 =				'deta'
  A__		 =				{
      'hidden_size': 'd_model',
      'num_attention_heads': 'encoder_attention_heads',
  }





  def __init__(		self      :	Tuple							,					_a      :	Optional[int]=None							,					_a      :	int=900							,					_a      :	Optional[Any]=2048							,					_a      :	int=6							,					_a      :	Tuple=2048							,					_a      :	Optional[int]=8							,					_a      :	Any=6							,					_a      :	str=1024							,					_a      :	int=8							,					_a      :	int=0.0							,					_a      :	Optional[Any]=True							,					_a      :	Tuple="relu"							,					_a      :	Union[str, Any]=256							,					_a      :	Tuple=0.1							,					_a      :	str=0.0							,					_a      :	Dict=0.0							,					_a      :	Tuple=0.02							,					_a      :	Union[str, Any]=1.0							,					_a      :	Any=True							,					_a      :	Tuple=False							,					_a      :	List[Any]="sine"							,					_a      :	str=5							,					_a      :	List[Any]=4							,					_a      :	str=4							,					_a      :	Union[str, Any]=True							,					_a      :	Optional[int]=300							,					_a      :	Dict=True							,					_a      :	List[Any]=True							,					_a      :	List[Any]=1							,					_a      :	List[str]=5							,					_a      :	int=2							,					_a      :	Dict=1							,					_a      :	str=1							,					_a      :	Optional[Any]=5							,					_a      :	Union[str, Any]=2							,					_a      :	List[str]=0.1							,					_a      :	List[Any]=0.25							,					**_a      :	Union[str, Any]							,					)	->    List[str]:





       '''simple docstring'''






       if backbone_config is None:
            logger.info('`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.' )
            _SCREAMING_SNAKE_CASE							    =CONFIG_MAPPING['resnet'](out_features=['stage2', 'stage3', 'stage4'] )
       else:
            if isinstance(_a							,					_a ):
                 _SCREAMING_SNAKE_CASE							    =backbone_config.pop('model_type' )
                 _SCREAMING_SNAKE_CASE							    =CONFIG_MAPPING[backbone_model_type]
                 _SCREAMING_SNAKE_CASE							    =config_class.from_dict(_a )

       _SCREAMING_SNAKE_CASE							    =backbone_config
       _SCREAMING_SNAKE_CASE							    =num_queries
       _SCREAMING_SNAKE_CASE							    =max_position_embeddings
       _SCREAMING_SNAKE_CASE							    =d_model
       _SCREAMING_SNAKE_CASE							    =encoder_ffn_dim
       _SCREAMING_SNAKE_CASE							    =encoder_layers
       _SCREAMING_SNAKE_CASE							    =encoder_attention_heads
       _SCREAMING_SNAKE_CASE							    =decoder_ffn_dim
       _SCREAMING_SNAKE_CASE							    =decoder_layers
       _SCREAMING_SNAKE_CASE							    =decoder_attention_heads
       _SCREAMING_SNAKE_CASE							    =dropout
       _SCREAMING_SNAKE_CASE							    =attention_dropout
       _SCREAMING_SNAKE_CASE							    =activation_dropout
       _SCREAMING_SNAKE_CASE							    =activation_function
       _SCREAMING_SNAKE_CASE							    =init_std
       _SCREAMING_SNAKE_CASE							    =init_xavier_std
       _SCREAMING_SNAKE_CASE							    =encoder_layerdrop
       _SCREAMING_SNAKE_CASE							    =auxiliary_loss
       _SCREAMING_SNAKE_CASE							    =position_embedding_type
       # deformable attributes
       _SCREAMING_SNAKE_CASE							    =num_feature_levels
       _SCREAMING_SNAKE_CASE							    =encoder_n_points
       _SCREAMING_SNAKE_CASE							    =decoder_n_points
       _SCREAMING_SNAKE_CASE							    =two_stage
       _SCREAMING_SNAKE_CASE							    =two_stage_num_proposals
       _SCREAMING_SNAKE_CASE							    =with_box_refine
       _SCREAMING_SNAKE_CASE							    =assign_first_stage
       if two_stage is True and with_box_refine is False:
            raise ValueError('If two_stage is True, with_box_refine must be True.' )
       # Hungarian matcher
       _SCREAMING_SNAKE_CASE							    =class_cost
       _SCREAMING_SNAKE_CASE							    =bbox_cost
       _SCREAMING_SNAKE_CASE							    =giou_cost
       # Loss coefficients
       _SCREAMING_SNAKE_CASE							    =mask_loss_coefficient
       _SCREAMING_SNAKE_CASE							    =dice_loss_coefficient
       _SCREAMING_SNAKE_CASE							    =bbox_loss_coefficient
       _SCREAMING_SNAKE_CASE							    =giou_loss_coefficient
       _SCREAMING_SNAKE_CASE							    =eos_coefficient
       _SCREAMING_SNAKE_CASE							    =focal_alpha
       super().__init__(is_encoder_decoder=_a							,					**_a )





  @property
  def 						A  (		self      :	Dict )	->    int:





       '''simple docstring'''






       return self.encoder_attention_heads





  @property
  def 						A  (		self      :	List[Any] )	->    int:





       '''simple docstring'''






       return self.d_model





  def 						A  (		self      :	Optional[int] )	->    List[str]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =copy.deepcopy(self.__dict__ )
       _SCREAMING_SNAKE_CASE							    =self.backbone_config.to_dict()
       _SCREAMING_SNAKE_CASE							    =self.__class__.model_type
       return output
