





'''simple docstring'''

import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import ViTConfig, ViTForImageClassification, ViTImageProcessor, ViTModel
from transformers.utils import logging


logging.set_verbosity_info()
lowercase__						:      Optional[Any]	=    logging.get_logger(__name__)







def  a__    (    lowercase :					Optional[Any], lowercase :					Union[str, Any]=False				)		->		List[str]:





     """simple docstring"""

     _UpperCamelCase       =	[]
     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""")				)

     # projection layer + position embeddings
     rename_keys.extend(
         [
             ('''cls_token''', '''vit.embeddings.cls_token'''),
             ('''patch_embed.proj.weight''', '''vit.embeddings.patch_embeddings.projection.weight'''),
             ('''patch_embed.proj.bias''', '''vit.embeddings.patch_embeddings.projection.bias'''),
             ('''pos_embed''', '''vit.embeddings.position_embeddings'''),
         ]				)

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

          # if just the base model, we should remove "vit" from all keys that start with "vit"
          _UpperCamelCase       =	[(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'''),
              ]				)

     return rename_keys







def  a__    (    lowercase :					Tuple, lowercase :					Tuple, lowercase :					List[Any]=False				)		->		str:





     """simple docstring"""

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







def  a__    (    lowercase :					int				)		->		str:





     """simple docstring"""

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







def  a__    (    lowercase :					List[Any], lowercase :					Union[str, Any], lowercase :					Optional[int]				)		->		Any:





     """simple docstring"""

     _UpperCamelCase       =	dct.pop(lowercase				)
     _UpperCamelCase       =	val







def  a__    (    )		->		str:





     """simple docstring"""

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







@torch.no_grad()
def  a__    (    lowercase :					Union[str, Any], lowercase :					Dict, lowercase :					str=True				)		->		str:





     """simple docstring"""

     _UpperCamelCase       =	ViTConfig()
     # patch_size
     if model_name[-1] == "8":
          _UpperCamelCase       =	8
     # set labels if required
     if not base_model:
          _UpperCamelCase       =	1000
          _UpperCamelCase       =	'''huggingface/label-files'''
          _UpperCamelCase       =	'''imagenet-1k-id2label.json'''
          _UpperCamelCase       =	json.load(open(hf_hub_download(lowercase, lowercase, repo_type='''dataset'''				), '''r'''				)				)
          _UpperCamelCase       =	{int(lowercase				): v for k, v in idalabel.items()}
          _UpperCamelCase       =	idalabel
          _UpperCamelCase       =	{v: k for k, v in idalabel.items()}
     # size of the architecture
     if model_name in ["dino_vits8", "dino_vits16"]:
          _UpperCamelCase       =	384
          _UpperCamelCase       =	1536
          _UpperCamelCase       =	12
          _UpperCamelCase       =	6

     # load original model from torch hub
     _UpperCamelCase       =	torch.hub.load('''facebookresearch/dino:main''', lowercase				)
     original_model.eval()

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

     # load HuggingFace model
     if base_model:
          _UpperCamelCase       =	ViTModel(lowercase, add_pooling_layer=lowercase				).eval()
     else:
          _UpperCamelCase       =	ViTForImageClassification(lowercase				).eval()
     model.load_state_dict(lowercase				)

     # Check outputs on an image, prepared by ViTImageProcessor
     _UpperCamelCase       =	ViTImageProcessor()
     _UpperCamelCase       =	image_processor(images=prepare_img(), return_tensors='''pt'''				)
     _UpperCamelCase       =	encoding['''pixel_values''']
     _UpperCamelCase       =	model(lowercase				)

     if base_model:
          _UpperCamelCase       =	original_model(lowercase				)
          assert torch.allclose(lowercase, outputs.last_hidden_state[:, 0, :], atol=1e-1				)
     else:
          _UpperCamelCase       =	original_model(lowercase				)
          assert logits.shape == outputs.logits.shape
          assert torch.allclose(lowercase, outputs.logits, atol=1e-3				)

     Path(lowercase				).mkdir(exist_ok=lowercase				)
     print(F"""Saving model {model_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__":
   lowercase__						:      Optional[int]	=    argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       '--model_name',
       default='dino_vitb16',
       type=str,
       help='Name of the model trained with DINO 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(
       '--base_model',
       action='store_true',
       help='Whether to only convert the base model (no projection head weights).',
   )

   parser.set_defaults(base_model=True)
   lowercase__						:      List[str]	=    parser.parse_args()
   convert_vit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.base_model)










'''simple docstring'''

import warnings

from ...utils import logging
from .image_processing_owlvit import OwlViTImageProcessor


lowercase__						:      Optional[Any]	=    logging.get_logger(__name__)
class     __lowerCAmelCase					(   __magic_name__			):






       """simple docstring"""





       def __init__(							self       :		List[Any]	,			*lowerCAmelCase__       :		List[str]	,			**lowerCAmelCase__       :		List[str]					)   ->					None:

            '''simple docstring'''




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



