





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()
SCREAMING_SNAKE_CASE_		     = logging.get_logger(__name__)



def      __lowercase     (   _SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE=False			)				->     Optional[int]:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE	=		[]
      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"
            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"""),
                ]			)

      return rename_keys



def      __lowercase     (   _SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE=False			)				->     Optional[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      __lowercase     (   _SCREAMING_SNAKE_CASE			)				->     List[str]:


      '''simple docstring'''


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



def      __lowercase     (   _SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE			)				->     List[str]:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE	=		dct.pop(_SCREAMING_SNAKE_CASE			)
      SCREAMING_SNAKE_CASE	=		val



def      __lowercase     (   )				->     Optional[int]:


      '''simple docstring'''


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



@torch.no_grad()
def      __lowercase     (   _SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE=True			)				->     List[str]:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE	=		ViTConfig()
      # patch_size
      if model_name[-1] == "8":
            SCREAMING_SNAKE_CASE	=		8
      # set labels if required
      if not base_model:
            SCREAMING_SNAKE_CASE	=		10_00
            SCREAMING_SNAKE_CASE	=		"""huggingface/label-files"""
            SCREAMING_SNAKE_CASE	=		"""imagenet-1k-id2label.json"""
            SCREAMING_SNAKE_CASE	=		json.load(open(hf_hub_download(_SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE				,		repo_type="""dataset"""			)				,		"""r"""			)			)
            SCREAMING_SNAKE_CASE	=		{int(_SCREAMING_SNAKE_CASE			): v for k, v in idalabel.items()}
            SCREAMING_SNAKE_CASE	=		idalabel
            SCREAMING_SNAKE_CASE	=		{v: k for k, v in idalabel.items()}
      # size of the architecture
      if model_name in ["dino_vits8", "dino_vits16"]:
            SCREAMING_SNAKE_CASE	=		3_84
            SCREAMING_SNAKE_CASE	=		15_36
            SCREAMING_SNAKE_CASE	=		12
            SCREAMING_SNAKE_CASE	=		6

      # load original model from torch hub
      SCREAMING_SNAKE_CASE	=		torch.hub.load("""facebookresearch/dino:main"""				,		_SCREAMING_SNAKE_CASE			)
      original_model.eval()

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

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

      # Check outputs on an image, prepared by ViTImageProcessor
      SCREAMING_SNAKE_CASE	=		ViTImageProcessor()
      SCREAMING_SNAKE_CASE	=		image_processor(images=prepare_img()				,		return_tensors="""pt"""			)
      SCREAMING_SNAKE_CASE	=		encoding["""pixel_values"""]
      SCREAMING_SNAKE_CASE	=		model(_SCREAMING_SNAKE_CASE			)

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

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


if __name__ == "__main__":
      SCREAMING_SNAKE_CASE_		     = 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)
      SCREAMING_SNAKE_CASE_		     = parser.parse_args()
      convert_vit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.base_model)










import warnings

from ...utils import logging
from .image_processing_deformable_detr import DeformableDetrImageProcessor


SCREAMING_SNAKE_CASE_		     = logging.get_logger(__name__)

class 	UpperCamelCase__				(   lowerCAmelCase_     ):



     '''simple docstring'''

     def __init__(							self		:					int		,*lowerCamelCase__		:					int		,**lowerCamelCase__		:					List[Any] )  ->	None:




           '''simple docstring'''


           warnings.warn(
               """The class DeformableDetrFeatureExtractor is deprecated and will be removed in version 5 of Transformers."""
               """ Please use DeformableDetrImageProcessor instead."""		,lowerCamelCase__		,)
           super().__init__(*lowerCamelCase__		,**lowerCamelCase__ )


