

"""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()
UpperCAmelCase													=logging.get_logger(__name__)



def   _A					(	_a	:						Any     ,   _a	:						Optional[Any]=False				):



      """simple docstring"""






      A				  =		[]
      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"
            A				  =		[(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					(	_a	:						List[Any]     ,   _a	:						List[str]     ,   _a	:						List[Any]=False				):



      """simple docstring"""






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



def   _A					(	_a	:						Dict				):



      """simple docstring"""






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



def   _A					(	_a	:						Union[str, Any]     ,   _a	:						List[str]     ,   _a	:						List[Any]				):



      """simple docstring"""






      A				  =		dct.pop(_a				)
      A				  =		val



def   _A					(	):



      """simple docstring"""






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



@torch.no_grad()
def   _A					(	_a	:						Dict     ,   _a	:						Any     ,   _a	:						List[str]=True				):



      """simple docstring"""






      A				  =		ViTConfig()
      # patch_size
      if model_name[-1] == "8":
            A				  =		8
      # set labels if required
      if not base_model:
            A				  =		1_0_0_0
            A				  =		"""huggingface/label-files"""
            A				  =		"""imagenet-1k-id2label.json"""
            A				  =		json.load(open(hf_hub_download(_a     ,   _a     ,   repo_type="""dataset"""				)     ,   """r"""				)				)
            A				  =		{int(_a				): v for k, v in idalabel.items()}
            A				  =		idalabel
            A				  =		{v: k for k, v in idalabel.items()}
      # size of the architecture
      if model_name in ["dino_vits8", "dino_vits16"]:
            A				  =		3_8_4
            A				  =		1_5_3_6
            A				  =		1_2
            A				  =		6

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

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

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

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

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

      Path(_a				).mkdir(exist_ok=_a				)
      print(f'Saving model {model_name} to {pytorch_dump_folder_path}'				)
      model.save_pretrained(_a				)
      print(f'Saving image processor to {pytorch_dump_folder_path}'				)
      image_processor.save_pretrained(_a				)


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







"""simple docstring"""


import re

from filelock import FileLock


try:
  import nltk

  UpperCAmelCase													=True
except (ImportError, ModuleNotFoundError):
  UpperCAmelCase													=False

if NLTK_AVAILABLE:
  with FileLock(".lock") as lock:
    nltk.download("punkt", quiet=True)



def   _A					(	_a	:						str				):



      """simple docstring"""






      re.sub("""<n>"""     ,   """"""     ,   _a				)  # remove pegasus newline char
      assert NLTK_AVAILABLE, "nltk must be installed to separate newlines between sentences. (pip install nltk)"
      return "\n".join(nltk.sent_tokenize(_a				)				)




