




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  (		lowerCAmelCase_	:   Tuple  ,							lowerCAmelCase_	:   Union[str, Any]=False     ):
     """simple docstring"""

     lowerCAmelCase__            =					[]
     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"
          lowerCAmelCase__            =					[(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  (		lowerCAmelCase_	:   str  ,							lowerCAmelCase_	:   Tuple  ,							lowerCAmelCase_	:   str=False     ):
     """simple docstring"""

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



def 				_A  (		lowerCAmelCase_	:   List[str]     ):
     """simple docstring"""

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



def 				_A  (		lowerCAmelCase_	:   Optional[Any]  ,							lowerCAmelCase_	:   Union[str, Any]  ,							lowerCAmelCase_	:   int     ):
     """simple docstring"""

     lowerCAmelCase__            =					dct.pop(lowerCAmelCase_     )
     lowerCAmelCase__            =					val



def 				_A  (		):
     """simple docstring"""

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



@torch.no_grad()
def 				_A  (		lowerCAmelCase_	:   Tuple  ,							lowerCAmelCase_	:   str  ,							lowerCAmelCase_	:   Any=True     ):
     """simple docstring"""

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

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

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

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

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

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

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


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"""






from __future__ import annotations

from random import random

class 					__a     :



							'''simple docstring'''







							def __init__(     self  ,    _lowerCamelCase = None       )			->							str:




										'''simple docstring'''
										__lowercase       =       value
										__lowercase       =       random()
										__lowercase       =       None
										__lowercase       =       None
							def __repr__(     self       )			->							str:




										'''simple docstring'''
										from pprint import pformat

										if self.left is None and self.right is None:
													return f'''\'{self.value}: {self.prior:.5}\''''
										else:
													return pformat(
													    {f'''{self.value}: {self.prior:.5}''': (self.left, self.right)}  ,    indent=1       )





							def __str__(     self       )			->							str:




										'''simple docstring'''
										__lowercase       =       str(self.value       ) + " "
										__lowercase       =       str(self.left or ""       )
										__lowercase       =       str(self.right or ""       )
										return value + left + right





def      lowerCAmelCase__			(		__magic_name__			,   __magic_name__      )						->tuple[Node | None, Node | None]:
			if root is None:  # None tree is split into 2 Nones
						return None, None
			elif root.value is None:
						return None, None
			else:
						if value < root.value:
									__lowercase   ,					__lowercase       =       split(root.left			,   __magic_name__      )
									return left, root
						else:
									__lowercase   ,					__lowercase       =       split(root.right			,   __magic_name__      )
									return root, right



def      lowerCAmelCase__			(		__magic_name__			,   __magic_name__      )						->Node | None:
			if (not left) or (not right):  # If one node is None, return the other
						return left or right
			elif left.prior < right.prior:
						__lowercase       =       merge(left.right			,   __magic_name__      )
						return left
			else:
						__lowercase       =       merge(__magic_name__			,   right.left      )
						return right



def      lowerCAmelCase__			(		__magic_name__			,   __magic_name__      )						->Node | None:
			__lowercase       =       Node(__magic_name__      )
			__lowercase   ,					__lowercase       =       split(__magic_name__			,   __magic_name__      )
			return merge(merge(__magic_name__			,   __magic_name__      )			,   __magic_name__      )



def      lowerCAmelCase__			(		__magic_name__			,   __magic_name__      )						->Node | None:
			__lowercase   ,					__lowercase       =       split(__magic_name__			,   value - 1      )
			__lowercase   ,					__lowercase       =       split(__magic_name__			,   __magic_name__      )
			return merge(__magic_name__			,   __magic_name__      )



def      lowerCAmelCase__			(		__magic_name__      )						->None:
			if not root:  # None
						return
			else:
						inorder(root.left      )
						print(root.value			,   end=","      )
						inorder(root.right      )



def      lowerCAmelCase__			(		__magic_name__			,   __magic_name__      )						->Node | None:
			for arg in args.split():
						if arg[0] == "+":
									__lowercase       =       insert(__magic_name__			,   int(arg[1:]      )      )

						elif arg[0] == "-":
									__lowercase       =       erase(__magic_name__			,   int(arg[1:]      )      )

						else:
									print("Unknown command"      )

			return root



def      lowerCAmelCase__			(		)						->None:
			__lowercase       =       None
			print(
			    "enter numbers to create a tree, + value to add value into treap, "
			    "- value to erase all nodes with value. 'q' to quit. "      )

			__lowercase       =       input()
			while args != "q":
						__lowercase       =       interact_treap(__magic_name__			,   __magic_name__      )
						print(__magic_name__      )
						__lowercase       =       input()

			print("good by!"      )


if __name__ == "__main__":
					import doctest

					doctest.testmod()
					main()





