



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()
a__    =	logging.get_logger(__name__)



def       lowercase       (   SCREAMING_SNAKE_CASE__       :	Any				,       SCREAMING_SNAKE_CASE__       :	Union[str, Any]=False				)  ->     List[str]:
 _snake_case    :  Dict					=					[]
 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"
  _snake_case    :  str					=					[(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__       :	str				,       SCREAMING_SNAKE_CASE__       :	Optional[int]				,       SCREAMING_SNAKE_CASE__       :	Optional[Any]=False				)  ->     Any:
 for i in range(config.num_hidden_layers				):
  if base_model:
   _snake_case    :  int					=					""""""
  else:
   _snake_case    :  int					=					"""vit."""
  # read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
  _snake_case    :  Dict					=					state_dict.pop(F'''blocks.{i}.attn.qkv.weight'''				)
  _snake_case    :  Any					=					state_dict.pop(F'''blocks.{i}.attn.qkv.bias'''				)
  # next, add query, keys and values (in that order) to the state dict
  _snake_case    :  Tuple					=					in_proj_weight[
      : config.hidden_size, :
  ]
  _snake_case    :  List[Any]					=					in_proj_bias[: config.hidden_size]
  _snake_case    :  List[Any]					=					in_proj_weight[
      config.hidden_size : config.hidden_size * 2, :
  ]
  _snake_case    :  Dict					=					in_proj_bias[
      config.hidden_size : config.hidden_size * 2
  ]
  _snake_case    :  List[str]					=					in_proj_weight[
      -config.hidden_size :, :
  ]
  _snake_case    :  Any					=					in_proj_bias[-config.hidden_size :]



def       lowercase       (   SCREAMING_SNAKE_CASE__       :	Optional[Any]				)  ->     Dict:
 _snake_case    :  Dict					=					["""head.weight""", """head.bias"""]
 for k in ignore_keys:
  state_dict.pop(SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__				)



def       lowercase       (   SCREAMING_SNAKE_CASE__       :	int				,       SCREAMING_SNAKE_CASE__       :	List[Any]				,       SCREAMING_SNAKE_CASE__       :	Optional[int]				)  ->     Union[str, Any]:
 _snake_case    :  List[Any]					=					dct.pop(SCREAMING_SNAKE_CASE__				)
 _snake_case    :  int					=					val



def       lowercase       (   )  ->     Optional[Any]:
 _snake_case    :  int					=					"""http://images.cocodataset.org/val2017/000000039769.jpg"""
 _snake_case    :  Optional[Any]					=					Image.open(requests.get(SCREAMING_SNAKE_CASE__				,       stream=SCREAMING_SNAKE_CASE__				).raw				)
 return im



@torch.no_grad()
def       lowercase       (   SCREAMING_SNAKE_CASE__       :	str				,       SCREAMING_SNAKE_CASE__       :	int				,       SCREAMING_SNAKE_CASE__       :	Any=True				)  ->     List[str]:
 _snake_case    :  str					=					ViTConfig()
 # patch_size
 if model_name[-1] == "8":
  _snake_case    :  Optional[Any]					=					8
 # set labels if required
 if not base_model:
  _snake_case    :  Any					=					1_000
  _snake_case    :  List[str]					=					"""huggingface/label-files"""
  _snake_case    :  List[Any]					=					"""imagenet-1k-id2label.json"""
  _snake_case    :  Tuple					=					json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__				,       repo_type="""dataset"""				)				,       """r"""				)				)
  _snake_case    :  Any					=					{int(SCREAMING_SNAKE_CASE__				): v for k, v in idalabel.items()}
  _snake_case    :  str					=					idalabel
  _snake_case    :  Tuple					=					{v: k for k, v in idalabel.items()}
 # size of the architecture
 if model_name in ["dino_vits8", "dino_vits16"]:
  _snake_case    :  Optional[Any]					=					384
  _snake_case    :  Optional[int]					=					1_536
  _snake_case    :  Any					=					12
  _snake_case    :  Optional[int]					=					6

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

 # load state_dict of original model, remove and rename some keys
 _snake_case    :  Optional[int]					=					original_model.state_dict()
 if base_model:
  remove_classification_head_(SCREAMING_SNAKE_CASE__				)
 _snake_case    :  Optional[int]					=					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:
  _snake_case    :  Dict					=					ViTModel(SCREAMING_SNAKE_CASE__				,       add_pooling_layer=SCREAMING_SNAKE_CASE__				).eval()
 else:
  _snake_case    :  str					=					ViTForImageClassification(SCREAMING_SNAKE_CASE__				).eval()
 model.load_state_dict(SCREAMING_SNAKE_CASE__				)

 # Check outputs on an image, prepared by ViTImageProcessor
 _snake_case    :  List[str]					=					ViTImageProcessor()
 _snake_case    :  Optional[Any]					=					image_processor(images=prepare_img()				,       return_tensors="""pt"""				)
 _snake_case    :  Optional[int]					=					encoding["""pixel_values"""]
 _snake_case    :  int					=					model(SCREAMING_SNAKE_CASE__				)

 if base_model:
  _snake_case    :  str					=					original_model(SCREAMING_SNAKE_CASE__				)
  assert torch.allclose(SCREAMING_SNAKE_CASE__				,       outputs.last_hidden_state[:, 0, :]				,       atol=1e-1				)
 else:
  _snake_case    :  Optional[int]					=					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__":
  a__    =	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)
  a__    =	parser.parse_args()
  convert_vit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.base_model)





from __future__ import annotations

from math import pi

# Define the Reduced Planck Constant ℏ (H bar), speed of light C, value of
# Pi and the function
a__    =	1.0_54_57_18_17E-34  # unit of ℏ : J * s

a__    =	3E8  # unit of c : m * s^-1



def       lowercase       (   SCREAMING_SNAKE_CASE__       :	float				,       SCREAMING_SNAKE_CASE__       :	float				,       SCREAMING_SNAKE_CASE__       :	float				)  ->     dict[str, float]:

 if (force, area, distance).count(0				) != 1:
  raise ValueError("""One and only one argument must be 0"""				)
 if force < 0:
  raise ValueError("""Magnitude of force can not be negative"""				)
 if distance < 0:
  raise ValueError("""Distance can not be negative"""				)
 if area < 0:
  raise ValueError("""Area can not be negative"""				)
 if force == 0:
  _snake_case    :  Union[str, Any]					=					(REDUCED_PLANCK_CONSTANT * SPEED_OF_LIGHT * pi**2 * area) / (
      240 * (distance) ** 4
  )
  return {"force": force}
 elif area == 0:
  _snake_case    :  Optional[Any]					=					(240 * force * (distance) ** 4) / (
      REDUCED_PLANCK_CONSTANT * SPEED_OF_LIGHT * pi**2
  )
  return {"area": area}
 elif distance == 0:
  _snake_case    :  str					=					(
      (REDUCED_PLANCK_CONSTANT * SPEED_OF_LIGHT * pi**2 * area) / (240 * force)
  ) ** (1 / 4)
  return {"distance": distance}
 raise ValueError("""One and only one argument must be 0"""				)


# Run doctest
if __name__ == "__main__":
  import doctest

  doctest.testmod()
