






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      :      Optional[int]     						=						logging.get_logger(__name__)





def 				snake_case  (     lowerCamelCase				,  lowerCamelCase=False					):




 '''simple docstring'''







 __lowercase             =  []
 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"
  __lowercase             =  [(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 				snake_case  (     lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase=False					):




 '''simple docstring'''







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





def 				snake_case  (     lowerCamelCase					):




 '''simple docstring'''







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





def 				snake_case  (     lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase					):




 '''simple docstring'''







 __lowercase             =  dct.pop(lowerCamelCase					)
 __lowercase             =  val





def 				snake_case  (     ):




 '''simple docstring'''







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





@torch.no_grad()
def 				snake_case  (     lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase=True					):




 '''simple docstring'''







 __lowercase             =  ViTConfig()
 # patch_size
 if model_name[-1] == "8":
  __lowercase             =  8
 # set labels if required
 if not base_model:
  __lowercase             =  1_000
  __lowercase             =  """huggingface/label-files"""
  __lowercase             =  """imagenet-1k-id2label.json"""
  __lowercase             =  json.load(open(hf_hub_download(lowerCamelCase				,  lowerCamelCase				,  repo_type="""dataset"""					)				,  """r"""					)					)
  __lowercase             =  {int(lowerCamelCase					): v for k, v in idalabel.items()}
  __lowercase             =  idalabel
  __lowercase             =  {v: k for k, v in idalabel.items()}
 # size of the architecture
 if model_name in ["dino_vits8", "dino_vits16"]:
  __lowercase             =  384
  __lowercase             =  1_536
  __lowercase             =  12
  __lowercase             =  6

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

 # load state_dict of original model, remove and rename some keys
 __lowercase             =  original_model.state_dict()
 if base_model:
  remove_classification_head_(lowerCamelCase					)
 __lowercase             =  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:
  __lowercase             =  ViTModel(lowerCamelCase				,  add_pooling_layer=lowerCamelCase					).eval()
 else:
  __lowercase             =  ViTForImageClassification(lowerCamelCase					).eval()
 model.load_state_dict(lowerCamelCase					)

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

 if base_model:
  __lowercase             =  original_model(lowerCamelCase					)
  assert torch.allclose(lowerCamelCase				,  outputs.last_hidden_state[:, 0, :]				,  atol=1e-1					)
 else:
  __lowercase             =  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      :      Optional[Any]     						=						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      :      Tuple     						=						parser.parse_args()
      convert_vit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.base_model)









def 				snake_case  (     lowerCamelCase					):




	'''simple docstring'''







	__lowercase             =  """"""
	for ch in key:
		if ch == " " or ch not in key_no_dups and ch.isalpha():
			key_no_dups += ch
	return key_no_dups





def 				snake_case  (     lowerCamelCase					):




	'''simple docstring'''







	__lowercase             =  [chr(i + 65					) for i in range(26					)]
	# Remove duplicate characters from key
	__lowercase             =  remove_duplicates(key.upper()					)
	__lowercase             =  len(lowerCamelCase					)
	# First fill cipher with key characters
	__lowercase             =  {alphabet[i]: char for i, char in enumerate(lowerCamelCase					)}
	# Then map remaining characters in alphabet to
	# the alphabet from the beginning
	for i in range(len(lowerCamelCase					)				,  26					):
		__lowercase             =  alphabet[i - offset]
		# Ensure we are not mapping letters to letters previously mapped
		while char in key:
			offset -= 1
			__lowercase             =  alphabet[i - offset]
		__lowercase             =  char
	return cipher_alphabet





def 				snake_case  (     lowerCamelCase				,  lowerCamelCase					):




	'''simple docstring'''







	return "".join(cipher_map.get(lowerCamelCase				,  lowerCamelCase					) for ch in message.upper()					)





def 				snake_case  (     lowerCamelCase				,  lowerCamelCase					):




	'''simple docstring'''







	__lowercase             =  {v: k for k, v in cipher_map.items()}
	return "".join(rev_cipher_map.get(lowerCamelCase				,  lowerCamelCase					) for ch in message.upper()					)





def 				snake_case  (     ):




	'''simple docstring'''







	__lowercase             =  input("""Enter message to encode or decode: """					).strip()
	__lowercase             =  input("""Enter keyword: """					).strip()
	__lowercase             =  input("""Encipher or decipher? E/D:"""					).strip()[0].lower()
	try:
		__lowercase             =  {"""e""": encipher, """d""": decipher}[option]
	except KeyError:
		raise KeyError("""invalid input option"""					)
	__lowercase             =  create_cipher_map(lowerCamelCase					)
	print(func(lowerCamelCase				,  lowerCamelCase					)					)


if __name__ == "__main__":
						import doctest

						doctest.testmod()
						main()

