




import argparse
import json

import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm.models import create_model

from transformers import (
    BeitImageProcessor,
    DataaVecVisionConfig,
    DataaVecVisionForImageClassification,
    DataaVecVisionModel,
)



def   lowerCAmelCase						(						_lowerCAmelCase							:				Any  ,							_lowerCAmelCase							:				Optional[int]=False  ,							_lowerCAmelCase							:				int=False  ,							_lowerCAmelCase							:				Tuple="data2vec."		):




 """simple docstring"""







 UpperCAmelCase__								=  "backbone." if is_semantic else ""

 UpperCAmelCase__								=  []
 for i in range(config.num_hidden_layers		):
  # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
  rename_keys.append(
      (F'''{prefix}blocks.{i}.norm1.weight''', F'''{hf_prefix}encoder.layer.{i}.layernorm_before.weight''')		)
  rename_keys.append((F'''{prefix}blocks.{i}.norm1.bias''', F'''{hf_prefix}encoder.layer.{i}.layernorm_before.bias''')		)
  rename_keys.append(
      (F'''{prefix}blocks.{i}.attn.proj.weight''', F'''{hf_prefix}encoder.layer.{i}.attention.output.dense.weight''')		)
  rename_keys.append(
      (F'''{prefix}blocks.{i}.attn.proj.bias''', F'''{hf_prefix}encoder.layer.{i}.attention.output.dense.bias''')		)
  rename_keys.append(
      (F'''{prefix}blocks.{i}.norm2.weight''', F'''{hf_prefix}encoder.layer.{i}.layernorm_after.weight''')		)
  rename_keys.append((F'''{prefix}blocks.{i}.norm2.bias''', F'''{hf_prefix}encoder.layer.{i}.layernorm_after.bias''')		)
  rename_keys.append(
      (F'''{prefix}blocks.{i}.mlp.fc1.weight''', F'''{hf_prefix}encoder.layer.{i}.intermediate.dense.weight''')		)
  rename_keys.append(
      (F'''{prefix}blocks.{i}.mlp.fc1.bias''', F'''{hf_prefix}encoder.layer.{i}.intermediate.dense.bias''')		)
  rename_keys.append((F'''{prefix}blocks.{i}.mlp.fc2.weight''', F'''{hf_prefix}encoder.layer.{i}.output.dense.weight''')		)
  rename_keys.append((F'''{prefix}blocks.{i}.mlp.fc2.bias''', F'''{hf_prefix}encoder.layer.{i}.output.dense.bias''')		)

 # projection layer + position embeddings
 rename_keys.extend(
     [
         (F'''{prefix}cls_token''', F'''{hf_prefix}embeddings.cls_token'''),
         (F'''{prefix}patch_embed.proj.weight''', F'''{hf_prefix}embeddings.patch_embeddings.projection.weight'''),
         (F'''{prefix}patch_embed.proj.bias''', F'''{hf_prefix}embeddings.patch_embeddings.projection.bias'''),
     ]		)

 if has_lm_head:
  # mask token + shared relative position bias + layernorm
  rename_keys.extend(
      [
          ("mask_token", F'''{hf_prefix}embeddings.mask_token'''),
          (
              "rel_pos_bias.relative_position_bias_table",
              F'''{hf_prefix}encoder.relative_position_bias.relative_position_bias_table''',
          ),
          (
              "rel_pos_bias.relative_position_index",
              F'''{hf_prefix}encoder.relative_position_bias.relative_position_index''',
          ),
          ("norm.weight", "layernorm.weight"),
          ("norm.bias", "layernorm.bias"),
      ]		)
 elif is_semantic:
  # semantic segmentation classification heads
  rename_keys.extend(
      [
          ("decode_head.conv_seg.weight", "decode_head.classifier.weight"),
          ("decode_head.conv_seg.bias", "decode_head.classifier.bias"),
          ("auxiliary_head.conv_seg.weight", "auxiliary_head.classifier.weight"),
          ("auxiliary_head.conv_seg.bias", "auxiliary_head.classifier.bias"),
      ]		)
 else:
  # layernorm + classification head
  rename_keys.extend(
      [
          ("fc_norm.weight", F'''{hf_prefix}pooler.layernorm.weight'''),
          ("fc_norm.bias", F'''{hf_prefix}pooler.layernorm.bias'''),
          ("head.weight", "classifier.weight"),
          ("head.bias", "classifier.bias"),
      ]		)

 return rename_keys



def   lowerCAmelCase						(						_lowerCAmelCase							:				List[Any]  ,							_lowerCAmelCase							:				Dict  ,							_lowerCAmelCase							:				Dict=False  ,							_lowerCAmelCase							:				List[str]=False  ,							_lowerCAmelCase							:				List[Any]="data2vec_vision."		):




 """simple docstring"""







 for i in range(config.num_hidden_layers		):
  UpperCAmelCase__								=  "backbone." if is_semantic else ""
  # queries, keys and values
  UpperCAmelCase__								=  state_dict.pop(F'''{prefix}blocks.{i}.attn.qkv.weight'''		)
  UpperCAmelCase__								=  state_dict.pop(F'''{prefix}blocks.{i}.attn.q_bias'''		)
  UpperCAmelCase__								=  state_dict.pop(F'''{prefix}blocks.{i}.attn.v_bias'''		)

  UpperCAmelCase__								=  in_proj_weight[
      : config.hidden_size, :
  ]
  UpperCAmelCase__								=  q_bias
  UpperCAmelCase__								=  in_proj_weight[
      config.hidden_size : config.hidden_size * 2, :
  ]
  UpperCAmelCase__								=  in_proj_weight[
      -config.hidden_size :, :
  ]
  UpperCAmelCase__								=  v_bias

  # gamma_1 and gamma_2
  # we call them lambda because otherwise they are renamed when using .from_pretrained
  UpperCAmelCase__								=  state_dict.pop(F'''{prefix}blocks.{i}.gamma_1'''		)
  UpperCAmelCase__								=  state_dict.pop(F'''{prefix}blocks.{i}.gamma_2'''		)

  UpperCAmelCase__								=  gamma_a
  UpperCAmelCase__								=  gamma_a

  # relative_position bias table + index
  if not has_lm_head:
   # each layer has its own relative position bias
   UpperCAmelCase__								=  state_dict.pop(F'''{prefix}blocks.{i}.attn.relative_position_bias_table'''		)
   UpperCAmelCase__								=  state_dict.pop(F'''{prefix}blocks.{i}.attn.relative_position_index'''		)

   UpperCAmelCase__								=  table
   UpperCAmelCase__								=  index



def   lowerCAmelCase						(						):




 """simple docstring"""







 UpperCAmelCase__								=  argparse.ArgumentParser(
     "Convert Data2VecVision to HF for image classification and pretraining"  ,							add_help=_lowerCAmelCase		)
 parser.add_argument("--hf_checkpoint_name"  ,							type=_lowerCAmelCase		)
 parser.add_argument("--input_size"  ,							default=224  ,							type=_lowerCAmelCase  ,							help="images input size"		)
 parser.add_argument("--beit_checkpoint"  ,							default=""  ,							help="beit checkpoint"		)

 return parser.parse_args()



def   lowerCAmelCase						(						_lowerCAmelCase							:				Tuple  ,							_lowerCAmelCase							:				Optional[int]  ,							_lowerCAmelCase							:				Dict		):




 """simple docstring"""







 def load_state_dict(_lowerCAmelCase							:				Tuple  ,							_lowerCAmelCase							:				List[str]  ,							_lowerCAmelCase							:				Dict=""  ,							_lowerCAmelCase							:				Optional[int]="relative_position_index"		):
  UpperCAmelCase__								=  []
  UpperCAmelCase__								=  []
  UpperCAmelCase__								=  []
  # copy state_dict so _load_from_state_dict can modify it
  UpperCAmelCase__								=  getattr(_lowerCAmelCase  ,							"_metadata"  ,							_lowerCAmelCase		)
  UpperCAmelCase__								=  state_dict.copy()
  if metadata is not None:
   UpperCAmelCase__								=  metadata

  def load(_lowerCAmelCase							:				Optional[Any]  ,							_lowerCAmelCase							:				Tuple=""		):
   UpperCAmelCase__								=  {} if metadata is None else metadata.get(prefix[:-1]  ,							{}		)
   module._load_from_state_dict(
       _lowerCAmelCase  ,							_lowerCAmelCase  ,							_lowerCAmelCase  ,							_lowerCAmelCase  ,							_lowerCAmelCase  ,							_lowerCAmelCase  ,							_lowerCAmelCase		)
   for name, child in module._modules.items():
    if child is not None:
     load(_lowerCAmelCase  ,							prefix + name + "."		)

  load(_lowerCAmelCase  ,							prefix=_lowerCAmelCase		)

  UpperCAmelCase__								=  []
  UpperCAmelCase__								=  []
  for key in missing_keys:
   UpperCAmelCase__								=  True
   for ignore_key in ignore_missing.split("|"		):
    if ignore_key in key:
     UpperCAmelCase__								=  False
     break
   if keep_flag:
    warn_missing_keys.append(_lowerCAmelCase		)
   else:
    ignore_missing_keys.append(_lowerCAmelCase		)

  UpperCAmelCase__								=  warn_missing_keys

  if len(_lowerCAmelCase		) > 0:
   print(
       "Weights of {} not initialized from pretrained model: {}".format(
           model.__class__.__name__  ,							_lowerCAmelCase		)		)
  if len(_lowerCAmelCase		) > 0:
   print("Weights from pretrained model not used in {}: {}".format(model.__class__.__name__  ,							_lowerCAmelCase		)		)
  if len(_lowerCAmelCase		) > 0:
   print(
       "Ignored weights of {} not initialized from pretrained model: {}".format(
           model.__class__.__name__  ,							_lowerCAmelCase		)		)
  if len(_lowerCAmelCase		) > 0:
   print("\n".join(_lowerCAmelCase		)		)

 UpperCAmelCase__								=  {
     "pretrained": False,
     "use_shared_rel_pos_bias": True,
     "use_abs_pos_emb": False,
     "init_values": 0.1,
 }

 if is_finetuned:
  model_kwargs.update(
      {
          "num_classes": 1000,
          "use_mean_pooling": True,
          "init_scale": 0.001,
          "use_rel_pos_bias": True,
      }		)

 UpperCAmelCase__								=  create_model(
     "beit_large_patch16_224" if is_large else "beit_base_patch16_224"  ,							**_lowerCAmelCase  ,							)
 UpperCAmelCase__								=  model.patch_embed.patch_size
 UpperCAmelCase__								=  (args.input_size // patch_size[0], args.input_size // patch_size[1])
 UpperCAmelCase__								=  torch.load(args.beit_checkpoint  ,							map_location="cpu"		)

 print(F'''Load ckpt from {args.beit_checkpoint}'''		)
 UpperCAmelCase__								=  None
 for model_key in ("model", "module"):
  if model_key in checkpoint:
   UpperCAmelCase__								=  checkpoint[model_key]
   print(F'''Load state_dict by model_key = {model_key}'''		)
   break

 UpperCAmelCase__								=  list(checkpoint_model.keys()		)
 for key in all_keys:
  if "relative_position_index" in key:
   checkpoint_model.pop(_lowerCAmelCase		)

  if "relative_position_bias_table" in key:
   UpperCAmelCase__								=  checkpoint_model[key]
   UpperCAmelCase__	,	UpperCAmelCase__								=  rel_pos_bias.size()
   UpperCAmelCase__	,	UpperCAmelCase__								=  model.state_dict()[key].size()
   UpperCAmelCase__								=  model.patch_embed.patch_shape
   if dst_patch_shape[0] != dst_patch_shape[1]:
    raise NotImplementedError()

 load_state_dict(_lowerCAmelCase  ,							_lowerCAmelCase  ,							prefix=""		)

 return model



def   lowerCAmelCase						(						):




 """simple docstring"""







 UpperCAmelCase__								=  get_args()

 UpperCAmelCase__								=  "ft1k" in args.hf_checkpoint_name
 UpperCAmelCase__								=  "large" in args.hf_checkpoint_name

 if is_finetuned:
  # To convert Beit's data2vec_vision to HF you need to copy
  # https://github.com/facebookresearch/data2vec_vision/blob/main/beit/modeling_finetune.py
  # into this folder.
  import modeling_finetune  # noqa: F401
 else:
  # To convert Beit's data2vec_vision to HF you need to copy
  # https://github.com/facebookresearch/data2vec_vision/blob/main/beit/modeling_cyclical.py
  # into this folder
  # IMPORTANT: Note that for now we've only converted the down-stream
  # model and not the full pretrained model. This means for the integration
  # test you need to add a `return x` after the following line:
  # https://github.com/facebookresearch/data2vec_vision/blob/af9a36349aaed59ae66e69b5dabeef2d62fdc5da/beit/modeling_cyclical.py#L197
  # to make the integration test pass.
  import modeling_cyclical  # noqa: F401

 # 1. Create model config
 UpperCAmelCase__								=  DataaVecVisionConfig()
 if is_finetuned:
  UpperCAmelCase__								=  True
  UpperCAmelCase__								=  False
  UpperCAmelCase__								=  True
  UpperCAmelCase__								=  1000

  UpperCAmelCase__								=  "huggingface/label-files"
  UpperCAmelCase__								=  "imagenet-1k-id2label.json"
  UpperCAmelCase__								=  json.load(open(hf_hub_download(_lowerCAmelCase  ,							_lowerCAmelCase  ,							repo_type="dataset"		)  ,							"r"		)		)
  UpperCAmelCase__								=  {int(_lowerCAmelCase		): v for k, v in idalabel.items()}
  UpperCAmelCase__								=  idalabel
  UpperCAmelCase__								=  {v: k for k, v in idalabel.items()}
 else:
  UpperCAmelCase__								=  False
  UpperCAmelCase__								=  True
  UpperCAmelCase__								=  False

 if is_large:
  UpperCAmelCase__								=  1024
  UpperCAmelCase__								=  4096
  UpperCAmelCase__								=  24
  UpperCAmelCase__								=  16

 # 2. Load Beit model
 UpperCAmelCase__								=  load_beit_model(_lowerCAmelCase  ,							_lowerCAmelCase  ,							_lowerCAmelCase		)
 orig_model.eval()

 # 3. Forward Beit model
 UpperCAmelCase__								=  BeitImageProcessor(size=config.image_size  ,							do_center_crop=_lowerCAmelCase		)
 UpperCAmelCase__								=  Image.open("../../../../tests/fixtures/tests_samples/COCO/000000039769.png"		)
 UpperCAmelCase__								=  image_processor(images=_lowerCAmelCase  ,							return_tensors="pt"		)
 UpperCAmelCase__								=  encoding["pixel_values"]

 UpperCAmelCase__								=  (pixel_values,) if is_finetuned else (pixel_values, None)
 with torch.no_grad():
  UpperCAmelCase__								=  orig_model(*_lowerCAmelCase		)

 # 4. Load HF Data2VecVision model
 if is_finetuned:
  UpperCAmelCase__								=  DataaVecVisionForImageClassification(_lowerCAmelCase		)
  hf_model.eval()
  UpperCAmelCase__								=  False
  UpperCAmelCase__								=  "data2vec_vision."
 else:
  UpperCAmelCase__								=  DataaVecVisionModel(_lowerCAmelCase		)
  hf_model.eval()
  UpperCAmelCase__								=  True
  UpperCAmelCase__								=  ""

 UpperCAmelCase__								=  create_rename_keys(_lowerCAmelCase  ,							hf_prefix=_lowerCAmelCase  ,							has_lm_head=_lowerCAmelCase		)
 UpperCAmelCase__								=  orig_model.state_dict()
 for src, dest in rename_keys:
  UpperCAmelCase__								=  state_dict.pop(_lowerCAmelCase		)
  UpperCAmelCase__								=  val

 read_in_q_k_v(_lowerCAmelCase  ,							_lowerCAmelCase  ,							hf_prefix=_lowerCAmelCase  ,							has_lm_head=_lowerCAmelCase		)
 UpperCAmelCase__	,	UpperCAmelCase__								=  hf_model.load_state_dict(_lowerCAmelCase  ,							strict=_lowerCAmelCase		)
 print("HF missing"  ,							_lowerCAmelCase		)
 print("HF unexpected_keys"  ,							_lowerCAmelCase		)

 # 5. Forward HF Data2VecVision model
 with torch.no_grad():
  UpperCAmelCase__								=  hf_model(_lowerCAmelCase		)

 UpperCAmelCase__								=  hf_model_output.logits if is_finetuned else hf_model_output.last_hidden_state

 # 6. Compare
 UpperCAmelCase__								=  torch.max(torch.abs(hf_output - orig_model_output		)		).item()

 print(F'''max_absolute_diff = {max_absolute_diff}'''		)
 UpperCAmelCase__								=  torch.allclose(_lowerCAmelCase  ,							_lowerCAmelCase  ,							atol=1E-3		)
 print("Do both models output the same tensors?"  ,							"🔥" if success else "💩"		)
 if not success:
  raise Exception("Something went wRoNg"		)

 # 7. Save
 print(F'''Saving to {args.hf_checkpoint_name}'''		)
 hf_model.save_pretrained(args.hf_checkpoint_name		)
 image_processor.save_pretrained(args.hf_checkpoint_name		)


if __name__ == "__main__":
  main()
  # Run the following to convert checkpoints
  #  python ./convert_data2vec_vision_original_pytorch_checkpoint_to_pytorch.py \
  #          --beit_checkpoint ./pretrained_base.pt \
  #          --hf_checkpoint_name "./data2vec-vision-base"
  #  python ./convert_data2vec_vision_original_pytorch_checkpoint_to_pytorch.py \
  #          --beit_checkpoint ./finetuned_base.pt \
  #          --hf_checkpoint_name "./data2vec-vision-base-ft1k"
  #  python ./convert_data2vec_vision_original_pytorch_checkpoint_to_pytorch.py \
  #          --beit_checkpoint ./pretrained_large.pt \
  #          --hf_checkpoint_name "./data2vec-vision-large"
  #  python ./convert_data2vec_vision_original_pytorch_checkpoint_to_pytorch.py \
  #          --beit_checkpoint ./finetuned_large.pt \
  #          --hf_checkpoint_name "./data2vec-vision-large-ft1k"











from __future__ import annotations

from string import ascii_letters



def   lowerCAmelCase						(						_lowerCAmelCase							:				str  ,							_lowerCAmelCase							:				int  ,							_lowerCAmelCase							:				str | None = None		):




 """simple docstring"""







 UpperCAmelCase__								=  alphabet or ascii_letters

 # The final result string
 UpperCAmelCase__								=  ""

 for character in input_string:
  if character not in alpha:
   # Append without encryption if character is not in the alphabet
   result += character
  else:
   # Get the index of the new key and make sure it isn't too large
   UpperCAmelCase__								=  (alpha.index(_lowerCAmelCase		) + key) % len(_lowerCAmelCase		)

   # Append the encoded character to the alphabet
   result += alpha[new_key]

 return result



def   lowerCAmelCase						(						_lowerCAmelCase							:				str  ,							_lowerCAmelCase							:				int  ,							_lowerCAmelCase							:				str | None = None		):




 """simple docstring"""







 key *= -1

 return encrypt(_lowerCAmelCase  ,							_lowerCAmelCase  ,							_lowerCAmelCase		)



def   lowerCAmelCase						(						_lowerCAmelCase							:				str  ,							_lowerCAmelCase							:				str | None = None		):




 """simple docstring"""







 UpperCAmelCase__								=  alphabet or ascii_letters

 # To store data on all the combinations
 UpperCAmelCase__								=  {}

 # Cycle through each combination
 for key in range(1  ,							len(_lowerCAmelCase		) + 1		):
  # Decrypt the message and store the result in the data
  UpperCAmelCase__								=  decrypt(_lowerCAmelCase  ,							_lowerCAmelCase  ,							_lowerCAmelCase		)

 return brute_force_data


if __name__ == "__main__":
  while True:
    print(F'''\n{"-" * 1_0}\n Menu\n{"-" * 1_0}''')
    print(*["1.Encrypt", "2.Decrypt", "3.BruteForce", "4.Quit"], sep="\n")

    # get user input
    _lowerCAmelCase  :				List[Any]						=				input("\nWhat would you like to do?: ").strip() or "4"

    # run functions based on what the user chose
    if choice not in ("1", "2", "3", "4"):
      print("Invalid choice, please enter a valid choice")
    elif choice == "1":
      _lowerCAmelCase  :				Any						=				input("Please enter the string to be encrypted: ")
      _lowerCAmelCase  :				str						=				int(input("Please enter off-set: ").strip())

      print(encrypt(input_string, key))
    elif choice == "2":
      _lowerCAmelCase  :				Optional[Any]						=				input("Please enter the string to be decrypted: ")
      _lowerCAmelCase  :				Any						=				int(input("Please enter off-set: ").strip())

      print(decrypt(input_string, key))
    elif choice == "3":
      _lowerCAmelCase  :				int						=				input("Please enter the string to be decrypted: ")
      _lowerCAmelCase  :				Dict						=				brute_force(input_string)

      for key, value in brute_force_data.items():
        print(F'''Key: {key} | Message: {value}''')

    elif choice == "4":
      print("Goodbye.")
      break





