




'''simple docstring'''



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 SCREAMING_SNAKE_CASE_				(							__A       :				Optional[Any] ,  __A       :				Dict=False ,  __A       :				Tuple=False ,  __A       :				List[str]="data2vec."						)				->				List[Any]:
   _SCREAMING_SNAKE_CASE =   "backbone." if is_semantic else ""

   _SCREAMING_SNAKE_CASE =   []
   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 SCREAMING_SNAKE_CASE_				(							__A       :				int ,  __A       :				Optional[int] ,  __A       :				str=False ,  __A       :				Optional[int]=False ,  __A       :				Dict="data2vec_vision."						)				->				Tuple:
   for i in range(config.num_hidden_layers						):
      _SCREAMING_SNAKE_CASE =   "backbone." if is_semantic else ""
      # queries, keys and values
      _SCREAMING_SNAKE_CASE =   state_dict.pop(f"""{prefix}blocks.{i}.attn.qkv.weight"""						)
      _SCREAMING_SNAKE_CASE =   state_dict.pop(f"""{prefix}blocks.{i}.attn.q_bias"""						)
      _SCREAMING_SNAKE_CASE =   state_dict.pop(f"""{prefix}blocks.{i}.attn.v_bias"""						)

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

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

      _SCREAMING_SNAKE_CASE =   gamma_a
      _SCREAMING_SNAKE_CASE =   gamma_a

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

         _SCREAMING_SNAKE_CASE =   table
         _SCREAMING_SNAKE_CASE =   index
def SCREAMING_SNAKE_CASE_				(							)				->				Dict:
   _SCREAMING_SNAKE_CASE =   argparse.ArgumentParser(
       "Convert Data2VecVision to HF for image classification and pretraining" ,  add_help=__A						)
   parser.add_argument("--hf_checkpoint_name" ,  type=__A						)
   parser.add_argument("--input_size" ,  default=2_24 ,  type=__A ,  help="images input size"						)
   parser.add_argument("--beit_checkpoint" ,  default="" ,  help="beit checkpoint"						)

   return parser.parse_args()
def SCREAMING_SNAKE_CASE_				(							__A       :				List[Any] ,  __A       :				Tuple ,  __A       :				Tuple						)				->				Union[str, Any]:
   def load_state_dict(__A       :				List[str] ,  __A       :				Tuple ,  __A       :				Any="" ,  __A       :				Tuple="relative_position_index"						):
      _SCREAMING_SNAKE_CASE =   []
      _SCREAMING_SNAKE_CASE =   []
      _SCREAMING_SNAKE_CASE =   []
      # copy state_dict so _load_from_state_dict can modify it
      _SCREAMING_SNAKE_CASE =   getattr(__A ,  "_metadata" ,  __A						)
      _SCREAMING_SNAKE_CASE =   state_dict.copy()
      if metadata is not None:
         _SCREAMING_SNAKE_CASE =   metadata

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

      load(__A ,  prefix=__A						)

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

      _SCREAMING_SNAKE_CASE =   warn_missing_keys

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

   _SCREAMING_SNAKE_CASE =   {
       "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": 10_00,
              "use_mean_pooling": True,
              "init_scale": 0.0_0_1,
              "use_rel_pos_bias": True,
          }						)

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

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

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

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

   load_state_dict(__A ,  __A ,  prefix=""						)

   return model
def SCREAMING_SNAKE_CASE_				(							)				->				str:
   _SCREAMING_SNAKE_CASE =   get_args()

   _SCREAMING_SNAKE_CASE =   "ft1k" in args.hf_checkpoint_name
   _SCREAMING_SNAKE_CASE =   "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
   _SCREAMING_SNAKE_CASE =   DataaVecVisionConfig()
   if is_finetuned:
      _SCREAMING_SNAKE_CASE =   True
      _SCREAMING_SNAKE_CASE =   False
      _SCREAMING_SNAKE_CASE =   True
      _SCREAMING_SNAKE_CASE =   10_00

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

   if is_large:
      _SCREAMING_SNAKE_CASE =   10_24
      _SCREAMING_SNAKE_CASE =   40_96
      _SCREAMING_SNAKE_CASE =   24
      _SCREAMING_SNAKE_CASE =   16

   # 2. Load Beit model
   _SCREAMING_SNAKE_CASE =   load_beit_model(__A ,  __A ,  __A						)
   orig_model.eval()

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

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

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

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

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

   # 5. Forward HF Data2VecVision model
   with torch.no_grad():
      _SCREAMING_SNAKE_CASE =   hf_model(__A						)

   _SCREAMING_SNAKE_CASE =   hf_model_output.logits if is_finetuned else hf_model_output.last_hidden_state

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

   print(f"""max_absolute_diff = {max_absolute_diff}"""						)
   _SCREAMING_SNAKE_CASE =   torch.allclose(__A ,  __A ,  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"







'''simple docstring'''



def 				SCREAMING_SNAKE_CASE_				(							__A       :				List[Any] ,  __A       :				Dict						)				->				Union[str, Any]:
   if n == 0:
      return 1

   elif n % 2 == 1:
      return binary_exponentiation(__A ,  n - 1						) * a

   else:
      _SCREAMING_SNAKE_CASE =   binary_exponentiation(__A ,  n / 2						)
      return b * b


if __name__ == "__main__":
 try:
  lowerCamelCase_	 =		int(input('Enter Base : ').strip())
  lowerCamelCase_	 =		int(input('Enter Power : ').strip())
 except ValueError:
  print('Invalid literal for integer')

 lowerCamelCase_	 =		binary_exponentiation(BASE, POWER)
 print(f'''{BASE}^({POWER}) : {RESULT}''')

