


"""simple docstring"""






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()
_lowerCAmelCase							:    str					   =	logging.get_logger(__name__)







def        lowerCamelCase_(		_lowerCamelCase			,		_lowerCamelCase=False       )    ->     List[Any]:
   '''simple docstring'''





   _lowerCamelCase   :				int							=     []
   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   :				List[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        lowerCamelCase_(		_lowerCamelCase			,		_lowerCamelCase			,		_lowerCamelCase=False       )    ->     Optional[int]:
   '''simple docstring'''





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







def        lowerCamelCase_(		_lowerCamelCase       )    ->     int:
   '''simple docstring'''





   _lowerCamelCase   :				Union[str, Any]							=     ["head.weight", "head.bias"]
   for k in ignore_keys:
      state_dict.pop(_lowerCamelCase			,		_lowerCamelCase       )







def        lowerCamelCase_(		_lowerCamelCase			,		_lowerCamelCase			,		_lowerCamelCase       )    ->     Any:
   '''simple docstring'''





   _lowerCamelCase   :				Any							=     dct.pop(_lowerCamelCase       )
   _lowerCamelCase   :				Dict							=     val







def        lowerCamelCase_(		)    ->     Optional[int]:
   '''simple docstring'''





   _lowerCamelCase   :				Optional[int]							=     "http://images.cocodataset.org/val2017/000000039769.jpg"
   _lowerCamelCase   :				List[str]							=     Image.open(requests.get(_lowerCamelCase			,		stream=_lowerCamelCase       ).raw       )
   return im







@torch.no_grad()
def        lowerCamelCase_(		_lowerCamelCase			,		_lowerCamelCase			,		_lowerCamelCase=True       )    ->     str:
   '''simple docstring'''





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

   # load original model from torch hub
   _lowerCamelCase   :				Union[str, Any]							=     torch.hub.load("facebookresearch/dino:main"			,		_lowerCamelCase       )
   original_model.eval()

   # load state_dict of original model, remove and rename some keys
   _lowerCamelCase   :				List[str]							=     original_model.state_dict()
   if base_model:
      remove_classification_head_(_lowerCamelCase       )
   _lowerCamelCase   :				Tuple							=     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   :				Optional[Any]							=     ViTModel(_lowerCamelCase			,		add_pooling_layer=_lowerCamelCase       ).eval()
   else:
      _lowerCamelCase   :				Union[str, Any]							=     ViTForImageClassification(_lowerCamelCase       ).eval()
   model.load_state_dict(_lowerCamelCase       )

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

   if base_model:
      _lowerCamelCase   :				List[str]							=     original_model(_lowerCamelCase       )
      assert torch.allclose(_lowerCamelCase			,		outputs.last_hidden_state[:, 0, :]			,		atol=1e-1       )
   else:
      _lowerCamelCase   :				Tuple							=     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__":
  _lowerCAmelCase							:    List[str]					   =	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)
  _lowerCAmelCase							:    List[Any]					   =	parser.parse_args()
  convert_vit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.base_model)

"""simple docstring"""




import math
import sys




def 			_lowerCAmelCase		(		_UpperCamelCase    ):






  """simple docstring"""



  _lowercase:      Any   			=  ''''''
  try:
    with open(_UpperCamelCase			,					'''rb'''    ) as binary_file:
      _lowercase:      Dict   			=  binary_file.read()
    for dat in data:
      _lowercase:      List[str]   			=  f'''{dat:08b}'''
      result += curr_byte
    return result
  except OSError:
    print('''File not accessible'''    )
    sys.exit()




def 			_lowerCAmelCase		(		_UpperCamelCase    ):






  """simple docstring"""



  _lowercase:      Any   			=  {'''0''': '''0''', '''1''': '''1'''}
  _lowercase			,					_lowercase:      Optional[int]   			=  '''''', ''''''
  _lowercase:      Dict   			=  len(_UpperCamelCase    )

  for i in range(len(_UpperCamelCase    )    ):
    curr_string += data_bits[i]
    if curr_string not in lexicon:
      continue

    _lowercase:      Any   			=  lexicon[curr_string]
    result += last_match_id
    _lowercase:      Optional[int]   			=  last_match_id + '''0'''

    if math.loga(_UpperCamelCase    ).is_integer():
      _lowercase:      str   			=  {}
      for curr_key in list(_UpperCamelCase    ):
        _lowercase:      List[Any]   			=  lexicon.pop(_UpperCamelCase    )
      _lowercase:      Tuple   			=  new_lex

    _lowercase:      List[Any]   			=  last_match_id + '''1'''
    index += 1
    _lowercase:      List[Any]   			=  ''''''
  return result




def 			_lowerCAmelCase		(		_UpperCamelCase			,					_UpperCamelCase    ):






  """simple docstring"""



  _lowercase:      Optional[Any]   			=  8
  try:
    with open(_UpperCamelCase			,					'''wb'''    ) as opened_file:
      _lowercase:      List[Any]   			=  [
          to_write[i : i + byte_length]
          for i in range(0			,					len(_UpperCamelCase    )			,					_UpperCamelCase    )
      ]

      if len(result_byte_array[-1]    ) % byte_length == 0:
        result_byte_array.append('''10000000'''    )
      else:
        result_byte_array[-1] += "1" + "0" * (
            byte_length - len(result_byte_array[-1]    ) - 1
        )

      for elem in result_byte_array[:-1]:
        opened_file.write(int(_UpperCamelCase			,					2    ).to_bytes(1			,					byteorder='''big'''    )    )
  except OSError:
    print('''File not accessible'''    )
    sys.exit()




def 			_lowerCAmelCase		(		_UpperCamelCase    ):






  """simple docstring"""



  _lowercase:      Any   			=  0
  for letter in data_bits:
    if letter == "1":
      break
    counter += 1

  _lowercase:      List[str]   			=  data_bits[counter:]
  _lowercase:      Union[str, Any]   			=  data_bits[counter + 1 :]
  return data_bits




def 			_lowerCAmelCase		(		_UpperCamelCase			,					_UpperCamelCase    ):






  """simple docstring"""



  _lowercase:      Tuple   			=  read_file_binary(_UpperCamelCase    )
  _lowercase:      Optional[Any]   			=  remove_prefix(_UpperCamelCase    )
  _lowercase:      Dict   			=  decompress_data(_UpperCamelCase    )
  write_file_binary(_UpperCamelCase			,					_UpperCamelCase    )


if __name__ == "__main__":
  compress(sys.argv[1], sys.argv[2])
