

'''simple docstring'''







import argparse
import json
from pathlib import Path

import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import DeiTConfig, DeiTForImageClassificationWithTeacher, DeiTImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
__lowerCamelCase		       =    logging.get_logger(__name__)
def       UpperCAmelCase__    (      UpperCAmelCase__,       UpperCAmelCase__=False				)			->			Tuple:
       A_						=      []
       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'''deit.encoder.layer.{i}.layernorm_before.weight''')				)
              rename_keys.append((F'''blocks.{i}.norm1.bias''', F'''deit.encoder.layer.{i}.layernorm_before.bias''')				)
              rename_keys.append((F'''blocks.{i}.attn.proj.weight''', F'''deit.encoder.layer.{i}.attention.output.dense.weight''')				)
              rename_keys.append((F'''blocks.{i}.attn.proj.bias''', F'''deit.encoder.layer.{i}.attention.output.dense.bias''')				)
              rename_keys.append((F'''blocks.{i}.norm2.weight''', F'''deit.encoder.layer.{i}.layernorm_after.weight''')				)
              rename_keys.append((F'''blocks.{i}.norm2.bias''', F'''deit.encoder.layer.{i}.layernorm_after.bias''')				)
              rename_keys.append((F'''blocks.{i}.mlp.fc1.weight''', F'''deit.encoder.layer.{i}.intermediate.dense.weight''')				)
              rename_keys.append((F'''blocks.{i}.mlp.fc1.bias''', F'''deit.encoder.layer.{i}.intermediate.dense.bias''')				)
              rename_keys.append((F'''blocks.{i}.mlp.fc2.weight''', F'''deit.encoder.layer.{i}.output.dense.weight''')				)
              rename_keys.append((F'''blocks.{i}.mlp.fc2.bias''', F'''deit.encoder.layer.{i}.output.dense.bias''')				)

       # projection layer + position embeddings
       rename_keys.extend(
           [
               ("""cls_token""", """deit.embeddings.cls_token"""),
               ("""dist_token""", """deit.embeddings.distillation_token"""),
               ("""patch_embed.proj.weight""", """deit.embeddings.patch_embeddings.projection.weight"""),
               ("""patch_embed.proj.bias""", """deit.embeddings.patch_embeddings.projection.bias"""),
               ("""pos_embed""", """deit.embeddings.position_embeddings"""),
           ]				)

       if base_model:
              # layernorm + pooler
              rename_keys.extend(
                  [
                      ("""norm.weight""", """layernorm.weight"""),
                      ("""norm.bias""", """layernorm.bias"""),
                      ("""pre_logits.fc.weight""", """pooler.dense.weight"""),
                      ("""pre_logits.fc.bias""", """pooler.dense.bias"""),
                  ]				)

              # if just the base model, we should remove "deit" from all keys that start with "deit"
              A_						=      [(pair[0], pair[1][4:]) if pair[1].startswith("""deit"""				) else pair for pair in rename_keys]
       else:
              # layernorm + classification heads
              rename_keys.extend(
                  [
                      ("""norm.weight""", """deit.layernorm.weight"""),
                      ("""norm.bias""", """deit.layernorm.bias"""),
                      ("""head.weight""", """cls_classifier.weight"""),
                      ("""head.bias""", """cls_classifier.bias"""),
                      ("""head_dist.weight""", """distillation_classifier.weight"""),
                      ("""head_dist.bias""", """distillation_classifier.bias"""),
                  ]				)

       return rename_keys
def       UpperCAmelCase__    (      UpperCAmelCase__,       UpperCAmelCase__,       UpperCAmelCase__=False				)			->			int:
       for i in range(config.num_hidden_layers				):
              if base_model:
                     A_						=      """"""
              else:
                     A_						=      """deit."""
              # read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
              A_						=      state_dict.pop(F'''blocks.{i}.attn.qkv.weight'''				)
              A_						=      state_dict.pop(F'''blocks.{i}.attn.qkv.bias'''				)
              # next, add query, keys and values (in that order) to the state dict
              A_						=      in_proj_weight[
                  : config.hidden_size, :
              ]
              A_						=      in_proj_bias[: config.hidden_size]
              A_						=      in_proj_weight[
                  config.hidden_size : config.hidden_size * 2, :
              ]
              A_						=      in_proj_bias[
                  config.hidden_size : config.hidden_size * 2
              ]
              A_						=      in_proj_weight[
                  -config.hidden_size :, :
              ]
              A_						=      in_proj_bias[-config.hidden_size :]
def       UpperCAmelCase__    (      UpperCAmelCase__,       UpperCAmelCase__,       UpperCAmelCase__				)			->			Any:
       A_						=      dct.pop(UpperCAmelCase__				)
       A_						=      val
def       UpperCAmelCase__    (      )			->			Optional[Any]:
       A_						=      """http://images.cocodataset.org/val2017/000000039769.jpg"""
       A_						=      Image.open(requests.get(UpperCAmelCase__,       stream=UpperCAmelCase__				).raw				)
       return im
@torch.no_grad()
def       UpperCAmelCase__    (      UpperCAmelCase__,       UpperCAmelCase__				)			->			str:
       A_						=      DeiTConfig()
       # all deit models have fine-tuned heads
       A_						=      False
       # dataset (fine-tuned on ImageNet 2012), patch_size and image_size
       A_						=      10_00
       A_						=      """huggingface/label-files"""
       A_						=      """imagenet-1k-id2label.json"""
       A_						=      json.load(open(hf_hub_download(UpperCAmelCase__,       UpperCAmelCase__,       repo_type="""dataset"""				),       """r"""				)				)
       A_						=      {int(UpperCAmelCase__				): v for k, v in idalabel.items()}
       A_						=      idalabel
       A_						=      {v: k for k, v in idalabel.items()}
       A_						=      int(deit_name[-6:-4]				)
       A_						=      int(deit_name[-3:]				)
       # size of the architecture
       if deit_name[9:].startswith("""tiny"""				):
              A_						=      1_92
              A_						=      7_68
              A_						=      12
              A_						=      3
       elif deit_name[9:].startswith("""small"""				):
              A_						=      3_84
              A_						=      15_36
              A_						=      12
              A_						=      6
       if deit_name[9:].startswith("""base"""				):
              pass
       elif deit_name[4:].startswith("""large"""				):
              A_						=      10_24
              A_						=      40_96
              A_						=      24
              A_						=      16

       # load original model from timm
       A_						=      timm.create_model(UpperCAmelCase__,       pretrained=UpperCAmelCase__				)
       timm_model.eval()

       # load state_dict of original model, remove and rename some keys
       A_						=      timm_model.state_dict()
       A_						=      create_rename_keys(UpperCAmelCase__,       UpperCAmelCase__				)
       for src, dest in rename_keys:
              rename_key(UpperCAmelCase__,       UpperCAmelCase__,       UpperCAmelCase__				)
       read_in_q_k_v(UpperCAmelCase__,       UpperCAmelCase__,       UpperCAmelCase__				)

       # load HuggingFace model
       A_						=      DeiTForImageClassificationWithTeacher(UpperCAmelCase__				).eval()
       model.load_state_dict(UpperCAmelCase__				)

       # Check outputs on an image, prepared by DeiTImageProcessor
       A_						=      int(
           (2_56 / 2_24) * config.image_size				)  # to maintain same ratio w.r.t. 224 images, see https://github.com/facebookresearch/deit/blob/ab5715372db8c6cad5740714b2216d55aeae052e/datasets.py#L103
       A_						=      DeiTImageProcessor(size=UpperCAmelCase__,       crop_size=config.image_size				)
       A_						=      image_processor(images=prepare_img(),       return_tensors="""pt"""				)
       A_						=      encoding["""pixel_values"""]
       A_						=      model(UpperCAmelCase__				)

       A_						=      timm_model(UpperCAmelCase__				)
       assert timm_logits.shape == outputs.logits.shape
       assert torch.allclose(UpperCAmelCase__,       outputs.logits,       atol=1e-3				)

       Path(UpperCAmelCase__				).mkdir(exist_ok=UpperCAmelCase__				)
       print(F'''Saving model {deit_name} to {pytorch_dump_folder_path}'''				)
       model.save_pretrained(UpperCAmelCase__				)
       print(F'''Saving image processor to {pytorch_dump_folder_path}'''				)
       image_processor.save_pretrained(UpperCAmelCase__				)


if __name__ == "__main__":
      __lowerCamelCase		       =    argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          '''--deit_name''',
          default='''vit_deit_base_distilled_patch16_224''',
          type=str,
          help='''Name of the DeiT timm model you\'d like to convert.''',
      )
      parser.add_argument(
          '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
      )

      __lowerCamelCase		       =    parser.parse_args()
      convert_deit_checkpoint(args.deit_name, args.pytorch_dump_folder_path)



'''simple docstring'''







import os

__lowerCamelCase		       =    {'''I''': 1, '''V''': 5, '''X''': 10, '''L''': 50, '''C''': 100, '''D''': 500, '''M''': 1000}
def       UpperCAmelCase__    (      UpperCAmelCase__				)			->			int:
       A_						=      0

       A_						=      0
       while index < len(UpperCAmelCase__				) - 1:
              A_						=      SYMBOLS[numerals[index]]
              A_						=      SYMBOLS[numerals[index + 1]]
              if current_value < next_value:
                     total_value -= current_value
              else:
                     total_value += current_value
              index += 1
       total_value += SYMBOLS[numerals[index]]

       return total_value
def       UpperCAmelCase__    (      UpperCAmelCase__				)			->			str:
       A_						=      """"""

       A_						=      num // 10_00
       numerals += m_count * "M"
       num %= 10_00

       A_						=      num // 1_00
       if c_count == 9:
              numerals += "CM"
              c_count -= 9
       elif c_count == 4:
              numerals += "CD"
              c_count -= 4
       if c_count >= 5:
              numerals += "D"
              c_count -= 5
       numerals += c_count * "C"
       num %= 1_00

       A_						=      num // 10
       if x_count == 9:
              numerals += "XC"
              x_count -= 9
       elif x_count == 4:
              numerals += "XL"
              x_count -= 4
       if x_count >= 5:
              numerals += "L"
              x_count -= 5
       numerals += x_count * "X"
       num %= 10

       if num == 9:
              numerals += "IX"
              num -= 9
       elif num == 4:
              numerals += "IV"
              num -= 4
       if num >= 5:
              numerals += "V"
              num -= 5
       numerals += num * "I"

       return numerals
def       UpperCAmelCase__    (      UpperCAmelCase__ = "/p089_roman.txt"				)			->			int:
       A_						=      0

       with open(os.path.dirname(UpperCAmelCase__				) + roman_numerals_filename				) as filea:
              A_						=      filea.readlines()

       for line in lines:
              A_						=      line.strip()
              A_						=      parse_roman_numerals(UpperCAmelCase__				)
              A_						=      generate_roman_numerals(UpperCAmelCase__				)
              savings += len(UpperCAmelCase__				) - len(UpperCAmelCase__				)

       return savings


if __name__ == "__main__":
      print(f"""{solution() = }""")
