


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()
UpperCAmelCase    =					logging.get_logger(__name__)






def  __lowerCAmelCase					(SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE=False      )->						List[str]:




      """simple docstring"""

      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'''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"
            snake_case_ =   [(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  __lowerCAmelCase					(SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE=False      )->						Optional[int]:




      """simple docstring"""

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






def  __lowerCAmelCase					(SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      )->						str:




      """simple docstring"""

      snake_case_ =   dct.pop(SCREAMING_SNAKE_CASE      )
      snake_case_ =   val






def  __lowerCAmelCase					()->						List[str]:




      """simple docstring"""

      snake_case_ =   '''http://images.cocodataset.org/val2017/000000039769.jpg'''
      snake_case_ =   Image.open(requests.get(SCREAMING_SNAKE_CASE      ,      stream=SCREAMING_SNAKE_CASE      ).raw      )
      return im






@torch.no_grad()
def  __lowerCAmelCase					(SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      )->						Tuple:




      """simple docstring"""

      snake_case_ =   DeiTConfig()
      # all deit models have fine-tuned heads
      snake_case_ =   False
      # dataset (fine-tuned on ImageNet 2012), patch_size and image_size
      snake_case_ =   1000
      snake_case_ =   '''huggingface/label-files'''
      snake_case_ =   '''imagenet-1k-id2label.json'''
      snake_case_ =   json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      ,      repo_type='''dataset'''      )      ,      '''r'''      )      )
      snake_case_ =   {int(SCREAMING_SNAKE_CASE      ): v for k, v in idalabel.items()}
      snake_case_ =   idalabel
      snake_case_ =   {v: k for k, v in idalabel.items()}
      snake_case_ =   int(deit_name[-6:-4]      )
      snake_case_ =   int(deit_name[-3:]      )
      # size of the architecture
      if deit_name[9:].startswith('''tiny'''      ):
            snake_case_ =   192
            snake_case_ =   768
            snake_case_ =   12
            snake_case_ =   3
      elif deit_name[9:].startswith('''small'''      ):
            snake_case_ =   384
            snake_case_ =   1536
            snake_case_ =   12
            snake_case_ =   6
      if deit_name[9:].startswith('''base'''      ):
            pass
      elif deit_name[4:].startswith('''large'''      ):
            snake_case_ =   1024
            snake_case_ =   4096
            snake_case_ =   24
            snake_case_ =   16

      # load original model from timm
      snake_case_ =   timm.create_model(SCREAMING_SNAKE_CASE      ,      pretrained=SCREAMING_SNAKE_CASE      )
      timm_model.eval()

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

      # load HuggingFace model
      snake_case_ =   DeiTForImageClassificationWithTeacher(SCREAMING_SNAKE_CASE      ).eval()
      model.load_state_dict(SCREAMING_SNAKE_CASE      )

      # Check outputs on an image, prepared by DeiTImageProcessor
      snake_case_ =   int(
          (256 / 224) * config.image_size      )  # to maintain same ratio w.r.t. 224 images, see https://github.com/facebookresearch/deit/blob/ab5715372db8c6cad5740714b2216d55aeae052e/datasets.py#L103
      snake_case_ =   DeiTImageProcessor(size=SCREAMING_SNAKE_CASE      ,      crop_size=config.image_size      )
      snake_case_ =   image_processor(images=prepare_img()      ,      return_tensors='''pt'''      )
      snake_case_ =   encoding['''pixel_values''']
      snake_case_ =   model(SCREAMING_SNAKE_CASE      )

      snake_case_ =   timm_model(SCREAMING_SNAKE_CASE      )
      assert timm_logits.shape == outputs.logits.shape
      assert torch.allclose(SCREAMING_SNAKE_CASE      ,      outputs.logits      ,      atol=1E-3      )

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


if __name__ == "__main__":
   UpperCAmelCase    =					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."""
   )

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



def  __lowerCAmelCase					(SCREAMING_SNAKE_CASE = 3      ,      SCREAMING_SNAKE_CASE = 7      ,      SCREAMING_SNAKE_CASE = 100_0000      )->						int:




      """simple docstring"""

      snake_case_ =   0
      snake_case_ =   1

      for current_denominator in range(1      ,      limit + 1      ):
            snake_case_ =   current_denominator * numerator // denominator
            if current_denominator % denominator == 0:
                  current_numerator -= 1
            if current_numerator * max_denominator > current_denominator * max_numerator:
                  snake_case_ =   current_numerator
                  snake_case_ =   current_denominator
      return max_numerator


if __name__ == "__main__":
   print(solution(numerator=3, denominator=7, limit=100_0000))