






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      :       Tuple    =						logging.get_logger(__name__)





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





def    __lowerCAmelCase   (    __snake_case       ,			__snake_case       ,			__snake_case							):
     __lowerCAmelCase             =       dct.pop(UpperCamelCase__							)
     __lowerCAmelCase             =       val





def    __lowerCAmelCase   (    ):
     __lowerCAmelCase             =       "http://images.cocodataset.org/val2017/000000039769.jpg"
     __lowerCAmelCase             =       Image.open(requests.get(UpperCamelCase__       ,			stream=UpperCamelCase__							).raw							)
     return im





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

     # load original model from timm
     __lowerCAmelCase             =       timm.create_model(UpperCamelCase__       ,			pretrained=UpperCamelCase__							)
     timm_model.eval()

     # load state_dict of original model, remove and rename some keys
     __lowerCAmelCase             =       timm_model.state_dict()
     __lowerCAmelCase             =       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
     __lowerCAmelCase             =       DeiTForImageClassificationWithTeacher(UpperCamelCase__							).eval()
     model.load_state_dict(UpperCamelCase__							)

     # Check outputs on an image, prepared by DeiTImageProcessor
     __lowerCAmelCase             =       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
     __lowerCAmelCase             =       DeiTImageProcessor(size=UpperCamelCase__       ,			crop_size=config.image_size							)
     __lowerCAmelCase             =       image_processor(images=prepare_img()       ,			return_tensors="pt"							)
     __lowerCAmelCase             =       encoding["pixel_values"]
     __lowerCAmelCase             =       model(UpperCamelCase__							)

     __lowerCAmelCase             =       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      :       List[Any]    =						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      :       Dict    =						parser.parse_args()
 convert_deit_checkpoint(args.deit_name, args.pytorch_dump_folder_path)













def 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				int = 600_851_475_143     ):

		try:
				SCREAMING_SNAKE_CASE__								=   int(UpperCamelCase__     )
		except (TypeError, ValueError):
				raise TypeError("""Parameter n must be int or castable to int."""     )
		if n <= 0:
				raise ValueError("""Parameter n must be greater than or equal to one."""     )
		SCREAMING_SNAKE_CASE__								=   1
		SCREAMING_SNAKE_CASE__								=   2
		while i * i <= n:
				while n % i == 0:
						SCREAMING_SNAKE_CASE__								=   i
						n //= i
				i += 1
		if n > 1:
				SCREAMING_SNAKE_CASE__								=   n
		return int(UpperCamelCase__     )


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