





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       :     List[str]   =     logging.get_logger(__name__)



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



def 		A_				(     _lowerCAmelCase							,   _lowerCAmelCase							,   _lowerCAmelCase     )						-> int:
 UpperCamelCase	:					Optional[Any]       	=   dct.pop(_lowerCAmelCase     )
 UpperCamelCase	:					Optional[Any]       	=   val



def 		A_				(     )						-> Optional[Any]:
 UpperCamelCase	:					Optional[int]       	=   "http://images.cocodataset.org/val2017/000000039769.jpg"
 UpperCamelCase	:					List[Any]       	=   Image.open(requests.get(_lowerCAmelCase							,   stream=_lowerCAmelCase     ).raw     )
 return im



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

 # load original model from timm
 UpperCamelCase	:					str       	=   timm.create_model(_lowerCAmelCase							,   pretrained=_lowerCAmelCase     )
 timm_model.eval()

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

 # load HuggingFace model
 UpperCamelCase	:					List[Any]       	=   DeiTForImageClassificationWithTeacher(_lowerCAmelCase     ).eval()
 model.load_state_dict(_lowerCAmelCase     )

 # Check outputs on an image, prepared by DeiTImageProcessor
 UpperCamelCase	:					Dict       	=   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
 UpperCamelCase	:					Union[str, Any]       	=   DeiTImageProcessor(size=_lowerCAmelCase							,   crop_size=config.image_size     )
 UpperCamelCase	:					List[str]       	=   image_processor(images=prepare_img()							,   return_tensors="pt"     )
 UpperCamelCase	:					List[Any]       	=   encoding["pixel_values"]
 UpperCamelCase	:					Tuple       	=   model(_lowerCAmelCase     )

 UpperCamelCase	:					Union[str, Any]       	=   timm_model(_lowerCAmelCase     )
 assert timm_logits.shape == outputs.logits.shape
 assert torch.allclose(_lowerCAmelCase							,   outputs.logits							,   atol=1e-3     )

 Path(_lowerCAmelCase     ).mkdir(exist_ok=_lowerCAmelCase     )
 print(F"""Saving model {deit_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       :     str   =     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       :     List[str]   =     parser.parse_args()
    convert_deit_checkpoint(args.deit_name, args.pytorch_dump_folder_path)







'''simple docstring'''




import math






def        _lowerCAmelCase			(  __snake_case  : int			)     ->     bool:

     if 1 < number < 4:
          # 2 and 3 are primes
          return True
     elif number < 2 or number % 2 == 0 or number % 3 == 0:
          # Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
          return False

     # All primes number are in format of 6k +/- 1
     for i in range(5 ,							int(math.sqrt(__snake_case			) + 1			) ,							6			):
          if number % i == 0 or number % (i + 2) == 0:
               return False
     return True






def        _lowerCAmelCase			(  __snake_case  : float = 0.1			)     ->     int:
     __A						: Tuple      =       3
     __A						: Optional[int]      =       3

     while primes / (2 * j - 1) >= ratio:
          for i in range(j * j + j + 1 ,							(j + 2) * (j + 2) ,							j + 1			):
               primes += is_prime(__snake_case			)
          j += 2
     return j


if __name__ == "__main__":
  import doctest

  doctest.testmod()