






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     :							Union[str, Any]    =logging.get_logger(__name__)







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







def 		SCREAMING_SNAKE_CASE							( __lowerCAmelCase ,   __lowerCAmelCase ,   __lowerCAmelCase      )			->     Any:
     UpperCamelCase__      :       List[Any]	    =  dct.pop(__lowerCAmelCase      )
     UpperCamelCase__      :       Any	    =  val







def 		SCREAMING_SNAKE_CASE							( )			->     Any:
     UpperCamelCase__      :       Union[str, Any]	    =  "http://images.cocodataset.org/val2017/000000039769.jpg"
     UpperCamelCase__      :       Tuple	    =  Image.open(requests.get(__lowerCAmelCase ,   stream=__lowerCAmelCase      ).raw      )
     return im







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

     # load original model from timm
     UpperCamelCase__      :       List[Any]	    =  timm.create_model(__lowerCAmelCase ,   pretrained=__lowerCAmelCase      )
     timm_model.eval()

     # load state_dict of original model, remove and rename some keys
     UpperCamelCase__      :       int	    =  timm_model.state_dict()
     UpperCamelCase__      :       Optional[int]	    =  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__      :       Tuple	    =  DeiTForImageClassificationWithTeacher(__lowerCAmelCase      ).eval()
     model.load_state_dict(__lowerCAmelCase      )

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

     UpperCamelCase__      :       Optional[int]	    =  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     :							int    =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)







import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
 from PIL import Image

 from transformers import CLIPImageProcessor, CLIPProcessor





@require_vision
class       __a  (   unittest.TestCase	):







 def  __lowercase (		self :       Union[str, Any]						):


      '''simple docstring'''






      UpperCamelCase__      :       Dict	    =  tempfile.mkdtemp()

      # fmt: off
      UpperCamelCase__      :       List[Any]	    =  ["l", "o", "w", "e", "r", "s", "t", "i", "d", "n", "lo", "l</w>", "w</w>", "r</w>", "t</w>", "low</w>", "er</w>", "lowest</w>", "newer</w>", "wider", "<unk>", "<|startoftext|>", "<|endoftext|>"]
      # fmt: on
      UpperCamelCase__      :       List[Any]	    =  dict(zip(SCREAMING_SNAKE_CASE							,       range(len(SCREAMING_SNAKE_CASE						)						)						)						)
      UpperCamelCase__      :       Tuple	    =  ["#version: 0.2", "l o", "lo w</w>", "e r</w>", ""]
      UpperCamelCase__      :       Tuple	    =  {"unk_token": "<unk>"}

      UpperCamelCase__      :       Tuple	    =  os.path.join(self.tmpdirname							,       VOCAB_FILES_NAMES["vocab_file"]						)
      UpperCamelCase__      :       Optional[int]	    =  os.path.join(self.tmpdirname							,       VOCAB_FILES_NAMES["merges_file"]						)
      with open(self.vocab_file							,       "w"							,       encoding="utf-8"						) as fp:
           fp.write(json.dumps(SCREAMING_SNAKE_CASE						) + "\n"						)
      with open(self.merges_file							,       "w"							,       encoding="utf-8"						) as fp:
           fp.write("\n".join(SCREAMING_SNAKE_CASE						)						)

      UpperCamelCase__      :       List[str]	    =  {
          "do_resize": True,
          "size": 20,
          "do_center_crop": True,
          "crop_size": 18,
          "do_normalize": True,
          "image_mean": [0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3],
          "image_std": [0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1],
      }
      UpperCamelCase__      :       int	    =  os.path.join(self.tmpdirname							,       SCREAMING_SNAKE_CASE						)
      with open(self.image_processor_file							,       "w"							,       encoding="utf-8"						) as fp:
           json.dump(SCREAMING_SNAKE_CASE							,       SCREAMING_SNAKE_CASE						)







 def  __lowercase (		self :       List[str]							,       **SCREAMING_SNAKE_CASE :       List[str]						):


      '''simple docstring'''






      return CLIPTokenizer.from_pretrained(self.tmpdirname							,       **SCREAMING_SNAKE_CASE						)







 def  __lowercase (		self :       List[Any]							,       **SCREAMING_SNAKE_CASE :       Union[str, Any]						):


      '''simple docstring'''






      return CLIPTokenizerFast.from_pretrained(self.tmpdirname							,       **SCREAMING_SNAKE_CASE						)







 def  __lowercase (		self :       Any							,       **SCREAMING_SNAKE_CASE :       Union[str, Any]						):


      '''simple docstring'''






      return CLIPImageProcessor.from_pretrained(self.tmpdirname							,       **SCREAMING_SNAKE_CASE						)







 def  __lowercase (		self :       Optional[Any]						):


      '''simple docstring'''






      shutil.rmtree(self.tmpdirname						)







 def  __lowercase (		self :       Any						):


      '''simple docstring'''






      UpperCamelCase__      :       int	    =  [np.random.randint(2_55							,       size=(3, 30, 4_00)							,       dtype=np.uinta						)]

      UpperCamelCase__      :       int	    =  [Image.fromarray(np.moveaxis(SCREAMING_SNAKE_CASE							,       0							,       -1						)						) for x in image_inputs]

      return image_inputs







 def  __lowercase (		self :       Tuple						):


      '''simple docstring'''






      UpperCamelCase__      :       Union[str, Any]	    =  self.get_tokenizer()
      UpperCamelCase__      :       List[str]	    =  self.get_rust_tokenizer()
      UpperCamelCase__      :       str	    =  self.get_image_processor()

      UpperCamelCase__      :       List[str]	    =  CLIPProcessor(tokenizer=SCREAMING_SNAKE_CASE							,       image_processor=SCREAMING_SNAKE_CASE						)
      processor_slow.save_pretrained(self.tmpdirname						)
      UpperCamelCase__      :       List[Any]	    =  CLIPProcessor.from_pretrained(self.tmpdirname							,       use_fast=SCREAMING_SNAKE_CASE						)

      UpperCamelCase__      :       Tuple	    =  CLIPProcessor(tokenizer=SCREAMING_SNAKE_CASE							,       image_processor=SCREAMING_SNAKE_CASE						)
      processor_fast.save_pretrained(self.tmpdirname						)
      UpperCamelCase__      :       Any	    =  CLIPProcessor.from_pretrained(self.tmpdirname						)

      self.assertEqual(processor_slow.tokenizer.get_vocab()							,       tokenizer_slow.get_vocab()						)
      self.assertEqual(processor_fast.tokenizer.get_vocab()							,       tokenizer_fast.get_vocab()						)
      self.assertEqual(tokenizer_slow.get_vocab()							,       tokenizer_fast.get_vocab()						)
      self.assertIsInstance(processor_slow.tokenizer							,       SCREAMING_SNAKE_CASE						)
      self.assertIsInstance(processor_fast.tokenizer							,       SCREAMING_SNAKE_CASE						)

      self.assertEqual(processor_slow.image_processor.to_json_string()							,       image_processor.to_json_string()						)
      self.assertEqual(processor_fast.image_processor.to_json_string()							,       image_processor.to_json_string()						)
      self.assertIsInstance(processor_slow.image_processor							,       SCREAMING_SNAKE_CASE						)
      self.assertIsInstance(processor_fast.image_processor							,       SCREAMING_SNAKE_CASE						)







 def  __lowercase (		self :       List[str]						):


      '''simple docstring'''






      UpperCamelCase__      :       str	    =  CLIPProcessor(tokenizer=self.get_tokenizer()							,       image_processor=self.get_image_processor()						)
      processor.save_pretrained(self.tmpdirname						)

      UpperCamelCase__      :       Union[str, Any]	    =  self.get_tokenizer(bos_token="(BOS)"							,       eos_token="(EOS)"						)
      UpperCamelCase__      :       int	    =  self.get_image_processor(do_normalize=SCREAMING_SNAKE_CASE							,       padding_value=1.0						)

      UpperCamelCase__      :       Tuple	    =  CLIPProcessor.from_pretrained(
          self.tmpdirname							,       bos_token="(BOS)"							,       eos_token="(EOS)"							,       do_normalize=SCREAMING_SNAKE_CASE							,       padding_value=1.0						)

      self.assertEqual(processor.tokenizer.get_vocab()							,       tokenizer_add_kwargs.get_vocab()						)
      self.assertIsInstance(processor.tokenizer							,       SCREAMING_SNAKE_CASE						)

      self.assertEqual(processor.image_processor.to_json_string()							,       image_processor_add_kwargs.to_json_string()						)
      self.assertIsInstance(processor.image_processor							,       SCREAMING_SNAKE_CASE						)







 def  __lowercase (		self :       List[Any]						):


      '''simple docstring'''






      UpperCamelCase__      :       List[str]	    =  self.get_image_processor()
      UpperCamelCase__      :       Union[str, Any]	    =  self.get_tokenizer()

      UpperCamelCase__      :       Any	    =  CLIPProcessor(tokenizer=SCREAMING_SNAKE_CASE							,       image_processor=SCREAMING_SNAKE_CASE						)

      UpperCamelCase__      :       Dict	    =  self.prepare_image_inputs()

      UpperCamelCase__      :       List[Any]	    =  image_processor(SCREAMING_SNAKE_CASE							,       return_tensors="np"						)
      UpperCamelCase__      :       Optional[Any]	    =  processor(images=SCREAMING_SNAKE_CASE							,       return_tensors="np"						)

      for key in input_image_proc.keys():
           self.assertAlmostEqual(input_image_proc[key].sum()							,       input_processor[key].sum()							,       delta=1e-2						)







 def  __lowercase (		self :       Union[str, Any]						):


      '''simple docstring'''






      UpperCamelCase__      :       str	    =  self.get_image_processor()
      UpperCamelCase__      :       int	    =  self.get_tokenizer()

      UpperCamelCase__      :       Any	    =  CLIPProcessor(tokenizer=SCREAMING_SNAKE_CASE							,       image_processor=SCREAMING_SNAKE_CASE						)

      UpperCamelCase__      :       List[Any]	    =  "lower newer"

      UpperCamelCase__      :       int	    =  processor(text=SCREAMING_SNAKE_CASE						)

      UpperCamelCase__      :       Tuple	    =  tokenizer(SCREAMING_SNAKE_CASE						)

      for key in encoded_tok.keys():
           self.assertListEqual(encoded_tok[key]							,       encoded_processor[key]						)







 def  __lowercase (		self :       int						):


      '''simple docstring'''






      UpperCamelCase__      :       List[str]	    =  self.get_image_processor()
      UpperCamelCase__      :       Dict	    =  self.get_tokenizer()

      UpperCamelCase__      :       Optional[Any]	    =  CLIPProcessor(tokenizer=SCREAMING_SNAKE_CASE							,       image_processor=SCREAMING_SNAKE_CASE						)

      UpperCamelCase__      :       Dict	    =  "lower newer"
      UpperCamelCase__      :       List[Any]	    =  self.prepare_image_inputs()

      UpperCamelCase__      :       Tuple	    =  processor(text=SCREAMING_SNAKE_CASE							,       images=SCREAMING_SNAKE_CASE						)

      self.assertListEqual(list(inputs.keys()						)							,       ["input_ids", "attention_mask", "pixel_values"]						)

      # test if it raises when no input is passed
      with pytest.raises(SCREAMING_SNAKE_CASE						):
           processor()







 def  __lowercase (		self :       Optional[int]						):


      '''simple docstring'''






      UpperCamelCase__      :       Optional[Any]	    =  self.get_image_processor()
      UpperCamelCase__      :       Optional[int]	    =  self.get_tokenizer()

      UpperCamelCase__      :       Optional[Any]	    =  CLIPProcessor(tokenizer=SCREAMING_SNAKE_CASE							,       image_processor=SCREAMING_SNAKE_CASE						)

      UpperCamelCase__      :       List[str]	    =  [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

      UpperCamelCase__      :       Optional[Any]	    =  processor.batch_decode(SCREAMING_SNAKE_CASE						)
      UpperCamelCase__      :       List[Any]	    =  tokenizer.batch_decode(SCREAMING_SNAKE_CASE						)

      self.assertListEqual(SCREAMING_SNAKE_CASE							,       SCREAMING_SNAKE_CASE						)







 def  __lowercase (		self :       List[str]						):


      '''simple docstring'''






      UpperCamelCase__      :       Dict	    =  self.get_image_processor()
      UpperCamelCase__      :       Tuple	    =  self.get_tokenizer()

      UpperCamelCase__      :       Dict	    =  CLIPProcessor(tokenizer=SCREAMING_SNAKE_CASE							,       image_processor=SCREAMING_SNAKE_CASE						)

      UpperCamelCase__      :       Dict	    =  "lower newer"
      UpperCamelCase__      :       List[str]	    =  self.prepare_image_inputs()

      UpperCamelCase__      :       str	    =  processor(text=SCREAMING_SNAKE_CASE							,       images=SCREAMING_SNAKE_CASE						)

      self.assertListEqual(list(inputs.keys()						)							,       processor.model_input_names						)