
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()
lowercase__          =logging.get_logger(__name__)




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




def        __UpperCamelCase					(    lowerCAmelCase__							:    int  ,       lowerCAmelCase__							:    Optional[Any]  ,       lowerCAmelCase__							:    Tuple  ):
 __a			:  List[Any]		    =  dct.pop(lowerCAmelCase__  )
 __a			:  List[str]		    =  val




def        __UpperCamelCase					(    ):
 __a			:  Any		    =  '''http://images.cocodataset.org/val2017/000000039769.jpg'''
 __a			:  Tuple		    =  Image.open(requests.get(lowerCAmelCase__  ,       stream=lowerCAmelCase__  ).raw  )
 return im




@torch.no_grad()
def        __UpperCamelCase					(    lowerCAmelCase__							:    Tuple  ,       lowerCAmelCase__							:    Union[str, Any]  ):
 __a			:  Tuple		    =  DeiTConfig()
 # all deit models have fine-tuned heads
 __a			:  Tuple		    =  False
 # dataset (fine-tuned on ImageNet 2012), patch_size and image_size
 __a			:  str		    =  1_0_0_0
 __a			:  List[str]		    =  '''huggingface/label-files'''
 __a			:  Tuple		    =  '''imagenet-1k-id2label.json'''
 __a			:  Any		    =  json.load(open(hf_hub_download(lowerCAmelCase__  ,       lowerCAmelCase__  ,       repo_type='''dataset'''  )  ,       '''r'''  )  )
 __a			:  Optional[int]		    =  {int(lowerCAmelCase__  ): v for k, v in idalabel.items()}
 __a			:  List[Any]		    =  idalabel
 __a			:  Any		    =  {v: k for k, v in idalabel.items()}
 __a			:  Dict		    =  int(deit_name[-6:-4]  )
 __a			:  str		    =  int(deit_name[-3:]  )
 # size of the architecture
 if deit_name[9:].startswith('''tiny'''  ):
  __a			:  List[str]		    =  1_9_2
  __a			:  List[Any]		    =  7_6_8
  __a			:  Optional[int]		    =  1_2
  __a			:  Optional[Any]		    =  3
 elif deit_name[9:].startswith('''small'''  ):
  __a			:  Optional[Any]		    =  3_8_4
  __a			:  List[str]		    =  1_5_3_6
  __a			:  Optional[int]		    =  1_2
  __a			:  Optional[Any]		    =  6
 if deit_name[9:].startswith('''base'''  ):
  pass
 elif deit_name[4:].startswith('''large'''  ):
  __a			:  List[str]		    =  1_0_2_4
  __a			:  List[Any]		    =  4_0_9_6
  __a			:  Any		    =  2_4
  __a			:  Optional[int]		    =  1_6

 # load original model from timm
 __a			:  Union[str, Any]		    =  timm.create_model(lowerCAmelCase__  ,       pretrained=lowerCAmelCase__  )
 timm_model.eval()

 # load state_dict of original model, remove and rename some keys
 __a			:  Union[str, Any]		    =  timm_model.state_dict()
 __a			:  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
 __a			:  Union[str, Any]		    =  DeiTForImageClassificationWithTeacher(lowerCAmelCase__  ).eval()
 model.load_state_dict(lowerCAmelCase__  )

 # Check outputs on an image, prepared by DeiTImageProcessor
 __a			:  List[Any]		    =  int(
     (2_5_6 / 2_2_4) * config.image_size  )  # to maintain same ratio w.r.t. 224 images, see https://github.com/facebookresearch/deit/blob/ab5715372db8c6cad5740714b2216d55aeae052e/datasets.py#L103
 __a			:  Tuple		    =  DeiTImageProcessor(size=lowerCAmelCase__  ,       crop_size=config.image_size  )
 __a			:  Union[str, Any]		    =  image_processor(images=prepare_img()  ,       return_tensors='''pt'''  )
 __a			:  List[Any]		    =  encoding['''pixel_values''']
 __a			:  str		    =  model(lowerCAmelCase__  )

 __a			:  Tuple		    =  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__":
      lowercase__          =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.'
      )

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


import os
import unittest

from transformers.models.phobert.tokenization_phobert import VOCAB_FILES_NAMES, PhobertTokenizer

from ...test_tokenization_common import TokenizerTesterMixin



class 		UpperCamelCase__  (	__lowercase   ,unittest.TestCase   ):
   _SCREAMING_SNAKE_CASE				:							Union[str, Any]     	=				PhobertTokenizer
   _SCREAMING_SNAKE_CASE				:							int     	=				False
   def 				lowerCAmelCase      (self			: Optional[int]       ):
    super().setUp()

    # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
    __a			:  Optional[int]		    =  ['''T@@''', '''i''', '''I''', '''R@@''', '''r''', '''e@@''']
    __a			:  Tuple		    =  dict(zip(snake_case_ ,    range(len(snake_case_       )       )       )       )
    __a			:  int		    =  ['''#version: 0.2''', '''l à</w>''']
    __a			:  List[Any]		    =  {'''unk_token''': '''<unk>'''}

    __a			:  Any		    =  os.path.join(self.tmpdirname ,    VOCAB_FILES_NAMES['''vocab_file''']       )
    __a			:  List[Any]		    =  os.path.join(self.tmpdirname ,    VOCAB_FILES_NAMES['''merges_file''']       )

    with open(self.vocab_file ,    '''w''' ,    encoding='''utf-8'''       ) as fp:
     for token in vocab_tokens:
      fp.write(f"{token} {vocab_tokens[token]}\n"       )
    with open(self.merges_file ,    '''w''' ,    encoding='''utf-8'''       ) as fp:
     fp.write('''\n'''.join(snake_case_       )       )
   def 				lowerCAmelCase      (self			: Union[str, Any] ,    **snake_case_			: List[str]       ):
    kwargs.update(self.special_tokens_map       )
    return PhobertTokenizer.from_pretrained(self.tmpdirname ,    **snake_case_       )
   def 				lowerCAmelCase      (self			: Any ,    snake_case_			: Dict       ):
    __a			:  Union[str, Any]		    =  '''Tôi là VinAI Research'''
    __a			:  int		    =  '''T<unk> i <unk> <unk> <unk> <unk> <unk> <unk> I Re<unk> e<unk> <unk> <unk> <unk>'''
    return input_text, output_text







   def 				lowerCAmelCase      (self			: Optional[Any]       ):
    __a			:  Optional[int]		    =  PhobertTokenizer(self.vocab_file ,    self.merges_file ,    **self.special_tokens_map       )
    __a			:  Any		    =  '''Tôi là VinAI Research'''
    __a			:  Union[str, Any]		    =  '''T@@ ô@@ i l@@ à V@@ i@@ n@@ A@@ I R@@ e@@ s@@ e@@ a@@ r@@ c@@ h'''.split()
    __a			:  List[str]		    =  tokenizer.tokenize(snake_case_       )
    print(snake_case_       )
    self.assertListEqual(snake_case_ ,    snake_case_       )

    __a			:  str		    =  tokens + [tokenizer.unk_token]

    __a			:  str		    =  [4, 3, 5, 3, 3, 3, 3, 3, 3, 6, 7, 9, 3, 9, 3, 3, 3, 3, 3]
    self.assertListEqual(tokenizer.convert_tokens_to_ids(snake_case_       ) ,    snake_case_       )
