




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__ :   str					=							logging.get_logger(__name__)

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

def 					UpperCAmelCase_							(      __UpperCAmelCase						:	str			,			__UpperCAmelCase						:	List[Any]			,			__UpperCAmelCase						:	Any							)					-> Optional[int]:
      SCREAMING_SNAKE_CASE_							=			dct.pop(__UpperCAmelCase							)
      SCREAMING_SNAKE_CASE_							=			val

def 					UpperCAmelCase_							(      )					-> List[Any]:
      SCREAMING_SNAKE_CASE_							=			'http://images.cocodataset.org/val2017/000000039769.jpg'
      SCREAMING_SNAKE_CASE_							=			Image.open(requests.get(__UpperCAmelCase			,			stream=__UpperCAmelCase							).raw							)
      return im

@torch.no_grad()
def 					UpperCAmelCase_							(      __UpperCAmelCase						:	Optional[Any]			,			__UpperCAmelCase						:	Optional[int]							)					-> Dict:
      SCREAMING_SNAKE_CASE_							=			DeiTConfig()
      # all deit models have fine-tuned heads
      SCREAMING_SNAKE_CASE_							=			False
      # dataset (fine-tuned on ImageNet 2012), patch_size and image_size
      SCREAMING_SNAKE_CASE_							=			10_00
      SCREAMING_SNAKE_CASE_							=			'huggingface/label-files'
      SCREAMING_SNAKE_CASE_							=			'imagenet-1k-id2label.json'
      SCREAMING_SNAKE_CASE_							=			json.load(open(hf_hub_download(__UpperCAmelCase			,			__UpperCAmelCase			,			repo_type='dataset'							)			,			'r'							)							)
      SCREAMING_SNAKE_CASE_							=			{int(__UpperCAmelCase							): v for k, v in idalabel.items()}
      SCREAMING_SNAKE_CASE_							=			idalabel
      SCREAMING_SNAKE_CASE_							=			{v: k for k, v in idalabel.items()}
      SCREAMING_SNAKE_CASE_							=			int(deit_name[-6:-4]							)
      SCREAMING_SNAKE_CASE_							=			int(deit_name[-3:]							)
      # size of the architecture
      if deit_name[9:].startswith('tiny'							):
            SCREAMING_SNAKE_CASE_							=			1_92
            SCREAMING_SNAKE_CASE_							=			7_68
            SCREAMING_SNAKE_CASE_							=			12
            SCREAMING_SNAKE_CASE_							=			3
      elif deit_name[9:].startswith('small'							):
            SCREAMING_SNAKE_CASE_							=			3_84
            SCREAMING_SNAKE_CASE_							=			15_36
            SCREAMING_SNAKE_CASE_							=			12
            SCREAMING_SNAKE_CASE_							=			6
      if deit_name[9:].startswith('base'							):
            pass
      elif deit_name[4:].startswith('large'							):
            SCREAMING_SNAKE_CASE_							=			10_24
            SCREAMING_SNAKE_CASE_							=			40_96
            SCREAMING_SNAKE_CASE_							=			24
            SCREAMING_SNAKE_CASE_							=			16

      # load original model from timm
      SCREAMING_SNAKE_CASE_							=			timm.create_model(__UpperCAmelCase			,			pretrained=__UpperCAmelCase							)
      timm_model.eval()

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

      # Check outputs on an image, prepared by DeiTImageProcessor
      SCREAMING_SNAKE_CASE_							=			int(
          (2_56 / 2_24) * config.image_size							)  # to maintain same ratio w.r.t. 224 images, see https://github.com/facebookresearch/deit/blob/ab5715372db8c6cad5740714b2216d55aeae052e/datasets.py#L103
      SCREAMING_SNAKE_CASE_							=			DeiTImageProcessor(size=__UpperCAmelCase			,			crop_size=config.image_size							)
      SCREAMING_SNAKE_CASE_							=			image_processor(images=prepare_img()			,			return_tensors='pt'							)
      SCREAMING_SNAKE_CASE_							=			encoding['pixel_values']
      SCREAMING_SNAKE_CASE_							=			model(__UpperCAmelCase							)

      SCREAMING_SNAKE_CASE_							=			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__ :   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__ :   Any					=							parser.parse_args()
   convert_deit_checkpoint(args.deit_name, args.pytorch_dump_folder_path)





import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_squeezebert import SqueezeBertTokenizer


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

lowerCamelCase__ :   Dict					=							{'vocab_file': 'vocab.txt', 'tokenizer_file': 'tokenizer.json'}

lowerCamelCase__ :   Tuple					=							{
    'vocab_file': {
        'squeezebert/squeezebert-uncased': (
            'https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/vocab.txt'
        ),
        'squeezebert/squeezebert-mnli': 'https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/vocab.txt',
        'squeezebert/squeezebert-mnli-headless': (
            'https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/vocab.txt'
        ),
    },
    'tokenizer_file': {
        'squeezebert/squeezebert-uncased': (
            'https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/tokenizer.json'
        ),
        'squeezebert/squeezebert-mnli': (
            'https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/tokenizer.json'
        ),
        'squeezebert/squeezebert-mnli-headless': (
            'https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/tokenizer.json'
        ),
    },
}

lowerCamelCase__ :   int					=							{
    'squeezebert/squeezebert-uncased': 512,
    'squeezebert/squeezebert-mnli': 512,
    'squeezebert/squeezebert-mnli-headless': 512,
}


lowerCamelCase__ :   str					=							{
    'squeezebert/squeezebert-uncased': {'do_lower_case': True},
    'squeezebert/squeezebert-mnli': {'do_lower_case': True},
    'squeezebert/squeezebert-mnli-headless': {'do_lower_case': True},
}





class        lowerCamelCase_							(      _SCREAMING_SNAKE_CASE					):


 '''simple docstring'''







 lowercase_				    =    VOCAB_FILES_NAMES
 lowercase_				    =    PRETRAINED_VOCAB_FILES_MAP
 lowercase_				    =    PRETRAINED_INIT_CONFIGURATION
 lowercase_				    =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 lowercase_				    =    SqueezeBertTokenizer



 def __init__(							self  :		Tuple				,				_lowerCAmelCase  :		Dict=None				,				_lowerCAmelCase  :		int=None				,				_lowerCAmelCase  :		List[str]=True				,				_lowerCAmelCase  :		str="[UNK]"				,				_lowerCAmelCase  :		Union[str, Any]="[SEP]"				,				_lowerCAmelCase  :		List[Any]="[PAD]"				,				_lowerCAmelCase  :		str="[CLS]"				,				_lowerCAmelCase  :		Dict="[MASK]"				,				_lowerCAmelCase  :		Optional[int]=True				,				_lowerCAmelCase  :		Dict=None				,				**_lowerCAmelCase  :		str				,				):
       super().__init__(
           _lowerCAmelCase				,				tokenizer_file=_lowerCAmelCase				,				do_lower_case=_lowerCAmelCase				,				unk_token=_lowerCAmelCase				,				sep_token=_lowerCAmelCase				,				pad_token=_lowerCAmelCase				,				cls_token=_lowerCAmelCase				,				mask_token=_lowerCAmelCase				,				tokenize_chinese_chars=_lowerCAmelCase				,				strip_accents=_lowerCAmelCase				,				**_lowerCAmelCase				,				)

       SCREAMING_SNAKE_CASE_							=			json.loads(self.backend_tokenizer.normalizer.__getstate__()						)
       if (
           normalizer_state.get('lowercase'				,				_lowerCAmelCase						) != do_lower_case
           or normalizer_state.get('strip_accents'				,				_lowerCAmelCase						) != strip_accents
           or normalizer_state.get('handle_chinese_chars'				,				_lowerCAmelCase						) != tokenize_chinese_chars
       ):
             SCREAMING_SNAKE_CASE_							=			getattr(_lowerCAmelCase				,				normalizer_state.pop('type'						)						)
             SCREAMING_SNAKE_CASE_							=			do_lower_case
             SCREAMING_SNAKE_CASE_							=			strip_accents
             SCREAMING_SNAKE_CASE_							=			tokenize_chinese_chars
             SCREAMING_SNAKE_CASE_							=			normalizer_class(**_lowerCAmelCase						)

       SCREAMING_SNAKE_CASE_							=			do_lower_case



 def       lowerCAmelCase_  (							self  :		Tuple				,				_lowerCAmelCase  :		str				,				_lowerCAmelCase  :		Optional[int]=None						):
       SCREAMING_SNAKE_CASE_							=			[self.cls_token_id] + token_ids_a + [self.sep_token_id]

       if token_ids_a:
             output += token_ids_a + [self.sep_token_id]

       return output



 def       lowerCAmelCase_  (							self  :		Optional[int]				,				_lowerCAmelCase  :		List[int]				,				_lowerCAmelCase  :		Optional[List[int]] = None						):
       SCREAMING_SNAKE_CASE_							=			[self.sep_token_id]
       SCREAMING_SNAKE_CASE_							=			[self.cls_token_id]
       if token_ids_a is None:
             return len(cls + token_ids_a + sep						) * [0]
       return len(cls + token_ids_a + sep						) * [0] + len(token_ids_a + sep						) * [1]




 def       lowerCAmelCase_  (							self  :		Tuple				,				_lowerCAmelCase  :		str				,				_lowerCAmelCase  :		Optional[str] = None						):
       SCREAMING_SNAKE_CASE_							=			self._tokenizer.model.save(_lowerCAmelCase				,				name=_lowerCAmelCase						)
       return tuple(_lowerCAmelCase						)