



'''simple docstring'''


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 				__lowerCamelCase				(   A__    ,						A__=False    )       ->	Optional[int]:






   """simple docstring"""
   UpperCamelCase					=  []
   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					=  [(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				(   A__    ,						A__    ,						A__=False    )       ->	Union[str, Any]:






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







def 				__lowerCamelCase				(   A__    ,						A__    ,						A__    )       ->	Optional[int]:






   """simple docstring"""
   UpperCamelCase					=  dct.pop(__UpperCamelCase    )
   UpperCamelCase					=  val







def 				__lowerCamelCase				(   )       ->	List[Any]:






   """simple docstring"""
   UpperCamelCase					=  'http://images.cocodataset.org/val2017/000000039769.jpg'
   UpperCamelCase					=  Image.open(requests.get(__UpperCamelCase    ,						stream=__UpperCamelCase    ).raw    )
   return im







@torch.no_grad()
def 				__lowerCamelCase				(   A__    ,						A__    )       ->	Optional[int]:






   """simple docstring"""
   UpperCamelCase					=  DeiTConfig()
   # all deit models have fine-tuned heads
   UpperCamelCase					=  False
   # dataset (fine-tuned on ImageNet 2012), patch_size and image_size
   UpperCamelCase					=  1_000
   UpperCamelCase					=  'huggingface/label-files'
   UpperCamelCase					=  'imagenet-1k-id2label.json'
   UpperCamelCase					=  json.load(open(hf_hub_download(__UpperCamelCase    ,						__UpperCamelCase    ,						repo_type='dataset'    )    ,						'r'    )    )
   UpperCamelCase					=  {int(__UpperCamelCase    ): v for k, v in idalabel.items()}
   UpperCamelCase					=  idalabel
   UpperCamelCase					=  {v: k for k, v in idalabel.items()}
   UpperCamelCase					=  int(deit_name[-6:-4]    )
   UpperCamelCase					=  int(deit_name[-3:]    )
   # size of the architecture
   if deit_name[9:].startswith('tiny'    ):
      UpperCamelCase					=  192
      UpperCamelCase					=  768
      UpperCamelCase					=  12
      UpperCamelCase					=  3
   elif deit_name[9:].startswith('small'    ):
      UpperCamelCase					=  384
      UpperCamelCase					=  1_536
      UpperCamelCase					=  12
      UpperCamelCase					=  6
   if deit_name[9:].startswith('base'    ):
      pass
   elif deit_name[4:].startswith('large'    ):
      UpperCamelCase					=  1_024
      UpperCamelCase					=  4_096
      UpperCamelCase					=  24
      UpperCamelCase					=  16

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

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

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

   UpperCamelCase					=  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	:       Tuple      	=	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)


import warnings

from transformers import AutoTokenizer
from transformers.utils import is_torch_available
from transformers.utils.generic import ExplicitEnum

from ...processing_utils import ProcessorMixin


if is_torch_available():
      import torch


class      lowerCamelCase    (SCREAMING_SNAKE_CASE__    ):
  """simple docstring"""







  lowerCamelCase__   =  '''char'''
  lowerCamelCase__   =  '''bpe'''
  lowerCamelCase__   =  '''wp'''



A   :     Tuple       =				(DecodeType.CHARACTER, DecodeType.BPE, DecodeType.WORDPIECE)


class      lowerCamelCase    (SCREAMING_SNAKE_CASE__    ):
  """simple docstring"""







  lowerCamelCase__   =  ['''image_processor''', '''char_tokenizer''']
  lowerCamelCase__   =  '''ViTImageProcessor'''
  lowerCamelCase__   =  '''MgpstrTokenizer'''







  def __init__(							self       :				Optional[Any]     ,   __magic_name__       :				Tuple=None     ,   __magic_name__       :				int=None     ,   **__magic_name__       :				Optional[Any]      )     ->		str:
        SCREAMING_SNAKE_CASE_      =     None
        if "feature_extractor" in kwargs:
              warnings.warn(
                  "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
                  " instead."     ,   __magic_name__     ,   )
              SCREAMING_SNAKE_CASE_      =     kwargs.pop("feature_extractor"      )

        SCREAMING_SNAKE_CASE_      =     image_processor if image_processor is not None else feature_extractor
        if image_processor is None:
              raise ValueError("You need to specify an `image_processor`."      )
        if tokenizer is None:
              raise ValueError("You need to specify a `tokenizer`."      )

        SCREAMING_SNAKE_CASE_      =     tokenizer
        SCREAMING_SNAKE_CASE_      =     AutoTokenizer.from_pretrained("gpt2"      )
        SCREAMING_SNAKE_CASE_      =     AutoTokenizer.from_pretrained("bert-base-uncased"      )

        super().__init__(__magic_name__     ,   __magic_name__      )







  def __call__(							self       :				Dict     ,   __magic_name__       :				Union[str, Any]=None     ,   __magic_name__       :				Union[str, Any]=None     ,   __magic_name__       :				Dict=None     ,   **__magic_name__       :				Tuple      )     ->		int:
        if images is None and text is None:
              raise ValueError("You need to specify either an `images` or `text` input to process."      )

        if images is not None:
              SCREAMING_SNAKE_CASE_      =     self.image_processor(__magic_name__     ,   return_tensors=__magic_name__     ,   **__magic_name__      )
        if text is not None:
              SCREAMING_SNAKE_CASE_      =     self.char_tokenizer(__magic_name__     ,   return_tensors=__magic_name__     ,   **__magic_name__      )

        if text is None:
              return inputs
        elif images is None:
              return encodings
        else:
              SCREAMING_SNAKE_CASE_      =     encodings["input_ids"]
              return inputs







  def       __A   (							self       :				Tuple     ,   __magic_name__       :				int      )     ->		Any:
        SCREAMING_SNAKE_CASE_   ,       SCREAMING_SNAKE_CASE_   ,       SCREAMING_SNAKE_CASE_      =     sequences
        SCREAMING_SNAKE_CASE_      =     char_preds.size(0      )

        SCREAMING_SNAKE_CASE_   ,       SCREAMING_SNAKE_CASE_      =     self._decode_helper(__magic_name__     ,   "char"      )
        SCREAMING_SNAKE_CASE_   ,       SCREAMING_SNAKE_CASE_      =     self._decode_helper(__magic_name__     ,   "bpe"      )
        SCREAMING_SNAKE_CASE_   ,       SCREAMING_SNAKE_CASE_      =     self._decode_helper(__magic_name__     ,   "wp"      )

        SCREAMING_SNAKE_CASE_      =     []
        SCREAMING_SNAKE_CASE_      =     []
        for i in range(__magic_name__      ):
              SCREAMING_SNAKE_CASE_      =     [char_scores[i], bpe_scores[i], wp_scores[i]]
              SCREAMING_SNAKE_CASE_      =     [char_strs[i], bpe_strs[i], wp_strs[i]]
              SCREAMING_SNAKE_CASE_      =     scores.index(max(__magic_name__      )      )
              final_strs.append(strs[max_score_index]      )
              final_scores.append(scores[max_score_index]      )

        SCREAMING_SNAKE_CASE_      =     {}
        SCREAMING_SNAKE_CASE_      =     final_strs
        SCREAMING_SNAKE_CASE_      =     final_scores
        SCREAMING_SNAKE_CASE_      =     char_strs
        SCREAMING_SNAKE_CASE_      =     bpe_strs
        SCREAMING_SNAKE_CASE_      =     wp_strs
        return out







  def       __A   (							self       :				int     ,   __magic_name__       :				List[Any]     ,   __magic_name__       :				str      )     ->		Any:
        if format == DecodeType.CHARACTER:
              SCREAMING_SNAKE_CASE_      =     self.char_decode
              SCREAMING_SNAKE_CASE_      =     1
              SCREAMING_SNAKE_CASE_      =     "[s]"
        elif format == DecodeType.BPE:
              SCREAMING_SNAKE_CASE_      =     self.bpe_decode
              SCREAMING_SNAKE_CASE_      =     2
              SCREAMING_SNAKE_CASE_      =     "#"
        elif format == DecodeType.WORDPIECE:
              SCREAMING_SNAKE_CASE_      =     self.wp_decode
              SCREAMING_SNAKE_CASE_      =     102
              SCREAMING_SNAKE_CASE_      =     "[SEP]"
        else:
              raise ValueError(F'''Format {format} is not supported.'''      )

        SCREAMING_SNAKE_CASE_   ,       SCREAMING_SNAKE_CASE_      =     [], []
        SCREAMING_SNAKE_CASE_      =     pred_logits.size(0      )
        SCREAMING_SNAKE_CASE_      =     pred_logits.size(1      )
        SCREAMING_SNAKE_CASE_   ,       SCREAMING_SNAKE_CASE_      =     pred_logits.topk(1     ,   dim=-1     ,   largest=__magic_name__     ,   sorted=__magic_name__      )
        SCREAMING_SNAKE_CASE_      =     preds_index.view(-1     ,   __magic_name__      )[:, 1:]
        SCREAMING_SNAKE_CASE_      =     decoder(__magic_name__      )
        SCREAMING_SNAKE_CASE_   ,       SCREAMING_SNAKE_CASE_      =     torch.nn.functional.softmax(__magic_name__     ,   dim=2      ).max(dim=2      )
        SCREAMING_SNAKE_CASE_      =     preds_max_prob[:, 1:]

        for index in range(__magic_name__      ):
              SCREAMING_SNAKE_CASE_      =     preds_str[index].find(__magic_name__      )
              SCREAMING_SNAKE_CASE_      =     preds_str[index][:pred_eos]
              SCREAMING_SNAKE_CASE_      =     preds_index[index].cpu().tolist()
              SCREAMING_SNAKE_CASE_      =     pred_index.index(__magic_name__      ) if eos_token in pred_index else -1
              SCREAMING_SNAKE_CASE_      =     preds_max_prob[index][: pred_eos_index + 1]
              SCREAMING_SNAKE_CASE_      =     pred_max_prob.cumprod(dim=0      )[-1] if pred_max_prob.nelement() != 0 else 0.0
              dec_strs.append(__magic_name__      )
              conf_scores.append(__magic_name__      )

        return dec_strs, conf_scores







  def       __A   (							self       :				Any     ,   __magic_name__       :				Dict      )     ->		List[str]:
        SCREAMING_SNAKE_CASE_      =     [seq.replace(" "     ,   ""      ) for seq in self.char_tokenizer.batch_decode(__magic_name__      )]
        return decode_strs







  def       __A   (							self       :				Any     ,   __magic_name__       :				Union[str, Any]      )     ->		Tuple:
        return self.bpe_tokenizer.batch_decode(__magic_name__      )







  def       __A   (							self       :				str     ,   __magic_name__       :				Dict      )     ->		List[Any]:
        SCREAMING_SNAKE_CASE_      =     [seq.replace(" "     ,   ""      ) for seq in self.wp_tokenizer.batch_decode(__magic_name__      )]
        return decode_strs


