

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




def        _lowercase    (			__lowerCAmelCase     ,    __lowerCAmelCase=False							)				-> Optional[int]:
      SCREAMING_SNAKE_CASE__      :					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"
            SCREAMING_SNAKE_CASE__      :					Union[str, 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        _lowercase    (			__lowerCAmelCase     ,    __lowerCAmelCase     ,    __lowerCAmelCase=False							)				-> Tuple:
      for i in range(config.num_hidden_layers							):
            if base_model:
                  SCREAMING_SNAKE_CASE__      :					str											=		""""""
            else:
                  SCREAMING_SNAKE_CASE__      :					Optional[int]											=		"""deit."""
            # read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
            SCREAMING_SNAKE_CASE__      :					Optional[Any]											=		state_dict.pop(F'''blocks.{i}.attn.qkv.weight'''							)
            SCREAMING_SNAKE_CASE__      :					str											=		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__      :					Optional[int]											=		in_proj_weight[
                : config.hidden_size, :
            ]
            SCREAMING_SNAKE_CASE__      :					Tuple											=		in_proj_bias[: config.hidden_size]
            SCREAMING_SNAKE_CASE__      :					Dict											=		in_proj_weight[
                config.hidden_size : config.hidden_size * 2, :
            ]
            SCREAMING_SNAKE_CASE__      :					List[Any]											=		in_proj_bias[
                config.hidden_size : config.hidden_size * 2
            ]
            SCREAMING_SNAKE_CASE__      :					str											=		in_proj_weight[
                -config.hidden_size :, :
            ]
            SCREAMING_SNAKE_CASE__      :					Dict											=		in_proj_bias[-config.hidden_size :]




def        _lowercase    (			__lowerCAmelCase     ,    __lowerCAmelCase     ,    __lowerCAmelCase							)				-> Tuple:
      SCREAMING_SNAKE_CASE__      :					Optional[Any]											=		dct.pop(__lowerCamelCase							)
      SCREAMING_SNAKE_CASE__      :					Tuple											=		val




def        _lowercase    (			)				-> Dict:
      SCREAMING_SNAKE_CASE__      :					Tuple											=		"""http://images.cocodataset.org/val2017/000000039769.jpg"""
      SCREAMING_SNAKE_CASE__      :					Tuple											=		Image.open(requests.get(__lowerCamelCase     ,    stream=__lowerCamelCase							).raw							)
      return im




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

      # load original model from timm
      SCREAMING_SNAKE_CASE__      :					int											=		timm.create_model(__lowerCamelCase     ,    pretrained=__lowerCamelCase							)
      timm_model.eval()

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

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

      SCREAMING_SNAKE_CASE__      :					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__":
  a    :List[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."
  )

  a    :Union[str, Any]			=       parser.parse_args()
  convert_deit_checkpoint(args.deit_name, args.pytorch_dump_folder_path)






"""simple docstring"""



import os
from shutil import copyfile
from typing import List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


UpperCAmelCase__          = logging.get_logger(__name__)

UpperCAmelCase__          = {'vocab_file': 'sentencepiece.model'}

UpperCAmelCase__          = {
    'vocab_file': {
        'google/rembert': 'https://huggingface.co/google/rembert/resolve/main/sentencepiece.model',
    },
}

UpperCAmelCase__          = {
    'google/rembert': 256,
}

class       lowerCAmelCase__				(						A_	):
     __a						=					VOCAB_FILES_NAMES
     __a						=					PRETRAINED_VOCAB_FILES_MAP
     __a						=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES







     def __init__(							self			:			Union[str, Any]    ,      _lowerCamelCase			:			Any    ,      _lowerCamelCase			:			Union[str, Any]=False    ,      _lowerCamelCase			:			Any=True    ,      _lowerCamelCase			:			Optional[Any]=True    ,      _lowerCamelCase			:			int="[CLS]"    ,      _lowerCamelCase			:			Optional[int]="[SEP]"    ,      _lowerCamelCase			:			Optional[int]="[UNK]"    ,      _lowerCamelCase			:			Optional[Any]="[SEP]"    ,      _lowerCamelCase			:			str="[PAD]"    ,      _lowerCamelCase			:			List[Any]="[CLS]"    ,      _lowerCamelCase			:			Any="[MASK]"    ,      **_lowerCamelCase			:			Optional[int]    ,      ):
       super().__init__(
           do_lower_case=_lowerCamelCase    ,      remove_space=_lowerCamelCase    ,      keep_accents=_lowerCamelCase    ,      bos_token=_lowerCamelCase    ,      eos_token=_lowerCamelCase    ,      unk_token=_lowerCamelCase    ,      sep_token=_lowerCamelCase    ,      pad_token=_lowerCamelCase    ,      cls_token=_lowerCamelCase    ,      mask_token=_lowerCamelCase    ,      **_lowerCamelCase    ,      )

       _snake_case					     =       do_lower_case
       _snake_case					     =       remove_space
       _snake_case					     =       keep_accents
       _snake_case					     =       vocab_file

       _snake_case					     =       spm.SentencePieceProcessor()
       self.sp_model.Load(_lowerCamelCase			)







     @property
     def      lowercase	(							self			:			int			):
       return len(self.sp_model			)







     def      lowercase	(							self			:			Any			):
       _snake_case					     =       {self.convert_ids_to_tokens(_lowerCamelCase			): i for i in range(self.vocab_size			)}
       vocab.update(self.added_tokens_encoder			)
       return vocab







     def __getstate__(							self			:			List[str]			):
       _snake_case					     =       self.__dict__.copy()
       _snake_case					     =       None
       return state







     def __setstate__(							self			:			List[str]    ,      _lowerCamelCase			:			Tuple			):
       _snake_case					     =       d
       _snake_case					     =       spm.SentencePieceProcessor()
       self.sp_model.Load(self.vocab_file			)







     def      lowercase	(							self			:			str    ,      _lowerCamelCase			:			List[str]    ,      _lowerCamelCase			:			Tuple=False			):
       _snake_case					     =       self.sp_model.EncodeAsPieces(_lowerCamelCase			)
       return pieces







     def      lowercase	(							self			:			str    ,      _lowerCamelCase			:			str			):
       return self.sp_model.PieceToId(_lowerCamelCase			)







     def      lowercase	(							self			:			List[str]    ,      _lowerCamelCase			:			int			):
       return self.sp_model.IdToPiece(_lowerCamelCase			)







     def      lowercase	(							self			:			Union[str, Any]    ,      _lowerCamelCase			:			Any			):
       _snake_case					     =       self.sp_model.decode_pieces(_lowerCamelCase			)
       return out_string







     def      lowercase	(							self			:			Optional[Any]    ,      _lowerCamelCase			:			List[int]    ,      _lowerCamelCase			:			Optional[List[int]] = None			):
       _snake_case					     =       [self.sep_token_id]
       _snake_case					     =       [self.cls_token_id]
       if token_ids_a is None:
         return cls + token_ids_a + sep
       return cls + token_ids_a + sep + token_ids_a + sep







     def      lowercase	(							self			:			Tuple    ,      _lowerCamelCase			:			List[int]    ,      _lowerCamelCase			:			Optional[List[int]] = None    ,      _lowerCamelCase			:			bool = False			):

       if already_has_special_tokens:
         if token_ids_a is not None:
           raise ValueError(
               '''You should not supply a second sequence if the provided sequence of '''
               '''ids is already formatted with special tokens for the model.'''			)
         return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]

       if token_ids_a is not None:
         return [1] + ([0] * len(_lowerCamelCase			)) + [1] + ([0] * len(_lowerCamelCase			)) + [1]
       return [1] + ([0] * len(_lowerCamelCase			)) + [1]







     def      lowercase	(							self			:			Optional[int]    ,      _lowerCamelCase			:			List[int]    ,      _lowerCamelCase			:			Optional[List[int]] = None			):
       _snake_case					     =       [self.sep_token_id]
       _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      lowercase	(							self			:			List[str]    ,      _lowerCamelCase			:			str    ,      _lowerCamelCase			:			Optional[str] = None			):
       if not os.path.isdir(_lowerCamelCase			):
         logger.error('''Vocabulary path ({}) should be a directory'''.format(_lowerCamelCase			)			)
         return
       _snake_case					     =       os.path.join(
           _lowerCamelCase    ,      (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']			)

       if os.path.abspath(self.vocab_file			) != os.path.abspath(_lowerCamelCase			):
         copyfile(self.vocab_file    ,      _lowerCamelCase			)

       return (out_vocab_file,)



