





from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_lowerCAmelCase 				=     logging.get_logger(__name__)

_lowerCAmelCase 				=     {
    """bert-base-uncased""": """https://huggingface.co/bert-base-uncased/resolve/main/config.json""",
    """bert-large-uncased""": """https://huggingface.co/bert-large-uncased/resolve/main/config.json""",
    """bert-base-cased""": """https://huggingface.co/bert-base-cased/resolve/main/config.json""",
    """bert-large-cased""": """https://huggingface.co/bert-large-cased/resolve/main/config.json""",
    """bert-base-multilingual-uncased""": """https://huggingface.co/bert-base-multilingual-uncased/resolve/main/config.json""",
    """bert-base-multilingual-cased""": """https://huggingface.co/bert-base-multilingual-cased/resolve/main/config.json""",
    """bert-base-chinese""": """https://huggingface.co/bert-base-chinese/resolve/main/config.json""",
    """bert-base-german-cased""": """https://huggingface.co/bert-base-german-cased/resolve/main/config.json""",
    """bert-large-uncased-whole-word-masking""": (
        """https://huggingface.co/bert-large-uncased-whole-word-masking/resolve/main/config.json"""
    ),
    """bert-large-cased-whole-word-masking""": (
        """https://huggingface.co/bert-large-cased-whole-word-masking/resolve/main/config.json"""
    ),
    """bert-large-uncased-whole-word-masking-finetuned-squad""": (
        """https://huggingface.co/bert-large-uncased-whole-word-masking-finetuned-squad/resolve/main/config.json"""
    ),
    """bert-large-cased-whole-word-masking-finetuned-squad""": (
        """https://huggingface.co/bert-large-cased-whole-word-masking-finetuned-squad/resolve/main/config.json"""
    ),
    """bert-base-cased-finetuned-mrpc""": """https://huggingface.co/bert-base-cased-finetuned-mrpc/resolve/main/config.json""",
    """bert-base-german-dbmdz-cased""": """https://huggingface.co/bert-base-german-dbmdz-cased/resolve/main/config.json""",
    """bert-base-german-dbmdz-uncased""": """https://huggingface.co/bert-base-german-dbmdz-uncased/resolve/main/config.json""",
    """cl-tohoku/bert-base-japanese""": """https://huggingface.co/cl-tohoku/bert-base-japanese/resolve/main/config.json""",
    """cl-tohoku/bert-base-japanese-whole-word-masking""": (
        """https://huggingface.co/cl-tohoku/bert-base-japanese-whole-word-masking/resolve/main/config.json"""
    ),
    """cl-tohoku/bert-base-japanese-char""": (
        """https://huggingface.co/cl-tohoku/bert-base-japanese-char/resolve/main/config.json"""
    ),
    """cl-tohoku/bert-base-japanese-char-whole-word-masking""": (
        """https://huggingface.co/cl-tohoku/bert-base-japanese-char-whole-word-masking/resolve/main/config.json"""
    ),
    """TurkuNLP/bert-base-finnish-cased-v1""": (
        """https://huggingface.co/TurkuNLP/bert-base-finnish-cased-v1/resolve/main/config.json"""
    ),
    """TurkuNLP/bert-base-finnish-uncased-v1""": (
        """https://huggingface.co/TurkuNLP/bert-base-finnish-uncased-v1/resolve/main/config.json"""
    ),
    """wietsedv/bert-base-dutch-cased""": """https://huggingface.co/wietsedv/bert-base-dutch-cased/resolve/main/config.json""",
    # See all BERT models at https://huggingface.co/models?filter=bert
}

class    _UpperCAmelCase							(		_lowerCamelCase     ):
      a						       =							'''bert'''
      def __init__(		self      ,       a__=30522      ,       a__=768      ,       a__=12      ,       a__=12      ,       a__=3072      ,       a__="gelu"      ,       a__=0.1      ,       a__=0.1      ,       a__=512      ,       a__=2      ,       a__=0.02      ,       a__=1E-12      ,       a__=0      ,       a__="absolute"      ,       a__=True      ,       a__=None      ,       **a__      ,       ):
         super().__init__(pad_token_id=a__      ,       **a__       )

         A_     :				Dict			      =	vocab_size
         A_     :				Dict			      =	hidden_size
         A_     :				str			      =	num_hidden_layers
         A_     :				Any			      =	num_attention_heads
         A_     :				Optional[Any]			      =	hidden_act
         A_     :				Union[str, Any]			      =	intermediate_size
         A_     :				Union[str, Any]			      =	hidden_dropout_prob
         A_     :				List[str]			      =	attention_probs_dropout_prob
         A_     :				Dict			      =	max_position_embeddings
         A_     :				str			      =	type_vocab_size
         A_     :				List[Any]			      =	initializer_range
         A_     :				int			      =	layer_norm_eps
         A_     :				Any			      =	position_embedding_type
         A_     :				Optional[Any]			      =	use_cache
         A_     :				Dict			      =	classifier_dropout






class    _UpperCAmelCase							(		_lowerCamelCase     ):
      @property
      def 		_lowerCamelCase      (		self       ):
         if self.task == "multiple-choice":
            A_     :				Tuple			      =	{0: """batch""", 1: """choice""", 2: """sequence"""}
         else:
            A_     :				List[str]			      =	{0: """batch""", 1: """sequence"""}
         return OrderedDict(
             [
                 ("""input_ids""", dynamic_axis),
                 ("""attention_mask""", dynamic_axis),
                 ("""token_type_ids""", dynamic_axis),
             ]       )













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 				=     logging.get_logger(__name__)



def     _lowerCAmelCase		(  _lowerCAmelCase  ,_lowerCAmelCase=False     ):
   '''simple docstring'''


   A_     :				List[Any]			      =	[]
   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_     :				Tuple			      =	[(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		(  _lowerCAmelCase  ,_lowerCAmelCase  ,_lowerCAmelCase=False     ):
   '''simple docstring'''


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



def     _lowerCAmelCase		(  _lowerCAmelCase  ,_lowerCAmelCase  ,_lowerCAmelCase     ):
   '''simple docstring'''


   A_     :				List[str]			      =	dct.pop(_lowerCAmelCase     )
   A_     :				Any			      =	val



def     _lowerCAmelCase		(  ):
   '''simple docstring'''


   A_     :				Union[str, Any]			      =	"""http://images.cocodataset.org/val2017/000000039769.jpg"""
   A_     :				Union[str, Any]			      =	Image.open(requests.get(_lowerCAmelCase  ,stream=_lowerCAmelCase     ).raw     )
   return im



@torch.no_grad()
def     _lowerCAmelCase		(  _lowerCAmelCase  ,_lowerCAmelCase     ):
   '''simple docstring'''


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

   # load original model from timm
   A_     :				int			      =	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_     :				Optional[Any]			      =	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_     :				List[str]			      =	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_     :				Any			      =	DeiTImageProcessor(size=_lowerCAmelCase  ,crop_size=config.image_size     )
   A_     :				Optional[int]			      =	image_processor(images=prepare_img()  ,return_tensors="""pt"""     )
   A_     :				List[str]			      =	encoding["""pixel_values"""]
   A_     :				List[Any]			      =	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__":
      _lowerCAmelCase 				=     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 				=     parser.parse_args()
      convert_deit_checkpoint(args.deit_name, args.pytorch_dump_folder_path)






