'''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: Optional[int]          =   logging.get_logger(__name__)
def 			__UpperCamelCase       (   UpperCAmelCase		,     UpperCAmelCase=False     ):
 lowercase__				:    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"
  lowercase__				:    Optional[int]  =					[(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		,     UpperCAmelCase		,     UpperCAmelCase=False     ):
 for i in range(config.num_hidden_layers     ):
  if base_model:
   lowercase__				:    Dict  =					''''''
  else:
   lowercase__				:    List[Any]  =					'''deit.'''
  # read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
  lowercase__				:    Optional[Any]  =					state_dict.pop(F"""blocks.{i}.attn.qkv.weight"""     )
  lowercase__				:    str  =					state_dict.pop(F"""blocks.{i}.attn.qkv.bias"""     )
  # next, add query, keys and values (in that order) to the state dict
  lowercase__				:    List[Any]  =					in_proj_weight[
      : config.hidden_size, :
  ]
  lowercase__				:    Any  =					in_proj_bias[: config.hidden_size]
  lowercase__				:    List[str]  =					in_proj_weight[
      config.hidden_size : config.hidden_size * 2, :
  ]
  lowercase__				:    Dict  =					in_proj_bias[
      config.hidden_size : config.hidden_size * 2
  ]
  lowercase__				:    Any  =					in_proj_weight[
      -config.hidden_size :, :
  ]
  lowercase__				:    Optional[Any]  =					in_proj_bias[-config.hidden_size :]
def 			__UpperCamelCase       (   UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase     ):
 lowercase__				:    Union[str, Any]  =					dct.pop(UpperCAmelCase     )
 lowercase__				:    List[Any]  =					val
def 			__UpperCamelCase       (   ):
 lowercase__				:    Union[str, Any]  =					'''http://images.cocodataset.org/val2017/000000039769.jpg'''
 lowercase__				:    Union[str, Any]  =					Image.open(requests.get(UpperCAmelCase		,     stream=UpperCAmelCase     ).raw     )
 return im
@torch.no_grad()
def 			__UpperCamelCase       (   UpperCAmelCase		,     UpperCAmelCase     ):
 lowercase__				:    List[str]  =					DeiTConfig()
 # all deit models have fine-tuned heads
 lowercase__				:    List[str]  =					False
 # dataset (fine-tuned on ImageNet 2012), patch_size and image_size
 lowercase__				:    Tuple  =					1000
 lowercase__				:    str  =					'''huggingface/label-files'''
 lowercase__				:    str  =					'''imagenet-1k-id2label.json'''
 lowercase__				:    Tuple  =					json.load(open(hf_hub_download(UpperCAmelCase		,     UpperCAmelCase		,     repo_type='''dataset'''     )		,     '''r'''     )     )
 lowercase__				:    Dict  =					{int(UpperCAmelCase     ): v for k, v in idalabel.items()}
 lowercase__				:    Dict  =					idalabel
 lowercase__				:    Any  =					{v: k for k, v in idalabel.items()}
 lowercase__				:    List[str]  =					int(deit_name[-6:-4]     )
 lowercase__				:    Dict  =					int(deit_name[-3:]     )
 # size of the architecture
 if deit_name[9:].startswith('''tiny'''     ):
  lowercase__				:    List[Any]  =					192
  lowercase__				:    Any  =					768
  lowercase__				:    Optional[int]  =					12
  lowercase__				:    Optional[Any]  =					3
 elif deit_name[9:].startswith('''small'''     ):
  lowercase__				:    List[str]  =					384
  lowercase__				:    Optional[Any]  =					1536
  lowercase__				:    int  =					12
  lowercase__				:    int  =					6
 if deit_name[9:].startswith('''base'''     ):
  pass
 elif deit_name[4:].startswith('''large'''     ):
  lowercase__				:    Union[str, Any]  =					1024
  lowercase__				:    List[str]  =					4096
  lowercase__				:    int  =					24
  lowercase__				:    Dict  =					16

 # load original model from timm
 lowercase__				:    Any  =					timm.create_model(UpperCAmelCase		,     pretrained=UpperCAmelCase     )
 timm_model.eval()

 # load state_dict of original model, remove and rename some keys
 lowercase__				:    Dict  =					timm_model.state_dict()
 lowercase__				:    Optional[int]  =					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
 lowercase__				:    Union[str, Any]  =					DeiTForImageClassificationWithTeacher(UpperCAmelCase     ).eval()
 model.load_state_dict(UpperCAmelCase     )

 # Check outputs on an image, prepared by DeiTImageProcessor
 lowercase__				:    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
 lowercase__				:    List[str]  =					DeiTImageProcessor(size=UpperCAmelCase		,     crop_size=config.image_size     )
 lowercase__				:    List[Any]  =					image_processor(images=prepare_img()		,     return_tensors='''pt'''     )
 lowercase__				:    Tuple  =					encoding['''pixel_values''']
 lowercase__				:    List[Any]  =					model(UpperCAmelCase     )

 lowercase__				:    Optional[Any]  =					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__":
  __a: str          =   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: Tuple          =   parser.parse_args()
  convert_deit_checkpoint(args.deit_name, args.pytorch_dump_folder_path)


'''simple docstring'''

import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging


__a: List[str]          =   logging.get_logger(__name__)



class 			UpperCAmelCase  (						a__					):





       '''simple docstring'''



       SCREAMING_SNAKE_CASE							=    "encoder-decoder"
       SCREAMING_SNAKE_CASE							=    True

       def __init__(						self	,   **__lowerCAmelCase )  ->  int:
        super().__init__(**__lowerCAmelCase )
        assert (
            "encoder" in kwargs and "decoder" in kwargs
        ), "Config has to be initialized with encoder and decoder config"
        lowercase__				:    Optional[int]  =					kwargs.pop('''encoder''' )
        lowercase__				:    Union[str, Any]  =					encoder_config.pop('''model_type''' )
        lowercase__				:    Any  =					kwargs.pop('''decoder''' )
        lowercase__				:    Any  =					decoder_config.pop('''model_type''' )

        from ..auto.configuration_auto import AutoConfig

        lowercase__				:    Union[str, Any]  =					AutoConfig.for_model(__lowerCAmelCase	,   **__lowerCAmelCase )
        lowercase__				:    Optional[Any]  =					AutoConfig.for_model(__lowerCAmelCase	,   **__lowerCAmelCase )
        lowercase__				:    Tuple  =					True

       @classmethod
       def _lowerCAmelCase(						cls	,   __lowerCAmelCase	,   __lowerCAmelCase	,   **__lowerCAmelCase )  ->  PretrainedConfig:
        logger.info('''Set `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config''' )
        lowercase__				:    Union[str, Any]  =					True
        lowercase__				:    Any  =					True

        return cls(encoder=encoder_config.to_dict()	,   decoder=decoder_config.to_dict()	,   **__lowerCAmelCase )


       def _lowerCAmelCase(						self )  ->  Any:
        lowercase__				:    Any  =					copy.deepcopy(self.__dict__ )
        lowercase__				:    Optional[Any]  =					self.encoder.to_dict()
        lowercase__				:    Tuple  =					self.decoder.to_dict()
        lowercase__				:    Dict  =					self.__class__.model_type
        return output

