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_    					=       logging.get_logger(__name__)





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





def    __lowercase     (						lowerCamelCase					:     int  ,  lowerCamelCase					:     Optional[Any]  ,  lowerCamelCase					:     Any	):
 UpperCamelCase_					:						List[Any] =					dct.pop(SCREAMING_SNAKE_CASE__	)
 UpperCamelCase_					:						Optional[Any] =					val





def    __lowercase     (						):
 UpperCamelCase_					:						List[Any] =					"""http://images.cocodataset.org/val2017/000000039769.jpg"""
 UpperCamelCase_					:						str =					Image.open(requests.get(SCREAMING_SNAKE_CASE__  ,  stream=SCREAMING_SNAKE_CASE__	).raw	)
 return im





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

 # load original model from timm
 UpperCamelCase_					:						List[str] =					timm.create_model(SCREAMING_SNAKE_CASE__  ,  pretrained=SCREAMING_SNAKE_CASE__	)
 timm_model.eval()

 # load state_dict of original model, remove and rename some keys
 UpperCamelCase_					:						Optional[int] =					timm_model.state_dict()
 UpperCamelCase_					:						List[Any] =					create_rename_keys(SCREAMING_SNAKE_CASE__  ,  SCREAMING_SNAKE_CASE__	)
 for src, dest in rename_keys:
  rename_key(SCREAMING_SNAKE_CASE__  ,  SCREAMING_SNAKE_CASE__  ,  SCREAMING_SNAKE_CASE__	)
 read_in_q_k_v(SCREAMING_SNAKE_CASE__  ,  SCREAMING_SNAKE_CASE__  ,  SCREAMING_SNAKE_CASE__	)

 # load HuggingFace model
 UpperCamelCase_					:						Tuple =					DeiTForImageClassificationWithTeacher(SCREAMING_SNAKE_CASE__	).eval()
 model.load_state_dict(SCREAMING_SNAKE_CASE__	)

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

 UpperCamelCase_					:						Optional[Any] =					timm_model(SCREAMING_SNAKE_CASE__	)
 assert timm_logits.shape == outputs.logits.shape
 assert torch.allclose(SCREAMING_SNAKE_CASE__  ,  outputs.logits  ,  atol=1e-3	)

 Path(SCREAMING_SNAKE_CASE__	).mkdir(exist_ok=SCREAMING_SNAKE_CASE__	)
 print(F"Saving model {deit_name} to {pytorch_dump_folder_path}"	)
 model.save_pretrained(SCREAMING_SNAKE_CASE__	)
 print(F"Saving image processor to {pytorch_dump_folder_path}"	)
 image_processor.save_pretrained(SCREAMING_SNAKE_CASE__	)


if __name__ == "__main__":
   a_    					=       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_    					=       parser.parse_args()
   convert_deit_checkpoint(args.deit_name, args.pytorch_dump_folder_path)





import inspect
import warnings
from typing import Any, Dict, Optional, Union

from packaging import version







def 						_snake_case(  *SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__ = None					,	SCREAMING_SNAKE_CASE__=True					,	SCREAMING_SNAKE_CASE__=2				) ->    Optional[Any]:
       from .. import __version__

       lowercase							:     int       	=      take_from
       lowercase							:     Tuple       	=      ()
       if not isinstance(args[0]					,	SCREAMING_SNAKE_CASE__				):
              lowercase							:     Dict       	=      (args,)

       for attribute, version_name, message in args:
              if version.parse(version.parse(SCREAMING_SNAKE_CASE__				).base_version				) >= version.parse(SCREAMING_SNAKE_CASE__				):
                     raise ValueError(
                         f"The deprecation tuple {(attribute, version_name, message)} should be removed since diffusers'"
                         f" version {__version__} is >= {version_name}"				)

              lowercase							:     int       	=      None
              if isinstance(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				) and attribute in deprecated_kwargs:
                     values += (deprecated_kwargs.pop(SCREAMING_SNAKE_CASE__				),)
                     lowercase							:     Union[str, Any]       	=      f"The `{attribute}` argument is deprecated and will be removed in version {version_name}."
              elif hasattr(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				):
                     values += (getattr(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				),)
                     lowercase							:     int       	=      f"The `{attribute}` attribute is deprecated and will be removed in version {version_name}."
              elif deprecated_kwargs is None:
                     lowercase							:     Dict       	=      f"`{attribute}` is deprecated and will be removed in version {version_name}."

              if warning is not None:
                     lowercase							:     Dict       	=      warning + """ """ if standard_warn else """"""
                     warnings.warn(warning + message					,	SCREAMING_SNAKE_CASE__					,	stacklevel=SCREAMING_SNAKE_CASE__				)

       if isinstance(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				) and len(SCREAMING_SNAKE_CASE__				) > 0:
              lowercase							:     str       	=      inspect.getouterframes(inspect.currentframe()				)[1]
              lowercase							:     List[str]       	=      call_frame.filename
              lowercase							:     Tuple       	=      call_frame.lineno
              lowercase							:     List[str]       	=      call_frame.function
              lowercase	,			lowercase							:     Optional[Any]       	=      next(iter(deprecated_kwargs.items()				)				)
              raise TypeError(f"{function} in {filename} line {line_number-1} got an unexpected keyword argument `{key}`"				)

       if len(SCREAMING_SNAKE_CASE__				) == 0:
              return
       elif len(SCREAMING_SNAKE_CASE__				) == 1:
              return values[0]
       return values
