




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      _SCREAMING_SNAKE_CASE     (				lowercase      :			str      ,				lowercase      :			str=False	):


      '''simple docstring'''



      lowerCamelCase_							=       []
      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"
            lowerCamelCase_							=       [(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      _SCREAMING_SNAKE_CASE     (				lowercase      :			Dict      ,				lowercase      :			Tuple      ,				lowercase      :			Any=False	):


      '''simple docstring'''



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

def      _SCREAMING_SNAKE_CASE     (				lowercase      :			List[str]      ,				lowercase      :			Any      ,				lowercase      :			Union[str, Any]	):


      '''simple docstring'''



      lowerCamelCase_							=       dct.pop(lowercase	)
      lowerCamelCase_							=       val

def      _SCREAMING_SNAKE_CASE     (				):


      '''simple docstring'''



      lowerCamelCase_							=       'http://images.cocodataset.org/val2017/000000039769.jpg'
      lowerCamelCase_							=       Image.open(requests.get(lowercase      ,				stream=lowercase	).raw	)
      return im

@torch.no_grad()
def      _SCREAMING_SNAKE_CASE     (				lowercase      :			Union[str, Any]      ,				lowercase      :			List[Any]	):


      '''simple docstring'''



      lowerCamelCase_							=       DeiTConfig()
      # all deit models have fine-tuned heads
      lowerCamelCase_							=       False
      # dataset (fine-tuned on ImageNet 2012), patch_size and image_size
      lowerCamelCase_							=       10_00
      lowerCamelCase_							=       'huggingface/label-files'
      lowerCamelCase_							=       'imagenet-1k-id2label.json'
      lowerCamelCase_							=       json.load(open(hf_hub_download(lowercase      ,				lowercase      ,				repo_type='dataset'	)      ,				'r'	)	)
      lowerCamelCase_							=       {int(lowercase	): v for k, v in idalabel.items()}
      lowerCamelCase_							=       idalabel
      lowerCamelCase_							=       {v: k for k, v in idalabel.items()}
      lowerCamelCase_							=       int(deit_name[-6:-4]	)
      lowerCamelCase_							=       int(deit_name[-3:]	)
      # size of the architecture
      if deit_name[9:].startswith('tiny'	):
            lowerCamelCase_							=       1_92
            lowerCamelCase_							=       7_68
            lowerCamelCase_							=       12
            lowerCamelCase_							=       3
      elif deit_name[9:].startswith('small'	):
            lowerCamelCase_							=       3_84
            lowerCamelCase_							=       15_36
            lowerCamelCase_							=       12
            lowerCamelCase_							=       6
      if deit_name[9:].startswith('base'	):
            pass
      elif deit_name[4:].startswith('large'	):
            lowerCamelCase_							=       10_24
            lowerCamelCase_							=       40_96
            lowerCamelCase_							=       24
            lowerCamelCase_							=       16

      # load original model from timm
      lowerCamelCase_							=       timm.create_model(lowercase      ,				pretrained=lowercase	)
      timm_model.eval()

      # load state_dict of original model, remove and rename some keys
      lowerCamelCase_							=       timm_model.state_dict()
      lowerCamelCase_							=       create_rename_keys(lowercase      ,				lowercase	)
      for src, dest in rename_keys:
            rename_key(lowercase      ,				lowercase      ,				lowercase	)
      read_in_q_k_v(lowercase      ,				lowercase      ,				lowercase	)

      # load HuggingFace model
      lowerCamelCase_							=       DeiTForImageClassificationWithTeacher(lowercase	).eval()
      model.load_state_dict(lowercase	)

      # Check outputs on an image, prepared by DeiTImageProcessor
      lowerCamelCase_							=       int(
          (2_56 / 2_24) * config.image_size	)  # to maintain same ratio w.r.t. 224 images, see https://github.com/facebookresearch/deit/blob/ab5715372db8c6cad5740714b2216d55aeae052e/datasets.py#L103
      lowerCamelCase_							=       DeiTImageProcessor(size=lowercase      ,				crop_size=config.image_size	)
      lowerCamelCase_							=       image_processor(images=prepare_img()      ,				return_tensors='pt'	)
      lowerCamelCase_							=       encoding['pixel_values']
      lowerCamelCase_							=       model(lowercase	)

      lowerCamelCase_							=       timm_model(lowercase	)
      assert timm_logits.shape == outputs.logits.shape
      assert torch.allclose(lowercase      ,				outputs.logits      ,				atol=1e-3	)

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


if __name__ == "__main__":
 lowerCamelCase							:						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."
 )

 lowerCamelCase							:						List[Any]					       =  parser.parse_args()
 convert_deit_checkpoint(args.deit_name, args.pytorch_dump_folder_path)










__UpperCamelCase:	str = {0: [2, 3], 1: [0], 2: [1], 3: [4], 4: []}

__UpperCamelCase:	Tuple = {0: [1, 2, 3], 1: [2], 2: [0], 3: [4], 4: [5], 5: [3]}


def       SCREAMING_SNAKE_CASE__  (       _lowercase     : dict[int, list[int]]   ,    _lowercase     : int   ,    _lowercase     : list[bool]			)   ->							list[int]:




		'''simple docstring'''
		lowercase__					:      str      =		True
		lowercase__					:      int      =		[]

		for neighbour in graph[vert]:
				if not visited[neighbour]:
						order += topology_sort(_lowercase   ,    _lowercase   ,    _lowercase			)

		order.append(_lowercase			)

		return order


def       SCREAMING_SNAKE_CASE__  (       _lowercase     : dict[int, list[int]]   ,    _lowercase     : int   ,    _lowercase     : list[bool]			)   ->							list[int]:




		'''simple docstring'''
		lowercase__					:      Union[str, Any]      =		True
		lowercase__					:      Optional[Any]      =		[vert]

		for neighbour in reversed_graph[vert]:
				if not visited[neighbour]:
						component += find_components(_lowercase   ,    _lowercase   ,    _lowercase			)

		return component





def       SCREAMING_SNAKE_CASE__  (       _lowercase     : dict[int, list[int]]			)   ->							list[list[int]]:




		'''simple docstring'''
		lowercase__					:      Optional[int]      =		len(_lowercase			) * [False]
		lowercase__					:      dict[int, list[int]]      =		{vert: [] for vert in range(len(_lowercase			)			)}

		for vert, neighbours in graph.items():
				for neighbour in neighbours:
						reversed_graph[neighbour].append(_lowercase			)

		lowercase__					:      Dict      =		[]
		for i, was_visited in enumerate(_lowercase			):
				if not was_visited:
						order += topology_sort(_lowercase   ,    _lowercase   ,    _lowercase			)

		lowercase__					:      Dict      =		[]
		lowercase__					:      Optional[Any]      =		len(_lowercase			) * [False]

		for i in range(len(_lowercase			)			):
				lowercase__					:      List[Any]      =		order[len(_lowercase			) - i - 1]
				if not visited[vert]:
						lowercase__					:      Tuple      =		find_components(_lowercase   ,    _lowercase   ,    _lowercase			)
						components_list.append(_lowercase			)

		return components_list


