




from __future__ import annotations

from math import pi, sqrt







def 			lowerCamelCase_		(       UpperCamelCase__				:      Optional[Any] ,     UpperCamelCase__				:      List[str]      )     ->       tuple:





				"""simple docstring"""


				if inductance <= 0:
								raise ValueError('Inductance cannot be 0 or negative'      )

				elif capacitance <= 0:
								raise ValueError('Capacitance cannot be 0 or negative'      )

				else:
								return (
								    "Resonant frequency",
								    float(1 / (2 * pi * (sqrt(inductance * capacitance      )))      ),
								)


if __name__ == "__main__":
					import doctest

					doctest.testmod()








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()
SCREAMING_SNAKE_CASE__       :      str  =   logging.get_logger(__name__)

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

def      A      (      _SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE    )	-> str:
       lowerCamelCase   :      List[str]			 =					dct.pop(_SCREAMING_SNAKE_CASE    )
       lowerCamelCase   :      Any			 =					val

def      A      (      )	-> List[str]:
       lowerCamelCase   :      Union[str, Any]			 =					"http://images.cocodataset.org/val2017/000000039769.jpg"
       lowerCamelCase   :      str			 =					Image.open(requests.get(_SCREAMING_SNAKE_CASE ,stream=_SCREAMING_SNAKE_CASE    ).raw    )
       return im

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

       # load original model from timm
       lowerCamelCase   :      List[Any]			 =					timm.create_model(_SCREAMING_SNAKE_CASE ,pretrained=_SCREAMING_SNAKE_CASE    )
       timm_model.eval()

       # load state_dict of original model, remove and rename some keys
       lowerCamelCase   :      Dict			 =					timm_model.state_dict()
       lowerCamelCase   :      Dict			 =					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
       lowerCamelCase   :      Optional[Any]			 =					DeiTForImageClassificationWithTeacher(_SCREAMING_SNAKE_CASE    ).eval()
       model.load_state_dict(_SCREAMING_SNAKE_CASE    )

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

       lowerCamelCase   :      Union[str, 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__":
       SCREAMING_SNAKE_CASE__       :      Optional[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.'
       )

       SCREAMING_SNAKE_CASE__       :      List[str]  =   parser.parse_args()
       convert_deit_checkpoint(args.deit_name, args.pytorch_dump_folder_path)






