

'''simple docstring'''

import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import ViTConfig, ViTForImageClassification, ViTImageProcessor, ViTModel
from transformers.utils import logging


logging.set_verbosity_info()
UpperCAmelCase					 =	logging.get_logger(__name__)

def 					__UpperCamelCase				(						lowercase__    :	List[str],       lowercase__    :	Any=False			):






     '''simple docstring'''





     __lowercase      		=[]
     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'''vit.encoder.layer.{i}.layernorm_before.weight''')			)
          rename_keys.append((F'''blocks.{i}.norm1.bias''', F'''vit.encoder.layer.{i}.layernorm_before.bias''')			)
          rename_keys.append((F'''blocks.{i}.attn.proj.weight''', F'''vit.encoder.layer.{i}.attention.output.dense.weight''')			)
          rename_keys.append((F'''blocks.{i}.attn.proj.bias''', F'''vit.encoder.layer.{i}.attention.output.dense.bias''')			)
          rename_keys.append((F'''blocks.{i}.norm2.weight''', F'''vit.encoder.layer.{i}.layernorm_after.weight''')			)
          rename_keys.append((F'''blocks.{i}.norm2.bias''', F'''vit.encoder.layer.{i}.layernorm_after.bias''')			)
          rename_keys.append((F'''blocks.{i}.mlp.fc1.weight''', F'''vit.encoder.layer.{i}.intermediate.dense.weight''')			)
          rename_keys.append((F'''blocks.{i}.mlp.fc1.bias''', F'''vit.encoder.layer.{i}.intermediate.dense.bias''')			)
          rename_keys.append((F'''blocks.{i}.mlp.fc2.weight''', F'''vit.encoder.layer.{i}.output.dense.weight''')			)
          rename_keys.append((F'''blocks.{i}.mlp.fc2.bias''', F'''vit.encoder.layer.{i}.output.dense.bias''')			)

     # projection layer + position embeddings
     rename_keys.extend(
         [
             ('cls_token', 'vit.embeddings.cls_token'),
             ('patch_embed.proj.weight', 'vit.embeddings.patch_embeddings.projection.weight'),
             ('patch_embed.proj.bias', 'vit.embeddings.patch_embeddings.projection.bias'),
             ('pos_embed', 'vit.embeddings.position_embeddings'),
         ]			)

     if base_model:
          # layernorm + pooler
          rename_keys.extend(
              [
                  ('norm.weight', 'layernorm.weight'),
                  ('norm.bias', 'layernorm.bias'),
              ]			)

          # if just the base model, we should remove "vit" from all keys that start with "vit"
          __lowercase      		=[(pair[0], pair[1][4:]) if pair[1].startswith('vit'			) else pair for pair in rename_keys]
     else:
          # layernorm + classification head
          rename_keys.extend(
              [
                  ('norm.weight', 'vit.layernorm.weight'),
                  ('norm.bias', 'vit.layernorm.bias'),
                  ('head.weight', 'classifier.weight'),
                  ('head.bias', 'classifier.bias'),
              ]			)

     return rename_keys

def 					__UpperCamelCase				(						lowercase__    :	List[str],       lowercase__    :	str,       lowercase__    :	Optional[int]=False			):






     '''simple docstring'''





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

def 					__UpperCamelCase				(						lowercase__    :	Tuple			):






     '''simple docstring'''





     __lowercase      		=['head.weight', 'head.bias']
     for k in ignore_keys:
          state_dict.pop(lowercase__,       lowercase__			)

def 					__UpperCamelCase				(						lowercase__    :	Dict,       lowercase__    :	List[str],       lowercase__    :	List[str]			):






     '''simple docstring'''





     __lowercase      		=dct.pop(lowercase__			)
     __lowercase      		=val

def 					__UpperCamelCase				(						):






     '''simple docstring'''





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

@torch.no_grad()
def 					__UpperCamelCase				(						lowercase__    :	int,       lowercase__    :	Any,       lowercase__    :	List[Any]=True			):






     '''simple docstring'''





     __lowercase      		=ViTConfig()
     # patch_size
     if model_name[-1] == "8":
          __lowercase      		=8
     # set labels if required
     if not base_model:
          __lowercase      		=10_00
          __lowercase      		='huggingface/label-files'
          __lowercase      		='imagenet-1k-id2label.json'
          __lowercase      		=json.load(open(hf_hub_download(lowercase__,       lowercase__,       repo_type='dataset'			),       'r'			)			)
          __lowercase      		={int(lowercase__			): v for k, v in idalabel.items()}
          __lowercase      		=idalabel
          __lowercase      		={v: k for k, v in idalabel.items()}
     # size of the architecture
     if model_name in ["dino_vits8", "dino_vits16"]:
          __lowercase      		=3_84
          __lowercase      		=15_36
          __lowercase      		=12
          __lowercase      		=6

     # load original model from torch hub
     __lowercase      		=torch.hub.load('facebookresearch/dino:main',       lowercase__			)
     original_model.eval()

     # load state_dict of original model, remove and rename some keys
     __lowercase      		=original_model.state_dict()
     if base_model:
          remove_classification_head_(lowercase__			)
     __lowercase      		=create_rename_keys(lowercase__,       base_model=lowercase__			)
     for src, dest in rename_keys:
          rename_key(lowercase__,       lowercase__,       lowercase__			)
     read_in_q_k_v(lowercase__,       lowercase__,       lowercase__			)

     # load HuggingFace model
     if base_model:
          __lowercase      		=ViTModel(lowercase__,       add_pooling_layer=lowercase__			).eval()
     else:
          __lowercase      		=ViTForImageClassification(lowercase__			).eval()
     model.load_state_dict(lowercase__			)

     # Check outputs on an image, prepared by ViTImageProcessor
     __lowercase      		=ViTImageProcessor()
     __lowercase      		=image_processor(images=prepare_img(),       return_tensors='pt'			)
     __lowercase      		=encoding['pixel_values']
     __lowercase      		=model(lowercase__			)

     if base_model:
          __lowercase      		=original_model(lowercase__			)
          assert torch.allclose(lowercase__,       outputs.last_hidden_state[:, 0, :],       atol=1E-1			)
     else:
          __lowercase      		=original_model(lowercase__			)
          assert logits.shape == outputs.logits.shape
          assert torch.allclose(lowercase__,       outputs.logits,       atol=1E-3			)

     Path(lowercase__			).mkdir(exist_ok=lowercase__			)
     print(F'''Saving model {model_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__":
       UpperCAmelCase					 =	argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           '''--model_name''',
           default='''dino_vitb16''',
           type=str,
           help='''Name of the model trained with DINO you\'d like to convert.''',
       )
       parser.add_argument(
           '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
       )
       parser.add_argument(
           '''--base_model''',
           action='''store_true''',
           help='''Whether to only convert the base model (no projection head weights).''',
       )

       parser.set_defaults(base_model=True)
       UpperCAmelCase					 =	parser.parse_args()
       convert_vit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.base_model)





'''simple docstring'''

from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import Features, Value
from .base import TaskTemplate


@dataclass(frozen=A )
class 	lowerCAmelCase  (   A ):
       # `task` is not a ClassVar since we want it to be part of the `asdict` output for JSON serialization
       lowerCAmelCase_												=			field(default="summarization" ,      metadata={"include_in_asdict_even_if_is_default": True} )
       lowerCAmelCase_												=			Features({"text": Value("string" )} )
       lowerCAmelCase_												=			Features({"summary": Value("string" )} )
       lowerCAmelCase_												=			"text"
       lowerCAmelCase_												=			"summary"







       @property
       def       snake_case (				self      :			int   ):







            """simple docstring"""







            return {self.text_column: "text", self.summary_column: "summary"}


