


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


logging.set_verbosity_info()
__UpperCamelCase				:  Any      					=							logging.get_logger(__name__)
def      snake_case_						( __lowercase			,       __lowercase=False       ):
   UpperCAmelCase_							:      str						=   []
   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'''),
              ('''pre_logits.fc.weight''', '''pooler.dense.weight'''),
              ('''pre_logits.fc.bias''', '''pooler.dense.bias'''),
          ]       )

      # if just the base model, we should remove "vit" from all keys that start with "vit"
      UpperCAmelCase_							:      Union[str, Any]						=   [(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      snake_case_						( __lowercase			,       __lowercase			,       __lowercase=False       ):




   for i in range(config.num_hidden_layers       ):
      if base_model:
         UpperCAmelCase_							:      str						=   ''''''
      else:
         UpperCAmelCase_							:      Union[str, Any]						=   '''vit.'''
      # read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
      UpperCAmelCase_							:      Optional[Any]						=   state_dict.pop(F'''blocks.{i}.attn.qkv.weight'''       )
      UpperCAmelCase_							:      Optional[Any]						=   state_dict.pop(F'''blocks.{i}.attn.qkv.bias'''       )
      # next, add query, keys and values (in that order) to the state dict
      UpperCAmelCase_							:      Any						=   in_proj_weight[
          : config.hidden_size, :
      ]
      UpperCAmelCase_							:      List[Any]						=   in_proj_bias[: config.hidden_size]
      UpperCAmelCase_							:      str						=   in_proj_weight[
          config.hidden_size : config.hidden_size * 2, :
      ]
      UpperCAmelCase_							:      int						=   in_proj_bias[
          config.hidden_size : config.hidden_size * 2
      ]
      UpperCAmelCase_							:      Union[str, Any]						=   in_proj_weight[
          -config.hidden_size :, :
      ]
      UpperCAmelCase_							:      Tuple						=   in_proj_bias[-config.hidden_size :]
def      snake_case_						( __lowercase       ):
   UpperCAmelCase_							:      Tuple						=   ['''head.weight''', '''head.bias''']
   for k in ignore_keys:
      state_dict.pop(__a			,       __a       )
def      snake_case_						( __lowercase			,       __lowercase			,       __lowercase       ):
   UpperCAmelCase_							:      Optional[int]						=   dct.pop(__a       )
   UpperCAmelCase_							:      str						=   val
def      snake_case_						( ):
   UpperCAmelCase_							:      Union[str, Any]						=   '''http://images.cocodataset.org/val2017/000000039769.jpg'''
   UpperCAmelCase_							:      Union[str, Any]						=   Image.open(requests.get(__a			,       stream=__a       ).raw       )
   return im
@torch.no_grad()
def      snake_case_						( __lowercase			,       __lowercase       ):
   UpperCAmelCase_							:      List[Any]						=   ViTConfig()
   UpperCAmelCase_							:      int						=   False
   # dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
   if vit_name[-5:] == "in21k":
      UpperCAmelCase_							:      Optional[Any]						=   True
      UpperCAmelCase_							:      List[str]						=   int(vit_name[-1_2:-1_0]       )
      UpperCAmelCase_							:      str						=   int(vit_name[-9:-6]       )
   else:
      UpperCAmelCase_							:      Any						=   1_0_0_0
      UpperCAmelCase_							:      Optional[int]						=   '''huggingface/label-files'''
      UpperCAmelCase_							:      Dict						=   '''imagenet-1k-id2label.json'''
      UpperCAmelCase_							:      Optional[Any]						=   json.load(open(hf_hub_download(__a			,       __a			,       repo_type='''dataset'''       )			,       '''r'''       )       )
      UpperCAmelCase_							:      List[Any]						=   {int(__a       ): v for k, v in idalabel.items()}
      UpperCAmelCase_							:      int						=   idalabel
      UpperCAmelCase_							:      Union[str, Any]						=   {v: k for k, v in idalabel.items()}
      UpperCAmelCase_							:      Tuple						=   int(vit_name[-6:-4]       )
      UpperCAmelCase_							:      Dict						=   int(vit_name[-3:]       )
   # size of the architecture
   if "deit" in vit_name:
      if vit_name[9:].startswith('''tiny'''       ):
         UpperCAmelCase_							:      Optional[Any]						=   1_9_2
         UpperCAmelCase_							:      Dict						=   7_6_8
         UpperCAmelCase_							:      List[Any]						=   1_2
         UpperCAmelCase_							:      Optional[Any]						=   3
      elif vit_name[9:].startswith('''small'''       ):
         UpperCAmelCase_							:      Union[str, Any]						=   3_8_4
         UpperCAmelCase_							:      Optional[int]						=   1_5_3_6
         UpperCAmelCase_							:      Union[str, Any]						=   1_2
         UpperCAmelCase_							:      Tuple						=   6
      else:
         pass
   else:
      if vit_name[4:].startswith('''small'''       ):
         UpperCAmelCase_							:      Any						=   7_6_8
         UpperCAmelCase_							:      str						=   2_3_0_4
         UpperCAmelCase_							:      Optional[int]						=   8
         UpperCAmelCase_							:      List[Any]						=   8
      elif vit_name[4:].startswith('''base'''       ):
         pass
      elif vit_name[4:].startswith('''large'''       ):
         UpperCAmelCase_							:      Tuple						=   1_0_2_4
         UpperCAmelCase_							:      Optional[Any]						=   4_0_9_6
         UpperCAmelCase_							:      Optional[Any]						=   2_4
         UpperCAmelCase_							:      Union[str, Any]						=   1_6
      elif vit_name[4:].startswith('''huge'''       ):
         UpperCAmelCase_							:      Tuple						=   1_2_8_0
         UpperCAmelCase_							:      Union[str, Any]						=   5_1_2_0
         UpperCAmelCase_							:      Optional[Any]						=   3_2
         UpperCAmelCase_							:      List[str]						=   1_6

    # load original model from timm
   UpperCAmelCase_							:      str						=   timm.create_model(__a			,       pretrained=__a       )
   timm_model.eval()

   # load state_dict of original model, remove and rename some keys
   UpperCAmelCase_							:      Tuple						=   timm_model.state_dict()
   if base_model:
      remove_classification_head_(__a       )
   UpperCAmelCase_							:      List[str]						=   create_rename_keys(__a			,       __a       )
   for src, dest in rename_keys:
      rename_key(__a			,       __a			,       __a       )
   read_in_q_k_v(__a			,       __a			,       __a       )

   # load HuggingFace model
   if vit_name[-5:] == "in21k":
      UpperCAmelCase_							:      Optional[Any]						=   ViTModel(__a       ).eval()
   else:
      UpperCAmelCase_							:      Optional[Any]						=   ViTForImageClassification(__a       ).eval()
   model.load_state_dict(__a       )

   # Check outputs on an image, prepared by ViTImageProcessor/DeiTImageProcessor
   if "deit" in vit_name:
      UpperCAmelCase_							:      str						=   DeiTImageProcessor(size=config.image_size       )
   else:
      UpperCAmelCase_							:      Union[str, Any]						=   ViTImageProcessor(size=config.image_size       )
   UpperCAmelCase_							:      Optional[Any]						=   image_processor(images=prepare_img()			,       return_tensors='''pt'''       )
   UpperCAmelCase_							:      List[Any]						=   encoding['''pixel_values''']
   UpperCAmelCase_							:      Dict						=   model(__a       )

   if base_model:
      UpperCAmelCase_							:      Tuple						=   timm_model.forward_features(__a       )
      assert timm_pooled_output.shape == outputs.pooler_output.shape
      assert torch.allclose(__a			,       outputs.pooler_output			,       atol=1e-3       )
   else:
      UpperCAmelCase_							:      int						=   timm_model(__a       )
      assert timm_logits.shape == outputs.logits.shape
      assert torch.allclose(__a			,       outputs.logits			,       atol=1e-3       )

   Path(__a       ).mkdir(exist_ok=__a       )
   print(F'''Saving model {vit_name} to {pytorch_dump_folder_path}'''       )
   model.save_pretrained(__a       )
   print(F'''Saving image processor to {pytorch_dump_folder_path}'''       )
   image_processor.save_pretrained(__a       )


if __name__ == "__main__":
       __UpperCamelCase				:  Union[str, Any]      					=							argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           '--vit_name',
           default='vit_base_patch16_224',
           type=str,
           help='Name of the ViT 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.'
       )

       __UpperCamelCase				:  List[Any]      					=							parser.parse_args()
       convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path)







import fire

from utils import calculate_rouge, save_json
def      snake_case_						( __lowercase			,       __lowercase			,       __lowercase=None			,       **__lowercase       ):
   UpperCAmelCase_							:      Tuple						=   [x.strip() for x in open(__lowercase       ).readlines()]
   UpperCAmelCase_							:      Dict						=   [x.strip() for x in open(__lowercase       ).readlines()][: len(__lowercase       )]
   UpperCAmelCase_							:      int						=   calculate_rouge(__lowercase			,       __lowercase			,       **__lowercase       )
   if save_path is not None:
      save_json(__lowercase			,       __lowercase			,       indent=__lowercase       )
   return metrics  # these print nicely


if __name__ == "__main__":
       fire.Fire(calculate_rouge_path)