





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 (
    BertTokenizer,
    ViltConfig,
    ViltForImageAndTextRetrieval,
    ViltForImagesAndTextClassification,
    ViltForMaskedLM,
    ViltForQuestionAnswering,
    ViltImageProcessor,
    ViltProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
_A :   List[str]  			=  logging.get_logger(__name__)



def 	_a						(   UpperCAmelCase      ,  UpperCAmelCase=False      ,  UpperCAmelCase=False      ,  UpperCAmelCase=False					)    ->    Any:







   """simple docstring"""
   lowerCamelCase__       :						Dict        =							[]
   for i in range(config.num_hidden_layers					):
      # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
      rename_keys.append((f"transformer.blocks.{i}.norm1.weight", f"vilt.encoder.layer.{i}.layernorm_before.weight")					)
      rename_keys.append((f"transformer.blocks.{i}.norm1.bias", f"vilt.encoder.layer.{i}.layernorm_before.bias")					)
      rename_keys.append(
          (f"transformer.blocks.{i}.attn.proj.weight", f"vilt.encoder.layer.{i}.attention.output.dense.weight")					)
      rename_keys.append(
          (f"transformer.blocks.{i}.attn.proj.bias", f"vilt.encoder.layer.{i}.attention.output.dense.bias")					)
      rename_keys.append((f"transformer.blocks.{i}.norm2.weight", f"vilt.encoder.layer.{i}.layernorm_after.weight")					)
      rename_keys.append((f"transformer.blocks.{i}.norm2.bias", f"vilt.encoder.layer.{i}.layernorm_after.bias")					)
      rename_keys.append(
          (f"transformer.blocks.{i}.mlp.fc1.weight", f"vilt.encoder.layer.{i}.intermediate.dense.weight")					)
      rename_keys.append((f"transformer.blocks.{i}.mlp.fc1.bias", f"vilt.encoder.layer.{i}.intermediate.dense.bias")					)
      rename_keys.append((f"transformer.blocks.{i}.mlp.fc2.weight", f"vilt.encoder.layer.{i}.output.dense.weight")					)
      rename_keys.append((f"transformer.blocks.{i}.mlp.fc2.bias", f"vilt.encoder.layer.{i}.output.dense.bias")					)

   # embeddings
   rename_keys.extend(
       [
           # text embeddings
           ('''text_embeddings.word_embeddings.weight''', '''vilt.embeddings.text_embeddings.word_embeddings.weight'''),
           (
               '''text_embeddings.position_embeddings.weight''',
               '''vilt.embeddings.text_embeddings.position_embeddings.weight''',
           ),
           ('''text_embeddings.position_ids''', '''vilt.embeddings.text_embeddings.position_ids'''),
           (
               '''text_embeddings.token_type_embeddings.weight''',
               '''vilt.embeddings.text_embeddings.token_type_embeddings.weight''',
           ),
           ('''text_embeddings.LayerNorm.weight''', '''vilt.embeddings.text_embeddings.LayerNorm.weight'''),
           ('''text_embeddings.LayerNorm.bias''', '''vilt.embeddings.text_embeddings.LayerNorm.bias'''),
           # patch embeddings
           ('''transformer.cls_token''', '''vilt.embeddings.cls_token'''),
           ('''transformer.patch_embed.proj.weight''', '''vilt.embeddings.patch_embeddings.projection.weight'''),
           ('''transformer.patch_embed.proj.bias''', '''vilt.embeddings.patch_embeddings.projection.bias'''),
           ('''transformer.pos_embed''', '''vilt.embeddings.position_embeddings'''),
           # token type embeddings
           ('''token_type_embeddings.weight''', '''vilt.embeddings.token_type_embeddings.weight'''),
       ]					)

   # final layernorm + pooler
   rename_keys.extend(
       [
           ('''transformer.norm.weight''', '''vilt.layernorm.weight'''),
           ('''transformer.norm.bias''', '''vilt.layernorm.bias'''),
           ('''pooler.dense.weight''', '''vilt.pooler.dense.weight'''),
           ('''pooler.dense.bias''', '''vilt.pooler.dense.bias'''),
       ]					)

   # classifier head(s)
   if vqa_model:
      # classification head
      rename_keys.extend(
          [
              ('''vqa_classifier.0.weight''', '''classifier.0.weight'''),
              ('''vqa_classifier.0.bias''', '''classifier.0.bias'''),
              ('''vqa_classifier.1.weight''', '''classifier.1.weight'''),
              ('''vqa_classifier.1.bias''', '''classifier.1.bias'''),
              ('''vqa_classifier.3.weight''', '''classifier.3.weight'''),
              ('''vqa_classifier.3.bias''', '''classifier.3.bias'''),
          ]					)
   elif nlvr_model:
      # classification head
      rename_keys.extend(
          [
              ('''nlvr2_classifier.0.weight''', '''classifier.0.weight'''),
              ('''nlvr2_classifier.0.bias''', '''classifier.0.bias'''),
              ('''nlvr2_classifier.1.weight''', '''classifier.1.weight'''),
              ('''nlvr2_classifier.1.bias''', '''classifier.1.bias'''),
              ('''nlvr2_classifier.3.weight''', '''classifier.3.weight'''),
              ('''nlvr2_classifier.3.bias''', '''classifier.3.bias'''),
          ]					)
   else:
      pass

   return rename_keys



def 	_a						(   UpperCAmelCase      ,  UpperCAmelCase					)    ->    List[Any]:







   """simple docstring"""
   for i in range(config.num_hidden_layers					):
      lowerCamelCase__       :						int        =							'''vilt.'''
      # read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
      lowerCamelCase__       :						int        =							state_dict.pop(f"transformer.blocks.{i}.attn.qkv.weight"					)
      lowerCamelCase__       :						Optional[Any]        =							state_dict.pop(f"transformer.blocks.{i}.attn.qkv.bias"					)
      # next, add query, keys and values (in that order) to the state dict
      lowerCamelCase__       :						Dict        =							in_proj_weight[
          : config.hidden_size, :
      ]
      lowerCamelCase__       :						Union[str, Any]        =							in_proj_bias[: config.hidden_size]
      lowerCamelCase__       :						Union[str, Any]        =							in_proj_weight[
          config.hidden_size : config.hidden_size * 2, :
      ]
      lowerCamelCase__       :						Optional[int]        =							in_proj_bias[
          config.hidden_size : config.hidden_size * 2
      ]
      lowerCamelCase__       :						Dict        =							in_proj_weight[
          -config.hidden_size :, :
      ]
      lowerCamelCase__       :						Union[str, Any]        =							in_proj_bias[-config.hidden_size :]



def 	_a						(   UpperCAmelCase					)    ->    Optional[int]:







   """simple docstring"""
   lowerCamelCase__       :						Optional[Any]        =							['''head.weight''', '''head.bias''']
   for k in ignore_keys:
      state_dict.pop(UpperCAmelCase      ,  UpperCAmelCase					)



def 	_a						(   UpperCAmelCase      ,  UpperCAmelCase      ,  UpperCAmelCase					)    ->    Optional[Any]:







   """simple docstring"""
   lowerCamelCase__       :						int        =							dct.pop(UpperCAmelCase					)
   lowerCamelCase__       :						str        =							val



@torch.no_grad()
def 	_a						(   UpperCAmelCase      ,  UpperCAmelCase					)    ->    List[str]:







   """simple docstring"""
   lowerCamelCase__       :						Union[str, Any]        =							ViltConfig(image_size=384      ,  patch_size=32      ,  tie_word_embeddings=UpperCAmelCase					)
   lowerCamelCase__       :						List[Any]        =							False
   lowerCamelCase__       :						Optional[int]        =							False
   lowerCamelCase__       :						Any        =							False
   lowerCamelCase__       :						Any        =							False
   if "vqa" in checkpoint_url:
      lowerCamelCase__       :						Optional[Any]        =							True
      lowerCamelCase__       :						Optional[int]        =							3129
      lowerCamelCase__       :						List[Any]        =							'''huggingface/label-files'''
      lowerCamelCase__       :						Any        =							'''vqa2-id2label.json'''
      lowerCamelCase__       :						Optional[int]        =							json.load(open(hf_hub_download(UpperCAmelCase      ,  UpperCAmelCase      ,  repo_type='''dataset'''					)      ,  '''r'''					)					)
      lowerCamelCase__       :						Union[str, Any]        =							{int(UpperCAmelCase					): v for k, v in idalabel.items()}
      lowerCamelCase__       :						Union[str, Any]        =							idalabel
      lowerCamelCase__       :						Any        =							{v: k for k, v in idalabel.items()}
      lowerCamelCase__       :						Optional[int]        =							ViltForQuestionAnswering(UpperCAmelCase					)
   elif "nlvr" in checkpoint_url:
      lowerCamelCase__       :						Dict        =							True
      lowerCamelCase__       :						List[str]        =							2
      lowerCamelCase__       :						Optional[int]        =							{0: '''False''', 1: '''True'''}
      lowerCamelCase__       :						Optional[Any]        =							{v: k for k, v in config.idalabel.items()}
      lowerCamelCase__       :						Optional[int]        =							3
      lowerCamelCase__       :						List[Any]        =							ViltForImagesAndTextClassification(UpperCAmelCase					)
   elif "irtr" in checkpoint_url:
      lowerCamelCase__       :						str        =							True
      lowerCamelCase__       :						Union[str, Any]        =							ViltForImageAndTextRetrieval(UpperCAmelCase					)
   elif "mlm_itm" in checkpoint_url:
      lowerCamelCase__       :						Optional[Any]        =							True
      lowerCamelCase__       :						List[str]        =							ViltForMaskedLM(UpperCAmelCase					)
   else:
      raise ValueError('''Unknown model type'''					)

   # load state_dict of original model, remove and rename some keys
   lowerCamelCase__       :						str        =							torch.hub.load_state_dict_from_url(UpperCAmelCase      ,  map_location='''cpu'''					)['''state_dict''']
   lowerCamelCase__       :						List[Any]        =							create_rename_keys(UpperCAmelCase      ,  UpperCAmelCase      ,  UpperCAmelCase      ,  UpperCAmelCase					)
   for src, dest in rename_keys:
      rename_key(UpperCAmelCase      ,  UpperCAmelCase      ,  UpperCAmelCase					)
   read_in_q_k_v(UpperCAmelCase      ,  UpperCAmelCase					)
   if mlm_model or irtr_model:
      lowerCamelCase__       :						Dict        =							['''itm_score.fc.weight''', '''itm_score.fc.bias''']
      for k in ignore_keys:
         state_dict.pop(UpperCAmelCase      ,  UpperCAmelCase					)

    # load state dict into HuggingFace model
   model.eval()
   if mlm_model:
      lowerCamelCase__       ,							lowerCamelCase__       :						Dict        =							model.load_state_dict(UpperCAmelCase      ,  strict=UpperCAmelCase					)
      assert missing_keys == ["mlm_score.decoder.bias"]
   else:
      model.load_state_dict(UpperCAmelCase					)

   # Define processor
   lowerCamelCase__       :						Tuple        =							ViltImageProcessor(size=384					)
   lowerCamelCase__       :						List[str]        =							BertTokenizer.from_pretrained('''bert-base-uncased'''					)
   lowerCamelCase__       :						Dict        =							ViltProcessor(UpperCAmelCase      ,  UpperCAmelCase					)

   # Forward pass on example inputs (image + text)
   if nlvr_model:
      lowerCamelCase__       :						List[str]        =							Image.open(requests.get('''https://lil.nlp.cornell.edu/nlvr/exs/ex0_0.jpg'''      ,  stream=UpperCAmelCase					).raw					)
      lowerCamelCase__       :						Any        =							Image.open(requests.get('''https://lil.nlp.cornell.edu/nlvr/exs/ex0_0.jpg'''      ,  stream=UpperCAmelCase					).raw					)
      lowerCamelCase__       :						int        =							(
          '''The left image contains twice the number of dogs as the right image, and at least two dogs in total are'''
          ''' standing.'''
      )
      lowerCamelCase__       :						Dict        =							processor(UpperCAmelCase      ,  UpperCAmelCase      ,  return_tensors='''pt'''					)
      lowerCamelCase__       :						str        =							processor(UpperCAmelCase      ,  UpperCAmelCase      ,  return_tensors='''pt'''					)
      lowerCamelCase__       :						str        =							model(
          input_ids=encoding_a.input_ids      ,  pixel_values=encoding_a.pixel_values      ,  pixel_values_a=encoding_a.pixel_values      ,  )
   else:
      lowerCamelCase__       :						Union[str, Any]        =							Image.open(requests.get('''http://images.cocodataset.org/val2017/000000039769.jpg'''      ,  stream=UpperCAmelCase					).raw					)
      if mlm_model:
         lowerCamelCase__       :						Dict        =							'''a bunch of [MASK] laying on a [MASK].'''
      else:
         lowerCamelCase__       :						str        =							'''How many cats are there?'''
      lowerCamelCase__       :						Optional[int]        =							processor(UpperCAmelCase      ,  UpperCAmelCase      ,  return_tensors='''pt'''					)
      lowerCamelCase__       :						List[str]        =							model(**UpperCAmelCase					)

   # Verify outputs
   if mlm_model:
      lowerCamelCase__       :						List[str]        =							torch.Size([1, 11, 30522]					)
      lowerCamelCase__       :						List[str]        =							torch.tensor([-12.50_61, -12.51_23, -12.51_74]					)
      assert outputs.logits.shape == expected_shape
      assert torch.allclose(outputs.logits[0, 0, :3]      ,  UpperCAmelCase      ,  atol=1E-4					)

      # verify masked token prediction equals "cats"
      lowerCamelCase__       :						int        =							outputs.logits[0, 4, :].argmax(-1					).item()
      assert tokenizer.decode([predicted_id]					) == "cats"
   elif vqa_model:
      lowerCamelCase__       :						Any        =							torch.Size([1, 3129]					)
      lowerCamelCase__       :						List[Any]        =							torch.tensor([-15.94_95, -18.14_72, -10.30_41]					)
      assert torch.allclose(outputs.logits[0, :3]      ,  UpperCAmelCase      ,  atol=1E-4					)
      assert outputs.logits.shape == expected_shape
      assert torch.allclose(outputs.logits[0, 0, :3]      ,  UpperCAmelCase      ,  atol=1E-4					)

      # verify vqa prediction equals "2"
      lowerCamelCase__       :						List[Any]        =							outputs.logits.argmax(-1					).item()
      assert model.config.idalabel[predicted_idx] == "2"
   elif nlvr_model:
      lowerCamelCase__       :						Union[str, Any]        =							torch.Size([1, 2]					)
      lowerCamelCase__       :						List[str]        =							torch.tensor([-2.87_21, 2.12_91]					)
      assert torch.allclose(outputs.logits[0, :3]      ,  UpperCAmelCase      ,  atol=1E-4					)
      assert outputs.logits.shape == expected_shape

   Path(UpperCAmelCase					).mkdir(exist_ok=UpperCAmelCase					)
   print(f"Saving model and processor to {pytorch_dump_folder_path}"					)
   model.save_pretrained(UpperCAmelCase					)
   processor.save_pretrained(UpperCAmelCase					)


if __name__ == "__main__":
       _A :   str  			=  argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           '--checkpoint_url',
           default='https://github.com/dandelin/ViLT/releases/download/200k/vilt_200k_mlm_itm.ckpt',
           type=str,
           help='URL of the checkpoint 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 :   Optional[int]  			=  parser.parse_args()
       convert_vilt_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)










import os
from pathlib import Path



def 	_a						(   )    ->    Tuple:







   """simple docstring"""
   from torch.utils.cpp_extension import load

   lowerCamelCase__       :						List[Any]        =							Path(UpperCAmelCase					).resolve().parent.parent.parent / '''kernels''' / '''deformable_detr'''
   lowerCamelCase__       :						Any        =							[
       root / filename
       for filename in [
           '''vision.cpp''',
           os.path.join('''cpu'''      ,  '''ms_deform_attn_cpu.cpp'''					),
           os.path.join('''cuda'''      ,  '''ms_deform_attn_cuda.cu'''					),
       ]
   ]

   load(
       '''MultiScaleDeformableAttention'''      ,  UpperCAmelCase      ,  with_cuda=UpperCAmelCase      ,  extra_include_paths=[str(UpperCAmelCase					)]      ,  extra_cflags=['''-DWITH_CUDA=1''']      ,  extra_cuda_cflags=[
           '''-DCUDA_HAS_FP16=1''',
           '''-D__CUDA_NO_HALF_OPERATORS__''',
           '''-D__CUDA_NO_HALF_CONVERSIONS__''',
           '''-D__CUDA_NO_HALF2_OPERATORS__''',
       ]      ,  )

   import MultiScaleDeformableAttention as MSDA

   return MSDA



