
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()
UpperCAmelCase__		:     Any     			=      logging.get_logger(__name__)






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






def   lowerCamelCase__							(					a      )      ->   Any:
   _A:  Dict					=       ['''head.weight''', '''head.bias''']
   for k in ignore_keys:
      state_dict.pop(a		,		a      )






def   lowerCamelCase__							(					a		,		a		,		a      )      ->   Union[str, Any]:
   _A:  Optional[Any]					=       dct.pop(a      )
   _A:  str					=       val






@torch.no_grad()
def   lowerCamelCase__							(					a		,		a      )      ->   Dict:
   _A:  List[str]					=       ViltConfig(image_size=3_84		,		patch_size=32		,		tie_word_embeddings=a      )
   _A:  Dict					=       False
   _A:  Tuple					=       False
   _A:  Optional[Any]					=       False
   _A:  Dict					=       False
   if "vqa" in checkpoint_url:
      _A:  List[Any]					=       True
      _A:  int					=       31_29
      _A:  List[str]					=       '''huggingface/label-files'''
      _A:  List[str]					=       '''vqa2-id2label.json'''
      _A:  Optional[Any]					=       json.load(open(hf_hub_download(a		,		a		,		repo_type='''dataset'''      )		,		'''r'''      )      )
      _A:  Any					=       {int(a      ): v for k, v in idalabel.items()}
      _A:  List[str]					=       idalabel
      _A:  Tuple					=       {v: k for k, v in idalabel.items()}
      _A:  str					=       ViltForQuestionAnswering(a      )
   elif "nlvr" in checkpoint_url:
      _A:  Any					=       True
      _A:  List[Any]					=       2
      _A:  Any					=       {0: '''False''', 1: '''True'''}
      _A:  Optional[int]					=       {v: k for k, v in config.idalabel.items()}
      _A:  List[str]					=       3
      _A:  Optional[Any]					=       ViltForImagesAndTextClassification(a      )
   elif "irtr" in checkpoint_url:
      _A:  Dict					=       True
      _A:  Optional[Any]					=       ViltForImageAndTextRetrieval(a      )
   elif "mlm_itm" in checkpoint_url:
      _A:  Union[str, Any]					=       True
      _A:  List[str]					=       ViltForMaskedLM(a      )
   else:
      raise ValueError('''Unknown model type'''      )

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

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

   # Define processor
   _A:  Any					=       ViltImageProcessor(size=3_84      )
   _A:  Any					=       BertTokenizer.from_pretrained('''bert-base-uncased'''      )
   _A:  Union[str, Any]					=       ViltProcessor(a		,		a      )

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

   # Verify outputs
   if mlm_model:
      _A:  Dict					=       torch.Size([1, 11, 3_05_22]      )
      _A:  Optional[Any]					=       torch.tensor([-12.5061, -12.5123, -12.5174]      )
      assert outputs.logits.shape == expected_shape
      assert torch.allclose(outputs.logits[0, 0, :3]		,		a		,		atol=1E-4      )

      # verify masked token prediction equals "cats"
      _A:  int					=       outputs.logits[0, 4, :].argmax(-1      ).item()
      assert tokenizer.decode([predicted_id]      ) == "cats"
   elif vqa_model:
      _A:  Optional[Any]					=       torch.Size([1, 31_29]      )
      _A:  List[str]					=       torch.tensor([-15.9495, -18.1472, -10.3041]      )
      assert torch.allclose(outputs.logits[0, :3]		,		a		,		atol=1E-4      )
      assert outputs.logits.shape == expected_shape
      assert torch.allclose(outputs.logits[0, 0, :3]		,		a		,		atol=1E-4      )

      # verify vqa prediction equals "2"
      _A:  List[Any]					=       outputs.logits.argmax(-1      ).item()
      assert model.config.idalabel[predicted_idx] == "2"
   elif nlvr_model:
      _A:  List[Any]					=       torch.Size([1, 2]      )
      _A:  List[Any]					=       torch.tensor([-2.8721, 2.1291]      )
      assert torch.allclose(outputs.logits[0, :3]		,		a		,		atol=1E-4      )
      assert outputs.logits.shape == expected_shape

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


if __name__ == "__main__":
   UpperCAmelCase__		:     List[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.'
   )

   UpperCAmelCase__		:     Optional[Any]     			=      parser.parse_args()
   convert_vilt_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)





def   lowerCamelCase__							(					a		,		a      )      ->   str:
   if a < 0 or b < 0:
      raise ValueError('''the value of both inputs must be positive'''      )

   _A:  Union[str, Any]					=       str(bin(a      )      )[2:]  # remove the leading "0b"
   _A:  Union[str, Any]					=       str(bin(a      )      )[2:]  # remove the leading "0b"

   _A:  Optional[int]					=       max(len(a      )		,		len(a      )      )

   return "0b" + "".join(
       str(int(char_a != char_b      )      )
       for char_a, char_b in zip(a_binary.zfill(a      )		,		b_binary.zfill(a      )      )      )


if __name__ == "__main__":
   import doctest

   doctest.testmod()



