



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


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

def 			a_	(				_lowerCAmelCase			,_lowerCAmelCase=False			,_lowerCAmelCase=False			,_lowerCAmelCase=False			) -> Union[str, Any]:
  __lowerCamelCase			:			Tuple       			=					[]
  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_	(				_lowerCAmelCase			,_lowerCAmelCase			) -> Any:
  for i in range(config.num_hidden_layers			):
    __lowerCamelCase			:			Dict       			=					'vilt.'
    # read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
    __lowerCamelCase			:			Any       			=					state_dict.pop(F'transformer.blocks.{i}.attn.qkv.weight'			)
    __lowerCamelCase			:			List[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			:			Optional[int]       			=					in_proj_weight[
        : config.hidden_size, :
    ]
    __lowerCamelCase			:			str       			=					in_proj_bias[: config.hidden_size]
    __lowerCamelCase			:			List[Any]       			=					in_proj_weight[
        config.hidden_size : config.hidden_size * 2, :
    ]
    __lowerCamelCase			:			int       			=					in_proj_bias[
        config.hidden_size : config.hidden_size * 2
    ]
    __lowerCamelCase			:			List[Any]       			=					in_proj_weight[
        -config.hidden_size :, :
    ]
    __lowerCamelCase			:			Optional[int]       			=					in_proj_bias[-config.hidden_size :]

def 			a_	(				_lowerCAmelCase			) -> Tuple:
  __lowerCamelCase			:			str       			=					['head.weight', 'head.bias']
  for k in ignore_keys:
    state_dict.pop(_lowerCAmelCase			,_lowerCAmelCase			)

def 			a_	(				_lowerCAmelCase			,_lowerCAmelCase			,_lowerCAmelCase			) -> List[Any]:
  __lowerCamelCase			:			Optional[Any]       			=					dct.pop(_lowerCAmelCase			)
  __lowerCamelCase			:			Any       			=					val

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

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

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

  # Define processor
  __lowerCamelCase			:			int       			=					ViltImageProcessor(size=384			)
  __lowerCamelCase			:			Tuple       			=					BertTokenizer.from_pretrained('bert-base-uncased'			)
  __lowerCamelCase			:			Dict       			=					ViltProcessor(_lowerCAmelCase			,_lowerCAmelCase			)

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

  # Verify outputs
  if mlm_model:
    __lowerCamelCase			:			str       			=					torch.Size([1, 11, 30522]			)
    __lowerCamelCase			:			List[str]       			=					torch.tensor([-12.5061, -12.5123, -12.5174]			)
    assert outputs.logits.shape == expected_shape
    assert torch.allclose(outputs.logits[0, 0, :3]			,_lowerCAmelCase			,atol=1E-4			)

    # verify masked token prediction equals "cats"
    __lowerCamelCase			:			Optional[int]       			=					outputs.logits[0, 4, :].argmax(-1			).item()
    assert tokenizer.decode([predicted_id]			) == "cats"
  elif vqa_model:
    __lowerCamelCase			:			Optional[Any]       			=					torch.Size([1, 3129]			)
    __lowerCamelCase			:			List[Any]       			=					torch.tensor([-15.9495, -18.1472, -10.3041]			)
    assert torch.allclose(outputs.logits[0, :3]			,_lowerCAmelCase			,atol=1E-4			)
    assert outputs.logits.shape == expected_shape
    assert torch.allclose(outputs.logits[0, 0, :3]			,_lowerCAmelCase			,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			:			str       			=					torch.Size([1, 2]			)
    __lowerCamelCase			:			List[Any]       			=					torch.tensor([-2.8721, 2.1291]			)
    assert torch.allclose(outputs.logits[0, :3]			,_lowerCAmelCase			,atol=1E-4			)
    assert outputs.logits.shape == expected_shape

  Path(_lowerCAmelCase			).mkdir(exist_ok=_lowerCAmelCase			)
  print(F'Saving model and processor to {pytorch_dump_folder_path}'			)
  model.save_pretrained(_lowerCAmelCase			)
  processor.save_pretrained(_lowerCAmelCase			)


if __name__ == "__main__":
   _UpperCamelCase											=		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											=		parser.parse_args()
   convert_vilt_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)











'''simple docstring'''





def 			a_	(				_lowerCAmelCase			) -> int:

  if not isinstance(_lowerCAmelCase			,_lowerCAmelCase			):
    raise TypeError('only integers accepted as input'			)
  else:
    __lowerCamelCase			:			Any       			=					str(abs(_lowerCAmelCase			)			)
    __lowerCamelCase			:			Tuple       			=					[list(_lowerCAmelCase			) for char in range(len(_lowerCAmelCase			)			)]
    for index in range(len(_lowerCAmelCase			)			):
      num_transpositions[index].pop(_lowerCAmelCase			)
    return max(
        int(''.join(list(_lowerCAmelCase			)			)			) for transposition in num_transpositions			)


if __name__ == "__main__":
   __import__('doctest').testmod()






