
"""simple docstring"""




from pathlib import Path

import fire




def  _lowerCAmelCase		(		_UpperCamelCase ,				_UpperCamelCase ,				_UpperCamelCase						):






  """simple docstring"""



  _lowercase:			List[str]       =     Path(lowerCamelCase_						)
  _lowercase:			Any       =     Path(lowerCamelCase_						)
  dest_dir.mkdir(exist_ok=lowerCamelCase_						)
  for path in src_dir.iterdir():
    _lowercase:			List[str]       =     [x.rstrip() for x in list(path.open().readlines()						)][:n]
    _lowercase:			Optional[Any]       =     dest_dir.joinpath(path.name						)
    print(lowerCamelCase_						)
    dest_path.open('''w'''						).write('''\n'''.join(lowerCamelCase_						)						)


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


"""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()
A__   :					str							=   logging.get_logger(__name__)




def  _lowerCAmelCase		(		_UpperCamelCase ,				_UpperCamelCase=False ,				_UpperCamelCase=False ,				_UpperCamelCase=False						):






  """simple docstring"""



  _lowercase:			Union[str, Any]       =     []
  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		(		_UpperCamelCase ,				_UpperCamelCase						):






  """simple docstring"""



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




def  _lowerCAmelCase		(		_UpperCamelCase						):






  """simple docstring"""



  _lowercase:			Optional[Any]       =     ['''head.weight''', '''head.bias''']
  for k in ignore_keys:
    state_dict.pop(_UpperCamelCase ,				_UpperCamelCase						)




def  _lowerCAmelCase		(		_UpperCamelCase ,				_UpperCamelCase ,				_UpperCamelCase						):






  """simple docstring"""



  _lowercase:			Tuple       =     dct.pop(_UpperCamelCase						)
  _lowercase:			Optional[Any]       =     val




@torch.no_grad()
def  _lowerCAmelCase		(		_UpperCamelCase ,				_UpperCamelCase						):






  """simple docstring"""



  _lowercase:			List[str]       =     ViltConfig(image_size=384 ,				patch_size=32 ,				tie_word_embeddings=_UpperCamelCase						)
  _lowercase:			List[Any]       =     False
  _lowercase:			List[str]       =     False
  _lowercase:			Optional[int]       =     False
  _lowercase:			Optional[int]       =     False
  if "vqa" in checkpoint_url:
    _lowercase:			Tuple       =     True
    _lowercase:			int       =     3_129
    _lowercase:			Union[str, Any]       =     '''huggingface/label-files'''
    _lowercase:			Optional[int]       =     '''vqa2-id2label.json'''
    _lowercase:			Optional[int]       =     json.load(open(hf_hub_download(_UpperCamelCase ,				_UpperCamelCase ,				repo_type='''dataset'''						) ,				'''r'''						)						)
    _lowercase:			str       =     {int(_UpperCamelCase						): v for k, v in idalabel.items()}
    _lowercase:			int       =     idalabel
    _lowercase:			Any       =     {v: k for k, v in idalabel.items()}
    _lowercase:			str       =     ViltForQuestionAnswering(_UpperCamelCase						)
  elif "nlvr" in checkpoint_url:
    _lowercase:			List[str]       =     True
    _lowercase:			Tuple       =     2
    _lowercase:			int       =     {0: '''False''', 1: '''True'''}
    _lowercase:			Any       =     {v: k for k, v in config.idalabel.items()}
    _lowercase:			Any       =     3
    _lowercase:			Optional[Any]       =     ViltForImagesAndTextClassification(_UpperCamelCase						)
  elif "irtr" in checkpoint_url:
    _lowercase:			Dict       =     True
    _lowercase:			Union[str, Any]       =     ViltForImageAndTextRetrieval(_UpperCamelCase						)
  elif "mlm_itm" in checkpoint_url:
    _lowercase:			Any       =     True
    _lowercase:			str       =     ViltForMaskedLM(_UpperCamelCase						)
  else:
    raise ValueError('''Unknown model type'''						)

  # load state_dict of original model, remove and rename some keys
  _lowercase:			Tuple       =     torch.hub.load_state_dict_from_url(_UpperCamelCase ,				map_location='''cpu'''						)['''state_dict''']
  _lowercase:			Union[str, 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:
    _lowercase:			Optional[int]       =     ['''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:
    _lowercase ,				_lowercase:			Optional[Any]       =     model.load_state_dict(_UpperCamelCase ,				strict=_UpperCamelCase						)
    assert missing_keys == ["mlm_score.decoder.bias"]
  else:
    model.load_state_dict(_UpperCamelCase						)

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

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

  # Verify outputs
  if mlm_model:
    _lowercase:			List[str]       =     torch.Size([1, 11, 30_522]						)
    _lowercase:			str       =     torch.tensor([-12.5_061, -12.5_123, -12.5_174]						)
    assert outputs.logits.shape == expected_shape
    assert torch.allclose(outputs.logits[0, 0, :3] ,				_UpperCamelCase ,				atol=1e-4						)

    # verify masked token prediction equals "cats"
    _lowercase:			Union[str, Any]       =     outputs.logits[0, 4, :].argmax(-1						).item()
    assert tokenizer.decode([predicted_id]						) == "cats"
  elif vqa_model:
    _lowercase:			int       =     torch.Size([1, 3_129]						)
    _lowercase:			Optional[Any]       =     torch.tensor([-15.9_495, -18.1_472, -10.3_041]						)
    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"
    _lowercase:			Optional[Any]       =     outputs.logits.argmax(-1						).item()
    assert model.config.idalabel[predicted_idx] == "2"
  elif nlvr_model:
    _lowercase:			List[Any]       =     torch.Size([1, 2]						)
    _lowercase:			int       =     torch.tensor([-2.8_721, 2.1_291]						)
    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__   :					int							=   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__   :					Union[str, Any]							=   parser.parse_args()
  convert_vilt_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)
