
"""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__ :       int				= logging.get_logger(__name__)




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






  """simple docstring"""



  _lowercase:      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 			_lowerCAmelCase		(		_UpperCamelCase			,					_UpperCamelCase    ):






  """simple docstring"""



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




def 			_lowerCAmelCase		(		_UpperCamelCase    ):






  """simple docstring"""



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




def 			_lowerCAmelCase		(		_UpperCamelCase			,					_UpperCamelCase			,					_UpperCamelCase    ):






  """simple docstring"""



  _lowercase:      List[str]   			=  dct.pop(_UpperCamelCase    )
  _lowercase:      Dict   			=  val




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






  """simple docstring"""



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

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

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

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

  # Verify outputs
  if mlm_model:
    _lowercase:      List[str]   			=  torch.Size([1, 11, 30_522]    )
    _lowercase:      Tuple   			=  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:      Optional[int]   			=  outputs.logits[0, 4, :].argmax(-1    ).item()
    assert tokenizer.decode([predicted_id]    ) == "cats"
  elif vqa_model:
    _lowercase:      Optional[int]   			=  torch.Size([1, 3_129]    )
    _lowercase:      List[str]   			=  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:      List[Any]   			=  outputs.logits.argmax(-1    ).item()
    assert model.config.idalabel[predicted_idx] == "2"
  elif nlvr_model:
    _lowercase:      Tuple   			=  torch.Size([1, 2]    )
    _lowercase:      Any   			=  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__ :       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__ :       str				= parser.parse_args()
  convert_vilt_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)


"""simple docstring"""




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


A__ :       List[str]				= {
    'configuration_biogpt': ['BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'BioGptConfig'],
    'tokenization_biogpt': ['BioGptTokenizer'],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  A__ :       Dict				= [
      'BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST',
      'BioGptForCausalLM',
      'BioGptForTokenClassification',
      'BioGptForSequenceClassification',
      'BioGptModel',
      'BioGptPreTrainedModel',
  ]


if TYPE_CHECKING:
  from .configuration_biogpt import BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP, BioGptConfig
  from .tokenization_biogpt import BioGptTokenizer

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_biogpt import (
        BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST,
        BioGptForCausalLM,
        BioGptForSequenceClassification,
        BioGptForTokenClassification,
        BioGptModel,
        BioGptPreTrainedModel,
    )


else:
  import sys

  A__ :       int				= _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
