





"""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()
lowerCamelCase_								=		logging.get_logger(__name__)



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



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



def      __lowerCamelCase   (							a_     :		Union[str, Any] ,   a_     :		Tuple ,   a_     :		Union[str, Any]							)			->      Tuple:
      __SCREAMING_SNAKE_CASE   :str						=  dct.pop(a_							)
      __SCREAMING_SNAKE_CASE   :List[str]						=  val



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

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

      # Define processor
      __SCREAMING_SNAKE_CASE   :int						=  ViltImageProcessor(size=3_84							)
      __SCREAMING_SNAKE_CASE   :List[Any]						=  BertTokenizer.from_pretrained('''bert-base-uncased'''							)
      __SCREAMING_SNAKE_CASE   :List[Any]						=  ViltProcessor(a_ ,   a_							)

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

      # Verify outputs
      if mlm_model:
            __SCREAMING_SNAKE_CASE   :Any						=  torch.Size([1, 11, 3_05_22]							)
            __SCREAMING_SNAKE_CASE   :Dict						=  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"
            __SCREAMING_SNAKE_CASE   :str						=  outputs.logits[0, 4, :].argmax(-1							).item()
            assert tokenizer.decode([predicted_id]							) == "cats"
      elif vqa_model:
            __SCREAMING_SNAKE_CASE   :Any						=  torch.Size([1, 31_29]							)
            __SCREAMING_SNAKE_CASE   :Tuple						=  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"
            __SCREAMING_SNAKE_CASE   :Optional[Any]						=  outputs.logits.argmax(-1							).item()
            assert model.config.idalabel[predicted_idx] == "2"
      elif nlvr_model:
            __SCREAMING_SNAKE_CASE   :List[Any]						=  torch.Size([1, 2]							)
            __SCREAMING_SNAKE_CASE   :List[str]						=  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__":
  lowerCamelCase_								=		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."
  )

  lowerCamelCase_								=		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_torch_available


lowerCamelCase_								=		{
    "configuration_blip_2": [
        "BLIP_2_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "Blip2Config",
        "Blip2QFormerConfig",
        "Blip2VisionConfig",
    ],
    "processing_blip_2": ["Blip2Processor"],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowerCamelCase_								=		[
      "BLIP_2_PRETRAINED_MODEL_ARCHIVE_LIST",
      "Blip2Model",
      "Blip2QFormerModel",
      "Blip2PreTrainedModel",
      "Blip2ForConditionalGeneration",
      "Blip2VisionModel",
  ]

if TYPE_CHECKING:
  from .configuration_blip_a import (
      BLIP_2_PRETRAINED_CONFIG_ARCHIVE_MAP,
      BlipaConfig,
      BlipaQFormerConfig,
      BlipaVisionConfig,
  )
  from .processing_blip_a import BlipaProcessor

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_blip_a import (
        BLIP_2_PRETRAINED_MODEL_ARCHIVE_LIST,
        BlipaForConditionalGeneration,
        BlipaModel,
        BlipaPreTrainedModel,
        BlipaQFormerModel,
        BlipaVisionModel,
    )

else:
  import sys

  lowerCamelCase_								=		_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)