




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	:		int							       =   logging.get_logger(__name__)







def SCREAMING_SNAKE_CASE				(       lowercase_ ,				lowercase_=False ,				lowercase_=False ,				lowercase_=False						)   ->       Optional[Any]:



       """simple docstring"""

       A__       =    []
       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 SCREAMING_SNAKE_CASE				(       lowercase_ ,				lowercase_						)   ->       Optional[int]:



       """simple docstring"""

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







def SCREAMING_SNAKE_CASE				(       lowercase_						)   ->       Optional[int]:



       """simple docstring"""

       A__       =    ['''head.weight''', '''head.bias''']
       for k in ignore_keys:
              state_dict.pop(lowercase_ ,				lowercase_						)







def SCREAMING_SNAKE_CASE				(       lowercase_ ,				lowercase_ ,				lowercase_						)   ->       Union[str, Any]:



       """simple docstring"""

       A__       =    dct.pop(lowercase_						)
       A__       =    val







@torch.no_grad()
def SCREAMING_SNAKE_CASE				(       lowercase_ ,				lowercase_						)   ->       Optional[int]:



       """simple docstring"""

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

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

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

       # Define processor
       A__       =    ViltImageProcessor(size=384						)
       A__       =    BertTokenizer.from_pretrained('''bert-base-uncased'''						)
       A__       =    ViltProcessor(lowercase_ ,				lowercase_						)

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

       # Verify outputs
       if mlm_model:
              A__       =    torch.Size([1, 11, 30_522]						)
              A__       =    torch.tensor([-12.50_61, -12.51_23, -12.51_74]						)
              assert outputs.logits.shape == expected_shape
              assert torch.allclose(outputs.logits[0, 0, :3] ,				lowercase_ ,				atol=1E-4						)

              # verify masked token prediction equals "cats"
              A__       =    outputs.logits[0, 4, :].argmax(-1						).item()
              assert tokenizer.decode([predicted_id]						) == "cats"
       elif vqa_model:
              A__       =    torch.Size([1, 3_129]						)
              A__       =    torch.tensor([-15.94_95, -18.14_72, -10.30_41]						)
              assert torch.allclose(outputs.logits[0, :3] ,				lowercase_ ,				atol=1E-4						)
              assert outputs.logits.shape == expected_shape
              assert torch.allclose(outputs.logits[0, 0, :3] ,				lowercase_ ,				atol=1E-4						)

              # verify vqa prediction equals "2"
              A__       =    outputs.logits.argmax(-1						).item()
              assert model.config.idalabel[predicted_idx] == "2"
       elif nlvr_model:
              A__       =    torch.Size([1, 2]						)
              A__       =    torch.tensor([-2.87_21, 2.12_91]						)
              assert torch.allclose(outputs.logits[0, :3] ,				lowercase_ ,				atol=1E-4						)
              assert outputs.logits.shape == expected_shape

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


if __name__ == "__main__":
  _lowerCamelCase	:		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."""
  )

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





from typing import Dict

from .base import GenericTensor, Pipeline


class  A						(							_UpperCAmelCase ):







    """simple docstring"""





    def 						snake_case__     (      self :       int,lowercase_ :       Dict=None,lowercase_ :       Tuple=None,lowercase_ :       List[Any]=None,**lowercase_ :       Any		)->    Optional[Any]:





        '''simple docstring'''







        if tokenize_kwargs is None:
            A__          =   {}

        if truncation is not None:
            if "truncation" in tokenize_kwargs:
                raise ValueError(
                    'truncation parameter defined twice (given as keyword argument as well as in tokenize_kwargs)'		)
            A__          =   truncation

        A__          =   tokenize_kwargs

        A__          =   {}
        if return_tensors is not None:
            A__          =   return_tensors

        return preprocess_params, {}, postprocess_params


    def 						snake_case__     (      self :       Dict,lowercase_ :       List[Any],**lowercase_ :       Tuple		)->    Dict[str, GenericTensor]:





        '''simple docstring'''







        A__          =   self.framework
        A__          =   self.tokenizer(lowercase_,return_tensors=lowercase_,**lowercase_		)
        return model_inputs


    def 						snake_case__     (      self :       Tuple,lowercase_ :       int		)->    Optional[Any]:





        '''simple docstring'''







        A__          =   self.model(**lowercase_		)
        return model_outputs


    def 						snake_case__     (      self :       Tuple,lowercase_ :       Tuple,lowercase_ :       List[str]=False		)->    Any:





        '''simple docstring'''







        if return_tensors:
            return model_outputs[0]
        if self.framework == "pt":
            return model_outputs[0].tolist()
        elif self.framework == "tf":
            return model_outputs[0].numpy().tolist()


    def __call__(      self :       List[Any],*lowercase_ :       int,**lowercase_ :       Optional[Any]		)->    int:





        '''simple docstring'''







        return super().__call__(*lowercase_,**lowercase_		)

