

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     :   List[str]						     =  logging.get_logger(__name__)

def    A_  (							a					,   a=False					,   a=False					,   a=False  ):

      """simple docstring"""



      SCREAMING_SNAKE_CASE_				:  Optional[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    A_  (							a					,   a  ):

      """simple docstring"""



      for i in range(config.num_hidden_layers  ):
            SCREAMING_SNAKE_CASE_				:  int           =     '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_				:  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
            SCREAMING_SNAKE_CASE_				:  str           =     in_proj_weight[
                : config.hidden_size, :
            ]
            SCREAMING_SNAKE_CASE_				:  Tuple           =     in_proj_bias[: config.hidden_size]
            SCREAMING_SNAKE_CASE_				:  str           =     in_proj_weight[
                config.hidden_size : config.hidden_size * 2, :
            ]
            SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     in_proj_bias[
                config.hidden_size : config.hidden_size * 2
            ]
            SCREAMING_SNAKE_CASE_				:  str           =     in_proj_weight[
                -config.hidden_size :, :
            ]
            SCREAMING_SNAKE_CASE_				:  int           =     in_proj_bias[-config.hidden_size :]

def    A_  (							a  ):

      """simple docstring"""



      SCREAMING_SNAKE_CASE_				:  Dict           =     ['head.weight', 'head.bias']
      for k in ignore_keys:
            state_dict.pop(lowercase__					,   lowercase__  )

def    A_  (							a					,   a					,   a  ):

      """simple docstring"""



      SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     dct.pop(lowercase__  )
      SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     val

@torch.no_grad()
def    A_  (							a					,   a  ):

      """simple docstring"""



      SCREAMING_SNAKE_CASE_				:  Any           =     ViltConfig(image_size=3_8_4					,   patch_size=3_2					,   tie_word_embeddings=lowercase__  )
      SCREAMING_SNAKE_CASE_				:  Any           =     False
      SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     False
      SCREAMING_SNAKE_CASE_				:  List[str]           =     False
      SCREAMING_SNAKE_CASE_				:  str           =     False
      if "vqa" in checkpoint_url:
            SCREAMING_SNAKE_CASE_				:  Any           =     True
            SCREAMING_SNAKE_CASE_				:  int           =     3_1_2_9
            SCREAMING_SNAKE_CASE_				:  List[str]           =     'huggingface/label-files'
            SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     'vqa2-id2label.json'
            SCREAMING_SNAKE_CASE_				:  Tuple           =     json.load(open(hf_hub_download(lowercase__					,   lowercase__					,   repo_type='dataset'  )					,   'r'  )  )
            SCREAMING_SNAKE_CASE_				:  Tuple           =     {int(lowercase__  ): v for k, v in idalabel.items()}
            SCREAMING_SNAKE_CASE_				:  Tuple           =     idalabel
            SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     {v: k for k, v in idalabel.items()}
            SCREAMING_SNAKE_CASE_				:  str           =     ViltForQuestionAnswering(lowercase__  )
      elif "nlvr" in checkpoint_url:
            SCREAMING_SNAKE_CASE_				:  str           =     True
            SCREAMING_SNAKE_CASE_				:  List[Any]           =     2
            SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     {0: 'False', 1: 'True'}
            SCREAMING_SNAKE_CASE_				:  Dict           =     {v: k for k, v in config.idalabel.items()}
            SCREAMING_SNAKE_CASE_				:  Optional[int]           =     3
            SCREAMING_SNAKE_CASE_				:  Tuple           =     ViltForImagesAndTextClassification(lowercase__  )
      elif "irtr" in checkpoint_url:
            SCREAMING_SNAKE_CASE_				:  int           =     True
            SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     ViltForImageAndTextRetrieval(lowercase__  )
      elif "mlm_itm" in checkpoint_url:
            SCREAMING_SNAKE_CASE_				:  str           =     True
            SCREAMING_SNAKE_CASE_				:  List[Any]           =     ViltForMaskedLM(lowercase__  )
      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(lowercase__					,   map_location='cpu'  )['state_dict']
      SCREAMING_SNAKE_CASE_				:  List[str]           =     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:
            SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     ['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:
            SCREAMING_SNAKE_CASE_       ,						SCREAMING_SNAKE_CASE_				:  int           =     model.load_state_dict(lowercase__					,   strict=lowercase__  )
            assert missing_keys == ["mlm_score.decoder.bias"]
      else:
            model.load_state_dict(lowercase__  )

      # Define processor
      SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     ViltImageProcessor(size=3_8_4  )
      SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     BertTokenizer.from_pretrained('bert-base-uncased'  )
      SCREAMING_SNAKE_CASE_				:  List[Any]           =     ViltProcessor(lowercase__					,   lowercase__  )

      # Forward pass on example inputs (image + text)
      if nlvr_model:
            SCREAMING_SNAKE_CASE_				:  int           =     Image.open(requests.get('https://lil.nlp.cornell.edu/nlvr/exs/ex0_0.jpg'					,   stream=lowercase__  ).raw  )
            SCREAMING_SNAKE_CASE_				:  str           =     Image.open(requests.get('https://lil.nlp.cornell.edu/nlvr/exs/ex0_0.jpg'					,   stream=lowercase__  ).raw  )
            SCREAMING_SNAKE_CASE_				:  int           =     (
                '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_				:  Optional[Any]           =     processor(lowercase__					,   lowercase__					,   return_tensors='pt'  )
            SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     processor(lowercase__					,   lowercase__					,   return_tensors='pt'  )
            SCREAMING_SNAKE_CASE_				:  str           =     model(
                input_ids=encoding_a.input_ids					,   pixel_values=encoding_a.pixel_values					,   pixel_values_a=encoding_a.pixel_values					,   )
      else:
            SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     Image.open(requests.get('http://images.cocodataset.org/val2017/000000039769.jpg'					,   stream=lowercase__  ).raw  )
            if mlm_model:
                  SCREAMING_SNAKE_CASE_				:  List[str]           =     'a bunch of [MASK] laying on a [MASK].'
            else:
                  SCREAMING_SNAKE_CASE_				:  List[str]           =     'How many cats are there?'
            SCREAMING_SNAKE_CASE_				:  Optional[int]           =     processor(lowercase__					,   lowercase__					,   return_tensors='pt'  )
            SCREAMING_SNAKE_CASE_				:  Any           =     model(**lowercase__  )

      # Verify outputs
      if mlm_model:
            SCREAMING_SNAKE_CASE_				:  str           =     torch.Size([1, 1_1, 3_0_5_2_2]  )
            SCREAMING_SNAKE_CASE_				:  Dict           =     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"
            SCREAMING_SNAKE_CASE_				:  List[str]           =     outputs.logits[0, 4, :].argmax(-1  ).item()
            assert tokenizer.decode([predicted_id]  ) == "cats"
      elif vqa_model:
            SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     torch.Size([1, 3_1_2_9]  )
            SCREAMING_SNAKE_CASE_				:  List[Any]           =     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"
            SCREAMING_SNAKE_CASE_				:  Optional[int]           =     outputs.logits.argmax(-1  ).item()
            assert model.config.idalabel[predicted_idx] == "2"
      elif nlvr_model:
            SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     torch.Size([1, 2]  )
            SCREAMING_SNAKE_CASE_				:  Optional[int]           =     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     :   List[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     :   Union[str, Any]						     =  parser.parse_args()
    convert_vilt_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)







from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


lowerCAmelCase     :   Dict						     =  logging.get_logger(__name__)

lowerCAmelCase     :   str						     =  {
    'microsoft/table-transformer-detection': (
        'https://huggingface.co/microsoft/table-transformer-detection/resolve/main/config.json'
    ),
}
class  _A		(  __magic_name__):
  SCREAMING_SNAKE_CASE    :      Union[str, Any]       =  '''table-transformer'''
  SCREAMING_SNAKE_CASE    :      Any       =  ['''past_key_values''']
  SCREAMING_SNAKE_CASE    :      Optional[int]       =  {
      '''hidden_size''': '''d_model''',
      '''num_attention_heads''': '''encoder_attention_heads''',
  }

  def __init__(				self ,					_SCREAMING_SNAKE_CASE=True ,					_SCREAMING_SNAKE_CASE=None ,					_SCREAMING_SNAKE_CASE=3 ,					_SCREAMING_SNAKE_CASE=100 ,					_SCREAMING_SNAKE_CASE=6 ,					_SCREAMING_SNAKE_CASE=2048 ,					_SCREAMING_SNAKE_CASE=8 ,					_SCREAMING_SNAKE_CASE=6 ,					_SCREAMING_SNAKE_CASE=2048 ,					_SCREAMING_SNAKE_CASE=8 ,					_SCREAMING_SNAKE_CASE=0.0 ,					_SCREAMING_SNAKE_CASE=0.0 ,					_SCREAMING_SNAKE_CASE=True ,					_SCREAMING_SNAKE_CASE="relu" ,					_SCREAMING_SNAKE_CASE=256 ,					_SCREAMING_SNAKE_CASE=0.1 ,					_SCREAMING_SNAKE_CASE=0.0 ,					_SCREAMING_SNAKE_CASE=0.0 ,					_SCREAMING_SNAKE_CASE=0.02 ,					_SCREAMING_SNAKE_CASE=1.0 ,					_SCREAMING_SNAKE_CASE=False ,					_SCREAMING_SNAKE_CASE="sine" ,					_SCREAMING_SNAKE_CASE="resnet50" ,					_SCREAMING_SNAKE_CASE=True ,					_SCREAMING_SNAKE_CASE=False ,					_SCREAMING_SNAKE_CASE=1 ,					_SCREAMING_SNAKE_CASE=5 ,					_SCREAMING_SNAKE_CASE=2 ,					_SCREAMING_SNAKE_CASE=1 ,					_SCREAMING_SNAKE_CASE=1 ,					_SCREAMING_SNAKE_CASE=5 ,					_SCREAMING_SNAKE_CASE=2 ,					_SCREAMING_SNAKE_CASE=0.1 ,					**_SCREAMING_SNAKE_CASE ,					):





        """simple docstring"""







        if backbone_config is not None and use_timm_backbone:
              raise ValueError('You can\'t specify both `backbone_config` and `use_timm_backbone`.'					)

        if not use_timm_backbone:
              if backbone_config is None:
                    logger.info('`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.'					)
                    SCREAMING_SNAKE_CASE_				:  List[Any]           =     CONFIG_MAPPING['resnet'](out_features=['stage4']					)
              elif isinstance(_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE					):
                    SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     backbone_config.get('model_type'					)
                    SCREAMING_SNAKE_CASE_				:  str           =     CONFIG_MAPPING[backbone_model_type]
                    SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     config_class.from_dict(_SCREAMING_SNAKE_CASE					)
              # set timm attributes to None
              SCREAMING_SNAKE_CASE_       ,						SCREAMING_SNAKE_CASE_       ,						SCREAMING_SNAKE_CASE_				:  Optional[int]           =     None, None, None

        SCREAMING_SNAKE_CASE_				:  Any           =     use_timm_backbone
        SCREAMING_SNAKE_CASE_				:  int           =     backbone_config
        SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     num_channels
        SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     num_queries
        SCREAMING_SNAKE_CASE_				:  int           =     d_model
        SCREAMING_SNAKE_CASE_				:  int           =     encoder_ffn_dim
        SCREAMING_SNAKE_CASE_				:  Any           =     encoder_layers
        SCREAMING_SNAKE_CASE_				:  str           =     encoder_attention_heads
        SCREAMING_SNAKE_CASE_				:  List[str]           =     decoder_ffn_dim
        SCREAMING_SNAKE_CASE_				:  str           =     decoder_layers
        SCREAMING_SNAKE_CASE_				:  Optional[int]           =     decoder_attention_heads
        SCREAMING_SNAKE_CASE_				:  List[Any]           =     dropout
        SCREAMING_SNAKE_CASE_				:  int           =     attention_dropout
        SCREAMING_SNAKE_CASE_				:  Dict           =     activation_dropout
        SCREAMING_SNAKE_CASE_				:  Optional[int]           =     activation_function
        SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     init_std
        SCREAMING_SNAKE_CASE_				:  int           =     init_xavier_std
        SCREAMING_SNAKE_CASE_				:  str           =     encoder_layerdrop
        SCREAMING_SNAKE_CASE_				:  Any           =     decoder_layerdrop
        SCREAMING_SNAKE_CASE_				:  Any           =     encoder_layers
        SCREAMING_SNAKE_CASE_				:  List[str]           =     auxiliary_loss
        SCREAMING_SNAKE_CASE_				:  List[str]           =     position_embedding_type
        SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     backbone
        SCREAMING_SNAKE_CASE_				:  Tuple           =     use_pretrained_backbone
        SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     dilation
        # Hungarian matcher
        SCREAMING_SNAKE_CASE_				:  List[Any]           =     class_cost
        SCREAMING_SNAKE_CASE_				:  List[Any]           =     bbox_cost
        SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     giou_cost
        # Loss coefficients
        SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     mask_loss_coefficient
        SCREAMING_SNAKE_CASE_				:  str           =     dice_loss_coefficient
        SCREAMING_SNAKE_CASE_				:  Dict           =     bbox_loss_coefficient
        SCREAMING_SNAKE_CASE_				:  int           =     giou_loss_coefficient
        SCREAMING_SNAKE_CASE_				:  Any           =     eos_coefficient
        super().__init__(is_encoder_decoder=_SCREAMING_SNAKE_CASE ,					**_SCREAMING_SNAKE_CASE					)

  @property
  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        return self.encoder_attention_heads



  @property
  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        return self.d_model

class  _A		(  __magic_name__):
  SCREAMING_SNAKE_CASE    :      List[Any]       =  version.parse('''1.11''')

  @property
  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        return OrderedDict(
            [
                ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
                ('pixel_mask', {0: 'batch'}),
            ]					)

  @property
  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        return 1e-5



  @property
  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        return 12






