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


def      UpperCAmelCase__			(							lowerCAmelCase__	:str   ,						lowerCAmelCase__	:Any=False   ,						lowerCAmelCase__	:Dict=False   ,						lowerCAmelCase__	:Dict=False						)					->       Union[str, Any]:
      '''simple docstring'''


      lowercase								=      []
      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      UpperCAmelCase__			(							lowerCAmelCase__	:Any   ,						lowerCAmelCase__	:Dict						)					->       int:
      '''simple docstring'''


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


def      UpperCAmelCase__			(							lowerCAmelCase__	:Union[str, Any]						)					->       int:
      '''simple docstring'''


      lowercase								=      ["""head.weight""", """head.bias"""]
      for k in ignore_keys:
            state_dict.pop(lowerCAmelCase__   ,						lowerCAmelCase__						)


def      UpperCAmelCase__			(							lowerCAmelCase__	:Tuple   ,						lowerCAmelCase__	:List[Any]   ,						lowerCAmelCase__	:List[Any]						)					->       Optional[Any]:
      '''simple docstring'''


      lowercase								=      dct.pop(lowerCAmelCase__						)
      lowercase								=      val


@torch.no_grad()
def      UpperCAmelCase__			(							lowerCAmelCase__	:Any   ,						lowerCAmelCase__	:Any						)					->       Any:
      '''simple docstring'''


      lowercase								=      ViltConfig(image_size=3_8_4   ,						patch_size=3_2   ,						tie_word_embeddings=lowerCAmelCase__						)
      lowercase								=      False
      lowercase								=      False
      lowercase								=      False
      lowercase								=      False
      if "vqa" in checkpoint_url:
            lowercase								=      True
            lowercase								=      3_1_2_9
            lowercase								=      """huggingface/label-files"""
            lowercase								=      """vqa2-id2label.json"""
            lowercase								=      json.load(open(hf_hub_download(lowerCAmelCase__   ,						lowerCAmelCase__   ,						repo_type="""dataset"""						)   ,						"""r"""						)						)
            lowercase								=      {int(lowerCAmelCase__						): v for k, v in idalabel.items()}
            lowercase								=      idalabel
            lowercase								=      {v: k for k, v in idalabel.items()}
            lowercase								=      ViltForQuestionAnswering(lowerCAmelCase__						)
      elif "nlvr" in checkpoint_url:
            lowercase								=      True
            lowercase								=      2
            lowercase								=      {0: """False""", 1: """True"""}
            lowercase								=      {v: k for k, v in config.idalabel.items()}
            lowercase								=      3
            lowercase								=      ViltForImagesAndTextClassification(lowerCAmelCase__						)
      elif "irtr" in checkpoint_url:
            lowercase								=      True
            lowercase								=      ViltForImageAndTextRetrieval(lowerCAmelCase__						)
      elif "mlm_itm" in checkpoint_url:
            lowercase								=      True
            lowercase								=      ViltForMaskedLM(lowerCAmelCase__						)
      else:
            raise ValueError("""Unknown model type"""						)

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

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

      # Define processor
      lowercase								=      ViltImageProcessor(size=3_8_4						)
      lowercase								=      BertTokenizer.from_pretrained("""bert-base-uncased"""						)
      lowercase								=      ViltProcessor(lowerCAmelCase__   ,						lowerCAmelCase__						)

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

      # Verify outputs
      if mlm_model:
            lowercase								=      torch.Size([1, 1_1, 3_0_5_2_2]						)
            lowercase								=      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]   ,						lowerCAmelCase__   ,						atol=1e-4						)

            # verify masked token prediction equals "cats"
            lowercase								=      outputs.logits[0, 4, :].argmax(-1						).item()
            assert tokenizer.decode([predicted_id]						) == "cats"
      elif vqa_model:
            lowercase								=      torch.Size([1, 3_1_2_9]						)
            lowercase								=      torch.tensor([-15.9_495, -18.1_472, -10.3_041]						)
            assert torch.allclose(outputs.logits[0, :3]   ,						lowerCAmelCase__   ,						atol=1e-4						)
            assert outputs.logits.shape == expected_shape
            assert torch.allclose(outputs.logits[0, 0, :3]   ,						lowerCAmelCase__   ,						atol=1e-4						)

            # verify vqa prediction equals "2"
            lowercase								=      outputs.logits.argmax(-1						).item()
            assert model.config.idalabel[predicted_idx] == "2"
      elif nlvr_model:
            lowercase								=      torch.Size([1, 2]						)
            lowercase								=      torch.tensor([-2.8_721, 2.1_291]						)
            assert torch.allclose(outputs.logits[0, :3]   ,						lowerCAmelCase__   ,						atol=1e-4						)
            assert outputs.logits.shape == expected_shape

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


if __name__ == "__main__":
       __lowerCAmelCase       :			Optional[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."""
       )

       __lowerCAmelCase       :			str   	=parser.parse_args()
       convert_vilt_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)




"""simple docstring"""






from ...configuration_utils import PretrainedConfig
from ...utils import logging


__lowerCAmelCase       :			int   	=logging.get_logger(__name__)

__lowerCAmelCase       :			Optional[Any]   	={
    """uw-madison/mra-base-512-4""": """https://huggingface.co/uw-madison/mra-base-512-4/resolve/main/config.json""",
}





class 					_A		(			lowerCAmelCase      ):
      snake_case__     :  Optional[int]				    =      'mra'

      def __init__(		self						,			__lowerCAmelCase=5_0265						,			__lowerCAmelCase=768						,			__lowerCAmelCase=12						,			__lowerCAmelCase=12						,			__lowerCAmelCase=3072						,			__lowerCAmelCase="gelu"						,			__lowerCAmelCase=0.1						,			__lowerCAmelCase=0.1						,			__lowerCAmelCase=512						,			__lowerCAmelCase=1						,			__lowerCAmelCase=0.0_2						,			__lowerCAmelCase=1E-5						,			__lowerCAmelCase="absolute"						,			__lowerCAmelCase=4						,			__lowerCAmelCase="full"						,			__lowerCAmelCase=0						,			__lowerCAmelCase=0						,			__lowerCAmelCase=1						,			__lowerCAmelCase=0						,			__lowerCAmelCase=2						,			**__lowerCAmelCase						,			):







            """simple docstring"""



            super().__init__(pad_token_id=__lowerCAmelCase						,			bos_token_id=__lowerCAmelCase						,			eos_token_id=__lowerCAmelCase						,			**__lowerCAmelCase		)

            lowercase								=      vocab_size
            lowercase								=      max_position_embeddings
            lowercase								=      hidden_size
            lowercase								=      num_hidden_layers
            lowercase								=      num_attention_heads
            lowercase								=      intermediate_size
            lowercase								=      hidden_act
            lowercase								=      hidden_dropout_prob
            lowercase								=      attention_probs_dropout_prob
            lowercase								=      initializer_range
            lowercase								=      type_vocab_size
            lowercase								=      layer_norm_eps
            lowercase								=      position_embedding_type
            lowercase								=      block_per_row
            lowercase								=      approx_mode
            lowercase								=      initial_prior_first_n_blocks
            lowercase								=      initial_prior_diagonal_n_blocks



