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_							=      logging.get_logger(__name__)




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




def       __lowerCAmelCase      (						A_   : Optional[int]					)    ->  Optional[int]:
  __UpperCAmelCase						    =       ["head.weight", "head.bias"]
  for k in ignore_keys:
    state_dict.pop(A_						,	A_					)




def       __lowerCAmelCase      (						A_   : Dict						,	A_   : List[Any]						,	A_   : int					)    ->  Dict:
  __UpperCAmelCase						    =       dct.pop(A_					)
  __UpperCAmelCase						    =       val




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

  # load state_dict of original model, remove and rename some keys
  __UpperCAmelCase						    =       torch.hub.load_state_dict_from_url(A_						,	map_location="cpu"					)["state_dict"]
  __UpperCAmelCase						    =       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:
    __UpperCAmelCase						    =       ["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:
    __UpperCAmelCase    ,		__UpperCAmelCase						    =       model.load_state_dict(A_						,	strict=A_					)
    assert missing_keys == ["mlm_score.decoder.bias"]
  else:
    model.load_state_dict(A_					)

  # Define processor
  __UpperCAmelCase						    =       ViltImageProcessor(size=3_84					)
  __UpperCAmelCase						    =       BertTokenizer.from_pretrained("bert-base-uncased"					)
  __UpperCAmelCase						    =       ViltProcessor(A_						,	A_					)

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

  # Verify outputs
  if mlm_model:
    __UpperCAmelCase						    =       torch.Size([1, 11, 3_05_22]					)
    __UpperCAmelCase						    =       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]						,	A_						,	atol=1e-4					)

    # verify masked token prediction equals "cats"
    __UpperCAmelCase						    =       outputs.logits[0, 4, :].argmax(-1					).item()
    assert tokenizer.decode([predicted_id]					) == "cats"
  elif vqa_model:
    __UpperCAmelCase						    =       torch.Size([1, 31_29]					)
    __UpperCAmelCase						    =       torch.tensor([-15.94_95, -18.14_72, -10.30_41]					)
    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"
    __UpperCAmelCase						    =       outputs.logits.argmax(-1					).item()
    assert model.config.idalabel[predicted_idx] == "2"
  elif nlvr_model:
    __UpperCAmelCase						    =       torch.Size([1, 2]					)
    __UpperCAmelCase						    =       torch.tensor([-2.87_21, 2.12_91]					)
    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__":
       a_							=      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_							=      parser.parse_args()
       convert_vilt_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)




import copy
from dataclasses import dataclass
from pathlib import Path
from typing import Dict, Optional, Union


@dataclass
class 			UpperCAmelCase__    :



  """simple docstring"""






  lowerCAmelCase__		:	Optional[Union[str, Path]]    		= None
  lowerCAmelCase__		:	bool    		= False
  lowerCAmelCase__		:	bool    		= False
  lowerCAmelCase__		:	bool    		= False
  lowerCAmelCase__		:	Optional[Dict]    		= None
  lowerCAmelCase__		:	Optional[str]    		= None
  lowerCAmelCase__		:	bool    		= False
  lowerCAmelCase__		:	bool    		= False
  lowerCAmelCase__		:	bool    		= False
  lowerCAmelCase__		:	bool    		= True
  lowerCAmelCase__		:	Optional[int]    		= None
  lowerCAmelCase__		:	int    		= 1
  lowerCAmelCase__		:	Optional[Union[str, bool]]    		= None
  lowerCAmelCase__		:	bool    		= False
  lowerCAmelCase__		:	Optional[Dict]    		= None
  lowerCAmelCase__		:	Optional[str]    		= None






  def 	_UpperCAmelCase				(				self:			Any   ) -> "DownloadConfig":




    '''simple docstring'''




    return self.__class__(**{k: copy.deepcopy(__lowerCAmelCase   ) for k, v in self.__dict__.items()}   )



