

'''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()
_snake_case         =  logging.get_logger(__name__)







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







def  _A		(     snake_case					)      ->     Tuple:
   _lowercase      :				Any      					=       ["head.weight", "head.bias"]
   for k in ignore_keys:
      state_dict.pop(snake_case		,							snake_case					)







def  _A		(     snake_case		,							snake_case		,							snake_case					)      ->     int:
   _lowercase      :				List[Any]      					=       dct.pop(snake_case					)
   _lowercase      :				str      					=       val







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

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

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

   # Define processor
   _lowercase      :				Union[str, Any]      					=       ViltImageProcessor(size=3_84					)
   _lowercase      :				Optional[Any]      					=       BertTokenizer.from_pretrained("bert-base-uncased"					)
   _lowercase      :				Tuple      					=       ViltProcessor(snake_case		,							snake_case					)

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

   # Verify outputs
   if mlm_model:
      _lowercase      :				List[str]      					=       torch.Size([1, 11, 3_05_22]					)
      _lowercase      :				Dict      					=       torch.tensor([-12.5061, -12.5123, -12.5174]					)
      assert outputs.logits.shape == expected_shape
      assert torch.allclose(outputs.logits[0, 0, :3]		,							snake_case		,							atol=1E-4					)

      # verify masked token prediction equals "cats"
      _lowercase      :				int      					=       outputs.logits[0, 4, :].argmax(-1					).item()
      assert tokenizer.decode([predicted_id]					) == "cats"
   elif vqa_model:
      _lowercase      :				List[Any]      					=       torch.Size([1, 31_29]					)
      _lowercase      :				str      					=       torch.tensor([-15.9495, -18.1472, -10.3041]					)
      assert torch.allclose(outputs.logits[0, :3]		,							snake_case		,							atol=1E-4					)
      assert outputs.logits.shape == expected_shape
      assert torch.allclose(outputs.logits[0, 0, :3]		,							snake_case		,							atol=1E-4					)

      # verify vqa prediction equals "2"
      _lowercase      :				Dict      					=       outputs.logits.argmax(-1					).item()
      assert model.config.idalabel[predicted_idx] == "2"
   elif nlvr_model:
      _lowercase      :				int      					=       torch.Size([1, 2]					)
      _lowercase      :				Union[str, Any]      					=       torch.tensor([-2.8721, 2.1291]					)
      assert torch.allclose(outputs.logits[0, :3]		,							snake_case		,							atol=1E-4					)
      assert outputs.logits.shape == expected_shape

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


if __name__ == "__main__":
    _snake_case         =  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.'
    )

    _snake_case         =  parser.parse_args()
    convert_vilt_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)









'''simple docstring'''
import warnings

from ...utils import logging
from .image_processing_chinese_clip import ChineseCLIPImageProcessor


_snake_case         =  logging.get_logger(__name__)


class        a__  (							lowerCamelCase_  ):


   def __init__(			self      ,    *_UpperCamelCase      ,    **_UpperCamelCase  ):





      """simple docstring"""





      warnings.warn(
          "The class ChineseCLIPFeatureExtractor is deprecated and will be removed in version 5 of Transformers."
          " Please use ChineseCLIPImageProcessor instead."      ,    _UpperCamelCase      ,    )
      super().__init__(*_UpperCamelCase      ,    **_UpperCamelCase  )






