






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	:							Union[str, Any]				=logging.get_logger(__name__)







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







def 		SCREAMING_SNAKE_CASE							(					__lowerCAmelCase			) ->	List[str]:
     UpperCamelCase__			:   str       				=  ["head.weight", "head.bias"]
     for k in ignore_keys:
          state_dict.pop(__lowerCAmelCase					,    __lowerCAmelCase			)







def 		SCREAMING_SNAKE_CASE							(					__lowerCAmelCase					,    __lowerCAmelCase					,    __lowerCAmelCase			) ->	Any:
     UpperCamelCase__			:   str       				=  dct.pop(__lowerCAmelCase			)
     UpperCamelCase__			:   Dict       				=  val







@torch.no_grad()
def 		SCREAMING_SNAKE_CASE							(					__lowerCAmelCase					,    __lowerCAmelCase			) ->	str:
     UpperCamelCase__			:   Tuple       				=  ViltConfig(image_size=384					,    patch_size=32					,    tie_word_embeddings=__lowerCAmelCase			)
     UpperCamelCase__			:   Dict       				=  False
     UpperCamelCase__			:   Optional[int]       				=  False
     UpperCamelCase__			:   List[Any]       				=  False
     UpperCamelCase__			:   Optional[Any]       				=  False
     if "vqa" in checkpoint_url:
          UpperCamelCase__			:   Tuple       				=  True
          UpperCamelCase__			:   Dict       				=  3129
          UpperCamelCase__			:   Dict       				=  "huggingface/label-files"
          UpperCamelCase__			:   int       				=  "vqa2-id2label.json"
          UpperCamelCase__			:   str       				=  json.load(open(hf_hub_download(__lowerCAmelCase					,    __lowerCAmelCase					,    repo_type="dataset"			)					,    "r"			)			)
          UpperCamelCase__			:   int       				=  {int(__lowerCAmelCase			): v for k, v in idalabel.items()}
          UpperCamelCase__			:   Optional[int]       				=  idalabel
          UpperCamelCase__			:   Optional[Any]       				=  {v: k for k, v in idalabel.items()}
          UpperCamelCase__			:   Optional[Any]       				=  ViltForQuestionAnswering(__lowerCAmelCase			)
     elif "nlvr" in checkpoint_url:
          UpperCamelCase__			:   Optional[int]       				=  True
          UpperCamelCase__			:   Dict       				=  2
          UpperCamelCase__			:   str       				=  {0: "False", 1: "True"}
          UpperCamelCase__			:   List[Any]       				=  {v: k for k, v in config.idalabel.items()}
          UpperCamelCase__			:   Tuple       				=  3
          UpperCamelCase__			:   List[Any]       				=  ViltForImagesAndTextClassification(__lowerCAmelCase			)
     elif "irtr" in checkpoint_url:
          UpperCamelCase__			:   Tuple       				=  True
          UpperCamelCase__			:   Optional[int]       				=  ViltForImageAndTextRetrieval(__lowerCAmelCase			)
     elif "mlm_itm" in checkpoint_url:
          UpperCamelCase__			:   str       				=  True
          UpperCamelCase__			:   Tuple       				=  ViltForMaskedLM(__lowerCAmelCase			)
     else:
          raise ValueError("Unknown model type"			)

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

     # Define processor
     UpperCamelCase__			:   Tuple       				=  ViltImageProcessor(size=384			)
     UpperCamelCase__			:   List[str]       				=  BertTokenizer.from_pretrained("bert-base-uncased"			)
     UpperCamelCase__			:   Union[str, Any]       				=  ViltProcessor(__lowerCAmelCase					,    __lowerCAmelCase			)

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

     # Verify outputs
     if mlm_model:
          UpperCamelCase__			:   Union[str, Any]       				=  torch.Size([1, 11, 3_0522]			)
          UpperCamelCase__			:   Dict       				=  torch.tensor([-1_2.5_0_6_1, -1_2.5_1_2_3, -1_2.5_1_7_4]			)
          assert outputs.logits.shape == expected_shape
          assert torch.allclose(outputs.logits[0, 0, :3]					,    __lowerCAmelCase					,    atol=1E-4			)

          # verify masked token prediction equals "cats"
          UpperCamelCase__			:   int       				=  outputs.logits[0, 4, :].argmax(-1			).item()
          assert tokenizer.decode([predicted_id]			) == "cats"
     elif vqa_model:
          UpperCamelCase__			:   int       				=  torch.Size([1, 3129]			)
          UpperCamelCase__			:   Union[str, Any]       				=  torch.tensor([-1_5.9_4_9_5, -1_8.1_4_7_2, -1_0.3_0_4_1]			)
          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"
          UpperCamelCase__			:   Optional[Any]       				=  outputs.logits.argmax(-1			).item()
          assert model.config.idalabel[predicted_idx] == "2"
     elif nlvr_model:
          UpperCamelCase__			:   Union[str, Any]       				=  torch.Size([1, 2]			)
          UpperCamelCase__			:   List[Any]       				=  torch.tensor([-2.8_7_2_1, 2.1_2_9_1]			)
          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	:							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	:							List[str]				=parser.parse_args()
 convert_vilt_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_speech_available, is_torch_available


lowerCamelCase	:							int				={
    '''configuration_audio_spectrogram_transformer''': [
        '''AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''ASTConfig''',
    ]
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase	:							Union[str, Any]				=[
     '''AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''ASTForAudioClassification''',
     '''ASTModel''',
     '''ASTPreTrainedModel''',
 ]

try:
 if not is_speech_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase	:							Optional[int]				=['''ASTFeatureExtractor''']

if TYPE_CHECKING:
 from .configuration_audio_spectrogram_transformer import (
     AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
     ASTConfig,
 )

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_audio_spectrogram_transformer import (
      AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
      ASTForAudioClassification,
      ASTModel,
      ASTPreTrainedModel,
  )

 try:
  if not is_speech_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .feature_extraction_audio_spectrogram_transformer import ASTFeatureExtractor


else:
 import sys

 lowerCamelCase	:							Optional[int]				=_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)