





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

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

def    __UpperCamelCase	(	a    : int     ) ->List[str]:
 snake_case				=			['''head.weight''', '''head.bias''']
 for k in ignore_keys:
  state_dict.pop(a					,   a     )

def    __UpperCamelCase	(	a    : Dict					,   a    : List[str]					,   a    : List[Any]     ) ->List[str]:
 snake_case				=			dct.pop(a     )
 snake_case				=			val

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

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

 # Define processor
 snake_case				=			ViltImageProcessor(size=384     )
 snake_case				=			BertTokenizer.from_pretrained('''bert-base-uncased'''     )
 snake_case				=			ViltProcessor(a					,   a     )

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

 # Verify outputs
 if mlm_model:
  snake_case				=			torch.Size([1, 11, 3_0522]     )
  snake_case				=			torch.tensor([-12.5061, -12.5123, -12.5174]     )
  assert outputs.logits.shape == expected_shape
  assert torch.allclose(outputs.logits[0, 0, :3]					,   a					,   atol=1e-4     )

  # verify masked token prediction equals "cats"
  snake_case				=			outputs.logits[0, 4, :].argmax(-1     ).item()
  assert tokenizer.decode([predicted_id]     ) == "cats"
 elif vqa_model:
  snake_case				=			torch.Size([1, 3129]     )
  snake_case				=			torch.tensor([-15.9495, -18.1472, -10.3041]     )
  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"
  snake_case				=			outputs.logits.argmax(-1     ).item()
  assert model.config.idalabel[predicted_idx] == "2"
 elif nlvr_model:
  snake_case				=			torch.Size([1, 2]     )
  snake_case				=			torch.tensor([-2.8721, 2.1291]     )
  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__":
   _lowercase       						=			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.'
   )

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








'''simple docstring'''







from __future__ import annotations

from collections.abc import Iterator
class 			_lowercase						:







     def __init__( self    ,   A__    ) ->	None:
      snake_case				=			value
      snake_case				=			None
      snake_case				=			None




class 			_lowercase						:







     def __init__( self    ,   A__    ) ->	None:
      snake_case				=			tree







     def     UpperCamelCase			( self    ,   A__    ) ->	int:
      if node is None:
       return 0
      return node.value + (
          self.depth_first_search(node.left    ) + self.depth_first_search(node.right    )
      )







     def __iter__( self    ) ->	Iterator[int]:
      yield self.depth_first_search(self.tree    )


if __name__ == "__main__":
   import doctest

   doctest.testmod()

