






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





def  _a(  UpperCamelCase__				:						int,	UpperCamelCase__				:						Optional[int]=False,	UpperCamelCase__				:						Any=False,	UpperCamelCase__				:						Any=False							):




      '''simple docstring'''






      SCREAMING_SNAKE_CASE__     :				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(  UpperCamelCase__				:						Tuple,	UpperCamelCase__				:						Any							):




      '''simple docstring'''






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





def  _a(  UpperCamelCase__				:						List[str]							):




      '''simple docstring'''






      SCREAMING_SNAKE_CASE__     :				Tuple									=["""head.weight""", """head.bias"""]
      for k in ignore_keys:
            state_dict.pop(UpperCAmelCase__,	UpperCAmelCase__							)





def  _a(  UpperCamelCase__				:						Tuple,	UpperCamelCase__				:						List[str],	UpperCamelCase__				:						Union[str, Any]							):




      '''simple docstring'''






      SCREAMING_SNAKE_CASE__     :				Optional[Any]									=dct.pop(UpperCAmelCase__							)
      SCREAMING_SNAKE_CASE__     :				Optional[Any]									=val





@torch.no_grad()
def  _a(  UpperCamelCase__				:						str,	UpperCamelCase__				:						Optional[int]							):




      '''simple docstring'''






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

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

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

      # Define processor
      SCREAMING_SNAKE_CASE__     :				Optional[Any]									=ViltImageProcessor(size=3_8_4							)
      SCREAMING_SNAKE_CASE__     :				Optional[int]									=BertTokenizer.from_pretrained('''bert-base-uncased'''							)
      SCREAMING_SNAKE_CASE__     :				Any									=ViltProcessor(UpperCAmelCase__,	UpperCAmelCase__							)

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

      # Verify outputs
      if mlm_model:
            SCREAMING_SNAKE_CASE__     :				List[str]									=torch.Size([1, 1_1, 3_0_5_2_2]							)
            SCREAMING_SNAKE_CASE__     :				List[Any]									=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],	UpperCAmelCase__,	atol=1e-4							)

            # verify masked token prediction equals "cats"
            SCREAMING_SNAKE_CASE__     :				Any									=outputs.logits[0, 4, :].argmax(-1							).item()
            assert tokenizer.decode([predicted_id]							) == "cats"
      elif vqa_model:
            SCREAMING_SNAKE_CASE__     :				Any									=torch.Size([1, 3_1_2_9]							)
            SCREAMING_SNAKE_CASE__     :				Optional[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],	UpperCAmelCase__,	atol=1e-4							)
            assert outputs.logits.shape == expected_shape
            assert torch.allclose(outputs.logits[0, 0, :3],	UpperCAmelCase__,	atol=1e-4							)

            # verify vqa prediction equals "2"
            SCREAMING_SNAKE_CASE__     :				List[Any]									=outputs.logits.argmax(-1							).item()
            assert model.config.idalabel[predicted_idx] == "2"
      elif nlvr_model:
            SCREAMING_SNAKE_CASE__     :				List[Any]									=torch.Size([1, 2]							)
            SCREAMING_SNAKE_CASE__     :				List[Any]									=torch.tensor([-2.8_7_2_1, 2.1_2_9_1]							)
            assert torch.allclose(outputs.logits[0, :3],	UpperCAmelCase__,	atol=1e-4							)
            assert outputs.logits.shape == expected_shape

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


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)







'''simple docstring'''
def  _a(  UpperCamelCase__				:						Optional[int],	UpperCamelCase__				:						int							):




      '''simple docstring'''






      SCREAMING_SNAKE_CASE__     :				Optional[int]									=0
      SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=len(UpperCamelCase__							) - 1

      while left <= right:
            # avoid divided by 0 during interpolation
            if sorted_collection[left] == sorted_collection[right]:
                  if sorted_collection[left] == item:
                        return left
                  else:
                        return None

            SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=left + ((item - sorted_collection[left]) * (right - left)) // (
                sorted_collection[right] - sorted_collection[left]
            )

            # out of range check
            if point < 0 or point >= len(UpperCamelCase__							):
                  return None

            SCREAMING_SNAKE_CASE__     :				Optional[int]									=sorted_collection[point]
            if current_item == item:
                  return point
            else:
                  if point < left:
                        SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=left
                        SCREAMING_SNAKE_CASE__     :				Optional[Any]									=point
                  elif point > right:
                        SCREAMING_SNAKE_CASE__     :				Optional[int]									=right
                        SCREAMING_SNAKE_CASE__     :				Tuple									=point
                  else:
                        if item < current_item:
                              SCREAMING_SNAKE_CASE__     :				str									=point - 1
                        else:
                              SCREAMING_SNAKE_CASE__     :				Tuple									=point + 1
      return None





def  _a(  UpperCamelCase__				:						List[str],	UpperCamelCase__				:						Optional[Any],	UpperCamelCase__				:						Union[str, Any],	UpperCamelCase__				:						Union[str, Any]							):




      '''simple docstring'''






      if sorted_collection[left] == sorted_collection[right]:
            if sorted_collection[left] == item:
                  return left
            else:
                  return None

      SCREAMING_SNAKE_CASE__     :				Dict									=left + ((item - sorted_collection[left]) * (right - left)) // (
          sorted_collection[right] - sorted_collection[left]
      )

      # out of range check
      if point < 0 or point >= len(UpperCamelCase__							):
            return None

      if sorted_collection[point] == item:
            return point
      elif point < left:
            return interpolation_search_by_recursion(UpperCamelCase__,	UpperCamelCase__,	UpperCamelCase__,	UpperCamelCase__							)
      elif point > right:
            return interpolation_search_by_recursion(UpperCamelCase__,	UpperCamelCase__,	UpperCamelCase__,	UpperCamelCase__							)
      else:
            if sorted_collection[point] > item:
                  return interpolation_search_by_recursion(
                      UpperCamelCase__,	UpperCamelCase__,	UpperCamelCase__,	point - 1							)
            else:
                  return interpolation_search_by_recursion(
                      UpperCamelCase__,	UpperCamelCase__,	point + 1,	UpperCamelCase__							)





def  _a(  UpperCamelCase__				:						Dict							):




      '''simple docstring'''






      if collection != sorted(UpperCamelCase__							):
            raise ValueError('''Collection must be ascending sorted'''							)
      return True


if __name__ == "__main__":
  import sys
  a_					= 0
  if debug == 1:
    a_					= [1_0, 3_0, 4_0, 4_5, 5_0, 6_6, 7_7, 9_3]
    try:
      __assert_sorted(collection)
    except ValueError:
      sys.exit('Sequence must be ascending sorted to apply interpolation search')
    a_					= 6_7

  a_					= interpolation_search(collection, target)
  if result is not None:
    print(F'''{target} found at positions: {result}''')
  else:
    print('Not found')