






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         =	logging.get_logger(__name__)





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





def       __lowerCAmelCase      (						__snake_case       ):
     __lowerCAmelCase								=					["""head.weight""", """head.bias"""]
     for k in ignore_keys:
          state_dict.pop(UpperCAmelCase__					, UpperCAmelCase__       )





def       __lowerCAmelCase      (						__snake_case					, __snake_case					, __snake_case       ):
     __lowerCAmelCase								=					dct.pop(UpperCAmelCase__       )
     __lowerCAmelCase								=					val





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

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

     # Define processor
     __lowerCAmelCase								=					ViltImageProcessor(size=384       )
     __lowerCAmelCase								=					BertTokenizer.from_pretrained("bert-base-uncased"       )
     __lowerCAmelCase								=					ViltProcessor(UpperCAmelCase__					, UpperCAmelCase__       )

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

     # Verify outputs
     if mlm_model:
          __lowerCAmelCase								=					torch.Size([1, 11, 3_0522]       )
          __lowerCAmelCase								=					torch.tensor([-12.5_061, -12.5_123, -12.5_174]       )
          assert outputs.logits.shape == expected_shape
          assert torch.allclose(outputs.logits[0, 0, :3]					, UpperCAmelCase__					, atol=1E-4       )

          # verify masked token prediction equals "cats"
          __lowerCAmelCase								=					outputs.logits[0, 4, :].argmax(-1       ).item()
          assert tokenizer.decode([predicted_id]       ) == "cats"
     elif vqa_model:
          __lowerCAmelCase								=					torch.Size([1, 3129]       )
          __lowerCAmelCase								=					torch.tensor([-15.9_495, -18.1_472, -10.3_041]       )
          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"
          __lowerCAmelCase								=					outputs.logits.argmax(-1       ).item()
          assert model.config.idalabel[predicted_idx] == "2"
     elif nlvr_model:
          __lowerCAmelCase								=					torch.Size([1, 2]       )
          __lowerCAmelCase								=					torch.tensor([-2.8_721, 2.1_291]       )
          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__":
 lowerCamelCase         =	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         =	parser.parse_args()
 convert_vilt_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)














from __future__ import annotations





def       __lowerCAmelCase      (						__snake_case					, __snake_case					, __snake_case       ):
     __lowerCAmelCase								=					list(range(len(__snake_case       )       )       )
     __lowerCAmelCase								=					[v / w for v, w in zip(__snake_case					, __snake_case       )]
     index.sort(key=lambda __snake_case       : ratio[i]					, reverse=__snake_case       )

     __lowerCAmelCase								=					0
     __lowerCAmelCase								=					[0] * len(__snake_case       )
     for i in index:
          if weight[i] <= capacity:
               __lowerCAmelCase								=					1
               max_value += value[i]
               capacity -= weight[i]
          else:
               __lowerCAmelCase								=					capacity / weight[i]
               max_value += value[i] * capacity / weight[i]
               break

     return max_value, fractions


if __name__ == "__main__":
 import doctest

 doctest.testmod()






