import os
import time
from dataclasses import dataclass, field
from enum import Enum
from typing import Dict, List, Optional, Union

import torch
from filelock import FileLock
from torch.utils.data import Dataset

from ...models.auto.modeling_auto import MODEL_FOR_QUESTION_ANSWERING_MAPPING
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
from ..processors.squad import SquadFeatures, SquadVaProcessor, SquadVaProcessor, squad_convert_examples_to_features


SCREAMING_SNAKE_CASE__		:					Tuple						=					logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__		:					int						=					list(MODEL_FOR_QUESTION_ANSWERING_MAPPING.keys())
SCREAMING_SNAKE_CASE__		:					List[str]						=					tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)

@dataclass
class       snake_case					:
  lowercase_			=     field(
      default=UpperCamelCase_   ,						metadata={'help': 'Model type selected in the list: ' + ', '.join(UpperCamelCase_						)}						)
  lowercase_			=     field(
      default=UpperCamelCase_   ,						metadata={'help': 'The input data dir. Should contain the .json files for the SQuAD task.'}						)
  lowercase_			=     field(
      default=128   ,						metadata={
          'help': (
              'The maximum total input sequence length after tokenization. Sequences longer '
              'than this will be truncated, sequences shorter will be padded.'
          )
      }   ,						)
  lowercase_			=     field(
      default=128   ,						metadata={'help': 'When splitting up a long document into chunks, how much stride to take between chunks.'}   ,						)
  lowercase_			=     field(
      default=64   ,						metadata={
          'help': (
              'The maximum number of tokens for the question. Questions longer than this will '
              'be truncated to this length.'
          )
      }   ,						)
  lowercase_			=     field(
      default=30   ,						metadata={
          'help': (
              'The maximum length of an answer that can be generated. This is needed because the start '
              'and end predictions are not conditioned on one another.'
          )
      }   ,						)
  lowercase_			=     field(
      default=UpperCamelCase_   ,						metadata={'help': 'Overwrite the cached training and evaluation sets'}						)
  lowercase_			=     field(
      default=UpperCamelCase_   ,						metadata={'help': 'If true, the SQuAD examples contain some that do not have an answer.'}						)
  lowercase_			=     field(
      default=0.0   ,						metadata={'help': 'If null_score - best_non_null is greater than the threshold predict null.'}						)
  lowercase_			=     field(
      default=20   ,						metadata={'help': 'If null_score - best_non_null is greater than the threshold predict null.'}						)
  lowercase_			=     field(
      default=0   ,						metadata={
          'help': (
              'language id of input for language-specific xlm models (see'
              ' tokenization_xlm.PRETRAINED_INIT_CONFIGURATION)'
          )
      }   ,						)
  lowercase_			=     field(default=1   ,						metadata={'help': 'multiple threads for converting example to features'}						)




class       snake_case					(      UpperCamelCase_						):
  lowercase_			=     'train'
  lowercase_			=     'dev'




class       snake_case					(      UpperCamelCase_						):
  lowercase_			=     42
  lowercase_			=     42
  lowercase_			=     42
  lowercase_			=     42




  def __init__(      self     :	int     , a_     :	SquadDataTrainingArguments     , a_     :	PreTrainedTokenizer     , a_     :	Optional[int] = None     , a_     :	Union[str, Split] = Split.train     , a_     :	Optional[bool] = False     , a_     :	Optional[str] = None     , a_     :	Optional[str] = "pt"     , )->       Optional[int]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				List[Any]			=		args
       SCREAMING_SNAKE_CASE__				:				List[Any]			=		is_language_sensitive
       SCREAMING_SNAKE_CASE__				:				Any			=		SquadVaProcessor() if args.version_2_with_negative else SquadVaProcessor()
       if isinstance(a_     , a_				):
            try:
                 SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		Split[mode]
            except KeyError:
                 raise KeyError('mode is not a valid split name'				)
       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		mode
       # Load data features from cache or dataset file
       SCREAMING_SNAKE_CASE__				:				Any			=		'v2' if args.version_2_with_negative else 'v1'
       SCREAMING_SNAKE_CASE__				:				int			=		os.path.join(
           cache_dir if cache_dir is not None else args.data_dir     , F'''cached_{mode.value}_{tokenizer.__class__.__name__}_{args.max_seq_length}_{version_tag}'''     , )

       # Make sure only the first process in distributed training processes the dataset,
       # and the others will use the cache.
       SCREAMING_SNAKE_CASE__				:				List[str]			=		cached_features_file + '.lock'
       with FileLock(a_				):
            if os.path.exists(a_				) and not args.overwrite_cache:
                 SCREAMING_SNAKE_CASE__				:				Any			=		time.time()
                 SCREAMING_SNAKE_CASE__				:				int			=		torch.load(a_				)

                 # Legacy cache files have only features, while new cache files
                 # will have dataset and examples also.
                 SCREAMING_SNAKE_CASE__				:				Tuple			=		self.old_features['features']
                 SCREAMING_SNAKE_CASE__				:				Tuple			=		self.old_features.get('dataset'     , a_				)
                 SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		self.old_features.get('examples'     , a_				)
                 logger.info(
                     F'''Loading features from cached file {cached_features_file} [took %.3f s]'''     , time.time() - start				)

                 if self.dataset is None or self.examples is None:
                      logger.warning(
                          F'''Deleting cached file {cached_features_file} will allow dataset and examples to be cached in'''
                          ' future run'				)
            else:
                 if mode == Split.dev:
                      SCREAMING_SNAKE_CASE__				:				List[Any]			=		self.processor.get_dev_examples(args.data_dir				)
                 else:
                      SCREAMING_SNAKE_CASE__				:				List[Any]			=		self.processor.get_train_examples(args.data_dir				)

                 SCREAMING_SNAKE_CASE__			,							SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		squad_convert_examples_to_features(
                     examples=self.examples     , tokenizer=a_     , max_seq_length=args.max_seq_length     , doc_stride=args.doc_stride     , max_query_length=args.max_query_length     , is_training=mode == Split.train     , threads=args.threads     , return_dataset=a_     , )

                 SCREAMING_SNAKE_CASE__				:				List[Any]			=		time.time()
                 torch.save(
                     {'features': self.features, 'dataset': self.dataset, 'examples': self.examples}     , a_     , )
                 # ^ This seems to take a lot of time so I want to investigate why and how we can improve.
                 logger.info(
                     F'''Saving features into cached file {cached_features_file} [took {time.time() - start:.3f} s]'''				)




  def __len__(      self     :	Union[str, Any]				)->       Any:




       """simple docstring"""
       return len(self.features				)




  def __getitem__(      self     :	List[Any]     , a_     :	str				)->       Dict[str, torch.Tensor]:




       """simple docstring"""
       # Convert to Tensors and build dataset
       SCREAMING_SNAKE_CASE__				:				int			=		self.features[i]

       SCREAMING_SNAKE_CASE__				:				List[Any]			=		torch.tensor(feature.input_ids     , dtype=torch.long				)
       SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		torch.tensor(feature.attention_mask     , dtype=torch.long				)
       SCREAMING_SNAKE_CASE__				:				Any			=		torch.tensor(feature.token_type_ids     , dtype=torch.long				)
       SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		torch.tensor(feature.cls_index     , dtype=torch.long				)
       SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		torch.tensor(feature.p_mask     , dtype=torch.float				)
       SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		torch.tensor(feature.is_impossible     , dtype=torch.float				)

       SCREAMING_SNAKE_CASE__				:				str			=		{
           'input_ids': input_ids,
           'attention_mask': attention_mask,
           'token_type_ids': token_type_ids,
       }

       if self.args.model_type in ["xlm", "roberta", "distilbert", "camembert"]:
            del inputs["token_type_ids"]

       if self.args.model_type in ["xlnet", "xlm"]:
            inputs.update({'cls_index': cls_index, 'p_mask': p_mask}				)
            if self.args.version_2_with_negative:
                 inputs.update({'is_impossible': is_impossible}				)
            if self.is_language_sensitive:
                 inputs.update({'langs': (torch.ones(input_ids.shape     , dtype=torch.intaa				) * self.args.lang_id)}				)

       if self.mode == Split.train:
            SCREAMING_SNAKE_CASE__				:				List[Any]			=		torch.tensor(feature.start_position     , dtype=torch.long				)
            SCREAMING_SNAKE_CASE__				:				Dict			=		torch.tensor(feature.end_position     , dtype=torch.long				)
            inputs.update({'start_positions': start_positions, 'end_positions': end_positions}				)

       return inputs




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()
SCREAMING_SNAKE_CASE__		:					int						=					logging.get_logger(__name__)







def     _a				(      lowercase__  :			Union[str, Any]     ,  lowercase__  :			Union[str, Any]=False     ,  lowercase__  :			str=False     ,  lowercase__  :			Dict=False ):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__				:				Optional[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     _a				(      lowercase__  :			List[str]     ,  lowercase__  :			Dict ):







     '''simple docstring'''


     for i in range(config.num_hidden_layers ):
          SCREAMING_SNAKE_CASE__				:				Dict			=		'vilt.'
          # read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
          SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		state_dict.pop(f'''transformer.blocks.{i}.attn.qkv.weight''' )
          SCREAMING_SNAKE_CASE__				:				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__				:				Union[str, Any]			=		in_proj_weight[
              : config.hidden_size, :
          ]
          SCREAMING_SNAKE_CASE__				:				List[str]			=		in_proj_bias[: config.hidden_size]
          SCREAMING_SNAKE_CASE__				:				int			=		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__				:				List[str]			=		in_proj_weight[
              -config.hidden_size :, :
          ]
          SCREAMING_SNAKE_CASE__				:				Tuple			=		in_proj_bias[-config.hidden_size :]







def     _a				(      lowercase__  :			Optional[int] ):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__				:				int			=		['head.weight', 'head.bias']
     for k in ignore_keys:
          state_dict.pop(lowercase__     ,  lowercase__ )







def     _a				(      lowercase__  :			int     ,  lowercase__  :			int     ,  lowercase__  :			Dict ):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__				:				Tuple			=		dct.pop(lowercase__ )
     SCREAMING_SNAKE_CASE__				:				Any			=		val







@torch.no_grad()
def     _a				(      lowercase__  :			Dict     ,  lowercase__  :			Optional[Any] ):







     '''simple docstring'''


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

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

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

     # Define processor
     SCREAMING_SNAKE_CASE__				:				str			=		ViltImageProcessor(size=3_84 )
     SCREAMING_SNAKE_CASE__				:				List[Any]			=		BertTokenizer.from_pretrained('bert-base-uncased' )
     SCREAMING_SNAKE_CASE__				:				List[Any]			=		ViltProcessor(lowercase__     ,  lowercase__ )

     # Forward pass on example inputs (image + text)
     if nlvr_model:
          SCREAMING_SNAKE_CASE__				:				List[str]			=		Image.open(requests.get('https://lil.nlp.cornell.edu/nlvr/exs/ex0_0.jpg'     ,  stream=lowercase__ ).raw )
          SCREAMING_SNAKE_CASE__				:				Any			=		Image.open(requests.get('https://lil.nlp.cornell.edu/nlvr/exs/ex0_0.jpg'     ,  stream=lowercase__ ).raw )
          SCREAMING_SNAKE_CASE__				:				Tuple			=		(
              '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__				:				List[Any]			=		processor(lowercase__     ,  lowercase__     ,  return_tensors='pt' )
          SCREAMING_SNAKE_CASE__				:				List[str]			=		processor(lowercase__     ,  lowercase__     ,  return_tensors='pt' )
          SCREAMING_SNAKE_CASE__				:				List[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__				:				Tuple			=		Image.open(requests.get('http://images.cocodataset.org/val2017/000000039769.jpg'     ,  stream=lowercase__ ).raw )
          if mlm_model:
               SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		'a bunch of [MASK] laying on a [MASK].'
          else:
               SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		'How many cats are there?'
          SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		processor(lowercase__     ,  lowercase__     ,  return_tensors='pt' )
          SCREAMING_SNAKE_CASE__				:				str			=		model(**lowercase__ )

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

          # verify masked token prediction equals "cats"
          SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		outputs.logits[0, 4, :].argmax(-1 ).item()
          assert tokenizer.decode([predicted_id] ) == "cats"
     elif vqa_model:
          SCREAMING_SNAKE_CASE__				:				str			=		torch.Size([1, 31_29] )
          SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		torch.tensor([-15.9495, -18.1472, -10.3041] )
          assert torch.allclose(outputs.logits[0, :3]     ,  lowercase__     ,  atol=1E-4 )
          assert outputs.logits.shape == expected_shape
          assert torch.allclose(outputs.logits[0, 0, :3]     ,  lowercase__     ,  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__				:				Optional[int]			=		torch.Size([1, 2] )
          SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		torch.tensor([-2.8721, 2.1291] )
          assert torch.allclose(outputs.logits[0, :3]     ,  lowercase__     ,  atol=1E-4 )
          assert outputs.logits.shape == expected_shape

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


if __name__ == "__main__":
      SCREAMING_SNAKE_CASE__		:					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."
      )

      SCREAMING_SNAKE_CASE__		:					List[Any]						=					parser.parse_args()
      convert_vilt_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)



