

"""simple docstring"""






import argparse
from collections import OrderedDict
from pathlib import Path

import torch

from transformers import (
    VisualBertConfig,
    VisualBertForMultipleChoice,
    VisualBertForPreTraining,
    VisualBertForQuestionAnswering,
    VisualBertForVisualReasoning,
)
from transformers.utils import logging


logging.set_verbosity_info()
__lowerCamelCase										=		logging.get_logger(__name__)

__lowerCamelCase										=		[
    ("bert.bert", "visual_bert"),
    ("bert.cls", "cls"),
    ("bert.classifier", "cls"),
    ("token_type_embeddings_visual", "visual_token_type_embeddings"),
    ("position_embeddings_visual", "visual_position_embeddings"),
    ("projection", "visual_projection"),
]

__lowerCamelCase										=		[
    "nlvr2_coco_pre_trained.th",
    "nlvr2_fine_tuned.th",
    "nlvr2_pre_trained.th",
    "vcr_coco_pre_train.th",
    "vcr_fine_tune.th",
    "vcr_pre_train.th",
    "vqa_coco_pre_trained.th",
    "vqa_fine_tuned.th",
    "vqa_pre_trained.th",
]





def 	lowercase      (   __UpperCamelCase						) ->      Optional[int]:
 __magic_name__     =      torch.load(__UpperCamelCase      ,   map_location='''cpu'''						)
 return sd





def 	lowercase      (   __UpperCamelCase      ,   __UpperCamelCase      ,   __UpperCamelCase=rename_keys_prefix						) ->      str:
 __magic_name__     =      OrderedDict()
 __magic_name__     =      torch.arange(config.max_position_embeddings						).expand((1, -1)						)
 # detector_d = OrderedDict()
 for key in d:
  if "detector" in key:
   # detector_d[key.replace('detector.','')] = d[key]
   continue
  __magic_name__     =      key
  for name_pair in rename_keys_prefix:
   __magic_name__     =      new_key.replace(name_pair[0]      ,   name_pair[1]						)
  __magic_name__     =      d[key]
  if key == "bert.cls.predictions.decoder.weight":
   # Old bert code didn't have `decoder.bias`, but was added separately
   __magic_name__     =      new_d['''cls.predictions.bias''']
 return new_d





@torch.no_grad()
def 	lowercase      (   __UpperCamelCase      ,   __UpperCamelCase						) ->      Optional[Any]:
 assert (
     checkpoint_path.split('''/'''						)[-1] in ACCEPTABLE_CHECKPOINTS
 ), f'''The checkpoint provided must be in {ACCEPTABLE_CHECKPOINTS}.'''

 # Get Config
 if "pre" in checkpoint_path:
  __magic_name__     =      '''pretraining'''
  if "vcr" in checkpoint_path:
   __magic_name__     =      {'''visual_embedding_dim''': 512}
  elif "vqa_advanced" in checkpoint_path:
   __magic_name__     =      {'''visual_embedding_dim''': 2048}
  elif "vqa" in checkpoint_path:
   __magic_name__     =      {'''visual_embedding_dim''': 2048}
  elif "nlvr" in checkpoint_path:
   __magic_name__     =      {'''visual_embedding_dim''': 1024}
  else:
   raise NotImplementedError(f'''No implementation found for `{checkpoint_path}`.'''						)
 else:
  if "vcr" in checkpoint_path:
   __magic_name__     =      {'''visual_embedding_dim''': 512}
   __magic_name__     =      '''multichoice'''
  elif "vqa_advanced" in checkpoint_path:
   __magic_name__     =      {'''visual_embedding_dim''': 2048}
   __magic_name__     =      '''vqa_advanced'''
  elif "vqa" in checkpoint_path:
   __magic_name__     =      {'''visual_embedding_dim''': 2048, '''num_labels''': 3129}
   __magic_name__     =      '''vqa'''
  elif "nlvr" in checkpoint_path:
   __magic_name__     =      {
       '''visual_embedding_dim''': 1024,
       '''num_labels''': 2,
   }
   __magic_name__     =      '''nlvr'''

 __magic_name__     =      VisualBertConfig(**__UpperCamelCase						)

 # Load State Dict
 __magic_name__     =      load_state_dict(__UpperCamelCase						)

 __magic_name__     =      get_new_dict(__UpperCamelCase      ,   __UpperCamelCase						)

 if model_type == "pretraining":
  __magic_name__     =      VisualBertForPreTraining(__UpperCamelCase						)
 elif model_type == "vqa":
  __magic_name__     =      VisualBertForQuestionAnswering(__UpperCamelCase						)
 elif model_type == "nlvr":
  __magic_name__     =      VisualBertForVisualReasoning(__UpperCamelCase						)
 elif model_type == "multichoice":
  __magic_name__     =      VisualBertForMultipleChoice(__UpperCamelCase						)

 model.load_state_dict(__UpperCamelCase						)
 # Save Checkpoints
 Path(__UpperCamelCase						).mkdir(exist_ok=__UpperCamelCase						)
 model.save_pretrained(__UpperCamelCase						)


if __name__ == "__main__":
 __lowerCamelCase										=		argparse.ArgumentParser()
 # Required parameters
 parser.add_argument("orig_checkpoint_path", type=str, help="A path to .th on local filesystem.")
 parser.add_argument("pytorch_dump_folder_path", type=str, help="Path to the output PyTorch model.")
 __lowerCamelCase										=		parser.parse_args()
 convert_visual_bert_checkpoint(args.orig_checkpoint_path, args.pytorch_dump_folder_path)




"""simple docstring"""






import os
import unittest

from transformers import FunnelTokenizer, FunnelTokenizerFast
from transformers.models.funnel.tokenization_funnel import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class        _lowercase					(     __UpperCAmelCase   , unittest.TestCase     ):
     _lowerCamelCase     =       FunnelTokenizer
     _lowerCamelCase     =       FunnelTokenizerFast
     _lowerCamelCase     =       True
     _lowerCamelCase     =       True







     def 	lowerCAmelCase__    (      self       ):
      super().setUp()

      __magic_name__     =      [
          '''<unk>''',
          '''<cls>''',
          '''<sep>''',
          '''want''',
          '''##want''',
          '''##ed''',
          '''wa''',
          '''un''',
          '''runn''',
          '''##ing''',
          ''',''',
          '''low''',
          '''lowest''',
      ]
      __magic_name__     =      os.path.join(self.tmpdirname ,						VOCAB_FILES_NAMES['''vocab_file''']       )
      with open(self.vocab_file ,						'''w''' ,						encoding='''utf-8'''       ) as vocab_writer:
       vocab_writer.write(''''''.join([x + '''\n''' for x in vocab_tokens]       )       )







     def 	lowerCAmelCase__    (      self ,						**UpperCamelCase_       ):
      return FunnelTokenizer.from_pretrained(self.tmpdirname ,						**UpperCamelCase_       )







     def 	lowerCAmelCase__    (      self ,						**UpperCamelCase_       ):
      return FunnelTokenizerFast.from_pretrained(self.tmpdirname ,						**UpperCamelCase_       )







     def 	lowerCAmelCase__    (      self ,						UpperCamelCase_       ):
      __magic_name__     =      '''UNwant\u00E9d,running'''
      __magic_name__     =      '''unwanted, running'''
      return input_text, output_text







     def 	lowerCAmelCase__    (      self       ):
      __magic_name__     =      self.tokenizer_class(self.vocab_file       )

      __magic_name__     =      tokenizer.tokenize('''UNwant\u00E9d,running'''       )
      self.assertListEqual(UpperCamelCase_ ,						['''un''', '''##want''', '''##ed''', ''',''', '''runn''', '''##ing''']       )
      self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCamelCase_       ) ,						[7, 4, 5, 10, 8, 9]       )







     def 	lowerCAmelCase__    (      self       ):
      __magic_name__     =      self.get_tokenizers(do_lower_case=UpperCamelCase_       )
      for tokenizer in tokenizers:
       __magic_name__     =      tokenizer('''UNwant\u00E9d,running'''       )
       __magic_name__     =      len(inputs['''input_ids''']       ) - 1
       self.assertListEqual(inputs['''token_type_ids'''] ,						[2] + [0] * sentence_len       )

       __magic_name__     =      tokenizer('''UNwant\u00E9d,running''' ,						'''UNwant\u00E9d,running'''       )
       self.assertListEqual(inputs['''token_type_ids'''] ,						[2] + [0] * sentence_len + [1] * sentence_len       )

