
"""simple docstring"""




import os
from itertools import chain
from random import randrange, shuffle

import pytest

from .sola import PokerHand

A__   :					Tuple							=   (
    '4S 3H 2C 7S 5H',
    '9D 8H 2C 6S 7H',
    '2D 6D 9D TH 7D',
    'TC 8C 2S JH 6C',
    'JH 8S TH AH QH',
    'TS KS 5S 9S AC',
    'KD 6S 9D TH AD',
    'KS 8D 4D 9S 4S',  # pair
    '8C 4S KH JS 4D',  # pair
    'QH 8H KD JH 8S',  # pair
    'KC 4H KS 2H 8D',  # pair
    'KD 4S KC 3H 8S',  # pair
    'AH 8S AS KC JH',  # pair
    '3H 4C 4H 3S 2H',  # 2 pairs
    '5S 5D 2C KH KH',  # 2 pairs
    '3C KH 5D 5S KH',  # 2 pairs
    'AS 3C KH AD KH',  # 2 pairs
    '7C 7S 3S 7H 5S',  # 3 of a kind
    '7C 7S KH 2H 7H',  # 3 of a kind
    'AC KH QH AH AS',  # 3 of a kind
    '2H 4D 3C AS 5S',  # straight (low ace)
    '3C 5C 4C 2C 6H',  # straight
    '6S 8S 7S 5H 9H',  # straight
    'JS QS 9H TS KH',  # straight
    'QC KH TS JS AH',  # straight (high ace)
    '8C 9C 5C 3C TC',  # flush
    '3S 8S 9S 5S KS',  # flush
    '4C 5C 9C 8C KC',  # flush
    'JH 8H AH KH QH',  # flush
    '3D 2H 3H 2C 2D',  # full house
    '2H 2C 3S 3H 3D',  # full house
    'KH KC 3S 3H 3D',  # full house
    'JC 6H JS JD JH',  # 4 of a kind
    'JC 7H JS JD JH',  # 4 of a kind
    'JC KH JS JD JH',  # 4 of a kind
    '2S AS 4S 5S 3S',  # straight flush (low ace)
    '2D 6D 3D 4D 5D',  # straight flush
    '5C 6C 3C 7C 4C',  # straight flush
    'JH 9H TH KH QH',  # straight flush
    'JH AH TH KH QH',  # royal flush (high ace straight flush)
)

A__   :					List[str]							=   (
    ('2H 3H 4H 5H 6H', 'KS AS TS QS JS', 'Loss'),
    ('2H 3H 4H 5H 6H', 'AS AD AC AH JD', 'Win'),
    ('AS AH 2H AD AC', 'JS JD JC JH 3D', 'Win'),
    ('2S AH 2H AS AC', 'JS JD JC JH AD', 'Loss'),
    ('2S AH 2H AS AC', '2H 3H 5H 6H 7H', 'Win'),
    ('AS 3S 4S 8S 2S', '2H 3H 5H 6H 7H', 'Win'),
    ('2H 3H 5H 6H 7H', '2S 3H 4H 5S 6C', 'Win'),
    ('2S 3H 4H 5S 6C', '3D 4C 5H 6H 2S', 'Tie'),
    ('2S 3H 4H 5S 6C', 'AH AC 5H 6H AS', 'Win'),
    ('2S 2H 4H 5S 4C', 'AH AC 5H 6H AS', 'Loss'),
    ('2S 2H 4H 5S 4C', 'AH AC 5H 6H 7S', 'Win'),
    ('6S AD 7H 4S AS', 'AH AC 5H 6H 7S', 'Loss'),
    ('2S AH 4H 5S KC', 'AH AC 5H 6H 7S', 'Loss'),
    ('2S 3H 6H 7S 9C', '7H 3C TH 6H 9S', 'Loss'),
    ('4S 5H 6H TS AC', '3S 5H 6H TS AC', 'Win'),
    ('2S AH 4H 5S 6C', 'AD 4C 5H 6H 2C', 'Tie'),
    ('AS AH 3H AD AC', 'AS AH 2H AD AC', 'Win'),
    ('AH AC 5H 5C QS', 'AH AC 5H 5C KS', 'Loss'),
    ('AH AC 5H 5C QS', 'KH KC 5H 5C QS', 'Win'),
    ('7C 7S KH 2H 7H', '3C 3S AH 2H 3H', 'Win'),
    ('3C 3S AH 2H 3H', '7C 7S KH 2H 7H', 'Loss'),
    ('6H 5H 4H 3H 2H', '5H 4H 3H 2H AH', 'Win'),
    ('5H 4H 3H 2H AH', '5H 4H 3H 2H AH', 'Tie'),
    ('5H 4H 3H 2H AH', '6H 5H 4H 3H 2H', 'Loss'),
    ('AH AD KS KC AC', 'AH KD KH AC KC', 'Win'),
    ('2H 4D 3C AS 5S', '2H 4D 3C 6S 5S', 'Loss'),
    ('2H 3S 3C 3H 2S', '3S 3C 2S 2H 2D', 'Win'),
    ('4D 6D 5D 2D JH', '3S 8S 3H TC KH', 'Loss'),
    ('4S 6C 8S 3S 7S', 'AD KS 2D 7D 7C', 'Loss'),
    ('6S 4C 7H 8C 3H', '5H JC AH 9D 9C', 'Loss'),
    ('9D 9H JH TC QH', '3C 2S JS 5C 7H', 'Win'),
    ('2H TC 8S AD 9S', '4H TS 7H 2C 5C', 'Win'),
    ('9D 3S 2C 7S 7C', 'JC TD 3C TC 9H', 'Loss'),
)

A__   :					Optional[int]							=   (
    ('2H 3H 4H 5H 6H', True),
    ('AS AH 2H AD AC', False),
    ('2H 3H 5H 6H 7H', True),
    ('KS AS TS QS JS', True),
    ('8H 9H QS JS TH', False),
    ('AS 3S 4S 8S 2S', True),
)

A__   :					Any							=   (
    ('2H 3H 4H 5H 6H', True),
    ('AS AH 2H AD AC', False),
    ('2H 3H 5H 6H 7H', False),
    ('KS AS TS QS JS', True),
    ('8H 9H QS JS TH', True),
)

A__   :					Union[str, Any]							=   (
    ('2H 4D 3C AS 5S', True, [5, 4, 3, 2, 1_4]),
    ('2H 5D 3C AS 5S', False, [1_4, 5, 5, 3, 2]),
    ('JH QD KC AS TS', False, [1_4, 1_3, 1_2, 1_1, 1_0]),
    ('9D 3S 2C 7S 7C', False, [9, 7, 7, 3, 2]),
)

A__   :					Union[str, Any]							=   (
    ('JH AH TH KH QH', 0),
    ('JH 9H TH KH QH', 0),
    ('JC KH JS JD JH', 7),
    ('KH KC 3S 3H 3D', 6),
    ('8C 9C 5C 3C TC', 0),
    ('JS QS 9H TS KH', 0),
    ('7C 7S KH 2H 7H', 3),
    ('3C KH 5D 5S KH', 2),
    ('QH 8H KD JH 8S', 1),
    ('2D 6D 9D TH 7D', 0),
)

A__   :					Dict							=   (
    ('JH AH TH KH QH', 2_3),
    ('JH 9H TH KH QH', 2_2),
    ('JC KH JS JD JH', 2_1),
    ('KH KC 3S 3H 3D', 2_0),
    ('8C 9C 5C 3C TC', 1_9),
    ('JS QS 9H TS KH', 1_8),
    ('7C 7S KH 2H 7H', 1_7),
    ('3C KH 5D 5S KH', 1_6),
    ('QH 8H KD JH 8S', 1_5),
    ('2D 6D 9D TH 7D', 1_4),
)




def  _lowerCAmelCase		(		):






  """simple docstring"""



  _lowercase ,				_lowercase:			Dict       =     randrange(len(_UpperCamelCase						)						), randrange(len(_UpperCamelCase						)						)
  _lowercase:			Any       =     ['''Loss''', '''Tie''', '''Win'''][(play >= oppo) + (play > oppo)]
  _lowercase ,				_lowercase:			Dict       =     SORTED_HANDS[play], SORTED_HANDS[oppo]
  return hand, other, expected




def  _lowerCAmelCase		(		_UpperCamelCase = 100						):






  """simple docstring"""



  return (generate_random_hand() for _ in range(_UpperCamelCase						))




@pytest.mark.parametrize('''hand, expected''' ,				_UpperCamelCase						)
def  _lowerCAmelCase		(		_UpperCamelCase ,				_UpperCamelCase						):






  """simple docstring"""



  assert PokerHand(_UpperCamelCase						)._is_flush() == expected




@pytest.mark.parametrize('''hand, expected''' ,				_UpperCamelCase						)
def  _lowerCAmelCase		(		_UpperCamelCase ,				_UpperCamelCase						):






  """simple docstring"""



  assert PokerHand(_UpperCamelCase						)._is_straight() == expected




@pytest.mark.parametrize('''hand, expected, card_values''' ,				_UpperCamelCase						)
def  _lowerCAmelCase		(		_UpperCamelCase ,				_UpperCamelCase ,				_UpperCamelCase						):






  """simple docstring"""



  _lowercase:			Optional[int]       =     PokerHand(_UpperCamelCase						)
  assert player._is_five_high_straight() == expected
  assert player._card_values == card_values




@pytest.mark.parametrize('''hand, expected''' ,				_UpperCamelCase						)
def  _lowerCAmelCase		(		_UpperCamelCase ,				_UpperCamelCase						):






  """simple docstring"""



  assert PokerHand(_UpperCamelCase						)._is_same_kind() == expected




@pytest.mark.parametrize('''hand, expected''' ,				_UpperCamelCase						)
def  _lowerCAmelCase		(		_UpperCamelCase ,				_UpperCamelCase						):






  """simple docstring"""



  assert PokerHand(_UpperCamelCase						)._hand_type == expected




@pytest.mark.parametrize('''hand, other, expected''' ,				_UpperCamelCase						)
def  _lowerCAmelCase		(		_UpperCamelCase ,				_UpperCamelCase ,				_UpperCamelCase						):






  """simple docstring"""



  assert PokerHand(_UpperCamelCase						).compare_with(PokerHand(_UpperCamelCase						)						) == expected




@pytest.mark.parametrize('''hand, other, expected''' ,				generate_random_hands()						)
def  _lowerCAmelCase		(		_UpperCamelCase ,				_UpperCamelCase ,				_UpperCamelCase						):






  """simple docstring"""



  assert PokerHand(_UpperCamelCase						).compare_with(PokerHand(_UpperCamelCase						)						) == expected




def  _lowerCAmelCase		(		):






  """simple docstring"""



  _lowercase:			List[Any]       =     [PokerHand(_UpperCamelCase						) for hand in SORTED_HANDS]
  _lowercase:			List[str]       =     poker_hands.copy()
  shuffle(_UpperCamelCase						)
  _lowercase:			Optional[Any]       =     chain(sorted(_UpperCamelCase						)						)
  for index, hand in enumerate(_UpperCamelCase						):
    assert hand == poker_hands[index]




def  _lowerCAmelCase		(		):






  """simple docstring"""



  _lowercase:			int       =     [PokerHand('''2D AC 3H 4H 5S'''						), PokerHand('''2S 3H 4H 5S 6C'''						)]
  pokerhands.sort(reverse=_UpperCamelCase						)
  assert pokerhands[0].__str__() == "2S 3H 4H 5S 6C"




def  _lowerCAmelCase		(		):






  """simple docstring"""



  _lowercase:			Optional[int]       =     PokerHand('''2C 4S AS 3D 5C'''						)
  _lowercase:			str       =     True
  _lowercase:			List[Any]       =     [5, 4, 3, 2, 14]
  for _ in range(10						):
    assert pokerhand._is_five_high_straight() == expected
    assert pokerhand._card_values == expected_card_values







def  _lowerCAmelCase		(		):






  """simple docstring"""



  _lowercase:			List[str]       =     0
  _lowercase:			Union[str, Any]       =     os.path.abspath(os.path.dirname(_UpperCamelCase						)						)
  _lowercase:			List[str]       =     os.path.join(_UpperCamelCase ,				'''poker_hands.txt'''						)
  with open(_UpperCamelCase						) as file_hand:
    for line in file_hand:
      _lowercase:			List[Any]       =     line[:14].strip()
      _lowercase:			Optional[int]       =     line[15:].strip()
      _lowercase ,				_lowercase:			Dict       =     PokerHand(_UpperCamelCase						), PokerHand(_UpperCamelCase						)
      _lowercase:			str       =     player.compare_with(_UpperCamelCase						)
      if output == "Win":
        answer += 1
  assert answer == 376


"""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__   :					str							=   logging.get_logger(__name__)




def  _lowerCAmelCase		(		_UpperCamelCase ,				_UpperCamelCase=False ,				_UpperCamelCase=False ,				_UpperCamelCase=False						):






  """simple docstring"""



  _lowercase:			Union[str, 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  _lowerCAmelCase		(		_UpperCamelCase ,				_UpperCamelCase						):






  """simple docstring"""



  for i in range(config.num_hidden_layers						):
    _lowercase:			Union[str, Any]       =     '''vilt.'''
    # read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
    _lowercase:			List[str]       =     state_dict.pop(f'''transformer.blocks.{i}.attn.qkv.weight'''						)
    _lowercase:			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
    _lowercase:			List[str]       =     in_proj_weight[
        : config.hidden_size, :
    ]
    _lowercase:			Dict       =     in_proj_bias[: config.hidden_size]
    _lowercase:			str       =     in_proj_weight[
        config.hidden_size : config.hidden_size * 2, :
    ]
    _lowercase:			Optional[Any]       =     in_proj_bias[
        config.hidden_size : config.hidden_size * 2
    ]
    _lowercase:			List[str]       =     in_proj_weight[
        -config.hidden_size :, :
    ]
    _lowercase:			Any       =     in_proj_bias[-config.hidden_size :]




def  _lowerCAmelCase		(		_UpperCamelCase						):






  """simple docstring"""



  _lowercase:			Optional[Any]       =     ['''head.weight''', '''head.bias''']
  for k in ignore_keys:
    state_dict.pop(_UpperCamelCase ,				_UpperCamelCase						)




def  _lowerCAmelCase		(		_UpperCamelCase ,				_UpperCamelCase ,				_UpperCamelCase						):






  """simple docstring"""



  _lowercase:			Tuple       =     dct.pop(_UpperCamelCase						)
  _lowercase:			Optional[Any]       =     val




@torch.no_grad()
def  _lowerCAmelCase		(		_UpperCamelCase ,				_UpperCamelCase						):






  """simple docstring"""



  _lowercase:			List[str]       =     ViltConfig(image_size=384 ,				patch_size=32 ,				tie_word_embeddings=_UpperCamelCase						)
  _lowercase:			List[Any]       =     False
  _lowercase:			List[str]       =     False
  _lowercase:			Optional[int]       =     False
  _lowercase:			Optional[int]       =     False
  if "vqa" in checkpoint_url:
    _lowercase:			Tuple       =     True
    _lowercase:			int       =     3_129
    _lowercase:			Union[str, Any]       =     '''huggingface/label-files'''
    _lowercase:			Optional[int]       =     '''vqa2-id2label.json'''
    _lowercase:			Optional[int]       =     json.load(open(hf_hub_download(_UpperCamelCase ,				_UpperCamelCase ,				repo_type='''dataset'''						) ,				'''r'''						)						)
    _lowercase:			str       =     {int(_UpperCamelCase						): v for k, v in idalabel.items()}
    _lowercase:			int       =     idalabel
    _lowercase:			Any       =     {v: k for k, v in idalabel.items()}
    _lowercase:			str       =     ViltForQuestionAnswering(_UpperCamelCase						)
  elif "nlvr" in checkpoint_url:
    _lowercase:			List[str]       =     True
    _lowercase:			Tuple       =     2
    _lowercase:			int       =     {0: '''False''', 1: '''True'''}
    _lowercase:			Any       =     {v: k for k, v in config.idalabel.items()}
    _lowercase:			Any       =     3
    _lowercase:			Optional[Any]       =     ViltForImagesAndTextClassification(_UpperCamelCase						)
  elif "irtr" in checkpoint_url:
    _lowercase:			Dict       =     True
    _lowercase:			Union[str, Any]       =     ViltForImageAndTextRetrieval(_UpperCamelCase						)
  elif "mlm_itm" in checkpoint_url:
    _lowercase:			Any       =     True
    _lowercase:			str       =     ViltForMaskedLM(_UpperCamelCase						)
  else:
    raise ValueError('''Unknown model type'''						)

  # load state_dict of original model, remove and rename some keys
  _lowercase:			Tuple       =     torch.hub.load_state_dict_from_url(_UpperCamelCase ,				map_location='''cpu'''						)['''state_dict''']
  _lowercase:			Union[str, Any]       =     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:
    _lowercase:			Optional[int]       =     ['''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:
    _lowercase ,				_lowercase:			Optional[Any]       =     model.load_state_dict(_UpperCamelCase ,				strict=_UpperCamelCase						)
    assert missing_keys == ["mlm_score.decoder.bias"]
  else:
    model.load_state_dict(_UpperCamelCase						)

  # Define processor
  _lowercase:			List[str]       =     ViltImageProcessor(size=384						)
  _lowercase:			Tuple       =     BertTokenizer.from_pretrained('''bert-base-uncased'''						)
  _lowercase:			Tuple       =     ViltProcessor(_UpperCamelCase ,				_UpperCamelCase						)

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

  # Verify outputs
  if mlm_model:
    _lowercase:			List[str]       =     torch.Size([1, 11, 30_522]						)
    _lowercase:			str       =     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"
    _lowercase:			Union[str, Any]       =     outputs.logits[0, 4, :].argmax(-1						).item()
    assert tokenizer.decode([predicted_id]						) == "cats"
  elif vqa_model:
    _lowercase:			int       =     torch.Size([1, 3_129]						)
    _lowercase:			Optional[Any]       =     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"
    _lowercase:			Optional[Any]       =     outputs.logits.argmax(-1						).item()
    assert model.config.idalabel[predicted_idx] == "2"
  elif nlvr_model:
    _lowercase:			List[Any]       =     torch.Size([1, 2]						)
    _lowercase:			int       =     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__":
  A__   :					int							=   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__   :					Union[str, Any]							=   parser.parse_args()
  convert_vilt_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)
