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()
lowercase__ 	=     logging.get_logger(__name__)







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







def 						__a       (     _SCREAMING_SNAKE_CASE       )     ->Optional[int]:
  a__:					List[str]      							=     ['head.weight', 'head.bias']
  for k in ignore_keys:
    state_dict.pop(_SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE       )







def 						__a       (     _SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE       )     ->List[str]:
  a__:					List[str]      							=     dct.pop(_SCREAMING_SNAKE_CASE       )
  a__:					Optional[int]      							=     val







@torch.no_grad()
def 						__a       (     _SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE       )     ->str:
  a__:					List[Any]      							=     ViltConfig(image_size=384		,					patch_size=32		,					tie_word_embeddings=_SCREAMING_SNAKE_CASE       )
  a__:					List[str]      							=     False
  a__:					Any      							=     False
  a__:					Dict      							=     False
  a__:					Optional[int]      							=     False
  if "vqa" in checkpoint_url:
    a__:					str      							=     True
    a__:					int      							=     3129
    a__:					Dict      							=     'huggingface/label-files'
    a__:					List[str]      							=     'vqa2-id2label.json'
    a__:					Optional[int]      							=     json.load(open(hf_hub_download(_SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE		,					repo_type='dataset'       )		,					'r'       )       )
    a__:					Any      							=     {int(_SCREAMING_SNAKE_CASE       ): v for k, v in idalabel.items()}
    a__:					str      							=     idalabel
    a__:					Union[str, Any]      							=     {v: k for k, v in idalabel.items()}
    a__:					Tuple      							=     ViltForQuestionAnswering(_SCREAMING_SNAKE_CASE       )
  elif "nlvr" in checkpoint_url:
    a__:					Optional[int]      							=     True
    a__:					str      							=     2
    a__:					Any      							=     {0: 'False', 1: 'True'}
    a__:					List[Any]      							=     {v: k for k, v in config.idalabel.items()}
    a__:					int      							=     3
    a__:					str      							=     ViltForImagesAndTextClassification(_SCREAMING_SNAKE_CASE       )
  elif "irtr" in checkpoint_url:
    a__:					Optional[int]      							=     True
    a__:					int      							=     ViltForImageAndTextRetrieval(_SCREAMING_SNAKE_CASE       )
  elif "mlm_itm" in checkpoint_url:
    a__:					List[str]      							=     True
    a__:					Tuple      							=     ViltForMaskedLM(_SCREAMING_SNAKE_CASE       )
  else:
    raise ValueError('Unknown model type'       )

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

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

  # Define processor
  a__:					Optional[Any]      							=     ViltImageProcessor(size=384       )
  a__:					Union[str, Any]      							=     BertTokenizer.from_pretrained('bert-base-uncased'       )
  a__:					int      							=     ViltProcessor(_SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE       )

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

  # Verify outputs
  if mlm_model:
    a__:					Dict      							=     torch.Size([1, 11, 30522]       )
    a__:					Tuple      							=     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]		,					_SCREAMING_SNAKE_CASE		,					atol=1e-4       )

    # verify masked token prediction equals "cats"
    a__:					Dict      							=     outputs.logits[0, 4, :].argmax(-1       ).item()
    assert tokenizer.decode([predicted_id]       ) == "cats"
  elif vqa_model:
    a__:					str      							=     torch.Size([1, 3129]       )
    a__:					Optional[int]      							=     torch.tensor([-15.9_495, -18.1_472, -10.3_041]       )
    assert torch.allclose(outputs.logits[0, :3]		,					_SCREAMING_SNAKE_CASE		,					atol=1e-4       )
    assert outputs.logits.shape == expected_shape
    assert torch.allclose(outputs.logits[0, 0, :3]		,					_SCREAMING_SNAKE_CASE		,					atol=1e-4       )

    # verify vqa prediction equals "2"
    a__:					List[Any]      							=     outputs.logits.argmax(-1       ).item()
    assert model.config.idalabel[predicted_idx] == "2"
  elif nlvr_model:
    a__:					List[Any]      							=     torch.Size([1, 2]       )
    a__:					Optional[Any]      							=     torch.tensor([-2.8_721, 2.1_291]       )
    assert torch.allclose(outputs.logits[0, :3]		,					_SCREAMING_SNAKE_CASE		,					atol=1e-4       )
    assert outputs.logits.shape == expected_shape

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


if __name__ == "__main__":
 lowercase__ 	=     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.'
 )

 lowercase__ 	=     parser.parse_args()
 convert_vilt_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)



"""simple docstring"""
def 						__a       (     _SCREAMING_SNAKE_CASE       )     ->bool:
  return credit_card_number.startswith(('34', '35', '37', '4', '5', '6')       )







def 						__a       (     _SCREAMING_SNAKE_CASE       )     ->bool:
  a__:					Any      							=     credit_card_number
  a__:					Tuple      							=     0
  a__:					List[str]      							=     len(_SCREAMING_SNAKE_CASE       ) - 2
  for i in range(_SCREAMING_SNAKE_CASE		,					-1		,					-2       ):
    #  double the value of every second digit
    a__:					Tuple      							=     int(cc_number[i]       )
    digit *= 2
    # If doubling of a number results in a two digit number
    # i.e greater than 9(e.g., 6 × 2 = 12),
    # then add the digits of the product (e.g., 12: 1 + 2 = 3, 15: 1 + 5 = 6),
    # to get a single digit number.
    if digit > 9:
      digit %= 10
      digit += 1
    a__:					Optional[Any]      							=     cc_number[:i] + str(_SCREAMING_SNAKE_CASE       ) + cc_number[i + 1 :]
    total += digit

  # Sum up the remaining digits
  for i in range(len(_SCREAMING_SNAKE_CASE       ) - 1		,					-1		,					-2       ):
    total += int(cc_number[i]       )

  return total % 10 == 0







def 						__a       (     _SCREAMING_SNAKE_CASE       )     ->bool:
  a__:					Optional[int]      							=     F'{credit_card_number} is an invalid credit card number because'
  if not credit_card_number.isdigit():
    print(F'{error_message} it has nonnumerical characters.'       )
    return False

  if not 13 <= len(_SCREAMING_SNAKE_CASE       ) <= 16:
    print(F'{error_message} of its length.'       )
    return False

  if not validate_initial_digits(_SCREAMING_SNAKE_CASE       ):
    print(F'{error_message} of its first two digits.'       )
    return False

  if not luhn_validation(_SCREAMING_SNAKE_CASE       ):
    print(F'{error_message} it fails the Luhn check.'       )
    return False

  print(F'{credit_card_number} is a valid credit card number.'       )
  return True


if __name__ == "__main__":
 import doctest

 doctest.testmod()
 validate_credit_card_number('4111111111111111')
 validate_credit_card_number('32323')


