"""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 BeitConfig, BeitForImageClassification, BeitForMaskedImageModeling, BeitImageProcessor
from transformers.image_utils import PILImageResampling
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       )     ->Union[str, Any]:
  a__:					str      							=     'backbone.' if is_semantic else ''

  a__:					Any      							=     []
  for i in range(config.num_hidden_layers       ):
    # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
    rename_keys.append((F'{prefix}blocks.{i}.norm1.weight', F'beit.encoder.layer.{i}.layernorm_before.weight')       )
    rename_keys.append((F'{prefix}blocks.{i}.norm1.bias', F'beit.encoder.layer.{i}.layernorm_before.bias')       )
    rename_keys.append(
        (F'{prefix}blocks.{i}.attn.proj.weight', F'beit.encoder.layer.{i}.attention.output.dense.weight')       )
    rename_keys.append(
        (F'{prefix}blocks.{i}.attn.proj.bias', F'beit.encoder.layer.{i}.attention.output.dense.bias')       )
    rename_keys.append((F'{prefix}blocks.{i}.norm2.weight', F'beit.encoder.layer.{i}.layernorm_after.weight')       )
    rename_keys.append((F'{prefix}blocks.{i}.norm2.bias', F'beit.encoder.layer.{i}.layernorm_after.bias')       )
    rename_keys.append((F'{prefix}blocks.{i}.mlp.fc1.weight', F'beit.encoder.layer.{i}.intermediate.dense.weight')       )
    rename_keys.append((F'{prefix}blocks.{i}.mlp.fc1.bias', F'beit.encoder.layer.{i}.intermediate.dense.bias')       )
    rename_keys.append((F'{prefix}blocks.{i}.mlp.fc2.weight', F'beit.encoder.layer.{i}.output.dense.weight')       )
    rename_keys.append((F'{prefix}blocks.{i}.mlp.fc2.bias', F'beit.encoder.layer.{i}.output.dense.bias')       )

  # projection layer + position embeddings
  rename_keys.extend(
      [
          (F'{prefix}cls_token', 'beit.embeddings.cls_token'),
          (F'{prefix}patch_embed.proj.weight', 'beit.embeddings.patch_embeddings.projection.weight'),
          (F'{prefix}patch_embed.proj.bias', 'beit.embeddings.patch_embeddings.projection.bias'),
          (F'{prefix}pos_embed', 'beit.embeddings.position_embeddings'),
      ]       )

  if has_lm_head:
    # mask token + layernorm
    rename_keys.extend(
        [
            ('mask_token', 'beit.embeddings.mask_token'),
            ('norm.weight', 'layernorm.weight'),
            ('norm.bias', 'layernorm.bias'),
        ]       )
  else:
    # layernorm + classification head
    rename_keys.extend(
        [
            ('fc_norm.weight', 'beit.pooler.layernorm.weight'),
            ('fc_norm.bias', 'beit.pooler.layernorm.bias'),
            ('head.weight', 'classifier.weight'),
            ('head.bias', 'classifier.bias'),
        ]       )

  return rename_keys







def 						__a       (     _SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE=False		,					_SCREAMING_SNAKE_CASE=False       )     ->str:
  for i in range(config.num_hidden_layers       ):
    a__:					int      							=     'backbone.' if is_semantic else ''
    # queries, keys and values
    a__:					Optional[int]      							=     state_dict.pop(F'{prefix}blocks.{i}.attn.qkv.weight'       )
    a__:					Optional[Any]      							=     state_dict.pop(F'{prefix}blocks.{i}.attn.q_bias'       )
    a__:					Any      							=     state_dict.pop(F'{prefix}blocks.{i}.attn.v_bias'       )

    a__:					List[Any]      							=     in_proj_weight[
        : config.hidden_size, :
    ]
    a__:					Any      							=     q_bias
    a__:					str      							=     in_proj_weight[
        config.hidden_size : config.hidden_size * 2, :
    ]
    a__:					Tuple      							=     in_proj_weight[
        -config.hidden_size :, :
    ]
    a__:					Dict      							=     v_bias

    # gamma_1 and gamma_2
    # we call them lambda because otherwise they are renamed when using .from_pretrained
    a__:					Dict      							=     state_dict.pop(F'{prefix}blocks.{i}.gamma_1'       )
    a__:					str      							=     state_dict.pop(F'{prefix}blocks.{i}.gamma_2'       )

    a__:					List[str]      							=     gamma_a
    a__:					Optional[int]      							=     gamma_a







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







def 						__a       (     )     ->Tuple:
  a__:					str      							=     'http://images.cocodataset.org/val2017/000000039769.jpg'
  a__:					Dict      							=     Image.open(requests.get(_SCREAMING_SNAKE_CASE		,					stream=_SCREAMING_SNAKE_CASE       ).raw       )
  return im







@torch.no_grad()
def 						__a       (     _SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE=False       )     ->str:
  a__:					Tuple      							=     False if 'rvlcdip' in checkpoint_url else True
  a__:					str      							=     BeitConfig(use_absolute_position_embeddings=_SCREAMING_SNAKE_CASE		,					use_mask_token=_SCREAMING_SNAKE_CASE       )

  # size of the architecture
  if "large" in checkpoint_url or "dit-l" in checkpoint_url:
    a__:					Union[str, Any]      							=     1024
    a__:					str      							=     4096
    a__:					List[str]      							=     24
    a__:					Union[str, Any]      							=     16

  # labels
  if "rvlcdip" in checkpoint_url:
    a__:					Optional[Any]      							=     16
    a__:					Optional[Any]      							=     'huggingface/label-files'
    a__:					List[Any]      							=     'rvlcdip-id2label.json'
    a__:					Any      							=     json.load(open(hf_hub_download(_SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE		,					repo_type='dataset'       )		,					'r'       )       )
    a__:					Optional[Any]      							=     {int(_SCREAMING_SNAKE_CASE       ): v for k, v in idalabel.items()}
    a__:					Any      							=     idalabel
    a__:					Union[str, Any]      							=     {v: k for k, v in idalabel.items()}

  # load state_dict of original model, remove and rename some keys
  a__:					Any      							=     torch.hub.load_state_dict_from_url(_SCREAMING_SNAKE_CASE		,					map_location='cpu'       )['model']

  a__:					Tuple      							=     create_rename_keys(_SCREAMING_SNAKE_CASE		,					has_lm_head=_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		,					has_lm_head=_SCREAMING_SNAKE_CASE       )

  # load HuggingFace model
  a__:					str      							=     BeitForMaskedImageModeling(_SCREAMING_SNAKE_CASE       ) if has_lm_head else BeitForImageClassification(_SCREAMING_SNAKE_CASE       )
  model.eval()
  model.load_state_dict(_SCREAMING_SNAKE_CASE       )

  # Check outputs on an image
  a__:					int      							=     BeitImageProcessor(
      size=config.image_size		,					resample=PILImageResampling.BILINEAR		,					do_center_crop=_SCREAMING_SNAKE_CASE       )
  a__:					int      							=     prepare_img()

  a__:					int      							=     image_processor(images=_SCREAMING_SNAKE_CASE		,					return_tensors='pt'       )
  a__:					Dict      							=     encoding['pixel_values']

  a__:					Any      							=     model(_SCREAMING_SNAKE_CASE       )
  a__:					int      							=     outputs.logits

  # verify logits
  a__:					Union[str, Any]      							=     [1, 16] if 'rvlcdip' in checkpoint_url else [1, 196, 8192]
  assert logits.shape == torch.Size(_SCREAMING_SNAKE_CASE       ), "Shape of logits not as expected"

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

  if push_to_hub:
    if has_lm_head:
      a__:					Tuple      							=     'dit-base' if 'base' in checkpoint_url else 'dit-large'
    else:
      a__:					Tuple      							=     'dit-base-finetuned-rvlcdip' if 'dit-b' in checkpoint_url else 'dit-large-finetuned-rvlcdip'
    image_processor.push_to_hub(
        repo_path_or_name=Path(_SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE       )		,					organization='nielsr'		,					commit_message='Add image processor'		,					use_temp_dir=_SCREAMING_SNAKE_CASE		,					)
    model.push_to_hub(
        repo_path_or_name=Path(_SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE       )		,					organization='nielsr'		,					commit_message='Add model'		,					use_temp_dir=_SCREAMING_SNAKE_CASE		,					)


if __name__ == "__main__":
 lowercase__ 	=     argparse.ArgumentParser()

 parser.add_argument(
     '--checkpoint_url',
     default='https://layoutlm.blob.core.windows.net/dit/dit-pts/dit-base-224-p16-500k-62d53a.pth',
     type=str,
     help='URL to the original PyTorch checkpoint (.pth file).',
 )
 parser.add_argument(
     '--pytorch_dump_folder_path', default=None, type=str, help='Path to the folder to output PyTorch model.'
 )
 parser.add_argument(
     '--push_to_hub',
     action='store_true',
 )
 lowercase__ 	=     parser.parse_args()
 convert_dit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)



"""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')


