"""simple docstring"""
import argparse
import json
from pathlib import Path

import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import DeiTConfig, DeiTForImageClassificationWithTeacher, DeiTImageProcessor
from transformers.utils import logging


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







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

  # projection layer + position embeddings
  rename_keys.extend(
      [
          ('cls_token', 'deit.embeddings.cls_token'),
          ('dist_token', 'deit.embeddings.distillation_token'),
          ('patch_embed.proj.weight', 'deit.embeddings.patch_embeddings.projection.weight'),
          ('patch_embed.proj.bias', 'deit.embeddings.patch_embeddings.projection.bias'),
          ('pos_embed', 'deit.embeddings.position_embeddings'),
      ]       )

  if base_model:
    # layernorm + pooler
    rename_keys.extend(
        [
            ('norm.weight', 'layernorm.weight'),
            ('norm.bias', 'layernorm.bias'),
            ('pre_logits.fc.weight', 'pooler.dense.weight'),
            ('pre_logits.fc.bias', 'pooler.dense.bias'),
        ]       )

    # if just the base model, we should remove "deit" from all keys that start with "deit"
    a__:					Any      							=     [(pair[0], pair[1][4:]) if pair[1].startswith('deit'       ) else pair for pair in rename_keys]
  else:
    # layernorm + classification heads
    rename_keys.extend(
        [
            ('norm.weight', 'deit.layernorm.weight'),
            ('norm.bias', 'deit.layernorm.bias'),
            ('head.weight', 'cls_classifier.weight'),
            ('head.bias', 'cls_classifier.bias'),
            ('head_dist.weight', 'distillation_classifier.weight'),
            ('head_dist.bias', 'distillation_classifier.bias'),
        ]       )

  return rename_keys







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







def 						__a       (     _SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE       )     ->int:
  a__:					Union[str, Any]      							=     dct.pop(_SCREAMING_SNAKE_CASE       )
  a__:					Dict      							=     val







def 						__a       (     )     ->Dict:
  a__:					Dict      							=     'http://images.cocodataset.org/val2017/000000039769.jpg'
  a__:					str      							=     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       )     ->Union[str, Any]:
  a__:					Tuple      							=     DeiTConfig()
  # all deit models have fine-tuned heads
  a__:					Optional[Any]      							=     False
  # dataset (fine-tuned on ImageNet 2012), patch_size and image_size
  a__:					int      							=     1000
  a__:					Dict      							=     'huggingface/label-files'
  a__:					List[str]      							=     'imagenet-1k-id2label.json'
  a__:					List[str]      							=     json.load(open(hf_hub_download(_SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE		,					repo_type='dataset'       )		,					'r'       )       )
  a__:					int      							=     {int(_SCREAMING_SNAKE_CASE       ): v for k, v in idalabel.items()}
  a__:					List[Any]      							=     idalabel
  a__:					Any      							=     {v: k for k, v in idalabel.items()}
  a__:					int      							=     int(deit_name[-6:-4]       )
  a__:					Optional[Any]      							=     int(deit_name[-3:]       )
  # size of the architecture
  if deit_name[9:].startswith('tiny'       ):
    a__:					Any      							=     192
    a__:					Any      							=     768
    a__:					Optional[Any]      							=     12
    a__:					List[Any]      							=     3
  elif deit_name[9:].startswith('small'       ):
    a__:					List[str]      							=     384
    a__:					Optional[int]      							=     1536
    a__:					Optional[int]      							=     12
    a__:					Union[str, Any]      							=     6
  if deit_name[9:].startswith('base'       ):
    pass
  elif deit_name[4:].startswith('large'       ):
    a__:					List[Any]      							=     1024
    a__:					int      							=     4096
    a__:					Optional[Any]      							=     24
    a__:					List[str]      							=     16

  # load original model from timm
  a__:					List[str]      							=     timm.create_model(_SCREAMING_SNAKE_CASE		,					pretrained=_SCREAMING_SNAKE_CASE       )
  timm_model.eval()

  # load state_dict of original model, remove and rename some keys
  a__:					Union[str, Any]      							=     timm_model.state_dict()
  a__:					Optional[Any]      							=     create_rename_keys(_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		,					_SCREAMING_SNAKE_CASE       )

  # load HuggingFace model
  a__:					Union[str, Any]      							=     DeiTForImageClassificationWithTeacher(_SCREAMING_SNAKE_CASE       ).eval()
  model.load_state_dict(_SCREAMING_SNAKE_CASE       )

  # Check outputs on an image, prepared by DeiTImageProcessor
  a__:					List[str]      							=     int(
      (256 / 224) * config.image_size       )  # to maintain same ratio w.r.t. 224 images, see https://github.com/facebookresearch/deit/blob/ab5715372db8c6cad5740714b2216d55aeae052e/datasets.py#L103
  a__:					int      							=     DeiTImageProcessor(size=_SCREAMING_SNAKE_CASE		,					crop_size=config.image_size       )
  a__:					Dict      							=     image_processor(images=prepare_img()		,					return_tensors='pt'       )
  a__:					Union[str, Any]      							=     encoding['pixel_values']
  a__:					Any      							=     model(_SCREAMING_SNAKE_CASE       )

  a__:					Any      							=     timm_model(_SCREAMING_SNAKE_CASE       )
  assert timm_logits.shape == outputs.logits.shape
  assert torch.allclose(_SCREAMING_SNAKE_CASE		,					outputs.logits		,					atol=1e-3       )

  Path(_SCREAMING_SNAKE_CASE       ).mkdir(exist_ok=_SCREAMING_SNAKE_CASE       )
  print(F'Saving model {deit_name} 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 __name__ == "__main__":
 lowercase__ 	=     argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     '--deit_name',
     default='vit_deit_base_distilled_patch16_224',
     type=str,
     help='Name of the DeiT timm model 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_deit_checkpoint(args.deit_name, 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')


