





"""simple docstring"""





from math import factorial

lowercase__				:dict[str, int]				   =       {str(digit): factorial(digit) for digit in range(1_0)}


def       lowerCamelCase_       (   UpperCAmelCase_  )						->int:





  """simple docstring"""


  if not isinstance(UpperCAmelCase_  ,			UpperCAmelCase_  ):
    raise TypeError('''Parameter number must be int'''  )

  if number < 0:
    raise ValueError('''Parameter number must be greater than or equal to 0'''  )

  # Converts number in string to iterate on its digits and adds its factorial.
  return sum(DIGIT_FACTORIAL[digit] for digit in str(UpperCAmelCase_  )  )


def       lowerCamelCase_       (   UpperCAmelCase_ = 60  ,			UpperCAmelCase_ = 1_00_00_00  )						->int:





  """simple docstring"""


  if not isinstance(UpperCAmelCase_  ,			UpperCAmelCase_  ) or not isinstance(UpperCAmelCase_  ,			UpperCAmelCase_  ):
    raise TypeError('''Parameters chain_length and number_limit must be int'''  )

  if chain_length <= 0 or number_limit <= 0:
    raise ValueError(
        '''Parameters chain_length and number_limit must be greater than 0'''  )

  # the counter for the chains with the exact desired length
  __UpperCAmelCase    :						List[str]       =						0
  # the cached sizes of the previous chains
  __UpperCAmelCase    :						dict[int, int]       =						{}

  for start_chain_element in range(1  ,			UpperCAmelCase_  ):
    # The temporary set will contain the elements of the chain
    __UpperCAmelCase    :						List[str]       =						set()
    __UpperCAmelCase    :						List[Any]       =						0

    # Stop computing the chain when you find a cached size, a repeating item or the
    # length is greater then the desired one.
    __UpperCAmelCase    :						List[str]       =						start_chain_element
    while (
        chain_element not in chain_sets_lengths
        and chain_element not in chain_set
        and chain_set_length <= chain_length
    ):
      chain_set.add(UpperCAmelCase_  )
      chain_set_length += 1
      __UpperCAmelCase    :						Optional[Any]       =						digit_factorial_sum(UpperCAmelCase_  )

    if chain_element in chain_sets_lengths:
      chain_set_length += chain_sets_lengths[chain_element]

    __UpperCAmelCase    :						Dict       =						chain_set_length

    # If chain contains the exact amount of elements increase the counter
    if chain_set_length == chain_length:
      chains_counter += 1

  return chains_counter


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  print(f"""{solution()}""")






"""simple docstring"""





import argparse
import re

import requests
import torch

# git clone https://github.com/salesforce/BLIP.git
from models.blip import blip_decoder
from models.blip_itm import blip_itm
from models.blip_vqa import blip_vqa
from PIL import Image
from torchvision import transforms
from torchvision.transforms.functional import InterpolationMode

from transformers import (
    BertTokenizer,
    BlipConfig,
    BlipForConditionalGeneration,
    BlipForImageTextRetrieval,
    BlipForQuestionAnswering,
)


def       lowerCamelCase_       (   UpperCAmelCase_  ,			UpperCAmelCase_  )						->Any:





  """simple docstring"""


  __UpperCAmelCase    :						Tuple       =						'''https://storage.googleapis.com/sfr-vision-language-research/BLIP/demo.jpg'''
  __UpperCAmelCase    :						Tuple       =						Image.open(requests.get(UpperCAmelCase_  ,			stream=UpperCAmelCase_  ).raw  ).convert('''RGB'''  )

  __UpperCAmelCase    :						Dict       =						transforms.Compose(
      [
          transforms.Resize((image_size, image_size)  ,			interpolation=InterpolationMode.BICUBIC  ),
          transforms.ToTensor(),
          transforms.Normalize((0.48_145_466, 0.4_578_275, 0.40_821_073)  ,			(0.26_862_954, 0.26_130_258, 0.27_577_711)  ),
      ]  )
  __UpperCAmelCase    :						Optional[Any]       =						transform(UpperCAmelCase_  ).unsqueeze(0  ).to(UpperCAmelCase_  )
  return image


def       lowerCamelCase_       (   UpperCAmelCase_  )						->Optional[Any]:





  """simple docstring"""


  if "visual_encoder" in key:
    __UpperCAmelCase    :						Tuple       =						re.sub('''visual_encoder*'''  ,			'''vision_model.encoder'''  ,			UpperCAmelCase_  )
  if "blocks" in key:
    __UpperCAmelCase    :						List[str]       =						re.sub(R'''blocks'''  ,			'''layers'''  ,			UpperCAmelCase_  )
  if "attn" in key:
    __UpperCAmelCase    :						Union[str, Any]       =						re.sub(R'''attn'''  ,			'''self_attn'''  ,			UpperCAmelCase_  )
  if "norm1" in key:
    __UpperCAmelCase    :						Optional[Any]       =						re.sub(R'''norm1'''  ,			'''layer_norm1'''  ,			UpperCAmelCase_  )
  if "norm2" in key:
    __UpperCAmelCase    :						Optional[int]       =						re.sub(R'''norm2'''  ,			'''layer_norm2'''  ,			UpperCAmelCase_  )
  if "encoder.norm" in key:
    __UpperCAmelCase    :						Union[str, Any]       =						re.sub(R'''encoder.norm'''  ,			'''post_layernorm'''  ,			UpperCAmelCase_  )
  if "encoder.patch_embed.proj" in key:
    __UpperCAmelCase    :						Optional[int]       =						re.sub(R'''encoder.patch_embed.proj'''  ,			'''embeddings.patch_embedding'''  ,			UpperCAmelCase_  )

  if "encoder.pos_embed" in key:
    __UpperCAmelCase    :						List[Any]       =						re.sub(R'''encoder.pos_embed'''  ,			'''embeddings.position_embedding'''  ,			UpperCAmelCase_  )
  if "encoder.cls_token" in key:
    __UpperCAmelCase    :						List[str]       =						re.sub(R'''encoder.cls_token'''  ,			'''embeddings.class_embedding'''  ,			UpperCAmelCase_  )

  if "self_attn" in key:
    __UpperCAmelCase    :						Any       =						re.sub(R'''self_attn.proj'''  ,			'''self_attn.projection'''  ,			UpperCAmelCase_  )

  return key


@torch.no_grad()
def       lowerCamelCase_       (   UpperCAmelCase_  ,			UpperCAmelCase_=None  )						->Dict:





  """simple docstring"""


  if config_path is not None:
    __UpperCAmelCase    :						Optional[int]       =						BlipConfig.from_pretrained(UpperCAmelCase_  )
  else:
    __UpperCAmelCase    :						Optional[int]       =						BlipConfig(projection_dim=5_12  ,			text_config={}  ,			vision_config={}  )

  __UpperCAmelCase    :						Optional[Any]       =						BlipForConditionalGeneration(UpperCAmelCase_  ).eval()

  __UpperCAmelCase    :						List[str]       =						'''https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_capfilt_large.pth'''

  __UpperCAmelCase    :						List[Any]       =						blip_decoder(pretrained=UpperCAmelCase_  ,			image_size=3_84  ,			vit='''base'''  )
  __UpperCAmelCase    :						str       =						pt_model.eval()

  __UpperCAmelCase    :						List[Any]       =						pt_model.state_dict()
  for key in modified_state_dict.copy():
    __UpperCAmelCase    :						List[str]       =						modified_state_dict.pop(UpperCAmelCase_  )
    __UpperCAmelCase    :						Optional[int]       =						rename_key(UpperCAmelCase_  )
    __UpperCAmelCase    :						List[Any]       =						value

  hf_model.load_state_dict(UpperCAmelCase_  )

  __UpperCAmelCase    :						str       =						3_84
  __UpperCAmelCase    :						Tuple       =						load_demo_image(image_size=UpperCAmelCase_  ,			device='''cpu'''  )
  __UpperCAmelCase    :						Optional[Any]       =						BertTokenizer.from_pretrained('''bert-base-uncased'''  )
  __UpperCAmelCase    :						Optional[int]       =						tokenizer(['''a picture of''']  ).input_ids

  __UpperCAmelCase    :						Union[str, Any]       =						hf_model.generate(UpperCAmelCase_  ,			UpperCAmelCase_  )

  assert out[0].tolist() == [3_05_22, 10_37, 38_61, 19_97, 10_37, 24_50, 35_64, 20_06, 19_96, 35_09, 20_07, 20_14, 38_99, 1_02]

  __UpperCAmelCase    :						Optional[int]       =						hf_model.generate(UpperCAmelCase_  )

  assert out[0].tolist() == [3_05_22, 10_37, 24_50, 35_64, 20_06, 19_96, 35_09, 20_07, 20_14, 38_99, 1_02]

  if pytorch_dump_folder_path is not None:
    hf_model.save_pretrained(UpperCAmelCase_  )

  # model_url = 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_vqa.pth'
  __UpperCAmelCase    :						Optional[Any]       =						(
      '''https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_vqa_capfilt_large.pth'''
  )

  __UpperCAmelCase    :						List[str]       =						blip_vqa(pretrained=UpperCAmelCase_  ,			image_size=UpperCAmelCase_  ,			vit='''base'''  )
  vqa_model.eval()

  __UpperCAmelCase    :						Dict       =						vqa_model.state_dict()
  for key in modified_state_dict.copy():
    __UpperCAmelCase    :						List[Any]       =						modified_state_dict.pop(UpperCAmelCase_  )
    __UpperCAmelCase    :						int       =						rename_key(UpperCAmelCase_  )
    __UpperCAmelCase    :						Union[str, Any]       =						value

  __UpperCAmelCase    :						List[str]       =						BlipForQuestionAnswering(UpperCAmelCase_  )

  hf_vqa_model.load_state_dict(UpperCAmelCase_  )

  __UpperCAmelCase    :						str       =						['''How many dogs are in this image?''']
  __UpperCAmelCase    :						Dict       =						tokenizer(UpperCAmelCase_  ,			return_tensors='''pt'''  ).input_ids

  __UpperCAmelCase    :						Any       =						hf_vqa_model.generate(UpperCAmelCase_  ,			UpperCAmelCase_  )
  print(tokenizer.decode(answer[0]  )  )

  assert tokenizer.decode(answer[0]  ) == "[UNK] 1 [SEP]"
  if pytorch_dump_folder_path is not None:
    hf_vqa_model.save_pretrained(pytorch_dump_folder_path + '''_vqa'''  )

  __UpperCAmelCase    :						List[Any]       =						'''https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_retrieval_coco.pth'''

  __UpperCAmelCase    :						List[str]       =						blip_itm(pretrained=UpperCAmelCase_  ,			image_size=UpperCAmelCase_  ,			vit='''base'''  )
  itm_model.eval()

  __UpperCAmelCase    :						Any       =						itm_model.state_dict()
  for key in modified_state_dict.copy():
    __UpperCAmelCase    :						List[str]       =						modified_state_dict.pop(UpperCAmelCase_  )
    __UpperCAmelCase    :						Dict       =						rename_key(UpperCAmelCase_  )
    __UpperCAmelCase    :						int       =						value

  __UpperCAmelCase    :						Optional[int]       =						BlipForImageTextRetrieval(UpperCAmelCase_  )

  __UpperCAmelCase    :						List[str]       =						['''A picture of a woman with a dog sitting in a beach''']
  __UpperCAmelCase    :						Optional[int]       =						tokenizer(
      UpperCAmelCase_  ,			return_tensors='''pt'''  ,			padding='''max_length'''  ,			truncation=UpperCAmelCase_  ,			max_length=35  ,			).input_ids

  hf_itm_model.load_state_dict(UpperCAmelCase_  )
  hf_itm_model.eval()

  __UpperCAmelCase    :						List[Any]       =						hf_itm_model(UpperCAmelCase_  ,			UpperCAmelCase_  ,			use_itm_head=UpperCAmelCase_  )
  __UpperCAmelCase    :						int       =						hf_itm_model(UpperCAmelCase_  ,			UpperCAmelCase_  ,			use_itm_head=UpperCAmelCase_  )

  assert out[0].item() == 0.2_110_687_494_277_954
  assert torch.nn.functional.softmax(out_itm[0]  ,			dim=1  )[:, 1].item() == 0.45_698_845_386_505_127

  if pytorch_dump_folder_path is not None:
    hf_itm_model.save_pretrained(pytorch_dump_folder_path + '''_itm'''  )


if __name__ == "__main__":
  lowercase__				:str				   =       argparse.ArgumentParser()
  parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
  parser.add_argument('--config_path', default=None, type=str, help='Path to hf config.json of model to convert')
  lowercase__				:int				   =       parser.parse_args()

  convert_blip_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)