
from __future__ import annotations

A__   :		List[str] 		=tuple[int, int, int]
A__   :		Dict 		=tuple[str, str, str]


# used alphabet --------------------------
# from string.ascii_uppercase
A__   :		Any 		='ABCDEFGHIJKLMNOPQRSTUVWXYZ'

# -------------------------- default selection --------------------------
# rotors --------------------------
A__   :		Optional[Any] 		='EGZWVONAHDCLFQMSIPJBYUKXTR'
A__   :		Dict 		='FOBHMDKEXQNRAULPGSJVTYICZW'
A__   :		List[Any] 		='ZJXESIUQLHAVRMDOYGTNFWPBKC'
# reflector --------------------------
A__   :		Union[str, Any] 		={
    'A': 'N',
    'N': 'A',
    'B': 'O',
    'O': 'B',
    'C': 'P',
    'P': 'C',
    'D': 'Q',
    'Q': 'D',
    'E': 'R',
    'R': 'E',
    'F': 'S',
    'S': 'F',
    'G': 'T',
    'T': 'G',
    'H': 'U',
    'U': 'H',
    'I': 'V',
    'V': 'I',
    'J': 'W',
    'W': 'J',
    'K': 'X',
    'X': 'K',
    'L': 'Y',
    'Y': 'L',
    'M': 'Z',
    'Z': 'M',
}

# -------------------------- extra rotors --------------------------
A__   :		Any 		='RMDJXFUWGISLHVTCQNKYPBEZOA'
A__   :		Dict 		='SGLCPQWZHKXAREONTFBVIYJUDM'
A__   :		Union[str, Any] 		='HVSICLTYKQUBXDWAJZOMFGPREN'
A__   :		str 		='RZWQHFMVDBKICJLNTUXAGYPSOE'
A__   :		Tuple 		='LFKIJODBEGAMQPXVUHYSTCZRWN'
A__   :		Any 		='KOAEGVDHXPQZMLFTYWJNBRCIUS'







def        A_     (						__SCREAMING_SNAKE_CASE						:  RotorPositionT			,     __SCREAMING_SNAKE_CASE						:  RotorSelectionT			,     __SCREAMING_SNAKE_CASE						:  str )				->       tuple[RotorPositionT, RotorSelectionT, dict[str, str]]:





  """simple docstring"""

  if (unique_rotsel := len(set(__SCREAMING_SNAKE_CASE ) )) < 3:
    __A		:     Optional[Any]												=		F"Please use 3 unique rotors (not {unique_rotsel})"
    raise Exception(__SCREAMING_SNAKE_CASE )

  # Checks if rotor positions are valid
  __A		:     Tuple												=		rotpos
  if not 0 < rotorposa <= len(__SCREAMING_SNAKE_CASE ):
    __A		:     Tuple												=		F"First rotor position is not within range of 1..26 ({rotorposa}"
    raise ValueError(__SCREAMING_SNAKE_CASE )
  if not 0 < rotorposa <= len(__SCREAMING_SNAKE_CASE ):
    __A		:     List[Any]												=		F"Second rotor position is not within range of 1..26 ({rotorposa})"
    raise ValueError(__SCREAMING_SNAKE_CASE )
  if not 0 < rotorposa <= len(__SCREAMING_SNAKE_CASE ):
    __A		:     Optional[Any]												=		F"Third rotor position is not within range of 1..26 ({rotorposa})"
    raise ValueError(__SCREAMING_SNAKE_CASE )

  # Validates string and returns dict
  __A		:     List[str]												=		_plugboard(__SCREAMING_SNAKE_CASE )

  return rotpos, rotsel, pbdict







def        A_     (						__SCREAMING_SNAKE_CASE						:  str )				->       dict[str, str]:





  """simple docstring"""

  if not isinstance(__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE ):
    __A		:     List[str]												=		F"Plugboard setting isn't type string ({type(__SCREAMING_SNAKE_CASE )})"
    raise TypeError(__SCREAMING_SNAKE_CASE )
  elif len(__SCREAMING_SNAKE_CASE ) % 2 != 0:
    __A		:     Any												=		F"Odd number of symbols ({len(__SCREAMING_SNAKE_CASE )})"
    raise Exception(__SCREAMING_SNAKE_CASE )
  elif pbstring == "":
    return {}

  pbstring.replace(""" """			,     """""" )

  # Checks if all characters are unique
  __A		:     Union[str, Any]												=		set()
  for i in pbstring:
    if i not in abc:
      __A		:     Tuple												=		F"'{i}' not in list of symbols"
      raise Exception(__SCREAMING_SNAKE_CASE )
    elif i in tmppbl:
      __A		:     Optional[Any]												=		F"Duplicate symbol ({i})"
      raise Exception(__SCREAMING_SNAKE_CASE )
    else:
      tmppbl.add(__SCREAMING_SNAKE_CASE )
  del tmppbl

  # Created the dictionary
  __A		:     int												=		{}
  for j in range(0			,     len(__SCREAMING_SNAKE_CASE ) - 1			,     2 ):
    __A		:     Optional[Any]												=		pbstring[j + 1]
    __A		:     List[str]												=		pbstring[j]

  return pb







def        A_     (						__SCREAMING_SNAKE_CASE						:  str			,     __SCREAMING_SNAKE_CASE						:  RotorPositionT			,     __SCREAMING_SNAKE_CASE						:  RotorSelectionT = (rotora, rotora, rotora)			,     __SCREAMING_SNAKE_CASE						:  str = ""			,     )				->       str:





  """simple docstring"""

  __A		:     List[str]												=		text.upper()
  __A		:     Tuple												=		_validator(
      __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     plugb.upper() )

  __A		:     Optional[Any]												=		rotor_position
  __A		:     Any												=		rotor_selection
  rotorposa -= 1
  rotorposa -= 1
  rotorposa -= 1

  __A		:     int												=		[]

  # encryption/decryption process --------------------------
  for symbol in text:
    if symbol in abc:
      # 1st plugboard --------------------------
      if symbol in plugboard:
        __A		:     Optional[Any]												=		plugboard[symbol]

      # rotor ra --------------------------
      __A		:     Union[str, Any]												=		abc.index(__SCREAMING_SNAKE_CASE ) + rotorposa
      __A		:     str												=		rotora[index % len(__SCREAMING_SNAKE_CASE )]

      # rotor rb --------------------------
      __A		:     Union[str, Any]												=		abc.index(__SCREAMING_SNAKE_CASE ) + rotorposa
      __A		:     Optional[Any]												=		rotora[index % len(__SCREAMING_SNAKE_CASE )]

      # rotor rc --------------------------
      __A		:     Optional[Any]												=		abc.index(__SCREAMING_SNAKE_CASE ) + rotorposa
      __A		:     Dict												=		rotora[index % len(__SCREAMING_SNAKE_CASE )]

      # reflector --------------------------
      # this is the reason you don't need another machine to decipher

      __A		:     Optional[Any]												=		reflector[symbol]

      # 2nd rotors
      __A		:     Optional[Any]												=		abc[rotora.index(__SCREAMING_SNAKE_CASE ) - rotorposa]
      __A		:     Union[str, Any]												=		abc[rotora.index(__SCREAMING_SNAKE_CASE ) - rotorposa]
      __A		:     str												=		abc[rotora.index(__SCREAMING_SNAKE_CASE ) - rotorposa]

      # 2nd plugboard
      if symbol in plugboard:
        __A		:     Optional[Any]												=		plugboard[symbol]

      # moves/resets rotor positions
      rotorposa += 1
      if rotorposa >= len(__SCREAMING_SNAKE_CASE ):
        __A		:     int												=		0
        rotorposa += 1
      if rotorposa >= len(__SCREAMING_SNAKE_CASE ):
        __A		:     int												=		0
        rotorposa += 1
      if rotorposa >= len(__SCREAMING_SNAKE_CASE ):
        __A		:     List[Any]												=		0

        # else:
        #    pass
        #    Error could be also raised
        #    raise ValueError(
        #       'Invalid symbol('+repr(symbol)+')')
    result.append(__SCREAMING_SNAKE_CASE )

  return "".join(__SCREAMING_SNAKE_CASE )


if __name__ == "__main__":
     A__   :		Optional[int] 		='This is my Python script that emulates the Enigma machine from WWII.'
     A__   :		str 		=(1, 1, 1)
     A__   :		Optional[int] 		='pictures'
     A__   :		Optional[int] 		=(rotora, rotora, rotora)
     A__   :		int 		=enigma(message, rotor_pos, rotor_sel, pb)

     print('Encrypted message:', en)
     print('Decrypted message:', enigma(en, rotor_pos, rotor_sel, pb))





'''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        A_     (						__SCREAMING_SNAKE_CASE						:  List[str]			,     __SCREAMING_SNAKE_CASE						:  Optional[Any] )				->       int:





  """simple docstring"""

  __A		:     Optional[int]												=		"""https://storage.googleapis.com/sfr-vision-language-research/BLIP/demo.jpg"""
  __A		:     Tuple												=		Image.open(requests.get(__SCREAMING_SNAKE_CASE			,     stream=__SCREAMING_SNAKE_CASE ).raw ).convert("""RGB""" )

  __A		:     Optional[Any]												=		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) ),
      ] )
  __A		:     Optional[int]												=		transform(__SCREAMING_SNAKE_CASE ).unsqueeze(0 ).to(__SCREAMING_SNAKE_CASE )
  return image







def        A_     (						__SCREAMING_SNAKE_CASE						:  int )				->       Optional[int]:





  """simple docstring"""

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

  if "encoder.pos_embed" in key:
    __A		:     Union[str, Any]												=		re.sub(R"""encoder.pos_embed"""			,     """embeddings.position_embedding"""			,     __SCREAMING_SNAKE_CASE )
  if "encoder.cls_token" in key:
    __A		:     Tuple												=		re.sub(R"""encoder.cls_token"""			,     """embeddings.class_embedding"""			,     __SCREAMING_SNAKE_CASE )

  if "self_attn" in key:
    __A		:     Tuple												=		re.sub(R"""self_attn.proj"""			,     """self_attn.projection"""			,     __SCREAMING_SNAKE_CASE )

  return key







@torch.no_grad()
def        A_     (						__SCREAMING_SNAKE_CASE						:  Any			,     __SCREAMING_SNAKE_CASE						:  List[str]=None )				->       int:





  """simple docstring"""

  if config_path is not None:
    __A		:     Any												=		BlipConfig.from_pretrained(__SCREAMING_SNAKE_CASE )
  else:
    __A		:     List[Any]												=		BlipConfig(projection_dim=512			,     text_config={}			,     vision_config={} )

  __A		:     List[Any]												=		BlipForConditionalGeneration(__SCREAMING_SNAKE_CASE ).eval()

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

  __A		:     List[str]												=		blip_decoder(pretrained=__SCREAMING_SNAKE_CASE			,     image_size=384			,     vit="""base""" )
  __A		:     List[str]												=		pt_model.eval()

  __A		:     int												=		pt_model.state_dict()
  for key in modified_state_dict.copy():
    __A		:     Tuple												=		modified_state_dict.pop(__SCREAMING_SNAKE_CASE )
    __A		:     Dict												=		rename_key(__SCREAMING_SNAKE_CASE )
    __A		:     Tuple												=		value

  hf_model.load_state_dict(__SCREAMING_SNAKE_CASE )

  __A		:     List[Any]												=		384
  __A		:     Dict												=		load_demo_image(image_size=__SCREAMING_SNAKE_CASE			,     device="""cpu""" )
  __A		:     Dict												=		BertTokenizer.from_pretrained("""bert-base-uncased""" )
  __A		:     Optional[Any]												=		tokenizer(["""a picture of"""] ).input_ids

  __A		:     int												=		hf_model.generate(__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE )

  assert out[0].tolist() == [3_0522, 1037, 3861, 1997, 1037, 2450, 3564, 2006, 1996, 3509, 2007, 2014, 3899, 102]

  __A		:     str												=		hf_model.generate(__SCREAMING_SNAKE_CASE )

  assert out[0].tolist() == [3_0522, 1037, 2450, 3564, 2006, 1996, 3509, 2007, 2014, 3899, 102]

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

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

  __A		:     List[Any]												=		blip_vqa(pretrained=__SCREAMING_SNAKE_CASE			,     image_size=__SCREAMING_SNAKE_CASE			,     vit="""base""" )
  vqa_model.eval()

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

  __A		:     Any												=		BlipForQuestionAnswering(__SCREAMING_SNAKE_CASE )

  hf_vqa_model.load_state_dict(__SCREAMING_SNAKE_CASE )

  __A		:     Tuple												=		["""How many dogs are in this image?"""]
  __A		:     Union[str, Any]												=		tokenizer(__SCREAMING_SNAKE_CASE			,     return_tensors="""pt""" ).input_ids

  __A		:     List[str]												=		hf_vqa_model.generate(__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE )
  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""" )

  __A		:     str												=		"""https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_retrieval_coco.pth"""

  __A		:     List[str]												=		blip_itm(pretrained=__SCREAMING_SNAKE_CASE			,     image_size=__SCREAMING_SNAKE_CASE			,     vit="""base""" )
  itm_model.eval()

  __A		:     List[str]												=		itm_model.state_dict()
  for key in modified_state_dict.copy():
    __A		:     Optional[Any]												=		modified_state_dict.pop(__SCREAMING_SNAKE_CASE )
    __A		:     str												=		rename_key(__SCREAMING_SNAKE_CASE )
    __A		:     Any												=		value

  __A		:     List[Any]												=		BlipForImageTextRetrieval(__SCREAMING_SNAKE_CASE )

  __A		:     Tuple												=		["""A picture of a woman with a dog sitting in a beach"""]
  __A		:     List[str]												=		tokenizer(
      __SCREAMING_SNAKE_CASE			,     return_tensors="""pt"""			,     padding="""max_length"""			,     truncation=__SCREAMING_SNAKE_CASE			,     max_length=35			,     ).input_ids

  hf_itm_model.load_state_dict(__SCREAMING_SNAKE_CASE )
  hf_itm_model.eval()

  __A		:     Any												=		hf_itm_model(__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     use_itm_head=__SCREAMING_SNAKE_CASE )
  __A		:     Optional[Any]												=		hf_itm_model(__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     use_itm_head=__SCREAMING_SNAKE_CASE )

  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__":
     A__   :		Tuple 		=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')
     A__   :		Any 		=parser.parse_args()

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



