


"""simple docstring"""



import string

import numpy







def     __lowerCAmelCase		(		lowercase    :    int		,				lowercase    :    int					)      ->		int:


    """simple docstring"""





    return b if a == 0 else greatest_common_divisor(b % a		,				lowercase					)







class _lowerCAmelCase							:
   __UpperCAmelCase					:			str				    =    string.ascii_uppercase + string.digits
   # This cipher takes alphanumerics into account
   # i.e. a total of 36 characters

   # take x and return x % len(key_string)
   __UpperCAmelCase					:			int				    =    numpy.vectorize(lambda snake_case_      : x % 36      )

   __UpperCAmelCase					:			str				    =    numpy.vectorize(snake_case_      )







   def __init__(      self		,      UpperCamelCase__		)      ->						None:

       '''simple docstring'''




       snake_case       : Optional[Any]    =       self.modulus(UpperCamelCase__		)  # mod36 calc's on the encrypt key
       self.check_determinant()  # validate the determinant of the encryption key
       snake_case       : int    =       encrypt_key.shape[0]







   def 					lowerCamelCase						(      self		,      UpperCamelCase__		)      ->						int:

       '''simple docstring'''




       return self.key_string.index(UpperCamelCase__		)







   def 					lowerCamelCase						(      self		,      UpperCamelCase__		)      ->						str:

       '''simple docstring'''




       return self.key_string[round(UpperCamelCase__		)]







   def 					lowerCamelCase						(      self		)      ->						None:

       '''simple docstring'''




       snake_case       : List[str]    =       round(numpy.linalg.det(self.encrypt_key		)		)

       if det < 0:
           snake_case       : int    =       det % len(self.key_string		)

       snake_case       : Union[str, Any]    =       len(self.key_string		)
       if greatest_common_divisor(UpperCamelCase__		,      len(self.key_string		)		) != 1:
           snake_case       : Optional[int]    =       (
               F'determinant modular {req_l} of encryption key({det}) '
               F'is not co prime w.r.t {req_l}.\nTry another key.'
           )
           raise ValueError(UpperCamelCase__		)







   def 					lowerCamelCase						(      self		,      UpperCamelCase__		)      ->						str:

       '''simple docstring'''




       snake_case       : Dict    =       [char for char in text.upper() if char in self.key_string]

       snake_case       : Dict    =       chars[-1]
       while len(UpperCamelCase__		) % self.break_key != 0:
           chars.append(UpperCamelCase__		)

       return "".join(UpperCamelCase__		)







   def 					lowerCamelCase						(      self		,      UpperCamelCase__		)      ->						str:

       '''simple docstring'''




       snake_case       : Union[str, Any]    =       self.process_text(text.upper()		)
       snake_case       : Dict    =       ""

       for i in range(0		,      len(UpperCamelCase__		) - self.break_key + 1		,      self.break_key		):
           snake_case       : Dict    =       text[i : i + self.break_key]
           snake_case       : Any    =       [self.replace_letters(UpperCamelCase__		) for char in batch]
           snake_case       : str    =       numpy.array([vec]		).T
           snake_case       : Optional[int]    =       self.modulus(self.encrypt_key.dot(UpperCamelCase__		)		).T.tolist()[
               0
           ]
           snake_case       : Union[str, Any]    =       "".join(
               self.replace_digits(UpperCamelCase__		) for num in batch_encrypted		)
           encrypted += encrypted_batch

       return encrypted







   def 					lowerCamelCase						(      self		)      ->						numpy.ndarray:

       '''simple docstring'''




       snake_case       : List[str]    =       round(numpy.linalg.det(self.encrypt_key		)		)

       if det < 0:
           snake_case       : Union[str, Any]    =       det % len(self.key_string		)
       snake_case       : Any    =       None
       for i in range(len(self.key_string		)		):
           if (det * i) % len(self.key_string		) == 1:
               snake_case       : Optional[Any]    =       i
               break

       snake_case       : str    =       (
           det_inv
           * numpy.linalg.det(self.encrypt_key		)
           * numpy.linalg.inv(self.encrypt_key		)
       )

       return self.to_int(self.modulus(UpperCamelCase__		)		)







   def 					lowerCamelCase						(      self		,      UpperCamelCase__		)      ->						str:

       '''simple docstring'''




       snake_case       : Union[str, Any]    =       self.make_decrypt_key()
       snake_case       : str    =       self.process_text(text.upper()		)
       snake_case       : Union[str, Any]    =       ""

       for i in range(0		,      len(UpperCamelCase__		) - self.break_key + 1		,      self.break_key		):
           snake_case       : int    =       text[i : i + self.break_key]
           snake_case       : Optional[int]    =       [self.replace_letters(UpperCamelCase__		) for char in batch]
           snake_case       : Optional[Any]    =       numpy.array([vec]		).T
           snake_case       : List[str]    =       self.modulus(decrypt_key.dot(UpperCamelCase__		)		).T.tolist()[0]
           snake_case       : int    =       "".join(
               self.replace_digits(UpperCamelCase__		) for num in batch_decrypted		)
           decrypted += decrypted_batch

       return decrypted







def     __lowerCAmelCase		(		)      ->		None:


    """simple docstring"""





    snake_case       : Dict    =       int(input("Enter the order of the encryption key: "					)					)
    snake_case       : Union[str, Any]    =       []

    print("Enter each row of the encryption key with space separated integers"					)
    for _ in range(lowercase					):
        snake_case       : Any    =       [int(lowercase					) for x in input().split()]
        hill_matrix.append(lowercase					)

    snake_case       : Tuple    =       HillCipher(numpy.array(lowercase					)					)

    print("Would you like to encrypt or decrypt some text? (1 or 2)"					)
    snake_case       : Any    =       input("\n1. Encrypt\n2. Decrypt\n"					)
    if option == "1":
        snake_case       : int    =       input("What text would you like to encrypt?: "					)
        print("Your encrypted text is:"					)
        print(hc.encrypt(lowercase					)					)
    elif option == "2":
        snake_case       : List[str]    =       input("What text would you like to decrypt?: "					)
        print("Your decrypted text is:"					)
        print(hc.decrypt(lowercase					)					)


if __name__ == "__main__":
  import doctest

  doctest.testmod()

  main()









"""simple docstring"""



import argparse
import json

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

from transformers import AutoImageProcessor, SwinConfig, SwinForImageClassification







def     __lowerCAmelCase		(		lowercase    :    Optional[int]					)      ->		Tuple:


    """simple docstring"""





    snake_case       : Optional[Any]    =       SwinConfig()
    snake_case       : Union[str, Any]    =       swin_name.split("_"					)

    snake_case       : str    =       name_split[1]
    snake_case       : Tuple    =       int(name_split[4]					)
    snake_case       : str    =       int(name_split[3][-1]					)

    if model_size == "tiny":
        snake_case       : str    =       96
        snake_case       : List[Any]    =       (2, 2, 6, 2)
        snake_case       : Union[str, Any]    =       (3, 6, 12, 24)
    elif model_size == "small":
        snake_case       : int    =       96
        snake_case       : Optional[Any]    =       (2, 2, 18, 2)
        snake_case       : Any    =       (3, 6, 12, 24)
    elif model_size == "base":
        snake_case       : List[str]    =       128
        snake_case       : int    =       (2, 2, 18, 2)
        snake_case       : Any    =       (4, 8, 16, 32)
    else:
        snake_case       : List[str]    =       192
        snake_case       : str    =       (2, 2, 18, 2)
        snake_case       : Any    =       (6, 12, 24, 48)

    if "in22k" in swin_name:
        snake_case       : List[Any]    =       2_1841
    else:
        snake_case       : str    =       1000
        snake_case       : Any    =       "huggingface/label-files"
        snake_case       : str    =       "imagenet-1k-id2label.json"
        snake_case       : Optional[int]    =       json.load(open(hf_hub_download(lowercase		,				lowercase		,				repo_type="dataset"					)		,				"r"					)					)
        snake_case       : List[Any]    =       {int(lowercase					): v for k, v in idalabel.items()}
        snake_case       : Optional[int]    =       idalabel
        snake_case       : Optional[int]    =       {v: k for k, v in idalabel.items()}

    snake_case       : Tuple    =       img_size
    snake_case       : Any    =       num_classes
    snake_case       : str    =       embed_dim
    snake_case       : Any    =       depths
    snake_case       : Dict    =       num_heads
    snake_case       : List[Any]    =       window_size

    return config







def     __lowerCAmelCase		(		lowercase    :    Optional[Any]					)      ->		Any:


    """simple docstring"""





    if "patch_embed.proj" in name:
        snake_case       : int    =       name.replace("patch_embed.proj"		,				"embeddings.patch_embeddings.projection"					)
    if "patch_embed.norm" in name:
        snake_case       : Union[str, Any]    =       name.replace("patch_embed.norm"		,				"embeddings.norm"					)
    if "layers" in name:
        snake_case       : Optional[Any]    =       "encoder." + name
    if "attn.proj" in name:
        snake_case       : List[Any]    =       name.replace("attn.proj"		,				"attention.output.dense"					)
    if "attn" in name:
        snake_case       : List[str]    =       name.replace("attn"		,				"attention.self"					)
    if "norm1" in name:
        snake_case       : List[str]    =       name.replace("norm1"		,				"layernorm_before"					)
    if "norm2" in name:
        snake_case       : Optional[int]    =       name.replace("norm2"		,				"layernorm_after"					)
    if "mlp.fc1" in name:
        snake_case       : int    =       name.replace("mlp.fc1"		,				"intermediate.dense"					)
    if "mlp.fc2" in name:
        snake_case       : Tuple    =       name.replace("mlp.fc2"		,				"output.dense"					)

    if name == "norm.weight":
        snake_case       : Optional[int]    =       "layernorm.weight"
    if name == "norm.bias":
        snake_case       : Tuple    =       "layernorm.bias"

    if "head" in name:
        snake_case       : Any    =       name.replace("head"		,				"classifier"					)
    else:
        snake_case       : Tuple    =       "swin." + name

    return name







def     __lowerCAmelCase		(		lowercase    :    List[Any]		,				lowercase    :    List[Any]					)      ->		Any:


    """simple docstring"""





    for key in orig_state_dict.copy().keys():
        snake_case       : int    =       orig_state_dict.pop(lowercase					)

        if "mask" in key:
            continue
        elif "qkv" in key:
            snake_case       : List[Any]    =       key.split("."					)
            snake_case       : Dict    =       int(key_split[1]					)
            snake_case       : Dict    =       int(key_split[3]					)
            snake_case       : str    =       model.swin.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size

            if "weight" in key:
                snake_case       : Dict    =       val[:dim, :]
                snake_case       : Tuple    =       val[
                    dim : dim * 2, :
                ]
                snake_case       : List[str]    =       val[-dim:, :]
            else:
                snake_case       : Dict    =       val[
                    :dim
                ]
                snake_case       : List[str]    =       val[
                    dim : dim * 2
                ]
                snake_case       : Union[str, Any]    =       val[
                    -dim:
                ]
        else:
            snake_case       : Any    =       val

    return orig_state_dict







def     __lowerCAmelCase		(		lowercase    :    Tuple		,				lowercase    :    Any					)      ->		Dict:


    """simple docstring"""





    snake_case       : str    =       timm.create_model(lowercase		,				pretrained=lowercase					)
    timm_model.eval()

    snake_case       : int    =       get_swin_config(lowercase					)
    snake_case       : Optional[int]    =       SwinForImageClassification(lowercase					)
    model.eval()

    snake_case       : int    =       convert_state_dict(timm_model.state_dict()		,				lowercase					)
    model.load_state_dict(lowercase					)

    snake_case       : Dict    =       "http://images.cocodataset.org/val2017/000000039769.jpg"

    snake_case       : Tuple    =       AutoImageProcessor.from_pretrained("microsoft/{}".format(swin_name.replace("_"		,				"-"					)					)					)
    snake_case       : Optional[int]    =       Image.open(requests.get(lowercase		,				stream=lowercase					).raw					)
    snake_case       : Dict    =       image_processor(images=lowercase		,				return_tensors="pt"					)

    snake_case       : str    =       timm_model(inputs["pixel_values"]					)
    snake_case       : Tuple    =       model(**lowercase					).logits

    assert torch.allclose(lowercase		,				lowercase		,				atol=1e-3					)

    print(F'Saving model {swin_name} to {pytorch_dump_folder_path}'					)
    model.save_pretrained(lowercase					)

    print(F'Saving image processor to {pytorch_dump_folder_path}'					)
    image_processor.save_pretrained(lowercase					)


if __name__ == "__main__":
  __snake_case       =       argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      """--swin_name""",
      default="""swin_tiny_patch4_window7_224""",
      type=str,
      help="""Name of the Swin 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."""
  )

  __snake_case       =       parser.parse_args()
  convert_swin_checkpoint(args.swin_name, args.pytorch_dump_folder_path)





