





'''simple docstring'''





from __future__ import annotations


def        __snake_case       (						lowerCAmelCase_   ,       lowerCAmelCase_  )							->	str:
    SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__       							= '''''', '''''', ''''''
    SCREAMING_SNAKE_CASE__       							= []

    for character in message_part:
        tmp.append(character_to_number[character]  )

    for each in tmp:
        one += each[0]
        two += each[1]
        three += each[2]

    return one + two + three


def        __snake_case       (						lowerCAmelCase_   ,       lowerCAmelCase_  )							->	tuple[str, str, str]:
    SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__       							= '''''', ''''''
    SCREAMING_SNAKE_CASE__       							= []

    for character in message_part:
        this_part += character_to_number[character]

    for digit in this_part:
        tmp += digit
        if len(lowerCAmelCase_  ) == len(lowerCAmelCase_  ):
            result.append(lowerCAmelCase_  )
            SCREAMING_SNAKE_CASE__       							= ''''''

    return result[0], result[1], result[2]


def        __snake_case       (						lowerCAmelCase_   ,       lowerCAmelCase_  )							->	tuple[str, str, dict[str, str], dict[str, str]]:
    # Validate message and alphabet, set to upper and remove spaces
    SCREAMING_SNAKE_CASE__       							= alphabet.replace(''' '''   ,       ''''''  ).upper()
    SCREAMING_SNAKE_CASE__       							= message.replace(''' '''   ,       ''''''  ).upper()

    # Check length and characters
    if len(lowerCAmelCase_  ) != 2_7:
        raise KeyError('''Length of alphabet has to be 27.'''  )
    for each in message:
        if each not in alphabet:
            raise ValueError('''Each message character has to be included in alphabet!'''  )

    # Generate dictionares
    SCREAMING_SNAKE_CASE__       							= (
        '''111''',
        '''112''',
        '''113''',
        '''121''',
        '''122''',
        '''123''',
        '''131''',
        '''132''',
        '''133''',
        '''211''',
        '''212''',
        '''213''',
        '''221''',
        '''222''',
        '''223''',
        '''231''',
        '''232''',
        '''233''',
        '''311''',
        '''312''',
        '''313''',
        '''321''',
        '''322''',
        '''323''',
        '''331''',
        '''332''',
        '''333''',
    )
    SCREAMING_SNAKE_CASE__       							= {}
    SCREAMING_SNAKE_CASE__       							= {}
    for letter, number in zip(lowerCAmelCase_   ,       lowerCAmelCase_  ):
        SCREAMING_SNAKE_CASE__       							= number
        SCREAMING_SNAKE_CASE__       							= letter

    return message, alphabet, character_to_number, number_to_character


def        __snake_case       (						lowerCAmelCase_   ,       lowerCAmelCase_ = "ABCDEFGHIJKLMNOPQRSTUVWXYZ."   ,       lowerCAmelCase_ = 5  )							->	str:
    SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__       							= __prepare(
        lowerCAmelCase_   ,       lowerCAmelCase_  )
    SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__       							= '''''', ''''''

    for i in range(0   ,       len(lowerCAmelCase_  ) + 1   ,       lowerCAmelCase_  ):
        encrypted_numeric += __encrypt_part(
            message[i : i + period]   ,       lowerCAmelCase_  )

    for i in range(0   ,       len(lowerCAmelCase_  )   ,       3  ):
        encrypted += number_to_character[encrypted_numeric[i : i + 3]]

    return encrypted


def        __snake_case       (						lowerCAmelCase_   ,       lowerCAmelCase_ = "ABCDEFGHIJKLMNOPQRSTUVWXYZ."   ,       lowerCAmelCase_ = 5  )							->	str:
    SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__       							= __prepare(
        lowerCAmelCase_   ,       lowerCAmelCase_  )
    SCREAMING_SNAKE_CASE__       							= []
    SCREAMING_SNAKE_CASE__       							= ''''''

    for i in range(0   ,       len(lowerCAmelCase_  ) + 1   ,       lowerCAmelCase_  ):
        SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__       							= __decrypt_part(message[i : i + period]   ,       lowerCAmelCase_  )

        for j in range(0   ,       len(lowerCAmelCase_  )  ):
            decrypted_numeric.append(a[j] + b[j] + c[j]  )

    for each in decrypted_numeric:
        decrypted += number_to_character[each]

    return decrypted


if __name__ == "__main__":
  _A				:							Union[str, Any]							= """DEFEND THE EAST WALL OF THE CASTLE."""
  _A				:							str							= encrypt_message(msg, """EPSDUCVWYM.ZLKXNBTFGORIJHAQ""")
  _A				:							int							= decrypt_message(encrypted, """EPSDUCVWYM.ZLKXNBTFGORIJHAQ""")
  print(F'Encrypted: {encrypted}\nDecrypted: {decrypted}')







import os
from pathlib import Path
from typing import Dict, List

import fire
import torch

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from transformers.utils.logging import get_logger


_A				:							Any							= get_logger(__name__)


def        __snake_case       (						lowerCAmelCase_   ,       lowerCAmelCase_  )							->	str:
    if text.startswith(lowerCAmelCase_  ):
        return text[len(lowerCAmelCase_  ) :]
    return text  # or whatever


def        __snake_case       (						lowerCAmelCase_  )							->	Union[str, Any]:
    return {remove_prefix(lowerCAmelCase_   ,       '''model.'''  ): v for k, v in sd.items()}


def        __snake_case       (						lowerCAmelCase_  )							->	Optional[int]:
    SCREAMING_SNAKE_CASE__       							= {}
    for k in state_dicts[0].keys():
        SCREAMING_SNAKE_CASE__       							= [sd[k] for sd in state_dicts]
        SCREAMING_SNAKE_CASE__       							= sum(lowerCAmelCase_  ) / len(lowerCAmelCase_  )
        assert isinstance(lowerCAmelCase_   ,       torch.Tensor  )
        SCREAMING_SNAKE_CASE__       							= new_t
    return new_sd


def        __snake_case       (						lowerCAmelCase_   ,       lowerCAmelCase_   ,       lowerCAmelCase_  )							->	None:
    SCREAMING_SNAKE_CASE__       							= AutoModelForSeqaSeqLM.from_pretrained(lowerCAmelCase_  )
    if os.path.isfile(lowerCAmelCase_  ):
        SCREAMING_SNAKE_CASE__       							= [pl_ckpt_path]
    else:
        assert os.path.isdir(lowerCAmelCase_  )
        SCREAMING_SNAKE_CASE__       							= list(Path(lowerCAmelCase_  ).glob('''*.ckpt'''  )  )
        assert ckpt_files, f'''could not find any ckpt files inside the {pl_ckpt_path} directory'''

    if len(lowerCAmelCase_  ) > 1:
        logger.info(f'''averaging the weights of {ckpt_files}'''  )

    SCREAMING_SNAKE_CASE__       							= [sanitize(torch.load(lowerCAmelCase_   ,       map_location='''cpu'''  )['''state_dict''']  ) for x in ckpt_files]
    SCREAMING_SNAKE_CASE__       							= average_state_dicts(lowerCAmelCase_  )

    SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__       							= hf_model.load_state_dict(lowerCAmelCase_   ,       strict=lowerCAmelCase_  )
    assert not missing, f'''missing keys: {missing}'''
    hf_model.save_pretrained(lowerCAmelCase_  )
    try:
        SCREAMING_SNAKE_CASE__       							= AutoTokenizer.from_pretrained(lowerCAmelCase_  )
        tok.save_pretrained(lowerCAmelCase_  )
    except Exception:
        pass
        # dont copy tokenizer if cant


if __name__ == "__main__":
  fire.Fire(convert_pl_to_hf)
