from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast
from ...utils import logging


lowerCAmelCase__     						=			logging.get_logger(__name__)

lowerCAmelCase__     						=			{
    'EleutherAI/gpt-neo-1.3B': 'https://huggingface.co/EleutherAI/gpt-neo-1.3B/resolve/main/config.json',
    # See all GPTNeo models at https://huggingface.co/models?filter=gpt_neo
}
class     a_			(						SCREAMING_SNAKE_CASE						):






 '''simple docstring'''






 UpperCAmelCase_     			= 'gpt_neo'
 UpperCAmelCase_     			= ['past_key_values']
 UpperCAmelCase_     			= {'num_attention_heads': 'num_heads', 'num_hidden_layers': 'num_layers'}




 def __init__(      self :  Tuple      ,   lowercase__ :  Optional[int]=50_257      ,   lowercase__ :  int=2_048      ,   lowercase__ :  Optional[Any]=2_048      ,   lowercase__ :  Optional[int]=24      ,   lowercase__ :  str=[[["global", "local"], 12]]      ,   lowercase__ :  Any=16      ,   lowercase__ :  Tuple=None      ,   lowercase__ :  List[Any]=256      ,   lowercase__ :  List[str]="gelu_new"      ,   lowercase__ :  Optional[int]=0.0      ,   lowercase__ :  int=0.0      ,   lowercase__ :  List[str]=0.0      ,   lowercase__ :  List[str]=0.1      ,   lowercase__ :  Union[str, Any]=1e-5      ,   lowercase__ :  int=0.02      ,   lowercase__ :  Optional[Any]=True      ,   lowercase__ :  Optional[Any]=50_256      ,   lowercase__ :  Any=50_256      ,   **lowercase__ :  Dict      ,   ):







        '''simple docstring'''



        lowerCAmelCase__					     = vocab_size
        lowerCAmelCase__					     = max_position_embeddings
        lowerCAmelCase__					     = hidden_size
        lowerCAmelCase__					     = num_layers
        lowerCAmelCase__					     = num_heads
        lowerCAmelCase__					     = intermediate_size
        lowerCAmelCase__					     = window_size
        lowerCAmelCase__					     = activation_function
        lowerCAmelCase__					     = resid_dropout
        lowerCAmelCase__					     = embed_dropout
        lowerCAmelCase__					     = attention_dropout
        lowerCAmelCase__					     = classifier_dropout
        lowerCAmelCase__					     = layer_norm_epsilon
        lowerCAmelCase__					     = initializer_range
        lowerCAmelCase__					     = use_cache

        lowerCAmelCase__					     = bos_token_id
        lowerCAmelCase__					     = eos_token_id

        lowerCAmelCase__					     = attention_types
        lowerCAmelCase__					     = self.expand_attention_types_params(lowercase__)

        if len(self.attention_layers) != self.num_layers:
               raise ValueError(
                   'Configuration for convolutional module is incorrect. '
                   'It is required that `len(config.attention_layers)` == `config.num_layers` '
                   F"""but is `len(config.attention_layers) = {len(self.attention_layers)}`, """
                   F"""`config.num_layers = {self.num_layers}`. """
                   '`config.attention_layers` is prepared using `config.attention_types`. '
                   'Please verify the value of `config.attention_types` argument.')

        super().__init__(bos_token_id=lowercase__      ,   eos_token_id=lowercase__      ,   **lowercase__)




 @staticmethod
 def 		__snake_case						(      lowercase__ :  Optional[Any]):







        '''simple docstring'''



        lowerCAmelCase__					     = []
        for item in attention_types:
               for _ in range(item[1]):
                      attentions.extend(item[0])
        return attentions






def 				__lowerCamelCase		(						lowerCAmelCase__     ,  lowerCAmelCase__     ,  lowerCAmelCase__     ,  lowerCAmelCase__     ):
       import torch

       lowerCAmelCase__					     = input.size()
       lowerCAmelCase__					     = len(lowerCAmelCase__     )
       lowerCAmelCase__					     = shape[dimension]

       lowerCAmelCase__					     = torch.arange(0     ,  lowerCAmelCase__     ,  lowerCAmelCase__     )
       lowerCAmelCase__					     = torch.div(sizedim - size     ,  lowerCAmelCase__     ,  rounding_mode='floor'     ) + 1
       lowerCAmelCase__					     = torch.arange(lowerCAmelCase__     ) + low_indices[:min_length][:, None]

       lowerCAmelCase__					     = [slice(lowerCAmelCase__     )] * rank
       lowerCAmelCase__					     = indices
       lowerCAmelCase__					     = input[s]

       lowerCAmelCase__					     = list(range(0     ,  rank + 1     )     )
       perm.append(perm.pop(dimension + 1     )     )

       return sliced.permute(lowerCAmelCase__     )






def 				__lowerCamelCase		(						lowerCAmelCase__     ,  lowerCAmelCase__     ):
       import torch

       lowerCAmelCase__					     = torch.arange(1     ,  lowerCAmelCase__     )
       lowerCAmelCase__					     = torch.remainder(lowerCAmelCase__     ,  lowerCAmelCase__     )
       lowerCAmelCase__					     = remainders == 0
       lowerCAmelCase__					     = candidates[divisor_indices]
       lowerCAmelCase__					     = torch.max(lowerCAmelCase__     )
       return largest_divisor, torch.div(lowerCAmelCase__     ,  lowerCAmelCase__     ,  rounding_mode='floor'     )



class     a_			(						SCREAMING_SNAKE_CASE						):






 '''simple docstring'''






 @property
 def 		__snake_case						(      self :  List[str]):







        '''simple docstring'''



        lowerCAmelCase__					     = OrderedDict({'input_ids': {0: 'batch', 1: 'sequence'}})
        if self.use_past:
               self.fill_with_past_key_values_(lowercase__      ,   direction='inputs')
               lowerCAmelCase__					     = {0: 'batch', 1: 'past_sequence + sequence'}
        else:
               lowerCAmelCase__					     = {0: 'batch', 1: 'sequence'}

        return common_inputs




 @property
 def 		__snake_case						(      self :  Optional[int]):







        '''simple docstring'''



        return self._config.num_heads




 def 		__snake_case						(      self :  Optional[int]      ,   lowercase__ :  PreTrainedTokenizer      ,   lowercase__ :  int = -1      ,   lowercase__ :  int = -1      ,   lowercase__ :  bool = False      ,   lowercase__ :  Optional[TensorType] = None      ,   ):







        '''simple docstring'''



        lowerCAmelCase__					     = super(lowercase__      ,   self).generate_dummy_inputs(
            lowercase__      ,   batch_size=lowercase__      ,   seq_length=lowercase__      ,   is_pair=lowercase__      ,   framework=lowercase__)

        # We need to order the input in the way they appears in the forward()
        lowerCAmelCase__					     = OrderedDict({'input_ids': common_inputs['input_ids']})

        # Need to add the past_keys
        if self.use_past:
               if not is_torch_available():
                      raise ValueError('Cannot generate dummy past_keys inputs without PyTorch installed.')
               else:
                      import torch

                      lowerCAmelCase__     , lowerCAmelCase__					     = common_inputs['input_ids'].shape
                      # Not using the same length for past_key_values
                      lowerCAmelCase__					     = seqlen + 2
                      lowerCAmelCase__					     = (
                          batch,
                          self.num_attention_heads,
                          past_key_values_length,
                          self._config.hidden_size // self.num_attention_heads,
                      )
                      lowerCAmelCase__					     = [
                          (torch.zeros(lowercase__), torch.zeros(lowercase__)) for _ in range(self.num_layers)
                      ]

        lowerCAmelCase__					     = common_inputs['attention_mask']
        if self.use_past:
               lowerCAmelCase__					     = ordered_inputs['attention_mask'].dtype
               lowerCAmelCase__					     = torch.cat(
                   [ordered_inputs['attention_mask'], torch.ones(lowercase__      ,   lowercase__      ,   dtype=lowercase__)]      ,   dim=1)

        return ordered_inputs




 @property
 def 		__snake_case						(      self :  List[Any]):







        '''simple docstring'''



        return 13

from __future__ import annotations

lowerCAmelCase__     						=			tuple[int, int, int]
lowerCAmelCase__     						=			tuple[str, str, str]


# used alphabet --------------------------
# from string.ascii_uppercase
lowerCAmelCase__     						=			'ABCDEFGHIJKLMNOPQRSTUVWXYZ'

# -------------------------- default selection --------------------------
# rotors --------------------------
lowerCAmelCase__     						=			'EGZWVONAHDCLFQMSIPJBYUKXTR'
lowerCAmelCase__     						=			'FOBHMDKEXQNRAULPGSJVTYICZW'
lowerCAmelCase__     						=			'ZJXESIUQLHAVRMDOYGTNFWPBKC'
# reflector --------------------------
lowerCAmelCase__     						=			{
    '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 --------------------------
lowerCAmelCase__     						=			'RMDJXFUWGISLHVTCQNKYPBEZOA'
lowerCAmelCase__     						=			'SGLCPQWZHKXAREONTFBVIYJUDM'
lowerCAmelCase__     						=			'HVSICLTYKQUBXDWAJZOMFGPREN'
lowerCAmelCase__     						=			'RZWQHFMVDBKICJLNTUXAGYPSOE'
lowerCAmelCase__     						=			'LFKIJODBEGAMQPXVUHYSTCZRWN'
lowerCAmelCase__     						=			'KOAEGVDHXPQZMLFTYWJNBRCIUS'






def 				__lowerCamelCase		(						lowerCAmelCase__     ,  lowerCAmelCase__     ,  lowerCAmelCase__     ):
       # Checks if there are 3 unique rotors

       if (unique_rotsel := len(set(lowerCAmelCase__     )     )) < 3:
              lowerCAmelCase__					     = F"""Please use 3 unique rotors (not {unique_rotsel})"""
              raise Exception(lowerCAmelCase__     )

       # Checks if rotor positions are valid
       lowerCAmelCase__     , lowerCAmelCase__     , lowerCAmelCase__					     = rotpos
       if not 0 < rotorposa <= len(lowerCAmelCase__     ):
              lowerCAmelCase__					     = F"""First rotor position is not within range of 1..26 ({rotorposa}"""
              raise ValueError(lowerCAmelCase__     )
       if not 0 < rotorposa <= len(lowerCAmelCase__     ):
              lowerCAmelCase__					     = F"""Second rotor position is not within range of 1..26 ({rotorposa})"""
              raise ValueError(lowerCAmelCase__     )
       if not 0 < rotorposa <= len(lowerCAmelCase__     ):
              lowerCAmelCase__					     = F"""Third rotor position is not within range of 1..26 ({rotorposa})"""
              raise ValueError(lowerCAmelCase__     )

       # Validates string and returns dict
       lowerCAmelCase__					     = _plugboard(lowerCAmelCase__     )

       return rotpos, rotsel, pbdict






def 				__lowerCamelCase		(						lowerCAmelCase__     ):

       # tests the input string if it
       # a) is type string
       # b) has even length (so pairs can be made)
       if not isinstance(lowerCAmelCase__     ,  lowerCAmelCase__     ):
              lowerCAmelCase__					     = F"""Plugboard setting isn't type string ({type(lowerCAmelCase__     )})"""
              raise TypeError(lowerCAmelCase__     )
       elif len(lowerCAmelCase__     ) % 2 != 0:
              lowerCAmelCase__					     = F"""Odd number of symbols ({len(lowerCAmelCase__     )})"""
              raise Exception(lowerCAmelCase__     )
       elif pbstring == "":
              return {}

       pbstring.replace(' '     ,  ''     )

       # Checks if all characters are unique
       lowerCAmelCase__					     = set()
       for i in pbstring:
              if i not in abc:
                     lowerCAmelCase__					     = F"""'{i}' not in list of symbols"""
                     raise Exception(lowerCAmelCase__     )
              elif i in tmppbl:
                     lowerCAmelCase__					     = F"""Duplicate symbol ({i})"""
                     raise Exception(lowerCAmelCase__     )
              else:
                     tmppbl.add(lowerCAmelCase__     )
       del tmppbl

       # Created the dictionary
       lowerCAmelCase__					     = {}
       for j in range(0     ,  len(lowerCAmelCase__     ) - 1     ,  2     ):
              lowerCAmelCase__					     = pbstring[j + 1]
              lowerCAmelCase__					     = pbstring[j]

       return pb






def 				__lowerCamelCase		(						lowerCAmelCase__     ,  lowerCAmelCase__     ,  lowerCAmelCase__ = (rotora, rotora, rotora)     ,  lowerCAmelCase__ = ""     ,  ):
       lowerCAmelCase__					     = text.upper()
       lowerCAmelCase__     , lowerCAmelCase__     , lowerCAmelCase__					     = _validator(
           lowerCAmelCase__     ,  lowerCAmelCase__     ,  plugb.upper()     )

       lowerCAmelCase__     , lowerCAmelCase__     , lowerCAmelCase__					     = rotor_position
       lowerCAmelCase__     , lowerCAmelCase__     , lowerCAmelCase__					     = rotor_selection
       rotorposa -= 1
       rotorposa -= 1
       rotorposa -= 1

       lowerCAmelCase__					     = []

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

                     # rotor ra --------------------------
                     lowerCAmelCase__					     = abc.index(lowerCAmelCase__     ) + rotorposa
                     lowerCAmelCase__					     = rotora[index % len(lowerCAmelCase__     )]

                     # rotor rb --------------------------
                     lowerCAmelCase__					     = abc.index(lowerCAmelCase__     ) + rotorposa
                     lowerCAmelCase__					     = rotora[index % len(lowerCAmelCase__     )]

                     # rotor rc --------------------------
                     lowerCAmelCase__					     = abc.index(lowerCAmelCase__     ) + rotorposa
                     lowerCAmelCase__					     = rotora[index % len(lowerCAmelCase__     )]

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

                     lowerCAmelCase__					     = reflector[symbol]

                     # 2nd rotors
                     lowerCAmelCase__					     = abc[rotora.index(lowerCAmelCase__     ) - rotorposa]
                     lowerCAmelCase__					     = abc[rotora.index(lowerCAmelCase__     ) - rotorposa]
                     lowerCAmelCase__					     = abc[rotora.index(lowerCAmelCase__     ) - rotorposa]

                     # 2nd plugboard
                     if symbol in plugboard:
                            lowerCAmelCase__					     = plugboard[symbol]

                     # moves/resets rotor positions
                     rotorposa += 1
                     if rotorposa >= len(lowerCAmelCase__     ):
                            lowerCAmelCase__					     = 0
                            rotorposa += 1
                     if rotorposa >= len(lowerCAmelCase__     ):
                            lowerCAmelCase__					     = 0
                            rotorposa += 1
                     if rotorposa >= len(lowerCAmelCase__     ):
                            lowerCAmelCase__					     = 0

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

       return "".join(lowerCAmelCase__     )


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

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