

from __future__ import annotations

snake_case_			  = tuple[int, int, int]
snake_case_			  = tuple[str, str, str]


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

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

def 	snake_case__			(    SCREAMING_SNAKE_CASE_		:    RotorPositionT	,     SCREAMING_SNAKE_CASE_		:    RotorSelectionT	,     SCREAMING_SNAKE_CASE_		:    str			):



     '''simple docstring'''





     if (unique_rotsel := len(set(UpperCAmelCase_			)			)) < 3:
          lowercase__    :   str      =       f"""Please use 3 unique rotors (not {unique_rotsel})"""
          raise Exception(UpperCAmelCase_			)

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

     # Validates string and returns dict
     lowercase__    :   Dict      =       _plugboard(UpperCAmelCase_			)

     return rotpos, rotsel, pbdict

def 	snake_case__			(    SCREAMING_SNAKE_CASE_		:    str			):



     '''simple docstring'''





     if not isinstance(UpperCAmelCase_	,     UpperCAmelCase_			):
          lowercase__    :   str      =       f"""Plugboard setting isn\'t type string ({type(UpperCAmelCase_			)})"""
          raise TypeError(UpperCAmelCase_			)
     elif len(UpperCAmelCase_			) % 2 != 0:
          lowercase__    :   Any      =       f"""Odd number of symbols ({len(UpperCAmelCase_			)})"""
          raise Exception(UpperCAmelCase_			)
     elif pbstring == "":
          return {}

     pbstring.replace(' '	,     ''			)

     # Checks if all characters are unique
     lowercase__    :   Tuple      =       set()
     for i in pbstring:
          if i not in abc:
               lowercase__    :   Dict      =       f"""\'{i}\' not in list of symbols"""
               raise Exception(UpperCAmelCase_			)
          elif i in tmppbl:
               lowercase__    :   List[Any]      =       f"""Duplicate symbol ({i})"""
               raise Exception(UpperCAmelCase_			)
          else:
               tmppbl.add(UpperCAmelCase_			)
     del tmppbl

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

     return pb

def 	snake_case__			(    SCREAMING_SNAKE_CASE_		:    str	,     SCREAMING_SNAKE_CASE_		:    RotorPositionT	,     SCREAMING_SNAKE_CASE_		:    RotorSelectionT = (rotora, rotora, rotora)	,     SCREAMING_SNAKE_CASE_		:    str = ""	,     ):



     '''simple docstring'''





     lowercase__    :   Tuple      =       text.upper()
     lowercase__    :   Union[str, Any]      =       _validator(
         UpperCAmelCase_	,     UpperCAmelCase_	,     plugb.upper()			)

     lowercase__    :   Optional[Any]      =       rotor_position
     lowercase__    :   List[str]      =       rotor_selection
     rotorposa -= 1
     rotorposa -= 1
     rotorposa -= 1

     lowercase__    :   Union[str, Any]      =       []

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

               # rotor ra --------------------------
               lowercase__    :   Dict      =       abc.index(UpperCAmelCase_			) + rotorposa
               lowercase__    :   List[str]      =       rotora[index % len(UpperCAmelCase_			)]

               # rotor rb --------------------------
               lowercase__    :   List[str]      =       abc.index(UpperCAmelCase_			) + rotorposa
               lowercase__    :   Tuple      =       rotora[index % len(UpperCAmelCase_			)]

               # rotor rc --------------------------
               lowercase__    :   List[Any]      =       abc.index(UpperCAmelCase_			) + rotorposa
               lowercase__    :   List[Any]      =       rotora[index % len(UpperCAmelCase_			)]

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

               lowercase__    :   Optional[Any]      =       reflector[symbol]

               # 2nd rotors
               lowercase__    :   Dict      =       abc[rotora.index(UpperCAmelCase_			) - rotorposa]
               lowercase__    :   Any      =       abc[rotora.index(UpperCAmelCase_			) - rotorposa]
               lowercase__    :   Optional[int]      =       abc[rotora.index(UpperCAmelCase_			) - rotorposa]

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

               # moves/resets rotor positions
               rotorposa += 1
               if rotorposa >= len(UpperCAmelCase_			):
                    lowercase__    :   str      =       0
                    rotorposa += 1
               if rotorposa >= len(UpperCAmelCase_			):
                    lowercase__    :   List[str]      =       0
                    rotorposa += 1
               if rotorposa >= len(UpperCAmelCase_			):
                    lowercase__    :   Any      =       0

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

     return "".join(UpperCAmelCase_			)


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

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





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

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...file_utils import TensorType, is_torch_available
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import logging


snake_case				:    List[str]       				=     logging.get_logger(__name__)

snake_case				:    int       				=     {
    '''facebook/blenderbot_small-90M''': '''https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/config.json''',
    # See all BlenderbotSmall models at https://huggingface.co/models?filter=blenderbot_small
}







class 		_snake_case  (      _snake_case							):
 SCREAMING_SNAKE_CASE__      =       'blenderbot-small'
 SCREAMING_SNAKE_CASE__      =       ['past_key_values']
 SCREAMING_SNAKE_CASE__      =       {'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}




 def __init__(		self					,  _lowerCamelCase=5_0265					,  _lowerCamelCase=512					,  _lowerCamelCase=8					,  _lowerCamelCase=2048					,  _lowerCamelCase=16					,  _lowerCamelCase=8					,  _lowerCamelCase=2048					,  _lowerCamelCase=16					,  _lowerCamelCase=0.0					,  _lowerCamelCase=0.0					,  _lowerCamelCase=True					,  _lowerCamelCase=True					,  _lowerCamelCase="gelu"					,  _lowerCamelCase=512					,  _lowerCamelCase=0.1					,  _lowerCamelCase=0.0					,  _lowerCamelCase=0.0					,  _lowerCamelCase=0.02					,  _lowerCamelCase=1					,  _lowerCamelCase=False					,  _lowerCamelCase=0					,  _lowerCamelCase=1					,  _lowerCamelCase=2					,  _lowerCamelCase=2					,  **_lowerCamelCase					,  ):
       a						:Dict 							=       vocab_size
       a						:Optional[Any] 							=       max_position_embeddings
       a						:str 							=       d_model
       a						:Any 							=       encoder_ffn_dim
       a						:Optional[int] 							=       encoder_layers
       a						:List[str] 							=       encoder_attention_heads
       a						:List[str] 							=       decoder_ffn_dim
       a						:Optional[int] 							=       decoder_layers
       a						:str 							=       decoder_attention_heads
       a						:List[str] 							=       dropout
       a						:Optional[int] 							=       attention_dropout
       a						:Dict 							=       activation_dropout
       a						:List[str] 							=       activation_function
       a						:List[Any] 							=       init_std
       a						:Optional[int] 							=       encoder_layerdrop
       a						:Tuple 							=       decoder_layerdrop
       a						:List[str] 							=       use_cache
       a						:int 							=       encoder_layers
       a						:Union[str, Any] 							=       scale_embedding  # scale factor will be sqrt(d_model) if True

       super().__init__(
           pad_token_id=_lowerCamelCase					,  bos_token_id=_lowerCamelCase					,  eos_token_id=_lowerCamelCase					,  is_encoder_decoder=_lowerCamelCase					,  decoder_start_token_id=_lowerCamelCase					,  forced_eos_token_id=_lowerCamelCase					,  **_lowerCamelCase					,  )







class 		_snake_case  (      _snake_case							):




 @property
 def        SCREAMING_SNAKE_CASE__				(		self    ):
       if self.task in ["default", "seq2seq-lm"]:
             a						:Optional[Any] 							=       OrderedDict(
                 [
                     ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
                     ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
                 ]    )

             if self.use_past:
                   a						:Union[str, Any] 							=       {0: '''batch'''}
                   a						:Tuple 							=       {0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
             else:
                   a						:Optional[int] 							=       {0: '''batch''', 1: '''decoder_sequence'''}
                   a						:str 							=       {0: '''batch''', 1: '''decoder_sequence'''}

             if self.use_past:
                   self.fill_with_past_key_values_(_lowerCamelCase					,  direction='''inputs'''    )
       elif self.task == "causal-lm":
             # TODO: figure this case out.
             a						:Optional[int] 							=       OrderedDict(
                 [
                     ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
                     ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
                 ]    )
             if self.use_past:
                   a							,				a						:str 							=       self.num_layers
                   for i in range(_lowerCamelCase    ):
                         a						:List[Any] 							=       {0: '''batch''', 2: '''past_sequence + sequence'''}
                         a						:List[str] 							=       {0: '''batch''', 2: '''past_sequence + sequence'''}
       else:
             a						:Optional[int] 							=       OrderedDict(
                 [
                     ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
                     ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
                     ('''decoder_input_ids''', {0: '''batch''', 1: '''decoder_sequence'''}),
                     ('''decoder_attention_mask''', {0: '''batch''', 1: '''decoder_sequence'''}),
                 ]    )

       return common_inputs




 @property
 def        SCREAMING_SNAKE_CASE__				(		self    ):
       if self.task in ["default", "seq2seq-lm"]:
             a						:List[Any] 							=       super().outputs
       else:
             a						:Union[str, Any] 							=       super(_lowerCamelCase					,  self    ).outputs
             if self.use_past:
                   a							,				a						:int 							=       self.num_layers
                   for i in range(_lowerCamelCase    ):
                         a						:int 							=       {0: '''batch''', 2: '''past_sequence + sequence'''}
                         a						:Optional[Any] 							=       {0: '''batch''', 2: '''past_sequence + sequence'''}
       return common_outputs




 def        SCREAMING_SNAKE_CASE__				(		self					,  _lowerCamelCase					,  _lowerCamelCase = -1					,  _lowerCamelCase = -1					,  _lowerCamelCase = False					,  _lowerCamelCase = None					,  ):
       a						:Tuple 							=       self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
           _lowerCamelCase					,  _lowerCamelCase					,  _lowerCamelCase					,  _lowerCamelCase					,  _lowerCamelCase    )

       # Generate decoder inputs
       a						:Dict 							=       seq_length if not self.use_past else 1
       a						:Dict 							=       self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
           _lowerCamelCase					,  _lowerCamelCase					,  _lowerCamelCase					,  _lowerCamelCase					,  _lowerCamelCase    )
       a						:List[Any] 							=       {F'''decoder_{name}''': tensor for name, tensor in decoder_inputs.items()}
       a						:List[str] 							=       dict(**_lowerCamelCase					,  **_lowerCamelCase    )

       if self.use_past:
             if not is_torch_available():
                   raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.'''    )
             else:
                   import torch
             a							,				a						:Optional[Any] 							=       common_inputs['''input_ids'''].shape
             a						:Tuple 							=       common_inputs['''decoder_input_ids'''].shape[1]
             a							,				a						:List[Any] 							=       self.num_attention_heads
             a						:List[Any] 							=       (
                 batch,
                 num_encoder_attention_heads,
                 encoder_seq_length,
                 self._config.hidden_size // num_encoder_attention_heads,
             )
             a						:int 							=       decoder_seq_length + 3
             a						:Union[str, Any] 							=       (
                 batch,
                 num_decoder_attention_heads,
                 decoder_past_length,
                 self._config.hidden_size // num_decoder_attention_heads,
             )

             a						:Union[str, Any] 							=       torch.cat(
                 [common_inputs['''decoder_attention_mask'''], torch.ones(_lowerCamelCase					,  _lowerCamelCase    )]					,  dim=1    )

             a						:List[Any] 							=       []
             # If the number of encoder and decoder layers are present in the model configuration, both are considered
             a							,				a						:Optional[int] 							=       self.num_layers
             a						:str 							=       min(_lowerCamelCase					,  _lowerCamelCase    )
             a						:str 							=       max(_lowerCamelCase					,  _lowerCamelCase    ) - min_num_layers
             a						:Tuple 							=       '''encoder''' if num_encoder_layers > num_decoder_layers else '''decoder'''

             for _ in range(_lowerCamelCase    ):
                   common_inputs["past_key_values"].append(
                       (
                           torch.zeros(_lowerCamelCase    ),
                           torch.zeros(_lowerCamelCase    ),
                           torch.zeros(_lowerCamelCase    ),
                           torch.zeros(_lowerCamelCase    ),
                       )    )
             # TODO: test this.
             a						:int 							=       encoder_shape if remaining_side_name == '''encoder''' else decoder_shape
             for _ in range(_lowerCamelCase					,  _lowerCamelCase    ):
                   common_inputs["past_key_values"].append((torch.zeros(_lowerCamelCase    ), torch.zeros(_lowerCamelCase    ))    )
       return common_inputs




 def        SCREAMING_SNAKE_CASE__				(		self					,  _lowerCamelCase					,  _lowerCamelCase = -1					,  _lowerCamelCase = -1					,  _lowerCamelCase = False					,  _lowerCamelCase = None					,  ):
       a						:Tuple 							=       self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
           _lowerCamelCase					,  _lowerCamelCase					,  _lowerCamelCase					,  _lowerCamelCase					,  _lowerCamelCase    )

       if self.use_past:
             if not is_torch_available():
                   raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.'''    )
             else:
                   import torch
             a							,				a						:Dict 							=       common_inputs['''input_ids'''].shape
             # Not using the same length for past_key_values
             a						:Optional[int] 							=       seqlen + 2
             a							,				a						:Union[str, Any] 							=       self.num_layers
             a							,				a						:Optional[Any] 							=       self.num_attention_heads
             a						:str 							=       (
                 batch,
                 num_encoder_attention_heads,
                 past_key_values_length,
                 self._config.hidden_size // num_encoder_attention_heads,
             )

             a						:Tuple 							=       common_inputs['''attention_mask'''].dtype
             a						:Any 							=       torch.cat(
                 [common_inputs['''attention_mask'''], torch.ones(_lowerCamelCase					,  _lowerCamelCase					,  dtype=_lowerCamelCase    )]					,  dim=1    )
             a						:Any 							=       [
                 (torch.zeros(_lowerCamelCase    ), torch.zeros(_lowerCamelCase    )) for _ in range(_lowerCamelCase    )
             ]
       return common_inputs




 def        SCREAMING_SNAKE_CASE__				(		self					,  _lowerCamelCase					,  _lowerCamelCase = -1					,  _lowerCamelCase = -1					,  _lowerCamelCase = False					,  _lowerCamelCase = None					,  ):
       # Copied from OnnxConfig.generate_dummy_inputs
       # Did not use super(OnnxConfigWithPast, self).generate_dummy_inputs for code clarity.
       # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
       a						:Optional[Any] 							=       compute_effective_axis_dimension(
           _lowerCamelCase					,  fixed_dimension=OnnxConfig.default_fixed_batch					,  num_token_to_add=0    )

       # If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
       a						:Optional[int] 							=       tokenizer.num_special_tokens_to_add(_lowerCamelCase    )
       a						:Tuple 							=       compute_effective_axis_dimension(
           _lowerCamelCase					,  fixed_dimension=OnnxConfig.default_fixed_sequence					,  num_token_to_add=_lowerCamelCase    )

       # Generate dummy inputs according to compute batch and sequence
       a						:List[str] 							=       [''' '''.join([tokenizer.unk_token]    ) * seq_length] * batch_size
       a						:Dict 							=       dict(tokenizer(_lowerCamelCase					,  return_tensors=_lowerCamelCase    )    )
       return common_inputs




 def        SCREAMING_SNAKE_CASE__				(		self					,  _lowerCamelCase					,  _lowerCamelCase = -1					,  _lowerCamelCase = -1					,  _lowerCamelCase = False					,  _lowerCamelCase = None					,  ):
       if self.task in ["default", "seq2seq-lm"]:
             a						:Tuple 							=       self._generate_dummy_inputs_for_default_and_seqaseq_lm(
                 _lowerCamelCase					,  batch_size=_lowerCamelCase					,  seq_length=_lowerCamelCase					,  is_pair=_lowerCamelCase					,  framework=_lowerCamelCase    )

       elif self.task == "causal-lm":
             a						:Dict 							=       self._generate_dummy_inputs_for_causal_lm(
                 _lowerCamelCase					,  batch_size=_lowerCamelCase					,  seq_length=_lowerCamelCase					,  is_pair=_lowerCamelCase					,  framework=_lowerCamelCase    )
       else:
             a						:Dict 							=       self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
                 _lowerCamelCase					,  batch_size=_lowerCamelCase					,  seq_length=_lowerCamelCase					,  is_pair=_lowerCamelCase					,  framework=_lowerCamelCase    )

       return common_inputs




 def        SCREAMING_SNAKE_CASE__				(		self					,  _lowerCamelCase					,  _lowerCamelCase					,  _lowerCamelCase					,  _lowerCamelCase    ):
       if self.task in ["default", "seq2seq-lm"]:
             a						:Optional[int] 							=       super()._flatten_past_key_values_(_lowerCamelCase					,  _lowerCamelCase					,  _lowerCamelCase					,  _lowerCamelCase    )
       else:
             a						:Any 							=       super(_lowerCamelCase					,  self    )._flatten_past_key_values_(
                 _lowerCamelCase					,  _lowerCamelCase					,  _lowerCamelCase					,  _lowerCamelCase    )





