



'''simple docstring'''





from __future__ import annotations

lowercase_	 =						tuple[int, int, int]
lowercase_	 =						tuple[str, str, str]


# used alphabet --------------------------
# from string.ascii_uppercase
lowercase_	 =						"ABCDEFGHIJKLMNOPQRSTUVWXYZ"

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




def lowerCAmelCase						(__A						,			__A						,			__A):



     """simple docstring"""



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

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

     # Validates string and returns dict
     _a											=						_plugboard(__A)

     return rotpos, rotsel, pbdict




def lowerCAmelCase						(__A):



     """simple docstring"""



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

     pbstring.replace(''' '''						,			'''''')

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

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

     return pb




def lowerCAmelCase						(__A						,			__A						,			__A = (rotora, rotora, rotora)						,			__A = ""						,			):



     """simple docstring"""



     _a											=						text.upper()
     _a						,					_a						,					_a											=						_validator(
         __A						,			__A						,			plugb.upper())

     _a						,					_a						,					_a											=						rotor_position
     _a						,					_a						,					_a											=						rotor_selection
     rotorposa -= 1
     rotorposa -= 1
     rotorposa -= 1

     _a											=						[]

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

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

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

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

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

               _a											=						reflector[symbol]

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

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

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

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

     return "".join(__A)


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

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








'''simple docstring'''





import math
from typing import Any, Callable, List, Optional, Tuple, Union

import numpy as np
import torch

from ...models import TaFilmDecoder
from ...schedulers import DDPMScheduler
from ...utils import is_onnx_available, logging, randn_tensor


if is_onnx_available():
  from ..onnx_utils import OnnxRuntimeModel

from ..pipeline_utils import AudioPipelineOutput, DiffusionPipeline
from .continous_encoder import SpectrogramContEncoder
from .notes_encoder import SpectrogramNotesEncoder


lowercase_	 =						logging.get_logger(__name__)  # pylint: disable=invalid-name

lowercase_	 =						256






class __A    (						A		):



 '''simple docstring'''







 __lowerCamelCase					: Optional[int]    = ['melgan']



 def __init__(self				,     A				,     A				,     A				,     A				,     A				,     )					->  None:


      """simple docstring"""



      super().__init__()

      # From MELGAN
      _a											=						math.log(1E-5 )  # Matches MelGAN training.
      _a											=						4.0  # Largest value for most examples
      _a											=						128

      self.register_modules(
          notes_encoder=A				,     continuous_encoder=A				,     decoder=A				,     scheduler=A				,     melgan=A				,     )



 def       a__						(self				,     A				,     A=(-1.0, 1.0)				,     A=False )					->  List[str]:


      """simple docstring"""



      _a						,					_a											=						output_range
      if clip:
           _a											=						torch.clip(A				,     self.min_value				,     self.max_value )
      # Scale to [0, 1].
      _a											=						(features - self.min_value) / (self.max_value - self.min_value)
      # Scale to [min_out, max_out].
      return zero_one * (max_out - min_out) + min_out



 def       a__						(self				,     A				,     A=(-1.0, 1.0)				,     A=False )					->  Optional[Any]:


      """simple docstring"""



      _a						,					_a											=						input_range
      _a											=						torch.clip(A				,     A				,     A ) if clip else outputs
      # Scale to [0, 1].
      _a											=						(outputs - min_out) / (max_out - min_out)
      # Scale to [self.min_value, self.max_value].
      return zero_one * (self.max_value - self.min_value) + self.min_value



 def       a__						(self				,     A				,     A				,     A )					->  Optional[int]:


      """simple docstring"""



      _a											=						input_tokens > 0
      _a						,					_a											=						self.notes_encoder(
          encoder_input_tokens=A				,     encoder_inputs_mask=A )

      _a						,					_a											=						self.continuous_encoder(
          encoder_inputs=A				,     encoder_inputs_mask=A )

      return [(tokens_encoded, tokens_mask), (continuous_encoded, continuous_mask)]



 def       a__						(self				,     A				,     A				,     A )					->  Any:


      """simple docstring"""



      _a											=						noise_time
      if not torch.is_tensor(A ):
           _a											=						torch.tensor([timesteps]				,     dtype=torch.long				,     device=input_tokens.device )
      elif torch.is_tensor(A ) and len(timesteps.shape ) == 0:
           _a											=						timesteps[None].to(input_tokens.device )

      # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
      _a											=						timesteps * torch.ones(input_tokens.shape[0]				,     dtype=timesteps.dtype				,     device=timesteps.device )

      _a											=						self.decoder(
          encodings_and_masks=A				,     decoder_input_tokens=A				,     decoder_noise_time=A )
      return logits



 @torch.no_grad()
 def __call__(self				,     A				,     A = None				,     A = 100				,     A = True				,     A = "numpy"				,     A = None				,     A = 1				,     )					->  Union[AudioPipelineOutput, Tuple]:


      """simple docstring"""



      if (callback_steps is None) or (
          callback_steps is not None and (not isinstance(A				,     A ) or callback_steps <= 0)
      ):
           raise ValueError(
               f'''`callback_steps` has to be a positive integer but is {callback_steps} of type'''
               f''' {type(A )}.''' )

      _a											=						np.zeros([1, TARGET_FEATURE_LENGTH, self.n_dims]				,     dtype=np.floataa )
      _a											=						np.zeros([1, 0, self.n_dims]				,     np.floataa )
      _a											=						torch.ones((1, TARGET_FEATURE_LENGTH)				,     dtype=A				,     device=self.device )

      for i, encoder_input_tokens in enumerate(A ):
           if i == 0:
                _a											=						torch.from_numpy(pred_mel[:1].copy() ).to(
                    device=self.device				,     dtype=self.decoder.dtype )
                # The first chunk has no previous context.
                _a											=						torch.zeros((1, TARGET_FEATURE_LENGTH)				,     dtype=A				,     device=self.device )
           else:
                # The full song pipeline does not feed in a context feature, so the mask
                # will be all 0s after the feature converter. Because we know we're
                # feeding in a full context chunk from the previous prediction, set it
                # to all 1s.
                _a											=						ones

           _a											=						self.scale_features(
               A				,     output_range=[-1.0, 1.0]				,     clip=A )

           _a											=						self.encode(
               input_tokens=torch.IntTensor([encoder_input_tokens] ).to(device=self.device )				,     continuous_inputs=A				,     continuous_mask=A				,     )

           # Sample encoder_continuous_inputs shaped gaussian noise to begin loop
           _a											=						randn_tensor(
               shape=encoder_continuous_inputs.shape				,     generator=A				,     device=self.device				,     dtype=self.decoder.dtype				,     )

           # set step values
           self.scheduler.set_timesteps(A )

           # Denoising diffusion loop
           for j, t in enumerate(self.progress_bar(self.scheduler.timesteps ) ):
                _a											=						self.decode(
                    encodings_and_masks=A				,     input_tokens=A				,     noise_time=t / self.scheduler.config.num_train_timesteps				,     )

                # Compute previous output: x_t -> x_t-1
                _a											=						self.scheduler.step(A				,     A				,     A				,     generator=A ).prev_sample

           _a											=						self.scale_to_features(A				,     input_range=[-1.0, 1.0] )
           _a											=						mel[:1]
           _a											=						mel.cpu().float().numpy()

           _a											=						np.concatenate([full_pred_mel, pred_mel[:1]]				,     axis=1 )

           # call the callback, if provided
           if callback is not None and i % callback_steps == 0:
                callback(A				,     A )

           logger.info('''Generated segment'''				,     A )

      if output_type == "numpy" and not is_onnx_available():
           raise ValueError(
               '''Cannot return output in \'np\' format if ONNX is not available. Make sure to have ONNX installed or set \'output_type\' to \'mel\'.''' )
      elif output_type == "numpy" and self.melgan is None:
           raise ValueError(
               '''Cannot return output in \'np\' format if melgan component is not defined. Make sure to define `self.melgan` or set \'output_type\' to \'mel\'.''' )

      if output_type == "numpy":
           _a											=						self.melgan(input_features=full_pred_mel.astype(np.floataa ) )
      else:
           _a											=						full_pred_mel

      if not return_dict:
           return (output,)

      return AudioPipelineOutput(audios=A )



