
import unittest

from transformers import BertGenerationConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
   import torch

   from transformers import BertGenerationDecoder, BertGenerationEncoder






class UpperCAmelCase   :





   '''simple docstring'''


   def __init__(					self     :							Any			,			lowerCAmelCase_     :							Optional[int]			,			lowerCAmelCase_     :							Tuple=1_3			,			lowerCAmelCase_     :							Union[str, Any]=7			,			lowerCAmelCase_     :							Tuple=True			,			lowerCAmelCase_     :							str=True			,			lowerCAmelCase_     :							int=9_9			,			lowerCAmelCase_     :							Tuple=3_2			,			lowerCAmelCase_     :							List[Any]=5			,			lowerCAmelCase_     :							Optional[Any]=4			,			lowerCAmelCase_     :							List[Any]=3_7			,			lowerCAmelCase_     :							Optional[int]="gelu"			,			lowerCAmelCase_     :							List[Any]=0.1			,			lowerCAmelCase_     :							Dict=0.1			,			lowerCAmelCase_     :							Any=5_0			,			lowerCAmelCase_     :							Optional[int]=0.02			,			lowerCAmelCase_     :							Optional[int]=True			,			lowerCAmelCase_     :							int=None			,			):

      """simple docstring"""
      _A:  Tuple					=       parent
      _A:  List[str]					=       batch_size
      _A:  List[Any]					=       seq_length
      _A:  str					=       is_training
      _A:  Dict					=       use_input_mask
      _A:  Union[str, Any]					=       vocab_size
      _A:  Union[str, Any]					=       hidden_size
      _A:  Optional[Any]					=       num_hidden_layers
      _A:  List[str]					=       num_attention_heads
      _A:  Optional[Any]					=       intermediate_size
      _A:  Union[str, Any]					=       hidden_act
      _A:  int					=       hidden_dropout_prob
      _A:  int					=       attention_probs_dropout_prob
      _A:  Union[str, Any]					=       max_position_embeddings
      _A:  Union[str, Any]					=       initializer_range
      _A:  List[Any]					=       use_labels
      _A:  List[Any]					=       scope


   def  __magic_name__				(					self     :							str     ):

      """simple docstring"""
      _A:  Dict					=       ids_tensor([self.batch_size, self.seq_length]			,			self.vocab_size     )

      _A:  Tuple					=       None
      if self.use_input_mask:
         _A:  Union[str, Any]					=       random_attention_mask([self.batch_size, self.seq_length]     )

      if self.use_labels:
         _A:  int					=       ids_tensor([self.batch_size, self.seq_length]			,			self.vocab_size     )

      _A:  Optional[int]					=       self.get_config()

      return config, input_ids, input_mask, token_labels


   def  __magic_name__				(					self     :							Optional[Any]     ):

      """simple docstring"""
      return BertGenerationConfig(
          vocab_size=self.vocab_size			,			hidden_size=self.hidden_size			,			num_hidden_layers=self.num_hidden_layers			,			num_attention_heads=self.num_attention_heads			,			intermediate_size=self.intermediate_size			,			hidden_act=self.hidden_act			,			hidden_dropout_prob=self.hidden_dropout_prob			,			attention_probs_dropout_prob=self.attention_probs_dropout_prob			,			max_position_embeddings=self.max_position_embeddings			,			is_decoder=__UpperCAmelCase			,			initializer_range=self.initializer_range			,			)


   def  __magic_name__				(					self     :							Any     ):

      """simple docstring"""
      (
          _A				
      ):  Optional[Any]					=       self.prepare_config_and_inputs()

      _A:  Dict					=       True
      _A:  Union[str, Any]					=       floats_tensor([self.batch_size, self.seq_length, self.hidden_size]     )
      _A:  Optional[Any]					=       ids_tensor([self.batch_size, self.seq_length]			,			vocab_size=2     )

      return (
          config,
          input_ids,
          input_mask,
          token_labels,
          encoder_hidden_states,
          encoder_attention_mask,
      )


   def  __magic_name__				(					self     :							int			,			lowerCAmelCase_     :							List[Any]			,			lowerCAmelCase_     :							Optional[Any]			,			lowerCAmelCase_     :							Any			,			lowerCAmelCase_     :							Tuple			,			**lowerCAmelCase_     :							Optional[Any]			,			):

      """simple docstring"""
      _A:  Tuple					=       BertGenerationEncoder(config=__UpperCAmelCase     )
      model.to(__UpperCAmelCase     )
      model.eval()
      _A:  str					=       model(__UpperCAmelCase			,			attention_mask=__UpperCAmelCase     )
      _A:  int					=       model(__UpperCAmelCase     )
      self.parent.assertEqual(result.last_hidden_state.shape			,			(self.batch_size, self.seq_length, self.hidden_size)     )


   def  __magic_name__				(					self     :							int			,			lowerCAmelCase_     :							str			,			lowerCAmelCase_     :							Optional[int]			,			lowerCAmelCase_     :							Tuple			,			lowerCAmelCase_     :							Dict			,			lowerCAmelCase_     :							Optional[Any]			,			lowerCAmelCase_     :							Union[str, Any]			,			**lowerCAmelCase_     :							Union[str, Any]			,			):

      """simple docstring"""
      _A:  str					=       True
      _A:  List[str]					=       BertGenerationEncoder(config=__UpperCAmelCase     )
      model.to(__UpperCAmelCase     )
      model.eval()
      _A:  List[str]					=       model(
          __UpperCAmelCase			,			attention_mask=__UpperCAmelCase			,			encoder_hidden_states=__UpperCAmelCase			,			encoder_attention_mask=__UpperCAmelCase			,			)
      _A:  str					=       model(
          __UpperCAmelCase			,			attention_mask=__UpperCAmelCase			,			encoder_hidden_states=__UpperCAmelCase			,			)
      self.parent.assertEqual(result.last_hidden_state.shape			,			(self.batch_size, self.seq_length, self.hidden_size)     )


   def  __magic_name__				(					self     :							int			,			lowerCAmelCase_     :							Optional[int]			,			lowerCAmelCase_     :							Union[str, Any]			,			lowerCAmelCase_     :							List[Any]			,			lowerCAmelCase_     :							List[Any]			,			lowerCAmelCase_     :							List[Any]			,			lowerCAmelCase_     :							Union[str, Any]			,			**lowerCAmelCase_     :							Optional[Any]			,			):

      """simple docstring"""
      _A:  Tuple					=       True
      _A:  Union[str, Any]					=       True
      _A:  Optional[int]					=       BertGenerationDecoder(config=__UpperCAmelCase     ).to(__UpperCAmelCase     ).eval()

      # first forward pass
      _A:  int					=       model(
          __UpperCAmelCase			,			attention_mask=__UpperCAmelCase			,			encoder_hidden_states=__UpperCAmelCase			,			encoder_attention_mask=__UpperCAmelCase			,			use_cache=__UpperCAmelCase			,			)
      _A:  Union[str, Any]					=       outputs.past_key_values

      # create hypothetical multiple next token and extent to next_input_ids
      _A:  Tuple					=       ids_tensor((self.batch_size, 3)			,			config.vocab_size     )
      _A:  Any					=       ids_tensor((self.batch_size, 3)			,			vocab_size=2     )

      # append to next input_ids and
      _A:  Tuple					=       torch.cat([input_ids, next_tokens]			,			dim=-1     )
      _A:  List[str]					=       torch.cat([input_mask, next_mask]			,			dim=-1     )

      _A:  Optional[int]					=       model(
          __UpperCAmelCase			,			attention_mask=__UpperCAmelCase			,			encoder_hidden_states=__UpperCAmelCase			,			encoder_attention_mask=__UpperCAmelCase			,			output_hidden_states=__UpperCAmelCase			,			)['hidden_states'][0]
      _A:  int					=       model(
          __UpperCAmelCase			,			attention_mask=__UpperCAmelCase			,			encoder_hidden_states=__UpperCAmelCase			,			encoder_attention_mask=__UpperCAmelCase			,			past_key_values=__UpperCAmelCase			,			output_hidden_states=__UpperCAmelCase			,			)['hidden_states'][0]

      # select random slice
      _A:  Tuple					=       ids_tensor((1,)			,			output_from_past.shape[-1]     ).item()
      _A:  Tuple					=       output_from_no_past[:, -3:, random_slice_idx].detach()
      _A:  Dict					=       output_from_past[:, :, random_slice_idx].detach()

      self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]     )

      # test that outputs are equal for slice
      self.parent.assertTrue(torch.allclose(__UpperCAmelCase			,			__UpperCAmelCase			,			atol=1e-3     )     )


   def  __magic_name__				(					self     :							int			,			lowerCAmelCase_     :							Tuple			,			lowerCAmelCase_     :							Optional[Any]			,			lowerCAmelCase_     :							Optional[Any]			,			lowerCAmelCase_     :							str			,			*lowerCAmelCase_     :							List[str]			,			):

      """simple docstring"""
      _A:  int					=       BertGenerationDecoder(__UpperCAmelCase     )
      model.to(__UpperCAmelCase     )
      model.eval()
      _A:  Tuple					=       model(__UpperCAmelCase			,			attention_mask=__UpperCAmelCase			,			labels=__UpperCAmelCase     )
      self.parent.assertEqual(result.logits.shape			,			(self.batch_size, self.seq_length, self.vocab_size)     )


   def  __magic_name__				(					self     :							str     ):

      """simple docstring"""
      _A:  Any					=       self.prepare_config_and_inputs()
      _A:  Union[str, Any]					=       {'input_ids': input_ids, 'attention_mask': input_mask}
      return config, inputs_dict






@require_torch
class UpperCAmelCase   (						SCREAMING_SNAKE_CASE__							,							SCREAMING_SNAKE_CASE__							,							SCREAMING_SNAKE_CASE__							,							unittest.TestCase   ):





   '''simple docstring'''
   __UpperCamelCase		:    Any 				=    (BertGenerationEncoder, BertGenerationDecoder) if is_torch_available() else ()
   __UpperCamelCase		:    Optional[Any] 				=    (BertGenerationDecoder,) if is_torch_available() else ()
   __UpperCamelCase		:    Optional[Any] 				=    (
       {"""feature-extraction""": BertGenerationEncoder, """text-generation""": BertGenerationDecoder}
       if is_torch_available()
       else {}
   )


   def  __magic_name__				(					self     :							str     ):

      """simple docstring"""
      _A:  int					=       BertGenerationEncoderTester(self     )
      _A:  int					=       ConfigTester(self			,			config_class=__UpperCAmelCase			,			hidden_size=3_7     )


   def  __magic_name__				(					self     :							Optional[Any]     ):

      """simple docstring"""
      self.config_tester.run_common_tests()


   def  __magic_name__				(					self     :							List[str]     ):

      """simple docstring"""
      _A:  Optional[int]					=       self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*__UpperCAmelCase     )


   def  __magic_name__				(					self     :							Optional[Any]     ):

      """simple docstring"""
      _A:  List[str]					=       self.model_tester.prepare_config_and_inputs()
      _A:  List[str]					=       'bert'
      self.model_tester.create_and_check_model(__UpperCAmelCase			,			__UpperCAmelCase			,			__UpperCAmelCase			,			__UpperCAmelCase     )


   def  __magic_name__				(					self     :							Optional[Any]     ):

      """simple docstring"""
      _A:  List[Any]					=       self.model_tester.prepare_config_and_inputs_for_decoder()
      self.model_tester.create_and_check_model_as_decoder(*__UpperCAmelCase     )


   def  __magic_name__				(					self     :							List[Any]     ):

      """simple docstring"""
      _A:  Tuple					=       self.model_tester.prepare_config_and_inputs_for_decoder()
      self.model_tester.create_and_check_decoder_model_past_large_inputs(*__UpperCAmelCase     )


   def  __magic_name__				(					self     :							str     ):

      """simple docstring"""
      (
          _A				
      ):  int					=       self.model_tester.prepare_config_and_inputs_for_decoder()

      _A:  int					=       None

      self.model_tester.create_and_check_model_as_decoder(
          __UpperCAmelCase			,			__UpperCAmelCase			,			__UpperCAmelCase			,			__UpperCAmelCase			,			__UpperCAmelCase			,			__UpperCAmelCase			,			)


   def  __magic_name__				(					self     :							int     ):

      """simple docstring"""
      _A:  Optional[Any]					=       self.model_tester.prepare_config_and_inputs_for_decoder()
      self.model_tester.create_and_check_for_causal_lm(*__UpperCAmelCase     )


   @slow
   def  __magic_name__				(					self     :							int     ):

      """simple docstring"""
      _A:  Dict					=       BertGenerationEncoder.from_pretrained('''google/bert_for_seq_generation_L-24_bbc_encoder'''     )
      self.assertIsNotNone(__UpperCAmelCase     )






@require_torch
class UpperCAmelCase   (						unittest.TestCase   ):





   '''simple docstring'''


   @slow
   def  __magic_name__				(					self     :							Union[str, Any]     ):

      """simple docstring"""
      _A:  str					=       BertGenerationEncoder.from_pretrained('''google/bert_for_seq_generation_L-24_bbc_encoder'''     )
      _A:  Tuple					=       torch.tensor([[1_0_1, 7_5_9_2, 1_0_1_0, 2_0_2_6, 3_8_9_9, 2_0_0_3, 1_0_1_4_0, 1_0_2]]     )
      with torch.no_grad():
         _A:  Dict					=       model(__UpperCAmelCase     )[0]
      _A:  Any					=       torch.Size([1, 8, 1_0_2_4]     )
      self.assertEqual(output.shape			,			__UpperCAmelCase     )
      _A:  Tuple					=       torch.tensor(
          [[[0.1775, 0.0083, -0.0321], [1.6002, 0.1287, 0.3912], [2.1473, 0.5791, 0.6066]]]     )
      self.assertTrue(torch.allclose(output[:, :3, :3]			,			__UpperCAmelCase			,			atol=1e-4     )     )






@require_torch
class UpperCAmelCase   (						unittest.TestCase   ):





   '''simple docstring'''


   @slow
   def  __magic_name__				(					self     :							List[Any]     ):

      """simple docstring"""
      _A:  Union[str, Any]					=       BertGenerationDecoder.from_pretrained('''google/bert_for_seq_generation_L-24_bbc_encoder'''     )
      _A:  str					=       torch.tensor([[1_0_1, 7_5_9_2, 1_0_1_0, 2_0_2_6, 3_8_9_9, 2_0_0_3, 1_0_1_4_0, 1_0_2]]     )
      with torch.no_grad():
         _A:  Any					=       model(__UpperCAmelCase     )[0]
      _A:  Tuple					=       torch.Size([1, 8, 5_0_3_5_8]     )
      self.assertEqual(output.shape			,			__UpperCAmelCase     )
      _A:  Tuple					=       torch.tensor(
          [[[-0.5788, -2.5994, -3.7054], [0.0438, 4.7997, 1.8795], [1.5862, 6.6409, 4.4638]]]     )
      self.assertTrue(torch.allclose(output[:, :3, :3]			,			__UpperCAmelCase			,			atol=1e-4     )     )










"""simple docstring"""





from __future__ import annotations

__A											=		tuple[int, int, int]
__A											=		tuple[str, str, str]


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

# -------------------------- default selection --------------------------
# rotors --------------------------
__A											=		"""EGZWVONAHDCLFQMSIPJBYUKXTR"""
__A											=		"""FOBHMDKEXQNRAULPGSJVTYICZW"""
__A											=		"""ZJXESIUQLHAVRMDOYGTNFWPBKC"""
# reflector --------------------------
__A											=		{
    """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 --------------------------
__A											=		"""RMDJXFUWGISLHVTCQNKYPBEZOA"""
__A											=		"""SGLCPQWZHKXAREONTFBVIYJUDM"""
__A											=		"""HVSICLTYKQUBXDWAJZOMFGPREN"""
__A											=		"""RZWQHFMVDBKICJLNTUXAGYPSOE"""
__A											=		"""LFKIJODBEGAMQPXVUHYSTCZRWN"""
__A											=		"""KOAEGVDHXPQZMLFTYWJNBRCIUS"""
def  __A   (_SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE			)					->tuple[RotorPositionT, RotorSelectionT, dict[str, str]]:





  """simple docstring"""


  if (unique_rotsel := len(set(_SCREAMING_SNAKE_CASE			)			)) < 3:
    lowerCAmelCase__			:Union[str, Any]											=							F"Please use 3 unique rotors (not {unique_rotsel})"
    raise Exception(_SCREAMING_SNAKE_CASE			)

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

  # Validates string and returns dict
  lowerCAmelCase__			:int											=							_plugboard(_SCREAMING_SNAKE_CASE			)

  return rotpos, rotsel, pbdict
def  __A   (_SCREAMING_SNAKE_CASE			)					->dict[str, str]:





  """simple docstring"""


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

  pbstring.replace(' '     ,   ''			)

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

  # Created the dictionary
  lowerCAmelCase__			:List[Any]											=							{}
  for j in range(0     ,   len(_SCREAMING_SNAKE_CASE			) - 1     ,   2			):
    lowerCAmelCase__			:Optional[int]											=							pbstring[j + 1]
    lowerCAmelCase__			:Union[str, Any]											=							pbstring[j]

  return pb
def  __A   (_SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE = (rotora, rotora, rotora)     ,   _SCREAMING_SNAKE_CASE = ""     ,   )					->str:





  """simple docstring"""


  lowerCAmelCase__			:Tuple											=							text.upper()
  lowerCAmelCase__							,				lowerCAmelCase__							,				lowerCAmelCase__			:Tuple											=							_validator(
      _SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE     ,   plugb.upper()			)

  lowerCAmelCase__							,				lowerCAmelCase__							,				lowerCAmelCase__			:Tuple											=							rotor_position
  lowerCAmelCase__							,				lowerCAmelCase__							,				lowerCAmelCase__			:Union[str, Any]											=							rotor_selection
  rotorposa -= 1
  rotorposa -= 1
  rotorposa -= 1

  lowerCAmelCase__			:Dict											=							[]

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

      # rotor ra --------------------------
      lowerCAmelCase__			:Optional[int]											=							abc.index(_SCREAMING_SNAKE_CASE			) + rotorposa
      lowerCAmelCase__			:str											=							rotora[index % len(_SCREAMING_SNAKE_CASE			)]

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

      # rotor rc --------------------------
      lowerCAmelCase__			:str											=							abc.index(_SCREAMING_SNAKE_CASE			) + rotorposa
      lowerCAmelCase__			:Optional[Any]											=							rotora[index % len(_SCREAMING_SNAKE_CASE			)]

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

      lowerCAmelCase__			:str											=							reflector[symbol]

      # 2nd rotors
      lowerCAmelCase__			:Tuple											=							abc[rotora.index(_SCREAMING_SNAKE_CASE			) - rotorposa]
      lowerCAmelCase__			:Optional[int]											=							abc[rotora.index(_SCREAMING_SNAKE_CASE			) - rotorposa]
      lowerCAmelCase__			:Any											=							abc[rotora.index(_SCREAMING_SNAKE_CASE			) - rotorposa]

      # 2nd plugboard
      if symbol in plugboard:
        lowerCAmelCase__			:Union[str, Any]											=							plugboard[symbol]

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

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

  return "".join(_SCREAMING_SNAKE_CASE			)


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

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