


"""simple docstring"""



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 						__lowerCAmelCase		(		lowercase						:						Tuple		,							lowercase						:						Tuple		,							lowercase						:						List[Any]				)			->		str:


    """simple docstring"""





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

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

    # Validates string and returns dict
    snake_case					:   Union[str, Any]         =					_plugboard(_lowerCAmelCase				)

    return rotpos, rotsel, pbdict







def 						__lowerCAmelCase		(		lowercase						:						Any				)			->		Optional[int]:


    """simple docstring"""





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

    pbstring.replace(" "		,							""				)

    # Checks if all characters are unique
    snake_case					:   str         =					set()
    for i in pbstring:
        if i not in abc:
            snake_case					:   List[Any]         =					F'\'{i}\' not in list of symbols'
            raise Exception(_lowerCAmelCase				)
        elif i in tmppbl:
            snake_case					:   int         =					F'Duplicate symbol ({i})'
            raise Exception(_lowerCAmelCase				)
        else:
            tmppbl.add(_lowerCAmelCase				)
    del tmppbl

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

    return pb







def 						__lowerCAmelCase		(		lowercase						:						int		,							lowercase						:						int		,							lowercase						:						Optional[int] = (rotora, rotora, rotora)		,							lowercase						:						Dict = ""		,							)			->		Any:


    """simple docstring"""





    snake_case					:   Dict         =					text.upper()
    snake_case      ,snake_case      ,snake_case					:   int         =					_validator(
        _lowerCAmelCase		,							_lowerCAmelCase		,							plugb.upper()				)

    snake_case      ,snake_case      ,snake_case					:   Optional[Any]         =					rotor_position
    snake_case      ,snake_case      ,snake_case					:   Dict         =					rotor_selection
    rotorposa -= 1
    rotorposa -= 1
    rotorposa -= 1

    snake_case					:   Union[str, Any]         =					[]

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

            # rotor ra --------------------------
            snake_case					:   Union[str, Any]         =					abc.index(_lowerCAmelCase				) + rotorposa
            snake_case					:   Any         =					rotora[index % len(_lowerCAmelCase				)]

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

            # rotor rc --------------------------
            snake_case					:   Any         =					abc.index(_lowerCAmelCase				) + rotorposa
            snake_case					:   Any         =					rotora[index % len(_lowerCAmelCase				)]

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

            snake_case					:   Tuple         =					reflector[symbol]

            # 2nd rotors
            snake_case					:   str         =					abc[rotora.index(_lowerCAmelCase				) - rotorposa]
            snake_case					:   Dict         =					abc[rotora.index(_lowerCAmelCase				) - rotorposa]
            snake_case					:   Optional[int]         =					abc[rotora.index(_lowerCAmelCase				) - rotorposa]

            # 2nd plugboard
            if symbol in plugboard:
                snake_case					:   List[str]         =					plugboard[symbol]

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

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

    return "".join(_lowerCAmelCase				)


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))



"""simple docstring"""



import os
import tempfile
import unittest
from pathlib import Path

from transformers import AutoConfig, is_torch_available
from transformers.testing_utils import require_torch, torch_device


if is_torch_available():
  from transformers import PyTorchBenchmark, PyTorchBenchmarkArguments







@require_torch
class _lowerCAmelCase       (   unittest.TestCase			):







   def 				lowerCamelCase	(			self		,			UpperCamelCase__		)			->	Dict:

       '''simple docstring'''




       for model_result in results.values():
           for batch_size, sequence_length in zip(model_result["bs"]		,			model_result["ss"]		):
               snake_case					:   Optional[int]         =					model_result["result"][batch_size][sequence_length]
               self.assertIsNotNone(UpperCamelCase__		)







   def 				lowerCamelCase	(			self		)			->	Any:

       '''simple docstring'''




       snake_case					:   List[Any]         =					"sshleifer/tiny-gpt2"
       snake_case					:   List[Any]         =					PyTorchBenchmarkArguments(
           models=[MODEL_ID]		,			training=UpperCamelCase__		,			inference=UpperCamelCase__		,			sequence_lengths=[8]		,			batch_sizes=[1]		,			multi_process=UpperCamelCase__		,			)
       snake_case					:   Dict         =					PyTorchBenchmark(UpperCamelCase__		)
       snake_case					:   Union[str, Any]         =					benchmark.run()
       self.check_results_dict_not_empty(results.time_inference_result		)
       self.check_results_dict_not_empty(results.memory_inference_result		)







   def 				lowerCamelCase	(			self		)			->	Union[str, Any]:

       '''simple docstring'''




       snake_case					:   str         =					"sgugger/tiny-distilbert-classification"
       snake_case					:   int         =					PyTorchBenchmarkArguments(
           models=[MODEL_ID]		,			training=UpperCamelCase__		,			inference=UpperCamelCase__		,			sequence_lengths=[8]		,			batch_sizes=[1]		,			multi_process=UpperCamelCase__		,			only_pretrain_model=UpperCamelCase__		,			)
       snake_case					:   Union[str, Any]         =					PyTorchBenchmark(UpperCamelCase__		)
       snake_case					:   Optional[int]         =					benchmark.run()
       self.check_results_dict_not_empty(results.time_inference_result		)
       self.check_results_dict_not_empty(results.memory_inference_result		)







   def 				lowerCamelCase	(			self		)			->	List[str]:

       '''simple docstring'''




       snake_case					:   List[Any]         =					"sshleifer/tiny-gpt2"
       snake_case					:   Tuple         =					PyTorchBenchmarkArguments(
           models=[MODEL_ID]		,			training=UpperCamelCase__		,			inference=UpperCamelCase__		,			torchscript=UpperCamelCase__		,			sequence_lengths=[8]		,			batch_sizes=[1]		,			multi_process=UpperCamelCase__		,			)
       snake_case					:   int         =					PyTorchBenchmark(UpperCamelCase__		)
       snake_case					:   int         =					benchmark.run()
       self.check_results_dict_not_empty(results.time_inference_result		)
       self.check_results_dict_not_empty(results.memory_inference_result		)







   @unittest.skipIf(torch_device == "cpu"		,			"Cant do half precision"		)
   def 				lowerCamelCase	(			self		)			->	Optional[Any]:

       '''simple docstring'''




       snake_case					:   List[Any]         =					"sshleifer/tiny-gpt2"
       snake_case					:   int         =					PyTorchBenchmarkArguments(
           models=[MODEL_ID]		,			training=UpperCamelCase__		,			inference=UpperCamelCase__		,			fpaa=UpperCamelCase__		,			sequence_lengths=[8]		,			batch_sizes=[1]		,			multi_process=UpperCamelCase__		,			)
       snake_case					:   List[Any]         =					PyTorchBenchmark(UpperCamelCase__		)
       snake_case					:   str         =					benchmark.run()
       self.check_results_dict_not_empty(results.time_inference_result		)
       self.check_results_dict_not_empty(results.memory_inference_result		)







   def 				lowerCamelCase	(			self		)			->	List[str]:

       '''simple docstring'''




       snake_case					:   Optional[int]         =					"sshleifer/tiny-gpt2"
       snake_case					:   int         =					AutoConfig.from_pretrained(UpperCamelCase__		)
       # set architectures equal to `None`
       snake_case					:   str         =					None
       snake_case					:   List[Any]         =					PyTorchBenchmarkArguments(
           models=[MODEL_ID]		,			training=UpperCamelCase__		,			inference=UpperCamelCase__		,			sequence_lengths=[8]		,			batch_sizes=[1]		,			multi_process=UpperCamelCase__		,			)
       snake_case					:   List[Any]         =					PyTorchBenchmark(UpperCamelCase__		,			configs=[config]		)
       snake_case					:   Optional[Any]         =					benchmark.run()
       self.check_results_dict_not_empty(results.time_inference_result		)
       self.check_results_dict_not_empty(results.memory_inference_result		)







   def 				lowerCamelCase	(			self		)			->	Dict:

       '''simple docstring'''




       snake_case					:   Tuple         =					"sshleifer/tiny-gpt2"
       snake_case					:   str         =					PyTorchBenchmarkArguments(
           models=[MODEL_ID]		,			training=UpperCamelCase__		,			inference=UpperCamelCase__		,			sequence_lengths=[8]		,			batch_sizes=[1]		,			multi_process=UpperCamelCase__		,			)
       snake_case					:   List[str]         =					PyTorchBenchmark(UpperCamelCase__		)
       snake_case					:   Dict         =					benchmark.run()
       self.check_results_dict_not_empty(results.time_train_result		)
       self.check_results_dict_not_empty(results.memory_train_result		)







   @unittest.skipIf(torch_device == "cpu"		,			"Can't do half precision"		)
   def 				lowerCamelCase	(			self		)			->	int:

       '''simple docstring'''




       snake_case					:   Optional[Any]         =					"sshleifer/tiny-gpt2"
       snake_case					:   Optional[Any]         =					PyTorchBenchmarkArguments(
           models=[MODEL_ID]		,			training=UpperCamelCase__		,			inference=UpperCamelCase__		,			sequence_lengths=[8]		,			batch_sizes=[1]		,			fpaa=UpperCamelCase__		,			multi_process=UpperCamelCase__		,			)
       snake_case					:   Optional[int]         =					PyTorchBenchmark(UpperCamelCase__		)
       snake_case					:   str         =					benchmark.run()
       self.check_results_dict_not_empty(results.time_train_result		)
       self.check_results_dict_not_empty(results.memory_train_result		)







   def 				lowerCamelCase	(			self		)			->	Optional[int]:

       '''simple docstring'''




       snake_case					:   Tuple         =					"sshleifer/tiny-gpt2"
       snake_case					:   List[Any]         =					AutoConfig.from_pretrained(UpperCamelCase__		)
       snake_case					:   Union[str, Any]         =					PyTorchBenchmarkArguments(
           models=[MODEL_ID]		,			training=UpperCamelCase__		,			inference=UpperCamelCase__		,			sequence_lengths=[8]		,			batch_sizes=[1]		,			multi_process=UpperCamelCase__		,			)
       snake_case					:   Union[str, Any]         =					PyTorchBenchmark(UpperCamelCase__		,			configs=[config]		)
       snake_case					:   int         =					benchmark.run()
       self.check_results_dict_not_empty(results.time_inference_result		)
       self.check_results_dict_not_empty(results.memory_inference_result		)







   def 				lowerCamelCase	(			self		)			->	Any:

       '''simple docstring'''




       snake_case					:   Optional[int]         =					"sshleifer/tinier_bart"
       snake_case					:   List[str]         =					AutoConfig.from_pretrained(UpperCamelCase__		)
       snake_case					:   Optional[int]         =					PyTorchBenchmarkArguments(
           models=[MODEL_ID]		,			training=UpperCamelCase__		,			inference=UpperCamelCase__		,			sequence_lengths=[8]		,			batch_sizes=[1]		,			multi_process=UpperCamelCase__		,			)
       snake_case					:   Union[str, Any]         =					PyTorchBenchmark(UpperCamelCase__		,			configs=[config]		)
       snake_case					:   Optional[Any]         =					benchmark.run()
       self.check_results_dict_not_empty(results.time_inference_result		)
       self.check_results_dict_not_empty(results.memory_inference_result		)







   def 				lowerCamelCase	(			self		)			->	Any:

       '''simple docstring'''




       snake_case					:   Optional[Any]         =					"sshleifer/tiny-gpt2"
       snake_case					:   str         =					AutoConfig.from_pretrained(UpperCamelCase__		)
       snake_case					:   List[str]         =					PyTorchBenchmarkArguments(
           models=[MODEL_ID]		,			training=UpperCamelCase__		,			inference=UpperCamelCase__		,			sequence_lengths=[8]		,			batch_sizes=[1]		,			multi_process=UpperCamelCase__		,			)
       snake_case					:   Optional[int]         =					PyTorchBenchmark(UpperCamelCase__		,			configs=[config]		)
       snake_case					:   int         =					benchmark.run()
       self.check_results_dict_not_empty(results.time_train_result		)
       self.check_results_dict_not_empty(results.memory_train_result		)







   def 				lowerCamelCase	(			self		)			->	Optional[int]:

       '''simple docstring'''




       snake_case					:   str         =					"sshleifer/tinier_bart"
       snake_case					:   Union[str, Any]         =					AutoConfig.from_pretrained(UpperCamelCase__		)
       snake_case					:   Any         =					PyTorchBenchmarkArguments(
           models=[MODEL_ID]		,			training=UpperCamelCase__		,			inference=UpperCamelCase__		,			sequence_lengths=[8]		,			batch_sizes=[1]		,			multi_process=UpperCamelCase__		,			)
       snake_case					:   Union[str, Any]         =					PyTorchBenchmark(UpperCamelCase__		,			configs=[config]		)
       snake_case					:   int         =					benchmark.run()
       self.check_results_dict_not_empty(results.time_train_result		)
       self.check_results_dict_not_empty(results.memory_train_result		)







   def 				lowerCamelCase	(			self		)			->	Any:

       '''simple docstring'''




       snake_case					:   Any         =					"sshleifer/tiny-gpt2"
       with tempfile.TemporaryDirectory() as tmp_dir:
           snake_case					:   Optional[int]         =					PyTorchBenchmarkArguments(
               models=[MODEL_ID]		,			training=UpperCamelCase__		,			inference=UpperCamelCase__		,			save_to_csv=UpperCamelCase__		,			sequence_lengths=[8]		,			batch_sizes=[1]		,			inference_time_csv_file=os.path.join(UpperCamelCase__		,			"inf_time.csv"		)		,			train_memory_csv_file=os.path.join(UpperCamelCase__		,			"train_mem.csv"		)		,			inference_memory_csv_file=os.path.join(UpperCamelCase__		,			"inf_mem.csv"		)		,			train_time_csv_file=os.path.join(UpperCamelCase__		,			"train_time.csv"		)		,			env_info_csv_file=os.path.join(UpperCamelCase__		,			"env.csv"		)		,			multi_process=UpperCamelCase__		,			)
           snake_case					:   List[Any]         =					PyTorchBenchmark(UpperCamelCase__		)
           benchmark.run()
           self.assertTrue(Path(os.path.join(UpperCamelCase__		,			"inf_time.csv"		)		).exists()		)
           self.assertTrue(Path(os.path.join(UpperCamelCase__		,			"train_time.csv"		)		).exists()		)
           self.assertTrue(Path(os.path.join(UpperCamelCase__		,			"inf_mem.csv"		)		).exists()		)
           self.assertTrue(Path(os.path.join(UpperCamelCase__		,			"train_mem.csv"		)		).exists()		)
           self.assertTrue(Path(os.path.join(UpperCamelCase__		,			"env.csv"		)		).exists()		)







   def 				lowerCamelCase	(			self		)			->	Any:

       '''simple docstring'''




       snake_case					:   Union[str, Any]         =					"sshleifer/tiny-gpt2"

       def _check_summary_is_not_empty(UpperCamelCase__		):
           self.assertTrue(hasattr(UpperCamelCase__		,			"sequential"		)		)
           self.assertTrue(hasattr(UpperCamelCase__		,			"cumulative"		)		)
           self.assertTrue(hasattr(UpperCamelCase__		,			"current"		)		)
           self.assertTrue(hasattr(UpperCamelCase__		,			"total"		)		)

       with tempfile.TemporaryDirectory() as tmp_dir:
           snake_case					:   Tuple         =					PyTorchBenchmarkArguments(
               models=[MODEL_ID]		,			training=UpperCamelCase__		,			inference=UpperCamelCase__		,			sequence_lengths=[8]		,			batch_sizes=[1]		,			log_filename=os.path.join(UpperCamelCase__		,			"log.txt"		)		,			log_print=UpperCamelCase__		,			trace_memory_line_by_line=UpperCamelCase__		,			multi_process=UpperCamelCase__		,			)
           snake_case					:   Dict         =					PyTorchBenchmark(UpperCamelCase__		)
           snake_case					:   List[Any]         =					benchmark.run()
           _check_summary_is_not_empty(result.inference_summary		)
           _check_summary_is_not_empty(result.train_summary		)
           self.assertTrue(Path(os.path.join(UpperCamelCase__		,			"log.txt"		)		).exists()		)





