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


				'''simple docstring'''
				# Checks if there are 3 unique rotors

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

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

				# Validates string and returns dict
				lowercase_	    =   _plugboard(snake_case__   )

				return rotpos, rotsel, pbdict




def 							a		(				snake_case__:   str   ):


				'''simple docstring'''

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

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

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

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

				return pb




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


				'''simple docstring'''
				lowercase_	    =   text.upper()
				lowercase_    ,      lowercase_    ,      lowercase_	    =   _validator(
				    snake_case__		,  snake_case__		,  plugb.upper()   )

				lowercase_    ,      lowercase_    ,      lowercase_	    =   rotor_position
				lowercase_    ,      lowercase_    ,      lowercase_	    =   rotor_selection
				rotorposa -= 1
				rotorposa -= 1
				rotorposa -= 1

				lowercase_	    =   []

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

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

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

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

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

												lowercase_	    =   reflector[symbol]

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

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

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

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

				return "".join(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))






'''simple docstring'''

import tempfile

import numpy as np
import torch
from transformers import AutoTokenizer, TaEncoderModel

from diffusers import DDPMScheduler, UNetaDConditionModel
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.pipelines.deepfloyd_if import IFWatermarker
from diffusers.utils.testing_utils import torch_device

from ..test_pipelines_common import to_np




class    lowerCAmelCase       :


 def    _A	(    self  : Union[str, Any]    ):
  '''simple docstring'''

  torch.manual_seed(0    )
  lowerCAmelCase__     :   List[str]     		=	TaEncoderModel.from_pretrained("hf-internal-testing/tiny-random-t5"    )

  torch.manual_seed(0    )
  lowerCAmelCase__     :   Optional[int]     		=	AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-t5"    )

  torch.manual_seed(0    )
  lowerCAmelCase__     :   Any     		=	UNetaDConditionModel(
      sample_size=32			,							layers_per_block=1			,							block_out_channels=[32, 64]			,							down_block_types=[
          "ResnetDownsampleBlock2D",
          "SimpleCrossAttnDownBlock2D",
      ]			,							mid_block_type="UNetMidBlock2DSimpleCrossAttn"			,							up_block_types=["SimpleCrossAttnUpBlock2D", "ResnetUpsampleBlock2D"]			,							in_channels=3			,							out_channels=6			,							cross_attention_dim=32			,							encoder_hid_dim=32			,							attention_head_dim=8			,							addition_embed_type="text"			,							addition_embed_type_num_heads=2			,							cross_attention_norm="group_norm"			,							resnet_time_scale_shift="scale_shift"			,							act_fn="gelu"			,							)
  unet.set_attn_processor(AttnAddedKVProcessor()    )  # For reproducibility tests

  torch.manual_seed(0    )
  lowerCAmelCase__     :   Union[str, Any]     		=	DDPMScheduler(
      num_train_timesteps=1000			,							beta_schedule="squaredcos_cap_v2"			,							beta_start=0.0001			,							beta_end=0.02			,							thresholding=a__			,							dynamic_thresholding_ratio=0.95			,							sample_max_value=1.0			,							prediction_type="epsilon"			,							variance_type="learned_range"			,							)

  torch.manual_seed(0    )
  lowerCAmelCase__     :   Tuple     		=	IFWatermarker()

  return {
      "text_encoder": text_encoder,
      "tokenizer": tokenizer,
      "unet": unet,
      "scheduler": scheduler,
      "watermarker": watermarker,
      "safety_checker": None,
      "feature_extractor": None,
  }


 def    _A	(    self  : Dict    ):
  '''simple docstring'''

  torch.manual_seed(0    )
  lowerCAmelCase__     :   Any     		=	TaEncoderModel.from_pretrained("hf-internal-testing/tiny-random-t5"    )

  torch.manual_seed(0    )
  lowerCAmelCase__     :   List[Any]     		=	AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-t5"    )

  torch.manual_seed(0    )
  lowerCAmelCase__     :   int     		=	UNetaDConditionModel(
      sample_size=32			,							layers_per_block=[1, 2]			,							block_out_channels=[32, 64]			,							down_block_types=[
          "ResnetDownsampleBlock2D",
          "SimpleCrossAttnDownBlock2D",
      ]			,							mid_block_type="UNetMidBlock2DSimpleCrossAttn"			,							up_block_types=["SimpleCrossAttnUpBlock2D", "ResnetUpsampleBlock2D"]			,							in_channels=6			,							out_channels=6			,							cross_attention_dim=32			,							encoder_hid_dim=32			,							attention_head_dim=8			,							addition_embed_type="text"			,							addition_embed_type_num_heads=2			,							cross_attention_norm="group_norm"			,							resnet_time_scale_shift="scale_shift"			,							act_fn="gelu"			,							class_embed_type="timestep"			,							mid_block_scale_factor=1.414			,							time_embedding_act_fn="gelu"			,							time_embedding_dim=32			,							)
  unet.set_attn_processor(AttnAddedKVProcessor()    )  # For reproducibility tests

  torch.manual_seed(0    )
  lowerCAmelCase__     :   Tuple     		=	DDPMScheduler(
      num_train_timesteps=1000			,							beta_schedule="squaredcos_cap_v2"			,							beta_start=0.0001			,							beta_end=0.02			,							thresholding=a__			,							dynamic_thresholding_ratio=0.95			,							sample_max_value=1.0			,							prediction_type="epsilon"			,							variance_type="learned_range"			,							)

  torch.manual_seed(0    )
  lowerCAmelCase__     :   Tuple     		=	DDPMScheduler(
      num_train_timesteps=1000			,							beta_schedule="squaredcos_cap_v2"			,							beta_start=0.0001			,							beta_end=0.02			,							)

  torch.manual_seed(0    )
  lowerCAmelCase__     :   Dict     		=	IFWatermarker()

  return {
      "text_encoder": text_encoder,
      "tokenizer": tokenizer,
      "unet": unet,
      "scheduler": scheduler,
      "image_noising_scheduler": image_noising_scheduler,
      "watermarker": watermarker,
      "safety_checker": None,
      "feature_extractor": None,
  }


 def    _A	(    self  : Optional[Any]    ):
  '''simple docstring'''

  lowerCAmelCase__     :   Union[str, Any]     		=	self.get_dummy_components()
  lowerCAmelCase__     :   Dict     		=	self.pipeline_class(**a__    )
  pipe.to(a__    )
  pipe.set_progress_bar_config(disable=a__    )

  lowerCAmelCase__     :   Tuple     		=	self.get_dummy_inputs(a__    )

  lowerCAmelCase__     :   str     		=	inputs["prompt"]
  lowerCAmelCase__     :   Optional[Any]     		=	inputs["generator"]
  lowerCAmelCase__     :   str     		=	inputs["num_inference_steps"]
  lowerCAmelCase__     :   int     		=	inputs["output_type"]

  if "image" in inputs:
   lowerCAmelCase__     :   Optional[int]     		=	inputs["image"]
  else:
   lowerCAmelCase__     :   str     		=	None

  if "mask_image" in inputs:
   lowerCAmelCase__     :   List[str]     		=	inputs["mask_image"]
  else:
   lowerCAmelCase__     :   str     		=	None

  if "original_image" in inputs:
   lowerCAmelCase__     :   Optional[int]     		=	inputs["original_image"]
  else:
   lowerCAmelCase__     :   Union[str, Any]     		=	None

  lowerCAmelCase__		,     lowerCAmelCase__     :   Dict     		=	pipe.encode_prompt(a__    )

  # inputs with prompt converted to embeddings
  lowerCAmelCase__     :   Dict     		=	{
      "prompt_embeds": prompt_embeds,
      "negative_prompt_embeds": negative_prompt_embeds,
      "generator": generator,
      "num_inference_steps": num_inference_steps,
      "output_type": output_type,
  }

  if image is not None:
   lowerCAmelCase__     :   Optional[Any]     		=	image

  if mask_image is not None:
   lowerCAmelCase__     :   Optional[int]     		=	mask_image

  if original_image is not None:
   lowerCAmelCase__     :   Optional[Any]     		=	original_image

  # set all optional components to None
  for optional_component in pipe._optional_components:
   setattr(a__			,							a__			,							a__    )

  lowerCAmelCase__     :   Optional[int]     		=	pipe(**a__    )[0]

  with tempfile.TemporaryDirectory() as tmpdir:
   pipe.save_pretrained(a__    )
   lowerCAmelCase__     :   Tuple     		=	self.pipeline_class.from_pretrained(a__    )
   pipe_loaded.to(a__    )
   pipe_loaded.set_progress_bar_config(disable=a__    )

  pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor()    )  # For reproducibility tests

  for optional_component in pipe._optional_components:
   self.assertTrue(
       getattr(a__			,							a__    ) is None			,							F'''`{optional_component}` did not stay set to None after loading.'''			,							)

  lowerCAmelCase__     :   Union[str, Any]     		=	self.get_dummy_inputs(a__    )

  lowerCAmelCase__     :   Any     		=	inputs["generator"]
  lowerCAmelCase__     :   Union[str, Any]     		=	inputs["num_inference_steps"]
  lowerCAmelCase__     :   str     		=	inputs["output_type"]

  # inputs with prompt converted to embeddings
  lowerCAmelCase__     :   int     		=	{
      "prompt_embeds": prompt_embeds,
      "negative_prompt_embeds": negative_prompt_embeds,
      "generator": generator,
      "num_inference_steps": num_inference_steps,
      "output_type": output_type,
  }

  if image is not None:
   lowerCAmelCase__     :   Tuple     		=	image

  if mask_image is not None:
   lowerCAmelCase__     :   int     		=	mask_image

  if original_image is not None:
   lowerCAmelCase__     :   List[Any]     		=	original_image

  lowerCAmelCase__     :   Dict     		=	pipe_loaded(**a__    )[0]

  lowerCAmelCase__     :   int     		=	np.abs(to_np(a__    ) - to_np(a__    )    ).max()
  self.assertLess(a__			,							1e-4    )


 def    _A	(    self  : Any    ):
  '''simple docstring'''

  lowerCAmelCase__     :   str     		=	self.get_dummy_components()
  lowerCAmelCase__     :   int     		=	self.pipeline_class(**a__    )
  pipe.to(a__    )
  pipe.set_progress_bar_config(disable=a__    )

  lowerCAmelCase__     :   Any     		=	self.get_dummy_inputs(a__    )
  lowerCAmelCase__     :   Union[str, Any]     		=	pipe(**a__    )[0]

  with tempfile.TemporaryDirectory() as tmpdir:
   pipe.save_pretrained(a__    )
   lowerCAmelCase__     :   str     		=	self.pipeline_class.from_pretrained(a__    )
   pipe_loaded.to(a__    )
   pipe_loaded.set_progress_bar_config(disable=a__    )

  pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor()    )  # For reproducibility tests

  lowerCAmelCase__     :   str     		=	self.get_dummy_inputs(a__    )
  lowerCAmelCase__     :   Union[str, Any]     		=	pipe_loaded(**a__    )[0]

  lowerCAmelCase__     :   Union[str, Any]     		=	np.abs(to_np(a__    ) - to_np(a__    )    ).max()
  self.assertLess(a__			,							1e-4    )





