






from __future__ import annotations

lowerCAmelCase_           = tuple[int, int, int]
lowerCAmelCase_           = tuple[str, str, str]


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

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







def      snake_case		(							UpperCAmelCase	:       RotorPositionT,						UpperCAmelCase	:       RotorSelectionT,						UpperCAmelCase	:       str   ):
				# Checks if there are 3 unique rotors

				if (unique_rotsel := len(set(UpperCAmelCase   )   )) < 3:
								A	 =      f'Please use 3 unique rotors (not {unique_rotsel})'
								raise Exception(UpperCAmelCase   )

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

				# Validates string and returns dict
				A	 =      _plugboard(UpperCAmelCase   )

				return rotpos, rotsel, pbdict







def      snake_case		(							UpperCAmelCase	:       str   ):

				# tests the input string if it
				# a) is type string
				# b) has even length (so pairs can be made)
				if not isinstance(UpperCAmelCase,						UpperCAmelCase   ):
								A	 =      f'Plugboard setting isn\'t type string ({type(UpperCAmelCase   )})'
								raise TypeError(UpperCAmelCase   )
				elif len(UpperCAmelCase   ) % 2 != 0:
								A	 =      f'Odd number of symbols ({len(UpperCAmelCase   )})'
								raise Exception(UpperCAmelCase   )
				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(UpperCAmelCase   )
								elif i in tmppbl:
												A	 =      f'Duplicate symbol ({i})'
												raise Exception(UpperCAmelCase   )
								else:
												tmppbl.add(UpperCAmelCase   )
				del tmppbl

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

				return pb







def      snake_case		(							UpperCAmelCase	:       str,						UpperCAmelCase	:       RotorPositionT,						UpperCAmelCase	:       RotorSelectionT = (rotora, rotora, rotora),						UpperCAmelCase	:       str = "",						):
				A	 =      text.upper()
				A ,		A ,		A	 =      _validator(
				    UpperCAmelCase,						UpperCAmelCase,						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(UpperCAmelCase   ) + rotorposa
												A	 =      rotora[index % len(UpperCAmelCase   )]

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

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

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

												A	 =      reflector[symbol]

												# 2nd rotors
												A	 =      abc[rotora.index(UpperCAmelCase   ) - rotorposa]
												A	 =      abc[rotora.index(UpperCAmelCase   ) - rotorposa]
												A	 =      abc[rotora.index(UpperCAmelCase   ) - rotorposa]

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

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

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

				return "".join(UpperCAmelCase   )


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

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













from typing import List, Union

from ..utils import (
    add_end_docstrings,
    is_tf_available,
    is_torch_available,
    is_vision_available,
    logging,
    requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
					from PIL import Image

					from ..image_utils import load_image

if is_tf_available():
					from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_VISION_2_SEQ_MAPPING

if is_torch_available():
					import torch

					from ..models.auto.modeling_auto import MODEL_FOR_VISION_2_SEQ_MAPPING

lowerCAmelCase_           = logging.get_logger(__name__)
@add_end_docstrings(snake_case__ )
class      UpperCamelCase (				snake_case__ ):







		"""simple docstring"""



		def __init__(  self		:       str						,*_SCREAMING_SNAKE_CASE		:       List[Any]						,**_SCREAMING_SNAKE_CASE		:       List[Any]		)    ->   Dict:


						'''simple docstring'''
						super().__init__(*_SCREAMING_SNAKE_CASE						,**_SCREAMING_SNAKE_CASE		)
						requires_backends(self						,'vision'		)
						self.check_model_type(
						    TF_MODEL_FOR_VISION_2_SEQ_MAPPING if self.framework == 'tf' else MODEL_FOR_VISION_2_SEQ_MAPPING		)
		def       A(  self		:       Union[str, Any]						,_SCREAMING_SNAKE_CASE		:       List[Any]=None						,_SCREAMING_SNAKE_CASE		:       List[str]=None						,_SCREAMING_SNAKE_CASE		:       int=None		)    ->   Any:


						'''simple docstring'''
						A	 =      {}
						A	 =      {}

						if prompt is not None:
										A	 =      prompt

						if generate_kwargs is not None:
										A	 =      generate_kwargs
						if max_new_tokens is not None:
										if "generate_kwargs" not in forward_kwargs:
														A	 =      {}
										if "max_new_tokens" in forward_kwargs["generate_kwargs"]:
														raise ValueError(
														    '\'max_new_tokens\' is defined twice, once in \'generate_kwargs\' and once as a direct parameter,'
														    ' please use only one'		)
										A	 =      max_new_tokens
						return preprocess_params, forward_kwargs, {}
		def __call__(  self		:       int						,_SCREAMING_SNAKE_CASE		:       Union[str, List[str], "Image.Image", List["Image.Image"]]						,**_SCREAMING_SNAKE_CASE		:       List[str]		)    ->   Union[str, Any]:


						'''simple docstring'''
						return super().__call__(_SCREAMING_SNAKE_CASE						,**_SCREAMING_SNAKE_CASE		)
		def       A(  self		:       Dict						,_SCREAMING_SNAKE_CASE		:       List[Any]						,_SCREAMING_SNAKE_CASE		:       Optional[Any]=None		)    ->   int:


						'''simple docstring'''
						A	 =      load_image(_SCREAMING_SNAKE_CASE		)

						if prompt is not None:
										if not isinstance(_SCREAMING_SNAKE_CASE						,_SCREAMING_SNAKE_CASE		):
														raise ValueError(
														    f'Received an invalid text input, got - {type(_SCREAMING_SNAKE_CASE		)} - but expected a single string. '
														    'Note also that one single text can be provided for conditional image to text generation.'		)

										A	 =      self.model.config.model_type

										if model_type == "git":
														A	 =      self.image_processor(images=_SCREAMING_SNAKE_CASE						,return_tensors=self.framework		)
														A	 =      self.tokenizer(text=_SCREAMING_SNAKE_CASE						,add_special_tokens=_SCREAMING_SNAKE_CASE		).input_ids
														A	 =      [self.tokenizer.cls_token_id] + input_ids
														A	 =      torch.tensor(_SCREAMING_SNAKE_CASE		).unsqueeze(0		)
														model_inputs.update({'input_ids': input_ids}		)

										elif model_type == "pix2struct":
														A	 =      self.image_processor(images=_SCREAMING_SNAKE_CASE						,header_text=_SCREAMING_SNAKE_CASE						,return_tensors=self.framework		)

										elif model_type != "vision-encoder-decoder":
														# vision-encoder-decoder does not support conditional generation
														A	 =      self.image_processor(images=_SCREAMING_SNAKE_CASE						,return_tensors=self.framework		)
														A	 =      self.tokenizer(_SCREAMING_SNAKE_CASE						,return_tensors=self.framework		)
														model_inputs.update(_SCREAMING_SNAKE_CASE		)

										else:
														raise ValueError(f'Model type {model_type} does not support conditional text generation'		)

						else:
										A	 =      self.image_processor(images=_SCREAMING_SNAKE_CASE						,return_tensors=self.framework		)

						if self.model.config.model_type == "git" and prompt is None:
										A	 =      None

						return model_inputs
		def       A(  self		:       str						,_SCREAMING_SNAKE_CASE		:       str						,_SCREAMING_SNAKE_CASE		:       Dict=None		)    ->   List[str]:


						'''simple docstring'''
						# Git model sets `model_inputs["input_ids"] = None` in `preprocess` (when `prompt=None`). In batch model, the
						# pipeline will group them into a list of `None`, which fail `_forward`. Avoid this by checking it first.
						if (
						    "input_ids" in model_inputs
						    and isinstance(model_inputs['input_ids']						,_SCREAMING_SNAKE_CASE		)
						    and all(x is None for x in model_inputs['input_ids']		)
						):
										A	 =      None

						if generate_kwargs is None:
										A	 =      {}
						# FIXME: We need to pop here due to a difference in how `generation.py` and `generation.tf_utils.py`
						#  parse inputs. In the Tensorflow version, `generate` raises an error if we don't use `input_ids` whereas
						#  the PyTorch version matches it with `self.model.main_input_name` or `self.model.encoder.main_input_name`
						#  in the `_prepare_model_inputs` method.
						A	 =      model_inputs.pop(self.model.main_input_name		)
						A	 =      self.model.generate(_SCREAMING_SNAKE_CASE						,**_SCREAMING_SNAKE_CASE						,**_SCREAMING_SNAKE_CASE		)
						return model_outputs







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


						'''simple docstring'''
						A	 =      []
						for output_ids in model_outputs:
										A	 =      {
										    'generated_text': self.tokenizer.decode(
										        _SCREAMING_SNAKE_CASE						,skip_special_tokens=_SCREAMING_SNAKE_CASE						,)
										}
										records.append(_SCREAMING_SNAKE_CASE		)
						return records





