
"""simple docstring"""





import numpy as np

_a									=				[
    ['a', 'b', 'c', 'd', 'e'],
    ['f', 'g', 'h', 'i', 'k'],
    ['l', 'm', 'n', 'o', 'p'],
    ['q', 'r', 's', 't', 'u'],
    ['v', 'w', 'x', 'y', 'z'],
]


class A_      :




  '''simple docstring'''







  def __init__(						self    ):

     """simple docstring"""
     UpperCAmelCase_   :   int     	=       np.array(lowercase_    )







  def 			UpperCamelCase__   (						self							,  lowercase_    ):

     """simple docstring"""
     UpperCAmelCase_	,  UpperCAmelCase_   :   int     	=       np.where(letter == self.SQUARE    )
     UpperCAmelCase_   :   Optional[Any]     	=       np.concatenate([indexa + 1, indexa + 1]    )
     return indexes







  def 			UpperCamelCase__   (						self							,  lowercase_							,  lowercase_    ):

     """simple docstring"""
     return self.SQUARE[indexa - 1, indexa - 1]







  def 			UpperCamelCase__   (						self							,  lowercase_    ):

     """simple docstring"""
     UpperCAmelCase_   :   Union[str, Any]     	=       message.lower()
     UpperCAmelCase_   :   int     	=       message.replace("j"							,  "i"    )

     UpperCAmelCase_   :   Union[str, Any]     	=       ""
     for letter_index in range(len(lowercase_    )    ):
        if message[letter_index] != " ":
           UpperCAmelCase_   :   List[Any]     	=       self.letter_to_numbers(message[letter_index]    )
           UpperCAmelCase_   :   Dict     	=       encoded_message + str(numbers[0]    ) + str(numbers[1]    )
        elif message[letter_index] == " ":
           UpperCAmelCase_   :   int     	=       encoded_message + " "

     return encoded_message







  def 			UpperCamelCase__   (						self							,  lowercase_    ):

     """simple docstring"""
     UpperCAmelCase_   :   Any     	=       message.replace(" "							,  "  "    )
     UpperCAmelCase_   :   List[Any]     	=       ""
     for numbers_index in range(int(len(lowercase_    ) / 2    )    ):
        if message[numbers_index * 2] != " ":
           UpperCAmelCase_   :   List[Any]     	=       message[numbers_index * 2]
           UpperCAmelCase_   :   Dict     	=       message[numbers_index * 2 + 1]

           UpperCAmelCase_   :   Tuple     	=       self.numbers_to_letter(int(lowercase_    )							,  int(lowercase_    )    )
           UpperCAmelCase_   :   List[Any]     	=       decoded_message + letter
        elif message[numbers_index * 2] == " ":
           UpperCAmelCase_   :   Optional[Any]     	=       decoded_message + " "

     return decoded_message






'''simple docstring'''

from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union

import numpy as np
import PIL
from PIL import Image

from ...utils import BaseOutput, is_torch_available, is_transformers_available


@dataclass
class    a__					(			__A   ):







				"""simple docstring"""



				__UpperCamelCase     :		Tuple						=						{
				    'sld_warmup_steps': 15,
				    'sld_guidance_scale': 20,
				    'sld_threshold': 0.0,
				    'sld_momentum_scale': 0.0,
				    'sld_mom_beta': 0.0,
				}
				__UpperCamelCase     :		Optional[Any]						=						{
				    'sld_warmup_steps': 10,
				    'sld_guidance_scale': 1_000,
				    'sld_threshold': 0.01,
				    'sld_momentum_scale': 0.3,
				    'sld_mom_beta': 0.4,
				}
				__UpperCamelCase     :		Tuple						=						{
				    'sld_warmup_steps': 7,
				    'sld_guidance_scale': 2_000,
				    'sld_threshold': 0.025,
				    'sld_momentum_scale': 0.5,
				    'sld_mom_beta': 0.7,
				}
				__UpperCamelCase     :		str						=						{
				    'sld_warmup_steps': 0,
				    'sld_guidance_scale': 5_000,
				    'sld_threshold': 1.0,
				    'sld_momentum_scale': 0.5,
				    'sld_mom_beta': 0.7,
				}


@dataclass
class    a__					(			__A   ):







				"""simple docstring"""



				__UpperCamelCase     :		Union[List[PIL.Image.Image], np.ndarray]
				__UpperCamelCase     :		Optional[List[bool]]
				__UpperCamelCase     :		Optional[Union[List[PIL.Image.Image], np.ndarray]]
				__UpperCamelCase     :		Optional[str]


if is_transformers_available() and is_torch_available():
	from .pipeline_stable_diffusion_safe import StableDiffusionPipelineSafe
	from .safety_checker import SafeStableDiffusionSafetyChecker

