





import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyImgaImgPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()

class       __A     (						a    ,					unittest.TestCase    ):
    __A      =				KandinskyImgaImgPipeline
    __A      =				["prompt", "image_embeds", "negative_image_embeds", "image"]
    __A      =				[
        "prompt",
        "negative_prompt",
        "image_embeds",
        "negative_image_embeds",
        "image",
    ]
    __A      =				[
        "generator",
        "height",
        "width",
        "strength",
        "guidance_scale",
        "negative_prompt",
        "num_inference_steps",
        "return_dict",
        "guidance_scale",
        "num_images_per_prompt",
        "output_type",
        "return_dict",
    ]
    __A      =				False


    @property
    def _snake_case       (		self							):
        return 32


    @property
    def _snake_case       (		self							):
        return 32


    @property
    def _snake_case       (		self							):
        return self.time_input_dim


    @property
    def _snake_case       (		self							):
        return self.time_input_dim * 4


    @property
    def _snake_case       (		self							):
        return 100


    @property
    def _snake_case       (		self							):
        lowerCamelCase	 =XLMRobertaTokenizerFast.from_pretrained("""YiYiXu/tiny-random-mclip-base"""							)
        return tokenizer


    @property
    def _snake_case       (		self							):
        torch.manual_seed(0							)
        lowerCamelCase	 =MCLIPConfig(
            numDims=self.cross_attention_dim   , transformerDimensions=self.text_embedder_hidden_size   , hidden_size=self.text_embedder_hidden_size   , intermediate_size=37   , num_attention_heads=4   , num_hidden_layers=5   , vocab_size=1005   , )

        lowerCamelCase	 =MultilingualCLIP(UpperCamelCase__							)
        lowerCamelCase	 =text_encoder.eval()

        return text_encoder


    @property
    def _snake_case       (		self							):
        torch.manual_seed(0							)

        lowerCamelCase	 ={
            """in_channels""": 4,
            # Out channels is double in channels because predicts mean and variance
            """out_channels""": 8,
            """addition_embed_type""": """text_image""",
            """down_block_types""": ("""ResnetDownsampleBlock2D""", """SimpleCrossAttnDownBlock2D"""),
            """up_block_types""": ("""SimpleCrossAttnUpBlock2D""", """ResnetUpsampleBlock2D"""),
            """mid_block_type""": """UNetMidBlock2DSimpleCrossAttn""",
            """block_out_channels""": (self.block_out_channels_a, self.block_out_channels_a * 2),
            """layers_per_block""": 1,
            """encoder_hid_dim""": self.text_embedder_hidden_size,
            """encoder_hid_dim_type""": """text_image_proj""",
            """cross_attention_dim""": self.cross_attention_dim,
            """attention_head_dim""": 4,
            """resnet_time_scale_shift""": """scale_shift""",
            """class_embed_type""": None,
        }

        lowerCamelCase	 =UNetaDConditionModel(**UpperCamelCase__							)
        return model


    @property
    def _snake_case       (		self							):
        return {
            "block_out_channels": [32, 64],
            "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
            "in_channels": 3,
            "latent_channels": 4,
            "layers_per_block": 1,
            "norm_num_groups": 8,
            "norm_type": "spatial",
            "num_vq_embeddings": 12,
            "out_channels": 3,
            "up_block_types": [
                "AttnUpDecoderBlock2D",
                "UpDecoderBlock2D",
            ],
            "vq_embed_dim": 4,
        }


    @property
    def _snake_case       (		self							):
        torch.manual_seed(0							)
        lowerCamelCase	 =VQModel(**self.dummy_movq_kwargs							)
        return model


    def _snake_case       (		self							):
        lowerCamelCase	 =self.dummy_text_encoder
        lowerCamelCase	 =self.dummy_tokenizer
        lowerCamelCase	 =self.dummy_unet
        lowerCamelCase	 =self.dummy_movq

        lowerCamelCase	 ={
            """num_train_timesteps""": 1000,
            """beta_schedule""": """linear""",
            """beta_start""": 0.0_0_0_8_5,
            """beta_end""": 0.0_1_2,
            """clip_sample""": False,
            """set_alpha_to_one""": False,
            """steps_offset""": 0,
            """prediction_type""": """epsilon""",
            """thresholding""": False,
        }

        lowerCamelCase	 =DDIMScheduler(**UpperCamelCase__							)

        lowerCamelCase	 ={
            """text_encoder""": text_encoder,
            """tokenizer""": tokenizer,
            """unet""": unet,
            """scheduler""": scheduler,
            """movq""": movq,
        }

        return components


    def _snake_case       (		self   , UpperCAmelCase_   , UpperCAmelCase_=0							):
        lowerCamelCase	 =floats_tensor((1, self.cross_attention_dim)   , rng=random.Random(UpperCamelCase__							)							).to(UpperCamelCase__							)
        lowerCamelCase	 =floats_tensor((1, self.cross_attention_dim)   , rng=random.Random(seed + 1							)							).to(UpperCamelCase__							)
        # create init_image
        lowerCamelCase	 =floats_tensor((1, 3, 64, 64)   , rng=random.Random(UpperCamelCase__							)							).to(UpperCamelCase__							)
        lowerCamelCase	 =image.cpu().permute(0   , 2   , 3   , 1							)[0]
        lowerCamelCase	 =Image.fromarray(np.uinta(UpperCamelCase__							)							).convert("""RGB"""							).resize((256, 256)							)

        if str(UpperCamelCase__							).startswith("""mps"""							):
            lowerCamelCase	 =torch.manual_seed(UpperCamelCase__							)
        else:
            lowerCamelCase	 =torch.Generator(device=UpperCamelCase__							).manual_seed(UpperCamelCase__							)
        lowerCamelCase	 ={
            """prompt""": """horse""",
            """image""": init_image,
            """image_embeds""": image_embeds,
            """negative_image_embeds""": negative_image_embeds,
            """generator""": generator,
            """height""": 64,
            """width""": 64,
            """num_inference_steps""": 10,
            """guidance_scale""": 7.0,
            """strength""": 0.2,
            """output_type""": """np""",
        }
        return inputs


    def _snake_case       (		self							):
        lowerCamelCase	 ="""cpu"""

        lowerCamelCase	 =self.get_dummy_components()

        lowerCamelCase	 =self.pipeline_class(**UpperCamelCase__							)
        lowerCamelCase	 =pipe.to(UpperCamelCase__							)

        pipe.set_progress_bar_config(disable=UpperCamelCase__							)

        lowerCamelCase	 =pipe(**self.get_dummy_inputs(UpperCamelCase__							)							)
        lowerCamelCase	 =output.images

        lowerCamelCase	 =pipe(
            **self.get_dummy_inputs(UpperCamelCase__							)   , return_dict=UpperCamelCase__   , )[0]

        lowerCamelCase	 =image[0, -3:, -3:, -1]
        lowerCamelCase	 =image_from_tuple[0, -3:, -3:, -1]

        assert image.shape == (1, 64, 64, 3)

        lowerCamelCase	 =np.array(
            [0.6_1_4_7_4_9_4_3, 0.6_0_7_3_5_3_9, 0.4_3_3_0_8_5_4_4, 0.5_9_2_8_2_6_9, 0.4_7_4_9_3_5_9_5, 0.4_6_7_5_5_9_7_3, 0.4_6_1_3_8_3_8, 0.4_5_3_6_8_7_9_7, 0.5_0_1_1_9_2_3_3]							)
        assert (
            np.abs(image_slice.flatten() - expected_slice							).max() < 1E-2
        ), f""" expected_slice {expected_slice}, but got {image_slice.flatten()}"""
        assert (
            np.abs(image_from_tuple_slice.flatten() - expected_slice							).max() < 1E-2
        ), f""" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}"""




@slow
@require_torch_gpu
class       __A     (						unittest.TestCase    ):


    def _snake_case       (		self							):
        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()


    def _snake_case       (		self							):
        lowerCamelCase	 =load_numpy(
            """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
            """/kandinsky/kandinsky_img2img_frog.npy"""							)

        lowerCamelCase	 =load_image(
            """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main""" """/kandinsky/cat.png"""							)
        lowerCamelCase	 ="""A red cartoon frog, 4k"""

        lowerCamelCase	 =KandinskyPriorPipeline.from_pretrained(
            """kandinsky-community/kandinsky-2-1-prior"""   , torch_dtype=torch.floataa							)
        pipe_prior.to(UpperCamelCase__							)

        lowerCamelCase	 =KandinskyImgaImgPipeline.from_pretrained(
            """kandinsky-community/kandinsky-2-1"""   , torch_dtype=torch.floataa							)
        lowerCamelCase	 =pipeline.to(UpperCamelCase__							)

        pipeline.set_progress_bar_config(disable=UpperCamelCase__							)

        lowerCamelCase	 =torch.Generator(device="""cpu"""							).manual_seed(0							)
        lowerCamelCase ,  lowerCamelCase	 =pipe_prior(
            UpperCamelCase__   , generator=UpperCamelCase__   , num_inference_steps=5   , negative_prompt=""""""   , ).to_tuple()

        lowerCamelCase	 =pipeline(
            UpperCamelCase__   , image=UpperCamelCase__   , image_embeds=UpperCamelCase__   , negative_image_embeds=UpperCamelCase__   , generator=UpperCamelCase__   , num_inference_steps=100   , height=768   , width=768   , strength=0.2   , output_type="""np"""   , )

        lowerCamelCase	 =output.images[0]

        assert image.shape == (768, 768, 3)

        assert_mean_pixel_difference(UpperCamelCase__   , UpperCamelCase__							)












from __future__ import annotations

UpperCAmelCase__						:			str									=tuple[int, int, int]
UpperCAmelCase__						:			str									=tuple[str, str, str]


# used alphabet --------------------------
# from string.ascii_uppercase
UpperCAmelCase__						:			int									='''ABCDEFGHIJKLMNOPQRSTUVWXYZ'''

# -------------------------- default selection --------------------------
# rotors --------------------------
UpperCAmelCase__						:			Union[str, Any]									='''EGZWVONAHDCLFQMSIPJBYUKXTR'''
UpperCAmelCase__						:			Union[str, Any]									='''FOBHMDKEXQNRAULPGSJVTYICZW'''
UpperCAmelCase__						:			Union[str, Any]									='''ZJXESIUQLHAVRMDOYGTNFWPBKC'''
# reflector --------------------------
UpperCAmelCase__						:			Optional[Any]									={
    '''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 --------------------------
UpperCAmelCase__						:			Dict									='''RMDJXFUWGISLHVTCQNKYPBEZOA'''
UpperCAmelCase__						:			Any									='''SGLCPQWZHKXAREONTFBVIYJUDM'''
UpperCAmelCase__						:			List[Any]									='''HVSICLTYKQUBXDWAJZOMFGPREN'''
UpperCAmelCase__						:			Tuple									='''RZWQHFMVDBKICJLNTUXAGYPSOE'''
UpperCAmelCase__						:			Optional[Any]									='''LFKIJODBEGAMQPXVUHYSTCZRWN'''
UpperCAmelCase__						:			Dict									='''KOAEGVDHXPQZMLFTYWJNBRCIUS'''





def      _lowercase					(						_UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase				)						->  tuple[RotorPositionT, RotorSelectionT, dict[str, str]]:
				# Checks if there are 3 unique rotors

				if (unique_rotsel := len(set(_UpperCAmelCase				)				)) < 3:
								lowerCamelCase	 =F"""Please use 3 unique rotors (not {unique_rotsel})"""
								raise Exception(_UpperCAmelCase				)

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

				# Validates string and returns dict
				lowerCamelCase	 =_plugboard(_UpperCAmelCase				)

				return rotpos, rotsel, pbdict





def      _lowercase					(						_UpperCAmelCase				)						->  dict[str, 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				):
								lowerCamelCase	 =F"""Plugboard setting isn't type string ({type(_UpperCAmelCase				)})"""
								raise TypeError(_UpperCAmelCase				)
				elif len(_UpperCAmelCase				) % 2 != 0:
								lowerCamelCase	 =F"""Odd number of symbols ({len(_UpperCAmelCase				)})"""
								raise Exception(_UpperCAmelCase				)
				elif pbstring == "":
								return {}

				pbstring.replace(""" """			,      """"""				)

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

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

				return pb





def      _lowercase					(						_UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase = (rotora, rotora, rotora)			,      _UpperCAmelCase = ""			,      )						->  str:
				lowerCamelCase	 =text.upper()
				lowerCamelCase ,  lowerCamelCase ,  lowerCamelCase	 =_validator(
				    _UpperCAmelCase			,      _UpperCAmelCase			,      plugb.upper()				)

				lowerCamelCase ,  lowerCamelCase ,  lowerCamelCase	 =rotor_position
				lowerCamelCase ,  lowerCamelCase ,  lowerCamelCase	 =rotor_selection
				rotorposa -= 1
				rotorposa -= 1
				rotorposa -= 1

				lowerCamelCase	 =[]

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

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

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

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

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

												lowerCamelCase	 =reflector[symbol]

												# 2nd rotors
												lowerCamelCase	 =abc[rotora.index(_UpperCAmelCase				) - rotorposa]
												lowerCamelCase	 =abc[rotora.index(_UpperCAmelCase				) - rotorposa]
												lowerCamelCase	 =abc[rotora.index(_UpperCAmelCase				) - rotorposa]

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

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

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

				return "".join(_UpperCAmelCase				)


if __name__ == "__main__":
							UpperCAmelCase__						:			Union[str, Any]									='''This is my Python script that emulates the Enigma machine from WWII.'''
							UpperCAmelCase__						:			List[Any]									=(1, 1, 1)
							UpperCAmelCase__						:			Optional[Any]									='''pictures'''
							UpperCAmelCase__						:			Any									=(rotora, rotora, rotora)
							UpperCAmelCase__						:			str									=enigma(message, rotor_pos, rotor_sel, pb)

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





