


'''simple docstring'''







import gc
import random
import unittest

import numpy as np
import torch
from transformers import (
    CLIPImageProcessor,
    CLIPTextConfig,
    CLIPTextModel,
    CLIPTokenizer,
    CLIPVisionConfig,
    CLIPVisionModelWithProjection,
)

from diffusers import AutoencoderKL, DDIMScheduler, DDPMScheduler, StableUnCLIPImgaImgPipeline, UNetaDConditionModel
from diffusers.pipelines.pipeline_utils import DiffusionPipeline
from diffusers.pipelines.stable_diffusion.stable_unclip_image_normalizer import StableUnCLIPImageNormalizer
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import (
    enable_full_determinism,
    floats_tensor,
    load_image,
    load_numpy,
    require_torch_gpu,
    skip_mps,
    slow,
    torch_device,
)

from ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import (
    PipelineKarrasSchedulerTesterMixin,
    PipelineLatentTesterMixin,
    PipelineTesterMixin,
    assert_mean_pixel_difference,
)


enable_full_determinism()


class 			__lowerCamelCase			(	a_    , a_    , a_    , unittest.TestCase	):
    """simple docstring"""
    a													=   StableUnCLIPImgaImgPipeline
    a													=   TEXT_GUIDED_IMAGE_VARIATION_PARAMS
    a													=   TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
    a													=   frozenset(
        []	)  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
    a													=   frozenset([]	)


    def        A   (  self      :							Union[str, Any]):
           _A					:     Optional[int]     = 32
           _A					:     str     = embedder_hidden_size

           # image encoding components

           _A					:     Union[str, Any]     = CLIPImageProcessor(crop_size=32				,							size=32)

           torch.manual_seed(0)
           _A					:     int     = CLIPVisionModelWithProjection(
               CLIPVisionConfig(
                   hidden_size=SCREAMING_SNAKE_CASE				,							projection_dim=SCREAMING_SNAKE_CASE				,							num_hidden_layers=5				,							num_attention_heads=4				,							image_size=32				,							intermediate_size=37				,							patch_size=1				,							))

           # regular denoising components

           torch.manual_seed(0)
           _A					:     List[str]     = StableUnCLIPImageNormalizer(embedding_dim=SCREAMING_SNAKE_CASE)
           _A					:     int     = DDPMScheduler(beta_schedule='squaredcos_cap_v2')

           torch.manual_seed(0)
           _A					:     List[str]     = CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip')

           torch.manual_seed(0)
           _A					:     Union[str, Any]     = CLIPTextModel(
               CLIPTextConfig(
                   bos_token_id=0				,							eos_token_id=2				,							hidden_size=SCREAMING_SNAKE_CASE				,							projection_dim=32				,							intermediate_size=37				,							layer_norm_eps=1e-05				,							num_attention_heads=4				,							num_hidden_layers=5				,							pad_token_id=1				,							vocab_size=1000				,							))

           torch.manual_seed(0)
           _A					:     List[str]     = UNetaDConditionModel(
               sample_size=32				,							in_channels=4				,							out_channels=4				,							down_block_types=('CrossAttnDownBlock2D', 'DownBlock2D')				,							up_block_types=('UpBlock2D', 'CrossAttnUpBlock2D')				,							block_out_channels=(32, 64)				,							attention_head_dim=(2, 4)				,							class_embed_type='projection'				,							projection_class_embeddings_input_dim=embedder_projection_dim * 2				,							cross_attention_dim=SCREAMING_SNAKE_CASE				,							layers_per_block=1				,							upcast_attention=SCREAMING_SNAKE_CASE				,							use_linear_projection=SCREAMING_SNAKE_CASE				,							)

           torch.manual_seed(0)
           _A					:     Union[str, Any]     = DDIMScheduler(
               beta_schedule='scaled_linear'				,							beta_start=0.0_0085				,							beta_end=0.012				,							prediction_type='v_prediction'				,							set_alpha_to_one=SCREAMING_SNAKE_CASE				,							steps_offset=1				,							)

           torch.manual_seed(0)
           _A					:     str     = AutoencoderKL()

           _A					:     int     = {
               # image encoding components
               'feature_extractor': feature_extractor,
               'image_encoder': image_encoder.eval(),
               # image noising components
               'image_normalizer': image_normalizer.eval(),
               'image_noising_scheduler': image_noising_scheduler,
               # regular denoising components
               'tokenizer': tokenizer,
               'text_encoder': text_encoder.eval(),
               'unet': unet.eval(),
               'scheduler': scheduler,
               'vae': vae.eval(),
           }

           return components


    def        A   (  self      :							Optional[int]				,							SCREAMING_SNAKE_CASE      :							List[Any]				,							SCREAMING_SNAKE_CASE      :							Optional[Any]=0				,							SCREAMING_SNAKE_CASE      :							str=True):
           if str(SCREAMING_SNAKE_CASE).startswith('mps'):
                  _A					:     Optional[Any]     = torch.manual_seed(SCREAMING_SNAKE_CASE)
           else:
                  _A					:     List[str]     = torch.Generator(device=SCREAMING_SNAKE_CASE).manual_seed(SCREAMING_SNAKE_CASE)

           _A					:     Tuple     = floats_tensor((1, 3, 32, 32)				,							rng=random.Random(SCREAMING_SNAKE_CASE)).to(SCREAMING_SNAKE_CASE)

           if pil_image:
                  _A					:     Dict     = input_image * 0.5 + 0.5
                  _A					:     Union[str, Any]     = input_image.clamp(0				,							1)
                  _A					:     Union[str, Any]     = input_image.cpu().permute(0				,							2				,							3				,							1).float().numpy()
                  _A					:     Optional[Any]     = DiffusionPipeline.numpy_to_pil(SCREAMING_SNAKE_CASE)[0]

           return {
               "prompt": "An anime racoon running a marathon",
               "image": input_image,
               "generator": generator,
               "num_inference_steps": 2,
               "output_type": "np",
           }


    @skip_mps
    def        A   (  self      :							int):
           _A					:     Any     = 'cpu'  # ensure determinism for the device-dependent torch.Generator
           _A					:     int     = self.get_dummy_components()
           _A					:     Dict     = StableUnCLIPImgaImgPipeline(**SCREAMING_SNAKE_CASE)
           _A					:     Optional[Any]     = sd_pipe.to(SCREAMING_SNAKE_CASE)
           sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE)

           _A					:     str     = self.get_dummy_inputs(SCREAMING_SNAKE_CASE)
           inputs.update({'image_embeds': None})
           _A					:     Tuple     = sd_pipe(**SCREAMING_SNAKE_CASE).images
           _A					:     str     = image[0, -3:, -3:, -1]

           assert image.shape == (1, 32, 32, 3)
           _A					:     str     = np.array([0.3872, 0.7224, 0.5601, 0.4741, 0.6872, 0.5814, 0.4636, 0.3867, 0.5078])

           assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3


    def        A   (  self      :							str):
           _A					:     Tuple     = torch_device in ['cpu', 'mps']

           self._test_attention_slicing_forward_pass(test_max_difference=SCREAMING_SNAKE_CASE)


    def        A   (  self      :							int):
           _A					:     Optional[Any]     = torch_device in ['cpu', 'mps']

           self._test_inference_batch_single_identical(test_max_difference=SCREAMING_SNAKE_CASE)


    @unittest.skipIf(
        torch_device != 'cuda' or not is_xformers_available()				,							reason='XFormers attention is only available with CUDA and `xformers` installed'				,							)
    def        A   (  self      :							Optional[int]):
           self._test_xformers_attention_forwardGenerator_pass(test_max_difference=SCREAMING_SNAKE_CASE)




@slow
@require_torch_gpu
class 			__lowerCamelCase			(	unittest.TestCase	):
    """simple docstring"""


    def        A   (  self      :							str):
           # clean up the VRAM after each test
           super().tearDown()
           gc.collect()
           torch.cuda.empty_cache()


    def        A   (  self      :							int):
           _A					:     Optional[int]     = load_image(
               'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/turtle.png')

           _A					:     Any     = load_numpy(
               'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_l_img2img_anime_turtle_fp16.npy')

           _A					:     int     = StableUnCLIPImgaImgPipeline.from_pretrained(
               'fusing/stable-unclip-2-1-l-img2img'				,							torch_dtype=torch.floataa)
           pipe.to(SCREAMING_SNAKE_CASE)
           pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE)
           # stable unclip will oom when integration tests are run on a V100,
           # so turn on memory savings
           pipe.enable_attention_slicing()
           pipe.enable_sequential_cpu_offload()

           _A					:     List[str]     = torch.Generator(device='cpu').manual_seed(0)
           _A					:     str     = pipe(SCREAMING_SNAKE_CASE				,							'anime turle'				,							generator=SCREAMING_SNAKE_CASE				,							output_type='np')

           _A					:     Union[str, Any]     = output.images[0]

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

           assert_mean_pixel_difference(SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE)


    def        A   (  self      :							Dict):
           _A					:     int     = load_image(
               'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/turtle.png')

           _A					:     List[Any]     = load_numpy(
               'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_h_img2img_anime_turtle_fp16.npy')

           _A					:     List[str]     = StableUnCLIPImgaImgPipeline.from_pretrained(
               'fusing/stable-unclip-2-1-h-img2img'				,							torch_dtype=torch.floataa)
           pipe.to(SCREAMING_SNAKE_CASE)
           pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE)
           # stable unclip will oom when integration tests are run on a V100,
           # so turn on memory savings
           pipe.enable_attention_slicing()
           pipe.enable_sequential_cpu_offload()

           _A					:     Optional[Any]     = torch.Generator(device='cpu').manual_seed(0)
           _A					:     str     = pipe(SCREAMING_SNAKE_CASE				,							'anime turle'				,							generator=SCREAMING_SNAKE_CASE				,							output_type='np')

           _A					:     Tuple     = output.images[0]

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

           assert_mean_pixel_difference(SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE)


    def        A   (  self      :							Any):
           _A					:     Dict     = load_image(
               'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/turtle.png')

           torch.cuda.empty_cache()
           torch.cuda.reset_max_memory_allocated()
           torch.cuda.reset_peak_memory_stats()

           _A					:     Dict     = StableUnCLIPImgaImgPipeline.from_pretrained(
               'fusing/stable-unclip-2-1-h-img2img'				,							torch_dtype=torch.floataa)
           _A					:     Union[str, Any]     = pipe.to(SCREAMING_SNAKE_CASE)
           pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE)
           pipe.enable_attention_slicing()
           pipe.enable_sequential_cpu_offload()

           _A					:     Optional[int]     = pipe(
               SCREAMING_SNAKE_CASE				,							'anime turtle'				,							num_inference_steps=2				,							output_type='np'				,							)

           _A					:     List[str]     = torch.cuda.max_memory_allocated()
           # make sure that less than 7 GB is allocated
           assert mem_bytes < 7 * 10**9










'''simple docstring'''







import string

import numpy






def 				lowerCAmelCase__	(    lowerCamelCase					:      int  ,lowerCamelCase					:      int       ):
       return b if a == 0 else greatest_common_divisor(b % a  ,lowerCamelCase       )


class 			__lowerCamelCase			:
    """simple docstring"""
    a													=   string.ascii_uppercase + string.digits
    # This cipher takes alphanumerics into account
    # i.e. a total of 36 characters

    # take x and return x % len(key_string)
    a													=   numpy.vectorize(lambda a_	: x % 36	)

    a													=   numpy.vectorize(a_	)


    def __init__(  self      :							List[Any]				,							SCREAMING_SNAKE_CASE      :							numpy.ndarray):
           _A					:     Union[str, Any]     = self.modulus(SCREAMING_SNAKE_CASE)  # mod36 calc's on the encrypt key
           self.check_determinant()  # validate the determinant of the encryption key
           _A					:     Optional[int]     = encrypt_key.shape[0]


    def        A   (  self      :							List[str]				,							SCREAMING_SNAKE_CASE      :							str):
           return self.key_string.index(SCREAMING_SNAKE_CASE)


    def        A   (  self      :							Union[str, Any]				,							SCREAMING_SNAKE_CASE      :							int):
           return self.key_string[round(SCREAMING_SNAKE_CASE)]


    def        A   (  self      :							List[Any]):
           _A					:     Optional[Any]     = round(numpy.linalg.det(self.encrypt_key))

           if det < 0:
                  _A					:     int     = det % len(self.key_string)

           _A					:     str     = len(self.key_string)
           if greatest_common_divisor(SCREAMING_SNAKE_CASE				,							len(self.key_string)) != 1:
                  _A					:     Optional[int]     = (
                      F'determinant modular {req_l} of encryption key({det}) '
                      F'is not co prime w.r.t {req_l}.\nTry another key.'
                  )
                  raise ValueError(SCREAMING_SNAKE_CASE)


    def        A   (  self      :							int				,							SCREAMING_SNAKE_CASE      :							str):
           _A					:     List[Any]     = [char for char in text.upper() if char in self.key_string]

           _A					:     List[str]     = chars[-1]
           while len(SCREAMING_SNAKE_CASE) % self.break_key != 0:
                  chars.append(SCREAMING_SNAKE_CASE)

           return "".join(SCREAMING_SNAKE_CASE)


    def        A   (  self      :							Optional[int]				,							SCREAMING_SNAKE_CASE      :							str):
           _A					:     Optional[int]     = self.process_text(text.upper())
           _A					:     List[str]     = ''

           for i in range(0				,							len(SCREAMING_SNAKE_CASE) - self.break_key + 1				,							self.break_key):
                  _A					:     Optional[Any]     = text[i : i + self.break_key]
                  _A					:     Optional[int]     = [self.replace_letters(SCREAMING_SNAKE_CASE) for char in batch]
                  _A					:     Tuple     = numpy.array([vec]).T
                  _A					:     List[str]     = self.modulus(self.encrypt_key.dot(SCREAMING_SNAKE_CASE)).T.tolist()[
                      0
                  ]
                  _A					:     str     = ''.join(
                      self.replace_digits(SCREAMING_SNAKE_CASE) for num in batch_encrypted)
                  encrypted += encrypted_batch

           return encrypted


    def        A   (  self      :							Union[str, Any]):
           _A					:     List[Any]     = round(numpy.linalg.det(self.encrypt_key))

           if det < 0:
                  _A					:     Optional[int]     = det % len(self.key_string)
           _A					:     List[str]     = None
           for i in range(len(self.key_string)):
                  if (det * i) % len(self.key_string) == 1:
                         _A					:     Union[str, Any]     = i
                         break

           _A					:     Dict     = (
               det_inv
               * numpy.linalg.det(self.encrypt_key)
               * numpy.linalg.inv(self.encrypt_key)
           )

           return self.to_int(self.modulus(SCREAMING_SNAKE_CASE))


    def        A   (  self      :							Any				,							SCREAMING_SNAKE_CASE      :							str):
           _A					:     List[str]     = self.make_decrypt_key()
           _A					:     Dict     = self.process_text(text.upper())
           _A					:     str     = ''

           for i in range(0				,							len(SCREAMING_SNAKE_CASE) - self.break_key + 1				,							self.break_key):
                  _A					:     Optional[int]     = text[i : i + self.break_key]
                  _A					:     Union[str, Any]     = [self.replace_letters(SCREAMING_SNAKE_CASE) for char in batch]
                  _A					:     Tuple     = numpy.array([vec]).T
                  _A					:     Optional[int]     = self.modulus(decrypt_key.dot(SCREAMING_SNAKE_CASE)).T.tolist()[0]
                  _A					:     Tuple     = ''.join(
                      self.replace_digits(SCREAMING_SNAKE_CASE) for num in batch_decrypted)
                  decrypted += decrypted_batch

           return decrypted






def 				lowerCAmelCase__	(    ):
       _A					:     List[Any]     = int(input('Enter the order of the encryption key: '       )       )
       _A					:     List[str]     = []

       print('Enter each row of the encryption key with space separated integers'       )
       for _ in range(lowerCamelCase       ):
              _A					:     str     = [int(lowerCamelCase       ) for x in input().split()]
              hill_matrix.append(lowerCamelCase       )

       _A					:     Dict     = HillCipher(numpy.array(lowerCamelCase       )       )

       print('Would you like to encrypt or decrypt some text? (1 or 2)'       )
       _A					:     List[str]     = input('\n1. Encrypt\n2. Decrypt\n'       )
       if option == "1":
              _A					:     int     = input('What text would you like to encrypt?: '       )
              print('Your encrypted text is:'       )
              print(hc.encrypt(lowerCamelCase       )       )
       elif option == "2":
              _A					:     int     = input('What text would you like to decrypt?: '       )
              print('Your decrypted text is:'       )
              print(hc.decrypt(lowerCamelCase       )       )


if __name__ == "__main__":
   import doctest

   doctest.testmod()

   main()






