





"""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      __SCREAMING_SNAKE_CASE    (						_lowerCAmelCase		,  _lowerCAmelCase		,  _lowerCAmelCase		,  unittest.TestCase   ):







  '''simple docstring'''


  SCREAMING_SNAKE_CASE__							:Any	     =					StableUnCLIPImgaImgPipeline
  SCREAMING_SNAKE_CASE__							:List[str]	     =					TEXT_GUIDED_IMAGE_VARIATION_PARAMS
  SCREAMING_SNAKE_CASE__							:Tuple	     =					TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
  SCREAMING_SNAKE_CASE__							:List[Any]	     =					frozenset(
      []   )  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
  SCREAMING_SNAKE_CASE__							:Dict	     =					frozenset([]   )

  def    __SCREAMING_SNAKE_CASE				(		self   :       Optional[Any]	)		->				str:
        _UpperCamelCase      :	Optional[Any]						=       32
        _UpperCamelCase      :	Union[str, Any]						=       embedder_hidden_size

        # image encoding components

        _UpperCamelCase      :	int						=       CLIPImageProcessor(crop_size=32					,   size=32	)

        torch.manual_seed(0	)
        _UpperCamelCase      :	List[Any]						=       CLIPVisionModelWithProjection(
            CLIPVisionConfig(
                hidden_size=_lowerCAmelCase					,   projection_dim=_lowerCAmelCase					,   num_hidden_layers=5					,   num_attention_heads=4					,   image_size=32					,   intermediate_size=37					,   patch_size=1					,   )	)

        # regular denoising components

        torch.manual_seed(0	)
        _UpperCamelCase      :	Optional[Any]						=       StableUnCLIPImageNormalizer(embedding_dim=_lowerCAmelCase	)
        _UpperCamelCase      :	List[Any]						=       DDPMScheduler(beta_schedule="squaredcos_cap_v2"	)

        torch.manual_seed(0	)
        _UpperCamelCase      :	Optional[int]						=       CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"	)

        torch.manual_seed(0	)
        _UpperCamelCase      :	Any						=       CLIPTextModel(
            CLIPTextConfig(
                bos_token_id=0					,   eos_token_id=2					,   hidden_size=_lowerCAmelCase					,   projection_dim=32					,   intermediate_size=37					,   layer_norm_eps=1e-0_5					,   num_attention_heads=4					,   num_hidden_layers=5					,   pad_token_id=1					,   vocab_size=1000					,   )	)

        torch.manual_seed(0	)
        _UpperCamelCase      :	Optional[int]						=       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=_lowerCAmelCase					,   layers_per_block=1					,   upcast_attention=_lowerCAmelCase					,   use_linear_projection=_lowerCAmelCase					,   )

        torch.manual_seed(0	)
        _UpperCamelCase      :	Union[str, Any]						=       DDIMScheduler(
            beta_schedule="scaled_linear"					,   beta_start=0.0_00_85					,   beta_end=0.0_12					,   prediction_type="v_prediction"					,   set_alpha_to_one=_lowerCAmelCase					,   steps_offset=1					,   )

        torch.manual_seed(0	)
        _UpperCamelCase      :	Optional[Any]						=       AutoencoderKL()

        _UpperCamelCase      :	Union[str, Any]						=       {
            # 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    __SCREAMING_SNAKE_CASE				(		self   :       List[Any]					,   __a   :       int					,   __a   :       Union[str, Any]=0					,   __a   :       Optional[int]=True	)		->				Tuple:







        if str(_lowerCAmelCase	).startswith("mps"	):
              _UpperCamelCase      :	str						=       torch.manual_seed(_lowerCAmelCase	)
        else:
              _UpperCamelCase      :	str						=       torch.Generator(device=_lowerCAmelCase	).manual_seed(_lowerCAmelCase	)

        _UpperCamelCase      :	Tuple						=       floats_tensor((1, 3, 32, 32)					,   rng=random.Random(_lowerCAmelCase	)	).to(_lowerCAmelCase	)

        if pil_image:
              _UpperCamelCase      :	List[str]						=       input_image * 0.5 + 0.5
              _UpperCamelCase      :	Tuple						=       input_image.clamp(0					,   1	)
              _UpperCamelCase      :	List[Any]						=       input_image.cpu().permute(0					,   2					,   3					,   1	).float().numpy()
              _UpperCamelCase      :	str						=       DiffusionPipeline.numpy_to_pil(_lowerCAmelCase	)[0]

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

  @skip_mps
  def    __SCREAMING_SNAKE_CASE				(		self   :       str	)		->				Any:
        _UpperCamelCase      :	List[str]						=       "cpu"  # ensure determinism for the device-dependent torch.Generator
        _UpperCamelCase      :	Dict						=       self.get_dummy_components()
        _UpperCamelCase      :	Dict						=       StableUnCLIPImgaImgPipeline(**_lowerCAmelCase	)
        _UpperCamelCase      :	Dict						=       sd_pipe.to(_lowerCAmelCase	)
        sd_pipe.set_progress_bar_config(disable=_lowerCAmelCase	)

        _UpperCamelCase      :	List[Any]						=       self.get_dummy_inputs(_lowerCAmelCase	)
        inputs.update({"image_embeds": None}	)
        _UpperCamelCase      :	Tuple						=       sd_pipe(**_lowerCAmelCase	).images
        _UpperCamelCase      :	int						=       image[0, -3:, -3:, -1]

        assert image.shape == (1, 32, 32, 3)
        _UpperCamelCase      :	int						=       np.array([0.38_72, 0.72_24, 0.56_01, 0.47_41, 0.68_72, 0.58_14, 0.46_36, 0.38_67, 0.50_78]	)

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

  def    __SCREAMING_SNAKE_CASE				(		self   :       List[str]	)		->				Optional[Any]:
        _UpperCamelCase      :	Tuple						=       torch_device in ["cpu", "mps"]

        self._test_attention_slicing_forward_pass(test_max_difference=_lowerCAmelCase	)

  def    __SCREAMING_SNAKE_CASE				(		self   :       Tuple	)		->				Optional[Any]:
        _UpperCamelCase      :	List[str]						=       torch_device in ["cpu", "mps"]

        self._test_inference_batch_single_identical(test_max_difference=_lowerCAmelCase	)


  @unittest.skipIf(
      torch_device != "cuda" or not is_xformers_available()					,   reason="XFormers attention is only available with CUDA and `xformers` installed"					,   )
  def    __SCREAMING_SNAKE_CASE				(		self   :       int	)		->				Tuple:
        self._test_xformers_attention_forwardGenerator_pass(test_max_difference=_lowerCAmelCase	)





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







  '''simple docstring'''


  def    __SCREAMING_SNAKE_CASE				(		self   :       int	)		->				List[Any]:
        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()

  def    __SCREAMING_SNAKE_CASE				(		self   :       List[str]	)		->				int:
        _UpperCamelCase      :	List[Any]						=       load_image(
            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/turtle.png"	)

        _UpperCamelCase      :	List[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"	)

        _UpperCamelCase      :	Dict						=       StableUnCLIPImgaImgPipeline.from_pretrained(
            "fusing/stable-unclip-2-1-l-img2img"					,   torch_dtype=torch.floataa	)
        pipe.to(_lowerCAmelCase	)
        pipe.set_progress_bar_config(disable=_lowerCAmelCase	)
        # 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()

        _UpperCamelCase      :	List[Any]						=       torch.Generator(device="cpu"	).manual_seed(0	)
        _UpperCamelCase      :	List[Any]						=       pipe(_lowerCAmelCase					,   "anime turle"					,   generator=_lowerCAmelCase					,   output_type="np"	)

        _UpperCamelCase      :	Optional[int]						=       output.images[0]

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

        assert_mean_pixel_difference(_lowerCAmelCase					,   _lowerCAmelCase	)

  def    __SCREAMING_SNAKE_CASE				(		self   :       str	)		->				Tuple:
        _UpperCamelCase      :	Tuple						=       load_image(
            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/turtle.png"	)

        _UpperCamelCase      :	int						=       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"	)

        _UpperCamelCase      :	str						=       StableUnCLIPImgaImgPipeline.from_pretrained(
            "fusing/stable-unclip-2-1-h-img2img"					,   torch_dtype=torch.floataa	)
        pipe.to(_lowerCAmelCase	)
        pipe.set_progress_bar_config(disable=_lowerCAmelCase	)
        # 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()

        _UpperCamelCase      :	List[str]						=       torch.Generator(device="cpu"	).manual_seed(0	)
        _UpperCamelCase      :	Optional[Any]						=       pipe(_lowerCAmelCase					,   "anime turle"					,   generator=_lowerCAmelCase					,   output_type="np"	)

        _UpperCamelCase      :	List[str]						=       output.images[0]

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

        assert_mean_pixel_difference(_lowerCAmelCase					,   _lowerCAmelCase	)


  def    __SCREAMING_SNAKE_CASE				(		self   :       Dict	)		->				List[str]:
        _UpperCamelCase      :	Any						=       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()

        _UpperCamelCase      :	Tuple						=       StableUnCLIPImgaImgPipeline.from_pretrained(
            "fusing/stable-unclip-2-1-h-img2img"					,   torch_dtype=torch.floataa	)
        _UpperCamelCase      :	Any						=       pipe.to(_lowerCAmelCase	)
        pipe.set_progress_bar_config(disable=_lowerCAmelCase	)
        pipe.enable_attention_slicing()
        pipe.enable_sequential_cpu_offload()

        _UpperCamelCase      :	List[Any]						=       pipe(
            _lowerCAmelCase					,   "anime turtle"					,   num_inference_steps=2					,   output_type="np"					,   )

        _UpperCamelCase      :	Dict						=       torch.cuda.max_memory_allocated()
        # make sure that less than 7 GB is allocated
        assert mem_bytes < 7 * 10**9







"""simple docstring"""


from __future__ import annotations

import string
from itertools import cycle, product
from pathlib import Path

lowerCamelCase__				  =   (
    string.ascii_letters + string.digits + string.punctuation + string.whitespace
)
lowerCamelCase__				  =   [ord(letter) for letter in string.ascii_lowercase]
lowerCamelCase__				  =   {ord(char) for char in VALID_CHARS}

lowerCamelCase__				  =   ["the", "be", "to", "of", "and", "in", "that", "have"]




def 					lowercase__    (					lowercase_		,lowercase_			)					->		str | None:




      """simple docstring"""

      _UpperCamelCase      :	str						=       ""
      _UpperCamelCase      :	int
      _UpperCamelCase      :	int
      _UpperCamelCase      :	int

      for keychar, cipherchar in zip(cycle(lowercase_			)		,lowercase_			):
            _UpperCamelCase      :	Dict						=       cipherchar ^ keychar
            if decodedchar not in VALID_INTS:
                  return None
            decoded += chr(lowercase_			)

      return decoded




def 					lowercase__    (					lowercase_			)					->		list[str]:




      """simple docstring"""

      _UpperCamelCase      :	list[str]						=       []
      for key in product(lowercase_		,repeat=3			):
            _UpperCamelCase      :	int						=       try_key(lowercase_		,lowercase_			)
            if encoded is not None:
                  possibles.append(lowercase_			)
      return possibles




def 					lowercase__    (					lowercase_		,lowercase_			)					->		list[str]:




      """simple docstring"""

      return [possible for possible in possibles if common_word in possible.lower()]




def 					lowercase__    (					lowercase_ = "p059_cipher.txt"			)					->		int:




      """simple docstring"""

      _UpperCamelCase      :	list[int]
      _UpperCamelCase      :	list[str]
      _UpperCamelCase      :	str
      _UpperCamelCase      :	str
      _UpperCamelCase      :	str						=       Path(lowercase_			).parent.joinpath(lowercase_			).read_text(encoding="utf-8"			)

      _UpperCamelCase      :	Optional[Any]						=       [int(lowercase_			) for number in data.strip().split(","			)]

      _UpperCamelCase      :	List[str]						=       filter_valid_chars(lowercase_			)
      for common_word in COMMON_WORDS:
            _UpperCamelCase      :	Union[str, Any]						=       filter_common_word(lowercase_		,lowercase_			)
            if len(lowercase_			) == 1:
                  break

      _UpperCamelCase      :	Union[str, Any]						=       possibles[0]
      return sum(ord(lowercase_			) for char in decoded_text			)


if __name__ == "__main__":
  print(f"""{solution() = }""")
