


"""simple docstring"""




import hashlib
import importlib.util
import json
import os
import pathlib
from types import ModuleType

import pytest
import requests

__SCREAMING_SNAKE_CASE					=						pathlib.Path.cwd().joinpath("""project_euler""")
__SCREAMING_SNAKE_CASE					=						pathlib.Path.cwd().joinpath(
    """scripts""", """project_euler_answers.json"""
)

with open(PROJECT_EULER_ANSWERS_PATH) as file_handle:
   __SCREAMING_SNAKE_CASE					=						json.load(file_handle)



def 						__a      (  a					):





       """simple docstring"""







       _a          =				importlib.util.spec_from_file_location(file_path.name    ,		str(a					)					)
       _a          =				importlib.util.module_from_spec(a					)  # type: ignore
       spec.loader.exec_module(a					)  # type: ignore
       return module



def 						__a      (  ):





       """simple docstring"""







       _a          =				[]
       for problem_dir_path in PROJECT_EULER_DIR_PATH.iterdir():
              if problem_dir_path.is_file() or problem_dir_path.name.startswith("_"					):
                     continue
              for file_path in problem_dir_path.iterdir():
                     if file_path.suffix != ".py" or file_path.name.startswith(("_", "test")					):
                            continue
                     solution_file_paths.append(a					)
       return solution_file_paths



def 						__a      (  ):





       """simple docstring"""







       with open(os.environ["GITHUB_EVENT_PATH"]					) as file:
              _a          =				json.load(a					)
       return event["pull_request"]["url"] + "/files"



def 						__a      (  ):





       """simple docstring"""







       _a          =				[]
       _a          =				{
           "Accept": "application/vnd.github.v3+json",
           "Authorization": "token " + os.environ["GITHUB_TOKEN"],
       }
       _a          =				requests.get(get_files_url()    ,		headers=a					).json()
       for file in files:
              _a          =				pathlib.Path.cwd().joinpath(file["filename"]					)
              if (
                  filepath.suffix != ".py"
                  or filepath.name.startswith(("_", "test")					)
                  or not filepath.name.startswith("sol"					)
              ):
                     continue
              solution_file_paths.append(a					)
       return solution_file_paths



def 						__a      (  ):





       """simple docstring"""







       if os.environ.get("CI"					) and os.environ.get("GITHUB_EVENT_NAME"					) == "pull_request":
              # Return only if there are any, otherwise default to all solutions
              if filepaths := added_solution_file_path():
                     return filepaths
       return all_solution_file_paths()



@pytest.mark.parametrize(
    "solution_path"    ,		collect_solution_file_paths()    ,		ids=lambda a					: F'{path.parent.name}/{path.name}'    ,		)
def 						__a      (  a					):





       """simple docstring"""







       _a          =				solution_path.parent.name[8:].zfill(3					)
       _a          =				PROBLEM_ANSWERS[problem_number]
       _a          =				convert_path_to_module(a					)
       _a          =				str(solution_module.solution()					)  # type: ignore
       _a          =				hashlib.shaaaa(answer.encode()					).hexdigest()
       assert (
           answer == expected
       ), F'Expected solution to {problem_number} to have hash {expected}, got {answer}'




"""simple docstring"""




from typing import Union

import torch
from PIL import Image
from torchvision import transforms as tfms
from tqdm.auto import tqdm
from transformers import CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)





class __snake_case		(					_SCREAMING_SNAKE_CASE      ):







   """simple docstring"""




   def __init__(		self							:List[Any]				,  UpperCamelCase__							:AutoencoderKL				,  UpperCamelCase__							:CLIPTextModel				,  UpperCamelCase__							:CLIPTokenizer				,  UpperCamelCase__							:UNetaDConditionModel				,  UpperCamelCase__							:Union[PNDMScheduler, LMSDiscreteScheduler, DDIMScheduler]				,  ):
          super().__init__()

          self.register_modules(vae=UpperCamelCase__				,  text_encoder=UpperCamelCase__				,  tokenizer=UpperCamelCase__				,  unet=UpperCamelCase__				,  scheduler=UpperCamelCase__       )




   def SCREAMING_SNAKE_CASE_      (		self							:Tuple				,  UpperCamelCase__							:str       ):
          with torch.no_grad():
                 _a          =				self.vae.encode(tfms.ToTensor()(UpperCamelCase__       ).unsqueeze(0       ).to(self.device       ) * 2 - 1       )
                 _a          =				0.18215 * latent.latent_dist.sample()
          return latent




   def SCREAMING_SNAKE_CASE_      (		self							:List[Any]				,  UpperCamelCase__							:Any       ):
          _a          =				(1 / 0.18215) * latent
          with torch.no_grad():
                 _a          =				self.vae.decode(UpperCamelCase__       ).sample
          _a          =				(img / 2 + 0.5).clamp(0				,  1       )
          _a          =				img.detach().cpu().permute(0				,  2				,  3				,  1       ).numpy()
          _a          =				(img * 255).round().astype("uint8"       )
          return Image.fromarray(img[0]       )




   def SCREAMING_SNAKE_CASE_      (		self							:List[str]				,  UpperCamelCase__							:List[Any]       ):
          _a          =				self.tokenizer(
              UpperCamelCase__				,  padding="max_length"				,  max_length=self.tokenizer.model_max_length				,  truncation=UpperCamelCase__				,  return_tensors="pt"				,  )

          _a          =				self.text_encoder(text_input.input_ids.to(self.device       )       )[0]

          _a          =				self.tokenizer(
              ""				,  padding="max_length"				,  max_length=self.tokenizer.model_max_length				,  truncation=UpperCamelCase__				,  return_tensors="pt"				,  )

          _a          =				self.text_encoder(uncond_input.input_ids.to(self.device       )       )[0]

          return torch.cat([uncond_embedding, text_embedding]       )




   def __call__(		self							:Tuple				,  UpperCamelCase__							:Image.Image				,  UpperCamelCase__							:str				,  UpperCamelCase__							:float = 0.3				,  UpperCamelCase__							:float = 0.6				,  UpperCamelCase__							:float = 0.5				,  UpperCamelCase__							:int = 42				,  UpperCamelCase__							:int = 50				,  UpperCamelCase__							:float = 7.5				,  ):
          _a          =				steps - int(kmin * steps       )
          _a          =				steps - int(kmax * steps       )

          _a          =				self.prep_text(UpperCamelCase__       )

          self.scheduler.set_timesteps(UpperCamelCase__       )

          _a   , _a          =				img.size
          _a          =				self.encode(UpperCamelCase__       )

          torch.manual_seed(UpperCamelCase__       )
          _a          =				torch.randn(
              (1, self.unet.config.in_channels, height // 8, width // 8)				,  ).to(self.device       )

          _a          =				self.scheduler.add_noise(
              UpperCamelCase__				,  UpperCamelCase__				,  timesteps=self.scheduler.timesteps[tmax]				,  )

          _a          =				torch.cat([latents] * 2       )

          _a          =				self.scheduler.scale_model_input(UpperCamelCase__				,  self.scheduler.timesteps[tmax]       )

          with torch.no_grad():
                 _a          =				self.unet(
                     UpperCamelCase__				,  self.scheduler.timesteps[tmax]				,  encoder_hidden_states=UpperCamelCase__				,  ).sample

          _a   , _a          =				pred.chunk(2       )
          _a          =				pred_uncond + guidance_scale * (pred_text - pred_uncond)

          _a          =				self.scheduler.step(UpperCamelCase__				,  self.scheduler.timesteps[tmax]				,  UpperCamelCase__       ).prev_sample

          for i, t in enumerate(tqdm(self.scheduler.timesteps       )       ):
                 if i > tmax:
                        if i < tmin:  # layout generation phase
                               _a          =				self.scheduler.add_noise(
                                   UpperCamelCase__				,  UpperCamelCase__				,  timesteps=UpperCamelCase__				,  )

                               _a          =				(mix_factor * latents) + (
                                   1 - mix_factor
                               ) * orig_latents  # interpolating between layout noise and conditionally generated noise to preserve layout sematics
                               _a          =				torch.cat([input] * 2       )

                        else:  # content generation phase
                               _a          =				torch.cat([latents] * 2       )

                        _a          =				self.scheduler.scale_model_input(UpperCamelCase__				,  UpperCamelCase__       )

                        with torch.no_grad():
                               _a          =				self.unet(
                                   UpperCamelCase__				,  UpperCamelCase__				,  encoder_hidden_states=UpperCamelCase__				,  ).sample

                        _a   , _a          =				pred.chunk(2       )
                        _a          =				pred_uncond + guidance_scale * (pred_text - pred_uncond)

                        _a          =				self.scheduler.step(UpperCamelCase__				,  UpperCamelCase__				,  UpperCamelCase__       ).prev_sample

          return self.decode(UpperCamelCase__       )
