from dataclasses import dataclass
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, logging, randn_tensor
from .scheduling_utils import SchedulerMixin


__lowerCamelCase     :					Union[str, Any]				  =						logging.get_logger(__name__)  # pylint: disable=invalid-name







@dataclass
class __snake_case							(		lowerCamelCase_						):
   lowerCAmelCase_								=				42







class __snake_case							(		lowerCamelCase_  ,						lowerCamelCase_						):
   lowerCAmelCase_								=				1




   @register_to_config
   def __init__(       self     :							Union[str, Any]			,      _lowercase     :							int = 40			,      _lowercase     :							float = 0.0_02			,      _lowercase     :							float = 80.0			,      _lowercase     :							float = 0.5			,      _lowercase     :							float = 1.0			,      _lowercase     :							float = 7.0			,      _lowercase     :							bool = True			,      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= sigma_max

     SCREAMING_SNAKE_CASE__     				= np.linspace(0			,      1			,      _lowercase )
     SCREAMING_SNAKE_CASE__     				= self._convert_to_karras(_lowercase )
     SCREAMING_SNAKE_CASE__     				= self.sigma_to_t(_lowercase )

     # setable values
     SCREAMING_SNAKE_CASE__     				= None
     SCREAMING_SNAKE_CASE__     				= torch.from_numpy(_lowercase )
     SCREAMING_SNAKE_CASE__     				= torch.from_numpy(_lowercase )
     SCREAMING_SNAKE_CASE__     				= False
     SCREAMING_SNAKE_CASE__     				= False




   def       __a		(       self     :							Union[str, Any]			,      _lowercase     :							Union[str, Any]			,      _lowercase     :							int=None ):



     """simple docstring"""






     if schedule_timesteps is None:
       SCREAMING_SNAKE_CASE__     				= self.timesteps

     SCREAMING_SNAKE_CASE__     				= (schedule_timesteps == timestep).nonzero()
     return indices.item()




   def       __a		(       self     :							Optional[Any]			,      _lowercase     :							torch.FloatTensor			,      _lowercase     :							Union[float, torch.FloatTensor] ):



     """simple docstring"""






     if isinstance(_lowercase			,      torch.Tensor ):
       SCREAMING_SNAKE_CASE__     				= timestep.to(self.timesteps.device )
     SCREAMING_SNAKE_CASE__     				= self.index_for_timestep(_lowercase )
     SCREAMING_SNAKE_CASE__     				= self.sigmas[step_idx]

     SCREAMING_SNAKE_CASE__     				= sample / ((sigma**2 + self.config.sigma_data**2) ** 0.5)

     SCREAMING_SNAKE_CASE__     				= True
     return sample




   def       __a		(       self     :							int			,      _lowercase     :							Union[float, np.ndarray] ):



     """simple docstring"""






     if not isinstance(_lowercase			,      np.ndarray ):
       SCREAMING_SNAKE_CASE__     				= np.array(_lowercase			,      dtype=np.floataa )

     SCREAMING_SNAKE_CASE__     				= 10_00 * 0.25 * np.log(sigmas + 1E-44 )

     return timesteps




   def       __a		(       self     :							Tuple			,      _lowercase     :							Optional[int] = None			,      _lowercase     :							Union[str, torch.device] = None			,      _lowercase     :							Optional[List[int]] = None			,      ):



     """simple docstring"""






     if num_inference_steps is None and timesteps is None:
       raise ValueError("""Exactly one of `num_inference_steps` or `timesteps` must be supplied.""" )

     if num_inference_steps is not None and timesteps is not None:
       raise ValueError("""Can only pass one of `num_inference_steps` or `timesteps`.""" )

     # Follow DDPMScheduler custom timesteps logic
     if timesteps is not None:
       for i in range(1			,      len(_lowercase ) ):
         if timesteps[i] >= timesteps[i - 1]:
           raise ValueError("""`timesteps` must be in descending order.""" )

       if timesteps[0] >= self.config.num_train_timesteps:
         raise ValueError(
             f"""`timesteps` must start before `self.config.train_timesteps`:"""
             f""" {self.config.num_train_timesteps}.""" )

       SCREAMING_SNAKE_CASE__     				= np.array(_lowercase			,      dtype=np.intaa )
       SCREAMING_SNAKE_CASE__     				= True
     else:
       if num_inference_steps > self.config.num_train_timesteps:
         raise ValueError(
             f"""`num_inference_steps`: {num_inference_steps} cannot be larger than `self.config.train_timesteps`:"""
             f""" {self.config.num_train_timesteps} as the unet model trained with this scheduler can only handle"""
             f""" maximal {self.config.num_train_timesteps} timesteps.""" )

       SCREAMING_SNAKE_CASE__     				= num_inference_steps

       SCREAMING_SNAKE_CASE__     				= self.config.num_train_timesteps // self.num_inference_steps
       SCREAMING_SNAKE_CASE__     				= (np.arange(0			,      _lowercase ) * step_ratio).round()[::-1].copy().astype(np.intaa )
       SCREAMING_SNAKE_CASE__     				= False

     # Map timesteps to Karras sigmas directly for multistep sampling
     # See https://github.com/openai/consistency_models/blob/main/cm/karras_diffusion.py#L675
     SCREAMING_SNAKE_CASE__     				= self.config.num_train_timesteps
     SCREAMING_SNAKE_CASE__     				= timesteps[::-1].copy()
     SCREAMING_SNAKE_CASE__     				= ramp / (num_train_timesteps - 1)
     SCREAMING_SNAKE_CASE__     				= self._convert_to_karras(_lowercase )
     SCREAMING_SNAKE_CASE__     				= self.sigma_to_t(_lowercase )

     SCREAMING_SNAKE_CASE__     				= np.concatenate([sigmas, [self.sigma_min]] ).astype(np.floataa )
     SCREAMING_SNAKE_CASE__     				= torch.from_numpy(_lowercase ).to(device=_lowercase )

     if str(_lowercase ).startswith("""mps""" ):
       # mps does not support float64
       SCREAMING_SNAKE_CASE__     				= torch.from_numpy(_lowercase ).to(_lowercase			,      dtype=torch.floataa )
     else:
       SCREAMING_SNAKE_CASE__     				= torch.from_numpy(_lowercase ).to(device=_lowercase )




   def       __a		(       self     :							int			,      _lowercase     :							Any ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= self.config.sigma_min
     SCREAMING_SNAKE_CASE__     				= self.config.sigma_max

     SCREAMING_SNAKE_CASE__     				= self.config.rho
     SCREAMING_SNAKE_CASE__     				= sigma_min ** (1 / rho)
     SCREAMING_SNAKE_CASE__     				= sigma_max ** (1 / rho)
     SCREAMING_SNAKE_CASE__     				= (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
     return sigmas




   def       __a		(       self     :							Union[str, Any]			,      _lowercase     :							int ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= self.config.sigma_data

     SCREAMING_SNAKE_CASE__     				= sigma_data**2 / (sigma**2 + sigma_data**2)
     SCREAMING_SNAKE_CASE__     				= sigma * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
     return c_skip, c_out




   def       __a		(       self     :							Optional[Any]			,      _lowercase     :							Optional[Any] ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= self.config.sigma_min
     SCREAMING_SNAKE_CASE__     				= self.config.sigma_data

     SCREAMING_SNAKE_CASE__     				= sigma_data**2 / ((sigma - sigma_min) ** 2 + sigma_data**2)
     SCREAMING_SNAKE_CASE__     				= (sigma - sigma_min) * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
     return c_skip, c_out




   def       __a		(       self     :							Optional[Any]			,      _lowercase     :							torch.FloatTensor			,      _lowercase     :							Union[float, torch.FloatTensor]			,      _lowercase     :							torch.FloatTensor			,      _lowercase     :							Optional[torch.Generator] = None			,      _lowercase     :							bool = True			,      ):



     """simple docstring"""






     if (
         isinstance(_lowercase			,      _lowercase )
         or isinstance(_lowercase			,      torch.IntTensor )
         or isinstance(_lowercase			,      torch.LongTensor )
     ):
       raise ValueError(
           (
               """Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to"""
               f""" `{self.__class__}.step()` is not supported. Make sure to pass"""
               """ one of the `scheduler.timesteps` as a timestep."""
           )			,      )

     if not self.is_scale_input_called:
       logger.warning(
           """The `scale_model_input` function should be called before `step` to ensure correct denoising. """
           """See `StableDiffusionPipeline` for a usage example.""" )

     if isinstance(_lowercase			,      torch.Tensor ):
       SCREAMING_SNAKE_CASE__     				= timestep.to(self.timesteps.device )

     SCREAMING_SNAKE_CASE__     				= self.config.sigma_min
     SCREAMING_SNAKE_CASE__     				= self.config.sigma_max

     SCREAMING_SNAKE_CASE__     				= self.index_for_timestep(_lowercase )

     # sigma_next corresponds to next_t in original implementation
     SCREAMING_SNAKE_CASE__     				= self.sigmas[step_index]
     if step_index + 1 < self.config.num_train_timesteps:
       SCREAMING_SNAKE_CASE__     				= self.sigmas[step_index + 1]
     else:
       # Set sigma_next to sigma_min
       SCREAMING_SNAKE_CASE__     				= self.sigmas[-1]

     # Get scalings for boundary conditions
     SCREAMING_SNAKE_CASE__				,	SCREAMING_SNAKE_CASE__     				= self.get_scalings_for_boundary_condition(_lowercase )

     # 1. Denoise model output using boundary conditions
     SCREAMING_SNAKE_CASE__     				= c_out * model_output + c_skip * sample
     if self.config.clip_denoised:
       SCREAMING_SNAKE_CASE__     				= denoised.clamp(-1			,      1 )

     # 2. Sample z ~ N(0, s_noise^2 * I)
     # Noise is not used for onestep sampling.
     if len(self.timesteps ) > 1:
       SCREAMING_SNAKE_CASE__     				= randn_tensor(
           model_output.shape			,      dtype=model_output.dtype			,      device=model_output.device			,      generator=_lowercase )
     else:
       SCREAMING_SNAKE_CASE__     				= torch.zeros_like(_lowercase )
     SCREAMING_SNAKE_CASE__     				= noise * self.config.s_noise

     SCREAMING_SNAKE_CASE__     				= sigma_next.clamp(min=_lowercase			,      max=_lowercase )

     # 3. Return noisy sample
     # tau = sigma_hat, eps = sigma_min
     SCREAMING_SNAKE_CASE__     				= denoised + z * (sigma_hat**2 - sigma_min**2) ** 0.5

     if not return_dict:
       return (prev_sample,)

     return CMStochasticIterativeSchedulerOutput(prev_sample=_lowercase )




   def       __a		(       self     :							List[str]			,      _lowercase     :							torch.FloatTensor			,      _lowercase     :							torch.FloatTensor			,      _lowercase     :							torch.FloatTensor			,      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= self.sigmas.to(device=original_samples.device			,      dtype=original_samples.dtype )
     if original_samples.device.type == "mps" and torch.is_floating_point(_lowercase ):
       # mps does not support float64
       SCREAMING_SNAKE_CASE__     				= self.timesteps.to(original_samples.device			,      dtype=torch.floataa )
       SCREAMING_SNAKE_CASE__     				= timesteps.to(original_samples.device			,      dtype=torch.floataa )
     else:
       SCREAMING_SNAKE_CASE__     				= self.timesteps.to(original_samples.device )
       SCREAMING_SNAKE_CASE__     				= timesteps.to(original_samples.device )

     SCREAMING_SNAKE_CASE__     				= [(schedule_timesteps == t).nonzero().item() for t in timesteps]

     SCREAMING_SNAKE_CASE__     				= sigmas[step_indices].flatten()
     while len(sigma.shape ) < len(original_samples.shape ):
       SCREAMING_SNAKE_CASE__     				= sigma.unsqueeze(-1 )

     SCREAMING_SNAKE_CASE__     				= original_samples + noise * sigma
     return noisy_samples




   def __len__(       self     :							Tuple ):



     """simple docstring"""






     return self.config.num_train_timesteps

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

import pytest
import requests

__lowerCamelCase     :					Optional[Any]				  =						pathlib.Path.cwd().joinpath('''project_euler''')
__lowerCamelCase     :					Dict				  =						pathlib.Path.cwd().joinpath(
    '''scripts''', '''project_euler_answers.json'''
)

with open(PROJECT_EULER_ANSWERS_PATH) as file_handle:
   __lowerCamelCase     :					dict[str, str]				  =						json.load(file_handle)



def       __SCREAMING_SNAKE_CASE    (   __UpperCamelCase       :			pathlib.Path      )				->   ModuleType:







  """simple docstring"""





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



def       __SCREAMING_SNAKE_CASE    (   )				->   list[pathlib.Path]:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__     				= []
  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(__UpperCamelCase      )
  return solution_file_paths



def       __SCREAMING_SNAKE_CASE    (   )				->   str:







  """simple docstring"""





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



def       __SCREAMING_SNAKE_CASE    (   )				->   list[pathlib.Path]:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__     				= []
  SCREAMING_SNAKE_CASE__     				= {
      """Accept""": """application/vnd.github.v3+json""",
      """Authorization""": """token """ + os.environ["""GITHUB_TOKEN"""],
  }
  SCREAMING_SNAKE_CASE__     				= requests.get(get_files_url()					,	headers=__UpperCamelCase      ).json()
  for file in files:
    SCREAMING_SNAKE_CASE__     				= 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(__UpperCamelCase      )
  return solution_file_paths



def       __SCREAMING_SNAKE_CASE    (   )				->   list[pathlib.Path]:







  """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 __UpperCamelCase      : f"""{path.parent.name}/{path.name}"""					,	)
def       __SCREAMING_SNAKE_CASE    (   __UpperCamelCase       :			pathlib.Path      )				->   None:







  """simple docstring"""





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