


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

import flax
import jax.numpy as jnp
from scipy import integrate

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import (
    CommonSchedulerState,
    FlaxKarrasDiffusionSchedulers,
    FlaxSchedulerMixin,
    FlaxSchedulerOutput,
    broadcast_to_shape_from_left,
)




@flax.struct.dataclass
class  A_ :







 """simple docstring"""





 _UpperCamelCase					:			Any     	=       42

 # setable values
 _UpperCamelCase					:			Optional[Any]     	=       42
 _UpperCamelCase					:			int     	=       42
 _UpperCamelCase					:			Optional[Any]     	=       42
 _UpperCamelCase					:			Dict     	=       None

 # running values
 _UpperCamelCase					:			Optional[int]     	=       None
 @classmethod
 def 	_UpperCamelCase     (      cls       :			List[str]							,     _a       :			CommonSchedulerState							,     _a       :			jnp.ndarray							,     _a       :			jnp.ndarray							,     _a       :			jnp.ndarray					)     ->      Any:
      return cls(common=_lowercase							,     init_noise_sigma=_lowercase							,     timesteps=_lowercase							,     sigmas=_lowercase					)




@dataclass
class  A_ (			lowerCamelCase_  ):







 """simple docstring"""





 _UpperCamelCase					:			Dict     	=       42




class  A_ (			lowerCamelCase_   ,    lowerCamelCase_  ):







 """simple docstring"""





 _UpperCamelCase					:			int     	=       [e.name for e in FlaxKarrasDiffusionSchedulers]

 _UpperCamelCase					:			Dict     	=       42
 @property
 def 	_UpperCamelCase     (      self       :			str					)     ->      List[str]:
      return True
 @register_to_config
 def __init__(      self       :			Optional[Any]							,     _a       :			int = 1_0_0_0							,     _a       :			float = 0.0_0_0_1							,     _a       :			float = 0.0_2							,     _a       :			str = "linear"							,     _a       :			Optional[jnp.ndarray] = None							,     _a       :			str = "epsilon"							,     _a       :			jnp.dtype = jnp.floataa							,     )     ->      int:
      UpperCAmelCase    :					Optional[int]			=      dtype
 def 	_UpperCamelCase     (      self       :			Dict							,     _a       :			Optional[CommonSchedulerState] = None					)     ->      int:






      if common is None:
           UpperCAmelCase    :					str			=      CommonSchedulerState.create(self					)

      UpperCAmelCase    :					Optional[int]			=      jnp.arange(0							,     self.config.num_train_timesteps					).round()[::-1]
      UpperCAmelCase    :					List[str]			=      ((1 - common.alphas_cumprod) / common.alphas_cumprod) ** 0.5

      # standard deviation of the initial noise distribution
      UpperCAmelCase    :					Tuple			=      sigmas.max()

      return LMSDiscreteSchedulerState.create(
          common=_lowercase							,     init_noise_sigma=_lowercase							,     timesteps=_lowercase							,     sigmas=_lowercase							,     )
 def 	_UpperCamelCase     (      self       :			Optional[int]							,     _a       :			LMSDiscreteSchedulerState							,     _a       :			jnp.ndarray							,     _a       :			int					)     ->      int:
      ((UpperCAmelCase	)	,				)    :					int			=      jnp.where(state.timesteps == timestep							,     size=1					)
      UpperCAmelCase    :					Optional[int]			=      step_index[0]

      UpperCAmelCase    :					str			=      state.sigmas[step_index]
      UpperCAmelCase    :					str			=      sample / ((sigma**2 + 1) ** 0.5)
      return sample
 def 	_UpperCamelCase     (      self       :			Any							,     _a       :			LMSDiscreteSchedulerState							,     _a       :			Dict							,     _a       :			Optional[Any]							,     _a       :			List[Any]					)     ->      int:






      def lms_derivative(_a       :			List[str]					):
           UpperCAmelCase    :					Optional[int]			=      1.0
           for k in range(_lowercase					):
                if current_order == k:
                     continue
                prod *= (tau - state.sigmas[t - k]) / (state.sigmas[t - current_order] - state.sigmas[t - k])
           return prod

      UpperCAmelCase    :					Dict			=      integrate.quad(_lowercase							,     state.sigmas[t]							,     state.sigmas[t + 1]							,     epsrel=1E-4					)[0]

      return integrated_coeff
 def 	_UpperCamelCase     (      self       :			Any							,     _a       :			LMSDiscreteSchedulerState							,     _a       :			int							,     _a       :			Tuple = ()					)     ->      int:
      UpperCAmelCase    :					Optional[Any]			=      jnp.linspace(self.config.num_train_timesteps - 1							,     0							,     _lowercase							,     dtype=self.dtype					)

      UpperCAmelCase    :					Union[str, Any]			=      jnp.floor(_lowercase					).astype(jnp.intaa					)
      UpperCAmelCase    :					Optional[Any]			=      jnp.ceil(_lowercase					).astype(jnp.intaa					)

      UpperCAmelCase    :					int			=      jnp.mod(_lowercase							,     1.0					)

      UpperCAmelCase    :					List[Any]			=      ((1 - state.common.alphas_cumprod) / state.common.alphas_cumprod) ** 0.5
      UpperCAmelCase    :					int			=      (1 - frac) * sigmas[low_idx] + frac * sigmas[high_idx]
      UpperCAmelCase    :					Tuple			=      jnp.concatenate([sigmas, jnp.array([0.0]							,     dtype=self.dtype					)]					)

      UpperCAmelCase    :					Dict			=      timesteps.astype(jnp.intaa					)

      # initial running values
      UpperCAmelCase    :					Optional[int]			=      jnp.zeros((0,) + shape							,     dtype=self.dtype					)

      return state.replace(
          timesteps=_lowercase							,     sigmas=_lowercase							,     num_inference_steps=_lowercase							,     derivatives=_lowercase							,     )
 def 	_UpperCamelCase     (      self       :			Any							,     _a       :			LMSDiscreteSchedulerState							,     _a       :			jnp.ndarray							,     _a       :			int							,     _a       :			jnp.ndarray							,     _a       :			int = 4							,     _a       :			bool = True							,     )     ->      Dict:






      if state.num_inference_steps is None:
           raise ValueError(
               "Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler"					)

      UpperCAmelCase    :					Any			=      state.sigmas[timestep]

      # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
      if self.config.prediction_type == "epsilon":
           UpperCAmelCase    :					Optional[Any]			=      sample - sigma * model_output
      elif self.config.prediction_type == "v_prediction":
           # * c_out + input * c_skip
           UpperCAmelCase    :					str			=      model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
      else:
           raise ValueError(
               f'prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`'					)

      # 2. Convert to an ODE derivative
      UpperCAmelCase    :					int			=      (sample - pred_original_sample) / sigma
      UpperCAmelCase    :					List[str]			=      state.replace(derivatives=jnp.append(state.derivatives							,     _lowercase					)					)
      if len(state.derivatives					) > order:
           UpperCAmelCase    :					List[str]			=      state.replace(derivatives=jnp.delete(state.derivatives							,     0					)					)

      # 3. Compute linear multistep coefficients
      UpperCAmelCase    :					Tuple			=      min(timestep + 1							,     _lowercase					)
      UpperCAmelCase    :					List[Any]			=      [self.get_lms_coefficient(_lowercase							,     _lowercase							,     _lowercase							,     _lowercase					) for curr_order in range(_lowercase					)]

      # 4. Compute previous sample based on the derivatives path
      UpperCAmelCase    :					Tuple			=      sample + sum(
          coeff * derivative for coeff, derivative in zip(_lowercase							,     reversed(state.derivatives					)					)					)

      if not return_dict:
           return (prev_sample, state)

      return FlaxLMSSchedulerOutput(prev_sample=_lowercase							,     state=_lowercase					)
 def 	_UpperCamelCase     (      self       :			Union[str, Any]							,     _a       :			LMSDiscreteSchedulerState							,     _a       :			jnp.ndarray							,     _a       :			jnp.ndarray							,     _a       :			jnp.ndarray							,     )     ->      Optional[Any]:
      UpperCAmelCase    :					Tuple			=      state.sigmas[timesteps].flatten()
      UpperCAmelCase    :					int			=      broadcast_to_shape_from_left(_lowercase							,     noise.shape					)

      UpperCAmelCase    :					Optional[int]			=      original_samples + noise * sigma

      return noisy_samples

 def __len__(      self       :			Union[str, Any]					)     ->      Dict:
      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}"""
