





"""simple docstring"""



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

import flax
import jax.numpy as jnp

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






@flax.struct.dataclass
class 	__snake_case      :





 """simple docstring"""


 _lowerCamelCase				 =  42
 _lowerCamelCase				 =  42

 # setable values
 _lowerCamelCase				 =  42
 _lowerCamelCase				 =  42
 _lowerCamelCase				 =  None

 @classmethod
 def       UpperCamelCase__(					cls      ,		__lowerCamelCase      ,		__lowerCamelCase      ,		__lowerCamelCase      ,		__lowerCamelCase      ,		):



     '''simple docstring'''




     return cls(
         common=__lowerCamelCase      ,		final_alpha_cumprod=__lowerCamelCase      ,		init_noise_sigma=__lowerCamelCase      ,		timesteps=__lowerCamelCase      ,		)






@dataclass
class 	__snake_case      (	SCREAMING_SNAKE_CASE__  ):





 """simple docstring"""


 _lowerCamelCase				 =  42






class 	__snake_case      (	SCREAMING_SNAKE_CASE__							,							SCREAMING_SNAKE_CASE__  ):





 """simple docstring"""


 _lowerCamelCase				 =  [e.name for e in FlaxKarrasDiffusionSchedulers]

 _lowerCamelCase				 =  42

 @property
 def       UpperCamelCase__(					self						):



     '''simple docstring'''




     return True

 @register_to_config
 def __init__(					self      ,		__lowerCamelCase = 1000      ,		__lowerCamelCase = 0.0_0_0_1      ,		__lowerCamelCase = 0.0_2      ,		__lowerCamelCase = "linear"      ,		__lowerCamelCase = None      ,		__lowerCamelCase = True      ,		__lowerCamelCase = 0      ,		__lowerCamelCase = "epsilon"      ,		__lowerCamelCase = jnp.floataa      ,		):



     '''simple docstring'''




     __A					:		Tuple						=  dtype

 def       UpperCamelCase__(					self      ,		__lowerCamelCase = None						):



     '''simple docstring'''




     if common is None:
         __A					:		Any						=  CommonSchedulerState.create(self						)

     # At every step in ddim, we are looking into the previous alphas_cumprod
     # For the final step, there is no previous alphas_cumprod because we are already at 0
     # `set_alpha_to_one` decides whether we set this parameter simply to one or
     # whether we use the final alpha of the "non-previous" one.
     __A					:		str						=  (
         jnp.array(1.0      ,		dtype=self.dtype						) if self.config.set_alpha_to_one else common.alphas_cumprod[0]
     )

     # standard deviation of the initial noise distribution
     __A					:		Tuple						=  jnp.array(1.0      ,		dtype=self.dtype						)

     __A					:		Dict						=  jnp.arange(0      ,		self.config.num_train_timesteps						).round()[::-1]

     return DDIMSchedulerState.create(
         common=__lowerCamelCase      ,		final_alpha_cumprod=__lowerCamelCase      ,		init_noise_sigma=__lowerCamelCase      ,		timesteps=__lowerCamelCase      ,		)

 def       UpperCamelCase__(					self      ,		__lowerCamelCase      ,		__lowerCamelCase      ,		__lowerCamelCase = None						):



     '''simple docstring'''




     return sample

 def       UpperCamelCase__(					self      ,		__lowerCamelCase      ,		__lowerCamelCase      ,		__lowerCamelCase = ()						):



     '''simple docstring'''




     __A					:		int						=  self.config.num_train_timesteps // num_inference_steps
     # creates integer timesteps by multiplying by ratio
     # rounding to avoid issues when num_inference_step is power of 3
     __A					:		Dict						=  (jnp.arange(0      ,		__lowerCamelCase						) * step_ratio).round()[::-1] + self.config.steps_offset

     return state.replace(
         num_inference_steps=__lowerCamelCase      ,		timesteps=__lowerCamelCase      ,		)

 def       UpperCamelCase__(					self      ,		__lowerCamelCase      ,		__lowerCamelCase      ,		__lowerCamelCase						):



     '''simple docstring'''




     __A					:		Dict						=  state.common.alphas_cumprod[timestep]
     __A					:		Union[str, Any]						=  jnp.where(
         prev_timestep >= 0      ,		state.common.alphas_cumprod[prev_timestep]      ,		state.final_alpha_cumprod						)
     __A					:		List[Any]						=  1 - alpha_prod_t
     __A					:		Union[str, Any]						=  1 - alpha_prod_t_prev

     __A					:		Optional[Any]						=  (beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev)

     return variance

 def       UpperCamelCase__(					self      ,		__lowerCamelCase      ,		__lowerCamelCase      ,		__lowerCamelCase      ,		__lowerCamelCase      ,		__lowerCamelCase = 0.0      ,		__lowerCamelCase = True      ,		):



     '''simple docstring'''




     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'''						)

     # See formulas (12) and (16) of DDIM paper https://arxiv.org/pdf/2010.02502.pdf
     # Ideally, read DDIM paper in-detail understanding

     # Notation (<variable name> -> <name in paper>
     # - pred_noise_t -> e_theta(x_t, t)
     # - pred_original_sample -> f_theta(x_t, t) or x_0
     # - std_dev_t -> sigma_t
     # - eta -> η
     # - pred_sample_direction -> "direction pointing to x_t"
     # - pred_prev_sample -> "x_t-1"

     # 1. get previous step value (=t-1)
     __A					:		str						=  timestep - self.config.num_train_timesteps // state.num_inference_steps

     __A					:		List[Any]						=  state.common.alphas_cumprod
     __A					:		Dict						=  state.final_alpha_cumprod

     # 2. compute alphas, betas
     __A					:		Tuple						=  alphas_cumprod[timestep]
     __A					:		List[Any]						=  jnp.where(prev_timestep >= 0      ,		alphas_cumprod[prev_timestep]      ,		__lowerCamelCase						)

     __A					:		Tuple						=  1 - alpha_prod_t

     # 3. compute predicted original sample from predicted noise also called
     # "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
     if self.config.prediction_type == "epsilon":
         __A					:		List[Any]						=  (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
         __A					:		Optional[Any]						=  model_output
     elif self.config.prediction_type == "sample":
         __A					:		str						=  model_output
         __A					:		List[Any]						=  (sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5
     elif self.config.prediction_type == "v_prediction":
         __A					:		str						=  (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
         __A					:		List[str]						=  (alpha_prod_t**0.5) * model_output + (beta_prod_t**0.5) * sample
     else:
         raise ValueError(
             F"""prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`, or"""
             ''' `v_prediction`'''						)

     # 4. compute variance: "sigma_t(η)" -> see formula (16)
     # σ_t = sqrt((1 − α_t−1)/(1 − α_t)) * sqrt(1 − α_t/α_t−1)
     __A					:		List[str]						=  self._get_variance(__lowerCamelCase      ,		__lowerCamelCase      ,		__lowerCamelCase						)
     __A					:		Optional[Any]						=  eta * variance ** 0.5

     # 5. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
     __A					:		int						=  (1 - alpha_prod_t_prev - std_dev_t**2) ** 0.5 * pred_epsilon

     # 6. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
     __A					:		Optional[Any]						=  alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction

     if not return_dict:
         return (prev_sample, state)

     return FlaxDDIMSchedulerOutput(prev_sample=__lowerCamelCase      ,		state=__lowerCamelCase						)

 def       UpperCamelCase__(					self      ,		__lowerCamelCase      ,		__lowerCamelCase      ,		__lowerCamelCase      ,		__lowerCamelCase      ,		):



     '''simple docstring'''




     return add_noise_common(state.common      ,		__lowerCamelCase      ,		__lowerCamelCase      ,		__lowerCamelCase						)

 def       UpperCamelCase__(					self      ,		__lowerCamelCase      ,		__lowerCamelCase      ,		__lowerCamelCase      ,		__lowerCamelCase      ,		):



     '''simple docstring'''




     return get_velocity_common(state.common      ,		__lowerCamelCase      ,		__lowerCamelCase      ,		__lowerCamelCase						)







 def __len__(					self						):



     '''simple docstring'''




     return self.config.num_train_timesteps











"""simple docstring"""



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

import pytest
import requests

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

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







def 	__lowercase  (     snake_case_						:						pathlib.Path		)     ->ModuleType:



    '''simple docstring'''







    __A					:		str						=  importlib.util.spec_from_file_location(file_path.name       ,str(snake_case_		)		)
    __A					:		Optional[int]						=  importlib.util.module_from_spec(snake_case_		)  # type: ignore
    spec.loader.exec_module(snake_case_		)  # type: ignore
    return module







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



    '''simple docstring'''







    __A					:		Optional[int]						=  []
    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(snake_case_		)
    return solution_file_paths







def 	__lowercase  (     )     ->str:



    '''simple docstring'''







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







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



    '''simple docstring'''







    __A					:		int						=  []
    __A					:		Optional[int]						=  {
        '''Accept''': '''application/vnd.github.v3+json''',
        '''Authorization''': '''token ''' + os.environ['''GITHUB_TOKEN'''],
    }
    __A					:		Optional[Any]						=  requests.get(get_files_url()       ,headers=snake_case_		).json()
    for file in files:
        __A					:		Union[str, Any]						=  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(snake_case_		)
    return solution_file_paths







def 	__lowercase  (     )     ->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 snake_case_		: F"""{path.parent.name}/{path.name}"""       ,)
def 	__lowercase  (     snake_case_						:						pathlib.Path		)     ->None:



    '''simple docstring'''







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




