
# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch

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

import flax
import jax.numpy as jnp
from jax import random

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import FlaxSchedulerMixin, FlaxSchedulerOutput, broadcast_to_shape_from_left

@flax.struct.dataclass
class 		lowercase_       :
 # setable values
 A__						:				Optional[jnp.ndarray]         =    None
 A__						:				Optional[jnp.ndarray]         =    None
 A__						:				Optional[jnp.ndarray]         =    None

 @classmethod
 def lowerCamelCase_				(      cls     ):




        """simple docstring"""


        return cls()



@dataclass
class 		lowercase_       (       __SCREAMING_SNAKE_CASE				):
 A__						:				ScoreSdeVeSchedulerState
 A__						:				jnp.ndarray
 A__						:				Optional[jnp.ndarray]         =    None



class 		lowercase_       (       __SCREAMING_SNAKE_CASE							,	__SCREAMING_SNAKE_CASE				):

 @property
 def lowerCamelCase_				(      self     ):




        """simple docstring"""


        return True

 @register_to_config
 def __init__(      self     ,	__UpperCamelCase = 2_0_0_0     ,	__UpperCamelCase = 0.15     ,	__UpperCamelCase = 0.01     ,	__UpperCamelCase = 1_348.0     ,	__UpperCamelCase = 1e-5     ,	__UpperCamelCase = 1     ,	):




        """simple docstring"""


        pass

 def lowerCamelCase_				(      self     ):




        """simple docstring"""


        UpperCamelCase_							       =       ScoreSdeVeSchedulerState.create()
        return self.set_sigmas(
            __UpperCamelCase     ,	self.config.num_train_timesteps     ,	self.config.sigma_min     ,	self.config.sigma_max     ,	self.config.sampling_eps     ,	)

 def lowerCamelCase_				(      self     ,	__UpperCamelCase     ,	__UpperCamelCase     ,	__UpperCamelCase = ()     ,	__UpperCamelCase = None     ):




        """simple docstring"""


        UpperCamelCase_							       =       sampling_eps if sampling_eps is not None else self.config.sampling_eps

        UpperCamelCase_							       =       jnp.linspace(1     ,	__UpperCamelCase     ,	__UpperCamelCase     )
        return state.replace(timesteps=__UpperCamelCase     )

 def lowerCamelCase_				(      self     ,	__UpperCamelCase     ,	__UpperCamelCase     ,	__UpperCamelCase = None     ,	__UpperCamelCase = None     ,	__UpperCamelCase = None     ,	):




        """simple docstring"""


        UpperCamelCase_							       =       sigma_min if sigma_min is not None else self.config.sigma_min
        UpperCamelCase_							       =       sigma_max if sigma_max is not None else self.config.sigma_max
        UpperCamelCase_							       =       sampling_eps if sampling_eps is not None else self.config.sampling_eps
        if state.timesteps is None:
               UpperCamelCase_							       =       self.set_timesteps(__UpperCamelCase     ,	__UpperCamelCase     ,	__UpperCamelCase     )

        UpperCamelCase_							       =       jnp.exp(jnp.linspace(jnp.log(__UpperCamelCase     )     ,	jnp.log(__UpperCamelCase     )     ,	__UpperCamelCase     )     )
        UpperCamelCase_							       =       jnp.array([sigma_min * (sigma_max / sigma_min) ** t for t in state.timesteps]     )

        return state.replace(discrete_sigmas=__UpperCamelCase     ,	sigmas=__UpperCamelCase     )

 def lowerCamelCase_				(      self     ,	__UpperCamelCase     ,	__UpperCamelCase     ,	__UpperCamelCase     ):




        """simple docstring"""


        return jnp.where(timesteps == 0     ,	jnp.zeros_like(__UpperCamelCase     )     ,	state.discrete_sigmas[timesteps - 1]     )

 def lowerCamelCase_				(      self     ,	__UpperCamelCase     ,	__UpperCamelCase     ,	__UpperCamelCase     ,	__UpperCamelCase     ,	__UpperCamelCase     ,	__UpperCamelCase = True     ,	):




        """simple docstring"""


        if state.timesteps is None:
               raise ValueError(
                   """`state.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"""     )

        UpperCamelCase_							       =       timestep * jnp.ones(
            sample.shape[0]     ,	)
        UpperCamelCase_							       =       (timestep * (len(state.timesteps     ) - 1)).long()

        UpperCamelCase_							       =       state.discrete_sigmas[timesteps]
        UpperCamelCase_							       =       self.get_adjacent_sigma(__UpperCamelCase     ,	__UpperCamelCase     ,	__UpperCamelCase     )
        UpperCamelCase_							       =       jnp.zeros_like(__UpperCamelCase     )
        UpperCamelCase_							       =       (sigma**2 - adjacent_sigma**2) ** 0.5

        # equation 6 in the paper: the model_output modeled by the network is grad_x log pt(x)
        # also equation 47 shows the analog from SDE models to ancestral sampling methods
        UpperCamelCase_							       =       diffusion.flatten()
        UpperCamelCase_							       =       broadcast_to_shape_from_left(__UpperCamelCase     ,	sample.shape     )
        UpperCamelCase_							       =       drift - diffusion**2 * model_output

        #  equation 6: sample noise for the diffusion term of
        UpperCamelCase_							       =       random.split(__UpperCamelCase     ,	num=1     )
        UpperCamelCase_							       =       random.normal(key=__UpperCamelCase     ,	shape=sample.shape     )
        UpperCamelCase_							       =       sample - drift  # subtract because `dt` is a small negative timestep
        # TODO is the variable diffusion the correct scaling term for the noise?
        UpperCamelCase_							       =       prev_sample_mean + diffusion * noise  # add impact of diffusion field g

        if not return_dict:
               return (prev_sample, prev_sample_mean, state)

        return FlaxSdeVeOutput(prev_sample=__UpperCamelCase     ,	prev_sample_mean=__UpperCamelCase     ,	state=__UpperCamelCase     )

 def lowerCamelCase_				(      self     ,	__UpperCamelCase     ,	__UpperCamelCase     ,	__UpperCamelCase     ,	__UpperCamelCase     ,	__UpperCamelCase = True     ,	):




        """simple docstring"""


        if state.timesteps is None:
               raise ValueError(
                   """`state.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"""     )

        # For small batch sizes, the paper "suggest replacing norm(z) with sqrt(d), where d is the dim. of z"
        # sample noise for correction
        UpperCamelCase_							       =       random.split(__UpperCamelCase     ,	num=1     )
        UpperCamelCase_							       =       random.normal(key=__UpperCamelCase     ,	shape=sample.shape     )

        # compute step size from the model_output, the noise, and the snr
        UpperCamelCase_							       =       jnp.linalg.norm(__UpperCamelCase     )
        UpperCamelCase_							       =       jnp.linalg.norm(__UpperCamelCase     )
        UpperCamelCase_							       =       (self.config.snr * noise_norm / grad_norm) ** 2 * 2
        UpperCamelCase_							       =       step_size * jnp.ones(sample.shape[0]     )

        # compute corrected sample: model_output term and noise term
        UpperCamelCase_							       =       step_size.flatten()
        UpperCamelCase_							       =       broadcast_to_shape_from_left(__UpperCamelCase     ,	sample.shape     )
        UpperCamelCase_							       =       sample + step_size * model_output
        UpperCamelCase_							       =       prev_sample_mean + ((step_size * 2) ** 0.5) * noise

        if not return_dict:
               return (prev_sample, state)

        return FlaxSdeVeOutput(prev_sample=__UpperCamelCase     ,	state=__UpperCamelCase     )

 def __len__(      self     ):




        """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

_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 				lowerCamelCase__				(						a__  :  pathlib.Path     )		->	ModuleType:
       UpperCamelCase_							       =       importlib.util.spec_from_file_location(file_path.name	,      str(a__     )     )
       UpperCamelCase_							       =       importlib.util.module_from_spec(a__     )  # type: ignore
       spec.loader.exec_module(a__     )  # type: ignore
       return module




def 				lowerCamelCase__				(						)		->	list[pathlib.Path]:
       UpperCamelCase_							       =       []
       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 				lowerCamelCase__				(						)		->	str:
       with open(os.environ["""GITHUB_EVENT_PATH"""]     ) as file:
              UpperCamelCase_							       =       json.load(a__     )
       return event["pull_request"]["url"] + "/files"




def 				lowerCamelCase__				(						)		->	list[pathlib.Path]:
       UpperCamelCase_							       =       []
       UpperCamelCase_							       =       {
           """Accept""": """application/vnd.github.v3+json""",
           """Authorization""": """token """ + os.environ["""GITHUB_TOKEN"""],
       }
       UpperCamelCase_							       =       requests.get(get_files_url()	,      headers=a__     ).json()
       for file in files:
              UpperCamelCase_							       =       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 				lowerCamelCase__				(						)		->	list[pathlib.Path]:
       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 				lowerCamelCase__				(						a__  :  pathlib.Path     )		->	None:
       UpperCamelCase_							       =       solution_path.parent.name[8:].zfill(3     )
       UpperCamelCase_							       =       PROBLEM_ANSWERS[problem_number]
       UpperCamelCase_							       =       convert_path_to_module(a__     )
       UpperCamelCase_							       =       str(solution_module.solution()     )  # type: ignore
       UpperCamelCase_							       =       hashlib.shaaaa(answer.encode()     ).hexdigest()
       assert (
           answer == expected
       ), f'''Expected solution to {problem_number} to have hash {expected}, got {answer}'''



