






# 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        SCREAMING_SNAKE_CASE	:
     '''simple docstring'''


     UpperCamelCase_							:						Optional[jnp.ndarray]								=		None
     UpperCamelCase_							:						Optional[jnp.ndarray]								=		None
     UpperCamelCase_							:						Optional[jnp.ndarray]								=		None


     @classmethod
     def  _A				( cls				:		Union[str, Any]					):
       return cls()





@dataclass
class        SCREAMING_SNAKE_CASE	(	lowerCAmelCase    ):
     '''simple docstring'''


     UpperCamelCase_							:						ScoreSdeVeSchedulerState
     UpperCamelCase_							:						jnp.ndarray
     UpperCamelCase_							:						Optional[jnp.ndarray]								=		None





class        SCREAMING_SNAKE_CASE	(	lowerCAmelCase					,						lowerCAmelCase    ):
     '''simple docstring'''


     @property
     def  _A				( self				:		Union[str, Any]					):
       return True


     @register_to_config
     def __init__( self				:		Union[str, Any]    ,       UpperCAmelCase_				:		int = 2000    ,       UpperCAmelCase_				:		float = 0.15    ,       UpperCAmelCase_				:		float = 0.01    ,       UpperCAmelCase_				:		float = 1_348.0    ,       UpperCAmelCase_				:		float = 1E-5    ,       UpperCAmelCase_				:		int = 1    ,       ):
       pass


     def  _A				( self				:		List[Any]					):
       SCREAMING_SNAKE_CASE				:					int		  =			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  _A				( self				:		Optional[Any]    ,       UpperCAmelCase_				:		ScoreSdeVeSchedulerState    ,       UpperCAmelCase_				:		int    ,       UpperCAmelCase_				:		Tuple = ()    ,       UpperCAmelCase_				:		float = None					):
       SCREAMING_SNAKE_CASE				:					List[str]		  =			sampling_eps if sampling_eps is not None else self.config.sampling_eps

       SCREAMING_SNAKE_CASE				:					List[str]		  =			jnp.linspace(1    ,       UpperCAmelCase_    ,       UpperCAmelCase_					)
       return state.replace(timesteps=UpperCAmelCase_					)


     def  _A				( self				:		int    ,       UpperCAmelCase_				:		ScoreSdeVeSchedulerState    ,       UpperCAmelCase_				:		int    ,       UpperCAmelCase_				:		float = None    ,       UpperCAmelCase_				:		float = None    ,       UpperCAmelCase_				:		float = None    ,       ):
       SCREAMING_SNAKE_CASE				:					List[str]		  =			sigma_min if sigma_min is not None else self.config.sigma_min
       SCREAMING_SNAKE_CASE				:					Optional[Any]		  =			sigma_max if sigma_max is not None else self.config.sigma_max
       SCREAMING_SNAKE_CASE				:					Tuple		  =			sampling_eps if sampling_eps is not None else self.config.sampling_eps
       if state.timesteps is None:
         SCREAMING_SNAKE_CASE				:					Any		  =			self.set_timesteps(UpperCAmelCase_    ,       UpperCAmelCase_    ,       UpperCAmelCase_					)

       SCREAMING_SNAKE_CASE				:					Optional[Any]		  =			jnp.exp(jnp.linspace(jnp.log(UpperCAmelCase_					)    ,       jnp.log(UpperCAmelCase_					)    ,       UpperCAmelCase_					)					)
       SCREAMING_SNAKE_CASE				:					Dict		  =			jnp.array([sigma_min * (sigma_max / sigma_min) ** t for t in state.timesteps]					)

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


     def  _A				( self				:		Any    ,       UpperCAmelCase_				:		List[str]    ,       UpperCAmelCase_				:		Optional[Any]    ,       UpperCAmelCase_				:		List[Any]					):
       return jnp.where(timesteps == 0    ,       jnp.zeros_like(UpperCAmelCase_					)    ,       state.discrete_sigmas[timesteps - 1]					)


     def  _A				( self				:		Union[str, Any]    ,       UpperCAmelCase_				:		ScoreSdeVeSchedulerState    ,       UpperCAmelCase_				:		jnp.ndarray    ,       UpperCAmelCase_				:		int    ,       UpperCAmelCase_				:		jnp.ndarray    ,       UpperCAmelCase_				:		random.KeyArray    ,       UpperCAmelCase_				:		bool = True    ,       ):
       if state.timesteps is None:
         raise ValueError(
             "`state.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"					)

       SCREAMING_SNAKE_CASE				:					int		  =			timestep * jnp.ones(
           sample.shape[0]    ,       )
       SCREAMING_SNAKE_CASE				:					Union[str, Any]		  =			(timestep * (len(state.timesteps					) - 1)).long()

       SCREAMING_SNAKE_CASE				:					Tuple		  =			state.discrete_sigmas[timesteps]
       SCREAMING_SNAKE_CASE				:					Optional[int]		  =			self.get_adjacent_sigma(UpperCAmelCase_    ,       UpperCAmelCase_    ,       UpperCAmelCase_					)
       SCREAMING_SNAKE_CASE				:					str		  =			jnp.zeros_like(UpperCAmelCase_					)
       SCREAMING_SNAKE_CASE				:					Optional[int]		  =			(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
       SCREAMING_SNAKE_CASE				:					Optional[int]		  =			diffusion.flatten()
       SCREAMING_SNAKE_CASE				:					Optional[int]		  =			broadcast_to_shape_from_left(UpperCAmelCase_    ,       sample.shape					)
       SCREAMING_SNAKE_CASE				:					Union[str, Any]		  =			drift - diffusion**2 * model_output

       #  equation 6: sample noise for the diffusion term of
       SCREAMING_SNAKE_CASE				:					List[Any]		  =			random.split(UpperCAmelCase_    ,       num=1					)
       SCREAMING_SNAKE_CASE				:					Union[str, Any]		  =			random.normal(key=UpperCAmelCase_    ,       shape=sample.shape					)
       SCREAMING_SNAKE_CASE				:					Any		  =			sample - drift  # subtract because `dt` is a small negative timestep
       # TODO is the variable diffusion the correct scaling term for the noise?
       SCREAMING_SNAKE_CASE				:					Any		  =			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  _A				( self				:		str    ,       UpperCAmelCase_				:		ScoreSdeVeSchedulerState    ,       UpperCAmelCase_				:		jnp.ndarray    ,       UpperCAmelCase_				:		jnp.ndarray    ,       UpperCAmelCase_				:		random.KeyArray    ,       UpperCAmelCase_				:		bool = True    ,       ):
       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
       SCREAMING_SNAKE_CASE				:					List[Any]		  =			random.split(UpperCAmelCase_    ,       num=1					)
       SCREAMING_SNAKE_CASE				:					Tuple		  =			random.normal(key=UpperCAmelCase_    ,       shape=sample.shape					)

       # compute step size from the model_output, the noise, and the snr
       SCREAMING_SNAKE_CASE				:					Optional[int]		  =			jnp.linalg.norm(UpperCAmelCase_					)
       SCREAMING_SNAKE_CASE				:					Any		  =			jnp.linalg.norm(UpperCAmelCase_					)
       SCREAMING_SNAKE_CASE				:					Tuple		  =			(self.config.snr * noise_norm / grad_norm) ** 2 * 2
       SCREAMING_SNAKE_CASE				:					Tuple		  =			step_size * jnp.ones(sample.shape[0]					)

       # compute corrected sample: model_output term and noise term
       SCREAMING_SNAKE_CASE				:					str		  =			step_size.flatten()
       SCREAMING_SNAKE_CASE				:					str		  =			broadcast_to_shape_from_left(UpperCAmelCase_    ,       sample.shape					)
       SCREAMING_SNAKE_CASE				:					str		  =			sample + step_size * model_output
       SCREAMING_SNAKE_CASE				:					Optional[int]		  =			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				:		Dict					):
       return self.config.num_train_timesteps














import csv
import os

import pytest

from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.csv import CsvDatasetReader, CsvDatasetWriter

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases


def      lowerCamelCase__	(   lowercase  ,   lowercase ):

  """simple docstring"""






  assert isinstance(lowercase  ,   lowercase )
  assert dataset.num_rows == 4
  assert dataset.num_columns == 3
  assert dataset.column_names == ["col_1", "col_2", "col_3"]
  for feature, expected_dtype in expected_features.items():
    assert dataset.features[feature].dtype == expected_dtype


@pytest.mark.parametrize("keep_in_memory"  ,   [False, True] )
def      lowerCamelCase__	(   lowercase  ,   lowercase  ,   lowercase ):

  """simple docstring"""






  SCREAMING_SNAKE_CASE				:					Union[str, Any]		  =			tmp_path / "cache"
  SCREAMING_SNAKE_CASE				:					Dict		  =			{"col_1": "int64", "col_2": "int64", "col_3": "float64"}
  with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
    SCREAMING_SNAKE_CASE				:					Optional[Any]		  =			CsvDatasetReader(lowercase  ,   cache_dir=lowercase  ,   keep_in_memory=lowercase ).read()
  _check_csv_dataset(lowercase  ,   lowercase )


@pytest.mark.parametrize(
    "features"  ,   [
        None,
        {"col_1": "string", "col_2": "int64", "col_3": "float64"},
        {"col_1": "string", "col_2": "string", "col_3": "string"},
        {"col_1": "int32", "col_2": "int32", "col_3": "int32"},
        {"col_1": "float32", "col_2": "float32", "col_3": "float32"},
    ]  ,   )
def      lowerCamelCase__	(   lowercase  ,   lowercase  ,   lowercase ):

  """simple docstring"""






  SCREAMING_SNAKE_CASE				:					Any		  =			tmp_path / "cache"
  # CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
  SCREAMING_SNAKE_CASE				:					Optional[Any]		  =			{"col_1": "int64", "col_2": "int64", "col_3": "float64"}
  SCREAMING_SNAKE_CASE				:					Any		  =			features.copy() if features else default_expected_features
  SCREAMING_SNAKE_CASE				:					List[Any]		  =			(
      Features({feature: Value(lowercase ) for feature, dtype in features.items()} ) if features is not None else None
  )
  SCREAMING_SNAKE_CASE				:					Tuple		  =			CsvDatasetReader(lowercase  ,   features=lowercase  ,   cache_dir=lowercase ).read()
  _check_csv_dataset(lowercase  ,   lowercase )


@pytest.mark.parametrize("split"  ,   [None, NamedSplit("train" ), "train", "test"] )
def      lowerCamelCase__	(   lowercase  ,   lowercase  ,   lowercase ):

  """simple docstring"""






  SCREAMING_SNAKE_CASE				:					Dict		  =			tmp_path / "cache"
  SCREAMING_SNAKE_CASE				:					Dict		  =			{"col_1": "int64", "col_2": "int64", "col_3": "float64"}
  SCREAMING_SNAKE_CASE				:					List[Any]		  =			CsvDatasetReader(lowercase  ,   cache_dir=lowercase  ,   split=lowercase ).read()
  _check_csv_dataset(lowercase  ,   lowercase )
  assert dataset.split == split if split else "train"


@pytest.mark.parametrize("path_type"  ,   [str, list] )
def      lowerCamelCase__	(   lowercase  ,   lowercase  ,   lowercase ):

  """simple docstring"""






  if issubclass(lowercase  ,   lowercase ):
    SCREAMING_SNAKE_CASE				:					Tuple		  =			csv_path
  elif issubclass(lowercase  ,   lowercase ):
    SCREAMING_SNAKE_CASE				:					int		  =			[csv_path]
  SCREAMING_SNAKE_CASE				:					Any		  =			tmp_path / "cache"
  SCREAMING_SNAKE_CASE				:					Optional[int]		  =			{"col_1": "int64", "col_2": "int64", "col_3": "float64"}
  SCREAMING_SNAKE_CASE				:					Tuple		  =			CsvDatasetReader(lowercase  ,   cache_dir=lowercase ).read()
  _check_csv_dataset(lowercase  ,   lowercase )


def      lowerCamelCase__	(   lowercase  ,   lowercase  ,   lowercase=("train",) ):

  """simple docstring"""






  assert isinstance(lowercase  ,   lowercase )
  for split in splits:
    SCREAMING_SNAKE_CASE				:					str		  =			dataset_dict[split]
    assert dataset.num_rows == 4
    assert dataset.num_columns == 3
    assert dataset.column_names == ["col_1", "col_2", "col_3"]
    for feature, expected_dtype in expected_features.items():
      assert dataset.features[feature].dtype == expected_dtype


@pytest.mark.parametrize("keep_in_memory"  ,   [False, True] )
def      lowerCamelCase__	(   lowercase  ,   lowercase  ,   lowercase ):

  """simple docstring"""






  SCREAMING_SNAKE_CASE				:					Union[str, Any]		  =			tmp_path / "cache"
  SCREAMING_SNAKE_CASE				:					Union[str, Any]		  =			{"col_1": "int64", "col_2": "int64", "col_3": "float64"}
  with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
    SCREAMING_SNAKE_CASE				:					Optional[Any]		  =			CsvDatasetReader({"train": csv_path}  ,   cache_dir=lowercase  ,   keep_in_memory=lowercase ).read()
  _check_csv_datasetdict(lowercase  ,   lowercase )


@pytest.mark.parametrize(
    "features"  ,   [
        None,
        {"col_1": "string", "col_2": "int64", "col_3": "float64"},
        {"col_1": "string", "col_2": "string", "col_3": "string"},
        {"col_1": "int32", "col_2": "int32", "col_3": "int32"},
        {"col_1": "float32", "col_2": "float32", "col_3": "float32"},
    ]  ,   )
def      lowerCamelCase__	(   lowercase  ,   lowercase  ,   lowercase ):

  """simple docstring"""






  SCREAMING_SNAKE_CASE				:					int		  =			tmp_path / "cache"
  # CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
  SCREAMING_SNAKE_CASE				:					Any		  =			{"col_1": "int64", "col_2": "int64", "col_3": "float64"}
  SCREAMING_SNAKE_CASE				:					Optional[Any]		  =			features.copy() if features else default_expected_features
  SCREAMING_SNAKE_CASE				:					List[Any]		  =			(
      Features({feature: Value(lowercase ) for feature, dtype in features.items()} ) if features is not None else None
  )
  SCREAMING_SNAKE_CASE				:					Optional[Any]		  =			CsvDatasetReader({"train": csv_path}  ,   features=lowercase  ,   cache_dir=lowercase ).read()
  _check_csv_datasetdict(lowercase  ,   lowercase )


@pytest.mark.parametrize("split"  ,   [None, NamedSplit("train" ), "train", "test"] )
def      lowerCamelCase__	(   lowercase  ,   lowercase  ,   lowercase ):

  """simple docstring"""






  if split:
    SCREAMING_SNAKE_CASE				:					Dict		  =			{split: csv_path}
  else:
    SCREAMING_SNAKE_CASE				:					str		  =			{"train": csv_path, "test": csv_path}
  SCREAMING_SNAKE_CASE				:					Optional[Any]		  =			tmp_path / "cache"
  SCREAMING_SNAKE_CASE				:					Dict		  =			{"col_1": "int64", "col_2": "int64", "col_3": "float64"}
  SCREAMING_SNAKE_CASE				:					Tuple		  =			CsvDatasetReader(lowercase  ,   cache_dir=lowercase ).read()
  _check_csv_datasetdict(lowercase  ,   lowercase  ,   splits=list(path.keys() ) )
  assert all(dataset[split].split == split for split in path.keys() )


def      lowerCamelCase__	(   lowercase ):

  """simple docstring"""






  with open(lowercase  ,   encoding="utf-8" ) as csvfile:
    yield from csv.reader(lowercase )


def      lowerCamelCase__	(   lowercase  ,   lowercase ):

  """simple docstring"""






  SCREAMING_SNAKE_CASE				:					List[Any]		  =			tmp_path / "cache"
  SCREAMING_SNAKE_CASE				:					Optional[int]		  =			os.path.join(lowercase  ,   "tmp.csv" )
  SCREAMING_SNAKE_CASE				:					Optional[int]		  =			CsvDatasetReader({"train": csv_path}  ,   cache_dir=lowercase ).read()
  CsvDatasetWriter(dataset["train"]  ,   lowercase  ,   num_proc=1 ).write()

  SCREAMING_SNAKE_CASE				:					Optional[Any]		  =			iter_csv_file(lowercase )
  SCREAMING_SNAKE_CASE				:					Optional[Any]		  =			iter_csv_file(lowercase )

  for rowa, rowa in zip(lowercase  ,   lowercase ):
    assert rowa == rowa


def      lowerCamelCase__	(   lowercase  ,   lowercase ):

  """simple docstring"""






  SCREAMING_SNAKE_CASE				:					Union[str, Any]		  =			tmp_path / "cache"
  SCREAMING_SNAKE_CASE				:					List[Any]		  =			os.path.join(lowercase  ,   "tmp.csv" )
  SCREAMING_SNAKE_CASE				:					List[str]		  =			CsvDatasetReader({"train": csv_path}  ,   cache_dir=lowercase ).read()
  CsvDatasetWriter(dataset["train"]  ,   lowercase  ,   num_proc=2 ).write()

  SCREAMING_SNAKE_CASE				:					Dict		  =			iter_csv_file(lowercase )
  SCREAMING_SNAKE_CASE				:					Tuple		  =			iter_csv_file(lowercase )

  for rowa, rowa in zip(lowercase  ,   lowercase ):
    assert rowa == rowa


def      lowerCamelCase__	(   lowercase  ,   lowercase ):

  """simple docstring"""






  SCREAMING_SNAKE_CASE				:					Optional[Any]		  =			tmp_path / "cache"
  SCREAMING_SNAKE_CASE				:					Union[str, Any]		  =			os.path.join(lowercase  ,   "tmp.csv" )
  SCREAMING_SNAKE_CASE				:					List[str]		  =			CsvDatasetReader({"train": csv_path}  ,   cache_dir=lowercase ).read()
  with pytest.raises(lowercase ):
    CsvDatasetWriter(dataset["train"]  ,   lowercase  ,   num_proc=0 )






