




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__				:
	lowerCamelCase__		: CommonSchedulerState

	# setable values
	lowerCamelCase__		: jnp.ndarray
	lowerCamelCase__		: jnp.ndarray
	lowerCamelCase__		: jnp.ndarray
	lowerCamelCase__		: Optional[int]				    =None

	# running values
	lowerCamelCase__		: Optional[jnp.ndarray]				    =None






	@classmethod
	def  lowercase      (	cls				,     lowerCamelCase				,     lowerCamelCase				,     lowerCamelCase				,     lowerCamelCase						)      ->   int:




						"""simple docstring"""






						return cls(common=lowerCamelCase				,     init_noise_sigma=lowerCamelCase				,     timesteps=lowerCamelCase				,     sigmas=lowerCamelCase						)





@dataclass
class 		A__				(   __SCREAMING_SNAKE_CASE ):
	lowerCamelCase__		: LMSDiscreteSchedulerState





class 		A__				(   __SCREAMING_SNAKE_CASE		,     __SCREAMING_SNAKE_CASE ):
	lowerCamelCase__		: Optional[Any]				    =[e.name for e in FlaxKarrasDiffusionSchedulers]

	lowerCamelCase__		: jnp.dtype






	@property
	def  lowercase      (	self						)      ->   Union[str, Any]:




						"""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 = "epsilon"				,     lowerCamelCase = jnp.floataa				,     )      ->   int:




						"""simple docstring"""






						__magic_name__					:							Optional[Any]		   =							dtype






	def  lowercase      (	self				,     lowerCamelCase = None						)      ->   LMSDiscreteSchedulerState:




						"""simple docstring"""






						if common is None:
											__magic_name__					:							Union[str, Any]		   =							CommonSchedulerState.create(self						)

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

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

						return LMSDiscreteSchedulerState.create(
						    common=lowerCamelCase				,     init_noise_sigma=lowerCamelCase				,     timesteps=lowerCamelCase				,     sigmas=lowerCamelCase				,     )






	def  lowercase      (	self				,     lowerCamelCase				,     lowerCamelCase				,     lowerCamelCase						)      ->   jnp.ndarray:




						"""simple docstring"""






						((__magic_name__)   , )					:							Optional[Any]		   =							jnp.where(state.timesteps == timestep				,     size=1						)
						__magic_name__					:							List[str]		   =							step_index[0]

						__magic_name__					:							Any		   =							state.sigmas[step_index]
						__magic_name__					:							List[Any]		   =							sample / ((sigma**2 + 1) ** 0.5)
						return sample






	def  lowercase      (	self				,     lowerCamelCase				,     lowerCamelCase				,     lowerCamelCase				,     lowerCamelCase						)      ->   Dict:




						"""simple docstring"""






						def lms_derivative(lowerCamelCase						):
											__magic_name__					:							List[Any]		   =							1.0
											for k in range(lowerCamelCase						):
																if current_order == k:
																					continue
																prod *= (tau - state.sigmas[t - k]) / (state.sigmas[t - current_order] - state.sigmas[t - k])
											return prod

						__magic_name__					:							Any		   =							integrate.quad(lowerCamelCase				,     state.sigmas[t]				,     state.sigmas[t + 1]				,     epsrel=1e-4						)[0]

						return integrated_coeff






	def  lowercase      (	self				,     lowerCamelCase				,     lowerCamelCase				,     lowerCamelCase = ()						)      ->   LMSDiscreteSchedulerState:




						"""simple docstring"""






						__magic_name__					:							Dict		   =							jnp.linspace(self.config.num_train_timesteps - 1				,     0				,     lowerCamelCase				,     dtype=self.dtype						)

						__magic_name__					:							Optional[Any]		   =							jnp.floor(lowerCamelCase						).astype(jnp.intaa						)
						__magic_name__					:							Any		   =							jnp.ceil(lowerCamelCase						).astype(jnp.intaa						)

						__magic_name__					:							Dict		   =							jnp.mod(lowerCamelCase				,     1.0						)

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

						__magic_name__					:							Tuple		   =							timesteps.astype(jnp.intaa						)

						# initial running values
						__magic_name__					:							List[str]		   =							jnp.zeros((0,) + shape				,     dtype=self.dtype						)

						return state.replace(
						    timesteps=lowerCamelCase				,     sigmas=lowerCamelCase				,     num_inference_steps=lowerCamelCase				,     derivatives=lowerCamelCase				,     )






	def  lowercase      (	self				,     lowerCamelCase				,     lowerCamelCase				,     lowerCamelCase				,     lowerCamelCase				,     lowerCamelCase = 4				,     lowerCamelCase = True				,     )      ->   Union[FlaxLMSSchedulerOutput, Tuple]:




						"""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'''						)

						__magic_name__					:							Tuple		   =							state.sigmas[timestep]

						# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
						if self.config.prediction_type == "epsilon":
											__magic_name__					:							Optional[int]		   =							sample - sigma * model_output
						elif self.config.prediction_type == "v_prediction":
											# * c_out + input * c_skip
											__magic_name__					:							int		   =							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
						__magic_name__					:							Tuple		   =							(sample - pred_original_sample) / sigma
						__magic_name__					:							Any		   =							state.replace(derivatives=jnp.append(state.derivatives				,     lowerCamelCase						)						)
						if len(state.derivatives						) > order:
											__magic_name__					:							List[str]		   =							state.replace(derivatives=jnp.delete(state.derivatives				,     0						)						)

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

						# 4. Compute previous sample based on the derivatives path
						__magic_name__					:							Optional[int]		   =							sample + sum(
						    coeff * derivative for coeff, derivative in zip(lowerCamelCase				,     reversed(state.derivatives						)						)						)

						if not return_dict:
											return (prev_sample, state)

						return FlaxLMSSchedulerOutput(prev_sample=lowerCamelCase				,     state=lowerCamelCase						)






	def  lowercase      (	self				,     lowerCamelCase				,     lowerCamelCase				,     lowerCamelCase				,     lowerCamelCase				,     )      ->   jnp.ndarray:




						"""simple docstring"""






						__magic_name__					:							int		   =							state.sigmas[timesteps].flatten()
						__magic_name__					:							Any		   =							broadcast_to_shape_from_left(lowerCamelCase				,     noise.shape						)

						__magic_name__					:							Optional[Any]		   =							original_samples + noise * sigma

						return noisy_samples






	def __len__(	self						)      ->   Any:




						"""simple docstring"""






						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  (	UpperCAmelCase,	UpperCAmelCase       )  ->str:






					"""simple docstring"""






					assert isinstance(UpperCAmelCase,	UpperCAmelCase       )
					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  (	UpperCAmelCase,	UpperCAmelCase,	UpperCAmelCase       )  ->Any:






					"""simple docstring"""






					__magic_name__					:							Dict		   =							tmp_path / '''cache'''
					__magic_name__					:							Tuple		   =							{'''col_1''': '''int64''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
					with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
										__magic_name__					:							str		   =							CsvDatasetReader(UpperCAmelCase,	cache_dir=UpperCAmelCase,	keep_in_memory=UpperCAmelCase       ).read()
					_check_csv_dataset(UpperCAmelCase,	UpperCAmelCase       )







@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  (	UpperCAmelCase,	UpperCAmelCase,	UpperCAmelCase       )  ->Tuple:






					"""simple docstring"""






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







@pytest.mark.parametrize('''split''',	[None, NamedSplit('''train'''       ), '''train''', '''test''']       )
def  lowerCAmelCase  (	UpperCAmelCase,	UpperCAmelCase,	UpperCAmelCase       )  ->List[Any]:






					"""simple docstring"""






					__magic_name__					:							str		   =							tmp_path / '''cache'''
					__magic_name__					:							List[str]		   =							{'''col_1''': '''int64''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
					__magic_name__					:							int		   =							CsvDatasetReader(UpperCAmelCase,	cache_dir=UpperCAmelCase,	split=UpperCAmelCase       ).read()
					_check_csv_dataset(UpperCAmelCase,	UpperCAmelCase       )
					assert dataset.split == split if split else "train"







@pytest.mark.parametrize('''path_type''',	[str, list]       )
def  lowerCAmelCase  (	UpperCAmelCase,	UpperCAmelCase,	UpperCAmelCase       )  ->str:






					"""simple docstring"""






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







def  lowerCAmelCase  (	UpperCAmelCase,	UpperCAmelCase,	UpperCAmelCase=("train",)       )  ->Tuple:






					"""simple docstring"""






					assert isinstance(UpperCAmelCase,	UpperCAmelCase       )
					for split in splits:
										__magic_name__					:							int		   =							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  (	UpperCAmelCase,	UpperCAmelCase,	UpperCAmelCase       )  ->int:






					"""simple docstring"""






					__magic_name__					:							Tuple		   =							tmp_path / '''cache'''
					__magic_name__					:							int		   =							{'''col_1''': '''int64''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
					with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
										__magic_name__					:							Optional[Any]		   =							CsvDatasetReader({'''train''': csv_path},	cache_dir=UpperCAmelCase,	keep_in_memory=UpperCAmelCase       ).read()
					_check_csv_datasetdict(UpperCAmelCase,	UpperCAmelCase       )







@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  (	UpperCAmelCase,	UpperCAmelCase,	UpperCAmelCase       )  ->List[str]:






					"""simple docstring"""






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







@pytest.mark.parametrize('''split''',	[None, NamedSplit('''train'''       ), '''train''', '''test''']       )
def  lowerCAmelCase  (	UpperCAmelCase,	UpperCAmelCase,	UpperCAmelCase       )  ->List[str]:






					"""simple docstring"""






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







def  lowerCAmelCase  (	UpperCAmelCase       )  ->int:






					"""simple docstring"""






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







def  lowerCAmelCase  (	UpperCAmelCase,	UpperCAmelCase       )  ->int:






					"""simple docstring"""






					__magic_name__					:							Dict		   =							tmp_path / '''cache'''
					__magic_name__					:							List[Any]		   =							os.path.join(UpperCAmelCase,	'''tmp.csv'''       )
					__magic_name__					:							int		   =							CsvDatasetReader({'''train''': csv_path},	cache_dir=UpperCAmelCase       ).read()
					CsvDatasetWriter(dataset['''train'''],	UpperCAmelCase,	num_proc=1       ).write()

					__magic_name__					:							Optional[int]		   =							iter_csv_file(UpperCAmelCase       )
					__magic_name__					:							Any		   =							iter_csv_file(UpperCAmelCase       )

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







def  lowerCAmelCase  (	UpperCAmelCase,	UpperCAmelCase       )  ->Union[str, Any]:






					"""simple docstring"""






					__magic_name__					:							Optional[int]		   =							tmp_path / '''cache'''
					__magic_name__					:							Optional[Any]		   =							os.path.join(UpperCAmelCase,	'''tmp.csv'''       )
					__magic_name__					:							List[Any]		   =							CsvDatasetReader({'''train''': csv_path},	cache_dir=UpperCAmelCase       ).read()
					CsvDatasetWriter(dataset['''train'''],	UpperCAmelCase,	num_proc=2       ).write()

					__magic_name__					:							Tuple		   =							iter_csv_file(UpperCAmelCase       )
					__magic_name__					:							List[str]		   =							iter_csv_file(UpperCAmelCase       )

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







def  lowerCAmelCase  (	UpperCAmelCase,	UpperCAmelCase       )  ->int:






					"""simple docstring"""






					__magic_name__					:							Optional[int]		   =							tmp_path / '''cache'''
					__magic_name__					:							Any		   =							os.path.join(UpperCAmelCase,	'''tmp.csv'''       )
					__magic_name__					:							Any		   =							CsvDatasetReader({'''train''': csv_path},	cache_dir=UpperCAmelCase       ).read()
					with pytest.raises(UpperCAmelCase       ):
										CsvDatasetWriter(dataset['''train'''],	UpperCAmelCase,	num_proc=0       )



