

"""simple docstring"""



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    			=       42

    # setable values
    UpperCAmelCase    			=       42
    UpperCAmelCase    			=       42
    UpperCAmelCase    			=       42
    UpperCAmelCase    			=       None

    # running values
    UpperCAmelCase    			=       None

    @classmethod
    def 					UpperCamelCase						(  cls:			str     ,    UpperCamelCase:			CommonSchedulerState     ,    UpperCamelCase:			jnp.ndarray     ,    UpperCamelCase:			jnp.ndarray     ,    UpperCamelCase:			jnp.ndarray							):






         """simple docstring"""


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



@dataclass
class        a    (							_lowerCamelCase					):



    """simple docstring"""



    UpperCAmelCase    			=       42



class        a    (							_lowerCamelCase,						_lowerCamelCase					):



    """simple docstring"""



    UpperCAmelCase    			=       [e.name for e in FlaxKarrasDiffusionSchedulers]

    UpperCAmelCase    			=       42

    @property
    def 					UpperCamelCase						(  self:			Union[str, Any]							):






         """simple docstring"""


         return True

    @register_to_config
    def __init__(  self:			List[Any]     ,    UpperCamelCase:			int = 10_00     ,    UpperCamelCase:			float = 0.0_001     ,    UpperCamelCase:			float = 0.02     ,    UpperCamelCase:			str = "linear"     ,    UpperCamelCase:			Optional[jnp.ndarray] = None     ,    UpperCamelCase:			str = "epsilon"     ,    UpperCamelCase:			jnp.dtype = jnp.floataa     ,    ):






         """simple docstring"""


         A__				      =	dtype

    def 					UpperCamelCase						(  self:			Tuple     ,    UpperCamelCase:			Optional[CommonSchedulerState] = None							):






         """simple docstring"""


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

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

         # standard deviation of the initial noise distribution
         A__				      =	sigmas.max()

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

    def 					UpperCamelCase						(  self:			List[str]     ,    UpperCamelCase:			LMSDiscreteSchedulerState     ,    UpperCamelCase:			jnp.ndarray     ,    UpperCamelCase:			int							):






         """simple docstring"""


         ((A__)      ,					)				      =	jnp.where(state.timesteps == timestep     ,    size=1							)
         A__				      =	step_index[0]

         A__				      =	state.sigmas[step_index]
         A__				      =	sample / ((sigma**2 + 1) ** 0.5)
         return sample

    def 					UpperCamelCase						(  self:			Union[str, Any]     ,    UpperCamelCase:			LMSDiscreteSchedulerState     ,    UpperCamelCase:			List[str]     ,    UpperCamelCase:			Union[str, Any]     ,    UpperCamelCase:			Optional[int]							):






         """simple docstring"""


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

         A__				      =	integrate.quad(UpperCamelCase     ,    state.sigmas[t]     ,    state.sigmas[t + 1]     ,    epsrel=1e-4							)[0]

         return integrated_coeff

    def 					UpperCamelCase						(  self:			int     ,    UpperCamelCase:			LMSDiscreteSchedulerState     ,    UpperCamelCase:			int     ,    UpperCamelCase:			Tuple = ()							):






         """simple docstring"""


         A__				      =	jnp.linspace(self.config.num_train_timesteps - 1     ,    0     ,    UpperCamelCase     ,    dtype=self.dtype							)

         A__				      =	jnp.floor(UpperCamelCase							).astype(jnp.intaa							)
         A__				      =	jnp.ceil(UpperCamelCase							).astype(jnp.intaa							)

         A__				      =	jnp.mod(UpperCamelCase     ,    1.0							)

         A__				      =	((1 - state.common.alphas_cumprod) / state.common.alphas_cumprod) ** 0.5
         A__				      =	(1 - frac) * sigmas[low_idx] + frac * sigmas[high_idx]
         A__				      =	jnp.concatenate([sigmas, jnp.array([0.0]     ,    dtype=self.dtype							)]							)

         A__				      =	timesteps.astype(jnp.intaa							)

         # initial running values
         A__				      =	jnp.zeros((0,) + shape     ,    dtype=self.dtype							)

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

    def 					UpperCamelCase						(  self:			Any     ,    UpperCamelCase:			LMSDiscreteSchedulerState     ,    UpperCamelCase:			jnp.ndarray     ,    UpperCamelCase:			int     ,    UpperCamelCase:			jnp.ndarray     ,    UpperCamelCase:			int = 4     ,    UpperCamelCase:			bool = 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"""							)

         A__				      =	state.sigmas[timestep]

         # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
         if self.config.prediction_type == "epsilon":
              A__				      =	sample - sigma * model_output
         elif self.config.prediction_type == "v_prediction":
              # * c_out + input * c_skip
              A__				      =	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
         A__				      =	(sample - pred_original_sample) / sigma
         A__				      =	state.replace(derivatives=jnp.append(state.derivatives     ,    UpperCamelCase							)							)
         if len(state.derivatives							) > order:
              A__				      =	state.replace(derivatives=jnp.delete(state.derivatives     ,    0							)							)

         # 3. Compute linear multistep coefficients
         A__				      =	min(timestep + 1     ,    UpperCamelCase							)
         A__				      =	[self.get_lms_coefficient(UpperCamelCase     ,    UpperCamelCase     ,    UpperCamelCase     ,    UpperCamelCase							) for curr_order in range(UpperCamelCase							)]

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

         if not return_dict:
              return (prev_sample, state)

         return FlaxLMSSchedulerOutput(prev_sample=UpperCamelCase     ,    state=UpperCamelCase							)

    def 					UpperCamelCase						(  self:			Optional[Any]     ,    UpperCamelCase:			LMSDiscreteSchedulerState     ,    UpperCamelCase:			jnp.ndarray     ,    UpperCamelCase:			jnp.ndarray     ,    UpperCamelCase:			jnp.ndarray     ,    ):






         """simple docstring"""


         A__				      =	state.sigmas[timesteps].flatten()
         A__				      =	broadcast_to_shape_from_left(UpperCamelCase     ,    noise.shape							)

         A__				      =	original_samples + noise * sigma

         return noisy_samples





    def __len__(  self:			Dict							):






         """simple docstring"""


         return self.config.num_train_timesteps









"""simple docstring"""



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





@pytest.mark.parametrize("""split"""       , [None, NamedSplit("""train"""      ), """train""", """test"""]      )
def 			_snake_case  (     UpperCAmelCase_	:       Optional[int]       , UpperCAmelCase_	:       Optional[int]       , UpperCAmelCase_	:       int      ):
     A__				      =	tmp_path / """cache"""
     A__				      =	{"""col_1""": """int64""", """col_2""": """int64""", """col_3""": """float64"""}
     A__				      =	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 			_snake_case  (     UpperCAmelCase_	:       Tuple       , UpperCAmelCase_	:       Tuple       , UpperCAmelCase_	:       Dict      ):
     if issubclass(UpperCAmelCase_       , UpperCAmelCase_      ):
          A__				      =	csv_path
     elif issubclass(UpperCAmelCase_       , UpperCAmelCase_      ):
          A__				      =	[csv_path]
     A__				      =	tmp_path / """cache"""
     A__				      =	{"""col_1""": """int64""", """col_2""": """int64""", """col_3""": """float64"""}
     A__				      =	CsvDatasetReader(UpperCAmelCase_       , cache_dir=UpperCAmelCase_      ).read()
     _check_csv_dataset(UpperCAmelCase_       , UpperCAmelCase_      )





def 			_snake_case  (     UpperCAmelCase_	:       Optional[Any]       , UpperCAmelCase_	:       Dict       , UpperCAmelCase_	:       int=("train",)      ):
     assert isinstance(UpperCAmelCase_       , UpperCAmelCase_      )
     for split in splits:
          A__				      =	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 			_snake_case  (     UpperCAmelCase_	:       int       , UpperCAmelCase_	:       Tuple       , UpperCAmelCase_	:       List[str]      ):
     A__				      =	tmp_path / """cache"""
     A__				      =	{"""col_1""": """int64""", """col_2""": """int64""", """col_3""": """float64"""}
     with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
          A__				      =	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 			_snake_case  (     UpperCAmelCase_	:       Dict       , UpperCAmelCase_	:       Any       , UpperCAmelCase_	:       Optional[Any]      ):
     A__				      =	tmp_path / """cache"""
     # CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
     A__				      =	{"""col_1""": """int64""", """col_2""": """int64""", """col_3""": """float64"""}
     A__				      =	features.copy() if features else default_expected_features
     A__				      =	(
         Features({feature: Value(UpperCAmelCase_      ) for feature, dtype in features.items()}      ) if features is not None else None
     )
     A__				      =	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 			_snake_case  (     UpperCAmelCase_	:       Any       , UpperCAmelCase_	:       List[str]       , UpperCAmelCase_	:       Optional[int]      ):
     if split:
          A__				      =	{split: csv_path}
     else:
          A__				      =	{"""train""": csv_path, """test""": csv_path}
     A__				      =	tmp_path / """cache"""
     A__				      =	{"""col_1""": """int64""", """col_2""": """int64""", """col_3""": """float64"""}
     A__				      =	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 			_snake_case  (     UpperCAmelCase_	:       Optional[Any]      ):
     with open(UpperCAmelCase_       , encoding="""utf-8"""      ) as csvfile:
          yield from csv.reader(UpperCAmelCase_      )





def 			_snake_case  (     UpperCAmelCase_	:       str       , UpperCAmelCase_	:       int      ):
     A__				      =	tmp_path / """cache"""
     A__				      =	os.path.join(UpperCAmelCase_       , """tmp.csv"""      )
     A__				      =	CsvDatasetReader({"""train""": csv_path}       , cache_dir=UpperCAmelCase_      ).read()
     CsvDatasetWriter(dataset["""train"""]       , UpperCAmelCase_       , num_proc=1      ).write()

     A__				      =	iter_csv_file(UpperCAmelCase_      )
     A__				      =	iter_csv_file(UpperCAmelCase_      )

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





def 			_snake_case  (     UpperCAmelCase_	:       Any       , UpperCAmelCase_	:       List[Any]      ):
     A__				      =	tmp_path / """cache"""
     A__				      =	os.path.join(UpperCAmelCase_       , """tmp.csv"""      )
     A__				      =	CsvDatasetReader({"""train""": csv_path}       , cache_dir=UpperCAmelCase_      ).read()
     CsvDatasetWriter(dataset["""train"""]       , UpperCAmelCase_       , num_proc=2      ).write()

     A__				      =	iter_csv_file(UpperCAmelCase_      )
     A__				      =	iter_csv_file(UpperCAmelCase_      )

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





def 			_snake_case  (     UpperCAmelCase_	:       Any       , UpperCAmelCase_	:       Any      ):
     A__				      =	tmp_path / """cache"""
     A__				      =	os.path.join(UpperCAmelCase_       , """tmp.csv"""      )
     A__				      =	CsvDatasetReader({"""train""": csv_path}       , cache_dir=UpperCAmelCase_      ).read()
     with pytest.raises(UpperCAmelCase_      ):
          CsvDatasetWriter(dataset["""train"""]       , UpperCAmelCase_       , num_proc=0      )






