



import multiprocessing
import os
from typing import BinaryIO, Optional, Union

from .. import Dataset, Features, NamedSplit, config
from ..formatting import query_table
from ..packaged_modules.csv.csv import Csv
from ..utils import logging
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader

class 			__snake_case   ( SCREAMING_SNAKE_CASE):







							'''simple docstring'''






							def __init__(				self	,	a_	,	a_ = None	,	a_ = None	,	a_ = None	,	a_ = False	,	a_ = False	,	a_ = None	,	**a_	,	):
											super().__init__(
											    a_	,	split=a_	,	features=a_	,	cache_dir=a_	,	keep_in_memory=a_	,	streaming=a_	,	num_proc=a_	,	**a_	,	)
											a__					   =		path_or_paths if isinstance(a_	,	a_		) else {self.split: path_or_paths}
											a__					   =		Csv(
											    cache_dir=a_	,	data_files=a_	,	features=a_	,	**a_	,	)







							def _a   (				self		):
											# Build iterable dataset
											if self.streaming:
															a__					   =		self.builder.as_streaming_dataset(split=self.split		)
											# Build regular (map-style) dataset
											else:
															a__					   =		None
															a__					   =		None
															a__					   =		None
															a__					   =		None

															self.builder.download_and_prepare(
															    download_config=a_	,	download_mode=a_	,	verification_mode=a_	,	base_path=a_	,	num_proc=self.num_proc	,	)
															a__					   =		self.builder.as_dataset(
															    split=self.split	,	verification_mode=a_	,	in_memory=self.keep_in_memory		)
											return dataset




class 			__snake_case   :







							'''simple docstring'''






							def __init__(				self	,	a_	,	a_	,	a_ = None	,	a_ = None	,	**a_	,	):
											if num_proc is not None and num_proc <= 0:
															raise ValueError(F'''num_proc {num_proc} must be an integer > 0.'''		)

											a__					   =		dataset
											a__					   =		path_or_buf
											a__					   =		batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
											a__					   =		num_proc
											a__					   =		"""utf-8"""
											a__					   =		to_csv_kwargs


							def _a   (				self		):
											a__					   =		self.to_csv_kwargs.pop("""path_or_buf"""	,	a_		)
											a__					   =		self.to_csv_kwargs.pop("""header"""	,	a_		)
											a__					   =		self.to_csv_kwargs.pop("""index"""	,	a_		)

											if isinstance(self.path_or_buf	,	(str, bytes, os.PathLike)		):
															with open(self.path_or_buf	,	"""wb+"""		) as buffer:
																			a__					   =		self._write(file_obj=a_	,	header=a_	,	index=a_	,	**self.to_csv_kwargs		)
											else:
															a__					   =		self._write(file_obj=self.path_or_buf	,	header=a_	,	index=a_	,	**self.to_csv_kwargs		)
											return written


							def _a   (				self	,	a_		):
											a__   ,      a__   ,      a__   ,      a__					   =		args

											a__					   =		query_table(
											    table=self.dataset.data	,	key=slice(a_	,	offset + self.batch_size		)	,	indices=self.dataset._indices	,	)
											a__					   =		batch.to_pandas().to_csv(
											    path_or_buf=a_	,	header=header if (offset == 0) else False	,	index=a_	,	**a_		)
											return csv_str.encode(self.encoding		)







							def _a   (				self	,	a_	,	a_	,	a_	,	**a_		):
											a__					   =		0

											if self.num_proc is None or self.num_proc == 1:
															for offset in logging.tqdm(
															    range(0	,	len(self.dataset		)	,	self.batch_size		)	,	unit="""ba"""	,	disable=not logging.is_progress_bar_enabled()	,	desc="""Creating CSV from Arrow format"""	,	):
																			a__					   =		self._batch_csv((offset, header, index, to_csv_kwargs)		)
																			written += file_obj.write(a_		)

											else:
															a__   ,      a__					   =		len(self.dataset		), self.batch_size
															with multiprocessing.Pool(self.num_proc		) as pool:
																			for csv_str in logging.tqdm(
																			    pool.imap(
																			        self._batch_csv	,	[(offset, header, index, to_csv_kwargs) for offset in range(0	,	a_	,	a_		)]	,	)	,	total=(num_rows // batch_size) + 1 if num_rows % batch_size else num_rows // batch_size	,	unit="""ba"""	,	disable=not logging.is_progress_bar_enabled()	,	desc="""Creating CSV from Arrow format"""	,	):
																							written += file_obj.write(a_		)

											return written





"""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"""

  lowercase		:	CommonSchedulerState

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

  # running values
  lowercase		:	Optional[jnp.ndarray]   =						None
  @classmethod
  def 							lowercase_     (						cls  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase						)				->					Tuple:
     return cls(common=__UpperCAmelCase  ,					init_noise_sigma=__UpperCAmelCase  ,					timesteps=__UpperCAmelCase  ,					sigmas=__UpperCAmelCase						)



@dataclass
class 	A_       (     _UpperCAmelCase   ):





  """simple docstring"""

  lowercase		:	LMSDiscreteSchedulerState



class 	A_       (     _UpperCAmelCase      ,  _UpperCAmelCase   ):





  """simple docstring"""

  lowercase		:	int   =						[e.name for e in FlaxKarrasDiffusionSchedulers]

  lowercase		:	jnp.dtype
  @property
  def 							lowercase_     (						self						)				->					Union[str, Any]:
     return True
  @register_to_config
  def __init__(						self  ,					__UpperCAmelCase = 10_00  ,					__UpperCAmelCase = 0.0001  ,					__UpperCAmelCase = 0.02  ,					__UpperCAmelCase = "linear"  ,					__UpperCAmelCase = None  ,					__UpperCAmelCase = "epsilon"  ,					__UpperCAmelCase = jnp.floataa  ,					)				->					Union[str, Any]:
     a   :	Dict						=    dtype
  def 							lowercase_     (						self  ,					__UpperCAmelCase = None						)				->					LMSDiscreteSchedulerState:
     if common is None:
        a   :	Dict						=    CommonSchedulerState.create(self						)

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

     # standard deviation of the initial noise distribution
     a   :	Union[str, Any]						=    sigmas.max()

     return LMSDiscreteSchedulerState.create(
         common=__UpperCAmelCase  ,					init_noise_sigma=__UpperCAmelCase  ,					timesteps=__UpperCAmelCase  ,					sigmas=__UpperCAmelCase  ,					)
  def 							lowercase_     (						self  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase						)				->					jnp.ndarray:
     ((a)	, )   :	Optional[int]						=    jnp.where(state.timesteps == timestep  ,					size=1						)
     a   :	Optional[Any]						=    step_index[0]

     a   :	Union[str, Any]						=    state.sigmas[step_index]
     a   :	Optional[int]						=    sample / ((sigma**2 + 1) ** 0.5)
     return sample
  def 							lowercase_     (						self  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase						)				->					Optional[int]:

     def lms_derivative(__UpperCAmelCase						):
        a   :	Optional[Any]						=    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   :	Union[str, Any]						=    integrate.quad(__UpperCAmelCase  ,					state.sigmas[t]  ,					state.sigmas[t + 1]  ,					epsrel=1E-4						)[0]

     return integrated_coeff
  def 							lowercase_     (						self  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase = ()						)				->					LMSDiscreteSchedulerState:
     a   :	Optional[int]						=    jnp.linspace(self.config.num_train_timesteps - 1  ,					0  ,					__UpperCAmelCase  ,					dtype=self.dtype						)

     a   :	Dict						=    jnp.floor(__UpperCAmelCase						).astype(jnp.intaa						)
     a   :	str						=    jnp.ceil(__UpperCAmelCase						).astype(jnp.intaa						)

     a   :	List[Any]						=    jnp.mod(__UpperCAmelCase  ,					1.0						)

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

     a   :	Any						=    timesteps.astype(jnp.intaa						)

     # initial running values
     a   :	Optional[int]						=    jnp.zeros((0,) + shape  ,					dtype=self.dtype						)

     return state.replace(
         timesteps=__UpperCAmelCase  ,					sigmas=__UpperCAmelCase  ,					num_inference_steps=__UpperCAmelCase  ,					derivatives=__UpperCAmelCase  ,					)
  def 							lowercase_     (						self  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase = 4  ,					__UpperCAmelCase = True  ,					)				->					Union[FlaxLMSSchedulerOutput, Tuple]:
     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   :	Dict						=    state.sigmas[timestep]

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

     # 3. Compute linear multistep coefficients
     a   :	Any						=    min(timestep + 1  ,					__UpperCAmelCase						)
     a   :	Dict						=    [self.get_lms_coefficient(__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase						) for curr_order in range(__UpperCAmelCase						)]

     # 4. Compute previous sample based on the derivatives path
     a   :	int						=    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 							lowercase_     (						self  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					)				->					jnp.ndarray:
     a   :	Dict						=    state.sigmas[timesteps].flatten()
     a   :	Union[str, Any]						=    broadcast_to_shape_from_left(__UpperCAmelCase  ,					noise.shape						)

     a   :	List[Any]						=    original_samples + noise * sigma

     return noisy_samples






  def __len__(						self						)				->					List[Any]:
     return self.config.num_train_timesteps





