
'''simple docstring'''

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

import flax
import jax.numpy as jnp

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import (
    CommonSchedulerState,
    FlaxKarrasDiffusionSchedulers,
    FlaxSchedulerMixin,
    FlaxSchedulerOutput,
    add_noise_common,
    get_velocity_common,
)







@flax.struct.dataclass
class   UpperCamelCase__   :


		"""simple docstring"""

		SCREAMING_SNAKE_CASE__  :     Any       =							42
		SCREAMING_SNAKE_CASE__  :     Dict       =							42

		# setable values
		SCREAMING_SNAKE_CASE__  :     Optional[int]       =							42
		SCREAMING_SNAKE_CASE__  :     Optional[Any]       =							42
		SCREAMING_SNAKE_CASE__  :     Dict       =							None







		@classmethod
		def 				A_	(			cls   ,      snake_case   ,      snake_case   ,      snake_case   ,      snake_case   ,      ):


							'''simple docstring'''

							return cls(
							    common=snake_case_   ,      final_alpha_cumprod=snake_case_   ,      init_noise_sigma=snake_case_   ,      timesteps=snake_case_   ,      )







@dataclass
class   UpperCamelCase__   (       A__       ):


		"""simple docstring"""

		SCREAMING_SNAKE_CASE__  :     str       =							42







class   UpperCamelCase__   (       A__							,							A__       ):


		"""simple docstring"""

		SCREAMING_SNAKE_CASE__  :     str       =							[e.name for e in FlaxKarrasDiffusionSchedulers]

		SCREAMING_SNAKE_CASE__  :     Any       =							42







		@property
		def 				A_	(			self      ):


							'''simple docstring'''

							return True







		@register_to_config
		def __init__(			self   ,      snake_case = 1_0_0_0   ,      snake_case = 0.0001   ,      snake_case = 0.02   ,      snake_case = "linear"   ,      snake_case = None   ,      snake_case = True   ,      snake_case = 0   ,      snake_case = "epsilon"   ,      snake_case = jnp.floataa   ,      ):


							'''simple docstring'''

							UpperCAmelCase		:							Dict        =       dtype







		def 				A_	(			self   ,      snake_case = None      ):


							'''simple docstring'''

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

							# At every step in ddim, we are looking into the previous alphas_cumprod
							# For the final step, there is no previous alphas_cumprod because we are already at 0
							# `set_alpha_to_one` decides whether we set this parameter simply to one or
							# whether we use the final alpha of the "non-previous" one.
							UpperCAmelCase		:							int        =       (
							    jnp.array(1.0   ,      dtype=self.dtype      ) if self.config.set_alpha_to_one else common.alphas_cumprod[0]
							)

							# standard deviation of the initial noise distribution
							UpperCAmelCase		:							Tuple        =       jnp.array(1.0   ,      dtype=self.dtype      )

							UpperCAmelCase		:							Tuple        =       jnp.arange(0   ,      self.config.num_train_timesteps      ).round()[::-1]

							return DDIMSchedulerState.create(
							    common=snake_case_   ,      final_alpha_cumprod=snake_case_   ,      init_noise_sigma=snake_case_   ,      timesteps=snake_case_   ,      )







		def 				A_	(			self   ,      snake_case   ,      snake_case   ,      snake_case = None      ):


							'''simple docstring'''

							return sample







		def 				A_	(			self   ,      snake_case   ,      snake_case   ,      snake_case = ()      ):


							'''simple docstring'''

							UpperCAmelCase		:							List[str]        =       self.config.num_train_timesteps // num_inference_steps
							# creates integer timesteps by multiplying by ratio
							# rounding to avoid issues when num_inference_step is power of 3
							UpperCAmelCase		:							List[Any]        =       (jnp.arange(0   ,      snake_case_      ) * step_ratio).round()[::-1] + self.config.steps_offset

							return state.replace(
							    num_inference_steps=snake_case_   ,      timesteps=snake_case_   ,      )







		def 				A_	(			self   ,      snake_case   ,      snake_case   ,      snake_case      ):


							'''simple docstring'''

							UpperCAmelCase		:							str        =       state.common.alphas_cumprod[timestep]
							UpperCAmelCase		:							List[str]        =       jnp.where(
							    prev_timestep >= 0   ,      state.common.alphas_cumprod[prev_timestep]   ,      state.final_alpha_cumprod      )
							UpperCAmelCase		:							str        =       1 - alpha_prod_t
							UpperCAmelCase		:							List[str]        =       1 - alpha_prod_t_prev

							UpperCAmelCase		:							List[str]        =       (beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev)

							return variance







		def 				A_	(			self   ,      snake_case   ,      snake_case   ,      snake_case   ,      snake_case   ,      snake_case = 0.0   ,      snake_case = 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"      )

							# See formulas (12) and (16) of DDIM paper https://arxiv.org/pdf/2010.02502.pdf
							# Ideally, read DDIM paper in-detail understanding

							# Notation (<variable name> -> <name in paper>
							# - pred_noise_t -> e_theta(x_t, t)
							# - pred_original_sample -> f_theta(x_t, t) or x_0
							# - std_dev_t -> sigma_t
							# - eta -> η
							# - pred_sample_direction -> "direction pointing to x_t"
							# - pred_prev_sample -> "x_t-1"

							# 1. get previous step value (=t-1)
							UpperCAmelCase		:							str        =       timestep - self.config.num_train_timesteps // state.num_inference_steps

							UpperCAmelCase		:							Tuple        =       state.common.alphas_cumprod
							UpperCAmelCase		:							Optional[Any]        =       state.final_alpha_cumprod

							# 2. compute alphas, betas
							UpperCAmelCase		:							Tuple        =       alphas_cumprod[timestep]
							UpperCAmelCase		:							Optional[Any]        =       jnp.where(prev_timestep >= 0   ,      alphas_cumprod[prev_timestep]   ,      snake_case_      )

							UpperCAmelCase		:							Any        =       1 - alpha_prod_t

							# 3. compute predicted original sample from predicted noise also called
							# "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
							if self.config.prediction_type == "epsilon":
												UpperCAmelCase		:							Tuple        =       (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
												UpperCAmelCase		:							List[Any]        =       model_output
							elif self.config.prediction_type == "sample":
												UpperCAmelCase		:							Optional[int]        =       model_output
												UpperCAmelCase		:							Tuple        =       (sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5
							elif self.config.prediction_type == "v_prediction":
												UpperCAmelCase		:							List[Any]        =       (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
												UpperCAmelCase		:							List[Any]        =       (alpha_prod_t**0.5) * model_output + (beta_prod_t**0.5) * sample
							else:
												raise ValueError(
												    f"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`, or"
												    " `v_prediction`"      )

							# 4. compute variance: "sigma_t(η)" -> see formula (16)
							# σ_t = sqrt((1 − α_t−1)/(1 − α_t)) * sqrt(1 − α_t/α_t−1)
							UpperCAmelCase		:							Union[str, Any]        =       self._get_variance(snake_case_   ,      snake_case_   ,      snake_case_      )
							UpperCAmelCase		:							Union[str, Any]        =       eta * variance ** 0.5

							# 5. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
							UpperCAmelCase		:							Tuple        =       (1 - alpha_prod_t_prev - std_dev_t**2) ** 0.5 * pred_epsilon

							# 6. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
							UpperCAmelCase		:							List[Any]        =       alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction

							if not return_dict:
												return (prev_sample, state)

							return FlaxDDIMSchedulerOutput(prev_sample=snake_case_   ,      state=snake_case_      )







		def 				A_	(			self   ,      snake_case   ,      snake_case   ,      snake_case   ,      snake_case   ,      ):


							'''simple docstring'''

							return add_noise_common(state.common   ,      snake_case_   ,      snake_case_   ,      snake_case_      )







		def 				A_	(			self   ,      snake_case   ,      snake_case   ,      snake_case   ,      snake_case   ,      ):


							'''simple docstring'''

							return get_velocity_common(state.common   ,      snake_case_   ,      snake_case_   ,      snake_case_      )







		def __len__(			self      ):


							'''simple docstring'''

							return self.config.num_train_timesteps



'''simple docstring'''

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   UpperCamelCase__   (       lowercase__       ):


		"""simple docstring"""

		def __init__(			self   ,      snake_case   ,      snake_case = None   ,      snake_case = None   ,      snake_case = None   ,      snake_case = False   ,      snake_case = False   ,      snake_case = None   ,      **snake_case   ,      ):


							'''simple docstring'''

							super().__init__(
							    snake_case   ,      split=snake_case   ,      features=snake_case   ,      cache_dir=snake_case   ,      keep_in_memory=snake_case   ,      streaming=snake_case   ,      num_proc=snake_case   ,      **snake_case   ,      )
							UpperCAmelCase		:							str        =       path_or_paths if isinstance(snake_case   ,      snake_case      ) else {self.split: path_or_paths}
							UpperCAmelCase		:							int        =       Csv(
							    cache_dir=snake_case   ,      data_files=snake_case   ,      features=snake_case   ,      **snake_case   ,      )







		def 				A_	(			self      ):


							'''simple docstring'''

							if self.streaming:
												UpperCAmelCase		:							Optional[Any]        =       self.builder.as_streaming_dataset(split=self.split      )
							# Build regular (map-style) dataset
							else:
												UpperCAmelCase		:							Union[str, Any]        =       None
												UpperCAmelCase		:							str        =       None
												UpperCAmelCase		:							Union[str, Any]        =       None
												UpperCAmelCase		:							int        =       None

												self.builder.download_and_prepare(
												    download_config=snake_case   ,      download_mode=snake_case   ,      verification_mode=snake_case   ,      base_path=snake_case   ,      num_proc=self.num_proc   ,      )
												UpperCAmelCase		:							Tuple        =       self.builder.as_dataset(
												    split=self.split   ,      verification_mode=snake_case   ,      in_memory=self.keep_in_memory      )
							return dataset







class   UpperCamelCase__   :


		"""simple docstring"""

		def __init__(			self   ,      snake_case   ,      snake_case   ,      snake_case = None   ,      snake_case = None   ,      **snake_case   ,      ):


							'''simple docstring'''

							if num_proc is not None and num_proc <= 0:
												raise ValueError(f"num_proc {num_proc} must be an integer > 0."      )

							UpperCAmelCase		:							Tuple        =       dataset
							UpperCAmelCase		:							Dict        =       path_or_buf
							UpperCAmelCase		:							List[Any]        =       batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
							UpperCAmelCase		:							str        =       num_proc
							UpperCAmelCase		:							List[Any]        =       "utf-8"
							UpperCAmelCase		:							Tuple        =       to_csv_kwargs







		def 				A_	(			self      ):


							'''simple docstring'''

							UpperCAmelCase		:							List[str]        =       self.to_csv_kwargs.pop("path_or_buf"   ,      snake_case      )
							UpperCAmelCase		:							Any        =       self.to_csv_kwargs.pop("header"   ,      snake_case      )
							UpperCAmelCase		:							Dict        =       self.to_csv_kwargs.pop("index"   ,      snake_case      )

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







		def 				A_	(			self   ,      snake_case      ):


							'''simple docstring'''

							UpperCAmelCase ,			UpperCAmelCase ,			UpperCAmelCase ,			UpperCAmelCase		:							Optional[int]        =       args

							UpperCAmelCase		:							List[str]        =       query_table(
							    table=self.dataset.data   ,      key=slice(snake_case   ,      offset + self.batch_size      )   ,      indices=self.dataset._indices   ,      )
							UpperCAmelCase		:							int        =       batch.to_pandas().to_csv(
							    path_or_buf=snake_case   ,      header=header if (offset == 0) else False   ,      index=snake_case   ,      **snake_case      )
							return csv_str.encode(self.encoding      )







		def 				A_	(			self   ,      snake_case   ,      snake_case   ,      snake_case   ,      **snake_case      ):


							'''simple docstring'''

							UpperCAmelCase		:							List[str]        =       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"   ,      ):
																	UpperCAmelCase		:							Any        =       self._batch_csv((offset, header, index, to_csv_kwargs)      )
																	written += file_obj.write(snake_case      )

							else:
												UpperCAmelCase ,			UpperCAmelCase		:							Union[str, Any]        =       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   ,      snake_case   ,      snake_case      )]   ,      )   ,      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(snake_case      )

							return written
