



# DISCLAIMER: This file is strongly influenced by https://github.com/ermongroup/ddim

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

import flax
import jax
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    :
	lowerCAmelCase	:   Optional[int]				=	42

	# setable values
	lowerCAmelCase	:   Optional[int]				=	42
	lowerCAmelCase	:   Optional[int]				=	42
	lowerCAmelCase	:   Tuple				=	None



	@classmethod
	def 			__A							(       cls			,	UpperCAmelCase__			,	UpperCAmelCase__			,	UpperCAmelCase__		):
							return cls(common=_SCREAMING_SNAKE_CASE			,	init_noise_sigma=_SCREAMING_SNAKE_CASE			,	timesteps=_SCREAMING_SNAKE_CASE		)






@dataclass
class      UpperCamelCase    (						_UpperCAmelCase     ):
	lowerCAmelCase	:   List[Any]				=	42






class      UpperCamelCase    (						_UpperCAmelCase       ,    _UpperCAmelCase     ):
	lowerCAmelCase	:   Any				=	[e.name for e in FlaxKarrasDiffusionSchedulers]

	lowerCAmelCase	:   Optional[Any]				=	42



	@property
	def 			__A							(       self		):
							return True



	@register_to_config
	def __init__(       self			,	UpperCAmelCase__ = 1_000			,	UpperCAmelCase__ = 0.0_001			,	UpperCAmelCase__ = 0.02			,	UpperCAmelCase__ = "linear"			,	UpperCAmelCase__ = None			,	UpperCAmelCase__ = "fixed_small"			,	UpperCAmelCase__ = True			,	UpperCAmelCase__ = "epsilon"			,	UpperCAmelCase__ = jnp.floataa			,	):
							A__							=       dtype



	def 			__A							(       self			,	UpperCAmelCase__ = None		):





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

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

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

							return DDPMSchedulerState.create(
							    common=_SCREAMING_SNAKE_CASE			,	init_noise_sigma=_SCREAMING_SNAKE_CASE			,	timesteps=_SCREAMING_SNAKE_CASE			,	)



	def 			__A							(       self			,	UpperCAmelCase__			,	UpperCAmelCase__			,	UpperCAmelCase__ = None		):
							return sample



	def 			__A							(       self			,	UpperCAmelCase__			,	UpperCAmelCase__			,	UpperCAmelCase__ = ()		):
							A__							=       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
							A__							=       (jnp.arange(0			,	_SCREAMING_SNAKE_CASE		) * step_ratio).round()[::-1]

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



	def 			__A							(       self			,	UpperCAmelCase__			,	UpperCAmelCase__			,	UpperCAmelCase__=None			,	UpperCAmelCase__=None		):
							A__							=       state.common.alphas_cumprod[t]
							A__							=       jnp.where(t > 0			,	state.common.alphas_cumprod[t - 1]			,	jnp.array(1.0			,	dtype=self.dtype		)		)

							# For t > 0, compute predicted variance βt (see formula (6) and (7) from https://arxiv.org/pdf/2006.11239.pdf)
							# and sample from it to get previous sample
							# x_{t-1} ~ N(pred_prev_sample, variance) == add variance to pred_sample
							A__							=       (1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * state.common.betas[t]

							if variance_type is None:
													A__							=       self.config.variance_type

							# hacks - were probably added for training stability
							if variance_type == "fixed_small":
													A__							=       jnp.clip(_SCREAMING_SNAKE_CASE			,	a_min=1e-2_0		)
							# for rl-diffuser https://arxiv.org/abs/2205.09991
							elif variance_type == "fixed_small_log":
													A__							=       jnp.log(jnp.clip(_SCREAMING_SNAKE_CASE			,	a_min=1e-2_0		)		)
							elif variance_type == "fixed_large":
													A__							=       state.common.betas[t]
							elif variance_type == "fixed_large_log":
													# Glide max_log
													A__							=       jnp.log(state.common.betas[t]		)
							elif variance_type == "learned":
													return predicted_variance
							elif variance_type == "learned_range":
													A__							=       variance
													A__							=       state.common.betas[t]
													A__							=       (predicted_variance + 1) / 2
													A__							=       frac * max_log + (1 - frac) * min_log

							return variance



	def 			__A							(       self			,	UpperCAmelCase__			,	UpperCAmelCase__			,	UpperCAmelCase__			,	UpperCAmelCase__			,	UpperCAmelCase__ = None			,	UpperCAmelCase__ = True			,	):
							A__							=       timestep

							if key is None:
													A__							=       jax.random.PRNGKey(0		)

							if model_output.shape[1] == sample.shape[1] * 2 and self.config.variance_type in ["learned", "learned_range"]:
													A__							=       jnp.split(_SCREAMING_SNAKE_CASE			,	sample.shape[1]			,	axis=1		)
							else:
													A__							=       None

							# 1. compute alphas, betas
							A__							=       state.common.alphas_cumprod[t]
							A__							=       jnp.where(t > 0			,	state.common.alphas_cumprod[t - 1]			,	jnp.array(1.0			,	dtype=self.dtype		)		)
							A__							=       1 - alpha_prod_t
							A__							=       1 - alpha_prod_t_prev

							# 2. compute predicted original sample from predicted noise also called
							# "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
							if self.config.prediction_type == "epsilon":
													A__							=       (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
							elif self.config.prediction_type == "sample":
													A__							=       model_output
							elif self.config.prediction_type == "v_prediction":
													A__							=       (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
							else:
													raise ValueError(
													    F"""prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample` """
													    " for the FlaxDDPMScheduler."		)

							# 3. Clip "predicted x_0"
							if self.config.clip_sample:
													A__							=       jnp.clip(_SCREAMING_SNAKE_CASE			,	-1			,	1		)

							# 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
							# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
							A__							=       (alpha_prod_t_prev ** 0.5 * state.common.betas[t]) / beta_prod_t
							A__							=       state.common.alphas[t] ** 0.5 * beta_prod_t_prev / beta_prod_t

							# 5. Compute predicted previous sample µ_t
							# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
							A__							=       pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

							# 6. Add noise
							def random_variance():
													A__							=       jax.random.split(_SCREAMING_SNAKE_CASE			,	num=1		)
													A__							=       jax.random.normal(_SCREAMING_SNAKE_CASE			,	shape=model_output.shape			,	dtype=self.dtype		)
													return (self._get_variance(_SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			,	predicted_variance=_SCREAMING_SNAKE_CASE		) ** 0.5) * noise

							A__							=       jnp.where(t > 0			,	random_variance()			,	jnp.zeros(model_output.shape			,	dtype=self.dtype		)		)

							A__							=       pred_prev_sample + variance

							if not return_dict:
													return (pred_prev_sample, state)

							return FlaxDDPMSchedulerOutput(prev_sample=_SCREAMING_SNAKE_CASE			,	state=_SCREAMING_SNAKE_CASE		)



	def 			__A							(       self			,	UpperCAmelCase__			,	UpperCAmelCase__			,	UpperCAmelCase__			,	UpperCAmelCase__			,	):
							return add_noise_common(state.common			,	_SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE		)



	def 			__A							(       self			,	UpperCAmelCase__			,	UpperCAmelCase__			,	UpperCAmelCase__			,	UpperCAmelCase__			,	):
							return get_velocity_common(state.common			,	_SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE		)



	def __len__(       self		):
							return self.config.num_train_timesteps










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

import fsspec

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






class      UpperCamelCase    (						_UpperCAmelCase     ):



	def __init__(       self			,	UpperCAmelCase__			,	UpperCAmelCase__ = None			,	UpperCAmelCase__ = None			,	UpperCAmelCase__ = None			,	UpperCAmelCase__ = False			,	UpperCAmelCase__ = False			,	UpperCAmelCase__ = None			,	UpperCAmelCase__ = None			,	**UpperCAmelCase__			,	):
							super().__init__(
							    UpperCAmelCase__			,	split=UpperCAmelCase__			,	features=UpperCAmelCase__			,	cache_dir=UpperCAmelCase__			,	keep_in_memory=UpperCAmelCase__			,	streaming=UpperCAmelCase__			,	num_proc=UpperCAmelCase__			,	**UpperCAmelCase__			,	)
							A__							=       field
							A__							=       path_or_paths if isinstance(UpperCAmelCase__			,	UpperCAmelCase__		) else {self.split: path_or_paths}
							A__							=       Json(
							    cache_dir=UpperCAmelCase__			,	data_files=UpperCAmelCase__			,	features=UpperCAmelCase__			,	field=UpperCAmelCase__			,	**UpperCAmelCase__			,	)



	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=UpperCAmelCase__			,	download_mode=UpperCAmelCase__			,	verification_mode=UpperCAmelCase__			,	base_path=UpperCAmelCase__			,	num_proc=self.num_proc			,	)
													A__							=       self.builder.as_dataset(
													    split=self.split			,	verification_mode=UpperCAmelCase__			,	in_memory=self.keep_in_memory		)
							return dataset






class      UpperCamelCase    :



	def __init__(       self			,	UpperCAmelCase__			,	UpperCAmelCase__			,	UpperCAmelCase__ = None			,	UpperCAmelCase__ = None			,	**UpperCAmelCase__			,	):
							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_json_kwargs



	def 			__A							(       self		):
							A__							=       self.to_json_kwargs.pop("path_or_buf"			,	UpperCAmelCase__		)
							A__							=       self.to_json_kwargs.pop("orient"			,	"records"		)
							A__							=       self.to_json_kwargs.pop("lines"			,	True if orient == "records" else False		)
							A__							=       self.to_json_kwargs.pop("index"			,	False if orient in ["split", "table"] else True		)
							A__							=       self.to_json_kwargs.pop("compression"			,	UpperCAmelCase__		)

							if compression not in [None, "infer", "gzip", "bz2", "xz"]:
													raise NotImplementedError(F"""`datasets` currently does not support {compression} compression"""		)

							if isinstance(self.path_or_buf			,	(str, bytes, os.PathLike)		):
													with fsspec.open(self.path_or_buf			,	"wb"			,	compression=UpperCAmelCase__		) as buffer:
																			A__							=       self._write(file_obj=UpperCAmelCase__			,	orient=UpperCAmelCase__			,	lines=UpperCAmelCase__			,	index=UpperCAmelCase__			,	**self.to_json_kwargs		)
							else:
													if compression:
																			raise NotImplementedError(
																			    F"""The compression parameter is not supported when writing to a buffer, but compression={compression}"""
																			    " was passed. Please provide a local path instead."		)
													A__							=       self._write(
													    file_obj=self.path_or_buf			,	orient=UpperCAmelCase__			,	lines=UpperCAmelCase__			,	index=UpperCAmelCase__			,	**self.to_json_kwargs		)
							return written



	def 			__A							(       self			,	UpperCAmelCase__		):
							A__						,       A__						,       A__						,       A__						,       A__							=       args

							A__							=       query_table(
							    table=self.dataset.data			,	key=slice(UpperCAmelCase__			,	offset + self.batch_size		)			,	indices=self.dataset._indices			,	)
							A__							=       batch.to_pandas().to_json(
							    path_or_buf=UpperCAmelCase__			,	orient=UpperCAmelCase__			,	lines=UpperCAmelCase__			,	index=UpperCAmelCase__			,	**UpperCAmelCase__		)
							if not json_str.endswith("\n"		):
													json_str += "\n"
							return json_str.encode(self.encoding		)



	def 			__A							(       self			,	UpperCAmelCase__			,	UpperCAmelCase__			,	UpperCAmelCase__			,	UpperCAmelCase__			,	**UpperCAmelCase__			,	):
							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 json from Arrow format"			,	):
																			A__							=       self._batch_json((offset, orient, lines, index, to_json_kwargs)		)
																			written += file_obj.write(UpperCAmelCase__		)
							else:
													A__						,       A__							=       len(self.dataset		), self.batch_size
													with multiprocessing.Pool(self.num_proc		) as pool:
																			for json_str in logging.tqdm(
																			    pool.imap(
																			        self._batch_json			,	[(offset, orient, lines, index, to_json_kwargs) for offset in range(0			,	UpperCAmelCase__			,	UpperCAmelCase__		)]			,	)			,	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 json from Arrow format"			,	):
																									written += file_obj.write(UpperCAmelCase__		)

							return written





