
# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch

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

import flax
import jax.numpy as jnp
from jax import random

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import FlaxSchedulerMixin, FlaxSchedulerOutput, broadcast_to_shape_from_left





@flax.struct.dataclass
class      _lowerCAmelCase :
				# setable values
				_lowercase      		=None
				_lowercase      		=None
				_lowercase      		=None




				@classmethod
				def 					__a						( cls					)    ->				Union[str, Any]:
							return cls()





@dataclass
class      _lowerCAmelCase (     __a	):
				_lowercase      		=42
				_lowercase      		=42
				_lowercase      		=None





class      _lowerCAmelCase (     __a ,				__a	):




				@property
				def 					__a						( self					)    ->				Tuple:
							return True




				@register_to_config
				def __init__( self    ,    _UpperCamelCase = 2_000    ,    _UpperCamelCase = 0.15    ,    _UpperCamelCase = 0.01    ,    _UpperCamelCase = 1348.0    ,    _UpperCamelCase = 1e-5    ,    _UpperCamelCase = 1    ,    )    ->				Any:
							pass




				def 					__a						( self					)    ->				str:
							lowerCAmelCase_      =     ScoreSdeVeSchedulerState.create()
							return self.set_sigmas(
							    _UpperCamelCase    ,    self.config.num_train_timesteps    ,    self.config.sigma_min    ,    self.config.sigma_max    ,    self.config.sampling_eps    ,    )




				def 					__a						( self    ,    _UpperCamelCase    ,    _UpperCamelCase    ,    _UpperCamelCase = ()    ,    _UpperCamelCase = None					)    ->				ScoreSdeVeSchedulerState:
							lowerCAmelCase_      =     sampling_eps if sampling_eps is not None else self.config.sampling_eps

							lowerCAmelCase_      =     jnp.linspace(1    ,    _UpperCamelCase    ,    _UpperCamelCase					)
							return state.replace(timesteps=_UpperCamelCase					)




				def 					__a						( self    ,    _UpperCamelCase    ,    _UpperCamelCase    ,    _UpperCamelCase = None    ,    _UpperCamelCase = None    ,    _UpperCamelCase = None    ,    )    ->				ScoreSdeVeSchedulerState:
							lowerCAmelCase_      =     sigma_min if sigma_min is not None else self.config.sigma_min
							lowerCAmelCase_      =     sigma_max if sigma_max is not None else self.config.sigma_max
							lowerCAmelCase_      =     sampling_eps if sampling_eps is not None else self.config.sampling_eps
							if state.timesteps is None:
										lowerCAmelCase_      =     self.set_timesteps(_UpperCamelCase    ,    _UpperCamelCase    ,    _UpperCamelCase					)

							lowerCAmelCase_      =     jnp.exp(jnp.linspace(jnp.log(_UpperCamelCase					)    ,    jnp.log(_UpperCamelCase					)    ,    _UpperCamelCase					)					)
							lowerCAmelCase_      =     jnp.array([sigma_min * (sigma_max / sigma_min) ** t for t in state.timesteps]					)

							return state.replace(discrete_sigmas=_UpperCamelCase    ,    sigmas=_UpperCamelCase					)




				def 					__a						( self    ,    _UpperCamelCase    ,    _UpperCamelCase    ,    _UpperCamelCase					)    ->				List[str]:
							return jnp.where(timesteps == 0    ,    jnp.zeros_like(_UpperCamelCase					)    ,    state.discrete_sigmas[timesteps - 1]					)




				def 					__a						( self    ,    _UpperCamelCase    ,    _UpperCamelCase    ,    _UpperCamelCase    ,    _UpperCamelCase    ,    _UpperCamelCase    ,    _UpperCamelCase = True    ,    )    ->				Union[FlaxSdeVeOutput, Tuple]:
							if state.timesteps is None:
										raise ValueError(
										    "`state.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"					)

							lowerCAmelCase_      =     timestep * jnp.ones(
							    sample.shape[0]    ,    )
							lowerCAmelCase_      =     (timestep * (len(state.timesteps					) - 1)).long()

							lowerCAmelCase_      =     state.discrete_sigmas[timesteps]
							lowerCAmelCase_      =     self.get_adjacent_sigma(_UpperCamelCase    ,    _UpperCamelCase    ,    _UpperCamelCase					)
							lowerCAmelCase_      =     jnp.zeros_like(_UpperCamelCase					)
							lowerCAmelCase_      =     (sigma**2 - adjacent_sigma**2) ** 0.5

							# equation 6 in the paper: the model_output modeled by the network is grad_x log pt(x)
							# also equation 47 shows the analog from SDE models to ancestral sampling methods
							lowerCAmelCase_      =     diffusion.flatten()
							lowerCAmelCase_      =     broadcast_to_shape_from_left(_UpperCamelCase    ,    sample.shape					)
							lowerCAmelCase_      =     drift - diffusion**2 * model_output

							#  equation 6: sample noise for the diffusion term of
							lowerCAmelCase_      =     random.split(_UpperCamelCase    ,    num=1					)
							lowerCAmelCase_      =     random.normal(key=_UpperCamelCase    ,    shape=sample.shape					)
							lowerCAmelCase_      =     sample - drift  # subtract because `dt` is a small negative timestep
							# TODO is the variable diffusion the correct scaling term for the noise?
							lowerCAmelCase_      =     prev_sample_mean + diffusion * noise  # add impact of diffusion field g

							if not return_dict:
										return (prev_sample, prev_sample_mean, state)

							return FlaxSdeVeOutput(prev_sample=_UpperCamelCase    ,    prev_sample_mean=_UpperCamelCase    ,    state=_UpperCamelCase					)




				def 					__a						( self    ,    _UpperCamelCase    ,    _UpperCamelCase    ,    _UpperCamelCase    ,    _UpperCamelCase    ,    _UpperCamelCase = True    ,    )    ->				Union[FlaxSdeVeOutput, Tuple]:
							if state.timesteps is None:
										raise ValueError(
										    "`state.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"					)

							# For small batch sizes, the paper "suggest replacing norm(z) with sqrt(d), where d is the dim. of z"
							# sample noise for correction
							lowerCAmelCase_      =     random.split(_UpperCamelCase    ,    num=1					)
							lowerCAmelCase_      =     random.normal(key=_UpperCamelCase    ,    shape=sample.shape					)

							# compute step size from the model_output, the noise, and the snr
							lowerCAmelCase_      =     jnp.linalg.norm(_UpperCamelCase					)
							lowerCAmelCase_      =     jnp.linalg.norm(_UpperCamelCase					)
							lowerCAmelCase_      =     (self.config.snr * noise_norm / grad_norm) ** 2 * 2
							lowerCAmelCase_      =     step_size * jnp.ones(sample.shape[0]					)

							# compute corrected sample: model_output term and noise term
							lowerCAmelCase_      =     step_size.flatten()
							lowerCAmelCase_      =     broadcast_to_shape_from_left(_UpperCamelCase    ,    sample.shape					)
							lowerCAmelCase_      =     sample + step_size * model_output
							lowerCAmelCase_      =     prev_sample_mean + ((step_size * 2) ** 0.5) * noise

							if not return_dict:
										return (prev_sample, state)

							return FlaxSdeVeOutput(prev_sample=_UpperCamelCase    ,    state=_UpperCamelCase					)




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





import json
import os
from dataclasses import dataclass

import numpy as np
import pyarrow as pa

import datasets
from utils import get_duration


_A       =					10_00_00_00_00_00
_A       =					1_00_00

_A,				_A       =					os.path.split(__file__)
_A       =					os.path.join(RESULTS_BASEPATH, "results", RESULTS_FILENAME.replace(".py", ".json"))






def        lowerCamelCase__    (    __lowerCAmelCase  :  int   ,							__lowerCAmelCase  :  int    ):




			"""simple docstring"""


			lowerCAmelCase_      =     pa.Table.from_pydict({"col": [0] * chunk_size}    )
			lowerCAmelCase_      =     pa.concat_tables([table] * (num_examples // chunk_size)    )
			return datasets.Dataset(__lowerCAmelCase   ,							fingerprint="table_100B"    )





@dataclass
class      _lowerCAmelCase :
				_lowercase      		=42
				_lowercase      		=42
				_lowercase      		=42
				_lowercase      		=42




				def 					__a						( self					)    ->				List[Any]:
							lowerCAmelCase_      =     np.random.default_rng(self.seed					)
							lowerCAmelCase_      =     rng.integers(low=self.low    ,    high=self.high    ,    size=self.size					).tolist()




				def __iter__( self					)    ->				List[str]:
							return iter(self._sampled_values					)




				def __len__( self					)    ->				Optional[int]:
							return self.size






@get_duration
def        lowerCamelCase__    (    __lowerCAmelCase  :  datasets.Dataset    ):




			"""simple docstring"""


			lowerCAmelCase_      =     dataset[0]






@get_duration
def        lowerCamelCase__    (    __lowerCAmelCase  :  datasets.Dataset    ):




			"""simple docstring"""


			lowerCAmelCase_      =     dataset[-1]






@get_duration
def        lowerCamelCase__    (    __lowerCAmelCase  :  datasets.Dataset    ):




			"""simple docstring"""


			lowerCAmelCase_      =     dataset[range(len(__lowerCAmelCase    ) // 2   ,							len(__lowerCAmelCase    ) // 2 + 1024    )]






@get_duration
def        lowerCamelCase__    (    __lowerCAmelCase  :  datasets.Dataset    ):




			"""simple docstring"""


			lowerCAmelCase_      =     dataset[RandIter(0   ,							len(__lowerCAmelCase    )   ,							1024   ,							seed=42    )]






def        lowerCamelCase__    (    ):




			"""simple docstring"""


			lowerCAmelCase_      =     {"num examples": SPEED_TEST_N_EXAMPLES}
			lowerCAmelCase_      =     (get_first_row, get_last_row, get_batch_of_aaaa_rows, get_batch_of_aaaa_random_rows)
			print("generating dataset"    )
			lowerCAmelCase_      =     generate_100B_dataset(num_examples=__lowerCAmelCase   ,							chunk_size=__lowerCAmelCase    )
			print("Functions"    )
			for func in functions:
						print(func.__name__    )
						lowerCAmelCase_      =     func(__lowerCAmelCase    )

			with open(__lowerCAmelCase   ,							"wb"    ) as f:
						f.write(json.dumps(__lowerCAmelCase    ).encode("utf-8"    )    )


if __name__ == "__main__":  # useful to run the profiler
			benchmark_table_100B()



