

'''simple docstring'''



import bisect
import copy
from collections import defaultdict

import numpy as np
from torch.utils.data import BatchSampler, Sampler

from utils import logger





def  _A		(							lowercase__		,		lowercase__	):
						lowercase__							 =       copy.deepcopy(lowercase__	)
						lowercase__							 =       sorted(lowercase__	)
						lowercase__							 =       [bisect.bisect_right(lowercase__		,		lowercase__	) for y in x]
						return quantized





def  _A		(							lowercase__		,		lowercase__=0	):
						lowercase__							 =       np.arange(start=3		,		stop=lowercase__		,		step=4	).tolist() if k > 0 else [10]
						lowercase__							 =       _quantize(lowercase__		,		lowercase__	)
						# count number of elements per group
						lowercase__							 =       np.unique(lowercase__		,		return_counts=lowercase__	)[1]
						lowercase__							 =       [0] + bins + [np.inf]
						logger.info("""Using {} as bins for aspect lengths quantization""".format(lowercase__	)	)
						logger.info("""Count of instances per bin: {}""".format(lowercase__	)	)
						return groups







class 			A							(       __UpperCAmelCase				):



			def __init__(					self       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       )			->		Optional[int]:




									'''simple docstring'''



									if not isinstance(lowerCamelCase__       ,     lowerCamelCase__       ):
															raise ValueError(
															    """sampler should be an instance of torch.utils.data.Sampler, but got sampler={}""".format(lowerCamelCase__       )       )
									lowercase__							 =       sampler
									lowercase__							 =       group_ids
									lowercase__							 =       batch_size



			def __iter__(					self       )			->		List[str]:




									'''simple docstring'''



									lowercase__							 =       defaultdict(lowerCamelCase__       )
									lowercase__							 =       defaultdict(lowerCamelCase__       )

									lowercase__							 =       0
									for idx in self.sampler:
															lowercase__							 =       self.group_ids[idx]
															buffer_per_group[group_id].append(lowerCamelCase__       )
															samples_per_group[group_id].append(lowerCamelCase__       )
															if len(buffer_per_group[group_id]       ) == self.batch_size:
																					yield buffer_per_group[group_id]  # TODO
																					num_batches += 1
																					del buffer_per_group[group_id]
															assert len(buffer_per_group[group_id]       ) < self.batch_size

									# now we have run out of elements that satisfy
									# the group criteria, let's return the remaining
									# elements so that the size of the sampler is
									# deterministic
									lowercase__							 =       len(self       )
									lowercase__							 =       expected_num_batches - num_batches
									if num_remaining > 0:
															# for the remaining batches, group the batches by similar lengths
															lowercase__							 =       []
															for group_id, idxs in sorted(buffer_per_group.items()       ,     key=lambda lowerCamelCase__       : x[0]       ):
																					batch_idx.extend(lowerCamelCase__       )
																					if len(lowerCamelCase__       ) >= self.batch_size:
																											yield batch_idx[: self.batch_size]
																											lowercase__							 =       batch_idx[self.batch_size :]
																											num_remaining -= 1
															if len(lowerCamelCase__       ) > 0:
																					yield batch_idx
																					num_remaining -= 1
									assert num_remaining == 0



			def __len__(					self       )			->		Any:




									'''simple docstring'''



									return (len(self.sampler       ) + self.batch_size - 1) // self.batch_size








'''simple docstring'''



# 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 			A							:
			# setable values
			lowerCamelCase							:							Optional[jnp.ndarray]       		=  None
			lowerCamelCase							:							Optional[jnp.ndarray]       		=  None
			lowerCamelCase							:							Optional[jnp.ndarray]       		=  None



			@classmethod
			def A__ (					cls       )			->		Optional[int]:




									'''simple docstring'''



									return cls()





@dataclass
class 			A							(       __UpperCAmelCase				):
			lowerCamelCase							:							ScoreSdeVeSchedulerState
			lowerCamelCase							:							jnp.ndarray
			lowerCamelCase							:							Optional[jnp.ndarray]       		=  None





class 			A							(       __UpperCAmelCase		,						__UpperCAmelCase				):



			@property
			def A__ (					self       )			->		Tuple:




									'''simple docstring'''



									return True



			@register_to_config
			def __init__(					self       ,     lowerCamelCase__ = 2_000       ,     lowerCamelCase__ = 0.15       ,     lowerCamelCase__ = 0.01       ,     lowerCamelCase__ = 13_48.0       ,     lowerCamelCase__ = 1e-5       ,     lowerCamelCase__ = 1       ,     )			->		str:




									'''simple docstring'''



									pass



			def A__ (					self       )			->		List[Any]:




									'''simple docstring'''



									lowercase__							 =       ScoreSdeVeSchedulerState.create()
									return self.set_sigmas(
									    lowerCamelCase__       ,     self.config.num_train_timesteps       ,     self.config.sigma_min       ,     self.config.sigma_max       ,     self.config.sampling_eps       ,     )



			def A__ (					self       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__ = ()       ,     lowerCamelCase__ = None       )			->		ScoreSdeVeSchedulerState:




									'''simple docstring'''



									lowercase__							 =       sampling_eps if sampling_eps is not None else self.config.sampling_eps

									lowercase__							 =       jnp.linspace(1       ,     lowerCamelCase__       ,     lowerCamelCase__       )
									return state.replace(timesteps=lowerCamelCase__       )



			def A__ (					self       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__ = None       ,     lowerCamelCase__ = None       ,     lowerCamelCase__ = None       ,     )			->		ScoreSdeVeSchedulerState:




									'''simple docstring'''



									lowercase__							 =       sigma_min if sigma_min is not None else self.config.sigma_min
									lowercase__							 =       sigma_max if sigma_max is not None else self.config.sigma_max
									lowercase__							 =       sampling_eps if sampling_eps is not None else self.config.sampling_eps
									if state.timesteps is None:
															lowercase__							 =       self.set_timesteps(lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       )

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

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



			def A__ (					self       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       )			->		List[str]:




									'''simple docstring'''



									return jnp.where(timesteps == 0       ,     jnp.zeros_like(lowerCamelCase__       )       ,     state.discrete_sigmas[timesteps - 1]       )



			def A__ (					self       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__ = True       ,     )			->		Union[FlaxSdeVeOutput, Tuple]:




									'''simple docstring'''



									if state.timesteps is None:
															raise ValueError(
															    """`state.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"""       )

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

									lowercase__							 =       state.discrete_sigmas[timesteps]
									lowercase__							 =       self.get_adjacent_sigma(lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       )
									lowercase__							 =       jnp.zeros_like(lowerCamelCase__       )
									lowercase__							 =       (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
									lowercase__							 =       diffusion.flatten()
									lowercase__							 =       broadcast_to_shape_from_left(lowerCamelCase__       ,     sample.shape       )
									lowercase__							 =       drift - diffusion**2 * model_output

									#  equation 6: sample noise for the diffusion term of
									lowercase__							 =       random.split(lowerCamelCase__       ,     num=1       )
									lowercase__							 =       random.normal(key=lowerCamelCase__       ,     shape=sample.shape       )
									lowercase__							 =       sample - drift  # subtract because `dt` is a small negative timestep
									# TODO is the variable diffusion the correct scaling term for the noise?
									lowercase__							 =       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=lowerCamelCase__       ,     prev_sample_mean=lowerCamelCase__       ,     state=lowerCamelCase__       )



			def A__ (					self       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__ = True       ,     )			->		Union[FlaxSdeVeOutput, Tuple]:




									'''simple docstring'''



									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
									lowercase__							 =       random.split(lowerCamelCase__       ,     num=1       )
									lowercase__							 =       random.normal(key=lowerCamelCase__       ,     shape=sample.shape       )

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

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

									if not return_dict:
															return (prev_sample, state)

									return FlaxSdeVeOutput(prev_sample=lowerCamelCase__       ,     state=lowerCamelCase__       )



			def __len__(					self       )			->		Union[str, Any]:




									'''simple docstring'''



									return self.config.num_train_timesteps






