
"""simple docstring"""




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 	UpperCAmelCase_      :


  '''simple docstring'''







  a__   	=		None
  a__   	=		None
  a__   	=		None
  @classmethod
  def 							_lowercase							(			cls      :			List[str]   )      ->     Tuple:







    """simple docstring"""






    return cls()







@dataclass
class 	UpperCAmelCase_      (			__lowerCAmelCase					):


  '''simple docstring'''







  a__   	=		42
  a__   	=		42
  a__   	=		None







class 	UpperCAmelCase_      (			__lowerCAmelCase  ,	__lowerCAmelCase					):


  '''simple docstring'''







  @property
  def 							_lowercase							(			self      :			Tuple   )      ->     str:







    """simple docstring"""






    return True
  @register_to_config
  def __init__(			self      :			Tuple      ,							UpperCamelCase__      :			Optional[int] = 2000      ,							UpperCamelCase__      :			str = 0.15      ,							UpperCamelCase__      :			List[Any] = 0.01      ,							UpperCamelCase__      :			Union[str, Any] = 1348.0      ,							UpperCamelCase__      :			Optional[Any] = 1E-5      ,							UpperCamelCase__      :			Union[str, Any] = 1      ,							)      ->     Union[str, Any]:







    """simple docstring"""






    pass
  def 							_lowercase							(			self      :			Optional[Any]   )      ->     Optional[Any]:







    """simple docstring"""






    __magic_name__    					=	ScoreSdeVeSchedulerState.create()
    return self.set_sigmas(
        __SCREAMING_SNAKE_CASE      ,							self.config.num_train_timesteps      ,							self.config.sigma_min      ,							self.config.sigma_max      ,							self.config.sampling_eps      ,							)
  def 							_lowercase							(			self      :			Optional[Any]      ,							UpperCamelCase__      :			str      ,							UpperCamelCase__      :			Dict      ,							UpperCamelCase__      :			str = ()      ,							UpperCamelCase__      :			int = None   )      ->     ScoreSdeVeSchedulerState:







    """simple docstring"""






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

    __magic_name__    					=	jnp.linspace(1      ,							__SCREAMING_SNAKE_CASE      ,							__SCREAMING_SNAKE_CASE   )
    return state.replace(timesteps=__SCREAMING_SNAKE_CASE   )
  def 							_lowercase							(			self      :			List[Any]      ,							UpperCamelCase__      :			Optional[int]      ,							UpperCamelCase__      :			Optional[Any]      ,							UpperCamelCase__      :			Tuple = None      ,							UpperCamelCase__      :			Dict = None      ,							UpperCamelCase__      :			List[Any] = None      ,							)      ->     ScoreSdeVeSchedulerState:







    """simple docstring"""






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

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

    return state.replace(discrete_sigmas=__SCREAMING_SNAKE_CASE      ,							sigmas=__SCREAMING_SNAKE_CASE   )
  def 							_lowercase							(			self      :			Dict      ,							UpperCamelCase__      :			List[str]      ,							UpperCamelCase__      :			Union[str, Any]      ,							UpperCamelCase__      :			List[Any]   )      ->     Union[str, Any]:







    """simple docstring"""






    return jnp.where(timesteps == 0      ,							jnp.zeros_like(__SCREAMING_SNAKE_CASE   )      ,							state.discrete_sigmas[timesteps - 1]   )
  def 							_lowercase							(			self      :			str      ,							UpperCamelCase__      :			Dict      ,							UpperCamelCase__      :			List[str]      ,							UpperCamelCase__      :			str      ,							UpperCamelCase__      :			List[str]      ,							UpperCamelCase__      :			Any      ,							UpperCamelCase__      :			List[str] = 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"""   )

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

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

    #  equation 6: sample noise for the diffusion term of
    __magic_name__    					=	random.split(__SCREAMING_SNAKE_CASE      ,							num=1   )
    __magic_name__    					=	random.normal(key=__SCREAMING_SNAKE_CASE      ,							shape=sample.shape   )
    __magic_name__    					=	sample - drift  # subtract because `dt` is a small negative timestep
    # TODO is the variable diffusion the correct scaling term for the noise?
    __magic_name__    					=	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=__SCREAMING_SNAKE_CASE      ,							prev_sample_mean=__SCREAMING_SNAKE_CASE      ,							state=__SCREAMING_SNAKE_CASE   )
  def 							_lowercase							(			self      :			Optional[int]      ,							UpperCamelCase__      :			Optional[int]      ,							UpperCamelCase__      :			Dict      ,							UpperCamelCase__      :			str      ,							UpperCamelCase__      :			Optional[int]      ,							UpperCamelCase__      :			Optional[int] = 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
    __magic_name__    					=	random.split(__SCREAMING_SNAKE_CASE      ,							num=1   )
    __magic_name__    					=	random.normal(key=__SCREAMING_SNAKE_CASE      ,							shape=sample.shape   )

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

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

    if not return_dict:
      return (prev_sample, state)

    return FlaxSdeVeOutput(prev_sample=__SCREAMING_SNAKE_CASE      ,							state=__SCREAMING_SNAKE_CASE   )





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







    """simple docstring"""






    return self.config.num_train_timesteps



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__   (	A_,					A_ ):






  '''simple docstring'''





  __magic_name__    					=	copy.deepcopy(A_ )
  __magic_name__    					=	sorted(A_ )
  __magic_name__    					=	[bisect.bisect_right(A_,					A_ ) for y in x]
  return quantized

def a__   (	A_,					A_=0 ):






  '''simple docstring'''





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





class 	UpperCAmelCase_      (			_A					):


  '''simple docstring'''







  def __init__(			self      :			Dict      ,							UpperCamelCase__      :			Dict      ,							UpperCamelCase__      :			Optional[Any]      ,							UpperCamelCase__      :			Tuple   )      ->     Union[str, Any]:







    """simple docstring"""






    if not isinstance(UpperCamelCase__      ,							UpperCamelCase__   ):
      raise ValueError(
          """sampler should be an instance of torch.utils.data.Sampler, but got sampler={}""".format(UpperCamelCase__   )   )
    __magic_name__    					=	sampler
    __magic_name__    					=	group_ids
    __magic_name__    					=	batch_size
  def __iter__(			self      :			Tuple   )      ->     Union[str, Any]:







    """simple docstring"""






    __magic_name__    					=	defaultdict(UpperCamelCase__   )
    __magic_name__    					=	defaultdict(UpperCamelCase__   )

    __magic_name__    					=	0
    for idx in self.sampler:
      __magic_name__    					=	self.group_ids[idx]
      buffer_per_group[group_id].append(UpperCamelCase__   )
      samples_per_group[group_id].append(UpperCamelCase__   )
      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
    __magic_name__    					=	len(self   )
    __magic_name__    					=	expected_num_batches - num_batches
    if num_remaining > 0:
      # for the remaining batches, group the batches by similar lengths
      __magic_name__    					=	[]
      for group_id, idxs in sorted(buffer_per_group.items()      ,							key=lambda UpperCamelCase__   : x[0]   ):
        batch_idx.extend(UpperCamelCase__   )
        if len(UpperCamelCase__   ) >= self.batch_size:
          yield batch_idx[: self.batch_size]
          __magic_name__    					=	batch_idx[self.batch_size :]
          num_remaining -= 1
      if len(UpperCamelCase__   ) > 0:
        yield batch_idx
        num_remaining -= 1
    assert num_remaining == 0





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







    """simple docstring"""






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

