


# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING, Dict, Optional

import numpy as np
import pyarrow as pa

from .. import config
from ..utils.logging import get_logger
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter


if TYPE_CHECKING:
						import jax
						import jaxlib

snake_case							: List[Any]      =   get_logger()

snake_case							: Optional[dict]      =   None







class  snake_case_     (TensorFormatter[Mapping, '''jax.Array''', Mapping]   ):
					def __init__(   self     :Tuple    ,__snake_case     :List[str]=None    ,__snake_case     :Dict=None    ,**__snake_case     :Union[str, Any]   )      ->					Tuple:
												super().__init__(features=__snake_case   )
												import jax
												from jaxlib.xla_client import Device

												if isinstance(__snake_case    ,__snake_case   ):
																			raise ValueError(
																			    F'Expected {device} to be a `str` not {type(__snake_case   )}, as `jaxlib.xla_extension.Device` '
																			    'is not serializable neither with `pickle` nor with `dill`. Instead you can surround '
																			    'the device with `str()` to get its string identifier that will be internally mapped '
																			    'to the actual `jaxlib.xla_extension.Device`.'   )
												a__    =  device if isinstance(__snake_case    ,__snake_case   ) else str(jax.devices()[0]   )
												# using global variable since `jaxlib.xla_extension.Device` is not serializable neither
												# with `pickle` nor with `dill`, so we need to use a global variable instead
												global DEVICE_MAPPING
												if DEVICE_MAPPING is None:
																			a__    =  self._map_devices_to_str()
												if self.device not in list(DEVICE_MAPPING.keys()   ):
																			logger.warning(
																			    F'Device with string identifier {self.device} not listed among the available '
																			    F'devices: {list(DEVICE_MAPPING.keys()   )}, so falling back to the default '
																			    F'device: {str(jax.devices()[0]   )}.'   )
																			a__    =  str(jax.devices()[0]   )
												a__    =  jnp_array_kwargs
					@staticmethod
					def 				lowerCamelCase__(   )      ->					Dict[str, "jaxlib.xla_extension.Device"]:
												import jax

												return {str(__snake_case   ): device for device in jax.devices()}
					def 				lowerCamelCase__(   self     :List[Any]    ,__snake_case     :List[str]   )      ->					List[Any]:
												import jax
												import jax.numpy as jnp

												if isinstance(__snake_case    ,__snake_case   ) and column:
																			if all(
																			    isinstance(__snake_case    ,jax.Array   ) and x.shape == column[0].shape and x.dtype == column[0].dtype for x in column   ):
																										return jnp.stack(__snake_case    ,axis=0   )
												return column
					def 				lowerCamelCase__(   self     :str    ,__snake_case     :Any   )      ->					List[str]:
												import jax
												import jax.numpy as jnp

												if isinstance(__snake_case    ,(str, bytes, type(__snake_case   ))   ):
																			return value
												elif isinstance(__snake_case    ,(np.character, np.ndarray)   ) and np.issubdtype(value.dtype    ,np.character   ):
																			return value.tolist()

												a__    =  {}

												if isinstance(__snake_case    ,(np.number, np.ndarray)   ) and np.issubdtype(value.dtype    ,np.integer   ):
																			# the default int precision depends on the jax config
																			# see https://jax.readthedocs.io/en/latest/notebooks/Common_Gotchas_in_JAX.html#double-64bit-precision
																			if jax.config.jax_enable_xaa:
																										a__    =  {'dtype': jnp.intaa}
																			else:
																										a__    =  {'dtype': jnp.intaa}
												elif isinstance(__snake_case    ,(np.number, np.ndarray)   ) and np.issubdtype(value.dtype    ,np.floating   ):
																			a__    =  {'dtype': jnp.floataa}
												elif config.PIL_AVAILABLE and "PIL" in sys.modules:
																			import PIL.Image

																			if isinstance(__snake_case    ,PIL.Image.Image   ):
																										a__    =  np.asarray(__snake_case   )

        # using global variable since `jaxlib.xla_extension.Device` is not serializable neither
        # with `pickle` nor with `dill`, so we need to use a global variable instead
												global DEVICE_MAPPING
												if DEVICE_MAPPING is None:
																			a__    =  self._map_devices_to_str()

												with jax.default_device(DEVICE_MAPPING[self.device]   ):
																			# calling jnp.array on a np.ndarray does copy the data
																			# see https://github.com/google/jax/issues/4486
																			return jnp.array(__snake_case    ,**{**default_dtype, **self.jnp_array_kwargs}   )
					def 				lowerCamelCase__(   self     :List[Any]    ,__snake_case     :Tuple   )      ->					Any:
												import jax

												# support for torch, tf, jax etc.
												if config.TORCH_AVAILABLE and "torch" in sys.modules:
																			import torch

																			if isinstance(__snake_case    ,torch.Tensor   ):
																										return self._tensorize(data_struct.detach().cpu().numpy()[()]   )
												if hasattr(__snake_case    ,'__array__'   ) and not isinstance(__snake_case    ,jax.Array   ):
																			a__    =  data_struct.__array__()
												# support for nested types like struct of list of struct
												if isinstance(__snake_case    ,np.ndarray   ):
																			if data_struct.dtype == object:  # jax arrays cannot be instantied from an array of objects
																										return self._consolidate([self.recursive_tensorize(__snake_case   ) for substruct in data_struct]   )
												elif isinstance(__snake_case    ,(list, tuple)   ):
																			return self._consolidate([self.recursive_tensorize(__snake_case   ) for substruct in data_struct]   )
												return self._tensorize(__snake_case   )
					def 				lowerCamelCase__(   self     :Union[str, Any]    ,__snake_case     :dict   )      ->					List[Any]:
												return map_nested(self._recursive_tensorize    ,__snake_case    ,map_list=__snake_case   )
					def 				lowerCamelCase__(   self     :Optional[int]    ,__snake_case     :pa.Table   )      ->					Mapping:
												a__    =  self.numpy_arrow_extractor().extract_row(__snake_case   )
												a__    =  self.python_features_decoder.decode_row(__snake_case   )
												return self.recursive_tensorize(__snake_case   )
					def 				lowerCamelCase__(   self     :int    ,__snake_case     :pa.Table   )      ->					"jax.Array":
												a__    =  self.numpy_arrow_extractor().extract_column(__snake_case   )
												a__    =  self.python_features_decoder.decode_column(__snake_case    ,pa_table.column_names[0]   )
												a__    =  self.recursive_tensorize(__snake_case   )
												a__    =  self._consolidate(__snake_case   )
												return column



					def 				lowerCamelCase__(   self     :Union[str, Any]    ,__snake_case     :pa.Table   )      ->					Mapping:
												a__    =  self.numpy_arrow_extractor().extract_batch(__snake_case   )
												a__    =  self.python_features_decoder.decode_batch(__snake_case   )
												a__    =  self.recursive_tensorize(__snake_case   )
												for column_name in batch:
																			a__    =  self._consolidate(batch[column_name]   )
												return batch








import math
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import SchedulerMixin, SchedulerOutput







class  snake_case_     (lowerCamelCase_			,		lowerCamelCase_   ):
					UpperCAmelCase__      :					Optional[Any]					   =    1
					@register_to_config
					def __init__(   self     :Optional[int]    ,__snake_case     :int = 10_00    ,__snake_case     :Optional[Union[np.ndarray, List[float]]] = None   )      ->					int:
												# set `betas`, `alphas`, `timesteps`
												self.set_timesteps(__snake_case   )

												# standard deviation of the initial noise distribution
												a__    =  1.0

												# For now we only support F-PNDM, i.e. the runge-kutta method
												# For more information on the algorithm please take a look at the paper: https://arxiv.org/pdf/2202.09778.pdf
												# mainly at formula (9), (12), (13) and the Algorithm 2.
												a__    =  4

												# running values
												a__    =  []
					def 				lowerCamelCase__(   self     :Union[str, Any]    ,__snake_case     :int    ,__snake_case     :Union[str, torch.device] = None   )      ->					Union[str, Any]:
												a__    =  num_inference_steps
												a__    =  torch.linspace(1    ,0    ,num_inference_steps + 1   )[:-1]
												a__    =  torch.cat([steps, torch.tensor([0.0]   )]   )

												if self.config.trained_betas is not None:
																			a__    =  torch.tensor(self.config.trained_betas    ,dtype=torch.floataa   )
												else:
																			a__    =  torch.sin(steps * math.pi / 2   ) ** 2

												a__    =  (1.0 - self.betas**2) ** 0.5

												a__    =  (torch.atana(self.betas    ,self.alphas   ) / math.pi * 2)[:-1]
												a__    =  timesteps.to(__snake_case   )

												a__    =  []
					def 				lowerCamelCase__(   self     :Any    ,__snake_case     :torch.FloatTensor    ,__snake_case     :int    ,__snake_case     :torch.FloatTensor    ,__snake_case     :bool = True    ,)      ->					Union[SchedulerOutput, Tuple]:
												if self.num_inference_steps is None:
																			raise ValueError(
																			    'Number of inference steps is \'None\', you need to run \'set_timesteps\' after creating the scheduler'   )

												a__    =  (self.timesteps == timestep).nonzero().item()
												a__    =  timestep_index + 1

												a__    =  sample * self.betas[timestep_index] + model_output * self.alphas[timestep_index]
												self.ets.append(__snake_case   )

												if len(self.ets   ) == 1:
																			a__    =  self.ets[-1]
												elif len(self.ets   ) == 2:
																			a__    =  (3 * self.ets[-1] - self.ets[-2]) / 2
												elif len(self.ets   ) == 3:
																			a__    =  (23 * self.ets[-1] - 16 * self.ets[-2] + 5 * self.ets[-3]) / 12
												else:
																			a__    =  (1 / 24) * (55 * self.ets[-1] - 59 * self.ets[-2] + 37 * self.ets[-3] - 9 * self.ets[-4])

												a__    =  self._get_prev_sample(__snake_case    ,__snake_case    ,__snake_case    ,__snake_case   )

												if not return_dict:
																			return (prev_sample,)

												return SchedulerOutput(prev_sample=__snake_case   )
					def 				lowerCamelCase__(   self     :Union[str, Any]    ,__snake_case     :torch.FloatTensor    ,*__snake_case     :int    ,**__snake_case     :Optional[int]   )      ->					torch.FloatTensor:
												return sample
					def 				lowerCamelCase__(   self     :Optional[Any]    ,__snake_case     :List[Any]    ,__snake_case     :Optional[int]    ,__snake_case     :Dict    ,__snake_case     :Any   )      ->					Optional[Any]:
												a__    =  self.alphas[timestep_index]
												a__    =  self.betas[timestep_index]

												a__    =  self.alphas[prev_timestep_index]
												a__    =  self.betas[prev_timestep_index]

												a__    =  (sample - sigma * ets) / max(__snake_case    ,1E-8   )
												a__    =  next_alpha * pred + ets * next_sigma

												return prev_sample



					def __len__(   self     :Any   )      ->					Union[str, Any]:
												return self.config.num_train_timesteps




