




# 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										=       get_logger()

_snake_case										=       None







class 						lowercase							(   TensorFormatter[Mapping, "jax.Array", Mapping]		):

     def __init__(  self		,     _a=None		,     _a=None		,     **_a				)						->							Dict:
           super().__init__(features=_a				)
           import jax
           from jaxlib.xla_client import Device

           if isinstance(_a		,     _a				):
                 raise ValueError(
                     F'''Expected {device} to be a `str` not {type(_a				)}, 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							:     Tuple			=						device if isinstance(_a		,     _a				) 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							:     int			=						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							:     Dict			=						str(jax.devices()[0]				)
           _A							:     int			=						jnp_array_kwargs

     @staticmethod
     def 					a__	(  )						->							Dict[str, "jaxlib.xla_extension.Device"]:
           import jax

           return {str(_a				): device for device in jax.devices()}

     def 					a__	(  self		,     _a				)						->							str:
           import jax
           import jax.numpy as jnp

           if isinstance(_a		,     _a				) and column:
                 if all(
                     isinstance(_a		,     jax.Array				) and x.shape == column[0].shape and x.dtype == column[0].dtype for x in column				):
                       return jnp.stack(_a		,     axis=0				)
           return column

     def 					a__	(  self		,     _a				)						->							Optional[int]:
           import jax
           import jax.numpy as jnp

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

           _A							:     Any			=						{}

           if isinstance(_a		,     (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							:     List[str]			=						{"""dtype""": jnp.intaa}
                 else:
                       _A							:     Optional[int]			=						{"""dtype""": jnp.intaa}
           elif isinstance(_a		,     (np.number, np.ndarray)				) and np.issubdtype(value.dtype		,     np.floating				):
                 _A							:     List[str]			=						{"""dtype""": jnp.floataa}
           elif config.PIL_AVAILABLE and "PIL" in sys.modules:
                 import PIL.Image

                 if isinstance(_a		,     PIL.Image.Image				):
                       _A							:     Union[str, Any]			=						np.asarray(_a				)

        # 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							:     Optional[Any]			=						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(_a		,     **{**default_dtype, **self.jnp_array_kwargs}				)

     def 					a__	(  self		,     _a				)						->							Optional[int]:
           import jax

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

                 if isinstance(_a		,     torch.Tensor				):
                       return self._tensorize(data_struct.detach().cpu().numpy()[()]				)
           if hasattr(_a		,     """__array__"""				) and not isinstance(_a		,     jax.Array				):
                 _A							:     Union[str, Any]			=						data_struct.__array__()
           # support for nested types like struct of list of struct
           if isinstance(_a		,     np.ndarray				):
                 if data_struct.dtype == object:  # jax arrays cannot be instantied from an array of objects
                       return self._consolidate([self.recursive_tensorize(_a				) for substruct in data_struct]				)
           elif isinstance(_a		,     (list, tuple)				):
                 return self._consolidate([self.recursive_tensorize(_a				) for substruct in data_struct]				)
           return self._tensorize(_a				)

     def 					a__	(  self		,     _a				)						->							Tuple:
           return map_nested(self._recursive_tensorize		,     _a		,     map_list=_a				)

     def 					a__	(  self		,     _a				)						->							Mapping:
           _A							:     Union[str, Any]			=						self.numpy_arrow_extractor().extract_row(_a				)
           _A							:     Any			=						self.python_features_decoder.decode_row(_a				)
           return self.recursive_tensorize(_a				)

     def 					a__	(  self		,     _a				)						->							"jax.Array":
           _A							:     Optional[int]			=						self.numpy_arrow_extractor().extract_column(_a				)
           _A							:     List[Any]			=						self.python_features_decoder.decode_column(_a		,     pa_table.column_names[0]				)
           _A							:     List[Any]			=						self.recursive_tensorize(_a				)
           _A							:     Optional[int]			=						self._consolidate(_a				)
           return column



     def 					a__	(  self		,     _a				)						->							Mapping:
           _A							:     List[Any]			=						self.numpy_arrow_extractor().extract_batch(_a				)
           _A							:     List[Any]			=						self.python_features_decoder.decode_batch(_a				)
           _A							:     Tuple			=						self.recursive_tensorize(_a				)
           for column_name in batch:
                 _A							:     Union[str, Any]			=						self._consolidate(batch[column_name]				)
           return batch







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

import math
from typing import Union

import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import randn_tensor
from .scheduling_utils import SchedulerMixin







class 						lowercase							(   UpperCamelCase__,UpperCamelCase__		):
     _a							 =						1

     @register_to_config
     def __init__(  self		,     _a=2000		,     _a=0.1		,     _a=20		,     _a=1e-3				)						->							List[Any]:
           _A							:     Dict			=						None
           _A							:     List[Any]			=						None
           _A							:     Dict			=						None

     def 					a__	(  self		,     _a		,     _a = None				)						->							Union[str, Any]:
           _A							:     Union[str, Any]			=						torch.linspace(1		,     self.config.sampling_eps		,     _a		,     device=_a				)

     def 					a__	(  self		,     _a		,     _a		,     _a		,     _a=None				)						->							Dict:
           if self.timesteps is None:
                 raise ValueError(
                     """`self.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"""				)

           # TODO(Patrick) better comments + non-PyTorch
           # postprocess model score
           _A							:     Any			=						(
               -0.25 * t**2 * (self.config.beta_max - self.config.beta_min) - 0.5 * t * self.config.beta_min
           )
           _A							:     List[Any]			=						torch.sqrt(1.0 - torch.exp(2.0 * log_mean_coeff				)				)
           _A							:     List[str]			=						std.flatten()
           while len(std.shape				) < len(score.shape				):
                 _A							:     List[Any]			=						std.unsqueeze(-1				)
           _A							:     int			=						-score / std

           # compute
           _A							:     Tuple			=						-1.0 / len(self.timesteps				)

           _A							:     str			=						self.config.beta_min + t * (self.config.beta_max - self.config.beta_min)
           _A							:     List[str]			=						beta_t.flatten()
           while len(beta_t.shape				) < len(x.shape				):
                 _A							:     Union[str, Any]			=						beta_t.unsqueeze(-1				)
           _A							:     Tuple			=						-0.5 * beta_t * x

           _A							:     Tuple			=						torch.sqrt(_a				)
           _A							:     Dict			=						drift - diffusion**2 * score
           _A							:     Dict			=						x + drift * dt

           # add noise
           _A							:     Any			=						randn_tensor(x.shape		,     layout=x.layout		,     generator=_a		,     device=x.device		,     dtype=x.dtype				)
           _A							:     str			=						x_mean + diffusion * math.sqrt(-dt				) * noise

           return x, x_mean



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

