



'''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 			_SCREAMING_SNAKE_CASE				:
			# setable values
			A__								=  None
			A__								=  None
			A__								=  None



			@classmethod
			def 				lowerCAmelCase (     cls		:						Union[str, Any]			)      ->      List[Any]:
							"""simple docstring"""





							return cls()






@dataclass
class 			_SCREAMING_SNAKE_CASE				(lowercase__      ):
			A__								=  42
			A__								=  42
			A__								=  None






class 			_SCREAMING_SNAKE_CASE				(lowercase__,	lowercase__      ):



			@property
			def 				lowerCAmelCase (     self		:						Dict			)      ->      str:
							"""simple docstring"""





							return True



			@register_to_config
			def __init__(     self		:						List[str]    ,			__UpperCamelCase		:						int = 2000    ,			__UpperCamelCase		:						float = 0.15    ,			__UpperCamelCase		:						float = 0.01    ,			__UpperCamelCase		:						float = 1348.0    ,			__UpperCamelCase		:						float = 1e-5    ,			__UpperCamelCase		:						int = 1    ,			)      ->      Optional[Any]:
							"""simple docstring"""





							pass



			def 				lowerCAmelCase (     self		:						Union[str, Any]			)      ->      List[Any]:
							"""simple docstring"""





							snake_case__      :   str      =    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 				lowerCAmelCase (     self		:						Union[str, Any]    ,			__UpperCamelCase		:						ScoreSdeVeSchedulerState    ,			__UpperCamelCase		:						int    ,			__UpperCamelCase		:						Tuple = ()    ,			__UpperCamelCase		:						float = None			)      ->      ScoreSdeVeSchedulerState:
							"""simple docstring"""





							snake_case__      :   str      =    sampling_eps if sampling_eps is not None else self.config.sampling_eps

							snake_case__      :   Dict      =    jnp.linspace(1    ,			__UpperCamelCase    ,			__UpperCamelCase			)
							return state.replace(timesteps=__UpperCamelCase			)



			def 				lowerCAmelCase (     self		:						Optional[int]    ,			__UpperCamelCase		:						ScoreSdeVeSchedulerState    ,			__UpperCamelCase		:						int    ,			__UpperCamelCase		:						float = None    ,			__UpperCamelCase		:						float = None    ,			__UpperCamelCase		:						float = None    ,			)      ->      ScoreSdeVeSchedulerState:
							"""simple docstring"""





							snake_case__      :   List[str]      =    sigma_min if sigma_min is not None else self.config.sigma_min
							snake_case__      :   int      =    sigma_max if sigma_max is not None else self.config.sigma_max
							snake_case__      :   Union[str, Any]      =    sampling_eps if sampling_eps is not None else self.config.sampling_eps
							if state.timesteps is None:
											snake_case__      :   int      =    self.set_timesteps(__UpperCamelCase    ,			__UpperCamelCase    ,			__UpperCamelCase			)

							snake_case__      :   Dict      =    jnp.exp(jnp.linspace(jnp.log(__UpperCamelCase			)    ,			jnp.log(__UpperCamelCase			)    ,			__UpperCamelCase			)			)
							snake_case__      :   str      =    jnp.array([sigma_min * (sigma_max / sigma_min) ** t for t in state.timesteps]			)

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



			def 				lowerCAmelCase (     self		:						Optional[int]    ,			__UpperCamelCase		:						Any    ,			__UpperCamelCase		:						Optional[int]    ,			__UpperCamelCase		:						List[Any]			)      ->      Optional[int]:
							"""simple docstring"""





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



			def 				lowerCAmelCase (     self		:						Tuple    ,			__UpperCamelCase		:						ScoreSdeVeSchedulerState    ,			__UpperCamelCase		:						jnp.ndarray    ,			__UpperCamelCase		:						int    ,			__UpperCamelCase		:						jnp.ndarray    ,			__UpperCamelCase		:						random.KeyArray    ,			__UpperCamelCase		:						bool = 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'''			)

							snake_case__      :   Optional[int]      =    timestep * jnp.ones(
							    sample.shape[0]    ,			)
							snake_case__      :   List[str]      =    (timestep * (len(state.timesteps			) - 1)).long()

							snake_case__      :   Any      =    state.discrete_sigmas[timesteps]
							snake_case__      :   List[Any]      =    self.get_adjacent_sigma(__UpperCamelCase    ,			__UpperCamelCase    ,			__UpperCamelCase			)
							snake_case__      :   Optional[Any]      =    jnp.zeros_like(__UpperCamelCase			)
							snake_case__      :   Any      =    (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
							snake_case__      :   Union[str, Any]      =    diffusion.flatten()
							snake_case__      :   List[str]      =    broadcast_to_shape_from_left(__UpperCamelCase    ,			sample.shape			)
							snake_case__      :   int      =    drift - diffusion**2 * model_output

							#  equation 6: sample noise for the diffusion term of
							snake_case__      :   List[Any]      =    random.split(__UpperCamelCase    ,			num=1			)
							snake_case__      :   Dict      =    random.normal(key=__UpperCamelCase    ,			shape=sample.shape			)
							snake_case__      :   Any      =    sample - drift  # subtract because `dt` is a small negative timestep
							# TODO is the variable diffusion the correct scaling term for the noise?
							snake_case__      :   int      =    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 				lowerCAmelCase (     self		:						Union[str, Any]    ,			__UpperCamelCase		:						ScoreSdeVeSchedulerState    ,			__UpperCamelCase		:						jnp.ndarray    ,			__UpperCamelCase		:						jnp.ndarray    ,			__UpperCamelCase		:						random.KeyArray    ,			__UpperCamelCase		:						bool = 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
							snake_case__      :   Union[str, Any]      =    random.split(__UpperCamelCase    ,			num=1			)
							snake_case__      :   int      =    random.normal(key=__UpperCamelCase    ,			shape=sample.shape			)

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

							# compute corrected sample: model_output term and noise term
							snake_case__      :   Any      =    step_size.flatten()
							snake_case__      :   Optional[Any]      =    broadcast_to_shape_from_left(__UpperCamelCase    ,			sample.shape			)
							snake_case__      :   int      =    sample + step_size * model_output
							snake_case__      :   List[str]      =    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[int]			)      ->      Tuple:
							"""simple docstring"""





							return self.config.num_train_timesteps







import sys
from collections.abc import Mapping

import numpy as np
import pyarrow as pa

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



class     lowercase_       (    TensorFormatter[Mapping, np.ndarray, Mapping]	):



      def __init__(     self :							Union[str, Any]     ,   A__ :							Dict=None     ,   **A__ :							str						)       ->	Any:
        super().__init__(features=A__						)
        _snake_case		=				np_array_kwargs



      def 		UpperCamelCase_						(     self :							str     ,   A__ :							Dict						)       ->	Dict:
        if isinstance(A__     ,   A__						):
          if column and all(
              isinstance(A__     ,   np.ndarray						) and x.shape == column[0].shape and x.dtype == column[0].dtype for x in column						):
            return np.stack(A__						)
          else:
            # don't use np.array(column, dtype=object)
            # since it fails in certain cases
            # see https://stackoverflow.com/q/51005699
            _snake_case		=				np.empty(len(A__						)     ,   dtype=A__						)
            _snake_case		=				column
            return out
        return column



      def 		UpperCamelCase_						(     self :							Tuple     ,   A__ :							Optional[int]						)       ->	Optional[Any]:
        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
        elif isinstance(A__     ,   np.number						):
          return value

        _snake_case		=				{}

        if isinstance(A__     ,   np.ndarray						) and np.issubdtype(value.dtype     ,   np.integer						):
          _snake_case		=				{'''dtype''': np.intaa}
        elif isinstance(A__     ,   np.ndarray						) and np.issubdtype(value.dtype     ,   np.floating						):
          _snake_case		=				{'''dtype''': np.floataa}
        elif config.PIL_AVAILABLE and "PIL" in sys.modules:
          import PIL.Image

          if isinstance(A__     ,   PIL.Image.Image						):
            return np.asarray(A__     ,   **self.np_array_kwargs						)

        return np.asarray(A__     ,   **{**default_dtype, **self.np_array_kwargs}						)



      def 		UpperCamelCase_						(     self :							Union[str, Any]     ,   A__ :							Union[str, Any]						)       ->	int:
        # 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__     ,   (np.ndarray, np.character, np.number)						):
          _snake_case		=				data_struct.__array__()
        # support for nested types like struct of list of struct
        if isinstance(A__     ,   np.ndarray						):
          if data_struct.dtype == object:
            return self._consolidate([self.recursive_tensorize(A__						) for substruct in data_struct]						)
        if isinstance(A__     ,   (list, tuple)						):
          return self._consolidate([self.recursive_tensorize(A__						) for substruct in data_struct]						)
        return self._tensorize(A__						)



      def 		UpperCamelCase_						(     self :							List[Any]     ,   A__ :							dict						)       ->	Tuple:
        return map_nested(self._recursive_tensorize     ,   A__     ,   map_list=A__						)



      def 		UpperCamelCase_						(     self :							str     ,   A__ :							pa.Table						)       ->	Mapping:
        _snake_case		=				self.numpy_arrow_extractor().extract_row(A__						)
        _snake_case		=				self.python_features_decoder.decode_row(A__						)
        return self.recursive_tensorize(A__						)



      def 		UpperCamelCase_						(     self :							Optional[Any]     ,   A__ :							pa.Table						)       ->	np.ndarray:
        _snake_case		=				self.numpy_arrow_extractor().extract_column(A__						)
        _snake_case		=				self.python_features_decoder.decode_column(A__     ,   pa_table.column_names[0]						)
        _snake_case		=				self.recursive_tensorize(A__						)
        _snake_case		=				self._consolidate(A__						)
        return column







      def 		UpperCamelCase_						(     self :							Dict     ,   A__ :							pa.Table						)       ->	Mapping:
        _snake_case		=				self.numpy_arrow_extractor().extract_batch(A__						)
        _snake_case		=				self.python_features_decoder.decode_batch(A__						)
        _snake_case		=				self.recursive_tensorize(A__						)
        for column_name in batch:
          _snake_case		=				self._consolidate(batch[column_name]						)
        return batch

