

# 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     lowercase_       :
      # setable values
      UpperCamelCase_     :						Optional[jnp.ndarray]		 =							None
      UpperCamelCase_     :						Optional[jnp.ndarray]		 =							None
      UpperCamelCase_     :						Optional[jnp.ndarray]		 =							None



      @classmethod
      def 		UpperCamelCase_						(     cls :							Dict						)       ->	int:
        return cls()



@dataclass
class     lowercase_       (    __lowercase	):
      UpperCamelCase_     :						ScoreSdeVeSchedulerState
      UpperCamelCase_     :						jnp.ndarray
      UpperCamelCase_     :						Optional[jnp.ndarray]		 =							None



class     lowercase_       (    __lowercase  ,				__lowercase	):



      @property
      def 		UpperCamelCase_						(     self :							int						)       ->	Union[str, Any]:
        return True



      @register_to_config
      def __init__(     self :							Any     ,   A__ :							int = 2000     ,   A__ :							float = 0.15     ,   A__ :							float = 0.01     ,   A__ :							float = 1348.0     ,   A__ :							float = 1e-5     ,   A__ :							int = 1     ,   )       ->	str:
        pass



      def 		UpperCamelCase_						(     self :							Tuple						)       ->	Any:
        _snake_case		=				ScoreSdeVeSchedulerState.create()
        return self.set_sigmas(
            A__     ,   self.config.num_train_timesteps     ,   self.config.sigma_min     ,   self.config.sigma_max     ,   self.config.sampling_eps     ,   )



      def 		UpperCamelCase_						(     self :							List[Any]     ,   A__ :							ScoreSdeVeSchedulerState     ,   A__ :							int     ,   A__ :							Tuple = ()     ,   A__ :							float = None						)       ->	ScoreSdeVeSchedulerState:
        _snake_case		=				sampling_eps if sampling_eps is not None else self.config.sampling_eps

        _snake_case		=				jnp.linspace(1     ,   A__     ,   A__						)
        return state.replace(timesteps=A__						)



      def 		UpperCamelCase_						(     self :							int     ,   A__ :							ScoreSdeVeSchedulerState     ,   A__ :							int     ,   A__ :							float = None     ,   A__ :							float = None     ,   A__ :							float = None     ,   )       ->	ScoreSdeVeSchedulerState:
        _snake_case		=				sigma_min if sigma_min is not None else self.config.sigma_min
        _snake_case		=				sigma_max if sigma_max is not None else self.config.sigma_max
        _snake_case		=				sampling_eps if sampling_eps is not None else self.config.sampling_eps
        if state.timesteps is None:
          _snake_case		=				self.set_timesteps(A__     ,   A__     ,   A__						)

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

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



      def 		UpperCamelCase_						(     self :							Union[str, Any]     ,   A__ :							Tuple     ,   A__ :							Optional[int]     ,   A__ :							Optional[Any]						)       ->	List[Any]:
        return jnp.where(timesteps == 0     ,   jnp.zeros_like(A__						)     ,   state.discrete_sigmas[timesteps - 1]						)



      def 		UpperCamelCase_						(     self :							Any     ,   A__ :							ScoreSdeVeSchedulerState     ,   A__ :							jnp.ndarray     ,   A__ :							int     ,   A__ :							jnp.ndarray     ,   A__ :							random.KeyArray     ,   A__ :							bool = True     ,   )       ->	Union[FlaxSdeVeOutput, Tuple]:
        if state.timesteps is None:
          raise ValueError(
              '''`state.timesteps` is not set, you need to run \'set_timesteps\' after creating the scheduler'''						)

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

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

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



      def 		UpperCamelCase_						(     self :							List[Any]     ,   A__ :							ScoreSdeVeSchedulerState     ,   A__ :							jnp.ndarray     ,   A__ :							jnp.ndarray     ,   A__ :							random.KeyArray     ,   A__ :							bool = True     ,   )       ->	Union[FlaxSdeVeOutput, Tuple]:
        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		=				random.split(A__     ,   num=1						)
        _snake_case		=				random.normal(key=A__     ,   shape=sample.shape						)

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

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

        if not return_dict:
          return (prev_sample, state)

        return FlaxSdeVeOutput(prev_sample=A__     ,   state=A__						)







      def __len__(     self :							str						)       ->	int:
        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

