

# DISCLAIMER: This code is strongly influenced by https://github.com/pesser/pytorch_diffusion
# and https://github.com/hojonathanho/diffusion

from dataclasses import dataclass
from typing import Optional, Tuple, Union

import flax
import jax.numpy as jnp

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import (
    CommonSchedulerState,
    FlaxKarrasDiffusionSchedulers,
    FlaxSchedulerMixin,
    FlaxSchedulerOutput,
    add_noise_common,
    get_velocity_common,
)



@flax.struct.dataclass
class     lowercase_       :
      UpperCamelCase_     :						CommonSchedulerState
      UpperCamelCase_     :						jnp.ndarray

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



      @classmethod
      def 		UpperCamelCase_						(     cls :							Dict     ,   A__ :							CommonSchedulerState     ,   A__ :							jnp.ndarray     ,   A__ :							jnp.ndarray     ,   A__ :							jnp.ndarray     ,   )       ->	str:
        return cls(
            common=A__     ,   final_alpha_cumprod=A__     ,   init_noise_sigma=A__     ,   timesteps=A__     ,   )



@dataclass
class     lowercase_       (    __lowercase	):
      UpperCamelCase_     :						DDIMSchedulerState



class     lowercase_       (    __lowercase  ,				__lowercase	):
      UpperCamelCase_     :						int		 =							[e.name for e in FlaxKarrasDiffusionSchedulers]

      UpperCamelCase_     :						jnp.dtype



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



      @register_to_config
      def __init__(     self :							Optional[Any]     ,   A__ :							int = 1000     ,   A__ :							float = 0.0001     ,   A__ :							float = 0.02     ,   A__ :							str = "linear"     ,   A__ :							Optional[jnp.ndarray] = None     ,   A__ :							bool = True     ,   A__ :							int = 0     ,   A__ :							str = "epsilon"     ,   A__ :							jnp.dtype = jnp.floataa     ,   )       ->	Union[str, Any]:
        _snake_case		=				dtype



      def 		UpperCamelCase_						(     self :							Union[str, Any]     ,   A__ :							Optional[CommonSchedulerState] = None						)       ->	DDIMSchedulerState:
        if common is None:
          _snake_case		=				CommonSchedulerState.create(self						)

        # At every step in ddim, we are looking into the previous alphas_cumprod
        # For the final step, there is no previous alphas_cumprod because we are already at 0
        # `set_alpha_to_one` decides whether we set this parameter simply to one or
        # whether we use the final alpha of the "non-previous" one.
        _snake_case		=				(
            jnp.array(1.0     ,   dtype=self.dtype						) if self.config.set_alpha_to_one else common.alphas_cumprod[0]
        )

        # standard deviation of the initial noise distribution
        _snake_case		=				jnp.array(1.0     ,   dtype=self.dtype						)

        _snake_case		=				jnp.arange(0     ,   self.config.num_train_timesteps						).round()[::-1]

        return DDIMSchedulerState.create(
            common=A__     ,   final_alpha_cumprod=A__     ,   init_noise_sigma=A__     ,   timesteps=A__     ,   )



      def 		UpperCamelCase_						(     self :							Any     ,   A__ :							DDIMSchedulerState     ,   A__ :							jnp.ndarray     ,   A__ :							Optional[int] = None						)       ->	jnp.ndarray:
        return sample



      def 		UpperCamelCase_						(     self :							Optional[int]     ,   A__ :							DDIMSchedulerState     ,   A__ :							int     ,   A__ :							Tuple = ()						)       ->	DDIMSchedulerState:
        _snake_case		=				self.config.num_train_timesteps // num_inference_steps
        # creates integer timesteps by multiplying by ratio
        # rounding to avoid issues when num_inference_step is power of 3
        _snake_case		=				(jnp.arange(0     ,   A__						) * step_ratio).round()[::-1] + self.config.steps_offset

        return state.replace(
            num_inference_steps=A__     ,   timesteps=A__     ,   )



      def 		UpperCamelCase_						(     self :							str     ,   A__ :							DDIMSchedulerState     ,   A__ :							int     ,   A__ :							Any						)       ->	Dict:
        _snake_case		=				state.common.alphas_cumprod[timestep]
        _snake_case		=				jnp.where(
            prev_timestep >= 0     ,   state.common.alphas_cumprod[prev_timestep]     ,   state.final_alpha_cumprod						)
        _snake_case		=				1 - alpha_prod_t
        _snake_case		=				1 - alpha_prod_t_prev

        _snake_case		=				(beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev)

        return variance



      def 		UpperCamelCase_						(     self :							Optional[Any]     ,   A__ :							DDIMSchedulerState     ,   A__ :							jnp.ndarray     ,   A__ :							int     ,   A__ :							jnp.ndarray     ,   A__ :							float = 0.0     ,   A__ :							bool = True     ,   )       ->	Union[FlaxDDIMSchedulerOutput, Tuple]:
        if state.num_inference_steps is None:
          raise ValueError(
              '''Number of inference steps is \'None\', you need to run \'set_timesteps\' after creating the scheduler'''						)

        # See formulas (12) and (16) of DDIM paper https://arxiv.org/pdf/2010.02502.pdf
        # Ideally, read DDIM paper in-detail understanding

        # Notation (<variable name> -> <name in paper>
        # - pred_noise_t -> e_theta(x_t, t)
        # - pred_original_sample -> f_theta(x_t, t) or x_0
        # - std_dev_t -> sigma_t
        # - eta -> η
        # - pred_sample_direction -> "direction pointing to x_t"
        # - pred_prev_sample -> "x_t-1"

        # 1. get previous step value (=t-1)
        _snake_case		=				timestep - self.config.num_train_timesteps // state.num_inference_steps

        _snake_case		=				state.common.alphas_cumprod
        _snake_case		=				state.final_alpha_cumprod

        # 2. compute alphas, betas
        _snake_case		=				alphas_cumprod[timestep]
        _snake_case		=				jnp.where(prev_timestep >= 0     ,   alphas_cumprod[prev_timestep]     ,   A__						)

        _snake_case		=				1 - alpha_prod_t

        # 3. compute predicted original sample from predicted noise also called
        # "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
        if self.config.prediction_type == "epsilon":
          _snake_case		=				(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
          _snake_case		=				model_output
        elif self.config.prediction_type == "sample":
          _snake_case		=				model_output
          _snake_case		=				(sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5
        elif self.config.prediction_type == "v_prediction":
          _snake_case		=				(alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
          _snake_case		=				(alpha_prod_t**0.5) * model_output + (beta_prod_t**0.5) * sample
        else:
          raise ValueError(
              f"""prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`, or"""
              ''' `v_prediction`'''						)

        # 4. compute variance: "sigma_t(η)" -> see formula (16)
        # σ_t = sqrt((1 − α_t−1)/(1 − α_t)) * sqrt(1 − α_t/α_t−1)
        _snake_case		=				self._get_variance(A__     ,   A__     ,   A__						)
        _snake_case		=				eta * variance ** 0.5

        # 5. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
        _snake_case		=				(1 - alpha_prod_t_prev - std_dev_t**2) ** 0.5 * pred_epsilon

        # 6. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
        _snake_case		=				alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction

        if not return_dict:
          return (prev_sample, state)

        return FlaxDDIMSchedulerOutput(prev_sample=A__     ,   state=A__						)



      def 		UpperCamelCase_						(     self :							Tuple     ,   A__ :							DDIMSchedulerState     ,   A__ :							jnp.ndarray     ,   A__ :							jnp.ndarray     ,   A__ :							jnp.ndarray     ,   )       ->	jnp.ndarray:
        return add_noise_common(state.common     ,   A__     ,   A__     ,   A__						)



      def 		UpperCamelCase_						(     self :							List[Any]     ,   A__ :							DDIMSchedulerState     ,   A__ :							jnp.ndarray     ,   A__ :							jnp.ndarray     ,   A__ :							jnp.ndarray     ,   )       ->	jnp.ndarray:
        return get_velocity_common(state.common     ,   A__     ,   A__     ,   A__						)







      def __len__(     self :							Union[str, Any]						)       ->	Union[str, Any]:
        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

