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

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, logging, randn_tensor
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin


a__				:      Any    		=     logging.get_logger(__name__)  # pylint: disable=invalid-name






@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->EulerAncestralDiscrete
class     a_       (			a__							):



       """simple docstring"""
       __SCREAMING_SNAKE_CASE       :					torch.FloatTensor
       __SCREAMING_SNAKE_CASE       :					Optional[torch.FloatTensor]						=  None







def 				UpperCAmelCase_(    a__ ,						a__=0.999 ,						a__="cosine" ,						):



    """simple docstring"""



    if alpha_transform_type == "cosine":

        def alpha_bar_fn(a__   ):
            return math.cos((t + 0.008) / 1.008 * math.pi / 2   ) ** 2

    elif alpha_transform_type == "exp":

        def alpha_bar_fn(a__   ):
            return math.exp(t * -12.0   )

    else:
        raise ValueError(F"""Unsupported alpha_tranform_type: {alpha_transform_type}"""   )

    SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = []
    for i in range(a__   ):
        SCREAMING_SNAKE_CASE				:				Tuple				      = i / num_diffusion_timesteps
        SCREAMING_SNAKE_CASE				:				List[Any]				      = (i + 1) / num_diffusion_timesteps
        betas.append(min(1 - alpha_bar_fn(a__   ) / alpha_bar_fn(a__   ) ,						a__   )   )
    return torch.tensor(a__ ,						dtype=torch.floataa   )






class     a_       (			a__				,    a__							):



       """simple docstring"""
       __SCREAMING_SNAKE_CASE       :					List[str]						=  [e.name for e in KarrasDiffusionSchedulers]
       __SCREAMING_SNAKE_CASE       :					int						=  1






       @register_to_config
       def __init__(		self				,       _lowerCamelCase = 1000				,       _lowerCamelCase = 0.0_0_0_1				,       _lowerCamelCase = 0.0_2				,       _lowerCamelCase = "linear"				,       _lowerCamelCase = None				,       _lowerCamelCase = "epsilon"				,       _lowerCamelCase = "linspace"				,       _lowerCamelCase = 0				,       )					->Tuple:
           if trained_betas is not None:
               SCREAMING_SNAKE_CASE				:				int				      = torch.tensor(_lowerCamelCase				,       dtype=torch.floataa   )
           elif beta_schedule == "linear":
               SCREAMING_SNAKE_CASE				:				List[Any]				      = torch.linspace(_lowerCamelCase				,       _lowerCamelCase				,       _lowerCamelCase				,       dtype=torch.floataa   )
           elif beta_schedule == "scaled_linear":
               # this schedule is very specific to the latent diffusion model.
               SCREAMING_SNAKE_CASE				:				str				      = (
                   torch.linspace(beta_start**0.5				,       beta_end**0.5				,       _lowerCamelCase				,       dtype=torch.floataa   ) ** 2
               )
           elif beta_schedule == "squaredcos_cap_v2":
               # Glide cosine schedule
               SCREAMING_SNAKE_CASE				:				Tuple				      = betas_for_alpha_bar(_lowerCamelCase   )
           else:
               raise NotImplementedError(F"""{beta_schedule} does is not implemented for {self.__class__}"""   )

           SCREAMING_SNAKE_CASE				:				str				      = 1.0 - self.betas
           SCREAMING_SNAKE_CASE				:				List[str]				      = torch.cumprod(self.alphas				,       dim=0   )

           SCREAMING_SNAKE_CASE				:				Optional[Any]				      = np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5   )
           SCREAMING_SNAKE_CASE				:				Tuple				      = np.concatenate([sigmas[::-1], [0.0]]   ).astype(np.floataa   )
           SCREAMING_SNAKE_CASE				:				Dict				      = torch.from_numpy(_lowerCamelCase   )

           # setable values
           SCREAMING_SNAKE_CASE				:				Optional[int]				      = None
           SCREAMING_SNAKE_CASE				:				Any				      = np.linspace(0				,       num_train_timesteps - 1				,       _lowerCamelCase				,       dtype=_lowerCamelCase   )[::-1].copy()
           SCREAMING_SNAKE_CASE				:				List[Any]				      = torch.from_numpy(_lowerCamelCase   )
           SCREAMING_SNAKE_CASE				:				Optional[int]				      = False






       @property
       def 	__lowerCAmelCase     (		self   )					->List[str]:
           # standard deviation of the initial noise distribution
           if self.config.timestep_spacing in ["linspace", "trailing"]:
               return self.sigmas.max()

           return (self.sigmas.max() ** 2 + 1) ** 0.5






       def 	__lowerCAmelCase     (		self				,       _lowerCamelCase				,       _lowerCamelCase   )					->torch.FloatTensor:
           if isinstance(_lowerCamelCase				,       torch.Tensor   ):
               SCREAMING_SNAKE_CASE				:				str				      = timestep.to(self.timesteps.device   )
           SCREAMING_SNAKE_CASE				:				List[Any]				      = (self.timesteps == timestep).nonzero().item()
           SCREAMING_SNAKE_CASE				:				List[str]				      = self.sigmas[step_index]
           SCREAMING_SNAKE_CASE				:				List[Any]				      = sample / ((sigma**2 + 1) ** 0.5)
           SCREAMING_SNAKE_CASE				:				int				      = True
           return sample






       def 	__lowerCAmelCase     (		self				,       _lowerCamelCase				,       _lowerCamelCase = None   )					->Optional[int]:
           SCREAMING_SNAKE_CASE				:				Optional[int]				      = num_inference_steps

           # "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
           if self.config.timestep_spacing == "linspace":
               SCREAMING_SNAKE_CASE				:				Optional[int]				      = np.linspace(0				,       self.config.num_train_timesteps - 1				,       _lowerCamelCase				,       dtype=_lowerCamelCase   )[
                   ::-1
               ].copy()
           elif self.config.timestep_spacing == "leading":
               SCREAMING_SNAKE_CASE				:				List[Any]				      = self.config.num_train_timesteps // self.num_inference_steps
               # creates integer timesteps by multiplying by ratio
               # casting to int to avoid issues when num_inference_step is power of 3
               SCREAMING_SNAKE_CASE				:				List[Any]				      = (np.arange(0				,       _lowerCamelCase   ) * step_ratio).round()[::-1].copy().astype(_lowerCamelCase   )
               timesteps += self.config.steps_offset
           elif self.config.timestep_spacing == "trailing":
               SCREAMING_SNAKE_CASE				:				Optional[Any]				      = self.config.num_train_timesteps / self.num_inference_steps
               # creates integer timesteps by multiplying by ratio
               # casting to int to avoid issues when num_inference_step is power of 3
               SCREAMING_SNAKE_CASE				:				Optional[Any]				      = (np.arange(self.config.num_train_timesteps				,       0				,       -step_ratio   )).round().copy().astype(_lowerCamelCase   )
               timesteps -= 1
           else:
               raise ValueError(
                   F"""{self.config.timestep_spacing} is not supported. Please make sure to choose one of 'linspace', 'leading' or 'trailing'."""   )

           SCREAMING_SNAKE_CASE				:				int				      = np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5   )
           SCREAMING_SNAKE_CASE				:				Optional[int]				      = np.interp(_lowerCamelCase				,       np.arange(0				,       len(_lowerCamelCase   )   )				,       _lowerCamelCase   )
           SCREAMING_SNAKE_CASE				:				List[Any]				      = np.concatenate([sigmas, [0.0]]   ).astype(np.floataa   )
           SCREAMING_SNAKE_CASE				:				Dict				      = torch.from_numpy(_lowerCamelCase   ).to(device=_lowerCamelCase   )
           if str(_lowerCamelCase   ).startswith('''mps'''   ):
               # mps does not support float64
               SCREAMING_SNAKE_CASE				:				str				      = torch.from_numpy(_lowerCamelCase   ).to(_lowerCamelCase				,       dtype=torch.floataa   )
           else:
               SCREAMING_SNAKE_CASE				:				Tuple				      = torch.from_numpy(_lowerCamelCase   ).to(device=_lowerCamelCase   )






       def 	__lowerCAmelCase     (		self				,       _lowerCamelCase				,       _lowerCamelCase				,       _lowerCamelCase				,       _lowerCamelCase = None				,       _lowerCamelCase = True				,       )					->Union[EulerAncestralDiscreteSchedulerOutput, Tuple]:

           if (
               isinstance(_lowerCamelCase				,       _lowerCamelCase   )
               or isinstance(_lowerCamelCase				,       torch.IntTensor   )
               or isinstance(_lowerCamelCase				,       torch.LongTensor   )
           ):
               raise ValueError(
                   (
                       '''Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to'''
                       ''' `EulerDiscreteScheduler.step()` is not supported. Make sure to pass'''
                       ''' one of the `scheduler.timesteps` as a timestep.'''
                   )				,       )

           if not self.is_scale_input_called:
               logger.warning(
                   '''The `scale_model_input` function should be called before `step` to ensure correct denoising. '''
                   '''See `StableDiffusionPipeline` for a usage example.'''   )

           if isinstance(_lowerCamelCase				,       torch.Tensor   ):
               SCREAMING_SNAKE_CASE				:				Optional[int]				      = timestep.to(self.timesteps.device   )

           SCREAMING_SNAKE_CASE				:				int				      = (self.timesteps == timestep).nonzero().item()
           SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = self.sigmas[step_index]

           # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
           if self.config.prediction_type == "epsilon":
               SCREAMING_SNAKE_CASE				:				str				      = sample - sigma * model_output
           elif self.config.prediction_type == "v_prediction":
               # * c_out + input * c_skip
               SCREAMING_SNAKE_CASE				:				List[str]				      = model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
           elif self.config.prediction_type == "sample":
               raise NotImplementedError('''prediction_type not implemented yet: sample'''   )
           else:
               raise ValueError(
                   F"""prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`"""   )

           SCREAMING_SNAKE_CASE				:				int				      = self.sigmas[step_index]
           SCREAMING_SNAKE_CASE				:				Optional[int]				      = self.sigmas[step_index + 1]
           SCREAMING_SNAKE_CASE				:				List[Any]				      = (sigma_to**2 * (sigma_from**2 - sigma_to**2) / sigma_from**2) ** 0.5
           SCREAMING_SNAKE_CASE				:				int				      = (sigma_to**2 - sigma_up**2) ** 0.5

           # 2. Convert to an ODE derivative
           SCREAMING_SNAKE_CASE				:				List[Any]				      = (sample - pred_original_sample) / sigma

           SCREAMING_SNAKE_CASE				:				List[str]				      = sigma_down - sigma

           SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = sample + derivative * dt

           SCREAMING_SNAKE_CASE				:				List[Any]				      = model_output.device
           SCREAMING_SNAKE_CASE				:				Optional[Any]				      = randn_tensor(model_output.shape				,       dtype=model_output.dtype				,       device=_lowerCamelCase				,       generator=_lowerCamelCase   )

           SCREAMING_SNAKE_CASE				:				List[str]				      = prev_sample + noise * sigma_up

           if not return_dict:
               return (prev_sample,)

           return EulerAncestralDiscreteSchedulerOutput(
               prev_sample=_lowerCamelCase				,       pred_original_sample=_lowerCamelCase   )






       def 	__lowerCAmelCase     (		self				,       _lowerCamelCase				,       _lowerCamelCase				,       _lowerCamelCase				,       )					->torch.FloatTensor:
           # Make sure sigmas and timesteps have the same device and dtype as original_samples
           SCREAMING_SNAKE_CASE				:				int				      = self.sigmas.to(device=original_samples.device				,       dtype=original_samples.dtype   )
           if original_samples.device.type == "mps" and torch.is_floating_point(_lowerCamelCase   ):
               # mps does not support float64
               SCREAMING_SNAKE_CASE				:				str				      = self.timesteps.to(original_samples.device				,       dtype=torch.floataa   )
               SCREAMING_SNAKE_CASE				:				List[Any]				      = timesteps.to(original_samples.device				,       dtype=torch.floataa   )
           else:
               SCREAMING_SNAKE_CASE				:				Optional[Any]				      = self.timesteps.to(original_samples.device   )
               SCREAMING_SNAKE_CASE				:				List[str]				      = timesteps.to(original_samples.device   )

           SCREAMING_SNAKE_CASE				:				str				      = [(schedule_timesteps == t).nonzero().item() for t in timesteps]

           SCREAMING_SNAKE_CASE				:				Optional[int]				      = sigmas[step_indices].flatten()
           while len(sigma.shape   ) < len(original_samples.shape   ):
               SCREAMING_SNAKE_CASE				:				Optional[Any]				      = sigma.unsqueeze(-1   )

           SCREAMING_SNAKE_CASE				:				List[Any]				      = original_samples + noise * sigma
           return noisy_samples






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


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

import numpy as np
import pyarrow as pa

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


if TYPE_CHECKING:
      import tensorflow as tf






class     a_       (			TensorFormatter[Mapping, 'tf.Tensor', Mapping]							):



       """simple docstring"""






       def __init__(		self				,       _lowerCamelCase=None				,       **_lowerCamelCase   )					->int:
           super().__init__(features=_lowerCamelCase   )
           SCREAMING_SNAKE_CASE				:				Any				      = tf_tensor_kwargs
           import tensorflow as tf  # noqa: import tf at initialization






       def 	__lowerCAmelCase     (		self				,       _lowerCamelCase   )					->Tuple:
           import tensorflow as tf

           if isinstance(_lowerCamelCase				,       _lowerCamelCase   ) and column:
               if all(
                   isinstance(_lowerCamelCase				,       tf.Tensor   ) and x.shape == column[0].shape and x.dtype == column[0].dtype for x in column   ):
                   return tf.stack(_lowerCamelCase   )
               elif all(
                   isinstance(_lowerCamelCase				,       (tf.Tensor, tf.RaggedTensor)   ) and x.ndim == 1 and x.dtype == column[0].dtype
                   for x in column   ):
                   # only rag 1-D tensors, otherwise some dimensions become ragged even though they were consolidated
                   return tf.ragged.stack(_lowerCamelCase   )

           return column






       def 	__lowerCAmelCase     (		self				,       _lowerCamelCase   )					->Union[str, Any]:
           import tensorflow as tf

           if value is None:
               return value

           SCREAMING_SNAKE_CASE				:				int				      = {}

           if isinstance(_lowerCamelCase				,       (np.number, np.ndarray)   ) and np.issubdtype(value.dtype				,       np.integer   ):
               SCREAMING_SNAKE_CASE				:				List[Any]				      = {'''dtype''': tf.intaa}
           elif isinstance(_lowerCamelCase				,       (np.number, np.ndarray)   ) and np.issubdtype(value.dtype				,       np.floating   ):
               SCREAMING_SNAKE_CASE				:				Optional[int]				      = {'''dtype''': tf.floataa}
           elif config.PIL_AVAILABLE and "PIL" in sys.modules:
               import PIL.Image

               if isinstance(_lowerCamelCase				,       PIL.Image.Image   ):
                   SCREAMING_SNAKE_CASE				:				Any				      = np.asarray(_lowerCamelCase   )

           return tf.convert_to_tensor(_lowerCamelCase				,       **{**default_dtype, **self.tf_tensor_kwargs}   )






       def 	__lowerCAmelCase     (		self				,       _lowerCamelCase   )					->Tuple:
           import tensorflow as tf

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

               if isinstance(_lowerCamelCase				,       torch.Tensor   ):
                   return self._tensorize(data_struct.detach().cpu().numpy()[()]   )
           if hasattr(_lowerCamelCase				,       '''__array__'''   ) and not isinstance(_lowerCamelCase				,       tf.Tensor   ):
               SCREAMING_SNAKE_CASE				:				int				      = data_struct.__array__()
           # support for nested types like struct of list of struct
           if isinstance(_lowerCamelCase				,       np.ndarray   ):
               if data_struct.dtype == object:  # tf tensors cannot be instantied from an array of objects
                   return self._consolidate([self.recursive_tensorize(_lowerCamelCase   ) for substruct in data_struct]   )
           elif isinstance(_lowerCamelCase				,       (list, tuple)   ):
               return self._consolidate([self.recursive_tensorize(_lowerCamelCase   ) for substruct in data_struct]   )
           return self._tensorize(_lowerCamelCase   )






       def 	__lowerCAmelCase     (		self				,       _lowerCamelCase   )					->Optional[int]:
           return map_nested(self._recursive_tensorize				,       _lowerCamelCase				,       map_list=_lowerCamelCase   )






       def 	__lowerCAmelCase     (		self				,       _lowerCamelCase   )					->Mapping:
           SCREAMING_SNAKE_CASE				:				str				      = self.numpy_arrow_extractor().extract_row(_lowerCamelCase   )
           SCREAMING_SNAKE_CASE				:				List[str]				      = self.python_features_decoder.decode_row(_lowerCamelCase   )
           return self.recursive_tensorize(_lowerCamelCase   )






       def 	__lowerCAmelCase     (		self				,       _lowerCamelCase   )					->"tf.Tensor":
           SCREAMING_SNAKE_CASE				:				Optional[int]				      = self.numpy_arrow_extractor().extract_column(_lowerCamelCase   )
           SCREAMING_SNAKE_CASE				:				str				      = self.python_features_decoder.decode_column(_lowerCamelCase				,       pa_table.column_names[0]   )
           SCREAMING_SNAKE_CASE				:				str				      = self.recursive_tensorize(_lowerCamelCase   )
           SCREAMING_SNAKE_CASE				:				int				      = self._consolidate(_lowerCamelCase   )
           return column






       def 	__lowerCAmelCase     (		self				,       _lowerCamelCase   )					->Mapping:
           SCREAMING_SNAKE_CASE				:				Tuple				      = self.numpy_arrow_extractor().extract_batch(_lowerCamelCase   )
           SCREAMING_SNAKE_CASE				:				Dict				      = self.python_features_decoder.decode_batch(_lowerCamelCase   )
           SCREAMING_SNAKE_CASE				:				int				      = self.recursive_tensorize(_lowerCamelCase   )
           for column_name in batch:
               SCREAMING_SNAKE_CASE				:				Optional[Any]				      = self._consolidate(batch[column_name]   )
           return batch
