

'''simple docstring'''

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


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







@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->EulerAncestralDiscrete
class lowerCamelCase   (    __lowerCAmelCase				):
				snake_case_							     = 42
				snake_case_							     = None

def      __magic_name__							(			A       , A=0.999       , A="cosine"       , )      ->		Optional[int]:
	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}'''				)

	snake_case							=					[]
	for i in range(A				):
		snake_case							=					i / num_diffusion_timesteps
		snake_case							=					(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 lowerCamelCase   (    __lowerCAmelCase				,				__lowerCAmelCase				):
				snake_case_							     = [e.name for e in KarrasDiffusionSchedulers]
				snake_case_							     = 1

				@register_to_config
				def __init__(       self,						lowercase_ = 1000,						lowercase_ = 0.0_001,						lowercase_ = 0.02,						lowercase_ = "linear",						lowercase_ = None,						lowercase_ = "epsilon",						lowercase_ = "linspace",						lowercase_ = 0,						)    ->							List[str]:
					if trained_betas is not None:
						snake_case							=					torch.tensor(lowercase_,						dtype=torch.floataa )
					elif beta_schedule == "linear":
						snake_case							=					torch.linspace(lowercase_,						lowercase_,						lowercase_,						dtype=torch.floataa )
					elif beta_schedule == "scaled_linear":
						# this schedule is very specific to the latent diffusion model.
						snake_case							=					(
						    torch.linspace(beta_start**0.5,						beta_end**0.5,						lowercase_,						dtype=torch.floataa ) ** 2
						)
					elif beta_schedule == "squaredcos_cap_v2":
						# Glide cosine schedule
						snake_case							=					betas_for_alpha_bar(lowercase_ )
					else:
						raise NotImplementedError(F'''{beta_schedule} does is not implemented for {self.__class__}''' )

					snake_case							=					1.0 - self.betas
					snake_case							=					torch.cumprod(self.alphas,						dim=0 )

					snake_case							=					np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5 )
					snake_case							=					np.concatenate([sigmas[::-1], [0.0]] ).astype(np.floataa )
					snake_case							=					torch.from_numpy(lowercase_ )

					# setable values
					snake_case							=					None
					snake_case							=					np.linspace(0,						num_train_timesteps - 1,						lowercase_,						dtype=lowercase_ )[::-1].copy()
					snake_case							=					torch.from_numpy(lowercase_ )
					snake_case							=					False

				@property
				def 					_lowerCamelCase    (       self )    ->							Any:
					# 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,						lowercase_,						lowercase_ )    ->							torch.FloatTensor:
					if isinstance(lowercase_,						torch.Tensor ):
						snake_case							=					timestep.to(self.timesteps.device )
					snake_case							=					(self.timesteps == timestep).nonzero().item()
					snake_case							=					self.sigmas[step_index]
					snake_case							=					sample / ((sigma**2 + 1) ** 0.5)
					snake_case							=					True
					return sample

				def 					_lowerCamelCase    (       self,						lowercase_,						lowercase_ = None )    ->							List[Any]:
					snake_case							=					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":
						snake_case							=					np.linspace(0,						self.config.num_train_timesteps - 1,						lowercase_,						dtype=lowercase_ )[
						    ::-1
						].copy()
					elif self.config.timestep_spacing == "leading":
						snake_case							=					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
						snake_case							=					(np.arange(0,						lowercase_ ) * step_ratio).round()[::-1].copy().astype(lowercase_ )
						timesteps += self.config.steps_offset
					elif self.config.timestep_spacing == "trailing":
						snake_case							=					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
						snake_case							=					(np.arange(self.config.num_train_timesteps,						0,						-step_ratio )).round().copy().astype(lowercase_ )
						timesteps -= 1
					else:
						raise ValueError(
						    F'''{self.config.timestep_spacing} is not supported. Please make sure to choose one of \'linspace\', \'leading\' or \'trailing\'.''' )

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

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

					if (
					    isinstance(lowercase_,						lowercase_ )
					    or isinstance(lowercase_,						torch.IntTensor )
					    or isinstance(lowercase_,						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(lowercase_,						torch.Tensor ):
						snake_case							=					timestep.to(self.timesteps.device )

					snake_case							=					(self.timesteps == timestep).nonzero().item()
					snake_case							=					self.sigmas[step_index]

					# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
					if self.config.prediction_type == "epsilon":
						snake_case							=					sample - sigma * model_output
					elif self.config.prediction_type == "v_prediction":
						# * c_out + input * c_skip
						snake_case							=					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`''' )

					snake_case							=					self.sigmas[step_index]
					snake_case							=					self.sigmas[step_index + 1]
					snake_case							=					(sigma_to**2 * (sigma_from**2 - sigma_to**2) / sigma_from**2) ** 0.5
					snake_case							=					(sigma_to**2 - sigma_up**2) ** 0.5

					# 2. Convert to an ODE derivative
					snake_case							=					(sample - pred_original_sample) / sigma

					snake_case							=					sigma_down - sigma

					snake_case							=					sample + derivative * dt

					snake_case							=					model_output.device
					snake_case							=					randn_tensor(model_output.shape,						dtype=model_output.dtype,						device=lowercase_,						generator=lowercase_ )

					snake_case							=					prev_sample + noise * sigma_up

					if not return_dict:
						return (prev_sample,)

					return EulerAncestralDiscreteSchedulerOutput(
					    prev_sample=lowercase_,						pred_original_sample=lowercase_ )

				def 					_lowerCamelCase    (       self,						lowercase_,						lowercase_,						lowercase_,						)    ->							torch.FloatTensor:
					# Make sure sigmas and timesteps have the same device and dtype as original_samples
					snake_case							=					self.sigmas.to(device=original_samples.device,						dtype=original_samples.dtype )
					if original_samples.device.type == "mps" and torch.is_floating_point(lowercase_ ):
						# mps does not support float64
						snake_case							=					self.timesteps.to(original_samples.device,						dtype=torch.floataa )
						snake_case							=					timesteps.to(original_samples.device,						dtype=torch.floataa )
					else:
						snake_case							=					self.timesteps.to(original_samples.device )
						snake_case							=					timesteps.to(original_samples.device )

					snake_case							=					[(schedule_timesteps == t).nonzero().item() for t in timesteps]

					snake_case							=					sigmas[step_indices].flatten()
					while len(sigma.shape ) < len(original_samples.shape ):
						snake_case							=					sigma.unsqueeze(-1 )

					snake_case							=					original_samples + noise * sigma
					return noisy_samples






				def __len__(       self )    ->							List[str]:
					return self.config.num_train_timesteps













from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available, is_tokenizers_available


lowerCamelCase					    =	{}

try:
   if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCamelCase					    =	['''MBart50Tokenizer''']

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCamelCase					    =	['''MBart50TokenizerFast''']


if TYPE_CHECKING:
   try:
      if not is_sentencepiece_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_mbartaa import MBartaaTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_mbartaa_fast import MBartaaTokenizerFast

else:
   import sys

   lowerCamelCase					    =	_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
