

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

@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->EulerAncestralDiscrete
class 					__UpperCamelCase					(       SCREAMING_SNAKE_CASE      ):
	_lowercase	: torch.FloatTensor
	_lowercase	: Optional[torch.FloatTensor]												=	None




def        _lowercase  (					a_					:       int       ,a_					:       Dict=0.999       ,a_					:       int="cosine"       ,)					->						Optional[int]:




					'''simple docstring'''







					if alpha_transform_type == "cosine":

										def alpha_bar_fn(a_					:       List[str]					):
															return math.cos((t + 0.008) / 1.008 * math.pi / 2					) ** 2

					elif alpha_transform_type == "exp":

										def alpha_bar_fn(a_					:       List[str]					):
															return math.exp(t * -12.0					)

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

					__magic_name__				=     []
					for i in range(a_					):
										__magic_name__				=     i / num_diffusion_timesteps
										__magic_name__				=     (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 					__UpperCamelCase					(       SCREAMING_SNAKE_CASE	,    SCREAMING_SNAKE_CASE      ):
	_lowercase	: Union[str, Any]												=	[e.name for e in KarrasDiffusionSchedulers]
	_lowercase	: int												=	1







	@register_to_config
	def __init__( self:			str	,			__UpperCamelCase:			int = 10_00	,			__UpperCamelCase:			float = 0.0001	,			__UpperCamelCase:			float = 0.02	,			__UpperCamelCase:			str = "linear"	,			__UpperCamelCase:			Optional[Union[np.ndarray, List[float]]] = None	,			__UpperCamelCase:			str = "epsilon"	,			__UpperCamelCase:			str = "linspace"	,			__UpperCamelCase:			int = 0	,			):

						'''simple docstring'''





						if trained_betas is not None:
											__magic_name__				=     torch.tensor(__UpperCamelCase	,			dtype=torch.floataa   )
						elif beta_schedule == "linear":
											__magic_name__				=     torch.linspace(__UpperCamelCase	,			__UpperCamelCase	,			__UpperCamelCase	,			dtype=torch.floataa   )
						elif beta_schedule == "scaled_linear":
											# this schedule is very specific to the latent diffusion model.
											__magic_name__				=     (
											    torch.linspace(beta_start**0.5	,			beta_end**0.5	,			__UpperCamelCase	,			dtype=torch.floataa   ) ** 2
											)
						elif beta_schedule == "squaredcos_cap_v2":
											# Glide cosine schedule
											__magic_name__				=     betas_for_alpha_bar(__UpperCamelCase   )
						else:
											raise NotImplementedError(F'{beta_schedule} does is not implemented for {self.__class__}'   )

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

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

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







	@property
	def 						_SCREAMING_SNAKE_CASE				( self:			str   ):

						'''simple docstring'''





						if self.config.timestep_spacing in ["linspace", "trailing"]:
											return self.sigmas.max()

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







	def 						_SCREAMING_SNAKE_CASE				( self:			Optional[int]	,			__UpperCamelCase:			torch.FloatTensor	,			__UpperCamelCase:			Union[float, torch.FloatTensor]   ):

						'''simple docstring'''





						if isinstance(__UpperCamelCase	,			torch.Tensor   ):
											__magic_name__				=     timestep.to(self.timesteps.device   )
						__magic_name__				=     (self.timesteps == timestep).nonzero().item()
						__magic_name__				=     self.sigmas[step_index]
						__magic_name__				=     sample / ((sigma**2 + 1) ** 0.5)
						__magic_name__				=     True
						return sample







	def 						_SCREAMING_SNAKE_CASE				( self:			str	,			__UpperCamelCase:			int	,			__UpperCamelCase:			Union[str, torch.device] = None   ):

						'''simple docstring'''





						__magic_name__				=     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":
											__magic_name__				=     np.linspace(0	,			self.config.num_train_timesteps - 1	,			__UpperCamelCase	,			dtype=__UpperCamelCase   )[
											    ::-1
											].copy()
						elif self.config.timestep_spacing == "leading":
											__magic_name__				=     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
											__magic_name__				=     (np.arange(0	,			__UpperCamelCase   ) * step_ratio).round()[::-1].copy().astype(__UpperCamelCase   )
											timesteps += self.config.steps_offset
						elif self.config.timestep_spacing == "trailing":
											__magic_name__				=     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
											__magic_name__				=     (np.arange(self.config.num_train_timesteps	,			0	,			-step_ratio   )).round().copy().astype(__UpperCamelCase   )
											timesteps -= 1
						else:
											raise ValueError(
											    F'{self.config.timestep_spacing} is not supported. Please make sure to choose one of \'linspace\', \'leading\' or \'trailing\'.'   )

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







	def 						_SCREAMING_SNAKE_CASE				( self:			Dict	,			__UpperCamelCase:			torch.FloatTensor	,			__UpperCamelCase:			Union[float, torch.FloatTensor]	,			__UpperCamelCase:			torch.FloatTensor	,			__UpperCamelCase:			Optional[torch.Generator] = None	,			__UpperCamelCase:			bool = True	,			):

						'''simple docstring'''





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

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

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

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

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

						__magic_name__				=     sigma_down - sigma

						__magic_name__				=     sample + derivative * dt

						__magic_name__				=     model_output.device
						__magic_name__				=     randn_tensor(model_output.shape	,			dtype=model_output.dtype	,			device=__UpperCamelCase	,			generator=__UpperCamelCase   )

						__magic_name__				=     prev_sample + noise * sigma_up

						if not return_dict:
											return (prev_sample,)

						return EulerAncestralDiscreteSchedulerOutput(
						    prev_sample=__UpperCamelCase	,			pred_original_sample=__UpperCamelCase   )







	def 						_SCREAMING_SNAKE_CASE				( self:			int	,			__UpperCamelCase:			torch.FloatTensor	,			__UpperCamelCase:			torch.FloatTensor	,			__UpperCamelCase:			torch.FloatTensor	,			):

						'''simple docstring'''





						__magic_name__				=     self.sigmas.to(device=original_samples.device	,			dtype=original_samples.dtype   )
						if original_samples.device.type == "mps" and torch.is_floating_point(__UpperCamelCase   ):
											# mps does not support float64
											__magic_name__				=     self.timesteps.to(original_samples.device	,			dtype=torch.floataa   )
											__magic_name__				=     timesteps.to(original_samples.device	,			dtype=torch.floataa   )
						else:
											__magic_name__				=     self.timesteps.to(original_samples.device   )
											__magic_name__				=     timesteps.to(original_samples.device   )

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

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

						__magic_name__				=     original_samples + noise * sigma
						return noisy_samples







	def __len__( self:			Dict   ):

						'''simple docstring'''





						return self.config.num_train_timesteps







from typing import Callable, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...utils import logging


A__							=				logging.get_logger(__name__)

A__							=				{
    "microsoft/prophetnet-large-uncased": (
        "https://huggingface.co/microsoft/prophetnet-large-uncased/resolve/main/config.json"
    ),
}

class 					__UpperCamelCase					(       SCREAMING_SNAKE_CASE      ):
	_lowercase	: Union[str, Any]												=	"prophetnet"
	_lowercase	: Any												=	["past_key_values"]
	_lowercase	: int												=	{
	    "num_attention_heads": "num_encoder_attention_heads",
	}







	def __init__( self:			Union[str, Any]	,			__UpperCamelCase:			Optional[float] = 0.1	,			__UpperCamelCase:			Optional[Union[str, Callable]] = "gelu"	,			__UpperCamelCase:			Optional[int] = 3_05_22	,			__UpperCamelCase:			Optional[int] = 10_24	,			__UpperCamelCase:			Optional[int] = 40_96	,			__UpperCamelCase:			Optional[int] = 12	,			__UpperCamelCase:			Optional[int] = 16	,			__UpperCamelCase:			Optional[int] = 40_96	,			__UpperCamelCase:			Optional[int] = 12	,			__UpperCamelCase:			Optional[int] = 16	,			__UpperCamelCase:			Optional[float] = 0.1	,			__UpperCamelCase:			Optional[float] = 0.1	,			__UpperCamelCase:			Optional[int] = 5_12	,			__UpperCamelCase:			Optional[float] = 0.02	,			__UpperCamelCase:			Optional[bool] = True	,			__UpperCamelCase:			Optional[bool] = True	,			__UpperCamelCase:			Optional[int] = 0	,			__UpperCamelCase:			Optional[int] = 2	,			__UpperCamelCase:			Optional[int] = 32	,			__UpperCamelCase:			Optional[int] = 1_28	,			__UpperCamelCase:			Optional[bool] = False	,			__UpperCamelCase:			Optional[float] = 0.0	,			__UpperCamelCase:			Optional[bool] = True	,			__UpperCamelCase:			Optional[int] = 0	,			__UpperCamelCase:			Optional[int] = 1	,			__UpperCamelCase:			Optional[int] = 2	,			**__UpperCamelCase:			Optional[int]	,			):

						'''simple docstring'''





						__magic_name__				=     vocab_size
						__magic_name__				=     hidden_size
						__magic_name__				=     encoder_ffn_dim
						__magic_name__				=     num_encoder_layers
						__magic_name__				=     num_encoder_attention_heads
						__magic_name__				=     decoder_ffn_dim
						__magic_name__				=     num_decoder_layers
						__magic_name__				=     num_decoder_attention_heads
						__magic_name__				=     max_position_embeddings
						__magic_name__				=     init_std  # Normal(0, this parameter)
						__magic_name__				=     activation_function

						# parameters for prophetnet
						__magic_name__				=     ngram
						__magic_name__				=     num_buckets
						__magic_name__				=     relative_max_distance
						__magic_name__				=     disable_ngram_loss
						__magic_name__				=     eps

						# 3 Types of Dropout
						__magic_name__				=     attention_dropout
						__magic_name__				=     activation_dropout
						__magic_name__				=     dropout

						__magic_name__				=     use_cache

						super().__init__(
						    pad_token_id=__UpperCamelCase	,			bos_token_id=__UpperCamelCase	,			eos_token_id=__UpperCamelCase	,			is_encoder_decoder=__UpperCamelCase	,			add_cross_attention=__UpperCamelCase	,			decoder_start_token_id=__UpperCamelCase	,			**__UpperCamelCase	,			)







	@property
	def 						_SCREAMING_SNAKE_CASE				( self:			Tuple   ):

						'''simple docstring'''





						return self.num_encoder_layers + self.num_decoder_layers







	@num_hidden_layers.setter
	def 						_SCREAMING_SNAKE_CASE				( self:			Union[str, Any]	,			__UpperCamelCase:			List[Any]   ):

						'''simple docstring'''





						raise NotImplementedError(
						    'This model does not support the setting of `num_hidden_layers`. Please set `num_encoder_layers` and'
						    ' `num_decoder_layers`.'   )




