



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

import numpy as np
import torch

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




@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->UnCLIP
class      __UpperCamelCase       (snake_case__			):
	__A			  =					42
	__A			  =					None







def      SCREAMING_SNAKE_CASE				(				lowercase_    :      Optional[int] ,  lowercase_    :      Optional[Any]=0.999 ,  lowercase_    :      List[Any]="cosine" ,  ):
						if alpha_transform_type == "cosine":

												def alpha_bar_fn(lowercase_    :      Optional[int]						):
																		return math.cos((t + 0.008) / 1.008 * math.pi / 2						) ** 2

						elif alpha_transform_type == "exp":

												def alpha_bar_fn(lowercase_    :      Optional[int]						):
																		return math.exp(t * -12.0						)

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

						lowercase        =							[]
						for i in range(_SCREAMING_SNAKE_CASE						):
												lowercase        =							i / num_diffusion_timesteps
												lowercase        =							(i + 1) / num_diffusion_timesteps
												betas.append(min(1 - alpha_bar_fn(_SCREAMING_SNAKE_CASE						) / alpha_bar_fn(_SCREAMING_SNAKE_CASE						) ,  _SCREAMING_SNAKE_CASE						)						)
						return torch.tensor(_SCREAMING_SNAKE_CASE ,  dtype=torch.floataa						)




class      __UpperCamelCase       (snake_case__			,   snake_case__			):







	@register_to_config
	def __init__(    self ,				_lowerCAmelCase = 1000 ,				_lowerCAmelCase = "fixed_small_log" ,				_lowerCAmelCase = True ,				_lowerCAmelCase = 1.0 ,				_lowerCAmelCase = "epsilon" ,				_lowerCAmelCase = "squaredcos_cap_v2" ,				)    ->    Optional[int]:
							'''simple docstring'''






							if beta_schedule != "squaredcos_cap_v2":
													raise ValueError("""UnCLIPScheduler only supports `beta_schedule`: 'squaredcos_cap_v2'"""						)

							lowercase        =							betas_for_alpha_bar(UpperCAmelCase_						)

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

							# standard deviation of the initial noise distribution
							lowercase        =							1.0

							# setable values
							lowercase        =							None
							lowercase        =							torch.from_numpy(np.arange(0 ,				UpperCAmelCase_						)[::-1].copy()						)

							lowercase        =							variance_type







	def   _a   (    self ,				_lowerCAmelCase ,				_lowerCAmelCase = None						)    ->    torch.FloatTensor:
							'''simple docstring'''






							return sample







	def   _a   (    self ,				_lowerCAmelCase ,				_lowerCAmelCase = None						)    ->    Tuple:
							'''simple docstring'''






							lowercase        =							num_inference_steps
							lowercase        =							(self.config.num_train_timesteps - 1) / (self.num_inference_steps - 1)
							lowercase        =							(np.arange(0 ,				UpperCAmelCase_						) * step_ratio).round()[::-1].copy().astype(np.intaa						)
							lowercase        =							torch.from_numpy(UpperCAmelCase_						).to(UpperCAmelCase_						)







	def   _a   (    self ,				_lowerCAmelCase ,				_lowerCAmelCase=None ,				_lowerCAmelCase=None ,				_lowerCAmelCase=None						)    ->    str:
							'''simple docstring'''






							if prev_timestep is None:
													lowercase        =							t - 1

							lowercase        =							self.alphas_cumprod[t]
							lowercase        =							self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
							lowercase        =							1 - alpha_prod_t
							lowercase        =							1 - alpha_prod_t_prev

							if prev_timestep == t - 1:
													lowercase        =							self.betas[t]
							else:
													lowercase        =							1 - alpha_prod_t / alpha_prod_t_prev

							# For t > 0, compute predicted variance βt (see formula (6) and (7) from https://arxiv.org/pdf/2006.11239.pdf)
							# and sample from it to get previous sample
							# x_{t-1} ~ N(pred_prev_sample, variance) == add variance to pred_sample
							lowercase        =							beta_prod_t_prev / beta_prod_t * beta

							if variance_type is None:
													lowercase        =							self.config.variance_type

							# hacks - were probably added for training stability
							if variance_type == "fixed_small_log":
													lowercase        =							torch.log(torch.clamp(UpperCAmelCase_ ,				min=1E-20						)						)
													lowercase        =							torch.exp(0.5 * variance						)
							elif variance_type == "learned_range":
													# NOTE difference with DDPM scheduler
													lowercase        =							variance.log()
													lowercase        =							beta.log()

													lowercase        =							(predicted_variance + 1) / 2
													lowercase        =							frac * max_log + (1 - frac) * min_log

							return variance







	def   _a   (    self ,				_lowerCAmelCase ,				_lowerCAmelCase ,				_lowerCAmelCase ,				_lowerCAmelCase = None ,				_lowerCAmelCase=None ,				_lowerCAmelCase = True ,				)    ->    Union[UnCLIPSchedulerOutput, Tuple]:
							'''simple docstring'''






							lowercase        =							timestep

							if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type == "learned_range":
													lowercase     ,   lowercase        =							torch.split(UpperCAmelCase_ ,				sample.shape[1] ,				dim=1						)
							else:
													lowercase        =							None

							# 1. compute alphas, betas
							if prev_timestep is None:
													lowercase        =							t - 1

							lowercase        =							self.alphas_cumprod[t]
							lowercase        =							self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
							lowercase        =							1 - alpha_prod_t
							lowercase        =							1 - alpha_prod_t_prev

							if prev_timestep == t - 1:
													lowercase        =							self.betas[t]
													lowercase        =							self.alphas[t]
							else:
													lowercase        =							1 - alpha_prod_t / alpha_prod_t_prev
													lowercase        =							1 - beta

							# 2. compute predicted original sample from predicted noise also called
							# "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
							if self.config.prediction_type == "epsilon":
													lowercase        =							(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
							elif self.config.prediction_type == "sample":
													lowercase        =							model_output
							else:
													raise ValueError(
													    F"""prediction_type given as {self.config.prediction_type} must be one of `epsilon` or `sample`"""
													    """ for the UnCLIPScheduler."""						)

							# 3. Clip "predicted x_0"
							if self.config.clip_sample:
													lowercase        =							torch.clamp(
													    UpperCAmelCase_ ,				-self.config.clip_sample_range ,				self.config.clip_sample_range						)

							# 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
							# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
							lowercase        =							(alpha_prod_t_prev ** 0.5 * beta) / beta_prod_t
							lowercase        =							alpha ** 0.5 * beta_prod_t_prev / beta_prod_t

							# 5. Compute predicted previous sample µ_t
							# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
							lowercase        =							pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

							# 6. Add noise
							lowercase        =							0
							if t > 0:
													lowercase        =							randn_tensor(
													    model_output.shape ,				dtype=model_output.dtype ,				generator=UpperCAmelCase_ ,				device=model_output.device						)

													lowercase        =							self._get_variance(
													    UpperCAmelCase_ ,				predicted_variance=UpperCAmelCase_ ,				prev_timestep=UpperCAmelCase_ ,				)

													if self.variance_type == "fixed_small_log":
																			lowercase        =							variance
													elif self.variance_type == "learned_range":
																			lowercase        =							(0.5 * variance).exp()
													else:
																			raise ValueError(
																			    F"""variance_type given as {self.variance_type} must be one of `fixed_small_log` or `learned_range`"""
																			    """ for the UnCLIPScheduler."""						)

													lowercase        =							variance * variance_noise

							lowercase        =							pred_prev_sample + variance

							if not return_dict:
													return (pred_prev_sample,)

							return UnCLIPSchedulerOutput(prev_sample=UpperCAmelCase_ ,				pred_original_sample=UpperCAmelCase_						)







	def   _a   (    self ,				_lowerCAmelCase ,				_lowerCAmelCase ,				_lowerCAmelCase ,				)    ->    torch.FloatTensor:
							'''simple docstring'''






							lowercase        =							self.alphas_cumprod.to(device=original_samples.device ,				dtype=original_samples.dtype						)
							lowercase        =							timesteps.to(original_samples.device						)

							lowercase        =							alphas_cumprod[timesteps] ** 0.5
							lowercase        =							sqrt_alpha_prod.flatten()
							while len(sqrt_alpha_prod.shape						) < len(original_samples.shape						):
													lowercase        =							sqrt_alpha_prod.unsqueeze(-1						)

							lowercase        =							(1 - alphas_cumprod[timesteps]) ** 0.5
							lowercase        =							sqrt_one_minus_alpha_prod.flatten()
							while len(sqrt_one_minus_alpha_prod.shape						) < len(original_samples.shape						):
													lowercase        =							sqrt_one_minus_alpha_prod.unsqueeze(-1						)

							lowercase        =							sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
							return noisy_samples







'''simple docstring'''


from ... import PretrainedConfig


lowercase_      :				int   					=    {
    '''sijunhe/nezha-cn-base''': '''https://huggingface.co/sijunhe/nezha-cn-base/resolve/main/config.json''',
}




class      __UpperCamelCase       (_UpperCAmelCase			):
	__A			  =					NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP
	__A			  =					'''nezha'''







	def __init__(    self ,				_lowerCAmelCase=2_1128 ,				_lowerCAmelCase=768 ,				_lowerCAmelCase=12 ,				_lowerCAmelCase=12 ,				_lowerCAmelCase=3072 ,				_lowerCAmelCase="gelu" ,				_lowerCAmelCase=0.1 ,				_lowerCAmelCase=0.1 ,				_lowerCAmelCase=512 ,				_lowerCAmelCase=64 ,				_lowerCAmelCase=2 ,				_lowerCAmelCase=0.02 ,				_lowerCAmelCase=1E-12 ,				_lowerCAmelCase=0.1 ,				_lowerCAmelCase=0 ,				_lowerCAmelCase=2 ,				_lowerCAmelCase=3 ,				_lowerCAmelCase=True ,				**_lowerCAmelCase ,				)    ->    int:
							'''simple docstring'''






							super().__init__(pad_token_id=_lowerCAmelCase ,				bos_token_id=_lowerCAmelCase ,				eos_token_id=_lowerCAmelCase ,				**_lowerCAmelCase						)

							lowercase        =							vocab_size
							lowercase        =							hidden_size
							lowercase        =							num_hidden_layers
							lowercase        =							num_attention_heads
							lowercase        =							hidden_act
							lowercase        =							intermediate_size
							lowercase        =							hidden_dropout_prob
							lowercase        =							attention_probs_dropout_prob
							lowercase        =							max_position_embeddings
							lowercase        =							max_relative_position
							lowercase        =							type_vocab_size
							lowercase        =							initializer_range
							lowercase        =							layer_norm_eps
							lowercase        =							classifier_dropout
							lowercase        =							use_cache


