





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       __lowerCAmelCase					(       _UpperCamelCase      ):


	'''simple docstring'''





	_A													=				42
	_A													=				None


def       SCREAMING_SNAKE_CASE__  (       _lowercase     : str   ,    _lowercase     : str=0.999   ,    _lowercase     : Any="cosine"   ,    )   ->							Tuple:




		'''simple docstring'''
		if alpha_transform_type == "cosine":

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

		elif alpha_transform_type == "exp":

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

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

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





class       __lowerCAmelCase					(       _UpperCamelCase   ,    _UpperCamelCase      ):


	'''simple docstring'''





	@register_to_config
	def __init__(						self:						Optional[Any],					lowerCamelCase_:						int = 1000,					lowerCamelCase_:						str = "fixed_small_log",					lowerCamelCase_:						bool = True,					lowerCamelCase_:						Optional[float] = 1.0,					lowerCamelCase_:						str = "epsilon",					lowerCamelCase_:						str = "squaredcos_cap_v2",					):
			if beta_schedule != "squaredcos_cap_v2":
					raise ValueError('UnCLIPScheduler only supports `beta_schedule`: \'squaredcos_cap_v2\''			)

			lowercase__					:      Optional[int]      =		betas_for_alpha_bar(lowerCamelCase_			)

			lowercase__					:      List[str]      =		1.0 - self.betas
			lowercase__					:      str      =		torch.cumprod(self.alphas,					dim=0			)
			lowercase__					:      Optional[Any]      =		torch.tensor(1.0			)

			# standard deviation of the initial noise distribution
			lowercase__					:      Optional[int]      =		1.0

			# setable values
			lowercase__					:      List[Any]      =		None
			lowercase__					:      Optional[int]      =		torch.from_numpy(np.arange(0,					lowerCamelCase_			)[::-1].copy()			)

			lowercase__					:      Any      =		variance_type

	def    snake_case__(						self:						Tuple,					lowerCamelCase_:						torch.FloatTensor,					lowerCamelCase_:						Optional[int] = None			):
			return sample

	def    snake_case__(						self:						Optional[int],					lowerCamelCase_:						int,					lowerCamelCase_:						Union[str, torch.device] = None			):
			lowercase__					:      Any      =		num_inference_steps
			lowercase__					:      List[str]      =		(self.config.num_train_timesteps - 1) / (self.num_inference_steps - 1)
			lowercase__					:      Dict      =		(np.arange(0,					lowerCamelCase_			) * step_ratio).round()[::-1].copy().astype(np.intaa			)
			lowercase__					:      Optional[int]      =		torch.from_numpy(lowerCamelCase_			).to(lowerCamelCase_			)

	def    snake_case__(						self:						Union[str, Any],					lowerCamelCase_:						List[str],					lowerCamelCase_:						Tuple=None,					lowerCamelCase_:						List[Any]=None,					lowerCamelCase_:						Union[str, Any]=None			):
			if prev_timestep is None:
					lowercase__					:      List[str]      =		t - 1

			lowercase__					:      int      =		self.alphas_cumprod[t]
			lowercase__					:      Optional[int]      =		self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
			lowercase__					:      Union[str, Any]      =		1 - alpha_prod_t
			lowercase__					:      List[Any]      =		1 - alpha_prod_t_prev

			if prev_timestep == t - 1:
					lowercase__					:      List[str]      =		self.betas[t]
			else:
					lowercase__					:      List[str]      =		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__					:      Union[str, Any]      =		beta_prod_t_prev / beta_prod_t * beta

			if variance_type is None:
					lowercase__					:      Tuple      =		self.config.variance_type

			# hacks - were probably added for training stability
			if variance_type == "fixed_small_log":
					lowercase__					:      Dict      =		torch.log(torch.clamp(lowerCamelCase_,					min=1E-20			)			)
					lowercase__					:      Union[str, Any]      =		torch.exp(0.5 * variance			)
			elif variance_type == "learned_range":
					# NOTE difference with DDPM scheduler
					lowercase__					:      List[str]      =		variance.log()
					lowercase__					:      str      =		beta.log()

					lowercase__					:      Optional[int]      =		(predicted_variance + 1) / 2
					lowercase__					:      Any      =		frac * max_log + (1 - frac) * min_log

			return variance

	def    snake_case__(						self:						Optional[Any],					lowerCamelCase_:						torch.FloatTensor,					lowerCamelCase_:						int,					lowerCamelCase_:						torch.FloatTensor,					lowerCamelCase_:						Optional[int] = None,					lowerCamelCase_:						Optional[Any]=None,					lowerCamelCase_:						bool = True,					):
			lowercase__					:      Any      =		timestep

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

			# 1. compute alphas, betas
			if prev_timestep is None:
					lowercase__					:      Optional[int]      =		t - 1

			lowercase__					:      Optional[int]      =		self.alphas_cumprod[t]
			lowercase__					:      Optional[int]      =		self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
			lowercase__					:      List[Any]      =		1 - alpha_prod_t
			lowercase__					:      Optional[int]      =		1 - alpha_prod_t_prev

			if prev_timestep == t - 1:
					lowercase__					:      Dict      =		self.betas[t]
					lowercase__					:      str      =		self.alphas[t]
			else:
					lowercase__					:      Tuple      =		1 - alpha_prod_t / alpha_prod_t_prev
					lowercase__					:      Optional[int]      =		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__					:      str      =		(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
			elif self.config.prediction_type == "sample":
					lowercase__					:      Tuple      =		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__					:      Union[str, Any]      =		torch.clamp(
					    lowerCamelCase_,					-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__					:      Tuple      =		(alpha_prod_t_prev ** 0.5 * beta) / beta_prod_t
			lowercase__					:      Union[str, Any]      =		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__					:      Optional[int]      =		pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

			# 6. Add noise
			lowercase__					:      Union[str, Any]      =		0
			if t > 0:
					lowercase__					:      Any      =		randn_tensor(
					    model_output.shape,					dtype=model_output.dtype,					generator=lowerCamelCase_,					device=model_output.device			)

					lowercase__					:      Optional[Any]      =		self._get_variance(
					    lowerCamelCase_,					predicted_variance=lowerCamelCase_,					prev_timestep=lowerCamelCase_,					)

					if self.variance_type == "fixed_small_log":
							lowercase__					:      int      =		variance
					elif self.variance_type == "learned_range":
							lowercase__					:      int      =		(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__					:      int      =		variance * variance_noise

			lowercase__					:      str      =		pred_prev_sample + variance

			if not return_dict:
					return (pred_prev_sample,)

			return UnCLIPSchedulerOutput(prev_sample=lowerCamelCase_,					pred_original_sample=lowerCamelCase_			)





	def    snake_case__(						self:						Union[str, Any],					lowerCamelCase_:						torch.FloatTensor,					lowerCamelCase_:						torch.FloatTensor,					lowerCamelCase_:						torch.IntTensor,					):
			# Make sure alphas_cumprod and timestep have same device and dtype as original_samples
			lowercase__					:      str      =		self.alphas_cumprod.to(device=original_samples.device,					dtype=original_samples.dtype			)
			lowercase__					:      Optional[int]      =		timesteps.to(original_samples.device			)

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

			lowercase__					:      Optional[int]      =		(1 - alphas_cumprod[timesteps]) ** 0.5
			lowercase__					:      Tuple      =		sqrt_one_minus_alpha_prod.flatten()
			while len(sqrt_one_minus_alpha_prod.shape			) < len(original_samples.shape			):
					lowercase__					:      Optional[Any]      =		sqrt_one_minus_alpha_prod.unsqueeze(-1			)

			lowercase__					:      List[str]      =		sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
			return noisy_samples









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


__UpperCamelCase:	List[Any] = logging.get_logger(__name__)

__UpperCamelCase:	Dict = {
    """studio-ousia/luke-base""": """https://huggingface.co/studio-ousia/luke-base/resolve/main/config.json""",
    """studio-ousia/luke-large""": """https://huggingface.co/studio-ousia/luke-large/resolve/main/config.json""",
}


class       __lowerCAmelCase					(       _UpperCamelCase      ):


	'''simple docstring'''





	_A													=				"luke"

	def __init__(						self:						Union[str, Any],					lowerCamelCase_:						str=50267,					lowerCamelCase_:						Dict=500000,					lowerCamelCase_:						Union[str, Any]=768,					lowerCamelCase_:						Dict=256,					lowerCamelCase_:						int=12,					lowerCamelCase_:						Union[str, Any]=12,					lowerCamelCase_:						str=3072,					lowerCamelCase_:						List[Any]="gelu",					lowerCamelCase_:						int=0.1,					lowerCamelCase_:						Optional[Any]=0.1,					lowerCamelCase_:						Any=512,					lowerCamelCase_:						List[str]=2,					lowerCamelCase_:						str=0.0_2,					lowerCamelCase_:						Union[str, Any]=1E-12,					lowerCamelCase_:						List[Any]=True,					lowerCamelCase_:						List[Any]=None,					lowerCamelCase_:						Optional[Any]=1,					lowerCamelCase_:						Dict=0,					lowerCamelCase_:						List[Any]=2,					**lowerCamelCase_:						Optional[Any],					):
			super().__init__(pad_token_id=lowerCamelCase_,					bos_token_id=lowerCamelCase_,					eos_token_id=lowerCamelCase_,					**lowerCamelCase_			)

			lowercase__					:      Tuple      =		vocab_size
			lowercase__					:      Any      =		entity_vocab_size
			lowercase__					:      Tuple      =		hidden_size
			lowercase__					:      List[str]      =		entity_emb_size
			lowercase__					:      Tuple      =		num_hidden_layers
			lowercase__					:      Optional[Any]      =		num_attention_heads
			lowercase__					:      Dict      =		hidden_act
			lowercase__					:      Optional[int]      =		intermediate_size
			lowercase__					:      List[str]      =		hidden_dropout_prob
			lowercase__					:      Dict      =		attention_probs_dropout_prob
			lowercase__					:      Union[str, Any]      =		max_position_embeddings
			lowercase__					:      int      =		type_vocab_size
			lowercase__					:      Optional[Any]      =		initializer_range
			lowercase__					:      Dict      =		layer_norm_eps
			lowercase__					:      List[Any]      =		use_entity_aware_attention
			lowercase__					:      Optional[Any]      =		classifier_dropout


