






from typing import TYPE_CHECKING

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


__magic_name__:					List[Any]							=     {
    "configuration_biogpt": ["BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP", "BioGptConfig"],
    "tokenization_biogpt": ["BioGptTokenizer"],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__magic_name__:					str							=     [
		    "BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST",
		    "BioGptForCausalLM",
		    "BioGptForTokenClassification",
		    "BioGptForSequenceClassification",
		    "BioGptModel",
		    "BioGptPreTrainedModel",
		]


if TYPE_CHECKING:
		from .configuration_biogpt import BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP, BioGptConfig
		from .tokenization_biogpt import BioGptTokenizer

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_biogpt import (
				    BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    BioGptForCausalLM,
				    BioGptForSequenceClassification,
				    BioGptForTokenClassification,
				    BioGptModel,
				    BioGptPreTrainedModel,
				)


else:
		import sys

		__magic_name__:					str							=     _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)








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 	snake_case__ (							_lowerCAmelCase				):
	lowercase__						:					torch.FloatTensor
	lowercase__						:					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}'			)

						__magic_name__      :       Optional[Any]       						= []
						for i in range(_A			):
												__magic_name__      :       Dict       						= i / num_diffusion_timesteps
												__magic_name__      :       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 	snake_case__ (							_lowerCAmelCase						,  _lowerCAmelCase				):
	@register_to_config
	def __init__(    self      ,       lowerCAmelCase__ = 10_00      ,       lowerCAmelCase__ = "fixed_small_log"      ,       lowerCAmelCase__ = True      ,       lowerCAmelCase__ = 1.0      ,       lowerCAmelCase__ = "epsilon"      ,       lowerCAmelCase__ = "squaredcos_cap_v2"      ,       )							->				Union[str, Any]:
							if beta_schedule != "squaredcos_cap_v2":
													raise ValueError("""UnCLIPScheduler only supports `beta_schedule`: 'squaredcos_cap_v2'"""			)

							__magic_name__      :       Tuple       						= betas_for_alpha_bar(lowerCAmelCase__			)

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

							# standard deviation of the initial noise distribution
							__magic_name__      :       Tuple       						= 1.0

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

							__magic_name__      :       List[Any]       						= variance_type
	def       __magic_name__    (    self      ,       lowerCAmelCase__      ,       lowerCAmelCase__ = None			)							->				torch.FloatTensor:
							return sample
	def       __magic_name__    (    self      ,       lowerCAmelCase__      ,       lowerCAmelCase__ = None			)							->				str:
							__magic_name__      :       List[Any]       						= num_inference_steps
							__magic_name__      :       Union[str, Any]       						= (self.config.num_train_timesteps - 1) / (self.num_inference_steps - 1)
							__magic_name__      :       List[Any]       						= (np.arange(0      ,       lowerCAmelCase__			) * step_ratio).round()[::-1].copy().astype(np.intaa			)
							__magic_name__      :       Dict       						= torch.from_numpy(lowerCAmelCase__			).to(lowerCAmelCase__			)
	def       __magic_name__    (    self      ,       lowerCAmelCase__      ,       lowerCAmelCase__=None      ,       lowerCAmelCase__=None      ,       lowerCAmelCase__=None			)							->				Tuple:
							if prev_timestep is None:
													__magic_name__      :       int       						= t - 1

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

							if prev_timestep == t - 1:
													__magic_name__      :       List[str]       						= self.betas[t]
							else:
													__magic_name__      :       List[Any]       						= 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
							__magic_name__      :       Dict       						= beta_prod_t_prev / beta_prod_t * beta

							if variance_type is None:
													__magic_name__      :       str       						= self.config.variance_type

							# hacks - were probably added for training stability
							if variance_type == "fixed_small_log":
													__magic_name__      :       str       						= torch.log(torch.clamp(lowerCAmelCase__      ,       min=1e-2_0			)			)
													__magic_name__      :       Optional[Any]       						= torch.exp(0.5 * variance			)
							elif variance_type == "learned_range":
													# NOTE difference with DDPM scheduler
													__magic_name__      :       List[str]       						= variance.log()
													__magic_name__      :       Optional[int]       						= beta.log()

													__magic_name__      :       Any       						= (predicted_variance + 1) / 2
													__magic_name__      :       Any       						= frac * max_log + (1 - frac) * min_log

							return variance
	def       __magic_name__    (    self      ,       lowerCAmelCase__      ,       lowerCAmelCase__      ,       lowerCAmelCase__      ,       lowerCAmelCase__ = None      ,       lowerCAmelCase__=None      ,       lowerCAmelCase__ = True      ,       )							->				Union[UnCLIPSchedulerOutput, Tuple]:
							__magic_name__      :       List[Any]       						= timestep

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

							# 1. compute alphas, betas
							if prev_timestep is None:
													__magic_name__      :       Union[str, Any]       						= t - 1

							__magic_name__      :       List[str]       						= self.alphas_cumprod[t]
							__magic_name__      :       Dict       						= self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
							__magic_name__      :       Any       						= 1 - alpha_prod_t
							__magic_name__      :       Dict       						= 1 - alpha_prod_t_prev

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

							# 6. Add noise
							__magic_name__      :       Tuple       						= 0
							if t > 0:
													__magic_name__      :       Any       						= randn_tensor(
													    model_output.shape      ,       dtype=model_output.dtype      ,       generator=lowerCAmelCase__      ,       device=model_output.device			)

													__magic_name__      :       Tuple       						= self._get_variance(
													    lowerCAmelCase__      ,       predicted_variance=lowerCAmelCase__      ,       prev_timestep=lowerCAmelCase__      ,       )

													if self.variance_type == "fixed_small_log":
																			__magic_name__      :       Tuple       						= variance
													elif self.variance_type == "learned_range":
																			__magic_name__      :       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."""			)

													__magic_name__      :       Tuple       						= variance * variance_noise

							__magic_name__      :       List[str]       						= pred_prev_sample + variance

							if not return_dict:
													return (pred_prev_sample,)

							return UnCLIPSchedulerOutput(prev_sample=lowerCAmelCase__      ,       pred_original_sample=lowerCAmelCase__			)







	def       __magic_name__    (    self      ,       lowerCAmelCase__      ,       lowerCAmelCase__      ,       lowerCAmelCase__      ,       )							->				torch.FloatTensor:
							# Make sure alphas_cumprod and timestep have same device and dtype as original_samples
							__magic_name__      :       List[str]       						= self.alphas_cumprod.to(device=original_samples.device      ,       dtype=original_samples.dtype			)
							__magic_name__      :       Any       						= timesteps.to(original_samples.device			)

							__magic_name__      :       int       						= alphas_cumprod[timesteps] ** 0.5
							__magic_name__      :       Union[str, Any]       						= sqrt_alpha_prod.flatten()
							while len(sqrt_alpha_prod.shape			) < len(original_samples.shape			):
													__magic_name__      :       int       						= sqrt_alpha_prod.unsqueeze(-1			)

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

							__magic_name__      :       Any       						= sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
							return noisy_samples
