
'''simple docstring'''





# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch

import math
from typing import Union

import torch

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

class 						a   (				__magic_name__				,__magic_name__    ):
				_snake_case		    = 1




				@register_to_config
				def __init__(							self	:				int,    SCREAMING_SNAKE_CASE_	:				Tuple=20_00,    SCREAMING_SNAKE_CASE_	:				int=0.1,    SCREAMING_SNAKE_CASE_	:				Dict=20,    SCREAMING_SNAKE_CASE_	:				List[str]=1e-3      ):
										snake_case       :     Any =    None
										snake_case       :     Tuple =    None
										snake_case       :     Optional[int] =    None




				def 	__snake_case							(							self	:				Union[str, Any],    SCREAMING_SNAKE_CASE_	:				List[str],    SCREAMING_SNAKE_CASE_	:				Union[str, torch.device] = None      ):
										snake_case       :     Optional[int] =    torch.linspace(1,    self.config.sampling_eps,    SCREAMING_SNAKE_CASE_,    device=SCREAMING_SNAKE_CASE_      )




				def 	__snake_case							(							self	:				Any,    SCREAMING_SNAKE_CASE_	:				Optional[int],    SCREAMING_SNAKE_CASE_	:				str,    SCREAMING_SNAKE_CASE_	:				Dict,    SCREAMING_SNAKE_CASE_	:				Union[str, Any]=None      ):
										if self.timesteps is None:
																raise ValueError(
																    '''`self.timesteps` is not set, you need to run \'set_timesteps\' after creating the scheduler'''      )

										# TODO(Patrick) better comments + non-PyTorch
										# postprocess model score
										snake_case       :     Tuple =    (
										    -0.25 * t**2 * (self.config.beta_max - self.config.beta_min) - 0.5 * t * self.config.beta_min
										)
										snake_case       :     int =    torch.sqrt(1.0 - torch.exp(2.0 * log_mean_coeff      )      )
										snake_case       :     List[Any] =    std.flatten()
										while len(std.shape      ) < len(score.shape      ):
																snake_case       :     Union[str, Any] =    std.unsqueeze(-1      )
										snake_case       :     Union[str, Any] =    -score / std

										# compute
										snake_case       :     int =    -1.0 / len(self.timesteps      )

										snake_case       :     str =    self.config.beta_min + t * (self.config.beta_max - self.config.beta_min)
										snake_case       :     Any =    beta_t.flatten()
										while len(beta_t.shape      ) < len(x.shape      ):
																snake_case       :     List[Any] =    beta_t.unsqueeze(-1      )
										snake_case       :     List[Any] =    -0.5 * beta_t * x

										snake_case       :     Optional[int] =    torch.sqrt(SCREAMING_SNAKE_CASE_      )
										snake_case       :     int =    drift - diffusion**2 * score
										snake_case       :     Union[str, Any] =    x + drift * dt

										# add noise
										snake_case       :     Union[str, Any] =    randn_tensor(x.shape,    layout=x.layout,    generator=SCREAMING_SNAKE_CASE_,    device=x.device,    dtype=x.dtype      )
										snake_case       :     Dict =    x_mean + diffusion * math.sqrt(-dt      ) * noise

										return x, x_mean




				def __len__(							self	:				Tuple      ):
										return self.config.num_train_timesteps



'''simple docstring'''





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCAmelCase    					=   {
    "configuration_convbert": ["CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "ConvBertConfig", "ConvBertOnnxConfig"],
    "tokenization_convbert": ["ConvBertTokenizer"],
}

try:
						if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						UpperCAmelCase    					=   ["ConvBertTokenizerFast"]

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						UpperCAmelCase    					=   [
						    "CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "ConvBertForMaskedLM",
						    "ConvBertForMultipleChoice",
						    "ConvBertForQuestionAnswering",
						    "ConvBertForSequenceClassification",
						    "ConvBertForTokenClassification",
						    "ConvBertLayer",
						    "ConvBertModel",
						    "ConvBertPreTrainedModel",
						    "load_tf_weights_in_convbert",
						]


try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						UpperCAmelCase    					=   [
						    "TF_CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "TFConvBertForMaskedLM",
						    "TFConvBertForMultipleChoice",
						    "TFConvBertForQuestionAnswering",
						    "TFConvBertForSequenceClassification",
						    "TFConvBertForTokenClassification",
						    "TFConvBertLayer",
						    "TFConvBertModel",
						    "TFConvBertPreTrainedModel",
						]


if TYPE_CHECKING:
						from .configuration_convbert import CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, ConvBertConfig, ConvBertOnnxConfig
						from .tokenization_convbert import ConvBertTokenizer

						try:
												if not is_tokenizers_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_convbert_fast import ConvBertTokenizerFast

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_convbert import (
												    CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
												    ConvBertForMaskedLM,
												    ConvBertForMultipleChoice,
												    ConvBertForQuestionAnswering,
												    ConvBertForSequenceClassification,
												    ConvBertForTokenClassification,
												    ConvBertLayer,
												    ConvBertModel,
												    ConvBertPreTrainedModel,
												    load_tf_weights_in_convbert,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_convbert import (
												    TF_CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TFConvBertForMaskedLM,
												    TFConvBertForMultipleChoice,
												    TFConvBertForQuestionAnswering,
												    TFConvBertForSequenceClassification,
												    TFConvBertForTokenClassification,
												    TFConvBertLayer,
												    TFConvBertModel,
												    TFConvBertPreTrainedModel,
												)


else:
						import sys

						UpperCAmelCase    					=   _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

