


from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_lowerCamelCase     =  logging.get_logger(__name__)

_lowerCamelCase     =  {
    'andreasmadsen/efficient_mlm_m0.40': (
        'https://huggingface.co/andreasmadsen/efficient_mlm_m0.40/resolve/main/config.json'
    ),
}


class 			lowerCamelCase_		(			lowercase   ):





				"""simple docstring"""







				_lowerCAmelCase      :      Optional[Any]      							=					"roberta-prelayernorm"



				def __init__(						self       ,		UpperCAmelCase__=5_0265       ,		UpperCAmelCase__=768       ,		UpperCAmelCase__=12       ,		UpperCAmelCase__=12       ,		UpperCAmelCase__=3072       ,		UpperCAmelCase__="gelu"       ,		UpperCAmelCase__=0.1       ,		UpperCAmelCase__=0.1       ,		UpperCAmelCase__=512       ,		UpperCAmelCase__=2       ,		UpperCAmelCase__=0.02       ,		UpperCAmelCase__=1e-12       ,		UpperCAmelCase__=1       ,		UpperCAmelCase__=0       ,		UpperCAmelCase__=2       ,		UpperCAmelCase__="absolute"       ,		UpperCAmelCase__=True       ,		UpperCAmelCase__=None       ,		**UpperCAmelCase__       ,		):
								super().__init__(pad_token_id=UpperCAmelCase__       ,		bos_token_id=UpperCAmelCase__       ,		eos_token_id=UpperCAmelCase__       ,		**UpperCAmelCase__     )

								SCREAMING_SNAKE_CASE__     							=				vocab_size
								SCREAMING_SNAKE_CASE__     							=				hidden_size
								SCREAMING_SNAKE_CASE__     							=				num_hidden_layers
								SCREAMING_SNAKE_CASE__     							=				num_attention_heads
								SCREAMING_SNAKE_CASE__     							=				hidden_act
								SCREAMING_SNAKE_CASE__     							=				intermediate_size
								SCREAMING_SNAKE_CASE__     							=				hidden_dropout_prob
								SCREAMING_SNAKE_CASE__     							=				attention_probs_dropout_prob
								SCREAMING_SNAKE_CASE__     							=				max_position_embeddings
								SCREAMING_SNAKE_CASE__     							=				type_vocab_size
								SCREAMING_SNAKE_CASE__     							=				initializer_range
								SCREAMING_SNAKE_CASE__     							=				layer_norm_eps
								SCREAMING_SNAKE_CASE__     							=				position_embedding_type
								SCREAMING_SNAKE_CASE__     							=				use_cache
								SCREAMING_SNAKE_CASE__     							=				classifier_dropout


class 			lowerCamelCase_		(			lowercase   ):





				"""simple docstring"""







				@property
				def   lowerCAmelCase__		(						self     ):
								if self.task == "multiple-choice":
												SCREAMING_SNAKE_CASE__     							=				{0: "batch", 1: "choice", 2: "sequence"}
								else:
												SCREAMING_SNAKE_CASE__     							=				{0: "batch", 1: "sequence"}
								return OrderedDict(
								    [
								        ("input_ids", dynamic_axis),
								        ("attention_mask", dynamic_axis),
								    ]     )




"""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 			lowerCamelCase_		(			lowercase       ,  lowercase   ):





				"""simple docstring"""







				_lowerCAmelCase      :      List[Any]      							=					1



				@register_to_config
				def __init__(						self       ,		UpperCAmelCase__=2000       ,		UpperCAmelCase__=0.1       ,		UpperCAmelCase__=20       ,		UpperCAmelCase__=1e-3     ):
								SCREAMING_SNAKE_CASE__     							=				None
								SCREAMING_SNAKE_CASE__     							=				None
								SCREAMING_SNAKE_CASE__     							=				None



				def   lowerCAmelCase__		(						self       ,		UpperCAmelCase__       ,		UpperCAmelCase__ = None     ):
								SCREAMING_SNAKE_CASE__     							=				torch.linspace(1       ,		self.config.sampling_eps       ,		UpperCAmelCase__       ,		device=UpperCAmelCase__     )



				def   lowerCAmelCase__		(						self       ,		UpperCAmelCase__       ,		UpperCAmelCase__       ,		UpperCAmelCase__       ,		UpperCAmelCase__=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
								SCREAMING_SNAKE_CASE__     							=				(
								    -0.25 * t**2 * (self.config.beta_max - self.config.beta_min) - 0.5 * t * self.config.beta_min
								)
								SCREAMING_SNAKE_CASE__     							=				torch.sqrt(1.0 - torch.exp(2.0 * log_mean_coeff     )     )
								SCREAMING_SNAKE_CASE__     							=				std.flatten()
								while len(std.shape     ) < len(score.shape     ):
												SCREAMING_SNAKE_CASE__     							=				std.unsqueeze(-1     )
								SCREAMING_SNAKE_CASE__     							=				-score / std

								# compute
								SCREAMING_SNAKE_CASE__     							=				-1.0 / len(self.timesteps     )

								SCREAMING_SNAKE_CASE__     							=				self.config.beta_min + t * (self.config.beta_max - self.config.beta_min)
								SCREAMING_SNAKE_CASE__     							=				beta_t.flatten()
								while len(beta_t.shape     ) < len(x.shape     ):
												SCREAMING_SNAKE_CASE__     							=				beta_t.unsqueeze(-1     )
								SCREAMING_SNAKE_CASE__     							=				-0.5 * beta_t * x

								SCREAMING_SNAKE_CASE__     							=				torch.sqrt(UpperCAmelCase__     )
								SCREAMING_SNAKE_CASE__     							=				drift - diffusion**2 * score
								SCREAMING_SNAKE_CASE__     							=				x + drift * dt

								# add noise
								SCREAMING_SNAKE_CASE__     							=				randn_tensor(x.shape       ,		layout=x.layout       ,		generator=UpperCAmelCase__       ,		device=x.device       ,		dtype=x.dtype     )
								SCREAMING_SNAKE_CASE__     							=				x_mean + diffusion * math.sqrt(-dt     ) * noise

								return x, x_mean






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