


from collections import OrderedDict
from typing import Mapping

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


snake_case							: Optional[int]      =   logging.get_logger(__name__)

snake_case							: List[str]      =   {
    '''YituTech/conv-bert-base''': '''https://huggingface.co/YituTech/conv-bert-base/resolve/main/config.json''',
    '''YituTech/conv-bert-medium-small''': (
        '''https://huggingface.co/YituTech/conv-bert-medium-small/resolve/main/config.json'''
    ),
    '''YituTech/conv-bert-small''': '''https://huggingface.co/YituTech/conv-bert-small/resolve/main/config.json''',
    # See all ConvBERT models at https://huggingface.co/models?filter=convbert
}







class  snake_case_     (lowerCamelCase_   ):
					UpperCAmelCase__      :					List[Any]					   =    '''convbert'''
					def __init__(   self     :int    ,__snake_case     :str=3_05_22    ,__snake_case     :Any=7_68    ,__snake_case     :List[Any]=12    ,__snake_case     :Tuple=12    ,__snake_case     :Tuple=30_72    ,__snake_case     :Tuple="gelu"    ,__snake_case     :Union[str, Any]=0.1    ,__snake_case     :Tuple=0.1    ,__snake_case     :Any=5_12    ,__snake_case     :int=2    ,__snake_case     :List[Any]=0.02    ,__snake_case     :List[Any]=1E-12    ,__snake_case     :Union[str, Any]=1    ,__snake_case     :Optional[Any]=0    ,__snake_case     :Tuple=2    ,__snake_case     :Tuple=7_68    ,__snake_case     :Dict=2    ,__snake_case     :str=9    ,__snake_case     :int=1    ,__snake_case     :int=None    ,**__snake_case     :List[str]    ,)      ->					Optional[Any]:
												super().__init__(
												    pad_token_id=__snake_case    ,bos_token_id=__snake_case    ,eos_token_id=__snake_case    ,**__snake_case    ,)

												a__    =  vocab_size
												a__    =  hidden_size
												a__    =  num_hidden_layers
												a__    =  num_attention_heads
												a__    =  intermediate_size
												a__    =  hidden_act
												a__    =  hidden_dropout_prob
												a__    =  attention_probs_dropout_prob
												a__    =  max_position_embeddings
												a__    =  type_vocab_size
												a__    =  initializer_range
												a__    =  layer_norm_eps
												a__    =  embedding_size
												a__    =  head_ratio
												a__    =  conv_kernel_size
												a__    =  num_groups
												a__    =  classifier_dropout







class  snake_case_     (lowerCamelCase_   ):
					@property
					def 				lowerCamelCase__(   self     :Optional[Any]   )      ->					Mapping[str, Mapping[int, str]]:
												if self.task == "multiple-choice":
																			a__    =  {0: 'batch', 1: 'choice', 2: 'sequence'}
												else:
																			a__    =  {0: 'batch', 1: 'sequence'}
												return OrderedDict(
												    [
												        ('input_ids', dynamic_axis),
												        ('attention_mask', dynamic_axis),
												        ('token_type_ids', dynamic_axis),
												    ]   )








import math
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import SchedulerMixin, SchedulerOutput







class  snake_case_     (lowerCamelCase_			,		lowerCamelCase_   ):
					UpperCAmelCase__      :					Optional[Any]					   =    1
					@register_to_config
					def __init__(   self     :Optional[int]    ,__snake_case     :int = 10_00    ,__snake_case     :Optional[Union[np.ndarray, List[float]]] = None   )      ->					int:
												# set `betas`, `alphas`, `timesteps`
												self.set_timesteps(__snake_case   )

												# standard deviation of the initial noise distribution
												a__    =  1.0

												# For now we only support F-PNDM, i.e. the runge-kutta method
												# For more information on the algorithm please take a look at the paper: https://arxiv.org/pdf/2202.09778.pdf
												# mainly at formula (9), (12), (13) and the Algorithm 2.
												a__    =  4

												# running values
												a__    =  []
					def 				lowerCamelCase__(   self     :Union[str, Any]    ,__snake_case     :int    ,__snake_case     :Union[str, torch.device] = None   )      ->					Union[str, Any]:
												a__    =  num_inference_steps
												a__    =  torch.linspace(1    ,0    ,num_inference_steps + 1   )[:-1]
												a__    =  torch.cat([steps, torch.tensor([0.0]   )]   )

												if self.config.trained_betas is not None:
																			a__    =  torch.tensor(self.config.trained_betas    ,dtype=torch.floataa   )
												else:
																			a__    =  torch.sin(steps * math.pi / 2   ) ** 2

												a__    =  (1.0 - self.betas**2) ** 0.5

												a__    =  (torch.atana(self.betas    ,self.alphas   ) / math.pi * 2)[:-1]
												a__    =  timesteps.to(__snake_case   )

												a__    =  []
					def 				lowerCamelCase__(   self     :Any    ,__snake_case     :torch.FloatTensor    ,__snake_case     :int    ,__snake_case     :torch.FloatTensor    ,__snake_case     :bool = True    ,)      ->					Union[SchedulerOutput, Tuple]:
												if self.num_inference_steps is None:
																			raise ValueError(
																			    'Number of inference steps is \'None\', you need to run \'set_timesteps\' after creating the scheduler'   )

												a__    =  (self.timesteps == timestep).nonzero().item()
												a__    =  timestep_index + 1

												a__    =  sample * self.betas[timestep_index] + model_output * self.alphas[timestep_index]
												self.ets.append(__snake_case   )

												if len(self.ets   ) == 1:
																			a__    =  self.ets[-1]
												elif len(self.ets   ) == 2:
																			a__    =  (3 * self.ets[-1] - self.ets[-2]) / 2
												elif len(self.ets   ) == 3:
																			a__    =  (23 * self.ets[-1] - 16 * self.ets[-2] + 5 * self.ets[-3]) / 12
												else:
																			a__    =  (1 / 24) * (55 * self.ets[-1] - 59 * self.ets[-2] + 37 * self.ets[-3] - 9 * self.ets[-4])

												a__    =  self._get_prev_sample(__snake_case    ,__snake_case    ,__snake_case    ,__snake_case   )

												if not return_dict:
																			return (prev_sample,)

												return SchedulerOutput(prev_sample=__snake_case   )
					def 				lowerCamelCase__(   self     :Union[str, Any]    ,__snake_case     :torch.FloatTensor    ,*__snake_case     :int    ,**__snake_case     :Optional[int]   )      ->					torch.FloatTensor:
												return sample
					def 				lowerCamelCase__(   self     :Optional[Any]    ,__snake_case     :List[Any]    ,__snake_case     :Optional[int]    ,__snake_case     :Dict    ,__snake_case     :Any   )      ->					Optional[Any]:
												a__    =  self.alphas[timestep_index]
												a__    =  self.betas[timestep_index]

												a__    =  self.alphas[prev_timestep_index]
												a__    =  self.betas[prev_timestep_index]

												a__    =  (sample - sigma * ets) / max(__snake_case    ,1E-8   )
												a__    =  next_alpha * pred + ets * next_sigma

												return prev_sample



					def __len__(   self     :Any   )      ->					Union[str, Any]:
												return self.config.num_train_timesteps




