
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 							_UpperCamelCase					(       _A  ,							_A	):






			'''simple docstring'''

			__UpperCamelCase   :    List[str]          =			1

			@register_to_config
			def __init__(							self			:							str    ,						snake_case_			:							int = 1000    ,						snake_case_			:							Optional[Union[np.ndarray, List[float]]] = None   ):
							# set `betas`, `alphas`, `timesteps`
							self.set_timesteps(_lowercase   )

							# standard deviation of the initial noise distribution
							UpperCamelCase_:				Dict						=    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.
							UpperCamelCase_:				Optional[int]						=    4

							# running values
							UpperCamelCase_:				Optional[Any]						=    []

			def    lowerCAmelCase__						(							self			:							int    ,						snake_case_			:							int    ,						snake_case_			:							Union[str, torch.device] = None   ):
							UpperCamelCase_:				Optional[int]						=    num_inference_steps
							UpperCamelCase_:				List[Any]						=    torch.linspace(1    ,						0    ,						num_inference_steps + 1   )[:-1]
							UpperCamelCase_:				Optional[Any]						=    torch.cat([steps, torch.tensor([0.0]   )]   )

							if self.config.trained_betas is not None:
											UpperCamelCase_:				List[Any]						=    torch.tensor(self.config.trained_betas    ,						dtype=torch.floataa   )
							else:
											UpperCamelCase_:				Dict						=    torch.sin(steps * math.pi / 2   ) ** 2

							UpperCamelCase_:				int						=    (1.0 - self.betas**2) ** 0.5

							UpperCamelCase_:				List[str]						=    (torch.atana(self.betas    ,						self.alphas   ) / math.pi * 2)[:-1]
							UpperCamelCase_:				Tuple						=    timesteps.to(_lowercase   )

							UpperCamelCase_:				Union[str, Any]						=    []

			def    lowerCAmelCase__						(							self			:							Dict    ,						snake_case_			:							torch.FloatTensor    ,						snake_case_			:							int    ,						snake_case_			:							torch.FloatTensor    ,						snake_case_			:							bool = True    ,						):
							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"""   )

							UpperCamelCase_:				Dict						=    (self.timesteps == timestep).nonzero().item()
							UpperCamelCase_:				int						=    timestep_index + 1

							UpperCamelCase_:				int						=    sample * self.betas[timestep_index] + model_output * self.alphas[timestep_index]
							self.ets.append(_lowercase   )

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

							UpperCamelCase_:				Union[str, Any]						=    self._get_prev_sample(_lowercase    ,						_lowercase    ,						_lowercase    ,						_lowercase   )

							if not return_dict:
											return (prev_sample,)

							return SchedulerOutput(prev_sample=_lowercase   )

			def    lowerCAmelCase__						(							self			:							Dict    ,						snake_case_			:							torch.FloatTensor    ,						*snake_case_			:							Optional[Any]    ,						**snake_case_			:							int   ):
							return sample

			def    lowerCAmelCase__						(							self			:							Optional[int]    ,						snake_case_			:							Optional[Any]    ,						snake_case_			:							Dict    ,						snake_case_			:							Union[str, Any]    ,						snake_case_			:							Union[str, Any]   ):
							UpperCamelCase_:				int						=    self.alphas[timestep_index]
							UpperCamelCase_:				Any						=    self.betas[timestep_index]

							UpperCamelCase_:				List[str]						=    self.alphas[prev_timestep_index]
							UpperCamelCase_:				Optional[Any]						=    self.betas[prev_timestep_index]

							UpperCamelCase_:				Dict						=    (sample - sigma * ets) / max(_lowercase    ,						1e-8   )
							UpperCamelCase_:				Tuple						=    next_alpha * pred + ets * next_sigma

							return prev_sample




			def __len__(							self			:							List[str]   ):
							return self.config.num_train_timesteps












'''simple docstring'''
import warnings

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


_SCREAMING_SNAKE_CASE			=						logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE			=						{
    "RUCAIBox/mvp": "https://huggingface.co/RUCAIBox/mvp/resolve/main/config.json",
}
class   __UpperCAmelCase				(     lowerCAmelCase				):



					'''simple docstring'''





					_UpperCamelCase							=		"""mvp"""
					_UpperCamelCase							=		["""past_key_values"""]
					_UpperCamelCase							=		{"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}



					def __init__(	self     :						List[str]      ,       _lowercase     :						int=50_267      ,       _lowercase     :						str=1_024      ,       _lowercase     :						int=12      ,       _lowercase     :						int=4_096      ,       _lowercase     :						Tuple=16      ,       _lowercase     :						Optional[Any]=12      ,       _lowercase     :						Union[str, Any]=4_096      ,       _lowercase     :						List[Any]=16      ,       _lowercase     :						str=0.0      ,       _lowercase     :						List[str]=0.0      ,       _lowercase     :						Dict="gelu"      ,       _lowercase     :						Union[str, Any]=1_024      ,       _lowercase     :						str=0.1      ,       _lowercase     :						List[Any]=0.0      ,       _lowercase     :						Any=0.0      ,       _lowercase     :						List[str]=0.02      ,       _lowercase     :						List[Any]=0.0      ,       _lowercase     :						List[str]=False      ,       _lowercase     :						Tuple=True      ,       _lowercase     :						Any=1      ,       _lowercase     :						Optional[Any]=0      ,       _lowercase     :						Dict=2      ,       _lowercase     :						int=True      ,       _lowercase     :						Any=2      ,       _lowercase     :						Tuple=2      ,       _lowercase     :						Optional[Any]=False      ,       _lowercase     :						List[str]=100      ,       _lowercase     :						List[str]=800      ,       **_lowercase     :						Dict      ,       )    ->							Dict:
								A_									= vocab_size
								A_									= max_position_embeddings
								A_									= d_model
								A_									= encoder_ffn_dim
								A_									= encoder_layers
								A_									= encoder_attention_heads
								A_									= decoder_ffn_dim
								A_									= decoder_layers
								A_									= decoder_attention_heads
								A_									= dropout
								A_									= attention_dropout
								A_									= activation_dropout
								A_									= activation_function
								A_									= init_std
								A_									= encoder_layerdrop
								A_									= decoder_layerdrop
								A_									= classifier_dropout
								A_									= use_cache
								A_									= encoder_layers
								A_									= scale_embedding  # scale factor will be sqrt(d_model) if True
								A_									= use_prompt
								A_									= prompt_length
								A_									= prompt_mid_dim

								super().__init__(
								    pad_token_id=_lowercase      ,       bos_token_id=_lowercase      ,       eos_token_id=_lowercase      ,       is_encoder_decoder=_lowercase      ,       decoder_start_token_id=_lowercase      ,       forced_eos_token_id=_lowercase      ,       **_lowercase      ,       )

								if self.forced_bos_token_id is None and kwargs.get('force_bos_token_to_be_generated'      ,       _lowercase):
											A_									= self.bos_token_id
											warnings.warn(
											    F'Please make sure the config includes `forced_bos_token_id={self.bos_token_id}` in future versions. '
											    'The config can simply be saved and uploaded again to be fixed.')




