


'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging


__snake_case							   =  logging.get_logger(__name__)

__snake_case							   =  {
    '''abeja/gpt-neox-japanese-2.7b''': '''https://huggingface.co/abeja/gpt-neox-japanese-2.7b/resolve/main/config.json''',
}
class      lowercase			(							A__      ):

		"""simple docstring"""

		_a									=       'gpt_neox_japanese'




		def __init__(	self					,     UpperCamelCase_=32000					,     UpperCamelCase_=2560					,     UpperCamelCase_=32					,     UpperCamelCase_=32					,     UpperCamelCase_=4					,     UpperCamelCase_="gelu"					,     UpperCamelCase_=1.00					,     UpperCamelCase_=10000					,     UpperCamelCase_=2048					,     UpperCamelCase_=0.02					,     UpperCamelCase_=1e-5					,     UpperCamelCase_=True					,     UpperCamelCase_=31996					,     UpperCamelCase_=31999					,     UpperCamelCase_=0.1					,     UpperCamelCase_=0.0					,     **UpperCamelCase_					,     ):

							'''simple docstring'''


							super().__init__(bos_token_id=UpperCamelCase_					,     eos_token_id=UpperCamelCase_					,     **UpperCamelCase_							)
							UpperCamelCase__     :Tuple							      =				vocab_size
							UpperCamelCase__     :Optional[int]							      =				max_position_embeddings
							UpperCamelCase__     :Dict							      =				hidden_size
							UpperCamelCase__     :Union[str, Any]							      =				num_hidden_layers
							UpperCamelCase__     :Union[str, Any]							      =				num_attention_heads
							UpperCamelCase__     :Optional[Any]							      =				intermediate_multiple_size
							UpperCamelCase__     :List[str]							      =				hidden_act
							UpperCamelCase__     :Optional[Any]							      =				rotary_pct
							UpperCamelCase__     :List[str]							      =				rotary_emb_base
							UpperCamelCase__     :int							      =				initializer_range
							UpperCamelCase__     :Dict							      =				layer_norm_eps
							UpperCamelCase__     :Dict							      =				use_cache
							UpperCamelCase__     :Optional[Any]							      =				attention_dropout
							UpperCamelCase__     :Dict							      =				hidden_dropout



'''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      lowercase			(							A__			,	A__      ):

		"""simple docstring"""

		_a									=       1




		@register_to_config
		def __init__(	self					,     UpperCamelCase_=2000					,     UpperCamelCase_=0.1					,     UpperCamelCase_=20					,     UpperCamelCase_=1e-3							):

							'''simple docstring'''


							UpperCamelCase__     :Dict							      =				None
							UpperCamelCase__     :Any							      =				None
							UpperCamelCase__     :List[str]							      =				None




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_					,     UpperCamelCase_ = None							):

							'''simple docstring'''


							UpperCamelCase__     :Union[str, Any]							      =				torch.linspace(1					,     self.config.sampling_eps					,     UpperCamelCase_					,     device=UpperCamelCase_							)




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_=None							):

							'''simple docstring'''


							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
							UpperCamelCase__     :Optional[int]							      =				(
							    -0.25 * t**2 * (self.config.beta_max - self.config.beta_min) - 0.5 * t * self.config.beta_min
							)
							UpperCamelCase__     :Tuple							      =				torch.sqrt(1.0 - torch.exp(2.0 * log_mean_coeff							)							)
							UpperCamelCase__     :Tuple							      =				std.flatten()
							while len(std.shape							) < len(score.shape							):
												UpperCamelCase__     :int							      =				std.unsqueeze(-1							)
							UpperCamelCase__     :List[Any]							      =				-score / std

							# compute
							UpperCamelCase__     :List[str]							      =				-1.0 / len(self.timesteps							)

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

							UpperCamelCase__     :int							      =				torch.sqrt(UpperCamelCase_							)
							UpperCamelCase__     :List[str]							      =				drift - diffusion**2 * score
							UpperCamelCase__     :Dict							      =				x + drift * dt

							# add noise
							UpperCamelCase__     :List[Any]							      =				randn_tensor(x.shape					,     layout=x.layout					,     generator=UpperCamelCase_					,     device=x.device					,     dtype=x.dtype							)
							UpperCamelCase__     :int							      =				x_mean + diffusion * math.sqrt(-dt							) * noise

							return x, x_mean




		def __len__(	self							):

							'''simple docstring'''


							return self.config.num_train_timesteps