
"""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__(				_UpperCAmelCase		,						_UpperCAmelCase   ):
	'''simple docstring'''

	UpperCamelCase				=  1





	@register_to_config
	def __init__(						self    :Dict		, lowerCamelCase_    :Optional[Any]=20_00		, lowerCamelCase_    :List[str]=0.1		, lowerCamelCase_    :Dict=20		, lowerCamelCase_    :Union[str, Any]=1E-3					)							->			List[str]:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     Tuple				= None
			SCREAMING_SNAKE_CASE       :     int				= None
			SCREAMING_SNAKE_CASE       :     List[str]				= None





	def   __lowerCAmelCase (						self    :Union[str, Any]		, lowerCamelCase_    :Optional[Any]		, lowerCamelCase_    :Union[str, torch.device] = None					)							->			str:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     Optional[int]				= torch.linspace(1		, self.config.sampling_eps		, lowerCamelCase_		, device=lowerCamelCase_					)





	def   __lowerCAmelCase (						self    :Any		, lowerCamelCase_    :Optional[Any]		, lowerCamelCase_    :Union[str, Any]		, lowerCamelCase_    :Optional[int]		, lowerCamelCase_    :Tuple=None					)							->			List[Any]:







			'''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
			SCREAMING_SNAKE_CASE       :     Optional[int]				= (
			    -0.2_5 * t**2 * (self.config.beta_max - self.config.beta_min) - 0.5 * t * self.config.beta_min
			)
			SCREAMING_SNAKE_CASE       :     Dict				= torch.sqrt(1.0 - torch.exp(2.0 * log_mean_coeff					)					)
			SCREAMING_SNAKE_CASE       :     Union[str, Any]				= std.flatten()
			while len(std.shape					) < len(score.shape					):
					SCREAMING_SNAKE_CASE       :     Optional[Any]				= std.unsqueeze(-1					)
			SCREAMING_SNAKE_CASE       :     Optional[Any]				= -score / std

			# compute
			SCREAMING_SNAKE_CASE       :     Union[str, Any]				= -1.0 / len(self.timesteps					)

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

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

			# add noise
			SCREAMING_SNAKE_CASE       :     str				= randn_tensor(x.shape		, layout=x.layout		, generator=lowerCamelCase_		, device=x.device		, dtype=x.dtype					)
			SCREAMING_SNAKE_CASE       :     Dict				= x_mean + diffusion * math.sqrt(-dt					) * noise

			return x, x_mean







	def __len__(						self    :Dict					)							->			Any:







			'''simple docstring'''




			return self.config.num_train_timesteps







"""simple docstring"""






import json
import os
from typing import Optional, Tuple

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


lowerCamelCase__ :       List[str]						     =			logging.get_logger(__name__)

lowerCamelCase__ :       Optional[int]						     =			{"vocab_file": "vocab.json"}

lowerCamelCase__ :       Dict						     =			{
    "vocab_file": {
        "mgp-str": "https://huggingface.co/alibaba-damo/mgp-str-base/blob/main/vocab.json",
    }
}

lowerCamelCase__ :       Optional[Any]						     =			{"mgp-str": 27}




class 				lowercase__(				_UpperCAmelCase   ):
	'''simple docstring'''

	UpperCamelCase				=  VOCAB_FILES_NAMES
	UpperCamelCase				=  PRETRAINED_VOCAB_FILES_MAP
	UpperCamelCase				=  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES





	def __init__(						self    :int		, lowerCamelCase_    :Optional[int]		, lowerCamelCase_    :Optional[int]="[GO]"		, lowerCamelCase_    :int="[GO]"		, lowerCamelCase_    :str="[s]"		, lowerCamelCase_    :Dict="[GO]"		, **lowerCamelCase_    :List[str]					)							->			Tuple:







			'''simple docstring'''




			super().__init__(
			    unk_token=lowerCamelCase_		, bos_token=lowerCamelCase_		, eos_token=lowerCamelCase_		, pad_token=lowerCamelCase_		, **lowerCamelCase_		, )

			with open(lowerCamelCase_		, encoding='''utf-8'''					) as vocab_handle:
					SCREAMING_SNAKE_CASE       :     int				= json.load(lowerCamelCase_					)
			SCREAMING_SNAKE_CASE       :     Union[str, Any]				= {v: k for k, v in self.vocab.items()}





	@property
	def   __lowerCAmelCase (						self    :int					)							->			Dict:







			'''simple docstring'''




			return len(self.vocab					)





	def   __lowerCAmelCase (						self    :List[str]					)							->			Dict:







			'''simple docstring'''




			return dict(self.vocab		, **self.added_tokens_encoder					)





	def   __lowerCAmelCase (						self    :List[str]		, lowerCamelCase_    :Optional[Any]					)							->			List[str]:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     Union[str, Any]				= []
			for s in text:
					char_tokens.extend(lowerCamelCase_					)
			return char_tokens





	def   __lowerCAmelCase (						self    :Dict		, lowerCamelCase_    :Tuple					)							->			Optional[int]:







			'''simple docstring'''




			return self.vocab.get(lowerCamelCase_		, self.vocab.get(self.unk_token					)					)





	def   __lowerCAmelCase (						self    :List[str]		, lowerCamelCase_    :Dict					)							->			Optional[int]:







			'''simple docstring'''




			return self.decoder.get(lowerCamelCase_					)







	def   __lowerCAmelCase (						self    :Optional[int]		, lowerCamelCase_    :str		, lowerCamelCase_    :Optional[str] = None					)							->			Tuple[str]:







			'''simple docstring'''




			if not os.path.isdir(lowerCamelCase_					):
					logger.error('''Vocabulary path ({}) should be a directory'''.format(lowerCamelCase_					)					)
					return
			SCREAMING_SNAKE_CASE       :     List[Any]				= os.path.join(
			    lowerCamelCase_		, (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']					)

			with open(lowerCamelCase_		, '''w'''		, encoding='''utf-8'''					) as f:
					f.write(json.dumps(self.vocab		, indent=2		, sort_keys=lowerCamelCase_		, ensure_ascii=lowerCamelCase_					) + '''\n'''					)

			return (vocab_file,)





