
"""simple docstring"""







import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


_SCREAMING_SNAKE_CASE	       =      logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE	       =      """▁"""

_SCREAMING_SNAKE_CASE	       =      {"""vocab_file""": """sentencepiece.bpe.model""", """monolingual_vocab_file""": """dict.txt"""}

_SCREAMING_SNAKE_CASE	       =      {
    """vocab_file""": {
        """vinai/bartpho-syllable""": """https://huggingface.co/vinai/bartpho-syllable/resolve/main/sentencepiece.bpe.model""",
    },
    """monolingual_vocab_file""": {
        """vinai/bartpho-syllable""": """https://huggingface.co/vinai/bartpho-syllable/resolve/main/dict.txt""",
    },
}

_SCREAMING_SNAKE_CASE	       =      {"""vinai/bartpho-syllable""": 1_024}







class 							__magic_name__     (		lowercase__   ):
					_SCREAMING_SNAKE_CASE       :					str     							=   VOCAB_FILES_NAMES
					_SCREAMING_SNAKE_CASE       :					Optional[int]     							=   PRETRAINED_VOCAB_FILES_MAP
					_SCREAMING_SNAKE_CASE       :					str     							=   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					_SCREAMING_SNAKE_CASE       :					Tuple     							=   ['input_ids', 'attention_mask']





					def __init__(				self   :							Union[str, Any]							,						snake_case_   :							Dict							,						snake_case_   :							Optional[int]							,						snake_case_   :							Union[str, Any]="<s>"							,						snake_case_   :							List[Any]="</s>"							,						snake_case_   :							List[Any]="</s>"							,						snake_case_   :							Optional[int]="<s>"							,						snake_case_   :							List[str]="<unk>"							,						snake_case_   :							Optional[Any]="<pad>"							,						snake_case_   :							List[str]="<mask>"							,						snake_case_   :							Optional[Dict[str, Any]] = None							,						**snake_case_   :							Optional[int]							,						):
												# Mask token behave like a normal word, i.e. include the space before it
												__snake_case     			=       AddedToken(snake_case_							,						lstrip=snake_case_							,						rstrip=snake_case_    ) if isinstance(snake_case_							,						snake_case_    ) else mask_token

												__snake_case     			=       {} if sp_model_kwargs is None else sp_model_kwargs

												super().__init__(
												    bos_token=snake_case_							,						eos_token=snake_case_							,						unk_token=snake_case_							,						sep_token=snake_case_							,						cls_token=snake_case_							,						pad_token=snake_case_							,						mask_token=snake_case_							,						sp_model_kwargs=self.sp_model_kwargs							,						**snake_case_							,						)

												__snake_case     			=       vocab_file
												__snake_case     			=       monolingual_vocab_file
												__snake_case     			=       spm.SentencePieceProcessor(**self.sp_model_kwargs    )
												self.sp_model.Load(str(snake_case_    )    )

												# Load the reduced vocab

												# Keep order of special tokens for backward compatibility
												__snake_case     			=       {}
												__snake_case     			=       0
												for token in [bos_token, pad_token, eos_token, unk_token, sep_token, cls_token]:
																			if str(snake_case_    ) not in self.fairseq_tokens_to_ids:
																										__snake_case     			=       cnt
																										cnt += 1
												with open(snake_case_							,						"r"							,						encoding="utf-8"    ) as f:
																			for line in f.readlines():
																										__snake_case     			=       line.strip().split()[0]
																										__snake_case     			=       len(self.fairseq_tokens_to_ids    )
												if str(snake_case_    ) not in self.fairseq_tokens_to_ids:
																			__snake_case     			=       len(self.fairseq_tokens_to_ids    )

												__snake_case     			=       {v: k for k, v in self.fairseq_tokens_to_ids.items()}





					def __getstate__(				self   :							str    ):
												__snake_case     			=       self.__dict__.copy()
												__snake_case     			=       None
												__snake_case     			=       self.sp_model.serialized_model_proto()
												return state





					def __setstate__(				self   :							List[str]							,						snake_case_   :							Tuple    ):
												__snake_case     			=       d

												# for backward compatibility
												if not hasattr(self							,						"sp_model_kwargs"    ):
																			__snake_case     			=       {}

												__snake_case     			=       spm.SentencePieceProcessor(**self.sp_model_kwargs    )
												self.sp_model.LoadFromSerializedProto(self.sp_model_proto    )





					def 							lowerCAmelCase      (				self   :							Dict							,						snake_case_   :							List[int]							,						snake_case_   :							Optional[List[int]] = None    ):

												if token_ids_a is None:
																			return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
												__snake_case     			=       [self.cls_token_id]
												__snake_case     			=       [self.sep_token_id]
												return cls + token_ids_a + sep + sep + token_ids_a + sep





					def 							lowerCAmelCase      (				self   :							int							,						snake_case_   :							List[int]							,						snake_case_   :							Optional[List[int]] = None							,						snake_case_   :							bool = False    ):

												if already_has_special_tokens:
																			return super().get_special_tokens_mask(
																			    token_ids_a=snake_case_							,						token_ids_a=snake_case_							,						already_has_special_tokens=snake_case_    )

												if token_ids_a is None:
																			return [1] + ([0] * len(snake_case_    )) + [1]
												return [1] + ([0] * len(snake_case_    )) + [1, 1] + ([0] * len(snake_case_    )) + [1]





					def 							lowerCAmelCase      (				self   :							int							,						snake_case_   :							List[int]							,						snake_case_   :							Optional[List[int]] = None    ):
												__snake_case     			=       [self.sep_token_id]
												__snake_case     			=       [self.cls_token_id]

												if token_ids_a is None:
																			return len(cls + token_ids_a + sep    ) * [0]
												return len(cls + token_ids_a + sep + sep + token_ids_a + sep    ) * [0]





					@property
					def 							lowerCAmelCase      (				self   :							int    ):
												return len(self.fairseq_ids_to_tokens    )





					def 							lowerCAmelCase      (				self   :							Any    ):
												__snake_case     			=       {self.convert_ids_to_tokens(snake_case_    ): i for i in range(self.vocab_size    )}
												vocab.update(self.added_tokens_encoder    )
												return vocab





					def 							lowerCAmelCase      (				self   :							Optional[int]							,						snake_case_   :							str    ):
												return self.sp_model.encode(snake_case_							,						out_type=snake_case_    )





					def 							lowerCAmelCase      (				self   :							Dict							,						snake_case_   :							List[str]    ):
												if token in self.fairseq_tokens_to_ids:
																			return self.fairseq_tokens_to_ids[token]
												else:
																			return self.unk_token_id





					def 							lowerCAmelCase      (				self   :							List[str]							,						snake_case_   :							str    ):
												return self.fairseq_ids_to_tokens[index]





					def 							lowerCAmelCase      (				self   :							Optional[Any]							,						snake_case_   :							Any    ):
												__snake_case     			=       "".join(snake_case_    ).replace(snake_case_							,						" "    ).strip()
												return out_string





					def 							lowerCAmelCase      (				self   :							Any							,						snake_case_   :							str							,						snake_case_   :							Optional[str] = None    ):
												if not os.path.isdir(snake_case_    ):
																			logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''    )
																			return
												__snake_case     			=       os.path.join(
												    snake_case_							,						(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]    )
												__snake_case     			=       os.path.join(
												    snake_case_							,						(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["monolingual_vocab_file"]							,						)

												if os.path.abspath(self.vocab_file    ) != os.path.abspath(snake_case_    ) and os.path.isfile(self.vocab_file    ):
																			copyfile(self.vocab_file							,						snake_case_    )
												elif not os.path.isfile(self.vocab_file    ):
																			with open(snake_case_							,						"wb"    ) as fi:
																										__snake_case     			=       self.sp_model.serialized_model_proto()
																										fi.write(snake_case_    )

												if os.path.abspath(self.monolingual_vocab_file    ) != os.path.abspath(
												    snake_case_    ) and os.path.isfile(self.monolingual_vocab_file    ):
																			copyfile(self.monolingual_vocab_file							,						snake_case_    )
												elif not os.path.isfile(self.monolingual_vocab_file    ):
																			with open(snake_case_							,						"w"							,						encoding="utf-8"    ) as fp:
																										for token in self.fairseq_tokens_to_ids:
																																	if token not in self.all_special_tokens:
																																								fp.write(F'''{str(snake_case_    )} \n'''    )

												return out_vocab_file, out_monolingual_vocab_file


"""simple docstring"""







# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch

import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union

import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin, SchedulerOutput







@dataclass
class 							__magic_name__     (		lowercase__   ):
					_SCREAMING_SNAKE_CASE       :					torch.FloatTensor
					_SCREAMING_SNAKE_CASE       :					torch.FloatTensor







class 							__magic_name__     (		lowercase__      ,		lowercase__   ):
					_SCREAMING_SNAKE_CASE       :					Optional[int]     							=   1





					@register_to_config
					def __init__(				self   :							List[Any]							,						snake_case_   :							int = 2000							,						snake_case_   :							float = 0.15							,						snake_case_   :							float = 0.01							,						snake_case_   :							float = 1348.0							,						snake_case_   :							float = 1e-5							,						snake_case_   :							int = 1							,						):
												# standard deviation of the initial noise distribution
												__snake_case     			=       sigma_max

												# setable values
												__snake_case     			=       None

												self.set_sigmas(snake_case_							,						snake_case_							,						snake_case_							,						snake_case_    )





					def 							lowerCAmelCase      (				self   :							Union[str, Any]							,						snake_case_   :							torch.FloatTensor							,						snake_case_   :							Optional[int] = None    ):
												return sample





					def 							lowerCAmelCase      (				self   :							Any							,						snake_case_   :							int							,						snake_case_   :							float = None							,						snake_case_   :							Union[str, torch.device] = None    ):
												__snake_case     			=       sampling_eps if sampling_eps is not None else self.config.sampling_eps

												__snake_case     			=       torch.linspace(1							,						snake_case_							,						snake_case_							,						device=snake_case_    )





					def 							lowerCAmelCase      (				self   :							Dict							,						snake_case_   :							int							,						snake_case_   :							float = None							,						snake_case_   :							float = None							,						snake_case_   :							float = None    ):
												__snake_case     			=       sigma_min if sigma_min is not None else self.config.sigma_min
												__snake_case     			=       sigma_max if sigma_max is not None else self.config.sigma_max
												__snake_case     			=       sampling_eps if sampling_eps is not None else self.config.sampling_eps
												if self.timesteps is None:
																			self.set_timesteps(snake_case_							,						snake_case_    )

												__snake_case     			=       sigma_min * (sigma_max / sigma_min) ** (self.timesteps / sampling_eps)
												__snake_case     			=       torch.exp(torch.linspace(math.log(snake_case_    )							,						math.log(snake_case_    )							,						snake_case_    )    )
												__snake_case     			=       torch.tensor([sigma_min * (sigma_max / sigma_min) ** t for t in self.timesteps]    )





					def 							lowerCAmelCase      (				self   :							Optional[Any]							,						snake_case_   :							str							,						snake_case_   :							Dict    ):
												return torch.where(
												    timesteps == 0							,						torch.zeros_like(t.to(timesteps.device    )    )							,						self.discrete_sigmas[timesteps - 1].to(timesteps.device    )							,						)





					def 							lowerCAmelCase      (				self   :							Dict							,						snake_case_   :							torch.FloatTensor							,						snake_case_   :							int							,						snake_case_   :							torch.FloatTensor							,						snake_case_   :							Optional[torch.Generator] = None							,						snake_case_   :							bool = True							,						):
												if self.timesteps is None:
																			raise ValueError(
																			    "`self.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"    )

												__snake_case     			=       timestep * torch.ones(
												    sample.shape[0]							,						device=sample.device    )  # torch.repeat_interleave(timestep, sample.shape[0])
												__snake_case     			=       (timestep * (len(self.timesteps    ) - 1)).long()

												# mps requires indices to be in the same device, so we use cpu as is the default with cuda
												__snake_case     			=       timesteps.to(self.discrete_sigmas.device    )

												__snake_case     			=       self.discrete_sigmas[timesteps].to(sample.device    )
												__snake_case     			=       self.get_adjacent_sigma(snake_case_							,						snake_case_    ).to(sample.device    )
												__snake_case     			=       torch.zeros_like(snake_case_    )
												__snake_case     			=       (sigma**2 - adjacent_sigma**2) ** 0.5

												# equation 6 in the paper: the model_output modeled by the network is grad_x log pt(x)
												# also equation 47 shows the analog from SDE models to ancestral sampling methods
												__snake_case     			=       diffusion.flatten()
												while len(diffusion.shape    ) < len(sample.shape    ):
																			__snake_case     			=       diffusion.unsqueeze(-1    )
												__snake_case     			=       drift - diffusion**2 * model_output

												#  equation 6: sample noise for the diffusion term of
												__snake_case     			=       randn_tensor(
												    sample.shape							,						layout=sample.layout							,						generator=snake_case_							,						device=sample.device							,						dtype=sample.dtype    )
												__snake_case     			=       sample - drift  # subtract because `dt` is a small negative timestep
												# TODO is the variable diffusion the correct scaling term for the noise?
												__snake_case     			=       prev_sample_mean + diffusion * noise  # add impact of diffusion field g

												if not return_dict:
																			return (prev_sample, prev_sample_mean)

												return SdeVeOutput(prev_sample=snake_case_							,						prev_sample_mean=snake_case_    )





					def 							lowerCAmelCase      (				self   :							Union[str, Any]							,						snake_case_   :							torch.FloatTensor							,						snake_case_   :							torch.FloatTensor							,						snake_case_   :							Optional[torch.Generator] = None							,						snake_case_   :							bool = True							,						):
												if self.timesteps is None:
																			raise ValueError(
																			    "`self.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"    )

												# For small batch sizes, the paper "suggest replacing norm(z) with sqrt(d), where d is the dim. of z"
												# sample noise for correction
												__snake_case     			=       randn_tensor(sample.shape							,						layout=sample.layout							,						generator=snake_case_    ).to(sample.device    )

												# compute step size from the model_output, the noise, and the snr
												__snake_case     			=       torch.norm(model_output.reshape(model_output.shape[0]							,						-1    )							,						dim=-1    ).mean()
												__snake_case     			=       torch.norm(noise.reshape(noise.shape[0]							,						-1    )							,						dim=-1    ).mean()
												__snake_case     			=       (self.config.snr * noise_norm / grad_norm) ** 2 * 2
												__snake_case     			=       step_size * torch.ones(sample.shape[0]    ).to(sample.device    )
												# self.repeat_scalar(step_size, sample.shape[0])

												# compute corrected sample: model_output term and noise term
												__snake_case     			=       step_size.flatten()
												while len(step_size.shape    ) < len(sample.shape    ):
																			__snake_case     			=       step_size.unsqueeze(-1    )
												__snake_case     			=       sample + step_size * model_output
												__snake_case     			=       prev_sample_mean + ((step_size * 2) ** 0.5) * noise

												if not return_dict:
																			return (prev_sample,)

												return SchedulerOutput(prev_sample=snake_case_    )





					def 							lowerCAmelCase      (				self   :							Dict							,						snake_case_   :							torch.FloatTensor							,						snake_case_   :							torch.FloatTensor							,						snake_case_   :							torch.FloatTensor							,						):
												# Make sure sigmas and timesteps have the same device and dtype as original_samples
												__snake_case     			=       timesteps.to(original_samples.device    )
												__snake_case     			=       self.discrete_sigmas.to(original_samples.device    )[timesteps]
												__snake_case     			=       (
												    noise * sigmas[:, None, None, None]
												    if noise is not None
												    else torch.randn_like(snake_case_    ) * sigmas[:, None, None, None]
												)
												__snake_case     			=       noise + original_samples
												return noisy_samples





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