
"""simple docstring"""







import os
import unicodedata
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 SPIECE_UNDERLINE, logging


_SCREAMING_SNAKE_CASE     :   List[Any]	       =      logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE     :   List[Any]	       =      {'''vocab_file''': '''spiece.model'''}

_SCREAMING_SNAKE_CASE     :   Tuple	       =      {
    '''vocab_file''': {
        '''xlnet-base-cased''': '''https://huggingface.co/xlnet-base-cased/resolve/main/spiece.model''',
        '''xlnet-large-cased''': '''https://huggingface.co/xlnet-large-cased/resolve/main/spiece.model''',
    }
}

_SCREAMING_SNAKE_CASE     :   Any	       =      {
    '''xlnet-base-cased''': None,
    '''xlnet-large-cased''': None,
}

# Segments (not really needed)
_SCREAMING_SNAKE_CASE     :   List[str]	       =      0
_SCREAMING_SNAKE_CASE     :   List[str]	       =      1
_SCREAMING_SNAKE_CASE     :   Dict	       =      2
_SCREAMING_SNAKE_CASE     :   List[str]	       =      3
_SCREAMING_SNAKE_CASE     :   List[Any]	       =      4







class 							__magic_name__     (		__a   ):
					_SCREAMING_SNAKE_CASE       :					Optional[Any]     							=   VOCAB_FILES_NAMES
					_SCREAMING_SNAKE_CASE       :					int     							=   PRETRAINED_VOCAB_FILES_MAP
					_SCREAMING_SNAKE_CASE       :					Optional[Any]     							=   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					_SCREAMING_SNAKE_CASE       :					str     							=   '''left'''





					def __init__(				self   :							Dict							,						snake_case_   :							int							,						snake_case_   :							Optional[Any]=False							,						snake_case_   :							List[str]=True							,						snake_case_   :							Tuple=False							,						snake_case_   :							int="<s>"							,						snake_case_   :							Tuple="</s>"							,						snake_case_   :							Tuple="<unk>"							,						snake_case_   :							str="<sep>"							,						snake_case_   :							Dict="<pad>"							,						snake_case_   :							int="<cls>"							,						snake_case_   :							Union[str, Any]="<mask>"							,						snake_case_   :							List[Any]=["<eop>", "<eod>"]							,						snake_case_   :							Optional[Dict[str, Any]] = None							,						**snake_case_   :							str							,						):
												# 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__(
												    do_lower_case=snake_case__							,						remove_space=snake_case__							,						keep_accents=snake_case__							,						bos_token=snake_case__							,						eos_token=snake_case__							,						unk_token=snake_case__							,						sep_token=snake_case__							,						pad_token=snake_case__							,						cls_token=snake_case__							,						mask_token=snake_case__							,						additional_special_tokens=snake_case__							,						sp_model_kwargs=self.sp_model_kwargs							,						**snake_case__							,						)

												__snake_case     			=       3

												__snake_case     			=       do_lower_case
												__snake_case     			=       remove_space
												__snake_case     			=       keep_accents
												__snake_case     			=       vocab_file

												__snake_case     			=       spm.SentencePieceProcessor(**self.sp_model_kwargs    )
												self.sp_model.Load(snake_case__    )





					@property
					def 							lowerCAmelCase      (				self   :							List[Any]    ):
												return len(self.sp_model    )





					def 							lowerCAmelCase      (				self   :							Dict    ):
												__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 __getstate__(				self   :							Optional[int]    ):
												__snake_case     			=       self.__dict__.copy()
												__snake_case     			=       None
												return state





					def __setstate__(				self   :							Union[str, Any]							,						snake_case_   :							Optional[int]    ):
												__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.Load(self.vocab_file    )





					def 							lowerCAmelCase      (				self   :							Optional[Any]							,						snake_case_   :							List[Any]    ):
												if self.remove_space:
																			__snake_case     			=       " ".join(inputs.strip().split()    )
												else:
																			__snake_case     			=       inputs
												__snake_case     			=       outputs.replace("``"							,						"\""    ).replace("''"							,						"\""    )

												if not self.keep_accents:
																			__snake_case     			=       unicodedata.normalize("NFKD"							,						snake_case__    )
																			__snake_case     			=       "".join([c for c in outputs if not unicodedata.combining(snake_case__    )]    )
												if self.do_lower_case:
																			__snake_case     			=       outputs.lower()

												return outputs





					def 							lowerCAmelCase      (				self   :							Dict							,						snake_case_   :							str    ):
												__snake_case     			=       self.preprocess_text(snake_case__    )
												__snake_case     			=       self.sp_model.encode(snake_case__							,						out_type=snake_case__    )
												__snake_case     			=       []
												for piece in pieces:
																			if len(snake_case__    ) > 1 and piece[-1] == str(","    ) and piece[-2].isdigit():
																										__snake_case     			=       self.sp_model.EncodeAsPieces(piece[:-1].replace(snake_case__							,						""    )    )
																										if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
																																	if len(cur_pieces[0]    ) == 1:
																																								__snake_case     			=       cur_pieces[1:]
																																	else:
																																								__snake_case     			=       cur_pieces[0][1:]
																										cur_pieces.append(piece[-1]    )
																										new_pieces.extend(snake_case__    )
																			else:
																										new_pieces.append(snake_case__    )

												return new_pieces





					def 							lowerCAmelCase      (				self   :							Tuple							,						snake_case_   :							int    ):
												return self.sp_model.PieceToId(snake_case__    )





					def 							lowerCAmelCase      (				self   :							Dict							,						snake_case_   :							Optional[Any]    ):
												return self.sp_model.IdToPiece(snake_case__    )





					def 							lowerCAmelCase      (				self   :							Any							,						snake_case_   :							int    ):
												__snake_case     			=       "".join(snake_case__    ).replace(snake_case__							,						" "    ).strip()
												return out_string





					def 							lowerCAmelCase      (				self   :							Tuple							,						snake_case_   :							List[int]							,						snake_case_   :							bool = False							,						snake_case_   :							bool = None							,						snake_case_   :							bool = True							,						**snake_case_   :							Optional[int]							,						):
												__snake_case     			=       kwargs.pop("use_source_tokenizer"							,						snake_case__    )

												__snake_case     			=       self.convert_ids_to_tokens(snake_case__							,						skip_special_tokens=snake_case__    )

												# To avoid mixing byte-level and unicode for byte-level BPT
												# we need to build string separately for added tokens and byte-level tokens
												# cf. https://github.com/huggingface/transformers/issues/1133
												__snake_case     			=       []
												__snake_case     			=       []
												for token in filtered_tokens:
																			if skip_special_tokens and token in self.all_special_ids:
																										continue
																			if token in self.added_tokens_encoder:
																										if current_sub_text:
																																	sub_texts.append(self.convert_tokens_to_string(snake_case__    )    )
																																	__snake_case     			=       []
																										sub_texts.append(snake_case__    )
																			else:
																										current_sub_text.append(snake_case__    )
												if current_sub_text:
																			sub_texts.append(self.convert_tokens_to_string(snake_case__    )    )

												# Mimic the behavior of the Rust tokenizer:
												# By default, there are no spaces between special tokens
												__snake_case     			=       "".join(snake_case__    )

												__snake_case     			=       (
												    clean_up_tokenization_spaces
												    if clean_up_tokenization_spaces is not None
												    else self.clean_up_tokenization_spaces
												)
												if clean_up_tokenization_spaces:
																			__snake_case     			=       self.clean_up_tokenization(snake_case__    )
																			return clean_text
												else:
																			return text





					def 							lowerCAmelCase      (				self   :							Tuple							,						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 token_ids_a + sep + cls
												return token_ids_a + sep + token_ids_a + sep + cls





					def 							lowerCAmelCase      (				self   :							Union[str, Any]							,						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 not None:
																			return ([0] * len(snake_case__    )) + [1] + ([0] * len(snake_case__    )) + [1, 1]
												return ([0] * len(snake_case__    )) + [1, 1]





					def 							lowerCAmelCase      (				self   :							str							,						snake_case_   :							List[int]							,						snake_case_   :							Optional[List[int]] = None    ):
												__snake_case     			=       [self.sep_token_id]
												__snake_case     			=       [2]

												if token_ids_a is None:
																			return len(token_ids_a + sep    ) * [0] + cls_segment_id
												return len(token_ids_a + sep    ) * [0] + len(token_ids_a + sep    ) * [1] + cls_segment_id





					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"]    )

												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__    )

												return (out_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
