"""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


_A   					=    logging.get_logger(__name__)

_A   					=    {'vocab_file': 'sentencepiece.bpe.model'}

_A   					=    {
    'vocab_file': {
        'camembert-base': 'https://huggingface.co/camembert-base/resolve/main/sentencepiece.bpe.model',
    }
}

_A   					=    {
    'camembert-base': 5_1_2,
}

_A   					=    '▁'




class lowerCamelCase		(a__      ):






					'''simple docstring'''


					a   	=		VOCAB_FILES_NAMES
					a   	=		PRETRAINED_VOCAB_FILES_MAP
					a   	=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					a   	=		["input_ids", "attention_mask"]



					def __init__(						self			:				Optional[int]   , _snake_case			:				Any   , _snake_case			:				str="<s>"   , _snake_case			:				int="</s>"   , _snake_case			:				Optional[int]="</s>"   , _snake_case			:				Dict="<s>"   , _snake_case			:				int="<unk>"   , _snake_case			:				int="<pad>"   , _snake_case			:				int="<mask>"   , _snake_case			:				str=["<s>NOTUSED", "</s>NOTUSED"]   , _snake_case			:				Any = None   , **_snake_case			:				List[Any]   , )							->       str:
									SCREAMING_SNAKE_CASE__		  =				AddedToken(_A   , lstrip=_A   , rstrip=_A		) if isinstance(_A   , _A		) else mask_token

									SCREAMING_SNAKE_CASE__		  =				{} if sp_model_kwargs is None else sp_model_kwargs

									super().__init__(
									    bos_token=_A   , eos_token=_A   , unk_token=_A   , sep_token=_A   , cls_token=_A   , pad_token=_A   , mask_token=_A   , additional_special_tokens=_A   , sp_model_kwargs=self.sp_model_kwargs   , **_A   , )
									SCREAMING_SNAKE_CASE__		  =				spm.SentencePieceProcessor(**self.sp_model_kwargs		)
									self.sp_model.Load(str(_A		)		)
									SCREAMING_SNAKE_CASE__		  =				vocab_file
									# HACK: These tokens were added by fairseq but don't seem to be actually used when duplicated in the actual
									# sentencepiece vocabulary (this is the case for <s> and </s>
									SCREAMING_SNAKE_CASE__		  =				{'<s>NOTUSED': 0, '<pad>': 1, '</s>NOTUSED': 2, '<unk>': 3}
									SCREAMING_SNAKE_CASE__		  =				len(self.fairseq_tokens_to_ids		)
									SCREAMING_SNAKE_CASE__		  =				len(self.sp_model		) + len(self.fairseq_tokens_to_ids		)
									SCREAMING_SNAKE_CASE__		  =				{v: k for k, v in self.fairseq_tokens_to_ids.items()}



					def 			lowerCAmelCase_					(						self			:				Any   , _snake_case			:				Optional[int]   , _snake_case			:				List[Any] = None		)							->       int:

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



					def 			lowerCAmelCase_					(						self			:				List[str]   , _snake_case			:				Tuple   , _snake_case			:				List[Any] = None   , _snake_case			:				int = False		)							->       Dict:

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

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



					def 			lowerCAmelCase_					(						self			:				int   , _snake_case			:				Union[str, Any]   , _snake_case			:				int = None		)							->       List[Any]:
									SCREAMING_SNAKE_CASE__		  =				[self.sep_token_id]
									SCREAMING_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		)							->       str:
									return len(self.fairseq_tokens_to_ids		) + len(self.sp_model		)



					def 			lowerCAmelCase_					(						self			:				Tuple		)							->       int:
									SCREAMING_SNAKE_CASE__		  =				{self.convert_ids_to_tokens(_A		): i for i in range(self.vocab_size		)}
									vocab.update(self.added_tokens_encoder		)
									return vocab



					def 			lowerCAmelCase_					(						self			:				Union[str, Any]   , _snake_case			:				int		)							->       Tuple:
									return self.sp_model.encode(_A   , out_type=_A		)



					def 			lowerCAmelCase_					(						self			:				List[Any]   , _snake_case			:				Optional[int]		)							->       str:

									if token in self.fairseq_tokens_to_ids:
													return self.fairseq_tokens_to_ids[token]
									elif self.sp_model.PieceToId(_A		) == 0:
													# Convert sentence piece unk token to fairseq unk token index
													return self.unk_token_id
									return self.fairseq_offset + self.sp_model.PieceToId(_A		)



					def 			lowerCAmelCase_					(						self			:				Union[str, Any]   , _snake_case			:				int		)							->       Optional[Any]:

									if index in self.fairseq_ids_to_tokens:
													return self.fairseq_ids_to_tokens[index]
									return self.sp_model.IdToPiece(index - self.fairseq_offset		)



					def 			lowerCAmelCase_					(						self			:				str   , _snake_case			:				Tuple		)							->       str:
									SCREAMING_SNAKE_CASE__		  =				[]
									SCREAMING_SNAKE_CASE__		  =				''
									SCREAMING_SNAKE_CASE__		  =				False
									for token in tokens:
													# make sure that special tokens are not decoded using sentencepiece model
													if token in self.all_special_tokens:
																	if not prev_is_special:
																					out_string += " "
																	out_string += self.sp_model.decode(_A		) + token
																	SCREAMING_SNAKE_CASE__		  =				True
																	SCREAMING_SNAKE_CASE__		  =				[]
													else:
																	current_sub_tokens.append(_A		)
																	SCREAMING_SNAKE_CASE__		  =				False
									out_string += self.sp_model.decode(_A		)
									return out_string.strip()



					def __getstate__(						self			:				Any		)							->       int:
									SCREAMING_SNAKE_CASE__		  =				self.__dict__.copy()
									SCREAMING_SNAKE_CASE__		  =				None
									return state



					def __setstate__(						self			:				Optional[int]   , _snake_case			:				List[Any]		)							->       int:
									SCREAMING_SNAKE_CASE__		  =				d

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

									SCREAMING_SNAKE_CASE__		  =				spm.SentencePieceProcessor(**self.sp_model_kwargs		)
									self.sp_model.Load(self.vocab_file		)



					def 			lowerCAmelCase_					(						self			:				Dict   , _snake_case			:				Dict   , _snake_case			:				int = None		)							->       str:

									if not os.path.isdir(_A		):
													logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""		)
													return
									SCREAMING_SNAKE_CASE__		  =				os.path.join(
									    _A   , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]		)

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

									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 lowerCamelCase		(_SCREAMING_SNAKE_CASE      ):






					'''simple docstring'''


					a   	=		42
					a   	=		42



class lowerCamelCase		(_SCREAMING_SNAKE_CASE    ,    _SCREAMING_SNAKE_CASE      ):






					'''simple docstring'''


					a   	=		1



					@register_to_config
					def __init__(						self			:				Tuple   , _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   , )							->       str:
									# standard deviation of the initial noise distribution
									SCREAMING_SNAKE_CASE__		  =				sigma_max

									# setable values
									SCREAMING_SNAKE_CASE__		  =				None

									self.set_sigmas(_snake_case   , _snake_case   , _snake_case   , _snake_case		)



					def 			lowerCAmelCase_					(						self			:				Tuple   , _snake_case			:				torch.FloatTensor   , _snake_case			:				Optional[int] = None		)							->       torch.FloatTensor:
									return sample



					def 			lowerCAmelCase_					(						self			:				int   , _snake_case			:				int   , _snake_case			:				float = None   , _snake_case			:				Union[str, torch.device] = None		)							->       Optional[int]:
									SCREAMING_SNAKE_CASE__		  =				sampling_eps if sampling_eps is not None else self.config.sampling_eps

									SCREAMING_SNAKE_CASE__		  =				torch.linspace(1   , _snake_case   , _snake_case   , device=_snake_case		)



					def 			lowerCAmelCase_					(						self			:				List[Any]   , _snake_case			:				int   , _snake_case			:				float = None   , _snake_case			:				float = None   , _snake_case			:				float = None		)							->       str:
									SCREAMING_SNAKE_CASE__		  =				sigma_min if sigma_min is not None else self.config.sigma_min
									SCREAMING_SNAKE_CASE__		  =				sigma_max if sigma_max is not None else self.config.sigma_max
									SCREAMING_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		)

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



					def 			lowerCAmelCase_					(						self			:				Any   , _snake_case			:				Optional[Any]   , _snake_case			:				List[str]		)							->       List[Any]:
									return torch.where(
									    timesteps == 0   , torch.zeros_like(t.to(timesteps.device		)		)   , self.discrete_sigmas[timesteps - 1].to(timesteps.device		)   , )



					def 			lowerCAmelCase_					(						self			:				str   , _snake_case			:				torch.FloatTensor   , _snake_case			:				int   , _snake_case			:				torch.FloatTensor   , _snake_case			:				Optional[torch.Generator] = None   , _snake_case			:				bool = True   , )							->       Union[SdeVeOutput, Tuple]:
									if self.timesteps is None:
													raise ValueError(
													    "`self.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"		)

									SCREAMING_SNAKE_CASE__		  =				timestep * torch.ones(
									    sample.shape[0]   , device=sample.device		)  # torch.repeat_interleave(timestep, sample.shape[0])
									SCREAMING_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
									SCREAMING_SNAKE_CASE__		  =				timesteps.to(self.discrete_sigmas.device		)

									SCREAMING_SNAKE_CASE__		  =				self.discrete_sigmas[timesteps].to(sample.device		)
									SCREAMING_SNAKE_CASE__		  =				self.get_adjacent_sigma(_snake_case   , _snake_case		).to(sample.device		)
									SCREAMING_SNAKE_CASE__		  =				torch.zeros_like(_snake_case		)
									SCREAMING_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
									SCREAMING_SNAKE_CASE__		  =				diffusion.flatten()
									while len(diffusion.shape		) < len(sample.shape		):
													SCREAMING_SNAKE_CASE__		  =				diffusion.unsqueeze(-1		)
									SCREAMING_SNAKE_CASE__		  =				drift - diffusion**2 * model_output

									#  equation 6: sample noise for the diffusion term of
									SCREAMING_SNAKE_CASE__		  =				randn_tensor(
									    sample.shape   , layout=sample.layout   , generator=_snake_case   , device=sample.device   , dtype=sample.dtype		)
									SCREAMING_SNAKE_CASE__		  =				sample - drift  # subtract because `dt` is a small negative timestep
									# TODO is the variable diffusion the correct scaling term for the noise?
									SCREAMING_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			:				List[Any]   , _snake_case			:				torch.FloatTensor   , _snake_case			:				torch.FloatTensor   , _snake_case			:				Optional[torch.Generator] = None   , _snake_case			:				bool = True   , )							->       Union[SchedulerOutput, Tuple]:
									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
									SCREAMING_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
									SCREAMING_SNAKE_CASE__		  =				torch.norm(model_output.reshape(model_output.shape[0]   , -1		)   , dim=-1		).mean()
									SCREAMING_SNAKE_CASE__		  =				torch.norm(noise.reshape(noise.shape[0]   , -1		)   , dim=-1		).mean()
									SCREAMING_SNAKE_CASE__		  =				(self.config.snr * noise_norm / grad_norm) ** 2 * 2
									SCREAMING_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
									SCREAMING_SNAKE_CASE__		  =				step_size.flatten()
									while len(step_size.shape		) < len(sample.shape		):
													SCREAMING_SNAKE_CASE__		  =				step_size.unsqueeze(-1		)
									SCREAMING_SNAKE_CASE__		  =				sample + step_size * model_output
									SCREAMING_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			:				List[str]   , _snake_case			:				torch.FloatTensor   , _snake_case			:				torch.FloatTensor   , _snake_case			:				torch.FloatTensor   , )							->       torch.FloatTensor:
									# Make sure sigmas and timesteps have the same device and dtype as original_samples
									SCREAMING_SNAKE_CASE__		  =				timesteps.to(original_samples.device		)
									SCREAMING_SNAKE_CASE__		  =				self.discrete_sigmas.to(original_samples.device		)[timesteps]
									SCREAMING_SNAKE_CASE__		  =				(
									    noise * sigmas[:, None, None, None]
									    if noise is not None
									    else torch.randn_like(_snake_case		) * sigmas[:, None, None, None]
									)
									SCREAMING_SNAKE_CASE__		  =				noise + original_samples
									return noisy_samples



					def __len__(						self			:				List[Any]		)							->       int:
									return self.config.num_train_timesteps




