



'''simple docstring'''
import math
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

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


snake_case_				:			Any						 =						logging.get_logger(__name__)  # pylint: disable=invalid-name






@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->EulerDiscrete
class 							lowercase__			(							lowercase     ):
	lowercase__						       =    42
	lowercase__						       =    None






def A__					(  UpperCAmelCase_						,						UpperCAmelCase_=0.999						,						UpperCAmelCase_="cosine"						,						):
						if alpha_transform_type == "cosine":

												def alpha_bar_fn(UpperCAmelCase_							):
																		return math.cos((t + 0.008) / 1.008 * math.pi / 2							) ** 2

						elif alpha_transform_type == "exp":

												def alpha_bar_fn(UpperCAmelCase_							):
																		return math.exp(t * -12.0							)

						else:
												raise ValueError(f'Unsupported alpha_tranform_type: {alpha_transform_type}'							)

						_UpperCamelCase      :	List[Any]				       =     []
						for i in range(UpperCAmelCase_							):
												_UpperCamelCase      :	str				       =     i / num_diffusion_timesteps
												_UpperCamelCase      :	Dict				       =     (i + 1) / num_diffusion_timesteps
												betas.append(min(1 - alpha_bar_fn(UpperCAmelCase_							) / alpha_bar_fn(UpperCAmelCase_							)						,						UpperCAmelCase_							)							)
						return torch.tensor(UpperCAmelCase_						,						dtype=torch.floataa							)






class 							lowercase__			(							lowercase		,      lowercase     ):
	lowercase__						       =    [e.name for e in KarrasDiffusionSchedulers]
	lowercase__						       =    1





	@register_to_config
	def __init__(				self      :       int				,lowerCamelCase__      :       int = 1000				,lowerCamelCase__      :       float = 0.0_0_0_1				,lowerCamelCase__      :       float = 0.0_2				,lowerCamelCase__      :       str = "linear"				,lowerCamelCase__      :       Optional[Union[np.ndarray, List[float]]] = None				,lowerCamelCase__      :       str = "epsilon"				,lowerCamelCase__      :       str = "linear"				,lowerCamelCase__      :       Optional[bool] = False				,lowerCamelCase__      :       str = "linspace"				,lowerCamelCase__      :       int = 0				,):







							'''simple docstring'''
							if trained_betas is not None:
													_UpperCamelCase      :	Optional[Any]				       =     torch.tensor(lowerCamelCase__				,dtype=torch.floataa				)
							elif beta_schedule == "linear":
													_UpperCamelCase      :	int				       =     torch.linspace(lowerCamelCase__				,lowerCamelCase__				,lowerCamelCase__				,dtype=torch.floataa				)
							elif beta_schedule == "scaled_linear":
													# this schedule is very specific to the latent diffusion model.
													_UpperCamelCase      :	Dict				       =     (
													    torch.linspace(beta_start**0.5				,beta_end**0.5				,lowerCamelCase__				,dtype=torch.floataa				) ** 2
													)
							elif beta_schedule == "squaredcos_cap_v2":
													# Glide cosine schedule
													_UpperCamelCase      :	str				       =     betas_for_alpha_bar(lowerCamelCase__				)
							else:
													raise NotImplementedError(F'{beta_schedule} does is not implemented for {self.__class__}'				)

							_UpperCamelCase      :	Optional[int]				       =     1.0 - self.betas
							_UpperCamelCase      :	Tuple				       =     torch.cumprod(self.alphas				,dim=0				)

							_UpperCamelCase      :	Optional[Any]				       =     np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5				)
							_UpperCamelCase      :	Union[str, Any]				       =     np.concatenate([sigmas[::-1], [0.0]]				).astype(np.floataa				)
							_UpperCamelCase      :	str				       =     torch.from_numpy(lowerCamelCase__				)

							# setable values
							_UpperCamelCase      :	Tuple				       =     None
							_UpperCamelCase      :	Optional[Any]				       =     np.linspace(0				,num_train_timesteps - 1				,lowerCamelCase__				,dtype=lowerCamelCase__				)[::-1].copy()
							_UpperCamelCase      :	List[str]				       =     torch.from_numpy(lowerCamelCase__				)
							_UpperCamelCase      :	str				       =     False
							_UpperCamelCase      :	int				       =     use_karras_sigmas





	@property
	def 			UpperCamelCase_						(				self      :       Dict				):







							'''simple docstring'''
							# standard deviation of the initial noise distribution
							if self.config.timestep_spacing in ["linspace", "trailing"]:
													return self.sigmas.max()

							return (self.sigmas.max() ** 2 + 1) ** 0.5





	def 			UpperCamelCase_						(				self      :       List[str]				,lowerCamelCase__      :       torch.FloatTensor				,lowerCamelCase__      :       Union[float, torch.FloatTensor]				):







							'''simple docstring'''
							if isinstance(lowerCamelCase__				,torch.Tensor				):
													_UpperCamelCase      :	List[Any]				       =     timestep.to(self.timesteps.device				)
							_UpperCamelCase      :	str				       =     (self.timesteps == timestep).nonzero().item()
							_UpperCamelCase      :	Union[str, Any]				       =     self.sigmas[step_index]

							_UpperCamelCase      :	Optional[int]				       =     sample / ((sigma**2 + 1) ** 0.5)

							_UpperCamelCase      :	Union[str, Any]				       =     True
							return sample





	def 			UpperCamelCase_						(				self      :       int				,lowerCamelCase__      :       int				,lowerCamelCase__      :       Union[str, torch.device] = None				):







							'''simple docstring'''
							_UpperCamelCase      :	int				       =     num_inference_steps

							# "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
							if self.config.timestep_spacing == "linspace":
													_UpperCamelCase      :	Tuple				       =     np.linspace(0				,self.config.num_train_timesteps - 1				,lowerCamelCase__				,dtype=lowerCamelCase__				)[
													    ::-1
													].copy()
							elif self.config.timestep_spacing == "leading":
													_UpperCamelCase      :	Tuple				       =     self.config.num_train_timesteps // self.num_inference_steps
													# creates integer timesteps by multiplying by ratio
													# casting to int to avoid issues when num_inference_step is power of 3
													_UpperCamelCase      :	Dict				       =     (np.arange(0				,lowerCamelCase__				) * step_ratio).round()[::-1].copy().astype(lowerCamelCase__				)
													timesteps += self.config.steps_offset
							elif self.config.timestep_spacing == "trailing":
													_UpperCamelCase      :	Optional[int]				       =     self.config.num_train_timesteps / self.num_inference_steps
													# creates integer timesteps by multiplying by ratio
													# casting to int to avoid issues when num_inference_step is power of 3
													_UpperCamelCase      :	int				       =     (np.arange(self.config.num_train_timesteps				,0				,-step_ratio				)).round().copy().astype(lowerCamelCase__				)
													timesteps -= 1
							else:
													raise ValueError(
													    F'{self.config.timestep_spacing} is not supported. Please make sure to choose one of \'linspace\', \'leading\' or \'trailing\'.'				)

							_UpperCamelCase      :	List[str]				       =     np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5				)
							_UpperCamelCase      :	Optional[Any]				       =     np.log(lowerCamelCase__				)

							if self.config.interpolation_type == "linear":
													_UpperCamelCase      :	Union[str, Any]				       =     np.interp(lowerCamelCase__				,np.arange(0				,len(lowerCamelCase__				)				)				,lowerCamelCase__				)
							elif self.config.interpolation_type == "log_linear":
													_UpperCamelCase      :	Union[str, Any]				       =     torch.linspace(np.log(sigmas[-1]				)				,np.log(sigmas[0]				)				,num_inference_steps + 1				).exp()
							else:
													raise ValueError(
													    F'{self.config.interpolation_type} is not implemented. Please specify interpolation_type to either'
													    ' \'linear\' or \'log_linear\''				)

							if self.use_karras_sigmas:
													_UpperCamelCase      :	List[str]				       =     self._convert_to_karras(in_sigmas=lowerCamelCase__				,num_inference_steps=self.num_inference_steps				)
													_UpperCamelCase      :	Optional[int]				       =     np.array([self._sigma_to_t(lowerCamelCase__				,lowerCamelCase__				) for sigma in sigmas]				)

							_UpperCamelCase      :	List[str]				       =     np.concatenate([sigmas, [0.0]]				).astype(np.floataa				)
							_UpperCamelCase      :	Tuple				       =     torch.from_numpy(lowerCamelCase__				).to(device=lowerCamelCase__				)
							if str(lowerCamelCase__				).startswith('mps'				):
													# mps does not support float64
													_UpperCamelCase      :	List[Any]				       =     torch.from_numpy(lowerCamelCase__				).to(lowerCamelCase__				,dtype=torch.floataa				)
							else:
													_UpperCamelCase      :	Optional[int]				       =     torch.from_numpy(lowerCamelCase__				).to(device=lowerCamelCase__				)





	def 			UpperCamelCase_						(				self      :       str				,lowerCamelCase__      :       Dict				,lowerCamelCase__      :       Any				):







							'''simple docstring'''
							# get log sigma
							_UpperCamelCase      :	Any				       =     np.log(lowerCamelCase__				)

							# get distribution
							_UpperCamelCase      :	Any				       =     log_sigma - log_sigmas[:, np.newaxis]

							# get sigmas range
							_UpperCamelCase      :	List[str]				       =     np.cumsum((dists >= 0)				,axis=0				).argmax(axis=0				).clip(max=log_sigmas.shape[0] - 2				)
							_UpperCamelCase      :	List[Any]				       =     low_idx + 1

							_UpperCamelCase      :	int				       =     log_sigmas[low_idx]
							_UpperCamelCase      :	List[str]				       =     log_sigmas[high_idx]

							# interpolate sigmas
							_UpperCamelCase      :	Dict				       =     (low - log_sigma) / (low - high)
							_UpperCamelCase      :	Any				       =     np.clip(lowerCamelCase__				,0				,1				)

							# transform interpolation to time range
							_UpperCamelCase      :	Any				       =     (1 - w) * low_idx + w * high_idx
							_UpperCamelCase      :	Union[str, Any]				       =     t.reshape(sigma.shape				)
							return t





	def 			UpperCamelCase_						(				self      :       Tuple				,lowerCamelCase__      :       torch.FloatTensor				,lowerCamelCase__      :       Optional[int]				):







							'''simple docstring'''
							_UpperCamelCase      :	float				       =     in_sigmas[-1].item()
							_UpperCamelCase      :	float				       =     in_sigmas[0].item()

							_UpperCamelCase      :	List[Any]				       =     7.0  # 7.0 is the value used in the paper
							_UpperCamelCase      :	List[Any]				       =     np.linspace(0				,1				,lowerCamelCase__				)
							_UpperCamelCase      :	Optional[int]				       =     sigma_min ** (1 / rho)
							_UpperCamelCase      :	Dict				       =     sigma_max ** (1 / rho)
							_UpperCamelCase      :	Any				       =     (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
							return sigmas





	def 			UpperCamelCase_						(				self      :       List[str]				,lowerCamelCase__      :       torch.FloatTensor				,lowerCamelCase__      :       Union[float, torch.FloatTensor]				,lowerCamelCase__      :       torch.FloatTensor				,lowerCamelCase__      :       float = 0.0				,lowerCamelCase__      :       float = 0.0				,lowerCamelCase__      :       float = float('inf'				)				,lowerCamelCase__      :       float = 1.0				,lowerCamelCase__      :       Optional[torch.Generator] = None				,lowerCamelCase__      :       bool = True				,):







							'''simple docstring'''

							if (
							    isinstance(lowerCamelCase__				,lowerCamelCase__				)
							    or isinstance(lowerCamelCase__				,torch.IntTensor				)
							    or isinstance(lowerCamelCase__				,torch.LongTensor				)
							):
													raise ValueError(
													    (
													        'Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to'
													        ' `EulerDiscreteScheduler.step()` is not supported. Make sure to pass'
													        ' one of the `scheduler.timesteps` as a timestep.'
													    )				,)

							if not self.is_scale_input_called:
													logger.warning(
													    'The `scale_model_input` function should be called before `step` to ensure correct denoising. '
													    'See `StableDiffusionPipeline` for a usage example.'				)

							if isinstance(lowerCamelCase__				,torch.Tensor				):
													_UpperCamelCase      :	Optional[Any]				       =     timestep.to(self.timesteps.device				)

							_UpperCamelCase      :	Dict				       =     (self.timesteps == timestep).nonzero().item()
							_UpperCamelCase      :	Optional[Any]				       =     self.sigmas[step_index]

							_UpperCamelCase      :	List[Any]				       =     min(s_churn / (len(self.sigmas				) - 1)				,2**0.5 - 1				) if s_tmin <= sigma <= s_tmax else 0.0

							_UpperCamelCase      :	int				       =     randn_tensor(
							    model_output.shape				,dtype=model_output.dtype				,device=model_output.device				,generator=lowerCamelCase__				)

							_UpperCamelCase      :	Union[str, Any]				       =     noise * s_noise
							_UpperCamelCase      :	Optional[int]				       =     sigma * (gamma + 1)

							if gamma > 0:
													_UpperCamelCase      :	List[str]				       =     sample + eps * (sigma_hat**2 - sigma**2) ** 0.5

							# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
							# NOTE: "original_sample" should not be an expected prediction_type but is left in for
							# backwards compatibility
							if self.config.prediction_type == "original_sample" or self.config.prediction_type == "sample":
													_UpperCamelCase      :	Tuple				       =     model_output
							elif self.config.prediction_type == "epsilon":
													_UpperCamelCase      :	Any				       =     sample - sigma_hat * model_output
							elif self.config.prediction_type == "v_prediction":
													# * c_out + input * c_skip
													_UpperCamelCase      :	Union[str, Any]				       =     model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
							else:
													raise ValueError(
													    F'prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`'				)

							# 2. Convert to an ODE derivative
							_UpperCamelCase      :	Union[str, Any]				       =     (sample - pred_original_sample) / sigma_hat

							_UpperCamelCase      :	List[Any]				       =     self.sigmas[step_index + 1] - sigma_hat

							_UpperCamelCase      :	str				       =     sample + derivative * dt

							if not return_dict:
													return (prev_sample,)

							return EulerDiscreteSchedulerOutput(prev_sample=lowerCamelCase__				,pred_original_sample=lowerCamelCase__				)





	def 			UpperCamelCase_						(				self      :       Optional[Any]				,lowerCamelCase__      :       torch.FloatTensor				,lowerCamelCase__      :       torch.FloatTensor				,lowerCamelCase__      :       torch.FloatTensor				,):







							'''simple docstring'''
							# Make sure sigmas and timesteps have the same device and dtype as original_samples
							_UpperCamelCase      :	List[str]				       =     self.sigmas.to(device=original_samples.device				,dtype=original_samples.dtype				)
							if original_samples.device.type == "mps" and torch.is_floating_point(lowerCamelCase__				):
													# mps does not support float64
													_UpperCamelCase      :	List[Any]				       =     self.timesteps.to(original_samples.device				,dtype=torch.floataa				)
													_UpperCamelCase      :	List[str]				       =     timesteps.to(original_samples.device				,dtype=torch.floataa				)
							else:
													_UpperCamelCase      :	Any				       =     self.timesteps.to(original_samples.device				)
													_UpperCamelCase      :	Union[str, Any]				       =     timesteps.to(original_samples.device				)

							_UpperCamelCase      :	str				       =     [(schedule_timesteps == t).nonzero().item() for t in timesteps]

							_UpperCamelCase      :	Optional[int]				       =     sigmas[step_indices].flatten()
							while len(sigma.shape				) < len(original_samples.shape				):
													_UpperCamelCase      :	List[str]				       =     sigma.unsqueeze(-1				)

							_UpperCamelCase      :	int				       =     original_samples + noise * sigma
							return noisy_samples





	def __len__(				self      :       Tuple				):







							'''simple docstring'''
							return self.config.num_train_timesteps







'''simple docstring'''
import os
from shutil import copyfile
from typing import List, Optional, Tuple

from tokenizers import processors

from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
					from .tokenization_mbartaa import MBartaaTokenizer
else:
					snake_case_				:			List[str]						 =						None


snake_case_				:			Union[str, Any]						 =						logging.get_logger(__name__)

snake_case_				:			Dict						 =						{'vocab_file': 'sentencepiece.bpe.model', 'tokenizer_file': 'tokenizer.json'}

snake_case_				:			str						 =						{
    'vocab_file': {
        'facebook/mbart-large-50-one-to-many-mmt': (
            'https://huggingface.co/facebook/mbart-large-50-one-to-many-mmt/resolve/main/sentencepiece.bpe.model'
        ),
    },
    'tokenizer_file': {
        'facebook/mbart-large-50-one-to-many-mmt': (
            'https://huggingface.co/facebook/mbart-large-50-one-to-many-mmt/resolve/main/tokenizer.json'
        ),
    },
}

snake_case_				:			List[Any]						 =						{
    'facebook/mbart-large-50-one-to-many-mmt': 1024,
}

# fmt: off
snake_case_				:			Union[str, Any]						 =						['ar_AR', 'cs_CZ', 'de_DE', 'en_XX', 'es_XX', 'et_EE', 'fi_FI', 'fr_XX', 'gu_IN', 'hi_IN', 'it_IT', 'ja_XX', 'kk_KZ', 'ko_KR', 'lt_LT', 'lv_LV', 'my_MM', 'ne_NP', 'nl_XX', 'ro_RO', 'ru_RU', 'si_LK', 'tr_TR', 'vi_VN', 'zh_CN', 'af_ZA', 'az_AZ', 'bn_IN', 'fa_IR', 'he_IL', 'hr_HR', 'id_ID', 'ka_GE', 'km_KH', 'mk_MK', 'ml_IN', 'mn_MN', 'mr_IN', 'pl_PL', 'ps_AF', 'pt_XX', 'sv_SE', 'sw_KE', 'ta_IN', 'te_IN', 'th_TH', 'tl_XX', 'uk_UA', 'ur_PK', 'xh_ZA', 'gl_ES', 'sl_SI']






class 							lowercase__			(							lowercase     ):
	lowercase__						       =    VOCAB_FILES_NAMES
	lowercase__						       =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	lowercase__						       =    PRETRAINED_VOCAB_FILES_MAP
	lowercase__						       =    ["""input_ids""", """attention_mask"""]
	lowercase__						       =    MBartaaTokenizer

	lowercase__						       =    []
	lowercase__						       =    []





	def __init__(				self      :       Optional[int]				,lowerCamelCase__      :       Union[str, Any]=None				,lowerCamelCase__      :       List[Any]=None				,lowerCamelCase__      :       Optional[Any]=None				,lowerCamelCase__      :       str=None				,lowerCamelCase__      :       List[str]="</s>"				,lowerCamelCase__      :       List[str]="</s>"				,lowerCamelCase__      :       Dict="<s>"				,lowerCamelCase__      :       int="<unk>"				,lowerCamelCase__      :       List[Any]="<pad>"				,lowerCamelCase__      :       Optional[int]="<mask>"				,**lowerCamelCase__      :       Optional[int]				,):







							'''simple docstring'''
							# Mask token behave like a normal word, i.e. include the space before it
							_UpperCamelCase      :	str				       =     AddedToken(lowerCamelCase__				,lstrip=lowerCamelCase__				,rstrip=lowerCamelCase__				) if isinstance(lowerCamelCase__				,lowerCamelCase__				) else mask_token

							_UpperCamelCase      :	Tuple				       =     kwargs.get('additional_special_tokens'				,[]				)
							kwargs["additional_special_tokens"] += [
							    code for code in FAIRSEQ_LANGUAGE_CODES if code not in kwargs["additional_special_tokens"]
							]

							super().__init__(
							    lowerCamelCase__				,src_lang=lowerCamelCase__				,tgt_lang=lowerCamelCase__				,tokenizer_file=lowerCamelCase__				,eos_token=lowerCamelCase__				,sep_token=lowerCamelCase__				,cls_token=lowerCamelCase__				,unk_token=lowerCamelCase__				,pad_token=lowerCamelCase__				,mask_token=lowerCamelCase__				,**lowerCamelCase__				,)

							_UpperCamelCase      :	Optional[int]				       =     vocab_file
							_UpperCamelCase      :	Union[str, Any]				       =     False if not self.vocab_file else True

							_UpperCamelCase      :	Union[str, Any]				       =     {
							    lang_code: self.convert_tokens_to_ids(lowerCamelCase__				) for lang_code in FAIRSEQ_LANGUAGE_CODES
							}

							_UpperCamelCase      :	Union[str, Any]				       =     src_lang if src_lang is not None else 'en_XX'
							_UpperCamelCase      :	Any				       =     tgt_lang
							_UpperCamelCase      :	Optional[Any]				       =     self.lang_code_to_id[self._src_lang]
							self.set_src_lang_special_tokens(self._src_lang				)





	@property
	def 			UpperCamelCase_						(				self      :       Optional[Any]				):







							'''simple docstring'''
							return self._src_lang





	@src_lang.setter
	def 			UpperCamelCase_						(				self      :       Optional[Any]				,lowerCamelCase__      :       str				):







							'''simple docstring'''
							_UpperCamelCase      :	str				       =     new_src_lang
							self.set_src_lang_special_tokens(self._src_lang				)





	def 			UpperCamelCase_						(				self      :       Tuple				,lowerCamelCase__      :       List[int]				,lowerCamelCase__      :       Optional[List[int]] = None				):







							'''simple docstring'''
							if token_ids_a is None:
													return self.prefix_tokens + token_ids_a + self.suffix_tokens
							# We don't expect to process pairs, but leave the pair logic for API consistency
							return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens





	def 			UpperCamelCase_						(				self      :       Optional[Any]				,lowerCamelCase__      :       List[str]				,lowerCamelCase__      :       str = "en_XX"				,lowerCamelCase__      :       Optional[List[str]] = None				,lowerCamelCase__      :       str = "ro_RO"				,**lowerCamelCase__      :       Dict				,):







							'''simple docstring'''
							_UpperCamelCase      :	List[str]				       =     src_lang
							_UpperCamelCase      :	List[str]				       =     tgt_lang
							return super().prepare_seqaseq_batch(lowerCamelCase__				,lowerCamelCase__				,**lowerCamelCase__				)





	def 			UpperCamelCase_						(				self      :       Tuple				):







							'''simple docstring'''
							return self.set_src_lang_special_tokens(self.src_lang				)





	def 			UpperCamelCase_						(				self      :       Optional[int]				):







							'''simple docstring'''
							return self.set_tgt_lang_special_tokens(self.tgt_lang				)





	def 			UpperCamelCase_						(				self      :       Dict				,lowerCamelCase__      :       str				):







							'''simple docstring'''
							_UpperCamelCase      :	Any				       =     self.convert_tokens_to_ids(lowerCamelCase__				)
							_UpperCamelCase      :	List[Any]				       =     [self.cur_lang_code_id]
							_UpperCamelCase      :	List[Any]				       =     [self.eos_token_id]

							_UpperCamelCase      :	Optional[int]				       =     self.convert_ids_to_tokens(self.prefix_tokens				)
							_UpperCamelCase      :	List[Any]				       =     self.convert_ids_to_tokens(self.suffix_tokens				)

							_UpperCamelCase      :	Union[str, Any]				       =     processors.TemplateProcessing(
							    single=prefix_tokens_str + ['$A'] + suffix_tokens_str				,pair=prefix_tokens_str + ['$A', '$B'] + suffix_tokens_str				,special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str				,self.prefix_tokens + self.suffix_tokens				)				)				,)





	def 			UpperCamelCase_						(				self      :       Union[str, Any]				,lowerCamelCase__      :       str				):







							'''simple docstring'''
							_UpperCamelCase      :	List[str]				       =     self.convert_tokens_to_ids(lowerCamelCase__				)
							_UpperCamelCase      :	int				       =     [self.cur_lang_code_id]
							_UpperCamelCase      :	int				       =     [self.eos_token_id]

							_UpperCamelCase      :	Union[str, Any]				       =     self.convert_ids_to_tokens(self.prefix_tokens				)
							_UpperCamelCase      :	int				       =     self.convert_ids_to_tokens(self.suffix_tokens				)

							_UpperCamelCase      :	Dict				       =     processors.TemplateProcessing(
							    single=prefix_tokens_str + ['$A'] + suffix_tokens_str				,pair=prefix_tokens_str + ['$A', '$B'] + suffix_tokens_str				,special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str				,self.prefix_tokens + self.suffix_tokens				)				)				,)





	def 			UpperCamelCase_						(				self      :       Any				,lowerCamelCase__      :       Optional[int]				,lowerCamelCase__      :       str				,lowerCamelCase__      :       Optional[str]				,lowerCamelCase__      :       Optional[str]				,**lowerCamelCase__      :       List[Any]				):







							'''simple docstring'''
							if src_lang is None or tgt_lang is None:
													raise ValueError('Translation requires a `src_lang` and a `tgt_lang` for this model'				)
							_UpperCamelCase      :	Optional[int]				       =     src_lang
							_UpperCamelCase      :	Optional[Any]				       =     self(lowerCamelCase__				,add_special_tokens=lowerCamelCase__				,return_tensors=lowerCamelCase__				,**lowerCamelCase__				)
							_UpperCamelCase      :	Tuple				       =     self.convert_tokens_to_ids(lowerCamelCase__				)
							_UpperCamelCase      :	Optional[int]				       =     tgt_lang_id
							return inputs





	def 			UpperCamelCase_						(				self      :       Optional[int]				,lowerCamelCase__      :       str				,lowerCamelCase__      :       Optional[str] = None				):







							'''simple docstring'''
							if not self.can_save_slow_tokenizer:
													raise ValueError(
													    'Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '
													    'tokenizer.'				)

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

							if os.path.abspath(self.vocab_file				) != os.path.abspath(lowerCamelCase__				):
													copyfile(self.vocab_file				,lowerCamelCase__				)

							return (out_vocab_file,)


