import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput





def 	__lowerCamelCase							(UpperCAmelCase__     :							Optional[int]      , UpperCAmelCase__     :							Tuple=0.999      , UpperCAmelCase__     :							int="cosine"      , ):
					if alpha_transform_type == "cosine":

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

					elif alpha_transform_type == "exp":

										def alpha_bar_fn(UpperCAmelCase__     :							Union[str, Any]  ):
															return math.exp(t * -12.0  )

					else:
										raise ValueError(F"Unsupported alpha_tranform_type: {alpha_transform_type}"  )

					SCREAMING_SNAKE_CASE    			=       []
					for i in range(UpperCAmelCase__  ):
										SCREAMING_SNAKE_CASE    			=       i / num_diffusion_timesteps
										SCREAMING_SNAKE_CASE    			=       (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    (     a				,       a     ):
							lowercase__						:       Union[str, Any]								=							[e.name for e in KarrasDiffusionSchedulers]
							lowercase__						:       str								=							2




							@register_to_config
							def __init__(						self   :					str	,							_UpperCamelCase   :					int = 1_000	,							_UpperCamelCase   :					float = 0.0_0_0_8_5	,							_UpperCamelCase   :					float = 0.0_1_2	,							_UpperCamelCase   :					str = "linear"	,							_UpperCamelCase   :					Optional[Union[np.ndarray, List[float]]] = None	,							_UpperCamelCase   :					str = "epsilon"	,							_UpperCamelCase   :					str = "linspace"	,							_UpperCamelCase   :					int = 0	,							)					->				str:



												'''simple docstring'''

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

												SCREAMING_SNAKE_CASE    			=       1.0 - self.betas
												SCREAMING_SNAKE_CASE    			=       torch.cumprod(self.alphas	,							dim=0      )

												#  set all values
												self.set_timesteps(_UpperCamelCase	,							_UpperCamelCase	,							_UpperCamelCase      )




							def 	__snake_case(						self   :					Optional[Any]	,							_UpperCamelCase   :					Optional[Any]	,							_UpperCamelCase   :					Optional[Any]=None      )					->				Dict:



												'''simple docstring'''

												if schedule_timesteps is None:
																	SCREAMING_SNAKE_CASE    			=       self.timesteps

												SCREAMING_SNAKE_CASE    			=       (schedule_timesteps == timestep).nonzero()

												# The sigma index that is taken for the **very** first `step`
												# is always the second index (or the last index if there is only 1)
												# This way we can ensure we don't accidentally skip a sigma in
												# case we start in the middle of the denoising schedule (e.g. for image-to-image)
												if len(self._index_counter      ) == 0:
																	SCREAMING_SNAKE_CASE    			=       1 if len(_UpperCamelCase      ) > 1 else 0
												else:
																	SCREAMING_SNAKE_CASE    			=       timestep.cpu().item() if torch.is_tensor(_UpperCamelCase      ) else timestep
																	SCREAMING_SNAKE_CASE    			=       self._index_counter[timestep_int]

												return indices[pos].item()




							@property
							def 	__snake_case(						self   :					Optional[Any]      )					->				Tuple:



												'''simple docstring'''

												if self.config.timestep_spacing in ["linspace", "trailing"]:
																	return self.sigmas.max()

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




							def 	__snake_case(						self   :					int	,							_UpperCamelCase   :					torch.FloatTensor	,							_UpperCamelCase   :					Union[float, torch.FloatTensor]	,							)					->				torch.FloatTensor:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       self.index_for_timestep(_UpperCamelCase      )

												if self.state_in_first_order:
																	SCREAMING_SNAKE_CASE    			=       self.sigmas[step_index]
												else:
																	SCREAMING_SNAKE_CASE    			=       self.sigmas_interpol[step_index]

												SCREAMING_SNAKE_CASE    			=       sample / ((sigma**2 + 1) ** 0.5)
												return sample




							def 	__snake_case(						self   :					Any	,							_UpperCamelCase   :					int	,							_UpperCamelCase   :					Union[str, torch.device] = None	,							_UpperCamelCase   :					Optional[int] = None	,							)					->				Union[str, Any]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       num_inference_steps

												SCREAMING_SNAKE_CASE    			=       num_train_timesteps or self.config.num_train_timesteps

												# "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
												if self.config.timestep_spacing == "linspace":
																	SCREAMING_SNAKE_CASE    			=       np.linspace(0	,							num_train_timesteps - 1	,							_UpperCamelCase	,							dtype=_UpperCamelCase      )[::-1].copy()
												elif self.config.timestep_spacing == "leading":
																	SCREAMING_SNAKE_CASE    			=       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
																	SCREAMING_SNAKE_CASE    			=       (np.arange(0	,							_UpperCamelCase      ) * step_ratio).round()[::-1].copy().astype(_UpperCamelCase      )
																	timesteps += self.config.steps_offset
												elif self.config.timestep_spacing == "trailing":
																	SCREAMING_SNAKE_CASE    			=       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
																	SCREAMING_SNAKE_CASE    			=       (np.arange(_UpperCamelCase	,							0	,							-step_ratio      )).round().copy().astype(_UpperCamelCase      )
																	timesteps -= 1
												else:
																	raise ValueError(
																	    F"{self.config.timestep_spacing} is not supported. Please make sure to choose one of 'linspace', 'leading' or 'trailing'."      )

												SCREAMING_SNAKE_CASE    			=       np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5      )
												SCREAMING_SNAKE_CASE    			=       torch.from_numpy(np.log(_UpperCamelCase      )      ).to(_UpperCamelCase      )

												SCREAMING_SNAKE_CASE    			=       np.interp(_UpperCamelCase	,							np.arange(0	,							len(_UpperCamelCase      )      )	,							_UpperCamelCase      )
												SCREAMING_SNAKE_CASE    			=       np.concatenate([sigmas, [0.0]]      ).astype(np.floataa      )
												SCREAMING_SNAKE_CASE    			=       torch.from_numpy(_UpperCamelCase      ).to(device=_UpperCamelCase      )

												# interpolate sigmas
												SCREAMING_SNAKE_CASE    			=       sigmas.log().lerp(sigmas.roll(1      ).log()	,							0.5      ).exp()

												SCREAMING_SNAKE_CASE    			=       torch.cat([sigmas[:1], sigmas[1:].repeat_interleave(2      ), sigmas[-1:]]      )
												SCREAMING_SNAKE_CASE    			=       torch.cat(
												    [sigmas_interpol[:1], sigmas_interpol[1:].repeat_interleave(2      ), sigmas_interpol[-1:]]      )

												if str(_UpperCamelCase      ).startswith("mps"      ):
																	# mps does not support float64
																	SCREAMING_SNAKE_CASE    			=       torch.from_numpy(_UpperCamelCase      ).to(_UpperCamelCase	,							dtype=torch.floataa      )
												else:
																	SCREAMING_SNAKE_CASE    			=       torch.from_numpy(_UpperCamelCase      ).to(_UpperCamelCase      )

												# interpolate timesteps
												SCREAMING_SNAKE_CASE    			=       self.sigma_to_t(_UpperCamelCase      ).to(_UpperCamelCase	,							dtype=timesteps.dtype      )
												SCREAMING_SNAKE_CASE    			=       torch.stack((timesteps_interpol[1:-1, None], timesteps[1:, None])	,							dim=-1      ).flatten()

												SCREAMING_SNAKE_CASE    			=       torch.cat([timesteps[:1], interleaved_timesteps]      )

												SCREAMING_SNAKE_CASE    			=       None

												# for exp beta schedules, such as the one for `pipeline_shap_e.py`
												# we need an index counter
												SCREAMING_SNAKE_CASE    			=       defaultdict(_UpperCamelCase      )




							def 	__snake_case(						self   :					Optional[Any]	,							_UpperCamelCase   :					List[Any]      )					->				Union[str, Any]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       sigma.log()

												# get distribution
												SCREAMING_SNAKE_CASE    			=       log_sigma - self.log_sigmas[:, None]

												# get sigmas range
												SCREAMING_SNAKE_CASE    			=       dists.ge(0      ).cumsum(dim=0      ).argmax(dim=0      ).clamp(max=self.log_sigmas.shape[0] - 2      )
												SCREAMING_SNAKE_CASE    			=       low_idx + 1

												SCREAMING_SNAKE_CASE    			=       self.log_sigmas[low_idx]
												SCREAMING_SNAKE_CASE    			=       self.log_sigmas[high_idx]

												# interpolate sigmas
												SCREAMING_SNAKE_CASE    			=       (low - log_sigma) / (low - high)
												SCREAMING_SNAKE_CASE    			=       w.clamp(0	,							1      )

												# transform interpolation to time range
												SCREAMING_SNAKE_CASE    			=       (1 - w) * low_idx + w * high_idx
												SCREAMING_SNAKE_CASE    			=       t.view(sigma.shape      )
												return t




							@property
							def 	__snake_case(						self   :					List[Any]      )					->				Optional[Any]:



												'''simple docstring'''

												return self.sample is None




							def 	__snake_case(						self   :					Dict	,							_UpperCamelCase   :					Union[torch.FloatTensor, np.ndarray]	,							_UpperCamelCase   :					Union[float, torch.FloatTensor]	,							_UpperCamelCase   :					Union[torch.FloatTensor, np.ndarray]	,							_UpperCamelCase   :					bool = True	,							)					->				Union[SchedulerOutput, Tuple]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       self.index_for_timestep(_UpperCamelCase      )

												# advance index counter by 1
												SCREAMING_SNAKE_CASE    			=       timestep.cpu().item() if torch.is_tensor(_UpperCamelCase      ) else timestep
												self._index_counter[timestep_int] += 1

												if self.state_in_first_order:
																	SCREAMING_SNAKE_CASE    			=       self.sigmas[step_index]
																	SCREAMING_SNAKE_CASE    			=       self.sigmas_interpol[step_index + 1]
																	SCREAMING_SNAKE_CASE    			=       self.sigmas[step_index + 1]
												else:
																	# 2nd order / KDPM2's method
																	SCREAMING_SNAKE_CASE    			=       self.sigmas[step_index - 1]
																	SCREAMING_SNAKE_CASE    			=       self.sigmas_interpol[step_index]
																	SCREAMING_SNAKE_CASE    			=       self.sigmas[step_index]

												# currently only gamma=0 is supported. This usually works best anyways.
												# We can support gamma in the future but then need to scale the timestep before
												# passing it to the model which requires a change in API
												SCREAMING_SNAKE_CASE    			=       0
												SCREAMING_SNAKE_CASE    			=       sigma * (gamma + 1)  # Note: sigma_hat == sigma for now

												# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
												if self.config.prediction_type == "epsilon":
																	SCREAMING_SNAKE_CASE    			=       sigma_hat if self.state_in_first_order else sigma_interpol
																	SCREAMING_SNAKE_CASE    			=       sample - sigma_input * model_output
												elif self.config.prediction_type == "v_prediction":
																	SCREAMING_SNAKE_CASE    			=       sigma_hat if self.state_in_first_order else sigma_interpol
																	SCREAMING_SNAKE_CASE    			=       model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
																	    sample / (sigma_input**2 + 1)
																	)
												elif self.config.prediction_type == "sample":
																	raise NotImplementedError("prediction_type not implemented yet: sample"      )
												else:
																	raise ValueError(
																	    F"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`"      )

												if self.state_in_first_order:
																	# 2. Convert to an ODE derivative for 1st order
																	SCREAMING_SNAKE_CASE    			=       (sample - pred_original_sample) / sigma_hat
																	# 3. delta timestep
																	SCREAMING_SNAKE_CASE    			=       sigma_interpol - sigma_hat

																	# store for 2nd order step
																	SCREAMING_SNAKE_CASE    			=       sample
												else:
																	# DPM-Solver-2
																	# 2. Convert to an ODE derivative for 2nd order
																	SCREAMING_SNAKE_CASE    			=       (sample - pred_original_sample) / sigma_interpol

																	# 3. delta timestep
																	SCREAMING_SNAKE_CASE    			=       sigma_next - sigma_hat

																	SCREAMING_SNAKE_CASE    			=       self.sample
																	SCREAMING_SNAKE_CASE    			=       None

												SCREAMING_SNAKE_CASE    			=       sample + derivative * dt

												if not return_dict:
																	return (prev_sample,)

												return SchedulerOutput(prev_sample=_UpperCamelCase      )




							def 	__snake_case(						self   :					Union[str, Any]	,							_UpperCamelCase   :					torch.FloatTensor	,							_UpperCamelCase   :					torch.FloatTensor	,							_UpperCamelCase   :					torch.FloatTensor	,							)					->				torch.FloatTensor:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       self.sigmas.to(device=original_samples.device	,							dtype=original_samples.dtype      )
												if original_samples.device.type == "mps" and torch.is_floating_point(_UpperCamelCase      ):
																	# mps does not support float64
																	SCREAMING_SNAKE_CASE    			=       self.timesteps.to(original_samples.device	,							dtype=torch.floataa      )
																	SCREAMING_SNAKE_CASE    			=       timesteps.to(original_samples.device	,							dtype=torch.floataa      )
												else:
																	SCREAMING_SNAKE_CASE    			=       self.timesteps.to(original_samples.device      )
																	SCREAMING_SNAKE_CASE    			=       timesteps.to(original_samples.device      )

												SCREAMING_SNAKE_CASE    			=       [self.index_for_timestep(_UpperCamelCase	,							_UpperCamelCase      ) for t in timesteps]

												SCREAMING_SNAKE_CASE    			=       sigmas[step_indices].flatten()
												while len(sigma.shape      ) < len(original_samples.shape      ):
																	SCREAMING_SNAKE_CASE    			=       sigma.unsqueeze(-1      )

												SCREAMING_SNAKE_CASE    			=       original_samples + noise * sigma
												return noisy_samples





							def __len__(						self   :					str      )					->				Optional[Any]:



												'''simple docstring'''

												return self.config.num_train_timesteps





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

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


_lowerCamelCase					:   Dict							=		logging.get_logger(__name__)

_lowerCamelCase					:   List[Any]							=		'''▁'''

_lowerCamelCase					:   Optional[int]							=		{'''vocab_file''': '''prophetnet.tokenizer'''}

_lowerCamelCase					:   str							=		{
    '''vocab_file''': {
        '''microsoft/xprophetnet-large-wiki100-cased''': (
            '''https://huggingface.co/microsoft/xprophetnet-large-wiki100-cased/resolve/main/prophetnet.tokenizer'''
        ),
    }
}

_lowerCamelCase					:   Optional[Any]							=		{
    '''microsoft/xprophetnet-large-wiki100-cased''': {'''do_lower_case''': False},
}

_lowerCamelCase					:   Optional[Any]							=		{
    '''microsoft/xprophetnet-large-wiki100-cased''': 5_12,
}





def 	__lowerCamelCase							(UpperCAmelCase__     :							Optional[Any]  ):
					SCREAMING_SNAKE_CASE    			=       collections.OrderedDict()
					with open(UpperCAmelCase__      , "r"      , encoding="utf-8"  ) as reader:
										SCREAMING_SNAKE_CASE    			=       reader.readlines()
					for index, token in enumerate(UpperCAmelCase__  ):
										SCREAMING_SNAKE_CASE    			=       token.rstrip("\n"  )
										SCREAMING_SNAKE_CASE    			=       index
					return vocab


class 						lowercase    (     a     ):
							lowercase__						:       Optional[int]								=							VOCAB_FILES_NAMES
							lowercase__						:       Optional[Any]								=							PRETRAINED_VOCAB_FILES_MAP
							lowercase__						:       Union[str, Any]								=							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							lowercase__						:       Any								=							["""input_ids""", """attention_mask"""]




							def __init__(						self   :					Optional[int]	,							_UpperCamelCase   :					List[str]	,							_UpperCamelCase   :					str="[SEP]"	,							_UpperCamelCase   :					str="[SEP]"	,							_UpperCamelCase   :					Dict="[SEP]"	,							_UpperCamelCase   :					Tuple="[UNK]"	,							_UpperCamelCase   :					Dict="[PAD]"	,							_UpperCamelCase   :					Any="[CLS]"	,							_UpperCamelCase   :					Optional[Any]="[MASK]"	,							_UpperCamelCase   :					Optional[Dict[str, Any]] = None	,							**_UpperCamelCase   :					Dict	,							)					->				None:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       {} if sp_model_kwargs is None else sp_model_kwargs

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

												try:
																	import sentencepiece as spm
												except ImportError:
																	logger.warning(
																	    "You need to install SentencePiece to use XLMRobertaTokenizer: https://github.com/google/sentencepiece"
																	    " pip install sentencepiece"      )
																	raise

												SCREAMING_SNAKE_CASE    			=       spm.SentencePieceProcessor(**self.sp_model_kwargs      )
												self.sp_model.Load(str(_UpperCamelCase      )      )
												SCREAMING_SNAKE_CASE    			=       vocab_file

												# Original fairseq vocab and spm vocab must be "aligned":
												# Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
												# -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
												# fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
												# spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

												# put special tokens and [unused] tokens into the vocab
												SCREAMING_SNAKE_CASE    			=       {"[PAD]": 0, "[CLS]": 1, "[SEP]": 2, "[UNK]": 3, "[MASK]": 4}

												for i in range(10      ):
																	SCREAMING_SNAKE_CASE    			=       F"[unused{i}]"
																	SCREAMING_SNAKE_CASE    			=       5 + i

												# The first "real" token "," has position 15 in the embedding vocab and position 3 in the spm vocab
												SCREAMING_SNAKE_CASE    			=       12
												SCREAMING_SNAKE_CASE    			=       {v: k for k, v in self.fairseq_tokens_to_ids.items()}
												for k in self.fairseq_tokens_to_ids.keys():
																	self.unique_no_split_tokens.append(_UpperCamelCase      )




							def __getstate__(						self   :					Dict      )					->				str:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       self.__dict__.copy()
												SCREAMING_SNAKE_CASE    			=       None
												return state




							def __setstate__(						self   :					List[Any]	,							_UpperCamelCase   :					Union[str, Any]      )					->				Dict:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       d
												try:
																	import sentencepiece as spm
												except ImportError:
																	logger.warning(
																	    "You need to install SentencePiece to use XLMRobertaTokenizer: https://github.com/google/sentencepiece"
																	    " pip install sentencepiece"      )
																	raise

												# 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 	__snake_case(						self   :					Dict	,							_UpperCamelCase   :					List[int]	,							_UpperCamelCase   :					Optional[List[int]] = None	,							_UpperCamelCase   :					bool = False      )					->				List[int]:



												'''simple docstring'''

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

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




							def 	__snake_case(						self   :					str	,							_UpperCamelCase   :					List[int]	,							_UpperCamelCase   :					Optional[List[int]] = None      )					->				List[int]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       [self.sep_token_id]

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




							@property
							def 	__snake_case(						self   :					Dict      )					->				Optional[Any]:



												'''simple docstring'''

												return len(self.sp_model      ) + self.fairseq_offset




							def 	__snake_case(						self   :					Optional[Any]      )					->				Union[str, Any]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       {self.convert_ids_to_tokens(_UpperCamelCase      ): i for i in range(self.vocab_size      )}
												vocab.update(self.added_tokens_encoder      )
												return vocab




							def 	__snake_case(						self   :					Union[str, Any]	,							_UpperCamelCase   :					str      )					->				str:



												'''simple docstring'''

												return self.sp_model.encode(_UpperCamelCase	,							out_type=_UpperCamelCase      )




							def 	__snake_case(						self   :					Optional[Any]	,							_UpperCamelCase   :					List[Any]      )					->				List[str]:



												'''simple docstring'''

												if token in self.fairseq_tokens_to_ids:
																	return self.fairseq_tokens_to_ids[token]
												SCREAMING_SNAKE_CASE    			=       self.sp_model.PieceToId(_UpperCamelCase      )

												# Need to return unknown token if the SP model returned 0
												return spm_id + self.fairseq_offset if spm_id else self.unk_token_id




							def 	__snake_case(						self   :					str	,							_UpperCamelCase   :					str      )					->				int:



												'''simple docstring'''

												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 	__snake_case(						self   :					List[str]	,							_UpperCamelCase   :					Dict      )					->				Optional[Any]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       "".join(_UpperCamelCase      ).replace(_UpperCamelCase	,							" "      ).strip()
												return out_string




							def 	__snake_case(						self   :					Optional[Any]	,							_UpperCamelCase   :					str	,							_UpperCamelCase   :					Optional[str] = None      )					->				Tuple[str]:



												'''simple docstring'''

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

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

												return (out_vocab_file,)





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



												'''simple docstring'''

												if token_ids_a is None:
																	return token_ids_a + [self.sep_token_id]
												SCREAMING_SNAKE_CASE    			=       [self.sep_token_id]
												return token_ids_a + sep + token_ids_a + sep




