


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

import sentencepiece as spm

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


snake_case							: List[str]      =   logging.get_logger(__name__)

snake_case							: Tuple      =   '''▁'''

snake_case							: int      =   {'''vocab_file''': '''sentencepiece.bpe.model'''}

snake_case							: List[str]      =   {
    '''vocab_file''': {
        '''facebook/mbart-large-en-ro''': (
            '''https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/sentencepiece.bpe.model'''
        ),
        '''facebook/mbart-large-cc25''': (
            '''https://huggingface.co/facebook/mbart-large-cc25/resolve/main/sentencepiece.bpe.model'''
        ),
    }
}

snake_case							: List[str]      =   {
    '''facebook/mbart-large-en-ro''': 10_24,
    '''facebook/mbart-large-cc25''': 10_24,
}

# fmt: off
snake_case							: Tuple      =   ['''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''']







class  snake_case_     (lowerCamelCase_   ):
					UpperCAmelCase__      :					Dict					   =    VOCAB_FILES_NAMES
					UpperCAmelCase__      :					List[Any]					   =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					UpperCAmelCase__      :					Optional[Any]					   =    PRETRAINED_VOCAB_FILES_MAP
					UpperCAmelCase__      :					Tuple					   =    ['''input_ids''', '''attention_mask''']

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

												a__    =  {} if sp_model_kwargs is None else sp_model_kwargs

												super().__init__(
												    bos_token=__snake_case    ,eos_token=__snake_case    ,unk_token=__snake_case    ,sep_token=__snake_case    ,cls_token=__snake_case    ,pad_token=__snake_case    ,mask_token=__snake_case    ,tokenizer_file=__snake_case    ,src_lang=__snake_case    ,tgt_lang=__snake_case    ,additional_special_tokens=__snake_case    ,sp_model_kwargs=self.sp_model_kwargs    ,**__snake_case    ,)

												a__    =  spm.SentencePieceProcessor(**self.sp_model_kwargs   )
												self.sp_model.Load(str(__snake_case   )   )
												a__    =  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'

												# Mimic fairseq token-to-id alignment for the first 4 token
												a__    =  {'<s>': 0, '<pad>': 1, '</s>': 2, '<unk>': 3}

												# The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
												a__    =  1

												a__    =  len(self.sp_model   )
												a__    =  {
												    code: self.sp_model_size + i + self.fairseq_offset for i, code in enumerate(__snake_case   )
												}
												a__    =  {v: k for k, v in self.lang_code_to_id.items()}
												a__    =  len(self.sp_model   ) + len(self.lang_code_to_id   ) + self.fairseq_offset

												self.fairseq_tokens_to_ids.update(self.lang_code_to_id   )
												a__    =  {v: k for k, v in self.fairseq_tokens_to_ids.items()}
												a__    =  list(self.lang_code_to_id.keys()   )

												if additional_special_tokens is not None:
																			# Only add those special tokens if they are not already there.
																			self._additional_special_tokens.extend(
																			    [t for t in additional_special_tokens if t not in self._additional_special_tokens]   )

												a__    =  src_lang if src_lang is not None else 'en_XX'
												a__    =  self.lang_code_to_id[self._src_lang]
												a__    =  tgt_lang
												self.set_src_lang_special_tokens(self._src_lang   )
					def __getstate__(   self     :List[Any]   )      ->					Union[str, Any]:
												a__    =  self.__dict__.copy()
												a__    =  None
												a__    =  self.sp_model.serialized_model_proto()
												return state
					def __setstate__(   self     :str    ,__snake_case     :List[str]   )      ->					List[str]:
												a__    =  d

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

												a__    =  spm.SentencePieceProcessor(**self.sp_model_kwargs   )
												self.sp_model.LoadFromSerializedProto(self.sp_model_proto   )
					@property
					def 				lowerCamelCase__(   self     :List[Any]   )      ->					Optional[int]:
												return len(self.sp_model   ) + len(self.lang_code_to_id   ) + self.fairseq_offset + 1  # Plus 1 for the mask token
					@property
					def 				lowerCamelCase__(   self     :Optional[int]   )      ->					str:
												return self._src_lang
					@src_lang.setter
					def 				lowerCamelCase__(   self     :Optional[int]    ,__snake_case     :str   )      ->					None:
												a__    =  new_src_lang
												self.set_src_lang_special_tokens(self._src_lang   )
					def 				lowerCamelCase__(   self     :Tuple    ,__snake_case     :List[int]    ,__snake_case     :Optional[List[int]] = None    ,__snake_case     :bool = False   )      ->					List[int]:

												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   )

												a__    =  [1] * len(self.prefix_tokens   )
												a__    =  [1] * len(self.suffix_tokens   )
												if token_ids_a is None:
																			return prefix_ones + ([0] * len(__snake_case   )) + suffix_ones
												return prefix_ones + ([0] * len(__snake_case   )) + ([0] * len(__snake_case   )) + suffix_ones
					def 				lowerCamelCase__(   self     :int    ,__snake_case     :List[int]    ,__snake_case     :Optional[List[int]] = None   )      ->					List[int]:
												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 				lowerCamelCase__(   self     :int    ,__snake_case     :List[int]    ,__snake_case     :Optional[List[int]] = None   )      ->					List[int]:
												a__    =  [self.sep_token_id]
												a__    =  [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]
					def 				lowerCamelCase__(   self     :List[Any]    ,__snake_case     :Dict    ,__snake_case     :str    ,__snake_case     :Optional[str]    ,__snake_case     :Optional[str]    ,**__snake_case     :List[Any]   )      ->					int:
												if src_lang is None or tgt_lang is None:
																			raise ValueError('Translation requires a `src_lang` and a `tgt_lang` for this model'   )
												a__    =  src_lang
												a__    =  self(__snake_case    ,add_special_tokens=__snake_case    ,return_tensors=__snake_case    ,**__snake_case   )
												a__    =  self.convert_tokens_to_ids(__snake_case   )
												a__    =  tgt_lang_id
												return inputs
					def 				lowerCamelCase__(   self     :Tuple   )      ->					List[Any]:
												a__    =  {self.convert_ids_to_tokens(__snake_case   ): i for i in range(self.vocab_size   )}
												vocab.update(self.added_tokens_encoder   )
												return vocab
					def 				lowerCamelCase__(   self     :Any    ,__snake_case     :str   )      ->					List[str]:
												return self.sp_model.encode(__snake_case    ,out_type=__snake_case   )
					def 				lowerCamelCase__(   self     :List[str]    ,__snake_case     :Dict   )      ->					List[str]:
												if token in self.fairseq_tokens_to_ids:
																			return self.fairseq_tokens_to_ids[token]
												a__    =  self.sp_model.PieceToId(__snake_case   )

												# 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 				lowerCamelCase__(   self     :Dict    ,__snake_case     :List[str]   )      ->					Optional[int]:
												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     :Dict    ,__snake_case     :Union[str, Any]   )      ->					List[Any]:
												a__    =  ''.join(__snake_case   ).replace(__snake_case    ,' '   ).strip()
												return out_string
					def 				lowerCamelCase__(   self     :Dict    ,__snake_case     :str    ,__snake_case     :Optional[str] = None   )      ->					Tuple[str]:
												if not os.path.isdir(__snake_case   ):
																			logger.error(F'Vocabulary path ({save_directory}) should be a directory'   )
																			return
												a__    =  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:
																										a__    =  self.sp_model.serialized_model_proto()
																										fi.write(__snake_case   )

												return (out_vocab_file,)
					def 				lowerCamelCase__(   self     :Dict    ,__snake_case     :List[str]    ,__snake_case     :str = "en_XX"    ,__snake_case     :Optional[List[str]] = None    ,__snake_case     :str = "ro_RO"    ,**__snake_case     :Tuple    ,)      ->					BatchEncoding:
												a__    =  src_lang
												a__    =  tgt_lang
												return super().prepare_seqaseq_batch(__snake_case    ,__snake_case    ,**__snake_case   )
					def 				lowerCamelCase__(   self     :int   )      ->					Any:
												return self.set_src_lang_special_tokens(self.src_lang   )
					def 				lowerCamelCase__(   self     :Optional[int]   )      ->					List[Any]:
												return self.set_tgt_lang_special_tokens(self.tgt_lang   )
					def 				lowerCamelCase__(   self     :str    ,__snake_case     :Optional[int]   )      ->					None:
												a__    =  self.lang_code_to_id[src_lang]
												a__    =  []
												a__    =  [self.eos_token_id, self.cur_lang_code]



					def 				lowerCamelCase__(   self     :Optional[int]    ,__snake_case     :str   )      ->					None:
												a__    =  self.lang_code_to_id[lang]
												a__    =  []
												a__    =  [self.eos_token_id, self.cur_lang_code]








import math
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 SchedulerMixin, SchedulerOutput







class  snake_case_     (lowerCamelCase_			,		lowerCamelCase_   ):
					UpperCAmelCase__      :					Optional[Any]					   =    1
					@register_to_config
					def __init__(   self     :Optional[int]    ,__snake_case     :int = 10_00    ,__snake_case     :Optional[Union[np.ndarray, List[float]]] = None   )      ->					int:
												# set `betas`, `alphas`, `timesteps`
												self.set_timesteps(__snake_case   )

												# standard deviation of the initial noise distribution
												a__    =  1.0

												# For now we only support F-PNDM, i.e. the runge-kutta method
												# For more information on the algorithm please take a look at the paper: https://arxiv.org/pdf/2202.09778.pdf
												# mainly at formula (9), (12), (13) and the Algorithm 2.
												a__    =  4

												# running values
												a__    =  []
					def 				lowerCamelCase__(   self     :Union[str, Any]    ,__snake_case     :int    ,__snake_case     :Union[str, torch.device] = None   )      ->					Union[str, Any]:
												a__    =  num_inference_steps
												a__    =  torch.linspace(1    ,0    ,num_inference_steps + 1   )[:-1]
												a__    =  torch.cat([steps, torch.tensor([0.0]   )]   )

												if self.config.trained_betas is not None:
																			a__    =  torch.tensor(self.config.trained_betas    ,dtype=torch.floataa   )
												else:
																			a__    =  torch.sin(steps * math.pi / 2   ) ** 2

												a__    =  (1.0 - self.betas**2) ** 0.5

												a__    =  (torch.atana(self.betas    ,self.alphas   ) / math.pi * 2)[:-1]
												a__    =  timesteps.to(__snake_case   )

												a__    =  []
					def 				lowerCamelCase__(   self     :Any    ,__snake_case     :torch.FloatTensor    ,__snake_case     :int    ,__snake_case     :torch.FloatTensor    ,__snake_case     :bool = True    ,)      ->					Union[SchedulerOutput, Tuple]:
												if self.num_inference_steps is None:
																			raise ValueError(
																			    'Number of inference steps is \'None\', you need to run \'set_timesteps\' after creating the scheduler'   )

												a__    =  (self.timesteps == timestep).nonzero().item()
												a__    =  timestep_index + 1

												a__    =  sample * self.betas[timestep_index] + model_output * self.alphas[timestep_index]
												self.ets.append(__snake_case   )

												if len(self.ets   ) == 1:
																			a__    =  self.ets[-1]
												elif len(self.ets   ) == 2:
																			a__    =  (3 * self.ets[-1] - self.ets[-2]) / 2
												elif len(self.ets   ) == 3:
																			a__    =  (23 * self.ets[-1] - 16 * self.ets[-2] + 5 * self.ets[-3]) / 12
												else:
																			a__    =  (1 / 24) * (55 * self.ets[-1] - 59 * self.ets[-2] + 37 * self.ets[-3] - 9 * self.ets[-4])

												a__    =  self._get_prev_sample(__snake_case    ,__snake_case    ,__snake_case    ,__snake_case   )

												if not return_dict:
																			return (prev_sample,)

												return SchedulerOutput(prev_sample=__snake_case   )
					def 				lowerCamelCase__(   self     :Union[str, Any]    ,__snake_case     :torch.FloatTensor    ,*__snake_case     :int    ,**__snake_case     :Optional[int]   )      ->					torch.FloatTensor:
												return sample
					def 				lowerCamelCase__(   self     :Optional[Any]    ,__snake_case     :List[Any]    ,__snake_case     :Optional[int]    ,__snake_case     :Dict    ,__snake_case     :Any   )      ->					Optional[Any]:
												a__    =  self.alphas[timestep_index]
												a__    =  self.betas[timestep_index]

												a__    =  self.alphas[prev_timestep_index]
												a__    =  self.betas[prev_timestep_index]

												a__    =  (sample - sigma * ets) / max(__snake_case    ,1E-8   )
												a__    =  next_alpha * pred + ets * next_sigma

												return prev_sample



					def __len__(   self     :Any   )      ->					Union[str, Any]:
												return self.config.num_train_timesteps




