



"""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


_lowercase			:			str										=						logging.get_logger(__name__)

_lowercase			:			Optional[Any]										=						"▁"

_lowercase			:			Any										=						{"vocab_file": "sentencepiece.bpe.model", "monolingual_vocab_file": "dict.txt"}

_lowercase			:			Union[str, Any]										=						{
    "vocab_file": {
        "vinai/bartpho-syllable": "https://huggingface.co/vinai/bartpho-syllable/resolve/main/sentencepiece.bpe.model",
    },
    "monolingual_vocab_file": {
        "vinai/bartpho-syllable": "https://huggingface.co/vinai/bartpho-syllable/resolve/main/dict.txt",
    },
}

_lowercase			:			str										=						{"vinai/bartpho-syllable": 1_0_2_4}

class 	__SCREAMING_SNAKE_CASE     (				lowerCAmelCase_       ):


					'''simple docstring'''





					_a             =							VOCAB_FILES_NAMES
					_a             =							PRETRAINED_VOCAB_FILES_MAP
					_a             =							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					_a             =							['input_ids', 'attention_mask']



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

								lowerCamelCase__		:		int		     ={} if sp_model_kwargs is None else sp_model_kwargs

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

								lowerCamelCase__		:		Optional[Any]		     =vocab_file
								lowerCamelCase__		:		Any		     =monolingual_vocab_file
								lowerCamelCase__		:		str		     =spm.SentencePieceProcessor(**self.sp_model_kwargs					)
								self.sp_model.Load(str(lowerCamelCase					)					)

								# Load the reduced vocab

								# Keep order of special tokens for backward compatibility
								lowerCamelCase__		:		Any		     ={}
								lowerCamelCase__		:		Optional[Any]		     =0
								for token in [bos_token, pad_token, eos_token, unk_token, sep_token, cls_token]:
											if str(lowerCamelCase					) not in self.fairseq_tokens_to_ids:
														lowerCamelCase__		:		Optional[int]		     =cnt
														cnt += 1
								with open(lowerCamelCase,  '''r''',  encoding='''utf-8'''					) as f:
											for line in f.readlines():
														lowerCamelCase__		:		List[str]		     =line.strip().split()[0]
														lowerCamelCase__		:		Dict		     =len(self.fairseq_tokens_to_ids					)
								if str(lowerCamelCase					) not in self.fairseq_tokens_to_ids:
											lowerCamelCase__		:		Tuple		     =len(self.fairseq_tokens_to_ids					)

								lowerCamelCase__		:		str		     ={v: k for k, v in self.fairseq_tokens_to_ids.items()}



					def __getstate__(	self							:  List[str]					)-> Optional[int]:
								lowerCamelCase__		:		Optional[Any]		     =self.__dict__.copy()
								lowerCamelCase__		:		Optional[int]		     =None
								lowerCamelCase__		:		int		     =self.sp_model.serialized_model_proto()
								return state



					def __setstate__(	self							:  List[Any],  lowerCamelCase							:  Tuple					)-> Any:
								lowerCamelCase__		:		Optional[int]		     =d

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

								lowerCamelCase__		:		str		     =spm.SentencePieceProcessor(**self.sp_model_kwargs					)
								self.sp_model.LoadFromSerializedProto(self.sp_model_proto					)



					def 		snake_case	(	self							:  Dict,  lowerCamelCase							:  List[int],  lowerCamelCase							:  Optional[List[int]] = None					)-> List[int]:

								if token_ids_a is None:
											return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
								lowerCamelCase__		:		Optional[Any]		     =[self.cls_token_id]
								lowerCamelCase__		:		Optional[int]		     =[self.sep_token_id]
								return cls + token_ids_a + sep + sep + token_ids_a + sep



					def 		snake_case	(	self							:  Optional[Any],  lowerCamelCase							:  List[int],  lowerCamelCase							:  Optional[List[int]] = None,  lowerCamelCase							:  bool = False					)-> List[int]:

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

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



					def 		snake_case	(	self							:  Dict,  lowerCamelCase							:  List[int],  lowerCamelCase							:  Optional[List[int]] = None					)-> List[int]:
								lowerCamelCase__		:		Optional[Any]		     =[self.sep_token_id]
								lowerCamelCase__		:		List[Any]		     =[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 		snake_case	(	self							:  int					)-> Optional[int]:
								return len(self.fairseq_ids_to_tokens					)



					def 		snake_case	(	self							:  Optional[int]					)-> Optional[int]:
								lowerCamelCase__		:		Optional[Any]		     ={self.convert_ids_to_tokens(lowerCamelCase					): i for i in range(self.vocab_size					)}
								vocab.update(self.added_tokens_encoder					)
								return vocab



					def 		snake_case	(	self							:  Optional[int],  lowerCamelCase							:  str					)-> List[str]:
								return self.sp_model.encode(lowerCamelCase,  out_type=lowerCamelCase					)



					def 		snake_case	(	self							:  int,  lowerCamelCase							:  Optional[Any]					)-> List[str]:
								if token in self.fairseq_tokens_to_ids:
											return self.fairseq_tokens_to_ids[token]
								else:
											return self.unk_token_id



					def 		snake_case	(	self							:  Any,  lowerCamelCase							:  Optional[int]					)-> Optional[int]:
								return self.fairseq_ids_to_tokens[index]



					def 		snake_case	(	self							:  int,  lowerCamelCase							:  Any					)-> Optional[int]:
								lowerCamelCase__		:		List[str]		     =''''''.join(lowerCamelCase					).replace(lowerCamelCase,  ''' '''					).strip()
								return out_string




					def 		snake_case	(	self							:  Union[str, Any],  lowerCamelCase							:  str,  lowerCamelCase							:  Optional[str] = None					)-> Tuple[str]:
								if not os.path.isdir(lowerCamelCase					):
											logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''					)
											return
								lowerCamelCase__		:		Dict		     =os.path.join(
								    lowerCamelCase,  (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']					)
								lowerCamelCase__		:		Tuple		     =os.path.join(
								    lowerCamelCase,  (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''monolingual_vocab_file'''],  )

								if os.path.abspath(self.vocab_file					) != os.path.abspath(lowerCamelCase					) and os.path.isfile(self.vocab_file					):
											copyfile(self.vocab_file,  lowerCamelCase					)
								elif not os.path.isfile(self.vocab_file					):
											with open(lowerCamelCase,  '''wb'''					) as fi:
														lowerCamelCase__		:		Any		     =self.sp_model.serialized_model_proto()
														fi.write(lowerCamelCase					)

								if os.path.abspath(self.monolingual_vocab_file					) != os.path.abspath(
								    lowerCamelCase					) and os.path.isfile(self.monolingual_vocab_file					):
											copyfile(self.monolingual_vocab_file,  lowerCamelCase					)
								elif not os.path.isfile(self.monolingual_vocab_file					):
											with open(lowerCamelCase,  '''w''',  encoding='''utf-8'''					) as fp:
														for token in self.fairseq_tokens_to_ids:
																	if token not in self.all_special_tokens:
																				fp.write(F'''{str(lowerCamelCase					)} \n'''					)

								return out_vocab_file, out_monolingual_vocab_file









"""simple docstring"""






import math

import flax.linen as nn
import jax.numpy as jnp






def 					snake_case__       (		__lowerCamelCase    :   jnp.ndarray							,		__lowerCamelCase    :   int							,		__lowerCamelCase    :   float = 1							,		__lowerCamelCase    :   float = 1							,		__lowerCamelCase    :   float = 1.0e4							,		__lowerCamelCase    :   bool = False							,		__lowerCamelCase    :   float = 1.0							,		):
			"""simple docstring"""
			assert timesteps.ndim == 1, "Timesteps should be a 1d-array"
			assert embedding_dim % 2 == 0, f'''Embedding dimension {embedding_dim} should be even'''
			lowerCamelCase__		:		Any		     =float(embedding_dim // 2     )
			lowerCamelCase__		:		List[str]		     =math.log(max_timescale / min_timescale     ) / (num_timescales - freq_shift)
			lowerCamelCase__		:		int		     =min_timescale * jnp.exp(jnp.arange(__lowerCamelCase							,		dtype=jnp.floataa     ) * -log_timescale_increment     )
			lowerCamelCase__		:		Tuple		     =jnp.expand_dims(__lowerCamelCase							,		1     ) * jnp.expand_dims(__lowerCamelCase							,		0     )

			# scale embeddings
			lowerCamelCase__		:		List[str]		     =scale * emb

			if flip_sin_to_cos:
						lowerCamelCase__		:		int		     =jnp.concatenate([jnp.cos(__lowerCamelCase     ), jnp.sin(__lowerCamelCase     )]							,		axis=1     )
			else:
						lowerCamelCase__		:		List[str]		     =jnp.concatenate([jnp.sin(__lowerCamelCase     ), jnp.cos(__lowerCamelCase     )]							,		axis=1     )
			lowerCamelCase__		:		str		     =jnp.reshape(__lowerCamelCase							,		[jnp.shape(__lowerCamelCase     )[0], embedding_dim]     )
			return signal

class 	__SCREAMING_SNAKE_CASE     (				nn.Module       ):


					'''simple docstring'''





					_a             =							3_2
					_a             =							jnp.floataa



					@nn.compact
					def __call__(	self							:  Optional[Any],  lowerCamelCase							:  int					)-> Any:
								lowerCamelCase__		:		Optional[Any]		     =nn.Dense(self.time_embed_dim,  dtype=self.dtype,  name='''linear_1'''					)(lowerCamelCase					)
								lowerCamelCase__		:		List[str]		     =nn.silu(lowerCamelCase					)
								lowerCamelCase__		:		Any		     =nn.Dense(self.time_embed_dim,  dtype=self.dtype,  name='''linear_2'''					)(lowerCamelCase					)
								return temb





class 	__SCREAMING_SNAKE_CASE     (				nn.Module       ):


					'''simple docstring'''





					_a             =							3_2
					_a             =							False
					_a             =							1



					@nn.compact
					def __call__(	self							:  Any,  lowerCamelCase							:  int					)-> int:
								return get_sinusoidal_embeddings(
								    lowerCamelCase,  embedding_dim=self.dim,  flip_sin_to_cos=self.flip_sin_to_cos,  freq_shift=self.freq_shift					)




